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Transformers for Limit Order Books

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
Year of publication:
2020
Authors: Wallbridge, James
Publisher: [S.l.] : SSRN
Subject: Wertpapierhandel | Securities trading | Theorie | Theory | Marktmikrostruktur | Market microstructure | Börsenhandel | Stock exchange trading
Description of contents: Abstract [papers.ssrn.com]
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