Reinforcement Learning Techniques for Stock Trading: A Survey of Current Research

Reinforcement learning (RL) has emerged as a promising approach for developing intelligent trading systems in the stock market. The intention of this survey article is to provide a comprehensive review of the state-of-the-art RL techniques used in stock market applications. This article begins with the introduction of basic concepts of RL and its applications in the stock market. A detailed analysis of the different RL algorithms, which includes Q-learning, deep Q-networks (DQN), actor–critic, and policy gradient methods, is presented later. These algorithms have been used for various tasks such as prediction of stock prices, portfolio management, and risk management. Further, the challenges of RL in stock trading are discussed, and potential areas for future research are highlighted. The strengths and limitations of each technique are analyzed to provide insights about their suitability for different trading scenarios. Overall, this study seeks to give a thorough summary of the RL research conducted to date in stock market trading. The analysis can serve as a useful guide for researchers, practitioners, and investors interested in applying RL for stock trading, as well as for those seeking to understand the potential of this emerging technology in financial markets.