Financial Technology (FinTech) services are rapidly expanding, arguably without being adequately supported by regulation. Robot advisory platforms that involve the provision of automated consultant and investment services with virtually no human contact may underestimate risks, causing a mismatch between investors’ expected and actual risk. Cryptocurrencies are a new asset class to be considered by robo-advisors in the near future. In this nascent market it is fundamental to understand the price dynamics in order to investigate in which exchange platforms the price formation process takes place and how they are interconnected. This chapter serves two aims: first, we propose an asset allocation strategy that takes individual users’ preference into account improving robot advisory portfolio allocation. In particular, random matrix theory filter and network metrics are combined in the minimum variance portfolio model in order to construct portfolios overperforming in terms of risk and realized risk with respect to the Markowitz model. Second, price discovery and interconnectedness of cryptocurrency market exchanges are studied in order to help investors in choosing the most suitable trading platforms to place profitable trades depending on their own strategy.