Activity of firmai/financial-machine-learning repository


Constant contribution activity

Activity badge for firmai/financial-machine-learning repository

Why firmai/financial-machine-learning is stable?

The result is based on ratio of number of commits and code additions from initial and final time ranges.

Initial time range – from 10 Feb, 2023 to 13 May, 2023

Final time range – from 11 Nov, 2023 to 10 Feb, 2024

From 6 to 6 commits per week
From 3879 to 4563 additions per week
From 3876 to 4560 deletions per week
Data calculated on 10 Feb, 2024

Summary of firmai/financial-machine-learning

The firmai/financial-machine-learning repository on GitHub is a comprehensive resource for machine learning in the field of finance. It contains a vast range of informational content, Python notebooks, and code examples related to financial machine learning concepts, techniques, and applications.

The main objective of this repository is to provide a comprehensive learning platform for data scientists, researchers, financial analysts, and developers who are involved in applying machine learning methodologies to solve financial problems. Various topics in financial concepts such as risk management, algorithmic trading, portfolio management, and financial data analysis are covered with practical code examples and references to pertinent literature and research papers.

The repository is actively maintained by contributors who specialize in financial machine learning, which enables continuously updating content keeping up with the fast-paced development of machine learning applications in finance.

Topics covered in the repository include but are not limited to:

  • Financial Investment Frameworks
  • Company Bankruptcies
  • Customer Analytics
  • Financial Machine Learning
  • Credit Scoring
  • Portfolio Management
  • Algorithmic Trading

Contributions to this repository are open, and improvements and additions are welcomed by the maintainer. It is recommended for those interested in the intersection of finance and machine learning.

Top 5 contributors