Retrieve nearly all data from Yahoo Finance for one or more ticker symbols
Project description
Yahooquery
Python wrapper around an unofficial Yahoo Finance API. Check out an interactive demo at (https://yahooquery-streamlit.herokuapp.com)
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 theget_endpoints
method
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