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portfolioanalytics's Introduction

PortfolioAnalytics Library

A python library that provides semi-analytical functions useful for testing the accuracy of credit portfolio simulation models

The basic formulas are reasonably simple and well known: They underpin the calculation of RWA (risk weighted assets), and in turn required capital, thus ensuring stability for the entire banking systems worldwide

The library provides support for the Monte Carlo testing framework

Dependencies: scipy, sympy

Examples

Check the jupyter notebook

Current Functions

  • vasicek_base
  • vasicek_base_el
  • vasicek_base_ul
  • vasicek_lim
  • vasicek_lim_el
  • vasicek_lim_ul
  • vasicek_lim_q

The Vasicek Base family produces finite pool loss probabilities and measures (EL, UL)

The Vasicek Lim family produces asymptotic pool loss probabities and measures (EL, UL, Quantile)

Risk Manual

Use the manual for documentation of use cases

Contributions

Contributions are welcome. Check the TODO list for ideas of where to take this library next

Portfolio Analytics Library (PAT)


Set of semi-analytical functions for testing the accuracy of credit portfolio simulation models

Contributions


Screenshot


Screenshot

Contributing


License


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portfolioanalytics's Issues

unable to find portfolioAnalytics package on pyPI

Hi

I would like to install the portfolioAnalytics package in an Anaconda environment but cannot find the package.

(vasicek) C:\Users\ruper>pip3 install portfolioAnalytics
ERROR: Could not find a version that satisfies the requirement portfolioAnalytics (from versions: none)
ERROR: No matching distribution found for portfolioAnalytics

I also tried to find it on pyPI.org but it could not find it there.

I see that the last update of the the portfolioAnalytics package was in 2019 but the readme file says its still underdevelopment.

Please what remains to be done and is further development planned?

improve input data validation

for all functions test input values for validity and provide helpful error or warning messages as appropriate. Examples

  • define acceptable type (ideally implement consistently type hinting across the library)
  • define acceptable range (e.g. negative, maximum etc)

Output

Where is output generated? Can you put up a walkthrough on where to enter data and where it gets put out? Sorry for lack of technicality and know-how. I'm brand new to this.

Feature request: Granularity Adjustment

Hi, please could you add granularity adjustment to the vasicek code. This would greatly help in understanding how the GA operates and its impact on the loss distribution. At present I feel lost in the GA maths which I can follow but find sufficiently complex to block my understanding/intuition! The existing vasicek code is excellent as it is sufficiently easy to follow as to be intuitive and so I would hope for something similar.

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