Guillaume Thibault

Trading Places: When Technical Analysts Meet Artificial Intelligence

Knowing that these images will be used for training the model, let’s simplify the graphics to reduce the memory and computing power needed for training. To do this, let’s lower the resolution of the image, without losing information, by increasing the size of the bands and by using several colors to distinguish the different parts of the candles.


The model

The chosen model is a convolutional neural network (CNN) model which is a deep neural network architecture used in computer vision tasks. Convolution filters are a key component of CNNs. They are filters that scan the image to extract important features.

Convolution filters are applied to every part of the image and are used to extract features such as edges, textures and shapes. Convolution filters are defined by their dimensions (width and height) and depth. During training, the CNN automatically adjusts the values of the convolution filters to optimize the performance of the classification or regression task.