# 策略核心类定义
class VolatilityFractalTrendStrategy:
    def __init__(self, initial_capital=100000):
        self.initial_capital = initial_capital
        self.capital = initial_capital
        self.position = 0
        
        # 策略参数
        self.fast_period = 8       # 快线周期
        self.slow_period = 21      # 慢线周期
        self.fractal_period = 13   # 分形计算周期
        self.volatility_period = 20  # 波动率计算周期
        
    def calculate_volatility_fractal(self, data):
        """计算波动率分形特征"""
        # 计算ATR
        high_low = data['high'] - data['low']
        high_close = np.abs(data['high'] - data['close'].shift())
        low_close = np.abs(data['low'] - data['close'].shift())
        true_range = pd.concat([high_low, high_close, low_close], axis=1).max(axis=1)
        atr = true_range.rolling(window=self.volatility_period).mean()
        
        # 计算分形特征
        volatility_short = true_range.rolling(window=self.fractal_period // 2).mean()
        volatility_long = true_range.rolling(window=self.fractal_period * 2).mean()
        fractal_ratio = volatility_short / volatility_long
        
        # 生成分形信号
        fractal_signal = pd.Series(0, index=data.index)
        fractal_signal[fractal_ratio > 1.2] = 1  # 分形扩张
        fractal_signal[fractal_ratio < 0.8] = -1 # 分形收缩
        
        return atr, fractal_signal
    
    def generate_signals(self, data):
        """生成综合交易信号"""
        atr, fractal_signal = self.calculate_volatility_fractal(data)
        
        # 计算趋势信号
        data['ma_fast'] = data['close'].rolling(window=self.fast_period).mean()
        data['ma_slow'] = data['close'].rolling(window=self.slow_period).mean()
        trend_signal = pd.Series(0, index=data.index)
        trend_signal[data['ma_fast'] > data['ma_slow']] = 1
        trend_signal[data['ma_fast'] < data['ma_slow']] = -1
        
        # 综合信号
        signals = pd.DataFrame(index=data.index)
        signals['final_signal'] = 0
        
        # 趋势与分形同向时增强信号
        same_direction = (trend_signal == fractal_signal) & (trend_signal != 0)
        signals.loc[same_direction, 'final_signal'] = trend_signal[same_direction] * 1.5
        
        return signals