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.

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 production track: live trading systems (Interactive Brokers, Alpaca, QuantConnect), MLOps and governance (drift detection, safe rollout, circuit breakers, feature stores, experiment tracking), and the operational reality of running strategies, not just building them.
• Generative AI: retrieval-augmented generation grounded in SEC filings, knowledge graphs and Graph RAG, and autonomous, multi-agent research systems.
• Causal machine learning: Double ML, Bayesian structural time series, and causal discovery for separating real effects from spurious correlation.
• Reinforcement learning: optimal execution, market making with inventory, and deep hedging.
• Synthetic financial data: TimeGAN, Tail-GAN, Sig-CWGAN, and diffusion-based generators for validation when history is short.

Methodological rigor is treated as a first-class topic rather than an afterthought. The book draws an explicit line between exploration and confirmation — the evidence boundary — uses walk-forward cross-validation throughout, and confronts the multiple-testing and overfitting problems that quietly invalidate most backtests, with tools like the Deflated Sharpe Ratio, the Rademacher Anti-Serum, and White's Reality Check, plus conformal prediction for honest uncertainty estimates.

The data layer moves to Polars for fast, expression-based manipulation, and every chapter ships in reproducible Docker environments so results repeat across machines; PyTorch, LightGBM, Optuna, and Plotly round out the modeling and visualization stack.

Nine Case Studies

The structural centerpiece of the third edition is nine case studies that run the length of the book. ETFs, crypto perpetuals, intraday equities, options, FX, futures, and equity factor panels are each carried through the same pipeline — from raw data and labels to features, models, backtests, costs, risk overlays, and a final deployment assessment. One disciplined process applied to nine very different markets shows where it works, where it breaks, and why.

112 Primer Topics

Free concept explainers for every idea the book relies on. Each part links to its full list; a few topics show the range:

• Foundations: 8 topics spanning limit order book mechanics, bitemporal data models, and the stylized facts a simulator must reproduce.
• Research Design and Feature Engineering: 21 topics, including multiple testing in factor research, fractional differencing, and path signatures for financial sequences.
• Model Development: 22 topics, among them regularization geometry, conformal prediction in finance, and the mechanism behind double machine learning.
• Strategy Implementation: 27 topics, from the deflated Sharpe ratio and hierarchical risk parity to Almgren-Chriss optimal execution.
• Advanced AI: 8 topics such as Markov decision processes, the policy-gradient theorem, and proper scoring rules for event forecasts.
• Production: 2 topics, champion-challenger evaluation and training-serving skew with feature stores.
• Cross-cutting concepts: 20 building blocks referenced across chapters, for example momentum and mean reversion, the bias-variance tradeoff, and walk-forward validation.

56 Agent Skills

Reusable, guard-railed tasks for coding agents, each with built-in defenses against lookahead bias, data leakage, and multiple-testing errors. Each category links to its full set; a few skills show the range:

• Concepts: 10 skills, including lookahead bias, data leakage, and the information coefficient.
• Data Acquisition: 7 skills spanning fetching data, building bars, and data validation.
• Feature Engineering: 10 skills, among them computing features, triple-barrier labels, and feature selection.
• Evaluation & Validation: 8 skills, from walk-forward CV and purging-and-embargo to the deflated Sharpe ratio.
• Backtesting: 5 skills such as running backtests, cost models, and tear sheets.
• Portfolio Management: 5 skills, including position sizing, risk metrics, and kill switches.
• Infrastructure: 4 skills, for example the canonical schema, the registry system, and Polars patterns.
• Workflows: 5 skills covering factor research, model validation, and production readiness.
• Production: 2 skills, live trading and monitoring & alerting.

The ML4T Libraries

The notebooks are built on six production Python packages, each documented and usable on its own — one per stage of the workflow:

An introduction and a closing chapter bookend six workflow-aligned parts. Chapter titles link to their guides as each part is published; the rest are added part by part over the coming weeks.

Introduction

1. The Process Is Your Edge

Why process discipline beats model sophistication. Introduces the ML4T workflow as a research-to-production system, regime detection on factor returns and macro indicators, and the evidence boundary that separates exploration from confirmation.

Part I — Financial Data (Chapters 2–5)

The markets, instruments, and infrastructure the rest of the book builds on: a taxonomy of sources, raw exchange messages turned into feature-ready bars, point-in-time fundamentals, and synthetic histories for robust validation.

2. The Financial Data Universe

A taxonomy of market, fundamental, and alternative data. Surveys eight asset classes, quantifies survivorship bias, benchmarks storage formats (Parquet, DuckDB, kdb+, TimescaleDB), and establishes the data-quality framework used throughout the book.

3. Market Microstructure

From raw exchange messages to feature-ready bars. Parses NASDAQ ITCH, reconstructs limit order books from multiple data sources, validates Lee-Ready trade classification, and compares bar-sampling methods — dollar bars deliver the best return normality.

4. Fundamental and Alternative Data

Point-in-time pipelines for SEC EDGAR filings, entity resolution across identifier systems, macro and commodity fundamentals, and alternative-data evaluation — including on-chain crypto fundamentals and prediction markets (Kalshi, Polymarket).

5. Synthetic Financial Data

Generating alternative market histories for robust validation. Implements TimeGAN, Tail-GAN, Sig-CWGAN, Diffusion-TS, and LLM-based tabular generation, evaluated through a fidelity–utility–privacy framework.

Part II — Research Design and Feature Engineering (Chapters 6–10)

Define the trading problem, then turn data into model-ready signals: research design, labels, features, and the evaluation that determines what any model can learn.

6. Strategy Research Framework

Defining the trading game before building models: universe rules, decision schedule, cost model, evaluation protocol, and run logging. Introduces the nine case studies and the walk-forward cross-validation discipline that anchors Chapters 7–20.

7. Defining the Learning Task

Label engineering (forward returns, triple-barrier, trend scanning), univariate feature evaluation (information coefficients, quantile analysis, feasibility screens), multiple-testing control (BH-FDR, Deflated Sharpe Ratio), and causal plausibility checks.

8. Financial Feature Engineering

Five feature families from price data (momentum, reversal, volatility, liquidity, microstructure), structural and cross-instrument features (yield curve, term structure, relative value), contextual features (macro regime, calendar, sentiment), and fea