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>
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jackyu66git
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"""
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regime_detector.py — Market regime detection (V1: 3 states).
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★ FACTOR-LOCKED: Regime = f(Price Structure, Breadth, Volatility) — forever.
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Fear, Liquidation, ETF, Funding are Context, NOT regime inputs.
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Adding new factors MUST NOT change regime definition.
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★ VERSIONED: regime_version = 'v1_price_breadth_vol'.
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Weight changes → new version. Multiple versions coexist.
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Query: WHERE regime_version = 'v1_price_breadth_vol'.
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★ CONFIDENCE-BASED: Each regime gets a continuous score. Highest wins.
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No hard thresholds (prevents boundary oscillation).
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"""
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from datetime import date as Date
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from typing import Optional
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from collections import deque
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from models import MarketRegime, RegimeResult
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from config import config
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class RegimeDetector:
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"""
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Detects market regime from Price + Breadth + Vol.
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V1: 3 regimes (TREND / RANGE / PANIC)
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V2+: Can split TREND→TREND_UP/TREND_DOWN/EUPHORIA when samples > 500/regime.
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"""
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def __init__(self, regime_version: Optional[str] = None):
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self.version = regime_version or config.regime_version
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self.w_price = config.regime_w_price
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self.w_breadth = config.regime_w_breadth
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self.w_vol = config.regime_w_vol
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self.panic_w_anti_trend = config.regime_panic_w_anti_trend
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self.panic_w_vol_extreme = config.regime_panic_w_vol_extreme
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# State persistence
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self._current_regime: Optional[MarketRegime] = None
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self._pending_regime: Optional[MarketRegime] = None
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self._confirmation_count: int = 0
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self._consecutive_days: int = 0
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self._regime_history: deque = deque(maxlen=100)
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# Confirmation: 2 days minimum
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self.MIN_CONFIRMATION = 2
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def load_state(self, db_path: str):
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"""Restore regime state from the most recent regime_history record."""
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import sqlite3
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try:
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conn = sqlite3.connect(db_path)
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conn.row_factory = sqlite3.Row
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row = conn.execute(
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"SELECT regime, confidence, confirmation_days, maturity_score "
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"FROM regime_history ORDER BY date DESC LIMIT 1"
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).fetchone()
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conn.close()
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if row:
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regime_str = row["regime"]
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if regime_str in ("TREND", "RANGE", "PANIC"):
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self._current_regime = MarketRegime(regime_str)
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self._consecutive_days = row["confirmation_days"] or 1
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except Exception:
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pass # DB not initialized yet, use defaults
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def detect(self, price_structure_score: float, breadth_score: float,
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volatility_regime: str, date: Date) -> RegimeResult:
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"""
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Detect regime from the 3 locked factors.
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Args:
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price_structure_score: 0-100 from PriceStructureScorer
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breadth_score: 0-100 from BreadthScorer
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volatility_regime: 'LOW_VOL'/'NORMAL_VOL'/'HIGH_VOL'/'EXPLOSIVE_VOL'
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date: Target date
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"""
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# ── Compute regime scores ────────────────────────
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# TREND: strong price + strong breadth + non-extreme vol
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trend_score = (
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price_structure_score * self.w_price +
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breadth_score * self.w_breadth +
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self._vol_to_trend(volatility_regime) * self.w_vol
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)
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# RANGE: neutral price + neutral breadth + low vol
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# Score how "range-like" each dimension is
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price_neutral = 60 - abs(price_structure_score - 50)
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breadth_neutral = 60 - abs(breadth_score - 50)
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vol_neutral = 80 if volatility_regime in ("LOW_VOL", "NORMAL_VOL") else 30
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range_score = (
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price_neutral * 0.40 +
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breadth_neutral * 0.40 +
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vol_neutral * 0.20
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)
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# PANIC: very weak trend + extreme vol (NO Fear/Liquidation!)
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anti_trend = 100 - trend_score
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vol_extreme = 100 if volatility_regime == "EXPLOSIVE_VOL" else (
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60 if volatility_regime == "HIGH_VOL" else 20
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)
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panic_score = (
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anti_trend * self.panic_w_anti_trend +
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vol_extreme * self.panic_w_vol_extreme
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)
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scores = {
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MarketRegime.TREND: round(trend_score, 1),
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MarketRegime.RANGE: round(range_score, 1),
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MarketRegime.PANIC: round(panic_score, 1),
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}
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best_regime = max(scores, key=scores.get)
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# ── Persistence check ────────────────────────────
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prior_regime = self._current_regime
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if best_regime == self._current_regime:
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self._consecutive_days += 1
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self._pending_regime = None
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self._confirmation_count = 0
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elif best_regime == self._pending_regime:
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self._confirmation_count += 1
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if self._confirmation_count >= self.MIN_CONFIRMATION:
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# Transition confirmed
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prior_regime = self._current_regime
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self._current_regime = best_regime
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self._consecutive_days = self.MIN_CONFIRMATION
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self._pending_regime = None
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self._confirmation_count = 0
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else:
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self._pending_regime = best_regime
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self._confirmation_count = 1
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# Fallback: if no current regime yet (first run)
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if self._current_regime is None:
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self._current_regime = best_regime
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self._consecutive_days = 1
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# ── Confidence: for the CONFIRMED regime, not the raw best ──
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confirmed_regime = self._current_regime
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confidence = scores[confirmed_regime] / 100.0
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# ── Maturity ─────────────────────────────────────
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maturity = self._compute_maturity(
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trend_score, breadth_score, volatility_regime
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)
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# Track history
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self._regime_history.append({
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"date": date,
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"regime": confirmed_regime.value,
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"confidence": round(confidence, 3),
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})
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return RegimeResult(
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date=date,
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regime=confirmed_regime,
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confidence=round(confidence, 3),
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prior_regime=prior_regime,
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regime_version=self.version,
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maturity_score=round(maturity, 1),
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all_scores={k.value: v for k, v in scores.items()},
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confirmation_days=self._consecutive_days,
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)
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@property
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def current_regime(self) -> Optional[MarketRegime]:
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return self._current_regime
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@property
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def pending_regime(self) -> Optional[MarketRegime]:
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return self._pending_regime
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@property
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def confirmation_progress(self) -> tuple[int, int]:
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"""(confirmed_days, required_days) for pending transition."""
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return (self._confirmation_count, self.MIN_CONFIRMATION)
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@staticmethod
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def _vol_to_trend(vol_regime: str) -> float:
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"""Convert volatility regime to trend-contributing score."""
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mapping = {
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"LOW_VOL": 50, # Low vol: neutral for trend
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"NORMAL_VOL": 70, # Normal vol: good for trend
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"HIGH_VOL": 60, # High vol: trending but risky
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"EXPLOSIVE_VOL": 30, # Explosive: anti-trend
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}
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return mapping.get(vol_regime, 50)
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@staticmethod
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def _compute_maturity(trend_score: float, breadth_score: float,
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vol_regime: str) -> float:
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"""
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Compute regime maturity: 0-100 continuous.
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0-30: EMERGING (trend accelerating, breadth expanding)
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30-70: CONFIRMED (stable)
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70-100: EXHAUSTING (decelerating, vol abnormal)
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"""
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# Trend strength contribution
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trend_contrib = trend_score * 0.50
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# Breadth contribution
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breadth_contrib = breadth_score * 0.30
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# Vol contribution (inverted: low vol = early, explosive = late)
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vol_contrib = {"LOW_VOL": 20, "NORMAL_VOL": 40, "HIGH_VOL": 60, "EXPLOSIVE_VOL": 85}
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vol_val = vol_contrib.get(vol_regime, 50) * 0.20
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return trend_contrib + breadth_contrib + vol_val
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