论文标题
限制顺序书籍的变压器
Transformers for Limit Order Books
论文作者
论文摘要
我们介绍了一种新的深度学习体系结构，以预测限制顺序书籍的价格变动。该体系结构使用因果卷积网络，与掩盖的自我注意力结合使用特征提取，以根据相关的上下文信息更新功能。该体系结构显示出明显胜过现有的架构，例如使用卷积网络（CNN）和长期术语内存（LSTM）建立FI-2010数据集的新最新基准测试的架构。
We introduce a new deep learning architecture for predicting price movements from limit order books. This architecture uses a causal convolutional network for feature extraction in combination with masked self-attention to update features based on relevant contextual information. This architecture is shown to significantly outperform existing architectures such as those using convolutional networks (CNN) and Long-Short Term Memory (LSTM) establishing a new state-of-the-art benchmark for the FI-2010 dataset.
