feat: 新增2h/6h时间框架支持，策略重构为增量指标计算
- 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
This commit is contained in:
Rekey
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"""
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多策略多级别分类回测报告
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日内交易 (30m/1h) ｜ 中线交易 (2h/4h/6h) ｜ 长线交易 (1d/1w)
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策略：牛熊自适应 / MACD / EMA双均线 / RSI / 布林突破
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币种：BTC / ETH / BNB / SOL
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数据：日内近两年，中线+长线全量
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用法：
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source .venv/bin/activate && python example/multi_strategy_report.py
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"""
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import asyncio
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import sys
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from collections import defaultdict
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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 BacktestEngine, BacktestConfig, BacktestResult
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from engine.indicators import macd, ema, rsi, bollinger, atr
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# ═══════════════════════════════════════════
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# 策略定义
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# ═══════════════════════════════════════════
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# --- MACD ---
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class MacdConfig(StrategyConfig):
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fast: int = 12; slow: int = 26; signal: int = 9
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class MacdStrategy(BaseStrategy):
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strategy_type = "macd"
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def __init__(self, c: MacdConfig):
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super().__init__(c); self.cfg = c; self._c: list[float] = []
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async def on_kline(self, k: Kline) -> Optional[Signal]:
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self._c.append(k.close)
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ml, sl, _ = macd(self._c, self.cfg.fast, self.cfg.slow, self.cfg.signal)
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if len(ml) < 3 or ml[-1] == 0: return None
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if ml[-2] <= sl[-2] and ml[-1] > sl[-1]:
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return Signal(symbol=self.cfg.symbol, side="BUY", reason="MACD金叉", timestamp=k.open_time)
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if ml[-2] >= sl[-2] and ml[-1] < sl[-1]:
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return Signal(symbol=self.cfg.symbol, side="SELL", reason="MACD死叉", timestamp=k.open_time)
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return None
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# --- EMA双均线 ---
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class EmaCrossConfig(StrategyConfig):
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fast: int = 20; slow: int = 50
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class EmaCrossStrategy(BaseStrategy):
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strategy_type = "ema_cross"
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def __init__(self, c: EmaCrossConfig):
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super().__init__(c); self.cfg = c; self._c: list[float] = []
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async def on_kline(self, k: Kline) -> Optional[Signal]:
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self._c.append(k.close)
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f = ema(self._c, self.cfg.fast); s = ema(self._c, self.cfg.slow)
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if len(f) < 3 or f[-1] == 0 or s[-1] == 0: return None
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if f[-2] <= s[-2] and f[-1] > s[-1]:
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return Signal(symbol=self.cfg.symbol, side="BUY", reason="EMA金叉", timestamp=k.open_time)
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if f[-2] >= s[-2] and f[-1] < s[-1]:
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return Signal(symbol=self.cfg.symbol, side="SELL", reason="EMA死叉", timestamp=k.open_time)
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return None
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# --- RSI ---
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class RsiConfig(StrategyConfig):
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period: int = 14; oversold: float = 30.0; overbought: float = 70.0
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class RsiStrategy(BaseStrategy):
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strategy_type = "rsi"
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def __init__(self, c: RsiConfig):
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super().__init__(c); self.cfg = c; self._c: list[float] = []; self._in = False
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async def on_kline(self, k: Kline) -> Optional[Signal]:
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self._c.append(k.close)
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v = rsi(self._c, self.cfg.period)[-1]
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if v == 0: return None
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if v < self.cfg.oversold and not self._in:
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self._in = True
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return Signal(symbol=self.cfg.symbol, side="BUY", reason=f"RSI超卖({v:.1f})", timestamp=k.open_time)
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if v > self.cfg.overbought and self._in:
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self._in = False
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return Signal(symbol=self.cfg.symbol, side="SELL", reason=f"RSI超买({v:.1f})", timestamp=k.open_time)
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return None
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# --- 布林突破 ---
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class BollConfig(StrategyConfig):
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period: int = 20; std: float = 2.0
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class BollStrategy(BaseStrategy):
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strategy_type = "boll"
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def __init__(self, c: BollConfig):
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super().__init__(c); self.cfg = c; self._c: list[float] = []
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async def on_kline(self, k: Kline) -> Optional[Signal]:
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self._c.append(k.close)
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upper, mid, lower = bollinger(self._c, self.cfg.period, self.cfg.std)
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if len(upper) < 3 or mid[-1] == 0: return None
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p, md = k.close, mid[-1]
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pp, pm = self._c[-2], mid[-2]
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if pp <= pm and p > md and upper[-1] > 0 and mid[-1] > mid[-2]:
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return Signal(symbol=self.cfg.symbol, side="BUY", reason=f"突破BB中轨", timestamp=k.open_time)
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if pp >= pm and p < md:
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return Signal(symbol=self.cfg.symbol, side="SELL", reason=f"跌破BB中轨", timestamp=k.open_time)
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return None
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# --- 牛熊自适应 (多空双向) ---
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class RegimeDetector:
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def __init__(self):
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self._ath = 0.0
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def update_ath(self, price: float):
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if price > self._ath: self._ath = price
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def ema200_slope(self, closes, idx):
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if idx < 210: return "unknown"
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e200 = ema(closes, 200)
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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, closes, idx):
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if idx < 210: return "unknown"
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e200 = ema(closes, 200)
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return "bull" if closes[idx] > e200[idx] else "bear"
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def ath_drawdown(self, closes, idx):
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if self._ath == 0: return "unknown"
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dd = (closes[idx] - 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, closes, idx):
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r1 = self.ema200_slope(closes, idx); r2 = self.price_vs_ema200(closes, idx); r3 = self.ath_drawdown(closes, idx)
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b = sum(1 for r in [r1,r2,r3] if r=="bull"); 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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class RegimeConfig(StrategyConfig):
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fast: int = 10; slow: int = 50; atr_stop: float = 2.5
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class RegimeStrategy(BaseStrategy):
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strategy_type = "regime"
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def __init__(self, c: RegimeConfig):
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super().__init__(c); 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 = RegimeDetector(); self._side = ""; self._hp = 0.0; self._lp = 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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self._detector.update_ath(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(self._c, n - 1)
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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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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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return Signal(symbol=self.cfg.symbol, side="SELL", reason="平多", 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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return Signal(symbol=self.cfg.symbol, side="BUY", reason="平空", 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", reason="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="熊市死叉", timestamp=k.open_time)
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return None
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# ═══════════════════════════════════════════
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# 注册
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# ═══════════════════════════════════════════
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STRATEGY_REGISTRY = {
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"牛熊自适应": (RegimeStrategy, RegimeConfig, "regime"),
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"MACD": (MacdStrategy, MacdConfig, "trend"),
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"EMA双均线": (EmaCrossStrategy, EmaCrossConfig, "trend"),
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"RSI超卖反弹": (RsiStrategy, RsiConfig, "reversal"),
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"布林突破": (BollStrategy, BollConfig, "breakout"),
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}
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SYMBOLS = ["BTCUSDT", "ETHUSDT", "BNBUSDT", "SOLUSDT"]
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REGIME_PARAMS = {
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"BTCUSDT": (10, 50), "ETHUSDT": (10, 75), "BNBUSDT": (20, 50), "SOLUSDT": (30, 50),
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}
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CATEGORIES = {
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"日内交易": {
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"intervals": ["30m", "1h"],
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"strategies": ["MACD", "EMA双均线", "RSI超卖反弹", "布林突破", "牛熊自适应"],
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"data": "recent",
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},
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"中线交易": {
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"intervals": ["2h", "4h", "6h"],
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"strategies": ["牛熊自适应", "MACD", "EMA双均线", "RSI超卖反弹", "布林突破"],
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"data": "full",
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},
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"长线交易": {
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"intervals": ["1d", "1w"],
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"strategies": ["牛熊自适应", "MACD", "EMA双均线"],
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"data": "full",
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},
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}
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RECENT_START = datetime(2024, 6, 1)
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RECENT_END = datetime(2026, 6, 12)
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FULL_DEFAULT = datetime(2017, 1, 1)
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async def run_simple(symbol, interval, strategy_cls, strategy_cfg, start, end) -> BacktestResult | None:
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"""使用 BacktestEngine（只做多）运行回测"""
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bt = BacktestConfig(
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symbol=symbol, interval=interval, start_time=start, end_time=end,
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initial_capital=10_000.0, warmup_bars=100,
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)
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strategy_cfg.symbol = symbol
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engine = BacktestEngine(bt, db_config=config.db)
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return await engine.run(strategy_cls, strategy_cfg)
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async def run_regime(symbol, interval, start, end) -> BacktestResult | None:
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"""使用 LongShortEngine（多空双向）运行牛熊自适应策略"""
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from engine.example.long_short import LongShortEngine
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fast, slow = REGIME_PARAMS.get(symbol, (10, 50))
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sc = RegimeConfig(symbol=symbol, fast=fast, slow=slow)
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bt = BacktestConfig(
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symbol=symbol, interval=interval, start_time=start, end_time=end,
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initial_capital=10_000.0, warmup_bars=250,
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)
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engine = LongShortEngine(bt, db_config=config.db)
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return await engine.run(RegimeStrategy, sc)
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# ═══════════════════════════════════════════
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# 主流程
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# ═══════════════════════════════════════════
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async def main():
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sem = asyncio.Semaphore(2) # 并发控制
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async def with_sem(coro):
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async with sem:
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return await coro
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out: list[str] = []
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def w(line=""):
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out.append(line); print(line)
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# 收集所有结果: (category, interval, symbol, strategy_name, result)
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all_results: list[dict] = []
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w("# 多策略多级别分类回测报告")
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w()
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w(f"> 生成时间：{datetime.now().strftime('%Y-%m-%d %H:%M')}")
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w(f"> 初始资金：10,000 USDT | 手续费：0.1% | 滑点：0.05%")
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w(f"> 日内交易使用近两年数据 (2024.06-2026.06)，中线/长线使用全量历史数据")
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w()
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for cat_name, cat_cfg in CATEGORIES.items():
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intervals = cat_cfg["intervals"]
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strategy_names = cat_cfg["strategies"]
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use_full = cat_cfg["data"] == "full"
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w(f"## {cat_name} ({'/'.join(intervals)})")
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w()
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for interval in intervals:
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w(f"### {interval}")
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w()
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# 表头
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cols = "| 币种 |"
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sep = "|------|"
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for sn in strategy_names:
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cols += f" {sn} 收益% | {sn} 夏普 | {sn} 回撤% | {sn} 交易 | {sn} 胜率% |"
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sep += "--------|------|------|------|------|"
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w(cols)
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w(sep)
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for symbol in SYMBOLS:
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row = f"| {symbol:<10} |"
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tasks = []
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for sn in strategy_names:
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if sn == "牛熊自适应":
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# 牛熊自适应使用 LongShortEngine
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if use_full:
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start, end = FULL_DEFAULT, RECENT_END
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else:
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start, end = RECENT_START, RECENT_END
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tasks.append((sn, with_sem(run_regime(symbol, interval, start, end))))
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else:
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cls, cfg_cls, _ = STRATEGY_REGISTRY[sn]
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if use_full:
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start, end = FULL_DEFAULT, RECENT_END
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else:
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start, end = RECENT_START, RECENT_END
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cfg = cfg_cls()
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tasks.append((sn, with_sem(run_simple(symbol, interval, cls, cfg, start, end))))
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# 并行执行当前币种的所有策略
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results_list = await asyncio.gather(*[t[1] for t in tasks], return_exceptions=True)
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for (sn, _), r in zip(tasks, results_list):
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if isinstance(r, Exception):
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row += f" ERR | — | — | — | — |"
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print(f" ✗ {symbol} {interval} {sn}: {r}")
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elif r is None:
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row += f" N/A | — | — | — | — |"
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else:
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m = r.metrics
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row += f" {m.total_return_pct:>+6.1f}% | {m.sharpe_ratio:>4.2f} | {m.max_drawdown_pct:>5.1f}% | {m.total_trades:>4} | {m.win_rate*100:>4.1f}% |"
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all_results.append({
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"category": cat_name, "interval": interval,
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"symbol": symbol, "strategy": sn,
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"return": m.total_return_pct, "sharpe": m.sharpe_ratio,
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"dd": m.max_drawdown_pct, "trades": m.total_trades,
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"win": m.win_rate, "pf": m.profit_factor,
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})
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w(row)
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w()
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# ═══════════════════════════════════════
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# 汇总分析
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# ═══════════════════════════════════════
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w("---")
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w()
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w("## 汇总分析")
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w()
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for cat_name in CATEGORIES:
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cat_results = [r for r in all_results if r["category"] == cat_name]
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if not cat_results:
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continue
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w(f"### {cat_name} — 各币种最优策略")
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w()
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w("| 币种 | 最佳周期 | 最佳策略 | 总收益% | 夏普 | 回撤% | 交易 | 胜率% |")
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w("|------|---------|---------|--------|------|------|------|------|")
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for symbol in SYMBOLS:
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candidates = [r for r in cat_results if r["symbol"] == symbol]
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if not candidates:
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continue
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best = max(candidates, key=lambda x: x["sharpe"])
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w(f"| {symbol} | {best['interval']} | {best['strategy']} | {best['return']:>+7.1f}% | {best['sharpe']:.2f} | {best['dd']:.1f}% | {best['trades']} | {best['win']*100:.1f}% |")
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w()
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# 全市场最优
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w("### 全市场 TOP 10（按夏普排序）")
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||||
w()
|
||||
w("| 排名 | 分类 | 币种 | 周期 | 策略 | 总收益% | 夏普 | 回撤% | 交易 | 胜率% |")
|
||||
w("|------|------|------|------|------|--------|------|------|------|------|")
|
||||
ranked = sorted(all_results, key=lambda x: x["sharpe"], reverse=True)
|
||||
for i, r in enumerate(ranked[:10]):
|
||||
w(f"| {i+1} | {r['category']} | {r['symbol']} | {r['interval']} | {r['strategy']} | {r['return']:>+7.1f}% | {r['sharpe']:.2f} | {r['dd']:.1f}% | {r['trades']} | {r['win']*100:.1f}% |")
|
||||
w()
|
||||
|
||||
# 按策略类型汇总
|
||||
w("### 各策略类型平均表现")
|
||||
w()
|
||||
w("| 策略 | 分类 | 平均收益% | 平均夏普 | 平均回撤% | 平均胜率% |")
|
||||
w("|------|------|---------|---------|---------|---------|")
|
||||
for sn in ["牛熊自适应", "MACD", "EMA双均线", "RSI超卖反弹", "布林突破"]:
|
||||
for cat_name in CATEGORIES:
|
||||
sr = [r for r in all_results if r["strategy"] == sn and r["category"] == cat_name]
|
||||
if not sr:
|
||||
continue
|
||||
avg_ret = sum(r["return"] for r in sr) / len(sr)
|
||||
avg_sh = sum(r["sharpe"] for r in sr) / len(sr)
|
||||
avg_dd = sum(r["dd"] for r in sr) / len(sr)
|
||||
avg_win = sum(r["win"] for r in sr) / len(sr)
|
||||
w(f"| {sn} | {cat_name} | {avg_ret:>+7.1f}% | {avg_sh:.2f} | {avg_dd:.1f}% | {avg_win*100:.1f}% |")
|
||||
w()
|
||||
|
||||
# 写出文件
|
||||
out_path = Path(__file__).resolve().parent.parent / "backtest" / "MULTI_STRATEGY_REPORT.md"
|
||||
with open(out_path, "w", encoding="utf-8") as f:
|
||||
f.write("\n".join(out) + "\n")
|
||||
print(f"\n✓ 报告已保存到: {out_path}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
Reference in New Issue
Block a user