Rekey
4294ec401d
- engine/indicators/regime.py: RegimeDetector(四法投票) + MultiTimeframeRegime(多周期并行) 四法: EMA200斜率 / 价格vsEMA200 / ATH回撤 / 窄幅盘整(<3%振幅) 全部 O(1)/bar 增量计算,适用于回测和实时 - engine/example/regime_display.py: 多周期牛熊矩阵展示脚本 独立加载各周期数据 → 运行判定 → 日线对齐矩阵 + 详细拆解 + 统计 输出 engine/backtest/REGIME_MATRIX_BTCUSDT.md - engine/example/regime_mtf_strategy.py: 多周期共识策略 + 四策略对比回测 MTF Consensus: 1w定方向 + 1d确认 + 4h EMA入场 vs Old Regime(单TF基线) vs Long/Short(无过滤) - engine/indicators/__init__.py: 导出 RegimeDetector, MultiTimeframeRegime
269 lines
8.7 KiB
Python
269 lines
8.7 KiB
Python
"""
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多周期牛熊判定模块
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基于 EMA200 斜率、价格 vs EMA200、ATH 回撤三法投票，支持单周期判定
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和多周期并行分析。每根 bar O(1) 增量更新，适用于回测和实时分析。
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用法:
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# 单周期
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det = RegimeDetector()
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for price in closes:
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det.update(price)
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regime = det.detect(closes[-1], len(closes) - 1)
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# 多周期
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mtf = MultiTimeframeRegime(['1h', '4h', '1d', '1w'])
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for price in closes:
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mtf.update(price)
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matrix = mtf.detect_all(closes[-1], len(closes) - 1)
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"""
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from .incremental import EmaInc
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class RegimeDetector:
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"""单周期牛熊判定器 — 四法投票
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四个独立判定维度：
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1. EMA200 斜率：近 20 根 bar 的 EMA200 变化率 → bull/bear/sideways
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2. 价格 vs EMA200：当前价在 EMA200 上方/下方 → bull/bear
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3. ATH 回撤：当前价距历史高点的回撤幅度 → bull/bear/sideways
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4. 窄幅盘整：近 20 根 bar 收盘价振幅 < 3% → sideways（否则 unknown，不投票）
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最终判定：四选二投票（>=2 票即定论）。
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"""
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def __init__(self, range_period: int = 20, range_threshold: float = 0.03):
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self._ath = 0.0
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self._e200 = EmaInc(200)
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self._range_period = range_period
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self._range_threshold = range_threshold
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self._recent_high: list[float] = []
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self._recent_low: list[float] = []
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def update(self, price: float) -> None:
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"""每根 bar 调一次：更新 ATH + EMA200 + 价格区间窗口"""
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if price > self._ath:
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self._ath = price
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self._e200.update(price)
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# 滚动窗口：维持最近 range_period 根 bar 的最高/最低（只用 close 做代理）
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self._recent_high.append(price)
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self._recent_low.append(price)
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if len(self._recent_high) > self._range_period:
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self._recent_high.pop(0)
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self._recent_low.pop(0)
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def detect(self, price: float, idx: int) -> str:
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"""返回当前 bar 的牛熊判定：bull / bear / sideways"""
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r1 = self._ema200_slope(idx)
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r2 = self._price_vs_ema200(price, idx)
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r3 = self._ath_drawdown(price)
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r4 = self._price_range()
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b = sum(1 for r in [r1, r2, r3, r4] if r == "bull")
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br = sum(1 for r in [r1, r2, r3, r4] if r == "bear")
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sx = sum(1 for r in [r1, r2, r3, r4] if r == "sideways")
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if b >= 2:
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return "bull"
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if br >= 2:
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return "bear"
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if sx >= 2:
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return "sideways"
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return "sideways"
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def detect_detail(self, price: float, idx: int) -> dict:
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"""返回详细判定（含各子法的结果 + 三态票数）"""
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r1 = self._ema200_slope(idx)
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r2 = self._price_vs_ema200(price, idx)
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r3 = self._ath_drawdown(price)
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r4 = self._price_range()
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b = sum(1 for r in [r1, r2, r3, r4] if r == "bull")
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br = sum(1 for r in [r1, r2, r3, r4] if r == "bear")
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sx = sum(1 for r in [r1, r2, r3, r4] if r == "sideways")
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if b >= 2:
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final = "bull"
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elif br >= 2:
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final = "bear"
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elif sx >= 2:
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final = "sideways"
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else:
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final = "sideways"
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return {
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"final": final,
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"ema200_slope": r1,
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"price_vs_ema200": r2,
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"ath_drawdown": r3,
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"price_range": r4,
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"bull_votes": b,
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"bear_votes": br,
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"sideways_votes": sx,
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"range_pct": self._range_pct(),
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}
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def _ema200_slope(self, idx: int) -> str:
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if idx < 220:
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return "unknown"
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e200 = self._e200
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if e200[idx - 20] == 0:
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return "unknown"
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slope = (e200[idx] - e200[idx - 20]) / e200[idx - 20]
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if slope > 0.002:
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return "bull"
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if slope < -0.002:
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return "bear"
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return "sideways"
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def _price_vs_ema200(self, price: float, idx: int) -> str:
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if idx < 210:
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return "unknown"
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e = self._e200[idx]
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if e == 0:
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return "unknown"
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return "bull" if price > e else "bear"
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def _ath_drawdown(self, price: float) -> str:
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if self._ath == 0:
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return "unknown"
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dd = (price - self._ath) / self._ath
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if dd > -0.20:
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return "bull"
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if dd < -0.40:
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return "bear"
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return "sideways"
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def _price_range(self) -> str:
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"""窄幅盘整检测：近 range_period 根 bar 振幅 < 阈值 → sideways"""
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if len(self._recent_high) < self._range_period:
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return "unknown"
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highest = max(self._recent_high)
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lowest = min(self._recent_low)
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if highest == 0:
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return "unknown"
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range_pct = (highest - lowest) / highest
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return "sideways" if range_pct < self._range_threshold else "unknown"
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def _range_pct(self) -> float:
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"""当前波动区间百分比（用于展示）"""
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if len(self._recent_high) < self._range_period:
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return 0.0
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highest = max(self._recent_high)
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if highest == 0:
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return 0.0
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return (highest - min(self._recent_low)) / highest
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@property
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def ema200(self) -> float:
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"""最新 EMA200 值"""
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return self._e200.current
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@property
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def ath(self) -> float:
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"""历史最高价"""
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return self._ath
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@property
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def ready(self) -> bool:
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"""是否已有足够数据做判定"""
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return len(self._e200) >= 220
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class MultiTimeframeRegime:
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"""多周期牛熊并行分析
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管理多个 RegimeDetector 实例，每个对应一个周期。
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所有检测器共用同一价格序列更新，保证 O(1)/bar 的增量计算。
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Attributes:
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timeframes: 周期列表（如 ['1h', '4h', '1d', '1w']）
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detectors: 周期 → RegimeDetector 映射
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_prices: 价格序列（所有检测器共享）
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"""
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def __init__(self, timeframes: list[str]):
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self.timeframes = timeframes
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self.detectors: dict[str, RegimeDetector] = {
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tf: RegimeDetector() for tf in timeframes
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}
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self._prices: list[float] = []
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def update(self, price: float) -> None:
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"""更新所有周期检测器"""
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self._prices.append(price)
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for det in self.detectors.values():
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det.update(price)
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def detect_all(self) -> dict[str, str]:
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"""返回各周期当前判定（需至少 220 根 bar）"""
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n = len(self._prices)
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if n < 220:
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return {tf: "unknown" for tf in self.timeframes}
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price = self._prices[-1]
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return {
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tf: self.detectors[tf].detect(price, n - 1)
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for tf in self.timeframes
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}
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def detect_all_detail(self) -> dict[str, dict]:
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"""返回各周期详细判定"""
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n = len(self._prices)
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if n < 220:
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return {tf: {"final": "unknown"} for tf in self.timeframes}
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price = self._prices[-1]
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return {
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tf: self.detectors[tf].detect_detail(price, n - 1)
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for tf in self.timeframes
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}
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def detect_at(self, idx: int) -> dict[str, str]:
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"""返回指定 bar 位置各周期的判定"""
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if idx < 0 or idx >= len(self._prices):
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raise IndexError(f"idx {idx} out of range [0, {len(self._prices)})")
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price = self._prices[idx]
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result = {}
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for tf in self.timeframes:
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if idx < 220:
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result[tf] = "unknown"
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else:
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result[tf] = self.detectors[tf].detect(price, idx)
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return result
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def score(self) -> float:
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"""加权综合评分 [-1, 1]
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各周期赋分：bull=+1, sideways=0, bear=-1
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权重：周期越长权重越大
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Returns:
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-1.0 (强熊) ~ +1.0 (强牛)
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"""
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n = len(self._prices)
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if n < 220:
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return 0.0
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# 周期权重：1h=1, 4h=2, 1d=3, 1w=4, 1mon=5
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tf_weights = {
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"1m": 1, "3m": 1, "5m": 1, "15m": 2, "30m": 2,
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"1h": 3, "2h": 3, "4h": 4, "6h": 4, "8h": 4,
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"1d": 5, "1w": 6, "1mon": 7,
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}
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score_map = {"bull": 1.0, "sideways": 0.0, "bear": -1.0, "unknown": 0.0}
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regimes = self.detect_all()
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total_weight = 0.0
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weighted_score = 0.0
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for tf, regime in regimes.items():
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w = tf_weights.get(tf, 1)
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weighted_score += score_map[regime] * w
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total_weight += w
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if total_weight == 0:
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return 0.0
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return weighted_score / total_weight
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@property
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def ready(self) -> bool:
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"""所有检测器是否都已就绪"""
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return len(self._prices) >= 220
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