[英]How to calculate the most common time for max value per day of week in pandas
使用python中的yahoo金融包,我可以下載相關數據以顯示OCHL。 我的目標是找出一天中股票平均最高的時間。
這是下載數據的代碼:
import yfinance as yf
import pandas as pd
df = yf.download(
tickers = "APPL",
period = "60d",
interval = "5m",
auto_adjust = True,
group_by = 'ticker',
prepost = True,
)
maxTimes = df.groupby([df.index.month, df.index.day, df.index.day_name()])['High'].idxmax()
這給了我這樣的東西:
Datetime Datetime Datetime
6 2 Tuesday 2020-06-02 19:45:00-04:00
3 Wednesday 2020-06-03 15:50:00-04:00
4 Thursday 2020-06-04 10:30:00-04:00
5 Friday 2020-06-05 11:30:00-04:00
...
8 3 Monday 2020-08-03 14:40:00-04:00
4 Tuesday 2020-08-04 18:10:00-04:00
5 Wednesday 2020-08-05 11:10:00-04:00
6 Thursday 2020-08-06 16:20:00-04:00
7 Friday 2020-08-07 15:50:00-04:00
Name: High, dtype: datetime64[ns, America/New_York]
我認為我創建的 maxTimes 對象應該給我每天發生一天中最高點的時間,但是我需要的是:
Monday 12:00
Tuesday 13:25
Wednesday 09:35
Thurs 16:10
Fri 12:05
有沒有人能幫我確定如何讓我的數據看起來像這樣?
這應該有效:
import yfinance as yf
import pandas as pd
df = yf.download(
tickers = "AAPL",
period = "60d",
interval = "5m",
auto_adjust = True,
group_by = 'ticker',
prepost = True,
)
maxTimes = df.groupby([df.index.month, df.index.day, df.index.day_name()])['High'].idxmax()
# Drop date
maxTimes = maxTimes.apply(lambda x: x.time())
# Drop unused sub-indexes
maxTimes = maxTimes.droplevel(level=[0,1])
# To seconds
maxTimes = maxTimes.apply(lambda t: (t.hour * 60 + t.minute) * 60 + t.second)
# Get average
maxTimes = maxTimes.groupby(maxTimes.index).mean()
# Back to time
maxTimes = pd.to_datetime(maxTimes, unit='s').apply(lambda x: x.time())
print (maxTimes)
'''
Output:
Datetime
Friday 11:59:32.727272
Monday 14:15:00
Thursday 13:21:40
Tuesday 10:35:00
Wednesday 11:53:45
Name: High, dtype: object
'''
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