用提示工程让AI模拟投资大师，实现可复现的量化策略
GuruAgents: Emulating Wise Investors with Prompt-Guided LLM Agents
- 通过提示词设计，将投资大师理念转化为AI推理流程
- 巴菲特型代理年化收益达42.2%，显著优于基准
- 适合对自动化投资策略感兴趣的金融从业者
本研究展示，基于提示词引导的AI代理GuruAgents能够系统化地实现传奇投资大师的投资策略。我们开发了五个不同GuruAgents，每个对应一位知名投资者，通过将他们的独特理念编码进大语言模型提示词，并结合金融工具与确定性推理流程，构建出可执行的策略。在2023年第四季度至2025年第二季度的纳斯达克100成分股回测中，各GuruAgents展现出由其提示人格驱动的独特行为。其中，巴菲特型代理表现最佳，实现42.2%的年化收益率（CAGR），显著优于基准。结果表明，提示工程可成功将投资大师的定性理念转化为可复现的定量策略，为自动化系统性投资开辟新路径。代码与数据已公开于https://github.com/yejining99/GuruAgents。
原文摘要 · Abstract (English)
This study demonstrates that GuruAgents, prompt-guided AI agents, can systematically operationalize the strategies of legendary investment gurus. We develop five distinct GuruAgents, each designed to emulate an iconic investor, by encoding their distinct philosophies into LLM prompts that integrate financial tools and a deterministic reasoning pipeline. In a backtest on NASDAQ-100 constituents from Q4 2023 to Q2 2025, the GuruAgents exhibit unique behaviors driven by their prompted personas. The Buffett GuruAgent achieves the highest performance, delivering a 42.2\% CAGR that significantly outperforms benchmarks, while other agents show varied results. These findings confirm that prompt engineering can successfully translate the qualitative philosophies of investment gurus into reproducible, quantitative strategies, highlighting a novel direction for automated systematic investing. The source code and data are available at https://github.com/yejining99/GuruAgents.
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