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.
Highlights
Over half a century, a significant amount of research has been done on trading volume and its relationship with good point returns [1,2,3,4,5,6,7,8,9,10,11,12]
One of the reasons for the great attention to these relationships is that many believe that price movements can bring sufficient income if it is right to decide on the volume of trading
Swing trading is a middle of the road investment strategy and should be considered when developing a personal approach
Summary
A significant amount of research has been done on trading volume and its relationship with good point returns [1,2,3,4,5,6,7,8,9,10,11,12]. Stenqvist and Lonno [21] utilized deep learning algorithms, on a much higher frequency time scale of every 30 min, to achieve a 79% accuracy in predicting bitcoin price fluctuations using 2.27 million tweets Neither of these strategies used data labeled directly based on the price fluctuations, nor did they analyze the average size of the price percent increases and percent decreases their models were predicting.
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