Search whether a stock has shown a profit gap in a given financial reporting period.
Screen and rank stocks using your own custom financial factors.
Build a candidate stock list and save the exact settings used to create it.
Review past research runs to see which conditions and weights were applied.
| mykernel/factor-lab | 920linjerry-stack/capital-studio | aahonarmand/neticu | |
|---|---|---|---|
| Stars | 16 | 16 | 16 |
| Language | — | Python | Swift |
| Setup difficulty | easy | easy | easy |
| Complexity | 2/5 | 3/5 | 2/5 |
| Audience | general | researcher | general |
Figures from each repo's GitHub metadata at analysis time.
It is a hosted web service with a mobile version, so there is no local install.
Factor Lab is a research workbench for Chinese A-share stocks, meaning shares traded on the mainland China stock exchanges in Shanghai and Shenzhen. It focuses on a specific investing concept called profit gaps, which are moments when a company's actual earnings come in higher or lower than what the market expected from a financial report. Researchers and individual investors use the platform to search for historical instances of these events and build their own stock screening rules around them. The tool lets you type in a stock ticker or company name to see whether that stock has ever appeared in the platform's profit-gap record database. From there, you can pick a specific financial reporting period and apply your own set of factors, numerical signals drawn from financial data, to filter or rank stocks and generate a candidate list. Each calculation run is saved with all its settings, so you can go back and review exactly which reporting period, filtering conditions, and scoring weights you used at any given time. Factor Lab adds a few extra layers on top of basic screening: current price data overlaid on past results, AI-generated summaries for individual stocks and stock pools, and access to publicly released research reports. There is also a built-in feedback channel for reporting data issues or requesting features. The platform is designed for people who want to document and repeat their own research process rather than receive automated buy or sell signals. The README is explicit that outputs do not constitute investment advice and carry no return promises. It is aimed at analysts who already understand financial statements and want a structured place to test factor combinations and keep a written record of their reasoning. The README does not describe the underlying technical stack or server setup. The platform is available as a web service and also works on mobile.
A web tool for researching Chinese A-share stocks by tracking earnings surprises, screening with custom factors, and saving research run history.
The README does not state a license.
Setup difficulty is rated easy, with roughly 5min to a first successful run.
Mainly general.
This repo across BitVibe Labs
Verify against the repo before relying on details.