yahooquery 1.0.4

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

Yahooquery

Python wrapper around an unofficial Yahoo Finance API.

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

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)

aapl.option_chain  # returns pandas.DataFrame

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

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()

dates	volume	open	low	high	close	ticker
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	AAPL
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	MSFT

Multiple Endpoints

Access more than one endpoint in one call using the get_multiple_endoints method of the Ticker class. This method ONLY returns dictionaries.

aapl = Ticker('aapl')
endpoints = ['assetProfile', 'esgScores', 'incomeStatementHistory']
aapl.get_multiple_endpoints(endpoints)
{'aapl': {'assetProfile': {...}, 'esgScores', {...}, 'incomeStatementHistory', {...}}}

Type Ticker._ENDPOINTS to view the list of endpoints supported through this method.