Cryptocurrency Portfolio Management Using Reinforcement Learning

Portfolio management is the science of choosing the best investment policies and strategies with the aim of getting maximum returns. Simply, it means managing the assets/stocks of a company, organization, or individual and taking into account the risks, and increasing the profit. This paper proposes portfolio management using a bot leveraging a reinforcement learning environment specifically for cryptocurrencies which are a hot topic in the current world of technology. The reinforcement Learning Environment gives the reward/penalty to the agent, which helps it train itself during the training process and make decisions based on the trial-and-error method. Dense and CNN networks are used for training the agent to taking the decision to either buy, hold or sell the coin. Various technical indicators, like MACD, SMA, etc., are also included in the dataset while making the decisions. The bot is trained on 3-year hourly data of Bitcoin, and results demonstrate that the Dense and CNN network models show a good amount of profit against a starting balance of 1,000, indicating that reinforcement learning environments can be efficacious and can be incorporated into the trading environments.

Keywords: Actor-Critic, Bitcoin, Cryptocurrency, CNN, Portfolio Management, Reinforcement Learning