  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.
