This book, "AI Quant Trading: From Zero to One," addresses the gap in current quantitative trading education by teaching readers how to build production-ready trading systems. It moves beyond basic API translations and overfitted strategies to tackle real-world complexities like data sourcing, backtesting integrity, multi-model integration, risk management, and deployment. Aimed at developers transitioning to quant, quant researchers, and investors, it provides a comprehensive path to understanding and constructing robust trading systems using a multi-agent architecture.

How It Works

The core approach centers on a multi-agent architecture, where distinct agents are responsible for specific functions such as signal generation, risk control, and trade execution. These agents collaborate to make informed trading decisions. This design is advantageous as it modularizes complex trading logic, allowing for better management of diverse market regimes, signal conflicts, and risk diversification, thereby building more resilient and production-ready systems compared to monolithic or simpler strategy-focused approaches.

Quick Start & Requirements

Access to the book's content is provided via links to its English or Chinese versions.

• Prerequisites: Basic programming knowledge (Python) is required. Familiarity with statistics and financial markets is helpful but not essential. No prior Machine Learning or Deep Learning background is needed.
• Recommended Path: Readers can follow tailored paths based on their background, starting from foundational concepts or diving into specific sections.
• Links:
  • English Version: ./manuscript/en/
  • Chinese Version: ./manuscript/cn/

Highlighted Details

• Focus