Large language models show promise for financial decision-making, yet deploying them as autonomous trading agents raises fundamental challenges: how to adapt instructions when rewards arrive late and obscured by market noise, how to synthesize heterogeneous information streams into coherent decisions, and how to bridge the gap between model outputs and executable market actions. We present ATLAS (Adaptive Trading with LLM AgentS), a unified multi-agent framework that integrates structured information from markets, news, and corporate fundamentals to support robust trading decisions. Within ATLAS, the central trading agent operates in an order-aware action space, ensuring that outputs correspond to executable market orders rather than abstract signals. The agent can incorporate feedback while trading using Adaptive-OPRO, a novel prompt-optimization technique that dynamically adapts the prompt by incorporating real-time, stochastic feedback, leading to increasing performance over time. Across regime-specific equity studies and multiple LLM families, Adaptive-OPRO consistently outperforms fixed prompts, while reflection-based feedback fails to provide systematic gains.

翻译：摘要：大语言模型在金融决策领域展现出潜力，但将其部署为自主交易代理面临根本性挑战：当奖励因市场噪声延迟且模糊时如何调整指令，如何将异构信息流综合为连贯决策，以及如何弥合模型输出与可执行市场行为之间的鸿沟。我们提出ATLAS（基于大语言模型的自适应交易系统），这是一个统一的多智能体框架，整合来自市场、新闻和公司基本面信息的结构化数据，以支持稳健的交易决策。在ATLAS中，中央交易代理在订单感知行动空间中运行，确保输出对应于可执行的市场订单而非抽象信号。该代理可在交易过程中通过自适应-OPRO技术纳入反馈——这是一种新型提示优化方法，通过整合实时的随机反馈动态调整提示，从而随时间推移持续提升性能。在跨制度特定股票研究和多个大语言模型家族的实验中，自适应-OPRO始终优于固定提示，而基于反思的反馈未能带来系统性改进。