yahoofinancials
A python module that returns stock, cryptocurrency, forex, mutual fund, commodity futures, ETF, and US Treasury financial data from Yahoo Finance.
Current Version: v1.20
Version Released: 12/17/2023
Report any bugs by opening an issue here: https://github.com/JECSand/yahoofinancials/issues
Overview
A powerful financial data module used for pulling both fundamental and technical data from Yahoo Finance.
- New analytic methods in v1.20:
- get_insights()
- returns data for:
‘instrumentInfo’
‘companySnapshot’
‘recommendation’
‘sigDevs’
‘secReports’
get_recommendations()
Example:
print(YahooFinancials('C').get_recommendations())Example Output:
{
"C": [
{
"recommendedSymbols": [
{
"score": 0.239602,
"symbol": "BAC"
},
{
"score": 0.225134,
"symbol": "JPM"
},
{
"score": 0.167669,
"symbol": "WFC"
},
{
"score": 0.145864,
"symbol": "GS"
},
{
"score": 0.134071,
"symbol": "F"
}
],
"symbol": "C"
}
]
}- As of Version 1.20, YahooFinancials supports a new optional parameter called flat_format.
When YahooFinancials(flat_format=True), financial statement data will return in a dict instead of a list. The keys of the dict will be the reporting dates.
Default is False, to ensure backwards compatibility.
As of Version 1.9, YahooFinancials supports optional parameters for asynchronous execution, proxies, and international requests.
from yahoofinancials import YahooFinancials
tickers = ['AAPL', 'GOOG', 'C']
yahoo_financials = YahooFinancials(tickers, concurrent=True, max_workers=8, country="US")
balance_sheet_data_qt = yahoo_financials.get_financial_stmts('quarterly', 'balance')
print(balance_sheet_data_qt)
proxy_addresses = [ "mysuperproxy.com:5000", "mysuperproxy.com:5001"]
yahoo_financials = YahooFinancials(tickers, concurrent=True, proxies=proxy_addresses)
balance_sheet_data_qt = yahoo_financials.get_financial_stmts('quarterly', 'balance')
print(balance_sheet_data_qt)Installation
yahoofinancials runs on Python 3.7, 3.8, 3.9, 3.10, 3.11, and 3.12
Installation using pip:
Linux/Mac:
$ pip install yahoofinancialsWindows (If python doesn’t work for you in cmd, try running the following command with just py):
> python -m pip install yahoofinancialsInstallation using github (Mac/Linux):
$ git clone https://github.com/JECSand/yahoofinancials.git
$ cd yahoofinancials
$ python setup.py installDemo using the included demo script:
$ cd yahoofinancials
$ python demo.py -h
$ python demo.py
$ python demo.py WFC C BACTest using the included unit testing script:
$ cd yahoofinancials
$ python test/test_yahoofinancials.pyModule Methods
The financial data from all methods is returned as JSON.
You can run multiple symbols at once using an inputted array or run an individual symbol using an inputted string.
YahooFinancials works with Python 3.7, 3.8, 3.9, 3.10, 3.11 and 3.12 and runs on all operating systems. (Windows, Mac, Linux).
Featured Methods
get_financial_stmts(frequency, statement_type, reformat=True)
frequency can be either ‘annual’ or ‘quarterly’.
statement_type can be ‘income’, ‘balance’, ‘cash’ or a list of several.
reformat optional value defaulted to true. Enter False for unprocessed raw data from Yahoo Finance.
get_stock_price_data(reformat=True)
get_stock_earnings_data()
reformat optional value defaulted to true. Enter False for unprocessed raw data from Yahoo Finance.
get_summary_data(reformat=True)
Returns financial summary data for cryptocurrencies, stocks, currencies, ETFs, mutual funds, U.S. Treasuries, commodity futures, and indexes.
reformat optional value defaulted to true. Enter False for unprocessed raw data from Yahoo Finance.
get_stock_quote_type_data()
get_historical_price_data(start_date, end_date, time_interval)
This method will pull historical pricing data for stocks, currencies, ETFs, mutual funds, U.S. Treasuries, cryptocurrencies, commodities, and indexes.
start_date should be entered in the ‘YYYY-MM-DD’ format and is the first day that data will be pulled for.
end_date should be entered in the ‘YYYY-MM-DD’ format and is the last day that data will be pulled for.
time_interval can be either ‘daily’, ‘weekly’, or ‘monthly’. This variable determines the time period interval for your pull.
Data response includes relevant pricing event data such as dividends and stock splits.
get_num_shares_outstanding(price_type=’current’)
price_type can also be set to ‘average’ to calculate the shares outstanding with the daily average price.
Additional Module Methods
get_daily_dividend_data(start_date, end_date)
get_stock_profile_data()
get_financial_data()
get_interest_expense()
get_operating_income()
get_total_operating_expense()
get_total_revenue()
get_cost_of_revenue()
get_income_before_tax()
get_income_tax_expense()
get_esg_score_data()
get_gross_profit()
get_net_income_from_continuing_ops()
get_research_and_development()
get_current_price()
get_current_change()
get_current_percent_change()
get_current_volume()
get_prev_close_price()
get_open_price()
get_ten_day_avg_daily_volume()
get_stock_exchange()
get_market_cap()
get_daily_low()
get_daily_high()
get_currency()
get_yearly_high()
get_yearly_low()
get_dividend_yield()
get_annual_avg_div_yield()
get_five_yr_avg_div_yield()
get_dividend_rate()
get_annual_avg_div_rate()
get_50day_moving_avg()
get_200day_moving_avg()
get_beta()
get_payout_ratio()
get_pe_ratio()
get_price_to_sales()
get_exdividend_date()
get_book_value()
get_ebit()
get_net_income()
get_earnings_per_share()
get_key_statistics_data()
get_stock_profile_data()
get_financial_data()
Usage Examples
The class constructor can take either a single ticker or a list of tickers as it’s parameter.
This makes it easy to initiate multiple classes for different groupings of financial assets.
Quarterly statement data returns the last 4 periods of data, while annual returns the last 3.
Single Ticker Example
from yahoofinancials import YahooFinancials
ticker = 'AAPL'
yahoo_financials = YahooFinancials(ticker)
balance_sheet_data_qt = yahoo_financials.get_financial_stmts('quarterly', 'balance')
income_statement_data_qt = yahoo_financials.get_financial_stmts('quarterly', 'income')
all_statement_data_qt = yahoo_financials.get_financial_stmts('quarterly', ['income', 'cash', 'balance'])
apple_earnings_data = yahoo_financials.get_stock_earnings_data()
apple_net_income = yahoo_financials.get_net_income()
historical_stock_prices = yahoo_financials.get_historical_price_data('2008-09-15', '2018-09-15', 'weekly')Lists of Tickers Example
from yahoofinancials import YahooFinancials
tech_stocks = ['AAPL', 'MSFT', 'INTC']
bank_stocks = ['WFC', 'BAC', 'C']
commodity_futures = ['GC=F', 'SI=F', 'CL=F']
cryptocurrencies = ['BTC-USD', 'ETH-USD', 'XRP-USD']
currencies = ['EURUSD=X', 'JPY=X', 'GBPUSD=X']
mutual_funds = ['PRLAX', 'QASGX', 'HISFX']
us_treasuries = ['^TNX', '^IRX', '^TYX']
yahoo_financials_tech = YahooFinancials(tech_stocks)
yahoo_financials_banks = YahooFinancials(bank_stocks)
yahoo_financials_commodities = YahooFinancials(commodity_futures)
yahoo_financials_cryptocurrencies = YahooFinancials(cryptocurrencies)
yahoo_financials_currencies = YahooFinancials(currencies)
yahoo_financials_mutualfunds = YahooFinancials(mutual_funds)
yahoo_financials_treasuries = YahooFinancials(us_treasuries)
tech_cash_flow_data_an = yahoo_financials_tech.get_financial_stmts('annual', 'cash')
bank_cash_flow_data_an = yahoo_financials_banks.get_financial_stmts('annual', 'cash')
banks_net_ebit = yahoo_financials_banks.get_ebit()
tech_stock_price_data = yahoo_financials_tech.get_stock_price_data()
daily_bank_stock_prices = yahoo_financials_banks.get_historical_price_data('2008-09-15', '2018-09-15', 'daily')
daily_commodity_prices = yahoo_financials_commodities.get_historical_price_data('2008-09-15', '2018-09-15', 'daily')
daily_crypto_prices = yahoo_financials_cryptocurrencies.get_historical_price_data('2008-09-15', '2018-09-15', 'daily')
daily_currency_prices = yahoo_financials_currencies.get_historical_price_data('2008-09-15', '2018-09-15', 'daily')
daily_mutualfund_prices = yahoo_financials_mutualfunds.get_historical_price_data('2008-09-15', '2018-09-15', 'daily')
daily_treasury_prices = yahoo_financials_treasuries.get_historical_price_data('2008-09-15', '2018-09-15', 'daily')Examples of Returned JSON Data
Annual Income Statement Data for Apple:
yahoo_financials = YahooFinancials('AAPL')
print(yahoo_financials.get_financial_stmts('annual', 'income')){
"incomeStatementHistory": {
"AAPL": [
{
"2016-09-24": {
"minorityInterest": null,
"otherOperatingExpenses": null,
"netIncomeFromContinuingOps": 45687000000,
"totalRevenue": 215639000000,
"totalOtherIncomeExpenseNet": 1348000000,
"discontinuedOperations": null,
"incomeTaxExpense": 15685000000,
"extraordinaryItems": null,
"grossProfit": 84263000000,
"netIncome": 45687000000,
"sellingGeneralAdministrative": 14194000000,
"interestExpense": null,
"costOfRevenue": 131376000000,
"researchDevelopment": 10045000000,
"netIncomeApplicableToCommonShares": 45687000000,
"effectOfAccountingCharges": null,
"incomeBeforeTax": 61372000000,
"otherItems": null,
"operatingIncome": 60024000000,
"ebit": 61372000000,
"nonRecurring": null,
"totalOperatingExpenses": 0
}
}
]
}
}Annual Balance Sheet Data for Apple:
yahoo_financials = YahooFinancials('AAPL')
print(yahoo_financials.get_financial_stmts('annual', 'balance')){
"balanceSheetHistory": {
"AAPL": [
{
"2016-09-24": {
"otherCurrentLiab": 8080000000,
"otherCurrentAssets": 8283000000,
"goodWill": 5414000000,
"shortTermInvestments": 46671000000,
"longTermInvestments": 170430000000,
"cash": 20484000000,
"netTangibleAssets": 119629000000,
"totalAssets": 321686000000,
"otherLiab": 36074000000,
"totalStockholderEquity": 128249000000,
"inventory": 2132000000,
"retainedEarnings": 96364000000,
"intangibleAssets": 3206000000,
"totalCurrentAssets": 106869000000,
"otherStockholderEquity": 634000000,
"shortLongTermDebt": 11605000000,
"propertyPlantEquipment": 27010000000,
"deferredLongTermLiab": 2930000000,
"netReceivables": 29299000000,
"otherAssets": 8757000000,
"longTermDebt": 75427000000,
"totalLiab": 193437000000,
"commonStock": 31251000000,
"accountsPayable": 59321000000,
"totalCurrentLiabilities": 79006000000
}
}
]
}
}Quarterly Cash Flow Statement Data for Citigroup:
yahoo_financials = YahooFinancials('C')
print(yahoo_financials.get_financial_stmts('quarterly', 'cash')){
"cashflowStatementHistoryQuarterly": {
"C": [
{
"2017-06-30": {
"totalCashFromOperatingActivities": -18505000000,
"effectOfExchangeRate": -117000000,
"totalCashFromFinancingActivities": 39798000000,
"netIncome": 3872000000,
"dividendsPaid": -760000000,
"salePurchaseOfStock": -1781000000,
"capitalExpenditures": -861000000,
"changeToLiabilities": -7626000000,
"otherCashflowsFromInvestingActivities": 82000000,
"totalCashflowsFromInvestingActivities": -22508000000,
"netBorrowings": 33586000000,
"depreciation": 901000000,
"changeInCash": -1332000000,
"changeToNetincome": 1444000000,
"otherCashflowsFromFinancingActivities": 8753000000,
"changeToOperatingActivities": -17096000000,
"investments": -23224000000
}
}
]
}
}Monthly Historical Stock Price Data for Wells Fargo:
yahoo_financials = YahooFinancials('WFC')
print(yahoo_financials.get_historical_price_data("2018-07-10", "2018-08-10", "monthly")){
"WFC": {
"currency": "USD",
"eventsData": {
"dividends": {
"2018-08-01": {
"amount": 0.43,
"date": 1533821400,
"formatted_date": "2018-08-09"
}
}
},
"firstTradeDate": {
"date": 76233600,
"formatted_date": "1972-06-01"
},
"instrumentType": "EQUITY",
"prices": [
{
"adjclose": 57.19147872924805,
"close": 57.61000061035156,
"date": 1533096000,
"formatted_date": "2018-08-01",
"high": 59.5,
"low": 57.08000183105469,
"open": 57.959999084472656,
"volume": 138922900
}
],
"timeZone": {
"gmtOffset": -14400
}
}
}Monthly Historical Price Data for EURUSD:
yahoo_financials = YahooFinancials('EURUSD=X')
print(yahoo_financials.get_historical_price_data("2018-07-10", "2018-08-10", "monthly")){
"EURUSD=X": {
"currency": "USD",
"eventsData": {},
"firstTradeDate": {
"date": 1070236800,
"formatted_date": "2003-12-01"
},
"instrumentType": "CURRENCY",
"prices": [
{
"adjclose": 1.1394712924957275,
"close": 1.1394712924957275,
"date": 1533078000,
"formatted_date": "2018-07-31",
"high": 1.169864296913147,
"low": 1.1365960836410522,
"open": 1.168961763381958,
"volume": 0
}
],
"timeZone": {
"gmtOffset": 3600
}
}
}Monthly Historical Price Data for BTC-USD:
yahoo_financials = YahooFinancials('BTC-USD')
print(yahoo_financials.get_historical_price_data("2018-07-10", "2018-08-10", "monthly")){
"BTC-USD": {
"currency": "USD",
"eventsData": {},
"firstTradeDate": {
"date": 1279321200,
"formatted_date": "2010-07-16"
},
"instrumentType": "CRYPTOCURRENCY",
"prices": [
{
"adjclose": 6285.02001953125,
"close": 6285.02001953125,
"date": 1533078000,
"formatted_date": "2018-07-31",
"high": 7760.740234375,
"low": 6133.02978515625,
"open": 7736.25,
"volume": 4334347882
}
],
"timeZone": {
"gmtOffset": 3600
}
}
}Weekly Historical Price Data for Crude Oil Futures:
yahoo_financials = YahooFinancials('CL=F')
print(yahoo_financials.get_historical_price_data("2018-08-01", "2018-08-10", "weekly")){
"CL=F": {
"currency": "USD",
"eventsData": {},
"firstTradeDate": {
"date": 1522555200,
"formatted_date": "2018-04-01"
},
"instrumentType": "FUTURE",
"prices": [
{
"adjclose": 68.58999633789062,
"close": 68.58999633789062,
"date": 1532923200,
"formatted_date": "2018-07-30",
"high": 69.3499984741211,
"low": 66.91999816894531,
"open": 68.37000274658203,
"volume": 683048039
},
{
"adjclose": 67.75,
"close": 67.75,
"date": 1533528000,
"formatted_date": "2018-08-06",
"high": 69.91999816894531,
"low": 66.13999938964844,
"open": 68.76000213623047,
"volume": 1102357981
}
],
"timeZone": {
"gmtOffset": -14400
}
}
}Apple Stock Quote Data:
yahoo_financials = YahooFinancials('AAPL')
print(yahoo_financials.get_stock_quote_type_data()){
"AAPL": {
"underlyingExchangeSymbol": null,
"exchangeTimezoneName": "America/New_York",
"underlyingSymbol": null,
"headSymbol": null,
"shortName": "Apple Inc.",
"symbol": "AAPL",
"uuid": "8b10e4ae-9eeb-3684-921a-9ab27e4d87aa",
"gmtOffSetMilliseconds": "-14400000",
"exchange": "NMS",
"exchangeTimezoneShortName": "EDT",
"messageBoardId": "finmb_24937",
"longName": "Apple Inc.",
"market": "us_market",
"quoteType": "EQUITY"
}
}U.S. Treasury Current Pricing Data:
yahoo_financials = YahooFinancials(['^TNX', '^IRX', '^TYX'])
print(yahoo_financials.get_current_price()){
"^IRX": 2.033,
"^TNX": 2.895,
"^TYX": 3.062
}BTC-USD Summary Data:
yahoo_financials = YahooFinancials('BTC-USD')
print(yahoo_financials.get_summary_data()){
"BTC-USD": {
"algorithm": "SHA256",
"ask": null,
"askSize": null,
"averageDailyVolume10Day": 545573809,
"averageVolume": 496761640,
"averageVolume10days": 545573809,
"beta": null,
"bid": null,
"bidSize": null,
"circulatingSupply": 17209812,
"currency": "USD",
"dayHigh": 6266.5,
"dayLow": 5891.87,
"dividendRate": null,
"dividendYield": null,
"exDividendDate": "-",
"expireDate": "-",
"fiftyDayAverage": 6989.074,
"fiftyTwoWeekHigh": 19870.62,
"fiftyTwoWeekLow": 2979.88,
"fiveYearAvgDividendYield": null,
"forwardPE": null,
"fromCurrency": "BTC",
"lastMarket": "CCCAGG",
"marketCap": 106325663744,
"maxAge": 1,
"maxSupply": 21000000,
"navPrice": null,
"open": 6263.2,
"openInterest": null,
"payoutRatio": null,
"previousClose": 6263.2,
"priceHint": 2,
"priceToSalesTrailing12Months": null,
"regularMarketDayHigh": 6266.5,
"regularMarketDayLow": 5891.87,
"regularMarketOpen": 6263.2,
"regularMarketPreviousClose": 6263.2,
"regularMarketVolume": 755834368,
"startDate": "2009-01-03",
"strikePrice": null,
"totalAssets": null,
"tradeable": false,
"trailingAnnualDividendRate": null,
"trailingAnnualDividendYield": null,
"twoHundredDayAverage": 8165.154,
"volume": 755834368,
"volume24Hr": 750196480,
"volumeAllCurrencies": 2673437184,
"yield": null,
"ytdReturn": null
}
}Apple Key Statistics Data:
yahoo_financials = YahooFinancials('AAPL')
print(yahoo_financials.get_key_statistics_data()){
"AAPL": {
"annualHoldingsTurnover": null,
"enterpriseToRevenue": 2.973,
"beta3Year": null,
"profitMargins": 0.22413999,
"enterpriseToEbitda": 9.652,
"52WeekChange": -0.12707871,
"morningStarRiskRating": null,
"forwardEps": 13.49,
"revenueQuarterlyGrowth": null,
"sharesOutstanding": 4729800192,
"fundInceptionDate": "-",
"annualReportExpenseRatio": null,
"totalAssets": null,
"bookValue": 22.534,
"sharesShort": 44915125,
"sharesPercentSharesOut": 0.0095,
"fundFamily": null,
"lastFiscalYearEnd": 1538179200,
"heldPercentInstitutions": 0.61208,
"netIncomeToCommon": 59531001856,
"trailingEps": 11.91,
"lastDividendValue": null,
"SandP52WeekChange": -0.06475246,
"priceToBook": 6.7582316,
"heldPercentInsiders": 0.00072999997,
"nextFiscalYearEnd": 1601337600,
"yield": null,
"mostRecentQuarter": 1538179200,
"shortRatio": 1,
"sharesShortPreviousMonthDate": "2018-10-31",
"floatShares": 4489763410,
"beta": 1.127094,
"enterpriseValue": 789555511296,
"priceHint": 2,
"threeYearAverageReturn": null,
"lastSplitDate": "2014-06-09",
"lastSplitFactor": "1/7",
"legalType": null,
"morningStarOverallRating": null,
"earningsQuarterlyGrowth": 0.318,
"priceToSalesTrailing12Months": null,
"dateShortInterest": 1543536000,
"pegRatio": 0.98,
"ytdReturn": null,
"forwardPE": 11.289103,
"maxAge": 1,
"lastCapGain": null,
"shortPercentOfFloat": 0.0088,
"sharesShortPriorMonth": 36469092,
"category": null,
"fiveYearAverageReturn": null
}
}Apple and Wells Fargo Daily Dividend Data:
start_date = '1987-09-15'
end_date = '1988-09-15'
yahoo_financials = YahooFinancials(['AAPL', 'WFC'])
print(yahoo_financials.get_daily_dividend_data(start_date, end_date)){
"AAPL": [
{
"date": 564157800,
"formatted_date": "1987-11-17",
"amount": 0.08
},
{
"date": 571674600,
"formatted_date": "1988-02-12",
"amount": 0.08
},
{
"date": 579792600,
"formatted_date": "1988-05-16",
"amount": 0.08
},
{
"date": 587655000,
"formatted_date": "1988-08-15",
"amount": 0.08
}
],
"WFC": [
{
"date": 562861800,
"formatted_date": "1987-11-02",
"amount": 0.3008
},
{
"date": 570724200,
"formatted_date": "1988-02-01",
"amount": 0.3008
},
{
"date": 578583000,
"formatted_date": "1988-05-02",
"amount": 0.3344
},
{
"date": 586445400,
"formatted_date": "1988-08-01",
"amount": 0.3344
}
]
}Apple key Financial Data:
yahoo_financials = YahooFinancials("AAPL")
print(yahoo_financials.get_financial_data()){
'AAPL': {
'ebitdaMargins': 0.29395,
'profitMargins': 0.21238,
'grossMargins': 0.37818,
'operatingCashflow': 69390999552,
'revenueGrowth': 0.018,
'operatingMargins': 0.24572,
'ebitda': 76476997632,
'targetLowPrice': 150,
'recommendationKey': 'buy',
'grossProfits': 98392000000,
'freeCashflow': 42914250752,
'targetMedianPrice': 270,
'currentPrice': 261.78,
'earningsGrowth': 0.039,
'currentRatio': 1.54,
'returnOnAssets': 0.11347,
'numberOfAnalystOpinions': 40,
'targetMeanPrice': 255.51,
'debtToEquity': 119.405,
'returnOnEquity': 0.55917,
'targetHighPrice': 300,
'totalCash': 100556996608,
'totalDebt': 108046999552,
'totalRevenue': 260174004224,
'totalCashPerShare': 22.631,
'financialCurrency': 'USD',
'maxAge': 86400,
'revenuePerShare': 56.341,
'quickRatio': 1.384,
'recommendationMean': 2.2
}
}1 maintainer
Metadata
1 maintainer
Release files for yahoofinancials-gemma-analytics 1.23
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Release files / yahoofinancials_gemma_analytics-1.23-py3-none-any.whl
| Download URL | yahoofinancials_gemma_analytics-1.23-py3-none-any.whl |
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| Size | 39.7 kB |
| Tags | Python 3 |
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