machine-learning-for-trading

What it is

A collection of Jupyter notebooks accompanying Stefan Jansen's Machine Learning for Trading book, covering the full path from data sourcing to live strategy execution. The code spans 27 chapters and nine case studies, including gradient boosting, deep time-series models, causal machine learning, reinforcement learning, and live trading via Interactive Brokers, Alpaca, and QuantConnect. Quant traders and developers who want to learn ML-driven strategy research and deployment end to end will find it most useful. As book companion material, the notebooks are research code for study rather than a production-ready trading product.