Deep Q-Network Based Reinforcement Learning for Bitcoin Future Price Prediction

Abstract

Bitcoin is the very first decentralized digital money in the entire world. Bitcoin is exceptionally reliable since it uses a block chain instead of a service provider like a banking institution. As a type of digital precious metal like gold, the price of a single bitcoin seems to have been significantly increasing since the year 2010. As a result, bitcoin is extremely risky for speculators as its value varies often. Since prices may now be predicted in real time, traditional techniques to price forecasting have failed to provide sufficient information and responses. This paper recommends Deep Q-Network based Reinforcement Learning technique (DQNRL) to predict the bitcoin price more effectively. The Binance Bitcoin Futures Price Dataset from Kaggle that contains 3082284 samples and is used towards the bitcoin future cost prediction for DQNRL. The DQNRL model starts with the data exploration that portray the bitcoin price with the standard deviation and mean, log transformation of data. After data preparation, the Binance Bitcoin Futures Price dataset is fitted with the existing regression technique and proposed DQNRL model and the performance is analyzed with MAE, MSE and RMSE. The proposed DQNRL exhibits low MAE with 0.2471, low MSE with 0.061 and low RMSE of 0.2469 when compared to other existing regression models after implementation.