根据上条的方法把以下代码修改后输出def trading_strategy(df, para): """ 根据给定的参数，计算交易信号 :param df: pandas.DataFrame, 包含股票价格数据的DataFrame :param para: list, 交易策略的参数 :return: int, 交易信号，1表示买入，-1表示卖出，0表示持有 """ # 策略参数 n = int(para[0]) # 取平均线和标准差的参数 m = para[1] # 标准差的倍数 ma_n = int(para[2]) # MA指标的参数 volatility_factor = 2 # 波动率因子，可以根据实际情况调整 # 计算均线和标准差 close = df['close'].values ma = np.mean(close[-n:]) std = np.std(close[-n:], ddof=1) # 计算上下轨道 upper = ma + volatility_factor * std lower = ma - volatility_factor * std # 计算MA指标 ma_values = df['close'].rolling(ma_n).mean().values ma_current = ma_values[-1] ma_previous = ma_values[-2] # 计算LLT指标 llt = np.zeros(ma_n) llt[0] = close[-1] alpha = 2 / (ma_n + 1) for i in range(1, ma_n): llt[i] = alpha * close[-i-1] + (1 - alpha) * llt[i-1] # 寻找交易信号 signal = 0 close_current = close[-1] close_previous = close[-2] # 做多信号 if (close_current > upper) and (close_previous <= upper) and \ (close_current > ma_current) and (close_previous <= ma_previous) and (std < volatility_factor * ma_current): # LLT指标过滤做多信号 if close_current < llt[-1]: signal = 0 else: signal = 1 # 做空信号 elif (close_current < lower) and (close_previous >= lower) and \ (close_current < ma_current) and (close_previous >= ma_previous) and (std < volatility_factor * ma_current): # LLT指标过滤做空信号 if close_current > llt[-1]: signal = 0 else: signal = -1 # 平仓信号 elif ((close_current < ma) and (close_previous >= ma)) or ((close_current > ma) and (close_previous <= ma)): signal = 0 return signal

根据上述方法，修改后的代码如下：

def trading_strategy(df, para_list):
    """
    根据给定的参数，计算交易信号
    :param df: pandas.DataFrame, 包含股票价格数据的DataFrame
    :param para_list: list, 包含交易策略的所有参数组合
    :return: list, 包含每个参数组合对应的交易信号列表
    """
    signals = []
    
    for para in para_list:
        # 策略参数
        n = int(para[0])  # 取平均线和标准差的参数
        m = para[1]  # 标准差的倍数
        ma_n = int(para[2])  # MA指标的参数
        volatility_factor = 2  # 波动率因子，可以根据实际情况调整
        
        # 计算均线和标准差
        close = df['close'].values
        ma = np.mean(close[-n:])
        std = np.std(close[-n:], ddof=1)
        
        # 计算上下轨道
        upper = ma + volatility_factor * std
        lower = ma - volatility_factor * std
        
        # 计算MA指标
        ma_values = df['close'].rolling(ma_n).mean().values
        ma_current = ma_values[-1]
        ma_previous = ma_values[-2]
        
        # 计算LLT指标
        llt = np.zeros(ma_n)
        llt[0] = close[-1]
        alpha = 2 / (ma_n + 1)
        for i in range(1, ma_n):
            llt[i] = alpha * close[-i-1] + (1 - alpha) * llt[i-1]
        
        # 寻找交易信号
        signal = 0
        close_current = close[-1]
        close_previous = close[-2]
        
        # 做多信号
        if (close_current > upper) and (close_previous <= upper) and \
           (close_current > ma_current) and (close_previous <= ma_previous) and \
           (std < volatility_factor * ma_current):
            # LLT指标过滤做多信号
            if close_current < llt[-1]:
                signal = 0
            else:
                signal = 1
        
        # 做空信号
        elif (close_current < lower) and (close_previous >= lower) and \
             (close_current < ma_current) and (close_previous >= ma_previous) and \
             (std < volatility_factor * ma_current):
            # LLT指标过滤做空信号
            if close_current > llt[-1]:
                signal = 0
            else:
                signal = -1
        
        # 平仓信号
        elif ((close_current < ma) and (close_previous >= ma)) or \
             ((close_current > ma) and (close_previous <= ma)):
            signal = 0
        
        signals.append(signal)
        
    return signals

这个函数的输入参数包括一个包含股票价格数据的DataFrame和一个参数列表`para_list`，其中`para_list`包含所有可能的参数组合。函数会遍历所有参数组合，对每个组合都计算一次交易信号，最后将每个参数组合对应的交易信号列表返回。