Quantitative Trading using Deep Q Learning

TL;DR

This paper explores the application of reinforcement learning, specifically Deep Q Learning, to quantitative trading, demonstrating its potential to outperform traditional algorithms and improve trading system efficiency.

Contribution

It introduces a reinforcement learning-based trading algorithm and provides a case study showing its effectiveness over traditional methods.

Findings

RL-based trading can outperform traditional algorithms

Reinforcement learning enhances trading system efficiency

Potential for more sophisticated trading strategies

Abstract

Reinforcement learning (RL) is a subfield of machine learning that has been used in many fields, such as robotics, gaming, and autonomous systems. There has been growing interest in using RL for quantitative trading, where the goal is to make trades that generate profits in financial markets. This paper presents the use of RL for quantitative trading and reports a case study based on an RL-based trading algorithm. The results show that RL can be a useful tool for quantitative trading and can perform better than traditional trading algorithms. The use of reinforcement learning for quantitative trading is a promising area of research that can help develop more sophisticated and efficient trading systems. Future research can explore the use of other reinforcement learning techniques, the use of other data sources, and the testing of the system on a range of asset classes. Together, our work shows the potential in the use of reinforcement learning for quantitative trading and the need for further research and development in this area. By developing the sophistication and efficiency of trading systems, it may be possible to make financial markets more efficient and generate higher returns for investors.