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.
@article{arxiv.2003.00130,
title = {Transformers for Limit Order Books},
author = {James Wallbridge},
journal= {arXiv preprint arXiv:2003.00130},
year = {2020}
}