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LLM agents turn code interpreters into portfolio sizing engines when you evolve the prompt

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内容摘要

KAIST EvolveTrade shows that frozen LLM trading agents improve Sharpe ratio not by writing new strategies, but by letting policy refinement turn Python outputs into explicit allocation math.
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