kensho_finance

kensho_finance.constants

class BusinessRelationshipType(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)

Bases: StrEnum

borrower = 'borrower'

client_services = 'client_services'

creditor = 'creditor'

customer = 'customer'

distributor = 'distributor'

franchisee = 'franchisee'

franchisor = 'franchisor'

investor_relations_client = 'investor_relations_client'

investor_relations_firm = 'investor_relations_firm'

landlord = 'landlord'

lessee = 'lessee'

lessor = 'lessor'

licensee = 'licensee'

licensor = 'licensor'

strategic_alliance = 'strategic_alliance'

supplier = 'supplier'

tenant = 'tenant'

transfer_agent = 'transfer_agent'

transfer_agent_client = 'transfer_agent_client'

vendor = 'vendor'

class CurrentPeriod

Bases: TypedDict

current_date: str

current_month: int

current_quarter: int

current_year: int

class HistoryMetadata

Bases: TypedDict

currency: str

exchange_name: str

first_trade_date: date

instrument_type: str

symbol: str

class IdentificationTriple

Bases: TypedDict

company_id: int

security_id: int

trading_item_id: int

class LatestAnnualPeriod

Bases: TypedDict

latest_year: int

class LatestPeriods

Bases: TypedDict

annual: LatestAnnualPeriod

now: CurrentPeriod

quarterly: LatestQuarterlyPeriod

class LatestQuarterlyPeriod

Bases: TypedDict

latest_quarter: int

latest_year: int

class LineItemType

Bases: TypedDict

aliases: set[str]

dataitemid: int

name: str

spgi_name: str

class YearAndQuarter

Bases: TypedDict

quarter: int

year: int

kensho_finance.fetch

class KFinanceApiClient(refresh_token: Optional[str] = None, client_id: Optional[str] = None, private_key: Optional[str] = None, api_host: str = 'https://kfinance.kensho.com', api_version: int = 1, okta_host: str = 'https://kensho.okta.com', okta_auth_server: str = 'default')

Bases: object

property access_token: str

Returns the client access token.

If the token is not set or has expired, a new token gets fetched and returned.

fetch(url: str) → dict

Does the request and auth

fetch_companies_from_business_relationship(company_id: int, relationship_type: BusinessRelationshipType) → dict[str, list[int]]

Fetches a dictionary of current and previous company IDs associated with a given company ID based on the specified relationship type.

The returned dictionary has the following structure: {

“current”: List[int], “previous”: List[int]

}

Example: fetch_companies_from_business_relationship(company_id=1234, relationship_type=”distributor”) returns a dictionary of company 1234’s current and previous distributors.

Parameters

• company_id (int) – The ID of the company for which associated companies are being fetched.

• relationship_type (BusinessRelationshipType) – The type of relationship to filter by. Valid relationship types are defined in the BusinessRelationshipType class.

Returns

A dictionary containing lists of current and previous company IDs that have the specified relationship with the given company_id.

Return type

dict[str, list[int]]

fetch_company_geography_groups(country_iso_code: str, state_iso_code: Optional[str] = None) → dict[str, list[int]]

Fetch company geography groups

fetch_company_simple_industry_groups(simple_industry: str) → dict[str, list[int]]

Fetch company simple industry groups

fetch_cusip(security_id: int) → dict

Get the CUSIP.

fetch_earnings_dates(company_id: int) → dict

Get the earnings dates.

fetch_exchange_groups(exchange_code: str, fetch_ticker: bool = True) → dict[str, list]

Fetch exchange groups

fetch_geography_groups(country_iso_code: str, state_iso_code: Optional[str] = None, fetch_ticker: bool = True) → dict[str, list]

Fetch geography groups

fetch_history(trading_item_id: int, is_adjusted: bool = True, start_date: Optional[str] = None, end_date: Optional[str] = None, periodicity: Optional[str] = None) → dict

Get the pricing history.

fetch_history_metadata(trading_item_id: int) → dict[str, str]

Get the pricing history metadata.

fetch_id_triple(identifier: str, exchange_code: Optional[str] = None) → dict

Get the ID triple from [identifier].

fetch_info(company_id: int) → dict

Get the company info.

fetch_isin(security_id: int) → dict

Get the ISIN.

fetch_line_item(company_id: int, line_item: str, period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → dict

Get a specified financial line item for a specified duration.

fetch_price_chart(trading_item_id: int, is_adjusted: bool = True, start_date: Optional[str] = None, end_date: Optional[str] = None, periodicity: Optional[str] = '') → bytes

Get the price chart.

fetch_primary_security(company_id: int) → dict

Get the primary security of a company.

fetch_primary_trading_item(security_id: int) → dict

Get the primary trading item of a security.

fetch_securities(company_id: int) → dict

Get the list of securities of a company.

fetch_simple_industry_groups(simple_industry: str, fetch_ticker: bool = True) → dict[str, list]

Fetch simple industry groups

fetch_statement(company_id: int, statement_type: str, period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → dict

Get a specified financial statement for a specified duration.

fetch_ticker_combined(country_iso_code: Optional[str] = None, state_iso_code: Optional[str] = None, simple_industry: Optional[str] = None, exchange_code: Optional[str] = None) → dict[str, list[kensho_finance.constants.IdentificationTriple]]

Fetch tickers using combined filters route

fetch_ticker_exchange_groups(exchange_code: str) → dict[str, list[kensho_finance.constants.IdentificationTriple]]

Fetch ticker exchange groups

fetch_ticker_geography_groups(country_iso_code: str, state_iso_code: Optional[str] = None) → dict[str, list[kensho_finance.constants.IdentificationTriple]]

Fetch ticker geography groups

fetch_ticker_simple_industry_groups(simple_industry: str) → dict[str, list[kensho_finance.constants.IdentificationTriple]]

Fetch ticker simple industry groups

fetch_trading_item_exchange_groups(exchange_code: str) → dict[str, list[int]]

Fetch company exchange groups

fetch_trading_items(security_id: int) → dict

Get the list of trading items of a security.

kensho_finance.kfinance

class BusinessRelationships(current: Companies, previous: Companies)

Bases: NamedTuple

Business relationships object that represents the current and previous companies of a given Company object.

Parameters

• current – A Companies set that represents the current company_ids.

• previous – A Companies set that represents the previous company_ids.

current: Companies

Alias for field number 0

previous: Companies

Alias for field number 1

class Client(refresh_token: Optional[str] = None, client_id: Optional[str] = None, private_key: Optional[str] = None, api_host: str = 'https://kfinance.kensho.com', api_version: int = 1, okta_host: str = 'https://kensho.okta.com', okta_auth_server: str = 'default')

Bases: object

Client class with LLM tools and a pre-credentialed Ticker object

Parameters

• tools (dict[str, Callable]) – A dictionary mapping function names to functions, where each function is an llm tool with the Client already passed in if applicable

• anthropic_tool_descriptions (list[dict]) – A list of dictionaries, where each dictionary is an Anthropic tool definition

• gemini_tool_descriptions (dict[list[dict]]) – A dictionary mapping “function_declarations” to a list of dictionaries, where each dictionary is a Gemini tool definition

• openai_tool_descriptions (list[dict]) – A list of dictionaries, where each dictionary is an OpenAI tool definition

property access_token: str

Returns the client access token.

Returns

A valid access token for use in API

Return type

str

company(company_id: int) → Company

Generate the Company object from company_id

Parameters

company_id (int) – CIQ company id

Returns

The Company specified by the the company id

Return type

Company

static get_latest() → LatestPeriods

Get the latest annual reporting year, latest quarterly reporting quarter and year, and current date.

Returns

A dict in the form of {“annual”: {“latest_year”: int}, “quarterly”: {“latest_quarter”: int, “latest_year”: int}, “now”: {“current_year”: int, “current_quarter”: int, “current_month”: int, “current_date”: str of Y-m-d}}

Return type

Latest

static get_n_quarters_ago(n: int) → YearAndQuarter

Get the year and quarter corresponding to [n] quarters before the current quarter

Parameters

n (int) – the number of quarters before the current quarter

Returns

A dict in the form of {“year”: int, “quarter”: int}

Return type

YearAndQuarter

prompt = '\nThink carefully before answering.\n\nRULES:\n- Always initialize the Ticker object first, e.g. amazon = client.ticker("AMZN"), e.g. microsoft = client.ticker("MSFT")\n- The client will always be initialized.\n- When provided with a common nickname for a company, identify the company and use its stock ticker.\n- Only use the functions given.\n- Follow the documentation carefully.\n- If a time range is not specified, do not index into the dataframe and provide all the rows and columns as they are.\n- If a temporal question is asked, make sure to include the latest date that you\'ve got information for. Its ok to reference the past.\n- If the question asks for quarterly updates, include the current year (2025).\n- Only output executable code with no comments.\n- Only output the code without any code block formatting (no ```python).\n- Do not index into balance_sheet, cashflow or income_statement and only use the functions provided.\n- Include import statements if needed in generated code, e.g. import datetime, or import pandas as pd.\n- Make sure that a metric is not provided in the function list before performing calculations.\n- If a question asks for a specific metric that is not provided in the function list, please calculate it with mathematical operations by utilizing the provided functions.\n- For calculations where each operand is a single year or quarter, make sure to index into the dataframe of each operand using .iloc, e.g. .iloc[0, -1] for the most recent item.\n- For ltm or ytd calculations, make sure to use .reset_index(drop=True).\n- Use dateutil.relativedelta instead of datetime.timedelta.\n- If the latest quarter is needed, use client.get_latest.\n- If the question asks for lowest or highest prices, use [\'low\'] or [\'high\'] to index into the dataframe.\nUse only the following functions to answer finance questions in concise, correct Python code.\n\nFUNCTIONS:\ndef get_latest(self) -> dict:\n    """\n    Get the latest quarter and year. The output is a dictionary with the following schema:\n        {\n            "quarterly": {\n                "latest_quarter": int,\n                "latest_year": int\n            }\n        }\n    Examples:\n        Question:\n        What was SPGI\'s total revenue in the last quarter?\n        Answer:\n        spgi = client.ticker("SPGI")\n        latest_periods = client.get_latest()\n        latest_year = latest_periods["quarterly"]["latest_year"]\n        latest_quarter = latest_periods["quarterly"]["latest_quarter"]\n        spgi_latest_quarter_revenue = spgi.total_revenue(start_year=latest_year, start_quarter=latest_quarter, end_year=latest_year, end_quarter=latest_quarter)\n        spgi_latest_quarter_revenue\n    """\ndef get_n_quarters_ago(self, n: int) -> dict:\n    """\n    Get the year and quarter corresponding to [n] quarters before the current quarter. The output is a dictionary with the following schema:\n        {\n            "year": int,\n            "quarter": int\n        }\n    Examples:\n        Question:\n        What is Microsoft\'s total revenue in the last 10 quarters?\n        Answer:\n        microsoft = client.ticker("MSFT")\n        latest_periods = client.get_latest()\n        latest_year = latest_periods["quarterly"]["latest_year"]\n        latest_quarter = latest_periods["quarterly"]["latest_quarter"]\n        last_10_quarters = client.get_n_quarters_ago(10)\n        total_revenue = microsoft.total_revenue(period_type="quarterly", start_year=last_10_quarters["year"], start_quarter=last_10_quarters["quarter"], end_year=latest_year, end_quarter=latest_quarter)\n        total_revenue\n    """\ndef ticker(self, identifier: str, exchange_code: Optional[str] = None) -> Ticker:\n    """\n    Returns the Ticker object\n    Param identifier (str): Provide either the ticker (the unique ticker symbol, the company\'s primary security ticker), the ISIN, or the CUSIP that can be used as an identifier for the ticker object.\n    Param exchange_code (str): Provide the stock exchange code.\n    For example, call the method with a ticker symbol, ISIN, or CUSIN with its respective company name:\n        amazon = client.ticker("AMZN")\n        Question:\n        What is Medibank Private Limited income statement?\n        Answer:\n        medibank = client.ticker("MPL", "ASX")\n        medibank.income_statement()\n\n        Question:\n        What is Tata Consultancy balance sheet?\n        Answer:\n        tata = client.ticker("TCS", "NSEI")\n        tata.balance_sheet()\n\n        Question:\n        What is Honda\'s cash flow statement?\n        Answer:\n        honda = client.ticker("7267")\n        honda.cashflow()\n\n        Question:\n        What was the EBITDA for CUSIP 550021109 in Q2 2023?\n        # Use CUSIP as ticker identifier\n        company1_ticker = client.ticker("550021109")\n        company1_ebitda = company1_ticker.net_income(start_year=2023, start_quarter=2, end_year=2023, end_quarter=2)\n        company1_ebitda\n\n        Question:\n        ISIN US45166V2051 net income 2023?\n        # Use ISIN as ticker identifier\n        company1_ticker = client.ticker("US45166V2051")\n        company1_net_income = company1_ticker.net_income(start_year=2023)\n        company1_net_income\n    """\nclass Ticker:\n    """\n    Attributes:\n        history_metadata (dict): A dictionary describing meta information about ticker history with the following keys:\n        - currency (str): The currency in which the ticker is traded.\n        - symbol (str): The symbol representing the ticker.\n        - exchange_name (str): The name of the exchange where the ticker is traded.\n        - instrument_type (str): The type of financial instrument.\n        - first_trade_date (datetime.date): The date when the ticker was first traded.\n        earnings_call_datetimes (list): A list of datetime objects representing company future and historical earnings dates with timezone information.\n        isin (str): Company ISIN\n        company_id (int)\n        security_id (int)\n        cusip (str)\n        trading_item_id (int): Trading item ID\n        name (str): The name of the company.\n        status (str): The operational status of the company.\n        type (str): The type of the company.\n        simple_industry (str): The industry in which the company operates.\n        number_of_employees (Decimal)\n        founding_date (datetime.date)\n        webpage (str)\n        address (str): The street address of the company.\n        city (str)\n        zip_code (str)\n        state (str)\n        country (str)\n        iso_country (str)\n        info (dict): A dictionary with the following keys:\n        - name (str): The name of the company.\n        - status (str): The operational status of the company.\n        - type (str): The type of the company.\n        - simple_industry (str): The industry in which the company operates.\n        - number_of_employees (Decimal)\n        - founding_date (datetime.date)\n        - webpage (str)\n        - address (str): The street address of the company.\n        - city (str)\n        - zip_code (str)\n        - state (str)\n        - country (str)\n        - iso_country (str)\n    """\n    Functions:\n    The following functions share the same signature, parameters and return shape.\n\n    Here is the general signature of the functions:\n    def function_name(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) -> pd.DataFrame:\n        """\n        Parameters:\n            period_type: Optional[str], default to None\n            The period type of the data requested.\n                Options:\n                "annual": For annual data set to "annual"\n                "quarterly": For quarterly data set to "quarterly"\n                "ytd": For year to date data also known as ytd, set to "ytd"\n                "ltm": For last twelve months data also known as LTM, set to "ltm"\n                If any other values are passed in, the function will error.\n            start_year (Optional[int]): The starting year for the data range.\n            end_year (Optional[int]): The ending year for the data range. If the question is about "the last x years" or the "latest", and end_year is not provided, put end_year as the current year - 1. Otherwise, put the end_year as the current year.\n            start_quarter (Optional[int]): The starting quarter (1-4) within the starting year.\n            end_quarter (Optional[int]): The ending quarter (1-4) within the ending year.\n\n        Function Names:\n            - balance_sheet()\n            - income_statement()\n            - cash_flow()\n            - revenue()\n            - finance_division_revenue()\n            - insurance_division_revenue()\n            - revenue_from_sale_of_assets()\n            - revenue_from_sale_of_investments()\n            - revenue_from_interest_and_investment_income()\n            - other_revenue()\n            - total_other_revenue()\n            - fees_and_other_income()\n            - total_revenue()\n            - cost_of_goods_sold()\n            - finance_division_operating_expense()\n            - insurance_division_operating_expense()\n            - finance_division_interest_expense()\n            - cost_of_revenue()\n            - gross_profit()\n            - selling_general_and_admin_expense()\n            - exploration_and_drilling_costs()\n            - provision_for_bad_debts()\n            - pre_opening_costs()\n            - total_selling_general_and_admin_expense()\n            - research_and_development_expense()\n            - depreciation_and_amortization()\n            - amortization_of_goodwill_and_intangibles()\n            - impairment_of_oil_gas_and_mineral_properties()\n            - total_depreciation_and_amortization()\n            - other_operating_expense()\n            - total_other_operating_expense()\n            - total_operating_expense()\n            - operating_income()\n            - interest_expense()\n            - interest_and_investment_income()\n            - net_interest_expense()\n            - income_from_affiliates()\n            - currency_exchange_gains()\n            - other_non_operating_income()\n            - total_other_non_operating_income()\n            - ebt_excluding_unusual_items()\n            - restructuring_charges()\n            - merger_charges()\n            - merger_and_restructuring_charges()\n            - impairment_of_goodwill()\n            - gain_from_sale_of_assets()\n            - gain_from_sale_of_investments()\n            - asset_writedown()\n            - in_process_research_and_development_expense()\n            - insurance_settlements()\n            - legal_settlements()\n            - other_unusual_items()\n            - total_other_unusual_items()\n            - total_unusual_items()\n            - ebt_including_unusual_items()\n            - income_tax_expense()\n            - earnings_from_continued_operations()\n            - earnings_from_discontinued_operations()\n            - extraordinary_item_and_accounting_change()\n            - net_income_to_company()\n            - minority_interest_in_earnings()\n            - net_income()\n            - premium_on_redemption_of_preferred_stock()\n            - preferred_stock_dividend()\n            - other_preferred_stock_adjustments()\n            - other_adjustments_to_net_income()\n            - preferred_dividends_and_other_adjustments()\n            - net_income_allocable_to_general_partner()\n            - net_income_to_common_shareholders_including_extra_items()\n            - net_income_to_common_shareholders_excluding_extra_items()\n            - cash_and_equivalents()\n            - short_term_investments()\n            - trading_asset_securities()\n            - total_cash_and_short_term_investments()\n            - accounts_receivable()\n            - other_receivables()\n            - notes_receivable()\n            - total_receivables()\n            - inventory()\n            - prepaid_expense()\n            - finance_division_loans_and_leases_short_term()\n            - finance_division_other_current_assets()\n            - loans_held_for_sale()\n            - deferred_tax_asset_current_portion()\n            - restricted_cash()\n            - other_current_assets()\n            - total_current_assets()\n            - gross_property_plant_and_equipment()\n            - accumulated_depreciation()\n            - net_property_plant_and_equipment()\n            - long_term_investments()\n            - goodwill()\n            - other_intangibles()\n            - finance_division_loans_and_leases_long_term()\n            - finance_division_other_non_current_assets()\n            - long_term_accounts_receivable()\n            - long_term_loans_receivable()\n            - long_term_deferred_tax_assets()\n            - long_term_deferred_charges()\n            - other_long_term_assets()\n            - total_assets()\n            - accounts_payable()\n            - accrued_expenses()\n            - short_term_borrowings()\n            - current_portion_of_long_term_debt()\n            - current_portion_of_capital_leases()\n            - current_portion_of_long_term_debt_and_capital_leases()\n            - finance_division_debt_current_portion()\n            - finance_division_other_current_liabilities()\n            - current_income_taxes_payable()\n            - current_unearned_revenue()\n            - current_deferred_tax_liability()\n            - other_current_liability()\n            - total_current_liabilities()\n            - long_term_debt()\n            - capital_leases()\n            - finance_division_debt_non_current_portion()\n            - finance_division_other_non_current_liabilities()\n            - non_current_unearned_revenue()\n            - pension_and_other_post_retirement_benefit()\n            - non_current_deferred_tax_liability()\n            - other_non_current_liabilities()\n            - total_liabilities()\n            - preferred_stock_redeemable()\n            - preferred_stock_non_redeemable()\n            - preferred_stock_convertible()\n            - preferred_stock_other()\n            - preferred_stock_additional_paid_in_capital()\n            - preferred_stock_equity_adjustment()\n            - treasury_stock_preferred_stock_convertible()\n            - treasury_stock_preferred_stock_non_redeemable()\n            - treasury_stock_preferred_stock_redeemable()\n            - total_preferred_equity()\n            - common_stock()\n            - additional_paid_in_capital()\n            - retained_earnings()\n            - treasury_stock()\n            - other_equity()\n            - total_common_equity()\n            - total_equity()\n            - total_liabilities_and_equity()\n            - common_shares_outstanding()\n            - adjustments_to_cash_flow_net_income()\n            - other_amortization()\n            - total_other_non_cash_items()\n            - net_decrease_in_loans_originated_and_sold()\n            - provision_for_credit_losses()\n            - loss_on_equity_investments()\n            - stock_based_compensation()\n            - tax_benefit_from_stock_options()\n            - net_cash_from_discontinued_operation()\n            - other_operating_activities()\n            - change_in_trading_asset_securities()\n            - change_in_accounts_receivable()\n            - change_in_inventories()\n            - change_in_accounts_payable()\n            - change_in_unearned_revenue()\n            - change_in_income_taxes()\n            - change_in_deferred_taxes()\n            - change_in_other_net_operating_assets()\n            - change_in_net_operating_assets()\n            - cash_from_operations()\n            - capital_expenditure()\n            - sale_of_property_plant_and_equipment()\n            - cash_acquisitions()\n            - divestitures()\n            - sale_of_real_estate()\n            - sale_of_intangible_assets()\n            - net_cash_from_investments()\n            - net_decrease_in_investment_loans_originated_and_sold()\n            - other_investing_activities()\n            - total_other_investing_activities()\n            - cash_from_investing()\n            - short_term_debt_issued()\n            - long_term_debt_issued()\n            - total_debt_issued()\n            - short_term_debt_repaid()\n            - long_term_debt_repaid()\n            - total_debt_repaid()\n            - issuance_of_common_stock()\n            - repurchase_of_common_stock()\n            - issuance_of_preferred_stock()\n            - repurchase_of_preferred_stock()\n            - common_dividends_paid()\n            - preferred_dividends_paid()\n            - total_dividends_paid()\n            - special_dividends_paid()\n            - other_financing_activities()\n            - cash_from_financing()\n            - foreign_exchange_rate_adjustments()\n            - miscellaneous_cash_flow_adjustments()\n            - net_change_in_cash()\n            - depreciation()\n            - depreciation_of_rental_assets()\n            - sale_proceeds_from_rental_assets()\n            - basic_eps()\n            - basic_eps_excluding_extra_items()\n            - basic_eps_from_accounting_change()\n            - basic_eps_from_extraordinary_items()\n            - basic_eps_from_accounting_change_and_extraordinary_items()\n            - weighted_average_basic_shares_outstanding()\n            - diluted_eps()\n            - diluted_eps_excluding_extra_items()\n            - weighted_average_diluted_shares_outstanding()\n            - normalized_basic_eps()\n            - normalized_diluted_eps()\n            - dividends_per_share()\n            - distributable_cash_per_share()\n            - diluted_eps_from_accounting_change_and_extraordinary_items()\n            - diluted_eps_from_accounting_change()\n            - diluted_eps_from_extraordinary_items()\n            - diluted_eps_from_discontinued_operations()\n            - funds_from_operations()\n            - ebitda()\n            - ebita()\n            - ebit()\n            - ebitdar()\n            - net_debt()\n            - effective_tax_rate()\n            - current_ratio()\n            - quick_ratio()\n            - total_debt_to_capital()\n            - net_working_capital()\n            - working_capital()\n            - change_in_net_working_capital()\n            - total_debt()\n            - total_debt_to_equity_ratio()\n        Returns:\n            A Pandas DataFrame with column headers as a string with the time period.\n            For quarterly, ytd, or ltm data: <Year>\'Q\'<Quarter>, such as \'2023Q4\'.\n            For annual data: <Year>, such as \'2021\'.\n            For example, to access the value for 2023Q3, use df[\'2023Q3\']. Or to access into year 2021, use df[\'2021\'].\n\n        Examples:\n            Question:\n            What is the return on equity for IBM from 2020 to 2023?\n            Answer:\n            ibm = client.ticker("IBM")\n            net_income = ibm.net_income(start_year=2020, end_year=2023)\n            total_equity = ibm.total_equity(start_year=2020, end_year=2023)\n            roe = net_income.reset_index(drop=True) / total_equity.reset_index(drop=True)\n            roe\n\n            Question:\n            What is the revenue CAGR for META from 2019 to 2023?\n            Answer:\n            meta = client.ticker("META")\n            revenue = meta.revenue(start_year=2019, end_year = 2023)\n            cagr = (revenue[\'2023\'] / revenue[\'2019\']) ** (1/4) - 1\n            cagr\n\n            Question:\n            What is BoFa\'s gross profit in the last 4 years?\n            Answer:\n            bac = client.ticker("BAC")\n            # Don\'t have information for the current year, so check last year\n            end_year = datetime.date.today().year - 1\n            gross_profit = bac.gross_profit(start_year=end_year - 4, end_year=end_year)\n            gross_profit\n\n            Question:\n            What is Chipotle\'s working capital from 2019 to 2021?\n            Answer:\n            chipotle = client.ticker("CMG")\n            working_capital = chipotle.working_capital(start_year=2019, end_year=2021)\n            working_capital\n\n            Question:\n            What is the percentage change in gross profit for Microsoft from 2021 to now?\n            Answer:\n            microsoft = client.ticker("MSFT")\n            end_year = datetime.date.today().year - 1\n            gross_profit = microsoft.gross_profit(start_year=2021, end_year=end_year)\n            percentage_change = (gross_profit[str(end_year)] - gross_profit["2021"]) / gross_profit["2021"] * 100\n            percentage_change\n\n            Question:\n            What is Airbnb\'s quick ratio quarterly for the last 4 years?\n            Answer:\n            airbnb = client.ticker("ABNB")\n            quick_ratio = airbnb.quick_ratio(period_type="quarterly", start_year=datetime.datetime.now().year - 4, end_year=datetime.datetime.now().year)\n            quick_ratio\n\n            Question:\n            LTM EBITDA from Q3 of 2022 to Q1 of 2024 for Exxon?\n            Answer:\n            exxon = client.ticker("XOM")\n            ebitda = exxon.ebitda(period_type="ltm", start_year=2022, start_quarter=3, end_year=2024, end_quarter=1)\n            ebitda\n\n            Question:\n            What is Verizon\'s year to date capex?\n            Answer:\n            verizon = client.ticker("VZ")\n            capital_expenditure = verizon.capital_expenditure(period_type="ytd")\n            capital_expenditure\n        """\n\n    Functions:\n    The following functions `history` and `price_chart` share the same parameters.\n\n    def history(self, periodicity: Optional[str] = "day", adjusted: Optional[bool] = True, start_date: Optional[str] = None, end_date: Optional[str] = None) -> pd.DataFrame:\n    Retrieves the historical price data for a given asset over a specified date range.\n        Returns:\n            A pd.DataFrame containing historical price data with columns corresponding to the specified periodicity,\n            with Date as the index, and columns "open", "high", "low", "close", "volume" in type decimal.\n            The Date index is a string that depends on the periodicity.\n            If periodicity="day", the Date index is the day in format "YYYY-MM-DD", eg "2024-05-13".\n            If periodicity="week", the Date index is the week number of the year in format "YYYY Week ##", eg "2024 Week 2".\n            If periodicity="month", the Date index is the month name of the year in format "<Month> YYYY", eg "January 2024".\n            If periodicity="year", the Date index is the year in format "YYYY", eg "2024".\n\n    def price_chart(self, periodicity: Optional[str] = "day", adjusted: Optional[bool] = True, start_date: Optional[str] = None, end_date: Optional[str] = None) -> Image:\n    Retrieves the plotted historical price data for a given asset over a specified date range as an Image.\n        Returns:\n            An Image object with the plotted price data.\n        """\n        Shared Parameters of function `history` and `price_chart`:\n            periodicity: Optional[str], default "day"\n            The frequency or interval at which the historical data points are sampled or aggregated. Periodicity is not the same as the date range. The date range specifies the time span over which the data is retrieved, while periodicity determines how the data within that date range is aggregated.\n                Options:\n                "day": Data points are returned for each day within the specified date range.\n                "week": Data points are aggregated weekly, with each row representing a week.\n                "month": Data points are aggregated monthly, with each row representing a month.\n                "year": Data points are aggregated yearly, with each row representing a year.\n            adjusted: Optional[bool], default True\n            Whether to retrieve adjusted prices that account for corporate actions such as dividends and splits.\n            start_date: Optional[str], default None\n            The start date for historical price retrieval in format "YYYY-MM-DD".\n            end_date: Optional[str], default None\n            The end date for historical price retrieval in format "YYYY-MM-DD".\n            If end_date is not specified, put end_date as today.\n\n        Examples:\n            Question:\n            What were Apple\'s prices for the the last 3 weeks?\n            Answer:\n            from dateutil.relativedelta import relativedelta\n            apple = client.ticker("AAPL")\n            end_date = datetime.date.today()\n            start_date = end_date - datetime.relativedelta(days=3*7)\n            prices = apple.history(periodicity="day", start_date=str(start_date), end_date=str(end_date))\n            prices\n\n            Question:\n            What was Nvidia\'s prices for the last 2 years on a weekly basis?\n            Answer:\n            from dateutil.relativedelta import relativedelta\n            nvda = client.ticker("NVDA")\n            end_date = datetime.date.today()\n            start_date = end_date - datetime.relativedelta(days=2*365)\n            prices = nvda.history(periodicity="week", start_date=str(start_date), end_date=str(end_date))\n            prices\n        """\n'

security(security_id: int) → Security

Generate Security object from security_id

Parameters

security_id (int) – CIQ security id

Returns

The Security specified by the the security id

Return type

Security

ticker(identifier: int | str, exchange_code: Optional[str] = None, function_called: Optional[bool] = False) → Ticker

Generate Ticker object from [identifier] that is a ticker, ISIN, or CUSIP.

Parameters

• identifier (str) – the ticker symbol, ISIN, or CUSIP

• exchange_code (str, optional) – The code representing the equity exchange the ticker is listed on.

• function_called (bool, optional) – Flag for use in signaling function calling

Returns

Ticker object from that corresponds to the identifier

Return type

Ticker

tickers(country_iso_code: Optional[str] = None, state_iso_code: Optional[str] = None, simple_industry: Optional[str] = None, exchange_code: Optional[str] = None) → Tickers

Generate tickers object representing the collection of Tickers that meet all the supplied parameters

One of country_iso_code, simple_industry, or exchange_code must be supplied. A parameter set to None is not used to filter on

Parameters

• country_iso_code (str, optional) – The ISO 3166-1 Alpha-2 or Alpha-3 code that represent the primary country the firm is based in. It default None

• state_iso_code (str, optional) – The ISO 3166-2 Alpha-2 code that represents the primary subdivision of the country the firm the based in. Not all ISO 3166-2 codes are supported as S&P doesn’t maintain the full list but a feature request for the full list is submitted to S&P product. Requires country_iso_code also to have a value other then None. It default None

• simple_industry (str, optional) – The S&P CIQ Simple Industry defined in ciqSimpleIndustry in XPF. It default None

• exchange_code (str, optional) – The exchange code for the primary equity listing exchange of the firm. It default None

Returns

A tickers object that is the group of Ticker objects meeting all the supplied parameters

Return type

Tickers

trading_item(trading_item_id: int) → TradingItem

Generate TradingItem object from trading_item_id

Parameters

trading_item_id (int) – CIQ trading item id

Returns

The trading item specified by the the trading item id

Return type

TradingItem

class Companies(kfinance_api_client: KFinanceApiClient, company_ids: Iterable[int])

Bases: set

Base class for representing a set of Companies

class Company(kfinance_api_client: KFinanceApiClient, company_id: int)

Bases: CompanyFunctionsMetaClass

Company class

Parameters

• kfinance_api_client (KFinanceApiClient) – The KFinanceApiClient used to fetch data

• company_id (int) – The S&P Global CIQ Company Id

property address: str

Get the address of the company’s HQ

Returns

address of the company’s HQ

Return type

str

property city: str

Get the city of the company’s HQ

Returns

city of the company’s HQ

Return type

str

property country: str

Get the country of the company’s HQ

Returns

country of the company’s HQ

Return type

str

property earnings_call_datetimes: list[datetime.datetime]

Get the datetimes of the companies earnings calls

Returns

a list of datetimes for the companies earnings calls

Return type

list[datetime]

property founding_date: date

Get the founding date for the company

Returns

founding date for the company

Return type

date

property info: dict

Get the company info

Returns

a dict with containing: name, status, type, simple industry, number of employees, founding date, webpage, address, city, zip code, state, country, & iso_country

Return type

dict

property iso_country: str

Get the ISO code for the country of the company’s HQ

Returns

iso code for the country of the company’s HQ

Return type

str

property latest_earnings_call: None

Set and return the latest earnings call item for the object

Raises

NotImplementedError – This function is not yet implemented

property name: str

Get the company name

Returns

The company name

Return type

str

property number_of_employees: str

Get the number of employees the company has

Returns

how many employees the company has

Return type

str

property primary_security: Security

Return the primary security item for the Company object

Returns

a Security object of the primary security of company_id

Return type

Security

property securities: Securities

Return the security items for the Company object

Returns

a Securities object containing the list of securities of company_id

Return type

Securities

property simple_industry: str

Get the simple industry for the company

Returns

The company’s simple_industry

Return type

str

property state: str

Get the state of the company’s HQ

Returns

state of the company’s HQ

Return type

str

property status: str

Get the company status

Returns

The company status

Return type

str

property type: str

Get the type of company

Returns

The company type

Return type

str

property webpage: str

Get the webpage for the company

Returns

webpage for the company

Return type

str

property zip_code: str

Get the zip code of the company’s HQ

Returns

zip code of the company’s HQ

Return type

str

class Securities(kfinance_api_client: KFinanceApiClient, security_ids: Iterable[int])

Bases: set

Base class for representing a set of Securities

class Security(kfinance_api_client: KFinanceApiClient, security_id: int)

Bases: object

Security class

Parameters

• kfinance_api_client (KFinanceApiClient) – The KFinanceApiClient used to fetch data

• security_id (int) – The S&P CIQ security id

property cusip: str

Get the CUSIP for the object

Returns

The CUSIP

Return type

str

property isin: str

Get the ISIN for the object

Returns

The ISIN

Return type

str

property primary_trading_item: TradingItem

Return the primary trading item for the Security object

Returns

a TradingItem object of the primary trading item of security_id

Return type

TradingItem

property trading_items: TradingItems

Return the trading items for the Security object

Returns

a TradingItems object containing the list of trading items of security_id

Return type

TradingItems

class Ticker(kfinance_api_client: KFinanceApiClient, identifier: Optional[str] = None, exchange_code: Optional[str] = None, company_id: Optional[int] = None, security_id: Optional[int] = None, trading_item_id: Optional[int] = None)

Bases: DelegatedCompanyFunctionsMetaClass

Base Ticker class for accessing data on company

Parameters

• kfinance_api_client (KFinanceApiClient) – The KFinanceApiClient used to fetch data

• exchange_code (str, optional) – The exchange code identifying which exchange the ticker is on

property address: str

Get the address of the company’s HQ

Returns

address of the company’s HQ

Return type

str

property city: str

Get the city of the company’s HQ

Returns

city of the company’s HQ

Return type

str

property company: Company

Set and return the company for the object

Returns

The company returned as Company object

Return type

Company

property company_id: int

Get the company id for the object

Returns

the CIQ company id

Return type

int

property country: str

Get the country of the company’s HQ

Returns

country of the company’s HQ

Return type

str

property cusip: str

Get the CUSIP for the object

Returns

The CUSIP

Return type

str

property earnings_call_datetimes: list[datetime.datetime]

Get the datetimes of the companies earnings calls

Returns

a list of datetimes for the companies earnings calls

Return type

list[datetime]

property founding_date: date

Get the founding date for the company

Returns

founding date for the company

Return type

date

history(periodicity: str = 'day', adjusted: bool = True, start_date: Optional[str] = None, end_date: Optional[str] = None) → DataFrame

Retrieves the historical price data for a given asset over a specified date range.

Parameters

• periodicity (str) – Determines the frequency of the historical data returned. Options are “day”, “week”, “month” and “year”. This default to “day”

• adjusted (bool, optional) – Whether to retrieve adjusted prices that account for corporate actions such as dividends and splits, it defaults True

• start_date (str, optional) – The start date for historical price retrieval in format “YYYY-MM-DD”, default to None

• end_date (str, optional) – The end date for historical price retrieval in format “YYYY-MM-DD”, default to None

Returns

A pd.DataFrame containing historical price data with columns corresponding to the specified periodicity, with Date as the index, and columns “open”, “high”, “low”, “close”, “volume” in type decimal. The Date index is a string that depends on the periodicity. If periodicity=”day”, the Date index is the day in format “YYYY-MM-DD”, eg “2024-05-13” If periodicity=”week”, the Date index is the week number of the year in format “YYYY Week ##”, eg “2024 Week 2” If periodicity=”month”, the Date index is the month name of the year in format “<Month> YYYY”, eg “January 2024”. If periodicity=”year”, the Date index is the year in format “YYYY”, eg “2024”.

Return type

pd.DataFrame

property history_metadata: HistoryMetadata

Get information about exchange and quotation

Returns

A dict containing data about the currency, symbol, exchange, type of instrument, and the first trading date

Return type

HistoryMetadata

property info: dict

Get the company info for the ticker

Returns

a dict with containing: name, status, type, simple industry, number of employees, founding date, webpage, address, city, zip code, state, country, & iso_country

Return type

dict

property isin: str

Get the ISIN for the object

Returns

The ISIN

Return type

str

property iso_country: str

Get the ISO code for the country of the company’s HQ

Returns

iso code for the country of the company’s HQ

Return type

str

property name: str

Get the company name

Returns

The company name

Return type

str

property number_of_employees: str

Get the number of employees the company has

Returns

how many employees the company has

Return type

str

price_chart(periodicity: str = 'day', adjusted: bool = True, start_date: Optional[str] = None, end_date: Optional[str] = None) → Image

Get the price chart.

Parameters

• periodicity (str) – Determines the frequency of the historical data returned. Options are “day”, “week”, “month” and “year”. This default to “day”

• adjusted (bool, optional) – Whether to retrieve adjusted prices that account for corporate actions such as dividends and splits, it defaults True

• start_date (str, optional) – The start date for historical price retrieval in format “YYYY-MM-DD”, default to None

• end_date (str, optional) – The end date for historical price retrieval in format “YYYY-MM-DD”, default to None

Returns

An image showing the price chart of the trading item

Return type

Image

property primary_security: Security

Set and return the primary security for the object

Returns

The primary security as a Security object

Return type

Security

property primary_trading_item: TradingItem

Set and return the trading item for the object

Returns

The trading item returned as TradingItem object

Return type

TradingItem

property security_id: int

Get the CIQ security id for the object

Returns

the CIQ security id

Return type

int

set_company_id() → int

Set the company id for the object

Returns

the CIQ company id

Return type

int

set_identification_triple() → None

Get & set company_id, security_id, & trading_item_id for ticker with an exchange

set_security_id() → int

Set the security id for the object

Returns

the CIQ security id

Return type

int

set_trading_item_id() → int

Set the trading item id for the object

Returns

the CIQ trading item id

Return type

int

property simple_industry: str

Get the simple industry for the company

Returns

The company’s simple_industry

Return type

str

property state: str

Get the state of the company’s HQ

Returns

state of the company’s HQ

Return type

str

property status: str

Get the company status

Returns

The company status

Return type

str

property ticker: str

Get the ticker if it isn’t available from initialization

property trading_item_id: int

Get the CIQ trading item id for the object

Returns

the CIQ trading item id

Return type

int

property type: str

Get the type of company

Returns

The company type

Return type

str

property webpage: str

Get the webpage for the company

Returns

webpage for the company

Return type

str

property zip_code: str

Get the zip code of the company’s HQ

Returns

zip code of the company’s HQ

Return type

str

class Tickers(kfinance_api_client: KFinanceApiClient, id_triples: Iterable[IdentificationTriple])

Bases: set

Base TickerSet class for representing a set of Tickers

companies() → Companies

Build a group of company objects from the group of tickers

Returns

The Companies corresponding to the Tickers

Return type

Companies

securities() → Securities

Build a group of security objects from the group of tickers

Returns

The Securities corresponding to the Tickers

Return type

Securities

trading_items() → TradingItems

Build a group of trading item objects from the group of ticker

Returns

The TradingItems corresponding to the Tickers

Return type

TradingItems

class TradingItem(kfinance_api_client: KFinanceApiClient, trading_item_id: int)

Bases: object

Trading Class

Parameters

• kfinance_api_client (KFinanceApiClient) – The KFinanceApiClient used to fetch data

• trading_item_id (int) – The S&P CIQ Trading Item ID

static from_ticker(kfinance_api_client: KFinanceApiClient, ticker: str, exchange_code: Optional[str] = None) → TradingItem

Return TradingItem object from ticker

Parameters

• kfinance_api_client (KFinanceApiClient) – The KFinanceApiClient used to fetch data

• ticker (str) – the ticker symbol

• exchange_code (str, optional) – The exchange code identifying which exchange the ticker is on.

history(periodicity: str = 'day', adjusted: bool = True, start_date: Optional[str] = None, end_date: Optional[str] = None) → DataFrame

Retrieves the historical price data for a given asset over a specified date range.

Parameters

• periodicity (str) – Determines the frequency of the historical data returned. Options are “day”, “week”, “month” and “year”. This default to “day”

• adjusted (Optional[bool]) – Whether to retrieve adjusted prices that account for corporate actions such as dividends and splits, it defaults True

• start_date (Optional[str]) – The start date for historical price retrieval in format “YYYY-MM-DD”, default to None

• end_date (Optional[str]) – The end date for historical price retrieval in format “YYYY-MM-DD”, default to None

Returns

A pd.DataFrame containing historical price data with columns corresponding to the specified periodicity, with Date as the index, and columns “open”, “high”, “low”, “close”, “volume” in type decimal. The Date index is a string that depends on the periodicity. If periodicity=”day”, the Date index is the day in format “YYYY-MM-DD”, eg “2024-05-13” If periodicity=”week”, the Date index is the week number of the year in format “YYYY Week ##”, eg “2024 Week 2” If periodicity=”month”, the Date index is the month name of the year in format “<Month> YYYY”, eg “January 2024”. If periodicity=”year”, the Date index is the year in format “YYYY”, eg “2024”.

Return type

pd.DataFrame

property history_metadata: HistoryMetadata

Get information about exchange and quotation

Returns

A dict containing data about the currency, symbol, exchange, type of instrument, and the first trading date

Return type

HistoryMetadata

price_chart(periodicity: str = 'day', adjusted: bool = True, start_date: Optional[str] = None, end_date: Optional[str] = None) → Image

Get the price chart.

Parameters

• periodicity (str) – Determines the frequency of the historical data returned. Options are “day”, “week”, “month” and “year”. This default to “day”

• adjusted (Optional[bool]) – Whether to retrieve adjusted prices that account for corporate actions such as dividends and splits, it defaults True

• start_date (Optional[str]) – The start date for historical price retrieval in format “YYYY-MM-DD”, default to None

• end_date (Optional[str]) – The end date for historical price retrieval in format “YYYY-MM-DD”, default to None

Returns

An image showing the price chart of the trading item

Return type

Image

property trading_item_id: int

Get the trading item id for the object

Returns

the CIQ trading item id

Return type

int

class TradingItems(kfinance_api_client: KFinanceApiClient, trading_item_ids: Iterable[int])

Bases: set

Base class for representing a set of Trading Items

kensho_finance.llm_tools

class Model(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)

Bases: Enum

Enum with values ANTHROPIC, GEMINI, OPENAI

ANTHROPIC = 1

GEMINI = 2

OPENAI = 3

get_business_relationship_from_identifier(self: Client, identifier: str, business_relationship: str) → dict

Get the current and previous company IDs having a business relationship with an identifier, where the identifier can be a ticker, ISIN or CUSIP.

Parameters

• identifier – A unique identifier, which can be a ticker symbol, ISIN, or CUSIP.

• business_relationship – the type of business relationship requested

get_company_id_from_identifier(self: Client, identifier: str) → int

Get the company id associated with an identifier, where the identifier can be a ticker, ISIN or CUSIP.

Parameters

identifier – A unique identifier, which can be a ticker symbol, ISIN, or CUSIP.

get_cusip_from_ticker(self: Client, ticker_str: str) → str

Get the CUSIP associated with a ticker, can also be an ISIN.

Parameters

ticker_str – The ticker

get_earnings_call_datetimes_from_identifier(self: Client, identifier: str) → str

Get earnings call datetimes associated with an identifier, where the identifier can be a ticker, ISIN or CUSIP.

Parameters

identifier – A unique identifier, which can be a ticker symbol, ISIN, or CUSIP.

get_financial_line_item_from_identifier(self: Client, identifier: str, line_item: str, period_type: str | None = None, start_year: int | None = None, end_year: int | None = None, start_quarter: int | None = None, end_quarter: int | None = None) → str

Get the financial line item associated with an identifier, where the identifier can be a ticker, ISIN or CUSIP.

Parameters

• identifier – A unique identifier, which can be a ticker symbol, ISIN, or CUSIP.

• line_item – The type of financial line_item requested

• period_type – time period type, valid inputs are [“annual”, “quarterly”, “ltm”, “ytd”]

• start_quarter – starting quarter, valid inputs are [1, 2, 3, 4]

• end_quarter – ending quarter, valid inputs are [1, 2, 3, 4]

• start_year – The starting year for the data range.

• end_year – The ending year for the data range.

get_financial_statement_from_identifier(self: Client, identifier: str, statement: str, period_type: str | None = None, start_year: int | None = None, end_year: int | None = None, start_quarter: int | None = None, end_quarter: int | None = None) → str

Get the financial statement associated with an identifier, where the identifier can be a ticker, ISIN or CUSIP.

Parameters

• identifier – A unique identifier, which can be a ticker symbol, ISIN, or CUSIP.

• statement – The type of financial statement, valid inputs are [“balance_sheet”, “income_statement”, “cashflow”]

• period_type – time period type, valid inputs are [“annual”, “quarterly”, “ltm”, “ytd”].

• start_quarter – starting quarter, valid inputs are [1, 2, 3, 4]

• end_quarter – ending quarter, valid inputs are [1, 2, 3, 4]

• start_year – The starting year for the data range.

• end_year – The ending year for the data range.

get_history_metadata_from_identifier(self: Client, identifier: str) → HistoryMetadata

Get the history metadata associated with an identifier, where the identifier can be a ticker, ISIN or CUSIP.

History metadata includes currency, symbol, exchange name, instrument type, and first trade date

Parameters

identifier – A unique identifier, which can be a ticker symbol, ISIN, or CUSIP.

get_info_from_identifier(self: Client, identifier: str) → str

Get the information associated with an identifier, where the identifier can be a ticker, ISIN or CUSIP.

Info includes company name, status, type, simple industry, number of employees, founding date, webpage, HQ address, HQ city, HQ zip code, HQ state, HQ country, and HQ country iso code

Parameters

identifier – A unique identifier, which can be a ticker symbol, ISIN, or CUSIP.

get_isin_from_ticker(self: Client, ticker_str: str) → str

Get the ISIN associated with a ticker, can also be CUSIP.

Parameters

ticker_str – The ticker

get_latest(use_local_timezone: bool = True) → LatestPeriods

Get the latest annual reporting year, latest quarterly reporting quarter and year, and current date. The output is a dictionary with the following schema:

{

“annual”: {

“latest_year”: int

}, “quarterly”: {

“latest_quarter”: int, “latest_year”: int

}, “now”: {

“current_year”: int, “current_quarter”: int, “current_month”: int, “current_date”: str # in format Y-m-d

}

}

Parameters

use_local_timezone – whether to use the local timezone of the user

get_n_quarters_ago(n: int) → YearAndQuarter

Get the year and quarter corresponding to [n] quarters before the current quarter. The output is a dictionary with the following schema:

{

“year”: int, “quarter”: int

}

Parameters

n – number of quarters before the current quarter

get_prices_from_identifier(self: Client, identifier: str, periodicity: str = 'day', adjusted: bool = True, start_date: str | None = None, end_date: str | None = None) → str

Get the historical open, high, low, and close prices, and volume of an identifier, where the identifier can be a ticker, ISIN or CUSIP, between inclusive start_date and inclusive end date.

Parameters

• identifier – A unique identifier, which can be a ticker symbol, ISIN, or CUSIP.

• start_date – The start date for historical price retrieval in format YYYY-MM-DD

• end_date – The end date for historical price retrieval in format YYYY-MM-DD

• periodicity – The frequency or interval at which the historical data points are sampled or aggregated. Periodicity is not the same as the date range. The date range specifies the time span over which the data is retrieved, while periodicity determines how the data within that date range is aggregated, valid inputs are [“day”, “week”, “month”, “year”].

• adjusted – Whether to retrieve adjusted prices that account for corporate actions such as dividends and splits.

get_security_id_from_identifier(self: Client, identifier: str) → int

Get the security id associated with an identifier, where the identifier can be a ticker, ISIN or CUSIP.

Parameters

identifier – A unique identifier, which can be a ticker symbol, ISIN, or CUSIP.

get_trading_item_id_from_identifier(self: Client, identifier: str) → int

Get the trading item id associated with an identifier, where the identifier can be a ticker, ISIN or CUSIP.

Parameters

identifier – A unique identifier, which can be a ticker symbol, ISIN, or CUSIP.

langchain_tools(tools: dict[str, Callable]) → list[langchain_core.tools.structured.StructuredTool]

Returns Langchain Tool callables

The Tool names and descriptions sent to the LLM are taken from the base tool dict. The Tool arguments and arg descriptions are taken from the Pydantic models with an input model corresponding to each tool. Any change to the base tool dict must be reflected in the input model

Parameters

tools – mapping of tool names and tool callables, to be converted to langchain tools

kensho_finance.meta_classes

class CompanyFunctionsMetaClass

Bases: object

accounts_payable(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 1018

accounts_receivable(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 1021

accrued_expenses(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 1016

accumulated_depreciation(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 1075

additional_paid_in_capital(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 1084

additional_paid_in_capital_preferred_stock(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for preferred_stock_additional_paid_in_capital

ciq data item 1085

adjustments_to_cash_flow_net_income(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 21523

amortization_of_goodwill_and_intangibles(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 31

asset_writedown(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 32

assets(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for total_assets

ciq data item 1007

balance_sheet(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

The templated balance sheet

basic_earning_per_share(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for basic_eps

ciq data item 9

basic_earning_per_share_excluding_extra_items(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for basic_eps_excluding_extra_items

ciq data item 3064

basic_earning_per_share_from_accounting_change(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for basic_eps_from_accounting_change

ciq data item 145

basic_earning_per_share_from_accounting_change_and_extraordinary_items(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for basic_eps_from_accounting_change_and_extraordinary_items

ciq data item 45

basic_earning_per_share_from_extraordinary_items(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for basic_eps_from_extraordinary_items

ciq data item 146

basic_earning_per_share_including_extra_items(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for basic_eps

ciq data item 9

basic_eps(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 9

basic_eps_excluding_extra_items(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 3064

basic_eps_from_accounting_change(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 145

basic_eps_from_accounting_change_and_extraordinary_items(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 45

basic_eps_from_extraordinary_items(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 146

basic_eps_including_extra_items(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for basic_eps

ciq data item 9

property borrower: BusinessRelationships

Returns the associated company’s current and previous borrowers

capex(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for capital_expenditure

ciq data item 2021

capital_expenditure(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 2021

capital_expenditures(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for capital_expenditure

ciq data item 2021

capital_leases(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 1183

capitalized_leases(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for capital_leases

ciq data item 1183

cash(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for cash_and_equivalents

ciq data item 1096

cash_acquisitions(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 2057

cash_and_cash_equivalents(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for cash_and_equivalents

ciq data item 1096

cash_and_equivalents(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 1096

cash_and_short_term_investments(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for total_cash_and_short_term_investments

ciq data item 1002

cash_flow(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

The templated cash flow statement

cash_flow_from_operations(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for cash_from_operations

ciq data item 2006

cash_from_discontinued_operation(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for net_cash_from_discontinued_operation

ciq data item 2081

cash_from_financing(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 2004

cash_from_financing_activities(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for cash_from_financing

ciq data item 2004

cash_from_investing(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 2005

cash_from_investing_activities(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for cash_from_investing

ciq data item 2005

cash_from_operating_activities(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for cash_from_operations

ciq data item 2006

cash_from_operations(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 2006

cashflow(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

The templated cash flow statement

cashflow_from_financing(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for cash_from_financing

ciq data item 2004

cashflow_from_financing_activities(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for cash_from_financing

ciq data item 2004

cashflow_from_investing(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for cash_from_investing

ciq data item 2005

cashflow_from_investing_activities(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for cash_from_investing

ciq data item 2005

change_in_accounts_payable(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 2017

change_in_accounts_receivable(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 2018

change_in_cash(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for net_change_in_cash

ciq data item 2093

change_in_deferred_taxes(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 2084

change_in_income_taxes(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 2101

change_in_inventories(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 2099

change_in_net_operating_assets(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 2010

change_in_net_working_capital(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 4421

change_in_other_net_operating_assets(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 2045

change_in_trading_asset_securities(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 2134

change_in_unearned_revenue(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 2139

property client_services: BusinessRelationships

Returns the associated company’s current and previous client_servicess

cogs(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for cost_of_goods_sold

ciq data item 34

common_dividends_paid(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 2074

common_equity(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for total_common_equity

ciq data item 1006

common_shares_outstanding(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 1100

common_stock(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 1103

property company_id: Any

Set and return the company id for the object

continued_operations_earnings(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for earnings_from_continued_operations

ciq data item 7

convertible_preferred_stock(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for preferred_stock_convertible

ciq data item 1214

cor(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for cost_of_revenue

ciq data item 1

cost_of_goods_sold(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 34

cost_of_revenue(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 1

property creditor: BusinessRelationships

Returns the associated company’s current and previous creditors

currency_exchange_gains(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 38

current_accounts_receivable(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for accounts_receivable

ciq data item 1021

current_assets(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for total_current_assets

ciq data item 1008

current_borrowing(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for short_term_borrowings

ciq data item 1046

current_borrowings(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for short_term_borrowings

ciq data item 1046

current_debt_issued(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for short_term_debt_issued

ciq data item 2043

current_debt_repaid(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for short_term_debt_repaid

ciq data item 2044

current_deferred_tax_asset(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for deferred_tax_asset_current_portion

ciq data item 1117

current_deferred_tax_liability(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 1119

current_income_taxes_payable(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 1094

current_liabilities(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for total_current_liabilities

ciq data item 1009

current_notes_receivable(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for notes_receivable

ciq data item 1048

current_other_receivables(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for other_receivables

ciq data item 1206

current_portion_of_cap_leases(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for current_portion_of_capital_leases

ciq data item 1090

current_portion_of_capital_leases(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 1090

current_portion_of_capitalized_leases(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for current_portion_of_capital_leases

ciq data item 1090

current_portion_of_income_taxes_payable(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for current_income_taxes_payable

ciq data item 1094

current_portion_of_leases(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for current_portion_of_capital_leases

ciq data item 1090

current_portion_of_long_term_debt(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 1297

current_portion_of_long_term_debt_and_capital_leases(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 1279

current_portion_of_long_term_debt_and_capitalized_leases(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for current_portion_of_long_term_debt_and_capital_leases

ciq data item 1279

current_portion_of_lt_debt(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for current_portion_of_long_term_debt

ciq data item 1297

current_portion_of_lt_debt_and_cap_leases(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for current_portion_of_long_term_debt_and_capital_leases

ciq data item 1279

current_portion_of_non_current_debt(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for current_portion_of_long_term_debt

ciq data item 1297

current_portion_of_non_current_debt_and_capital_leases(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for current_portion_of_long_term_debt_and_capital_leases

ciq data item 1279

current_portion_of_non_current_debt_and_capitalized_leases(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for current_portion_of_long_term_debt_and_capital_leases

ciq data item 1279

current_portion_of_unearned_revenue(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for current_unearned_revenue

ciq data item 1074

current_ratio(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 4030

current_total_receivable(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for total_receivables

ciq data item 1001

current_total_receivables(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for total_receivables

ciq data item 1001

current_unearned_revenue(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 1074

property customer: BusinessRelationships

Returns the associated company’s current and previous customers

d_and_a(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for depreciation_and_amortization

ciq data item 41

debt_ratio(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for total_debt_to_equity_ratio

ciq data item 4034

deferred_tax_asset_current_portion(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 1117

depreciation(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 2143

depreciation_and_amortization(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 41

depreciation_of_rental_assets(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 42409

diluted_earning_per_share(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for diluted_eps

ciq data item 8

diluted_earning_per_share_excluding_extra_items(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for diluted_eps_excluding_extra_items

ciq data item 142

diluted_earning_per_share_from_accounting_change(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for diluted_eps_from_accounting_change

ciq data item 141

diluted_earning_per_share_from_accounting_change_and_extraordinary_items(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for diluted_eps_from_accounting_change_and_extraordinary_items

ciq data item 44

diluted_earning_per_share_from_discontinued_operations(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for diluted_eps_from_discontinued_operations

ciq data item 143

diluted_earning_per_share_from_extraordinary_items(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for diluted_eps_from_extraordinary_items

ciq data item 144

diluted_earning_per_share_including_extra_items(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for diluted_eps

ciq data item 8

diluted_eps(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 8

diluted_eps_excluding_extra_items(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 142

diluted_eps_from_accounting_change(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 141

diluted_eps_from_accounting_change_and_extraordinary_items(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 44

diluted_eps_from_discontinued_operations(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 143

diluted_eps_from_extraordinary_items(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 144

diluted_eps_including_extra_items(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for diluted_eps

ciq data item 8

discontinued_operations_earnings(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for earnings_from_discontinued_operations

ciq data item 40

distributable_cash_per_share(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 23317

property distributor: BusinessRelationships

Returns the associated company’s current and previous distributors

divestitures(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 2077

dividends_paid(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for total_dividends_paid

ciq data item 2022

dividends_per_share(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 3058

dna(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for depreciation_and_amortization

ciq data item 41

earnings_before_interest_and_taxes(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for ebit

ciq data item 400

earnings_before_interest_taxes_and_amortization(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for ebita

ciq data item 100689

earnings_before_interest_taxes_depreciation_amortization_and_rental_expense(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for ebitdar

ciq data item 21674

earnings_before_interest_taxes_depreciation_and_amortization(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for ebitda

ciq data item 4051

earnings_before_taxes_excluding_unusual_items(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for ebt_excluding_unusual_items

ciq data item 4

earnings_before_taxes_including_unusual_items(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for ebt_including_unusual_items

ciq data item 139

earnings_from_continued_operations(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 7

earnings_from_discontinued_operations(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 40

ebit(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 400

ebita(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 100689

ebitda(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 4051

ebitdar(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 21674

ebt_excluding_unusual_items(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 4

ebt_including_unusual_items(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 139

effective_tax_rate(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 4376

equity(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for total_equity

ciq data item 1275

equity_adjustment_preferred_stock(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for preferred_stock_equity_adjustment

ciq data item 1215

exploration_and_drilling_costs(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 49

exploration_and_drilling_expense(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for exploration_and_drilling_costs

ciq data item 49

extraordinary_item_and_accounting_change(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 42

fees_and_other_income(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 168

ffo(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for funds_from_operations

ciq data item 3074

finance_division_debt_current_portion(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 1030

finance_division_debt_long_term_portion(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for finance_division_debt_non_current_portion

ciq data item 1035

finance_division_debt_non_current_portion(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 1035

finance_division_interest_expense(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 50

finance_division_loans_and_leases_long_term(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 1033

finance_division_loans_and_leases_short_term(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 1032

finance_division_long_term_debt(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for finance_division_debt_non_current_portion

ciq data item 1035

finance_division_long_term_loans_and_leases(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for finance_division_loans_and_leases_long_term

ciq data item 1033

finance_division_non_current_debt(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for finance_division_debt_non_current_portion

ciq data item 1035

finance_division_operating_expense(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 51

finance_division_other_current_assets(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 1029

finance_division_other_current_liabilities(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 1031

finance_division_other_long_term_assets(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for finance_division_other_non_current_assets

ciq data item 1034

finance_division_other_long_term_liabilities(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for finance_division_other_non_current_liabilities

ciq data item 1036

finance_division_other_non_current_assets(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 1034

finance_division_other_non_current_liabilities(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 1036

finance_division_other_short_term_assets(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for finance_division_other_current_assets

ciq data item 1029

finance_division_revenue(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 52

finance_division_short_term_loans_and_leases(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for finance_division_loans_and_leases_short_term

ciq data item 1032

foreign_exchange_adjustments(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for foreign_exchange_rate_adjustments

ciq data item 2144

foreign_exchange_rate_adjustments(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 2144

property franchisee: BusinessRelationships

Returns the associated company’s current and previous franchisees

property franchisor: BusinessRelationships

Returns the associated company’s current and previous franchisors

funds_from_operations(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 3074

fx_adjustments(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for foreign_exchange_rate_adjustments

ciq data item 2144

gain_from_sale_of_assets(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 62

gain_from_sale_of_investments(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 56

goodwill(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 1171

gppe(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for gross_property_plant_and_equipment

ciq data item 1169

gross_ppe(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for gross_property_plant_and_equipment

ciq data item 1169

gross_profit(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 10

gross_property_plant_and_equipment(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 1169

impairment_o_and_g(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for impairment_of_oil_gas_and_mineral_properties

ciq data item 71

impairment_of_goodwill(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 209

impairment_of_oil_and_gas(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for impairment_of_oil_gas_and_mineral_properties

ciq data item 71

impairment_of_oil_gas_and_mineral_properties(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 71

in_process_r_and_d_cost(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for in_process_research_and_development_expense

ciq data item 72

in_process_r_and_d_expense(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for in_process_research_and_development_expense

ciq data item 72

in_process_research_and_development_cost(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for in_process_research_and_development_expense

ciq data item 72

in_process_research_and_development_expense(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 72

in_process_rnd_cost(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for in_process_research_and_development_expense

ciq data item 72

in_process_rnd_expense(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for in_process_research_and_development_expense

ciq data item 72

income_from_affiliates(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 47

income_statement(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

The templated income statement

income_stmt(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

The templated income statement

income_tax(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for income_tax_expense

ciq data item 75

income_tax_expense(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 75

income_taxes(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for income_tax_expense

ciq data item 75

insurance_division_operating_expense(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 69

insurance_division_revenue(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 70

insurance_settlements(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 73

interest_and_investment_income(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 65

interest_expense(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 82

interest_expense_finance_division(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for finance_division_interest_expense

ciq data item 50

inventories(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for inventory

ciq data item 1043

inventory(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 1043

property investor_relations_client: BusinessRelationships

Returns the associated company’s current and previous investor_relations_clients

property investor_relations_firm: BusinessRelationships

Returns the associated company’s current and previous investor_relations_firms

issuance_of_common_stock(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 2169

issuance_of_preferred_stock(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 2181

property landlord: BusinessRelationships

Returns the associated company’s current and previous landlords

legal_settlements(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 77

property lessee: BusinessRelationships

Returns the associated company’s current and previous lessees

property lessor: BusinessRelationships

Returns the associated company’s current and previous lessors

liabilities(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for total_liabilities

ciq data item 1276

liabilities_and_equity(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for total_liabilities_and_equity

ciq data item 1013

property licensee: BusinessRelationships

Returns the associated company’s current and previous licensees

property licensor: BusinessRelationships

Returns the associated company’s current and previous licensors

line_item(line_item: str, period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

Get a DataFrame of a financial line item according to the date ranges.

loans_held_for_sale(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 1185

loans_receivable(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for long_term_loans_receivable

ciq data item 1050

long_term_accounts_receivable(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 1088

long_term_debt(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 1049

long_term_debt_issued(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 2034

long_term_debt_repaid(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 2036

long_term_deferred_charges(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 1025

long_term_deferred_tax_assets(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 1026

long_term_finance_division_loans_and_leases(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for finance_division_loans_and_leases_long_term

ciq data item 1033

long_term_investments(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 1054

long_term_leases(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for capital_leases

ciq data item 1183

long_term_loans_and_leases_of_the_finance_division(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for finance_division_loans_and_leases_long_term

ciq data item 1033

long_term_loans_receivable(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 1050

long_term_other_assets(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for other_long_term_assets

ciq data item 1060

long_term_other_liabilities(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for other_non_current_liabilities

ciq data item 1062

long_term_unearned_revenue(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for non_current_unearned_revenue

ciq data item 1256

loss_on_equity_investments(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 2086

merger_and_restructuring_charges(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 363

merger_charges(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 80

minority_interest_in_earnings(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 83

misc_cash_flow_adj(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for miscellaneous_cash_flow_adjustments

ciq data item 2149

miscellaneous_cash_flow_adjustments(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 2149

net_cash_from_discontinued_operation(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 2081

net_cash_from_investments(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 2027

net_change_in_cash(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 2093

net_debt(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 4364

net_decrease_in_investment_loans_originated_and_sold(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 2032

net_decrease_in_loans_originated_and_sold(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 2033

net_income(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 15

net_income_allocable_to_general_partner(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 249

net_income_to_common_shareholders_excluding_extra_items(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 379

net_income_to_common_shareholders_including_extra_items(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 16

net_income_to_company(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 41571

net_income_to_minority_interest(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for minority_interest_in_earnings

ciq data item 83

net_interest_expense(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 368

net_ppe(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for net_property_plant_and_equipment

ciq data item 1004

net_property_plant_and_equipment(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 1004

net_working_capital(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 1311

non_current_accounts_receivable(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for long_term_accounts_receivable

ciq data item 1088

non_current_debt(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for long_term_debt

ciq data item 1049

non_current_debt_issued(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for long_term_debt_issued

ciq data item 2034

non_current_debt_repaid(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for long_term_debt_repaid

ciq data item 2036

non_current_deferred_charges(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for long_term_deferred_charges

ciq data item 1025

non_current_deferred_tax_assets(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for long_term_deferred_tax_assets

ciq data item 1026

non_current_deferred_tax_liability(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 1027

non_current_investments(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for long_term_investments

ciq data item 1054

non_current_loans_receivable(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for long_term_loans_receivable

ciq data item 1050

non_current_other_assets(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for other_long_term_assets

ciq data item 1060

non_current_other_liabilities(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for other_non_current_liabilities

ciq data item 1062

non_current_unearned_revenue(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 1256

non_redeemable_preferred_stock(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for preferred_stock_non_redeemable

ciq data item 1216

normal_revenue(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for revenue

ciq data item 112

normalized_basic_earning_per_share(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for normalized_basic_eps

ciq data item 4379

normalized_basic_eps(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 4379

normalized_diluted_earning_per_share(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for normalized_diluted_eps

ciq data item 4380

normalized_diluted_eps(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 4380

notes_receivable(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 1048

nppe(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for net_property_plant_and_equipment

ciq data item 1004

operating_expense(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for total_operating_expense

ciq data item 373

operating_expense_finance_division(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for finance_division_operating_expense

ciq data item 51

operating_expense_insurance_division(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for insurance_division_operating_expense

ciq data item 69

operating_income(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 21

other_adjustments_to_net_income(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 259

other_amortization(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 2014

other_current_assets(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 1055

other_current_assets_of_the_finance_division(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for finance_division_other_current_assets

ciq data item 1029

other_current_liabilities(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for other_current_liability

ciq data item 1057

other_current_liability(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 1057

other_equity(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 1028

other_financing_activities(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 2050

other_intangibles(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 1040

other_investing_activities(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 2051

other_long_term_assets(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 1060

other_long_term_assets_of_the_finance_division(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for finance_division_other_non_current_assets

ciq data item 1034

other_long_term_liabilities(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for other_non_current_liabilities

ciq data item 1062

other_non_current_assets(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for other_long_term_assets

ciq data item 1060

other_non_current_assets_of_the_finance_division(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for finance_division_other_non_current_assets

ciq data item 1034

other_non_current_liabilities(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 1062

other_non_operating_income(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 85

other_operating_activities(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 2047

other_operating_expense(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 260

other_preferred_stock(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for preferred_stock_other

ciq data item 1065

other_preferred_stock_adjustments(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 281

other_receivables(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 1206

other_revenue(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 90

other_short_term_assets_of_the_finance_division(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for finance_division_other_current_assets

ciq data item 1029

other_unusual_items(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 87

pension_and_other_post_retirement_benefit(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 1213

ppe(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for net_property_plant_and_equipment

ciq data item 1004

pre_opening_costs(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 96

pre_opening_expense(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for pre_opening_costs

ciq data item 96

preferred_dividends_and_other_adjustments(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 97

preferred_dividends_paid(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 2116

preferred_equity(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for total_preferred_equity

ciq data item 1005

preferred_stock(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for total_preferred_equity

ciq data item 1005

preferred_stock_additional_paid_in_capital(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 1085

preferred_stock_convertible(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 1214

preferred_stock_dividend(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 280

preferred_stock_equity_adjustment(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 1215

preferred_stock_non_redeemable(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 1216

preferred_stock_other(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 1065

preferred_stock_redeemable(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 1217

premium_on_redemption_of_preferred_stock(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 279

prepaid_expense(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 1212

prepaid_expenses(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for prepaid_expense

ciq data item 1212

property_plant_and_equipment(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for net_property_plant_and_equipment

ciq data item 1004

provision_for_bad_debt(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for provision_for_bad_debts

ciq data item 95

provision_for_bad_debts(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 95

provision_for_credit_losses(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 2112

quick_ratio(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 4121

r_and_d_cost(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for research_and_development_expense

ciq data item 100

r_and_d_expense(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for research_and_development_expense

ciq data item 100

redeemable_preferred_stock(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for preferred_stock_redeemable

ciq data item 1217

regular_revenue(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for revenue

ciq data item 112

relationships(relationship_type: BusinessRelationshipType) → BusinessRelationships

Returns a BusinessRelationships object that includes the current and previous Companies associated with company_id and filtered by relationship_type. The function calls fetch_companies_from_business_relationship.

Parameters

relationship_type (BusinessRelationshipType) – The type of relationship to filter by. Valid relationship types are defined in the BusinessRelationshipType class.

Returns

A BusinessRelationships object containing a tuple of Companies objects that lists current and previous company IDs that have the specified relationship with the given company_id.

Return type

BusinessRelationships

repurchase_of_common_stock(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 2164

repurchase_of_preferred_stock(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 2172

research_and_development_cost(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for research_and_development_expense

ciq data item 100

research_and_development_expense(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 100

restricted_cash(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 1104

restructuring_charges(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 98

retained_earnings(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 1222

revenue(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 112

revenue_from_interest_and_investment_income(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 110

revenue_from_sale_of_assets(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 104

revenue_from_sale_of_investments(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 106

rnd_cost(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for research_and_development_expense

ciq data item 100

rnd_expense(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for research_and_development_expense

ciq data item 100

sale_of_intangible_asset(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for sale_of_intangible_assets

ciq data item 2029

sale_of_intangible_assets(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 2029

sale_of_intangibles(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for sale_of_intangible_assets

ciq data item 2029

sale_of_ppe(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for sale_of_property_plant_and_equipment

ciq data item 2042

sale_of_property_plant_and_equipment(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 2042

sale_of_real_estate(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 2040

sale_of_real_estate_properties(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for sale_of_real_estate

ciq data item 2040

sale_of_real_properties(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for sale_of_real_estate

ciq data item 2040

sale_proceeds_from_rental_assets(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 42411

selling_general_and_admin(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for selling_general_and_admin_expense

ciq data item 102

selling_general_and_admin_cost(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for selling_general_and_admin_expense

ciq data item 102

selling_general_and_admin_expense(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 102

sg_and_a(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for selling_general_and_admin_expense

ciq data item 102

sga(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for selling_general_and_admin_expense

ciq data item 102

shareholders_equity(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for total_equity

ciq data item 1275

short_term_accounts_receivable(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for accounts_receivable

ciq data item 1021

short_term_assets(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for total_current_assets

ciq data item 1008

short_term_borrowing(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for short_term_borrowings

ciq data item 1046

short_term_borrowings(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 1046

short_term_debt_issued(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 2043

short_term_debt_repaid(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 2044

short_term_deferred_tax_asset(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for deferred_tax_asset_current_portion

ciq data item 1117

short_term_finance_division_loans_and_leases(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for finance_division_loans_and_leases_short_term

ciq data item 1032

short_term_investments(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 1069

short_term_loans_and_leases_of_the_finance_division(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for finance_division_loans_and_leases_short_term

ciq data item 1032

short_term_notes_receivable(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for notes_receivable

ciq data item 1048

short_term_other_receivables(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for other_receivables

ciq data item 1206

short_term_total_receivable(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for total_receivables

ciq data item 1001

short_term_total_receivables(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for total_receivables

ciq data item 1001

special_dividends_paid(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 2041

statement(statement_type: str, period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

Get the company’s financial statement

stock_based_compensation(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 2127

property strategic_alliance: BusinessRelationships

Returns the associated company’s current and previous strategic_alliances

property supplier: BusinessRelationships

Returns the associated company’s current and previous suppliers

tax_benefit_from_stock_options(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 2135

tax_rate(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for effective_tax_rate

ciq data item 4376

property tenant: BusinessRelationships

Returns the associated company’s current and previous tenants

total_assets(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 1007

total_cash_and_short_term_investments(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 1002

total_common_equity(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 1006

total_current_assets(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 1008

total_current_liabilities(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 1009

total_current_portion_of_long_term_debt_and_capital_leases(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for current_portion_of_long_term_debt_and_capital_leases

ciq data item 1279

total_current_portion_of_long_term_debt_and_capitalized_leases(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for current_portion_of_long_term_debt_and_capital_leases

ciq data item 1279

total_current_portion_of_lt_debt_and_cap_leases(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for current_portion_of_long_term_debt_and_capital_leases

ciq data item 1279

total_current_portion_of_non_current_debt_and_capital_leases(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for current_portion_of_long_term_debt_and_capital_leases

ciq data item 1279

total_current_portion_of_non_current_debt_and_capitalized_leases(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for current_portion_of_long_term_debt_and_capital_leases

ciq data item 1279

total_d_and_a(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for total_depreciation_and_amortization

ciq data item 2

total_debt(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 4173

total_debt_issued(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 2161

total_debt_ratio(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for total_debt_to_equity_ratio

ciq data item 4034

total_debt_repaid(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 2166

total_debt_to_capital(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 43907

total_debt_to_equity(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for total_debt_to_equity_ratio

ciq data item 4034

total_debt_to_equity_ratio(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 4034

total_debt_to_total_equity(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for total_debt_to_equity_ratio

ciq data item 4034

total_depreciation_and_amortization(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 2

total_dividends_paid(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 2022

total_dna(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for total_depreciation_and_amortization

ciq data item 2

total_equity(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 1275

total_liabilities(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 1276

total_liabilities_and_equity(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 1013

total_operating_expense(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 373

total_other_investing_activities(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 2177

total_other_non_cash_items(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 2179

total_other_non_operating_income(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 371

total_other_operating_expense(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 380

total_other_revenue(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 357

total_other_unusual_items(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 374

total_preferred_equity(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 1005

total_preferred_stock(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for total_preferred_equity

ciq data item 1005

total_receivable(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for total_receivables

ciq data item 1001

total_receivables(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 1001

total_revenue(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 28

total_selling_general_and_admin(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for total_selling_general_and_admin_expense

ciq data item 23

total_selling_general_and_admin_cost(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for total_selling_general_and_admin_expense

ciq data item 23

total_selling_general_and_admin_expense(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 23

total_sga(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for total_selling_general_and_admin_expense

ciq data item 23

total_shareholders_equity(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for total_equity

ciq data item 1275

total_short_term_assets(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for total_current_assets

ciq data item 1008

total_unusual_items(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 19

trading_asset_securities(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 1244

property transfer_agent: BusinessRelationships

Returns the associated company’s current and previous transfer_agents

property transfer_agent_client: BusinessRelationships

Returns the associated company’s current and previous transfer_agent_clients

treasury_convertible_preferred_stock(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for treasury_stock_preferred_stock_convertible

ciq data item 1249

treasury_non_redeemable_preferred_stock(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for treasury_stock_preferred_stock_non_redeemable

ciq data item 1250

treasury_preferred_stock_convertible(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for treasury_stock_preferred_stock_convertible

ciq data item 1249

treasury_preferred_stock_non_redeemable(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for treasury_stock_preferred_stock_non_redeemable

ciq data item 1250

treasury_preferred_stock_redeemable(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for treasury_stock_preferred_stock_redeemable

ciq data item 1251

treasury_redeemable_preferred_stock(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for treasury_stock_preferred_stock_redeemable

ciq data item 1251

treasury_stock(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 1248

treasury_stock_convertible_preferred_stock(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for treasury_stock_preferred_stock_convertible

ciq data item 1249

treasury_stock_non_redeemable_preferred_stock(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for treasury_stock_preferred_stock_non_redeemable

ciq data item 1250

treasury_stock_preferred_stock_convertible(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 1249

treasury_stock_preferred_stock_non_redeemable(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 1250

treasury_stock_preferred_stock_redeemable(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 1251

treasury_stock_redeemable_preferred_stock(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for treasury_stock_preferred_stock_redeemable

ciq data item 1251

unusual_items(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

alias for total_unusual_items

ciq data item 19

validate_inputs(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → None

Test the time inputs for validity.

property vendor: BusinessRelationships

Returns the associated company’s current and previous vendors

weighted_average_basic_shares_outstanding(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 3217

weighted_average_diluted_shares_outstanding(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 342

working_capital(period_type: Optional[str] = None, start_year: Optional[int] = None, end_year: Optional[int] = None, start_quarter: Optional[int] = None, end_quarter: Optional[int] = None) → DataFrame

ciq data item 4165

class DelegatedCompanyFunctionsMetaClass

Bases: CompanyFunctionsMetaClass

all methods in CompanyFunctionsMetaClass delegated to company attribute

property company: Any

Set and return the company for the object

kensho_finance.prompt

kensho_finance.server_thread

class ServerThread(daemon: bool = True)

Bases: Thread

run() → None

Run the server, but only until the refresh token is written to.

class WebRequestHandler(thread: ServerThread)

Bases: SimpleHTTPRequestHandler

do_GET() → None

This should never come up, but don’t serve files or anything regardless.

do_OPTIONS() → None

OPTIONS is needed for a preflight check, apparently.

do_POST() → None

Receive the refresh token from the client webpage, which will shut off the server and the thread.

end_headers() → None

The headers you needs for a CORS check.

kensho_finance.tool_schemas

class GetBusinessRelationshipFromIdentifier(*, identifier: str, business_relationship: BusinessRelationshipType)

Bases: BaseModel

business_relationship: BusinessRelationshipType

identifier: str

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class GetCompanyIdFromIdentifier(*, ticker_str: str)

Bases: BaseModel

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

ticker_str: str

class GetCusipFromTicker(*, ticker_str: str)

Bases: BaseModel

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

ticker_str: str

class GetEarningsCallDatetimesFromIdentifier(*, identifier: str)

Bases: BaseModel

identifier: str

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class GetFinancialLineItemFromIdentifier(*, identifier: str, line_item: Literal['revenue', 'regular_revenue', 'normal_revenue', 'finance_division_revenue', 'insurance_division_revenue', 'revenue_from_sale_of_assets', 'revenue_from_sale_of_investments', 'revenue_from_interest_and_investment_income', 'other_revenue', 'total_other_revenue', 'fees_and_other_income', 'total_revenue', 'cost_of_goods_sold', 'cogs', 'finance_division_operating_expense', 'operating_expense_finance_division', 'insurance_division_operating_expense', 'operating_expense_insurance_division', 'finance_division_interest_expense', 'interest_expense_finance_division', 'cost_of_revenue', 'cor', 'gross_profit', 'selling_general_and_admin_expense', 'selling_general_and_admin', 'selling_general_and_admin_cost', 'sg_and_a', 'sga', 'exploration_and_drilling_costs', 'exploration_and_drilling_expense', 'provision_for_bad_debts', 'provision_for_bad_debt', 'pre_opening_costs', 'pre_opening_expense', 'total_selling_general_and_admin_expense', 'total_selling_general_and_admin', 'total_selling_general_and_admin_cost', 'total_sga', 'research_and_development_expense', 'r_and_d_expense', 'rnd_expense', 'research_and_development_cost', 'rnd_cost', 'r_and_d_cost', 'depreciation_and_amortization', 'dna', 'd_and_a', 'amortization_of_goodwill_and_intangibles', 'impairment_of_oil_gas_and_mineral_properties', 'impairment_o_and_g', 'impairment_of_oil_and_gas', 'total_depreciation_and_amortization', 'total_d_and_a', 'total_dna', 'other_operating_expense', 'total_other_operating_expense', 'total_operating_expense', 'operating_expense', 'operating_income', 'interest_expense', 'interest_and_investment_income', 'net_interest_expense', 'income_from_affiliates', 'currency_exchange_gains', 'other_non_operating_income', 'total_other_non_operating_income', 'ebt_excluding_unusual_items', 'earnings_before_taxes_excluding_unusual_items', 'restructuring_charges', 'merger_charges', 'merger_and_restructuring_charges', 'impairment_of_goodwill', 'gain_from_sale_of_assets', 'gain_from_sale_of_investments', 'asset_writedown', 'in_process_research_and_development_expense', 'in_process_research_and_development_cost', 'in_process_rnd_cost', 'in_process_r_and_d_expense', 'in_process_r_and_d_cost', 'in_process_rnd_expense', 'insurance_settlements', 'legal_settlements', 'other_unusual_items', 'total_other_unusual_items', 'total_unusual_items', 'unusual_items', 'ebt_including_unusual_items', 'earnings_before_taxes_including_unusual_items', 'income_tax_expense', 'income_tax', 'income_taxes', 'earnings_from_continued_operations', 'continued_operations_earnings', 'earnings_from_discontinued_operations', 'discontinued_operations_earnings', 'extraordinary_item_and_accounting_change', 'net_income_to_company', 'minority_interest_in_earnings', 'net_income_to_minority_interest', 'net_income', 'premium_on_redemption_of_preferred_stock', 'preferred_stock_dividend', 'other_preferred_stock_adjustments', 'other_adjustments_to_net_income', 'preferred_dividends_and_other_adjustments', 'net_income_allocable_to_general_partner', 'net_income_to_common_shareholders_including_extra_items', 'net_income_to_common_shareholders_excluding_extra_items', 'cash_and_equivalents', 'cash', 'cash_and_cash_equivalents', 'short_term_investments', 'trading_asset_securities', 'total_cash_and_short_term_investments', 'cash_and_short_term_investments', 'accounts_receivable', 'short_term_accounts_receivable', 'current_accounts_receivable', 'other_receivables', 'current_other_receivables', 'short_term_other_receivables', 'notes_receivable', 'current_notes_receivable', 'short_term_notes_receivable', 'total_receivables', 'current_total_receivable', 'total_receivable', 'short_term_total_receivables', 'current_total_receivables', 'short_term_total_receivable', 'inventory', 'inventories', 'prepaid_expense', 'prepaid_expenses', 'finance_division_loans_and_leases_short_term', 'short_term_finance_division_loans_and_leases', 'short_term_loans_and_leases_of_the_finance_division', 'finance_division_short_term_loans_and_leases', 'finance_division_other_current_assets', 'other_current_assets_of_the_finance_division', 'finance_division_other_short_term_assets', 'other_short_term_assets_of_the_finance_division', 'loans_held_for_sale', 'deferred_tax_asset_current_portion', 'short_term_deferred_tax_asset', 'current_deferred_tax_asset', 'restricted_cash', 'other_current_assets', 'total_current_assets', 'short_term_assets', 'total_short_term_assets', 'current_assets', 'gross_property_plant_and_equipment', 'gppe', 'gross_ppe', 'accumulated_depreciation', 'net_property_plant_and_equipment', 'property_plant_and_equipment', 'nppe', 'net_ppe', 'ppe', 'long_term_investments', 'non_current_investments', 'goodwill', 'other_intangibles', 'finance_division_loans_and_leases_long_term', 'long_term_finance_division_loans_and_leases', 'long_term_loans_and_leases_of_the_finance_division', 'finance_division_long_term_loans_and_leases', 'finance_division_other_non_current_assets', 'other_non_current_assets_of_the_finance_division', 'finance_division_other_long_term_assets', 'other_long_term_assets_of_the_finance_division', 'long_term_accounts_receivable', 'non_current_accounts_receivable', 'long_term_loans_receivable', 'non_current_loans_receivable', 'loans_receivable', 'long_term_deferred_tax_assets', 'non_current_deferred_tax_assets', 'long_term_deferred_charges', 'non_current_deferred_charges', 'other_long_term_assets', 'non_current_other_assets', 'other_non_current_assets', 'long_term_other_assets', 'total_assets', 'assets', 'accounts_payable', 'accrued_expenses', 'short_term_borrowings', 'short_term_borrowing', 'current_borrowings', 'current_borrowing', 'current_portion_of_long_term_debt', 'current_portion_of_non_current_debt', 'current_portion_of_lt_debt', 'current_portion_of_capital_leases', 'current_portion_of_capitalized_leases', 'current_portion_of_cap_leases', 'current_portion_of_leases', 'current_portion_of_long_term_debt_and_capital_leases', 'total_current_portion_of_non_current_debt_and_capitalized_leases', 'current_portion_of_lt_debt_and_cap_leases', 'current_portion_of_non_current_debt_and_capital_leases', 'total_current_portion_of_long_term_debt_and_capitalized_leases', 'current_portion_of_non_current_debt_and_capitalized_leases', 'total_current_portion_of_lt_debt_and_cap_leases', 'current_portion_of_long_term_debt_and_capitalized_leases', 'total_current_portion_of_non_current_debt_and_capital_leases', 'total_current_portion_of_long_term_debt_and_capital_leases', 'finance_division_debt_current_portion', 'finance_division_other_current_liabilities', 'current_income_taxes_payable', 'current_portion_of_income_taxes_payable', 'current_unearned_revenue', 'current_portion_of_unearned_revenue', 'current_deferred_tax_liability', 'other_current_liability', 'other_current_liabilities', 'total_current_liabilities', 'current_liabilities', 'long_term_debt', 'non_current_debt', 'capital_leases', 'long_term_leases', 'capitalized_leases', 'finance_division_debt_non_current_portion', 'finance_division_non_current_debt', 'finance_division_debt_long_term_portion', 'finance_division_long_term_debt', 'finance_division_other_non_current_liabilities', 'finance_division_other_long_term_liabilities', 'non_current_unearned_revenue', 'long_term_unearned_revenue', 'pension_and_other_post_retirement_benefit', 'non_current_deferred_tax_liability', 'other_non_current_liabilities', 'non_current_other_liabilities', 'other_long_term_liabilities', 'long_term_other_liabilities', 'total_liabilities', 'liabilities', 'preferred_stock_redeemable', 'redeemable_preferred_stock', 'preferred_stock_non_redeemable', 'non_redeemable_preferred_stock', 'preferred_stock_convertible', 'convertible_preferred_stock', 'preferred_stock_other', 'other_preferred_stock', 'preferred_stock_additional_paid_in_capital', 'additional_paid_in_capital_preferred_stock', 'preferred_stock_equity_adjustment', 'equity_adjustment_preferred_stock', 'treasury_stock_preferred_stock_convertible', 'treasury_preferred_stock_convertible', 'treasury_stock_convertible_preferred_stock', 'treasury_convertible_preferred_stock', 'treasury_stock_preferred_stock_non_redeemable', 'treasury_preferred_stock_non_redeemable', 'treasury_non_redeemable_preferred_stock', 'treasury_stock_non_redeemable_preferred_stock', 'treasury_stock_preferred_stock_redeemable', 'treasury_stock_redeemable_preferred_stock', 'treasury_preferred_stock_redeemable', 'treasury_redeemable_preferred_stock', 'total_preferred_equity', 'total_preferred_stock', 'preferred_stock', 'preferred_equity', 'common_stock', 'additional_paid_in_capital', 'retained_earnings', 'treasury_stock', 'other_equity', 'total_common_equity', 'common_equity', 'total_equity', 'equity', 'shareholders_equity', 'total_shareholders_equity', 'total_liabilities_and_equity', 'liabilities_and_equity', 'common_shares_outstanding', 'adjustments_to_cash_flow_net_income', 'other_amortization', 'total_other_non_cash_items', 'net_decrease_in_loans_originated_and_sold', 'provision_for_credit_losses', 'loss_on_equity_investments', 'stock_based_compensation', 'tax_benefit_from_stock_options', 'net_cash_from_discontinued_operation', 'cash_from_discontinued_operation', 'other_operating_activities', 'change_in_trading_asset_securities', 'change_in_accounts_receivable', 'change_in_inventories', 'change_in_accounts_payable', 'change_in_unearned_revenue', 'change_in_income_taxes', 'change_in_deferred_taxes', 'change_in_other_net_operating_assets', 'change_in_net_operating_assets', 'cash_from_operations', 'cash_from_operating_activities', 'cash_flow_from_operations', 'capital_expenditure', 'capex', 'capital_expenditures', 'sale_of_property_plant_and_equipment', 'sale_of_ppe', 'cash_acquisitions', 'divestitures', 'sale_of_real_estate', 'sale_of_real_estate_properties', 'sale_of_real_properties', 'sale_of_intangible_assets', 'sale_of_intangibles', 'sale_of_intangible_asset', 'net_cash_from_investments', 'net_decrease_in_investment_loans_originated_and_sold', 'other_investing_activities', 'total_other_investing_activities', 'cash_from_investing', 'cashflow_from_investing', 'cash_from_investing_activities', 'cashflow_from_investing_activities', 'short_term_debt_issued', 'current_debt_issued', 'long_term_debt_issued', 'non_current_debt_issued', 'total_debt_issued', 'short_term_debt_repaid', 'current_debt_repaid', 'long_term_debt_repaid', 'non_current_debt_repaid', 'total_debt_repaid', 'issuance_of_common_stock', 'repurchase_of_common_stock', 'issuance_of_preferred_stock', 'repurchase_of_preferred_stock', 'common_dividends_paid', 'preferred_dividends_paid', 'total_dividends_paid', 'dividends_paid', 'special_dividends_paid', 'other_financing_activities', 'cash_from_financing', 'cashflow_from_financing', 'cashflow_from_financing_activities', 'cash_from_financing_activities', 'foreign_exchange_rate_adjustments', 'fx_adjustments', 'foreign_exchange_adjustments', 'miscellaneous_cash_flow_adjustments', 'misc_cash_flow_adj', 'net_change_in_cash', 'change_in_cash', 'depreciation', 'depreciation_of_rental_assets', 'sale_proceeds_from_rental_assets', 'basic_eps', 'basic_earning_per_share', 'basic_eps_including_extra_items', 'basic_earning_per_share_including_extra_items', 'basic_eps_excluding_extra_items', 'basic_earning_per_share_excluding_extra_items', 'basic_eps_from_accounting_change', 'basic_earning_per_share_from_accounting_change', 'basic_eps_from_extraordinary_items', 'basic_earning_per_share_from_extraordinary_items', 'basic_eps_from_accounting_change_and_extraordinary_items', 'basic_earning_per_share_from_accounting_change_and_extraordinary_items', 'weighted_average_basic_shares_outstanding', 'diluted_eps', 'diluted_earning_per_share_including_extra_items', 'diluted_earning_per_share', 'diluted_eps_including_extra_items', 'diluted_eps_excluding_extra_items', 'diluted_earning_per_share_excluding_extra_items', 'weighted_average_diluted_shares_outstanding', 'normalized_basic_eps', 'normalized_basic_earning_per_share', 'normalized_diluted_eps', 'normalized_diluted_earning_per_share', 'dividends_per_share', 'distributable_cash_per_share', 'diluted_eps_from_accounting_change_and_extraordinary_items', 'diluted_earning_per_share_from_accounting_change_and_extraordinary_items', 'diluted_eps_from_accounting_change', 'diluted_earning_per_share_from_accounting_change', 'diluted_eps_from_extraordinary_items', 'diluted_earning_per_share_from_extraordinary_items', 'diluted_eps_from_discontinued_operations', 'diluted_earning_per_share_from_discontinued_operations', 'funds_from_operations', 'ffo', 'ebitda', 'earnings_before_interest_taxes_depreciation_and_amortization', 'ebita', 'earnings_before_interest_taxes_and_amortization', 'ebit', 'earnings_before_interest_and_taxes', 'ebitdar', 'earnings_before_interest_taxes_depreciation_amortization_and_rental_expense', 'net_debt', 'effective_tax_rate', 'tax_rate', 'current_ratio', 'quick_ratio', 'total_debt_to_capital', 'net_working_capital', 'working_capital', 'change_in_net_working_capital', 'total_debt', 'total_debt_to_equity_ratio', 'debt_ratio', 'total_debt_ratio', 'total_debt_to_equity', 'total_debt_to_total_equity'], period_type: Optional[Literal['annual', 'quarterly', 'ltm', 'ytd']] = None, start_year: int | None = None, end_year: int | None = None, start_quarter: Optional[Literal[1, 2, 3, 4]] = None, end_quarter: Optional[Literal[1, 2, 3, 4]] = None)

Bases: BaseModel

end_quarter: Optional[Literal[1, 2, 3, 4]]

end_year: int | None

identifier: str

line_item: Literal['revenue', 'regular_revenue', 'normal_revenue', 'finance_division_revenue', 'insurance_division_revenue', 'revenue_from_sale_of_assets', 'revenue_from_sale_of_investments', 'revenue_from_interest_and_investment_income', 'other_revenue', 'total_other_revenue', 'fees_and_other_income', 'total_revenue', 'cost_of_goods_sold', 'cogs', 'finance_division_operating_expense', 'operating_expense_finance_division', 'insurance_division_operating_expense', 'operating_expense_insurance_division', 'finance_division_interest_expense', 'interest_expense_finance_division', 'cost_of_revenue', 'cor', 'gross_profit', 'selling_general_and_admin_expense', 'selling_general_and_admin', 'selling_general_and_admin_cost', 'sg_and_a', 'sga', 'exploration_and_drilling_costs', 'exploration_and_drilling_expense', 'provision_for_bad_debts', 'provision_for_bad_debt', 'pre_opening_costs', 'pre_opening_expense', 'total_selling_general_and_admin_expense', 'total_selling_general_and_admin', 'total_selling_general_and_admin_cost', 'total_sga', 'research_and_development_expense', 'r_and_d_expense', 'rnd_expense', 'research_and_development_cost', 'rnd_cost', 'r_and_d_cost', 'depreciation_and_amortization', 'dna', 'd_and_a', 'amortization_of_goodwill_and_intangibles', 'impairment_of_oil_gas_and_mineral_properties', 'impairment_o_and_g', 'impairment_of_oil_and_gas', 'total_depreciation_and_amortization', 'total_d_and_a', 'total_dna', 'other_operating_expense', 'total_other_operating_expense', 'total_operating_expense', 'operating_expense', 'operating_income', 'interest_expense', 'interest_and_investment_income', 'net_interest_expense', 'income_from_affiliates', 'currency_exchange_gains', 'other_non_operating_income', 'total_other_non_operating_income', 'ebt_excluding_unusual_items', 'earnings_before_taxes_excluding_unusual_items', 'restructuring_charges', 'merger_charges', 'merger_and_restructuring_charges', 'impairment_of_goodwill', 'gain_from_sale_of_assets', 'gain_from_sale_of_investments', 'asset_writedown', 'in_process_research_and_development_expense', 'in_process_research_and_development_cost', 'in_process_rnd_cost', 'in_process_r_and_d_expense', 'in_process_r_and_d_cost', 'in_process_rnd_expense', 'insurance_settlements', 'legal_settlements', 'other_unusual_items', 'total_other_unusual_items', 'total_unusual_items', 'unusual_items', 'ebt_including_unusual_items', 'earnings_before_taxes_including_unusual_items', 'income_tax_expense', 'income_tax', 'income_taxes', 'earnings_from_continued_operations', 'continued_operations_earnings', 'earnings_from_discontinued_operations', 'discontinued_operations_earnings', 'extraordinary_item_and_accounting_change', 'net_income_to_company', 'minority_interest_in_earnings', 'net_income_to_minority_interest', 'net_income', 'premium_on_redemption_of_preferred_stock', 'preferred_stock_dividend', 'other_preferred_stock_adjustments', 'other_adjustments_to_net_income', 'preferred_dividends_and_other_adjustments', 'net_income_allocable_to_general_partner', 'net_income_to_common_shareholders_including_extra_items', 'net_income_to_common_shareholders_excluding_extra_items', 'cash_and_equivalents', 'cash', 'cash_and_cash_equivalents', 'short_term_investments', 'trading_asset_securities', 'total_cash_and_short_term_investments', 'cash_and_short_term_investments', 'accounts_receivable', 'short_term_accounts_receivable', 'current_accounts_receivable', 'other_receivables', 'current_other_receivables', 'short_term_other_receivables', 'notes_receivable', 'current_notes_receivable', 'short_term_notes_receivable', 'total_receivables', 'current_total_receivable', 'total_receivable', 'short_term_total_receivables', 'current_total_receivables', 'short_term_total_receivable', 'inventory', 'inventories', 'prepaid_expense', 'prepaid_expenses', 'finance_division_loans_and_leases_short_term', 'short_term_finance_division_loans_and_leases', 'short_term_loans_and_leases_of_the_finance_division', 'finance_division_short_term_loans_and_leases', 'finance_division_other_current_assets', 'other_current_assets_of_the_finance_division', 'finance_division_other_short_term_assets', 'other_short_term_assets_of_the_finance_division', 'loans_held_for_sale', 'deferred_tax_asset_current_portion', 'short_term_deferred_tax_asset', 'current_deferred_tax_asset', 'restricted_cash', 'other_current_assets', 'total_current_assets', 'short_term_assets', 'total_short_term_assets', 'current_assets', 'gross_property_plant_and_equipment', 'gppe', 'gross_ppe', 'accumulated_depreciation', 'net_property_plant_and_equipment', 'property_plant_and_equipment', 'nppe', 'net_ppe', 'ppe', 'long_term_investments', 'non_current_investments', 'goodwill', 'other_intangibles', 'finance_division_loans_and_leases_long_term', 'long_term_finance_division_loans_and_leases', 'long_term_loans_and_leases_of_the_finance_division', 'finance_division_long_term_loans_and_leases', 'finance_division_other_non_current_assets', 'other_non_current_assets_of_the_finance_division', 'finance_division_other_long_term_assets', 'other_long_term_assets_of_the_finance_division', 'long_term_accounts_receivable', 'non_current_accounts_receivable', 'long_term_loans_receivable', 'non_current_loans_receivable', 'loans_receivable', 'long_term_deferred_tax_assets', 'non_current_deferred_tax_assets', 'long_term_deferred_charges', 'non_current_deferred_charges', 'other_long_term_assets', 'non_current_other_assets', 'other_non_current_assets', 'long_term_other_assets', 'total_assets', 'assets', 'accounts_payable', 'accrued_expenses', 'short_term_borrowings', 'short_term_borrowing', 'current_borrowings', 'current_borrowing', 'current_portion_of_long_term_debt', 'current_portion_of_non_current_debt', 'current_portion_of_lt_debt', 'current_portion_of_capital_leases', 'current_portion_of_capitalized_leases', 'current_portion_of_cap_leases', 'current_portion_of_leases', 'current_portion_of_long_term_debt_and_capital_leases', 'total_current_portion_of_non_current_debt_and_capitalized_leases', 'current_portion_of_lt_debt_and_cap_leases', 'current_portion_of_non_current_debt_and_capital_leases', 'total_current_portion_of_long_term_debt_and_capitalized_leases', 'current_portion_of_non_current_debt_and_capitalized_leases', 'total_current_portion_of_lt_debt_and_cap_leases', 'current_portion_of_long_term_debt_and_capitalized_leases', 'total_current_portion_of_non_current_debt_and_capital_leases', 'total_current_portion_of_long_term_debt_and_capital_leases', 'finance_division_debt_current_portion', 'finance_division_other_current_liabilities', 'current_income_taxes_payable', 'current_portion_of_income_taxes_payable', 'current_unearned_revenue', 'current_portion_of_unearned_revenue', 'current_deferred_tax_liability', 'other_current_liability', 'other_current_liabilities', 'total_current_liabilities', 'current_liabilities', 'long_term_debt', 'non_current_debt', 'capital_leases', 'long_term_leases', 'capitalized_leases', 'finance_division_debt_non_current_portion', 'finance_division_non_current_debt', 'finance_division_debt_long_term_portion', 'finance_division_long_term_debt', 'finance_division_other_non_current_liabilities', 'finance_division_other_long_term_liabilities', 'non_current_unearned_revenue', 'long_term_unearned_revenue', 'pension_and_other_post_retirement_benefit', 'non_current_deferred_tax_liability', 'other_non_current_liabilities', 'non_current_other_liabilities', 'other_long_term_liabilities', 'long_term_other_liabilities', 'total_liabilities', 'liabilities', 'preferred_stock_redeemable', 'redeemable_preferred_stock', 'preferred_stock_non_redeemable', 'non_redeemable_preferred_stock', 'preferred_stock_convertible', 'convertible_preferred_stock', 'preferred_stock_other', 'other_preferred_stock', 'preferred_stock_additional_paid_in_capital', 'additional_paid_in_capital_preferred_stock', 'preferred_stock_equity_adjustment', 'equity_adjustment_preferred_stock', 'treasury_stock_preferred_stock_convertible', 'treasury_preferred_stock_convertible', 'treasury_stock_convertible_preferred_stock', 'treasury_convertible_preferred_stock', 'treasury_stock_preferred_stock_non_redeemable', 'treasury_preferred_stock_non_redeemable', 'treasury_non_redeemable_preferred_stock', 'treasury_stock_non_redeemable_preferred_stock', 'treasury_stock_preferred_stock_redeemable', 'treasury_stock_redeemable_preferred_stock', 'treasury_preferred_stock_redeemable', 'treasury_redeemable_preferred_stock', 'total_preferred_equity', 'total_preferred_stock', 'preferred_stock', 'preferred_equity', 'common_stock', 'additional_paid_in_capital', 'retained_earnings', 'treasury_stock', 'other_equity', 'total_common_equity', 'common_equity', 'total_equity', 'equity', 'shareholders_equity', 'total_shareholders_equity', 'total_liabilities_and_equity', 'liabilities_and_equity', 'common_shares_outstanding', 'adjustments_to_cash_flow_net_income', 'other_amortization', 'total_other_non_cash_items', 'net_decrease_in_loans_originated_and_sold', 'provision_for_credit_losses', 'loss_on_equity_investments', 'stock_based_compensation', 'tax_benefit_from_stock_options', 'net_cash_from_discontinued_operation', 'cash_from_discontinued_operation', 'other_operating_activities', 'change_in_trading_asset_securities', 'change_in_accounts_receivable', 'change_in_inventories', 'change_in_accounts_payable', 'change_in_unearned_revenue', 'change_in_income_taxes', 'change_in_deferred_taxes', 'change_in_other_net_operating_assets', 'change_in_net_operating_assets', 'cash_from_operations', 'cash_from_operating_activities', 'cash_flow_from_operations', 'capital_expenditure', 'capex', 'capital_expenditures', 'sale_of_property_plant_and_equipment', 'sale_of_ppe', 'cash_acquisitions', 'divestitures', 'sale_of_real_estate', 'sale_of_real_estate_properties', 'sale_of_real_properties', 'sale_of_intangible_assets', 'sale_of_intangibles', 'sale_of_intangible_asset', 'net_cash_from_investments', 'net_decrease_in_investment_loans_originated_and_sold', 'other_investing_activities', 'total_other_investing_activities', 'cash_from_investing', 'cashflow_from_investing', 'cash_from_investing_activities', 'cashflow_from_investing_activities', 'short_term_debt_issued', 'current_debt_issued', 'long_term_debt_issued', 'non_current_debt_issued', 'total_debt_issued', 'short_term_debt_repaid', 'current_debt_repaid', 'long_term_debt_repaid', 'non_current_debt_repaid', 'total_debt_repaid', 'issuance_of_common_stock', 'repurchase_of_common_stock', 'issuance_of_preferred_stock', 'repurchase_of_preferred_stock', 'common_dividends_paid', 'preferred_dividends_paid', 'total_dividends_paid', 'dividends_paid', 'special_dividends_paid', 'other_financing_activities', 'cash_from_financing', 'cashflow_from_financing', 'cashflow_from_financing_activities', 'cash_from_financing_activities', 'foreign_exchange_rate_adjustments', 'fx_adjustments', 'foreign_exchange_adjustments', 'miscellaneous_cash_flow_adjustments', 'misc_cash_flow_adj', 'net_change_in_cash', 'change_in_cash', 'depreciation', 'depreciation_of_rental_assets', 'sale_proceeds_from_rental_assets', 'basic_eps', 'basic_earning_per_share', 'basic_eps_including_extra_items', 'basic_earning_per_share_including_extra_items', 'basic_eps_excluding_extra_items', 'basic_earning_per_share_excluding_extra_items', 'basic_eps_from_accounting_change', 'basic_earning_per_share_from_accounting_change', 'basic_eps_from_extraordinary_items', 'basic_earning_per_share_from_extraordinary_items', 'basic_eps_from_accounting_change_and_extraordinary_items', 'basic_earning_per_share_from_accounting_change_and_extraordinary_items', 'weighted_average_basic_shares_outstanding', 'diluted_eps', 'diluted_earning_per_share_including_extra_items', 'diluted_earning_per_share', 'diluted_eps_including_extra_items', 'diluted_eps_excluding_extra_items', 'diluted_earning_per_share_excluding_extra_items', 'weighted_average_diluted_shares_outstanding', 'normalized_basic_eps', 'normalized_basic_earning_per_share', 'normalized_diluted_eps', 'normalized_diluted_earning_per_share', 'dividends_per_share', 'distributable_cash_per_share', 'diluted_eps_from_accounting_change_and_extraordinary_items', 'diluted_earning_per_share_from_accounting_change_and_extraordinary_items', 'diluted_eps_from_accounting_change', 'diluted_earning_per_share_from_accounting_change', 'diluted_eps_from_extraordinary_items', 'diluted_earning_per_share_from_extraordinary_items', 'diluted_eps_from_discontinued_operations', 'diluted_earning_per_share_from_discontinued_operations', 'funds_from_operations', 'ffo', 'ebitda', 'earnings_before_interest_taxes_depreciation_and_amortization', 'ebita', 'earnings_before_interest_taxes_and_amortization', 'ebit', 'earnings_before_interest_and_taxes', 'ebitdar', 'earnings_before_interest_taxes_depreciation_amortization_and_rental_expense', 'net_debt', 'effective_tax_rate', 'tax_rate', 'current_ratio', 'quick_ratio', 'total_debt_to_capital', 'net_working_capital', 'working_capital', 'change_in_net_working_capital', 'total_debt', 'total_debt_to_equity_ratio', 'debt_ratio', 'total_debt_ratio', 'total_debt_to_equity', 'total_debt_to_total_equity']

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

period_type: Optional[Literal['annual', 'quarterly', 'ltm', 'ytd']]

start_quarter: Optional[Literal[1, 2, 3, 4]]

start_year: int | None

class GetFinancialStatementFromIdentifier(*, identifier: str, statement: Literal['balance_sheet', 'income_statement', 'cashflow'], period_type: Optional[Literal['annual', 'quarterly', 'ltm', 'ytd']] = None, start_year: int | None = None, end_year: int | None = None, start_quarter: Optional[Literal[1, 2, 3, 4]] = None, end_quarter: Optional[Literal[1, 2, 3, 4]] = None)

Bases: BaseModel

end_quarter: Optional[Literal[1, 2, 3, 4]]

end_year: int | None

identifier: str

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

period_type: Optional[Literal['annual', 'quarterly', 'ltm', 'ytd']]

start_quarter: Optional[Literal[1, 2, 3, 4]]

start_year: int | None

statement: Literal['balance_sheet', 'income_statement', 'cashflow']

class GetHistoryMetadataFromIdentifier(*, identifier: str)

Bases: BaseModel

identifier: str

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class GetInfoFromIdentifier(*, identifier: str)

Bases: BaseModel

identifier: str

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class GetIsinFromTicker(*, ticker_str: str)

Bases: BaseModel

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

ticker_str: str

class GetLatestInput(*, use_local_timezone: bool = True)

Bases: BaseModel

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

use_local_timezone: bool

class GetNQuartersAgoInput(*, n: int)

Bases: BaseModel

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

n: int

class GetPricesFromIdentifier(*, identifier: str, periodicity: Literal['day', 'week', 'month', 'year'] = 'day', adjusted: bool, start_date: str | None = None, end_date: str | None = None)

Bases: BaseModel

adjusted: bool

end_date: str | None

identifier: str

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

periodicity: Literal['day', 'week', 'month', 'year']

start_date: str | None

class GetSecurityIdFromIdentifier(*, identifier: str)

Bases: BaseModel

identifier: str

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class GetTradingItemIdFromIdentifier(*, identifier: str)

Bases: BaseModel

identifier: str

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].