Is there a way to automatically download historical prices of stocks from yahoo finance or google finance (csv format)? Preferably in Python.
This question is related to
pandas
finance
yahoo-finance
google-finance
stockquotes
The answer is
Short answer: Yes. Use Python's urllib to pull the historical data pages for the stocks you want. Go with Yahoo! Finance; Google is both less reliable, has less data coverage, and is more restrictive in how you can use it once you have it. Also, I believe Google specifically prohibits you from scraping the data in their ToS.
Longer answer: This is the script I use to pull all the historical data on a particular company. It pulls the historical data page for a particular ticker symbol, then saves it to a csv file named by that symbol. You'll have to provide your own list of ticker symbols that you want to pull.
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()
You can check out the yahoo_fin package. It was initially created after Yahoo Finance changed their API (documentation is here: http://theautomatic.net/yahoo_fin-documentation).
from yahoo_fin import stock_info as si
aapl_data = si.get_data("aapl")
nflx_data = si.get_data("nflx")
aapl_data.head()
nflx_data.head()
aapl.to_csv("aapl_data.csv")
nflx_data.to_csv("nflx_data.csv")
When you're going to work with such time series in Python, pandas is indispensable. And here's the good news: it comes with a historical data downloader for Yahoo: 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.
Update for pandas >= 0.19:
The pandas.io.data module has been removed from pandas>=0.19 onwards. Instead, you should use the separate pandas-datareader package. Install with:
pip install pandas-datareader
And then you can do this in Python:
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'])
Extending @Def_Os's answer with an actual demo...
As @Def_Os has already said - using Pandas Datareader makes this task a real fun
In [12]: from pandas_datareader import data
pulling all available historical data for AAPL starting from 1980-01-01
#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')
first 5 rows
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
last 5 rows
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
save all data as CSV file
In [16]: aapl.to_csv('d:/temp/aapl_data.csv')
d:/temp/aapl_data.csv - 5 first rows
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
...
It's trivial when you know how:
import yfinance as yf
df = yf.download('CVS', '2015-01-01')
df.to_csv('cvs-health-corp.csv')
If you wish to plot it:
import finplot as fplt
fplt.candlestick_ochl(df[['Open','Close','High','Low']])
fplt.show()
There is already a library in Python called yahoo_finance so you'll need to download the library first using the following command line:
sudo pip install yahoo_finance
Then once you've installed the yahoo_finance library, here's a sample code that will download the data you need from Yahoo Finance:
#!/usr/bin/python
import yahoo_finance
import pandas as pd
symbol = yahoo_finance.Share("GOOG")
google_data = symbol.get_historical("1999-01-01", "2016-06-30")
google_df = pd.DataFrame(google_data)
# Output data into CSV
google_df.to_csv("/home/username/google_stock_data.csv")
This should do it. Let me know if it works.
UPDATE: The yahoo_finance library is no longer supported.
Source: Stackoverflow.com