This notebook presents a simple deep learning approach to predict movements of a stock. It is based on high-frequency limit order book data and information about former movements. Based on this and the order imbalance we build a fully-connected neural network to run a prediction on an unlabelled dataset.
For the implementation of the model we use the Keras library.