README.rst

Yahoo! Finance-ng python3 / pandas market data downloader

Ever since Yahoo! finance decommissioned their historical data API, many programs that relied on it to stop working.

yfinanceng aimes 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 yfinance, but I've since renamed it to yfinanceng as I no longer consider it a mere "fix", and the author is not promptly maintaining it or merging PRs.

Changelog »

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Quick Start

The Ticker module

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

import yfinanceng 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 heet
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.puts

If you want to use a proxy server for downloading data, use:

import yfinanceng 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")
msgt.option_chain(..., proxy="PROXY_SERVER")
...

To initialize multiple Ticker objects, use

import yfinanceng as yf

tickers = yf.Tickers('msft aapl goog')
# ^ returns a named tuple of Ticker objects

# access each ticker using (example)
tickers.msft.info
tickers.aapl.history(period="1mo")
tickers.goog.actions

Fetching data for multiple tickers

import yfinanceng 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
    )

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 yfinanceng 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 yfinanceng 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 yfinanceng using pip:

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

Install yfinanceng using conda:

$ conda install -c larroy yfinanceng

Requirements

• Python >= 3.6+
• Pandas (tested to work with >=0.23.1)
• Numpy >= 1.11.1
• requests >= 2.14.2

Optional (if you want to use pandas_datareader)

• pandas_datareader >= 0.4.0

Legal Stuff

yfinanceng is distributed under the Apache Software License. See the LICENSE.txt file in the release for details.

Based on yfinance from Ran Aroussi.