# Jack-Cherish/quantitative

量化交易：python3

Repository: https://github.com/Jack-Cherish/quantitative
Canonical: https://ross.abutalabs.com/products/quantitative
Language: Python
License Family: other
Last push: 2023-11-25T07:55:53+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": 1820, "days_push": 1012, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2712, forks 381 (observed 2026-08-28T04:07:11.920653+00:00)

## What it is
A Chinese-language open-source tutorial series on quantitative (algorithmic) trading with Python 3, accompanying a popular Bilibili video course. It provides lesson-by-lesson code for building automated stock trading strategies.

## Use cases
- learn quantitative trading with python
- build an automated stock trading bot
- backtest trading strategies in python
- algorithmic trading tutorial for beginners
- automate stock buying and selling
- learn python for finance

## When to choose
- you want a beginner-friendly, video-backed introduction to quant trading in Python
- you prefer Chinese-language learning materials
- you want example code for automated stock trading lessons

## When to avoid
- you need production-grade, licensed trading software
- you need guaranteed up-to-date broker API integrations
- you require English documentation or enterprise support

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: trading, data-science, developer-tools
- domain: fintech, tutorials
- platform: python, cross-platform
- tags: quantitative-trading, algorithmic-trading, stock-trading, tutorial, chinese-language, backtesting, cryptocurrency, automation

## Member repositories
- Jack-Cherish/quantitative (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:11.920653+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 2026-08-30T02:15:33.129406+00:00, confidence not recorded.
  - readme: https://github.com/Jack-Cherish/quantitative (fetched 2026-08-28T04:07:11.920653+00:00, sha 528583031fdb)
- Data as of 2026-08-30T08:39:29.467469+00:00.