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
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"""
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市场状态识别 — 牛市/熊市判定方法对比 + 自适应策略回测
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方法：
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1. EMA200 斜率 — EMA200 向上=牛，向下=熊
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2. 价格 vs EMA200 — Price > EMA200 = 牛
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3. ATH 回撤 — 距历史高点 < 20% = 牛，> 20% = 熊
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4. 综合投票 — 三选二
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根据识别结果自动偏多/偏空：
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牛市：只做多（金叉买入，死叉平仓）
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熊市：只做空（死叉做空，金叉平仓）
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震荡（票数2:1或无共识）：空仓等待
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BTC 2017-2026 全周期测试
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用法：
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source .venv/bin/activate && python example/regime_detect.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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# 市场状态识别器
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# ════════════════════════════════════════════════════════
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class RegimeDetector:
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"""市场状态识别：牛/熊/震荡"""
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def __init__(self, closes: list[float]):
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self._c = closes
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self._ath = 0.0
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self._ath_tracking = [] # 追踪历史高点序列
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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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def ema200_slope(self, idx: int) -> str:
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"""EMA200 斜率判定"""
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if idx < 202:
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return "unknown"
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e200 = ema(self._c, 200)
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# 最近5根EMA200的斜率
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if e200[idx] == 0 or e200[max(0, idx - 5)] == 0:
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return "unknown"
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slope = (e200[idx] - e200[max(0, idx - 5)]) / e200[max(0, idx - 5)]
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if slope > 0.001:
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return "bull"
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elif slope < -0.001:
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return "bear"
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return "sideways"
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def price_vs_ema200(self, idx: int) -> str:
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"""价格 vs EMA200"""
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if idx < 202:
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return "unknown"
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e200 = ema(self._c, 200)
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if e200[idx] == 0:
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return "unknown"
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return "bull" if self._c[idx] > e200[idx] else "bear"
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def ath_drawdown(self, idx: int) -> str:
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"""ATH 回撤判定（经典加密牛熊指标）"""
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if not self._ath_tracking or idx >= len(self._ath_tracking):
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return "unknown"
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curr_ath = self._ath_tracking[idx]
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if curr_ath == 0:
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return "unknown"
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dd = (self._c[idx] - curr_ath) / curr_ath
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if dd > -0.20:
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return "bull"
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elif dd < -0.40:
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return "bear"
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return "sideways"
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def combined(self, idx: int) -> tuple[str, str, str, str]:
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"""综合判定"""
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r1 = self.ema200_slope(idx)
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r2 = self.price_vs_ema200(idx)
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r3 = self.ath_drawdown(idx)
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votes = {"bull": 0, "bear": 0}
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for r in [r1, r2, r3]:
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if r in votes:
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votes[r] += 1
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if votes["bull"] >= 2:
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return "bull", r1, r2, r3
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elif votes["bear"] >= 2:
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return "bear", r1, r2, r3
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return "sideways", r1, r2, r3
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# ════════════════════════════════════════════════════════
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# 自适应策略（根据市场状态偏多/偏空）
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# ════════════════════════════════════════════════════════
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class AdaptiveEmaConfig(StrategyConfig):
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fast: int = 10
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slow: int = 50
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atr_stop: float = 2.5
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class AdaptiveEmaStrategy(BaseStrategy):
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"""牛市只做多、熊市只做空、震荡空仓"""
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strategy_type = "adaptive_ema"
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def __init__(self, c: AdaptiveEmaConfig):
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super().__init__(c)
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self.cfg = c
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self._closes: list[float] = []
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self._highs: list[float] = []
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self._lows: list[float] = []
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self._detector: Optional[RegimeDetector] = None
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self._position_side: str = "" # "long" / "short"
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self._highest: float = 0.0
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self._lowest: float = float('inf')
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async def on_kline(self, k: Kline) -> Optional[Signal]:
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self._closes.append(k.close)
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self._highs.append(k.high)
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self._lows.append(k.low)
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if self._detector is None:
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self._detector = RegimeDetector(self._closes)
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self._detector.update_ath(k.close)
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n = len(self._closes)
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if n < 210:
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return None
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regime, r1, r2, r3 = self._detector.combined(n - 1)
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fast = ema(self._closes, self.cfg.fast)
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slow = ema(self._closes, self.cfg.slow)
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atr_vals = atr(self._highs, self._lows, self._closes, 14)
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cur_f, cur_s, cur_atr = fast[-1], slow[-1], atr_vals[-1]
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prev_f, prev_s = fast[-2], slow[-2]
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if cur_f == 0 or cur_s == 0 or cur_atr == 0:
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return None
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golden = prev_f <= prev_s and cur_f > cur_s
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death = prev_f >= prev_s and cur_f < cur_s
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# ── 多头持仓 ──
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if self._position_side == "long":
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self._highest = max(self._highest, k.high)
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stop = self._highest - self.cfg.atr_stop * cur_atr
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if death or k.close < stop or regime == "bear":
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self._position_side = ""
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reason = "死叉" if death else ("ATR止损" if k.close < stop else "转熊市")
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return Signal(symbol=self.cfg.symbol, side="SELL", reason=reason, timestamp=k.open_time)
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# ── 空头持仓 ──
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elif self._position_side == "short":
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self._lowest = min(self._lowest, k.low)
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stop = self._lowest + self.cfg.atr_stop * cur_atr
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if golden or k.close > stop or regime == "bull":
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self._position_side = ""
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reason = "金叉" if golden else ("ATR止损" if k.close > stop else "转牛市")
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return Signal(symbol=self.cfg.symbol, side="BUY", reason=reason, timestamp=k.open_time)
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# ── 空仓等待 ──
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else:
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if regime == "bull" and golden:
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self._position_side = "long"
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self._highest = k.close
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return Signal(symbol=self.cfg.symbol, side="BUY",
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reason=f"牛市金叉 ({r1}/{r2}/{r3})", timestamp=k.open_time)
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elif regime == "bear" and death:
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self._position_side = "short"
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self._lowest = k.close
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return Signal(symbol=self.cfg.symbol, side="SELL",
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reason=f"熊市死叉 ({r1}/{r2}/{r3})", timestamp=k.open_time)
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# 震荡市：不开仓
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return None
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# ════════════════════════════════════════════════════════
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# 对比测试
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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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async def main():
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print()
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print("═" * 120)
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print(" 市场状态自适应策略 — 牛市只做多 / 熊市只做空 / 震荡空仓")
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print("═" * 120)
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# ── BTC 自适应 vs 多空 vs 只做多 ──
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print("\n ■ BTC 全周期 2017-2026 对比")
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print(f" {'策略':<18} {'总收益%':>7} {'年化%':>7} {'夏普':>6} {'回撤%':>7} {'交易':>5} {'多头P&L':>10} {'空头P&L':>10}")
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print(" " + "─" * 105)
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# 自适应
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sc = AdaptiveEmaConfig(symbol="BTCUSDT")
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bt = BacktestConfig(symbol="BTCUSDT", interval="4h",
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start_time=DATE_START, end_time=DATE_END, initial_capital=10_000.0)
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engine = LongShortEngine(bt, db_config=config.db)
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r = await engine.run(AdaptiveEmaStrategy, sc)
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m = r.metrics
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long_t = [t for t in r.trades if t.pnl is not None and t.side == "SELL"]
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short_t = [t for t in r.trades if t.pnl is not None and t.side == "BUY"]
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print(f" {'自适应(牛多熊空)':<18} {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} {sum(t.pnl for t in long_t) if long_t else 0:>+9.0f} {sum(t.pnl for t in short_t) if short_t else 0:>+9.0f}")
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# 始终多空（之前结果）
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from engine.example.long_short import LongShortEmaStrategy, LongShortEmaConfig as LSCfg
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sc2 = LSCfg(symbol="BTCUSDT", fast=10, slow=50)
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r2 = await engine.run(LongShortEmaStrategy, sc2)
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m2 = r2.metrics
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print(f" {'始终多空':<18} {m2.total_return_pct:>6.1f}% {m2.annual_return_pct:>6.1f}% {m2.sharpe_ratio:>6.2f} {m2.max_drawdown_pct:>6.1f}% {m2.total_trades:>5}")
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# 只做多
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from engine.backtest import BacktestEngine as OrigEngine
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class LongOnlyS(BaseStrategy):
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strategy_type = "lo"; _in = False; _hp = 0.0
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def __init__(self, c): super().__init__(c); self._c = []; self._h = []; self._l = []
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async def on_start(self): await super().on_start()
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async def on_kline(self, k):
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self._c.append(k.close); self._h.append(k.high); self._l.append(k.low)
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n = len(self._c)
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if n < 55: return None
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f = ema(self._c, 10); s = ema(self._c, 50); 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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if self._in:
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self._hp = max(self._hp, k.high); stop = self._hp - 2.5 * ca
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if (pf >= ps and cf < cs) or k.close < stop:
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self._in = False
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return Signal(symbol="BTCUSDT", side="SELL",
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reason="死叉" if pf>=ps else "ATR止损", timestamp=k.open_time)
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else:
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if pf <= ps and cf > cs:
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self._in = True; self._hp = k.close
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return Signal(symbol="BTCUSDT", side="BUY", reason="金叉", timestamp=k.open_time)
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return None
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from engine.common.base import StrategyConfig as SC
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lo_bt = BacktestConfig(symbol="BTCUSDT", interval="4h", start_time=DATE_START, end_time=DATE_END, initial_capital=10_000.0)
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lo_e = OrigEngine(lo_bt, db_config=config.db)
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lo_r = await lo_e.run(LongOnlyS, SC(symbol="BTCUSDT"))
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lo_m = lo_r.metrics
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long_only_t = [t for t in lo_r.trades if t.pnl is not None]
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print(f" {'只做多':<18} {lo_m.total_return_pct:>6.1f}% {lo_m.annual_return_pct:>6.1f}% {lo_m.sharpe_ratio:>6.2f} {lo_m.max_drawdown_pct:>6.1f}% {lo_m.total_trades:>5}")
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# ── 分段对比 ──
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PERIODS = [
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("2017 牛市", datetime(2017,1,1), datetime(2018,1,1)),
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("2018 熊市", datetime(2018,1,1), datetime(2019,1,1)),
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("2019 反弹", datetime(2019,1,1), datetime(2020,1,1)),
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("2020 牛初", datetime(2020,1,1), datetime(2021,1,1)),
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("2021 牛市", datetime(2021,1,1), datetime(2022,1,1)),
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("2022 熊市", datetime(2022,1,1), datetime(2023,1,1)),
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("2023 震荡", datetime(2023,1,1), datetime(2024,1,1)),
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("2024-25 牛", datetime(2024,1,1), datetime(2026,1,1)),
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]
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print(f"\n ■ BTC 分段：自适应 vs 始终多空")
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print(f" {'阶段':<16} {'自适应':>8} {'始终多空':>8} {'只做多':>8}")
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print(" " + "─" * 50)
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for name, s, e in PERIODS:
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try:
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bt_p = BacktestConfig(symbol="BTCUSDT", interval="4h", start_time=s, end_time=e, initial_capital=10_000.0)
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# 自适应
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sc_p = AdaptiveEmaConfig(symbol="BTCUSDT")
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e_p = LongShortEngine(bt_p, db_config=config.db)
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r_p = await e_p.run(AdaptiveEmaStrategy, sc_p)
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# 始终多空
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sc_p2 = LSCfg(symbol="BTCUSDT", fast=10, slow=50)
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r_p2 = await e_p.run(LongShortEmaStrategy, sc_p2)
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# 只做多
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lo_e2 = OrigEngine(bt_p, db_config=config.db)
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lo_r2 = await lo_e2.run(LongOnlyS, SC(symbol="BTCUSDT"))
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print(f" {name:<16} {r_p.metrics.total_return_pct:>+7.1f}% {r_p2.metrics.total_return_pct:>+7.1f}% {lo_r2.metrics.total_return_pct:>+7.1f}%")
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except Exception as ex:
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print(f" {name:<16} 错误: {ex}")
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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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Reference in New Issue
Block a user