<abstract xmlns="http://www.w3.org/1999/xhtml"><p>This study investigates the profitability of an 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 the lowest quintile. 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. The portfolio is formed by ranking coins using the 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 of how sensitive the portfolio performance is to the parameters set in the SVM model is also addressed in this study.</p></abstract>

This study investigates the profitability of an 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 the lowest quintile. 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. The portfolio is formed by ranking coins using the 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 of how sensitive the portfolio performance is to the parameters set in the SVM model is also addressed in this study.

Robustness of Support Vector Machines in Algorithmic Trading on Cryptocurrency Market

Central European Economic Journal

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

This study investigates the profitability of an algorithmic trading strategy based on training SVM model to identify cryptocurrencies with high or low...

Robustness of Support Vector Machines in Algorithmic Trading on Cryptocurrency Market

Published Online: Aug 07, 2019

Page range: 186 - 205

DOI: https://doi.org/10.1515/ceej-2018-0022

Keywords
Machine learning, support vector machines, investment algorithm, algorithmic trading, strategy, optimization, cross-validation, overfitting, cryptocurrency market, technical analysis, meta parameters

© 2018 R. Ślepaczuk, M. Zenkova, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Robustness of Support Vector Machines in Algorithmic Trading on Cryptocurrency Market

Published Online: Aug 07, 2019

Page range: 186 - 205

DOI: https://doi.org/10.1515/ceej-2018-0022

KeywordsMachine learning, support vector machines, investment algorithm, algorithmic trading, strategy, optimization, cross-validation, overfitting, cryptocurrency market, technical analysis, meta parameters

© 2018 R. Ślepaczuk, M. Zenkova, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Keywords
Machine learning, support vector machines, investment algorithm, algorithmic trading, strategy, optimization, cross-validation, overfitting, cryptocurrency market, technical analysis, meta parameters