Machine Learning for Trading — 3rd Edition

Build, test, and deploy ML-driven trading strategies — from data sourcing to live execution.

This repository hosts the code for Machine Learning for Trading, 3rd Edition by Stefan Jansen — a ground-up rebuild, organized around one end-to-end workflow: how you define a research idea and develop it iteratively into a strategy you can actually run, and keep running, in a live market.

• Nine case studies illustrate the workflow throughout the 27 chapters of the book, from raw data through features, models, backtests, costs, and risk to deployment.
• Generative AI and autonomous agents are new to this edition and cut across that workflow, bringing retrieval-augmented generation, knowledge graphs, and multi-agent systems to financial research.
• The companion website features 112 primers, 56 agent skills, and six production Python libraries that facilitate substantial parts of the workflow.

🎓 New: Live Courses & Lightning Lessons

For the first time, the third edition comes with a live cohort course, hands-on workshops, and free lightning lessons taught by Stefan on Maven — full schedule on the courses page.

• ▶ Machine Learning for Trading: From Research to Production — the flagship live cohort course: take a research idea all the way to a deployed, monitored strategy, working through the book's end-to-end workflow with direct feedback. The first cohort starts Monday, July 6, 2026 — enrollment closes Friday, July 3.
• Getting Stuff Done with Coding Agents — a free lightning lesson on putting coding agents to work.
• Building Multi-Agent Forecasting Systems — a hands-on workshop on engineering the forecasting-agent loop: building auditable, debate-driven multi-agent systems for financial research.

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What's New in the Third Edition

The whole book traces one path: from data infrastructure and strategy research, across an evidence boundary that separates tuning from evaluation, to deployment and monitoring — with a feedback loop that retrains, pauses, or retires a strategy as its edge decays.

Where earlier editions moved technique by technique, the third edition runs that one process end to end — and adds substantial new material:

• A wider model toolkit: from gradient boosting (XGBoost, LightGBM, CatBoost) to deep time-series architectures (PatchTST, iTransformer, TSMixer, TCN, Mamba) and newer tabular and latent-factor models (TabPFN, TabM, conditional and supervised autoencoders).
• Dedicated strategy-design chapters: transaction costs and risk management are now full chapters, neither of which existed before, joining portfolio construction and strategy synthesis so a raw signal is carried through to a sized, cost- and risk-aware portfolio.
• **A full produc