from backtesting import engine, statistics


def test_moving_block_bootstrap_is_deterministic_and_handles_constant_series():
    first = statistics.moving_block_bootstrap_ci(
        [12.5] * 120,
        block_size=15,
        n_resamples=200,
        seed=7,
    )
    second = statistics.moving_block_bootstrap_ci(
        [12.5] * 120,
        block_size=15,
        n_resamples=200,
        seed=7,
    )

    assert first == second
    assert first == {"estimate": 12.5, "ci_low": 12.5, "ci_high": 12.5, "n": 120}


def test_summarize_returns_reports_observations_and_block_bootstrap_interval():
    summary = statistics.summarize_returns(
        [10.0, -5.0, 20.0, -10.0],
        block_size=2,
        n_resamples=200,
        seed=3,
    )

    assert summary["n"] == 4
    assert summary["mean"] == 3.75
    assert summary["median"] == 2.5
    assert summary["win_rate"] == 50.0
    assert summary["mean_ci_low"] <= summary["mean"] <= summary["mean_ci_high"]


def test_backtest_brackets_publish_bootstrap_confidence_intervals():
    stats = {}

    engine._add_return_statistics(stats, "90d", [10.0, -5.0, 20.0, -10.0])

    assert stats["avg_90d"] == 3.75
    assert stats["median_90d"] == 2.5
    assert stats["win_rate_90d"] == 50.0
    assert stats["avg_90d_ci_low"] <= stats["avg_90d"] <= stats["avg_90d_ci_high"]


def test_long_horizon_returns_use_a_matching_dependence_block(monkeypatch):
    observed = {}

    def fake_summary(values, *, block_size, n_resamples):
        observed.update(block_size=block_size, n_resamples=n_resamples)
        return {
            "mean": 1.0, "median": 1.0, "win_rate": 100.0,
            "mean_ci_low": 0.5, "mean_ci_high": 1.5, "n": len(values),
        }

    monkeypatch.setattr(engine, "summarize_returns", fake_summary)
    engine._add_return_statistics({}, "365d", [1.0] * 500)

    assert observed == {"block_size": 365, "n_resamples": 400}