X Trend

Abstract

1. Introduction

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.

2. Objective

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

This project demonstrates how Deep Learning, Machine Learning can be applied to a real-world Data Science problem.

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• See the project README for exact requirements
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/kieranjwood/x-trend.git
cd x-trend

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Turn the analysis into an interactive dashboard
• Automate data refresh with a scheduled job
• Add a predictive model on top of the analysis

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What is the source of the dataset and how was missing data handled?
2. Which exploratory analysis steps revealed the most useful insight?
3. Why were these particular charts chosen to present the data?
4. Which statistical or ML technique supports the conclusions?
5. How could the analysis be automated or refreshed with new data?

9. Source Code & License

This project is developed by kieranjwood and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.