Recommending Cryptocurrency Trading Points with Deep Reinforcement Learning Approach

Abstract

The net profit of investors can rapidly increase if they correctly decide to take one of these three actions: buying, selling, or holding the stocks. The right action is related to massive stock market measurements. Therefore, defining the right action requires specific knowledge from investors. The economy scientists, following their research, have suggested several strategies and indicating factors that serve to find the best option for trading in a stock market. However, several investors’ capital decreased when they tried to trade the basis of the recommendation of these strategies. That means the stock market needs more satisfactory research, which can give more guarantee of success for investors. To address this challenge, we tried to apply one of the machine learning algorithms, which is called deep reinforcement learning (DRL) on the stock market. As a result, we developed an application that observes historical price movements and takes action on real-time prices. We tested our proposal algorithm with three—Bitcoin (BTC), Litecoin (LTC), and Ethereum (ETH)—crypto coins’ historical data. The experiment on Bitcoin via DRL application shows that the investor got 14.4% net profits within one month. Similarly, tests on Litecoin and Ethereum also finished with 74% and 41% profit, respectively.

Cryptocurrency trading: a comprehensive survey

“…By setting rewards functions as Sharpe ratio and profit, the double Q-learning method demonstrated to be the most profitable approach in trading cryptocurrency. Sattarov et al (2020) applied deep reinforcement learning and used historical data from BTC, LTC and ETH to observe historical price movements and acted on real-time prices. The model proposed by the authors helped traders to correctly choose one of the following three actions: buy, sell and hold stocks and get advice on the correct option.…”

FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance

“…Cryptocurrencies are rising in the digital financial market, such as Bitcoin (BTC) [40], and are considered more volatile than stocks. DRL is also being actively explored in automated trading, portfolio allocation, and market making for cryptocurrencies [20,39,41].…”

Econophysics of cryptocurrency crashes: an overview

“…Equally interesting is the development of the theory of quantum computation and quantum information [375][376][377][378] where it would be interesting to see their influence on blockchain and cryptocurrencies. Moreover, the research in the field of artificial intelligence, machine, and deep learning does not remain without attention [51,[379][380][381][382][383][384][385][386][387][388][389][390].…”