quant-statistics
Quant-statistics provides time-series testing and volatility modeling tools for quantitative investing. It covers stationarity detection via ADF tests, cointegration analysis for pair trading, GARCH volatility forecasting, and regression diagnostics including heteroskedasticity and autocorrelation checks.
Quant-statistics performs stationarity and cointegration tests to identify tradable pair relationships for strategy development.
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Install
HKUDS/Vibe-Trading/quant-statistics · repository language: Python
git clone https://github.com/HKUDS/Vibe-Trading
cp -r Vibe-Trading/agent/src/skills/quant-statistics ~/.claude/skills/quant-statisticsFrequently asked questions
AI-generated answers based on this skill's SKILL.md and metadata
What ADF unit root test and stationarity capabilities does quant-statistics offer?
Quant-statistics provides ADF (Augmented Dickey-Fuller) unit root testing to detect stationarity in time series data. This is essential for identifying mean-reverting assets and avoiding spurious regression in quantitative strategies. The skill enables researchers to validate whether price series or spreads are stationary before building statistical arbitrage models.
How does quant-statistics support cointegration testing for pair trading?
Quant-statistics includes cointegration analysis tools designed for pair trading strategy development. These tests identify long-term equilibrium relationships between two assets, calculate hedge ratios, and detect mean-reverting spreads. Cointegration testing helps traders establish when two securities move together and when deviations signal trading opportunities.
Can quant-statistics model and forecast volatility using GARCH?
Yes, quant-statistics supports GARCH and variant models for volatility forecasting and risk management. The toolkit enables modeling of time-varying volatility, capturing volatility clustering in financial returns, and generating forward-looking volatility estimates. EGARCH variants are also available for capturing asymmetric volatility effects in downturns.
What regression diagnostics does quant-statistics provide for model validation?
Quant-statistics validates regression models through comprehensive diagnostic tests including heteroskedasticity detection, autocorrelation checks via Ljung-Box testing, and residual analysis. These diagnostics ensure regression assumptions hold and help identify model specification issues before deployment in quantitative strategies.
How does quant-statistics estimate confidence intervals and test significance?
Quant-statistics employs bootstrap methods to estimate confidence intervals and assess statistical significance without relying on parametric assumptions. This approach is particularly valuable for testing Sharpe ratio significance, factor returns, and other non-normally distributed financial metrics in quantitative research.
Does quant-statistics support multiple-testing corrections for factor research?
Quant-statistics includes hypothesis testing and multiple-testing correction methods such as FDR (False Discovery Rate) for factor research. These corrections prevent false discoveries when testing many factors simultaneously, ensuring robust factor analysis and reducing the risk of overfitting in quantitative finance applications.
SKILL.md
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Quantitative Statistical Methods
Overview
Common statistical methodology used in quantitative investing, covering time-series testing, volatility modeling, regression diagnostics, and statistical inference. Provides the statistical foundation for strategy development and factor research.
Time-Series Tests
1. ADF Unit-Root Test (Stationarity Test)
Why it matters: regressing non-stationary series directly can produce spurious regression, making conclusions unreliable.
```python from statsmodels.tsa.stattools import adfuller
def adf_test(series: pd.Series, significance: float = 0.05) -> dict: """ ADF test: H0 = unit root exists (non-stationary), H1 = stationary
Args:
series: Time series
significance: Significance level
Returns:
Test result
"""
result = adfuller(series.dropna(), autolag='AIC')
return {
'adf_statistic': result[0],
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