Skip to main content

Retrieve nearly all data from Yahoo Finance for one or more ticker symbols

Project description

Yahooquery

CodeFactor PyPi download total PyPI version shields.io PyPI license PyPI pyversions Build Status codecov

Python wrapper around an unofficial Yahoo Finance API. Check out an interactive demo at (https://yahooquery-streamlit.herokuapp.com)

Yahooquery Demo

Install

pip install yahooquery

Ticker

The Ticker module is the access point to the Yahoo Finance API. Pass a ticker symbol to the Ticker class.

from yahooquery import Ticker

aapl = Ticker('aapl')

Or pass a list of tickers.

tickers = Ticker(['aapl', 'msft'])

Data

Based on the data you'd like, the result will either be accessed through a dict or as a pandas.DataFrame. Accessing data is incredibly easy and pythonic.

Dictionaries

aapl = Ticker('aapl')

# Asset Profile
aapl.asset_profile
{'aapl': {'address1': 'One Apple Park Way', 'city': 'Cupertino', ... }}

# ESG Scores
aapl.esg_scores
{'aapl': {'totalEsg': 72.27, 'environomentScore': 89.81, ... }}

# Financial Data
aapl.financial_data
{'aapl': {'currentPrice': 275.15, 'targetHighPrice': 342.4, ... }}

# Key Statistics
aapl.key_stats
{'aapl': {'priceHint': 2, 'enterpriseValue': 1230054359040, ... }}

# Price Information
aapl.price
{'aapl': {'preMarketChange': {}, 'preMarketPrice': {}, ... }}

# Quote Type
aapl.quote_type
{'aapl': {'exchange': 'NMS', 'quoteType': 'EQUITY', ... }}

# Share Purchase Activity
aapl.share_purchase_activity
{'aapl': {'period': '6m', 'buyInfoCount': 20, ... }}

# Summary Information
aapl.summary_detail
{'aapl': {'priceHint': 2, 'previousClose': 271.46, ... }}
aapl.summary_profile
{'aapl': {'address1': 'One Apple Park Way', 'city': 'Cupertino', ... }}

How about more than one ticker?

# Pass a list of tickers to the Ticker class
tickers = Ticker(['aapl', 'msft'])

tickers.asset_profile
{'aapl': {'address1': 'One Apple Park Way', 'city': 'Cupertino', ... }, 'msft': {'address1': 'One Microsoft Way', 'city': 'Redmond', ... }}

tickers.esg_scores
{'aapl': {'totalEsg': 72.27, 'environomentScore': 89.81, ... }, 'msft': {'totalEsg': 74.8, 'environmentScore': 84.17, ... }}

tickers.financial_data
{'aapl': {'currentPrice': 275.15, 'targetHighPrice': 342.4, ... }, 'msft': {'currentPrice': 154.53, 'targetHighPrice': 174.0, ... }}

tickers.key_stats
{'aapl': {'priceHint': 2, 'enterpriseValue': 1230054359040, ... }, 'msft': {'priceHint': 2, 'enterpriseValue': 1127840350208, ... }}

tickers.price
{'aapl': {'preMarketChange': {}, 'preMarketPrice': {}, ... }, 'msft': {'preMarketChange': {}, 'preMarketPrice': {}, ... }}

tickers.quote_type
{'aapl': {'exchange': 'NMS', 'quoteType': 'EQUITY', ... }, 'msft': {'exchange': 'NMS', 'quoteType': 'EQUITY', ... }}

tickers.share_purchase_activity
{'aapl': {'period': '6m', 'buyInfoCount': 20, ... }, 'msft': {'period': '6m', 'buyInfoCount': 30, ... }}

tickers.summary_detail
{'aapl': {'priceHint': 2, 'previousClose': 271.46, ... }, 'msft': {'priceHint': 2, 'previousClose': 153.24, ... }}

tickers.summary_profile
{'aapl': {'address1': 'One Apple Park Way', 'city': 'Cupertino', ... }, 'msft': {'address1': 'One Microsoft Way', 'city': 'Redmond', ... }}

Dataframes

aapl.company_officers
aapl.earning_history
aapl.grading_history
aapl.insider_holders
aapl.insider_transactions
aapl.institution_ownership
aapl.recommendation_trend
aapl.sec_filings
aapl.fund_ownership
aapl.major_holders
aapl.earnings_trend

# The following methods take a frequency argument.  If nothing is provided, annual data will be returned.  To return quarterly data, pass "q" as an argument.
aapl.balance_sheet()  # Defaults to Annual
aapl.balance_sheet(frequency="q")
aapl.balance_sheet("q")
aapl.cash_flow()
aapl.income_statement()

Fund Specific

Mutual Funds have many of the accessors detailed above as well as the additional ones below:

fund = Ticker('rpbax')

fund.fund_category_holdings  # pandas.DataFrame
fund.fund_bond_ratings  # pandas.DataFrame
fund.fund_sector_weightings  # pandas.DataFrame
fund.fund_performance  # dict
fund.fund_bond_holdings  # dict
fund.fund_equity_holdings  # dict

Options

Retrieve option pricing for every expiration date for given ticker(s)

import pandas as pd
df = aapl.option_chain  # returns pandas.DataFrame

# The dataframe contains a MultiIndex
df.index.names
FrozenList(['symbol', 'expiration_date', 'option_type', 'row'])

# Get all options for specified symbol
df.loc['aapl']

# Get specific expiration date for specified symbol
df.loc['aapl', '2020-01-02']

# Get specific option type for expiration date for specified symbol
df.loc['aapl', '2020-01-02', 'calls']

# Works with multiple tickers as well
tickers = Ticker(['aapl', 'msft', 'fb'])
df = tickers.option_chain

# Retrieve options for only one symbol
df.loc['aapl']

# Retrieve only calls for all symbols
df.xs('calls', level=2)

# Retrieve only puts for fb
df.xs(('fb', 'puts'), level=[0, 2])
# or
df.xs(('fb', 'puts'), level=['symbol', 'option_type'])

# Filter dataframe by options that in the money
df.loc[df['inTheMoney'] == True]

# Only include Apple in the money options
df.loc[df['inTheMoney'] == True].xs('aapl') 

Historical Pricing

Historical price data can be retrieved for one or more tickers through the history method.

aapl.history()

If no arguments are provided, as above, default values will be supplied for both period and interval, which are ytd and 1d, respectively. Additional arguments you can provide to the method are start and end. Start and end dates can be either strings with a date format of yyyy-mm-dd or as a datetime.datetime object.

aapl.history(period='max')
aapl.history(start='2019-05-01')  # Default end date is now
aapl.history(end='2018-12-31')  # Default start date is 1900-01-01

# Period options = 1d, 5d, 1mo, 3mo, 6mo, 1y, 2y, 5y, 10y, ytd, max
# Interval options = 1m, 2m, 5m, 15m, 30m, 60m, 90m, 1h, 1d, 5d, 1wk, 1mo, 3mo

Available periods and intervals can be seen through Ticker.PERIODS and Ticker.INTERVALS, respectively.

If trying to retrieve more than one ticker, one dataframe will be returned and the column ticker can be used to identify each row appropriately.

tickers = Ticker(['aapl', 'msft'])
tickers.history()
symbol dates volume open low high close
AAPL 2019-01-02 07:30:00 37039700 154.89 154.23 158.85 157.92
AAPL 2019-01-03 07:30:00 91312200 143.98 142 145.72 142.19
MSFT 2019-12-12 07:30:00 24612100 151.65 151.02 153.44 153.24
MSFT 2019-12-13 14:00:01 23850062 153.003 152.85 154.89 154.53

Multiple Endpoints

Multiple endpoints can be accessed in one call for a given symbol through two separate endpoints: get_endpoints and all_endpoints. The get_endpoints method takes in a list of allowable endpoints. Conversely, the all_endpoints property will retrieve all base endpoints.

aapl = Ticker('aapl')
endpoints = ['assetProfile', 'esgScores', 'incomeStatementHistory']
data = aapl.get_endpoints(endpoints)

# or

data = aapl.all_endpoints

# The symbol(s) and endpoints become the keys in the dictionary

data['aapl']['assetProfile']
data['aapl']['esgScores']
data['aapl']['incomeStatementHistory']

Notes

  • The data will always be returned as a dictionary
  • Ticker.ENDPOINTS will show you the list of allowable endpoints you can pass to the get_endpoints method

Project details


Download files

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

Files for yahooquery, version 1.0.15
Filename, size File type Python version Upload date Hashes
Filename, size yahooquery-1.0.15-py3-none-any.whl (15.9 kB) File type Wheel Python version py3 Upload date Hashes View
Filename, size yahooquery-1.0.15.tar.gz (16.1 kB) File type Source Python version None Upload date Hashes View

Supported by

Pingdom Pingdom Monitoring Google Google Object Storage and Download Analytics Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page