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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 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, then subsequently take 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.

References

Please cite our papers with:

@article{wood2023fewshot,
  title={Few-Shot Learning Patterns in Financial Time-Series for Trend-Following Strategies},
  author={Wood, Kieran and Kessler, Samuel and Roberts, Stephen J and Zohren, Stefan},
  journal={arXiv preprint arXiv:2310.10500},
  year={2023}
}

@article{wood2021trading,
  title={Trading with the Momentum Transformer: An Intelligent and Interpretable Architecture},
  author={Wood, Kieran and Giegerich, Sven and Roberts, Stephen and Zohren, Stefan},
  journal={arXiv preprint arXiv:2112.08534},
  year={2021}
}

@article {wood22slowmomfastrev,
  author = {Wood, Kieran and Roberts, Stephen and Zohren, Stefan},
  title = {Slow Momentum with Fast Reversion: A Trading Strategy Using Deep Learning and Changepoint Detection},
  volume = {4},
  number = {1},
  pages = {111--129},
  year = {2022},
  doi = {10.3905/jfds.2021.1.081},
  publisher = {Institutional Investor Journals Umbrella},
  issn = {2640-3943},
  URL = {https://jfds.pm-research.com/content/4/1/111},
  eprint = {https://jfds.pm-research.com/content/4/1/111.full.pdf},
  journal = {The Journal of Financial Data Science}
}