Activity of firmai/financial-machine-learning repository

Stable ✅

Constant contribution activity

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 14 Aug, 2023 to 14 Nov, 2023

Final time range – from 15 May, 2024 to 14 Aug, 2024

From 6 to 6 commits per week

From 4324 to 4802 additions per week

11%

From 4321 to 4799 deletions per week

11%

Data calculated on 14 Aug, 2024

Bus factor

What is Bus factor?

It is basically a number of most active contributors responsible for 80% of contributions.

Bus factor tries to assess "What happens if a key member of the team is hit by a bus?". The more there are key members, the lower the risk.

The firmai/financial-machine-learning repository has a bus factor of 1.

High risk, a lot of knowledge concentrated in a few people

Bus factor was measured on 14 Aug 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.