标题： 交易还是不交易：一种代理方法用于估计市场风险以改进交易决策 显示英文标题

标题： To Trade or Not to Trade: An Agentic Approach to Estimating Market Risk Improves Trading Decisions

摘要： 大型语言模型（LLMs）越来越多地被部署在代理框架中，在这种框架中，提示会触发复杂的基于工具的分析以实现目标。 尽管这些框架在包括金融在内的多个领域显示出潜力，但它们通常缺乏一种有原则的模型构建步骤，而是依赖于基于情感或趋势的分析。 我们通过开发一个代理系统来解决这一差距，该系统使用LLMs迭代地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，用于指导日常交易决策。 我们在传统的回测和使用市场模拟器中评估了我们的系统，该模拟器引入了合成但因果上合理的价格路径和新闻事件。 我们发现，基于模型的交易策略优于标准的LLM代理，在多个股票中提高了夏普比率。 我们的结果表明，将LLMs与代理模型发现相结合可以增强市场风险估计，并实现更有利可图的交易决策。 显示英文摘要

摘要： 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.