Review
Version 1
Preserved in Portico This version is not peer-reviewed
Deep Reinforcement Learning For Trading - A Critical Survey
Version 1
: Received: 28 October 2021 / Approved: 2 November 2021 / Online: 2 November 2021 (10:57:23 CET)
A peer-reviewed article of this Preprint also exists.
Millea, A. Deep Reinforcement Learning For Trading—A Critical Survey. Data 2021, 6, 119. Millea, A. Deep Reinforcement Learning For Trading—A Critical Survey. Data 2021, 6, 119.
Abstract
Deep reinforcement learning (DRL) has achieved significant results in many Machine Learning (ML) benchmarks. In this short survey we provide an overview of DRL applied to trading on financial markets, including a short meta-analysis using Google Scholar, with an emphasis on using hierarchy for dividing the problem space as well as using model-based RL to learn a world model of the trading environment which can be used for prediction. In addition, multiple risk measures are defined and discussed, which not only provide a way of quantifying the performance of various algorithms, but they can also act as (dense) reward-shaping mechanisms for the agent. We discuss in detail the various state representations used for financial markets, which we consider critical for the success and efficiency of such DRL agents. The market in focus for this survey is the cryptocurrency market.
Keywords
deep reinforcement learning; model-based RL; hierarchy; trading; cryptocurrency; foreign exchange; stock market; risk; prediction; reward shaping
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Comments (0)
We encourage comments and feedback from a broad range of readers. See criteria for comments and our Diversity statement.
Leave a public commentSend a private comment to the author(s)
* All users must log in before leaving a comment
