feat: sharpen the event study — more events, fair baseline, per-event view

The first run gave only 2 events (N=2 is anecdote, not evidence) and an unfairly
weak coincident baseline, so the +42d lead couldn't be trusted. This makes the
measurement meaningful:

- More, cleaner events: default drawdown threshold 15%→10%, and dedup switched
  from "recover to the high" to a rising-edge + cooldown (40d), so distinct
  drawdowns each register instead of merging.
- Fair comparison: each indicator now warns at its OWN 80th percentile instead of
  a shared absolute 60, removing the artifact that muted the coincident baseline.
- Per-event breakdown (date · depth · breadth lead · coincident lead) so a median
  over a tiny sample can't hide an apples-to-oranges comparison — you see whether
  both warned on the same drawdown.
- Surface precision/recall (best row) + base rate per indicator — the honest edge
  read, not just lead time.

Re-run the Event Study job to regenerate the cached report in the new shape.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

tests/unit/test_event_study.py

@@ -6,6 +6,8 @@ from datetime import date, timedelta
 
 from app.services.breadth_service import _breadth_from_closes, compute_divergence_series
 from app.services.event_study_service import (
+    _lead,
+    _percentile,
     detect_events,
     event_centered,
     signal_centered,
@@ -40,6 +42,29 @@ def test_detect_events_two_after_recovery():
     assert len(events) == 2
 
 
+def test_detect_events_cooldown_suppresses_close_recross():
+    # Dips below threshold then re-crosses only a few bars later.
+    closes = [100.0] * 300 + [85.0] * 3 + [100.0] * 3 + [85.0] * 3
+    dates = _days(len(closes))
+    assert len(detect_events(closes, dates, threshold_pct=15.0, cooldown=40)) == 1
+    assert len(detect_events(closes, dates, threshold_pct=15.0, cooldown=3)) == 2
+
+
+def test_percentile_interpolation():
+    vals = [float(v) for v in range(0, 101, 10)]  # 0,10,...,100
+    assert _percentile(vals, 50) == 50.0
+    assert _percentile(vals, 80) == 80.0
+    assert _percentile([], 50) is None
+
+
+def test_lead_earliest_crossing():
+    dates = _days(200)
+    t0 = 120
+    indicator = {dates[i]: (70.0 if t0 - 30 <= i <= t0 else 10.0) for i in range(len(dates))}
+    assert _lead(indicator, t0, dates, pre=60, threshold=60.0) == 30
+    assert _lead(indicator, t0, dates, pre=60, threshold=80.0) is None
+
+
 # ---------------------------------------------------------------------------
 # Event-centered lead time
 # ---------------------------------------------------------------------------