  Predictions of short-term directional movement of the futures contract can be
challenging as its pricing is often based on multiple complex dynamic
conditions. This work presents a method for predicting the short-term
directional movement of an underlying futures contract. We engineered a set of
features from technical analysis, order flow, and order-book data. Then,
Tabnet, a deep learning neural network, is trained using these features. We
train our model on the Silver Futures Contract listed on Shanghai Futures
Exchange and achieve an accuracy of 0.601 on predicting the directional change
during the selected period.
