Portfolio backtest engine with montecarlo simulation, walk-forward, efficient frontier, FIRE, charts, performance optimization, max drowdown with Ai suggest!
Hybrid LSTM-XGBoost model for stock return prediction and portfolio optimization with backtesting and Explainable AI.
End-to-end ML pipeline for systematic equity trading: 25 years of Bloomberg risk factors → feature engineering → model selection (XGBoost/BayesianRidge) → SHAP explainability → walk-forward backtesting. Finds that an adaptive blended model produces +439% cumulative long-short alpha with 0.38 Sharpe over 1999–2025.
From-scratch mean-variance portfolio optimization toolkit reproducing canonical literature results.
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