from __future__ import annotations

import random

from mcp_common.stats import cointegration_test


def test_cointegrated_synthetic_pair():
    """Costruisco coppia cointegrata: B random walk, A = 2*B + noise stazionario."""
    r = random.Random(1)
    b = [100.0]
    for _ in range(300):
        b.append(b[-1] + r.gauss(0, 1))
    a = [2 * b[i] + r.gauss(0, 0.5) for i in range(len(b))]
    out = cointegration_test(a, b)
    assert out["cointegrated"] is True
    assert out["beta"] == pytest_approx(2.0, rel=0.05)
    assert out["adf_t_stat"] is not None
    assert out["adf_t_stat"] < -2.86


def test_not_cointegrated_independent_walks():
    """Due random walk indipendenti → spread non stazionario → no cointegration."""
    r = random.Random(2)
    a = [100.0]
    b = [100.0]
    for _ in range(300):
        a.append(a[-1] + r.gauss(0, 1))
        b.append(b[-1] + r.gauss(0, 1))
    out = cointegration_test(a, b)
    # Per due RW indipendenti, t-stat ADF è solitamente > -2.86 → non cointegrate
    assert out["cointegrated"] is False or out["adf_t_stat"] > -3.0


def test_cointegration_short_series():
    out = cointegration_test([1.0, 2.0], [3.0, 4.0])
    assert out["cointegrated"] is None
    assert out["beta"] is None


def test_cointegration_mismatched_length():
    out = cointegration_test([1.0, 2.0, 3.0], [1.0, 2.0])
    assert out["cointegrated"] is None


def pytest_approx(value, rel):
    """Tiny helper to avoid importing pytest just for approx."""
    class _Approx:
        def __eq__(self, other):
            return abs(other - value) <= abs(value) * rel
    return _Approx()