Deep Reinforcement Learning with Function Properties in Mean Reversion Strategies

Abstract

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, whereas others are still building RL agents from scratch based on classical theories. To address the aforementioned gaps in adopting the latest DRL methods, the author is particularly interested in testing out whether 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, the author shows precisely how to use a DRL library that is initially built for games in a commonly used trading strategy—mean reversion. And by introducing a framework that incorporates economically motivated function properties, they also demonstrate, through the library, a highly performant and convergent DRL solution to decision-making financial problems in general.