Stock market forecasting has been a quite popular challenge in machine learning researches. Most investors want to make decisions based on criteria that will provide greater returns in their operations. Recently, studies have been using Deep Learning techniques, such as Convolutional Neural Networks (CNN), to perform price regression or trade signal classification in financial market. In this work, a system architecture that uses a CNN model is proposed to perform the indication of the best operation for each moment in the stock market, this system was called CNN Trading Classifier (CNN-TC). This system consists of data pre-processing, classification by the model and decision making in the market. It was evaluated based on data from the Brazilian and American stock market in three different periods. For this, statistical evaluation was performed, using the metrics of accuracy, precision, recall and F1 classification, and financial based on the classifications performed by the model. In addition, a test on a simulated environment using the MetaTrader software was evaluated in order to attest to the effectiveness of this approach. The results show that the system had better statistical and financial results in most evaluations compared to the use of other Deep Learning models and overcame the strategy Buy and Hold (BH) and fixed income returns
Combining Deep Learning and Multiresolution Analysis for Stock Market Forecasting
