Making decisions based on the times of datetime in a dataframe-CodePudding

I am new to programming and python. After many hours of research I want to ask the community for help. I want to use data to backtest a trading strategy based on the beginning of the London Session.

I have a dataframe:

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 28766 entries, 0 to 28765
Data columns (total 6 columns):
| #  | Column | Non-Null Count  |Dtype   |      
|--- |------ |--------------  |----- |        
| 0  |Time   |28766 non-null  |datetime64[ns]|
| 1  |Open   |28766 non-null  |float64 |
| 2 |...|

|    |Time                  |Open    | High   | Low    |Close   |Volume  |
|--- |--------------------- |------- |------- |------- |------- | ------|
|8   |2017-05-23 08:00:00   |2180.7  |2187.2  |2139.2  | 2170.2 | 97.0  |

And now I want to define a function which recognizes every row with 08:00 GMT in it:

def LondonSession(df):
df = df.copy()
for t in df['Time']:
    if df[df['Time'].dt.hour == (8)]:
        df['StopLoss'] = technicals(df)['atr'] * (-1)
        df['TakeProfit'] = technicals(df)['atr'] * (2)
    else: 
        df['StopLoss'] = technicals(df)['atr'] * (0)
        df['TakeProfit'] = technicals(df)['atr'] * (0)
return df

print(LondonSession(df)[0:10])

Unfortunately I have no more ideas how to solve the error messages:

ValueError                                Traceback (most recent call last) Input In [204], in <module>
      9             df['TakeProfit'] = technicals(df)['atr'] * (0)
     10     return df
---> 11 print(LondonSession(df)[0:10])

Input In [204], in LondonSession(df)
      2 df = df.copy()
      3 for t in df['Time']:
----> 4     if df[df['Time'].dt.hour == (8)]:
      5         df['StopLoss'] = technicals(df)['atr'] * (-1)
      6         df['TakeProfit'] = technicals(df)['atr'] * (2)

File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\pandas\core\generic.py:1537, in NDFrame.__nonzero__(self)    1535 @final    1536 def
__nonzero__(self):
-> 1537     raise ValueError(    1538         f"The truth value of a {type(self).__name__} is ambiguous. "    1539         "Use a.empty, a.bool(), a.item(), a.any() or a.all()."    1540     )

ValueError: The truth value of a DataFrame is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

Any input is recommended. Thanks in advance!

CodePudding user response：

The problem here is what you wrote inside the first if condition : df[df['Time'].dt.hour == (8)]. This code doesn't return a boolean but rather a dataframe in which the selected rows are the ones where the attribute Time is equal to 8.

If you want to keep your for-loop structure, you should therefore change the condition with if t.hour == 8. But keep in mind that with pandas you should rather avoid for-loops and use row filtering (you can read more on that matter on this pandas official tutorial).

CodePudding user response：

The solution for me is:

test2.loc[test2['Time'].dt.hour == (8), 'TakeProfit'] = (test2['atr'] * (1.5)) test2.loc[test2['Time'].dt.hour == (8), 'StopLoss'] = (test2['atr'] * (-1)) test2.loc[test2['Time'].dt.hour != (8), 'TakeProfit'] = (0) test2.loc[test2['Time'].dt.hour != (8), 'StopLoss'] = (0)

I read about in this article: enter link description here

The article describes more options for my problem. But I had success with the first one.