EvolveTrade framework improves LLM trading agents' Sharpe Ratio and Cumulative Return

The EvolveTrade framework improves LLM trading agents' Sharpe Ratio and cumulative return by holding model weights fixed and updating only the system prompt and tool-use policy after each trading interval using decision traces plus realized portfolio feedback, according to a paper posted to arXiv. The framework's authors report the adaptive prompt policies beat fixed policies in most tested regimes, while noting that prompts become live model parameters requiring versioning, backtesting, rollback, and risk controls like any other trading logic.

arXiv https://arxiv.org/abs/2609.17632 EvolveTrade framework improves LLM trading agents' Sharpe Ratio and Cumulative Return Which summary reads better? Pick one — models revealed after.Both summaries are AI-generated. EvolveTrade keeps the LLM weights fixed and updates only the system prompt/tool-use policy after each trading interval using decision traces plus realized portfolio feedback. For production agents, the takeaway is that adaptive prompt policies can improve Sharpe Ratio and cumulative return versus fixed policies in most tested regimes, but they also turn prompts into live model parameters that need versioning, backtesting, rollback, and risk controls like any other trading logic. Treating an LLM agent's system prompt as a dynamic, text-parameterized policy updated via a secondary feedback loop enables autonomous optimization of tool-use and decision strategies without fine-tuning the underlying model. For production systems operating in volatile domains, this framework allows agents to self-evolve and adapt to shifting real-world regimes using historical execution traces and performance feedback, eliminating manual prompt engineering and expensive model retraining cycles.