  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.
