Open Source Risk Models

Open Source Risk Models

The Tools of the Trade

Open Risk promotes and supports the use of open source and non-proprietary frameworks, standards and languages for the development of open, transparent risk modelling tools and solutions. There is already a fantastic set of building blocks available for supporting an open source risk modelling universe, including but not limited to:

  • The Python language, tools and libraries
  • R, the statistics and modelling language and libraries
  • The Julia language, tools and libraries
  • The C++ language and Libraries

We advocate a toolkit view that uses the best tool for each task. For an in-depth list of functionality comparing (at-present) the Python and R ecosystems take a look at this side-by-side catalog

Python as the glue for the ecosystem

Open Risk promotes, in particular, the use of Python, a modern, free, powerful and widely available computing platform for the prototyping, documenting and validating of risk analytics relevant for risk management. One of the many benefits of adopting Python is that it can easily integrate already available specialized libraries such as those provided by R or C++.

Our long-term objective is to build a high quality, open source risk management base (models & documentation, annotated datasets) that will be widely available.

Current Projects

Anybody interested in the development of open source risk management tools is welcome to join our gitter channels where we coordinate our activities

We are operating a Github repository for the collaborative development of open source risk management tools

Logo of openriskpy

The number and maturity of available libraries is monotonically growing, here is a sampler of the current list (check out the repository for various other projects in alpha stage):

  • transitionMatrixis a Python powered library for the statistical analysis and visualization of state transition phenomena. It can be used to analyze any dataset that captures timestamped transitions in a discrete state space. Use cases include credit rating transitions, system state event logs etc.
  • 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
  • concentrationMetrics is a python library for the computation of various concentration, diversification and inequality indices.
  • portfolioAnalytics is a python library that provides semi-analytical functions useful for testing the accuracy of credit portfolio simulation models
  • openSecuritisation a proposal for a simple, open and transparent securitisation modeling framework