Authors: Jerry Loh @jlohding, Lian Kah Seng @sunroofgod
- View the source code here
- View the project report here
- In this project, we attempt to search for pairs of securities suitable for a mean-reversion pairs trading strategy using machine learning methods. In particular, we apply unsupervised dimensionality reduction and clustering techniques to historical daily price data for a set of 4088 ETFs.
- These methods include Principal Component Analysis (PCA), k-Means Clustering, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), and Hierarchical Clustering.
- This project is part of our graded coursework in the National University of Singapore module IT1244: Artificial Intelligence: Technology and Impact.
git clone https://github.com/jlohding/pca-cluster.git
Make sure you have the following dependencies in requirements.txt
python3==3.9.2
pandas==1.3.0
numpy==1.20.3
sklearn==1.0.2
Run the Jupyter Notebook main.ipynb on your local machine or Google Colab
