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/trend_detector.py

@@ -0,0 +1,81 @@
+"""
+trend_detector.py — Trend strength and maturity helpers.
+
+Utility functions for computing trend alignment, acceleration, persistence.
+Used by regime_detector and price_structure scorer.
+"""
+
+import numpy as np
+import pandas as pd
+
+
+def ema_alignment_score(close: float, ema20: float, ema60: float, ema120: float) -> float:
+    """Score EMA alignment: 0=bearish, 50=neutral, 100=bullish."""
+    if any(pd.isna(x) for x in [ema20, ema60, ema120]):
+        return 50.0
+
+    alignments = 0
+    if ema20 > ema60:
+        alignments += 1
+    if ema60 > ema120:
+        alignments += 1
+    if ema20 > ema120:
+        alignments += 1
+
+    if alignments == 3:
+        return 85.0
+    elif alignments == 2:
+        return 65.0
+    elif alignments == 1:
+        return 35.0
+    else:
+        return 15.0
+
+
+def adx_trend_score(adx: float) -> float:
+    """Convert ADX value to trend score: 0-100."""
+    if pd.isna(adx):
+        return 50.0
+    if adx > 40:
+        return 90.0
+    elif adx > 25:
+        return 60.0 + (adx - 25) / 15 * 30
+    elif adx > 15:
+        return 40.0 + (adx - 15) / 10 * 20
+    else:
+        return max(10.0, adx / 15 * 40)
+
+
+def breadth_persistence(breadth_scores: list[float], window: int = 5) -> float:
+    """How consistently has breadth stayed at its current level? 0-100."""
+    if len(breadth_scores) < window:
+        return 50.0
+    recent = breadth_scores[-window:]
+    mean_val = np.mean(recent)
+    std_val = np.std(recent) if len(recent) > 1 else 0
+    # Low std = high persistence
+    persistence = 100 - min(std_val * 5, 100)
+    # Bias: higher breadth = higher persistence score
+    return persistence * 0.5 + mean_val * 0.5
+
+
+def trend_strength_composite(ema_score: float, adx_score: float,
+                              breadth_score: float) -> float:
+    """Composite trend strength 0-100."""
+    return ema_score * 0.25 + adx_score * 0.25 + breadth_score * 0.50
+
+
+def compute_maturity(trend_strength: float, breadth_persistence: float,
+                     vol_expansion: float) -> float:
+    """
+    Compute regime maturity score 0-100.
+
+    EMERGING (0-30): trend accelerating, breadth expanding
+    CONFIRMED (30-70): trend stable, breadth stable
+    EXHAUSTING (70-100): trend decelerating, breadth contracting, vol abnormal
+    """
+    return (
+        trend_strength * 0.50 +
+        breadth_persistence * 0.30 +
+        (100 - vol_expansion) * 0.20  # inverted: low vol = early stage
+    )