Yahoo Finance

Yahoo! Finance is a component of Yahoo’s network. It is the most widely used business news website in the United States, featuring stock quotes, press announcements, financial reports, and original content, as well as financial news, data, and commentary. They provide market data, fundamental and option data, market analysis, and news for cryptocurrencies, fiat currencies, commodities futures, equities, and bonds, as well as fundamental and option data, market analysis, and news.

Note

Refer to Yahoo Finance Jupyter Notebook for more details.

Table of Contents

• Installation

• Usage

• Historical Price and Volume for 1 Stock

• Adding Time Periods

• Frequency Setting

• Stock Split and Dividends

• Importing Many Stocks

• Financial Indices

• Currencies

• Cryptocurrencies

• Mutual Funds

• Treasury Rates

• Stock Fundamentals

• Financials

• Put Call Options

• Stream Realtime Data

Installation

Install yfinance using pip:

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

Note

To install yfinance using conda, see this

Usage

Note

YFinance automatically uses Pandas DataFrames.

Import all necessary libraries:

import numpy as np
import yfinance as yf

Historical Price and Volume for 1 Stock

Outputs a Pandas DataFrame containing the values for open, high, low, close, and volume (OHLCV) of an equity.

ticker = 'GE'
yf.download(ticker)

Adding Time Periods

Uses start and end to denote a time period to get the data from above between.

yf.download(ticker, start = "2014-01-01", end = "2018-12-31")
GE = yf.download(ticker, start = "2014-01-01", end = "2018-12-31")
GE.info()

Output structure:

<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 1257 entries, 2014-01-02 to 2018-12-28
Data columns (total 6 columns):
Open         1257 non-null float64
High         1257 non-null float64
Low          1257 non-null float64
Close        1257 non-null float64
Adj Close    1257 non-null float64
Volume       1257 non-null int64
dtypes: float64(5), int64(1)
memory usage: 68.7 KB

Alternative, static time periods:

yf.download(ticker, period = "ytd")
yf.download(ticker, period = "1mo")
yf.download(ticker, period = "5d")
yf.download(ticker, period = "10y")

Frequency Setting

Outputs a similar Pandas DataFrame that breaks the OHLCV down into smaller minute or hour intervals.

yf.download('GE',period='1mo',interval='1h')
yf.download('GE',period='1mo',interval='5m')
GE = yf.download('GE',period='5d',interval='5m')

You can even get pre and post market data using prepost:

GE=yf.download('GE',prepost=True,period='5d',interval='5m')

Stock Split and Dividends

Gets the quarterly dividend data for the given ticker.

ticker = "AAPL"
# action = True for dividend and Stock Split
AAPL = yf.download(ticker, period="10y", actions = True)
AAPL.head()

You can use Pandas to narrow the data down by date or other features, such as stock splits.

AAPL[AAPL["Dividends"]>0]
AAPL.loc["2019-08-05":"2019-08-15"].diff()
AAPL[AAPL["Stock Splits"] > 0]

Importing Many Stocks

Use an array to get data on more than one stock.

ticker = ['GE', 'AAPL','FB']
yf.download(ticker, period="5y")

stock=yf.download(ticker, period="5y").Close

Financial Indices

Getting OHLCV data on multiple indices with the download function.

index = ['^DJI', '^GSPC']

stock = yf.download(index,period='10y').Close

#Total Return
index = ['^DJITR', '^SP500TR']

indexes = yf.download(index,period='10y').Close

Currencies

Getting currency OHLCV data with the download function.

#Tickers
ticker1 = "EURUSD=X"
ticker2 = "USDEUR=X"

yf.download(ticker1,period='5y')

yf.download(ticker2,period='5y')

Cryptocurrencies

Getting crypto OHLCV data with the download function.

#Tickers
ticker1 = ["BTC-USD", "ETH-USD"]

data = yf.download(ticker1,start='2019-08-01',end='2020-05-01')

Mutual Funds

Getting mutual fund data with the download function.

#Tickers
#20+Y Treasury Bobd ETF and Vivoldi Multi-Strategy Fund Class
ticker1 = ["TLT", "OMOIX"]

data = yf.download(ticker1,start='2019-08-01',end='2020-05-01')

Treasury Rates

Getting treasury rates with the download function.

#10Y and 5Y Treasury Rates
ticker1 = ["^TNX", "^FVX"]

data = yf.download(ticker1,period="5y")

Stock Fundamentals

To get fundamentals, use the Ticker object to instantiate new values.

ticker ="DIS"
dis = yf.Ticker(ticker)

Simply list the current ticker

dis.ticker

'DIS'

Outputs 150+ features on the ticker, including: sector, website, ebitda, targetLowPrice, currentRatio, currentPrice, debtToEquity, and totalRevenue.

data=dis.info

Summary of the information from the Ticker object.

ticker = ["MSFT","FB"]

for i in ticker:
    df.loc["{}".format(i)] = pd.Series(yf.Ticker(i).info)

df.info()

Financials

Designate your desired ticker.

ticker ="DIS"
dis = yf.Ticker(ticker)

Gets the balance sheet.

dis.balance_sheet

Gets the income statement.

dis.financials

Gets the statement of cash flows.

dis.cashflow

Put Call Options

Note

This output does not default to a Pandas DataFrame.

Designate your desired ticker.

ticker = "DIS"
dis = yf.Ticker(ticker)

Gets the call, contractSymbol, lastTradeDate, strike, lastPrice, bid, and ask.

dis.option_chain()

calls = dis.option_chain()[0]
calls

puts = dis.option_chain()[1]
puts

Stream Realtime Data

Continuously gets the latest data in 1 minute intervals.

import time

ticker1 ="EURUSD=X"
data = yf.download(ticker1,interval = '1m', period='1d')
print(data.index[-1], data.iloc[-1,3])
#Every 5 second data corresponding to 5 seconds
while True:
    time.sleep(5)
    data = yf.download(ticker1,interval = '1m', period='1d')
    print(data.index[-1], data.iloc[-1,3])