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
265 lines
9.9 KiB
Python
265 lines
9.9 KiB
Python
"""
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策略对比回测 — 4 个策略 × 4 个币种
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策略：
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1. MACD 金叉死叉 — MACD(12,26,9) 金叉买入，死叉卖出
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2. EMA 双均线 — EMA20 上穿 EMA50 买入，下穿卖出
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3. RSI 超卖反弹 — RSI(14)<30 买入，RSI>70 卖出
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4. 布林带突破 — 价格突破上轨买入，跌破中轨卖出
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币种：BTCUSDT / ETHUSDT / BNBUSDT / SOLUSDT
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周期：4h，最近两年 (2024-2026)
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用法：
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source .venv/bin/activate && python example/strategy_battle.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.indicators import macd, ema, rsi, bollinger
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# ════════════════════════════════════════════════════════
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# 策略 1：MACD 金叉死叉
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# ════════════════════════════════════════════════════════
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class MacdConfig(StrategyConfig):
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fast: int = 12
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slow: int = 26
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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)
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self.cfg = c
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self._closes: list[float] = []
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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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macd_line, sig_line, _ = macd(self._closes, self.cfg.fast, self.cfg.slow, self.cfg.signal)
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if len(macd_line) < 3:
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return None
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cur_m, cur_s = macd_line[-1], sig_line[-1]
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prev_m, prev_s = macd_line[-2], sig_line[-2]
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if cur_m == 0:
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return None
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if prev_m <= prev_s and cur_m > cur_s:
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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 prev_m >= prev_s and cur_m < cur_s:
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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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# ════════════════════════════════════════════════════════
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# 策略 2：EMA 双均线
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# ════════════════════════════════════════════════════════
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class EmaCrossConfig(StrategyConfig):
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fast: int = 20
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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)
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self.cfg = c
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self._closes: list[float] = []
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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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fast = ema(self._closes, self.cfg.fast)
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slow = ema(self._closes, self.cfg.slow)
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if len(fast) < 3 or fast[-1] == 0 or slow[-1] == 0:
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return None
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if fast[-2] <= slow[-2] and fast[-1] > slow[-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 fast[-2] >= slow[-2] and fast[-1] < slow[-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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# ════════════════════════════════════════════════════════
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# 策略 3：RSI 超卖反弹
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# ════════════════════════════════════════════════════════
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class RsiConfig(StrategyConfig):
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period: int = 14
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oversold: float = 30.0
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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)
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self.cfg = c
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self._closes: list[float] = []
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self._in_position = False
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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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vals = rsi(self._closes, self.cfg.period)
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v = vals[-1]
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if v == 0:
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return None
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if v < self.cfg.oversold and not self._in_position:
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self._in_position = 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_position:
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self._in_position = 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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# ════════════════════════════════════════════════════════
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# 策略 4：布林带突破
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# ════════════════════════════════════════════════════════
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class BollConfig(StrategyConfig):
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period: int = 20
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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)
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self.cfg = c
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self._closes: list[float] = []
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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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upper, mid, lower = bollinger(self._closes, self.cfg.period, self.cfg.std)
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if mid[-1] == 0 or len(upper) < 3:
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return None
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p, up, md = k.close, upper[-1], mid[-1]
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pp, prev_md = self._closes[-2], mid[-2]
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# 突破上轨+中轨向上 → 买入
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if pp <= prev_md and p > md and up > 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 P={p:.0f}>M={md:.0f}", timestamp=k.open_time)
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# 跌破中轨 → 卖出
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if pp >= prev_md and p < md:
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return Signal(symbol=self.cfg.symbol, side="SELL", reason=f"跌破BB中轨 P={p:.0f}<M={md:.0f}", timestamp=k.open_time)
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return None
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# ════════════════════════════════════════════════════════
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# 注册策略
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# ════════════════════════════════════════════════════════
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STRATEGIES = [
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("MACD金叉死叉", MacdStrategy, MacdConfig()),
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("EMA双均线", EmaCrossStrategy, EmaCrossConfig()),
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("RSI超卖反弹", RsiStrategy, RsiConfig()),
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("布林突破", BollStrategy, BollConfig()),
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]
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SYMBOLS = ["BTCUSDT", "ETHUSDT", "BNBUSDT", "SOLUSDT"]
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async def run_one(
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symbol: str,
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strategy_name: str,
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strategy_cls,
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strategy_config: StrategyConfig,
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) -> BacktestResult:
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bt = BacktestConfig(
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symbol=symbol,
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interval="4h",
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start_time=datetime(2024, 1, 1),
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end_time=datetime(2026, 1, 1),
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initial_capital=10_000.0,
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commission_pct=0.001,
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slippage_pct=0.0005,
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warmup_bars=100,
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)
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strategy_config.symbol = symbol
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strategy_config.name = f"{strategy_name}_{symbol}"
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engine = BacktestEngine(bt, db_config=config.db)
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return await engine.run(strategy_cls, strategy_config)
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async def main():
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print()
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print("═" * 98)
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print(" 策略对比回测 — 4 策略 × 4 币种 | 4h 周期 | 2024-2026")
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print("═" * 98)
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print(f" {'策略':<16} {'币种':<10} {'总收益%':>8} {'夏普':>6} {'最大回撤%':>8} {'交易数':>6} {'胜率%':>6}")
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print("─" * 98)
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results: list[tuple[str, str, BacktestResult]] = []
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# 创建引擎（每个币种一个，复用连接）
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for symbol in SYMBOLS:
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for s_name, s_cls, s_cfg in STRATEGIES:
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cfg = s_cfg.model_copy() if hasattr(s_cfg, 'model_copy') else s_cfg.__class__(**s_cfg.model_dump())
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r = await run_one(symbol, s_name, s_cls, cfg)
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results.append((s_name, symbol, r))
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m = r.metrics
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print(
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f" {s_name:<16} {symbol:<10} "
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f"{m.total_return_pct:>7.1f}% "
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f"{m.sharpe_ratio:>6.2f} "
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f"{m.max_drawdown_pct:>7.1f}% "
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f"{m.total_trades:>6} "
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f"{m.win_rate*100:>5.1f}%"
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)
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# ── 汇总排名 ──
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print("─" * 98)
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print("\n ■ 按总收益排名 TOP 5:")
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ranked = sorted(results, key=lambda x: x[2].metrics.total_return_pct, reverse=True)
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for i, (s_name, symbol, r) in enumerate(ranked[:5]):
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m = r.metrics
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print(f" {i+1}. {symbol} {s_name:<16} 收益={m.total_return_pct:+.1f}% 夏普={m.sharpe_ratio:.2f} 回撤={m.max_drawdown_pct:.1f}% 胜率={m.win_rate*100:.0f}%")
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print("\n ■ 按夏普排名 TOP 5:")
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by_sharpe = sorted(results, key=lambda x: x[2].metrics.sharpe_ratio, reverse=True)
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for i, (s_name, symbol, r) in enumerate(by_sharpe[:5]):
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m = r.metrics
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print(f" {i+1}. {symbol} {s_name:<16} 夏普={m.sharpe_ratio:.2f} 收益={m.total_return_pct:+.1f}% 回撤={m.max_drawdown_pct:.1f}%")
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print("\n ■ 各币种最佳策略:")
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for symbol in SYMBOLS:
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sym_results = [(s, r) for s, sym, r in results if sym == symbol]
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best = max(sym_results, key=lambda x: x[1].metrics.sharpe_ratio)
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m = best[1].metrics
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print(f" {symbol}: {best[0]:<16} 夏普={m.sharpe_ratio:.2f} 收益={m.total_return_pct:+.1f}% 交易={m.total_trades}")
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print("\n═" * 98)
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print(" 全部回测完成。")
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print("═" * 98)
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if __name__ == "__main__":
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asyncio.run(main())
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