1

我有以下代码：

import yfinance as yf
import pandas as pd
import datetime as dt
end=dt.datetime.today()
start=end-dt.timedelta(59)
tickers=['WBA', 'HD']
ohlcv={}
df=pd.DataFrame
df = yf.download(tickers,group_by=tickers,start=start,end=end,interval='5m')

df['h-l']=abs(df.High-df.Low)
df['h-pc']=abs (df.High-df['Adj Close'].shift(1))
df['l-pc']=abs(df.Low-df['Adj Close'].shift(1))
df['tr']=df[['h-l','h-pc','l-pc']].max(axis=1)
df['atr']=df['tr'].rolling(window=n, min_periods=n).mean()

当我尝试运行它时，我收到以下提到的错误：

return object.__getattribute__(self, name)
AttributeError: 'DataFrame' object has no attribute 'High'

我尝试使用此代码：

df = df.stack(level=0).rename_axis(['Date', 'Ticker']).reset_index(level=1)

提取的报告存在数学错误，因为代码之间没有分隔。

当我真正需要的是代码列表中提到的每个代码时，它应该创建一个名为“hl”的列，在该列中减去该行的高位和该行的低位，依此类推。

4

2 回答 2

0

选项 1：多级列名

  • 通过传递元组访问多级列
    • df[('WMB', 'High')]
  • 使用的软件包版本
    • print(pd.__version__)至少'1.0.5'
    • print(yf.__version__)是'0.1.54'
import yfinance as yf
import pandas as pd
from datetime import datetime, timedelta

end = datetime.today()
start = end - timedelta(59)
tickers = ['WBA', 'HD']

df = yf.download(tickers,group_by=tickers,start=start,end=end,interval='5m')

# iterate over level 0 ticker names
for ticker in tickers:
    df[(ticker, 'h-l')] = abs(df[(ticker, 'High')] - df[(ticker, 'Low')])
    df[(ticker, 'h-pc')] = abs(df[(ticker, 'High')] - df[(ticker, 'Adj Close')].shift(1))
    df[(ticker, 'l-pc')] = abs(df[(ticker, 'Low')] - df[(ticker, 'Adj Close')].shift(1))
    df[(ticker, 'tr')] = df[[(ticker, 'h-l'), (ticker, 'h-pc'), (ticker, 'l-pc')]].max(axis=1)
#     df[(ticker, 'atr')] = df[(ticker, 'tr')].rolling(window=n, min_periods=n).mean()  # not included becasue n is not defined

# sort the columns
df = df.reindex(sorted(df.columns), axis=1)

# display(df.head())
                                   HD                                                                                                          WBA                                                                                              
                            Adj Close       Close        High         Low        Open    Volume       h-l      h-pc      l-pc        tr  Adj Close      Close       High        Low       Open    Volume       h-l      h-pc      l-pc        tr
Datetime                                                                                                                                                                                                                                        
2020-06-08 09:30:00-04:00  253.937500  253.937500  253.960007  252.360001  252.490005  210260.0  1.600006       NaN       NaN  1.600006  46.049999  46.049999  46.070000  45.490002  45.490002  239860.0  0.579998       NaN       NaN  0.579998
2020-06-08 09:35:00-04:00  253.470001  253.470001  254.339996  253.220093  253.990005   95906.0  1.119904  0.402496  0.717407  1.119904  46.330002  46.330002  46.330002  46.040001  46.070000  104259.0  0.290001  0.280003  0.009998  0.290001
2020-06-08 09:40:00-04:00  253.580002  253.580002  253.829895  252.955002  253.429993   55868.0  0.874893  0.359894  0.514999  0.874893  46.610001  46.610001  46.660000  46.240002  46.330002  113174.0  0.419998  0.329998  0.090000  0.419998
2020-06-08 09:45:00-04:00  253.740005  253.740005  253.929993  253.289993  253.529999   61892.0  0.639999  0.349991  0.290009  0.639999  46.880001  46.880001  46.950001  46.624100  46.624100  121388.0  0.325901  0.340000  0.014099  0.340000
2020-06-08 09:50:00-04:00  253.703400  253.703400  253.910004  253.419998  253.740005   60809.0  0.490005  0.169998  0.320007  0.490005  46.919998  46.919998  46.990002  46.820000  46.880001  154239.0  0.170002  0.110001  0.060001  0.170002

选项 2：单级列名

import yfinance as yf
import pandas as pd
from datetime import datetime, timedelta

tickerStrings = ['WBA', 'HD']
df = yf.download(tickers, group_by='Ticker', start=start ,end=end, interval='5m')

# create single level column names
df = df.stack(level=0).rename_axis(['Date', 'Ticker']).reset_index(level=1)

# function with calculations
def my_calculations(df):
    df['h-l']=abs(df.High-df.Low)
    df['h-pc']=abs(df.High-df['Adj Close'].shift(1))
    df['l-pc']=abs(df.Low-df['Adj Close'].shift(1))
    df['tr']=df[['h-l','h-pc','l-pc']].max(axis=1)
#     df['atr']=df['tr'].rolling(window=n, min_periods=n).mean()  # n is not defined in the question
    return df

# apply the function
df_updated = df.reset_index().groupby('Ticker').apply(my_calculations).sort_values(['Ticker', 'Date'])
于 2020-08-05T20:01:54.240 回答
0

这是我创建的一些列。查找与前一天相比的百分比变化，查找范围和百分比范围。

df['% Change'] = (df['Adj Close'] / df['Adj Close'].shift(1))-1

df['Range'] = df['High'] - df['Low']

df['% Range'] = df['Range'] / df['Open']
于 2020-12-13T17:59:29.803 回答