Problem
Research questions and friction points this paper is trying to address.
Large Language Model
Trading Agents
Static Policy
Market Regimes
EvolveTrade: Experience-Driven Policy Refinement for Self-Evolving LLM Trading Agents
🤖 AI Summary
为解决LLM交易代理在变化市场中适应性不足的问题，提出EvolveTrade框架，通过累积决策轨迹和投资组合反馈动态调整策略，提高夏普比率和累计回报。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Experience-Driven Policy Refinement
Self-Evolving LLM Trading Agents
Text-Parameterized Policy
🔎 Similar Papers
No similar papers found.
