From Natural Language to Executable Option Strategies via Large Language Models

TL;DR

This paper presents a neuro-symbolic approach using a domain-specific language and LLMs to accurately translate natural language trading intents into executable option strategies, improving reliability and consistency.

Contribution

Introduction of OQL, a domain-specific language, and a neuro-symbolic pipeline that enhances the translation of natural language into executable option strategies.

Findings

Significant improvement in execution accuracy over baseline methods

Enhanced logical consistency in generated strategies

Effective validation and execution of strategies via a deterministic engine

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.