AI Research
EvolveTrade: Experience-Driven Policy Refinement for Self-Evolving LLM Trading Agents
Bridging the gap between static LLM logic and dynamic financial market adaptation.
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 ...
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ArXiv CS.AI
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ArXiv CS.AI
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EvolveTrade addresses the critical limitation of static trading agents by enabling self-optimizing tool-use policies that adapt to shifting market dynamics. By shifting from fixed heuristics to experience-driven refinement, this research significantly narrows the gap between static LLM reasoning and real-time financial agility.
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