如何在Python中使用yfinance获得汇总4Hour酒吧历史股票数据?

Question

yfinance似乎没有提供免费的预聚合栏下载(据我所知)。相反，我需要自己汇总数据。我已经尝试了下面的代码，但它在聚合的数据框架中产生了很多NaN值，这似乎是不对的!我想知道是否有更好的方法来做这件事。

代码:

import yfinance as yf
df = yf.download("SPY", period="2y", interval='1h')
ohlc_dict = {'Open': 'first', 'High': 'max', 'Low': 'min', 'Close': 'last', 'Adj Close': 'last', 'Volume': 'sum'}
df = df.resample('4H', label='left').agg(ohlc_dict)
print(df)

结果:

[*********************100%***********************]  1 of 1 completed
Open        High  ...   Adj Close    Volume
2020-11-17 12:00:00-05:00  361.36499  361.920013  ...  360.640015  22304320
2020-11-17 16:00:00-05:00        NaN         NaN  ...         NaN         0
2020-11-17 20:00:00-05:00        NaN         NaN  ...         NaN         0
2020-11-18 00:00:00-05:00        NaN         NaN  ...         NaN         0
2020-11-18 04:00:00-05:00        NaN         NaN  ...         NaN         0
[5 rows x 6 columns]

是否有一种简单的方法可以从雅虎财经服务器获得已经预聚合的数据?

Answer 1

我就是这样汇总数据的;

import yfinance as yf
import pandas as pd
# Download data from 'Yahoo! Finance' using yfinance
df = yf.download('AAPL', period='2y', interval='1h')
# group in 4Hours chunks.
df_agg = df.groupby(pd.Grouper(freq='4H')).agg({"Open": "first", "High": "max", "Low": "min", "Close": "last",
"Adj Close": "last"})
# Remove the NaN rows
df_final = df_agg.dropna(how='all')
# Label the dataframe columns 
df_final.columns = ["open", "high", "low", "close", "Adj Close"]
# Print the dataframe
print(df_final.to_string())

结果看起来像这样:

open        high         low       close   Adj Close
2020-12-14 08:00:00-05:00  122.599998  123.349998  122.489998  122.914497  122.914497
2020-12-14 12:00:00-05:00  122.910004  122.964203  121.540001  121.699997  121.699997
2020-12-15 08:00:00-05:00  124.339996  126.919998  124.129997  126.370003  126.370003
2020-12-15 12:00:00-05:00  126.371696  127.900002  126.239998  127.892998  127.892998
2020-12-16 08:00:00-05:00  127.410004  128.020004  126.599998  127.555000  127.555000
2020-12-16 12:00:00-05:00  127.550003  128.369995  127.260002  127.750000  127.750000
2020-12-17 08:00:00-05:00  128.899994  129.580002  128.044998  128.413605  128.413605
2020-12-17 12:00:00-05:00  128.410004  128.830002  128.119995  128.649994  128.649994
2020-12-18 08:00:00-05:00  128.960007  129.100006  127.080002  127.649300  127.649300
2020-12-18 12:00:00-05:00  127.645203  128.000000  126.120003  126.650002  126.650002
2020-12-21 08:00:00-05:00  125.430000  126.618401  123.448997  125.695900  125.695900
2020-12-21 12:00:00-05:00  125.695000  128.309998  125.578499  128.240005  128.240005
2020-12-22 08:00:00-05:00  131.610001  134.404999  129.649994  131.580002  131.580002
2020-12-22 12:00:00-05:00  131.586105  133.149994  131.221497  131.845001  131.845001
2020-12-23 08:00:00-05:00  131.918503  132.259796  130.850006  132.068298  132.068298
2020-12-23 12:00:00-05:00  132.050003  132.259995  130.919998  130.960007  130.960007
2020-12-24 08:00:00-05:00  131.320007  133.460007  131.100006  131.880005  131.880005
2020-12-24 12:00:00-05:00  131.880005  132.050003  131.610001  131.990005  131.990005