Predicting Trends of Bitcoin Prices Based on Machine Learning Methods

Abstract

Due to its characteristics of decentralization, no counterfeit currency, and anonymity, Bitcoin has developed incredibly rapidly, gradually realizing free exchange with real currency, and stepping into the purchase of real goods and services. This paper applied historical daily frequency data of Bitcoin and constructed traditional technical indicator factors such as CCI, AROON, MA, PSY, etc. Then logistic regression and XGBoost were leveraged to predict the rise and fall of the price of Bitcoin. The results showed that XGBoost classifier obtained a higher score than two logistic regressions, therefore the XGBoost method performed better until this stage: 163% of the return, 32% of the maximum retracement. This paper helps to buy and sell Bitcoin better, get a higher positive return, and may provide ideas for the stock market research.