得到下一个n个交易日的函数太慢了

此检查next_day不在mcal.get_calendar('NYSE')中。valid_days(start_date='2000-12-20'， end_date='2020-01-10')非常耗时，因为它需要从7000天的数组中查找。你需要对每一个操作都这样做，所以我认为这是效率低下的主要原因。 您可以通过转换mcal.get_calendar('NYSE')来加快这种检查。valid_days(start_date='2000-12-20'， end_date='2020-01-10')设置，将查找从O(N)降低到O(log N)。 但我会选择另一种策略: 编辑:允许任意数量的滞后和领先

import pandas as pd
import pandas_market_calendars as mcal

def get_next_trading_day(df1, n):
    trading_days = pd.DataFrame({"date": mcal.get_calendar('NYSE').valid_days(start_date='2016-11-10', end_date='2016-12-01')})
    trading_days['date'] = trading_days['date'].dt.tz_convert(None)
    trading_days = trading_days[~trading_days.date.dt.weekday.isin([5,6])]
    trading_days['next_trading_day'] = trading_days.date.shift(-n)
    # extract unique  date from df1
    df2 = pd.DataFrame({"date": pd.unique(df1['date'])})

    # merge with the trading days data (non-trading day will have NA fields)
    df2 = df2.merge(trading_days, on='date', how='outer')

    # impute NA values
    df2.sort_values(by='date', inplace=True)

    df2['next_trading_day'].fillna(method=  'ffill' if n>0 else 'bfill', inplace=True)

    return df1.merge(df2, on='date', how='left')

dict1 = [
        {'date': '2016-11-27'},
        {'date': '2016-11-28'},
        {'date': '2016-11-27'},
        ]
df1= pd.DataFrame(dict1)
df1['date']      = pd.to_datetime(df1['date'])


print("Next trading day")
print(get_next_trading_day(df1, 1))
print()

print("Previous trading day")
print(get_next_trading_day(df1, -1))
print()

print("Next next trading day")
print(get_next_trading_day(df1, 2))
print()

print("Previous previous trading day")
print(get_next_trading_day(df1, -2))
print()

输出

Next trading day
        date next_trading_day
0 2016-11-27       2016-11-28
1 2016-11-28       2016-11-29
2 2016-11-27       2016-11-28

Previous trading day
        date next_trading_day
0 2016-11-27       2016-11-25
1 2016-11-28       2016-11-25
2 2016-11-27       2016-11-25

Next next trading day
        date next_trading_day
0 2016-11-27       2016-11-29
1 2016-11-28       2016-11-30
2 2016-11-27       2016-11-29

Previous previous trading day
        date next_trading_day
0 2016-11-27       2016-11-23
1 2016-11-28       2016-11-23
2 2016-11-27       2016-11-23