Deep Reinforcement Learning for Quantitative Trading: Challenges and Opportunities

被引：11

作者：

An, Bo ^{[1
]}

Sun, Shuo ^{[1
]}

Wang, Rundong ^{[1
]}

机构：

[1] Nanyang Technol Univ, Singapore 639798, Singapore

来源：

IEEE INTELLIGENT SYSTEMS | 2022年 / 37卷 / 02期

关键词：

Decision-making problem - Financial industry - Go-game - Performance - Research interests - Sequential decision making - Stellars - Video-games;

D O I：

10.1109/MIS.2022.3165994

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Quantitative trading (QT) has been a popular topic in both academia and the financial industry since the 1970s. In the last decade, deep reinforcement learning (DRL) has garnered significant research interest with stellar performance in solving complex sequential decision-making problems, such as Go and video games. The impact of DRL is pervasive, recently demonstrating its ability to conquer some challenging QT tasks. In this article, we outline several key challenges and opportunities that manifest in DRL-basedQT to shed light on future research in this field.

引用

页码：23 / 26

页数：4

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