Rekey
515e61c517
- 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
231 lines
8.7 KiB
Python
231 lines
8.7 KiB
Python
"""
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多时间框架 v3 — 1d 定趋势 / 4h 找买点
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策略：
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1d EMA(20,50) → 日线金叉=多头趋势，死叉=空头
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4h 回调入场 → 日线多头中，4h 价格回调到 EMA20 附近 + 收阳 → 买入
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4h EMA死叉 或 日线转空 → 卖出
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ATR 动态止损
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币种：BTC/ETH/BNB/SOL | 2024-2026
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用法：
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source .venv/bin/activate && python example/multi_tf_v3.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 BacktestEngine, BacktestConfig, BacktestResult
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from engine.data import DataService
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from engine.indicators import ema, atr
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class DailyTrendConfig(StrategyConfig):
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"""1d趋势+4h交易 策略配置"""
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# 1d 趋势参数
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trend_fast: int = 20
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trend_slow: int = 50
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# 4h 交易参数
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entry_ema: int = 20 # 4h 回调到该均线附近买入
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atr_period: int = 14
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atr_stop_mult: float = 2.5
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# 数据范围
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data_start: Optional[datetime] = None
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data_end: Optional[datetime] = None
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class DailyTrendStrategy(BaseStrategy):
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"""日线定大势，4h 抓回调
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┌──────────────────────────────────────────────┐
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│ 1d EMA(20,50) │
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│ ├─ 金叉 → 多头区间 │
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│ └─ 死叉 → 空仓等待 │
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│ │
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│ 4h K线（引擎推送） │
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│ ├─ 多头区间 + 回调到 EMA20 + 收阳 → 买入 │
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│ ├─ EMA死叉 → 卖出 │
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│ └─ ATR 动态止损 │
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└──────────────────────────────────────────────┘
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"""
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strategy_type = "daily_trend"
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def __init__(self, c: DailyTrendConfig):
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super().__init__(c)
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self.cfg = c
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# 1d 预加载
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self._klines_1d: list[Kline] = []
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self._ema_fast_1d: list[float] = []
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self._ema_slow_1d: list[float] = []
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# 4h 积累
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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._highest: float = 0.0
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self._in_position = False
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async def on_start(self):
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from engine.common.config import config as app_config
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ds = DataService(app_config.db)
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await ds.connect()
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try:
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self._klines_1d = await ds.fetch_klines(
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symbol=self.cfg.symbol, interval="1d",
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start_time=self.cfg.data_start, end_time=self.cfg.data_end, limit=1_000_000,
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)
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closes_1d = [k.close for k in self._klines_1d]
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self._ema_fast_1d = ema(closes_1d, self.cfg.trend_fast)
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self._ema_slow_1d = ema(closes_1d, self.cfg.trend_slow)
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finally:
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await ds.close()
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await super().on_start()
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def _get_1d_state(self, ts: float) -> tuple[bool, bool, float, float]:
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"""获取日线趋势状态（仅已完成K线）
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Returns: (is_bull, is_golden_cross, price, ema_fast)
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"""
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if not self._klines_1d:
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return False, False, 0.0, 0.0
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for i in range(len(self._klines_1d) - 1, -1, -1):
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if self._klines_1d[i].close_time <= ts:
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ef = self._ema_fast_1d[i]
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es = self._ema_slow_1d[i]
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if ef == 0 or es == 0:
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return False, False, self._klines_1d[i].close, ef
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# 金叉 = EMA20 > EMA50
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golden = ef > es
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# 趋势向上 = 价格在EMA20上方（额外确认）
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price_above = self._klines_1d[i].close > ef
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return golden and price_above, golden, self._klines_1d[i].close, ef
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return False, False, 0.0, 0.0
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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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n = len(self._closes)
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if n < self.cfg.entry_ema + 20:
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return None
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ema_4h = ema(self._closes, self.cfg.entry_ema)
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atr_vals = atr(self._highs, self._lows, self._closes, self.cfg.atr_period)
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cur_ema, cur_atr = ema_4h[-1], atr_vals[-1]
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if cur_ema == 0 or cur_atr == 0:
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return None
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is_bull, is_golden, price_1d, ema_1d = self._get_1d_state(k.open_time)
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# ── 出场 ──
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if self._in_position:
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self._highest = max(self._highest, k.high)
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stop = self._highest - self.cfg.atr_stop_mult * cur_atr
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# 4h EMA死叉（用EMA9代替，更快反应）
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ema9_vals = ema(self._closes, 9)
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ema20_vals = ema(self._closes, 20)
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if len(ema9_vals) > 2 and ema9_vals[-2] >= ema20_vals[-2] and ema9_vals[-1] < ema20_vals[-1]:
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self._in_position = False
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return Signal(symbol=self.cfg.symbol, side="SELL", reason="4h EMA死叉", timestamp=k.open_time)
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if k.close < stop:
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self._in_position = False
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return Signal(symbol=self.cfg.symbol, side="SELL", reason=f"ATR止损", timestamp=k.open_time)
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# ── 入场 ──
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if not self._in_position and is_bull:
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# 4h 价格回调到 EMA20 附近（偏离不超过 1.5 倍 ATR）
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distance = abs(k.close - cur_ema) / cur_atr
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near_ema = distance < 1.5
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# K线收阳
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bullish_bar = k.close > k.open
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# EMA20 走平或向上
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ema_rising = n >= 4 and ema_4h[-1] >= ema_4h[-3]
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if near_ema and bullish_bar and ema_rising:
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self._in_position = True
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self._highest = k.close
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return Signal(
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symbol=self.cfg.symbol, side="BUY",
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confidence=0.8,
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reason=f"1d多头+4h回调 P={k.close:.0f}≈EMA={cur_ema:.0f} dist={distance:.1f}σ",
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timestamp=k.open_time,
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)
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return None
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# ════════════════════════════════════════════════════════
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# 运行
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# ════════════════════════════════════════════════════════
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SYMBOLS = ["BTCUSDT", "ETHUSDT", "BNBUSDT", "SOLUSDT"]
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DATE_START = datetime(2024, 1, 1)
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DATE_END = datetime(2026, 1, 1)
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# v3 EMA最优结果（用于对比）
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EMA_V3 = {
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"BTCUSDT": (39.9, 1.03, -11.5, 20, 55.0),
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"ETHUSDT": (53.6, 1.04, -15.3, 18, 38.9),
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"BNBUSDT": (26.0, 0.64, -23.4, 23, 34.8),
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"SOLUSDT": (73.6, 1.18, -25.7, 13, 46.2),
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}
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async def main():
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print()
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print("═" * 110)
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print(" 多时间框架 v3 — 1d 定趋势 / 4h 找回调买点 | 2024-2026")
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print("═" * 110)
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print(f" {'币种':<10} {'版本':<22} {'收益%':>7} {'夏普':>6} {'回撤%':>7} {'交易':>5} {'胜率%':>6} {'盈亏比':>6}")
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print("─" * 110)
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for symbol in SYMBOLS:
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sc = DailyTrendConfig(
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symbol=symbol,
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data_start=DATE_START, data_end=DATE_END,
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)
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bt = BacktestConfig(symbol=symbol, interval="4h",
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start_time=DATE_START, end_time=DATE_END, initial_capital=10_000.0)
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engine = BacktestEngine(bt, db_config=config.db)
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r = await engine.run(DailyTrendStrategy, sc)
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m = r.metrics
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# v3 对比
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v3 = EMA_V3[symbol]
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print(f" {symbol:<10} {'1d趋势+4h回调':<22} {m.total_return_pct:>6.1f}% {m.sharpe_ratio:>6.2f} {m.max_drawdown_pct:>6.1f}% {m.total_trades:>5} {m.win_rate*100:>5.1f}% {m.profit_factor:>6.2f}")
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print(f" {'':<10} {'(对比) EMA v3 最优':<22} {v3[0]:>6.1f}% {v3[1]:>6.2f} {v3[2]:>6.1f}% {v3[3]:>5} {v3[4]:>5.1f}%")
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# 打印最近几笔出场
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sells = [t for t in r.trades if t.side == "SELL" and t.pnl is not None]
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if sells:
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recent = sells[-3:]
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for t in recent:
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dt = datetime.fromtimestamp(t.timestamp / 1000, tz=timezone.utc).strftime("%m-%d %H:%M")
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print(f" {'':<10} └ {dt} {t.pnl:>+8.2f} USDT {t.reason}")
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print()
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print("═" * 110)
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if __name__ == "__main__":
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asyncio.run(main())
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