Introduction

ModelFlow is written in Python. Python comes “batteries included” and is the basis of a very rich ecosystem, which consists of a wide array of libraries. ModelFlow is just another library. It supplements the existing libraries regarding modeling language and solving and allows the use of Python as a model management framework.

Data handling and wrangling is done in the Pandas library. This library is the Swiss army knife of data science in Python. It can import and export data to most systems and it is very powerful in manipulating and transforming data. The core element of Pandas is the Dataframe. A Dataframe is a two-dimensional tabular data structure. Each column consists of cells of the same type – it can be a number, a string, a matrix or another Python data object.This includes matrices and other dataframes. Each row is indexed. The index can basically be any type of variable including dates, which is especially relevant for economic and financial models.

ModelFlow gives the user tools for more than solving models. This includes:

  • Visualization and comparison of results

  • Integration of models from different sources

  • Analyze the logical structure of a model. By applying graph theory, ModelFlow can find data lineage, find a suitable calculating sequence and trace causes of changes through the calculations.

  • Inverting the model to calculating the necessary instruments to achieve a desired target.

  • Calculating the attributions from input to the results of a model.

  • Calculating the attribution from input to the result of each formula.

  • Finding and calculating partial derivatives of formulas

  • Integrating user defined python functions in the Business logic language (like optimization, calculating risk weights or to make a matrices consistent with the RAS algorithm )

  • Wrap matlab models so they can be used in the Business logic language.

  • Speed up solving using “Just in time compilation”

  • Analyze the model structure through tools from graph theory

  • Handle large models. 1,000,000 formulas is not a problem.

  • Integrate model management in Jupyter notebooks for agile and user friendly model use

The core code of ModelFlow is small Thus it can easily be modified and expanded to the specific need of the user. ModelFlow is a toolset. It can handle models, which conform to the tools.

If you need a feature or have a model which can’t be handled in ModelFlow, you are encouraged to improve ModelFlow. Please share the improvement, other users may have the same need, or can be inspired by your work.

Also bear in mind that ModelFlow is experimental. It is provided ”as is”, without any representation or warranty of any kind either express or implied.