2021/05/14 by Mohsen Asgari, Asgari, Mohsen, Hossein Khasteh +1
Decision Sciences · Economics, Econometrics and Finance · #Artificial Intelligence (cs.AI) #Complex Systems and Time Series Analysis #FOS: Computer and information sciences #FOS: Economics and business #Financial Markets and Investment Strategies #Stock Market Forecasting Methods #Trading and Market Microstructure (q-fin.TR)
paper · pdf · doi:10.48550/arxiv.2105.06827
openalex publication_date 2021/05/14 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28
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.