OptionsPricerLib 0.1.2

A library for pricing options using different models

OptionsPricerLib

OptionsPricerLib is a Python library for pricing financial options using various european and american models. The library provides options pricing, implied volatility calculation, and the Greeks for options, covering models such as Barone-Adesi Whaley, Black-Scholes, Leisen-Reimer, Jarrow-Rudd, and Cox-Ross-Rubinstein.

You can find the library on PyPI and on GitHub

Installation

pip install OptionsPricerLib

Or, install directly from the GitHub repository:

pip install git+https://github.com/hedge0/OptionsPricerLib.git

Usage

After installation, you can import and use any of the models. Here's a quick example:

from OptionsPricerLib import BaroneAdesiWhaley, BlackScholes, CoxRossRubinstein, LeisenReimer, JarrowRudd

# Define parameters
S = 100        # Current stock price
K = 100        # Strike price
T = 1          # Time to maturity (in years)
r = 0.05       # Risk-free interest rate
q = 0.01       # Dividend yield
sigma = 0.2    # Volatility
option_type = 'calls'  # Option type ('calls' or 'puts')

# Barone-Adesi Whaley
price = BaroneAdesiWhaley.price(sigma, S, K, T, r, q, option_type)
delta = BaroneAdesiWhaley.calculate_delta(sigma, S, K, T, r, q, option_type)
gamma = BaroneAdesiWhaley.calculate_gamma(sigma, S, K, T, r, q, option_type)
vega = BaroneAdesiWhaley.calculate_vega(sigma, S, K, T, r, q, option_type)
theta = BaroneAdesiWhaley.calculate_theta(sigma, S, K, T, r, q, option_type)
rho = BaroneAdesiWhaley.calculate_rho(sigma, S, K, T, r, q, option_type)
sigma = BaroneAdesiWhaley.calculate_implied_volatility(price, S, K, T, r, q, option_type)
print(f"Barone-Adesi Whaley {option_type}: Price={price:.2f}, Delta={delta:.4f}, Gamma={gamma:.4f}, Vega={vega:.4f}, Theta={theta:.4f}, Rho={rho:.4f}, Sigma={sigma:.4f}")

# Black-Scholes
price = BlackScholes.price(sigma, S, K, T, r, q, option_type)
delta = BlackScholes.calculate_delta(sigma, S, K, T, r, q, option_type)
gamma = BlackScholes.calculate_gamma(sigma, S, K, T, r, q, option_type)
vega = BlackScholes.calculate_vega(sigma, S, K, T, r, q, option_type)
theta = BlackScholes.calculate_theta(sigma, S, K, T, r, q, option_type)
rho = BlackScholes.calculate_rho(sigma, S, K, T, r, q, option_type)
sigma = BlackScholes.calculate_implied_volatility(price, S, K, T, r, q, option_type)
print(f"Black-Scholes {option_type}: Price={price:.2f}, Delta={delta:.4f}, Gamma={gamma:.4f}, Vega={vega:.4f}, Theta={theta:.4f}, Rho={rho:.4f}, Sigma={sigma:.4f}")

# Leisen-Reimer
price = LeisenReimer.price(sigma, S, K, T, r, q, option_type, steps=100)
delta = LeisenReimer.calculate_delta(sigma, S, K, T, r, q, option_type, steps=100)
gamma = LeisenReimer.calculate_gamma(sigma, S, K, T, r, q, option_type, steps=100)
vega = LeisenReimer.calculate_vega(sigma, S, K, T, r, q, option_type, steps=100)
theta = LeisenReimer.calculate_theta(sigma, S, K, T, r, q, option_type, steps=100)
rho = LeisenReimer.calculate_rho(sigma, S, K, T, r, q, option_type, steps=100)
sigma = LeisenReimer.calculate_implied_volatility(price, S, K, T, r, q, option_type, steps=100)
print(f"Leisen-Reimer {option_type}: Price={price:.2f}, Delta={delta:.4f}, Gamma={gamma:.4f}, Vega={vega:.4f}, Theta={theta:.4f}, Rho={rho:.4f}, Sigma={sigma:.4f}")

# Jarrow-Rudd
price = JarrowRudd.price(sigma, S, K, T, r, q, option_type, steps=100)
delta = JarrowRudd.calculate_delta(sigma, S, K, T, r, q, option_type, steps=100)
gamma = JarrowRudd.calculate_gamma(sigma, S, K, T, r, q, option_type, steps=100)
vega = JarrowRudd.calculate_vega(sigma, S, K, T, r, q, option_type, steps=100)
theta = JarrowRudd.calculate_theta(sigma, S, K, T, r, q, option_type, steps=100)
rho = JarrowRudd.calculate_rho(sigma, S, K, T, r, q, option_type, steps=100)
sigma = JarrowRudd.calculate_implied_volatility(price, S, K, T, r, q, option_type, steps=100)
print(f"Jarrow-Rudd {option_type}: Price={price:.2f}, Delta={delta:.4f}, Gamma={gamma:.4f}, Vega={vega:.4f}, Theta={theta:.4f}, Rho={rho:.4f}, Sigma={sigma:.4f}")

# Cox-Ross-Rubinstein (CRR)
price = CoxRossRubinstein.price(sigma, S, K, T, r, q, option_type, steps=100)
delta = CoxRossRubinstein.calculate_delta(sigma, S, K, T, r, q, option_type, steps=100)
gamma = CoxRossRubinstein.calculate_gamma(sigma, S, K, T, r, q, option_type, steps=100)
vega = CoxRossRubinstein.calculate_vega(sigma, S, K, T, r, q, option_type, steps=100)
theta = CoxRossRubinstein.calculate_theta(sigma, S, K, T, r, q, option_type, steps=100)
rho = CoxRossRubinstein.calculate_rho(sigma, S, K, T, r, q, option_type, steps=100)
sigma = CoxRossRubinstein.calculate_implied_volatility(price, S, K, T, r, q, option_type, steps=100)
print(f"Cox-Ross-Rubinstein {option_type}: Price={price:.2f}, Delta={delta:.4f}, Gamma={gamma:.4f}, Vega={vega:.4f}, Theta={theta:.4f}, Rho={rho:.4f}, Sigma={sigma:.4f}")

Available Models

1. Barone-Adesi Whaley: American, approximation model.
2. Black-Scholes: European.
3. Leisen-Reimer: American, binomial model.
4. Jarrow-Rudd: American, binomial model.
5. Cox-Ross-Rubinstein (CRR): American, binomial model.

Running Tests

The package includes unit tests in the tests/ folder. You can run them using unittest:

python -m unittest discover -s tests -p "test_options_models.py"

This command will execute the test suite and verify the functionality of each pricing model. If all tests pass, it confirms that each model in OptionsPricerLib is performing as expected.

Contributing

Contributions to OptionsPricerLib are welcome! If you find a bug or have suggestions for improvements, please open an issue or submit a pull request. Make sure to follow these guidelines:

1. Fork the repository and clone it locally.
2. Create a new branch for your feature or fix.
3. Add your changes and include tests for any new functionality.
4. Run the test suite to ensure all tests pass.
5. Submit a pull request describing your changes.

License

This project is licensed under the MIT License. See the LICENSE file for more details.

Contact

For any questions or feedback, feel free to reach out to the author via GitHub: hedge0.