Few-Shot Learning Patterns in Financial Time-Series for Trend-Following Strategies: X-Trend Architecture About This is a placeholder for the code that accompanies our paper Few-Shot Learning Patterns in Financial Time-Series for Trend-Following Strategies. We intend to release the code in the coming weeks. This work builds upon our previous papers Trading with the Momentum Transformer: An Intelligent and Interpretable Architecture (code) and Slow Momentum with Fast Reversion: A Trading Strategy Using Deep Learning and Changepoint Detection (code). All papers and updates can be found on my website. 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 u

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