AlphaQuanter：一个端到端工具编排的自主强化学习框架，用于股票交易
AlphaQuanter: An End-to-End Tool-Orchestrated Agentic Reinforcement Learning Framework for Stock Trading
October 16, 2025
作者: Zheye Deng, Jiashu Wang
cs.AI
摘要
尽管大型语言模型（LLM）代理在自动化交易中展现出潜力，它们仍面临关键局限。主流的多元代理框架常存在效率低下、信号不一致的问题，且缺乏从市场反馈中学习连贯策略所需的端到端优化能力。为此，我们推出了AlphaQuanter，一个采用强化学习（RL）的单代理框架，该框架在透明、工具增强的决策流程上学习动态策略，使单一代理能够自主协调工具并按需主动获取信息，从而建立透明且可审计的推理过程。大量实验表明，AlphaQuanter在关键金融指标上达到了业界领先水平。此外，其可解释的推理揭示了复杂策略，为人类交易者提供了新颖且有价值的洞见。我们的数据获取与代理训练代码已公开于：https://github.com/AlphaQuanter/AlphaQuanter。
English
While Large Language Model (LLM) agents show promise in automated trading,
they still face critical limitations. Prominent multi-agent frameworks often
suffer from inefficiency, produce inconsistent signals, and lack the end-to-end
optimization required to learn a coherent strategy from market feedback. To
address this, we introduce AlphaQuanter, a single-agent framework that uses
reinforcement learning (RL) to learn a dynamic policy over a transparent,
tool-augmented decision workflow, which empowers a single agent to autonomously
orchestrate tools and proactively acquire information on demand, establishing a
transparent and auditable reasoning process. Extensive experiments demonstrate
that AlphaQuanter achieves state-of-the-art performance on key financial
metrics. Moreover, its interpretable reasoning reveals sophisticated
strategies, offering novel and valuable insights for human traders. Our code
for data acquisition and agent training is publicly available at:
https://github.com/AlphaQuanter/AlphaQuanter