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        "author": "Emanuele Fabbiani",
        "author_email": "emanuele.fabbiani@xtreamers.io",
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            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3"
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        "description": "# Vanilla Option Pricing\n[![Build Status](https://travis-ci.org/donlelef/vanilla-option-pricing.svg?branch=master)](https://travis-ci.org/donlelef/vanilla-option-pricing) \n[![Coverage Status](https://coveralls.io/repos/github/donlelef/vanilla-option-pricing/badge.svg?branch=master)](https://coveralls.io/github/donlelef/vanilla-option-pricing?branch=master)\n[![Documentation Status](https://readthedocs.org/projects/vanilla-option-pricing/badge/?version=latest)](https://vanilla-option-pricing.readthedocs.io/en/latest/?badge=latest)\n\nA simple Python package implementing stochastic models to price financial options.  \nTheoretical background and comprehensive explanation of models and their paramenters\ncan be found is the paper *Fast calibration of two-factor models for energy option pricing*\nby Emanuele Fabbiani, Andrea Marziali and Giuseppe De Nicolao, available on \n[arXiv](https://arxiv.org/abs/1809.03941)  \n\n### Installing\nThe preferred way to install the package is using pip,\nbut you can also download the code and install the package from source\n\nTo install the package using pip:\n\n```bash\npip install vanilla_option_pricing\n```\n\n### Quickstart\nLet's create a sample call option\n\n```python\nfrom datetime import datetime, timedelta\nfrom vanilla_option_princing.option import VanillaOption\nfrom vanilla_option_princing.models import BlackScholes\nfrom vanilla_option_pricing.calibration import ModelCalibration\n\noption = VanillaOption(\n    spot=100,\n    strike=101,\n    dividend=0,\n    date=datetime.today(),\n    maturity=datetime.today() + timedelta(days=30),\n    option_type='c',\n    price=1,\n    instrument='TTF'\n)\n```\n\nWe can compute the implied volatility and create a Black-Sholes model \nwith it. Of course, if now we ask the model to price the option, we'll\nget the real option price.\n\n```python\nvolatility = option.implied_volatility_of_undiscounted_price\nmodel = BlackScholes(volatility).as_option_pricing_model()\nmodel_price = model.price_option_black(option)\nprint(f'Actual price: {option.price}, model price: {model_price}')\n```\n\nWe can also try and calibrate the parameters of a model against \nlisted options.\n\n```python\ndata_set = [\n    VanillaOption('TTF', 'c', date(2018, 1, 1), 2, 101, 100, date(2018, 2, 1)),\n    VanillaOption('TTF', 'p', date(2018, 1, 1), 2, 98, 100, date(2018, 2, 1)),\n    VanillaOption('TTF', 'c', date(2018, 1, 1), 5, 101, 100, date(2018, 5, 31))\n]\n\nprint(f'Implied volatilities: {[o.implied_volatility_of_undiscounted_price for o in data_set]}\\n')\n\nmodel = BlackScholes(0.2).as_option_pricing_model()\ncalibration = ModelCalibration(data_set)\n\nresult, trained_model = calibration.calibrate_model(model)\nprint(result)\nprint(f'Calibrated implied volatility: {trained_model.parameters[0]}') \n```",
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