Resumo:
Aiming to achieve the goal of training neural networks to enable them to identify trends
in the price of a stock, data were collected on the stocks of 7,145 companies during
the period of one month. These data were transformed into candlestick charts that, using
technical analysis concepts, were classified as being charts with high, low and consolidation
tendencies and formed the bases used to train, validate and test ResNet and Xception
network models. It was possible to conclude that both obtained satisfactory results, but
with advantage to ResNet, that obtained greater precisions from the training phase until
the final tests. In the final tests ResNet reached 87% of accuracy and F1 Score, while
Xception reached 86%.
