  AI and data driven solutions have been applied to different fields and
achieved 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. We use these
classifiers to design a strategy to trade in those markets. Our input data in
the experiments include price data with and without technical indicators in
separate tests to see the effect of using them. 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. We also discuss
limitations of these approaches and their potential impact on Efficient Market
Hypothesis.
