How to calculate vwap (volume weighted average price) using groupby and apply?

user_5575597 0
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I have read multiple post similar to my question, but I still can't figure it out. I have a pandas df that looks like the following (for multiple days): 

Out[1]: 
                     price  quantity
time                                
2016-06-08 09:00:22  32.30    1960.0
2016-06-08 09:00:22  32.30     142.0
2016-06-08 09:00:22  32.30    3857.0
2016-06-08 09:00:22  32.30    1000.0
2016-06-08 09:00:22  32.35     991.0
2016-06-08 09:00:22  32.30     447.0
...

To calculate the vwap I could do:

df['vwap'] = (np.cumsum(df.quantity * df.price) / np.cumsum(df.quantity))

However, I would like to start over every day (groupby), but I can't figure out how to make it work with a (lambda?) function.

df['vwap_day'] = df.groupby(df.index.date)['vwap'].apply(lambda ...

Speed is of essence. Would appreciate any help:)

pandas-groupby pandas lambda python
2 Answers

I also used this method before but it's not working quite accurately if you're trying to limit the window period. Instead I found the TA python library to work really well: https://technical-analysis-library-in-python.readthedocs.io/en/latest/index.html

from ta.volume import VolumeWeightedAveragePrice

# ...
def vwap(dataframe, label='vwap', window=3, fillna=True):
        dataframe[label] = VolumeWeightedAveragePrice(high=dataframe['high'], low=dataframe['low'], close=dataframe["close"], volume=dataframe['volume'], window=window, fillna=fillna).volume_weighted_average_price()
        return dataframe
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