Cryptocurrency portfolio optimization using genetic algorithms

Project Work presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data Science ; Blockchain most discussed application has been in cryptocurrency, being Bitcoin its first. Unbeknownst to many, Bitcoin not only introduced a new digital means of exchange creating fertile ground for others trying to emulate it. Cryptocurrencies took relevance beyond computer science spheres to reach a place of relevance as a security for several investors, making it relevant to study beyond computer science spheres. Economists, statisticians, and portfolio managers, have taken the subject of cryptocurrencies as case study for open digital finance, opening questions regarding price behaviors and objective investment strategies, creating opportunities of research on fields such as machine learning. Nevertheless, each cryptocurrency has its technicalities, and different value proposals, making the subject relatable, in some sense, to traditional financial instruments, from which some lessons can be of use, for example, how it is difficult to come up with a unique approach or toolset that forecasts prices and optimizes investments, at least in a general sense. Here the objective is to tackle a cryptocurrency portfolio optimization by means of genetic algorithms. Two approaches are suggested, the first Limited Trading approach, consists on using genetic algorithms to find how much of the coins to invest for profit considering to sell at the last day. Second approach is called Open Trading, much like the first, intends to find profit but it allows buying and selling at each timestep. In both cases respecting budgeting limitations and using forecast price values of each coin, but for this, instead of delving into the complexities of thousands of possible coins and algorithms, it was used a combination of machine learning methods to forecast prices of the coins in the portfolio. It was found that Limited trading outperforms its counterpart in fitness (expected ...