feat: add standalone AI/Tech regime-change monitor tab

A new /regime tab scoring how far the AI/Tech bull regime has deteriorated
toward a re-rating as a single 0-100 index with per-signal breakdown and a
7/30-day trend. Intentionally decoupled: nothing reads its output to gate or
score trades — the daily-pipeline membership is scheduling only.

- regime_monitor_service: price sub-scores (P1-P6 via Alpaca, like
  market_regime), VIX + HY credit spreads via a small FRED helper, weighted
  aggregation over available signals (missing source -> n/a, dropped from the
  denominator), one snapshot row/day, and a ~90-day history backfill by
  replaying the already-fetched series as-of each past day.
- F1/F3 fundamentals proposed by the configured grounded LLM (reuses
  sentiment_provider_service config resolution), with a manual override + lock.
- regime_snapshots table (migration 011); endpoints on the existing market
  router; admin-editable weights/threshold; standalone /regime page.

Data needs: prices via Alpaca, VIX/credit via FRED (optional key — signals show
n/a without it). No LLM needed for history.

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

tests/unit/test_regime_monitor.py

@@ -0,0 +1,139 @@
+"""Unit tests for the regime-monitor pure functions and aggregation."""
+
+from __future__ import annotations
+
+from datetime import date, timedelta
+
+from app.services.regime_monitor_service import (
+    DEFAULT_CONFIG,
+    band_for,
+    compute_regime_score,
+    f2_credit_spreads,
+    p1_trend_break,
+    p2_death_cross,
+    p3_drawdown,
+    p4_relative_strength,
+    p5_volatility,
+    p6_canary,
+    _compute_index,
+)
+
+
+def _dated(values: list[float], end: date = date(2026, 6, 26)) -> list[tuple[date, float]]:
+    n = len(values)
+    return [(end - timedelta(days=(n - 1 - i)), v) for i, v in enumerate(values)]
+
+
+# ---------------------------------------------------------------------------
+# Bands
+# ---------------------------------------------------------------------------
+
+def test_band_for():
+    assert band_for(10) == "stable"
+    assert band_for(45) == "watch"
+    assert band_for(70) == "elevated"
+    assert band_for(90) == "breaking"
+
+
+# ---------------------------------------------------------------------------
+# Price sub-scores
+# ---------------------------------------------------------------------------
+
+def test_p1_blends_leader_double():
+    smh_under = [100.0] * 199 + [50.0]   # last below its 200-DMA
+    qqq_above = [100.0] * 200            # last at/above its 200-DMA -> healthy
+    score = p1_trend_break(smh_under, qqq_above, leader_weight=2.0)
+    # leader(100) weighted 2, confirm(0) weighted 1 -> 66.7
+    assert round(score, 1) == 66.7
+
+
+def test_p1_none_without_history():
+    assert p1_trend_break([100.0] * 50, [100.0] * 50, 2.0) is None
+
+
+def test_p2_death_cross_bearish_vs_healthy():
+    bearish = [300.0 - i for i in range(260)]      # falling: 50 < 200, slope down
+    healthy = [100.0 + i * 0.5 for i in range(260)]  # rising: 50 > 200
+    assert p2_death_cross(bearish, bearish, 2.0) > 0
+    assert p2_death_cross(healthy, healthy, 2.0) == 0
+
+
+def test_p3_drawdown_linear():
+    closes = [100.0] * 252 + [80.0]   # 20% below the 52w high -> 100
+    assert p3_drawdown(closes, [100.0] * 253) == 100.0
+
+
+def test_p4_relative_strength_direction():
+    falling = [100.0 - i * 0.5 for i in range(70)]   # SMH underperforms flat SPY
+    rising = [100.0 + i * 0.5 for i in range(70)]
+    spy = [100.0] * 70
+    assert p4_relative_strength(falling, spy, 60) > 50
+    assert p4_relative_strength(rising, spy, 60) < 50
+
+
+def test_p5_volatility_linear():
+    assert p5_volatility(15) == 0
+    assert p5_volatility(30) == 100
+    assert p5_volatility(22.5) == 50
+    assert p5_volatility(None) is None
+
+
+def test_f2_credit_percentile():
+    rising = [float(i) for i in range(1, 31)]   # latest is the max -> ~100th pct
+    assert f2_credit_spreads(rising) == 100.0
+    falling = [float(i) for i in range(30, 0, -1)]  # latest is the min
+    assert f2_credit_spreads(falling) < 10
+    assert f2_credit_spreads([1.0] * 5) is None  # too short
+
+
+def test_p6_canary_divergence():
+    nvda_weak = [100.0] * 49 + [80.0]            # below its 50-DMA
+    smh_intact = [100.0] * 199 + [120.0]         # above its 200-DMA
+    assert p6_canary(nvda_weak, smh_intact) == 100.0
+    assert p6_canary([100.0] * 50, smh_intact) == 0.0
+
+
+# ---------------------------------------------------------------------------
+# Aggregation
+# ---------------------------------------------------------------------------
+
+def test_compute_regime_score_excludes_na_and_zero_weight():
+    weights = {"P1": 10, "P2": 0, "F2": 5}
+    subs = {"P1": 80.0, "P2": 50.0, "F2": None}
+    result = compute_regime_score(subs, weights)
+    # Only P1 counts: P2 weight 0, F2 unavailable.
+    assert result["total_score"] == 80.0
+    ids = {row["id"]: row for row in result["breakdown"]}
+    assert "P2" not in ids            # zero-weight signals are hidden
+    assert ids["F2"]["available"] is False
+    assert ids["P1"]["contribution"] == 80.0
+
+
+def test_compute_regime_score_contributions_sum_to_total():
+    weights = {"P1": 10, "F2": 10}
+    subs = {"P1": 80.0, "F2": 40.0}
+    result = compute_regime_score(subs, weights)
+    assert result["total_score"] == 60.0
+    total = sum(row["contribution"] for row in result["breakdown"])
+    assert round(total, 1) == 60.0
+
+
+# ---------------------------------------------------------------------------
+# As-of index replay (backfill mechanics)
+# ---------------------------------------------------------------------------
+
+def test_compute_index_as_of_truncates_history():
+    rising = [100.0 + i * 0.2 for i in range(260)]
+    prices = {sym: _dated(rising) for sym in ("SMH", "QQQ", "SPY", "RSP", "NVDA")}
+    overrides = {"f1_score": 50.0, "f3_score": 50.0}
+
+    full = _compute_index(prices, None, None, overrides, DEFAULT_CONFIG, date(2026, 6, 26))
+    by_id = {r["id"]: r for r in full["breakdown"]}
+    assert by_id["P1"]["available"] is True          # 200-DMA computable on full history
+    assert 0 <= full["total_score"] <= 100
+    assert full["band"] in {"stable", "watch", "elevated", "breaking"}
+
+    # As-of 250 days earlier: only ~10 bars are in scope -> long-lookback signals n/a.
+    early = _compute_index(prices, None, None, overrides, DEFAULT_CONFIG, date(2026, 6, 26) - timedelta(days=250))
+    early_by_id = {r["id"]: r for r in early["breakdown"]}
+    assert early_by_id["P1"]["available"] is False

tests/unit/test_scheduler.py

@@ -87,6 +87,7 @@ class TestConfigureScheduler:
             "outcome_evaluator",
             "alerts",
             "market_regime",
+            "regime_monitor",
             "backtest",
             "daily_pipeline",
             "intraday_pipeline",
@@ -107,6 +108,7 @@ class TestConfigureScheduler:
             "data_backfill",
             "fundamental_collector",
             "market_regime",
+            "regime_monitor",
             "outcome_evaluator",
             "rr_scanner",
             "sentiment_collector",