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RetailAgent: Structured Adverse Timing in Self-Conditioned Multimodal LLM Trading Agents

arXiv · AI, language, vision and robotics · article · Aug 28, 2026 · UTC

In financial markets, a sequential policy that reacts systematically to price movements may become predictable to other market participants. This paper studies whether large language model (LLM) agents exhibit such directional structure through RetailAgent, an experimental framework in which an LLM observes anonymized intraday equity price histories and permitted state, then repeatedly chooses long (hold the stock) or flat (stay out) before the subsequent interval return is revealed. We compare returns during long and flat intervals along the same stock's intraday path after removing the overa

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