LiveOption: Evaluating LLM Agents in Structured Option Trading with Nonlinear Payoffs
By Haochen Luo, Yifan Li, Binh Minh An, Xiaolong Luo, Zhengzhao Lai, Yuan Zhang, Chen Liu
Rating
1500
Battle Count: 0
Relevance
9/10
Highly relevant as it addresses the structural complexity of derivatives markets, which are central to quantitative trading. It provides a rigorous testbed for evaluating AI agents in realistic, constraint-heavy trading environments, moving beyond simple directional equity prediction.
Implementation Complexity
8/10
High complexity due to the need for a realistic backtesting engine handling option mechanics (Greeks, margin, multi-leg strategies), data integration pipelines for licensed financial data, and agent orchestration.
Reproducibility
4/5
The paper provides a reproducible environment with standardized interaction protocols and a web interface. However, raw financial data is not redistributed due to licensing; users must provide their own licensed data sources (e.g., from MASSIVE Inc. or Benzinga) and use the provided preprocessing pipeline.
About this paper
Methodology: LiveOption Framework. Problem types: Reinforcement Learning, Portfolio Optimization, Risk Management, Algorithmic Execution, Structured Prediction.
The interactive Everscope explorer (charts, battles, favorites) loads below.