Question

我使用python pandas计算以下公式（https://i.stack.imgur.com/XIKBz.png）我是这样在python中完成的：

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

当我计算TMA 100时，问题是长编码，因此我需要使用“ for loop”来轻松更改TMA周期。预先感谢

编辑：我找到了代码，但是有一个错误：

值= []

对于范围（1,201）中的i：values.append（eurusd ['Close']）。rolling（window = i）.mean（）values.mean（）

Answer 1

TMA是平均值的平均值。

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)

或者您可以轻松打印它。

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

这是它的外观：

          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

如何使用for循环在python中创建Traingular移动平均

1 个答案: