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

Jakub Drahokoupil

No 4.006, FFA Working Papers from Prague University of Economics and Business

Abstract: 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 (search for similar items in EconPapers)
JEL-codes: C11 C39 C61 G11 (search for similar items in EconPapers)
Pages: 35 pages
Date: 2022-03-24, Revised 2022-05-09
New Economics Papers: this item is included in nep-big, nep-cmp and nep-pay
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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