Reinforcement Learning for Quantitative Trading

TL;DR

This paper surveys the application of reinforcement learning techniques in quantitative trading, highlighting recent advances, challenges, and future research directions in this interdisciplinary field.

Contribution

It provides a comprehensive taxonomy and summary of RL-based models for quantitative trading, consolidating recent research efforts and identifying key challenges.

Findings

RL has shown success in solving complex QT tasks

The survey categorizes RL models used in QT

Future research directions are proposed

Abstract

Quantitative trading (QT), which refers to the usage of mathematical models and data-driven techniques in analyzing the financial market, has been a popular topic in both academia and financial industry since 1970s. In the last decade, reinforcement learning (RL) has garnered significant interest in many domains such as robotics and video games, owing to its outstanding ability on solving complex sequential decision making problems. RL's impact is pervasive, recently demonstrating its ability to conquer many challenging QT tasks. It is a flourishing research direction to explore RL techniques' potential on QT tasks. This paper aims at providing a comprehensive survey of research efforts on RL-based methods for QT tasks. More concretely, we devise a taxonomy of RL-based QT models, along with a comprehensive summary of the state of the art. Finally, we discuss current challenges and propose future research directions in this exciting field.