Artificial Intelligence for Quantitative Finance: A RAG-Augmented Multi-Agent Framework for Robust Equity Strategy Discovery

Abstract

This paper introduces an AI-driven multi-agent framework for automated quantitative strategy generation and validation, integrating large language model (LLM) agents with Retrieval-Augmented Generation (RAG) to enhance factual accuracy and research reliability. The system orchestrates specialized agents for market analysis, financial knowledge retrieval, feature engineering, strategy construction, backtesting, and performance interpretation, establishing a comprehensive autonomous investment research pipeline. By grounding each agent's reasoning in a curated financial knowledge base, the framework mitigates hallucination risks, improves decision consistency, and provides transparent explanations of trading logic and risk characteristics. Experimental evaluation on simulated equity market data demonstrates that the RAG-enhanced multi-agent system achieves superior performance, generating a +3.25% return with improved consistency compared to single-agent (+2.15%) and non-RAG multi-agent (+1.82%) variants. The RAG-augmented approach exhibits higher win rates (51.2% vs 48-49%) and more robust risk-adjusted returns, validating its effectiveness in producing interpretable and reliable trading strategies. These results underscore the transformative potential of knowledge-grounded multi-agent AI systems in modern quantitative finance, while highlighting the framework's adaptability across different market environments without dependency on specific asset classes or historical periods.