dennisthiessen
ebff19940b
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>
140 lines
5.1 KiB
Python
140 lines
5.1 KiB
Python
"""Unit tests for the regime-monitor pure functions and aggregation."""
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from __future__ import annotations
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from datetime import date, timedelta
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from app.services.regime_monitor_service import (
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DEFAULT_CONFIG,
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band_for,
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compute_regime_score,
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f2_credit_spreads,
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p1_trend_break,
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p2_death_cross,
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p3_drawdown,
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p4_relative_strength,
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p5_volatility,
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p6_canary,
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_compute_index,
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)
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def _dated(values: list[float], end: date = date(2026, 6, 26)) -> list[tuple[date, float]]:
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n = len(values)
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return [(end - timedelta(days=(n - 1 - i)), v) for i, v in enumerate(values)]
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# ---------------------------------------------------------------------------
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# Bands
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# ---------------------------------------------------------------------------
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def test_band_for():
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assert band_for(10) == "stable"
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assert band_for(45) == "watch"
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assert band_for(70) == "elevated"
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assert band_for(90) == "breaking"
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# ---------------------------------------------------------------------------
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# Price sub-scores
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# ---------------------------------------------------------------------------
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def test_p1_blends_leader_double():
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smh_under = [100.0] * 199 + [50.0] # last below its 200-DMA
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qqq_above = [100.0] * 200 # last at/above its 200-DMA -> healthy
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score = p1_trend_break(smh_under, qqq_above, leader_weight=2.0)
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# leader(100) weighted 2, confirm(0) weighted 1 -> 66.7
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assert round(score, 1) == 66.7
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def test_p1_none_without_history():
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assert p1_trend_break([100.0] * 50, [100.0] * 50, 2.0) is None
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def test_p2_death_cross_bearish_vs_healthy():
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bearish = [300.0 - i for i in range(260)] # falling: 50 < 200, slope down
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healthy = [100.0 + i * 0.5 for i in range(260)] # rising: 50 > 200
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assert p2_death_cross(bearish, bearish, 2.0) > 0
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assert p2_death_cross(healthy, healthy, 2.0) == 0
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def test_p3_drawdown_linear():
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closes = [100.0] * 252 + [80.0] # 20% below the 52w high -> 100
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assert p3_drawdown(closes, [100.0] * 253) == 100.0
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def test_p4_relative_strength_direction():
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falling = [100.0 - i * 0.5 for i in range(70)] # SMH underperforms flat SPY
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rising = [100.0 + i * 0.5 for i in range(70)]
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spy = [100.0] * 70
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assert p4_relative_strength(falling, spy, 60) > 50
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assert p4_relative_strength(rising, spy, 60) < 50
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def test_p5_volatility_linear():
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assert p5_volatility(15) == 0
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assert p5_volatility(30) == 100
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assert p5_volatility(22.5) == 50
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assert p5_volatility(None) is None
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def test_f2_credit_percentile():
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rising = [float(i) for i in range(1, 31)] # latest is the max -> ~100th pct
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assert f2_credit_spreads(rising) == 100.0
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falling = [float(i) for i in range(30, 0, -1)] # latest is the min
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assert f2_credit_spreads(falling) < 10
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assert f2_credit_spreads([1.0] * 5) is None # too short
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def test_p6_canary_divergence():
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nvda_weak = [100.0] * 49 + [80.0] # below its 50-DMA
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smh_intact = [100.0] * 199 + [120.0] # above its 200-DMA
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assert p6_canary(nvda_weak, smh_intact) == 100.0
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assert p6_canary([100.0] * 50, smh_intact) == 0.0
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# ---------------------------------------------------------------------------
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# Aggregation
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# ---------------------------------------------------------------------------
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def test_compute_regime_score_excludes_na_and_zero_weight():
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weights = {"P1": 10, "P2": 0, "F2": 5}
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subs = {"P1": 80.0, "P2": 50.0, "F2": None}
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result = compute_regime_score(subs, weights)
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# Only P1 counts: P2 weight 0, F2 unavailable.
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assert result["total_score"] == 80.0
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ids = {row["id"]: row for row in result["breakdown"]}
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assert "P2" not in ids # zero-weight signals are hidden
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assert ids["F2"]["available"] is False
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assert ids["P1"]["contribution"] == 80.0
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def test_compute_regime_score_contributions_sum_to_total():
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weights = {"P1": 10, "F2": 10}
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subs = {"P1": 80.0, "F2": 40.0}
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result = compute_regime_score(subs, weights)
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assert result["total_score"] == 60.0
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total = sum(row["contribution"] for row in result["breakdown"])
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assert round(total, 1) == 60.0
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# ---------------------------------------------------------------------------
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# As-of index replay (backfill mechanics)
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# ---------------------------------------------------------------------------
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def test_compute_index_as_of_truncates_history():
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rising = [100.0 + i * 0.2 for i in range(260)]
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prices = {sym: _dated(rising) for sym in ("SMH", "QQQ", "SPY", "RSP", "NVDA")}
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overrides = {"f1_score": 50.0, "f3_score": 50.0}
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full = _compute_index(prices, None, None, overrides, DEFAULT_CONFIG, date(2026, 6, 26))
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by_id = {r["id"]: r for r in full["breakdown"]}
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assert by_id["P1"]["available"] is True # 200-DMA computable on full history
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assert 0 <= full["total_score"] <= 100
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assert full["band"] in {"stable", "watch", "elevated", "breaking"}
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# As-of 250 days earlier: only ~10 bars are in scope -> long-lookback signals n/a.
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early = _compute_index(prices, None, None, overrides, DEFAULT_CONFIG, date(2026, 6, 26) - timedelta(days=250))
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early_by_id = {r["id"]: r for r in early["breakdown"]}
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assert early_by_id["P1"]["available"] is False
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