FinanceToolkit v1.9.8

This release features a fix to issues with Yahoo Finance which is used as a fallback whenever FinancialModelingPrep doesn't have the data or because your plan limits you from accessing this data. E.g. if you have the Free plan you can only get historical data on U.S. listed stocks so all other historical data comes from Yahoo Finance. I used to directly query from Yahoo Finance myself but given that it can break over time, it makes more sense to add in an optional dependency (yfinance). This means that by default this dependency is not installed given that the Finance Toolkit doesn't have to rely on this package if you have the proper FMP plan.

This is what I am referring to (see "The following tickers acquired historical data..")

In case you've used pip install financetoolkit -U in a new environment you will receive the following message when you have a Free plan and try to download data on stocks not on U.S. exchanges:

As can be read, this can be resolved by using pip install 'financetoolkit[yfinance]' as shown below:

FinanceToolkit v1.9.6

The last couple of releases were all minor bugfixes therefore it didn't merit a proper release. However, for this one quite a fix things under the hood have changed entirely inside the Economy menu. Given that OECD recently updated their databases, all of the original links broke and therefore needed fixing. This unfortunately also resulted in some functionality being scrapped given that the datapoints were archived altogether.

Furthermore, I am aware of issues with Yahoo Finance's data download. You won't notice any to this when using an API Key from FMP but if you don't, I had built a fallback method that went to Yahoo Finance. Given that Yahoo Finance doesn't officially support the collection of data for now this functionality within the Finance Toolkit won't function (it will return 0s). I have still to consider whether to remove the functionality altogether or come up with a fix (however given that it is unofficial, things can break at any time again). Given that the package yfinance exists, I might introduce it as an add-on if you still wish to have this functionality.

Please let me know if you do.

FinanceToolkit v1.9.0

The release of the Finance Toolkit v1.9.0 includes an entirely new 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 Convexity, the Yield to Maturity and models such as Black and Bachelier to valuate derivative instruments such as Swaptions.

For example, it is possible to perform Bond Calculations with collect_bond_statistics which can valuate a variety of bonds based on used input:

	Bond 1	Bond 2	Bond 3	Bond 4	Bond 5	Bond 6
Par Value	100	250	50	1000	85	320
Coupon Rate	0.05	0.02	0.075	0	0.15	0.015
Years to Maturity	5	10	2	10	3	1
Yield to Maturity	0.08	0.021	0.03	0	0.16	0.04
Frequency	1	1	4	1	2	12
Present Value	88.0219	247.766	54.3518	1000	83.0353	312.171
Current Yield	0.0568	0.0202	0.069	0	0.1535	0.0154
Effective Yield	0.05	0.02	0.0771	0	0.1556	0.0151
Macaulay's Duration	4.5116	9.1576	1.8849	10	2.5667	0.9932
Modified Duration	4.1774	8.9693	1.8709	10	2.3766	0.9899
Effective Duration	4.0677	8.5181	1.8477	9.4713	2.2952	0.9844
Dollar Duration	3.677	22.2228	1.0168	100	1.9734	3.0902
DV01	0.0004	0.0022	0	0.01	0.0001	0
Convexity	22.4017	93.7509	4.0849	110	7.0923	1.0662

This can be done with the following code:

from financetoolkit import FixedIncome

fixedincome = FixedIncome()

# Valuate a specific bond, for example Bond 1
fixedincome.collect_bond_statistics(
    par_value=100,
    coupon_rate=0.05,
    years_to_maturity=5,
    yield_to_maturity=0.08,
    frequency=1,
)

Not only Bonds can be valuated, the module can also be used to valudate derivatives such as Swaptions utilizing the Black and Bachelier models. For example, the Black model can be used to valuate a Swaption:

from financetoolkit import FixedIncome

fixedincome = FixedIncome()

# You can also provide lists of values for the strike rate and years to maturity
# to define your own strike rates and years to maturity to display in the DataFrame
fixedincome.get_derivative_price(model_type='black', forward_rate=0.0325)

This returns a large DataFrame that shows the present value of such a swaption contract at a variety of strike rates and maturities.

Strike Rate	2025-04-21	2026-04-21	2027-04-21	2028-04-20	2029-04-20	2030-04-20	2031-04-20	2032-04-19	2033-04-19	2034-04-19
0.005	0	0	0	0	0	0	0	0	0	0
0.01	0	0	0	0	0	0	0	0	0	0
0.015	0	0	0	0	0	0	0	0	0	0
0.02	0	0	0	0	0	0	0	0	0	0
0.025	0	0	0	0	0	0	0	0	0	0
0.03	0	0	0	0.04	0.25	0.9	2.3	4.68	8.22	12.98
0.035	24200.6	23426.7	22677.6	21952.5	21251.2	20573.2	19918.5	19286.4	18676.5	18088
0.04	72601.7	70280.1	68032.7	65857.2	63751.2	61712.6	59739.2	57828.9	55979.6	54189.6
0.045	121003	117133	113388	109762	106252	102854	99565.3	96381.4	93299.4	90315.9
0.05	169404	163987	158743	153667	148753	143996	139391	134934	130619	126442
0.055	217805	210840	204098	197571	191254	185138	179218	173487	167939	162569
0.06	266206	257694	249453	241476	233754	226280	219044	212039	205259	198695
0.065	314607	304547	294808	285381	276255	267421	258870	250592	242578	234821
0.07	363008	351400	340163	329286	318756	308563	298696	289144	279898	270948
0.075	411410	398254	385518	373191	361257	349705	338522	327697	317218	307074
0.08	459811	445107	430874	417095	403758	390846	378348	366250	354538	343200
0.085	508212	491960	476229	461000	446258	431988	418174	404802	391858	379327
0.09	556613	538814	521584	504905	488759	473130	458000	443355	429177	415453
0.095	605014	585667	566939	548810	531260	514272	497827	481907	466497	451580
0.1	653415	632521	612294	592714	573761	555413	537653	520460	503817	487706
0.105	701816	679374	657649	636619	616262	596555	577479	559012	541137	523832
0.11	750217	726227	703004	680524	658762	637697	617305	597565	578456	559959
0.115	798619	773081	748359	724429	701263	678839	657131	636118	615776	596085
0.12	847020	819934	793715	768334	743764	719980	696957	674670	653096	632211
0.125	895421	866787	839070	812238	786265	761122	736783	713223	690416	668338
0.13	943822	913641	884425	856143	828766	802264	776609	751775	727735	704464

It is also possible to access Government Bond rates from 30+ countries. For example, using get_government_bond_yields(short_term=False, period='monthly'):

	Australia	Austria	Belgium	Brazil	Bulgaria	Canada	Chile	China	Colombia	Costa Rica	Croatia	Czech Republic	Denmark	Estonia	Euro Area 19	Finland	France	Germany	Greece	Hungary	Iceland	India	Indonesia	Ireland	Israel	Italy	Japan	Latvia	Lithuania	Luxembourg	Mexico	Netherlands	New Zealand	Norway	Poland	Portugal	Romania	Russia	Slovakia	Slovenia	South Africa	South Korea	Spain	Sweden	Switzerland	United Kingdom	United States
2023-05	0.035	0.0303	0.0304	0.0728	0.0403	0.0305	0.0555	0.0269	0.1111	nan	0.0371	0.045	0.0254	0.0394	0.0321	0.0298	0.0294	0.0234	0.0398	0.0778	nan	0.0701	0.0636	0.0279	0.0379	0.0431	0.0043	0.0383	0.0288	0.0295	0.0876	0.0273	0.0423	0.0323	0.0593	0.0316	0.063	nan	0.0359	0.0315	0.1194	0.034	0.0341	0.0232	0.0085	0.0396	0.0357
2023-06	0.0392	0.0305	0.0305	0.0728	0.0403	0.0334	0.0531	0.0264	0.1015	nan	0.0373	0.0435	0.0257	0.0394	0.0316	0.03	0.0293	0.0238...

FinanceToolkit v1.8.5

This release contains new Fixed Income metrics as part of the Economics module, these contain Option-Adjusted Spreads, Effective Yields, Total Returns and Yield to Worst. For example, see the Option-Adjusted Spread below (see documentation here):

The Option-Adjusted Spread (OAS) is the spread relative to a risk-free interest rate, usually measured in basis points (bp), that equates the theoretical present value of a series of uncertain cash flows to the market price of a fixed-income investment. The spread is added to the risk-free rate to compensate for the uncertainty of the cash flows. This is usually a better alternative than the usual Z-spread used to price derivatives.

Furthermore, @northern-64bit (maintainer of scikit-multilearn-ng) introduced new AR and MA models that can be utilised with any type of time series. For more information, see #117. @northern-64bit actually introduced many more models in the Finance Toolkit as seen here.

Furthermore, I've introduced bug fixes solving #128 and #129.

FinanceToolkit v1.8.3

This release includes the Binomial Model, a mathematical model for pricing both American as well as European options. The Binomial Model is a discrete-time model that calculates the price of an option by creating a riskless hedge portfolio that replicates the payoff of the option. The model is based on the assumption that the price of the underlying asset follows a binomial distribution. The Binomial Model is a simple and intuitive model that is widely used in practice. It is also the basis for more complex models. See for an elaborate explanation the documentation as found here.

For example, when using the following code:

from financetoolkit import Toolkit

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

companies.options.get_binomial_model(show_input_info=True)

It returns a large DataFrame with the binomial tree for each company and each strike price around the current price (as defined by the start_date parameter).

The resulting output is a DataFrame containing the tickers, strike prices and movements as the index and the time to expiration as the columns. The movements index contains the number of up movements and the number of down movements. The output is the binomial tree displayed in a table. E.g. when using 10 time steps, the table for each strike price from each company will contain the actual binomial tree as also depicted in the image as seen below. Find the documentation here.

When selecting for example Apple at a Strike Price of 140 you will get the actual Binomial Tree depicted as a table, this represents the tree you see in the image at the top.

Movement	2024-02-02	2024-03-09	2024-04-15	2024-05-21	2024-06-27	2024-08-02	2024-09-08	2024-10-14	2024-11-20	2024-12-26	2025-02-01
UUUUUUUUUU	54.7747	69.9327	87.4757	107.31	129.344	153.573	180.122	209.208	241.069	275.965	314.18
UUUUUUUUUD	nan	39.9569	52.8423	68.2288	86.0206	106.037	128.14	152.365	178.911	207.994	239.852
UUUUUUUUDD	nan	nan	27.3011	37.7763	50.8718	66.5774	84.6651	104.825	126.925	151.146	177.689
UUUUUUUDDD	nan	nan	nan	16.9659	24.8886	35.4656	48.9066	65.0645	83.4462	103.602	125.698
UUUUUUDDDD	nan	nan	nan	nan	9.1158	14.4288	22.208	33.0259	47.083	63.8384	82.2161
UUUUUDDDDD	nan	nan	nan	nan	nan	3.8311	6.7007	11.4806	19.124	30.5822	45.85
UUUUDDDDDD	nan	nan	nan	nan	nan	nan	0.9669	1.9327	3.8631	7.722	15.4353
UUUDDDDDDD	nan	nan	nan	nan	nan	nan	nan	0	0	0	0
UUDDDDDDDD	nan	nan	nan	nan	nan	nan	nan	nan	0	0	0
UDDDDDDDDD	nan	nan	nan	nan	nan	nan	nan	nan	nan	0	0
DDDDDDDDDD	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0

The model contains parameters to lengthen the time steps, change the risk-free rate and the dividend yield but more importantly, make it possible to calculate the price of both American and European options as well as Call and Put options. For example, let's calculate the price of a American Put option with a strike price of 140 for Apple again:

from financetoolkit import Toolkit

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

companies.options.get_binomial_model(
    show_input_info=True,
    put_option=True,
    american_option=True,
    timesteps=12,
    risk_free_rate=0.01)

Which returns the Option valuations for an American Put Option.

Movement	2024-02-02	2024-03-03	2024-04-02	2024-05-03	2024-06-02	2024-07-03	2024-08-02	2024-09-01	2024-10-02	2024-11-01	2024-12-02	2025-01-01	2025-02-01
UUUUUUUUUUUU	2.3581	1.1115	0.4236	0.116	0.0171	0	0	0	0	0	0	0	0
UUUUUUUUUUUD	nan	3.7011	1.8524	0.7546	0.2225	0.0355	0	0	0	0	0	0	0
UUUUUUUUUUDD	nan	nan	5.6933	3.0346	1.3274	0.4238	0.0736	0	0	0	0	0	0
UUUUUUUUUDDD	nan	nan	nan	8.5588	4.8737	2.3002	0.8005	0.1529	0	0	0	0	0
UUUUUUUUDDDD	nan	nan	nan	nan	12.5322	7.6463	3.9148	1.4975	0.3173	0	0	0	0
UUUUUUUDDDDD	nan	nan	nan	nan	nan	17.8023	11.6676	6.518	2.7676	0.6586	0	0	0
UUUUUUDDDDDD	nan	nan	nan	nan	nan	nan	24.4233	17.2193	10.5575	5.0375	1.3671	0	0
UUUUUDDDDDDD	nan	nan	nan	nan	nan	nan	nan	32.2051	24.4052	16.5049	8.9884	2.8376	0
UUUUDDDDDDDD	nan	nan	nan	nan	nan	nan	nan	nan	40.6414	32.9347	24.6074	15.6102	5.89
UUUDDDDDDDDD	nan	nan	nan	nan	nan	nan	nan	nan	nan	48.9936	41.9389	34.3151	26.0772
UUDDDDDDDDDD	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	56.6615	50.2044	43.2257
UDDDDDDDDDDD	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	63.702	57.7929
DDDDDDDDDDDD	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	70.1673

Under the hood of this model the stock prices are simulated based on up and down movements. These can be graphically depicted as a binomial tree and help in understanding the calculated option prices for each node in. More information about these stock price simulations can be found in the documentation here and as follows:

from financetoolkit import Toolkit

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

companies.options.get_stock_price_simulations(show_input_info=True, timesteps=10)

Which would return for Apple the following graph when plotted:

Given that the Finance Toolkit is modular, you do not have to use the Toolkit functionality directly and can also call each functionality of the model separately. For example, this shows the output of using the model directly, specifying each parameter yourself.

FinanceToolkit v1.8.1

This new release contains intraday historical data which makes it possible to calculate Risk, Performance and Technical metrics based on a 1 min, 5 min, 15 min, 30 min or 1 hour interval. For example, you can obtain the Capital Asset Pricing Model (CAPM) per model in which it calculates the beta for each hour.

Or for example Williams %R on an hourly basis as follows:

Next to that, I've done a lot of polishing. A user noticed that there were some issues with working with delisted companies. As Financial Modeling Prep allows you to get data on these companies whereas Yahoo Finance doesn't, I needed to make sure that the Finance Toolkit didn't attempt to query the Yahoo Finance and return an error. This has been resolved (all of these companies are delisted):

Furthermore, I've fixed small bugs, updated the documentation and more. All in all it should be a more pleasant experience!

FinanceToolkit v1.8.0

It's time for another major release which consists of a full blown Options menu including all First, Second and Third Order Greeks such as Delta, Vega, Gamma, Theta and Ultima. Find the entire list of Greeks here. Update your Finance Toolkit now with:

pip install financetoolkit -U

Based on the Black Scholes formula, it is now possible to get theoretical option prices and greeks for a range of stocks being able to plot charts such as the following:

For example, the following code gets you all Greeks for Tesla.

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 (Stock Price: 238.45, Volatility: 55.4%, Dividend Yield: 0.0% and Risk Free Rate: 3.91%):

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	0.4931	-0.6533	4.0782	0	0	-0	0	0	-0	0	0	-0	0	0	0
185	1	-0.9999	0	-0.0198	0.5068	-0.6533	4.4595	0	0	-0	0	0	-0	0	0	-0	0	0	0
190	1	-0.9999	0	-0.0204	0.5205	-0.6533	4.9195	0	0	-0	0	0	-0	0	0	-0	0	0	0
195	1	-0.9999	0	-0.0209	0.5342	-0.6533	5.4853	0	0	-0	0	0	-0	0	0	-0	0	0	0
200	1	-0.9999	0	-0.0214	0.5479	-0.6533	6.1981	0	0	-0	0	0	-0	0.0012	0	-0	0	0	0
205	1	-0.9999	0	-0.022	0.5616	-0.6533	7.1239	0	0	-0	0.0004	0.0003	-0	0.1071	0	-0	0	0.0003	0.0001
210	1	-0.9999	0	-0.0226	0.5753	-0.6533	8.3747	0	0	-0.0002	0.0199	0.0108	-0.0001	4.1973	0	-0	0.0001	0.012	0.0032
215	0.9998	-0.9997	0.0001	-0.0252	0.5889	-0.6532	10.1567	0.0001	0.0001	-0.0041	0.414	0.1838	-0.0027	72.9841	0.0001	-0	0.002	0.198	0.0324
220	0.9974	-0.9971	0.001	-0.0512	0.601	-0.6516	12.8704	0.0012	0.0014	-0.04	4.0384	1.3972	-0.0264	580.934	0.0014	-0.0005	0.0141	1.4208	0.1193
225	0.9783	-0.9767	0.0065	-0.2027	0.6021	-0.6391	17.2415	0.0075	0.0084	-0.1863	18.7659	4.6975	-0.1235	2158.08	0.0084	-0.0022	0.0409	4.115	0.0867
230	0.8966	-0.8912	0.0224	-0.6437	0.5616	-0.5857	24.2406	0.026	0.028	-0.4003	40.2357	6.3078	-0.2677	3809	0.028	-0.0049	0.0261	2.5995	-0.1644
235	0.6987	-0.6885	0.0435	-1.2217	0.4433	-0.4565	34.5702	0.0504	0.0519	-0.3092	30.7944	2.0098	-0.2139	3626.75	0.0519	-0.004	-0.0676	-6.8748	-0.0997
240	0.4187	-0.4074	0.0488	-1.361	0.2679	-0.2735	48.191	0.0565	0.0558	0.1652	-17.2254	0.4231	0.0945	3408.71	0.0558	0.0014	-0.0971	-9.7985	-0.0227
245	0.1798	-0.1722	0.0327	-0.911	0.1156	-0.1174	64.4551	0.0379	0.0359	0.4473	-45.5831	5.1158	0.2833	4082.47	0.0359	0.0049	-0.0092	-0.8794	-0.1971
250	0.0534	-0.0503	0.0136	-0.3769	0.0344	-0.0349	82.5525	0.0157	0.0143	0.322	-32.7	6.4816	0.2066	3305.97	0.0143	0.0036	0.0467	4.7605	-0.0412
255	0.0108	-0.01	0.0036	-0.0992	0.007	-0.0071	101.798	0.0041	0.0036	0.12	-12.1728	3.4389	0.0774	1510.91	0.0036	0.0014	0.0324	3.2868	0.1451
260	0.0015	-0.0014	0.0006	-0.017	0.001	-0.001	121.702	0.0007	0.0006	0.0266	-2.6915	0.9828	0.0172	404.445	0.0006	0.0003	0.0101	1.0246	0.1043
265	0.0001	-0.0001	0.0001	-0.002	0.0001	-0.0001	141.935	0.0001	0.0001	0.0037	-0.3769	0.1682	0.0024	66.8956	0.0001	0	0.0018	0.1826	0.031
270	0	-0	0	-0.0002	0	-0	162.286	0	0	0.0003	-0.0349	0.0183	0.0002	7.148	0	0	0.0002	0.0203	0.0051
275	0	-0	0	-0	0	-0	182.618	0	0	0	-0.0022	0.0013	0	0.5115	0	0	0	0.0015	0.0005
280	0	-0	0	-0	0	-0	202.841	0	0	0	-0.0001	0.0001	0	0.0253	0	0	0	0.0001	0
285	0	-0	0	-0	0	-0	222.899	0	0	0	-0	0	0	0.0009	0	0	0	0	0
290	0	-0	0	-0	0	-0	242.753	0	0	0	-0	0	0	0	0	0	0	0	0
295	0	-0	0	-0	0	-0	262.382	0	0	0	-0	0	0	0	0	0	0	0	0

Here, it automatically creates Strike Prices around the current stock price (or any price in the past if you change the start_date) parameter as well as plots forward for the time to expiration. Things that you can change with ease yourself if you like. For example:

from financetoolkit import Toolkit

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

rho = toolkit.options.get_rho(
    start_date="2020-01-02",
    strike_price_range=0.15,
    expiration_time_range=20,
    put_option=True,
    show_input_info=True
)

rho.loc['MSFT']

Which returns:

Based on the period 2013-01-22 to 2024-01-17 the following parameters were used:
Stock Price: ASML (291.79), Benchmark (305.06), MSFT (154.78)
Volatility: ASML (34.77%), Benchmark (17.46%), MSFT (26.99%)
Dividend Yield: MSFT (0.96%), ASML (0.6%)
Risk Free Rate: 1.88%

Strike Price	2020-01-03	2020-01-04	2020-01-05	2020-01-06	2020-01-07	2020-01-08	2020-01-09	2020-01-10	2020-01-11	2020-01-12	2020-01-13	2020-01-14	2020-01-15	2020-01-16	2020-01-17	2020-01-18	2020-01-19	2020-01-20	2020-01-21
130	-0	-0	-0	-0	-0	-0	-0	-0	-0.0001	-0.0002	-0.0004	-0.0008	-0.0015	-0.0026	-0.0041	-0.0062	-0.0089	-0.0124	-0.0167
135	-0	-0	-0	-0	-0	-0.0001	-0.0003	-0.001	-0.0022	-0.0043	-0.0076	-0.0122	-0.0185	-0.0265	-0.0365	-0.0486	-0.0629	-0.0794	-0.0982
140	-0	-0	-0	-0.0003	-0.0015	-0.0045	-0.0101	-0.0192	-0.0322	-0.0493	-0.0709	-0.0968	-0.1271	-0.1616	-0.2003	-0.2429	-0.2895	-0.3397	-0.3934
145	-0	-0.0004	-0.0047	-0.017	-0.0396	-0.0727	-0.1156	-0.1676	-0.2277	-0.295	-0.3689	-0.4486	-0.5335	-0.6233	-0.7174	-0.8154	-0.917	-1.022	-1.13
150	-0.0055	-0.0485	-0.1251	-0.223	-0.3353	-0.4579	-0.5883	-0.7249	-0.8666	-1.0125	-1.162	-1.3145	-1.4698	-1.6275	-1.7873	-1.949	-2.1125	-2.2776	-2.4442
155	-0.23...

FinanceToolkit v1.7.4

This release features the Black Scholes Model, Present Value of Growth Opportunities (PVGO) and a variety of bug fixes.

Starting with the Black Scholes model, I've built in a method that automatically calculates the theoretical options value with strike prices that are near the current stock price and for a lengthy period of time. All of this you can expand yourself if desired with the strike_price_range, strike_step_size and expiration_time_range parameters. By default, the strike prices between 75% and 125% of the current stock price are used and the time to expiration is calculated for the upcoming 30 days.

For example:

from financetoolkit import Toolkit

companies = Toolkit(
    tickers=['GOOGL', "MSFT", 'AAPL'],
    api_key="FMP_KEY",
    start_date='2022-01-01',
    end_date='2023-01-01'
)

companies.risk.get_black_scholes_model()

Which returns:

I've also added in the Present Value of Growth Opportunities (PVGO), a metric that uses WACC and Earnings per Share to determine the attractiveness of companies in the near future. This was requested by #88.

from financetoolkit import Toolkit

companies = Toolkit(
    tickers=['GOOGL', "MSFT", 'AAPL'],
    api_key="FMP_KEY",
    quarterly=True
)

companies.models.get_present_value_of_growth_opportunities(calculate_daily=True)

Which returns:

Next to that, #94 noted that the Average Shares got adjusted through currency conversions. This has been corrected. Read in the new Q&A why numbers can sometimes deviate from FinancialModelingPrep.

FinanceToolkit v1.7.3

Happy New Year Everyone 🍾

This release includes an entirely new class which is the Discovery class. This class will help in discovering new instruments that can be inputted directly into the Finance Toolkit for further analysis. Find the documentation here.

For example, by importing the Discovery module directly, it is possible to obtain a large list of companies.

from financetoolkit import Discovery

discovery = Discovery(api_key="FINANCIAL_MODELING_PREP_KEY")

stock_list = discovery.get_stock_list()

# The total list equals over 60.000 rows
stock_list.iloc[48000:48010]

Which returns:

Symbol	Name	Price	Exchange	Exchange Code
RBL.AX	Redbubble Limited	0.54	Australian Securities Exchange	ASX
RBL.BO	Rane Brake Lining Limited	870.05	Bombay Stock Exchange	BSE
RBL.NS	Rane Brake Lining Limited	870.05	National Stock Exchange of India	NSE
RBLAY	Robinsons Land Corporation	4.61	Other OTC	PNK
RBLBANK.BO	RBL Bank Limited	280.9	Bombay Stock Exchange	BSE
RBLBANK.NS	RBL Bank Limited	280.9	National Stock Exchange of India	NSE
RBLN-B.CO	Roblon A/S	91.8	Copenhagen	CPH
RBLX	Roblox Corporation	45.72	New York Stock Exchange	NYSE
RBMNF	Rugby Resources Ltd.	0.065	Other OTC	PNK
RBMS.JK	PT Ristia Bintang Mahkotasejati Tbk	50	Jakarta Stock Exchange	JKT

It also enables stock screeners:

from financetoolkit import Discovery

discovery = Discovery(api_key="FINANCIAL_MODELING_PREP_KEY")

discovery.get_stock_screener(
    market_cap_higher=1000000,
    market_cap_lower=200000000000,
    price_higher=100,
    price_lower=200,
    beta_higher=1,
    beta_lower=1.5,
    volume_higher=100000,
    volume_lower=2000000,
    dividend_higher=1,
    dividend_lower=2,
    is_etf=False
)

Which returns:

Symbol	Name	Market Cap	Sector	Industry	Beta	Price	Dividend	Volume	Exchange	Exchange Code	Country
NKE	NIKE, Inc.	163403295604	Consumer Cyclical	Footwear & Accessories	1.079	107.36	1.48	1045865	New York Stock Exchange	NYSE	US
SAF.PA	Safran SA	66234006559	Industrials	Aerospace & Defense	1.339	160.16	1.35	119394	Paris	EURONEXT	FR
ROST	Ross Stores, Inc.	46724188589	Consumer Cyclical	Apparel Retail	1.026	138.785	1.34	169879	NASDAQ Global Select	NASDAQ	US
HES	Hess Corporation	44694706090	Energy	Oil & Gas E&P	1.464	145.51	1.75	123147	New York Stock Exchange	NYSE	US

Which then can be directly inputted into the Finance Toolkit:

from financetoolkit import Toolkit

companies = Toolkit(
    tickers=['NKE', 'SAF.PA', 'ROST', 'HES'],
    api_key="FINANCIAL_MODELING_PREP_KEY",
)

companies.ratios.get_price_earnings_ratio()

Which returns:

	2014	2015	2016	2017	2018	2019	2020	2021	2022	2023
HES	8.4355	-3.9366	-2.8064	-3.3368	-39.7483	-79.129	-5.4733	25.1712	19.5719
NKE	29.4181	31.0527	21.9158	23.5325	60.6729	39.2127	86.2596	45.9014	30.8813	33.6161
ROST	19.5859	19.8813	21.7047	21.3971	18.6637	24.4335	493.771	22.9419	26.2563
SAF.PA	-152.997	-57.6348	14.3119	7.1287	34.6723	24.0264	142.461	686.616	-21.0077

Next to that, I've also grouped the TQDM statements when using Ratios or Models. So instead of 5 bars, it displays just 2. Furthermore, I've extended the custom ratios functionality, see here.

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!