"""Test statistici puri (cointegration, ADF, half-life già in indicators).
Nessuna dipendenza esterna: pure-Python.
"""
from __future__ import annotations

import math


def _ols_slope_intercept(xs: list[float], ys: list[float]) -> tuple[float, float] | None:
    if len(xs) != len(ys) or len(xs) < 3:
        return None
    n = len(xs)
    mx = sum(xs) / n
    my = sum(ys) / n
    num = sum((xs[i] - mx) * (ys[i] - my) for i in range(n))
    den = sum((xs[i] - mx) ** 2 for i in range(n))
    if den == 0:
        return None
    slope = num / den
    intercept = my - slope * mx
    return slope, intercept


def _adf_t_stat(series: list[float]) -> float | None:
    """Augmented Dickey-Fuller test stat semplificato (lag=0 → DF):
    Δy_t = a + b*y_{t-1} + eps. t-stat di b vs zero.
    Più negativo = più stazionario. Approssimazione: critical value ~ -2.86 al 5%.
    """
    if len(series) < 30:
        return None
    y_lag = series[:-1]
    delta = [series[i] - series[i - 1] for i in range(1, len(series))]
    res = _ols_slope_intercept(y_lag, delta)
    if res is None:
        return None
    b, a = res
    n = len(y_lag)
    mx = sum(y_lag) / n
    den = sum((x - mx) ** 2 for x in y_lag)
    if den == 0:
        return None
    fitted = [a + b * y_lag[i] for i in range(n)]
    resid = [delta[i] - fitted[i] for i in range(n)]
    rss = sum(r * r for r in resid)
    if n - 2 <= 0:
        return None
    sigma2 = rss / (n - 2)
    se_b = math.sqrt(sigma2 / den)
    if se_b == 0:
        return None
    return b / se_b


def cointegration_test(
    series_a: list[float],
    series_b: list[float],
    significance_t: float = -2.86,
) -> dict[str, float | bool | None]:
    """Engle-Granger cointegration:
    1. OLS: y_t = alpha + beta * x_t + eps
    2. ADF su residui: se t-stat < critical (-2.86 @ 5%) → cointegrate.
    """
    if len(series_a) != len(series_b) or len(series_a) < 50:
        return {
            "cointegrated": None,
            "beta": None,
            "alpha": None,
            "adf_t_stat": None,
            "spread_mean": None,
            "spread_std": None,
        }
    res = _ols_slope_intercept(series_b, series_a)
    if res is None:
        return {
            "cointegrated": None,
            "beta": None,
            "alpha": None,
            "adf_t_stat": None,
            "spread_mean": None,
            "spread_std": None,
        }
    beta, alpha = res
    spread = [series_a[i] - alpha - beta * series_b[i] for i in range(len(series_a))]
    t_stat = _adf_t_stat(spread)
    cointegrated = (t_stat is not None and t_stat < significance_t)
    n = len(spread)
    mean = sum(spread) / n
    var = sum((s - mean) ** 2 for s in spread) / (n - 1) if n > 1 else 0.0
    return {
        "cointegrated": cointegrated,
        "beta": beta,
        "alpha": alpha,
        "adf_t_stat": t_stat,
        "spread_mean": mean,
        "spread_std": math.sqrt(var),
    }