Intro

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

Key Information

• Author: Open Risk
• License: Apache 2.0
• Mathematical Documentation: Open Risk Manual
• Development website: Github

NB: correlationMatrix is still in active development. If you encounter issues please raise them in our github repository

Examples

The examples directory contains a large sample of examples illustrating the current functionality

Display correlation matrix

Display dependency dendrogram