🐍 Python Stock Market Analysis

A hands-on learning project for financial data analysis using Python and Jupyter Notebooks. This repository covers stock market data retrieval, exploration, visualization, and technical analysis with popular Python libraries.

📂 Repository Structure

├── 00_setup.ipynb                    # Environment check + download OHLCV + basic price/volume plots + CSV export
├── 01_stock_data_exploration.ipynb   # Deeper exploration of stock data + indicators/visuals (work-in-progress)
├── finance_knowledgebase.docx        # Finance concepts and interactive learning activities
├── requirements.txt                  # Python dependencies
├── LICENSE                           # MIT License
└── README.md                         # Project overview

📓 Notebooks

• 00_setup.ipynb

  • Validates your environment (imports + version checks)
  • Downloads adjusted OHLCV using yfinance
  • Plots:
    • individual close-price charts
    • normalised close-price comparison (rebased to 100)
    • volume bar charts
  • Exports per-ticker OHLCV CSVs to data/

• 01_stock_data_exploration.ipynb

  • Explores downloaded OHLCV data in more detail (inspection, cleaning, visualisation)
  • Intended place for technical indicators (e.g., RSI/MACD via stockstats / ta) and further analysis

🚀 Getting Started

Prerequisites

• Python 3.11+
• Git

Option A — venv + pip (recommended)

# from the repo root
python -m venv .venv

# activate (Git Bash)
source .venv/Scripts/activate

python -m pip install --upgrade pip
pip install -r requirements.txt

# start Jupyter
jupyter notebook

Option B — conda

conda create -n stock-analysis python=3.11 -y
conda activate stock-analysis
pip install -r requirements.txt
jupyter notebook

📦 Dependencies

The project uses a small set of common Python data-science libraries:

Library	Purpose
pandas	Data manipulation and tabular display
numpy	Numerical computation
matplotlib	Core plotting and visualisation
seaborn	Statistical visualisations (optional but useful)
plotly	Interactive charts (optional but useful)
yfinance	Downloading stock market data from Yahoo Finance
multitasking	Multithreading support used by yfinance
stockstats	Technical indicators (e.g., MACD, RSI)
ta	Additional technical analysis indicators
scikit-learn	Machine learning experiments (optional)
ipykernel	Jupyter kernel support

📖 Usage Example

import yfinance as yf
import pandas as pd
import matplotlib.pyplot as plt

ticker = "AAPL"
df = pd.DataFrame(yf.download(ticker, start="2025-01-01", end="2026-01-01", auto_adjust=True))

plt.figure(figsize=(14, 4))
plt.plot(df.index, df["Close"])
plt.title("AAPL adjusted close")
plt.xlabel("Date")
plt.ylabel("Price (USD)")
plt.grid(True, alpha=0.3)
plt.show()

📝 License

This project is open source and available under the MIT License (see LICENSE).