| Description
| This course explores the application of machine learning in algorithmic trading and systematic investment strategies. Grounded in rigorous data engineering, it teaches students to process market and alternative data (e.g., news sentiment) to extract predictive Alpha factors.The first half focuses on feature engineering and financial time-series cross-validation (e.g., Purged CV) to ensure model robustness. The second half systematically introduces various predictive models, including tree-based ensembles (Random Forest, LightGBM) and deep learning architectures (CNN, RNN/LSTM), applied to asset pricing and volatility prediction. Finally, the course covers portfolio optimization and rigorous backtesting. Students will use the Python ecosystem (Zipline, Alphalens, Pyfolio) to evaluate risk-adjusted performance metrics. The course culminates in a final project where students develop an end-to-end quantitative trading strategy.
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