How to resample and calculate amount weighted average price in Pandas?

Question

I have a dataframe of trades in which each trade has a corresponding amount and price. How can I resample the dataframe in a 1 minute period and calculate the amount weighted average prices of each period ?

>df
                          amount     price
datetime                                  
2023-06-23 10:55:40.420  0.04657  30106.01
2023-06-23 10:55:42.348  0.00085  30104.54
2023-06-23 10:55:42.491  0.00368  30104.54
2023-06-23 10:55:43.211  0.03008  30104.54
2023-06-23 10:55:45.910  0.00035  30101.56
...                          ...       ...
2023-06-23 10:58:06.401  0.00863  30108.00
2023-06-23 10:58:06.661  0.00829  30108.00
2023-06-23 10:58:07.474  0.00305  30108.00
2023-06-23 10:58:07.599  0.00048  30108.00
2023-06-23 10:58:08.393  0.00041  30108.00

[428 rows x 2 columns]

I can resample trade amounts with :

>>> df['amount'].resample("1Min").sum()
datetime
2023-06-23 10:55:00     0.78885
2023-06-23 10:56:00    12.84216
2023-06-23 10:57:00     9.56456
2023-06-23 10:58:00     0.08334
Freq: T, Name: amount, dtype: float64

But what is the best solution to calculate the average price of each periods based on the amount of each trade ?

Asked By: Florent

||

Source

Answer 1

You can compute the average of amount*price and divide by the sum of amount per period:

(df.eval('product = amount*price')[['amount', 'product']]
   .resample("1Min").sum()
   .eval('product/amount')
)

Output:

datetime
2023-06-23 10:55:00    30105.366872
2023-06-23 10:56:00             NaN
2023-06-23 10:57:00             NaN
2023-06-23 10:58:00    30108.000000
Freq: T, dtype: float64

Intermediate before the last step (.eval('product/amount')):

                      amount      product
datetime                                 
2023-06-23 10:55:00  0.08153  2454.490561
2023-06-23 10:56:00  0.00000     0.000000
2023-06-23 10:57:00  0.00000     0.000000
2023-06-23 10:58:00  0.02086   628.052880

Answered By: mozway

How to resample and calculate amount weighted average price in Pandas?

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