用注意力机制改进LSTM，提升股票动量交易表现。
Enhanced Momentum with Momentum Transformers
- 融合注意力与LSTM，直接访问历史时间步数据。
- 平均年化收益4.14%，但波动率更高导致夏普比为1.12。
- 适合研究量化交易中长周期依赖建模的从业者。
本研究旨在构建一种超越基准时序动量与均值回归交易策略的动量Transformer模型。我们将原始论文中仅针对期货和股指的框架拓展至个股领域。与传统LSTM模型仅处理局部模式不同，注意力机制使模型能直接访问训练窗口内所有历史时间步。该混合设计兼顾长期依赖捕捉能力，在考虑交易成本的场景下表现更优，并可适应市场剧烈变化，如新冠疫情时期。模型平均年化收益达4.14%，与原论文结果相当；但因个股固有高波动性，夏普比降至1.12，反映出风险上升。
原文摘要 · Abstract (English)
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.
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