用大模型自动发现金融时间序列模型，提升交易决策收益
To Trade or Not to Trade: An Agentic Approach to Estimating Market Risk Improves Trading Decisions
- 让大模型迭代构建随机微分方程来建模金融数据
- 在多只股票上提升夏普比率，优于传统LLM代理
- 适合量化交易与风险建模研究者参考
大型语言模型（LLMs）正被部署于智能体框架中，通过提示触发基于工具的复杂分析以达成目标。尽管该框架在多个领域（包括金融）已展现潜力，但通常缺乏严谨的建模步骤，依赖情绪或趋势分析。本文提出一种智能体系统，利用LLMs迭代发现金融时间序列的随机微分方程。这些模型生成风险指标，指导每日交易决策。我们在传统回测和市场模拟器中评估系统，后者引入合成但因果合理的价格路径与新闻事件。结果表明，基于模型的交易策略优于标准LLM代理，在多只股票上提升夏普比率。研究显示，将LLMs与智能体建模发现结合，可增强市场风险估计，实现更优交易决策。
原文摘要 · Abstract (English)
Large language models (LLMs) are increasingly deployed in agentic frameworks, in which prompts trigger complex tool-based analysis in pursuit of a goal. While these frameworks have shown promise across multiple domains including in finance, they typically lack a principled model-building step, relying instead on sentiment- or trend-based analysis. We address this gap by developing an agentic system that uses LLMs to iteratively discover stochastic differential equations for financial time series. These models generate risk metrics which inform daily trading decisions. We evaluate our system in both traditional backtests and using a market simulator, which introduces synthetic but causally plausible price paths and news events. We find that model-informed trading strategies outperform standard LLM-based agents, improving Sharpe ratios across multiple equities. Our results show that combining LLMs with agentic model discovery enhances market risk estimation and enables more profitable trading decisions.
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