arXiv cs.AI· Haochen Luo, Yifan Li, Binh Minh An, Xiaolong Luo, Zhengzhao Lai, Yuan Zhang, Chen Liu·· 14 小时前AI 评分31
LiveOption：在结构化期权交易中评估 LLM 智能体
LiveOption: Evaluating LLM Agents in Structured Option Trading with Nonlinear Payoffs
AI 导读
LiveOption 是一个面向 LLM 智能体的期权交易评估框架，将期权交易建模为受真实执行与资金约束的结构化序贯决策问题，并提供可复现环境和标准化交互协议。框架包含投资组合叠加、事件驱动财报交易和 0DTE 日内交易三个任务套件，并配套分层指标评估动作有效性、决策质量、风险特征与结果表现。实验显示当前智能体在多数场景下难以取得有竞争力的收益。
正文 · 原文
Abstract:Large language models (LLMs) and multi-agent systems (MAS) have shown promise in financial decision-making, yet existing evaluations focus on equity trading and primarily assess directional prediction, overlooking the structural complexity of derivative markets. Option trading introduces fundamentally different challenges, including nonlinear payoffs and multi-leg strategy construction, requiring structured decisions rather than simple directional bets. We introduce LiveOption, an evaluation framework for LLM-based agents in option trading. LiveOption formulates the problem as structured sequential decision-making under realistic execution and capital constraints, and provides a reproducible environment with standardized interaction protocols. The framework includes three task suites covering portfolio overlays, event-driven earnings trading, and 0DTE intraday trading. We further propose a hierarchical metric suite that evaluates action validity, decision quality, risk characteristics, and outcome-level performance. Experiments show that current agents often fail to achieve competitive returns in most scenarios. LiveOption offers a principled testbed for evaluating structured decision-making beyond outcome-based metrics. Project Website: this https URL
| Subjects: | Artificial Intelligence (cs.AI); Computational Finance (q-fin.CP) |
| Cite as: | arXiv:2609.33470 [cs.AI] |
| (or arXiv:2609.33470v2 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2609.33470 arXiv-issued DOI via DataCite |
Submission history
From: Haochen Luo [view email]
[v1]
Sun, 27 Sep 2026 11:29:16 UTC (1,648 KB)
[v2]
Sun, 25 Oct 2026 11:22:19 UTC (1,762 KB)
来源：arXiv cs.AI · arxiv.org