AlphaQuanter：一個端到端工具協調的代理強化學習框架，用於股票交易
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
儘管大型語言模型（LLM）代理在自動化交易中展現出潛力，它們仍面臨關鍵限制。主流的多元代理框架常存在效率低下、產生不一致信號，以及缺乏從市場反饋中學習連貫策略所需的端到端優化等問題。為解決這些問題，我們引入了AlphaQuanter，這是一個單一代理框架，利用強化學習（RL）在一個透明、工具增強的決策流程上學習動態策略，使單一代理能夠自主協調工具並按需主動獲取信息，從而建立一個透明且可審計的推理過程。大量實驗表明，AlphaQuanter在關鍵金融指標上達到了最先進的性能。此外，其可解釋的推理揭示了複雜的策略，為人類交易者提供了新穎且有價值的洞見。我們的數據獲取與代理訓練代碼已公開於：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