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SOTA: Stock Options Trading Agents Guided by Option-Implied Return Distributions

Yizhen Xie, Mengyang Liu · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.10407 · cited by 0 works (OpenAlex) · open-access PDF

Spanish

A medida que crecen los mercados de opciones y avanza la IA, los sistemas de agentes para el comercio de opciones están ganando cada vez más atención. Los agentes basados en el modelo de lenguaje pueden razonar sobre la información contextual, como las noticias, pero el comercio de opciones presenta un problema de decisión particularmente desafiante: una sola acción puede tener miles de contratos, y el agente debe decidir qué contratos deben comerciar y cómo combinarlos. Los enfoques existentes a menudo limitan esta complejidad al restringir la política a una estructura de estrategia fija, como un estradillo, limitando su capacidad para cambiar las estrategias a medida que cambian las condiciones del mercado. Presentamos SOTA (Agentes de Comercio de Opciones de Stock), un marco de comercio de agentes para la selección de opciones estructuradas y la estrategia de

Machine translation (Meta M2M100); the English original follows.

Abstract

As option markets grow and AI advances, agentic systems for option trading are gaining increasing attention. Language-model-based agents can reason over contextual information such as news, but option trading presents a particularly challenging decision problem: a single stock can have thousands of contracts, and the agent must decide both which contracts to trade and how to combine them. Existing approaches often sidestep this complexity by restricting the policy to a fixed strategy structure, such as a straddle, limiting their ability to switch strategies as market conditions change. We present SOTA (Stock Options Trading Agents), an agentic trading framework for structured option-strategy selection. SOTA abstracts the large option universe into strategy-level decisions while deterministic resolvers handle portfolio implementation. We develop SOTA by post-training Qwen3.8-27B with supervised fine-tuning followed by reinforcement learning. SOTA is evaluated on options on nine large-cap U.S. equities and SPY against rule-based and machine-learning strategy selectors in the same trading environment. Over a six-month out-of-sample period, SOTA earns an 18.3% total return with a Sharpe ratio of 1.60 and a maximum drawdown of 8.96%. We also document an asymmetric role of news: news improves frontier-teacher trajectories, but retaining news during reinforcement learning reduces out-of-sample return from 18.3% to -2.7%.

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Topics: Reinforcement learning, Portfolio, Trading strategy, Sharpe ratio, Economics, Limiting

Sources: OpenAlex (CC0), arXiv, Crossref. Agent Exchange