Deep Reinforcement Learning in Quantitative Algorithmic Trading: A Review

TL;DR

This review discusses the progress and challenges of applying deep reinforcement learning to automated stock trading, highlighting its potential and current limitations in real-world applications.

Contribution

It provides a comprehensive overview of DRL applications in quantitative trading, emphasizing the gap between promising research results and practical, real-time trading implementations.

Findings

Most studies are proof-of-concept with unrealistic settings

Significant performance improvements over baseline strategies

Lack of real-time, online trading experiments

Abstract

Algorithmic stock trading has become a staple in today's financial market, the majority of trades being now fully automated. Deep Reinforcement Learning (DRL) agents proved to be to a force to be reckon with in many complex games like Chess and Go. We can look at the stock market historical price series and movements as a complex imperfect information environment in which we try to maximize return - profit and minimize risk. This paper reviews the progress made so far with deep reinforcement learning in the subdomain of AI in finance, more precisely, automated low-frequency quantitative stock trading. Many of the reviewed studies had only proof-of-concept ideals with experiments conducted in unrealistic settings and no real-time trading applications. For the majority of the works, despite all showing statistically significant improvements in performance compared to established baseline strategies, no decent profitability level was obtained. Furthermore, there is a lack of experimental testing in real-time, online trading platforms and a lack of meaningful comparisons between agents built on different types of DRL or human traders. We conclude that DRL in stock trading has showed huge applicability potential rivalling professional traders under strong assumptions, but the research is still in the very early stages of development.