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将这段代码加入名为'trading_strategy_para_list'的属性或函数

抱歉，我可能理解错了你的意图。如果你想将这段代码添加到名为'trading_strategy_para_list'的属性或函数中，你可以按照以下步骤操作：

1. 打开Signals模块，找到一个合适的位置添加该属性或函数。
2. 添加该属性或函数，并将该函数的参数名称修改为'para_list'。
3. 在该函数中添加该代码段，并注意要调整缩进以确保代码正确。
4. 保存并关闭该模块。

以下是可能的实现代码：

def trading_strategy_para_list():
    para_list = [
        {'name': 'n', 'default': 20, 'description': '计算均线和标准差的参数'},
        {'name': 'm', 'default': 2, 'description': '标准差的倍数'},
        {'name': 'ma_n', 'default': 5, 'description': 'MA指标的参数'}
    ]
    return para_list

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

请注意，以上代码只是一个示例，你可能需要根据实际情况进行适当的修改和调整。