"""Tests for v3 correction events, warning alarm episodes, and report caveats."""

from __future__ import annotations

from datetime import date, timedelta

from app.services.breadth_service import _breadth_from_closes, compute_divergence_series
from app.services.event_study_service import (
    MIN_EVENTS_FOR_CONFIDENCE,
    _percentile,
    _reliability,
    alarm_episodes,
    detect_events,
    evaluate_alarms,
)


def _days(count: int, start: date = date(2021, 1, 1)) -> list[date]:
    return [start + timedelta(days=index) for index in range(count)]


def test_detect_events_uses_rising_edge_and_cooldown():
    closes = [100.0] * 300 + [85.0] * 5 + [100.0] * 50 + [85.0] * 5
    events = detect_events(closes, _days(len(closes)), threshold_pct=15.0, cooldown=40)
    assert [event["index"] for event in events] == [300, 355]


def test_reliability_flags_a_thin_holdout():
    """2/4 must not read like a property of the score."""
    dates = _days(100)
    backing = dict.fromkeys(dates, 3)

    thin = _reliability(dates, 70, backing, events_detected=11, events_in_holdout=4)
    assert thin["underpowered"] is True
    assert thin["events_detected"] == 11
    assert thin["events_in_holdout"] == 4
    assert thin["minimum_events"] == MIN_EVENTS_FOR_CONFIDENCE

    ample = _reliability(dates, 70, backing, events_detected=20, events_in_holdout=12)
    assert ample["underpowered"] is False


def test_reliability_flags_a_sensor_coverage_split():
    """The threshold must not be frozen on a different construct than it is tested on.

    Credit history starts partway through the training window, so the score
    renormalises over two sensors early and three later.
    """
    dates = _days(100)
    matched = dict.fromkeys(dates, 3)
    assert _reliability(dates, 70, matched, 20, 12)["sensor_coverage_mismatch"] is False

    # Training is 40% three-sensor; the holdout is entirely three-sensor.
    split_backing = {d: (3 if index >= 42 else 2) for index, d in enumerate(dates)}
    mismatched = _reliability(dates, 70, split_backing, 20, 12)
    assert mismatched["sensor_coverage_mismatch"] is True
    assert mismatched["train_full_sensor_share"] == 40.0
    assert mismatched["holdout_full_sensor_share"] == 100.0
    assert mismatched["sensors_expected"] == 3


def test_percentile_is_fixed_from_supplied_values():
    values = [float(value) for value in range(0, 101, 10)]
    assert _percentile(values, 50) == 50.0
    assert _percentile(values, 80) == 80.0
    assert _percentile([], 80) is None


def test_alarm_requires_upward_crossing_and_reset():
    dates = _days(10)
    values = [10, 70, 80, 75, 20, 70, 80, 20, 20, 70]
    indicator = dict(zip(dates, values))
    assert alarm_episodes(indicator, dates, threshold=60) == [1, 5, 9]


def test_holdout_start_does_not_invent_crossing_when_already_high():
    dates = _days(6)
    indicator = dict(zip(dates, [10, 70, 80, 80, 20, 70]))
    assert alarm_episodes(indicator, dates, threshold=60, start_index=3) == [5]


def test_evaluate_alarms_counts_episodes_not_alarm_days():
    dates = _days(100)
    result = evaluate_alarms([10, 50, 80], [25, 70], dates, horizon=20)
    assert result["events_warned"] == 2
    assert result["events_missed"] == 0
    assert result["false_alarms"] == 1
    assert result["median_lead_days"] == 17.5


def test_breadth_from_fixed_closes_and_tapered_divergence():
    dates = _days(10)
    closes_by_symbol = {
        "A": list(zip(dates, [1.0 + index for index in range(10)])),
        "B": list(zip(dates, [10.0 - index for index in range(10)])),
        "C": list(zip(dates, [5.0] * 10)),
    }
    breadth = _breadth_from_closes(closes_by_symbol, window=3, min_tickers=2)
    assert breadth[dates[2]] == round(1 / 3 * 100, 2)

    falling_breadth = {dates[index]: 80.0 - index * 3 for index in range(10)}
    rising_benchmark = list(zip(dates, [100.0 + index for index in range(10)]))
    divergence = compute_divergence_series(falling_breadth, rising_benchmark, lookback=3)
    assert divergence[dates[-1]] > 0

    # v3: breadth loss with price confirming it is still deterioration, scored at
    # DIVERGENCE_CONFIRMED_FLOOR of the masked case rather than discarded. v2's
    # hard gate zeroed this and left Warning at 0 through every selloff.
    falling_benchmark = list(zip(dates, [100.0 - index for index in range(10)]))
    confirmed = compute_divergence_series(falling_breadth, falling_benchmark, lookback=3)
    assert 0 < confirmed[dates[-1]] < divergence[dates[-1]]

    # Flat breadth is not deterioration regardless of price direction.
    flat_breadth = {day: 60.0 for day in dates}
    assert compute_divergence_series(flat_breadth, falling_benchmark, lookback=3)[dates[-1]] == 0