Neural Network and Order Flow, Technical Analysis: Predicting short-term direction of futures contract

TL;DR

This paper introduces a neural network-based method utilizing technical analysis, order flow, and order-book features to predict short-term futures contract directions, achieving over 60% accuracy on Shanghai Futures Exchange data.

Contribution

It presents a novel feature engineering approach combined with Tabnet neural network for short-term futures prediction, demonstrating practical effectiveness.

Findings

Achieved 60.1% accuracy in directional prediction.

Utilized combined technical and order flow features.

Validated on Shanghai Futures Exchange data.

Abstract

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.