vanilla-option-pricing 0.0.7

Stochastic models to price financial options

Vanilla Option Pricing

A simple Python package implementing stochastic models to price financial options.
Theoretical background and comprehensive explanation of models and their paramenters can be found is the paper Fast calibration of two-factor models for energy option pricing by Emanuele Fabbiani, Andrea Marziali and Giuseppe De Nicolao, available on arXiv

Installing

The preferred way to install the package is using pip, but you can also download the code and install the package from source

To install the package using pip:

pip install vanilla_option_pricing

Quickstart

Let's create a sample call option

from datetime import datetime, timedelta
from vanilla_option_princing.option import VanillaOption
from vanilla_option_princing.models import BlackScholes
from vanilla_option_pricing.calibration import ModelCalibration

option = VanillaOption(
    spot=100,
    strike=101,
    dividend=0,
    date=datetime.today(),
    maturity=datetime.today() + timedelta(days=30),
    option_type='c',
    price=1,
    instrument='TTF'
)

We can compute the implied volatility and create a Black-Sholes model with it. Of course, if now we ask the model to price the option, we'll get the real option price.

volatility = option.implied_volatility_of_undiscounted_price
model = BlackScholes(volatility).as_option_pricing_model()
model_price = model.price_option_black(option)
print(f'Actual price: {option.price}, model price: {model_price}')

We can also try and calibrate the parameters of a model against listed options.

data_set = [
    VanillaOption('TTF', 'c', date(2018, 1, 1), 2, 101, 100, date(2018, 2, 1)),
    VanillaOption('TTF', 'p', date(2018, 1, 1), 2, 98, 100, date(2018, 2, 1)),
    VanillaOption('TTF', 'c', date(2018, 1, 1), 5, 101, 100, date(2018, 5, 31))
]

print(f'Implied volatilities: {[o.implied_volatility_of_undiscounted_price for o in data_set]}\n')

model = BlackScholes(0.2).as_option_pricing_model()
calibration = ModelCalibration(data_set)

result, trained_model = calibration.calibrate_model(model)
print(result)
print(f'Calibrated implied volatility: {trained_model.parameters[0]}')