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Robustness of Support Vector Machines in Algorithmic Trading on Cryptocurrency Market

Author

Listed:
  • Maryna Zenkova

    (Quantitative Finance Research Group, Faculty of Economic Sciences, University of Warsaw)

  • Robert Ślepaczuk

    (Quantitative Finance Research Group, Faculty of Economic Sciences, University of Warsaw)

Abstract

This study investigates the profitability of a algorithmic trading strategy based on training SVM model to identify cryptocurrencies with high or low predicted returns. A tail set is defined to be a group of coins whose volatility-adjusted returns are in the highest or lowest quantile. Each cryptocurrency is represented by a set of six technical features. SVM is trained on historical tail sets and tested on the current data. The classifier is chosen to be a nonlinear support vector machine. Portfolio is formed by ranking coins using SVM output. The highest ranked coins are used for long positions to be included in the portfolio for one reallocation period. The following metrics were used to estimate the portfolio profitability: %ARC (the annualized rate of change), %ASD (the annualized standard deviation of daily returns), MDD (the maximum drawdown coefficient), IR1, IR2 (the information ratio coefficients). The performance of the SVM portfolio is compared to the performance of the four benchmark strategies based on the values of the information ratio coefficient IR1 which quantifies the risk-weighted gain. The question on how sensitive the portfolio performance is to the parameters set in the SVM model is also addressed in this study.

Suggested Citation

  • Maryna Zenkova & Robert Ślepaczuk, 2019. "Robustness of Support Vector Machines in Algorithmic Trading on Cryptocurrency Market," Working Papers 2019-02, Faculty of Economic Sciences, University of Warsaw.
    • Handle: RePEc:war:wpaper:2019-02
    as

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    File URL: https://www.wne.uw.edu.pl/index.php/download_file/4735/
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    References listed on IDEAS

    as
    1. Kosc, Krzysztof & Sakowski, Paweł & Ślepaczuk, Robert, 2019. "Momentum and contrarian effects on the cryptocurrency market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 691-701.
    2. Robert Ślepaczuk & Grzegorz Zakrzewski & Paweł Sakowski, 2012. "Investment strategies beating the market. What can we squeeze from the market?," Working Papers 2012-04, Faculty of Economic Sciences, University of Warsaw.
    3. Huerta, Ramon & Corbacho, Fernando & Elkan, Charles, 2013. "Nonlinear support vector machines can systematically identify stocks with high and low future returns," Algorithmic Finance, IOS Press, vol. 2(1), pages 45-58.
    4. Tay, Francis E. H. & Cao, Lijuan, 2001. "Application of support vector machines in financial time series forecasting," Omega, Elsevier, vol. 29(4), pages 309-317, August.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Bartosz Bieganowski & Robert 'Slepaczuk, 2024. "Supervised Autoencoders with Fractionally Differentiated Features and Triple Barrier Labelling Enhance Predictions on Noisy Data," Papers 2411.12753, arXiv.org, revised Nov 2024.
    2. Mahmut Bağcı & Pınar Kaya Soylu & Selçuk Kıran, 2024. "The Symmetric and Asymmetric Algorithmic Trading Strategies for the Stablecoins," Computational Economics, Springer;Society for Computational Economics, vol. 64(5), pages 2663-2684, November.
    3. Maudud Hassan Uzzal & Robert Ślepaczuk, 2023. "The performance of time series forecasting based on classical and machine learning methods for S&P 500 index," Working Papers 2023-05, Faculty of Economic Sciences, University of Warsaw.
    4. Yanzhao Zou & Dorien Herremans, 2022. "PreBit -- A multimodal model with Twitter FinBERT embeddings for extreme price movement prediction of Bitcoin," Papers 2206.00648, arXiv.org, revised Oct 2023.
    5. Bartosz Bieganowski & Robert Slepaczuk, 2024. "Supervised Autoencoder MLP for Financial Time Series Forecasting," Papers 2404.01866, arXiv.org, revised Jun 2024.

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    More about this item

    Keywords

    machine learning; support vector machines; investment algorithm; algorithmic trading; strategy; optimization; cross-validation; overfitting; cryptocurrency market; technical analysis; meta parameters;
    All these keywords.

    JEL classification:

    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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