Deep Reinforcement Learning with Function Properties in Mean Reversion Strategies
This work provides a practical framework for practitioners to apply advanced DRL methods, typically used in games, to fundamental trading problems, potentially offering a more robust approach to financial decision-making.
This paper explores the application of a deep reinforcement learning (DRL) library, originally designed for strategic games, to mean reversion trading problems. By integrating economically-motivated function properties, the author demonstrates a highly-performant and convergent DRL solution for financial decision-making.
Over the past decades, researchers have been pushing the limits of Deep Reinforcement Learning (DRL). Although DRL has attracted substantial interest from practitioners, many are blocked by having to search through a plethora of available methodologies that are seemingly alike, while others are still building RL agents from scratch based on classical theories. To address the aforementioned gaps in adopting the latest DRL methods, I am particularly interested in testing out if any of the recent technology developed by the leads in the field can be readily applied to a class of optimal trading problems. Unsurprisingly, many prominent breakthroughs in DRL are investigated and tested on strategic games: from AlphaGo to AlphaStar and at about the same time, OpenAI Five. Thus, in this writing, I want to show precisely how to use a DRL library that is initially built for games in a fundamental trading problem; mean reversion. And by introducing a framework that incorporates economically-motivated function properties, I also demonstrate, through the library, a highly-performant and convergent DRL solution to decision-making financial problems in general.