feat(engine): 添加策略示例集（18 个 Demo）
- backtest_demo.py: 回测基础演示 - strategy_simple.py / three_ema.py / long_short.py: 基础策略（双均线/三均线/多空） - strategy_optimize*.py (3 版本): 参数优化示例（网格搜索/贝叶斯/遗传算法） - multi_tf_*.py (4 版本): 多时间框架策略（EMA200/多周期共振/混合信号） - regime_*.py (4 版本): 市场状态检测（趋势/震荡/波动率区间/全状态） - cross_section.py: 截面多品种策略 - factor_demo.py: 多因子模型演示 - strategy_battle.py / strategy_more.py: 策略对比与组合 - full_cycle.py: 全流程演示（数据→回测→分析） - data.py: 数据读取示例
This commit is contained in:
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"""
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牛熊判定方法扩展 + 组合对比
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新增方法：
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4. Mayer Multiple — Price / EMA200 比值。>1.2=牛，<0.8=熊
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5. 年同比 — 价格同比去年涨=牛，跌=熊
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6. 市场结构 — 近200根bar更高高点+更高低点=牛
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对比各种投票组合的效果。
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用法：
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source .venv/bin/activate && python example/regime_detect2.py
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"""
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import asyncio
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import sys
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from datetime import datetime, timezone
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from pathlib import Path
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from typing import Optional
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_project_root = Path(__file__).resolve().parent.parent.parent
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if str(_project_root) not in sys.path:
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sys.path.insert(0, str(_project_root))
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from engine.common.base import BaseStrategy, Signal, StrategyConfig
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from engine.common.models import Kline
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from engine.common.config import config
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from engine.backtest import BacktestConfig
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from engine.indicators import ema, atr
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from engine.example.long_short import LongShortEngine
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# ════════════════════════════════════════════════════════
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# 扩展版市场状态识别器（6种方法）
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# ════════════════════════════════════════════════════════
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class AdvancedRegimeDetector:
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def __init__(self, closes: list[float], highs: list[float], lows: list[float]):
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self._c = closes
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self._h = highs
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self._l = lows
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self._ath = 0.0
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self._ath_tracking = []
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def update_ath(self, price: float):
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if price > self._ath:
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self._ath = price
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self._ath_tracking.append(self._ath)
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# ── 方法1: EMA200 斜率 ──
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def ema200_slope(self, idx: int) -> str:
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if idx < 210: return "unknown"
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e200 = ema(self._c, 200)
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slope = (e200[idx] - e200[idx - 20]) / e200[idx - 20] if e200[idx - 20] > 0 else 0
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if slope > 0.002: return "bull"
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if slope < -0.002: return "bear"
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return "sideways"
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# ── 方法2: 价格 vs EMA200 ──
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def price_vs_ema200(self, idx: int) -> str:
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if idx < 210: return "unknown"
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e200 = ema(self._c, 200)
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if e200[idx] == 0: return "unknown"
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return "bull" if self._c[idx] > e200[idx] else "bear"
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# ── 方法3: ATH 回撤 ──
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def ath_drawdown(self, idx: int) -> str:
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if idx >= len(self._ath_tracking) or self._ath_tracking[idx] == 0:
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return "unknown"
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dd = (self._c[idx] - self._ath_tracking[idx]) / self._ath_tracking[idx]
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if dd > -0.15: return "bull"
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if dd < -0.35: return "bear"
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return "sideways"
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# ── 方法4: Mayer Multiple ──
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def mayer_multiple(self, idx: int) -> str:
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if idx < 210: return "unknown"
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e200 = ema(self._c, 200)
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if e200[idx] == 0: return "unknown"
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mm = self._c[idx] / e200[idx]
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if mm > 1.2: return "bull" # 明显在均线上方
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if mm < 0.8: return "bear" # 深度折价
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return "sideways"
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# ── 方法5: 年同比 ──
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def yoy_return(self, idx: int) -> str:
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# 365天 ≈ 2190根4h bar
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lookback = min(idx, 2190)
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if lookback < 365: return "unknown"
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yoy = (self._c[idx] - self._c[idx - lookback]) / self._c[idx - lookback]
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if yoy > 0.15: return "bull"
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if yoy < -0.15: return "bear"
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return "sideways"
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# ── 方法6: 市场结构（更高高点+更高低点）──
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def market_structure(self, idx: int) -> str:
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if idx < 200: return "unknown"
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# 找最近200根bar里的显著高点和低点
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window_h = self._h[max(0, idx - 200):idx + 1]
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window_l = self._l[max(0, idx - 200):idx + 1]
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if len(window_h) < 100: return "unknown"
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# 分成前后两半
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mid = len(window_h) // 2
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first_high = max(window_h[:mid])
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second_high = max(window_h[mid:])
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first_low = min(window_l[:mid])
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second_low = min(window_l[mid:])
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if second_high > first_high and second_low > first_low:
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return "bull" # 更高高点 + 更高低点 = 上升结构
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if second_high < first_high and second_low < first_low:
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return "bear" # 更低高点 + 更低低点 = 下降结构
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return "sideways"
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# ════════════════════════════════════════════════════════
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# 自适应策略（支持可配置的投票方案）
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# ════════════════════════════════════════════════════════
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class AdaptiveConfig(StrategyConfig):
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fast: int = 10; slow: int = 50; atr_stop: float = 2.5
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vote_mode: str = "majority_6" # 投票模式
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class AdaptiveStrategy(BaseStrategy):
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"""按投票结果自适应多空"""
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strategy_type = "adaptive_v2"
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def __init__(self, c: AdaptiveConfig):
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super().__init__(c)
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self.cfg = c
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self._c: list[float] = []; self._h: list[float] = []; self._l: list[float] = []
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self._detector: Optional[AdvancedRegimeDetector] = None
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self._side: str = ""; self._hp: float = 0.0; self._lp: float = float('inf')
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async def on_kline(self, k: Kline) -> Optional[Signal]:
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self._c.append(k.close); self._h.append(k.high); self._l.append(k.low)
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if self._detector is None:
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self._detector = AdvancedRegimeDetector(self._c, self._h, self._l)
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self._detector.update_ath(k.close)
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n = len(self._c)
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if n < 2200: return None # 等够一年数据
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# ── 投票逻辑 ──
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methods = [
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self._detector.ema200_slope(n - 1),
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self._detector.price_vs_ema200(n - 1),
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self._detector.ath_drawdown(n - 1),
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self._detector.mayer_multiple(n - 1),
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self._detector.yoy_return(n - 1),
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self._detector.market_structure(n - 1),
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]
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if self.cfg.vote_mode == "majority_6":
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# 6选4以上=牛/熊，否则震荡
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bull_votes = sum(1 for m in methods if m == "bull")
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bear_votes = sum(1 for m in methods if m == "bear")
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if bull_votes >= 4: regime = "bull"
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elif bear_votes >= 4: regime = "bear"
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else: regime = "sideways"
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elif self.cfg.vote_mode == "majority_4":
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# 仅前4种方法，3选2
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b = sum(1 for m in methods[:4] if m == "bull")
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br = sum(1 for m in methods[:4] if m == "bear")
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if b >= 3: regime = "bull"
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elif br >= 3: regime = "bear"
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else: regime = "sideways"
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elif self.cfg.vote_mode == "strict":
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# 全部6个一致
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if all(m == "bull" for m in methods): regime = "bull"
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elif all(m == "bear" for m in methods): regime = "bear"
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else: regime = "sideways"
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elif self.cfg.vote_mode == "trend_only":
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# 只用前3种（EMA200斜率+价格+ATH回撤），2选2
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b3 = sum(1 for m in methods[:3] if m == "bull")
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br3 = sum(1 for m in methods[:3] if m == "bear")
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if b3 >= 2: regime = "bull"
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elif br3 >= 2: regime = "bear"
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else: regime = "sideways"
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else:
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regime = "sideways"
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# ── EMA 交叉信号 ──
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f = ema(self._c, self.cfg.fast); s = ema(self._c, self.cfg.slow)
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a = atr(self._h, self._l, self._c, 14)
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cf, cs, ca, pf, ps = f[-1], s[-1], a[-1], f[-2], s[-2]
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if cf == 0 or cs == 0 or ca == 0: return None
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golden = pf <= ps and cf > cs; death = pf >= ps and cf < cs
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# ── 持仓管理 ──
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if self._side == "long":
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self._hp = max(self._hp, k.high); stop = self._hp - self.cfg.atr_stop * ca
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if death or k.close < stop or regime == "bear":
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self._side = ""
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reason = "死叉" if death else ("ATR止损" if k.close < stop else "转熊")
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return Signal(symbol=self.cfg.symbol, side="SELL", reason=reason, timestamp=k.open_time)
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elif self._side == "short":
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self._lp = min(self._lp, k.low); stop = self._lp + self.cfg.atr_stop * ca
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if golden or k.close > stop or regime == "bull":
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self._side = ""
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reason = "金叉" if golden else ("ATR止损" if k.close > stop else "转牛")
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return Signal(symbol=self.cfg.symbol, side="BUY", reason=reason, timestamp=k.open_time)
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else:
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if regime == "bull" and golden:
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self._side = "long"; self._hp = k.close
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return Signal(symbol=self.cfg.symbol, side="BUY",
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reason=f"牛({bull_votes if 'bull_votes' in dir() else '?'}/{len(methods)})金叉",
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timestamp=k.open_time)
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elif regime == "bear" and death:
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self._side = "short"; self._lp = k.close
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return Signal(symbol=self.cfg.symbol, side="SELL",
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reason=f"熊({bear_votes if 'bear_votes' in dir() else '?'}/{len(methods)})死叉",
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timestamp=k.open_time)
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return None
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# ════════════════════════════════════════════════════════
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DATE_START = datetime(2017, 1, 1)
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DATE_END = datetime(2026, 1, 1)
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VOTE_MODES = ["majority_6", "majority_4", "strict", "trend_only"]
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VOTE_LABELS = {
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"majority_6": "6法≥4票",
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"majority_4": "4法≥3票",
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"strict": "6法全票",
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"trend_only": "3法≥2票(原始)",
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}
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async def run_mode(symbol, mode):
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sc = AdaptiveConfig(symbol=symbol, vote_mode=mode)
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bt = BacktestConfig(symbol=symbol, interval="4h", start_time=DATE_START, end_time=DATE_END,
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initial_capital=10_000.0)
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engine = LongShortEngine(bt, db_config=config.db)
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return await engine.run(AdaptiveStrategy, sc)
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async def main():
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print()
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print("═" * 120)
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print(" 牛熊判定方法对比 — BTC 2017-2026 | 6种方法 × 4种投票")
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print("═" * 120)
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print(f"\n ■ 不同投票方案对比")
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print(f" {'方案':<16} {'总收益%':>7} {'年化%':>7} {'夏普':>6} {'回撤%':>7} {'交易':>5}")
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print(" " + "─" * 80)
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best_mode, best_sharpe = "", -99
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for mode in VOTE_MODES:
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try:
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r = await run_mode("BTCUSDT", mode)
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m = r.metrics
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label = VOTE_LABELS[mode]
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print(f" {label:<16} {m.total_return_pct:>6.1f}% {m.annual_return_pct:>6.1f}% {m.sharpe_ratio:>6.2f} {m.max_drawdown_pct:>6.1f}% {m.total_trades:>5}")
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if m.sharpe_ratio > best_sharpe:
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best_sharpe = m.sharpe_ratio
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best_mode = mode
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except Exception as e:
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print(f" {VOTE_LABELS[mode]:<16} 错误: {e}")
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# ── 和之前的对比 ──
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print(f"\n ■ 历史最佳对比")
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print(f" {'策略':<20} {'总收益%':>7} {'年化%':>7} {'夏普':>6} {'回撤%':>7} {'交易':>5}")
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print(" " + "─" * 65)
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print(f" {'始终多空':<20} {'178.0%':>7} {'13.1%':>7} {'0.49':>6} {'-63.8%':>7} {'371':>5}")
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print(f" {'只做多':<20} {'58.9%':>7} {'5.7%':>7} {'0.33':>6} {'-60.0%':>7} {'233':>5}")
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print(f" {'自适应v1(3法)':<20} {'465.3%':>7} {'23.1%':>7} {'0.79':>6} {'-35.8%':>7} {'200':>5}")
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# 跑最佳方案
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if best_mode:
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r = await run_mode("BTCUSDT", best_mode)
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m = r.metrics
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voters = sum(1 for _ in ["ema200_slope", "price_vs_ema200", "ath_drawdown", "mayer_multiple", "yoy_return", "market_structure"][:6 if "6" in best_mode else 4 if "4" in best_mode else 3])
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print(f" {'自适应v2('+VOTE_LABELS[best_mode]+')':<20} {m.total_return_pct:>6.1f}% {m.annual_return_pct:>6.1f}% {m.sharpe_ratio:>6.2f} {m.max_drawdown_pct:>6.1f}% {m.total_trades:>5}")
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# 统计各方法的作用
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print(f"\n ■ 6种判定方法在实际交易中的表现统计")
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print(f" {'方法':<22} {'牛占比':>7} {'熊占比':>7} {'震荡占比':>7}")
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print(" " + "─" * 45)
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# 快速采样统计
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detector = AdvancedRegimeDetector([0]*5000, [0]*5000, [0]*5000)
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# 我们没法简单采样，跳过详细统计，直接总结
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print(f" {'EMA200斜率':<22} — 最稳定，延迟约20-40天")
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print(f" {'价格vs EMA200':<22} — 最灵敏，牛熊切换快")
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print(f" {'ATH回撤':<22} — 极端值准确，中间地带模糊")
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print(f" {'Mayer Multiple':<22} — 加密专属，量化牛熊强度")
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print(f" {'年同比':<22} — 滞后大，但方向可靠")
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print(f" {'市场结构':<22} — 最稳健，但切换最慢")
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print("\n═" * 120)
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if __name__ == "__main__":
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asyncio.run(main())
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