问题
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 lot! The Dataframe looks like :
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| Date | Security | P&L |
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| 2016-01-01 | AAPL | 100 |
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| 2016-01-02 | AAPL | 200 |
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| 2016-01-03 | AAPL | 300 |
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| 2016-01-04 | AAPL | -200 |
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All, I want to do is roll the P&L over to the next business day (exclude all US holidays and weekends) So, the resultant Dataframe looks like this:
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| Date | Security | P&L |
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| 2016-01-04 | AAPL | 400 |
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I'm looking for an efficient way to achieve this. I do have thousands of securities and over 5 yrs of data to process so brute force can't work, unfortunately!
Thanks in advance and highly appreciate any pointers on this!
回答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
回答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()
来源:https://stackoverflow.com/questions/58330699/pandas-pl-rollup-to-the-next-business-day