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
- The Process is Your Edge
- The Financial Data Universe
- Market Microstructure
- Fundamental and Alternative Data
- Synthetic Financial Data
- Strategy Research Framework
- Defining the Learning Task
- Financial Feature Engineering
- Model-Based Feature Extraction
- Text Feature Engineering
- The ML Pipeline
- Advanced Models for Tabular Data
- Deep Learning for Time Series
- Latent Factor Models
- Causal Machine Learning
- Strategy Simulation
- Portfolio Construction
- Transaction Costs
- Risk Management
- Strategy Synthesis
- Reinforcement Learning
- RAG for Financial Research
- Knowledge Graphs
- Autonomous Agents
- Live Trading Systems
- MLOps and Governance
- The Systematic Edge
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.
