Quantitative Trading using Deep Q Learning

TL;DR

This work investigates reinforcement learning for quantitative trading, focusing on a Q-learning/DQN-based trading agent trained on historical stock data. The methodology preprocesses daily stock returns, normalizes features, and defines the agent's state as the past $n$ days of returns, with actions Buy/Sell/Hold and rewards tied to portfolio wealth changes; training uses $10{,}000$ episodes with $\alpha=0.001$ and $\gamma=0.99$, and evaluation compares against buy-and-hold and simple moving-average baselines. Results indicate the RL approach improves cumulative return ($CR$) and risk-adjusted metrics such as the Sharpe ratio, while reducing maximum drawdown, though challenges like data requirements and overfitting remain. The paper discusses limitations and outlines future work, including exploring additional data sources, alternative RL algorithms, regime adaptation, cross-asset evaluation, and portfolio optimization integration, underscoring the potential of RL to yield more efficient and higher-return trading systems in financial markets.

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.