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

Source: DEV Community·

Summary

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.
CategoryAI Tutorials & Practice
TierOrdinary
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Indexed by AIQB
SourceDEV Community
AIQB record IDintel-d637df8e8ce75d4dbe0027c1
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