手动计算波动率与内置函数不一样

Question

有人可以帮助我了解我错在哪里吗？我不知道为什么每列的波动率都不同...

这是我的代码示例：

from math import sqrt
from numpy import around
from numpy.random import uniform
from pandas import DataFrame
from statistics import stdev

data = around(a=uniform(low=1.0, high=50.0, size=(500, 1)), decimals=3)
df = DataFrame(data=data, columns=['close'], dtype='float64')
df.loc[:, 'delta'] = df.loc[:, 'close'].pct_change().fillna(0).round(3)

volatility = []

for index in range(df.shape[0]):
    if index < 90:
        volatility.append(0)
    else:
        start = index - 90
        stop = index + 1
        volatility.append(stdev(df.loc[start:stop, 'delta']) * sqrt(252))

df.loc[:, 'volatility1'] = volatility
df.loc[:, 'volatility2'] = df.loc[:, 'delta'].rolling(window=90).std(ddof=0) * sqrt(252)

print(df)

      close   delta  volatility1  volatility2
0    10.099   0.000     0.000000          NaN
1    26.331   1.607     0.000000          NaN
2    32.361   0.229     0.000000          NaN
3     2.068  -0.936     0.000000          NaN
4    36.241  16.525     0.000000          NaN
..      ...     ...          ...          ...
495  48.015  -0.029    46.078037    46.132943
496   6.988  -0.854    46.036210    46.178820
497  23.331   2.339    46.003184    45.837245
498  25.551   0.095    45.608260    45.792188
499  46.248   0.810    45.793012    45.769787

[500 rows x 4 columns]

非常感谢！

Answer 1

需要进行三处小改动。添加内联评论。 89 是必需的，因为包含端点（与许多其他 python 不同）。 ddof=1 是必需的，因为 stdev 默认使用它。 This article 谈论的是 numpy std 而不是 stdev，但 ddof 所做的理论仍然是一样的。

此外，将来尝试将大小更改为 95 之类的值。调试时不需要其他 405 行，很高兴看到从 0/NaN 到实际波动率的转换，您需要 89不是 90.

0 与 NaN 的区别仍然存在。这是您附加 0 和滚动的默认行为的结果。我不确定那是不是故意的，所以我离开了。

from math import sqrt
from numpy import around
from numpy.random import uniform
from pandas import DataFrame
from statistics import stdev

data = around(a=uniform(low=1.0, high=50.0, size=(500, 1)), decimals=3)
df = DataFrame(data=data, columns=['close'], dtype='float64')
df['delta'] = df['close'].pct_change().fillna(0).round(3)

volatility = []

for index in range(df.shape[0]):
    if index < 89: #change to 89
        volatility.append(0)
    else:
        start = index - 89 #change to 89
        stop = index
        volatility.append(stdev(df.loc[start:stop, 'delta']) * sqrt(252))

df['volatility1'] = volatility
df['volatility2'] = df.loc[:, 'delta'].rolling(window=90).std(ddof=1) * sqrt(252) #change to ddof=1

print(df)

手动计算波动率与内置函数不一样

Calculating volatility manually vs built-in functions are not the same

python

volatility

pandas