使用Pandas实现加权移动平均(WMA)迭代26次后值归零的BUG求助

问题概述 #

测试价格指标计算用的WMA函数时，迭代至索引27后，WMA值突然降至0.0，无法定位原因，请求协助排查。

数据处理规范 #

测试用CSV列名均为小写，需用以下代码预处理：

data = pd.read_csv(csv_path)
data = data.drop(['symbol'], axis=1)
data.rename(columns={'open': 'Open', 'high': 'High', 'low': 'Low', 'close': 'Close', 'volume': 'Volume'}, inplace=True)

CSV仅包含Open/High/Low/Close/Volume字段，input_mode参数不可超过4（无HL2、HLC3等衍生价格数据）。

WMA计算函数代码 #

使用默认参数测试以下函数：

def wma(price_df: PandasDataFrame, n: int = 14, input_mode: int = 2, from_price: bool = True, *,
        indicator_name: str = 'None') -> PandasDataFrame:
    if from_price:
        name_var, state = input_type(__input_mode__=input_mode)
    else:
        if indicator_name == 'None':
            raise TypeError('Invalid input argument. indicator_name cannot be set to None if from_price is False.')
        else:
            name_var = indicator_name

    wma_n = pd.DataFrame(index=range(price_df.shape[0]), columns=range(1))
    wma_n.rename(columns={0: f'WMA{n}'}, inplace=True)
    weight = np.arange(1, (n + 1)).astype('float64')
    weight = weight * n
    norm = sum(weight)
    weight_df = pd.DataFrame(weight)
    weight_df.rename(columns={0: 'weight'}, inplace=True)
    product = pd.DataFrame()
    product_sum = 0
    for i in range(price_df.shape[0]):
        if i < (n - 1):
            # 创建无法计算WMA的NaN值
            wma_n[f'WMA{n}'].iloc[i] = np.nan
        elif i == (n - 1):
            product = price_df[f'{name_var}'].iloc[:(i + 1)] * weight_df['weight']
            product_sum = product.sum()
            wma_n[f'WMA{n}'].iloc[i] = product_sum / norm
            print(f'index: {i}, wma: ', wma_n[f'WMA{n}'].iloc[i])
            print(product_sum)
            print(norm)
            product = product.iloc[0:0]
            product_sum = 0

        elif i > (n - 1):
            product = price_df[f'{name_var}'].iloc[(i - (n - 1)): (i + 1)] * weight_df['weight']
            product_sum = product.sum()
            wma_n[f'WMA{n}'].iloc[i] = product_sum / norm
            print(f'index: {i}, wma: ', wma_n[f'WMA{n}'].iloc[i])
            print(product_sum)
            print(norm)
            product = product.iloc[0:0]
            product_sum = 0

    return wma_n

辅助函数代码 #

def input_type(__input_mode__: int) -> (str, bool):
    list_of_inputs = ['Open', 'Close', 'High', 'Low', 'HL2', 'HLC3', 'OHLC4', 'HLCC4']
    if __input_mode__ in range(1, 10, 1):
        input_name = list_of_inputs[__input_mode__ - 1]
        state = True
        return input_name, state
    else:
        raise TypeError('__input_mode__ out of range.')

实际输出结果 #

index: 13, wma:  14467.42857142857
product_sum:  21267120.0
norm 1470.0
index: 14, wma:  14329.609523809524
product_sum:  21064526.0
norm 1470.0
index: 15, wma:  14053.980952380953
product_sum:  20659352.0
norm 1470.0
index: 16, wma:  13640.480952380953
product_sum:  20051507.0
norm 1470.0
index: 17, wma:  13089.029523809522
product_sum:  19240873.4
norm 1470.0
index: 18, wma:  12399.72
product_sum:  18227588.4
norm 1470.0
index: 19, wma:  11572.234285714285
product_sum:  17011184.4
norm 1470.0
index: 20, wma:  10607.100952380953
product_sum:  15592438.4
norm 1470.0
index: 21, wma:  9504.32
product_sum:  13971350.4
norm 1470.0
index: 22, wma:  8263.905714285715
product_sum:  12147941.4
norm 1470.0
index: 23, wma:  6885.667619047619
product_sum:  10121931.4
norm 1470.0
index: 24, wma:  5369.710476190477
product_sum:  7893474.4
norm 1470.0
index: 25, wma:  3716.270476190476
product_sum:  5462917.6
norm 1470.0
index: 26, wma:  1926.48
product_sum:  2831925.6
norm 1470.0
index: 27, wma:  0.0
product_sum:  0.0
norm 1470.0
index: 28, wma:  0.0
product_sum:  0.0
norm 1470.0

内容的提问来源于stack exchange，提问作者Jakub Szurlej