Skip to main content

Download market data from Yahoo! Finance API

Project description

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.


Python version PyPi version PyPi status PyPi downloads Travis-CI build status CodeFactor Star this repo Follow me on twitter

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 »


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 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

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 = "1m",

        # 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,

        # 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

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")

Installation

Install yfinance using pip:

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

To install yfinance using conda, see this.

Requirements

Optional (if you want to use pandas_datareader)


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.


P.S.

Please drop me an note with any feedback you have.

Ran Aroussi

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

yfinance-0.1.91.tar.gz (30.3 kB view details)

Uploaded Source

Built Distribution

yfinance-0.1.91-py2.py3-none-any.whl (31.2 kB view details)

Uploaded Python 2 Python 3

File details

Details for the file yfinance-0.1.91.tar.gz.

File metadata

  • Download URL: yfinance-0.1.91.tar.gz
  • Upload date:
  • Size: 30.3 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.1

File hashes

Hashes for yfinance-0.1.91.tar.gz
Algorithm Hash digest
SHA256 27ef933b1af6396aeb408e7474ab64c178ae854fdf1cab97feac1130cb30520a
MD5 576a49b07dfc37e7fadc212d3c3f71e4
BLAKE2b-256 c553bf0525d91c0f8c95e03f6cee9e3885d0b50013e914195187fbb6a34d4dff

See more details on using hashes here.

File details

Details for the file yfinance-0.1.91-py2.py3-none-any.whl.

File metadata

  • Download URL: yfinance-0.1.91-py2.py3-none-any.whl
  • Upload date:
  • Size: 31.2 kB
  • Tags: Python 2, Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.1

File hashes

Hashes for yfinance-0.1.91-py2.py3-none-any.whl
Algorithm Hash digest
SHA256 da7c24df1edbda8c75cff7421e2a6de7cb22f93174781b9634a77d97beba902e
MD5 a4d43dbc1e2c62c2a7575cac9126fa58
BLAKE2b-256 f3230e28fa29eba03f33d74c58296f301064930340622be34b008ed02d4486de

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page