Documentation

This page includes all the documentation for the Finance Toolkit, an open-source toolkit in which all relevant financial ratios (150+), indicators and performance measurements are written down in the most simplistic way allowing for complete transparency of the calculation method. Each functionality includes an example of how to use it and is therefore an excellent way to better understand how to use each functionality. These examples are also directly embedded in the code. For simplicity sake, only the controller modules are included here given that the models themselves should be relatively straightforward. Make sure to also have a look at the example notebooks as found here.

To install the FinanceToolkit it simply requires the following:

pip install financetoolkit -U

The Toolkit Module is meant to be a collection of useful functions that collect and parse data. These are historical data, fundamental data (balance, income and cash flow statements) as well as several others metrics from Financial Modeling Prep like enterprise values, company profiles and more. From this module, you are able to access the related modules as well.

If you are looking for documentation regarding the discovery, ratios, models, technical indicators, fixed income, risk metrics, performance metrics and economic indicators, please have a look below:

Toolkit Discovery Ratios Models Options Technicals Fixed Income Risk Performance Economics

init

Initializes an Toolkit object with a ticker or a list of tickers. The way the Toolkit is initialized will define how the data is collected. For example, if you enable the quarterly flag, you will be able to collect quarterly data. Next to that, you can define the start and end date to specify a specific range. Another option is to define the custom ratios you want to calculate. This can be done by passing a dictionary.

See for more information on all of this, the following link: https://www.jeroenbouma.com/projects/financetoolkit

Args:

tickers (str or list): A string or a list of strings containing the company ticker(s). E.g. ‘TSLA’ or ‘MSFT’ Find the tickers on a variety of websites or via the FinanceDatabase: https://github.com/JerBouma/financedatabase
api_key (str): An API key from FinancialModelingPrep. Obtain one here: https://www.jeroenbouma.com/fmp
start_date (str): A string containing the start date of the data. This needs to be formatted as YYYY-MM-DD. The default is today minus 10 years which can be freely changed to extend the period.
end_date (str): A string containing the end date of the data. This needs to be formatted as YYYY-MM-DD. The default is today which can be freely changed to extend the period.
quarterly (bool): A boolean indicating whether to collect quarterly data. This defaults to False and thus collects yearly financial statements. Note that historical data can still be collected for any period and interval.
risk_free_rate (str): A string containing the risk free rate. This can be 13w, 5y, 10y or 30y. This is based on the US Treasury Yields and is used to calculate various ratios and Excess Returns.
benchmark_ticker (str): A string containing the benchmark ticker. Defaults to SPY (S&P 500). This is meant to calculate ratios and indicators such as the CAPM and Jensen’s Alpha but also serves as purpose to give insights in the performance of a stock compared to a benchmark.
historical_source (str): A string containing the historical source. This can be either FinancialModelingPrep or YahooFinance. Defaults to FinancialModelingPrep. It is automatically defined if you enter an API Key from FinancialModelingPrep. You can overwrite this by filling this parameter. Note that for the Free plan the amount of historical data is limited to 5 years. If you want to collect more data, you need to upgrade to a paid plan.
historical (pd.DataFrame): A DataFrame containing historical data. This is a custom dataset only relevant if you are looking to use custom data. See for more information the following Notebook: https://www.jeroenbouma.com/projects/financetoolkit/external-datasets
balance (pd.DataFrame): A DataFrame containing balance sheet data. This is a custom dataset only relevant if you are looking to use custom data. See for more information the notebook as mentioned at historical.
cash (pd.DataFrame): A DataFrame containing cash flow statement data. This is a custom dataset only relevant if you are looking to use custom data. See for more information the notebook as mentioned at historical.
format_location (str): A string containing the location of the normalization files.
convert_currency (bool): A boolean indicating whether to convert the currency of the financial statements to match that of the related historical data. This is an important conversion when comparing the financial statements between each ticker as well as for calculations that are done with the historical data. If you are using a Free plan from FinancialModelingPrep, this will be set to False. If you are using a Premium plan from FinancialModelingPrep, this will be set to True. Defaults to None and can thus be overridden.
reverse_dates (bool): A boolean indicating whether to reverse the dates in the financial statements.
intraday_period (str): A string containing the intraday period. This can be 1min, 5min, 15min, 30min or 1hour. This is used to collect intraday data. Note that this is only relevant if you have are looking to utilize intraday data through the Toolkit and wish to access Risk, Performance and Technicals for very short timeframes. Defaults to None which means it will not use intraday data.
rounding (int): An integer indicating the number of decimals to round the results to.
remove_invalid_tickers (bool): A boolean indicating whether to remove invalid tickers. Defaults to False.
sleep_timer (bool): Whether to set a sleep timer when the rate limit is reached. Note that this only works if you have a Premium subscription (Starter or higher) from FinancialModelingPrep. Defaults to None which means it is determined by the model (Free plan = False, Premium plan = True).
progress_bar (bool): Whether to enable the progress bar when ticker amount is over 10. Defaults to True.

As an example:

from financetoolkit import Toolkit

# Simple example
toolkit = Toolkit(["TSLA", "ASML"], api_key="FINANCIAL_MODELING_PREP_KEY")

# Obtaining quarterly data
toolkit = Toolkit(["AAPL", "GOOGL"], quarterly=True, api_key="FINANCIAL_MODELING_PREP_KEY")

# Including a start and end date
toolkit = Toolkit(["MSFT", "MU"], start_date="2020-01-01", end_date="2023-01-01", quarterly=True, api_key="FINANCIAL_MODELING_PREP_KEY")

# Changing the benchmark and risk free rate
toolkit = Toolkit("AMZN", benchmark_ticker="^DJI", risk_free_rate="30y", api_key="FINANCIAL_MODELING_PREP_KEY")

ratios

The Ratios Module contains over 50+ ratios that can be used to analyse companies. These ratios are divided into 5 categories which are efficiency, liquidity, profitability, solvency and valuation. Each ratio is calculated using the data from the Toolkit module.

Some examples of ratios are the Current Ratio, Debt to Equity Ratio, Return on Assets (ROA), Return on Equity (ROE), Return on Invested Capital (ROIC), Return on Capital Employed (ROCE), Price to Earnings Ratio (P/E), Price to Book Ratio (P/B), Price to Sales Ratio (P/S), Price to Cash Flow Ratio (P/CF), Price to Free Cash Flow Ratio (P/FCF), Dividend Yield and Dividend Payout Ratio.

Next to that, it is also possible to define custom ratios.

See the following link for more information: https://www.jeroenbouma.com/projects/financetoolkit/docs/ratios

from financetoolkit import Toolkit

toolkit = Toolkit(["AAPL", "TSLA"], api_key="FINANCIAL_MODELING_PREP_KEY")

profitability_ratios = toolkit.ratios.collect_profitability_ratios()

profitability_ratios.loc['AAPL']

Which returns:

	2018	2019	2020	2021	2022
Gross Margin	0.383437	0.378178	0.382332	0.417794	0.433096
Operating Margin	0.26694	0.24572	0.241473	0.297824	0.302887
Net Profit Margin	0.224142	0.212381	0.209136	0.258818	0.253096
Interest Burden Ratio	1.02828	1.02827	1.01211	1.00237	0.997204
Income Before Tax Profit Margin	0.274489	0.252666	0.244398	0.298529	0.30204
Effective Tax Rate	0.183422	0.159438	0.144282	0.133023	0.162045
Return on Assets (ROA)	0.162775	0.16323	0.177256	0.269742	0.282924
Return on Equity (ROE)	0.555601	0.610645	0.878664	1.50071	1.96959
Return on Invested Capital (ROIC)	0.269858	0.293721	0.344126	0.503852	0.562645
Return on Capital Employed (ROCE)	0.305968	0.297739	0.320207	0.495972	0.613937
Return on Tangible Assets	0.555601	0.610645	0.878664	1.50071	1.96959
Income Quality Ratio	1.30073	1.25581	1.4052	1.09884	1.22392
Net Income per EBT	0.816578	0.840562	0.855718	0.866977	0.837955
Free Cash Flow to Operating Cash Flow Ratio	0.828073	0.848756	0.909401	0.893452	0.912338
EBT to EBIT Ratio	0.957448	0.948408	0.958936	0.976353	0.975982
EBIT to Revenue	0.286688	0.26641	0.254864	0.305759	0.309473

models

Gives access to the Models module. The Models module is meant to execute well -known models such as DUPONT and the Discounted Cash Flow (DCF) model. These models are also directly related to the data retrieved from the Toolkit module.

See the following link for more information: https://www.jeroenbouma.com/projects/financetoolkit/docs/models

from financetoolkit import Toolkit

toolkit = Toolkit(["TSLA", "AMZN"], api_key="FINANCIAL_MODELING_PREP_KEY", quarterly=True, start_date='2022-12-31')

dupont_analysis = toolkit.models.get_extended_dupont_analysis()

dupont_analysis.loc['AMZN']

Which returns:

	2022Q2	2022Q3	2022Q4	2023Q1	2023Q2
Interest Burden Ratio	-1.24465	0.858552	-2.88409	1.20243	1.01681
Tax Burden Ratio	-0.611396	1.13743	0.101571	0.640291	0.878792
Operating Profit Margin	-0.0219823	0.0231391	-0.00636042	0.0323498	0.0562125
Asset Turnover	nan	0.299735	0.3349	0.274759	0.285319
Equity Multiplier	nan	3.15403	3.14263	3.08433	2.91521
Return on Equity	nan	0.0213618	0.00196098	0.0211066	0.0417791

options

This gives access to the Options module. The Options Module is meant to provide Options valuations based on real market data. This includes the Black -Scholes model and in the future the Binomial model and the Monte Carlo model. It also includes all available first -order, second -order and third -order Greeks such as Delta, Gamma, Theta, Vega, Rho, Charm, Vanna, Vomma, Veta, Speed and Zomma.

It gives insights in the sensitivity of an option to changes in the underlying asset price, volatility, years to maturity, dividend yilds and interest rates and several derivatives of these sensitivities.

See the following link for more information: https://www.jeroenbouma.com/projects/financetoolkit/docs/options

from financetoolkit import Toolkit

toolkit = Toolkit(["TSLA", "MU"], api_key="FINANCIAL_MODELING_PREP_KEY")

all_greeks = toolkit.options.collect_all_greeks(start_date='2024-01-03')

all_greeks.loc['TSLA', '2024-01-04']

Which returns:

Strike Price	Delta	Dual Delta	Vega	Theta	Rho	Epsilon	Lambda	Gamma	Dual Gamma	Vanna	Charm	Vomma	Vera	Veta	PD	Speed	Zomma	Color	Ultima
180	1	-0.9999	0	-0.0193	0.0049	-0.6533	0.0408	0	0	-0	0	0	-0	0	0	-0	0	0	0
185	1	-0.9999	0	-0.0198	0.0051	-0.6533	0.0446	0	0	-0	0	0	-0	0	0	-0	0	0	0
190	1	-0.9999	0	-0.0204	0.0052	-0.6533	0.0492	0	0	-0	0	0	-0	0	0	-0	0	0	0
195	1	-0.9999	0	-0.0209	0.0053	-0.6533	0.0549	0	0	-0	0	0	-0	0	0	-0	0	0	0
200	1	-0.9999	0	-0.0214	0.0055	-0.6533	0.062	0	0	-0	0	0	-0	0.0014	0	-0	0	0	0
205	1	-0.9999	0	-0.022	0.0056	-0.6533	0.0712	0	0	-0	0.0005	0.0003	-0	0.1236	0	-0	0	0.0004	0.0001
210	1	-0.9999	0	-0.0226	0.0058	-0.6533	0.0837	0	0	-0.0002	0.0221	0.0119	-0.0001	4.6313	0	-0	0.0001	0.0132	0.0034
215	0.9998	-0.9997	0.0001	-0.0254	0.0059	-0.6532	0.1016	0.0001	0.0001	-0.0044	0.4426	0.1942	-0.0029	77.6496	0.0001	-0.0001	0.0021	0.209	0.0336
220	0.9973	-0.9969	0.001	-0.0526	0.006	-0.6515	0.1287	0.0012	0.0014	-0.0414	4.1955	1.4351	-0.0273	600.92	0.0014	-0.0005	0.0144	1.4569	0.1196
225	0.9777	-0.976	0.0066	-0.2079	0.006	-0.6387	0.1723	0.0076	0.0086	-0.1884	19.0888	4.7244	-0.1249	2187.89	0.0086	-0.0022	0.0407	4.1228	0.0829
230	0.8953	-0.8898	0.0226	-0.6528	0.0056	-0.5849	0.2419	0.0261	0.028	-0.3993	40.3564	6.2557	-0.267	3816.31	0.028	-0.0048	0.0253	2.5239	-0.1641
235	0.6978	-0.6874	0.0435	-1.2304	0.0044	-0.4558	0.3442	0.0502	0.0516	-0.306	30.653	1.9785	-0.2119	3623.7	0.0516	-0.0039	-0.0672	-6.8719	-0.0977
240	0.4192	-0.4078	0.0488	-1.3691	0.0027	-0.2739	0.4789	0.0562	0.0555	0.1634	-17.1438	0.4159	0.0934	3407.79	0.0555	0.0014	-0.096	-9.7512	-0.0222
245	0.1812	-0.1736	0.0329	-0.9207	0.0012	-0.1184	0.6396	0.0379	0.0359	0.4445	-45.5549	5.0536	0.2814	4080.87	0.0359	0.0048	-0.0098	-0.9474	-0.1945
250	0.0544	-0.0513	0.0138	-0.3848	0.0004	-0.0355	0.8183	0.0159	0.0144	0.3232	-33.01	6.468	0.2073	3328.37	0.0144	0.0036	0.0461	4.7176	-0.0443
255	0.0112	-0.0104	0.0037	-0.1028	0.0001	-0.0073	1.0084	0.0042	0.0037	0.1223	-12.477	3.4845	0.0789	1542.52	0.0037	0.0014	0.0325	3.3216	0.1424
260	0.0016	-0.0015	0.0006	-0.018	0	-0.001	1.205	0.0007	0.0006	0.0276	-2.8148	1.0161	0.0179	421.028	0.0006	0.0003	0.0104	1.0578	0.1054
265	0.0002	-0.0001	0.0001	-0.0021	0	-0.0001	1.4049	0.0001	0.0001	0.004	-0.4041	0.1783	0.0026	71.3544	0.0001	0	0.0019	0.1933	0.0322
270	0	-0	0	-0.0002	0	-0	1.6059	0	0	0.0004	-0.0385	0.02	0.0002	7.8471	0	0	0.0002	0.0222	0.0054
275	0	-0	0	-0	0	-0	1.8068	0	0	0	-0.0025	0.0015	0	0.5804	0	0	0	0.0017	0.0006
280	0	-0	0	-0	0	-0	2.0066	0	0	0	-0.0001	0.0001	0	0.0297	0	0	0	0.0001	0
285	0	-0	0	-0	0	-0	2.2048	0	0	0	-0	0	0	0.0011	0	0	0	0	0
290	0	-0	0	-0	0	-0	2.401	0	0	0	-0	0	0	0	0	0	0	0	0
295	0	-0	0	-0	0	-0	2.595	0	0	0	-0	0	0	0	0	0	0	0	0

technicals

This gives access to the Technicals module. The Technicals Module contains nearly 50 Technical Indicators that can be used to analyse companies. These indicators are divided into 3 categories: breadth, overlap and volatility. Each indicator is calculated using the data from the Toolkit module.

Some examples of technical indicators are the Average Directional Index (ADX), the Accumulation/Distribution Line (ADL), the Average True Range (ATR), the Bollinger Bands (BBANDS), the Commodity Channel Index (CCI), the Chaikin Oscillator (CHO), the Chaikin Money Flow (CMF), the Double Exponential Moving Average (DEMA), the Exponential Moving Average (EMA) and the Moving Average Convergence Divergence (MACD).

See the following link for more information: https://www.jeroenbouma.com/projects/financetoolkit/docs/technicals

from financetoolkit import Toolkit

toolkit = Toolkit(["AAPL", "TSLA"], api_key="FINANCIAL_MODELING_PREP_KEY")

average_directional_index = toolkit.technicals.get_average_directional_index()

Which returns:

Date	AAPL	MSFT
2023-08-21	62.8842	36.7468
2023-08-22	65.7063	36.5525
2023-08-23	67.3596	35.5149
2023-08-24	66.4527	35.4399
2023-08-25	63.4837	32.3323

performance

This gives access to the Performance module. The Performance Module is meant to calculate metrics related to the risk -return relationship. These are things such as Beta, Sharpe Ratio, Sortino Ratio, CAPM, Alpha and the Treynor Ratio.

It gives insights in the performance a stock has to e.g. a benchmark that is not easily identified by looking at the raw data. This class is closely related to the Risk class which highlights things such as Value at Risk (VaR) and Maximum Drawdown.

See the following link for more information: https://www.jeroenbouma.com/projects/financetoolkit/docs/performance

from financetoolkit import Toolkit

toolkit = Toolkit(["AAPL", "TSLA"], api_key="FINANCIAL_MODELING_PREP_KEY")

toolkit.performance.get_capital_asset_pricing_model(period='quarterly')

Which returns:

Date	AAPL	TSLA
2022Q3	-0.0684	-0.1047
2022Q4	0.0857	0.0828
2023Q1	0.075	0.1121
2023Q2	0.0922	0.1342
2023Q3	0.0052	-0.0482

risk

This gives access to the Risk module. The Risk Module is meant to calculate metrics related to risk such as Value at Risk (VaR), Conditional Value at Risk (cVaR), EMWA/GARCH models and similar models.

It gives insights in the risk a stock composes that is not perceived as easily by looking at the data. This class is closely related to the Performance class which highlights things such as Sharpe Ratio and Sortino Ratio.

See the following link for more information: https://www.jeroenbouma.com/projects/financetoolkit/docs/risk

from financetoolkit import Toolkit

toolkit = Toolkit(["AAPL", "TSLA"], api_key="FINANCIAL_MODELING_PREP_KEY")

toolkit.risk.get_value_at_risk(period='yearly')

Which returns:

Date	AAPL	TSLA
2012	0	0
2013	0.1754	4.96
2014	1.7515	0.9481
2015	-0.1958	0.1454
2016	0.4177	-0.3437
2017	2.6368	1.2225
2018	-0.2786	0.0718
2019	3.2243	0.4707
2020	1.729	8.3319
2021	1.3179	0.8797
2022	-0.8026	-1.0046
2023	1.8549	1.8238

fixedincome

This gives access to the Fixed Income module. This module contains a wide variety of fixed income related calculations such as the Effective Yield, the Macaulay Duration, the Modified Duration, the Convexity, the Yield to Maturity and models such as Black and Bachelier to valuate derivative instruments such as Swaptions.

Next to that, it is also possible to acquire Central Bank Rates and ICE BofA Indices such as the ICE BofA US High Yield Index, the ICE BofA US Corporate Index and the ICE BofA US Treasury Index.

Note that this class can also be directly accessed by importing the FixedIncome class directly via from financetoolkit import FixedIncome. This is useful if you only want to use the FixedIncome class and not the other classes within the Toolkit module.

See the following link for more information: https://www.jeroenbouma.com/projects/financetoolkit/docs/fixedincome

from financetoolkit import FixedIncome

fixedincome = FixedIncome(
start_date='2024-01-01',
end_date='2024-01-15',
)

fixedincome.get_effective_yield(maturity=False)

Which returns:

Date	AAA	AA	A	BBB	BB	B	CCC
2024-01-01	0.0456	0.047	0.0505	0.054	0.0613	0.0752	0.1319
2024-01-02	0.0459	0.0473	0.0509	0.0543	0.0622	0.0763	0.1333
2024-01-03	0.0459	0.0474	0.051	0.0544	0.0634	0.0779	0.1358
2024-01-04	0.0466	0.0481	0.0518	0.0551	0.0639	0.0784	0.1367
2024-01-05	0.047	0.0485	0.0521	0.0554	0.0641	0.0787	0.137
2024-01-08	0.0465	0.0481	0.0517	0.055	0.0633	0.0776	0.1365
2024-01-09	0.0464	0.048	0.0516	0.0548	0.0629	0.0771	0.1359
2024-01-10	0.0464	0.048	0.0515	0.0547	0.0622	0.0762	0.1351
2024-01-11	0.0456	0.0472	0.0507	0.054	0.0619	0.076	0.1344
2024-01-12	0.0451	0.0467	0.0502	0.0534	0.0613	0.0753	0.1338
2024-01-15	0.0451	0.0467	0.0501	0.0533	0.0611	0.0751	0.1328

economics

This gives access to the Economics module. This module contains a wide variety of economic data obtained from OECD. These include things such as the Consumer Price Index (CPI), the Producer Price Index (PPI), the Unemployment Rate, the GDP Growth Rate, the Long and Short Term Interest Rate and the Consumer Confidence Index.

Note that this class can also be directly accessed by importing the Economics class directly via from financetoolkit import Economics. This is useful if you only want to use the Economics class and not the other classes within the Toolkit module.

See the following link for more information: https://www.jeroenbouma.com/projects/financetoolkit/docs/economics

from financetoolkit import Toolkit

toolkit = Toolkit(["AMZN", "ASML"])

cpi = toolkit.economics.get_consumer_price_index(period='yearly')

cpi.loc['2015':, ['United States', 'Netherlands', 'Japan']]

Which returns:

	United States	Netherlands	Japan
2015	100	100	100
2016	101.262	100.317	99.8727
2017	103.419	101.703	100.356
2018	105.945	103.435	101.349
2019	107.865	106.159	101.824
2020	109.195	107.51	101.799
2021	114.325	110.387	101.561
2022	123.474	121.427	104.098

get_profile

Obtain the profile of the specified tickers. These include important metrics such as the beta, market capitalization, currency, isin, industry, and ipo date that give an overall understanding about the company.

Args:

progress_bar (bool, optional): Whether to show a progress bar. Defaults to None.

As an example:

from financetoolkit import Toolkit

toolkit = Toolkit(["MSFT", "AAPL"], api_key="FINANCIAL_MODELING_PREP_KEY")

toolkit.get_profile()

Which returns:

	MSFT	AAPL
Symbol	MSFT	AAPL
Price	316.48	174.49
Beta	0.903706	1.286802
Average Volume	28153120	57348456
Market Capitalization	2353183809372	2744500935588
Last Dividend	2.7199999999999998	0.96
Range	213.43-366.78	124.17-198.23
Changes	-0.4	0.49
Company Name	Microsoft Corporation	Apple Inc.
Currency	USD	USD
CIK	789019	320193
ISIN	US5949181045	US0378331005
CUSIP	594918104	37833100
Exchange	NASDAQ Global Select	NASDAQ Global Select
Exchange Short Name	NASDAQ	NASDAQ
Industry	Software—Infrastructure	Consumer Electronics
Website	https://www.microsoft.com	https://www.apple.com
CEO	Mr. Satya Nadella	Mr. Timothy D. Cook
Sector	Technology	Technology
Country	US	US
Full Time Employees	221000	164000
Phone	425 882 8080	408 996 1010
Address	One Microsoft Way	One Apple Park Way
City	Redmond	Cupertino
State	WA	CA
ZIP Code	98052-6399	95014
DCF Difference	4.56584	4.15176
DCF	243.594	150.082
IPO Date	1986-03-13	1980-12-12

get_quote

Get the quote of the specified tickers. These include important metrics such as the price, changes, day low, day high, year low, year high, market capitalization, volume, average volume, open, previous close, earnings per share (EPS), price to earnings ratio (PE), earnings announcement, shares outstanding and timestamp that give an overall understanding about the company.

Args:

progress_bar (bool, optional): Whether to show a progress bar. Defaults to None.

As an example:

from financetoolkit import Toolkit

toolkit = Toolkit(["TSLA", "AAPL"], api_key="FINANCIAL_MODELING_PREP_KEY")

toolkit.get_quote()

Which returns:

	TSLA	AAPL
Symbol	TSLA	AAPL
Name	Tesla, Inc.	Apple Inc.
Price	215.49	174.49
Changes Percentage	-1.7015	0.2816
Change	-3.73	0.49
Day Low	212.36	171.96
Day High	217.58	175.1
Year High	313.8	198.23
Year Low	101.81	124.17
Market Capitalization	682995534313	2744500935588
Price Average 50 Days	258.915	187.129
Price Average 200 Days	196.52345	161.4698
Exchange	NASDAQ	NASDAQ
Volume	136276584	61172150
Average Volume	133110158	57348456
Open	214.12	172.3
Previous Close	219.22	174
EPS	3.08	5.89
PE	69.96	29.62
Earnings Announcement	2023-10-17T20:00:00.000+0000	2023-10-25T10:59:00.000+0000
Shares Outstanding	3169499904	15728700416
Timestamp	2023-08-18 20:00:00	2023-08-18 20:00:01

get_rating

Get the rating of the specified tickers. These scores and recommendations are categorized as follows:

An overall rating
Discounted Cash Flow (DCF)
Return on Equity (ROE)
Return on Assets (ROA)
Debt to Equity (DE)
Price Earnings (PE)
Price to Book (PB)

Args:

progress_bar (bool, optional): Whether to show a progress bar. Defaults to None.

Raises: ValueError: If an API key is not defined for FinancialModelingPrep.

Returns: pd.DataFrame: The stock rating information for the specified tickers.

As an example:

from financetoolkit import Toolkit

toolkit = Toolkit(["AMZN", "TSLA"], api_key="FINANCIAL_MODELING_PREP_KEY")

rating = toolkit.get_rating()

rating.loc['AMZN', 'Rating Recommendation'].tail()

Which returns:

date	Rating Recommendation
2023-08-01 00:00:00	Strong Buy
2023-08-02 00:00:00	Strong Buy
2023-08-03 00:00:00	Strong Buy
2023-08-04 00:00:00	Strong Buy
2023-08-07 00:00:00	Strong Buy

get_analyst_estimates

Obtain analyst estimates regarding revenues, EBITDA, EBIT, Net Income SGA Expenses and EPS. The number of analysts are also reported.

Note that this information requires a Premium FMP subscription.

Args:

limit (int): Defines the maximum years or quarters to obtain.
overwrite (bool): Defines whether to overwrite the existing data.
rounding (int): Defines the number of decimal places to round the data to.
growth (bool): Defines whether to return the growth of the data.
lag (int | str): Defines the number of periods to lag the growth data by.
trailing (int): Defines whether to select a trailing period. E.g. when selecting 4 with quarterly data, the TTM is calculated.

Returns: pandas.DataFrame: The analyst estimates for the specified tickers.

As an example:

from financetoolkit import Toolkit

toolkit = Toolkit(
["AAPL", "MSFT", "GOOGL", "AMZN"], api_key="FINANCIAL_MODELING_PREP_KEY", start_date="2021-05-01", quarterly=False
)

analyst_estimates = toolkit.get_analyst_estimates()

analyst_estimates.loc['AAPL']

Which returns:

	2021	2022	2023	2024
Estimated Revenue Low	2.98738e+11	3.07919e+11	3.3871e+11	2.93633e+11
Estimated Revenue High	4.48107e+11	4.61878e+11	5.08066e+11	4.4045e+11
Estimated Revenue Average	3.73422e+11	3.84898e+11	4.23388e+11	3.67042e+11
Estimated EBITDA Low	8.50991e+10	1.00742e+11	1.10816e+11	1.07415e+11
Estimated EBITDA High	1.27649e+11	1.51113e+11	1.66224e+11	1.61122e+11
Estimated EBITDA Average	1.06374e+11	1.25928e+11	1.3852e+11	1.34269e+11
Estimated EBIT Low	7.62213e+10	9.05428e+10	9.9597e+10	9.81566e+10
Estimated EBIT High	1.14332e+11	1.35814e+11	1.49396e+11	1.47235e+11
Estimated EBIT Average	9.52766e+10	1.13178e+11	1.24496e+11	1.22696e+11
Estimated Net Income Low	6.54258e+10	7.62265e+10	8.38492e+10	8.23371e+10
Estimated Net Income High	9.81387e+10	1.1434e+11	1.25774e+11	1.23506e+11
Estimated Net Income Average	8.17822e+10	9.52832e+10	1.04811e+11	1.02921e+11
Estimated SGA Expense Low	1.48491e+10	1.85317e+10	2.03848e+10	2.04857e+10
Estimated SGA Expense High	2.22737e+10	2.77975e+10	3.05772e+10	3.07286e+10
Estimated SGA Expense Average	1.85614e+10	2.31646e+10	2.5481e+10	2.56072e+10
Estimated EPS Average	4.26	5.465	6.01	6.2612
Estimated EPS High	5.12	6.56	7.21	7.5135
Estimated EPS Low	3.4	4.37	4.81	5.009
Number of Analysts	14	16	12	10

get_earnings_calendar

Obtain Earnings Calendars for any range of companies. You have the option to obtain the actual dates or to convert to the corresponding quarters.

Note that this information requires a Premium FMP subscription.

Args:

actual_dates (bool): Defines whether to return the actual dates or the corresponding quarters.
overwrite (bool): Defines whether to overwrite the existing data.

Returns: pd.DataFrame: The earnings calendar for the specified tickers.

As an example:

from financetoolkit import Toolkit

toolkit = Toolkit(
["AAPL", "MSFT", "GOOGL", "AMZN"], api_key="FINANCIAL_MODELING_PREP_KEY", start_date="2022-08-01", quarterly=False
)

earning_calendar = toolkit.get_earnings_calendar()

earning_calendar.loc['AMZN']

Which returns:

date	EPS	Estimated EPS	Revenue	Estimated Revenue	Fiscal Date Ending	Time
2022-10-27	0.17	0.22	1.27101e+11	nan	2022-09-30	amc
2023-02-02	0.25	0.18	1.49204e+11	1.5515e+11	2022-12-31	amc
2023-04-27	0.31	0.21	1.27358e+11	1.24551e+11	2023-03-31	amc
2023-08-03	0.65	0.35	1.34383e+11	1.19573e+11	2023-06-30	amc
2023-10-25	nan	0.56	nan	1.41407e+11	2023-09-30	amc
2024-01-31	nan	nan	nan	nan	2023-12-30	amc
2024-04-25	nan	nan	nan	nan	2024-03-30	amc
2024-08-01	nan	nan	nan	nan	2024-06-30	amc

get_revenue_geographic_segmentation

Obtain revenue by geographic segmentation (e.g. United States, Europe, Asia).

Note that this information requires a Premium FMP subscription.

Args:

overwrite (bool): Defines whether to overwrite the existing data.
progress_bar (bool, optional): Whether to show a progress bar. Defaults to None.

Returns: pd.DataFrame: The revenue by geographic segmentation for the specified tickers.

As an example:

from financetoolkit import Toolkit

toolkit = Toolkit(
["AAPL", "MSFT", "GOOGL", "AMZN"], api_key="FINANCIAL_MODELING_PREP_KEY", start_date="2021-05-01", quarterly=False
)

geographic_segmentation = toolkit.get_revenue_geographic_segmentation()

geographic_segmentation.loc['AAPL']

Which returns:

	2020	2021	2022	2023
Americas	4.631e+10	5.1496e+10	4.9278e+10	3.5383e+10
Asia Pacific	8.225e+09	9.81e+09	9.535e+09	5.63e+09
China	2.1313e+10	2.5783e+10	2.3905e+10	1.5758e+10
Europe	2.7306e+10	2.9749e+10	2.7681e+10	2.0205e+10
Japan	8.285e+09	7.107e+09	6.755e+09	4.821e+09

get_revenue_product_segmentation

Obtain revenue by product segmentation (e.g. iPad, Advertisement, Windows).

Note that this information requires a Premium FMP subscription.

Args:

overwrite (bool): Defines whether to overwrite the existing data.
progress_bar (bool, optional): Whether to show a progress bar. Defaults to None.

Returns: pd.DataFrame: The revenue by product segmentation for the specified tickers.

As an example:

from financetoolkit import Toolkit

toolkit = Toolkit(
["AAPL", "MSFT", "GOOGL", "AMZN"], api_key="FINANCIAL_MODELING_PREP_KEY", start_date="2021-05-01", quarterly=False
)

product_segmentation = toolkit.get_revenue_product_segmentation()

product_segmentation.loc['MSFT']

Which returns:

	2022Q2	2022Q3	2022Q4	2023Q1	2023Q2
Devices	1.581e+09	1.448e+09	1.43e+09	1.282e+09	1.361e+09
Enterprise Services	1.902e+09	1.876e+09	1.862e+09	2.007e+09	1.977e+09
Gaming	3.455e+09	3.61e+09	4.758e+09	3.607e+09	3.491e+09
Linked In Corporation	3.712e+09	3.663e+09	3.876e+09	3.697e+09	3.909e+09
Office Products And Cloud Services	1.1639e+10	1.1548e+10	1.1837e+10	1.2438e+10	1.2905e+10
Other Products And Services	1.403e+09	1.348e+09	1.359e+09	1.428e+09	-3.924e+09
Search And News Advertising	2.926e+09	2.928e+09	3.223e+09	3.045e+09	3.012e+09
Server Products And Cloud Services	1.8839e+10	1.8388e+10	1.9594e+10	2.0025e+10	2.1963e+10
Windows	6.408e+09	5.313e+09	4.808e+09	5.328e+09	6.058e+09

get_historical_data

Returns historical data for the specified tickers. This contains the following columns:

Open: The opening price for the period.
High: The highest price for the period.
Low: The lowest price for the period.
Close: The closing price for the period.
Adj Close: The adjusted closing price for the period.
Volume: The volume for the period.
Dividends: The dividends for the period.
Return: The return for the period.
Volatility: The volatility for the period.
Excess Return: The excess return for the period. This is defined as the return minus the a predefined risk free rate. Only calculated when excess_return is True.
Excess Volatility: The excess volatility for the period. This is defined as the volatility of the excess return. Only calculated when excess_return is True.
Cumulative Return: The cumulative return for the period.

If a benchmark ticker is selected, it also calculates the benchmark ticker together with the results. By default this is set to “SPY” (S&P 500 Index) but can be any ticker. This is relevant for calculations for models such as CAPM, Alpha and Beta.

Important to note is that when an api_key is included in the Toolkit initialization that the data collection defaults to FinancialModelingPrep which is a more stable source and utilises your subscription. However, if this is undesired, it can be disabled by setting historical_source to “YahooFinance”. If data collection fails from FinancialModelingPrep it automatically reverts back to YahooFinance.

Args:

start (str): The start date for the historical data. Defaults to None.
end (str): The end date for the historical data. Defaults to None.
period (str): The interval at which the historical data should be returned - daily, weekly, monthly, quarterly, or yearly. Defaults to “daily”.
return_column (str): The column to use for the return calculation. Defaults to “Adj Close”.
include_dividends (bool): Defines whether to include dividends in the return calculation. Defaults to True.
fill_nan (bool): Defines whether to forward fill NaN values. This defaults to True to prevent holes in the dataset. This is especially relevant for technical indicators.
overwrite (bool): Defines whether to overwrite the existing data. If this is not enabled, the function will return the earlier retrieved data. This is done to prevent too many API calls. Defaults to False.
rounding (int): Defines the number of decimal places to round the data to.
sleep_timer (bool): Defines whether to include a sleep timer to prevent overloading the API. Defaults to True.
show_ticker_seperation (bool, optional): A boolean representing whether to show which tickers acquired data from FinancialModelingPrep and which tickers acquired data from YahooFinance.
progress_bar (bool, optional): Whether to show a progress bar. Defaults to None.

Raises: ValueError: If an invalid value is specified for period.

Returns: pandas.DataFrame: The historical data for the specified tickers.

As an example:

from financetoolkit import Toolkit

toolkit = Toolkit("AAPL", api_key="FINANCIAL_MODELING_PREP_KEY")

toolkit.get_historical_data(period="yearly")

Which returns:

Date	Open	High	Low	Close	Adj Close	Volume	Dividends	Return	Volatility	Excess Return	Excess Volatility	Cumulative Return
2013	19.7918	20.0457	19.7857	20.0364	17.5889	2.23084e+08	0.108929	0	0.240641	0	0.244248	1
2014	28.205	28.2825	27.5525	27.595	24.734	1.65614e+08	0.461429	0.406225	0.216574	0.384525	0.219536	1.40623
2015	26.7525	26.7575	26.205	26.315	23.9886	1.63649e+08	0.5075	-0.0301373	0.267373	-0.0528273	0.269845	1.36385
2016	29.1625	29.3	28.8575	28.955	26.9824	1.22345e+08	0.5575	0.124804	0.233383	0.100344	0.240215	1.53406
2017	42.63	42.6475	42.305	42.3075	40.0593	1.04e+08	0.615	0.484644	0.176058	0.460594	0.17468	2.27753
2018	39.6325	39.84	39.12	39.435	37.9	1.40014e+08	0.705	-0.0539019	0.287421	-0.0807619	0.289905	2.15477
2019	72.4825	73.42	72.38	73.4125	71.615	1.00806e+08	0.76	0.889578	0.261384	0.870388	0.269945	4.0716
2020	134.08	134.74	131.72	132.69	130.559	9.91166e+07	0.8075	0.823067	0.466497	0.813897	0.470743	7.4228
2021	178.09	179.23	177.26	177.57	175.795	6.40623e+07	0.865	0.346482	0.251019	0.331362	0.251429	9.99467
2022	128.41	129.95	127.43	129.93	129.378	7.70342e+07	0.91	-0.264042	0.356964	-0.302832	0.377293	7.35566
2023	187.84	188.51	187.68	188.108	188.108	4.72009e+06	0.71	0.453941	0.213359	0.412901	0.22327	10.6947

get_intraday_data

Returns intraday historical data for the specified tickers. This contains the following columns:

Open: The opening price for the period.
High: The highest price for the period.
Low: The lowest price for the period.
Close: The closing price for the period.
Volume: The volume for the period.
Return: The return for the period.
Volatility: The volatility for the period.
Cumulative Return: The cumulative return for the period.

Keep in mind that this data is available for a shorter period. This means that the start date is ignored if the difference between the start and end date is bigger than the maximum period.

Please note that this functionality is only available through Financial Modeling Prep. Therefore, an api_key is required to use this functionality.

Args:

start (str): The start date for the historical data. Defaults to None.
end (str): The end date for the historical data. Defaults to None.
period (str): The interval at which the historical data should be returned - daily, weekly, monthly, quarterly, or yearly. Defaults to “daily”.
return_column (str): The column to use for the return calculation. Defaults to “Close”.
fill_nan (bool): Defines whether to forward fill NaN values. This defaults to True to prevent holes in the dataset. This is especially relevant for technical indicators.
rounding (int): Defines the number of decimal places to round the data to.
progress_bar (bool, optional): Whether to show a progress bar. Defaults to None.

Returns: pandas.DataFrame: The intraday data for the specified tickers.

As an example:

from financetoolkit import Toolkit

toolkit = Toolkit("MSFT", api_key="FINANCIAL_MODELING_PREP_KEY")

toolkit.get_intraday_data(period="1min")

Which returns:

date	Open	High	Low	Close	Volume	Return	Volatility	Cumulative Return
2024-01-19 15:45	397.64	397.88	397.63	397.88	49202	0.0006	0.0005	1.0266
2024-01-19 15:46	397.86	397.93	397.788	397.82	68913	-0.0002	0.0005	1.0264
2024-01-19 15:47	397.81	397.97	397.76	397.78	62605	-0.0001	0.0005	1.0263
2024-01-19 15:48	397.78	397.85	397.675	397.845	62146	0.0002	0.0005	1.0265
2024-01-19 15:49	397.85	397.97	397.8	397.94	72700	0.0002	0.0005	1.0267
2024-01-19 15:50	397.92	398.27	397.9	398.04	140754	0.0003	0.0005	1.027
2024-01-19 15:51	398.04	398.15	397.96	398	122208	-0.0001	0.0005	1.0269
2024-01-19 15:52	397.99	398.26	397.98	398.05	83546	0.0001	0.0005	1.027
2024-01-19 15:53	398.04	398.12	397.98	398.09	85098	0.0001	0.0005	1.0271
2024-01-19 15:54	398.1	398.52	398.03	398.45	187358	0.0009	0.0005	1.028
2024-01-19 15:55	398.45	398.62	398.25	398.335	237902	-0.0003	0.0005	1.0278
2024-01-19 15:56	398.33	398.44	398.3	398.415	149157	0.0002	0.0005	1.028
2024-01-19 15:57	398.42	398.5	398.29	398.43	181074	0	0.0005	1.028
2024-01-19 15:58	398.46	398.47	398.29	398.35	278802	-0.0002	0.0005	1.0278
2024-01-19 15:59	398.35	398.66	398.22	398.66	586344	0.0008	0.0005	1.0286

get_dividend_calendar

Obtain Dividend Calendars for any range of companies. It includes the following columns:

Date: The date of the dividend.
Adj Dividend: The adjusted dividend amount.
Dividend: The dividend amount.
Record Date: The record date of the dividend.
Payment Date: The payment date of the dividend.
Declaration Date: The declaration date of the dividend.

If a company does not pay any dividend, the function will mention that it was not able to find any dividend data for that company.

Args:

overwrite (bool): Defines whether to overwrite the existing data.
rounding (int): Defines the number of decimal places to round the data to.
progress_bar (bool, optional): Whether to show a progress bar. Defaults to None.

Returns: pd.DataFrame: The earnings calendar for the specified tickers.

As an example:

from financetoolkit import Toolkit

toolkit = Toolkit(
["AAPL", "MSFT", "GOOGL", "AMZN"], api_key="FINANCIAL_MODELING_PREP_KEY", start_date="2022-08-01", quarterly=False
)

dividend_calendar = toolkit.get_dividend_calendar()

dividend_calendar.loc['AAPL']

Which returns:

date	Adj Dividend	Dividend	Record Date	Payment Date	Declaration Date
2022-08-05	0.23	0.23	2022-08-08	2022-08-11	2022-07-28
2022-11-04	0.23	0.23	2022-11-07	2022-11-10	2022-10-27
2023-02-10	0.23	0.23	2022-12-28	2023-02-16	2022-12-19
2023-05-12	0.24	0.24	2023-05-15	2023-05-18	2023-05-04
2023-08-11	0.24	0.24	2023-08-14	2023-08-17	2023-08-03

get_esg_scores

ESG scores, which stands for Environmental, Social, and Governance scores, are a crucial metric used by investors and organizations to assess a company’s sustainability and ethical practices. These scores provide valuable insights into a company’s performance in three key areas:

Environmental (E): The environmental component evaluates a company’s impact on the planet and its efforts to mitigate environmental risks. It includes factors like carbon emissions, energy efficiency, water management, and waste reduction. A high environmental score indicates a company’s commitment to eco -friendly practices and reducing its ecological footprint.
Social (S): The social component focuses on how a company interacts with its employees, customers, suppliers, and the communities in which it operates. Key factors in the social score include labor practices, diversity and inclusion, human rights, product safety, and community engagement. A strong social score reflects a company’s dedication to fostering positive relationships and contributing positively to society.
Governance (G): Governance examines a company’s internal structures, policies, and leadership. It assesses aspects such as board independence, executive compensation, transparency, and the presence of anti -corruption measures. A high governance score signifies strong leadership and a commitment to maintaining high ethical standards and accountability

ESG scores provide investors with a holistic view of a company’s sustainability and ethical practices, allowing them to make more informed investment decisions. These scores are increasingly used to identify socially responsible investments and guide capital towards companies that prioritize long -term sustainability and responsible business practices. As the importance of ESG considerations continues to grow, companies are motivated to improve their ESG scores, not only for ethical reasons but also to attract investors who value sustainable and responsible business practices.

Args:

overwrite (bool): Defines whether to overwrite the existing data.
rounding (int): Defines the number of decimal places to round the data to.

Returns: pd.DataFrame: The ESG scores for the specified tickers.

As an example:

from financetoolkit import Toolkit

toolkit = Toolkit(
["MSFT", "TSLA", "AMZN"], api_key="FINANCIAL_MODELING_PREP_KEY", start_date="2022-08-01", quarterly=False
)

esg_scores = toolkit.get_esg_scores()

esg_scores.xs("MSFT", level=1, axis=1)

Which returns:

date	Environmental Score	Social Score	Governance Score	ESG Score
2022Q3	72.42	58.39	61.13	63.98
2022Q4	72.22	58.05	61.27	63.85
2023Q1	72.6	58.74	61.88	64.41
2023Q2	73.54	60.73	63.44	65.9

get_historical_statistics

Retrieve statistics about each ticker’s historical data. This is especially useful to understand why certain tickers might fluctuate more than others as it could be due to local regulations or the currency the instrument is denoted in. It returns:

Currency: The currency the instrument is denoted in.
Symbol: The symbol of the instrument.
Exchange Name: The name of the exchange the instrument is listed on.
Instrument Type: The type of instrument.
First Trade Date: The date the instrument was first traded.
Regular Market Time: The time the instrument is traded.
GMT Offset: The GMT offset.
Timezone: The timezone the instrument is traded in.
Exchange Timezone Name: The name of the timezone the instrument is traded in.

Args:

progress_bar (bool): Defines whether to show a progress bar. Defaults to None.

Returns: pd.DataFrame: A DataFrame containing the statistics for each ticker.

As an example:

from financetoolkit import Toolkit

companies = Toolkit(["AMZN", "^HSI", "IWDA.AS", "0P0000Z8RO.T"])

companies.get_historical_statistics()

Which returns:

	AMZN	^HSI	IWDA.AS	0P0000Z8RO.T
Currency	USD	HKD	EUR	JPY
Symbol	AMZN	^HSI	IWDA.AS	0P0000Z8RO.T
Exchange Name	NMS	HKG	AMS	JPX
Instrument Type	EQUITY	INDEX	ETF	MUTUALFUND
First Trade Date	1997-05-15	1986-12-31	2009-09-25	2018-01-04
Regular Market Time	2023-09-22	2023-09-22	2023-09-22	2023-09-21
GMT Offset	-14400	28800	7200	32400
Timezone	EDT	HKT	CEST	JST
Exchange Timezone Name	America/New_York	Asia/Hong_Kong	Europe/Amsterdam	Asia/Tokyo

get_treasury_data

Retrieve daily, weekly, monthly, quarterly or yearly treasury data. This can be from FinancialModelingPrep or from YahooFinance. FinancialModelingPrep is by far a more extensive dataset containing daily data from 1 month to 30 years. YahooFinance only contains daily data for 5, 10 and 30 years but is a free alternative.

Args:

period (str): The interval at which the treasury data should be returned - daily, weekly, monthly, quarterly, or yearly.
fill_nan (bool): Defines whether to forward fill NaN values. This defaults to True to prevent holes in the dataset. This is especially relevant for technical indicators.

Returns: pd.DataFrame: A DataFrame containing the treasury data.

As an example:

from financetoolkit import Toolkit

companies = Toolkit(["AAPL", "MSFT"], api_key="FINANCIAL_MODELING_PREP_KEY", start_date="2023-08-10")

companies.get_treasury_data()

Which returns:

date	13 Week	5 Year	10 Year	30 Year
2023-10-16	0.0533	0.0472	0.0471	0.0487
2023-10-17	0.0534	0.0487	0.0485	0.0495
2023-10-18	0.0533	0.0492	0.049	0.05
2023-10-19	0.0531	0.0496	0.0499	0.051
2023-10-20	0.053	0.0491	0.0496	0.0512

get_exchange_rates

This functionality looks at the exchange rates between the currency of the historical data and the currency of the financial statements. Given that these can deviate from each other, e.g. the historical data is in USD but the financial statements are in EUR, it is important to adjust for this. This is especially relevant for models that use the historical data and the financial statements.

This function therefore shows the exchange rates that are used to convert the financial statements to the currency of the historical data. The historical market data is quote currency and the financial statements are base currency.

Note that you can get currency data from any currency as well by supplying the currency as a ticker. For example, if you want to get the exchange rates between USD and EUR you can use USDEUR=X as a ticker.

Args:

start (str): The start date for the exchange data. Defaults to None.
end (str): The end date for the exchange data. Defaults to None.
period (str): The interval at which the historical data should be returned - daily, weekly, monthly, quarterly, or yearly. Defaults to “daily”.
return_column (str): The column to use for the return calculation. Defaults to “Adj Close”.
fill_nan (bool): Defines whether to forward fill NaN values. This defaults to True to prevent holes in the dataset. This is especially relevant for technical indicators.
overwrite (bool): Defines whether to overwrite the existing data.
rounding (int): Defines the number of decimal places to round the data to.
show_ticker_seperation (bool, optional): A boolean representing whether to show which tickers acquired data from FinancialModelingPrep and which tickers acquired data from YahooFinance.

Raises: ValueError: If an invalid value is specified for period.

Returns: pandas.DataFrame: The historical exchange rate data.

As an example:

from financetoolkit import Toolkit

toolkit = Toolkit("ASML", api_key="FINANCIAL_MODELING_PREP_KEY")

toolkit.get_exchange_rates(period="monthly")

Which returns:

Date	Open	High	Low	Close	Adj Close	Volume	Return	Volatility	Cumulative Return
2023-03	1.0905	1.0926	1.0861	1.0905	1.0905	0	0.0277	0.0298	0.7896
2023-04	1.1011	1.1037	1.0963	1.0969	1.0969	131812	0.0059	0.0278	0.7943
2023-05	1.0693	1.0771	1.066	1.076	1.0733	162069	-0.0215	0.0143	0.7772
2023-06	1.09	1.09	1.08	1.09	1.0868	0	0.0126	0.0179	0.787
2023-07	1.0996	1.102	1.0952	1.1007	1.1024	183278	0.0144	0.0225	0.7983
2023-08	1.0842	1.0882	1.077	1.0796	1.09	171695	-0.0112	0.0219	0.7893
2023-09	1.06	1.06	1.06	1.06	1.06	0	-0.0275	0.0264	0.7676
2023-10	1.0614	1.0674	1.0556	1.0578	1.0615	184667	0.0014	0.0232	0.7686
2023-11	1.0973	1.0984	1.0878	1.0892	1.0974	173646	0.0338	0.0202	0.7946
2023-12	1.088	1.0898	1.0848	1.0871	1.0871	90494	-0.0094	0.0173	0.7872

get_balance_sheet_statement

Retrieves the balance sheet statement financial data for the company(s) from the specified source.

Args:

limit (int): Defines the maximum years or quarters to obtain.
overwrite (bool): Defines whether to overwrite the existing data.
rounding (int): Defines the number of decimal places to round the data to.
growth (bool): Defines whether to return the growth of the data.
lag (int | str): Defines the number of periods to lag the growth data by.
trailing (int): Defines whether to select a trailing period. E.g. when selecting 4 with quarterly data, the TTM is calculated.
progress_bar (bool): Defines whether to show a progress bar.

Returns: pd.DataFrame: A pandas DataFrame with the retrieved balance sheet statement data.

As an example:

from financetoolkit import Toolkit

toolkit = Toolkit(["MSFT", "MU"], api_key="FINANCIAL_MODELING_PREP_KEY", quarterly=True, start_date='2022-05-01')

balance_sheet_statements = toolkit.get_balance_sheet_statement()

balance_sheet_statements.loc['MU']

Which returns:

	2022Q2	2022Q3	2022Q4	2023Q1	2023Q2
Cash and Cash Equivalents	9.157e+09	8.262e+09	9.574e+09	9.798e+09	9.298e+09
Short Term Investments	1.07e+09	1.069e+09	1.007e+09	1.02e+09	1.054e+09
Cash and Short Term Investments	1.0227e+10	9.331e+09	1.0581e+10	1.0818e+10	1.0352e+10
Accounts Receivable	6.229e+09	5.13e+09	3.318e+09	2.278e+09	2.429e+09
Inventory	5.629e+09	6.663e+09	8.359e+09	8.129e+09	8.238e+09
Other Current Assets	6.08e+08	6.44e+08	6.63e+08	6.73e+08	7.15e+08
Total Current Assets	2.2708e+10	2.1781e+10	2.2921e+10	2.1898e+10	2.1734e+10
Property, Plant and Equipment	3.7355e+10	3.9227e+10	4.0028e+10	3.9758e+10	3.9382e+10
Goodwill	1.228e+09	1.228e+09	1.228e+09	1.228e+09	1.252e+09
Intangible Assets	4.15e+08	4.21e+08	4.28e+08	4.1e+08	4.1e+08
Long Term Investments	1.646e+09	1.647e+09	1.426e+09	1.212e+09	9.73e+08
Tax Assets	6.82e+08	7.02e+08	6.72e+08	6.97e+08	7.08e+08
Other Fixed Assets	1.262e+09	1.277e+09	1.171e+09	1.317e+09	1.221e+09
Fixed Assets	4.2588e+10	4.4502e+10	4.4953e+10	4.4622e+10	4.3946e+10
Other Assets	0	0	0	0	0
Total Assets	6.5296e+10	6.6283e+10	6.7874e+10	6.652e+10	6.568e+10
Accounts Payable	2.019e+09	2.142e+09	1.789e+09	1.689e+09	1.64e+09
Short Term Debt	1.07e+08	1.03e+08	1.71e+08	2.37e+08	2.59e+08
Tax Payables	3.82e+08	4.2e+08	4.19e+08	2.41e+08	1.48e+08
Deferred Revenue	0	0	0	0	-1.64e+09
Other Current Liabilities	4.883e+09	5.294e+09	4.565e+09	3.329e+09	4.845e+09
Total Current Liabilities	7.009e+09	7.539e+09	6.525e+09	5.255e+09	5.104e+09
Long Term Debt	7.485e+09	7.413e+09	1.0719e+10	1.2647e+10	1.3589e+10
Deferred Revenue Non Current	6.63e+08	5.89e+08	5.16e+08	5.29e+08	6.32e+08
Deferred Tax Liabilities	0	0	0	0	0
Other Non Current Liabilities	8.58e+08	8.35e+08	8.08e+08	8.32e+08	9.5e+08
Total Non Current Liabilities	9.006e+09	8.837e+09	1.2043e+10	1.4008e+10	1.5171e+10
Other Liabilities	0	0	0	0	0
Capital Lease Obligations	6.29e+08	6.1e+08	6.25e+08	6.1e+08	6.03e+08
Total Liabilities	1.6015e+10	1.6376e+10	1.8568e+10	1.9263e+10	2.0275e+10
Preferred Stock	0	0	0	0	0
Common Stock	1.22e+08	1.23e+08	1.23e+08	1.23e+08	1.24e+08
Retained Earnings	4.5916e+10	4.7274e+10	4.6873e+10	4.4426e+10	4.2391e+10
Accumulated Other Comprehensive Income	-3.64e+08	-5.6e+08	-4.73e+08	-3.73e+08	-3.4e+08
Other Total Shareholder Equity	3.607e+09	3.07e+09	2.783e+09	3.081e+09	3.23e+09
Total Shareholder Equity	4.9281e+10	4.9907e+10	4.9306e+10	4.7257e+10	4.5405e+10
Total Equity	4.9281e+10	4.9907e+10	4.9306e+10	4.7257e+10	4.5405e+10
Total Liabilities and Shareholder Equity	6.5296e+10	6.6283e+10	6.7874e+10	6.652e+10	6.568e+10
Minority Interest	0	0	0	0	0
Total Liabilities and Equity	6.5296e+10	6.6283e+10	6.7874e+10	6.652e+10	6.568e+10
Total Investments	2.716e+09	2.716e+09	2.433e+09	2.232e+09	2.027e+09
Total Debt	7.592e+09	7.516e+09	1.089e+10	1.2884e+10	1.3848e+10
Net Debt	-1.565e+09	-7.46e+08	1.316e+09	3.086e+09	4.55e+09

get_income_statement

Retrieves the income statement financial data for the company(s) from the specified source.

Args:

limit (int): Defines the maximum years or quarters to obtain.
overwrite (bool): Defines whether to overwrite the existing data.
rounding (int): Defines the number of decimal places to round the data to.
growth (bool): Defines whether to return the growth of the data.
lag (int | str): Defines the number of periods to lag the growth data by.
trailing (int): Defines whether to select a trailing period. E.g. when selecting 4 with quarterly data, the TTM is calculated.
progress_bar (bool): Defines whether to show a progress bar.

Returns: pd.DataFrame: A pandas DataFrame with the retrieved income statement data.

As an example:

from financetoolkit import Toolkit

toolkit = Toolkit(["TSLA", "MU"], api_key="FINANCIAL_MODELING_PREP_KEY", quarterly=True, start_date='2022-05-01')

income_sheet_statements = toolkit.get_income_statement()

income_sheet_statements.loc['TSLA']

Which returns:

	2022Q2	2022Q3	2022Q4	2023Q1	2023Q2
Revenue	1.6934e+10	2.1454e+10	2.4318e+10	2.3329e+10	2.4927e+10
Cost of Goods Sold	1.27e+10	1.6072e+10	1.8541e+10	1.8818e+10	2.0394e+10
Gross Profit	4.234e+09	5.382e+09	5.777e+09	4.511e+09	4.533e+09
Gross Profit Ratio	0.25003	0.250862	0.237561	0.193364	0.181851
Research and Development Expenses	6.67e+08	7.33e+08	8.1e+08	7.71e+08	9.43e+08
General and Administrative Expenses	0	0	0	0	0
Selling and Marketing Expenses	0	0	0	0	0
Selling, General and Administrative Expenses	9.61e+08	9.61e+08	1.032e+09	1.076e+09	1.191e+09
Other Expenses	2.8e+07	-8.5e+07	-4.2e+07	-4.8e+07	3.28e+08
Operating Expenses	1.628e+09	1.694e+09	1.842e+09	1.847e+09	2.134e+09
Cost and Expenses	1.4328e+10	1.7766e+10	2.0383e+10	2.0665e+10	2.2528e+10
Interest Income	2.6e+07	8.6e+07	1.57e+08	2.13e+08	2.38e+08
Interest Expense	4.4e+07	5.3e+07	3.3e+07	2.9e+07	2.8e+07
Depreciation and Amortization	1.118e+09	9.57e+08	1.138e+09	1.211e+09	1.72e+09
EBITDA	3.582e+09	4.645e+09	5.039e+09	3.875e+09	4.119e+09
EBITDA Ratio	0.211527	0.21651	0.207213	0.166102	0.165243
Operating Income	2.464e+09	3.688e+09	3.901e+09	2.664e+09	2.399e+09
Operating Income Ratio	0.145506	0.171903	0.160416	0.114193	0.096241
Total Other Income	1e+07	-5.2e+07	8.2e+07	1.36e+08	5.38e+08
Income Before Tax	2.474e+09	3.636e+09	3.983e+09	2.8e+09	2.937e+09
Income Before Tax Ratio	0.146097	0.169479	0.163788	0.120022	0.117824
Income Tax Expense	2.05e+08	3.05e+08	2.76e+08	2.61e+08	3.23e+08
Net Income	2.259e+09	3.292e+09	3.687e+09	2.513e+09	2.703e+09
Net Income Ratio	0.1334	0.153445	0.151616	0.10772	0.108437
EPS	0.73	1.05	1.18	0.8	0.85
EPS Diluted	0.65	0.95	1.07	0.73	0.78
Weighted Average Shares	3.111e+09	3.146e+09	3.16e+09	3.166e+09	3.171e+09
Weighted Average Shares Diluted	3.465e+09	3.468e+09	3.471e+09	3.468e+09	3.478e+09

get_cash_flow_statement

Retrieves the cash flow statement financial data for the company(s) from the specified source.

Args:

limit (int): Defines the maximum years or quarters to obtain.
overwrite (bool): Defines whether to overwrite the existing data.
rounding (int): Defines the number of decimal places to round the data to.
growth (bool): Defines whether to return the growth of the data.
lag (int | str): Defines the number of periods to lag the growth data by.
trailing (int): Defines whether to select a trailing period. E.g. when selecting 4 with quarterly data, the TTM is calculated.
progress_bar (bool): Defines whether to show a progress bar.

Returns: pd.DataFrame: A pandas DataFrame with the retrieved cash flow statement data.

As an example:

from financetoolkit import Toolkit

toolkit = Toolkit(["MU", "AMZN"], api_key="FINANCIAL_MODELING_PREP_KEY", quarterly=True, start_date='2022-09-01')

cash_flow_statements = toolkit.get_cash_flow_statement()

cash_flow_statements.loc['AMZN']

Which returns:

	2022Q3	2022Q4	2023Q1	2023Q2
Net Income	2.872e+09	2.78e+08	3.172e+09	6.75e+09
Depreciation and Amortization	1.0204e+10	1.2685e+10	1.1123e+10	1.1589e+10
Deferred Income Tax	-8.25e+08	-3.367e+09	-4.72e+08	-2.744e+09
Stock Based Compensation	5.556e+09	5.606e+09	4.748e+09	7.127e+09
Change in Working Capital	-5.254e+09	1.0526e+10	-1.4317e+10	-6.293e+09
Accounts Receivables	-4.794e+09	-8.788e+09	1.521e+09	-5.167e+09
Inventory	7.32e+08	3.18e+09	3.71e+08	-2.373e+09
Accounts Payables	-1.226e+09	9.852e+09	-1.1264e+10	3.029e+09
Other Working Capital	3.4e+07	6.282e+09	-4.945e+09	-1.782e+09
Other Non Cash Items	-1.149e+09	3.445e+09	5.34e+08	4.7e+07
Cash Flow from Operations	1.1404e+10	2.9173e+10	4.788e+09	1.6476e+10
Property, Plant and Equipment	-1.6378e+10	-1.6592e+10	-1.4207e+10	-1.1455e+10
Acquisitions	-8.85e+08	-8.31e+08	-3.513e+09	-3.16e+08
Purchases of Investments	-2.39e+08	-2.33e+08	-3.38e+08	-4.96e+08
Sales of Investments	5.57e+08	5.683e+09	1.115e+09	1.551e+09
Other Investing Activities	1.337e+09	1.152e+09	1.137e+09	1.043e+09
Cash Flow from Investing	-1.5608e+10	-1.0821e+10	-1.5806e+10	-9.673e+09
Debt Repayment	-9.429e+09	-1.8756e+10	-6.369e+09	-1.0861e+10
Common Stock Issued	0	0	0	0
Common Stock Purchased	0	6e+09	0	0
Dividends Paid	0	0	0	0
Other Financing Activities	1.2445e+10	1.2842e+10	1.2723e+10	4.322e+09
Cash Flow from Financing	3.016e+09	8.6e+07	6.354e+09	-6.539e+09
Forex Changes on Cash	-1.334e+09	6.37e+08	1.45e+08	6.9e+07
Net Change in Cash	-2.522e+09	1.9075e+10	-4.519e+09	3.33e+08
Cash End of Period	3.5178e+10	5.4253e+10	4.9734e+10	5.0067e+10
Cash Beginning of Period	3.77e+10	3.5178e+10	5.4253e+10	4.9734e+10
Operating Cash Flow	1.1404e+10	2.9173e+10	4.788e+09	1.6476e+10
Capital Expenditure	-1.6378e+10	-1.6592e+10	-1.4207e+10	-1.1455e+10
Free Cash Flow	-4.974e+09	1.2581e+10	-9.419e+09	5.021e+09

get_statistics_statement

Retrieves the balance, cash and income statistics for the company(s) from the specified source.

Note that this also obtains the balance sheet statement at the same time given that it’s the same API call. This is done to reduce the number of API calls to FinancialModelingPrep.

Args:

limit (int): Defines the maximum years or quarters to obtain.
overwrite (bool): Defines whether to overwrite the existing data.
progress_bar (bool): Defines whether to show a progress bar.

Returns: pd.DataFrame: A pandas DataFrame with the retrieved statistics statement data.

As an example:

from financetoolkit import Toolkit

toolkit = Toolkit("TSLA", api_key="FINANCIAL_MODELING_PREP_KEY", quarterly=True, start_date='2023-05-01')

toolkit.get_statistics_statement()

Which returns:

	2023Q2
Reported Currency	USD
CIK ID	1318605
Filling Date	2023-07-24
Accepted Date	2023-07-21 18:08:29
Calendar Year	2023
Period	Q2
SEC Link	https://www.sec.gov/Archives/edgar/data/1318605/000095017023033872/0000950170-23-033872-index.htm
Document Link	https://www.sec.gov/Archives/edgar/data/1318605/000095017023033872/tsla-20230630.htm

get_normalization_files

Copies the normalization files to a folder based on path. By default, this is the path of the ‘Downloads’ folder.

This function is relevant if you want to supply your own datasets. See for a proper guide the following notebook: https://www.jeroenbouma.com/projects/financetoolkit/external -datasets

Args:

path (str, optional): The path where to save the files to.

Returns: Three csv files saved to the desired location.