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
228 lines
9.0 KiB
Python
228 lines
9.0 KiB
Python
"""
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策略深度优化 — 成交量确认 + 时间止损 + 参数扫描
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优化点 v3：
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1. 成交量确认：金叉当日成交量 > 前20根均量 × 1.3，过滤缩量假突破
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2. 时间止损：持仓超过48根K线自动平仓，避免死扛
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3. 参数扫描：对 EMA(fast, slow) 组合做网格搜索，每个币种找最优参数
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用法：
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source .venv/bin/activate && python example/strategy_optimize2.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 ema, atr
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# ════════════════════════════════════════════════════════
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# EMA v3: 趋势 + ATR止损 + 时间止损 + 成交量确认
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# ════════════════════════════════════════════════════════
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class EmaV3Config(StrategyConfig):
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fast: int = 20
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slow: int = 50
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atr_period: int = 14
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atr_stop_mult: float = 3.0
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time_stop_bars: int = 48 # 时间止损（30m下48根=1天，4h下48根=8天）
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vol_factor: float = 1.3 # 成交量确认倍数
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class EmaV3Strategy(BaseStrategy):
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"""EMA v3: 金叉+放量买入，ATR止损+时间止损出场"""
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strategy_type = "ema_v3"
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def __init__(self, c: EmaV3Config):
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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._volumes: list[float] = []
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self._highest_since_entry: float = 0.0
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self._bars_held: int = 0
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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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self._highs.append(k.high)
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self._lows.append(k.low)
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self._volumes.append(k.volume)
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n = len(self._closes)
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if n < self.cfg.slow + 20:
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return None
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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, self.cfg.atr_period)
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cur_f, cur_s, cur_atr = fast[-1], slow[-1], atr_vals[-1]
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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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# 成交量确认：最近bar成交量 > 前20根均量 × factor
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avg_vol = sum(self._volumes[-21:-1]) / 20 if n > 21 else 0
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vol_confirmed = k.volume > avg_vol * self.cfg.vol_factor if avg_vol > 0 else True
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if self._in_position:
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self._bars_held += 1
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self._highest_since_entry = max(self._highest_since_entry, k.high)
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stop_price = self._highest_since_entry - self.cfg.atr_stop_mult * cur_atr
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death_cross = fast[-2] >= slow[-2] and cur_f < cur_s
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time_up = self._bars_held >= self.cfg.time_stop_bars
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if k.close < stop_price:
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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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if death_cross:
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self._in_position = False
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return Signal(symbol=self.cfg.symbol, side="SELL", reason=f"EMA死叉", timestamp=k.open_time)
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if time_up:
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self._in_position = False
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return Signal(symbol=self.cfg.symbol, side="SELL", reason=f"时间止损({self._bars_held}bar)", timestamp=k.open_time)
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if not self._in_position:
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golden = fast[-2] <= slow[-2] and cur_f > cur_s
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if golden and vol_confirmed:
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self._in_position = True
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self._entry_price = k.close
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self._highest_since_entry = k.close
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self._bars_held = 0
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return Signal(symbol=self.cfg.symbol, side="BUY",
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reason=f"金叉+放量{'✓' if vol_confirmed else ''} V={k.volume:.0f}",
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timestamp=k.open_time)
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return None
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# ════════════════════════════════════════════════════════
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# 参数扫描 + 对比
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# ════════════════════════════════════════════════════════
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SYMBOLS = ["BTCUSDT", "ETHUSDT", "BNBUSDT", "SOLUSDT"]
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PARAM_GRID = [
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# (fast, slow)
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(10, 40), (10, 50), (10, 75),
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(20, 40), (20, 50), (20, 75),
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(30, 40), (30, 50), (30, 75),
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]
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# 原始 v1 基线
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BASELINE = {
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"BTCUSDT": (20, 50, 45.5, 0.74, -31.6),
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"ETHUSDT": (20, 50, 24.4, 0.47, -54.8),
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"BNBUSDT": (20, 50, 52.0, 0.71, -39.8),
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"SOLUSDT": (20, 50, 27.8, 0.49, -39.5),
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}
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async def scan_one(symbol: str, fast: int, slow: int) -> dict:
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"""单次参数扫描"""
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bt = BacktestConfig(
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symbol=symbol, interval="4h",
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start_time=datetime(2024, 1, 1), end_time=datetime(2026, 1, 1),
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initial_capital=10_000.0,
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)
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sc = EmaV3Config(symbol=symbol, fast=fast, slow=slow)
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engine = BacktestEngine(bt, db_config=config.db)
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r = await engine.run(EmaV3Strategy, sc)
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m = r.metrics
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return {"fast": fast, "slow": slow, "return": m.total_return_pct,
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"sharpe": m.sharpe_ratio, "dd": m.max_drawdown_pct,
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"trades": m.total_trades, "wr": m.win_rate}
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async def main():
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print()
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print("═" * 105)
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print(" EMA v3 深度优化 — 成交量+时间止损+参数扫描 | 4h 周期 | 2024-2026")
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print("═" * 105)
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all_results: dict[str, list[dict]] = {}
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for symbol in SYMBOLS:
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print(f"\n ▸ {symbol} 参数扫描 (9 组)...")
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symbol_results = []
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for fast, slow in PARAM_GRID:
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r = await scan_one(symbol, fast, slow)
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symbol_results.append(r)
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print(f" EMA({fast},{slow}) 收益={r['return']:>+6.1f}% 夏普={r['sharpe']:>6.2f} 回撤={r['dd']:>6.1f}% 交易={r['trades']:>3} 胜率={r['wr']*100:>5.1f}%")
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all_results[symbol] = symbol_results
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# ── 每个币种最佳参数 ──
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print("\n" + "═" * 105)
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print(" ■ 各币种最优参数:")
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print(f" {'币种':<10} {'最优参数':<16} {'收益%':>7} {'夏普':>6} {'回撤%':>7} {'交易':>5} {'胜率%':>6} vs 基线")
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print("─" * 105)
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best_params = {}
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for symbol in SYMBOLS:
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# 按夏普排序
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sorted_r = sorted(all_results[symbol], key=lambda x: x["sharpe"], reverse=True)
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best = sorted_r[0]
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best_params[symbol] = (best["fast"], best["slow"])
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base = BASELINE[symbol]
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delta = best["return"] - base[2]
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print(f" {symbol:<10} EMA({best['fast']},{best['slow']}){'':>8} {best['return']:>6.1f}% {best['sharpe']:>6.2f} {best['dd']:>6.1f}% {best['trades']:>5} {best['wr']*100:>5.1f}% Δ={delta:+.1f}%")
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# ── 汇总对比 ──
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print("\n" + "═" * 105)
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print(" ■ 三层对比：基线(v1) → 止损(v2) → 成交量+时间止损+最优参数(v3)")
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print(f" {'币种':<10} {'版本':<20} {'收益%':>7} {'夏普':>6} {'回撤%':>7} {'交易':>5} {'胜率%':>6}")
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print("─" * 105)
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# v2 数据（从上次运行）
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V2 = {
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"BTCUSDT": (20, 50, 7.0, 0.27, -27.8),
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"ETHUSDT": (20, 50, 41.3, 0.76, -29.6),
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"BNBUSDT": (20, 50, 43.6, 0.81, -26.1),
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"SOLUSDT": (20, 50, 48.1, 0.70, -26.6),
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}
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for symbol in SYMBOLS:
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# 基线
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b = BASELINE[symbol]
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print(f" {symbol:<10} {'v1 原始 EMA双均线':<20} {b[2]:>6.1f}% {b[3]:>6.2f} {b[4]:>6.1f}%")
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# v2
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v2 = V2[symbol]
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print(f" {symbol:<10} {'v2 +ATR止损':<20} {v2[2]:>6.1f}% {v2[3]:>6.2f} {v2[4]:>6.1f}%")
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# v3 最佳
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best = [r for r in all_results[symbol] if r["fast"] == best_params[symbol][0] and r["slow"] == best_params[symbol][1]][0]
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print(f" {symbol:<10} {'v3 全优化+最优参数':<20} {best['return']:>6.1f}% {best['sharpe']:>6.2f} {best['dd']:>6.1f}%")
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print()
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# ── 组合收益 ──
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print("─" * 105)
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print(" ■ 等权组合（4币种各投入2500 USDT）:")
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total_baseline = sum(BASELINE[s][2] for s in SYMBOLS) / 4 * 100
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total_v2 = sum(V2[s][2] for s in SYMBOLS) / 4 * 100
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total_v3 = sum(
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sorted(all_results[s], key=lambda x: x["sharpe"], reverse=True)[0]["return"]
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for s in SYMBOLS
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) / 4 * 100
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print(f" v1 基线组合: {total_baseline:+.0f} USDT")
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print(f" v2 止损组合: {total_v2:+.0f} USDT")
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print(f" v3 全优化组合: {total_v3:+.0f} USDT")
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print("\n═" * 105)
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print(" 扫描完成。")
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print("═" * 105)
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if __name__ == "__main__":
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asyncio.run(main())
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