feat(engine): 添加策略示例集（18 个 Demo）
- 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
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"""
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横截面动量 — 选强弃弱 + 趋势/均值回归入场
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策略：
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1. 每根 4h K 线，计算 4 个币种过去 N 根 K 线的收益率
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2. 按收益率排名，只有前 2 名允许做多
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3. 趋势入场：EMA(10,50) 金叉 + 排名前2 → 买入
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4. 回归入场：RSI < 35 + 排名前2 → 回调买入
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5. 出场：排名跌出前2 或 EMA死叉 或 ATR止损
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币种：BTC/ETH/BNB/SOL | 4h | 2024-2026
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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
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from engine.data import DataService
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from engine.indicators import ema, atr, rsi as calc_rsi
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ALL_SYMBOLS = ["BTCUSDT", "ETHUSDT", "BNBUSDT", "SOLUSDT"]
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class CrossSectionConfig(StrategyConfig):
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lookback: int = 20 # 排名回溯周期
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rank_threshold: int = 2 # 只做前N名
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ema_fast: int = 10
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ema_slow: int = 50
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rsi_period: int = 14
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rsi_entry: float = 35.0
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atr_stop: float = 2.5
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data_start: Optional[datetime] = None
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data_end: Optional[datetime] = None
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class CrossSectionStrategy(BaseStrategy):
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"""横截面动量 — 只做强势币种"""
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strategy_type = "cross_section"
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def __init__(self, c: CrossSectionConfig):
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super().__init__(c)
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self.cfg = c
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# 所有币种的数据 {symbol: [Kline]}
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self._all_klines: dict[str, list[Kline]] = {}
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self._all_closes: dict[str, list[float]] = {}
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# 当前币种的数据
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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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for sym in ALL_SYMBOLS:
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klines = await ds.fetch_klines(
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symbol=sym, interval="4h",
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start_time=self.cfg.data_start, end_time=self.cfg.data_end,
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limit=1_000_000,
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)
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self._all_klines[sym] = klines
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self._all_closes[sym] = [k.close for k in klines]
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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_rank(self, ts: float) -> dict[str, float]:
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"""计算所有币种在指定时间戳的排名收益率，返回 {symbol: return%}"""
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scores = {}
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for sym in ALL_SYMBOLS:
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klines = self._all_klines.get(sym, [])
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if not klines:
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scores[sym] = -999
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continue
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# 找到时间戳 <= ts 的最新K线索引
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idx = len(klines) - 1
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for i in range(len(klines) - 1, -1, -1):
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if klines[i].open_time <= ts:
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idx = i
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break
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# 计算过去 lookback 根K线的收益率
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start_idx = max(0, idx - self.cfg.lookback)
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if start_idx >= idx:
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scores[sym] = 0
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else:
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start_price = self._all_closes[sym][start_idx]
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end_price = self._all_closes[sym][idx]
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scores[sym] = (end_price / start_price - 1) * 100 if start_price > 0 else 0
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return scores
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def _my_rank(self, ts: float) -> int:
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"""当前币种在全部币种中的排名（1=最强）"""
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scores = self._get_rank(ts)
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my_score = scores.get(self.cfg.symbol, -999)
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# 高于我的分数有几个
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better = sum(1 for s in scores.values() if s > my_score)
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return better + 1
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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.ema_slow + 10:
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return None
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fast = ema(self._closes, self.cfg.ema_fast)
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slow = ema(self._closes, self.cfg.ema_slow)
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atr_vals = atr(self._highs, self._lows, self._closes, 14)
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rsi_vals = calc_rsi(self._closes, self.cfg.rsi_period)
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cur_f, cur_s = fast[-1], slow[-1]
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prev_f, prev_s = fast[-2], slow[-2]
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cur_atr = atr_vals[-1]
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cur_rsi = rsi_vals[-1]
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if cur_f == 0 or cur_s == 0 or cur_atr == 0 or cur_rsi == 0:
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return None
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rank = self._my_rank(k.open_time)
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is_top = rank <= self.cfg.rank_threshold
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golden = prev_f <= prev_s and cur_f > cur_s # 趋势入场
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death = prev_f >= prev_s and cur_f < cur_s # 趋势出场
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oversold = cur_rsi < self.cfg.rsi_entry # 均值回归入场
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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 * cur_atr
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if not is_top:
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self._in_position = False
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return Signal(symbol=self.cfg.symbol, side="SELL",
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reason=f"排名跌出前{self.cfg.rank_threshold}(#{rank})", timestamp=k.open_time)
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if death:
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self._in_position = False
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return Signal(symbol=self.cfg.symbol, side="SELL", reason="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="ATR止损", timestamp=k.open_time)
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# ── 入场 ──
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if not self._in_position and is_top:
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# 趋势信号：金叉
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if golden:
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self._in_position = True
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self._highest = k.close
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return Signal(symbol=self.cfg.symbol, side="BUY",
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reason=f"金叉+#{rank}横截面动量", timestamp=k.open_time)
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# 回归信号：RSI超卖
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if oversold:
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self._in_position = True
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self._highest = k.close
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return Signal(symbol=self.cfg.symbol, side="BUY",
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confidence=0.7, # 回归信号稍降仓位
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reason=f"RSI超卖+#{rank}横截面动量 RSI={cur_rsi:.0f}",
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timestamp=k.open_time)
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return None
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# ═══════════════════════════════════════════════
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DATE_START = datetime(2024, 1, 1)
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DATE_END = datetime(2026, 1, 1)
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async def main():
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print()
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print("═" * 110)
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print(" 横截面动量 — 只做最强 + 趋势/回归双信号 | 4h | 2024-2026")
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print("═" * 110)
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print(f" {'币种':<10} {'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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results = {}
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for symbol in ALL_SYMBOLS:
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sc = CrossSectionConfig(symbol=symbol, data_start=DATE_START, data_end=DATE_END)
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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(CrossSectionStrategy, sc)
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m = r.metrics
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results[symbol] = (m, r)
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# 统计排名分布和信号类型
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trend_signals = sum(1 for t in r.trades if t.side == "BUY" and "金叉" in t.reason)
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meanrev_signals = sum(1 for t in r.trades if t.side == "BUY" and "RSI" in t.reason)
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exits_rank = sum(1 for t in r.trades if t.side == "SELL" and "排名" in t.reason)
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print(f" {symbol:<10} {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} └ 趋势入场:{trend_signals} 回归入场:{meanrev_signals} 排名出场:{exits_rank}")
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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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for t in sells[-2:]:
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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} {t.reason}")
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# ── 对比 ──
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print("─" * 110)
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print("\n ■ 对比：纯趋势跟踪 vs 横截面动量")
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TREND = {
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"BTCUSDT": ("EMA v3(10,50)", 39.9, 1.03, 20),
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"ETHUSDT": ("EMA v3(10,75)", 53.6, 1.04, 18),
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"BNBUSDT": ("EMA v1(20,50)", 52.0, 0.71, 41),
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"SOLUSDT": ("EMA v3(30,50)", 73.6, 1.18, 13),
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}
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print(f" {'币种':<10} {'纯趋势':>24} → {'横截面动量':>24}")
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print(f" {'':<10} {'收益% 夏普 交易':>24} → {'收益% 夏普 交易':>24}")
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for sym in ALL_SYMBOLS:
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t_name, t_ret, t_sh, t_tr = TREND[sym]
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m, r = results[sym]
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print(f" {sym:<10} {t_ret:>5.1f}% {t_sh:>5.2f} {t_tr:>4}次 → {m.total_return_pct:>5.1f}% {m.sharpe_ratio:>5.2f} {m.total_trades:>4}次")
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print("\n═" * 110)
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if __name__ == "__main__":
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asyncio.run(main())
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