v1.8.0 - New lessons and Lint Formatting

[MeridianAlgo-Developer]

Learn-Quant: Master Quantitative Finance & Python (v1.8.0)

Welcome to Learn-Quant! Your all-in-one, comprehensive toolkit for mastering algorithmic trading, quantitative finance theory, and professional Python software engineering.

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Overview

Learn-Quant is a massive, curated collection of over 50+ self-contained modules designed to bridge the gap between academic theory and production-grade code. Whether you are a student, a software engineer moving into finance, or a trader learning to code, this repository provides the building blocks you need.

Key Learning Outcomes

• Master Quant Strategies: Implement Pairs Trading, Momentum, Mean Reversion, and more.
• Engineer Robust Systems: Learn AsyncIO, Context Managers, Decorators, and advanced OOP.
• Deep Dive into Math: Kalman Filters, Stochastic Processes, Linear Algebra for Portfolio Theory.
• Build Core Tools: Create your own Option Pricers, Risk Engines (VaR), and Backtesting Simulators.
• CS Algorithms: Understand how Sorting, Graph Theory, and Dynamic Programming apply to market data.

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Repository Structure

Every folder is a fully functional lesson. Pick a topic and run the code.

Level 1: Python Fundamentals

Essential coding skills for financial analysis.

• UTILS - Python Basics - Numbers: Floating point precision & financial math.
• UTILS - Python Basics - Strings: Ticker manipulation & news parsing.
• UTILS - Python Basics - Control Flow: Implementing trading logic & rules.
• UTILS - Python Basics - Functions: Building reusable quant libraries.

Level 2: Data Structures & Algorithms

Optimizing performance for high-frequency environments.

• UTILS - Data Structures: Efficient use of Lists, Sets, Tuples, and Dictionaries.
• UTILS - Algorithms - Sorting: Algorithmic efficiency (Quicksort, Mergesort).
• UTILS - Algorithms - Searching: Binary search on time-series data.
• UTILS - Algorithms - Graph: Arbitrage detection using shortest paths.
• UTILS - Algorithms - Dynamic Programming: Optimizing execution paths.

Level 3: Advanced Engineering

Writing professional, production-ready code.

• UTILS - Advanced Python - AsyncIO: Building high-throughput data pipelines.
• UTILS - Advanced Python - OOP: Designing scalable Trading Engines & Portfolio Managers.
• UTILS - Advanced Python - Context Managers: Handling database locks and atomic transactions.
• UTILS - Advanced Python - Decorators: Custom logging, timing, and error handling wrappers.
• UTILS - Advanced Python - Error Handling: Robust systems that never crash mid-trade.

Level 4: Quantitative Methods

The mathematics of the markets.

• UTILS - Quantitative Methods - Kalman Filter: Dynamic hedge ratios & noise filtering.
• UTILS - Quantitative Methods - Stochastic Processes: Geometric Brownian Motion & Monte Carlo.
• UTILS - Quantitative Methods - Statistics: Hypothesis testing, stationarity, and cointegration.
• UTILS - Quantitative Methods - Regression: Factor models & Alpha generation.
• UTILS - Quantitative Methods - Linear Algebra: Portfolio optimization & risk modelling.

Level 5: Strategies & Finance

Applied quantitative finance.

• UTILS - Strategies - Pairs Trading: Statistical arbitrage & mean reversion.
• UTILS - Strategies - Momentum Trading: Trend following & signal generation.
• UTILS - Black-Scholes Option Pricing: Greeks, implied volatility, & derivatives pricing.
• UTILS - Finance - Volatility Calculator: Parkinson, Garman-Klass, & EWMA estimators.
• UTILS - Portfolio Optimizer: Efficient Frontier, Sharpe Ratio, & Markowitz optimization.
• UTILS - Risk Metrics: Value at Risk (VaR), CVaR, Drawdown, & Sortino Ratio.
• UTILS - Technical Indicators: Custom implementations of RSI, MACD, Bollinger Bands.

Level 6: AI & Alternative Data

Modern approaches to trading.

• UTILS - AI Development: Basic market prediction models.
• UTILS - Sentiment Analysis on News: NLP for fundamental analysis.
• UTILS - Websocket Connection: Real-time market data streaming.

Level 7: Market Microstructure

Understanding order book dynamics and market impact.

• UTILS - Market Microstructure: Order book implementation, spread analysis, and market impact models.
• UTILS - High Frequency Trading: Latency optimization, execution algorithms, and HFT strategies.

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Usage

1. Installation

Clone the repository and install the required dependencies.

git clone https://github.com/MeridianAlgo/Learn-Quant
pip install -r requirements.txt

2. Running a Module

Navigate to any directory and run the tutorial script.

Example: Running the Momentum Strategy

cd "UTILS - Strategies - Momentum Trading"
python momentum_strategy.py

Example: Learning Context Managers

cd "UTILS - Advanced Python - Context Managers"
python context_managers_tutorial.py

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Contributing

We believe in open-source knowledge. Contributions are welcome!

• Found a bug? Open an Issue.
• Have a new strategy? Fork the repo and submit a Pull Request.
• Documentation improvements? We love those too.

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License

This project is open-sourced under the MIT License.

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Learn-Quant v1.8.0
Quantitative Finance | Algorithmic Trading | Python Mastery
Maintained by MeridianAlgo

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This discussion was created from the release v1.8.0 - New lessons and Lint Formatting.