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We introduce the Momentum Transformer, an attention-based deep-learningarchitecture, which outperforms benchmark time-series momentum andmean-reversion trading strategies. Unlike state-of-the-art Long Short-TermMemory (LSTM) architectures, which are sequential in nature and tailored tolocal processing, an attention mechanism provides our architecture with adirect connection to all previous time-steps. Our architecture, anattention-LSTM hybrid, enables us to learn longer-term dependencies, improvesperformance when considering returns net of transaction costs and naturallyadapts to new market regimes, such as during the SARS-CoV-2 crisis. Via theintroduction of multiple attention heads, we can capture concurrent regimes, ortemporal dynamics, which are occurring at different timescales. The MomentumTransformer is inherently interpretable, providing us with greater insightsinto our deep-learning momentum trading strategy, including the importance ofdifferent factors over time and the past time-steps which are of the greatestsignificance to the model.

Trading with the Momentum Transformer: An Intelligent and Interpretable Architecture