Rolling means in Pandas dataframe - pandas

Question

I am trying to run some computations on DataFrames. I want to compute the average difference between two sets of rolling mean. To be more specific, the average of the difference between a long-term mean (lst) and a smaller-one (lst_2). I am trying to combine the calculation with a double for loop as follows:
import pandas as pd
import numpy as pd
def main(df):
df=df.pct_change()
lst=[100,150,200,250,300]
lst_2=[5,10,15,20]
result=pd.DataFrame(np.sum([calc(df,T,t) for T in lst for t in lst_2]))/(len(lst)+len(lst_2))
return result
def calc(df,T,t):
roll=pd.DataFrame(np.sign(df.rolling(t).mean()-df.rolling(T).mean()))
return roll
Overall I should have 20 differences (5 and 100, 10 and 100, 15 and 100 ... 20 and 300); I take the sign of the difference and I want the average of these differences at each point in time. Ideally the result would be a dataframe result.
I got the error: cannot copy sequence with size 3951 to array axis with dimension 1056 when it runs the double for loops. Obviously I understand that due to rolling of different T and t, the dimensions of the dataframes are not equal when it comes to the array conversion (with np.sum), but I thought it would put "NaN" to align the dimensions.
Hope I have been clear enough. Thank you.
As requested in the comments, here is an example. Let's suppose the following
dataframe:
df = pd.DataFrame({'A': [100,101.4636,104.9477,106.7089,109.2701,111.522,113.3832,113.8672,115.0718,114.6945,111.7446,108.8154]},index=[0, 1, 2, 3,4,5,6,7,8,9,10,11])
df=df.pct_change()
and I have the following 2 sets of mean I need to compute:
lst=[8,10]
lst_1=[3,4]
Then I follow these steps:
1/
I want to compute the rolling mean(3) - rolling mean(8), and get the sign of it:
roll=np.sign(df.rolling(3).mean()-df.rolling(8).mean())
This should return the following:
roll = pd.DataFrame({'A': ['NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN',-1,-1,-1,-1},index=[0, 1, 2, 3,4,5,6,7,8,9,10,11])
2/
I redo step 1 with the combination of differences 3-10 ; 4-8 ; 4-10. So I get overall 4 roll dataframes.
roll_3_8 = pd.DataFrame({'A': ['NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN',-1,-1,-1,-1},index=[0, 1, 2, 3,4,5,6,7,8,9,10,11])
roll_3_10 = pd.DataFrame({'A': ['NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN',-1,-1},index=[0, 1, 2, 3,4,5,6,7,8,9,10,11])
roll_4_8 = pd.DataFrame({'A': ['NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN',-1,-1,-1,-1},index=[0, 1, 2, 3,4,5,6,7,8,9,10,11])
roll_4_10 = pd.DataFrame({'A': ['NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN',-1,-1},index=[0, 1, 2, 3,4,5,6,7,8,9,10,11])
3/
Now that I have all the diffs, I simply want the average of them, so I sum all the 4 rolling dataframes, and I divide it by 4 (number of differences computed). The results should be (before dropping all N/A values):
result = pd.DataFrame({'A': ['NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN','NaN',-1,-1},index=[0, 1, 2, 3,4,5,6,7,8,9,10,11])