  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.
