Machine Learning for Trading: A disciplined workflow from research to live execution, with nine case studies and AI agents, 3rd Edition

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Build and deploy AI-driven trading systems using the 7-Stage workflow with pandas, Polars, LightGBM, PyTorch, Optuna, zipline-reloaded, MLflow, Feast, and SHAP

Key Features

Build point-in-time pipelines, integrate alternative data, and ensure data integrity
Build and validate predictive models using GBMs, Transformers, and causal inference frameworks to create robust, interpretable alpha signals
Deploy RAG systems, autonomous financial agents, and diffusion-based synthetic data generators
The rapid rise of AI and the growing complexity of financial markets have transformed quantitative trading into a data-driven, process-oriented discipline. This third edition provides a comprehensive blueprint for designing, validating, and deploying systematic trading strategies powered by modern machine learning.

It introduces the 7 stage ML4T Workflow, a professional framework that unites data engineering, model development, validation, and live deployment into one cohesive process. It demonstrates how to turn raw market, fundamental, and alternative data into predictive signals and robust, production-ready trading systems.

You’ll learn to build advanced pipelines for feature engineering, model evaluation, and portfolio optimization using libraries such as Polars, LightGBM, PyTorch, and Optuna.

Practical notebooks illustrate every stage of the workflow, from factor testing and backtesting with zipline reloaded to live deployment with MLOps tools such as MLflow, Feast, and Prometheus. Additional coverage of synthetic data generation, Graph Neural Networks, and Reinforcement Learning extends the toolkit for building resilient, adaptive strategies that thrive in dynamic markets.

By the end of this book, you’ll be proficient to build your own industrial-grade “alpha factory”.

What you will learn

Transform raw data into predictive alpha factors, validated with leak-proof cross-validation
Master advanced models, from Gradient Boosting Machines to Transformers, Graph Neural Networks, and Reinforcement Learning agents
Harness Generative AI, Retrieval Augmented Generation, and Causal Inference to make models interpretable, auditable, and compliant with regulatory standards
Build production-ready trading infrastructure using MLOps, feature stores, and model monitoring to transition research into live capital deployment safely