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.

Implementation

Every test below is already implemented and unit-tested in src.quantlib.timeseries. Import and call it — do not retype these formulas into throwaway code, which is how sign errors and double-sqrt bugs get into results.

from src.quantlib.timeseries import (
    adf_test, cointegration_test, find_hedge_ratio, compute_half_life,
    granger_test, fit_garch, heteroscedasticity_test, autocorrelation_test,
    vif_test, bootstrap_statistic, bootstrap_sharpe,
)

Optional backends: statsmodels powers everything except the two bootstrap helpers (which are pure numpy); arch powers fit_garch only. Neither is declared as a dependency of vibe-trading-ai, so both are imported lazily inside the functions. Importing the module always works; calling a function whose backend is missing raises an ImportError naming the package and the install command (pip install "statsmodels>=0.14" / pip install "arch>=6.0"). If you hit that error, report it to the user rather than silently substituting a different method.