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.

翻译：AI和数据驱动的解决方案已应用于不同领域,并取得了优异和有希望的结果。在这个研究工作中,我们运用k-Nearest nearneghears, extreme Gradient 推动器和随机森林分类器来探测三个加密货币市场的趋势问题。我们利用这些分类器来设计一个在这些市场上进行交易的战略。我们实验中的输入数据包括价格数据,有和没有技术指标,进行不同的测试,以了解使用这些数据的效果。我们对隐蔽数据的测试结果非常有希望,并显示出这一方法在帮助拥有专家体系的投资者利用市场和获得利润方面的巨大潜力。我们无法预见的66天跨度的最大利润系数是1.60。我们还讨论了这些方法的局限性及其对高效市场假说的潜在影响。