Next Article in Journal
Entropic Uncertainty in Spin XY Model with Long-Range Interactions
Previous Article in Journal
Risk-Neutrality of RND and Option Pricing within an Entropy Framework
Open AccessEditor’s ChoiceArticle

Forecasting Bitcoin Trends Using Algorithmic Learning Systems

Department of Management, Western Galilee Academic College, P.O.Box, 2125, Acre 2412101, Israel
Entropy 2020, 22(8), 838; https://doi.org/10.3390/e22080838
Received: 1 July 2020 / Revised: 24 July 2020 / Accepted: 28 July 2020 / Published: 30 July 2020
This research has examined the ability of two forecasting methods to forecast Bitcoin’s price trends. The research is based on Bitcoin—USA dollar prices from the beginning of 2012 until the end of March 2020. Such a long period of time that includes volatile periods with strong up and downtrends introduces challenges to any forecasting system. We use particle swarm optimization to find the best forecasting combinations of setups. Results show that Bitcoin’s price changes do not follow the “Random Walk” efficient market hypothesis and that both Darvas Box and Linear Regression techniques can help traders to predict the bitcoin’s price trends. We also find that both methodologies work better predicting an uptrend than a downtrend. The best setup for the Darvas Box strategy is six days of formation. A Darvas box uptrend signal was found efficient predicting four sequential daily returns while a downtrend signal faded after two days on average. The best setup for the Linear Regression model is 42 days with 1 standard deviation. View Full-Text
Keywords: Bitcoin; algorithmic trading; Darvas box; swarm optimizations Bitcoin; algorithmic trading; Darvas box; swarm optimizations
Show Figures

Figure 1

MDPI and ACS Style

Cohen, G. Forecasting Bitcoin Trends Using Algorithmic Learning Systems. Entropy 2020, 22, 838. https://doi.org/10.3390/e22080838

AMA Style

Cohen G. Forecasting Bitcoin Trends Using Algorithmic Learning Systems. Entropy. 2020; 22(8):838. https://doi.org/10.3390/e22080838

Chicago/Turabian Style

Cohen, Gil. 2020. "Forecasting Bitcoin Trends Using Algorithmic Learning Systems" Entropy 22, no. 8: 838. https://doi.org/10.3390/e22080838

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop