Sales forecast is a key issue in pharmacy wholesales and has a direct effect in inventory cost, it also represents a difficult problem, affected by factors like promotions, price changes and seasonal preferences. Traditional sales forecast techniques use historical sales data and recursive formulas, which limit their accuracy. More recently learning-based methods have expanded data features capture capabilities. This paper presents a deep architecture which explores a few years of weekly sales data and learns to makes assertive predictions. The system is assembled with ReLU neurons, whose learning behavior makes possible the effective training of sparse coded deep sub-nets. The developed learning algorithm uses two learning stages where the first produces sparse representation of the studied time series and the second harvest one week ahead predictions using this sparse data. In both cases the reward system is future-focused and favors search for future capacities. To achieving successful network training we develop an algorithms that deals with exploding gradient problem, typical of ReLU networks. The fully assembled predictor automatically learn features from structured data and produces inventories with improved dollar cost. The system has been tested in real time with real data.