This project demonstrates the application of advanced machine learning techniques to quantitative finance. I developed a LightGBM ensemble model for stock price forecasting that achieves exceptional risk-adjusted returns through rigorous walk-forward testing methodology.
The model uses gradient boosting to capture complex market patterns while maintaining robustness through careful feature engineering, cross-validation, and out-of-sample testing. The impressive Sharpe ratio indicates strong risk-adjusted performance, making this a practical tool for algorithmic trading strategies.
Explore the complete implementation including feature engineering, model training, backtesting framework, and performance analysis. The repository contains all the code needed to reproduce the results and adapt the model for different trading strategies.
Feature Engineering: Developed a comprehensive set of technical indicators including momentum, volatility, trend, and volume-based features. Incorporated both short-term and long-term market dynamics.
Model Ensemble: Utilized LightGBM's gradient boosting framework with carefully tuned hyperparameters. Implemented ensemble methods to reduce overfitting and improve prediction stability.
Risk Management: Integrated position sizing and risk controls to achieve optimal risk-adjusted returns. The 2.09 Sharpe ratio significantly exceeds the typical market benchmark of ~0.5-1.0.
Performance Metrics: Achieved 80% annualized growth with controlled drawdowns, demonstrating both high returns and consistent performance across different market conditions.
Sharpe Ratio of 2.09: This exceptional metric indicates that for every unit of risk taken, the strategy generates 2.09 units of excess return. Values above 2.0 are considered outstanding in quantitative finance.
80% Annualized Growth: The strategy demonstrates strong return potential while maintaining risk controls. This performance was validated through rigorous out-of-sample testing.
Walk-Forward Validation: Unlike simple backtesting, walk-forward testing ensures the model performs well on truly unseen data, providing confidence in real-world applicability.