A2C Reinforcement Learning for Cryptocurrency Trading: Analyzing the Impact of Price Prediction Accuracy and Data Granularity

This paper investigates the application of the Advantage Actor–Critic (A2C) reinforcement learning model in cryptocurrency trading and portfolio management, focusing on the impact of price prediction accuracy and data granularity on model performance. The highly volatile and 24/7 operating cryptocurrency market presents unique challenges that require sophisticated trading strategies. Our study examines how varying levels of noise in price predictions affect the A2C model's ability to manage a diversified portfolio and generate returns, revealing that higher prediction accuracy leads to improved performance. Additionally, we explore the role of data granularity, finding that overly fine‐grained data introduce excessive noise that impairs the model's performance, whereas data processed at 6‐ and 12‐h intervals optimize trading efficiency and profitability. These findings show the importance of maintaining sufficient prediction accuracy and selecting appropriate data granularity to enhance the effectiveness of reinforcement learning models in cryptocurrency trading, providing valuable insights for developing robust artificial intelligence (AI)‐driven trading strategies in volatile markets.