yahoofinancials-gemma-analytics 1.22

A powerful financial data module used for pulling both fundamental and technical data from Yahoo Finance

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

1. Installation using pip:

• Linux/Mac:

$ pip install yahoofinancials

• Windows (If python doesn’t work for you in cmd, try running the following command with just py):

> python -m pip install yahoofinancials

2. Installation using github (Mac/Linux):

$ git clone https://github.com/JECSand/yahoofinancials.git
$ cd yahoofinancials
$ python setup.py install

3. Demo using the included demo script:

$ cd yahoofinancials
$ python demo.py -h
$ python demo.py
$ python demo.py WFC C BAC

4. Test using the included unit testing script:

$ cd yahoofinancials
$ python test/test_yahoofinancials.py

Module 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

1. 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.

2. get_stock_price_data(reformat=True)

3. get_stock_earnings_data()

   • reformat optional value defaulted to true. Enter False for unprocessed raw data from Yahoo Finance.

4. 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.

5. get_stock_quote_type_data()

6. 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.

7. 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

1. 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
                }
            }
        ]
    }
}

2. 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
                }
            }
        ]
    }
}

3. 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
                }
            }
        ]
    }
}

4. 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
        }
    }
}

5. 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
        }
    }
}

6. 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
        }
    }
}

7. 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
        }
    }
}

8. 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"
    }
}

9. 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
}

10. 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
    }
}

11. 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
    }
}

12. 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
        }
    ]
}

13. 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
    }
}