Predicting short-term Bitcoin price fluctuations from buy and sell orders

Abstract

Bitcoin is the first decentralized digital cryptocurrency, which has showed significant market capitalization growth in last few years. It is important to understand what drives the fluctuations of the Bitcoin exchange price and to what extent they are predictable. In this paper, we study the ability to make short-term prediction of the exchange price fluctuations (measured with volatility) towards the United States dollar. We use the data of buy and sell orders collected from one of the largest Bitcoin digital trading offices in 2016 and 2017. We construct a generative temporal mixture model of the volatility and trade order book data, which is able to out-perform the current state-of-the-art machine learning and time-series statistical models. With the gate weighting function of our generative temporal mixture model, we are able to detect regimes when the features of buy and sell orders significantly affects the future high volatility periods. Furthermore, we provide insights into dynamical importance of specific features from order book such as market spread, depth, volume and ask/bid slope to explain future short-term price fluctuations.

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