Standardization between float and None types-CodePudding

I have converted the code from Pine Script to Python, but I am facing some problems. I'm not good in Python.

import pandas as pd
import pandas_ta as ta

close = pd.read_csv('close-MATIC.csv')

def Z_4(src, length, smooth):
    mean = ta.sma(src, length)
    vlo = src - mean
    std = ta.stdev(vlo, length)
    value = (src - mean) / std
    Wema = ta.ema(ta.ema(value, smooth), smooth)
    return [Wema, value]

R4 = Z_4(close, 8, 5)

print(R4)

Sample close-MATIC.csv:

1.1154
1.1133
1.114
1.1131666666666669
1.1158444444444446
1.1207629629629632
1.1231753086419756
1.1252835390946503
1.1277223593964336
1.1305149062642892
1.1350099375095262
1.1363399583396843
1.1352933055597896
1.1354622037065263
1.132108135804351
1.1314387572029008
1.1310258381352671
1.1331172254235113
1.1350114836156744
1.135007655743783

The original Pine Script:

f_zscore(_src, _length, _smooth)=>
    _mean = sma(_src, _length)
    _std = stdev(_src-_mean, _length)
    _value = (_src - _mean) / _std
    _dema = ema(ema(_value, _smooth), _smooth)
    [_value, _dema]

smooth = input(3)
[z0, d0] = f_zscore(close, input(6), smooth)

The errors:

Traceback (most recent call last):
  File "c:/Users/MO/OneDrive/Desktop/New folder (21)/TOP4.py", line 14, in <module>
    R4= Z_4(close,8,5)
  File "c:/Users/MO/OneDrive/Desktop/New folder (21)/TOP4.py", line 8, in Z_4
    vlo=(src-mean)
  File "C:\Users\MO\AppData\Local\Programs\Python\Python38\lib\site-packages\pandas\core\ops\common.py", line 65, in new_method     
    return method(self, other)
  File "C:\Users\MO\AppData\Local\Programs\Python\Python38\lib\site-packages\pandas\core\arraylike.py", line 97, in __sub__
    return self._arith_method(other, operator.sub)
  File "C:\Users\MO\AppData\Local\Programs\Python\Python38\lib\site-packages\pandas\core\frame.py", line 5982, in _arith_method
    new_data = self._dispatch_frame_op(other, op, axis=axis)
  File "C:\Users\MO\AppData\Local\Programs\Python\Python38\lib\site-packages\pandas\core\frame.py", line 6008, in _dispatch_frame_op    bm = self._mgr.apply(array_op, right=right)
  File "C:\Users\MO\AppData\Local\Programs\Python\Python38\lib\site-packages\pandas\core\internals\managers.py", line 425, in apply 
    applied = b.apply(f, **kwargs)
  File "C:\Users\MO\AppData\Local\Programs\Python\Python38\lib\site-packages\pandas\core\internals\blocks.py", line 378, in apply   
    result = func(self.values, **kwargs)
  File "C:\Users\MO\AppData\Local\Programs\Python\Python38\lib\site-packages\pandas\core\ops\array_ops.py", line 189, in arithmetic_op
    res_values = _na_arithmetic_op(lvalues, rvalues, op)
  File "C:\Users\MO\AppData\Local\Programs\Python\Python38\lib\site-packages\pandas\core\ops\array_ops.py", line 149, in _na_arithmetic_op
    result = _masked_arith_op(left, right, op)
  File "C:\Users\MO\AppData\Local\Programs\Python\Python38\lib\site-packages\pandas\core\ops\array_ops.py", line 111, in _masked_arith_op
    result[mask] = op(xrav[mask], y)
TypeError: unsupported operand type(s) for -: 'float' and 'NoneType'

I would be thankful and grateful for all the help.

CodePudding user response：

Here is your code fixed, but I'm not sure if the results are valid though:

import pandas as pd
import pandas_ta as ta


def Z_4(src, length, smooth):
    mean = ta.sma(src, length)
    vlo = src - mean
    std = ta.stdev(vlo, length)
    value = vlo / std
    Wema = ta.ema(
        ta.ema(value, smooth),
        smooth
    )

    return [Wema, value]


close = pd.read_csv('close-MATIC.csv', header=None, names=['Sources'])

R4 = Z_4(close.Sources, 8, 5)

print(R4)

The main problem is that you are using a DataFrame everywhere a Series is expected.

For the sample CSV below:

1.1154
1.1133
1.114
1.1131666666666669
1.1158444444444446
1.1207629629629632
1.1231753086419756
1.1252835390946503
1.1277223593964336
1.1305149062642892
1.1350099375095262
1.1363399583396843
1.1352933055597896
1.1354622037065263
1.132108135804351
1.1314387572029008
1.1310258381352671
1.1331172254235113
1.1350114836156744
1.135007655743783

The code will return the following:

[0          NaN
1          NaN
2          NaN
3          NaN
4          NaN
5          NaN
6          NaN
7          NaN
8          NaN
9          NaN
10         NaN
11         NaN
12         NaN
13         NaN
14   -0.030459
15   -0.063999
16   -0.121392
17   -0.146357
18   -0.106888
19   -0.021776
Name: EMA_5, dtype: float64, 0          NaN
1          NaN
2          NaN
3          NaN
4          NaN
5          NaN
6          NaN
7          NaN
8          NaN
9          NaN
10         NaN
11         NaN
12         NaN
13         NaN
14   -0.030459
15   -0.332315
16   -0.446383
17   -0.116503
18    0.308728
19    0.501244
dtype: float64]