pandas 使用熊猫滚动法计算加权移动平均线
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Calculating weighted moving average using pandas Rolling method
提问by anilca
I calculate simple moving average:
我计算简单的移动平均线：
def sma(data_frame, length=15):
# TODO: Be sure about default values of length.
smas = data_frame.Close.rolling(window=length, center=False).mean()
return smas
Using the rolling function is it possible to calculate weighted moving average? As I read in the documentation, I think that I have to pass win_typeparameter. But I'm not sure which one I have to choose.
使用滚动函数是否可以计算加权移动平均值？正如我在文档中读到的，我认为我必须传递win_type参数。但我不确定我必须选择哪一个。
Here is a definitionfor weighted moving average.
这是加权移动平均线的定义。
Thanks in advance,
提前致谢，
回答by Dthal
Yeah, that part of pandas really isn't very well documented. I think you might have to use rolling.apply() if you aren't using one of the standard window types. I poked at it and got this to work:
是的，pandas 的那部分确实没有很好的记录。如果您不使用标准窗口类型之一，我认为您可能必须使用rolling.apply()。我戳了一下它并让它起作用：
>>> import numpy as np
>>> import pandas as pd
>>> d = pd.DataFrame({'a':range(10), 'b':np.random.random(size=10)})
>>> d.b = d.b.round(2)
>>> d
a b
0 0 0.28
1 1 0.70
2 2 0.28
3 3 0.99
4 4 0.72
5 5 0.43
6 6 0.71
7 7 0.75
8 8 0.61
9 9 0.14
>>> wts = np.array([-1, 2])
>>> def f(w):
def g(x):
return (w*x).mean()
return g
>>> d.rolling(window=2).apply(f(wts))
a b
0 NaN NaN
1 1.0 0.560
2 1.5 -0.070
3 2.0 0.850
4 2.5 0.225
5 3.0 0.070
6 3.5 0.495
7 4.0 0.395
8 4.5 0.235
9 5.0 -0.165
I think that is correct. The reason for the closure there is that the signature for rolling.apply is rolling.apply(func, *args, **kwargs), so the weights get tuple-unpacked if you just send them to the function directly, unless you send them as a 1-tuple (wts,), but that's weird.
我认为这是正确的。关闭的原因是 rolling.apply 的签名是rolling.apply(func, *args, **kwargs)，所以如果你直接将它们发送到函数，权重会被元组解包，除非你将它们作为 1-tuple 发送(wts,)，但这很奇怪。
