Trading with the momentum transformer: an interpretable deep learning architecture
An attention-based deep learning model for trading is presented
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Kieran Wood, Sven Giegerich, Stephen Roberts and Stefan Zohren introduce the ‘momentum transformer’, an attention-based deep-learning architecture that outperforms benchmark time series momentum and mean-reversion trading strategies. Unlike state of-the-art long short-term memory architectures, which are sequential in nature and tailored to local processing, an attention mechanism provides our architecture with a direct connection to all previous time steps
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