问题描述
我有一个时间序列"Ser"，我想用一个滚动窗口来计算波动率(标准差).我当前的代码以这种形式正确执行了此操作:
I have a time series "Ser" and I want to compute volatilities (standard deviations) with a rolling window. My current code correctly does it in this form:
w=10
for timestep in range(length):
subSer=Ser[timestep:timestep+w]
mean_i=np.mean(subSer)
vol_i=(np.sum((subSer-mean_i)**2)/len(subSer))**0.5
volList.append(w_i)
在我看来，这是非常低效的.熊猫是否具有内置功能可用于执行此类操作?
This seems to me very inefficient. Does Pandas have built-in functionality for doing something like this?
推荐答案
您似乎正在寻找 Series.rolling .您可以应用 std 计算到结果对象:
It looks like you are looking for Series.rolling. You can apply the std calculations to the resulting object:
roller = Ser.rolling(w)
volList = roller.std(ddof=0)
如果您不打算再次使用滚动窗口对象，则可以编写单行代码:
If you don't plan on using the rolling window object again, you can write a one-liner:
volList = Ser.rolling(w).std(ddof=0)
请记住，在这种情况下必须使用ddof=0，因为标准差的归一化是通过len(Ser)-ddof进行的，并且ddof在熊猫中默认为1.
Keep in mind that ddof=0 is necessary in this case because the normalization of the standard deviation is by len(Ser)-ddof, and that ddof defaults to 1 in pandas.
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