Deep Reinforcement Learning to Automate Cryptocurrency Trading

Abstract

This research produces a deep reinforcement learning model for algorithmic trading of cryptocurrencies. The model aims to help traders earn greater profits than using traditional strategies. Although traditional strategies can generate profits, they require considerable knowledge, experience, and time to generate optimal profits. Models are trained to trade on the cryptocurrency market. Model inputs are 1 minute interval candlestick data and technical indicators for the BTC/USDT cryptocurrency pair. The model produces an output in the form of a buy, hold, or sell signal. Models are created with the PPO algorithm and a custom environment that follows the gym interface. The performance of the model is compared to the Buy and Hold strategy. The tests that have been carried out show that the model produced in this study still cannot beat the Buy and Hold strategy.