[英]Calculating weighted moving average using pandas Rolling method
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? 使用滚动函数可以计算加权移动平均值吗? As I read in the documentation , I think that I have to pass win_type parameter. 正如我在文档中看到的那样 ,我认为我必须传递win_type参数。 But I'm not sure which one I have to choose. 但我不确定我必须选择哪一个。
Here is a definition for weighted moving average. 这是加权移动平均线的定义 。
Thanks in advance, 提前致谢,
Yeah, that part of pandas really isn't very well documented. 是的,那部分大熊猫真的没有很好的记录。 I think you might have to use rolling.apply() if you aren't using one of the standard window types. 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()。 I poked at it and got this to work: 我戳了戳它并让它工作:
>>> 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. 我认为这是正确的。 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. 关闭的原因是rolling.apply(func, *args, **kwargs)
的签名是rolling.apply(func, *args, **kwargs)
,所以如果你只是直接将它们发送到函数,权重会得到元组解压缩,除非你发送它们作为1元组(wts,)
,但这很奇怪。
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