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小样本学习助力自适应趋势跟踪预测

文章 arXiv papers · 作者: Kieran Wood et al.
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总结

本文提出X-Trend，这是一种趋势跟踪时间序列预测器，旨在市场进入新状态时快速适应。它通过小样本学习和对金融时间序列状态上下文集的交叉注意力，将相似状态中的模式迁移到目标状态以预测头寸。交叉注意力机制也可用于解释哪些上下文模式与预测相关。

报告的评估涵盖了从 2018 年到 2023 年的动荡时期。作者称，X-Trend的夏普比率高于神经网络预测器和传统时间序列动量策略；从 COVID-19 回撤中恢复得更快；并为未见过的资产生成头寸，报告的夏普比率高于神经网络预测器。以上均为文中报告的结果；此处未提供数据构建、交易成本、统计不确定性或样本外防护措施的细节，因此难以独立评估。

核心观点

  • X-Trend利用小样本学习，使趋势预测适应新的市场状态。
  • 交叉注意力将上下文集中相关状态的模式迁移到目标状态。
  • 作者报告称，在 2018–2023 年期间，该方法的夏普比率优于神经网络和传统动量基准。
  • 据报告，该方法从 COVID-19 回撤中恢复更快，并能处理未见过的资产。
  • 交叉注意力可用于检查上下文模式与预测之间的关系。

标签

趋势跟踪机器学习动量回测
全文
# Few-Shot Learning Patterns in Financial Time-Series for Trend-Following Strategies


# Few-Shot Learning Patterns in Financial Time-Series for Trend-Following Strategies









Forecasting models for systematic trading strategies do not adapt quickly when financial market conditions rapidly change, as was seen in the advent of the COVID-19 pandemic in 2020, causing many forecasting models to take loss-making positions. To deal with such situations, we propose a novel time-series trend-following forecaster that can quickly adapt to new market conditions, referred to as regimes. We leverage recent developments from the deep learning community and use few-shot learning. We propose the Cross Attentive Time-Series Trend Network -- X-Trend -- which takes positions attending over a context set of financial time-series regimes. X-Trend transfers trends from similar patterns in the context set to make forecasts, then subsequently takes positions for a new distinct target regime. By quickly adapting to new financial regimes, X-Trend increases Sharpe ratio by 18.9% over a neural forecaster and 10-fold over a conventional Time-series Momentum strategy during the turbulent market period from 2018 to 2023. Our strategy recovers twice as quickly from the COVID-19 drawdown compared to the neural-forecaster. X-Trend can also take zero-shot positions on novel unseen financial assets obtaining a 5-fold Sharpe ratio increase versus a neural time-series trend forecaster over the same period. Furthermore, the cross-attention mechanism allows us to interpret the relationship between forecasts and patterns in the context set.

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。