Since the release of Bitcoin, cryptocurrencies have gained more and more attention, becoming an important financial reality. Market capitalization exploded when the Bitcoin/USD pair reached its all-time high in December 2017, attracting investors from retail, professional and institutional markets in a novel gold run. 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. This work proposes the analysis of multiple cryptocurrencies among the most popular by market capitalization between 01/2011 and 01/2019, combining daily market data with a selection of technical analysis indicators and blockchain-derived metrics to build and analyse different trading systems based on popular machine learning algorithms and ensemble methods both in terms of performance and relevant feature interactions.