machine-learning-for-trading

Why we included this project

Anyone learning to apply machine learning to financial markets will find this repository a practical companion to the third edition of Stefan Jansen's Machine Learning for Trading. The code follows one end-to-end path, from managing data and engineering features through training models, backtesting, and accounting for costs and risk, to deploying a strategy that runs live. Nine case studies tie the chapters together. The new edition adds generative AI and autonomous agents, covering retrieval-augmented generation, knowledge graphs, and multi-agent systems for financial research. Because the code is organized around a single pipeline rather than isolated snippets, it also works as a reference for how the pieces of a quant research stack fit together, and the companion site adds primers and production libraries that extend the notebooks.