SCRiPTMAFiA.ORG > Machine Learning for Trading, 3rd Edition

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

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

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