AI Quantitative Trading

Build a production-ready quantitative trading system with multi-agent architecture

This book systematically explains how to build a multi-agent quantitative trading system from scratch, covering market fundamentals, data engineering, machine learning, multi-agent architecture, risk control, and production deployment.

⚠️ Disclaimer

This book is for educational and research purposes only and does not constitute investment advice. Quantitative trading involves significant risks and may result in loss of principal. Readers should fully understand the risks before making independent investment decisions. The author assumes no responsibility for any losses incurred from using the content of this book.

Table of Contents

Part 1: Quick Start

Establish a global perspective and understand the quantitative world and multi-agent architecture

Background Knowledge: Alpha and Beta · US vs China Quant Market Differences · Top Quantitative Hedge Funds · Famous Quantitative Disasters · Recommended Reading List

Part 2: Quant Fundamentals

Build a solid foundation in markets, data, strategies, and backtesting

Background Knowledge: Exchanges and Order Book Mechanics · HF Market Microstructure · Tick-Level Backtesting Framework · Statistical Traps of Sharpe Ratio · Candlestick Patterns and Volume Analysis · Cryptocurrency Trading Characteristics · Data Sources and API Comparison

Part 3: Machine Learning

From traditional models to decision-making agents

Background Knowledge: LLM in Quantitative Research · Triple Barrier Labeling Method · Time Series Cross-Validation (Purged CV) · Reinforcement Learning in Trading · Alternative Data (NLP and Satellite) · Meta-Labeling Method · Feature Engineering Pitfalls · Limitations of ML in Finance · Model Architecture Selection Guide · Model Drift and Retraining · MLOps Infrastructure · Cutting-Edge ML and RL Methods (2025)

Part 4: Multi-Agent Systems

Build collaborative agent architectures for specialization and risk control

Background Knowledge: Multi-Agent Framework Comparison · Quantitative Open-Source Framework Comparison · Mean-Variance Portfolio Optimization

Part 5: Production and Practice

From backtesting to live trading, deploy a running trading system

Background Knowledge: Execution Simulator Implementation · Strategy Homogenization and Capacity Bottlenecks · Algorithmic Trading Regulations (2024-2025)

Appendices

References

→ Resources and Links

Statistics

Reading Recommendations