Option Pricing

Models uses pydantic to create a data model for option inputs. Refer the following implementation of the Black Scholes amd Monte Carlo option pricing methods.

import numpy as np
from tabulate import tabulate
from quantmod.models import OptionInputs, BlackScholesOptionPricing, MonteCarloOptionPricing


# Get the option inputs parameters
inputs=OptionInputs(
    spot=100,
    strike=100,
    ttm=1,
    rate=0.05,
    volatility=0.2,
    callprice=8.0
    )


# 1. Black Scholes Option Pricing
# Initialize the BS Option Pricing Engine
option = BlackScholesOptionPricing(inputs=inputs)

# Print the BS option price
header = ['Option Price', 'Delta', 'Gamma', 'Theta', 'Vega', 'Rho', 'IV']
table = [[option.call_price, option.call_delta, option.gamma, option.call_theta, option.vega, option.call_rho, option.impvol],]
print(tabulate(np.around(table, 4), headers=header))


# 2. Binomial Option Pricing
# Define input parameters
inputs=OptionInputs(
    spot=100,
    strike=100,
    ttm=1,
    rate=0.05,
    volatility=0.2
    )

# Instantiate the BinomialOptionPricing Engine
binomial = BinomialOptionPricing(
    inputs=inputs,
    nsteps=4,
    option_type=OptionType.CALL,
    exercise_style=ExerciseStyle.EUROPEAN,
    output="delta",
)

# Print the tree values
print(f"\nOption {binomial.output} tree:")
print(binomial.binomialoption)

# Plot the tree
print(f"\n\nPlot the tree:")
binomial.plot_tree()

# 3. Monte Carlo Option Pricing
# Instantiate the MonteCarloPricing class
mc = MonteCarloOptionPricing(
    inputs=inputs,
    nsims=50000,
    timestep=252,
    option_type=OptionType.CALL,
    exercise_style=ExerciseStyle.EUROPEAN,
    barrier_level=None,
    barrier_rebate=None,
    barrier_type=None,
)

print(f"\nMonte Carlo price for {mc.exercise_style} {mc.option_type} option:")
print(f"Option Price: {mc.option_price:.4f}")