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：大型语言模型在金融决策中展现出潜力，然而将其部署为自主交易智能体仍面临根本性挑战：如何在延迟且被市场噪声掩盖的回报信号下调整指令，如何将异构信息流综合为连贯的决策，以及如何弥合模型输出与可执行市场操作之间的鸿沟。本文提出ATLAS（基于LLM智能体的自适应交易框架），这是一个统一的多智能体框架，通过整合来自市场行情、新闻资讯与企业基本面的结构化信息，以支撑稳健的交易决策。在ATLAS框架内，核心交易智能体运行于订单感知的动作空间，确保其输出对应于可执行的市场订单而非抽象信号。该智能体能够在交易过程中通过Adaptive-OPRO——一种创新的提示优化技术——融入实时随机反馈以动态调整提示，从而实现随时间推移的性能持续提升。在针对特定市场机制的股票研究及跨多种LLM模型系列的实验中，Adaptive-OPRO始终优于固定提示方法，而基于反思的反馈机制则未能带来系统性收益。