SOTA：由期权隐含收益分布引导的股票期权交易智能体

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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%.

Comments:	Accepted at the NeurIPS 2026 Agenthon Workshop
Subjects:	Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Portfolio Management (q-fin.PM); Trading and Market Microstructure (q-fin.TR)
Cite as:	arXiv:2610.10407 [cs.AI]
	(or arXiv:2610.10407v1 [cs.AI] for this version)
	https://doi.org/10.48550/arXiv.2610.10407 arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Yizhen Xie [view email]
[v1] Wed, 7 Oct 2026 16:55:05 UTC (96 KB)