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    Academy News

    Latest version of openNPL starts support for Fannie Mae Loan Performance Data

    by Ad Min -
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    The openNPL credit portfolio management platform implements and builds on the detailed European Banking Authority loan templates for NPL data. It thereby enables the collection and easy management of non-performing loan data according to best-practices.

    In the the latest update (0.5.3) the platform is expanded to enable working also with US Agency (Fannie Mae) Mortgage Loan Performance data. This functionality is still under development and will mature with the upcoming 0.6 release of openNPL (along with other related resources) so stay tuned! The updated code documentation is available here. The corresponding data dictionaries and concepts are documented with a new category at the Open Risk Manual.

    OpenNPL snapshot

    Another year in permacrisis, yet the future is in our hands

    by Ad Min -
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    Dear users and friends of the Open Risk Academy, it is this time of the year again... Where the, all too-visible, signs of fragility and failure in how we collectively organize our affairs and communities across the globe must be internalized and transformed into positive learnings towards a more resilient future.

    Not an easy task: our infatuations, lazy habits, poor information access and poor decisions that make no rational or emotional sense time and again take the upper hand and create exhaustion and a sense of impossibility. Yet there is no option but persist with an agenda that empowers invididuals and organizations to enhance welfare at all levels. After all, everything positive that has been achieved was due to somebody acting.

    At Open Risk we will continue to try contibute our small effort towards the open future of risk management. We wish you too, a regenerative break this holiday season and a healthy and productive 2023.

    new years 2023 card by Open Risk

    Solstice - a new framework for simulating economic networks

    by Ad Min -
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    Dear all,

    we've started  releasing bits and pieces of a new open source tool we are building (the Solstice simulation framework) on our github repo and more is comming over the next weeks. You can check out the first installment of the Solstice manual which describes the analytic framework behind Solstice. 

    The analytic framework has the following core components:

    • A representation of economic entities as property graphs (nodes and edges with associated attributes) as discussed in our earlier white papers (number 8 and 10 in particular)
    • A representation of network-wide factors and dynamics drivers as macro variables
    • Sources of uncertainty (both macro (system-wide) and idiosyncratic (entity-specific)
    • A discrete temporal grid where future states of the economic network and (any) macro variables are modeled
    • Network evolution along the temporal grid due to both deterministic and stochastic elements that follows by a variety of risk distributions / models using potentially a combination of model components (satellite models)
    • Bottom up composition of network state changes using elementary "system updates"
    • Ability to condition on specific realizations of (in particular) macro factors, which emulates scenario analysis / stress testing
    • Introducing special entities that interact with the network in systemic ways (e.g. financial intermediaries, sovereigns or regulatory entities) with specialized internal states
    • Collection of sampling statistics of network evolution and distillation of useful risk metrics and reports

    If you are interested to use and/or contribute to our open source projects please follow and star them on github and (if appropriate) raise issues with ideas or functionality that you would like to see.

    the logo of the Solstice framework

    Step into python data science the hacktoberfest way. Two small libraries that can help you get started

    by Ad Min -
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    Machine learning and data science turned python from a niche scripting language into one of the most popular developer ecosystems. From numpy and pandas to scikit-learn and pytorch and tensorflow to name but a few, there are some amazing python open source frameworks out there. These projects have completely transformed what people can do with data. What used to be expensive, proprietary and arcane software is now one pip install away!
    Hacktoberfest is a great excuse to get involved with python data science and learn what all the excitement is about.

    What is hactoberfest??

    Hacktoberfest is an annual global hackathon event celebrating open source software hosted by DigitalOcean in partnership with Github. The event was created to raise awareness for the open source community and encourage participation in open source projects. Participants are challenged to contribute a specific amount of pull requests during the month of October to public open source repositories on GitHub in order to earn a limited-edition T-shirt and swag from the host and sponsors.

    The catch is that these are sophisticated and mature frameworks, frequently using also optimized C/C++ code underneath the hood. But there is also the "long tail" of niche python libraries and tools that focus on some specific data science task and these might be an easier stepping stone for aspiring data scientists.
    Two such libraries you can contribute to this hactoberfest are https://github.com/open-risk/transitionMatrix and concentrationMetrics. Here is a brief description of what they are about and how you can contribute:


    transitionMatrix is a library for the statistical analysis and visualization of state transition phenomena. It can be used to analyze (produce a transition matrix) for any dataset that captures timestamped transitions in a discrete state space. You can use the library to:
    • Estimate transition matrices from historical event data using a variety of estimators
    • Manipulate transition matrices (generators, comparisons etc.)
    • Visualize event data and transition matrices
    • Provide standardized data sets for testing
    • Model transitions using threshold processes
    • Map credit ratings using mapping tables between popularly used rating systems
    Use cases include credit rating transitions in finance, system state event logs etc.


    concentrationMetrics is a python library for the computation of various concentration, diversification and inequality indices. You can use concentrationMetrics to
    • access an exhaustive collection of such indexes and metrics
    • perform file input/output in both json and csv formats
    • compute indexes with confidence intervals via bootstraping
    • visualize using matplotlib

    How you can contribute

    First things first, make sure you read the hactoberfest participation guidelines!
    • fork the repos from the above links
    • look at the code / documentation and / try the examples
    • find bugs or other issues and raise issues
    • think and work on possible extensions, better documentation or any other ideas that fit within the scope of each library
    • eventually contribute via a pull request
    • get a tree planted in your name, or the Hacktoberfest 2022 t-shirt :-)
    Good luck, enjoy hactoberfest and hope to see you around the python metaverse!

    New season, new members and new Open Risk White Paper

    by Ad Min -
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    Dear Academy users,

    we are officially past the Fall Equinox for 2022 and the new season is in full swing. We have also reached a milestone of more than 1500 registered users on the site. A warm welcome to all new Open Risk Academy enthousiasts!

    A new Open Risk White Paper that might be of interest to some of you focuses on sustainability accounting and in particular how to elevate the reporting of non-financial data to the same level of rigor offered by double-entry bookkeeping practices. The central design is the use of multidimensional double-entry bookkeeping which tracks additional quantitative information characterizing economic objects beyond their monetary values. This choice ensures the enforcement of both classic balance sheet constraints and the applicable energy conservation laws. The blog post has a summary overview, while the pdf is available here.


    Pictogram of a balance, with an energy amount on the left and a monetary amount on the right
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