Autonomous agents based on Large Language Models (LLMs) that devise plans and tackle real-world challenges have gained prominence.However, tailoring these agents for specialized domains like quantitative investment remains a formidable task. The core challenge involves efficiently building and integrating a domain-specific knowledge base for the agent's learning process. This paper introduces a principled framework to address this challenge, comprising a two-layer loop.In the inner loop, the agent refines its responses by drawing from its knowledge base, while in the outer loop, these responses are tested in real-world scenarios to automatically enhance the knowledge base with new insights.We demonstrate that our approach enables the agent to progressively approximate optimal behavior with provable efficiency.Furthermore, we instantiate this framework through an autonomous agent for mining trading signals named QuantAgent. Empirical results showcase QuantAgent's capability in uncovering viable financial signals and enhancing the accuracy of financial forecasts.

翻译：基于大语言模型的自主智能体在制定计划和应对现实挑战方面已展现出显著优势。然而，将这些智能体适配至量化投资等专业领域仍是一项艰巨任务，其核心挑战在于高效构建并整合领域专业知识库以支持智能体的学习过程。本文提出一个包含双层回路的原理性框架以应对该挑战：在内层回路中，智能体通过调用知识库优化其输出；在外层回路中，这些输出在真实场景中接受测试，从而自动向知识库注入新见解。我们证明，该框架能够使智能体以可证明的效率渐进逼近最优行为。此外，我们通过一个名为QuantAgent的交易信号挖掘自主智能体实现了该框架。实验结果表明，QuantAgent具备发现有效金融信号并提升金融预测准确性的能力。