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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. |
yfinance offers a threaded and Pythonic way to download market data from Yahoo!Ⓡ finance.
→ 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 all earnings dates msft.earnings_dates # show ISIN code - *experimental* # ISIN = International Securities Identification Number msft.isin # show options expirations msft.options # show news msft.news # get option chain for specific expiration opt = msft.option_chain('YYYY-MM-DD') # data available via: opt.calls, opt.puts
If 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.actions
To 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.actions
import 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-dir
To 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
- appdirs >=1.4.4
- pandas_datareader >= 0.4.0
yfinance is distributed under the Apache Software License. See the LICENSE.txt file in the release for details.
AGAIN - 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 detailes on your rights to use the actual data downloaded.
Please drop me an note with any feedback you have.
Ran Aroussi