correlationMatrix is a Python powered library for the statistical analysis and visualization of correlation phenomena. It can be used to analyze any dataset that captures timestamped values (timeseries) The present use cases focus on typical analyses of market correlations, e.g via factor models
You can use correlationMatrix to
- Estimate correlation matrices from historical timeseries using a variety of models
- Visualize correlation matrices
- Manipulate correlation matrices (stress matrices, fix problematic matrices etc)
- Provide standardized data sets for testing
- Author: Open Risk
- License: Apache 2.0
- Mathematical Documentation: Open Risk Manual
- Development website: Github
- General Discussions: Open Risk Commons
NB: correlationMatrix is still in active development. If you encounter issues please raise them in our GitHub repository
The examples directory contains examples illustrating the current functionality
Display correlation matrix
Display dependency dendrogram
