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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

Indonesian

Sebagai pasar opsi berkembang dan AI maju, sistem agen untuk perdagangan opsi mendapatkan perhatian yang semakin meningkat. Agen berbasis bahasa dapat berpendapat tentang informasi kontekstual seperti berita, tetapi perdagangan opsi menyajikan masalah keputusan yang sangat menantang: satu saham dapat memiliki ribuan kontrak, dan agen harus memutuskan kontrak mana yang harus diperdagangkan dan bagaimana untuk menggabungkan mereka. Pendekatan yang ada sering mengecualikan kompleksitas ini dengan membatasi kebijakan ke struktur strategi tetap, seperti straddle, membatasi kemampuan mereka untuk mengubah strategi sebagai kondisi pasar berubah. Kami menyajikan SOTA (Stock Options Trading Agents), kerangka kerja perdagangan agen untuk pilihan-strategi pilihan terstruktur yang dipilih. SOTA mengevaluasi dunia opsi besar ke dalam keputusan tingkat strategi sementara menentukan implementasi portofolio tangan. Kami mengembangkan SOTA dengan mengembalikan setelah pelatihan Q3.8-27B dengan pengawasan

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