
Machine Learning for Trading: Integrate GenAI, Causal Inference, and Reinforcement Learning into Real World Trading Systems
Author(s): Stefan Jansen (Author)
- Publisher: Packt Publishing - ebooks Account
- Publication Date: October 9, 2026
- Language: English
- Print length: 865 pages
- ISBN-10: 1803246979
- ISBN-13: 9781803246970
Book Description
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
Book Description
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
Who this book is for
If you are a data analyst, data scientist, Python developer, investment analyst, or portfolio manager interested in getting hands-on machine learning knowledge for trading, this book is for you. This book is for you if you want to learn how to extract value from a diverse set of data sources using machine learning to design your own systematic trading strategies.
Some understanding of Python and machine learning techniques is required.
Table of Contents
- Machine Learning for Trading – From Idea to Execution
- ML4T Workflow: End-to-end strategy development and evaluation
- Market and Fundamental Data
- Alternative Data for Finance
- Alpha Factor Research
- Portfolio Management & Strategy Evaluation
- Linear Models
- Linear Time Series Models
- Bayesian Machine Learning
- Tree-Based Ensembles
- Intro to Deep Learning & Feedforward NN
- Linear & Non-linear Time Series Models
- Text Data: Sentiment Analysis & Topic Models
- Text Data: Word2Ved & Transformers
- Convolutional Neural Networks
- Autoencoders
- Generative Adversarial Nets
- Reinforcement Learning
- Live Trading
- Next Steps
- Appendix
Editorial Reviews
About the Author
Stefan is the founder and CEO of Applied AI. He advises Fortune 500 companies, investment firms, and startups across industries on data & AI strategy, building data science teams, and developing end to end machine learning solutions.
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