StockBench：LLM智能体能否在现实市场中进行盈利性股票交易？

StockBench: Can LLM Agents Trade Stocks Profitably In Real-world Markets?

October 2, 2025

作者: Yanxu Chen, Zijun Yao, Yantao Liu, Jin Ye, Jianing Yu, Lei Hou, Juanzi Li

cs.AI

摘要

大型语言模型（LLMs）近期作为自主代理展现了强大的能力，在推理、工具使用及序列决策方面表现出潜力。尽管先前基准测试已在软件工程和科学发现等领域评估了LLM代理，但金融领域却鲜有探索，尽管其与经济价值和高风险决策直接相关。现有的金融基准主要通过问答测试静态知识，却未能捕捉交易的动态与迭代特性。为填补这一空白，我们推出了StockBench，一个无污染的基准测试，旨在评估LLM代理在真实、多月的股票交易环境中的表现。代理每日接收市场信号——包括价格、基本面数据和新闻——并需做出连续的买入、卖出或持有决策。性能通过累计收益、最大回撤和索提诺比率等金融指标进行评估。我们对顶尖专有模型（如GPT-5、Claude-4）和开源权重模型（如Qwen3、Kimi-K2、GLM-4.5）的评估显示，尽管多数LLM代理难以超越简单的买入持有基准，但部分模型展现出实现更高收益和更有效管理风险的潜力。这些发现既揭示了开发LLM驱动的金融代理的挑战，也指明了机遇，表明在静态金融知识任务上的优异表现未必能转化为成功的交易策略。我们开源StockBench，以支持可重复性并推动该领域未来的研究进展。

English

Large language models (LLMs) have recently demonstrated strong capabilities as autonomous agents, showing promise in reasoning, tool use, and sequential decision-making. While prior benchmarks have evaluated LLM agents in domains such as software engineering and scientific discovery, the finance domain remains underexplored, despite its direct relevance to economic value and high-stakes decision-making. Existing financial benchmarks primarily test static knowledge through question answering, but they fall short of capturing the dynamic and iterative nature of trading. To address this gap, we introduce StockBench, a contamination-free benchmark designed to evaluate LLM agents in realistic, multi-month stock trading environments. Agents receive daily market signals -- including prices, fundamentals, and news -- and must make sequential buy, sell, or hold decisions. Performance is assessed using financial metrics such as cumulative return, maximum drawdown, and the Sortino ratio. Our evaluation of state-of-the-art proprietary (e.g., GPT-5, Claude-4) and open-weight (e.g., Qwen3, Kimi-K2, GLM-4.5) models shows that while most LLM agents struggle to outperform the simple buy-and-hold baseline, several models demonstrate the potential to deliver higher returns and manage risk more effectively. These findings highlight both the challenges and opportunities in developing LLM-powered financial agents, showing that excelling at static financial knowledge tasks does not necessarily translate into successful trading strategies. We release StockBench as an open-source resource to support reproducibility and advance future research in this domain.