Learning to Play the Trading Game: Exploring Reinforcement Learning-Based Stock Trading Bots

Developing a stock trading bot capable of navigating high-entropy environments like stock markets presents both challenges and opportunities in the field of machine learning. While quantitative algorithms currently dominate global stock trading, creating a consistently profitable prediction model remains elusive due to market unpredictability. This paper explores the feasibility of training an agent using reinforcement learning (RL) and artificial neural networks (ANN) to make optimal decisions in stock trading. We evaluate various RL techniques and their modified variants to assess the agent's performance across different stock prices and portfolio settings. While our approaches show promise in specific historical periods, they also exhibit fragility, with inconsistent results across different time periods and uncertain performance in future data. Nevertheless, this work provides valuable insights into the challenges and potential of employing RL in stock trading algorithms.