Rekey
edc50e8809
- 数据层: build_aggregates_sql 新增 2h/6h 聚合视图，默认起始时间调整为 2017-05 - 模型层: KlineInterval 类型扩展 2h/6h，DataService 新增对应表名和毫秒映射 - 指标层: 新增 incremental.py 增量指标模块 (EmaInc/AtrInc/RsiInc/BbInc)，O(1) per bar - 策略重构: long_short.py 和 regime_all.py 从批量 ema/atr 迁移至增量指标，避免每 bar 重复全量计算 - regime 探测器: RegimeDetector3 改为增量 EMA200，detect() 接口简化 - 回测扩展: regime_timeframe_comparison 从 4h/1d 扩展至 2h/4h/6h/1d - 新增示例: multi_strategy_report, vol_break_compare/periods, intraday_explore, top3_trades 等分析脚本
248 lines
9.8 KiB
Python
248 lines
9.8 KiB
Python
"""
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最佳牛熊判定 — 全币种全周期回测
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方法：EMA200斜率 + 价格vs EMA200 + ATH回撤，3选2投票
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策略：牛市只做多 / 熊市只做空 / 震荡空仓
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币种：BTC / ETH / BNB / SOL，各自最早有数据到2026
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用法：
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source .venv/bin/activate && python example/regime_all.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.incremental import EmaInc, AtrInc
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from engine.data import DataService
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from engine.example.long_short import LongShortEngine
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# ════════════════════════════════════════════════════════
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# 3法判定器（增量 EMA200，O(1) per bar）
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# ════════════════════════════════════════════════════════
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class RegimeDetector3:
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"""牛熊判定器，内部维护增量 EMA(200)，避免每次从头重算"""
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def __init__(self):
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self._ath = 0.0
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self._e200 = EmaInc(200)
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def update(self, price: float):
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"""每根 bar 调一次：更新 ATH + EMA(200)"""
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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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def _ema200_slope(self, idx: int) -> str:
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if idx < 220: return "unknown"
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e200 = self._e200
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if e200[idx - 20] == 0: return "unknown"
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slope = (e200[idx] - e200[idx - 20]) / e200[idx - 20]
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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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def _price_vs_ema200(self, price: float, idx: int) -> str:
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if idx < 210: return "unknown"
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e = self._e200[idx]
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if e == 0: 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: return "unknown"
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dd = (price - self._ath) / self._ath
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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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def detect(self, price: float, idx: int) -> str:
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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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b = sum(1 for r in [r1, r2, r3] if r == "bull")
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br = sum(1 for r in [r1, r2, r3] if r == "bear")
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if b >= 2: return "bull"
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if br >= 2: return "bear"
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return "sideways"
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# ════════════════════════════════════════════════════════
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# 自适应策略
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# ════════════════════════════════════════════════════════
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class RegimeEmaConfig(StrategyConfig):
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fast: int = 10; slow: int = 50; atr_stop: float = 2.5
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class RegimeEmaStrategy(BaseStrategy):
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"""按市场状态自适应做多/做空 — 全部指标增量计算，O(1) per bar"""
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strategy_type = "regime_ema"
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def __init__(self, c: RegimeEmaConfig):
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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 = RegimeDetector3()
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self._ema_fast = EmaInc(c.fast)
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self._ema_slow = EmaInc(c.slow)
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self._atr = AtrInc(14)
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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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# 增量更新所有指标（O(1) each）
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self._detector.update(k.close)
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self._ema_fast.update(k.close)
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self._ema_slow.update(k.close)
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self._atr.update(k.high, k.low, k.close)
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n = len(self._c)
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if n < 220: return None
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regime = self._detector.detect(k.close, n - 1)
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cf, cs = self._ema_fast[-1], self._ema_slow[-1]
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ca = self._atr[-1]
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pf, ps = self._ema_fast[-2], self._ema_slow[-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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# 多头持仓
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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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# 空头持仓
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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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# 空仓等信号
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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", reason=f"牛市金叉", 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", reason=f"熊市死叉", timestamp=k.open_time)
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return None
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# ════════════════════════════════════════════════════════
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SYMBOLS = ["BTCUSDT", "ETHUSDT", "BNBUSDT", "SOLUSDT"]
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PARAMS = {
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"BTCUSDT": (10, 50),
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"ETHUSDT": (10, 75),
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"BNBUSDT": (20, 50),
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"SOLUSDT": (30, 50),
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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 get_actual_range(symbol: str) -> tuple[datetime, datetime]:
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"""获取币种实际数据范围"""
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ds = DataService(config.db)
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await ds.connect()
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try:
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start, end = await ds.fetch_symbol_date_range(symbol, "4h")
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return start, end
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except:
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return DATE_START, DATE_END
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finally:
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await ds.close()
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async def main():
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print()
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print("═" * 125)
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print(" 牛熊自适应策略 — 全币种全周期 | 牛市只多/熊市只空/震荡空仓")
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print("═" * 125)
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print(f"\n ■ 全周期汇总")
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print(f" {'币种':<10} {'数据范围':<22} {'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(" " + "─" * 115)
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for symbol in SYMBOLS:
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fast, slow = PARAMS[symbol]
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sc = RegimeEmaConfig(symbol=symbol, fast=fast, slow=slow)
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# 获取实际数据范围
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try:
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act_start, act_end = await get_actual_range(symbol)
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range_str = f"{act_start.date()}~{act_end.date()}"
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except:
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act_start, act_end = DATE_START, DATE_END
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range_str = "2017-2026"
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bt = BacktestConfig(symbol=symbol, interval="4h",
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start_time=act_start, end_time=act_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(RegimeEmaStrategy, 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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long_pnl = sum(t.pnl for t in long_t) if long_t else 0
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short_pnl = sum(t.pnl for t in short_t) if short_t else 0
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print(f" {symbol:<10} {range_str:<22} {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} {long_pnl:>+9.0f} {short_pnl:>+9.0f}")
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# ── BTC 分段 ──
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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 分段表现")
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print(f" {'阶段':<16} {'总收益%':>7} {'夏普':>6} {'多头P&L':>9} {'空头P&L':>9}")
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print(" " + "─" * 65)
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for name, s, e in PERIODS:
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try:
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sc = RegimeEmaConfig(symbol="BTCUSDT", fast=10, slow=50)
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bt = BacktestConfig(symbol="BTCUSDT", interval="4h", start_time=s, end_time=e, initial_capital=10_000.0)
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eng = LongShortEngine(bt, db_config=config.db)
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r = await eng.run(RegimeEmaStrategy, sc)
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lt = [t for t in r.trades if t.pnl is not None and t.side == "SELL"]
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st = [t for t in r.trades if t.pnl is not None and t.side == "BUY"]
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print(f" {name:<16} {r.metrics.total_return_pct:>+6.1f}% {r.metrics.sharpe_ratio:>6.2f} {sum(t.pnl for t in lt) if lt else 0:>+8.0f} {sum(t.pnl for t in st) if st else 0:>+8.0f}")
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except Exception as ex:
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print(f" {name:<16} 数据不足")
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print("\n═" * 125)
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if __name__ == "__main__":
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asyncio.run(main())
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