Ever since Yahoo decommissioned their historical data API, Python developers looked for a reliable workaround. As a result, my library, yfinance, gained momentum and gets 300k+ 3M+ installs per month, acording to PyPi!

Legal note:

Yahoo!, Y!Finance, and Yahoo! finance are registered trademarks of Yahoo, Inc.

yfinance is not affiliated, endorsed, or vetted by Yahoo, Inc. It's an open-source tool that uses Yahoo's publicly available APIs, and is intended for research and educational purposes.

You should refer to Yahoo!'s terms of use (here, here, and here) for details on your rights to use the actual data downloaded. Remember - the Yahoo! finance API is intended for personal use only.


fix-yahoo-finance aimed to offer a temporary fix to the problem by getting data from Yahoo! Finance and returning it in the same format as pandas_datareader's get_data_yahoo(), thus keeping the code changes in exisiting software to minimum.

The problem was, that this hack was a bit unreliable, causing data to not being downloaded and required developers to force session re-initialization and re-fetching of cookies, by calling yf.get_yahoo_crumb(force=True).

yfinance is a complete re-write of the libray, offering a reliable method of downloading historical market data from Yahoo! Finance's API, up to 1 minute granularity, in a more Pythonic way.

Introducing the Ticker() module:

The Ticker() module allows you get market and meta data for a security, using a Pythonic way:

import yfinance as yf

msft = yf.Ticker("MSFT")
print(msft)
"""
returns
<yfinance.Ticker object at 0x1a1715e898>
"""

# get stock info
msft.info

"""
returns:
{
 'quoteType': 'EQUITY',
 'quoteSourceName': 'Nasdaq Real Time Price',
 'currency': 'USD',
 'shortName': 'Microsoft Corporation',
 'exchangeTimezoneName': 'America/New_York',
  ...
 'symbol': 'MSFT'
}
"""

# get historical market data
msft.history(period="max")
"""
returns:
              Open    High    Low    Close      Volume  Dividends  Splits
Date
1986-03-13    0.06    0.07    0.06    0.07  1031788800        0.0     0.0
1986-03-14    0.07    0.07    0.07    0.07   308160000        0.0     0.0
...
2019-04-15  120.94  121.58  120.57  121.05    15792600        0.0     0.0
2019-04-16  121.64  121.65  120.10  120.77    14059700        0.0     0.0
"""

# show actions (dividends, splits)
msft.actions
"""
returns:
            Dividends  Splits
Date
1987-09-21       0.00     2.0
1990-04-16       0.00     2.0
...
2018-11-14       0.46     0.0
2019-02-20       0.46     0.0
"""

# show dividends
msft.dividends
"""
returns:
Date
2003-02-19    0.08
2003-10-15    0.16
...
2018-11-14    0.46
2019-02-20    0.46
"""

# show splits
msft.splits
"""
returns:
Date
1987-09-21    2.0
1990-04-16    2.0
...
1999-03-29    2.0
2003-02-18    2.0
"""

Available paramaters for the history() method are:

  • period: data period to download (Either Use period parameter or use start and end) Valid periods are: 1d, 5d, 1mo, 3mo, 6mo, 1y, 2y, 5y, 10y, ytd, max
  • interval: data interval (intraday data cannot extend last 60 days) Valid intervals are: 1m, 2m, 5m, 15m, 30m, 60m, 90m, 1h, 1d, 5d, 1wk, 1mo, 3mo
  • start: If not using period - Download start date string (YYYY-MM-DD) or datetime.
  • end: If not using period - Download end date string (YYYY-MM-DD) or datetime.
  • prepost: Include Pre and Post market data in results? (Default is False)
  • auto_adjust: Adjust all OHLC automatically? (Default is True)
  • actions: Download stock dividends and stock splits events? (Default is True)

Mass download of market data:

You can also download data for multiple tickers at once, like before.

import yfinance as yf
data = yf.download("SPY AAPL", start="2017-01-01", end="2017-04-30")

To access the closing price data for SPY, you should use: data['Close']['SPY'].

If, however, you want to group data by Symbol, use:

import yfinance as yf
data = yf.download("SPY AAPL", start="2017-01-01", end="2017-04-30",
                   group_by="ticker")

To access the closing price data for SPY, you should use: data['SPY']['Close'].

The download() method accepts an additional parameter - threads for faster completion when downloading a lot of symbols at once.

* NOTE: To keep compatibility with older versions, auto_adjust defaults to False when using mass-download.

Using pandas_datareader:

If your legacy code is using pandas_datareader and you wand to keep the code changes to minimum, you can simply call the override method and keep your code as it was:

from pandas_datareader import data as pdr

import yfinance as yf
yf.pdr_override() # <== that's all it takes :-)

# download dataframe using pandas_datareader
data = pdr.get_data_yahoo("SPY", start="2017-01-01", end="2017-04-30")

To install/upgrade yfinance using pip, run:

$ pip install yfinance --upgrade --no-cache-dir

The Github repository has more information and issue tracking.

Enjoy!