I calculate simple moving average:
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_type parameter. But I'm not sure which one I have to choose.
Here is a definition for weighted moving average.
Thanks in advance,
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:
>>> 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.