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结合变点检测、动量与快速均值回归

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

该策略将在线变点检测与基于LSTM的深度动量网络相结合。网络学习趋势估计和仓位规模，检测模块则提供疑似转折点及其严重程度。系统可以在较慢的时间序列动量与较快的均值回归之间进行平衡：前者旨在跟随持续趋势，后者则可在局部行情波动时反转敞口。该设计旨在应对市场状态急剧变化时的动量损失。

覆盖1995至2020的回测报告称，加入变点检测后，夏普比率提高了三分之一；在2015至2020期间，提升约为三分之二。报告的提升在非平稳时期最为明显。这些数字来自回测，并非实盘交易表现的证据；摘录未说明市场、交易成本、验证设计或稳健性检验。因此，该方法提供了一种调整动量敞口的思路，但重要的实施和泛化问题仍未解答。

核心观点

  • 在线变点检测被加入基于LSTM的动量和仓位规模模型。
  • 严重程度评分帮助模型根据检测到的市场状况变化调整反应。
  • 该策略结合了跟随持续趋势的敞口与更快的均值回归行为。
  • 回测报告称，加入检测模块后夏普比率提高，近期测试年份的提升尤为明显。
  • 摘录未提供评估实盘适用性所需的市场、成本和稳健性细节。

标签

动量趋势跟踪均值回归机器学习回测
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# Slow Momentum with Fast Reversion: A Trading Strategy Using Deep Learning and Changepoint Detection


# Slow Momentum with Fast Reversion: A Trading Strategy Using Deep Learning and Changepoint Detection









Momentum strategies are an important part of alternative investments and are at the heart of commodity trading advisors (CTAs). These strategies have, however, been found to have difficulties adjusting to rapid changes in market conditions, such as during the 2020 market crash. In particular, immediately after momentum turning points, where a trend reverses from an uptrend (downtrend) to a downtrend (uptrend), time-series momentum (TSMOM) strategies are prone to making bad bets. To improve the response to regime change, we introduce a novel approach, where we insert an online changepoint detection (CPD) module into a Deep Momentum Network (DMN) [1904.04912] pipeline, which uses an LSTM deep-learning architecture to simultaneously learn both trend estimation and position sizing. Furthermore, our model is able to optimise the way in which it balances 1) a slow momentum strategy which exploits persisting trends, but does not overreact to localised price moves, and 2) a fast mean-reversion strategy regime by quickly flipping its position, then swapping it back again to exploit localised price moves. Our CPD module outputs a changepoint location and severity score, allowing our model to learn to respond to varying degrees of disequilibrium, or smaller and more localised changepoints, in a data driven manner. Back-testing our model over the period 1995-2020, the addition of the CPD module leads to an improvement in Sharpe ratio of one-third. The module is especially beneficial in periods of significant nonstationarity, and in particular, over the most recent years tested (2015-2020) the performance boost is approximately two-thirds. This is interesting as traditional momentum strategies have been underperforming in this period.

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。