Chan/crypto_wyckoff/decision.py

"""Decision Engine — multi-timeframe fusion and tradability (Architecture v1.0)."""

from __future__ import annotations

from crypto_wyckoff.domain_models import (
    DecisionSignal,
    EngineResult,
    RiskLevel,
    WyckoffCycle,
    WyckoffEvent,
    WyckoffPhase,
)

BULL_CYCLES = {
    WyckoffCycle.ACCUMULATION.value,
    WyckoffCycle.RE_ACCUMULATION.value,
    WyckoffCycle.MARKUP.value,
}
BEAR_CYCLES = {
    WyckoffCycle.DISTRIBUTION.value,
    WyckoffCycle.RE_DISTRIBUTION.value,
    WyckoffCycle.MARKDOWN.value,
}


class DecisionEngine:
    name = "Decision"
    version = "1.0.0"

    def run(
        self,
        monthly_cycle: EngineResult,
        weekly_cycle: EngineResult,
        weekly_phase: EngineResult,
        weekly_event: EngineResult,
        daily_event: EngineResult,
        daily_signal: EngineResult,
    ) -> EngineResult:
        m_cycle = monthly_cycle.payload.get("cycle", WyckoffCycle.UNKNOWN.value)
        w_cycle = weekly_cycle.payload.get("cycle", WyckoffCycle.UNKNOWN.value)
        w_phase = weekly_phase.payload.get("phase", WyckoffPhase.NONE.value)
        w_event = weekly_event.payload.get("current_event", WyckoffEvent.NONE.value)
        d_event = daily_event.payload.get("current_event", WyckoffEvent.NONE.value)

        trend_score = float(monthly_cycle.payload.get("trend_score", monthly_cycle.score))
        structure_score = float(weekly_phase.payload.get("structure_score", weekly_phase.score))
        entry_score = float(daily_event.payload.get("entry_score", daily_event.score))

        overall_score = 0.30 * trend_score + 0.30 * structure_score + 0.40 * entry_score

        reasons: list[str] = []
        warnings: list[str] = []
        alignment = 50.0

        m_bull = m_cycle in BULL_CYCLES
        m_bear = m_cycle in BEAR_CYCLES
        w_bull = w_cycle in BULL_CYCLES
        d_bullish_event = d_event in {
            WyckoffEvent.SPRING.value,
            WyckoffEvent.TEST.value,
            WyckoffEvent.SOS.value,
            WyckoffEvent.LPS.value,
            WyckoffEvent.JUMP.value,
            WyckoffEvent.BACKUP.value,
        }
        d_bearish_event = d_event in {
            WyckoffEvent.UTAD.value,
            WyckoffEvent.SOW.value,
            WyckoffEvent.LPSY.value,
        }

        # Alignment scoring
        if m_bull and w_bull and d_bullish_event:
            alignment = 92.0
            reasons.append("✓ 月/周多头结构与日线多头事件一致")
        elif m_bull and d_bullish_event:
            alignment = 78.0
            reasons.append("✓ 月线支持，日线有入场事件")
            if not w_bull:
                warnings.append("周线结构未完全确认")
                alignment -= 8
        elif m_bear and d_bullish_event:
            alignment = 35.0
            reasons.append("✗ 月线派发/下跌，日线弹簧可能只是反弹")
        elif m_bear and d_bearish_event:
            alignment = 85.0
            reasons.append("✓ 空头多周期一致")
        else:
            alignment = 55.0
            reasons.append("○ 多周期部分一致，需观察")

        if w_phase in (WyckoffPhase.D.value, WyckoffPhase.E.value) and m_bull:
            alignment = min(98.0, alignment + 6)
            reasons.append(f"✓ 周线阶段 {w_phase} 结构成熟（{w_event}）")
        active = daily_event.payload.get("active_events") or daily_event.payload.get("recent_events") or []
        if d_event == WyckoffEvent.SPRING.value and len(active) >= 3:
            alignment = min(98.0, alignment + 4)
            reasons.append("✓ 日线多重事件同时确认")

        # Decision signal — hard gate on monthly bear + daily spring
        decision = DecisionSignal.WATCH.value
        risk = RiskLevel.MEDIUM.value

        if m_bear and d_event == WyckoffEvent.SPRING.value:
            decision = DecisionSignal.WATCH.value
            risk = RiskLevel.HIGH.value
            overall_score = min(overall_score, 55.0)
            reasons.append("→ 决策：观察（月线不支持，禁止追日线弹簧）")
        elif m_bear and d_bullish_event:
            decision = DecisionSignal.AVOID.value
            risk = RiskLevel.HIGH.value
            overall_score = min(overall_score, 48.0)
            reasons.append("→ 决策：回避（逆大周期多头事件）")
        elif (
            m_bull
            and w_phase in (WyckoffPhase.D.value, WyckoffPhase.E.value, WyckoffPhase.C.value)
            and d_event in (WyckoffEvent.SPRING.value, WyckoffEvent.LPS.value, WyckoffEvent.SOS.value)
            and alignment >= 85
            and overall_score >= 80
        ):
            decision = DecisionSignal.STRONG_BUY.value
            risk = RiskLevel.LOW.value
            reasons.append("→ 决策：强烈买入（三级共振）")
        elif m_bull and d_bullish_event and overall_score >= 68 and alignment >= 70:
            decision = DecisionSignal.BUY.value
            risk = RiskLevel.LOW.value if alignment >= 80 else RiskLevel.MEDIUM.value
            reasons.append("→ 决策：买入")
        elif m_bear and d_bearish_event and overall_score >= 65:
            decision = DecisionSignal.SELL.value
            risk = RiskLevel.MEDIUM.value
            reasons.append("→ 决策：卖出")
        else:
            decision = DecisionSignal.WATCH.value
            reasons.append("→ 决策：观察")

        # Stars from score + alignment
        combo = 0.6 * overall_score + 0.4 * alignment
        if combo >= 90:
            stars = 5
        elif combo >= 80:
            stars = 4
        elif combo >= 65:
            stars = 3
        elif combo >= 50:
            stars = 2
        else:
            stars = 1

        overall_confidence = (
            0.25 * monthly_cycle.confidence
            + 0.25 * weekly_phase.confidence
            + 0.25 * daily_event.confidence
            + 0.25 * daily_signal.confidence
        )
        # Weak event pulls overall down
        if daily_event.confidence < 60:
            overall_confidence = min(overall_confidence, daily_event.confidence + 15)

        return EngineResult(
            name=self.name,
            version=self.version,
            confidence=overall_confidence,
            score=overall_score,
            reasons=reasons,
            warnings=warnings,
            metrics={
                "trend_score": trend_score,
                "structure_score": structure_score,
                "entry_score": entry_score,
                "alignment": alignment,
                "stars": stars,
            },
            payload={
                "decision_signal": decision,
                "alignment": alignment,
                "stars": stars,
                "risk": risk,
                "overall_score": overall_score,
                "overall_confidence": overall_confidence,
                "trend_score": trend_score,
                "structure_score": structure_score,
                "entry_score": entry_score,
                "m_cycle": m_cycle,
                "w_cycle": w_cycle,
                "w_phase": w_phase,
                "w_event": w_event,
                "d_event": d_event,
                # Facts preserved — never overwritten
                "facts": {
                    "monthly": {"cycle": m_cycle},
                    "weekly": {"cycle": w_cycle, "phase": w_phase, "event": w_event},
                    "daily": {"event": d_event},
                },
            },
        )