SOTA: Stock Options Trading Agents Guided by Option-Implied Return Distributions
Training AI to pick winning option trades from thousands of contracts
Researchers built an AI trading agent that learned to select and combine stock options strategies by reading financial news and market data. Over six months of live trading, the system returned 18.3% while beating simpler rule-based competitors—but only when news was used during training, not during actual trading; keeping news in the live trading phase actually flipped returns to negative.
Options markets are opaque and sprawling—a single stock can have thousands of active contracts at any moment, making manual strategy selection impractical for most traders. An AI that can navigate this complexity and outperform conventional approaches could democratize access to sophisticated trading strategies. However, the finding that news helps training but hurts live performance is a cautionary sign: the agent may be learning to chase news-driven patterns that don't repeat in the real market, a common pitfall in automated trading that developers will need to solve before deploying such systems with real money.