"""Tests for v2 correction events and warning alarm episodes.""" 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 ( _percentile, 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_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