Can news text data add a significant value in a multi-task neural network scenario? Which preprocessing method is best suitable to predict price movements? Which time granularity is the best in an intraday trading system? This work addresses these questions. In the first experiment, we have explored different news sentiment methods, both human-rated and word embedding. Provided the first result, we focus on different time granularities and different quantile labelling. The first trading system has let us choose the 2-hours granularity as this decreases the number of signals and the trading costs. Worth mentioning that it has both a selling strategy based only on the predictions and one based on technical trading signals.