Clustering Approaches for GMVP

This is the source code used for experiments for the research paper "Clustering Approaches for Global Minimum Variance Portfolio"

Example

python3 main.py --data_period 'test' --max_cluster_size 75 --scaling_method 'none' --dim_reduction_method 'none'

Parameters

• data_period: Daily returns of stocks from validation period or test period (validation or test)
  • We use validation period to choose the parameters which produces the best portfolio optimization performance.
  • Portfolio performance from test period is the true score of the proposed algorithm.
• max_cluster_size: Maximum clustering size allowed for individual clusters (integer numbers)
• scaling_method : Whether scaling data to follow a normal distribution or not (standard_scale or none)
• dim_reduction_method : Whether reducing dimensionality of 252-long vectors of daily returns of stocks with PCA or T-SNE or not (PCA, tsne or none)

Datasets

Datasets should be downloaded and preprocessed in accordance with instructions in 0. preparing_data.ipynb, located in data folder.