@inproceedings{luo-etal-2026-qfinzero,
    title = "{QF}in{Z}ero: A Unified Financial Toolchain for {LLM}-Based Trading Agents",
    author = "Luo, Haochen  and
      LI, Yifan  and
      Ko, Ho Tin  and
      Minh, An Binh  and
      Xu, Junjie  and
      Hin, Tang Pok  and
      Wong, Wang Chak  and
      Yuan, Gao  and
      Lai, Zhengzhao  and
      Zhang, Yuan  and
      Liu, Chen",
    editor = "Durrett, Greg  and
      Jian, Ping",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 3: System Demonstrations)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.acl-demo.7/",
    doi = "10.18653/v1/2026.acl-demo.7",
    pages = "68--77",
    ISBN = "979-8-89176-392-0",
    abstract = "Large language model (LLM) agents are increasingly applied to financial decision-making tasks that require interaction with external tools, including market data retrieval, news analysis, and trade execution. However, existing trading systems rely on fragmented and task-specific APIs, resulting in inconsistent schemas, complex integration, and limited reproducibility. We present QFinZero, a unified trading environment for LLM-based financial agents. QFinZero standardizes three core capabilities: (i) multi-frequency market and derivatives data access, (ii) structured news and event retrieval, and (iii) stateful brokerage simulation with explicit order lifecycle management. All tools adopt consistent JSON schemas and time-aligned interfaces, enabling agents to acquire information and execute trades within a coherent framework. By abstracting financial interaction into composable, agent-invokable primitives, QFinZero reduces engineering overhead and supports reproducible evaluation through comprehensive logging and deterministic replay. We argue that such a standardized trading environment is essential for scalable research on LLM-based financial agents."
}