CODEWITHSUNDEEP
pythonpandaspython-2.7finance
Mohamed Abass
Mohamed Abass

Reputation: 81

How to create Traingular moving average in python using for loop

I use python pandas to caculate the following formula (https://i.sstatic.net/XIKBz.png) I do it in python like this :

EURUSD['SMA2']= EURUSD['Close']. rolling (2).mean()
EURUSD['TMA2']= ( EURUSD['Close'] + EURUSD[SMA2']) / 2

The proplem is long coding when i calculated TMA 100 , so i need to use " for loop " to easy change TMA period . Thanks in advance

Edited : I had found the code but there is an error :

values = []

for i in range(1,201): values.append(eurusd['Close']).rolling(window=i).mean() values.mean()

Upvotes: 0

Views: 214

Answers (1)

prosti
prosti

Reputation: 46331

TMA is average of averages.

import numpy as np
import pandas as pd

df = pd.DataFrame(np.random.rand(10, 5))
print(df)
# df['mean0']=df.mean(0)
df['mean1']=df.mean(1)
print(df)

df['TMA'] = df['mean1'].rolling(window=10,center=False).mean()
print(df)

Or you can easily print it.

print(df["mean1"].mean())

Here is how it looks:

          0         1         2         3         4
0  0.643560  0.412046  0.072525  0.618968  0.080146
1  0.018226  0.222212  0.077592  0.125714  0.595707
2  0.652139  0.907341  0.581802  0.021503  0.849562
3  0.129509  0.315618  0.711265  0.812318  0.757575
4  0.881567  0.455848  0.470282  0.367477  0.326812
5  0.102455  0.156075  0.272582  0.719158  0.266293
6  0.412049  0.527936  0.054381  0.587994  0.442144
7  0.063904  0.635857  0.244050  0.002459  0.423960
8  0.446264  0.116646  0.990394  0.678823  0.027085
9  0.951547  0.947705  0.080846  0.848772  0.699036
          0         1         2         3         4     mean1
0  0.643560  0.412046  0.072525  0.618968  0.080146  0.365449
1  0.018226  0.222212  0.077592  0.125714  0.595707  0.207890
2  0.652139  0.907341  0.581802  0.021503  0.849562  0.602470
3  0.129509  0.315618  0.711265  0.812318  0.757575  0.545257
4  0.881567  0.455848  0.470282  0.367477  0.326812  0.500397
5  0.102455  0.156075  0.272582  0.719158  0.266293  0.303313
6  0.412049  0.527936  0.054381  0.587994  0.442144  0.404901
7  0.063904  0.635857  0.244050  0.002459  0.423960  0.274046
8  0.446264  0.116646  0.990394  0.678823  0.027085  0.451842
9  0.951547  0.947705  0.080846  0.848772  0.699036  0.705581
          0         1         2         3         4     mean1       TMA
0  0.643560  0.412046  0.072525  0.618968  0.080146  0.365449       NaN
1  0.018226  0.222212  0.077592  0.125714  0.595707  0.207890       NaN
2  0.652139  0.907341  0.581802  0.021503  0.849562  0.602470       NaN
3  0.129509  0.315618  0.711265  0.812318  0.757575  0.545257       NaN
4  0.881567  0.455848  0.470282  0.367477  0.326812  0.500397       NaN
5  0.102455  0.156075  0.272582  0.719158  0.266293  0.303313       NaN
6  0.412049  0.527936  0.054381  0.587994  0.442144  0.404901       NaN
7  0.063904  0.635857  0.244050  0.002459  0.423960  0.274046       NaN
8  0.446264  0.116646  0.990394  0.678823  0.027085  0.451842       NaN
9  0.951547  0.947705  0.080846  0.848772  0.699036  0.705581  0.436115

Upvotes: 1

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