correlationMatrix 0.2.0

A Python powered library for statistical analysis and visualization of correlation phenomena

correlationMatrix

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)

• Author: Open Risk

• License: Apache 2.0

• Mathematical Documentation: Open Risk Manual

• Training: Open Risk Academy

• Development Website: Github

Functionality

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, stress matrices etc)

• Provide standardized data sets for testing

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

Architecture

• correlationMatrix supports file input/output in json and csv formats

• provides intuitive objects for handling correlation matrices individually and as sets (based on numpy)

• supports visualization using matplotlib

Links to other open source software

• correlationMatrix makes use of lower level methods available in numpy, scipy and statsmodels

• There is a sister project for estimating transition rates transitionMatrix

Installation

You can install and use the correlationMatrix package in any system that supports the Scipy ecosystem of tools

Dependencies

• correlationMatrix requires Python 3

• It depends on numerical and data processing Python libraries (Numpy, Scipy, Pandas, stastmodels)

• The Visualization API depends on Matplotlib

• The precise dependencies are listed in the requirements.txt file.

• correlationMatrix may work with earlier versions of these packages but this has not been tested.

From PyPi

TODO

pip3 install pandas
pip3 install matplotlib
pip3 install correlationMatrix

From sources

Download the sources to your preferred directory:

git clone https://github.com/open-risk/correlationMatrix

Using virtualenv

It is advisable to install the package in a virtualenv so as not to interfere with your system’s python distribution

virtualenv -p python3 tm_test
source tm_test/bin/activate

If you do not have pandas already installed make sure you install it first (will also install numpy)

pip3 install pandas
pip3 install matplotlib
pip3 install -r requirements.txt

Finally issue the install command and you are ready to go!

python3 setup.py install

File structure

The distribution has the following structure:

correlationMatrix The library source code

model.py Main data structures

utils Helper classes and methods

settings.py Settings

examples Usage examples

datasets Contains a variety of datasets useful for getting started with correlationMatrix

tests Testing suite

Testing

It is a good idea to run the test-suite. Before you get started:

• Adjust the source directory path in correlationMatrix/__init__ and then issue the following in at the root of the distribution

• Unzip the data files in the datasets directory

python3 test.py

Getting Started

Check the Usage pages in this documentation

Look at the examples directory for a variety of typical workflows.

For more in depth study, the Open Risk Academy has courses elaborating on the use of the library

• How to estimate an Equity Correlation Matrix using correlationMatrix: https://www.openriskacademy.com/course/view.php?id=44