A Forecasting Approach to Cryptocurrency Price Index Using Reinforcement Learning

Abstract

Cryptocurrency has emerged as a well-known significant component with both economic and financial potential in recent years. Unfortunately, Bitcoin acquisition is not simple, due to uneven business and significant rate fluctuations. Traditional approaches to price forecasting have proven incapable of proving adequate data and solutions because prices can now be forecast in real time. We recommended a machine learning-based alternative for a mortgage lender based on highlighted problems in forecasting the price of Bitcoin. The proposed system included a reinforcement learning algorithm for price estimation and forecasting, as well as a blockchain framework for an efficient and secure environment. The proposed prediction, compared to other state-of-the-art strategies in this sector, demonstrated better performance. In this system, the proposed prediction reached improved consistency, in comparison to other systems, with respect to Monero (XMR), Litecoin (LTC), Oryen (ORY), and Bitcoin (BTC).