feat(ECR-009): Crypto Wyckoff Screener 独立页（D/W/M）

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Co-authored-by: Cursor <cursoragent@cursor.com>

crypto_wyckoff/cycle.py

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+"""Cycle Engine — monthly/weekly macro cycle via Rule Registry."""
+
+from __future__ import annotations
+
+from crypto_wyckoff.domain_models import EngineResult, WyckoffCycle
+from crypto_wyckoff.rules.base import RuleHit
+from crypto_wyckoff.rules.registry import rule_registry
+
+
+def _resolve_range_conflict(hits: list[RuleHit], features: dict) -> list[RuleHit]:
+    """Accumulation vs Distribution overlap → mutually exclusive by MA120 position."""
+    accum = [h for h in hits if h.cycle == WyckoffCycle.ACCUMULATION.value]
+    dist = [h for h in hits if h.cycle == WyckoffCycle.DISTRIBUTION.value]
+    if not (accum and dist):
+        return hits
+
+    close = float(features.get("close") or 0)
+    ma120 = float(features.get("ma120") or close) or close
+    others = [
+        h for h in hits
+        if h.cycle not in (WyckoffCycle.ACCUMULATION.value, WyckoffCycle.DISTRIBUTION.value)
+    ]
+    # Below MA120 → accumulation; above → distribution; equal band uses relative position
+    if close < ma120 * 0.995:
+        return others + accum
+    if close > ma120 * 1.005:
+        return others + dist
+    # Tight band: keep higher confidence only
+    best_a = max(accum, key=lambda h: h.confidence)
+    best_d = max(dist, key=lambda h: h.confidence)
+    return others + ([best_a] if best_a.confidence >= best_d.confidence else [best_d])
+
+
+class CycleEngine:
+    name = "Cycle"
+    version = "1.0.0"
+
+    def run(self, feature: EngineResult, timeframe: str) -> EngineResult:
+        features = feature.payload
+        if features.get("insufficient"):
+            return EngineResult(
+                name=self.name,
+                version=self.version,
+                confidence=15.0,
+                score=40.0,
+                reasons=[f"{timeframe} 数据不足，Cycle=Unknown"],
+                warnings=["insufficient_features"],
+                payload={
+                    "cycle": WyckoffCycle.UNKNOWN.value,
+                    "timeframe": timeframe,
+                    "trend_score": 40.0,
+                },
+            )
+
+        context = {"features": features, "timeframe": timeframe}
+        hits: list[RuleHit] = []
+        for rule in rule_registry.by_category("cycle", timeframe):
+            hit = rule.evaluate(context)
+            if hit and hit.cycle:
+                hits.append(hit)
+
+        hits = _resolve_range_conflict(hits, features)
+
+        if not hits:
+            return EngineResult(
+                name=self.name,
+                version=self.version,
+                confidence=30.0,
+                score=40.0,
+                reasons=["无匹配周期规则，标记 Unknown"],
+                payload={
+                    "cycle": WyckoffCycle.UNKNOWN.value,
+                    "timeframe": timeframe,
+                    "trend_score": 40.0,
+                },
+            )
+
+        best = max(hits, key=lambda h: h.confidence)
+        trend_score = best.score
+        if best.cycle == WyckoffCycle.MARKUP.value:
+            trend_score = max(trend_score, 75.0)
+        elif best.cycle == WyckoffCycle.ACCUMULATION.value:
+            trend_score = max(60.0, trend_score * 0.9)
+        elif best.cycle == WyckoffCycle.DISTRIBUTION.value:
+            trend_score = min(45.0, 100 - trend_score * 0.5)
+        elif best.cycle == WyckoffCycle.MARKDOWN.value:
+            trend_score = min(30.0, 100 - trend_score)
+
+        return EngineResult(
+            name=self.name,
+            version=self.version,
+            confidence=best.confidence,
+            score=trend_score,
+            reasons=best.reasons,
+            metrics=best.metrics,
+            payload={
+                "cycle": best.cycle,
+                "timeframe": timeframe,
+                "rule_id": best.rule_id,
+                "trend_score": trend_score,
+            },
+        )