Data

Tools

Indexes

Applications

Experiments

Open Source Science

gingado

A machine learning library for economics and finance

https://github.com/bis-med-it/gingado

Science Score: 67.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ✓
    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ✓
    DOI references
    Found 2 DOI reference(s) in README
  • ✓
    Academic publication links
    Links to: arxiv.org
  • ○
    Academic email domains
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (17.3%) to scientific vocabulary
Last synced: 6 months ago · JSON representation ·

Repository

A machine learning library for economics and finance

Basic Info
Statistics
  • Stars: 23
  • Watchers: 1
  • Forks: 5
  • Open Issues: 5
  • Releases: 6
Created about 2 years ago · Last pushed 10 months ago
Metadata Files
Readme Contributing License Citation Security

README.md

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 data from official sources, improving the machine models being trained by the user;

  • relevant datasets, both real and simulated, to allow for easier model development and comparison;

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

  • machine learning-based estimators, to help answer questions of academic or practical importance;

  • 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.

Install

> **Note** > > Please make sure you have read and understood the license disclaimer > in the NOTES.md file in our [GitHub > repository](https://github.com/bis-med-it/gingado) before using > gingado.

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

$ pip install gingado

Attribution

If you use this package in your work, please consider citing Araujo (2023).

In BibTeX format:

@techreport{gingado,
    author = {Araujo, Douglas KG},
    title = {gingado: a machine learning library focused on economics and finance},
    series = {BIS Working Paper},
    type = {Working Paper},
    institution = {Bank for International Settlements},
    year = {2023},
    number = {1122}
}

Over time, new tools that are described in specific papers might be added (eg, a machine learning-based econometric estimator). Please consider citing them as well if used in your work. Specific information, if any, can be found in the documentation.

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.

For more information about gingado, please read the paper.

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 quarto.

References

Araujo, Douglas KG. 2023. “Gingado: A Machine Learning Library Focused on Economics and Finance.” Working Paper 1122. BIS Working Paper. Bank for International Settlements.
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. .
Howard, Jeremy, and Sylvain Gugger. 2020. “Fastai: A Layered API for Deep Learning.” *Information* 11 (2). .

Owner

  • Name: MEDIT
  • Login: bis-med-it
  • Kind: organization

Citation (CITATION.cff) 

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
  - family-names: Araujo
    given-names: Douglas
    orcid: https://orcid.org/0000-0001-8070-6828
title: "gingado: a machine learning library for economics and finance"
version: 0.0.3
date-released: 2022-09-21
preferred-citation:
  type: report
  authors:
  - family-names: Araujo
    given-names: Douglas
    orcid: https://orcid.org/0000-0001-8070-6828
  journal: "BIS Working Paper"
  month: 9
  title: "gingado: a machine learning library for economics and finance"
  issue: 1122
  year: 2023

GitHub Events

Total
  • Create event: 9
  • Release event: 2
  • Issues event: 13
  • Watch event: 7
  • Delete event: 2
  • Issue comment event: 17
  • Push event: 39
  • Pull request review comment event: 5
  • Pull request event: 11
  • Pull request review event: 8
  • Fork event: 1
Last Year
  • Create event: 9
  • Release event: 2
  • Issues event: 13
  • Watch event: 7
  • Delete event: 2
  • Issue comment event: 17
  • Push event: 39
  • Pull request review comment event: 5
  • Pull request event: 11
  • Pull request review event: 8
  • Fork event: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 4
  • Total pull requests: 3
  • Average time to close issues: 2 months
  • Average time to close pull requests: 2 months
  • Total issue authors: 1
  • Total pull request authors: 2
  • Average comments per issue: 1.25
  • Average comments per pull request: 1.67
  • Merged pull requests: 2
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 4
  • Pull requests: 3
  • Average time to close issues: 2 months
  • Average time to close pull requests: 2 months
  • Issue authors: 1
  • Pull request authors: 2
  • Average comments per issue: 1.25
  • Average comments per pull request: 1.67
  • Merged pull requests: 2
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • dkgaraujo (11)
  • ccantug (1)
  • Tevdokimova (1)
Pull Request Authors
  • jdamp-bis (7)
  • dkgaraujo (7)
  • robin-mader-bis (2)
  • stephprobst (1)
Top Labels
Issue Labels
bug (2) enhancement (2) good first issue (1)
Pull Request Labels
documentation (1)

Packages

  • Total packages: 1
  • Total downloads:
    • pypi 113 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 1
  • Total versions: 27
  • Total maintainers: 1
pypi.org: gingado

A machine learning library for economics and finance

  • Versions: 27
  • Dependent Packages: 0
  • Dependent Repositories: 1
  • Downloads: 113 Last month
Rankings
Dependent packages count: 10.1%
Forks count: 19.1%
Average: 20.1%
Dependent repos count: 21.6%
Stargazers count: 23.1%
Downloads: 26.7%
Maintainers (1)
dkgaraujo
Last synced: 6 months ago

Dependencies

dev_requirements.txt pypi
  • nbclient * development
  • nbformat * development
  • pre-commit * development
  • statsmodels * development
  • tensorflow * development
  • torch * development
requirements.txt pypi
  • matplotlib >=3.5
  • numpy >=1.21
  • pandas >=1.3.5
  • pandasdmx >=1.8.1
  • requests_cache >=0.9.1
  • scikit-learn >=1.0.2
setup.py pypi