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In the present work, we tackle the problem of fnding the optimal price tarif to be set by a risk-averse electric retailer participating in the pool and whose customers are price sensitive. We assume that the retailer has access to a sufciently large smart-meter dataset from which it can statistically characterize the relationship between the tarif price and the demand load of its clients. Three diferent models are analyzed to predict the aggregated load as a function of the electricity prices and other parameters, as humidity or temperature. More specifcally, we train linear regression (predictive) models to forecast the resulting demand load as a function of the retail price. Then, we will insert this model in a quadratic optimization problem which evaluates the optimal price to be ofered. This optimization problem accounts for diferent sources of uncertainty including consumer's response, pool prices and renewable source availability, and relies on a stochastic and risk-averse formulation. In particular, one important contribution of this work is to base the scenario generation and reduction procedure on the statistical properties of the resulting predictive model. This allows us to properly quantify (data-driven) not only the expected value but the level of uncertainty associated with the main problem parameters. Moreover, we consider both standard forward-based contracts and the recently introduced power purchase agreement contracts as risk-hedging tools for the retailer. The results are promising as profts are found for the retailer with highly competitive prices and some possible improvements are shown if richer datasets could be available in the future. A realistic case study and multiple sensitivity analyses have been performed to characterize the risk-aversion behavior of the retailer considering price-sensitive consumers. It has been assumed that the energy procurement of the retailer can be satisfed from the pool and diferent types of contracts. The obtained results reveal that the risk-aversion degree of the retailer strongly infuences contracting decisions, whereas the price sensitiveness of consumers has a higher impact on the selling price ofered.