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Yahoo! Finance market data downloader

Project description

Yahoo! Finance-ng python3 / pandas market data downloader

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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.


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 »

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

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

# show actions (dividends, splits)

# show dividends

# show splits

# show financials

# show major holders

# show institutional holders

# show balance heet

# show cashflow

# show earnings

# show sustainability

# show analysts recommendations

# show next event (earnings, etc)

# show ISIN code - *experimental*
# ISIN = International Securities Identification Number

# show options expirations

# 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")
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)

Fetching data for multiple tickers

import yfinanceng as yf
data ="SPY AAPL", start="2017-01-01", end="2017-04-30")

I’ve also added some options to make life easier :)

data =  # 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” 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")


Install yfinanceng using pip:

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

Install yfinanceng using conda:

$ conda install -c larroy yfinanceng


Optional (if you want to use pandas_datareader)

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