hugo2046/QuantsPlaybook resource
量化研究-券商金工研报复现 observed · 2026-08-28
Health v2 · maintenance only
69/100
- Activity 81
- Release rhythm 35
- Longevity 100
Flags: no_releases no_license
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-02. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 2249
- days_rel: n/a
- days_push: 117
- n_releases_24m: 0
Adoption not part of the score
5892 stars · 1391 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded
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
learning-resource · maturity active
trading data-science machine-learning data-visualization etl fintech data-science machine-learning analytics python jvm-scripting quantitative-finance backtesting jupyter-notebooks chinese-market research-reports stock-analysis a-share factor-investing market-timing qlib backtrader tushare
1 source
- readme: https://github.com/hugo2046/QuantsPlaybook · fetched 2026-08-28 · e634be2ac677
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| hugo2046/QuantsPlaybook | main | 69 |
For agents
markdown · JSON · MCP: product_card(name="hugo2046/QuantsPlaybook")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem