3rd Edition — Now Available
Machine Learning
for Trading
A structured workflow for building systematic trading strategies. From hypothesis formulation through production deployment.
Foundations
Data & Strategy Setup
Ch 1-6
Features
Feature Engineering
Ch 7-10
Models
ML Pipeline to Synthesis
Ch 11-15
Strategy
Backtest to Execution
Ch 16-20
Advanced AI
RL, RAG & Agents
Ch 21-24
Production
Deploy & Monitor
Ch 25-27
Foundations
Ch 1-6
Features
Ch 7-10
Models
Ch 11-15
Strategy
Ch 16-20
Advanced AI
Ch 21-24
Production
Ch 25-27
More than a book
An integrated learning system: structured content, production software, and AI-powered research tools.
27 Chapters
Six parts from foundations to production. Covers data infrastructure, feature engineering, ML models, backtesting, GenAI, and live deployment.
9 Case Studies
End-to-end strategies across equities, ETFs, crypto, options, futures, forex, and commodities. Each case study is a complete, runnable implementation.
5 Python Libraries
Production packages covering data acquisition, feature engineering, model diagnostics, event-driven backtesting, and live trading with broker integrations.
ML Primer
Foundational concepts in machine learning, statistics, and quantitative finance. Glossary, prerequisites, and background for each chapter topic.
Agent Skills
Autonomous workflow tasks with built-in guardrails against lookahead bias, data leakage, and multiple testing errors. From data fetching to strategy evaluation.
Agent Lab
AI-powered research environment where agents generate forecasts, analyze signals, and surface market insights. Try live forecasting in real time.
From hypothesis to production
Each part of the book maps to a stage of the ML4T workflow.
Foundation
Data & Strategy Setup Ch 1-6ML4T workflow, data infrastructure, and evaluation protocols
Feature Engineering and Evaluation
Feature Engineering Ch 7-10Alpha factors, text features, and label construction
Machine Learning Models
ML Pipeline to Synthesis Ch 11-15Time series, boosting, deep learning, causal inference
Strategy Implementation
Backtest to Execution Ch 16-20Backtesting, portfolio, risk, and strategy synthesis
Advanced AI
RL, RAG & Agents Ch 21-24Reinforcement learning, RAG for finance, knowledge graphs, autonomous agents
Production Deployment
Deploy & Monitor Ch 25-27Live trading, MLOps, and systematic edge
Purpose-built Python libraries
Five packages covering the full workflow, from data acquisition to live trading.
Start with Chapter 1
Begin the ML4T workflow from the ground up, or jump to the topic that matters most to you.
Start ReadingOr explore: Workflow · Case Studies · Libraries · Documentation