Hello, I’m a quantitative analyst at ActiveViam and in this blog, I’d like to illustrate how to create a BI app for xVA aggregation with Python and atoti. This notebook will be of particular interest to professionals working with xVA in the finance industry, but other atoti users may find…

IFRS 9 portfolio analytics with Python and atoti — In this post, I would like to present examples of visualizations for IFRS 9 using the BI app built in the previous post with Python and atoti: “Tutorial: How to Build an IFRS 9 BI app with Python and atoti”. Getting started Please refer to the previous post for a guide on…

IFRS 9 portfolio analytics — This blog provides a guide on how to address daily credit portfolio monitoring needs, in particular, to track and explain expected credit losses for IFRS 9, perform vintage analysis, drill-down to loan-level data, analyze changes between periods and determine the main drivers behind the portfolio risks. I will furnish you…

In the post “How to explain non-additive measures” we talked about how to use “parent” and “child” data relationships in atoti to navigate data and implement your on-the-fly allocation rules. Today let’s make use of these atoti functions to define the percentage of parent calculation.

A product catalogue example — In this atoti tutorial, I will walk you through how you can create a hierarchy — aka parent child data structure — to interactively aggregate and drill down using python libraries Treelib and atoti. The example I’ll be using is an e-commerce product catalog, and the same technique can be…

In this post, I’d like to show how to quickly create a Tableau-like BI application on top of your data in a Jupyter Notebook using the atoti python module. It can give your users an easy way to: explore the data produced by your model with an interactive UI, play…

Interactive decomposition with atoti — This article is a part of a series. Check also: Part 1: Pro-rata allocation for a generic example of allocation into additive components and Part 2: Marginal contribution for an incremental analysis example. …

Building regulatory risk application with python and atoti — In this post, I want to discuss a faster approach to compute the variance-covariance formulae present by regulatory capital models — FRTB SBM and CVA Risk Framework — as well as ISDA SIMM and internal sensitivity-based VaR-type models, and illustrate this approach with a sample implementation of SBM Equity Delta…

Interactive decomposition with atoti — This article is the second part of a series of tutorials about interactive analytics in in atoti’s dynamic pivot tables. Check out Part 1: Pro-rata allocation if you wish to read what non-additive measures are and why we may want to have them decomposed. Solution #2: Incremental contribution In the previous post we were…