FFA Working Papers 4:006 (2022)3723

Application of the XGBoost algorithm and Bayesian optimization for the Bitcoin price prediction during the COVID-19 period

Jakub Drahokoupil

Prague University of Economics and Business

Aim of this paper is to use Machine Learning algorithm called XGBoost developed by  Tianqi Chen and Carlos Guestrin in 2016 to predict future development of the Bitcoin (BTC) price and build an algorithmic trading strategy based on the predictions from the model. For the final algorithmic strategy, six XGBoost models are estimated in total, estimating following n-th day BTC Close predictions: 1,2,5,10,20,30. Bayesian optimization techniques are used twice during the development of the trading strategy. First, when appropriate hyperparameters of the XGBoost model are selected. Second, for the optimization of each model prediction weight, in order to obtain the most profitable trading strategy. The paper shows, that even though the XGBoost model has several limitations, it can fairly accurately predict future development of the BTC price, even for further predictions. The paper aims specifically for the potential of algorithmic trading during the COVID-19 period, where BTC cryptocurrency suffered extremely volatile period, reaching its new all-time highest prices as well as 50% losses during few consecutive months. The applied trading strategy shows promising results, as it beats the B&H strategy both from the perspective of total profit, Sharpe ratio or Sortino ratio.

Keywords: XGBoost, Bayesian Optimization, Bitcoin, Algorithmic trading

JEL classification: C11, C39, C61, G11

Received: March 24, 2022; Revised: May 9, 2022; Accepted: May 10, 2022; Published online: February 21, 2022  Show citation