Machine Learning for Trading
Integrate GenAI, Causal Inference, and Reinforcement Learning into Real World Trading Systems
2027
|
3rd Revised edition
Packt Publishing Limited (Verlag)
978-1-80324-697-0 (ISBN)
Packt Publishing Limited (Verlag)
978-1-80324-697-0 (ISBN)
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 DescriptionThe 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 forIf 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.
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 DescriptionThe 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 forIf 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.
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.
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
| Erscheinungsdatum | 08.01.2026 |
|---|---|
| Verlagsort | Birmingham |
| Sprache | englisch |
| Maße | 191 x 235 mm |
| Themenwelt | Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik |
| ISBN-10 | 1-80324-697-9 / 1803246979 |
| ISBN-13 | 978-1-80324-697-0 / 9781803246970 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
| Haben Sie eine Frage zum Produkt? |
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