gingado 0.1.1

A machine learning library for economics and finance

Welcome to gingado!

gingado seeks to facilitate the use of machine learning in economic and finance use cases, while promoting good practices. This package aims to be suitable for beginners and advanced users alike. Use cases may range from simple data retrievals to experimentation with machine learning algorithms to more complex model pipelines used in production.

Overview

gingado is a free, open source library built different functionalities:

• data augmentation, to add more data from official sources, improving the machine models being trained by the user;

• automatic benchmark model, to assess candidate models against a reasonably well-performant model;

• (new!) relevant datasets, both real and simulamed, to allow for easier model development and comparison;

• support for model documentation, to embed documentation and ethical considerations in the model development phase; and

• utilities, including tools to allow for lagging variables in a straightforward way.

Each of these functionalities builds on top of the previous one. They can be used on a stand-alone basis, together, or even as part of a larger pipeline from data input to model training to documentation!

Tip

New functionalities are planned over time, so consider checking frequently on gingado for the latest toolsets.

Design principles

The choices made during development of gingado derive from the following principles, in no particular order:

• flexibility: users can use gingado out of the box or build custom processes on top of it;

• compatibility: gingado works well with other widely used libraries in machine learning, such as scikit-learn and pandas; and

• responsibility: gingado facilitates and promotes model documentation, including ethical considerations, as part of the machine learning development workflow.

Acknowledgements

gingado’s API is inspired on the following libraries:

• scikit-learn (Buitinck et al. 2013)

• keras (website here and also, this essay)

• fastai (Howard and Gugger 2020)

In addition, gingado is developed and maintained using nbdev.

Presentations, talks, papers

The most current version of the paper describing gingado is here. The paper and other material about gingado (ie, slide decks, papers) in this dedicated repository. Interested users are welcome to visit the repository and comment on the drafts or slide decks, preferably by opening an issue. I also store in this repository suggestions I receive as issues, so users can see what others commented (anonymously unless requested) and comment along as well!

Install

To install gingado, simply run the following code on the terminal:

$ pip install gingado

Citation

If you use this package in your work, please cite it as below:

Araujo, Douglas (2022): “gingado: A machine learning library for economics and finance”, Irving Fisher Committee on Central Bank Statistics Workshop on “Data science in central banking” - Part 2: Data Science in Central Banking: Applications and tools.

References

Buitinck, Lars, Gilles Louppe, Mathieu Blondel, Fabian Pedregosa, Andreas Mueller, Olivier Grisel, Vlad Niculae, et al. 2013. “API Design for Machine Learning Software: Experiences from the Scikit-Learn Project.” CoRR abs/1309.0238. http://arxiv.org/abs/1309.0238.

Howard, Jeremy, and Sylvain Gugger. 2020. “Fastai: A Layered API for Deep Learning.” Information 11 (2). https://doi.org/10.3390/info11020108.