kfinance

class BusinessRelationships(current: Companies, previous: Companies)[source]

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.

count(value, /): Return number of occurrences of value.

current: Companies: Alias for field number 0

index(value, start=0, stop=9223372036854775807, /)

Return first index of value.

Raises ValueError if the value is not present.

previous: Companies: Alias for field number 1

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

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

property anthropic_tool_descriptions: list[dict[str, Any]]: Return tool descriptions for anthropic

company(company_id: int) → Company[source]

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

property gemini_tool_descriptions: Tool

Return tool descriptions for gemini.

The conversion from BaseTool -> openai tool description -> google tool mirrors the langchain implementation.

static get_latest(use_local_timezone: bool = True) → LatestPeriods[source]

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

Parameters:: use_local_timezone (bool) – whether to use the local timezone of the user
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[source]

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

property langchain_tools: list[KfinanceTool]: Return a list of all Kfinance tools for tool calling.

property openai_tool_descriptions: list[dict[str, Any]]: Return tool descriptions for gemini

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[source]

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: str | None = None, function_called: bool | None = False) → Ticker[source]

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

Generate a 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, or one of sic, naics, nace, anzsic, spcapiqetf, spratings, or gics.

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 defaults to 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 defaults to None
simple_industry (str, optional) – The S&P CIQ Simple Industry defined in ciqSimpleIndustry in XPF. It defaults to None
exchange_code (str, optional) – The exchange code for the primary equity listing exchange of the firm. It defaults to None
sic (str, optional) – The SIC industry code. It defaults to None
naics (str, optional) – The NAICS industry code. It defaults to None
nace (str, optional) – The NACE industry code. It defaults to None
anzsic (str, optional) – The ANZSIC industry code. It defaults to None
spcapiqetf (str, optional) – The S&P CapitalIQ ETF industry code. It defaults to None
spratings (str, optional) – The S&P Ratings industry code. It defaults to None
gics (str, optional) – The GICS code. It defaults to None

Returns:

A Tickers object that is the intersection of Ticker objects meeting all the supplied parameters.

Return type:

Tickers

property tools: dict[str, Callable]

Return a mapping of tool calling function names to the corresponding functions.

tools is intended for running without langchain. When running with langchain, use langchain_tools.

trading_item(trading_item_id: int) → TradingItem[source]

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

transcript(key_dev_id: int) → Transcript[source]

Generate Transcript object from key_dev_id

Parameters:: key_dev_id (int) – The key dev ID for the earnings
Returns:: The transcript specified by the key dev id
Return type:: Transcript

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

Base class for representing a set of Companies

accounts_payable(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) → DataFrame: ciq data item 1018

accounts_receivable(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) → DataFrame: ciq data item 1021

accrued_expenses(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) → DataFrame: ciq data item 1016

accumulated_depreciation(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) → DataFrame: ciq data item 1075

add()

Add an element to a set.

This has no effect if the element is already present.

additional_paid_in_capital(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) → DataFrame: ciq data item 1084

alias for preferred_stock_additional_paid_in_capital

ciq data item 1085

property address: str

Get the address of the company’s HQ

Returns:: address of the company’s HQ
Return type:: str

adjustments_to_cash_flow_net_income(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) → DataFrame: ciq data item 21523

property all_earnings: list[Earnings]: Retrieve and cache all earnings items for this company

amortization_of_goodwill_and_intangibles(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) → DataFrame: ciq data item 31

asset_writedown(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) → DataFrame: ciq data item 32

alias for total_assets

ciq data item 1007

balance_sheet(period_type: PeriodType | None = None, start_year: int | None = None, end_year: int | None = None, start_quarter: int | None = None, end_quarter: int | None = None) → DataFrame: The templated balance sheet

alias for basic_eps

ciq data item 9

alias for basic_eps_excluding_extra_items

ciq data item 3064

alias for basic_eps_from_accounting_change

ciq data item 145

alias for basic_eps_from_accounting_change_and_extraordinary_items

ciq data item 45

alias for basic_eps_from_extraordinary_items

ciq data item 146

alias for basic_eps

ciq data item 9

basic_eps(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) → DataFrame: ciq data item 9

basic_eps_excluding_extra_items(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) → DataFrame: ciq data item 3064

basic_eps_from_accounting_change(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) → DataFrame: ciq data item 145

basic_eps_from_accounting_change_and_extraordinary_items(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) → DataFrame: ciq data item 45

basic_eps_from_extraordinary_items(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) → DataFrame: ciq data item 146

alias for basic_eps

ciq data item 9

property borrower: BusinessRelationships: Returns the associated company’s current and previous borrowers

Retrieves the templated line of business segments for a given period_type, start_year, start_quarter, end_year and end_quarter.

Parameters:

period_type – The period_type requested for. Can be “annual”, “quarterly”, “ytd”. Defaults to “annual” when start_quarter and end_quarter are None.
start_year (int, optional) – The starting calendar year, defaults to None
end_year (int, optional) – The ending calendar year, defaults to None
start_quarter (int, optional) – The starting calendar quarter, defaults to None
end_quarter (int, optional) – The ending calendar quarter, defaults to None

Returns:

A dictionary containing the templated line of business segments for each time period, segment name, line item, and value.

Return type:

dict

alias for capital_expenditure

ciq data item 2021

capital_expenditure(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) → DataFrame: ciq data item 2021

alias for capital_expenditure

ciq data item 2021

capital_leases(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) → DataFrame: ciq data item 1183

alias for capital_leases

ciq data item 1183

alias for cash_and_equivalents

ciq data item 1096

cash_acquisitions(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) → DataFrame: ciq data item 2057

alias for cash_and_equivalents

ciq data item 1096

cash_and_equivalents(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) → DataFrame: ciq data item 1096

alias for total_cash_and_short_term_investments

ciq data item 1002

cash_flow(period_type: PeriodType | None = None, start_year: int | None = None, end_year: int | None = None, start_quarter: int | None = None, end_quarter: int | None = None) → DataFrame: The templated cash flow statement

alias for cash_from_operations

ciq data item 2006

alias for net_cash_from_discontinued_operation

ciq data item 2081

cash_from_financing(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) → DataFrame: ciq data item 2004

alias for cash_from_financing

ciq data item 2004

cash_from_investing(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) → DataFrame: ciq data item 2005

alias for cash_from_investing

ciq data item 2005

alias for cash_from_operations

ciq data item 2006

cash_from_operations(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) → DataFrame: ciq data item 2006

cashflow(period_type: PeriodType | None = None, start_year: int | None = None, end_year: int | None = None, start_quarter: int | None = None, end_quarter: int | None = None) → DataFrame: The templated cash flow statement

alias for cash_from_financing

ciq data item 2004

alias for cash_from_financing

ciq data item 2004

alias for cash_from_investing

ciq data item 2005

alias for cash_from_investing

ciq data item 2005

change_in_accounts_payable(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) → DataFrame: ciq data item 2017

change_in_accounts_receivable(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) → DataFrame: ciq data item 2018

alias for net_change_in_cash

ciq data item 2093

change_in_deferred_taxes(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) → DataFrame: ciq data item 2084

change_in_income_taxes(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) → DataFrame: ciq data item 2101

change_in_inventories(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) → DataFrame: ciq data item 2099

change_in_net_operating_assets(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) → DataFrame: ciq data item 2010

change_in_net_working_capital(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) → DataFrame: ciq data item 4421

change_in_other_net_operating_assets(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) → DataFrame: ciq data item 2045

change_in_trading_asset_securities(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) → DataFrame: ciq data item 2134

change_in_unearned_revenue(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) → DataFrame: ciq data item 2139

property city: str

Get the city of the company’s HQ

Returns:: city of the company’s HQ
Return type:: str

clear(): Remove all elements from this set.

property client_services: BusinessRelationships: Returns the associated company’s current and previous client_servicess

alias for cost_of_goods_sold

ciq data item 34

common_dividends_paid(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) → DataFrame: ciq data item 2074

alias for total_common_equity

ciq data item 1006

common_shares_outstanding(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) → DataFrame: ciq data item 1100

common_stock(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) → DataFrame: ciq data item 1103

property company_id: Any: Set and return the company id for the object

alias for earnings_from_continued_operations

ciq data item 7

alias for preferred_stock_convertible

ciq data item 1214

copy(): Return a shallow copy of a set.

alias for cost_of_revenue

ciq data item 1

cost_of_goods_sold(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) → DataFrame: ciq data item 34

cost_of_revenue(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) → DataFrame: ciq data item 1

property country: str

Get the country of the company’s HQ

Returns:: country of the company’s HQ
Return type:: str

property creditor: BusinessRelationships: Returns the associated company’s current and previous creditors

currency_exchange_gains(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) → DataFrame: ciq data item 38

alias for accounts_receivable

ciq data item 1021

alias for total_current_assets

ciq data item 1008

alias for short_term_borrowings

ciq data item 1046

alias for short_term_borrowings

ciq data item 1046

alias for short_term_debt_issued

ciq data item 2043

alias for short_term_debt_repaid

ciq data item 2044

alias for deferred_tax_asset_current_portion

ciq data item 1117

current_deferred_tax_liability(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) → DataFrame: ciq data item 1119

current_income_taxes_payable(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) → DataFrame: ciq data item 1094

alias for total_current_liabilities

ciq data item 1009

alias for notes_receivable

ciq data item 1048

alias for other_receivables

ciq data item 1206

alias for current_portion_of_capital_leases

ciq data item 1090

current_portion_of_capital_leases(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) → DataFrame: ciq data item 1090

alias for current_portion_of_capital_leases

ciq data item 1090

alias for current_income_taxes_payable

ciq data item 1094

alias for current_portion_of_capital_leases

ciq data item 1090

current_portion_of_long_term_debt(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) → DataFrame: ciq data item 1297

current_portion_of_long_term_debt_and_capital_leases(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) → DataFrame: ciq data item 1279

alias for current_portion_of_long_term_debt_and_capital_leases

ciq data item 1279

alias for current_portion_of_long_term_debt

ciq data item 1297

alias for current_portion_of_long_term_debt_and_capital_leases

ciq data item 1279

alias for current_portion_of_long_term_debt

ciq data item 1297

alias for current_portion_of_long_term_debt_and_capital_leases

ciq data item 1279

alias for current_portion_of_long_term_debt_and_capital_leases

ciq data item 1279

alias for current_unearned_revenue

ciq data item 1074

current_ratio(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) → DataFrame: ciq data item 4030

alias for total_receivables

ciq data item 1001

alias for total_receivables

ciq data item 1001

current_unearned_revenue(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) → DataFrame: ciq data item 1074

property customer: BusinessRelationships: Returns the associated company’s current and previous customers

alias for depreciation_and_amortization

ciq data item 41

alias for total_debt_to_equity_ratio

ciq data item 4034

deferred_tax_asset_current_portion(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) → DataFrame: ciq data item 1117

depreciation(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) → DataFrame: ciq data item 2143

depreciation_and_amortization(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) → DataFrame: ciq data item 41

depreciation_of_rental_assets(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) → DataFrame: ciq data item 42409

difference()

Return the difference of two or more sets as a new set.

(i.e. all elements that are in this set but not the others.)

difference_update(): Remove all elements of another set from this set.

alias for diluted_eps

ciq data item 8

alias for diluted_eps_excluding_extra_items

ciq data item 142

alias for diluted_eps_from_accounting_change

ciq data item 141

alias for diluted_eps_from_accounting_change_and_extraordinary_items

ciq data item 44

alias for diluted_eps_from_discontinued_operations

ciq data item 143

alias for diluted_eps_from_extraordinary_items

ciq data item 144

alias for diluted_eps

ciq data item 8

diluted_eps(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) → DataFrame: ciq data item 8

diluted_eps_excluding_extra_items(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) → DataFrame: ciq data item 142

diluted_eps_from_accounting_change(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) → DataFrame: ciq data item 141

diluted_eps_from_accounting_change_and_extraordinary_items(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) → DataFrame: ciq data item 44

diluted_eps_from_discontinued_operations(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) → DataFrame: ciq data item 143

diluted_eps_from_extraordinary_items(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) → DataFrame: ciq data item 144

alias for diluted_eps

ciq data item 8

discard()

Remove an element from a set if it is a member.

If the element is not a member, do nothing.

alias for earnings_from_discontinued_operations

ciq data item 40

distributable_cash_per_share(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) → DataFrame: ciq data item 23317

property distributor: BusinessRelationships: Returns the associated company’s current and previous distributors

divestitures(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) → DataFrame: ciq data item 2077

alias for total_dividends_paid

ciq data item 2022

dividends_per_share(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) → DataFrame: ciq data item 3058

alias for depreciation_and_amortization

ciq data item 41

earnings(start_date: date | None = None, end_date: date | None = None) → list[Earnings]

Get earnings for the company within date range sorted in descending order by date

Parameters:

start_date (date, optional) – Start date filter, defaults to None
end_date (date, optional) – End date filter, defaults to None

Returns:

List of earnings objects

Return type:

list[Earnings]

alias for ebit

ciq data item 400

alias for ebita

ciq data item 100689

alias for ebitdar

ciq data item 21674

alias for ebitda

ciq data item 4051

alias for ebt_excluding_unusual_items

ciq data item 4

alias for ebt_including_unusual_items

ciq data item 139

property earnings_call_datetimes: list[datetime]

Get the datetimes of the companies earnings calls

Returns:: a list of datetimes for the companies earnings calls
Return type:: list[datetime]

earnings_from_continued_operations(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) → DataFrame: ciq data item 7

earnings_from_discontinued_operations(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) → DataFrame: ciq data item 40

ebit(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) → DataFrame: ciq data item 400

ebita(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) → DataFrame: ciq data item 100689

ebitda(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) → DataFrame: ciq data item 4051

ebitdar(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) → DataFrame: ciq data item 21674

ebt_excluding_unusual_items(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) → DataFrame: ciq data item 4

ebt_including_unusual_items(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) → DataFrame: ciq data item 139

effective_tax_rate(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) → DataFrame: ciq data item 4376

alias for total_equity

ciq data item 1275

alias for preferred_stock_equity_adjustment

ciq data item 1215

exploration_and_drilling_costs(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) → DataFrame: ciq data item 49

alias for exploration_and_drilling_costs

ciq data item 49

extraordinary_item_and_accounting_change(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) → DataFrame: ciq data item 42

fees_and_other_income(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) → DataFrame: ciq data item 168

alias for funds_from_operations

ciq data item 3074

finance_division_debt_current_portion(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) → DataFrame: ciq data item 1030

alias for finance_division_debt_non_current_portion

ciq data item 1035

finance_division_debt_non_current_portion(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) → DataFrame: ciq data item 1035

finance_division_interest_expense(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) → DataFrame: ciq data item 50

finance_division_loans_and_leases_long_term(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) → DataFrame: ciq data item 1033

finance_division_loans_and_leases_short_term(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) → DataFrame: ciq data item 1032

alias for finance_division_debt_non_current_portion

ciq data item 1035

alias for finance_division_loans_and_leases_long_term

ciq data item 1033

alias for finance_division_debt_non_current_portion

ciq data item 1035

finance_division_operating_expense(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) → DataFrame: ciq data item 51

finance_division_other_current_assets(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) → DataFrame: ciq data item 1029

finance_division_other_current_liabilities(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) → DataFrame: ciq data item 1031

alias for finance_division_other_non_current_assets

ciq data item 1034

alias for finance_division_other_non_current_liabilities

ciq data item 1036

finance_division_other_non_current_assets(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) → DataFrame: ciq data item 1034

finance_division_other_non_current_liabilities(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) → DataFrame: ciq data item 1036

alias for finance_division_other_current_assets

ciq data item 1029

finance_division_revenue(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) → DataFrame: ciq data item 52

alias for finance_division_loans_and_leases_short_term

ciq data item 1032

alias for foreign_exchange_rate_adjustments

ciq data item 2144

foreign_exchange_rate_adjustments(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) → DataFrame: ciq data item 2144

property founding_date: date

Get the founding date for the company

Returns:: founding date for the company
Return type:: date

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: str | None = None, start_year: int | None = None, end_year: int | None = None, start_quarter: int | None = None, end_quarter: int | None = None) → DataFrame: ciq data item 3074

alias for foreign_exchange_rate_adjustments

ciq data item 2144

gain_from_sale_of_assets(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) → DataFrame: ciq data item 62

gain_from_sale_of_investments(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) → DataFrame: ciq data item 56

Retrieves the templated geographic segments for a given period_type, start_year, start_quarter, end_year and end_quarter.

Parameters:

period_type – The period_type requested for. Can be “annual”, “quarterly”, “ytd”. Defaults to “annual” when start_quarter and end_quarter are None.
start_year (int, optional) – The starting calendar year, defaults to None
end_year (int, optional) – The ending calendar year, defaults to None
start_quarter (int, optional) – The starting calendar quarter, defaults to None
end_quarter (int, optional) – The ending calendar quarter, defaults to None

Returns:

A dictionary containing the templated geographic segments for each time period, segment name, line item, and value.

Return type:

dict

goodwill(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) → DataFrame: ciq data item 1171

alias for gross_property_plant_and_equipment

ciq data item 1169

alias for gross_property_plant_and_equipment

ciq data item 1169

gross_profit(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) → DataFrame: ciq data item 10

gross_property_plant_and_equipment(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) → DataFrame: ciq data item 1169

alias for impairment_of_oil_gas_and_mineral_properties

ciq data item 71

impairment_of_goodwill(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) → DataFrame: ciq data item 209

alias for impairment_of_oil_gas_and_mineral_properties

ciq data item 71

impairment_of_oil_gas_and_mineral_properties(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) → DataFrame: ciq data item 71

alias for in_process_research_and_development_expense

ciq data item 72

alias for in_process_research_and_development_expense

ciq data item 72

alias for in_process_research_and_development_expense

ciq data item 72

in_process_research_and_development_expense(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) → DataFrame: ciq data item 72

alias for in_process_research_and_development_expense

ciq data item 72

alias for in_process_research_and_development_expense

ciq data item 72

income_from_affiliates(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) → DataFrame: ciq data item 47

income_statement(period_type: PeriodType | None = None, start_year: int | None = None, end_year: int | None = None, start_quarter: int | None = None, end_quarter: int | None = None) → DataFrame: The templated income statement

income_stmt(period_type: PeriodType | None = None, start_year: int | None = None, end_year: int | None = None, start_quarter: int | None = None, end_quarter: int | None = None) → DataFrame: The templated income statement

alias for income_tax_expense

ciq data item 75

income_tax_expense(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) → DataFrame: ciq data item 75

alias for income_tax_expense

ciq data item 75

property info: dict

Get the company info

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

insurance_division_operating_expense(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) → DataFrame: ciq data item 69

insurance_division_revenue(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) → DataFrame: ciq data item 70

insurance_settlements(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) → DataFrame: ciq data item 73

interest_and_investment_income(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) → DataFrame: ciq data item 65

interest_expense(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) → DataFrame: ciq data item 82

alias for finance_division_interest_expense

ciq data item 50

intersection()

Return the intersection of two sets as a new set.

(i.e. all elements that are in both sets.)

intersection_update(): Update a set with the intersection of itself and another.

alias for inventory

ciq data item 1043

inventory(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) → 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

isdisjoint(): Return True if two sets have a null intersection.

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

issuance_of_common_stock(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) → DataFrame: ciq data item 2169

issuance_of_preferred_stock(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) → DataFrame: ciq data item 2181

issubset(): Report whether another set contains this set.

issuperset(): Report whether this set contains another set.

property landlord: BusinessRelationships: Returns the associated company’s current and previous landlords

property latest_earnings: Earnings | None

Get the most recent past earnings

Returns:: The most recent earnings or None if no data available
Return type:: Earnings | None

legal_settlements(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) → 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

alias for total_liabilities

ciq data item 1276

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: PeriodType | None = None, start_year: int | None = None, end_year: int | None = None, start_quarter: int | None = None, end_quarter: int | None = None) → DataFrame: Get a DataFrame of a financial line item according to the date ranges.

loans_held_for_sale(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) → DataFrame: ciq data item 1185

alias for long_term_loans_receivable

ciq data item 1050

long_term_accounts_receivable(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) → DataFrame: ciq data item 1088

long_term_debt(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) → DataFrame: ciq data item 1049

long_term_debt_issued(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) → DataFrame: ciq data item 2034

long_term_debt_repaid(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) → DataFrame: ciq data item 2036

long_term_deferred_charges(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) → DataFrame: ciq data item 1025

long_term_deferred_tax_assets(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) → DataFrame: ciq data item 1026

alias for finance_division_loans_and_leases_long_term

ciq data item 1033

long_term_investments(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) → DataFrame: ciq data item 1054

alias for capital_leases

ciq data item 1183

alias for finance_division_loans_and_leases_long_term

ciq data item 1033

long_term_loans_receivable(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) → DataFrame: ciq data item 1050

alias for other_long_term_assets

ciq data item 1060

alias for other_non_current_liabilities

ciq data item 1062

alias for non_current_unearned_revenue

ciq data item 1256

loss_on_equity_investments(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) → DataFrame: ciq data item 2086

market_cap(start_date: str | None = None, end_date: str | None = None) → DataFrame

Retrieves market caps for a company between start and end date.

Parameters:

start_date (str, optional) – The start date in format “YYYY-MM-DD”, default to None
end_date (str, optional) – The end date in format “YYYY-MM-DD”, default to None

Returns:

A DataFrame with a market_cap column. The dates are the index.

Return type:

pd.DataFrame

merger_and_restructuring_charges(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) → DataFrame: ciq data item 363

merger_charges(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) → DataFrame: ciq data item 80

minority_interest_in_earnings(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) → DataFrame: ciq data item 83

alias for miscellaneous_cash_flow_adjustments

ciq data item 2149

miscellaneous_cash_flow_adjustments(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) → DataFrame: ciq data item 2149

property name: str

Get the company name

Returns:: The company name
Return type:: str

net_cash_from_discontinued_operation(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) → DataFrame: ciq data item 2081

net_cash_from_investments(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) → DataFrame: ciq data item 2027

net_change_in_cash(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) → DataFrame: ciq data item 2093

net_debt(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) → DataFrame: ciq data item 4364

net_decrease_in_investment_loans_originated_and_sold(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) → DataFrame: ciq data item 2032

net_decrease_in_loans_originated_and_sold(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) → DataFrame: ciq data item 2033

net_income(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) → DataFrame: ciq data item 15

net_income_allocable_to_general_partner(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) → DataFrame: ciq data item 249

net_income_to_common_shareholders_excluding_extra_items(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) → DataFrame: ciq data item 379

net_income_to_common_shareholders_including_extra_items(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) → DataFrame: ciq data item 16

net_income_to_company(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) → DataFrame: ciq data item 41571

alias for minority_interest_in_earnings

ciq data item 83

net_interest_expense(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) → DataFrame: ciq data item 368

alias for net_property_plant_and_equipment

ciq data item 1004

net_property_plant_and_equipment(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) → DataFrame: ciq data item 1004

net_working_capital(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) → DataFrame: ciq data item 1311

property next_earnings: Earnings | None

Get the next upcoming earnings

Returns:: The next earnings or None if no data available
Return type:: Earnings | None

alias for long_term_accounts_receivable

ciq data item 1088

alias for long_term_debt

ciq data item 1049

alias for long_term_debt_issued

ciq data item 2034

alias for long_term_debt_repaid

ciq data item 2036

alias for long_term_deferred_charges

ciq data item 1025

alias for long_term_deferred_tax_assets

ciq data item 1026

non_current_deferred_tax_liability(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) → DataFrame: ciq data item 1027

alias for long_term_investments

ciq data item 1054

alias for long_term_loans_receivable

ciq data item 1050

alias for other_long_term_assets

ciq data item 1060

alias for other_non_current_liabilities

ciq data item 1062

non_current_unearned_revenue(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) → DataFrame: ciq data item 1256

alias for preferred_stock_non_redeemable

ciq data item 1216

alias for revenue

ciq data item 112

alias for normalized_basic_eps

ciq data item 4379

normalized_basic_eps(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) → DataFrame: ciq data item 4379

alias for normalized_diluted_eps

ciq data item 4380

normalized_diluted_eps(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) → DataFrame: ciq data item 4380

notes_receivable(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) → DataFrame: ciq data item 1048

alias for net_property_plant_and_equipment

ciq data item 1004

property number_of_employees: str | None

Get the number of employees the company has (if available)

Returns:: how many employees the company has
Return type:: str | None

alias for total_operating_expense

ciq data item 373

alias for finance_division_operating_expense

ciq data item 51

alias for insurance_division_operating_expense

ciq data item 69

operating_income(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) → DataFrame: ciq data item 21

other_adjustments_to_net_income(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) → DataFrame: ciq data item 259

other_amortization(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) → DataFrame: ciq data item 2014

other_current_assets(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) → DataFrame: ciq data item 1055

alias for finance_division_other_current_assets

ciq data item 1029

alias for other_current_liability

ciq data item 1057

other_current_liability(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) → DataFrame: ciq data item 1057

other_equity(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) → DataFrame: ciq data item 1028

other_financing_activities(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) → DataFrame: ciq data item 2050

other_intangibles(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) → DataFrame: ciq data item 1040

other_investing_activities(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) → DataFrame: ciq data item 2051

other_long_term_assets(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) → DataFrame: ciq data item 1060

alias for finance_division_other_non_current_assets

ciq data item 1034

alias for other_non_current_liabilities

ciq data item 1062

alias for other_long_term_assets

ciq data item 1060

alias for finance_division_other_non_current_assets

ciq data item 1034

other_non_current_liabilities(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) → DataFrame: ciq data item 1062

other_non_operating_income(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) → DataFrame: ciq data item 85

other_operating_activities(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) → DataFrame: ciq data item 2047

other_operating_expense(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) → DataFrame: ciq data item 260

alias for preferred_stock_other

ciq data item 1065

other_preferred_stock_adjustments(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) → DataFrame: ciq data item 281

other_receivables(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) → DataFrame: ciq data item 1206

other_revenue(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) → DataFrame: ciq data item 90

alias for finance_division_other_current_assets

ciq data item 1029

other_unusual_items(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) → DataFrame: ciq data item 87

pension_and_other_post_retirement_benefit(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) → DataFrame: ciq data item 1213

pop(): Remove and return an arbitrary set element. Raises KeyError if the set is empty.

alias for net_property_plant_and_equipment

ciq data item 1004

pre_opening_costs(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) → DataFrame: ciq data item 96

alias for pre_opening_costs

ciq data item 96

preferred_dividends_and_other_adjustments(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) → DataFrame: ciq data item 97

preferred_dividends_paid(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) → DataFrame: ciq data item 2116

alias for total_preferred_equity

ciq data item 1005

alias for total_preferred_equity

ciq data item 1005

preferred_stock_additional_paid_in_capital(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) → DataFrame: ciq data item 1085

preferred_stock_convertible(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) → DataFrame: ciq data item 1214

preferred_stock_dividend(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) → DataFrame: ciq data item 280

preferred_stock_equity_adjustment(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) → DataFrame: ciq data item 1215

preferred_stock_non_redeemable(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) → DataFrame: ciq data item 1216

preferred_stock_other(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) → DataFrame: ciq data item 1065

preferred_stock_redeemable(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) → DataFrame: ciq data item 1217

premium_on_redemption_of_preferred_stock(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) → DataFrame: ciq data item 279

prepaid_expense(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) → DataFrame: ciq data item 1212

alias for prepaid_expense

ciq data item 1212

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

alias for net_property_plant_and_equipment

ciq data item 1004

alias for provision_for_bad_debts

ciq data item 95

provision_for_bad_debts(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) → DataFrame: ciq data item 95

provision_for_credit_losses(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) → DataFrame: ciq data item 2112

quick_ratio(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) → DataFrame: ciq data item 4121

alias for research_and_development_expense

ciq data item 100

alias for research_and_development_expense

ciq data item 100

alias for preferred_stock_redeemable

ciq data item 1217

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

remove()

Remove an element from a set; it must be a member.

If the element is not a member, raise a KeyError.

repurchase_of_common_stock(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) → DataFrame: ciq data item 2164

repurchase_of_preferred_stock(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) → DataFrame: ciq data item 2172

alias for research_and_development_expense

ciq data item 100

research_and_development_expense(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) → DataFrame: ciq data item 100

restricted_cash(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) → DataFrame: ciq data item 1104

restructuring_charges(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) → DataFrame: ciq data item 98

retained_earnings(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) → DataFrame: ciq data item 1222

revenue(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) → DataFrame: ciq data item 112

revenue_from_interest_and_investment_income(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) → DataFrame: ciq data item 110

revenue_from_sale_of_assets(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) → DataFrame: ciq data item 104

revenue_from_sale_of_investments(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) → DataFrame: ciq data item 106

alias for research_and_development_expense

ciq data item 100

alias for research_and_development_expense

ciq data item 100

alias for sale_of_intangible_assets

ciq data item 2029

sale_of_intangible_assets(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) → DataFrame: ciq data item 2029

alias for sale_of_intangible_assets

ciq data item 2029

alias for sale_of_property_plant_and_equipment

ciq data item 2042

sale_of_property_plant_and_equipment(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) → DataFrame: ciq data item 2042

sale_of_real_estate(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) → DataFrame: ciq data item 2040

alias for sale_of_real_estate

ciq data item 2040

alias for sale_of_real_estate

ciq data item 2040

sale_proceeds_from_rental_assets(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) → DataFrame: ciq data item 42411

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

alias for selling_general_and_admin_expense

ciq data item 102

alias for selling_general_and_admin_expense

ciq data item 102

selling_general_and_admin_expense(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) → DataFrame: ciq data item 102

alias for selling_general_and_admin_expense

ciq data item 102

alias for selling_general_and_admin_expense

ciq data item 102

alias for total_equity

ciq data item 1275

shares_outstanding(start_date: str | None = None, end_date: str | None = None) → DataFrame

Retrieves shares outstanding for a company between start and end date.

Parameters:

start_date (str, optional) – The start date in format “YYYY-MM-DD”, default to None
end_date (str, optional) – The end date in format “YYYY-MM-DD”, default to None

Returns:

A DataFrame with a shares_outstanding column. The dates are the index.

Return type:

pd.DataFrame

alias for accounts_receivable

ciq data item 1021

alias for total_current_assets

ciq data item 1008

alias for short_term_borrowings

ciq data item 1046

short_term_borrowings(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) → DataFrame: ciq data item 1046

short_term_debt_issued(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) → DataFrame: ciq data item 2043

short_term_debt_repaid(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) → DataFrame: ciq data item 2044

alias for deferred_tax_asset_current_portion

ciq data item 1117

alias for finance_division_loans_and_leases_short_term

ciq data item 1032

short_term_investments(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) → DataFrame: ciq data item 1069

alias for finance_division_loans_and_leases_short_term

ciq data item 1032

alias for notes_receivable

ciq data item 1048

alias for other_receivables

ciq data item 1206

alias for total_receivables

ciq data item 1001

alias for total_receivables

ciq data item 1001

property simple_industry: str

Get the simple industry for the company

Returns:: The company’s simple_industry
Return type:: str

special_dividends_paid(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) → DataFrame: ciq data item 2041

property state: str

Get the state of the company’s HQ

Returns:: state of the company’s HQ
Return type:: str

statement(statement_type: str, period_type: PeriodType | None = None, start_year: int | None = None, end_year: int | None = None, start_quarter: int | None = None, end_quarter: int | None = None) → DataFrame: Get the company’s financial statement

property status: str

Get the company status

Returns:: The company status
Return type:: str

stock_based_compensation(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) → 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

symmetric_difference()

Return the symmetric difference of two sets as a new set.

(i.e. all elements that are in exactly one of the sets.)

symmetric_difference_update(): Update a set with the symmetric difference of itself and another.

tax_benefit_from_stock_options(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) → DataFrame: ciq data item 2135

alias for effective_tax_rate

ciq data item 4376

property tenant: BusinessRelationships: Returns the associated company’s current and previous tenants

tev(start_date: str | None = None, end_date: str | None = None) → DataFrame

Retrieves TEV (total enterprise value) for a company between start and end date.

Parameters:

start_date (str, optional) – The start date in format “YYYY-MM-DD”, default to None
end_date (str, optional) – The end date in format “YYYY-MM-DD”, default to None

Returns:

A DataFrame with a tev column. The dates are the index.

Return type:

pd.DataFrame

total_assets(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) → DataFrame: ciq data item 1007

total_cash_and_short_term_investments(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) → DataFrame: ciq data item 1002

total_common_equity(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) → DataFrame: ciq data item 1006

total_current_assets(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) → DataFrame: ciq data item 1008

total_current_liabilities(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) → DataFrame: ciq data item 1009

alias for current_portion_of_long_term_debt_and_capital_leases

ciq data item 1279

alias for current_portion_of_long_term_debt_and_capital_leases

ciq data item 1279

alias for current_portion_of_long_term_debt_and_capital_leases

ciq data item 1279

alias for current_portion_of_long_term_debt_and_capital_leases

ciq data item 1279

alias for current_portion_of_long_term_debt_and_capital_leases

ciq data item 1279

alias for total_depreciation_and_amortization

ciq data item 2

total_debt(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) → DataFrame: ciq data item 4173

total_debt_issued(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) → DataFrame: ciq data item 2161

alias for total_debt_to_equity_ratio

ciq data item 4034

total_debt_repaid(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) → DataFrame: ciq data item 2166

total_debt_to_capital(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) → DataFrame: ciq data item 43907

alias for total_debt_to_equity_ratio

ciq data item 4034

total_debt_to_equity_ratio(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) → DataFrame: ciq data item 4034

alias for total_debt_to_equity_ratio

ciq data item 4034

total_depreciation_and_amortization(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) → DataFrame: ciq data item 2

total_dividends_paid(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) → DataFrame: ciq data item 2022

alias for total_depreciation_and_amortization

ciq data item 2

total_equity(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) → DataFrame: ciq data item 1275

total_liabilities(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) → DataFrame: ciq data item 1276

total_liabilities_and_equity(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) → DataFrame: ciq data item 1013

total_operating_expense(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) → DataFrame: ciq data item 373

total_other_investing_activities(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) → DataFrame: ciq data item 2177

total_other_non_cash_items(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) → DataFrame: ciq data item 2179

total_other_non_operating_income(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) → DataFrame: ciq data item 371

total_other_operating_expense(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) → DataFrame: ciq data item 380

total_other_revenue(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) → DataFrame: ciq data item 357

total_other_unusual_items(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) → DataFrame: ciq data item 374

total_preferred_equity(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) → DataFrame: ciq data item 1005

alias for total_preferred_equity

ciq data item 1005

alias for total_receivables

ciq data item 1001

total_receivables(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) → DataFrame: ciq data item 1001

total_revenue(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) → DataFrame: ciq data item 28

alias for total_selling_general_and_admin_expense

ciq data item 23

alias for total_selling_general_and_admin_expense

ciq data item 23

total_selling_general_and_admin_expense(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) → DataFrame: ciq data item 23

alias for total_selling_general_and_admin_expense

ciq data item 23

alias for total_equity

ciq data item 1275

alias for total_current_assets

ciq data item 1008

total_unusual_items(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) → DataFrame: ciq data item 19

trading_asset_securities(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) → 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

alias for treasury_stock_preferred_stock_convertible

ciq data item 1249

alias for treasury_stock_preferred_stock_non_redeemable

ciq data item 1250

alias for treasury_stock_preferred_stock_convertible

ciq data item 1249

alias for treasury_stock_preferred_stock_non_redeemable

ciq data item 1250

alias for treasury_stock_preferred_stock_redeemable

ciq data item 1251

alias for treasury_stock_preferred_stock_redeemable

ciq data item 1251

treasury_stock(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) → DataFrame: ciq data item 1248

alias for treasury_stock_preferred_stock_convertible

ciq data item 1249

alias for treasury_stock_preferred_stock_non_redeemable

ciq data item 1250

treasury_stock_preferred_stock_convertible(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) → DataFrame: ciq data item 1249

treasury_stock_preferred_stock_non_redeemable(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) → DataFrame: ciq data item 1250

treasury_stock_preferred_stock_redeemable(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) → DataFrame: ciq data item 1251

alias for treasury_stock_preferred_stock_redeemable

ciq data item 1251

property type: str

Get the type of company

Returns:: The company type
Return type:: str

union()

Return the union of sets as a new set.

(i.e. all elements that are in either set.)

alias for total_unusual_items

ciq data item 19

update(): Update a set with the union of itself and others.

validate_inputs(start_year: int | None = None, end_year: int | None = None, start_quarter: int | None = None, end_quarter: int | None = None) → None: Test the time inputs for validity.

property vendor: BusinessRelationships: Returns the associated company’s current and previous vendors

property webpage: str

Get the webpage for the company

Returns:: webpage for the company
Return type:: str

weighted_average_basic_shares_outstanding(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) → DataFrame: ciq data item 3217

weighted_average_diluted_shares_outstanding(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) → DataFrame: ciq data item 342

working_capital(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) → DataFrame: ciq data item 4165

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 Company(kfinance_api_client: KFinanceApiClient, company_id: int)[source]

Company class

Parameters:

kfinance_api_client (KFinanceApiClient) – The KFinanceApiClient used to fetch data
company_id (int) – The S&P Global CIQ Company Id

accounts_payable(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) → DataFrame: ciq data item 1018

accounts_receivable(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) → DataFrame: ciq data item 1021

accrued_expenses(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) → DataFrame: ciq data item 1016

accumulated_depreciation(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) → DataFrame: ciq data item 1075

additional_paid_in_capital(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) → DataFrame: ciq data item 1084

alias for preferred_stock_additional_paid_in_capital

ciq data item 1085

property address: str

Get the address of the company’s HQ

Returns:: address of the company’s HQ
Return type:: str

adjustments_to_cash_flow_net_income(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) → DataFrame: ciq data item 21523

property all_earnings: list[Earnings]: Retrieve and cache all earnings items for this company

amortization_of_goodwill_and_intangibles(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) → DataFrame: ciq data item 31

asset_writedown(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) → DataFrame: ciq data item 32

alias for total_assets

ciq data item 1007

balance_sheet(period_type: PeriodType | None = None, start_year: int | None = None, end_year: int | None = None, start_quarter: int | None = None, end_quarter: int | None = None) → DataFrame: The templated balance sheet

alias for basic_eps

ciq data item 9

alias for basic_eps_excluding_extra_items

ciq data item 3064

alias for basic_eps_from_accounting_change

ciq data item 145

alias for basic_eps_from_accounting_change_and_extraordinary_items

ciq data item 45

alias for basic_eps_from_extraordinary_items

ciq data item 146

alias for basic_eps

ciq data item 9

basic_eps(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) → DataFrame: ciq data item 9

basic_eps_excluding_extra_items(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) → DataFrame: ciq data item 3064

basic_eps_from_accounting_change(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) → DataFrame: ciq data item 145

basic_eps_from_accounting_change_and_extraordinary_items(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) → DataFrame: ciq data item 45

basic_eps_from_extraordinary_items(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) → DataFrame: ciq data item 146

alias for basic_eps

ciq data item 9

property borrower: BusinessRelationships: Returns the associated company’s current and previous borrowers

Retrieves the templated line of business segments for a given period_type, start_year, start_quarter, end_year and end_quarter.

Parameters:

period_type – The period_type requested for. Can be “annual”, “quarterly”, “ytd”. Defaults to “annual” when start_quarter and end_quarter are None.
start_year (int, optional) – The starting calendar year, defaults to None
end_year (int, optional) – The ending calendar year, defaults to None
start_quarter (int, optional) – The starting calendar quarter, defaults to None
end_quarter (int, optional) – The ending calendar quarter, defaults to None

Returns:

A dictionary containing the templated line of business segments for each time period, segment name, line item, and value.

Return type:

dict

alias for capital_expenditure

ciq data item 2021

capital_expenditure(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) → DataFrame: ciq data item 2021

alias for capital_expenditure

ciq data item 2021

capital_leases(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) → DataFrame: ciq data item 1183

alias for capital_leases

ciq data item 1183

alias for cash_and_equivalents

ciq data item 1096

cash_acquisitions(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) → DataFrame: ciq data item 2057

alias for cash_and_equivalents

ciq data item 1096

cash_and_equivalents(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) → DataFrame: ciq data item 1096

alias for total_cash_and_short_term_investments

ciq data item 1002

cash_flow(period_type: PeriodType | None = None, start_year: int | None = None, end_year: int | None = None, start_quarter: int | None = None, end_quarter: int | None = None) → DataFrame: The templated cash flow statement

alias for cash_from_operations

ciq data item 2006

alias for net_cash_from_discontinued_operation

ciq data item 2081

cash_from_financing(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) → DataFrame: ciq data item 2004

alias for cash_from_financing

ciq data item 2004

cash_from_investing(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) → DataFrame: ciq data item 2005

alias for cash_from_investing

ciq data item 2005

alias for cash_from_operations

ciq data item 2006

cash_from_operations(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) → DataFrame: ciq data item 2006

cashflow(period_type: PeriodType | None = None, start_year: int | None = None, end_year: int | None = None, start_quarter: int | None = None, end_quarter: int | None = None) → DataFrame: The templated cash flow statement

alias for cash_from_financing

ciq data item 2004

alias for cash_from_financing

ciq data item 2004

alias for cash_from_investing

ciq data item 2005

alias for cash_from_investing

ciq data item 2005

change_in_accounts_payable(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) → DataFrame: ciq data item 2017

change_in_accounts_receivable(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) → DataFrame: ciq data item 2018

alias for net_change_in_cash

ciq data item 2093

change_in_deferred_taxes(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) → DataFrame: ciq data item 2084

change_in_income_taxes(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) → DataFrame: ciq data item 2101

change_in_inventories(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) → DataFrame: ciq data item 2099

change_in_net_operating_assets(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) → DataFrame: ciq data item 2010

change_in_net_working_capital(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) → DataFrame: ciq data item 4421

change_in_other_net_operating_assets(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) → DataFrame: ciq data item 2045

change_in_trading_asset_securities(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) → DataFrame: ciq data item 2134

change_in_unearned_revenue(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) → DataFrame: ciq data item 2139

property city: str

Get the city of the company’s HQ

Returns:: city of the company’s HQ
Return type:: str

property client_services: BusinessRelationships: Returns the associated company’s current and previous client_servicess

alias for cost_of_goods_sold

ciq data item 34

common_dividends_paid(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) → DataFrame: ciq data item 2074

alias for total_common_equity

ciq data item 1006

common_shares_outstanding(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) → DataFrame: ciq data item 1100

common_stock(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) → DataFrame: ciq data item 1103

property company_id: Any: Set and return the company id for the object

alias for earnings_from_continued_operations

ciq data item 7

alias for preferred_stock_convertible

ciq data item 1214

alias for cost_of_revenue

ciq data item 1

cost_of_goods_sold(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) → DataFrame: ciq data item 34

cost_of_revenue(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) → DataFrame: ciq data item 1

property country: str

Get the country of the company’s HQ

Returns:: country of the company’s HQ
Return type:: str

property creditor: BusinessRelationships: Returns the associated company’s current and previous creditors

currency_exchange_gains(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) → DataFrame: ciq data item 38

alias for accounts_receivable

ciq data item 1021

alias for total_current_assets

ciq data item 1008

alias for short_term_borrowings

ciq data item 1046

alias for short_term_borrowings

ciq data item 1046

alias for short_term_debt_issued

ciq data item 2043

alias for short_term_debt_repaid

ciq data item 2044

alias for deferred_tax_asset_current_portion

ciq data item 1117

current_deferred_tax_liability(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) → DataFrame: ciq data item 1119

current_income_taxes_payable(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) → DataFrame: ciq data item 1094

alias for total_current_liabilities

ciq data item 1009

alias for notes_receivable

ciq data item 1048

alias for other_receivables

ciq data item 1206

alias for current_portion_of_capital_leases

ciq data item 1090

current_portion_of_capital_leases(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) → DataFrame: ciq data item 1090

alias for current_portion_of_capital_leases

ciq data item 1090

alias for current_income_taxes_payable

ciq data item 1094

alias for current_portion_of_capital_leases

ciq data item 1090

current_portion_of_long_term_debt(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) → DataFrame: ciq data item 1297

current_portion_of_long_term_debt_and_capital_leases(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) → DataFrame: ciq data item 1279

alias for current_portion_of_long_term_debt_and_capital_leases

ciq data item 1279

alias for current_portion_of_long_term_debt

ciq data item 1297

alias for current_portion_of_long_term_debt_and_capital_leases

ciq data item 1279

alias for current_portion_of_long_term_debt

ciq data item 1297

alias for current_portion_of_long_term_debt_and_capital_leases

ciq data item 1279

alias for current_portion_of_long_term_debt_and_capital_leases

ciq data item 1279

alias for current_unearned_revenue

ciq data item 1074

current_ratio(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) → DataFrame: ciq data item 4030

alias for total_receivables

ciq data item 1001

alias for total_receivables

ciq data item 1001

current_unearned_revenue(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) → DataFrame: ciq data item 1074

property customer: BusinessRelationships: Returns the associated company’s current and previous customers

alias for depreciation_and_amortization

ciq data item 41

alias for total_debt_to_equity_ratio

ciq data item 4034

deferred_tax_asset_current_portion(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) → DataFrame: ciq data item 1117

depreciation(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) → DataFrame: ciq data item 2143

depreciation_and_amortization(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) → DataFrame: ciq data item 41

depreciation_of_rental_assets(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) → DataFrame: ciq data item 42409

alias for diluted_eps

ciq data item 8

alias for diluted_eps_excluding_extra_items

ciq data item 142

alias for diluted_eps_from_accounting_change

ciq data item 141

alias for diluted_eps_from_accounting_change_and_extraordinary_items

ciq data item 44

alias for diluted_eps_from_discontinued_operations

ciq data item 143

alias for diluted_eps_from_extraordinary_items

ciq data item 144

alias for diluted_eps

ciq data item 8

diluted_eps(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) → DataFrame: ciq data item 8

diluted_eps_excluding_extra_items(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) → DataFrame: ciq data item 142

diluted_eps_from_accounting_change(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) → DataFrame: ciq data item 141

diluted_eps_from_accounting_change_and_extraordinary_items(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) → DataFrame: ciq data item 44

diluted_eps_from_discontinued_operations(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) → DataFrame: ciq data item 143

diluted_eps_from_extraordinary_items(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) → DataFrame: ciq data item 144

alias for diluted_eps

ciq data item 8

alias for earnings_from_discontinued_operations

ciq data item 40

distributable_cash_per_share(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) → DataFrame: ciq data item 23317

property distributor: BusinessRelationships: Returns the associated company’s current and previous distributors

divestitures(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) → DataFrame: ciq data item 2077

alias for total_dividends_paid

ciq data item 2022

dividends_per_share(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) → DataFrame: ciq data item 3058

alias for depreciation_and_amortization

ciq data item 41

earnings(start_date: date | None = None, end_date: date | None = None) → list[Earnings][source]

Get earnings for the company within date range sorted in descending order by date

Parameters:

start_date (date, optional) – Start date filter, defaults to None
end_date (date, optional) – End date filter, defaults to None

Returns:

List of earnings objects

Return type:

list[Earnings]

alias for ebit

ciq data item 400

alias for ebita

ciq data item 100689

alias for ebitdar

ciq data item 21674

alias for ebitda

ciq data item 4051

alias for ebt_excluding_unusual_items

ciq data item 4

alias for ebt_including_unusual_items

ciq data item 139

property earnings_call_datetimes: list[datetime]

Get the datetimes of the companies earnings calls

Returns:: a list of datetimes for the companies earnings calls
Return type:: list[datetime]

earnings_from_continued_operations(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) → DataFrame: ciq data item 7

earnings_from_discontinued_operations(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) → DataFrame: ciq data item 40

ebit(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) → DataFrame: ciq data item 400

ebita(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) → DataFrame: ciq data item 100689

ebitda(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) → DataFrame: ciq data item 4051

ebitdar(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) → DataFrame: ciq data item 21674

ebt_excluding_unusual_items(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) → DataFrame: ciq data item 4

ebt_including_unusual_items(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) → DataFrame: ciq data item 139

effective_tax_rate(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) → DataFrame: ciq data item 4376

alias for total_equity

ciq data item 1275

alias for preferred_stock_equity_adjustment

ciq data item 1215

exploration_and_drilling_costs(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) → DataFrame: ciq data item 49

alias for exploration_and_drilling_costs

ciq data item 49

extraordinary_item_and_accounting_change(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) → DataFrame: ciq data item 42

fees_and_other_income(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) → DataFrame: ciq data item 168

alias for funds_from_operations

ciq data item 3074

finance_division_debt_current_portion(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) → DataFrame: ciq data item 1030

alias for finance_division_debt_non_current_portion

ciq data item 1035

finance_division_debt_non_current_portion(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) → DataFrame: ciq data item 1035

finance_division_interest_expense(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) → DataFrame: ciq data item 50

finance_division_loans_and_leases_long_term(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) → DataFrame: ciq data item 1033

finance_division_loans_and_leases_short_term(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) → DataFrame: ciq data item 1032

alias for finance_division_debt_non_current_portion

ciq data item 1035

alias for finance_division_loans_and_leases_long_term

ciq data item 1033

alias for finance_division_debt_non_current_portion

ciq data item 1035

finance_division_operating_expense(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) → DataFrame: ciq data item 51

finance_division_other_current_assets(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) → DataFrame: ciq data item 1029

finance_division_other_current_liabilities(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) → DataFrame: ciq data item 1031

alias for finance_division_other_non_current_assets

ciq data item 1034

alias for finance_division_other_non_current_liabilities

ciq data item 1036

finance_division_other_non_current_assets(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) → DataFrame: ciq data item 1034

finance_division_other_non_current_liabilities(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) → DataFrame: ciq data item 1036

alias for finance_division_other_current_assets

ciq data item 1029

finance_division_revenue(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) → DataFrame: ciq data item 52

alias for finance_division_loans_and_leases_short_term

ciq data item 1032

alias for foreign_exchange_rate_adjustments

ciq data item 2144

foreign_exchange_rate_adjustments(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) → DataFrame: ciq data item 2144

property founding_date: date

Get the founding date for the company

Returns:: founding date for the company
Return type:: date

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: str | None = None, start_year: int | None = None, end_year: int | None = None, start_quarter: int | None = None, end_quarter: int | None = None) → DataFrame: ciq data item 3074

alias for foreign_exchange_rate_adjustments

ciq data item 2144

gain_from_sale_of_assets(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) → DataFrame: ciq data item 62

gain_from_sale_of_investments(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) → DataFrame: ciq data item 56

Retrieves the templated geographic segments for a given period_type, start_year, start_quarter, end_year and end_quarter.

Parameters:

period_type – The period_type requested for. Can be “annual”, “quarterly”, “ytd”. Defaults to “annual” when start_quarter and end_quarter are None.
start_year (int, optional) – The starting calendar year, defaults to None
end_year (int, optional) – The ending calendar year, defaults to None
start_quarter (int, optional) – The starting calendar quarter, defaults to None
end_quarter (int, optional) – The ending calendar quarter, defaults to None

Returns:

A dictionary containing the templated geographic segments for each time period, segment name, line item, and value.

Return type:

dict

goodwill(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) → DataFrame: ciq data item 1171

alias for gross_property_plant_and_equipment

ciq data item 1169

alias for gross_property_plant_and_equipment

ciq data item 1169

gross_profit(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) → DataFrame: ciq data item 10

gross_property_plant_and_equipment(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) → DataFrame: ciq data item 1169

alias for impairment_of_oil_gas_and_mineral_properties

ciq data item 71

impairment_of_goodwill(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) → DataFrame: ciq data item 209

alias for impairment_of_oil_gas_and_mineral_properties

ciq data item 71

impairment_of_oil_gas_and_mineral_properties(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) → DataFrame: ciq data item 71

alias for in_process_research_and_development_expense

ciq data item 72

alias for in_process_research_and_development_expense

ciq data item 72

alias for in_process_research_and_development_expense

ciq data item 72

in_process_research_and_development_expense(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) → DataFrame: ciq data item 72

alias for in_process_research_and_development_expense

ciq data item 72

alias for in_process_research_and_development_expense

ciq data item 72

income_from_affiliates(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) → DataFrame: ciq data item 47

income_statement(period_type: PeriodType | None = None, start_year: int | None = None, end_year: int | None = None, start_quarter: int | None = None, end_quarter: int | None = None) → DataFrame: The templated income statement

income_stmt(period_type: PeriodType | None = None, start_year: int | None = None, end_year: int | None = None, start_quarter: int | None = None, end_quarter: int | None = None) → DataFrame: The templated income statement

alias for income_tax_expense

ciq data item 75

income_tax_expense(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) → DataFrame: ciq data item 75

alias for income_tax_expense

ciq data item 75

property info: dict

Get the company info

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

insurance_division_operating_expense(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) → DataFrame: ciq data item 69

insurance_division_revenue(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) → DataFrame: ciq data item 70

insurance_settlements(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) → DataFrame: ciq data item 73

interest_and_investment_income(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) → DataFrame: ciq data item 65

interest_expense(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) → DataFrame: ciq data item 82

alias for finance_division_interest_expense

ciq data item 50

alias for inventory

ciq data item 1043

inventory(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) → 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

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

issuance_of_common_stock(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) → DataFrame: ciq data item 2169

issuance_of_preferred_stock(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) → DataFrame: ciq data item 2181

kfinance_api_client: KFinanceApiClient

property landlord: BusinessRelationships: Returns the associated company’s current and previous landlords

property latest_earnings: Earnings | None

Get the most recent past earnings

Returns:: The most recent earnings or None if no data available
Return type:: Earnings | None

legal_settlements(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) → 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

alias for total_liabilities

ciq data item 1276

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: PeriodType | None = None, start_year: int | None = None, end_year: int | None = None, start_quarter: int | None = None, end_quarter: int | None = None) → DataFrame: Get a DataFrame of a financial line item according to the date ranges.

loans_held_for_sale(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) → DataFrame: ciq data item 1185

alias for long_term_loans_receivable

ciq data item 1050

long_term_accounts_receivable(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) → DataFrame: ciq data item 1088

long_term_debt(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) → DataFrame: ciq data item 1049

long_term_debt_issued(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) → DataFrame: ciq data item 2034

long_term_debt_repaid(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) → DataFrame: ciq data item 2036

long_term_deferred_charges(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) → DataFrame: ciq data item 1025

long_term_deferred_tax_assets(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) → DataFrame: ciq data item 1026

alias for finance_division_loans_and_leases_long_term

ciq data item 1033

long_term_investments(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) → DataFrame: ciq data item 1054

alias for capital_leases

ciq data item 1183

alias for finance_division_loans_and_leases_long_term

ciq data item 1033

long_term_loans_receivable(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) → DataFrame: ciq data item 1050

alias for other_long_term_assets

ciq data item 1060

alias for other_non_current_liabilities

ciq data item 1062

alias for non_current_unearned_revenue

ciq data item 1256

loss_on_equity_investments(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) → DataFrame: ciq data item 2086

market_cap(start_date: str | None = None, end_date: str | None = None) → DataFrame

Retrieves market caps for a company between start and end date.

Parameters:

start_date (str, optional) – The start date in format “YYYY-MM-DD”, default to None
end_date (str, optional) – The end date in format “YYYY-MM-DD”, default to None

Returns:

A DataFrame with a market_cap column. The dates are the index.

Return type:

pd.DataFrame

merger_and_restructuring_charges(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) → DataFrame: ciq data item 363

merger_charges(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) → DataFrame: ciq data item 80

minority_interest_in_earnings(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) → DataFrame: ciq data item 83

alias for miscellaneous_cash_flow_adjustments

ciq data item 2149

miscellaneous_cash_flow_adjustments(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) → DataFrame: ciq data item 2149

property name: str

Get the company name

Returns:: The company name
Return type:: str

net_cash_from_discontinued_operation(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) → DataFrame: ciq data item 2081

net_cash_from_investments(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) → DataFrame: ciq data item 2027

net_change_in_cash(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) → DataFrame: ciq data item 2093

net_debt(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) → DataFrame: ciq data item 4364

net_decrease_in_investment_loans_originated_and_sold(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) → DataFrame: ciq data item 2032

net_decrease_in_loans_originated_and_sold(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) → DataFrame: ciq data item 2033

net_income(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) → DataFrame: ciq data item 15

net_income_allocable_to_general_partner(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) → DataFrame: ciq data item 249

net_income_to_common_shareholders_excluding_extra_items(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) → DataFrame: ciq data item 379

net_income_to_common_shareholders_including_extra_items(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) → DataFrame: ciq data item 16

net_income_to_company(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) → DataFrame: ciq data item 41571

alias for minority_interest_in_earnings

ciq data item 83

net_interest_expense(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) → DataFrame: ciq data item 368

alias for net_property_plant_and_equipment

ciq data item 1004

net_property_plant_and_equipment(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) → DataFrame: ciq data item 1004

net_working_capital(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) → DataFrame: ciq data item 1311

property next_earnings: Earnings | None

Get the next upcoming earnings

Returns:: The next earnings or None if no data available
Return type:: Earnings | None

alias for long_term_accounts_receivable

ciq data item 1088

alias for long_term_debt

ciq data item 1049

alias for long_term_debt_issued

ciq data item 2034

alias for long_term_debt_repaid

ciq data item 2036

alias for long_term_deferred_charges

ciq data item 1025

alias for long_term_deferred_tax_assets

ciq data item 1026

non_current_deferred_tax_liability(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) → DataFrame: ciq data item 1027

alias for long_term_investments

ciq data item 1054

alias for long_term_loans_receivable

ciq data item 1050

alias for other_long_term_assets

ciq data item 1060

alias for other_non_current_liabilities

ciq data item 1062

non_current_unearned_revenue(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) → DataFrame: ciq data item 1256

alias for preferred_stock_non_redeemable

ciq data item 1216

alias for revenue

ciq data item 112

alias for normalized_basic_eps

ciq data item 4379

normalized_basic_eps(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) → DataFrame: ciq data item 4379

alias for normalized_diluted_eps

ciq data item 4380

normalized_diluted_eps(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) → DataFrame: ciq data item 4380

notes_receivable(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) → DataFrame: ciq data item 1048

alias for net_property_plant_and_equipment

ciq data item 1004

property number_of_employees: str | None

Get the number of employees the company has (if available)

Returns:: how many employees the company has
Return type:: str | None

alias for total_operating_expense

ciq data item 373

alias for finance_division_operating_expense

ciq data item 51

alias for insurance_division_operating_expense

ciq data item 69

operating_income(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) → DataFrame: ciq data item 21

other_adjustments_to_net_income(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) → DataFrame: ciq data item 259

other_amortization(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) → DataFrame: ciq data item 2014

other_current_assets(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) → DataFrame: ciq data item 1055

alias for finance_division_other_current_assets

ciq data item 1029

alias for other_current_liability

ciq data item 1057

other_current_liability(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) → DataFrame: ciq data item 1057

other_equity(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) → DataFrame: ciq data item 1028

other_financing_activities(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) → DataFrame: ciq data item 2050

other_intangibles(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) → DataFrame: ciq data item 1040

other_investing_activities(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) → DataFrame: ciq data item 2051

other_long_term_assets(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) → DataFrame: ciq data item 1060

alias for finance_division_other_non_current_assets

ciq data item 1034

alias for other_non_current_liabilities

ciq data item 1062

alias for other_long_term_assets

ciq data item 1060

alias for finance_division_other_non_current_assets

ciq data item 1034

other_non_current_liabilities(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) → DataFrame: ciq data item 1062

other_non_operating_income(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) → DataFrame: ciq data item 85

other_operating_activities(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) → DataFrame: ciq data item 2047

other_operating_expense(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) → DataFrame: ciq data item 260

alias for preferred_stock_other

ciq data item 1065

other_preferred_stock_adjustments(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) → DataFrame: ciq data item 281

other_receivables(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) → DataFrame: ciq data item 1206

other_revenue(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) → DataFrame: ciq data item 90

alias for finance_division_other_current_assets

ciq data item 1029

other_unusual_items(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) → DataFrame: ciq data item 87

pension_and_other_post_retirement_benefit(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) → DataFrame: ciq data item 1213

alias for net_property_plant_and_equipment

ciq data item 1004

pre_opening_costs(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) → DataFrame: ciq data item 96

alias for pre_opening_costs

ciq data item 96

preferred_dividends_and_other_adjustments(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) → DataFrame: ciq data item 97

preferred_dividends_paid(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) → DataFrame: ciq data item 2116

alias for total_preferred_equity

ciq data item 1005

alias for total_preferred_equity

ciq data item 1005

preferred_stock_additional_paid_in_capital(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) → DataFrame: ciq data item 1085

preferred_stock_convertible(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) → DataFrame: ciq data item 1214

preferred_stock_dividend(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) → DataFrame: ciq data item 280

preferred_stock_equity_adjustment(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) → DataFrame: ciq data item 1215

preferred_stock_non_redeemable(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) → DataFrame: ciq data item 1216

preferred_stock_other(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) → DataFrame: ciq data item 1065

preferred_stock_redeemable(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) → DataFrame: ciq data item 1217

premium_on_redemption_of_preferred_stock(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) → DataFrame: ciq data item 279

prepaid_expense(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) → DataFrame: ciq data item 1212

alias for prepaid_expense

ciq data item 1212

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

alias for net_property_plant_and_equipment

ciq data item 1004

alias for provision_for_bad_debts

ciq data item 95

provision_for_bad_debts(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) → DataFrame: ciq data item 95

provision_for_credit_losses(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) → DataFrame: ciq data item 2112

quick_ratio(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) → DataFrame: ciq data item 4121

alias for research_and_development_expense

ciq data item 100

alias for research_and_development_expense

ciq data item 100

alias for preferred_stock_redeemable

ciq data item 1217

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: str | None = None, start_year: int | None = None, end_year: int | None = None, start_quarter: int | None = None, end_quarter: int | None = None) → DataFrame: ciq data item 2164

repurchase_of_preferred_stock(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) → DataFrame: ciq data item 2172

alias for research_and_development_expense

ciq data item 100

research_and_development_expense(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) → DataFrame: ciq data item 100

restricted_cash(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) → DataFrame: ciq data item 1104

restructuring_charges(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) → DataFrame: ciq data item 98

retained_earnings(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) → DataFrame: ciq data item 1222

revenue(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) → DataFrame: ciq data item 112

revenue_from_interest_and_investment_income(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) → DataFrame: ciq data item 110

revenue_from_sale_of_assets(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) → DataFrame: ciq data item 104

revenue_from_sale_of_investments(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) → DataFrame: ciq data item 106

alias for research_and_development_expense

ciq data item 100

alias for research_and_development_expense

ciq data item 100

alias for sale_of_intangible_assets

ciq data item 2029

sale_of_intangible_assets(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) → DataFrame: ciq data item 2029

alias for sale_of_intangible_assets

ciq data item 2029

alias for sale_of_property_plant_and_equipment

ciq data item 2042

sale_of_property_plant_and_equipment(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) → DataFrame: ciq data item 2042

sale_of_real_estate(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) → DataFrame: ciq data item 2040

alias for sale_of_real_estate

ciq data item 2040

alias for sale_of_real_estate

ciq data item 2040

sale_proceeds_from_rental_assets(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) → DataFrame: ciq data item 42411

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

alias for selling_general_and_admin_expense

ciq data item 102

alias for selling_general_and_admin_expense

ciq data item 102

selling_general_and_admin_expense(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) → DataFrame: ciq data item 102

alias for selling_general_and_admin_expense

ciq data item 102

alias for selling_general_and_admin_expense

ciq data item 102

alias for total_equity

ciq data item 1275

shares_outstanding(start_date: str | None = None, end_date: str | None = None) → DataFrame

Retrieves shares outstanding for a company between start and end date.

Parameters:

start_date (str, optional) – The start date in format “YYYY-MM-DD”, default to None
end_date (str, optional) – The end date in format “YYYY-MM-DD”, default to None

Returns:

A DataFrame with a shares_outstanding column. The dates are the index.

Return type:

pd.DataFrame

alias for accounts_receivable

ciq data item 1021

alias for total_current_assets

ciq data item 1008

alias for short_term_borrowings

ciq data item 1046

short_term_borrowings(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) → DataFrame: ciq data item 1046

short_term_debt_issued(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) → DataFrame: ciq data item 2043

short_term_debt_repaid(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) → DataFrame: ciq data item 2044

alias for deferred_tax_asset_current_portion

ciq data item 1117

alias for finance_division_loans_and_leases_short_term

ciq data item 1032

short_term_investments(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) → DataFrame: ciq data item 1069

alias for finance_division_loans_and_leases_short_term

ciq data item 1032

alias for notes_receivable

ciq data item 1048

alias for other_receivables

ciq data item 1206

alias for total_receivables

ciq data item 1001

alias for total_receivables

ciq data item 1001

property simple_industry: str

Get the simple industry for the company

Returns:: The company’s simple_industry
Return type:: str

special_dividends_paid(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) → DataFrame: ciq data item 2041

property state: str

Get the state of the company’s HQ

Returns:: state of the company’s HQ
Return type:: str

statement(statement_type: str, period_type: PeriodType | None = None, start_year: int | None = None, end_year: int | None = None, start_quarter: int | None = None, end_quarter: int | None = None) → DataFrame: Get the company’s financial statement

property status: str

Get the company status

Returns:: The company status
Return type:: str

stock_based_compensation(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) → 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: str | None = None, start_year: int | None = None, end_year: int | None = None, start_quarter: int | None = None, end_quarter: int | None = None) → DataFrame: ciq data item 2135

alias for effective_tax_rate

ciq data item 4376

property tenant: BusinessRelationships: Returns the associated company’s current and previous tenants

tev(start_date: str | None = None, end_date: str | None = None) → DataFrame

Retrieves TEV (total enterprise value) for a company between start and end date.

Parameters:

start_date (str, optional) – The start date in format “YYYY-MM-DD”, default to None
end_date (str, optional) – The end date in format “YYYY-MM-DD”, default to None

Returns:

A DataFrame with a tev column. The dates are the index.

Return type:

pd.DataFrame

total_assets(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) → DataFrame: ciq data item 1007

total_cash_and_short_term_investments(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) → DataFrame: ciq data item 1002

total_common_equity(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) → DataFrame: ciq data item 1006

total_current_assets(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) → DataFrame: ciq data item 1008

total_current_liabilities(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) → DataFrame: ciq data item 1009

alias for current_portion_of_long_term_debt_and_capital_leases

ciq data item 1279

alias for current_portion_of_long_term_debt_and_capital_leases

ciq data item 1279

alias for current_portion_of_long_term_debt_and_capital_leases

ciq data item 1279

alias for current_portion_of_long_term_debt_and_capital_leases

ciq data item 1279

alias for current_portion_of_long_term_debt_and_capital_leases

ciq data item 1279

alias for total_depreciation_and_amortization

ciq data item 2

total_debt(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) → DataFrame: ciq data item 4173

total_debt_issued(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) → DataFrame: ciq data item 2161

alias for total_debt_to_equity_ratio

ciq data item 4034

total_debt_repaid(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) → DataFrame: ciq data item 2166

total_debt_to_capital(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) → DataFrame: ciq data item 43907

alias for total_debt_to_equity_ratio

ciq data item 4034

total_debt_to_equity_ratio(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) → DataFrame: ciq data item 4034

alias for total_debt_to_equity_ratio

ciq data item 4034

total_depreciation_and_amortization(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) → DataFrame: ciq data item 2

total_dividends_paid(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) → DataFrame: ciq data item 2022

alias for total_depreciation_and_amortization

ciq data item 2

total_equity(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) → DataFrame: ciq data item 1275

total_liabilities(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) → DataFrame: ciq data item 1276

total_liabilities_and_equity(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) → DataFrame: ciq data item 1013

total_operating_expense(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) → DataFrame: ciq data item 373

total_other_investing_activities(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) → DataFrame: ciq data item 2177

total_other_non_cash_items(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) → DataFrame: ciq data item 2179

total_other_non_operating_income(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) → DataFrame: ciq data item 371

total_other_operating_expense(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) → DataFrame: ciq data item 380

total_other_revenue(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) → DataFrame: ciq data item 357

total_other_unusual_items(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) → DataFrame: ciq data item 374

total_preferred_equity(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) → DataFrame: ciq data item 1005

alias for total_preferred_equity

ciq data item 1005

alias for total_receivables

ciq data item 1001

total_receivables(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) → DataFrame: ciq data item 1001

total_revenue(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) → DataFrame: ciq data item 28

alias for total_selling_general_and_admin_expense

ciq data item 23

alias for total_selling_general_and_admin_expense

ciq data item 23

total_selling_general_and_admin_expense(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) → DataFrame: ciq data item 23

alias for total_selling_general_and_admin_expense

ciq data item 23

alias for total_equity

ciq data item 1275

alias for total_current_assets

ciq data item 1008

total_unusual_items(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) → DataFrame: ciq data item 19

trading_asset_securities(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) → 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

alias for treasury_stock_preferred_stock_convertible

ciq data item 1249

alias for treasury_stock_preferred_stock_non_redeemable

ciq data item 1250

alias for treasury_stock_preferred_stock_convertible

ciq data item 1249

alias for treasury_stock_preferred_stock_non_redeemable

ciq data item 1250

alias for treasury_stock_preferred_stock_redeemable

ciq data item 1251

alias for treasury_stock_preferred_stock_redeemable

ciq data item 1251

treasury_stock(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) → DataFrame: ciq data item 1248

alias for treasury_stock_preferred_stock_convertible

ciq data item 1249

alias for treasury_stock_preferred_stock_non_redeemable

ciq data item 1250

treasury_stock_preferred_stock_convertible(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) → DataFrame: ciq data item 1249

treasury_stock_preferred_stock_non_redeemable(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) → DataFrame: ciq data item 1250

treasury_stock_preferred_stock_redeemable(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) → DataFrame: ciq data item 1251

alias for treasury_stock_preferred_stock_redeemable

ciq data item 1251

property type: str

Get the type of company

Returns:: The company type
Return type:: str

alias for total_unusual_items

ciq data item 19

validate_inputs(start_year: int | None = None, end_year: int | None = None, start_quarter: int | None = None, end_quarter: int | None = None) → None: Test the time inputs for validity.

property vendor: BusinessRelationships: Returns the associated company’s current and previous vendors

property webpage: str

Get the webpage for the company

Returns:: webpage for the company
Return type:: str

weighted_average_basic_shares_outstanding(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) → DataFrame: ciq data item 3217

weighted_average_diluted_shares_outstanding(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) → DataFrame: ciq data item 342

working_capital(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) → DataFrame: ciq data item 4165

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 Earnings(kfinance_api_client: KFinanceApiClient, name: str, datetime: datetime, key_dev_id: int)[source]

Earnings class that represents an earnings item

property transcript: Transcript

Get the transcript for this earnings

Returns:: The transcript object containing all components
Return type:: Transcript

exception NoEarningsDataError[source]

Exception raised when no earnings data is found for a company.

args

with_traceback(): Exception.with_traceback(tb) – set self.__traceback__ to tb and return self.

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

Base class for representing a set of Securities

add()

Add an element to a set.

This has no effect if the element is already present.

clear(): Remove all elements from this set.

copy(): Return a shallow copy of a set.

property cusip: str

Get the CUSIP for the object

Returns:: The CUSIP
Return type:: str

difference()

Return the difference of two or more sets as a new set.

(i.e. all elements that are in this set but not the others.)

difference_update(): Remove all elements of another set from this set.

discard()

Remove an element from a set if it is a member.

If the element is not a member, do nothing.

intersection()

Return the intersection of two sets as a new set.

(i.e. all elements that are in both sets.)

intersection_update(): Update a set with the intersection of itself and another.

isdisjoint(): Return True if two sets have a null intersection.

property isin: str

Get the ISIN for the object

Returns:: The ISIN
Return type:: str

issubset(): Report whether another set contains this set.

issuperset(): Report whether this set contains another set.

pop(): Remove and return an arbitrary set element. Raises KeyError if the set is empty.

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

remove()

Remove an element from a set; it must be a member.

If the element is not a member, raise a KeyError.

symmetric_difference()

Return the symmetric difference of two sets as a new set.

(i.e. all elements that are in exactly one of the sets.)

symmetric_difference_update(): Update a set with the symmetric difference of itself and another.

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

union()

Return the union of sets as a new set.

(i.e. all elements that are in either set.)

update(): Update a set with the union of itself and others.

class Security(kfinance_api_client: KFinanceApiClient, security_id: int)[source]

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

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

accounts_payable(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) → DataFrame: ciq data item 1018

accounts_receivable(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) → DataFrame: ciq data item 1021

accrued_expenses(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) → DataFrame: ciq data item 1016

accumulated_depreciation(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) → DataFrame: ciq data item 1075

additional_paid_in_capital(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) → DataFrame: ciq data item 1084

alias for preferred_stock_additional_paid_in_capital

ciq data item 1085

property address: str

Get the address of the company’s HQ

Returns:: address of the company’s HQ
Return type:: str

adjustments_to_cash_flow_net_income(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) → DataFrame: ciq data item 21523

amortization_of_goodwill_and_intangibles(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) → DataFrame: ciq data item 31

asset_writedown(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) → DataFrame: ciq data item 32

alias for total_assets

ciq data item 1007

balance_sheet(period_type: PeriodType | None = None, start_year: int | None = None, end_year: int | None = None, start_quarter: int | None = None, end_quarter: int | None = None) → DataFrame: The templated balance sheet

alias for basic_eps

ciq data item 9

alias for basic_eps_excluding_extra_items

ciq data item 3064

alias for basic_eps_from_accounting_change

ciq data item 145

alias for basic_eps_from_accounting_change_and_extraordinary_items

ciq data item 45

alias for basic_eps_from_extraordinary_items

ciq data item 146

alias for basic_eps

ciq data item 9

basic_eps(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) → DataFrame: ciq data item 9

basic_eps_excluding_extra_items(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) → DataFrame: ciq data item 3064

basic_eps_from_accounting_change(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) → DataFrame: ciq data item 145

basic_eps_from_accounting_change_and_extraordinary_items(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) → DataFrame: ciq data item 45

basic_eps_from_extraordinary_items(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) → DataFrame: ciq data item 146

alias for basic_eps

ciq data item 9

property borrower: BusinessRelationships: Returns the associated company’s current and previous borrowers

Retrieves the templated line of business segments for a given period_type, start_year, start_quarter, end_year and end_quarter.

Parameters:

period_type – The period_type requested for. Can be “annual”, “quarterly”, “ytd”. Defaults to “annual” when start_quarter and end_quarter are None.
start_year (int, optional) – The starting calendar year, defaults to None
end_year (int, optional) – The ending calendar year, defaults to None
start_quarter (int, optional) – The starting calendar quarter, defaults to None
end_quarter (int, optional) – The ending calendar quarter, defaults to None

Returns:

A dictionary containing the templated line of business segments for each time period, segment name, line item, and value.

Return type:

dict

alias for capital_expenditure

ciq data item 2021

capital_expenditure(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) → DataFrame: ciq data item 2021

alias for capital_expenditure

ciq data item 2021

capital_leases(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) → DataFrame: ciq data item 1183

alias for capital_leases

ciq data item 1183

alias for cash_and_equivalents

ciq data item 1096

cash_acquisitions(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) → DataFrame: ciq data item 2057

alias for cash_and_equivalents

ciq data item 1096

cash_and_equivalents(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) → DataFrame: ciq data item 1096

alias for total_cash_and_short_term_investments

ciq data item 1002

cash_flow(period_type: PeriodType | None = None, start_year: int | None = None, end_year: int | None = None, start_quarter: int | None = None, end_quarter: int | None = None) → DataFrame: The templated cash flow statement

alias for cash_from_operations

ciq data item 2006

alias for net_cash_from_discontinued_operation

ciq data item 2081

cash_from_financing(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) → DataFrame: ciq data item 2004

alias for cash_from_financing

ciq data item 2004

cash_from_investing(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) → DataFrame: ciq data item 2005

alias for cash_from_investing

ciq data item 2005

alias for cash_from_operations

ciq data item 2006

cash_from_operations(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) → DataFrame: ciq data item 2006

cashflow(period_type: PeriodType | None = None, start_year: int | None = None, end_year: int | None = None, start_quarter: int | None = None, end_quarter: int | None = None) → DataFrame: The templated cash flow statement

alias for cash_from_financing

ciq data item 2004

alias for cash_from_financing

ciq data item 2004

alias for cash_from_investing

ciq data item 2005

alias for cash_from_investing

ciq data item 2005

change_in_accounts_payable(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) → DataFrame: ciq data item 2017

change_in_accounts_receivable(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) → DataFrame: ciq data item 2018

alias for net_change_in_cash

ciq data item 2093

change_in_deferred_taxes(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) → DataFrame: ciq data item 2084

change_in_income_taxes(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) → DataFrame: ciq data item 2101

change_in_inventories(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) → DataFrame: ciq data item 2099

change_in_net_operating_assets(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) → DataFrame: ciq data item 2010

change_in_net_working_capital(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) → DataFrame: ciq data item 4421

change_in_other_net_operating_assets(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) → DataFrame: ciq data item 2045

change_in_trading_asset_securities(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) → DataFrame: ciq data item 2134

change_in_unearned_revenue(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) → DataFrame: ciq data item 2139

property city: str

Get the city of the company’s HQ

Returns:: city of the company’s HQ
Return type:: str

property client_services: BusinessRelationships: Returns the associated company’s current and previous client_servicess

alias for cost_of_goods_sold

ciq data item 34

common_dividends_paid(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) → DataFrame: ciq data item 2074

alias for total_common_equity

ciq data item 1006

common_shares_outstanding(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) → DataFrame: ciq data item 1100

common_stock(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) → DataFrame: ciq data item 1103

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

alias for earnings_from_continued_operations

ciq data item 7

alias for preferred_stock_convertible

ciq data item 1214

alias for cost_of_revenue

ciq data item 1

cost_of_goods_sold(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) → DataFrame: ciq data item 34

cost_of_revenue(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) → DataFrame: ciq data item 1

property country: str

Get the country of the company’s HQ

Returns:: country of the company’s HQ
Return type:: str

property creditor: BusinessRelationships: Returns the associated company’s current and previous creditors

currency_exchange_gains(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) → DataFrame: ciq data item 38

alias for accounts_receivable

ciq data item 1021

alias for total_current_assets

ciq data item 1008

alias for short_term_borrowings

ciq data item 1046

alias for short_term_borrowings

ciq data item 1046

alias for short_term_debt_issued

ciq data item 2043

alias for short_term_debt_repaid

ciq data item 2044

alias for deferred_tax_asset_current_portion

ciq data item 1117

current_deferred_tax_liability(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) → DataFrame: ciq data item 1119

current_income_taxes_payable(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) → DataFrame: ciq data item 1094

alias for total_current_liabilities

ciq data item 1009

alias for notes_receivable

ciq data item 1048

alias for other_receivables

ciq data item 1206

alias for current_portion_of_capital_leases

ciq data item 1090

current_portion_of_capital_leases(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) → DataFrame: ciq data item 1090

alias for current_portion_of_capital_leases

ciq data item 1090

alias for current_income_taxes_payable

ciq data item 1094

alias for current_portion_of_capital_leases

ciq data item 1090

current_portion_of_long_term_debt(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) → DataFrame: ciq data item 1297

current_portion_of_long_term_debt_and_capital_leases(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) → DataFrame: ciq data item 1279

alias for current_portion_of_long_term_debt_and_capital_leases

ciq data item 1279

alias for current_portion_of_long_term_debt

ciq data item 1297

alias for current_portion_of_long_term_debt_and_capital_leases

ciq data item 1279

alias for current_portion_of_long_term_debt

ciq data item 1297

alias for current_portion_of_long_term_debt_and_capital_leases

ciq data item 1279

alias for current_portion_of_long_term_debt_and_capital_leases

ciq data item 1279

alias for current_unearned_revenue

ciq data item 1074

current_ratio(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) → DataFrame: ciq data item 4030

alias for total_receivables

ciq data item 1001

alias for total_receivables

ciq data item 1001

current_unearned_revenue(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) → DataFrame: ciq data item 1074

property cusip: str

Get the CUSIP for the object

Returns:: The CUSIP
Return type:: str

property customer: BusinessRelationships: Returns the associated company’s current and previous customers

alias for depreciation_and_amortization

ciq data item 41

alias for total_debt_to_equity_ratio

ciq data item 4034

deferred_tax_asset_current_portion(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) → DataFrame: ciq data item 1117

depreciation(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) → DataFrame: ciq data item 2143

depreciation_and_amortization(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) → DataFrame: ciq data item 41

depreciation_of_rental_assets(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) → DataFrame: ciq data item 42409

alias for diluted_eps

ciq data item 8

alias for diluted_eps_excluding_extra_items

ciq data item 142

alias for diluted_eps_from_accounting_change

ciq data item 141

alias for diluted_eps_from_accounting_change_and_extraordinary_items

ciq data item 44

alias for diluted_eps_from_discontinued_operations

ciq data item 143

alias for diluted_eps_from_extraordinary_items

ciq data item 144

alias for diluted_eps

ciq data item 8

diluted_eps(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) → DataFrame: ciq data item 8

diluted_eps_excluding_extra_items(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) → DataFrame: ciq data item 142

diluted_eps_from_accounting_change(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) → DataFrame: ciq data item 141

diluted_eps_from_accounting_change_and_extraordinary_items(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) → DataFrame: ciq data item 44

diluted_eps_from_discontinued_operations(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) → DataFrame: ciq data item 143

diluted_eps_from_extraordinary_items(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) → DataFrame: ciq data item 144

alias for diluted_eps

ciq data item 8

alias for earnings_from_discontinued_operations

ciq data item 40

distributable_cash_per_share(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) → DataFrame: ciq data item 23317

property distributor: BusinessRelationships: Returns the associated company’s current and previous distributors

divestitures(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) → DataFrame: ciq data item 2077

alias for total_dividends_paid

ciq data item 2022

dividends_per_share(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) → DataFrame: ciq data item 3058

alias for depreciation_and_amortization

ciq data item 41

alias for ebit

ciq data item 400

alias for ebita

ciq data item 100689

alias for ebitdar

ciq data item 21674

alias for ebitda

ciq data item 4051

alias for ebt_excluding_unusual_items

ciq data item 4

alias for ebt_including_unusual_items

ciq data item 139

property earnings_call_datetimes: list[datetime]

Get the datetimes of the companies earnings calls

Returns:: a list of datetimes for the companies earnings calls
Return type:: list[datetime]

earnings_from_continued_operations(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) → DataFrame: ciq data item 7

earnings_from_discontinued_operations(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) → DataFrame: ciq data item 40

ebit(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) → DataFrame: ciq data item 400

ebita(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) → DataFrame: ciq data item 100689

ebitda(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) → DataFrame: ciq data item 4051

ebitdar(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) → DataFrame: ciq data item 21674

ebt_excluding_unusual_items(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) → DataFrame: ciq data item 4

ebt_including_unusual_items(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) → DataFrame: ciq data item 139

effective_tax_rate(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) → DataFrame: ciq data item 4376

alias for total_equity

ciq data item 1275

alias for preferred_stock_equity_adjustment

ciq data item 1215

exploration_and_drilling_costs(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) → DataFrame: ciq data item 49

alias for exploration_and_drilling_costs

ciq data item 49

extraordinary_item_and_accounting_change(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) → DataFrame: ciq data item 42

fees_and_other_income(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) → DataFrame: ciq data item 168

alias for funds_from_operations

ciq data item 3074

finance_division_debt_current_portion(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) → DataFrame: ciq data item 1030

alias for finance_division_debt_non_current_portion

ciq data item 1035

finance_division_debt_non_current_portion(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) → DataFrame: ciq data item 1035

finance_division_interest_expense(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) → DataFrame: ciq data item 50

finance_division_loans_and_leases_long_term(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) → DataFrame: ciq data item 1033

finance_division_loans_and_leases_short_term(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) → DataFrame: ciq data item 1032

alias for finance_division_debt_non_current_portion

ciq data item 1035

alias for finance_division_loans_and_leases_long_term

ciq data item 1033

alias for finance_division_debt_non_current_portion

ciq data item 1035

finance_division_operating_expense(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) → DataFrame: ciq data item 51

finance_division_other_current_assets(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) → DataFrame: ciq data item 1029

finance_division_other_current_liabilities(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) → DataFrame: ciq data item 1031

alias for finance_division_other_non_current_assets

ciq data item 1034

alias for finance_division_other_non_current_liabilities

ciq data item 1036

finance_division_other_non_current_assets(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) → DataFrame: ciq data item 1034

finance_division_other_non_current_liabilities(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) → DataFrame: ciq data item 1036

alias for finance_division_other_current_assets

ciq data item 1029

finance_division_revenue(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) → DataFrame: ciq data item 52

alias for finance_division_loans_and_leases_short_term

ciq data item 1032

alias for foreign_exchange_rate_adjustments

ciq data item 2144

foreign_exchange_rate_adjustments(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) → DataFrame: ciq data item 2144

property founding_date: date

Get the founding date for the company

Returns:: founding date for the company
Return type:: date

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: str | None = None, start_year: int | None = None, end_year: int | None = None, start_quarter: int | None = None, end_quarter: int | None = None) → DataFrame: ciq data item 3074

alias for foreign_exchange_rate_adjustments

ciq data item 2144

gain_from_sale_of_assets(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) → DataFrame: ciq data item 62

gain_from_sale_of_investments(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) → DataFrame: ciq data item 56

Retrieves the templated geographic segments for a given period_type, start_year, start_quarter, end_year and end_quarter.

Parameters:

period_type – The period_type requested for. Can be “annual”, “quarterly”, “ytd”. Defaults to “annual” when start_quarter and end_quarter are None.
start_year (int, optional) – The starting calendar year, defaults to None
end_year (int, optional) – The ending calendar year, defaults to None
start_quarter (int, optional) – The starting calendar quarter, defaults to None
end_quarter (int, optional) – The ending calendar quarter, defaults to None

Returns:

A dictionary containing the templated geographic segments for each time period, segment name, line item, and value.

Return type:

dict

goodwill(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) → DataFrame: ciq data item 1171

alias for gross_property_plant_and_equipment

ciq data item 1169

alias for gross_property_plant_and_equipment

ciq data item 1169

gross_profit(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) → DataFrame: ciq data item 10

gross_property_plant_and_equipment(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) → DataFrame: ciq data item 1169

history(periodicity: Periodicity = Periodicity.day, adjusted: bool = True, start_date: str | None = None, end_date: str | None = None) → DataFrame[source]

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

Parameters:

periodicity (Periodicity) – Determines the frequency of the historical data returned. Defaults to Periodicity.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 id_triple: IdentificationTriple

Returns a unique identification triple for the Ticker object.

Returns:: an identification triple consisting of company_id, security_id, and trading_item_id
Return type:: IdentificationTriple

alias for impairment_of_oil_gas_and_mineral_properties

ciq data item 71

impairment_of_goodwill(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) → DataFrame: ciq data item 209

alias for impairment_of_oil_gas_and_mineral_properties

ciq data item 71

impairment_of_oil_gas_and_mineral_properties(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) → DataFrame: ciq data item 71

alias for in_process_research_and_development_expense

ciq data item 72

alias for in_process_research_and_development_expense

ciq data item 72

alias for in_process_research_and_development_expense

ciq data item 72

in_process_research_and_development_expense(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) → DataFrame: ciq data item 72

alias for in_process_research_and_development_expense

ciq data item 72

alias for in_process_research_and_development_expense

ciq data item 72

income_from_affiliates(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) → DataFrame: ciq data item 47

income_statement(period_type: PeriodType | None = None, start_year: int | None = None, end_year: int | None = None, start_quarter: int | None = None, end_quarter: int | None = None) → DataFrame: The templated income statement

income_stmt(period_type: PeriodType | None = None, start_year: int | None = None, end_year: int | None = None, start_quarter: int | None = None, end_quarter: int | None = None) → DataFrame: The templated income statement

alias for income_tax_expense

ciq data item 75

income_tax_expense(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) → DataFrame: ciq data item 75

alias for income_tax_expense

ciq data item 75

property info: dict

Get the company info for the ticker

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

insurance_division_operating_expense(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) → DataFrame: ciq data item 69

insurance_division_revenue(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) → DataFrame: ciq data item 70

insurance_settlements(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) → DataFrame: ciq data item 73

interest_and_investment_income(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) → DataFrame: ciq data item 65

interest_expense(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) → DataFrame: ciq data item 82

alias for finance_division_interest_expense

ciq data item 50

alias for inventory

ciq data item 1043

inventory(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) → 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

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

issuance_of_common_stock(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) → DataFrame: ciq data item 2169

issuance_of_preferred_stock(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) → DataFrame: ciq data item 2181

kfinance_api_client: KFinanceApiClient

property landlord: BusinessRelationships: Returns the associated company’s current and previous landlords

legal_settlements(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) → 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

alias for total_liabilities

ciq data item 1276

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: PeriodType | None = None, start_year: int | None = None, end_year: int | None = None, start_quarter: int | None = None, end_quarter: int | None = None) → DataFrame: Get a DataFrame of a financial line item according to the date ranges.

loans_held_for_sale(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) → DataFrame: ciq data item 1185

alias for long_term_loans_receivable

ciq data item 1050

long_term_accounts_receivable(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) → DataFrame: ciq data item 1088

long_term_debt(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) → DataFrame: ciq data item 1049

long_term_debt_issued(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) → DataFrame: ciq data item 2034

long_term_debt_repaid(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) → DataFrame: ciq data item 2036

long_term_deferred_charges(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) → DataFrame: ciq data item 1025

long_term_deferred_tax_assets(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) → DataFrame: ciq data item 1026

alias for finance_division_loans_and_leases_long_term

ciq data item 1033

long_term_investments(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) → DataFrame: ciq data item 1054

alias for capital_leases

ciq data item 1183

alias for finance_division_loans_and_leases_long_term

ciq data item 1033

long_term_loans_receivable(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) → DataFrame: ciq data item 1050

alias for other_long_term_assets

ciq data item 1060

alias for other_non_current_liabilities

ciq data item 1062

alias for non_current_unearned_revenue

ciq data item 1256

loss_on_equity_investments(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) → DataFrame: ciq data item 2086

market_cap(start_date: str | None = None, end_date: str | None = None) → DataFrame

Retrieves market caps for a company between start and end date.

Parameters:

start_date (str, optional) – The start date in format “YYYY-MM-DD”, default to None
end_date (str, optional) – The end date in format “YYYY-MM-DD”, default to None

Returns:

A DataFrame with a market_cap column. The dates are the index.

Return type:

pd.DataFrame

merger_and_restructuring_charges(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) → DataFrame: ciq data item 363

merger_charges(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) → DataFrame: ciq data item 80

minority_interest_in_earnings(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) → DataFrame: ciq data item 83

alias for miscellaneous_cash_flow_adjustments

ciq data item 2149

miscellaneous_cash_flow_adjustments(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) → DataFrame: ciq data item 2149

property name: str

Get the company name

Returns:: The company name
Return type:: str

net_cash_from_discontinued_operation(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) → DataFrame: ciq data item 2081

net_cash_from_investments(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) → DataFrame: ciq data item 2027

net_change_in_cash(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) → DataFrame: ciq data item 2093

net_debt(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) → DataFrame: ciq data item 4364

net_decrease_in_investment_loans_originated_and_sold(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) → DataFrame: ciq data item 2032

net_decrease_in_loans_originated_and_sold(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) → DataFrame: ciq data item 2033

net_income(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) → DataFrame: ciq data item 15

net_income_allocable_to_general_partner(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) → DataFrame: ciq data item 249

net_income_to_common_shareholders_excluding_extra_items(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) → DataFrame: ciq data item 379

net_income_to_common_shareholders_including_extra_items(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) → DataFrame: ciq data item 16

net_income_to_company(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) → DataFrame: ciq data item 41571

alias for minority_interest_in_earnings

ciq data item 83

net_interest_expense(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) → DataFrame: ciq data item 368

alias for net_property_plant_and_equipment

ciq data item 1004

net_property_plant_and_equipment(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) → DataFrame: ciq data item 1004

net_working_capital(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) → DataFrame: ciq data item 1311

alias for long_term_accounts_receivable

ciq data item 1088

alias for long_term_debt

ciq data item 1049

alias for long_term_debt_issued

ciq data item 2034

alias for long_term_debt_repaid

ciq data item 2036

alias for long_term_deferred_charges

ciq data item 1025

alias for long_term_deferred_tax_assets

ciq data item 1026

non_current_deferred_tax_liability(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) → DataFrame: ciq data item 1027

alias for long_term_investments

ciq data item 1054

alias for long_term_loans_receivable

ciq data item 1050

alias for other_long_term_assets

ciq data item 1060

alias for other_non_current_liabilities

ciq data item 1062

non_current_unearned_revenue(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) → DataFrame: ciq data item 1256

alias for preferred_stock_non_redeemable

ciq data item 1216

alias for revenue

ciq data item 112

alias for normalized_basic_eps

ciq data item 4379

normalized_basic_eps(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) → DataFrame: ciq data item 4379

alias for normalized_diluted_eps

ciq data item 4380

normalized_diluted_eps(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) → DataFrame: ciq data item 4380

notes_receivable(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) → DataFrame: ciq data item 1048

alias for net_property_plant_and_equipment

ciq data item 1004

property number_of_employees: str | None

Get the number of employees the company has (if available)

Returns:: how many employees the company has
Return type:: str | None

alias for total_operating_expense

ciq data item 373

alias for finance_division_operating_expense

ciq data item 51

alias for insurance_division_operating_expense

ciq data item 69

operating_income(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) → DataFrame: ciq data item 21

other_adjustments_to_net_income(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) → DataFrame: ciq data item 259

other_amortization(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) → DataFrame: ciq data item 2014

other_current_assets(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) → DataFrame: ciq data item 1055

alias for finance_division_other_current_assets

ciq data item 1029

alias for other_current_liability

ciq data item 1057

other_current_liability(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) → DataFrame: ciq data item 1057

other_equity(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) → DataFrame: ciq data item 1028

other_financing_activities(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) → DataFrame: ciq data item 2050

other_intangibles(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) → DataFrame: ciq data item 1040

other_investing_activities(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) → DataFrame: ciq data item 2051

other_long_term_assets(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) → DataFrame: ciq data item 1060

alias for finance_division_other_non_current_assets

ciq data item 1034

alias for other_non_current_liabilities

ciq data item 1062

alias for other_long_term_assets

ciq data item 1060

alias for finance_division_other_non_current_assets

ciq data item 1034

other_non_current_liabilities(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) → DataFrame: ciq data item 1062

other_non_operating_income(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) → DataFrame: ciq data item 85

other_operating_activities(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) → DataFrame: ciq data item 2047

other_operating_expense(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) → DataFrame: ciq data item 260

alias for preferred_stock_other

ciq data item 1065

other_preferred_stock_adjustments(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) → DataFrame: ciq data item 281

other_receivables(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) → DataFrame: ciq data item 1206

other_revenue(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) → DataFrame: ciq data item 90

alias for finance_division_other_current_assets

ciq data item 1029

other_unusual_items(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) → DataFrame: ciq data item 87

pension_and_other_post_retirement_benefit(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) → DataFrame: ciq data item 1213

alias for net_property_plant_and_equipment

ciq data item 1004

pre_opening_costs(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) → DataFrame: ciq data item 96

alias for pre_opening_costs

ciq data item 96

preferred_dividends_and_other_adjustments(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) → DataFrame: ciq data item 97

preferred_dividends_paid(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) → DataFrame: ciq data item 2116

alias for total_preferred_equity

ciq data item 1005

alias for total_preferred_equity

ciq data item 1005

preferred_stock_additional_paid_in_capital(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) → DataFrame: ciq data item 1085

preferred_stock_convertible(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) → DataFrame: ciq data item 1214

preferred_stock_dividend(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) → DataFrame: ciq data item 280

preferred_stock_equity_adjustment(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) → DataFrame: ciq data item 1215

preferred_stock_non_redeemable(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) → DataFrame: ciq data item 1216

preferred_stock_other(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) → DataFrame: ciq data item 1065

preferred_stock_redeemable(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) → DataFrame: ciq data item 1217

premium_on_redemption_of_preferred_stock(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) → DataFrame: ciq data item 279

prepaid_expense(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) → DataFrame: ciq data item 1212

alias for prepaid_expense

ciq data item 1212

price_chart(periodicity: Periodicity = Periodicity.day, adjusted: bool = True, start_date: str | None = None, end_date: str | None = None) → Image[source]

Get the price chart.

Parameters:

periodicity (Periodicity) – Determines the frequency of the historical data returned. Defaults to Periodicity.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

alias for net_property_plant_and_equipment

ciq data item 1004

alias for provision_for_bad_debts

ciq data item 95

provision_for_bad_debts(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) → DataFrame: ciq data item 95

provision_for_credit_losses(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) → DataFrame: ciq data item 2112

quick_ratio(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) → DataFrame: ciq data item 4121

alias for research_and_development_expense

ciq data item 100

alias for research_and_development_expense

ciq data item 100

alias for preferred_stock_redeemable

ciq data item 1217

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: str | None = None, start_year: int | None = None, end_year: int | None = None, start_quarter: int | None = None, end_quarter: int | None = None) → DataFrame: ciq data item 2164

repurchase_of_preferred_stock(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) → DataFrame: ciq data item 2172

alias for research_and_development_expense

ciq data item 100

research_and_development_expense(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) → DataFrame: ciq data item 100

restricted_cash(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) → DataFrame: ciq data item 1104

restructuring_charges(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) → DataFrame: ciq data item 98

retained_earnings(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) → DataFrame: ciq data item 1222

revenue(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) → DataFrame: ciq data item 112

revenue_from_interest_and_investment_income(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) → DataFrame: ciq data item 110

revenue_from_sale_of_assets(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) → DataFrame: ciq data item 104

revenue_from_sale_of_investments(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) → DataFrame: ciq data item 106

alias for research_and_development_expense

ciq data item 100

alias for research_and_development_expense

ciq data item 100

alias for sale_of_intangible_assets

ciq data item 2029

sale_of_intangible_assets(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) → DataFrame: ciq data item 2029

alias for sale_of_intangible_assets

ciq data item 2029

alias for sale_of_property_plant_and_equipment

ciq data item 2042

sale_of_property_plant_and_equipment(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) → DataFrame: ciq data item 2042

sale_of_real_estate(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) → DataFrame: ciq data item 2040

alias for sale_of_real_estate

ciq data item 2040

alias for sale_of_real_estate

ciq data item 2040

sale_proceeds_from_rental_assets(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) → DataFrame: ciq data item 42411

property security_id: int

Get the CIQ security id for the object

Returns:: the CIQ security id
Return type:: int

alias for selling_general_and_admin_expense

ciq data item 102

alias for selling_general_and_admin_expense

ciq data item 102

selling_general_and_admin_expense(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) → DataFrame: ciq data item 102

alias for selling_general_and_admin_expense

ciq data item 102

alias for selling_general_and_admin_expense

ciq data item 102

alias for total_equity

ciq data item 1275

shares_outstanding(start_date: str | None = None, end_date: str | None = None) → DataFrame

Retrieves shares outstanding for a company between start and end date.

Parameters:

start_date (str, optional) – The start date in format “YYYY-MM-DD”, default to None
end_date (str, optional) – The end date in format “YYYY-MM-DD”, default to None

Returns:

A DataFrame with a shares_outstanding column. The dates are the index.

Return type:

pd.DataFrame

alias for accounts_receivable

ciq data item 1021

alias for total_current_assets

ciq data item 1008

alias for short_term_borrowings

ciq data item 1046

short_term_borrowings(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) → DataFrame: ciq data item 1046

short_term_debt_issued(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) → DataFrame: ciq data item 2043

short_term_debt_repaid(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) → DataFrame: ciq data item 2044

alias for deferred_tax_asset_current_portion

ciq data item 1117

alias for finance_division_loans_and_leases_short_term

ciq data item 1032

short_term_investments(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) → DataFrame: ciq data item 1069

alias for finance_division_loans_and_leases_short_term

ciq data item 1032

alias for notes_receivable

ciq data item 1048

alias for other_receivables

ciq data item 1206

alias for total_receivables

ciq data item 1001

alias for total_receivables

ciq data item 1001

property simple_industry: str

Get the simple industry for the company

Returns:: The company’s simple_industry
Return type:: str

special_dividends_paid(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) → DataFrame: ciq data item 2041

property state: str

Get the state of the company’s HQ

Returns:: state of the company’s HQ
Return type:: str

statement(statement_type: str, period_type: PeriodType | None = None, start_year: int | None = None, end_year: int | None = None, start_quarter: int | None = None, end_quarter: int | None = None) → DataFrame: Get the company’s financial statement

property status: str

Get the company status

Returns:: The company status
Return type:: str

stock_based_compensation(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) → 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: str | None = None, start_year: int | None = None, end_year: int | None = None, start_quarter: int | None = None, end_quarter: int | None = None) → DataFrame: ciq data item 2135

alias for effective_tax_rate

ciq data item 4376

property tenant: BusinessRelationships: Returns the associated company’s current and previous tenants

tev(start_date: str | None = None, end_date: str | None = None) → DataFrame

Retrieves TEV (total enterprise value) for a company between start and end date.

Parameters:

start_date (str, optional) – The start date in format “YYYY-MM-DD”, default to None
end_date (str, optional) – The end date in format “YYYY-MM-DD”, default to None

Returns:

A DataFrame with a tev column. The dates are the index.

Return type:

pd.DataFrame

property ticker: str: Get the ticker if it isn’t available from initialization

total_assets(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) → DataFrame: ciq data item 1007

total_cash_and_short_term_investments(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) → DataFrame: ciq data item 1002

total_common_equity(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) → DataFrame: ciq data item 1006

total_current_assets(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) → DataFrame: ciq data item 1008

total_current_liabilities(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) → DataFrame: ciq data item 1009

alias for current_portion_of_long_term_debt_and_capital_leases

ciq data item 1279

alias for current_portion_of_long_term_debt_and_capital_leases

ciq data item 1279

alias for current_portion_of_long_term_debt_and_capital_leases

ciq data item 1279

alias for current_portion_of_long_term_debt_and_capital_leases

ciq data item 1279

alias for current_portion_of_long_term_debt_and_capital_leases

ciq data item 1279

alias for total_depreciation_and_amortization

ciq data item 2

total_debt(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) → DataFrame: ciq data item 4173

total_debt_issued(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) → DataFrame: ciq data item 2161

alias for total_debt_to_equity_ratio

ciq data item 4034

total_debt_repaid(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) → DataFrame: ciq data item 2166

total_debt_to_capital(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) → DataFrame: ciq data item 43907

alias for total_debt_to_equity_ratio

ciq data item 4034

total_debt_to_equity_ratio(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) → DataFrame: ciq data item 4034

alias for total_debt_to_equity_ratio

ciq data item 4034

total_depreciation_and_amortization(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) → DataFrame: ciq data item 2

total_dividends_paid(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) → DataFrame: ciq data item 2022

alias for total_depreciation_and_amortization

ciq data item 2

total_equity(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) → DataFrame: ciq data item 1275

total_liabilities(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) → DataFrame: ciq data item 1276

total_liabilities_and_equity(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) → DataFrame: ciq data item 1013

total_operating_expense(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) → DataFrame: ciq data item 373

total_other_investing_activities(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) → DataFrame: ciq data item 2177

total_other_non_cash_items(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) → DataFrame: ciq data item 2179

total_other_non_operating_income(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) → DataFrame: ciq data item 371

total_other_operating_expense(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) → DataFrame: ciq data item 380

total_other_revenue(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) → DataFrame: ciq data item 357

total_other_unusual_items(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) → DataFrame: ciq data item 374

total_preferred_equity(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) → DataFrame: ciq data item 1005

alias for total_preferred_equity

ciq data item 1005

alias for total_receivables

ciq data item 1001

total_receivables(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) → DataFrame: ciq data item 1001

total_revenue(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) → DataFrame: ciq data item 28

alias for total_selling_general_and_admin_expense

ciq data item 23

alias for total_selling_general_and_admin_expense

ciq data item 23

total_selling_general_and_admin_expense(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) → DataFrame: ciq data item 23

alias for total_selling_general_and_admin_expense

ciq data item 23

alias for total_equity

ciq data item 1275

alias for total_current_assets

ciq data item 1008

total_unusual_items(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) → DataFrame: ciq data item 19

trading_asset_securities(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) → DataFrame: ciq data item 1244

property trading_item_id: int

Get the CIQ trading item id for the object

Returns:: the CIQ trading item id
Return type:: int

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

alias for treasury_stock_preferred_stock_convertible

ciq data item 1249

alias for treasury_stock_preferred_stock_non_redeemable

ciq data item 1250

alias for treasury_stock_preferred_stock_convertible

ciq data item 1249

alias for treasury_stock_preferred_stock_non_redeemable

ciq data item 1250

alias for treasury_stock_preferred_stock_redeemable

ciq data item 1251

alias for treasury_stock_preferred_stock_redeemable

ciq data item 1251

treasury_stock(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) → DataFrame: ciq data item 1248

alias for treasury_stock_preferred_stock_convertible

ciq data item 1249

alias for treasury_stock_preferred_stock_non_redeemable

ciq data item 1250

treasury_stock_preferred_stock_convertible(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) → DataFrame: ciq data item 1249

treasury_stock_preferred_stock_non_redeemable(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) → DataFrame: ciq data item 1250

treasury_stock_preferred_stock_redeemable(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) → DataFrame: ciq data item 1251

alias for treasury_stock_preferred_stock_redeemable

ciq data item 1251

property type: str

Get the type of company

Returns:: The company type
Return type:: str

alias for total_unusual_items

ciq data item 19

validate_inputs(start_year: int | None = None, end_year: int | None = None, start_quarter: int | None = None, end_quarter: int | None = None) → None: Test the time inputs for validity.

property vendor: BusinessRelationships: Returns the associated company’s current and previous vendors

property webpage: str

Get the webpage for the company

Returns:: webpage for the company
Return type:: str

weighted_average_basic_shares_outstanding(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) → DataFrame: ciq data item 3217

weighted_average_diluted_shares_outstanding(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) → DataFrame: ciq data item 342

working_capital(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) → DataFrame: ciq data item 4165

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])[source]

Base class for representing a set of Tickers

accounts_payable(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) → DataFrame: ciq data item 1018

accounts_receivable(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) → DataFrame: ciq data item 1021

accrued_expenses(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) → DataFrame: ciq data item 1016

accumulated_depreciation(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) → DataFrame: ciq data item 1075

add()

Add an element to a set.

This has no effect if the element is already present.

additional_paid_in_capital(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) → DataFrame: ciq data item 1084

alias for preferred_stock_additional_paid_in_capital

ciq data item 1085

property address: str

Get the address of the company’s HQ

Returns:: address of the company’s HQ
Return type:: str

adjustments_to_cash_flow_net_income(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) → DataFrame: ciq data item 21523

amortization_of_goodwill_and_intangibles(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) → DataFrame: ciq data item 31

asset_writedown(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) → DataFrame: ciq data item 32

alias for total_assets

ciq data item 1007

balance_sheet(period_type: PeriodType | None = None, start_year: int | None = None, end_year: int | None = None, start_quarter: int | None = None, end_quarter: int | None = None) → DataFrame: The templated balance sheet

alias for basic_eps

ciq data item 9

alias for basic_eps_excluding_extra_items

ciq data item 3064

alias for basic_eps_from_accounting_change

ciq data item 145

alias for basic_eps_from_accounting_change_and_extraordinary_items

ciq data item 45

alias for basic_eps_from_extraordinary_items

ciq data item 146

alias for basic_eps

ciq data item 9

basic_eps(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) → DataFrame: ciq data item 9

basic_eps_excluding_extra_items(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) → DataFrame: ciq data item 3064

basic_eps_from_accounting_change(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) → DataFrame: ciq data item 145

basic_eps_from_accounting_change_and_extraordinary_items(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) → DataFrame: ciq data item 45

basic_eps_from_extraordinary_items(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) → DataFrame: ciq data item 146

alias for basic_eps

ciq data item 9

property borrower: BusinessRelationships: Returns the associated company’s current and previous borrowers

Retrieves the templated line of business segments for a given period_type, start_year, start_quarter, end_year and end_quarter.

Parameters:

period_type – The period_type requested for. Can be “annual”, “quarterly”, “ytd”. Defaults to “annual” when start_quarter and end_quarter are None.
start_year (int, optional) – The starting calendar year, defaults to None
end_year (int, optional) – The ending calendar year, defaults to None
start_quarter (int, optional) – The starting calendar quarter, defaults to None
end_quarter (int, optional) – The ending calendar quarter, defaults to None

Returns:

A dictionary containing the templated line of business segments for each time period, segment name, line item, and value.

Return type:

dict

alias for capital_expenditure

ciq data item 2021

capital_expenditure(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) → DataFrame: ciq data item 2021

alias for capital_expenditure

ciq data item 2021

capital_leases(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) → DataFrame: ciq data item 1183

alias for capital_leases

ciq data item 1183

alias for cash_and_equivalents

ciq data item 1096

cash_acquisitions(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) → DataFrame: ciq data item 2057

alias for cash_and_equivalents

ciq data item 1096

cash_and_equivalents(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) → DataFrame: ciq data item 1096

alias for total_cash_and_short_term_investments

ciq data item 1002

cash_flow(period_type: PeriodType | None = None, start_year: int | None = None, end_year: int | None = None, start_quarter: int | None = None, end_quarter: int | None = None) → DataFrame: The templated cash flow statement

alias for cash_from_operations

ciq data item 2006

alias for net_cash_from_discontinued_operation

ciq data item 2081

cash_from_financing(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) → DataFrame: ciq data item 2004

alias for cash_from_financing

ciq data item 2004

cash_from_investing(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) → DataFrame: ciq data item 2005

alias for cash_from_investing

ciq data item 2005

alias for cash_from_operations

ciq data item 2006

cash_from_operations(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) → DataFrame: ciq data item 2006

cashflow(period_type: PeriodType | None = None, start_year: int | None = None, end_year: int | None = None, start_quarter: int | None = None, end_quarter: int | None = None) → DataFrame: The templated cash flow statement

alias for cash_from_financing

ciq data item 2004

alias for cash_from_financing

ciq data item 2004

alias for cash_from_investing

ciq data item 2005

alias for cash_from_investing

ciq data item 2005

change_in_accounts_payable(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) → DataFrame: ciq data item 2017

change_in_accounts_receivable(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) → DataFrame: ciq data item 2018

alias for net_change_in_cash

ciq data item 2093

change_in_deferred_taxes(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) → DataFrame: ciq data item 2084

change_in_income_taxes(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) → DataFrame: ciq data item 2101

change_in_inventories(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) → DataFrame: ciq data item 2099

change_in_net_operating_assets(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) → DataFrame: ciq data item 2010

change_in_net_working_capital(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) → DataFrame: ciq data item 4421

change_in_other_net_operating_assets(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) → DataFrame: ciq data item 2045

change_in_trading_asset_securities(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) → DataFrame: ciq data item 2134

change_in_unearned_revenue(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) → DataFrame: ciq data item 2139

property city: str

Get the city of the company’s HQ

Returns:: city of the company’s HQ
Return type:: str

clear(): Remove all elements from this set.

property client_services: BusinessRelationships: Returns the associated company’s current and previous client_servicess

alias for cost_of_goods_sold

ciq data item 34

common_dividends_paid(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) → DataFrame: ciq data item 2074

alias for total_common_equity

ciq data item 1006

common_shares_outstanding(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) → DataFrame: ciq data item 1100

common_stock(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) → DataFrame: ciq data item 1103

companies() → Companies[source]

Build a group of company objects from the group of tickers

Returns:: The Companies corresponding to the Tickers
Return type:: Companies

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

alias for earnings_from_continued_operations

ciq data item 7

alias for preferred_stock_convertible

ciq data item 1214

copy(): Return a shallow copy of a set.

alias for cost_of_revenue

ciq data item 1

cost_of_goods_sold(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) → DataFrame: ciq data item 34

cost_of_revenue(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) → DataFrame: ciq data item 1

property country: str

Get the country of the company’s HQ

Returns:: country of the company’s HQ
Return type:: str

property creditor: BusinessRelationships: Returns the associated company’s current and previous creditors

currency_exchange_gains(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) → DataFrame: ciq data item 38

alias for accounts_receivable

ciq data item 1021

alias for total_current_assets

ciq data item 1008

alias for short_term_borrowings

ciq data item 1046

alias for short_term_borrowings

ciq data item 1046

alias for short_term_debt_issued

ciq data item 2043

alias for short_term_debt_repaid

ciq data item 2044

alias for deferred_tax_asset_current_portion

ciq data item 1117

current_deferred_tax_liability(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) → DataFrame: ciq data item 1119

current_income_taxes_payable(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) → DataFrame: ciq data item 1094

alias for total_current_liabilities

ciq data item 1009

alias for notes_receivable

ciq data item 1048

alias for other_receivables

ciq data item 1206

alias for current_portion_of_capital_leases

ciq data item 1090

current_portion_of_capital_leases(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) → DataFrame: ciq data item 1090

alias for current_portion_of_capital_leases

ciq data item 1090

alias for current_income_taxes_payable

ciq data item 1094

alias for current_portion_of_capital_leases

ciq data item 1090

current_portion_of_long_term_debt(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) → DataFrame: ciq data item 1297

current_portion_of_long_term_debt_and_capital_leases(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) → DataFrame: ciq data item 1279

alias for current_portion_of_long_term_debt_and_capital_leases

ciq data item 1279

alias for current_portion_of_long_term_debt

ciq data item 1297

alias for current_portion_of_long_term_debt_and_capital_leases

ciq data item 1279

alias for current_portion_of_long_term_debt

ciq data item 1297

alias for current_portion_of_long_term_debt_and_capital_leases

ciq data item 1279

alias for current_portion_of_long_term_debt_and_capital_leases

ciq data item 1279

alias for current_unearned_revenue

ciq data item 1074

current_ratio(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) → DataFrame: ciq data item 4030

alias for total_receivables

ciq data item 1001

alias for total_receivables

ciq data item 1001

current_unearned_revenue(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) → DataFrame: ciq data item 1074

property cusip: str

Get the CUSIP for the object

Returns:: The CUSIP
Return type:: str

property customer: BusinessRelationships: Returns the associated company’s current and previous customers

alias for depreciation_and_amortization

ciq data item 41

alias for total_debt_to_equity_ratio

ciq data item 4034

deferred_tax_asset_current_portion(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) → DataFrame: ciq data item 1117

depreciation(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) → DataFrame: ciq data item 2143

depreciation_and_amortization(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) → DataFrame: ciq data item 41

depreciation_of_rental_assets(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) → DataFrame: ciq data item 42409

difference()

Return the difference of two or more sets as a new set.

(i.e. all elements that are in this set but not the others.)

difference_update(): Remove all elements of another set from this set.

alias for diluted_eps

ciq data item 8

alias for diluted_eps_excluding_extra_items

ciq data item 142

alias for diluted_eps_from_accounting_change

ciq data item 141

alias for diluted_eps_from_accounting_change_and_extraordinary_items

ciq data item 44

alias for diluted_eps_from_discontinued_operations

ciq data item 143

alias for diluted_eps_from_extraordinary_items

ciq data item 144

alias for diluted_eps

ciq data item 8

diluted_eps(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) → DataFrame: ciq data item 8

diluted_eps_excluding_extra_items(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) → DataFrame: ciq data item 142

diluted_eps_from_accounting_change(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) → DataFrame: ciq data item 141

diluted_eps_from_accounting_change_and_extraordinary_items(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) → DataFrame: ciq data item 44

diluted_eps_from_discontinued_operations(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) → DataFrame: ciq data item 143

diluted_eps_from_extraordinary_items(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) → DataFrame: ciq data item 144

alias for diluted_eps

ciq data item 8

discard()

Remove an element from a set if it is a member.

If the element is not a member, do nothing.

alias for earnings_from_discontinued_operations

ciq data item 40

distributable_cash_per_share(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) → DataFrame: ciq data item 23317

property distributor: BusinessRelationships: Returns the associated company’s current and previous distributors

divestitures(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) → DataFrame: ciq data item 2077

dividends_paid(period_type: str | None = None, start_year: int | None