Preprint Review Version 1 Preserved in Portico This version is not peer-reviewed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.

Journal reference: Data 2021, 6, 119
DOI: 10.3390/data6110119

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

MATHEMATICS & COMPUTER SCIENCE, Artificial Intelligence & Robotics

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