Ever since Yahoo! finance decommissioned their historical data API, many programs that relied on it to stop working.
yfinance aims to solve this problem by offering a reliable, threaded, and Pythonic way to download historical market data from Yahoo! finance.
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-compatibility, fix-yahoo-finance now import and
uses yfinance, but you should install and use yfinance directly.
==> Check out this Blog post for a detailed tutorial with code examples.
The Ticker module, which allows you to access
ticker data in a more Pythonic way:
Note: yahoo finance datetimes are received as UTC.
import yfinance as yf
msft = yf.Ticker("MSFT")
# get stock info
msft.info
# get historical market data
hist = msft.history(period="max")
# show actions (dividends, splits)
msft.actions
# show dividends
msft.dividends
# show splits
msft.splits
# show financials
msft.financials
msft.quarterly_financials
# show major holders
msft.major_holders
# show institutional holders
msft.institutional_holders
# show balance sheet
msft.balance_sheet
msft.quarterly_balance_sheet
# show cashflow
msft.cashflow
msft.quarterly_cashflow
# show earnings
msft.earnings
msft.quarterly_earnings
# show sustainability
msft.sustainability
# show analysts recommendations
msft.recommendations
# show next event (earnings, etc)
msft.calendar
# show ISIN code - *experimental*
# ISIN = International Securities Identification Number
msft.isin
# show options expirations
msft.options
# get option chain for specific expiration
opt = msft.option_chain('YYYY-MM-DD')
# data available via: opt.calls, opt.putsIf you want to use a proxy server for downloading data, use:
import yfinance as yf
msft = yf.Ticker("MSFT")
msft.history(..., proxy="PROXY_SERVER")
msft.get_actions(proxy="PROXY_SERVER")
msft.get_dividends(proxy="PROXY_SERVER")
msft.get_splits(proxy="PROXY_SERVER")
msft.get_balance_sheet(proxy="PROXY_SERVER")
msft.get_cashflow(proxy="PROXY_SERVER")
msft.option_chain(..., proxy="PROXY_SERVER")
...To use a custom requests session (for example to cache calls to the API
or customize the User-agent header), pass a session= argument to the
Ticker constructor.
import requests_cache
session = requests_cache.CachedSession('yfinance.cache')
session.headers['User-agent'] = 'my-program/1.0'
ticker = yf.Ticker('msft aapl goog', session=session)
# The scraped response will be stored in the cache
ticker.actionsTo initialize multiple Ticker objects, use
import yfinance as yf
tickers = yf.Tickers('msft aapl goog')
# ^ returns a named tuple of Ticker objects
# access each ticker using (example)
tickers.tickers.MSFT.info
tickers.tickers.AAPL.history(period="1mo")
tickers.tickers.GOOG.actionsimport yfinance as yf
data = yf.download("SPY AAPL", start="2017-01-01", end="2017-04-30")I've also added some options to make life easier :)
data = yf.download( # or pdr.get_data_yahoo(...
# tickers list or string as well
tickers = "SPY AAPL MSFT",
# use "period" instead of start/end
# valid periods: 1d,5d,1mo,3mo,6mo,1y,2y,5y,10y,ytd,max
# (optional, default is '1mo')
period = "ytd",
# fetch data by interval (including intraday if period < 60 days)
# valid intervals: 1m,2m,5m,15m,30m,60m,90m,1h,1d,5d,1wk,1mo,3mo
# (optional, default is '1d')
interval = "1m",
# group by ticker (to access via data['SPY'])
# (optional, default is 'column')
group_by = 'ticker',
# adjust all OHLC automatically
# (optional, default is False)
auto_adjust = True,
# download pre/post regular market hours data
# (optional, default is False)
prepost = True,
# use threads for mass downloading? (True/False/Integer)
# (optional, default is True)
threads = True,
# proxy URL scheme use use when downloading?
# (optional, default is None)
proxy = None
)The following answer on Stack Overflow is for How to deal with multi-level column names downloaded with yfinance?
yfinancereturns apandas.DataFramewith multi-level column names, with a level for the ticker and a level for the stock price data- The answer discusses:
- How to correctly read the the multi-level columns after saving the dataframe to a csv with
pandas.DataFrame.to_csv - How to download single or multiple tickers into a single dataframe with single level column names and a ticker column
- How to correctly read the the multi-level columns after saving the dataframe to a csv with
- The answer discusses:
If your code uses pandas_datareader and you want to download data faster,
you can "hijack" pandas_datareader.data.get_data_yahoo() method to use
yfinance while making sure the returned data is in the same format as
pandas_datareader's get_data_yahoo().
from pandas_datareader import data as pdr
import yfinance as yf
yf.pdr_override() # <== that's all it takes :-)
# download dataframe
data = pdr.get_data_yahoo("SPY", start="2017-01-01", end="2017-04-30")Install yfinance using pip:
$ pip install yfinance --upgrade --no-cache-dirTo install yfinance using conda, see this.
- Python >= 2.7, 3.4+
- Pandas (tested to work with >=0.23.1)
- Numpy >= 1.11.1
- requests >= 2.14.2
- lxml >= 4.5.1
- pandas_datareader >= 0.4.0
yfinance is distributed under the Apache Software License. See the LICENSE.txt file in the release for details.
Please drop me an note with any feedback you have.
Ran Aroussi