Pandas 每周重采样

Question

I have a dataframe with daily market data (OHLCV) and am resampling it to weekly.

My specific requirement is that the weekly dataframe's index labels must be the index labels of the first day of that week , whose data is present in the daily dataframe.

For example, in July 2022, the trading week beginning 4th July (for US stocks) should be labelled 5th July, since 4th July was a holiday and not found in the daily dataframe, and the first date in that week found in the daily dataframe is 5th July.

The usual weekly resampling offset aliases and anchored offsets do not seem to have such an option.

I can achieve my requirement specifically for US stocks by importing USFederalHolidayCalendar from pandas.tseries.holiday and then using

bday_us = pd.offsets.CustomBusinessDay(calendar=USFederalHolidayCalendar())
dfw.index = dfw.index.map(lambda idx: bday_us.rollforward(idx))

where dfw is the already resampled weekly dataframe with W-MON as option.

However, this would mean that I'd have to use different trading calendars for each different exchange/market, which I'd very much like to avoid.

Any pointers on how to do this simply so that the index label in the weekly dataframe is the index label of the first day of that week available in the daily dataframe would be much appreciated.

Answer 1

You want to group all days by calendar week (Mon-Sun), then aggregate the data, and use the first observed date as the index, correct?

If so, W-MON is not applicable because you will group dates from Tuesday through Monday. Using W-SUN instead, you group by the calendar week where the index is the Sunday. However, you can use method first on the date column to obtain the first observed date in this week and replace the index with this result.

This is possible with either groupby or resample :

import numpy as np
import pandas as pd

# simulate daily data, drop a monday
date_range = pd.bdate_range(start='2022-06-06',end='2022-07-31')
date_range = date_range[~(date_range=='2022-07-04')]

# simulate data
df = pd.DataFrame(data = {
    'date': date_range,
    'return': np.random.random(size=len(date_range))
})

# resample with groupby
g = df.groupby([pd.Grouper(key='date', freq='W-SUN')])
result_groupby = g[['return']].mean() # example aggregation method
result_groupby['date_first_observed'] = g['date'].first()
result_groupby['date_last_observed'] = g['date'].last()
result_groupby.set_index('date_first_observed', inplace=True)

# resample with resample
df.index = df['date']
g = df.resample('W-SUN')
result_resample = g[['return']].mean() # example aggregation method
result_resample['date_first_observed'] = g['date'].first()
result_resample['date_last_observed'] = g['date'].last()
result_resample.set_index('date_first_observed', inplace=True)

This gives

>>> result_groupby
                       return date_last_observed
date_first_observed                             
2022-06-06           0.704949         2022-06-10
2022-06-13           0.460946         2022-06-17
2022-06-20           0.578682         2022-06-24
2022-06-27           0.361004         2022-07-01
2022-07-05           0.692309         2022-07-08
2022-07-11           0.569810         2022-07-15
2022-07-18           0.435222         2022-07-22
2022-07-25           0.454765         2022-07-29
>>> result_resample
                       return date_last_observed
date_first_observed                             
2022-06-06           0.704949         2022-06-10
2022-06-13           0.460946         2022-06-17
2022-06-20           0.578682         2022-06-24
2022-06-27           0.361004         2022-07-01
2022-07-05           0.692309         2022-07-08
2022-07-11           0.569810         2022-07-15
2022-07-18           0.435222         2022-07-22
2022-07-25           0.454765         2022-07-29

One row shows 2022-07-05 (Tuesday) instead of 2022-07-04 (Monday).