Contribution activity and bus factor of guanquann/Stocksera

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Inactive 🧊

Significant drop in contribution activity

Why guanquann/Stocksera is inactive?

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

Initial time range – from 5 Jul, 2023 to 5 Oct, 2023

Final time range – from 5 Apr, 2024 to 5 Jul, 2024

Additions and deletions stats are not available for this repository due to GitHub API limitations.

Data calculated on 5 Jul, 2024

Bus factor of guanquann/Stocksera

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 guanquann/Stocksera 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

Repository summary

The guanquann/Stocksera GitHub repository is a resource designed to provide information on stocks, including Wall Street Bets mention trends, insider trading, institutional ownership, and more.

The main highlights of this repository include:

WallStreetBets Tracker: This is perhaps one of the main reasons why people use Stocksera. It effectively analyses mentions of the most popular stocks on Reddit's WallStreetBets and shows their trends.
Insider Trading: The repository also tracks insider trading as per US SEC Form 4, which could be a crucial piece of information for those looking to make investments based on insider decisions.
Institutional Ownership: It displays changes in institutional ownership to show what companies or stocks are experiencing increased or decreased attention from big institutional investors.
Earnings Call Sentiment Analysis: It analyses and brings forth the sentiment involved in earnings calls, providing the user with an easy understanding of the general sentiment.
Twitter Mention: The tracker can be used to get data on Twitter mentions of specific stocks in real-time.

This repository is built with Python and uses several libraries like Pandas, Numpy, Flask etc. It also makes use of web scraping, natural language processing, and data visualization to provide helpful tools for stock analysis.