correlationMatrix is a Python powered library for the statistical analysis and visualization of timeseries correlation phenomena. It can be used to analyze any dataset that captures timestamped values (timeseries) The present use cases focuses on typical analysis of market correlations
You can use correlationMatrix to
- Estimate correlation matrices from historical timeseries using a variety of models
- Visualize correlation matrices
- Manipulate correlation matrices (fix problematic matrices etc)
- Provide standardized data sets for testing
- Author: Open Risk
- License: Apache 2.0
- Code Documentation: Read The Docs
- 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
- The Open Risk Academy has free courses demonstrating the use of the library. The current list is:
- Commercial Support for correlationMatrix is provided as part of OpenCPM
The code documentation includes a large number of examples, jupyter notebooks and more.
Plotting individual correlation trajectories
Sampling correlation data
Estimation of correlation matrices using standard estimators
Estimation of correlation matrices using factor models
Visualization of a correlation matrix
Stressing correlation Matrices
Computation and Visualization of multi-period correlation matrices
