FinanceToolkit v1.7.2

Added a lot more economic metrics (over 50) into the Finance Toolkit, find the total list here. For example, get insights into how the United States Government spend their income:

from financetoolkit import Economics

economics = Economics()

central_government_spending = economics.get_central_government_spending()

central_government_spending['United States']

Which returns:

	General Public Services	Defence	Public Order and Safety	Economic Affairs	Environmental Protection	Housing and Community Amenities	Health	Recreation, Culture and Religion	Education	Social Protection
2007	0.1578	0.186	0.0148	0.0578	0	0.0192	0.2486	0.0015	0.0222	0.2921
2008	0.1392	0.1861	0.0148	0.069	0	0.0188	0.2369	0.0014	0.0221	0.3117
2009	0.1198	0.1772	0.0148	0.0639	0	0.0406	0.2417	0.0014	0.028	0.3127
2010	0.1211	0.1759	0.0148	0.0585	0	0.0309	0.2416	0.0015	0.0346	0.3211
2011	0.1324	0.1754	0.0149	0.0562	0	0.0271	0.2416	0.0014	0.0337	0.3173
2012	0.1348	0.173	0.0154	0.056	0	0.0216	0.2491	0.0014	0.0296	0.3191
2013	0.1333	0.162	0.0145	0.055	0	0.0172	0.2589	0.0013	0.0281	0.3296
2014	0.135	0.1525	0.0143	0.0524	0	0.0168	0.2765	0.0013	0.0269	0.3244
2015	0.1294	0.1461	0.0141	0.0473	0	0.0161	0.2919	0.0012	0.026	0.3279
2016	0.1321	0.141	0.0141	0.0526	0	0.0156	0.2952	0.0013	0.0244	0.3238
2017	0.1325	0.1408	0.0137	0.0526	0	0.0171	0.2961	0.0013	0.0233	0.3226
2018	0.1407	0.1427	0.0135	0.0519	0	0.014	0.2967	0.0012	0.0226	0.3165
2019	0.1417	0.1461	0.013	0.052	0	0.0133	0.2971	0.0012	0.021	0.3147

Or plotted:

Or see the Income Inequality in the Netherlands:

from financetoolkit import Toolkit

toolkit = Toolkit("MSFT")

income_inequality = toolkit.economics.get_income_inequality()

income_inequality['Netherlands']

Which returns:

	Gini Coefficient	P90/P10	P90/P50	P50/P10	Palma Ratio	S80/S20
2013	0.287	3.4	1.8	1.9	1.05	4.3
2014	0.305	3.4	1.8	1.9	1.16	4.6
2015	0.305	3.4	1.8	1.9	1.06	4.4
2016	0.292	3.4	1.8	1.9	1.07	4.4
2017	0.298	3.4	1.8	1.9	1.11	4.5
2018	0.295	3.3	1.8	1.9	1.09	4.4
2019	0.312	3.4	1.8	1.9	1.21	4.7
2020	0.295	3.3	1.8	1.9	1.09	4.4
2021	0.297	3.4	1.8	1.9	1.1	4.5

Or plotted:

And so much more!

FinanceToolkit v1.7.1

In addition to the many new Economics indicators in the previous release, I've added some new key economic rates.

I've added in the European Central Banks which include:

• The main refinancing operations (MRO) rate is the interest rate banks pay when they borrow money from the ECB for one week. When they do this, they have to provide collateral to guarantee that the money will be paid back.
• The marginal lending facility rate is the interest rate banks pay when they borrow from the ECB overnight. When they do this, they have to provide collateral, for example securities, to guarantee that the money will be paid back.
• The deposit facility rate which is one of the three interest rates the ECB sets every six weeks as part of its monetary policy. The rate defines the interest banks receive for depositing money with the central bank overnight.

These can be shown by using:

from financetoolkit import Economics

economics = Economics(start_date='2000-01-01')

economics.get_european_central_bank_rates()

Which returns (when plotted):

I've also added in the Federal Funds rates which include:

• The effective federal funds rate (EFFR) which is calculated as a volume-weighted median of overnight federal funds transactions reported in the FR 2420 Report of Selected Money Market Rates.
• The overnight bank funding rate (OBFR) which is calculated as a volume-weighted median of overnight federal funds transactions, Eurodollar transactions, and the domestic deposits reported as “Selected Deposits” in the FR 2420 Report.
• The TGCR which is calculated as a volume-weighted median of transaction-level tri-party repo data collected from the Bank of New York Mellon.
• The BGCR which is calculated as a volume-weighted median of transaction-level tri-party repo data collected from the Bank of New York Mellon as well as GCF Repo transaction data obtained from the U.S. Department of the Treasury’s Office of Financial Research (OFR).
• The SOFR which is calculated as a volume-weighted median of transaction-level tri-party repo data collected from the Bank of New York Mellon as well as GCF Repo transaction data and data on bilateral Treasury repo transactions cleared through FICC's DVP service, which are obtained from the U.S. Department of the Treasury’s Office of Financial Research (OFR).

These can be shown by using (and defining the rate):

from financetoolkit import Economics

economics = Economics(start_date='2016-01-01')

# Effective Federal Funds Rate
economics.get_federal_reserve_rates(rate='EFFR')

# Secured Overnight Financing Rate
economics.get_federal_reserve_rates(rate='SOFR')

Which returns (when plotted):

FinanceToolkit v1.7.0

This release features an entirely new module called Economics. This is meant to collect key economic indicators such as the Gross Domestic Product (GDP), Consumer Price Index (CPI) and Long and Short Term Interest Rates for 60+ countries. Find a complete overview here. This module can be called via the Toolkit:

from financetoolkit import Toolkit

companies = Toolkit(tickers=["AAPL", "TSLA"], api_key="FMP_KEY")

companies.economics.get_house_prices()

But also as standalone module (given that it doesn't require a company ticker):

from financetoolkit import Economics

economics = Economics()

economics.get_house_prices()

Both return the same dataset:

	Australia	Austria	Belgium	Brazil	Bulgaria	Canada	Chile	China	Colombia	Croatia	Czech Republic	Denmark	EA	Estonia	Euro Area 17	Finland	France	Germany	Greece	Hungary	Iceland	India	Indonesia	Ireland	Israel	Italy	Japan	Latvia	Lithuania	Luxembourg	Mexico	Netherlands	New Zealand	Norway	OECD - Total	Poland	Portugal	Romania	Russia	Saudi Arabia	Slovakia	Slovenia	South Africa	South Korea	Spain	Sweden	Switzerland	Turkey	United Kingdom	United States
2013	84.1065	92.1	98.962	98.269	95.915	89.9958	78.0367	96.4912	85.1748	104.627	93.875	90.0977	98.267	82.2933	98.4009	100.353	103.8	92.5982	113.816	84.87	85.2075	76.6863	88.5178	77.0045	88.8228	109.1	96.148	97.6225	90.645	90.8947	88.6885	95.7715	83.5338	91.75	92.1431	97.4165	93.085	99.26	95.574	nan	93.5868	106.21	87.4525	95.7696	96.2393	80.785	94.867	75.946	87.3505	90.7087
2014	91.7202	95.33	98.417	99.1345	97.29	94.828	88.4455	101.132	91.9212	102.975	96.175	93.4987	98.482	93.5795	98.5693	99.995	101.925	95.499	105.32	88.4275	92.3855	88.032	94.73	89.7232	94.4668	103.95	97.648	103.475	96.47	94.8777	92.4655	96.5565	88.9588	94.25	95.5783	98.4538	97.0375	97.22	97.9095	101.189	94.9068	99.2025	94.146	97.2716	96.5393	88.4025	97.4497	86.1545	94.3388	95.1076
2015	100	100	100	100	100	100	100	100	100	100	100	100	100	100	100	100	100	100	100	100	100	100	100	100	100	100	100	100	100	100	100	100	100	100	100	100	100	100	100	100	100	100	100	100	100	100	100	100	100	100
2016	105.5	108.532	102.641	100.868	107.02	110.589	106.573	112.23	109.995	100.888	107.15	104.727	104.01	104.745	104.012	101.285	100.925	107.527	97.623	113.377	109.767	106.686	103.159	107.484	107.459	100.25	102.213	108.487	105.4	106.006	107.282	105.052	114.178	107.025	105.352	101.859	107.115	105.95	102.856	95.091	106.703	103.252	105.616	101.622	104.623	108.243	101.627	113.615	106.975	105.586
2017	114.308	114.26	106.373	101.733	116.295	123.751	115.275	126.989	117.332	104.745	119.7	109.378	108.513	110.511	108.454	102.388	103.975	114.104	96.6342	127.24	131.196	115.658	106.415	119.162	111.649	99.175	104.805	118.013	114.797	111.96	115.687	112.978	121.596	112.35	111.13	105.768	117.015	112.355	108.782	88.9675	112.99	111.785	110.064	102.854	111.105	115.427	103.591	125.392	111.834	112.078
2018	112.664	119.64	109.415	102.599	123.955	128.378	126.812	134.268	123.825	111.14	130	114.529	113.668	117.066	113.604	103.335	107.15	121.681	98.379	145.495	142.006	122.248	109.895	131.363	110.724	98.625	106.849	129.3	123.18	119.862	125.951	123.656	125.966	113.975	116.592	112.765	129.032	118.618	116.147	86.4625	121.32	121.547	114.246	104.275	118.58	114.35	106.728	134.891	115.496	118.951
2019	108.084	126.6	113.779	103.464	131.417	129.959	137.5	139.503	130.601	121.103	141.9	117.53	118.641	125.267	118.505	103.77	110.675	128.707	105.481	170.167	148.291	126.309	111.91	134.431	112.812	98.525	108.553	140.875	131.61	131.987	136.739	132.658	129.9	116.8	121.327	122.55	141.882	122.69	119.822	83.5023	132.395	129.69	118.273	103.968	124.715	117.188	110.572	141.024	116.592	124.942
2020	113.916	136.311	118.595	104.332	137.412	138.454	146.659	144.577	133.834	130.38	153.85	123.531	124.813	132.792	124.761	105.595	116.825	138.685	110.223	178.577	157.788	129.465	113.646	134.847	116.261	100.4	108.63	145.768	141.178	151.115	144.687	142.761	143.109	121.775	128.945	135.335	154.327	128.435	144.26	84.833	145.061	135.657	121.228	107.451	127.505	122.11	115.355	173.709	119.904	134.761
2021	133.764	153.262	127.058	105.198	149.333	159.222	164.029	150.101	141.909	139.912	184.225	138.01	134.675	152.778	134.765	110.448	124.625	154.739	118.606	208.037	177.563	132.749	115.27	146	126.076	102.95	115.102	161.645	163.84	172.151	156.073	164.241	180.981	134.525	145.113	147.76	168.84	134.088	175.268	85.7263	154.334	151.317	126.269	116.89	132.195	134.463	123.334	237.534	130.515	157.24
2022	142.65	170.765	134.194	106.063	169.917	177.235	175.906	151.385	152.542	160.685	215.275	137.36	144.219	186.76	144.178	111.745	132.5	162.941	132.69	254.345	214.848	136.925	nan	163.977	147.852	106.875	124.755	184.005	195.048	188.6	169.973	186.321	182.022	141.575	165.45	165.18	190.17	143.697	225.506	87.5558	175.471	173.648	130.758	119.32	141.978	139.25	132.603	601.714	143.214	179.097

It is also possible to plot things such as the Employment Rate as also found in the README:

Or the Long Term (10 year) and Short Term (3 month) Interest Rates:

Or any of the other metrics:

• Gross Domestic Product (GDP) including Growth and Forecasts
• Consumer Confidence Index (CCI)
• Business Confidence Index (BCI)
• Composite Leading Indicator (CLI)
• Consumer Price Index (CPI)
• Producer Price Index (PPI)
• House and Rent Prices
• Unemployment Rates
• Long Term Interest Rates (10 year)
• Short Term Interest Rates (3 month)
• Purchasing Power Parity (PPP)
• Exchange Rates

Are you looking for any specific economic parameter? Let me know and I'll start working on it.

FinanceToolkit v1.6.6

This releases introduces the GARCH model including volatility forecasting. Next to that, it includes a bugfix for the currency conversion.

Designed by @northern-64bit (LinkedIn) in #82, this release introduces GARCH (Generalized autoregressive conditional heteroskedasticity) which is stochastic model for time series, which is for instance used to model volatility clusters, stock return and inflation. It is a generalisation of the ARCH models.

It can be found inside the risk module and can be ran with the following code:

from financetoolkit import Toolkit

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

toolkit.risk.get_garch()

Which produces the following result:

Date	AMZN	TSLA	Benchmark
2012	0	0	0
2013	0.2038	5.027	0.024
2014	0.4016	10.2307	0.0537
2015	0.53	13.2345	0.0688
2016	0.7664	15.6152	0.079
2017	0.8181	17.5204	0.0887
2018	0.8896	19.0642	0.0997
2019	0.9235	20.2789	0.104
2020	0.9479	21.2567	0.1169
2021	1.0203	27.5585	0.1209
2022	1.0201	27.0986	0.129
2023	1.0445	26.7482	0.1305

You can also forecast in the future for any period and for any interval. For example, the quarterly expected volatility estimations can be shown with:

from financetoolkit import Toolkit

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

toolkit.risk.get_garch_forecast(period='quarterly')

Which returns:

	AMZN	TSLA	Benchmark
2024Q1	0	0	0
2024Q2	0	0	0
2024Q3	0.006	0.3185	0.0003
2024Q4	0.0114	0.6051	0.0006
2025Q1	0.0162	0.8631	0.0008
2025Q2	0.0206	1.0953	0.001
2025Q3	0.0245	1.3042	0.0012
2025Q4	0.0281	1.4923	0.0014
2026Q1	0.0312	1.6615	0.0015
2026Q2	0.0341	1.8138	0.0017

Next to that, a bug fix went in related to the currency conversions which makes it more robust when there is data missing. If you didn't know, when you have a Premium FMP plan the Finance Toolkit will now automatically convert currencies that do not match up with the historical data. For more see here: https://github.com/JerBouma/FinanceToolkit/releases/tag/v1.6.3

FinanceToolkit v1.6.3

This release features currency conversions. A user noticed that the financial statements are not always in the expected currency (different from the historical data):

This could lead to confusion when comparing numbers between companies and or when calculating ratios that require both data from the financial statement and the historical market data. This has now been resolved with an automatic converter that converts the financial statements accordingly. With this, the end of quarter or end of year exchange rate is used.

This is also saved in a seperate function called get_exchange_rates as shown below:

This is quite a major change given that there are plenty of financial statements that report their financial statements in their local currency while the listed historical prices are in USD. This ensures that those numbers can be compared. This can be disabled by setting convert_currency to False in the Toolkit initialisation. Note that by default it is automatically turned off when using the Free plan of FinancialModelingPrep as it could drain the API calls for that specific day.

I've also added in a parameter that recognises if you are using a Free or Premium plan from FinancialModelingPrep. This is a dependency for the sleep_timer which will wait a maximum of 60 seconds before making a new request to FMP. This is only relevant for the Premium plans since there is a rate limit per minute. For the Free plan it doesn't matter given that you are limited to 250 requests per day. In any case, this should make the Free experience smoother since you are not waiting for no reason. This solves #80.

Note: this consumes 1 API call so if you don't want that, just set sleep_timer to True or False.

if sleep_timer is None:
    # This tests the API key to determine the subscription plan. This is relevant for the sleep timer
    # but also for other components of the Toolkit. This prevents wait timers from occurring while
    # it wouldn't result to any other answer than a rate limit error.
    determine_plan = helpers.get_financial_data(
        url=f"https://financialmodelingprep.com/api/v3/income-statement/AAPL?period=quarter&apikey={api_key}",
        sleep_timer=False,
    )

    self._fmp_plan = "Premium"

    for option in ["NOT AVAILABLE", "LIMIT REACH", "INVALID API KEY"]:
        if option in determine_plan:
            self._fmp_plan = "Free"
            break
else:
    self._fmp_plan = "Premium"

FinanceToolkit v1.6.0

I did a new release last week but for everything I've added in it made sense to do another full release. This release includes new models and ESG scores and a lot of the code has been refactored, many new tests have been added and a lot of bug fixes went in. For example, @brianrasmusson noticed in #76 that Apple's data was a bit out of whack. This has now been resolved. I've also added in a lot pretty graphs into the README to show what you can really do with the data, e.g. see this one on the Value of Risk:

It is now possible to view ESG Scores for any range of companies with get_esg_scores:

Next to that, within the Models module, two new models are included. These are Altman Z-Score and Piotroski F-score. The former calculates the chance of bankruptcy based on a set of criteria:

The latter scores the financial strength of the company:

Furthermore, I've added a performance measurement which shows the period growth rate that has occurred (taking out fluctuations over time as shown in the plot below):

Other than that, I've extended the examples and gave more explanation how to add in your own datasets.

FinanceToolkit v1.5.0

Introducing the power of Threading into the FinanceToolkit. Update now via pip install financetoolkit -U.

As an example, obtaining the balance sheet statements of 345 different companies for the period 1983 to 2023 takes less than 40 seconds. This makes data collection extremely quick and makes the FinanceToolkit an invaluable tool for any financial specialist.

Next to that, given that there are rate limits, these are correctly dealt with through sleep timers. If you have a subscription from FinancialModelingPrep that limits you to 300 requests per minute, the FinanceToolkit will deal with this and will wait until it can make requests again.

Furthermore, if some of the historical data is not available from FinancialModelingPrep it will try YahooFinance instead making sure that you don't lose vital data for your calculations.

Of course, all of this extends in all functionality. E.g. see Jensen's Alpha for all 345 tickers on a quarterly basis. Given that the calculations are done by the FinanceToolkit, this takes just a few seconds.

It's time to level up. 🚀

FinanceToolkit v1.4.1

I've added in the Dividend Calendar which can be shown by get_dividend_calendar():

from financetoolkit import Toolkit

API_KEY = "FINANCIAL_MODELING_PREP_KEY"

# Initialize the Toolkit with company tickers
companies = Toolkit(
    ["AAPL", "MSFT", "GOOGL", "AMZN"], api_key=API_KEY, start_date="2005-01-01"
)

companies.get_dividend_calendar()

Which returns:

Next to that, this release introduces historical data from FinancialModelingPrep. This is enabled by default when you provide an API key but can be changed back by setting historical_source to "YahooFinance" when initialising the Toolkit. When you don't provide an API key it defaults to Yahoo Finance.

This is done to prevent rate limits from becoming an issue when you have purchased a FinancialModelingPrep subscription given that Yahoo Finance will rate limit at some point. It of course makes little sense that you are paying and still get rate limited!

E.g. the following will net you the same result:

from financetoolkit import Toolkit

API_KEY = "FINANCIAL_MODELING_PREP_KEY"

# Use FinancialModelingPrep to obtain Historical Data
companies = Toolkit(
    ["AAPL", "MSFT", "GOOGL", "AMZN"], api_key=API_KEY, start_date="2005-01-01"
)

companies.get_historical_data()

# Use Yahoo Finance to obtain Historical Data even though you set a FinancialModelingPrep API Key
companies = Toolkit(
    ["AAPL", "MSFT", "GOOGL", "AMZN"], api_key=API_KEY, start_date="2005-01-01", historical_source='YahooFinance'
)

companies.get_historical_data()

# Use Yahoo Finance to obtain Historical Data without needing to set an API Key
companies = Toolkit(
    ["AAPL", "MSFT", "GOOGL", "AMZN"], start_date="2005-01-01"
)

companies.get_historical_data()

Which returns:

FinanceToolkit v1.4.0

This releases includes the Fama and French 5-factor model. Something I've been wanting to add into the Finance Toolkit for some time now. This allows you to understand the explanatory power of the following factors on each ticker included in the Toolkit on any period, let it be yearly, quarterly, monthly or weekly:

• Market Risk Premium (Mkt-RF): Represents the additional return that investors expect to earn for taking
  on the risk of investing in the overall market as opposed to a risk-free asset.
• Size Premium (SMB): Reflects the historical excess return of small-cap stocks over large-cap stocks.
• Value Premium (HML): Captures the historical excess return of value stocks over growth stocks.
• Profitability (RMW): Measures the historical excess return of high profitability stocks over
  low profitability stocks.
• Investment (CMA): Quantifies the historical excess return of low investment stocks over
  high investment stocks.

I've added correlation functions that help you understand how factors correlate with each other over time. For example, the following GIF is created with data from the function performance.get_factor_correlations.

Next to that, you can perform Linear Regressions on each factor and each factor combination to discover sensitivities of individual assets to each factor (this is not only limited to companies but can also be currencies, commodities, ETFs and more):

from financetoolkit import Toolkit

# Initialize the Finance Toolkit
companies = Toolkit(
    tickers=["MSFT", 'AAPL', 'TSLA', 'GOOG', "AMZN", 'MU'], api_key=FMP_KEY, quarterly=False
)
companies.performance.get_fama_and_french_model()

Which returns regression coefficients for all factors for each individual stock:

You have the option to show both the results from a simple linear regression (which is defined as Excess Return = Intercept + Slope * Factor Value + Residuals) or a multi linear regression (which is defined as Excess Return = Intercept + Beta1 * Mkt-RF + Beta2 * SMB + Beta3 * HML + Beta4 * RMW + Beta5 * CMA + Residuals) the latter is the default. E.g. you can show the explanatory power of each individual factor for example for Microsoft as follows:

from financetoolkit import Toolkit

# Initialize the Finance Toolkit
companies = Toolkit(
    tickers=["MSFT"], api_key=FREE_FMP_KEY, quarterly=False
)
result = companies.performance.get_fama_and_french_model(period='quarterly', method='simple')

result['MSFT'].xs('R Squared', level=1, axis=1).plot(figsize=(15, 5), title=f'Factor Sensitivities of Microsoft')

Which returns the following:

FinanceToolkit v1.3.10

This releases features a couple of Quality of Life improvements and some bug fixes:

• A user noticed that for some tickers, e.g. currencies, it returned an error since the index wasn't matched up correctly. This has now been corrected by only keeping the first duplicate if any are found. The issue here was that the same index was reported twice (e.g. 2020-05-06 being reported twice in the index) which will cause an issue if you try to combine such a DataFrame with other DataFrames.
• For all functionality, if the ticker doesn't correspond to the correct type, the Toolkit will mention this to you. E.g. if you would try to obtain an Income Statement from a ticker such as "^GSPC" (which is the S&P 500 index) it will return a well-written error. This also applies to if a subset of your tickers is not marked as an Equity.

If you didn't know, any type of asset class works with the Finance Toolkit (e.g. Equities, Currencies, Cryptocurrencies, ETFs, Mutual Funds, Indices, Money Markets, Commodities and more) which also allows you to obtain detailed calculations such as the Conditional Value at Risk (cVaR).