Pairs Trading Strategies in Cryptocurrency Markets: A Comparative Study between Statistical Methods and Evolutionary Algorithms

Engineering Proceedings

article

Po-Chang Ko¹  Ping-Chen Lin²  Hoang-Thu Do¹  Yuan-Heng Kuo¹  You-Fu Huang²  Wen-Hsien Chen³ 

[1] Department of Intelligent Commerce, National Kaohsiung University of Science and Technology;AI Fintech Center, National Kaohsiung University of Science and Technology;Department of Finance and Information, National Kaohsiung University of Science and Technology

关键词: pairs trading;    cryptocurrency markets;    NSGA-II;   

DOI  :  10.3390/engproc2023038074

来源: mdpi

【 摘 要 】

Pairs trading is a popular quantitative trading strategy with the advantage of a similarity in price movement to financial assets. Assuming that the price spreads of trading pairs are mean-reverting, this strategy exploits the disequilibrium in financial markets to find arbitrage investment opportunities. Pairs trading has been widely applied to stock, ETF, and commodity markets. However, the effectiveness of this method for cryptocurrency markets has yet to be properly explored. Therefore, we examine the profitability of pairs trading for 26 cryptocurrencies traded on the Binance exchange at high frequencies of 1, 5, and 60 min. In addition to the traditional statistical methods of distance, correlation, cointegration, and stochastic differential residual (SDR), we focus on two evolutionary algorithms: genetic algorithm (GA) and non-dominated sorting genetic algorithm II (NSGA-II). During the 79-trading-day period from 11 January to 31 March 2018, NSGA-II showed the best results at all frequencies, with an average return of 2.84%. Among the statistical models, SDR ranks first, whereas Correlation ranks last, with average returns of 1.63% and −0.48%, respectively. The z-test results show that the models are statistically significantly different. We propose NSGA-II as the best candidate for use in pairs trading strategies in cryptocurrency markets.

【 授权许可】

Unknown