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

问题

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:)

回答1:

Option 0
plain vanilla approach

def vwap(df):
    q = df.quantity.values
    p = df.price.values
    return df.assign(vwap=(p * q).cumsum() / q.cumsum())

df = df.groupby(df.index.date, group_keys=False).apply(vwap)
df

                     price  quantity       vwap
time                                           
2016-06-08 09:00:22  32.30    1960.0  32.300000
2016-06-08 09:00:22  32.30     142.0  32.300000
2016-06-08 09:00:22  32.30    3857.0  32.300000
2016-06-08 09:00:22  32.30    1000.0  32.300000
2016-06-08 09:00:22  32.35     991.0  32.306233
2016-06-08 09:00:22  32.30     447.0  32.305901

Option 1
Throwing in a little eval

df = df.assign(
    vwap=df.eval(
        'wgtd = price * quantity', inplace=False
    ).groupby(df.index.date).cumsum().eval('wgtd / quantity')
)
df

                     price  quantity       vwap
time                                           
2016-06-08 09:00:22  32.30    1960.0  32.300000
2016-06-08 09:00:22  32.30     142.0  32.300000
2016-06-08 09:00:22  32.30    3857.0  32.300000
2016-06-08 09:00:22  32.30    1000.0  32.300000
2016-06-08 09:00:22  32.35     991.0  32.306233
2016-06-08 09:00:22  32.30     447.0  32.305901

来源：https://stackoverflow.com/questions/44854512/how-to-calculate-vwap-volume-weighted-average-price-using-groupby-and-apply

标签

python

pandas

lambda

pandas-groupby