Robustness of Support Vector Machines in Algorithmic Trading on Cryptocurrency Market

Maryna Zenkova and Robert Ślepaczuk
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Maryna Zenkova: Quantitative Finance Research Group, Faculty of Economic Sciences, University of Warsaw

No 2019-02, Working Papers from 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.

Keywords: machine learning; support vector machines; investment algorithm; algorithmic trading; strategy; optimization; cross-validation; overfitting; cryptocurrency market; technical analysis; meta parameters (search for similar items in EconPapers)
JEL-codes: C15 C4 C45 C61 G14 G17 (search for similar items in EconPapers)
Pages: 35 pages
Date: 2019
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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https://www.wne.uw.edu.pl/index.php/download_file/4735/ First version, 2019 (application/pdf)

Related works:
Journal Article: Robustness of Support Vector Machines in Algorithmic Trading on Cryptocurrency Market (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:war:wpaper:2019-02

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