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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

Korean

옵션 시장이 성장하고 AI가 진보함에 따라 옵션 거래를위한 에이전트 시스템이 점점 더 많은 관심을 얻고 있습니다. 언어 모델 기반 에이전트는 뉴스와 같은 컨텍스트 정보에 대해 논의 할 수 있지만 옵션 거래는 특히 도전적인 결정 문제를 제시합니다 : 단일 주식은 수천 개의 계약을 가질 수 있으며 에이전트는 거래를위한 계약을 모두 결정해야합니다. 기존 접근 방식은 종종 정책을 고정된 전략 구조로 제한함으로써 이러한 복잡성을 측정합니다. 우리는 SOTA (주식 옵션 거래 에이전트)를 제시합니다. SOTA는 구조화 된 옵션 전략 선택을위한 에이전트 거래 프레임 워크입니다. SOTA는 전략 수준의 결정을 결정하는 동안 큰

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