论文标题
神经网络和秩序流，技术分析：预测期货合约的短期方向
Neural Network and Order Flow, Technical Analysis: Predicting short-term direction of futures contract
论文作者
论文摘要
预测期货合约的短期方向运动可能会具有挑战性，因为其定价通常基于多个复杂的动态条件。这项工作提出了一种预测基础期货合约的短期方向运动的方法。我们从技术分析，订单流和订单数据数据中设计了一组功能。然后，使用这些功能对深度学习的神经网络TabNet进行了训练。我们将模型培训在上海期货交易所上列出的银牌期货合约上，并在预测选定时期的方向变化方面的准确性为0.601。
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.
