SOTA: Stock Options Trading Agents Guided by Option-Implied Return Distributions
We present SOTA (Stock Options Trading Agents), an agentic trading framework for structured option-strategy selection.
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Key points
- 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.
- 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.
Sources (1)
- [1]SOTA: Stock Options Trading Agents Guided by Option-Implied Return DistributionsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 7, 04:55 PM
We present SOTA (Stock Options Trading Agents), an agentic trading framework for structured option-strategy selection.
As option markets grow and AI advances, agentic systems for option trading are gaining increasing attention.
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