发表机构
City University of Hong Kong; The Chinese University of Hong Kong (Shenzhen); Shanghai University of Finance and Economics（香港城市大学; 香港中文大学（深圳）; 上海财经大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对现有金融评估忽视衍生品结构复杂性的问题，提出LiveOption框架，通过三个任务套件和分层指标，评估LLM智能体在期权交易中的结构化决策能力，实验表明当前智能体多数场景下难以获得有竞争力回报。
AI 中文摘要
大型语言模型（LLMs）和多智能体系统（MAS）在金融决策中已展现出潜力，然而现有评估主要聚焦于股票交易，且主要评估方向性预测，忽视了衍生品市场的结构性复杂性。期权交易引入了根本不同的挑战，包括非线性收益和多腿策略构建，需要结构化决策而非简单的方向性押注。我们提出了LiveOption，一个用于评估基于LLM的智能体在期权交易中表现的评估框架。LiveOption将问题形式化为在现实执行和资本约束下的结构化序列决策，并提供了一个具有标准化交互协议的可复现环境。该框架包含三个任务套件，涵盖投资组合叠加、事件驱动的财报交易和0DTE日内交易。我们进一步提出了一个分层指标套件，用于评估行动有效性、决策质量、风险特征和结果层面表现。实验表明，当前智能体在大多数场景下往往无法获得有竞争力的回报。LiveOption为评估超越基于结果的指标之外的结构化决策提供了一个原则性的测试平台。
英文摘要
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.
