Zusammenfassung
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.
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