用动态提示优化让大模型自动交易更聪明，实时学习市场反馈。
ATLAS: Adaptive Trading with LLM AgentS Through Dynamic Prompt Optimization and Multi-Agent Coordination
- 设计多智能体框架，融合行情、新闻与财报数据决策
- 提出自适应提示优化技术，交易中持续改进表现
- 适合金融算法研究者与量化交易系统开发者
大型语言模型在金融决策中展现出潜力，但将其部署为自主交易代理仍面临根本挑战：如何在奖励延迟且受市场噪声干扰时调整指令，如何将异构信息流整合为一致决策，以及如何弥合模型输出与可执行市场操作之间的差距。我们提出ATLAS（Adaptive Trading with LLM AgentS），一个统一的多智能体框架，整合市场、新闻和公司基本面的结构化信息，支持稳健的交易决策。在ATLAS中，核心交易智能体运行于订单感知的动作空间，确保输出对应可执行的市场订单而非抽象信号。该智能体可通过自适应OPRO（一种新颖的提示优化技术）在交易过程中结合实时随机反馈动态调整提示，实现性能随时间持续提升。在特定市场周期的股权研究及多种大模型家族测试中，自适应提示始终优于固定提示，而基于反思的反馈未能带来系统性增益。
原文摘要 · Abstract (English)
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.
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