finpie - a simple library to download some financial data

For recreational and educational purposes. Creating easier access to some financial and news 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 news module enables historical news headline collection from the FT, NYT, WSJ, Barrons and Seeking Alpha 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 v0.1364

Changes for v0.1362

Changes for v0.1362

Changes for v0.1361

Changes for v0.136

Changes for v0.133

Changes for v0.13

Changes for v0.12

Changes for v0.11

To do list:

• Refactor the news scrape..
• Rework economic data module
• Add USDA data
• 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
   • Financial statements
   • Financial ratios and key metrics
   • Earnings and revenue estimates
   • Earnings call transcripts
   • Insider transactions and analyst ratings
   • ESG scores
   • Company profile
4. Price data
   • Stock prices
   • Option prices
   • Futures prices
5. News data
6. Other data
7. Sources
8. License

Installation

Python3 is required. Google Chrome version 89.*.****.*** 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

Company Fundamental data

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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

---

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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

To index

---

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

To index

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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

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Financial ratios and key metrics

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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

To index

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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	...

To index

Earnings and revenue estimates

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Back to top

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

To index

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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

To index

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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

To index

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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

To index

---

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

To index

---

Earnings Call Transcripts

---

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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	Prepared Remarks: Operator 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	Prepared Remarks: Operator 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	Prepared Remarks: Operator 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 ...
...	...	...	...	...	...	...	...	...	...

To index

---

Insider transactions and analyst ratings

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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
...	...	...	...	...	...	...	...	...	...

To index

---

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
...	...	...	...	...

To index

---

ESG scores

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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	...

To index

---

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

To index

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Company info

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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

To index

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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

To index

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Price data

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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
...	...	...	...	...	...	...	...

To index

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Option prices

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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	...
...	...	...	...	...	...

To index

---

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
...	...	...	...	...	...	...	...	...	...	...	...	...	...

To index

Futures prices

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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)
...	...	...	...	...	...	...	...	...	...	...	...

To index

---

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)
...	...	...	...	...	...	...	...	...	...	...	...

To index

---

News data

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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
...	...	...	...	...	...	...	...	...	...	...	...	...

To index

---

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
...	...	...	...	...	...	...	...	...	...	...	...	...

To index

---

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
...	...	...	...	...	...	...	...	...	...	...	...	...

To index

---

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
...	...	...	...	...	...	...	...	...	...	...	...	...

To index

---

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
...	...	...	...	...	...	...	...	...	...	...	...	...

To index

---

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
...	...	...	...	...	...	...	...	...	...	...	...	...

To index

---

Other data

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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
...	...	...

To index

---

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
...	...	...

To index

---

---

cftc( report_type = 'futures_traders', year = 2000 )

• Returns the CFTC reports for the given report type and period. Information on the data can be found here.
• report_type options:
  • 'disaggregated_futures': Disaggregated Futures Only Reports
  • 'disaggregated_combined': Disaggregated Futures-and-Options Combined Reports
  • 'futures_traders' (default): Traders in Financial Futures ; Futures Only Reports
  • 'futures_traders_combined': Traders in Financial Futures ; Futures-and-Options Combined Reports
  • 'futures': Futures Only Reports
  • 'futures_combined': Futures-and-Options Combined Reports
  • 'commodity_index': Commodity Index Trader Supplement
• If the year parameter is smaller than 2016, all available data will be returned.

Example

cftc(report_type = 'futures', year = 2020)

	market_and_exchange_names	cftc_contract_market_code	cftc_market_code_in_initials	cftc_region_code	cftc_commodity_code	open_interest_(all)	noncommercial_positions-long_(all)	noncommercial_positions-short_(all)	noncommercial_positions-spreading_(all)	commercial_positions-long_(all)	commercial_positions-short_(all)	_total_reportable_positions-long_(all)	total_reportable_positions-short_(all)	nonreportable_positions-long_(all)	nonreportable_positions-short_(all)	open_interest_(old)	noncommercial_positions-long_(old)	noncommercial_positions-short_(old)	noncommercial_positions-spreading_(old)	commercial_positions-long_(old)	commercial_positions-short_(old)	total_reportable_positions-long_(old)	total_reportable_positions-short_(old)	nonreportable_positions-long_(old)	nonreportable_positions-short_(old)	open_interest_(other)	noncommercial_positions-long_(other)	noncommercial_positions-short_(other)	noncommercial_positions-spreading_(other)	commercial_positions-long_(other)	commercial_positions-short_(other)	total_reportable_positions-long_(other)	total_reportable_positions-short_(other)	nonreportable_positions-long_(other)	nonreportable_positions-short_(other)	change_in_open_interest_(all)	change_in_noncommercial-long_(all)	change_in_noncommercial-short_(all)	change_in_noncommercial-spreading_(all)	change_in_commercial-long_(all)	change_in_commercial-short_(all)	change_in_total_reportable-long_(all)	change_in_total_reportable-short_(all)	change_in_nonreportable-long_(all)	change_in_nonreportable-short_(all)	%_of_open_interest_(oi)_(all)	%_of_oi-noncommercial-long_(all)	%_of_oi-noncommercial-short_(all)	%_of_oi-noncommercial-spreading_(all)	%_of_oi-commercial-long_(all)	%_of_oi-commercial-short_(all)	%_of_oi-total_reportable-long_(all)	%_of_oi-total_reportable-short_(all)	%_of_oi-nonreportable-long_(all)	%_of_oi-nonreportable-short_(all)	%_of_open_interest_(oi)(old)	%_of_oi-noncommercial-long_(old)	%_of_oi-noncommercial-short_(old)	%_of_oi-noncommercial-spreading_(old)	%_of_oi-commercial-long_(old)	%_of_oi-commercial-short_(old)	%_of_oi-total_reportable-long_(old)	%_of_oi-total_reportable-short_(old)	%_of_oi-nonreportable-long_(old)	%_of_oi-nonreportable-short_(old)	%_of_open_interest_(oi)_(other)	%_of_oi-noncommercial-long_(other)	%_of_oi-noncommercial-short_(other)	%_of_oi-noncommercial-spreading_(other)	%_of_oi-commercial-long_(other)	%_of_oi-commercial-short_(other)	%_of_oi-total_reportable-long_(other)	%_of_oi-total_reportable-short_(other)	%_of_oi-nonreportable-long_(other)	%_of_oi-nonreportable-short_(other)	traders-total_(all)	traders-noncommercial-long_(all)	traders-noncommercial-short_(all)	traders-noncommercial-spreading_(all)	traders-commercial-long_(all)	traders-commercial-short_(all)	traders-total_reportable-long_(all)	traders-total_reportable-short_(all)	traders-total_(old)	traders-noncommercial-long_(old)	traders-noncommercial-short_(old)	traders-noncommercial-spreading_(old)	traders-commercial-long_(old)	traders-commercial-short_(old)	traders-total_reportable-long_(old)	traders-total_reportable-short_(old)	traders-total_(other)	traders-noncommercial-long_(other)	traders-noncommercial-short_(other)	traders-noncommercial-spreading_(other)	traders-commercial-long_(other)	traders-commercial-short_(other)	traders-total_reportable-long_(other)	traders-total_reportable-short_(other)	concentration-gross_lt_=_4_tdr-long_(all)	concentration-gross_lt_=4_tdr-short_(all)	concentration-gross_lt_=8_tdr-long_(all)	concentration-gross_lt_=8_tdr-short_(all)	concentration-net_lt_=4_tdr-long_(all)	concentration-net_lt_=4_tdr-short_(all)	concentration-net_lt_=8_tdr-long_(all)	concentration-net_lt_=8_tdr-short_(all)	concentration-gross_lt_=4_tdr-long_(old)	concentration-gross_lt_=4_tdr-short_(old)	concentration-gross_lt_=8_tdr-long_(old)	concentration-gross_lt_=8_tdr-short_(old)	concentration-net_lt_=4_tdr-long_(old)	concentration-net_lt_=4_tdr-short_(old)	concentration-net_lt_=8_tdr-long_(old)	concentration-net_lt_=8_tdr-short_(old)	concentration-gross_lt_=4_tdr-long_(other)	concentration-gross_lt_=4_tdr-short(other)	concentration-gross_lt_=8_tdr-long_(other)	concentration-gross_lt_=8_tdr-short(other)	concentration-net_lt_=4_tdr-long_(other)	concentration-net_lt_=4_tdr-short_(other)	concentration-net_lt_=8_tdr-long_(other)	concentration-net_lt_=8_tdr-short_(other)	contract_units	cftc_contract_market_code_(quotes)	cftc_market_code_in_initials_(quotes)	cftc_commodity_code_(quotes)
date
2020-11-17	WHEAT-SRW - CHICAGO BOARD OF TRADE	001602	CBT	0	1	432714	127007	108586	108258	162186	168899	397451	385743	35263	46971	290010	100877	91806	55396	107265	114199	263538	261401	26472	28609	142704	49751	40401	29241	54921	54700	133913	124342	8791	18362	-8804	-14010	1027	6421	-1417	-14619	-9006	-7171	202	-1633	100	29.4	25.1	25	37.5	39	91.9	89.1	8.1	10.9	100	34.8	31.7	19.1	37	39.4	90.9	90.1	9.1	9.9	100	34.9	28.3	20.5	38.5	38.3	93.8	87.1	6.2	12.9	392	113	120	114	83	127	268	301	373	106	121	89	73	122	233	281	223	54	55	39	49	87	128	161	16.8	12.4	25.5	19.5	14.1	8.2	21.6	13.4	15.9	9.2	25.9	17.1	15.4	8	25.1	14.9	24.4	27.7	36.3	37.9	17.3	16.3	26.4	23.8	(CONTRACTS OF 5,000 BUSHELS)	001602	CBT	1
2020-11-10	WHEAT-SRW - CHICAGO BOARD OF TRADE	001602	CBT	0	1	441518	141017	107559	101837	163603	183518	406457	392914	35061	48604	309149	117977	91684	53973	110678	132654	282628	278311	26521	30838	132369	45187	38022	25717	52925	50864	123829	114603	8540	17766	-17695	-3704	4074	-3266	-11418	-16834	-18388	-16026	693	-1669	10000	3109	2404	2301	3701	4106	9201	8900	709	1100	10000	3802	2907	1705	3508	4209	9104	9000	806	1000	10000	3401	2807	1904	4000	3804	9305	8606	605	1304	389	120	106	108	87	127	273	286	373	111	111	86	77	121	239	270	220	52	52	35	49	88	125	155	1605	1301	2504	2008	1308	900	2106	1405	1500	1000	2504	1802	1405	809	2406	1600	2508	2709	3805	3808	1709	1601	2706	2404	(CONTRACTS OF 5,000 BUSHELS)	001602	CBT	1
2020-11-03	WHEAT-SRW - CHICAGO BOARD OF TRADE	001602	CBT	0	1	459213	144721	103485	105103	175021	200352	424845	408940	34368	50273	335930	124897	93364	58310	126302	151768	309509	303442	26421	32488	123283	43831	34128	22786	48719	48584	115336	105498	7947	17785	1309	-6915	-4209	-2235	11389	7396	2239	952	-930	357	100	31.5	22.5	22.9	38.1	43.6	92.5	89.1	7.5	10.9	100	37.2	27.8	17.4	37.6	45.2	92.1	90.3	7.9	9.7	100	35.6	27.7	18.5	39.5	39.4	93.6	85.6	6.4	14.4	393	119	108	106	91	130	275	290	382	111	114	88	76	122	243	274	204	46	47	31	51	84	116	146	15.7	13.7	24.5	21.7	12.4	8.7	20.2	14.6	14.5	11.9	24.3	19.6	12.9	9.4	22.4	16.3	25.8	28.7	38.9	39.2	17.1	16.7	27.5	24.4	(CONTRACTS OF 5,000 BUSHELS)	001602	CBT	1
2020-10-27	WHEAT-SRW - CHICAGO BOARD OF TRADE	001602	CBT	0	1	457904	151636	107694	107338	163632	192956	422606	407988	35298	49916	340631	132614	101843	60118	120105	145840	312837	307801	27794	32830	117273	45561	32390	20681	43527	47116	109769	100187	7504	17086	13223	2593	5188	788	7089	7666	10470	13642	2753	-419	100	33.1	23.5	23.4	35.7	42.1	92.3	89.1	7.7	10.9	100	38.9	29.9	17.6	35.3	42.8	91.8	90.4	8.2	9.6	100	38.9	27.6	17.6	37.1	40.2	93.6	85.4	6.4	14.6	402	129	115	106	82	125	271	294	388	119	120	88	68	118	240	276	206	49	48	29	49	82	112	149	14.8	13.2	23.8	21.4	11.8	8.9	19.9	15.6	14.3	11.6	23.9	20.5	13.2	10.1	22.3	16.9	25.6	29.2	38.6	39	16.9	18.2	28.2	25.7	(CONTRACTS OF 5,000 BUSHELS)	001602	CBT	1
2020-10-20	WHEAT-SRW - CHICAGO BOARD OF TRADE	001602	CBT	0	1	444681	149043	102506	106550	156543	185290	412136	394346	32545	50335	334451	132530	99016	60133	116411	141422	309074	300571	25377	33880	110230	43092	30069	19838	40132	43868	103062	93775	7168	16455	28174	13140	9401	1205	11140	14955	25485	25561	2689	2613	100	33.5	23.1	24	35.2	41.7	92.7	88.7	7.3	11.3	100	39.6	29.6	18	34.8	42.3	92.4	89.9	7.6	10.1	100	39.1	27.3	18	36.4	39.8	93.5	85.1	6.5	14.9	390	122	109	105	79	126	261	291	379	117	113	84	69	121	232	275	200	49	42	29	46	83	108	145	14.9	13.5	24.2	21.1	11.7	9.4	20	15.9	14.3	10.9	24.7	19.1	13.4	9.9	22.3	17.3	27.4	30.6	39.7	40.8	18	18.9	27.9	27.1	(CONTRACTS OF 5,000 BUSHELS)	001602	CBT	1
2020-10-13	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...

To index

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Sources

Barrons, www.barrons.com

CBOE, www.cboe.com

CFTC, www.cftc.gov

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

Motley Fool, www.fool.com

NASDAQ, www.nasdaq.com

Seeking Alpha, www.seekingalpha.com

Wall Street Journal, www.wsj.com

Yahoo Finance, www.finance.yahoo.com

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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