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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scoring/oi_matrix.py — OI × Price 2×2 state machine.
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Discrete states, NOT a continuous score:
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NEW_LONGS: Price↑ OI↑ → new money entering, trend continuation
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SHORT_COVERING: Price↑ OI↓ → shorts covering, rally fragile
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NEW_SHORTS: Price↓ OI↑ → new shorts entering, trend continuation
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LONG_EXIT: Price↓ OI↓ → longs stopping out, panic (possible bottom)
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NEUTRAL: flat → noise, don't force classification
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"""
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from datetime import date as Date
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import sqlite3
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from .base import BaseScorer
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from .constants import OI_PRICE_THRESHOLD, OI_OI_THRESHOLD, OI_STATE_SCORES
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from models import FactorScore, OIMatrixScore, OIState, MacroDirection
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from config import config
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class OIMatrixScorer(BaseScorer):
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"""Classifies OI × Price state and assigns score."""
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def compute(self, target_date: Date) -> OIMatrixScore:
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conn = self.get_connection()
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try:
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row = conn.execute(
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"SELECT * FROM derivatives WHERE date = ? AND symbol = 'BTC/USDT:USDT'",
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(str(target_date),)
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).fetchone()
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if row is None:
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return OIMatrixScore(
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name="OI Matrix",
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score=50.0,
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label="No Data",
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oi_state=OIState.NEUTRAL,
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)
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row = dict(row)
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oi_change = row.get("oi_24h_change_pct") or 0
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# Get price change from OHLCV
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price_change = self._get_price_change(conn, str(target_date))
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# Classify state
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oi_state = self._classify(price_change, oi_change)
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# Score from state
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score = OI_STATE_SCORES.get(oi_state.value, 50)
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# Direction
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if oi_state == OIState.NEW_LONGS:
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direction = MacroDirection.BULLISH
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elif oi_state == OIState.SHORT_COVERING:
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direction = MacroDirection.BULLISH # bullish but fragile
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elif oi_state == OIState.NEW_SHORTS:
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direction = MacroDirection.BEARISH
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elif oi_state == OIState.LONG_EXIT:
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direction = MacroDirection.BEARISH # bearish but possible bottom
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else:
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direction = MacroDirection.NEUTRAL
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# Narrative
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narrative = self._build_narrative(oi_state, price_change, oi_change)
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return OIMatrixScore(
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name="OI Matrix",
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score=float(score),
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label=oi_state.value,
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direction=direction,
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oi_state=oi_state,
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price_change_pct=round(price_change, 2),
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oi_change_pct=round(oi_change, 2),
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sub_scores={
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"price_change_pct": round(price_change, 2),
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"oi_change_pct": round(oi_change, 2),
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},
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narrative=narrative,
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)
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finally:
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conn.close()
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def _get_price_change(self, conn: sqlite3.Connection, date_str: str) -> float:
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"""Get BTC 24h price change % for a given date."""
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row = conn.execute(
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"SELECT close FROM ohlcv_daily WHERE date = ? AND symbol = 'BTC/USDT:USDT'",
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(date_str,)
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).fetchone()
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if row is None:
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return 0.0
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# Get previous day close
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prev = conn.execute(
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"SELECT close FROM ohlcv_daily WHERE date < ? AND symbol = 'BTC/USDT:USDT' ORDER BY date DESC LIMIT 1",
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(date_str,)
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).fetchone()
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if prev is None:
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return 0.0
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current_close = float(row["close"])
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prev_close = float(prev["close"])
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if prev_close == 0:
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return 0.0
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return (current_close - prev_close) / prev_close * 100
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@staticmethod
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def _classify(price_change_pct: float, oi_change_pct: float) -> OIState:
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"""Classify OI × Price into discrete state."""
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price_up = price_change_pct > OI_PRICE_THRESHOLD
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price_down = price_change_pct < -OI_PRICE_THRESHOLD
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oi_up = oi_change_pct > OI_OI_THRESHOLD
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oi_down = oi_change_pct < -OI_OI_THRESHOLD
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if price_up and oi_up:
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return OIState.NEW_LONGS
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elif price_up and oi_down:
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return OIState.SHORT_COVERING
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elif price_down and oi_up:
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return OIState.NEW_SHORTS
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elif price_down and oi_down:
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return OIState.LONG_EXIT
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else:
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return OIState.NEUTRAL
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@staticmethod
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def _build_narrative(state: OIState, price_chg: float, oi_chg: float) -> str:
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mapping = {
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OIState.NEW_LONGS: f"新多进场: 价格+{price_chg:.1f}%, OI+{oi_chg:.1f}%, 真金白银推动",
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OIState.SHORT_COVERING: f"空头回补: 价格+{price_chg:.1f}%, OI{oi_chg:.1f}%, 上涨脆弱",
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OIState.NEW_SHORTS: f"新空进场: 价格{price_chg:.1f}%, OI+{oi_chg:.1f}%, 趋势延续",
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OIState.LONG_EXIT: f"多头止损: 价格{price_chg:.1f}%, OI{oi_chg:.1f}%, 恐慌(可能见底)",
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OIState.NEUTRAL: "OI/价格变化不显著, 噪音区",
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}
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return mapping.get(state, "Unknown")
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