Ever since Yahoo! Finance decommissioned their historical data API, Python developers looked for a reliable workaround. As a result, my library, yfinance, gained momentum and was downloaded over 100,000 acording to PyPi.

UPDATE (2019-05-26): The library was originally named fix-yahoo-finance, but I've since renamed it to yfinance as I no longer consider it a mere "fix". For reasons of backward-competability, fix-yahoo-finance now import and uses yfinance, but you should install and use yfinance directly.

yfinance aimed to offer a temporary fix to the problem by scraping the data from Yahoo! Finance and returning a the data 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).

The latest version of yfinance is a complete re-write of the libray, offering a reliable method of downloading historical market data from Yahoo! Finance, up to 1 minute granularity, with 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")
<yfinance.Ticker object at 0x1a1715e898>

# get stock info

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

# get historical market data
              Open    High    Low    Close      Volume  Dividends  Splits
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)
            Dividends  Splits
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
2003-02-19    0.08
2003-10-15    0.16
2018-11-14    0.46
2019-02-20    0.46

# show splits
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",

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.