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.
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。 在这项研究中,我们运用k-Nearest nearneghears, extreme Gradient 推动和随机森林分类器来探测三个加密货币市场的趋势问题。 我们的投入数据包括价格数据和技术指标。 我们用这些分类器来设计这些市场的贸易战略。 我们对未见数据的测试结果非常有希望,并显示出在帮助投资者利用专家系统来利用市场和获取利润方面的巨大潜力。 我们无法见的66天的最大利润系数是1.60(60%利润 ) 。 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。