# hugo2046/QuantsPlaybook

量化研究-券商金工研报复现

Repository: https://github.com/hugo2046/QuantsPlaybook
Canonical: https://ross.abutalabs.com/products/quantsplaybook
Language: Jupyter Notebook
License Family: other
Topics: quant, quantitative-finance, stock-analysis, trading-algorithms
Last push: 2026-05-08T07:02:40+00:00

## Health v2 (maintenance only)
Score: 69/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 81, release rhythm 35, longevity 100
- inputs: {"age_days": 2249, "days_push": 117, "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 5892, forks 1391 (observed 2026-08-28T04:09:32.009935+00:00)

## What it is
A collection of Jupyter Notebook reproductions of 100+ quantitative investment strategies from Chinese brokerage financial engineering research reports, covering market timing, factor construction, quantitative value, and portfolio optimization. It uses Python with real A-share market data from JoinQuant and Tushare, plus frameworks like Qlib and Backtrader for backtesting.

## Use cases
- reproduce quantitative strategies from Chinese brokerage research reports
- learn market timing indicators like RSRS and QRS
- build and backtest stock factors for A-share market
- study quantitative finance with real market data
- implement machine learning models for stock trading
- backtest trading strategies in Python
- learn factor mining and multi-factor models

## When to choose
- you want to learn quantitative investing through worked notebook examples
- you need reproductions of specific Chinese brokerage research reports
- you trade Chinese A-share markets and want strategy ideas with code
- you want examples combining machine learning with trading strategies

## When to avoid
- you need a production-ready trading system or live execution engine
- you need a maintained software library with an API rather than notebooks
- you need a permissively licensed codebase (no license is specified)
- you trade non-Chinese markets and need localized data sources

## Facets
- artifact type: learning-resource
- maturity: active
- function: trading, data-science, machine-learning, data-visualization, etl
- domain: fintech, data-science, machine-learning, analytics
- platform: python, jvm-scripting
- tags: quantitative-finance, backtesting, jupyter-notebooks, chinese-market, research-reports, stock-analysis, a-share, factor-investing, market-timing, qlib, backtrader, tushare

## Member repositories
- hugo2046/QuantsPlaybook (main) score 69

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:32.009935+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 2026-08-29T17:51:32.692252+00:00, confidence not recorded.
  - readme: https://github.com/hugo2046/QuantsPlaybook (fetched 2026-08-28T04:09:32.009935+00:00, sha e634be2ac677)
- Data as of 2026-08-30T08:39:29.467469+00:00.