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股票市场中的注意力机制与 LSTM 动量交易

文章 arXiv papers · 作者: Max Mason et al.
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总结

本研究将 Momentum Transformer 架构应用于股票，并将其预期用途与时间序列动量和均值回归策略进行比较。模型结合注意力机制（可关联训练窗口内的信息）与 LSTM（用于处理序列模式）。作者称，这种组合能够捕捉更长期的依赖关系，并应对包括新冠疫情在内的市场状况变化。

报告的平均收益率为 4.14%，作者称其与早期论文的结果相近；平均夏普比率为 1.12。作者将夏普比率低于早期研究归因于股票的波动性高于期货和股票指数。简要描述没有提供测试时期、股票范围、交易成本假设、基准结果或统计不确定性等细节，因此仅凭这些数值不足以评估稳健性或实盘交易表现。

核心观点

  • 本研究将 Momentum Transformer 架构应用于股票。
  • 模型结合注意力机制与 LSTM，以表示序列模式和长期模式。
  • 作者将该方法与时间序列动量和均值回归策略进行比较。
  • 作者报告平均收益率为 4.14%，平均夏普比率为 1.12。
  • 现有结果缺少评估稳健性或实施成本所需的细节。

标签

股票动量均值回归机器学习
全文
# Enhanced Momentum with Momentum Transformers


# Enhanced Momentum with Momentum Transformers









The primary objective of this research is to build a Momentum Transformer that is expected to outperform benchmark time-series momentum and mean-reversion trading strategies. We extend the ideas introduced in the paper Trading with the Momentum Transformer: An Intelligent and Interpretable Architecture to equities as the original paper primarily only builds upon futures and equity indices. Unlike conventional Long Short-Term Memory (LSTM) models, which operate sequentially and are optimized for processing local patterns, an attention mechanism equips our architecture with direct access to all prior time steps in the training window. This hybrid design, combining attention with an LSTM, enables the model to capture long-term dependencies, enhance performance in scenarios accounting for transaction costs, and seamlessly adapt to evolving market conditions, such as those witnessed during the Covid Pandemic. We average 4.14% returns which is similar to the original papers results. Our Sharpe is lower at an average of 1.12 due to much higher volatility which may be due to stocks being inherently more volatile than futures and indices.

在遵守原作品许可的前提下，附作者信息全文展示。 许可协议: abstract CC0

此摘要由 Stratmill 研究智能体根据原文撰写，并非原文副本。