# From Natural Language to Executable Option Strategies via Large Language Models Source: [From Natural Language to Executable Option Strategies via Large Language Models]() Collection index: [llms.txt]() Data updated: 2026-03-19T05:00:05.030Z Authors: Haochen Luo, Zhengzhao Lai, Junjie Xu, Yifan Li, Tang Pok Hin, Yuan Zhang, Chen Liu Published: 2026-03-17T12:14:47.000Z ## Abstract Large Language Models (LLMs) excel at general code generation, yet translating natural-language trading intents into correct option strategies remains challenging. Real-world option design requires reasoning over massive, multi-dimensional option chain data with strict constraints, which often overwhelms direct generation methods. We introduce the Option Query Language (OQL), a domain-specific intermediate representation that abstracts option markets into high-level primitives under grammatical rules, enabling LLMs to function as reliable semantic parsers rather than free-form programmers. OQL queries are then validated and executed deterministically by an engine to instantiate executable strategies. We also present a new dataset for this task and demonstrate that our neuro-symbolic pipeline significantly improves execution accuracy and logical consistency over direct baselines. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Option Strategy Performance Evaluation on Option Strategy Evaluation Set](): Option Strategy Performance Evaluation; Option Strategy Evaluation Set; Win Rate: 60.9. - [Option Query Language (OQL) Generation on OQL Query Quality (evaluation set)](): Option Query Language (OQL) Generation; OQL Query Quality (evaluation set); VR: 95. ## Original sources and implementation [Original paper]() [PDF]()