Download market data from Yahoo! Finance's API

*** IMPORTANT LEGAL DISCLAIMER *** 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.

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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.

Changelog »

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Quick Start

The Ticker module

The Ticker module, which allows you to access ticker data in a more Pythonic way:

import yfinance as yf

msft = yf.Ticker("MSFT")

# get stock info
msft.info

# get historical market data
hist = msft.history(period="max")

# show meta information about the history (requires history() to be called first)
msft.history_metadata

# show actions (dividends, splits, capital gains)
msft.actions

# show dividends
msft.dividends

# show splits
msft.splits


# show capital gains (for mutual funds & etfs)
msft.capital_gains

# show share count
msft.shares

# show financials:
# - income statement
msft.income_stmt
msft.quarterly_income_stmt
# - balance sheet
msft.balance_sheet
msft.quarterly_balance_sheet
# - cash flow statement
msft.cashflow
msft.quarterly_cashflow
# see `Ticker.get_income_stmt()` for more options

# show major holders
msft.major_holders

# show institutional holders
msft.institutional_holders

# show mutualfund holders
msft.mutualfund_holders

# show earnings
msft.earnings
msft.quarterly_earnings

# show sustainability
msft.sustainability

# show analysts recommendations
msft.recommendations
msft.recommendations_summary
# show analysts other work
msft.analyst_price_target
msft.revenue_forecasts
msft.earnings_forecasts
msft.earnings_trend

# show next event (earnings, etc)
msft.calendar

# Show future and historic earnings dates, returns at most next 4 quarters and last 8 quarters by default. 
# Note: If more are needed use msft.get_earnings_dates(limit=XX) with increased limit argument.
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_capital_gains(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', 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')

# access each ticker using (example)
tickers.tickers['MSFT'].info
tickers.tickers['AAPL'].history(period="1mo")
tickers.tickers['GOOG'].actions

Fetching data for multiple tickers

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 = "5d",

        # Whether to ignore timezone when aligning ticker data from 
        # different timezones. Default is True. False may be useful for 
        # minute/hourly data.
        ignore_tz = False,

        # 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,

        # identify and attempt repair of currency unit mixups e.g. $/cents
        repair = False,

        # 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
    )

Timezone cache store

When fetching price data, all dates are localized to stock exchange timezone. But timezone retrieval is relatively slow, so yfinance attemps to cache them in your users cache folder. You can direct cache to use a different location with set_tz_cache_location():

import yfinance as yf
yf.set_tz_cache_location("custom/cache/location")
...

Managing Multi-Level Columns

The following answer on Stack Overflow is for How to deal with multi-level column names downloaded with yfinance?

• yfinance returns a pandas.DataFrame with 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

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pandas_datareader override

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")

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Installation

Install yfinance using pip:

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

To install yfinance using conda, see this.

Requirements

• Python >= 2.7, 3.4+
• Pandas >= 1.3.0
• Numpy >= 1.16.5
• requests >= 2.26
• lxml >= 4.9.1
• appdirs >= 1.4.4
• pytz >=2022.5
• frozendict >= 2.3.4
• beautifulsoup4 >= 4.11.1
• html5lib >= 1.1

Optional (if you want to use pandas_datareader)

• pandas_datareader >= 0.4.0

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Legal Stuff

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.

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P.S.

Please drop me an note with any feedback you have.

Ran Aroussi