Stock Analysis Engine with Machine Learning

A comprehensive Python-based financial analysis platform that combines traditional statistical analysis with advanced machine learning techniques for in-depth stock market insights.

Features

Traditional Financial Analysis

• Statistical Metrics: Historical Volatility, Skewness, Kurtosis, Sharpe Ratio
• Return Analysis: Logarithmic returns calculation and distribution analysis
• Visualization Suite: Professional-quality charts with matplotlib and seaborn
• Multi-Stock Comparison: Side-by-side statistical comparison

Machine Learning Capabilities

• Regression Analysis: Pairwise regression models with beta coefficients and R² values
• Correlation Analysis: Static and rolling correlation matrices with heatmap visualizations
• Cointegration Testing: Engle-Granger tests for long-term relationship identification
• Predictive Modeling: Foundation for price prediction and portfolio optimization

Advanced Visualizations

• Returns distribution plots with normal distribution overlay
• Rolling volatility trend analysis
• Correlation heatmaps with statistical significance
• Regression scatter plots with trend lines
• Time series correlation evolution

Project Structure

stock-analysis-engine/
├── README.md                     # This file
├── LICENSE.txt                    # MIT License
├── requirements.txt              # Python dependencies
├── main.py                      # Main application entry point
├── __init__.py                  # Package initialization
│
├── analysis/                    # Core financial analysis
│   ├── __init__.py
│   ├── financial_metrics.py    # Statistical calculations
│   └── test_metrics.py         # Testing module
│
├── utils/                       # Data handling utilities
│   ├── __init__.py
│   ├── data_fetcher.py         # Yahoo Finance data retrieval
│   └── data_validator.py       # Data quality validation
│
├── visualization/               # Charting and plotting
│   ├── __init__.py
│   ├── stats_visualizer.py     # Traditional financial charts
│   └── test_visualizer.py      # Visualization testing
│
├── ml/                         # Machine Learning module
│   ├── __init__.py
│   └── correlation_analyzer.py # ML correlation & regression analysis
│
└── output/                     # Generated files
    ├── plots/                  # Traditional analysis charts
    └── ml_plots/              # Machine learning visualizations

Statistical Metrics Explained

Traditional Metrics

• Historical Volatility: Annualized standard deviation of returns (risk measure)
• Skewness: Asymmetry of return distribution (negative = more left tail risk)
• Kurtosis: Tail heaviness compared to normal distribution (higher = more extreme events)
• Sharpe Ratio: Risk-adjusted return measure (return per unit of risk)

ML Metrics

• Beta Coefficient: Stock's sensitivity to market movements (β > 1 = more volatile than market)
• R-Squared: Percentage of variance explained by the model (0-1 scale)
• Correlation: Linear relationship strength between two stocks (-1 to +1)
• Cointegration: Long-term equilibrium relationship between stock prices

Technical Implementation

Data Source

• Yahoo Finance API via yfinance library
• Real-time and historical stock data
• Reliable day-end pricing information

Core Libraries

• pandas & numpy: Data manipulation and numerical computations
• scipy: Advanced statistical calculations
• scikit-learn: Machine learning algorithms
• statsmodels: Econometric analysis and cointegration testing
• matplotlib & seaborn: Professional visualization

Machine Learning Algorithms

• Linear Regression: For beta calculation and relationship modeling
• Pearson/Spearman Correlation: For relationship strength measurement
• Engle-Granger Test: For cointegration analysis
• Rolling Window Analysis: For time-varying correlation patterns

Future Enhancements

Planned ML Features

1. LSTM Price Prediction: Neural networks for price forecasting
2. Portfolio Optimization: Modern Portfolio Theory implementation
3. Sentiment Analysis: News and social media impact analysis
4. RAG Integration: Natural language query interface

Advanced Analytics

1. Options Pricing Models: Black-Scholes implementation
2. Risk Management: VaR and CVaR calculations
3. Backtesting Framework: Strategy performance evaluation
4. Real-time Data Streaming: Live market analysis

License

This project is licensed under the MIT License - see the LICENSE file for details. Feel free to open a PR