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

Author

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  • Jakub Drahokoupil

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.

Suggested Citation

  • Jakub Drahokoupil, 2022. "Application of the XGBoost algorithm and Bayesian optimization for the Bitcoin price prediction during the COVID-19 period," FFA Working Papers 4.006, Prague University of Economics and Business, revised 09 May 2022.
    • Handle: RePEc:prg:jnlwps:v:4:y:2022:id:4.006
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    References listed on IDEAS

    as
    1. Rohitash Chandra & Yixuan He, 2021. "Bayesian neural networks for stock price forecasting before and during COVID-19 pandemic," PLOS ONE, Public Library of Science, vol. 16(7), pages 1-32, July.
    2. Ekaterina Zolotareva, 2021. "Aiding Long-Term Investment Decisions with XGBoost Machine Learning Model," Papers 2104.09341, arXiv.org.
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    Citations

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    Cited by:

    1. Karol Chojnacki & Robert Ślepaczuk, 2023. "This study compares well-known tools of technical analysis (Moving Average Crossover MAC) with Machine Learning based strategies (LSTM and XGBoost) and Ensembled Machine Learning Strategies (LSTM ense," Working Papers 2023-15, Faculty of Economic Sciences, University of Warsaw.

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    More about this item

    Keywords

    XGBoost; Bayesian Optimization; Bitcoin; Algorithmic trading;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C39 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Other
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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