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
