Application of Three Different Machine Learning Methods on Strategy
Creation for Profitable Trades on Cryptocurrency Markets
- URL: http://arxiv.org/abs/2105.06827v1
- Date: Fri, 14 May 2021 13:42:46 GMT
- Title: Application of Three Different Machine Learning Methods on Strategy
Creation for Profitable Trades on Cryptocurrency Markets
- Authors: Mohsen Asgari, Hossein Khasteh
- Abstract summary: We apply k-Nearest Neighbours, eXtreme Gradient Boosting and Random Forest classifiers to direction detection problem of three cryptocurrency markets.
Our test results on unseen data shows a great potential for this approach in helping investors with an expert system to exploit the market and gain profit.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: AI and data driven solutions have been applied to different fields with
outperforming and promising results. In this research work we apply k-Nearest
Neighbours, eXtreme Gradient Boosting and Random Forest classifiers to
direction detection 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
shows a great potential for this approach in helping investors with an expert
system to exploit the market and gain profit. Our highest gain for an unseen 66
day span is 860 dollars per 1800 dollars investment. We also discuss
limitations of these approaches and their potential impact to Efficient Market
Hypothesis.
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