chanmacro: Signal Expectancy Engine V1 — Market Memory System

Phase A-C complete: 4 core factors, regime detection, signal tracking, Bayesian expectancy.

chanmacro/ (32 files, ~4000 lines):
- models: 12 enums + 15 Pydantic v2 models (DateAwareModel, MarketStateVector, etc.)
- fetchers: OHLCV + Breadth (from data_provider) + Derivatives (new endpoint)
- scoring: Price Structure / Breadth (quantile buckets) / OI Matrix (5 discrete states) / Volatility Regime
- regime_detector: 3-state (TREND/RANGE/PANIC), factor-locked (Price+Breadth+Vol), versioned, 2-day confirmation
- expectancy: SignalTracker (record+outcomes), TimeDecay (half-life=180d), BayesianExpectancyEngine (Empirical Bayes, Leveled, SufficiencyGuard)
- validation: FactorValidator (IC/ICIR/Hit Ratio), RegimeValidator (MI/KL/ANOVA), TransitionValidator (stability)
- CLI: fetch|score|regime|track|backfill|expectancy|validate|serve
- tests: 52 passing (models, scoring, regime, expectancy)

data_provider:
- /api/derivatives endpoint: funding rate, OI, OI change, basis
- _derivatives storage: same persist pattern as K-line (merge→lock→snapshot→atomic write)
- background refresh every 60s

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

ChanMacro/tests/test_regime.py

@@ -0,0 +1,109 @@
+"""Test regime detector and validation."""
+import pytest
+from datetime import date
+import pandas as pd
+import numpy as np
+
+
+class TestRegimeDetector:
+    def test_detects_trend(self):
+        from regime_detector import RegimeDetector
+        from models import MarketRegime
+        d = RegimeDetector()
+        r = d.detect(75.0, 80.0, "NORMAL_VOL", date(2026, 6, 24))
+        assert r.regime == MarketRegime.TREND
+        assert r.confidence > 0.5
+
+    def test_detects_range(self):
+        from regime_detector import RegimeDetector
+        from models import MarketRegime
+        d = RegimeDetector()
+        r = d.detect(50.0, 50.0, "LOW_VOL", date(2026, 6, 24))
+        assert r.regime in (MarketRegime.RANGE, MarketRegime.TREND)
+
+    def test_detects_panic(self):
+        from regime_detector import RegimeDetector
+        from models import MarketRegime
+        d = RegimeDetector()
+        r = d.detect(15.0, 10.0, "EXPLOSIVE_VOL", date(2026, 6, 24))
+        assert r.regime == MarketRegime.PANIC
+
+    def test_2day_confirmation(self):
+        from regime_detector import RegimeDetector
+        from models import MarketRegime
+        d = RegimeDetector()
+        # Day 1: RANGE
+        r1 = d.detect(50.0, 50.0, "LOW_VOL", date(2026, 6, 24))
+        assert r1.regime == MarketRegime.RANGE  # first run, no confirmation needed
+        # Day 2: still RANGE
+        r2 = d.detect(50.0, 50.0, "LOW_VOL", date(2026, 6, 25))
+        assert r2.regime == MarketRegime.RANGE
+        assert r2.confirmation_days == 2
+
+    def test_transition_needs_confirmation(self):
+        from regime_detector import RegimeDetector
+        from models import MarketRegime
+        d = RegimeDetector()
+        # Establish TREND
+        d.detect(75.0, 80.0, "NORMAL_VOL", date(2026, 6, 24))
+        d.detect(75.0, 80.0, "NORMAL_VOL", date(2026, 6, 25))
+        # Day 3: weak scores → raw best = RANGE, but TREND should persist
+        r3 = d.detect(35.0, 40.0, "NORMAL_VOL", date(2026, 6, 26))
+        # First day of pending transition — should still be TREND
+        assert r3.regime == MarketRegime.TREND
+        assert d.pending_regime is not None
+
+    def test_version_is_stored(self):
+        from regime_detector import RegimeDetector
+        d = RegimeDetector(regime_version="v1_price_breadth_vol")
+        r = d.detect(75.0, 80.0, "NORMAL_VOL", date(2026, 6, 24))
+        assert r.regime_version == "v1_price_breadth_vol"
+
+    def test_load_state(self, db_path):
+        from regime_detector import RegimeDetector
+        from models import MarketRegime
+        d = RegimeDetector()
+        d.load_state(db_path)
+        # DB has TREND for first 100 days, so most recent should load
+        assert d.current_regime is not None
+
+    def test_confidence_for_confirmed_regime(self):
+        """Confidence should be for the confirmed regime, not raw best."""
+        from regime_detector import RegimeDetector
+        from models import MarketRegime
+        d = RegimeDetector()
+        # Establish TREND
+        d.detect(75.0, 80.0, "NORMAL_VOL", date(2026, 6, 24))
+        d.detect(75.0, 80.0, "NORMAL_VOL", date(2026, 6, 25))
+        # Now feed weak scores → raw best would be PANIC or RANGE
+        r = d.detect(15.0, 10.0, "EXPLOSIVE_VOL", date(2026, 6, 26))
+        # Should still report TREND (need 2 confirmations to switch)
+        assert r.regime == MarketRegime.TREND
+
+
+class TestTransitionValidator:
+    def test_stable_regime_passes(self):
+        from validation.transition_validator import TransitionValidator
+        # Create stable regime sequence: long periods
+        seq = pd.Series(
+            ["TREND"] * 50 + ["RANGE"] * 50 + ["PANIC"] * 40,
+            index=pd.date_range("2026-01-01", periods=140),
+        )
+        tv = TransitionValidator()
+        report = tv.validate(seq)
+        assert report.is_stable
+        assert report.avg_duration > 20
+        assert report.flip_rate < 0.05
+
+    def test_unstable_regime_fails(self):
+        from validation.transition_validator import TransitionValidator
+        # Create unstable sequence: flips every 2 days
+        seq = pd.Series(
+            ["TREND", "TREND", "RANGE", "RANGE", "TREND", "TREND",
+             "PANIC", "PANIC", "RANGE", "RANGE"] * 5,
+            index=pd.date_range("2026-01-01", periods=50),
+        )
+        tv = TransitionValidator()
+        report = tv.validate(seq)
+        assert not report.is_stable
+        assert report.flip_rate > 0.15