# EvolveTrade rewrites a trading agent's system prompt after each interval

The EvolveTrade paper treats a trading agent's system prompt as its policy and has a separate Policy Agent rewrite it after each interval from decision traces and realized returns, while the backbone model stays frozen. Across several market regimes and two backbone models, it reports higher Sharpe ratios and cumulative returns than fixed-prompt baselines in most settings.

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Source: DAIR.AI · Published 9/22/2026, 09:52:27

The [EvolveTrade paper](https://academy.dair.ai/papers/evolvetrade-experience-driven-policy-refinement-for-self-evolving-llm-trading-ag-2609.17632) treats a trading agent's system prompt as its policy. After each trading interval, a separate Policy Agent reads the decision traces and realized returns and rewrites the prompt. The backbone model stays frozen.

The paper reports that the evolved agent beat fixed-prompt baselines on Sharpe ratio and cumulative return in most settings, across several market regimes and two backbone models. The rewritten prompts also led the agent to run more code-based analysis and to compute signals that fit the current market regime.

Tags: EvolveTrade, AI agents, System prompts, Trading, Research

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