This thesis work is carried out within the context of financial time series forecasting, a subject that has undergone innovations over the years from the point of view of tools. The task consists in leveraging historical and current stock prices to predict future values over a period of time or a specific point in the future. For years the most common method used by traders and analysts have been technical indicators calculated from time series, but lately machine learning is increasingly being exploited. In this thesis work, the objective is to generate deep representations of financial time series by exploiting Contrastive Learning frameworks, explain them by means of technical indicators (i.e., a new dataset) and compare performances of the two approaches (contrastive vs technical indicators) on the same downstream task.