LiveOption: Evaluating LLM Agents in Structured Option Trading with Nonlinear Payoffs

Paper: arXiv 2609.33470

Authors: Haochen Luo, Yifan Li, Binh Minh An, Xiaolong Luo, Zhengzhao Lai, Yuan Zhang, Chen Liu

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.

Complexity vs Empirical Score

• Math Complexity: 6.0/10
• Empirical Rigor: 8.0/10
• Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: This paper introduces a novel and much-needed evaluation framework for LLM agents in the complex domain of option trading, moving beyond simple equity predictions. It demonstrates strong empirical rigor through its detailed backtesting engine, multi-scenario evaluation, and hierarchical metric suite. The framework’s design for reproducibility and its focus on structured decision-making under realistic constraints are significant strengths.

Research Flowchart

  flowchart TD
    A[Research Goal: Evaluate LLM Agents in Structured Option Trading] --> B(Methodology: LiveOption Framework Development)
    B --> C{Data/Inputs: Realistic Market Data, Execution & Capital Constraints}
    C --> D[Computational Processes: LLM Agent Decision-Making, Multi-leg Strategy Construction, Nonlinear Payoff Calculation]
    D --> E{Evaluation: Hierarchical Metric Suite - Validity, Quality, Risk, Outcome}
    E --> F[Key Finding: Current LLM Agents Struggle to Achieve Competitive Returns in Option Trading]

Cited References (86)

1. Leveraging large language models for predicting stock option valuation and financial risk mitigation (2024)
2. Deep reinforcement learning for active high frequency trading (2021) — link
3. Beat the Market An Effective Intraday Momentum Strategy for S&P500 ETF (SPY) (2024)
4. Does 0DTE Options Trading Increase Volatility? (2023)
5. Trading Options at Expiration: Strategies and Models for Winning the Endgame (2009)
6. Day Trading Options: Profiting from Price Distortions in Very Brief Time Frames (2009)
7. Simulating Dynamics of the Recognition-Primed Decision Model on Financial Markets Using LLM (2026)
8. Machine learning methods for pricing financial derivatives — → 2406.00459

8 of 86 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.