Porfolio Selection In The Crytocurrency Market With Genetic Algorithm

Abstract

Trading has always been an essential trait of humans. This thesis studies a portfolio selection problem in which an investor may find a reasonable trade-off between return and risk by combining a variety of risky assets with distinguished weights. The rapidly-growing cryptocurrency market is also the focus of this paper. In order to provide a suitable solution to the problem, two crossover approaches of the Genetic Algorithm are proposed. Interesting findings of the paper reveal an enormous gap in efficiency between the two methods of crossovers.