Profitable Strategy Design for Trades on Cryptocurrency Markets with Machine Learning Techniques

AI and data driven solutions have been applied to different fields and achieve outperforming and promising results. In this research work we apply k-Nearest Neighbours, eXtreme Gradient Boosting and Random Forest classifiers for detecting the trend problem of three cryptocurrency markets. Our input data includes price data and technical indicators. We use these classifiers to design a strategy to trade in those markets. Our test results on unseen data are very promising and show a great potential for this approach in helping investors with an expert system to exploit the market and gain profit. Our highest profit factor for an unseen 66 day span is 1.60 (60% profit). We also discuss limitations of these approaches and their potential impact on Efficient Market Hypothesis.