Forecasting models for systematic trading strategies do not adapt quickly when financial market conditions change, as was seen in the advent of the COVID-19 pandemic in 2020, when market conditions changed dramatically causing many forecasting models to take loss-making positions. To deal with such situations, we propose a novel time-series trend-following forecaster that is able to 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 predictions and take positions for a new distinct target regime. X-Trend is able to quickly adapt to new financial regimes with a Sharpe ratio increase of 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. X-Trend both forecasts next-day prices and outputs a trading signal. Furthermore, the cross-attention mechanism allows us to interpret the relationship between forecasts and patterns in the context set.

翻译：系统性交易策略的预测模型在金融市场条件变化时无法快速适应，正如2020年新冠疫情爆发期间市场环境剧烈变化导致许多预测模型出现亏损仓位的情况。为应对此类问题，我们提出一种能够快速适应新市场状态（称为制度）的新型时间序列趋势跟踪预测器。我们利用深度学习领域的最新进展，采用小样本学习方法。具体而言，我们提出交叉注意力时间序列趋势网络（X-Trend），该网络通过对一组金融时间序列制度（称为上下文集）进行注意力机制处理来构建仓位。X-Trend将上下文集中相似模式的趋势迁移至新的目标制度，以进行预测并建立仓位。在2018至2023年市场动荡期间，X-Trend能快速适应新金融制度，其夏普比率相比神经网络预测器提升18.9%，相比传统时间序列动量策略提升10倍。与神经网络预测器相比，我们的策略从新冠疫情回撤中恢复的速度快一倍。此外，X-Trend可对未见过的全新金融资产进行零样本仓位操作，同期相比神经网络时间序列趋势预测器获得5倍的夏普比率提升。X-Trend既可预测次日价格，亦可输出交易信号。值得关注的是，交叉注意力机制使我们能够解释预测结果与上下文集中模式之间的关系。