SOTA: Stock Options Trading Agents Guided by Option-Implied Return Distributions· SOTA: 基于期权隐含收益分布的交易代理
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 determinist
基于期权隐含收益分布的交易代理，解决多合约选择问题。
- 核心方法
- SOTA将大量期权合约抽象成策略级别的决策问题，利用期权隐含收益分布指导交易。
- 适合谁读
- 研究者 / 工程师
- 要解决的问题
- 现有AI交易代理在处理期权市场时，因合约数量庞大而难以选择最优策略。
- 关键实验
- 未提供
- 主要贡献
- 提供了一种能够动态适应市场条件变化的期权交易策略选择框架。
- 意义与局限
- SOTA框架提高了AI交易代理在期权市场的决策能力，但可能受限于模型假定的准确性。