# Rgveda/996Quant

35岁程序员退路之量化投资学习笔记

Repository: https://github.com/Rgveda/996Quant
Canonical: https://ross.abutalabs.com/products/996quant
Language: Jupyter Notebook
License: MIT
License Family: permissive
Last push: 2021-01-31T10:39:58+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2128, "days_push": 2040, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1215, forks 173 (observed 2026-08-28T04:04:01.094410+00:00)

## What it is
GolemQ (996Quant) is a personal quantitative trading learning project built as a fork-style subproject of QUANTAXIS, focused on fetching and resampling live A-share (Chinese stock) market data from Sina Finance. It provides CLI tools and Jupyter notebook examples for retrieving realtime L1 snapshots and converting them into minute, hourly, and daily K-line data.

## Use cases
- learn quantitative investing with Python
- fetch realtime Chinese A-share market data
- resample tick data into minute and daily K-lines
- get stock kline data from Sina Finance
- build a personal quant trading study setup
- mimic Youku/JoinQuant data APIs

## When to choose
- you want a lightweight Python tool to pull live A-share quotes outside the QUANTAXIS Docker frontend
- you are learning quant trading with Jupyter notebooks and Chinese market data
- you need CLI-based subscription to Sina L1 market snapshots

## When to avoid
- you need a stable, well-tested production trading system
- you require non-Chinese market data
- you want polished APIs like JoinQuant or Youku - those interfaces are incomplete
- you need active maintenance - the project has not been updated since early 2021 and documents known bugs

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: data-science, trading, http-client, cli
- domain: fintech, data-science, developer-tools
- platform: python, cli
- tags: quantitative-finance, quantaxis, chinese-stock-market, jupyter-notebook, market-data, sina-finance, kline-data, personal-project, docker

## Member repositories
- Rgveda/996Quant (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:01.094410+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T06:17:13.979068+00:00, confidence not recorded.
  - readme: https://github.com/Rgveda/996Quant (fetched 2026-08-28T04:04:01.094410+00:00, sha 859f393c2444)
- Data as of 2026-08-30T08:39:29.467469+00:00.