OPHR: Mastering Volatility Trading with Multi-Agent Deep Reinforcement Learning

Zeting Chen, Xinyu Cai, Molei Qin, Bo An

Keywords: Reinforcement Learning, Quantatitive Trading, Option Trading

TL;DR: We introduce OPHR, a multi-agent reinforcement learning framework that outperforms traditional approaches in volatility trading by dynamically selecting options positions and optimizing hedging strategies.

Abstract: Options markets represent one of the most sophisticated segments of the financial ecosystem, with prices that directly reflect market uncertainty. In this paper, we introduce the first reinforcement learning (RL) framework specifically designed for volatility trading through options, focusing on profit from the difference between implied volatility and realized volatility. Our multi-agent architecture consists of an Option Position Agent (OP-Agent) responsible for volatility timing by controlling long/short volatility positions, and a Hedger Routing Agent (HR-Agent) that manages risk and maximizes path-dependent profits by selecting optimal hedging strategies with different risk preferences. Evaluating our approach using cryptocurrency options data from 2021-2024, we demonstrate superior performance on BTC and ETH, significantly outperforming traditional strategies and machine learning baselines across all profit and risk-adjusted metrics while exhibiting sophisticated trading behavior. The code framework and sample data of this paper have been released on https://github.com/Edwicn/OPHR-MasteringVolatilityTradingwithMultiAgentDeepReinforcementLearning

Primary Area: Applications (e.g., vision, language, speech and audio, Creative AI)

Submission Number: 7551