I developed this project using a dataset that, after cleaning, contained approximately 1,800 investment funds. The model uses the following inputs: mean\_return, median\_return, std\_return, avg\_time\_drawdown, sharpe, and max\_drawdown. I trained two clustering algorithms (KMeans and Gaussian Mixture Models), evaluating them with and without scaling and PCA.
The dataset was split chronologically into training, testing, and validation sets of roughly 1.5 years each. The validation set was only used at the end to assess whether the clusters remained stable and meaningful out of sample.
To evaluate the consistency of the clusters, I used the Wasserstein Distance on the original variables, comparing train and test distributions. This allowed me to measure how similar the cluster behavior remained when moving from training / test to REAL unseen data.
the code is here : [https://github.com/B0scos/Financial-Funds-Clustering](https://github.com/B0scos/Financial-Funds-Clustering)