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Simple library to download some financial data.

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Build Status PyPi Status

finpie - a simple library to download some financial data

For recreational and educational purposes. Creating easier access to some financial and economic data.

This library is an ongoing project designed to facilitate access to financial and economic data. It tries to cover potentially useful or interesting data points but unfortunately some functions will only return single point data which however could be aggregated over time to construct a limited time series. On the other hand, some functions that retrieve large amounts of data or depending on the data source will take some time to run. See the function index for more information on issues of data availability and relative run time.

The company fundamentals module includes functions to retrive data from Yahoo Finance, MarketWatch, The Motley Fool, Finviz and Macrotrends. The price data module retrieves data from Yahoo Finance and CBOE. The economic data is collected from the OECD database at this point and the news module enables historical news headline collection from the FT, NYT, WSJ, Barrons, Seeking Alpha and Reuters based on keyword searches. The library also provides a function to get all Nasdaq-listed stock tickers as well as worldwide stock symbols (these need some cleaning still once retrieved).

If there are any issues, ideas or recommendations please feel free to reach out.


Changes for v0.13

  • Restructured the fundamental data module to reduce clutter and simplify the repository
  • Excluded Bloomberg news headline scrape because of the automation detection
  • Debugged news headline scrape
  • Removed third party price data API wrappers
  • v0.1312: added option to get press releases from Seeking Alpha, updated WSJ script and Yahoo executive info

    Changes for v0.12

  • Added a section to download earnings call transcripts
  • Added a test file and Travis CI
  • Debugged failing tests

    • Changes for v0.11

  • Added function to get option prices from CBOE to the price_data module
  • Added EIA Petroleum data section to the economic_data module
  • Updated the Windows and Mac Chromedrivers to 85.\*.\*\*\*\*.\*\*\* (had included an older Windows Chromedriver before)


    • To do list:

    • Refactor the news scrape..
    • Improve EIA data representation ( same column names for PADDs or crude and products across different series for easier cross reference; add more granularity to some functions ), add other EIA data sets
    • Add EIA bulk download option and support EIA API
    • Add USDA data, CFTC COT and potentially add weather data sources (e.g. heating degree days, cooling degree days in NE US)
    • Add social media data (Twitter, Stocktwits, Weibo, Reddit WSB?)
    • Add async requests, multiple/batch download options, proxies..


    Documentation

    1. Installation
    2. Function index
    3. Company fundamental data
    4. Price data
    5. Economic data
      6. OECD data EIA petroleum data
    6. News data
    7. Other data
    8. Sources
    9. License

    Installation

    Python3 is required. Google Chrome version 85.*.****.*** or higher is required for some functions involving Selenium (can be found here).

    Note that Selenium may not be able to use Chrome in combination with Firewalls and the functions may fail to execute..

    $ pip install finpie

    Requirements

    beautifulsoup4>=4.9.1
dask>=2.11.0
numpy>=1.18.2
pandas>=1.0.1
requests>=2.22.0
requests_html>=0.10.0
selenium>=3.141.0
tqdm>=4.32.1

    Index

    Output Data Output Runtime
    Company Fundamentals
    fd = Fundamentals( ticker, source, freq )
    Financial statements
  • fd.income_statement()
    		• up to 2005 Slow, depends on source
  • fd.balance_sheet()
    		• up to 2005 Slow, depends on source
  • fd.cashflow_statement()
    		• up to 2005 Slow, depends on source
    Financial ratios and key metrics
  • fd.ratios()
    		• up to 2005 Slow
  • fd.key_metrics()
    		• Most recent data Fast
    Earnings and revenue estimates
  • fd.earnings_estimates()
    		• Most recent data Fast
  • fd.earnings_estimates_trends()
    		• Recent trend Fast
  • fd.earnings_history()
    		• 4 quarters Fast
  • fd.revenue_estimates()
    		• Most recent data Fast
  • fd.growth_estimates()
    		• Most recent data Fast
    Earnings call transcripts
  • fd.transcripts()
    		• up to 2018 Slow
    Insider transactions and analyst ratings
  • fd.insider_transactions()
    		• Most recent data Fast
  • fd.analyst_ratings()
    		• Most recent data Fast
    ESG data
  • fd.esg_score()
    		• Most recent data Fast
  • fd.corporate_governance_score()
    		• Most recent data Fast
    Company profile
  • fd.profile()
    		• Most recent data Fast
  • fd.executives_info()
    		• Most recent data Fast
    Price data
  • historical_prices(ticker)
    		• Timeseries Fast
  • yahoo_option_chain(ticker)
    		• Most recent data Fast
  • cboe_option_chain(ticker)
    		• Most recent data Fast
  • historical_futures_contracts(date_range)
    		• Timeseries Very slow
  • futures_contracts(date)
    		• Any date Fast
    Economic data
    OECD Data
    oecd = OecdData( country_code, **args )
    Composite leading indicators
  • oecd.cli(subject = 'amplitude)
    		• Timeseries Not that slow
  • oecd.cci()
    		• Timeseries Not that slow
  • oecd.bci()
    		• Timeseries Not that slow
    Financial indicators
  • oecd.monetary_aggregates_m1()
    		• Timeseries Not that slow
  • oecd.monetary_aggregates_m3()
    		• Timeseries Not that slow
  • oecd.interbank_rates()
    		• Timeseries Not that slow
  • oecd.short_term_rates()
    		• Timeseries Not that slow
  • oecd.long_term_rates()
    		• Timeseries Not that slow
  • oecd.all_share_prices( )
    		• Timeseries Not that slow
  • oecd.share_prices_industrials()
    		• Timeseries Not that slow
  • oecd.usd_exchange_rates_spot()
    		• Timeseries Not that slow
  • oecd.usd_exchange_rates_average( )
    		• Timeseries Not that slow
  • oecd.rer_overall()
    		• Timeseries Not that slow
    Trade indicators
  • oecd.exports_value()
    		• Timeseries Not that slow
  • oecd.imports_value( )
    		• Timeseries Not that slow
    Labour market indicators
  • oecd.unemployment_rate( )
    		• Timeseries Not that slow
    Price indices
  • oecd.cpi_total()
    		• Timeseries Not that slow
  • oecd.cpi_city_total( )
    		• Timeseries Not that slow
  • oecd.cpi_non_food_non_energy()
    		• Timeseries Not that slow
  • oecd.cpi_energy( )
    		• Timeseries Not that slow
    Business tendency and consumer opinion 
  • oecd.business_tendency_survey( sector )
    		• Timeseries Not that slow
  • oecd.consumer_opinion_survey( measure = ‘national' )
    		• Timeseries Not that slow
    National accounts 
  • oecd.gdp_deflator()
    		• Timeseries Not that slow
  • oecd.gdp_total()
    		• Timeseries Not that slow
  • oecd.gdp_final_consumption()
    		• Timeseries Not that slow
  • oecd.gdp_government_consumption()
    		• Timeseries Not that slow
  • oecd.gdp_fixed_capital_formation()
    		• Timeseries Not that slow
  • oecd.gdp_exports()
    		• Timeseries Not that slow
  • oecd.gdp_imports()
    		• Timeseries Not that slow
    Production and sales 
  • oecd.total_manufacturing_index()
    		• Timeseries Not that slow
  • oecd.total_industry_production_ex_construction()
    		• Timeseries Not that slow
  • oecd.total_construction()
    		• Timeseries Not that slow
  • oecd.total_retail_trade()
    		• Timeseries Not that slow
  • oecd.passenger_car_registration()
    		• Timeseries Not that slow
  • oecd.construction_permits_issued()
    		• Timeseries Not that slow
    OECD Business Tendency Survey 
  • oecd.economic_situation_survey(sector)
    		• Timeseries Not that slow
  • oecd.consumer_confidence_survey()
    		• Timeseries Not that slow
  • oecd.consumer_prices_inflation_survey()
    		• Timeseries Not that slow
    OECD Balance of Payments 
    Current Account
  • oecd.current_account(percent_of_gdp = False)
    		• Timeseries Not that slow
  • oecd.goods_balance( xm = ‘balance’ )
    		• Timeseries Not that slow
  • oecd.services_balance( xm = ‘balance’ )
    		• Timeseries Not that slow
    Financial account
  • oecd.financial_account(assets_or_liabs = None)
    		• Timeseries Not that slow
  • oecd.direct_investment(assets_or_liabs = None)
    		• Timeseries Not that slow
  • oecd.portfolio_investment(assets_or_liabs = None)
    		• Timeseries Not that slow
  • oecd.other_investment(assets_or_liabs = None)
    		• Timeseries Not that slow
  • oecd.financial_derivatives()
    		• Timeseries Not that slow
  • oecd.reserve_assets()
    		• Timeseries Not that slow
    EIA Data
    eia = EiaData()
  • eia.eia_petroleum_series()
    		• Timeseries Not that slow
    Weekly balances
  • eia.weekly_balance()
    		• Timeseries Not that slow
  • eia.last_weekly_balance()
    		• Most recent data Not that slow
    Crude oil supply
  • eia.crude_production()
    		• Timeseries Not that slow
  • eia.crude_supply_and_disposition()
    		• Timeseries Not that slow
  • eia.rig_count()
    		• Timeseries Not that slow
  • eia.crude_reserves()
    		• Timeseries Not that slow
    Refining and Processing
  • eia.weekly_refinery_inputs()
    		• Timeseries Not that slow
  • eia.refinery_utilisation()
    		• Timeseries Not that slow
  • eia.refinery_yield()
    		• Timeseries Not that slow
  • eia.crude_acquisition_cost()
    		• Timeseries Not that slow
  • eia.crude_inputs_quality()
    		• Timeseries Not that slow
  • eia.refineries()
    		• Timeseries Not that slow
    Imports and Exports
  • eia.weekly_xm()
    		• Timeseries Not that slow
  • eia.monthly_xm()
    		• Timeseries Not that slow
  • eia.weekly_imports_by_country()
    		• Timeseries Not that slow
  • eia.crude_imports_quality()
    		• Timeseries Not that slow
    Stocks
  • eia.weekly_stocks()
    		• Timeseries Not that slow
  • eia.monthly_product_stocks()
    		• Timeseries Not that slow
  • eia.monthly_refinery_stocks()
    		• Timeseries Not that slow
  • eia.monthly_tank_and_pipeline_stocks()
    		• Timeseries Not that slow
    Consumption and sales
  • eia.weekly_product_supplied()
    		• Timeseries Not that slow
  • eia.monthly_product_supplied()
    		• Timeseries Not that slow
  • eia.product_prices_sales_and_stock()
    		• Timeseries Not that slow
    News data
    news = NewsData( ticker, keyword_string )
  • news.barrons()
    		• Timeseries Slow
  • news.cnbc()
    		• Timeseries Very slow
  • news.ft()
    		• Timeseries Very slow
  • news.nyt()
    		• Timeseries Very slow
  • news.reuters()
    		• Timeseries Very slow
  • news.seeking_alpha()
    		• Timeseries Slow
  • news.wsj()
    		• Timeseries Very slow
    Other data
  • nasdaq_tickers()
    		• List of stock tickers Fast
  • global_tickers()
    		• List of stock tickers Slow

    Company Fundamental data

    The functions below enable you to download financial statements, valuation ratios and key financial statistics as well as analyst ratings, insider transactions, ESG scores and company profiles.

    The data is pulled from Yahoo Finance, Marketwatch.com , Finviz.com and Macrotrends.com. The macrotrends scrape runs on Selenium and the website might sometimes fail to load. The function may just need to be re-run to work (assuming the ticker is available on the website). As a remedy it might sometimes help to set macrotrends().head = True which will then open a browser window while scraping the data.

    import finpie # or import finpie.fundamental_data

# default:
# source = 'macrotrends' 
# freq = 'A'
fd = finpie.Fundamentals(ticker, source = 'macrotrends', freq = 'A') 

# source options for financial statements and key metrics:
# 'yahoo', 'marketwatch', 'macrotrends'
# freq options:
# 'A', 'Q'

# default key metrics for marketwatch and macrotrends come from Finviz

  • Financial statements

    • Fundamentals(ticker, source, freq).income_statement()

    • Returns a dataframe with income statements from either Macrotrends.com, Yahoo Finance or Marketwatch. Default source is 'macrotrends'.
      • Class Arguments:
        • ticker: valid company ticker
        • source: 'yahoo', 'marketwatch', 'macrotrends', default: 'macrotrends'
        • freq: 'A' (annual data), 'Q' (quarterly data), default: 'A'

    Default Example 
    fd = finpie.Fundamentals('AAPL', freq = 'A')
fd.income_statement()
    revenue cost_of_goods_sold gross_profit research_and_development_expenses sganda_expenses other_operating_income_or_expenses operating_expenses operating_income total_nonoperating_income_to_expense pretax_income income_taxes income_after_taxes other_income income_from_continuous_operations income_from_discontinued_operations net_income ebitda ebit basic_shares_outstanding shares_outstanding basic_eps eps__earnings_per_share
    date
    2005-09-30 13931.0 9889.0 4042.0 535.0 1864.0 NaN 12288.0 1643.0 165.0 1808.0 480.0 1328.0 NaN 1328.0 NaN 1328.0 1822.0 1643.0 22636.0 23993.0 0.06 0.06
    2006-09-30 19315.0 13717.0 5598.0 712.0 2433.0 NaN 16862.0 2453.0 365.0 2818.0 829.0 1989.0 NaN 1989.0 NaN 1989.0 2678.0 2453.0 23634.0 24571.0 0.08 0.08
    2007-09-30 24578.0 16426.0 8152.0 782.0 2963.0 NaN 20171.0 4407.0 599.0 5006.0 1511.0 3495.0 NaN 3495.0 NaN 3495.0 4734.0 4407.0 24209.0 24900.0 0.14 0.14
    2008-09-30 37491.0 24294.0 13197.0 1109.0 3761.0 NaN 29164.0 8327.0 620.0 8947.0 2828.0 6119.0 NaN 6119.0 NaN 6119.0 8823.0 8327.0 24685.0 25260.0 0.25 0.24
    2009-09-30 42905.0 25683.0 17222.0 1333.0 4149.0 NaN 31165.0 11740.0 326.0 12066.0 3831.0 8235.0 NaN 8235.0 NaN 8235.0 12474.0 11740.0 25004.0 25396.0 0.33 0.32
    Yahoo Example 
    fd = finpie.Fundamentals('AAPL', source = 'yahoo') # no frequency choice for Yahoo...
fd.income_statement()
    total_revenue cost_of_revenue gross_profit operating_expense operating_income net_non_operating_interest_income_expense other_income_expense pretax_income tax_provision net_income_common_stockholders diluted_ni_available_to_com_stockholders basic_eps diluted_eps basic_average_shares diluted_average_shares total_operating_income_as_reported total_expenses net_income_from_continuing_and_discontinued_operation normalized_income interest_income interest_expense net_interest_income ebit ebitda reconciled_cost_of_revenue reconciled_depreciation net_income_from_continuing_operation_net_minority_interest normalized_ebitda tax_rate_for_calcs tax_effect_of_unusual_items ticker
    date
    2019-09-30 260174000 161782000 98392000 34462000 63930000 1385000 422000 65737000 10481000 55256000 55256000 0.003 0.003 18471336 18595652 63930000 196244000 55256000 55256000 4961000 3576000 1385000 69313000 NaN 161782000 12547000 55256000 81860000 0 0 AAPL
    2018-09-30 265595000 163756000 101839000 30941000 70898000 2446000 -441000 72903000 13372000 59531000 59531000 0.003 0.003 19821508 20000436 70898000 194697000 59531000 59531000 5686000 3240000 2446000 76143000 NaN 163756000 10903000 59531000 87046000 0 0 AAPL
    2017-09-30 229234000 141048000 88186000 26842000 61344000 2878000 -133000 64089000 15738000 48351000 48351000 0.0023 0.0023 20868968 21006768 61344000 167890000 48351000 48351000 5201000 2323000 2878000 66412000 NaN 141048000 10157000 48351000 76569000 0 0 AAPL
    Marketwatch Example 
    fd = Fundamentals('AAPL', source = 'marketwatch', freq = 'Q')
fd.income_statement()
    sales_revenue sales_growth cost_of_goods_sold_(cogs)_incl_danda cogs_excluding_danda depreciation_and_amortization_expense depreciation amortization_of_intangibles cogs_growth gross_income gross_income_growth gross_profit_margin sganda_expense research_and_development other_sganda sga_growth other_operating_expense unusual_expense ebit_after_unusual_expense non_operating_income_expense non-operating_interest_income equity_in_affiliates_(pretax) interest_expense interest_expense_growth gross_interest_expense interest_capitalized pretax_income pretax_income_growth pretax_margin income_tax income_tax_-_current_domestic income_tax_-_current_foreign income_tax_-_deferred_domestic income_tax_-_deferred_foreign income_tax_credits equity_in_affiliates other_after_tax_income_(expense) consolidated_net_income minority_interest_expense net_income net_income_growth net_margin_growth extraordinaries_and_discontinued_operations extra_items_and_gain_loss_sale_of_assets cumulative_effect_-_accounting_chg discontinued_operations net_income_after_extraordinaries preferred_dividends net_income_available_to_common eps_(basic) eps_(basic)_growth basic_shares_outstanding eps_(diluted) eps_(diluted)_growth diluted_shares_outstanding ebitda ebitda_growth ebitda_margin
    date
    2019-06-30 5.374e+10 nan 3.357e+10 3.064e+10 2.93e+09 2.93e+09 nan nan 2.017e+10 nan nan 8.68e+09 4.26e+09 4.43e+09 nan nan nan nan 9.8e+07 1.19e+09 nan 8.66e+08 nan 8.66e+08 nan 1.191e+10 nan nan 1.87e+09 nan nan nan nan nan nan nan 1.004e+10 nan 1.004e+10 nan nan nan nan nan nan 1.004e+10 nan 1.004e+10 0.55 nan 1.828e+10 0.55 nan 1.841e+10 1.442e+10 nan nan
    2019-09-30 6.394e+10 0.1897 3.977e+10 3.784e+10 1.93e+09 1.93e+09 nan 0.1848 2.417e+10 0.1979 nan 8.69e+09 4.11e+09 4.58e+09 0.0006 nan nan nan 3.54e+08 1.11e+09 nan 8.1e+08 -0.0647 8.1e+08 nan 1.613e+10 0.354 nan 2.44e+09 nan nan nan nan nan nan nan 1.369e+10 nan 1.369e+10 0.3626 nan nan nan nan nan 1.369e+10 nan 1.369e+10 0.76 0.3868 1.796e+10 0.76 0.387 1.808e+10 1.741e+10 0.2071 nan
    2019-12-31 9.172e+10 0.4346 5.677e+10 5.396e+10 2.82e+09 2.82e+09 nan 0.4275 3.495e+10 0.4463 nan 9.65e+09 4.45e+09 5.2e+09 0.1105 nan -1.28e+08 1.28e+08 2.29e+08 1.05e+09 nan 7.85e+08 -0.0309 7.85e+08 nan 2.592e+10 0.6071 nan 3.68e+09 nan nan nan nan nan nan nan 2.224e+10 nan 2.224e+10 0.6247 nan nan nan nan nan 2.224e+10 nan 2.224e+10 1.26 0.6526 1.766e+10 1.25 0.6487 1.782e+10 2.812e+10 0.6151 nan
    2020-03-31 5.835e+10 -0.3639 3.593e+10 3.315e+10 2.79e+09 2.79e+09 nan -0.3671 2.242e+10 -0.3586 nan 9.52e+09 4.57e+09 4.95e+09 -0.0136 nan -1.26e+08 1.26e+08 -1.82e+08 1.05e+09 nan 7.57e+08 -0.0357 7.57e+08 nan 1.314e+10 -0.4932 nan 1.89e+09 nan nan nan nan nan nan nan 1.125e+10 nan 1.125e+10 -0.4941 nan nan nan nan nan 1.125e+10 nan 1.125e+10 0.65 -0.4877 1.744e+10 0.64 -0.4883 1.762e+10 1.569e+10 -0.4422 nan
    2020-06-30 5.942e+10 0.0183 3.737e+10 3.462e+10 2.75e+09 2.75e+09 nan 0.04 2.205e+10 -0.0164 0.3711 9.59e+09 4.76e+09 4.83e+09 0.0076 nan -1.19e+08 1.19e+08 3.55e+08 901M nan 6.97e+08 -0.0793 6.97e+08 nan 1.314e+10 0.0002 0.2211 1.88e+09 nan nan nan nan nan nan nan 1.125e+10 nan 1.125e+10 0.0004 0.1894 nan nan nan nan 1.125e+10 nan 1.125e+10 0.65 0.0113 1.725e+10 0.65 0.0117 1.742e+10 1.521e+10 -0.0302 0.256

    Fundamentals(ticker, source, freq).balance_sheet()

    • Returns a dataframe with balance sheets from either Macrotrends.com, Yahoo Finance or Marketwatch. Default source is 'macrotrends'.
      • Class Arguments:
        • ticker: valid company ticker
        • source: 'yahoo', 'marketwatch', 'macrotrends', default: 'macrotrends'
        • freq: 'A' (annual data), 'Q' (quarterly data), default: 'A'

    Default Example 
    fd = Fundamentals('AAPL', freq = 'A')
fd.balance_sheet()
    cash_on_hand receivables inventory prepaid_expenses other_current_assets total_current_assets property,_plant,_and_equipment longterm_investments goodwill_and_intangible_assets other_longterm_assets total_longterm_assets total_assets total_current_liabilities long_term_debt other_noncurrent_liabilities total_long_term_liabilities total_liabilities common_stock_net retained_earnings_(accumulated_deficit) comprehensive_income other_share_holders_equity share_holder_equity total_liabilities_and_share_holders_equity
    date
    2005-09-30 8261.0 895.0 165.0 NaN 648.0 10300.0 817.0 NaN 96.0 303.0 1216.0 11516.0 3487.0 NaN 601.0 601.0 4088.0 3564.0 3925.0 NaN NaN 7428.0 11516.0
    2006-09-30 10110.0 1252.0 270.0 NaN 2270.0 14509.0 1281.0 NaN 177.0 1238.0 2696.0 17205.0 6443.0 NaN 778.0 778.0 7221.0 4355.0 5607.0 22.0 NaN 9984.0 17205.0
    2007-09-30 15386.0 1637.0 346.0 NaN 3805.0 21956.0 1832.0 NaN 337.0 1222.0 3391.0 25347.0 9280.0 NaN 1535.0 1535.0 10815.0 5368.0 9101.0 63.0 NaN 14532.0 25347.0
    2008-09-30 22111.0 2422.0 509.0 NaN 3920.0 30006.0 2455.0 2379.0 492.0 839.0 6165.0 36171.0 11361.0 NaN 1745.0 2513.0 13874.0 7177.0 15129.0 9.0 NaN 22297.0 36171.0
    2009-09-30 23464.0 5057.0 455.0 NaN 1444.0 31555.0 2954.0 10528.0 453.0 2011.0 15946.0 47501.0 11506.0 NaN 3502.0 4355.0 15861.0 8210.0 23353.0 77.0 NaN 31640.0 47501.0
    Yahoo Example 
    fd = Fundamentals('AAPL', source = 'yahoo') # no frequency choice for Yahoo...
fd.balance_sheet()
    total_assets total_liabilities_net_minority_interest total_equity_gross_minority_interest total_capitalization common_stock_equity net_tangible_assets working_capital invested_capital tangible_book_value total_debt net_debt share_issued ordinary_shares_number ticker
    date
    2019-09-30 338516000 248028000 90488000 182295000 90488000 90488000 57101000 198535000 90488000 108047000 59203000 17772944 17772944 AAPL
    2018-09-30 365725000 258578000 107147000 200882000 107147000 107147000 14473000 221630000 107147000 114483000 88570000 19019944 19019944 AAPL
    2017-09-30 375319000 241272000 134047000 231254000 134047000 126032000 27831000 249727000 126032000 115680000 95391000 20504804 20504804 AAPL
    Marketwatch Example 
    fd = Fundamentals('AAPL', source = 'marketwatch', freq = 'Q')
fd.balance_sheet()
    cash_and_short_term_investments cash_only short-term_investments cash_and_short_term_investments_growth cash_and_st_investments___total_assets total_accounts_receivable accounts_receivables_net accounts_receivables_gross bad_debt_doubtful_accounts other_receivables accounts_receivable_growth accounts_receivable_turnover inventories finished_goods work_in_progress raw_materials progress_payments_and_other other_current_assets miscellaneous_current_assets total_current_assets net_property_plant_and_equipment property_plant_and_equipment_-_gross buildings land_and_improvements computer_software_and_equipment other_property_plant_and_equipment accumulated_depreciation total_investments_and_advances other_long-term_investments long-term_note_receivable intangible_assets net_goodwill net_other_intangibles other_assets tangible_other_assets total_assets assets_-_total_-_growth st_debt_and_current_portion_lt_debt short_term_debt current_portion_of_long_term_debt accounts_payable accounts_payable_growth income_tax_payable other_current_liabilities dividends_payable accrued_payroll miscellaneous_current_liabilities total_current_liabilities long-term_debt long-term_debt_excl_capitalized_leases non-convertible_debt convertible_debt capitalized_lease_obligations provision_for_risks_and_charges deferred_taxes deferred_taxes_-_credit deferred_taxes_-_debit other_liabilities other_liabilities_(excl_deferred_income) deferred_income total_liabilities non-equity_reserves total_liabilities___total_assets preferred_stock_(carrying_value) redeemable_preferred_stock non-redeemable_preferred_stock common_equity_(total) common_stock_par_carry_value retained_earnings esop_debt_guarantee cumulative_translation_adjustment_unrealized_for_exch_gain unrealized_gain_loss_marketable_securities revaluation_reserves treasury_stock common_equity___total_assets total_shareholders\'_equity total_shareholders\'_equity___total_assets accumulated_minority_interest total_equity liabilities_and_shareholders\'_equity
    date
    2019-06-30 9.488e+10 2.29e+10 nan nan 0.2944 2.647e+10 1.415e+10 1.415e+10 nan 1.233e+10 nan 2.03 3.36e+09 3.36e+09 nan nan nan 1.026e+10 1.026e+10 1.3497e+11 3.764e+10 9.398e+10 nan nan nan nan 5.635e+10 1.1735e+11 1.1735e+11 nan nan nan nan 3.228e+10 3.228e+10 3.2224e+11 nan 2.348e+10 9.95e+09 1.353e+10 2.912e+10 nan nan 3.711e+10 nan nan 3.711e+10 8.97e+10 8.494e+10 8.494e+10 nan nan nan 3.052e+10 1.61e+10 1.61e+10 nan 4.52e+09 4.52e+09 nan 2.2578e+11 nan 0.7007 nan nan nan 9.646e+10 4.337e+10 5.372e+10 nan -1.18e+09 328M nan nan 0.2993 9.646e+10 0.2993 nan 9.646e+10 3.2224e+11
    2019-09-30 1.0058e+11 2.812e+10 nan 0.0601 0.2971 4.58e+10 2.293e+10 2.293e+10 nan 2.288e+10 0.7302 1.40 4.11e+09 4.11e+09 nan nan nan 1.233e+10 1.233e+10 1.6282e+11 3.738e+10 9.596e+10 nan nan nan nan 5.858e+10 1.067e+11 1.067e+11 nan nan nan nan 3.162e+10 3.162e+10 3.3852e+11 0.0505 1.624e+10 5.98e+09 1.026e+10 4.624e+10 0.588 nan 4.324e+10 nan nan 4.324e+10 1.0572e+11 9.181e+10 9.181e+10 nan nan nan 2.955e+10 1.692e+10 1.692e+10 nan 4.04e+09 4.04e+09 nan 2.4803e+11 nan 0.7327 nan nan nan 9.049e+10 4.517e+10 4.59e+10 nan -1.46e+09 707M nan nan 0.2673 9.049e+10 0.2673 nan 9.049e+10 3.3852e+11
    2019-12-31 1.0723e+11 2.299e+10 nan 0.0661 0.3148 3.995e+10 2.097e+10 2.097e+10 nan 1.898e+10 -0.1279 2.30 4.1e+09 4.1e+09 nan nan nan 1.196e+10 1.196e+10 1.6323e+11 4.429e+10 1.0525e+11 nan nan nan nan 6.096e+10 1.0173e+11 1.0173e+11 nan nan nan nan 3.137e+10 3.137e+10 3.4062e+11 0.0062 1.647e+10 6.24e+09 1.024e+10 4.511e+10 -0.0243 nan 4.058e+10 nan nan 4.058e+10 1.0216e+11 1.0028e+11 9.308e+10 nan nan 6.27e+08 2.82e+10 nan nan nan 2.045e+10 2.045e+10 nan 2.5109e+11 nan 0.7372 nan nan nan 8.953e+10 4.597e+10 4.398e+10 nan -1.26e+09 822M nan nan 0.2628 8.953e+10 0.2628 nan 8.953e+10 3.4062e+11
    2020-03-31 9.513e+10 2.996e+10 nan -0.1129 0.2969 3.068e+10 1.572e+10 1.572e+10 nan 1.496e+10 -0.232 1.90 3.33e+09 3.33e+09 nan nan nan 1.461e+10 1.461e+10 1.4375e+11 4.399e+10 1.0684e+11 nan nan nan nan 6.285e+10 1.0059e+11 1.0059e+11 nan nan nan nan 3.207e+10 3.207e+10 3.204e+11 -0.0594 2.163e+10 1.121e+10 1.041e+10 3.242e+10 -0.2813 nan 4.205e+10 nan nan 4.205e+10 9.609e+10 9.714e+10 8.909e+10 nan nan 6.29e+08 2.819e+10 nan nan nan 2.056e+10 2.056e+10 nan 2.4198e+11 nan 0.7552 nan nan nan 7.843e+10 4.803e+10 3.318e+10 nan -1.83e+09 -1.47e+09 nan nan 0.2448 7.843e+10 0.2448 nan 7.843e+10 3.204e+11
    2020-06-30 9.305e+10 2.73e+10 nan -0.0218 0.2932 3.208e+10 1.788e+10 1.788e+10 nan 1.419e+10 0.0456 1.85 3.98e+09 3.98e+09 nan nan nan 1.096e+10 1.096e+10 1.4007e+11 4.385e+10 1.0908e+11 nan nan nan nan 6.523e+10 1.0222e+11 1.0222e+11 nan nan nan nan 3.121e+10 3.121e+10 3.1734e+11 -0.0095 2.005e+10 1.252e+10 7.53e+09 3.533e+10 0.0896 nan 3.995e+10 nan nan 3.995e+10 9.532e+10 1.0214e+11 9.405e+10 nan nan 6.3e+08 2.819e+10 nan nan nan 1.942e+10 1.942e+10 nan 2.4506e+11 nan 0.7722 nan nan nan 7.228e+10 4.87e+10 2.414e+10 nan -1.63e+09 1.61e+09 nan nan 0.2278 7.228e+10 0.2278 nan 7.228e+10 3.1734e+11

    Fundamentals(ticker, source, freq).cashflow_statement()

    • Returns a dataframe with cashflow statements from either Macrotrends.com, Yahoo Finance or Marketwatch. Default source is 'macrotrends'.
      • Class Arguments:
        • ticker: valid company ticker
        • source: 'yahoo', 'marketwatch', 'macrotrends', default: 'macrotrends'
        • freq: 'A' (annual data), 'Q' (quarterly data), default: 'A'

    Default Example 
    fd = Fundamentals('AAPL', freq = 'A')
fd.cashflow_statement()
    net_income_to_loss total_depreciation_and_amortization__cash_flow other_noncash_items total_noncash_items change_in_accounts_receivable change_in_inventories change_in_accounts_payable change_in_assets_to_liabilities total_change_in_assets_to_liabilities cash_flow_from_operating_activities net_change_in_property,_plant,_and_equipment net_change_in_intangible_assets net_acquisitions_to_divestitures net_change_in_shortterm_investments net_change_in_longterm_investments net_change_in_investments__total investing_activities__other cash_flow_from_investing_activities net_longterm_debt net_current_debt debt_issuance_to_retirement_net__total net_common_equity_issued_to_repurchased net_total_equity_issued_to_repurchased total_common_and_preferred_stock_dividends_paid financial_activities__other cash_flow_from_financial_activities net_cash_flow stockbased_compensation common_stock_dividends_paid
    date
    2005-09-30 1328.0 179.0 536.0 715.0 121.0 64.0 328.0 349.0 492.0 2535.0 260.0 NaN NaN 2861.0 586.0 2275.0 21.0 2556.0 NaN NaN NaN 543.0 543.0 NaN NaN 543.0 522.0 49.0 NaN
    2006-09-30 1989.0 225.0 231.0 456.0 357.0 105.0 1611.0 1374.0 225.0 2220.0 657.0 28.0 NaN 1057.0 25.0 1032.0 10.0 357.0 NaN NaN NaN 318.0 318.0 NaN 6.0 324.0 2901.0 163.0 NaN
    2007-09-30 3495.0 327.0 327.0 654.0 385.0 76.0 1494.0 288.0 1321.0 5470.0 735.0 251.0 NaN 2295.0 17.0 2312.0 49.0 3249.0 NaN NaN NaN 365.0 365.0 NaN 374.0 739.0 2960.0 242.0 NaN
    2008-09-30 6119.0 496.0 936.0 1432.0 785.0 163.0 596.0 2397.0 2045.0 9596.0 1091.0 108.0 220.0 6722.0 38.0 6760.0 10.0 8189.0 NaN NaN NaN 483.0 483.0 NaN 633.0 1116.0 2523.0 516.0 NaN
    2009-09-30 8235.0 734.0 1750.0 2484.0 939.0 54.0 92.0 233.0 560.0 10159.0 1144.0 69.0 NaN 16046.0 NaN 16046.0 175.0 17434.0 NaN NaN NaN 475.0 475.0 NaN 188.0 663.0 6612.0 710.0 NaN
    2010-09-30 14013.0 1027.0 2319.0 3346.0 2142.0 596.0 6307.0 2333.0 1236.0 18595.0 2005.0 116.0 638.0 11075.0 NaN 11075.0 20.0 13854.0 NaN NaN NaN 912.0 912.0 NaN 345.0 1257.0 5998.0 879.0 NaN
    2011-09-30 25922.0 1814.0 4036.0 5850.0 143.0 275.0 2515.0 2824.0 5757.0 37529.0 4260.0 3192.0 244.0 32464.0 NaN 32464.0 259.0 40419.0 NaN NaN NaN 831.0 831.0 NaN 613.0 1444.0 1446.0 1168.0 NaN
    2012-09-30 41733.0 3277.0 6145.0 9422.0 5551.0 15.0 4467.0 800.0 299.0 50856.0 8295.0 1107.0 350.0 38427.0 NaN 38427.0 48.0 48227.0 NaN NaN NaN 665.0 665.0 2488.0 125.0 1698.0 931.0 1740.0 2488.0
    2013-09-30 37037.0 6757.0 3394.0 10151.0 2172.0 973.0 2340.0 7283.0 6478.0 53666.0 8165.0 911.0 496.0 24042.0 NaN 24042.0 160.0 33774.0 16896.0 NaN 16896.0 22330.0 22330.0 10564.0 381.0 16379.0 3513.0 2253.0 10564.0
    2014-09-30 39510.0 7946.0 5210.0 13156.0 4232.0 76.0 5938.0 5417.0 7047.0 59713.0 9571.0 242.0 3765.0 9017.0 10.0 9027.0 26.0 22579.0 11960.0 6306.0 18266.0 44270.0 44270.0 11126.0 419.0 37549.0 415.0 2863.0 11126.0
    2015-09-30 53394.0 11257.0 5353.0 16610.0 417.0 238.0 5001.0 6082.0 11262.0 81266.0 11247.0 241.0 343.0 44417.0 NaN 44417.0 26.0 56274.0 27114.0 2191.0 29305.0 34710.0 34710.0 11561.0 750.0 17716.0 7276.0 3586.0 11561.0
    2016-09-30 45687.0 10505.0 9634.0 20139.0 527.0 217.0 2117.0 2456.0 405.0 66231.0 12734.0 297.0 NaN 30634.0 1388.0 32022.0 924.0 45977.0 22454.0 397.0 22057.0 30797.0 30797.0 12150.0 NaN 20890.0 636.0 4210.0 12150.0
    2017-09-30 48351.0 10157.0 10640.0 20797.0 2093.0 2723.0 8966.0 9073.0 4923.0 64225.0 12451.0 NaN 329.0 33147.0 395.0 33542.0 124.0 46446.0 25162.0 3852.0 29014.0 32345.0 32345.0 12769.0 1874.0 17974.0 195.0 4840.0 12769.0
    2018-09-30 59531.0 10903.0 27694.0 16791.0 5322.0 828.0 9175.0 30013.0 34694.0 77434.0 13313.0 NaN 721.0 32363.0 1518.0 30845.0 745.0 16066.0 469.0 37.0 432.0 72069.0 72069.0 13712.0 2527.0 87876.0 5624.0 5340.0 13712.0
    2019-09-30 55256.0 12547.0 5076.0 17623.0 245.0 289.0 1923.0 1521.0 3488.0 69391.0 10495.0 NaN 624.0 57460.0 633.0 58093.0 1078.0 45896.0 1842.0 5977.0 7819.0 66116.0 66116.0 14119.0 2922.0 90976.0 24311.0 6068.0 14119.0
    Yahoo Example 
    fd = Fundamentals('AAPL', source = 'yahoo') # no frequency choice for Yahoo...
fd.cashflow_statement()
    operating_cash_flow investing_cash_flow financing_cash_flow end_cash_position income_tax_paid_supplemental_data interest_paid_supplemental_data capital_expenditure issuance_of_capital_stock issuance_of_debt repayment_of_debt repurchase_of_capital_stock free_cash_flow ticker
    date
    2019-09-30 69391000 45896000 -90976000 50224000 15263000 3423000 -10495000 781000 6963000 -8805000 -66897000 58896000 AAPL
    2018-09-30 77434000 16066000 -87876000 25913000 10417000 3022000 -13313000 669000 6969000 -6500000 -72738000 64121000 AAPL
    2017-09-30 63598000 -46446000 -17347000 20289000 11591000 2092000 -12795000 555000 28662000 -3500000 -32900000 50803000 AAPL
    Marketwatch Example 
    fd = Fundamentals('AAPL', source = 'marketwatch', freq = 'Q')
fd.cashflow_statement()
    net_income_before_extraordinaries net_income_growth depreciation_depletion_and_amortization depreciation_and_depletion amortization_of_intangible_assets deferred_taxes_and_investment_tax_credit deferred_taxes investment_tax_credit other_funds funds_from_operations extraordinaries changes_in_working_capital receivables accounts_payable other_assets_liabilities net_operating_cash_flow net_operating_cash_flow_growth net_operating_cash_flow___sales capital_expenditures capital_expenditures_(fixed_assets) capital_expenditures_(other_assets) capital_expenditures_growth capital_expenditures___sales net_assets_from_acquisitions sale_of_fixed_assets_and_businesses purchase_sale_of_investments purchase_of_investments sale_maturity_of_investments other_uses other_sources net_investing_cash_flow net_investing_cash_flow_growth net_investing_cash_flow___sales cash_dividends_paid_-_total common_dividends preferred_dividends change_in_capital_stock repurchase_of_common_and_preferred_stk sale_of_common_and_preferred_stock proceeds_from_stock_options other_proceeds_from_sale_of_stock issuance_reduction_of_debt_net change_in_current_debt change_in_long-term_debt issuance_of_long-term_debt reduction_in_long-term_debt other_funds1 other_uses1 other_sources1 net_financing_cash_flow net_financing_cash_flow_growth net_financing_cash_flow___sales exchange_rate_effect miscellaneous_funds net_change_in_cash free_cash_flow free_cash_flow_growth free_cash_flow_yield
    date
    2019-06-30 1.004e+10 nan 2.93e+09 2.93e+09 nan nan 8.6e+07 nan 1.37e+09 1.443e+10 nan -2.8e+09 -214M 220M -4.31e+09 1.164e+10 nan 0.2165 -2e+09 -2e+09 nan nan -0.0372 -3.2e+08 nan 3.012e+10 -8.19e+09 3.831e+10 -2.68e+08 -3e+07 2.75e+10 nan 0.5118 -3.63e+09 -3.63e+09 nan -1.695e+10 -1.696e+10 1e+06 1e+06 nan -5.03e+09 -2.03e+09 -3e+09 nan -3e+09 -1.2e+09 -1.2e+09 nan -2.68e+10 nan -0.4988 nan nan 1.233e+10 9.64e+09 nan nan
    2019-09-30 1.369e+10 0.3626 3.18e+09 3.18e+09 nan nan -3.02e+08 nan 1.19e+09 1.775e+10 nan 2.16e+09 -1.932e+10 1.788e+10 4.38e+09 1.991e+10 0.7111 0.3114 -2.78e+09 -2.78e+09 nan -0.3885 -0.0434 -1.3e+07 nan 2.8e+09 -1.81e+10 2.09e+10 -8.1e+08 nan -798M -1.029 -0.0125 -3.48e+09 -3.48e+09 nan -1.705e+10 -1.744e+10 3.9e+08 3.9e+08 nan -293M -3.95e+09 3.66e+09 6.96e+09 -3.31e+09 -213M -213M nan -2.104e+10 0.2151 -0.3291 nan nan -1.93e+09 1.713e+10 0.778 nan
    2019-12-31 2.224e+10 0.6247 2.82e+09 2.82e+09 nan nan -3.49e+08 nan 1.57e+09 2.627e+10 nan 4.25e+09 5.92e+09 -1.09e+09 -555M 3.052e+10 0.5327 0.3327 -2.11e+09 -2.11e+09 nan 0.2413 -0.023 -9.58e+08 nan -1.047e+10 -3.749e+10 2.702e+10 -1.3e+08 nan -1.367e+10 -16.1278 -0.149 -3.54e+09 -3.54e+09 nan -2.07e+10 -2.071e+10 2e+06 2e+06 nan 231M -979M 1.21e+09 2.21e+09 -1e+09 -1.4e+09 -1.4e+09 nan -2.541e+10 -0.2076 -0.277 nan nan -8.56e+09 2.841e+10 0.6581 nan
    2020-03-31 1.125e+10 -0.4941 2.79e+09 2.79e+09 nan nan -3.02e+08 nan 1.58e+09 1.531e+10 nan -2e+09 9.29e+09 -1.243e+10 412M 1.331e+10 -0.5638 0.2281 -1.85e+09 -1.85e+09 nan 0.1206 -0.0318 -1.76e+08 nan 1.134e+10 -2.914e+10 4.048e+10 -2.96e+08 nan 9.01e+09 1.6594 0.1545 -3.38e+09 -3.38e+09 nan -1.815e+10 -1.857e+10 4.28e+08 4.28e+08 nan 803M 5.05e+09 -4.25e+09 nan -4.25e+09 -222M -222M nan -2.094e+10 0.1758 -0.3589 nan nan 1.38e+09 1.146e+10 -0.5967 nan
    2020-06-30 1.125e+10 0.0004 2.75e+09 2.75e+09 nan nan 8.33e+08 nan 1.86e+09 1.67e+10 nan -430M -1.37e+09 2.73e+09 -1.1e+09 1.627e+10 0.2224 0.2739 -1.57e+09 -1.57e+09 nan 0.1554 -0.0263 -3.39e+08 nan -3e+09 -3.018e+10 2.718e+10 -2.63e+08 nan -5.17e+09 -1.5731 -0.0869 -3.66e+09 -3.66e+09 nan -1.589e+10 -1.589e+10 nan nan nan 2.17e+09 1.12e+09 1.05e+09 8.43e+09 -7.38e+09 -1.74e+09 -1.74e+09 nan -1.912e+10 0.0871 -0.3217 nan nan -8.01e+09 1.471e+10 0.2835 0.044


  • Financial ratios and key metrics

    • Fundamentals(ticker, source, freq).ratios()

    • Returns a dataframe with annual or quarterly financial ratios up to 2005 from Macrotrends.com.
      • Class Arguments:
        • ticker: valid company ticker
        • source: choice has no effect, data is from Macrotrends.com
        • freq: 'A' (annual data), 'Q' (quarterly data), default: 'A'

    Example 
    fd = Fundamentals('AAPL', freq = 'A')
fd.ratios()
    current_ratio longterm_debt_to_capital debt_to_equity_ratio gross_margin operating_margin ebit_margin ebitda_margin pretax_profit_margin net_profit_margin asset_turnover inventory_turnover_ratio receiveable_turnover days_sales_in_receivables roe__return_on_equity return_on_tangible_equity roa__return_on_assets roi__return_on_investment book_value_per_share operating_cash_flow_per_share free_cash_flow_per_share
    date
    2005-09-30 2.9538 NaN NaN 29.0144 11.7938 11.7938 13.0787 12.9783 9.5327 1.2097 59.9333 15.5654 23.4495 17.8783 18.1124 11.5318 17.8783 0.3177 0.1057 0.0948
    2006-09-30 2.2519 NaN NaN 28.9827 12.7000 12.7000 13.8649 14.5897 10.2977 1.1226 50.8037 15.4273 23.6593 19.9219 20.2814 11.5606 19.9219 0.4169 0.0153 0.0312
    2007-09-30 2.3659 NaN NaN 33.1679 17.9307 17.9307 19.2611 20.3678 14.2200 0.9697 47.4740 15.0141 24.3106 24.0504 24.6214 13.7886 24.0504 0.5950 0.1293 0.1266
    2008-09-30 2.6411 NaN NaN 35.2005 22.2107 22.2107 23.5337 23.8644 16.3213 1.0365 47.7289 15.4794 23.5798 27.4432 28.0624 16.9169 27.4432 0.8964 0.1602 0.1465
    2009-09-30 2.7425 NaN NaN 40.1399 27.3628 27.3628 29.0735 28.1226 19.1936 0.9032 56.4462 8.4843 43.0207 26.0272 26.4052 17.3365 26.0272 1.2558 0.0201 0.0183

    Fundamentals(ticker, source, freq).key_metrics()

    • Returns a dataframe with current key statistics and financial ratios from either Yahoo Finance or Finviz. Default key metrics for the 'macrotrends' and 'marketwatch' source is from Finviz.
      • Class Arguments:
        • ticker: valid company ticker
        • source: 'yahoo', 'marketwatch', 'macrotrends', default: 'macrotrends'
        • freq: choice has no effect, most recent data is returned

    Default Example 
    fd = Fundamentals('AAPL')
fd.key_metrics()
    index market_cap income sales book_to_sh ..
    0 DJIA S&P500 1.94097e+12 5.842e+10 2.7386e+11 4.19 ...

    Yahoo Example 
    fd = Fundamentals('AAPL', source = 'yahoo')
fd.key_metrics()
    payout_ratio profit_margin operating_margin_(ttm) return_on_assets_(ttm) return_on_equity_(ttm) ...
    0 0.2373 0.2133 0.2452 0.1312 0.6925 ...


  • Earnings and revenue estimates

    • Fundamentals( ticker, source, freq ).earnings_estimates()

    • Returns current earnings estimates for the current quarter, next quarter, current year and the next year from Yahoo Finance.
      • Class Arguments:
        • ticker: valid company ticker
        • source: choice has no effect, data is from Yahoo Finance
        • freq: choice has no effect, most recent data is returned

    Example 
    fd = Fundamentals('AAPL')
fd.earnings_estimates('AAPL')
    date no_of_analysts avg_estimate low_estimate high_estimate year_ago_eps
    1 Current Qtr. (Sep 2020) 28 2.8 2.18 3.19 3.03
    2 Next Qtr. (Dec 2020) 24 5.45 4.76 6.82 4.99
    3 Current Year (2020) 35 12.97 12.36 13.52 11.89
    4 Next Year (2021) 35 15.52 12.67 18 12.97

    Fundamentals( ticker, source, freq ).earnings_estimate_trends()

    • Returns earnings estimates for the current quarter, next quarter, current year and the next year for the current date, 7 days ago, 30 days ago, 60 days ago and 90 days ago from Yahoo Finance.
      • Class Arguments:
        • ticker: valid company ticker
        • source: choice has no effect, data is from Yahoo Finance
        • freq: choice has no effect, most recent data is returned

    Example 
    fd = Fundamentals('AAPL')
fd.earnings_estimate_trends()
    date current_estimate 7_days_ago 30_days_ago 60_days_ago 90_days_ago
    1 Current Qtr. (Sep 2020) 2.8 2.84 2.79 2.82 2.8
    2 Next Qtr. (Dec 2020) 5.45 5.44 5.22 5.21 5.22
    3 Current Year (2020) 12.97 13 12.41 12.39 12.32
    4 Next Year (2021) 15.52 15.54 14.94 14.86 14.73

    Fundamentals( ticker, source, freq ).earnings_history()

    • Returns earnings estimates and actual earnings for the past 4 quarters from Yahoo Finance.
      • Class Arguments:
        • ticker: valid company ticker
        • source: choice has no effect, data is from Yahoo Finance
        • freq: choice has no effect, most recent data is returned

    Example 
    fd = Fundamentals('AAPL')
fd.earnings_history()
    date eps_est eps_actual difference surprise_%
    1 9/29/2019 2.84 3.03 0.19 0.067
    2 12/30/2019 4.55 4.99 0.44 0.097
    3 3/30/2020 2.26 2.55 0.29 0.128
    4 6/29/2020 2.04 2.58 0.54 0.265

    Fundamentals(ticker, source, freq).revenue_estimates()

    • Returns revenue estimates for the current quarter, next quarter, current year and the next year from Yahoo Finance.
      • Class Arguments:
        • ticker: valid company ticker
        • source: choice has no effect, data is from Yahoo Finance
        • freq: choice has no effect, most recent data is returned

    Example 
    fd = Fundamentals('AAPL')
fd.revenue_estimates()
    date no_of_analysts avg_estimate low_estimate high_estimate year_ago_sales sales_growth_(yearest)
    1 Current Qtr. (Sep 2020) 26 6.351e+10 5.255e+10 6.85e+10 6.404e+10 -0.008
    2 Next Qtr. (Dec 2020) 24 1.0036e+11 8.992e+10 1.157e+11 8.85e+10 0.134
    3 Current Year (2020) 33 2.7338e+11 2.6236e+11 2.8089e+11 2.6017e+11 0.051
    4 Next Year (2021) 33 3.0734e+11 2.7268e+11 3.3153e+11 2.7338e+11 0.124

    Fundamentals( ticker, source, freq ).growth_estimates()

    • Returns earnings estimates and actual earnings for the past 4 quarters from Yahoo Finance.
      • Class Arguments:
        • ticker: valid company ticker
        • source: choice has no effect, data is from Yahoo Finance
        • freq: choice has no effect, most recent data is returned

    Example 
    fd = Fundamentals('AAPL')
fd.growth_estimates()

    Output

    aapl industry sector(s) sandp_500
    Current_Qtr. -0.079 nan nan nan
    Next_Qtr. 0.088 nan nan nan
    Current_Year 0.088 nan nan nan
    Next_Year 0.195 nan nan nan
    Next_5_Years_(per_annum) 0.1246 nan nan nan
    Past_5_Years_(per_annum) 0.0842 nan nan nan


  • Earnings Call Transcripts

    • The earnings call transcripts are collected from The Motley Fool and are available until Q1 2018. The data returns a simple breakdown of the sections of the earnings call which will still need to be processed further. The full html of the call is also available.

    Fundamentals( ticker, source, freq ).transcripts(html = True)

    • Returns recent history (up to Q1 2018) of earnings call transcripts.
      • Function Arguments:
        • html = True returns additional columns with html of transcript from Motley Fool.
      • Class Arguments:
        • ticker: valid company ticker
        • source: choice has no effect, data is the Motley Fool
        • freq: choice has no effect

    Example 
    fd = Fundamentals('AAPL')
fd.transcripts(html = True)
    prepared_remarks questions_and_answers call_participants ticker html date time quarter link
    2020-07-31 Operator Good day, everyone. Welcome to the Apple Inc. third-quarter fiscal year 2020 earnings con ... Operator [Operator instructions] Luca Maestri -- Chief Financial Officer Operator, may we plea ... Tejas Gala -- Senior Manager, Corporate Finance, and Investor Relations Tim Cook -- Chief Executiv ... AAPL <h2>Prepared Remarks:</h2> <p><strong>Operator</strong></p> <p>Good day, everyone. Welcome to the Ap ... 2020/07/31 5:00 p.m. ET Q3 2020 https://www.fool.com/earnings/call-transcripts/2020/07/31/apple-aapl-q3-2020-earnings-call-transcrip ...
    2020-04-30 Operator Good day, everyone. Welcome to the Apple Inc. Second Quarter Fiscal Year 2020 Earnings Co ... Operator Yes. That will come from Shannon Cross, Cross Research. Shannon Cross -- Cross Research ... Tejas Gala -- Senior Manager, Corporate Finance and Investor Relations Tim Cook -- Chief Executive ... AAPL <h2>Prepared Remarks:</h2> <p><strong>Operator</strong></p> <p>Good day, everyone. Welcome to the Ap ... 2020/04/30 5:00 p.m. ET Q2 2020 https://www.fool.com/earnings/call-transcripts/2020/04/30/apple-inc-aapl-q2-2020-earnings-call-trans ...
    2020-01-28 Operator Good day everyone. Welcome to the Apple Incorporated First Quarter Fiscal Year 2020 Earni ... Operator Yes. That will be from Amit Daryanani with Evercore. Amit Daryanani -- Evercore ISI -- ... Tejas Gala -- Senior Analyst, Corporate Finance and Investor Relations Tim Cook -- Chief Executive ... AAPL <h2>Prepared Remarks:</h2> <p><strong>Operator</strong></p> <p>Good day everyone. Welcome to the App ... 2020/01/28 5:00 p.m. ET Q1 2020 https://www.fool.com/earnings/call-transcripts/2020/01/28/apple-inc-aapl-q1-2020-earnings-call-trans ...
    ... ... ... ... ... ... ... ... ... ...

  • Insider transactions and analyst ratings

    • Fundamentals( ticker, source, freq ).insider_transactions()

    • Returns company insider transactions for the past year from Finviz.
      • Class Arguments:
        • ticker: valid company ticker
        • source: choice has no effect, data is from Finviz
        • freq: choice has no effect, most recent data is returned

    Example 
    fd = Fundamentals('AAPL')
fd.insider_transactions()
    insider_trading relationship date transaction cost #shares value_($) #shares_total sec_form_4
    0 COOK TIMOTHY D Chief Executive Officer Aug 25 Sale 496.91 265160 131761779 837374 Aug 25 06:45 PM
    1 KONDO CHRIS Principal Accounting Officer May 08 Sale 305.62 4491 1372539 7370 May 12 06:30 PM
    2 JUNG ANDREA Director Apr 28 Option Exercise 48.95 9590 469389 33548 Apr 30 09:30 PM
    3 O'BRIEN DEIRDRE Senior Vice President Apr 16 Sale 285.12 9137 2605141 33972 Apr 17 06:31 PM
    4 Maestri Luca Senior Vice President, CFO Apr 07 Sale 264.44 41062 10858445 27568 Apr 09 06:30 PM
    ... ... ... ... ... ... ... ... ... ...

    Fundamentals( ticker, source, freq ).analyst_ratings()

    • Returns recent history of analyst ratings from Finviz.
      • Class Arguments:
        • ticker: valid company ticker
        • source: choice has no effect, data is from Finviz
        • freq: choice has no effect, most recent data is returned

    Example 
    fd = Fundamentals('AAPL')
fd.analyst_ratings()

    Output

    date action rating_institution rating price_target
    2020-09-01 00:00:00 Reiterated JP Morgan Overweight $115 → $150
    2020-09-01 00:00:00 Reiterated Cowen Outperform $530 → $133
    2020-08-31 00:00:00 Reiterated Monness Crespi & Hardt Buy $117.50 → $144
    2020-08-26 00:00:00 Reiterated Wedbush Outperform $515 → $600
    2020-08-25 00:00:00 Reiterated Cowen Outperform $470 → $530
    ... ... ... ... ...


  • ESG scores

    • Fundamentals( ticker, source, freq ).esg_score()

    • Returns current ESG scores from Sustainalytics published on Yahoo Finance.
      • Class Arguments:
        • ticker: valid company ticker
        • source: choice has no effect, data is from Yahoo Finance
        • freq: choice has no effect, most recent data is returned

    Example 
    fd = Fundamentals('AAPL')
fd.esg_score()
    date total_esg_risk_score risk_category risk_percentile environment_risk_score social_risk_score ...
    0 2020-08-25 24 Medium 33rd 0.5 13 ...

    Fundamentals( ticker, source, freq ).corporate_governance_score()

    • Returns current corporate governance scores from Institutional Shareholder Services (ISS) published on Yahoo Finance.
      • Class Arguments:
        • ticker: valid company ticker
        • source: choice has no effect, data is from Yahoo Finance
        • freq: choice has no effect, most recent data is returned

    Example 
    fd = Fundamentals('AAPL')
fd.corporate_governance_score()

    Output

    audit board shareholder_rights compensation quality_score ticker date
    0 1 1 1 3 1 AAPL 2020-08-25


  • Company info

    • Fundamentals( ticker, source, freq ).profile()

    • Returns company sector, industry, current number of employees and a company description.
      • Class Arguments:
        • ticker: valid company ticker
        • source: choice has no effect, data is from Yahoo Finance
        • freq: choice has no effect

    Example 
    fd = Fundamentals('AAPL')
fd.profile()
    company_name sector industry number_of_employees description ticker
    0 Apple Inc. Technology Consumer Electronics 137000 Apple Inc. designs, manufactures, and markets smartphones, personal computers, tablets, wearables, and accessories worldwide... AAPL

    Fundamentals( ticker, source, freq ).executives_info()

    • Returns current company executives with name, title, salary, age and their gender.
      • Class Arguments:
        • ticker: valid company ticker
        • source: choice has no effect, data is from Yahoo Finance
        • freq: choice has no effect

    Example 
    fd = Fundamentals('AAPL')
fd.executives_info()
    name title pay exercised year_born gender age_at_end_of_year
    0 Mr. Timothy D. Cook CEO & Director 1.156e+07 nan 1961 male 59
    1 Mr. Luca Maestri CFO & Sr. VP 3.58e+06 nan 1964 male 56
    2 Mr. Jeffrey E. Williams Chief Operating Officer 3.57e+06 nan 1964 male 56
    3 Ms. Katherine L. Adams Sr. VP, Gen. Counsel & Sec. 3.6e+06 nan 1964 female 56
    4 Ms. Deirdre O'Brien Sr. VP of People & Retail 2.69e+06 nan 1967 female 53


    Price data

    The functions below help to retrieve daily historical price data from Yahoo Finance as well as most recent option prices from Yahoo Finance or CBOE.

    Furthermore, the historical_futures_contracts function enables a bulk download of historical monthly futures contracts up to the year 2000 for currencies, indices, interest rates and commodities including energy, metals and agricultural contracts. The data is downloaded from www.mrci.com but the data is not completely cleaned (yet).

    import finpie.price_data

# Historical price data from Yahoo Finance, most recent option prices from Yahoo Finance and CBOE, and futures prices bulk-download...
# from finpie.price_data import price_data
import finpie

  • Stock and ETF prices

    • historical_prices( ticker )

    • Returns dataframe with daily historical prices from Yahoo Finance.
    Example 
    historical_prices('AAPL')
    Date Open High Low Close Adj Close Volume
    0 1993-01-29 43.9688 43.9688 43.75 43.9375 26.1841 1003200
    1 1993-02-01 43.9688 44.25 43.9688 44.25 26.3703 480500
    2 1993-02-02 44.2188 44.375 44.125 44.3438 26.4262 201300
    3 1993-02-03 44.4062 44.8438 44.375 44.8125 26.7055 529400
    4 1993-02-04 44.9688 45.0938 44.4688 45 26.8172 531500
    ... ... ... ... ... ... ... ...


  • Option prices

    • yahoo_option_chain( ticker )

    • Returns two dataframes for current put and call options from Yahoo Finance.
    Example 
    calls, puts = yahoo_option_chain('AAPL')

    Call options chain

    contract_name last_trade_date strike last_price ...
    0 AAPL200828C00190000 2020-08-25 3:40PM EDT 190 310.29 ...
    1 AAPL200828C00195000 2020-08-25 12:36PM EDT 195 300.7 ...
    2 AAPL200828C00200000 2020-08-25 12:13PM EDT 200 294.8 ...
    3 AAPL200828C00205000 2020-08-06 3:07PM EDT 205 249.54 ...
    ... ... ... ... ... ...

    Put options chain

    contract_name last_trade_date strike last_price bid
    0 AAPL200828P00190000 2020-08-24 2:05PM EDT 190 0.01 ...
    1 AAPL200828P00195000 2020-08-10 10:38AM EDT 195 0.02 ...
    2 AAPL200828P00200000 2020-08-24 1:36PM EDT 200 0.01 ...
    3 AAPL200828P00205000 2020-08-24 10:08AM EDT 205 0.02 ...
    ... ... ... ... ... ...

    cboe_option_chain( ticker, head = False )

    • Returns two dataframes for current put and call options from CBOE.
    Example 
    calls, puts = cboe_option_chain('AAPL')

    Call options chain

    expiration calls last_sale net bid ask vol iv delta gamma open_int strike underlying
    0 09/25/2020 AAPL200925C00058750 46.75 -2.375 50.1 52.65 15 0.02 1 0.0002 0 58.75 110.13
    1 09/25/2020 AAPL200925C00060000 49.2 1.325 48.85 51.4 33 0.02 1 0.0001 38 60 110.13
    2 09/25/2020 AAPL200925C00061250 49.3 0 47.6 50.2 0 0.02 1 0.0002 0 61.25 110.13
    3 09/25/2020 AAPL200925C00062500 43.1 -2.3 47.2 48.05 2 0.02 0.9989 0.0002 6 62.5 110.13
    ... ... ... ... ... ... ... ... ... ... ... ... ... ...

    Put options chain

    expiration puts last_sale net bid ask vol iv delta gamma open_int strike underlying
    0 09/25/2020 AAPL200925P00058750 0.06 0 0 0.01 0 2.001 -0.001 0.0001 76 58.75 110.13
    1 09/25/2020 AAPL200925P00060000 0.01 0 0 0.01 0 1.876 -0.0008 0.0001 505 60 110.13
    2 09/25/2020 AAPL200925P00061250 0.03 0 0 0.01 0 1.8406 -0.0009 0.0001 17 61.25 110.13
    3 09/25/2020 AAPL200925P00062500 0.01 -0.005 0 0.03 10 1.8178 -0.0011 0.0002 123 62.5 110.13
    ... ... ... ... ... ... ... ... ... ... ... ... ... ...

  • Futures prices

    • historical_futures_contracts( pandas.date_range )

    • Returns daily price data for a number of monthly future contracts including open interest of each contract for the given date range.
    Example 
    historical_futures_contracts( pd.date_range('2020-01-01', '2020-09-01') )
    month date open high low close change volume open_interest change_in_oi future
    2020-01-06 Jan20 200106 296.2 299.4 296.2 297.7 1.6 4103 2459 -811 Soybean Meal(CBOT)
    2020-01-06 Mar20 200106 301.5 304.5 300.6 302.9 1.7 58930 222007 3,678 Soybean Meal(CBOT)
    2020-01-06 May20 200106 305.3 308.3 304.6 306.9 1.7 23500 92983 2,616 Soybean Meal(CBOT)
    ... ... ... ... ... ... ... ... ... ... ... ...

    futures_contracts( date )

    • Returns daily price data for a number of monthly future contracts including open interest of each contract for the given date.
    Example 
    futures_prices('2020-01-06')
    month date open high low close change volume open_interest change_in_oi future
    2020-01-06 Jan20 200106 296.2 299.4 296.2 297.7 1.6 4103 2459 -811 Soybean Meal(CBOT)
    2020-01-06 Mar20 200106 301.5 304.5 300.6 302.9 1.7 58930 222007 3,678 Soybean Meal(CBOT)
    2020-01-06 May20 200106 305.3 308.3 304.6 306.9 1.7 23500 92983 2,616 Soybean Meal(CBOT)
    ... ... ... ... ... ... ... ... ... ... ... ...

    Economic data

    The functions below retrieve economic data from the OECD nad EIA database.


    OECD data

    The available OECD timeseries so far include the OECD composite leading indicators, OECD business surveys, OECD main economic indicators and OECD balance of payments.

    The data can be accessed by country or for list of countries and for timeseries specific keyword arguments. Not all timeseries are available for all countries at all frequencies.

    For available country codes see here.

    from finpie.economic_data import oecd_data # or import finpie

# Example for instantiating class for Australia and the USA at monthly frequency with national currencies
oecd = oecd_data.OecdData( country_code = 'USA', freq = 'M', currency_code = 'NXCU')
# or oecd = finpie.OecdData(...) 

# Example for instantiating class for all available countries at quarterly frequency with dollar converted currencies
oecd = oecd_data.OecdData( country_code = 'all', freq = 'Q', currency_code = 'CXCU')
# or oecd = finpie.OecdData(...)

  • OECD Composite Leading Indicators

    • OecdData( country_code, **args ).cli( subject = 'amplitude' )

    • Returns the OECD composite leading indicator with a given measure. Only monthly data available.
    • Subject options:
      • (default) amplitude adjusted
      • LOLITONO - normalised
      • LOLITOTR_STSA - trend restored
      • LOLITOTR_GYSA - 12-month rate of change of the trend restored
      • BSCICP03 - OECD standardised BCI, amplitude adjusted
      • CSCICP03 - OECD standardised CCI, amplitude adjusted
      • LORSGPRT - ratio to trend (gdp)
      • LORSGPNO - normalised ( gdp )
      • LORSGPTD - trend ( gdp )
      • LORSGPOR_IXOBSA - original seasonally adjusted (gdp)
    Example 
    oecd = oecd_data.OecdData( country_code = 'USA', freq = 'M' )
oecd.cli(subject = 'amplitude')
    TIME SUBJECT Subject Country FREQUENCY TIME Unit Code PowerCode Code Value
    1955-01-01 00:00:00 LOLITOAA Amplitude adjusted (CLI) United States M 1955-01 IDX 0 101.484
    1955-02-01 00:00:00 LOLITOAA Amplitude adjusted (CLI) United States M 1955-02 IDX 0 101.838
    1955-03-01 00:00:00 LOLITOAA Amplitude adjusted (CLI) United States M 1955-03 IDX 0 102.131
    ... ... ... ... ... ... ... ... ...

    OecdData( country_code, **args ).cci()

    • Returns the OECD consumer confidence indicator. Only monthly data available.
    Example 
    oecd = oecd_data.OecdData( country_code = 'USA', freq = 'M' )
oecd.cci()
    TIME SUBJECT Subject Country FREQUENCY TIME Unit Code PowerCode Code Value
    1960-01-01 00:00:00 CSCICP03 OECD Standardised CCI, Amplitude adjusted (Long term average=100), sa United States M 1960-01 IDX 0 101.498
    1960-02-01 00:00:00 CSCICP03 OECD Standardised CCI, Amplitude adjusted (Long term average=100), sa United States M 1960-02 IDX 0 101.243
    1960-03-01 00:00:00 CSCICP03 OECD Standardised CCI, Amplitude adjusted (Long term average=100), sa United States M 1960-03 IDX 0 101.023
    ... ... ... ... ... ... ... ... ...

    OecdData( country_code, **args ).bci()

    • Returns the OECD business confidence indicator. Only monthly data available.
    Example 
    oecd = oecd_data.OecdData( country_code = 'USA', freq = 'M' )
oecd.bci()
    TIME SUBJECT Subject Country FREQUENCY TIME Unit Code PowerCode Code Value
    1950-01-01 00:00:00 BSCICP03 OECD Standardised BCI, Amplitude adjusted (Long term average=100), sa United States M 1950-01 IDX 0 101.071
    1950-02-01 00:00:00 BSCICP03 OECD Standardised BCI, Amplitude adjusted (Long term average=100), sa United States M 1950-02 IDX 0 101.59
    1950-03-01 00:00:00 BSCICP03 OECD Standardised BCI, Amplitude adjusted (Long term average=100), sa United States M 1950-03 IDX 0 102.282
    ... ... ... ... ... ... ... ... ...


  • OECD Main Economic Indicators


  • Financial indicators


    • OecdData( country_code, **args ).monetary_aggregates_m1( index = True, seasonally_adjusted = True )

    • Returns the M1 monetary aggregate. Not available for all countries.
    • Arguments:
      • index = True returns an index, index = False returns level values
      • seasonally_adjusted = True returns seasonally adjusted index
    Example 
    oecd = oecd_data.OecdData( country_code = 'all', freq = 'M' )
oecd.monetary_aggregates_m1(index = True, seasonally_adjusted = True)
    TIME SUBJECT Subject Country FREQUENCY TIME Unit Code PowerCode Code Value
    1992-01-01 00:00:00 MANMM101 Monetary aggregates and their components > Narrow money and components > M1 and components > M1 Czech Republic M 1992-01 IDX 0 10.4902
    1992-02-01 00:00:00 MANMM101 Monetary aggregates and their components > Narrow money and components > M1 and components > M1 Czech Republic M 1992-02 IDX 0 10.4718
    1992-03-01 00:00:00 MANMM101 Monetary aggregates and their components > Narrow money and components > M1 and components > M1 Czech Republic M 1992-03 IDX 0 10.7145
    ... ... ... ... ... ... ... ... ...

    OecdData( country_code, **args ).monetary_aggregates_m3(index = True, seasonally_adjuted = True)

    • Returns the M3 monetary aggregate. Not available for all countries.
    • Arguments:
      • index = True returns an index, index = False returns level values
      • seasonally_adjusted = True returns seasonally adjusted index
    Example 
    oecd = oecd_data.OecdData( country_code = 'all', freq = 'M' )
oecd.monetary_aggregates_m3( index = True, seasonally_adjuted = True )
    TIME SUBJECT Subject Country FREQUENCY TIME Unit Code PowerCode Code Value
    1980-02-01 00:00:00 MABMM301 Monetary aggregates and their components > Broad money and components > M3 > M3 Korea M 1980-02 IDX 0 0.461489
    1980-03-01 00:00:00 MABMM301 Monetary aggregates and their components > Broad money and components > M3 > M3 Korea M 1980-03 IDX 0 0.47687
    1980-04-01 00:00:00 MABMM301 Monetary aggregates and their components > Broad money and components > M3 > M3 Korea M 1980-04 IDX 0 0.488449
    ... ... ... ... ... ... ... ... ...

    OecdData( country_code, **args ).interbank_rates()

    • Returns interbank interest rates. Not available for all countries.
    Example 
    oecd = oecd_data.OecdData( country_code = 'all', freq = 'M' )
oecd.interbank_rates()
    TIME SUBJECT Subject Country FREQUENCY TIME Unit Code PowerCode Code Value
    1990-08-01 00:00:00 IRSTCI01 Interest Rates > Immediate rates (< 24 hrs) > Call money/interbank rate > Total Australia M 1990-08 PC 0 14
    1990-09-01 00:00:00 IRSTCI01 Interest Rates > Immediate rates (< 24 hrs) > Call money/interbank rate > Total Australia M 1990-09 PC 0 14
    1990-10-01 00:00:00 IRSTCI01 Interest Rates > Immediate rates (< 24 hrs) > Call money/interbank rate > Total Australia M 1990-10 PC 0 13.43
    ... ... ... ... ... ... ... ... ...

    OecdData( country_code, **args ).short_term_rates()

    • Returns short-term interest rates. Not avaialable for all countries.
    Example 
    oecd = oecd_data.OecdData( country_code = 'all', freq = 'M' )
oecd.short_term_rates()
    TIME SUBJECT Subject Country FREQUENCY TIME Unit Code PowerCode Code Value
    1968-01-01 00:00:00 IR3TBB01 Interest Rates > 3-month or 90-day rates and yields > Bank bills > Total Australia M 1968-01 PC 0 5.1
    1968-02-01 00:00:00 IR3TBB01 Interest Rates > 3-month or 90-day rates and yields > Bank bills > Total Australia M 1968-02 PC 0 5.15
    1968-03-01 00:00:00 IR3TBB01 Interest Rates > 3-month or 90-day rates and yields > Bank bills > Total Australia M 1968-03 PC 0 5.15
    ... ... ... ... ... ... ... ... ...

    OecdData( country_code, **args ).long_term_rates()

    • Returns long-term interest rates. Not available for all countries.
    Example 
    oecd = oecd_data.OecdData( country_code = 'all', freq = 'M' )
oecd.long_term_rates()
    TIME SUBJECT Subject Country FREQUENCY TIME Unit Code PowerCode Code Value
    1969-07-01 00:00:00 IRLTLT01 Interest Rates > Long-term government bond yields > 10-year > Main (including benchmark) Australia M 1969-07 PC 0 5.8
    1969-08-01 00:00:00 IRLTLT01 Interest Rates > Long-term government bond yields > 10-year > Main (including benchmark) Australia M 1969-08 PC 0 5.79
    1969-09-01 00:00:00 IRLTLT01 Interest Rates > Long-term government bond yields > 10-year > Main
    ... ... ... ... ... ... ... ... ...

    OecdData( country_code, **args ).all_share_prices()

    • Returns aggregate share prices of a given country. Not available for all countries.
    Example 
    oecd = oecd_data.OecdData( country_code = 'all', freq = 'M' )
oecd.all_share_prices()
    TIME SUBJECT Subject Country FREQUENCY TIME Unit Code PowerCode Code Value
    1958-01-01 00:00:00 SPASTT01 Share Prices > All shares/broad > Total > Total Australia M 1958-01 IDX 0 2.46886
    1958-02-01 00:00:00 SPASTT01 Share Prices > All shares/broad > Total > Total Australia M 1958-02 IDX 0 2.55808
    1958-03-01 00:00:00 SPASTT01 Share Prices > All shares/broad > Total > Total Australia M
    ... ... ... ... ... ... ... ... ...

    OecdData( country_code, **args ).share_prices_industrials()

    • Returns aggregate share prices of industrial companies from a given country. Not available for all countries.
    Example 
    oecd = oecd_data.OecdData( country_code = 'all', freq = 'M' )
oecd.share_prices_industrials()
    TIME SUBJECT Subject Country FREQUENCY TIME Unit Code PowerCode Code Value
    1955-01-01 00:00:00 SPINTT01 Share Prices > Industrials > Total > Total Norway M 1955-01 IDX 0 2.38957
    1955-02-01 00:00:00 SPINTT01 Share Prices > Industrials > Total > Total Norway M 1955-02 IDX 0 2.29226
    1955-03-01 00:00:00 SPINTT01 Share Prices > Industrials > Total > Total Norway M 1955-03 IDX 0 2.34632
    ... ... ... ... ... ... ... ... ...

    OecdData( country_code, **args ).usd_exchange_rates_spot()

    • Returns USD spot exchange rates at end of month/quarter.
    Example 
    oecd = oecd_data.OecdData( country_code = 'all', freq = 'M' )
usd_exchange_rates_spot()
    TIME SUBJECT Subject Country FREQUENCY TIME Unit Code PowerCode Code Value
    1983-10-01 00:00:00 LCEATT02 Labour Compensation > Earnings > All activities > Weekly Australia Q 1983-Q4 AUD 0 311.822
    1984-01-01 00:00:00 LCEATT02 Labour Compensation > Earnings > All activities > Weekly Australia Q 1984-Q1 AUD 0 321.838
    1984-04-01 00:00:00 LCEATT02 Labour Compensation > Earnings > All activities > Weekly Australia Q 1984-Q2 AUD 0 333.959
    ... ... ... ... ... ... ... ... ...

    OecdData( country_code, **args ).usd_exchange_rates_average()

    • Returns monthly/quarterly average USD exchange rates.
    Example 
    oecd = oecd_data.OecdData( country_code = 'all', freq = 'M' )
oecd.usd_exchange_rates_average()
    TIME SUBJECT Subject Country FREQUENCY TIME Unit Code PowerCode Code Value
    1957-01-01 00:00:00 CCUSMA02 Currency Conversions > US$ exchange rate > Average of daily rates > National currency:USD Australia M 1957-01 AUD 0 0.598516
    1957-02-01 00:00:00 CCUSMA02 Currency Conversions > US$ exchange rate > Average of daily rates > National currency:USD Australia M 1957-02 AUD 0 0.598015
    1957-03-01 00:00:00 CCUSMA02 Currency Conversions > US$ exchange rate > Average of daily rates > National currency:USD Australia M 1957-03 AUD 0 0.599125
    ... ... ... ... ... ... ... ... ...

    OecdData( country_code, **args ).rer_overall()

    • Returns overall real exchange rates.
    Example 
    oecd = oecd_data.OecdData( country_code = 'all', freq = 'M' )
oecd.rer_overall()
    TIME SUBJECT Subject Country FREQUENCY TIME Unit Code PowerCode Code Value
    1972-01-01 00:00:00 CCRETT01 Currency Conversions > Real effective exchange rates > Overall Economy > CPI Australia M 1972-01 IDX 0 110.762
    1972-02-01 00:00:00 CCRETT01 Currency Conversions > Real effective exchange rates > Overall Economy > CPI Australia M 1972-02 IDX 0 109.613
    1972-03-01 00:00:00 CCRETT01 Currency Conversions > Real effective exchange rates > Overall Economy > CPI Australia M 1972-03 IDX 0 108.894
    ... ... ... ... ... ... ... ... ...


  • Trade indicators

    • OecdData( country_code, **args ).exports_value(growth = False, seasonally_adjusted = True)

    • Returns value of exports in national currency or dollar converted, etc..
    • Arguments:
      • growth = True returns seasonally adjusted growth
      • growth = False returns monthly level values in specified currency conversion (national or dollar converted)
      • seasonally_adjusted = True returns seasonally adjusted monthly level values in specified currency conversion (national or dollar converted)
    Example 
    oecd = oecd_data.OecdData( country_code = 'all', currency_code = 'CXCU', freq = 'M' )
oecd.exports_value(growth = False, seasonally_adjusted = True)
    TIME SUBJECT Subject Country FREQUENCY TIME Unit Code PowerCode Code Value
    1958-01-01 00:00:00 XTEXVA01 International Trade > Exports > Value (goods) > Total Australia M 1958-01 USD 9 0.149812
    1958-02-01 00:00:00 XTEXVA01 International Trade > Exports > Value (goods) > Total Australia M 1958-02 USD 9 0.133962
    1958-03-01 00:00:00 XTEXVA01 International Trade > Exports > Value (goods) > Total Australia M 1958-03 USD 9 0.131655
    ... ... ... ... ... ... ... ... ...

    OecdData( country_code, **args ).imports_value(growth = False, seasonally_adjusted = True)

    • Returns value of imports in national currency or dollar converted, etc..
    • Arguments:
      • growth = True returns seasonally adjusted growth
      • growth = False returns monthly level values in specified currency conversion (national or dollar converted)
      • seasonally_adjusted = True returns seasonally adjusted monthly level values in specified currency conversion (national or dollar converted)
    Example 
    oecd = oecd_data.OecdData( country_code = 'all', currency_code = 'CXCU', freq = 'M' )
oecd.imports_value(growth = False, seasonally_adjusted = True)
    TIME SUBJECT Subject Country FREQUENCY TIME Unit Code PowerCode Code Value
    1958-01-01 00:00:00 XTIMVA01 International Trade > Imports > Value (goods) > Total Australia M 1958-01 USD 9 0.155267
    1958-02-01 00:00:00 XTIMVA01 International Trade > Imports > Value (goods) > Total Australia M 1958-02 USD 9 0.150965
    1958-03-01 00:00:00 XTIMVA01 International Trade > Imports > Value (goods) > Total Australia M 1958-03 USD 9 0.138973
    ... ... ... ... ... ... ... ... ...


  • Labour market indicators

    • OecdData( country_code, **args ).unemployment_rate()

    • Returns unemployment rates.
    Example 
    oecd = oecd_data.OecdData( country_code = 'all', freq = 'M' )
oecd.unemployment_rate()
    TIME SUBJECT Subject Country FREQUENCY TIME Unit Code PowerCode Code Value
    1978-02-01 00:00:00 LRHUTTTT Labour Force Survey - quarterly rates > Harmonised unemployment - monthly rates > Total > All persons Australia M 1978-02 PC 0 6.64535
    1978-03-01 00:00:00 LRHUTTTT Labour Force Survey - quarterly rates > Harmonised unemployment - monthly rates > Total > All persons Australia M 1978-03 PC 0 6.30344
    1978-04-01 00:00:00 LRHUTTTT Labour Force Survey - quarterly rates > Harmonised unemployment - monthly rates > Total > All persons Australia M 1978-04 PC 0 6.26811
    ... ... ... ... ... ... ... ... ...


  • Price indices

    • OecdData( country_code, **args ).cpi_total(growth = False, seasonally_adjusted = True)

    • Returns the consumer price index.
    • Arguments:
      • growth = True returns yoy growth
      • growth = False returns index
      • growth = False and seasonally_adjusted = True returns seasonally adjusted index
    Example 
    oecd = oecd_data.OecdData( country_code = 'all', freq = 'M' )
oecd.cpi_total(growth = False, seasonally_adjusted = True)
    TIME SUBJECT Subject Country FREQUENCY TIME Unit Code PowerCode Code Value
    1985-01-01 00:00:00 CPALTT01 Consumer Price Index > All items > Total > Total Japan M 1985-01 IDX 0 85.7678
    1985-02-01 00:00:00 CPALTT01 Consumer Price Index > All items > Total > Total Japan M 1985-02 IDX 0 85.6816
    1985-03-01 00:00:00 CPALTT01 Consumer Price Index > All items > Total > Total Japan M 1985-03 IDX 0 85.6816
    ... ... ... ... ... ... ... ... ...

    OecdData( country_code, **args ).cpi_city_total()

    • Returns the consumer price index for cities.
    Example 
    oecd = oecd_data.OecdData( country_code = 'all', freq = 'M' )
oecd.cpi_city_total()
    TIME SUBJECT Subject Country FREQUENCY TIME Unit Code PowerCode Code Value
    1961-01-01 00:00:00 CPALCY01 Consumer Price Index > All items > All items: City > Total Canada M 1961-01 IDX 0 13.4288
    1961-02-01 00:00:00 CPALCY01 Consumer Price Index > All items > All items: City > Total Canada M 1961-02 IDX 0 13.4288
    1961-03-01 00:00:00 CPALCY01 Consumer Price Index > All items > All items: City > Total Canada M 1961-03 IDX 0 13.3779
    ... ... ... ... ... ... ... ... ...

    OecdData( country_code, **args ).cpi_non_food_non_energy(growth = False, seasonally_adjusted = True)

    • Returns non-food and non-energy consumer price index .
    • Arguments:
      • growth = True returns yoy growth
      • growth = False returns index
      • growth = False and seasonally_adjusted = True returns seasonally adjusted index
    Example 
    oecd = oecd_data.OecdData( country_code = 'all', freq = 'M' )
oecd.cpi_non_food_non_energy(growth = False, seasonally_adjusted = True)
    TIME SUBJECT Subject Country FREQUENCY TIME Unit Code PowerCode Code Value
    1957-01-01 00:00:00 CPGRLE01 Consumer Price Index > OECD Groups > All items non-food non-energy > Total United States M 1957-01 IDX 0 11.7649
    1957-02-01 00:00:00 CPGRLE01 Consumer Price Index > OECD Groups > All items non-food non-energy > Total United States M 1957-02 IDX 0 11.8062
    1957-03-01 00:00:00 CPGRLE01 Consumer Price Index > OECD Groups > All items non-food non-energy > Total United States M 1957-03 IDX 0 11.8474
    ... ... ... ... ... ... ... ... ...

    OecdData( country_code, **args ).cpi_energy(growth = False, seasonally_adjusted = True)

    • Returns consumer price index for energy (fuel, electricity, etc.).
    • Arguments:
      • growth = True returns yoy growth
      • growth = False returns index
      • growth = False and seasonally_adjusted = True returns seasonally adjusted index
    Example 
    oecd = oecd_data.OecdData( country_code = 'all', freq = 'M' )
oecd.cpi_energy(growth = False, seasonally_adjusted = True)
    TIME SUBJECT Subject Country FREQUENCY TIME Unit Code PowerCode Code Value
    1991-01-01 00:00:00 CPGREN01 Consumer Price Index > OECD Groups > Energy (Fuel, electricity & gasoline) > Total Germany M 1991-01 IDX 0 46.028
    1991-02-01 00:00:00 CPGREN01 Consumer Price Index > OECD Groups > Energy (Fuel, electricity & gasoline) > Total Germany M 1991-02 IDX 0 45.7485
    1991-03-01 00:00:00 CPGREN01 Consumer Price Index > OECD Groups > Energy (Fuel, electricity & gasoline) > Total Germany M 1991-03 IDX 0 44.0713
    ... ... ... ... ... ... ... ... ...


  • Business tendency and consumer opinion

    • OecdData( country_code, **args ).business_tendency_survey( sector )

    • Returns national business tendency survey for given sector.
    • Sector arguments:
      • (default) retail
      • construction
      • services
      • manufacturing
    Example 
    oecd = oecd_data.OecdData( country_code = 'all', freq = 'M' )
oecd.business_tendency_survey('retail')
    TIME SUBJECT Subject Country FREQUENCY TIME Unit Code PowerCode Code Value
    1996-01-01 00:00:00 BRCICP02 Business tendency surveys (retail trade) > Confidence indicators > Composite indicators > National indicator Austria M 1996-01 PC 0 -19.4
    1996-02-01 00:00:00 BRCICP02 Business tendency surveys (retail trade) > Confidence indicators > Composite indicators > National indicator Austria M 1996-02 PC 0 -15.1
    1996-03-01 00:00:00 BRCICP02 Business tendency surveys (retail trade) > Confidence indicators > Composite indicators > National indicator Austria M 1996-03 PC 0 -13.4
    ... ... ... ... ... ... ... ... ...

    OecdData( country_code, **args ).consumer_opinion_survey( measure = 'national' )

    • Returns national consumer opinion survey.
    Example 
    oecd = oecd_data.OecdData( country_code = 'all', freq = 'M' )
oecd.consumer_opinion_survey()
    TIME SUBJECT Subject Country FREQUENCY TIME Unit Code PowerCode Code Value
    1974-09-01 00:00:00 CSCICP02 Consumer opinion surveys > Confidence indicators > Composite indicators > National indicator Australia M 1974-09 PC 0 -9
    1974-10-01 00:00:00 CSCICP02 Consumer opinion surveys > Confidence indicators > Composite indicators > National indicator Australia M 1974-10 PC 0 -9
    1974-11-01 00:00:00 CSCICP02 Consumer opinion surveys > Confidence indicators > Composite indicators > National indicator Australia M 1974-11 PC 0 -8
    ... ... ... ... ... ... ... ... ...


  • National accounts

    • OecdData( country_code, **args ).gdp_deflator()

    • Returns the quarterly GDP deflator. Not available for all countries.
    Example 
    oecd = oecd_data.OecdData( country_code = 'all', freq = 'Q' )
oecd.gdp_deflator()
    TIME SUBJECT Subject Country FREQUENCY TIME Unit Code PowerCode Code Value
    1960-01-01 00:00:00 NAGIGP01 National Accounts > National Accounts Deflators > Gross Domestic Product > GDP Deflator Australia Q 1960-Q1 IDX 0 6.78408
    1960-04-01 00:00:00 NAGIGP01 National Accounts > National Accounts Deflators > Gross Domestic Product > GDP Deflator Australia Q 1960-Q2 IDX 0 6.93289
    1960-07-01 00:00:00 NAGIGP01 National Accounts > National Accounts Deflators > Gross Domestic Product > GDP Deflator Australia Q 1960-Q3 IDX 0 6.9521
    ... ... ... ... ... ... ... ... ...

    OecdData( country_code, **args ).gdp_total( growth = False, index = False )

    • Returns total GDP at constant prices.
    • Arguments:
      • growth = True returns seasonally adjusted yoy growth
      • growth = False and index = True returns seasonally adjusted index
      • growth = False and index = False returns seasonally adjusted level values
    Example 
    oecd = oecd_data.OecdData( country_code = 'all', freq = 'Q' )
oecd.gdp_total(growth = False, index = False)
    TIME SUBJECT Subject Country FREQUENCY TIME Unit Code PowerCode Code Value
    1959-07-01 00:00:00 NAEXKP01 National Accounts > GDP by Expenditure > Constant Prices > Gross Domestic Product - Total Australia Q 1959-Q3 AUD 9 62.496
    1959-10-01 00:00:00 NAEXKP01 National Accounts > GDP by Expenditure > Constant Prices > Gross Domestic Product - Total Australia Q 1959-Q4 AUD 9 63.043
    1960-01-01 00:00:00 NAEXKP01 National Accounts > GDP by Expenditure > Constant Prices > Gross Domestic Product - Total Australia Q 1960-Q1 AUD 9 64.683
    ... ... ... ... ... ... ... ... ...

    OecdData( country_code, **args ).gdp_final_consumption()

    • Returns GDP final consumption at constant prices.
    • Arguments:
      • growth = True returns seasonally adjusted yoy growth
      • growth = False and index = True returns seasonally adjusted index
      • growth = False and index = False returns seasonally adjusted level values
    Example 
    oecd = oecd_data.OecdData( country_code = 'all', freq = 'Q' )
oecd.gdp_final_consumption(growth = False, index = False)
    TIME SUBJECT Subject Country FREQUENCY TIME Unit Code PowerCode Code Value
    1959-07-01 00:00:00 NAEXKP02 National Accounts > GDP by Expenditure > Constant Prices > Private Final Consumption Expenditure Australia Q 1959-Q3 AUD 9 33.383
    1959-10-01 00:00:00 NAEXKP02 National Accounts > GDP by Expenditure > Constant Prices > Private Final Consumption Expenditure Australia Q 1959-Q4 AUD 9 34.303
    1960-01-01 00:00:00 NAEXKP02 National Accounts > GDP by Expenditure > Constant Prices > Private Final Consumption Expenditure Australia Q 1960-Q1 AUD 9 35.111
    ... ... ... ... ... ... ... ... ...

    OecdData( country_code, **args ).gdp_government_consumption()

    • Returns government consumption at constant prices.
    • Arguments:
      • growth = True returns seasonally adjusted yoy growth
      • growth = False and index = True returns seasonally adjusted index
      • growth = False and index = False returns seasonally adjusted level values
    Example 
    oecd = oecd_data.OecdData( country_code = 'all', freq = 'Q' )
oecd.gdp_government_consumption(growth = False, index = False)
    TIME SUBJECT Subject Country FREQUENCY TIME Unit Code PowerCode Code Value
    1959-07-01 00:00:00 NAEXKP03 National Accounts > GDP by Expenditure > Constant Prices > Government Final Consumption Expenditure Australia Q 1959-Q3 AUD 9 9.626
    1959-10-01 00:00:00 NAEXKP03 National Accounts > GDP by Expenditure > Constant Prices > Government Final Consumption Expenditure Australia Q 1959-Q4 AUD 9 9.56
    1960-01-01 00:00:00 NAEXKP03 National Accounts > GDP by Expenditure > Constant Prices > Government Final Consumption Expenditure Australia Q 1960-Q1 AUD 9 10.004
    ... ... ... ... ... ... ... ... ...

    OecdData( country_code, **args ).gdp_fixed_capital_formation(growth = False, index = False)

    • Returns fixed capital formation at constant prices.
    • Arguments:
      • growth = True returns seasonally adjusted yoy growth
      • growth = False and index = True returns seasonally adjusted index
      • growth = False and index = False returns seasonally adjusted level values
    Example 
    oecd = oecd_data.OecdData( country_code = 'all', freq = 'Q' )
oecd.gdp_fixed_capital_formation(growth = False, index = False)
    TIME SUBJECT Subject Country FREQUENCY TIME Unit Code PowerCode Code Value
    1959-07-01 00:00:00 NAEXKP04 National Accounts > GDP by Expenditure > Constant Prices > Gross Fixed Capital Formation Australia Q 1959-Q3 AUD 9 10.278
    1959-10-01 00:00:00 NAEXKP04 National Accounts > GDP by Expenditure > Constant Prices > Gross Fixed Capital Formation Australia Q 1959-Q4 AUD 9 9.984
    1960-01-01 00:00:00 NAEXKP04 National Accounts > GDP by Expenditure > Constant Prices > Gross Fixed Capital Formation Australia Q 1960-Q1 AUD 9 10.25
    ... ... ... ... ... ... ... ... ...

    OecdData( country_code, **args ).gdp_exports(growth = False, index = False)

    • Returns export value for GDP calculation.
    • Arguments:
      • growth = True returns seasonally adjusted yoy growth
      • growth = False and index = True returns seasonally adjusted index
      • growth = False and index = False returns seasonally adjusted level values
    Example 
    oecd = oecd_data.OecdData( country_code = 'all', freq = 'Q' )
oecd.gdp_exports(growth = False, index = False)
    TIME SUBJECT Subject Country FREQUENCY TIME Unit Code PowerCode Code Value
    1959-07-01 00:00:00 NAEXKP06 National Accounts > GDP by Expenditure > Constant Prices > Exports of Goods and Services Australia Q 1959-Q3 AUD 9 3.991
    1959-10-01 00:00:00 NAEXKP06 National Accounts > GDP by Expenditure > Constant Prices > Exports of Goods and Services Australia Q 1959-Q4 AUD 9 5.172
    1960-01-01 00:00:00 NAEXKP06 National Accounts > GDP by Expenditure > Constant Prices > Exports of Goods and Services Australia Q 1960-Q1 AUD 9 4.603
    ... ... ... ... ... ... ... ... ...

    OecdData( country_code, **args ).gdp_imports(growth = False, index = False)

    • Returns import value for GDP calculation.
    • Arguments:
      • growth = True returns seasonally adjusted yoy growth
      • growth = False and index = True returns seasonally adjusted index
      • growth = False and index = False returns seasonally adjusted level values
    Example 
    oecd = oecd_data.OecdData( country_code = 'all', freq = 'Q' )
oecd.gdp_imports(growth = False, index = False)
    TIME SUBJECT Subject Country FREQUENCY TIME Unit Code PowerCode Code Value
    1959-07-01 00:00:00 NAEXKP07 National Accounts > GDP by Expenditure > Constant Prices > Less: Imports of Goods and Services Australia Q 1959-Q3 AUD 9 3.226
    1959-10-01 00:00:00 NAEXKP07 National Accounts > GDP by Expenditure > Constant Prices > Less: Imports of Goods and Services Australia Q 1959-Q4 AUD 9 3.422
    1960-01-01 00:00:00 NAEXKP07 National Accounts > GDP by Expenditure > Constant Prices > Less: Imports of Goods and Services Australia Q 1960-Q1 AUD 9 3.58
    ... ... ... ... ... ... ... ... ...


  • Production and sales

    • OecdData( country_code, **args ).total_manufacturing_index( index = True, seasonally_adjusted = True )

    • Returns total manufacturing index.
    • Arguments:
      • index = True returns index index = False returns monthly or quarterly levels depending on frequency
      • seasonally\_adjusted = True returns seasonally adjusted values
    Example 
    oecd = oecd_data.OecdData( country_code = 'all', freq = 'M' )
oecd.total_manufacturing_index(index = True, seasonally_adjusted = True)
    TIME SUBJECT Subject Country FREQUENCY TIME Unit Code PowerCode Code Value
    1956-01-01 00:00:00 PRMNTO01 Production > Manufacturing > Total manufacturing > Total manufacturing Austria M 1956-01 IDX 0 11.2315
    1956-02-01 00:00:00 PRMNTO01 Production > Manufacturing > Total manufacturing > Total manufacturing Austria M 1956-02 IDX 0 11.0611
    1956-03-01 00:00:00 PRMNTO01 Production > Manufacturing > Total manufacturing > Total manufacturing Austria M 1956-03 IDX 0 11.2976
    ... ... ... ... ... ... ... ... ...

    OecdData( country_code, **args ).total_industry_production_ex_construction(index = True, seasonally_adjusted = True)

    • Returns total industry production excluding construction.
    • Arguments:
      • index = True returns index index = False returns monthly or quarterly levels depending on frequency
      • seasonally\_adjusted = True returns seasonally adjusted values
    Example 
    oecd = oecd_data.OecdData( country_code = 'all', freq = 'M' )
oecd.total_industrial_production_ex_construction(index = True, seasonally_adjusted = True)
    TIME SUBJECT Subject Country FREQUENCY TIME Unit Code PowerCode Code Value
    1955-01-01 00:00:00 PRINTO01 Production > Industry > Total industry > Total industry excluding construction Austria M 1955-01 IDX 0 10.7655
    1955-02-01 00:00:00 PRINTO01 Production > Industry > Total industry > Total industry excluding construction Austria M 1955-02 IDX 0 10.7772
    1955-03-01 00:00:00 PRINTO01 Production > Industry > Total industry > Total industry excluding construction Austria M 1955-03 IDX 0 10.7544
    ... ... ... ... ... ... ... ... ...

    OecdData( country_code, **args ).total_construction(index = True, seasonally_adjusted = True)

    • Returns total construction index.
    • Arguments:
      • index = True returns index index = False returns monthly or quarterly levels depending on frequency
      • seasonally\_adjusted = True returns seasonally adjusted values
    Example 
    oecd = oecd_data.OecdData( country_code = 'all', freq = 'M' )
oecd.total_construction(index = True, seasonally_adjusted = True)
    TIME SUBJECT Subject Country FREQUENCY TIME Unit Code PowerCode Code Value
    1996-01-01 00:00:00 PRCNTO01 Production > Construction > Total construction > Total Austria M 1996-01 IDX 0 56.1
    1996-02-01 00:00:00 PRCNTO01 Production > Construction > Total construction > Total Austria M 1996-02 IDX 0 57.8
    1996-03-01 00:00:00 PRCNTO01 Production > Construction > Total construction > Total Austria M 1996-03 IDX 0 57
    ... ... ... ... ... ... ... ... ...

    OecdData( country_code, **args ).total_retail_trade(index = True, seasonally_adjusted = True)

    • Returns total retail trade index.
    • Arguments:
      • index = True returns index index = False returns monthly or quarterly levels depending on frequency
      • seasonally\_adjusted = True returns seasonally adjusted values
    Example 
    oecd = oecd_data.OecdData( country_code = 'all', freq = 'M' )
oecd.total_retail_trade(index = True, seasonally_adjusted = True)
    TIME SUBJECT Subject Country FREQUENCY TIME Unit Code PowerCode Code Value
    1955-01-01 00:00:00 SLRTTO02 Sales > Retail trade > Total retail trade > Value Austria M 1955-01 IDX 0 5.65006
    1955-02-01 00:00:00 SLRTTO02 Sales > Retail trade > Total retail trade > Value Austria M 1955-02 IDX 0 5.72288
    1955-03-01 00:00:00 SLRTTO02 Sales > Retail trade > Total retail trade > Value Austria M 1955-03 IDX 0 5.63975
    ... ... ... ... ... ... ... ... ...

    OecdData( country_code, **args ).passenger_car_registrations(index = True, seasonally_adjusted = True)

    • Returns index for passenger car registrations.
    • Arguments:
      • index = True returns index index = False returns monthly or quarterly levels depending on frequency
      • seasonally\_adjusted = True returns seasonally adjusted values
    Example 
    oecd = oecd_data.OecdData( country_code = 'all', freq = 'M' )
oecd.passenger_car_registrations(index = True, seasonally_adjusted = True)
    TIME SUBJECT Subject Country FREQUENCY TIME Unit Code PowerCode Code Value
    1994-01-01 00:00:00 SLRTCR03 Sales > Retail trade > Car registration > Passenger cars Australia M 1994-01 IDX 0 83.9795
    1994-02-01 00:00:00 SLRTCR03 Sales > Retail trade > Car registration > Passenger cars Australia M 1994-02 IDX 0 86.7998
    1994-03-01 00:00:00 SLRTCR03 Sales > Retail trade > Car registration > Passenger cars Australia M 1994-03 IDX 0 85.8574
    ... ... ... ... ... ... ... ... ...

    OecdData( country_code, **args ).construction_permits_issued(index = True, seasonally_adjusted = True)

    • Returns index for construction permits issued.
    • Arguments:
      • index = True returns index index = False returns monthly or quarterly levels depending on frequency
      • seasonally\_adjusted = True returns seasonally adjusted values
    Example 
    oecd = oecd_data.OecdData( country_code = 'all', freq = 'M' )
oecd.construction_permits_issued(index = True, seasonally_adjusted = True)
    TIME SUBJECT Subject Country FREQUENCY TIME Unit Code PowerCode Code Value
    1955-01-01 00:00:00 ODCNPI03 Orders > Construction > Permits issued > Dwellings / Residential buildings Australia M 1955-01 IDX 0 32.3003
    1955-02-01 00:00:00 ODCNPI03 Orders > Construction > Permits issued > Dwellings / Residential buildings Australia M 1955-02 IDX 0 40.88
    1955-03-01 00:00:00 ODCNPI03 Orders > Construction > Permits issued > Dwellings / Residential buildings Australia M 1955-03 IDX 0 35.8331
    ... ... ... ... ... ... ... ... ...


  • OECD Business Tendency Survey

    • OecdData( country_code, **args ).economic_situation_survey()

    • Returns national economic situation survey.
    Example 
    oecd = oecd_data.OecdData( country_code = 'USA', freq = 'M' )
oecd.economic_situation_survey()
    TIME SUBJECT Subject Country FREQUENCY TIME Unit Code PowerCode Code Value
    1978-01-01 00:00:00 CSESFT Future tendency United States M 1978-01 PC 0 8
    1978-02-01 00:00:00 CSESFT Future tendency United States M 1978-02 PC 0 11
    1978-03-01 00:00:00 CSESFT Future tendency United States M 1978-03 PC 0 -3
    ... ... ... ... ... ... ... ... ...

    OecdData( country_code, **args ).consumer_confidence_survey()

    • Returns national consumer confidence survey.
    Example 
    oecd = oecd_data.OecdData( country_code = 'USA', freq = 'M' )
oecd.consumer_confidence_survey()
    TIME SUBJECT Subject Country FREQUENCY TIME Unit Code PowerCode Code Value
    1960-01-01 00:00:00 CSCICP02 National indicator United States M 1960-01 PC 0 107.594
    1960-02-01 00:00:00 CSCICP02 National indicator United States M 1960-02 PC 0 105.191
    1960-03-01 00:00:00 CSCICP02 National indicator United States M 1960-03 PC 0 102.788
    ... ... ... ... ... ... ... ... ...

    OecdData( country_code, **args ).consumer_price_inflation_survey()

    • Returns consumer price inflation survey.
    Example 
    oecd = oecd_data.OecdData( country_code = 'USA', freq = 'M' )
oecd.consumer_price_inflation_survey()
    TIME SUBJECT Subject Country FREQUENCY TIME Unit Code PowerCode Code Value
    1978-01-01 00:00:00 CSINFT Future tendency United States M 1978-01 PC 0 6.1
    1978-02-01 00:00:00 CSINFT Future tendency United States M 1978-02 PC 0 8.5
    1978-03-01 00:00:00 CSINFT Future tendency United States M 1978-03 PC 0 7.5
    ... ... ... ... ... ... ... ... ...


  • OECD Balance of Payments

    • Current account

    OecdData( country_code, **args ).current_account( percent_of_gdp = False )

    • Returns the current account as value or as percent of GDP.
    Example 
    oecd = oecd_data.OecdData( country_code = 'USA', freq = 'Q' )
oecd.current_account(percent_of_gdp = True)
    TIME SUBJECT Subject Country MEASURE Measure FREQUENCY TIME Unit Code PowerCode Code Value
    1960-01-01 00:00:00 B6BLTT02 Current account balance as % of GDP United States STSA Indicators in percentage Q 1960-Q1 PC 0 0.257994
    1960-04-01 00:00:00 B6BLTT02 Current account balance as % of GDP United States STSA Indicators in percentage Q 1960-Q2 PC 0 0.391809
    1960-07-01 00:00:00 B6BLTT02 Current account balance as % of GDP United States STSA Indicators in percentage Q 1960-Q3 PC 0 0.612899
    ... ... ... ... ... ... ... ... ... ... ...

    OecdData( country_code, **args ).goods_balance( xm = 'balance' )

    • Returns the imported, exported goods or good balance of the current account.
    • xm arguments:
      • (default) balance
      • exports
      • imports
    Example 
    oecd = oecd_data.OecdData( country_code = 'USA', freq = 'Q' )
oecd.goods_balance(xm = 'exports')
    TIME SUBJECT Subject Country MEASURE Measure FREQUENCY TIME Unit Code PowerCode Code Value
    1960-01-01 00:00:00 B6CRTD01 Goods, credits (exports) United States CXCU US-Dollar converted Q 1960-Q1 USD 6 4664
    1960-04-01 00:00:00 B6CRTD01 Goods, credits (exports) United States CXCU US-Dollar converted Q 1960-Q2 USD 6 5058
    1960-07-01 00:00:00 B6CRTD01 Goods, credits (exports) United States CXCU US-Dollar converted Q 1960-Q3 USD 6 4736
    ... ... ... ... ... ... ... ... ... ... ...

    OecdData( country_code, **args ).services_balance( xm = 'balance' )

    • Returns the imported, exported services or services balance of the current account.
    • xm arguments:
      • (default) balance
      • exports
      • imports
    Example 
    oecd = oecd_data.OecdData( country_code = 'USA', freq = 'Q' )
oecd.goods_balance(xm = 'balance')
    TIME SUBJECT Subject Country MEASURE Measure FREQUENCY TIME Unit Code PowerCode Code Value
    1960-01-01 00:00:00 B6BLSE01 Services, balance United States CXCU US-Dollar converted Q 1960-Q1 USD 6 -239
    1960-04-01 00:00:00 B6BLSE01 Services, balance United States CXCU US-Dollar converted Q 1960-Q2 USD 6 -205
    1960-07-01 00:00:00 B6BLSE01 Services, balance United States CXCU US-Dollar converted Q 1960-Q3 USD 6 -758
    ... ... ... ... ... ... ... ... ... ... ...

    Financial account

    OecdData( country_code, **args ).financial_account( assets_or_liabs = None )

    • Returns the assets, liabilities or net financial account in specified currency.
    • assets\_or\_liabs arguments:
      • (default) None
      • assets
      • liabs
    Example 
    oecd = oecd_data.OecdData( country_code = 'USA', freq = 'Q', currency = 'CXCU' )
oecd.financial_account(assets_or_liabs = None)
    TIME SUBJECT Subject Country MEASURE Measure FREQUENCY TIME Unit Code PowerCode Code Value
    1960-01-01 00:00:00 B6FATT01 Financial account, net United States CXCU US-Dollar converted Q 1960-Q1 USD 6 358
    1960-04-01 00:00:00 B6FATT01 Financial account, net United States CXCU US-Dollar converted Q 1960-Q2 USD 6 414
    1960-07-01 00:00:00 B6FATT01 Financial account, net United States CXCU US-Dollar converted Q 1960-Q3 USD 6 159
    ... ... ... ... ... ... ... ... ... ... ...

    OecdData( country_code, **args ).direct_investment( assets_or_liabs = None )

    • Returns the assets, liabilities or net direct investment of the financial account.
      • (default) None
      • assets
      • liabs
    Example 
    oecd = oecd_data.OecdData( country_code = 'USA', freq = 'Q', currency = 'CXCU' )
oecd.direct_investment(assets_or_liabs = None)
    TIME SUBJECT Subject Country MEASURE Measure FREQUENCY TIME Unit Code PowerCode Code Value
    1960-01-01 00:00:00 B6FADI01 Direct investment, net United States CXCU US-Dollar converted Q 1960-Q1 USD 6 591
    1960-04-01 00:00:00 B6FADI01 Direct investment, net United States CXCU US-Dollar converted Q 1960-Q2 USD 6 560
    1960-07-01 00:00:00 B6FADI01 Direct investment, net United States CXCU US-Dollar converted Q 1960-Q3 USD 6 595
    ... ... ... ... ... ... ... ... ... ... ...

    OecdData( country_code, **args ).portfolio_investment( assets_or_liabs = None )

    • Returns the assets, liabilities or net portfolio investment of the financial account.
      • (default) None
      • assets
      • liabs
    Example 
    oecd = oecd_data.OecdData( country_code = 'USA', freq = 'Q', currency = 'CXCU' )
oecd.portfolio_investment(assets_or_liabs = None)
    TIME SUBJECT Subject Country MEASURE Measure FREQUENCY TIME Unit Code PowerCode Code Value
    1960-01-01 00:00:00 B6FAPI10 Portfolio investment, net United States CXCU US-Dollar converted Q 1960-Q1 USD 6 69
    1960-04-01 00:00:00 B6FAPI10 Portfolio investment, net United States CXCU US-Dollar converted Q 1960-Q2 USD 6 139
    1960-07-01 00:00:00 B6FAPI10 Portfolio investment, net United States CXCU US-Dollar converted Q 1960-Q3 USD 6 -27
    ... ... ... ... ... ... ... ... ... ... ...

    OecdData( country_code, **args ).other_investment( assets_or_liabs = None )

    • Returns the assets, liabilities or net other investments of the financial account.
      • (default) None
      • assets
      • liabs
    Example 
    oecd = oecd_data.OecdData( country_code = 'USA', freq = 'Q', currency = 'CXCU' )
oecd.other_investment(assets_or_liabs = None)
    TIME SUBJECT Subject Country MEASURE Measure FREQUENCY TIME Unit Code PowerCode Code Value
    1960-01-01 00:00:00 B6FAOI01 Other investment, net United States CXCU US-Dollar converted Q 1960-Q1 USD 6 -143
    1960-04-01 00:00:00 B6FAOI01 Other investment, net United States CXCU US-Dollar converted Q 1960-Q2 USD 6 -110
    1960-07-01 00:00:00 B6FAOI01 Other investment, net United States CXCU US-Dollar converted Q 1960-Q3 USD 6 331
    ... ... ... ... ... ... ... ... ... ... ...

    OecdData( country_code, **args ).financial_derivatives()

    • Returns the net financial derivatives of the financial account.
    Example 
    oecd = oecd_data.OecdData( country_code = 'USA', freq = 'Q', currency = 'CXCU' )
oecd.financial_derivatives()
    TIME SUBJECT Subject Country MEASURE Measure FREQUENCY TIME Unit Code PowerCode Code Value
    1960-01-01 00:00:00 B6FAFD01 Financial derivatives, net United States CXCU US-Dollar converted Q 1960-Q1 USD 6 0
    1960-04-01 00:00:00 B6FAFD01 Financial derivatives, net United States CXCU US-Dollar converted Q 1960-Q2 USD 6 0
    1960-07-01 00:00:00 B6FAFD01 Financial derivatives, net United States CXCU US-Dollar converted Q 1960-Q3 USD 6 0
    ... ... ... ... ... ... ... ... ... ... ...

    OecdData( country_code, **args ).reserve_assets()

    • Returns the net reserve assets of the financial account.
    Example 
    oecd = oecd_data.OecdData( country_code = 'USA', freq = 'Q', currency = 'CXCU' )
oecd.reserve_assets()
    TIME SUBJECT Subject Country MEASURE Measure FREQUENCY TIME Unit Code PowerCode Code Value
    1960-01-01 00:00:00 B6FARA01 Reserve assets, net acquisition of financial assets United States CXCU US-Dollar converted Q 1960-Q1 USD 6 -159
    1960-04-01 00:00:00 B6FARA01 Reserve assets, net acquisition of financial assets United States CXCU US-Dollar converted Q 1960-Q2 USD 6 -175
    1960-07-01 00:00:00 B6FARA01 Reserve assets, net acquisition of financial assets United States CXCU US-Dollar converted Q 1960-Q3 USD 6 -740
    ... ... ... ... ... ... ... ... ... ... ...

    EIA Petroleum data

    The available EIA data so far only includes timeseries from the EIA petroleum data set. No API key is required but a future version may feature the option to use the EIA API directly.

    Not all timeseries are available at all frequencies.

    For available petroleum time series codes see here.

    from finpie.economic_data import eia_data # or import finpie

# Example for instantiating class for Australia and the USA at monthly frequency with national currencies
eia = eia_data.EiaData()
# or eia = finpie.EiaData(...) 
eia.freq = 'm' # for monthly frequency if available (default)
# eia.freq = 'a' for annual frequency if available
eia.barrels = 'mbblpd' # (default)
# eia.barrels = 'mbbl'
eia.id = False # default, id = True returns EIA series id for column names

    EiaData().eia_petroleum_series( series = 'all', sheet_name = 'all' )

    • Returns timeseries for the given series id.
    • series options: any EIA petroleum series id
    Example 
    eia = eia_data.EiaData()
eia.eia_petroleum_series( series_id, sheet_name = 'all')

  • Weekly balances

    • EiaData().weekly_balance( series = 'all', sma = False )

    • Returns timeseries for the weekly EIA balance.
    • series options:
      • 'all' - returns all series (default)
      • 'crude oil production' - returns weekly crude oil production
      • 'refiner inputs and utilisation' - returns refinery inputs, weekly capacity and utilisation
      • 'refiner and blender net inputs' - returns net input of blending components
      • 'refiner and blender net production' - returns net refinery product production
      • 'ethanol plant production' - returns fuel ethanol production
      • 'stocks' - returns weekly crude and product stocks
      • 'days of supply' - returns number of days of supply available
      • 'imports' - returns weekly imports of crude and products
      • 'exports' - returns weekly exports of crude and products
      • 'imports' - returns weekly imports of crude and products
      • 'net imports incl spr' - returns weekly net imports of crude and total products
      • 'product supplied' - returns volume of supplied products
    • sma options:
      • sma = True returns 4 week averages
      • sma = False returns actual values
    Example 
    eia = eia_data.EiaData()
eia.weekly_balance(series = 'all')
    crude_oil_production refiner_inputs_and_utilisation refiner_and_blender_net_inputs ...
    Weekly U.S. Field Production of Crude Oil (Thousand Barrels per Day) Weekly U.S. Refiner Net Input of Crude Oil (Thousand Barrels per Day) Weekly U.S. Gross Inputs into Refineries (Thousand Barrels per Day) Weekly U. S. Operable Crude Oil Distillation Capacity (Thousand Barrels per Calendar Day) Weekly U.S. Percent Utilization of Refinery Operable Capacity (Percent) Weekly U.S. Imports of Crude Oil and Petroleum Products (Thousand Barrels per Day) Weekly U.S. Imports of Crude Oil (Thousand Barrels per Day) Weekly U.S. Commercial Crude Oil Imports Excluding SPR (Thousand Barrels per Day) Weekly U.S. Crude Oil Imports by SPR (Thousand Barrels per Day) Weekly U.S. Crude Oil Imports for SPR by Others (Thousand Barrels per Day) ...
    date ...
    1982-08-20 NaN 11722 NaN NaN NaN NaN NaN 3459 172 NaN ...
    1982-08-27 NaN 11918 NaN NaN NaN NaN NaN 3354 339 NaN ...
    1982-09-24 NaN 12375 NaN NaN NaN NaN NaN 3494 176 NaN ...
    ... ... ... ... ... ... ... ... ... ... ... ...

    EiaData().last_weekly_balance( breakdown = False )

    • Returns timeseries for the weekly EIA balance.
    • Arguments:
      • breakdown = False returns week ending stocks
      • breakdown = True returns a breakdown by production, imports, exports, etc. of crude and products
    Example 
    eia = eia_data.EiaData()
eia.last_weekly_balance( breakdown = False )
    supply 9/11/20 9/4/20 difference_week_ago percent_change_week_ago 9/13/19 difference_year_ago percent_change_year_ago
    0 Crude Oil 1141.778 1148.294 -6.516 -0.6 1061.944 79.833 7.5
    1 Commercial (Excluding SPR) 496.045 500.434 -4.389 -0.9 417.126 78.918 18.9
    2 Strategic Petroleum Reserve (SPR) 645.733 647.860 -2.127 -0.3 644.818 0.915 0.1
    3 Total Motor Gasoline 231.524 231.905 -0.381 -0.2 229.685 1.840 0.8
    ... ... ... ... ... ... ... ... ...

  • Crude oil supply

    • EiaData().crude_production()

    • Returns monthly crude production by PADD and state.
    Example 
    eia = eia_data.EiaData()
eia.crude_production()

    Output

    U.S. Field Production of Crude Oil (Thousand Barrels per Day) East Coast (PADD 1) Field Production of Crude Oil (Thousand Barrels per Day) Florida Field Production of Crude Oil (Thousand Barrels per Day) New York Field Production of Crude Oil (Thousand Barrels per Day) Pennsylvania Field Production of Crude Oil (Thousand Barrels per Day) Virginia Field Production of Crude Oil (Thousand Barrels per Day) ...
    date ...
    1920-01-15 1097.0 NaN NaN NaN NaN NaN ...
    1920-02-15 1145.0 NaN NaN NaN NaN NaN ...
    1920-03-15 1167.0 NaN NaN NaN NaN NaN ...
    ... ... ... ... ... ... ... ...

    EiaData().crude_supply_and_disposition( series = 'all' )

    • Returns monthly crude supply and disposition.
    • series options:
      • 'all' - returns all series (default)
      • 'supply' - returns weekly crude oil production
      • 'disposition' - returns stock change, exports, refinery and blender net input of crude oil, etc.
      • 'ending stocks' - returns monthly crude ending stocks
      • 'spr stocks' - returns month end SPR stocks
      • 'spr imports' - returns monthly SPR imports
    Example 
    eia = eia_data.EiaData()
eia.crude_supply_and_disposition(series = 'supply')
    U.S. Field Production of Crude Oil (Thousand Barrels) Alaska Field Production of Crude Oil (Thousand Barrels) Lower 48 States Field Production of Crude Oil (Thousand Barrels) U.S. Imports of Crude Oil (Thousand Barrels) U.S. Crude Oil Imports Excluding SPR (Thousand Barrels) U.S. Crude Oil SPR Imports from All Countries (Thousand Barrels) U.S. Supply Adjustment of Crude Oil (Thousand Barrels)
    date
    1920-01-15 34008.0 NaN NaN 6294.0 NaN NaN NaN
    1920-02-15 33193.0 NaN NaN 4940.0 NaN NaN NaN
    1920-03-15 36171.0 NaN NaN 6503.0 NaN NaN NaN
    1920-04-15 34945.0 NaN NaN 6186.0 NaN NaN NaN
    ... ... ... ... ... ... ... ...

    EiaData().rig_count()

    • Returns monthly rig counts.
    Example 
    eia = eia_data.EiaData()
eia.rig_count()
    U.S. Crude Oil and Natural Gas Rotary Rigs in Operation (Count) U.S. Onshore Crude Oil and Natural Gas Rotary Rigs in Operation (Count) U.S. Offshore Crude Oil and Natural Gas Rotary Rigs in Operation (Count) U.S. Crude Oil Rotary Rigs in Operation (Count) U.S. Natural Gas Rotary Rigs in Operation (Count) U.S. Crude Oil and Natural Gas Active Well Service Rigs in operation (Count)
    date
    1973-01-15 1219.0 1120.0 99.0 NaN NaN 1549.0
    1973-02-15 1126.0 1037.0 89.0 NaN NaN 1677.0
    1973-03-15 1049.0 959.0 90.0 NaN NaN 1805.0
    1973-04-15 993.0 914.0 79.0 NaN NaN 1898.0
    ... ... ... ... ... ... ...

    EiaData().crude_reserves()

    • Returns annual proven crude reserves and discoveries (last data point is from 2018).
    Example 
    eia = eia_data.EiaData()
eia.crude_reserves()

    Output

    U.S. Crude Oil Proved Reserves (Million Barrels) U.S. Crude Oil Reserves Adjustments (Million Barrels) U.S. Crude Oil Reserves Revision Increases (Million Barrels) U.S. Crude Oil Reserves Revision Decreases (Million Barrels) U.S. Crude Oil Reserves Sales (Million Barrels) U.S. Crude Oil Reserves Acquisitions (Million Barrels) U.S. Crude Oil Reserves Extensions and Discoveries (Million Barrels) U.S. Crude Oil Reserves Extensions (Million Barrels) U.S. Crude Oil Reserves New Field Discoveries (Million Barrels) U.S. Crude Oil New Reservoir Discoveries in Old Fields (Million Barrels) U.S. Crude Oil Estimated Production from Reserves (Million Barrels)
    date
    1900-06-30 2900 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
    1901-06-30 3000 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
    1902-06-30 3200 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
    1903-06-30 3400 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
    ... ... ... ... ... ... ... ... ... ... ... ...

  • Refining and processing

    • EiaData().weekly_refinery_inputs( series = 'all', sma = False )

    • Returns weekly import and export data.
        • Arguments:
      • series = 'all' - returns weekly refinery inputs and net inputs
      • series = 'inputs' - returns weekly crude inputs, capcaity and utilisation
      • series = 'net' - returns weekly net inputs of blending components
      • sma = True - returns 4 week average
    Example 
    eia = eia_data.EiaData()
eia.weekly_refinery_inputs( series = 'inputs' )
    Weekly U.S. Refiner Net Input of Crude Oil (Thousand Barrels per Day) Weekly U.S. Gross Inputs into Refineries (Thousand Barrels per Day) Weekly U. S. Operable Crude Oil Distillation Capacity (Thousand Barrels per Calendar Day) Weekly U.S. Percent Utilization of Refinery Operable Capacity (Percent)
    date
    1982-08-20 11722.0 NaN NaN NaN
    1982-08-27 11918.0 NaN NaN NaN
    1982-09-24 12375.0 NaN NaN NaN
    1982-10-01 12303.0 NaN NaN NaN
    ... ... ... ... ...

    EiaData().refinery_utilisation()

    • Returns monthly refinery utilisation.
    Example 
    eia = eia_data.EiaData()
eia.refinery_utilisation()
    U.S. Gross Inputs to Refineries (Thousand Barrels Per Day) U. S. Operable Crude Oil Distillation Capacity (Thousand Barrels per Calendar Day) U. S. Operating Crude Oil Distillation Capacity (Thousand Barrels per Day) U. S. Idle Crude Oil Distillation Capacity (Thousand Barrels per Day) U.S. Percent Utilization of Refinery Operable Capacity
    date
    1985-01-15 11583.0 15659.0 14361.0 1298.0 74.0
    1985-02-15 11485.0 15559.0 14293.0 1266.0 73.8
    1985-03-15 11484.0 15582.0 14268.0 1314.0 73.7
    1985-04-15 11969.0 15640.0 14605.0 1035.0 76.5
    ... ... ... ... ... ...

    EiaData().refinery_yield()

    • Returns monthly refinery yield by product.
    Example 
    eia = eia_data.EiaData()
eia.refinery_yield()
    U.S. Refinery Yield of Hydrocarbon Gas Liquids (Percent) U.S. Refinery Yield of Finished Motor Gasoline (Percent) U.S. Refinery Yield of Aviation Gasoline (Percent) U.S. Refinery Yield of Kerosene-Type Jet Fuel (Percent) U.S. Refinery Yield of Kerosene (Percent) U.S. Refinery Yield of Distillate Fuel Oil (Percent) ...
    date
    1993-01-15 NaN 47.5 0.1 9.7 0.5 21.6 ...
    1993-02-15 NaN 47.1 0.1 9.7 0.5 20.8 ...
    1993-03-15 NaN 45.4 0.2 9.7 0.4 21.3 ...
    ... ... ... ... ... ... ... ...

    EiaData().crude_acquistion_cost()

    • Returns monthly crude acquistion cost of refiners.
    Example 
    eia = eia_data.EiaData()
eia.crude_acquisition_cost()
    U.S. Crude Oil Composite Acquisition Cost by Refiners (Dollars per Barrel) U.S. Crude Oil Domestic Acquisition Cost by Refiners (Dollars per Barrel) U.S. Crude Oil Imported Acquisition Cost by Refiners (Dollars per Barrel)
    date
    1974-01-15 7.46 6.72 9.59
    1974-02-15 8.57 7.08 12.45
    1974-03-15 8.68 7.05 12.73
    1974-04-15 9.13 7.21 12.72
    ... ... ... ...

    EiaData().crude_inputs_quality()

    • Returns monthly crude inputs quality.
    Example 
    eia = eia_data.EiaData()
eia.crude_inputs_quality()
    U.S. Sulfur Content (Weighted Average) of Crude Oil Input to Refineries (Percent) U.S. API Gravity (Weighted Average) of Crude Oil Input to Refineries (Degrees)
    date
    1985-01-15 0.88 32.64
    1985-02-15 0.88 32.87
    1985-03-15 0.93 32.75
    1985-04-15 0.90 32.58
    ... ... ...

    EiaData().refineries()

    • Returns annual number of U.S. refineries and capacity by refinery unit.
    Example 
    eia = eia_data.EiaData()
eia.refineries()
    U.S. Number of Operable Refineries as of January 1 (Count) U.S. Number of Operating Refineries as of January 1 (Count) U.S. Number of Idle Refineries as of January 1 (Count) U.S. Refinery Annual Operable Atmospheric Crude Oil Distillation Capacity as of January 1 (Barrels per Calendar Day) U.S. Refinery Annual Operating Atmospheric Crude Oil Distillation Capacity as of January 1 (Barrels per Calendar Day) U.S. Refinery Annual Idle Atmospheric Crude Oil Distillation Capacity as of January 1 (Barrels per Calendar Day) ...
    date
    1982-06-30 301.0 254.0 47.0 17889734.0 16103579.0 1786155.0 ...
    1983-06-30 258.0 233.0 25.0 16859337.0 14960647.0 1898690.0 ...
    1984-06-30 247.0 214.0 33.0 16137141.0 14837685.0 1299456.0 ...
    ... ... ... ... ... ... ... ...

  • Imports and Exports

    • EiaData().weekly_xm( padds = False, sma = False )

    • Returns weekly import and export data.
        • Arguments:
      • padds = True - returns weekly imports by PADD
      • padds = True - returns weekly imports and exports by product
      • sma = True - returns 4-week average
    Example 
    eia = eia_data.EiaData()
eia.weekly_xm( padds = True )
    Weekly U.S. Imports of Crude Oil and Petroleum Products (Thousand Barrels per Day) Weekly East Coast (PADD 1) Imports of Crude Oil and Petroleum Products (Thousand Barrels per Day) Weekly Midwest (PADD 2) Imports of Crude Oil and Petroleum Products (Thousand Barrels per Day) Weekly Gulf Coast (PADD 3) Imports of Crude Oil and Petroleum Products (Thousand Barrels per Day) Weekly Rocky Mountain (PADD 4) Imports of Crude Oil and Petroleum Products (Thousand Barrels per Day) Weekly West Coast (PADD 5) Imports of Crude Oil and Petroleum Products (Thousand Barrels per Day)
    date
    1991-02-08 6877.0 NaN NaN NaN NaN NaN
    1991-02-15 6573.0 NaN NaN NaN NaN NaN
    1991-02-22 6221.0 NaN NaN NaN NaN NaN
    1991-03-01 6188.0 NaN NaN NaN NaN NaN
    ... ... ... ... ... ... ...

    EiaData().monthly_xm( net = False, xm = 'both', by = False )

    • Returns monthly import and export data.
        • Arguments:
      • net = True - returns monthly net imports by country of origin
      • net = False and xm = 'both' - returns monthly imports and exports by product
      • net = False and xm = 'm' - returns monthly imports by product
      • net = False and xm = 'x' - returns monthly exports by product
      • net = False and xm = 'm' and by = True - returns monthly imports by country of origin
      • net = False and xm = 'x' and by = True - returns monthly exports by destination
    Example 
    eia = eia_data.EiaData()
eia.monthly_xm( net = True )
    U.S. Net Imports of Crude Oil and Petroleum Products (Thousand Barrels per Day) U.S. Net Imports from Persian Gulf Countries of Crude Oil and Petroleum Products (Thousand Barrels per Day) U.S. Net Imports from OPEC Countries of Crude Oil and Petroleum Products (Thousand Barrels per Day) U.S. Net Imports from Algeria of Crude Oil and Petroleum Products (Thousand Barrels per Day) U.S. Net Imports from Angola of Crude Oil and Petroleum Products (Thousand Barrels per Day) U.S. Net Imports from Congo (Brazzaville) of Crude Oil and Petroleum Products (Thousand Barrels per Day)
    date
    1973-01-15 5646.0 NaN NaN NaN NaN NaN
    1973-02-15 6246.0 NaN NaN NaN NaN NaN
    1973-03-15 6386.0 NaN NaN NaN NaN NaN
    ... ... ... ... ... ... ...

    EiaData().weekly_imports_by_country( sma = False )

    • Returns weekly imports by country.
        • Arguments:
      • sma = True - returns 4 week average
    Example 
    eia = eia_data.EiaData()
eia.weekly_top_imports_by_country( sma = False )
    Weekly U.S. Imports from Canada of Crude Oil (Thousand Barrels per Day) Weekly U.S. Imports from Saudi Arabia of Crude Oil (Thousand Barrels per Day) Weekly U.S. Imports from Mexico of Crude Oil (Thousand Barrels per Day) Weekly U.S. Imports from Iraq of Crude Oil (Thousand Barrels per Day) Weekly U.S. Imports from Venezuela of Crude Oil (Thousand Barrels per Day) Weekly U.S. Imports from Colombia of Crude Oil (Thousand Barrels per Day) ...
    date
    2010-06-04 1869.0 1230.0 1284.0 538.0 638.0 259.0 ...
    2010-06-11 2320.0 488.0 871.0 369.0 630.0 243.0 ...
    2010-06-18 1875.0 1048.0 1289.0 1069.0 542.0 448.0 ...
    ... ... ... ... ... ... ... ...

    EiaData().crude_imports_quality()

    • Returns monthly crude import quality.
    Example 
    eia = eia_data.EiaData()
eia.crude_quality()
    U.S. Percent Total Imported by API Gravity of Crude Gravity 20.0 percent or less (%) U.S. Percent Total Imported by API Gravity of Crude Gravity 20.1 to 25.0 percent (%) U.S. Percent Total Imported by API Gravity of Crude Gravity 25.1 to 30.0 percent (%) U.S. Percent Total Imported by API Gravity of Crude Gravity 30.1 to 35.0 percent (%) U.S. Percent Total Imported by API Gravity of Crude Gravity 35.1 to 40.0 percent (%) U.S. Percent Total Imported by API Gravity of Crude Gravity 40.1 to 45.0% U.S. Percent Total Imported by API Gravity of Crude Gravity 45.1% or more (%)
    date
    1983-01-15 2.72 32.83 6.44 30.73 15.98 9.30 2.00
    1983-02-15 5.92 27.70 10.92 23.09 19.97 8.65 3.75
    1983-03-15 4.10 26.62 9.17 23.10 26.10 8.07 2.83
    1983-04-15 3.76 21.87 10.50 20.91 27.77 10.31 4.88
    ... ... ... ... ... ... ... ...

  • Stocks

    • EiaData().weekly_stocks( padds = False, sma = False )

    • Returns weekly crude and product stocks.
        • Arguments:
      • padds = True - returns weekly stocks by PADD for crude and a coarse product categories
      • sma = True - returns 4-week average
    Example 
    eia = eia_data.EiaData()
eia.weekly_stocks( padds = True )
    commercial_crude distillates
    Weekly Cushing, OK Ending Stocks excluding SPR of Crude Oil (Thousand Barrels) Weekly East Coast (PADD 1) Ending Stocks excluding SPR of Crude Oil (Thousand Barrels) Weekly Gulf Coast (PADD 3) Ending Stocks excluding SPR of Crude Oil (Thousand Barrels) Weekly Midwest (PADD 2) Ending Stocks excluding SPR of Crude Oil (Thousand Barrels) Weekly Rocky Mountain (PADD 4) Ending Stocks excluding SPR of Crude Oil (Thousand Barrels) Weekly U.S. Ending Stocks excluding SPR of Crude Oil (Thousand Barrels) Weekly West Coast (PADD 5) Ending Stocks excluding SPR of Crude Oil (Thousand Barrels) Weekly Central Atlantic (PADD 1B) Ending Stocks of Distillate Fuel Oil (Thousand Barrels) ...
    date ...
    1982-08-20 NaN NaN NaN NaN NaN 338764.0 NaN NaN ...
    1982-08-27 NaN NaN NaN NaN NaN 336138.0 NaN NaN ...
    1982-09-24 NaN NaN NaN NaN NaN 335586.0 NaN NaN ...
    ... ... ... ... ... ... ... ... ... ...

    EiaData().monthly_product_stocks( padds = False )

    • Returns monthly product stocks.
        • Arguments:
      • padds = True - returns monthly stocks by PADD for crude and a coarse product categories
    Example 
    eia = eia_data.EiaData()
eia.monthly_product_stocks( padds = False )
    U.S. Ending Stocks of Total Gasoline (Thousand Barrels) U.S. Ending Stocks of Distillate Fuel Oil (Thousand Barrels) U.S. Ending Stocks of Distillate Fuel Oil, 0 to 15 ppm Sulfur (Thousand Barrels) U.S. Ending Stocks of Distillate Fuel Oil, Greater than 15 to 500 ppm Sulfur (Thousand Barrels) U.S. Ending Stocks of Distillate Fuel Oil, Greater Than 500 ppm Sulfur (Thousand Barrels) U.S. Ending Stocks of Residual Fuel Oil (Thousand Barrels) U.S. Ending Stocks of Propane and Propylene (Thousand Barrels) ...
    date
    1936-01-15 NaN NaN NaN NaN NaN 83083.0 NaN ...
    1936-02-15 NaN NaN NaN NaN NaN 81563.0 NaN ...
    1936-03-15 NaN NaN NaN NaN NaN 80870.0 NaN ...
    ... ... ... ... ... ... ... ... ...

    EiaData().monthly_refinery_stocks()

    • Returns monthly refinery stocks.
    Example 
    eia = eia_data.EiaData()
eia.monthly_refinery_stocks()
    U.S. Crude Oil and Petroleum Products Stocks at Refineries (Thousand Barrels) U.S. Crude Oil Stocks at Refineries (Thousand Barrels) U.S. Total Petroleum Products Stocks at Refineries (Thousand Barrels) U.S. Hydrocarbon Gas Liquids Stocks at Refineries (Thousand Barrels) U.S. Refinery Stocks of Natural Gas Liquids (Thousand Barrels) U.S. Refinery Stocks of Ethane (Thousand Barrels) U.S. Refinery Stocks of Propane (Thousand Barrels) U.S. Refinery Stocks of Normal Butane (Thousand Barrels) ...
    date
    1981-01-15 NaN 119156.0 NaN NaN NaN NaN NaN NaN ...
    1981-02-15 NaN 125167.0 NaN NaN NaN NaN NaN NaN ...
    1981-03-15 NaN 128448.0 NaN NaN NaN NaN NaN NaN ...
    ... ... ... ... ... ... ... ... ... ...

    EiaData().monthly_tank_and_pipeline_stocks()

    • Returns monthly tank and pipeline stocks.
    Example 
    eia = eia_data.EiaData()
eia.monthly_tank_and_pipeline_stocks()
    U.S. Crude Oil Stocks at Tank Farms and Pipelines (Thousand Barrels) East Coast (PADD 1) Crude Oil Stocks at Tank Farms and Pipelines (Thousand Barrels) Midwest (PADD 2) Crude Oil Stocks at Tank Farms and Pipelines (Thousand Barrels) Cushing, OK Ending Stocks of Crude Oil (Thousand Barrels) Gulf Coast (PADD 3) Crude Oil Stocks at Tank Farms and Pipelines (Thousand Barrels) Rocky Mountain (PADD 4) Crude Oil Stocks at Tank Farms and Pipelines (Thousand Barrels) West Coast (PADD 5) Crude Oil Stocks at Tank Farms and Pipelines (Thousand Barrels) ...
    date
    1981-01-15 211030.0 2615.0 70627.0 NaN 94631.0 13169.0 29988.0 ...
    1981-02-15 212835.0 3684.0 67137.0 NaN 96435.0 13458.0 32121.0 ...
    1981-03-15 222457.0 2570.0 71186.0 NaN 101831.0 13872.0 32998.0 ...
    ... ... ... ... ... ... ... ... ...

  • Consumption and sales

    • EiaData().weekly_product_supplied( sma = False )

    • Returns weekly product supplied.
        • Arguments:
      • sma = True - returns 4-week average
    Example 
    eia = eia_data.EiaData()
eia.weekly_product_supplied()
    Weekly U.S. Product Supplied of Petroleum Products (Thousand Barrels per Day) Weekly U.S. Product Supplied of Finished Motor Gasoline (Thousand Barrels per Day) Weekly U.S. Product Supplied of Kerosene-Type Jet Fuel (Thousand Barrels per Day) Weekly U.S. Product Supplied of Distillate Fuel Oil (Thousand Barrels per Day) Weekly U.S. Product Supplied of Residual Fuel Oil (Thousand Barrels per Day) Weekly U.S. Product Supplied of Propane and Propylene (Thousand Barrels per Day) Weekly U.S. Product Supplied of Other Oils (Thousand Barrels per Day) ...
    date
    1990-11-09 16588.0 NaN NaN NaN NaN NaN NaN ...
    1990-11-16 17019.0 NaN NaN NaN NaN NaN NaN ...
    1990-11-23 15686.0 NaN NaN NaN NaN NaN NaN ...
    ... ... ... ... ... ... ... ... ...

    EiaData().monthly_product_supplied()

    • Returns monthly product supplied.
    Example 
    eia = eia_data.EiaData()
eia.monthly_product_supplied()
    U.S. Product Supplied of Crude Oil and Petroleum Products (Thousand Barrels per Day) U.S. Product Supplied of Crude Oil (Thousand Barrels per Day) U.S. Product Supplied of Hydrocarbon Gas Liquids (Thousand Barrels per Day) U.S. Product Supplied of Natural Gas Liquids (Thousand Barrels per Day) U.S. Product Supplied of Ethane (Thousand Barrels per Day) U.S. Product Supplied of Propane (Thousand Barrels per Day) U.S. Product Supplied of Normal Butane (Thousand Barrels per Day) U.S. Product Supplied of Isobutane (Thousand Barrels per Day) ...
    date
    1936-01-15 NaN NaN NaN NaN NaN NaN NaN NaN ...
    1936-02-15 NaN NaN NaN NaN NaN NaN NaN NaN ...
    1936-03-15 NaN NaN NaN NaN NaN NaN NaN NaN ...
    ... ... ... ... ... ... ... ... ... ...

    EiaData().product_prices_sales_and_stock( series = 'all' )

    • Returns monthly sales prices, sales volume (in thousand gallons per day) and product stocks.
        • Arguments:
      • series = 'all' - returns sales prices, sales volume and product stocks
      • series = 'retail' - returns sales prices as dollars per gallon
      • series = 'volume' - returns sales volume in thousand gallons per day
      • series = 'stocks' - returns product stocks in thousand barrels
    Example 
    eia = eia_data.EiaData()
eia.product_prices_sales_and_stock('retail')
    U.S. Total Gasoline Through Company Outlets Price by All Sellers (Dollars per Gallon) U.S. Regular Gasoline Through Company Outlets Price by All Sellers (Dollars per Gallon) U.S. Gasoline Midgrade Through Company Outlets Price by All Sellers (Dollars per Gallon) U.S. Premium Gasoline Through Company Outlets Price by All Sellers (Dollars per Gallon) U.S. Aviation Gasoline Retail Sales by Refiners (Dollars per Gallon) U.S. Kerosene-Type Jet Fuel Retail Sales by Refiners (Dollars per Gallon) U.S. Propane Retail Sales by All Sellers (Dollars per Gallon) U.S. Kerosene Retail Sales by Refiners (Dollars per Gallon) ...
    date
    1975-07-15 NaN NaN NaN NaN NaN 0.292 NaN NaN ...
    1975-08-15 NaN NaN NaN NaN NaN 0.295 NaN NaN ...
    1975-09-15 NaN NaN NaN NaN NaN 0.296 NaN NaN ...
    ... ... ... ... ... ... ... ... ... ...


    News data

    The functions below retrieve news headlines based on keyword searches from Barrons, CNBC, the Financial Times, the New York Times, Reuters, Seeking Alpha and the Wall Street Journal. The keyword for Seeking Alpha is simply the relevant stock ticker.

    The scrape is based on Selenium and may not be very stable if the website layouts change.

    Furthermore, some of the functions can run for a long-time so it is recommended to use a reasonable datestop value.

    Some downloads may fail occasionally as access to the website could be blocked.

    # Importing the NewsData class
from finpie import NewsData # 
news = NewsData('XOM', 'exxon mobil')
news.head = False # default = false, ensures selenium headless mode
news.verbose = True # default = False, prints total number of collected articles

    NewsData(ticker, keywords).barrons()

    • Returns the news headlines from Barrons.com for the specified keywords.
    Example 
    # retrieve news article for a given search term
news = NewsData('XOM', 'exxon mobil')
df = news.barrons(datestop = '2020-06-01')
# filter news headlines with a keyword list
news.filterz = [ 'exxon', 'mobil', 'oil', 'energy' ]
df = news.filter_data(df)
    date link headline description newspaper author date_retrieved ticker comments tag search_term id source
    15/09/2020 https://www.barrons.com/articles/options-traders-are-pricing-in-an-exxon-dividend-cut-analyst-says-51600181938 Options Traders Are Pricing In an Exxon Dividend Cut, Analyst Says Whether Exxon can maintain its dividend is one of the most active debates right now among energy investors. The company has a strong incentive to keep making payments at current levels. Barrons.com Avi Salzman 2020-09-16 13:35:26.574289 XOM nan nan exxon mobil Barrons.comOptions Traders Are Pricing In an Exxon Dividend Cut, Analyst Sayshttps://www.barrons.com/articles/options-traders-are-pricing-in-an-exxon-dividend-cut-analyst-says-51600181938 barrons
    13/09/2020 https://www.wsj.com/articles/exxon-used-to-be-americas-most-valuable-company-what-happened-oil-gas-11600037243 Exxon Used to Be America’s Most Valuable Company. What Happened? The oil giant doubled down on oil and gas at what now looks to be the worst possible time. Investors are fleeing and workers are grumbling about the direction of a company some see as out of touch. WSJ.com Christopher M. Matthews 2020-09-16 13:35:26.574289 XOM nan nan exxon mobil WSJ.comExxon Used to Be America’s Most Valuable Company. What Happened?https://www.wsj.com/articles/exxon-used-to-be-americas-most-valuable-company-what-happened-oil-gas-11600037243 barrons
    11/09/2020 https://www.barrons.com/articles/where-to-find-bargains-in-oil-stocks-51599837910 Where to Find Bargains in Oil Stocks Now Goldman Sachs analyst likes certain refiners and Canadian oil companies. Barrons.com Avi Salzman 2020-09-16 13:35:26.574289 XOM nan nan exxon mobil Barrons.comWhere to Find Bargains in Oil Stocks Nowhttps://www.barrons.com/articles/where-to-find-bargains-in-oil-stocks-51599837910 barrons
    ... ... ... ... ... ... ... ... ... ... ... ... ...

    NewsData(ticker, keywords).cnbc()

    • Returns the news headlines from CNBC for the specified keywords.
    Example 
    # retrieve news article for a given search term
news = NewsData('XOM', 'exxon mobil')
df = news.cnbc(datestop = '2020-06-01')
# filter news headlines with a keyword list
news.filterz = [ 'exxon', 'mobil', 'oil', 'energy' ]
df = news.filter_data(df)
    date link headline description tag author date_retrieved ticker comments newspaper search_term id source
    2020-09-10 00:00:00 https://www.cnbc.com/video/2020/09/10/honeywell-ceo-darius-adamczyk-on-rejoining-the-dow.html?&qsearchterm=exxon mobil Honeywell CEO Darius Adamczyk on rejoining the Dow S&P Dow Jones Indices said Monday that three new companies will be joining the 30-stock benchmark. Salesforce.com will replace Exxon Mobil, Amgen will replace Pfizer ... Squawk Box U.S. nan 2020-09-16 14:14:43.533664 XOM nan CNBC exxon mobil CNBCHoneywell CEO Darius Adamczyk on rejoining the Dowhttps://www.cnbc.com/video/2020/09/10/honeywell-ceo-darius-adamczyk-on-rejoining-the-dow.html?&qsearchterm=exxon mobil cnbc
    2020-09-09 00:00:00 https://www.cnbc.com/2020/09/09/options-market-predicts-exxon-mobils-dividend-could-be-in-danger.html?&qsearchterm=exxon mobil Options market predicts Exxon Mobil’s dividend could be in danger One of the most consistent dividend payers in the history of the energy trade could be in danger of having to slash its payout, according ... Options Action Tyler Bailey 2020-09-16 14:14:43.533664 XOM nan CNBC exxon mobil CNBCOptions market predicts Exxon Mobil’s dividend could be in dangerhttps://www.cnbc.com/2020/09/09/options-market-predicts-exxon-mobils-dividend-could-be-in-danger.html?&qsearchterm=exxon mobil cnbc
    2020-09-08 00:00:00 https://www.cnbc.com/2020/09/08/exxon-downsizes-global-empire-as-wall-street-worries-about-dividend.html?&qsearchterm=exxon mobil Exxon downsizes global empire as Wall Street worries about dividend Ill-timed bets on rising demand have Exxon Mobil facing a shortfall of about $48 billion through 2021, according to a Reuters tally and Wall Street ... Oil and Gas nan 2020-09-16 14:14:43.533664 XOM nan CNBC exxon mobil CNBCExxon downsizes global empire as Wall Street worries about dividendhttps://www.cnbc.com/2020/09/08/exxon-downsizes-global-empire-as-wall-street-worries-about-dividend.html?&qsearchterm=exxon mobil cnbc
    ... ... ... ... ... ... ... ... ... ... ... ... ...

    NewsData(ticker, keywords).ft()

    • Returns the news headlines from the Financial Times for the specified keywords.
    Example 
    # retrieve news article for a given search term
news = NewsData('XOM', 'exxon mobil')
df = news.ft(datestop = '2020-06-01')
# filter news headlines with a keyword list
news.filterz = [ 'exxon', 'mobil', 'oil', 'energy' ]
df = news.filter_data(df)
    date link headline description tag date_retrieved ticker comments author newspaper search_term id source
    2020-07-31 00:00:00 /content/64d7e86e-079c-4502-a9a4-5ab7439c732f Big Oil gets smaller as Chevron and Exxon losses mount to $9.4bn ...destruction in the second quarter was unprecedented in the history of modern oil markets,” Neil Chapman, Exxon senior vice-president, told analysts on an investor call. “To put it in context, absolute... Oil & Gas industry 2020-09-16 14:20:31.865540 XOM nan nan FT exxon mobil FTBig Oil gets smaller as Chevron and Exxon losses mount to $9.4bn/content/64d7e86e-079c-4502-a9a4-5ab7439c732f ft
    2020-05-27 00:00:00 /content/c43ead81-5af3-44de-af1e-b108d6491354 Exxon shareholders vote against splitting chair and CEO roles ...Exxon, said the appointment of a lead director had helped improve oversight. A separate resolution calling for increased transparency about Exxon’s lobbying activity won 37.5 per cent support, a... Oil & Gas industry 2020-09-16 14:20:31.865540 XOM nan nan FT exxon mobil FTExxon shareholders vote against splitting chair and CEO roles/content/c43ead81-5af3-44de-af1e-b108d6491354 ft
    2020-05-12 00:00:00 /content/c54ee229-f4e7-43c8-87a5-e383099542fb Big Exxon shareholder to vote against chief ...company to disclose its lobbying activities, arguing it was falling behind global peers by failing to act on climate change. Wednesday’s move by LGIM, whose roughly $1bn stake makes it a top-20 Exxon... Corporate governance 2020-09-16 14:20:31.865540 XOM nan nan FT exxon mobil FTBig Exxon shareholder to vote against chief/content/c54ee229-f4e7-43c8-87a5-e383099542fb ft
    ... ... ... ... ... ... ... ... ... ... ... ... ...

    NewsData(ticker, keywords).nyt()

    • Returns the news headlines from the New York Times for the specified keywords.
    Example 
    # retrieve news article for a given search term
news = NewsData('XOM', 'exxon mobil')
df = news.nyt(datestop = '2020-06-01')
# filter news headlines with a keyword list
news.filterz = [ 'exxon', 'mobil', 'oil', 'energy' ]
df = news.filter_data(df)
    date link headline description tag author comments date_retrieved ticker newspaper search_term id source
    2020-09-08 00:00:00 /aponline/2020/09/08/business/ap-financial-markets-stocks.html?searchResultPosition=2 Exxon, Tesla Fall; Nikola, Beyond Meat Rise Stocks that moved heavily or traded substantially Tuesday: Business The Associated Press nan 2020-09-16 14:22:13.032245 XOM NYT exxon mobil NYTExxon, Tesla Fall; Nikola, Beyond Meat Rise/aponline/2020/09/08/business/ap-financial-markets-stocks.html?searchResultPosition=2 nyt
    2020-09-08 00:00:00 /reuters/2020/09/08/business/08reuters-exxon-mobil-spending-exclusive.html?searchResultPosition=3 Exclusive: Exxon Downsizes Global Empire as Wall Street Worries About Dividend Ill-timed bets on rising demand have Exxon Mobil Corp facing a shortfall of about $48 billion through 2021, according to a Reuters tally and Wall Street estimates, a situation that will require the top U.S. oil company to make deep cuts to its staff and projects. Business Reuters nan 2020-09-16 14:22:13.032245 XOM NYT exxon mobil NYTExclusive: Exxon Downsizes Global Empire as Wall Street Worries About Dividend/reuters/2020/09/08/business/08reuters-exxon-mobil-spending-exclusive.html?searchResultPosition=3 nyt
    2020-09-03 00:00:00 /reuters/2020/09/03/business/03reuters-refinery-operations-exxon-beaumont.html?searchResultPosition=4 Exxon Beaumont, Texas, Refinery Restarts Large Crude Unit: Sources Exxon Mobil Corp restarted the large crude distillation unit (CDU) at its 369,024 barrel-per-day (bpd) Beaumont, Texas, refinery on Thursday, said sources familiar with plant operations. Business Reuters nan 2020-09-16 14:22:13.032245 XOM NYT exxon mobil NYTExxon Beaumont, Texas, Refinery Restarts Large Crude Unit: Sources/reuters/2020/09/03/business/03reuters-refinery-operations-exxon-beaumont.html?searchResultPosition=4 nyt
    ... ... ... ... ... ... ... ... ... ... ... ... ...

    NewsData(ticker, keywords).reuters()

    • Returns the news headlines from Reuters for the specified keywords.
    Example 
    # retrieve news article for a given search term
news = NewsData('XOM', 'exxon mobil')
df = news.reuters()
# filter news headlines with a keyword list
news.filterz = [ 'exxon', 'mobil', 'oil', 'energy' ]
df = news.filter_data(df)
df.drop_duplicates('headline', inplace = True) # Reuters returns duplicate articles if articles were updated after publication...
    date link headline description date_retrieved ticker comments author tag newspaper search_term id source
    2020-09-16 00:00:00 /article/idUSL4N2GD12G FACTBOX-Oil refiners shut plants as demand losses may never return Plc, Exxon Mobil Corp,Viva Energy Group and Ampol Ltd - all welcomed 2020-09-16 15:37:54.994138 XOM nan nan nan Reuters exxon mobil ReutersFACTBOX-Oil refiners shut plants as demand losses may never return/article/idUSL4N2GD12G reuters
    2020-09-15 00:00:00 /article/idUSKBN26707N U.S. presidential candidate Biden rips Trump's record on ethanol Exxon Mobil Corp and billionaire investor Carl Icahn.Biden's team has 2020-09-16 15:37:54.994138 XOM nan nan nan Reuters exxon mobil ReutersU.S. presidential candidate Biden rips Trump's record on ethanol/article/idUSKBN26707N reuters
    2020-09-15 00:00:00 /article/idUSKBN2660I3 Column: Australia still addicted to fossil fuel with oil, gas subsidies - Russell for subsidising the four oil refineries, owned by BP Plc, Exxon Mobil 2020-09-16 15:37:54.994138 XOM nan nan nan Reuters exxon mobil ReutersColumn: Australia still addicted to fossil fuel with oil, gas subsidies - Russell/article/idUSKBN2660I3 reuters
    ... ... ... ... ... ... ... ... ... ... ... ... ...

    NewsData(ticker, keywords).seeking_alpha(datestop, press_releases = False)

    • Returns the news headlines from Seeking Alpha for the specified keywords.
    • It can happen that access to SeekingAlpha requires to solve a captcha by pressing and holding a button when run for the first time in a program. Will try to fix this in future versions. press_releases = True will get press releases instead of news headlines.
    Example 
    # retrieve news article for a given search term
news = NewsData('XOM', 'exxon mobil')
df = news.seeking_alpha(datestop = '2020-06-01')
# filter news headlines with a keyword list
news.filterz = [ 'exxon', 'mobil', 'oil', 'energy' ]
df = news.filter_data(df)
    date link headline author comments date_retrieved ticker description tag newspaper search_term id source
    2020-09-15 00:00:00 /news/3614409-options-traders-pricing-in-exxon-dividend-cut-analyst-says?source=content_type:react|section:News|sectionAsset:News|first_level_url:symbol|button:Author|lock_status:No|line:1 Options traders pricing in Exxon dividend cut, analyst says SA News 0 comments 2020-09-16 15:14:23.575898 XOM nan nan SA - News exxon mobil SA - NewsOptions traders pricing in Exxon dividend cut, analyst says/news/3614409-options-traders-pricing-in-exxon-dividend-cut-analyst-says?source=content_type:react|section:News|sectionAsset:News|first_level_url:symbol|button:Author|lock_status:No|line:1 sa
    2020-09-14 00:00:00 /news/3613801-connecticut-latest-state-to-sue-exxon-over-climate-change?source=content_type:react|section:News|sectionAsset:News|first_level_url:symbol|button:Author|lock_status:No|line:2 Connecticut latest state to sue Exxon over climate change SA News 0 comments 2020-09-16 15:14:23.575898 XOM nan nan SA - News exxon mobil SA - NewsConnecticut latest state to sue Exxon over climate change/news/3613801-connecticut-latest-state-to-sue-exxon-over-climate-change?source=content_type:react|section:News|sectionAsset:News|first_level_url:symbol|button:Author|lock_status:No|line:2 sa
    2020-09-10 00:00:00 /news/3612953-exxon-rated-new-buy-mkm-shares-slip?source=content_type:react|section:News|sectionAsset:News|first_level_url:symbol|button:Author|lock_status:No|line:3 Exxon rated new Buy at MKM but shares slip SA News 0 comments 2020-09-16 15:14:23.575898 XOM nan nan SA - News exxon mobil SA - NewsExxon rated new Buy at MKM but shares slip/news/3612953-exxon-rated-new-buy-mkm-shares-slip?source=content_type:react section:News
    ... ... ... ... ... ... ... ... ... ... ... ... ...

    NewsData(ticker, keywords).wsj()

    • Returns the news headlines from the Wall Street Journal for the specified keywords.
    Example 
    # retrieve news article for a given search term
news = NewsData('XOM', 'exxon mobil')
df = news.wsj(datestop = '2020-06-01')
# filter news headlines with a keyword list
news.filterz = [ 'exxon', 'mobil', 'oil', 'energy' ]
df = news.filter_data(df)
    date link headline description author tag date_retrieved ticker newspaper search_term id comments source
    2020-09-13 00:00:00 /articles/exxon-used-to-be-americas-most-valuable-company-what-happened-oil-gas-11600037243?mod=searchresults&page=1&pos=1 Exxon Used to Be America’s Most Valuable Company. What Happened? The oil giant doubled down on oil and gas at what now looks to be the worst possible time. Investors are fleeing and workers are grumbling about the direction of a company some see as out of touch. Christopher M. Matthews Business 2020-09-16 15:19:39.733511 XOM WSJ exxon mobil WSJExxon Used to Be America’s Most Valuable Company. What Happened?/articles/exxon-used-to-be-americas-most-valuable-company-what-happened-oil-gas-11600037243?mod=searchresults&page=1&pos=1 nan wsj
    2020-09-10 00:00:00 /articles/oil-major-bp-gives-a-taste-of-how-it-will-go-green-11599745648?mod=searchresults&page=1&pos=2 Oil Major BP Gives a Taste of How It Will Go Green A deal to buy into wind farms off the coast of New York and Massachusetts showcases the British company’s ambitions in the clean-energy sector—and the risks it is taking. Rochelle Toplensky Heard on the Street 2020-09-16 15:19:39.733511 XOM WSJ exxon mobil WSJOil Major BP Gives a Taste of How It Will Go Green/articles/oil-major-bp-gives-a-taste-of-how-it-will-go-green-11599745648?mod=searchresults&page=1&pos=2 nan wsj
    2020-09-08 00:00:00 /articles/oil-prices-drop-on-faltering-recovery-in-demand-11599562101?mod=searchresults&page=1&pos=3 Oil Prices Tumble on Faltering Recovery in Demand Oil prices slumped to their lowest level in nearly three months, under pressure from a stalling recovery in demand and planned production expansions by OPEC that threaten to add to an existing glut of crude. Joe Wallace Oil Markets 2020-09-16 15:19:39.733511 XOM WSJ exxon mobil WSJOil Prices Tumble on Faltering Recovery in Demand/articles/oil-prices-drop-on-faltering-recovery-in-demand-11599562101?mod=searchresults&page=1&pos=3 nan wsj
    ... ... ... ... ... ... ... ... ... ... ... ... ...

    Other data

    nasdaq_tickers()

    • Returns dataframe of tickers traded on the Nasdaq exchange.
    Example 
    nasdaq_tickers()
    Symbol Security Name
    0 AACG ATA Creativity Global - American Depositary Shares, each representing two common shares
    1 AACQ Artius Acquisition Inc. - Class A Common Stock
    2 AACQU Artius Acquisition Inc. - Unit consisting of one ordinary share and one third redeemable warrant
    3 AACQW Artius Acquisition Inc. - Warrant
    4 AAL American Airlines Group, Inc. - Common Stock
    ... ... ...

    global_tickers()

    • Returns 100.000+ global tickers from Gurufocus.com. Note that companies are listed in different countries or exchanges with different ticker symbols.
    Example 
    global_tickers()
    Symbol Company
    0 QNCO.Israel (Y.Z) Queenco Ltd
    1 ONE.Canada 01 Communique Laboratory Inc
    2 DFK.Germany 01 Communique Laboratory Inc
    3 OCQLF 01 Communique Laboratory Inc
    4 01C.Poland 01Cyberaton SA
    5 1PG.Australia 1 Page Ltd
    6 I8Y.Germany 1 Page Ltd
    8 8458.Taiwan 1 Production Film Co
    9 DRI.Austria 1&1 Drillisch AG
    10 DRI.Switzerland 1&1 Drillisch AG
    ... ... ...


    Sources

  • Barrons, www.barrons.com
  • CBOE, www.cboe.com
  • CNBC, www.cnbc.com
  • Financial Times, www.ft.com
  • Finviz, www.finviz.com
  • Gurufocus, www.gurufocus.com
  • Investing.com, www.investing.com
  • MarketWatch, www.marketwatch.com
  • Macrotrends, www.macrotrends.net
  • Moore Research Center, www.mrci.com
  • NASDAQ, www.nasdaq.com
  • OECD, www.oecd.org
  • Reuters, www.reuters.com
  • Seeking Alpha, www.seekingalpha.com
  • Wall Street Journal, www.wsj.com
  • Yahoo Finance, www.finance.yahoo.com

    • License

    Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

    Copyright (c) 2020 Peter la Cour

    About

    Simple library to download some financial data.

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