Is There A Way To Get Pandas Ewm To Function On Fixed Windows?

I am trying to use Pandas ewm function to calculating exponentially weighted moving averages. However i've noticed that information seems to carry through your entire time series.

Solution 1:

IIUC, you are asking for ewm in a rolling window, which means, every 10 rows return a single number. If that is the case, then we can use a stride trick:

Edit: update function works on series only

def EMA(arr, window=10, alpha=0.5):
    ret = pd.Series(index=arr.index, name=arr.name)

    arr=np.array(arr)
    l = len(arr)
    stride = arr.strides[0]

    ret.iloc[window-1:] = (pd.DataFrame(np.lib.stride_tricks.as_strided(arr, 
                                                                       (l-window+1,window), 
                                                                       (stride,stride)))
                          .T.ewm(alpha)
                          .mean()
                          .iloc[-1]
                          .values
                           )
    return ret

Test:

a = pd.Series([x for x in range(100)])

EMA(a).tail(2)
# 98    97.500169# 99    98.500169# Name: 9, dtype: float64

EMA(a[:50]).tail(2)
# 98    97.500169# 99    98.500169# Name: 9, dtype: float64

EMA(a, 2).tail(2)
98    97.75
99    98.75
dtype: float64

Test on random data:

a = pd.Series(np.random.uniform(0,1,10000))
fig, ax = plt.subplots(figsize=(12,6))
a.plot(ax=ax)
EMA(a,alpha=0.99, window=2).plot(ax=ax)
EMA(a,alpha=0.99, window=1500).plot(ax=ax)

plt.show()

Output: we can see that the larger window (green) is less volatile than the smaller window (orange).