用自然语言生成期权策略，提升准确性和逻辑一致性。
From Natural Language to Executable Option Strategies via Large Language Models
- 设计领域专用语言OQL，将期权市场抽象为语法化高阶指令
- 新数据集上策略执行准确率显著优于直接生成基线
- 适合金融工程、量化交易开发者快速构建可执行策略
大型语言模型（LLMs）在通用代码生成方面表现优异，但将自然语言交易意图转化为正确期权策略仍具挑战。现实中的期权设计需对海量多维期权链数据进行推理并满足严格约束，直接生成方法常因复杂性而失效。我们提出期权查询语言（OQL），一种领域特定的中间表示，将期权市场抽象为符合语法规则的高层级原子操作，使LLM能作为可靠的语义解析器而非自由编程者。OQL查询由引擎验证并确定性执行，生成可运行策略。我们还构建了一个新数据集，并证明该神经符号管道在执行准确率和逻辑一致性上显著优于直接基线方法。
原文摘要 · Abstract (English)
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.
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