LLM-Powered Quantitative Finance: A Practical Guide to Building AI Agents for Trading and Risk Management

Reactive Publishing

Discover how large language models are transforming quantitative finance in this practical guide to building intelligent AI agents.

This book explores the integration of LLMs with quantitative trading strategies, risk modeling, and decision-making systems. Readers will learn the fundamentals of designing, training, and deploying AI agents capable of analyzing market data, processing financial news and sentiment, and supporting robust risk management frameworks.

Key topics include:
• Architecting LLM-based trading agents
• Integrating real-time market data and alternative datasets
• Implementing sentiment analysis pipelines for financial decision-making
• Developing risk forecasting and portfolio management systems
• Best practices for testing, backtesting, and safely deploying autonomous AI systems in live trading environments

Whether you are a quantitative analyst, developer, or finance professional interested in the intersection of artificial intelligence and capital markets, this book provides clear explanations, code examples, and architectural patterns to help you build production-ready solutions.

Ideal for readers with basic knowledge of Python, machine learning concepts, and financial markets.