Profitable Strategy Design for Trades on Cryptocurrency Markets with Machine Learning Techniques
This work addresses the challenge of profitable trading for cryptocurrency investors, but it is incremental as it applies existing machine learning methods to this domain.
The researchers tackled the problem of designing profitable trading strategies for cryptocurrency markets by applying k-Nearest Neighbours, eXtreme Gradient Boosting, and Random Forest classifiers to detect trends, achieving a highest profit factor of 1.60 over a 66-day unseen data span.
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.