让AI炒股策略自己进化：不动模型，只改操作手册
AI炒股代理通常用一套写死的操作规则，市场一变就失灵。EvolveTrade 的思路是：模型本身不动，只让一个“策略代理”根据过往交易记录和实际盈亏，定期重写系统提示词里的操作手册——比如该查哪些数据、怎么验证信号、怎么控风险。实验显示，在多种市场环境下，它多数时候比固定策略的基线获得更高夏普比率和累计收益，而且进化出的策略确实更爱用代码分析、更贴合当前市场状态。它不是你明天就能拿来赚钱的东西，但它指出了一个方向：让AI自己改“做事流程”，可能比换更大的模型更划算。
📄 原文摘要(英文)
Large language model (LLM) trading agents can combine market data, news, and executable analysis, but their behavior is often controlled by static hand-written tool-use policies that are fixed before deployment. This limits their ability to adapt how they gather evidence, invoke tools, verify signals, and manage risk under changing market regimes. We introduce EvolveTrade, a self-evolving framework that treats the system prompt of a tool-using trading agent as a text-parameterized policy. After each update interval, a Policy Agent revises this policy using accumulated decision traces and realized portfolio feedback, while keeping the backbone LLM fixed. The updated policy is then used for the next batch of trading decisions, enabling the agent to refine its information-acquisition and portfolio-construction procedure over time. Experiments across multiple market regimes and two LLM backbones show that EvolveTrade often improves Sharpe Ratio and Cumulative Return over fixed-policy LLM baselines, achieving the improved SR and CR in most evaluated settings. Behavioral analyses further show that self-evolved policies increase code-mediated analysis and activate regime-relevant computations; case-level policy-to-return attributions trace how policy-induced allocation changes contribute to realized return differences. These results suggest that adapting the reusable procedure governing tool use is a key direction for building more robust LLM trading agents.