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.
