Download 6MB Preview Abstract: Since the release of Bitcoin, cryptocurrencies have gained more and more attention, becoming an important financial reality. Existing studies on Decision Support Systems DSS and Automated Trading Systems based show pertinence of such techniques to traditional markets, while their application to cryptocurrencies is still a study subject.
However, when analysed through metrics who take input bias into account such as index-based accuracy IBAvery few models reach the skill threshold, implicating class imbalance in the training data affects classification results.
Trading simulation shows how the proposed systems are profitable in both bear and bullish markets yet fail to identify patterns leading to high volatility events characterising the cryptocurrency markets, giving the baseline strategy a lead over longer timespans.
The work also explores the reasons behind machine learning algorithms' decisions.
It applies a state-of-the-art explainable model, namely SHAP, to machine learning bitcoin trading the features that mostly influence the performance of cryptocurrency price forecasting.