Pandas P&l Rollup To The Next Business Day

I'm having a hard time trying to do this efficiently. I have some stocks and daily P&L info in a dataframe. In reality, I have millions of rows of data so efficiency matters a

Solution 1:

We can create the DataFrame of business dates then merge_asof. Then we can group on this to get the sums.

import pandas as pd
from pandas.tseries.holiday import USFederalHolidayCalendar

#df['Date'] = pd.to_datetime(df.Date)
date_min = '2015-01-01'
date_max = '2016-12-31'

cal = USFederalHolidayCalendar()
holidays = cal.holidays(date_min, date_max).tolist()
df2 = pd.DataFrame({'bdate': pd.bdate_range(date_min, date_max, 
                                            holidays=holidays, freq='C')})

res = pd.merge_asof(df, df2, left_on='Date', right_on='bdate', direction='forward')
#        Date Security  P&L      bdate#0 2016-01-01     AAPL  100 2016-01-04#1 2016-01-02     AAPL  200 2016-01-04#2 2016-01-03     AAPL  300 2016-01-04#3 2016-01-04     AAPL -200 2016-01-04

res.groupby(['Security', 'bdate'])['P&L'].sum()
#Security  bdate     #AAPL      2016-01-04    400

Solution 2:

IIUC you can do something like:

import pandas as pd
from pandas.tseries.holiday import USFederalHolidayCalendar
import numpy as np

date_min = '2015-01-01'
date_max = '2016-12-31'

cal = USFederalHolidayCalendar()
holidays = cal.holidays(date_min, date_max).tolist()

df = pd.DataFrame({"Date":pd.date_range(date_min, date_max)})
df["Security"] ="APPL"
df["P&L"] = np.random.randint(-1000, 1000, len(df))

df[~df["Date"].isin(holidays)].groupby("Security")\
                              .agg({"Date":"max",
                                    "P&L":"sum"})\
                              .reset_index()