@inproceedings{deng-etal-2026-alphaquanter,
    title = "{A}lpha{Q}uanter: An End-to-End Tool-Augmented Agentic Reinforcement Learning Framework for Stock Trading",
    author = "Deng, Zheye  and
      Yan, Weixiang  and
      Yu, Changlong  and
      Wang, Jiashu",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.456/",
    pages = "9373--9394",
    ISBN = "979-8-89176-395-1",
    abstract = "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 reasoning process. Extensive experiments demonstrate that AlphaQuanter achieves state-of-the-art performance on key financial metrics. Besides, human evaluation shows the learned reasoning patterns reveal more faithful and coherent tool-usage behaviors, providing steps toward verifiable LLM-driven trading. Our code and data can be found at https://github.com/horizon-llm/AlphaQuanter."
}