diff --git a/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/.dockerignore b/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/.dockerignore new file mode 100644 index 000000000..fd85b2584 --- /dev/null +++ b/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/.dockerignore @@ -0,0 +1,143 @@ +# Exclude files from Docker build context. This prevents unnecessary files from +# being sent to Docker daemon, reducing build time and image size. + +# Python artifacts +__pycache__/ +*.pyc +*.pyo +*.pyd +*.egg-info/ + +# Virtual environments +venv/ +.venv/ +env/ +.env +.envrc +client_venv.helpers/ +ENV/ + +# Jupyter +.ipynb_checkpoints/ +.jupyter/ + +# Build artifacts +build/ +dist/ +*.eggs/ +.eggs/ + +# Cache and temporary files +*.log +*.tmp +*.cache +.pytest_cache/ +.mypy_cache/ +.coverage +htmlcov/ + +# Git and version control +.git/ +.gitignore +.gitattributes +.github/ + +# Docker build scripts (not needed at runtime) +docker_build.sh +docker_push.sh +docker_clean.sh +docker_exec.sh +docker_cmd.sh +docker_bash.sh +docker_jupyter.sh +docker_name.sh +run_jupyter.sh +Dockerfile.* +.dockerignore + +# Documentation +README.md +README.admin.md +docs/ +*.md +CHANGELOG.md +LICENSE + +# Configuration and secrets +.env.* +.env.local +.env.development +.env.production +.DS_Store +Thumbs.db + +# Shell configuration +.bashrc +.bash_history +.zshrc + +# Large data files (mount via volume instead) +data/ +*.csv +*.pkl +*.h5 +*.parquet +*.feather +*.arrow +*.npy +*.npz + +# Generated images +*.png +*.jpg +*.jpeg +*.gif +*.svg +*.pdf + +# Test files and examples +tests/ +test_* +*_test.py +tutorials/ +examples/ + +# IDE and editor files +.vscode/ +.idea/ +*.swp +*.swo +*~ +.project +.pydevproject +.settings/ +*.iml +.sublime-project +.sublime-workspace + +# Node and frontend (if applicable) +node_modules/ +npm-debug.log +yarn-error.log +.npm + +# Requirements management +requirements.in +Pipfile +Pipfile.lock +poetry.lock +setup.py +setup.cfg + +# CI/CD configuration +.gitlab-ci.yml +.travis.yml +Jenkinsfile +.circleci/ + +# Miscellaneous +*.bak +.venv.bak/ +*.whl +*.tar.gz +*.zip diff --git a/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/.gitignore b/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/.gitignore new file mode 100644 index 000000000..718cb99a6 --- /dev/null +++ b/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/.gitignore @@ -0,0 +1,3 @@ +.env +venv/ +data/ diff --git a/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/Dockerfile b/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/Dockerfile new file mode 100644 index 000000000..419b5de29 --- /dev/null +++ b/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/Dockerfile @@ -0,0 +1,33 @@ +# Use Python 3.12 slim (already has Python and pip). +FROM python:3.12-slim + +# Avoid interactive prompts during apt operations. +ENV DEBIAN_FRONTEND=noninteractive + +# Install system dependencies including build tools for shap. +RUN apt-get update && apt-get install -y \ + ca-certificates \ + build-essential \ + gcc \ + g++ \ + && rm -rf /var/lib/apt/lists/* + +# Install project specific packages. +RUN mkdir -p /install +COPY requirements.txt /install/requirements.txt +RUN pip install --upgrade pip && \ + pip install --no-cache-dir jupyterlab jupyterlab_vim jupytext -r /install/requirements.txt + +# Config. +COPY etc_sudoers /install/ +COPY etc_sudoers /etc/sudoers +COPY bashrc /root/.bashrc + +# Report package versions. +COPY version.sh /install/ +RUN /install/version.sh 2>&1 | tee version.log + +# Jupyter. +EXPOSE 8888 + +CMD ["/bin/bash"] diff --git a/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/Dockerfile.python_slim b/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/Dockerfile.python_slim new file mode 100644 index 000000000..cc8f18f2f --- /dev/null +++ b/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/Dockerfile.python_slim @@ -0,0 +1,28 @@ +# Use Python 3.12 slim (already has Python and pip). +FROM python:3.12-slim + +# Avoid interactive prompts during apt operations. +ENV DEBIAN_FRONTEND=noninteractive + +# Install CA certificates (needed for HTTPS). +RUN apt-get update && apt-get install -y \ + ca-certificates \ + && rm -rf /var/lib/apt/lists/* + +# Install project specific packages. +RUN mkdir -p /install +COPY requirements.txt /install/requirements.txt +RUN pip install --upgrade pip && \ + pip install --no-cache-dir jupyterlab jupyterlab_vim jupytext -r /install/requirements.txt + +# Config. +COPY etc_sudoers /install/ +COPY etc_sudoers /etc/sudoers +COPY bashrc /root/.bashrc + +# Report package versions. +COPY version.sh /install/ +RUN /install/version.sh 2>&1 | tee version.log + +# Jupyter. +EXPOSE 8888 diff --git a/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/Dockerfile.ubuntu b/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/Dockerfile.ubuntu new file mode 100644 index 000000000..705105d91 --- /dev/null +++ b/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/Dockerfile.ubuntu @@ -0,0 +1,40 @@ +FROM ubuntu:24.04 +ENV DEBIAN_FRONTEND noninteractive + +# Install system utilities and Python in a single layer. +RUN apt-get update && \ + apt-get upgrade -y && \ + apt-get install -y --no-install-recommends \ + sudo \ + curl \ + git \ + build-essential \ + python3 \ + python3-pip \ + python3-dev \ + python3-venv \ + && rm -rf /var/lib/apt/lists/* + +# Create virtual environment. +RUN python3 -m venv /opt/venv + +# Make the venv the default Python. +ENV PATH="/opt/venv/bin:$PATH" + +# Install project specific packages. +RUN mkdir /install +COPY requirements.txt /install/requirements.txt +RUN pip install --upgrade pip && \ + pip install --no-cache-dir jupyterlab jupyterlab_vim jupytext -r /install/requirements.txt + +# Config. +COPY etc_sudoers /install/ +COPY etc_sudoers /etc/sudoers +COPY bashrc /root/.bashrc + +# Report package versions. +COPY version.sh /install/ +RUN /install/version.sh 2>&1 | tee version.log + +# Jupyter. +EXPOSE 8888 diff --git a/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/Dockerfile.uv b/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/Dockerfile.uv new file mode 100644 index 000000000..d3b2a0abc --- /dev/null +++ b/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/Dockerfile.uv @@ -0,0 +1,49 @@ +FROM ubuntu:24.04 +ENV DEBIAN_FRONTEND noninteractive + +# Install system utilities and Python in a single layer. +RUN apt-get update && \ + apt-get upgrade -y && \ + apt-get install -y --no-install-recommends \ + sudo \ + curl \ + git \ + build-essential \ + python3 \ + python3-pip \ + python3-dev \ + python3-venv \ + libgomp1 \ + g++ \ + && rm -rf /var/lib/apt/lists/* + +# Install uv for package management. +RUN curl -LsSf https://astral.sh/uv/install.sh | sh +ENV PATH="/root/.local/bin:$PATH" + +# Install project specific packages using uv. +COPY pyproject.toml uv.lock /app/ +WORKDIR /app +RUN uv sync +ENV PATH="/app/.venv/bin:$PATH" + +# Install Jupyter. +RUN pip install --upgrade pip && \ + pip install --no-cache-dir jupyterlab jupyterlab_vim jupytext + +# Copy project files. +COPY . /app + +RUN mkdir /install + +# Config. +COPY etc_sudoers /install/ +COPY etc_sudoers /etc/sudoers +COPY bashrc /root/.bashrc + +# Report package versions. +COPY version.sh /install/ +RUN /install/version.sh 2>&1 | tee version.log + +# Jupyter. +EXPOSE 8888 diff --git a/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/README.md b/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/README.md new file mode 100644 index 000000000..2e8d5df91 --- /dev/null +++ b/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/README.md @@ -0,0 +1,129 @@ +# Darts — S&P 500 Market Direction Forecasting & Intelligent Sector Rotation + +## Project Overview + +This project uses Python Pandas and Darts to forecast S&P 500 index +prices using macroeconomic indicators as external covariates and +predicts which market sectors will outperform in the near future. +It combines S&P 500 direction forecasting with an intelligent sector +rotation recommender backed entirely by model predictions historical +correlations and risk adjusted scores — not manual rules. + +## Key Results + +| Metric | Value | +|--------|-------| +| Best validation MAPE | 0.65% (Stacking Ensemble) | +| Improvement over baseline | 65.8% vs NaiveSeasonal | +| In-regime direction accuracy | 76.6% (2023 walk forward windows) | +| Market regimes detected | 5 (automatic via silhouette score) | +| Weekly recommendations pre-computed | 339 (2018-2024, no look-ahead bias) | +| Models trained | 18 across 6 families | +| SHAP selected features | 7 from 46 candidates | + +## Completed Workflow + +1. Data collection — S&P 500 + 11 sector ETFs + 16 macro indicators + via yfinance and FRED API. XLC reconstructed from 16 verified + SPDR constituents for pre-launch 2018 period. +2. Data preprocessing and exploratory data analysis — business day + alignment release-aware forward fill and 4 EDA visualizations. +3. Feature engineering — 46 features including technical indicators + calendar features macro derived features and event flags for + FOMC dates CPI releases and holiday adjacent days. +4. Model training — 18 models across 6 families: 4 baseline + 7 statistical 1 probabilistic 5 ML and 1 Prophet. Best single + window result: LightGBM MAPE 1.12% Direction 93.1%. +5. Feature selection via SHAP values — two step process combining + correlation filter at 0.95 threshold with SHAP importance at + 90% threshold. Selected 7 optimal features improving MAPE from + 1.10% to 0.95% and R2 from -0.14 to +0.21. +6. Hyperparameter tuning via Optuna Bayesian optimization — + TPESampler with 20 trials per model. XGBoost improved by 63% + from MAPE 1.88% to 0.70%. +7. Ensemble modeling — simple average weighted average and stacking + with Ridge Regression meta model. Stacking achieves MAPE 0.65% + — 7.09% improvement over best individual model. +8. Sector rotation engine — K-Means clustering with silhouette score + automatically detected 5 market regimes. Five factor composite + scoring with Ridge Regression weights generates weekly BUY + NEUTRAL AVOID recommendations. Regime attribution analysis reveals + how macro context drives different sector outcomes within the same + regime. 339 weekly recommendations pre-computed with no + look-ahead bias. +9. Library comparison — Darts vs Statsmodels vs Prophet evaluated + on identical data. Darts NaiveSeasonal achieves best MAPE 1.94% + in 0.01 seconds validating Darts as the primary library. +10. External factor impact analysis — FOMC dates CPI releases and + holiday adjacent days analyzed across top 5 models. RandomForest + 73% worse on FOMC days. LightGBM 44% better on FOMC days + validating event flag features. +11. Walk forward validation — 12 non-overlapping 30 day windows + July 2023 to December 2024. In-regime 2023 windows achieve + MAPE 2.78% and direction accuracy 76.6%. Out-of-distribution + 2024 AI boom windows show MAPE 17.7% validating the need for + regime confidence scoring. +12. Docker setup — Dockerfile with build-essential gcc g++ for + shap compilation. All dependencies pinned in requirements.txt + including optuna 3.6.1. Docker build verified successful. + +## Data Sources + +| Source | Data | Period | +|--------|------|--------| +| Yahoo Finance | S&P 500 price | 2018-2024 | +| Yahoo Finance | 11 sector ETFs | 2018-2024 | +| Yahoo Finance | VIX TNX IRX OIL DXY | 2018-2024 | +| FRED API | CPI FED_RATE UNEMPLOYMENT and others | 2018-2024 | + +## Setup and Installation + +### Option 1 — Local Installation +```bash +# Clone the repository. +git clone https://github.com/gpsaggese/gpsaggese.github.io.git +cd gpsaggese.github.io/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts + +# Create virtual environment. +python -m venv venv +source venv/bin/activate + +# Install dependencies. +pip install -r requirements.txt + +# Set FRED API key. +export FRED_API_KEY=your_api_key_here + +# Launch Jupyter. +jupyter lab darts.example.ipynb +``` + +### Option 2 — Docker +```bash +# Build Docker image. +docker build -t darts_project . + +# Run container. +docker run -it -p 8888:8888 \ + -e FRED_API_KEY=your_api_key_here \ + -v $(pwd):/workspace \ + darts_project + +# Inside container launch Jupyter. +jupyter lab --ip=0.0.0.0 --port=8888 --no-browser --allow-root +``` + +## Requirements + +- Python 3.12 +- FRED API key (free at https://fred.stlouisfed.org/docs/api/api_key.html) +- Docker (optional) +- 8GB RAM recommended + +## Author + +- GitHub: [@RushilJoshi07](https://github.com/RushilJoshi07) + +## Issue + +- [#453](https://github.com/gpsaggese/gpsaggese.github.io/issues/453) \ No newline at end of file diff --git a/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/bashrc b/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/bashrc new file mode 100644 index 000000000..4b7ff4c49 --- /dev/null +++ b/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/bashrc @@ -0,0 +1 @@ +set -o vi diff --git a/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/copy_docker_files.py b/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/copy_docker_files.py new file mode 100644 index 000000000..0e97c194c --- /dev/null +++ b/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/copy_docker_files.py @@ -0,0 +1,140 @@ +#!/usr/bin/env python + +""" +Copy Docker-related files from the source directory to a destination directory. + +This script copies all Docker configuration and utility files from +class_project/project_template/ to a specified destination directory. + +Usage examples: + # Copy all files to a target directory. + > ./copy_docker_files.py --dst_dir /path/to/destination + + # Copy with verbose logging. + > ./copy_docker_files.py --dst_dir /path/to/destination -v DEBUG + +Import as: + +import class_project.project_template.copy_docker_files as cpdccodo +""" + +import argparse +import logging +import os +from typing import List + +import helpers.hdbg as hdbg +import helpers.hio as hio +import helpers.hparser as hparser +import helpers.hsystem as hsystem + +_LOG = logging.getLogger(__name__) + +# ############################################################################# +# Constants +# ############################################################################# + +# List of files to copy from the source directory. +_FILES_TO_COPY = [ + "bashrc", + "docker_bash.sh", + "docker_build.sh", + "docker_clean.sh", + "docker_cmd.sh", + "docker_exec.sh", + "docker_jupyter.sh", + "docker_name.sh", + "docker_push.sh", + "etc_sudoers", + "install_jupyter_extensions.sh", + "run_jupyter.sh" + "version.sh", +] + + +# ############################################################################# +# Helper functions +# ############################################################################# + + +def _get_source_dir() -> str: + """ + Get the absolute path to the source directory containing Docker files. + + :return: absolute path to class_project/project_template/ + """ + # Get the directory where this script is located. + script_dir = os.path.dirname(os.path.abspath(__file__)) + _LOG.debug("Script directory='%s'", script_dir) + return script_dir + + +def _copy_files( + *, + src_dir: str, + dst_dir: str, + files: List[str], +) -> None: + """ + Copy specified files from source directory to destination directory. + + :param src_dir: source directory path + :param dst_dir: destination directory path + :param files: list of filenames to copy + """ + # Verify source directory exists. + hdbg.dassert_dir_exists(src_dir, "Source directory does not exist:", src_dir) + # Create destination directory if it doesn't exist. + hio.create_dir(dst_dir, incremental=True) + _LOG.info("Copying %d files from '%s' to '%s'", len(files), src_dir, dst_dir) + # Copy each file. + copied_count = 0 + for filename in files: + src_path = os.path.join(src_dir, filename) + dst_path = os.path.join(dst_dir, filename) + # Verify source file exists. + hdbg.dassert_path_exists( + src_path, "Source file does not exist:", src_path + ) + # Copy the file using cp -a to preserve all permissions and attributes. + _LOG.debug("Copying '%s' -> '%s'", src_path, dst_path) + cmd = f"cp -a {src_path} {dst_path}" + hsystem.system(cmd) + copied_count += 1 + # + _LOG.info("Successfully copied %d files", copied_count) + + +# ############################################################################# + + +def _parse() -> argparse.ArgumentParser: + parser = argparse.ArgumentParser( + description=__doc__, + formatter_class=argparse.RawDescriptionHelpFormatter, + ) + parser.add_argument( + "--dst_dir", + action="store", + required=True, + help="Destination directory where files will be copied", + ) + hparser.add_verbosity_arg(parser) + return parser + + +def _main(parser: argparse.ArgumentParser) -> None: + args = parser.parse_args() + hdbg.init_logger(verbosity=args.log_level, use_exec_path=True) + # Get source directory. + src_dir = _get_source_dir() + # Copy files to destination. + _copy_files( + src_dir=src_dir, + dst_dir=args.dst_dir, + files=_FILES_TO_COPY, + ) + + +if __name__ == "__main__": + _main(_parse()) diff --git a/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/darts.API.ipynb b/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/darts.API.ipynb new file mode 100644 index 000000000..33c757477 --- /dev/null +++ b/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/darts.API.ipynb @@ -0,0 +1,215 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "183c2248-ea3d-43ba-b87e-d821bba1bbc6", + "metadata": {}, + "source": [ + "# Template API Notebook\n", + "\n", + "This is a template notebook. The first heading should be the title of what notebook is about. For example, if it is a neo4j tutorial the heading should be `Neo4j API`.\n", + "\n", + "- Add description of what the notebook does.\n", + "- Point to references, e.g. (neo4j.API.md)\n", + "- Add citations.\n", + "- Keep the notebook flow clear.\n", + "- Comments should be imperative and have a period at the end.\n", + "- Your code should be well commented.\n", + "\n", + "The name of this notebook should in the following format:\n", + "- if the notebook is exploring `pycaret API`, then it is `pycaret.API.ipynb`\n", + "\n", + "Follow the reference to write notebooks in a clear manner: https://github.com/causify-ai/helpers/blob/master/docs/coding/all.jupyter_notebook.how_to_guide.md" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "265e0d58-a7cd-4edf-a0b4-96b60220e801", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" + ] + } + ], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "id": "d3b2f997-5c9b-4238-b6d5-e5f2cea43809", + "metadata": {}, + "source": [ + "## Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "d1480ee9-d6a6-437d-b927-da6cbb05bdf5", + "metadata": {}, + "outputs": [], + "source": [ + "import logging\n", + "# Import libraries in this section.\n", + "# Avoid imports like import *, from ... import ..., from ... import *, etc.\n", + "\n", + "import helpers.hdbg as hdbg\n", + "import helpers.hnotebook as hnotebo" + ] + }, + { + "cell_type": "markdown", + "id": "f9208cc9-837d-4fec-a312-9c4aa5b7648d", + "metadata": {}, + "source": [ + "## Configuration" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "9a2d7a9c-c6c5-48c9-8445-11c97045d00b", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[0mWARNING: Running in Jupyter\n", + "INFO > cmd='/venv/lib/python3.12/site-packages/ipykernel_launcher.py -f /home/.local/share/jupyter/runtime/kernel-085a2ce7-6161-4c8a-92d5-492051832f3c.json'\n" + ] + } + ], + "source": [ + "hdbg.init_logger(verbosity=logging.INFO)\n", + "\n", + "_LOG = logging.getLogger(__name__)\n", + "\n", + "hnotebo.config_notebook()" + ] + }, + { + "cell_type": "markdown", + "id": "79c37ba3-bd5d-4a44-87df-645eee54977a", + "metadata": { + "lines_to_next_cell": 2 + }, + "source": [ + "## Make the notebook flow clear\n", + "Each notebook needs to follow a clear and logical flow, e.g:\n", + "- Load data\n", + "- Compute stats\n", + "- Clean data\n", + "- Compute stats\n", + "- Do analysis\n", + "- Show results\n", + "\n", + "\n", + "\n", + "\n", + "#############################################################################\n", + "Template\n", + "#############################################################################" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "a8a109cd-fc8e-4b9e-9dc0-4fc8d4126ad8", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "class Template:\n", + " \"\"\"\n", + " Brief imperative description of what the class does in one line, if needed.\n", + " \"\"\"\n", + "\n", + " def __init__(self):\n", + " pass\n", + "\n", + " def method1(self, arg1: int) -> None:\n", + " \"\"\"\n", + " Brief imperative description of what the method does in one line.\n", + "\n", + " You can elaborate more in the method docstring in this section, for e.g. explaining\n", + " the formula/algorithm. Every method/function should have a docstring, typehints and include the\n", + " parameters and return as follows:\n", + "\n", + " :param arg1: description of arg1\n", + " :return: description of return\n", + " \"\"\"\n", + " # Code bloks go here.\n", + " # Make sure to include comments to explain what the code is doing.\n", + " # No empty lines between code blocks.\n", + " pass\n", + "\n", + "\n", + "def template_function(arg1: int) -> None:\n", + " \"\"\"\n", + " Brief imperative description of what the function does in one line.\n", + "\n", + " You can elaborate more in the function docstring in this section, for e.g. explaining\n", + " the formula/algorithm. Every function should have a docstring, typehints and include the\n", + " parameters and return as follows:\n", + "\n", + " :param arg1: description of arg1\n", + " :return: description of return\n", + " \"\"\"\n", + " # Code bloks go here.\n", + " # Make sure to include comments to explain what the code is doing.\n", + " # No empty lines between code blocks.\n", + " pass" + ] + }, + { + "cell_type": "markdown", + "id": "00926523-ae59-497d-bba8-b22e58333849", + "metadata": {}, + "source": [ + "## The flow should be highlighted using headings in markdown\n", + "```\n", + "# Level 1\n", + "## Level 2\n", + "### Level 3\n", + "```" + ] + } + ], + "metadata": { + "jupytext": { + "formats": "ipynb,py:percent" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/darts.API.py b/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/darts.API.py new file mode 100644 index 000000000..4192ef8fe --- /dev/null +++ b/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/darts.API.py @@ -0,0 +1,129 @@ +# --- +# jupyter: +# jupytext: +# formats: ipynb,py:percent +# text_representation: +# extension: .py +# format_name: percent +# format_version: '1.3' +# jupytext_version: 1.19.0 +# kernelspec: +# display_name: Python 3 (ipykernel) +# language: python +# name: python3 +# --- + +# %% [markdown] +# # Template API Notebook +# +# This is a template notebook. The first heading should be the title of what notebook is about. For example, if it is a neo4j tutorial the heading should be `Neo4j API`. +# +# - Add description of what the notebook does. +# - Point to references, e.g. (neo4j.API.md) +# - Add citations. +# - Keep the notebook flow clear. +# - Comments should be imperative and have a period at the end. +# - Your code should be well commented. +# +# The name of this notebook should in the following format: +# - if the notebook is exploring `pycaret API`, then it is `pycaret.API.ipynb` +# +# Follow the reference to write notebooks in a clear manner: https://github.com/causify-ai/helpers/blob/master/docs/coding/all.jupyter_notebook.how_to_guide.md + +# %% +# %load_ext autoreload +# %autoreload 2 +# %matplotlib inline + +# %% [markdown] +# ## Imports + +# %% +import logging +# Import libraries in this section. +# Avoid imports like import *, from ... import ..., from ... import *, etc. + +import helpers.hdbg as hdbg +import helpers.hnotebook as hnotebo + +# %% [markdown] +# ## Configuration + +# %% +hdbg.init_logger(verbosity=logging.INFO) + +_LOG = logging.getLogger(__name__) + +hnotebo.config_notebook() + + +# %% [markdown] +# ## Make the notebook flow clear +# Each notebook needs to follow a clear and logical flow, e.g: +# - Load data +# - Compute stats +# - Clean data +# - Compute stats +# - Do analysis +# - Show results +# +# +# +# + + +# ############################################################################# +# Template +# ############################################################################# + + +# %% +class Template: + """ + Brief imperative description of what the class does in one line, if needed. + """ + + def __init__(self): + pass + + def method1(self, arg1: int) -> None: + """ + Brief imperative description of what the method does in one line. + + You can elaborate more in the method docstring in this section, for e.g. explaining + the formula/algorithm. Every method/function should have a docstring, typehints and include the + parameters and return as follows: + + :param arg1: description of arg1 + :return: description of return + """ + # Code bloks go here. + # Make sure to include comments to explain what the code is doing. + # No empty lines between code blocks. + pass + + +def template_function(arg1: int) -> None: + """ + Brief imperative description of what the function does in one line. + + You can elaborate more in the function docstring in this section, for e.g. explaining + the formula/algorithm. Every function should have a docstring, typehints and include the + parameters and return as follows: + + :param arg1: description of arg1 + :return: description of return + """ + # Code bloks go here. + # Make sure to include comments to explain what the code is doing. + # No empty lines between code blocks. + pass + + +# %% [markdown] +# ## The flow should be highlighted using headings in markdown +# ``` +# # Level 1 +# ## Level 2 +# ### Level 3 +# ``` diff --git a/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/darts.example.ipynb b/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/darts.example.ipynb new file mode 100644 index 000000000..dfa43d07f --- /dev/null +++ b/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/darts.example.ipynb @@ -0,0 +1,7344 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "b2f8e698-38fd-41c1-b2db-58be8cdd4179", + "metadata": {}, + "source": [ + "# Description" + ] + }, + { + "cell_type": "markdown", + "id": "c1a0a5cb-1ef2-4141-84ad-f7cd19066b43", + "metadata": {}, + "source": [ + "This notebook implements a full time series forecasting pipeline using the\n", + "Darts library to predict S&P 500 index prices and identify which market\n", + "sectors will outperform in the near future.\n", + "\n", + "The notebook covers data collection for S&P 500, 11 sector ETFs, and 22\n", + "macroeconomic dimensions including yield curve, VIX, CPI, Fed rate, oil,\n", + "gold, and dollar index. It performs data preprocessing with release-date-aware\n", + "forward fill, feature engineering including technical indicators, calendar\n", + "features, and event flags for FOMC meetings, CPI release dates, and US\n", + "holidays. It trains the full Darts model suite across baseline, statistical,\n", + "probabilistic, and machine learning model groups, applies SHAP based feature\n", + "selection, performs hyperparameter tuning, builds ensemble models, and\n", + "benchmarks results against Facebook Prophet and Statsmodels. The sector\n", + "rotation engine recommends which sectors to rotate into based entirely on\n", + "model predictions, historical correlations, and risk adjusted scores.\n", + "\n", + "References:\n", + "- Darts documentation: https://unit8co.github.io/darts/\n", + "- FRED API documentation: https://fred.stlouisfed.org/docs/api/fred/\n", + "- yfinance documentation: https://pypi.org/project/yfinance/\n", + "- Prophet documentation: https://facebook.github.io/prophet/" + ] + }, + { + "cell_type": "markdown", + "id": "62cccba5-e59e-4a9f-8e9a-26208afd00ec", + "metadata": {}, + "source": [ + "# Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "9bdc4ff9-90e7-4ac3-9c30-e1affbddcb14", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Support for PyTorch based likelihood models not available. To enable them, install \"darts[torch]\" or \"darts[all]\" (with pip); or \"u8darts-torch\" or \"u8darts-all\" (with conda).\n", + "Support for Torch based models not available. To enable them, install \"darts[torch]\" or \"darts[all]\" (with pip); or \"u8darts-torch\" or \"u8darts-all\" (with conda).\n" + ] + } + ], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "%matplotlib inline\n", + "\n", + "import logging\n", + "import os\n", + "import warnings\n", + "\n", + "import darts\n", + "import darts.dataprocessing.transformers\n", + "import darts.metrics\n", + "import darts.models\n", + "import darts.timeseries\n", + "import dotenv\n", + "import fredapi\n", + "import holidays\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import optuna\n", + "import pandas as pd\n", + "import prophet\n", + "import seaborn as sns\n", + "import shap\n", + "import sklearn.preprocessing\n", + "import statsmodels.tsa.arima.model\n", + "import statsmodels.tsa.holtwinters\n", + "import tqdm\n", + "import utils\n", + "import yfinance as yf\n", + "\n", + "warnings.filterwarnings(\"ignore\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "4d1f1b75-4d94-41c5-b14c-d969d1330e6c", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-04-07 09:22:20,776 - INFO - Environment configured successfully.\n", + "2026-04-07 09:22:20,776 - INFO - Darts version: 0.43.0\n" + ] + } + ], + "source": [ + "# Configure the logger to track notebook execution.\n", + "logging.basicConfig(\n", + " level=logging.INFO,\n", + " format=\"%(asctime)s - %(levelname)s - %(message)s\",\n", + ")\n", + "_LOG = logging.getLogger(__name__)\n", + "# Log the environment setup confirmation and Darts version.\n", + "_LOG.info(\"Environment configured successfully.\")\n", + "_LOG.info(\"Darts version: %s\", darts.__version__)" + ] + }, + { + "cell_type": "markdown", + "id": "c243c428-23b3-4350-bd76-b1c4a8730d78", + "metadata": {}, + "source": [ + "# Configuration" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "76f1b533-cac9-4b23-a703-2e3795a14297", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-04-07 09:22:20,807 - INFO - Configuration loaded successfully.\n", + "2026-04-07 09:22:20,808 - INFO - Date range: 2018-01-01 to 2024-12-31\n", + "2026-04-07 09:22:20,808 - INFO - Sectors: ['Technology', 'Healthcare', 'Financials', 'Energy', 'Consumer Discretionary', 'Consumer Staples', 'Industrials', 'Utilities', 'Materials', 'Real Estate', 'Communication Services']\n" + ] + } + ], + "source": [ + "# Define the date range for all data downloads.\n", + "START_DATE = \"2018-01-01\"\n", + "END_DATE = \"2024-12-31\"\n", + "# Define the S&P 500 ticker symbol.\n", + "SP500_TICKER = \"^GSPC\"\n", + "# Define all 11 sector ETF ticker symbols.\n", + "SECTOR_TICKERS = [\n", + " \"XLK\",\n", + " \"XLV\",\n", + " \"XLF\",\n", + " \"XLE\",\n", + " \"XLY\",\n", + " \"XLP\",\n", + " \"XLI\",\n", + " \"XLU\",\n", + " \"XLB\",\n", + " \"XLRE\",\n", + " \"XLC\",\n", + "]\n", + "# Define sector names mapped to their ticker symbols.\n", + "SECTOR_NAMES = {\n", + " \"XLK\": \"Technology\",\n", + " \"XLV\": \"Healthcare\",\n", + " \"XLF\": \"Financials\",\n", + " \"XLE\": \"Energy\",\n", + " \"XLY\": \"Consumer Discretionary\",\n", + " \"XLP\": \"Consumer Staples\",\n", + " \"XLI\": \"Industrials\",\n", + " \"XLU\": \"Utilities\",\n", + " \"XLB\": \"Materials\",\n", + " \"XLRE\": \"Real Estate\",\n", + " \"XLC\": \"Communication Services\",\n", + "}\n", + "# Define daily macro indicator ticker symbols from yfinance.\n", + "DAILY_MACRO_TICKERS = {\n", + " \"VIX\": \"^VIX\",\n", + " \"TNX\": \"^TNX\",\n", + " \"IRX\": \"^IRX\",\n", + " \"OIL\": \"CL=F\",\n", + " \"GOLD\": \"GC=F\",\n", + " \"DXY\": \"DX-Y.NYB\",\n", + "}\n", + "# Define monthly macro indicator codes from FRED API.\n", + "MONTHLY_MACRO_CODES = {\n", + " \"CPI\": \"CPIAUCSL\",\n", + " \"CORE_CPI\": \"CPILFESL\",\n", + " \"FED_RATE\": \"FEDFUNDS\",\n", + " \"UNEMPLOYMENT\": \"UNRATE\",\n", + " \"NFP\": \"PAYEMS\",\n", + " \"RETAIL_SALES\": \"RSAFS\",\n", + " \"INDUSTRIAL_PROD\": \"INDPRO\",\n", + " \"PCE\": \"PCEPI\",\n", + " \"PPI\": \"PPIACO\",\n", + "}\n", + "# Define the FRED daily indicator code for breakeven inflation.\n", + "BREAKEVEN_INFLATION_CODE = \"T10YIE\"\n", + "# Define the data directory path for saving raw CSV files.\n", + "DATA_DIR = \"data\"\n", + "# Define the test set size in trading days.\n", + "TEST_SIZE = 120\n", + "# Define the validation set size in trading days.\n", + "VAL_SIZE = 240\n", + "# Define the forecast horizon in trading days.\n", + "FORECAST_HORIZON = 30\n", + "# Use closing price as prediction target.\n", + "TARGET_COL = \"Close\"\n", + "# Create the data directory if it does not exist.\n", + "os.makedirs(DATA_DIR, exist_ok=True)\n", + "_LOG.info(\"Configuration loaded successfully.\")\n", + "_LOG.info(\"Date range: %s to %s\", START_DATE, END_DATE)\n", + "_LOG.info(\"Sectors: %s\", list(SECTOR_NAMES.values()))" + ] + }, + { + "cell_type": "markdown", + "id": "b5d3eff2-f001-4366-87bf-3e9e04c9c089", + "metadata": {}, + "source": [ + "# Data collection\n", + "\n", + "This section downloads all required data from Yahoo Finance and the FRED\n", + "API and saves it as raw CSV files in the `data` directory. The data\n", + "includes S&P 500 historical prices, 11 sector ETF prices, 6 daily\n", + "macroeconomic indicators, and 10 monthly macroeconomic indicators covering\n", + "the period from 2018 to 2024." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "4a4d7f92-34db-431f-a360-a9f45105478b", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-04-07 09:22:20,828 - INFO - FRED API key loaded successfully.\n" + ] + } + ], + "source": [ + "# Load environment variables from the .env file.\n", + "dotenv.load_dotenv()\n", + "# Read the FRED API key from the environment variables.\n", + "FRED_API_KEY = os.environ.get(\"FRED_API_KEY\")\n", + "# Verify the FRED API key was loaded successfully.\n", + "if FRED_API_KEY is None:\n", + " raise ValueError(\"FRED_API_KEY not found in .env file.\")\n", + "_LOG.info(\"FRED API key loaded successfully.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "22c29808-bd0f-4c04-9719-0d1c5f51c69a", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-04-07 09:22:20,861 - INFO - Loaded 1760 rows from data/sp500_raw.csv.\n" + ] + }, + { + "data": { + "text/html": [ + "
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CloseHighLowOpenVolume
Date
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" + ], + "text/plain": [ + " XLK XLV XLF XLE XLY XLP \\\n", + "Date \n", + "2018-01-02 29.872921 72.723335 23.884142 25.747267 46.275246 45.314571 \n", + "2018-01-03 30.122097 73.419182 24.012455 26.132853 46.487698 45.298527 \n", + "2018-01-04 30.274364 73.523575 24.234877 26.290606 46.640129 45.426777 \n", + "\n", + " XLI XLU XLB XLRE XLC \n", + "Date \n", + "2018-01-02 66.254837 20.132584 25.995680 24.837584 NaN \n", + "2018-01-03 66.611710 19.974422 26.177759 24.845165 NaN \n", + "2018-01-04 67.099136 19.808550 26.406427 24.420458 NaN " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Load sector ETF data from CSV if available otherwise download fresh.\n", + "if os.path.exists(os.path.join(DATA_DIR, \"sectors_raw.csv\")):\n", + " sectors = utils.load_data(\"sectors_raw.csv\", DATA_DIR)\n", + "else:\n", + " sectors = utils.download_sectors(SECTOR_TICKERS, START_DATE, END_DATE)\n", + " utils.save_data(sectors, \"sectors_raw.csv\", DATA_DIR)\n", + "sectors.head(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "16bea255-f9f0-442e-ada5-194087ded0e3", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-04-07 09:22:20,930 - INFO - Loaded 1760 rows from data/macro_daily_raw.csv.\n" + ] + }, + { + "data": { + "text/html": [ + "
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VIXTNXIRXOILGOLDDXY
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2018-01-03NaNNaNNaNNaNNaNNaNNaNNaNNaN1.98
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" + ], + "text/plain": [ + " CPI CORE_CPI FED_RATE UNEMPLOYMENT NFP RETAIL_SALES \\\n", + "Date \n", + "2018-01-01 248.859 255.204 1.41 4.0 147660.0 481414.0 \n", + "2018-01-02 NaN NaN NaN NaN NaN NaN \n", + "2018-01-03 NaN NaN NaN NaN NaN NaN \n", + "\n", + " INDUSTRIAL_PROD PCE PPI BREAKEVEN \n", + "Date \n", + "2018-01-01 101.4625 101.199 197.9 NaN \n", + "2018-01-02 NaN NaN NaN 2.00 \n", + "2018-01-03 NaN NaN NaN 1.98 " + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Load monthly macro data from CSV if available otherwise download fresh.\n", + "if os.path.exists(os.path.join(DATA_DIR, \"macro_monthly_raw.csv\")):\n", + " macro_monthly = utils.load_data(\"macro_monthly_raw.csv\", DATA_DIR)\n", + "else:\n", + " macro_monthly = utils.download_monthly_macro(\n", + " MONTHLY_MACRO_CODES,\n", + " BREAKEVEN_INFLATION_CODE,\n", + " START_DATE,\n", + " END_DATE,\n", + " FRED_API_KEY,\n", + " )\n", + " utils.save_data(macro_monthly, \"macro_monthly_raw.csv\", DATA_DIR)\n", + "macro_monthly.head(3)" + ] + }, + { + "cell_type": "markdown", + "id": "f6dcd269-52e8-4a3f-a04b-737df6593767", + "metadata": {}, + "source": [ + "# Data preprocessing and EDA\n", + "\n", + "This section cleans and aligns all downloaded datasets, applies\n", + "release-date-aware forward fill to monthly macro indicators, adds\n", + "binary release flags to distinguish actual data release days from\n", + "forward filled values, and performs exploratory data analysis to\n", + "understand the structure and patterns in the data before modeling." + ] + }, + { + "cell_type": "markdown", + "id": "170009f3-91eb-45b8-ba9d-d9849db8956f", + "metadata": {}, + "source": [ + "## Align datasets to common date index" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "6c178b05-a7cb-4d79-bbb0-8bd0b8453329", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-04-07 09:22:20,990 - INFO - S&P 500 shape: (1760, 5)\n", + "2026-04-07 09:22:20,991 - INFO - Sectors shape: (1760, 11)\n", + "2026-04-07 09:22:20,991 - INFO - Macro daily shape: (1760, 6)\n", + "2026-04-07 09:22:20,991 - INFO - Macro monthly shape: (1760, 10)\n" + ] + } + ], + "source": [ + "# Use S&P 500 date index as the master index for all datasets.\n", + "master_index = sp500.index\n", + "# Reindex daily datasets directly — no monthly values to preserve.\n", + "sectors = sectors.reindex(master_index)\n", + "macro_daily = macro_daily.reindex(master_index)\n", + "# Preserve monthly values before reindexing to avoid losing data\n", + "# that falls on non trading days like weekends and holidays.\n", + "macro_monthly = utils.preserve_month_start_values(\n", + " macro_monthly, master_index\n", + ")\n", + "# Verify all datasets have the same shape after alignment.\n", + "_LOG.info(\"S&P 500 shape: %s\", sp500.shape)\n", + "_LOG.info(\"Sectors shape: %s\", sectors.shape)\n", + "_LOG.info(\"Macro daily shape: %s\", macro_daily.shape)\n", + "_LOG.info(\"Macro monthly shape: %s\", macro_monthly.shape)" + ] + }, + { + "cell_type": "markdown", + "id": "2d5b4c3c-a99f-44da-b7a3-d489f6f8ef85", + "metadata": {}, + "source": [ + "## Apply release-date-aware forward fill to monthly indicators" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "9bca7193-fc22-4069-8061-0931c7aac513", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-04-07 09:22:21,010 - INFO - Applying release-date-aware forward fill using vintage dates.\n", + "Forward filling indicators: 100%|█████████████████| 9/9 [00:02<00:00, 3.31it/s]\n", + "2026-04-07 09:22:23,745 - INFO - Forward fill complete for 9 indicators.\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " CPI CORE_CPI FED_RATE UNEMPLOYMENT NFP RETAIL_SALES \\\n", + "Date \n", + "2018-01-02 NaN NaN 1.41 NaN NaN NaN \n", + "2018-01-03 NaN NaN 1.41 NaN NaN NaN \n", + "2018-01-04 NaN NaN 1.41 NaN NaN NaN \n", + "2018-01-05 NaN NaN 1.41 4.0 147660.0 NaN \n", + "2018-01-08 NaN NaN 1.41 4.0 147660.0 NaN \n", + "2018-01-09 NaN NaN 1.41 4.0 147660.0 NaN \n", + "2018-01-10 NaN NaN 1.41 4.0 147660.0 NaN \n", + "2018-01-11 NaN NaN 1.41 4.0 147660.0 NaN \n", + "2018-01-12 248.859 255.204 1.41 4.0 147660.0 481414.0 \n", + "2018-01-16 248.859 255.204 1.41 4.0 147660.0 481414.0 \n", + "2018-01-17 248.859 255.204 1.41 4.0 147660.0 481414.0 \n", + "2018-01-18 248.859 255.204 1.41 4.0 147660.0 481414.0 \n", + "2018-01-19 248.859 255.204 1.41 4.0 147660.0 481414.0 \n", + "2018-01-22 248.859 255.204 1.41 4.0 147660.0 481414.0 \n", + "2018-01-23 248.859 255.204 1.41 4.0 147660.0 481414.0 \n", + "2018-01-24 248.859 255.204 1.41 4.0 147660.0 481414.0 \n", + "2018-01-25 248.859 255.204 1.41 4.0 147660.0 481414.0 \n", + "2018-01-26 248.859 255.204 1.41 4.0 147660.0 481414.0 \n", + "2018-01-29 248.859 255.204 1.41 4.0 147660.0 481414.0 \n", + "2018-01-30 248.859 255.204 1.41 4.0 147660.0 481414.0 \n", + "\n", + " INDUSTRIAL_PROD PCE PPI BREAKEVEN CPI_released \\\n", + "Date \n", + "2018-01-02 NaN NaN NaN 2.00 0 \n", + "2018-01-03 NaN NaN NaN 1.98 0 \n", + "2018-01-04 NaN NaN NaN 2.01 0 \n", + "2018-01-05 NaN NaN NaN 2.01 0 \n", + "2018-01-08 NaN NaN NaN 2.02 0 \n", + "2018-01-09 NaN NaN NaN 2.03 0 \n", + "2018-01-10 NaN NaN NaN 2.03 0 \n", + "2018-01-11 NaN NaN 197.9 2.00 0 \n", + "2018-01-12 NaN NaN 197.9 2.01 1 \n", + "2018-01-16 NaN NaN 197.9 2.03 0 \n", + "2018-01-17 101.4625 NaN 197.9 2.04 0 \n", + "2018-01-18 101.4625 NaN 197.9 2.05 0 \n", + "2018-01-19 101.4625 NaN 197.9 2.06 0 \n", + "2018-01-22 101.4625 NaN 197.9 2.06 0 \n", + "2018-01-23 101.4625 NaN 197.9 2.06 0 \n", + "2018-01-24 101.4625 NaN 197.9 2.07 0 \n", + "2018-01-25 101.4625 NaN 197.9 2.07 0 \n", + "2018-01-26 101.4625 NaN 197.9 2.09 0 \n", + "2018-01-29 101.4625 101.199 197.9 2.09 0 \n", + "2018-01-30 101.4625 101.199 197.9 2.10 0 \n", + "\n", + " CORE_CPI_released FED_RATE_released UNEMPLOYMENT_released \\\n", + "Date \n", + "2018-01-02 0 1 0 \n", + "2018-01-03 0 0 0 \n", + "2018-01-04 0 0 0 \n", + "2018-01-05 0 0 1 \n", + "2018-01-08 0 0 0 \n", + "2018-01-09 0 0 0 \n", + "2018-01-10 0 0 0 \n", + "2018-01-11 0 0 0 \n", + "2018-01-12 1 0 0 \n", + "2018-01-16 0 0 0 \n", + "2018-01-17 0 0 0 \n", + "2018-01-18 0 0 0 \n", + "2018-01-19 0 0 0 \n", + "2018-01-22 0 0 0 \n", + "2018-01-23 0 0 0 \n", + "2018-01-24 0 0 0 \n", + "2018-01-25 0 0 0 \n", + "2018-01-26 0 0 0 \n", + "2018-01-29 0 0 0 \n", + "2018-01-30 0 0 0 \n", + "\n", + " NFP_released RETAIL_SALES_released INDUSTRIAL_PROD_released \\\n", + "Date \n", + "2018-01-02 0 0 0 \n", + "2018-01-03 0 0 0 \n", + "2018-01-04 0 0 0 \n", + "2018-01-05 1 0 0 \n", + "2018-01-08 0 0 0 \n", + "2018-01-09 0 0 0 \n", + "2018-01-10 0 0 0 \n", + "2018-01-11 0 0 0 \n", + "2018-01-12 0 1 0 \n", + "2018-01-16 0 0 0 \n", + "2018-01-17 0 0 1 \n", + "2018-01-18 0 0 0 \n", + "2018-01-19 0 0 0 \n", + "2018-01-22 0 0 0 \n", + "2018-01-23 0 0 0 \n", + "2018-01-24 0 0 0 \n", + "2018-01-25 0 0 0 \n", + "2018-01-26 0 0 0 \n", + "2018-01-29 0 0 0 \n", + "2018-01-30 0 0 0 \n", + "\n", + " PCE_released PPI_released \n", + "Date \n", + "2018-01-02 0 0 \n", + "2018-01-03 0 0 \n", + "2018-01-04 0 0 \n", + "2018-01-05 0 0 \n", + "2018-01-08 0 0 \n", + "2018-01-09 0 0 \n", + "2018-01-10 0 0 \n", + "2018-01-11 0 1 \n", + "2018-01-12 0 0 \n", + "2018-01-16 0 0 \n", + "2018-01-17 0 0 \n", + "2018-01-18 0 0 \n", + "2018-01-19 0 0 \n", + "2018-01-22 0 0 \n", + "2018-01-23 0 0 \n", + "2018-01-24 0 0 \n", + "2018-01-25 0 0 \n", + "2018-01-26 0 0 \n", + "2018-01-29 1 0 \n", + "2018-01-30 0 0 " + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Apply release-date-aware forward fill to monthly macro indicators.\n", + "macro_monthly = utils.apply_release_aware_forward_fill(\n", + " macro_monthly,\n", + " FRED_API_KEY,\n", + " MONTHLY_MACRO_CODES,\n", + ")\n", + "# Preview the result to verify forward fill worked correctly.\n", + "macro_monthly.head(20)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "ef48a2c3-79a0-4d55-82d2-9eb2dde5d3cf", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-04-07 09:22:23,772 - INFO - First valid date per indicator:\n", + "CPI 2018-01-12\n", + "CORE_CPI 2018-01-12\n", + "FED_RATE 2018-01-02\n", + "UNEMPLOYMENT 2018-01-05\n", + "NFP 2018-01-05\n", + "RETAIL_SALES 2018-01-12\n", + "INDUSTRIAL_PROD 2018-01-17\n", + "PCE 2018-01-29\n", + "PPI 2018-01-11\n", + "BREAKEVEN 2018-01-02\n", + "CPI_released 2018-01-02\n", + "CORE_CPI_released 2018-01-02\n", + "FED_RATE_released 2018-01-02\n", + "UNEMPLOYMENT_released 2018-01-02\n", + "NFP_released 2018-01-02\n", + "RETAIL_SALES_released 2018-01-02\n", + "INDUSTRIAL_PROD_released 2018-01-02\n", + "PCE_released 2018-01-02\n", + "PPI_released 2018-01-02\n" + ] + } + ], + "source": [ + "# Check when the first non-NaN values appear for each indicator.\n", + "first_valid = macro_monthly.apply(lambda col: col.first_valid_index())\n", + "_LOG.info(\"First valid date per indicator:\\n%s\", first_valid.to_string())" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "276a28cf-f65c-48e1-8a01-8b1d0f3bd60f", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-04-07 09:22:23,790 - INFO - Macro monthly shape: (1760, 19)\n", + "2026-04-07 09:22:23,792 - INFO - Total NaN values remaining: 65\n", + "2026-04-07 09:22:23,792 - INFO - Release flag columns: ['CPI_released', 'CORE_CPI_released', 'FED_RATE_released', 'UNEMPLOYMENT_released', 'NFP_released', 'RETAIL_SALES_released', 'INDUSTRIAL_PROD_released', 'PCE_released', 'PPI_released']\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " CPI CPI_released\n", + "Date \n", + "2018-01-02 NaN 0\n", + "2018-01-03 NaN 0\n", + "2018-01-04 NaN 0\n", + "2018-01-05 NaN 0\n", + "2018-01-08 NaN 0\n", + "2018-01-09 NaN 0\n", + "2018-01-10 NaN 0\n", + "2018-01-11 NaN 0\n", + "2018-01-12 248.859 1\n", + "2018-01-16 248.859 0" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Verify the forward fill and release flags worked correctly.\n", + "_LOG.info(\"Macro monthly shape: %s\", macro_monthly.shape)\n", + "_LOG.info(\"Total NaN values remaining: %d\", macro_monthly.isnull().sum().sum())\n", + "_LOG.info(\"Release flag columns: %s\", \n", + " [col for col in macro_monthly.columns if col.endswith(\"_released\")]\n", + ")\n", + "# Show a sample around a known release date to verify flags.\n", + "macro_monthly.loc[\"2018-01-01\":\"2018-02-05\", [\"CPI\", \"CPI_released\"]].head(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "90b211df-6021-4806-bc49-975d870d9f88", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-04-07 09:22:23,818 - INFO - Number of actual CPI release days: 90\n", + "2026-04-07 09:22:23,819 - INFO - First few CPI release dates:\n", + "Date\n", + "2018-01-12 248.859\n", + "2018-02-14 249.529\n", + "2018-03-13 249.577\n", + "2018-04-11 250.227\n", + "2018-05-10 250.792\n" + ] + } + ], + "source": [ + "# Verify CPI release flag shows 1 on actual release days.\n", + "cpi_releases = macro_monthly[macro_monthly[\"CPI_released\"] == 1][\"CPI\"]\n", + "_LOG.info(\n", + " \"Number of actual CPI release days: %d\", len(cpi_releases)\n", + ")\n", + "_LOG.info(\n", + " \"First few CPI release dates:\\n%s\", cpi_releases.head(5).to_string()\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "ee503030-9bd9-42bd-b3d8-0dadf52adaf9", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-04-07 09:22:23,840 - INFO - fredapi version: 0.5.2\n", + "2026-04-07 09:22:23,841 - INFO - Available methods: ['api_key', 'earliest_realtime_start', 'get_series', 'get_series_all_releases', 'get_series_as_of_date', 'get_series_first_release', 'get_series_info', 'get_series_latest_release', 'get_series_vintage_dates', 'latest_realtime_end', 'max_results_per_request', 'nan_char', 'proxies', 'root_url', 'search', 'search_by_category', 'search_by_release']\n" + ] + } + ], + "source": [ + "# Check fredapi version and available methods.\n", + "import fredapi\n", + "_LOG.info(\"fredapi version: %s\", fredapi.__version__)\n", + "fred_temp = fredapi.Fred(api_key=FRED_API_KEY)\n", + "fred_methods = [m for m in dir(fred_temp) if not m.startswith(\"_\")]\n", + "_LOG.info(\"Available methods: %s\", fred_methods)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "894d69af-b0b3-4c0d-b4c0-5e6aed809434", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-04-07 09:22:23,869 - INFO - Number of actual CPI release days: 90\n", + "2026-04-07 09:22:23,870 - INFO - First few CPI release dates:\n", + "Date\n", + "2018-01-12 248.859\n", + "2018-02-14 249.529\n", + "2018-03-13 249.577\n", + "2018-04-11 250.227\n", + "2018-05-10 250.792\n", + "2026-04-07 09:22:23,871 - INFO - Total NaN values remaining: 65\n" + ] + } + ], + "source": [ + "# Verify CPI release flag shows 1 on true release days.\n", + "cpi_releases = macro_monthly[macro_monthly[\"CPI_released\"] == 1][\"CPI\"]\n", + "_LOG.info(\"Number of actual CPI release days: %d\", len(cpi_releases))\n", + "_LOG.info(\n", + " \"First few CPI release dates:\\n%s\", cpi_releases.head(5).to_string()\n", + ")\n", + "# Verify total NaN values remaining.\n", + "_LOG.info(\n", + " \"Total NaN values remaining: %d\",\n", + " macro_monthly.isnull().sum().sum()\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "fedc0f57-b7de-4890-82f4-73fa9e8537f7", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-04-07 09:22:23,891 - INFO - Columns with remaining NaN values:\n", + "CPI 8\n", + "CORE_CPI 8\n", + "UNEMPLOYMENT 3\n", + "NFP 3\n", + "RETAIL_SALES 8\n", + "INDUSTRIAL_PROD 10\n", + "PCE 18\n", + "PPI 7\n" + ] + } + ], + "source": [ + "# Identify which columns still have NaN values and how many.\n", + "nan_counts = macro_monthly.isnull().sum()\n", + "nan_counts = nan_counts[nan_counts > 0]\n", + "_LOG.info(\"Columns with remaining NaN values:\\n%s\", nan_counts.to_string())" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "49995b00-3b9c-4086-98eb-ca26659b5d32", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-04-07 09:22:23,910 - INFO - First date with complete macro data: 2018-01-29 00:00:00\n", + "2026-04-07 09:22:23,911 - INFO - Rows dropped: 18 (1.02% of total)\n" + ] + } + ], + "source": [ + "# Find the first date where all monthly macro indicators have values.\n", + "first_complete_date = macro_monthly.dropna().index[0]\n", + "_LOG.info(\n", + " \"First date with complete macro data: %s\", first_complete_date\n", + ")\n", + "# Count how many rows we lose by starting from this date.\n", + "rows_before = len(macro_monthly)\n", + "rows_after = len(macro_monthly.loc[first_complete_date:])\n", + "rows_dropped = rows_before - rows_after\n", + "_LOG.info(\n", + " \"Rows dropped: %d (%.2f%% of total)\",\n", + " rows_dropped,\n", + " rows_dropped / rows_before * 100,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "f879f645-f354-4830-97cd-94c0f74e3494", + "metadata": {}, + "source": [ + "## Trim datasets to first complete macro data date" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "97cb2140-9d92-428c-b2e7-fc29a1cc123b", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-04-07 09:22:23,929 - INFO - Rows dropped: 18 (1.02% of total)\n", + "2026-04-07 09:22:23,930 - INFO - S&P 500 shape after trim: (1742, 5)\n", + "2026-04-07 09:22:23,930 - INFO - Sectors shape after trim: (1742, 11)\n", + "2026-04-07 09:22:23,930 - INFO - Macro daily shape after trim: (1742, 6)\n", + "2026-04-07 09:22:23,930 - INFO - Macro monthly shape after trim: (1742, 19)\n", + "2026-04-07 09:22:23,932 - INFO - NaN values remaining in macro monthly: 0\n" + ] + } + ], + "source": [ + "# Trim all datasets to start from the first date where all macro\n", + "# indicators have complete values to eliminate initial NaN period.\n", + "sp500 = sp500.loc[first_complete_date:]\n", + "sectors = sectors.loc[first_complete_date:]\n", + "macro_daily = macro_daily.loc[first_complete_date:]\n", + "macro_monthly = macro_monthly.loc[first_complete_date:]\n", + "# Show percentage of rows dropped across all datasets.\n", + "_LOG.info(\n", + " \"Rows dropped: %d (%.2f%% of total)\",\n", + " rows_dropped,\n", + " rows_dropped / rows_before * 100,\n", + ")\n", + "# Verify all datasets have the same shape after trimming.\n", + "_LOG.info(\"S&P 500 shape after trim: %s\", sp500.shape)\n", + "_LOG.info(\"Sectors shape after trim: %s\", sectors.shape)\n", + "_LOG.info(\"Macro daily shape after trim: %s\", macro_daily.shape)\n", + "_LOG.info(\"Macro monthly shape after trim: %s\", macro_monthly.shape)\n", + "# Verify no NaN values remain in monthly macro data.\n", + "_LOG.info(\n", + " \"NaN values remaining in macro monthly: %d\",\n", + " macro_monthly.isnull().sum().sum()\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "37155cf0-16ae-4faf-826a-190310b7f37f", + "metadata": {}, + "source": [ + "## Reconstruct XLC pre-launch history\n", + "\n", + "XLC (Communication Services ETF) launched on June 19 2018. To preserve\n", + "the full analysis period from January 2018, pre-launch XLC values are\n", + "reconstructed using historical prices of the top 12 XLC constituent\n", + "companies weighted by market capitalization at each point in time.\n", + "\n", + "The reconstruction is validated against actual XLC prices for the\n", + "overlap period from June 2018 onwards. If the correlation exceeds\n", + "0.90 the reconstruction replaces NaN values in the sectors DataFrame.\n", + "If correlation falls below 0.90 the dataset is trimmed to June 2018\n", + "as a fallback — ensuring we never use an unreliable reconstruction.\n", + "\n", + "References:\n", + "- XLC constituent data: https://www.ssga.com/us/en/intermediary/etfs/funds/the-communication-services-select-sector-spdr-fund-xlc" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "c7a9e0fa-9346-441b-9c70-5f045e82f7df", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-04-07 09:22:23,951 - INFO - Downloading XLC constituent stock prices.\n", + "Downloading XLC constituents: 0%| | 0/16 [00:00\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
XLC
Date
2018-06-0544.523975
2018-06-0644.625142
2018-06-0744.287412
2018-06-0844.416398
2018-06-1144.915994
2018-06-1245.284426
2018-06-1345.258194
2018-06-1446.302532
2018-06-1546.281125
2018-06-1846.519409
\n", + "" + ], + "text/plain": [ + " XLC\n", + "Date \n", + "2018-06-05 44.523975\n", + "2018-06-06 44.625142\n", + "2018-06-07 44.287412\n", + "2018-06-08 44.416398\n", + "2018-06-11 44.915994\n", + "2018-06-12 45.284426\n", + "2018-06-13 45.258194\n", + "2018-06-14 46.302532\n", + "2018-06-15 46.281125\n", + "2018-06-18 46.519409" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Define XLC launch date as the first valid date in the sectors data.\n", + "xlc_first_date = pd.Timestamp(\"2018-06-19\")\n", + "# Reconstruct XLC pre-launch history using verified constituent weights\n", + "# from the official SPDR ETF filing on June 19 2018.\n", + "sectors = utils.reconstruct_xlc(\n", + " sectors,\n", + " START_DATE,\n", + " str(xlc_first_date.date()),\n", + " correlation_threshold=0.90,\n", + ")\n", + "# Verify XLC NaN values are resolved after reconstruction.\n", + "xlc_nan_remaining = sectors[\"XLC\"].isnull().sum()\n", + "_LOG.info(\n", + " \"XLC NaN values remaining after reconstruction: %d\",\n", + " xlc_nan_remaining,\n", + ")\n", + "# Show percentage of rows that were reconstructed.\n", + "_LOG.info(\n", + " \"XLC rows reconstructed: %d (%.2f%% of total)\",\n", + " 98 - xlc_nan_remaining,\n", + " (98 - xlc_nan_remaining) / len(sectors) * 100,\n", + ")\n", + "# Preview XLC values around the reconstruction period.\n", + "sectors[[\"XLC\"]].loc[\n", + " sectors.index[sectors.index < pd.Timestamp(xlc_first_date)]\n", + "].tail(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "fca19632-6685-4491-91b0-ca704df6477e", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-04-07 09:22:27,858 - INFO - Total NaN values in sectors: 0\n", + "2026-04-07 09:22:27,859 - INFO - Sectors date range: 2018-01-29 to 2024-12-30\n" + ] + }, + { + "data": { + "text/html": [ + "
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XLKXLVXLFXLEXLYXLPXLIXLUXLBXLREXLC
Date
2018-01-2931.63557279.40335125.70624926.76032650.20578046.86939669.73643519.43822526.82988424.10952246.636041
2018-01-3031.34948277.73335325.37262026.22049949.95638346.55683169.18809519.47294426.63508624.01092546.672748
2018-01-3131.58483376.62870825.44105726.23801849.79472046.34042769.39698819.68896926.65626324.50388946.854583
2018-02-0131.61250776.65481625.68058226.50794049.24047546.14006869.27510819.38036726.29209324.05642947.396780
2018-02-0230.67581275.57625625.11598825.40023048.76936045.25846567.88248419.24149125.60187323.78340146.879372
2018-02-0529.39303672.22757023.84992824.33457247.12508043.61547164.80125418.92516524.62369523.16909644.847898
2018-02-0630.23745373.02774824.28620024.52737848.33057444.09634865.98503118.64356625.42824923.12359645.537886
2018-02-0729.84985972.94079624.30330824.11724748.18277443.90398866.10685718.55483825.19958723.01742044.635573
2018-02-0828.61321470.40968323.23399923.38812146.26137542.95827163.55660618.34653124.31457122.29694742.559564
2018-02-0929.31459471.40995023.67883123.37059246.57084743.04643264.24424018.73228824.73802622.82024243.490041
\n", + "
" + ], + "text/plain": [ + " XLK XLV XLF XLE XLY XLP \\\n", + "Date \n", + "2018-01-29 31.635572 79.403351 25.706249 26.760326 50.205780 46.869396 \n", + "2018-01-30 31.349482 77.733353 25.372620 26.220499 49.956383 46.556831 \n", + "2018-01-31 31.584833 76.628708 25.441057 26.238018 49.794720 46.340427 \n", + "2018-02-01 31.612507 76.654816 25.680582 26.507940 49.240475 46.140068 \n", + "2018-02-02 30.675812 75.576256 25.115988 25.400230 48.769360 45.258465 \n", + "2018-02-05 29.393036 72.227570 23.849928 24.334572 47.125080 43.615471 \n", + "2018-02-06 30.237453 73.027748 24.286200 24.527378 48.330574 44.096348 \n", + "2018-02-07 29.849859 72.940796 24.303308 24.117247 48.182774 43.903988 \n", + "2018-02-08 28.613214 70.409683 23.233999 23.388121 46.261375 42.958271 \n", + "2018-02-09 29.314594 71.409950 23.678831 23.370592 46.570847 43.046432 \n", + "\n", + " XLI XLU XLB XLRE XLC \n", + "Date \n", + "2018-01-29 69.736435 19.438225 26.829884 24.109522 46.636041 \n", + "2018-01-30 69.188095 19.472944 26.635086 24.010925 46.672748 \n", + "2018-01-31 69.396988 19.688969 26.656263 24.503889 46.854583 \n", + "2018-02-01 69.275108 19.380367 26.292093 24.056429 47.396780 \n", + "2018-02-02 67.882484 19.241491 25.601873 23.783401 46.879372 \n", + "2018-02-05 64.801254 18.925165 24.623695 23.169096 44.847898 \n", + "2018-02-06 65.985031 18.643566 25.428249 23.123596 45.537886 \n", + "2018-02-07 66.106857 18.554838 25.199587 23.017420 44.635573 \n", + "2018-02-08 63.556606 18.346531 24.314571 22.296947 42.559564 \n", + "2018-02-09 64.244240 18.732288 24.738026 22.820242 43.490041 " + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Verify the full sectors DataFrame has zero NaN values.\n", + "_LOG.info(\n", + " \"Total NaN values in sectors: %d\",\n", + " sectors.isnull().sum().sum()\n", + ")\n", + "# Verify all sectors start from the same date.\n", + "_LOG.info(\"Sectors date range: %s to %s\",\n", + " sectors.index[0].date(),\n", + " sectors.index[-1].date()\n", + ")\n", + "# Preview sectors DataFrame.\n", + "sectors.head(10)" + ] + }, + { + "cell_type": "markdown", + "id": "e4e56fad-9fc4-4677-bce1-e90df839e04f", + "metadata": {}, + "source": [ + "## Check and handle missing values in daily macro indicators" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "87e90cff-8571-4132-808b-955fa87c9209", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-04-07 09:22:27,882 - INFO - Checking missing values across all datasets.\n", + "2026-04-07 09:22:27,884 - INFO - S&P 500 missing values:\n", + "Series([], )\n", + "2026-04-07 09:22:27,884 - INFO - Sectors missing values:\n", + "Series([], )\n", + "2026-04-07 09:22:27,885 - INFO - Macro daily missing values:\n", + "GOLD 1\n", + "2026-04-07 09:22:27,885 - INFO - Macro monthly missing values:\n", + "Series([], )\n", + "2026-04-07 09:22:27,886 - INFO - Total missing values across all datasets: 1\n" + ] + } + ], + "source": [ + "# Check missing values across all datasets before proceeding.\n", + "_LOG.info(\"Checking missing values across all datasets.\")\n", + "# Count NaN values per column for each dataset.\n", + "sp500_nan = sp500.isnull().sum()\n", + "sectors_nan = sectors.isnull().sum()\n", + "macro_daily_nan = macro_daily.isnull().sum()\n", + "macro_monthly_nan = macro_monthly.isnull().sum()\n", + "# Log results for each dataset.\n", + "_LOG.info(\n", + " \"S&P 500 missing values:\\n%s\", sp500_nan[sp500_nan > 0].to_string()\n", + ")\n", + "_LOG.info(\n", + " \"Sectors missing values:\\n%s\",\n", + " sectors_nan[sectors_nan > 0].to_string()\n", + ")\n", + "_LOG.info(\n", + " \"Macro daily missing values:\\n%s\",\n", + " macro_daily_nan[macro_daily_nan > 0].to_string()\n", + ")\n", + "_LOG.info(\n", + " \"Macro monthly missing values:\\n%s\",\n", + " macro_monthly_nan[macro_monthly_nan > 0].to_string()\n", + ")\n", + "# Log total missing values across all datasets.\n", + "total_nan = (\n", + " sp500_nan.sum()\n", + " + sectors_nan.sum()\n", + " + macro_daily_nan.sum()\n", + " + macro_monthly_nan.sum()\n", + ")\n", + "_LOG.info(\"Total missing values across all datasets: %d\", total_nan)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "f41f7ded-badf-49cf-92ee-38347304a3cf", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-04-07 09:22:27,906 - INFO - Missing values after forward fill: 1\n" + ] + } + ], + "source": [ + "# Forward fill any remaining missing values in daily macro indicators.\n", + "# Single missing values in daily data are caused by occasional gaps\n", + "# in yfinance data — forward fill is the correct approach here.\n", + "macro_daily = macro_daily.ffill()\n", + "# Verify no missing values remain after forward fill.\n", + "_LOG.info(\n", + " \"Missing values after forward fill: %d\",\n", + " macro_daily.isnull().sum().sum(),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "3cd2a6f4-eaa5-4617-b495-9ff0c07e2158", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-04-07 09:22:27,927 - INFO - Missing GOLD value location:\n", + " GOLD\n", + "Date \n", + "2018-01-29 NaN\n" + ] + } + ], + "source": [ + "# Find the exact location of the remaining missing value.\n", + "missing_location = macro_daily[macro_daily[\"GOLD\"].isnull()]\n", + "_LOG.info(\n", + " \"Missing GOLD value location:\\n%s\",\n", + " missing_location[[\"GOLD\"]].to_string()\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "396b5089-68ff-498b-817f-ecd66f7f8b07", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-04-07 09:22:27,952 - INFO - Missing values after forward and backward fill: 0\n" + ] + } + ], + "source": [ + "# Forward fill any remaining missing values in daily macro indicators.\n", + "# Single missing values in daily data are caused by occasional gaps\n", + "# in yfinance data — forward fill is the correct approach here.\n", + "macro_daily = macro_daily.ffill()\n", + "# Backward fill any remaining missing values at the start of the\n", + "# dataset where forward fill cannot work due to no prior values.\n", + "macro_daily = macro_daily.bfill()\n", + "# Verify no missing values remain after both fill operations.\n", + "_LOG.info(\n", + " \"Missing values after forward and backward fill: %d\",\n", + " macro_daily.isnull().sum().sum(),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "3542129e-e2fa-4a4b-bcb5-19c1b77223c5", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-04-07 09:22:27,979 - INFO - Saved 1742 rows to data/sp500_processed.csv.\n", + "2026-04-07 09:22:27,992 - INFO - Saved 1742 rows to data/sectors_processed.csv.\n", + "2026-04-07 09:22:27,999 - INFO - Saved 1742 rows to data/macro_daily_processed.csv.\n", + "2026-04-07 09:22:28,007 - INFO - Saved 1742 rows to data/macro_monthly_processed.csv.\n", + "2026-04-07 09:22:28,007 - INFO - All processed datasets saved successfully.\n" + ] + } + ], + "source": [ + "# Save all processed datasets to CSV so preprocessing never needs\n", + "# to be rerun on every kernel restart.\n", + "utils.save_data(sp500, \"sp500_processed.csv\", DATA_DIR)\n", + "utils.save_data(sectors, \"sectors_processed.csv\", DATA_DIR)\n", + "utils.save_data(macro_daily, \"macro_daily_processed.csv\", DATA_DIR)\n", + "utils.save_data(macro_monthly, \"macro_monthly_processed.csv\", DATA_DIR)\n", + "_LOG.info(\"All processed datasets saved successfully.\")" + ] + }, + { + "cell_type": "markdown", + "id": "e1c73720-d55c-4039-948c-ffa8ba0545c6", + "metadata": {}, + "source": [ + "# Exploratory data analysis\n", + "\n", + "This section visualizes the structure and patterns in our cleaned\n", + "datasets before modeling. The analysis covers S&P 500 price history\n", + "and returns, sector performance comparison, macroeconomic indicator\n", + "trends, and correlations between macro signals and market returns.\n", + "These insights directly inform our feature engineering and model\n", + "selection decisions." + ] + }, + { + "cell_type": "markdown", + "id": "105be147-5241-4d94-b38e-862786d418a7", + "metadata": {}, + "source": [ + "## S&P 500 price history and daily returns" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "c971c593-a789-44e1-8154-18def6b5c868", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot S&P 500 price history and daily returns.\n", + "fig = utils.plot_sp500_history(sp500)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "23286de6-4517-4a18-a57b-27c89ff12a66", + "metadata": {}, + "source": [ + "## Sector performance comparison" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "618fd8e2-b6e3-44d0-94ad-5578d5cba370", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot normalized cumulative performance of all 11 sector ETFs.\n", + "fig = utils.plot_sector_performance(sectors, SECTOR_NAMES)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "e3646546-3393-444d-b442-545a1105e880", + "metadata": {}, + "source": [ + "## Macroeconomic indicator trends" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "30ee0226-1227-431e-8bde-e69b72d8be7f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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faw+Ky3ILy2nFzqO0PVt/VV5QgB9VnqoW5w/mFdON7y8xXdcpNYbapURjkwM0cRhXAAD4jqpqw4LVQH/XrHM+XV1D+SVVlBAZ5JL7BwBQIWPD2bhBX1VVw098P3ZwY2/uWzFjxgzTZVyuacuWLXTLLbfQJ598Is6z7Oxsev755+nee++1WF11/fXX05gxYyyCCLZw8OF///uf+D9a3Nvj448/Fuc5WMEBDC1+rklJSSKowUGM48ePi8sPHDhAycnJdNNNN4mG57LHBgAAANRu9e4cUVbqxZ/X04Wv/CWCGm2TosR1L/y8gT6ev8MiqJEcEyp+JkaH0qe3jaDfHhlPcx8/hx44r6fVfa/fn0dVpw1BDwDw0nFFLWMKhnEFAIDvOHXaENgI8G/mkvv/fMURuuKDrbQ7ByVOAcD1kLHhLH5+REFBPLNP5KzsAr4/vl8dXKLpl19+Ef0tnn76aQoJCRGNw6dPny4CCBzw4ObbHGTgjAe+jdp34+KLLxY9LDhDwhZu8M1loPg+uAE5l63q2rWr1e3efPNNEfB46623KDAwkL799lur21x++eU0ceJEUYpq2LBh4v4YNxl/+eWXxf+LiYkRPTfKy8vrucEAAACaNu6F8euaAzS0UzKdKK6g+ZstS06FBwfQfy7pR1dOX2C6bEzPljS8czJ1TYujiJBAEazgoWyAslJvdI+WItiRX1RBfdo0p4tenUcf/b2Dvly8m0b1aEET+7aiVbty6MiJUurYIobO7tmSggL8yZMcOl5CKXFh5NfMNQN18G5LliwRx5zcA+7IkSP0888/0+TJk8V1VVVV9Nhjj9Hs2bNp3759FB0dLSb7X3jhBVNZVZafn0933HEH/fbbb+Tn50cXXHCBOA6OiIjw7HGFnTEFw7gCAMD3AhuBLgpsLNpxQvycvSmP7hidhuMyAHCpZjW2GieAVcYBD3JOnjxJUVFRYvI9MzOTMjIyRFBB4IFHtRNXNvIAxN+zJg28ne77Bp6JVxlmZnLjFqLAQPc/dkUFUUaG+x8bQKu6iqg4k8gvmMgPn8em4vW5B6l9chhN7Nnc9vteXUHHa1Jp1ppsSowJpffnbrOZQTG6Rwua1K81tU6IoOBAf/po/g7y92tGFw9pK4IZdXXtO4voUL51BqYaLOESBsXlVRQa5E/n9c+giJAACg8JJI4rlFWcppjwIIvgiS38mvi5zlyxVwRtzumTTlOHt6fD+SW0YmeOKJfFQZeB7RPFBKy0dNsROnyilPYfK6T9ucW0L6dQXJ4QFUI9WsWL3/l8bEQwVZ2qpmOF5VRQUkFJ0aHistKKU2LbhAT506VD21F8JI4LnH287En+/PNPkYHct29fOv/88y0CG/x8L7zwQrrhhhuoZ8+edOLECbrrrrtEKdQ1a9aY7mP8+PEiKMJZzBwMueaaa0S/uK+//rpB28jl4wqMKZwOYwovgjEFgFl1FX05fw19sSqf0uKC6aNrDOXKnen/ftxDa/YXifP3j0unMV3j8Q40JuOYgiIyMJaEJjmmQMaGM3EQAoEIAAAAsKO6pobmbDkuTjYDG8bbvTl7K63ek2e6rENqNHVuEUuZxwopKSaMzu3Xijqkxlj93xtGd27Qe8ClqbKPl9Cxk2X0+eJdpstbJ0TS/twimrcx2+L2czcYfg8J9KfyqtMiUME1lvn2j17Qm8KDA6l5lDlwMGf9QfptzQHR34N7e3CWBb9e9tmiXTR73UHRH0Q1qnsLumRoWzpRUiF+f+bHdbrPnf+fzGTJPGYYWLPgAD+KiQimtfvM21PauP+4eA4Vp06LlYz8/NskRtLdE3tQVBhqRDcFHJTgkx4eOM2bN8/isrfffpsGDBhABw8eFJnG27dvpzlz5ogedf369RO34WzlCRMm0CuvvGKR2aGqqKgQJ3Wg5hCMKwAAwAWqXFyKKjzYvDj3wHFU4wAA10JgAwAAAMCNZLNulplbRhkJhr4XWm8tOEar95gnQe86pztN6GMo5ehqnVvGihO7dFg7mvXvfmoZF04D2ifSS79sMAUOOFujrNJcKoeDGoyDGoyDILIhOWd4cHZGr9bxtGG/odeWJIMa15zVkT5duFMEJ7qmxdLkARnEycWv/75JPKa29BbjbTJtRAcRmMgtLKOi8io6fbqGkmJCKTOnSGRkcBAoLT5cZHyUVZ6in1dnimyT/u0SafHWw/Tn+iyr++WyW5GhO+jioW0pOSZMBDvAd/AKMf68cKlUtnLlSnFeBjUYl6viklSrV6+mKVOm6N4P97l76qmn3Pa8AQAAHCtF5ZqWu2WV5uPcojInlWkHALABgQ0AAAAAN1mxu4Ay88yr19YfLLIKbGzKKqKwwBr6Z7+h6eJ7Nw4XE/YD2yc1yvvEE/rnD8ww/X796E40oF0iDe6YJEpNLdxyiDISI+mN3zfTriMnTbd7buoAev+vbSIjg8kyWjKo8fSl/UUz8+PFFSI7JDosSPQCSY0LF/eX1tzct2BghyRasPkQffj3dlFCigMqt43rRiO6pVoMzLXZFS3jrXsfhAYFiFJXUvf0OEqJDRNZG22SokRmSF5ROT32zb80Z0OWOHHvkmenDjAFe6DplxniXnSXXXaZKf396NGjlJiYaHG7gIAAiouLE9fZ8sgjj9C9995rkbGRlpbmwmcPAABgW6kx8BAS4PzAxtJdJ+ifzEKKCw+ggtJTVFh+Cm8FALgUAhsAAAAAbvLUr5kWv8/flk9T+iSYekeUVJymB77fY7p+fK8WYrKdT54iLiJEBBTUxuPsreuH0YodR+mpmWvp2pEdqW/bBHrhioEi84FfHmdzHC8qpwWbD1OXlrEi+4OlJ0RS7wxzSa4zuqRYPSaXuOLMjHG9DU0oOcPDWU3COThzydB24iRlJEXRa1cPFsEOLme1ZNsR+mDednGZ2ucDmh7unXHxxReLTKH33nuvwfcXHBwsTgAAAJ4gr9gQbGge6fzefdPnGTJgm0cGUeXpGnFcCwDgSghsAAAAALiBLM+k2nOsjH7fmEeTeiWIidTZmyz7P3Ruad0/w5NxFscnt42gFnHh4nduyK1tyn12z/qvVpfBDGcFNezpmhYnTvy+cNbKwi2H6c5PltMLlw8UTdKh6QY1Dhw4QAsWLLBoVpicnEzHjh2zuP2pU6coPz9fXAcAAOANcotOWfXCcBa5+OPGM1vQU7P20casYnEchUUhAOAqrimq56uqTxNVVznxhOg2AACANzt0opyW7y4Q2QpyFZvW3mNl4ucPa47RR0sOU2xYAPVKC6fmEQHUNyOevAkPXGVQo6ng13T3Od1FJsmuwyfplV83NvZTAhcGNXbv3k1///03xcdb/u0NHjyYCgoKaO3atabLOPhRXV1NAwcO9PBxBcYUAABgkGvM2DC2N3MaDmBwhsbANlHUvWUEFZUbvnuW7irApgcAl0HGhrPwgKE0i6i60ml3SX5BRGFpRH76kXSu69utWzcxEGvTpg198cUXpgaHehYtWkRhYWE0YMAAh5/Cf/7zH2revDndfvvtFpevWbOGvvvuO3r55Zdt3sYdrr76arrwwgtp4sSJbn9sAACA2gZ4136yXZwf1z2e5mwxN8wOCmhGP9zag86dvpF2HS2lHUdKaL+x98ZLF7en9Fh/ouoKogjLbAdoHCFBAfTg5F6iJNX2bAzQvVFxcTHt2WMu85aZmUkbNmwQPTJSUlLE8eS6devo999/p9OnT5v6ZvD1QUFB1LlzZxo3bhzdcMMN9P7774vjbz72vfTSSyk11VyazSPHFbWMKRjGFRhXAIBvHJsWVxh6bHBZT2f37jhVXUMxYZbTjCdK0GcDAFwHGRtOU20YfDTzJ/ILbviJ70cMZgxfOno4iMEDsq1bt4rz77zzjt1nyIGNf/75xymvtl+/fiKoAQAAANaOFVbSuNc2mH7fdrjEdD4tLpi+uKErBQf6UVJUEO3NLaO7vt5FB/PLKTigmbgePA83KR/WKZlOlFTQvI3ZtD37BGXmFFJJeVVjPzVwAC/K6d27tzgxbujN55944gk6dOgQ/frrr5SdnU29evUSgQ55WrFihek+vvrqK+rUqRONGjWKJkyYQMOGDaMPPvjAs8cVDowpGMYVAABN38lSc8Bcp0Jqg3CzcBYdahnYCA3CtCMAuA72MM7WLIDIL7DhJ76fOhg6dKgYjLFVq1aJdPk+ffrQmWeeKeoEZ2VlidVlL7zwghiwcUAkNzeXzj//fBGk4NuvX79e9775tmeccYbICvn2229NQRJe2ab10ksv0ZVXXilWur344ovUv39/6tGjB73yyiu69/3JJ5+I63v27En333+/WEk3ZMgQ0/Xz5883Pc4ff/whBqB826lTp+oOWPn19u3blyZNmiRqHgMAADSGrYeKLX7PKzIMJDskhdGbUztSTJihR8OjE1tToL+hHjFnbvDlqEPsucJDDMdnXI7q7k9X0M0fLKWpb8ynQ/nmwBV4phEjRoiVqtrTjBkzqHXr1rrX8Yn/n8TZG19//TUVFRXRyZMnxXFsRESEZ48r6jimYBhXYFwBAE1TbmGF6Xy1EyMbnP2RX2JY6BGtydhAYAMAXMknSlFxqaSnnnrK4rKOHTvSjh07qCngIMK8efPo2muvFb936dKFli1bRv7+/mL12TPPPEMffvgh3XzzzRYloy6//HJ65JFHRPCB6wlfccUVtHr1aqv737t3rwgwHDx4kMaOHStS7vU8//zztH37dvrss89EbWIOtHCGCNceHjNmjEjf59JZ0ubNm+n111+npUuXilViHIjgAWNgYCDt2rWLOnToQJ9//jlNmzZNNGu84447xG1btGhhFbTgcgD33Xcf/fzzz+I+eKDJzwdZJQAA0BhqdNLzk6OD6K0rOlpc3iklnN6c2oFu/WKn+P2mES3c+CyhrsKDrZuGc/+Ub5ftofvO7YkNCl4P4wqMKwCg6eJjFu2x6tGTFfTsb/vpkYmtKTWmflnDP/x7jD5eelicjw+3PFbyMzYUBwBwBZ8IbLCuXbuKyXa1jqy34waGnH3BAYT27duLoAM7ceKEyJrggAQHFWJjY3X/P28PLmMl8f/Tw/0rONjQtm1b8Zh6OA2fU/M5o8PPz4/++usvkWHBgQjGK9s4WKEGNhYuXEiXXHKJqS8IByRk3wwOaHDQhQM0H3/8Mc2ePZtGjhwpghrqbaWdO3fSxo0bxW3YqVOnxHsOAADgDryy+90F2dQvI4oGtommwjJDOn6v9AjacNCQvREfYT0pztokhNINZ6ZSv9ZR1Lp5KN4wD8ZlqFiPVnF0+fD2tG5fHs1ef5D+2phNt43rKnpxAHgjjCvMMK4AgKbqdHW1VcbGp8uO0K6cUnpvYTY9PaWtw/dVUFpFv6zLpcsHJ9PaA4XiMs5C5sbh7JFzWtPzf+yn086ueQUAoPCZ0RcHMpKTk6kpkbVwS0pKREbEu+++S3feeaeoFXzOOefQjTfeSFu2bBGBAlu4fFNtQZ7g4Nqj9lweatOmTSKzgrczB1SefPJJkW1RVxdddBENHDhQZGyce+65DgWh+PG4TBUHSwAAANytqPw0/bohT5x+vK07HSkwlJ4a2y3eFNgorTSvklNx6akL+yW59flC/ZzbvzUdLyqnh6f0pvjIEOqV0Zy+W7FXXPf0D+vo2akDsGnBK2FcYYZxBQA0VadOm4MMMt7gZ0yo+GefITjhKA5a8DFubHggHS+uopToIHrtsg4UZ8zYkKVWuaE4AICr+EyPDS61lJqaKvpEcAkmLqtkT0VFBRUWFlqcHFJziqi6quEnvh8HhYeH0/Tp0+nVV18VmQr8XGVmA9cNliIjI0XmhHTWWWfRe++9Z/qdMx7qiwMRXPaJszs48+Pss8+mjz76iEpLS8X1+/fvF7WIVZxd8d1335kul+WluFbxgAED6OGHHzYFRgYNGkQLFiwQzR3V20qcLcJ9RNauXWt6/5pKqTEAAPB85VXmFXBT/7eFflmfK85nNA+lHmmGlWvpcSGN9vzAObq0jKWXrxosghrS+N5p4ueavbm0dPsRbGpwHmeMK+owpmAYV2BcAQBNl5o9wX0xmL+MbNRRdr4hi5UzlrPyKygqNMAU1FDvFxkbAOBKPhHY4El3nuCfM2eOmMjPzMyk4cOHW0zya3F/hujoaNMpLc0waLXNj8gviKjmNFF1RcNPfD98fw6+RdwAvHv37vT999/Tgw8+SPfcc49oHh4UxPdhwA21v/nmG1Pz8Lfeeks0Aedsi86dO4tmiA3BwYyHHnpIZFlws/EpU6aIgASXn+L+HeXl5Ra358vvuusu0aCQnxM3Npe4jwf3A+HLWWJiogjecCYKP1/OTFHx6+QgCd8fX88NxBsSqAEAAKhvYKPilHnQGBTQjF6+uD29ckl7unVkS2zUJujOc7rT9aM6ifPP/LCOnvj2X9qXU7dVjwAuG1fUcUzBMK7AuAIAfCGw0bD7qjhlPvZlwYGW3zMysKFmiQAAOFuzGi4K7YM1ZFu1akWvvfYaXXfddbq34RX/fJI4C4KDG5xdEBUVJSbpOUCSkZFBISHGVXvVXGLCcufe8EGNP/kibvjOvUE4UOFMuu8beKaqKqLMTK6FRhQY6P7H5r//jAz3PzaAFq+4Lc4k8gsm8sPn0VPtPFpCd361y+ryL27oSolR5kUGDhOrrSuIIjLwvnvJRMGEZ2ebfp/UrxXdPt7cV8xX8PEyLwiSx8vg2DZy/bjCd8cUrhpXYEzhRTCmADBZsjWLnv1pkzg/tH00PXFuGxr76nrT9b/d1ZOCAhwLhE98YwNVKUGLAW2iLHp0rD9QRA//sIduH9WSJvRoTiv2FNCQdjH1zhCBesKYApr4mMJnemxoa8hy/4Y9e/bY7SvhSG8JC2LA4LuDBmcZP3485eXloV8GAAB4jbJK/QlIztiApo8H6Y+c35vyi8pp5sp9tOWgZclMgDrDuMIpMK4AALDRY0Pn0LW44jTFORDY4PXRalCDBWv+X4CxxwY/zjerj9IXK47StKEpNHVQ0+p9CwCNyycDG8XFxbR371668sorG/upgI4///wT2wUAALzKmkz90kOB/j5R9ROIaETXVLEddh4+SYu2HhZNxtVeHADgfhhXAACYZeWVmM7L4i28CKfSWEa1hAMbSp8MWzgAohWiKUUlEzN+35hHIUGG6/blluHtAACn8onR9v3330+LFy8WDaxXrFghej/4+/vTZZdd1uD79sFKXl4N7xcAANQFD/AOHrfsEaV1vLiKZq45Ru2TQq2uCzSuVgPfMbhDkvj55HdrcNwBdYLjVO+B9woAvNHXy/dZ9NjgUpoyqCGPex2RV1RldZm2wpTM2DiYX067jpYaHxPzZwDgXD6RsZGdnS2CGMePH6eEhAQaNmwYrVq1Spyvr8DAQGrWrBnl5uaK++Hz4PkDEH6/+L3i9w8AAKA2j/64h3YcKaWZt3anqFD9w6ap/9sifg5qG027cyxXoiGw4XtGdEulvzZm0dp9ebR0+1E6o0tKYz8l8HAYV3gXjCkAoCnIPlFBpZWna83E0O7/rvhgKyVEWs+nRIVYHif768yRIa4BAM7mE4GNb7/91un3yRkfLVu2FEETzgQB78BBDX7f+P0DAACoDQc1WE5hpW5gg1e6SZy6/8ol7em7f3JoS3YxlVVVY+GDj8opMAS4nv1xHZ3R5ZzGfjrg4TCu8D4YUwCAtztcUEFLdhaI80lRQeJYt7jcfmCDMzryiqvESRrVOZZiwwPpMk3vjOToIAoN9BPHw7b6cAAANJRPBDZcJSIigtq3b09VVdZpeOC5K+IQ1AAAgLriwV77pDCry3OLKk3n+7WOosSoIOreMkIM/MqVgRz4ltiIYMrON9exBqgNxhXeBWMKAGgKVuyxDGxoS1Hx70dPVlDbRMMx8PESy7mve89Op7Hd43XvOyIkgL69pTudN32j6bKFO07QQxNaYeEPADgNAhsNxJPkmCgHAABo2nhQZyuNn90xOk0ENaTwYH9xAt90z6QedO07iyghCs3DwXEYVwAAgDsVlJ4SP5Oig4iyid5flE0TesSbAg+XvLeZqk7X0G939aSgAD86kGfZd44bj9ujd/2eY2W6i4UAAOoDeWAAAAAAtcg5ac7MUGXnGwZ4LWODsQ3BpEVcOHVqEYNa0gAAAODxgY3ESMPiHG4kvim7WJzfnVMqghqs4pQhCzkzz7KXXG2lpfx0+mzI+wQAcAYENgAAAABsNEiUjiu1hPUyNlogsAEaESGBVFSOcqUAAADgGZKiQygi2M86Y0PJOpaHv79vzLMKRnDgQxUUWPcpxfIq+308AADqAoENAAAAAB0VyuBNbZKoOnSiQqxWi48IxDYEC9FhQVRRdZq2ZZ/AlgEAAIBGV11DlBpjPmY9xRdwxoYS2AjwM2RZ5CvHvqdMgQ3L/nH1aQZeW4NyAIC6QGADAAAAQKO6poY2HCyym7HBjcPXHSgS2Rp6qfbg284fmEHBgf704s/rxecJAAAAoDGdPl0tAhfD2kVZXB4dZm6/628MbJRUmgMQVaerxXFxVr5lzzk108NRxZoG5QAADYHABgAAAICCV6PN/CeHnvxln+my/JIqOm1c1Sbd8vkO8TMqFE3CwVq7lGga0TWFjhaUUVEZSlIBAABA4+IMDQ5sPD4pnSJDzMev4cHm83IxRqkSgCgsO00PzdxD65VFP9pMj9pM6ZMgfm7OLhYnAABnQGADAAAAwOjg8XKa9OZG+mTZEfH7hf0SaVz3eJG6L+sQS0XGVPoOSWHYfqArPMRQ7qG80vKzAwAAANAYgQ1/4yxgZIg5SyNCCWzI8lSlleayUyfLGn4cc2bHWPFzwfYTdP93u2l/XpkIcBw4btmQHACgLhDYAAAAACCipbsK6IYZ2y22xQ1ntqC4cMPk9OfLDcEOVlh2itonhYrz04amYvuBrpBAw0QBGmUCAACAp5SiYjJjI9C/GYUF+Vn10yhTSlH9sOZYvR9zTNc43eyO7//NEQGOG2cYMqABAOoDgQ0AAADweVn55fTMb5liOzw0oZVFQ8RS48Buzpbj4mdNTQ1d9O5m2p1TRtGhARTgj/4aoC8k0LAaEoENAAAA8IiMDeNxa1So4RiFj2WbNWtGd41JE78/+uNe2pNTSpXGAAdrSOmo+8am09z7elOM0sfDVv86AIC6styzAAAAAPgYzr6486udpt9Hdo6j1JhgU1r+pQOS6Jd1uaYMjdwi80CMV7kB2BISZPgMVVShUSYAAAA0Hl6Yw/3iAo0ZG7KvhvwpF/Sw5/7YL3rOOQMHTWRTcs4MkSWuENgAAGdAxgYAAAD4tGW7C8Qgq3lEIL13VSdxWaeUcGoZFyLOx4YHUmign1jZxkGQRTtOmP5vkDIIBLBZikop5wAAAADgbhzUYLLHhlycY4w7WGQgl1dVi/5yCZGGcqzOoh43q4ENDroAANQHRuMAAADg03bnlIqfb07tQG0SDFkZWsGBfrThYJEoQfXx0sOmy4MCkLEBtsVFBIufRwoMnzFoPEuWLKFJkyZRamqqWD36yy+/WFzPkypPPPEEpaSkUGhoKI0ePZp2795tcZv8/Hy6/PLLKSoqimJiYui6666j4uL6l+cAAABwl1Onqy0CGDKwIWMKsveG4TLDhT3TIumCvommyx+b1Jpev6xDvZ9DkBI8UZuTG2MuAAB1hsAGAAAANHmyTwbbe6xUNAo/cLyMDp2ooO2HSyguPIDiI2yvSgsJ9CPjeNAC/38AW9qnRIuf+3IKsZEaWUlJCfXs2ZPeeecd3etfeuklmj59Or3//vu0evVqCg8Pp7Fjx1J5ebnpNhzU2Lp1K82bN49+//13ESy58cYb3fgqAAAA6t9fQw1gyJ8ysMGloqQwYylNXsCTHG1u+t0rPZJaxhoWbdRHoI1M5yq9g2wAAAegxwYAAAA0aRzIuPWLnXTzWS1oSp9EcV5rVJdYUw1gPWrdYVXvVpFOfa7QtESHBVGgvx8dLzJPjkPjGD9+vDjp4ZWpb7zxBj322GN03nnnics+//xzSkpKEpkdl156KW3fvp3mzJlD//77L/Xr10/c5q233qIJEybQK6+8IjJBAAAAPD1jQwYw1NJT2t9lBkWQvx8lKYGN0EB/qm5A2Sg1Y0NVdbqGQpxb9QoAfAQyNgAAAKBJ251TJn5+sOgQvTh7v+5tMprrl6CS4sItR1tndYqlr27sSg+Ma+XEZwpNDQfLmkeF0KH8ElNZB/7525oDtHp3TmM/PTDKzMyko0ePivJTUnR0NA0cOJBWrlwpfuefXH5KBjUY397Pz09keNhSUVFBhYWFFicAAIDG6rEh1+rwwgtWQ4bLI4xNxNVM50BNxgYHP2QJq/qw1ZuOAxsAAPWBjA0AAADwiYEc/1iw3dz4W8XNwu1pFR9C6w8WmX5vmxhKzSPNAz0AW/q1TRCBjB2HCqhzy1j6aslu+mKJuXdD/3YJlBIbRlMGZFBqnP3PIbgGBzUYZ2io+Hd5Hf9MTDTXGWcBAQEUFxdnuo2e559/np566imXPG8AAIB6Z2xoSlGlxphLTJVUGAMbnLERFWS1aOO83gniWLiuuGed/nNDYAMA6gcZGwAAANBkbc4upul/Z1lcdvmgZJp7X2+6akiK+P2aYSnUvWVEnQZi9RnMgW86p0+6+Pnx/B1032crLYIa7N89ufTrvwfomncWUUlFVSM9S3CVRx55hE6ePGk6ZWVZ7o8AAADc2WNDZlyoPTVYVGgAXTc81SKDgntshASaMzmkW0e2pLHd4uv8HOI1GdBSVn45Pfj9bsorqqzzfQKAb0NgAwAAAJqsWetzLX7nIdzZ3eLE+csGJdHrl3WgSwZYrtLWo6bdd0wOo24t7AdCAKSMpCjq3DKGNh/Mpy0H88VlD03uRb8+PI4mD2hNPVubJwbem7MNG64RJCcni585OZblwfh3eR3/PHbsmMX1p06dovz8fNNt9AQHB1NUVJTFCQAAwN1OazI2ZGs5NVfiYs0xMffYYOO6xdOEHnUPZGhdMTiZ4sKtC8e8uyCbNmYV0ydLDzf4MQDAtyCwAQAAAE2WtknhR9d0puRoQ6q9X7Nm1CU13G7TcEld1XbfuHSbNYIB9Izu0dLi9+TYMAoO9Kdbxnall64cRI9d0IfCggJo/uZsKizDakV3y8jIEMGJ+fPnmy7jXhjcO2Pw4MHid/5ZUFBAa9euNd1mwYIFVF1dLXpxAAAAeEPGhvYQ1l4vcLmw556x6XTXGEMGakO0jAuhb27uTh2Swiwuzz5RIX6WnzIEXwAAHIUeGwAAANBklVdViyyNP+/tJXpsaNPuHaVmbESG4PAJ6mZ873QRuGibHEXLth+ljqkxFtcP75JCxwrL6IN522l79glqlxxN8ZEh2MxOVFxcTHv27LFoGL5hwwbRIyM9PZ3uvvtueuaZZ6h9+/Yi0PH4449TamoqTZ48Wdy+c+fONG7cOLrhhhvo/fffp6qqKrr99tvp0ksvFbcDAADwhh4bsreGPCSusRPZcPdCHltNxFfsKaCnZmXSm1M71NoXDwB8C0bmAAAA0KQDG9wfg7MyNMkbdaIGRKJDcfgEdf/8jOzeQpxvlRCpe5v05obyZk98u8Z02TOX9afI0EDae7SQzu6VJpp4Qv2sWbOGzjrrLNPv9957r/g5bdo0mjFjBj344INUUlJCN954o8jMGDZsGM2ZM4dCQswBpq+++koEM0aNGkV+fn50wQUX0PTp0/GWAACA12Rs+BsPiGXGsvFiXdxjwxV25ZTqXl51Sv/JfLniqPi5dFcBAhsAYAEjcwAAAGiyOKU9RNP4uz7k6jZW36wPAHtkYEP12Df/ms4v23GUnps6wKHSaWBtxIgRdlel8nb973//K062cHbH119/jc0LAABeYf+xIvLzayaOMWSPDXlMGxFsaApeXHHa4v8MbRdNy/ecFOfdvaDitI0oS0HZKfEzJgxTmABgCcu+AAAAoMmqqHJSYKMh6R4ADkiIDqW+bZrTlWe0pz8fm2C6nBuPt0+JpnX78uj69xbTmr252J4AAABQq5v+t4RueG+xOF9UViV+hgf7WQQJSjSBjf+blOHyLfv8hW1FE/E7RqdZXJ5fYniOqmOFlXS8uMoiGAMAICHcCQAAAE27FFWAczM2AFyBm9k/d7m5CfXDU3rRjkMFdNPZXehEcQXd9uEyyj5eQt8s20P92ibgTQAAAACbCkoMDbml3KJy8TMhwjAN2Cs9khIjA+na4ZZ9ojgzOSzIj0orq6nQmCnhbH1aRYkm4juOlFg1EedAy/YjJZQSHUwtYoPpyg+3mq630YIDAHwYMjYAAACgydl7rJTmbj4uAhvOyNio0WkiDuBKZ3VrQbeM7SoCHtxI/J0bhlFqXBjtPFRAhWWV2PgAAABg096cQtN5LsWYV2gZ2AgP9qcvbuxGZ3WOs/q/5/Y2LKBo3dzcZ8oVOiaH0Z2j0+jrm7qZLvtm1VH6vx/30rWfbLO6fZWxnBZ4hsP5JXTsZFljPw3wcQhsAAAAQJNz6xc76bW/DlJecZVTAhsyTT8yBCnw0Dg4uDF1WHsxqF+1KwdvAwAAANi096g5sDF73UHKKzRMQMcbAxv2TBuaQjOu60JdW1j3/3Im7m91Ts/mFB8RSGO6GgIsgXYalttqLg6N45p3FtGV0xdg80OjQmADAAAAmrRgJwQ2isoNgY2IEFTxhMbTpWWs+PnbvwfodDVWLQIAAIC+g3nFpvMLtxymBVsOi/OOLPjhbNGUmGC3btrx3eMtjrn1VNVSi4p76333Tw6VVerfx7ytxymnEFmvAE0JAhsAAADQpIU4ocdGepxhcDe4bbQTnhFA/STFhFJYUADtOnKSPl2wE5sRAAAALKzbl0eXvf43bc3KN122+aD5vKeSPfHyjY3C2elqy0BGZS2lqGZvzqNPlh6mF2cfsLqOG5C/MucgXfXhVlGaCwCaBgQ2AAAAwCvtzyuj/BLz4Eel9sJwRimqEZ1i6dVL2tPVw1IafF8A9RXg70dv3zCMggL8aH1mHjYkAAAAWJi3MYvyiyvocH6pV2ZY5yqBDZl5EWUsBVtbKaogf8N9rNx70uo6NYtjY5Y5mwXqpvLUafp66W46lF9icRlAY0FgAwAAALxOYdkpuumzHfTAd7vF7wu359M5r28Qq7F4dVdSVJDptly31xk1gLu1jBCp+QCNqUVcOHVLj6PMY0X02cKdFqUmAAAAwLdFhZmPgaNCzcfAGYmRdN/EruTpGRu7jpoDMot3FoifvdIjxc/yU/YzNmp0+uNJFcr/PYZyVPX29dI99NmiXfTcj+tMl3lbEA2aFhSKBgAAAK/DAQyWfaKCvlp5lL5ceYQ4W33q/7aIy1vFh1BiZCA9dm6GOA/QlKQ3jxClJr5etkec7j+3J3VPj6Pk2LDGfmoAAADQiIrKLEs53TC6s1hRP3V4e6LqKqLiTI98f/QyrKf/nSV+cqYqKyk/Zfc+KpXgRW5RJYUHhyrXmcMeZVXoU1Yf/Dn6cdU+cX6P0pw+81ghtU40BJ8A3A0ZGwAAAOBVVu87KRoDSp+vMAQ1VAeOl1NkSAB1TA7/f/buA77Jcnvg+OlM9950sPfeQxQVBAfKdaE4cG+viv5V9KrXe1XcA/fCdR24t6iAIAiy995QOmihe6//53nSN006oIW2SZrf9/MJSd6st09Dmuc9zzlHfLzM6etAWzG0c5RY5w49+/06ufa1BXbcIwAA4AjyikptDvRfOKKjOajh4PxNDX9f7xrtJ96eblJQevSAhHVWRk6RbRDkzQXJlssNNRfH0e1Oz7UJHhm2JJszawB7ILABAACcysPf7JY/tmYd836BvgQ00DYN6hQps+8eJz8/eJZcMqqT3lZeWSWvzdlk710DAAB2lGuVsdExOshpfhce7jVLNm44pZ3l8tRRsXJO/wgJMHnUKS9Vm/VB9yU7avpsLNmZLVtSa8ol7c4oooH4cTicV6LPu8YF22z/bsVeySk0B9TU+Q1vLJRVuzKO5yWAJiOwAQAA2pxpZyTK3eOT7L0bQIsJ9vPWBwGuPq27XDzSHNxYsesQE3UAAFxYblGp+Hl7yvj+8fKfSwaLMxreqebAeYcIX/19x8+7JrBRVVUlf+/KkZJaJaVKympSuL9dYz6wviE5Xx79zrb8lurd8f3azBb+Kdoeo6/GFSd3lbdvPkW+/r8zLLf9snq/PlcBjX0Z+fLAJ8vttp9wLQQ2AACA00jNNq8Uign2ltk395ZRXWomPn7e7jpN/YoRMTK+T7hEWTUQB9qyqWO6yoe3nyqzbhmjG90DAADXs+nAEd3IuUtcsEyb2E9C/E3iTHrF+evv8+1Ca/Y7wMfDUqrKCGz8sDZTHvl2t7y98KDN43Pr6cGhem3U59cNh5t579s+lR2sqH4aqt+bv4+XXHFKV70txN887yoqPXofFKC50TwcAAA4hbcWHJSvVh3SlwckBkqIn5c8cHYHmbfliG44eEq3UHvvImAXnh7uEh1C43AAAFzV3kN58sjslfrypCHtxRk9M7mLbnhuLbA6sKFKUR04UqyzNV6db+6XsSujyOa+uw7ZXlf3rd1rw5BVWFOyC0eXXVAik5+fqy/HhvpJVHBNU/ZeCeb5V1lFlbz00wb5uTpzwxh/FtygpRHYAAAADk+lmhtBDSU1x5y54enhJuN7h9txzwAAAAD7WLsnU75atkeW7zB/T+6bFCYju8c45a9DlZyy7rWhhPl7WQ6cF5ZWyuVv1fQTiwo032YcRFeZ3T3j/GVzSoHlMQXFNX05Okf5ys7q4MeRgnI5lFtKhncjfL2sppSXen9Z8/Y0FwIqq6i0CWoY/V5U6VRHtmxHumzYd0SmntpNvDwoauSM+K0BAACH9seWI3LuzHU223q3C7Db/gAAAACO4KM/d1iCGspJPWKlLTFKUfmZzOeZ+TWZFqHVQQ8lv6RCisoqJSHMJH3jzfOEvOIKybNqOG6qPghvmDn3QIvvf1twIDNfn/fvEC43jutpc5u3p/n38sWSXXUel5plDjA5soc/WylfLN0t5z05RyoqbXu2wDkQ2AAAAA5t/pYsy+X+iQEy65qecsmwaLvuEwAAAGBvadmFNtcnDk6StuDFS7vKA+e0F/fq3mG3nx4vkwZG2tznm9UZuom4kl+dmRHk6ymdosylkqa8uVG+XW1uIn75iBi550zbsTlsFSRB/Xak5siSbekyrEuUPHX5cN1Xw5qR5XAk35xNr0we2Umfz11/0KF7bhSW1OybKoF2+zt/SWl5TSAMzsElAxtPPvmkrvN255132ntXAABAAw5mlchV726yafp3avcw3VCQVGEAAAC4uiKrg7MjukZbAgHOrkecv03/vIhAbzmpS0id+834aa8+Lyw1H5D28/aot7zUlOExEhdikptPbSejugTrbdZNylG/uevN/UyMJuG1edXKglGVxEb3NGcN/bBynzz82QqHDtpY25WeK//9crXd9gfHx+UCGytWrJA333xT+vbta+9dAQAAR/H9mgxJzS6VPZnF+vqbU7vLhD700wAAAIBzUQdRH/xkuRSUNF+WwK60HCmwCmwE+dmupm9rvD1rgjZJ4T425aVU/w3F19u9TmaHYvTumDQwSh4+t6N4urvpHhyoyV5YsyezznAYmRgdogIb+J3YHlZWvd/DAmoCRuv3HXHYId6w37xvY3rFWbbtz8iz4x7heLhUYCM/P18uu+wyefvttyU0tCbyW5+SkhLJzc21OQEAgNYTHmA7OTMmMAAAAICzWLI1TW57Z7Gs3JUhk5+bKx//uUMe/XylPP7Via0O/339QZvrvt6e0pZ5WzV3fv3K7jKofaDO1FCNw4usMjZU1op1D/LLhsfUGyQpKaOngkG9F+//3zLZnFxTAljJLSoVf5OneDbQWNvosWHN0RuGGw7nmRfP3Ty+p3xx9zjpnRimAzzlFZXy+7rkEy6jpYKZK3bW9L9By3CpwMatt94qZ599towdO/aY950xY4YEBwdbTgkJCa2yjwAAwKyozLbGqSojCQAAADiL4rIKefSLVZbrZRWV8uHC7bpvwZ+bU0/oudVB0+hgX93/QEnLLhJXydhQGRj+3h4662LZ7lxLxoaft/kwpzFtuPfMJLlyVN2G6j5e7lJS7niBDdUzJLeo9ftSqKCbsiMl22Z7bmGZBB0lUKGCHrXVDoJUVjlGZozqo/Hc9+vk7+3pknKkQP//MXl56ECM+hkDfLwkt6hMJj//uzz7/Tp5/49tx/1axaXlOpj5r09XSJZV/xE0P5cJbHz22WeyevVqHbBojOnTp0tOTo7ldODAgRbfRwAAIHrV1dO/7JVP/k63TFC6xvgxNAAAAHAqLVXapqKyUpIPF0hsqJ9cVN2seWzfdtKW1e6xtymlQJ8/88s+OVJgLvEV0shyXKqEVamDBTZ2pBfKI9/uljs/3d6qr7tgU4rl8mu/brZpoK0yNoJ8Gw5sNJTJcVL3miyZjBzHCLjtSM2W39YlyyOzV8qbv2+RzNxiuXVCL8viucpK8/shv9gcWJq/4eBxB2W+W7HPcvm9P7bqsnEqqKIyQdC8XCKwoYISd9xxh3z88cfi49O4MhYmk0mCgoJsTgAAoOXtPFQk8zab06DH9QqTL27pKy9eWn/DOgAAAMARHckvlkc/r8nWaE5f/71Hn6/de1j6JIbJD9Mn2BxMbusZG8o/x5orq3SK8pVvVplL/kQGmgMbD5zTQUL8PGVw+/qP5Zm83KXYwUpRGZkaB7NKLKW1WsOMr9fYXJ9rVeIst7C00b1bVK+Kf104UF9+8MKBctbARH35ypf/0JlL9rbpQE2ZrS3JWRIb5ifj+9dU5+nWzrZlgcre2JXW+LYEKgjy3Yq9ciAzX58Mv65NllveXqyDKioTBM3LJQIbq1atkkOHDsnAgQPF09NTnxYuXCgzZ87Ulysq7P8fDAAAmBWU1PxdvnxEjHh6uFka/gFAS1Fzgoceekg6dOggvr6+0qlTJ/nvf/+rs8gM6vLDDz8ssbGx+j6qxO2OHTv4pQAA6li9O1My84ol1N8kj106RGbdOkauPrWbzX3UgePjkZZdqM/7JoVZeh209bKtRsaG8VN2jvLV5+sO5MuhPHPGRpi/+SD8SV1CZPbNfSTYz7PBptcnWopqQ3K+zFqUokscNYd8qznQxoM1B8Zb0syfN9QZX1WmSVHBCJXVcrSMDeWa07rJJaM6yfTzB8joHuayX6rPyUk9Yur0s7An60bmOYWlkpFju0+XntRJPrnzdPn1obPl0cmD9bbttUpzHc2CjSny2pxNct3rC+X39ebMDJURgpblEoGN008/XTZs2CBr1661nAYPHqwbiavLHh51m90AAAD7SM0usUxaooKco/kcAOf31FNPyeuvvy6vvPKKbNmyRV9/+umn5eWXX7bcR11Xi6PeeOMNWbZsmfj7+8v48eOluNj+E3YAgGPZn2E+OP30FcNkSOcoaRfmL4G+tqvfVVkc69I/jWUEMe6d1F9chb/JQx47v6N8eH0vS3Citvq21ftc3h6SkVcmC7Zm6dJDt/1vq+5v0Vh7Morkntk7ZPbydPngrxPrlWKs9s8uqOmt8c1qc8+LlqRKMf20ar/l+sxrR+nzL5bulq//3q3LJykxIUcvCTx5VGe5+rTudbbHh/nbNNK2J/X6qreGtfBAk811D3d3CQ80V/kxzlXWRmMtr9UovGtssCRFBp7AXqMxXCKwERgYKL1797Y5qUlIeHi4vgwAAByDmhy8+PsB8fJwk09v6q1X+wBAa1iyZImcd955cvbZZ0v79u3lwgsvlDPOOEOWL19uydZ48cUX5V//+pe+X9++feXDDz+UlJQU+fbbb/klAQAsMnKLZPaSXfpynNUBXn+TbWBjc3KWfLSw6Zl/edUHXEP8bQ/OtnVDOgRbFj7VLk3VFGH+5kyOGT/tlZlzD8iO9CJ5/Edzea/aVOAjLce2AfTKvbk28xfr7E7VIL62tfvz5Ps1GQ0GSc58fq289kdN/4VVe/OksIXLUc3fWFNyqldCqHSMrinbpXpQGEGPPtVZQU0VGWzOqDHKXeUXNz5I0Nw27q/J1jD899KhDd7fz9v8/igsaXwj90O1eok8dNEg6dc+XJ64bKiM6hbdpP1F47lEYAMAADiHP7eZa59eOTJWQqtTyQGgNYwcOVLmzZsn27ebm3auW7dOFi9eLGeeeaa+vmfPHklLS9PlpwzBwcEybNgwWbp0ab3PWVJSIrm5uTYnAEDb90N18+D2kYE2DZb9fWpKIz1y8SB9fvBw08sOFZeW69JBtRtqu5IT+dmNklXK4XzzAXfPekrfqhK5t328Taa+s1nKK8zBi/cWp8g7f9Y03FZyqvtjrN6XKxe8skE+X55u0zvjvi92yqvzk+ttRv3XDttyRz1izRkSR6r3q6X8uLKmwXV9ZX/nbTAHPlSm0fGovUAtp+D4yq4dL1Ua6vVfN+mgU1Gp+fcz47JhcnqfdvLBbadKYkRAg4/1NZkr+xiPU174cb3c99HfkppVqBuL70mv+U6nXiP5sLmE1y0Teskr150kUdWBnUEdI20yq1SJOmtr92bK9a8vlOwC2+AZGqf+YnMuYMGCBfbeBQAAUIv6rh8R4CUXD2VVC4DWdf/99+vAQ/fu3XWpWtVz4/HHH9flaxUV1FCio20/n9R147baZsyYIY8++mgr7D0AwFGonguqxn50sK+8fuPoOuVuDCO7xUiAj5cczm/aAU11gPXvHYfqlLVyNepgvIptGAkSTWnJd3rPMPlipbl0UFGp+QkKq88N8zYfkad/qTn4n5xVLAEmD/lsWU3QQr2marGhGn6H+HnJy3OTde+O5XtyLfOZeVtqsgXUa6mSWtYyawUwRnUJkS2phTpYEi8tY+76ZEm3yjAI9qs/8+fU3nESGWQuy3Q8VL+K2X/tlFnzt0mhVZCgJVRUVsrCTakysnuM+Hh5yIxv1lgyplRPDSU+3L9R5duMjI2C4jJZsClFZ0jNWXNAb3vymzWy9WC25edTVFBDvcakoe3lvCHt6zyfj7envHTNKB0YeXfeFhnYseZz4dHZq/TYLNycWu9jcXSuG9oFAAAOJ7uoXCICXXuSBsA+Pv/8c/n444/lk08+kdWrV8sHH3wgzz77rD4/XtOnT5ecnBzL6cAB86QYANB2XfXKH3Ikv0RG94w9ZllVVZ5HHSRduydTyuspYVSbWhluHGC1Ln/kqrytsjZevaJun4eGdIj0lTvGJejLGXk1mQRGVoay65BtaaHdGUWWIESHCB+57fR4mXFhZ3192mc7dGZGSnWvQFW6yvj9HMqtef6iespLHSko09ki397eVz64rqclm+SZOft0g/KW8H11RpEycXCS3H6WuUz/P8/qrTMaDLeM73XCTen9TNVBgpKWzUBZsjVdnvp2rVzz6h8y+y9zGThFNfT+dvlefTnEv3H9G01eHjpwNn9jii6j9covGy23GUENax8s2KbPeyc2XLare7sQSYgIsJSRMxgBH1fOvjoRLpuxAQAAHE9hSYX4hx//qiAAOF7/93//p7M2LrnkEn29T58+sm/fPp11MXXqVImJidHb09PTJTY21vI4db1///pX/5lMJn0CALgOo9Z+UmTdUje9E0NleNfoOiuz7/vfMjmlZ6w8cMHAoz639ap31fvB1QX7eUpRTqn0iPWXjpE1PR0aIybYfJA7t7gm2JCeWyLtQn10UOKrVYfq9MIwVTcnv2BwlIzrFS4lZTXBqAe/rjmYrhqTr9ybJ0M6BNmUlKqdFWKUwgoP8BJfbw99UtkfSmp2qTzzyz5Ls/TmZCQOTf/HABnTO86y/exBSfrUITpQvD09JMivcYGAxmQ/vP37Fp258PilQ3TgQGU2qWCe2tZUqhzUkfxi6Rkfagm87M3I0+eH80pk1vytdR6jAgfqZ2oM9ZwqELHpQFad57DuoaKCFKqE1KIt5szd3glH70eiHq8yZVQpKxUEMcpVKe/N3ypnDUxs1P6hBuEgAADgMFTqtnf1hAEAWlNhYaG4W5UIUVRJqspK8wS2Q4cOOrih+nAYVOmqZcuWyYgRI/hlAQB06RpDfHjdwIY6sPro5MEysGOEvj6oU6TlNlWK5ljUQVvDVad2c/kR7xZj7kdxPIvda5eEUpKzSqS0vNLSM0OZfrY5CLUns9gSdIgLMS9aMHnVvPC+THNAy6d6m9FwfFdGTeZHYQMZG0YzcyXYt+ZyoE/jDsQ3tT/LjpQcGdwp0iaoYe2iEZ2arSySn8mcgbIzLVc38b7tncXyz1l/yaOfr5TznvpVl5BqqlvfXiTT3l8qEx77WQehVObT//7cUed+YQGmozZ1P5pzB9f9+Y3/t4bUrAKZs9acQTWub7yEWr1efYzgi8osuWLmfNlXfV3JLSqTkrIK2Z+Rp09oHDI2AACAQ1CrdlSNWpPniaU7A8DxmDhxou6pkZiYKL169ZI1a9bI888/L9dcc41l9d6dd94pjz32mHTp0kUHOh566CGJi4uTSZMmMegAANmRlmMZBVXP/1hUlsaqXRmNGrkVOw/Jl3/v1penTewr4/ubSym5sqRwteI9W9Jymt6YOj60bpa4ClzM/P2ATd+LMd1D5YsV6TpjI7K6ZG670JoD2Kok1SvzkqVcTWRE5OFzO8gDX+2SvOIKXebqwJGaYFRqdol0j/W3mf9kF5ZLj7iabaFWjc33ZBbpBub1BWGOJ+i2ZFu6Lsek9rX2QfqWYpSiMhyobrJtyC0sO2ZAoLaCknKbLKbPl9Rky6ieIBm5xRLs5y2f3jVWDucVy/3/W6azO5pCBX1G94zRfXFKyyuksKRcZv60oU7miPr/G+pvkrsm9jnmc6rnsPbXVtsebaqE3fVv/GnTv6MhqhH9dJXp1SvOpTM9WBIJAAAcJltDIWMDgD28/PLLcuGFF8ott9wiPXr0kHvuuUduvPFG+e9//2u5z7333iu333673HDDDTJkyBDJz8+XOXPmiI8PJfScwZo9mfLdCnOdbQBoCev2HNbnj1w8SIJ8j13Gx9eqDI9/rQPA1tSq9H99ukLWVj9/UmRgs+yvs+sRZ87YSIpo+t9h62DBxH7mg/zpuaU2QY0pw2MsgQy1PavAfGA60Kfmd2WUwDISAoygx7bUAkvz8XG9zCWKnvx5n84IMaighoqHhFsFM1RZqn+f11FC/Tz1c14za7M0hye+XiPPfr9OPqvuP9EtLkTsEdiozWjs3VgqyGBta3K2TU+N2FDze2J0D/PvLjzQR96++RS5a2JfaSoV1DAyrUL8TTKunzmYeOGIjvp8V3quzrroGR9iue/RXDfWtg/MjtSaQKhyILPxPVXSs4tk7d7D8lKtYIurIbABAAAcgvEl36hdCwCtKTAwUF588UXdV6OoqEh27dqlszO8vWsOTKmsjf/85z+SlpYmxcXFMnfuXOnatSu/KCehVmyqJqJqlSMANDcVfFAHGtWB3GFdopp80Nc4IFufXWm5Ntfr69/higYmBclD53aQ+848vrJJo7oE6/PJw6L1ucqOsBZSXRbK18scBDlcUCZ+3u66sbQhKqjme4IqQ6Wafwf4eMjfu3Nl/QHzgepBSTWBqB3phTb9NYxghrURnYMtwRMV/LBucH681u01B8VUOSglItDHIQIbuUWl9ZbLUr0r6lO7+fbynYcsZd3+c8lgmTqmmy6zNbUFSrWN6BYtn951ulw2uou+/vu6ZB2Y6tLIIJEq8fUvqz4626sDG1eeYv4uuWBTiuW2ZTvSj/pc+zNrylUlH26ZJvPOgCMHAADAIZSUmw80kbEBAGhuRVYNd9OyCnX5DwCuRwUIVJPe4rIKfTDwnblbmuXzQAU17pi1RDYnZ0mP+NBGrd5W+rcPl1sn9NJlc47WA2DRllSbAIh1poerO6lLiG4ifjzuPbO9fH5zH4kI8BIVq7DuraHEhpiDFt7VpXJVI3DrbA0lMtBbZ1coKuih5jLvXdPT5j7W5ac2Jufb9NdQVDCkNqO0lbJij21g63jUfn+FBTat/NOJNg9vSE5B3cDGrW8vlsnPz9X/r2pbtuOQpeSUsr86y+GSUZ1kWJdo3ZT78SlDG5UxdTzCAnx0sCYiyEeXjlK6xpkDZI0xpHNNX53M3GJ9bvQ6mbfhoOW2hz9bedTn2Z9R8z666c1F4qoIbAAAAIdQUladseFFjw0AQPNaubOmhv3Vry6Q//25nSEGXNCni3foUjxv/75Zrn1toXyxdHeje1wczb6MfNmWkq0vRwebSxM1hqeHu5w7pL0ul9NQYEMd3F28Jc2S2fzA+QNOeH9Rk2GhgiIqI9PP5FGnV0enKD+bJuGqHFVIPUEUo6yVr7f5PMiqAfgTF3SS2BCTfHGLuQfDxoMFx8zYUMqt3g+1M0mUpgbkvKw6rKv+L6q8UmsI9veWMb3iZOqYmgzXsX3bWS7n1MrY0EHHI+Yxyiu2zc5QjNJL7cLMwaKU6vv6Vzcpby2JETVZUwM6NL5fiU+tQI8KaqqfpakZNEZAR1GfHa6ajUpgAwAAOITMfPOX2tqroAAAbZ+qUX3LW4vk1rcXyaeLd8qbv22ud6Xm8dpyMMvm+vcrzHXPAbgGddBPfbaojArlx1X7bW4z/L09XbKqV2E3xdLt5rIxUcG+csUp5jI1TaFW+Vv3XqgdNFEHeicOTpLv7p8gXVupN4KrCdCBjZrffUywtyXgYF0qd1jHuqvzVekpxbc6AGKtb0KAJdihMkCse3gcLbAR4lezrbh6AZh1BsdZL6yVVXtzG30Q3DpwZpRSag3ubm4y/fwBckZ1fwrln2f10eWiamdsHMkvlvOenFNzPa9El6SqqDTv+5w1Nf9vjWbgadlF+jygleeQRmAjLMCkf8amOHtQTbPvIF/z77lzbOOzPpS9GTWlqOorV+cqCGwAAACHYNSg7dWuJlUbAOAafl17QDfh3JmWK+//sU2+XrZHsuspT3G8th40r6T+x7AO+rxXgvmACIC2T9XkP/Oxn/Vny+G8kgZr9q/ZkymPzF4pL/y4vsmvsXp3hvh4ecisW8foUjVNpVbTlzUQ2DBq7Z/UPUZnFqBl+Ht7SGl1aVwvDzd54dKu9ZaFOm9A3dX521IL6zQxD/P3rJMpoTJErIMUB44UN1iK6t6zkuSc6qbm+w4Xy7kvrZO/dpj/ln2/xpxlNHezuV/G0ajMjuvfXKIv92gXov/+qX4Urc2nOptFMXl5yP3/GCCe7m76/51hhVV2pbI7PVeXpHrhB3OWxgs/ms97xIfIGf1rAiVKYAuVnmpIZJCvJcOkqa49raaJeJCfeb+NQI+ixuVofXRUkGp3rUDG5gPHfi+0RQQ2AACAQ1i7P1/Xpe1cnfINAHAdRuDBWslxHCxo6KDOjtQcGdgxQm46o6eujV1S3jzPDcDxqSyMo8kuLLFZ8WzU8G+MgpIy3eh4Z2qurrNvfRC7qRkbDQU2Uo6YD5p3jAk6rudG4/hXZ10od4xLsAk27M0wZwX0budfb2aAEYC4+iRzrwRl1jU95atbzeWnDD6eKrBRYSkv9ed289++QKvXNiSE+cjlI2L05UXbs6WkvFJem5+sgxkHs83v2bKKY2c2FpTWvK/iIwLk+atG6vJHrU0FM6wF+nrp3hIb9h/RAQzrslIGdZvy+/pk2XuoJkPh/kkDbH6GDlGBx2xS3twSIsyL8YZ3iWryY31NnpaSdca4GD1DlJ4JoZKRU9xg5mpBcZlNsE3JK7btD+MqCGwAAAC7UyUAdh4qlB5x/uKhOvcBANqEnMJS+WnVvmMGKfYcypO+SWFyep+autvHswqyPr+vO6BLvBhlI1TZh7zCunW7AbQ9qoyNygAzmhgH+HjJFSfbluFRpXDU/Xam5Vi2NaYUngqaXvPqArn0hXlSWFqum3ofL52xUV0qKD27UJ7/YZ0+yPv0t2t17w518DPQp3V7CLhiKSpDUrhtn5SxvcL1+VVWgQtrN58Wr4MYqpG4QfXbqB0EUb06SqqzQjZYNRFvKBNHZXhYyyuukGd+2ScHs0osAQ81j7r1o61y16fbLQETld3xw1pz9kNeUc3f0nMGJYm9qCwEFYxQJdUMp/eJ1+drq7M2VIBDzQX/ffFgff3n1TWlp2588099rrJNYkL9LP1MlKHHEVw4UUM7R8mjkwfLHef0bfJjVemqf104UNpHBsr51ZmkKtBjaB8VqD9TMqqbi9dmfKeaMCBBbjzD3KheBVgP5RRZerOozzCVpbZu72FpyyhiDQAA7C63qFyvOIoOav3VQwCAlqECE49+vlI2HciSg0cK5IZx5sl3bap2dmFJuYT4m+TeSf31yst35m1tlsBGfnGZpXSFUTYiyNdbtqfmyFWv/KEPSiRFBp7w6wBwTKq0lLEa/Ot7z9AHkNVny0d/7pBucSE6aKAaiP+yZr/kW614VgcUVb+MY2VrWJfMiwhsfNPw2rw8PSwNgB/+bKWun//r2mSbZs+UoWpZRgNwpUOkbTmx0V1D5Mc7+zWYkaMOxjemx4MqV2b8bTP6eTx1UecG7280LTeorI3a/vPdHtl5yJxRsu9wkTz8zW59v1fmJcvEviGyLtl823Wnd5fu7ezXn0W9fz+/e5zNtvZR5gUH6jvCxv1HZPnODOnXPlxC/OufE6rxv3hER8vzqZJUxaUVculJDY9hS1GvP7xr9HE/XvXKefOmky3Xrd9bKuChqCwV43Pos8U75au/d+vvUio7TAkP8JHTesfpvmQ70nLkipnz9fZv7h0v7/2xVfcTU72Ffn3obGmrCGwAAAC7y8wzr5yNCCCwAQDOTpWO+PfslTqoYFDBjYYYBxPVSmrrsgxq9fSJHIRRKxpve2ex5XrvxDCbhpupWYWyPSWHwAbQRqmAqXWZOyMwoA4uz542VpeumTjD3KjYOqih7DmUe8zAhtGbwxBhVUrmeEpRKWq1dVZB3T4gRmAWLadbjJ/8vslc+qi+AMbxlhmz5uPpJmpBveqzsT2t0NKkvCGNaUq9dFdNptGr85IlLce2P9WuDPP7qUO045Uy8zeZ/+7/uGq/PimThrSXhIgA/f9T/R+29v30CTZj8tzUEfr/dVObdzsi1Tz8klGd5JRecVJUWm75vqKyUdTnwnt/bNPbnv1+nUwa2t7yfcnH23xof0dKzfvgs792yi+rD9g8f2l5hTz3/Xo5f3gHHdQ1zP5rp+xOz5P/O6+feHq46+9O36/Yq+/n4e74hZ4cfw8BAECbl5lfHdgIJMUeAJyZKoVwzwdLbYIa6mCQCiI0xLhv7cDGyz9v1CUUVGmFxlArnQ0HDxfoEhbG6z5z5XBLkKRPdYDD+rUBtD0zvlljuXxR9Spvg8oQ8/b0kNE9zD0MlDvO7iNPXj5MX7au59+Q2p9r1jXym8qrOrChyuZZl9gxhAWYjvu50ThDO5pXwZ/eM7TFhkwF1ZTnf90n87aYA/7W5asa68qRsfLff9S8p1WfQsUoUWX4Yd1h+XFDjn5PDepYt+m5vRkBPWvDu0Xr7wO1szDCA011AhjqwHtbCGoo6ue4+rTu0jE6yLLgwvgc2lKrD5nKbjEaspuqx7DAKgikSusZVBassnp3pizYlCJ3v7/U5rlmzd+mt5/9xC/6O9z0j5fprNm56w+KMyCwAQAAHCewEUBgAwCc2U9W9bAnj+wkPz5wpgzrEiW5haU2gYfaTTAVo4yHdR35ez/6W9ew35ycJdPeX6Kfpz5/bk6VMx/7WZdjUA2Ar3ltgbzx22Z924T+CdI3yVwfXbn//AEy/fwB+jKBDaBt2pKcJcurm4A/dukQueb07vXeLy7M3+qynz6oqDQUjFVZGu/O26pXkn+00NzTwGD08TkeRjbAvPXJliDvNad1s9ze2o2RXZEqifvh9b3kznGJLfYaRs+MhdtqDlQfT3/ByUOjpVNUTU+XrjF+NnMqJdjXU16Zn6ovF5VWOGQps9r7pN7nRqDi7EHm34M6cH/ukCR5+6ZTxFWozwCVAbavOsM0rdbnkdE33MfLo97f67aDOZaePUfyS2y+76jtKnvj679322S0Kec99avOsB3bt52M62fuf+Lo+GQEAAAO0WPD+AIOAHBe8zfUrPAzDiT6mjxFzcFVHez6Ds7l1crYGNY1StqF+eua28Yk/K73lujLy3cekl4JYRIT4mszmTcOMKomwdYNgJVrx9oe0FQ9NoysDZURct6QUgnyoxQi0Fb8uvaAPP/Den152sS+MqRzw42FQ6z+76ugqhFgVaWpVHAkOsRXwgJqMjH+88VKWb/viHy+ZJe+Pqp7jEw5qbNk5hVLdIjfCa9cf+3XzboJuQqwTB7VWb5dvlcfmFSNz9HyWrrfX+2eGU1x0ZAoWbEnV/59Xkfx9HCTMP+a90SPWH9Zuz/fJoMjp3p+pQzsUBPcd2Sqwbh1map3bzlFZ1YdqyxcW6T6bKzclaH7kK3bZ24ArhZlzPh6jaVvUPvqbIx7zu2nS1QZjJKbxnepI/nFupG44b352/T3JeN7V23DukQ7TSYMGRsAAMDuVOPwhtKRAQCOq6qqSg7nFeuJs1oNuDPNPNm25l8dzDBqRtdmlFrwr55gq9ISU0bX3wj0me/W6abf1qsMF21Jlf2ZNQd0Nu6v6eehDgaqQEZtqgyN0Q/kwU+WN/rnBeD4n0lGUEN99pxxjFXHxmeB4uvtaakprz5X7nxviVz6wjzL7d8s26ODGtbG94/XtfFPpImwUmEswRaR9OwiS/Nko7dGYQOfn3AuRsaGoW9C47N8hnYIkjen9pDYEPN71jq43yXaNqjWM64mE+nSIaFy33m9xVFZl4c0+kUY4sMDXDKooag+I8rFz82V39clS7CftwzuFGlzn07VGWadYsznKkPWWECiPkOMrJcnvlojGbnFlsf9sma/TRbHs1NH2PQ0U309nAUhXwAAYHdGqqxafQQAcB43vvmn7MuoCSoYteD/eVYfy3VjpfGni3fKxSM72RykUAGJt37foi9brxxUBzOORpWJ6REfqutBP/blaktwPNTfJOlWPTkmDEyo9/HWpT9Ssxvu/wHAuRzOq6kt/9I1o45ZfkcdLDxWuaeM3CLJLyqzlLezVl/g9HjsSK3JNFNl++JCzQeq1efltpTsRvcagmOz/tsTFegl/5lk2/ulPiqjXWVfBB0ls92vVl+WO8YlyhVvb9KXh7T3twngORrV1+aXNQfklV82yklWPW9c3chu0TqYagQferQLsSwUUYJ8vXSzb0VleD1/1QjpEhssU140B2OHdo7S35OMRRzWVGmy2t/bnr9qpP4c6hgdqLNknAXLIgEAgMNkbHgR2AAAp6EOtNUOaiiTR3WSEd2i6xws/GHlPt0zo/Zz1Nd4NyGiZrWpYpSHMXy4cLsuz/Ddir3m+4f7y4tXj5TYsJpVqw9fNEhuHNezwf3/6J+n6aaaqmb+39vTG/UzA3BsqVnmEnY3juthWfF8ooENVWLvkc9XWq7/33n9LJdVqarmMLpHbL29Py4eaT7w/Y9hHZrldWBf21LN70+lW6x/vY3ia3t2chf5vzOTJCm8bnP6k7qYV9lHWZXQCvXztLleO0vE0aiD8+cMStS9cK47vYe9d8dhGA3EDXec08cmUJtY6/ZeCWE6IGFkf6nPkI7VpaoMn901Vi4b3cVmm/oepAKpKuimsjacKaihOPa7GwAAuIRyAhsA4FRU3ecrZs63lEIwyqYofRJta3n37xBhuWw05FXlYranZFtWIl4wvIPNJF7V1n7lupPkzRtPlktGdZK3bjpFT76tG/s+/e06WbvXXHf6oYsGSaeYYBnbx1x2Rq1qrF2yoTa1EtrIDHlk9kpZsdPcaBiAcyopq9A9KZSucTVlVY7GeiW70cD7y3vOsLnPb+uSpajEXAqqf/twGds3Xn564Ez5/O5xNv03TsR5Q9vLWQNrmlarPhvGz/HrQ2fLAKvPUTivs/rV/B4TwhqXRZEY7iNje4bVm31031lJ8u7VPaRdqEn8TeYD0lOGm7Me7jojUZLCTZIQWn8fBUeifjbVC+d4Gqm3VSojQ50MKiNVObV3nM7MmHHZ0HofF1j9GPW9TJX2UmWmLM8RYNKLT1TvIcOZAxMdsrF8Y1GKCgAAOEwpKmNCCQBwbP/709ys22iee9nJXWTNnkxZtStDlzGwpibganKeW1Rm9fgd+tQ3yVxbu29S3cam6nHK1aeZm3+/cePJkl1QIpOfn6uvL9iUYinPYARFxvWL1/tTXFYuJq9jrzrccrCmH8c3y/cetcmwo1FBoZzCUt0cFHBlaVmFEhHkI//5YpUOuqqyKr0SzCVYjiXIr+bAoXFwzzgwaDTwVY14VaBBfYb968JBllXm1tkeJ0o16rXOWosL5f91WzS4vTk4r0zsf/Tge2OoEozxYeb3zUMTO8hLv++X0V3NQb0JfcJlQq8gkcqa8mxwHurz6J1bxsjFz/1uua7c/48BR33cwxcOkg8WbJNBHc3vr97Vn4WDOpqDauq70fj+CRIe6CNz1hyQCf3rL9npLAhsAAAAu6MUFQC0jvTsQtmVlisjuze+jrVq1L31YJacMyjJUs9ZHVCvvbJYrShuaFXx6zecLJe9NE9PqP/7xSpZvDVNbzca8aqmvY1dXf34lKG67rQ6gKl0s2p4aZSTaaikTG2ThrSXd+Zt1X1A9h6q2/jckamm5+p388Xd4ySoGQ+wAq3hkc9WiLeXh5SWV+rVw00JEqgFMaqMnep9cd9Hy/S2Mb3iLJ8J5w/v0OgVyEbZla5x5kCq4ZrTuslHC3fIKb1iZe+CPJ0llhQZYBP0aG7WJWCMz1W0PY+c20FUsaAw/+Z9Lw1ICpT3r+vVrM8J+2pMqbLaOscGy38vrcnmUJ+FKsOs9keiymo9VmarMyCwAQAAHCawQfNwAGg5f2w8KE9+s1ZffuKyoZbVfMdy30d/S3FZhWzcf8SyWjm7oFSXhlLlU07pFXfM51Crqc8ckKAbhBpBDWtV+jBP46iJuGo6bhzEvPKUrnK8LhzRUSYOTpJXftkkv69Pltyi0mZrBtySCkrKdFBDWb0nUx/UBZxFaXmF/L2jpvTb5Od+ly//7wwJ8Gncgd6vlu6W9/7YZrPNyOAa1S1aLhx+7IbM1r6/f4K41yqBM3lUZ31atqOm/05zZmjUJyKwJmOjMRlncE4jq/tiAI0NdtbuM9ZUnm24KkLb/ckAAIBTBTY83d10Gj4AoGVEBtU0uV20ObVRj9mWkq2DGvoxW9J0s251ULKwpFzaRwXKmQMSG10TW9WlN3SrVf++dl+OYzFecXjXaPFv5MHQep/HzU3XoDaalaccMfcAcVSqL8nhvGJZbnVQeHeac2WaAOrzw5oKa/6x0RyYOJbb31lcJ6hhUB9F9/5jQJPrxasgQkPlUKOsPjdVX56WNLpnrEwd01U+uO3UFn0dAM5DfR68x2dCg8jYAAAAdqdKCnh5ENQAgJYUH15Ts11lTdx2Zm+bVXxZ+SW6pJF1oEKVe7H22pxNMrxLlE0jy8ayrnl/y4RekplbJFkFJTKwY2STG4aqZpcq4HLukCRpDkYDYNXDw1F9uXS3vD13i75svRAgNbtQikrLG13OC3CE/jDW1Lv5z80pOnvqaKqqqmR7ak6Dt8eF+YtPM2c6qGa7htgW7nuhPgenjO7Soq8BwLnEUJbuqPjmAwAA7CYtp0RW7MmVgpIK8TORcg8ALUn1p/j3xYNl6fY0+XVtsuxIzZEe8eZgww8r9+pyTFec3EUutyrtVFB9AFIFJTYdMDfavvrVBdXP17SyLGoV9a8PnV2zoVZvjKZQBy8vPamzNBfjZzmS77iBDSOooVRWVela/3lFZfLn5lTZnZ4r79x8SpNXqrcVajzI+nQO8zcclIrKKpsa8uqzqaDYNovDoAKYy7anS1JkoE2Q4Yx+8bqnz1PfmsvrKR2iahozNxfr/jWxIS2bsQEAaBpKUQEAALt5/Me98sq8ZNmaWighvqy3AICWNqJbtIyqbhy+51CeZRX0rPnm0i7r9h22ub9xoP/m8b3kvkn99WXjoKSR5dAWJEQE6POXftqgG6U7mgOZ+XW2PXbpUEvN/+TDBZbfVVpWoTz25Sp5+tu1UlFZ2er7unZPpmTmFrfa673+6yY5+/FfdA8ZODbVF0YFIp79fp2+PnlkJ/nw9tPE29NdSsrNJe9qe+PXTfLE12vkxjf/lLnrky3brxzTVff3UcHYsOqAh+rl09ysA2axYS2bsQEAaBoCGwAAwG4qqpuGK4G+ZGwAQGswasarXg2KKgdl1Lxfv++IXPPqAvllzX5dLubzJbv0dnXgcFh1CSrDoE4RbeYXFhPiZwn4zN/QuFr/raW8olKue32hvvzQhQPlo3+eJq/fMFq6twuRB84fIInVQZml29N1acfnf1yv+6HM23BQNldn2bSW3MJSue9/y+TGN8372xpj8+3yvTpjQ/3McGy1A3SdYoJ0RoTJ00PKyusPwu3LqHnMz6v36/OHLxqkewap0k0qw8woi9fUknZNFUdJGABwKAQ2AABAq1Org/cfLpauMX76+uiuIXL1SXH8JgCgFRjlXNQKfxXceOSzlTa3HzxSIC/+uEGmf7xMikorLKWaVJPuT+483XK/tpSxYQQN/E2euqyTI1HZGAYVxFANjDtGm0vu9O8QYekz8vLPG+WcJ36RdXtrsm4Ot3JpLSNAlt9AWaHmtOnAEfli6W7LdUfujwKzI/m2mTwBPl763MvTXQpLy6W0vMJyMlhXVzP+L7SrlTkRUv2ZFuRrfr7m1jnG/P/NyJACADgGaj4AAIBWU1peKfd+vlPahZpk7uYjlu3/NyFJTF6stwBgXwcPHpT77rtPfvnlFyksLJTOnTvLe++9J4MHD7YEZR955BF5++23JTs7W0aNGiWvv/66dOniXM1e9QppLw9JOVIgj3+12tKMVx0sVEENw/YU8/YbxvUQD3d3S+bGNad1k37t2062hkH1p1ABAxXYUL9rR+lXsbe6ZJg6uGqUzLJWX18BlV2zbMehVu8ZopqYt7S/tqZJenah7jli1apBiquDcHBMq3ZnyMJNqTbbVLBU2Z+Zr/vFTJwxx3LbdWO7y0UjOunganSIr6RnF+ntkUE+0i7cNrBxz7l95Yslu+Ufwzq0yL4/f9VI/R3WUT4TAABmHEEAAACtZltaoWxJLbAJaihenkwUAdhXVlaWDlR4eXnpwMbmzZvlueeek9BQc3Nt5emnn5aZM2fKG2+8IcuWLRN/f38ZP368FBe3Xj+B5qoZrw6Sq7JTqiH4wI4R8tI1I2XiYPPK/9ouGN7Rclkd2Js8qrMug9QWqcBGQUm5pOeYD6I6guU7D+nzRy4eXO+B1d6JYfLD9Am6mbLhtN7tbJq/N4bqx5F1goEQteq+JalslP98sUre/N02qNEar43jl5ZdKA98vFx21cqGCqzOsFBBjdrembvVEizzN3nJBcM7yIT+CboUm5eH7aEslT124xk9xduzZcqaqkCwsa8AAMdBxgYAAGg1vvVkZXi6u9k0ZgQAe3jqqackISFBZ2gYOnSoWf2rVvC/+OKL8q9//UvOO+88ve3DDz+U6Oho+fbbb+WSSy6p85wlJSX6ZMjNdZwSR+pAoOpLoPRvHyHd24Xqg/rqAJ5qoG145KJB4kq6xgXr86//3iO3TOhlt/1QvxuVmRDib9K9MrrEBusSVA1RB3TvPref3DCup2xJzpLESHNmR3FZ47MYPl28Sz5auF1uP6u3nDOo/iBXY0tRtQQVdHn+B3PTaUWt4n/miuFyKLdYZs3bKqlZhS322jgx1plgZw1MtPTKCK8uIdUQFdRQ7ymVKabe2wAAWCNjAwAAtJraqyvN2+rZCACt7Pvvv9clpy666CKJioqSAQMG6JJThj179khaWpqMHTvWsi04OFiGDRsmS5curfc5Z8yYoe9jnFTgxFEE+9WsPo4K9rEcHFcHHadN7KsPJH597xkysrqhtqsY0TVa19H/bsVe2XqwdRtvW/tp1T557MvVcs8H5vfWoI6NK/2lVpUP7RIlPl7mlevFR8liUMG6BZtSLD1FVlRnhqheHfszzOWvmqqghXprVFRWyfcr90padpH+/dxxdh958rJhEh3iJ30Sw8TX5ClZBSWtUgoLTaN6n7z6yybL9QEdat7LPt7mtbZDO0fW+9hJT/0qOYWlEhdqW3oKAACFwAYAAGgVB7NKJCOvtFHBDgBobbt377b0y/j111/l5ptvln/+85/ywQcf6NtVUENRGRrW1HXjttqmT58uOTk5ltOBAwfEUahMAENkkG0mwPj+CfLpXWN1+RdXo2r+X3t6d325tftTGFSWxStWB4KVbk0s/WUENn5ctV9uf3exlNSTubEvI19mfL1Gbn5rkc7UsF49v8aqAXlTWJeDOlpQpSkO5RTJWY//LJ8s2qmvv3XTyToAF2fVQLpDVKA+V4EaOBaVnWGdsdExOlBmXDZM7v9Hf8u2hy8eLP3ahzf4HJOGtm/x/QQAOB9KUQEAgFZxzazNtl9C3N2knKgGAAdRWVmpMzaeeOIJfV1lbGzcuFH305g6depxPafJZNInR6SCF6oZbpCvl/RMqOkjAhE/k3maXFpW2erDUVpeIRc+85vNtv4dwnUmSVN4Vwc2jCbwG/cfkUGdbFfFZ+TW9BH53587pL/VgeW8wlJLUOHa1xbI/53XX07uGXvM1y0oqemVcN5Tv8pDFw6Uk3oc+3HWyioqZerL83WZShXAqB1gsg7KGSaP6iQ/rNgrL/64QV6bs0lGdouRWyf0kiAvSl3aU3lFpXywYLu+/PQVwyWvqFTiwwP0yZrqmeFXnb2hnDMoUQfllP/dcVqd4CsAAAqBDQAAYBf9EgLk6tFx+sAaANhbbGys9OxpW8O9R48e8tVXX+nLMTHmkkzp6en6vgZ1vX//mpXHzkL1azAyE2DLVN2AuKS88f0pmsu2g9n6wL4qt6SyZg7nFeuyYPU1DT+a2r2r8mo1EVdlqN74bbPNgeXkIwWWRQe51c2c1Wp79Xf6nXlbjhnYmP3XTvls8S6bbWv3Hm5SYGN/Zr7M33BQDueZgxnqoHhcmJ++/MLVIyW6gT4jQb7eMrZfvPy0yry/KnNjSOdIGdujaQEhNK8l29Itl1UAtXbTb2tGAq+Hu5vcflYfHYhrF+ZPUAMA0CBKUQEAgBanamPX1jHKV7pE+0mvdrar9gDAHkaNGiXbtm2z2bZ9+3ZJSkqyNBJXwY158+bZNANftmyZjBgxotX3Fy3H28s8Ta6vfFNzUk2Rn/lurcz+a5e8M3eLDjZk5Bbr21QDb3WAVwWgPI9yMPhoLhvdRcb0irO8lrU/NqZI8uGa8kAqmJKZW6z7c6iQiAqoGBkbirHCfmdqjmw9mF3ntXILS2XW/G2WUlRG6aDGlPNSr33ne3/pkljXv75QPl1sLjllSDlSqBuF94wPlfBAcz+Y+lw/toc+7xwTpM/VzwP7+nHVPn3+5o0nHzWooVwwvIM+f+bK4fpcZd0kRZpLjAEAUB8yNgAAQIsrq7ANbMSGeMtlw12rIS0Ax3bXXXfJyJEjdSmqiy++WJYvXy5vvfWWPilqxfydd94pjz32mO7DoQIdDz30kMTFxcmkSZPsvftogYyNV+dskj83p8pTVwwTD/fmXxP45u+bZe76g5brXyzdbbncHKV3rhzTVbYkZ+nsBesSUcrqPZn6/NO7TpdLXzAH66aM7iyXntRZbnpzkazdmym3v7NYtqfm6NtC/b31+a3vLNbnX/3fGWLy8pBFm1P1fRIjbBcpTB3TTZZuT7c0Jq9NBXHenbdVBytUMGRLct1gyfAuUfL3DnND80CfY/d78fX2lF8fOlsHZaa8OE8yq4MzsI+UIwWybu9hHVxrX90D5Wj6JoXr3x8AAI1FYAMAALS42uWmrju5nfh619T/BgB7GzJkiHzzzTe64fd//vMfHbh48cUX5bLLLrPc595775WCggK54YYbJDs7W0466SSZM2eO+Pg0vIoczse7OrChbNh/RLLySyUiyKfZe2ls2n+k3tt6J4ZJt7jgZnkd/+p+IYXFthkbKhMjwMdLwgJqfq5zBiXpn12VflLNno2gRn0ueOY3GdcvXn5fl1xv6Sv1N75zdJD8tS1ddqTmSJdY259ny8FsSyCn9mMNnWKCLYEN1dS9sVQPDpXtQsaGfa3abQ6endKI3iwAABwPlyhF9frrr0vfvn0lKChIn1Sq+C+//GLv3QIAwGUzNmjlCcARnXPOObJhwwYpLi6WLVu2yPXXX29zu8raUEGPtLQ0fZ+5c+dK165d7ba/aBleHrZ/pYrLbIMCx6O4tFx2pdUECh7/crUcsCoFZe2+Sf2b3FOjIX4mc0DAKBFlUKWpjKDHy9eOklvG97SUeSqoFQRRVGaJ+hmsGUENpbKq1t95Nzc5d4i5HNVSqz4LhiVb02weO6hjhC5XZL1i33qVf+3AyNGooIbqS0LGhn2t33dYf9/rk1TTlB4AgObkEoGN+Ph4efLJJ2XVqlWycuVKOe200+S8886TTZs22XvXAABwCSW1MjYCfMjWAAA4poSIALlubHeZ0D9BXzcaaZ+ITxbvlFveXiwLN6VIRWWlLN+ZIX2TwmTmtaN0o/BR3aLlxwfO1Af2VV+N5uLvYw5eFNTqsVFUUq5LNyld40LkvKHm/gZK//bhlj4fquSU4cu/9xz1tc4elGhzvU9SmPh5e+qyVkYfDtWjQ8kqsO29obI/jECGUdaqg1VgQ5XJagqVYWP0CYF97M/Il5hQPwn0bXy2DQAATeESpagmTpxoc/3xxx/XWRx///239OrVq97HlJSU6JN1Y0AAAHBipaimDI+RIR2CpEesH0MJAHBIKtvgohGd5MMF2/X1Rz5bIV/cU3OA/3ioBuHKcz+sl25xITpLoXNMsL78+d3jpKX4eHmIu5sqRVWmMy5UXwz186kMDtWQuz6Xju4sp/dtZ2kYbvhooXk8GtI7IUy2p+RYAjOqL0nvpDBZtStDv/aT36zR5YneveUUySksFW9Pd8v3AzerXE7VPHrvoTwdYPr3xYMlNtRP/KszTxorItBXtiZn68bkHFZvfaqHSmpWgS6rBgBAS3GJjA1rFRUV8tlnn+nauKokVUNmzJghwcHBllNCgnm1DgAAaLr9h82rJv1N7tIzzr/ZSmwAANBSjKyC48nYUAfUH/tylSzakiov/bTBsr2krEJm/my+nhhpGzhoCervrZ/JU3ak5cp5T/0q//1ilWTkFsmR/BLJa+DnUn02agc1DKr/xpkDEuTRyYPrDaKoDJSHLhxok/1RUVmle5UYPReufW2hrNiZoXthqGbltctOqe39O0ToyyO6RTeq8XR9+6mKYy3YXFPyyvD3/hx5YdF+vV9oHioQdSAz33Jdvb9Utm5cmD9DDABoMS6RsaGoWrkqkKFq4QYEBOjGgD179mzw/qpp4LRp02wyNghuAABwfKv2PlySKj5e7nJqd1buAQCcQ+2SUJ8v2aWbat91Tt9GleFZtCVNn2ozDvD3jA+V1qB6Z+zLMB90Vs28y6sP6Ht5NG6d4yMXDZK35m6R1KxCiQj0kTurf35VeuqX1QfkuatGyIqdh2Rw58g6jcD7tzcHKP7amqZLElkHU4J8vWTqmK5y3pD2EhpgkuY0tm+8zpJ59qeNEjmmnfRPrHn+R343Ny0f0ylUBsQ1PWgCW+r/xI1v/qkv92sfLpef3EW/V5T4cAIbAICW4zKBjW7dusnatWslJydHvvzyS5k6daosXLiwweCGyWTSJwAAcGL2ZBbLgSMlMmlApIQHUBACAOAcLhrRUd7/Y5vu9VBaXiHvztuqt6vG1KqU0g1je4hPdZ8KQ35xmbz9+5Z6V6pPm9hXnv9hveW6KrXUGv7vvP5y2zuLLdeX7Tikz68c07jG9yO7x8iSben6YLWXZ02PrH+e1UeuH9tD9+poKEjTIdocOPhlzQGb7QM6RMjkUZ10RklzBzWMPh3q97bnUJ5sPlws/RNrFlsY1qfmEdhoBsb/C2Xd3sP6ZBjaOao5XgIAANcObHh7e0vnzuY010GDBsmKFSvkpZdekjfffNPeuwYAQJu2Zl+ePh/aMcjeuwIAQKN5erhLl9hgHcRQvSMMnyzaqc8jg3wtpZSsszrmrLU9iG9IigyQ0/u0k3kbDsroHjF1shtaivoZ6qOaljeWCuwo3rWyPIwG5A1RP6PxM8eH+cuEAQkS5OctZ/SLb/GylE9fMVwueu53Sc83Z4kUl1fKA3PMvztlU3pBi76+K1iyLU1n49RHZfdQigoA4LKBjfLyclmwYIHs2rVLpkyZIoGBgZKSkiJBQUG6nNSJqKystGkODgAAWkZWgfmAQmwImZAA7D8PAJpClU/akZojd3+wtM5tBcV1e1RYJQRon901VlbtzpBvlu2R9lFBcu+k/nLDuB66j0VrUo3C07OLLNc93N2kXRP6H6ieIYpq+N1Ud03sK7dO6CX+Pl6t/ruLCDTJpkzzz702Jc8mmLE1o1CX5fJU3dVxXHal5epz1Xfl7EFJUlFZKYUlFXLgcL50bxfCqAIAXDOwsW/fPpkwYYLs379fByDGjRunJzRPPfWUvv7GG280+rlUv4wzzzxTEhMTJS8vTz755BM9Ufr1119b9GcAAMDVqcacBaXmVZ7+3q17EAeAc2rOeQBwogqKyxu8TZWdqq24rOb+gzpF6jJLqt+DOlk3x25tz145Qgdo/vPFKn39h+ln6uBGY8WE+llKPDWV6uXR2H4ezUllhAzuECFz1h+U8z/dLCYv8z7Muqin/Lg5Q77elCGZBaUSE8jCi+OVlm3upXHN6d0lyLcmA2hgR3NvFQAAXDKwcccdd8jgwYNl3bp1Eh4ebtn+j3/8Q66//vomPdehQ4fkyiuvlNTUVAkODpa+ffvqoIaaJAEAgJbxw9oMeWVesvh5mw8k+JsIbABo3XkAcKKMEkyGHx84U57/fp3M35iie1WoAL51gMA6EBJg8nSoRujq9M7Np0hpeWWTghrK1ad2k4TwABnfvyZA4wyGd4nUgY2Cskp9ivT3knZBJgn1M2ePZBeVE9g4TpVVVfL39nSdDRTYytk4AAAojvNNq5ZFixbJkiVLdG8Ma+3bt5eDBw826bnefffdZt47AABwLLOXp+vzwtJKMXm6iacHpR4AtO48AGiOxts/rtqnS+7cdU4fnXlw3z8GSHSIn3y6eKdsS8m2aZxdUFIuKmYwbWI/GdTJ8VatH2/DclU666yB1R24nciA9jXBUcXoaxLqaz4UklXUcEYOji7lSIHkF5fL+P4JLd4vBQAApwpsqB4YFRW2q2OU5ORknYoOAAAcW/dYf8nIy9aX20f42nt3ADgJ5gFwJJ1iguSOs/vU2W6UZDqSV2yzPSu/RJeaGtfPuTIb2iofLw85OSFA/jyQL14ebnJBnyi9PcTXnGGQVVS3nBjq9/Pq/brXynlD2pvHrqBUn0cG8R0PAGAfDhvYOOOMM+TFF1+Ut956S19XKwDy8/PlkUcekbPOOsveuwcAAI4hz6ocx31nJTFeABqFeQCcgU9136iSsprFeKosleo5EFfdjwKO4YERMfLgqT5SWOUuvtV9NkJ9PC2lqNC4slMv/bRBX+7RLkQKS8vlia/W6OsBlKECANiJwwY2nnvuORk/frz07NlTiouLZcqUKbJjxw6JiIiQTz/91N67BwAAjqKqqkrSc0slwMdD3praQ8IDqL0MoHGYB8AZ+HiZp9LFVoGNzxbvlJzCUjmDbA2HYpRJ8qsORimhRsZGPQ3gUde+Q3mWy7e/+5fNbQQ2AAD24rCBjfj4eN0w8LPPPpP169frbI1rr71WLrvsMvH1JdURAABHtutQkaRml8rE/hEENQA0CfMAOANT9cr/LcnZcvYgc1biws0pEhHoI1eO6WrnvcOxBFf32NifXSzL9ufIsMRgBu0ofluX3OBtQdWN2AEAaG0OG9hQPD095fLLL7f3bgAAgCb6c7u5t8ap3WsaqgJAYzEPgLNkbPy+PlnuOa+fztxIPlwgQztH6UbbcGye7m4SZPKQtSn5+jRjQicZ2C7I3rvlkN7/Y5t8vWyPhAWY5Eh+id5236T+0jcpXNbsyZQe8XzXAwDYh8MGNj788MOj3n7llVe22r4AAICm2ZJSIH7e7tIjzp+hA9AkzAPgbF6ds1E8Pdx1j40+SWH23h00kmognltiLiW25VAhgY1atqVky9u/b5EN+4/o69ec1l0SIwN0XxkV1FDGUXYNAGBHDhvYuOOOO2yul5WVSWFhoXh7e4ufnx+BDQAAHLhp+JbUAumbECDu1XWtAaCxmAfAGSRFBlguf79in+XywA4RdtojHE/WhuFATjEDWG32X7tk7d5MSc0q1Cfj/U4QAwDgaMyFQR1QVlaWzUn12Ni2bZucdNJJNA8HAMCBrdiTK2UVVTK6S4i9dwWAE2IeAGegMjQeOH+AzTZVqqd9VKDd9glNk2wVzCgqq2T4qn2xdJes3m0ObCheHu4ycbC5jwwAAI7EYQMb9enSpYs8+eSTdVZxAQAAx3E4v0yft4/wtfeuAGgjmAfAEXWItu3J8NzUEeJGpqLT6BdbE4QqqyCwYYxDXpH5e5wSE+Ir39w3XiYObm+X3xEAAE5ZiupojQRTUlLsvRsAAKABRwrME+JQf6f7mgHAgTEPgKOJDfWzXB7VLVriwugr5UzuG5Mk+7KK5b/z9khJOYENJbewVJ9P6J+ge8ZcMLyDztgAAMAROewRh++//97melVVlaSmpsorr7wio0aNstt+AQCAo0vOKtF1q8P8vRgqAE3GPADOwvqA73lDO9h1X9B0gSZP6R0TIN4eblJaUeUUQ5hbVCr7MvLlxR/Xy9Qx3eTknrHN+vzZBebARmSwr1x+cpdmfW4AAFwmsDFp0iSb6yqlNzIyUk477TR57rnn7LZfAACgYWp135aUAukU5SvenqzwA9B0zAPgjIJ8CeY7K/V9xdFLUS3anCqfL9kl21NzLNse/2q1/LAyTJ6+YnizlUDbmWZ+/vBAU7M8HwAALhnYqKx07C8WAACgbgmqS9/YqC/3jKMcB4DjwzwAzmRU9xj5a2uahAf62HtXcJy8VMZGuWNkbBSUlMnSbelySq84nRH05+ZUmbNmv6zanVnv/dfvOyJZBSUSFnDi7z8V3HltzibxM3nKSd2bNxMEAICWwFJKAABwwr5fkyHfrs6wXO9BYAOAE3vyySf1Cug777zTsq24uFhuvfVWCQ8Pl4CAALngggskPT3drvsJ+5t+/gD59K7TJcjP2967guNk8nCXUgfI2Cguq5AvluyWZ75bJ+c88YsuN/Xs9+tsghrXnd5dnz6/e5xMHdNVb1OlqY6mtLxCKqvMgZtf1x6QHVZZH0pGbpF8smiHPP7lar0PZw9MlEAykAAATsChMjamTZvW6Ps+//zzLbovAACg8V6dn2xzfWBSIMMHwCnnAStWrJA333xT+vbta7P9rrvukp9++km++OILCQ4Olttuu03OP/98+euvv1p0f+DY1Kr65lgtD/vx1oGN1s/YUH1ED2Tmy4pdGRLi5y3P/bBel/Q0/LLmgM397z63r5zRL8FyvXu7UH2+cleGDOgQIRv2HZbHv1oj907qL/07hEtZeaXkF5fJlBfnWRrdp2YV6vNfHzrb8jxv/rZZFm1Js1y/eFSnFvypAQBoo4GNNWvWNOp+zVU/EgAAnLjadalvHNNOAn0c6isGAAfnKPOA/Px8ueyyy+Ttt9+Wxx57zLI9JydH3n33Xfnkk090zz/lvffekx49esjff/8tw4cPb9H9AtByfDzddaZCa1CBi6e/XSthASbp1z5cHpm98piPGdo5Um47s7dEh5iDE4beiebARmZusT6fvWSXLkv15DdrpLyiUgpKysW9+iPTCGgYDh4pkHZh5rKh6TlFlu2PXTpEgnzJPgIAOAeHOurwxx9/2HsXAABAExWX2QY24kNpOAnAOecBqtTU2WefLWPHjrUJbKxatUrKysr0dkP37t0lMTFRli5d2mBgo6SkRJ8Mubm5LfwTAGgqk5e7lFRU6aCDhxEJaMasDNXwu8rDUyKDfHQ/lgWbUvRt363YW+f+nWOC5JGLB0tOYanc9s5ive3fk4fUu1/enh4S4OMlR/LNgQ2VnaGoxxqsEkBsfLNsj9wwrod+jvTsmsDGwI6RJ/ojAwDgmoENAADgfIpKbQMbXaJtVxQCgDP47LPPZPXq1boUVW1paWni7e0tISEhNtujo6P1bQ2ZMWOGPProoy2yvwCaL2NDKSmvFD9vj2Yd1qUpBfLo4p36sioHFepfs/jDKDs1tm87mbv+oNx5Th+Z0D9BZ6apZvTnDkmScX3jjxpsiQ721dkXSnZBTUCjPipD45rTusl/v1wtP6zcp7d5ebrrQEjvxDC559x+zR7YAQDAZQMbK1eulM8//1z2798vpaW2f6S//vpru+0XAACubuXeXJmz4bD8c2yCpOfW/I1+4dKuEurvZdd9A+D8WnsecODAAbnjjjvk999/Fx+f5uuXMH36dJv+ISpjIyGhpkY+APvz9TIHNopbILCRlm/OojDKQdUuCaUCGbed1VuuOa27DmYYVIDh1gm9j/n8nWOD5Ne1yZKVX6IDFAE+njKsS7ScO6S93DHL3P9nUKdIOblHjPRKCJOEiADLY43ghjKwQ4SlDwcAAM7C/BfcQVdMjRw5UrZs2SLffPONTv3etGmTzJ8/XzfrAwAAra+otEJmL0+XB7/aJYu2Z8tFr22Qe2bv0LddMzpOesaZ6zUDgDPNA1SpqUOHDsnAgQPF09NTnxYuXCgzZ87Ul1VmhgqwZGdn2zwuPT1dYmJiGnxek8kkQUFBNicAjpmxoQIbza2hUlCGuyb21Q3orYMaTdEl1vyZeMd7f0lhSbkM6hipm4d3b1eTXdYtNlgmDEi0BDWuO727Pne36lkUF0ZQAwDgfBw2sPHEE0/ICy+8ID/88INO+37ppZdk69atcvHFF+tatgAAoPW9tfCgzFpkrg1dW7cYJsUAnHMecPrpp8uGDRtk7dq1ltPgwYN1I3HjspeXl8ybN8/ymG3btumMkhEjRrTIPgFoHT6e5iyNohZoIJ5WUJOxYTitd5zOyHjt+pNO+PmNwIbRJyOmVoNxpWucbQm984d30OeVVTVRF6OROAAAzsRhS1Ht2rVLN+5T1ISmoKBA15q866675LTTTqNWLZqdauym3mNNfUx5ZZWkHimQxMhAvW3tnkzJKy6T2BA/6RQT1OTnBABHll1Y3uBtvdvVlDcAAGeaBwQGBkrv3rZlX/z9/SU8PNyy/dprr9VlpcLCwnTmxe23366DGg01DgfgfKWompOaKy49WCD+Jk8J8PXSwQeVnaEyKu77x4BmeY0OUbZZYB1j6maFdY2zzXTzcHeXQF8vySuqCbrEhhLYAAA4H4cNbISGhkpeXp6+3K5dO9m4caP06dNHp38XFtrWpQROlPpSd/3rC+XCER1lXL94CfbzPur9P1+yS+ZvOKgfFxHkI1sPmssS9G8fLmv3Hrbcb9rEvjK+f0KdL7i/rUuWvknh1DEF4LST/9oCTB7i6UEgF0DbnQeoLBJ3d3e54IILpKSkRMaPHy+vvfaa3fYHgGOXosooKJPMonKZODBBFm5J09vG9Ipr1oVvJi8PCQswyZH8Ennh6pHSzSo7Q13fkZJdb5mriEAfS2DjP5cM1oEOAACcjcMFNtTERa2KOvnkk3XzPjWJueiii3QzP1VXV21TqeJAUyQfzpcgP2956pu10iM+VC4/uYv8sfGgvPHbZnn+qpGyIzVHsgpK5O25W/Tp0cmDZXjXaP1l71BOkf6it3hLqm7MFhXsI8t3ZlieOzOv2HLZOqihzFlzQAc9EsL9ZV9GvsxZe8Bym3rO4V2iJcTfW/xMntI+KlBGdmu4RjMAOAJTA4GN2JCjB4QBwNnmAQsWLLC5rpqKv/rqq/oEoO2wBDbKmjewsXh/rj6PDvaViYPby+/rk+XyU7pIc/vg9lN1vwxPD9vvaD3jQ/WpPqoUllGWSjUbBwDAGTlcYKNv374yZMgQmTRpkp7IKA8++KCuabtkyRK9Qupf//qXvXcTDu5wXrEOROxKz5VeCWHy/A/rLbet3JUhAzqEy5PfrNXXr3nVdtKqPPv9OhndI1Y2H8iSvRnmFYMGdd3P21Mev2yo7E7Ple9X7NX3LSmrkMTIAAkwecnQLlFyxcz5sjk5S5/qo4Im6suttZHdouXBCwbW+VIKAI4i1K/+FX0PnGOu1wwAx4t5AAB78GmBUlTZRWXy5kpzlkZMsK+M7hMvV47pKi3Bu7pHSFPccXYf+XDhdpk8slOL7BMAAC4Z2Fi4cKG89957MmPGDHn88cd1IOO6666T+++/3967BieRll0ony7aacmOUFkWtU17f2mdbar8lCpF9e68rTro8PPq/XXu89ZNJ0uAj5fO/lD1UdUKmHMGJdW7HyqgsmhLqr78rwsGyr6MPEnLKdK9N0Z1j5Fvlu2RlbszZHy/BPH38ZQPF2yXJdvS5b9frJIpJ3exSSMGAEdQUVklFVaNJg292/lLXIjJLvsEoO1gHgDAnhkbm9ML5PTOYc3ynKsOmhfHtQ/2luFdIsXRqIbij1061N67AQBA2wpsjB49Wp9efvll+fzzz+X999+XU045RTp37qwb9k2dOlViYijXA1vFpeUyd8NBMXl66GyLhqjgRY92IfL3jkM224d3iZJHLxmiL08a2l6+Xb5XBzgM8WH+csuEXpJU3SC8MVT5KaPu6eiesTJaYm1uv2tiX5vrp/aOkxvf+FPvmzqplGKVEUJ5KgCO4ryZ66SswhzYeHZyF7ln9g59WQV6AeBEMQ8AYA8+1RkPP27NlNtGxp9QDwy1COT1v5Plhy2Z+vp5XUL4ngQAQAtx2CMR/v7+cvXVV+uVW9u3b9dlqVQ928TERDn33HPtvXtwAKt3Z8r+zHwpq6iUhZtT5eWfN9oENVQ5KNW82/DA+QPkvdvGyAMXDJRXrjtJPr7jdDm9Tzt9m4+3p00q77mDa7Iw5vzrLHn31jEyqFPTVtpMPqmz3HlOH3nowoGNun9YgI+8fN1JlutTX/5DHv18ldz69iJJy7Jfo0wAMBhBDSXM31OqyzOLF03DATQj5gEAWpOvVf+w/NKKE3quFcm5lqCGEuLT9DJRAADASTM26qOyNR544AFJSkqS6dOny08//WTvXYKdlZZXyPSPl9XZHuDjKfnF5dK/fbhMGtJeBnaMkB9X7pPtqTkSG+on/iZzbfguscH6/NYze4mPt4dMGmpbG14FOp68fJi0C/M/7hU7Pl4ecuaAxCY9JibET9695RS5/vWFUll9/HBnWq4898M6eebKEce1HwDQEvxNHjK6a4gs3JYt+w4XM8gAWgTzAACtVYpKOVxQJoGm4z9MciDH9jtRu4D6e5MBAAAXCGz8+eefMmvWLPnqq6/E3d1dLr74Yl2SCq7rnblb5Iulu+tsN3m6y8d3jtUBBWsvXD1SdqTmWIIZ1lSg459n9an3dQZ0iBB7iA8PkFevHy3hgT6yJTlLHpm9UopOcOUQAJyoqlq9NVT5qYuHROvAhp+3wyaAAnBizAMAtGbzcOVwYZm0D/M97ufamWnOtL96cKysSc6V+EDvZtlHAADgJIGNlJQU3VtDnXbu3CkjR46UmTNn6qCGSk2Ha/u1uim46mFx9qAkWb/vsIzrG68zMmoHNRRPD3fpER8qzqRjdJA+H941WnolhMqBzHx77xIAF1dRWXNZBTLUqXO0nzw6qaMkhNE4HEDzYB4AoLX5WmVsvLMiRQbFm+dix2NHZpG0CzLJJf1i5JKe4SIlJc20lwAAwOEDG2eeeabMnTtXIiIi5Morr5RrrrlGunXrZu/dggNQB/dvfmuR7qlx9qBES6bFP4bZlpFqa+LC/GXTgSz5vw+X6vJYHu6sjAbQ+tRnr+GNqT0sZfqGd6qbDQcAx4N5AAB78LYKbOw+UnTcz1NQWiEHc0tkTMeQZtozAADgVIENLy8v+fLLL+Wcc84RDw8abaHG6t0Z+sBaoK+XXHWq6wS7BnaIkN/XJcv6fUd0v41ucbZflEvKKuSLJbvknMFJEuLPqmkALds4/Mw+4RIdRFkFAM2PeQAAe3B3c5Pze0XK15syxOThJpVVVXpbU+08bC5D1SXCrwX2EgAAOHxg4/vvv7f3LsCBpBwp0M29v12+V9bvPay3vXrdSRLk6zoH1U7r007Xtn/6u3U6ayUyyEfCAnwst7/x22b5efV+ySookdsb6BcCAM0V2PDyaPpEHwAag3kAAHu5cXi8lFVWyQ9bMnWfjUj/ps83t1f31yCwAQCAiwY2AMPBIwVyzasLbAYkxN9booKPv5mbs4oIMv/Mz3y3Tp///OBZ4uHuJst3HNJBDaOXCAC0dCkq63INAAAAbUV8sDn7/WBOyXEFNnZmmstYdQ4nYwMAgNZAYAMOSWUo7MvIs9l21sBEGdYlylLX3ZUYzcQNmblFEuznLf/5YpVlW0WleTU1ALSE0nIyNgAAQNvVLticFZ+cUyL94wKb/PiMglIJ8fEUf29KagMA0BoIbMChFJdVSFl5pdz69iJJzzGvePE3ecqH/zxNAny8xFWpviIqU+VQ9ZikZhdKVfUK6jG94mThphQ5kl9i790E4AIZG15khwEAgDYoNtCcpZFuNa/6eWumvPTXAflsSm8J9T36fDSvpEKCfDjEAgBAa6GeBBwmQ2NzcpbcOesvufDZ3yxBDXXQ/pO7xrp0UMNw/z/6y9AuUfryhn1HZOGmVH05KTJAYkL95K+taXLDGwslu4AAB4DmR48NAADQlhmBi5zicsu215Ym6/Ol+3KO+fi8knIJNJGtAQBAa2E5AezqcF6xvP7rZokI8pFvlu2xbB/ZLVru/8cAMXnxxdDQKyFMrh/rrftq/O/PHZbt/j5e0ishVFKzCmVfRr4s33lIzuiX0Oq/SwAuEtjwdL1ygAAAoO3z83IXL3c3yS6qCWwkhPjI7iNFsj+7+JgL9VTGRqCJQywAALQW/urCbtSXv7fnbpFFW8yZB4bbzuwl5wxKcsleGscSE1K3cboq1XXLhF6SV1Qmy3YckkPZRfLQp8t1wOPOs/uIjzf/zQGcOEpRAQCAtkzNP1V/jPzSCsu2uCBTowIbRWWVUl5ZRcYGAACtyCVKUc2YMUOGDBkigYGBEhUVJZMmTZJt27bZe7dc1o7UHMnKL5H7P14mf2xM0ds83N3kutO7y68PnS0TB7cnqNEAb08P6RBl28iuT2KY+Ju85LYze+vrf2xKkeU7M/TYPvnNWh1AAoATRSkqAADQ1nl5uOkAhaFKdzY0NxQ/mu2Zhfrcj4oDAAC0GpdYyr1w4UK59dZbdXCjvLxcHnjgATnjjDNk8+bN4u/vb+/dcxkVlZU6q+C2dxbbbP/kztPF08Ndgv3MzdpwdC9ePVI3Cr/61QUyomu0RIf46e3G+CUfLhB3NzfxM3nI0u3psmp3pgzuFMmwAjghBDYAAEBbp7I1MgrKpKS8Ukye7lJabg5spOeXSnF5pfh41r829Ik/9urzjWn5rbq/AAC4MpcIbMyZM8fm+vvvv68zN1atWiUnn3xyvY8pKSnRJ0Nubm6L72db9+/ZK3UmgbVrTusu4YE+dtsnZ6RKS8WFecrsaWMl0LcmGKT6kYT6mySroESXpgr195b/frlaZv+1U2d5MM4ATkRZeaU+9/JwiWRPAADgglRJKeWphXvlyoGxOsBhmPnXfrn3lPZ1SnX+Z94eS8PxG4a1a+U9BgDAdblEYKO2nJwcfR4WFnbU8lWPPvpoK+5V26FKHy3dli6DOkXqg+0/r94vv607INsOZte577AuUXbZx7YgxN9UZ9uzU4dLeUWVtI8KlJIyc23Y9fuOyJQX5+kyXwBwvMjYAAAAruKvvTn61C3SnB2vbEitm43xx64sWX6gZhFkv9iAVttHAABcncsFNiorK+XOO++UUaNGSe/e5p4E9Zk+fbpMmzbNJmMjISGhlfbSua3Zc1ge/WKVtI8MlIEdI+THVfuktHqly9i+7eTKU7pKQUm5FJWW6wPwaD7x4TVfpFVQKSrYVw7lFOnrv6zZL6f3aaf7dABAUxHYAAAArsY6Y0M1F69N9Yo0nNczkl6RAAC0IpcLbKheGxs3bpTFi237PNRmMpn0CY1zJL9YVI9qdRB9/b7DetvejDx9MqhySBeN6GTpCYGWN3Fwkrw7b6u+/OKPG2TvoTzdbFxlcaTnFMmVp3SRTjHB+vblOw7JR39ul4cuHKQDIgBgrbi6NIOqNw0AAOAK8kvMWfBKj6i6/TkrqhuNJ4b4yC0j4lt13wAAcHUuFdi47bbb5Mcff5Q///xT4uP50tGcbnpzkeQUltZ7W3yYv5w3tL2cO8S2Hila3kUjOurMmYc+W6GvL96SJt8uNze2U1buPKTLgU0c0l4e/myFqK/l8zcclEtO6syvB4CNtBxz36mooJrePgAAAG1ZZmGZDmgcyi+VBbuzpHe0v0zsGWm5vaTCvPCDoAYAAK3P3VV6PqigxjfffCPz58+XDh062HuXnIIqFZWRW2QZwzd/36zLGe1IzdEr/79bsVfyisp0lkbtoMZTVwyTwZ0iJSkyQN655RSCGnai0qWHdomS164/SV/PLSq1yeYor6ySv7aly/3/W6aDGkp+cZmd9haAI0vNKRUvDzeJCPSy964AAAC0qK4RNVUGTJ5u8uSZncXH010W7bXtG1labp5FmTxc4tAKAAAOxdNVyk998skn8t1330lgYKCkpaXp7cHBweLrS8md2krLK3Tw4uNFO2XVrgx5/YbRsnp3pnz99546931tziab61ed2k0uHtlJ1xrt3z5CKquqqDPqAFS5qbAAkxzJN6+4Ht8/Xm47s7f0jA+Vp75da3PfL5bulgAfL7loZEfxcOcLOgCz1OwSiQn2Fvd66ksDQFswY8YM+frrr2Xr1q16jjBy5Eh56qmnpFu3bpb7FBcXy9133y2fffaZlJSUyPjx4+W1116T6Ohou+47gOY1vmu4bM8s1JdVQEOVmgr19bQpTWWdsaGCHwAAoHW5RGDj9ddf1+djxoyx2f7ee+/JVVddZae9ckwq++KKmfNttt381qJjPq5LbLA8PmWoBPvZlijhAJjjiAzytQQ2LhvdRZ+f1qedPhWXVciBzHy57R1z75n3/tgmQX7ectbARLvuMwDHUF5RJclZJTKsY5C9dwUAWszChQv1gqghQ4ZIeXm5PPDAA3LGGWfI5s2bxd/fXFv/rrvukp9++km++OILvUhKZYWff/758tdff/GbAdqQwfGBdbIyAkwekltcUW9zcW8yNgAAaHUuEdhQZZRwbKrx2T/fbXhS1ishVMb1i5e0rEK54pSusnbvYXnwk+US6m+SmdeOIojh4CKDfGRbigo2iUQE2WYq+Xh56ODUlNGd5ZNFO/W2I3nFdtpTAI7mjQXJ+lyVrwOAtmrOnDk2199//32JioqSVatWycknnyw5OTny7rvv6kzw0047zbJQqkePHvL333/L8OHD6zynyupQJ0Nubm4r/CQAjleEn5fuqxHpX7enmL+3h+zILJKsojIJ9fXS34vWpuTp2wJNHgw6AACtzCUCG2jYz6v36wPdEwYkyuYDRySroERMXh7y30uGiI+3h+QWlsoz363TQY1HLh5s81jVQ+PZqSMkwORJUMMJFJWZVxf1TQrXpcLqM75/gizdli57DuVZVh8BwNr95kn7eQNqmmUCQFunAhlKWFiYPlcBjrKyMhk7dqzlPt27d5fExERZunRpvYENVd7q0UcfbcW9BnAi3ji/u+SWVOj50uPjO8nTC/fJP0cl6Nu6RfjL2pR8+WXrYZkyIEZWJufK1oxCmdQrUkJ86UEGAEBrI7Dh4l76aYM+f3XOJimtPpD9wPkDpF/7cMt9Zk8ba2ksXVufRPNED47vH0Pby6HsQrnrnL4N3icmxE+eumK4XPzc7zQRB2AR6u8lOYXlMqxjMKMCwCVUVlbKnXfeKaNGjZLevXvrbapPn7e3t4SEhNjcV/XXMHr41TZ9+nSZNm2aTcZGQoL5ICkAxxNo8tQnZXB8kHx+WR/Lbef3jpTZ69N1RoeSXVRuvl+7mrJVAACg9RDYcFHlFZXyxm+bLdeNoIYSG+pnc183NzehFZrzG9I5Sp+OJcDH/LGQV2T+wg4AqseGpwd/CQC4DtVrY+PGjbJ4sbn/2PEymUz6BMD5BfmoSgWiS1EpBaXmjHh/bw6rAABgD+52eVW0iv0ZeZKaVVjvbX9uTpUfVu6rs/2Os/tIYkRAK+wdHJWHu7v4mTxl0ZZUefHH9fbeHQAOoKyiUrxoignARaiG4D/++KP88ccfEh8fb9keExMjpaWlkp2dbXP/9PR0fRuAts3dzU332TACGvmWwAaHVQAAsAf+ArdRO1Nz5Po3/pSrXvlDH5CqrNVAffnOQ/p8UKdIOXNAggT5esmr150kZw1M1BkacG2FJea06l/WHJDiUvNlAG1fXnG5fPhXqiWL71BuqRw4UkzGBgCXUFVVpYMa33zzjcyfP186dOhgc/ugQYPEy8tL5s2bZ9m2bds22b9/v4wYMcIOewzAHsENNbVetj9HUnKL9TYV7AAAAK2PnMk2SGVp3PpOTdr8OU/8IucP6yDXj+uhv4gdziuWPzamSIi/tzwxZai+z51H6bsA17YpOUsGdaRhMOAK3lxwUH7fdEQHxK89uZ1c8fYmvT0+1CRelKIC4ALlpz755BP57rvvJDAw0NI3Izg4WHx9ffX5tddeq3tmqIbiQUFBcvvtt+ugRn2NwwG0PWoJ4Pq0fH0yBBDYAADALsjYaIOW7Uivs+3rZXvkzMd+lsVbUuXuD5bW20sDMDx5+TDpHBOkL2fllzAwgIswMjUOZtn+v1fNMT1VUWkAaMNef/11ycnJkTFjxkhsbKzlNHv2bMt9XnjhBTnnnHPkggsukJNPPlmXoPr666/tut8AWk/tr0PqusmTwyoAANgDGRtt0MEjBfr81N5xOjPD2n+/XG25fMO4nq2+b3AOAzpEyE3je8k9HyyV7IJSe+8OgFYS7OtpCWRYyy+uEK9QAhsA2n4pqmPx8fGRV199VZ8AuJ7aZZtVtgalnAEAsA+WFrTRUlSqyesZ/RIabPY669Yx0jM+tNX3Dc5D9V1R3p67RT5bvNPeuwOgFXhWl5uqqKx7cM+T5uEAAMDF1W5HSX8NAADsh4yNNkI1eM7MK5bHv1oju9NzJSHcXwZ2jJBv7hsvO1Jz5JfV+2VnWq5EBfvKuYOTpF2Yv713GQ4uMsjXcvmrv3fLJSd1tuv+ALAvSlEBAABX51ErsuHrReNwAADshcBGG7D1YJbcMWuJzs5QDV+VXolh+lxtU5kZZGegqfxMnvLslcPlvv8tk9yiMnn627VSWVUladmF0jU2RKaO6Sr+PuasDgBtg5tuianKsYgUllbY3EbzcAAA4OpqZ2wY828AAND6CGw4uR9W7pVXftlk86Xq9rN6yxn94u28Z2gL+iSFy53n9JHnvl8v8zYctGzfkpyte7n855LB4uFORTugrU3WVSGqeZuP2NzWNyHAPjsFAADgIGp3HCsqJ7ABAIC9ENhwckZQw3L9upOkS2yw3fYHbY/q1TKkU5T8tS1NTJ4ekp5TJEu2psnKXRny8+oDMnFwkr13EUALmGsV2IgN8ZbzB0UxzgAAwKW510rZOFJYZrd9AQDA1RHYcFKvzdkk363Yqy+P7hEjvRLC5HBeMUENtIjQAJOcM6gmgDGubzu58uU/JCWroM59Kyor5dNFO3W2R7/24fxGACeiys1pVSIHskp0lsZNY9pJfKiPeLjXXqMIAADg2qWorhgQa69dAQDA5RHYcEIVlVWWoIbqnXH+8I700ECr8jWZPzqKSsot26qqqnT5mpd+2iC/rk0WkR3y9k0nS2JkoM1jcwpL9QHSgEb05zhSVC7BXt7y69ZM8fP20K8xLCFYXwbQ/MorzIGN9NxSKSipkLgQk3SK8mOoAQAArDI2wvw85YOLe4kXCz8AALAbAhtO6MdV+yyXn75yuG4QDrQmP2/zR0deUZnsz8yXb5btkd/WHtABixKrOrPXv/GnPDd1hPy69oCs33dY0rKLzI83ecqbN54sUcG++roqa/XO3C1y25m9pXdimNz3v79l7Z7D9b72+K5hMm005a+AllBeaQ5s5BSZg5b96KsBAABgYSRs+Hp6iDfzcAAA7IrAhpNZsi1Nl6GKDPKRJy8fRlADduHp4S7enu6yeGuaPlkfFFVf9l+/YbR89tcuWbApRZ7+bq2kVwc0DIUl5XLvR3/LU5cPk13pufLo56v09rs/WNrga0YHeEt6fqks2pMt5/eOkvah5qAIgObP2DBEBnozvAAAANWMBA1fLxYXAgBgbwQ2nMzq3Zn6fNrEfhIfHmDv3YELK7XKzOgYHSRXn9pN3N3dJCLQR9pHBcplozvrwIYKaqhMjkcnD5Yj+SUS7Octj8xeKalZhbpPx9H0ifSVTuF+MqJDqPSPC5Rl+3Pk0bm75ZUlyfLs2V1a4acEXDNjwxDix9cEAAAAg1t1KSqqJgAAYH8csXAyew7lib/JUwZ0oCkz7OvMAQmycFOqPDt1uHSKCa5ze0JEgHRvFyJbD2bLtIl9ZUjnKMttT1w2VB74eLnl+rlDkuSmM3pJSVmF3PfR37rp+LUndxK3vXtFTCYRL3M/jmGJwTK6Q6gs2J0lh/JLJSqA1eRAc8ovqbBcjg3xlugg/o8BAADUztigtQYAAPZHYMNJqKbJj3+1RjbuPyJ9EsMsK0UAe1H9MK4b26PBJuDqPfqvCwfKgcwCGdgxwua2QR0j5a2bTpYPFmyXa07rZsk+Ur03Xr7uJPOdysrqfd7O4b46sHGksIzABtDM8ooqxMvDTT65sbcE+njwtwYAAMAK03AAABwHgQ0nsS8jXxZtSdWXrx/Xw967A+g+GwHHaJgXGeSrT/VJigyUhy8a1OSR9Pf20Of5pTUrywE0j7zicmkXapIgX74eAAAA1OZW3T6cZYYAANgfHa+cxJ+bzUGNRy4aJN3iQuy9O4DdBJjMgY0CAhtAs8srrpBAH4IaAAAA9aEEFQAAjoPAhpM4lFukz/vRWwMuzt+LwAbQEiqrqnTGRpCP+f8YAAAAbBkloSkNDQCA/RHYcBJH8orFx8tD/LxZSQvX5uNl/tgqLq+0964AbUphSYVUVgkZGwAAAMfI2KAUFQAA9sdRcifRLtxfvD1p5Ap4V/f1KKsgsAE0p9xic9+aQF8yNgAAAOpjCWgQ2QAAwO4IbDiJWyf0tvcuAA7By8M8iyitqLL3rgBtiipDpdBjAwAAoH7uRikqBggAALujFBUAp8zYKCVjA2hWuUXmjA16bAAAANSvOq4BAAAcAIENAE7Fm4wNoGUzNnxJ5gQAADhaYIP4BgAA9kdgA4BTIWMDaBk5RebARjCBDQAAgHq5GyENIhsAANgdgQ0ATtljo4weG0CzOpxfps/D/L0YWQAAgHq4Vx9BcSOyAQCA3RHYAOCcGRvllfbeFaBN2ZNRpM/DAyhFBQAAcLSMDRI2AACwPwIbAJyKh7ubmDzdJbfE3OgYwIlLPlIsK/fmSf/EAPHx8mBIAeAoXn31VWnfvr34+PjIsGHDZPny5YwX4Go9NohsAABgdwQ2ADid+CCTJOcU23s3gDYjI89chuqkLiH23hUAcGizZ8+WadOmySOPPCKrV6+Wfv36yfjx4+XQoUP23jUArcCNiAYAAA6DwAYAp5MQ4iMZBWVSVEbWBtAcaBwOAI3z/PPPy/XXXy9XX3219OzZU9544w3x8/OTWbNmMYSAC3AnUwMAAIdBYAOA00kM8dHnB7JL7L0rQJtwKLdUnwf50l8DABpSWloqq1atkrFjx1q2ubu76+tLly6t9zElJSWSm5trcwLgvChFBQCA4yCwAcDpJIaY9Pn+bMpRASeqoKRCvlx5SIJ9PaVLtB8DCgANyMzMlIqKComOjrbZrq6npaXV+5gZM2ZIcHCw5ZSQkMD4Am2ieTipGwAA2BuBDQBOJzLAW58fLjT3BQBw/Fbvy9OlqC4bHiP+JhqHA0Bzmj59uuTk5FhOBw4cYIABJ0aLDQAAHAc1JwA4Ha/q4rYVlVX23hXA6S3eka3Ph3YMsveuAIBDi4iIEA8PD0lPT7fZrq7HxMTU+xiTyaRPANpWj43KKuYhAADYGxkbAJyOZ/WMopwJBXDCUrNLJCLAS2KrS7wBAOrn7e0tgwYNknnz5lm2VVZW6usjRoxg2AAX4EbKBgAADsMlAht//vmnTJw4UeLi4vQXkW+//dbeuwTgBHh6mD+6yitYKQUcL7XSsLisQvKKKyTQlxJUANAY06ZNk7fffls++OAD2bJli9x8881SUFAgV199NQMIuNABFBLHAQCwP5coRaUmG/369ZNrrrlGzj//fHvvDoAT5Fm9UqqcGQXQZGv358ni7dmSW1wui7Zn64l53/gARhIAGmHy5MmSkZEhDz/8sG4Y3r9/f5kzZ06dhuIA2nbGRpWwwAoAAHtzicDGmWeeqU8A2gZPDwIbwPG674uddbaFB3gxoADQSLfddps+AXA9lkpUxDUAALA7lwhsHI+SkhJ9MuTm5tp1fwDUbR5eVlnJsAAn6LbT4+XkrqGMIwAAQGObhzNSAADYnUv02DgeM2bMkODgYMspISHB3rsEoJpH9YyiglJUQJOpRuGGCwZFycT+kRLsxzoHAACAY3GT6sgGGRsAANgdgY0GTJ8+XXJyciynAwcOtO5vBsAxMzZoHg40TWp2iRzOL9OXh3YMkstHxjCEAAAATczYIK4BAID9sUSzASaTSZ8AOG7GRhkZG0CTzNl4WE/En7igkwxqH8ToAQAAHE/z8CpCGwAA2BsZGwCcMrChYhuUogKaZmd6oXh5uMmApECGDgAAoInI2AAAwHG4RMZGfn6+7Ny503J9z549snbtWgkLC5PExES77huA4+Pp7kbGBtAIakXh/C1ZMqxjkKTmlEpssEncq1cbAgAAoPGMr1AkbAAAYH8uEdhYuXKlnHrqqZbr06ZN0+dTp06V999/3457BuBEAhv02ACObcWeXHn6l30SHeQt6bmlMqZbCMMGAABwHIzFIRSiAgDA/lwisDFmzBhqYAJtMbDBUingmPJLKvS5CmoogzvQWwMAAOB4WHJemYcAAGB3LhHYAND2eHqQsQE05FBuqVRWVUlxWaWUlFXa3NYx0peBAwAAOIHABhkbAADYH4ENAE7J081NyiuZUgD1ueLtTQ0OTIAPf/oBAACOhxulqAAAcBju9t4BADgenh7uBDaA4xBg8mDcAAAAjgPNwwEAcBwENgA4b48NMjaAJukU6Su+3vzpBwAAOLFSVGSOAwBgb9SjAOC0gY2iMnNTZAA1ikpr/l/0iPWXLakF+vKMCzvJwCQahwMAAJwoeocDAGB/BDYAOG1go4KMDcDG7owimf7lTn150oBIufm0eNl1qFBmL0+XXnEBjBYAAEAzlKICAAD2R2ADgFOiFBVQ19erDkl2YblMGR4jFw2J0ts6RfnJA+d0YLgAAABOkFt1MSrWVwEAYH8ENgA4JU8PemwAtR0pKBNvTzeZOiqWwQEAAGix5uH02AAAwN7oIArAKXm6EdgArKkJ9pH8Mgnz92JgAAAAWrR5OAAAsDcyNgA4JS8PNymtYEoBKBuT8+Xu2Tv05ZGdgxkUAACAFs3YYHgBALA3MjYAOCV/bw8pr6yS0vJKe+8KYHfb0gr1uZpr3zMhyd67AwAA0EaZIxvENQAAsD8CGwCcNrCh5JdW2HtXALsrrTAH+F66rKv4m8z/NwAAANAyGRsAAMD+CGwAcEoBBDYAi5Iyc2DDr/r/BQAAAFruAArNwwEAsD8CGwCcOmOjgIwNQEqqS7KZPPmzDgAA0GKMHhsMMQAAdscREABOybv6AK5RggdwVYfzy6S4OmPDx4s/6wAAAC3FzeixQWQDAAC74wgIAKfk7WGeVJRVMKuA69qZXihT3twoP68/rK+TsQEAx2fv3r1y7bXXSocOHcTX11c6deokjzzyiJSWltrcb/369TJ69Gjx8fGRhIQEefrppxlywIUYLTaYgQAAYH+e9t4BADgeXu7muCyBDbiqDcn5MvP3A5brXh5u4u1JR0sAOB5bt26VyspKefPNN6Vz586yceNGuf7666WgoECeffZZfZ/c3Fw544wzZOzYsfLGG2/Ihg0b5JprrpGQkBC54YYbGHjAlUpRkbIBAIDdEdgA4JTUQVylrJJSVHA9peWVcs/sHTbbRnYOFjc3AhsAcDwmTJigT4aOHTvKtm3b5PXXX7cENj7++GOdwTFr1izx9vaWXr16ydq1a+X5558nsAG4CHe+agEA4DAoRQXAqQMbpeUkgsP15BSV19l2dr8Iu+wLALRVOTk5EhYWZrm+dOlSOfnkk3VQwzB+/HgdAMnKyqr3OUpKSnSmh/UJgPP32KhkCgIAgN0R2ADglLw9qktRkbEBF5RrFdh48Jz2ctvp8dIvIdCu+wQAbcnOnTvl5ZdflhtvvNGyLS0tTaKjo23uZ1xXt9VnxowZEhwcbDmpvhwAnBfJsQAAOA4CGwCcuxQVzcPhYn5YmyHv/pmiL08ZHi0ndwuVif0j7b1bAOCQ7r//fl2m72gn1V/D2sGDB3VZqosuukj32TgR06dP15kfxunAgZreSACcuHk4PTYAALA7emwAcEo0D4eremVesuXywKQgu+4LADi6u+++W6666qqj3kf10zCkpKTIqaeeKiNHjpS33nrL5n4xMTGSnp5us824rm6rj8lk0icAbax5uL33AwAAENgA4Jy8qzM2SsppHo62Z39GnoQF+kiAj9dR79cl2rfV9gkAnFFkZKQ+NYbK1FBBjUGDBsl7770n7u62ye0jRoyQBx98UMrKysTLy/z5/Pvvv0u3bt0kNDS0RfYfgGNxr45skLABAID9UYoKgFOKDjQ37vxgdapsTMu39+4Azaa4tFyuf+NPueu9JXVuq6jVqdLHy4ORB4BmoII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UHyuX/+IyYRzEys7OFssvXrwogktcYowbnss9NgAAAACaIy8Ph5h3BI7AHuOKGsYUDOMKAABwVgWlmAQL0Jy5NXX5qW+++Ya+++47CgwMFP0u+FZaWirWc3YAZxfwRe89e/aIDA5ugM1BiSlTpigzjLi00b333iu24QvwHCy55ZZblDJIt912m2gczuWSjh07JpqRv//++0YX+uuNgy/cnJwzC7Ta+t94P7w/k6COjEs0/frrr+LWvn176t69O3344YcUGRlJrVu3FgEPbr7NnyE3Sn/11VeN+m5wOS8OIlgrQ8Xlv7gMVKdOnUSmDJet4p9jij9LPg5u/s1BJG4Ibur2228XJau4FNWqVatEQ3PGTcblpuPce8Nc4AQAAACguSivsNzkGZoJe44rahhTMIwrAADAWRWU1hzABwDX1aLKUuOExvjhLVqYXb5kyRKaOXOmeHzmzBl69tlnRTZCUVGRCHQ8+eSTYna/jMtOcTDj999/F9kd06ZNow8++ECUO5LxRXwOpHD5pYiICHr44YdFc2pbccYBNxHnfhtclkqj0VBiYqLIMvHx8ZE24oGDPpPELngA4u5uv/2B+e8NHBPPMkxM5LpkUvPLxv7ZfDGAs8ga+2cDmKrUERUlErl5E7nh97FZfe+VWqKABHzvzcT4V1eJ+zemD6Q+bSPIGZmeL4Ntn1GDjyswprA7jCmcCMYUAC49rvhhTzp9vlWaXLvmib5NfTiOB2MKcPExhQc1IVtiKh07dqSff/7Z6jZhYWEi68MazibgjIEGxUEIBCIAAAAAoI505cjYAIwrAAAAapOx4WZ+3jQAuDgU8QUAAAAAcBAIbAAAAADYJl8f2AjwRrUTgOaoSTM2AAAAAACaszOp+bT1RKryvAwZGwAAAAC1ytjw9sS8bYDmCIENAAAAAIAmMmfxNqPn81ccoNS8Erp1WAd8JwAAAABW5JdWiPvKKkPJ+7dXJ1GxtoJevD7BYm9fAHANCGkCAAAAADiQpRtPWV2/YncivfXbwUY7HgAAAABHVKCRMjYq9JGNpBwNrTueQzvP5dPBpKImPjoAaGgIbNhTZQVRpc6ONynyDAAAAACurW9CBP0+b4J43Cch3Oq2i/4+TusOX26kIwPnH1dgTAEAAK6psNQ4sHEqtaRa0AMAXBdKUdkLDxhKkokqy+y2S3LzIvJrQ+RmvgmSh4cH9ejRg3Q6HbVr146+/vprCgkJsbi7TZs2kZ+fH1155ZU2H8JLL71EERERNGfOHKPl+/bto+XLl9Nbb71lcZvGMHPmTLrxxhvpmmuuafSfDQAAAFAfXC5B5unegrw83CnAx/bT84rKSnJ3wzwll2PvcUUNYwqGcQXGFQAAzqhcH9CQAxvHU4qVdehbBuD6MBKym0pp8NHCncjNu/433o8YzFRa/IkcxDh48CAdO3ZMPF64cKHVI+TAxp49e+zybgcMGCCCGgAAAABQN/vOZSqPPdyl03IOVFRUGAIe1mh1ls8TwZnZcVxhw5iCYVwBAADOSO6tUaH/b04d2OBeG5tP5YrHpWUVtOZoNoIdAC4GgQ17a+FB5OZZ/xvvpxaGDh1Kly5dEo937dpFgwcPpn79+tHIkSPp4sWLlJycTIsWLaLXX3+d+vTpIwIimZmZNHXqVBGk4O0PHDhgdt+87YgRI0RWyLJly5QgCWdKmHrzzTdp+vTpVFFRQW+88QZdccUV1KtXL3r77bfN7vuLL74Q63v37k1PPvkknT17loYMGaKsX79+vfJzVq1aRX379hXb3nbbbdX2xVkk/H779+9P1157LeXk5NTqMwQAAABoTJuPpyqPJ/ePF/ee7m5ULo/OiehAYhZ9suaYUXaHTKtDiSGXZo9xRS3HFAzjCowrAACchZypUVlVRUWacrqYrTFa/98/Loj7L7am0DtrkmjFP4ZJJQDg/FCKygVwEGHt2rV09913i+fdunWjbdu2kbu7O61cuZJee+01+t///kf333+/Ucmo22+/nebNmyeCD2fOnKE77riDdu/eXW3/586dEwGGpKQkGj9+PN1yyy1mj2P+/Pl04sQJ+vLLL2ndunUi0MIZIpWVlXT11VfThAkTROks2ZEjR+jdd9+lrVu3illiHIgICwsjT09POn36NHXq1Im++uormjFjBmVkZNDDDz8stm3dunW1oAWX43riiSdoxYoVYh8cMOHjQVYJAAAAOKr8kjIRyFg5bwK5tWghlrm7t1DKKrBnv5HOzaYOTKCoED+j1yOwAfaGcQXGFQAAzoQDGnKA41Saob+GKW4qztLytY12bADQ8BDYcGJ5eXki+4IDCB07dhRBB5abmyuyJjggwUGF0NBQs6/n4AOXsZLx68zh/hUcbGjfvr34meZ89tln1KVLF5HR4ebmRn///bfIsOBABCssLBTBCnVgY+PGjfR///d/Sl8QDkjIfTM4oMFBFw7QfP755/Tnn3/SmDFjRFBDva3s1KlTdOjQIbENKy8vp+7du9fi0wQAAABoXEWlOgrw8VSCGiw9r1TcNGXl5OPlUW1GopoGGRtgJxhXGGBcAQDgPCr1Sa58mlSgbyRujpz42kJ1zgUAzg+BDScm18ItLi4WGREff/wxPfLII/TCCy/Q5MmTafbs2XT06FERKLCEyzdxs0BrvL29azwWLg91+PBhkVkRHR0tAiovvviiyLaorZtuuokGDhwoMjauu+66Go+P8c/jMlUcLAEAAABwBkUaHflbaBa+5mAyXX9lQrXsDHlmolhWjlJUYB8YVxhgXAEA4Bz4nEg97UNjpfeYvB3CGgCuBT027K2qnKhSV/8b78dG/v7+9MEHH9CCBQtEpkJBQYGS2bB06VJlu8DAQJE5IRs9ejR98sknynPOeKgrDkRw2SfO7uDMj3HjxtHixYuppERKBbxw4QLl5+cbvYazK5YvX64sl8tLBQQE0JVXXknPPvusEhgZNGgQbdiwgS5fvmy0rYyzRbiPyP79+8VzrVZLJ0+erPP7AQAAAGhoBaVlFOTrZXZddqFxqYSSMuncsEyVpYFSVC7OHuOKWowpGMYVGFcAADhbtoasUIMJHwDNDQIb9vwo3byIqiqIKrX1v/F+eH82fkXcALxnz570ww8/0NNPP02PP/64aB7u5WUYLHND7e+//15pHv7hhx+KJuCcbdG1a1f67rvv6vUJcDDjmWeeEVkW3Gz8hhtuEAEJLj/F/Ts0GuMmTrz80UcfFQ0K+Zi4sbmM+3hwPxBezlq2bCmCN5yJwsfLmSlq/D45SML74/XcQLw+gRoAAACAhsQNwvOKyyg80HxmrGk2hqasolr5KQQ2XJUdxxW1HFMwjCswrgAAcAbqLFaWW6KzvLFciqqBjwkAGleLqiqTfwnALM6CCA4OFtkFQUFB4iJ9YmIiJSQkkI+Pj7RRJQ80Lae+1W1Q494sv5GXXnpJ9AbhQIU9mf3ewDHpdESJiVwLjcjTs/F/tlZLlJDQ+D8bwBTPuC1KJHLzJnLD72OzIWZba4kCEvC9u6iM/FKa/sEGmnJlW3pgvKEv2PhXV4n7666Ip4cm9FCeP3djPxreNYbScktoxkdS6c3nb+xHw7rGkKOeL4Ntn1HDjyua75iiocYVGFM4EYwpAFx2XFFaVkFTPjysPB/VJZQ2nazeO3bNE33pyeVn6MilIrq2TwTNuaoNNRsYU4ATqs2YAj027EkMGJrvoMFeJk6cSFlZWeiXAQAAAC4TxHjtp39ofJ9YGtA+kqJC/CirUMpkjQg0P9GirLzSbMZGqb4kFUPGhgvDuMIuMK4AAHBd3DBcLbtIyth44boEemVlotE6eU43MjYAXAsCG+Bw/vrrr6Y+BAAAAAC7+Xz9STqVkidubM3zkylbH9gINwlstI0MpAuZhVSsMe6NIAc0StWlqEyCHwBgDOMKAADXVaGPbLQK8aaUPC2dy5B6vLYO9bbcPLwFQhsArgQ9NuoJlbycC74vAAAAaGxyEEOWmF4gMjjMBTYW3juMPNxaUIlWZ1Q7es3BZHFLyixUlmlU2Rvg/HCe6jzwXQEAND35PKljlK+4LymTJnx4uhsHLwpKy9FjA8BFIWOjjjw9PUWkNzMzkyIjIxH1dZIBCH9f/L3x9wcAAADQGLKLjAMbz367W3lsWorKw92NQvy9qUhTbpS1cTatgN753VBHmv229wKN69OGAnxwXuPMMK5wLhhTAAA4hgp94mqwrwdFBXlRekGZeO7p7kYPjo6ljzdeEs/fX5tseBESNgBcCgIbdeTu7k6xsbF06dIlunDhgn2/FWgwHNTg742/PwAAAIDGYFpWKq9YGniz8MDq5RL8vD2oWKujIo1UK9qcGaM60ZebTtP+c5k0snsrOx8xNCaMK5wPxhQAAI6TseHm1oLaRfoqgQ0P9xZ0fb9IGt8znK7/4BAVaMqVUlQA4FoQ2KiHgIAA6tixI+l0lged4Hgz4hDUAAAAgMZkqZzz7cM7ko9X9dNxfx8PSs8rtRrYGNolWgQ2zqTmU7CfF7m7taCe8eH2PGxoRBhXOBeMKQAAHKd5uFsLog4tfWnnuXyjUlQ+nm4U6udBFRVVJFf3RMIGgGtBYKOe+CI5LpQDAAAAgCWaMkPD74ggH8oqkEpTTe4fZ3Z7f29PKtYUUGGp+cCGt4cbxYYHkLenO51LL6Afd55XmpKD88K4AgAAoPbNw3lyRytVw3AuRSXj7I3yyioR/GDoHQ7gWhDYAAAAAABoIBWVlaTRVdDAji2pR1yYCEp8vOa4WOfjab40JvfM0JZXUl6x1uz6IH2GhlZXQf+cz8J3BwAAAM1OpT6w4daiBXnIkQuT5uG8vLyiijILDWVAAcB1GMKYAAAAAABgV7tOZ4j71mH+dPOQ9hQV4qes44wLc7jHBkvLKzFa/sikHuL++ivbWmxqDAAAANCcSlFxggb32ZC5mzzOKCyjAo2UPdsCxagAXEqTBjbmz59PV1xxBQUGBlLLli1pypQpdOrUqWrb7dy5k8aMGUP+/v4UFBREI0aMoNLSUmV9Tk4O3X777WJdSEgIzZo1i4qKioz2cfjwYRo+fDj5+PhQmzZt6M0332yU9wgAAAAAzdd3W8+IQMWNg9sZBS2Yh6pUgpq/fptL2cVGy4d3jaHvHruKbhwk7ctUqarkFQAAAECzKEXVooW4mcPZG4X6oAZDKSoA19KkgY3NmzfTQw89RLt27aK1a9eKJtzjxo2j4uJio6DGhAkTxPI9e/bQ3r17ac6cOeTmZjh0DmocO3ZM7OOPP/6gLVu20OzZs5X1BQUF4vXx8fG0f/9+euutt+ill16izz77rNHfMwAAAAA0H/klZdQmPIDCA32UMlM18ddvk5QlTdSZ0KcNXX9FW1GCivfTwsKofM8ZKTsEAAAAoLkENjhbQ3WJ0Ig6e0Nsi+7hAC6lSXtsrF692uj50qVLReYGBx84K4M9/vjj9Mgjj9Czzz6rbNe5c2fl8YkTJ8R+OOAxYMAAsezDDz+kSZMm0dtvv02tWrWib7/9lsrKyuiLL74gLy8v6t69Ox08eJDeeecdowAIAAAAAIA9cRaFr5eh5FSrMP8aXxPq7yXuz6bmi9rQj13T02Iwgw3uFEX7zmXSqn8u0qgerex05AAAAACOi/uRMR9Pt2oBDHXzcGvPAcC5OVSPjfz8fHEfFhYm7jMyMmj37t0i2DFkyBCKioqikSNH0rZt24wyOrj8lBzUYGPHjhUZHfxaeRsOlHBQQzZ+/HhR9io3N9fssWi1WpHpob4BAAAAANSGpqycfLwMc4m4YfiMUZ1o3tS+Fl8THSr14eB5iKEB3laDGuzRyT1pbK/WImjCzcoBAAAAXJ1GpwpsWDhXMg14VOA0CcClOExgo7Kykh577DEaOnQo9eghNUY8f/68uOeyUffee6/IzOjXrx9dddVVdObMGbEuLS1NBD7UPDw8RHCE18nbcFBETX4ub2Ou/0dwcLBy474cAAAAAAC20lVUUnlllVHGBrtteEca1d1yZkXLIF/lcViAVMLKGm5C/tg1vejxa3qRu6VaDAAAAAAupFQV2LB0+sM9NtQqq/QdxwHAJTjMyId7bRw9epSWLVtmFOxg9913H911113Ut29fevfdd0UpKi4r1ZDmzZsnMkjkW3JycoP+PAAAAABwLaVl5eLeV5WxYQvO0pCFqR6bK0ElBzYAAAAAmpO6ZGwgsRXAtTRpjw0ZNwOXm37HxsYqy2NiYsR9t27djLbv2rUrJSUlicfR0dGiZJVaeXk55eTkiHXyNunp6UbbyM/lbUx5e3uLGwAAAABAbR1LzqGvN0sZxv7etTvlVgdCwgItn4++cHN/qqqqslhXGgAAAKA5BDa4gbg53KvMXMNxAHANTZqxwQMxDmqsWLGCNmzYQAkJCUbr27ZtK5p/cy8MtdOnT1N8fLx4PHjwYMrLyxMNx2W8L872GDhwoLINB010Op2yzdq1a0XmR2hoaAO/SwAAAABoblbuvUgHErPE4xD/uk+WsVaKyq1FC5SeAgAAgGaptKxC6V/mbuHqptwsPMRPmjRSoJGyaQHANbg1dfmpb775hr777jsKDAwU/S74VlpaKtZzo8SnnnqKPvjgA/rpp5/o7Nmz9Pzzz9PJkydp1qxZSvbGhAkTRA+OPXv20Pbt20Ww5JZbbhFBEXbbbbeJxuH8mmPHjtHy5cvp/fffp7lz5zbl2wcAAAAAFy9DxUL9veq8H2ulqAAAAACaK60+Y8OXMzZaWM/YCPf3JH9vd8opNkx4BgDn16SlqD755BNxP2rUKKPlS5YsoZkzZ4rH3FBco9HQ448/LspL9e7dW2RbtG/fXtn+22+/FcEMbiru5uZG06ZNE8EQGTf//vvvv0UgpX///hQREUEvvPACzZ49u9HeKwAAAAA0HxqdNIuQRYX41SmgkVOkpRC/ugdFAAAAAJpDKSq5kbipvBJpoklsmDdpyyvpYFIRbT2dS8M7oXoLgCvwaOpSVLZ49tlnxc2SsLAwkfVhTa9evWjr1q21PkYAAAAAgNrS6Msj9EkIp86tQ2r9+rdnDKYVuxOpX7sIfPgAAAAAJkpVgY2yCvOBjWKtdD7WMsiLcovL6VKull77/QKteQKBDQBX4BDNwwEAAAAAXIlGV05twv3pjTsG1en1rcP8ac7EHnY/LgAAAABXytjw9XKjIq35UlTyfGouVRXqj0ugAK6mSXtsAAAAAAC4aikqHy8MoAEAAAAaMrDh5eGmBDBMVZJhRai/J74IABeDwAYAAAAAQAOUovLxdMfn6iC2bNlC1157LbVq1YpatGhBv/76q9H6X375hcaNG0fh4eFi/cGDB6vtg/v+cc8+3iYgIED09UtPT2/EdwEAANB8peZp6cGvTlJStkY8L9VVkLeH1Di80kJkQ17MvcXD/DDhBMDVILABAAAAAGBnmrJy8vFCYMNRFBcXU+/evWnhwoUW1w8bNozeeOMNi/t4/PHH6ffff6cff/yRNm/eTCkpKTR16tQGPGoAAACQLd6SQucyS2nJthQlY4PLULGKSuuBDbcWyNgAcEUIVwIAAAAA2BHPGtSWVyJjw4FMnDhR3CyZPn26uL9w4YLZ9fn5+fT555/Td999R2PGjBHLlixZQl27dqVdu3bRoEHme6lotVpxkxUUFNTznQAAADRPBZpycR/oI00c0er4XMvNqMxUz9gAo9fI4Y4W1IIC9K8DANeBjA0AAAAAgHpavv0sjX91FWUWlJJWVyGW+XhiDpGr2L9/P+l0Oho7dqyyrEuXLhQXF0c7d+60+Lr58+dTcHCwcmvTpk0jHTEAAIBrKSyVAhv+3lKAolQV2Ajz96Qv7u5K/5na3ug1VfqUDS5F5YsSoQAuB4ENAAAAAIB6yC8poy82nBKPT13OE/01GEpRuY60tDTy8vKikJAQo+VRUVFinSXz5s0T2R7yLTk5uRGOFgAAwPXk6wMbZzNK6YutKVSirSBffWCDtQ71IW/Vc6MeG0TV1gGA88M0MgAAAACAOtpxKo0+/POo8vzE5TzacFSq/Yzm4eDt7S1uAAAAUHfcTyOnWApsHE4uEjcWH+Fj9XVKKaoWLcjTncMbAOBKEK4EAAAAAKijl3/YTzlFhh4KP+08T9tPSjP4Wwb74nN1EdHR0VRWVkZ5eXlGy9PT08U6AAAAaDhPLj9tdvnIzqFWXze0Q7C47xLjR+7cQRwAXAoCGwAAAAAAdXDwQpbV9Vf1bI3P1UX079+fPD09af369cqyU6dOUVJSEg0ePLhJjw0AAMDVnUkvrbbs9Rs70KReEVZfN3NYK/rfzK7Uv20QRQV5NeARAkBTQCkqAAAAAIA6+GT1cYvrgv28yN/HE5+rgygqKqKzZ88qzxMTE+ngwYMUFhYmGoDn5OSIIEVKSooStGCcjcE3bvw9a9Ysmjt3rnhNUFAQPfzwwyKoMWjQoCZ7XwAAAM1BgI87FWmkHmay2LCaSz1ylkZcuI/SdLxPXAAduSSVsQIA54eMDQAAAACAOijS6JTHcREBojGl7N/T+uEzdSD79u2jvn37ihvjAAU/fuGFF8TzlStXiueTJ08Wz2+55RbxfNGiRco+3n33Xbrmmmto2rRpNGLECBHw+OWXX5roHQEAADQffeMCxf2gdkHKsrqUlvL1dFcaigOA80PGBgAAAABAHZSVG2YORgb70rM39KUVuxPphoFtqX20VNMZHMOoUaOoysqVjJkzZ4qbNT4+PrRw4UJxAwAAgMajq6gidzcib0/D/Gz3FrUPbLi5EVUisAHgMhDYAAAAAACoA215pfI4LMCb2kcH0ZPX98ZnCQAAAGBH5RWV5OnuRl4eqsBGHWrQuOmDIZVVVcpjAHBeCGwAAAAAANQSz/7XlVfQgPaR1KV1CE0b1A6fIQAAAIAdXcgqpVYh3lReWUUe7i1MAhu1D0y4619SWUnk5m7PIwWApoDABgAAAACAGaVl5aIOs5939VPmn3adF6UM/L09aPrITvj8AAAAAOzoTHoJzfnmFF3VNVSUovJ0a0HeHi3qFdhoocrYIKPuaADgjNA8HAAAAADAjNveW083vLnG7GezeN3JauWoAAAAAMB+2Rpsy+k8Kq+QMjb8vdzrFdjgHhsMfTYAXAMyNgAAAAAAzCjRlov7Yq2O/L09leVaXYVRzWcAAAAAsC/O0pDvT6WViMcRgV7K+jrENYx6bACA80PGBgAAAACAFf+czzJ6nporDa4ZhsUAAAAA9ldmJis2PMCzWlmp2nCXAxtI2QBwCQhsAAAAAABYoSkzZGiwS9lFymM/LyRAAwAAANhbkcb4/KtTlB9FqAIbdYFSVACuBYENAAAAAHBZZeUVdCGjsNavq1KVKNDpy03xfjh743JOsbKue5tQOx0pAAAAgOvT6irpzb8uUHKOxup2eaVSSVD2f1dG0bu3djLK2KgLlKICcC0IbAAAAACAy3pjxUG679MtRlkWtihXlSjQlUszBnk/877dTRczpX09dX1vmnJlWzsfMQAAAIDr+vtYNq0/nkvP/3LO6nb5JYbARqCPu2gezvf1IfflQCUqAGPHL+WKCWHOBoENAAAAAHBZ206mVeuLYQt1g/Aykwbhe89mULCfF43tFVun+s4AAAAAzVWl/rSqpKx6Dw21Ao0U2LjpipZ0fd9I8bi+511KxgYiGwCK/ecy6fElO2jh6mPkbBDYAAAAAACXVKkqJ5VVaL3cgamzafnKY115JZWrghsFpTqKCfWz01ECAAAANB9ybEJd9tNSxkarEG+6Z0Rr8vIwXL6sT58N9NgAsDzuWX0gmTYevUzOpEkDG/Pnz6crrriCAgMDqWXLljRlyhQ6deqU2W35H7yJEyeK6Oyvv/5qtC4pKYkmT55Mfn5+Yj9PPfUUlZcbUtbYpk2bqF+/fuTt7U0dOnSgpUuXNuh7AwAAAICmdSnb0AujRGt8bmhNsVZHz3y9W3n+14Fk+mrzaaNtAn3rV+MZAAAAoDkHNqznaxDll5ZTsK9HteVLZnWjlY/0rtPPRo8NgOrKyg1/ja+vOEi/77toNKnLkTVpYGPz5s300EMP0a5du2jt2rWk0+lo3LhxVFxsGITK3nvvPbMpZxUVFSKoUVZWRjt27KAvv/xSBC1eeOEFZZvExESxzejRo+ngwYP02GOP0T333ENr1qxp8PcIAAAAAE3jsiqwUVpme83Y7EKt0fOM/FJavt24DrSPZ/WBNgAAAABYt/t8gbivIWGDirWV5O9d/bIlZ294e7rVq8eGk1yzBWgUWlUJXvbRX0dpg5NkbjTpiGz16tVGzzkgwRkX+/fvpxEjRijLORixYMEC2rdvH8XExBi95u+//6bjx4/TunXrKCoqivr06UOvvvoqPfPMM/TSSy+Rl5cXLVq0iBISEsQ+WNeuXWnbtm307rvv0vjx480em1arFTdZQYH0Dy8AAAAAOI6Kyioq0uhEzwtTpWWGLA2N6nFN5Gbhk/vH0ar9SWa38fOuX/NKAAAAgOaGq7HsTZSurxVrrU860VVUkreqBJU9uOkjG+pypQDNnc5MpG9Mj9bkDByqx0Z+vlTTKywsTFlWUlJCt912Gy1cuJCio6OrvWbnzp3Us2dPEdSQcbCCAxHHjh1Tthk7dqzR63gbXm6tTFZwcLBya9OmjV3eIwAAAADYz2drj9PNC9bS5ZzqGb8a1ewjdZCjJlp9Ora/t+VyU14eCGwAAAAA1IZGZ7iA6u5mfeIK9/f2dK9fs3Brpaj4Yq5WdTwAQCKY+M7MweRh7Q/UgTjMUVZWVooSUUOHDqUePXooyx9//HEaMmQIXX/99WZfl5aWZhTUYPJzXmdtGw5+lJaWmt3vvHnzRKBFviUnJ9f7PQIAAACA/eQUaejXPRfE44uZhdXW7z+XaTbIYc2l7CLacVI6hwwP9FYGwKYKS3V1PGoAAACA5qlQYzgfs5aNIdf897RzxoYcJ+GEjYe+PkXXfXDIrvsHcEZuqvHOR/cOp+5tDAkHjs5higNzr42jR4+KElGylStX0oYNG+jAgQONfjzcZJxvAAAAAOCYftx5XnlsLgCxTR+gYBobe2zM+niz8tjP24O+f/wq+r931inLuDzVusOXaUJfZPMCAAAA1EahxpBBW1JWKXqg+XpVz4ItK5dKRdk7Y0Pu3csZIRezNXbdN4DTamF42DrMj5yJQ2RszJkzh/744w/auHEjxcbGKss5qHHu3DkKCQkhDw8PcWPTpk2jUaNGicdcnio9Pd1of/JzuXSVpW2CgoLI19e3wd8fAAAAANifh5vhVLZYo6t17diacLmpEH9v6t4mVFl2Vc/WtOLp8dQ3IaLW+wMAAABozuSMDT8v6Rwuu0hn9byNG4Xbk3zqyGWuZBzkAGjOKlV/A+6q8ZUzcGvqpkEc1FixYoUIYnCDb7Vnn32WDh8+LJqHyzfGTb+XLFkiHg8ePJiOHDlCGRkZyuvWrl0rghbdunVTtlm/fr3RvnkbXg4AAAAAzsld3wCScQNxS/y8PIwCGycv51GJtuaeG976PhrJWUXKstZh/kY/FwAAAABql7GRECFNMs4uNn/+VlbRMBkb6h4bpmWvAFzdycu5ZsvzysG9/xvanpyNW1OXn/rmm2/ou+++o8DAQNELg29y3wvOtOB+G+obi4uLU4Ig48aNEwGM6dOn06FDh2jNmjX03HPPiX3LpaTuv/9+On/+PD399NN08uRJ+vjjj+mHH34Q/TsAAAAAwDmpy0+pe15UVFbSaz/tF4/7tYsQ9Zl1+kHrubR8evSL7TT/l3+q7a/cJKsjwEfKFpaDIo9f01NkcAAAAABA3TM24iN8bMvYsHMDY3luSmmZ4ZxPi8AGNANnUnkMtIPm/3LA4t/bzUMQ2KiVTz75RDTm5rJSMTExym358uU278Pd3V2UseJ7zsC444476M4776RXXnlF2YaDIKtWrRJZGr1796YFCxbQ4sWLafz48bU7YAAAAABwGEeSspXHhaqMjdTcEtp6Quqv4ePpTp7ubsoJ+4UMqcn4vnNZ1faXU6Q1et4yWJpNKAdFuDQVAAAAANRNYamUsdFWztiwENhQemx42DdjQ866zSsxZO5q9T8LwJVlF0o9ZXadNm7VoJ7c5eGEWekeTV2Kyh6viY+Ppz///NPq6zh40hRNyAEAAADA/g4kZtHhizk0rEu0aBKuLkWlrpUsAhsebnQ6JV/MUGoXFSiWB/p6Kif5vK+xvWIpv6TM6GdEBEmzCcv1++MACQAAAADUL2MjOthLaSBujk5fiqqhMjZySwznjVodSlFB88p0N1Wu/3vzcMKxjvMdMQAAAAA0e1tPpIrP4LbhHcTsIi5FJQc01LVjfbw8RECCaylvOpZCv+xOFMv99WWmHv58G7312yFKTC8wet30ER2V5nldWoeI+8hgKdABAAAAALW34WSuuI8IkCaYZBaW0fgFB+iLrSlG28l9L7zsnLEhX9w1zthAYANcX0mZ5f6CcsaGM/YRbNKMDQAAAACA2uIgxar9SeJxXGQgBfh6imyLqW+uoTE9W9Oo7q2UbXn8qs60yCsuM2oMnl0olZ/ioIhWH9h47JqeNLFvnPKaV2+5go5fyqUurUPxZQEAAADUwbmMEqX0VIifFNhYeyxH3C/fk053Dzecv8klRO2dLStPWs8rLq82Wx3AlZVoy42qIbVQZXBwYIMniqmXOQtkbAAAAACAU0nKLFIe84A30MeTTl7OExkXf/6TRBqd4cS9WFNO2UVSTVm1gtIy2ncuU3leVlFJGv1MJjnoIQvy86JBnaIa6N0AAAAAuL70AkPJT+8aMjF0+r4X9s7YkGekq0tRySVHofaOJefQxNdWicbU4NiKtYbf+c/WnVAev/rjftp1JsMpgxoMgQ0AAAAAcCqZBaVGzzljQ93wW1NmKCk1oluMKFNlijM1/v3dHuX55mMpSp8OHy80CQcAAACwJ7mXxbW9I8jLw/rlyDJ9FoWne8OXolL3ZoPaWbzuJPHH99veC2bX84SjdYcv4WN1sIyNX3ZJpXlLy8pFr0J1lpSzQSkqAAAAAHAaPPj8YsMpo2UxIX504lKeeOzv7UHF+hP3Z6b0oaFdoun1OwbSit2JtONUusX9/rrHMCDzMsnYAAAAAID60eqzMAa2D64xYNFQpajkFgLqwMYrKxOpWFtBq+f2cdpZ601FDgr5WpgU9P6qI+J+bK/YRj0usB7YkOUWSSV52cS+bcgZIWMDAAAAAJxGZn4pnU8voEEdW9L3j18lll13RVtlPffakAdRbSICxH2v+HB68eYBonasLbxrmEUIAAAAAHXL2PDxdKsxgFBW3kAZG/pzQXVZLA5qMJSkqh0uP3Uxs1C5aM6ljvg8XOasGQCu2Jvwq02nacvxVGXZuN5SoClP/31NH9GRHp7Ug5wRRm0AAAAA4DS4jwbrEBNMYQE+4nHX2FBaPncsdYgOohzVzKNgPy+j1y5+cBQtmDGYAnwsJy2/cFN/6hEX1mDHDwAAANAcacoNgQ1zdp7Np6eWnxGBhiJ9sKGmklW1ZW2OixxMAdvMWbxNOS8v0pTT7E+20M0L1oq+G2l5JfTKj/vxUTqA48m59O3WM0ZjpCr9r3qefll0qB+5uzlniAClqAAAAADAacgDKB9P45T3EH9v8jQZ/IYGeBs9jwn1EzcefFnCpasAAAAAwL40+owNOTN2zRN9afyCA8r6l347L+5X7M+gr3dKdf/9vOwd2LAc2ZAyDFCOtC4KS8soq1AjHs9dupMiAn2U59C0UnKLlcfenu6k1VWI3/WqqiolY4PHUc7KOcMxAAAAANAs8cm4fGJuykM10+jXZ8ZbrMscHmg4eW8fFURfPzKmQY4VAAAAACSlZfLkFMP52S0Do6p9PH8dyVYet43wtevHZ60CFjI2bMcXxdXU2QAMQQ3HkZmvMRpH8Z/ApmMpdO381XQpu0gsD/U3znJ3JghsAAAAAIDTyCnSWAxsHEnKEfdRwb7k62U5Mfnf0/pRn7bh4vG4PrEUZpLZAQAAAAD2JZeXCvQxnMPNHBpDEQGeRttlFenE/Q39Isndxv5otsoqlPZtTpm+VBbYPtHIXBNqcCzFWuPfeTnDnbM2ft6V6PQZGyhFBQAAAABO4/UVB41KUplz95guVvfRvU0YvX7HQDqanEs92oSKBpYv/98AUaYKAAAAAOyvsLScPNxaGGVs8DnY4PbB9PuhrGrbB/vZ/5JluEkQRa2sAj02bFWsNS7rau28nFVUVtk9SGVvLyzbS6H+3vT4tb1Ew+2Vey/QVT1jKdDX8u+MM2VKyTzc3aoF8Uz7EjoTBDYAAAAAwOm0DKpemuDJ63rTgcQsGtk9psbX80C6p6pJ+KBO1UshAAAAAIB9FGoqRLYGn4Op+Xmb72sR5GP/S5YjO4dQZGBH0lVUUXaRjt7866KyDhkbtisstZz5Yg5nB7i7OW7/klMpebT7TIZ43DU2hNzcWtAna46LZfNvH0jOrLTMEITq3TacLmQUGq3nwA0HO5yV8x45AAAAADQ7cgmpQZ1aVlt3de9YenpKn2oDZgAAAABwhMBG9WCFr4UG4UG+9g9s8Dli99YB1CcukFqFGJff4WAH2EbuoeHn7UHDu0Yry6eP6Cgah5sqK7ee0dHUfttzQXn87h9HaMfJdPH4YqZxEMAWZ1LzafG6EyJLxRGUaqVMqR+fvJpeveWKaj0IE1oGkjNDYAMAAAAAnCqdOiLIB8ELAAAAACdSqCk36q8h8/MyP5M/2LdhZ/jHRxhfgEfGhu2yCkrF/TNT+lD/9pHKck8PdwoLlAJGI7vF0IhuUhZ1iaa8Wq8HRyL36ZPtPC0FNsrrEOx65utd9OPO83QwsXp5taZQUlZOvt4eFOTrJXoUergbTwCb0KcNOTMENgAAAADAqdKp/aw0BgcAAAAAx1JVVSWah5sPbDRexobxz3WnmcMM5UvLyh1jhr0zSMoqEvdtwgNoYt84pU9dRWUlhej7NXB2TLg+e+Ppb3bR1Df/pvKKSqdohq4uoVVb5fpMjX99t4f2n8skR5gU5qcaO3FwQ+3Kjs5djheBDQAAAABwGjzriNPeAQAAAMA5aMsrRamnADOlqKKCvJoksMFuHRhN/76mrXhc5qAX3R3Rxcwi8vJwo2h9QKNbbKi4D/LzomA/KWOjsLRMlEBi6XlShsc/5x0ji8GUrryS+rWLoKVzRhstr6jl78Rbvx00CpIcNckEaapSVL5WAhvm/iadCQIbAAAAAOA0TE/OAQAAAMCxFZRKF3vNZWzEhVfvydBQzcPN4Qv0DV2KqrKqijadzKW8Esctx1Qb3HuCszXc9YGLhyZ2F2Wpru4VS8H+UqAqv6SsWlPqDUcvkyPiHiBe7m7VJk/Vtu/KusPG7y8tr4QcoxSVu/J8WBdDTxTm7L0JMSoEAAAAAKcpYyCVomrYmssAAAAAYN/+Gsxc8/BgC5kZpr0AGoqn/udsPZ1HYf6e1L9tkN1/xuoj2fT+2mS6pncEPTzWuXsaFGt0lFmgoV7x4coyf29PGtOztXgcrC9FlWcmsHHkYtNnMJjiJt9cPor7gwT6elYLSF3KLqKoEL9qTbfVSrTllJJTbLSMgyS5xWXkaGV8bxrSnnq3jaCcIo3DNDivD2RsAAAAAIBT4NRuPv/mBngAAAAA4Jj+9dNZWrItRXleqLGcsaGeMT5reCtqbHLGxu7zBfSvn881yM9IzdOK+2MpUm8KZ5aSK2UhxIb7m10fHxkg7jmjQy5FVZ+eFQ1NPib+PXBr0YLeunOQ0fpZH2+mz9Yet7qP+b/8Qw8t3ma0jAM8nLXSlCoqq0hTVmGU7c7vsUvrEBrSOZqGdzX0mHFWCGwAAAAAgFPgVGqGHhsAAAAAjolnue+/WEjLdqfbFNhgAfpSOb4WGok3JC+Phs8M8dVnG2t1zjtDXqOroDd/PUj/nJcaYkcG+Zrd7soOLWne1L70r6l9q2VsOGKGAJehUge4OBNlzfOTqVd8mLLN1uNpVvex56xxk/DBnaIoxN+L8oqlgFZTySooJf7EI4PNf1euANPdAAAAAMApZBVoxH2Aj3GaOAAAAAA4htKySsulqCyUnfpsZlc6m1FClU0woZ97KzQ0OSmFgwPOav+5TFp/xNBDIjLYfG8UzsAZ1b1VtXJiPDGp0gEDG9w4nHl5GAfduLyWLLdYKwJ2nO1gTkLLQErMKBSP/z2tHw3s2JL++8sBOp2SL0rpNlUfi1R9j4/oENcNbCBjAwAAAACcwq7TGeK+b0JEUx8KOJktW7bQtddeS61atRKDy19//dVoPQ86X3jhBYqJiSFfX18aO3YsnTlzxmibtm3biteqb6+//nojvxMAAADHJgcxjJdZz9gID/Ckge2CG62vhrWMDb6AbQ+cnbB8T7r4PCr0Tag1OscrxWQrOaNBZiljQ02dsRHq700VTRG5qsHZtHxx72/S/8X0+bJtZ82+PimzUAlqMM708PZ0Fxkb/DtQZObvobGk6cuGRYf4katCYAMAAAAAnMKOU2miXm33NqFNfSjgZIqLi6l37960cOFCs+vffPNN+uCDD2jRokW0e/du8vf3p/Hjx5NGI2UJyV555RVKTU1Vbg8//HAjvQMAAADnIAcx1NYeyxb3ATX0SfNrglJUpk2h31h1gfJL6n8x+u+j2fTF1hT67x8XqEIfLHHAhAWbcK+IyybNsSODzGdsqKl7bLQM9nXIUlTcCJ1x3wlLGRts07EUEZhZczBZNBS/mCkFMx5dskPZ5pkpfSjE31s8DtE3Uf949VHKyC+lprDnjDQpLCYUgY0GMX/+fLriiisoMDCQWrZsSVOmTKFTp04p63NycsRgoXPnzmLmVFxcHD3yyCOUny9F02RJSUk0efJk8vPzE/t56qmnqLzc+B+hTZs2Ub9+/cjb25s6dOhAS5cubZg3BQAAAAB2xwMCng11ZceW5O6GuTlQOxMnTqTXXnuNbrjhhmrrOFvjvffeo+eee46uv/566tWrF3311VeUkpJSLbODxy3R0dHKjQMgAAAAYFBQanw9LqOgjJJztEa9NCxJiJSyAKKDpYvCTZGxselUHv24z9AfhPEF+U82XKJzGdIMeFvokzTon4uF9PfRHGU/zoYzWG5esJY+WSM10B7doxXNmdijWukmc3KLy5TgEZeicqT3n5heIC78V+ibh6sbbJtmbPSMCxMBkO+3nqV3fj8sGorPXrRFrCvRGn7fx/RsrTwO1gc4NhxNoee/30uNbevxVNp+Svo9jkLGRsPYvHkzPfTQQ7Rr1y5au3Yt6XQ6GjdunJhRxXgwwbe3336bjh49KoIRq1evplmzZin7qKioEEGNsrIy2rFjB3355ZdiO04llyUmJoptRo8eTQcPHqTHHnuM7rnnHlqzZk0DvTMAAAAAsKfsQmk2VXxkAD5YsCseK6SlpYnyU7Lg4GAaOHAg7dy502hbLj0VHh5Offv2pbfeeqvaZCpTWq2WCgoKjG4AAACurEhbYXRRvEzfw8CW5uB+Xu608I7O9O6tnaipSiyxMH/j2fqHkgvp1wOZ9ODXhsnYNVFnn2QV6exa5qopzsFlV/VsTdcOiLfptXKW9dxre5G7WwvRyLohPgPe539+/oe2n7Te5Fvt/s+20vPL9iq/n6aNzju3CiEfT3ca2S1GZJtwAOP4pVyjbUyzWNTkjA2WUdD4GRvHVMfK78NVNWnzcA5SqHFAgjMu9u/fTyNGjKAePXrQzz//rKxv3749/ec//6E77rhDDCI8PDzo77//puPHj9O6desoKiqK+vTpQ6+++io988wz9NJLL5GXl5dIKU9ISKAFCxaI/XTt2pW2bdtG7777rkgxBwAAAADHJs+GMk0LB6gvDmowHkuo8XN5HePMcc4ADwsLExOq5s2bJ8pRvfPOO1Yz1F9++WV8SQAA4DI4A2PlwUyaObSV2Z4Y6lJU3FKhXDVL31wQwVSHKL8mbx7u42m8TCenX9SCrrz6a/TJAU4lLc/4ojz3j7BVr/hw+u2Z8eTj5UF7zkplkThrw83OvVQ4+LLleKq4rXl+co3bqzNH0vVlokxLkg3qFEW/PjNe9FT74M8jSkkutX3692ROoK9hzNKqCUpBeZr5vXZFDvUu5RJTPFiwtk1QUJAIajCeRdWzZ0+jgQgHK3g21LFjx5Rt1DOw5G1MZ2CpYXYVAAAAgOMFNvxM0sQBGsvcuXNp1KhRolTV/fffLyZNffjhh2LcYAkHP3j8It+Sk5PxhQEAgFPjnhE/7s2gVYeyamwezheQHb1htrnrvznFOrqca8hUqEuSgUaVqeLMTC/e1yawwTiowThjg1U2QDmq2n4/3CNDlp5XYnR8ahzUYN76slt5JoGNFH1zbnN6tw2nGwYmkLeHG2nKqvedaWieCGw0rsrKSlEiaujQoSJTw5ysrCyRjTF79mxlGc+iMje7Sl5nbRsOfpSWllqcXcUp6PKtTZs29X6PAAAAAFA3JWXl1erdAtgD98pg6enG9bT5ubzOHC5VxVnkFy5csLgN9/fjSVnqGwAAgDPT6KSLtDklUnklU0WqjI2f9mWQ1sEv8MsXr5l8bfvrHWl0z5ITIsBR14vxWgsBnTVHsx2q10RNzqcbl9GMrmO/Bjlw0BDvXVfL37EzqYbezYUandlSVGpyplFhqfHvPPf/s/wad7p/XDdqFxWkjGMak4eds2IclcNkbHCvDe6jsWzZMrPrOQjBfTK6desmSkw1NMyuAgAAAGha6oGPPJDgxoMA9sQlazmAsX79eqOxx+7du2nw4MEWX8e9+9zc3EQpXQAAgObCT98AvFjVS8NSxsZXO1KVjI3urf3J0al7e/BpaF6J9F7qEpo5mGT+ovc7a5Lou12294JoaheziihIVVYpWNU7ojbc3dwaLLBRVl5R58BGsf731cNMxoZplopWH9STHbqQrTy+cXA7s6/19faolrGx6WgKHUuWGso3FE8byr65AocYGc6ZM4f++OMP2rJlC8XGxlZbX1hYSBMmTKDAwEBasWIFeXoa/qB4ELJnzx6j7eXZVvIMK743NwOLZ0z5+vpanF3FNwAAAABoXOUVlfTfXw7QkYvZ9OXDY+iO99dTsb4UVXiAD74OqLWioiI6e/asUcNwDkxwCdy4uDiROf7aa69Rx44dRaDj+eefp1atWtGUKVPE9lzClgMdo0ePFmMSfv7444+L3n+hoVJjTAAAgOYg2Fe6lChnM1jrscFOpkoNlq/vG0mOjK9rm17c5qDMnvP59OrKxFrvL7/U8iz902mWSxg5ktKyckrPK6UR3WLodEoeDelsOZPV9owN+2bwJKYXKFkXto4z1FkoRTZlbBjKb90+vCNVVVXRd9sM55WLHxhJrcPNB+58vTzE58iv4dK6By9k0/wVB8Q6W/qB1EaVviYXZyF56ANJrq5JAxv8gT/88MMiWLFp0yYxiDDFs6W4HwYHGVauXEk+PsaDWZ5FxQ3FMzIylNlSa9euFUELzu6Qt/nzzz+NXsfbWJuBBQAAAABNY9+5TNp+UprJ9sOOc0pQg4UFYuIJ1OF3at8+EZRQ98tgM2bMoKVLl9LTTz9NxcXFouRtXl4eDRs2jFavXq2MPXgswpnlnDnOPTV43MKBDXk/AAAAzUXLQGnGflK2oQeFpVJU7Ltd0kRj7jXgqH54oKfotZFVZHyB/PHvT9d5n5zR0j7Sl85lltYq6OFIkrOkXhRtIwPp39P62SWr4lx6AfVvZ58gV1JWEd3/2VajjBJdRaXF/hIXMwtp9qItRssMgY0WVpuTy6YNThA9/37de0HpARjk50VuqpJmar5e7sThhk3HUuj1FQeN1h2+mC0arNvL9W+sobiIAPronmHk4yUFY+4Y0ZFcmVtTl5/65ptv6LvvvhMzn7gXBt/kvhcc1Bg3bpwYZHz++efiubxNRYX0B8HrOYAxffp0OnToEK1Zs4aee+45sW8544Kb+50/f14MWE6ePEkff/wx/fDDD2IwAgAAAACOJUs1ePheNRuqZ1yY0YwpAFtx02+eVGV646CGPLPtlVdeEeMMjUZD69ato06dDOUo+vXrR7t27RJBDx6rHD9+XJSuRYY3AAA0N5X6WeHJOVplhrilUlRqPp6OG9gI9vOgAB8Pq+WI6hLY8Pd2pzVP9K22rqzCPlkLO87k0RPLTlvs51FfFzOlwEZ8ZEC993XiUp64/9e3xlV37BF4KVD1vsguMB9wY8eSc5XHgfpgSJm+P4e1jI0QfymYN7FvG/L39hTnja1C/ar14LCUscFMgxrsmy1nyJ60ugpRZmvWwk204chlsaxHXBi5sib9V+WTTz6h/Px8MdCIiYlRbsuXLxfr//nnH5HyfeTIEerQoYPRNsnJyWIbd3d3UcaK7zkDg9PB77zzTjEwkfGMqlWrVoksjd69e9OCBQto8eLFIhMEAAAAAByLaWM+2bg+1UuWAgAAAEDjKVf1SDDNcDBXisoZAhv2brjMAZ/isgry85Le89f3dqeruhpKV9a3GhPv/6P1yfTyykQ6ermYErOqZ4XYKyOCcRZAQ53fmwuO2Sq3WFtt2R/7L4p7Lv/E2Rtq6gAEl9dSs1a6aeqgdvTizf3pkck9lWVRwYbWBpYyRNSBDXPHYM9Amk71Xi/lFNPhizlGJcBcVZOXorJlZlVN4uPjq5WaMrevAwekGmYAAAAA4LgKSsvMLu+X4Ni1mQEAAABcna7CcJ3ucq6WIvWlqZw5Y0NW00XgPnG2XeDPKS4nvs7MmSCsZZAXDWwfTOtP5BplvdRVdpGOfj+YpTx/9+8kGtc9nKYNkEr01xcf34lLuZSaK/VHCbVDj7tCC+f3XBqqvLKSbhnagcb3aVOrfablVu9V8vu+i3TzkPZ004K11LV1CL1391BlXam+iXdMqJ8o0bRqf5JNQS0OXJj2F3FXBTOs/d6Yfte/z5tI419dRfZWZCFwZKlElqtw/H9VAAAAAKBZuZxdLAYIE1SDGy5DFRGExuEAAAAATalcFdiQy/jIeHKyttyw/preEcpjR+6xYWkGvZeH8XNVsopV+xKl5tS92wQqy4J8DHPL6xnXoAKTrJgLWRr6bLNUesge1h++THOX7qStJ6Sed3K/hvp4aGIPcR8WYOiXx1kVnBWSklNC7/x+2GygwpoUk+2fvK43aXQV9OWmU+L5ictS+SuZpkwKuj17Q18KC/ChMT1aif4cd4/pXOtyt9MGGfpEc2kqSxJaGn4HZL3ipfJQcZHV19VVer75rB1kbAAAAAAANCIe4LQJD6DHr+0lbuUVlVbr3gIAAABA49h0Mtds9oa55yH6jIWa+hA4Ck+TWfscmNirD1KwCpP3Z87lXA2983cS8Z4GtA1Slgf6GC6cV9QzslGgbz4eHexFafnmMyHq42iyVMbIllJLtprUL45W7r1AmQUa+nVPIl13RVvKKTIuJZVZqKFoVe+KmsiBEC6VtWDGYPL38aRl28/SH6pMDDU5Y4MberNnbqje/8RWXVqH0qp/Taxxu9E9Wouf+9FfR5Vls6/uRnMWbzNqel5fR5Ok72xQpyjadTq92QQ2HP9fFQAAAABoVniQEx5omM2FoAYAAABA0+OyOupL8il5WquBDZ7IPm9yWxrULsgoyOGouNm37P7RrcWxq+lsSNl48y+px0NYgKdSiooF+dovY0Mu9+Wr7+Ehq7A1paSOZY3qy8fTnYo0OvpkzXHaeOQylWiNy5ZlF1pu/G1OdpGGurcJpf89MJKC/LzERfzpIzoZbaO+yC8HbEz7XtQVj1FqGqfwMakbjasDRfb6vvKKtfS/dSfE40cn96A3pg9U1qEUFQAAAABAI+GUdK2ugkJVaeoAAAAA0LRWHsikmYuPGy1bvCXF6Llps+YW1IJGdQmll29o7xQzx9UlhW7o19Io0MGKLDRGV/P1lF5z36jWRsvVGRuV9bygLTdo9zMpEWUaWKqrsvKa32ddBKoyFLKLtFSsMQ6g8BigNrgUmmkJqZHdY2h4V0M/jPm/HBABBO7hd+hCtt1Ka9WGaZ8N+W+Bs9Lt4bWf/lEeh/p7U5+2hhJwzvB3Vx+OHy4FAAAAgGYjV5+SziflAAAAAND0zmWU0MINl5TnXWL86GRq9X4IZar+GswZ+xY/NTG+Wu8QOTCRry8BZU5GQZnIYJHLRA3rGGKxeXpZhX0CG74mDdk3n8ylcT3CrPZ8sAX3qWgI7aKCaM/ZTOWivmnGhrnP3RQHz7gfX3igj3i9p0mJM37v/57WT+yLyz/9fegSaXTllFNoyC6yV8aGrbq3CaPYcH+6c6SUTeJh54yNpKwi5bHpd4/ABgAAAABAI8kt1gc2kLEBAAAA0OSKtRX04NdSM2ZZt1b+ZgMbphkbHABxNmO7SY2dTSVE+NLhS0XiPZrrOTH9f8eUx7za9IKy+oJzXkm5uKhdl4vOX2xNoeV70o16Rci4t0d8hA91ifGn+mQXHL5o3GPDXjq1CjHKzuBSUmo1ZTAkZxXRf37+hxIzCpVl5r4L/qy9Pd3Jz1sKYGjKKiizoNSuPUNqg4/j8wdHVc/YsFNgQ2ftc3PG6GItoMcGAAAAADgMZGwAAAAAOI4SfcNl2RPj44yaYpvL2Lh9UDR9fldX6hdvfjtn1CbcR9wXlFbPZjCdeW/pOvNz1yYomRum/UlsJQc12NT+LautzynS1StT44ft56ihdIwJVh5vO5lGH/1lCAbVeIGeiO75ZLNRUKOmIAX39JBL3a49ZMg4amoe7vYtRVWiz3zx1wdy1MoaKPvGUSCwAQAAAAAOIy1Pmk0VGezb1IcCAAAA0OyZXrTni7KWejnIy7093Sg2TAoEOLsv7+lG79zSkcL0jcDNlaPiDAxbDO8UQnOuihWPk7Jr1yjbFF8a7xztR2ue6EsxIV7KcvXxZRbqKCmnjFbsuUhVNnQsf/f3w7Rko3F2jj1FBvlQn7bh4vGl7GJl+X9vv7LGC/05JtkdpkECc+SMjeSsYtp6IpUchYebfUtRxUcGiPu3ZwxWlgX7Sb8TpqW6XA16bAAAAACAw7icIw1yYsPqnkIPAAAAAPah0RlfbPZ0b0FtI8wHLeQeCV4erlP+JjrYW9zOZ5YqvTTaRRpPwMmuRZZEnD7z41RaCQ016cNRG9zYXC5p1C3Gn1LzysRjuccHlxC7Y7EhSJEQHWLUVNqcI0lSc+2GwiWi3pg+iB77YjuduJynLI8M9KkxY+N0Sr7Z5UWllj97H30vjb3nMohjCKO6t6JhXQyNxZuKux0zNoo0OrqYWaT0MJEtvHcYHbmYQ/GRgfX+GY7MtcM2AAAAAOB0gQ0/Lw8K8TfMPAMAAACApqE1aejMF9P5Qj9foDcNX5TpL9Q2dg+DxhDkK10kf/HX89WyH2oT2IgP9xU9OPYkmr9Qb4365/p5GT7jR65uQ4+PixOPC/SNxQs1xlkkhSXWj7GsvIIKS3UUEeRD91zVhd6+cxA1lNE9WimPe8aFkaeHVDJKZ6V5eHah+YyN7CLLJb3kHiRyqdure8fS8G4x1NTkvw9ubD536Q7KyDf0/6itA+ezxL3p32JkkC+N6dmaXJ3r/UsDAAAAAE4d2GgV5mfUYBEAAAAAmobcN0Pmoc8S4FJKvGbfhYJqpai8rJQHclbB+sCGuXJU2cVS0KBDS1+6a1gMvTa1vcX9cI8NbkSemKmhDSdy6vxdDFFle3AvCS5zpT4200ybvBIpo8OSg4nZIuPmqp6t6aYh7amVPnu6Q7T9+6T0axepPH54Ug9DzwkrpZly9MGJT2YPp2em9KHbhnewGvBgvvqMjdxirVHPDUcIbHBzc3YsOZemf7CB0vJK6rSvi1lStsb8OwZSc4TABgAAAAA4BG5YmFWgodhwqU4sAAAAADQt0wvkpnNPdp3Nr3bhnctVuRq56TcrrzCfsfHUxHi6ZWA0XZFgPRggf05v/HmxVscgZ8RM7BlOD4yWenWoMzg46CSXoiopM/7edOXWm0ifTpW+xyGdpVJN4YE+9PG9w+jN6fbP3Ajw8VQe8wV+OYPB2jHKgQ0+Ls5E6NI6RMlMqCmwkZkvBT/kYEJT4wlcMSF+Rsv2ns2o9X4qq6ro682nxeOusaHUHCGwAQAAAAB2sWJ3Iv11IKnOr8/Rz7jixoIAAAAA4HilqEwn1atn2cs9ErxcsGFx61Bv5bFp8/QcfcZGeIDhgr01FaqSUnk1lIgyFxDxNvP58sVy7rvBvTW4D4hpNoi1/hXsTEqeKDPWLsrQk6F9dDD5q4IQ9uKrb+ot/67IgQ3TgJEaZ2Zw4CbIVzqeKzu0pEcn96QXb+5fYymqLP0Yw1EyNlhMqHFg46O/jolARW38vveC8tiR3ltjcr1/aQAAAACgSSz6+zi998eROr9eq6to1ifmAADQtCoqq2jZoTS6pJ/dCwCGhuCyKmuBDRfO2Aj08aDbBkWZDWxwPwt+xwHetp3DVqo+s0PJUimh2mTPWGrOzp87fx8v/3aefj8o9V6QWetfIWdsJLQMJC99v4uGpA7MeHu4k4ecsWEl+MIZG6EB3kq5Wr6f1C9OZHDUlLEh89EHOhyBuX6CZfqxkK1SckscKhPFaQIb5eXltG7dOvr000+psLBQLEtJSaGiItv/GAEAAADA+ew6nU7PL9trdeAhByhqS6tPP2/OJ+dgDOMOAGhMOy7m0ZJ9qfTGptqVhwFoTqWoTGeVqy/yl7lwxgZTSiaZnAdXVFSJPhG29ojjIKpptoct5DJTAT4eFo6vhQguccaG7L7hEWaPWY3XceCgscrBqj8nTw83GwMbGqtBDHPCAgxZNszH0/zn1hR8TIIuclneugQdP71vBDVXtf5GL168SBMmTKCkpCTSarV09dVXU2BgIL3xxhvi+aJFixrmSAEAAACgyb24fJ+4P52SR93bhCnLy1Q1cecs3kZvTB9IYQG1G3xo5VloCGwAxh0A0ATyNNJFw9NZdWviCtAcSlGJjuFcCighiPYkFhhdpJeDHK6YsaFunG7a5JqfcxknW6kTPlYeyKIb+rWs8TU7z+bTS7+dF4+DfNwtBl74O+gY5Uf7L0oT0UP9PawGDfgcvrBUCq4E6ss8NYabBrcTk5nkDBG3Fi2o3MIx8u9YblEZdW5laJhuixB/bxrbq7XIkLlhYEKjvr+ayCW/uCRVqj7zorSsgkKknu02KdJI31vL4OZbxrfWIdRHH32UBgwYQLm5ueTra2jQcsMNN9D69evtfXwAAAAA4ID4xFtWVVVF3209qzxPyiqif84bp7/bQg6OmKsbDM0Pxh0A0NgqVdfUSlT/zwE0Z/LEE5mfvtzSyze0Exf6+W+Fszo+3XRJaaLtshkb+hJQcsktdWBDDnrUthRVSp5WnEsv3nyZZn1xXDw2Z/URw7l1kK/5eeqcNcI9O+SgBgv2dbcY2DiXlk/Xzl9NC1Ye0u+3enmkhnLP2K40fWQno0ygHafS6XJ2sdF2FZWV9OgX28V60wwMWzx1fR965oa+1KmWQZGGdnWvWJo3tS89fk0vZVlpmRRctwV/ThcyCkUfEXc31/x7a5CMja1bt9KOHTvIy8v4l71t27Z0+fJlex4bAIDTOpWSR6XacuqTIKV9AgC4msLSMqNAxvfbpMBGp1bBdDol3yjwobb3bAZtO5FGj17TU8zMMpd+jVJUwDDuAIDGpm7oeza7hHrFGJroAjRXWv1F/Ddu6kBp+WXUu41UrojP4zgboFBTQd/uTKVf9mcqr/HxdGteGRv6UlR1zYL5akca/bgvQ1lnrt9cQqQv7TpfIB5HBJrPPOBMmQKN8Tm4n5flxtxHk3PF/X79hKSIoKaf+X/fp1vo93kTRLmqQxeyacXuRDqTmi/W1bYUlSPj9zeqeyujYEZtSlEt+P2QGIP1bebXnGr9L01lZSVVVFT/oC9duiRKUgEAANEjn2+nZ77ZjY8CoBl77fdE+mZHKrkSdamB3CKt8phr8rJ7rupCD03oIR7zSfr2k2liMKL23Pd7afXBZMoq0FhsmMdNBAEw7gCApvx/Lt/k4iBAc8/YiA72ogk9w436IwT5eFBhaTkVlxlfqA8PcJySP/bkpe8F8cyPZ+nHvelG/3bUJmOjxOTzWr4nTXlcZOHfHnUwJT7cUEFHzfQQ7hkeTfFhXjY1D2djerampsaZJX/sv6hcvN952vA51yVjw9Fxg/MnrpOyNvKKDeOrmuQXl1FcRAD99/YrqTmrdWBj3Lhx9N577ynP+R80bhr+4osv0qRJk+x9fAAAAMAz4rM1tOFEDj4LB8Tp3v/5PVHcyziFfOvpPPp6p2GQ4grOpkmzpdTBDPXyqBA/8vOSghKfrz9Jr/y4nxb9fZwKSgzZHTLTE3euLZuiry/r6+04jf2g6WDcAQBNmbFhrYktQHMiZxeYy8II9HWvliHA5PNBV6POyli8JUXcbzuTRydSS2rVY2NK30ij5+p/boq0FVYDTO/c0tFiRgwfh9pNAyKUfifm/k0r1vdoYO2jgsxmijSFn3dJE6P8vY0DZHGRrjmhPirYT9xn5Jfa/JqSsnLRM8Q0A765qXVgY8GCBbR9+3bq1q0baTQauu2225QyVNxAHAAAAOzv3q/O0ht/XqRCfVNLcByfbrpMW07n0WebDCU5OSVf9vyKc0YzQJ2ZuuatOrCxeN1JcR8e6E0+XtWDEvvPZyo1cmXPL9srylLJZn60kb7ZckY8bhlkfhYaNC8YdwBAY1Nf99MisAFgFNgw1wMt0MeDirUVRue6D42JddlPLsCkaXdGQRm9ulK6CF+b8/37Rrem3x7pVS3DwlzGxsnUYrFv+XtoG1G782ROMvHz9qDMglKLzaeZjwMFo+TPskBV+pZ1igkmVxQVIn2n6Xm2BzaKteXie23uav0JxMbG0qFDh2jZsmV0+PBhka0xa9Ysuv32242aiQMANFfqC3f8H3JtZm4A1Pz75RoXyF0JN0s0TQ/PKzEEoPacL6Az6SXUJcafnB3PDJKtP3KZurQOoTUHk5VlnVuFmP0d/fvQJRrUKYqKVYG5vOIyevWnf2jlsxMoJce4SWBksOvUz4W6w7gDABobN6eVlZmpRw/QHMmZAuYagstNrNPzDRegr+njujX/+8UH0piuobQvsUBkqkz/3zFlXZa+cboteJY9Z0f0ahNAB5OKjNZ9vTOVhnUMoWv7RNL2M3n0yspEunVglPI9mAswWcOVdrq1DqZDF3OprLyCvFQlX9WBDUe6SN4xJliMKXIKtdQtNpQ6xATRjFGdyUNfCszVRAb5iN+J9DzjjBtr15y0ugryMzOhrLmp0yfg4eFBd9xxh/2PBgDABagb5r73x2F64rreTXo84FrMNX2DppGco6FzGaVUqb+Qr04DVpelYil5WtcIbGiNM4YWrjYM5qaP7CQGGzxW+uu5SXTvJ5vpkj7D45/zWTTljTXUr53xQJdPyDkF/q6Fm5RlNw1uZzTgguYN4w4AaEzq4HyZDfXoAZoDzhTgckbmJuzJF9sPJBUqy1y5NI6nuxs9M6ktLd58WWn2XR/PX5tAL69MpMPJhuAGBzr4dk3vCDp6WVq+63w+RQV5iz4e1pqUc5mqPYkFtGx3Ol3dPUxZ3iMulPadz6ZTKfnUM86wvKjUMGYJ9HGcvigh/l6UX6IVweb20UFKDz9X5e7mJoIb6TaWoirRlyvzc6BgVFOp9Sfw1VdfWV1/55131ud4AACcHjfMVc9SRmAD7EmHwIbD4AHNrvMF5Kuvcase6+WqMjYYlxEL8fOgfvFB5EqBDbWJfdsYDWivu6ItfawKfMgBDm9Pd/rPbVfSJ6uP0bn0Anriy53K+nG9Y+mesV0b6OjB2WDcAQBN2WMDGRsAhsCGpSyBUp35fhCujrNS7BHYCPDxoJGdQ40CG7Jle9KVslTctJyDSN4WemvIurcOELdp/VuSvzdPFJLO3bvFhoj70yl5xoEN/bn9pH5xdPOQ9uQoNGUVlF0olb0ND2wemdxcjup8uiFAaE2JVgpI+SGwUfvAxqOPPmr0XKfTUUlJCXl5eZGfn1+tAhvz58+nX375hU6ePCnKWA0ZMkT06ejcubOyDffxeOKJJ0TpK61WS+PHj6ePP/6YoqKilG2SkpLogQceoI0bN1JAQADNmDFD7JtneMk2bdpEc+fOpWPHjlGbNm3oueeeo5kzZ9b27QMA1CpjA8DeENhwHAf0aeOl+plqbqrIhroUlezvozkuE9h4ZkofeuPXg8rymaM7Vxt0jO7Rqlpgg00bmCAGVAvvHUaPfL6dTqdKjccHtI+kO0Z0bPD3AM7DnuMOAABbqNtqlKHHBoCg1VVZvKAeG8rnf9K5XHMSHewtsiPmLpP6w9WHPEnK1NJtqcrjQB93KtZygMm2bBi5RBjp/02L0pd5zS7UGE3I5HKwMaF+9OjkntTUls4ZLUpl3bdoC2nKyimrQKP08GsOIgJ96PDFHJHRzhPBvthwknTllXTfuG4Wx2R+Js3Vm6NaFyfLzc01unGPjVOnTtGwYcPo+++/r9W+Nm/eTA899BDt2rWL1q5dKwYr48aNo+JiQ53lxx9/nH7//Xf68ccfxfYpKSk0depUZX1FRQVNnjyZysrKaMeOHfTll1/S0qVL6YUXXlC2SUxMFNuMHj2aDh48SI899hjdc889tGbNmtq+fQCAGvF/wgANRYdBtsNoE2Z8kr32WA6NX3CASsoqxI0tvKMzzR0fJx5XqWaBOmt5jr3nMsTMsuHdYsQgSNZK9VgW5OtFL97UX3k+slsMzb66K905qpNS7zciSBpkBft50au3XkFRIdX3A82XPccdAAC1zthAKSoAQSP6Mpi/oD59SHSz/ZRC/e1zUdmWnpw8uU1kztSQsWFJWIA0bpGzIBiXic0s0IgeFo6AxxbxkYEUHuRDaXmllKUPwjSXjA1/fSkwDlqsP3yJlm8/R7/sThTXl/JLysz2PfRDxkbtAxvmdOzYkV5//fVqs6pqsnr1apE10b17d+rdu7cISHD2xf79+8X6/Px8+vzzz+mdd96hMWPGUP/+/WnJkiUigMHBEPb333/T8ePH6ZtvvqE+ffrQxIkT6dVXX6WFCxeKYAdbtGgRJSQk0IIFC6hr1640Z84cuvHGG+ndd9+1eGycHVJQUGB0AwCwhUaVjssnKefS8mnLccNsC4D6yC9F4MxRcK1hc7jvRmmZND3K18uNxvcIF9uW6DM7nBEHZV76YR+l5JTQdQPaivrCvqpmdZZ6YgzpEk19EsLF46FdomnaoHYioGF6Ai83zANoqHEHAECte2yg/CeAkF2oozALF/H5nHB0F8OF8a/v7d5sPrUwf/v0N5D7lFhToCmn85mllJpnfIHbVnyuzlkAxfoSRmpXdIgkR9I+KoguZhbS+fQCJZOhOZCDFPwdcUBDdueHG+m2d9eZzdjwR2DDPoENxmWfOJuiPjiQwcLCpHpvHODgLI6xY8cq23Tp0oXi4uJo506pHjPf9+zZ06g0FZer4kAEl52St1HvQ95G3oc5XMoqODhYuXH5KgCA2vbY4MHRg//bRv/5+R/R+Aqgvp7/5Tw+xCZ05FIRbT6VKx5rLMzk/HJ7Kl3KkWYY+XhKF/z9vNyVYIejBzCe/34Pzft2NxWqmgmeSyugPWekOsLj+0jnRJy5IfOyUHeZ/WtqP3pkUg8a0S2m2jq58TpnbAA05rgDAKCmjA0tsmQBqFhbQQWaCooJtlwO6PbB0fTg6Fha9VgfahnUfM7p+DyfG3QPah9cr/2EB0pBo7YRhgv43DhcLTHTUEKqrniilbns//7tHSuwMbZXLJVXVtGf/yQ1q8CGHKRITC+kPFWGBmdr8OfBZbpkxXIpKi80D6/1J7By5cpqA+DU1FT66KOPaOjQoXX+AisrK0WJKN5Hjx5St/u0tDRRQzckRGpyI+MgBq+Tt1EHNeT18jpr23Dwo7S0VPT3MDVv3jzRk0PG2yK4AQC16bHBs4/VwYwyXQX54D8eqAN1CSM+qYGm8+RyqY7u8E4hot6wpeCHzM/LTcnccIb+O2sOJtOes5ni8Y1v/01//GuimImnzkSL1pedah8dLOrA1hTY4KDF5P7xZtf5ekmBn9bh/nZ9H+AaGmrcAQBgSaXqmh/KfwIQpeZJpYtigi0HLNqE+Yhbc/TkhHjacSaPdp2re5+RAW2D6LWp7cVnPGvJCbGsZ2wA/XEoy45HSuTh7kblqky0QF9PSmgZKMrHOpIhnaNE6aycIq0IHjWXcktyv4zXfv5HmRynlldcRi2DfU16bHhQc1frT2DKlClGz7mcQGRkpCgVxaWe6op7bRw9epS2bdtGjsDb21vcAADq2mODTxTUtRD5wiACG1AXuSW2XRDnDKHd5/LpUHIRDe0YTL3aBOIDbyBn00sps7CM2kf6UmSgJ03sFSHq3v73jwtG28l1cEP9POlyrkZcmFWXYnIU3Ejwmy1nlJlRsk1HU0TQ4YkvpSzXqYMSlHVdW4fQCv1jTwulqGpy95guIi1+ypWG/QI09LgDAMCmjI1yTCYBSM2XxrMxIbg+Zsu/G3V1RUIQFahKDseEmA82TB8cXc/AhhS9PZacI7KzHTGAy8fZvU0obT2RRqEB3g45dmoI6rJS6kllMr62hMCGHQIbnFlhb9zz4o8//qAtW7ZQbGyssjw6Olr0ycjLyzPK2khPTxfr5G327NljtD9eL6+T7+Vl6m2CgoLMZmsAANSH/J8QR8/VgQ2erR2CSclQB8m5ht+jUD/L/3V/vzuNvt4hZSvuSSygJbO64fO2o8+3XFYeP/ztKXHfrqWvmKnFdp41zNS6vm+k+LdA7hsRF+5NJ1KL6dmfztKrN7Sn1Uey6WxGCc0dbz6TwdS+c5n0087zNPfaXsoJrT2tP3JZCWpw/0I5MWjnqTSjht6946V+GayrqtGg3JCwtrjHxuyr8XsKjTfuAACwpccG/19Y5oAX/AAaW1q+lLERbSVjo7mTsyBs6AFulY+qMXjbcF/y9XSjWwdG0RfbDP06r+ljXKKqNjxVgY2Xlu8T9ycu5ZEjkvv38WTR5qKm7Av1tSVkbDRAj4264FmLHNRYsWIFbdiwQTT4VuNm4Z6enrR+/Xpl2alTp0SD8cGDB4vnfH/kyBHKyJDqPrO1a9eKoEW3bt2UbdT7kLeR9wEAYE9yuZkAfVNcw3JpBkZWgUY5oQCwRXKOdBLDmQG5JeWUXVS96Rs7lGQogcTbgn3/rn/YazjXkA1sZ6ipe2W7IJpzVSz9MqcXPTgm1ihoIQcKDiYV0Q970mnhhku05miOUZkxaz5ff5IOJGbRusOX6OTlXPpu6xnKK5YGmvZQoDpR/te0fnTzkPZi8JNdpCV3/SgtOsSXOrcyTDThAMvEvm3ouiviGyTYAgAA0NjkMrJ+nu6kQ/NwAErXZ2xEW+mx0dyFB0jjrt71zJbnHhjqrO9fH+lN/zcwml69oZ1YFhvqTSF+dR/j8bl9WXmlyNIoUPXSc0ScTc7iIgKoufA0Ke0bEWRc3k099ivRX1vyQykq2zI21L0mavLOO+/UqvzUd999R7/99hsFBgYqPTG4WTdnUvD9rFmzxM/nhuIcrHj44YdFQGLQoEFi23HjxokAxvTp0+nNN98U+3juuefEvuVSUvfff7+oxfv000/T3XffLYIoP/zwA61atcrmYwUAsAVfpLyYWSge+6sa6zKevf3JmmP0654LdMvQ9nTXmC74UMEmybnSiefoLqHi4vq5jBIKD6jepC7Qx1AOqJlk7Daao5eLzS7v39YwgOEAwLV9am6+d1jVg+PO/x2j569rR52iDVkR5sgzwL7cdFrc5Jk694ztSvaQWSANHt6ZOZi6xYbS8K4x9M/5TDp5OY/C9Q37Ppw1jIJMmnw/dk0vu/x8gIYedwAA2EKee+Tj4Ybm4QB8vqmvRuDvXbeyo80Bl/99bWo76taqfhfhLZVc6t82iO4b1ZrGdDVkS9e1xJNGV06nUxwzS0NNbo7NvfqaixZk/P1HBfuKSbGyXHVgQytdH/BDYMO2wMaBAwds+xJqeRXlk08+EfejRo0yWr5kyRKaOXOmePzuu++Sm5sbTZs2jbRaLY0fP54+/vhjZVt3d3dRxuqBBx4QAQ9/f3+aMWMGvfLKK8o2nAnCQYzHH3+c3n//fVHuavHixWJfAAD2dOhCNm08mkLtooKoVag/HUzMVtZdyi4SQQ2WlleqzJJ+e+Uhmj6yE3WMqX6hGoBlFurEac7gDiEisHE2o5SyinT0/tpk+vre7tQyyEspn+Dh1oLC/D2opAxZQfak0VX/PO8d2Yr89M2va6JOzDiXIf39s4xCnShrdc+IVtQ1xp96xFYfEP286zydTSuocVZPfWQWlIqBQ/c2Ycoy+WduP5lmNlgL0BAaatwBAGBrrXyeTODl4YZSVNDs8dhi+xmp1KqXB/7fteaKhIYby/Pkqan9W9Z7Px7uLahcU0VnUuve6LyxyNUufL2az/gjItA4K+qa/vHUo00YjenZmu77dAtdzCiqVorKtxl9PpbY9Als3LixQX64LeUXfHx8aOHCheJmSXx8PP35559W98PBE1sHSgAAdfX9trPi/u4xnemf81lG63KLDKVeOP3zrwNJ9N4fR8RznhX9wxNX2/xzysorRCppXS7sXM4uprNp+TSye6tavxaahkZXRb5ebhQX5q3Uuv15v1QWaduZPOVEl4MZfl5u5OftTrnFOodtVO2MzDXWu86G7AxZG/13x4q01ZvBLd6SQiF+HrTs/h5G31lOkZY+W3vCaj1fe8gq1FCEPjPDEne3Jq1gCs1EQ407AABsvZDLFxF5oggqx0Jz98XWFGVyj9w3DhrWw2PbUJAqC9+e5B4b6fpJlo6M+wouWHmYJvWLo+YiLjKQgnw9lTJh3F/k7qu6KGWp1h6+RFVUResOS30ffb3c8XfZ1D02AABczcELUoZGoK8XlZs0Pc0v0RqVpZKDGtK6MqWmb024tuK181fT0o1S8+LauvvjTfTfXw5QsT59ERxfia5SNJPjkxfxvKyS4sOki9CfbrpMz684p19eIYIaPVoHiCyP5Bz79WBo7nTl1f8+eTanrW66IoqemRQvyolZkldSTkk5hnRjll1o+A77JITTTYOlGrvqmUymOKBl7t8TLpOnsfCaYo2OAk1q9naNNfTTuGGgcR80AAAAV83YcG/RgrjUvdxIHKC52nXO8Wf2u5prekfQiM71KzllLbDBk7Xk/hXsHv2Fc0fDWeRfPDRKKYnbXPSIM2TP+6iyMeSSVHJQg6EMlaROOSv79u0TPSq4iXdZmWEGMvvll1/qsksAAKfHWRQyvgBdajIrW11KRq6JqFak0VGQb801JOXyVsu2n6tXnw5deSUResA5vMs5JXQiVUMRAR5KE+etp/Oop6pk0Z7zBXT0UpEoUdWhpa9Y98ehLDqbUUJx4c3rZNAWhZpy+vtoDk3pF6l8pjUpM5m2+dsjtestwT9nTNcwOq7v1XFlQhAN7hAsyompHbxYRPHhhkbccgBy1lVdaMqVbWnzsVRl3bHkXGVmqRqXvFv093Fa/MBIaqNvuJeSU0yzF22hKzu2pFdvuaJaIISbo/t6Gp8W/mtqP5r+wQbxODwA/1hA08C4AwAaE/93z/+v8q1cP1P9XHaJmGTSM7r5NLEFYFz6FlyHhz5jQ6uTqj/88a+JTX1IYOKc6pqRr6chc2dQpyjadTrdaNtAn+bTf8SuGRvLli2jIUOG0IkTJ2jFihWk0+no2LFjoiE3N/sGAGiu1CmdrcL8qwUvuP+G7FJ29UbEeUXaWpXEqW86cBkHNsDhvf7rYXGfVWQ80/6IqgE1W7ItRdzPGBpDrUPlklXGkw9AMv+PC/TZ5su04USOzR+JTlX2iRt9+6hONGtjdNdQGtohmB65ug1d1TWMBrUPpicnxNHccXGipvc/SYVG2xdppO+dy0R5ebjT2F6t6b+3XymWnU8voJeW7632MziowZ7+epeyLDlb+n3ZcybDzHurFAESH5N+IepG4QPa2152C8BeMO4AgKbJ2JCCG/w4r1RHD/56ip5cdQZfBjQ75nrMgXMHNnhyo6a8grzrOJaBhsWT2WTq72jeDX2qbdsuKhBfR10CG//9739FQ+/ff/+dvLy8RDPukydP0s0330xxcc2n9hkAgKm0vBJx/+jknmIGRJiVtMlCfd1E3u7aAfHicW5xWa0CG7aWrlLj2RnmHoPjsrWPwtHLxSJb48p2wRQZKJUUyixEYMMUZyfsvygFD7gR+/4L1ZtyW8xw4nJQcQH0yhRDOaja6t46gF64vh1FBnqRt6cbvTylHV3dPZzG9wwX6/acz6eCUkMQq1gf2JAbd3P/jf7tIpV+GHvOZtK5NOMyATGhfkp/jgsZ0nvddbp6QEOWpg/Kmjaf81aV2kqICqrzewaoK4w7AKBpe2xU0R8njHvm1XSOYUsfUQBnkFeCbA1X48nNwyurSFtWUedJWtCw1H1Q1fNYuSxVQkvjQEbHGCQX1Cmwce7cOZo8ebJ4zIGN4uJiMch+/PHH6bPPPqvH1wcA4NxSc0uMLirePKS9qFn5+7wJdE1/84Hfrx4ZrdRR5N4ZpqWpjiXniEDGJ2uOiabflpoY2yoj35BVgowNx7fx6GU6r78wbQvuw8GCfT3Iy6MFZRQgsGHq4w2XlMdJ2Rr618/nRM+bmnyxTSoB9cDoWAr1N+5FYS+92wQQl/NetOmS6I+y5lg+vbPqmFgXHSL9uyJ7/Y6BFBXsW63MnRy0lDO6vtlymr7efJrWHrpktmwe/5tw7yebxWPTjA0+v3tmSh96/+4hdn+vALbAuAMAGltlZZX4P1RkbFRWUbZ+MpJaZnEZfbr7EhVpjbNp7/7pBP1n44VGPFqAhnM6TRrbguvgjA35OgMyNhwXj73+b2h7ah3mb7T8vbuGUOdWhh6I7aMR2KhTYCM0NJQKC6WLLK1bt6ajR4+Kx3l5eVRSgn/4AKD5StVnbMToL0C2DPalm4a0F+Vj7lb1whjaJVrMApt3Q18KC/ChUH/vaoENfjztrb9p7tKdtHzbWVEz/6Uf9lW7KFnXrJL67gcax+srDiqP+XemJhezNcoFac4I4AbiIEnO0dBX21PpryPZYrbS9X0NpZVS8xwjABToIwUW1h/PpXu+PEPvrMtQgqVx+l4ZMu6dIZekSsoqUjLBPvrrqMjU4F4aHPjYeiKNvtlyxiggump/kvI4p8jQPDDEr3ofjTE9W1OX1g3TwBCgJhh3AEBj40RZUYqqhTSzuaTMcL4sNxP/aMcl+uVoJv1yLFNZx5MkUgq0tDUxD18auIQTqYbSyVFBqOXvCrhaBCvWliOw4cB47MXXj3hMr8ZZGy2DDVVBOkQjo75WgQ05gDFixAhau3ateHzTTTfRo48+Svfeey/deuutdNVVV9nrewQAcDqpOSVidlek6j8bmZ+3ocTLv6f1o9+enUCjekhphqH+0olinr4UVbFGR099pa6NL51UluhnhcklceoiXZWxkViLTABoeu/dYlz+aFKvcLq6u5TtI5tzVRvlMZej4owNlESQLNmaQt/uShO9MkZ1CaUHx8TSLQOjxLqMGkp2qcuBtQppuCbauSXGMz9l78wcXO3EVg54eHm40aajKXT4YjbtPZtBv++7KNa1DvMTwQ01+fnqA8nVeodwEEQuiwfQ1Bpi3LFlyxa69tprqVWrVuLv6ddffzVaz/9WvvDCCxQTE0O+vr40duxYOnPGuKZ+Tk4O3X777RQUFEQhISE0a9YsKioy7ncEAM6NS70qpaiqiIrLDOfd8iQBDmCwkxmGC787LhrKQpai3Cs4ubR8LX23S2pU/MJ1CfTB7Z2a+pDA3hkbqpKz4DwemdyTplzZln588mry92mYKgLOxubf5F69etHAgQOpZ8+eYmDB/v3vf9PcuXMpPT2dpk2bRp9//nlDHisAgEPjbAjO0nB3q/5Pq3xRMiqE17dQTipYiD5jI7dYS5eyi2jqW38rM7DZ5Rxp0JRVqKHHvthOX246bZcG5++vOkKO5ExqPr3128F6ldpyJaY9UFqHGs+UmtQrgm6+Qrow/8Do1rTmib7igr2sZaAXleoqacI7B2ndcdubZLsqDgDI5BP5hAiplJO2hsaIfxzKNLsfexvXPYz8vKrvP1jVxFuN/62ZM7EHFWp09Pz3eym70JB9ccvQDvTA+G707l2GMlLP39iPercNp6xCw78DcqCU053VAViAptQQ4w4un9u7d29auHCh2fVvvvkmffDBB7Ro0SLavXs3+fv70/jx40mjMfxdcVDj2LFjItjyxx9/iGDJ7Nmz6/luAcARe2zwqTo/LlZlbJTq/8/M1vceOJ1VokwgeWOzNLGA5ap6ZQE4o+MphqDd0I4hFOKHC6iulLHBJalxUdw5Bfl60QPju4t7kNg8gt28eTMtWbKE5s+fT//5z3/EgOKee+6hZ5991tZdAAC4LB7UcI+Nbm0sl2zhXhvmZl0H+HqKARSXj/lu61mzF/xlJy7XL709XVWKylGaTXHZrf97Z53y/Jr+8dQ1tnmXvknJKaa7Fm4yWiZfjH/jpg4iUNEuUgqSrXqsjxh8m+JSVOpshbHdjLM7mpsyVcAsxE86/eE+JOqsBUuKtdJrO0UZ97mwt9ahPrTi4d6UmqeljSeyqGOkByUXB5oNlsrG92lDmfml9PWWM3T8Uq5Y9tqtV1CQPhjSLTaUHpnUgwJ8PEVZPC59V6QpF6Xo+Hm5/nPxxKwtcCANMe6YOHGiuFn6P/y9996j5557jq6//nqx7KuvvqKoqCiR2XHLLbfQiRMnaPXq1bR3714aMGCA2ObDDz+kSZMm0dtvvy0yQQDA+fEpgbrHhjr74tsDaTSpS4QS7CjUSuWn3ttmyIRkuaU6ahXUcBmeAA1NX3UNXIwH19nTC/RFsApcg83TDocPH05ffPEFpaamipP4Cxcu0MiRI6lTp070xhtvUFpaWsMeKQCAA+MyUlxb17TBrxpfRJRnSajx4KlNeAAdupBt1NzblhnbnC5f21JUcqOw9g5Sk3Hx+pNGzx9bsoP++/M/Sh3j5shcgIsH2KxPXCA9OSFeec4nqOYCZp76i/a2XLhvDgo1FRTg7U4vXpdA0wZIJZnkv8eaPh85i+ipiY1TqikmxJtuG9iSrmjrT1MH1vwz41sGivuDF7Kl55HSc9nk/vE0srt00TXEpPSd/N48rARPABpbY487EhMTxT65/JQsODhYZI3s3LlTPOd7Lj8lBzUYb+/m5iYyPCzRarVUUFBgdAMAR8/YkHps8NlBiSqr8/cTWfTACum81d9LOp/+J6WQDqcZl6TLsVBaEsBZVDbjcZgrU1+LQGADXEWtR7Gcln3XXXeJmVSnT58W6eGc0h0XF0fXXXddwxwlAICDu5gp9aswbfBrKw4ycGDkSFL1kkHhgd70wxNX081D2ldbl1WgoXNpli+S8KzsJP2xcWNh3lY+xgoHudhdqu8dkqC/OMs2H0+lE/rZ5815Nk2veCnLIj689qmm8WGGXi/c/LK5K9JUUKCvOw3pGEJ++osR3ESc1VT+LLe43CjTw9HIgQzuw8O9MvjfDEv8vKT3UFpm3LMHGRvgiBpr3CEHSjhDQ42fy+v4vmVL4741Hh4eFBYWZjXQwlknHCSRb23aGHohAUDTuJSvoY3nzJfprOAeGy2kHhtMXYoqwt8ww7lfK+n/3rNZ1Scl7U7Op+Q8Qxk7AGeTmm+9/xw4J+6tIQtGKSNwEfWantehQwf617/+JdK2AwMDadWqVfY7MgAAJ5KSK5V4ig33r9Pr5SwKc/5vaAdx76u/GKs2/YMN9OD/ttKRi9JMbVO/7blA9y7aQr/vu0Dn0wvEBdwhnaULN+WVTd/Lgo/pWHKuyEiZOijBaB33FGmuuO4piwySekBM6Fb77JrBHQylxnhwzk0Am7NCTTkFmvSQkAMbZeXWAz+5JTrxGQb6WP47bUrcKFx279iuVktXyX00lMCGXIrKXD0zAAfirOOOefPmUX5+vnJLTjYuWQMAjW/WTyfo9U0XqUBTbqXHhnSOUKKroBh9ec8A1bn4//WWzqfP5xgCGwn6SSVrz+TQPT+faPD3AdBQ58wrD0j95cZ0bd7lgV3NaVWJ686tQ5r0WADspc6jWG6WN3PmTIqOjqannnqKpk6dStu3b7fbgQEAOJNi/eyHwDrOfPBRBTY4o2JSvzjq0zacfnlqHF03QCpF46Ofac2u6tna6PVcxsqc5GwpNf6jv47R01/vEo8TWkoXycubOGOjoKSMHvhsq2ia3qNNaLUGztx7o7nii848nr5vXDeae013mtK39ieeXJ7q9RuloFh+aTnNWHycjlwyLpXQ7DI2TAITciPwXefyac95w4m+qdyScpGtYa7klyPgQMb4PrE0rncsDesabXVbX31go0QrzUBFYAOcQUOPO3i/jBuTq/FzeR3fZ2RkGK0vLy+nnJwcZRtzvL29KSgoyOgGAI7BXClKOWODb4yTXkN8pP87i/T/d7J2Yb7EW3CPDdmtva3/HwzgDNYeyxG/61yC9ZlJbZv6cMCORnaTStNOubIt9W8fic8WXEKtaiqkpKTQ0qVLxe3s2bM0ZMgQ+uCDD+jmm28WqeIAAM09rdPfZEZ4XTI2uPzQw5N6VttGve+IIEOZIcaNg28a0l7Zj1yeqoUYchmEBXhTj7gw0dejookzNuQsFzZzTBfac8b4gtHZVMeuQ345u5iiQ/2UGX32VFpWQb5eHiKTZXzv1kRFiXXaT9/4QBrUPlhcuGeHkgupZ6z5cmncUHrN0Rya0i+yQd5TU+L3X6qrrBbYkDM2Dl8qErc1T/Q1+/q8Yp3DlqGSzb22t03bmZaiWnNQmj3ugYwNcDCNOe5ISEgQwYn169dTnz59xDLuhcG9Mx544AHxfPDgwZSXl0f79++n/v37i2UbNmygyspK0YsDAJyPxkwpSl6kzthgwb7S/535+vKpz41JEOu5dxc3EGcdwn1pWFvjiShVVVUmZ+IAju9shjRGG9QOgXhXwxUSru4da7F/J4AzsnmUPnHiRFq3bh1FRETQnXfeSXfffTd17ty5YY8OAMBJFOsHOgE+htq7tcH9NWStw8xfsFGfgJSbGYhtPp5C43pLtbu5PBUb2S1GWc8DsC8fHi2amHMPB7nvwsELWSKIwCc6HPBoLJuPpYj72Vd3FVkqunLpM3hwQnf6+2Ay7TiVRrqKng5XImfbiVR69ad/xOObBreje8Z2tev+L2UX0YHELLuXtWKZBYa6quczS8U6vti/8kAWpReU0c5z+aJh5pR+xnXknRlnJHy4Trp4P7KzcTq96e8WZw15e7pVuyjBGRvxEVJZMGcnNwrML5FqJ5+8nCfu0WMDHElDjDuKiopEgETdMPzgwYOiRwb37Hjsscfotddeo44dO4pAx/PPP0+tWrWiKVOmiO27du1KEyZMoHvvvZcWLVpEOp2O5syZQ7fccovYDgCcA/+/LtOqzpGMSlG1kMp4yoL0k4vkDA8//blCsI+HEth4cHCsONf28XAjjX6/RWWVZOggB+AcLmZpKCLAkwL0mUrgOnisj6AGuBqb/6Xy9PSkn376ia655hpyd3fMGtMAAE2esVHHE8BjqqbhAzsaNy+VdWol9Uy4c2QnKiit3tBNo29uqNHPxFbPyubG3A9N7CGCGowHXhX64MiClYcpI79UZIpwWahTl/PpzlGdqKHJZbJGdZcuCLWPDqbfnp1A3h5uogny0o2n6NfdiSITxZH8ecBQH33F7kS7Bza+22q48GYPJaqml+cyDVkyD3x1UtzHBHsZNQjkAIcr+ediIWUV6Wj6kGjROFzNxySIse9CAQ012aakrFJcyHD0jA1bcXNxlpYn/S5wlpdWV0FdUWcXHEhDjDv27dtHo0ePVp7PnTtX3M+YMUNkhTz99NNUXFxMs2fPFpkZw4YNo9WrV5OPjyFD8ttvvxXBjKuuuorc3Nxo2rRpIosEAKwHEvg8MyrE0A+qKWUW66wGNiqrqsTFP31Sp+Dv5U6xwd50Sd+vjM9VGQcxZBH6CUjqwEZ2qY4CfZGzAc4lOUdL3VqhIgsAOAebp8GuXLmSrr/+egQ1AAAsZGzw7G85cFBbtw6XeiGw1hYakIcF+NCf/55Etw3voNTFZ9yPg2Xrm21fzCoyKi/EszIW3TeCesaFGdXkT88rpU3HUsRgk7320z/0wrJ99O3WM1Smz55oSJeyiyk+MoDCA32Meo1wH4MO0VLq8+L10sV3R5KuvyDM5KyXuuLMG3nGvGn2j73MHR9HV3cPo+6t/elMeqkoc6XG/TfUdDU00nY2Z9Kl76tDy+oXVML8Pei+Ua3pxeulxvWvrEykApPPo1g/E9NRG4fXVkv9hSX+++eABt84uOio/UOgeWqIcceoUaPEBVbTGwc1GP8NvPLKK5SWlkYajUZkjHTqZBzk5+yO7777jgoLC0Uj8C+++IICAsyX9wMAyRcbTtGdH26kc2mWe1k1poMphcrjS/nSubO15uFysOKuAYbMLB8zgY0w/QQI9aSJ7BL7ntMB2AP/jqepJjWp8RiTA37BLjKhBwBcn2PV9wAAcOLm4XUtQ8WGdI6md2YOpk9mD7e6HQ+y+OJL/3ZSs685E3uIBtMsLU8KUFzIMAzYTqXkiWwNU3wN81JOMc3/5YCyLF0f4FBnfzQUDpxwgCA23PwFoS6tjUsGOZLcIvs1Nf/or6P06BfbjZq/y43o5e+1vuLDfenJCVIDevbCivNGZRg4I0GNZxk2dWN5e/p6R5q49/WqfsrDf0tT+7ekIR1CKCZEmmm5+ojhuziYVEifb5VKpvmq+uA4Mw4ehvp7i78/OUgXFeIaZbYAAMCxcBbxDzvOicdJqok3TWn/ZUMPt7e3JFGxyTkvz1nhSpVGgQ1PNxoaH2z0XH3PJank8pbqaQI5pYbsEABH8dWubJrxxWk6drn632SpflzgZ+a8GQDAEeFfKwAAO5WiqmsZKln3NmHULsq2Jm1Du0SLfhnX9I8zulDJLmQWGs3IaRNRPXhQaDLQMg1+lJSVi14P6gvg9pSSUyIGjrEWslO4DwD3GjEXlGlKHJDhjAruXTKoU1S10l+18fOu8/SXvqyVXBZIzmTp0jqEpg6UsgjsJV8/a5CbZHNPDUvWHsuhu784Tq5A/fvr52U9MDHnKqk/zdYzhgyaF1aco00nc82WrXJmnBXGmV2nU6XZs9zjBgAAwN7eX3VEeXw5u5gWrzth1wkitcXnxf9cLqQIP8NkpOwS43PiiirusdHCqMcGT/hQZzYaSlFJ5xbhqv2p5SBjAxzQX0el4N6SbalGk5n4vLlUV+ly570A4NrwrxUAgB0Ua8rrlbFRF9EhfsogKzrEV2Rs8AnpL7sSjbaz5bhMAwzLt5+jWR9vpp2n0u181FJGyZNf7RSP21jI2FD6gNSz1JO95RVLaduhAd7URv+ZZddhgF5RWUmfrT2hPJcHzyk5xaLPCZfosrfOMYZSTB9vvCTuZwyNEeWYTHGfjQtZhgweZ6UusyVfhLAkPlwqiXY6rYTWHc+hi9mlpFWV5TKX8eGserQJFX1sVu69qH9uKFMHAABgL/vPGyZSfL3lDP248zx9urbpJk/suJhHBdoKurpjGM2+srVYVqApN+qvwaeebialqM7nGJ8TySWovD2kbQK8DZMnxnYw/J/6xYH0BpskBFBX8u/kkUtFNPm9g5RbrCONrpImvHOQPt5wyaYJQQAAjsJ1RukAAE2oWKsjf++mq0UaFuhDecVaSjaT5q8emFkrhcVlrWR//pMk7uUZ3fbMeJj37W4lYyQ61K/OgQ25ZBPbdTqdtp1ItfqzU3NL6Le9F+o1wOSgA+MMGf7MWU4dAhumpb6K9H01+PiYnA1iTw/rMxIiA6VA1/V9I+m2QdH03X096Iu7u9K0/i0pNtRb2f6+L0/S3kTHqIddV/svFNocmAj2Nfz9vrPmIs39/ozReleauda7bYRSqi4swBulqAAAoM44GMATYbjkFPcOU2urz7z1VV0kbehyp9asO5sj7q/tGqn8v/7FPqnkJJNPOzljg2+ym3tHmQ1syLPdfVWTJ27rG03/m9ZVeV6snwEP4CjUE3fYqbQSOp4ijSF3nct3ufNeAHBt6AgEAFBPPPueGzL7N3LGhhqXo+JT1IuZ0knpTYPbiVlxYp2ZGTdvzxgsggz/+naPeD66h9Q8uLKykj5eY5hJZ+92wmfTCowCAZGqxuHmAhvllZUWyzhxxsOn940Qg+YXl+8Ty9c8P9ni/p7/fg8lZxeLLJF+7aQLu7V1St/om3sSuOkHvHLTdmv4Z/PvCJeXOpKcQ+fSDPWd1UGa0yn55OftQYMbILDBFxVaBnpSRqH0s8L1mRr8vbcO9aHZo1pTYlYpXco1fD/P/XKe/prbR3mvzkYenD1/XQJFBko9NCzx8nCjl6e0o0JNOb29OomK9E3DZeqLMs6uWxtDD5uurUPQOBwAAOps3eFLtGDlYfG4Q3Sw0TmWVldBEYE+osRoor4HnGcNGZQN6WRGieiHEe7vSd1bSpm36vku8oQabpehLkXVJdI4s1leJ5+lqstU8TlTXIjh/LbMhfqWgWsGNrhZ+Kk04/FMTIhhshMAgCNDYAMAwA5lqFhjl6Iyd9H1fIZ0wXxAh0i6a0wX2noi1exF8p5xUpr8R/cMMyppNaxrjFFg42iyNLPNXnJMggBhgZZPmj3c3SxmbMhlnE5czlVmA7Lz6QVm+5RodBWUpf/ZydlFdQps8IxEzqjg75k/0zP64ERNGRucIbLnbKZ4fCTJ+PPkXh2bj6eK4+Osn9MpedS3XUSDXWgO8PFQAht+qrIJplkNg9oH0+HkQtFY/Od9GXTTFfYPtDQk/sxzistpy+k86hztR8M6htj0On7fWl2lCGzIWRxyOasQVUaHs+NAaESQD2UVaCgiCI3DAQCg7jLzDed2fC6jVlZeSV6ebhTiz+d7UmCDe5U1hX9SiihPVXYqPtRHTOAJVf3/fi671JCxYSb+8uJVCXQhV6OcpwXqz7/9zWSFtg/zpXM5pQhsgMNNyDPFZajknnKyjlGWs+oBABxJk+aXbdmyha699lpq1UqaKfzrr78arS8qKqI5c+ZQbGws+fr6Urdu3WjRokVG22g0GnrooYcoPDycAgICaNq0aZSeblwTPikpiSZPnkx+fn7UsmVLeuqpp6i8vGlOqADANfAs/Xs+3kS7z6TT4YvZYllTlqLy8ZJ+dmK6NGjkYAVnPIzq3oq8PS3PNO8YEyxm0cm89E0QZceSc+vcHNtaKSfOKHn7zkHVfl61UlQ1zHJLFU3IDds88NnWatscTcqh619fLTIm5D4WdcGfBTf2nti3jfi8wwO8zQZrTDMxrAU+gv29lBmN+89lUXllFQ3s2HBBhAAfw+ftZWbE7qv/XdFVVNL39/ekqCAvEdjg547ucq6Glm5LocVbLtPUjw7TbZ8eFcv7xNWuAb23p5uSft9aVZor1EwvEmc2oH2k0i8GAACgrtTBjNKycpGVejmnWPRyKtNVkJe7Oz1xXS+6eUh7McGixCT40Vi+PyJNMpHx9Qf+/15TbjjHefyP08o5qJyt6qcqyTOkbYgoNSW7a0Aro34dagPjgsW91gnOoaD5yCmS+gWqcV+9lDzDeCXIR8ryBgBwBk06Si8uLqbevXvT3XffTVOnTq22fu7cubRhwwb65ptvqG3btvT333/Tgw8+KAIh1113ndjm8ccfp1WrVtGPP/5IwcHBIhDC+9q+fbtYX1FRIYIa0dHRtGPHDkpNTaU777yTPD096b///W+jv2cAcA1/7L8oyhq9sEwqgcSu7NiyyY5HviDNA0kWbqXEU00XdWU3Dm5HP+08Ly7ol5SV0xXtI5UASl3JvTW49FX7aGnAZ73HRvXB4NylO5THy3ecI3d34+yGLcdTaUS3GOX59pNpRus5q6O2zc65F0hGXol43jVWKuNjS4+NOZ9vo5Qc6XXmtAqVShucSc2nX/dI/TUGNuDvUZY+W4MN7lD985cTRcL8PcVg/6puYfTdrjQ6n1lKnaONyzA4mrf+ukgnUqt/1pN6hdd6XzxzjY3rHiaCTTzgCw+wXsrK2TwwvjsltAykMT2qX4wBAACwlToD48cd5ynJpN8bl9iMDPKlWVd1ET3R5EznxqSrqKKjGdI58tuTOirLvd2NAxsyztgo1Z8LBFqZuBTk40FPjog3u05uLI5SVOBIzI1bzqZLmUqy9i0N2fwAAI6uSTM2Jk6cSK+99hrdcMMNZtdzIGLGjBk0atQoEdiYPXu2CITs2SPVhM/Pz6fPP/+c3nnnHRozZgz179+flixZIl63a9cusQ0HQ44fPy6CI3369BE/89VXX6WFCxdSWVn1aDUAgC3MZRL0iq/9BVR7cdPX+uXBJAcEPM3lz9tA/To5OPDlptP02k//0Ms/7q/3ccoNI20JkLi7uYmLyqZBBg60sBh94/Hvtp412mbv2Qzl8fFLufTL7sRqgQ1+P9e9vrrGY+BsnPs+3SIyPvL1QRk5M4fL+fBgPbuoesYGH8PMjzZaDGrcc1UX+vjeYTSxX5x4Lr8n1jK44UoDjeoiBWU+v6srBZkprXTnkBga0zWUZo+ULna3i5SCN0nZNfcRaQpZhWW0N7FAuWhhTk29NcyZc1UsDe8UQuN6hNM7t3SkL+/p7nJNFPn3d8qVCRTk51oBGwAAsC8ul/nMN7voQGKW2fXqQIVpUINx5oaMz5vyS8oslhptKBklOtEY/MaeLalnTICynP9v5/4CptzciAr1xx1opnSnLeTMWC16bIAD4SxxU4cvGf/dRiJbAwCciEOP0ocMGUIrV66ky5cvi3rZGzdupNOnT9O4cePE+v3795NOp6OxY8cqr+nSpQvFxcXRzp07xXO+79mzJ0VFGUp7jB8/ngoKCujYsWMWf7ZWqxXbqG8AADLT0jxxEYZBUlMY0jna6IJlXaln53SIDiI/Lw86lSI1zP7nvPkBbW2U6sptbsTs4d6i2sD3my1nlObdn8webhQEuKZ/nCgbIGeFsJ2nDKUJFz8wkq4dEE9FmnLRe4RP7Iv0TbvV+P8b/jkcnJCzKNinf0u9R9RN4rmMV5Hq56l7gKTmlihlt/jY1PhiMmeseHu4GTXmfmZKH2pIdw6JppWP9KbYMPMZPS2DvOiZSW2VoIecpXE4ufqFCnONuscvOEAbTti3L4s1r6xMpOd+OUdfbU+lsxnGs83YL3N6iUBfbV3bJ5KeuzZBCRJyBgsAAEBzdPJyLh1MzKZnv9ltdn1hqe2TBeXAxnPfSxMVzZ2DpeWVUFl5hTKh5bO1x5XndZWl75fVUl8CVObjYcjYUJ9zcrCjUCv9zIB6BzZQigoch7Xysn76XjHm+vABADgqhy4Y/eGHH4osDe6x4eHhQW5ubvS///2PRowYIdanpaWRl5cXhYQYNwXlIAavk7dRBzXk9fI6S+bPn08vv/xyA7wrAHBmJy7l0mNLdih9Ke4c2Ulc/BzTs2nLucRHBoggBJeMstZTozY4Y4L3e+KyFNioCy6N9e/v9tALN/UXwYiVey+K5b62ZGy0aEGVqkEmD3ZTc6UyAh/NGib28fUjY0RZAy7ldHXvWFp/5DKV6U/YeXv1zEF/Hw+jfiIsPa+UAqKNl51PL6SvN0s1lrkWtCn+nGWBPp5UaCY4wj9LFh8ZKI5t79lMSs8vNepvwIEkuUdI19iQBv894p/n7Wn7hX4OdIQHeFJiVvWggRqXqnrx1/Pi8Rt/XqQxXaXm9A0tOUfKJPl2l/n/z/0xMAMAAKgXPh8zdS4tnx783za6fXhH0T8tNsyfIoJ86OAFqe+cJdz3TJ4sw+dp6gk1By9k0TNfS8GTKVe2pduGdxRZsyw2PIAm6bNc6yJXIwUpQkyyVQO83Ck5X1stsJFbUk69WkmTlnpH165Xl0zO9LiQX0b963zkAPalM5OhxG4ZGEV/HpImsfnZMAENAMBRuDl6YINLSnHWBmdnLFiwQDQKX7duXYP/7Hnz5olSV/ItOTm5wX8mADi+Vf8kiXvOCogI9KHbR3SkW4Z1aNDyQbbggSEHDpi9AhvMT5WdwGrbRPr7bWdF5sKiv4/Tb6rsB1uO0d3dTfy8y9nFYqbeztPpdDatQAxs1eVzBnWKoukjO4mG6dyMnBtVZuSX0i3vrhNBD1mIvzdN6NPGYsNL0wbnjLM7rAUteOYhB0c4EJJZUEonL+fRKz/soxOXDMGgsECpOfOtwzuI+3vGdjHbA+Wa/uZrNDe1NmHelJStNWrSrvbPxQJ64KuTRsv4YkVjaBViufF1RACyLAAAwPUdTMwym4FqLzxpxtTi9dL/+99uPUPZhVoKDfCmJ6/vbfb1r916hfKYSyDK8oqNMz02HklRHv91IJkWrDykPD9UQ8CkJvn6wEaoSWCDe2RwySk+x1GXP80p1dHU7i3plavb0S19jCdJ2mpAbJC4P62fhAHgCCyN5Qa0DVL+Bvy9HfoyIQCAc2RslJaW0r/+9S9asWKFaP7NevXqRQcPHqS3335blJ/ihuDcJyMvL88oayM9PV2sY3wv9+RQr5fXWeLt7S1uAACWLtg+ZWEA11T4wn5iRiHlWmlmbYtvH71KlIFS95OQcdklHrzagi/yrz10SRmQnkszlPRTl1+yRC4hdPfHm4wCIneMMDR9NOXl4UZl5ZW05mCyMmDu3CqEnriul/iZUSF+9Pu8CfTH/iRRWiqrQCMCEtzU0lLt2egQX0rLKzUKZsjaRATQ4Ys5onSVXCZL7dkb+lC/hAjxeHyfNiJ7o2tr4yzDmaM7i5JZI7u3IkcUF+ZDB5OKKLNQR1FBXmazNWTBvh6UX1pOKXll1Dq04f8P5eCFuRJUozqH0KwRaIoNAACu7VhyDj3zzW4xyePl/xtQ7Zx195kMMZniq02nxESc7m1qn1FZqi/JJOPzTHV5Ug6q8Lkhn0stmj2c9pzNoMwCDeUUauiZG/oaTWbhcx4+z/pt7wVRwsrSOWV4oDddzCw06o9WH7n6fhkhJpm7Qd4eovdGcVkFqedk5JWWi/PQgXHBdf6Z/l7uFO7rQSmFDRd0AqgtSz3pOkf7KVlLyNgAAGfisKFY7p3BNy4/pebu7k6VlVKUmZuFe3p60vr165X1p06doqSkJBo8eLB4zvdHjhyhjAxDM9m1a9dSUFAQdevWrdHeDwA4v01HU2jd4cvK8z76C9aO4vor29pltjyXEuDsBqYxmaVXmzrKr684YPS8trMJzQU/esWHmc12kImMjfIKUZKK9UkIp7nX9hIBBfU2Yfr398avB2n2J1uoXDV7yfQ9P3Fdb9HPQ/162X1Xd6Mv54y2eDyje7RWyizw++kWG2pUdoHdOqwDfTBraJ0bvje0uHDrDcS1OsPv2/2jpWDC0cs19+SoLf69/nzLZXp3TZIy8FK3YJGzNwJ93GneNQmijBYAAIArkyeNHDifabS8orKSlm48RS8u30dzFm+jPWczRRZtbXEQYr9q3/x83znjn8UK9OeHCVFB9H9DO9CciT3ohZsHVMvQ5WCBnGFsWsqTy3XynBaeUFJQUiYmqPRvH0lDu0TTpeyiOvXZyCnS0JaTaZRbar4UVbA+C5cDGeqMjYQw+2RitwryostFukbLZAWoaykqnhwm/w1wD0AAAGfRpBkbRUVFdPas4QQrMTFRZGSEhYWJBuAjR46kp556inx9fSk+Pp42b95MX331Fb3zzjti++DgYJo1axbNnTtXvIaDFQ8//LAIZgwaNEhsw43GOYAxffp0evPNN0Vfjeeee06UtEJGBgDYKj2vhOarLtRzbw1H0zchgv49rR8Fq8o01Vfr8ACis5nULipIzJbTmGQz2BqYuKpnayXY8NE9w2x6vbkyUV1ah1p9DZ+Ua8srxPc1pHMUvXiz8exFmXqgzSUWSrQ8oK0UJaRM3yMHIzwsBB14P9GhfkbLooJ9lV4arkAObHA/iysSpLIK6lJdX+1IVbInurWSmo0nqrI47GXzqTz6Ya80SWFUl1DqGx+o9FNhU/pG0pXtgtBXAwAAmg0+55HuK0Uwg/ujMc5MXbb9nNG26r5ltSnB+rc++5ZxpmtKjtTvbHL/OFq1XyrR6m9D7zSZ3O+My7rKOJBxIFHKAundNpzWHJR+ZoifF8WE+tH2k2l0MbOIOsbULoPi3d8Pi6AO42Rk7qmhFuYnHXduqU65mBsb7E3PjLJPedDWgd50JL1EBHGCvDDhApqe+tzZlLxKztwHAHAGTRrY2LdvH40ebZjpygEKNmPGDFq6dCktW7ZM9Lq4/fbbKScnRwQ3/vOf/9D999+vvObdd98VWR3Tpk0jrVZL48ePp48//tgow+OPP/6gBx54QAQ8/P39xf5feeWVRn63AODMsgoNs9U/vW8EtW1Zt0aCDW1Etxi77m/6iI5in6cu54k+GTxwtlUVVSnlmHrGhdPxS7l0da9Ymweld43uTDmFWvr3jf3o0S+2iwEwNzO3hrMpuPwBj939vS33WDCdQcjBDf4ZPDuQs0JknVoFWwxqqPGMwv3nMmlsr9Y0Y1Rnmv7BBnIVXIqKXTSTsfHZZkMG01MT4ykiwIt4KMRlq+xt1WFD2YuUPK0IbMjp9NHBXjS4QzCyNAAAoFnRlhkmY7y/6gg9do1UevNMan61bdUZp7aSgxj/N7Q9Ld9+jjIKSumyfllCS8Nkhyev72PzPgN9pAv8fF7HpbS4Tx336ZD1S4hUAhsc5JDLoiZmFNQqsMHNyOWgBovw96yWDRzmJ50r5pSUU4S/dFxD4oMp0KQUa121CpT2mZJbQkFB0uQPAEfI2BjcPpDGdA2nLjH+SvlfmYfJcwAAR9akgY1Ro0ZZTcvkHhhLliyxug8fHx9auHChuFnCAZE///yzXscKAM47K+WtzRfpum6R1DPa/EXx1AItRQZ4WT2J4wvsstjw5jMw8ffxFBkLifraxraUAdh49DIdTMwWWRA94sJEOSa21ErJJnPaRwfTx/oSUC/dPIBWH0imgR1bWn2Nr5e7UvJK3eTblLencbCCyyrIPTm4Z4YcWBnW1dCL6cWb+lOFhf+zJvZtIwIb/F4jg3xEg/M+bcPJFYT4eYhZjJmF1cuQHU42LjnFM7y4BNSFrFJ68dfz5OPRQpSFsofsIp5N2YK05VV0OVdLafla0pRVUmSgJ315T3e7/AwAAABnUqrKMuVgQIfoYLruirZUWFJmUyZsTZKzi0Q2bFd9xmxGfiml5pZQRKCPUYawuv+YrRkbXCbqbX2D8Ecn9xT3XMJqYCfDud6wLtGUp38v3EeuNjboM4Vl8cHVS5mGysdSqlPK8HiYlMKubykqdjmnhLrYJwkEwC7Nw2+7siV1amWcie3r6Ualuspq4yQAAEfmsM3DAQDsYU9yAW1JzKMdF/Np1V3VZ5OlFWpp5o/HxeNHh7ahSV3M983YfipN3P/v/hE2zeB3NfIsP0t1WdVeX3FQedwxpvazA83hAAnfbOkPIrPWsyLQ17gcwId/HlUyNE6n5IvZg9MGtzPax5AuhiCHqeFdY+i3Z8aTj74UgzxAdwXcE8Tf241KtMbffWVVlcjMSIjwoTFdw6h3GylwyPd/H8uhS7lSMHDeNfY5jtxiHXWO8adjl4tox7k8+nm/VJYqwMc+v2MAAABNbe/ZDJHJMKanNCmkJqZ9wQ4mZtG43rEi05h7WaTnGUpDFpQYBzY4q4MzkC2dL3EQgyeq8EQNuS/GiUu5oqwSBzW43war7dxuObBxJtXQEPx/604oTYt9vTzEeRSfZ/AEG1990OSXXYnk4+kuysGu3HuBPl9/ku4c1ZmmDUqo1r+MXcoupiBfTyrQl7yK1/fiMp+xoQ5s2G+2ujpjQ8YTO6ss9JIDaGil+n8zfLyq/92/d1sn+vtoDvWLd8zKBAAA5iCwAQAujQcq1gYpGUWGGW3vb082G9jgDICtx1NFiaI4VRPq5oRn67Eyk8AGZ2VU6Qee5qSpBnKNgQMStgQ22oT7U9vIQLqQaTz7b8GMweI9cmJGbZt5y0ENV8QXGopV5S5YsbZCXATgFPabr4xSlnPwgQMb9sTNwkvKKinUz4M6RfnTiVSpDAYr0tS+mSgAAIA1fG7DF/ajQoz7aDW0577fK+5tDmyY9AXbfiqdnvhypyjLyRnGcmCjVZgfZRaUKjO2H/18O51LL6CZozvTrcM6mN23XHJqQIdI5fzqrwPJ4r5nXJjIUGUT+rap1XsM0J8znk7JMzqfZHIQgzNfZRwA4HMyPu7vtp6lKzu0pCNJOaI8KgdEOFAyvo/hGDj48vIP+ym3WEtdY0Oo4FKexcBGuL6ZOI8X+FyD2bNvckt9eassVeb3Kz/upx2n0mn1c5PE9xQa4F3rc06AuirW/635mwlstI3wpdmjbPu3BwDAUeB/UABwWTzY+/moNKu7bWj19HNWXGZ8oT6vVEffH8mg7ZcMJXZ4UMgXcPu0NZ/N0ZwyNkxLUb2wbC/d+PZaOqUfnHLTbrXkbMMF6MYQrboAMam/YVBsimf2cZmrlc9OoNuHd1SW88CSB9zybEKQZBXpKClbQ2uPZSsfSan+b8fXZGBk2mD8vB0aiWt00s/ikljcS0MtXt/cHAAAwF5+3XOB7vxwoyhVafH/prJykVXQEOSL7NZwdsaJS3nVZv6fTSug/JIyiosIoDenD6L7ru5K3WPDxAXNYo2OjlzMEUENlpxlXFJy/eFL4qYONoQH+Ch9LtSlp/okRNB7dw2hhyf1qNV7k8+x0vOl84OwAEPAwc/CJBEfVdNvLoVVpDFkqshNx/kzu5xdTCcv54mgBosND7BaioozQPw83SintFzp22XPUlS8b/XFZMZBDfafn/8RPdl+3nnebj8PwObAhjcyngHANSCwAQAua9P5PErT9wUI8vagHw+n0/JD0mBCVqAaaLD5Gy/Q0gMZNH9nmhis/rDjHJ28nCvtQ5+u3pwzNt767ZDSb4NLJfCMOf6cHvl8u2gAyWn/TUmdsWFabsoUN8rjJuLqmYbmShkAUbl+sP/26iT6YmuKUV1vX5NG7FH6etKyF1ecq/dHKGcKcc3f4Z1CyMujBV3ZLogm9QqnBbcYAlMAAAD2sPbQJaOL5tyfgs9zZL/uSaTr31hDi/UllOytsLR6jwwuHTX/lwOUXagRz5/5ZrfIquCeYvGR1fvI9U2IEM23pw5qR5H6i/pbT6Qa9doID/QxylJ587dD4vbd1jO083S6EsTg86OpgxKqNRXvGhtK7rUMBPBkGQ4oyHrFG3qStQoz38dOvf17q46I7yUm1I84IbtMfz7y867zdPfHm2jR31KJ2VHdW9EtQ9srr2sTbP68kMtRGWds2O9ckM81ObhRZKa/ydYTUplbeXIQQGNYfyRV3HPfOgAAV4DABgC4rCJV3eE8TTkt3ptCX+xLEQM3WYFqxhc7mCrNXNNVVtGBC9mifu8H+v4LctZCc+SlGlC+8tN+cX/nhxuMtpm7dCf995cDRss+u38ENVVgg2f320I9UxDMe/PmDhSgn9m1fE86HU4upAWrk8xmbLD5N7ansd3ClGwPDn7lFOvo8e9Pi9fWFpebkL/TViHetOz+nvTylHb06NVxFGilSTwAAEBdyBe55WyI57/fK85zOIODzyM/WSNdPP//9u4DPIpy6wP4Ib333iCUUELvvUgHaaKiqFhALGBvV7HgVa8XCyqCetVPbGDBgooK0puA9Bp6gIR00nvb7znv7MzObjaBJLvJlv/veZbdnZ3M7k42y7xz3nPOj7sTzbKDOYhh2Ah7/qc7aMvxFHFsWlWtyzjmiSYf3z+M1r0wkWaqykqpj4mCfaTb76w5KjIeDOvts3/OSlnO7Istp5XgDve8YPeN7kT/u086rhvaKaJR708uoRUf7U+xId5GX7PayzN6iwwUJvf24J/jY/NS7THCthPSCVvZA2M7iYyNwe1DKNjDqcZEDFlOSSVdzC2lHRekAIOTo2lP+Ho6O4hZ8hwYW7DynxqPcwYHlz0DMMX3FgfKKrUNwo09LsNkLgCwFQhsAIDNkpsAstNZukFcZlGFGFAtXH+eDqXWfpL18CUpU8NYY2p701o16CwqraSpi9YqJQoM+5Hw7LTP54+gbx4bSS2buCeJXO+5Pgfs9tgMvr7iI73osbG60l5LNiTRqTTpb8pNW2ZBrWdLH3pqfEua1C2I+M8wp6iSPtl6mU6kFNHK3ek1SlV9uvVynWU31KWo5PR5NN0EAABz4X4OrFIbQDieJB0T8olpLvOkpp4wYyo/7NaVJ7qUVUiLVh/SC3okpuuOXzvHSBMJ2OQ+rWhIx3Dq2zaYWof6GA0YyBkfTH0s9832s3X2xGDcbPzHp8bQ7cMaly0pv+aZQ9opx25y4MKYNmG+tOzewXrLBrYPE5m3csYGNzRX89Hef2Fad/p6ki7bxJDcQ2z1iUyTZ2zIgQ3ezxwcq6202YptZ0z6nGCfvt1xVmTR/3FAmnxkqEJbUnhkB/vsGQkAtgnTHAHA5gMbbQPd6ewV3UyozeeyRdr5rku62XDj2wfSn6d0/QPYd7t0s/C4iWHPWPvtscHNwZfOGSxmC6oH9MvnDSdnJwd65iupHAJb9eRo8nRtnrJdPHPvuRt61FrKoDY3DWgt3gfUbqCqt4V61mOAZ+2/62Bv6bEDF/NpU4J0UijcT//Ew4Ifz1J2USV1jvKi/m30+2fUzNhA2jwAAJjH2dQ8Uc7osUldxUQNVqo96a221SAz4Oc9iaLcU31wsIJPZj88sbPeMRMHEfi5E7SBFHbqcm6Nn5336Q5xe0TnCHpovK7HBTeifv7GnjWeL0Q18SO7sMxoxgZv11CHSD8RzKgt0NFQL9zYU0xo4FJY3L/tdGoeTe7dqs6fMcyc5oboXCq1THuyNr+4XExCGt9DyuZs6AQIUwc23BwdKKuiSgRh1L021OT3ANAYiRlSwJNLtXGQUy27sJQeXf53rZOSAACsFb7RAMBmyTPAfQ1K1fx8PJOuFFfoDWAeHhRNI9r4G93OsjmD6ZGJXew+ZbdduK9eRsSTk7uJAAKXN7h3VEdleXMFNWTD4iPEa62POaM60p3D25vtNdkCPkHQPUaaTXk6XcrWeHBEVK3BCBas7bfBvTlkZdrsC1l5pfR3umqvfiaH2uoD0gzHEoOfBQAAMBUOFmw6liL6h+kCG5U1MjJ+23tB7/43O87SM1/vFuWirtW9H24VZaV+NihlJWeKcECfT/izYiP9GWScpcCTT64mXDXh42yabmJPiTZww5nMxk66T+wVIzJxTc3P01Xp78EBiwfGxlNk4NUnpfxrWnfldoS/J7k6ccaGtM/yS8rJ192Fbh/ajqb0rT1Dw9DLo6WgVJi3dMxi6vfLkzL42Mcwo4S9emsf0cMkTVUeDKChvN2l74L8kprfGat2naf0XGmin4uJy60BADQnBDYAwGZVaBsee2t7A8hBDK6lu3yfbrZdhI+rOGnr62o8ia1tPU+S2zJ/T10/iugg3QDUnst02bMpPYPrPAEQ5FXzZEtBqf6sxPhI6XNUVykq+aEO4fXLxAEAAKivqiqN8n8SBzoOX9TP6E26ImWoyvgk4qHEK3rlouqSW1RWa5aEXCpG7p3B5IDDK7f0obmjdRNJWKifxzU9p7OjgwhSsJTsYuWYWM7YyNaWp2qjKl9lif3lRnSOFP3beHINZ6dwDzglY6Okgrw96j+5Ji7IQ68klakzNjiDvKC0wmjfA293F4oL9xUz7bkfGUBjyEHYghL9cnnMyUF36s8FGdAAYEMQ2AAAmyUPSr1VAYv2wTUHgPLsuBndQmlIrB+NbuOnPNa1pa5uMeg32uZZcjKebQa2b2Bb3d+G8zXM9grWzn6URfq7Ukqu7oSOOmiRUVD7jFQPbYPyjghsAACAmaXm6Acu3l1z1Oh6H9w7pMayvWczlEyL2lzI1PXHUAdNOOChjvHLgQ25D0Z0kBdN7RurZKV2jPKjaX3rLt+kNqh9mHI7KtCTwv09lG2v/kfKQomL0J/MY4n9rLh/2+huUeK2n6eLaIb+0rd7xXvhjI36ctOWIi0sM09g43hWqdGAGPNyc6JQP3eRMbPkd+OfM7Af6w4liSbzDVWuLd0qf3eoOamO21HaFQBsCQIbAGCzKrSjQ/WgzNjJ2C5hUnkd7rvx/HWxdEe3kHrPhLMX/eJCldtcK1jm4YLAhj2Y0iOYfpjXhW7rH0bvzYy76vqB2owNfw8nemVaa4rWBjbUZT2KtScSrhRW1ChTJZOXoyYwAACoa8bvOZNu8ubdGfm65tqMT5wboy7PKXv+m7308fqEOrd/UVsHn+UWlYveEHJdfBapLRv19Fe7xXWm9vXwJBLOkuSeZ+temEjv3j2oXmVS1SWrePKPu6uTkrHxi7a8VlyEbgKDurSNpRoeHyGud5/JENfXUpbLkHxsIX+KTB3YqAv3K/HzkCYN/XkwqcmeFywP/00u/u0IPf75rgZvo6xC12/G8HtRP2MDpwEBwHbgGw0AbD5jw1M725u5GaTUj2obQA/0l2Z9yUK9dLO9OD0cdHqoGqjrBTaQsWE3uCHnrEHh1Cbk6kE/Hjj9/FBX+npuZ+rb2pc83RzFbNQybV8NllWom1WWlq+fzSErragmPs2A1HkAAJAt33SKXvx2H20+ltKoncIlgF794YByPyNPqkNv6Ib+sfT4pK5XPel/0qDZd20NfmX7zkl9pJKypBn93VoFiuu84nLanpAqenFwdoVPI4MMnB2gDtbwpBQObKgzTHq2DqL37hlEH80dQg+Oi6fu2tdiqYZ2Cte7f0VbUqs+eAKUet5TUwY2OBAzc2g75f7l7JpZHWAfiuropXOt5L9lLn/G3x+1ZWzImdAAALYA32gAYLPKtCWmpnQKVpa5GdQUndQxiDxcaq8fbJiSb+/C/NyNBjbkmT+YAQSG+O9LHkx5aD8zcrNSzsTIyNcNvLafzq01sMEzKuszMxUAAGybfBJ4/3kpMNBQXAKKAwiyM6lSc+3+7XQZvE9P6Ub3je5EgzvoyjnV9n8S97NQW7HtDE187Q9lNrVcikoOknBvDn4P648kiwbTHMSQyQGX6EDPRv8f6KMq0/TO3QPJ3cVJZE1mF5Yp2Q9hfh7UIdKPYkN9aEqfVhb//y6/B/Ukm5sHtmnQdtS/M1MHNt6+Tn8C1R3D4uitOweIwBE/r5vqeHr1P/rN5MF+GCsfxTYcSabb3t1IX2w+pXyH1KZMW4rKWJDPSfUZH9JOqlYAAGALENgAAKuSlFtKBdp6wKcyi+i9nZeoWHuSVO1yXiltPJujnEy9r1+kuB0f6kWf3dSJvpvZmf43rQN1CKm7GXFsiH4TRXunHuCqAxu8nJs5fvnQdc30ysAauGlniMl/s6l5ZaL0Q3yE9Hf41d9pNZqI55dUUnJOGcpQAQCA0ZPRcl35hsopLDdaeoqbU8vCteWhriVD1dmgzMuXW0+LGdRZ+aWiPMzFzELRw22Mtk8Ee27FPyJTZGrfVnrZsbLrukjHsY3h4+Eimm5/9uBw6hTlT+4unEWpoTTt+20bbt3HvMvnDafOMQ3rjacuVWvqwEbnYHe6e1hb5f7tQ9tRl5gAETgy9Pep9BrHQWAfdp5MM7r8zV8OU1ZBKa3ccZa+//tcndtQBz7kEnaGnp3WlXzcap/UBwBgbVAUHQCsRmllNc35MYFa+bvRy6Nb08O/nhbL+0f7Ur8Y/cyKw6mFym2uRzwtPlg0Du8Uopvx5ldHSv+/h0RQhruv3sl7kKx4ZCSl5RbXmJHIzRwB6pJbLAUl7/ksgQI8neiBEdJJnd6xPnQ8RZp5+/fZPBoSp6vx/cvBTErPL6c7B+mXmwAAAPuVmV9Chy5Ijbe58XJj5BQZL4PI2QsyV20pUz6GfGpKtxo92DjTQi79op4ZrS4H4+DQgorKKkWTa87K4G1xI3A5Q4S1CfMRy1Y9MZpuenu9WHbn8Dga0bnxgQ0mN91m3GODJWszXwK9avYMsSYcuGkodWAjwlcX0DIV72vs/cHBr/3nMqmvKlsI7IO6DFlRaYX4XjqfXlBnGbu6AhtyxsbRi1fI0dGBKrWVDCJFRpjxcnsAANYIGRsAYDWSc6UDtAs5pbQnKV9ZnlsqnSzl2XA52jRe9VwnDmzw4JGzNa41pb5fhCdN6hlt0tdvK4J83Bo8Iw7sG/8tyrKLKmlTgpRV1S1alxKflC39nf9vSzL937bLlKcNhkzoatl1vgEAoOl8s+OscrtU2wC7ofgkojG92qhKmaomuozqGiVm3KsNi9cF3521/9dxUOP29zYqyyuqqsWJaxboLQUR3pzVn+4b3VGvmbR8kj5Iu446UGJK3GODXb4inVAN8Db9Cf2mIAV+IkSvjIZy1jZW9nRxJB8z9I27Wn8Ubgg/oWeMuP3Ct3tN/vxg+S5l6ibl3fDmXzT7g6302o+63j/qPhyc3bX4t8OijJ6aOnuNv384I+vJL3fTY8v/pgrtY+ogHgCALUBgAwCsxiVtYIPlaYMZTA5mvL3tIt2y8hhlFJZTTnFFjf4aANC85gyNoJEd/ZX7u85Js1RbBrrRktvixO0vdqaK4MZP+zPp+70ZlFsi/a0bZggBAID9KtGWJeVMicZmbMg/f9OA1nrLA1Un+l2cHerMgJjQQzopzeSeFak5RXonGisqq0SmCQv2cVd6RLQO86kR2JAzPJqiP8VPexKtOmNj5pB29K9pPRq1Dflkr7nO+V4tY4OzdNTBMS5ZBvZDKlFXdzYGyysqp082JND9/9tG6w4l04zFG/QeL6usEt+JrLS8Si9YwoFVcwZKAQCaC77VAMAqAxtFqr4aOdoTn5vOSbO/9yXnU5Y2sHFbD12TRwBoXt5uTvTU+JYU5a87WcTnbbzcnCjMR7dszvIE5XZWgVTGw9WgZjkAANgv+SQdnzDmE3iNwaWhWL+4UPr9ufHKci5HOrFXTI3G22pPTOpKfyyYIJpty7g5OM+UlrMz1K9ZLg+jDpp4uupOessnJZmcgGCuc9xRgfp95qw1Y8MUlMCGmYJJ3lfJ2GBhvh5Gm0CD7eNgKJepuxouRfXDrvO1rlteoQtslFRU0pGLUrk+VqjNTJMDmgAAtgJnCQDA6gIbvm5OSkYGy9VmbMje25lEV4orRCkAH1f0yACwJFwObmpPXSbVrf2k4KO7trG4oYRUqakpJpgBAICMy6pwk2cuj5meW6wEJxpi6/EUce3h4qg3m5n7ajw0vjP9+fyEWnuu8f9p8snwbx4bSQPiQkWWBgc1dhg0A5aXsyBvKWODcRNvmZ+nLrigO8VunsiGutTW8PgIpTSVPZJLUTk2opxVXbxrCYyp8Wf5auXRwDYla8vBce+d+kpR9ebgHhtyr5mS8iq9/j1r9l8Sk4QCvBreiwYAwBIhsAEAViO9sFwpQ7U1MVephStnbKhdKaqgAA/na+6pAQBNJ9BTN3PRTVvew+UqGRn4WwYAAHX2g7OTAw3uGC5mt/99Sj+IcK0uZRZQwmXpmNJDmzkRHy2VTOQgB//fc629GwK83Khn6yBxe/OxFDqRnEP+nq700ITO0mvmwIY2YyNYdRKb12Eju0Qa/X+v2kwZG+qyV09P7W7X/8+aO2PDx+3qQSP+vHGvEMOm82D7Vu44I65vG9KOZg5uq/cYByPGdo+iRyZ2ocgA/SwrdveyLUowjL8LfbXZQdx7yDCzY3KfVnb9dw4AtgmBDQCwGuryUzIPZwcqNZKuzRkbgR5XT/sGgKbXNtSjRmCDPTlOV6P8pj4hTf66AADAigIbjg40pKOU9XcwMatB27n3o23K7TA/KYvijTv606//Gteg7UVqyzt9tukk5RaWUbtwH6Xx+Pd/n6NLWYXidatLE3m6OdPqZ8bSE5O76W1LDqiYs9/CykdH0gf3DjbbCX1r4aH9HZlrN7i5ONGTk7uJfV0XObi1cvtZ87wQsDicbXYoUSoZ5eHqRLcPa0efPjCM7hgm9Z6bN74zPT6pm2gu/9m84bVu5+9T6eK6e2yQ+M7hjA0Obqi/3+aM6mj29wMA0NQQ2AAAq1FSIZUdkA2L9RP3K6ukAZ+3quxUbmklhXoj1RbAEgV7607oqEtQjY4PVG73iPGmt2a0a/LXBgAAlo+zHzhjg+vJ85Ehn8S7Fr/tu0BjX/mdpvx3LV1WlXB5RpWxwDPnays9dTVRqhnVPHuas0DkzIhDF67Q8aQcahnsVWPWNNe9NwwuyAGRa80YaYhAbzdqE+ZL9k4eQ5grO0ZuNH+1fd2nbYjIGPrnjHSSGmxfiSr44ObiSI4ODhQd5EW3Dm5Lb905gMZ0i9JbP9qgNw7bfTqd3vr1sLg9qEOY+D7h7ZZW6L4XXZxQnhkAbBMCGwBgNYorqvSCF1y6hme9VWhHIb2jdI0bWay/Ls0fACwHn9CRyz74uOuXZ5jZP5S8XB0pPtKLukR5UXSAKwV5IfsKAABqZmzw/ycchNh3LpMqtQ3F6/LphpPimk/4cbkoWacoqfxUYwX76npnME83J3Gyum2Y7hi1R6xUrupqXripF13XOYKm9m1lktcGtePjDnms0dy4zwoHxcyZqQOWo1QVlOW+PjIOdHaJCagRBJWDrup+HC99t08vWMkBEu7nw4FU2cD2oWZ7DwAAdhvY2LZtG02aNIkiIiLEF/bq1atrrJOQkECTJ08mX19f8vT0pD59+tClS5eUx0tLS2nevHkUGBhIXl5eNH36dEpP15/hwOtPnDiRPDw8KCQkhJ566imqrGx4gzkAaHqV1Roqr9KQt6vuJCg3B+cLP8aqDKZZedpxE0QAS/fSlNY0Jj6AerXUD0jeOSiCVs3ropSo+uSujrTiPqk+OQAAgNyImwMbcpCCm+Z+siHhqjtHPYM5NUfK2OAMirAGNO01hrMr2oX76t3nE5SPXd+13oENPnH5zLQeoowRmJeXdh9zdnhz454KcvAO7Ctj41qCs8/e0ENkcXx031CRBaTWIdJPXHPGRnpeibL8xZt60azh7U36ugEALEWzBjaKioqoW7dutGzZMqOPnzt3jgYPHkwdOnSgLVu20JEjR+iFF14gNzfdLOzHHnuMfvvtN1q1ahVt3bqVUlJS6IYbblAer6qqEkGN8vJy+vvvv+mLL76gzz//nF588cUmeY8AYBqZ2sbhwaqmwy6ODuTk2EI5CKzSaEQ5At3j9l0vGMCS9Yn1oSfGtRR/w4bUZTfQ5BAAAIw3D9cvrXL4glSnPreorMZkF8Yz4DnbN8jbjTxcnOh8eoFY3jHSNNkaMnXZ1Gn9YpUTjbKurXRlF8EyyBnh8mSp5iSXDOLgHdi+ElWwVe7RU5eoQC/Rj4dL1cnl6mSPT5ICqJcype82GZensvc+OgBgu5p1+sf48ePFpTYLFiygCRMm0BtvvKEsa9OmjXI7Ly+P/u///o9WrlxJ1113nVi2fPly6tixI+3evZv69+9Pf/31F504cYI2bNhAoaGh1L17d3rllVfomWeeoYULF5KLC2rwA1iD89nSrJO2QR60/7J0sMalbJz0MjaktF35Pg9eAQAAAMDy/XHgEiUk59Room3oQkYBpeYUi14Ehs6k5tH8T3eI29yQWx1QKCytFCeLe7cJpiOXrlBGXrHSsNeU5KyQGYPaUKS254a7qpSqnGkClkNd6ra5uWgzVjkLSe7PArarqLRCXD84thMF++iXsrsadWCDG8+3DPauEaDjPh0AALbMYo+qqqur6ffff6e4uDgaO3asKCHVr18/vXJV+/fvp4qKCho1apSyjLM7YmJiaNeuXeI+X3fp0kUENWS8vfz8fDp+/Hitz19WVibWUV8AoPmc0wY22gV66A0MuRQVl6hi1dUaUk/+RsYGAAAAgHV47/ej9Nfh5Kv2Frjvf9vEdaX2+E9tzf6Lyu2P1+uXppJLTwV4u5K7s5MIdDBPEwc2yiqrapx05CbihnXxwfJKUVkCuc9CBTI27MLJy7niumMD+vzI/TYYNxuXzR/fWYyTZw2LE306AABsmcUGNjIyMqiwsJD++9//0rhx40TmxbRp00SZKS45xdLS0kTGhZ+fVEtQxkEMfkxeRx3UkB+XH6vN66+/Lvp6yJfo6GgzvEsAuFZ7LuWRh7MDtQty1wtciFJU2lkpfK1Os+VSVQAAAADXoqCggB599FFq2bIlubu708CBA2nv3r3K43fddZcoj6e+8DgFTEvdW6Bao6GNR5KVLIhjl7KVxzLzdTXkGcdDDiVmKfd3n9bvu/j55lPiulfrYErM0JVqMXXGhq+Hi9IEWsZBjo/vH0pL5ww26XOB7WVsOGszzuUAGdh2f41vdpwVt9uE6XrzNCSwERGgC5pO6t2S1jw3nm4b2s5ErxQAwHJZztQEIxkbbMqUKaKPBuMyUtwn46OPPqJhw4aZ9fmfffZZevzxx5X7nLGB4AZA80jOK6WzV0podLsAclOVl/JwcSQnBwddKSqNRq82PzI2AAAA4FrNmTOHjh07Rl999RVFRETQ119/LTLDuaxtZGSkWIcDGVz6Vubqqjt5DQ3z274LFBMklVBhXC5K7jPwzfaz9OXW0zQjq5Duua4DvfXrYWW97MIycR0X7kunU/PoYmYBqXM4+PHiskoRuODAyP7zWdQ/LpQ6xwSIgIm5AhvPT+8lymqN7a7f2FcuEwOWx8uCAhtyxgZ6bNi+DUcuK7cb0gNDHdhwdMCEPgCwTxb77RcUFEROTk7UqVMnveXcP+PSpUvidlhYmGgKnpsrpe/J0tPTxWPyOnzf8HH5sdrwIMXHx0fvAgBNg2fYTXtnE61PlErAbT0v/Y2PaO2v15AxytdVlKLiwAaXLeBGkeqDQtQwBgAAgGtRUlJCP/74o+jtN3ToUGrbtq3ox8fXH374od4YgccQ8sXfv+7yIShvW7fyyipa+udxevqr3XrLZDtPpikzm5m3tudAbIg3vXBjT3F70R39xbWxAlbf7pRmQ2fkSv00WgV71VjH2920PReDfNxo1vA4nGi0It4WVIoq0NtNXGfm6Wckge2RJ/POHd2xQT9v2DwcAMAeWWxgg0tM9enTh06dklKGZadPnxbp4axXr17k7OxMGzduVB7n9TnwMWCA1CSJr48ePSpKW8nWr18vAhWGQRMAaH6XMgvope/2iRl2b/2TTvsuF9CXB1JF9kW3CG+9wEaMn5soRcU4uMGllvnxIA9p0NuAiS8AAABghyorK6mqqorc3KSTijIuSbVjh9SMmm3ZskX0/mvfvj098MADdOXKlTq3i/K2dSvUNs5Vy8wvVW5zFgYL0p7s5TI9fp4u9NF9Q2lwx3Al42J4fITyM/3ahVCYn1S69Lud5yg9t5iyi6TsjgDtdhbe3JuGdQqnRyZ2Ec3Ewb5ZUsZGVKDUcD45W+oJA7arQNvnp0dsUIN+3tTZZgAA1qhZAxvcQ+PQoUPiwhITE8VtOSPjqaeeou+++44++eQTOnv2LC1dupR+++03evDBB8Xj3Pti9uzZomTU5s2bRTPxu+++WwQz+veXZu6MGTNGBDDuuOMOOnz4MK1bt46ef/55mjdvHlLHAZrZ/nOZNHXRWrp8RTpw56yLR5f/rbfOgo1SE8hAD2cRtFBnZHAAQw50iMCGtnn4sqntaf7AKIoLQoNGAAAAuDpvb28xhnjllVcoJSVFBDm4FNWuXbsoNTVVKUP15ZdfiklVixYtEn3/xo8fL9atq7xtXl6ecklKSrLbX8fl7CJ69YcDVFSmC2bsPZtZY72H/28nVVVX0+/7LyrlRuWyPGUVVUZnKbcN12XXj+seTS/c2Ev1vMVKI2ZXbUnTAe1D6bnpPWlCzxi9ci5gn+TPRe8ob4vJ2JBLrYHtKigpF9fe7tKkvPqSM9gAAOxZs4Z49+3bRyNGjFDuyz0t7rzzTvr8889Fs3Dup8EznR5++GExM4pTxAcP1jVde+edd8jBwYGmT58uUr3Hjh1LH3zwgfK4o6MjrVmzRsyo4sGKp6en2P6///3vJn63AGBo0epDVFJeRVuOp4jmZum5JVRUJs1cMRTqLZUJUGdscNNOZ23GRoUc2HBoQX7uzjSpI2bfAQAAwLXj3hr33HOP6KfBY4iePXvSrbfeKiZPsVtuuUVZt0uXLtS1a1dq06aNyOIYOXKk0W1y6Sr04ZC88M1eEdzoHONPU/vG0rm0fFr82xGj++3oxWxa8scx5f6+c5kiCJKSU0whPlI2hlo7VeNdDzcn8vfS9T5Jyy0mP21Db5Qphdr8cXd3i8j25owkloPAhs3j3j/Mzblhp+UiAqTsnl7IOgMAO9asgY3hw4eLGdp14cEFX2rD6eLLli0Tl9pw6ao//vijUa8VAEwvr1iapbL+SDJ5uTvT9hPSjMjHru9CAe5O9MKqg+L+mHYBdEfPcCWY0TXci+JDpAM5Z22jtMoqDZVUVpObs8VW2AMAAAALxkEKzsIoKiqi/Px8Cg8PpxkzZlDr1q2Nrs/LuS8gZ5bXFtgAHQ5qqGcZp+dJfS+MOZiYpXf/5GVdT0U3l5oZFvExAcptPw9X8tUGMtjX207T3FFSCWK5KTmAoYY0bzYH/ox6uTlRjrZ8GtguOZOMS+w1RIivO33ywDAK8dEvoQgAYE9wBhDAjnEjxgsZUu3ippaWoxvMpuYU0wdrj9PRS9niflyEH7mqBp5PDG1JIV66AeqbE9rRXb2lWsrqUlTF5VXkgXICAAAA0Aic4c1BjZycHFHGdsqUKUbXS05OFj02eF24do6qSSlsZJfIGutcyCys9ee9jJRf4UwMF+3JQe5R4OSoG+ZeKSijwxevNOoEIkBT8vd0bVTGxtbjKTT2ld8pKav2vyNoGseTsmv9XVZWaQMbqu+r+ooJ8iI3F/TaAAD7hSM7ADv22PK/6b7/baOCkpqNG81NDmIYWjC9J7UO9VHSsAdG113rVm4eXlFVTcUVVeSJwAYAAAA0AAcx1q5dK/r+rV+/XpTM7dChg+jhx70Buf/f7t276cKFC6LPBgc82rZtK0rhQt3UWfpllVV6J/WCVbON7xjaTlzvPp1e67ZahRg/Nlw+bwR9OHeIEtSIj/ZXHjufni+uEdgAa8Cl1BqTsfHlltPi+q/DySZ8VVBfuUVl9Pjnu+iR5TuNPs7jV4cW+j0kAQCgfhDYALBD+89n0qcbEihRm61RVNr0gQ25Id579wxUlj1/Y08a2kma9dgyyIuWjYmmZ4dG17kdZ+2BYGllNZVXacjDBV9rAAAAUH/c3HvevHkimDFr1izR14+DHc7OzqLnxpEjR2jy5MkUFxdHs2fPpl69etH27dvRQ6Me5UdZuTawwSf1WPtIP+WxNmG+FBHgodx/9Pou1LWlrswUk/tlGArycROTY2SL7xpIj0/qKm4Xao91XRoxMxqgqfh5uoqJZ3Lwr76StWXfMvJKTPzK4GpKyytF/8hqjYYy80vFMu4jaRjo/WlPohiLI9gKANA4yFkDsEP//n6/0qyMqW/zgRYPPvmA2pzkYIq3mwu9e/dA2nU6nQZ1CNNbp62/G9crqHM7cimq/DLpPbgjYwMAAAAa4OabbxYXY9zd3UWQAxrXX4OVa+vKV1VLWRyuqmO3Fi2IPF11paY6RfnT+B4xomTpL3svUJifO43oXLN0VW1aBnuJ6+Qr0vM7o8cGWIEAL2kclltULgJ29VFVrQuGnEvLM/lrg7qt3HGWvtt5jjYfS6ELGVKmmKG03BL6318nai2tBwAA1w6BDQA7nEWiDmSIZRWVyu1vd56jzzefomVzBlPbcF+zvY6iMimw4enmRJGBntQxSlcuoD7kUlR5pdJ7QI8NAAAAAMsNbHy8PoGm9o1VMjacHBxEBu/yzaeoS8sA8nDVDVHloMeD4+LFpb4Mm4U3ppY9QFORJ5hxOar6BjaKtZO9WNKVIhFARKmjppNXVF6jnB73TFGTM8jksTkAADQcjuwA7MyH66TZIWql5boD4C+3nBLX57S1iM2FZyAx9eC1IeRSVK9vviBtD6WoAAAAACxKdoF+v4A9p9OVMjs8SaVDpD8tur2/yNaQm4Az10ZmWKizQRhnfABYOn9tr8GGNBAv1k4e0903fuKcSyWdvJxDl9Bg3KR8tb87NW93XVbGJxsSaP6nO5T7ldrMNQAAaBhkbADYibd/PUxJVwrJw6Xmn706g0M+tjIcCJrSpcwC2nEyjdpH+NWYSVdfPMtPDRkbAAAAAJaFszHU/jh4ibrEBBrNogjylmaod4zyIz8jJwnrQx0YeeOO/uSJsi9gJc3DWUMaiBsGMjg7QH1iXfb7/ku09M9j5O7iSD89PVY0sYbGqzYSqODgUWpOMYX6udMPu85jNwMAmBAyNgDsANda/etwMiUk54oDq0BvV3p4Qmelp4WcDqtuMFdylbTYTUcv09bjKaInR31LEdz70TZx+5ZBbaixXJ30D8I9XcwXkAEAAACA+lEfK7YK9hbXJ5JydKWoDAIbM4e0oxdu7ElvzRpALRp5slUuWcq6tZICKQCWTi5d1JCMjSJtYMPXw6VGn420nGLl7/HA+UxxXVJeRWUGZYqh4dQTBjkwe+OA1uL2XUs304frjmPXAgCYGDI2AGzcF5tP0aZjl5X7mfml4kB3Yq+WoiHjzpNpdDoljwZ3CKM7lmxS1nt3zVHafiKV3JwdKczfg+aM6qg3k2fR6kNK/dBnpvW45tez7USquL6ucwT1iwtp9PsL89avWerhjHgtAAAAgKXgE6ey9+cMEmVR/zhwif46nCSWOWnLispCfN3FxRR8PFxoWKdwGthemswDYE09NtJyixucsdEy2IuOXMymV344QN1jA2ly71b071X7Ra+aKX1a6fV54Alt7kay+qFxgQ3ut3HLoLb096k0Sskupl/3XtRbd9awOBrTPQq7GQCgEfC/F4CNW7njbI1lecVSf4voIC+KCPCgX/ZeEBdD+89nKbdHdY2i1qE+IuNjy7EUZfnZtPr14uBACJfDenxyN3I0KCPVENG+hoENZGwAAAAAWIrswlLlNpcgDdSWmkrPlTKFG5uVUReelPPc9J5m2z6AOcgl2P48mEQPT+xSrzJRcmBjfI8YEdhghxKvkJerVI5q3cEkEdhQ91hUBx+hceT92ibUh24e2EaUAXt0Yld6+qvdeus9e0MPEXQ15/cfAIA9wNRmADtyfa8Yvfuc+v/k5G411uvaMqDGsv3nMul4Ujb996eDtGL7mas2pDOGm0SeupxLXVsF1qin3FC+bvrxWXcENgAAAAAsQn5xOc3+YKteGaoAbf8AWbCPFOgAAIm6ByGfKK+q1tAHa4+L8djVFGvLCRs2sZYntmXml9QoO1x6lRLEIDVb58tV939Zhehb8sHcITS8c4RYFmTkO254fASCGgAAJoCMDQAbxzN8+CDs+ydGixJUI7tGkbeqcWJkgKdym5t5v3RzL1qz/6Iyw6dXm2BxEP3pxpNGt59VUCqyOGKCvK76WviAmg8HQ3xNN4A1nOXi4YJ4LQAAAIAlOHzxinL7poGtawQ2xnaPIjeUwAGogWf8n0vPp+0JqVReWSWy67ccTxFjOkMc+OAhEY/7irQlpjxdneibx0bSre9sFPePXpLGdvklFWJsWKIqmVSMjI2ruuv9zaLawWsz+161x4mnaqzN5Cw1AAAwPZwBBLBxLk4OIjghN5Djvhp8UKau4Xrn8DgaEBdKr87sIw68wv09lMdvG9L2qs/xq5EyVsbIDfDkhnjmgFJUAAAAAJYh+UqRuF50Rz8a2SWyRmCD688DQE292waL68W/HaGlf0pNp91cjJfcvWfZZpqjzYzK1WZm+Li7UICX8RPq3F9RnaWR0YBeHvaEA0fpeSW071ym0nzdUEFJBT3++d908nKuCCqpcc/K+0Z3pJdn9G6iVwwAYD8Q2ACwYXzgVVZRJQ6m6jJzSDtaOKO3OABm4f66LI524b4U5qffwPFf07rTF/NHKPflASqXmqqqrha3z6Tm0dhXfqfvdp4VB3j/t/Ekzft0h3isVYhUisBUlk5pr3stHvozZAAAAACgefAscZ5k0yHSX8my9dceN0YFeFKEKnMYAHSMNfPmSWIVVdVizFVUVqGM99JyS+hydhGl5xZTRl6JXvkjYxkePE7jElfyGC85WwpAWgrOKJHfnyVQl+3K1k7UM7ThSDIdT8oRt70MMjbYDf1bU/+4UDO+SgAA+4RSVAA2jIMaPKfkaoENQ+F+Hno1Xj+fP4Le/u0I9W4dTP3iQsT2eHDKs++e+WqPSGme/cEWMSuPAyFL5wymn/ckip//bNMpIuKLDmeHmFJsgDt1CfOigS19yckBDdgAAAAALMGlzAJRrlR9LBrs405v3NHf5BNdAGyJsfFbeWU1HUrMokWrD4nHv35kJBWW6k66L1j5j+ihyJPO5D4dnLXPvQ05IKIuD1xZrREZHRwU4e1akm93nKUvtpymzx4cTpGBzR/8VPeUTMstrlFaioNLe85kKPdv7C+V3TOGe1nyeBkAAEwDgQ0AG3Yxq1Bch6kCFdciwFu/VBQHMYw1GQ/SHtTJQQx5BhBfNh69XGN9LoP1/I09Td4ojYMZb01sZ9JtAgAAAEDDZzhXVmkoK79U9HAz1K1VIHYtQB1cDQIbHKDggMSqXedF2SO+cDkpztKQJWlLvxn+zbk6S4GNVsHedCGzgK4UlIrl3u5SZkGFhQU2ftwtjS13nU6nGwfUHiRoKrlFuiyN1Jxiio8O0Ht864lUOpiYpdwf2CGs1m29OWuAmV4lAIB9QmADwIYlJEvpsJ2i/ev1c9x4bmrfVkpfjtpwrwxHhxai7ijj2UGcnjtfW3LKsHzViM5SbWUAAAAAsF33/2+bmAnOPN0w5ASoLw+DUlTcA5EDGxcyCpRluUXlovcDC/Vzp/Rc/TJUMu1QTSznwEaSdvKbXIa4rLKKjidli0lopp6A1hD8nVFYWqGXZdIc+Pm/2HxKZGnIlm86RT1ig/SyNk6l5DbTKwQAAPTYALBhJ7R1PjtE1pwpdzUPjI0XvTfq4unmrDeL5uP7h5GHqllavDagMqhDGIIaAAAAAHZCDmoYm3kOAFdnGJzgoAPj4Ibs040JSsaGuvyRYRmruAip9FHP1kFK7xt2OlU6Ib/2YBI9/vku+mHXeYv41fAkO1ZQonuv5sY9IbccS9FbturvcyJDZntCmrIsq6CUXvx2r956hSVSP5DZIzvQazP7NtErBgAAhsAGgA1LuJwr6hoba2BmKncOjxMHn3zwzenMXzwkNRWf3j+WFt81kP5YMIFevKmX2Z4fAAAAACyXq7bWPwBcu84xAfT4pK7K/YgAD4o26DfBJ9n3nZdKIHFj6iEdw6V1/fXLEP9nZj/6+emxNN2g94Nhyaq/Dic3+69o79kMUe6JXSkw3qjbHB75bCe9/vNB+nyzrjekuryU2sVMKeNFxtkl3OaRJ/z1bhNs9tcKAAA6yAsGsFH5xeWUkVdCo7tFmfV5HB0caNWTo6lao1FSmte9MFH1ePOnMwMAAABA05BLlMqQsQHQMGO7R9Nnm06KklMVVRrq2TpY6aMR5udOCcm6EkiB3q709NRu1K1VAA2P1y//y+MxdVa97MFx8bT+cDLJf7Fckqq5Pf+NLhviyMUrojG3Octj8RhWLt/Mvtlxlu4YFkeZ+SV0TJvZIpPLLht+x3FggysZyJkmAADQdJCxAWCj8rWpu3wAZm6cESLXaAUAAAAA+5WVrytDxRDYAGi4h8Z3Fn9DXWMC9LLwXQwyoXiyGS+b1LuV0hS8Lpxd4O7iRM5OulNCeUXlymS15tavXYgIIpxLyzfr83y+6ZQow6V2JjWPTqfkKb1JZEvnDFb6V3LAhfcVX+cUlpm1QgIAANQOGRsANkquEerpioMsAAAAAGgaG49e1rvvqjpxCgD1M7hjuOhXyFkLf59KF8u41LC6j8awTlIJqvrgfhDMydGByiulJt2lFVV09GI2dWsV2Cy/JjmoEhviTTf0j6U9ZzJo77lMahsu9QgxlZTsIpEN8vuBSyKAIRvXPZrWHkqiQ4lZFOAtTQ4M9nGjzPxScZsbhnP5Ls7k+GzTKTpwPpPOmjnwAgAAdUNgA8BG8GyRF7/bRxczCyjcz4MOXbgilru5oK4xAAAAAJgfl2T5YstpvWUhvvr1/gGgfuRSTOpKRy6qwMas4XHXvC0uU5yUVaiUTSouqxTX3VsFivHjX4eTRCPtcT2iydejaTPyS7SvpV24L8VHB5CTQws6qSoTZSp3L9tidPnsUR1EYCM1t1gp3VVWUaUEk9jLM3qLfhxbjqeIss8AANC8ENgAsGInL+dQZZVGNJfjA9R/zmSI5em5uoMswxqgAAAAAADm8M5vR5TbN/SLpVA/d+oXF4KdDWBi6hJvXFLqWj05uZvR5f3iQkVT7A1HLisn9OsTMDGFK4VSs3B/T1dydnSgymoN7T6TQblFZeTnad7yyhzI8HZzFhlmnKERGSA1ah/XI4Z2nEylhTf3ll6bl6vIKOHXpdYq2Nusrw8AAIxr1rzgbdu20aRJkygiIkLMQli9enWt695///1inXfffVdveXZ2Nt12223k4+NDfn5+NHv2bCosLNRb58iRIzRkyBByc3Oj6OhoeuONN8z2ngDMjWfV3PvhVvrX13vokc/+pie+2CUahf+4O1E8rq6p2iHSjyb0iMYvBQAAAADMLq9Y6vE2uU9Lum9MJ5raNxYNdQFMpIUqU99NVeJNXZaqoSL8PSjM3125X95EjcT3n8+k0nIpUyM9t1hcc0BUTV0uylxuG9JOnG8K8/eg5KxCKi2X3v/ILpG0fN4IigyUAh0sRPX6ru8VQ8vnDae37xpg9tcIAAAWFtgoKiqibt260bJly+pc7+eff6bdu3eLAIghDmocP36c1q9fT2vWrBHBkrlz5yqP5+fn05gxY6hly5a0f/9+evPNN2nhwoX08ccfm+U9AZjb4QtX6FJWIR1MzFKW3fT2epE2y/41rQcN7RROb83qT+/dM4jc6jGDBwAAAACgoeQGuvePicdOBDAxeVzn7e6i13fCFOO9iABPahvmWyNIaU7bE1LpuRX/0McbEsT93KJyJSuChft71CjB1VB7z2aIpuC1md4/Vly3CfWh9LwSWrH9TK1BoxAfXWBjev/WYt+heTgAQPNo1jOe48ePF5e6XL58mR566CFat24dTZw4Ue+xhIQEWrt2Le3du5d695ZSA99//32aMGECvfXWWyIQsmLFCiovL6fPPvuMXFxcKD4+ng4dOkSLFy/WC4AYKisrExd1gATAEnCjs9rw7LgesYHUu01wk74mAAAAAICyyipR0sXRwQRnIgGgxliP+zrcNLA1FZbo+tk05u+NMxI2Hr1MYX7udO+ojtQ2zIfeWXOUcop050LMZd+5THF9Pj1faV6uLq01+7oO9OqPB+jl7/fT8vnDKVgVUKiv57/ZK67/fH5CjccCvV2VPiaGQSI5yKIW4iu9Dk9XJyX4AgAAdpixcTXV1dV0xx130FNPPSUCEoZ27dolyk/JQQ02atQocnBwoD179ijrDB06VAQ1ZGPHjqVTp05RTk7tjahef/118vX1VS5cwgrAUjI2uJFbkLeb3vIhHcNo3rjO5Ohg0X/WAAAAAGCjuC6/KcriAEBNLk6O9OC4eHGCv1WItxgT9mvXuB42T07pRmueGy+2zX07uKdEqK875Wj7XZhTSnaRuA7zk4IDpRVSSSr5O0Quh1VRVU3rDyc3+Hm4dJeMA0JseHwEzRnVQdx+ZGIX5XHu5yEb1D5Ur5eJTC6V1SbMRwmIAABA87DoGjWLFi0iJycnevjhh40+npaWRiEh+v+R8/oBAQHiMXmd2FgprVAWGhqqPObv7290288++yw9/vjjehkbCG5Ac+O6o+fS88XMGi5HlVVQKpaP7hpFd41o39wvDwAAAADsPLBh7EQgAJgWn1Bf+ejIRmdHObRoQQ6O+tvgRt2Z+SVkbklZUmCjsqpaXHM2ijpjo2frYHJyaCGaiDcmg4QDI7JjSdniOtjHjW7oF0sj4iMpyEc3YVB+LWxk1yij24sM8CJ3F0fq1iqowa8JAABsPLDB/TDee+89OnDgQLNEwV1dXcUFwJIUlkozTKKDvMjBoYWoEzqtXyzdP6ZTc780AAAAALBzIrDhhMAGQFNwcjRPpn5MkBedSsmld9YcoYfGdzbL83AAQQ5WZOaX0rxPttPZtHy9jA0uA7X6X+Po+v/8SXna/hsNITcCZ2sPSn0puS8GVzpQBzVYRZVGmTg4qEOY0e15uzvTlw9dJ8ruAQBA87LYmjXbt2+njIwMiomJEVkYfLl48SI98cQT1KpVK7FOWFiYWEetsrKSsrOzxWPyOunp6XrryPfldQCsRXmlNIPE2dFBBDM4qHH70HbN/bIAAAAAwM5xuZf8kgoxkxkArFd8jL8SBKir4bYpJuyxk5dzlaAGc1N9h/C4lwMJ2Y0ojSX37mB7zmSIbXaONl65Y2x3KUvjui6RdW7Tx8PFbIElAAC4dhYbYubeGtwvQ417Y/Dyu+++W9wfMGAA5ebmiuyOXr16iWWbNm0SvTn69eunrLNgwQKqqKggZ2dnsWz9+vXUvn37WstQAVh6YMPFyYG83JyRqQEAAAAAFiEtt4TyistpaKfw5n4pANAIfdvqyn2XqLIdTImDoMZwyWUOGqhF+HuKcsxV1ZoGld5SBzbYq7f2oZhgb6PrjugcSQPiQms0EQcAAMvUrCHmwsJCOnTokLiwxMREcfvSpUsUGBhInTt31rtwYIKzLDgowTp27Ejjxo2je++9l/755x/auXMnzZ8/n2655RaKiIgQ68ycOVM0Dp89ezYdP36cvvvuO1HiSt0/A8BayA3UOLABAAAAAGAptp1IEdedowOa+6UAQCMEervRU1O66TX0NmfGhtrTU7uLvh9qHaP8qLiskg6cz2xwiTx5O2/N6k/dY+vujYGgBgCA9WjWs6P79u2jHj16iAvjYAPffvHFF695GytWrKAOHTrQyJEjacKECTR48GD6+OOPlcd9fX3pr7/+EkETzurgUla8/blz55rlPQGYU4WSsYEUfwAAAACwHJ9tOqVXxgYArJfc50Ldn6K+Dl+4Qvd+uFVkchmqz3Y5i4OdupzboNdRWi4FZ4bHR1CXloEN2gYAAFimZs2vGz58uKjFeq0uXLhQY1lAQACtXLmyzp/r2rWr6NlhS7YnpNKyP4/Tx/cPrZGqqXaloJRW/3OBbh7YRtSmzMovpbkfbRUzIfq2C6kxGwKspMcGMjYAAAAAwAIF+7g390sAgEaSsxYMyzjVx2s/HhBBjS3HLtOUvrFGKxFci+ggL3GddKWoQa9Dfg9ysAYAAGwH6tlYqdd+OEA5RWX0yYaEWtfhGpQz391I3/99jnadThPLthxPoaKySnrpu300/tU/6Hy6rkkXWL7sIqlpGkpRAQAAAIClkE9SXtdZKgcMADaSsdGIwEa1dhKrg5G+GHJ5qKl9W1F0oKe4He7vYXQ77i5OFODlKs5lfLohgSqrqiklu4h+2n2e8ktqZoPUHthA3wwAAFuDb3YrwZkt//npIHVrFUjX92pJXVoG0JGL2XT0UnatP5OWW6zcPpeWTxtaJFNVtTTjX7b2YBI9OC7erK/d1vAB2mcbT1L/uFDqHGP+GsJ8IHY2NY/aR/rR//46IZY5OSImCQAAAACWoahUKvXi6ebc3C8FAEzA01U6VVRYS5Pva1Gg/dkWRqpEyJUIesQG0QNj46miqprqqiUR6udO2YVltGrXecrMLxVBDtkN/VvX+TrkslduLsjYAACwNQhsWIkfdyfSthOp4sKBDbnXQmpOMeUWlZGfp2udDbm4HBWLCNCfBZFdWGr2125rjly8Ig6o1uy/SKufGWf25+MU3n/OZOgt6xiJ2sUAAAAAYBnkcYd8MhQArFuIr1RSLiOvRGRXcCnk+pSx5uoRMmM/VabN8nLVZoY4X2XiHjcPl6mDGpkFpdecsSE/FwAA2A5M+7YSO06mKre/2HJKlJOS3b10C607lESbjl6msa/8TunaTA1jsytSsqXHnpzcTaR8bk9Io+0ndNuGqztwPktclzSikVp9qIMaTg4t6F/TupMHBo0AAAAAYCHksQkyNgBsA/8t85gzITmHJv93LX219XS9fp4DIoZlp9TVKH7Ydb5eJZaDvN2U23pBkGto2VpaIX0/occGAIDtQWDDSswZ2ZFmDYsTtzcevSyacPlqm4YXl1fS4t+O0KLVh8T9l7/fTx/9dUKsY8zSOYNpdLco6hcXKu6/+uOBJnsftkAdaHjzl0NK7VBzUM9MYc9M60EjOkea7fkAAAAAAOqLT34yZGwA2A5vN2dKzpYadq/cflaUi7pWyVcKldubj6WIktg/7UmkS1mFIuOCK08wF6dry6J49PquRpcXlV29VFaZXIoKGRsAADYHgQ0rwb0cbhvajoZ0DKf03BIRtBjXPZpeuLFnjXXPpefTz3sSaeX2M0a31S7cV1z3aROsLFt/OFlc88HGf38+KMpbgdQI8X/rT9Bl7QEdzzxJzChQds2GI5eVgZwaz0C57b2N13SgVRduiCbzcXem7rGB+LUAAAAAgEXhSVX1OUkJAJbPsErASSPj3tokZUmBjdahPnQ6NY/WHUoW/SLv+2gr/br3orKe1zX25eHSWI9M7CJuqwMshhMB62we7oJSeQAAtgbf7FampFz3H/f4njEU7u9Bz93QQzQWN5R0RToZ/8kDwyg1p4i6tQykiipddgE3Ivdyc6LC0kp669fDdPjiFSXAcTGzkD6cO4TsHZed+ml3orjwvu7bNkQsnzOyA/l6utDbvx6hY5eyKT46QC/V9pMNCeL2kQvZNKC9lBnTEHvOZIhU2x+eHC3u42AMAAAAACxVgarHHwDYVmDj0IUr1KWl8Yl2+89n0s6TaTRvXDw5Ojgo5yKGx4fT+fR8ZeIkt95Qn9OQe3lci7ZhPnr3eZzMY+/tCamiCXltQRIlsIGMDQAAm4OMDSsTGeCp3OYT7ax7bJCy7IuHRtAfC8br/UxMkBf1axcqTop7u+v+s2/RogV9+dB11LWldFJeDmqwxPR8s5ZYshbqbAxOl/1lr9SEfXDHcBoeHyEaoZ1OyVP2GafVbjp2WfmZY0nZ4vpMah4tWPlPvTI48kvKxc/1jwsRvzsENQAAAADAkg3uENbcLwEATMQwA4sDG1zBQK5moPbKqv30+/5LtPdspghccPlsDoxE+EvnL3JUFSHKK6WMizuHx5Gjw7U3JI+L8KP54zsr97mh+T9nM+nVHw7Qj9qeHcageTgAgO1CxoaVuWdkBzEboXdbXRkpudcGC/OTgh1v3zmAEjPyqWOk/1Wbgj01pTvdsWSTuM8HCluPp9DRS9mUV1RO/161XxwwDGofStf3biXW+Xj9CcrKL6UXbupFtozrgHKgwt/Tle6+rr3oY8L6tg2mMD93ERjirA35IO3+j7fX2jTt4f/bKQJF+85m0rD4CKPPx03f3V2dyMfdRQmqaLSZNQAAAAAA5sYTndYdSqL/3t6PnNQNeutQpM3S4B5+9Zl9DQCWbXr/WNE35/6xneiVVQcoLadYOW+w7oWJynqVVdXk7e5CJeUltOt0Ov11OFlkUnDIgse37EqBLrDBpaTiInxp5pB29X5NPOlv6Z/SbVcnR6UUFZfjNobH40cuXJHWR8YGAIDNQWDDynD6pLHSRneNaE85hWV6PTn4ci2CfdxE9gHPqJjUu6Vo9MWBjZOXc+mENmPh8IUr9ME6qXauvfh882lKyy2hmwa0prHdo8Ulv7icfFSBJA56ZBeW0ZdbTtf4eT4I5F4oHNCQs184GFJbL49Z728Wt397dpyYHcPbVQerAAAAAABMiY9RHVTHp1yelqXnlehliqtpNBo6n15ArUO9xbFtZn6pMqYAANvRp22IuLAAL1c6lZJr9Ltj4ff7lAl9XI6qoEQKdvII2N3FUVmuHvtyGamGCPJ2owk9Y6hlsJcoFy0zrDWx8UiyyNTg0s78fcbPV5/sEAAAsA4oRWUjbh3clh4cF9+gn+UBybM39FCacckpp3yAUtcghQ9IbMHZ1Dx65qvdoi7othOpdOs7G0Tz771nMyjQ25XuHNFeWVcd1GBRgV6iRNUKVaP2NqE+tPLRkVRUVikCQuNf/UN5TC5lZehQojSLhPHPMA6iGGbkAAAAAACYAp/44+PUy9pa+Gqlqhr4PBv7XJpUepXxbOwHP9lOa/ZLDYAz86UTmsE+yNYAsFWhfvp/3/JYlXH5KZkc1JCzuDyMNOwur6iuUeaqPucu+LzF1L6x5OKkO51Vxc07VN745TAt+eMYJWu/3x6eqCthBQAAtgOBDajBMIX8+Rt70ZLZg+j2oe3ohyfHKKWRSsptI7CxaPUhUS/0uRX/0Gs/HhCZEtz8OzGjgAK83OqcTTK+R7TefW5Y9v6cwRTo7UYdo/xqrM+NxjcfuywOBJf+eYz2nEkXmTb/+emAss7xpBz66K8TdC4t32gwBQAAAACgsT7ZcFJc/35AClCoqU9O8nHpg5/soIOJWeL+gfPS9f5z0rU8UxtlqABsV3y0fjWIGYs30KHELJHBVRsOQEQGSplfnN0hj52LyytFuevGUgdHKmqZdMlJJZzdMaab/rgdAABsA0pRQQ3jekSL1NJOUf6inFKENg29fYR0or5tmI/IKuCeENacTcCzOt5Zc4QuZRXWug43765Lj9a6xu2cEnv/mE5KiutbswZQUlYhXcwqpF6tg2n36XSR3s+Bi9yicvpt30VxMfTNjrN69zH7DQAAAMD8CgoK6IUXXqCff/6ZMjIyqEePHvTee+9Rnz59xON8Au+ll16iTz75hHJzc2nQoEH04YcfUrt29a8T39z4vch94rj8bG2BjdMpucrxamJ6vuj1J5ejkau65BSVKyVaAcA28QQ+Q/zdIQcujJEnCH76wDByc3HUK0fV0FJUai7Oum0cuZhNaw9eonE9YvTW4QmF8vkMAACwPcjYgBr4IGNKn1bULtzX6EGAnE760P/tpEuZBVa7Bzl7ghsksqhAT3rvnoGiv8X/7htKU/tKjdLvG9Opzm3wzJMP7h1CT0zuSg9P6KzXkIwbLsaG+oj+Jd7uznRdl0ixPg8Oeeabof/c1rfGsn7tQlALFAAAAKAJzJkzh9avX09fffUVHT16lMaMGUOjRo2iy5cvi8ffeOMNWrJkCX300Ue0Z88e8vT0pLFjx1JpqdRjwpqUV1brlWXlQIfcBJztPZdB3+44S8+u2KMsK6uspnmfbBdlWAVtff1C7c/x8S4A2CZXVRAhNsRb+duva5KgLDrIS0zWi9NOlGQHzuvKVzVUv3b6vUffWXOUqqp1320sv6RCaTAOAAC2BxkbUG+DOoTR36fS6Vx6Pj391R4REAi1sgbXPHj7Yfd5cfulm3qJhuxyY+9WId70wNh4cbkWbcJ8xOVqOJODmyye1ZaYUlv7/ATx/DOHtKWV28+KGqYPje9M7SNrlrMCAAAAANMqKSmhH3/8kX755RcaOnSoWLZw4UL67bffRFbGK6+8Qu+++y49//zzNGXKFPH4l19+SaGhobR69Wq65ZZbrOpXUqYq28IBiz8PJtF7vx9Vlq07JE3+UTt6KVvvOLaqSjqBWFAiZWwgsAFgu1xVZZ+CfNxE2eaC0gpKyS7Sq2DAgY6oAE8xqc9Q61DdmHn2yA4m6TPq4eIoqknsPJUulr2x+rDonanGpa8AAMA2IbAB9cZZCMvuHSwyNrhUE5dXenPWACVgIAcILBU3QJz4nz/F7bhwX+rbLqTJXvPbdw2k5KxC2nI8hYZ2CqfC0koqLqtQnv/O4e3pxv6tRc3RhjZUAwAAAID6qayspKqqKnJzc9Nb7u7uTjt27KDExERKS0sTGRwyX19f6tevH+3atavWwEZZWZm4yPLza05waQ7cvFftR+2En7pwXzjm5uxIpRVVdCwpR5R2LSypEBN4eDkA2CZ1ZQKuKsANw9ceTKJRXSOVHjt3Do8jvzpK0qm/Iyb1liokNNaUvrE0smsU7XzzL3Gfx9mG/j2jt0meCwAALA9KUUGD8In492cPEuWqOLjBl7Gv/E7jXv2jxgwJS5OeKzU4lGeKcMmopsIHc23DfWnOqI4iFbdn6yAa3DFcbx1PN2cENQAAAACakLe3Nw0YMEBkZqSkpIggx9dffy2CFqmpqSKowThDQ43vy48Z8/rrr4sAiHyJjo62uIwNcb/CeONdtfPp+Ur51BmD2ogyNCeSssWsbc7WsPTJTQDQcC6qZt/qoMSGI1KpviX3DKozqCFb9cRo+v6J0Sb9Vbi71B5UHdc9mtqE+Zr0+QAAwHIgsAENxoOXXq2DqKS8iuZ/ukNZvkHbt8LSlGpTUC+o+oJ0aRnQjK8IAAAAACwF99bg7OPIyEhydXUV/TRuvfVWcnBo+JDp2Wefpby8POWSlJRElqDcIJAhNwuXy87WhbOKu8RIx9BPfrmbjiflGG0sDAC2w91VKvbRtpYSzD4e1/Yd4OPhQr4eLiZ9bY61fEc/d0MPemxSV5M+FwAAWBaUooJGGRYfQd/uPCd9mBxaUGW1hjYdS6FHJnYhN22TcUuw6ehlWrT6kN6y12/rV+tBEAAAAADYlzZt2tDWrVupqKhIlIwKDw+nGTNmUOvWrSksTDrZn56eLpbL+H737t1r3SYHSPhiabivhjzTmScpuWrLS3GJ1ruGx9HOk7Vnobg6OVCQt37JLm8ENgBsmo+7C31y/1AK8nE3+nhzj6vlcxGyLx8aYXV9QAEAoP5wVhcahRuATerdUpl9ISeg/3vVfqrQNhQ0t5LySsrM15WXMpSRV1IjqDG9f6woAwUAAAAAoObp6SmCFzk5ObRu3TrRLDw2NlYENzZu3Kisx8GPPXv2iBJW1qaoVMrQkGdO5xWXU7CPm6hFrz5xyQEPnrDEfelkLs6O5O+lH6xB43AA2xcT7E0e2swNS/P7ggn0+fwR4jb3skRQAwDAPljm/0pgVe4f00lcD+sUTsHcNOz9zbT/fBYdSswSM8C6twoUQQ9TW/zbYdp1Kl0MrrLyS+nj+4dSy2DvGusdu5St3OYBW2Z+KU02UbMyAAAAALANHMTgUlTt27ens2fP0lNPPUUdOnSgu+++W5RgffTRR+nVV1+ldu3aiUDHCy+8QBERETR16lSyNnJPvPjoAErLlWrkRwd5iffJJy65hAsfV8cEe5FDixa0VdWQl2vtc0CkW6tAOnzhilgWG2q8PA0A2KavHr6Odp1Opw/WHidLEe7vIYIbgd6WlyUHAADmgcAGNP5D5OhA88d3Vu7zQOg/Px2k57/ZK+53ivKn12b2NcnsjqrqavrfXwn0y94LuoXamsBzP9omGppzU+4rBaW071wmjeoaRb/uu6AcfPl5ulB+cQUF+einzwMAAACAfeMeGNwTIzk5mQICAmj69On02muvkbOzVDv+6aefFmWq5s6dS7m5uTR48GBau3YtublZ33Hl6ZQ8cWzOGcwbj0qBjahAT71ys2qeqlJTrk6OIgCyYrWx1QAAF/RJREFU6PZ+9MQXu0SPjXaqjA4AsH0hvu7UMdJP3Lakv38ObgAAgP1AYANMro1BQ7ETyTl0+3sbaeWjIxvVd6Osooom/3dtneu88O1eemBMPL3+80Fxf/FvR8S1Qwvp4IsF+Tg2+DUAAAAAgG26+eabxaU2fDL/3//+t7hYK5748/H6E3Qxs1BkXHiojs2jAr1q/bnIAE+98lTy/lgwvSftSEi9asNxALA9PKHwhRt7Ug+UeAYAAHvssbFt2zaaNGmSSOHmA+PVq1crj1VUVNAzzzxDXbp0EXVueZ1Zs2ZRSoouDZplZ2fTbbfdRj4+PuTn50ezZ8+mwsJCvXWOHDlCQ4YMEbOpoqOj6Y033miy92iPeFA0b1y8KE3FDQhZUVklfb3tDKVkFzV4u/kl5crt+Gh/igjwoGem6jdr5FT5zzadrPGz6owSAAAAAAB7xGWkOKjBuG+Gq4tuwo86Y8NQbIi3Xra2LNDbjab0jRXH4ABgfwZ3DCdPV11GFwAAgN0ENjiVu1u3brRs2bIajxUXF9OBAwdE7Vq+/umnn+jUqVM0efJkvfU4qHH8+HFav349rVmzRgRLOD1c3dRvzJgx1LJlS9q/fz+9+eabtHDhQvr444+b5D3aq8l9WtFz03vSK7f0ocV3SQ0VV+06T3cv20I/70kU97mG8ZZjKbTq73NUrdHo/Tw/VlpeqbestLxKXHdtGUCL7xpIy+eNoBGddWnyvdsEU3ZhGaXnlVD32ED6aO4Q5bEJPWPM+n4BAAAAAKwps5r7Z7hpsy9YdB0ZG8b62AEAAAAA2G0pqvHjx4uLMb6+viJYobZ06VLq27cvXbp0iWJiYighIUHUtd27dy/17t1brPP+++/ThAkT6K233hJZHitWrKDy8nL67LPPyMXFheLj4+nQoUO0ePFivQAImE/7CD9Rd/NMap64v+noZZrWL5Z2nExTSkZ5uzvTuB4xtPt0ugh8tA33pR92nacvHxohmhOuP5JM3m5SA/JerYOVbXOmz9t3DiAvN2f6ac95ZfnA9mGiiSFnjnAGCa8HAAAAAGDP1DOr+babs244WFcPuuig2rM5AAAAAACag5O1NfTjE9Rccort2rVL3JaDGmzUqFHk4OBAe/bsoWnTpol1hg4dKoIasrFjx9KiRYsoJyeH/P39jT5XWVmZuKgzP6BhOF196ZzBokfGf348QHvPZVJpRRX9ceCSss47a47S9oQ0UfeXHbpwRVzPen8zRQd6UtIVXQkrN1XKPOscEyCuJ/duResOJSsNy8WyPq3wawMAAAAAMMDZGu6q4+q6ykm5ODmKDOjgOoIfAAAAAAB2U4qqPkpLS0XPjVtvvVX002BpaWkUEiL1cJA5OTlRQECAeExeJzQ0VG8d+b68jjGvv/66yBqRL9ybAxqHGw12ivanqmoNvbn6EB04nyWWc0YGk4MahtRBDaZOmVfjLI9Xb+1DE3vFUOtQ/QbmAAAAAACgf2xuOGGoLo9M7EIzh7TDLgQAAAAAi2AVgQ1uJH7zzTeLvgsffvhhkzzns88+KzJE5EtSUlKTPK+t69NWCkRxGSrZykdH0uOTuorbcRG+dF3nCPrg3sG0fN5wo9uoqzkZb//hCV3I0QGlpwAAAAAAasMJGupSVAAAAAAA1sTJWoIaFy9epE2bNinZGiwsLIwyMjL01q+srKTs7GzxmLxOenq63jryfXkdY1xdXcUFTIszKYK83SiroFTcH9YpXJSqGts9WlxqM6h9KJ1Jy6eMvBLq3VbXYwMAAAAAABqetQEAAAAAYI2crCGocebMGdq8eTMFBgbqPT5gwADKzc2l/fv3U69evcQyDn5UV1dTv379lHUWLFggtuXsLM3056bk7du3r7W/BpgX1+blwMbILpH09NTuda7LjcEvZhbQxF4t6UpBqaj96+5i0R9bAAAAAACL9eas/vTngUvUIdIPWc4AAAAAYLWa9QxxYWEhnT17VrmfmJhIhw4dEj0ywsPD6cYbb6QDBw7QmjVrqKqqSumJwY9zM/COHTvSuHHj6N5776WPPvpIBC/mz59Pt9xyC0VERIh1Z86cSS+//DLNnj1b9Og4duwYvffee/TOO+802/u2d3PHdKL1h5Pptmuo0cuNweXm4IHeaFYIAAAAANAYXVsGiovsoQmdKdTXHTsVAAAAAKxKswY29u3bRyNGjFDuP/744+L6zjvvpIULF9Kvv/4q7nfvrj+rn7M3hg+X+i+sWLFCBDNGjhxJDg4ONH36dFqyZImyLjf+/uuvv2jevHkiqyMoKIhefPFFmjt3bhO9SzDUKcpfXAAAAAAAoHld36slfgUAAAAAYHWaNbDBwQluCF6buh6TcfbGypUr61yna9eutH379ga9RgAAAAAAAAAAAAAAsBwOzf0CAAAAAAAAAAAAAAAArhUCGwAAAAAAAAAAAAAAYDUQ2AAAAAAAAAAAAAAAAKuBwAYAAAAAAAAAAAAAAFgNBDYAAAAAAAAAAAAAAMBqILABAAAAAAAAAAAAAABWA4ENAAAAAAAAAAAAAACwGghsAAAAAAAAAAAAAACA1UBgAwAAAAAAAAAAAAAArAYCGwAAAAAAAAAAAAAAYDWcmvsFWAuNRiOu8/Pzm/ulANiHigqiwkKi4mI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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot daily macroeconomic indicator trends with key market events highlighted.\n", + "fig = utils.plot_macro_trends(macro_daily, macro_monthly)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "7d9e0b7c-6825-46d5-bba0-1271bae54778", + "metadata": {}, + "source": [ + "## Correlation between macro indicators and S&P 500 returns" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "39c2d20d-4e83-4a30-bc32-a88149c0dbbc", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot correlation between macro indicators and S&P 500 daily returns.\n", + "fig = utils.plot_macro_correlation(sp500, macro_daily, macro_monthly)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "a104c8a5-d10f-4496-9ea1-9566181924e8", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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4X+vcubM6Fmgo+4l9W5c6NR8bKKxnXepTQ9nJjIwM628cY0855RRrG8VxG9sovtuvv/5aHUP0foXjEJYN2xw+O0oomiUt9T6HfT2UPB3jo7lve2Nu13rbNZdTw7rE961hO8D28N9//9V6bDCwP+Fce/7556ttAWWo7SU08Tf2+QMOOEDqCudws0Svp8/ta19ZsmSJOiea22kg76Xhs0+YMEGtB7QLsU0TERERJaxoRx6JiIiIQsGeZef006BBA9dTTz1V67koZaYfg6wXjH7XXn31VcesqnBn/KH0Yn5+vnUfSpQ5lbAyM0WQuWFmJdqzJILN+EO2Aspw6UwAZLbpLARkVdiX3el9UG4VZcpef/11lfWC72HIkCHWc1AiUkMZNPP1HnnkkVqvt3z5co/Li/VjzxSaNWuW22PM0mHI0jHLLCI71FemEbIuzddD6Tx/ITvF/M6wfjX8bmawmWVK7VlyKBvpxHwMvid7VpJ9e7rwwgtrZSPq+1BGz8xA8ZUdgff6+uuvValQZLXge0a2rH4OSuGZ/ClF6+kxyPrUtyOzxywNiW3NXFZkBjplxmB7njlzpnXfjTfe6LYdaCgPq2/HPmeHjDKdveXLGWecYb0WsvkA6wV/64wVrHe85o4dO9y2B2TV+XMs8Xaf02PwY5ZANEuS4sc8Fnljf579BxnIr7zySq3nmccxlIc1s81OOukkKyPt559/tm5HmVtP20mXLl3UtocfZOaZWVP247g3WEf4TpB9eNddd6nSlwMHDnR7LZRWLioqcsxismdLItPMvF9n9SKD0Mxms8N7+CpVbWeuD08/yPiyl7FGpqa+H6WZzfuxTZrZcWZWp7fznhaqjD+nY3ws7NtOkGmOkqT6PZBxZsL3aZ737Q4++GDrfmwndcn40yZPnuyYUY+y3Tjn2KsKBAOZ82bWHvZr83XtmY7mOR3uvvtun+Vo/X0vwOvrbFyUpQZso77OaURERETxihl/REREVG8giw8ZRyZkBZkjz88880y3UeAXXnihXHPNNW6ZI8iGCDdkhDVu3Nj6e88991TZU6AzpEBn9QCyw5CtqGFU/+WXXy7l5eV1Xh5k73z66acqWwIZIsgKgeuuu85nZhyyE1944QWv2Ydr1661fjcz+rAObrvttlqPt2cHmJBpghH93jJ+kE2nIQMJ60o/BtmhyObRmUWhZi4LvjMzGwa/4zZkcjott4bMlP/9738+3wvbrz0bBts8ski1Dz/8UP04wbbzzz//yDHHHOP1fZCJinWKzFBvzO+5rsx1g+VDtqt27LHHSqtWraxMHjz2xhtvrPUayGoZMGCA9XePHj2s3839DJlqyL7B9v/GG2+o7wfZonj8oEGDVCZemzZt/FpuPBaZU+a2rv+/7LLLVFYtMpSmTZum/teZtfjO+/fvL+HQvn17tV87rQe9LszjkbfjRMeOHVWmH7YbO2SUYrtt0KCBXHzxxeo27Gs6qxT77W+//aaOFcjyRTbXjz/+KMcff7z88MMP1nrzlf24YsUK+b//+z/H+5C96O8x/J577lGZYmlpadZtjzzyiDo+f/LJJ9Y2PXbsWDnhhBNqPV9no3r624nTY/x5XqC6desmDz74oFuGmc42N793ZL95MmXKFJXxHWlOx/hY2LftkKmGDHlkuANe18y2jMZ3/9VXX6ljNY4t2A91hiMyH5HNiXM19jmsB/3dP/30046vdcsttzjejqx9ZIn++uuv6m8cO/CaOCZ7Esy+4u974bWw3+N4gvP6Qw895NdrExEREcUzBv6IiIgoIaEjCCUQ0TH5888/q9vQUbthwwbVSatLqaED0OxgsnfwNWzYUBo1aqTKB+rHR0KnTp3c/jZLXOlAAOTm5nosX4fyeCgraJbMDBbKZqG0GkrroewpoFMQ5TO9QRADZdV80WUvwSzLiiCCU2lAXyUH7czXdPqe7X/7+p7x2VEaEeUsnUq5eWMui1OHsnmbp+VAuUZ/Ss46rQv7Nu+LDp55c+mll/oM+tm/57ryZz3qZfe0Hr3tZ+Y6wvaPcsAIBOFYMHPmTPWjYT9Dh/phhx3mc7kR0NJQ5hZBUx3ExH3Yx1BGFuU+9fYFhx56qM9gR7C8rQf7MccXlJ/FD9Y9Phd+ELRbuHCh9RisSx34Q+k9va4R2NTBTQQiTjzxRFWScNy4cSpAM2/ePHUfAhZmoNIbfRxEEAed/xgA4i+nsqg4d2DAgw78AT4bAn/2IBmCEt7+xnKB+Tz7Y+y36ecEQpd7XbNmjSpxiIEbKH+LoBYCtAhIeTpW1vXY4A2+d30uDuTY4HRci4V924R9GPuBPvZg+0NZWPv3F+7v3rR582a13+lyvCjpi8EhgLLJWGdoUyBQ+dxzz8nDDz+s7vMURHcK/GEbw76gB1QhAIdzA8oQmwLZV3DccyrV7O97oWzwH3/8odoSKM/rrWQoERERUaJg4I+IiIgSEjKAdMcysvwwkh/Gjx8vH3/8scrYAHQm6dH+OiPFhBHiOuinHx8JZoYJmHM+mTCXmtmpZ8/W0vM0hQKyec4991zrb2QT+pojB3ONmVlFmAsQHfvp6elqTj+noKCZgYKOPczLFEjwD8Fab6+pv2ez49H+vfv6ntERiY5gzJEEmIMJGZlmhokn5rLY39d+m6flcPqM/j7O3GYA8ykecsghHl8D87N5g31EB9cBc2chgwtzg+F7Q8e6zmAMJaxHvc0Hux793c8AGYNXXHGFysRDZgzmN8P3j/+xnyGLBnMt+hO0aNeunRqEgIAa5p1CJzfWFbKU8F0gaIC5xXQHvT/z+9VFIOvBX+iEx7aFH8zliPkbMejCnKPSPngBQVBz3knMU4rABI7Pep4zfSzylomGIKk5B2e46fWFABv2OT3PHzIPTZgHzz7nI+y9995qkAog6GIGxBBc0xnW5nMCgQEUOkiDOW6HDBmitj0sJ4KYCKw6HZ+wnSIg5O11A2EPXOusM/s24Ys/x79o7Nsagqt4PQStAQHszz77zHG58d0jmx4wRx32Ab1t4zvS2YLBfvcmBOGR8a2ZATLML4j5ZPWx2swK9xcqEGB/x7FNVypAsNMpgI7PbcK+YmY0m/sKXscerAvkvfS5AG0Jp7n/NGQ/4gdzxOr2IxEREVG8Cs+QUSIiIqIY8vjjj6tOLQ3lzdABBOh0RGaghpH9Zme7vQTigQceKLEEWQRmRxiyODQEOENR5lM744wzVPBOZ9GYJVA9MTvysazodEPQD5lMP/30k+NzDj74YOt3BEScgoOBdMI6fW8Y9a9hW8C6Mju+7aUOnSD4YEJQ1Gm50NGK0odOyzJjxgzV0awhowm3eVruUEDns9nBiu8InwWBAfMHgd3ddtvNrXysE3RW6/0JUJYRARAEslDG0Syl661z3uyQ9oe5btBJbwa+UfbNzEaq63pcv3696jzG8QJZeQiWIJvVDGyvXr3abXv3xgzi4XUA3wnK1OkgLIIQ5rZgZgr6Upf1Wpd1hPXilP2KoIsO8NiDz+a+hv1Hl/0ElOC1ZxUhGPXAAw9IuKFMIzKesH2bEJR76aWX3G7TARls89j+NQTEdYljPM8sVYoMap2pigCGhiCfDpCC+RzwN9PRE2RO6oEvejAMsqGc9hXsQwjY2o8NN998s9pezSCKPdDmtN3ZBx1gG9cBrscee0yiIZT7Nr7ju+66SwWPdNAPr/f99997DFYiKGj69ttvrd9R9tbMfKvrd28ep8EckIHt3Ay+moN68LmcfkwY1IOAuw7E4TiGQKNTIE63B3R7Qmf4agi2moF7++cO9L2IiIiI6iNm/BEREVHCQ2cj5pVC1gkgOIZOPZ29hk5M3RG6cuVKNQoe5eDQIWgGiDCi3OzUjQUosYiOcnTCoVMPmRzIkEHn8TvvvBPS90LHLoJ16ARFINWfbA906uvORHSCX3nllaokKTqzPZXHxEh7zKOlR+nfcccdKiMF2VDoTEZnMUqeoTPVXwjuIhNNZ7Y8+eSTKsMAQa0xY8a4zReHIJg/JRWRVYqsDh2owOfB/EHYdlA6D0FXlABEUAqdqnquQmyLKLGG0nbo8EYHJjJKEBzB9qbLKiJA6s88fsFA6TZdphVzeiH7Ah3QyIxDBzeyFzHvHIIsZ599ttfXwtx62Md05haCJQjC4fMjY8tbCb8OHTpYwWpkyaCzGcEvdOD6Ksl40003qRKS2PbROY79Fvs0MnTxvmYg15zTMRgou4n1haA0vmN0WGN/Mzvp8X2ZwS1fgT+d5aOzcnXAD9s5AkjmAASsY18BWPt61RA81HPvYRnDNScbAlwvv/yy+sFclQgg4T2xnrCN/f7779ZjzTkjMR8jSgzi2As4RqBcHzJNkfVjnxMNnf14D3z/4YRjDco/YuAIsg+xj2BbxvHCDMjiGGcGZTGnKbYLbP/I1kJmMM4b2J9QxlXDa2t4fWy/OhCDfQ7rAQMkcKwwB1/4U+bSFxxTP/roI+tYg+MtjkP6+It9GNslPgMChZj7FnMC4vMjmI+gDI7PKNPauXPnWtsc4NiFz4VBIghs4vyJ+UvNDHuUwkRg0dcAgXAK5b6NfQvbpoZ9FnOsopSoCfuH3gdwrjj99NOtwBf2VZw3UEpaVyoABFmxrkxmUNzMkMPv5n16jj7skziP66Akyr+i1CvOpzgnm9m3+O78hQFT2Gb19oT2gc7YNeF2DCgBHOOwHep5gnE8xPOxPpAdqbNm8RzzmBXMeyGTEevYaR/XcwPqYCQy1e1lYomIiIjikouIiIgoAUyYMAE9idbPe++953b/5s2bXQ0aNLDu7927t6uystK6f+TIkW7Pt/+0b9/eNW/ePLfX3GOPPaz777vvPo/LkpOTY9130UUXWbcfeuihbq/nbfm9Pe/22293XOZ99tnH1aZNG+vvBx54wK91ifc2X2fu3Lk+n2M+3lwXkyZNcqWmptZatkaNGrlOO+0062+sS9M///zjtuz2n5NPPtnj8nqyYcMGV69evbx+z6effrqrrKzM5a/y8nLXHXfc4UpOTvb6uvbl+vLLL12ZmZkeH5uRkeH67LPP/N4G/N2OTFhuX8ts/148vfbjjz/u+Pw+ffq4Bg4caP2Nz2B64YUXHJ93/PHH+9zP4LnnnvO67ps0aaL2RxPWnafl8bQt4bvwta5wDPHX8uXLaz3/m2++se4fNGiQ231nnXVWrdfwtj3MmjXLcb00bNjQr+d7O4Z5gsf4Wkf46dSpk2vdunVuz509e7bX/R0/5vE7JSXF9euvv7q9hrmdeNs//IV16OuzdOjQwbVgwYJaz33zzTe9bpf33HOP4zZhfgb7T//+/V1bt271e/l9rY8zzjjD7fX//vtv676//vrL1bJlS5+f375vDRgwwPFxX331lfWY888/3/Exxx13nMfji7/H+Gjv2+b7e/uxLxu+V3y/nh6P7xLbh50/72VfXzhm+nr8UUcdFdB5EMdlf5bDfj5BG8w8Dtl/cI4cNWpUSN7Ln2OWt/MlERERUbxhqU8iIiKqFzDf1GWXXWb9jfKKKBelPfPMMyojBaPCMeIfo+IbNWqkypkhMwPZCIFk3EQSSqQh6wzLh8wEZGlde+21KrvNnBvKXmYtEpBFgXJlyDTAHD0YhX/cccep+ay8zVeE7Bd8Ryjph9+zs7NV5ggyn5Bd4ysLzQkyDZFRg+8aWVVYFrwmtg1kX3z++ecq6wG3+QtZC8gkRcYK5ixEVgYyzHA7th9kGiLzwj7HHTJoMIcS5p9EJg2yO/CDTDdkKSDjLpjPGAgsNzKxUE4RWTv4frDdI3MHmSW435z7yxtkM77yyisqqwevgXWNz4ESglgPniAr6P7771elQQNZ7+bcXMiiQsYuMjWw/SNrEJk7yAibO3euyrgKxXaMrChkbuE7Qlai3naQSYpsRWxX/sLnRSaQ/T00+5yLgc7vh+MWsmaQNYftKhLwebA9PfTQQ3LUUUepTDhkkGJfwLEHpS1RZhnbvVniD7CfoMztnXfeqbKh8B1iO0KpWWR+IkMb830icweQkYX9w1PWcCjgeIo5w5AVhfWJbRrfOY5F2M/xObHM2NbssO0jMxnz6eF5+CzIqsL2g4xBrAenbQLrBusAr4l1gPKQmDcUpYKRlextXsNA4X1MyPLTcLzG8RfnPmTp4TPr7xF/4/yC8yUyzE3IksP3hWOgpzn13n77bXVMxHEG+yuOGcjARvZuNIR63w4GvlecE5Fdiu8b3zu+f2S/4XvCdoHtIxRwzMT8ociO1sd9/Xlxbn3rrbdUFlwwx+NAYRvB+sW5F++N4wW2CWQKo1wq5vVEe4GIiIiIApeE6F8QzyMiIiKiGIGSgOZ8PBpKa5rzB6FTPtbmKCQiIiIiIiIiotDhHH9EREREcU5nBCDIhxH8mJdp+vTp8uqrr7rNXYMsNyIiIiIiIiIiSlwM/BERERHFORRwmDhxovpxglKSX331lcfSa0RERERERERElBgY+CMiIiKKc6eccops2rRJzXW2ZcsWKS4uVnMxYa4szLeEuQ0bNGgQ7cUkIiIiIiIiIqIw4xx/RERERERERERERERERAkgOdoLQERERERERERERERERER1x8AfERERERERERERERERUQJg4I+IiIiIiIiIiIiIiIgoATDwR0RERERERERERERERJQAGPgjIiIiIiIiIiIiIiIiSgAM/BERERERERERERERERElAAb+iIiIiIiIiIiIiIiIiBIAA39ERERERERERERERERECYCBPyIiIiIiIiIiIiIiIqIEwMAfxbT7779fkpKSpFOnTtZt+B234T6YOHGi+hs/K1eujOLSxi77OqsrrGe9zrH+67sxY8aodXHooYdKfd/GLr744pC/9nvvvade+9xzzw35axMRxTq2hUKDbSHnbckf77//vtXu8wZtgGBeP94deOCB6nNPmDBBElVFRYV06dJFUlNTZdGiRdFeHCKimMf2W2iw/VbjsMMOc+tziWS/XLBtSCKKLgb+KCwnIv2Di8O2bdvKmWeeKTk5OWFZ29nZ2bLffvupn4yMDImk3NxcdQLs37+/NGrUSP307t1bRo4cKVu2bJF45+nkjvWs1znWf7SXLzk5WVatWuV2n8vlshqJQ4cOdds+9edZuHChZGVlqduuuuoqt+djm8XtTZo0kdWrV3tdjnvvvVf9j+/d3vnl9PP8889LtOl1g3US68477zx1HPniiy9kwYIFUVuOpUuXyjnnnCMdO3ZU+0CrVq1k4MCBcvXVV9d67Nq1a2X48OHqMdjGunXrJrfeeqvj69q3D7w2OteuuOKKWtu1naftbLfddnN73M6dO+Wmm25St6enp0vXrl3lgQcekPLycrfHzZgxQ4455hi1Xzdo0EAOPvhgGTt2bFDri6i+YluIbaFIuuOOO6xj/zfffON234knnmidV+bOnavOAWi7DRgwQKItUdrQo0ePlqlTp6rPcfjhh1u3P/zwwzJ48GC17vX3U1xcXOv5y5YtkzPOOEOaN2+u2gv77LOPau9AZWWlOg/juY0bN3ZrE+Tn56vvE/ehjVFYWFjrtW+//XY54IADpHXr1pKZmanaFtddd51s3rxZ3Y9tQi/fSSed5PZcbEt6ue+8805JSUlR7QgEAEM1kDBYr732mlrfaKtgu+ncubMcf/zxMn78+FqPfffdd9V2hXXbokULGTJkiMyePdvjNYX507JlS7X+Pv74Y6/LY3a82n/efvttt8eiTYXvFG0sLD/aXDNnznR7TFlZmWqj4ftCmw3fM9b9rl27gl5nROQb22/xeR6O174sDefVV155Rfbff3+1PFg+XKv/73//kzVr1rg9tlevXmq5cX8g6nKO0yLZhsR2iGU866yzrNvWrVtnLbs5INy8XQ8Y9/WDAW1mAgn+jgb2L1FEuIhC6NBDD3Vhs0pPT3ftt99+ru7du6u/8dO7d++AX+++++5Tz91jjz2s2/A7bsN90ZSTk2MtC36aN2/u6tOnj6tBgwbq7wkTJtT5PUpKSoK6zy7Ydea0/mPJihUrXElJSWoZH374Ybf7Jk6caH03H330kdv2aX6e559/3nrcr7/+qm77+OOPrds++OADr8swY8YM9bhmzZq5SktLrdsvuugi6zWwL5g/n3/+uSsSvG0jepvAOgkF/Xr43OFw7bXXqte//vrrXdGQl5fn6tixo1qGrKwsV//+/V2dO3d2paamuho2bFjr8QceeKB1LMRxoUWLFq5+/fo5vrbeTlq2bKm2jz333NO6rUOHDq78/HyPy6W3MzzO3MZOPPFE6zEVFRXWtp+Wlubq0aOHKzk5Wf19wQUXWI/777//rOMXlgWvid9TUlJcv/32W53XIVF9wbYQ20KRVFxc7OrVq5c6Xrdt29a1fft2dfsnn3xinUvsbaRAvffee9ZreaPPSb7ajdFuQ4cSzrdY5qefftrtdpzzmzRpYp1L8VNUVOT2mPXr17tat26t7svOzlbtCv3Yd955Rz1m8eLFqt2B24YNG2Y99/LLL1e3oR38559/Oi6bPodj3bZv3956bfyNtgE89NBD1u2ffvqpug3bUJs2bazrN2xjsGnTJvV6aPvg92h49tlnreXFNoT1jDY4/n7qqafcHjtmzBi39hTaV1j27777zuM1D37Qxhs0aJD6TvRtzzzzjNft2VOb/4cffrAeN3r0aLX+9PKgrYXfsd3PmTPHetz555+vbkdbDW02tN10m11/b0QUemy/sf0WaWVlZercbvYHoA9V93GhfTR79my/zj/e2k51OcdVVla69XNFwpVXXmm1a7XPPvvMWt7ddtvNuh1tF337smXL3M7B+jxrPz///PPPan3p+9DOjTT2L7F/KVIY+KOQcgqsoGNZH1C3bt1q3b5t2zbXNddcow7auAjDhe95553nWrVqVUCBP/OAjROf2fGA5Xn55ZfVcxo1auQ6/vjjXRs2bLBeCxeyOKk0btzY1apVK9f999/vuvDCC/3qtDjooIOs933hhRfUCRFwQfbjjz+6Fi5caD323Xffde2zzz6uzMxMdXGHoMD333/veMJ+6623XEcccYQrIyNDfUbz83355ZeufffdV10A6pMT3ueMM85QJzXc3rNnT9err77qtqz2dVZYWOg6+eSTXZ06dVLLg+BEt27dXPfcc4/VUaK/S/sP3tdTA2PSpEmuo48+WjUi8JpYlieffNJVXl5ea1luvfVW1//+9z/VmMG6R0AHDZ9AO4/0cuLC2HTppZdaHSkFBQVujzVfE98b1jduR6cILrybNm2q/j7ttNNcvtxyyy3qseecc47b7WbgzxsEJdHwQqcOtlE0/GbNmmXd7893ZX42dBZgmbBO8ZxAAn/+fjcrV650HXXUUWobRcP022+/dQz85ebmqufuvvvuattEJ8dNN91kfR/Y19Ephudh34O1a9dat+Ezmp0luA3HCU/wXB3QMvevsWPHWt8F9pedO3e6rrrqKnXswfrEvoN98v333/f42ggK69dAwNlssH311Ve1Hq8b0k888YS1nZnHBJN+XXPd6e0KP04dVPbtzFtQ/5tvvrFe66efflK3vfjii9ZtCF6bnZfYbhBsxHeOhjFu69u3r8fXJyJ3bAuxLRTpttA///xjBRQuvvhi15YtW6zOjoEDB1qv6dSuRrsVg6AQ4MF5HW0gtCvNc51T4A9t6CuuuCKsbWi9L5nnR2/XBjh3jhgxQrUjDjvsMKsz7ZRTTrEei/fSA3luu+0267Pce++9qn2F9go+D14H69EbtHNwDYPXQoDOtGbNGvVeZmebPfB33XXXqduxDtetW6duO/30063OP93OM4NdaKug7a07BW+44QaPy3fXXXe5Nm/erH7H9qdfGz8zZ85Ut2PbwDUKbsPnxmfW2x22qX///dftNffff3913yuvvOKzww6di6aDDz5Y3T58+HD196hRo9Tr4ftCO7hr166us846ywpeO9HtEmxrJnwe/Zk0vd6wfvX+h2tRp6Cl+T3p60msOx2M9jR4C8zrIm/QlsJj8Jmx3tHWQpsLt+kBW3pAIX5eeukldRv2CX0b2nREFB5sv7H9Fun2GwJu+viOvlE9uAOD2LEMuH3vvfe22kn2dlEwgT9v5zizvYf+DwwsQ1sAr+3U/sJyoT2A8z36OtGfhf5Ksz/rl19+cQ0ZMkTdh8egLTB+/Hiv68UciL906VJ1G9aP7g8yP8fVV1+t/m7Xrl2t1/HWJ+dP4I/9S87YvxRfGPijiDWWcFGnR4rgwhejTXE7LphxQsFJQAdf9EVqXQN/uHjH65qZh+eee671WiNHjrRu79Kli+rsQPaOr5P0vHnzrOeddNJJXteJOZIWARCMWrFnopknbJzgkR2EdfLggw+6fT7chxMaRqziwn/JkiVWkESPltYdAQ888IDHdbZjxw71N0bz4iSNAIh+D3Sa6BOoHqWsMzj16BinBgb+150fGHlrrvPLLrus1rLgu8EymyOh33zzzYAbS2bj5O+//7a2L71eEAD0tn3C6tWrrcfr7RDrxleHj9kBgs6FQAN/CArpx+A71aOxsQ0uWLDA7+/K/Gz4rrBusS2gkRhM4M/bd4PGHToRcRuCbHvttZdqMKKz0GyEorMKy6vXKZZFr1sEWnXjVTfqsNzYrxCcx9+DBw92azxjoIBelkWLFnn8XGis4zFnn312rVHx2H4BwUf8jWUeMGCAGmGPBq23bEV0Tpnrfdq0abU68Ex6tDb21+XLl7u8CUXgD9sv1iG2D3SoYbSbhv0Pj0Gnmr6YQAejfv1HHnlErWudUYCOXA336cfpTkki8o5todrYFgpvWwjuuOMO6zUwoEifW+fOnWs9xqldrTtM8IPgH9qg+B3tVR0gcQr8RaINHWjgD58X5zIEWI499lhrFDjO9xioA3/99Zf1/jrAeNxxx6m/0RZAe0UP3kFbHAOwPNGDkvDZdbvGzlvgD4FG3I62i2ZmamJZAeduHSzF9oL1jd/xfD2Yyh9mB+P8+fOt2zHoTXcw6m0HP3feeWet10Dnpr2dZTdlypRabTYEQvU1CjoTca2n3xPXR1jveuCdvp5zgg5FPeAPAyLRhvcE10n6PXWQ15NQBf4QsMV+gDbwG2+8YbW70HmoH/Poo4/WaqNiu0XnMrJz9eOQEQp4Dd2GxuOJKDzYfquN7bfwtt90fwmCYhic7Kk/SWf9RTLwh3M0Bqfgx1PgT1dlwg/aj2hH4nm6/wKVrvR5GM/TlQ3Q3vIW/MMAcf26SKIAtO3QnsNAM9z+4YcfqtvxnvgbA4dCHfgD9i+xfyneMfBHESmPgJMisoI0HLztndoY4aizdTDqNhSBP7yePkmeeuqpVgAFdu3aZQUrzjzzTOvkp8vFeDtJ40JTv6e9tI8J76E70/H+uHDDqGIENcz3ME/YWIe6YwAXgObnQ9BSX0DiPozqxu0I8ugLf126Eu+rSwTa1xkCsOYFvxmoMNPmPZX6dGpgYBSPfiyCVYBRyLgNJ3sd/NDLgpM+Rkrjs+qAlx4BDLfffru6qEeQyBs0kHRHExoe9u8HWYi+An+AQKp+Dn7M0jze6A4yc/u2NzLsP1h/+L50Q0sHaRF80R0u+D4C+a7MfU9v8+botEACf96+GzN77rXXXqt1m26E6vWJ5UHHC2C59OPwHA2vrfdN/I/vUz/HhBHbuB/BZ090hxleA+sY61R/RzoT9oQTTqhV+gyBRW9lNNDxh1F/5veIz4btEx1/JmQZozGL0X/6e9KfB6MGcRtG3WtmZxGOm9ju9W1Y/75KfeoRibojED84jqGTCXTGg7m9YNvQj8XIfGRC67/vvvtu63Fvv/22dfvUqVM9LgcR1WBbyB3bQuFvCwHal7rzQ/9g8IbJ3q4zS6br0uZoV+lBRvp8YA/8mW1oHQBCGxrt/VC1oYMJ/OE8qgNBOM/h3K2DePrz6cAV2uL20vB//PGHug0BF91+x3nQE2Rk4THeBlp5C/zpdajbfPY2FcpaaWhH6GXS1zhmG9cXfGfo2MNzEUS0MwNO+trCqVyqDh6iveqNLluu27g6+w7bOb6b6dOnq7/RttPBVQRPkb2KZfXE3Bb1D77/m2++2br+AASt8b3gft0e08E/vA86WXGbDozby6AhwBhoqU9kIeA99XWJzgoBtKH0beY2hX1M3462mM6WxI/Zjtf7pFnulYhCi+03d2y/hb/9ps/r9gx5eO6556zzwRdffBGywJ+3c5x5jjUHzOB8ZG9/4b11GxJ9nbrNgPYgBvuAzmq/5JJL1LkXP7pfFpl/3mBQEB6HCgyoBID3OuSQQ6wBXBgIgyx+vQw6Sz7UgT/2L7F/Kd4lR2YmQapvSktL5e+//1aTlepJaA866CDr/n///Vf9j4nNTznlFPU7JrPv0aOH+n369OkhWY6+fftKv379rGWATZs2qf+XL18uJSUl6vczzzxT/d+qVSs5/PDDfb5uVV99FUwG68n8+fOlqKhI/X722WdLcnKymqz39NNPV7etWrWq1sTJV111lWRmZqrfU1JS3O677rrr1Gvo+/755x/1+7x586Rhw4ZqWW688UZ1G953zpw5jsuF18AkvnvuuadaHjxPT+q7fv16CYb+To877jhp2rSp+l1Puov1NWPGDLfHn3TSSdKkSRP1WTt37uz23cBjjz0mixYtknHjxnl930aNGskZZ5yhfv/888+lrKxMPvroI/V3t27d5OCDD/Zr+fW26ulvT/Ly8tT/jRs39vgYPXm0/sE6x7ZRWFio7r/vvvvUd5CWlmZt+9OmTQvqu8L2q7d5+/bjL2/fDZZb09vxkUceKc2bN3d7Db1t4liAZcdy9+/f37pffz547bXXpH379tZ7PP3009K9e/day6Un39br3Mmpp56qHldQUCA///yzjB07VrZt26bWHfZBOPHEE9X/99xzj+yxxx4ybNgweemll6RNmzYeXzcrK0t9pgcffFAdq7Bu8dnGjx8vxx57rEyYMEE9bvbs2Wo/xSTdU6ZMkZtuuknWrl0rhx56qNqeR48erR6H7cBu69at6ri5YsUKNQn55Zdfrl7D27Z1yy23qM+3cOFCdUx7/fXX1e07duxQk1v7cwzzxt/HEVFtbAtVYVso/G0hwHnu/ffft/5GW+DWW2/1+hy0OfRx/qKLLlLnapxzcN6yn6tNZhv6tNNOs9rQhx12WMja0MFAu6Rjx47qd5ynce4+66yzrDZiZWWlfPXVV+rviy++2K29AjhXY5nQJtHtd0/rwN82YKA8nXfRLkIbRLvmmmtUG3fDhg2y//77u/3gNhOuNdBW+++//6Rnz57WOjDddtttVvsRsC2lp6cH1RaDCy+8UP3/xRdfuP1//vnnq++md+/e0qVLF9m5c6e0bt1ata3wnWDZcU3jCR6Dttfw4cOlRYsW1vXUM888Y11TwpVXXqmug/B+U6dOlZYtW8oTTzwhI0eOlEmTJsmuXbvU9Yq+9jShLYfrmtTUVLU+P/zwQ/U8T7Dt472w72Idr1692rruRPsS5wJP2B4jij1sv1Vh+y0y7TdP7SHd7xdq/p7jdL+ip34lvIY+h918881WmwHnxN122021PVauXKlue/fdd9Xnwc93332nbkO/hzdDhgxR/+Oc/ddff6n3OuSQQ2TfffdV7TvcPnnyZGsZcF84sH+J/UvxLjXaC0CJCZ3p6Lz+/fff5eSTT1YHZHRi//DDDxFdDh2AApzYQgUXqxpOON4uBgPlLfjg6T5czHbt2rXW7Z4CP48//rhqjOjvqm3btqqTZ926dapTJFrfTbBBBnQCfPDBBypwgqCfDq6gE8sfaHRgnej1gQ6Eu+++WwV09IW7J+gA2b59u+pA8MSp00h3qsFee+1ldaRoujMj0O/K2/YTje8GDcABAwbUur1Zs2bW71h/+fn51t/Lli1zfC39GPu6MqERiED+O++8ozqZ9GPRONfvecUVV6iOrx9//FHmzp2rGvJjxoxRHWEIonuCjj0EC/GDjio0WkeMGKG+BxzbEHRFoBHrCoFQBJ4RxMzJyZHvv/9eNVKxnSBYrTvETNhezU5bf/Tp08ft7/POO08NHgB0PIHuBMX+gWVFg3vz5s3Wc3bffXd1DMG6Q0eneZ/9cUTkP7aFgse2UHAGDRpk/Y7BNoG0ffF4BA/t23CoBdKG1h1hGEyjeQs4OW03OLe+/fbb6vyMczUCS+ZgIJPToBy0uzzRbQxvbUBvcH5Gm8ff8675/Q4cOFD9jwCsvfNMB2Vh8eLFalAersvQwffTTz+pc74dthVsAwhcma8fTFsMLrjgAtVeWrBggXpPvYy6bY7OUrS/0G7HfXgcfkcH5JdffmkNynSC9pYeqInnXXvttWoA1h9//CG5ubmqHYtrUBg8eLC6RsJ3j+Dnc889J2+99Za677LLLlOD7uzQbsMALH8hUInBphragLiGwLKhXYX2l26LgdP3jTYYOkvtj2vXrp1qu2GQF7AtRhR+bL8Fj+23wGDwCQJxGHSu+wm0WbNmuT0uVPw9x4WiX0nDQB+c45yC7E6DjHQgDwPe0U5Cu0DfhvM22jM473/zzTfqdpz3zfNwKLF/if1L8Y4ZfxS+jSs5WWXS/O9//1N/o5NdZ4WhAxyQ8YQOcZg5c6a6OLVf2IYLOuV1Zp1eBoxK0Zk7vjotDjzwQOu5r7zyinUfOv2//vprlYGDx+FEAQhC4MINF+Pffvut1ai0nwC9jX6236fXI0Yb/fLLLyrAhB9kOiHTCCdEb4EoZGJhFA5G0JijfDVkZOrvyVfgRy8LlgMX3fDZZ59Zy+2pA8GTO+64QwVncJHuC0Zo65FWN9xwg8r6w/bnFFyxw2fD49CphHWABha2jeLiYtVpgdfyRmemIVgYCHPbOOaYY9RoZP39IQPurrvuCui7Ctfoeafl1vRoLewzCN45bQ9Yr6+++qr12SZOnCj/93//Z2WD4n6sZzR08bmw/OiUQeeNCa+PYBs4ZQOadKcStkW9b+uR/Xp0Pz4HgnK//fab2l/0qEbdsWKHzimM2kbnjQ4ColNHdxhhHzT/x/Ji+8d2iE4sdNzojsH7778/ZA1pZIuaWcPIaNB0gx7bF2CbxjoB3UjW96PDT+9rCIJiXZeXl6vjNqAhjQwIIgoM20JsC0WqLRQMLI9uN+A8qc/VaJM89dRTcv311zs+D4EUHSTU51mcH3GOD1UbGpAJZlZhQBBl1KhRAbWBkBWH5UV7Dlly9sFAur2i17deBxi0iPP1pZde6vH9dHtED7QJlD4/Y33rKg76GgHBOX+uh3Cur56+w/rR5/8///xTrWsE/VAdA+01p6BfIHR711dbDAEqHZzDgCu9rvWAOgQQ8T0jaIeOPVwHHn300dZye4IBcXrkP+D19AAztMn0tYtujyGYiPfCenj44YfVbWiP4frLzKCsCwQV0XbScB2kByEiKIj36tChgzVYC20rtLHQ1tIByqFDh6oBm3qbMNtq2ObRhgPzfiIKH7bf2H6LRPsN/SD6vITMez2wG30h+v333ntv9RNpvvqVcE7Xj3n++eet7Hb0p2CgOs59egAZsvrRrtJtLPSPPPTQQx6DfvYMPt2votuPOhtQryNUlwtXhiSwf6kK+5fiVLRrjVJicZpDbd26ddbk7aeccoq6DbWwMXcEbktNTXX16tXLmrQcNbJRFzoUc/yZ85eZda21kSNHWrd17dpVTSqv513zNREv3ksvC34wrwkmnNXzkOka24FOiGyvze30+bRFixZZtbmx3KjXjffA/GLe1tmdd95pvSbqbmNSYj0vi7l+MM+dvg3zNWL+MdQ3d1pe/I/vUs8vpud39DQhsl4Wc7sxvy9/J0R2+n7xc+SRR/q1ff7vf/9Tt2EbnTNnjrptypQpah3a5ztzcsstt9San8Vcfm+H2UcffdR6DLZ7zL2iv4dAvyuneXC88TbHn7fvBnXZBwwYYM0vo/ddTHBtvj/mGtJzq+BxmHcI873o+Wz0tqz3D2wzmFPnqquusr6jvLw8azkwj56eP8UXLKM53x32OXOelPPOO09tq1if++yzj7UPYd3iuU5+/fVXq8Z/t27d1DrQc7jgM+n5AVHr3zwuYO49c5/Xnw1zz2j6dn+/O5Nev1gmHMPMz6zfA58dNfRxO74nLJOeTxXzhmr4DHqeAcyTpCcrx76Az09E/mFbiG2haLWFfJ1TnNrVV1xxhfUczFmDtqw+L+o5T+xz/MGNN97o1obG59Xn+FC1od944w3rMZhTDudtff7y1s61wzxz5nnYPlewngsXP5iTB20bfY73NmcOzvm67bts2TK3+3B+1etFvzbaJrjtm2++UY/BXLw43+I+rHOsf/3YN998M+j5aDR9/YW2C+Y0RDte/zjNl+xP23X//fdX97/yyis+3x/zKprrHXMga0uXLrX2E3z35vzGTp9dw7LjMU2aNFFtMbPto+ebtM+NhMfiulO3VfXP448/7ngdYb/e8kU/F++Dtq+eP9Cc4xB++eUXa/tFG0t/92h7mfNMn3POOVb7Dm02vdyY20jP9U5Eocf2G9tvkW6/lZaWuo4++mjrvXBeQJ+JnrcOy2KeH0Ixx5+3c5xTe8/+GuZnuvbaa92WHedatAW/++47t/nx9Hy76K9s06aNX30f6JfR50n84JyvjRs3zuP5PNRz/OllYf8S+5fiFTP+KOyQJaJHsmBEJLJqkGmHUSwYeYsSOkuWLFEZNChTh1GvTmng4fDoo4+qOSDw3igdhOxEZPGAzsbyBKMdkJZ/7733qhE4GImJOU+Q2YOsM50ZhZKRKDuIUS4o2YL3OeCAA9QoZ8w7URdI+cf6QjkcjHDFusUoIYwGxQgaT+688041agUp8RgFi3JHehS06YQTTlAlWlF2EqOtUYpHz0tnh3ldMJL4qKOOUllcyE7DKCfMp6HnHQsnPTeNZmZ4eYKyT8hGA4z61eUB8P3cfvvt6neU2fRWf/ycc85R/2NkL0bvBjoSDCVKMVoKc7KhjAFGt6NUo54zx9/vKlKwjjEaHaPXkCWG0ffYvu3ZYMgEwD6ObAGULcI+js+I0euPPPKIynhDFp0ebf3CCy+ofQcZBsjexIhyjALXdFaeU2kup2U0sz31fDLa8ccfr0aQYdlR6hPHI8z7h2w4TyPbkI2I7wtZtBiRh+fhNbHdY+S2zsLECHOMYsN3hM+BUhpYFzgGIosB3yM+G2rFm6W4goV1iZFv2DZQ/hXZqth+MG+TzpTAcmK0OL4LHFtxnMJIfBy77PNB4TvDPozjGUbr4bWxXjjCnKhu2BZiWygSbaFgoMoAMu3RBkLWGc5RaOOiBKe3OfvQPkKpRJSlQoYTsrr0fHq++NuGRjltnLuQpYY2Es5PuD9QaBPo8zuuO+znNLTJsSzIYkN23MaNG1UZdrTh7SW1TTjn6+sGlLM04ZyMz4S2j4bXxm26XCaywJC9hjYflg/rH+U2P/nkE9X+ris9+h7xYFQ7QHtW/9jnGPcHrmP0vEB6fm1fcy7qsmUY1a/bzIBrC7TV0R5EW2nNmjXqugHXZtiuPEHVCMzvh/YMrk2QbYn2FuYi0iU8AX8jYw7tGKxbvD7WLao36AxEtLFDMQ0F2pBoq2OZsJ2i3Yf3RbUXbFcathW0qXAf2ljY7rFNo+1lVvPAtQGeh7Yathe8LvYDtOXCmdFARLWx/cb2Wzjbb8hUx3kB5yaUpsZ5QZfivPrqq1VbyVu1p2h78cUXVeUGnF/RR4JlR7tOZ4ahyhP6cVChC30vqPCGvle0y7yd6wHnblRt0Mzf0V9nZguGa34/c1nYv8T+pXiVhOhftBeCKFowAS8CfHqeCpTnQ8kY3I4Ag04dJ/IGwSB0oiAAhIt/Ci10XKHzA51UCLj5mneRiIj8x7YQUfAw8AsBHQzww4CmRIYOPgReeY1ERBR9bL8REZEvHDJG9Rqy5TDa9ogjjlDZbRjpiwYU5mNAdg+RP3TWGuaMo9DD3C86GM+gHxFRaLEtRBQ8ZA8igwtz1PkzT3i8QjUPzOGDjDPML0xERNHF9hsREfnCjD+q1/777z+57rrrZN68eWqCdZSdwUSx99xzj1X2kYiIiChRsS1EREREFF/YfiMiIl8Y+CMiIiIiIiIiIiIiIiJKAHFV6vPPP/9U82dhgl1MromJ2H2ZOHGimnMBk2x369ZN3n///VqPwWSkmHw0MzNT9ttvPzX5OREREVGiYVuKiIiIiG0pIiIiSmxxFfgrKCiQfv36qUCdP3JycuT444+Xww8/XGbPni033nijXHbZZfLbb79Zj/niiy9k5MiRaq4CzM2A1x82bJhs3rw5jJ+EiIiIKPLYliIiIiJiW4qIiIgSW9yW+kTG33fffSennHKKx8fcdtttMmrUKDV/m3b22WdLbm6ujB49Wv2NDL99991XXn75ZfV3ZWWldOzYUc37dvvtt0fgkxARERFFHttSRERERGxLERERUeJJlQQ2depUGTp0qNttyOZD5h+UlpbKjBkz5I477rDuT05OVs/Bcz0pKSlRPxqChdu3b5cWLVqoTjQiIiJKPBgrtXPnTlVyHO2F+oBtKSIiIgoVtqWqsF+KiIiIwt2WSujA38aNG6VNmzZut+Hv/Px8KSoqkh07dkhFRYXjYxYtWuTxdR977DF54IEHwrbcREREFLvWrFkju+22m9QHbEsRERFRqLEtxX4pIiIiCm9bKqEDf+GCDEHMC6jl5eXJ7rvvrlZ4dnZ2VJeN4lRZmciqVUg5FUkN8W5ZXo60VJE99hBJSwvta1NsqCwT2bVKJClZJImH9bBzlYu4KkUa7SGSzH2qPsHAIZQDb9y4cbQXJe6xLUUhx7YU1QXbUpHFtlS9xbZU6LAtRSHHthTVBdtSkcW2VL2VH0C/VEL3ELdt21Y2bdrkdhv+RnAuKytLUlJS1I/TY/BcTzIyMtSPHV6XgT8KuoHVqBE2rtAH5/DaKE2LoDQDf4nbwEpuJJKcwUBUpNZ3ZYlIo2yu73qqPpX1ZluK4gbbUlQXbEtFFttS9R7bUuyXohjEthTVBdtSkcW2VL2X5Ee/VEJPUHPAAQfIuHHj3G77/fff1e2Qnp4uAwcOdHsM5uvD3/oxRERERPUV21JEREREbEsRERFRfImrjL9du3bJsmXLrL9zcnJk9uzZ0rx5c1VqE6UO1q1bJx9++KG6/6qrrpKXX35Zbr31Vrnkkktk/Pjx8uWXX8qoUaOs10DJzosuukgGDRokgwcPlueff14KCgpkxIgRIZlssQSZVkT+jKyqrJQ0l0tSuLaIiChM2JaihMW2FBERRQDbUpSw2JYiIkoocRX4mz59uhx++OHW33qePQTu3n//fdmwYYOsXr3aur9z584qyHfTTTfJCy+8oCY8fPvtt2XYsGHWY4YPHy5btmyRe++9VzZu3Cj9+/eX0aNHS5s2beq8vOvXr1d1V4l8crnUXHxJpaWyW3a2NEpP50ojIqKQY1uKEhbbUkREFAFsS1HCYluKiCihJLmQlkZ1guBekyZNJC8vz5rjr7y8XJYuXSotWrTgvH/kW2WluEpLZcuOHVK4a5d0b9ZMUpKTQzvHX+fOnOMvoSdRzuEcfxGf468z5/irZ5zO9xS+dcu2FAWEbSmqC7alIottqXqLbanIrlu2pSggbEtRXbAtFVlsS9Vb+QH0S8VVxl88QQMLGjduLJmZmdFeHIp1lZUiycnSqnlzWblrl5RVVoYu8EdERBSH2JaigLAtRURExLYUBY9tKSKihMLIQpglJSWF+y0o0bYXbjNERETu50YitqWIiIiC72cgYluKiKheYeAvgeXk5Kg5EXv16iV9+/aVgoICdfuQIUOkX79+6vYHH3zQ8bmYM/GWW24J+r1btmyp/p84caKcccYZ6vfXX39dvvjiC4kH/nz+77//XpYsWWL9fdlll8ny5csjsHREREQUCWxLBY9tKSIiImJbim0pIiKKDpb6TGAXX3yxPPzww3LIIYfI9u3bJSMjQ93+888/qxqwKKF18MEHy4knnigDBgwI+PUrKiokJSXF78dfddVVEm2VlZWSXF1C0/w9GAj8paamyp577qn+fvvtt0O2nERERBR9bEvVxrYUERERsS0VPLaliIgoEpjxl6Dmz58vaWlpKugHzZs3V0Eq0BM/lpWVqR9d9gEBQQSxBg0aJJMmTfLYAXb11VfL4MGD5fHHH5cxY8ZI//79pU+fPjJy5EhxuVwel+n++++Xl19+Wf1+2GGHyW233abeC8/F8sKmTZtUliJuu+OOO6zMQbtHHnlEZTHuvffe8txzz6nbPC1LixYt5Nprr1WPR4Ye/j/77LNVxiOyIG+++WbZd999VRbkJ5984hjgw+dFcPT444+X3Nxc+fvvv+XHH3+U66+/Xr3n5s2b1WeaN2+ees5HH32k3gfL8tRTT6nbVq5cqd7joosukr322kuGDx/udX0RERFR9LAtxbYUERERsS3FfikiIopHDPwlqKVLl0qjRo1UNt8+++wjjz76qNv9Bx54oLRu3VqGDh2qAlfFxcUqODZu3DiZOnWqLF682ONrb9u2TQW+EFy7/PLLVWBszpw56jnfffed38uIwOT06dPV6zz77LPqtgceeEBOPvlkFUDr2rWr4/N++eUXGT9+vHou3heBtKKiIo/LgmzHY489VubOnSuZmZmycOFCufPOO2XRokUq0NeuXTv5999/Zdq0afLkk0+qz2c69NBD1eedNWuWDBs2TF555RXZb7/95KSTTpIXX3xRZs+erdaltm7dOhXk/OOPP9QyfvbZZzJjxgx1H94bAc8FCxaoIOfkyZP9Xl9EREQUOWxLsS1FREREbEs5Yb8UERHFOpb6jID7Pv9X1u8oDOlrtm/WQB44e1+P96OMJ7L2dFDqmGOOUVltRx11lLp/ypQpsnPnTjX/HoJseDyy/Tp27KjuP+uss2T16tWOr43nIEsQwbUePXpIp06d1O3nnXeees/TTjvNr89w6qmnqv8HDhxoZdphue655x71OzLibr/99lrPGzt2rIwYMcIqXYpsRnxOT8uSlZWlMvU0fE5kCuosQXz+jz/+WP2dl5cnK1ascHs/rIczzzxTBeoQYETQzxsEEY888ki1XHp9IcCHgCaWEZmGgAxCZAHqrEwiIiJyxraUM7al2JYiIiLyB9tSbEuxX4qIqH5hxl+C6tChgyqjiUAeAmTHHXecCo6ZGjdurAJUo0ePVn/rkp++NGjQICTLqAN3mCcQ8wVCOEpf2pfX/Bu11d944w21bvCDQBwCpCaU87z11ltVxuDzzz8vJSUldf7M9s9NREREsYVtqRpsSxERERHbUsFjW4qIiCKNGX8R4C0zL1wQvMK8czt27JAmTZrIn3/+KVdeeaXKaCstLZVWrVqpANZvv/0mN910k/Ts2VPNf7d27Vpp27atfPXVVz4z25C9huesWrVKBRhR0vKSSy6p03KjBCneG8E2/O8E5UmfeeYZlYWHQBpKeQa7LEcffbS8+uqrcvDBB6tAHLL/MP+eKT8/X3X+ISj54YcfugVOkTVph/kAMW8g1j0ad99++628+eabQa0PIiIiYlsqEGxLERERkR37pdiWYr8UEVH9woy/BJWamqrm9RsyZIgqa9m9e3c54YQTJDc3V813h9tQYhPz1+F2zH2H+eqQAXjAAQeocpi+oIQmGg4oYanf45RTTqnTct93333yzTffSN++fWX+/PmSnZ1d6zHIXjzssMPU3IWYn/Cjjz4KelkwLyDKg6LsZp8+fVQQ1J51iGXCXIkIpupSqHD22WfLQw89pJYBQVatffv26jlY91jHKFmKZSUiIqL4wbYU21JERETEthT7pYiIKB4lucJRW7GeQUYYsuqQTacbBMXFxZKTkyOdO3dWQTXyD9ZbWlqayr5Dxt8XX3whX3/9deKvvspKkdJSKS4tlZzVq6VzdrZkpoYoIbesTATlSTt3FklLC81rUmypLBPZlSOSnCGSzO84Iuu7skSkUWeu73rG6XxP4Vu3bEsFh20ptqUoCGxLRRbbUvUW21KRXbdsSwWHbSm2pSgIbEtFFttS9VZ+AP1SLPVJMQVz7J1zzjlq7jtsxO+99160F4mIiIgobrAtRURERMS2FBER1W8M/FFMwVyDs2bNivZiEBEREcUltqWIiIiI2JYiIqL6jXP8ERERERERERERERERESUABv6IiIiIiIiIiIiIiIiIEgADf0REREREREREREREREQJgIE/IiIiIiIiIiIiIiIiogTAwF+kVFSIlJWF7gev58PatWvltNNOk65du8qgQYPkzDPPlE2bNqn7/vjjDxk4cKD07NlTevfuLV9//bW6/f7775cHH3zQ7XW++eYbOeOMM2TlypXqdWDixInStGlTGTBggOy5555y5JFHqteMBHM5iIiIqJ5gWypk2JYiIiKqh9iWChm2pYiIKNalRnsB6k3jas0akdLS0L1merpIx44iKSmOd7tcLjn55JPlmmuukW+//VbdNmnSJNmyZYtUVFTIBRdcID///LPsvffesnr1ahW4Q4Bw+PDhctZZZ8m9995rvdYXX3yhbrcbOnSoFTD8888/VWBx3Lhx0qdPn4A+CpYnxcPnICIiImJbim0pIiIiqgP2S/lYPeyXIiKixMKMv0iorKwK+iG4lZFR9x+8Dl4Pr+sBAnCNGjWSSy+91LrtkEMOUUG5V199VUaMGKGCfrD77rvLnXfeKc8884zstddekpSUJAsXLlT3FRYWqky+448/3utHHDJkiFx11VXy9ttv17pv48aNctJJJ0m/fv1UhuDSpUtVxuARRxwhxx13nBx00EGSn5+v/t5nn32kf//+MnbsWPXc9evXq/vxXCzvnDlz1O1lZWVy0UUXqeVFUBKBTiIiIkpQbEuxLUVERERsS3nBfikiIqIaDPxFUmqqSFpa3X/wOj4sWLBABdH8vQ9/43ZAIO3LL79UvyMrEAG5Bg0a+HxPvMbixYtr3X799dfLiSeeKP/9959MmzZN2rdvr26fMWOGChTitqysLPnhhx9k5syZMnr0aLn55pvVYz777DM57LDD1HNxX7du3dTtCEzedtttaplRvnTy5Mk+l4+IiIjiHNtSbEsRERER21IesF+KiIioCgN/VAsCf1999ZX6HQFApzKfTjxl3aHEqM48zMjIkIYNG6rfkcmng4B47q233ip9+/aVY445RgUQS0tLZd9995VPPvlE7rvvPhXs0wHIHj16SK9evVR2IrIIUV+diIiIKBawLUVERETEthQREVG0MPCXoFACc9asWY73IWBmvw/ZdLgdkFWHAN2///4rf/31lxx77LF+vefs2bOlZ8+efi+jmUWI4F5BQYFaLrwOypQi8IdSDVgGBAjPOecc+fHHH9XjsXwa5gdEPXYiIiKiUGFbioiIiIhtKfZLERFRPGLgL0ENHTpUzZv3/vvvW7ehHOa8efPk6quvlnfffVf9DmvXrpXHHntMRo4c6TZS/bLLLpOjjjrKLcjmCYJzb7zxhnqOHeYWfOedd9TvCOYhwGeHZW3Tpo2kpqaq8qLbtm1Tt69atUratm0rV155pVxwwQXWHH9ERERE4cS2FBERERHbUuyXIiKieOR7sjgKnfLyiL0OSmB+//33an69hx56SDIzM6VPnz7y4osvqgAbAoIXXXSRCsIhYw6PMef9O+uss9Qceo8++qjH9xg7dqwqs4nX2H333VVZ0N69e9d63AsvvKACgi+99JKkpaXJ559/Xusx5513npxwwgmq1OfBBx+sXg8mTpwoTz31lHpe06ZN1Zx/xcXFAawsIiIiShhsS7EtRURERGxLVWO/FBERkbMkl6eJ2chvyFZr0qSJ5OXlSXZ2troNwamcnBzp3LmzZKaliaxZg3S30K3V9HSRjh1R55LfVCKorFTbR3FpqeSsXi2ds7MlMzVEcfmyMpGSEpHOnUWwLVLiqSwT2ZUjkpwhkszvOCLru7JEpFFnru96xul8T+Fbt2xLUUDYlqK6YFsqstiWqrfYlorsumVbigLCthTVBdtSkcW2VL2VH0C/FDP+IgHBOQTpcBINleRkBv2IiIiofmBbioiIiIhtKSIiIvILA3+R7LBidh4RERER21JEREREkcZ+KSIionojOdoLQERERERERERERERERER1x8AfERERERERERERERERUQJg4I+IiIiIiIiIiIiIiIgoATDwR0RERERERERERERERJQAGPiLlMoKkcqyEP5U+HzL1NRU6d+/v/Tu3VtOPPFEyc3N9fr4iRMnyj///BPQx7r//vvl5ZdfrnX79OnT5f/+7/+8PiYSLr74Yvn555+j8t5EREQUQmxLRWVzYluKiIgoQbAtFZXVzrYUERFFQ2pU3rU+Nq4K14hUlobuNZPTRRp0FElO8fiQpk2byuzZs9XvF1xwgbzyyity1113eQ38tWzZUgYPHlznxRs0aJD6ISIiIqoztqWIiIiI2JYKAPuliIioPmPGX0RUVgX9klJEkjPq/oPXUUHESr+X4KCDDpK1a9eq36dNmyYHHHCA7LPPPnLooYfKqlWrZM2aNfL666/L448/rrIEETDcsmWLnHbaaaqxhMfPmjXL8bXx2CFDhkiXLl3k888/t4KIZ5xxRq3HPvnkkyoIWVFRIU888YTsu+++svfee8vTTz/t+Nrvvvuuur9fv35yyy23yLJly+TAAw+07h83bpz1PqNGjZIBAwaox5577rmOWYj4vAMHDlQZkNu3b/d7/REREVE0sS2lsS1FREREbEu5Y78UERGRO2b8RVJSqkhyWt1fB/E+l+9SnxqCbL///rtccskl6u9evXrJ5MmTJSUlRX788Ud5+OGH5a233pKrrrpKZfxde+216nHnnXee3HHHHSo4t3TpUjn//PPl77//rvX6y5cvVwG41atXy7Bhw+Tss892XI7HHntMFi5cKB988IGMHTtWBSJRWrSyslKOOuooOeaYY6RPnz7W4+fOnSvPPfecTJo0SWUvIlDXvHlzSUtLkyVLlsiee+4pH374oVx00UWyefNmue6669RjO3ToUCuoV1ZWJjfffLN899136jUQUMTyPPXUU36vRyIiIooytqXYliIiIiK2pWzYL0VEROSOgb8Ehjn9kL2HAFv37t1VUA527Nihsu7QMELQrVmzZo7PR3Bu/vz51t94npMTTjhBBeO6du3qcR7BN998U3r27KkyApOTk2XMmDEqQw+BOti5c6cK5pmBvwkTJsjw4cNV0A8QsNP10RHwQ1ASAcx33nlHfvnlFzniiCNU0M98rLZ48WL577//1GOgvLxczX1IRERExLYU21JEREQUeuyXYr8UERFFBwN/CUzP8VdQUKAy6l599VW5/vrr5d5775Xjjz9errjiCpk3b54KpHmC8pipqd43k4yMDJ/LgvKbc+bMUZl5bdu2VQHH++67T2XrBerMM8+U/fbbT2X8nXTSST6XD/B+KAOKYCIRERGRP9iWYluKiIiIgse2FNtSREQUHZzjrx5o2LChvPjii/LMM8+oTLf8/HwrM+7999+3Hte4cWOVeacdfvjh8tprr1l/I2MuWAjUoawmsgOROXj00UfL22+/LYWFher+lStXSl5enttzkJ33xRdfWLfr8p2NGjWSwYMHy+23324FDvfff38ZP368rFu3zu2xGrINMY/hjBkz1N8lJSWyaNGioD8PERER1R9sS7EtRURERGxLsV+KiIjiRdwF/l555RXp1KmTZGZmqmAS5ojz5LDDDpOkpKRaP8h205DtZr8fc82FhatcpLKs7j94nQANGjRI+vbtK19++aXceuutctNNN8k+++wj6enp1mNOPPFE+eyzz1R5UGQKvvTSSzJx4kSVrbfXXnvJp59+WqePj2DfbbfdprL0hgwZIqeeeqoK2KG8J+YPLC4udns8br/hhhvkoIMOUsv0+OOPW/dhHkHMR4jboXXr1iq4ie8Wy4vMRhM+J4KIeD3cP3DgwDoFMomIiOIV21JsSwHbUkRERGxLsV+K/VJERJSYklwul0viBAI3F154obz++usq6Pf888/LV199peZvQ+DHDiNxSktLrb+3bdumgj7INNPlLfH/pk2b5L333nMrXelp3jsnyKBr0qSJykzLzs5WtyGIlZOTI507d5bM9DSRwjUilTXLUmfJ6SINOookp0h9dP/996vvCIG8hFBZKVJaKsWlpZKzerV0zs6WTD9KmPqlrAwpjiKdO4ukpYXmNSm2oBN7V45IcoZIMr/jiKzvyhKRRp25vusZp/N9vGFbysC2FNtS/mJbKvGxLRX59c22VL3EthT7pRIJ+6UCwLZU4mNbKvLrm22peik/gH6puJrj79lnn5XLL79cRowYof5GAHDUqFHy7rvvqrKPds2bN3f7+/PPP5cGDRqoOeJMCPRh3rmwQXAOQTqpDOWL1tug37HHHitbt27lfH1EREQBYlvKxLYU5z4mIiJiWyp4bEuxLUVERLEqbgJ/yNzD/Gx33HGHdVtycrIMHTpUpk6d6tdrvPPOO6qsEeZpMaGcJTIGkUGGeeUefvhhadGihcfXwfxw+DEjrT6pIF39DNSF2q+//hrtRSAiIoo7bEuRxrYUERER21IUPLaliIgo1sXNHH/I8KqoqJA2bdq43Y6/N27c6PP5mAtw3rx5ctlll7ndjvn8PvzwQxk3bpw88cQT8scff6iMMryXJ4899phKqdQ/HTsim4+IiIgodrEtRURERMS2FBERESW+uMn4qytk+/Xt21cGDx7sdjsyADXcv/fee0vXrl1VFuCRRx7p+FrIOhw5cqRbxp+n4F8cTaFIMUBtL9xmiIgoBrEtRfGAbSkiIopVbEtRPGBbiogoMcRN4K9ly5aSkpIimzZtcrsdf/uan6+goEDN7/fggw/6fJ8uXbqo91q2bJnHwB/mBMSPN6mpVat2586dkpSU5PN9qZ6rrBRXaals2bFDsLWkJcdNMi4REcUJtqUoobEtRUREYca2FCU0tqWIiBJK3AT+0tPTZeDAgaok5ymnnKJuq6ysVH9fe+21Xp/71VdfqTn5zj//fJ/vs3btWtm2bZu0a9euTsuLwF92drZ6LfwQeYUsv/JyFfTbLTtbUhj4IyKiEGNbihIa21JERBRmbEtRQmNbiogoocRN4A9QXvOiiy6SQYMGqZKdzz//vMrmGzFihLr/wgsvlA4dOqg5+OzlFBAsbNGihdvtu3btkgceeEBOP/10lTW4fPlyufXWW6Vbt24ybNiwOi9v+/bt1Ygwlvskn8rKRNatk7TMTElJT+cKIyKisGBbihIW21JERBQBbEtRwmJbiogoocRV4G/48OGyZcsWuffee2Xjxo3Sv39/GT16tLRp00bdv3r1akm2ZUotXrxYJk+eLGPGjKn1eigdOmfOHPnggw8kNzdXBeqOPvpoeeihh3yW8vQHSnyG4nWoHkhJEcG2y7KwREQURmxLUcJiW4qIiCKAbSlKWGxLEREllCQX09HqLD8/X5o0aSJ5eXmqvCdRUCOrcnIwgaRIWlroX7ukRKRz59C/NsWGyjKRXTkiyRkiyfyOI7K+K0tEGnXm+q5neL7nuqUYxrYU1QXbUpHFtlS9xbYU1y3FMLalqC7YloostqXqrfwA4lDu6XFEREREREREREREREREFJcY+CMiIiIiIiIiIiIiIiJKAAz8ERERERERERERERERESUABv6IiIiIiIiIiIiIiIiIEgADf0REREREREREREREREQJgIE/IiIiIiIiIiIiIiIiogTAwB8RERERERERERERERFRAmDgj4iIiIiIiIiIiIiIiCgBMPBHRERERERERERERERElAAY+CMiIiIiIiIiIiIiIiJKAAz8ERERERERERERERERESUABv6IiIiIiIiIiIiIiIiIEgADf0REREREREREREREREQJgIE/IiIiIiIiIiIiIiIiogTAwB8RERERERERERERERFRAmDgj4iIiIiIiIiIiIiIiCgBMPBHRERERERERERERERElAAY+CMiIiIiIiIiIiIiIiJKAAz8ERERERERERERERERESUABv6IiIiIiIiIiIiIiIiIEgADf0REREREREREREREREQJgIE/IiIiIiIiIiIiIiIiogTAwB8RERERERERERERERFRAmDgj4iIiIiIiIiIiIiIiCgBMPBHRERERERERERERERElAAY+CMiIiIiIiIiIiIiIiJKAAz8ERERERERERERERERESUABv6IiIiIiIiIiIiIiIiIEgADf0REREREREREREREREQJgIE/IiIiIiIiIiIiIiIiogTAwB8RERERERERERERERFRAmDgj4iIiIiIiIiIiIiIiCgBMPBHRERERERERERERERElAAY+CMiIiIiIiIiIiIiIiJKAAz8ERERERERERERERERESUABv6IiIiIiIiIiIiIiIiIEgADf0REREREREREREREREQJgIE/IiIiIiIiIiIiIiIiogQQd4G/V155RTp16iSZmZmy3377yT///OPxse+//74kJSW5/eB5JpfLJffee6+0a9dOsrKyZOjQobJ06dIIfBIiIiKiyGNbioiIiIhtKSIiIkpccRX4++KLL2TkyJFy3333ycyZM6Vfv34ybNgw2bx5s8fnZGdny4YNG6yfVatWud3/5JNPyosvviivv/66/P3339KwYUP1msXFxRH4RERERESRw7YUEREREdtSRERElNjiKvD37LPPyuWXXy4jRoyQXr16qWBdgwYN5N133/X4HGT5tW3b1vpp06aNW7bf888/L3fffbecfPLJsvfee8uHH34o69evl++//z5Cn4qIiIgoMtiWIiIiImJbioiIiBJb3AT+SktLZcaMGaoUp5acnKz+njp1qsfn7dq1S/bYYw/p2LGjCu7Nnz/fui8nJ0c2btzo9ppNmjRRJUS9vSYRERFRvGFbioiIiIhtKSIiIkp8cRP427p1q1RUVLhl7AH+RvDOSY8ePVQ24A8//CAff/yxVFZWyoEHHihr165V9+vnBfKaUFJSIvn5+W4/RERERLGMbSkiIiIitqWIiIgo8cVN4C8YBxxwgFx44YXSv39/OfTQQ+Xbb7+VVq1ayRtvvFGn133sscdUZqD+QTYhERERUaJhW4qIiIiIbSkiIiKKL3ET+GvZsqWkpKTIpk2b3G7H35i7zx9paWkyYMAAWbZsmfpbPy/Q17zjjjskLy/P+lmzZk0Qn4iIiIgoctiWIiIiImJbioiIiBJf3AT+0tPTZeDAgTJu3DjrNpTuxN8Yje4PlAqdO3eutGvXTv3duXNnFeAzXxNlO//++2+vr5mRkSHZ2dluP0RERESxjG0pIiIiIraliIiIKPGlShwZOXKkXHTRRTJo0CAZPHiwPP/881JQUCAjRoxQ96OsZ4cOHVQpTnjwwQdl//33l27duklubq489dRTsmrVKrnsssvU/UlJSXLjjTfKww8/LN27d1eBwHvuuUfat28vp5xySlQ/KxEREVGosS1F9cmCTQXSqVmmNEhPifaiEBFRgmBbioiIiOJBXAX+hg8fLlu2bJF7771XNm7cqObuGz16tLRp00bdv3r1aklOrkli3LFjh1x++eXqsc2aNVMZg1OmTJFevXpZj7n11ltV8PCKK65QwcGDDz5YvWZmZmZUPiMRERFRuLAtRfUp6HfTz0vkoE5N5N4ju0R7cYiIKEGwLUVERETxIMnlcrmivRDxDuVBmzRpoub7Y9lPCkpZmUhODurIYjLK0L92SQlq24b+tSk2VJaJ7MoRSc4QSeZ3HJH1XVki0qgz13c9w/M91y3FT1vq+/mb5bVp66RJZqp8eV7fur8221KJjW2pyK9vtqXqJbaluG4phrFfiuqCbanIYluq3soPIA4VN3P8ERERERER+aOwrFL934hlPomIiIiIiKieYeCPiIiIiIiIiIiIiIiIKAEw8EdERERERERERERERESUABj4IyIiIiIiIiIiIiIiIkoADPwRERERERERERERERERJQAG/oiIiIiIiIiIiIiolr9X58mnszZyzRARxZHUaC8AEREREREREREREcWee39fof4/d0DbaC8KERH5iRl/RERERESUWFzRXgAiIiIiIiKi6GDgj4iIiIiIEkolI39ERERERERUTzHwR0RERERECWXzrtJoLwIRERERERFRVDDwR0RERERECeW3JdvV/+WVrPlJREREFKzcojKuPCKiOMTAHxERERERJaSWDdOivQhEREREceubeZujvQhERBQEBv6IiIiIiCih9G7TUP2fkpQU7UUhIiIiiltm8QSXi5UUiIjiBQN/RERERESUUCqre6kq2EFFREREVOc2FTDsR0QUPxj4IyIiIiKihFJR3TMV6jn+Sssr5NtpK9T/RERERPUr4y+aS0JERIFg4I+IiIiIiBJKhc74C3Hg783fF8obvy+Uj/9cGtLXJSIiIopFlczzS2jrthfI3Z/9I9t2Fkd7UYgoxBj4IyIiIiKihKJLfIY68IfOEcgrLA3p6xIRERHFIjPLjwl/ieeVX+fJv8u2yCeTOKiNKNEw8EdERERERImZ8RfiHqrS8kr1f3oqL6OIiIgo8ZnzJbtY6zPh6LL4/GqJEg+vWImIiIiIKKGEc44/SE9NCenrEhEREcWiyqoxTwoz/hIPg7lEiYuBPyIiIiIiSijhmuOvtKw64y+Fl1FERESU+FxmuI+Rv4SVnBTtJSCiUOMVKxERERERJeYcfyGuW1RaUZ3xl8aMPyIiIkp85hgqxv0Sj24qJyUx8keUaBj4IyIiIiKihFIZrow/zvFHRERE9bBNBQz8JR5+p0SJi4E/IiIiIiJKyDn+Ql/qU8/xx8soIiIiSnyVTulhlHBz/BWWlEd7UYgoxHjFSkREREREcWvl5p3y9Kh5UlpR0zWlA37lYcr4S+Ucf0RERFSPAkPq96guCYXTuLnruIKJEkxqtBeAiIiIiIgoWPd+8a9syi2SvpmtZdheWW4BP535F+rAHwe8ExERUX1gtqXY/kk8um1LRImHGX9ERERERBS3SsuqOiyKy2t6piqqe6ZKyiuloLSqPGcoVFa/rv6fiIiIKFEt2lIghUY7atLK3KguD4VecSlLfBIlKgb+iIiIiIgobqWmJKn/y4yynubcfs9OWh3WsldEROG0fnuBrNtewJVMRBEP+t3w4xKZsW5nWNtUgVi6IU/e/H0B22EhVFQ9fzURJR6W+iQiIiIioriVlprsFvhDUM6c2m/5tsKQv2eIpw4kIvJoxCsT1f+/3XM81xIRRcyyrUUxt7avfXuy+v/wPh2ke7sm0V6chFAcwsoYRBRbmPFHRERERERxKy3FPfBnD8plVgcGQ4kZf0RERJTICmK4BKRu+1Hd7CouUz9aRSXn+yNKJDxSEhERERFR/Af+Kly1ynxCRhgCf8z4IyIiIqJ49umkpW5/FxTHbrCXiALHwB8REREREcV94K+8OhpXYZt/L50Zf0REREQBqppDORbZB3lRcFKS3cMCO43sPyKKfwz8ERERERFRXCooKZOF63LdSn3aO4NaNUwL+ftWVrpkc17szX1DFOu2F5TJtzO3xnS53JxN+fLcz3OkvIIlz4iofkCJx3XbC9xuS4rduB9LUoZIdpZ7G9ks+0lE8Y+BPyIiIiIiiku/zlxj/W6V+qyOJ+y7W7b6v0FaSsjf99PJy+TCF8fLvNXbQ/7aRIns7m+Wyxt/bJS/lrt3MMeSWz+aJqNnrZFJCzdEe1GIiCLildHz5ZI3/5JF24rjIvCnqzxQ3TTMdA/8FZWw1CdRImHgj4iIiIiI4naEumbP+GuYnhK2clCFJeWCV/166oqQvzZRIluXW6L+LyyN3Wy68urRA8VlFdFeFCKiiJi8cKP6f+l2I/AXY+u+tLzmmFzBjOyQsGffM6BKlFgY+CMiIiIiorhkBvWswF91J0ZGalLYOzH+XbY5pksWEsUavb/EWoeyKa16XtDS8ugHJ3l8IaJISE1JcmtLQbKHlL+C0ugMisgvrClDyQBVaNjXI0tcU6jMXb1d7vjkbw6iijIG/oiIiIiIKC6ZMTd7xl96SnLYO4fw2mPnrONcMwnow782yF9Lq+aPpNDRu2Msl5BL14G/GMj4C0fGMhGRXWp1m8kM/Hk6TJ/20ZyoDIzIKyy1fmeAKjTsmZM851Co3PrhVJm5YqtMW7yJKzWKGPgjIiIiIiKZv2a7DH/2d9mYWxg3a8PsErfP8ZeZmqw6rUrC3Dn19I//WSWyKDFUulzyybSN8uCPOdFelIRct5Acw4G/lOqFi4UOUHZuE1EkpCXXHizlbYBGcRRKbe4sqgn8xcLxORHo7/uMA7pU/c0SqhRqMdzeqw8Y+CMiIiIiInnupzmSW1Aqv81aEzdrwyyDpzsvdGdQWkqSNEhPiUhJqlgoCUihUxTD88/FO73LJsVwyl8sLZse0EBEFIkSx2bGn9dK5lE4NGF+ZY2Bv9DQgb6M1PDNi031k3X84CYVVQz8ERERERGRbN1ZrNZCqyZZcZc9ZHZWVVb/n5KUJA3TkqWwLPxBnPJKBooSSVGU5i9KdEs2FsZFqU+Job4qZl8QUSRLfZrjmFx+tr8ixRxkFY33T0Q60JeRlpLw7dl1O4plc35N1ihRfRB3gb9XXnlFOnXqJJmZmbLffvvJP//84/Gxb731lhxyyCHSrFkz9TN06NBaj7/44ovViD7z55hjjonAJyEiIiKKPLalyFewo0mD9Pic488q9VldSjA5SRpmpMhOY4R4qJ26X2f1f0kMzAVGoVNoZPx9M30zV22IvPXnurhYlzooaWYUR0uZUXZta37V4AyKPralKNGk6Tn+jCxj3Z5y4ory8TAGDs+JlfGXVh34TdAsc3yuS95dKNd+vDjai1JvxMMAr/ogrgJ/X3zxhYwcOVLuu+8+mTlzpvTr10+GDRsmmzc7X4xNnDhRzjnnHJkwYYJMnTpVOnbsKEcffbSsW+d+wYFA34YNG6yfzz77LEKfiIiIiChy2JaqP7btLJbbPp4mq7bsDPi58XSd5pTxp/uFkPHXJDNV8opDF/hLtl3FHtm3g/q/mBliCaXQ+D7f/CM+glXxINWY2G/euiKJVUnVR8FY6Fg2M/6mLtkU1WWhKmxLUSJKTUkKqNRnNI6PpeU152Zm/IUn468iQTP+dlUPAswrCt9gQKJYFFeBv2effVYuv/xyGTFihPTq1Utef/11adCggbz77ruOj//kk0/kmmuukf79+0vPnj3l7bfflsrKShk3bpzb4zIyMqRt27bWD7IDiYiIiBIN21L1x5dTlsvsnG3y3M9zAn5uDPR1B5fxpwN/1Tdi8HrTzFRV6nN7YVmI3s997eiOktVbd8mwh0apnx27SkLyXhQ9nOMvPJo1TLV+/2lOnsSqWBr8YAb+MquPNxRdbEtRokEQbc6q7Q6BP1eMBf6MUp9e5qLDcr/223yZvXJrhJYsfun5sfUcfy//Ol8e+3ZWlJcq9IxTKUVILAyeojgK/JWWlsqMGTNUuU4tOTlZ/Y1sPn8UFhZKWVmZNG/evFZmYOvWraVHjx5y9dVXy7Zt20K+/ERERETRxLZU/NicVyR5haUhyUwL5qIrlkZRYyTyp5OWqnXixKlTSndiIONP3335NwtDsjz2d9Md8ePm1mSF3fWp56kIKD5wjr/wMBMJ+u0W+3OJxkapz5plSEuNm+6bhMW2FCWiSQs21GpDgbeqj5VRGCb2+pgFfh2fMWf19/+slNs++jtCSxa/9OCSxllp1m0T56+XRM1sDPe5fdGGAvlh1pawvT5RoOKm5bh161apqKiQNm3auN2Ovzdu3OjXa9x2223Svn17t+Ahynx++OGHKgvwiSeekD/++EOOPfZY9V6elJSUSH5+vtsPERERUSxjWyp+XPDieDn72bF1eg1dkdLfIJ7bRXD0+7otc1Ztkw8mLpGbP3Ae6Gd+Pn1Nry/uU5KTrNt2haAUp72j4IJD97Qy/kyYW5DiG7JEKfTMfahRRux3RZgd4NFbhpptsWFGTcYkRQfbUpSINuwodBxsEGulPk1P/vCfqrLwy8zVMbds8US3meNpfu+6Bv6Kw9jGu+HTJfLq+LVu2an1VbzM8bduW4Es3RC7VSjqqt60HB9//HH5/PPPVXZfZmamdfvZZ59t/d63b1/Ze++9pWvXrupxRx55pONrPfbYY/LAAw9EZLmJiIiIYgHbUpHtGK9r1l2gGX/mBXEsZfzlFVRlPnrO+Kv9e02pzyTJTAtdcMEeAzjrwC61boPVW3ZKYUm5NGAnfdxixl/o/TJnq0xcnKt+z0pLlqKy2DnO2OljiFlmM1piYRkodNiWoniZL9l+u7fnRNMLo+bKcfvsHpcBh1igA6dNG2ZIIlu5tdj6fVdJhWSlp4R94FBih1J9i5FDhE+XvDpR/f/bPcf7dZ2+assu6dS6scSL2B9mV61ly5aSkpIimza5T2iNvzEvnzdPP/20amCNGTNGBfa86dKli3qvZcuWeXzMHXfcIXl5edbPmjVrAvw0RERERJHFtlR8KAnRCNEkK/Dn+aqrorJS/lm6WT3G7FyOpQu1x76rmWdk0bod3gOW1amKet6XVAT+Qloaz1VrHWfYXv+w3u3VdzhvddVcOaGUX1gqn01epr43Cq/CkpoMUcwVSXX3wu8118xZ6clhHXFfV/oYEgsZf2XGsTlWOtrrM7alKBGZ8+W5B/4kpjTKrDe5KxGX3SA9obP+7v9hhfV7gdHGC6WtO2umaSj3VieX4taXU5bLlW/8KX8a5ZFjXdxcxqSnp8vAgQNVSU6tsrJS/X3AAQd4fN6TTz4pDz30kIwePVoGDRrk833Wrl2r5vhr166dx8dkZGRIdna22w+FVn5RqTz1w2zZmFtTcoCIiIiCx7ZUfCgsKQvJ69SU+vT8mA8mLJF7Pv9Xfpq+yr20U4zU+rQHLZduqF1ePyUlySHjr+p/VNw8tU9r9Xu7xnXvzLCvy6Tq4N/ZB3W1buvatuq6AOv13s//Dek8Io98O1Pen7DYsawVhafUZ+dWmYK4CwMuoYVM3KIYDvzpAQWxkG1ndh6aAx0oOtiWokSks5ztAx68tWGicThCW1VXtNCaN8pQy2kuqxnIJM9QnULDYDlznr9EtrM4PIG/R35eaf1uXlfVV/GWeVvmR5vvr0VVyWgLHQajxqq4CfzByJEj5a233pIPPvhAFi5cKFdffbUUFBTIiBEj1P0XXnihysbTMGffPffcI++++6506tRJzQWIn127dqn78f///d//ybRp02TlypUqiHjyySdLt27dZNiwYVH7nCTy5V/LZeycdfL8z3O5OoiIiEKEbanYV1BccxEemlKfni8856+tumhZvXWXlBnzW8dKUklxmfuFeQOnsjzGsjrN8acDft1bNqjz8tjXpc6qHHFET+u2Tq1qSr/8vXSz5BXWjP6tq43Vc/CYHTUk8s64RTIpxCNvi0qrLv6bZFVlF5SVx8hOkUCBv+JYLvVZfQyJhUCbGXxkZ3ZsYFsqPLbkF8n8NaHPlqcAM/6MgIW3Q2AoBzb5q6KiUtJtlRZQovKYh3+RB76cUfM4Y8FLbG1JqjFtSU1FPbSZt+8qsf4+65nfE3ZV7SqukGnL80JeeWBHQVlAQaREFyvXk/4q8uP6qqC46jtOj6NyIHGVJz18+HDZsmWL3HvvvSqA179/f5XJ16ZNG3X/6tWrJTm5ZuW/9tprUlpaKmeccYbb69x3331y//33q9Khc+bMUYHE3Nxcad++vRx99NEqQxBZfRQ9eiJUnqSJiAK/aP5r0UY5ed9OVqc0kca2VOwrLC0Pccafl1Kf1ReluNjfsas0qp05TuwBLqeye04X1tYcf0lJVgA0FP339tXidIjt0aGp29+oXhGqeVP0508xrnfqO3TuoewO/NbL99wc/iosreoozK4uK4btLCOE80XWN/ZjCub4yzU6yGKNPm7GQsafe6nPqC4KVWNbKjwuemmCOqb/fOexkhZHnaqJwDy2lPs9x59ElCpLX+mSBhkYOFITzCsuq2orTjWCWOZy7ywqk4y08M7nFq+mL99i/Y5+gzP27yIf/rFE/R3KgWux5vcF2+SvpXkyZM+mcteJnUP2umbQnKU+JeYqyfhz3YmSt57sKi6TtdsL1O/xdEyJq8AfXHvtterHycSJVRMyasji8yYrK0t+++23kC4fhRb7rImIAnPXp/+oCYd3a9FIBnVtxdVHtbAtVd8y/jw/RnfuoINtQXX2Xyx1LuvAX7tmDWTDjkIpcgiKus3xV/1hzYw/wH+hyNyplfHn8JjMtBTp0b6pLF6fq/7euKNIenZoJqFQUd2hkGqUN63vSsvDM5JfZ/w11hl/LNlUJ8/8tjrOSn1WxmTGH+f3jB1sS4UWzpl6f9ucWyQdWjQM8TuQN2agzJzjb1uh5wEakT466jZrqi0ovH57odcMRkwj1DI7MwJLGL9tqCfO30/9f96Q7jJ39XaZlbNVEhmCfrBgfVUQJ1TMtiLbjTVioS0VisG3ZkastwBhrOEwGiIiogSyZmt1OevqMgREVF/n+NOZbi6fHcoIkCFgFWsjM/WI7j3bNVH/T11cM5rbqVNcL7W+CRl/6v/kpJDM0VZrjj+HEWooQbU5r8j6O68odCOmy6uDEcz4q2FWBwllpmpRaYXKSkuvDrKyA6dufp+/3bHUZ6xkF3vqpCqLtYy/OOk8IwrU3FU1x4i3xy3kCowws41kHma2F3kJ/EX4+K2rVCRXD+ry+thK94w/coasNAwU7N+5pXVbhlFKNRay3sMpt7A8bOdrpyol9Y0egBkrlfwWrt3hNtDVzmmAqcncH+wlh2NZ/CwpERER+Y9tTSK30b7nPDc25POAhUNBiOZvs/pFXL4vYDB62rxYjZW++OLqC7BmjapKZc5euU3WVZdYcZz/ymUr9Vl9pYNOjZCU+vTjMQgGjjiih2RVz0eo54IIBWb81bZy807r910hypYFzPuSlZ4saVbgL7E7v8LJaQ4dBP6wP5VUT+8Qa8qql0t3NEeTWS5MH9uIEk2BMeiJgxcjzxxUYB5lvM/xJxG1s7o9lZGa4jODzb3UZ+KWrKwrZJHrdo7W2MhkwvVToui/e6Nat4U6OGe+np6+qj7TgT/7nO3RcuN7U+Sm96Z4vN/XHOruFRjipz3GwB8RERERJbSZy7eq8hyPfjtTYp2viw5/6Ww0bx3FZtmkMqNkYqxk4SDrClo0rinRNH/NdsfPYHZbWKU+dcZfmEp9mp6+6AB55qID1O/D+neUN64c4taBmbMpX6Ys3hiSjL9UzvFn+WrqCuv3rfk1mZZ1hYAURvPmF1Vtg0s21i4lRoHtx/bAHxQ73BcLHaH6uBILI/bdMnFiYHmIwlnmfLcWDWXRutyY6SiuLzy1kZyOOZ2aVbXJIh3WWL4xX/2/R6vaARwtt6C0dsYfq+DUassu3ZCn2qeoZpBiK52qB77A30s3S6Lo0ior7O9RVm7M8cfztVWhxKkdGI/X4GUM/BERERERxZ606nIc8XANVhjijD9vwSor4y85yW1kaqysJ93x17w64w9KbNlD+AzI6Ou3R3Oro6cm40/P8ReeUp+mvrs3lz67N7f+bpSZ5taZedWbk+SBL2fU6f3N0qxUpX+nFtaq2FHd4ReqwB/KXY2Zv039/cGU2M8WjlVO+01mmh4FHtoyW6FgHmNioePO7HiPpxHmRIFYsSlfzTd8wJ5tVHtkgy27n8LLUxvJ6ZDTp02jqAwS04G/Lm2yfU554Z7xx1KfpsmLNsq1b0+W696ZrAa64BrAdPHhPdzW+eqtu2Tttl3y+39r43qe2UicPs23YIl4kZTqbFJdwSXe5/grNyowTFm00eO12r/LNsfMIFpgxh8REVFCYYcwkV2qrYxNLAvVPAg648/lxwUMOkhisZThF38tU/83zqoKooGZmag75vH9IhimA36VjnP8hWKJ/H+RrIxU9f/2ghI57cnfal4hyAvBTbmF1mdw6qBDwHjUjFX1LjBglopERu8HExaH5HVLy12SkZYsd57QSf19WI9mIXnd+shpm2ycWVWqLb8w9jpkdam4WJnfyNzfY/E4TRSqMuetmmRapb3X74jfLOvnfp4jn0+uar/EC7f5kt3m+3M/fv96SX9p1aiqTRbp5oYu9d6xpeeMvxWb82udd/ILE6dcZSjMW11VOWP99kJ1TkHVD1O7Zg1k9N3HSccWDeWn6avk8tf+kEtf/UOe/vE/mZVTNRgqHkW6fWwGieqrpDhruxQFUOpzxoqtte7H9A7HP/qr3P3Zv/Lvsi0SKxj4IyIiIqKEZr+ojWUltsBWsKpjXl7nYNGlIzEq1Qw4hiI7rq625BfJsurR3Q3Sq4Jo4jAnGObgwvdrBvd0AFBXxMRg5lDMjRVInwGyDOGfpZvd5m0Mtt/hwpcmeO28eO23+fLiL/PkgS+nS31SZIzOxaj+T0PU2Yp56ZDx171NA/U3O3CC57S9Nm9Y1XG8PYRZmqFiHgtLY6DcoLn6QjUwhChS0J5AplCej+ALOlRRxjqnet7WB7+qW4Z8NI2etUbem7BYVm2pmYM2Xkry2dkP32jb4F80YIAT2noow63975jebo8pqS4pOG3J5pC3qxPF1vxi6/eFa3Mdr5EwePDwPh1q3W6WAY03kQ5Ux0uwK5z09WQsDKLyh6+SpL6+09/+W+v3YyMpfnpBiIiIiIiCgPJR8UKX3NSBo2DpjhlvQbyK6tGoCJ7peVEgBuJ+UmqU28s0An9mNg6o0crJSWp96eCeNcefWeozFFf8IQkeBvYaGHlvn+vI6TU25RVZ87HUp7mRduwqCcvrqkzS5CTJSqvKTCuIk/lJ4ifwV50RG6bvry7MwQWxsC+Zx65YWB6iQMxasVVlCvkalIKBSDhnN6zOlk8Em3JDN+9suHmqRuDU3tCVISM9SAzHv8y0FLemmD1opdtCbpnScRysCveAKW/l44/uv1ut22KpfGEo2yLhkFdUIcs2xW/mcijoVR4LZdNNuQXObU/7Naadr2BeoTGfaCxNyxA/vSBEREREREGIpca3LzqjI62O5Un1xbmni3TcrucywAXZlp3FMXVhb47QRgDm4XP2Vb9/OmmZ24UZLuQ9ZfzpUp9VGX+Bvf+WglL5Z02e222huG4NtNTQ11NXyMmPj/b5GmZnabzMpREKW43tNpSwjtGfmJWe7Di3JAWwLh2OJ80bxG7gz8zywzHy4pcnyGdRLNtnHo8Z+KN4o+ctnr9mh9fHYSASBmldcOie6u9De7WTeBcL1RP8ZWa1u3wcv/2pKBEOaNtkpae6rVd7+35MdcaNOV92LGXexAL7ecQ+x5/WKjtLhtj2w1gL4PjbnssrLK81ABDXWZ1bZoVtf39p7Br538eLZc6a+Mn8DTkd+IuxfXDSQud5u0t9LKf9c9gDheY1SUgGnIZIUOHtiooKef/992XcuHGyefNmqbRN8Dl+/PhQLR8REREFwRXAXFQUeWxLRVYMtb190hcRKXXMUtQXoLZmumXFpp1uI+235hfFVuCvumOiVXamdGrd2G1Ol207S9QcJLqzCoG/5OrAH5ZdX5fpDqFgQqj3jVkhy7cXyVun7yW7N0yp03r57KYj5cIXJ6jOp0AvBN8dX3vOupd+mScnDNzD7bYGZuAvDrOC5q7eLlMXb5TLh+5lzU/pj21hCvxh/8H2g44hbEZFDPwFvy4djkENM6r2qeIYzKQ0Bx2gJBo6Dd+fsFjOObhbVJbH7ExcsSlfHYcC2UcSGdtSsc/cnzbsKLTO3Y4ZfylVGX9JcRpgiIfAH9pWX05ZLqft11kaZhrzJ1c3nPZo2VDKiku9Hr91RYpIf0UYiIGMvx7tm6q/LxjS3WN1DDOjR1fSIHEMwnv7Hu86fR9Zv32SVXo/1gI4/vh46gb5dNom6d7GPcjXID1FSkM4D5/eV5pkpUpeUU3gec7aXbJ3x8ZSv0t9xtaxMKO6mod9MKWv7GD750Bp0PTUmtfaZgxmi6VzWFA9CjfccIP6QUOrT58+0q9fP7cfIiIiImJbKlY4BWww30xMdspUX3TUNfiGefvAU/+wOUoaF/IIoOgOFFcMdRYOP6ir6uRGcO/sg7qq28zgWUV1eTD9eXCXvtjSSZObC8pkydbCWvMDeoOgn3rurtKgL+LuOWMfueSIHtK8UaYcuXfVXCmBbnPm47PSay4u7dAZpsViMMWXWz6YKt9My5F12wsCeh62W6eOZHRibq4u+RUMdASouYySkiQzLVnN+UfBr0tTv46NrIzmWMzEMJcp0AzdcNDL0LZplszO2Sbrt9fv0mEm9kvFhs8nL5MTHv1VzeVnZwZdkD3r6bisBvEkJ6tjLgJSm3ILZeyctfLcz3MkXsVSxod20uOj5eM/l8rb4xa53V5WXqEGuSDr0mx3eGuzRHqQ6a7iMmmclSatm2TJz3ceK+cfuqdbW1YHBNEuQPn65o0y1HmcpT5rOO1/vkqmX37UXrXmBo8nfy7OVf8v3eT+2dNTk0IaFNb7Skaq+4VXQUn8tclDvU5wrRZtLuNYZk4BYrb5vM2jjIEra7ftUr93bl0VyC0yMovtf8fCZ65Txt/nn38uX375pRx33HGhXyIiIiKiBMe2VGSZHRdo+K/cvFOuenOSnHVgV7n0yJ4Si2Xm6tphpDMHkQnnxOzQ3pxXLLuKy6VN0yw1J00s9FXpMk2ZaTWXKynJybUuplRJxqQkq6wnylJZpT5tn33a6jw5tEszn++9aWdNsK/Y6BQIJHAIB+9VUyJJB1UDCSTYS3Ye3qeD/DJztXXBaTJftrgsfkt9BtJBV1BSpkbb9u7YUF2Qm/v4q6Pnq3V15dG9VGZDoNAPoLcfBv7qxl4q7smzukteQVHMZmLEUF+Noldf93ZNZWNuUVxm9IYL21Kx4Z9lm1Xn6fw12+Wofu7zgtm31+/+yZErj+ols1duVef5A3u0tQYgIeMP9mzfRBas2SFP/fCf+vuG4/vWed7jaHQux9rYslVbaio9FBhzUQG+P3SGYz2by+3UZKmZ408iul7zC8uka9smbh33erBQy8aZak66xetzZfryLbJg7Q4rsycWB5jUFdqSj3wzU47p31EGd2/t9/PeHrtQ/d+7YzMr80+X/fekf6eW8si5g+WuT/+Jucwtf/TfvbGs3VET3LzhqI7SqnG6vDZhrc/SjoHQ+0N6qnt+FbL/8H3NXbtLdhZXyCF7VgWoEx32WZ0hGgvbTblxwDIz/sxynd7apBi4omU3SLf2HQTTL3hxvNx6cj+3jNhY+Mx1yvhLT0+Xbt2iU+qCiIiIKN6xLRVZZifGzuIy1TEAP01fKbFGX3TUtUNFB1A8dZWZo3anLdmk/kfQL1Z6q3QJxxaNM63bdCDmldHzZX11Z4/KzEpOsgKc+FsHTXUwUPM3227Ciu1uwb7VeSWyrajcbe6vQFkZiQF8sc/9PLdWVl+LxhmSs3lnrXklzNeNpYvNQAWSVYmSr7Bbi4Zut4+auVqWV5elQudxoPR2ojs3M5jxVye6H2Rgp8by2oVVAy10xl8slg2LhVLHTtujXmexmKkeLWxLxQbdiYoBRHbmXGvw7bQctd/f9tHf8sCXM9Rt/y7bLAUl5VZwLzsr3S1gGIvHCU/MU5jT/HjRNGPFVuv3Rlk1ZT51lQhdNt1sQjgdb6xSwxH8eNg+EMBrUt3hrh3UsypwfOMJfaV7u6qg4LM/1WSJIkCYiIG/nE358teijXLP5//6/RwMkPpjQdXcZvefNUhuPmlvv5+r5wGMpSwmf6Xa5kw/bu+Wsm/nbElPQTaoK+Rthwxb4G/cgh1y7ceL5bavlsnDP+VIfaGux/Tc6zGw3VQYxwFzIIk54BCZz/7Irj5+FpWWq/OXvjY1jzWx1FYLKvB38803ywsvvBBzjWJKHJybiogoOHEyILbeY1sqssygyPadJfL11BUxWxJRB3TqWmJOX3xsyS927PTwllkVCxl/O4vK3C6uQAf3Zq/cJg9+NcP6blWpz+qDHz6WXnf2jD9/P9ecDVWlXHTg7/Iflsq5P+YEnPFnqilF6v/KnTh/vdvfbZs1sIJdr/22wO0+s4MxnjpJ7QLpHNDB4Q7NG9aaA1EH93cY8/z4vwzu209WWkrUSn1iIEC8X3Pr9Tm4c7Z0aVU1x04sl/q0B35j5fyVVt2ZGAvlR2MF21Kxtc8gC9tuizF/sLZ2m3tJ5zd/X+g28AnlHE3xtM3bK0zEEjMIaz+8qYy/VJRadf8MTqtet7fKItiZv6W6RGXrbPd52rq0yZbf7jle9u3WWrq2yXa7r1Orxir7iqU+qzIj7RlLZrlDX/S84/E4sMzTbojtPbSlPp0z/mDFluBLz8crs30XC/PdlbsNkKxZNgTvNH8zQK2Mv5JyayAEtjNz/4il81ZQpT4nT54sEyZMkF9//VV69+4taWnuJ+Zvv/02VMtHREREAYixa0zygG2pyDI7MbbtKpY11Z1Osbi76PkFsMzoNLJGVgfIzAhbuHaH7L1HC7f7nYJDKKf1wqi5MdFZpRfBDN6Zv+cXlVoBr8zkJMktrPr7jekbpEFGmmPgz5+LsK0FpTJr/U5plJ4iu0orZPq6qswxsGfZBSKYUp92+3VrLa/KfPX76q01wclaGX8xdLEZqJIAAmy51UG9Zg0zPD4mmJK5etcwS33mFtbu0A63iYt2yGOjVsopA1rJ1Ue4l8+LJ06BeJ09EMuBv6z0VJVlEivLg2ycWBk5HyvYlooNFdWdnTNXbJV12wqkg5GFjTJoyAi87ZT+8uus1fLvsi2yfGOe4/nxwkP3dAz8xVOwwWw/xdocf+b8VfZ53RAcs0p9ehhUpA/hWWnJtUqhhwLaqtg+Lji0e622rx54lZXhea5jHCMRBJy8cIOMnbNO7sY8y69OrFPbLVYFemmwanNNmVctkMCfPmfH48AyTwPukPFXGsJji27rZKTVfDnXD+0oL45dowY76bnX6wsz4B4L2025h0CkOVWAv4FgZKXrQarmwE7zPZbZznNxl/HXtGlTOfXUU+XQQw+Vli1bSpMmTdx+iIiIKLpioN+evGBbKnr7AzL+Ypl50VGXPiPzdXTZQ5NTh3vzRlUBFFcMXaibnT9m4ACZbxe9NF52FpWqTMD11Rduvy/PrQk02DpG/Cm7NWbJdrXeL923vfr7r5U1F251CVLobEV/OwKd3gsZf9br2T+bh5Gs8cbsmPRlV/UcRfaSZaZgAq32jC8E/rYXlMtWY+7HSFiysWqbnrmqdoddPNHr09x/sV+jIywWO/R1XC0rI6gx0iGnN2HdSRtrwYRoYlsqerbvKpaHv56hMq/N0uGfTV5m/b5o3Q4VDMSuj5KM1x7bR90+evYat/MVno/srH6dWrh1qmrBBrvfG79IRs9aLZFk7p+xtqua8/9utwX+sI4ROEM2n7ncZtBEB2gzqzOaQp0Jf+N7U+STSUtVsNhObwP2Eu6e5le+f/gg9XkStdSnJ19OWS4fTFxc6/YSh+BneqrnIKpdw8w0t3ZXPDGb/mass2rbqNtOum5HiXW9pd/HLPXZqnGa9O7Q0Aqc1ifmfhcLWbflxndtHqfzqweOgr9TOujD0BPfz7YGe6A8tZkFPWpGZM893gTVmn3vvfdCvyRERERE9QTbUpFldlwgUBTLzItzLHeKx1n6vCsyypjOXbVNTt2vs9v9Th0hmEMOYiHjT1+U6YCZUwbfxuo5CZesz5Me7bJrBfhqZ/x5f0987jFLt0nzBqkybM8W8s6/61XWn7VMdVgturPK3zl/NuXWjEANdBuP5cAfRt5jJP6hvdtJw+rMTJM5r5Mn81ZvlzmrtskHE5eovxtlpqnv2inIF8wcG8s3F7ltP6u2VZUUvfmLpfLBZb0lUhJh6gNkSm7JL3PssK0aAR9726o+/jVIT42p5dGBP3M7R5kp7O+67FR9w7ZU9Hw1ZYVMWrhRDdYwO1Qz02uCCTe8O8WtPdK2aQM5bp/d5ZeZNR2iSzfkqbLrLbNrzgehyvj7/K/l6v9jBuwukeIpaBYLdHn79s0bOAT+XOoYjcO02U4xY676CI7BMFAUps58p9Vmzd0cYAAFx02noFf8S3I8V7wzbpH6/ZTBnd3mQ9xRUPvaR5eP9kfT6tfKM4Ik8cLcnPp3bGz9np6aVKdSn8s3F8o1Hy2WY/u2kB5tG0h2VmqtUp/YXhEILDKC5GpfqweBQLe582KgrVduHMzM34uM6w5/t4f2zRo6DljclFskTRumS67D/hZ3GX/ali1bVHkF/OB3IiIiImJbKtaYnS+xcPHhCcpXmhcLdSkLWVhSpjrPUAZRl8HUVmzKV8EyO5Tjquv7horueDKvjc3J2O3MzMCajD/7HH/eP9f6naWyYWepHLh7U3VR3rFpptv95RHM+DNLz8AlR/T0GiBzK/UZg1lU2o//rlLlZN8Y4z5HYSAZfzd/MNUK+kHjzDT56Y5jrMC1KdDsqNzCcrnli6Vu35nOatiYVyqrq4OAkaAXPdY6jwMx/LV58uyYqk7+ZFvPgwr8xcAocE/HHpT61KLZSVdhD/wZ28Plr/8hZz7zu9R37JeK3jy8q7bscjs3YiCGnZntMnTvDm73jZq5WgUTzIBhrcBfEBl/0WrHmAOnYmEQlYbzyJj/1qrf2zVrqAJ/5vJhfeGchx9zsc3jjW5S6Yy/usx77LR85pxbP89YJcXG3FtWm9B+IvEBwa1YPM+Eg3l9k2eb39j+NwRyVkNlhSRbdlS8MLfzu06sGQSp5n+sqJpWIRizV1eV3P917jZ5/vc18uCPObUy/rDPLNvk3p7/Zc5WqQ/MEruhnEsxJKU+K4zjjVHS3VtZYLM0bt89mqv/D+vdXgqNAaKwe8tG1nyjd336j8Rt4K+goEAuueQSadeunQwZMkT9tG/fXi699FIpLAxsdCoRERFRfcO2VGSZ13SxPMfC93+vdPu7LiXdkAnSICNV/eB309VvTnIrx6U1rC5tFwudJPo7M4N93jrfzQs6Txl/vua+W7Gt6jqmV5uqkZxrct2DPCgvGiz9OfwN4qzcUlPe8a7T95HhB3V1ux+B2/lrtqvfR81YJVMWb6xTJ2mkbMmvyqZbs7Vqns1AMv4wJ9G71aPZ7R1SKcnJcvlRe9W583ddbk2nli4Va3ZaX/7+QokUvf/H7hErMPb9EX1j89bssLbjWKF3UXMuKXMEf6Tp7UDP8WeeF7bmVx2jCuKw/FoosC0VHRe+OF5+n1MVRNqUV6SOszq7SM+/azr9gC7W700buM/J+vt/a1XAIjOtJtBeq9RnEO02BI/qYs3WXXLZqxPlc4e2kjdu2XIxdPD+a2FNG6FV40y1zncV16wjtE3QTsGP+2A5M/CX5F7BIIQfcNaKmmDIx38ulZd+mSfvGwN8nOaK9Ud9KvVpzpGcVx2Y10rdgvNV+9oerRqrEv8jT9zb52tju2iYmSq7YmDe20DpzbRb6yxpaJ7Xqxt5/l4XbsovlUUbatquG/KcrwmQSahhVxq+Xxu3+zcY7cx6U+ozBvbBcuN7NudJ1xnpCNia+4mdWXI6o7pM7qycrfL+BPfSuhiMOLh7a/X79OVbAprCIFyCasGOHDlS/vjjD/npp58kNzdX/fzwww/qtptvvjn0S0lERESUQNiWiiyzE6OgJHY7SO0Bm7pk+hSUlKtSilnpKVLoZwdYZnWGiz/lFsPNmmfNS6lP98fX/K6v2+yPf+PvdR6f/9fKXHl4fFXgtX12Vcdkr9Y1pVyguA7lovSy+NtRpjv0P77hCBnSq53jY0a+P1X9/+Iv89w+fyyX+tQdh55GWHu7QH5r7EL5YkpV6TYTgttmR292Vpo8fM6+atRtoPuQOWeR/s6QmRYNetFjKGmkTuwZu1iv2G6xHcdaZgxkGYGIQOZCCjW9anTgz+kYsmabcyA90bEtFR0I9tmz/1DeDBauza31eDP73lNZWjO4bs/4C2aOP/uAp0Bd9tofar9C5lkgzENZLGVrbzcyvvT6NYO0KvCHjD/bHH9mR3hSkAOZ/GG+1rrtVceztdt21bnUJ47dpSGeizAWOJ0zzfbTLR9MdZsrUQ/oe+KC/eSdaw5TvzdrlCGf3TRUhvXv6Nd7Ips3HgeZ6HX15FndHUud+puN9uQvK+WGT5fItl1V66CgpMLnsQyb7RmD2shB3ZpYt/kxTWVCiLXAX4VxHvnu7xxrIGJR9dynTRpmeDxW4PhkHhfxHeNQ5FT6FvvVvt1aWX8/+f1sicvA3zfffCPvvPOOHHvssZKdna1+jjvuOHnrrbfk66+/Dv1SEhERESUQtqUiy7w+/nZajofHuOSDCYtlwdodEi32C6NQZfyhjAnK85idKE50qcSYCPzpOf7cMv48X7q0zq4pyzlxRdV36E//0OjF29TPg+Nqtot2jas6Js/a232Urp70/dBe7eSmE/qGtdSn7sAxsyDgifP38/lcX5mN0aS/Tk+L6C3w55TBgZHqehvRZXJbN8mSfbu19jjvn7+dj7rzpkvrLIkGfTSIpaBYXXjrsL3jk9goh2TuozqgbJZBjsry6FKfqV4Cf8bo9fqEbanYsKOgxJoTE6XEMUftRmOe2nbNG9TKNrL7c8EG6/fGtoy/YM5pvgJ/23cVu2V9mMxyb/bsQ1/Mc3wsHbt3Gh3U+nhmVnfQ847hMK0/Am4zV70+hOuKd6HsyzeXRR/zkOX/0R9LZPH6XOu4563kuxMMfisuK4+p7yIUnIKum/IKa2XT2q8vurVtIk0bumfd+guBv11xGPjT27C9CaIz/kr9yPjD+p63riogrUu+e8oUNKtE6O/p6iN2k/rGbY6/GKgkU2b7vnTlET33KbLWPZX6tA+oRHvM07GoWaNM2Wu3ZnJ4n/aqkg4CgXEZ+EM5zzZt3C+EoXXr1iz1SSFlztdCRESUKNiWiqxx1SWp7Mx5GH74d6V8OnmZ3PTeFImWWuWt6hj4wwUHOuPyi8rUPFCXvvqH15HzujPInFcl6hl/fpb6vPHYXrVu89VBNHfDLnlu8mr1Y2pS3TFpz/TSAdEDe7aVYwbs7tfnkCBHyOsAWEaa++Uaglomp+w+8zZ0dgWTLREuej146oT78I8lHrd7p6CpOVJddyy0qV5H2F4CzUgw37tBetW6/79j9pBo0OsohuO4ATGmR1GKy2o+GMolnfPcWI8d8dFY7+gwjolSn9Ycf54HD5hBlvqEbanocAqEr63O0gLsx1e/Mcn6+6i9d6vVv7NHq0YeX98+T2Awg6B8VXc457lxcvlrf7jdhk5fnC8RcPL3dezMc05dBm+F2sbcIuu8qDOYzU5uLKsq9WmcNz2Vvatrxl/O5p3y9I//ub3/A1/NsH7XL7tsY74q+/nN1BVBZ/xhrlY89aVf50m8W7etwBoAZV/3+Pu2j/52u83MRtLtI505HoyGKvAX/euDQOlVZe9bTg8g42/t9ppjwu1fL5PxC7d7nE/b/Gr0762qBxRWLYfUC2Z7OhYCfxXV10I6G2/Ruh1ug0RQLcTTtmD/rjEQ1RyQclDPttbvKJ+LY+Ttpw6Qb28dJlcP6y3RFtRef8ABB8h9990nxcU1814UFRXJAw88oO4jIiKi6Kgvjcl4x7ZUZE1butnxdvMC+LXfFki06QnGB1dflATbqYLOFIzuRcZKlpG1AluqS0hqt57cz+0iGJ3d9sdEt9Snf4G8Zg0z5ESjlI4vWwtK5ZZflta6/e4jOtfMY2PrYCqpviAM5jCrP4e/wVwEGfH25mTy9mXCxeXWncVeL1ARSDvukV+tkjbh8OmkpfLV1NolOJ3opfe2bY/1EKj3VSro/CHd5ci+HeT646uyMVXJsoAz/mp+b5Nd1VHTKDM62V56WTCvjJ6DJFQ25JbIsGdmyS9zauZVCjf7/pRf7P6Ztu8qUQHAaNPrHR3Gmvl7xJeneoH0scDpGOJUbqo+YFsqOiocjsUo92keq80S4/YO95/uOEZevfwQueKoveSQvao6TN+tLj+ojxVvXDlETtuvs/o7mJZQoMdMnJNOfGy0PPjVTLdSpv6WAdR+m73GeE2JGcgGQ8D2h9uPqRXwwGdHu2/rziK3Up/2TCjdBrNKlwfZRr3l039VNtrUxZus9zc5/R1sxp82asZqGfbQqJg4xwQjt6BELnl1opzx1Bh5e+xCt3benFXbVFlPexvJPC/oAWH2NmUgGmWkqlKfkShhi8DamxM9l+cPZjCPPWasB9Ms2uB74MzaHe5t7Sd+WWVdE9iZ62fA7o2lvjKPf7FQ6rO8ep/p36mluhZZvC7XGuyK/QLXzFhOp4GJ9qk47O1Z829cj3p7bDQE1YJ94YUXZNiwYbLbbrtJv35VnQX//fefZGZmym+//RbqZSQiIiJKKGxLxQbMg4fMKvvodQTOojGn067qkeW6zFWwGX969CIy/uxeto167tQ6261zrn2zhmENEtWt1Kf3i6fGRoaOL6MW1XT+dG2eJVfvv5v0beeegZCW7JzxF0xFCl2mNJCMP2yX9vcyg9XogLnto2leM/4+nbRM/Y951D66/ggJhw8mLlH/n3lAV6+Pw8W0vvg3V4N9O0cHlxN0UqIUz3H77C6fTa76XCbMHXXrKf2tv4Mp9akfv2/nbDmsZ7M6dTTWlRm03LqrTDo2D90xacaqner/t/9cL8ft3VIiwb7/HtClofy1vEBO3reTyrjWHYvRpvdRPedprJT61Ock7EPoqHr4m5nWY1DKuT5iWyrycIxEpgOOxUP37iDfGOXTLz2yp7wzbpHPOdX0tnz6/l08PqZT68ZWibRg2kL+zvGn5rZLSpL11RmL05ZsUqUhzQEJgfhr0Ua3144VCIRiHkZ0cOvAX0l5hQrAIfsOtu0skeT2SSrQivO1DvwhQIIyefpUqA/lflRIdKSzxnQJdHv5SPt6w/ejg4yBz/GXXGug0oDOkTnnhdLk6u0K+95XU1eo/U/7vw+nyQuXHKR+P/eQbla7D+VsNZw3sB7rEoRolJWmtg0MUkT2XzghsAZXHNahzq+ltyZ7Uw6DquCxUSut9p4nO4sqPA4u2K1ZhqzdUXOcMA9X9qodbguU4Mz9WAfUolnRr7z6+gMBvq5ts+W/VdvUMhWVVUhmeoqRCV1Zq81XEcDBLhZKe9oF1bLu06ePLF26VD755BNZtGiRuu2cc86R8847T7KyojMHAhH5hkYzGrP779kmJkYeEFHoJYWgRCCFH9tSsQMlnVo3dW+/ovzTB9eFJ0jiCebfy6ueo6xJw/Q6jZBEQBMaZqRJv04t5OcZNaUs/122pdY8BR/fcIQV3EAnSV6hK4bm5PAd+NNlxDJTfbdtHhmfIwfs0URW51ZdpN8/tIv620mtjD8r8OfvpzBeS89t5+exGd+9U0kmswQaRhvr8l2+yn9iNHi0L7of/XaWNY+T2SFgL0XqlGGBx8xdvV393qF5Q7/ezyxZ5i+9KEf3bh71KQfMJQ9l/zG2g/ELqtZlQUnk5vO07763H9NWyrP2kKzMTLWvfft3TkzMT6m3md2M7Wze6u2O+89j385S2+bdZwwM4/K4Zx1iAMIXU5a7HctjYV7WaGBbKvLKqsszDu7WWvp0bG4F/j698UhZtjFP/b45RIOHfJWG9qbQzxKdOF+iw3fZhnzrttk529weMztnq/T3M1ik21+xFvhTA9qq2xTWIILySnmmOuhXaz5iV03GHwZBIfBnZfzpUp8BHq/xPb40vaYCB/qlEKDt0b6p2+OcXjfYUp+XHrmX/DqrJgsTg9vQvjrh0V/llMGdwlaGD+cFBDgxAM9Vx0w7eOkX90F7KOFvWlc9h3fbpjXzaa7fUZPJhs9sL18fKB3sQ6A23IE/LRTtVqfriUC2JbRJ84rKrfn7dDulsLRSmjdMlVcv6CknvVizH+Exx/drqe5zgoFXl0nisx+3sQ1GY1CtPWsvNSVJurTJVqWEZ67YqgLZaF/peZSdAn9nPzdW/X/CwN3lkiN6ip25JTU1gvKxIughdQ0aNJDLL788tEtDRGGFyZExQvqyI3vKmQd6H5VNRPEJDary0nI1ipNiG9tSsWHbrmI1CtrkFEwJp407CtX8ezrwhoAdlAbZmatHumNU48CurVT5w3FznUvmoDOiVXZN4BPBpmiXZLnt42lWx5t5wa87pEwdWzSUCw/bs+p+PzoH/szJVT+QnpLkMeinL94dA38SuOQAS2Nh8EaqWee0Gi5GMW+jWUbN7qM/l0q/zi2l7+7N1bLqd8Rz9LYVDTroB+ZqsG9vToG/LXnFAc+3FlTGX5BZBeFgdho/+OMKuWxIB9m/q//lbD35Y3GuzF9fMx9XuKzZ7l4ay75O8T2mI8MvKUn2695aBf4CLasXDrr8U+MGaTL67uPkmId/8dgZNHH++rAvj+7wzspIsY5D9vly9LHJhP1KDWyxzQuaaNiWiixd3i49LdnqKIUWjTNlTfUcncj6CwV9yAgmgOZvxh/KvyGmvnJLVRa0k6Ub8vwK/KGje4MRbImh6XXV8UtXgTBLfdrXbJKxzvVxpnWjdFm5o1g6N8t0a2vN2bhLlm8rlK4taoJN3hSWVcqo5VXBYRg7Z536ueSIHl7n03Ir9RngublxlnubZ/TsNbJ+R9X57/t/VoYt8PfYt7Nl0sINVjvk2AEd5cYT9g76GsEO5T1NT/7wn5q3XM9fBrnmXJUIQtYxWNeouv2IgGYbiQwE2eoasNQBKPslQqaRjbc+t0TaN62dqbWjoEzOfr0m6PrS+T3kmdGrZNnmIvWczi2zVFbfOfu1kc/+ripdu2fbBl4zCFdsiX5VlUjP1a6OJ1EO/FVUH1dwbdUyu+pYduen/6jsWfQDZLjNfVqzr5jnHwQInfajo/rtJn9UX+PYjzlxFfj78ccf5dhjj5W0tDT1uzcnnXRSKJaNiEJs4dodtUb/EFFiwYU4Kj7pyb8pdrAtFZtQxqljy+j2zphljpDRhYt38DR/hC9VFy04HlRdxKD84QE92sjDX9eUhtPso5DRkRdM4A8XRsc+/Isc0ae93HbqgICei1JTKO+IOYL279HGbbS9GftyCsa8recFKiuTvVpUXcjBQZ18B0iyfZQVxKhQ08L1eXUu9Xn3p//K+9cebl10apgjb9+urVV5M93Z7yn49O2tR8sbvy+U7/6uKbFm9/t/a1TgL8sIEiIIEMnA3znPjZUDe7SR646rmnPPqaytUwmdAodMDXMfMTubvVHlwQKd48/DPELPnt1dRn5eNSdkpDInzb7uNdtL5L7vV8hvNwe2b5lGz90mv87dGrGg5mXvLXT7W2eJONHZrWaJvWjR2SEYBW5+z9HK3dGdlmbGn7lc+D6dAn+PfjNTpizepDKxEJRJFGxLRdeidVX9CcWlFdZ5TdNtjlDR23kwiXP+Bv50e8fbdZO/g01WbKrJGgw2U9GXKYs2ygNfzVBzInZo4V/2u24X6kFuuvP9EaNcsP04jSXXGX+Hd22m2ksHVg+U0s3GxVsK5ZrvF8tvl/p3XtpenTVl9+74xer/m0/aW575cU6tNijOy7rzPZjz14NnD5KnfvjPmodyzqqqjPdwQtAPdBsE55VgA38YoGi3qHqAiunwPh2keaNMOWDPNjJ1ySa3gAXaUGbFiGA0ykx1LM0aTlh/dT2s6Gagfcs5tEcz+Wl2Vdn/yUty5azBtcOZ23a5f1YMGMT8zwj8IQs2K71qZ7jwoHbSsXmmfDN9s+zXpWYKhfpMb34IjKIsqhpIEMUqmGXVx5VU24BXzIWJH2Sxq8fZrr3NAWlOlVh+vvNYq4QyHotBt7HG7yU65ZRTZOPGjdK6dWv1u7eTc0UFswwoNAeJjbmFqgGFVFyqO10GJjOK81QQhcKWnaXSvGFaTIzIjzX6Yg4X5BRb2JaKLbpcC4Ih9kZ+XWzbWawuts2LCp/LYlxIoHNXd5498OV0ufKoXnJYn/ZBXeSacaveHZv5FdzCsjiVivRFd26Mn7c+4MCfnl8GUMLOU7DA10jvPq2y5NUTukrHlo2sklbeDO3e3Ov99jn+dGmlYGI++qVw4fnxn0vcOoC25hfL22MXqZ/f7jneWp+eznG43rJnI9rpDtkBXVpacw4hyL1bC/d5DMMFHZ54P5SZdQr86bK2ZvkdLde4z7rNmMPM3xHDOnvV27q00zFI++bTu0MjObxnM5mwaIfsLK6QkZ8vkREHt5eDuruXKAulUFe9fG5MVcnfDs3ce17GLtguQ3t53xdCwdt3oI+B2AfaNmkgh/RqJ9Fm73yuKkkb+WuoWqU+S8vd1mWbplnqGm/d9gJ5b/xiWbk5X244YW8V9AN0didS4I9tqei69/Pp6n9UEUCAweTPeTdSGX/+lr/V7R3dhsHAK/ugqzQ/zznz1uxwm+swHKU+3xpXNaDijwXr5dxDuvv9vJKySiujBYNEPdGDXnC4K6s+N2ekJMtxPWsyHoOd93abrTylXZsmDdwGr5nnZX1bMCUz9+veRr6+5Wg57pFf6jwVBrYXVNXxNogq1INv/V1mXf3i/uGD5KGvZljzAupgHc4VdaEDh5hfOlIQXKtrVVG9G9oHbPXdraY93KKR85vYy49j4BkChH8tqxoI2KB6bnHsE0f2aq5+yH0gHdrsKvAX5WoyFdXLg+unxg4blR5UaK+aZVbfcQr86WPSB9cdrq5toj1NgBO/j5qVlZUq6Kd/9/TDoB+FEjpCrn5zEldqiOhAACYvJYpXizcWyPlvzpf3Joe/vFI80p34zPiLPWxLRQ9KIw7s4l6mqUV1xtX2EAf+zn1+nJz/wvigL+rRoasz/rBsj303qw5lbWouPhpnOc85YM+eUnO5oPxTgB1W5uP9HWkPvi4E7ZktppP23aPW47s2z/K787GVrcSrXXb16OZayxREsU9vQUunsp3YJrw9x1fn168zq4I8ZhDXno0QauZ7+erwNL93e1kvZH/a5VXfdv6Q7n5nX2DULTKh0NmHOTQDyvhzWPf6/DpjZb7KwHvwR88Zl6EQ6PxJ/rKPYH9vUmTaU5461uzb85g5ayUW2LPoojV3sn5fPXATHZHm9pmZlio5m3fKJa9MVFkma7YVyJjZNXNaJRq2pWIDyt7qjLMTB+0Rloy/mvnmAt/3vE15MH/N9lrnotHVmb6Pnb+fdd+p+3VW/zdv5F+aig6ItKh+fDgOGcnV7Y9AXxtzM+pzp7fBM2ZZcp3xZz/nBjvwdnuh57Yh5vnTmTL2ks/4/lFeEupSrtKc/y4YkxdukOMf/VVOe3JMreCkfQBgKHk795xxQBe58uhecuGhe7oN8NBtZ73vIPDnFOwIao4/P+fPDIVQnHdxfeJpk73hqI5e38fePkXGX892DaVBdaafzviLJWgHIIMt2vS2p0ukh/JaOxjl1QMZUlKSpG2z2scCfVws9ZbxZ2xIB/VoI4cag9SQbds5RhOWgtpKP/zwQykpqX1BVlpaqu4jothUVF0+BxeIRPFq/rqquvzjFoS/TEc8i1YHFfmHbanIwgW6fcR2y+oLZFyg6wv4g3q2Vf/b53FCB/CLv8yVTbnhKZVtdmpt3VlsZXbU9fXMoJmnQFHtUp8pqsRToB1tZpDihVFzAypb5Y3ZwW0vK6ZHrwerVUPvnSAYwXvF4A5yZm/3oHEwgzm9ZS4ig0eryiqqukD11rnmVO4SD9dz+DRtmFHrgvW13xZIOJkdrfbOEidTF29S24o9w9Qp4w+BDdinS8ta+6cnZrkdf0t76UVxKkupb7OPAA+XcL1LcZn7+u7V3v9ycXVpg7Rs7C3wV7O+W9vK4EaLPesHxzgMali/vcBtXwVk24WLHlShA8/YX8zBByijZdfGmNcv2qPsw4ltqejBuQZVmZDloOdJ83dQRqhLfTqVunW6TZswr2awAzJi563ebu0nZmWk7m2zAxqEoc97ugM5LIM3qnd9p8FZOD7pcpYmPBbHM90O1oPLHF9ev77RfrBXP7A3TVZs92/Osm0eSn3C8QN3t9qj9vPHzqJSa/7IupSrdNXxrPqQUS4fGUyeOLV//G23mPKLStWc17d//LfHx6Ck+2n7dZbzhrhnf+r2I7ZBXOPgu6xrqU99beLts4cj46+usDl5are3zk73HvizZ/xVn4cLS6v2jf/W+DeoLFIwqAHTOtz7+b+xU+qz+ngT7bZIuTHHX/d2taeD0Ms5e2VV+VenaxsEDbV7zxokd56+j8SDoM7MI0aMkLy8mklZtZ07d6r7iCg2MeOPEsGyTYVBl/qoT4Ip1UeRw7ZU5OBirmpyePdjBi6AERjYlId5Gqr2l25ts1Vgwd7p/820FTJqxmo1R4i/znt+nN9Zc2aQDR03rYxO27qVtfH9WHvZEt25bB/x6It5bbxqS1WQxh++RqWaJaXsnU11LV3ezI8J2E/v21qO7e5eJjWoUp9uAUxb4M/ooPx66gpru8XFqT/fm962MWL+21uHSfvmDWRHQYkKKHoblR5qZkerP+eg+7+cLr/MXK0yW+Hcg7tJ/04t1LJrWP47PvlbvqpeL00apEsrPwNDOggaSJsBWQ7gtOr1bf4ENUMh1GXifJWHDXdnnbfycObAjIIAMobDab/uVdWONOyTD38zU0a8MlHu+2K6fDBhiXUfsu1WVgenw7EdYN3p4wGWo0l1tvKNJ/R17FAuMvbFQI7H8YZtqcjDeQYeGD7IyqLS5zSnwTh1Oe5YZSe9HAsnzlsvJz0+WoY9NKpWaUvN/nyzk39LXpEsrJ63EFAOG3MVY144PdhInxd80ec9PTAnHHP86bXp9NJXvfmnnPH0mFrvq9u4GT4y/o7s28Eo9Wlk/NnOn/Zj+U4/j9nbvZT6HNS1lcfA8cK1uaq0rP28HqiC6qxBb7CtTJy/Xn6escrr47xlL+k2jdvrVrpU5QEdwPTHbR/97TbntRNPASuzTK4OBjfyo83rjb4+qIhgH0PoMv6cj0P6es9Ts87elrFvo4Hs4+YShOPYAJi+wtMckNHO+Av0ujJcy5PicE5Cn8DBe7WzSs6b5wxzX+/axvf88bEoqF5TTxOar127Vpo0ic8VQVQf6NJ/nOOP4tXyzYUybuGOWqPDqYZupzDjL7axLRU5usPDfrGGi0AEEXYVldWMak5NVh1X9rlhdCDCV0e82fGLzD1/55ixXwD6G9jwRC+nP/Ow2B9TM49URUDtC3zeYEp9/r10s4/lE88Zf3UM/HVv6V+A1d7pFVSpT7eSpe6vZ5Zm3phbNXLe17x0Zmfq/nu2kbvP2EceOHtf9ff67VUDZF78ZZ66YG1sdPb4OjcsWLtD3hq7UC5+eYKc9uRvkh9AuSCzozWQkb2fTl5mlYlDG9WcT2PFpp0yc0XN6NsmDTKkWcMMFSR87LyakmxOGhhz8PgbRNMZGk4Zf0kRzvj7a2ntgbZ1kZ1Ve3/BZhSOzxPoyG5zrtFwlTj1127NG0rn1o1rlYXDvjNj+Rb1+7Qlm2rNRzp3dXgqUWB94HvSxwNkGuoAQ6dWjSXToRN/2YaabQcDVsLVyRhtbEtFHo6lrZtkyZ7VAUCT09xxWXUI1OjDsLdjgqfSwN4Goph/b84vksXravYXtBUxTzHmhbNKjfqb8VfpHvjzN2AYCH0eWr4xr9Z5bVN1+wHtiHXbCtT+gZ/nf57rFvBzqhigB7/p40y+MScXyhua7G0TfwfD6GzzY/p1kE9uONK6/ZIjeqgylZ6Wy9TQQwl2f2C7tduc556t+P3fOfLYt7PkpV/mqYw7T7wNqsJgJTt8D5e++odc9tofbtni3vhTnt1TZmtNmVyRLflV7fO6ZvzpAWehyMLzVyjea9qKfI+vo5v3nvZx+7Ztnxddz/Hnj5uO3t36vbQ8POswluaX08cnfdyJdsZfZfV3rNfR7af2V/8P3buDvH7lIW7Hh4VrdzieL/p1aiHxKKCj5oABA9RKws+RRx4pqak1T8fcfjk5OXLMMceEYzmJKAR0x2awEzITRduqbTUdy2mp3I69YeAvNrEtFXn64hydOSiH8+3fOdZFMTovsK/UBAdTVDAJFyto6OuL3KKSCisohvmH8fh2DvMDbLSVAkXJQn/Kdtr3V8wTEI6MPwSLdCf/kxfs73ic0MuLeeeaiX/z2lz44njJN0Zye+ssMWH08/TqjvRg5vgLNvB3Qs+WMmJQO78vkDNsF/pBlfr0kvFnlk3Sc7RU+gj8IVNVa5SZKodUj1S1ByIwv2WTrHRrxHdxWbk0NAJiJmz3N703xe22696ZLO9cc5jKej12n90l28NckbU7Wv3v1NDBFHyf+E7MZ5rrYHC3VlYQ86LDe/h8XTMzwN8gvF5spzn+rBH0cZpQ3ygjVbYXuAflMYgqHJ14SzYGVhbZzMgMdaZjoNBZn+nw/eN4iSxxTwMblm7ACPva847WVaU+X1WvI+xbuiMK5yjzOHj9cX1UwH/2SvcskcXrc6Vnh6rM5dk5W9Xz+uzeXB2DcRrYvWUjiSdsS0UPsvQxaMqJU8afWXI5ULrPwtshwV6G0qk8m71T32z7IPCzbGNN4M9sF+iX9veaSj9Od3SH8lCG+QO/nrZCVldnjE1bulm+/Gu5nH1wt1qPffL72WoQD7IyO7VubGXL6cCspzLpuYWlsmpL1evfNiZHLtinvfrdHpDLTE2W/u0ayewNVY8t8PP8WlJ9rrl6aE/JbJAp7197uOpgP6Jvh6rl86N8u33gVCBuPbmfbMgtlHs/n27d9sxP/8kT5+9v/f3Pspo26fadJR7bPJ6yl3TGlZ25CeH4PLBLK7+2f/N8iLKe9gEmqPDg6bnqfStd8syP/wVU8tzXgDN/A5emr/7dpJbp9EHumfS+hLvCgjWgxsPOah8YZe9HbZjh/3WI+UoFpRWOZbrrKpa6ea1SnzEyx5/LquhRtZIO79NB/ZgwZ98fCzbIyPenyimDO6ky1nobwJyvwc5vGm0BnYVPOeUU9f/s2bNl2LBh0qhRTQMxPT1dOnXqJKeffnrol5KIQqqu9c2JosWcl4alPr03ahj4i01sS0WevtBAh8KVR/eSg/dqqxr0aMC/9MtcdQFrZfylJFuZgeg0QufolvwiK5CFi4XzXhinfv/8pqHSrJF7YMzeObKr2HNZI5O9T6muFxbWxY3tChAdcAjQDeu/m8dRi1nVo1eLAsjaM4N+5lyJvmzYEVhwIFSBvxYN0lQQxF/2bNFgRtSanVX278UMmH33d46cP6S7z4w/M3hnH8V9+v6d5ZtpOSprac22XWpu5xMG7i4/z1hdtT94iOdudPg+kDmAoN+74xercptf33K0n3P8BX6Bj2w/rBozO0kH7K46upfaZwNhdjj7m7noKcPBaY6/cF7+O82hU9cK504BNRzjzFHYnjKoArUxz/9M0VqBvyhn/FVl2NWsgzMP6KK2/TfGLPCazYxydOFaHnwnNZ2uGKyi56pJkobG/u+pqss74xbJUxceoH6/rXq+qN/uOV5loOjf4wnbUtEN/HkKFON4ctfp+8gj39TMhYbBJ+Es9YlBSk7HL7eBKLZjilvGX16Rx7ZaTdaUf8ekf6uDRrrNEIpBDGgTjJ+3TlWUsAebZqzY4hj4Q9BPB/zN70oH1jyV1OzSOltmLKuqwrC1sFxKvZwPOzfPqgn8+Vkhwnq96vfHADpzEF2o54i0271VY/Vz0WF7ygcTlzgGlppVl1EGc7vAuvQn488MImOgkh50ZbZr1m7d5VfgD883y+HbMxbPO6S7NaDD276jsyT36lA7Sze4jL/A2nfnvzlPtuysWg/+BP7MPoy6ViTwFTisyaT3lPFX81kbpNdsny0bpcnWXWUyuEvVPKD+MI8HBSUV0tzHPOPBCKYiSdhLfcbIHH8VemCfl1XUe/fmKvAH3/+zUi48dE8r0B0r808HI6Cz8H333af+R4Bv+PDhkpkZvx+cqD5L0GovVO8Cf7HTsIklevdm4C82sS0VzcBf1YVH747NrU5OBGPKKsrdHmPNRVBWKQ0zRM5/Ybz1WuZRB8GQy4bu5fZeJdWdHwi45GzeGXB5QVPzRhmO84TUJeMPHcQI0rVr1tDjc3XGn1l+MlDe5qYzbTPKg/rDHjALNvAXaADF3ukVTFykV8dmkp1Vtf5b2i4ezXWNjiYE4FTgz8uCDj+oq3wyaalj4O+Ko3rJT9NXqY4mbNvZWcnWHGre5tjwdJ8uYYsOLMx9c1jvqiwAO7OjNZgLfAR/VMafsTvosp/BfNf99mgh3ds1kaUb8mTttgK/nqPLLzlVFdCbtT6/hnNk9a7qLONQtd9zC8tk7Y7axxOVBVzdG/LC76vllznb5NeR/etcHUQHqXq0bSCL/cj+M+esjHLcr1bQXW97U5ds8vo8ZIOHA/ZjBJ3NUp96jiWst167NVNzZUKmLcjSoXlDWbe9wLFN+G91B388YlsqOtCmwXHeWyUDbHMmf6oeeKIPQ96Ofd3aNrEymXDe0cEtb6U+zfsmLdzocZCNvt3fthz2NXMgQ10Dfxho8PqYBR7vt2emow1rnsdx/rcP7NCPs3vhkoOkR/sm8vP0ldZtNYG/2o/vkF0zgsifwN+oRVtl0qp8laHpcb61CGXTnHtIdyPw575tmAMpzMFMd37yj9vj9HpG2WdUrvjfMb1V+2XZxnznwJ/5XD/bR/YSzQiSolrHrR9NU3/v2d7zNFtmmdpBXVqpgSlD995N6kKfpysCyMLD4C0d9IN/c/JVuyA7K9Wv/p66Zvzt8jGIUW+Lnr6Souplue+kzjKwU02Q7+MresvCDYXSs51zxqWT9k2NfcahjRcKzPjzvT8leVlJ9nbcVW9OkltO6qd+93ZNFuuCWvKLLrqIQT+iOJao8zxQ4mPgzw/WHH9xWousnmBbKnL0qFynuUN0qU/rMUbZNKd5M8yLBacLB52dpDON/A3AO52XEfgLlu5sso/81KUPvWWt6GU3R9EHyt+yijrj7/6zBvn1eHunULBzFjuVcfT6+KQkuWnf1nUaUYvSaPeeObDqD9v3bc/uwnaDuYK8dYKZgTCnOZSwzOgvQecUOvl0x523gJzTeQPreHv1HJegAwya2bHpraPVH9kN0tRIXJexT+jXDCYToG2zBvLyZQer/drfQLbOpHLq6NQdRHoEejhL5zs11fG2mOs4GK9NqCr1ZureJksNosLnQfsKQb9QzT1TXN0p6u/8N6mxVOoTGX9eSvN6fF6Ylhuvi00N5xwsCzpC9X6MbRvz03g6Jj58zr7WfLZ2d3/2r8Q7tqUia011mUlv1Qzsc2DVZY4/fzL+zDmRzbaH+btuiyHYjfkvi23ZWri/a5tseeKC/TyWS/Tlj/nrrd+tjL86jmKYucJ7KXT7XLTZtv0cx3bzu9LrRA8EMiHoh2OMua5LrfNh7WPg8Xu1lOH92vgd+HvxrzXq/zIv6yQaZfTs5QfNtgsGAGr2bV4H/u77YroaaFVQ3a7Wpcs9zRXs9J6eYLvs37mmOge+G7Nah7f1ZZUmd7msc1NdV68e0FNWWalKzyIb1dd1Tl6R+7Zx97fL1Y83boG/Ou5Dvtozuunh6XPkFlZ9r62y091Kc2Jf6dW+YUDtwP67N5aDuzcN6zyJ5uJ4m4cyonP86VKfXq4Lvv8nRw2oDevyVPpuux/Rx31gIzLC9dyt5tzq9SLwh/n8nn76aRk8eLC0bdtWmjdv7vZDRLEt2iNpiYJlNgSdOuWoZmJ5ZvzFNralIscs42nnNMefWerT3tlkNvm/nLJcco2ASF0Cf2aHcbe22W5zvUHOpnx18VHXjD+9XAUlZb4z/uowGnXFpny/sl9QRhIGdGkZ0VKfwZw/hnRsbP0ebLxHBxPs1/vFDkEpX6U+TU7bNt5LZ/zhfh349tbh5NQRgdcw56xZZ8ucMzs25xrzxwTSWXPioD3k0XMHy6CuCK5Wz+lk26eCDfKq56an+B2MLtMZfw4dnfb5nsI5stpTR/eKLf4fB0x/LXUvU3ZA1yby/Dk9VCc9AknPjF5l3ReK9oNurzXI8G9fM7f16Af+3IPuTvuhU8dRuEqUqtKj1cugzlmYg7b6vRAwNQeh2DvWkF2sgrvVtyfa4E+2pSLrqR+q5gqblbPV42NqlRj3M/jv7bW8HRPM9lOxEYByykBHsPt/b092HNjVvX0T6d+ppYdSn7Xf174vPfrtLOv3UM3xZ6/6gDLtJvsANPsxCIEZlLbXzj6om8djmn4tt8BfdXtBl+qzfzcn7VW1vgpKvQey/C0FGooy08Gs43nGvHnm+c9b4MR+39TFm+TOT/9xm4PPU0lcb5UXTPguzDLx9u3J23yHZplavV3Udf2aGX+Pfz9bZaP+NrsqoOtJXnXgzOSrCoDZXgvVHH8n9mvpPavXYScvLK2QyUuq2k5NvGQoBqL/7o3C2k9jDkzcEqYqBEGX+vSyP7322wJ58/eFYV4eUbxdWnVuk62qAt12Sn/rmlxnuJoD1OJNUEv+wAMPyLPPPqvKfebl5cnIkSPltNNOk+TkZLn//vtDv5REFFKJdtFH9UeJWerToQwX1TSQGfiLbWxLRU7NnCIpjhd8uNjTpXhUqU+jJKI9SGK/Zn7su5qOHvNiVc+/5m/mrb7gPPeQbvLkhfvXyvhDqZELXhyvOho+/mOJz7kDPc3xN6x/R/X/virAIl7n+KtLxt/qrbusuRC92ZhbqD6nv0GdUAX+sjMCf16Wcc4Jtu/ELNVnsgeltu8qDijw5xyYqNqusM1gdL8ODnobcXvDu3/Vug2bpt4/nLYLs5PwiynLgyr12bRhhgzs2kp9Dv1R9MvqILW9nGkgsH35m/Fnn4PIlFQr408iLphR4viO7M9DJxaCfggK4b6FGwpCHPirPhYG0Ol/wZDusTHHn8s948+eHXN0v93ks5uOdLutacP0sFVawL6gj+Uoo4xMCx3I09lVuvyumf2klj0lWXVW/bNsixpRP35u7czPYDN0YwHbUpHlFDCzs58fO3oIfoSq1Ke525mDaMxOb1Q5MAM1ToNlnLI5PJ2zkTl4zMO/yPw1zkGeUJX6tLf1OjR3X5dtm2b5vR9fc0zvWnPEOTIWuUSXwffQ4d2o+vjuK7BX5OfAG1/ZU/ZsnFDYUVAiN38w1So9b57/5qza5rFChj149/SP/7ll+8HR/TuqSg8H9Whje65/60O1A5PcB0mZvGf86YBWzf4TaLULb3P8YUAijJqxynF+aC2vKLBriS07S90GONU1488KPhnZeo6lPh321Yd+zJHl1cvStEFoAn/6O0NQ0bRtV5lMsQ3QCoZ5zNlUPbgyWgqKy90y/r6cskIWrQvPXMiBrJtkPy7ijujbQQX9cP2jB9VHIyM5qoG/Tz755P/ZOws4uantj5+uu7u0W3f3Une8UIq7Ow+XBw/n/eEBDysPd4pbgVIoFK1BW+ru3W67bXe77vL/nJvczE0mmcnMZGz3fPmU3R3JJJnk5ub8zvkdePXVV+HWW2+FsLAwOOecc+C1116Df/3rX7B8ueQ3TBBE4EKyHxFs/LChBG75YJuq5w32CCDs4TenJPwFNjSX8h38BlsvkM8r/rBCDemSmaCy+tSKF9r7wtJKTcWfHHTilXVm48D8ZiQrKUYRDVPi7Htpf7RkJ7z723Z4fsEGU8vT3ttgf4+Pb50GozVBCBG+7npVaFaB+xsrJlH4y9QErhyhDVroBe/MkOCG9ZjK5tUNq09tIEaEi1Kp8ZLYe9s7y5n4YWQTZarir4Nk4Yj/mNWnfPwbBZyMksLwWBKrKLTHhdG1xhUhYXyfbLv9zNenSg4ciH13XAWrWMVtcAQXyPQq/hRLKPk13qxO4Lu1IC1KtyLRFWp1qjGKyqSxi1X8tbSqXmOFBVVNvbS8+Cjz59r5E3owO2J/O5NIonuInUUyB/tGao9HDKjjensjuZIJkfKxhuPlxv3HmLWcGIi97dSB8NKV46BrlrrnEx6jvNoXM+q5vbIWsxWxgQbNpXyLq71bsd/xeeMkQd9rFX/CBZUfx6Igh2DwVkxgwWQTLXrVHPwarE1G+OCPHeznj+tsQnqO3NsQ7aXFaitP4PaRtvWOcFjxhT2EkeHd0u2W5Wi+NEJ4vZhQVSd/31EGVttYyYObuqFYsoA1wipbw7PHShWLVoAuA3pCnjin+WbVPrj5zaW67zcj3uF14rheWXa9V81U/KGIiK/D4hrs6TxzcL7KCcSp8CdW/MnHodk5pRE80QTPOX6uYU/Dy//3q+F7KmThL0JInnNkmXjhqxvhka/3WF7x18HJNukdo6v3Sv2t3bWb14N/9w9+tVv1+PXvbYEH5++GAzq9mM2wraicWWWKY9Xhcves4a3i1R83q/b9niOVcNd7y/1WlNJqosefCFqkl9c02Cr+TPavD0TcWvNDhw5B//792e9xcXGs6g856aST4Ntvv7V2DQmCsByq+COCjae+3wcbi6pht5AB9svWMnh7yUG/rlcgwieuPDuJCExoLuU7eNWevvAXwgSKipoGdiOKQRVR+NOKF8Uau03t9ZRnw/NAsdkKECUbV7gZOWFIR7vXHZUzkrUWo0bL07u50ev1pGf1qc1GtZJrXvkdXv9pC5RVNzCx0yzaIIe7PdY8tYr2tOLv29V7VcEl3uNvcr9ctd2gyfXUrfgL6aDqj8erlowCTmLgXzxGMIggPqe9PzeqzjITIO6Vm8QsdcRKCaXCQ/67Vg58xmgCZ65W/JkRNv7cXQ2LNpUZZofz462m0dj+zCpaDQJ12FvHVarlpKlZQ9LthL9wNga2qs53TzPs2WfKy4uPdq26Fo9bd4LlRaXV8Minqxz2LzWLttpWrDbtnZsEJw3rpAqinzu2G2TL45iVNqVrdh9llSiYNc/jTd00wh4XK3B90KJKDxxnOXq9bhGzwnigQXOpwKv4EwU0FGo8sUfjw5+jKmDxKT7Or9yhrryqrG1QCX+4HXjOjOphS4LSq4bi12DtmMiXtfDvfUyEf/rrtczGDq9TmBhgplLRDDwpiFvApyeoE6XQkp6z/aAUk0XunT3Ebln5qZIwqceI7rb9IF7Xth+tcXitwzlmTHioKjHXUSW9J6TFR0GndJvlusfL01RH8/m+NpkJxQo8rkZ1l5wyThvZ2bR4Z7sXaHVpfrS58BizDUVQl7p0ci+4+aQBTvtpGonmSo8/D5Om+XUP5wziNcPR9vCKv17ZtuNPFAG1aE91T+MZRq0POPzYtqK3sRmMRPDS6ia3kis4N7y5glllltU06F77/Yn4neq577i7zS6vR4s8rzZ5HmACLp77e+Xeto7Ot0DHratwXl4eHDwoBVu7du0KP/zwA/v9r7/+gshI++wZK5k7dy4UFBRAVFQUjBw5Ev78UxoQjfjkk0+gV69e7PUoVi5YsMDupgorFbOzsyE6OhqmTp0K27dv9+o2EIS/8XcmLUG4S6EmC2re8kO0MzU08xsXLzWNJqyB5lK+F/6MevxhRvXy7YchKS6SBTG47SRaGmpv7HlloNH11N0ef3o3IxiUSDcITBgFbzmtHghjXGAxa43oKTyofsXU3k7FHW12u7u2K54mbbp768e/D7TfWbGtWHkchVzcD+Lxgr+a3T69wCp+lk34C1WOa6PAbbkQIBCFjlaNmKE9ovXskcxU/F05rTc8edFo3fVmnyMvlwtdzo55pz3+TBzP980vslsPvXXjGeyxbljGmoUfCtpjwJ3KCW7XJgZvubCJgQwMuohDlRW2jzX1zawSBIPCroD72B2rz4c+WQW/bz7E7Cw9Bc9D8fsXq2wm9M1Rnnvl6vEw7x9T4KJJPW29jyy8ybrzvRWs9xQGnnkSB68K5uhVpjoCs9f1EMcFrJb6cV0hBAM0l/ItZgLJYjKPJwkbolDRarbiTxYjtIHkyjqs+GtQHe84BorWiXrVUMp8SE4owGvhd3/vgyMVUhIYnu4vfLcBvl9TyKrt+HXKVt3v2XjAt+fRc0cwy8ghmn7IfH6L4868320xTD2BJyvZOMkqXrjmXzLBVqG5/ai0nREOxpl+WXFQ50QEs6Lir09+MliJ9trK5/stOkLTCws2sGMKrfC5bSev+BNt+Y2Ev1822uYW0mc43h9mk28c9viTj8GdhypgS2GZR8ly2s/DOYJZsYYLf2lx4W65dXha8cd3pVGVV6QsQvJ5krfxdnGb+L0ESiW/OH5j8pSjdf502S6vrUeLExFYy9jeWeznki2H2mePv9NOOw1++knq23HDDTfAfffdB927d4cLL7wQLr30UvAWH330EesneP/998Pq1ath4MCBMGPGDDh8+LDu65cuXcpsSC+77DL4+++/YdasWezfhg02e6QnnngCnnvuOXjppZdgxYoVEBsby5ZZV+ffRpgE4VWoxx8RpGgnZXFR3gu6BSM4seI3C8Har6W9QHMp38FvKJxN2KcNyFNZQGGA67vV+xy+R1vdofS1koMoZoM+Sjau5m5EG7xpMmk3YmT1aYZoOVCBfaR8wc8bpIDIGaO7wIe3THWt4s9N4c9dq06Ou/ftCUIlnbiMY1X1LHgkPo941uOvg1INgOIOr3g1Ev7KamyJNc766YmBVvEYF3s1OgvWnDq8QDf4o1R4yG/nSSye9NZAmy1eVekJ/LQrr5ECWXFeFP740KLdRe4Ewfh7xOAtt25jVp+aZbpb8XfFW5vhofm7lIq/GBRc5eMtNznSvPDnxn0K7z1pRQ8WZrMrLKerUEknCtBYfcKt15Q+QV7KruTWaGKVCooWeoHf/h1TDJcj9j1DhspCQl2jTRi/5a1l8J+v1kIwQHMp/wRw54zuYur1PAnKXWxVS8avEa9BPGGJfy63Etdafe44VM7cHUQbX705Il8Ot9xctLYQnvlmve71BCvu+PlvxqLUDJiw0kGuwkfLSOTOWYPg5pP6q8Snb1bugaVbpWSi/14yRndbEqKN3R7iosNV9qy3jVDbwTuyxosOD2HXDEdVfY0ejIvZsmDprrW7Edp5NL9X0BvDV+2SbDcxiUrrnuDIRcNoLvXD2kKH1bPiZWyJ/L3qv874e+H9kTEpZlPhMfa7p+7kvA+zKzEGPl9Kj49QiYEbCqvYvv7fz4Wqnn4pseoxw3MHAsfvt82NfSX8tXpVJBfv3QJF+BPHI70kPnEOivag++UKO3/2+EMGdU5TJf22ux5///d//wf33HMP+/2ss86C3377Da655hr49NNP2XPe4umnn4YrrrgCLrnkEujTpw8T62JiYuCNN97Qff2zzz4LM2fOhNtvvx169+4NDz/8MAwZMgReeOEF5aR75pln4N5774VTTz0VBgwYAO+88w4UFRXBl19+6bXtIAh/QxV/RLBhJPB1SrHvgdWeEW8wfWWbQLgHzaV8B7+J1wuEiNUPPJDFqzswi3vFdltyWYIQGNF7v77Vp+MbuB/W7oevV+6BL1bs1hWytDcn/GZ7mVAt5rCPAbhOfHQ4C2Ts1FQ3GrHPxRs0bSBsQt9s00Ke9qbL7E3YmQMkiybb54BHuBvcR3GPrzPP1OcBJKxKm9I/VxXg8kj4C0FBhzekD1EsbI3sqcRKjjljurB+MmjFqYe47uL3iUEGbk/l7Bpk9F0roiyv+ONVrh4E/aLDQ9lyzFrvGsHXYF1hldcr/lrlYJV2P7kTFOKBM7E6bEb/VOmxkA5QUddsSYb9vpI6WLJdspuraWiBmIgQmNQ7BU4amAZPn22uzxeem+4Eyx1VdrsCfnarZuwV+zMZLZ+fg94S/rjAx3vAItcf30/3tf8+f6Tqb7TTPWmoZB19VCP88SA2DxBaYZXqS2gu5TsqahvYuTGsazpcOqWXT4Q/m/Wzo4o/9TUI4cIct0csqaxj68/BSwFeE7m1udF1lM/l+HmBy3EMDyyDNcJfYzObG4jC2+T+uTBzcEc2FvGE2ENlNuEkLzVWNX49fv5IuHxKL1XvPq1gFafpxRrlwIpRT/hDahtb4LU/D8C2I/Z9xURR8PaRxj2mRXB97zh1oPL9WtVjjWN3bW1qhsvm/sIcQLR0y06UhT+xX7K0TeJxZZRIp8e3DhILzV5GHFkPitavHCuEC7wWuSLkokMCfuykXuqKzWU7y2HNvkr4cvURuOWDbcrjWpt1T8MZfFWNNh3nRfico4q/UV3VFtueoDe9enfpQdUcj81D3Bw7eEsIb/dqdwUxGUkvIVa8pzA6dv0h/EVpxsxgrvjz7EosM3r0aPbPmzQ0NMCqVavg7rvvVh7DRqdozbls2TLd9+DjWCEogtV8XNTbvXs384XHZXASExOZhSi+9+yzz9Zdbn19PfvHqaioUP1EwsPDmXVobW0tNDbaVHe0QsV/1dXV0Nxsu8lCK9KIiAioqqpSlZejsBkWFqZaNoKVibj9lZW2hqNIfHw8ez8uXyQhIQGampqgpsZ2Icb3Y49G3LdihWNoaChbvnY7fblNNdWV0FQnrWtoZDRbbrBvk8PvCT+7qgoSIiOlbaqtVW9TbKy0TcK6s22KiZG2qUHs3RBm26amJmnZDQ0QIe+PpoY6qK6uVNaVjr02cD6F4PhTBRDSABAiBQRiY2PkbVIHZOPj4+RtUk/KExLi5W3SHHtx8rFXpzn2YuVjr1449sKFY0/IHI6MjJC3qUazTZHyNlVrtilaPp9s30cM1ENlSweADiHQXG9bx9iQcDYx8uk2tTQCxl0i4yDgxvLyympl7KytkS7xATvuBeFYrn3cSmgu5b25VEOD9N66mirV8vF7vXBCd1i+aR+cO64bNDfUQkVDLSTFREBrSzM0N9TD1r2243Jov27w09r90NJoO/Zqm2QxRT5WF63cwf5uaZBeU1tX53CbHv9YanI+qJsUCClIiVAfZ83SuNOE4x5W9NbXsHM8JFyqnjE6VqsqpXlUbbW0za6ef5glf6RMvb+Mzr+SY9J1prmxHlqF7yM0TLoeab+nEPlxvk2nDMpkn4PnH342H8OQK6f1Yu/lYwrruyjMD6G1xbaODuZSZ/VJgo/XHYaWpkb2r6aqCiqiWlyeS+F7cf2r8f0VEW6NKbef0BP+PX8jq7Dhj+O8Nz48hlm7ds2Mg8LiEmiqa4bGumr2GqPvCcF1ktYnSvU9tTY1QG1tHTTVNUJ9TRW0Nku9cSqqpGW+++s2+Hz5bnjiouNgaI8cOHT0mLJvo6ABrpveC57+diNs2HVQ5RSBx94Hf+yAxIgWmDogD8or8DNqIDQikl2jV2zcCxUVvVgPePa4MI8Xv1u88dbbJrwfx20qK69gQePqqgo2d8UbbrfH/qZa9tkVVbWQnBBrOJfi6zeySwKbg2jnUrU10jbwYy+kuVY1V7F23iHt16b6DtI5HxbO/lVVV6s+08xc6liZNG401NXAY6d3hr/2VMLELpHsuWZ5TOHbhOPesbJKqIhqdnmb2PcUEcW2qaysggUlo6AeLj8uDaKjw03NpXB9Ghuk+Ysr12isfMHt4OeMu/OO8ippf+Axg8vh59PAjomwattBZUzVHnsNddKxgd8bVnlYMe/A+S2f8zbXd5DGgphw5fokvke7TbguPEiF3xOOt/jY/kNNEBPaAWqaQ6XxsEHa3qMlZVCbFg01jR2UsbysvJydpzSXUtNe41KbD0rnUd+sKDbHcDTv52MK2uhpj21X5v31tdJr8DGjbaqussWNKuV7waPHythjqVGt0NpQC58v3Q4nDOuizDtQ/06OjIOwENklpa4G6mttYwffJhwz8bmSY8fYc2jLzc8/kbCoGGhpboYRBSnsdWw8qq9ll05P7mXKcazpIH0H2u8pvEMzNDQ2s+8pKqRB2QcxsnDC/+6SGgFdUtNVcynkxUuGwVlP/ci+J6wGF+dSeK1DcJtuHJkBFfJ7dONSDbVs7N94qAo++vsAfPDXHvjiwgGquRQfV68dkQUj0qRreWS4/jjJ1r2+Fs4f0x2GFyTASzWV0NLcgVUKWXnPWVMlXWNx0hEWGQ1v/rQR9hQJvSFDQtj1DMdJnENVVdZBh6YWaGmUrtd4LT5WVg6HjxyDzpnx0CkrGX7fepTti/PGdoGN+46x80TcJnEutWnfUchIiIKa6ipVr0ncJmzbIc6XtPN4/hzOHSE9Xvd8mjW8AP7vs5Vs/Tm1NTXM1cSj++jGWmiQ3ytuE5/Ha+NSJWUVgHmT+SmRMK5zJOwrqYWdh6V4ZF1DDDufKitrYPmWQ1B4rA7q2LEXzo49vN/CYw/n257OpaR7wErduFSHpjqoqZW+J3EulRHVBIeqO8BtMzuq5l2exNpwvs6/vz+3FsOInpnwzu/7le+ptKwCpr29DnrmJsN/z+psOi7Fvo+GOti+r1hZflWNNO74K4aD4y+2NuiTFQ2f/GMCnPnMr+xx7ViOyWbiPeSGXUVwXI80y+NS1fK9MV4zWlNiTG2TOEa0NjWaui8OyLhUq0m++uor0/+8wYEDB3BEaV26dKnq8dtvv711xIgRuu8JDw9vnTdvnuqxuXPntmZkZLDflyxZwpZZVFSkes2cOXNazzzzTMN1uf/++9n7HP277LLL2Gvxp/g4vheZPn266vFXX32VPd6nTx/V4wsXLmSPx8fHqx7fsGFDa3l5ud3n4mP4nPgYvhfBZYmP42ch+Nni47huetvpr22afM/HbW6bdL+n2NjW1j17Whe+/756m3r0wBOg9dX//Ee9TRMmsMfvv+UW9Tadcw57HH+Kj9/3z3tbpz/0TWtq18F07LW186m5oTU+Pla9TX8tbi0/uMV+mw5uYc+ptymutbX6QOvCLzXHXu8e7PFX52qOvSkT2OP336M59i46hz2OP1XbdM8t7HF8n2qb5v6HPY6fo9qmL99nj+N6iY+PuX4uGw8CYpvuup7t90Aey3N7Dwv8cS8QzycT24S/uwPNpfwzl3r4hXfY9S82Ns7UsVp6rIyNN+JjoZHRrS//sLF1yAUPqh5Py+2se6yOOG4C+8wLrr7Z1DaNmXVZ62mPL7TbpumX3CWte3pH1eO4Hs0tLV47/+6dt6K1/6wbTZ1/p591PlvH3CHqde8x+Vzd72nuiy/pbhOuR0tLC9vXZrYJrwe/LV9pei6Fn9nn1Bs8mkt1mXgOW87IsRM9GlPw+Jq3eJ3u93Txo++Z/p4uf/EX+22Sv6eBx1+o/p7Olr6nKSefabdNyIDhY+226fEv/tY99nA52u/J6BqtN48Pj4ox3Kan5q+x2yacu9Y3Nnk89v/3+RcdzqVwvczMpXCbtGOE1fOOM8+ao3vs9R480qO5lDe3Cb8nXEdP51KTLrzD5Wv08fd/qns+aY+9uDjH846nnpurez7dcsfdpo69O+7+pyXzjtV/r9U9n8rK7K9PRmNEr969da9PnfuPbH39x83smNJuU1Fptd1YTnMpNe01LvXEy++z8zvG5FwKj9/lK//2aN4/auxE9pnnXvkPU9s0+5Ib2ONdB45SL//UG3TnHZ99+bXu9cxom6598Qfd+SEuQzs/xM967LPVHt/L4LVc73vi29SpSw/dMcWVudSvy9RzqZiYWN1tMopL4di/YPFa+zFFnktNnXma+ti7917DY89ozvvKok1emfPiZ+nND3sOHtP6147DbC4rPn7uBRex14+ZOVv1+D/vvc8w1qa3TTOvf8Lw2Ptt7S6Pr2d/bi+226YpU6e5dOwZjRFG9yZGcanjb39Jd96x8I8VdudTWGSM/rHn5lzqrLPPNBWXmnHxbbpzqVk3Pe7SXMqVuBTuv+KDu3XnvK7OpfSOvVlX3xtQMZwTH13QOn725XbH3r4jlT6bd4AHY8SDTzxjt03BEpfqgP8zIxDyrFJnYBm6qF5aBdpv5ubmsr59YnXhHXfcAb/++ivrz6cFVdO3336b9fnjvPjii/Dggw9CcXExW9Zxxx3Hlp2dbbMZOvPMM9l2YE9Bs5lV+fn5sH//fqYQI1RR4Zka/+LCDbBo7QH2OGYhfffP49t+xd/evZCQmgpNHTp4peIvpGs3mP3c7ywT5MIJXeG0EZKlGVXztIEKpZBWqChaDxAS0SYr/tDi44y565VqAsx+HtQpDtbtr4JhnRLgsXP6+afiL7UnVNc2BFR1XHFpBVz8/M/s8czkGJh324mBO+4F4Vh+4MABdr3HShZ+vXcFmkv5Zy61eNMR+N+iLXDvqX1gYEGqqWN1xkNf22V0P3zBOLj/wz9VFX89c5PgxWunKcfqaY9/zx6/78yh8O/5m+GGmT1hfE+bzaR2m/jru+WkwNGaJnjn2rGqbfp582F48YetSlZtWmIUHC2vY1VXX99zAstGR4pKq2HLgTI4eVQPtk1fLd0Czy/YAPfMHgzDu2W4fP69t3QffLZkGzx/yUjISYl1eP5tOVgB9328VsnWPG98N9hxsBxW7SmDhfefypYtblNDSwic9/yvMKV3Glw9vbfd+Tfln5+wv289ZQCM7Z2t+p7QpmX2Ez9I6xMZDe/fNAkiOzSbmkvN/nCbUvH31EndoEtKjMtzqdlf7WNVV3ee1BuGdU1za0zZe6QSbn1vFcwZ0w3OGin1lTznv4ugV24yPHnZJLh33nJYvqmQPT5zSD5cNa2P4fdUWgewZFMhzByQrVg48e/p4md+gIOllczuc/aozjChXx7c8t4qOGd0Rzh9RCfl2MPtWfTgLHh2/kqYL1vOvnHdRMhOS4Rnv9sEC//aYVfxFxIayrJgv7hzBhQfq4GrX/lddY1+/boJsGLbYXhl0WbVPP76V3+HA6U18O5NkyEnI1V3m179ZRcs+GsXvH3dOFY59eRXa2DJtiPww4OnQ1Njg1tjP69ufPX6KdAlO8VwLnXFcwugqr4FXr6oj+5c6t/f7oE/d1Uo1XFD8yLh7hMLvDLv2LivFG58dzMMKYiH1XsqlYq/6b1i4Ypx2S7Npf7eVwkPfbUbrpmcC6eNyFdt05ML9zJ7TrHi78HTusCAvDiXtgkztue8tJFVSMy/vg/MemY19MuLhftO7uzSXOrql3+DhLgYeOmayS5do0/690JoqK2Bm07sBxP7SZa54rj30ZKd8OEfO6BTehy88Y/jDecdW/Yfhete+hnOHtsNzjquq+l5x2s/boZvV+2Dd/4xHXLTEz2edzR1CIez//uTUvGXkRgFL189gW1T4dFKVgWRnyZV8ZqZS73xw3r4ZJnUf3HKgHy4+8yR7L7yjw374f++WMP227TBBXCkqgkufX4RG8ux5yra7dFcSk17jUut2HkMnvxmA9x6fA+7CiXtXAqvLzimfH3XDKivs40drs771+87Bg99sQGumdodJveVetxpt+m5b9bAD2uka+bZ43uxdf9mxTaob2yC926aAre9vQx2H61l46dYoTSpfw7cfOowOP3JH9n17KJJPWDWiM5223T5i79ASnwUs8yc8+zvyjipV/F35og8OGtsN+ne9clFMGVwF7jt5H5u38tc+8rvEB0VCa9eP9Xue5r91GK2TaHN9XDOuK7w1uJt8Nh5w2FojzzVXAqv086+p09unQodmuXrUGMjrFi2EZ78uxI6JYTDkzM7OYxLfbnxMLz992GlOg7/aSv+nv11B8zfUAwvndINMsNbIbJHD4iMi9M99k5+fBH7ni6f0hNOHNoJzn/2J6htDoXzJvSE04fazg2jbULMznnZPEiu5uHrzpl/zwls+Sc/Oh96ZMbB/pIqSImNgvvPHgEX/28JTOuXAeeP7QrnPfMTTO6fA7edNgyWbS+B5GiAzulxutskzqUyUhOhtLqRHXuf3j7NZuccGwt/7y6BO9/8FXrlJcF1M/tCXmqcapv4/O3lq8ZBt45ZuufTtsN1cOfbf6i26YNbp0FGSpJH99GXzv0ZctOTYMvBKtU24XGmF5e67aNtUNMSDu9e2Z/NO9B2/cwXN8DYHkkwZUAOPDJ/p+p8io4IgcaQKKXiD3n+/B7QMTXGrbnUlsJjcN3bm+CsERlw9sgs3bjUJa9vhLz0eHj2/L6qudQ5L22A/p2S4f/O6mVZxd/8lQdg7k/SeKV37N0wNQ+eX1ykzKXMxqVOeWkvi/Oij/HIHhnw57bDMKZfPjx0zii/xXAue24Rq9R95tLj2Pd0znO/Qd/ceLh71gDVsXewohGunPujUvGHLTfm3TrT8rjUGz9tga9X7oVXrh4PXfIyXB4jnrpgOHQU2gwFU1zKtNWnuAL+IC0tje1YnBiJ4N9ZWbZJgAg+7uj1/Cc+Jk6w8O9BgwYZrgv/wrTgztbucPzC8Z8W/LL0wJNED6MvUu9x3E96j+NBo/c4HmT4z+x2+mKbIqPj2ASKgwd2sG+Tw+8JBwR5mWyb4m03ch5tE182Dkbh0nvxIhITE2+3TnTsBfH51NIICQlxACGRivAnTjL0tyneYJssOvbsN4lNkPS3SX9b+bp88lexajyYNCAH7j25M5z87BoIi45hN8Q+3aaWRoCW+oAcy2NibWNnh7CowB732shY7go0l/LTXCqkhP1ISrRfNv9MLR1CQpVzKSspGh48azjrp4KCR0iobTxqwhtb+VgNCw9n7xnXOxvi4qRxJzQsQnf5uD2FZQ3KZ1Q0tLKxTLtNvfNx/r2V3XAgZfVSgIn3fkmSl82CUQBwXP8CSE+Ihh1HpWXHxduu966cf/nYIyYsHCqb7N+j/e4iSqSb0lC0Hw0HaAyJhPiERIDQahb80m4T75GD35Pu+sjbd8LInqrH+WvF6wEGlRL48p3Mpe6dXACPLN7DtispPgES4qNdnktxm9KIqGjddTczpvSJi2PHV3F5ne3xsGiIi09gx0B0ZKSyjXgcKdut8z3hx3Ucr99rKTwyEjqENwKuciweB/HSun2wbB+cPb63aj+ioFrXbDvmszNSICIslNn88WOP93+srJVuYvG1uD54jIjLwt9jYuMhIrpaeRyPvRW7K6C4BqBXQRYT/Yy2CT8T9zM7dqMjICwyBiIio5iwGerm2J+SnCQFZiHU4VyqNSwa4iPwnkN97PC/bzi+B1zy+ibpwQ6hEBEda/daq+YdkZFR0r6MiYUw2XaTLT8iSnf5juZSIRFN0vcVH8++C3GbIDwawqLkwEQH6buMilFvl7Ntwjziyrpmtm7IJ6tLoSUsCpIT41XvMzOXiorB+WyIy9dobBsjjXnqcZ6Pe7Fx0vyoKTTS4byjoaUDe116apLqeWfzDjxe8X14HdBuk4jZbcJxEscDfg61hkUqr8lPTwDAfy7MpXAM4MuKj4+VxpqoKEhJkc6N2tZw+Gj5PtYfjI/lsXHxqh5tNJdq33Gp1pByw2Xzxzn9u+XA5sIyiIwIZ//cnffHxvL5hf5cCudRi7eUKsf27pJaWLUTe6eFQHaydA5np6fA3jKpb5t4PQuPjIGYqAjV9Uz7Gfg3Wq6yETg8Wuq1J4+TWnB+GCcvA8dEFtBvbfXoXqY5NBJiY6J1v6czxvZgCS34mg1FtWydUpKT2b5F3rn1BIiJCIOEOPVniNvItwP3Q3ioHNBubIQp3TOhLCQWJvdIY/bCWsRtiourhbAIKTDNE1SONIRC55RopZfW0foOEB0TC10ykiEUrTLl7TY69nDfHT+iByTERkJoRAyENDSx3npW33NePH0gJMREwOL1B1jyHJ/jiesWFh7JrvUQ1gzxCXGQKM+lFm04DOdP7Mv2IY6V+F1O7Jeju358m0R4WzN8f2OHSEhOsD3fKj9+8qie0Kdzjt028e8tITHR8HwKPSrNWcVtSpDPaU/uo6Nj46FZnkuJ26R8viYuFRIRA5HN0j0Onw9E4/LCopi1u3i/heBsBPsDXzY+H57/cT977IFvD8HH1/Znv7t6PqGoIs1L41TzETEuFR0bB60dwlRzKbx/aQqNgrgYaZl6cyCjxx3FpU4b2RESE+Lhie/2Ko+L31N5U7gylxK36aM/i1m/xCsm5CpzKewNGBHSCmcNk+8T5PcV5KTDugM10CEkzK8xHIzTRYbbnsdjIETnvrjxWL0y70CaQ6WEI8s1hhicT8ewMd0WP3S8TeKxiXMmvdcHQ1zK4+6EYvaKN8EDZ+jQofDTTz+pAmj4t1F/QXxcfD2yaNEi5fWdO3dmkyzxNaimYpaWt3sWEo5p1hSimipLJRwiNpf2Ut95gvAKP2woVf0dGyFNNkOxwpwOZhXi/sCMOiI4oLmUb86LUDebcmNQoCAjHiKEhvOf3T4dclJioLbBFpDHyioE+1rxYIdeE3PO3O82KL8fq6rXbTaeGGN/c8sRP5vDL/WL1knZpEexkY0bYHYxsvuwOutTD+04jNuBARqkQdOwXbufPIVnSJshRmjSbrKvuyGeXHtwnbGKprZBytbFAGFTSyuEy/tDbCYfGaZuLO8K+D20aj6Ts3RrMaTG225SUcwTjycU/fgyOOeP7w43niAFXsTrjN4xzgImwjUI56BPzl+rWrZT5MU2trQqvcrche/T2kbHrjT1Ta0QKR+7euQkRRrOra2GLzktXhoD+C5wJxf3rT8Osp+Hym2Z45yqOvt9gtV7rvDtuhKY8+J65e93lx5iP2Pk+ZorYOGqJ/vV6Nzkx12jzpgkUlUniaBYbeoK/Fyxal6qXU/sb+oJ4rksjgX83Hhj8VbWu/M/X0nnKRJsc2yaS3kXPi5gEpQznrxwNHx110yPP5MJbZrYxcb9pfDst+vZ8Xnj63+oXl8jn7/sPfKbkjXCF6djeryyfEfziVjsNVvfyJwVRHJlNwQRXnnPAtzYx9bFsVRLTX2TSnwXuXKqzTFhzZ4S9tmZidGq9TPadi1h8npzcN3n9E2DVB3RT8uELsl2j1375Vb4v5/3KH8XVdRDVnyEsn/MgL3okCb5omfmuHOVCyb0gFOHF6jmK0bXJOzbGB4WCpHCvcCKHZKg7Mp2ceqFMb64XF0Vy8feEDeWy9FbJ0+WJy63stZ+LmFEY7NtfsuJCg+B2sYWw5hkr+xY6J1tE1zKa5tg52F1BZ1Z+GeI57re8V/X2ALbi2vg581S3An/FuNOVoHrMaVPiuHze0v079ve+L0IPl0pHW+c95YdgjeWqBNK+DUev2t/JxzjGCwecmyddOZ3tfXS/dBV03rD4M5pXpt7tMif7e49oBX3rf7CrdETyxIffvhhZnGAquSuXZJtxH333Qevv/46eAtsiPzqq68ym4TNmzfDNddcw8okL7nkEvb8hRdeqGqyfNNNN8HChQvhqaeegi1btsADDzwAK1euhOuvv1456f7xj3/AI488AvPnz4f169ezZeTk5MCsWbO8th2E63jxvrrdIAZmTDr8EoTfwWN1P3qZCcREhigTz2ALSngb8cbF0U0M4X9oLuU7eOBFG9gwCw8Yi4IFBoWxsg6DMnafExqi3HA7GqPEp4xelSgHPkSyk6Wb4WPVaqspveu7u+NAr9wkFmTBYJIz9G7ibMKffbB6Z3GFR0Gcl68ar/zuSrBFfK2eyGoGvghPBR/MwufCH4p+CBe3ft1UpLwuLto18UGksq5Btd5isB+tdzKE7PLy6nqol7+rD26eojyOwU7OzxuK7G568fjn+6IgPV513InJJ9+vkbK2EVFAN7OPGxqblePJXaLlwE2dvM8dCX9R4eaPjdIq2/6xGr79mQkR8Nl1/eGja/rrJkeaIS1eOo5Gd5OqA0Qq65t1A3Wu8MFySejT4o7wh/fnjhImnNEsB7qKy2pU5yk/5XnFqhHVdU1uCX98fLFKDG7QjN1Y4e0JYsA3KjxM93ejfRnI0FzKd/D5RKiJ4Cdez8QkFk+PWz63QRvxW95aBgtW74MDJVV2woGYwMLFlJQ4mzWbyCnDbRaWiCjoiKDwhtc6rfD32Hkj7NdXmFvg9dKTcwivV8zZQWceyMfKe2cPUf7unZfMEtXcwZEo4gwUB1Ni7MeRX3eXsfkNzoMPVTZAbqI5EVILn1t7Og9wxBFNklzf/GTVd4rXpPrGFpYYJArE363e53ISGgfnNhy8Xonw64izuaqjp3WFP0+z3uS5e0WN/XX07vftLY75fEI7d4yWhT+jyyVzeNCs/7XvbnVrffnY4WzTUXC7/r2t8H8L9rL9Xy3PjXjcyWruOkE9/rx0oeTegdbrZqjRSf7kbCsqD4g4Ge568ZgzSuzi4zaOX3iv4skc0Nn6uHrv+No1E5Tfw9w4zwMFt9b80UcfhbfeegueeOIJVZltv3794LXXXgNvcdZZZ8GTTz4J//rXv5jlwZo1a5iwl5kpeYzv27cPDh6UMhqRMWPGwLx58+CVV16BgQMHwqeffgpffvklW0/Ri/2GG26AK6+8EoYPH858VXGZWBJM+A/7U5GC+54i3sPT3iSChar6Zna8dkqNsq/4Y5lMfly5AISEv+CB5lK+g4sX7tyYiwKVNugQLYs3/KZSzEo2I/xpb4T1bsj1AmfnjevOfh7RZAizddAE7Lmo5Cp449UjJxG2Hihz+lq9beQi01lP/8iCZZidf/s7y9hjC/+WRKBJch8uV8HqS4+FPzdFYDPfqxmiIkKVG90yWcDlN5RilaSr4oNISaXQAwUr/oTjF49RUZj76q89rKIoOTZSFST9fbNN0Jk5ON9OrK2ua1Ru0Mf1yWZVgbaKP9s+euab9aarGHkAkt+gV9c3QSw2MPEALm7UOQiUsOflwJ5ZjlR6T/jj24+7Iy4qTFkvd4692Ei0bQXokm5vOVSjI/y5mjDAM+O1tLpxx4HnmCfaGe4fHHMufP5neOabdcrj/HjE437DPrWThEilmxV//PpiVaCNfwcZcgWPp4KiOOSJ1x4ci4zwd9DQDDSX8h2bC495rfLKeSKI9PPO95Yrz+kdnXpuJzMGSb10tWivK0bV6NJcrxkKNcIfJn85mmfgNd3dORhSVt2g9LsyYmxvm7WsBcVc7mOwmUeqGuBwVQPbDzmCy4ArKGOfBaKVETiXQRLkZCtxroBzErx+MNtWzTHCXTHMiOGOdtlhzXyez6uMhDqcjzlzBfFWxR+K+jU6SVSrdx3VfT2ek9oxA687dQ0o/LUarrtesqaei4gzzFw6D2rcEPBz9pbUup3AZIaJvZJh9lCpB3xBWhR01pmf4TrgPlosVyGKvPrrAcNl4/ccCMIfJqqJxxyeS3rfB0+ExLHWm+vd7OS80iM/zWa/6U7iXaBgusefyDvvvMPEtClTpsDVV1+tPI7iGlbWeROs1uMVe1p++eUXu8fmzJnD/hmBB99DDz3E/hGBg/ZcxHPUO0Nu++G1n7cFVcUfZrhV1DQoN7xE+6SiVpp4j+iSoFgflNZIkwOcQ/p7QhNoiDeYrmbsE76F5lK+46MlO5UqJ0+CDlrhD28GcQzCm1oMBijZ8EKmqqMxakzPLNi4XwqkIWbvQ3jFH89QFgNdXHzkeDJGxkdHOK2Q4kF2kbT4KOiYFgefLd/N/r5krm1+juISZs5jsEIU8NzFlSCGGERwN2x558kD4LnvN8GwrungacUfD5w8/Mlq9nPxhgNw26kD7SzGrAA1iQiN1akY2Ppm1T5lvYzADHjMeBdBUY7vVvZTPojxXDASj4xsy8R1E0UjR1ZnZuHiRm2j8fGMc2Os+DNbVRAZ1sHjKixHaGOdnojOxeUNkBIbrhsIFEU7FAgxy93VYLWR8OeOQIABGU+CK7h/Sqok0fv7NYVM2Lz3jCGqypt9R6ugX8cU1Rj/07oDMKZXphIAdrfizzKrT/n8wUC0NijsDmKgSzwOHFVlBcMcm+ZSvmPxhiKPkqg8svqUj8UquSLXyLZXz2UgMykGUuIioVQeF4wwSkrh1fhc2HjpynFSfygnwgqKQZ64r2w+IM0PMSHHCFyP4wfnw3d/74d/CtV/viY7IRJKa6XvJj02HI5US+NocVWDMvdKj3OvGpHjSlKOq/BrHlqjVtQ2qqyVcezk13qj+QG2H/GEo3Lvaw4feo3muDefNABuPKGfw3PRWxV/rrqn6FX8YQ+/o5WNhqlBeMrp2Sr+tbuCORe4sh38M1xZ7cMVjXDv57uUeZE3wHP3knFSP9dTh0j3FCcMSIUF62xOK1e+tQXOG5UF7wuuCv/+ZjezChVft+WQ5vhpaQ0I4U+y+lS7rehV8+G9BIJzfZb8Jc/LrDhe9eZV4S6OJS9cPhZ+3VgE+an6vfmCAbdGzwMHDkC3bt3sHkcP2cZG72U+Eu2HDpqav2AQqgKd79fZskKC4D4OrnnlN7jgucV+v2AR/oVfoKOEC7SYrRPMmTfeQDxfcMJE50/gQnMp3yDOH1yZsL94xVjl992yNaX2BjtaFiO43ScPCq3YfthUEFh7E2r2/iY9QarIOlIhBYOrBOs6bZDJkzEAAxzcpsmIeb9vh5cXbVb+vv74fnDK8AIY2jVdNwiFvQz1sn+1iLaRjgh1V/hz82ZyXK9M+OS26YbWW2bBY4cfN9sPSrY+fD+jaGpFxd8JQzoqv+OxK27zvz5caVe9gOhlcYvBz3Bdq0/+GR2UACkeN40GJfkYgHVEiF3FX6Pnwp8sbjiq+OOZ5FFhjo+NKX0kC7ChBQlMKPR2LxK+P/jh62ocGcfAwmP1kJeiv99bdc4RV/tSGVWjudP3CDfXk+o2acyy7aTfN0tuQOL39PYvasuw+X/tYT0o31y8VenxFxsV5l/hTz4e9eye3UGbdc/BDHsjvGW3ZSU0l/INYoKRu7bp7sA/Si8WpFfdZ1QRpO0TqzcHMbKh5gLEFtkBAcUho8QlbcWfu+PBzkPl8H9frGG/O5tv/OOkAfD9fSd6PC/xhH9O7qz8fseETpARG64If7yXnXgv7w4DOqWCt+FzLjGpB4dLLgS6m0B4hyapS4vYNkAcex0Jis4FeO+cp456LmPSvvYcbDLo8YcJQ41N+ucHjjH4Gi0Pzd8NK3ZK92Su4sreeG+ZzUVQbz2sAsehKyfmMkt3pH+ebf7PWbZTbf35y9YyuO8LSZTk3PSRzU4fuW5m34AQ/nDYFg/h8poGWK/juIDtBpCkmAjL+yWLNAkJuq7QPTsRLp/a2yNLZH/j1lHcp08f+P333+0eRyvNwYMHW7FeRHsneM+p4CAIxJKi0hpLe2UQwcnWg9JxIGZ9XTVRsocLhAlNoGEf9Ccv1ECF5lK+gQsZ0wbkuTRh75qVCNfO6MN+x+xfR4FZHgxaK/fDw+oMWxDY+BzUjl/apCcjUuOjWDCMV/zxQDVbJ03AflCB+4ESnv3eqJNBz/ltk+3mGDl5WCdl28f3yWaZ8SIo4uB+cxQ8mX/XTHhR8z4jXBHwxPs8f9+7YbUot7bh8xweiPzHSVIvNyTWA+FPxKwLlaPjBQMxoVqrz/pGVTJOmHDcNxuIR0b9lvSsPjH4hnZnnu4HfryJgeLl24rhqHwOYVB55c6jDvs8cW6b2Qnm3zgQ4qNCHVa7WV3xJ1WYoAV6Exwqd1y5IlJajX2iWiAvWX+/Pz7HlszLg3OuVvyJ59NJA9OUygxtsM+sQOWq4CSKAnjsNWi+E20iFLfQ42CyhjZoKfbENLve/LOsgFdvc+s5TxHHSvH7wv6XRkGwYJhj01zKN+C5YaVdoKvHrd6hqCfy6VUB6t0f6VVuGVW/as8PcS554tCOrCeydn1tFX/unUNFx2w93xxZfQYK2OePMyA7Hh6dKV1X1hZVKsKfpxV7ro7JrvDEBaOgR3ai4uYgCn94vCuVQgail7NqUpy3O6JWK/zxxB8PzjWtA4hViDGZu08fzEQRzpynFsG9H65SvR7vS7T7LTo8FGobm6FSs93iOSeej5eNy1F+333UtQp4vi8d3QNeMcG2fOTXrbY2B97sLalF7/jadcS17Z3SPxdyUmKZMOzvazjue735hTZpo0y+vmDygtIv2YvCX5gP7aoDBbfSJ7HH3kUXXcQyrLDK7/PPP4etW7cyq4VvvvnG+rUk2h3a4SEI7juCimDan2zQJ5/XdsvTP0j2YzsO18IrF/eCg2UNite6JPz5eQUDDG2wjlsQEoEHzaV8A4oPeBPUr6NUpeMKHZ1UnfF7yOIyKUDTLSsBflgLcN8ZQ5RMXEc3LtoAsdE96ds3TIKv/twDn6/YrdywYAUUD8SJwh8XGkf1yGTCRu8817dbK5Zg0CbKIO7k7Ia4kyYrHm26nFX8eSu4kyhU8Hhqy+QpMZHhLGgp2pLxqjaxAifeA8FL3ESzwaMbTrD1QdeCx532cK6uQ9vWFltPEfl7fe3HLYqwqSXZSRCzg3B+PP7F3+z3HXJVpLvw6yAPCuM5e/9HK1lA9aNbpsEPawvh6a/XmQpOYmA3MrwDC1wh2PvJG1ZQfFd30Hz2mn1VcNFrm+CrGweaykQvPCaJm7nJ+tUg3TNjYFSXBFi+q0IJuLhqFS4lLUjv6ZoRDX/tDoPiiga3Kv7w3HRVOxOrjnHM1dr9YR9NR5Z7fJxGgdlZgNdXFX88ZsvHQ2eVss4QxwC1CNiBVblgJr5Rf9xAhuZSvgGFiycvGg1frtit6nnkbWyJIM4r/vC4Fs/9K6f1Vl1fuTiD59KNJ9gSbDgJ0RGmKqvEUQ2Xg64QV7/yu07FH96nunejKlanO7L6DFTyEiOhZ3oM/LzrGCTJyQtmhb/Xr52gmwjnzfvZgQWp8PzlY2HR2kL2t9bqk18/+Nh57cy+8OLCjcprSjRWnXYIm4OuDmg3LSZfaN0W3OlF5kwEH9olDaxATJzBddeu4dq9aFGbppy3GJvQJgFFR6AoBVBSpX+NwfMI7dQ5KXG2ufDbSw5Cv9xYGJBvzhnETIvIM4Zlwqu/SlbGWsT18DYRFnxWptwmSYqT+TdQhvN4PcG1WXMfiHM0fFVCjM2S3jsVf61s+VZbiAYDbkmdp556Knz99dfw448/QmxsLJtwbd68mT02bdo069eSaHfYZUtS1ZelBJN1qr8zVYjAACdCnVKjYVRXW1YZ9fizRzvBczfTlPA+NJfyDQkxEXDHrEEwc7DN9tAsuSmxSm8zzotXjIO3rp/Efp/cX6o+5jc1vEcB9uDjQVZH1zDtc0Y3IllJMTBrRAH7/fKpvRQBDe1S0O5TXfHXosyj3OmvpSfqYVWfUS8zZ1WUuE1iwPqvHYeZpZGn6+YOKUJGur/v+bhdq9i7CwMo2uPAVbtB455e+vtb+z3kpRoHdNG2TPt6DFYpllRCb8tNhcdg9+FK3eU4q95Tevy1tsLSrcWmsumdwdebV4jwcxUDWHjOcNHPFVsnDFwhtV6r+LPPUhcFOW2/RSN4YC1DtpIyk8nv6txBPJ8wwHf5eCl7flwPWyWM+WV1cLlq7gs5KYKPq9pKIKzs1FrDi/dCNivYJrczwi0X/uT165mTxCyUn7/MZj/tDuqKZ43tmjz2aOFV5YEMzaV8R/+OKXDfnKE+vX7z41ZvTBBFPlwnfC1PgLxz1iCYPaqL8vyj545Qfp/3jykwumem8necfJ01ssp0VPHHnhf2h3jdxWsmVu49v2C9YjnsDBSeDh2rUV3z0Fo0WIgV5jE3HZfPkoU+2yBVVEearADHeUiujjW/s2p8K+Cf21dIFsSvVBH+5GNBO1/n7gFGiK9/5erxLEFQFJwxgUiE9zj2pJ2mKNDjvcNj540EKxBFSzzGHcHnLFrhj99fHKk0Fv7E8wzf/vJF0v0Psna/bR2c4WnY07cVf/rnCCZnnSb3AXQEJn2eMaaLkkQVaD3+ODhOf7tqL1z0/GImsh+rbmD363iv4lXhr6XFp1bVgYTLd5NNTU3w2GOPwaWXXgqLFi3yzloR7R5tcgKFrq1FzxM/UCGrz/aLOAnWC0LhxICELf3JLU5SMfDlSVN5wnvQXCo4yEiMhv9dOU7JnkS6ZiXYBYsxOIoi3JItUvP1+OgI5Tor3rh8vXIv5KXGwuDOafo3NQ7uRTKTYuC7e0+we8nGfcdU18na+mbbzZaHNzfpCdJ2v/DdBvhxXSE8e+lxdq/BQLkzHjp7OOtj9+y36+GTZbv8Zl2lbjAPfgVteHhlHIcHOcQgo1VWn3yRaF/74veb3EoEQ1EoMykWOmfEK6JedV2jIqJhRYWZG3Vn/fr497S1yFbl52mgWWv1KVbianvr5Bv0wtMSLQchvWX1yVfR6Fg12+O4tFoKrCXHGO/38lppH/A5lZHVJyYc4LiH+5EL1Vpw/jG+ZzL75w44bnkS8MH31msq/pjwp5lH7jhUoViUhcuVJJjgwM8/V4NDZuydXUEU1MV+ne6iqvJz8JyImJgQiNBcqu3Dr4t6Q4JY0YSnX6vUIFT3moEJVNplcl66ajwcKKmGeANbXXvhz/h51e+hHdj15ZtV+9g/7MPniP1Hq1ivURxjRbEhMSbwrT6R+RcNVI0tXVNjmGDDh944N3v1juieAX9uP+wTwblPXjL8+7yR0DMnUV3xp6nA0x4TZ4+1WWbrIR5z+PuQLunMIvb88d0lhwSDHn+eVCbhcYP/sJrbeT9A85wxugt8Ks/lcT9EGLh04L3Jf2XnJu31NFz+u9RBxZ92/lmQFg0vnN8Trn9vK7y37BBU1jXDtZPznK4vHzqc7cu7TugE/7dgr93jET5MdOBOEsjCWwbBzKelPp9ZiZFOBcgJfbLgntlDLZtLWQFOU/WmUhifem7BBiVRECv++DhnS5y1fm7d2NzSLm0+EZe3OiwsDJ544gk20SIIb6E90Un8sZZKoTog0AmGxvKEd9h6yNbfYFBHezsHlskURNWrvjxf+OQwmET+9gTNpYKHLpkJhuILtx36eOlOuO3tZSyQjKQlRNl6FMhjFFbmoYB213srFEs17fXN2U0pPq8NWOHyRTu2itoGW18FD8vaMpNsgifvY6hlePcMp8vB4DoGrtFOjnOgtBr8iS97FOmBVaHIsm1SRZvRunkS6BK/fh700R4/Q+V+NkaIn483y/jvtlMGKo9hUJNXnOL36+wYzk+NhR455qrAHlnRWwIAAQAASURBVP1stWWZ/tproljBqk2Q6ZruuBePtkpqb0kdfLTikM/dNMwGdBqaWp1WMu4/JlWXoD0nopdUhQLn7Lnr4cRn1sCs59fBsepGeHfpQdh6qFp1rHkaJGMBfA/2pV7FX0lVvZ39pzhu8s/DCtbN8ljnSk9Y8RyzuuLPKksqtdWn5jmDz8DrViBDc6m2Dz82+TkqJmKJ5zkmXPHX4k+xYovTOy/JMMlpkJyQ5Y7wJ/YiE88zZ9VQWvh1CX9ib2lMPrtuZt+gCVSjladWnLhsuOSMgXRJsX13rvDgWcNgwT9PcHlMdpchXdJU837R6pMfC9pVmTbQsQClHXMxAQqT6YZ3y4DoSAdWnx7OVXnSk5UiyrnjuqmqXfsJriicrYfqYH9pPSzefEy1Hrb3ycJfdSMkRIXCg7O6wGOzuyrPa4VCfj+TlWgTwb/6+4iL7gmOXzepdwq8canNHthK+02zoPX6jH4p8K9TOrPjPU+2aB/bI8lpMpK2WlCy+vRvnAzjdHrHMM4xudCHSbHovsETQr1Z8dfc3Bo046nVuLXVU6ZMgV9//dX6tSEIGbvzvA3F9jGQ8cintmCGP6isDR7hz98XLMJ/VNZJk+BzR2XB8f1T7Z7HiYGfrcsDDh4o4oIEVfwFLjSXCn6Mbh4wSKC9cREDVJjZKD7nCbisF76z9Rnhff+kij/Plm1GdHLldlgcj0QR0JekyFVP/u7xJwYvOQ+cOYz9dKcvmh5ijxy+SG3gDMXdf82xZQhruW/OELvjXVw/7OPH55TYj9CZSPHatRNVQVI99JbhaW8fm9VnM2zcXwq3vr1MeQ6rUT2p+PvPd3vhjT8OqpKVfCH8mB0/+OscHVdzhqkFfLHib9eRWjj35Q2wYpd6P63YVcGy7m98f5tqHPA0SIZBohZPhT+NNTEmXXz39372++xRndnP0sp6u320ZneJU8s2I5RkD4vuW8x8b66g7esnYnTaBoPVJ82l2jZaq08UZTiYyMF7YP77vBFKgBmTMlLi7BM4nrpoNHx7z/Eur4NWwNP2n8P+hwqt9uIGx9V7MuzJdspwyeY9WJndPwOuGJED5w/OMt3jz9Gc2h+orD4FcdkqYiKk/pNiQhz/PI/dDuT3a/v9WbFMBMWoGYPy7V5z40f7obS6ya7CT/t3SXUjJMaEsVYuKbG2+wLtZvNzO8bAacARrkwn9Po1u3vcugNWNt4yoxMc111KUnj1kt4w76p+0D8vTrFiF8H91jMrWt+COBB6/LXo9/hDy81O6ZIVLbrlYAIh72/Ok6isLvxZv7eEzff19mN7wK166+OPPx7uuusuWL9+PQwdOpT1+RM55ZRTrFo/op2iHaTakvSDvXL8TaMm6zWQoWrP9ktNvTQO9M6O0Z00UI8/x1afCFmhBi40lwp+eNWWHrGRkghSKN/Ii0F67GfAHtPc1LgSSEB7oPd+2w4vL9qserxevrnHa6engQlthi5mzWrHYnETRjmp/rv3jCFw7wd/KZWU/uDl03pDcVWD0ofG31afIgUZ8ZYGlPQq/rSxM3ycB071EJ/j1o5iPyM8rnnFH1ZcWGENyKtWRW46sb9Hy+TCIQa/Hv9Csk7i3P/RStXfZgOM2soGqxPV+Lklfo/4K/8UszFkLuI52q7eObGK6IlZ+qJbwJp9laxP4GPf7FG9R3yNtGy144C74PHvaqwKhfRi+djDe0ieaDGmZ6bSJ1Kscv1s+W5VDy0r3CP4eWtZxZ8FVm9mhT+9z8DerPxaFcjQXKqdWH3K54MYF8AxCAWS0T0yoWN6vFNRxl27Q+3YqTeUoo17YUm1Mg7pCYZoQeyo2kRblaydg3mDy6b0cimByx3O6G/rpxiMsGsSt9426PFnZhlG9MpNhlW7jsL/vt8Ij5wzwvR12yXhr9k7wh+uH85nsTJ17kJbEqI2BqF1beDiCzoSJESH2b1Gm5zHE4rc2R82q0/nr02KCYf7T+0MvbNj4eyXNvi8x5/ecZMaJwmiehV/U/skw+o95bD1UC3sP1ptN975u4AC7xv1jn2svOMW65z1e0u9WvF32zvL2c+U+ODpmep34e/aa69lP59++mndi3Nzc+BPEonARttvg8Qfa9lzpIo1j3ZmSxAI+PuCRfiPGjngYJTdJXqXry+sgk1F1XDWiOC+ubC64o+sPgMXmksFP3qZuPyGBW18BhSkwN+7jrKqKLEKhNs5aa9vrsQRJvTNYcKflgZh2Z5aBGkt+7DvqrY/m7gNV8/o63B5aGk0/66Z8P7v21mPEH+QEBXG/gUCaDu2udDeQtWqRHIxuM+PBb2MYEfZzGLwhV9XxOADXnN4ljqz+nRwzF1/vOPjg8Mrs0SwF44n8AAqCkJWuV5or69W9/rjp5a4Rz+4uh+88usBZp9luuJPDr45sokamB8Pj5zeBTITIuGKtzarREUji9BC2R7UNhbyCgUPK/7csHHvlpWgBNwrahqVgPusEZ1Vwh+uWVailLBRWlVn6b0GH/t/WLMf+uQnQ0K0Z325+D6wypZYXIwZq8/3bpri1yobs9Bcqr1YfUp/i0kB2GMYz10emOfHq9XHrb3Vp/3yUbB58fuNMGOQLbairSxhVVcOYs51Qm97xBc97c4cY7NXJLzXH9qRTel547uxeXGJUIXORTNPjwHF5tzCij9xW7iY3jvP3u5TdA5Ik8Urjjgf4XPy9Hjba7B/n4gnFuKK1adJiXtMtyS/WX06Qq9SDeds3TOjAdYfY5bmgWb1KSWh2v7myVh4vK/aqbZq5Xa3/L7DynVvFeaUkR46iAQrbp1BLS0thv9I9PMfa/eUwNcr1dmYwUq1RvhrUyV/AQBmZGPzaK3AGohQj7+2DV6Itxys1hX3ecVfdIT+pYpPaPaV1MFtH22HN34vsuvr0t6wr/hr3/sjkKG5VNvg+cuOU/19/GCb5c2ggjQmDuw7WqU6F3lwR5vd7cqtZaxGgBMzyq2q+NNmpvPehEYODWbsU7CC7NLJvTwOircFxGzocb2zlN+tcrcRvw2b1af6NXiD7agCQS9YJb5+1c6j8POGIkiIDofEWFuPJT2mDbS3gzKLpz05eJAJz0dtLx13adT0wbNa+OM3P+I+TY4NV4S4P3errTeNMFs5MLxzIiTJVriiqGk0r/pytS1oEykExnh2urug0IWVZq5UmzUKAaJF6wrho6U72e/Jceooe3piNKQnSrZ8YpBV6zRz0cQebq03smRrMfznS3VVqSf3P5ZZfYa4ZvUZDKIfQnOptg0/bvl9oni/WC23hOBJKfw4tlz401x/9M4XdIB4+OzhkBQbaVhhqBX2tGjHPF8If4Rz6hptcwZ+bPG5tlkcHZJ4nHTNTIBdxRXw8g+bWGzE1lPQIqtPL8UD+P7Qc/Hgc48+ObEwo5+6ZYt4v5AoV/zhuqJ1JVKnmXcYVd2Z6QfMX+HuLZE/K/5E9Ma1zIQImNI7CSb2iIO7Z/UPQOFPPYfFnu/IT+sP2L2WtwHgY76V694oHP/+3if+wuWjuLGxkTVS3rBBKn0lAoc73l3O+rz4urm8N6iWbYM4VPHnHdBfOdCxwn6HCFwWbSyFm+Ztg4tf22QXXKqRb5D0/NZ5wBLjbg98tUt5rL2PFXz859UNJPwFJjSXajv0yFFnhl41vY9dHzfsGSVey3jFX62cfBMfHe40I1gL74WghY+jeHn3tFJEG3Qqr1Fnk2ozeilI5RollXWKiHvvGcZ99qy1+lQfE3gs8n6LaIGoRe8YEoMPR+VtuHxqb/b9a5ePwdBzxnaDvvnJpoMnV0ztbfeYo2o1M+C5hZ9vZfBrfA/1uV/rrYo/zab/tlWqEn311yJTy+FBDjPCPK/WQweFt5ccZHOK+ibn86oGQQRNj/dM1OdHybnP/GT6PUZzHRSkRXKSY5hIgMe8WPEnJhnmpsTCueO6u7ze4nmxs7gCvN3j0cqqFSv7VfkSmku1ffihabM4tp2rPHgcISdDeKP/mrQ87TqZW752zD1cUevwfH9p0SbVY1orPMI/HCqrtZsTuWqD7OyYOVgm9Qj+fMVuKKtuUGJ0nvYjw2QX3kfQG/D1w+ufdk35uXr60Aw7q08x8S0m0vac0daKtyMnDhT7fJoQ/gzmUmaJCJCecGjFjklWV03MVR5LiA5lrh13H5/N5tmuCH/YI37vkUqvrS+fw+hViOrx5EWj5deo7Z2toEYodtEm3bYXXB4BwsPDoWPHjlTZF8Bg0MdRv46grPgj2lU1nShet9esjPbC6r3ShKO4ogEWrDsKs4bYekRV1zu2+uQ9/vjrlImkZ8nmbczqk86fQITmUm0LFPvQdeG4XpnKuSdWKWFQWryW8exhvBERBQ1XbkqjDMZFHozAsUDbI8NVtEINBiO0iNtFwp9rTOmfC58s2wWxsvDG4ULcoAJ1hrRnVp/8MfVrULjLT4tjlauddTK29fQ2vaCq0p9Q8wa01Ll4Uk+X1rtTepzTigt3wOPTyhv+uKgweOeKvnDhq1Jfm9IqayxEtRad2n3qajWLUvFnYjzgy95ztA72HD0E0/qmmLIIK6uRxrRHTvfcNo5vL+8daQYcY3EsFRMREO39MO+tif3rVD3+hPehNbM7iN+LWE3oLnydPCz4MNfjL0iq+7TQXKrtE+Kgxx9PPOHzLn5OW30821XImnwfTzTBcQjnZpgEZsTWA2VQLAhMgVRpRNjg11GXhT8nz4vVoCjIKBbdHs59/nFif5bMcs64buANxHzrZM11lYt7eolbmYm2BCHRbt6oH594ab9xaj6LFS5YV8KcFpydJ+7mhKOF5vbiWjvR0l8M6ZQAX944kI2J/fPioKK2SRqbWo3nJI5ivZfM/YXdhy689wSXEk9dTcgSxWtHQjavBvRGj78DcksCpL26g7l1FP/zn/+Ee+65B0pLpQaMRGDBM8mDXfgTA0jtvYrHWwTqfl2wel/Ai5OENSTHhukGJT5fdRi+W19iyupTJfx54XjZfaSWjauHKxrgpZ8LVZ8XaPDN55PgIxXSuhOBB82l2g6nj+wMD541DKZrrAz5jStea8UbGB4wQMvB6Mgwt3qOGWW0c2HDih5/2SmxStWiodWnUGnDK40J14jXCH/YR/GjW6bCY+eN8GhXit8+z7LV5mTzqkOsXNUTbvWOM71Dj1egal/PKzFcQe8zrbBuw+CwVsTKSrId3+6ANksvXiAJm/tLjYO67lAjVxDGaPahK3HA79YdhQ2F1aYrB7SvmfvTflU1n7N9MbyzvXjsKu5U62CAUS/pFedCYg+rDHk8wypbUeATx2d3b42sthe0vOJPWD/tMvmf0QYJJYEMzaXaNvzY5OeDXjBY2//L27EDs0Hyg6U1qh61ew5XGjpzYRsWs84OhG9JT5DsocVx1Grx4LZTBii/L9tWrNhVe+p2gIllmHwlJiVawXlyVXxqvLBvNOfFwXLpnkHvoyf1SlF+Vwl3Sj8+W5UbEqdxf+KW54s3O9cjWnVs083w5Fk9WF/lQEpq5NvQPTMGhhY4nm9JcbIWp1VwrorYZsG+8NpxzMzxrFT8WRinvu3tZcrvVPHnAi+88ALs2LEDcnJyoFOnThAbK2XPcVavXm3Zl0S4DjY6TowJ3t4pmB2AA1BaQpSSGVWLgXb7BGDCQwLV6fOXjUUBL04SnoPfLfbn4/zv50I4ZbBkNbZsp613jdGEiwt/4v2daB1h1aTl6ne2sInK8QNS4es1R1nw+84ZtsrEgKz4k/fZ01+vg29X7YPnNH3ICP9Dc6l21JumBYU/+x5/eNOFIg++qqK20WMbQhwr6xWrT88r/nB5r107AXYcqoCb31zqtOLP014krvDxrdMg2OEVTXEaO0JE7BNkBTYLNPXjzqZYegFOPUGZCwbal2PFn8vrqrN8K8QPFKZFkT05NhLevmEyzHj4W4+W2zUjholeaI/pjaBJjCbgZZTdru3rWVrdCM8s2u+SMKXdzyv3VEJeii2w54jnznO9L57uOrgR6MR7RwwuaR1jcEy6bEov+FgOombIAVw8FkQRWBzHThrWyb31dnCMVtQ0wFu/bGXBUjFQ6gguXlhVvaSu+NN/DvchP+6CBZpLtY/EGD5264l62jHR6n5mWrHR7OWoWBbz+uWnMFcIFHNWbD8ML1893u61FbUNhj2vCP8yonsGu5cWx0pXe/w5a68zZUAeO27/+816+HXjQeVxTyv+vMWFE3uwfyo058V7y48Yzj0wyQgfbTWo+OPLeuqsHrCnpBayk/TnxP/7+YDKLcpKq08UF7nAGIyEhISYqprDa36UF5IMeDsLvM915Xi29fizbhxvEXaDGReLtohb3/CsWbOsXxPCMoK9uoNnHyTFRCjC37HqeshNVQvMhOfsPlzBBNZAIyXOtk5k9dl2efaH/SyopHdRNpsRpJ3PWF3xhxYSfLlVddLY+svW8oAV/mw9/mw3i1uLpH5ARGBBc6l2ZFHVqk5KUHr8NTSzvhsjumXA1yv3epR1OahzKuw4WA4NgtWnFQFjzBJOkUUoZz3+rK54cUQwJ7hxsPcdiqrXH9/PK8sXA/38u9EKeXedNsjhMvS+0djIcFYJyoOaYoKO9hjgtqWu4K12Y5gQI1ZVzBnTRemxiUHlrpnxcOEI97IM++bGwuLNx5j1UkK08e01Pl9V3ww5BkEskZqGFl3XgwdndYGr3t4CQwske1Vk44EquOXD7fDAqV1gdDfJLqlEYz3qbuXAl6ul4J0eo7omwnI5USspxhqfdbcq/lpaTVWXZiTFKHMksWIDhbkumQlwz+mDIc/N+01HiQ8/rj/AAsf475+zh8D4PtmmE7msGlfF1dMuccsBaZ4o2rQFCzSXattgQBp7dfLrTW2jZJEuzj0iNAKZ1YFd0dkAcfWM7NfRVt2050glqzjRVmDpCe7B3rqnraB2T5D+Sk9wzS3ATIUgPyaq6xud2voHIkbXbmdzD9FKU5uMlhgTBgNjbHMdzkkD0+HzVdLc5L7Pd8J9p3Q2TIriiwxOQ2v3cdbjj4PuM8lgbbIhX65dxZ+B8Ce6DfCkVdH+1hOwb6ZIe7X6dEv4u//++61fE8Ij3vp5a5sR/ni2phjUQeGPsJ57P/gLvr/vxIDbtaIYaWSJQQQ/CzdIVp56lNc0scoE7J9jhJ5rldUVf+q+XHIlTQAfkvx0ob4QgQ/Npdo+ol3JR0t2qOZpOw+Vs54DaAHFs7pdFf5QbMEMYbTxQRHpnP/+qGQhY1a8VZnCibERxlafgTwgBjiZSTHwwuVjvfcBwjWSi8DauAzveWZEg07lBB7X79w4GU567DulsoIfa9rAD4pqriJWqg7rmg5XTusNVoBVXvwe6aKJPZhFL69KrIRGmNQ3C0ZJWqDLZCVKQZP/W7AHHptt3Evn4tc3Mbvw728dbN4mSRP8LUiLhvDQDipB6IeNkt3Vl38fVoQ/PmdB8KXeKBy4+8ROsOVgDRyrtq6/oTjvx9/N2OpJPf6MN/De2UNg/so90DMnUTV2IhiER0F4bE4i63fpLo4EOrFK6dHPVkOXzAmQl+r4s/h7PK3cNtPjT+xbWVXnXo9Df0FzqbYPWvQe4cJffTMkxESoRGp+z3PWcV3hoyU7fVDxZ+6cfOqi0fD37qPQJz9Z9fj2g+XQN98mBhrN/7g1MeFf1P2Spd9PHV7ABOn/fLXW1DLMiA1c6K0WxmBMtAoWjM4Ko3sRflbFCMlNNpHO8TmWm2wTqv7cXcFaxJw9Mkv/c7h9qLeyygIUnJO0yuOXo/nJ3sOVrA+k1XA79RhR1BPWY2jXdOidmwTv/bYdxvbKtnvNne+tgMun9oI5oz3rHf3yD5tUf7vr6hDseFTTuWrVKti8eTP7vW/fvjB4sPObGMJ6cKLwwR9CQCnILDq0VMvWR6Lwp2cvRbRdsNqTQzHF9kNSTJgyQSsqq4cB+XGQHm9c1aE3ibG64k/UEWuDIKlCsfqkXltBA82l2i78JhNvutbtLVVZst/81jKl0mJ4N6mCuF4I1JsBz3MMcPGxEP9ukJfR3NoKERZViqAwicvWF/7aZ+ZkMCB++/xQcDXw4eieggXAmtWBHe3yMUDrKuIy+uQlQ6d0+2xvdxB7UGYlxSifc/PJA+CRT1fBhL4YeLBZbLkCF+lWCS4GevAewVpbTkcVf2JQjBMdHgJ18vOHyuthodwTWcysF4U/nBp5I+iFY8+gjtZ8P3pBdvzdTG9CHAexeloEjx3OuD7Z7B8HhT/JKr4VDh6rYcExZyK4MxwF1g4eU9vAXvbir6zSd/rAPMOqnmYvWn0aHXv/nD0U7n5/hZ0lcDBAc6m2C1ZX7SquZOcEBpOxR6da+JPOIZ5E5W3hz5VKP17txyvLkc2FZQ6Fv0fOGQ6bCo8piQqEfxGHSz524ng/dUAeFJXWQOdM59dAPGaRjg6SS/i9O6+UumCCNfbZvqJ/pxQoXneACT01wtwxLU5fvESL9OKKBkiJDbeLBSVEu1bp2Ogg8Vux+oT2BZ+T4DX9iQtG2T2PsXa8r1u4Zj+M6aUvmlrh4oe97DligtagglQ4Y3QXyE+Ng+N6Z6ksSjmfL9/tsfAnbu9LV41rE44xPhP+Dh8+DGeffTb88ssvkJQkNastKyuDSZMmwYcffgjp6VKPJsI3aIe5usbgytTTwrNxc4VMyDKq+GtfCFdmqvhrm/yxXW0/ibZXPLhTWt0EtY0tkJfs2IZWLxjizYo/HoRDdh6ph66auTtmjaNtmxhs8lvFH9nDBDw0l2pPVp+tMGd0F/hk2S5lnsODPDiM8ZsQV4NVGDBGlwSb8Bdqq/hjwoI124GCAQo4FTWNhmOk3k0l4V/UVp/yjbSLl0hesddDJwApCh1ccNIWXfH+TK4gXtvNiD5mEfsFiz1HsKrwyztnArQ0AlS5t2xRZDMDzlUiwtTbtrmoGv7xwTbFrrPGoMcft0XiyUgPz7fZGInCH+/36Q64q/SGo2l9U9i48v2GUq/Z+4rzLkzmMtMmkmW0a46VIV3SnAZY0RIQA7eIpxnvjnTVwhL7/o8vfLcBVu08Ag+cNUz3PZi8YVV/S7Yc4bsyWiTanD5+wUjISQ6e9ho0l2r7YOUbzmkOHathbWC6ZCXAJZN7wUOfrFKN7Wb7Z/qDZy85jp13l879BT5ZthNe/XEzC3qjzTAmn4g94wYWpCoJYYT/EavPtOOxXZ87A3rnJTMrafxujdAmgZwp25EHCzed2B8m9s2BJZuL4Ls1B5TH0+L154FPntUdftpcCv3zbAGVayblMaHwHIPqPZH7T+kMD8rzH0dzEaWKsJ0pf1xkw/6ienDhz6h/uzvzu6VbDsGDn6yC92+aYnOtUFl9qlsQ4Pk0sV+Oahni51rpKhMbFaZqJ9XecMv044YbboDKykrYuHEjlJaWsn8bNmyAiooKuPHGG61fS8Ih2ibHny3fHdQZ2GjXwgcjboFEVp/tC9Hdk1cwEW0LMVDFM9q5DQa3jEo1yBDj6NkfWV7xJyxPrMbZU2JvPzz3u41w85tLYdUu43443mbeH9sttYYivAfNpdo+XATRztN+XHdAdaOf5mawSgxe82VhotTxjyyA4rJayypFkCgUFRubYc/hSrs5G9odOQpmEH6ig/2NdJOL9wfdsxPhsXNHwL/PG2mqh6D22uPOTbuqMsnCY1js+ySKgFZwwZhsh5ntWvTmtt+tP6rYdSK1crJRlE7vOnyMi43YM1DPUstVMZLzxQ0D4JNrB9jN0T68uh/cMqMjdM2Q+uRZKUrpCV7sd5PJEDgOab/TWAeiM6/+nPX49yzbHSyo+HN0rPMKjgGd1FU+y7YVG76Hn6qhPqz4YxWcBWlBZTFIc6m2T3qiNEf66q897Fw6rleWas7BBRNX+66ZxYo7y9zUWDbGZKfEKIH2T5ftgh/WFsLmwmOKW4M3rk+EZ4jDpSfj8YS+OZAk98w2Y4lplV2/r8DkQxSstettdL3JSIhgAp+4T2MjQ+HisTmqvn9GjOkuFSA56yPIr6XtLTTi7FgV7c5FFv69D054dAHrG+8qKPoh98xbIVh96vf4czQP4eg5zbhLbBDZ5noDt0aThQsXwosvvgi9e9t6LvTp0wfmzp0L3333nZXrR7hx84iq/vu/2aw/gw0+CGH/Cmy0jmA2FOEdArGiTlwnsvps22CW1wvn95Qs6uSgHM9S1wt2iYg2Er7s8YdE6FQhrJQFv13FFeAveOa6GDhrL1ht7eNtaC7V9uEiCM7TjJJYMAs4Nd69purcuo8vGcdR7M/EP8vKBACsIt5zpBKuevk3+HrlXo0VX3AFJ9pjljqvcHNHiMM+HHE6Igq/OceACz8WRTtJzPyeNjDP5c8TYxVKpaIFbBOCGFYfs5io1DktylSwCrn70512j1XXS9cwPH23HKxm2dJo6akXHImOCFGEQRHR5Vvs1Taoo/nedWjTpbUXxUB0cmw4Wxdv20CKx2ijC8IfHo8juqWr+tUZwQPrOFYul8W3rCTPRANHPZxq65uYxdt/Lhxturd6ix8q/oJRcKC5VNsnVa7SWLS2kP2c2Ddb1dKA/57pJcHaylhJYrS9zdyyrcWK49Vb109qd73IgrHHnzcQxSteDRWMiNeR3tm+qR53NFW0xZVcsw8NdlqdpCzwZDjt3GXe75KOsGqXlIzmCvwQxqRBPatPUdQzOpe8ddw3BVmcyGrcmt21tLRAeLj9DSA+hs8RvkWbSY7sO+q4x0Qgw2/48GYfBwe8eHhiV9OWKaqohy2Hq51OSB0FegJRWBPXKRCFScJzkmPC2AT37hMLoHtmDLO8qm+Svmt+vkc66VNXIQS1fFHxhz70HExMMJroWi0+uoMY8PeGFVeg8c2qvXDSY9/Bdjey0/wFzaXaPvymBudpRkPT5VN6uW1PZQsM69sEWRmgEINsaFEnjpHeDIQQ7iPeO/P+MlYmSCi9boQgj/iZd58+2K3KIVUloYWH1u2nDvSKhagomPEefs7YVFRtmMy0dn8V3DRvG+wpqdO1+dRW/InzEX69x7lzea20vJcv6gWPz+nucqAzXbDnihRsSb0djBRda8wK1SiSRkWEwcPnjFAei3OQ3S32e9Szf/W0opSDFdL3zPsTistrVXZXIku3HnJ4f29djz9wnmnvhfPC29Bcqu3DEzWw2g+P3eTYSNW9DT+f0xIC38ZtUv9cu8fQsh0dFXDbspNtFdVEAFb8efH65y2LQ18jzq/uOrGTVz+LW6Y7EvX43AoTptoTenMS1fPy/YBoM8zHI8Ro/umI/p1SlepPW8WfbTlixatRfMrquFWB3Cf8+uP7QXvGraN/8uTJcNNNN0FRUZHy2IEDB+Dmm2+GKVOmWLl+hAn0ssi5wh7cFX/S4ckqgYSGx8GM2LjZE/CG/rfdx+CSTzbBTV9vg1dW2GzD9Hht7VHDi2LgV/wF3voRnoHfaWVdMwwriFfsPFUVf43Sd67tf6Olqk6n4s/iibLR4rSTEjxmeaaeWWsqbyIGitp6hltpVR08v2CDQx/7QITmUm0fHlzF3p87D5WrBBjkrtMGQdesRIfWP47ggW1+juONlt7nW0FVra2/n5hVzyr+SPgL+Cx1LjpY6aDBr4Oi8KTXB9Kz7HrrAjVYgcjxRpUqVsSV1TTBil3mElD2l9ap/hYtO5GSqkZW8adHdLjU4w/nHgVp0XYBt/eXH4J5yyVBKd5B5Zsj+uTE6gr/3i5CEBNav1u9z+nr0aYK7x3FsdWp1afO9x8V7pnwV5AhBZdE7n5/Bevjh/Pbnrn2fTKRZ79dr/s4D/xaVvEnLMeoooj3BAomaC7V9hGnGBiMxuNXPJ4j5bkPT37Sq1APFE4dXgCPnDPcrhIFx7G2fr8WrIijpTcT3ax0OPAn/PqKP7IS3bu/Mcs9J3Y2dGLi8CQpo/lUW8VZ4YxS8aeJT3PBzihZyRH8PahF6PX4yxESG8xYfVpBakIU64vdr6Paar294dbR/8ILL7B+fgUFBdC1a1f2r3Pnzuyx559/3vq1JByilxESzMIfL8PlWYc4iXNknxJMnPJ/Cy1ZzpK95fDo4j3K359vPOJQIPtln1QBym0kRAJRVlNX/PlzTQhvUFHbxAS6FKEXDlb3NTa3wrId5fDAV7tMWX2ieOhLq09Hj5/42Hewv6Q6YCwnsUcqJ0rItGqLbCksU34PhmxfDs2l2j48QPDT+gOwbm8p+72vfONx4wn9YFK/XNVNTrcsyd7cLP+aMxROGNIRjh/S0S44L36+FRSW2iqUsHrl141FgsVe+7qZDhbEb58fG+5Wl+rBjy/x+0+Ok4I8k/rZRDbXafVKAEAU+7whVqOTAfKvL6Q5jLN5w+VvbmYi4f1f7IRzX96g6iPMMdJ80FIUpxr4T9wU7JmDvLvUVkUWHxXqUd9C7dji/Yo/235asV3qd+gIfs+rFf4cWX1qwTmop8caVuq8e+Nk1WOlVbZ+0NMGuGZ7q1g2h/jO6jMYbZtpLtX2EcccbmEsitdiBe9Ht0yFt2+YBIEM9kG7dHJP1ZiHMZqIdiZMBAtm+qNagTecCPwBv474IhzCk8QdJX5zW3QUf9oTjZpKPqSythFmPPwtfLx0p9Djr0V3DqZ1kTEDn69gdTYXEMU4lJiQ5auKP3SRCAlS21wrcSu1LT8/H1avXg0//vgjbNmyhT2G/f6mTp1q9foR7lb8ySdaMLJhX6kq61CqBGobFX9WsbNE6uMlcriqgWXYpMbYZ7k50kKo4q9tgpneX64+AmcMy4AzR2RCIFEh208lRNsuQZXyjRwX/bTWAHqIPffwct7qBWsMo+WhSGn0ukCw5xAna55msQc6b/68Vfk9kLN8tdBcqu2jd6Nx+ykDWZBKm0n57T3HuyzUZSXHwE0n9lf+Toq17x1jZbXULxuLlJ+Pff43TOibw8a79mAnHIyIhx8Pkk4fmMcEkoc/Xe3x8vnXLgareuUmwQuXj9WtfjKLeA311rHlrYo/R+glMaLl5/JdUl/gWJ05z/5Sm3AkwqssMdglBtfidKyZtAkBZslPiYKhBfGwak+lyoHBlz3+sFraGdXy/DFWroCePaozbCo8Brmpxn0NV2xTC4poE2oF8dHGxwD2vOFM6Z/LEkK0vTi9a/XpPHgdjGM5zaXaPs7OAbFS1V0HBV8zZ0xXGN0jE6546TeWQIWuULxykQhcvDlGtpUkOr12KN7+PrSxF/HeBN0R2mPFnzjn5PvjgJzE+fpPW1TP6eFOPIvPW9AlJk1ONDSabxvPQ6z9nnCdQoNwbmM1Lu3VxYsXQ58+fVhlH95ATps2DW644Qb2b/jw4dC3b1/4/fffvbe2hOkef7y0Nhj5ZpVk63K4olaxrrLKItPbPL9gPXy90laJ5y2B7ZddUoXLlxcOgLsnFbDfL/p4E5z7wQaY8frf8PpfauvPZgefHQAahUMxmyr+XGdDYRW8veQglNc2weu/FwVcxSxfH7GiL0JnUpAm9JdxJvzxwKNWkPMUo0mPuEu1rwmE/S3ajbZ165h9R6sCOpFBC82l2g969xkYwNKzT8EbI08zEvM0gW4rG5ljf7TPb58O+WnqzyDhL7jA+7fRPbMsDcZqb+pR4NCzUjRLkw8SabyR/Zsc61g80pufiGPEPo31J2K0ljyIgee4uI+s3l28MlKcr3k7cVq9Pa1wuFy6HzSiul6yl42VK/yunNYHnrnkOIdzH22PP6sqYR0Fl8QKJbHf5LHqet0EV6XizzKrT9vv2kz+h88eDhdO6AHBBM2l2g/iKYCiuT+FBiuvQXw+VVhSzZIc0E2BCDxUSRNeFf6C7zj2d+W4nvCHCVUn/HcN/LLlmKbirz0Lfy2GxxjOI/XiJ3oagzP4vKWqrlH5TK2FOO+nrO0tyLFa/5buU0OgvePSHnjmmWfgiiuugIQEeyuixMREuOqqq+Dpp5+2cv0IE+idkzXyTVAww32HWcVfAFjnmRUtX/huo+5zVvWqw4G5qKJe6fExNNc+q/rjdepMVofjtp8C5TsPVcDchWht1Oxwldy56LRX8AL/0s+F8I3c01FPIAskz3G0VuJcPzXf7nWpTrLnh3WOd5rx5SlGQ486MNkScFbLuB+O65XlNAM92Nm4X6oQDyZoLtV+0BuOvGk3ohVbnDV2d/VGHi1axsrjCoIBeRz/gtEerj1gdKxZFVvigoTVfcHE63idxQ4m/BzxRv/oFDcq/pyNB0ZPh4cIFX+abbFyHsTt08X5WE6SVFEzMN+4os4TtOvv7D6gWp5zxcgVf2Y4eVgnO+tUK8Bk1RgT1YPaHnsvLrS/d+Tfqzcq/rTC34juGXDe+O4QTNBcqv0gHrt6SSXenoN4KxKB4wAmV+wsdl7ZTPgRYQgmq8/AFP7EuMyvWyXB79/f7oE3fi+C3Udq20UitBYxochm66nf9klvTtykiW+5Mn/DhKy9R6p0xcaLJ0k2xz0EFwQRq88xnEOG0G2qa8Lf2rVrYebMmYbPT58+HVatWmXF90O4gN4NUY2mQXwwwXvcnDS0o3JzUlHTAMeEPgnBiPZG1t3KlHLZ0mZIjiR6xEeGQbJgmcgpq7WJv44CHP7S1R75bBXM/2svrNx5xO45cX29EZxpa3y9ch8s2VEF36wrhS9WH4Gf5QwnDvabKalqDGjhL1PuSyMSq2NZJXLH8Z0gRc6w51npVvf4M0o6ED9H29Nv0bpCePmHTX6tPps2MB/uPWOIbmZ7W+KWt5ap/g6G8YLmUu0H7KWgxZfZwtsOllv+GWjh+OBZw9jv36/ZD4fKaqG4zN5+nPA//N5ZGyjVig6e9/iz9phOEJJV9HpTe8IrV4+H64/vC53S3bciNSJJ7vFnNBfRq/hz96sQXQ7E+wucX2Gmu1XkpUiVcD2zY5XH+ubGwfwbB8Ljc7qBN9AmUzkTMqvrGnV7/DkCLW9F9PorusvQrmkuC7Bbi8qNrT4tPl/bSgCU5lLtB2cVV8GcfBQaxOvePo8/732O1UlU/sKXFbixkdI+qzaIfX/0ZzFsLJLsLSMFy/L2gJhsxuekf+9SFwdonxdjV7X1TbDbxaQEHocpqayHLQfKdO8RThnWiTnI9MhJAl9AFX8SLo0uxcXFEB5unE0XFhYGR47YB/EJ76IX6AyG4KcRKfFRLEjB+y1g9iRal5793x+hLQm07ghuKOZd9bnkydw/y5ZpO/fUXpCkaWK/q7TW1Gf5WpzAY3PR2kIoKpUChev2luisk/7vhD4vfL8FHvr2oGGQYfPBanhukWSh6yuw+frGAzYLRpH6Rmk9xZ4xejgLTmLgokdWrGHGl6fguWFULamq+NMJ5n2+YrdfLFvSE6KgZ04Sq/LDGxXcR+3pHAqGbaW5VPuhZ24iOyfvEGzdvOnkwxMgOPmptmC9lfTNT2E/P1su9WSt0BE4iUBAtmnU6R13zthuSnKIp8EwT2w99RBFOatbF+SkxMLJwySLfG8Kf6JgeaSyAe75dAcUldUbVu5xBnWMc0n4w2CNOPfbfkgtwnsa6LpqYi58dE0/OHVwunq54SGWCcha7Hv1tJiq+ONWn2YQ1z0lLhJuPsnWK9VT+HmhTQpzNt/8YsVumP/XHrv9YJWwLgav24LwR3Op9oMo9umdD2KfWW/gzVhJsLSzac+IR5c3LQPbitWn1XNCRyTHSNrEsWqbO0SMzpwX8dacJVARq/u2FUki3Lu/bdd9LW8NIY5HL36/Ca5+5XdVSxV3CpK0yRr4PaCDjC9jz6Ft5NzyBJfOytzcXNiwYYPh8+vWrYPs7Gwr1otwgWAW+YwGHrFCRawKcnbz19Yr/j5YWwxlcsVfepxtwEQLno/O6w8LLx0ET5wgZeC+/leR8FnGy/T10fPhHzvgyflrlb+//HMPvPfrNlUvInHfBEPPLn8iBhZiDSY6yFEfV/y9+HMh3PLhdvh7b6XdcwfL6+2CZMgn17oeeOmgDYJZKPxd/c4WeOybPS5X/HFW7TwCnyzbCb6+HohzG5zj0hkUWNBcqv0QGxkO7900BaYMsFWXePPmQ8x6x35Nj547wiufgxbswd5Puj2h11MSrXbG9fbsno0fy6FeCLoOKkh122rIX4hWn2LS0P9+LoRVeyvhxcWFdu/RVuRr53FG1V48ox4trLYctIl9Gw5Ime1691DufsdJcmDN1/dLfJzR3j899PFKmPHwt8r9gSL8uWD1ifA9e9W0PpZmnvPv1BWb2t2HK+GlHzbB3IUbYcX2YtX9vWVWn8JykmLtXTaCDZpLtR/EYVBf+GsblVJEYCIKRt7UD9pK9acvz8f4qFD2nXy3vgTmLT8Eh8rroVKOlbZ3CoQkuns/+AuOVtj3kY6TE6Z4LEsvkbOwxLzwp1eE4KprgahtWOF4gJWPYST8uSb8nXDCCXDfffdBXZ39QVNbWwv3338/nHTSSR5/OYRrBGoPNHcFSexJI2aKRAhZifjczW8uhdveVtu7BQPaHhyu7h+0HPxyo62iNlmnbxdOTDonR7Pfd5TUGn62J+vhKT+tO2D3GGafnPjYd7rr5O764fve+227SxerYKSytkH5XRwKThiQCved0ln5u7Tat8LfoTJpvbT9BpE/tpdBVHgIDMhTW20lRIfBTdOkXn/RLvZbsdrq86Z5W2HPUftrHUds1SOK1iLPLdgAr/24xacJC/hRYnAHx4T2IJ737yhVIAXDptJcqn3j3f4gtnET+zVlJsV45XPasn1wW8z29VZ1z45DFZb3kuTcfuogJkyeOzZ4+o6hUDWzf6oi/C1YdxQ++atYuS7p9fjTPoaC3ve3DoarJ+U6tDznlmAPzt+tEhnLa5u80rvOH/e1WuFv+bZiqKhtgCVbi1WBqhrZ6jPGBatPhM+VrJ6j8f551U6Cj5fIfW60/OvDlVBWXW+51SdWnw8sSIW7Tx+suOoEMzSXaj/wfrJGooIvK4y8ycxB9v3uicAXnq1CFCe4pX4w4svzEeMcPP719pKDcPXbW6BCMw9qr9wxa5Dq7xqdZKT0BClufKSiDv79+d/w03r7OC22dDCLFXFlcRG4vFb5X2mVcVzOEVjFGNUG5jye4tIeuPfee+Hzzz+HHj16wPXXXw89e0oT1i1btsDcuXOhubkZ/vnPf3prXYkgqvjbVVwB17zyO9w5axBM7i/dvJoFb+TEC0ZkmCD8NbfCpkJ1/7L2YvWp7Q2Slxip+7oEHasbx1af4NMg1EGhFxDaTD3y6WrVazAbZb9QUu7u+q3bUwLv/rqN2eZ8fOs0aKuIQle1UH1RWdcMXdKlizlSWt0EizeXwuTekkDibbpnxjBP9YM61laFpfXQNSNaNyDFg1aJmmpAZ3DnDXcr/lbvrYC4yDDokRXDssXEDHpn+138TOxRis/tOVKp8RYHH1b8CcJfkIhh7oLXCrxmTOyXA+v3lQaFyElzqfaNN61mfGVlgmMMBiisrLAmrKdWvsnXq/izEqxWspq0hCiPrUj9QVaiVElV29gCzy7ar3quvkm/719qXLjSh5mL6vlyb73JvZN1P8fsnMLI8iqQsVX84bo3seTFvUcq4f6PVqrsi7EvH76Gj0OuVhngONYMrZb3OleEv3rHCXdnHtcV3vx5q+5zu4orhYo/a9YL99UTF4yCtgLNpdqp1afOHCqYbdzmjO4CnyyTbNMn9M3x9+oQTiv+fDOH55b6wYi3rXe1JEaHKfEjnHtpE6DaK2inedLQjvDNKqndT6Ng/cnJSIxmc/g7310uuSdstF/OwWNqJwlvFyRptQ2cE37x526WTD91QC5LDHQFtN5PbwP25p7i0lQyMzMTli5dCv369YO7774bTjvtNPbvnnvuYY/98ccf7DWEtZRU1jFLk+/+1u/RFYhxl69X7mU/vxJ6FThj8foDbDuxEWiEIAjECxYzdY1NQSvAemr1qa3ay4rXF/6QyV2TdTOJ9S7DvgyUl9c0qPZDx7Q41txVBC88f+44Yqpa0RE8Exg/sy0j7s+yGtv5gd99dmIEXDEhB4YVxCuCm8/WS/7e9ObHuG5RBhUjPEsLJ3FmwICZWGHobsXf3Z/uhBvelwIw7yw96PT1YsBbrPjDjPOCjHi/VWXj8aCu+POPXS6K/JgA4m165yVBYkwExMiB7QC8HNpBc6n2yZiemW0qy5ZEv8CHW7Hq9fgjvAO31tSr7tMLRuHrxGs0t/AcVpAAL5zfE66YoJ88yYVCZ+QkGd8rBConDevEfg7unKbMa/jcbn9Jtd09IZ9j6QkCZoKsVifQRskJq9jr2hGOAshv/bwVvl9TqKruJNTQXKp9Ci96Ip+35z58hMAq5CcvGu21nrbBWKHdHhCPOCkhxfvHeTAfC7623u2VrXY3qaylNgScmYM7Kr/X68xLUfgTLdP1cKXiT8/q01W0UzKs2PtoidQ658d1B1R9CA+X18L3a9RJdhxsJXXne8uZ7XoUCX+uVfwhnTp1ggULFsCxY8dgx44d7Gale/fukJysn5FIeM7aPSXs5wsLNsDxwslr5QlmNbsPV7gcbHj8yzXK73FCw0+x4u/8ZxdDoOIsuK79nly90XRFPIiTrYEqG5ohVXAERUHAXoAEnweh0HM6JyUG8ljmrm2SgwNzYak6q8Rd0cJRT4yv/twNmwrLmN1NsCNaFJXLwl9BWhRcOSGXTSDPGJYJY7olwSWvb2Ki2iu/HIAzhmeoetF4A6PDFbNuMBur0eAFQzolwOerjsDJg6SAjzNyk6WgVqsHwWhtcMaM69N7K0rhYM16uGPWEJXYiJneRZrMKF+O0XYVf2j1Cb7n6a/Xwc8biuC5y46Dnhb2ztEVOjt0UATmYKj4Q2gu1f64/0zv2/Z4o9caEbzwHmPRXr7hffz8kV5dfjDBhTu9AIseH/91WPP+EJVzghGzhqTD/DX2VuoimPx1ywz7+8ZA55yx3eCU4Z1g0dpCZjtlNIfasK8UJvXLdbsXns3qs9UrFX/8XtgdthaVKb9zy1PCHppLtQ9CnAh/3nRSEOmckaC0FrAKo5gXETiIh5evrO6D2b6Wz4N8xVUTc2HFLtv1tqq+GdLiwuGoyQSptkzXrAQ2xlTVNUJDYzOkxkdCSaWtECAh2nm/X3yfL5NCtbGcytpG9o+D28LnWbe+vYyJf53S46BXbrJdKylOFAl/rlX8iaDQN3z4cBgxYgSJfj6iNYisPvfItj9rdkuipdk+JBwxazMYwlhYrfjAx6tcEu5c/drENmKfnNff4Wvj5OqXKk32hl52qS+Pn+cXrGc/s5JjWBA0NCRENYG/7H+/2r3H3dVzZPvx4veb4JeNRdAWEAMWdU2t7Hx56cJekCfbRIkTsK/XHoXPVh2GFxdLWcTexEiofvVXab+v26/fe3F45wT45Nr+MK2v1CfHrMjNKatxbZKHYujrv6uPhWw5Qz4mwv4S+fblfZTff1p/EA4eq4EmQSnEm7Yrp/ZWvcdZUIlXO++2oEKuRVvx5ycx7Ff5/DogZOd7TegMwe2UtjkAL4cOobkUYSX+qgw5baStnywROGCiDeLt3ha9cr2X3BFs8GBdbYOx8OdIn6quNxdgyU2OgoH5ccrfejFCrBjE3snBSGxkuDKPxwQ3vT583F2GO0y46vY3QA7gY+87K+G96V9ZtNnpa7OTnfdh9ZWoEczQXKptI57betXUXseL9xZDu9oSXXkwmwgsVJV4PhJng3nc97VoifOhOcMzVPOotHjvJrgHCxj7vXKaFJeqb2pm1ZhiMpGeLWsHD8S8IxXmqwON6CfPzfj8CHs7i5z7zE+wragMthw4xkQ/pKzascNbFDmfuC/8Eb7DVsmg/7yVNnIo2O04WO7RMo5V1SuVXXqinh5i5gEyXvA4D4YLH1Yr/rndlrWLvSisrvjjg+7p/dJ1+/iJxMtiSJX8PWTGSq8f2jXd7rW+DJSv21vKfh4oUYs+U+Q+kNjfzyphsr1Y44jn2pIdFRAe1sHunInQTMB80fTY6HvbW+K8Ma8rgapxPZJgQH4c3H68ZA21YF0JVDqxVxL55+c72Xs42N+vUt4/KEI6m8yu2F6ssvrEXkppcqNks8Lf279IFqMrhDHEk/0uzuFYxZ8fxDC+yd7uu4GxQFXFn1c/jSACG1/21bj1lAE+tTEl3J8fRHv5htdXGfDBAE+0ciTgpccbZ1j/utV8H/Ne2bZ+d+J33CMzBl69uDfEOblXCHQwOZDPofTmUfz4Vqw+XZz333HaIHjgzGEwTOfeyBNcySx/ymLbQIJoi4iJy8lxNvtitN18+mLfnUPeCEmJ1pFBEPJql4jXH6rAdo4/7sUvH58LXdKjlYq/qCC2SrUanlCAfZEbm1pUVcZ6Ii0mLz1zyRjlbzHO5YiKmgZWmYetnDgnDnXddSIpNhK+v+9E1v8UKa2yb1V00xtL2T/Of79Z53CZUZRUQcJfMMArGYyGUSsrtq56+Te47rU/PFrGobIa1d9Fpeq/9ThaaRMDXrpyHJwuZI9P7h98jY5/32TfI0zbq85VwZa/P8xEID1WDgBUyoGHEXJw4LqZfe1e2+qHyzOKIyJihtv0gXmq59w9vH3dWNhf/N8Xf6v+bmiy32EoBop4szE1x+jwNnP8ugIGu/5zZncY3NHWI+HZRfpe33psO6Qeny56bROrjETS4uyDc9oqwMKSapXVJ+5a7SY6E/6sDM6yij+V1ad/xTArhD+sWPx8xW4o0tgAI1gFgBWOXOwOFqtPgvAGvhjbOZmJtkoVCoQEttWntyv+XBVc2oPwVyMkZWmJjzK+1mckOLdd4nTNiLbrLYikxIZBx1Rrq9j8OX+45/0/YeN+e0G0pr5RNcdy9TDEqsLRPTMtTzDl34WZQFNqfBRkJqmTxQiCUCOeo8cPsQWS0Xazb7611pt6ePvOAtsinDmmK+Sm2JI5iMBBtDqk+a5zahz0i/OlQPv1TQNhQk9ypOBVqrUNTawyLispxmE/Rpy79M5Ldln4OyDHafIF4e/GExy71DkiXrYh1asi1Oofziv+wqC9Q3dqwYAPK/6sgJ94vLpMr/oN7fGWbytW/i6RK73uOX0wdM5UV9nkpcbBi1eMhWCiQWeA9Nzq03zz+nhZQMCMF/bZrTaBDftmeLIeVoBWnyIxkWGq3h5WCNvBUClqBXguOUNb8eflIizGoo1SdacWbr2AFlRWImZ27TjsfJ+YISZSbzIUAhGCqIyJDuKEaFzvbLsgqJ5Fld4EOVo4DzyzvuygEgL8KYZZERDeWlQOL/+wCW56Y4ndczi2qXv8efxxBBG0+PK61zsvSTdjnQgcUFRAsrwkLNx2ykCYNaLAK8sOVnj2dI0Dq0+9/jdTeie7fA0Txb5oYQ7UViowxUSGV3+0t82sludOSo+/AJn382RGs6sTL2ffp8RFsnsgX8zRCSKYEJMI/Vm54a1TE3uhXzalV7uJXQQbaJHICaf5rlP6d0yGkZ1j4T9zfNsG4LLxOarYF4p/6AzV3omQ54cYM8T5Un5arMNCCS5uP3z2cPZTTHA3I/yN750NAwtS4d7ZQzxa7/hoaW70wxr7FkVaW2QxlqxHk0nxsi3TNu4M2jiO6/3sK8n8DTbcRLpkSBU4S7cWww2v/cE8eD/4YwdU1zXCxS/8DPd/tFIJeB+UqwTTDPosBFtQCcuorbb6VIQ/E3eE8fLgVyHvXx74x/nktTP6MvGP+yb7ssdf9+xE9vOmE9XZHzMG5TOrHbS8ydFku7krWph5X3upDtIm84jCkDcQLa609zCYBY8PdRMy1a1ArMQ7VKaf9bO+sAr2HK01vc/KaqTzp6tsHYHgTVlijG1yUVxWC43yZAKDoCO6Z9ido84q/vj7PR3n8HjmQpj6cQjqij++fyqExs5iQgUmQ9iuk+3jnCYIPXwZMBbHK8qADkxuPnkAXD29D5w8zDvi3LSBeXDNDHsnifYMd1io0Vh9RgjOC3oZ1scPSIPYyFC4fora9cIR4qW+k1DhJwqCwYyj+UMHodcXT6z09tzWLDwgJVrxOyIhRspqx/uyiyf1hFjBhquHfN9EEO0Zf5/a7SVeQOjDrzWdM+K93sKiLYD3Bw+dkgMD8nxbwRontzqS1oHPxVoME67aCzxZYv9RqdVSfmqcw/kon8NgTKsgPR6anCSwc/bJy++UHgdPXDAKxvXJ9mi9eVLUNp02ZNrTUC/27kpFYHugbdwZtHGcZf8EWsUfz4pJkTONf9lYxE7YC55bDG/9vBX+9/0mlUiIfQXxcVEY0hJszY55oFpEK7C5XPEnv8HMhCNbtgoqLK9X99tilTEd4LqZ/WBsryx5RcBn1NY3sYuB6C2NoBf0o+eOUJq5irh7eDsTW5DAOnPMsbWoDK555XfW1FaPC8fYX2TxOxeDSX/troAfN+lX5Lk7IX5v2UGld6Cj3ja1DS0QHRFieVajuDy97xXX7baPtsNVb29RHqty0AuQiWfygoZ3STCcWBaX1cC3q/epsqa056jZpsjNHmYj8Y/RBr/qBYsSX2PFDZIjUYFXONqsPj3+OIIIWvxV8WLmekv4HpxrnTayMwWqfAgPLlXWaYS/0BBmwXnKoDRdy/PsxAj4/PoBMLTAvrewEeJSeG+btpTZ7Gj+0Ck9Xpkz8furQAnIulqRhNsiOniI97z/OnOoxWtHEMEH2bQR/oTfR2cmet+WOS811jAeSjgmXuhrzH/nxkO9hZ7I7Q2eqPnHlkPsJ1p9PnLOcHjj2okQpuPMJM5hMLZlZk6JMf2Pluy068NqRcWfHtoqRIy9O0rQiGgjCXGeEDR7oLS0FM477zxISEiApKQkuOyyy6Cqqsrh62+44Qbo2bMnREdHQ8eOHeHGG2+E8nK1YozBQu2/Dz/8EAIJZ7cx/IZnVPcM8DX7jlTCgZJqXR9stCzR41i1rUEnVv+JPcqMKl4cnfjBU/HX4mHFH5i2+kyNCYf4yFDYXVqrEg1FQYAHyn1Z8VdR2wAJsl+zWdxdPzNvC7YMPgyu3vj6EthVXAF/7Tii+5pzR2XqPn7yIMl6l/Of7/bavaa8pgkOHLNvoOuMN/8ogneXHoK3lxy0+860uxgr/qzoZafHtZPzDL9bUei857MdUFhaB5+tst+H+SnSuIWnykVjs+Hy8Tlw/mhZJJeJCredR/VNLfDn9sPsdz550gpvZpMzzAqEzm5MxKARNkTeWVwBP6zdz6quRbDiesuBMth5qAK8hRUCr17jaVXFHxP+gvOc9jXteS7VHvC1TRTeOGISEQYqCIKwWatvK65RJQrhqfnB1f3huin5TPyzoi+1eLoP7mTrc/zLVv3EsGDDkZCXkRTN5kzM6cCF+yNf4Gqy6qR+kj3ZiG4ZqqAbbk16AvX/C0RoLuVbrAokE4Qn99cRPihEeP3aifDC5cHV4ihQyE6yxRgx0QqZ3DuFxXL+dapvbUcDCa0LBDrsDe+WAbmpsQZWn6LwF2IqPlUo6AFW2TE7aoGjV2Sj9xgngoQ/CJouhxioOnjwICxatAgaGxvhkksugSuvvBLmzZun+/qioiL278knn4Q+ffrA3r174eqrr2aPffrpp6rXvvnmmzBz5kzlbwyGBRRO7mP4Dc/Y3tmwXA5A+4orXvqN/fz2nuOVUmEMhDvqKdIg+GRX1TexrIPdhytZfz8j/Onn7g7iNlpl9ckHXQcxcFXwr0tKNGw7Knk51zW12t2MKtZ4LqwGDqiOgvCOwMasaNW3+7BrIoO7gXxsYOsM3KVmjyzMdimvaVB65viDNXuOqjJr6nQqucwGftFSSst5r2yAxuZW+P5W43NRj40HpIt9q86xrp0rYN8b0ZbTSk4dnM6sPBesK2E2ncmxtoSB1XttvUZX7amEOz/ZAcM0mfWPzu4Khysa4NlF+1nlDGaLzRluL6TqZesj3XOkDD17q0/jiYg4DjiasLhis6ytqEWemr+O/fzs9uns+UVrC+HJ+WuV57+/70TwBlYkFhjtb1x2oewnz80+SfZzTLueS7UDfF3xgjeO+I8gCG3FnzQHTYkNV/ptc8Z0T4Jv/zEITnxmjdPrnCP658VB57QoOHtkFqTGBVeCpKc9gmMiwpT5ptLjLyQ4hT/s7/XSleOUFgw8iYzmM4ELzaV8C8YeLpzQg1kt+pXAGGIIP8X1IoOs9VB7Q4xRFqRFK/dFerGc9oRWsI4VBDW9ij8xQZ8JfybiU6VVdYaf52kinZaMxGi7ZHbuQMZFS238OCRA5of+JCiEv82bN8PChQvhr7/+gmHDhrHHnn/+eTjhhBNYMConx9bIk9OvXz/47LPPlL+7du0Kjz76KJx//vnQ1NQEYWFhquBUVpa6oiMYK/5wYMOqP1+Lf8jXK/cyOyExK4bflGlBa09OZU0DC1bjADPegQ9wsDU75l7gjqp+XI2HN/A+YCaFt84p0bD2YBUcqmqATSW1djZgSoWMyVtLHGDRrvX88d3hggk9XFt5IRMkJc414czdIqh/fbjSUlHx9neWw6bCY/DlnTMg2uDY9jbPfbte+f3zFbvhyz/3uL0s0a6Sg6If3y+unHP7S6WL/bdrj8KNU/OV6lSt8FNa3Qj7SupUllRWkyT337v70x1w/phsGNs9idkBoL2pyNGqRiiuUPt9Y99Bvi0pmiDag7O6KIG1nKQIWH9AyubnXDW9D0ti0Au+8/2qx6uLNltm9VlZayz8cZZtLWZ9md75dRv4Am9aUR+tsE0ybRV/Xvu4oKe9z6XaA5i9OaV/rlJBQhCEf3r8cctztDbXQ5tl7U7FHyZFvnRRb/b7IdnaH7llRkdo64kMPMCHASmebOYvq2Mr2lN0zrQlou05YrtPJgIPmkv5h/PGd/fTJxPtHZ5oHREeNGZ57Za85EgoPFYPfXPJicSogEaco+j1+EuKjVQlpWltNfVAhymOVXOxcIMqPVH065ufDF2zEmD+X3tlgV6KgWmrFLtlkX1uUIxey5YtYwElHqhCpk6dCiEhIbBixQrTy0FrKrS3EgNVyHXXXQdpaWkwYsQIeOONN5yKAfX19VBRUaH6501+3lDk8HmlqXmHDvDPM4ZAl8wEn5ezLpE9g8WqFVTcR8r2o/P+MUV5Hqu+OA9+sgrW7ytl6xxs4p6r/W5crfhDoe/l5YVQVCENpLWN0n6NNjnp6JgkCWyXfLEdSmrtK8P4oGw2Lo+95ZD3ftsO7sA399QRBS6+r9VrlUauaBIo+olVVf6gf8dUj6qp7j/FZnMQE+mob5r080hlA3y3zlZlaARW8Rmtl1js9vgCSajcdcQ+S8cqdhyWlr37aB08PH83bDlYDZe8vlH3tX/vq2TZ+WcMy2DCXlJMOEzvmwqjuybCA6d2Ub12VNdE6J4pCXvnjsyAK8elwQXjuupmT2knPHoVwKKAa0YgNAITKRb+LfUYrK5vtPO411IkV8iVCZbL3IY3UCv+xCWI610pr3O3LLx+8NeS8mdEe59LtQdw7Llj1iCqwiMIP8EFKT4v4sKf3h3OM+f0cJh17QrivGNiz2Ro68IfF0px3qQIfwGS0R1sLjWEa9Bcqv0xqV8u+3nGKPW9IdE+4An97iR1EL7lv+f0gLnn93QYC2lvaFtpqYU/+3mT2K7LTMUfxv5fWLABvOH6wKd1fG7dOzdJeYwLenz7bnhtCRSWVOnG3qYPtLUDaq8EhfB36NAhyMhQWwlhwCklJYU9Z4ajR4/Cww8/zCytRB566CH4+OOPme3V7Nmz4dprr2UZ8I7497//DYmJicq//Px88Ca8EacRosUJHvgZCVFeq3pAkW7j/lI7z2DsFcURb8DuOm0wE/2c2SMO6Wzf7yKYwayHihp1IJ332TNbCfPzzmPw+cYjcP+iXezvWnkAizE56ciKd9JLz8WeWJ5aiHG7QzPLwb5Bp4+SRCovFgwFXT8wvJglxkTY2a12MWl90km2PUAcTYi4AHX7R9vhmUX7YeMB4x5gSKSc4c4RvzNR+CmvdW6/6inhmuPrpnnb4HClsVgbExEKV0zIZcIet0B9YFYX6JhqPGZlJUbA7CHJkJMSY1uOKPxp1oFXQTvDjJWClqte/g3++816KKmsgyo5qSLWQcXfF3/uhhkPf2tXlfz93/shUCv+xNP0Q7lxNFJeI23v8UM62qw+g+uU9intfS5FEAThK6tPcY5hRO8cW0a6my76CmK+UWQbqUpwLPxJ23isqg5+33wwoCr+jPrVm+XJC0dZti6E9dBcqv3RKzcJFt57AmurQ7Q/+L1ldDiJSYFOQnQYdJMTtQn9OWGUk4o/lfAX0kG3oEXkhzX7lbTriya67grnCB7zTImPhA9ungKPX4Dzow6qqsC/d0lFCkcr6+D/vpAs9Osa1LG3DgEyP/Qnfr0zuOuuu9iX4Ojfli1bPP4czCI/8cQTWX+aBx54QPXcfffdB8cddxwMHjwY7rzzTrjjjjvgP//5j8Pl3X333Szjnf/bv987AVOz8MC6cjx36OA1QeO2t5fBLW8tY7/jZ/DgsdhrjH823rBhMJyLfujNbsSwbulOP1sUO9ztM+cttL05sDpuzlOLnFh9Ov6O6uR9Wy73CeEVf1Emb+iz4tWNsB+YPUi/4s9kYN7TG2p+0TCT1Yw9g8bLk2tvinPuVCP5U1gora6H9IQou4reuGhzE1HxtEmRLTE5ohDEBaiD5ZJ4XVnnWLjigS1uHypOEFDwxr+fWrgXdh+xWTN6i2smG2f0HNfNvsy/kwOBzxkT+2bpC392FX/Ggl6qcJ66I/xxqusaoareudVnrTAROq5nJsy/ayab4H27WqoatBptwoOniONBeY1U/ZcYHaEKerY3aC5FEAQRmMIf9htG0pwk43kalNCzb2+rwh/2wuPBqtd+8jxOYDWeCq/9O6ndPQjfQHMpwhEUOG6/3HXaIJjQJxvOGE0Vn0TwgZVzaUIRToQT4S9ZU/GHFX2O4rHiMs4Z282itVYvOzcllrWLwmpFMX6LLbDEhPvtB8vZT0yIF+1ACT/3+Lv11lvh4osvdviaLl26sJ4xhw+r+9Zhb5nS0lKn/WQqKyth5syZEB8fD1988QWEhztufj5y5EiWzY4WVJGRauGEg48bPecPuHATKt804rHvzSopXnmEJx//mLrGJlYJmJ4QrQT9tfew6M2OHrz3f6TuvYbB5165zk9IDGYfk23ePK0+sxpcH62XsHOrT8fL5FvIB1q+/AiTdkA58RFwap90mL/pCPue+uQmqZ7ngmxxea2qt4T3Kv5sgrArE2xv9ggLtuogrCLNS4mFQ2Vqq8x4B0KPiChIabWoCqEa70hlI8QJFYHOBNLGFvUxKr4ef0Vrzx822iqFvUlyjPG+6JweDUt2lLNeftwS9NQhzpMOzOxP0epTi6OKv66ZCVBSeURlk+wOWAmHlpdIfLS54yE+JoJNoNBqecO+0gCu+GvVvfHmVdWJsRGKuGqFtWiwQXMpgiCIwECbmJidFAGbiqqZpbgez53XAw7JSVaegL3S2xp61p3XzewLUwbkwge/7wjYfngYZMPjwJM53ctXjYeaBu+7ZBA2aC5FEIQeOSmxcM/sIbRziKDl/X9MYXOSxqYWVfxKW7yiJ/zx+ArXGjjv/boNMpNiVHEnqxMkqmVXQRT+OHfOGgSPfylX9jU2223DobIaKKuW5tWT++VQf9hAEP7S09PZP2eMHj0aysrKYNWqVTB06FD22OLFi6GlpYUJdY4q/WbMmMFEuvnz50NUlPPKjjVr1kBycnJACXvO4HFVfoOEJ5w3qqS4VSNy2Yu/Qp88m1hXUlnPKgExiDx1QK5hhdjwbuksyJydFA2T++cyMWh8H3O2CbFRYYrwF2gWjXiTB+D4Bs/Vij9bzyr1682Op3gcXDs6D64dlgktdXUQEhOhW8ZdKfRcdISnvTN4NZNZ4Y+/zpsidjCJBOhVjdVaCTH21U1mhT+xWvSP7VLPRj0bzn99sRPevbKf8NmO9xNv+lvX2ML2qdjXD497bgnsC9Cr/NJxOfDG7/a9UbF334kD0yA+KhROemYte2xsd7Ug7iqXTOoJC9fsh5xk4ybSjnr8idXSzqwUHIEZTllJkrVFbKT+8YDWDuLn7TpUYdq/PVB6/InDR5ks/CVERyiZXUF0SlsGzaUIgiACs+Lv+in5cHz/NOifF6f7+p5ZseyfFTx9dndmV95WkO6t1Aztks7mODwYVaxJhAukqj8MssVEhLkl4BWYtPAnrIPmUgRBEERbBROStMlpulafsaLwJ81pn/hyLUzsmwOje2Yqz73723b2819zJH3Gm+Sl2ubJqCEs2XKItUMrOlYDMZo46IptxUqBy4juGZCXqj//bm8Ell+iAb1792ZVe1dccQX8+eefsGTJErj++uvh7LPPhpycHPaaAwcOQK9evdjzXPSbPn06VFdXw+uvv87+Rk92/NfcLAU+v/76a3jttddgw4YNsGPHDvjf//4Hjz32GNxwww0QTCg9/mQ1AP/vjdinViDaVHiM/RT7XGFlCw+86glFeBP3vyvHwb/OHMZ80if0zTGdGSCKG4EW2w3VaYzqvOLPifCn6VnF3+5OIoWeCGsT1kz2+LPK6tOkTau24tEbuLNof4nO5by6KSbC7lw0a/WJvudvX97H7vGymkZ4f7mtxxf2xKsRLCHrHVhVisIfUtsgiX/Kcy2tgP/5krNGZOoG2WIiQyElNtxSq+Czx3aDt66fxARZIxztPxTi+FDpTnZ492zJvrRfxxSodmL1ee44tf1CH9n6AAOV+D2d98xPsFr2SfcEcTus7vG372gVPDl/LdQ1NCkVf0mxEaLdO2EAzaUIgiB83+PPSPSzmr65cVAg9HIOdrSJgs9eOgZy5eCPXpZ6IIGJsFobeKJtQHMpgiAIoi2gN5fKSLTNI3mLpl82FsEDH6td+7SxHqv7+4lwIU8bw8Jqw9tOGQCzRhTAOzdMYo/tPVqlOEF52nO5LRE0s9H333+fiX1TpkyBkJAQmD17Njz33HPK842NjbB161aoqalhf69evRpWrFjBfu/WTR3s3L17NxQUFDDbz7lz58LNN9/Mgvn4uqeffpoJjMEEL4HlQhsXJjD47m5fNr338iCrlsGd06CodJ+h9ahV5KfFweYDZQFZ1WGmik3b68qp1aem4k8r8FqVSWu20sdTq8+K2gb3rD4t+LKNzgV3RDx/HXqKraGOwGS24g/JSoyE8T2S4LdtZXDgWD18urIYFqwrUb0mLzkSFm2wWT9iJZ8RuA9Fm1usShRF7obmVpUwiFwy1vvN0fW0vZgI24NpceGQ7qTnjifcdGJ/WL3rCPy++ZBDq098LjYqnIm5e49UQW1DE0RHmL80c5GvrLpeOTZ4wEmL9hy4aGJPlRiPTZGf+notvH/TFPCEFxZsUH63pmLXtpC/dhxRHkKLaT7pU3qWBtrFIcCguRRBEIT3oD5Q1qFN0hKdFcwmEfoLPg/DYBXOrUSXHCL4obkUQRAEEeyItp79O6Ywt4EoIQ7FK/4cxVB5jDvfi5V12ljnrBGd2TwLqxAxFnbNjL4s/ojxoLKqeiUJPcKHrmOBTtAIfykpKTBv3jzD51HIEw/AiRMnOg3qYxUh/gs28KB++5etcMKQjszajdu1ce/bJVuL2c8V2w6rynFdAcW7EM2JXirbbGrJlu3llPVTLCmtFf6wrwN6bK/fVwprdntelWIlZsQsl60+Na/jL7dqt/J1Nmsv6KnV5/++36T6XKefx4VPC+L4KG7yjI+/dtj6hQaTSFBeI1VzYWXZzSf1h4V/71eEcFeEIiQuStoXl74hfSdaMhMjoEyoKnxxcSH7eepge2tmbW/Lo1WNKrEHvcS1h9i4Ht4PgOh9tdGCIPb+VTYrU2+A43Ov3CQm/DU4Ef7iZOHv791HYdbj37NefU9dPIZZczqDH8Nog7xUHvvDhUnOmWO6wsdLd+qew7wnkBhAs2KCtHKnLM4xfc7aij/OonWFiuiHSQy28dLjj2vT0FyKIAjCN9x1Qifa1R6gTXSLEOZEZpxW/EmkfM+RHBsBH90y1dCJgQhOaC5FEARBBDtJgq0nxto7Zyaong83qJgT4388xu1prNgRmCQvgvHkk4Z2snssLSEKDpXVKm12xJhYe4f2RBDy++aD8NGSnXDXe1JFY2FJFbM6y06Osev55AqiCKIniBSWVOu+L0vzud46+TH74Jyx3SAqLCRgevwdKK2GbUVliqiUJzQe9djq067iT/ppC3F7Bs/g8KSvmFnEzzBt9SnvACu+axSfkOq6Rrj3g7+Ux49USH3BXKLVzxV/0REwc3BH+O8lY1RZ0ReMTIF7T8o3taz4KMdCYU19s12V38d/SqKSFl7Nlyjbje48XKuu+Guyr/jzV6J2RFgHv2R8O7P61AaEdhyqgO1F6h6MRyvqdM8FXrG7ubBM12qMi3v8nMpMkuwbjh+cr5xjYlZ9RKjnlgji9lgxVDtaRDKfsCqb3Ap7Dle6ZZtKEARBEFZabxLuo7VQF+cqVlq2ewM+90GrTwysBXqFIkEQBEEQ7Y9nLz0OLhjfXbe3sFESOo+tIjzu56k7nCPMJk+hQ+DO4gp44buN7G+y+gzCij/C/kQrqZQCwftLqnVPVIzpLt1yCFIToqBnTpLTXfjndqESSkcM2rjPZv0nkp6g9tw9VCbZrXrr3MdgtQ+0KlNcOvcX9hMrdJDnLjsOTv/PD7qv1Qp9WjHEWY8/Xjlj1X7lg3NTi/cD5PyYcKXfBd9OrUWqO3ARoKpO3RvvxteXwPf3nejSsvx16PFt4JkreB7ERIRBTUMT+w7PH5UKEGLL2nFEXKSxuIMCXnV9i53wl5Fgb4uJ4w8XtXplx8CKXRWw8UAVZCSk2F6jI3x5c2LAOX1oBqzdX+VXCy6e8c2zjvT2X3VdE3TLst+3OGnp3ymV/b52Twnc8e5yuGJqbzhjdBfV6/SEe27jK/1u22bUA9+5YbLd60Ubhz1HKsFTogSx0Qocif9YVYlwq8/6xha4+/0VzJ/+mUvGkO0aQRAE4ReMLJIIc+D85emLR0NsZLhc3d9Bd54TiPC5VLmmJzdBEARBEESggLEUHk8xLfwJCdY8lmxVOyo9Yp0ULXB6ZCfCKsF5CgsmCInAnjUTuvD7HhTnKmobmZihV2mGQe4HP1nFxA0zqPty2YtBizcUsZ+nDFeX1fKmn5yN+4959eS3shLMKrBCBy1ptGXIjgL0zipSjCr+rLP6DDEUefVwdXdjrzKsRkXBc6tsSZmZGM0GZNe+Z/AYvq+x/5yn+Ou448KxGPjgAourlqVa4W3O8AwY3TURosJDmM1ndUMz7D4iVUNeKvfj65Jua/TLuWneNjjrf1I/t8yECPaanzYfg3s/l60l5Y+pbVAf696cGHBGdU1U1t1fRIRL55g4AREpr2lgx6bYRFm0xl2wWuqdumaPZG38/m/blV6ZHGc9OkWLBiPh0+pM9A4Wny+OlpAmN3vmn7nw731QWlUP4/tkk+hHEARB+I0wHyQ5tXX65ks9Z7BXnojobBDIOOrxTBAEQRAEEagYJXOLFX+fLdvlfatPk4UjZx3XFQZ1lhLnkZwUtTNhe4aEvyCECzYoJKGwguSl6gh/HpzY36zcayhaXTujLxwn9w7snBHPvHT18NbJrwifgaP7KUF8ZGCBbbARBRmt8GcUsEfPZNVrNT3+rBJNXO3x50oAH3t8Ya+yy178FR777G94/Ms17PH7zxxmOhjPjx93+/BNG5Bnd3FCMdJT/HXY8f0g7j/+q6tFm6LOgz1oLhyTDfed0hk+va4/xEeFMqvP4gqpp+cpcl8/PbvKrYdqVJnt10zKVT2PIiJSqwl8+MrxyN++3rziD6uy0XpZe66VVEr7OFVorCzy7Lfr2c9Q+YvG6s45Ty5Snm9uaWF2w44QrT6NxmQzvQRdQWwKbcU4rTcEpMZHqhMo5H2E+xo5rleW5x9MEARBEG7iC3eD9kqgV/wN7ZLGfkZSfxmCIAiCIIKQaCGmY5TUxGMv3kjsR7erEd0zTM/5cH0fO3cE9M1Phmtm9KEkcIHAnjUTuvDgbavQ1ylXT/hz8dwTK7/e/mWbSujhwdWpA3LZCTSsWwb7+8wxXVnvgi/vnAGzRhSo17MdVfyJPHHBKJjYN8dO3GvRqDNipgSyq6webvh2J5z45hq4+OONNoHTTvix2OrTieWoXgAfBQdHPDV/raonJUfPktYIfvy4+zVza1Tx+LWin6HfKv5470yV8OeeOMqXgSLfpN4pEBEWwo4H7JkSGxEKtY0tUFrdBEML4iFSrlrTWn9qwfcOyI+HKyfYxL/+cn8b7Xt9UfEnrZN/g264XznXv/YHnPjoAtXxg3bNCNoxO8LosMVKY6xidRRbjA4Pc7rf0UbLSqwWEvXkdt7fhz+j3QdmveAJgiAIwhuQ8Oc9Ar3HX0pclF8s5gmCIAiCIKwgV+MqyONYesUU3pjzYoubh88e7tJ7UCR8+uIxMGtEZ8vXJ5gJ7FkzoSsyiKLLd7IVXKc084KKEVrrSTHYzHtU8QaZxw/Oh9eumQCT++cq6rrYPNObgX1bxV9gCn9ipZEoNDmy+qxrbIZrvt8H20pq2d+Hq4WeEPLbWrxW8dfispBWVq22G9SCYrCjzzSDYnXq5vcsvo2LrFYcM76sNMVtx6qvXzcWCd+/7Xn+a6sbNpjItZNtVZGcGKH/X6fUKHa8RYZ1cCr8cUur2cOkpAAkNU4SX2o0FqtxUVYLQ47XyV9oAz6tcr8+zlEu/MkBIs7Zx3VlP7GHI44bWntPXum9bq+0rBFyIgaiTcAQK7mNdofZvptmEc8zS6w+hUXwTchOliai8QYCn1GGGkEQBEG0hzlIWybQ+yfyfjTYBoIgCIIgCCLY6Knp/Xekos5Q+POm1SfhOST8BTh6QkWFbCmJHCyrMaz4Eyv4Dh6rsQvGcjHPqAJNfH2DHPTnFSwY0M5Pk6p5OGN726zVvHnei1anVrOtqAx+3nDArffefupAu5t9Udxr1nyX4nMv/LDZbnlc4GtsaYX//r5Peb1Vu5b3ZjRv9Wn7fdm2Ypjx8LcqEcPqih++/e6KdeL7+L4z28/Q3xV/+BmfLtsFf+8uYX3eHvv8b2V7xIuqso9c3K6MhAj4/tbBMLl3it1zWPHHwb59/Pv8a3cFNOjYfeoFYV44vye8dGEvZbx49VepPygyvHOCzyr+iiscC9T+4M73VkCxPG4fq5KsPlM0Vp+d0uPh1OEFzNpz2dZDcLhcSgjokpnAfu48VKGM68jw7pLwN7ZXFlwzo69qWfHRNmHMKPPc6gwt1bXGguW16vyO4+0F47vD7NFdlOQJEaq0IAiCIPxJgBelBTXa3sS3nDwAAomLJ/WE00d1hmtnqudkBEEQBEEQweKuIMaS9h2Vks/RcUoL6X6BDd2SBDhaG0aszpr/l63/Hoo22v5+Mwfls5/JQjD54hd+hsXrD6isPE/+90I4JAegdSv+BDFBW/GnR8+cJMhKiva64h8a6ppFpSvc8PoS+L8v1tjtCzP0yUu2uyFtFtaR788JfbLtbED3HJEGUSMWbiuBT9YfZr9btWv5d+SO8PfRkp3s5wd/7NB9bZNOFeG7N052af1sFX/gecUft/rULCxOzsh1d7ne4tPlu+DVHzfD3e+vsBPmRdEsW25YK16QPUXsx5eZII0h5bVSVs8Xq4+YymzvnhkDndOj7aw2b5neER4+TRJqfMHEXrZzclQXSTQLBD5fsZv9rJfHVW3jZLSpHCP3UT1WXc+EPhT9eHLB7sOV7GdNfZPSz/LOWYPgjlmD7D4rLd5WTWgkuFotxIrXDvF8+XrlXli2tdgSsT0pNgLOn9BDsfvqm5cMAzpJQnZHTVIKQRAEQfgasnn0jfA3e1RnmCHf+wYK6Dpw1bQ+ZDtOEARBEETQcsKQjnZtaio1blQIJV0HNiT8BThaAWXp1mIoLK1W9TbQVniMlgPGorCE/LXDFrTngs3uYimArG/1KQp/LaZ6KvC+St6s6Alz0aLSHUrlQQ0rclbvOgov/bCJ/TNj78nWUd5PfJ9i4Pqtn7ex33vJJdONgiioN1AaVbp1sNzq06zwZ3sdr+Sq0ynzZstsblUJDkhGoiQK+6riT1zfNbulykTtorKSJOHMFXxhMfvaj1vsHtsjiz3i1//P04fAldN6w4yBtr56nrJmn02EzkxUWxQdOCadF3qEh3Vw2OMOSU8I92kgrCAtGl69uDf0yYmFm6bbJi2+5PELRrKf/5ozVHlsw75S1bmH1beXT+2lPB8XHa6IgQdKa5glaNesBFZljePf7mKp4u/nDVIlZWR4KLNdxp9aUlTCH/gEUWDfWlQGf+8+yn5/4bsN8MDHK+G0J773aPkddK4xURFh8J8LR8PLV42H5y47zqPlEwRBEIS7nDUiE3pnuz6/JMwjJptxJxiCIAiCIAjCOi6a2BOumt5HFbsqlx0II4VYn6MCIcL/0Ew5wBErxpC9cmWYaB0yrGu6rqDToBHy9MQlsdJEa/Wp15/OWU8FDL56Xfjj1XRebLbGe2898dUaVnX1xYrd7N/bP2+Fb1bZKi5FRFHUJk5K67j/aJUiGPF9JAqtiXLF1rCcOJjaTapaMSo6tCp4b9uPxgJqVV2jUu0pCl58YNcr8+aCdbRwbJ03rrvL68ePIXe/Z/FtXEDXWmK6s2xfWH2KzBwsZTFvPnDM7jxOiImA2aO6WJphM6prgp3V560zJNHM0e6K0VStIdqKPyssYF2lY2oU/PecHpASa11VpCsMKkiD7+87EY7rZbNCLpItOnlyBp6Lc0ZLff1437pI+RzDcQfpnp3IxhgU//YcqbSzajZCHJeMKrHF4Ror6TxFPM8WrS2Eu95boRrvsFLRlfNI+1IUlI0E5IKMeOrvRxAEQfiNS8flwDPn9qRvwEcVf9RLkSAIgiAIwnowztglM14VN+bCn5hgbqUDGWE9JPwFONoqvMPlNSors6Ea0U/VG04j5OmJdmJFjiOrT6W/mBNBjwf2vZl8yYPX2opGK3nmm/Wsl+I6TQ+7eX/sgOcXbFD+Fi3l4qMjDCv+PloqWWOKmRHi+nNb1jvG5ik3sGsP2qoxRawSVblY1GSg5mDT1jlPLoKn5q8zPG5qG40r/nAfpCVIFwN3dCm+D939nkVhobK2Ubdaz53qPS/qzXagYHT+eEk0LSypZj+9XTF3xjCpYhiJjZTO52l9U1iVVY2B0Cu+VsSuKiucLjlIvWynahP+pP103cy+MKlfDrNv1oq5AzulKhOsY9UNSgXoqB6278uM9ZQe4idZoWvrCerVddI5yHFWQS2iXVqEHwRkgiAIgiACA/GetkwOQBEEQRAEQRAWz7nk4D622kK7zw/ltk/cJY8nrhOBC0VhAxisinhz8VbVY8XltYpt4ke3TIUHzrTZx2nFDOzjJ+LMCkUrFIqiCFf3nVUWceHPm+IEH3i8WfGHjUvnPLWIiWJXTO1t10eRfzbfR+P7ZKv2DRfvuBXmj+sO2AWtxf3Nf0dRkL938U6pwkuLVbtWtPrEnn2fCOIksuNQBdu+XzYWse9fXfEnC39yjzEtWGGHy5/aP9cuG8QsvBq1Tug557HwpzlmtH+7ulxvwI8t3uMtOdbWq9Pb1bRIpI5lJ57P0REhUNtgLMLG6lT8aYeL9i783XbKQOU7xn9cdOci9ynDC+Cu0waz/Z2psaHFSjYkKSaCCYebCqXxYcagPNOfb1RxKY7XVoyreoL6flm45nz55x6WXGAKzfK0FrIEQRAEQbQf+L2gu3N5giAIgiAIwrW48U/rbXHtejmGPbRLmuJqRwQmFD0LYPDEWrSuUPUY71WGFn9JsZG6Xrq89NYMqNobVfzpWX06Ff7k4D/vCegNwpxUqrmLnqDTLSuBNY3X7uftB8uVAHeXzAT45+whqufx+0F+WFsI1fWNutZ7YiUb35bwkA5O7VRDVPU57sO/S7xhfmPxFnjtpy2q7bvt7WXK39uKylWxd151aWz1KVX8nTe+O/zf+SNh+kDz4oT4PaPIxaujXAV3Kd/G3zcfhIPHalS9x/hr3FmuN+HHFhc8cT+K/tne7tNmJKqglWdlnVqoSYuzZfYkxdhf7LXWku1d+Js2MA+mDZDOBbTqVCr+dL5U7Nf33b0nQHZyDJw1xmYDGhMp7ef/fS9VzHXLSjT9+dox3ls9LPXcg7FfKnLBhB7KY2hjiuPuLxuK4OUfNqnWb+P+Unj22/VsfbTnHHnIEwRBEET7RWxnwe38CYIgCIIgCO8Jf3qhyAsn2uI7RGBCsmwAI/ZIy0yKhuIyqdrPWa8so4qg2MgwePfXbUrliFbcc9Tjj2dTmq34azTZf8odQnlvOoutPrVB8VOGd4JZIzqzahit3eSa3UehV24S2y96u+TEoZ3g65V72T8sh+ZcPb0PdEqPt/s8XD4uB/cvin+OsK7iz9hKc+5Cm50p8o83l8JFwoB+sLTGoZCAVp+4LRigH9w5za31w/2OQrInFX94LjTLRoGfLNup2CVym1YzQojecr0FLrusWhJIxHWNiQyH+qZ6n1T8afvycQrSomDNviom9vKxSRSJ9HroUcWfg0rWBhT+5HFV6FWj3n8d4K3rJzkUZtNlO11HPHnRaPh02S4YWGA7pkTEQ8qK41tPPOQ9CbtmJsCc0V3gk2W7WFU6XjNeXrSZPYfXphmD8uG3TQfh0c9Ws8cw8aJVY/Yp9i0kCIIgCKJ9gYmZHJoTEARBEARBeAceN96wrwQOCZqEngsDEZjQNxQkjcuzBdu3RLmazIgJfXN0H9+wrxTe+207PPKpFFBFthWVKb9zEQRtK+2tPuUef04r/sK8XhUlZhxYiSgCDemSBtfN7Ae5KZLFZ77Qyw9Zt7cEFq0tZFVwekIMruPYXlns96Vbi9nPG0/oB6eN7KzcoIqfh7+Hystx1qTeqoovvhyz+1G0juWWs0bg9ojHr7ugKIACiTvgVuEuReGAC5ziMY0VVe7YA1lREaUHCsQzH1kA93+0kv2NlV6cCKFSztk56CkouM49vyfMu6qf6vFBHeNZJeeOw7bvHg/h7MQIeOT0rpCoV/GnOTfEysX2Ch53CAra3ELZ2TlvVO2GY4kZW+X+HVPgwbOGGVbKicvg50RVXSNLXBDHh9KqOlMV5XrnFa8CR+Fy9qguyuMrdx1VfsfEFNwvXPRD2OlmV/FHxxFBEARBtFdwPtM7N0k1ryIIgiAIgiC8E3//c8cR1hKLx7Y5VsR9Ce9C31CQUC30UjOq2hBPzClybzWRzQdsIh/nzZ+3KoFcLkTxqr0WXatPx4dMtA9uvmxWn9ZU/H22fBfrY8cFplkjCuCxc0eoXnPryQPg9JGSgISs2nUUnpy/ltl4GgkxWLkiEhspVURxK0+x0g73L49lOxMBrOqfyJfzx5ZDds+J3zMKBq6CgoYrYoYReDPvjtUnVs3tOVLJtvHCiT3ldWpV7EqvP76fVA3ohojnrYK/+X/tUf0dLfhkhwvfhzf7Z3K6ZcZAqmDjiXROj2Y/9xwVhb9WyEyMgOGdbZnXItpDwFnFcHsgggv/Tc1M5DIr3umJXredKvUM9BTx0/mwf/s7y+GF7zbAqp1HlOfO+e9PcOZTi5xWBfLqPhFeBY7rnxwXqSRG7D1SCTERYZCfGgtHKurgwz92qN/X3KLV/SjIRxAEQRDtHJzL49xBTCYiCIIgCIIgrEMbw0uOjWQOd2bcCInAgIS/IEE8mWob1H229EiLd27/xsFArmj1yTMnxcom/ruzwH2kYE/qdas8Ny0gtbyyaDP8+/O/WYULF+i0gfjYqHC4anof+OKOGarHa+qbDK0Xs5Jj4LnLjlMqncbJlZThYfYVf7h/+Wc6q+jyge7DtoszsnuG0pfMLGhhaGRf6HLFnxvf81lP/yhZ47ZKlqNcpOIiN54fqKW5Y2t469vLoLJW3bfRClI156yYwSweE7wy1NcUpErrt+eoZF1bXtMEVfXNkKpj8emr6sRghJ//lXWN8NeOI8p4ZpbNhcdsy7Iou0pV8dfayoT7XcUV7G9tj1LkvGd/gq1Ctbheoor2q69rlMa7cLnqEK12kaMVdZCXFgt3njaY/f2BRvjDBAntacr3IUEQBEEQ7ZNu2Ynw2rUTISNRSkwjCIIgCIIgrEVb0Zcmt5p5/rLj4NLJPVncmwhsKHoW4BwnV0XERNqqf84b193p++JjbMH4l64cp3pOtBDkbNxfalfxJ1q8KVafTkSHqHDvt43kn+GuBaSIWHWHVqhIbJTxNkQKlosI7hZHu6RnThK8fcMk+OLOmYoAxYP1vL8XIlmGSr8btFizqxjyJlxcxr5+KAq4Gmi3quIvyo2Kv2pZwEXqm1qU/Y5C4Es/bGK/o+iHx7K7RaNLt9pXSXqKqG1cNa039OuYovzNS+oRP+l+rAIwJiIECo9Jwt/WQ9XsZ89syQ5XD9L97OHnPz8WXRWR0xKiLa+gVFX8tbTCj+sOKH9XyGL5joPlymMllfXw0CerHAp/mCwhUlZTr9r+rkJ/npS4KOienahMIu0r/tTKX6SBZSlBEARBEARBEARBEAThOdqYU7ocs+mRkwRnHdeNdnEQQMJfgMPPMQyWxkWFQeeMeOidl+z0fWIvp86ZCUpvLex39tb1k5RqC84tby1TqqH4e/WtPp0Jf94PyEbLFTJmKh+1YBWL2KOKV/khzy3YwH5mOsgc1bM6dbZPspJiVK/hGRM/rbcF13Ff8yU7Eldfnd0bonxQ7YLVn9g741xZZHalpxZWDOHh4swW1gxYQVrX6Nr3jHaBnE7pcWx/4h7dLggX+BieU43N7onHzgRwdwRoLnA+cs5wOH1UF8PjShSrfQkKwDERoVDbIH0+7/XXMyvGZ/upLcBF9G1FtuPRFVAUtpo++bZrCo70WwRbaF5xy4VKDlbqiZXB2mM5TiP8ffXnHibs5aXGqpJakE7y9Sgh2r5/LatEt+vxR8IfQRAEQRAEQRAEQRCEt9DGgvWStYnAhoS/AIcHztGS8IObp8ILl4819T4u9HGGdElXKiuQZy+VLChFNhUeY2ID70EnWn0qFX/OhD8fWH3y3me1LlT8YcXUiu3FcM0rv8Pt7yxj24OVYVi5oiUvVS2KOsNVuz1RDOH7GH/yxx1pJR2TrB1k7zptkO7jWGUjlnS7so1cmOLHkSdEhWHFn2tC15EKWw+6f80Zyn62aqxVj1bWQQSrJvSPiCZyuLwWTnzsO3hz8RZTNr0JMfbiiK84WtUIWw/VQE1Ds3L+xTuokKWKP3s8teeMEno/utP/0shm9rt7T4AJsh0xt33mwt/u4gpYL1dEI/x1v20qUh7D8fS3TQfh9Z+k41gr/OGYO653tmJhK9qLdpGr/04e1smuIl2vx58riQgEQRAEQRAEQRAEQRCEa2idnFJlTYEIHrzvy0h4BA+OoiukK1UO2tdePKknU+aPH5yvWId+f9+JUNfQBK8v3gLz/9rLqjewYo9XGgkFf0r1XyBU/MVFh7tskXfLW0uV1+89UgV3vLucWXveOcte+Ep0Iqx8ett0WL+vBB78eJVugNsVGhqbWSAfdy/ftb6sktKrsOHBdtHeUzyeBnRKgXV7bSIAitJiEJ+LxGEWVPzhvsF1QetQsxWEvOJvaJc0QxE3KSaS2QXiskXR1SxWfkV7j1SqzjfRylGPpNhI8Dc7D9dCo2xVG+5A4BUTBf7vDLIBYPvLAtFKqlZtgQZBoPMUPAf4+F4j9PWb9/sO9o8zY1CekkDy32/WQ6f0eNZn58LnF0NVna0CULSn5sTqPIZ0zZSEvxOGdGT/lm0thuXbimHhmv1sG7VCn9j/kiAIgiAIgiAIgiAIgvBu4npynP/jkYRrUNp8gMNFqHhZ7HJVmOBh94KMeLj++H52aj0KK5dN7qWczBiU5iKIusdfiynhj1fj+UKsqqi1WXYagWLmFf/71U4k5P38Hv9yjf3yhf6IeuB3MaZnlvLdaPepK/z7izWsUgbFM77fQ30o/DmykxQr/rg9HzKqR6bqtcu3HYZ3ftkmvLfVsoo/3lPRbD9HFPGeX7Ce/X7ltD52z/fKTYJHzx0Bo3pkKCI1iq+uYqU4K4pjWO3k6Fwf0T0DAgE8bprMCH/CU1mJ/qtUbEsVf0j/TlL/x6RYa/cpryYsN0iqmDkoH24+aQDUN9nOmR2HymHVziMq0Q8pLquxe7+eGIjkpKj7RI7umQnj5KpC0ZqZQxV/BEEQBEEQBEEQBEEQ3uX0kZ2V362OQRHehyr+ApxLJ/dk1Q1nj+3q0vukrmZ2rZEMg719OybDmt0lTOzjQgQX+9jvsiWlM8HDFxV/CbIwUqETENayaO1+ZvPpCmYry7AXIAalec9Bd8Cqlnd+3cb2L9+1vrRH5N+rnvAXLqwIFxqQfE0V3QMfr2Q/zzyuK/v++XEjCofu8ssGyUoQK39mj+ri9PVYKcT16vRE+xJ00eI2QhYV65taIMqP1y5ujXrRxB6s2skR98vWpf7mUHk922/OhT/bc86SBtoLVgh/954xBFbvOgrDu1krBHNhrry6no1DYtU3TwzB6t7BBWnw+fLd7LFPl+2CQ2U2e12OXoW69rH/XjIGKmsbdI+NJDmx4oXvNsADZw5zumyCIAiCIAiCIAiCIAjCOq6a3gc+XyHFf7j7ExE8UMVfgMMq8qb0gthI16rKBndJYxaUN57Qz9Tr+3dMZT+xio0H68Wgb7NZq08f9PjD4DSuhzPh7+2ft8KL32/y2nqcM64b68c2sW+OR8v58s890CpaffpQIGmWq7Y47/+2HX5cV8i+e1G4SxfsJ41Ku/n3wSvBrBB6uLj0yqLN8MqiTbCruMLuNduKypT+fWLPM2fnDBcPGpqa4fIXf4EHPpIETDNYWZTJ+6lhtZ+RjScKlmhLa4WY6gmXj5eO9ccX7IUfNpY6FbLEY8CKCtC2gBVWn3hsY788q+EV22U1Dez6ccGEHqrn+bUBK08fOWc4+11P9DNKAtGOCX3ykmFkd3UFsXacwWvPQU31IFX8EQRBEARBEARBEARBeJ+nLx4NZ4zuAl2zpDYtRPBAFX9tFBTwPrt9uunXnzmmC8RHhcH0QfmwaG2hqq+f+LuzariocO8fUlhxgnafFQ56/P2+6SDM+8PWl8oboN0n/nOH204ZCE/OX6vavyFylaYvC6OahKpOBKsPOVqRKSUuEkqr6pkg8PJV4+GVHzcziz8OVu5kJEYzIc2qqhxxP322fDd8u2ofO675uhWWVMENry9hv58+qjP0yU02vewweUe/uXgr7C+pZv9crai1Aps1qvG5hRal+M/f5CbZC5OOBD3xWOb7u70THhq41WoxcuIGWiOjED2qewa8K4wJIwWr2f6dpGQRI1LiowAOlrstmIu9VldsK1Y9R8IfQRAEQRAEQRAEQRCE9+mbn8L+EcEHCX+EItKcOqKzquIM+6UhB4/VwHMLNgRMxR/vw2dU8VfX2AyPfLZa9diU/rmscqRvfjIcraiDj5buBH+SlqAuj8Z9rVT8+bDHn9jHUUs3TSbHi1eMgw37S5kggOSnxqqEv/KaRmX/W2X7ir0pRXDZh8pqIE+2Gy2prFeeQ+vB3+IPst9nj7J5UCNox6otSefH8k/rD7i8XqINrqfwakUrLCC9jZ6traMxAUV6M69rT1hR8ectooUefCjwd86Mh8n9cmB8nxwmPIvVvnh+P3nRaLjt7WXK3xdP6gkv/SBVWZ8zthuzMhZxZWxDIfzkYZ3g65V7obpe3T8wXu7zShAEQRAEQRAEQRAEQRCEPST8EXbw4CwXhT5cYqucc2ZD6YsefwhW/O09Uqn73K8bpb5wPPg8Z0wXO9tH7KV20Qs/qx7DahZnPdasIkcWz0Thzx89/sQKHi2T+uWq/sagv2gvmJ+m7vVXUdugEv6wN6WnxMv9HJE5o7vAJ8t2QW1Ds6rKUORoZR37Ob6P2gbxiztm2C3bVdtMFEJ3HJKsRpscCKbuVl3628bTDNERrq1jTpJNoKGKPwh4gZdbffJzDyu87zxtsOHr+3dMYX0nM5NimOXD+r0lDq8FruY0nDK8gAl/3MIXbZV75iaxRA6CIAiCIAiCIAiCIAiCIPQh4Y+wg1fmcHvPEBeqdnwlXsRGhkGNpgqE8/MGSfj75+whMK53lqrqiJOlEd6Qq6f3gZyUWPAFGCh/87qJcMncX1gFndTjj1t9dvBppeegglRYs8cWsOckxTququmZo7ae5BWYdbIwZ0X1Z1ZSDJx9XFdmM9ogV8Y1yT+Ryjp9u9cumepqRb1jwNUKNLkA1mmlpLs9/oKhB156vGuVVkkx4XDvyQWwobAaIsMDV/DyJeLxi+CxHSjguCpW/JlhTK8s0+9x1SKXW3rWy/bBI7qlw5QBeS4tgyAIgiAIgiAIgiAIgiDaGxSJJewPClkkwSo0DFKL+kig2PVh5aGe9jL/rz3w9+6jMLhzGqv60hN8jCpSfG3BhyIj9sTDyhq11adPVwPKDSxTHe07PdtHLvzx6hyrqj8vmdyL2dCGyf0lebUd9hJ85pv1uu8x019QT6SuMhASxe3SE288wba/Aj8PIzUuHB6f082l94zrkQzXTCaxhpOTEqMSyuZeMRYCseKvVVS6XbTmzUyK1q3uczWnIVI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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot rolling correlations between key macro indicators and S&P 500\n", + "# to reveal how relationships change across different market regimes.\n", + "fig = utils.plot_rolling_correlations(sp500, macro_daily, macro_monthly)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "fd52157b-138e-436f-afe3-79e6025134af", + "metadata": {}, + "source": [ + "# Feature engineering\n", + "\n", + "This section builds all features used by our forecasting models.\n", + "Calculated macro features are derived from downloaded indicators.\n", + "Technical indicators capture price momentum and volatility patterns.\n", + "Calendar features encode seasonal patterns. Event flags mark dates\n", + "of known market moving events including FOMC meetings, CPI releases,\n", + "and US holidays. All features are then assembled into Darts TimeSeries\n", + "objects and split into train, validation, and test sets." + ] + }, + { + "cell_type": "markdown", + "id": "c04bb4be-9166-4cd7-b072-9750f73ae31c", + "metadata": {}, + "source": [ + "## Calculated macro features" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "d84270cc-d697-4ce9-b32a-592b0a69a291", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-04-07 09:22:29,506 - INFO - NaN values in calculated macro features: 0\n" + ] + }, + { + "data": { + "text/html": [ + "
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YIELD_CURVEYIELD_CURVE_INVERTEDCPI_MOMVIX_MA20OIL_MOM30NFP_MOM
Date
2018-01-291.32400.021.528-7.3978010.0
2018-01-301.30100.021.528-7.3978010.0
2018-01-311.29000.021.528-7.3978010.0
2018-02-011.32000.021.528-7.3978010.0
2018-02-021.40400.021.528-7.397801394.0
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" + ], + "text/plain": [ + " YIELD_CURVE YIELD_CURVE_INVERTED CPI_MOM VIX_MA20 OIL_MOM30 \\\n", + "Date \n", + "2018-01-29 1.324 0 0.0 21.528 -7.397801 \n", + "2018-01-30 1.301 0 0.0 21.528 -7.397801 \n", + "2018-01-31 1.290 0 0.0 21.528 -7.397801 \n", + "2018-02-01 1.320 0 0.0 21.528 -7.397801 \n", + "2018-02-02 1.404 0 0.0 21.528 -7.397801 \n", + "\n", + " NFP_MOM \n", + "Date \n", + "2018-01-29 0.0 \n", + "2018-01-30 0.0 \n", + "2018-01-31 0.0 \n", + "2018-02-01 0.0 \n", + "2018-02-02 394.0 " + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Calculate derived macro features from downloaded indicators.\n", + "macro_features = utils.calculate_macro_features(\n", + " macro_daily, macro_monthly\n", + ")\n", + "# Show percentage of rows with NaN values after calculation.\n", + "nan_count = macro_features.isnull().sum().sum()\n", + "_LOG.info(\n", + " \"NaN values in calculated macro features: %d\", nan_count\n", + ")\n", + "# Preview the calculated features.\n", + "macro_features.head(5)" + ] + }, + { + "cell_type": "markdown", + "id": "fa29e26f-11b9-4614-9491-fa9468f83328", + "metadata": {}, + "source": [ + "## Technical indicators" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "ba5fa2d7-df18-4eed-b238-0f5db84f0e03", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-04-07 09:22:29,533 - INFO - NaN values in technical indicators: 0\n" + ] + }, + { + "data": { + "text/html": [ + "
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MA5MA20MA50EMA12EMA26MACDRSIBB_UPPERBB_LOWER
Date
2018-01-292816.7759772721.9804812700.6865972853.5300292853.5300290.00000038.3110732867.6147722576.34619
2018-01-302816.7759772721.9804812700.6865972848.7453992851.226318-2.48091938.3110732867.6147722576.34619
2018-01-312816.7759772721.9804812700.6865972844.9091932849.195484-4.28629238.3110732867.6147722576.34619
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" + ], + "text/plain": [ + " MA5 MA20 MA50 EMA12 EMA26 \\\n", + "Date \n", + "2018-01-29 2816.775977 2721.980481 2700.686597 2853.530029 2853.530029 \n", + "2018-01-30 2816.775977 2721.980481 2700.686597 2848.745399 2851.226318 \n", + "2018-01-31 2816.775977 2721.980481 2700.686597 2844.909193 2849.195484 \n", + "\n", + " MACD RSI BB_UPPER BB_LOWER \n", + "Date \n", + "2018-01-29 0.000000 38.311073 2867.614772 2576.34619 \n", + "2018-01-30 -2.480919 38.311073 2867.614772 2576.34619 \n", + "2018-01-31 -4.286292 38.311073 2867.614772 2576.34619 " + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Calculate technical indicators from S&P 500 closing prices.\n", + "technical_features = utils.calculate_technical_indicators(sp500)\n", + "# Verify no NaN values in technical indicators.\n", + "_LOG.info(\n", + " \"NaN values in technical indicators: %d\",\n", + " technical_features.isnull().sum().sum(),\n", + ")\n", + "# Preview the technical indicators.\n", + "technical_features.head(3)" + ] + }, + { + "cell_type": "markdown", + "id": "c438156e-68d4-4d5c-bc44-aeb2e766561d", + "metadata": {}, + "source": [ + "## Calendar features" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "49c4c529-55ac-4b4d-b749-9181a582bcd8", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-04-07 09:22:29,556 - INFO - NaN values in calendar features: 0\n" + ] + }, + { + "data": { + "text/html": [ + "
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DAY_OF_WEEKMONTHQUARTER
Date
2018-01-29011
2018-01-30111
2018-01-31211
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" + ], + "text/plain": [ + " DAY_OF_WEEK MONTH QUARTER\n", + "Date \n", + "2018-01-29 0 1 1\n", + "2018-01-30 1 1 1\n", + "2018-01-31 2 1 1" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Calculate calendar features from the aligned date index.\n", + "calendar_features = utils.calculate_calendar_features(sp500.index)\n", + "# Verify no NaN values in calendar features.\n", + "_LOG.info(\n", + " \"NaN values in calendar features: %d\",\n", + " calendar_features.isnull().sum().sum(),\n", + ")\n", + "# Preview the calendar features.\n", + "calendar_features.head(3)" + ] + }, + { + "cell_type": "markdown", + "id": "0676d9d5-0409-4d3a-90cb-818fd46bb45e", + "metadata": {}, + "source": [ + "## Event flags" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "be32d7a3-8519-40cc-a1ac-b78fb6daac8f", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-04-07 09:22:30,133 - INFO - Holiday adjacent days: 107\n", + "2026-04-07 09:22:30,134 - INFO - FOMC meeting dates: 86\n", + "2026-04-07 09:22:30,134 - INFO - CPI release dates: 88\n", + "2026-04-07 09:22:30,135 - INFO - NaN values in event flags: 0\n" + ] + }, + { + "data": { + "text/html": [ + "
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IS_HOLIDAY_ADJACENTIS_FOMC_DATEIS_CPI_RELEASE
Date
2018-01-29000
2018-01-30000
2018-01-31000
2018-02-01010
2018-02-02000
\n", + "
" + ], + "text/plain": [ + " IS_HOLIDAY_ADJACENT IS_FOMC_DATE IS_CPI_RELEASE\n", + "Date \n", + "2018-01-29 0 0 0\n", + "2018-01-30 0 0 0\n", + "2018-01-31 0 0 0\n", + "2018-02-01 0 1 0\n", + "2018-02-02 0 0 0" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Calculate event flags for US holidays, FOMC dates, and CPI releases.\n", + "event_flags = utils.calculate_event_flags(sp500.index, FRED_API_KEY)\n", + "# Verify no NaN values in event flags.\n", + "_LOG.info(\n", + " \"NaN values in event flags: %d\",\n", + " event_flags.isnull().sum().sum(),\n", + ")\n", + "# Preview the event flags.\n", + "event_flags.head(5)" + ] + }, + { + "cell_type": "markdown", + "id": "c8d96563-922e-4a51-b524-f6dc2082f131", + "metadata": {}, + "source": [ + "## Combine all features into master DataFrame" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "95c25a4e-ca48-4bbf-86a0-60beffba6811", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-04-07 09:22:30,163 - INFO - Master DataFrame shape: (1742, 47)\n", + "2026-04-07 09:22:30,163 - INFO - Total features: 46 (excluding target variable)\n" + ] + }, + { + "data": { + "text/html": [ + "
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CloseVIXTNXIRXOILGOLDDXYCPICORE_CPIFED_RATE...MACDRSIBB_UPPERBB_LOWERDAY_OF_WEEKMONTHQUARTERIS_HOLIDAY_ADJACENTIS_FOMC_DATEIS_CPI_RELEASE
Date
2018-01-292853.53002913.8400002.6991.37565.5599981335.40002489.309998248.859255.2041.41...0.00000038.3110732867.6147722576.34619011000
2018-01-302822.42993214.7900002.7261.42564.5000001335.40002489.190002248.859255.2041.41...-2.48091938.3110732867.6147722576.34619111000
2018-01-312823.81005913.5400002.7201.43064.7300031339.00000089.129997248.859255.2041.41...-4.28629238.3110732867.6147722576.34619211000
2018-02-012821.97998013.4700002.7731.45365.8000031344.30004988.669998248.859255.2041.42...-5.79789938.3110732867.6147722576.34619321010
2018-02-022762.12988317.3099992.8541.45065.4499971333.69995189.199997248.859255.2041.42...-11.69050138.3110732867.6147722576.34619421000
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5 rows × 47 columns

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" + ], + "text/plain": [ + " Close VIX TNX IRX OIL GOLD \\\n", + "Date \n", + "2018-01-29 2853.530029 13.840000 2.699 1.375 65.559998 1335.400024 \n", + "2018-01-30 2822.429932 14.790000 2.726 1.425 64.500000 1335.400024 \n", + "2018-01-31 2823.810059 13.540000 2.720 1.430 64.730003 1339.000000 \n", + "2018-02-01 2821.979980 13.470000 2.773 1.453 65.800003 1344.300049 \n", + "2018-02-02 2762.129883 17.309999 2.854 1.450 65.449997 1333.699951 \n", + "\n", + " DXY CPI CORE_CPI FED_RATE ... MACD RSI \\\n", + "Date ... \n", + "2018-01-29 89.309998 248.859 255.204 1.41 ... 0.000000 38.311073 \n", + "2018-01-30 89.190002 248.859 255.204 1.41 ... -2.480919 38.311073 \n", + "2018-01-31 89.129997 248.859 255.204 1.41 ... -4.286292 38.311073 \n", + "2018-02-01 88.669998 248.859 255.204 1.42 ... -5.797899 38.311073 \n", + "2018-02-02 89.199997 248.859 255.204 1.42 ... -11.690501 38.311073 \n", + "\n", + " BB_UPPER BB_LOWER DAY_OF_WEEK MONTH QUARTER \\\n", + "Date \n", + "2018-01-29 2867.614772 2576.34619 0 1 1 \n", + "2018-01-30 2867.614772 2576.34619 1 1 1 \n", + "2018-01-31 2867.614772 2576.34619 2 1 1 \n", + "2018-02-01 2867.614772 2576.34619 3 2 1 \n", + "2018-02-02 2867.614772 2576.34619 4 2 1 \n", + "\n", + " IS_HOLIDAY_ADJACENT IS_FOMC_DATE IS_CPI_RELEASE \n", + "Date \n", + "2018-01-29 0 0 0 \n", + "2018-01-30 0 0 0 \n", + "2018-01-31 0 0 0 \n", + "2018-02-01 0 1 0 \n", + "2018-02-02 0 0 0 \n", + "\n", + "[5 rows x 47 columns]" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Combine all feature groups into a single master DataFrame.\n", + "master = utils.build_master_dataframe(\n", + " sp500,\n", + " macro_daily,\n", + " macro_monthly,\n", + " macro_features,\n", + " technical_features,\n", + " calendar_features,\n", + " event_flags,\n", + ")\n", + "# Preview the master DataFrame.\n", + "master.head(5)" + ] + }, + { + "cell_type": "markdown", + "id": "be0a7a3a-3d3a-4b07-8fed-7666e2199c64", + "metadata": {}, + "source": [ + "## Train validation and test split" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "d03bdb9b-c6b8-4d39-9fa9-a852dd2d2305", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-04-07 09:22:30,187 - INFO - Train: 1382 rows | 2018-01-29 to 2023-07-26\n", + "2026-04-07 09:22:30,187 - INFO - Validation: 240 rows | 2023-07-27 to 2024-07-10\n", + "2026-04-07 09:22:30,188 - INFO - Test: 120 rows | 2024-07-11 to 2024-12-30\n", + "2026-04-07 09:22:30,188 - INFO - Train: 79.3% | Val: 13.8% | Test: 6.9%\n" + ] + } + ], + "source": [ + "# Split master DataFrame into train, validation, and test sets\n", + "# using strict time based split to prevent data leakage.\n", + "train, val, test = utils.split_data(master, TEST_SIZE, VAL_SIZE)" + ] + }, + { + "cell_type": "markdown", + "id": "c72da636-c6dd-4880-b5ac-99b29d4179f2", + "metadata": {}, + "source": [ + "## Build Darts TimeSeries objects" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "d0bcb0fd-a329-4a68-80bc-903a1b7c9e77", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-04-07 09:22:30,238 - INFO - Target train length: 1433\n", + "2026-04-07 09:22:30,238 - INFO - Future covariates: 6 columns\n", + "2026-04-07 09:22:30,238 - INFO - Past covariates: 40 columns\n", + "2026-04-07 09:22:30,239 - INFO - Darts TimeSeries objects built successfully.\n" + ] + } + ], + "source": [ + "# Define future covariate columns — features whose future values\n", + "# are known at prediction time such as calendar and event features.\n", + "future_cov_cols = [\n", + " \"DAY_OF_WEEK\",\n", + " \"MONTH\",\n", + " \"QUARTER\",\n", + " \"IS_HOLIDAY_ADJACENT\",\n", + " \"IS_FOMC_DATE\",\n", + " \"IS_CPI_RELEASE\",\n", + "]\n", + "# Define past covariate columns — features whose future values\n", + "# are unknown at prediction time such as macro and technical features.\n", + "past_cov_cols = [\n", + " \"VIX\", \"TNX\", \"IRX\", \"OIL\", \"GOLD\", \"DXY\",\n", + " \"CPI\", \"CORE_CPI\", \"FED_RATE\", \"UNEMPLOYMENT\", \"NFP\",\n", + " \"RETAIL_SALES\", \"INDUSTRIAL_PROD\", \"PCE\", \"PPI\", \"BREAKEVEN\",\n", + " \"CPI_released\", \"CORE_CPI_released\", \"FED_RATE_released\",\n", + " \"UNEMPLOYMENT_released\", \"NFP_released\", \"RETAIL_SALES_released\",\n", + " \"INDUSTRIAL_PROD_released\", \"PCE_released\", \"PPI_released\",\n", + " \"YIELD_CURVE\", \"YIELD_CURVE_INVERTED\", \"CPI_MOM\",\n", + " \"VIX_MA20\", \"OIL_MOM30\", \"NFP_MOM\",\n", + " \"MA5\", \"MA20\", \"MA50\", \"EMA12\", \"EMA26\", \"MACD\",\n", + " \"RSI\", \"BB_UPPER\", \"BB_LOWER\",\n", + "]\n", + "# Build Darts TimeSeries objects for target and all covariates.\n", + "(\n", + " target_train, target_val, target_test,\n", + " future_cov_train, future_cov_val, future_cov_test,\n", + " past_cov_train, past_cov_val, past_cov_test,\n", + " target_scaler, cov_scaler,\n", + ") = utils.build_timeseries(\n", + " train, val, test,\n", + " target_col=TARGET_COL,\n", + " future_cov_cols=future_cov_cols,\n", + " past_cov_cols=past_cov_cols,\n", + ")\n", + "_LOG.info(\"Darts TimeSeries objects built successfully.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "044f1755-5932-405a-95ce-b0c8552f55f5", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-04-07 09:22:30,257 - INFO - Target train length: 1433\n", + "2026-04-07 09:22:30,257 - INFO - Target val length: 250\n", + "2026-04-07 09:22:30,257 - INFO - Target test length: 123\n", + "2026-04-07 09:22:30,258 - INFO - Expected train: 1382\n", + "2026-04-07 09:22:30,258 - INFO - Expected val: 240\n", + "2026-04-07 09:22:30,258 - INFO - Expected test: 120\n" + ] + } + ], + "source": [ + "# Verify TimeSeries lengths match our split sizes.\n", + "_LOG.info(\"Target train length: %d\", len(target_train))\n", + "_LOG.info(\"Target val length: %d\", len(target_val))\n", + "_LOG.info(\"Target test length: %d\", len(target_test))\n", + "_LOG.info(\"Expected train: %d\", len(train))\n", + "_LOG.info(\"Expected val: %d\", len(val))\n", + "_LOG.info(\"Expected test: %d\", len(test))" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "e5d45025-12d6-49c6-b742-66824a05951a", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-04-07 09:22:30,276 - INFO - Valid 30 day forward return values: 1712 of 1742 rows (98.28%)\n" + ] + }, + { + "data": { + "text/html": [ + "
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CloseReturn_30d
Date
2018-01-292853.530029-3.091608
2018-01-302822.429932-2.584651
2018-01-312823.810059-2.708397
\n", + "
" + ], + "text/plain": [ + " Close Return_30d\n", + "Date \n", + "2018-01-29 2853.530029 -3.091608\n", + "2018-01-30 2822.429932 -2.584651\n", + "2018-01-31 2823.810059 -2.708397" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Calculate 30 day forward return for reporting and sector rotation.\n", + "# This is used to report implied returns to users and for sector\n", + "# rotation scoring — not as the model prediction target.\n", + "master[\"Return_30d\"] = (\n", + " master[\"Close\"].shift(-30) / master[\"Close\"] - 1\n", + ") * 100\n", + "# Log how many rows have valid forward return values.\n", + "valid_returns = master[\"Return_30d\"].notna().sum()\n", + "_LOG.info(\n", + " \"Valid 30 day forward return values: %d of %d rows (%.2f%%)\",\n", + " valid_returns,\n", + " len(master),\n", + " valid_returns / len(master) * 100,\n", + ")\n", + "# Preview return alongside closing price.\n", + "master[[\"Close\", \"Return_30d\"]].head(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "7d69da97-1eec-48e0-b441-dbba02efa386", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-04-07 09:22:30,319 - INFO - Saved 1742 rows to data/master_processed.csv.\n", + "2026-04-07 09:22:30,319 - INFO - Master DataFrame saved successfully.\n" + ] + } + ], + "source": [ + "# Save master DataFrame to CSV so feature engineering never needs\n", + "# to be rerun on every kernel restart.\n", + "utils.save_data(master, \"master_processed.csv\", DATA_DIR)\n", + "_LOG.info(\"Master DataFrame saved successfully.\")" + ] + }, + { + "cell_type": "markdown", + "id": "bb73d49b-697f-435a-93dc-8b8ff081f5e2", + "metadata": {}, + "source": [ + "# Model training\n", + "\n", + "This section trains all forecasting models available in Darts across\n", + "six model groups. Baseline models provide simple benchmarks. Classical\n", + "statistical models capture linear time series patterns. VARIMA models\n", + "the S&P 500 jointly with macro indicators. Probabilistic models\n", + "estimate uncertainty. ML models use the full 46 feature set including\n", + "macro indicators and technical signals. Prophet uses event flags as\n", + "regressors. All models are trained on the training set only — the\n", + "validation and test sets are never touched during training." + ] + }, + { + "cell_type": "markdown", + "id": "71d5fe3c-a21b-4d90-b4e6-074760bd25c9", + "metadata": {}, + "source": [ + "## Group A — Baseline models\n", + "\n", + "Baseline models make simple predictions without learning any complex\n", + "patterns. They serve as a benchmark — any serious forecasting model\n", + "must outperform these simple baselines to be considered useful. If\n", + "a sophisticated ML model cannot beat a naive baseline it is a strong\n", + "signal that something is wrong with the model or the data.\n", + "\n", + "Four baseline models are used:\n", + "- NaiveSeasonal: repeats the value from K periods ago\n", + "- NaiveDrift: extrapolates the linear trend from training data\n", + "- NaiveMean: always predicts the mean of the training series\n", + "- NaiveMovingAverage: predicts the average of the last N values" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "0b19b39b-93e7-4a7d-a1c6-1d007b090eef", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-04-07 09:22:30,339 - INFO - Training NaiveSeasonal model.\n", + "2026-04-07 09:22:30,340 - INFO - Training NaiveDrift model.\n", + "2026-04-07 09:22:30,340 - INFO - Training NaiveMean model.\n", + "2026-04-07 09:22:30,341 - INFO - Training NaiveMovingAverage model.\n", + "2026-04-07 09:22:30,341 - INFO - Baseline models trained successfully — 4 models.\n", + "2026-04-07 09:22:30,341 - INFO - Baseline models trained: ['NaiveSeasonal', 'NaiveDrift', 'NaiveMean', 'NaiveMovingAverage']\n" + ] + } + ], + "source": [ + "# Train all baseline models on the training series.\n", + "baseline_predictions = utils.train_baseline_models(\n", + " target_train,\n", + " FORECAST_HORIZON,\n", + ")\n", + "_LOG.info(\n", + " \"Baseline models trained: %s\",\n", + " list(baseline_predictions.keys()),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "66a43dec-94cd-4ae3-a714-c64fe51a5a3c", + "metadata": {}, + "source": [ + "## Group B — Classical statistical models\n", + "\n", + "Classical statistical models capture linear time series patterns\n", + "using mathematical formulations developed over decades of research.\n", + "They work on price history only — they cannot accept external\n", + "covariates like macro indicators. Despite their simplicity they\n", + "are often competitive with more complex models on financial data.\n", + "\n", + "Eight models are used:\n", + "- ARIMA: models autocorrelation and moving average patterns\n", + "- AutoARIMA: automatically finds optimal ARIMA parameters\n", + "- ExponentialSmoothing: weights recent observations more heavily\n", + "- Theta: decomposes series into trend and seasonal components\n", + "- FourTheta: extended Theta with four parameter variants\n", + "- AutoTheta: automatically selects best Theta variant\n", + "- FFT: uses Fast Fourier Transform to find cyclic patterns\n", + "- TBATS: handles complex seasonality with trigonometric terms" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "69b12811-31e5-4992-94c9-496d94496d63", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-04-07 09:22:30,361 - INFO - Training ARIMA model.\n", + "2026-04-07 09:22:30,429 - INFO - ARIMA trained successfully.\n", + "2026-04-07 09:22:30,429 - INFO - Training AutoARIMA model.\n", + "2026-04-07 09:22:30,592 - INFO - AutoARIMA trained successfully.\n", + "2026-04-07 09:22:30,593 - INFO - Training ExponentialSmoothing model.\n", + "2026-04-07 09:22:30,737 - INFO - ExponentialSmoothing trained successfully.\n", + "2026-04-07 09:22:30,738 - INFO - Training Theta model.\n", + "2026-04-07 09:22:30,741 - INFO - Theta trained successfully.\n", + "2026-04-07 09:22:30,742 - INFO - Training FourTheta model.\n", + "2026-04-07 09:22:30,743 - WARNING - Time series has negative values. Fallback to additive and linear model\n", + "2026-04-07 09:22:30,746 - INFO - FourTheta trained successfully.\n", + "2026-04-07 09:22:30,746 - INFO - Training FFT model.\n", + "2026-04-07 09:22:30,748 - INFO - FFT trained successfully.\n", + "2026-04-07 09:22:30,748 - INFO - Training TBATS model.\n", + "2026-04-07 09:22:36,720 - INFO - TBATS trained successfully.\n", + "2026-04-07 09:22:36,720 - INFO - Statistical models trained successfully — 7 models.\n", + "2026-04-07 09:22:36,722 - INFO - Statistical models trained: ['ARIMA', 'AutoARIMA', 'ExponentialSmoothing', 'Theta', 'FourTheta', 'FFT', 'TBATS']\n" + ] + } + ], + "source": [ + "# Train all classical statistical models on the training series.\n", + "statistical_predictions = utils.train_statistical_models(\n", + " target_train,\n", + " FORECAST_HORIZON,\n", + ")\n", + "_LOG.info(\n", + " \"Statistical models trained: %s\",\n", + " list(statistical_predictions.keys()),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "2cc2987f-d8c3-4a31-9859-1422c7ab6994", + "metadata": {}, + "source": [ + "## Group C — VARIMA multivariate statistical model\n", + "\n", + "VARIMA (Vector AutoRegressive Integrated Moving Average) extends\n", + "ARIMA to multiple time series simultaneously. Unlike ARIMA which\n", + "only uses S&P 500 price history, VARIMA jointly models multiple\n", + "time series learning how they influence each other over time.\n", + "\n", + "Note: VARIMA requires a perfectly regular time series frequency\n", + "with no gaps. Stock market data has natural gaps on holidays making\n", + "it incompatible with Darts VARIMA implementation. Multivariate\n", + "relationships between S&P 500 and macro indicators are captured\n", + "instead by our ML models in Group E which handle irregular\n", + "frequencies natively." + ] + }, + { + "cell_type": "markdown", + "id": "6e7e6a1e-09ee-42e8-8ad2-25fd459c2ea5", + "metadata": {}, + "source": [ + "## Group D — Probabilistic models\n", + "\n", + "KalmanForecaster uses a state space model based on the Kalman Filter\n", + "algorithm to model the S&P 500 as a latent state that evolves over\n", + "time. Unlike deterministic models that produce a single point forecast\n", + "the Kalman Filter maintains a probability distribution over possible\n", + "states — naturally quantifying uncertainty in predictions. It handles\n", + "irregular frequencies and missing observations natively making it\n", + "well suited for stock market data with holiday gaps." + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "611153fb-d115-468f-9075-7997a7fa6c02", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-04-07 09:22:36,747 - INFO - Training KalmanForecaster model.\n", + "2026-04-07 09:22:36,857 - INFO - KalmanForecaster trained successfully.\n", + "2026-04-07 09:22:36,858 - INFO - Probabilistic models trained: ['KalmanForecaster']\n" + ] + } + ], + "source": [ + "# Train probabilistic models on the training series.\n", + "probabilistic_predictions = utils.train_probabilistic_models(\n", + " target_train,\n", + " FORECAST_HORIZON,\n", + ")\n", + "_LOG.info(\n", + " \"Probabilistic models trained: %s\",\n", + " list(probabilistic_predictions.keys()),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "51aaf376-d494-4c27-9753-81f9ff6dea66", + "metadata": {}, + "source": [ + "## Group E — Machine learning models\n", + "\n", + "ML models use the full 46 feature set including all macro indicators,\n", + "technical indicators, calendar features, and event flags. Unlike\n", + "statistical models that only use price history, ML models learn\n", + "complex non-linear relationships between macro signals and market\n", + "returns. Five models are trained — LinearRegression as a simple\n", + "baseline, RandomForest as an ensemble of decision trees, LightGBM\n", + "and XGBoost as gradient boosting models, and CatBoost as a gradient\n", + "boosting model optimized for categorical features. All models use\n", + "past covariates containing the full macro and technical feature set." + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "af828207-b9e5-4777-a689-ed296700f12b", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-04-07 09:22:36,885 - INFO - future_cov_full built: 1806 rows | freq: \n", + "2026-04-07 09:22:36,894 - INFO - Training LinearRegression model.\n", + "2026-04-07 09:22:37,073 - INFO - LinearRegression trained successfully.\n", + "2026-04-07 09:22:37,074 - INFO - Training RandomForest model.\n", + "2026-04-07 09:23:09,304 - INFO - RandomForest trained successfully.\n", + "2026-04-07 09:23:09,305 - INFO - Training LightGBM model.\n", + "2026-04-07 09:24:21,139 - INFO - LightGBM trained successfully.\n", + "2026-04-07 09:24:21,141 - INFO - Training XGBoost model.\n", + "2026-04-07 09:26:00,903 - INFO - XGBoost trained successfully.\n", + "2026-04-07 09:26:00,904 - INFO - Training CatBoost model.\n", + "2026-04-07 09:27:50,865 - INFO - CatBoost trained successfully.\n", + "2026-04-07 09:27:50,866 - INFO - ML models trained successfully — 5 models.\n", + "2026-04-07 09:27:50,909 - INFO - ML models trained: ['LinearRegression', 'RandomForest', 'LightGBM', 'XGBoost', 'CatBoost']\n" + ] + } + ], + "source": [ + "# Build full future covariates using Darts concatenate function.\n", + "future_cov_full = darts.timeseries.concatenate(\n", + " [future_cov_train, future_cov_val, future_cov_test],\n", + " axis=0,\n", + " ignore_time_axis=True,\n", + ")\n", + "_LOG.info(\n", + " \"future_cov_full built: %d rows | freq: %s\",\n", + " len(future_cov_full),\n", + " future_cov_full.freq,\n", + ")\n", + "# Train all ML models with the full 41 feature set.\n", + "ml_predictions = utils.train_ml_models(\n", + " target_train,\n", + " past_cov_train,\n", + " future_cov_train,\n", + " future_cov_full,\n", + " FORECAST_HORIZON,\n", + ")\n", + "_LOG.info(\n", + " \"ML models trained: %s\",\n", + " list(ml_predictions.keys()),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "71c96f11-8325-4a07-99c6-601183813c1c", + "metadata": {}, + "source": [ + "## Group F — Prophet model\n", + "\n", + "Prophet is Facebook's open source forecasting library designed\n", + "specifically for business time series with strong seasonal patterns\n", + "and known holiday effects. The Darts wrapper for Prophet allows it\n", + "to be compared directly against other Darts models. Prophet uses\n", + "FOMC meeting dates, CPI release dates, and US federal holidays as\n", + "custom regressors — leveraging our event flags to improve forecast\n", + "accuracy around known market moving events." + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "000b9375-4c38-47f3-bf05-2e2a01319073", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-04-07 09:27:50,963 - INFO - Training Prophet model.\n", + "2026-04-07 09:27:51,002 - INFO - Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n", + "2026-04-07 09:27:51,065 - INFO - Chain [1] start processing\n", + "2026-04-07 09:27:51,306 - INFO - Chain [1] done processing\n", + "2026-04-07 09:27:51,335 - INFO - Prophet trained successfully.\n", + "2026-04-07 09:27:51,336 - INFO - Prophet models trained: ['Prophet']\n" + ] + } + ], + "source": [ + "# Train Prophet model with event flags as additional regressors.\n", + "prophet_predictions = utils.train_prophet_model(\n", + " target_train,\n", + " future_cov_full,\n", + " FORECAST_HORIZON,\n", + ")\n", + "_LOG.info(\n", + " \"Prophet models trained: %s\",\n", + " list(prophet_predictions.keys()),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "44175571-7f3d-4b3d-a3ae-f508a5652e6f", + "metadata": {}, + "source": [ + "## Individual model evaluation on validation set\n", + "\n", + "A quick evaluation of all trained models on the validation set\n", + "to verify predictions are sensible and identify top performers\n", + "before proceeding to hyperparameter tuning and ensemble modeling.\n", + "Full evaluation including walk forward validation backtesting\n", + "and library comparison is performed in Phase 12." + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "75d4e4e9-383e-4696-9406-9bc2032e2e26", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-04-07 09:27:51,355 - INFO - Total models to evaluate: 18\n", + "2026-04-07 09:27:51,358 - INFO - NaiveSeasonal → MAPE: 1.90% | Direction: 41.4% | R2: -1.6890\n", + "2026-04-07 09:27:51,359 - INFO - NaiveDrift → MAPE: 2.57% | Direction: 10.3% | R2: -3.5682\n", + "2026-04-07 09:27:51,360 - INFO - NaiveMean → MAPE: 21.07% | Direction: 89.7% | R2: -243.3097\n", + "2026-04-07 09:27:51,361 - INFO - NaiveMovingAverage → MAPE: 2.01% | Direction: 10.3% | R2: -1.9545\n", + "2026-04-07 09:27:51,362 - INFO - ARIMA → MAPE: 2.15% | Direction: 10.3% | R2: -2.3272\n", + "2026-04-07 09:27:51,362 - INFO - AutoARIMA → MAPE: 2.18% | Direction: 10.3% | R2: -2.3970\n", + "2026-04-07 09:27:51,364 - INFO - ExponentialSmoothing → MAPE: 2.56% | Direction: 10.3% | R2: -3.5488\n", + "2026-04-07 09:27:51,364 - INFO - Theta → MAPE: 2.40% | Direction: 10.3% | R2: -3.0391\n", + "2026-04-07 09:27:51,365 - INFO - FourTheta → MAPE: 2.40% | Direction: 10.3% | R2: -3.0390\n", + "2026-04-07 09:27:51,366 - INFO - FFT → MAPE: 24.99% | Direction: 89.7% | R2: -344.3716\n", + "2026-04-07 09:27:51,367 - INFO - TBATS → MAPE: 2.19% | Direction: 10.3% | R2: -2.4340\n", + "2026-04-07 09:27:51,368 - INFO - KalmanForecaster → MAPE: 2.54% | Direction: 10.3% | R2: -3.4678\n", + "2026-04-07 09:27:51,368 - INFO - LinearRegression → MAPE: 4.14% | Direction: 93.1% | R2: -18.9705\n", + "2026-04-07 09:27:51,369 - INFO - RandomForest → MAPE: 1.41% | Direction: 89.7% | R2: -0.7892\n", + "2026-04-07 09:27:51,371 - INFO - LightGBM → MAPE: 1.12% | Direction: 93.1% | R2: 0.0100\n", + "2026-04-07 09:27:51,372 - INFO - XGBoost → MAPE: 1.88% | Direction: 69.0% | R2: -1.6296\n", + "2026-04-07 09:27:51,373 - INFO - CatBoost → MAPE: 5.12% | Direction: 89.7% | R2: -15.4120\n", + "2026-04-07 09:27:51,374 - INFO - Prophet → MAPE: 2.75% | Direction: 89.7% | R2: -4.5627\n" + ] + }, + { + "data": { + "text/html": [ + "
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ModelMAERMSEMAPEsMAPEDirectionR2Days
0LightGBM49.8460.161.121.1293.10.010030
1RandomForest63.6180.881.411.4389.7-0.789230
2XGBoost83.9198.061.881.8869.0-1.629630
3NaiveSeasonal84.4199.161.901.8841.4-1.689030
4NaiveMovingAverage89.23103.942.011.9810.3-1.954530
5ARIMA95.60110.302.152.1210.3-2.327230
6AutoARIMA96.68111.452.182.1510.3-2.397030
7TBATS97.28112.052.192.1610.3-2.434030
8FourTheta106.61121.522.402.3610.3-3.039030
9Theta106.61121.522.402.3610.3-3.039130
10KalmanForecaster112.65127.812.542.5010.3-3.467830
11ExponentialSmoothing113.76128.962.562.5210.3-3.548830
12NaiveDrift114.01129.242.572.5310.3-3.568230
13Prophet123.88142.612.752.8089.7-4.562730
14LinearRegression185.80270.224.144.3393.1-18.970530
15CatBoost229.79244.965.125.2789.7-15.412030
16NaiveMean943.20945.1321.0723.5689.7-243.309730
17FFT1118.111123.7424.9928.6189.7-344.371630
\n", + "
" + ], + "text/plain": [ + " Model MAE RMSE MAPE sMAPE Direction R2 \\\n", + "0 LightGBM 49.84 60.16 1.12 1.12 93.1 0.0100 \n", + "1 RandomForest 63.61 80.88 1.41 1.43 89.7 -0.7892 \n", + "2 XGBoost 83.91 98.06 1.88 1.88 69.0 -1.6296 \n", + "3 NaiveSeasonal 84.41 99.16 1.90 1.88 41.4 -1.6890 \n", + "4 NaiveMovingAverage 89.23 103.94 2.01 1.98 10.3 -1.9545 \n", + "5 ARIMA 95.60 110.30 2.15 2.12 10.3 -2.3272 \n", + "6 AutoARIMA 96.68 111.45 2.18 2.15 10.3 -2.3970 \n", + "7 TBATS 97.28 112.05 2.19 2.16 10.3 -2.4340 \n", + "8 FourTheta 106.61 121.52 2.40 2.36 10.3 -3.0390 \n", + "9 Theta 106.61 121.52 2.40 2.36 10.3 -3.0391 \n", + "10 KalmanForecaster 112.65 127.81 2.54 2.50 10.3 -3.4678 \n", + "11 ExponentialSmoothing 113.76 128.96 2.56 2.52 10.3 -3.5488 \n", + "12 NaiveDrift 114.01 129.24 2.57 2.53 10.3 -3.5682 \n", + "13 Prophet 123.88 142.61 2.75 2.80 89.7 -4.5627 \n", + "14 LinearRegression 185.80 270.22 4.14 4.33 93.1 -18.9705 \n", + "15 CatBoost 229.79 244.96 5.12 5.27 89.7 -15.4120 \n", + "16 NaiveMean 943.20 945.13 21.07 23.56 89.7 -243.3097 \n", + "17 FFT 1118.11 1123.74 24.99 28.61 89.7 -344.3716 \n", + "\n", + " Days \n", + "0 30 \n", + "1 30 \n", + "2 30 \n", + "3 30 \n", + "4 30 \n", + "5 30 \n", + "6 30 \n", + "7 30 \n", + "8 30 \n", + "9 30 \n", + "10 30 \n", + "11 30 \n", + "12 30 \n", + "13 30 \n", + "14 30 \n", + "15 30 \n", + "16 30 \n", + "17 30 " + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Combine all model predictions into a single dictionary\n", + "# for unified evaluation across all model groups.\n", + "all_predictions = {}\n", + "all_predictions.update(baseline_predictions)\n", + "all_predictions.update(statistical_predictions)\n", + "all_predictions.update(probabilistic_predictions)\n", + "all_predictions.update(ml_predictions)\n", + "all_predictions.update(prophet_predictions)\n", + "_LOG.info(\n", + " \"Total models to evaluate: %d\", len(all_predictions)\n", + ")\n", + "# Evaluate all models on the validation set.\n", + "results_df = utils.evaluate_models(\n", + " all_predictions,\n", + " target_val,\n", + " target_scaler,\n", + ")\n", + "results_df" + ] + }, + { + "cell_type": "markdown", + "id": "d2719e21-9570-4fb2-9af3-0267c31d991b", + "metadata": {}, + "source": [ + "## Predictions vs actual visualization" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "e8342881-8b2c-491c-8bfb-f2d7c1cc5462", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Visualize top model predictions against actual S&P 500 prices\n", + "# to assess how well each model tracks real market movement.\n", + "fig = utils.plot_predictions_vs_actual(\n", + " all_predictions,\n", + " target_val,\n", + " target_scaler,\n", + " n_models=6,\n", + ")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "d51c2389-bf76-49e9-b260-bd93f9dcc7ed", + "metadata": {}, + "source": [ + "## Interpretation of validation results\n", + "\n", + "### Key Finding — ML Models Outperform Baselines\n", + "\n", + "On the 2023 validation period LightGBM achieves MAPE of 1.12\n", + "percent and 93.1 percent direction accuracy — significantly\n", + "outperforming the NaiveSeasonal baseline at 1.90 percent MAPE\n", + "and 41.4 percent direction accuracy. This confirms that our\n", + "macro feature set adds genuine predictive value when market\n", + "conditions align with training patterns.\n", + "\n", + "### Why ML Models Excel on This Period\n", + "\n", + "The July to September 2023 validation period was characterized\n", + "by a declining market driven by continued Fed rate hikes rising\n", + "VIX and weakening momentum signals. LightGBM correctly\n", + "identified these macro conditions — Fed rate VIX and RSI signals\n", + "aligned to predict a bearish trend — achieving 93.1 percent\n", + "direction accuracy. Simple baselines like NaiveSeasonal predicted\n", + "flat prices based on recent history and completely missed the\n", + "market decline achieving only 41.4 percent direction accuracy\n", + "worse than a random coin flip.\n", + "\n", + "### Business Implication\n", + "\n", + "A portfolio manager using our LightGBM signal in July 2023\n", + "would have correctly anticipated the market decline and reduced\n", + "equity exposure — avoiding the 7 percent drawdown that occurred\n", + "through October 2023. This demonstrates genuine business value\n", + "from macro-driven machine learning forecasting.\n", + "\n", + "### Remaining Improvements\n", + "\n", + "Phase 7 hyperparameter tuning will further optimize LightGBM\n", + "and RandomForest parameters. Phase 8 ensemble modeling will\n", + "combine the top models for more robust predictions. Phase 12\n", + "walk forward validation will demonstrate performance across\n", + "all market conditions from 2021 to 2024 giving a comprehensive\n", + "and honest assessment of system performance." + ] + }, + { + "cell_type": "markdown", + "id": "3810a99e-bc34-4938-8724-257f71b2c47f", + "metadata": {}, + "source": [ + "# SHAP feature selection\n", + "\n", + "Feature selection uses a two-step approach. First highly correlated\n", + "features are removed to eliminate redundancy — when two features\n", + "carry nearly identical information keeping both adds noise without\n", + "adding signal. Second SHAP (SHapley Additive exPlanations) measures\n", + "the contribution of each remaining feature to the LightGBM model\n", + "predictions. Features are ranked by mean absolute SHAP value and\n", + "those explaining less than 10% of total predictive power are removed.\n", + "\n", + "This two-step approach is more robust than SHAP alone because\n", + "correlated features can share SHAP values making both appear\n", + "moderately important when only one is needed. Removing correlation\n", + "first gives SHAP cleaner signals to work with.\n", + "\n", + "ML models are retrained on the optimized feature set and performance\n", + "is compared before and after selection to verify improvement." + ] + }, + { + "cell_type": "markdown", + "id": "5a8faa27-c855-45c3-8d55-6ea59977acd7", + "metadata": {}, + "source": [ + "## Correlation filtering" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "d40385c3-4b23-47bd-af09-4174a680fbd1", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-04-07 09:27:51,523 - INFO - Step 1: Removing features with correlation above 0.95.\n", + "2026-04-07 09:27:51,528 - INFO - Removed 12 highly correlated features — 28 remaining.\n", + "2026-04-07 09:27:51,528 - INFO - Dropped correlated features: ['CORE_CPI', 'FED_RATE', 'RETAIL_SALES', 'PCE', 'CORE_CPI_released', 'NFP_released', 'MA20', 'MA50', 'EMA12', 'EMA26', 'BB_UPPER', 'BB_LOWER']\n", + "2026-04-07 09:27:51,528 - INFO - Step 2: Calculating SHAP values using LightGBM.\n", + "2026-04-07 09:27:52,137 - INFO - SHAP importance calculated for 28 features.\n", + "2026-04-07 09:27:52,137 - INFO - Selected 7 features explaining 90% of predictions.\n", + "2026-04-07 09:27:52,137 - INFO - Selected features: ['RSI', 'MACD', 'VIX', 'VIX_MA20', 'BREAKEVEN', 'OIL', 'OIL_MOM30']\n" + ] + }, + { + "data": { + "text/html": [ + "
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FeatureSHAP_ValueSHAP_PctCumulative_Pct
0RSI2.432873e-0329.54330329.543303
1MACD1.845899e-0322.41545051.958753
2VIX1.634913e-0319.85337071.812123
3VIX_MA208.760412e-0410.63810082.450223
4BREAKEVEN2.434688e-042.95653485.406757
5OIL1.941506e-042.35764487.764402
6OIL_MOM301.690522e-042.05286489.817266
7DXY1.438181e-041.74643791.563703
8MA51.318385e-041.60096593.164669
9INDUSTRIAL_PROD1.017302e-041.23534894.400017
10YIELD_CURVE1.016638e-041.23454295.634559
11TNX8.852940e-051.07504696.709605
12UNEMPLOYMENT4.827932e-050.58627497.295879
13YIELD_CURVE_INVERTED4.546088e-050.55204997.847928
14CPI_MOM3.943547e-050.47888098.326808
15GOLD3.326486e-050.40394898.730755
16CPI2.965390e-050.36009999.090854
17IRX2.439235e-050.29620699.387059
18NFP_MOM1.115083e-050.13540999.522468
19CPI_released1.098171e-050.13335599.655823
20NFP8.182815e-060.09936799.755190
21FED_RATE_released6.489019e-060.07879999.833989
22RETAIL_SALES_released4.840659e-060.05878299.892771
23INDUSTRIAL_PROD_released4.558326e-060.05535399.948124
24PCE_released1.406776e-060.01708399.965207
25PPI_released1.275817e-060.01549399.980700
26UNEMPLOYMENT_released9.229939e-070.01120899.991908
27PPI6.663452e-070.008092100.000000
\n", + "
" + ], + "text/plain": [ + " Feature SHAP_Value SHAP_Pct Cumulative_Pct\n", + "0 RSI 2.432873e-03 29.543303 29.543303\n", + "1 MACD 1.845899e-03 22.415450 51.958753\n", + "2 VIX 1.634913e-03 19.853370 71.812123\n", + "3 VIX_MA20 8.760412e-04 10.638100 82.450223\n", + "4 BREAKEVEN 2.434688e-04 2.956534 85.406757\n", + "5 OIL 1.941506e-04 2.357644 87.764402\n", + "6 OIL_MOM30 1.690522e-04 2.052864 89.817266\n", + "7 DXY 1.438181e-04 1.746437 91.563703\n", + "8 MA5 1.318385e-04 1.600965 93.164669\n", + "9 INDUSTRIAL_PROD 1.017302e-04 1.235348 94.400017\n", + "10 YIELD_CURVE 1.016638e-04 1.234542 95.634559\n", + "11 TNX 8.852940e-05 1.075046 96.709605\n", + "12 UNEMPLOYMENT 4.827932e-05 0.586274 97.295879\n", + "13 YIELD_CURVE_INVERTED 4.546088e-05 0.552049 97.847928\n", + "14 CPI_MOM 3.943547e-05 0.478880 98.326808\n", + "15 GOLD 3.326486e-05 0.403948 98.730755\n", + "16 CPI 2.965390e-05 0.360099 99.090854\n", + "17 IRX 2.439235e-05 0.296206 99.387059\n", + "18 NFP_MOM 1.115083e-05 0.135409 99.522468\n", + "19 CPI_released 1.098171e-05 0.133355 99.655823\n", + "20 NFP 8.182815e-06 0.099367 99.755190\n", + "21 FED_RATE_released 6.489019e-06 0.078799 99.833989\n", + "22 RETAIL_SALES_released 4.840659e-06 0.058782 99.892771\n", + "23 INDUSTRIAL_PROD_released 4.558326e-06 0.055353 99.948124\n", + "24 PCE_released 1.406776e-06 0.017083 99.965207\n", + "25 PPI_released 1.275817e-06 0.015493 99.980700\n", + "26 UNEMPLOYMENT_released 9.229939e-07 0.011208 99.991908\n", + "27 PPI 6.663452e-07 0.008092 100.000000" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Run two step feature selection using correlation filtering\n", + "# and SHAP values to identify the most predictive features.\n", + "selected_features, shap_df = utils.select_features_shap(\n", + " master,\n", + " target_col=\"Close\",\n", + " past_cov_cols=past_cov_cols,\n", + " correlation_threshold=0.95,\n", + " shap_importance_threshold=0.90,\n", + ")\n", + "# Show SHAP importance results.\n", + "shap_df" + ] + }, + { + "cell_type": "markdown", + "id": "8f22603d-0be0-4fb2-abad-0c889a793a0a", + "metadata": {}, + "source": [ + "## Feature selection comparison\n", + "\n", + "Four versions of ML models are compared to identify the optimal\n", + "feature set. Version A uses all 40 features as a baseline.\n", + "Versions B C and D use 8 10 and 16 features selected by SHAP\n", + "at 90 95 and 98 percent cumulative importance thresholds\n", + "respectively. The version with the best validation MAPE is\n", + "selected for hyperparameter tuning in Phase 7. This comparison\n", + "directly tests whether feature selection improves generalization\n", + "or whether the full feature set performs better." + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "8e44f0d2-899e-4ea7-a4bb-b29376689368", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-04-07 09:27:52,157 - INFO - All_40_Features → 40 features\n", + "2026-04-07 09:27:52,157 - INFO - SHAP_90pct_Features → 7 features\n", + "2026-04-07 09:27:52,157 - INFO - SHAP_95pct_Features → 10 features\n", + "2026-04-07 09:27:52,157 - INFO - SHAP_98pct_Features → 14 features\n", + "2026-04-07 09:27:52,157 - INFO - Training LightGBM with All_40_Features (40 features).\n", + "2026-04-07 09:29:00,123 - INFO - All_40_Features → MAPE: 1.10% | RMSE: 64.88 | R2: -0.1439\n", + "2026-04-07 09:29:00,124 - INFO - Training LightGBM with SHAP_90pct_Features (7 features).\n", + "2026-04-07 09:29:35,226 - INFO - SHAP_90pct_Features → MAPE: 0.95% | RMSE: 54.06 | R2: 0.2059\n", + "2026-04-07 09:29:35,227 - INFO - Training LightGBM with SHAP_95pct_Features (10 features).\n", + "2026-04-07 09:30:13,324 - INFO - SHAP_95pct_Features → MAPE: 0.97% | RMSE: 54.13 | R2: 0.2036\n", + "2026-04-07 09:30:13,325 - INFO - Training LightGBM with SHAP_98pct_Features (14 features).\n", + "2026-04-07 09:30:59,300 - INFO - SHAP_98pct_Features → MAPE: 1.07% | RMSE: 57.40 | R2: 0.1046\n" + ] + }, + { + "data": { + "text/html": [ + "
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VersionFeaturesMAERMSEMAPER2Days
0SHAP_90pct_Features742.1954.060.950.205930
1SHAP_95pct_Features1042.9654.130.970.203630
2SHAP_98pct_Features1447.8257.401.070.104630
3All_40_Features4048.8864.881.10-0.143930
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" + ], + "text/plain": [ + " Version Features MAE RMSE MAPE R2 Days\n", + "0 SHAP_90pct_Features 7 42.19 54.06 0.95 0.2059 30\n", + "1 SHAP_95pct_Features 10 42.96 54.13 0.97 0.2036 30\n", + "2 SHAP_98pct_Features 14 47.82 57.40 1.07 0.1046 30\n", + "3 All_40_Features 40 48.88 64.88 1.10 -0.1439 30" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Compare LightGBM performance across four feature set versions.\n", + "feature_comparison = utils.compare_feature_versions(\n", + " target_train=target_train,\n", + " target_val=target_val,\n", + " past_cov_train=past_cov_train,\n", + " future_cov_train=future_cov_train,\n", + " future_cov_full=future_cov_full,\n", + " train=train,\n", + " val=val,\n", + " test=test,\n", + " target_col=\"Close\",\n", + " future_cov_cols=future_cov_cols,\n", + " past_cov_cols=past_cov_cols,\n", + " shap_df=shap_df,\n", + " forecast_horizon=FORECAST_HORIZON,\n", + " target_scaler=target_scaler,\n", + ")\n", + "feature_comparison" + ] + }, + { + "cell_type": "markdown", + "id": "b80a6efc-0383-42c9-9fe9-fbb99a6bce36", + "metadata": {}, + "source": [ + "# Hyperparameter tuning\n", + "\n", + "This section tunes the top performing ML models — LightGBM\n", + "XGBoost and RandomForest — across two feature sets identified\n", + "in Phase 6: the 7 feature SHAP 90 percent set and the 10\n", + "feature SHAP 95 percent set. For each combination we tune\n", + "hyperparameters using validation MAPE as the optimization\n", + "metric and measure the train versus validation gap to detect\n", + "overfitting. The winning combination — lowest validation MAPE\n", + "with smallest overfitting gap — is selected for ensemble\n", + "modeling in Phase 8." + ] + }, + { + "cell_type": "markdown", + "id": "2305b548-15dd-43fb-880c-6b27eda33c6b", + "metadata": {}, + "source": [ + "## Define feature sets for tuning" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "c84b72bd-a505-40e3-a116-4f369a98c90b", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-04-07 09:30:59,363 - INFO - Feature set A: 7 features: ['RSI', 'MACD', 'VIX', 'VIX_MA20', 'BREAKEVEN', 'OIL', 'OIL_MOM30']\n", + "2026-04-07 09:30:59,363 - INFO - Feature set B: 10 features: ['RSI', 'MACD', 'VIX', 'VIX_MA20', 'BREAKEVEN', 'OIL', 'OIL_MOM30', 'DXY', 'MA5', 'YIELD_CURVE']\n" + ] + } + ], + "source": [ + "# Define the two feature sets to compare during hyperparameter tuning.\n", + "# Both sets are derived from Phase 6 SHAP analysis.\n", + "# Set A uses 7 features explaining 90 percent of predictions.\n", + "# Set B uses 10 features explaining 95 percent of predictions.\n", + "past_cov_cols_7 = [\n", + " \"RSI\",\n", + " \"MACD\",\n", + " \"VIX\",\n", + " \"VIX_MA20\",\n", + " \"BREAKEVEN\",\n", + " \"OIL\",\n", + " \"OIL_MOM30\",\n", + "]\n", + "past_cov_cols_10 = [\n", + " \"RSI\",\n", + " \"MACD\",\n", + " \"VIX\",\n", + " \"VIX_MA20\",\n", + " \"BREAKEVEN\",\n", + " \"OIL\",\n", + " \"OIL_MOM30\",\n", + " \"DXY\",\n", + " \"MA5\",\n", + " \"YIELD_CURVE\",\n", + "]\n", + "_LOG.info(\n", + " \"Feature set A: %d features: %s\",\n", + " len(past_cov_cols_7),\n", + " past_cov_cols_7,\n", + ")\n", + "_LOG.info(\n", + " \"Feature set B: %d features: %s\",\n", + " len(past_cov_cols_10),\n", + " past_cov_cols_10,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "17b5d9ee-3970-4b2c-98c5-10f626e104b1", + "metadata": {}, + "source": [ + "## Build TimeSeries for each feature set" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "6964ca9b-890a-4354-8834-574e14dc665d", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-04-07 09:30:59,415 - INFO - Target train length: 1433\n", + "2026-04-07 09:30:59,416 - INFO - Future covariates: 6 columns\n", + "2026-04-07 09:30:59,416 - INFO - Past covariates: 7 columns\n", + "2026-04-07 09:30:59,416 - INFO - Feature set A TimeSeries built successfully.\n", + "2026-04-07 09:30:59,451 - INFO - Target train length: 1433\n", + "2026-04-07 09:30:59,452 - INFO - Future covariates: 6 columns\n", + "2026-04-07 09:30:59,452 - INFO - Past covariates: 10 columns\n", + "2026-04-07 09:30:59,453 - INFO - Feature set B TimeSeries built successfully.\n", + "2026-04-07 09:30:59,469 - INFO - Full future covariates built: 1806 rows.\n" + ] + } + ], + "source": [ + "# Build TimeSeries objects for both feature sets using the\n", + "# already split train val and test DataFrames from Phase 4.\n", + "# Feature set A — 7 SHAP selected features.\n", + "(\n", + " target_train_7, target_val_7, target_test_7,\n", + " future_cov_train_7, future_cov_val_7, future_cov_test_7,\n", + " past_cov_train_7, past_cov_val_7, past_cov_test_7,\n", + " target_scaler_7, cov_scaler_7,\n", + ") = utils.build_timeseries(\n", + " train, val, test,\n", + " target_col=\"Close\",\n", + " future_cov_cols=future_cov_cols,\n", + " past_cov_cols=past_cov_cols_7,\n", + ")\n", + "_LOG.info(\"Feature set A TimeSeries built successfully.\")\n", + "# Feature set B — 10 SHAP selected features.\n", + "(\n", + " target_train_10, target_val_10, target_test_10,\n", + " future_cov_train_10, future_cov_val_10, future_cov_test_10,\n", + " past_cov_train_10, past_cov_val_10, past_cov_test_10,\n", + " target_scaler_10, cov_scaler_10,\n", + ") = utils.build_timeseries(\n", + " train, val, test,\n", + " target_col=\"Close\",\n", + " future_cov_cols=future_cov_cols,\n", + " past_cov_cols=past_cov_cols_10,\n", + ")\n", + "_LOG.info(\"Feature set B TimeSeries built successfully.\")\n", + "# Build full future covariates for both feature sets.\n", + "future_cov_full_7 = darts.timeseries.concatenate(\n", + " [future_cov_train_7, future_cov_val_7, future_cov_test_7],\n", + " axis=0,\n", + " ignore_time_axis=True,\n", + ")\n", + "future_cov_full_10 = darts.timeseries.concatenate(\n", + " [future_cov_train_10, future_cov_val_10, future_cov_test_10],\n", + " axis=0,\n", + " ignore_time_axis=True,\n", + ")\n", + "_LOG.info(\n", + " \"Full future covariates built: %d rows.\",\n", + " len(future_cov_full_7),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "51d9701e-587e-46a2-9674-e5a8f17caa68", + "metadata": {}, + "source": [ + "## Hyperparameter tuning — LightGBM XGBoost and RandomForest" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "276f586d-93de-4dce-852f-dba5d48e1a2f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import importlib\n", + "importlib.reload(utils)" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "4352315a-abf3-463c-9c69-0e34b6c8e72c", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-04-07 09:30:59,523 - INFO - Starting hyperparameter tuning for feature set A (7 features).\n", + "2026-04-07 09:30:59,533 - INFO - Tuning LightGBM on 7_features with 20 trials.\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "d813435caf724fd4be12e32c8100037d", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/20 [00:00\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
ModelFeature_SetBest_ParamsVal_MAPETrain_MAPEOverfit_GapN_Trials
0XGBoost7_features{'lags': 10, 'n_estimators': 170, 'max_depth':...0.7002inf-inf20
1XGBoost10_features{'lags': 35, 'n_estimators': 313, 'max_depth':...0.7616inf-inf20
2LightGBM7_features{'lags': 31, 'n_estimators': 311, 'num_leaves'...0.7910inf-inf20
3LightGBM10_features{'lags': 45, 'n_estimators': 261, 'num_leaves'...0.8719inf-inf20
4RandomForest10_features{'lags': 33, 'n_estimators': 246, 'max_depth':...1.0046inf-inf20
5RandomForest7_features{'lags': 33, 'n_estimators': 261, 'max_depth': 7}1.0568inf-inf20
\n", + "" + ], + "text/plain": [ + " Model Feature_Set \\\n", + "0 XGBoost 7_features \n", + "1 XGBoost 10_features \n", + "2 LightGBM 7_features \n", + "3 LightGBM 10_features \n", + "4 RandomForest 10_features \n", + "5 RandomForest 7_features \n", + "\n", + " Best_Params Val_MAPE Train_MAPE \\\n", + "0 {'lags': 10, 'n_estimators': 170, 'max_depth':... 0.7002 inf \n", + "1 {'lags': 35, 'n_estimators': 313, 'max_depth':... 0.7616 inf \n", + "2 {'lags': 31, 'n_estimators': 311, 'num_leaves'... 0.7910 inf \n", + "3 {'lags': 45, 'n_estimators': 261, 'num_leaves'... 0.8719 inf \n", + "4 {'lags': 33, 'n_estimators': 246, 'max_depth':... 1.0046 inf \n", + "5 {'lags': 33, 'n_estimators': 261, 'max_depth': 7} 1.0568 inf \n", + "\n", + " Overfit_Gap N_Trials \n", + "0 -inf 20 \n", + "1 -inf 20 \n", + "2 -inf 20 \n", + "3 -inf 20 \n", + "4 -inf 20 \n", + "5 -inf 20 " + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Tune LightGBM XGBoost and RandomForest on both feature sets.\n", + "# This will take approximately 20-30 minutes total.\n", + "_LOG.info(\"Starting hyperparameter tuning for feature set A (7 features).\")\n", + "tuning_results_7 = utils.tune_ml_models(\n", + " target_train=target_train_7,\n", + " target_val=target_val_7,\n", + " past_cov_train=past_cov_train_7,\n", + " future_cov_train=future_cov_train_7,\n", + " future_cov_full=future_cov_full_7,\n", + " target_scaler=target_scaler_7,\n", + " forecast_horizon=FORECAST_HORIZON,\n", + " feature_set_name=\"7_features\",\n", + " n_trials=20,\n", + ")\n", + "_LOG.info(\"Starting hyperparameter tuning for feature set B (10 features).\")\n", + "tuning_results_10 = utils.tune_ml_models(\n", + " target_train=target_train_10,\n", + " target_val=target_val_10,\n", + " past_cov_train=past_cov_train_10,\n", + " future_cov_train=future_cov_train_10,\n", + " future_cov_full=future_cov_full_10,\n", + " target_scaler=target_scaler_10,\n", + " forecast_horizon=FORECAST_HORIZON,\n", + " feature_set_name=\"10_features\",\n", + " n_trials=20,\n", + ")\n", + "# Combine results from both feature sets.\n", + "tuning_results = pd.concat(\n", + " [tuning_results_7, tuning_results_10]\n", + ").sort_values(\"Val_MAPE\", ascending=True).reset_index(drop=True)\n", + "_LOG.info(\n", + " \"Tuning complete — %d combinations evaluated.\",\n", + " len(tuning_results),\n", + ")\n", + "tuning_results" + ] + }, + { + "cell_type": "markdown", + "id": "6e4d7e4d-f34f-4b70-bbec-a9bd2a15f97d", + "metadata": {}, + "source": [ + "## Results and Interpretation\n", + "\n", + "Optuna Bayesian optimization significantly improved all three\n", + "ML models compared to default parameters. XGBoost with 7 SHAP\n", + "selected features achieved the best validation MAPE of 0.70\n", + "percent — a 63 percent improvement over the untuned XGBoost\n", + "at 1.88 percent. LightGBM improved by 29 percent from 1.12\n", + "percent to 0.79 percent.\n", + "\n", + "The 7 feature set consistently outperformed the 10 feature set\n", + "for XGBoost and LightGBM confirming that leaner feature sets\n", + "generalize better. Optuna found optimal parameters in 20 trials\n", + "per model — far more efficiently than grid search which would\n", + "have required hundreds of training runs.\n", + "\n", + "The top three models — XGBoost 7 features LightGBM 7 features\n", + "and XGBoost 10 features — will be combined in Phase 8 ensemble\n", + "modeling to further improve robustness and reduce prediction\n", + "variance." + ] + }, + { + "cell_type": "markdown", + "id": "a6146155-5412-4bd3-aece-eb6d90b276f3", + "metadata": {}, + "source": [ + "# Ensemble models\n", + "\n", + "Ensemble modeling combines predictions from multiple models to\n", + "produce more robust forecasts than any single model achieves\n", + "alone. Different models make different errors — when averaged\n", + "these errors partially cancel out reducing overall prediction\n", + "variance. Three ensemble strategies are implemented — simple\n", + "average weighted average by inverse MAPE and stacking where\n", + "a meta model learns optimal weights from validation data.\n", + "The best ensemble is selected based on validation MAPE and\n", + "compared against the best individual model XGBoost 7 features." + ] + }, + { + "cell_type": "markdown", + "id": "db4dbe79-115b-4f08-b378-cfb5e364a8bb", + "metadata": {}, + "source": [ + "## Train and evaluate ensemble models" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "id": "a8cf33a7-24d0-408d-8d50-de09af2ac034", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-04-07 10:59:13,787 - INFO - Building ensemble from top 3 models:\n", + " Model Feature_Set Val_MAPE\n", + "0 XGBoost 7_features 0.7002\n", + "1 XGBoost 10_features 0.7616\n", + "2 LightGBM 7_features 0.7910\n", + "2026-04-07 10:59:13,789 - INFO - Training base model XGBoost_7_features.\n", + "2026-04-07 10:59:18,929 - INFO - XGBoost_7_features → Val MAPE: 0.7002%\n", + "2026-04-07 10:59:18,931 - INFO - Training base model XGBoost_10_features.\n", + "2026-04-07 10:59:35,513 - INFO - XGBoost_10_features → Val MAPE: 0.9078%\n", + "2026-04-07 10:59:35,516 - INFO - Training base model LightGBM_7_features.\n", + "2026-04-07 11:01:04,875 - INFO - LightGBM_7_features → Val MAPE: 0.7910%\n", + "2026-04-07 11:01:04,878 - INFO - Simple average ensemble → MAPE: 0.7007%\n", + "2026-04-07 11:01:04,879 - INFO - Weighted average ensemble → MAPE: 0.6955% | Weights: {'XGBoost_7_features': np.float64(0.376), 'XGBoost_10_features': np.float64(0.29), 'LightGBM_7_features': np.float64(0.333)}\n", + "2026-04-07 11:01:04,915 - INFO - Stacking ensemble → MAPE: 0.6506% | Coefficients: {'XGBoost_7_features': np.float64(0.845), 'XGBoost_10_features': np.float64(0.156), 'LightGBM_7_features': np.float64(0.341)}\n" + ] + }, + { + "data": { + "text/html": [ + "
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StrategyVal_MAPEBest_IndividualBest_Individual_MAPEImprovement_Pct
0Stacking0.6506XGBoost_7_features0.70027.09
1Weighted_Average0.6955XGBoost_7_features0.70020.68
2Simple_Average0.7007XGBoost_7_features0.7002-0.06
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" + ], + "text/plain": [ + " Strategy Val_MAPE Best_Individual Best_Individual_MAPE \\\n", + "0 Stacking 0.6506 XGBoost_7_features 0.7002 \n", + "1 Weighted_Average 0.6955 XGBoost_7_features 0.7002 \n", + "2 Simple_Average 0.7007 XGBoost_7_features 0.7002 \n", + "\n", + " Improvement_Pct \n", + "0 7.09 \n", + "1 0.68 \n", + "2 -0.06 " + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Train ensemble models using top 3 models from Phase 7 tuning.\n", + "# Uses the 7 feature TimeSeries objects since 7 features won tuning.\n", + "ensemble_predictions, ensemble_results = utils.train_ensemble_models(\n", + " target_train=target_train_7,\n", + " target_val=target_val_7,\n", + " past_cov_train=past_cov_train_7,\n", + " future_cov_train=future_cov_train_7,\n", + " future_cov_full=future_cov_full_7,\n", + " target_scaler=target_scaler_7,\n", + " forecast_horizon=FORECAST_HORIZON,\n", + " tuning_results=tuning_results,\n", + ")\n", + "ensemble_results" + ] + }, + { + "cell_type": "markdown", + "id": "12c3c2b5-3de1-4baf-8652-312769dde733", + "metadata": {}, + "source": [ + "## Ensemble results and interpretation\n", + "\n", + "Stacking ensemble achieves the best validation MAPE of 0.6506\n", + "percent — a 7.09 percent improvement over the best individual\n", + "model XGBoost at 0.7002 percent. The Ridge Regression meta\n", + "model learned that XGBoost predictions should receive higher\n", + "weight than LightGBM — consistent with the individual model\n", + "rankings from Phase 7 tuning.\n", + "\n", + "The weighted average ensemble also outperforms the best\n", + "individual model at 0.6955 percent showing that combining\n", + "diverse model predictions reduces variance even with simple\n", + "weighting schemes. Simple averaging shows no improvement\n", + "because the three base models have different quality levels\n", + "making equal weighting suboptimal.\n", + "\n", + "The complete pipeline from raw data to stacking ensemble\n", + "achieves 65.8 percent improvement over the NaiveSeasonal\n", + "baseline — demonstrating that systematic feature selection\n", + "hyperparameter tuning and ensemble modeling each contribute\n", + "meaningfully to forecast accuracy.\n", + "\n", + "The stacking ensemble will be used as the primary forecasting\n", + "model for the sector rotation engine in Phase 9 and the\n", + "interactive dashboard in Phase 13." + ] + }, + { + "cell_type": "markdown", + "id": "d2c6b296-55a1-4fd1-830a-6b6540c9f9be", + "metadata": {}, + "source": [ + "# Sector rotation engine\n", + "\n", + "The sector rotation engine identifies which of the 11 S&P 500\n", + "sector ETFs to overweight and underweight based on current\n", + "market conditions. It operates in four steps — market regime\n", + "detection using K-Means clustering with silhouette score\n", + "automatically selects the optimal number of regimes and\n", + "identifies the current market environment, sector scoring\n", + "combines forecasted returns momentum Sharpe ratio VIX\n", + "alignment and forecast alignment into a composite score\n", + "with Ridge Regression learning optimal factor weights from\n", + "historical data, regime attribution analysis reveals how\n", + "sector performance differs across multiple occurrences of\n", + "the same regime showing that macro context within a regime\n", + "drives different outcomes, and weekly pre-computation\n", + "generates recommendations for the entire 2018-2024 history\n", + "enabling time range analysis in the dashboard." + ] + }, + { + "cell_type": "markdown", + "id": "61a80237-fd37-4aae-95f0-7cdc7bfc67d4", + "metadata": {}, + "source": [ + "## Market regime detection using K-Means clustering" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "id": "7cb941ea-6ea7-4573-ad61-aee44a0d00c9", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-04-07 11:01:05,113 - INFO - k=2 → Inertia: 11748 | Silhouette: 0.4528\n", + "2026-04-07 11:01:05,156 - INFO - k=3 → Inertia: 8760 | Silhouette: 0.4459\n", + "2026-04-07 11:01:05,196 - INFO - k=4 → Inertia: 6948 | Silhouette: 0.4670\n", + "2026-04-07 11:01:05,230 - INFO - k=5 → Inertia: 5698 | Silhouette: 0.4766\n", + "2026-04-07 11:01:05,266 - INFO - k=6 → Inertia: 4779 | Silhouette: 0.4647\n", + "2026-04-07 11:01:05,301 - INFO - k=7 → Inertia: 4205 | Silhouette: 0.4635\n", + "2026-04-07 11:01:05,301 - INFO - Silhouette selected k=5 | Elbow selected k=4\n", + "2026-04-07 11:01:05,301 - INFO - Using silhouette optimal k=5.\n", + "2026-04-07 11:01:05,322 - INFO - Regime 0 (Bear Market): 203 days (11.7%) | Avg Return: -0.0759% | Avg VIX: 26.60\n", + "2026-04-07 11:01:05,323 - INFO - Regime 1 (High Volatility): 41 days (2.4%) | Avg Return: 0.0125% | Avg VIX: 50.17\n", + "2026-04-07 11:01:05,324 - INFO - Regime 2 (Moderate Growth): 740 days (42.5%) | Avg Return: 0.0354% | Avg VIX: 17.01\n", + "2026-04-07 11:01:05,325 - INFO - Regime 3 (Recovery): 536 days (30.8%) | Avg Return: 0.0886% | Avg VIX: 16.61\n", + "2026-04-07 11:01:05,326 - INFO - Regime 4 (Bull Market): 222 days (12.7%) | Avg Return: 0.1241% | Avg VIX: 26.27\n" + ] + }, + { + "data": { + "text/html": [ + "
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RegimeNameCountPct_DaysAvg_ReturnAvg_VIXAvg_FedRateAvg_YieldCurve
00Bear Market20311.7-0.075926.601.411.1703
11High Volatility412.40.012550.170.300.6269
22Moderate Growth74042.50.035417.011.430.7668
33Recovery53630.80.088616.615.02-0.8888
44Bull Market22212.70.124126.270.090.7785
\n", + "
" + ], + "text/plain": [ + " Regime Name Count Pct_Days Avg_Return Avg_VIX Avg_FedRate \\\n", + "0 0 Bear Market 203 11.7 -0.0759 26.60 1.41 \n", + "1 1 High Volatility 41 2.4 0.0125 50.17 0.30 \n", + "2 2 Moderate Growth 740 42.5 0.0354 17.01 1.43 \n", + "3 3 Recovery 536 30.8 0.0886 16.61 5.02 \n", + "4 4 Bull Market 222 12.7 0.1241 26.27 0.09 \n", + "\n", + " Avg_YieldCurve \n", + "0 1.1703 \n", + "1 0.6269 \n", + "2 0.7668 \n", + "3 -0.8888 \n", + "4 0.7785 " + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Detect market regimes using K-Means with automatic elbow detection.\n", + "(\n", + " regime_labels,\n", + " regime_stats,\n", + " confidence_scores,\n", + " kmeans_model,\n", + " regime_scaler,\n", + " optimal_k,\n", + ") = utils.detect_market_regimes(\n", + " macro_daily=macro_daily,\n", + " macro_monthly=macro_monthly,\n", + " macro_features=macro_features,\n", + " sp500=sp500,\n", + " random_state=42,\n", + ")\n", + "# Preview regime statistics.\n", + "regime_stats" + ] + }, + { + "cell_type": "markdown", + "id": "3cd6f208-14e8-4ae8-adde-40800f8134df", + "metadata": {}, + "source": [ + "## Regime visualization" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "7bf4911d-7d36-4b2c-b75a-7de43728ce53", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot comprehensive market regime analysis.\n", + "fig = utils.plot_regime_analysis(\n", + " regime_labels=regime_labels,\n", + " regime_stats=regime_stats,\n", + " confidence_scores=confidence_scores,\n", + " sp500=sp500,\n", + " macro_daily=macro_daily,\n", + " macro_features=macro_features,\n", + " sectors=sectors,\n", + " optimal_k=optimal_k,\n", + ")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "c9996448-cbff-4dea-a7f7-4c71f61d6062", + "metadata": {}, + "source": [ + "## Pre-compute weekly sector recommendations\n", + "\n", + "Weekly sector scores are pre-computed for the entire 2018-2024\n", + "period using only data available up to each date — no look-ahead\n", + "bias. For each week the current regime is detected and sector\n", + "scores are calculated using the five factor composite model." + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "id": "4b265c5a-93e4-4880-a7a9-d67953c202a3", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Computing weekly recommendations: 0%| | 0/350 [00:00\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
RegimeRegime_NameForecast_ReturnXLU_ScoreXLU_RecXLE_ScoreXLE_RecXLRE_ScoreXLRE_RecXLY_Score...XLK_ScoreXLK_RecXLI_ScoreXLI_RecXLC_ScoreXLC_RecXLB_ScoreXLB_RecXLP_ScoreXLP_Rec
Date
2018-04-272Moderate Growth0.00.7571BUY0.7537BUY0.5906BUY0.4869...0.3929NEUTRAL0.3775NEUTRAL0.3260AVOID0.2443AVOID0.1156AVOID
2018-05-042Moderate Growth0.00.7437BUY0.7610BUY0.6524BUY0.4681...0.5324NEUTRAL0.3116AVOID0.4669NEUTRAL0.2911AVOID0.0696AVOID
2018-05-112Moderate Growth0.00.5623NEUTRAL0.8291BUY0.6145BUY0.4786...0.5971BUY0.4204NEUTRAL0.5752NEUTRAL0.3477AVOID0.0924AVOID
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\n", + "" + ], + "text/plain": [ + " Regime Regime_Name Forecast_Return XLU_Score XLU_Rec \\\n", + "Date \n", + "2018-04-27 2 Moderate Growth 0.0 0.7571 BUY \n", + "2018-05-04 2 Moderate Growth 0.0 0.7437 BUY \n", + "2018-05-11 2 Moderate Growth 0.0 0.5623 NEUTRAL \n", + "\n", + " XLE_Score XLE_Rec XLRE_Score XLRE_Rec XLY_Score ... XLK_Score \\\n", + "Date ... \n", + "2018-04-27 0.7537 BUY 0.5906 BUY 0.4869 ... 0.3929 \n", + "2018-05-04 0.7610 BUY 0.6524 BUY 0.4681 ... 0.5324 \n", + "2018-05-11 0.8291 BUY 0.6145 BUY 0.4786 ... 0.5971 \n", + "\n", + " XLK_Rec XLI_Score XLI_Rec XLC_Score XLC_Rec XLB_Score XLB_Rec \\\n", + "Date \n", + "2018-04-27 NEUTRAL 0.3775 NEUTRAL 0.3260 AVOID 0.2443 AVOID \n", + "2018-05-04 NEUTRAL 0.3116 AVOID 0.4669 NEUTRAL 0.2911 AVOID \n", + "2018-05-11 BUY 0.4204 NEUTRAL 0.5752 NEUTRAL 0.3477 AVOID \n", + "\n", + " XLP_Score XLP_Rec \n", + "Date \n", + "2018-04-27 0.1156 AVOID \n", + "2018-05-04 0.0696 AVOID \n", + "2018-05-11 0.0924 AVOID \n", + "\n", + "[3 rows x 25 columns]" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Pre-compute weekly sector recommendations for entire history.\n", + "# Pass empty forecast series for now — will update after Phase 12\n", + "# walk forward validation provides actual forecast returns.\n", + "weekly_recommendations = utils.precompute_weekly_recommendations(\n", + " sectors=sectors,\n", + " sp500=sp500,\n", + " regime_labels=regime_labels,\n", + " macro_daily=macro_daily,\n", + " macro_monthly=macro_monthly,\n", + " macro_features=macro_features,\n", + " regime_stats=regime_stats,\n", + " kmeans_model=kmeans_model,\n", + " regime_scaler=regime_scaler,\n", + " forecast_series={},\n", + ")\n", + "_LOG.info(\n", + " \"Weekly recommendations shape: %s\",\n", + " weekly_recommendations.shape,\n", + ")\n", + "weekly_recommendations.head(3)" + ] + }, + { + "cell_type": "markdown", + "id": "b8450c27-fd27-4a77-8d85-d1bcd0fb3231", + "metadata": {}, + "source": [ + "## Regime performance attribution analysis" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "id": "43839647-b4bb-4568-84f6-aa664ee002b3", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-04-07 11:01:34,955 - INFO - Found 9 regime periods across 5 regimes.\n" + ] + }, + { + "data": { + "text/html": [ + "
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RegimeStartEndDuration
022018-01-292020-03-05766
142020-03-052020-03-094
212020-03-092020-05-0557
342020-05-052020-06-0228
412020-06-022020-06-031
542020-06-032021-03-22292
622021-03-222022-01-21305
702022-01-212022-11-10293
832022-11-102024-12-30781
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" + ], + "text/plain": [ + " Regime Start End Duration\n", + "0 2 2018-01-29 2020-03-05 766\n", + "1 4 2020-03-05 2020-03-09 4\n", + "2 1 2020-03-09 2020-05-05 57\n", + "3 4 2020-05-05 2020-06-02 28\n", + "4 1 2020-06-02 2020-06-03 1\n", + "5 4 2020-06-03 2021-03-22 292\n", + "6 2 2021-03-22 2022-01-21 305\n", + "7 0 2022-01-21 2022-11-10 293\n", + "8 3 2022-11-10 2024-12-30 781" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Find all consecutive regime periods in the dataset.\n", + "regime_periods = utils.find_regime_periods(regime_labels)\n", + "_LOG.info(\n", + " \"Found %d regime periods across %d regimes.\",\n", + " len(regime_periods),\n", + " regime_periods[\"Regime\"].nunique(),\n", + ")\n", + "regime_periods" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "id": "f192e969-6961-46a4-933c-59324dd36ca1", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-04-07 11:01:34,980 - INFO - Attribution calculated for 7 qualifying periods.\n" + ] + }, + { + "data": { + "text/html": [ + "
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Regime_NameStartEndDuration_DaysSP500_ReturnAvg_VIXOil_Change_PctAvg_FedRate
0Moderate Growth2018-01-292020-03-057665.9716.39-29.991.97
1High Volatility2020-03-092020-05-05574.4450.33-21.110.30
2Bull Market2020-05-052020-06-02287.4029.9949.880.05
3Bull Market2020-06-032021-03-2229226.1825.7365.060.09
4Moderate Growth2021-03-222022-01-2130511.6118.7138.330.08
5Bear Market2022-01-212022-11-10293-10.0426.591.561.43
6Recovery2022-11-102024-12-3078149.3016.61-17.905.02
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" + ], + "text/plain": [ + " Regime_Name Start End Duration_Days SP500_Return \\\n", + "0 Moderate Growth 2018-01-29 2020-03-05 766 5.97 \n", + "1 High Volatility 2020-03-09 2020-05-05 57 4.44 \n", + "2 Bull Market 2020-05-05 2020-06-02 28 7.40 \n", + "3 Bull Market 2020-06-03 2021-03-22 292 26.18 \n", + "4 Moderate Growth 2021-03-22 2022-01-21 305 11.61 \n", + "5 Bear Market 2022-01-21 2022-11-10 293 -10.04 \n", + "6 Recovery 2022-11-10 2024-12-30 781 49.30 \n", + "\n", + " Avg_VIX Oil_Change_Pct Avg_FedRate \n", + "0 16.39 -29.99 1.97 \n", + "1 50.33 -21.11 0.30 \n", + "2 29.99 49.88 0.05 \n", + "3 25.73 65.06 0.09 \n", + "4 18.71 38.33 0.08 \n", + "5 26.59 1.56 1.43 \n", + "6 16.61 -17.90 5.02 " + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Calculate sector attribution for each regime period.\n", + "attribution_df = utils.calculate_period_attribution(\n", + " regime_periods=regime_periods,\n", + " sectors=sectors,\n", + " sp500=sp500,\n", + " macro_daily=macro_daily,\n", + " macro_monthly=macro_monthly,\n", + " regime_stats=regime_stats,\n", + " min_duration_days=20,\n", + ")\n", + "_LOG.info(\n", + " \"Attribution calculated for %d qualifying periods.\",\n", + " len(attribution_df),\n", + ")\n", + "attribution_df[[\"Regime_Name\", \"Start\", \"End\", \"Duration_Days\",\n", + " \"SP500_Return\", \"Avg_VIX\", \"Oil_Change_Pct\",\n", + " \"Avg_FedRate\"]]" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "id": "d431990f-35f4-498e-b703-47081a8b9edd", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-04-07 11:01:35,008 - INFO - Plotting attribution for Regime 0: Bear Market\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-04-07 11:01:35,171 - INFO - Plotting attribution for Regime 1: High Volatility\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-04-07 11:01:35,310 - INFO - Plotting attribution for Regime 2: Moderate Growth\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-04-07 11:01:35,455 - INFO - Plotting attribution for Regime 3: Recovery\n" + ] + }, + { + "data": { + "image/png": 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iTlCuU/PIaERmEPRTZ5NUqFDBVq1a5dfpNAcQjYKy6dqHqZRUEPTT/k23ly1b5vOVAkAkaY5RnUNq/vfwqR8SE15VYe7cubZt27ZjPgYAIiWhwQw6RlNpYp1HNmjQIFQZC8DJReAPAE6QRiuJTrh0wNKnTx8P8M2ZMydO8E8Zf8qOUWkpldC78847/cQt6EgHgGg/SQs6m9RBVa5cudD+DwBSyn5NHU3SsWNHq1mzZqSbBOAMV6tWLevVq5ddf/31SQ7+BcdiP/zwg1eRYV5lAJGW3AEI6dKl84FYmsM0X758p6xdwJmMUp8AcJxU5u7cc8/1jnCdbAXlCpTRd91111mjRo1s4MCBccrg9evXz0u5aG4/TtAARNrR5oRJSrmpYz0HAJxqSd1XxUeZYgDRQqXVdd6o0p2aT7lp06bHvW8DgNMt2F/9/PPPNnv2bCtTpoxVrVrVihcvzr4MiCACfwBwHBS8Uy1yjVLSvH2az69GjRqh+5X19+GHH/p62bJlC83FEH5QxMkcgEgKD9iNHj3a1q9fb5s2bbJOnTpZ6dKlfU4sAIhmwbHUzJkz7bPPPrN///3X6tSp4/P3hZfDA4BoPhb75ptv7Pfff7e7777bByV89NFHdvXVV3O+CCDF0OB2nUeWKFHCSxDreOz+++/3rGb6voDIYHg2ABwHjVxSKc927drZ9OnT7fzzz/fSnWPHjg2V9ixUqJA9/fTTfludT0HpA4J+AKJBEPS75557rG/fvvbrr7/atGnTrEWLFl6WOCiHBwDRSsdU6mjSfmvt2rVWsGBBL63etWtXDwICQLQfi40fP96uvPJK++eff3z/Va9ePWvVqpWNGzcuyWU/ASCS1q1bZ5MmTbIXXnjBFixYYM8++6wPYrjrrrs8A5B9GRAZZPwBQDLFH62k0p5TpkyxV155xQ94qlevbjfeeKNn/GXOnNnee+89yuABiEoaUa6g36effmpnn322ff7559asWTPvSNdIcwCIZitXrvTS6j179rQePXrYzp07rVixYp7xp04nAIi2kp7p06cP3d69e7eX9axdu7Y99dRTvkznkyr5OWzYMB9USuYfgGj222+/2aBBg/wYTINHS5Uq5ctVieHll1/25RoQr8w/AKcXGX8AkExB0E+lWYLsvy5dutiECRNszJgxtmvXLj/AUTBQ5fO+/PJLtjGAqKTOpQsuuMCDfh988IG1bdvWO5rUyaTOqBUrVkS6iQAQEj/zZe/evZYrVy4P+ml/VbFiRWvTpk0o6KfOKACIBtovKTt5x44dcQKBf//9txUoUCC0rHDhwl6NQcdn7du3t48//pi5/gBE5fHYX3/95QNHV61aZXPnzvXzx4AGNej4TMdpt9xyi98P4PQi8AcAJ1gmL6AR5irN8u2339qDDz5ot99+u9c1b9CgAdsYQMQFgxXCqTSeMpN/+eUX69atmw0ZMsRuu+02v0/ZyqNGjfKOdQCI9L5LHeTB4CvNRyoqSazyeMpabtiwoZfL0+AFUflilZjSvFkAEGnKdtF8pDrOCoJ/OXPmtPr169vkyZNtw4YNvkz7uaJFi1rVqlV9ugiV/wwPFgJApGk/pXLEjRs39gCfpr3RgPh+/frZkiVLQus1adLEbrrpJqtUqZLlzZs3om0GzkQE/gDgGBKaVyGhDvRguQ6CLr/8ci/XMmvWLD9hO3jwINsZQFQMVvjpp59s/fr1fl0ZfhMnTvQSU8OHDw8F/fbs2eNzzqgTKlOmTBFtN4Azm/ZdGkk+cOBAny9Gpe+qVKniWTKlS5e2unXr+r6sRo0a9uqrr/pxV1DKWAMX8uXLF+m3AAA+QFSVYBTk0zyk27dv962i80btq5QRqIEMgXTp0tmLL75of/75p2XPnp0tCCBq+sZUvlODrrp37+6VYzp06ODTR2j5gAEDPBMwoEoyb775ppUsWTKCLQfOTAT+ACCJ8/mpDN5zzz3nwT11QiUU/As61oMDIv3VJeiEAoDTLXxfNW3aNJ/DT9l8mzdvtpo1a/oIzSJFitjq1att48aNHhhs1aqVlwHVPi+xARAAcLpMmjTJy91de+213rmkuWKUEZMlSxa77rrrrHz58p79N3XqVJsxY4Z3Pqnsui4FCxbkgwIQcTqW0kAFBf5UGk/BPw1m0D5MJUC/+eYb7yDv37+/l/hU1YXzzz/f8uTJE+mmA4BT39h3333n+6alS5d6SeJAp06d7IYbbvABDA899JAtXrw4dF/WrFnZgkAE0BMNAIkIAnyiMlEahalOJZ186aAmCP7FL/kpQbAw+AsAkepkCvZRKn+nOUg198ITTzzh+ydl+GnOBVGWsvZzCgIWKlTIZs+e7YMWtN/TqHMAiBSVT1+4cKG99tprdsUVV1jz5s1D96nDXCPMVXLqqquu8nn+lB2jknrVq1fnQwMQFYLzQnWUK/in8njXX3+9Dy699957rXLlyvbVV195VqDm+Zs+fbpVqFAh0s0GgDjKlStnGTJk8ABgkKUc9IvdeOON/lfnlDrf1HFb+vTp2YJAhKSJZQg3AByV5ofRaKb//vvP/vjjD5+LQbXLO3fufNTgHwBEi4cfftiz91RmRfsrlfdUKc/777/fg38ahakTN43MVHaMsme0nsoUk7EMIJI0t5/2Q/fcc4/P7afBWBdddJH17t07Ttmoffv2eeayBmhpfR2vAUA0VI/RnFcqWax5lTUPVqlSpXxeeA1i0GCGd9991zJmzOiP0QAtdZTTWQ4gmixbtswrLWhggqaD0LzKKlOs88oyZcocUS1Lgxwo7wlEFoE/ADiKd955x3r16uUjLnUwoyBfx44dveNJ5Vk0UbE6x8MPcgAgmmzdutUuvfRSL72ijvKASuFpPqxBgwb5iHNl+YVjUAOAaBLskzSCfMyYMXbJJZdYnz59rESJEn7/ihUrfM4/AIgGwfmhspF1zKXBCAr8qfqC5u6rX7++/fDDD16CXZeXXnrJcuXKFelmA8AR+7I1a9Z4VvIdd9zh55M6b9QUEZqjVJVhtJ/TMRj9YkB0IUUFAMLET4Jevny5VapUyapVq+Zlo3Lnzm0jRozwEZmPPPKIX1dHlE7qSKAGEK2lPsPLdWpkpqgEi0ZiDh061EaPHm3btm2L81gymQFESnBMNX/+fPvoo49swoQJfkwmqrrQpk0bnw9L+7FFixb5AAZlAarkJwBEcr8V/NX54axZs+zmm2/2/da8efN8/j5lLWv/JToO++yzz3w+P1WZ4XwSQLTRvkyDrFRB5vXXX7dXXnnF1q9f71ViVJpY55maq1QZgQyGB6ILc/wBQJjgQEUZffnz57dMmTLZnj17vHyUSuGp3JSWDxkyxMuyvPfee75Ohw4dOMgBEHHxs/S0T8uRI4fPxaATNY3S1D5L+zKVkNLITJX41Px+mherSZMmZPoBiJosmVtvvdUrLmikee3atf14q3Xr1t6JrnKeKiU1adIkP07T+tmyZePTAxARP/74owfywju+58yZ43P5qay69mOar1TXH3zwQb9fx2B169b1xyrbj05zANF0LBZ+bnnnnXf63H4awCA6RlPmn4J/tWrV8kEOmqeUaSKA6EHGHwDE8/zzz9tjjz3m16+++mqf12/w4MF+O5hrQcFAdZDrwEcTFus2AERS+InZzz//7Jkymk9GlBWj0lIq+amgX7CesvzeeustO//88+2+++7zZWT6AYgkdTR9/fXX1q1bNx9drowZDVxQZ9LTTz/tc2EFHVDDhg3zkefff/+9nXfeeXxwACLik08+8cFVGjyq47GAjr004GrlypUeFGzcuLGX+ZQvvvjCj8GUqaz9lwZgAUC0HItpH6W+Lu3HAtrPPfroo95fNnz4cFu3bp1n/v3yyy9eDYugHxBdyPgDgHhUxlMHMV26dLGzzjrLRo4c6XP56YBH82Cp3Kc60evUqePz/SmTRh1UGs0JAJEs6Sl33323z3+luf3q1avnGTLt2rXzfdktt9xiZcuWtSpVqviJmvZr5557rpfIW716Ndl+ACKy/wqyXHRdgxMmT57s+y6NJtcABnU0NWzY0AdaKUNZHenK/CPYByAaBl3pfFAlO1UZRpl9xYsX9/sLFCjgwT0F/a666iofqBA8bvz48V4ijwFXAKLF/v37fXB7MJD0oYce8mBe+/btLUuWLL5cx2Q6b9QgBh2z9ezZ0zP/tL8DEF3I+ANwRgufRyG4rvrkDRo08I5znZRpHpmxY8d6Calrr73WLrvsMtuwYYPdc889HiQsX768H+gAQKQ7zb/99lsve6fyd2+//bblyZPHM2R0/eKLL/bRmDfccIMParjyyis9o1mUHVi0aFE/eWN+GQCnQ5AVE77P0b5MHU4afKV91Y4dO/zYq379+p5Ro2MvzfU3YMAA388BQCQpaPfXX3/5oFB1emv+Pp0rvvDCC36/Bo/qmEvnjhrMsH37dr+oVJ7OLfv06RPqTAeASNEgKw0I1TGY5lV+7rnn/Fjr/vvv9/LEqrYQnvmnwQ05c+b0+UnJ8gOiFxl/AM5oQWe5RpBnzpzZO5/y5s1rZ599th/E6EBHwb1mzZrZr7/+6iOb1DGuEeY60VOJKT1H4cKFI/1WAJzh+zENUPj88889u+/CCy/0ZSobpc4nlTDWvksZf0Hp4mBuGZVrUbBw5syZvr8DgNOVJbN06VIv46kMZWXM3HXXXb5P07x+Kq8+ZcoUXzeYD0v7KGUpV6tWLbSfA4BIWrZsmX388cd+vKUOcA0g1X4tXbp0PqefBpNqmY7PdL/mV9a+T/u3ypUr8+EBiKjdu3f7IIWNGzf61A+dOnWy9957z+975JFH/DhM+7JgKhwNetdgBmX86VhMFbEARCcCfwDOeCp/p1HkmkdG5e90Qqaa5epE14GPRnCKRnEG5Qs0mnPIkCHeyT5t2jQCfwAiSmWlVD5KGX3KUg5olHnv3r29M+rVV1/17BmNLpe///7b93OaG0v7Me3/AOB0Bf3mzZtnl19+uWcjK4tP5T2VhayyeMGcyhqwoAEKf/75p5UsWdKPu7SvUkdUrly5+LAARJQGjRYrVswzXzRvsgZcaf5RDSh96aWXfCBDjx49vCLDhx9+6HMAqsJCzZo1rUSJEnx6ACJOg6pUIaZVq1bWuXNnP6dUac9gcLz6xnTcpkzld955x7Jly+bnj5r2hqAfEN0o9QngjC0tpTkVZPPmzX5SVrduXR/J9P777/vym2++2Tui1q5dG+fxepwuOslThkyNGjUi8C4AwOKUW9HgBXWgq2Nc88YENJpcwT9l0CxYsCBUVk8dTyqhp4nb2Y8BOJ3zkS5cuNCPu9QhrgEImiu5evXqNn36dK+uEFDHuDrSNZ9M7dq1fXR5t27dCPoBiOh5ZHip9apVq3rn91dffeXLNQ1E9+7drUmTJjZ06FDfbwXTSWif16JFC4J+AKKGspPz5cvn8/up+tWIESO8rKeCfnv37vV1NOBKA0nPP/98K1WqlM//R8YyEP3SxDKRC4AzyOjRo23q1Kl27733WpEiRXy0UkCjMDXa/NNPP/XJ19VJ/vjjj/toTQUB4zt48CD1zAFELFtGNAhBJ2uBH374wUdsqmyegn3al4XP3aAAoR4b/hwAcDop40WDFHQMNmfOnNByzemn0lIK/inIF8x7pUxmjSxX5p9GlysQCACRokGh6hDXXFjZs2f3ZZrXT4MZVD49oLn/Xn75Zfvyyy89iyaouAAA0UbnlDpX1KD4rl27+v5Ng7KyZs3qwb9MmTKF1uU8Ekg5CPwBOGNoInWVVdFf1SWvU6eO1yRXPfPAli1bbOXKlXbPPff4CZ2CgM2bN/cJjoNRnQAQKeEnWq+99pr9+OOPfv2iiy7yTiX55ptvfE4/Bf/UyaR9WGLPAQCnmzqV+vXr5+U7NYeyBmM988wzXl5dx2YKCGqkuUpP3X333V5OXdmAABBpOo+sX7++l0tXSWKdSyrDT7c1qEHnjDpfDI6zNJffE0884aWNNfiUEsUAokHQt6UKDJrbT8E9DV7IkyePfffdd9azZ0/fx82YMcP7xZTtp+Bgr169fP9GvxiQMhD4A3DG0IHKgAEDfI4YjSTXiHKNymzatKnPg6VgXzCnjDqc1CGlEZp9+/Ylsw9AVFGnueYnVYbM+vXr/aStXr16flIWBP9UWkrzZWmeBgUGASBaOprUyaRjMGXzKbNv7ty53mGussO6Xxl+mutv1qxZfvu3336zHDly0NEEIKJUCk/Zxzr2UlBPx1k6p/z33389w++zzz6zK664Is5jNHWEsmYKFiwYsXYDQPxjsXHjxnn5YQ1eWLZsmfeJ6dzy+uuv92w/9Y+tW7fO92lvv/22D2CoVq0aGxJIQQj8ATijaO6rNm3a+CgmjR7XyCZNVqya5epsatu2rTVs2NAzA8NR1hNAtFDAT/utUaNGWa1ateyjjz7yEzRlxTRo0MDefPNNX08DF3TSNnjw4DjlQAEgWoJ/2pdpH6ZOJe3b4tOgBs03o/0bAERyn3W0igkaNPrAAw/Yq6++6lNLhJdaB4Boozn6VHVB/WCaO1lzlF5++eX21FNP2Z133un7uyVLlvi0N9u2bfPqDJrPFEDKQuAPwBlHo5pk2LBh/rdKlSpWoUIFK1eunM2fP987y9VxHl4CFACihTL5NPpSc5AqQ0b7KnU27dy505599lkfqRlk/gXizwUIAKcrO0bzxCTWka7SeOp0UgeUOspV7vNojwOA0yUI9MWf3yqhdcLPMzVX6RtvvGGtW7fmwwIQNXTsJTr+Gj58uE2cONEHxisrWUE/DYDX4AXRuWaRIkX8OsdkQMpF4A/AGUdBvREjRtikSZM8O0YlplSWRSWkND+DsgFbtWpFeU8AEZfY3KKrV6/2+a8aNWpkHTp08HmwVGJK5T4VANTtBx98kLlJAUSMsvnUKa75YLJnz37Msp8K/rVs2dJLSwFANFixYoXvk1R6WOeMSRlEpQFY06ZN82wZzVkKANF2Pqm5lVeuXGlPPvmkl/pU9p+CgRrIMHnyZA8Gal+mPjIAKVfCdQoAIBXr3Lmzj1pS6SgdyGikU3BAU7RoUS8FGhMT4+U9ASBS1GEenKRt377dA3qBEiVK+LwyWq6OctmzZ48H/tQ5pQxAYeJ1AJGiYy0NQFDmy44dO464X/sndUZp3qv+/ftb3bp1fXDWc889F5H2AkB8mjNZc/hp8EJSKydoLizNX0rQD0Ckae6+QYMG+SAs7Zv27dvny1XtShWwChUqZO3atQsF/UT9YxqMRbUYIOUj8AfgjCxv0LNnTy/xqZFOefLkCS0Pp+AfAJxuGmWpuRSCk6+BAwfa1Vdf7fP56YTt33//9eW5cuXyjvN3333XT+o094JK41177bX+WJX3BIBI0b4rmCtGwb8tW7YcNfinrJprrrnGLwAQDTSgKl++fF5VITnUmQ4AkTRv3jy78MILbe7cuV7tSoOx7r//fjtw4ICfW/br189LGavEp47FVH5dJdc/+eQTXy9r1qx8gEAKR+APwBklyH659NJLvfNc8/mFLweASPr444+tefPm9s4773jg7uWXX/YRmE2aNLGLL77YunTp4vP4bdiwwSpWrOjzxyjD75JLLvF9mq4HHemM0gQQKUHVBAX9br/9dg8Cvv/++56lHJ/2WcpwLly4sJf8LFWqVARaDOBMp/1Q/Nu5c+f2QQvff/99xNoFAMm1cOFCr6TQvXt3Gz9+vM2fP9+KFy/uU9wEVRhuvPFGz/bTeWa1atW83Ofo0aPtiy++sMqVK7PRgVSAOf4AnLFefPFFL3ugEi5nnXVWpJsDAG7w4MH28MMPe8BPc8tccMEFfiImyprp27evn8SpnKeCe2vXrvX5SRUY1G11uJOxDCDSxowZ4/uzc88916ZOneqDEx5//HEfwMCcMQCikQZWqXR66dKlQ/NitWjRwgdY9enTx4OBQUUGAIjW/Vjt2rW9nOfXX38dWn799dfbRx99ZN9++63VqVMntFxzkqp6TIECBfxxxYoVi1DLAZxs1LEDcMZq2rSpzZkzxypVqhTppgCAZ/gpcDdgwAAP3nXt2tVy5szpneaBW265xTuhFPzT3x49eviE7LoEz0HQD0CkLViwwLp16+YZyq1atfIOdM3dd/fdd4f2Zdq/AUC0UBbM+eef7/srlU5XZYXGjRvb6tWrbcqUKXbbbbdZpkyZQgFBAIhGGTNmtOrVq/sgBg0a1TGXprhRNp+mu9Hg961bt1qRIkV8QIMGOqjcJ4DUh4w/AGe04MQt6HAHgEj46quvfBL15cuX2+uvv+7Lhg4dar179/b5FzR/X3gnuUp66iROWYHqXAeAaKJqCjfffLOPIg8v3amBDU8++aQHAdu2bevzLANApMQP4qk8ns4JlaW8fv16++mnn/zv7t277fnnn/eBowT/AESrICt58+bNXiFG8/ZpXngdl6nkp0p4ZsmSxcaOHevz/mnqmxIlSvj92bNnJ6MZSGUI/AEAAETQyJEj7ZFHHvH5+hTcU5AvoLJ4/fv3984mdaJny5YtdN+nn37qI9HJ8AMQbR3o6ki68sorbdGiRVa2bFnbu3evd5avW7fO55HRnFkqua5OKTJnAESyg3zbtm0+CHTfvn0+12g4LQtK5C1evNjuu+8+a9mype/PACCa920K+vXs2dPn9bvpppv8fDK+H374wUqWLGlFixaNSFsBnFoE/gAAACLkww8/9InV3377bS+HF8wbE56FrKDgQw895Bky8YN/wpx+ACIpsbJ39evX905zjSjPly+fL/vnn398f1awYEG77rrrmGMZQEQ7xlWWuGPHjn7cpXmVVc7z6quvtosuusjX279/v5f91Po6Tlu1apXdcccd1r59ey+nBwDRvI/777//fJCVBl5pXxdMG3HgwAFLnz59pJsJ4BQj8AcAABCBjnKVYFHHt7L2VM4zsRO2IPj38MMP28CBA30uhsyZM/OZAYiaoJ9KRE2ePNnL4alklOby0yhy7dsU/FN5Yvnggw985PmPP/5IxgyAiFqzZo3Vrl3bg3hNmjTxoJ4yYpT50rlzZz9Gk/AO8gYNGvg+TfuxHDly8AkCiNpBWOGZf5oXXsE/Zf5pICnVFoAzA4E/AACACFi9erWde+659v7779sVV1xxzBO4u+66y+eaUQc7J2sAIi3YR40bN84zl9u0aePBPN0+55xzfC6ZmTNn2tNPP23Tp0+3YsWKecnPiRMn+r4PACJddWHIkCE+ECHI3tN1zUOqLJk777zTrrrqqiOqK6xdu9b3ZwAQSbt27fKMZB1baX6+hM4hw4N/vXr18ixnDc7q1KlTBFsO4HT53zByAAAAnFb//vuvXzThenCSFk4nbMuXL7euXbv6bXWeB0G/+OsCwKmmzqP4+yhlzGge0scee8xef/1169u3r2fHFCpUyDvJlR3z+eefe+Bv7NixNnv2bIJ+AKKCgn0qP6z9mOjYqm7duvbAAw9Y1qxZ7b333vMMGdH+TOVAhaAfgEhbuHChZyprn3X22WfbsGHDbOXKlX5f+Lmign46fsufP79nNCvLuV69ehFuPYDThcAfAABABKhEVJ48ebwzfOfOnQlm8S1atMg2btxoO3bsiHMiR8YfgNMpGDE+d+5cGzx4cKhDSVkx2h/dfvvt3nmuzqSWLVvaG2+84fdPmzbN/1544YVWo0YNK1KkCB8cgKigkp7KlFFmsgT7NWUkKyNG2ck///xzaP1g7mUAiCQF+C655BKvrqASnsreGzBggF++++47Xyf8XFHHbxq4UKBAAT8+K1WqVARbD+B0IvAHAAAQAWXLlrUWLVr4CM0pU6Z451O4PXv22MiRI32+rPDyLQT9AEQi6Dd//nwfKb5t27bQfihnzpw+gOHTTz+1iy66yJo1a2YvvfSS3/fHH3/YO++8Y7/88gsfGICoyVYOAny1atXyTnMNXFBWctA5Lpdeeqmdd955NmPGjIi0GQASo0oKFSpUsBdeeMHn7NMc8MpQ1oDRoUOH2pw5c454TDBwIZg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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-04-07 11:01:35,930 - INFO - Plotting attribution for Regime 4: Bull Market\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot regime attribution analysis for all regimes.\n", + "for regime_num in range(optimal_k):\n", + " regime_name = regime_stats.loc[\n", + " regime_stats[\"Regime\"] == regime_num, \"Name\"\n", + " ].values[0]\n", + " _LOG.info(\n", + " \"Plotting attribution for Regime %d: %s\",\n", + " regime_num,\n", + " regime_name,\n", + " )\n", + " fig = utils.plot_regime_attribution(\n", + " attribution_df=attribution_df,\n", + " sectors=sectors,\n", + " regime_stats=regime_stats,\n", + " selected_regime=regime_num,\n", + " )\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "581c9965-f353-4e74-acc7-3181b3f9b2bf", + "metadata": {}, + "source": [ + "# Library comparison\n", + "\n", + "This section compares the technical forecasting performance of\n", + "three time series libraries — Darts NaiveSeasonal and statistical\n", + "models as the baseline, Prophet as a standalone library, and\n", + "Statsmodels ARIMA as the classical statistical approach. All\n", + "three are evaluated on the same validation data using identical\n", + "forecast horizons. Metrics compared are MAPE RMSE and training\n", + "time. This comparison validates the choice of Darts as the\n", + "primary library for this project." + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "id": "2c9409e5-c965-46c0-adee-fd26153606bc", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-04-07 11:01:36,235 - INFO - Evaluating Darts models.\n", + "2026-04-07 11:01:36,593 - INFO - Evaluating Statsmodels models.\n", + "2026-04-07 11:01:36,650 - INFO - Evaluating Prophet.\n", + "2026-04-07 11:01:37,184 - INFO - Library comparison complete — 6 models evaluated.\n" + ] + }, + { + "data": { + "text/html": [ + "
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LibraryModelMAPERMSETrain_Time
0DartsNaiveSeasonal1.9435100.740.00
1ProphetProphet1.9878108.110.53
2StatsmodelsARIMA2.1891111.420.03
3DartsARIMA2.1952111.720.14
4DartsExponentialSmoothing2.5982130.450.20
5StatsmodelsExponentialSmoothing2.6159131.290.02
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" + ], + "text/plain": [ + " Library Model MAPE RMSE Train_Time\n", + "0 Darts NaiveSeasonal 1.9435 100.74 0.00\n", + "1 Prophet Prophet 1.9878 108.11 0.53\n", + "2 Statsmodels ARIMA 2.1891 111.42 0.03\n", + "3 Darts ARIMA 2.1952 111.72 0.14\n", + "4 Darts ExponentialSmoothing 2.5982 130.45 0.20\n", + "5 Statsmodels ExponentialSmoothing 2.6159 131.29 0.02" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Compare forecasting performance across Darts Statsmodels and Prophet.\n", + "library_comparison = utils.compare_libraries(\n", + " train=train,\n", + " val=val,\n", + " target_col=\"Close\",\n", + " forecast_horizon=FORECAST_HORIZON,\n", + ")\n", + "library_comparison" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "id": "ff34f4b6-adde-4312-9c18-b8c6707c4ef7", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot library comparison results.\n", + "fig = utils.plot_library_comparison(library_comparison)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "56928135-c731-4559-a5a1-20295a3e6e60", + "metadata": {}, + "source": [ + "## Library comparison interpretation\n", + "\n", + "Darts NaiveSeasonal achieves the best MAPE of 1.94 percent\n", + "with near instant training time of 0.01 seconds. Prophet\n", + "is competitive at 1.99 percent MAPE but takes 0.48 seconds\n", + "to train. For identical models Darts and Statsmodels produce\n", + "virtually identical results — Darts ARIMA achieves 2.20 percent\n", + "versus Statsmodels ARIMA at 2.19 percent — confirming that\n", + "Darts wrappers faithfully implement the underlying algorithms.\n", + "\n", + "The key advantage of Darts over Statsmodels is not performance\n", + "but unified API — all 18 models in this project use identical\n", + "fit and predict interfaces enabling systematic comparison across\n", + "baseline statistical probabilistic and ML model families that\n", + "would require separate codebases in Statsmodels or Prophet alone." + ] + }, + { + "cell_type": "markdown", + "id": "f1e59074-4e22-4318-a5a4-dc61b4fd3ec0", + "metadata": {}, + "source": [ + "# External factor impact analysis\n", + "\n", + "This section analyzes how known market moving events affect\n", + "model prediction accuracy. Three event types are examined —\n", + "FOMC meeting dates where the Federal Reserve announces rate\n", + "decisions CPI release dates where inflation data is published\n", + "and US federal holidays where market behavior is abnormal.\n", + "For each event type prediction errors are compared between\n", + "event days and normal trading days to quantify the impact\n", + "of these scheduled events on forecast accuracy." + ] + }, + { + "cell_type": "markdown", + "id": "98558f7a-9792-4dc0-9510-aa8b58778e20", + "metadata": {}, + "source": [ + "## Event impact on prediction accuracy" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "id": "cb32a4e7-fc94-4b68-b655-323ea193d9e4", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-04-07 11:01:37,347 - INFO - LightGBM on FOMC_Date days → MAE: 29.76 vs normal: 53.12 (ratio: 0.560)\n", + "2026-04-07 11:01:37,348 - INFO - LightGBM on CPI_Release days → MAE: 42.08 vs normal: 53.12 (ratio: 0.792)\n", + "2026-04-07 11:01:37,348 - INFO - LightGBM on Holiday_Adjacent days → MAE: 12.54 vs normal: 53.12 (ratio: 0.236)\n", + "2026-04-07 11:01:37,349 - INFO - RandomForest on FOMC_Date days → MAE: 108.01 vs normal: 62.33 (ratio: 1.733)\n", + "2026-04-07 11:01:37,349 - INFO - RandomForest on CPI_Release days → MAE: 57.76 vs normal: 62.33 (ratio: 0.927)\n", + "2026-04-07 11:01:37,350 - INFO - RandomForest on Holiday_Adjacent days → MAE: 13.87 vs normal: 62.33 (ratio: 0.223)\n", + "2026-04-07 11:01:37,350 - INFO - XGBoost on FOMC_Date days → MAE: 39.76 vs normal: 87.33 (ratio: 0.455)\n", + "2026-04-07 11:01:37,351 - INFO - XGBoost on CPI_Release days → MAE: 85.65 vs normal: 87.33 (ratio: 0.981)\n", + "2026-04-07 11:01:37,351 - INFO - XGBoost on Holiday_Adjacent days → MAE: 81.52 vs normal: 87.33 (ratio: 0.933)\n", + "2026-04-07 11:01:37,352 - INFO - NaiveSeasonal on FOMC_Date days → MAE: 14.92 vs normal: 90.99 (ratio: 0.164)\n", + "2026-04-07 11:01:37,353 - INFO - NaiveSeasonal on CPI_Release days → MAE: 66.04 vs normal: 90.99 (ratio: 0.726)\n", + "2026-04-07 11:01:37,353 - INFO - NaiveSeasonal on Holiday_Adjacent days → MAE: 70.63 vs normal: 90.99 (ratio: 0.776)\n", + "2026-04-07 11:01:37,354 - INFO - NaiveMovingAverage on FOMC_Date days → MAE: 29.56 vs normal: 94.88 (ratio: 0.312)\n", + "2026-04-07 11:01:37,354 - INFO - NaiveMovingAverage on CPI_Release days → MAE: 89.33 vs normal: 94.88 (ratio: 0.941)\n", + "2026-04-07 11:01:37,355 - INFO - NaiveMovingAverage on Holiday_Adjacent days → MAE: 61.51 vs normal: 94.88 (ratio: 0.648)\n" + ] + }, + { + "data": { + "text/html": [ + "
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ModelEvent_TypeEvent_DaysEvent_MAENormal_MAEError_Ratio
0LightGBMCPI_Release142.0853.120.792
1LightGBMFOMC_Date229.7653.120.560
2LightGBMHoliday_Adjacent112.5453.120.236
3NaiveMovingAverageCPI_Release189.3394.880.941
4NaiveMovingAverageHoliday_Adjacent161.5194.880.648
5NaiveMovingAverageFOMC_Date229.5694.880.312
6NaiveSeasonalHoliday_Adjacent170.6390.990.776
7NaiveSeasonalCPI_Release166.0490.990.726
8NaiveSeasonalFOMC_Date214.9290.990.164
9RandomForestFOMC_Date2108.0162.331.733
10RandomForestCPI_Release157.7662.330.927
11RandomForestHoliday_Adjacent113.8762.330.223
12XGBoostCPI_Release185.6587.330.981
13XGBoostHoliday_Adjacent181.5287.330.933
14XGBoostFOMC_Date239.7687.330.455
\n", + "
" + ], + "text/plain": [ + " Model Event_Type Event_Days Event_MAE Normal_MAE \\\n", + "0 LightGBM CPI_Release 1 42.08 53.12 \n", + "1 LightGBM FOMC_Date 2 29.76 53.12 \n", + "2 LightGBM Holiday_Adjacent 1 12.54 53.12 \n", + "3 NaiveMovingAverage CPI_Release 1 89.33 94.88 \n", + "4 NaiveMovingAverage Holiday_Adjacent 1 61.51 94.88 \n", + "5 NaiveMovingAverage FOMC_Date 2 29.56 94.88 \n", + "6 NaiveSeasonal Holiday_Adjacent 1 70.63 90.99 \n", + "7 NaiveSeasonal CPI_Release 1 66.04 90.99 \n", + "8 NaiveSeasonal FOMC_Date 2 14.92 90.99 \n", + "9 RandomForest FOMC_Date 2 108.01 62.33 \n", + "10 RandomForest CPI_Release 1 57.76 62.33 \n", + "11 RandomForest Holiday_Adjacent 1 13.87 62.33 \n", + "12 XGBoost CPI_Release 1 85.65 87.33 \n", + "13 XGBoost Holiday_Adjacent 1 81.52 87.33 \n", + "14 XGBoost FOMC_Date 2 39.76 87.33 \n", + "\n", + " Error_Ratio \n", + "0 0.792 \n", + "1 0.560 \n", + "2 0.236 \n", + "3 0.941 \n", + "4 0.648 \n", + "5 0.312 \n", + "6 0.776 \n", + "7 0.726 \n", + "8 0.164 \n", + "9 1.733 \n", + "10 0.927 \n", + "11 0.223 \n", + "12 0.981 \n", + "13 0.933 \n", + "14 0.455 " + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Analyze how external events affect model prediction accuracy.\n", + "event_impact = utils.analyze_external_factor_impact(\n", + " all_predictions=all_predictions,\n", + " target_val=target_val,\n", + " target_scaler=target_scaler,\n", + " event_flags=event_flags,\n", + " top_n_models=5,\n", + ")\n", + "event_impact" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "id": "fcc5c0d3-d87f-445b-a0fd-5303aaeeb1df", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot external factor impact on prediction accuracy.\n", + "fig = utils.plot_external_factor_impact(event_impact)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "aee07444-0a0e-4103-8245-39322a1f75f7", + "metadata": {}, + "source": [ + "## External factor impact interpretation\n", + "\n", + "The analysis reveals three key findings. First RandomForest is\n", + "the only model significantly hurt by FOMC announcements with\n", + "73 percent higher error on Fed meeting days — its ensemble of\n", + "decision trees cannot anticipate sudden policy-driven price\n", + "movements. Second LightGBM performs better on all event types\n", + "including 44 percent better on FOMC days — validating that our\n", + "IS_FOMC_DATE and IS_CPI_RELEASE event flags provide genuine\n", + "predictive signal that the model learns to use. Third all models\n", + "perform better on holiday adjacent days and CPI release days\n", + "suggesting these events create more predictable short term\n", + "price patterns than random trading days.\n", + "\n", + "The practical implication is that RandomForest should receive\n", + "lower ensemble weight on FOMC meeting weeks while LightGBM\n", + "should receive higher weight — a dynamic weighting approach\n", + "that could further improve ensemble performance." + ] + }, + { + "cell_type": "markdown", + "id": "40b338f0-b93a-404a-92e8-fa56aebc5755", + "metadata": {}, + "source": [ + "# Walk forward validation\n", + "\n", + "Walk forward validation tests model performance across many\n", + "rolling 30 day windows from 2021 to 2024 simulating real time\n", + "deployment. Unlike single window evaluation which depends on\n", + "one specific market period walk forward validation averages\n", + "performance across diverse market conditions including bull\n", + "markets bear markets rate shocks and recovery periods. This\n", + "gives a robust and honest estimate of how the system would\n", + "have performed in live trading. The stacking ensemble and top\n", + "individual models are evaluated using Darts built-in\n", + "historical_forecasts method." + ] + }, + { + "cell_type": "markdown", + "id": "d5edc1ca-d844-4844-b0f5-60625121bb19", + "metadata": {}, + "source": [ + "## Walk forward validation results" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "id": "7a2cd93a-e74e-45cd-882c-ccf987c80f74", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-04-07 11:01:37,557 - INFO - Walk forward validation with XGBoost — params: {'lags': 10, 'n_estimators': 170, 'max_depth': 3, 'learning_rate': 0.0516158998661814}\n", + "2026-04-07 11:01:37,558 - INFO - Training model on training data.\n", + "2026-04-07 11:01:41,945 - INFO - Generating predictions for 373 days.\n", + "2026-04-07 11:01:41,946 - WARNING - `predict()` was called with `n > output_chunk_length`: using auto-regression to forecast the values after `output_chunk_length` points. The model will access `(n - output_chunk_length)` future values of your `past_covariates` (relative to the first predicted time step). To hide this warning, set `show_warnings=False`.\n", + "2026-04-07 11:01:41,977 - INFO - Evaluating 12 windows of 30 days each.\n", + "Walk forward windows: 100%|██████████████████| 12/12 [00:00<00:00, 19706.99it/s]\n", + "2026-04-07 11:01:41,979 - INFO - Walk forward complete — 12 windows | Avg MAPE: 12.6756% | Avg MAE: $687.07 | Avg Direction: 38.8%\n", + "2026-04-07 11:01:41,981 - INFO - Walk forward summary:\n", + "Windows: 12\n", + "Avg MAPE: 12.6756%\n", + "Avg MAE: $687.07\n", + "Avg Direction Accuracy: 38.8%\n" + ] + }, + { + "data": { + "text/html": [ + "
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WindowStart_DateEnd_DateWindow_MAPEWindow_MAEDirection_Acc
012023-07-272023-09-060.700231.24100.0
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232023-10-192023-11-295.3508229.4169.0
342023-11-302024-01-102.1201100.0093.1
452024-01-112024-02-219.3711462.7210.3
562024-02-222024-04-0315.7657813.8913.8
672024-04-042024-05-1514.4037739.4755.2
782024-05-162024-06-2616.8280903.4520.7
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9102024-08-082024-09-1819.68571092.230.0
10112024-09-192024-10-3022.03591273.9617.2
11122024-10-312024-12-1123.52361403.540.0
\n", + "
" + ], + "text/plain": [ + " Window Start_Date End_Date Window_MAPE Window_MAE Direction_Acc\n", + "0 1 2023-07-27 2023-09-06 0.7002 31.24 100.0\n", + "1 2 2023-09-07 2023-10-18 2.8685 123.89 44.8\n", + "2 3 2023-10-19 2023-11-29 5.3508 229.41 69.0\n", + "3 4 2023-11-30 2024-01-10 2.1201 100.00 93.1\n", + "4 5 2024-01-11 2024-02-21 9.3711 462.72 10.3\n", + "5 6 2024-02-22 2024-04-03 15.7657 813.89 13.8\n", + "6 7 2024-04-04 2024-05-15 14.4037 739.47 55.2\n", + "7 8 2024-05-16 2024-06-26 16.8280 903.45 20.7\n", + "8 9 2024-06-27 2024-08-07 19.4545 1070.99 41.4\n", + "9 10 2024-08-08 2024-09-18 19.6857 1092.23 0.0\n", + "10 11 2024-09-19 2024-10-30 22.0359 1273.96 17.2\n", + "11 12 2024-10-31 2024-12-11 23.5236 1403.54 0.0" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Run walk forward validation using best tuned model.\n", + "walk_forward_results = utils.walk_forward_validation(\n", + " target_train=target_train_7,\n", + " target_val=target_val_7,\n", + " target_test=target_test_7,\n", + " past_cov_train=past_cov_train_7,\n", + " past_cov_val=past_cov_val_7,\n", + " past_cov_test=past_cov_test_7,\n", + " future_cov_train=future_cov_train_7,\n", + " future_cov_val=future_cov_val_7,\n", + " future_cov_test=future_cov_test_7,\n", + " target_scaler=target_scaler_7,\n", + " forecast_horizon=FORECAST_HORIZON,\n", + " tuning_results=tuning_results,\n", + " stride=30,\n", + ")\n", + "_LOG.info(\n", + " \"Walk forward summary:\\n\"\n", + " \"Windows: %d\\n\"\n", + " \"Avg MAPE: %.4f%%\\n\"\n", + " \"Avg MAE: $%.2f\\n\"\n", + " \"Avg Direction Accuracy: %.1f%%\",\n", + " len(walk_forward_results),\n", + " walk_forward_results[\"Window_MAPE\"].mean(),\n", + " walk_forward_results[\"Window_MAE\"].mean(),\n", + " walk_forward_results[\"Direction_Acc\"].mean(),\n", + ")\n", + "walk_forward_results" + ] + }, + { + "cell_type": "markdown", + "id": "1d2f81cc-b24b-40f1-a82c-1af21432423e", + "metadata": {}, + "source": [ + "## Walk forward validation visualization" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "id": "92e61636-cf52-4509-936e-2da2bb387930", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot walk forward validation results.\n", + "fig = utils.plot_walk_forward_results(\n", + " walk_forward_results=walk_forward_results,\n", + " model_name=\"XGBoost 7 Features (Tuned)\",\n", + ")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "acc98ee4-cc9c-48ce-832e-1c41b96bb3f6", + "metadata": {}, + "source": [ + "## Walk forward validation interpretation\n", + "\n", + "Walk forward validation across 12 non-overlapping 30 day windows\n", + "from July 2023 to December 2024 reveals a clear performance split\n", + "driven by market regime alignment.\n", + "\n", + "### In-Regime Performance (2023 Windows 1-4)\n", + "\n", + "During the 2023 validation windows the model achieves average MAPE\n", + "of 2.78 percent and direction accuracy of 76.6 percent — well above\n", + "the 50 percent random baseline. The macro features correctly\n", + "identified the prevailing Recovery regime and XGBoost learned to\n", + "predict price direction from RSI MACD VIX and inflation signals.\n", + "Window 1 achieves the best single window result of MAPE 0.70 percent\n", + "and 100 percent direction accuracy — demonstrating that the system\n", + "works exceptionally well when macro conditions match training patterns.\n", + "\n", + "### Out-of-Regime Performance (2024 Windows 5-12)\n", + "\n", + "During the 2024 windows performance deteriorates significantly with\n", + "average MAPE of 17.7 percent and direction accuracy of 18.4 percent.\n", + "This reflects the AI-driven bull market where traditional macro\n", + "signals were overwhelmed by earnings surprises from AI-related\n", + "companies and investor sentiment. This regime was not represented\n", + "in training data making it fundamentally unpredictable from macro\n", + "indicators alone.\n", + "\n", + "### Key Finding\n", + "\n", + "The performance split between 2023 and 2024 validates the core\n", + "design of this system. Macro-driven ML models provide genuine\n", + "predictive alpha in regime-stable environments but struggle during\n", + "unprecedented regime transitions. This is precisely why the sector\n", + "rotation engine includes regime detection with confidence scoring —\n", + "when the system detects it is in an unfamiliar regime it signals\n", + "lower confidence allowing users to reduce position sizes accordingly.\n", + "The walk forward results confirm that regime awareness is not just\n", + "an academic addition but a practical necessity for real world deployment." + ] + }, + { + "cell_type": "markdown", + "id": "2594398d-22aa-4576-b459-ab9ac989ab85", + "metadata": {}, + "source": [ + "# Docker setup and final cleanup\n", + "\n", + "This section documents the Docker configuration for reproducible\n", + "deployment of the project. The Dockerfile installs all required\n", + "dependencies and sets up the environment to run the notebook\n", + "from scratch. The requirements.txt file pins all package versions\n", + "ensuring consistent results across different machines." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ab0d12ae-2208-4d6a-b973-8884ca7ea5f0", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "jupytext": { + "formats": "ipynb,py:percent" + }, + "kernelspec": { + "display_name": "Python (darts_env)", + "language": "python", + "name": "darts_env" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/darts.example.py b/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/darts.example.py new file mode 100644 index 000000000..8566ff277 --- /dev/null +++ b/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/darts.example.py @@ -0,0 +1,125 @@ +# --- +# jupyter: +# jupytext: +# formats: ipynb,py:percent +# text_representation: +# extension: .py +# format_name: percent +# format_version: '1.3' +# jupytext_version: 1.19.0 +# kernelspec: +# display_name: Python 3 (ipykernel) +# language: python +# name: python3 +# --- + +# %% [markdown] +# # Template Example Notebook +# +# This is a template notebook. The first heading should be the title of what notebook is about. For example, if it is a project on neo4j tutorial the heading should be `Project Title`. +# +# - Add description of what the notebook does. +# - Point to references, e.g. (neo4j.example.md) +# - Add citations. +# - Keep the notebook flow clear. +# - Comments should be imperative and have a period at the end. +# - Your code should be well commented. +# +# The name of this notebook should in the following format: +# - if the notebook is exploring `pycaret API`, then it is `pycaret.example.ipynb` +# +# Follow the reference to write notebooks in a clear manner: https://github.com/causify-ai/helpers/blob/master/docs/coding/all.jupyter_notebook.how_to_guide.md + +# %% +# %load_ext autoreload +# %autoreload 2 +# %matplotlib inline + +# %% +import logging +# Import libraries in this section. +# Avoid imports like import *, from ... import ..., from ... import *, etc. + +import helpers.hdbg as hdbg +import helpers.hnotebook as hnotebo + +# %% +hdbg.init_logger(verbosity=logging.INFO) + +_LOG = logging.getLogger(__name__) + +hnotebo.config_notebook() + + +# %% [markdown] +# ## Make the notebook flow clear +# Each notebook needs to follow a clear and logical flow, e.g: +# - Load data +# - Compute stats +# - Clean data +# - Compute stats +# - Do analysis +# - Show results +# +# +# +# + + +# ############################################################################# +# Template +# ############################################################################# + + +# %% +class Template: + """ + Brief imperative description of what the class does in one line, if needed. + """ + + def __init__(self): + pass + + def method1(self, arg1: int) -> None: + """ + Brief imperative description of what the method does in one line. + + You can elaborate more in the method docstring in this section, for e.g. explaining + the formula/algorithm. Every method/function should have a docstring, typehints and include the + parameters and return as follows: + + :param arg1: description of arg1 + :return: description of return + """ + # Code bloks go here. + # Make sure to include comments to explain what the code is doing. + # No empty lines between code blocks. + pass + + +def template_function(arg1: int) -> None: + """ + Brief imperative description of what the function does in one line. + + You can elaborate more in the function docstring in this section, for e.g. explaining + the formula/algorithm. Every function should have a docstring, typehints and include the + parameters and return as follows: + + :param arg1: description of arg1 + :return: description of return + """ + # Code bloks go here. + # Make sure to include comments to explain what the code is doing. + # No empty lines between code blocks. + pass + + +# %% [markdown] +# ## The flow should be highlighted using headings in markdown +# ``` +# # Level 1 +# ## Level 2 +# ### Level 3 +# ``` + +# %% diff --git a/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/docker_bash.sh b/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/docker_bash.sh new file mode 100755 index 000000000..0025e81f4 --- /dev/null +++ b/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/docker_bash.sh @@ -0,0 +1,34 @@ +#!/bin/bash +# """ +# This script launches a Docker container with an interactive bash shell for +# development. +# """ + +# Exit immediately if any command exits with a non-zero status. +set -e + +# Import the utility functions from the project template. +GIT_ROOT=$(git rev-parse --show-toplevel) +source $GIT_ROOT/class_project/project_template/utils.sh + +# Parse default args (-h, -v) and enable set -x if -v is passed. +parse_default_args "$@" + +# Load Docker configuration variables for this script. +get_docker_vars_script ${BASH_SOURCE[0]} +source $DOCKER_NAME +print_docker_vars + +# List the available Docker images matching the expected image name. +run "docker image ls $FULL_IMAGE_NAME" + +# Configure and run the Docker container with interactive bash shell. +# - Container is removed automatically on exit (--rm) +# - Interactive mode with TTY allocation (-ti) +# - Port forwarding for Jupyter or other services +# - Git root mounted to /git_root inside container +CONTAINER_NAME=${IMAGE_NAME}_bash +PORT= +DOCKER_CMD=$(get_docker_bash_command) +DOCKER_CMD_OPTS=$(get_docker_bash_options $CONTAINER_NAME $PORT) +run "$DOCKER_CMD $DOCKER_CMD_OPTS $FULL_IMAGE_NAME" diff --git a/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/docker_build.sh b/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/docker_build.sh new file mode 100755 index 000000000..5b0957a99 --- /dev/null +++ b/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/docker_build.sh @@ -0,0 +1,40 @@ +#!/bin/bash +# """ +# Build a Docker container image for the project. +# +# This script sets up the build environment with error handling and command +# tracing, loads Docker configuration from docker_name.sh, and builds the +# Docker image using the build_container_image utility function. It supports +# both single-architecture and multi-architecture builds via the +# DOCKER_BUILD_MULTI_ARCH environment variable. +# """ + +# Exit immediately if any command exits with a non-zero status. +set -e + +# Import the utility functions. +GIT_ROOT=$(git rev-parse --show-toplevel) +source $GIT_ROOT/class_project/project_template/utils.sh + +# Parse default args (-h, -v) and enable set -x if -v is passed. +# Shift processed option flags so remaining args are passed to the build. +parse_default_args "$@" +shift $((OPTIND-1)) + +# Load Docker configuration variables (REPO_NAME, IMAGE_NAME, FULL_IMAGE_NAME). +get_docker_vars_script ${BASH_SOURCE[0]} +source $DOCKER_NAME +print_docker_vars + +# Configure Docker build settings. +# Enable BuildKit for improved build performance and features. +export DOCKER_BUILDKIT=1 +#export DOCKER_BUILDKIT=0 + +# Configure single-architecture build (set to 1 for multi-arch build). +#export DOCKER_BUILD_MULTI_ARCH=1 +export DOCKER_BUILD_MULTI_ARCH=0 + +# Build the container image. +# Pass extra arguments (e.g., --no-cache) via command line after -v. +build_container_image "$@" diff --git a/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/docker_build.version.log b/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/docker_build.version.log new file mode 100644 index 000000000..8315eefe2 --- /dev/null +++ b/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/docker_build.version.log @@ -0,0 +1 @@ +the input device is not a TTY diff --git a/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/docker_clean.sh b/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/docker_clean.sh new file mode 100755 index 000000000..7e40839ae --- /dev/null +++ b/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/docker_clean.sh @@ -0,0 +1,26 @@ +#!/bin/bash +# """ +# Remove Docker container image for the project. +# +# This script cleans up Docker images by removing the container image +# matching the project configuration. Useful for freeing disk space or +# ensuring a fresh build. +# """ + +# Exit immediately if any command exits with a non-zero status. +set -e + +# Import the utility functions. +GIT_ROOT=$(git rev-parse --show-toplevel) +source $GIT_ROOT/class_project/project_template/utils.sh + +# Parse default args (-h, -v) and enable set -x if -v is passed. +parse_default_args "$@" + +# Load Docker configuration variables for this script. +get_docker_vars_script ${BASH_SOURCE[0]} +source $DOCKER_NAME +print_docker_vars + +# Remove the container image. +remove_container_image diff --git a/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/docker_cmd.sh b/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/docker_cmd.sh new file mode 100755 index 000000000..906d7a77b --- /dev/null +++ b/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/docker_cmd.sh @@ -0,0 +1,41 @@ +#!/bin/bash +# """ +# Execute a command in a Docker container. +# +# This script runs a specified command inside a new Docker container instance. +# The container is removed automatically after the command completes. The +# git root is mounted to /git_root inside the container. +# """ + +# Exit immediately if any command exits with a non-zero status. +set -e + +# Import the utility functions. +GIT_ROOT=$(git rev-parse --show-toplevel) +source $GIT_ROOT/class_project/project_template/utils.sh + +# Parse default args (-h, -v) and enable set -x if -v is passed. +# Shift processed option flags so remaining args form the command. +parse_default_args "$@" +shift $((OPTIND-1)) + +# Capture the command to execute from remaining arguments. +CMD="$@" +echo "Executing: '$CMD'" + +# Load Docker configuration variables for this script. +get_docker_vars_script ${BASH_SOURCE[0]} +source $DOCKER_NAME +print_docker_vars + +# List available Docker images matching the expected image name. +run "docker image ls $FULL_IMAGE_NAME" +#(docker manifest inspect $FULL_IMAGE_NAME | grep arch) || true + +# Configure and run the Docker container with the specified command. +CONTAINER_NAME=$IMAGE_NAME +DOCKER_CMD=$(get_docker_cmd_command) +PORT="" +DOCKER_RUN_OPTS="" +DOCKER_CMD_OPTS=$(get_docker_bash_options $CONTAINER_NAME $PORT $DOCKER_RUN_OPTS) +run "$DOCKER_CMD $DOCKER_CMD_OPTS $FULL_IMAGE_NAME bash -c '$CMD'" diff --git a/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/docker_exec.sh b/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/docker_exec.sh new file mode 100755 index 000000000..24f8e401a --- /dev/null +++ b/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/docker_exec.sh @@ -0,0 +1,25 @@ +#!/bin/bash +# """ +# Execute a bash shell in a running Docker container. +# +# This script connects to an already running Docker container and opens an +# interactive bash session for debugging or inspection purposes. +# """ + +# Exit immediately if any command exits with a non-zero status. +set -e + +# Import the utility functions. +GIT_ROOT=$(git rev-parse --show-toplevel) +source $GIT_ROOT/class_project/project_template/utils.sh + +# Parse default args (-h, -v) and enable set -x if -v is passed. +parse_default_args "$@" + +# Load Docker configuration variables for this script. +get_docker_vars_script ${BASH_SOURCE[0]} +source $DOCKER_NAME +print_docker_vars + +# Execute bash shell in the running container. +exec_container diff --git a/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/docker_jupyter.sh b/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/docker_jupyter.sh new file mode 100755 index 000000000..1a60dfd3a --- /dev/null +++ b/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/docker_jupyter.sh @@ -0,0 +1,39 @@ +#!/bin/bash +# """ +# Execute Jupyter Lab in a Docker container. +# +# This script launches a Docker container running Jupyter Lab with +# configurable port, directory mounting, and vim bindings. It passes +# command-line options to the run_jupyter.sh script inside the container. +# +# Usage: +# > docker_jupyter.sh [options] +# """ + +# Exit immediately if any command exits with a non-zero status. +set -e + +# Import the utility functions. +GIT_ROOT=$(git rev-parse --show-toplevel) +source $GIT_ROOT/class_project/project_template/utils.sh + +# Parse command-line options and set Jupyter configuration variables. +parse_docker_jupyter_args "$@" + +# Load Docker configuration variables for this script. +get_docker_vars_script ${BASH_SOURCE[0]} +source $DOCKER_NAME +print_docker_vars + +# List available Docker images and inspect architecture. +list_and_inspect_docker_image + +# Run the Docker container with Jupyter Lab. +CMD=$(get_run_jupyter_cmd "${BASH_SOURCE[0]}" "$OLD_CMD_OPTS") +CONTAINER_NAME=$IMAGE_NAME +# Kill existing container if -f flag is set. +kill_existing_container_if_forced + +DOCKER_CMD=$(get_docker_jupyter_command) +DOCKER_CMD_OPTS=$(get_docker_jupyter_options $CONTAINER_NAME $JUPYTER_HOST_PORT $JUPYTER_USE_VIM) +run "$DOCKER_CMD $DOCKER_CMD_OPTS $FULL_IMAGE_NAME $CMD" diff --git a/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/docker_name.sh b/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/docker_name.sh new file mode 100644 index 000000000..32a546cf3 --- /dev/null +++ b/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/docker_name.sh @@ -0,0 +1,12 @@ +#!/bin/bash +# """ +# Docker image naming configuration. +# +# This file defines the repository name, image name, and full image name +# variables used by all docker_*.sh scripts in the project template. +# """ + +REPO_NAME=gpsaggese +# The file should be all lower case. +IMAGE_NAME=umd_project_template +FULL_IMAGE_NAME=$REPO_NAME/$IMAGE_NAME diff --git a/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/docker_push.sh b/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/docker_push.sh new file mode 100755 index 000000000..27d752dd9 --- /dev/null +++ b/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/docker_push.sh @@ -0,0 +1,25 @@ +#!/bin/bash +# """ +# Push Docker container image to Docker Hub or registry. +# +# This script authenticates with the Docker registry using credentials from +# ~/.docker/passwd.$REPO_NAME.txt and pushes the locally built container +# image to the remote repository. +# """ + +# Exit immediately if any command exits with a non-zero status. +set -e + +# Import the utility functions. +GIT_ROOT=$(git rev-parse --show-toplevel) +source $GIT_ROOT/class_project/project_template/utils.sh + +# Parse default args (-h, -v) and enable set -x if -v is passed. +parse_default_args "$@" + +# Load Docker image naming configuration. +SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +source $SCRIPT_DIR/docker_name.sh + +# Push the container image to the registry. +push_container_image diff --git a/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/etc_sudoers b/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/etc_sudoers new file mode 100644 index 000000000..ee0816a15 --- /dev/null +++ b/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/etc_sudoers @@ -0,0 +1,31 @@ +# +# This file MUST be edited with the 'visudo' command as root. +# +# Please consider adding local content in /etc/sudoers.d/ instead of +# directly modifying this file. +# +# See the man page for details on how to write a sudoers file. +# +Defaults env_reset +Defaults mail_badpass +Defaults secure_path="/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/snap/bin" + +# Host alias specification + +# User alias specification + +# Cmnd alias specification + +# User privilege specification +root ALL=(ALL:ALL) ALL + +# Members of the admin group may gain root privileges +%admin ALL=(ALL) ALL + +# Allow members of group sudo to execute any command +%sudo ALL=(ALL:ALL) ALL + +# See sudoers(5) for more information on "#include" directives: +postgres ALL=(ALL) NOPASSWD:ALL + +#includedir /etc/sudoers.d diff --git a/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/requirements.txt b/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/requirements.txt new file mode 100644 index 000000000..3bca91ab7 --- /dev/null +++ b/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/requirements.txt @@ -0,0 +1,30 @@ +# Core data manipulation +pandas==2.2.0 +numpy==1.26.4 + +# Data download +yfinance==0.2.37 +fredapi==0.5.1 + +# Darts and forecasting +darts==0.30.0 +prophet==1.1.5 +statsmodels==0.14.1 + +# Machine learning +lightgbm==4.3.0 +xgboost==2.0.3 +catboost==1.2.3 +scikit-learn==1.4.0 + +# Model explainability +shap==0.44.1 + +# Visualization +matplotlib==3.8.2 +seaborn==0.13.2 +plotly==5.18.0 + +# Holidays +holidays==0.46 +optuna==3.6.1 diff --git a/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/run_jupyter.sh b/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/run_jupyter.sh new file mode 100755 index 000000000..d725c3fe7 --- /dev/null +++ b/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/run_jupyter.sh @@ -0,0 +1,35 @@ +#!/bin/bash +# """ +# Launch Jupyter Lab server. +# +# This script starts Jupyter Lab on port 8888 with the following configuration: +# - No browser auto-launch (useful for Docker containers) +# - Accessible from any IP address (0.0.0.0) +# - Root user allowed (required for Docker environments) +# - No authentication token or password (for development convenience) +# - Vim keybindings can be enabled via JUPYTER_USE_VIM environment variable +# """ + +# Exit immediately if any command exits with a non-zero status. +set -e + +# Print each command to stdout before executing it. +#set -x + +# Import the utility functions from /git_root. +GIT_ROOT=/git_root +source $GIT_ROOT/class_project/project_template/utils.sh + +# Load Docker configuration variables for this script. +get_docker_vars_script ${BASH_SOURCE[0]} +source $DOCKER_NAME +print_docker_vars + +# Setup Jupyter Lab environment. +setup_jupyter_environment + +# Initialize Jupyter Lab command with base configuration. +JUPYTER_ARGS=$(get_jupyter_args) + +# Start Jupyter Lab with development-friendly settings. +run "jupyter lab $JUPYTER_ARGS" diff --git a/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/template_utils.py b/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/template_utils.py new file mode 100644 index 000000000..f8916102e --- /dev/null +++ b/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/template_utils.py @@ -0,0 +1,72 @@ +""" +template_utils.py + +This file contains utility functions that support the tutorial notebooks. + +- Notebooks should call these functions instead of writing raw logic inline. +- This helps keep the notebooks clean, modular, and easier to debug. +- Students should implement functions here for data preprocessing, + model setup, evaluation, or any reusable logic. + +Import as: + +import class_project.project_template.template_utils as cpptteut +""" + +import pandas as pd +import logging +from sklearn.model_selection import train_test_split +from pycaret.classification import compare_models + +# ----------------------------------------------------------------------------- +# Logging +# ----------------------------------------------------------------------------- + +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + +# ----------------------------------------------------------------------------- +# Example 1: Split the dataset into train and test sets +# ----------------------------------------------------------------------------- + + +def split_data(df: pd.DataFrame, target_column: str, test_size: float = 0.2): + """ + Split the dataset into training and testing sets. + + :param df: full dataset + :param target_column: name of the target column + :param test_size: proportion of test data (default = 0.2) + + :return: X_train, X_test, y_train, y_test + """ + logger.info("Splitting data into train and test sets") + X = df.drop(columns=[target_column]) + y = df[target_column] + return train_test_split(X, y, test_size=test_size, random_state=42) + + +# ----------------------------------------------------------------------------- +# Example 2: PyCaret classification pipeline +# ----------------------------------------------------------------------------- + + +def run_pycaret_classification( + df: pd.DataFrame, target_column: str +) -> pd.DataFrame: + """ + Run a basic PyCaret classification experiment. + + :param df: dataset containing features and target + :param target_column: name of the target column + + :return: comparison of top-performing models + """ + logger.info("Initializing PyCaret classification setup") + ... + + logger.info("Comparing models") + results = compare_models() + ... + + return results diff --git a/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/test/test_docker_all.py b/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/test/test_docker_all.py new file mode 100644 index 000000000..904cdd7af --- /dev/null +++ b/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/test/test_docker_all.py @@ -0,0 +1,48 @@ +""" +Run each notebook in class_project/project_template/ inside Docker using docker_cmd.sh. + +Import as: + +import class_project.project_template.test.test_docker_all as tptdal +""" + +import logging + +import pytest + +import helpers.hdocker_tests as hdoctest + +_LOG = logging.getLogger(__name__) + + +# ############################################################################# +# Test_docker +# ############################################################################# + + +class Test_docker(hdoctest.DockerTestCase): + """ + Run all Docker tests for class_project/project_template/. + """ + + _test_file = __file__ + + @pytest.mark.slow + def test1(self) -> None: + """ + Test that template.example.ipynb runs without error inside Docker. + """ + # Prepare inputs. + notebook_name = "template.example.ipynb" + # Run test. + self._helper(notebook_name) + + @pytest.mark.slow + def test2(self) -> None: + """ + Test that template.API.ipynb runs without error inside Docker. + """ + # Prepare inputs. + notebook_name = "template.API.ipynb" + # Run test. + self._helper(notebook_name) diff --git a/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/utils.py b/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/utils.py new file mode 100644 index 000000000..6372979fe --- /dev/null +++ b/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/utils.py @@ -0,0 +1,4808 @@ +""" +Utility functions for S&P 500 market direction forecasting and intelligent +sector rotation using Darts. + +This module contains all reusable helper functions for data download, +preprocessing, feature engineering, model training, evaluation, and +visualization. Functions are imported and called from `darts.example.ipynb`. +""" + +import logging +import os + +import darts.dataprocessing.transformers +import darts.timeseries +import fredapi +import holidays +import matplotlib +import matplotlib.pyplot as plt +import numpy as np +import optuna +import pandas as pd +import prophet +import seaborn as sns +import shap +import sklearn.ensemble +import tqdm +import yfinance as yf + +_LOG = logging.getLogger(__name__) + +def download_sp500( + ticker: str, + start_date: str, + end_date: str, +) -> pd.DataFrame: + """ + Download S&P 500 historical price data from Yahoo Finance. + + The data is downloaded for the given date range and returned as a + DataFrame with a DatetimeIndex. The `Close` column contains the + daily closing price used for forecasting. + + :param ticker: Yahoo Finance ticker symbol for S&P 500 index + :param start_date: start date in 'YYYY-MM-DD' format + :param end_date: end date in 'YYYY-MM-DD' format + :return: DataFrame with OHLCV columns indexed by date + """ + # Download raw S&P 500 price data from Yahoo Finance. + _LOG.info("Downloading S&P 500 data from %s to %s.", start_date, end_date) + df = yf.download( + ticker, + start=start_date, + end=end_date, + auto_adjust=True, + progress=False, + ) + # Flatten the column MultiIndex that yfinance returns by default. + df.columns = df.columns.get_level_values(0) + # Convert the index to a DatetimeIndex for Darts compatibility. + df.index = pd.DatetimeIndex(df.index) + # Rename the index to Date for clarity. + df.index.name = "Date" + _LOG.info("Downloaded %d rows of S&P 500 data.", len(df)) + return df + +def download_sectors( + tickers: list, + start_date: str, + end_date: str, +) -> pd.DataFrame: + """ + Download historical price data for all sector ETFs from Yahoo Finance. + + Each ticker is downloaded individually and the closing prices are + combined into a single DataFrame where each column represents one + sector ETF. A progress bar tracks the download of each ticker. + + :param tickers: list of sector ETF ticker symbols to download + :param start_date: start date in 'YYYY-MM-DD' format + :param end_date: end date in 'YYYY-MM-DD' format + :return: DataFrame with one closing price column per sector ETF, + indexed by date + """ + # Initialize an empty dictionary to collect each sector's closing price. + sector_data = {} + # Download closing price for each sector ETF individually. + _LOG.info("Downloading %d sector ETFs.", len(tickers)) + for ticker in tqdm.tqdm(tickers, desc="Downloading sectors"): + # Download raw price data for the current ticker. + df = yf.download( + ticker, + start=start_date, + end=end_date, + auto_adjust=True, + progress=False, + ) + # Flatten the MultiIndex columns that yfinance returns. + df.columns = df.columns.get_level_values(0) + # Store only the closing price column named after the ticker. + sector_data[ticker] = df["Close"] + # Combine all sector closing prices into a single DataFrame. + sectors_df = pd.DataFrame(sector_data) + # Convert the index to a DatetimeIndex for Darts compatibility. + sectors_df.index = pd.DatetimeIndex(sectors_df.index) + # Rename the index to Date for clarity. + sectors_df.index.name = "Date" + _LOG.info( + "Downloaded %d rows for %d sectors.", + len(sectors_df), + len(tickers), + ) + return sectors_df + +def download_daily_macro( + tickers: dict, + start_date: str, + end_date: str, +) -> pd.DataFrame: + """ + Download daily macroeconomic indicators from Yahoo Finance. + + Each indicator is downloaded individually using its Yahoo Finance + ticker symbol and combined into a single DataFrame. The columns are + named using the clean indicator names defined in `tickers` dictionary + rather than the raw Yahoo Finance symbols. + + :param tickers: dictionary mapping clean indicator names to Yahoo + Finance ticker symbols, e.g. `{'VIX': '^VIX', 'OIL': 'CL=F'}` + :param start_date: start date in 'YYYY-MM-DD' format + :param end_date: end date in 'YYYY-MM-DD' format + :return: DataFrame with one column per macro indicator indexed by date + """ + # Initialize an empty dictionary to collect each indicator's data. + macro_data = {} + # Download closing price for each macro indicator individually. + _LOG.info("Downloading %d daily macro indicators.", len(tickers)) + for name, symbol in tqdm.tqdm( + tickers.items(), desc="Downloading daily macro" + ): + # Download raw price data for the current macro indicator. + df = yf.download( + symbol, + start=start_date, + end=end_date, + auto_adjust=True, + progress=False, + ) + # Flatten the MultiIndex columns that yfinance returns. + df.columns = df.columns.get_level_values(0) + # Store only the closing price using the clean indicator name. + macro_data[name] = df["Close"] + # Combine all macro indicators into a single DataFrame. + macro_df = pd.DataFrame(macro_data) + # Convert the index to a DatetimeIndex for Darts compatibility. + macro_df.index = pd.DatetimeIndex(macro_df.index) + # Rename the index to Date for clarity. + macro_df.index.name = "Date" + _LOG.info( + "Downloaded %d rows for %d daily macro indicators.", + len(macro_df), + len(tickers), + ) + return macro_df + +def download_monthly_macro( + codes: dict, + breakeven_code: str, + start_date: str, + end_date: str, + fred_api_key: str, +) -> pd.DataFrame: + """ + Download monthly macroeconomic indicators from the FRED API. + + Each indicator is downloaded individually using its FRED series code + and combined into a single DataFrame. The 10Y breakeven inflation rate + is also downloaded as it is available daily from FRED. No reindexing + is applied here — alignment to business day frequency is handled + in the preprocessing phase to preserve values that fall on + non-trading days like weekends and holidays. + + :param codes: dictionary mapping clean indicator names to FRED series + codes e.g. `{'CPI': 'CPIAUCSL', 'FED_RATE': 'FEDFUNDS'}` + :param breakeven_code: FRED series code for 10Y breakeven inflation + rate which is available at daily frequency + :param start_date: start date in 'YYYY-MM-DD' format + :param end_date: end date in 'YYYY-MM-DD' format + :param fred_api_key: FRED API key for authentication + :return: DataFrame with one column per macro indicator indexed by + original FRED dates without business day reindexing + """ + # Initialize the FRED API client with the provided key. + fred = fredapi.Fred(api_key=fred_api_key) + # Initialize an empty dictionary to collect each indicator's data. + macro_data = {} + # Download each monthly macro indicator from FRED individually. + _LOG.info("Downloading %d monthly macro indicators from FRED.", len(codes)) + for name, code in tqdm.tqdm( + codes.items(), desc="Downloading monthly macro" + ): + # Download the raw series data for the current indicator. + series = fred.get_series( + code, + observation_start=start_date, + observation_end=end_date, + ) + # Store the series using the clean indicator name as the key. + macro_data[name] = series + # Download the daily breakeven inflation rate separately. + _LOG.info("Downloading 10Y breakeven inflation from FRED.") + macro_data["BREAKEVEN"] = fred.get_series( + breakeven_code, + observation_start=start_date, + observation_end=end_date, + ) + # Combine all indicators into a single DataFrame. + macro_df = pd.DataFrame(macro_data) + # Convert the index to a DatetimeIndex for Darts compatibility. + macro_df.index = pd.DatetimeIndex(macro_df.index) + # Rename the index to Date for clarity. + macro_df.index.name = "Date" + _LOG.info( + "Downloaded %d rows for %d monthly macro indicators.", + len(macro_df), + len(codes) + 1, + ) + return macro_df + +def save_data( + df: pd.DataFrame, + file_name: str, + data_dir: str, +) -> None: + """ + Save a DataFrame to a CSV file in the specified data directory. + + The file is saved with the index included so the date column is + preserved when the file is read back. If the file already exists + it is overwritten to ensure the saved data is always up to date. + + :param df: DataFrame to save to CSV + :param file_name: name of the CSV file including `.csv` extension + :param data_dir: path to the directory where the file is saved + :return: None + """ + # Build the full file path from the directory and file name. + file_path = os.path.join(data_dir, file_name) + # Save the DataFrame to CSV with the date index included. + df.to_csv(file_path, index=True) + _LOG.info("Saved %d rows to %s.", len(df), file_path) + +def load_data( + file_name: str, + data_dir: str, +) -> pd.DataFrame: + """ + Load a DataFrame from a CSV file in the specified data directory. + + The date column is parsed as a DatetimeIndex automatically. This + function is used to load previously downloaded or processed data + instead of re-downloading from external APIs on every notebook run. + Using saved CSV files makes the notebook idempotent and eliminates + unnecessary API calls on every kernel restart. + + :param file_name: name of the CSV file including `.csv` extension + :param data_dir: path to the directory where the file is saved + :return: DataFrame with DatetimeIndex loaded from CSV file + """ + # Build the full file path from the directory and file name. + file_path = os.path.join(data_dir, file_name) + # Load the CSV file with the Date column parsed as DatetimeIndex. + df = pd.read_csv(file_path, index_col="Date", parse_dates=True) + # Convert index to DatetimeIndex for Darts compatibility. + df.index = pd.DatetimeIndex(df.index) + _LOG.info("Loaded %d rows from %s.", len(df), file_path) + return df + +def preserve_month_start_values( + df: pd.DataFrame, + master_index: pd.DatetimeIndex, +) -> pd.DataFrame: + """ + Preserve monthly indicator values that fall on non-trading days. + + FRED releases monthly data on the first of each month. When the + first of the month is a weekend or holiday, those values get lost + during reindexing to business day frequency. This function carries + those values forward to the next available trading day before + reindexing. + + :param df: DataFrame with monthly macro indicators containing + values on non-trading days like weekends and holidays + :param master_index: business day DatetimeIndex from S&P 500 + used as the target index for alignment + :return: DataFrame reindexed to master index with monthly values + preserved on the next available trading day + """ + # Forward fill before reindexing to preserve non-trading day values. + df = df.ffill() + # Reindex to master index after forward fill to preserve values. + df = df.reindex(master_index) + return df + +def apply_release_aware_forward_fill( + df: pd.DataFrame, + fred_api_key: str, + codes: dict, +) -> pd.DataFrame: + """ + Apply release-date-aware forward fill to monthly macro indicators. + + For each monthly indicator, the official FRED vintage dates are + used as true release dates. Vintage dates represent the exact dates + data was first published to the public — not the observation period + start date. Values are forward filled only from those dates onwards + to prevent data leakage. A binary `_released` flag column is added + for each indicator to distinguish actual release days from forward + filled values. + + :param df: DataFrame with monthly macro indicators indexed by + business day frequency containing values from + `preserve_month_start_values` + :param fred_api_key: FRED API key for fetching vintage dates + :param codes: dictionary mapping clean indicator names to FRED + series codes e.g. `{'CPI': 'CPIAUCSL'}` + :return: DataFrame with forward filled values and binary + `_released` flag columns added for each indicator + """ + # Initialize the FRED API client with the provided key. + fred = fredapi.Fred(api_key=fred_api_key) + # Create a copy to avoid modifying the original DataFrame. + filled_df = df.copy() + # Reset all monthly indicator values to NaN before refilling. + # This ensures forward fill only starts from true release dates. + for name in codes.keys(): + if name in filled_df.columns: + filled_df[name] = np.nan + # Process each monthly indicator individually. + _LOG.info("Applying release-date-aware forward fill using vintage dates.") + for name, code in tqdm.tqdm( + codes.items(), desc="Forward filling indicators" + ): + try: + # Fetch the official vintage dates for this indicator. + vintage_dates = fred.get_series_vintage_dates(code) + # Convert vintage dates to DatetimeIndex for comparison. + vintage_dates = pd.DatetimeIndex(vintage_dates) + # Filter vintage dates to our analysis period only. + vintage_dates = vintage_dates[ + (vintage_dates >= df.index[0]) + & (vintage_dates <= df.index[-1]) + ] + # Get the original series values from the input DataFrame. + original_series = df[name].dropna() + # Place each observation value on its true release date. + for vintage_date in vintage_dates: + # Find the closest business day on or after release date. + business_day = df.index[df.index >= vintage_date] + if len(business_day) == 0: + continue + business_day = business_day[0] + # Find the observation value closest to this vintage date. + obs_dates = original_series.index[ + original_series.index <= vintage_date + ] + if len(obs_dates) == 0: + continue + obs_value = original_series[obs_dates[-1]] + # Place the value on the true release business day. + filled_df.loc[business_day, name] = obs_value + # Add binary flag marking true release days. + filled_df[f"{name}_released"] = ( + filled_df[name].notna() + & filled_df[name].diff().ne(0) + ).astype(int) + # Forward fill values from true release dates onwards. + filled_df[name] = filled_df[name].ffill() + except Exception as e: + # Fall back to simple forward fill if vintage dates unavailable. + _LOG.warning( + "Vintage dates unavailable for %s: %s. " + "Falling back to simple forward fill.", + name, + str(e), + ) + filled_df[name] = df[name].ffill() + filled_df[f"{name}_released"] = ( + filled_df[name].notna() + & filled_df[name].diff().ne(0) + ).astype(int) + # Log the number of indicators processed. + _LOG.info( + "Forward fill complete for %d indicators.", len(codes) + ) + return filled_df + +def reconstruct_xlc( + sectors: pd.DataFrame, + start_date: str, + xlc_launch_date: str, + correlation_threshold: float, +) -> pd.DataFrame: + """ + Reconstruct XLC pre-launch history using constituent stock prices. + + XLC (Communication Services ETF) launched in June 2018. This + function reconstructs XLC values using historical prices of the + top XLC constituents with verified weights from the official SPDR + ETF filing on the first day of trading June 19 2018. The + reconstruction is first validated against actual XLC prices for + the post-launch overlap period by extending the reconstruction + through the full analysis period. If correlation exceeds the + threshold the pre-launch NaN values are replaced — otherwise the + function returns the original DataFrame unchanged. + + :param sectors: DataFrame with sector ETF prices including XLC + column with NaN values before launch date + :param start_date: start date of the full analysis period in + 'YYYY-MM-DD' format + :param xlc_launch_date: date XLC was officially launched in + 'YYYY-MM-DD' format + :param correlation_threshold: minimum acceptable correlation + between reconstruction and actual XLC e.g. `0.90` + :return: DataFrame with XLC NaN values replaced by reconstruction + if correlation threshold is met otherwise original DataFrame + """ + # Define XLC constituents with verified weights from June 19 2018 + # first day holdings sourced from SPDR ETF public filing. + # Tickers use current names where companies were renamed. + xlc_constituents = { + "META" : 0.1865, # Facebook Class A + "GOOGL" : 0.1120, # Alphabet Class A + "GOOG" : 0.1095, # Alphabet Class C + "DIS" : 0.0520, # Walt Disney + "CMCSA" : 0.0505, # Comcast Class A + "NFLX" : 0.0480, # Netflix + "T" : 0.0445, # AT&T + "VZ" : 0.0435, # Verizon + "CHTR" : 0.0380, # Charter Communications + "EA" : 0.0235, # Electronic Arts + "TTWO" : 0.0215, # Take-Two Interactive + "OMC" : 0.0180, # Omnicom Group + "TMUS" : 0.0155, # T-Mobile + "TRIP" : 0.0145, # TripAdvisor + "NWSA" : 0.0160, # News Corp Class A + "LUMN" : 0.0135, # CenturyLink now Lumen Technologies + } + # Download constituent prices over the FULL analysis period + # including post-launch dates for overlap validation. + _LOG.info("Downloading XLC constituent stock prices.") + constituent_prices = {} + total_weight = 0.0 + for ticker, weight in tqdm.tqdm( + xlc_constituents.items(), desc="Downloading XLC constituents" + ): + try: + # Download adjusted closing prices for the full period. + df = yf.download( + ticker, + start=start_date, + end=sectors.index[-1].strftime("%Y-%m-%d"), + auto_adjust=True, + progress=False, + ) + # Flatten MultiIndex columns that yfinance returns. + df.columns = df.columns.get_level_values(0) + if len(df) > 0: + constituent_prices[ticker] = df["Close"] + total_weight += weight + _LOG.info("Downloaded %s successfully.", ticker) + else: + _LOG.warning( + "No data for %s — excluding from reconstruction.", + ticker, + ) + except Exception as e: + _LOG.warning( + "Failed to download %s: %s — excluding.", + ticker, + str(e), + ) + # Normalize weights to sum to 1.0 after excluding failed downloads. + normalized_weights = { + ticker: xlc_constituents[ticker] / total_weight + for ticker in constituent_prices + } + _LOG.info( + "Using %d constituents with total normalized weight: %.4f.", + len(constituent_prices), + sum(normalized_weights.values()), + ) + # Combine all constituent prices into a single DataFrame. + prices_df = pd.DataFrame(constituent_prices) + # Convert index to DatetimeIndex for alignment. + prices_df.index = pd.DatetimeIndex(prices_df.index) + # Reindex to match sectors DataFrame index for alignment. + prices_df = prices_df.reindex(sectors.index) + # Forward fill and backward fill any gaps in constituent prices. + prices_df = prices_df.ffill().bfill() + # Calculate weighted reconstruction over the full period. + reconstruction = pd.Series(0.0, index=prices_df.index) + for ticker, weight in normalized_weights.items(): + # Scale each constituent price by its normalized weight. + reconstruction += prices_df[ticker] * weight + # Scale reconstruction to match actual XLC price at launch date + # so the reconstructed series connects seamlessly to actual data. + actual_xlc_at_launch = sectors.loc[ + sectors.index >= pd.Timestamp(xlc_launch_date), "XLC" + ].iloc[0] + reconstruction_at_launch = reconstruction.loc[ + pd.Timestamp(xlc_launch_date) + ] + scale_factor = actual_xlc_at_launch / reconstruction_at_launch + reconstruction = reconstruction * scale_factor + _LOG.info( + "Scale factor applied: %.4f to match XLC price at launch.", + scale_factor, + ) + # Validate reconstruction against actual XLC post-launch period. + # Use daily returns for correlation — more reliable than price levels. + actual_xlc = sectors.loc[ + sectors.index >= pd.Timestamp(xlc_launch_date), "XLC" + ].dropna() + reconstruction_post = reconstruction.loc[ + reconstruction.index >= pd.Timestamp(xlc_launch_date) + ] + # Align both series to common dates. + common_index = actual_xlc.index.intersection( + reconstruction_post.index + ) + if len(common_index) > 10: + # Calculate daily returns for both series. + actual_returns = actual_xlc.loc[common_index].pct_change().dropna() + recon_returns = reconstruction_post.loc[common_index].pct_change().dropna() + # Align returns to same index after pct_change drops first row. + common_returns_index = actual_returns.index.intersection( + recon_returns.index + ) + correlation = actual_returns.loc[common_returns_index].corr( + recon_returns.loc[common_returns_index] + ) + _LOG.info( + "Return correlation with actual XLC: %.4f over %d days.", + correlation, + len(common_returns_index), + ) + else: + correlation = 0.0 + _LOG.warning("Insufficient overlap for validation.") + # Replace XLC NaN values only if correlation meets threshold. + result_df = sectors.copy() + if correlation >= correlation_threshold: + _LOG.info( + "Correlation %.4f exceeds threshold %.2f — " + "using reconstruction for pre-launch period.", + correlation, + correlation_threshold, + ) + # Get only the pre-launch reconstruction values. + pre_launch_recon = reconstruction.loc[ + reconstruction.index < pd.Timestamp(xlc_launch_date) + ] + # Replace only NaN values in XLC pre-launch period. + result_df.loc[pre_launch_recon.index, "XLC"] = ( + result_df.loc[pre_launch_recon.index, "XLC"].fillna( + pre_launch_recon + ) + ) + else: + _LOG.warning( + "Correlation %.4f below threshold %.2f — " + "reconstruction not reliable. " + "Consider trimming to XLC launch date instead.", + correlation, + correlation_threshold, + ) + return result_df + +def plot_sp500_history( + sp500: pd.DataFrame, + ax: plt.Axes = None, +) -> plt.Figure: + """ + Plot S&P 500 price history and daily returns in a single figure. + + Two subplots are created — the top panel shows the closing price + over time and the bottom panel shows the daily percentage returns. + This gives a clear picture of both the price trend and the + volatility pattern over the analysis period. + + :param sp500: DataFrame with S&P 500 OHLCV data indexed by date + :param ax: optional matplotlib Axes object for embedding in a + larger figure — if None a new figure is created + :return: matplotlib Figure object with both subplots + """ + # Calculate daily percentage returns from closing prices. + returns = sp500["Close"].pct_change().dropna() * 100 + # Create a figure with two vertically stacked subplots. + fig, axes = plt.subplots(2, 1, figsize=(14, 8)) + # Plot closing price on the top subplot. + axes[0].plot( + sp500.index, + sp500["Close"], + color="steelblue", + linewidth=1.2, + label="S&P 500 Close", + ) + axes[0].set_title( + "S&P 500 Closing Price 2018-2024", + fontsize=13, + fontweight="bold", + ) + axes[0].set_ylabel("Price (USD)") + axes[0].set_xlabel("") + axes[0].legend(loc="upper left", fontsize=9) + # Plot daily returns on the bottom subplot. + axes[1].bar( + returns.index, + returns.values, + color=["crimson" if r < 0 else "steelblue" for r in returns], + width=1.0, + alpha=0.7, + label="Daily Return %", + ) + axes[1].axhline(0, color="black", linewidth=0.8, linestyle="--") + axes[1].set_title( + "S&P 500 Daily Returns 2018-2024", + fontsize=13, + fontweight="bold", + ) + axes[1].set_ylabel("Return (%)") + axes[1].set_xlabel("Date") + axes[1].legend(loc="upper left", fontsize=9) + fig.tight_layout() + return fig + +def plot_sector_performance( + sectors: pd.DataFrame, + sector_names: dict, + ax: plt.Axes = None, +) -> plt.Figure: + """ + Plot sector ETF performance using a heatmap and bar chart. + + Two visualizations are created — a heatmap showing annual returns + per sector per year and a horizontal bar chart showing total + return over the full period ranked from best to worst. This + layout clearly shows which sectors led and lagged each year + and over the full analysis period. + + :param sectors: DataFrame with sector ETF closing prices indexed + by date with one column per sector ticker + :param sector_names: dictionary mapping ticker symbols to full + sector names e.g. `{'XLK': 'Technology'}` + :param ax: optional matplotlib Axes object for embedding in a + larger figure — if None a new figure is created + :return: matplotlib Figure object with heatmap and bar chart + """ + # Calculate annual returns for each sector. + annual_returns = sectors.resample("YE").last().pct_change() * 100 + # Drop the first row which is NaN after pct_change. + annual_returns = annual_returns.dropna() + # Rename columns to full sector names for readability. + annual_returns.columns = [ + sector_names.get(col, col) for col in annual_returns.columns + ] + # Format index to show year only. + annual_returns.index = annual_returns.index.year + # Calculate total return over full period for bar chart. + total_returns = ( + (sectors.iloc[-1] / sectors.iloc[0] - 1) * 100 + ) + # Rename total returns index to full sector names. + total_returns.index = [ + sector_names.get(col, col) for col in total_returns.index + ] + # Sort total returns from highest to lowest. + total_returns = total_returns.sort_values(ascending=True) + # Create figure with two subplots side by side. + fig, axes = plt.subplots(1, 2, figsize=(20, 7)) + # Plot annual returns heatmap on the left subplot. + sns.heatmap( + annual_returns.T, + annot=True, + fmt=".1f", + cmap="RdYlGn", + center=0, + linewidths=0.5, + linecolor="white", + ax=axes[0], + cbar_kws={"label": "Annual Return (%)"}, + ) + axes[0].set_title( + "Sector Annual Returns by Year (%)", + fontsize=13, + fontweight="bold", + ) + axes[0].set_xlabel("Year") + axes[0].set_ylabel("Sector") + # Plot total returns horizontal bar chart on right subplot. + colors = [ + "crimson" if r < 0 else "steelblue" + for r in total_returns.values + ] + axes[1].barh( + total_returns.index, + total_returns.values, + color=colors, + edgecolor="white", + height=0.6, + ) + axes[1].axvline(0, color="black", linewidth=0.8, linestyle="--") + # Add value labels on each bar. + for idx, val in enumerate(total_returns.values): + axes[1].text( + val + (2 if val >= 0 else -2), + idx, + f"{val:.1f}%", + va="center", + ha="left" if val >= 0 else "right", + fontsize=9, + fontweight="bold", + ) + axes[1].set_title( + "Total Return by Sector 2018-2024 (%)", + fontsize=13, + fontweight="bold", + ) + axes[1].set_xlabel("Total Return (%)") + axes[1].set_ylabel("") + fig.tight_layout() + return fig + +def plot_macro_trends( + macro_daily: pd.DataFrame, + macro_monthly: pd.DataFrame, +) -> plt.Figure: + """ + Plot key macroeconomic indicator trends over the analysis period. + + Two separate figures are created — one for daily indicators and + one for monthly indicators. Each figure uses subplots to show + each indicator individually with its own scale so trends are + clearly visible without compression from different value ranges. + + :param macro_daily: DataFrame with daily macro indicators indexed + by date including VIX, yields, oil, gold, and DXY + :param macro_monthly: DataFrame with monthly macro indicators + indexed by date including CPI, Fed rate, and unemployment + :return: matplotlib Figure object with all macro trend subplots + """ + # Define which daily indicators to plot with their display names. + daily_indicators = { + "VIX" : "VIX (Fear Index)", + "TNX" : "10Y Treasury Yield (%)", + "IRX" : "2Y Treasury Yield (%)", + "OIL" : "Oil Price WTI (USD)", + "GOLD" : "Gold Price (USD)", + "DXY" : "Dollar Index (DXY)", + } + # Define which monthly indicators to plot with display names. + monthly_indicators = { + "CPI" : "CPI Inflation", + "FED_RATE" : "Fed Funds Rate (%)", + "UNEMPLOYMENT" : "Unemployment Rate (%)", + "NFP" : "Non-Farm Payrolls (K)", + } + # Create figure for daily macro indicators with 6 subplots. + fig, axes = plt.subplots(3, 2, figsize=(16, 12)) + axes = axes.flatten() + # Plot each daily indicator in its own subplot. + for idx, (col, title) in enumerate(daily_indicators.items()): + axes[idx].plot( + macro_daily.index, + macro_daily[col], + color="steelblue", + linewidth=1.2, + ) + axes[idx].set_title(title, fontsize=11, fontweight="bold") + axes[idx].set_ylabel("Value") + axes[idx].set_xlabel("Date") + # Add shaded region for COVID crash period. + axes[idx].axvspan( + pd.Timestamp("2020-02-01"), + pd.Timestamp("2020-06-01"), + alpha=0.1, + color="red", + label="COVID crash", + ) + # Add shaded region for rate hiking cycle. + axes[idx].axvspan( + pd.Timestamp("2022-03-01"), + pd.Timestamp("2023-07-01"), + alpha=0.1, + color="orange", + label="Rate hike cycle", + ) + axes[idx].legend(fontsize=7, loc="upper left") + fig.suptitle( + "Daily Macroeconomic Indicators 2018-2024", + fontsize=14, + fontweight="bold", + ) + fig.tight_layout() + return fig + +def plot_macro_correlation( + sp500: pd.DataFrame, + macro_daily: pd.DataFrame, + macro_monthly: pd.DataFrame, +) -> plt.Figure: + """ + Plot correlation heatmap between macro indicators and S&P 500 returns. + + Daily S&P 500 returns are calculated and correlated against all + macro indicators. The heatmap shows which indicators have the + strongest positive or negative relationship with market returns + providing insight into which features will be most valuable + for our forecasting models. + + :param sp500: DataFrame with S&P 500 OHLCV data indexed by date + :param macro_daily: DataFrame with daily macro indicators + :param macro_monthly: DataFrame with monthly macro indicators + excluding binary release flag columns + :return: matplotlib Figure object with correlation heatmap + """ + # Calculate daily S&P 500 returns as the target variable. + sp500_returns = sp500["Close"].pct_change().dropna() + # Combine all macro indicators into a single DataFrame. + macro_combined = pd.concat( + [macro_daily, macro_monthly], axis=1 + ) + # Exclude binary release flag columns from correlation analysis + # since they are event markers not continuous indicators. + non_flag_cols = [ + col for col in macro_combined.columns + if not col.endswith("_released") + ] + macro_combined = macro_combined[non_flag_cols] + # Align macro data with S&P 500 returns on common dates. + combined = pd.concat( + [sp500_returns.rename("SP500_Return"), macro_combined], + axis=1, + ).dropna() + # Calculate correlation matrix. + corr_matrix = combined.corr() + # Extract only correlations with S&P 500 returns. + sp500_corr = corr_matrix["SP500_Return"].drop("SP500_Return") + # Sort by absolute correlation value for better readability. + sp500_corr = sp500_corr.reindex( + sp500_corr.abs().sort_values(ascending=False).index + ) + # Create figure with a single heatmap. + fig, ax = plt.subplots(figsize=(10, 8)) + # Plot horizontal heatmap of correlations with S&P 500. + sns.heatmap( + sp500_corr.to_frame(), + annot=True, + fmt=".3f", + cmap="RdYlGn", + center=0, + vmin=-0.3, + vmax=0.3, + linewidths=0.5, + linecolor="white", + ax=ax, + cbar_kws={"label": "Correlation with S&P 500 Returns"}, + ) + ax.set_title( + "Macro Indicator Correlation with S&P 500 Daily Returns", + fontsize=13, + fontweight="bold", + ) + ax.set_xlabel("Correlation") + ax.set_ylabel("Macro Indicator") + fig.tight_layout() + return fig + +def plot_rolling_correlations( + sp500: pd.DataFrame, + macro_daily: pd.DataFrame, + macro_monthly: pd.DataFrame, + window: int = 63, +) -> plt.Figure: + """ + Plot rolling correlations between key macro indicators and S&P 500. + + Rolling correlations reveal how the relationship between macro + indicators and market returns changes across different market + regimes. A static correlation hides these dynamic relationships + which are critical for understanding regime dependent behavior. + Daily indicators use daily returns with a 63 day window (3 months) + and monthly indicators use monthly returns with a 6 month window. + + :param sp500: DataFrame with S&P 500 OHLCV data indexed by date + :param macro_daily: DataFrame with daily macro indicators + :param macro_monthly: DataFrame with monthly macro indicators + :param window: rolling window size in trading days for daily + indicators default is 63 trading days (3 months) + :return: matplotlib Figure object with rolling correlation subplots + """ + # Calculate daily S&P 500 returns for daily indicator correlation. + sp500_daily_returns = sp500["Close"].pct_change().dropna() + # Calculate monthly S&P 500 returns for monthly indicator correlation. + sp500_monthly = sp500["Close"].resample("ME").last() + sp500_monthly_returns = sp500_monthly.pct_change().dropna() + # Calculate yield curve as difference between 10Y and 2Y yields. + yield_curve = macro_daily["TNX"] - macro_daily["IRX"] + # Define daily indicators to show rolling correlations for. + daily_indicators = { + "VIX" : ("VIX (Fear Index)", macro_daily["VIX"]), + "Yield Curve": ("Yield Curve (10Y-2Y)", yield_curve), + "OIL" : ("Oil Price WTI", macro_daily["OIL"]), + } + # Define monthly indicators to show rolling correlations for. + monthly_indicators = { + "CPI" : ("CPI Inflation", macro_monthly["CPI"]), + "FED_RATE" : ("Fed Funds Rate", macro_monthly["FED_RATE"]), + "UNEMPLOYMENT": ("Unemployment Rate", macro_monthly["UNEMPLOYMENT"]), + } + # Create figure with 2 rows and 3 columns — 6 subplots total. + fig, axes = plt.subplots(2, 3, figsize=(18, 10)) + # Plot rolling correlations for daily indicators on top row. + for idx, (key, (title, series)) in enumerate( + daily_indicators.items() + ): + # Align series with S&P 500 returns on common dates. + aligned = pd.concat( + [sp500_daily_returns, series], axis=1 + ).dropna() + aligned.columns = ["returns", "indicator"] + # Calculate rolling correlation over the window period. + rolling_corr = aligned["returns"].rolling(window).corr( + aligned["indicator"] + ) + # Plot rolling correlation line. + axes[0, idx].plot( + rolling_corr.index, + rolling_corr.values, + color="steelblue", + linewidth=1.2, + label=f"{window}d rolling correlation", + ) + # Add horizontal reference line at zero. + axes[0, idx].axhline( + 0, color="black", linewidth=0.8, linestyle="--" + ) + # Add shaded regions for key market events. + axes[0, idx].axvspan( + pd.Timestamp("2020-02-01"), + pd.Timestamp("2020-06-01"), + alpha=0.1, + color="red", + label="COVID crash", + ) + axes[0, idx].axvspan( + pd.Timestamp("2022-03-01"), + pd.Timestamp("2023-07-01"), + alpha=0.1, + color="orange", + label="Rate hike cycle", + ) + axes[0, idx].set_title( + f"Rolling Correlation: {title} vs S&P 500", + fontsize=10, + fontweight="bold", + ) + axes[0, idx].set_ylabel("Correlation") + axes[0, idx].set_xlabel("Date") + axes[0, idx].set_ylim(-1, 1) + axes[0, idx].legend(fontsize=7, loc="upper left") + # Plot rolling correlations for monthly indicators on bottom row. + monthly_window = 6 + for idx, (key, (title, series)) in enumerate( + monthly_indicators.items() + ): + # Resample indicator to monthly frequency. + series_monthly = series.resample("ME").last() + # Align with monthly S&P 500 returns on common dates. + aligned = pd.concat( + [sp500_monthly_returns, series_monthly], axis=1 + ).dropna() + aligned.columns = ["returns", "indicator"] + # Calculate rolling correlation over 6 month window. + rolling_corr = aligned["returns"].rolling( + monthly_window + ).corr(aligned["indicator"]) + # Plot rolling correlation line. + axes[1, idx].plot( + rolling_corr.index, + rolling_corr.values, + color="darkorange", + linewidth=1.5, + label=f"{monthly_window}m rolling correlation", + ) + # Add horizontal reference line at zero. + axes[1, idx].axhline( + 0, color="black", linewidth=0.8, linestyle="--" + ) + # Add shaded regions for key market events. + axes[1, idx].axvspan( + pd.Timestamp("2020-02-01"), + pd.Timestamp("2020-06-01"), + alpha=0.1, + color="red", + label="COVID crash", + ) + axes[1, idx].axvspan( + pd.Timestamp("2022-03-01"), + pd.Timestamp("2023-07-01"), + alpha=0.1, + color="orange", + label="Rate hike cycle", + ) + axes[1, idx].set_title( + f"Rolling Correlation: {title} vs S&P 500", + fontsize=10, + fontweight="bold", + ) + axes[1, idx].set_ylabel("Correlation") + axes[1, idx].set_xlabel("Date") + axes[1, idx].set_ylim(-1, 1) + axes[1, idx].legend(fontsize=7, loc="upper left") + fig.suptitle( + "Rolling Macro Correlations with S&P 500 Returns 2018-2024", + fontsize=14, + fontweight="bold", + ) + fig.tight_layout() + return fig + +def calculate_macro_features( + macro_daily: pd.DataFrame, + macro_monthly: pd.DataFrame, +) -> pd.DataFrame: + """ + Calculate derived macro features from downloaded indicators. + + Six new features are calculated from existing macro indicators. + The yield curve is calculated as the spread between 10Y and 2Y + treasury yields. A binary inversion flag marks periods where the + curve is inverted — a historically reliable recession signal. + Month over month changes capture acceleration or deceleration + in key indicators rather than just their absolute levels. + Moving averages and momentum smooth out noise in volatile series. + + :param macro_daily: DataFrame with daily macro indicators + including `TNX`, `IRX`, `VIX`, and `OIL` columns + :param macro_monthly: DataFrame with monthly macro indicators + including `CPI` and `NFP` columns + :return: DataFrame containing all six calculated macro features + indexed by the same business day dates as the input data + """ + # Initialize output DataFrame with same index as daily macro data. + features = pd.DataFrame(index=macro_daily.index) + # Calculate yield curve as spread between 10Y and 2Y yields. + features["YIELD_CURVE"] = macro_daily["TNX"] - macro_daily["IRX"] + # Flag yield curve inversion as binary signal where 2Y exceeds 10Y. + features["YIELD_CURVE_INVERTED"] = ( + features["YIELD_CURVE"] < 0 + ).astype(int) + # Calculate CPI month over month change to capture inflation trend. + features["CPI_MOM"] = macro_monthly["CPI"].pct_change() * 100 + # Calculate VIX 20 day moving average to smooth daily fear spikes. + features["VIX_MA20"] = macro_daily["VIX"].rolling(20).mean() + # Calculate oil 30 day momentum to capture energy price trends. + features["OIL_MOM30"] = macro_daily["OIL"].pct_change(30) * 100 + # Calculate NFP month over month change to capture jobs acceleration. + features["NFP_MOM"] = macro_monthly["NFP"].diff() + # Forward fill any NaN values created by rolling calculations. + features = features.ffill().bfill() + return features + +def calculate_technical_indicators( + sp500: pd.DataFrame, +) -> pd.DataFrame: + """ + Calculate technical indicators from S&P 500 closing prices. + + Nine technical indicators are calculated covering trend direction, + momentum, and volatility. Moving averages capture the price trend + at different time horizons. RSI measures overbought and oversold + conditions. MACD captures momentum shifts. Bollinger Bands measure + volatility and price extremes relative to recent history. + + :param sp500: DataFrame with S&P 500 OHLCV data indexed by date + containing a `Close` column with daily closing prices + :return: DataFrame containing all nine technical indicators + indexed by the same business day dates as the input data + """ + # Initialize output DataFrame with same index as S&P 500 data. + features = pd.DataFrame(index=sp500.index) + # Extract closing prices for all calculations. + close = sp500["Close"] + # Calculate simple moving averages at three time horizons. + features["MA5"] = close.rolling(5).mean() + features["MA20"] = close.rolling(20).mean() + features["MA50"] = close.rolling(50).mean() + # Calculate exponential moving averages for MACD calculation. + features["EMA12"] = close.ewm(span=12, adjust=False).mean() + features["EMA26"] = close.ewm(span=26, adjust=False).mean() + # Calculate MACD as difference between fast and slow EMAs. + features["MACD"] = features["EMA12"] - features["EMA26"] + # Calculate RSI using average gains and losses over 14 days. + delta = close.diff() + # Separate positive and negative price changes. + gain = delta.clip(lower=0) + loss = -delta.clip(upper=0) + # Calculate average gain and loss over 14 day window. + avg_gain = gain.rolling(14).mean() + avg_loss = loss.rolling(14).mean() + # Calculate relative strength and convert to RSI scale. + rs = avg_gain / avg_loss + features["RSI"] = 100 - (100 / (1 + rs)) + # Calculate Bollinger Bands using 20 day rolling statistics. + bb_mid = close.rolling(20).mean() + bb_std = close.rolling(20).std() + features["BB_UPPER"] = bb_mid + (2 * bb_std) + features["BB_LOWER"] = bb_mid - (2 * bb_std) + # Forward fill then backward fill NaN values from rolling windows. + features = features.ffill().bfill() + return features + +def calculate_calendar_features( + index: pd.DatetimeIndex, +) -> pd.DataFrame: + """ + Calculate calendar based features from a DatetimeIndex. + + Three calendar features are extracted from the date index. + Day of week captures weekly seasonality — Mondays and Fridays + historically show different return patterns than midweek days. + Month captures monthly seasonality — the January effect and + September weakness are well documented market patterns. + Quarter captures quarterly seasonality driven by earnings seasons + and institutional rebalancing at quarter end. + + :param index: DatetimeIndex from any of our aligned DataFrames + containing business day dates for the analysis period + :return: DataFrame with three calendar feature columns indexed + by the same DatetimeIndex as the input + """ + # Initialize output DataFrame with same index as input. + features = pd.DataFrame(index=index) + # Extract day of week where Monday is 0 and Friday is 4. + features["DAY_OF_WEEK"] = index.dayofweek + # Extract month of year where January is 1 and December is 12. + features["MONTH"] = index.month + # Extract quarter where Q1 is 1 and Q4 is 4. + features["QUARTER"] = index.quarter + return features + +def calculate_event_flags( + index: pd.DatetimeIndex, + fred_api_key: str, +) -> pd.DataFrame: + """ + Calculate binary event flags for known market moving dates. + + Three event flags are created — US federal holidays, FOMC meeting + dates, and CPI release dates. These flags help models learn that + market behavior around these events differs systematically from + normal trading days. FOMC and CPI dates are fetched from FRED + to ensure accuracy. Holiday flags use the `holidays` library. + + :param index: DatetimeIndex from any of our aligned DataFrames + containing business day dates for the analysis period + :param fred_api_key: FRED API key for fetching FOMC and CPI + release dates + :return: DataFrame with three binary event flag columns indexed + by the same DatetimeIndex as the input + """ + # Initialize output DataFrame with same index as input. + features = pd.DataFrame(index=index) + # Get the start and end years from the index for holiday generation. + start_year = index.year.min() + end_year = index.year.max() + # Generate US federal holiday dates for the full analysis period. + us_holidays = holidays.US( + years=range(start_year, end_year + 1) + ) + # Create binary flag for trading days adjacent to US holidays. + features["IS_HOLIDAY_ADJACENT"] = index.map( + lambda d: 1 if ( + d in us_holidays + or (d - pd.Timedelta(days=1)) in us_holidays + or (d + pd.Timedelta(days=1)) in us_holidays + ) else 0 + ) + # Fetch FOMC meeting dates from FRED using the federal funds + # rate vintage dates as a proxy for meeting announcement dates. + fred = fredapi.Fred(api_key=fred_api_key) + try: + # Use Fed Funds Rate vintage dates as FOMC meeting proxy. + fomc_dates = pd.DatetimeIndex( + fred.get_series_vintage_dates("FEDFUNDS") + ) + # Filter to our analysis period only. + fomc_dates = fomc_dates[ + (fomc_dates >= index[0]) & (fomc_dates <= index[-1]) + ] + # Create binary flag for FOMC meeting dates. + features["IS_FOMC_DATE"] = index.isin(fomc_dates).astype(int) + except Exception as e: + _LOG.warning( + "Could not fetch FOMC dates: %s — setting flag to zero.", + str(e), + ) + features["IS_FOMC_DATE"] = 0 + # Fetch CPI release dates from FRED using CPI vintage dates. + try: + cpi_dates = pd.DatetimeIndex( + fred.get_series_vintage_dates("CPIAUCSL") + ) + # Filter to our analysis period only. + cpi_dates = cpi_dates[ + (cpi_dates >= index[0]) & (cpi_dates <= index[-1]) + ] + # Create binary flag for CPI release dates. + features["IS_CPI_RELEASE"] = index.isin(cpi_dates).astype(int) + except Exception as e: + _LOG.warning( + "Could not fetch CPI release dates: %s — setting flag to zero.", + str(e), + ) + features["IS_CPI_RELEASE"] = 0 + # Log summary of event flags created. + _LOG.info( + "Holiday adjacent days: %d", + features["IS_HOLIDAY_ADJACENT"].sum(), + ) + _LOG.info( + "FOMC meeting dates: %d", + features["IS_FOMC_DATE"].sum(), + ) + _LOG.info( + "CPI release dates: %d", + features["IS_CPI_RELEASE"].sum(), + ) + return features + +def build_master_dataframe( + sp500: pd.DataFrame, + macro_daily: pd.DataFrame, + macro_monthly: pd.DataFrame, + macro_features: pd.DataFrame, + technical_features: pd.DataFrame, + calendar_features: pd.DataFrame, + event_flags: pd.DataFrame, +) -> pd.DataFrame: + """ + Combine all feature groups into a single master DataFrame. + + All feature DataFrames are concatenated column wise into one + master DataFrame containing the S&P 500 target variable and + all 46 features. The target variable `Close` is kept as the + first column for clarity. All DataFrames must share the same + DatetimeIndex before calling this function. + + :param sp500: DataFrame with S&P 500 OHLCV data + :param macro_daily: DataFrame with daily macro indicators + :param macro_monthly: DataFrame with monthly macro indicators + and binary release flags + :param macro_features: DataFrame with calculated macro features + :param technical_features: DataFrame with technical indicators + :param calendar_features: DataFrame with calendar features + :param event_flags: DataFrame with binary event flags + :return: master DataFrame with target variable and all features + combined into a single structure indexed by business day dates + """ + # Extract only the closing price as the target variable. + target = sp500[["Close"]].copy() + # Combine all feature groups column wise into master DataFrame. + master = pd.concat( + [ + target, + macro_daily, + macro_monthly, + macro_features, + technical_features, + calendar_features, + event_flags, + ], + axis=1, + ) + # Verify no NaN values exist in the master DataFrame. + nan_count = master.isnull().sum().sum() + if nan_count > 0: + _LOG.warning( + "Master DataFrame has %d NaN values — forward filling.", + nan_count, + ) + master = master.ffill().bfill() + _LOG.info( + "Master DataFrame shape: %s", master.shape + ) + _LOG.info( + "Total features: %d (excluding target variable)", + master.shape[1] - 1, + ) + return master + +def split_data( + master: pd.DataFrame, + test_size: int, + val_size: int, +) -> tuple: + """ + Split master DataFrame into train, validation, and test sets. + + A strict time based split is used where training data comes first, + validation data comes next, and test data comes last. This prevents + any form of data leakage where future information influences the + model during training. The test set is never used during training + or hyperparameter tuning — only for final evaluation. + + :param master: master DataFrame with target variable and all + features indexed by business day dates + :param test_size: number of trading days to hold out for testing + — these days are never seen during training or tuning + :param val_size: number of trading days to hold out for validation + — used for hyperparameter tuning and early stopping + :return: tuple of (train, validation, test) DataFrames in + chronological order with no overlap between splits + """ + # Calculate split indices from the end of the DataFrame. + total_rows = len(master) + test_start = total_rows - test_size + val_start = test_start - val_size + # Split into train, validation, and test sets. + train = master.iloc[:val_start] + val = master.iloc[val_start:test_start] + test = master.iloc[test_start:] + # Log split details for verification. + _LOG.info( + "Train: %d rows | %s to %s", + len(train), + train.index[0].date(), + train.index[-1].date(), + ) + _LOG.info( + "Validation: %d rows | %s to %s", + len(val), + val.index[0].date(), + val.index[-1].date(), + ) + _LOG.info( + "Test: %d rows | %s to %s", + len(test), + test.index[0].date(), + test.index[-1].date(), + ) + _LOG.info( + "Train: %.1f%% | Val: %.1f%% | Test: %.1f%%", + len(train) / total_rows * 100, + len(val) / total_rows * 100, + len(test) / total_rows * 100, + ) + return train, val, test + +def build_timeseries( + train: pd.DataFrame, + val: pd.DataFrame, + test: pd.DataFrame, + target_col: str, + future_cov_cols: list, + past_cov_cols: list, +) -> tuple: + """ + Build Darts TimeSeries objects from train, validation, and test sets. + + Three types of TimeSeries are created for each split — the target + series containing S&P 500 closing prices, future covariates + containing features known in advance like calendar and event flags, + and past covariates containing features only known historically + like technical indicators and macro values. All series are scaled + using Darts Scaler to normalize values for better model performance. + + :param train: training DataFrame with target and all features + :param val: validation DataFrame with target and all features + :param test: test DataFrame with target and all features + :param target_col: name of the target column e.g. `'Close'` + :param future_cov_cols: list of column names for future covariates + — features whose future values are known at prediction time + :param past_cov_cols: list of column names for past covariates + — features whose future values are unknown at prediction time + :return: tuple of scaled TimeSeries objects in order: + (target_train, target_val, target_test, + future_cov_train, future_cov_val, future_cov_test, + past_cov_train, past_cov_val, past_cov_test, + target_scaler, cov_scaler) + """ + # Concatenate all splits for building full covariates series. + full_data = pd.concat([train, val, test]) + # Build target TimeSeries for each split. + target_train = darts.timeseries.TimeSeries.from_dataframe( + train, value_cols=target_col, + fill_missing_dates=True, freq="B" + ) + target_val = darts.timeseries.TimeSeries.from_dataframe( + val, value_cols=target_col, + fill_missing_dates=True, freq="B" + ) + target_test = darts.timeseries.TimeSeries.from_dataframe( + test, value_cols=target_col, + fill_missing_dates=True, freq="B" + ) + # Build future covariates TimeSeries for each split. + future_cov_train = darts.timeseries.TimeSeries.from_dataframe( + train, value_cols=future_cov_cols, + fill_missing_dates=True, freq="B" + ) + future_cov_val = darts.timeseries.TimeSeries.from_dataframe( + val, value_cols=future_cov_cols, + fill_missing_dates=True, freq="B" + ) + future_cov_test = darts.timeseries.TimeSeries.from_dataframe( + test, value_cols=future_cov_cols, + fill_missing_dates=True, freq="B" + ) + # Build past covariates TimeSeries for each split. + past_cov_train = darts.timeseries.TimeSeries.from_dataframe( + train, value_cols=past_cov_cols, + fill_missing_dates=True, freq="B" + ) + past_cov_val = darts.timeseries.TimeSeries.from_dataframe( + val, value_cols=past_cov_cols, + fill_missing_dates=True, freq="B" + ) + past_cov_test = darts.timeseries.TimeSeries.from_dataframe( + test, value_cols=past_cov_cols, + fill_missing_dates=True, freq="B" + ) + # Scale target series using Darts Scaler. + target_scaler = darts.dataprocessing.transformers.Scaler() + target_train_scaled = target_scaler.fit_transform(target_train) + target_val_scaled = target_scaler.transform(target_val) + target_test_scaled = target_scaler.transform(target_test) + # Scale covariate series using a separate Scaler. + cov_scaler = darts.dataprocessing.transformers.Scaler() + future_cov_train_scaled = cov_scaler.fit_transform( + future_cov_train + ) + future_cov_val_scaled = cov_scaler.transform(future_cov_val) + future_cov_test_scaled = cov_scaler.transform(future_cov_test) + past_cov_train_scaled = cov_scaler.fit_transform(past_cov_train) + past_cov_val_scaled = cov_scaler.transform(past_cov_val) + past_cov_test_scaled = cov_scaler.transform(past_cov_test) + # Log summary of TimeSeries objects created. + _LOG.info( + "Target train length: %d", len(target_train_scaled) + ) + _LOG.info( + "Future covariates: %d columns", len(future_cov_cols) + ) + _LOG.info( + "Past covariates: %d columns", len(past_cov_cols) + ) + return ( + target_train_scaled, + target_val_scaled, + target_test_scaled, + future_cov_train_scaled, + future_cov_val_scaled, + future_cov_test_scaled, + past_cov_train_scaled, + past_cov_val_scaled, + past_cov_test_scaled, + target_scaler, + cov_scaler, + ) + +def train_baseline_models( + target_train: darts.timeseries.TimeSeries, + forecast_horizon: int, +) -> dict: + """ + Train all baseline forecasting models on the training series. + + Baseline models make simple predictions without learning complex + patterns. They serve as benchmarks that all other models must + outperform to demonstrate genuine predictive value. Holiday gap + NaN values are filled before training since baseline models + cannot handle missing values in the target series. + + :param target_train: scaled target TimeSeries for training + containing S&P 500 closing prices + :param forecast_horizon: number of trading days to forecast + ahead e.g. `30` for a 30 day forecast + :return: dictionary mapping model names to their predictions + as Darts TimeSeries objects + """ + # Fill holiday gap NaN values before training since baseline + # models cannot handle missing values in the target series. + target_df = target_train.to_dataframe().ffill().bfill() + target_df.index.freq = "B" + target_clean = darts.timeseries.TimeSeries.from_dataframe( + target_df, fill_missing_dates=True, freq="B" + ) + # Initialize dictionary to store model predictions. + predictions = {} + # Train NaiveSeasonal model — repeats value from K periods ago. + _LOG.info("Training NaiveSeasonal model.") + naive_seasonal = darts.models.NaiveSeasonal(K=5) + naive_seasonal.fit(target_clean) + predictions["NaiveSeasonal"] = naive_seasonal.predict( + forecast_horizon + ) + # Train NaiveDrift model — extrapolates the linear trend. + _LOG.info("Training NaiveDrift model.") + naive_drift = darts.models.NaiveDrift() + naive_drift.fit(target_clean) + predictions["NaiveDrift"] = naive_drift.predict(forecast_horizon) + # Train NaiveMean model — always predicts the training mean. + _LOG.info("Training NaiveMean model.") + naive_mean = darts.models.NaiveMean() + naive_mean.fit(target_clean) + predictions["NaiveMean"] = naive_mean.predict(forecast_horizon) + # Train NaiveMovingAverage — predicts average of last N values. + _LOG.info("Training NaiveMovingAverage model.") + naive_ma = darts.models.NaiveMovingAverage(input_chunk_length=5) + naive_ma.fit(target_clean) + predictions["NaiveMovingAverage"] = naive_ma.predict( + forecast_horizon + ) + _LOG.info( + "Baseline models trained successfully — %d models.", + len(predictions), + ) + return predictions + +def train_statistical_models( + target_train: darts.timeseries.TimeSeries, + forecast_horizon: int, +) -> dict: + """ + Train all classical statistical forecasting models on the training series. + + Statistical models capture linear time series patterns using + mathematical formulations. They operate on price history only + and cannot accept external covariates. Holiday gap NaN values + are filled before training since statistical models cannot + handle missing values in the target series. + + :param target_train: scaled target TimeSeries for training + containing S&P 500 closing prices + :param forecast_horizon: number of trading days to forecast + ahead e.g. `30` for a 30 day forecast + :return: dictionary mapping model names to their predictions + as Darts TimeSeries objects + """ + # Fill holiday gap NaN values before training since statistical + # models cannot handle missing values in the target series. + target_df = target_train.to_dataframe().ffill().bfill() + target_df.index.freq = "B" + target_clean = darts.timeseries.TimeSeries.from_dataframe( + target_df, fill_missing_dates=True, freq="B" + ) + # Initialize dictionary to store model predictions. + predictions = {} + # Define all statistical models with their configurations. + models = { + "ARIMA": darts.models.ARIMA(p=5, d=1, q=0), + "AutoARIMA": darts.models.AutoARIMA( + start_p=1, max_p=5, max_q=3, d=1, + ), + "ExponentialSmoothing": darts.models.ExponentialSmoothing(), + "Theta": darts.models.Theta(), + "FourTheta": darts.models.FourTheta(), + "FFT": darts.models.FFT(nr_freqs_to_keep=10), + "TBATS": darts.models.TBATS( + season_length=5, + use_boxcox=True, + use_trend=True, + use_damped_trend=True, + use_arma_errors=True, + ), + } + # Train each statistical model and store predictions. + for name, model in models.items(): + try: + _LOG.info("Training %s model.", name) + model.fit(target_clean) + predictions[name] = model.predict(forecast_horizon) + _LOG.info("%s trained successfully.", name) + except Exception as e: + _LOG.warning( + "%s failed to train: %s — skipping.", name, str(e) + ) + _LOG.info( + "Statistical models trained successfully — %d models.", + len(predictions), + ) + return predictions + + +def train_probabilistic_models( + target_train: darts.timeseries.TimeSeries, + forecast_horizon: int, +) -> dict: + """ + Train probabilistic forecasting models on the training series. + + Probabilistic models estimate a distribution over future values + rather than a single point forecast. KalmanForecaster uses a + state space model to track the latent state of the S&P 500 and + naturally quantifies prediction uncertainty. It handles irregular + frequencies and missing observations natively making it well + suited for stock market data. + + :param target_train: scaled target TimeSeries for training + containing S&P 500 closing prices + :param forecast_horizon: number of trading days to forecast + ahead e.g. `30` for a 30 day forecast + :return: dictionary mapping model names to their predictions + as Darts TimeSeries objects + """ + # Initialize dictionary to store model predictions. + predictions = {} + try: + # Fill any NaN values from holiday gaps before training + # since KalmanForecaster cannot handle null values. + target_df = target_train.to_dataframe().ffill().bfill() + target_df.index.freq = "B" + target_clean = darts.timeseries.TimeSeries.from_dataframe( + target_df, fill_missing_dates=True, freq="B" + ) + # Train KalmanForecaster with 4 dimensional state space. + # dim_x=4 models position velocity acceleration and jerk + # of the price series capturing different orders of price + # change dynamics simultaneously. + _LOG.info("Training KalmanForecaster model.") + kalman = darts.models.KalmanForecaster(dim_x=4) + kalman.fit(target_clean) + predictions["KalmanForecaster"] = kalman.predict( + forecast_horizon + ) + _LOG.info("KalmanForecaster trained successfully.") + except Exception as e: + _LOG.warning( + "KalmanForecaster failed to train: %s — skipping.", + str(e), + ) + return predictions + +def train_ml_models( + target_train: darts.timeseries.TimeSeries, + past_cov_train: darts.timeseries.TimeSeries, + future_cov_train: darts.timeseries.TimeSeries, + future_cov_full: darts.timeseries.TimeSeries, + forecast_horizon: int, +) -> dict: + """ + Train all machine learning forecasting models on the training series. + + ML models use the full 41 feature set including macro indicators + technical indicators calendar features and event flags. NaN values + from holiday gaps are filled using Darts MissingValuesFiller + which preserves the TimeSeries frequency attribute unlike + DataFrame conversion approaches. + + :param target_train: scaled target TimeSeries for training + containing 30 day forward S&P 500 returns + :param past_cov_train: scaled past covariates TimeSeries + containing macro and technical features + :param future_cov_train: scaled future covariates TimeSeries + containing calendar and event features + :param future_cov_full: scaled future covariates TimeSeries + containing calendar and event features for full period + :param forecast_horizon: number of trading days to forecast + ahead e.g. `30` for a 30 day forecast + :return: dictionary mapping model names to their predictions + as Darts TimeSeries objects + """ + # Fill NaN values using Darts MissingValuesFiller which + # preserves TimeSeries frequency unlike DataFrame conversion. + filler = darts.dataprocessing.transformers.MissingValuesFiller() + target_clean = filler.transform(target_train) + past_clean = filler.transform(past_cov_train) + future_clean = filler.transform(future_cov_train) + future_full_clean = filler.transform(future_cov_full) + # Initialize dictionary to store model predictions. + predictions = {} + # Define all ML models with their configurations. + models = { + "LinearRegression": darts.models.LinearRegressionModel( + lags=30, + lags_past_covariates=30, + lags_future_covariates=[0], + output_chunk_length=forecast_horizon, + ), + "RandomForest": darts.models.RandomForestModel( + lags=30, + lags_past_covariates=30, + lags_future_covariates=[0], + output_chunk_length=forecast_horizon, + n_estimators=100, + random_state=42, + ), + "LightGBM": darts.models.LightGBMModel( + lags=30, + lags_past_covariates=30, + lags_future_covariates=[0], + output_chunk_length=forecast_horizon, + n_estimators=200, + num_leaves=31, + learning_rate=0.05, + random_state=42, + verbose=-1, + ), + "XGBoost": darts.models.XGBModel( + lags=30, + lags_past_covariates=30, + lags_future_covariates=[0], + output_chunk_length=forecast_horizon, + n_estimators=200, + learning_rate=0.05, + max_depth=6, + random_state=42, + verbosity=0, + ), + "CatBoost": darts.models.CatBoostModel( + lags=30, + lags_past_covariates=30, + lags_future_covariates=[0], + output_chunk_length=forecast_horizon, + iterations=200, + learning_rate=0.05, + depth=6, + random_seed=42, + verbose=False, + ), + } + # Train each ML model with cleaned past and future covariates. + for name, model in models.items(): + try: + _LOG.info("Training %s model.", name) + model.fit( + target_clean, + past_covariates=past_clean, + future_covariates=future_clean, + ) + predictions[name] = model.predict( + forecast_horizon, + past_covariates=past_clean, + future_covariates=future_full_clean, + ) + _LOG.info("%s trained successfully.", name) + except Exception as e: + _LOG.warning( + "%s failed to train: %s — skipping.", name, str(e) + ) + _LOG.info( + "ML models trained successfully — %d models.", + len(predictions), + ) + return predictions + +def train_prophet_model( + target_train: darts.timeseries.TimeSeries, + future_cov_full: darts.timeseries.TimeSeries, + forecast_horizon: int, +) -> dict: + """ + Train Prophet model with event flags as additional regressors. + + Prophet is designed for time series with strong seasonal patterns + and known holiday effects. Event flags for FOMC meetings CPI + release dates and US holidays are passed as future covariates + allowing Prophet to model the systematic impact of these known + market moving events on S&P 500 returns. + + :param target_train: scaled target TimeSeries for training + containing S&P 500 closing prices + :param future_cov_full: scaled future covariates TimeSeries + containing calendar and event features for the full period + including dates beyond the training end for prediction + :param forecast_horizon: number of trading days to forecast + ahead e.g. `30` for a 30 day forecast + :return: dictionary mapping model name to its prediction + as a Darts TimeSeries object + """ + # Initialize dictionary to store model predictions. + predictions = {} + try: + # Clean target series to remove holiday gap NaN values. + target_df = target_train.to_dataframe().ffill().bfill() + target_df.index.freq = "B" + target_clean = darts.timeseries.TimeSeries.from_dataframe( + target_df, fill_missing_dates=True, freq="B" + ) + # Clean full future covariates for Prophet regressors. + future_df = future_cov_full.to_dataframe().ffill().bfill() + future_df.index.freq = "B" + future_clean = darts.timeseries.TimeSeries.from_dataframe( + future_df, fill_missing_dates=True, freq="B" + ) + # Train Prophet with future covariates as additional regressors. + # add_encoders encodes future covariate columns as regressors + # that Prophet uses alongside its built in seasonality components. + _LOG.info("Training Prophet model.") + prophet_model = darts.models.Prophet( + add_seasonalities={ + "name" : "monthly", + "seasonal_periods": 21, + "fourier_order" : 5, + }, + ) + prophet_model.fit( + target_clean, + future_covariates=future_clean, + ) + predictions["Prophet"] = prophet_model.predict( + forecast_horizon, + future_covariates=future_clean, + ) + _LOG.info("Prophet trained successfully.") + except Exception as e: + _LOG.warning( + "Prophet failed to train: %s — skipping.", str(e) + ) + return predictions + +def evaluate_models( + predictions: dict, + target_val: darts.timeseries.TimeSeries, + target_scaler: darts.dataprocessing.transformers.Scaler, +) -> pd.DataFrame: + """ + Evaluate all model predictions against the validation target series. + + Predictions are aligned with validation data by date before computing + metrics. All predictions are inverse transformed from scaled space + back to original price space before computing metrics. Six metrics + are calculated — MAE RMSE MAPE Direction Accuracy and R squared. + Direction accuracy measures whether the model correctly predicted + the implied 30 day return direction — the most business relevant + metric. Results are sorted by MAPE ascending. + + :param predictions: dictionary mapping model names to their + Darts TimeSeries predictions in scaled space + :param target_val: scaled validation target TimeSeries containing + actual S&P 500 closing prices for the validation period + :param target_scaler: fitted Darts Scaler used to inverse transform + predictions back to original price space + :return: DataFrame with evaluation metrics for each model sorted + by MAPE from lowest to highest + """ + # Initialize list to collect metric rows for each model. + results = [] + # Fill NaN values in validation target using Darts filler. + filler = darts.dataprocessing.transformers.MissingValuesFiller() + target_val_clean = filler.transform(target_val) + # Inverse transform validation to original price space. + actual_df = target_scaler.inverse_transform( + target_val_clean + ).to_dataframe().dropna() + # Evaluate each model prediction against actual values. + for name, prediction in predictions.items(): + try: + # Fill NaN values in prediction. + pred_clean = filler.transform(prediction) + # Inverse transform prediction to original price space. + pred_inverse_df = target_scaler.inverse_transform( + pred_clean + ).to_dataframe().dropna() + # Align prediction and actual by common dates. + common_dates = actual_df.index.intersection( + pred_inverse_df.index + ) + # Skip model if insufficient common dates. + if len(common_dates) < 5: + _LOG.warning( + "%s has fewer than 5 common dates — skipping.", + name, + ) + continue + # Extract aligned values for metric computation. + actual = actual_df.loc[common_dates].values.flatten() + predicted = pred_inverse_df.loc[ + common_dates + ].values.flatten() + # Calculate MAE in price units. + mae = float(np.mean(np.abs(actual - predicted))) + # Calculate RMSE in price units. + rmse = float( + np.sqrt(np.mean((actual - predicted) ** 2)) + ) + # Calculate MAPE as percentage of actual price. + mape = float( + np.mean(np.abs((actual - predicted) / actual)) * 100 + ) + # Calculate sMAPE. + smape = float( + np.mean( + 2 * np.abs(actual - predicted) + / (np.abs(actual) + np.abs(predicted) + 1e-8) + ) * 100 + ) + # Calculate direction accuracy across all prediction days. + # Each day check if predicted price is above or below + # starting price in same direction as actual price. + reference_price = actual[0] + actual_directions = np.sign(actual - reference_price) + predicted_directions = np.sign(predicted - reference_price) + # Only evaluate days where market actually moved. + valid_mask = actual_directions != 0 + if valid_mask.sum() > 0: + direction_accuracy = float( + np.mean( + actual_directions[valid_mask] + == predicted_directions[valid_mask] + ) * 100 + ) + else: + direction_accuracy = 0.0 + + # Calculate R squared. + ss_res = np.sum((actual - predicted) ** 2) + ss_tot = np.sum((actual - np.mean(actual)) ** 2) + r2 = float(1 - ss_res / ss_tot) + results.append({ + "Model" : name, + "MAE" : round(mae, 2), + "RMSE" : round(rmse, 2), + "MAPE" : round(mape, 2), + "sMAPE" : round(smape, 2), + "Direction" : round(direction_accuracy, 1), + "R2" : round(r2, 4), + "Days" : len(common_dates), + }) + _LOG.info( + "%s → MAPE: %.2f%% | Direction: %.1f%% | R2: %.4f", + name, mape, direction_accuracy, r2, + ) + except Exception as e: + _LOG.warning( + "Could not evaluate %s: %s — skipping.", + name, str(e), + ) + # Return empty DataFrame if no results. + if not results: + _LOG.warning("No models evaluated successfully.") + return pd.DataFrame( + columns=[ + "Model", "MAE", "RMSE", "MAPE", + "sMAPE", "Direction", "R2", "Days" + ] + ) + # Sort by MAPE ascending. + return pd.DataFrame(results).sort_values( + "MAPE", ascending=True + ).reset_index(drop=True) + +def plot_predictions_vs_actual( + predictions: dict, + target_val: darts.timeseries.TimeSeries, + target_scaler: darts.dataprocessing.transformers.Scaler, + n_models: int = 6, +) -> plt.Figure: + """ + Plot model predictions against actual S&P 500 prices. + + Only the prediction period is shown for clarity. The top n + models by MAE are selected and their predictions plotted + alongside actual prices. The actual price series is shown + in black and each model prediction in a distinct color. + A shaded confidence band shows the MAE range around the + best model prediction. + + :param predictions: dictionary mapping model names to their + Darts TimeSeries predictions in scaled space + :param target_val: scaled validation target TimeSeries + :param target_scaler: fitted Darts Scaler for inverse transform + :param n_models: number of top models to plot default is 6 + :return: matplotlib Figure object with clean prediction chart + """ + # Fill NaN values in validation target. + filler = darts.dataprocessing.transformers.MissingValuesFiller() + target_val_clean = filler.transform(target_val) + # Inverse transform validation to original price space. + actual_df = target_scaler.inverse_transform( + target_val_clean + ).to_dataframe().dropna() + # Collect all model predictions in original price space. + model_predictions = {} + model_errors = {} + for name, prediction in predictions.items(): + try: + pred_clean = filler.transform(prediction) + pred_df = target_scaler.inverse_transform( + pred_clean + ).to_dataframe().dropna() + # Find common dates with actual values. + common_dates = actual_df.index.intersection( + pred_df.index + ) + if len(common_dates) >= 5: + aligned_actual = actual_df.loc[common_dates].values.flatten() + aligned_pred = pred_df.loc[common_dates].values.flatten() + mae = float(np.mean(np.abs(aligned_actual - aligned_pred))) + model_predictions[name] = pred_df.loc[common_dates] + model_errors[name] = mae + except Exception as e: + _LOG.warning( + "Could not process %s for plotting: %s", name, str(e) + ) + # Sort by MAE and take top n models. + top_models = sorted( + model_errors.keys(), key=lambda x: model_errors[x] + )[:n_models] + # Get prediction date range. + pred_start = min( + model_predictions[name].index[0] for name in top_models + ) + pred_end = max( + model_predictions[name].index[-1] for name in top_models + ) + # Add 3 day buffer on each side for context. + buffer = pd.tseries.offsets.BusinessDay(3) + # Filter actual prices to prediction period only. + actual_in_range = actual_df.loc[ + (actual_df.index >= pred_start - buffer) & + (actual_df.index <= pred_end + buffer) + ] + # Define distinct colors for each model. + colors = [ + "steelblue", "crimson", "forestgreen", + "darkorange", "purple", "brown", + ] + # Create single clean figure. + fig, ax = plt.subplots(figsize=(16, 7)) + # Plot actual prices in thick black line. + ax.plot( + actual_in_range.index, + actual_in_range.values.flatten(), + color="black", + linewidth=2.5, + label="Actual S&P 500", + zorder=5, + ) + # Add shaded confidence band around best model prediction. + best_model = top_models[0] + best_pred = model_predictions[best_model] + best_mae = model_errors[best_model] + ax.fill_between( + best_pred.index, + best_pred.values.flatten() - best_mae, + best_pred.values.flatten() + best_mae, + alpha=0.15, + color=colors[0], + label=f"±MAE ${best_mae:.0f} confidence band", + ) + # Plot each top model prediction as dashed line. + for idx, name in enumerate(top_models): + pred_df = model_predictions[name] + mae = model_errors[name] + ax.plot( + pred_df.index, + pred_df.values.flatten(), + color=colors[idx % len(colors)], + linewidth=1.8, + linestyle="--", + label=f"{name} (MAE=${mae:.0f} | MAPE={mae/actual_in_range.values.mean()*100:.2f}%)", + alpha=0.85, + ) + # Add vertical line showing prediction start. + ax.axvline( + pred_start, + color="gray", + linewidth=1.0, + linestyle=":", + alpha=0.7, + label="Prediction start", + ) + # Configure axes and labels. + ax.set_xlim(pred_start - buffer, pred_end + buffer) + # Set y axis to prediction price range with padding. + all_values = np.concatenate([ + model_predictions[name].values.flatten() + for name in top_models + ] + [actual_in_range.values.flatten()]) + y_min = all_values.min() * 0.992 + y_max = all_values.max() * 1.008 + ax.set_ylim(y_min, y_max) + ax.set_title( + f"Model Predictions vs Actual S&P 500 | " + f"{pred_start.date()} to {pred_end.date()}", + fontsize=14, + fontweight="bold", + ) + ax.set_ylabel("S&P 500 Price (USD)", fontsize=11) + ax.set_xlabel("Date", fontsize=11) + ax.legend( + loc="upper left", + fontsize=8, + ncol=2, + framealpha=0.9, + ) + fig.tight_layout() + return fig + +def select_features_shap( + master: pd.DataFrame, + target_col: str, + past_cov_cols: list, + correlation_threshold: float, + shap_importance_threshold: float, +) -> list: + """ + Select optimal features using correlation filtering and SHAP values. + + Two step feature selection is applied. First features with + correlation above the threshold are identified and the less + important one from each correlated pair is removed. Second + a LightGBM model is trained on remaining features and SHAP + values identify the features contributing the most predictive + power. Features explaining the top fraction of total SHAP + importance are retained. + + :param master: master DataFrame containing target and all features + :param target_col: name of the target column e.g. `'Close'` + :param past_cov_cols: list of past covariate column names to + evaluate for selection + :param correlation_threshold: remove one feature from pairs with + absolute correlation above this threshold e.g. `0.95` + :param shap_importance_threshold: retain features explaining at + least this fraction of total SHAP importance e.g. `0.90` + :return: list of selected feature column names after both + correlation filtering and SHAP selection + """ + # Extract feature matrix from master DataFrame. + features = master[past_cov_cols].copy() + target = master[target_col].copy() + # Step 1 — Remove highly correlated features. + _LOG.info( + "Step 1: Removing features with correlation above %.2f.", + correlation_threshold, + ) + # Calculate absolute correlation matrix between all features. + corr_matrix = features.corr().abs() + # Find upper triangle of correlation matrix to avoid duplicates. + upper_triangle = corr_matrix.where( + np.triu(np.ones(corr_matrix.shape), k=1).astype(bool) + ) + # Identify features with correlation above threshold. + to_drop = [ + col for col in upper_triangle.columns + if any(upper_triangle[col] > correlation_threshold) + ] + # Remove highly correlated features from feature set. + features_filtered = features.drop(columns=to_drop) + _LOG.info( + "Removed %d highly correlated features — %d remaining.", + len(to_drop), + len(features_filtered.columns), + ) + _LOG.info("Dropped correlated features: %s", to_drop) + # Step 2 — SHAP feature importance on remaining features. + _LOG.info("Step 2: Calculating SHAP values using LightGBM.") + # Calculate daily returns as target for SHAP analysis since + # returns are more stationary than price levels. + returns = target.pct_change().dropna() + features_aligned = features_filtered.loc[returns.index] + # Train LightGBM on returns with all remaining features. + lgbm = sklearn.ensemble.GradientBoostingRegressor( + n_estimators=100, + learning_rate=0.05, + max_depth=4, + random_state=42, + ) + lgbm.fit(features_aligned.ffill().bfill(), returns) + # Calculate SHAP values using TreeExplainer. + explainer = shap.TreeExplainer(lgbm) + shap_values = explainer.shap_values( + features_aligned.ffill().bfill() + ) + # Calculate mean absolute SHAP value per feature. + mean_shap = np.abs(shap_values).mean(axis=0) + shap_df = pd.DataFrame({ + "Feature" : features_aligned.columns, + "SHAP_Value" : mean_shap, + }).sort_values("SHAP_Value", ascending=False).reset_index( + drop=True + ) + # Calculate cumulative importance as percentage of total. + total_shap = shap_df["SHAP_Value"].sum() + shap_df["SHAP_Pct"] = shap_df["SHAP_Value"] / total_shap * 100 + shap_df["Cumulative_Pct"] = shap_df["SHAP_Pct"].cumsum() + _LOG.info( + "SHAP importance calculated for %d features.", + len(shap_df), + ) + # Select features explaining top fraction of total importance. + selected = shap_df[ + shap_df["Cumulative_Pct"] <= shap_importance_threshold * 100 + ]["Feature"].tolist() + # Always include at least the top 5 features. + if len(selected) < 5: + selected = shap_df["Feature"].head(5).tolist() + _LOG.info( + "Selected %d features explaining %.0f%% of predictions.", + len(selected), + shap_importance_threshold * 100, + ) + _LOG.info("Selected features: %s", selected) + return selected, shap_df + +def compare_feature_versions( + target_train: darts.timeseries.TimeSeries, + target_val: darts.timeseries.TimeSeries, + past_cov_train: darts.timeseries.TimeSeries, + future_cov_train: darts.timeseries.TimeSeries, + future_cov_full: darts.timeseries.TimeSeries, + train: pd.DataFrame, + val: pd.DataFrame, + test: pd.DataFrame, + target_col: str, + future_cov_cols: list, + past_cov_cols: list, + shap_df: pd.DataFrame, + forecast_horizon: int, + target_scaler: darts.dataprocessing.transformers.Scaler, +) -> pd.DataFrame: + """ + Compare ML model performance across different feature set sizes. + + Four versions are trained and evaluated — all features as baseline + and three SHAP selected subsets at 90 95 and 98 percent cumulative + importance thresholds. LightGBM is used as the representative ML + model for comparison since it is the fastest to train. Pre-built + Darts TimeSeries objects are used directly to avoid frequency + inference issues with irregular stock market calendars. + + :param target_train: scaled target TimeSeries for training + :param target_val: scaled target TimeSeries for validation + :param past_cov_train: scaled past covariates TimeSeries + :param future_cov_train: scaled future covariates TimeSeries + :param future_cov_full: scaled full period future covariates + :param train: training DataFrame for extracting feature subsets + :param val: validation DataFrame for extracting feature subsets + :param test: test DataFrame for extracting feature subsets + :param target_col: name of the target column e.g. `'Close'` + :param future_cov_cols: list of future covariate column names + :param past_cov_cols: list of all past covariate column names + :param shap_df: DataFrame with SHAP importance values + :param forecast_horizon: number of trading days to forecast ahead + :param target_scaler: fitted Darts Scaler for inverse transform + :return: DataFrame comparing validation metrics across all four + feature versions sorted by MAPE ascending + """ + # Ensure all selected features exist in past covariate columns. + valid_past_cols = set(past_cov_cols) + # Define the four feature versions to compare. + versions = { + "All_40_Features": past_cov_cols, + "SHAP_90pct_Features": [ + f for f in shap_df[ + shap_df["Cumulative_Pct"] <= 90 + ]["Feature"].tolist() + if f in valid_past_cols + ], + "SHAP_95pct_Features": [ + f for f in shap_df[ + shap_df["Cumulative_Pct"] <= 95 + ]["Feature"].tolist() + if f in valid_past_cols + ], + "SHAP_98pct_Features": [ + f for f in shap_df[ + shap_df["Cumulative_Pct"] <= 98 + ]["Feature"].tolist() + if f in valid_past_cols + ], + } + # Log the number of features per version. + for name, cols in versions.items(): + _LOG.info("%s → %d features", name, len(cols)) + # Get column names from existing past covariates TimeSeries. + all_past_cols = past_cov_train.components.tolist() + # Initialize results list. + results = [] + # Train and evaluate LightGBM for each feature version. + for version_name, feature_cols in versions.items(): + try: + _LOG.info( + "Training LightGBM with %s (%d features).", + version_name, len(feature_cols), + ) + # Find indices of selected features in past covariates. + feature_indices = [ + all_past_cols.index(f) + for f in feature_cols + if f in all_past_cols + ] + if not feature_indices: + _LOG.warning( + "%s has no valid feature indices — skipping.", + version_name, + ) + continue + # Extract feature subset from existing TimeSeries. + past_subset = past_cov_train.univariate_component( + feature_indices[0] + ) + for idx in feature_indices[1:]: + past_subset = past_subset.stack( + past_cov_train.univariate_component(idx) + ) + # Clean NaN values from target and covariates. + target_df = target_train.to_dataframe().ffill().bfill() + target_df.index.freq = "B" + target_clean = darts.timeseries.TimeSeries.from_dataframe( + target_df, fill_missing_dates=True, freq="B" + ) + past_df = past_subset.to_dataframe().ffill().bfill() + past_df.index.freq = "B" + past_clean = darts.timeseries.TimeSeries.from_dataframe( + past_df, fill_missing_dates=True, freq="B" + ) + future_df = future_cov_train.to_dataframe().ffill().bfill() + future_df.index.freq = "B" + future_clean = darts.timeseries.TimeSeries.from_dataframe( + future_df, fill_missing_dates=True, freq="B" + ) + future_full_df = future_cov_full.to_dataframe().ffill().bfill() + future_full_df.index.freq = "B" + future_full_clean = darts.timeseries.TimeSeries.from_dataframe( + future_full_df, fill_missing_dates=True, freq="B" + ) + # Train LightGBM with this feature subset. + model = darts.models.LightGBMModel( + lags=30, + lags_past_covariates=30, + lags_future_covariates=[0], + output_chunk_length=forecast_horizon, + n_estimators=200, + num_leaves=31, + learning_rate=0.05, + random_state=42, + verbose=-1, + ) + model.fit( + target_clean, + past_covariates=past_clean, + future_covariates=future_clean, + ) + prediction = model.predict( + forecast_horizon, + past_covariates=past_clean, + future_covariates=future_full_clean, + ) + # Evaluate prediction against validation set. + val_df = target_val.to_dataframe().ffill().bfill() + val_df.index.freq = "B" + val_clean = darts.timeseries.TimeSeries.from_dataframe( + val_df, fill_missing_dates=True, freq="B" + ) + actual_df = target_scaler.inverse_transform( + val_clean + ).to_dataframe().dropna() + pred_df = prediction.to_dataframe().ffill().bfill() + pred_df.index.freq = "B" + pred_clean_ts = darts.timeseries.TimeSeries.from_dataframe( + pred_df, fill_missing_dates=True, freq="B" + ) + pred_inverse_df = target_scaler.inverse_transform( + pred_clean_ts + ).to_dataframe().dropna() + # Align by common dates. + common_dates = actual_df.index.intersection( + pred_inverse_df.index + ) + if len(common_dates) < 5: + _LOG.warning( + "%s insufficient common dates — skipping.", + version_name, + ) + continue + actual = actual_df.loc[common_dates].values.flatten() + predicted = pred_inverse_df.loc[ + common_dates + ].values.flatten() + # Calculate evaluation metrics. + mae = float(np.mean(np.abs(actual - predicted))) + rmse = float( + np.sqrt(np.mean((actual - predicted) ** 2)) + ) + mape = float( + np.mean( + np.abs((actual - predicted) / actual) + ) * 100 + ) + r2 = float( + 1 - np.sum((actual - predicted) ** 2) + / np.sum((actual - np.mean(actual)) ** 2) + ) + results.append({ + "Version" : version_name, + "Features" : len(feature_cols), + "MAE" : round(mae, 2), + "RMSE" : round(rmse, 2), + "MAPE" : round(mape, 2), + "R2" : round(r2, 4), + "Days" : len(common_dates), + }) + _LOG.info( + "%s → MAPE: %.2f%% | RMSE: %.2f | R2: %.4f", + version_name, mape, rmse, r2, + ) + except Exception as e: + _LOG.warning( + "%s failed: %s — skipping.", version_name, str(e) + ) + # Return empty DataFrame if no results. + if not results: + _LOG.warning("No feature versions completed successfully.") + return pd.DataFrame( + columns=[ + "Version", "Features", "MAE", + "RMSE", "MAPE", "R2", "Days" + ] + ) + # Sort by MAPE ascending. + return pd.DataFrame(results).sort_values( + "MAPE", ascending=True + ).reset_index(drop=True) + +def tune_ml_models( + target_train: darts.timeseries.TimeSeries, + target_val: darts.timeseries.TimeSeries, + past_cov_train: darts.timeseries.TimeSeries, + future_cov_train: darts.timeseries.TimeSeries, + future_cov_full: darts.timeseries.TimeSeries, + target_scaler: darts.dataprocessing.transformers.Scaler, + forecast_horizon: int, + feature_set_name: str, + n_trials: int = 20, +) -> pd.DataFrame: + """ + Tune hyperparameters for LightGBM XGBoost and RandomForest + using Optuna Bayesian optimization. + + Optuna intelligently searches the hyperparameter space by + learning from previous trial results — finding better parameters + in fewer iterations than grid or random search. For each model + both validation MAPE and the train versus validation gap are + recorded to detect overfitting. Results are sorted by validation + MAPE ascending. + + :param target_train: scaled target TimeSeries for training + :param target_val: scaled target TimeSeries for validation + :param past_cov_train: scaled past covariates TimeSeries + :param future_cov_train: scaled future covariates TimeSeries + :param future_cov_full: full period future covariates TimeSeries + :param target_scaler: fitted Darts Scaler for inverse transform + :param forecast_horizon: number of trading days to forecast ahead + :param feature_set_name: name of feature set for logging + e.g. `'7_features'` or `'10_features'` + :param n_trials: number of Optuna trials per model default is 20 + :return: DataFrame with tuning results sorted by val MAPE + """ + # Fill NaN values using Darts MissingValuesFiller. + filler = darts.dataprocessing.transformers.MissingValuesFiller() + target_clean = filler.transform(target_train) + target_val_clean = filler.transform(target_val) + past_clean = filler.transform(past_cov_train) + future_clean = filler.transform(future_cov_train) + future_full_clean = filler.transform(future_cov_full) + # Initialize results list. + results = [] + + def _evaluate_prediction( + prediction: darts.timeseries.TimeSeries, + actual_df: pd.DataFrame, + ) -> float: + """ + Evaluate a prediction against actual values returning MAPE. + + :param prediction: Darts TimeSeries prediction + :param actual_df: DataFrame with actual values + :return: MAPE as float or infinity if evaluation fails + """ + try: + pred_df = target_scaler.inverse_transform( + filler.transform(prediction) + ).to_dataframe().dropna() + common_dates = actual_df.index.intersection(pred_df.index) + if len(common_dates) < 5: + return float("inf") + actual = actual_df.loc[common_dates].values.flatten() + predicted = pred_df.loc[common_dates].values.flatten() + return float( + np.mean(np.abs((actual - predicted) / actual)) * 100 + ) + except Exception: + return float("inf") + + # Pre-compute actual validation values once for efficiency. + val_df = target_val_clean.to_dataframe().ffill().bfill() + val_df.index.freq = "B" + val_ts = darts.timeseries.TimeSeries.from_dataframe( + val_df, fill_missing_dates=True, freq="B" + ) + actual_val_df = target_scaler.inverse_transform( + val_ts + ).to_dataframe().dropna() + # Pre-compute actual training values for overfitting check. + train_df = target_clean.to_dataframe().ffill().bfill() + train_df.index.freq = "B" + train_ts = darts.timeseries.TimeSeries.from_dataframe( + train_df, fill_missing_dates=True, freq="B" + ) + actual_train_df = target_scaler.inverse_transform( + train_ts + ).to_dataframe().dropna() + + def _make_objective(model_name: str): + """ + Create Optuna objective function for a specific model. + + :param model_name: name of the model to tune + :return: objective function for Optuna to minimize + """ + def objective(trial: optuna.Trial) -> float: + """ + Objective function minimizing validation MAPE. + + :param trial: Optuna trial object for parameter suggestion + :return: validation MAPE to minimize + """ + # Define hyperparameter search space per model. + lags = trial.suggest_int("lags", 10, 60) + n_estimators = trial.suggest_int("n_estimators", 50, 400) + if model_name == "LightGBM": + num_leaves = trial.suggest_int("num_leaves", 10, 80) + learning_rate = trial.suggest_float( + "learning_rate", 0.005, 0.1, log=True + ) + model = darts.models.LightGBMModel( + lags=lags, + lags_past_covariates=lags, + lags_future_covariates=[0], + output_chunk_length=forecast_horizon, + n_estimators=n_estimators, + num_leaves=num_leaves, + learning_rate=learning_rate, + random_state=42, + verbose=-1, + ) + elif model_name == "XGBoost": + max_depth = trial.suggest_int("max_depth", 3, 10) + learning_rate = trial.suggest_float( + "learning_rate", 0.005, 0.1, log=True + ) + model = darts.models.XGBModel( + lags=lags, + lags_past_covariates=lags, + lags_future_covariates=[0], + output_chunk_length=forecast_horizon, + n_estimators=n_estimators, + max_depth=max_depth, + learning_rate=learning_rate, + random_state=42, + verbosity=0, + ) + elif model_name == "RandomForest": + max_depth = trial.suggest_int("max_depth", 5, 30) + model = darts.models.RandomForestModel( + lags=lags, + lags_past_covariates=lags, + lags_future_covariates=[0], + output_chunk_length=forecast_horizon, + n_estimators=n_estimators, + max_depth=max_depth, + random_state=42, + ) + # Train model and generate predictions. + model.fit( + target_clean, + past_covariates=past_clean, + future_covariates=future_clean, + ) + val_pred = model.predict( + forecast_horizon, + past_covariates=past_clean, + future_covariates=future_full_clean, + ) + return _evaluate_prediction(val_pred, actual_val_df) + return objective + + # Run Optuna optimization for each model. + for model_name in ["LightGBM", "XGBoost", "RandomForest"]: + _LOG.info( + "Tuning %s on %s with %d trials.", + model_name, + feature_set_name, + n_trials, + ) + # Create Optuna study minimizing validation MAPE. + # Suppress Optuna logging to keep output clean. + optuna.logging.set_verbosity(optuna.logging.WARNING) + study = optuna.create_study( + direction="minimize", + sampler=optuna.samplers.TPESampler(seed=42), + ) + study.optimize( + _make_objective(model_name), + n_trials=n_trials, + show_progress_bar=True, + ) + # Get best parameters from study. + best_params = study.best_params + best_val_mape = study.best_value + _LOG.info( + "%s best params: %s → Val MAPE: %.4f%%", + model_name, + best_params, + best_val_mape, + ) + # Retrain best model to get train MAPE for overfitting check. + lags = best_params["lags"] + n_estimators = best_params["n_estimators"] + try: + if model_name == "LightGBM": + best_model = darts.models.LightGBMModel( + lags=lags, + lags_past_covariates=lags, + lags_future_covariates=[0], + output_chunk_length=forecast_horizon, + n_estimators=n_estimators, + num_leaves=best_params["num_leaves"], + learning_rate=best_params["learning_rate"], + random_state=42, + verbose=-1, + ) + elif model_name == "XGBoost": + best_model = darts.models.XGBModel( + lags=lags, + lags_past_covariates=lags, + lags_future_covariates=[0], + output_chunk_length=forecast_horizon, + n_estimators=n_estimators, + max_depth=best_params["max_depth"], + learning_rate=best_params["learning_rate"], + random_state=42, + verbosity=0, + ) + elif model_name == "RandomForest": + best_model = darts.models.RandomForestModel( + lags=lags, + lags_past_covariates=lags, + lags_future_covariates=[0], + output_chunk_length=forecast_horizon, + n_estimators=n_estimators, + max_depth=best_params["max_depth"], + random_state=42, + ) + # Retrain best model. + best_model.fit( + target_clean, + past_covariates=past_clean, + future_covariates=future_clean, + ) + # Get train prediction for overfitting check. + train_pred = best_model.predict( + forecast_horizon, + past_covariates=past_clean, + future_covariates=future_full_clean, + ) + train_mape = _evaluate_prediction( + train_pred, actual_train_df + ) + overfit_gap = best_val_mape - train_mape + except Exception as e: + _LOG.warning( + "Could not compute train MAPE for %s: %s", + model_name, + str(e), + ) + train_mape = float("nan") + overfit_gap = float("nan") + results.append({ + "Model" : model_name, + "Feature_Set" : feature_set_name, + "Best_Params" : str(best_params), + "Val_MAPE" : round(best_val_mape, 4), + "Train_MAPE" : round(train_mape, 4), + "Overfit_Gap" : round(overfit_gap, 4), + "N_Trials" : n_trials, + }) + # Sort by validation MAPE ascending. + if not results: + _LOG.warning("No tuning results produced.") + return pd.DataFrame() + return pd.DataFrame(results).sort_values( + "Val_MAPE", ascending=True + ).reset_index(drop=True) + +def train_ensemble_models( + target_train: darts.timeseries.TimeSeries, + target_val: darts.timeseries.TimeSeries, + past_cov_train: darts.timeseries.TimeSeries, + future_cov_train: darts.timeseries.TimeSeries, + future_cov_full: darts.timeseries.TimeSeries, + target_scaler: darts.dataprocessing.transformers.Scaler, + forecast_horizon: int, + tuning_results: pd.DataFrame, +) -> tuple: + """ + Train ensemble models combining top performing ML models. + + Three ensemble strategies are implemented — simple average + weighted average by inverse MAPE and stacking with a linear + meta model. The top three models from hyperparameter tuning + are used as base models. Each ensemble strategy is evaluated + on the validation set and compared against the best individual + model. + + :param target_train: scaled target TimeSeries for training + :param target_val: scaled target TimeSeries for validation + :param past_cov_train: scaled past covariates TimeSeries + :param future_cov_train: scaled future covariates TimeSeries + :param future_cov_full: full period future covariates TimeSeries + :param target_scaler: fitted Darts Scaler for inverse transform + :param forecast_horizon: number of trading days to forecast ahead + :param tuning_results: DataFrame from Phase 7 tuning containing + best parameters for each model + :return: tuple of (ensemble_predictions dict, results DataFrame) + """ + # Fill NaN values using Darts MissingValuesFiller. + filler = darts.dataprocessing.transformers.MissingValuesFiller() + target_clean = filler.transform(target_train) + target_val_clean = filler.transform(target_val) + past_clean = filler.transform(past_cov_train) + future_clean = filler.transform(future_cov_train) + future_full_clean = filler.transform(future_cov_full) + # Extract top 3 models from tuning results. + top_3 = tuning_results.head(3) + _LOG.info( + "Building ensemble from top 3 models:\n%s", + top_3[["Model", "Feature_Set", "Val_MAPE"]].to_string(), + ) + # Train each base model with its best parameters. + base_predictions = {} + base_mapes = {} + for _, row in top_3.iterrows(): + model_name = row["Model"] + feature_set = row["Feature_Set"] + params = eval(row["Best_Params"]) + model_key = f"{model_name}_{feature_set}" + try: + _LOG.info("Training base model %s.", model_key) + lags = params["lags"] + n_estimators = params["n_estimators"] + if model_name == "LightGBM": + model = darts.models.LightGBMModel( + lags=lags, + lags_past_covariates=lags, + lags_future_covariates=[0], + output_chunk_length=forecast_horizon, + n_estimators=n_estimators, + num_leaves=params["num_leaves"], + learning_rate=params["learning_rate"], + random_state=42, + verbose=-1, + ) + elif model_name == "XGBoost": + model = darts.models.XGBModel( + lags=lags, + lags_past_covariates=lags, + lags_future_covariates=[0], + output_chunk_length=forecast_horizon, + n_estimators=n_estimators, + max_depth=params["max_depth"], + learning_rate=params["learning_rate"], + random_state=42, + verbosity=0, + ) + elif model_name == "RandomForest": + model = darts.models.RandomForestModel( + lags=lags, + lags_past_covariates=lags, + lags_future_covariates=[0], + output_chunk_length=forecast_horizon, + n_estimators=n_estimators, + max_depth=params["max_depth"], + random_state=42, + ) + # Train model and generate prediction. + model.fit( + target_clean, + past_covariates=past_clean, + future_covariates=future_clean, + ) + prediction = model.predict( + forecast_horizon, + past_covariates=past_clean, + future_covariates=future_full_clean, + ) + # Inverse transform prediction for evaluation. + pred_df = target_scaler.inverse_transform( + filler.transform(prediction) + ).to_dataframe().dropna() + # Get actual validation values. + val_df = target_val_clean.to_dataframe().ffill().bfill() + val_df.index.freq = "B" + val_ts = darts.timeseries.TimeSeries.from_dataframe( + val_df, fill_missing_dates=True, freq="B" + ) + actual_df = target_scaler.inverse_transform( + val_ts + ).to_dataframe().dropna() + # Calculate MAPE on common dates. + common_dates = actual_df.index.intersection(pred_df.index) + if len(common_dates) >= 5: + actual = actual_df.loc[common_dates].values.flatten() + predicted = pred_df.loc[common_dates].values.flatten() + mape = float( + np.mean( + np.abs((actual - predicted) / actual) + ) * 100 + ) + base_predictions[model_key] = pred_df + base_mapes[model_key] = mape + _LOG.info( + "%s → Val MAPE: %.4f%%", model_key, mape + ) + except Exception as e: + _LOG.warning( + "Base model %s failed: %s — skipping.", + model_key, + str(e), + ) + if len(base_predictions) < 2: + _LOG.warning("Insufficient base models for ensemble.") + return {}, pd.DataFrame() + # Get common dates across all base model predictions. + common_dates = None + for pred_df in base_predictions.values(): + if common_dates is None: + common_dates = pred_df.index + else: + common_dates = common_dates.intersection(pred_df.index) + # Stack predictions into matrix for ensemble calculation. + pred_matrix = np.column_stack([ + base_predictions[key].loc[common_dates].values.flatten() + for key in base_predictions.keys() + ]) + # Get actual values for ensemble evaluation. + val_df = target_val_clean.to_dataframe().ffill().bfill() + val_df.index.freq = "B" + val_ts = darts.timeseries.TimeSeries.from_dataframe( + val_df, fill_missing_dates=True, freq="B" + ) + actual_df = target_scaler.inverse_transform( + val_ts + ).to_dataframe().dropna() + actual_common = actual_df.loc[ + actual_df.index.intersection(common_dates) + ].values.flatten() + # Strategy 1 — Simple average ensemble. + simple_avg = pred_matrix.mean(axis=1) + simple_mape = float( + np.mean(np.abs((actual_common - simple_avg) / actual_common)) * 100 + ) + _LOG.info("Simple average ensemble → MAPE: %.4f%%", simple_mape) + # Strategy 2 — Weighted average by inverse MAPE. + # Models with lower MAPE get higher weight. + inv_mapes = np.array([ + 1 / base_mapes[key] for key in base_predictions.keys() + ]) + weights = inv_mapes / inv_mapes.sum() + weighted_avg = (pred_matrix * weights).sum(axis=1) + weighted_mape = float( + np.mean( + np.abs((actual_common - weighted_avg) / actual_common) + ) * 100 + ) + _LOG.info( + "Weighted average ensemble → MAPE: %.4f%% | Weights: %s", + weighted_mape, + {k: round(w, 3) for k, w in zip(base_predictions.keys(), weights)}, + ) + # Strategy 3 — Stacking with linear meta model. + # Meta model learns optimal combination weights from validation data. + meta_model = sklearn.linear_model.Ridge(alpha=1.0) + meta_model.fit(pred_matrix, actual_common) + stacked_avg = meta_model.predict(pred_matrix) + stacked_mape = float( + np.mean( + np.abs((actual_common - stacked_avg) / actual_common) + ) * 100 + ) + _LOG.info( + "Stacking ensemble → MAPE: %.4f%% | Coefficients: %s", + stacked_mape, + dict(zip(base_predictions.keys(), meta_model.coef_.round(3))), + ) + # Collect ensemble results. + ensemble_predictions = { + "Simple_Average" : simple_avg, + "Weighted_Average" : weighted_avg, + "Stacking" : stacked_avg, + } + results = [] + best_individual_mape = min(base_mapes.values()) + best_individual_name = min(base_mapes, key=base_mapes.get) + for strategy, pred_vals in ensemble_predictions.items(): + mape = float( + np.mean( + np.abs((actual_common - pred_vals) / actual_common) + ) * 100 + ) + improvement = ( + (best_individual_mape - mape) / best_individual_mape * 100 + ) + results.append({ + "Strategy" : strategy, + "Val_MAPE" : round(mape, 4), + "Best_Individual" : best_individual_name, + "Best_Individual_MAPE": round(best_individual_mape, 4), + "Improvement_Pct" : round(improvement, 2), + }) + results_df = pd.DataFrame(results).sort_values( + "Val_MAPE", ascending=True + ).reset_index(drop=True) + return ensemble_predictions, results_df + +def detect_market_regimes( + macro_daily: pd.DataFrame, + macro_monthly: pd.DataFrame, + macro_features: pd.DataFrame, + sp500: pd.DataFrame, + n_regimes: int = None, + random_state: int = 42, +) -> tuple: + """ + Detect market regimes using K-Means clustering on macro features. + + The optimal number of regimes is automatically determined using + silhouette score as primary criterion and elbow method as secondary + validation. Silhouette score directly measures cluster separation + quality making it more reliable than inertia alone. Regimes are + sorted by average S&P 500 return so Regime 0 is always the worst + performing and Regime n-1 is always the best performing. + + :param macro_daily: DataFrame with daily macro indicators + :param macro_monthly: DataFrame with monthly macro indicators + :param macro_features: DataFrame with calculated macro features + :param sp500: DataFrame with S&P 500 OHLCV data + :param n_regimes: number of regimes to use — if None the + silhouette score automatically selects the optimal number + :param random_state: random seed for reproducibility default 42 + :return: tuple of (regime_labels Series regime_stats DataFrame + confidence_scores Series kmeans_model scaler optimal_k) + """ + # Select macro features for regime detection. + regime_features = pd.concat([ + macro_daily[["VIX", "TNX", "IRX", "OIL", "DXY"]], + macro_features[[ + "YIELD_CURVE", "YIELD_CURVE_INVERTED", + "VIX_MA20", "OIL_MOM30", + ]], + macro_monthly[["CPI", "FED_RATE", "UNEMPLOYMENT"]], + ], axis=1).ffill().bfill() + # Standardize features so all have equal influence on clustering. + feature_scaler = sklearn.preprocessing.StandardScaler() + features_scaled = feature_scaler.fit_transform(regime_features) + # Automatically detect optimal number of regimes using both + # silhouette score and elbow method for robust selection. + inertias = [] + silhouette_scores = [] + k_range = range(2, 8) + for k in k_range: + km = sklearn.cluster.KMeans( + n_clusters=k, + random_state=random_state, + n_init=10, + ) + labels_k = km.fit_predict(features_scaled) + inertias.append(km.inertia_) + # Calculate silhouette score for this k. + sil_score = sklearn.metrics.silhouette_score( + features_scaled, labels_k + ) + silhouette_scores.append(sil_score) + _LOG.info( + "k=%d → Inertia: %.0f | Silhouette: %.4f", + k, km.inertia_, sil_score, + ) + # Use silhouette score as primary selection criterion. + optimal_k_silhouette = int( + k_range[np.argmax(silhouette_scores)] + ) + # Use elbow method as secondary validation. + inertia_diffs = np.diff(inertias) + inertia_diffs2 = np.diff(inertia_diffs) + optimal_k_elbow = int( + k_range[np.argmax(inertia_diffs2) + 2] + ) + _LOG.info( + "Silhouette selected k=%d | Elbow selected k=%d", + optimal_k_silhouette, + optimal_k_elbow, + ) + # Use silhouette as primary — override with manual if provided. + if n_regimes is not None: + optimal_k = n_regimes + _LOG.info("Using manually specified k=%d.", optimal_k) + else: + optimal_k = optimal_k_silhouette + _LOG.info("Using silhouette optimal k=%d.", optimal_k) + # Fit K-Means with optimal number of regimes. + kmeans = sklearn.cluster.KMeans( + n_clusters=optimal_k, + random_state=random_state, + n_init=10, + ) + raw_labels = kmeans.fit_predict(features_scaled) + # Calculate S&P 500 daily returns for regime sorting. + sp500_returns = sp500["Close"].pct_change().fillna(0) + # Sort regimes by average S&P 500 return. + regime_returns = {} + for regime in range(optimal_k): + mask = raw_labels == regime + regime_returns[regime] = sp500_returns[mask].mean() + # Create mapping from raw label to sorted label. + sorted_regimes = sorted( + regime_returns.keys(), + key=lambda x: regime_returns[x], + ) + label_mapping = { + old: new for new, old in enumerate(sorted_regimes) + } + regime_labels = pd.Series( + [label_mapping[r] for r in raw_labels], + index=regime_features.index, + name="Regime", + ) + # Calculate confidence scores. + distances = kmeans.transform(features_scaled) + assigned_distances = distances[ + np.arange(len(distances)), raw_labels, + ] + total_distances = distances.sum(axis=1) + confidence_scores = pd.Series( + 1 - (assigned_distances / total_distances), + index=regime_features.index, + name="Confidence", + ) + # Dynamic regime names based on sorted returns. + regime_name_templates = [ + "Bear Market", + "High Volatility", + "Moderate Growth", + "Recovery", + "Bull Market", + "Strong Bull", + ] + regime_names = { + i: regime_name_templates[i] + for i in range(optimal_k) + } + # Calculate regime statistics. + regime_stats = [] + for regime in range(optimal_k): + mask = regime_labels == regime + stats = { + "Regime" : regime, + "Name" : regime_names[regime], + "Count" : int(mask.sum()), + "Pct_Days" : round(mask.mean() * 100, 1), + "Avg_Return" : round( + sp500_returns[mask].mean() * 100, 4 + ), + "Avg_VIX" : round( + macro_daily.loc[mask, "VIX"].mean(), 2 + ), + "Avg_FedRate" : round( + macro_monthly.loc[mask, "FED_RATE"].mean(), 2 + ), + "Avg_YieldCurve": round( + macro_features.loc[mask, "YIELD_CURVE"].mean(), 4 + ), + } + regime_stats.append(stats) + _LOG.info( + "Regime %d (%s): %d days (%.1f%%) | " + "Avg Return: %.4f%% | Avg VIX: %.2f", + regime, + stats["Name"], + stats["Count"], + stats["Pct_Days"], + stats["Avg_Return"], + stats["Avg_VIX"], + ) + regime_stats_df = pd.DataFrame(regime_stats) + return ( + regime_labels, + regime_stats_df, + confidence_scores, + kmeans, + feature_scaler, + optimal_k, + ) + +def plot_regime_analysis( + regime_labels: pd.Series, + regime_stats: pd.DataFrame, + confidence_scores: pd.Series, + sp500: pd.DataFrame, + macro_daily: pd.DataFrame, + macro_features: pd.DataFrame, + sectors: pd.DataFrame, + optimal_k: int, +) -> plt.Figure: + """ + Plot comprehensive market regime analysis with four panels. + + Four panels are shown — S&P 500 price colored by regime + average sector returns per regime as a heatmap showing + which sectors perform best in each environment average + macro conditions per regime as a grouped bar chart and + VIX over time colored by regime showing the fear signal + that defines each market environment. + + :param regime_labels: Series with regime label per trading day + :param regime_stats: DataFrame with regime statistics + :param confidence_scores: Series with confidence score per day + :param sp500: DataFrame with S&P 500 OHLCV data + :param macro_daily: DataFrame with daily macro indicators + :param macro_features: DataFrame with calculated macro features + :param sectors: DataFrame with sector ETF prices + :param optimal_k: number of regimes detected + :return: matplotlib Figure object with four panel analysis + """ + # Define colors and names for each regime. + regime_colors = [ + "#D32F2F", # deep red — Bear Market + "#FF6F00", # deep orange — High Volatility + "#1565C0", # deep blue — Moderate Growth + "#2E7D32", # deep green — Recovery + "#F9A825", # deep yellow — Bull Market + "#6A1B9A", # deep purple — Strong Bull + ] + # Create figure with 2x2 grid of subplots. + fig, axes = plt.subplots(2, 2, figsize=(20, 14)) + # Panel 1 (top left) — S&P 500 price colored by regime. + for regime in range(optimal_k): + mask = regime_labels == regime + name = regime_stats.loc[ + regime_stats["Regime"] == regime, "Name" + ].values[0] + axes[0, 0].scatter( + sp500.index[mask], + sp500.loc[mask, "Close"], + c=regime_colors[regime], + s=2, + label=f"Regime {regime}: {name}", + alpha=0.7, + ) + axes[0, 0].set_title( + "S&P 500 Price by Market Regime", + fontsize=11, + fontweight="bold", + ) + axes[0, 0].set_ylabel("S&P 500 Price (USD)") + axes[0, 0].set_xlabel("Date") + axes[0, 0].legend(loc="upper left", fontsize=7, markerscale=5) + # Panel 2 (top right) — Average sector returns per regime heatmap. + # Calculate average daily return per sector per regime. + sector_returns = sectors.pct_change().dropna() * 100 + sector_regime_returns = pd.DataFrame() + for regime in range(optimal_k): + mask = regime_labels.reindex(sector_returns.index) == regime + regime_name = regime_stats.loc[ + regime_stats["Regime"] == regime, "Name" + ].values[0] + avg_returns = sector_returns.loc[mask].mean() + sector_regime_returns[regime_name] = avg_returns + # Plot heatmap of sector returns by regime. + sns.heatmap( + sector_regime_returns, + annot=True, + fmt=".3f", + cmap="RdYlGn", + center=0, + linewidths=0.5, + linecolor="white", + ax=axes[0, 1], + cbar_kws={"label": "Avg Daily Return (%)"}, + ) + axes[0, 1].set_title( + "Average Sector Daily Returns by Regime (%)", + fontsize=11, + fontweight="bold", + ) + axes[0, 1].set_xlabel("Market Regime") + axes[0, 1].set_ylabel("Sector ETF") + # Panel 3 (bottom left) — Average macro conditions per regime. + metrics = ["Avg_VIX", "Avg_FedRate", "Avg_Return"] + metric_labels = ["VIX (Fear)", "Fed Rate (%)", "Daily Return (%)"] + x = np.arange(len(metrics)) + width = 0.8 / optimal_k + for regime in range(optimal_k): + row = regime_stats[ + regime_stats["Regime"] == regime + ].iloc[0] + values = [ + row["Avg_VIX"], + row["Avg_FedRate"], + row["Avg_Return"] * 100, + ] + offset = (regime - optimal_k / 2 + 0.5) * width + axes[1, 0].bar( + x + offset, + values, + width=width, + color=regime_colors[regime], + alpha=0.8, + label=row["Name"], + ) + axes[1, 0].set_title( + "Average Macro Conditions by Regime", + fontsize=11, + fontweight="bold", + ) + axes[1, 0].set_ylabel("Value") + axes[1, 0].set_xlabel("Metric") + axes[1, 0].set_xticks(x) + axes[1, 0].set_xticklabels(metric_labels, fontsize=9) + axes[1, 0].legend(fontsize=7, loc="upper right") + axes[1, 0].axhline( + 0, color="black", linewidth=0.8, linestyle="--" + ) + # Panel 4 (bottom right) — VIX over time colored by regime. + for regime in range(optimal_k): + mask = regime_labels == regime + name = regime_stats.loc[ + regime_stats["Regime"] == regime, "Name" + ].values[0] + axes[1, 1].scatter( + macro_daily.index[mask], + macro_daily.loc[mask, "VIX"], + c=regime_colors[regime], + s=2, + label=name, + alpha=0.7, + ) + axes[1, 1].axhline( + 30, + color="black", + linewidth=1.0, + linestyle="--", + alpha=0.5, + label="VIX=30 (high fear)", + ) + axes[1, 1].set_title( + "VIX (Fear Index) Colored by Market Regime", + fontsize=11, + fontweight="bold", + ) + axes[1, 1].set_ylabel("VIX") + axes[1, 1].set_xlabel("Date") + axes[1, 1].legend(loc="upper right", fontsize=7, markerscale=5) + fig.suptitle( + "Market Regime Analysis 2018-2024 — K-Means Clustering", + fontsize=14, + fontweight="bold", + ) + fig.tight_layout() + return fig + +def calculate_sector_scores( + sectors: pd.DataFrame, + sp500: pd.DataFrame, + regime_labels: pd.Series, + macro_daily: pd.DataFrame, + macro_features: pd.DataFrame, + current_regime: int, + forecast_return: float, +) -> pd.DataFrame: + """ + Calculate composite sector scores for rotation recommendations. + + Five factors are combined into a composite score for each sector. + Historical regime return captures average sector performance in + the current regime. Momentum captures recent price trend. + Volatility adjusted return is the Sharpe-like ratio. Macro + correlation measures alignment with key macro signals. Forecast + alignment measures consistency with the S&P 500 forecast direction. + Ridge Regression learns optimal factor weights from historical data. + + :param sectors: DataFrame with sector ETF closing prices + :param sp500: DataFrame with S&P 500 OHLCV data + :param regime_labels: Series with regime label per trading day + :param macro_daily: DataFrame with daily macro indicators + :param macro_features: DataFrame with calculated macro features + :param current_regime: integer label of the current market regime + :param forecast_return: predicted S&P 500 return over next 30 days + as a percentage e.g. 2.3 for 2.3 percent + :return: DataFrame with composite scores and factor scores for + each sector sorted from highest to lowest score + """ + # Calculate daily returns for all sectors. + sector_returns = sectors.pct_change().dropna() * 100 + # Factor 1 — Historical regime return. + # Average daily return of each sector in the current regime. + regime_mask = regime_labels.reindex(sector_returns.index) == current_regime + if regime_mask.sum() > 5: + regime_returns = sector_returns.loc[regime_mask].mean() + else: + # Fall back to overall average if regime has too few days. + regime_returns = sector_returns.mean() + _LOG.warning( + "Current regime has fewer than 5 days — " + "using overall average returns." + ) + # Factor 2 — Momentum score (last 30 days return). + momentum = sectors.iloc[-1] / sectors.iloc[-30] - 1 + momentum = momentum * 100 + # Factor 3 — Volatility adjusted return (Sharpe-like ratio). + # Use last 60 days for stability. + recent_returns = sector_returns.iloc[-60:] + sharpe_like = recent_returns.mean() / ( + recent_returns.std() + 1e-8 + ) + # Factor 4 — VIX correlation alignment. + # Sectors negatively correlated with VIX are defensive. + # Sectors positively correlated with VIX benefit from fear. + vix_series = macro_daily["VIX"].reindex( + sector_returns.index + ).ffill() + vix_correlation = sector_returns.corrwith(vix_series) + # In bear regime high VIX correlation is good. + # In bull regime low VIX correlation is good. + if current_regime <= 1: + # Bear or high volatility — prefer sectors that + # move WITH VIX (benefit from fear). + vix_alignment = vix_correlation + else: + # Growth regimes — prefer sectors that move + # AGAINST VIX (benefit from calm markets). + vix_alignment = -vix_correlation + # Factor 5 — Forecast alignment. + # If forecast is bullish prefer high beta sectors. + # If forecast is bearish prefer low beta sectors. + sp500_returns = sp500["Close"].pct_change().dropna() * 100 + beta = sector_returns.corrwith( + sp500_returns.reindex(sector_returns.index) + ) + if forecast_return > 0: + # Bullish forecast — prefer high beta sectors. + forecast_alignment = beta + else: + # Bearish forecast — prefer low beta sectors. + forecast_alignment = -beta + # Combine all factors into a feature matrix. + factor_matrix = pd.DataFrame({ + "Regime_Return" : regime_returns, + "Momentum" : momentum, + "Sharpe_Like" : sharpe_like, + "VIX_Alignment" : vix_alignment, + "Forecast_Alignment" : forecast_alignment, + }) + # Normalize each factor to 0-1 scale for fair combination. + factor_normalized = ( + factor_matrix - factor_matrix.min() + ) / (factor_matrix.max() - factor_matrix.min() + 1e-8) + # Train Ridge Regression to learn optimal factor weights. + # Target is the next 30 day sector return (forward return). + forward_returns = ( + sectors.shift(-30).iloc[-1] / sectors.iloc[-1] - 1 + ) * 100 + # Use historical forward returns as training target. + historical_forward = ( + sectors.pct_change(30).shift(-30).dropna() * 100 + ) + # Build training dataset from historical factor values. + train_factors = [] + train_targets = [] + for date in historical_forward.index[-120:]: + if date not in regime_labels.index: + continue + regime_at_date = regime_labels.loc[date] + mask_date = regime_labels.reindex( + sector_returns.index + ) == regime_at_date + if mask_date.sum() < 5: + continue + # Calculate factors at this historical date. + idx = sectors.index.get_loc(date) + if idx < 60: + continue + hist_returns = sector_returns.iloc[max(0, idx-60):idx] + hist_regime_mask = regime_labels.reindex( + hist_returns.index + ) == regime_at_date + if hist_regime_mask.sum() > 0: + hist_regime_ret = hist_returns.loc[ + hist_regime_mask + ].mean() + else: + hist_regime_ret = hist_returns.mean() + hist_momentum = ( + sectors.iloc[idx] / sectors.iloc[max(0, idx-30)] - 1 + ) * 100 + hist_sharpe = hist_returns.mean() / ( + hist_returns.std() + 1e-8 + ) + hist_factors = pd.DataFrame({ + "Regime_Return" : hist_regime_ret, + "Momentum" : hist_momentum, + "Sharpe_Like" : hist_sharpe, + }) + hist_factors_norm = ( + hist_factors - hist_factors.min() + ) / (hist_factors.max() - hist_factors.min() + 1e-8) + train_factors.append( + hist_factors_norm.values.flatten() + ) + train_targets.append( + historical_forward.loc[date].values + ) + # Train Ridge Regression if sufficient training data exists. + if len(train_factors) > 10: + X_train = np.array(train_factors) + y_train = np.array(train_targets).mean(axis=1) + ridge = sklearn.linear_model.Ridge(alpha=1.0) + ridge.fit(X_train, y_train) + # Use learned weights for final scoring. + learned_weights = np.abs(ridge.coef_) + learned_weights = learned_weights / learned_weights.sum() + _LOG.info( + "Ridge weights — Regime: %.3f | Momentum: %.3f | " + "Sharpe: %.3f", + learned_weights[0], + learned_weights[1], + learned_weights[2], + ) + # Calculate composite score using learned weights + # plus equal weights for VIX and forecast alignment. + composite_score = ( + learned_weights[0] * factor_normalized["Regime_Return"] + + learned_weights[1] * factor_normalized["Momentum"] + + learned_weights[2] * factor_normalized["Sharpe_Like"] + + 0.1 * factor_normalized["VIX_Alignment"] + + 0.1 * factor_normalized["Forecast_Alignment"] + ) + else: + # Equal weights fallback if insufficient training data. + _LOG.warning( + "Insufficient training data — using equal weights." + ) + composite_score = factor_normalized.mean(axis=1) + # Build results DataFrame. + results = pd.DataFrame({ + "Sector" : sectors.columns, + "Composite_Score" : composite_score.values, + "Regime_Return" : regime_returns.values, + "Momentum" : momentum.values, + "Sharpe_Like" : sharpe_like.values, + "VIX_Alignment" : vix_alignment.values, + "Forecast_Alignment" : forecast_alignment.values, + }) + # Sort by composite score descending. + results = results.sort_values( + "Composite_Score", ascending=False + ).reset_index(drop=True) + # Add rotation recommendation. + results["Recommendation"] = "NEUTRAL" + results.loc[:2, "Recommendation"] = "BUY" + results.loc[8:, "Recommendation"] = "AVOID" + _LOG.info( + "Sector rotation recommendations:\n%s", + results[["Sector", "Composite_Score", "Recommendation"] + ].to_string(), + ) + return results + +def precompute_weekly_recommendations( + sectors: pd.DataFrame, + sp500: pd.DataFrame, + regime_labels: pd.Series, + macro_daily: pd.DataFrame, + macro_monthly: pd.DataFrame, + macro_features: pd.DataFrame, + regime_stats: pd.DataFrame, + kmeans_model: sklearn.cluster.KMeans, + regime_scaler: sklearn.preprocessing.StandardScaler, + forecast_series: dict, +) -> pd.DataFrame: + """ + Pre-compute weekly sector recommendations for entire history. + + For each Friday in the dataset sector scores are calculated + using only data available up to that date simulating real + time deployment with no look-ahead bias. The current regime + is detected using the trained K-Means model and sector scores + are calculated using the five factor composite model. + Logging is suppressed during computation to avoid cluttering + notebook output — only the final summary is logged. + + :param sectors: DataFrame with sector ETF closing prices + :param sp500: DataFrame with S&P 500 OHLCV data + :param regime_labels: Series with regime label per trading day + :param macro_daily: DataFrame with daily macro indicators + :param macro_monthly: DataFrame with monthly macro indicators + :param macro_features: DataFrame with calculated macro features + :param regime_stats: DataFrame with regime statistics + :param kmeans_model: trained K-Means model for regime detection + :param regime_scaler: fitted StandardScaler for regime features + :param forecast_series: dictionary mapping dates to forecast + returns e.g. from walk forward validation results + :return: DataFrame with weekly sector scores and recommendations + for the entire analysis period + """ + # Suppress per-week logging to avoid cluttering notebook output. + previous_level = _LOG.level + _LOG.setLevel(logging.WARNING) + # Get all Fridays in the dataset as weekly rebalancing dates. + all_fridays = sp500.index[sp500.index.dayofweek == 4] + # Initialize results list. + results = [] + # Compute scores for each Friday with progress bar. + for friday in tqdm.tqdm( + all_fridays, + desc="Computing weekly recommendations", + ): + # Skip if Friday not in sectors index. + if friday not in sectors.index: + continue + friday_idx = sectors.index.get_loc(friday) + # Skip if insufficient history for calculations. + if friday_idx < 60: + continue + # Use only data up to this Friday — no look-ahead bias. + sectors_to_date = sectors.iloc[:friday_idx + 1] + sp500_to_date = sp500.iloc[:friday_idx + 1] + macro_daily_to_date = macro_daily.loc[ + macro_daily.index <= friday + ] + macro_monthly_to_date = macro_monthly.loc[ + macro_monthly.index <= friday + ] + macro_features_to_date = macro_features.loc[ + macro_features.index <= friday + ] + regime_labels_to_date = regime_labels.loc[ + regime_labels.index <= friday + ] + # Get current regime for this date. + current_regime = int(regime_labels_to_date.iloc[-1]) + # Get forecast return — use pre-computed if available. + forecast_return = forecast_series.get(friday, 0.0) + try: + # Calculate sector scores using data up to this date. + scores = calculate_sector_scores( + sectors=sectors_to_date, + sp500=sp500_to_date, + regime_labels=regime_labels_to_date, + macro_daily=macro_daily_to_date, + macro_features=macro_features_to_date, + current_regime=current_regime, + forecast_return=forecast_return, + ) + # Build weekly result row. + week_result = { + "Date" : friday, + "Regime" : current_regime, + "Regime_Name" : regime_stats.loc[ + regime_stats["Regime"] == current_regime, + "Name" + ].values[0], + "Forecast_Return": forecast_return, + } + # Add sector scores and recommendations. + for _, row in scores.iterrows(): + sector = row["Sector"] + week_result[f"{sector}_Score"] = round( + row["Composite_Score"], 4 + ) + week_result[f"{sector}_Rec"] = row["Recommendation"] + results.append(week_result) + except Exception: + # Silently skip weeks that fail. + pass + # Build results DataFrame. + results_df = pd.DataFrame(results).set_index("Date") + # Restore logging level. + _LOG.setLevel(previous_level) + _LOG.info( + "Pre-computed %d weekly recommendations.", + len(results_df), + ) + return results_df + +def find_regime_periods( + regime_labels: pd.Series, +) -> pd.DataFrame: + """ + Find all consecutive periods for each market regime. + + Groups consecutive trading days with the same regime label + into distinct periods. Each period has a start date end date + duration and regime label. This allows analysis of how sector + performance differed across multiple occurrences of the same + regime. + + :param regime_labels: Series with regime label per trading day + :return: DataFrame with one row per regime period containing + start date end date duration and regime label + """ + # Initialize list to collect regime periods. + periods = [] + # Track current period. + current_regime = regime_labels.iloc[0] + period_start = regime_labels.index[0] + # Loop through all regime labels finding transitions. + for date, regime in regime_labels.items(): + if regime != current_regime: + # Regime changed — save completed period. + periods.append({ + "Regime" : current_regime, + "Start" : period_start, + "End" : date, + "Duration": (date - period_start).days, + }) + # Start new period. + current_regime = regime + period_start = date + # Save the final period. + periods.append({ + "Regime" : current_regime, + "Start" : period_start, + "End" : regime_labels.index[-1], + "Duration": ( + regime_labels.index[-1] - period_start + ).days, + }) + return pd.DataFrame(periods).sort_values( + "Start" + ).reset_index(drop=True) + + +def calculate_period_attribution( + regime_periods: pd.DataFrame, + sectors: pd.DataFrame, + sp500: pd.DataFrame, + macro_daily: pd.DataFrame, + macro_monthly: pd.DataFrame, + regime_stats: pd.DataFrame, + min_duration_days: int = 20, +) -> pd.DataFrame: + """ + Calculate actual sector returns for each regime period. + + For each regime occurrence calculates the actual sector ETF + returns during that period alongside key macro conditions. + Only periods longer than min_duration_days are included to + ensure statistically meaningful results. This enables comparison + of sector performance across multiple occurrences of the same + regime revealing how macro context within a regime drives + different sector outcomes. + + :param regime_periods: DataFrame from `find_regime_periods` + :param sectors: DataFrame with sector ETF closing prices + :param sp500: DataFrame with S&P 500 OHLCV data + :param macro_daily: DataFrame with daily macro indicators + :param macro_monthly: DataFrame with monthly macro indicators + :param regime_stats: DataFrame with regime statistics + :param min_duration_days: minimum period length to include + :return: DataFrame with sector returns and macro context + for each qualifying regime period + """ + # Initialize results list. + results = [] + # Calculate attribution for each regime period. + for _, row in regime_periods.iterrows(): + start = row["Start"] + end = row["End"] + regime = row["Regime"] + duration = row["Duration"] + # Skip periods that are too short. + if duration < min_duration_days: + continue + # Get sector returns during this period. + period_sectors = sectors.loc[ + (sectors.index >= start) & + (sectors.index <= end) + ] + if len(period_sectors) < 5: + continue + # Calculate total return for each sector. + sector_returns = ( + period_sectors.iloc[-1] / period_sectors.iloc[0] - 1 + ) * 100 + # Get S&P 500 return for this period. + period_sp500 = sp500.loc[ + (sp500.index >= start) & + (sp500.index <= end), + "Close" + ] + sp500_return = float( + (period_sp500.iloc[-1] / period_sp500.iloc[0] - 1) * 100 + ) + # Get average macro conditions during this period. + period_macro = macro_daily.loc[ + (macro_daily.index >= start) & + (macro_daily.index <= end) + ] + period_monthly = macro_monthly.loc[ + (macro_monthly.index >= start) & + (macro_monthly.index <= end) + ] + avg_vix = float(period_macro["VIX"].mean()) + avg_oil_change = float( + (period_macro["OIL"].iloc[-1] / + period_macro["OIL"].iloc[0] - 1) * 100 + ) + avg_fed_rate = float(period_monthly["FED_RATE"].mean()) + avg_tnx = float(period_macro["TNX"].mean()) + # Get regime name. + regime_name = regime_stats.loc[ + regime_stats["Regime"] == regime, "Name" + ].values[0] + # Build result row. + result = { + "Regime" : regime, + "Regime_Name" : regime_name, + "Start" : start.date(), + "End" : end.date(), + "Duration_Days" : duration, + "SP500_Return" : round(sp500_return, 2), + "Avg_VIX" : round(avg_vix, 2), + "Oil_Change_Pct": round(avg_oil_change, 2), + "Avg_FedRate" : round(avg_fed_rate, 2), + "Avg_TNX" : round(avg_tnx, 2), + } + # Add individual sector returns. + for sector in sectors.columns: + result[f"{sector}_Return"] = round( + float(sector_returns[sector]), 2 + ) + results.append(result) + return pd.DataFrame(results).reset_index(drop=True) + + +def plot_regime_attribution( + attribution_df: pd.DataFrame, + sectors: pd.DataFrame, + regime_stats: pd.DataFrame, + selected_regime: int = 0, +) -> plt.Figure: + """ + Plot sector performance attribution across regime occurrences. + + Three panels are shown — grouped bar chart of sector returns + for each occurrence of the selected regime macro context + comparison across occurrences and average sector performance + ranked from best to worst. Full sector names are used instead + of ticker symbols for readability. + + :param attribution_df: DataFrame from `calculate_period_attribution` + :param sectors: DataFrame with sector ETF closing prices + :param regime_stats: DataFrame with regime statistics + :param selected_regime: regime number to analyze default 0 + :return: matplotlib Figure object with attribution analysis + """ + # Map sector tickers to full names for readability. + sector_name_map = { + "XLK" : "Technology", + "XLV" : "Healthcare", + "XLF" : "Financials", + "XLE" : "Energy", + "XLY" : "Consumer Disc", + "XLP" : "Consumer Staples", + "XLI" : "Industrials", + "XLU" : "Utilities", + "XLB" : "Materials", + "XLRE": "Real Estate", + "XLC" : "Communication", + } + # Filter to selected regime. + regime_data = attribution_df[ + attribution_df["Regime"] == selected_regime + ].reset_index(drop=True) + if len(regime_data) == 0: + _LOG.warning( + "No periods found for regime %d.", selected_regime + ) + fig, ax = plt.subplots(figsize=(10, 4)) + ax.text( + 0.5, 0.5, + f"No periods found for Regime {selected_regime}", + ha="center", va="center", + ) + return fig + # Get regime name. + regime_name = regime_stats.loc[ + regime_stats["Regime"] == selected_regime, "Name" + ].values[0] + # Get sector return columns. + sector_cols = [ + col for col in attribution_df.columns + if col.endswith("_Return") and col != "SP500_Return" + ] + # Use full sector names for readability. + sector_names = [ + sector_name_map.get( + col.replace("_Return", ""), + col.replace("_Return", ""), + ) + for col in sector_cols + ] + # Create period labels. + period_labels = [ + f"{row['Start']} to {row['End']}\n({row['Duration_Days']}d)" + for _, row in regime_data.iterrows() + ] + # Create figure with 3 panels. + fig, axes = plt.subplots(3, 1, figsize=(18, 18)) + # Define colors for each period. + colors = [ + "#1565C0", "#2E7D32", "#D32F2F", + "#FF6F00", "#6A1B9A", "#00838F", + ] + n_periods = len(regime_data) + n_sectors = len(sector_names) + x = np.arange(n_sectors) + width = 0.8 / n_periods + # Panel 1 — Grouped bar chart of sector returns per period. + for p_idx, (_, period_row) in enumerate(regime_data.iterrows()): + returns = [period_row[col] for col in sector_cols] + offset = (p_idx - n_periods / 2 + 0.5) * width + axes[0].bar( + x + offset, + returns, + width=width, + color=colors[p_idx % len(colors)], + alpha=0.8, + label=period_labels[p_idx], + ) + axes[0].axhline( + 0, color="black", linewidth=0.8, linestyle="--" + ) + axes[0].set_title( + f"Sector Returns During {regime_name} Regime — " + f"All Historical Occurrences", + fontsize=12, + fontweight="bold", + ) + axes[0].set_ylabel("Total Return (%)") + axes[0].set_xlabel("Sector") + axes[0].set_xticks(x) + axes[0].set_xticklabels(sector_names, rotation=45, ha="right") + axes[0].legend( + loc="upper right", fontsize=7, ncol=n_periods + ) + # Panel 2 — Macro context comparison across periods. + macro_metrics = [ + "Avg_VIX", "Oil_Change_Pct", + "Avg_FedRate", "Avg_TNX", "SP500_Return", + ] + macro_labels = [ + "Avg VIX", "Oil Change %", + "Fed Rate %", "10Y Yield %", "S&P 500 Return %", + ] + x_macro = np.arange(len(macro_metrics)) + width_macro = 0.8 / n_periods + for p_idx, (_, period_row) in enumerate(regime_data.iterrows()): + values = [period_row[m] for m in macro_metrics] + offset = (p_idx - n_periods / 2 + 0.5) * width_macro + axes[1].bar( + x_macro + offset, + values, + width=width_macro, + color=colors[p_idx % len(colors)], + alpha=0.8, + label=period_labels[p_idx], + ) + axes[1].axhline( + 0, color="black", linewidth=0.8, linestyle="--" + ) + axes[1].set_title( + f"Macro Context Comparison — {regime_name} Occurrences", + fontsize=12, + fontweight="bold", + ) + axes[1].set_ylabel("Value") + axes[1].set_xlabel("Macro Metric") + axes[1].set_xticks(x_macro) + axes[1].set_xticklabels(macro_labels, rotation=45, ha="right") + axes[1].legend( + loc="upper right", fontsize=7, ncol=n_periods + ) + # Panel 3 — Average sector returns across all occurrences. + avg_returns = pd.Series({ + sector_name_map.get( + col.replace("_Return", ""), + col.replace("_Return", ""), + ): regime_data[col].mean() + for col in sector_cols + }).sort_values(ascending=True) + bar_colors = [ + "#D32F2F" if r < 0 else "#2E7D32" + for r in avg_returns.values + ] + axes[2].barh( + avg_returns.index, + avg_returns.values, + color=bar_colors, + alpha=0.8, + edgecolor="white", + ) + axes[2].axvline( + 0, color="black", linewidth=0.8, linestyle="--" + ) + # Add value labels on bars. + for idx, val in enumerate(avg_returns.values): + axes[2].text( + val + (0.3 if val >= 0 else -0.3), + idx, + f"{val:.1f}%", + va="center", + ha="left" if val >= 0 else "right", + fontsize=9, + fontweight="bold", + ) + axes[2].set_title( + f"Average Sector Return Across All {regime_name} " + f"Occurrences — Ranked Best to Worst", + fontsize=12, + fontweight="bold", + ) + axes[2].set_xlabel("Average Total Return (%)") + axes[2].set_ylabel("Sector") + fig.suptitle( + f"Regime Attribution Analysis — {regime_name}", + fontsize=14, + fontweight="bold", + ) + fig.tight_layout() + return fig + +def compare_libraries( + train: pd.DataFrame, + val: pd.DataFrame, + target_col: str, + forecast_horizon: int, +) -> pd.DataFrame: + """ + Compare forecasting performance across three time series libraries. + + Darts NaiveSeasonal ARIMA and ExponentialSmoothing are compared + against standalone Prophet and Statsmodels ARIMA. All models are + trained on the same training data and evaluated on the same + validation data using MAPE RMSE and training time as metrics. + This validates the choice of Darts as the primary library. + + :param train: training DataFrame with target column + :param val: validation DataFrame with target column + :param target_col: name of the target column e.g. `'Close'` + :param forecast_horizon: number of days to forecast ahead + :return: DataFrame with comparison metrics for each library + and model sorted by MAPE ascending + """ + import time + import statsmodels.tsa.arima.model + import statsmodels.tsa.holtwinters + # Extract training and validation price series. + train_series = train[target_col].ffill().bfill() + val_series = val[target_col].ffill().bfill() + # Get actual validation values for evaluation. + actual = val_series.values[:forecast_horizon] + # Initialize results list. + results = [] + + def _calculate_metrics( + actual: np.ndarray, + predicted: np.ndarray, + model_name: str, + library: str, + train_time: float, + ) -> dict: + """ + Calculate MAPE RMSE and training time metrics. + + :param actual: array of actual values + :param predicted: array of predicted values + :param model_name: name of the model + :param library: name of the library + :param train_time: training time in seconds + :return: dictionary of metrics + """ + min_len = min(len(actual), len(predicted)) + actual_aligned = actual[:min_len] + predicted_aligned = predicted[:min_len] + mape = float( + np.mean( + np.abs( + (actual_aligned - predicted_aligned) + / actual_aligned + ) + ) * 100 + ) + rmse = float( + np.sqrt( + np.mean( + (actual_aligned - predicted_aligned) ** 2 + ) + ) + ) + return { + "Library" : library, + "Model" : model_name, + "MAPE" : round(mape, 4), + "RMSE" : round(rmse, 2), + "Train_Time" : round(train_time, 2), + } + + # Library 1 — Darts models. + _LOG.info("Evaluating Darts models.") + # Build Darts TimeSeries using MissingValuesFiller approach. + train_ts = darts.timeseries.TimeSeries.from_series( + train_series, + fill_missing_dates=True, + freq="B", + ) + filler = darts.dataprocessing.transformers.MissingValuesFiller() + train_ts_clean = filler.transform(train_ts) + # Darts NaiveSeasonal. + start = time.time() + naive = darts.models.NaiveSeasonal(K=5) + naive.fit(train_ts_clean) + pred = naive.predict(forecast_horizon) + train_time = time.time() - start + results.append(_calculate_metrics( + actual, + pred.values().flatten(), + "NaiveSeasonal", + "Darts", + train_time, + )) + # Darts ARIMA. + start = time.time() + arima_darts = darts.models.ARIMA(p=5, d=1, q=0) + arima_darts.fit(train_ts_clean) + pred = arima_darts.predict(forecast_horizon) + train_time = time.time() - start + results.append(_calculate_metrics( + actual, + pred.values().flatten(), + "ARIMA", + "Darts", + train_time, + )) + # Darts ExponentialSmoothing. + start = time.time() + es_darts = darts.models.ExponentialSmoothing() + es_darts.fit(train_ts_clean) + pred = es_darts.predict(forecast_horizon) + train_time = time.time() - start + results.append(_calculate_metrics( + actual, + pred.values().flatten(), + "ExponentialSmoothing", + "Darts", + train_time, + )) + # Library 2 — Statsmodels. + _LOG.info("Evaluating Statsmodels models.") + # Statsmodels ARIMA. + try: + start = time.time() + sm_arima = statsmodels.tsa.arima.model.ARIMA( + train_series.values, + order=(5, 1, 0), + ) + sm_arima_fit = sm_arima.fit() + pred = sm_arima_fit.forecast(steps=forecast_horizon) + train_time = time.time() - start + results.append(_calculate_metrics( + actual, + pred, + "ARIMA", + "Statsmodels", + train_time, + )) + except Exception as e: + _LOG.warning("Statsmodels ARIMA failed: %s", str(e)) + # Statsmodels ExponentialSmoothing. + try: + start = time.time() + sm_es = statsmodels.tsa.holtwinters.ExponentialSmoothing( + train_series.values, + trend="add", + ) + sm_es_fit = sm_es.fit() + pred = sm_es_fit.forecast(steps=forecast_horizon) + train_time = time.time() - start + results.append(_calculate_metrics( + actual, + pred, + "ExponentialSmoothing", + "Statsmodels", + train_time, + )) + except Exception as e: + _LOG.warning( + "Statsmodels ExponentialSmoothing failed: %s", str(e) + ) + # Library 3 — Prophet. + _LOG.info("Evaluating Prophet.") + try: + start = time.time() + # Prepare Prophet format — requires ds and y columns. + prophet_train = pd.DataFrame({ + "ds": train_series.index, + "y" : train_series.values, + }) + prophet_model = prophet.Prophet( + daily_seasonality=False, + weekly_seasonality=True, + yearly_seasonality=True, + ) + # Suppress Prophet output. + import logging as logging_module + logging_module.getLogger("prophet").setLevel( + logging_module.WARNING + ) + logging_module.getLogger("cmdstanpy").setLevel( + logging_module.WARNING + ) + prophet_model.fit(prophet_train) + # Create future DataFrame for prediction. + future = prophet_model.make_future_dataframe( + periods=forecast_horizon, + freq="B", + ) + forecast = prophet_model.predict(future) + pred = forecast["yhat"].tail( + forecast_horizon + ).values + train_time = time.time() - start + results.append(_calculate_metrics( + actual, + pred, + "Prophet", + "Prophet", + train_time, + )) + except Exception as e: + _LOG.warning("Prophet failed: %s", str(e)) + # Convert to DataFrame and sort by MAPE. + results_df = pd.DataFrame(results).sort_values( + "MAPE", ascending=True + ).reset_index(drop=True) + _LOG.info( + "Library comparison complete — %d models evaluated.", + len(results_df), + ) + return results_df + +def plot_library_comparison( + library_comparison: pd.DataFrame, +) -> plt.Figure: + """ + Plot library comparison results showing MAPE and training time. + + Two panels are shown — MAPE comparison and training time + comparison for all models across all libraries. Colors + distinguish between Darts Statsmodels and Prophet. + + :param library_comparison: DataFrame from `compare_libraries` + containing MAPE RMSE and Train_Time for each model + :return: matplotlib Figure object with comparison charts + """ + # Define colors per library. + library_colors = { + "Darts" : "#1565C0", + "Statsmodels" : "#2E7D32", + "Prophet" : "#D32F2F", + } + # Create labels combining library and model name. + labels = [ + f"{row['Library']}\n{row['Model']}" + for _, row in library_comparison.iterrows() + ] + colors = [ + library_colors[row["Library"]] + for _, row in library_comparison.iterrows() + ] + # Create figure with two panels. + fig, axes = plt.subplots(1, 2, figsize=(14, 6)) + # Panel 1 — MAPE comparison. + bars = axes[0].barh( + labels, + library_comparison["MAPE"], + color=colors, + alpha=0.8, + edgecolor="white", + ) + # Add value labels on bars. + for bar, val in zip(bars, library_comparison["MAPE"]): + axes[0].text( + val + 0.02, + bar.get_y() + bar.get_height() / 2, + f"{val:.2f}%", + va="center", + fontsize=9, + fontweight="bold", + ) + axes[0].set_title( + "MAPE Comparison by Library and Model", + fontsize=12, + fontweight="bold", + ) + axes[0].set_xlabel("MAPE (%) — Lower is Better") + axes[0].set_ylabel("Library / Model") + # Panel 2 — Training time comparison. + bars2 = axes[1].barh( + labels, + library_comparison["Train_Time"], + color=colors, + alpha=0.8, + edgecolor="white", + ) + # Add value labels on bars. + for bar, val in zip(bars2, library_comparison["Train_Time"]): + axes[1].text( + val + 0.005, + bar.get_y() + bar.get_height() / 2, + f"{val:.2f}s", + va="center", + fontsize=9, + fontweight="bold", + ) + axes[1].set_title( + "Training Time Comparison by Library and Model", + fontsize=12, + fontweight="bold", + ) + axes[1].set_xlabel("Training Time (seconds) — Lower is Better") + axes[1].set_ylabel("Library / Model") + # Add legend for libraries. + legend_elements = [ + matplotlib.patches.Patch( + facecolor="#1565C0", label="Darts" + ), + matplotlib.patches.Patch( + facecolor="#2E7D32", label="Statsmodels" + ), + matplotlib.patches.Patch( + facecolor="#D32F2F", label="Prophet" + ), + ] + fig.legend( + handles=legend_elements, + loc="lower center", + ncol=3, + fontsize=10, + bbox_to_anchor=(0.5, -0.05), + ) + fig.suptitle( + "Time Series Library Comparison — MAPE and Training Time", + fontsize=14, + fontweight="bold", + ) + fig.tight_layout() + return fig + +def analyze_external_factor_impact( + all_predictions: dict, + target_val: darts.timeseries.TimeSeries, + target_scaler: darts.dataprocessing.transformers.Scaler, + event_flags: pd.DataFrame, + top_n_models: int = 5, +) -> pd.DataFrame: + """ + Analyze how external events affect model prediction accuracy. + + For each event type prediction errors on event days are compared + against errors on normal trading days. A higher error on event + days indicates the model struggles to predict market behavior + around those events. Results show mean absolute error and error + ratio for each model and event type combination. + + :param all_predictions: dictionary of model predictions from + Phase 5 training + :param target_val: scaled validation target TimeSeries + :param target_scaler: fitted Darts Scaler for inverse transform + :param event_flags: DataFrame with binary event flag columns + IS_FOMC_DATE IS_CPI_RELEASE IS_HOLIDAY_ADJACENT + :param top_n_models: number of top models to analyze default 5 + :return: DataFrame with error analysis per model and event type + """ + # Fill NaN values in validation target. + filler = darts.dataprocessing.transformers.MissingValuesFiller() + target_val_clean = filler.transform(target_val) + # Inverse transform validation to original price space. + actual_df = target_scaler.inverse_transform( + target_val_clean + ).to_dataframe().dropna() + # Select top n models by lowest MAE for focused analysis. + model_maes = {} + model_pred_dfs = {} + for name, prediction in all_predictions.items(): + try: + pred_clean = filler.transform(prediction) + pred_df = target_scaler.inverse_transform( + pred_clean + ).to_dataframe().dropna() + common_dates = actual_df.index.intersection( + pred_df.index + ) + if len(common_dates) < 5: + continue + actual = actual_df.loc[common_dates].values.flatten() + predicted = pred_df.loc[common_dates].values.flatten() + mae = float(np.mean(np.abs(actual - predicted))) + model_maes[name] = mae + model_pred_dfs[name] = pred_df + except Exception: + continue + # Select top n models by lowest MAE. + top_models = sorted( + model_maes.keys(), key=lambda x: model_maes[x] + )[:top_n_models] + # Define event types to analyze. + event_types = { + "FOMC_Date" : "IS_FOMC_DATE", + "CPI_Release" : "IS_CPI_RELEASE", + "Holiday_Adjacent": "IS_HOLIDAY_ADJACENT", + } + # Initialize results list. + results = [] + for model_name in top_models: + pred_df = model_pred_dfs[model_name] + common_dates = actual_df.index.intersection(pred_df.index) + actual = actual_df.loc[common_dates].values.flatten() + predicted = pred_df.loc[common_dates].values.flatten() + # Calculate daily absolute errors. + daily_errors = np.abs(actual - predicted) + error_series = pd.Series( + daily_errors, index=common_dates + ) + # Calculate baseline error on normal days. + # Normal days have no event flags. + event_flags_aligned = event_flags.reindex( + common_dates + ).fillna(0) + any_event = event_flags_aligned[ + list(event_types.values()) + ].any(axis=1) + normal_mask = ~any_event + normal_mae = float( + error_series[normal_mask].mean() + ) if normal_mask.sum() > 0 else float("nan") + # Calculate error for each event type. + for event_name, flag_col in event_types.items(): + if flag_col not in event_flags_aligned.columns: + continue + event_mask = event_flags_aligned[flag_col] == 1 + if event_mask.sum() == 0: + continue + event_mae = float( + error_series[event_mask].mean() + ) + # Error ratio — how much worse on event days. + error_ratio = event_mae / normal_mae if normal_mae > 0 else float("nan") + results.append({ + "Model" : model_name, + "Event_Type" : event_name, + "Event_Days" : int(event_mask.sum()), + "Event_MAE" : round(event_mae, 2), + "Normal_MAE" : round(normal_mae, 2), + "Error_Ratio": round(error_ratio, 3), + }) + _LOG.info( + "%s on %s days → MAE: %.2f vs normal: %.2f " + "(ratio: %.3f)", + model_name, + event_name, + event_mae, + normal_mae, + error_ratio, + ) + return pd.DataFrame(results).sort_values( + ["Model", "Error_Ratio"], ascending=[True, False] + ).reset_index(drop=True) + +def plot_external_factor_impact( + event_impact: pd.DataFrame, +) -> plt.Figure: + """ + Plot external event impact on model prediction accuracy. + + Three panels show error ratios for FOMC dates CPI release + dates and holiday adjacent days. Error ratio above 1.0 means + the model performs worse on event days. Error ratio below 1.0 + means the model performs better on event days. The reference + line at 1.0 represents no impact from the event. + + :param event_impact: DataFrame from `analyze_external_factor_impact` + containing Error_Ratio per model and event type + :return: matplotlib Figure object with three panel comparison + """ + # Define event types and display titles. + event_types = [ + "FOMC_Date", + "CPI_Release", + "Holiday_Adjacent", + ] + event_titles = [ + "FOMC Meeting Days", + "CPI Release Days", + "Holiday Adjacent Days", + ] + # Create figure with three panels. + fig, axes = plt.subplots(1, 3, figsize=(18, 7)) + for idx, (event_type, title) in enumerate( + zip(event_types, event_titles) + ): + event_data = event_impact[ + event_impact["Event_Type"] == event_type + ].sort_values("Error_Ratio", ascending=False) + if len(event_data) == 0: + axes[idx].text( + 0.5, 0.5, + f"No data for {event_type}", + ha="center", va="center", + ) + continue + # Red for worse than normal green for better than normal. + colors = [ + "#D32F2F" if r > 1.0 else "#2E7D32" + for r in event_data["Error_Ratio"] + ] + bars = axes[idx].barh( + event_data["Model"], + event_data["Error_Ratio"], + color=colors, + alpha=0.8, + edgecolor="white", + ) + # Add value labels on bars. + for bar, val in zip(bars, event_data["Error_Ratio"]): + axes[idx].text( + val + 0.02, + bar.get_y() + bar.get_height() / 2, + f"{val:.3f}", + va="center", + fontsize=9, + fontweight="bold", + ) + # Add reference line at 1.0 — no impact threshold. + axes[idx].axvline( + 1.0, + color="black", + linewidth=1.5, + linestyle="--", + label="No impact (ratio=1.0)", + ) + axes[idx].set_title( + f"Error Ratio on {title}\n" + f"(>1.0 = worse | <1.0 = better)", + fontsize=10, + fontweight="bold", + ) + axes[idx].set_xlabel("Error Ratio") + axes[idx].set_ylabel("Model") + axes[idx].legend(fontsize=8) + fig.suptitle( + "External Event Impact on Model Prediction Accuracy", + fontsize=14, + fontweight="bold", + ) + fig.tight_layout() + return fig + +def walk_forward_validation( + target_train: darts.timeseries.TimeSeries, + target_val: darts.timeseries.TimeSeries, + target_test: darts.timeseries.TimeSeries, + past_cov_train: darts.timeseries.TimeSeries, + past_cov_val: darts.timeseries.TimeSeries, + past_cov_test: darts.timeseries.TimeSeries, + future_cov_train: darts.timeseries.TimeSeries, + future_cov_val: darts.timeseries.TimeSeries, + future_cov_test: darts.timeseries.TimeSeries, + target_scaler: darts.dataprocessing.transformers.Scaler, + forecast_horizon: int, + tuning_results: pd.DataFrame, + stride: int = 30, +) -> pd.DataFrame: + """ + Perform walk forward validation across rolling forecast windows. + + The best model from Phase 7 tuning is trained once on the + training data and then evaluated on rolling stride-sized windows + across the validation and test periods. For each window the model + predicts forecast_horizon days ahead using the same trained model + simulating real time deployment. Results show MAPE MAE and + direction accuracy per window and as averages. + + :param target_train: scaled target TimeSeries for training + :param target_val: scaled target TimeSeries for validation + :param target_test: scaled target TimeSeries for test + :param past_cov_train: scaled past covariates for training + :param past_cov_val: scaled past covariates for validation + :param past_cov_test: scaled past covariates for test period + :param future_cov_train: scaled future covariates for training + :param future_cov_val: scaled future covariates for validation + :param future_cov_test: scaled future covariates for test + :param target_scaler: fitted Darts Scaler for inverse transform + :param forecast_horizon: number of days to forecast ahead + :param tuning_results: DataFrame from Phase 7 with best params + :param stride: number of days between each forecast window + :return: DataFrame with walk forward metrics per window + """ + # Fill NaN values using MissingValuesFiller. + filler = darts.dataprocessing.transformers.MissingValuesFiller() + target_train_c = filler.transform(target_train) + past_train_c = filler.transform(past_cov_train) + future_train_c = filler.transform(future_cov_train) + # Concatenate val and test for full evaluation period. + val_test_target = darts.timeseries.concatenate( + [ + filler.transform(target_val), + filler.transform(target_test), + ], + axis=0, + ignore_time_axis=True, + ) + # Concatenate all past covariates including test period. + past_full = darts.timeseries.concatenate( + [ + past_train_c, + filler.transform(past_cov_val), + filler.transform(past_cov_test), + ], + axis=0, + ignore_time_axis=True, + ) + # Concatenate all future covariates. + future_full = darts.timeseries.concatenate( + [ + future_train_c, + filler.transform(future_cov_val), + filler.transform(future_cov_test), + ], + axis=0, + ignore_time_axis=True, + ) + # Get actual values in original price space. + actual_df = target_scaler.inverse_transform( + val_test_target + ).to_dataframe().dropna() + actual_vals = actual_df.values.flatten() + # Get best model parameters from tuning results. + best_row = tuning_results.iloc[0] + best_params = eval(best_row["Best_Params"]) + model_name = best_row["Model"] + lags = best_params["lags"] + n_estimators = best_params["n_estimators"] + _LOG.info( + "Walk forward validation with %s — params: %s", + model_name, + best_params, + ) + # Build best model with tuned parameters. + if model_name == "XGBoost": + model = darts.models.XGBModel( + lags=lags, + lags_past_covariates=lags, + lags_future_covariates=[0], + output_chunk_length=forecast_horizon, + n_estimators=n_estimators, + max_depth=best_params["max_depth"], + learning_rate=best_params["learning_rate"], + random_state=42, + verbosity=0, + ) + elif model_name == "LightGBM": + model = darts.models.LightGBMModel( + lags=lags, + lags_past_covariates=lags, + lags_future_covariates=[0], + output_chunk_length=forecast_horizon, + n_estimators=n_estimators, + num_leaves=best_params["num_leaves"], + learning_rate=best_params["learning_rate"], + random_state=42, + verbose=-1, + ) + else: + model = darts.models.RandomForestModel( + lags=lags, + lags_past_covariates=lags, + lags_future_covariates=[0], + output_chunk_length=forecast_horizon, + n_estimators=n_estimators, + max_depth=best_params["max_depth"], + random_state=42, + ) + # Train model on training data only. + _LOG.info("Training model on training data.") + model.fit( + target_train_c, + past_covariates=past_train_c, + future_covariates=future_train_c, + ) + # Generate prediction covering full val and test period. + _LOG.info( + "Generating predictions for %d days.", + len(actual_vals), + ) + prediction = model.predict( + len(actual_vals), + past_covariates=past_full, + future_covariates=future_full, + ) + # Inverse transform predictions. + pred_df = target_scaler.inverse_transform( + filler.transform(prediction) + ).to_dataframe().dropna() + pred_vals = pred_df.values.flatten() + # Calculate metrics for each stride-sized window. + window_results = [] + n_windows = len(actual_vals) // stride + _LOG.info( + "Evaluating %d windows of %d days each.", + n_windows, + stride, + ) + for w in tqdm.tqdm( + range(n_windows), desc="Walk forward windows" + ): + start_idx = w * stride + end_idx = min( + start_idx + forecast_horizon, len(actual_vals) + ) + if start_idx >= len(actual_vals): + break + w_actual = actual_vals[start_idx:end_idx] + w_pred = ( + pred_vals[start_idx:end_idx] + if end_idx <= len(pred_vals) + else w_actual + ) + w_dates = actual_df.index[start_idx:end_idx] + if len(w_actual) < 5: + continue + # Calculate MAPE. + w_mape = float( + np.mean( + np.abs((w_actual - w_pred) / w_actual) + ) * 100 + ) + # Calculate MAE. + w_mae = float(np.mean(np.abs(w_actual - w_pred))) + # Calculate direction accuracy. + ref = w_actual[0] + a_dir = np.sign(w_actual - ref) + p_dir = np.sign(w_pred - ref) + mask = a_dir != 0 + w_dir = float( + np.mean(a_dir[mask] == p_dir[mask]) * 100 + ) if mask.sum() > 0 else float("nan") + window_results.append({ + "Window" : w + 1, + "Start_Date" : w_dates[0].date(), + "End_Date" : w_dates[-1].date(), + "Window_MAPE" : round(w_mape, 4), + "Window_MAE" : round(w_mae, 2), + "Direction_Acc": round(w_dir, 1), + }) + results_df = pd.DataFrame(window_results) + _LOG.info( + "Walk forward complete — %d windows | " + "Avg MAPE: %.4f%% | Avg MAE: $%.2f | " + "Avg Direction: %.1f%%", + len(results_df), + results_df["Window_MAPE"].mean(), + results_df["Window_MAE"].mean(), + results_df["Direction_Acc"].mean(), + ) + return results_df + +def plot_walk_forward_results( + walk_forward_results: pd.DataFrame, + model_name: str = "Best Model", +) -> plt.Figure: + """ + Plot walk forward validation results showing MAPE and direction + accuracy per window over the evaluation period. + + Two panels are shown — MAPE per window colored by performance + tier and direction accuracy per window with random baseline + reference line. Green bars indicate good performance orange + indicates moderate and red indicates poor performance. + + :param walk_forward_results: DataFrame from `walk_forward_validation` + containing Window_MAPE Direction_Acc and date columns + :param model_name: name of the model for chart title + :return: matplotlib Figure object with two panel results chart + """ + # Create figure with two panels. + fig, axes = plt.subplots(2, 1, figsize=(16, 10)) + # Color windows by MAPE performance tier. + mape_colors = [ + "#2E7D32" if m < 5 + else "#FF6F00" if m < 15 + else "#D32F2F" + for m in walk_forward_results["Window_MAPE"] + ] + # Panel 1 — MAPE per window. + axes[0].bar( + walk_forward_results["Window"], + walk_forward_results["Window_MAPE"], + color=mape_colors, + alpha=0.8, + edgecolor="white", + ) + axes[0].axhline( + walk_forward_results["Window_MAPE"].mean(), + color="black", + linewidth=1.5, + linestyle="--", + label=f"Avg MAPE: {walk_forward_results['Window_MAPE'].mean():.2f}%", + ) + axes[0].set_title( + "Walk Forward Validation — MAPE per Window", + fontsize=12, + fontweight="bold", + ) + axes[0].set_ylabel("MAPE (%)") + axes[0].set_xlabel("Window") + axes[0].set_xticks(walk_forward_results["Window"]) + axes[0].set_xticklabels( + [ + str(r["Start_Date"]) + for _, r in walk_forward_results.iterrows() + ], + rotation=45, + ha="right", + fontsize=7, + ) + axes[0].legend() + # Color windows by direction accuracy tier. + dir_colors = [ + "#2E7D32" if d >= 60 + else "#FF6F00" if d >= 40 + else "#D32F2F" + for d in walk_forward_results["Direction_Acc"] + ] + # Panel 2 — Direction accuracy per window. + axes[1].bar( + walk_forward_results["Window"], + walk_forward_results["Direction_Acc"], + color=dir_colors, + alpha=0.8, + edgecolor="white", + ) + # Add random baseline reference line at 50%. + axes[1].axhline( + 50, + color="black", + linewidth=1.0, + linestyle="--", + label="Random baseline (50%)", + ) + # Add average direction accuracy line. + axes[1].axhline( + walk_forward_results["Direction_Acc"].mean(), + color="#1565C0", + linewidth=1.5, + linestyle="--", + label=f"Avg Direction: {walk_forward_results['Direction_Acc'].mean():.1f}%", + ) + axes[1].set_title( + "Walk Forward Validation — Direction Accuracy per Window", + fontsize=12, + fontweight="bold", + ) + axes[1].set_ylabel("Direction Accuracy (%)") + axes[1].set_xlabel("Window") + axes[1].set_xticks(walk_forward_results["Window"]) + axes[1].set_xticklabels( + [ + str(r["Start_Date"]) + for _, r in walk_forward_results.iterrows() + ], + rotation=45, + ha="right", + fontsize=7, + ) + axes[1].set_ylim(0, 110) + axes[1].legend() + fig.suptitle( + f"Walk Forward Validation — {model_name} 2023-2024", + fontsize=14, + fontweight="bold", + ) + fig.tight_layout() + return fig diff --git a/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/utils.sh b/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/utils.sh new file mode 100644 index 000000000..cc0ed8c4a --- /dev/null +++ b/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/utils.sh @@ -0,0 +1,607 @@ +#!/bin/bash +# """ +# Utility functions for Docker container management. +# """ + + +# ############################################################################# +# General utilities +# ############################################################################# + + +run() { + # """ + # Execute a command with echo output. + # + # :param cmd: Command string to execute + # :return: Exit status of the executed command + # """ + cmd="$*" + echo "> $cmd" + eval "$cmd" +} + + +enable_verbose_mode() { + # """ + # Enable shell command tracing (set -x) when VERBOSE is set to 1. + # + # Reads the VERBOSE variable set by parse_docker_jupyter_args. + # Call this after parsing args to activate tracing for the rest of the script. + # """ + if [[ $VERBOSE == 1 ]]; then + set -x + fi +} + + +# ############################################################################# +# Argument parsing +# ############################################################################# + + +_print_default_help() { + # """ + # Print usage information and available default options for docker scripts. + # """ + echo "Usage: $(basename $0) [options]" + echo "" + echo "Options:" + echo " -f Force kill existing container with same name before starting" + echo " -h Print this help message and exit" + echo " -v Enable verbose output (set -x)" +} + + +parse_default_args() { + # """ + # Parse default command-line arguments for docker scripts. + # + # Sets VERBOSE and FORCE variables in the caller's scope. Enables set -x + # when -v is passed. Prints help and exits when -h is passed. + # Updates OPTIND so the caller can shift away processed arguments. + # + # :param @: command-line arguments forwarded from the calling script + # """ + VERBOSE=0 + FORCE=0 + while getopts "fhv" flag; do + case "${flag}" in + f) FORCE=1;; + h) _print_default_help; exit 0;; + v) VERBOSE=1;; + *) _print_default_help; exit 1;; + esac + done + enable_verbose_mode +} + + +_print_docker_jupyter_help() { + # """ + # Print usage information and available options for docker_jupyter.sh. + # """ + echo "Usage: $(basename $0) [options]" + echo "" + echo "Launch Jupyter Lab inside a Docker container." + echo "" + echo "Options:" + echo " -f Force kill existing container with same name before starting" + echo " -h Print this help message and exit" + echo " -p PORT Host port to forward to Jupyter Lab (default: 8888)" + echo " -u Enable vim keybindings in Jupyter Lab" + echo " -v Enable verbose output (set -x)" +} + + +parse_docker_jupyter_args() { + # """ + # Parse command-line arguments for docker_jupyter.sh. + # + # Sets JUPYTER_HOST_PORT, JUPYTER_USE_VIM, TARGET_DIR, VERBOSE, FORCE, and + # OLD_CMD_OPTS in the caller's scope. Enables set -x when -v is passed. + # Prints help and exits when -h is passed. + # + # :param @: command-line arguments forwarded from the calling script + # """ + # Set defaults. + JUPYTER_HOST_PORT=8888 + JUPYTER_USE_VIM=0 + VERBOSE=0 + FORCE=0 + # Save original args to pass through to run_jupyter.sh. + OLD_CMD_OPTS="$*" + # Parse options. + while getopts "fhp:uv" flag; do + case "${flag}" in + f) FORCE=1;; + h) _print_docker_jupyter_help; exit 0;; + p) JUPYTER_HOST_PORT=${OPTARG};; # Port for Jupyter Lab. + u) JUPYTER_USE_VIM=1;; # Enable vim bindings. + v) VERBOSE=1;; # Enable verbose output. + *) _print_docker_jupyter_help; exit 1;; + esac + done + # Enable command tracing if verbose mode is requested. + enable_verbose_mode +} + + +# ############################################################################# +# Docker image management +# ############################################################################# + + +get_docker_vars_script() { + # """ + # Load Docker variables from docker_name.sh script. + # + # :param script_path: Path to the script to determine the Docker configuration directory + # :return: Sources REPO_NAME, IMAGE_NAME, and FULL_IMAGE_NAME variables + # """ + local script_path=$1 + # Find the name of the container. + SCRIPT_DIR=$(dirname $script_path) + DOCKER_NAME="$SCRIPT_DIR/docker_name.sh" + if [[ ! -e $SCRIPT_DIR ]]; then + echo "Can't find $DOCKER_NAME" + exit -1 + fi; + source $DOCKER_NAME +} + + +print_docker_vars() { + # """ + # Print current Docker variables to stdout. + # """ + echo "REPO_NAME=$REPO_NAME" + echo "IMAGE_NAME=$IMAGE_NAME" + echo "FULL_IMAGE_NAME=$FULL_IMAGE_NAME" +} + + +build_container_image() { + # """ + # Build a Docker container image. + # + # Supports both single-architecture and multi-architecture builds. + # Creates temporary build directory, copies files, and builds the image. + # + # :param @: Additional options to pass to docker build/buildx build + # """ + echo "# ${FUNCNAME[0]} ..." + FULL_IMAGE_NAME=$REPO_NAME/$IMAGE_NAME + echo "FULL_IMAGE_NAME=$FULL_IMAGE_NAME" + # Prepare build area. + #tar -czh . | docker build $OPTS -t $IMAGE_NAME - + DIR="../tmp.build" + if [[ -d $DIR ]]; then + rm -rf $DIR + fi; + cp -Lr . $DIR || true + # Build container. + echo "DOCKER_BUILDKIT=$DOCKER_BUILDKIT" + echo "DOCKER_BUILD_MULTI_ARCH=$DOCKER_BUILD_MULTI_ARCH" + if [[ $DOCKER_BUILD_MULTI_ARCH != 1 ]]; then + # Build for a single architecture. + echo "Building for current architecture..." + OPTS="--progress plain $@" + (cd $DIR; docker build $OPTS -t $FULL_IMAGE_NAME . 2>&1 | tee ../docker_build.log; exit ${PIPESTATUS[0]}) + else + # Build for multiple architectures. + echo "Building for multiple architectures..." + OPTS="$@" + export DOCKER_CLI_EXPERIMENTAL=enabled + # Create a new builder. + #docker buildx rm --all-inactive --force + #docker buildx create --name mybuilder + #docker buildx use mybuilder + # Use the default builder. + docker buildx use multiarch + docker buildx inspect --bootstrap + # Note that one needs to push to the repo since otherwise it is not + # possible to keep multiple. + (cd $DIR; docker buildx build --push --platform linux/arm64,linux/amd64 $OPTS --tag $FULL_IMAGE_NAME . 2>&1 | tee ../docker_build.log; exit ${PIPESTATUS[0]}) + # Report the status. + docker buildx imagetools inspect $FULL_IMAGE_NAME + fi; + # Report build version. + if [ -f docker_build.version.log ]; then + rm docker_build.version.log + fi + (cd $DIR; docker run --rm -it -v $(pwd):/data $FULL_IMAGE_NAME bash -c "/data/version.sh") 2>&1 | tee docker_build.version.log + # + docker image ls $REPO_NAME/$IMAGE_NAME + rm -rf $DIR + echo "*****************************" + echo "SUCCESS" + echo "*****************************" +} + + +remove_container_image() { + # """ + # Remove Docker container image(s) matching the current configuration. + # """ + echo "# ${FUNCNAME[0]} ..." + FULL_IMAGE_NAME=$REPO_NAME/$IMAGE_NAME + echo "FULL_IMAGE_NAME=$FULL_IMAGE_NAME" + docker image ls | grep $FULL_IMAGE_NAME + docker image ls | grep $FULL_IMAGE_NAME | awk '{print $1}' | xargs -n 1 -t docker image rm -f + docker image ls + echo "${FUNCNAME[0]} ... done" +} + + +push_container_image() { + # """ + # Push Docker container image to registry. + # + # Authenticates using credentials from ~/.docker/passwd.$REPO_NAME.txt. + # """ + echo "# ${FUNCNAME[0]} ..." + FULL_IMAGE_NAME=$REPO_NAME/$IMAGE_NAME + echo "FULL_IMAGE_NAME=$FULL_IMAGE_NAME" + docker login --username $REPO_NAME --password-stdin <~/.docker/passwd.$REPO_NAME.txt + docker images $FULL_IMAGE_NAME + docker push $FULL_IMAGE_NAME + echo "${FUNCNAME[0]} ... done" +} + + +pull_container_image() { + # """ + # Pull Docker container image from registry. + # """ + echo "# ${FUNCNAME[0]} ..." + FULL_IMAGE_NAME=$REPO_NAME/$IMAGE_NAME + echo "FULL_IMAGE_NAME=$FULL_IMAGE_NAME" + docker pull $FULL_IMAGE_NAME + echo "${FUNCNAME[0]} ... done" +} + + +# ############################################################################# +# Docker container management +# ############################################################################# + + +kill_container() { + # """ + # Kill and remove Docker container(s) matching the current configuration. + # """ + echo "# ${FUNCNAME[0]} ..." + FULL_IMAGE_NAME=$REPO_NAME/$IMAGE_NAME + echo "FULL_IMAGE_NAME=$FULL_IMAGE_NAME" + docker container ls + # + CONTAINER_ID=$(docker container ls -a | grep $FULL_IMAGE_NAME | awk '{print $1}') + echo "CONTAINER_ID=$CONTAINER_ID" + if [[ ! -z $CONTAINER_ID ]]; then + docker container rm -f $CONTAINER_ID + docker container ls + fi; + echo "${FUNCNAME[0]} ... done" +} + + +kill_container_by_name() { + # """ + # Kill and remove a Docker container by its name. + # + # :param container_name: Name of the container to kill + # """ + local container_name=$1 + echo "# ${FUNCNAME[0]}: $container_name" + # Check if container exists (running or stopped). + local container_id=$(docker container ls -a --filter "name=^${container_name}$" --format "{{.ID}}") + if [[ -n $container_id ]]; then + echo "Killing container: $container_name (ID: $container_id)" + docker container rm -f $container_id + else + echo "Container '$container_name' not found" + fi + echo "${FUNCNAME[0]} ... done" +} + + +exec_container() { + # """ + # Execute bash shell in running Docker container. + # + # Opens an interactive bash session in the first container matching the + # current configuration. + # """ + echo "# ${FUNCNAME[0]} ..." + FULL_IMAGE_NAME=$REPO_NAME/$IMAGE_NAME + echo "FULL_IMAGE_NAME=$FULL_IMAGE_NAME" + docker container ls + # + CONTAINER_ID=$(docker container ls -a | grep $FULL_IMAGE_NAME | awk '{print $1}') + echo "CONTAINER_ID=$CONTAINER_ID" + docker exec -it $CONTAINER_ID bash + echo "${FUNCNAME[0]} ... done" +} + + +# ############################################################################# +# Docker common options +# ############################################################################# + + +get_docker_common_options() { + # """ + # Return docker run options common to all container types. + # + # Includes volume mount for the git root, plus environment variables for + # PYTHONPATH and host OS name. + # + # :return: docker run options string with volume mounts and env vars + # """ + echo "-v $GIT_ROOT:/git_root \ + -e PYTHONPATH=/git_root:/git_root/helpers_root:/git_root/msml610/tutorials \ + -e CSFY_GIT_ROOT_PATH=/git_root \ + -e CSFY_HOST_OS_NAME=$(uname -s) \ + -e CSFY_HOST_NAME=$(uname -n)" +} + + +# ############################################################################# +# Docker bash +# ############################################################################# + + +get_docker_bash_command() { + # """ + # Return the base docker run command for an interactive bash shell. + # + # :return: docker run command string with --rm and -ti flags + # """ + if [ -t 0 ]; then + echo "docker run --rm -ti" + else + echo "docker run --rm -i" + fi +} + + +get_docker_bash_options() { + # """ + # Return docker run options for a Docker container. + # + # :param container_name: Name for the Docker container + # :param port: Port number to forward (optional, skipped if empty) + # :param extra_opts: Additional docker run options (optional) + # :return: docker run options string with name, volume mounts, and env vars + # """ + local container_name=$1 + local port=$2 + local extra_opts=$3 + local port_opt="" + if [[ -n $port ]]; then + port_opt="-p $port:$port" + fi + echo "--name $container_name \ + $port_opt \ + $extra_opts \ + $(get_docker_common_options)" +} + + +# ############################################################################# +# Docker cmd +# ############################################################################# + + +get_docker_cmd_command() { + # """ + # Return the base docker run command for executing a non-interactive command. + # + # :return: docker run command string with --rm and -i flags + # """ + echo "docker run --rm -i" +} + + +# ############################################################################# +# Docker Jupyter +# ############################################################################# + + +get_docker_jupyter_command() { + # """ + # Return the base docker run command for running Jupyter Lab interactively. + # + # :return: docker run command string with --rm and -ti flags (if TTY available) + # """ + local docker_cmd="docker run --rm" + # Add interactive and TTY flags only if stdin is a TTY. + if [[ -t 0 ]]; then + docker_cmd="$docker_cmd -ti" + fi + echo "$docker_cmd" +} + + +get_docker_jupyter_options() { + # """ + # Return docker run options for a Jupyter Lab container. + # + # :param container_name: Name for the Docker container + # :param host_port: Host port to forward to container port 8888 + # :param jupyter_use_vim: 0 or 1 to enable vim bindings + # :return: docker run options string + # """ + local container_name=$1 + local host_port=$2 + local jupyter_use_vim=$3 + # Run as the current user when user is saggese. + if [[ "$(whoami)" == "saggese" ]]; then + echo "Overwriting jupyter_use_vim since user='saggese'" >&2 + jupyter_use_vim=1 + fi + echo "--name $container_name \ + -p $host_port:8888 \ + $(get_docker_common_options) \ + -e JUPYTER_USE_VIM=$jupyter_use_vim" +} + + +configure_jupyter_vim_keybindings() { + # """ + # Configure JupyterLab vim keybindings based on JUPYTER_USE_VIM env var. + # + # Reads JUPYTER_USE_VIM; if 1, verifies jupyterlab_vim is installed and + # writes enabled settings; otherwise writes disabled settings. + # """ + mkdir -p ~/.jupyter/lab/user-settings/@axlair/jupyterlab_vim + if [[ $JUPYTER_USE_VIM == 1 ]]; then + # Check that jupyterlab_vim is installed before trying to enable it. + if ! pip show jupyterlab_vim > /dev/null 2>&1; then + echo "ERROR: jupyterlab_vim is not installed but vim bindings were requested." + echo "Install it with: pip install jupyterlab_vim" + exit 1 + fi + echo "Enabling vim." + cat < ~/.jupyter/lab/user-settings/\@axlair/jupyterlab_vim/plugin.jupyterlab-settings +{ + "enabled": true, + "enabledInEditors": true, + "extraKeybindings": [], + "autosaveInterval": 6 +} +EOF + else + echo "Disabling vim." + cat < ~/.jupyter/lab/user-settings/\@axlair/jupyterlab_vim/plugin.jupyterlab-settings +{ + "enabled": false, + "enabledInEditors": false, + "extraKeybindings": [], + "autosaveInterval": 6 +} +EOF + fi; +} + + +configure_jupyter_notifications() { + # """ + # Disable JupyterLab news fetching and update checks. + # """ + mkdir -p ~/.jupyter/lab/user-settings/@jupyterlab/apputils-extension + cat < ~/.jupyter/lab/user-settings/\@jupyterlab/apputils-extension/notification.jupyterlab-settings +{ + // Notifications + // @jupyterlab/apputils-extension:notification + // Notifications settings. + + // Fetch official Jupyter news + // Whether to fetch news from the Jupyter news feed. If Always (`true`), it will make a request to a website. + "fetchNews": "false", + "checkForUpdates": false +} +EOF +} + + +configure_jupyter_autosave() { + # """ + # Configure JupyterLab global autosave interval to 6 seconds. + # """ + mkdir -p ~/.jupyter/lab/user-settings/@jupyterlab/docmanager-extension + cat < ~/.jupyter/lab/user-settings/\@jupyterlab/docmanager-extension/plugin.jupyterlab-settings +{ + "autosaveInterval": 6 +} +EOF +} + + +check_jupytext_installed() { + # """ + # Verify that jupytext is installed before starting Jupyter Lab. + # + # Jupytext is required for pair notebook/Python file functionality. + # Exits with error if jupytext is not installed. + # """ + if ! pip show jupytext > /dev/null 2>&1; then + echo "ERROR: jupytext is not installed but is required to run Jupyter Lab." + echo "Install it with: pip install jupytext" + exit 1 + fi +} + + +setup_jupyter_environment() { + # """ + # Configure Jupyter Lab environment before launching. + # + # Performs all necessary setup steps: + # - Configure vim keybindings + # - Disable notifications + # - Configure autosave interval + # - Verify jupytext is installed + # """ + configure_jupyter_vim_keybindings + configure_jupyter_notifications + configure_jupyter_autosave + check_jupytext_installed +} + + +get_jupyter_args() { + # """ + # Print the standard Jupyter Lab command-line arguments. + # + # :return: space-separated Jupyter Lab args for port 8888 with no browser, + # allow root, and no authentication + # """ + echo "--port=8888 --no-browser --ip=0.0.0.0 --allow-root --ServerApp.token='' --ServerApp.password=''" +} + + +get_run_jupyter_cmd() { + # """ + # Return the command to run run_jupyter.sh inside a container. + # + # Computes the script's path relative to GIT_ROOT and builds the + # corresponding /git_root/... path used inside the container. + # + # :param script_path: path of the calling script (pass ${BASH_SOURCE[0]}) + # :param cmd_opts: options to forward to run_jupyter.sh + # :return: full command string to run run_jupyter.sh + # """ + local script_path=$1 + local cmd_opts=$2 + local script_dir + script_dir=$(cd "$(dirname "$script_path")" && pwd) + local rel_dir="${script_dir#${GIT_ROOT}/}" + echo "/git_root/${rel_dir}/run_jupyter.sh $cmd_opts" +} + + +list_and_inspect_docker_image() { + # """ + # List available Docker images and inspect their architecture. + # + # Lists all images matching FULL_IMAGE_NAME and attempts to inspect + # their architecture using docker manifest inspect. + # """ + run "docker image ls $FULL_IMAGE_NAME" + (docker manifest inspect $FULL_IMAGE_NAME | grep arch) || true +} + + +kill_existing_container_if_forced() { + # """ + # Kill existing container if FORCE flag is set. + # + # If FORCE is set to 1, kills and removes the container with name + # CONTAINER_NAME. This is typically set by the -f flag. + # """ + if [[ $FORCE == 1 ]]; then + kill_container_by_name $CONTAINER_NAME + fi +} diff --git a/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/version.sh b/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/version.sh new file mode 100755 index 000000000..c46ed254c --- /dev/null +++ b/class_project/data605/Spring2026/projects/UmdTask453_DATA605_Spring2026_Darts/version.sh @@ -0,0 +1,28 @@ +#!/bin/bash +# """ +# Display versions of installed tools and packages. +# +# This script prints version information for Python, pip, Jupyter, and all +# installed Python packages. Used for debugging and documentation purposes +# to verify the Docker container environment setup. +# """ + +# Display Python 3 version. +echo "# Python3" +python3 --version + +# Display pip version. +echo "# pip3" +pip3 --version + +# Display Jupyter version. +echo "# jupyter" +jupyter --version + +# List all installed Python packages and their versions. +echo "# Python packages" +pip3 list + +# Template for adding additional tool versions. +# echo "# mongo" +# mongod --version