longbridge-quant-stats
Quantitative statistics framework for time-series analysis using Longbridge price data — ADF unit root test (stationarity), cointegration (Engle-Granger / Johansen), GARCH volatility modelling (conditional heteroskedasticity), regression diagnostics (Durbin-Watson / Breusch-Pagan), bootstrap confidence intervals, hypothesis tests (t-test / F-test). Requires statsmodels and scipy. Triggers: "量化统计", "ADF检验", "单位根", "协整检验", "GARCH", "自相关", "异方差", "Bootstrap", "假设检验", "量化統計", "ADF檢驗", "單位根", "協整檢驗", "異方差", "假設檢驗", "quantitative statistics", "ADF test", "unit root", "cointegration", "GARCH", "autoc
npx skills add majiayu000/claude-skill-registry --skill longbridge-quant-stats --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# longbridge-quant-stats Apply rigorous statistical methods to financial time-series data retrieved from Longbridge — test assumptions before modelling, diagnose residuals, and produce statistically sound inferences. > **Response language**: match the user's input language — Simplified Chinese / Traditional Chinese / English. ## When to use - *"帮我做 ADF 单位根检验"*, *"run an ADF test on this price series"*, *"幫我做 ADF 單位根檢驗"* - *"AAPL 和 MSFT 有没有协整关系"*, *"are AAPL and MSFT cointegrated"* - *"用 GARCH 建模波动率"*, *"model volatility with GARCH"* - *"回归残差有没有自相关"*, *"check residual autocorrelation (Durbin-Watson)"* - *"用 Bootstrap 估计置信区间"*, *"bootstrap confidence interval for Sharpe ratio"* For factor IC/IR testing, use `longbridge-factor-research`. For pairs-trading cointegration application, use `longbridge-pairs-trading`. ## Prerequisites ```bash pip install statsmodels scipy numpy pandas ``` ## Workflow and test catalogue ### Step 1 — Fetch price data ```bash longbridge kline --help longbridge kline <SYMBOL> --period day --count 252 --format json ``` Extract the `close` price series. Compute log returns: `r_t = ln(P_t / P_{t-1})`. ### Step 2 — Stationarity: ADF Unit Root Test **When to use**:
- When to use
- Prerequisites
- Workflow and test catalogue
- Step 1 — Fetch price data
- Step 2 — Stationarity: ADF Unit Root Test
- Step 3 — Cointegration Test
- Step 4 — GARCH Volatility Modelling
- Step 5 — Regression Diagnostics
- Step 6 — Bootstrap Confidence Intervals
- Step 7 — Hypothesis Tests
- CLI
- Output
- Error handling
- Related skills
pip install statsmodels scipy numpy pandas longbridge kline --help longbridge kline <SYMBOL> --period day --count 252 --format json
What does the longbridge-quant-stats skill do?
Quantitative statistics framework for time-series analysis using Longbridge price data — ADF unit root test (stationarity), cointegration (Engle-Granger / Johansen), GARCH volatility modelling (conditional heteroskedasticity), regression diagnostics (Durbin-Watson / Breusch-Pagan), bootstrap confidence intervals, hypothesis tests (t-test / F-test). Requires statsmodels and scipy. Triggers: "量化统计", "ADF检验", "单位根", "协整检验", "GARCH", "自相关", "异方差", "Bootstrap", "假设检验", "量化統計", "ADF檢驗", "單位根", "協整檢驗", "異方差", "假設檢驗", "quantitative statistics", "ADF test", "unit root", "cointegration", "GARCH", "autoc
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill longbridge-quant-stats --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.