Prediction of Bitcoin Price Movements with Machine Learning Algorithms

Abstract

We study the limits of prediction accuracy of Bitcoin price data in CNY currency using tick data from the OKCoin Bitcoin exchange (source: Kaiko data). The tick data contain the price, volume, and trade direction, and are transformed to the OHLCV format using standard methods. In this report, we deploy the Support Vector Machine algorithm by Vapnik to estimate the sign of the hour-to-hour transaction return using a sampling moving window of varying size on the past data. Several kernel functions are validated. Our first results for all months of the year 2015 show that the hit ratio accuracy level (the fraction of correctly predicted upward or downward events) does not exceed 60%. It remains to be established whether this low result corresponds to the causal extraction limit inherent in the data, or whether it can be improved by deploying other methods, such as LSTM networks in deep learning.