Transformers for Limit Order Books

2020/02/29 by Wallbridge, James · 1 citation
#Computational Finance (q-fin.CP) #FOS: Economics and business #Trading and Market Microstructure (q-fin.TR)

paper · doi:10.48550/arxiv.2003.00130

Abstract

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.

Cited by

• Detecting Multilevel Manipulation from Limit Order Book via Cascaded Contrastive Representation Learning

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