Deep Options: Trading Options, Strategy Selection, and Risk Management Framework with Multi-Agent and Deep Reinforcement Learning

Abstract

Achieving consistent profitability and outperforming the underlying index are long-standing challenges in financial markets. While most AI-driven research focuses on stock trading and portfolio optimization, options trading—comprising nearly 90% of total exchange-traded volume—has remained relatively underexplored. Options trading strategies involve complex, sequential decision-making steps, from gauging market direction, volatility, and momentum to selecting strikes, managing risk, sizing positions, and determining entry/exit timing. Recent advances in Agentic AI and Deep Reinforcement Learning (DRL) have shown significant potential for tackling such high-dimensional, dynamic problems.
In this thesis, we propose an autonomous framework for options trading built on Agentic AI, comparing two distinct approaches. The first is a multi-agent collaborative system that orchestrates five specialized agents: a Generative Adversarial Network (GAN) for strategy generation, a dedicated strategy selection module, a Transformer-based market regime prediction agent, a risk management agent, and a data acquisition and technical analysis agent. The second approach leverages a DRL-driven pipeline to dynamically learn and execute options strategies.
Both methods are benchmarked against 15 different option strategies across various market conditions. Experimental results demonstrate that our proposed framework consistently delivers robust performance and significantly outperforms the underlying index. This research closes a critical gap in AI-based decision-making for options trading and provides a scalable, adaptable,
and empirically validated solution for real-world market environments.
Keywords: Deep Reinforcemen