Yahoo! Finance market data downloader
Project description
Yahoo! Finance market data downloader
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.
NOTE
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.
Quick Start
The Ticker module
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
# show news
msft.news
# 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.actionsFetching 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",
# 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
)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-dirTo install yfinance using conda, see this.
Requirements
Optional (if you want to use pandas_datareader)
pandas_datareader >= 0.4.0
Legal Stuff
yfinance is distributed under the Apache Software License. See the LICENSE.txt file in the release for details.
P.S.
Please drop me an note with any feedback you have.
Ran Aroussi
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