We introduce the Momentum Transformer, an attention-based deep-learning architecture, which outperforms benchmark time-series momentum and mean-reversion trading strategies. Unlike state-of-the-art Long Short-Term Memory (LSTM) 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. Our architecture, an attention-LSTM hybrid, enables us to learn longer-term dependencies, improves performance when considering returns net of transaction costs and naturally adapts to new market regimes, such as during the SARS-CoV-2 crisis. Via the introduction of multiple attention heads, we can capture concurrent regimes, or temporal dynamics, which are occurring at different timescales. The Momentum Transformer is inherently interpretable, providing us with greater insights into our deep-learning momentum trading strategy, including the importance of different factors over time and the past time-steps which are of the greatest significance to the model.

翻译：我们引入了“动力变换器 ” ( Momentum 变换器), 这是一种基于关注的深层次学习结构,它比基准时间序列势头和平均反转交易战略要好得多。 与最先进的长期短期记忆(LSTM)结构不同,这些结构是相继性质的,适合本地加工。 一个关注机制为我们的结构提供了与以往所有时间步骤的直接关联。 我们的架构,即关注-LSTM混合结构,使我们能够了解长期依赖性,在考虑回报时,在扣除交易成本后,改善业绩,并自然适应新的市场制度,如SARS-COV-2危机期间。 通过引入多位关注头,我们可以捕捉到在不同时间尺度上出现的同时制度或时间动态。 动动变换器本质上是可以解释的,让我们更深入地了解我们深层次学习的势头交易战略,包括不同因素在时间和过去时间步骤中的重要性,这些对模型具有最重大的意义。