QuantAgent: Seeking Holy Grail in Trading by Self-Improving Large Language Model
QuantAgent uses a self-improving LLM framework to autonomously mine financial signals and enhance trading forecasts.
What it examines
This paper introduces a framework for autonomous agents using Large Language Models (LLMs) in quantitative investment. It addresses the challenge of integrating domain-specific knowledge efficiently, proposing a two-layer loop system for self-improvement and optimal behavior approximation.
What it concludes
The research demonstrates the potential of LLM-based autonomous agents in quantitative investment, suggesting applications in finance, healthcare, and logistics. Future work will focus on enhancing learning efficiency and real-time adaptation to dynamic environments.
Evidence objects
The research demonstrates the potential of LLM-based autonomous agents in quantitative investment, suggesting applications in finance, healthcare, and logistics. Future work will focus on enhancing learning efficiency and real-time adaptation to dynamic environments.
key_findings bullet 1 · key_findings · validation V0
Raw abstract and provenance
Abstract: 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 pr… ▽ More 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. △ Less
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