StockBench：LLM代理能否在現實市場中實現盈利性股票交易？

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

大型語言模型（LLMs）近期展現出作為自主代理的強大能力，在推理、工具使用及序列決策方面顯示出潛力。儘管先前的基準測試已在軟件工程和科學發現等領域對LLM代理進行了評估，但金融領域仍未被充分探索，儘管其與經濟價值和高風險決策直接相關。現有的金融基準測試主要通過問答來測試靜態知識，但未能捕捉到交易的動態和迭代特性。為填補這一空白，我們引入了StockBench，這是一個無污染的基準測試，旨在評估LLM代理在現實的多月股票交易環境中的表現。代理接收每日市場信號——包括價格、基本面和新聞——並必須做出連續的買入、賣出或持有決策。性能通過累計回報、最大回撤和索提諾比率等金融指標進行評估。我們對最先進的專有模型（如GPT-5、Claude-4）和開放權重模型（如Qwen3、Kimi-K2、GLM-4.5）的評估顯示，雖然大多數LLM代理難以超越簡單的買入並持有基準，但部分模型展示了提供更高回報和更有效管理風險的潛力。這些發現凸顯了開發LLM驅動的金融代理所面臨的挑戰與機遇，表明在靜態金融知識任務上的優異表現並不一定轉化為成功的交易策略。我們將StockBench作為開源資源發布，以支持可重複性並推動該領域的未來研究。

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.