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>
127 lines
4.4 KiB
Python
127 lines
4.4 KiB
Python
"""Cycle classification rules (monthly / weekly)."""
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from __future__ import annotations
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from typing import Any
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from crypto_wyckoff.domain_models import WyckoffCycle
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from crypto_wyckoff.rules.base import RuleHit, WyckoffRule
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def _f(ctx: dict[str, Any], key: str, default: float = 0.0) -> float:
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v = ctx.get("features", {}).get(key, default)
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try:
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return float(v) if v is not None else default
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except (TypeError, ValueError):
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return default
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class MarkupCycleRule(WyckoffRule):
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rule_id = "cycle_markup"
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category = "cycle"
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timeframes = ("1M", "1w")
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def evaluate(self, context: dict[str, Any]) -> RuleHit | None:
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close = _f(context, "close")
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ma20 = _f(context, "ma20")
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ma60 = _f(context, "ma60")
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ma120 = _f(context, "ma120")
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adx = _f(context, "adx")
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slope = _f(context, "ma60_slope")
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if close > ma20 > ma60 and (ma60 >= ma120 or slope > 0) and adx >= 18:
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conf = min(95.0, 55 + adx + (10 if close > ma120 else 0))
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return RuleHit(
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rule_id=self.rule_id,
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cycle=WyckoffCycle.MARKUP.value,
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confidence=conf,
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score=conf,
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reasons=["价格位于均线多头排列", f"ADX={adx:.1f}"],
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metrics={"adx": adx, "slope": slope},
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)
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return None
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class MarkdownCycleRule(WyckoffRule):
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rule_id = "cycle_markdown"
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category = "cycle"
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timeframes = ("1M", "1w")
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def evaluate(self, context: dict[str, Any]) -> RuleHit | None:
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close = _f(context, "close")
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ma20 = _f(context, "ma20")
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ma60 = _f(context, "ma60")
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ma120 = _f(context, "ma120")
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adx = _f(context, "adx")
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slope = _f(context, "ma60_slope")
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if close < ma20 < ma60 and (ma60 <= ma120 or slope < 0) and adx >= 18:
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conf = min(95.0, 55 + adx + (10 if close < ma120 else 0))
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return RuleHit(
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rule_id=self.rule_id,
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cycle=WyckoffCycle.MARKDOWN.value,
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confidence=conf,
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score=conf,
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reasons=["价格位于均线空头排列", f"ADX={adx:.1f}"],
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metrics={"adx": adx},
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)
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return None
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class AccumulationCycleRule(WyckoffRule):
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rule_id = "cycle_accumulation"
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category = "cycle"
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timeframes = ("1M", "1w")
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def evaluate(self, context: dict[str, Any]) -> RuleHit | None:
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adx = _f(context, "adx")
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range_pct = _f(context, "range_pct_60")
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close = _f(context, "close")
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ma120 = _f(context, "ma120")
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vol_trend = _f(context, "volume_trend")
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# Range-bound after decline: strictly at/below MA120 (mutually exclusive vs Distribution)
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if adx < 22 and range_pct < 0.28 and close <= ma120:
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conf = 60 + (10 if vol_trend > 0 else 0) + (10 if close < ma120 else 0)
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return RuleHit(
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rule_id=self.rule_id,
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cycle=WyckoffCycle.ACCUMULATION.value,
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confidence=min(90.0, conf),
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score=min(90.0, conf),
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reasons=["低趋势强度区间震荡", "疑似吸筹区间"],
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metrics={"adx": adx, "range_pct_60": range_pct},
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)
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return None
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class DistributionCycleRule(WyckoffRule):
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rule_id = "cycle_distribution"
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category = "cycle"
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timeframes = ("1M", "1w")
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def evaluate(self, context: dict[str, Any]) -> RuleHit | None:
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adx = _f(context, "adx")
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range_pct = _f(context, "range_pct_60")
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close = _f(context, "close")
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ma120 = _f(context, "ma120")
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vol_trend = _f(context, "volume_trend")
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# Range-bound near highs: strictly above MA120 (mutually exclusive vs Accumulation)
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if adx < 22 and range_pct < 0.28 and close > ma120:
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conf = 60 + (10 if vol_trend < 0 else 0) + (10 if close > ma120 else 0)
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return RuleHit(
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rule_id=self.rule_id,
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cycle=WyckoffCycle.DISTRIBUTION.value,
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confidence=min(90.0, conf),
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score=min(90.0, conf),
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reasons=["高位低趋势震荡", "疑似派发区间"],
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metrics={"adx": adx, "range_pct_60": range_pct},
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)
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return None
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def build_rules() -> list[WyckoffRule]:
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# Order: trend cycles first (more decisive), then range cycles
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return [
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MarkupCycleRule(),
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MarkdownCycleRule(),
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AccumulationCycleRule(),
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DistributionCycleRule(),
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]
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