用大模型自动发现量化投资策略，提升收益与风控能力
Automate Strategy Finding with LLM in Quant Investment
- 三阶段框架结合大模型生成因子，多智能体评估筛选
- SSE50指数2023年1月至2024年1月累计收益达53.17%
- 适合追求自动化、风险敏感的量化交易研究者
我们提出一种新颖的三阶段框架，利用大语言模型（LLMs）在风险感知的多智能体系统中实现量化金融中的策略自动生成。该方法通过提示工程驱动的LLM，在多种金融数据中生成可执行的阿尔法因子；采用多模态智能体评估机制，基于市场状态和预测质量筛选因子，并保持类别平衡；部署动态权重优化，适应市场变化。实验表明，该策略在中国及美国市场环境下均显著优于现有基准。在SSE50指数（2023年1月至2024年1月）上实现53.17%的累计收益率，展现出优异的风险调整性能与下行保护能力。本工作拓展了大模型在量化交易中的应用，提供了一套可扩展的金融信号提取与组合构建架构。
原文摘要 · Abstract (English)
We present a novel three-stage framework leveraging Large Language Models (LLMs) within a risk-aware multi-agent system for automate strategy finding in quantitative finance. Our approach addresses the brittleness of traditional deep learning models in financial applications by: employing prompt-engineered LLMs to generate executable alpha factor candidates across diverse financial data, implementing multimodal agent-based evaluation that filters factors based on market status, predictive quality while maintaining category balance, and deploying dynamic weight optimization that adapts to market conditions. Experimental results demonstrate the robust performance of the strategy in Chinese & US market regimes compared to established benchmarks. Our work extends LLMs capabilities to quantitative trading, providing a scalable architecture for financial signal extraction and portfolio construction. The overall framework significantly outperforms all benchmarks with 53.17% cumulative return on SSE50 (Jan 2023 to Jan 2024), demonstrating superior risk-adjusted performance and downside protection on the market.
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