Reinforcement Learning for Automatic Cryptocurrency Trading

Abstract

Reinforcement learning as machine learning algorithms can construct software agents, and machines work automatically to determine the superior manners to maximize the algorithm. On the other hand, in recent years, cryptocurrencies are increasingly known because numerous people have used them for investment and trading. People have created automated cryptocurrency trading systems to save time in trading activities. Therefore, researchers are interested in inventing a computerized trading system by implementing a reinforcement learning algorithm. This implementation uses a stable baseline and OpenAi gym with three methods of RNN, such as A2C, ACER, and PPO. The result is that A2C is the best method for low-volume trade like BTC/USDT, while for higher-volume trade in ETH/USDT, the ACER method proved more beneficial. The best BTC/USDT trading method is A2C, with a reward in the testing phase of 0.332. Meanwhile, for ETH/USDT, the best approach is ACER, with the testing phase’s reward is 0.257.