jackyu66gitandCursor
ec08de098e
移植 A_Share_DP 引擎；本地缓存与 60s tip；月线由日线 UTC 聚合；不碰主站 analyze/缠论叠层。 Co-authored-by: Cursor <cursoragent@cursor.com>
103 lines
3.7 KiB
Python
103 lines
3.7 KiB
Python
"""Cycle Engine — monthly/weekly macro cycle via Rule Registry."""
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from __future__ import annotations
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from crypto_wyckoff.domain_models import EngineResult, WyckoffCycle
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from crypto_wyckoff.rules.base import RuleHit
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from crypto_wyckoff.rules.registry import rule_registry
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def _resolve_range_conflict(hits: list[RuleHit], features: dict) -> list[RuleHit]:
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"""Accumulation vs Distribution overlap → mutually exclusive by MA120 position."""
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accum = [h for h in hits if h.cycle == WyckoffCycle.ACCUMULATION.value]
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dist = [h for h in hits if h.cycle == WyckoffCycle.DISTRIBUTION.value]
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if not (accum and dist):
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return hits
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close = float(features.get("close") or 0)
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ma120 = float(features.get("ma120") or close) or close
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others = [
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h for h in hits
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if h.cycle not in (WyckoffCycle.ACCUMULATION.value, WyckoffCycle.DISTRIBUTION.value)
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]
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# Below MA120 → accumulation; above → distribution; equal band uses relative position
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if close < ma120 * 0.995:
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return others + accum
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if close > ma120 * 1.005:
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return others + dist
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# Tight band: keep higher confidence only
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best_a = max(accum, key=lambda h: h.confidence)
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best_d = max(dist, key=lambda h: h.confidence)
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return others + ([best_a] if best_a.confidence >= best_d.confidence else [best_d])
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class CycleEngine:
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name = "Cycle"
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version = "1.0.0"
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def run(self, feature: EngineResult, timeframe: str) -> EngineResult:
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features = feature.payload
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if features.get("insufficient"):
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return EngineResult(
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name=self.name,
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version=self.version,
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confidence=15.0,
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score=40.0,
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reasons=[f"{timeframe} 数据不足，Cycle=Unknown"],
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warnings=["insufficient_features"],
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payload={
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"cycle": WyckoffCycle.UNKNOWN.value,
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"timeframe": timeframe,
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"trend_score": 40.0,
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},
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)
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context = {"features": features, "timeframe": timeframe}
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hits: list[RuleHit] = []
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for rule in rule_registry.by_category("cycle", timeframe):
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hit = rule.evaluate(context)
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if hit and hit.cycle:
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hits.append(hit)
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hits = _resolve_range_conflict(hits, features)
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if not hits:
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return EngineResult(
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name=self.name,
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version=self.version,
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confidence=30.0,
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score=40.0,
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reasons=["无匹配周期规则，标记 Unknown"],
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payload={
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"cycle": WyckoffCycle.UNKNOWN.value,
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"timeframe": timeframe,
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"trend_score": 40.0,
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},
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)
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best = max(hits, key=lambda h: h.confidence)
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trend_score = best.score
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if best.cycle == WyckoffCycle.MARKUP.value:
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trend_score = max(trend_score, 75.0)
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elif best.cycle == WyckoffCycle.ACCUMULATION.value:
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trend_score = max(60.0, trend_score * 0.9)
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elif best.cycle == WyckoffCycle.DISTRIBUTION.value:
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trend_score = min(45.0, 100 - trend_score * 0.5)
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elif best.cycle == WyckoffCycle.MARKDOWN.value:
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trend_score = min(30.0, 100 - trend_score)
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return EngineResult(
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name=self.name,
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version=self.version,
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confidence=best.confidence,
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score=trend_score,
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reasons=best.reasons,
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metrics=best.metrics,
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payload={
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"cycle": best.cycle,
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"timeframe": timeframe,
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"rule_id": best.rule_id,
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"trend_score": trend_score,
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},
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)
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