Cryptocurrency Trading Agent Using Deep Reinforcement Learning

Abstract

Cryptocurrencies are peer-to-peer digital assets monitored and organised by a blockchain network. Price prediction has been a significant focus point with various machine learning algorithms, especially concerning cryptocurrency. This work addresses the challenge faced by traders of short-term profit maximisation. The study presents a deep reinforcement learning algorithm to trade in cryptocurrency markets, Duelling DQN. The environment has been designed to simulate actual trading behaviour, observing historical price movements and taking action on real-time prices. The proposed algorithm was tested with Bitcoin, Ethereum, and Litecoin. The respective portfolio returns are used as a metric to measure the algorithm's performance against the buy-and-hold benchmark, with the buy-and-hold outperforming the results produced by the Duelling DQN agent.