This work presents an intelligent tool developed utilizing a deep learning technique to trade energy stocks. The tool forecasts energy prices and recommends when to buy and how much energies from different households. First, we introduce the world of time series, showing examples of fields where they are being successfully used as well as their growing importance nowadays. We then describe how they have been classified through the years and the different technologies that have been experimented with for solving forecasting problems in different ways. Then, we discuss the world of recommending systems and how they are becoming more and more important in the context of classification.