这是我存储在 df1 中的每日 ohlc 数据的小样本。
date open close high low
2019-01-01 00:00:00 3700 3800 3806 3646
2019-01-02 00:00:00 3800 3857 3880 3750
2019-01-03 00:00:00 3858 3766 3863 3729
2019-01-04 00:00:00 3768 3791 3821 3706
2019-01-05 00:00:00 3789 3772 3839 3756
2019-01-06 00:00:00 3776 3988 4023 3747
2019-01-07 00:00:00 3985 3972 4018 3928
我想创建一个数据框 (df2)，它代表活动年份蜡烛在进展过程中的样子。收盘以当日收盘价为准，最高价为1月1日至当天的最高价，最低价为1月1日至当天的最低价，开盘价为当年开盘价。应该是这样的：
date open close high low
2019-01-01 00:00:00 3700 3800 3806 3646
2019-01-02 00:00:00 3700 3857 3880 3646
2019-01-03 00:00:00 3700 3766 3880 3646
2019-01-04 00:00:00 3700 3791 3880 3646
2019-01-05 00:00:00 3700 3772 3880 3646
2019-01-06 00:00:00 3700 3988 4023 3646
2019-01-07 00:00:00 3700 3972 4023 3646
很想放一些代码，但我在这里迷路了，我认为重新采样会对我有所帮助，但它只是将整年总结为一行数据。我也想认为我可以通过每天迭代和重新采样来解决这个问题，但我知道这会减慢计算速度，所以我希望看看这是否可以通过矢量化实现。这是我第一次发帖，如果有任何需要改进的指导方针，请告诉我。
- - - - - - - -编辑 - - - - - - - - -
这是我工作年份的完整代码，但其他时间框架不工作，希望当我从公共来源 yfinance 提取数据时，复制糟糕的结果会更容易。
import pandas as pd
import yfinance as yf
#not working
def resample_active_week(df):
df2 = pd.DataFrame()
# high is the max from Jan1 to current day
df2['high'] = df.groupby(df.index.isocalendar().week)['high'].cummax()
# low is the min from Jan1 to current day
df2['low'] = df.groupby(df.index.isocalendar().week)['low'].cummin()
#close
df2['close'] = df['close']
# open is based on the open of the current week
df2['open'] = df.groupby(df.index.isocalendar().week)['open'].head(1)
df2=df2.fillna(method='ffill')
return df2
#not working
def resample_active_month(df):
df2 = pd.DataFrame()
# high is the max from Jan1 to current day
df2['high'] = df.groupby(df.index.month)['high'].cummax()
# low is the min from Jan1 to current day
df2['low'] = df.groupby(df.index.month)['low'].cummin()
#close
df2['close'] = df['close']
# open is based on the open of the current month
df2['open'] = df.groupby(df.index.month)['open'].head(1)
df2=df2.fillna(method='ffill')
return df2
#not working
def resample_active_quarter(df):
df2 = pd.DataFrame()
# high is the max from Jan1 to current day
df2['high'] = df.groupby(df.index.quarter)['high'].cummax()
# low is the min from Jan1 to current day
df2['low'] = df.groupby(df.index.quarter)['low'].cummin()
#close
df2['close'] = df['close']
# open is based on the open of the current quarter
df2['open'] = df.groupby(df.index.quarter)['open'].head(1)
df2=df2.fillna(method='ffill')
return df2
#working
def resample_active_year(df):
df2 = pd.DataFrame()
# high is the max from Jan1 to current day
df2['high'] = df.groupby(df.index.year)['high'].cummax()
# low is the min from Jan1 to current day
df2['low'] = df.groupby(df.index.year)['low'].cummin()
#close
df2['close'] = df['close']
# open is based on the open of the current year
df2['open'] = df.groupby(df.index.year)['open'].head(1)
df2=df2.fillna(method='ffill')
return df2
df = yf.download(tickers='BTC-USD', period = 'max', interval = '1d',auto_adjust = True)
df.rename(columns={'Open':'open', 'High':'high','Low':'low','Close':'close'}, inplace=True)
df = df.drop(['Volume'],axis=1)
df2 = resample_active_week(df)
df3 = resample_active_month(df)
df4 = resample_active_quarter(df)
df5 = resample_active_year(df)
with pd.ExcelWriter('ResampleOut.xlsx', engine="openpyxl", mode="w") as writer:
df.to_excel(writer, sheet_name='df_original')
df2.to_excel(writer, sheet_name='df2_week')
df3.to_excel(writer, sheet_name='df3_month')
df4.to_excel(writer, sheet_name='df4_quarter')
df5.to_excel(writer, sheet_name='df5_year')