A2C Reinforcement Learning for Cryptocurrency Trading and Asset Management

Unlike the traditional stock markets, the 24/7 nature of the cryptocurrency market poses unique challenges and opportunities, particularly in asset trading and management. These dynamic market conditions have accelerated the development of sophisticated trading strategies, increasingly leveraging the power of Artificial Intelligence (AI). Among these, AI-driven trading bots have become a prominent tool, offering enhanced decision-making capabilities over conventional methods. This paper proposes the application of the Advantage Actor-Critic (A2C) model, a reinforcement learning technique ideally suited for the unpredictable nature of the cryptocurrency market. Our research aims to optimize asset allocation within a diverse portfolio, including both high-volatility cryptocurrencies and the more stable US Dollar. The proposed A2C model strategically leverages current and predicted price data of cryptocurrencies with current asset allocation to make new asset allocation decisions. Our experiments demonstrate the A2C model’s efficacy in managing asset allocations under varying market conditions. We particularly focus on how the model responds to alterations in the loss penalty factor within its reward function, which enables a shift between aggressive and conservative investment strategies. The model effectively balances risk and return, showing promising potential in achieving stable asset growth in rising markets while mitigating losses during market downturns.