Few-Shot Learning Patterns in Financial Time-Series for Trend-Following Strategies

arXiv:2310.10500v2 Announce Type: replace-cross
Abstract: 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 …

abstract adapt arxiv change covid covid-19 covid-19 pandemic cs.lg deal few-shot few-shot learning financial financial market forecasting loss making market novel pandemic patterns q-fin.pm q-fin.tr series strategies trading trend type