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, the authors propose a novel time-series trend-following forecaster that can quickly adapt to new market conditions, referred to as regimes. The authors leverage recent developments from the deep learning community and use few-shot learning. They 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 and then 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. The authors' 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 fivefold Sharpe ratio increase versus a neural time-series trend forecaster over the same period. Furthermore, the cross-attention mechanism allows for interpretation of the relationship between forecasts and patterns in the context set.