Intro

concentrationMetrics is a python library for the computation of various concentration, diversification and inequality indices. The library aims to implement the computation of all well known indexes of inequality and concentration along with confidence intervals.

You can use concentrationMetrics to:

• Access an exhaustive collection of concentration, inequality and diversity indexes and metrics
• Perform file input/output in both json and csv formats
• Compute indexes with confidence intervals via bootstraping
• Visualize using matplotlib

Key Information

• Author: Open Risk
• License: MIT
• Mathematical Documentation: Open Risk Manual
• Code Documentation: Read The Docs
• Development website: Github
• Discussions: Open Risk Commons

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

List of Implemented Indexes

An overview of the implemented metrics (indexes) and their relationships is available at the Open Risk Manual

The below list provides more specific documentation URL's for each one of the implement indexes:

• Atkinson Index
• Berger-Parker Index
• Concentration Ratio
• Ellison-Glaeser Index
• Gini Index
• Theil Index
• Hannah-Kay Index
• Hoover Index
• Herfindahl-Hirschman Index and related indexes such as Simpson and Inverse Simpson
• Shannon Index
• Generalized Entropy Index (Renyi)
• Kolm Index

Support and Training

The Open Risk Academy has free courses demonstrating the use of the library: Open Risk Academy

Examples

Comparing two indexes across a range of input portfolio data

Calculating industrial and geographic concentrations

Contributions

• Check the TODO list for immediate TODO items
• Check the Roadmap for the bigger picture