Learn to extract signals from financial and alternative data
Design and backtest systematic strategies using machine learning. The most comprehensive guide to applying ML in financial markets.
Unlock Free Tier 2 Content
Get detailed section summaries, code examples, best practices, and case study introductions delivered to your inbox. No purchase required.
- Section summaries
- Code examples
- Best practices
- Case studies
Join 5,000+ readers • Free forever • Unsubscribe anytime
The Evolution of ML4T
Three editions, continuous innovation
Trusted by thousands of quantitative analysts and traders since 2018
From foundational ML algorithms to cutting-edge GenAI and autonomous agents, ML4T has continuously evolved to keep pace with the rapidly changing landscape of machine learning in quantitative finance.
From theory to practice with dozens of examples
Everything from fundamentals to cutting edge techniques
65+ Applications
Real-world examples covering every aspect of ML for trading
40+ Data Sources
Market data, fundamental data, and alternative data examples
150+ Notebooks
Jupyter notebooks with complete, runnable code examples
On over 800 pages, this revised and expanded 2nd edition demonstrates how ML can add value to algorithmic trading through a broad range of applications. Organized in four parts and 24 chapters, it covers the end-to-end workflow from data sourcing and model development to strategy backtesting and evaluation.
More specifically, it covers:
- • Key aspects of data sourcing, financial feature engineering, and portfolio management
- • The design and evaluation of long-short strategies based on a broad range of ML algorithms
- • How to extract tradeable signals from financial text data like SEC filings, earnings call transcripts or financial news
- • Using deep learning models like CNN and RNN with financial and alternative data
- • How to generate synthetic data with Generative Adversarial Networks
- • Training a trading agent using deep reinforcement learning
Updated Strategy Development Software
We have updated the libraries used in the book to analyze alpha factors, backtest trading strategies, and evaluate their performance.
The End-to-End ML4T Workflow
The book introduces the complete machine learning for trading workflow, from data sourcing to strategy deployment
