用python从雅虎财经自动下载历史股票价格
有没有办法从雅虎财经或谷歌财经(csv格式)自动下载股票的历史价格？最好是用Python编写。
回答 6
Stack Overflow用户
发布于 2012-09-15 07:29:55
简短的回答是:是的。使用Python的urllib来获取您想要的股票的历史数据页面。使用Yahoo！金融；谷歌既不太可靠，数据覆盖率也更低，而且一旦拥有它，你如何使用它就会受到更多限制。此外，我相信谷歌明确禁止你在他们的ToS中抓取数据。
更长的答案:这是我用来提取特定公司所有历史数据的脚本。它拉取特定股票代码的历史数据页面，然后将其保存到以该符号命名的csv文件。你必须提供你自己的股票代码列表，你想拉。
import urllib
base_url = "http://ichart.finance.yahoo.com/table.csv?s="
def make_url(ticker_symbol):
return base_url + ticker_symbol
output_path = "C:/path/to/output/directory"
def make_filename(ticker_symbol, directory="S&P"):
return output_path + "/" + directory + "/" + ticker_symbol + ".csv"
def pull_historical_data(ticker_symbol, directory="S&P"):
try:
urllib.urlretrieve(make_url(ticker_symbol), make_filename(ticker_symbol, directory))
except urllib.ContentTooShortError as e:
outfile = open(make_filename(ticker_symbol, directory), "w")
outfile.write(e.content)
outfile.close()Stack Overflow用户
发布于 2012-09-20 18:05:42
当您要在Python中处理这样的时间序列时，pandas是必不可少的。好消息是:它为雅虎提供了一个历史数据下载器：pandas.io.data.DataReader。
from pandas.io.data import DataReader
from datetime import datetime
ibm = DataReader('IBM', 'yahoo', datetime(2000, 1, 1), datetime(2012, 1, 1))
print(ibm['Adj Close'])Here's an example from the pandas documentation.
pandas >= 0.19的更新：
从pandas>=0.19开始，pandas.io.data模块已被删除。相反，您应该使用单独的pandas-datareader package。安装时使用：
pip install pandas-datareader然后你可以在Python中做到这一点：
import pandas_datareader as pdr
from datetime import datetime
ibm = pdr.get_data_yahoo(symbols='IBM', start=datetime(2000, 1, 1), end=datetime(2012, 1, 1))
print(ibm['Adj Close'])Downloading from Google Finance is also supported.
There's more in the documentation of pandas-datareader.
Stack Overflow用户
发布于 2016-06-14 16:30:57
使用实际演示扩展@Def_Os's答案...
正如@Def_Os已经说过的--使用Pandas Datareader让这项任务变得非常有趣
In [12]: from pandas_datareader import data从1980-01-01拉取AAPL的所有可用历史数据
#In [13]: aapl = data.DataReader('AAPL', 'yahoo', '1980-01-01')
# yahoo api is inconsistent for getting historical data, please use google instead.
In [13]: aapl = data.DataReader('AAPL', 'google', '1980-01-01')前5行
In [14]: aapl.head()
Out[14]:
Open High Low Close Volume Adj Close
Date
1980-12-12 28.750000 28.875000 28.750 28.750 117258400 0.431358
1980-12-15 27.375001 27.375001 27.250 27.250 43971200 0.408852
1980-12-16 25.375000 25.375000 25.250 25.250 26432000 0.378845
1980-12-17 25.875000 25.999999 25.875 25.875 21610400 0.388222
1980-12-18 26.625000 26.750000 26.625 26.625 18362400 0.399475最后5行
In [15]: aapl.tail()
Out[15]:
Open High Low Close Volume Adj Close
Date
2016-06-07 99.250000 99.870003 98.959999 99.029999 22366400 99.029999
2016-06-08 99.019997 99.559998 98.680000 98.940002 20812700 98.940002
2016-06-09 98.500000 99.989998 98.459999 99.650002 26419600 99.650002
2016-06-10 98.529999 99.349998 98.480003 98.830002 31462100 98.830002
2016-06-13 98.690002 99.120003 97.099998 97.339996 37612900 97.339996将所有数据保存为CSV文件
In [16]: aapl.to_csv('d:/temp/aapl_data.csv')d:/temp/aapl_data.csv -前5行
Date,Open,High,Low,Close,Volume,Adj Close
1980-12-12,28.75,28.875,28.75,28.75,117258400,0.431358
1980-12-15,27.375001,27.375001,27.25,27.25,43971200,0.408852
1980-12-16,25.375,25.375,25.25,25.25,26432000,0.378845
1980-12-17,25.875,25.999999,25.875,25.875,21610400,0.38822199999999996
1980-12-18,26.625,26.75,26.625,26.625,18362400,0.399475
...https://stackoverflow.com/questions/12433076
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