auto-sync: 2026-05-11 09:02:47
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cfdaily
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"""
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Dual Moving Average (双均线) Strategy
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Classic trend-following strategy:
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- Golden Cross: Short MA crosses above Long MA → BUY
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- Death Cross: Short MA crosses below Long MA → SELL
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Author: Zhang Fei
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Date: 2026-05-11
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"""
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import numpy as np
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import pandas as pd
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from typing import Dict, List, Tuple, Optional
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from dataclasses import dataclass
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from datetime import datetime
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import logging
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logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
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logger = logging.getLogger(__name__)
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@dataclass
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class Trade:
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code: str
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entry_date: datetime
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exit_date: Optional[datetime]
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entry_price: float
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exit_price: Optional[float]
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direction: int
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shares: int
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entry_value: float
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exit_value: Optional[float]
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profit: Optional[float]
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profit_pct: Optional[float]
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hold_days: Optional[int]
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strategy: str
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@dataclass
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class BacktestResult:
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strategy: str
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start_date: datetime
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end_date: datetime
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initial_capital: float
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final_capital: float
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total_return: float
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annual_return: float
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max_drawdown: float
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sharpe_ratio: float
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win_rate: float
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total_trades: int
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win_trades: int
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loss_trades: int
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avg_profit_pct: float
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avg_win_pct: float
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avg_loss_pct: float
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trades: List[Trade]
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class DualMAStrategy:
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def __init__(self, short_period: int = 5, long_period: int = 20):
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self.short_period = short_period
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self.long_period = long_period
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self.name = f"Dual_MA_{short_period}_{long_period}"
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def calculate_indicators(self, df: pd.DataFrame) -> pd.DataFrame:
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df = df.copy()
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df['ma_short'] = df['close'].rolling(window=self.short_period, min_periods=1).mean()
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df['ma_long'] = df['close'].rolling(window=self.long_period, min_periods=1).mean()
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return df
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def generate_signals(self, df: pd.DataFrame) -> pd.DataFrame:
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df = self.calculate_indicators(df)
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# Generate cross signals
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df['ma_prev_short'] = df['ma_short'].shift(1)
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df['ma_prev_long'] = df['ma_long'].shift(1)
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# Golden Cross: Short MA crosses above Long MA
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df['golden_cross'] = (
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(df['ma_prev_short'] <= df['ma_prev_long']) &
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(df['ma_short'] > df['ma_long'])
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).astype(int)
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# Death Cross: Short MA crosses below Long MA
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df['death_cross'] = (
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(df['ma_prev_short'] >= df['ma_prev_long']) &
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(df['ma_short'] < df['ma_long'])
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).astype(int)
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df['signal'] = 0
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df.loc[df['golden_cross'] == 1, 'signal'] = 1
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df.loc[df['death_cross'] == 1, 'signal'] = -1
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return df
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class DualMABacktester:
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def __init__(self, initial_capital: float = 100000.0,
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short_period: int = 5,
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long_period: int = 20,
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commission: float = 0.0003,
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slipage: float = 0.001):
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self.initial_capital = initial_capital
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self.commission = commission
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self.slipage = slipage
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self.strategy = DualMAStrategy(short_period, long_period)
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def run_single_stock(self, df: pd.DataFrame, code: str) -> BacktestResult:
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df = df.sort_values('date').reset_index(drop=True)
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df = self.strategy.generate_signals(df)
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capital = self.initial_capital
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position = 0
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trades = []
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current_trade = None
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for i, row in df.iterrows():
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# Entry
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if row['signal'] == 1 and position == 0:
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entry_price = row['close'] * (1 + self.slipage)
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shares = int(capital / entry_price)
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if shares > 0:
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entry_value = shares * entry_price
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commission_fee = entry_value * self.commission
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capital -= (entry_value + commission_fee)
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position = shares
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current_trade = Trade(
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code=code,
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entry_date=row['date'],
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exit_date=None,
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entry_price=entry_price,
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exit_price=None,
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direction=1,
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shares=shares,
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entry_value=entry_value,
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exit_value=None,
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profit=None,
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profit_pct=None,
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hold_days=None,
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strategy=self.strategy.name
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)
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# Exit
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elif row['signal'] == -1 and position > 0 and current_trade is not None:
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exit_price = row['close'] * (1 - self.slipage)
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exit_value = position * exit_price
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commission_fee = exit_value * self.commission
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capital += (exit_value - commission_fee)
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current_trade.exit_date = row['date']
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current_trade.exit_price = exit_price
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current_trade.exit_value = exit_value
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current_trade.profit = exit_value - current_trade.entry_value - commission_fee - (current_trade.entry_value * self.commission)
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current_trade.profit_pct = (exit_price - current_trade.entry_price) / current_trade.entry_price
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current_trade.hold_days = (row['date'] - current_trade.entry_date).days
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trades.append(current_trade)
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position = 0
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current_trade = None
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# Close remaining position at end
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if position > 0 and current_trade is not None:
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last_row = df.iloc[-1]
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exit_price = last_row['close']
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exit_value = position * exit_price
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commission_fee = exit_value * self.commission
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capital += (exit_value - commission_fee)
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current_trade.exit_date = last_row['date']
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current_trade.exit_price = exit_price
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current_trade.exit_value = exit_value
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current_trade.profit = exit_value - current_trade.entry_value - commission_fee - (current_trade.entry_value * self.commission)
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current_trade.profit_pct = (exit_price - current_trade.entry_price) / current_trade.entry_price
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current_trade.hold_days = (last_row['date'] - current_trade.entry_date).days
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trades.append(current_trade)
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return self._calculate_metrics(trades, df)
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def _calculate_metrics(self, trades: List[Trade], df: pd.DataFrame) -> BacktestResult:
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if len(trades) == 0:
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return BacktestResult(
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strategy=self.strategy.name,
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start_date=df['date'].min(),
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end_date=df['date'].max(),
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initial_capital=self.initial_capital,
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final_capital=self.initial_capital,
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total_return=0.0,
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annual_return=0.0,
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max_drawdown=0.0,
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sharpe_ratio=0.0,
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win_rate=0.0,
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total_trades=0,
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win_trades=0,
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loss_trades=0,
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avg_profit_pct=0.0,
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avg_win_pct=0.0,
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avg_loss_pct=0.0,
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trades=[]
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)
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final_capital = self.initial_capital + sum(t.profit or 0 for t in trades)
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total_return = (final_capital - self.initial_capital) / self.initial_capital
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days = (df['date'].max() - df['date'].min()).days
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annual_return = (1 + total_return) ** (365 / days) - 1 if days > 0 else 0
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win_trades = [t for t in trades if (t.profit or 0) > 0]
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loss_trades = [t for t in trades if (t.profit or 0) <= 0]
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win_rate = len(win_trades) / len(trades) if trades else 0
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avg_profit_pct = np.mean([t.profit_pct or 0 for t in trades])
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avg_win_pct = np.mean([t.profit_pct or 0 for t in win_trades]) if win_trades else 0
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avg_loss_pct = np.mean([t.profit_pct or 0 for t in loss_trades]) if loss_trades else 0
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# Simple max drawdown calculation
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cumulative = self.initial_capital
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max_cumulative = cumulative
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max_drawdown = 0.0
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for trade in trades:
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cumulative += trade.profit or 0
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if cumulative > max_cumulative:
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max_cumulative = cumulative
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drawdown = (max_cumulative - cumulative) / max_cumulative
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if drawdown > max_drawdown:
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max_drawdown = drawdown
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# Sharpe ratio (simplified)
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daily_returns = [t.profit_pct / t.hold_days for t in trades if t.hold_days and t.hold_days > 0]
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sharpe_ratio = np.sqrt(252) * np.mean(daily_returns) / np.std(daily_returns) if len(daily_returns) > 1 and np.std(daily_returns) > 0 else 0
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return BacktestResult(
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strategy=self.strategy.name,
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start_date=df['date'].min(),
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end_date=df['date'].max(),
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initial_capital=self.initial_capital,
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final_capital=final_capital,
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total_return=total_return,
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annual_return=annual_return,
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max_drawdown=max_drawdown,
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sharpe_ratio=sharpe_ratio,
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win_rate=win_rate,
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total_trades=len(trades),
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win_trades=len(win_trades),
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loss_trades=len(loss_trades),
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avg_profit_pct=avg_profit_pct,
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avg_win_pct=avg_win_pct,
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avg_loss_pct=avg_loss_pct,
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trades=trades
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)
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def load_sample_data(code: str = '510300.SH',
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start_date: str = '2023-01-01',
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end_date: str = '2024-12-31') -> pd.DataFrame:
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"""Load sample data for testing"""
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np.random.seed(42)
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dates = pd.date_range(start=start_date, end=end_date, freq='B')
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n = len(dates)
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# Generate synthetic price data with trend
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base_price = 4.0
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returns = np.random.normal(0.0005, 0.015, n)
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prices = base_price * np.cumprod(1 + returns)
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df = pd.DataFrame({
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'date': dates,
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'open': prices * (1 + np.random.normal(0, 0.002, n)),
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'high': prices * (1 + np.random.normal(0.005, 0.003, n)),
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'low': prices * (1 - np.random.normal(0.005, 0.003, n)),
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'close': prices,
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'volume': np.random.randint(1000000, 10000000, n)
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})
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return df
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def main():
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# Sample backtest
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logger.info("Starting Dual MA Strategy Backtest...")
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df = load_sample_data()
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logger.info(f"Loaded {len(df)} days of data")
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backtester = DualMABacktester(
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initial_capital=100000.0,
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short_period=5,
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long_period=20
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)
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result = backtester.run_single_stock(df, '510300.SH')
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logger.info(f"Strategy: {result.strategy}")
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logger.info(f"Period: {result.start_date.date()} to {result.end_date.date()}")
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logger.info(f"Initial Capital: {result.initial_capital:.2f}")
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logger.info(f"Final Capital: {result.final_capital:.2f}")
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logger.info(f"Total Return: {result.total_return*100:.2f}%")
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logger.info(f"Annual Return: {result.annual_return*100:.2f}%")
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logger.info(f"Max Drawdown: {result.max_drawdown*100:.2f}%")
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logger.info(f"Sharpe Ratio: {result.sharpe_ratio:.2f}")
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logger.info(f"Win Rate: {result.win_rate*100:.2f}%")
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logger.info(f"Total Trades: {result.total_trades}")
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logger.info(f"Win/Loss: {result.win_trades}/{result.loss_trades}")
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logger.info(f"Avg Profit %: {result.avg_profit_pct*100:.2f}%")
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# Print first 5 trades
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logger.info("\nFirst 5 Trades:")
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for i, trade in enumerate(result.trades[:5]):
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logger.info(f" {i+1}. {trade.entry_date.date()} -> {trade.exit_date.date()}: "
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f"{trade.profit_pct*100:+.2f}% (hold {trade.hold_days} days)")
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if __name__ == '__main__':
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main()
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# 双均线策略回测报告
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## 策略信息
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- **策略名称**: Dual_MA_5_20
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- **回测期间**: 2023-01-02 至 2024-12-31
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- **初始资金**: 100000.00 元
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## 绩效指标
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| 指标 | 值 |
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|------|-----|
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| 最终资金 | 104436.44 元 |
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| 总收益率 | 4.44% |
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| 年化收益率 | 2.20% |
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| 最大回撤 | 14.31% |
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| 夏普比率 | -3.22 |
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| 胜率 | 44.44% |
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| 总交易次数 | 18 |
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| 盈利/亏损 | 8/10 |
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| 平均盈亏比 | 0.46% |
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| 平均盈利 | 5.75% |
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| 平均亏损 | -3.77% |
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## 交易明细
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| 序号 | 入场日期 | 出场日期 | 持仓天数 | 收益率 |
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|------|----------|----------|----------|--------|
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| 1 | 2023-01-09 | 2023-01-23 | 14 | -3.46% |
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| 2 | 2023-03-24 | 2023-03-31 | 7 | -3.17% |
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| 3 | 2023-04-05 | 2023-04-26 | 21 | +1.62% |
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| 4 | 2023-05-09 | 2023-05-16 | 7 | -3.79% |
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| 5 | 2023-06-12 | 2023-07-05 | 23 | -1.28% |
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| 6 | 2023-08-09 | 2023-09-28 | 50 | +6.52% |
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| 7 | 2023-10-16 | 2023-11-15 | 30 | +11.41% |
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| 8 | 2023-11-30 | 2023-12-01 | 1 | +1.13% |
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| 9 | 2023-12-20 | 2024-01-05 | 16 | -6.63% |
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| 10 | 2024-01-31 | 2024-02-16 | 16 | -1.30% |
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| 11 | 2024-02-21 | 2024-04-22 | 61 | +13.10% |
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| 12 | 2024-05-22 | 2024-06-25 | 34 | +4.83% |
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| 13 | 2024-06-28 | 2024-07-04 | 6 | +0.09% |
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| 14 | 2024-07-05 | 2024-07-19 | 14 | -3.20% |
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| 15 | 2024-08-08 | 2024-09-02 | 25 | +7.29% |
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| 16 | 2024-09-03 | 2024-09-06 | 3 | -4.70% |
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| 17 | 2024-10-31 | 2024-11-13 | 13 | -7.47% |
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| 18 | 2024-11-20 | 2024-12-10 | 20 | -2.65% |
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