FinanceToolkit v2.0.0

It has been a long time since I made a significant update as I was working on the v2.0.0 of the Finance Toolkit featuring numerous improvements to the code as well as major new additions.

Portfolio Functionalities

I've added a brand new class to the Finance Toolkit called Portfolio (use from financetoolkit import Portfolio) which allows you to load in your own transactions data and see performance over any period of time (as opposed to some benchmark). For a full list of details see here. Some things that are now possible:

• Load in your own transactions data, based on a custom template that is provided automatically when initializing the class.
• See an overview of your entire portfolio including invested amount, current value, return, benchmark return, alpha, beta and more.
• Explore individual transactions and see how they have contributed to the overall performance of your portfolio. This also allows you to get a breakdown per period whether it be weekly, monthly, quarterly or yearly.
• Delve deeper into how your portfolio performed with daily (cumulative) returns, portfolio weights and more.

As a simple example:

This is however only minor as opposed to the integration with the Finance Toolkit itself meaning that you are able to use all of the 150+ metrics directly with your own portfolio. It also includes a "Portfolio" ticker which represents the weighted sum over time of your assets in the portfolio. As an example

from financetoolkit import Portfolio

instance = Portfolio(example=True, api_key="FMP_KEY")

profit_margin = instance.toolkit.ratios.get_net_profit_margin()

Which returns when plotted for the Portfolio the following:

This makes it so that you can not only evaluate your portfolio through basic OHLC data but also understand the financial health of the companies you are invested in and how this impacts your overall portfolio.

Caching Capabilities

I've had this request a couple of times now and given the ever-growing size of the project it made sense to integrate caching functionalities. When using use_cached_data=True when you initialize the Finance Toolkit any of the core functionalities will automatically be saved to a pickle file (e.g. if you collect a balance sheet statement, these will be saved to the cached folder under balance_sheet_statement.pickle). If you then re-run the Finance Toolkit you will notice the data collection is instant, this is because the data is loaded from the pickle file.

As an example:

If I wish to receive this data again, I no longer need an API key or set the tickers and can simply keep use_cached_data=True.

Please note that it will force the settings as found in the pickle files so if you wish to use a different time period, you will have to recollect.

You can also change the folder by entering a string instead of a boolean for the use_cached_data parameter.

Global Macro Database Integration

I've seen the work from Karsten Muller related to the "Global Macro Database" project (see LinkedIn and the GitHub project here) which has Economics metrics going back all the way to the 1800s. An invaluable resource which I've integrated into the Finance Toolkit as well. You will see that the Key Economic Indicators list is therefore greatly expanded.

The current list of Economic parameters is:

• Gross Domestic Product (GDP)
• Total Consumption
• Total Investment
• Total Fixed Investment
• Exports
• Imports
• Current Account Balance
• Government Debt
• Government (Tax) Revenue
• Government Expenditure
• Government Deficit
• Trust in Government
• Consumer Price Index (CPI)
• Inflation Rate
• Consumer Confidence Index (CCI)
• Business Confidence Index (BCI)
• Composite Leading Indicator (CLI)
• House Prices
• Rent Prices
• Share Prices
• Exchange Rates
• Money Supply
• Central Bank Policy Rate
• Long Term Interest Rates (10 year)
• Short Term Interest Rates (3 month)
• Renewable Energy as % of Total Energy
• Carbon Footprint
• Unemployment Rate
• Labour Productivity
• Income Inequality
• Population
• Poverty Rate

Many Bugfixes and Improvements

I've added better error handling for Bandwidth Limits, more information when data is collected from a different source, numerous minor fixes e.g. recalculating ratios found in the financial statements when applying trailing (#172 and #173) and cleaned up a lot of other details.

The Streamlit Dashboard is also updated with new additions including a brand new Portfolio Dashboard, see below:

FinanceToolkit v1.9.9

Functionality for the Economics menu broke due to the fact I forgot to include headers into my request towards OECD, thanks for reporting #162. Furthermore, the Close and Adj Close price were the same for data acquired from Yahoo Finance, this has now been fixed and is correct. I've also suppressed Yahoo Finance logging messages as I'll handle all of it myself as well.

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.