Notebooks/Derivatives_Analytics___Inheritance_and_Polymorphism.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Derivatives Analytics - Inheritance and Polymorphism"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Author**: [Gabriele Pompa](https://www.linkedin.com/in/gabrielepompa/): gabriele.pompa@unisi.com"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Table of contents\n",
    "\n",
    "[Executive Summary](#executive-summary)\n",
    "\n",
    "1. [Cash-Or-nothing Digital Options](#digitals)\n",
    "2. [Polymorphism in functions and class methods](#poly)\n",
    "3. [Inheritance](#inheritance)\\\n",
    "    3.1. [Abstract classes and interfaces](#abstract)\\\n",
    "    3.2. [Inheritance and Polymorphism](#inheritance-and-poly)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Resources**: \n",
    "\n",
    "- [_Python for Finance (2nd ed.)_](http://shop.oreilly.com/product/0636920117728.do): Chapter 6 Object-Oriented Programming.\n",
    "\n",
    "\n",
    "- [Object-Oriented Programming in Python](https://python-textbok.readthedocs.io/en/1.0/index.html): Section [Object-oriented programming](https://python-textbok.readthedocs.io/en/1.0/Object_Oriented_Programming.html)\n",
    "\n",
    "\n",
    "- [Polymorphism in Python](https://www.programiz.com/python-programming/polymorphism)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Executive Summary <a name=\"executive-summary\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook we introduce inheritance and polymorphism which are two milestones in object-oriented programming. We present these concepts introducing digital cash-or-nothing options and observing their similarities with plain-vanilla options. Inheritance and polymorphism allow us to leverage on the financial similarities between these two contracts and eventually represent them more efficiently as classes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following sections are organized as follows:\n",
    "\n",
    "- In Sec. [1](#digitals) we introduce cash-or-nothing digital options, modeling them as a `DigitalOption` class.\n",
    "- In Sec. [2](#poly) we introduce polymorphism in Python in functions and classes methods.\n",
    "- In Sec. [3](#inheritance) we introduce inheritance and model plain-vanilla options and digitals as `PlainVaillaOption` and `DigitalOption` sub-classes of a common base class `EuropeanOption`, representing generically contracts with european-style exercise."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These are the basic imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for NumPy arrays\n",
    "import numpy as np\n",
    "\n",
    "# for Pandas Series and DataFrame\n",
    "import pandas as pd\n",
    "\n",
    "# for statistical functions\n",
    "from scipy import stats\n",
    "\n",
    "# for Matplotlib plotting\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# to do inline plots in the Notebook\n",
    "%matplotlib inline\n",
    "\n",
    "# for Operating System operations\n",
    "import os\n",
    "\n",
    "# for some mathematical functions\n",
    "import math\n",
    "\n",
    "# for date management\n",
    "import datetime as dt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Cash-Or-nothing Digital Options <a name=\"digitals\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A digital cash-or-nothing (CON) call/put option of maturity $T$ and strike $K$ on an asset $S$ is a contract paying to the owner a certain amount of cash $Q$ should the asset settle above/below the strike at maturity. The payoff of a digital call option contract at time $T$ is then\n",
    "\n",
    "$$\n",
    "Q  \\times \\mathcal{I}_{\\left\\{S_T > K\\right\\}}\n",
    "$$\n",
    "\n",
    "and, symmetrically, for a put option\n",
    "\n",
    "$$\n",
    "Q  \\times \\mathcal{I}_{\\left\\{S_T \\leq K\\right\\}}\n",
    "$$\n",
    "\n",
    "where $\\mathcal{I}_{x}$ is the indicator function for event $x$, worth $1$ in case $x$ materializes and $0$ otherwise."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As before, we consider non-dividend paying underlying asset $S$. At any time $t \\leq T$, the price $c_t$ of the digital CON call option under the Black-Scholes model is given by\n",
    "\n",
    "$$\n",
    "c^{CON}_t = Q e^{-r \\tau} N(d_2)\n",
    "$$\n",
    "\n",
    "where $\\tau = T-t$ is the option time-to-maturity, $r$ is the continuously compounding short-rate, $N(z)$ is the cumulative distribution function of a standard normal random variable and the $d_2$ argument has been already defined for plain-vanilla options. For theoretical background (an notation) we refer to [_Financial Modeling 1_ lecture notes](https://finmod1.wordpress.com/lecture-notes/) from Professor Pacati."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Independently from the valuation model used to price the option (we assume constant deterministic short-rate $r$ here and everywhere in this notebook), there are non-arbitrage arguments leading to upper and lower limits for the price of the CON options. At any time $t \\leq T$, the price $\\text{price}_t$ of both call and put CONs satisfies:\n",
    "\n",
    "$$\n",
    "0 \\leq \\text{price}_t \\leq Q e^{-r \\tau}\n",
    "$$\n",
    "\n",
    "This can be easily understood: since both type of CON options, at most, pay an amount of cash $Q$ in $T$, at any time $t \\leq T$ they cannot be worth more than the discounted (from $T$ to $t$) amount of cash, that is $Q e^{-r (T-t)} = Q e^{-r \\tau}$. Moreover, since the worst that can happen owning a CON option is that it expires worthless in $T$ (that is, not paying any cash), its current market value cannot be negative, then $0$ has to be the lower bound. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Moreover, from the financial point of view, CON call and put prices are related by the following _parity_ relation\n",
    "\n",
    "$$\n",
    "c^{CON}_t + p^{CON}_t = Qe^{-r\\tau}\n",
    "$$\n",
    "\n",
    "which we can use to compute the put price $p^{CON}_t$, once we know the call's price $c^{CON}_t$ and the other contract variables."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the spirit of the `PlainVaillaOption` class developed before, we implement a Black-Scholes pricer for digital options as a `DigitalOption` class"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For convenience, we report here `PlainVanillaOption` class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class PlainVanillaOption:\n",
    "    \"\"\"\n",
    "    PlainVanillaOption class implementing payoff and pricing of plain-vanilla call and put options.\n",
    "    Put price is calculated using put-call parity\n",
    "    \n",
    "    Attributes:\n",
    "    -----------\n",
    "        type (str):               type of the options. Can be either 'call' or 'put';\n",
    "        S_t (float):              spot price of the underlying asset at the valuation date 't';\n",
    "        K (float):                strike price;\n",
    "        t (str; dt.datetime):     valuation date. Can be either a \"dd-mm-YYYY\" String or a pd.datetime() object\n",
    "        T (str; dt.datetime):     expiration date. Can be either a \"dd-mm-YYYY\" String or a pd.datetime() object\n",
    "        tau (float):              time to maturity in years, computed as tau=T-t by time_to_maturity() method\n",
    "        r (float):                continuously compounded short-rate;\n",
    "        sigma (float):            volatility of underlying asset;\n",
    "    \n",
    "    Public Methods:\n",
    "    --------   \n",
    "    \n",
    "        getters and setters for all attributes\n",
    "        \n",
    "        payoff: float\n",
    "            Computes the payoff of the option and returns it\n",
    "\n",
    "        price_upper_limit: float \n",
    "            Returns the upper limit for a vanilla option price.\n",
    "\n",
    "        price_lower_limit: float \n",
    "            Returns the lower limit for a vanilla option price.\n",
    "            \n",
    "        price: float\n",
    "            Computes the exact price of the option and returns it, using call_price() or put_price()\n",
    "\n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(self, option_type, S_t, K, t, T, r, sigma):\n",
    "\n",
    "        # option type check\n",
    "        if option_type not in ['call', 'put']:\n",
    "            raise NotImplementedError(\"Option Type: '{}' does not exist!\".format(option_type))\n",
    "        \n",
    "        self.__type  = option_type\n",
    "        self.__S     = S_t\n",
    "        self.__K     = K\n",
    "        self.__t     = dt.datetime.strptime(t, \"%d-%m-%Y\") if isinstance(t, str) else t\n",
    "        self.__T     = dt.datetime.strptime(T, \"%d-%m-%Y\") if isinstance(T, str) else T\n",
    "        self.__tau   = self.__time_to_maturity()\n",
    "        self.__r     = r\n",
    "        self.__sigma = sigma\n",
    "        \n",
    "        # informations dictionary\n",
    "        self.__docstring_dict = {\n",
    "            'call':{\n",
    "                'price_upper_limit': r\"Upper limit: $S_t$\",\n",
    "                'payoff':            r\"Payoff: $max(S-K, 0)$\",\n",
    "                'price_lower_limit': r\"Lower limit: $max(S_t - K e^{-r \\tau}, 0)$\"\n",
    "            },\n",
    "            'put': {\n",
    "                'price_upper_limit': r\"Upper limit: $K e^{-r \\tau}$\",\n",
    "                'payoff':            r\"Payoff: $max(K-S, 0)$\",\n",
    "                'price_lower_limit': r\"Lower limit: $max(K e^{-r \\tau} - S_t, 0)$\"}\n",
    "        }\n",
    "                \n",
    "    def __repr__(self):\n",
    "        return r\"PlainVanillaOption('{}', S_t={:.1f}, K={:.1f}, t={}, T={}, tau={:.2f}y, r={:.1f}%, sigma={:.1f}%)\".\\\n",
    "                format(self.get_type(), self.get_S(), self.get_K(), self.get_t().strftime(\"%d-%m-%Y\"), \n",
    "                       self.get_T().strftime(\"%d-%m-%Y\"), self.get_tau(), self.get_r()*100, self.get_sigma()*100)\n",
    "    \n",
    "    # getters\n",
    "    def get_type(self):\n",
    "        return self.__type\n",
    "    \n",
    "    def get_S(self):\n",
    "        return self.__S\n",
    "    \n",
    "    def get_K(self):\n",
    "        return self.__K\n",
    "    \n",
    "    def get_t(self):\n",
    "        return self.__t\n",
    "\n",
    "    def get_T(self):\n",
    "        return self.__T\n",
    "\n",
    "    def get_tau(self):\n",
    "        return self.__tau\n",
    "    \n",
    "    def get_r(self):\n",
    "        return self.__r\n",
    "\n",
    "    def get_sigma(self):\n",
    "        return self.__sigma\n",
    "    \n",
    "    def get_docstring(self, label):\n",
    "        return self.__docstring_dict[self.get_type()][label]    \n",
    "    \n",
    "    # setters\n",
    "    def set_type(self, option_type):\n",
    "        self.__type = option_type\n",
    "        \n",
    "        # option type check\n",
    "        if option_type not in ['call', 'put']:\n",
    "            raise NotImplementedError(\"Option Type: '{}' does not exist!\".format(option_type))\n",
    "        \n",
    "    def set_S(self, S):\n",
    "        self.__S = S\n",
    "        \n",
    "    def set_K(self, K):\n",
    "        self.__K = K\n",
    "    \n",
    "    def set_t(self, t):\n",
    "        self.__t = dt.datetime.strptime(t, \"%d-%m-%Y\") if isinstance(t, str) else t\n",
    "        # update time to maturity, given changed t, to keep internal consistency\n",
    "        self.__update_tau() \n",
    "\n",
    "    def set_T(self, T):\n",
    "        self.__T = dt.datetime.strptime(T, \"%d-%m-%Y\") if isinstance(T, str) else T\n",
    "        # update time to maturity, given changed T, to keep internal consistency\n",
    "        self.__update_tau() \n",
    "    \n",
    "    def set_tau(self, tau):\n",
    "        self.__tau = tau\n",
    "        # update expiration date, given changed tau, to keep internal consistency\n",
    "        # we could have updated valuation date as well, but this is a stylistic choice\n",
    "        self.__update_T()\n",
    "\n",
    "    def set_r(self, r):\n",
    "        self.__r = r\n",
    "\n",
    "    def set_sigma(self, sigma):\n",
    "        self.__sigma = sigma\n",
    "        \n",
    "    # update methods (private)\n",
    "    def __update_tau(self):\n",
    "        self.__tau = self.__time_to_maturity()\n",
    "\n",
    "    def __update_T(self):\n",
    "        self.__T = self.__t + dt.timedelta(days=math.ceil(self.__tau*365))\n",
    "\n",
    "    # time to maturity calculation\n",
    "    def __time_to_maturity(self):\n",
    "        return (self.__T - self.__t).days / 365.0\n",
    "    \n",
    "    # payoff calculation\n",
    "    def payoff(self):\n",
    "        # call case\n",
    "        if self.get_type() == 'call':\n",
    "            return max(0.0, self.get_S() - self.get_K())\n",
    "        # put case\n",
    "        else:\n",
    "            return max(0.0, self.get_K() - self.get_S())\n",
    "\n",
    "    # upper price limit\n",
    "    def price_upper_limit(self):\n",
    "        # call case\n",
    "        if self.get_type() == 'call':\n",
    "            return self.get_S()\n",
    "        # put case\n",
    "        else:\n",
    "            return self.get_K()*np.exp(-self.get_r() * self.get_tau())\n",
    "\n",
    "    # lower price limit\n",
    "    def price_lower_limit(self):\n",
    "        # call case\n",
    "        if self.get_type() == 'call':\n",
    "            return max(self.get_S() - self.get_K()*np.exp(-self.get_r() * self.get_tau()), 0)\n",
    "        # put case\n",
    "        else:\n",
    "            return max(self.get_K()*np.exp(-self.get_r() * self.get_tau()) - self.get_S(), 0)\n",
    "            \n",
    "    # price calculation\n",
    "    def price(self):\n",
    "        # call case\n",
    "        if self.get_type() == 'call':\n",
    "            return self.__call_price()\n",
    "        # put case\n",
    "        else:\n",
    "            return self.__put_price()\n",
    "          \n",
    "    def __call_price(self):\n",
    "        \n",
    "        # some local variables retrieved to be used repeatedly\n",
    "        S = self.get_S()\n",
    "        tau = self.get_tau()\n",
    "        \n",
    "        if S == 0: # this is to avoid log(0) issues\n",
    "            return 0.0\n",
    "        elif tau == 0.0: # this is to avoid 0/0 issues\n",
    "            return self.payoff()\n",
    "        else:\n",
    "            K     = self.get_K()\n",
    "            r     = self.get_r()\n",
    "            sigma = self.get_sigma()\n",
    "\n",
    "            d1 = (np.log(S/K) + (r + 0.5 * sigma ** 2) * tau) / (sigma * np.sqrt(tau))\n",
    "            d2 = d1 - sigma * np.sqrt(tau)\n",
    "\n",
    "            price = S * stats.norm.cdf(d1, 0.0, 1.0) - K * np.exp(-r * tau) * stats.norm.cdf(d2, 0.0, 1.0)\n",
    "\n",
    "            return price\n",
    "    \n",
    "    def __put_price(self):\n",
    "        \"\"\" Put price from Put-Call parity relation: Call + Ke^{-r*tau} = Put + S\"\"\"\n",
    "        return self.__call_price() + self.get_K() * np.exp(- self.get_r() * self.get_tau()) - self.get_S()        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The similarity with plain-vanilla options is apparent. The only difference is:\n",
    "\n",
    "- the additional parameter `self.__Q` needed to initialize the digital option class with the cash amount $Q$;\n",
    "\n",
    "- the functional form of the payoff, price boundaries and price."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DigitalOption(object):\n",
    "    \"\"\"\n",
    "    DigitalOption class implementing payoff and pricing of digital call and put options.\n",
    "    Put price is calculated using put-call parity\n",
    "    \n",
    "    Attributes:\n",
    "    -----------\n",
    "        type (str):               type of the options. Can be either 'call' or 'put';\n",
    "        Q (float):                cash amount\n",
    "        S_t (float):              spot price of the underlying asset at the valuation date 't';\n",
    "        K (float):                strike price;\n",
    "        t (str; dt.datetime):     valuation date. Can be either a \"dd-mm-YYYY\" String or a pd.datetime() object\n",
    "        T (str; dt.datetime):     expiration date. Can be either a \"dd-mm-YYYY\" String or a pd.datetime() object\n",
    "        tau (float):              time to maturity in years, computed as tau=T-t by time_to_maturity() method\n",
    "        r (float):                continuously compounded short-rate;\n",
    "        sigma (float):            volatility of underlying asset;\n",
    "    \n",
    "    Public Methods:\n",
    "    --------   \n",
    "    \n",
    "        getters and setters for all attributes\n",
    "        \n",
    "        payoff: float\n",
    "            Computes the payoff of the option and returns it\n",
    "\n",
    "        price_upper_limit: float \n",
    "            Returns the upper limit for a CON digital option price.\n",
    "\n",
    "        price_lower_limit: float \n",
    "            Returns the lower limit for a CON digital option price\n",
    "            .\n",
    "        price: float\n",
    "            Computes the exact price of the option and returns it, using call_price() or put_price()\n",
    "\n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(self, option_type, cash_amount, S_t, K, t, T, r, sigma):\n",
    "\n",
    "        # option type check\n",
    "        if option_type not in ['call', 'put']:\n",
    "            raise NotImplementedError(\"Option Type: '{}' does not exist!\".format(option_type))\n",
    "        \n",
    "        self.__type  = option_type\n",
    "        self.__Q     = cash_amount\n",
    "        self.__S     = S_t\n",
    "        self.__K     = K\n",
    "        self.__t     = dt.datetime.strptime(t, \"%d-%m-%Y\") if isinstance(t, str) else t\n",
    "        self.__T     = dt.datetime.strptime(T, \"%d-%m-%Y\") if isinstance(T, str) else T\n",
    "        self.__tau   = self.__time_to_maturity()\n",
    "        self.__r     = r\n",
    "        self.__sigma = sigma\n",
    "        \n",
    "        # informations dictionary\n",
    "        self.__docstring_dict = {\n",
    "            'call':{\n",
    "                'price_upper_limit': r\"Upper limit: $Q e^{-r \\tau}$\",\n",
    "                'payoff':            r\"Payoff: $Q$ $I(S > K)$\",\n",
    "                'price_lower_limit': r\"Lower limit: $0$\"\n",
    "            },\n",
    "            'put': {\n",
    "                'price_upper_limit': r\"Upper limit: $Q e^{-r \\tau}$\",\n",
    "                'payoff':            r\"Payoff: $Q$ $I(S \\leq K)$\",\n",
    "                'price_lower_limit': r\"Lower limit: $0$\"}\n",
    "        }        \n",
    "                \n",
    "    def __repr__(self):\n",
    "        return r\"DigitalOption('{}', cash={:.1f}, S_t={:.1f}, K={:.1f}, t={}, T={}, tau={:.2f}y, r={:.1f}%, sigma={:.1f}%)\".\\\n",
    "                format(self.get_type(), self.get_Q(), self.get_S(), self.get_K(), self.get_t().strftime(\"%d-%m-%Y\"), \n",
    "                       self.get_T().strftime(\"%d-%m-%Y\"), self.get_tau(), self.get_r()*100, self.get_sigma()*100)\n",
    "    \n",
    "    # getters\n",
    "    def get_type(self):\n",
    "        return self.__type\n",
    "    \n",
    "    def get_Q(self):\n",
    "        return self.__Q\n",
    "    \n",
    "    def get_S(self):\n",
    "        return self.__S\n",
    "    \n",
    "    def get_K(self):\n",
    "        return self.__K\n",
    "    \n",
    "    def get_t(self):\n",
    "        return self.__t\n",
    "\n",
    "    def get_T(self):\n",
    "        return self.__T\n",
    "\n",
    "    def get_tau(self):\n",
    "        return self.__tau\n",
    "    \n",
    "    def get_r(self):\n",
    "        return self.__r\n",
    "\n",
    "    def get_sigma(self):\n",
    "        return self.__sigma\n",
    "    \n",
    "    def get_docstring(self, label):\n",
    "        return self.__docstring_dict[self.get_type()][label]    \n",
    "    \n",
    "    # setters\n",
    "    def set_type(self, option_type):\n",
    "        self.__type = option_type\n",
    "        \n",
    "        # option type check\n",
    "        if option_type not in ['call', 'put']:\n",
    "            raise NotImplementedError(\"Option Type: '{}' does not exist!\".format(option_type))\n",
    "    \n",
    "    def set_Q(self, cash_amount):\n",
    "        self.__Q = cash_amount\n",
    "        \n",
    "    def set_S(self, S):\n",
    "        self.__S = S\n",
    "        \n",
    "    def set_K(self, K):\n",
    "        self.__K = K\n",
    "    \n",
    "    def set_t(self, t):\n",
    "        self.__t = dt.datetime.strptime(t, \"%d-%m-%Y\") if isinstance(t, str) else t\n",
    "        # update time to maturity, given changed t, to keep internal consistency\n",
    "        self.__update_tau() \n",
    "\n",
    "    def set_T(self, T):\n",
    "        self.__T = dt.datetime.strptime(T, \"%d-%m-%Y\") if isinstance(T, str) else T\n",
    "        # update time to maturity, given changed T, to keep internal consistency\n",
    "        self.__update_tau() \n",
    "    \n",
    "    def set_tau(self, tau):\n",
    "        self.__tau = tau\n",
    "        # update expiration date, given changed tau, to keep internal consistency\n",
    "        # we could have updated valuation date as well, but this is a stylistic choice\n",
    "        self.__update_T()\n",
    "\n",
    "    def set_r(self, r):\n",
    "        self.__r = r\n",
    "\n",
    "    def set_sigma(self, sigma):\n",
    "        self.__sigma = sigma\n",
    "        \n",
    "    # update methods (private)\n",
    "    def __update_tau(self):\n",
    "        self.__tau = self.__time_to_maturity()\n",
    "\n",
    "    def __update_T(self):\n",
    "        self.__T = self.__t + dt.timedelta(days=math.ceil(self.__tau*365))\n",
    "\n",
    "    # time to maturity method (private)\n",
    "    def __time_to_maturity(self):\n",
    "        return (self.__T - self.__t).days / 365.0\n",
    "    \n",
    "    # payoff calculation\n",
    "    def payoff(self):\n",
    "        # call case\n",
    "        if self.get_type() == 'call':\n",
    "            return self.get_Q() * int(self.get_S() > self.get_K())\n",
    "        # put case\n",
    "        else:\n",
    "            return self.get_Q() * int(self.get_S() <= self.get_K())\n",
    "        \n",
    "    # upper price limit\n",
    "    def price_upper_limit(self):\n",
    "        # call case\n",
    "        if self.get_type() == 'call':\n",
    "            return self.get_Q()*np.exp(-self.get_r() * self.get_tau())\n",
    "        # put case\n",
    "        else:\n",
    "            return self.get_Q()*np.exp(-self.get_r() * self.get_tau())\n",
    "\n",
    "    # lower price limit\n",
    "    def price_lower_limit(self):\n",
    "        # call case\n",
    "        if self.get_type() == 'call':\n",
    "            return 0.0\n",
    "        # put case\n",
    "        else:\n",
    "            return 0.0\n",
    "        \n",
    "    # price calculation\n",
    "    def price(self):\n",
    "        # call case\n",
    "        if self.get_type() == 'call':\n",
    "            return self.__call_price()\n",
    "        # put case\n",
    "        else:\n",
    "            return self.__put_price()\n",
    "          \n",
    "    def __call_price(self):\n",
    "        \n",
    "        # some local variables retrieved to be used repeatedly\n",
    "        S = self.get_S()\n",
    "        tau = self.get_tau()\n",
    "        \n",
    "        if S == 0: # this is to avoid log(0) issues\n",
    "            return 0.0\n",
    "        elif tau == 0.0: # this is to avoid 0/0 issues\n",
    "            return self.payoff()\n",
    "        else:\n",
    "            Q     = self.get_Q()\n",
    "            K     = self.get_K()\n",
    "            r     = self.get_r()\n",
    "            sigma = self.get_sigma()\n",
    "\n",
    "            d1 = (np.log(S/K) + (r + 0.5 * sigma ** 2) * tau) / (sigma * np.sqrt(tau))\n",
    "            d2 = d1 - sigma * np.sqrt(tau)\n",
    "\n",
    "            price = Q * np.exp(-r * tau) * stats.norm.cdf(d2, 0.0, 1.0)\n",
    "\n",
    "            return price\n",
    "    \n",
    "    def __put_price(self):\n",
    "        \"\"\" Put price from Put-Call parity relation: CON_Call + CON_Put = Qe^{-r*tau}\"\"\"\n",
    "        return self.get_Q() * np.exp(- self.get_r() * self.get_tau()) - self.__call_price()        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is a `DigitalOption` object representing a digital CON call option"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DigitalOption('call', cash=1.0, S_t=90.0, K=100.0, t=19-04-2020, T=31-12-2020, tau=0.70y, r=5.0%, sigma=20.0%)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CON_Call = DigitalOption(option_type='call', cash_amount = 1.0, S_t=90.0, K=100.0, t=\"19-04-2020\", T=\"31-12-2020\", r=0.05, sigma=0.2)\n",
    "CON_Call"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "__main__.DigitalOption"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(CON_Call)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "isinstance(CON_Call, DigitalOption)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.29674605684957245"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CON_Call.price()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and here another one representing a CON put option"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DigitalOption('put', cash=1.0, S_t=90.0, K=100.0, t=19-04-2020, T=31-12-2020, tau=0.70y, r=5.0%, sigma=20.0%)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CON_Put = DigitalOption(option_type='put', cash_amount = 1.0, S_t=90.0, K=100.0, t=\"19-04-2020\", T=\"31-12-2020\", r=0.05, sigma=0.2)\n",
    "CON_Put"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "__main__.DigitalOption"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(CON_Put)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "isinstance(CON_Put, DigitalOption)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6687932243156424"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CON_Put.price()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CON_Put.payoff()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Polymorphism in functions and class methods <a name=\"poly\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Polymorphism (from ancient Greek: $\\pi \\omicron \\lambda \\upsilon$ + $\\mu \\omicron \\rho \\phi \\eta'$ = $multiple$ + $shape$) is the condition of occurrence of the same thing in different forms. For a good treatment of polymorhism in Python refer to [this blog post](https://www.programiz.com/python-programming/polymorphism).\n",
    "\n",
    "When a function can works with different data-types, that is it can be called with input variables of different data-type, that function is implementing polymorphism. Think for example to function `len()`, defined in the Python standard library: `len()` can works:\n",
    "- on Python Lists: `len(List)` returns the number of elements of the List;\n",
    "- on Python Strings: `len(String)` returns the number of characters of the String;\n",
    "- on Pthon Dicts: `len(Dict)` returns the number of keys of the Dictionary.\n",
    "\n",
    "Thus `len()` can works on different data-types, with specific implementations for each data-type."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can have polymorphism in class methods too. The fact that both `PlainVanillaOption` and `DigitalOption` classes define methods with the same name (like `.payoff()`, `.price()` as well as getters and setters for the attributes they have in common), is an example of _polymorphism._ Of course the implementations can be completely different, what matters is the fact that they share the same name. Indeed, as long as we are concerned with the output of the `.price()` (as well as other methods with same name in the two classes), we can call those method disregarding the object on which we are calling it (should it be a `PlainVanillaOption` and `DigitalOption` object). We say that `.price()` method (as well as other methods with same name in the two classes) is _polymorphic_ and the two classes are implementing polymorphism through it. \n",
    "\n",
    "Let's see this in practice. We can leverage the polymorphism of `PlainVanillaOption` and `DigitalOption` classes to use our plotting functions `plot_multi_tau(option,...)` and `plot_single_tau(option,...)` with an object `option` of any of the two classes. This simply because `option.price()` is a valid method call disregarding whether `option` is a `PlainVanillaOption` or `DigitalOption` object. In other words, we can plot without any additional specifications both plain-vanilla and CON options somehow _agnostically._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In general, as long as two - otherwise unrelated - classes `A` and `B` define a method `.method()` with the same name - but possibly implemented in completely different ways by `A` and `B` (as is differently implemented the Black-Scholes `.price()` of a plain-vanilla from the Black-Scholes `.price()` of a CON option) - we can use the method `.method()` elsewhere (e.g. in other functions outside the classes) disregarding whether we are calling that method on objects of class `A` or `B`. We then say that classes `A` and `B` are implementing _polymorphism_ through the (polymorphic) method `.method()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_multi_tau(option, S_list, tau_list):\n",
    "    \"\"\"\n",
    "    plot_multi_tau(option, S_list, tau_list) plot Plain-Vanilla or Cash-Or-Nothing option prices for underlying and \n",
    "    maturities in 'S_list' and 'tau_list', respectively.\n",
    "\n",
    "    Parameters:\n",
    "        option (PlainVanillaOption; DigitalOption): instance of PlainVanillaOption or DigitalOption class;\n",
    "        S_list (List):                              list of underlying values;\n",
    "        tau_list (List):                            list of times to maturity (in years);\n",
    "        \n",
    "    Returns:\n",
    "        None;\n",
    "\n",
    "    \"\"\"\n",
    "\n",
    "    # color cycle setup: basically a cycle of different shades of blue as many time to maturity there are\n",
    "    plt.rcParams[\"axes.prop_cycle\"] = plt.cycler(\"color\", plt.cm.Blues(np.linspace(0,1,len(tau_list)+1)))\n",
    "    \n",
    "    # setting legend labels\n",
    "    # plain vanilla case:\n",
    "    if isinstance(option, PlainVanillaOption):\n",
    "        title_label = \"Plain Vanilla\"\n",
    "    # digital case:\n",
    "    else:\n",
    "        title_label = \"Cash-Or-Nothing ($Q={}$)\".format(option.get_Q())\n",
    "\n",
    "    # define the figure\n",
    "    fig, ax = plt.subplots(figsize=(10,6))\n",
    "\n",
    "    # auxiliary variables\n",
    "    numS = len(S_list)\n",
    "    numTau = len(tau_list)\n",
    "\n",
    "    # plot a dot to highlight the strike position\n",
    "    ax.plot(K, 0, 'k.', ms=15, label=\"Strike $K$\")\n",
    "\n",
    "    # plot the price for different underlying values, one line for each different time to maturity tau\n",
    "    for i in np.arange(numTau)[::-1]: # loop over reversed range\n",
    "        \n",
    "        option.set_tau(tau_list[i])\n",
    "        price = np.zeros(numS)\n",
    "        \n",
    "        for j in np.arange(numS):\n",
    "            option.set_S(S_list[j]) # reset of underlying value\n",
    "            price[j] = option.price()\n",
    "        \n",
    "        ax.plot(S_list, price, '-', lw=1.5, label=r\"$\\tau={}$\".format(option.get_tau()))\n",
    "    \n",
    "    # plot the red payoff line for different underlying values\n",
    "    payoff = np.zeros(numS)\n",
    "    for i in np.arange(numS):\n",
    "        option.set_S(S_list[i]) # reset of underlying value\n",
    "        payoff[i] = option.payoff()\n",
    "    \n",
    "    ax.plot(S_list, payoff, 'r-',  lw=1.5, label=option.get_docstring('payoff'))\n",
    "    \n",
    "    # set axis labels \n",
    "    ax.set_xlabel('Underlying $S_t$', fontsize=12) \n",
    "    ax.set_ylabel('Black-Scholes Price', fontsize=12) \n",
    "\n",
    "    # set title\n",
    "    ax.set_title(r\"Price of a {} {} Option $(S_t, K={}, \\tau=T-t, r={}\\%, \\sigma={}\\%)$ Vs $S$ (at different $\\tau$)\".\n",
    "                 format(title_label, option.get_type(), option.get_K(), option.get_r()*100, option.get_sigma()*100), fontsize=12) \n",
    "\n",
    "    # add the legend ('best' loc parameters places the legend in the best position automatically)\n",
    "    ax.legend(loc='best', ncol=1)\n",
    "        \n",
    "    # add a gride to ease visualization\n",
    "    plt.grid(True)\n",
    "\n",
    "    # show the plot\n",
    "    fig.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So let's plot a `CON_Call` CON call option"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DigitalOption('call', cash=1.0, S_t=90.0, K=100.0, t=19-04-2020, T=31-12-2020, tau=0.70y, r=5.0%, sigma=20.0%)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CON_Call = DigitalOption(option_type='call', cash_amount = 1.0, S_t=90.0, K=100.0, t=\"19-04-2020\", T=\"31-12-2020\", r=0.05, sigma=0.2)\n",
    "CON_Call"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 80.        ,  80.4040404 ,  80.80808081,  81.21212121,\n",
       "        81.61616162,  82.02020202,  82.42424242,  82.82828283,\n",
       "        83.23232323,  83.63636364,  84.04040404,  84.44444444,\n",
       "        84.84848485,  85.25252525,  85.65656566,  86.06060606,\n",
       "        86.46464646,  86.86868687,  87.27272727,  87.67676768,\n",
       "        88.08080808,  88.48484848,  88.88888889,  89.29292929,\n",
       "        89.6969697 ,  90.1010101 ,  90.50505051,  90.90909091,\n",
       "        91.31313131,  91.71717172,  92.12121212,  92.52525253,\n",
       "        92.92929293,  93.33333333,  93.73737374,  94.14141414,\n",
       "        94.54545455,  94.94949495,  95.35353535,  95.75757576,\n",
       "        96.16161616,  96.56565657,  96.96969697,  97.37373737,\n",
       "        97.77777778,  98.18181818,  98.58585859,  98.98989899,\n",
       "        99.39393939,  99.7979798 , 100.        , 100.2020202 ,\n",
       "       100.60606061, 101.01010101, 101.41414141, 101.81818182,\n",
       "       102.22222222, 102.62626263, 103.03030303, 103.43434343,\n",
       "       103.83838384, 104.24242424, 104.64646465, 105.05050505,\n",
       "       105.45454545, 105.85858586, 106.26262626, 106.66666667,\n",
       "       107.07070707, 107.47474747, 107.87878788, 108.28282828,\n",
       "       108.68686869, 109.09090909, 109.49494949, 109.8989899 ,\n",
       "       110.3030303 , 110.70707071, 111.11111111, 111.51515152,\n",
       "       111.91919192, 112.32323232, 112.72727273, 113.13131313,\n",
       "       113.53535354, 113.93939394, 114.34343434, 114.74747475,\n",
       "       115.15151515, 115.55555556, 115.95959596, 116.36363636,\n",
       "       116.76767677, 117.17171717, 117.57575758, 117.97979798,\n",
       "       118.38383838, 118.78787879, 119.19191919, 119.5959596 ,\n",
       "       120.        ])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "K = 100.0\n",
    "\n",
    "S_strip = np.linspace(80, 120, 100)\n",
    "S_strip = np.append(S_strip, K)\n",
    "S_strip = np.sort(S_strip)\n",
    "S_strip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.05, 0.1 , 0.25, 0.5 , 0.75, 1.  ])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tau_strip = np.array([0.05, 0.1, 0.25, 0.5, 0.75, 1.0])\n",
    "tau_strip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_multi_tau(CON_Call, S_strip, tau_strip)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and now a plain-vanilla put option"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PlainVanillaOption('put', S_t=90.0, K=100.0, t=19-04-2020, T=31-12-2020, tau=0.70y, r=5.0%, sigma=20.0%)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Vanilla_Put = PlainVanillaOption(option_type='put', S_t=90.0, K=100.0, t=\"19-04-2020\", T=\"31-12-2020\", r=0.05, sigma=0.2)\n",
    "Vanilla_Put"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_multi_tau(Vanilla_Put, S_strip, tau_strip)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_single_tau(option, S_list, tau):\n",
    "    \"\"\"\n",
    "    plot_single_tau(option, S_list, tau) plot option prices for underlying in 'S_list' and time to maturity 'tau'.\n",
    "\n",
    "    Parameters:\n",
    "        option (PlainVanillaOption): instance of PlainVanillaOption class;\n",
    "        S_list (List):               list of underlying values;\n",
    "        tau (float):                 time to maturity (in years);\n",
    "        \n",
    "    Returns:\n",
    "        None;\n",
    "\n",
    "    \"\"\"\n",
    "\n",
    "    # define the figure\n",
    "    fig, ax = plt.subplots(figsize=(10,6))\n",
    "    \n",
    "    # setting legend labels\n",
    "    # plain vanilla case:\n",
    "    if isinstance(option, PlainVanillaOption):\n",
    "        title_label = \"Plain Vanilla\"\n",
    "    # digital case:\n",
    "    else:\n",
    "        title_label = \"Cash-Or-Nothing ($Q={}$)\".format(option.get_Q())\n",
    "            \n",
    "    # auxiliary variable\n",
    "    numS = len(S_list)\n",
    "\n",
    "    # plot a dot to highlight the strike position\n",
    "    ax.plot(option.get_K(), 0, 'k.', ms=15, label=\"Strike $K$\")\n",
    "\n",
    "    # plot the upper limit, the price and the lower limit for different underlying values\n",
    "    upper_limit = np.zeros(numS)\n",
    "    price       = np.zeros(numS)\n",
    "    lower_limit = np.zeros(numS)\n",
    "    payoff      = np.zeros(numS)\n",
    "\n",
    "    for i in np.arange(numS):\n",
    "        option.set_S(S_list[i]) # reset of underlying value\n",
    "        \n",
    "        upper_limit[i] = option.price_upper_limit()\n",
    "        price[i]       = option.price()\n",
    "        lower_limit[i] = option.price_lower_limit()\n",
    "        payoff[i]      = option.payoff()\n",
    "\n",
    "    ax.plot(S_list, upper_limit, 'k-.', lw=1.5, label=option.get_docstring('price_upper_limit'))\n",
    "    ax.plot(S_list, price,       'b-',  lw=1.5, label=r\"Price\")\n",
    "    ax.plot(S_list, lower_limit, 'k--', lw=1.5, label=option.get_docstring('price_lower_limit'))\n",
    "    ax.plot(S_list, payoff,      'r-',  lw=1.5, label=option.get_docstring('payoff'))\n",
    "\n",
    "    # set axis labels \n",
    "    ax.set_xlabel('Underlying $S_t$', fontsize=12) \n",
    "    ax.set_ylabel('Black-Scholes Price', fontsize=12) \n",
    "\n",
    "    # set title\n",
    "    ax.set_title(r\"Price of a {} {} Option $(S_t, K={}, \\tau=T-t={:.1f}y, r={}\\%, \\sigma={}\\%)$ Vs $S$ (with price limits)\".\n",
    "                 format(title_label, option.get_type(), option.get_K(), option.get_tau(), option.get_r()*100, \n",
    "                        option.get_sigma()*100), fontsize=12) \n",
    "\n",
    "    # add the legend ('best' loc parameters places the legend in the best position automatically)\n",
    "    ax.legend(loc='best', ncol=1)\n",
    "\n",
    "    # add a gride to ease visualization\n",
    "    plt.grid(True)\n",
    "\n",
    "    # show the plot\n",
    "    fig.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.        ,  0.1010101 ,  0.2020202 ,  0.3030303 ,  0.4040404 ,\n",
       "        0.50505051,  0.60606061,  0.70707071,  0.80808081,  0.90909091,\n",
       "        1.01010101,  1.11111111,  1.21212121,  1.31313131,  1.41414141,\n",
       "        1.51515152,  1.61616162,  1.71717172,  1.81818182,  1.91919192,\n",
       "        2.02020202,  2.12121212,  2.22222222,  2.32323232,  2.42424242,\n",
       "        2.52525253,  2.62626263,  2.72727273,  2.82828283,  2.92929293,\n",
       "        3.03030303,  3.13131313,  3.23232323,  3.33333333,  3.43434343,\n",
       "        3.53535354,  3.63636364,  3.73737374,  3.83838384,  3.93939394,\n",
       "        4.04040404,  4.14141414,  4.24242424,  4.34343434,  4.44444444,\n",
       "        4.54545455,  4.64646465,  4.74747475,  4.84848485,  4.94949495,\n",
       "        5.        ,  5.05050505,  5.15151515,  5.25252525,  5.35353535,\n",
       "        5.45454545,  5.55555556,  5.65656566,  5.75757576,  5.85858586,\n",
       "        5.95959596,  6.06060606,  6.16161616,  6.26262626,  6.36363636,\n",
       "        6.46464646,  6.56565657,  6.66666667,  6.76767677,  6.86868687,\n",
       "        6.96969697,  7.07070707,  7.17171717,  7.27272727,  7.37373737,\n",
       "        7.47474747,  7.57575758,  7.67676768,  7.77777778,  7.87878788,\n",
       "        7.97979798,  8.08080808,  8.18181818,  8.28282828,  8.38383838,\n",
       "        8.48484848,  8.58585859,  8.68686869,  8.78787879,  8.88888889,\n",
       "        8.98989899,  9.09090909,  9.19191919,  9.29292929,  9.39393939,\n",
       "        9.49494949,  9.5959596 ,  9.6969697 ,  9.7979798 ,  9.8989899 ,\n",
       "       10.        ])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tau = 2.0\n",
    "\n",
    "K_focus = 5\n",
    "\n",
    "S_strip_focus = np.linspace(0, 10, 100)\n",
    "S_strip_focus = np.append(S_strip_focus, K_focus)\n",
    "S_strip_focus = np.sort(S_strip_focus)\n",
    "S_strip_focus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DigitalOption('call', cash=1.0, S_t=3.0, K=5.0, t=19-04-2020, T=19-04-2022, tau=2.00y, r=5.0%, sigma=20.0%)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CON_Call_focus = DigitalOption(option_type='call', \n",
    "                               cash_amount=1.0, \n",
    "                               S_t=3.0, \n",
    "                               K=5.0, \n",
    "                               t=\"19-04-2020\", \n",
    "                               T=\"19-04-2022\", \n",
    "                               r=0.05, \n",
    "                               sigma=0.2)\n",
    "\n",
    "CON_Call_focus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_single_tau(CON_Call_focus, S_strip_focus, CON_Call_focus.get_tau())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "Vanilla_Put_focus = PlainVanillaOption(option_type='put', \n",
    "                                       S_t=3.0, \n",
    "                                       K=5.0, \n",
    "                                       t=\"19-04-2020\", \n",
    "                                       T=\"19-04-2022\", \n",
    "                                       r=0.05, \n",
    "                                       sigma=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_single_tau(Vanilla_Put_focus, S_strip_focus, Vanilla_Put_focus.get_tau())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Inheritance <a name=\"inheritance\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Given all the financial similarities between plain-vanilla and digital options, \n",
    "\n",
    "- don't you think that modeling `PlainVanillaOption` and `DigitalOption` as two completely unrelated classes is somehow at odds with their analogies? \n",
    "\n",
    "- And, looking at the body of the two classes, don't you think that there are a lot of repetitions?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_Inheritance_ is a way of arranging objects in a hierarchy from the most general one to the most specific one. It's a logical and programming paradygm modeling an _\"is a\"_ relationship between the most specific object and the most general one. At the end of the day, since both kind of options share european exercise (they cannot be exercised before expiration date $T$):\n",
    "\n",
    "- a plain-vanilla option _is a_ european option,\n",
    "\n",
    "- a digital option _is a_ european option."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can translate this considerations defining a general `EuropeanOption` class and making `PlainVanillaOption` and `DigitalOption` _sub-classes_ of `EuropeanOption`. In this way, we are modeling the fact that:\n",
    "\n",
    "- a `PlainVanillaOption` _is a_ `EuropeanOption`\n",
    "\n",
    "- a `DigitalOption` _is a_ `EuropeanOption`.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then refer to `EuropeanOption` class as the _parent_ (aka _mother_ or _base_ ) class and to any of `PlainVanillaOption` and `DigitalOption` as the _derived_ (aka _child_ or _sub-class_ ) class. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The syntax is\n",
    "\n",
    "```python\n",
    "class BaseClass:\n",
    "    ...\n",
    "```\n",
    "```python\n",
    "class Child1(BaseClass):\n",
    "    ...\n",
    "```\n",
    "```python\n",
    "class Child2(BaseClass):\n",
    "    ...\n",
    "```\n",
    "and you can have as many sub-classes as you want."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The idea is to put all the attributes and methods that the classes have in common in a base class. These will be inherited by sub-classes. Then define one or more subclasses with their own custom attributes and methods."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is our `EuropeanOption` base class implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "class EuropeanOption:\n",
    "    \"\"\"\n",
    "    EuropeanOption abstract class: an interface setting the template for any option with european-style exercise.\n",
    "    This class is not meant to be instantiated.\n",
    "    \n",
    "    Attributes:\n",
    "    -----------\n",
    "        type (str):               type of the options. Can be either 'call' or 'put';\n",
    "        S_t (float):              spot price of the underlying asset at the valuation date 't';\n",
    "        K (float):                strike price;\n",
    "        t (str; dt.datetime):     valuation date. Can be either a \"dd-mm-YYYY\" String or a pd.datetime() object\n",
    "        T (str; dt.datetime):     expiration date. Can be either a \"dd-mm-YYYY\" String or a pd.datetime() object\n",
    "        tau (float):              time to maturity in years, computed as tau=T-t by time_to_maturity() method\n",
    "        r (float):                continuously compounded short-rate;\n",
    "        sigma (float):            volatility of underlying asset;\n",
    "    \n",
    "    Public Methods:\n",
    "    --------   \n",
    "    \n",
    "        getters and setters for all common attributes\n",
    "        \n",
    "        payoff: float\n",
    "            Template method for payoff. Raises NotImplementedError if called.\n",
    "\n",
    "        price_upper_limit: float \n",
    "            Template method for upper limit. Raises NotImplementedError if called.\n",
    "\n",
    "        price_lower_limit: float \n",
    "            Template method for lower limit. Raises NotImplementedError if called.\n",
    "            \n",
    "        price: float\n",
    "            Template method for price. Raises NotImplementedError if called.\n",
    "\n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(self, option_type, S_t, K, t, T, r, sigma):\n",
    "        \n",
    "        print(\"Calling the EuropeanOption constructor!\")\n",
    "\n",
    "        # option type check\n",
    "        if option_type not in ['call', 'put']:\n",
    "            raise NotImplementedError(\"Option Type: '{}' does not exist!\".format(option_type))\n",
    "        \n",
    "        self.__type  = option_type\n",
    "        self.__S     = S_t\n",
    "        self.__K     = K\n",
    "        self.__t     = dt.datetime.strptime(t, \"%d-%m-%Y\") if isinstance(t, str) else t\n",
    "        self.__T     = dt.datetime.strptime(T, \"%d-%m-%Y\") if isinstance(T, str) else T\n",
    "        self.__tau   = self.__time_to_maturity()\n",
    "        self.__r     = r\n",
    "        self.__sigma = sigma\n",
    "        \n",
    "        # empty informations dictionary\n",
    "        self.__docstring_dict = {}        \n",
    "        \n",
    "    # string representation method template\n",
    "    def __repr__(self):\n",
    "        raise NotImplementedError()\n",
    "    \n",
    "    # getters\n",
    "    def get_type(self):\n",
    "        return self.__type\n",
    "        \n",
    "    def get_S(self):\n",
    "        return self.__S\n",
    "    \n",
    "    def get_K(self):\n",
    "        return self.__K\n",
    "    \n",
    "    def get_t(self):\n",
    "        return self.__t\n",
    "\n",
    "    def get_T(self):\n",
    "        return self.__T\n",
    "\n",
    "    def get_tau(self):\n",
    "        return self.__tau\n",
    "    \n",
    "    def get_r(self):\n",
    "        return self.__r\n",
    "\n",
    "    def get_sigma(self):\n",
    "        return self.__sigma\n",
    "    \n",
    "    # doctring getter template\n",
    "    def get_docstring(self, label):\n",
    "        raise NotImplementedError()\n",
    "\n",
    "    # setters\n",
    "    def set_type(self, option_type):\n",
    "        self.__type = option_type\n",
    "        \n",
    "        # option type check\n",
    "        if option_type not in ['call', 'put']:\n",
    "            raise NotImplementedError(\"Option Type: '{}' does not exist!\".format(option_type))\n",
    "            \n",
    "    def set_S(self, S):\n",
    "        self.__S = S\n",
    "        \n",
    "    def set_K(self, K):\n",
    "        self.__K = K\n",
    "    \n",
    "    def set_t(self, t):\n",
    "        self.__t = dt.datetime.strptime(t, \"%d-%m-%Y\") if isinstance(t, str) else t\n",
    "        # update time to maturity, given changed t, to keep internal consistency\n",
    "        self.__update_tau() \n",
    "\n",
    "    def set_T(self, T):\n",
    "        self.__T = dt.datetime.strptime(T, \"%d-%m-%Y\") if isinstance(T, str) else T\n",
    "        # update time to maturity, given changed T, to keep internal consistency\n",
    "        self.__update_tau() \n",
    "    \n",
    "    def set_tau(self, tau):\n",
    "        self.__tau = tau\n",
    "        # update expiration date, given changed tau, to keep internal consistency\n",
    "        # we could have updated valuation date as well, but this is a stylistic choice\n",
    "        self.__update_T()\n",
    "\n",
    "    def set_r(self, r):\n",
    "        self.__r = r\n",
    "\n",
    "    def set_sigma(self, sigma):\n",
    "        self.__sigma = sigma\n",
    "        \n",
    "    # update methods (private)\n",
    "    def __update_tau(self):\n",
    "        self.__tau = self.__time_to_maturity()\n",
    "\n",
    "    def __update_T(self):\n",
    "        self.__T = self.__t + dt.timedelta(days=math.ceil(self.__tau*365))\n",
    "\n",
    "    # time to maturity method (private)\n",
    "    def __time_to_maturity(self):\n",
    "        return (self.__T - self.__t).days / 365.0\n",
    "    \n",
    "    # payoff template\n",
    "    def payoff(self):\n",
    "        raise NotImplementedError()     \n",
    "        \n",
    "    # upper price limit template\n",
    "    def price_upper_limit(self):\n",
    "        raise NotImplementedError()     \n",
    "\n",
    "    # lower price limit template\n",
    "    def price_lower_limit(self):\n",
    "        raise NotImplementedError()     \n",
    "        \n",
    "    # price template\n",
    "    def price(self):\n",
    "        raise NotImplementedError()     "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that `EuropeanOption` defines all common attributes and methods. This avoid a lot of code repetition. Look for example at the time-to-maturity computation\n",
    "\n",
    "```python\n",
    "def __time_to_maturity(self):\n",
    "    return (self.__T - self.__t).days / 365.0\n",
    "```\n",
    "\n",
    "since all european options derived from `EuropeanOption` should feature a time-to-maturity, it definitely makes sense to implement it here once-for-all and make it _inherited_ by all the sub-classes of `EuropeanOption`.\n",
    "\n",
    "On the other side, since the cash amount $Q$ paid at maturity is a parameter of CON options, but not plain-vanilla ones, it makes sense to implement it as an attribute `self.__Q` in `DigitalOption` class only."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here are the derived classes: look how much code we are saving w.r.t. their previous implementation as independent classes. Also, look how armhonically they integrate: we are leveraging on the financial similarities between the two kind of contracts, translating them in a considerable sharing of properties."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "class PlainVanillaOption(EuropeanOption):\n",
    "    \"\"\"\n",
    "    PlainVanillaOption class implementing payoff and pricing of plain-vanilla call and put options.\n",
    "    Put price is calculated using put-call parity\n",
    "    \n",
    "    Attributes:\n",
    "    -----------\n",
    "        type (str):               type of the options. Can be either 'call' or 'put';\n",
    "        S_t (float):              spot price of the underlying asset at the valuation date 't';\n",
    "        K (float):                strike price;\n",
    "        t (str; dt.datetime):     valuation date. Can be either a \"dd-mm-YYYY\" String or a pd.datetime() object\n",
    "        T (str; dt.datetime):     expiration date. Can be either a \"dd-mm-YYYY\" String or a pd.datetime() object\n",
    "        tau (float):              time to maturity in years, computed as tau=T-t by time_to_maturity() method\n",
    "        r (float):                continuously compounded short-rate;\n",
    "        sigma (float):            volatility of underlying asset;\n",
    "    \n",
    "    Methods:\n",
    "    --------   \n",
    "    \n",
    "        payoff: float\n",
    "            Computes the payoff of the option and returns it\n",
    "\n",
    "        price_upper_limit: float \n",
    "            Returns the upper limit for a vanilla option price.\n",
    "\n",
    "        price_lower_limit: float \n",
    "            Returns the lower limit for a vanilla option price.\n",
    "            \n",
    "        price: float\n",
    "            Computes the exact price of the option and returns it, using call_price() or put_price()\n",
    "\n",
    "    \"\"\"\n",
    "    \n",
    "    # initializer with default arguments\n",
    "    def __init__(self, option_type='call', S_t=90.0, K=100.0, t=\"19-04-2020\", T=\"31-12-2020\", r=0.05, sigma=0.2):    \n",
    "        # calling the EuropeanOption constructor\n",
    "        super(PlainVanillaOption, self).__init__(option_type, S_t, K, t, T, r, sigma)\n",
    "        \n",
    "        # additional stuff - PlainVanillaOption-specific\n",
    "\n",
    "        # informations dictionary\n",
    "        self.__docstring_dict = {\n",
    "            'call':{\n",
    "                'price_upper_limit': r\"Upper limit: $S_t$\",\n",
    "                'payoff':            r\"Payoff: $max(S-K, 0)$\",\n",
    "                'price_lower_limit': r\"Lower limit: $max(S_t - K e^{-r \\tau}, 0)$\"\n",
    "            },\n",
    "            'put': {\n",
    "                'price_upper_limit': r\"Upper limit: $K e^{-r \\tau}$\",\n",
    "                'payoff':            r\"Payoff: $max(K-S, 0)$\",\n",
    "                'price_lower_limit': r\"Lower limit: $max(K e^{-r \\tau} - S_t, 0)$\"}\n",
    "        }\n",
    "                \n",
    "    def __repr__(self):\n",
    "        return r\"PlainVanillaOption('{}', S_t={:.1f}, K={:.1f}, t={}, T={}, tau={:.2f}y, r={:.1f}%, sigma={:.1f}%)\".\\\n",
    "                format(self.get_type(), self.get_S(), self.get_K(), self.get_t().strftime(\"%d-%m-%Y\"), \n",
    "                       self.get_T().strftime(\"%d-%m-%Y\"), self.get_tau(), self.get_r()*100, self.get_sigma()*100)\n",
    "    \n",
    "    # docstring getter\n",
    "    def get_docstring(self, label):\n",
    "        return self.__docstring_dict[self.get_type()][label] \n",
    "    \n",
    "    # payoff calculation\n",
    "    def payoff(self):\n",
    "        # call case\n",
    "        if self.get_type() == 'call':\n",
    "            return max(0.0, self.get_S() - self.get_K())\n",
    "        # put case\n",
    "        else:\n",
    "            return max(0.0, self.get_K() - self.get_S())\n",
    "\n",
    "    # upper price limit\n",
    "    def price_upper_limit(self):\n",
    "        # call case\n",
    "        if self.get_type() == 'call':\n",
    "            return self.get_S()\n",
    "        # put case\n",
    "        else:\n",
    "            return self.get_K()*np.exp(-self.get_r() * self.get_tau())\n",
    "\n",
    "    # lower price limit\n",
    "    def price_lower_limit(self):\n",
    "        # call case\n",
    "        if self.get_type() == 'call':\n",
    "            return max(self.get_S() - self.get_K()*np.exp(-self.get_r() * self.get_tau()), 0)\n",
    "        # put case\n",
    "        else:\n",
    "            return max(self.get_K()*np.exp(-self.get_r() * self.get_tau()) - self.get_S(), 0)\n",
    "            \n",
    "    # price calculation\n",
    "    def price(self):\n",
    "        # call case\n",
    "        if self.get_type() == 'call':\n",
    "            return self.__call_price()\n",
    "        # put case\n",
    "        else:\n",
    "            return self.__put_price()\n",
    "          \n",
    "    def __call_price(self):\n",
    "        \n",
    "        # some local variables retrieved to be used repeatedly\n",
    "        S = self.get_S()\n",
    "        tau = self.get_tau()\n",
    "        \n",
    "        if S == 0: # this is to avoid log(0) issues\n",
    "            return 0.0\n",
    "        elif tau == 0.0: # this is to avoid 0/0 issues\n",
    "            return self.payoff()\n",
    "        else:\n",
    "            K     = self.get_K()\n",
    "            r     = self.get_r()\n",
    "            sigma = self.get_sigma()\n",
    "\n",
    "            d1 = (np.log(S/K) + (r + 0.5 * sigma ** 2) * tau) / (sigma * np.sqrt(tau))\n",
    "            d2 = d1 - sigma * np.sqrt(tau)\n",
    "\n",
    "            price = S * stats.norm.cdf(d1, 0.0, 1.0) - K * np.exp(-r * tau) * stats.norm.cdf(d2, 0.0, 1.0)\n",
    "\n",
    "            return price\n",
    "    \n",
    "    def __put_price(self):\n",
    "        \"\"\" Put price from Put-Call parity relation: Call + Ke^{-r*tau} = Put + S\"\"\"\n",
    "        return self.__call_price() + self.get_K() * np.exp(- self.get_r() * self.get_tau()) - self.get_S()        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DigitalOption(EuropeanOption):\n",
    "    \"\"\"\n",
    "    DigitalOption class implementing payoff and pricing of digital call and put options.\n",
    "    Put price is calculated using put-call parity\n",
    "    \n",
    "    Attributes:\n",
    "    -----------\n",
    "        type (str):               type of the options. Can be either 'call' or 'put';\n",
    "        Q (float):                cash amount\n",
    "        S_t (float):              spot price of the underlying asset at the valuation date 't';\n",
    "        K (float):                strike price;\n",
    "        t (str; dt.datetime):     valuation date. Can be either a \"dd-mm-YYYY\" String or a pd.datetime() object\n",
    "        T (str; dt.datetime):     expiration date. Can be either a \"dd-mm-YYYY\" String or a pd.datetime() object\n",
    "        tau (float):              time to maturity in years, computed as tau=T-t by time_to_maturity() method\n",
    "        r (float):                continuously compounded short-rate;\n",
    "        sigma (float):            volatility of underlying asset;\n",
    "    \n",
    "    Public Methods:\n",
    "    --------   \n",
    "    \n",
    "        getter and setter for cash amount attribute\n",
    "        \n",
    "        payoff: float\n",
    "            Computes the payoff of the option and returns it\n",
    "\n",
    "        price_upper_limit: float \n",
    "            Returns the upper limit for a CON digital option price.\n",
    "\n",
    "        price_lower_limit: float \n",
    "            Returns the lower limit for a CON digital option price\n",
    "            .\n",
    "        price: float\n",
    "            Computes the exact price of the option and returns it, using call_price() or put_price()\n",
    "\n",
    "    \"\"\"\n",
    "    \n",
    "    # initializer with default arguments    \n",
    "    def __init__(self, option_type='call', cash_amount = 1.0, S_t=90.0, K=100.0, t=\"19-04-2020\", T=\"31-12-2020\", r=0.05, sigma=0.2):\n",
    "        # calling the EuropeanOption constructor\n",
    "        super(DigitalOption, self).__init__(option_type, S_t, K, t, T, r, sigma)        \n",
    "\n",
    "        # additional stuff - DigitalOption-specific\n",
    "        self.__Q = cash_amount\n",
    "        \n",
    "        # informations dictionary\n",
    "        self.__docstring_dict = {\n",
    "            'call':{\n",
    "                'price_upper_limit': r\"Upper limit: $Q e^{-r \\tau}$\",\n",
    "                'payoff':            r\"Payoff: $Q$ $I(S > K)$\",\n",
    "                'price_lower_limit': r\"Lower limit: $0$\"\n",
    "            },\n",
    "            'put': {\n",
    "                'price_upper_limit': r\"Upper limit: $Q e^{-r \\tau}$\",\n",
    "                'payoff':            r\"Payoff: $Q$ $I(S \\leq K)$\",\n",
    "                'price_lower_limit': r\"Lower limit: $0$\"}\n",
    "        }        \n",
    "                \n",
    "    def __repr__(self):\n",
    "        return r\"DigitalOption('{}', cash={:.1f}, S_t={:.1f}, K={:.1f}, t={}, T={}, tau={:.2f}y, r={:.1f}%, sigma={:.1f}%)\".\\\n",
    "                format(self.get_type(), self.get_Q(), self.get_S(), self.get_K(), self.get_t().strftime(\"%d-%m-%Y\"), \n",
    "                       self.get_T().strftime(\"%d-%m-%Y\"), self.get_tau(), self.get_r()*100, self.get_sigma()*100)\n",
    "    \n",
    "    # getters\n",
    "    def get_Q(self):\n",
    "        return self.__Q\n",
    "    \n",
    "    # docstring getter\n",
    "    def get_docstring(self, label):\n",
    "        return self.__docstring_dict[self.get_type()][label] \n",
    "    \n",
    "    # setters\n",
    "    def set_Q(self, cash_amount):\n",
    "        self.__Q = cash_amount\n",
    "    \n",
    "    # payoff calculation\n",
    "    def payoff(self):\n",
    "        # call case\n",
    "        if self.get_type() == 'call':\n",
    "            return self.get_Q() * int(self.get_S() > self.get_K())\n",
    "        # put case\n",
    "        else:\n",
    "            return self.get_Q() * int(self.get_S() <= self.get_K())\n",
    "        \n",
    "    # upper price limit\n",
    "    def price_upper_limit(self):\n",
    "        # call case\n",
    "        if self.get_type() == 'call':\n",
    "            return self.get_Q()*np.exp(-self.get_r() * self.get_tau())\n",
    "        # put case\n",
    "        else:\n",
    "            return self.get_Q()*np.exp(-self.get_r() * self.get_tau())\n",
    "\n",
    "    # lower price limit\n",
    "    def price_lower_limit(self):\n",
    "        # call case\n",
    "        if self.get_type() == 'call':\n",
    "            return 0.0\n",
    "        # put case\n",
    "        else:\n",
    "            return 0.0\n",
    "        \n",
    "    # price calculation\n",
    "    def price(self):\n",
    "        # call case\n",
    "        if self.get_type() == 'call':\n",
    "            return self.__call_price()\n",
    "        # put case\n",
    "        else:\n",
    "            return self.__put_price()\n",
    "          \n",
    "    def __call_price(self):\n",
    "        \n",
    "        # some local variables retrieved to be used repeatedly\n",
    "        S = self.get_S()\n",
    "        tau = self.get_tau()\n",
    "        \n",
    "        if S == 0: # this is to avoid log(0) issues\n",
    "            return 0.0\n",
    "        elif tau == 0.0: # this is to avoid 0/0 issues\n",
    "            return self.payoff()\n",
    "        else:\n",
    "            Q     = self.get_Q()\n",
    "            K     = self.get_K()\n",
    "            r     = self.get_r()\n",
    "            sigma = self.get_sigma()\n",
    "\n",
    "            d1 = (np.log(S/K) + (r + 0.5 * sigma ** 2) * tau) / (sigma * np.sqrt(tau))\n",
    "            d2 = d1 - sigma * np.sqrt(tau)\n",
    "\n",
    "            price = Q * np.exp(-r * tau) * stats.norm.cdf(d2, 0.0, 1.0)\n",
    "\n",
    "            return price\n",
    "    \n",
    "    def __put_price(self):\n",
    "        \"\"\" Put price from Put-Call parity relation: CON_Call + CON_Put = Qe^{-r*tau}\"\"\"\n",
    "        return self.get_Q() * np.exp(- self.get_r() * self.get_tau()) - self.__call_price()        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that the `__init__` method of the `EuropeanOption` base class initialises all the instance variables that are common to both `PlainVanillaOption` and `DigitalOption`. In each sub-class we define another `__init__` method (it's then an _overriden_ method, see below) so that we can use it to initialize that class's attributes and from its body we call the `EuropeanOption` `__init__` method to initialize common attributes. The base class initializer can be called from a derived class using the `super()` function like\n",
    "\n",
    "```python\n",
    "super(DerivedClass, self).__init__(parameters_of_the_BaseClass_initializer)    \n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see this instantiating a plain-vanilla option"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calling the EuropeanOption constructor!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "PlainVanillaOption('call', S_t=90.0, K=100.0, t=19-04-2020, T=31-12-2020, tau=0.70y, r=5.0%, sigma=20.0%)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Vanilla_Call = PlainVanillaOption(option_type='call', S_t=90.0, K=100.0, t=\"19-04-2020\", T=\"31-12-2020\", r=0.05, sigma=0.2)\n",
    "Vanilla_Call"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Do you see the message print on screen? It comes from the `__init__` method of `EuropeanOption`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_En passant,_ notice that we can initialize `PlainVanillaOption` (and `DigitalOption` as well) using default parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calling the EuropeanOption constructor!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "PlainVanillaOption('call', S_t=90.0, K=100.0, t=19-04-2020, T=31-12-2020, tau=0.70y, r=5.0%, sigma=20.0%)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Vanilla_Call = PlainVanillaOption()\n",
    "Vanilla_Call"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "__main__.PlainVanillaOption"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(Vanilla_Call)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "isinstance(Vanilla_Call, PlainVanillaOption)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100.0"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Vanilla_Call.get_K()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.487402470943657"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Vanilla_Call.price()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly, let's instantiate a digital CON option"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calling the EuropeanOption constructor!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "DigitalOption('call', cash=1.0, S_t=90.0, K=100.0, t=19-04-2020, T=31-12-2020, tau=0.70y, r=5.0%, sigma=20.0%)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CON_Call = DigitalOption()\n",
    "CON_Call"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "__main__.DigitalOption"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(CON_Call)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "isinstance(CON_Call, DigitalOption)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100.0"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CON_Call.get_K()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CON_Call.get_Q()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.29674605684957245"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CON_Call.price()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.1. Abstract classes and interfaces <a name=\"abstract\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's go back `EuropeanOption` class. Notice that most of its methods are left empty (eventually raising a `NotImplementedError()` to prevent from being called). Let's see this in practice, instantiating a generic european option and considering for example the `.price()` method\n",
    "\n",
    "```python\n",
    "    # price template\n",
    "    def price(self):\n",
    "        raise NotImplementedError()     \n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calling the EuropeanOption constructor!\n"
     ]
    }
   ],
   "source": [
    "European_Call_generic = EuropeanOption(option_type='call', S_t=90.0, K=100.0, t=\"19-04-2020\", T=\"31-12-2020\", r=0.05, sigma=0.2)\n",
    "European_Call_generic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# raises NotImplementedError:\n",
    "#\n",
    "# European_Call_generic.price()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This modeling choice has been taken because our `EuropeanOption` class is not meant to be instantiated. It is meant only to serves as a _template_ (or _interface_ ) according to which all options with european-style exercise (that we derive from her) should conform. \n",
    "\n",
    "Here we are making all classes derived from `EuropeanOption` _inheriting_ this method and we are imposing that each of them should _override_ (see next section) it providing a specific implementation for the method according to their needs.\n",
    "\n",
    "We say that `EuropeanOption` is an _abstract_ class. We instantiate options through its sub-classes only."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.2. Inheritance and Polymorphism <a name=\"inheritance-and-poly\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Given a `.method()` implemented in the base class, sub-classes are also free to re-implement the same method (that is, keeping its name). Methods defined in the base class and re-implemented by the derived classes are known as _overriden_ methods. Therefore sub-classes implement polymorphism through the methods they override from the base class. \n",
    "\n",
    "An example we have already encountered is the `.price()` method, that is defined in `EuropeanOption` class as an empty method (raising a `NotImplementedError()` error message if called) and that both `PlainVaillaOption` and `DigitalOption` classes re-implement according to their specific needs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can se this in practice again using our plotting functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_multi_tau(Vanilla_Call, S_strip, tau_strip)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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bvDYaF4j708C03Sh+0xZ43+5LtlWxxCqQ4M7D64Nd3lmLcNyLt5OaAUwI/F/0hoMpeEnYTrwzc7c651aGMz5wZurnEZQVatkLrcJLuvaa16etupTZ/7QyAgnGH4EvzGxLNRRZbpuFaOMyP8/A9rIcyHXOlTwzWFbbV2f7NMTbLnfhbWtT8bpkBbeBSNcZMxuGdzPMPwCcc58B/w9vHzEM72azQqWW0Tm3BK+/4ht4+412wMgSZ0UjZt4Z6sl42+72Imdkx0ewbEXboqJtJ5K2GIX3fPOiZw5LKetYhNf/cRbeZ7gd7wbWn5ecn4r3f6EU3X/ivJvP8vCSVZxzB/A+p0WBY1AoZe7vI1TeMSWk6sp9aljJ9inv8wxrXQGwMq5WiUgJZpaL9xidhdGOBcDMfgXsdM79roLpLge6FE5n3nNnlwHJVT1o1jcn87LXlkAbr8N7hNziEsNP6LY/GZYxXGb2KXCj827gqnPC3X/WcAy51KFjSrREsq4oaRUJU33dwZhZb7xHyyx0zulnX6VGmdnDwBnOuXCfrCFyUqqvx5RoOtF/ZUHkpBfoRlHd/ZJFijEzP/A+3k0uP6xgchGRiOlMq4iIiIjUefX6RiwREREROTmcMN0D2rZt67p06VJr9X377bc0b17VG6pPLGqT0tQmoaldSlOblKY2KU1tEprapbTabpOlS5fuds6dUpN1nDBJa5cuXViyZEmt1ZeRkUFaWlqt1VcfqE1KU5uEpnYpTW1SmtqkNLVJaGqX0mq7TcxsY03Xoe4BIiIiIlLnKWkVERERkTpPSauIiIiI1HlKWkVERESkzlPSKiIiIiJ1npJWEREREanzlLSKiIiISJ2npFVERERE6jwlrSIiIiJS5ylpFREREZE6T0mriIiIiNR5tZK0mtmfzWynma0oMuzXZvalmeWY2T/MrGWRcfeY2TozW21mF9ZGjCIiIiJSd9XWmdYXgItKDHsHSHLOJQNrgHsAzKw3cBXQJzDPHDOLraU4RURERKQOqpWk1Tn3f8CeEsPeds4dD7xdDHQM/H8ZMN85951z7itgHTCwNuIUERERqc+OHj3Kxo0box1GjWgQ7QACbgAWBP4/HS+JLbQlMExEREREAg4dOsTy5cvJzMwkMzOTrKwsli9fTseOHXnuueeiHV61M+dc7VRk1gV43TmXVGL4fwP9gdHOOWdmTwGfOOfmBsY/B7zhnPt7iDJvAW4BOO200/rNnz+/ZheiiLy8POLi4mqtvvpAbVKa2iQ0tUtpapPS1CalqU1COxnaJS8vj127dtG1a1cA7r77bpYsWUJBQQEALVq0oHv37sHXgAEDiI+Pr7X4hg0bttQ5178m64jqmVYzmwRcAqS777PnLUCnIpN1BL4ONb9z7k/AnwD69+/v0tLSai7YEjIyMqjN+uoDtUlpapPQ1C6lqU1KU5uUpjYJ7URrl507d5KVlcXKlSuZPn06ZsbVV1/Nxx9/HLz0f/nll3PhhRfi9/vx+/106tQJMwuWcaK1CUQxaTWzi4C7gR845w4VGfVvYJ6Z/QboAHQHPotCiCIiIiI1xjnHli1bil3ez8zMZOvWrcFprrnmGtq1a8e0adOYNGkSzjnMjHvuuSeKkUdHrSStZvZXIA1oa2ZbgAfwnhbQGHgn8M1gsXPuR865lWb2N+AL4Dhwm3MuvzbiFBEREakJBQUFrF+/ntNPP51mzZoxd+5cpk+fzu7duwGIiYmhZ8+epKWlBc+epqam0rKl90TQQYMGRTP8OqFWklbn3NUhBpfZQ9g59zDwcM1FJCIiIlIzjh8/zqpVq8jMzKR///706dOH9957j+HDh7Nw4ULS09Pp2rUro0aNol+/fvh8PpKTk2nevHm0Q6/T6srTA0RERETqncOHD7N8+fLgpf2srCxycnL47rvvAJg5cyZ9+vRhwIABPPfcc/Tu3RuAIUOGMGTIkGiGXu8oaRUREREJw4EDB8jLy6NDhw4cPHiQIUOG8MUXX5Cf7/VibNmyJT6fj9tvvx2/34/P56NHjx4AJCQkcMMNN0Qz/HpPSauIiIhICbt37yYrK4uDBw8yevRoAJKTkxk8eDDz5s0jLi6OpKQkRo0aFeyDmpiYWOwOfqleSlpFRETkpOWc4+uvvy52935mZiabN28GoEuXLsGk9bHHHqNdu3YAmBnz5s2LWtwnIyWtIiIiclJwzrF582Y6d+4MwH333cfTTz/Nrl27AC8RPeussxg6dGixO/gLXXnllVGJWzxKWkVEROSEc/z4cVavXk1mZiaXXXYZLVq04Fe/+hX33XcfBw4cIC4ujvbt23PJJZcE+5+mpKSc8L+sVZ8paRUREZF67ejRo8HL+oWX+ZctW8bhw4cBePfddzn//PO59NJLOfXUU4PzTZkyJVohSyUoaRUREZF649tvv6WgoID4+HiWLFnCzTffzIoVKzh+/DgALVq0wOfzMXny5OAZ1J49ewLejVTJycnRDF+qQEmriIiI1El79+4lKyuLFi1a0L9/f7Zv306HDh144oknuP3222nbti2nnXYavXv35vLLL8fv99O1a1diYmKiHbrUACWtIiIiEnXbtm0r9oD+zMxMcnNzARg/fjxz587ltNNO4+GHHw4+lL9Lly689dZbZGRkkJaWFr3gpVYoaRUREZFa45xj7969tG7dGoCrr76ajIwMtm/fHpyme/fuDBw4kMmTJwd/5hS8u/vvueeeqMQt0aekVURERGpEfn4+a9asYe3atYwaNQqAMWPGkJubS2ZmJgBxcXGMGDEi+IiplJQUWrRoEc2wpY5S0ioiIiJVdvToUVauXFnsEv+yZcv49ttviYmJ4cCBAzRv3pyJEyeyd+/e4HzPPPNMFKOW+kRJq4iIiETk0KFDNG7cmNjYWBYsWMBjjz3G8uXLOXbsGOCdPU1NTeXGG28MnkFt0qQJAD/84Q+jGbrUY0paRUREpEz79u0jOzub7t27c/rpp/PGG29w6aWXsmTJEnw+Hw0aNKBVq1ZMnz4dn8+H3++nW7duuoNfqp2SVhEREQFg586dxR7Qn5mZyYYNGwB46qmnmDJlCn379uXee++lVatWgNdHdcyYMdEMW04SSlpFREROMs45vvvuO5o0acLu3bu5/vrrycrKYuvWrcFpzjzzTPx+PzfddBM+n4+BAwcC0KlTJx566KFohS4nMSWtIiIiJ7CCggLWrVvH/v37GTBgAM45zjzzTEaOHMmTTz5Jy5Yt2bp1K8OGDQv+glRqaiotW7aMdugixShpFREROUEcO3aMVatWBS/tZ2Zmkp2dTV5eHn6/n6VLl2JmTJ48mR49egDQoEGD4OOnROoyJa0iIiL10LFjx2jYsCEAjz76KK+88grLly/nu+++A6BZs2akpqZy3XXX4fP56N+/f3Deu+++Oyoxi1SFklYREZE67sCBA2RnZ3P8+HEAHnnkER599FG++eYbYmNj2bNnDy1atGDq1Kn4fD58Ph89evQgNjY2ypGLVB8lrSIiInXI7t27i13ez8zMZN26dcD3D+IfOHAgU6dO5ciRIzRv3pxHH300miGL1AolrSIiIlHgnMM5R0xMDEuXLuWXv/wlmZmZbN68OThNly5d8Pv9wUv8hdLT00lPT49G2CJRo6RVRESkhjnn2LBhA7GxsXTp0oWvvvqKs88+m8cff5yrr74a5xyrV69m6NCh+Hw++vXrR2pqKq1bty5WTkZGRnQWQKQOUNIqIiJSjY4fP87q1auLPaA/Ozub/fv3M3XqVJ544gk6duzIqFGj6Ny5MwD9+/dn1apVUY5cpG5T0ioiIlJJzjnMDOcc06ZN47PPPmPZsmUcOXIEgKZNm5KcnMzVV19Nv379GDp0KAANGzbk2WefjWboIvWOklYREZEw5OXlsWHDBpKTkwGYMGECe/bs4Y033sDMyMrKokmTJtx6663Bh/SfddZZNGigQ61IddCWJCIiUsKePXvIysoKXt7Pyspi9erVNG/enP379xMTE8PAgQP59ttvg/N8+OGHUYxY5MSnpFVERE5qhZf4//3vf/PCCy+QmZnJxo0bg+M7deqE3+/nqquuwu/3U1BQQExMDHfccUcUoxY5+ShpFRGRk4JzjtzcXNq0aUOLFi14/fXXuemmm/j0009JTEwkNzeXlStXMmjQIKZMmRJ8SH/btm2jHbqIoKRVREROQPn5+axZs6bYQ/qzsrLYt28f8+fPZ9y4cXTu3JmLLrqI/Px8AKZOnaqzpyJ1mJJWERE5IezYsYNf/vKXZGVlkZ2dzaFDhwBo3LgxycnJjBs3Dr/fz6BBgwBITk7mhRdeCM5vZtEIW0TCpKRVRETqjUOHDvHNN9/QqVMn8vPzOeecc7jsssv47//+b5o0acJf/vIXUlJSuOmmm4J38Pfq1YuGDRtGO3QRqSIlrSIiUifl5eWRkZFR7PL+l19+SXp6Om+//TaxsbGkpKTQsWNHABISEti7dy8xMTFRjlxEaoKSVhERqTOeeeYZ3n77bTIzM9mwYUNw+Omnn47f7+eKK65g8ODBxaYvSgmryIlLSauIiNQa5xybN2+mU6dOmBn/8z//w/z58/n8888BWLhwIVlZWfj9foYNG8bYsWPx+XyceuqpUY5cRKJNSauIiNSIgoIC1q1bV+zyfmZmJnv27GHTpk106tSJU089lZ49e3L06FEaNWrEyy+/HPwFqYyMDNLS0qK7ECJSZyhpFRGRapOTk8Ozzz4bvIM/Ly8PgEaNGtG3b19Gjx6N3++nefPmAEycOJGJEycG59dPnopIWbR3EBGRiBw+fJijR4+SkJDAmjVrGD9+PLNmzSI9PZ1t27bx5z//mZSUFCZNmkS/fv3w+/306tWLRo0aRTt0EanHlLSKiEiZDhw4QHZ2drHL+6tWreLee+/lwQcf5NRTT6VFixbBZ5ymp6ezf/9+YmNjoxy5iJxolLSKiEhQfn4+s2fPDvZDXbduXXBcu3bt6NevH5dffjkXXnghAC1btuTdd98NTqPL+yJSU7R3ERE5yTjn2LdvH61atQJgypQpxMbG8vvf/z74t2HDhvj9fq677rrgQ/rbtWsX5chF5GSmpFVE5ARWUFDAhg0bgpf2C/82adKETZs2AdCkSZNil/PXrFlDs2bNohWyiEhISlpFRE4w77//Pv/+97/JysoiKyuLAwcOAN6l+6SkJC699FJ8Ph8FBQXExMTwm9/8ptj8SlhFpC5S0ioiUg999913xMTE0LBhQ958800eeugh3n77bVq0aMH777/P008/TUpKCuPHjw9e3k9KSqJx48bRDl1EpFJqJWk1sz8DlwA7nXNJgWGtgQVAFyAXuNI5t9e8W1AfB0YCh4DrnHOZtRGniEhdlJeXx7Jly4pd3l+5ciVvvPEGw4cPp1GjRjRt2pRvvvmGFi1acPfdd3P//ffrpigROaHU1h7tBeBJ4KUiw2YA7zrnZpnZjMD7u4GLge6B19nAHwJ/RUROCjt37uTFF18MJqlr1qzBOQfAKaecgt/vZ+TIkXTq1AnwHjOVnp4enL/wwf0iIieSWklanXP/Z2ZdSgy+DEgL/P8ikIGXtF4GvOS8PfRiM2tpZu2dc9tqI1YRkdpy5MgRmjRpwpEjR7jvvvu46aabmDRpEocPH+ZnP/sZnTt3xufzcc011+Dz+fD7/XTo0CH4TFQRkZOJFX57r/GKvKT19SLdA/Y551oWGb/XOdfKzF4HZjnnPgoMfxe42zm3JESZtwC3AJx22mn95s+fX/MLEpCXl0dcXFyt1VcfqE1KU5uEdrK1i3OO7du3s2bNGtatW8fatWtZs2YNqamp3H///YD32KkRI0Zw+eWX45zjwIEDJCQkRDny6DrZ1pNwqE1CU7uUVtttMmzYsKXOuf41WUdd7PAU6hRCyMzaOfcn4E8A/fv3d2lpaTUYVnEZGRnUZn31gdqkNLVJaCdDu/zjH/9g0aJFwUv8+/btAyA2NpY+ffowatQohg8fHmyHOXPmnPBtEqmTYT2JlNokNLVLaSdim0Qzad1ReNnfzNoDOwPDtwCdikzXEfi61qMTEanAsWPHaNiwIQB//OMf+fjjj3npJa/r/pNPPsmiRYtITk7myiuvxO/34/f76du3L02aNELcoOAAACAASURBVIlm2CIi9VI0k9Z/A5OAWYG//yoy/HYzm493A9Z+9WcVkWj79ttvycnJKXYH/6pVq9i5cyfx8fHs3buXrVu3Bp99+te//pVWrVoFk1oREama2nrk1V/xbrpqa2ZbgAfwktW/mdmNwCZgbGDyN/Aed7UO75FX19dGjCIiRa1du5bXXnstmKR++eWXFBQUANCmTRv8fj/Tpk3j6NGjANxzzz3cc889wflPPfXUqMQtInKiqq2nB1xdxqj0kgMCTw24rWYjEhHxFJ4ZXb16Nffeey/33XcfycnJfP755/zkJz/h9NNPx+/3M3bs2OAd/B07dtQd/CIitawu3oglIlLtnHNs2rSp2OX9rKwsZsyYwdSpU2nUqBGZmZns2LEDgFGjRrFjxw6dMRURqSOUtIrICeno0aO8+uqrxZLUPXv2ABATE0OvXr1IT0/nrLPOAqBr166sX78+OH9cXJweoSMiUocoaRWRes05F7xUf++999K6dWt+/OMfExsby4033sjx48fp27cvY8aMCV7e79u3L82aNYty5CIiEgklrSJSbxw+fJicnJzgmdPMzEwaNGjA4sWLAVi2bBkdOnQAvOehZmdnk5iYSKNGjaIZtoiIVAMlrSJSZ2VlZfHBBx8EL/GvWrWK/Px8AFq3bo3P5+Pss88OTv/aa68Vm7979+61Gq+IiNQcJa0iUme89957PPvss7z00ks0aNCA5557jqeeeor27dvj9/u5/PLL8fl89OvXj86dO+sOfhGRk4iSVhGpNc45tm7dyqJFi4JnUDMzM/nnP/9Jv379+Prrr1m8eDHbt2+nY8eOzJgxg3vvvZd27dpFO3QREYkyJa0iUmP27t3LO++8U+wRU7t27QLAzOjZsyfnnXdesM/p+PHjmTBhQnD+jh07RiVuERGpe5S0iki1OXjwIPfffz8jR45k+PDhfPXVV4wbN46GDRvSp08fLr30Unw+HwDXXXddqUdK6XK/iIiURUmriETkyJEjrFixotgd/Oeeey6zZ8+madOm/OUvfyExMZHhw4eTlJTE0qVL6dOnD40bNw6WkZGRoWegiohIRJS0iki5Fi9ezOeffx5MUleuXMnx48cBSEhIIDU1lcTERAAaNGjAzp07iYmJAaBRo0b4/f6oxS4iIicOJa0iUsy8efNYt24d999/PwC33nor2dnZnHLKKfj9fkaOHInf78fv99O1a9dSl/QLE1YREZHqpKRV5CTjnGPbtm3FLu9/+eWXrFixgtjYWD788EM+/vjjYNL6wgsv0LZtWzp06KA+pyIiEjVKWkVOcNu3b2fRokXBu/czMzPZsWNHcHz37t3p168fBw4coFWrVvz+97+nQYPvdw0pKSnRCFtERKQYJa0iJ5i1a9fyxz/+kenTp9OxY0deeeUVpk6dSmxsLH369OHiiy/G5/Ph9/tJSUkhPj6+2PxFE1YREZG6QkcnkXrou+++Y+XKlcWef/rjH/+YK664gv379zNnzhwuvvhiOnbsyJgxYxg0aBBJSUk0adIk2qGLiIhUipJWkTru2LFjLFmypNjl/RUrVnDs2DEA4uPj8fl8NGzYEACfz8fBgweDZ0zbt29P+/btoxa/iIhIdVDSKlIHPfHEE7Rv356xY8dy5MgRhgwZgnOOtm3b4vf7+fGPf4zf78fn83HmmWcWu2M/NjY2ipGLiIjUDCWtIlGyY8eO4N37WVlZNGvWjJdeegmAZ599luTkZMaOHUt8fDz/+c9/6NmzJx07dtQd/CIiclJS0ipSC7Zs2RK8xF/42rZtW3B8t27dGDZsWPD9J598QvPmzYPvhw8fXqvxioiI1DVKWkVqwOLFi3nrrbdIS0sD4K677mL+/PnExMTQu3dvLrjgAnw+H/369SMlJYWEhIRi8xdNWEVERERJq0ilHTt2jC+++KLYDVIvv/wyiYmJfPrppzzyyCP4fD4A7r77bu6880769u1Ls2bNohy5iIhI/aOkVSQMR44cYdmyZcUS1OXLl3P06FHAOzOamprKvn37SExM5Oabb2bKlCksWrQIgNTU1GiGLyIiUu8paRUJYf/+/Tz//POkpaWRmprKRx99FOxX2rp1a3w+H9OmTQs+pL9bt27F7trX2VQREZHqpaRVTmq7du0q9oD+oUOHMnXqVMyM6dOnM3v2bFJTUxk4cCCvvvoqfr+fzp076w5+ERGRWqakVU4aW7ZsKXZ5PzMzky1btgTHd+nSJXgZv0WLFuzcuZNTTjkl+P6HP/xhVOIWERERJa1yAnvrrbfYvXs3EyZMAGDw4MFs3rwZM6Nnz56cd955+P1+/H4/qamptGrVqtj8hQmriIiIRJ+SVqnXjh8/zpdffhk8g7px40ZeffVVAJ5//nmysrKCSesf/vAHWrVqRXJyMnFxcdEMW0RERCKkpFXqjSNHjrBixYpil/dzcnI4cuQIAE2bNiU1NZVDhw7RrFkznnzySVq0aBGc/7/+67+iFbqIiIhUkZJWqbM2bdrEv/71L6699loSEhJ49NFHefDBBwFISEjA5/MxZcoU/H4/Pp+Ps846q9gd/Lq8LyIicuJQ0ipR980335CVlRU8g3rbbbcxdOhQVq1axR133EFycjI/+MEPGDt2LH369MHn83HGGWfoDn4REZGTiJJWqTXOObZt21bqDv5NmzYFp+ncuTOjR48G4LzzzmPr1q20b98egN69e9O7d++oxC4iIiLRpaRVaoxzjr///e+0a9eOoUOHsnXrVjp16gSAmdG9e3cGDx7MbbfdFrzE36ZNm+D8TZs2pWnTptEKX0REROoQJa1SZfn5+axevZp33nmH1157jYSEBO6//37MjDvvvJO0tDSGDh3K6aefzpw5c+jbty8pKSnEx8dHO3QRERGpJ5S0SkS+++47Vq5cWeoO/kOHDgHQpEkTLrnkkuD0GRkZdOzYEfDOrt56661RiVtERETqNyWtUq4vvviCzMzM4LNOL7/8ct566y0A4uPj8fl83Hzzzfj9fvLz85k4cSINGny/WnXr1i0qcYuIiMiJRUmrALB3716ys7ODN0k99dRTJCQksGDBAmbOnMno0aNp1qwZ06ZN4/rrr8fv93PGGWcQExMTLCMjI6NYwioiIiJSXZRhnIR27NgRvLRfmKR+9dVXwfEdO3Zk8+bNJCQkcOutt3LzzTcHb4i66KKLohW2iIiInMSUtJ7g8vLyeOedd/D7/SQmJvLqq68yZsyY4Phu3boxYMAAJk+ejM/nw+fzFXsof7t27aIRtoiIiEgxSlpPEAUFBaxduzZ45nTQoEGMHj2ab775htGjRzNnzhxuvfVWzj77bH7729/i9/tJSUkhISEh2qGLiIiIVEhJaz107Ngxvvjii2J38GdnZ/Ptt98C0KhRIxo3bszo0aPp3Lkzn376KX379gXg9NNP584774xm+CIiIiIRU9JaD2RmZnLgwAHS0tJwztGhQwd2794NQPPmzfH5fNxwww3BB/T37t2bhg0bAt5jpgYOHBjN8EVERESqTElrHbJ//36ys7PJyspi+/btzJo1C4Cf/exn7Nu3jyVLlmBmPPTQQ7Ru3Rqfz0f37t2L3cEvIiIiciJS0holu3btKnZ5Pysri3Xr1gXHd+7cmZkzZ9KgQQMef/xx4uLiguOmTJkSjZBFREREokZJay3YvXs3ixYtYuTIkTRs2JB7772Xhx9+ODj+jDPOwOfzcf311wfv4C96136fPn2iEbaIiIhInRH1pNXMpgM3AQ5YDlwPtAfmA62BTGCic+5o1IIMU0FBAevXrw+eQZ00aRK9evXinXfe4ZprrmHZsmUkJydz4YUXBi/vp6am0qpVq2iHLiIiIlKnhZ20mlkv4AqgnXPuNjPrCTRyzuVUtnIzOx24A+jtnDtsZn8DrgJGAr91zs03sz8CNwJ/qGw9NSE/P5/ly5cHE9SsrCyysrI4ePAgAA0bNsTv99OrVy9GjBjBxx9/TI8ePQA499xzOffcc6MZvoiIiEi9ElbSamZjgaeAV4FrgNuAOGAWcEE1xNDUzI4BzYBtwPmBegBeBB6kDiWta9euZeTIkRw96p38bdq0KampqUycOBG/34/f76dPnz40atQIgDZt2nDOOedEM2QRERGRes2ccxVPZLYKuNo5l21me51zrcysIfC1c+6UiuavoOxpwMPAYeBtYBqw2DnXLTC+E/Cmcy4pxLy3ALcAnHbaaf3mz59flVDCdvz4cZ566il69+5Njx496NixI7GxsbVSd12Wl5dX7IYxUZuURe1SmtqkNLVJaWqT0NQupdV2mwwbNmypc65/TdYRbveAU4Flgf9dkb8VZ7zlMLNWwGVAV2Af8L/AxSEmDVmPc+5PwJ8A+vfv79LS0qoSTkQaNGhAbdZXH2RkZKhNSlCbhKZ2KU1tUprapDS1SWhql9JOxDYJ9wGfS4GJJYZdBXxWxfovAL5yzu1yzh3D634wGGhpZoUJdUfg6yrWIyIiIiL1WLhnWu8A3jazG4HmZvYfoAcwoor1bwIGmVkzvO4B6cAS4H28m77mA5OAf1WxHhERERGpx8JKWp1zXwaeFnAJ8DqwGXjdOZdXlcqdc5+a2St4j7U6DmThXe7/f8B8M5sZGPZcVeoRERERkfot3KcHnA4ccs79rciwVmbWwTlXpUv3zrkHgAdKDN4ADKxKuSIiIiJy4gi3T+s/8fqWFtUR+Ef1hiMiIiIiUlq4fVp7OOeWFx3gnFse6DIgIiJy0jp27BhbtmzhyJEjNV5XQkICq1atqvF66hu1S2k11SZNmjShY8eONGzYsNrLrki4SesuM+vmnFtXOMDMugHf1ExYIiIi9cOWLVuIj4+nS5cumFmN1nXw4EHi4+NrtI76SO1SWk20iXOOb775hi1bttC1a9dqLTsc4XYP+DPwdzO7xMx6m9mlwCvAszUXmoiISN135MgR2rRpU+MJq0i0mRlt2rSplasKoYR7pnUWcAyYDXTCe3rAs8BvaiguERGRekMJq5wsormuh/vIqwLg14GXiIiIiEitKjNpNbPznHP/F/j//LKmc869VxOBiYiIiIgUKu9M6xwgKfB/WQ/3d8AZ1RqRiIiIiEgJZd6I5ZxLKvK2m3Oua4iXElYREZEILF68mKuvvpp+/fpx9dVXs3jx4mop9+GHH6ZPnz4kJyeTmprKp59+yr59+5gzZ0658w0ePBiA3NxckpKSyp22PE8//TQ/+tGPAO8xYBMnTmTSpEkcO3as0mWKFFXh0wPMLBbIM7PGtRCPiIjICeuBBx4gPT2dBQsWkJmZyd/+9jfS09N54IGSPwwZmU8++YTXX3+dzMxMcnJyWLhwIZ06dSo3aXXOUVBQwMcff1ylugvl5OSQnJzMgQMHuPjii+ncuTMvvvhiVJ7nKSemCpNW51w+sAZoU/PhiIiInJgWL17M7NmzOXToEM45AAoKCjh06BCzZ8+u0hnXbdu20bZtWxo39s4vtW3blg4dOjBjxgzWr19Pamoqd911F7m5ufTq1YspU6bg9/vZvHkzcXFxpcrbsGEDPp+Pzz//nLlz5zJw4EBSU1OZPHky+fn5IWNYvnw5rVu3Ji0tjbFjx/Lwww9XenlEQgn3Oa0vA6+b2SQzSzez8wtfNRmciIjIieLxxx/n8OHDIccdOXKExx9/vNJljxgxgs2bN9OjRw+mTJnCBx98AMCsWbM488wzyc7O5te/9h4AtHr1aq699lqysrJITEwsVdbq1asZM2YMzz//PHFxcSxYsIBFixaRnZ1NbGwsL7/8csgYli9fztSpU3nooYeYPHlypZdFpCzhPqf11sDfB0sM141YIiIiYVizZk3wDGtJBQUFrF27ttJlx8XFsXTpUj788EPef/99xo0bx6xZs0hLSys1bWJiIoMGDQpZzq5du7jsssv4+9//Tp8+fXjyySdZunQpAwYMAODw4cOceuqppeYrPGPbvXt3tm3bVunlEClPuM9prf3f6hIRETmB9OjRg+zsbAoKCkqNi4mJoUePHlUqPzY2lrS0NNLS0ujbty8vvvhiyKS1efPmZZaRkJBAp06dWLRoEX369ME5x6RJk3jkkUfKrTsnJ4eUlBSeeeYZBg0axIABA/D5fFVaHpGSyu0eYJ5bzOwJM7uulmISERE54UybNo0mTZqEHNekSRPuuOOOSpe9evXqYmdqs7OzSUxMJD4+noMHD4ZdTqNGjfjnP//JSy+9xLx580hPT+eVV15h586dAOzZs4eNGzeWmm/58uX07duX9u3b8+yzzzJu3Dj2799f6eURCaWiPq2zgYeAdsAjZvZQzYckIiJy4hk0aBA//elPadasGTEx3uE3JiaGZs2a8dOf/rTMS/bhyMvLY9KkSfTu3Zvk5GS++OILHnzwQdq0acOQIUNISkrirrvuCqus5s2b8/rrr/Pb3/6WtWvXMnPmTEaMGEFycjLDhw8Pefm/MGkFGD58OFdeeSU33HBDpZdHJJSKugdcCfzAObfGzHoD/wKq9lwOERGRk9RDDz3ExRdfzOOPP87atWvp3r0706ZNq1LCCtCvX78yH101b968Yu9XrFhR7H1eXh4AXbp0CY5r2bIln3/+eXCacePGlVt/yZuzZs6cGV7gIhGoKGlNcM6tAXDOfWFmrWshJhERkRPWoEGDqpykipyMKkpazcy6AhZ4H1viPc65DTUVnIiIiIgIVJy0NgfWUSRJBdYX+d8BsdUdlIiIiIhIUeUmrc65cH98QERERESkxigpFREREZE6T0mriIiIiNR5SlpFREREpM5T0ioiIiIidV6lklYzG2Zm51V3MCIiIhK53NxckpKSig178MEHmT17dq3GERcXF/x/8ODBEc1bOP2+ffuYM2dO2PNt27aN6667jv79+9OjRw+GDRsWUb1Sf4SVtJrZB2Y2JPD/3cB84K9m9vOaDE5ERETqJuccBQUFZY4v6xe6Kpo+0qR14sSJXHrppSxZsoQ1a9bwxBNPRFSv1B/hnmlNAhYH/r8ZSAMGAT+qgZhERESkmuTm5tKzZ08mTZpEcnIyV1xxBYcOHSpzeKG5c+cycOBAUlNTmTx5Mvn5+eTm5tKrVy+mTJmC3+9n8+bNZdZbeNa1sJ6bbrqJpKQkxo8fz8KFCxkyZAjdu3fns88+Kzb9jBkzWL9+Pampqdx1113lLlt+fj4ZGRkMHTo0OKxv376Vbiup28JNWmMAZ2ZnAuacW+Wc2wy0qrnQRERE6p+0tLQKX0Uv26elpfHCCy8AsHv37lLTVofVq1dzyy23kJOTQ4sWLYJnMssavmrVKhYsWMCiRYvIzs4mNjaWl19+OTjPtddeS1ZWFomJiWHVv27dOqZNm0ZOTg5ffvkl8+bN46OPPmL27Nn86le/KjbtrFmzOPPMM8nOzubXv/41ACNHjuTrr78uVW5sbCwXXHABgwcPZvLkySxatCg4bu/evZE3lNRp4SatHwFPArOBfwAEEtjdNRSXiIiIhMnMyh3eqVMnhgwZAsCECRP46KOPyh3+7rvvsnTpUgYMGEBqairvvvsuGzZ4v9qemJjIoEGDIoqva9eu9O3bl5iYGPr06UN6ejpmRt++fcnNza1w/jfeeIMOHTqEHPfmm28yd+5cEhISuOiii/jnP/8JwPTp0yOKUeq+in7GtdB1wE+AXcCvA8N6Ao/XQEwiIiL1VkZGRqWnb9u2bcTzA7Rp06bUmcU9e/bQtWtXoHRSW/i+rOHOOSZNmsQjjzxSbHxubi7NmzePOL7GjRsH/4+JiQm+j4mJ4fjx4xGXV5SZcc455zBixAj27t1LTk4OTZo04csvv2T27Nn89Kc/rVL5UneEdabVOfeNc+7nzrkHnHN5gWH/zzn3u5oNT0RERCoSFxdH+/bteffddwEvYX3rrbeCfT03bdrEJ598AsBf//rXCoenp6fzyiuvsHPnzmB5GzdurJVliY+P5+DBg2FN+5///IejR48CsHPnTj766COGDx9O27ZtmTBhghLWE0y4Tw9obGYPm9kGM9sfGDbCzG6v2fBEREQkHC+99BIzZ84kNTWV888/nwceeIAzzzwTgF69evHiiy+SnJzMnj17uPXWW8sd3rt3b2bOnMmIESNITk5m+PDhbNu2rVaWo02bNgwZMoSkpKTgjVhl9Wl95ZVX6NWrF4MHD+aSSy7hl7/8Jeeccw45OTmkpKTUSrxSe8LtHvBb4HRgPPBmYNjKwPAnayAuERERiUDv3r15//33Q46LiYnhj3/8Y9jDAcaNG8e4ceNKDV+xYkWZMeTl5ZX6v0uXLsXmKbzprOS4ovPOmzevWLlvvPFGyPqeeeYZAA4ePEh8fHxweNu2bXn22Wdp27YtvXr1KjNeqV/CTVp/CHRzzn1rZgUAzrmtZnZ6zYUmIiIiErlRo0YxatSoaIch1SzcpwccpUSCa2anAN9Ue0QiIiJSbUqe6axouEhdFW7S+r/Ai2bWFcDM2uN1C5hfU4GJiIiIiBQKN2n9OZALLAdaAmuBr4GHaiYsEREREZHvhdWn1Tl3FLgTuDPQLWC3c87VaGQiIiIiIgFlJq1mdkY588UXeQDxhuoOSkRERESkqPLOtK4DHBD6t+E8Doit1ohEREREREooM2l1zoXb31VEREREpEZFlJiaWWczO8fMOtVUQCIiIhKZ2NhYUlNTSUpKYuzYsRw6dCjkdCNHjmTfvn21HJ1I9Qj3Z1zbm9kHeF0GXgXWm9n/mVmHGo1OREREKtS0aVOys7NZsWIFjRo1KvUrV845CgoKeOONN2jZsmWUohSpmnDPtP4BWAa0cs61B1oBWUDo336LgJm1NLNXzOxLM1sVOJPb2szeMbO1gb+tqlqPiIjIyeDcc89l3bp15Obm0qtXL6ZMmYLf72fz5s106dKF3bt3A/DSSy+RnJxMSkoKEydOBGDXrl2MGTOGAQMGMGDAABYtWhTNRREpJtyfcR0KtHfOHQMI/Jzrz4Ct1RDD48BbzrkrzKwR0AzvubDvOudmmdkMYAZwdzXUJSIiUmPuvBOys6u3zNRU+N3vwpv2+PHjvPnmm1x00UUArF69mueff545c+YUm27lypU8/PDDLFq0iLZt27Jnzx4Apk2bxvTp0xk6dCibNm3iwgsvZNWqVdW6PCKVFW7SuhfojXe2tdBZQJU6xphZC+A84DoIPg/2qJldBqQFJnsRyEBJq4iISEiHDx8mNTUV8M603njjjXz99dckJiYyaNCgUtO/9957XHHFFbRt2xaA1q1bA7Bw4UK++OKL4HQHDhzg4MGDxMfH18JSiJQv3KT1MWChmT0HbAQSgeuB+6pY/xnALuB5M0sBlgLTgNOcc9sAnHPbzOzUKtYjIiJS48I9I1rdCvu0ltS8efOQ0zvnKHzeelEFBQV88sknNG3atNpjFKkqC/eHrczsfOAaoAPeT7jOc869V6XKzfoDi4EhzrlPzexx4AAw1TnXssh0e51zpfq1mtktwC0Ap512Wr/58+dXJZyI5OXlERcXV2v11Qdqk9LUJqGpXUpTm5RWX9okISGBbt261Upd+fn5xMaWfjx6+/bt2bZtW7FhGzdu5Morr+TTTz8NDktKSuKDDz5g586dXHPNNSxcuJA2bdqwZ88eWrduzQ033EBKSgrTpk0DICcnh+Tk5JpdqGpQVruczGqyTdatW8f+/fuLDRs2bNhS51z/GqkwINwzrQQS1ColqSFsAbY45wq3qFfw+q/uMLP2gbOs7YGdZcT0J+BPAP3793dpaWnVHF7ZMjIyqM366gO1SWlqk9DULqWpTUqrL22yatWqWrt8Xt6l+pLD4+LiiImJKTbczIiLi6NLly7cd999XHLJJcTGxuLz+XjhhRf4wx/+wG233caQIUM4fvw45513XqknEdRF6sJQWk22SZMmTfD5fDVSdnnCSloDN0hdB6QCxb72OueurWzlzrntZrbZzM5yzq0G0oEvAq9JwKzA339Vtg4REZETXV5eXqlhXbp0YcWKFcWG5ebmBv+fNGkSkyZNKja+bdu2LFiwoEZiFKmqcM+0vgikAK8BO6o5hqnAy4HEeANeX9kY4G9mdiOwCRhbzXWKiIiISD0SbtJ6EdDVOVftP6PhnMsGQvWBSK/uukRERESkfgr3xwU2AY1rMhARERERkbKUeaY18LSAQi8B/wrc3V+se0BVnyAgIiIiIlKR8roHPBdi2K9KvHd4z1oVEREREakxZSatzrmutRmIiIiIiEhZwurTamapZtapxLBOgV+xEhERERGpUeHeiDUXaFhiWCPgL9UbjoiIiIhUVri/dFofhZu0dnbObSg6wDm3HuhS7RGJiIiISNi2b9/O4MGDmTVrFosXL2bChAkkJyczYcIENm3aFO3wqk24SesWM/MXHRB4/3X1hyQiIiKRiouLq3iiWoxh8ODBEc1bOP2+ffuYM2dOtcYVqcOHD/ODH/yA/Px8ALZt28ZVV11F//796dGjB8OGDauWep5++ml+9KMfAXDs2DEmTpzIpEmTOHbsWMjply9fziWXXFLstXPnTrKysrjqqquYMWMG55xzDr/4xS8YO3Ysc+fOpXPnziHLeuuttzjrrLPo1q0bs2bNCg4/evQo5513HsePH6+WZaxO4f64wG/xHnn1GLAeOBP4KfBwTQUmIiIidZdzDuccMTGhz399/PHHEZVXOH1h0jplypQqx1hZf/7znxk9ejSxsbEATJw4kZtvvplx48YBXvJYHXJyckhOTubAgQOMHj2as88+m4cfLju16tu3L6+//nqp4dnZ2Vx++eXB98uXLycpKanMcvLz87ntttt455136NixIwMGDGDUqFH07t2bRo0akZ6ezoIFCxg/fnzVFrCahXWm1Tn3DPBj4L+AXwf+/sQ596cajE1ERESq4De/+Q1JSUkkJSXxu9/9DoDHHnuMJ554AoDp06dz/vneY9nfffddJkyYAMDcuXMZOHAggexRDAAAIABJREFUqampTJ48OXjGMTc3l169ejFlyhT8fj+bN28us+64uDhyc3Pp2bMnN910E0lJSYwfP56FCxcyZMgQunfvzmeffVZseoAZM2awfv16UlNTueuuu8pdvrFjx3L77bczYsQIEhMT+eijj7j22mvp0aMHN954Y3C6V155hUGDBpGSksLQoUPZtWsXAMOGDeOdd94B4N57/397dx4fZX3u//91ZbIvQCAQWQVkC1sWdhWEIi64sFiXHq1a8adt7XE5x56eLlrt3u9RTw/HWlutuxatytK6UBdi4SggBEgA2QQkQNiXhGyT5fP7454MCRMgLMkM8H4+HvdjZu7PvVz3xU1y5b4/n3t+wr333gvAq6++yqRJkwCvwMvNzeWSSy4Jbm/QoEHHjKupCgoKaNu2LWPHjuX6668PFqybNm1i0qRJDB06lOHDh7N27dpjbmf9+vX07ds3+Dk1NZWZM2dSW1vb6PKLFy+mV69e9OzZk9jYWG666SZmz54dbJ88eTKvvvrqaTjC06zuL6UzfRoyZIhrSfPmzWvR/Z0JlJNQyknjlJdQykmoMyUnq1evbvD5kksuCZl+//vfO+ecKy0tbbT9+eefd845t3v37pC2+oqLi48aR1JSUoPPS5YscQMHDnSHDh1yJSUlrn///i4vL8999tln7utf/7pzzrmLL77YDRs2zPn9fvfII4+4p59+2q1evdpdffXVzu/3O+ec+853vuNefPFF55xzmzZtcmbmPvvss+PGkJSU5DZt2uR8Pp/Lz893NTU1Licnx33rW99ytbW1btasWW7SpEkh627atMkNGDCgwXavvPJKt23btpD99e3b1z3++OOuuLjYPfTQQ65Pnz5u+/btrrKy0rVp08ZVVFQ455zbs2dPcJ1HHnnEPfnkk8455z755BN3ySWXuFdeecVNnDjRVVdXu8rKSpeent5gP5dffrnr0KGDu+uuu9yCBQuC8/ft29doHpqqTZs2Li0tzc2ZMyc4z+/3u6997Wtuw4YNzjnn3nnnHXf77bef8LaPda789a9/ddOmTQt+fumll9w999wT/FxdXe3S0tKOuv6R57xzzgFLXDPXese80mpmQ8xsYL3P7c3sVTNbYWZPm1n4O9CIiIhIiAULFjBlyhSSkpJITk5m6tSpzJ8/nyFDhrB06VJKSkqIi4tj1KhRLFmyhPnz5zN69Gg++ugjli5dyrBhw8jKyuKjjz5i48bDY7HPP/98Ro4c2eQ4evTowaBBg4iKimLAgAGMHz8eM2PQoEFs3ry5Sdt499136dSpU4N5FRUVHDhwgPvvvx+AhIQEpk2bRseOHYmNjSUxMZHY2FgAXnjhBYYPH05mZiZPPfUU8fHxAIwZMwbnHE888QQzZszA5/OxZ88e2rRp02Bf7733Hm+99RatW7fmiiuuYNasWYB3pfpIl156afDqdv2p/pVMgMLCQpKTkxk0aBBFRUXB+bNmzWLVqlVcd911ZGVl8R//8R/BeE8X18gTBsws+N7n8xEbG0tJSclp3e+pOl6f1t8BjwIrA5+fBToBfwK+Afw/IHydTkRERCJMbm7uUdsSExOP2Z6WlnbM9hPRWGECEBMTQ/fu3Xn++ee58MILGTx4MPPmzePLL78kIyODjz76iNtuu41f//rXja6flJR0QnHExcUF30dFRQU/R0VFndJgn1WrVpGTkxPsU7tixQq+853vALB161Y6deqEmfHSSy+xePFiPv74Y5KTkxkzZgwDBgwAvNvzRUVFpKWlkZKSAnjFb0VFRYN9mRkXX3wxF198Mfv37yc/P5/4+HjWrFnDY489xoMPPhhc9sMPP2xS/Pn5+WRmZvLMM88wcuRIhg0bRnZ2NitWrOCXv/xlg+4Np1uXLl0adO2oy1d9lZWVp71YPlXH69OaAcwHMLM2wJXAzc653+MVrdc0b3giIiJyMsaMGcOsWbMoKyujtLSUmTNnMnr06GDbY489xpgxYxg9ejRPP/00WVlZmBnjx4/nzTffZNeuXQDs27ePr776qsXiTklJadIVvoKCAjIzD3/HUd2gJvAK2Lr3BQUFXHjhhSQnJ/PWW2/x6aefBq9u3nzzzcyePZukpCTmzp0LeP1Ba2pqgoXr3Llz8fv9AOzatYsFCxYwYcIE0tLSuOWWWxoUrCeioKCAQYMG0bFjR5599lluvPFGDh48SMeOHZk7d26wP2pBQcEpP3t1/PjxbNu2Lfh52LBhrF+/nk2bNuH3+5kxYwbXXnttsH3v3r20b9+emJgjH9EfXscrWqMBf+D9SGCHc24dgHOuEGhztBVFRESk5ZSVldGlS5fglJuby+23387w4cMZMWIEd955J9nZ2QCMHj2aoqIiRo0aRXp6OvHx8cGCtn///vziF7/gsssuY/DgwUyYMKHB7evm1q5dOy666CIGDhwYHIg1ceJEtm9v+JTNgoICsrKyAK+rQHl5OampqUDDAva2225j+vTpjB49mnXr1tGzZ0/MjKlTp/L444+TkZHBQw89xCOPPBLc9mWXXcaCBQsAbxBXRkYGmZmZXH311fz85z9n1KhRwSulJ6uuaAWYMGECN9xwA3fccQd33HEHtbW1ZGRkkJWVxW9/+9sGt+5PVG1tLRs2bKBt27bBedHR0Tz55JNcfvnlZGRkcMMNNwSvPgPMmzePiRMnnvQ+m82xOrwC/wfcEHj/AvBcvbbOwNbm7nTb1EkDscJPOQmlnDROeQmlnIQ6U3LS2KCU5nKswTXnstOdl7y8PHfLLbccc5nZs2e7W2+9tUX//U9EXU4KCgrcAw88cELrTpkyxa1Zs+ao7eEaiHW8Pq0/AP5mZk8DNcDF9dpuDBS1IiIiImeN7Oxsxo0bR01NTfBZrUe69tprG9xSj1QDBw7kiSeeaPLyfr+fyZMnN3iEVqQ4ZtHqnFtgZt2APsA651z9TibvADOaMzgRERGRcLjjjjvCHUJYxMbGcuutt4Y7jEYd98sFnHMlzrmldQWrmX0jMH+tc05f4yoiIiIiza5J34h1hD+e9ihERERERI7hZIrWkx/CJiIiIiJyEk6maJ1/2qMQERERETmGJhWtZhb8mgTn3MR687ObIygRERERkfqaeqX1H2bWtv4MMxsOvHv6QxIRERERaaipReuf8ArXZAAzuxCYAzTfF+OKiIiIiAQc78sFAHDOTTez1sC7ZvZrvG/Hutk592FzBiciIiIiAicwEMs593Pgc+B14HoVrCIiIpHD5/ORlZXFwIEDuf766ykrKzut258+fToZGRncfPPNDd6HQ3l5OZdccgk1NTUA7Nixg5tuuomhQ4fSp08fxo0bd9Lb/uMf/8i3v/1tAKqqqvjmN7/JbbfdRlVVVciy77//Pn379qVXr1785je/Cc73+/2MGTOG6urqk45DQh21aDWzQjPbUn/C++rWKOCVevNEREQkzBISEli+fDkrV64kNjaWp59++rRu/6mnnuLdd9/l1VdfbfA+HJ577jmmTp0a/IrVu+66iylTprBkyRLWrVvH9OnTT3rb+fn5DB48mOLiYq688kq6devGiy++SExMTIPlampquOeee3jvvfdYvXo1f/nLX1i9ejXgfavU+PHjef3110/+ICXEsboH3NJiUYiIiJwN7r8fli8/vdvMyoLf/e6EVhk9ejT5+fkATJ48mcLCQioqKrjvvvu46667eOihh0hLS+O+++4D4Mc//jHp6ence++9PPHEEzz33HMA3Hnnndx///18+9vfZuPGjVx77bWsW7cO5xzXXnstd9xxBw888MBR47j++utJT09n+fLlFBYW8uqrr/KnP/2JhQsXMnr0aP785z8D8Oabb/LYY49RXl5OSkoKM2fOpH379owbN44f/ehHTJgwgZ/85CcUFxczffp0Xn31VV577TXAKx7nz5/PX/7yl+B+Bw0adEL5qq+goIDRo0czduxY7r77bu6+++5Gl1u8eDG9evWiZ8+eANx0003Mnj2b/v37B/P+wx/+MGxXo89GRy1anXOftGQgIiIicuqqq6t57733uOKKKwDvqmTbtm0pLy9n2LBhXHfddUybNo2pU6dy3333UVtby4wZM1i8eDFLly7l+eefZ9GiRTjnGDFiBJdccglPP/0077//PvPmzSMtLY3u3bsH3wNMnDiRZ599lk6dOjWIpaCggFGjRvHkk0/y8MMPM23aNHJzc2nXrh3p6ek89dRTxMXFMW7cOL7+9a8D8Oijj/LGG29wzz338Oijj/Lwww+za9culi1bxpw5c/D7/WzcuJHu3bsDXreIsWPHkpmZyeTJk7n11lu56KKLTjp/BQUF/Ou//ivPPfcc11xzzVGX27ZtG127dg1+7tKlC4sWLQp+HjhwIJ9//vlJxyGhmjQQy8zeBv7bOTe/3rzRwH3Oua83V3AiIiJnlBO8Ino6lZeXk5WVBXhXWqdN8x7wM336dGbOnAlAYWEh69evZ+TIkbRr145ly5axc+dOsrOzadeuHa+88gpTpkwhKSkJgKlTpzJ//nyys4/9WPZ33w19AmZFRQUHDhzg/vvvB7zuC9OmTaNjx44AJCYmEhsbC8ALL7zA66+/TmVlJTt27OBXv/oVAGPGjME5xxNPPEFubi4+n4+dO3fSpk2bBvt6++23yc/PZ86cOVxxxRW8/PLLTJ48Odh+6aWXsmPHjpAYf/nLXzJp0qTg58LCQpKTk+nduzdFRUXHPGbnXMg8s8NfGurz+YiNjaWkpISUlJRjbkuapklFK3AJcP0R8z4DZp3ecERERORk1PVprS83N5cPP/yQzz77jMTERMaOHUtFRQXg3fp/4YUX2LFjB3fccQfQeCF2slatWkVOTg5RUd7wmRUrVvCd73wHgK1bt9KpUyfMjJdeeonFixfz8ccfk5yczJgxYxgwYADgXfUsKioiLS0tWPglJCQEj6GOmXHxxRdz8cUXs3//fvLz8xsUrR9+2LSx4/n5+WRmZvLMM88wcuRIhg0bdtSCvUuXLhQWFgY/1x1TfZWVlcTHxzdp33J8TX16QAWQdMS8ZCB0KJ2IiIhEhIMHD5KamkpiYiJr1qxh4cKFwbYpU6bw/vvv8/nnn3P55ZcD3pXNWbNmUVZWRmlpKTNnzmT06NEnte+CggIyMzODn+sGOIFXwNa9Lygo4MILLyQ5OZm33nqLTz/9lEGDBlFUVMTNN9/M7NmzSUpKYu7cuQCkpqZSU1MTLFznzp2L3+8HYNeuXSxYsIAJEyacdMyDBg2iY8eOPPvss9x4440cPHgQgPHjx7Nt27bgssOGDWP9+vVs2rQJv9/PjBkzuPbaa4Pte/fupX379iEDuOTkNbVonQv80cxaAQRenwTeb67ARERE5NRcccUVVFdXM3jwYB566CFGjhwZbIuNjWXcuHHccMMNwVH4OTk53H777QwfPpwRI0Zw5513HrdrAHh9Wrdv395gXkFBQbC7QkVFBeXl5aSmpgINC9jbbruN6dOnM3r0aNatW0fPnj0xM6ZOncrjjz9ORkYGDz30EI888khw25dddhkLFiwAvEFcQ4cOJTMzk6uvvpqf//znjBo16qTyVVe0AkyYMIEbbriBO+64g9raWjZs2EDbtoe/HDQ6Oponn3ySyy+/nIyMDG644YbgFWKAefPmMXHixJOKQ47COXfcCUgF3gGqgV2B178BbZqyfktMQ4YMcS1p3rx5Lbq/M4FyEko5aZzyEko5CXWm5GT16tUttq/i4uLTtq2amhqXmZnp1q1bd9q22VLy8vLcLbfcEvx8OvPSmIKCAvfAAw+c0DpTpkxxa9asaaaIjq85c9LYOQ8scc1c6zXpSqtzbr9z7iqgK3AV0MU5d41z7kBzFNIiIiLSfFavXk2vXr0YP348vXv3Dnc4Jyw7O5tx48YFv1yguQ0cOJAnnniiycv7/X4mT55M3759mzGqc09TB2IB4JwrMrMdgJlZVGBebbNEJiIiIs2if//+bNy4MdxhnJK6wWORKDY2lltvvTXcYZx1mnSl1cw6mdlMM9uL1zWgqt4kIiIiItKsmjoQ64+AHxgPHAJygDnAt5spLhERERGRoKZ2D7gQ6OacKzUz55xbYWbTgE+BZ5ovPBERkcjnnGvwYHmRs5U7jc/yPVFNvdJag9ctAOCAmbUHSoHOzRKViIjIGSI+Pp69e/eG9Ze5SEtwzrF3796wfWFCU6+0LgImAjPxntn6OlAOLGmmuERERM4IXbp0YevWrezevbvZ91VRUaFvWGqE8hKquXISHx9Ply5dTvt2m6KpRes3OXxV9n7gQbxvxArflyyLiIhEgJiYGHr06NEi+8rNzW3Sw/7PNcpLqLMxJ00qWus/j9U5Vw78vNkiEhERERE5wlGLVjP7WVM24Jx7+PSFIyIiIiIS6lhXWru2VBBm5sPrH7vNOXe1mfUAZgBtgTzgm845f0vFIyIiIiKR5ahFq3PuWy0Yx33AF0CrwOffAv/tnJthZk8D04A/tGA8IiIiIhJBmvrIqyAz62BmU82s3+kIwMy6AFcBzwY+G/A14M3AIi8Ck0/HvkRERETkzGTHeq5coKD8XyAD+Ax4DPgn3nNb2wC3OudmnFIAZm8CvwZS8J5KcDuw0DnXK9DeFXjPOTewkXXvAu4CSE9PHzJjximFckIOHTpEcnJyi+3vTKCchFJOGqe8hFJOQiknoZSTxikvoVo6J+PGjVvqnBvanPs43tMD/gDsAh4AbsR7RuudzrmZZjYJ7ykCJ10pmtnVwC7n3FIzG1s3u5FFG62snXN/Av4EMHToUDd27NjGFmsWubm5tOT+zgTKSSjlpHHKSyjlJJRyEko5aZzyEupszMnxitYLgY7OOb+ZfQIcBGYBOOdmm9lLp7j/i4BrzWwiEI/Xp/V3QBszi3bOVQNdgO2nuB8REREROYMdr09rTN2ofedcGVDiGvYnOKUvWnbO/dA518U51x24CfjYOXczMA/4emCx24DZp7IfERERETmzHe9Ka7SZjeNwcXrkZ18zxfUDYIaZ/QJYBvy5mfYjIiIiImeA4xWtu4Dn6n3ee8TnXacrEOdcLpAbeL8RGH66ti0iIiIiZ7ZjFq2B2/YiIiIiImF1ws9pFRERERFpaSpaRURERCTiqWgVERERkYinolVEREREIp6KVhERERGJeCpaRURERCTiqWgVERERkYinolVEREREIp6KVhERERGJeCpaRURERCTiqWgVERERkYinolVEREREIp6KVhERERGJeCpaRURERCTiqWgVERERkYinolVEREREIp6KVhERERGJeCpaRURERCTiqWgVERERkYinolVEREREIp6KVhERERGJeCpaRURERCTiqWgVERERkYinolVEREREIp6KVhERERGJeCpaRURERCTiqWgVERERkYinolVEREREIp6KVhERERGJeCpaRURERCTiqWgVERERkYinolVEREREIp6KVhERERGJeCpaRURERCTiqWgVERERkYinolVEREREIp6KVhERERGJeCpaRURERCTiqWgVERERkYinolVEREREIp6KVhERERGJeCpaRURERCTiqWgVERERkYinolVEREREIl5Yi1Yz62pm88zsCzNbZWb3Bea3NbMPzGx94DU1nHGKiIiISHiF+0prNfDvzrkMYCRwj5n1B/4T+Mg51xv4KPBZRERERI6irKyKN9/M5/vf/4ClS8++633R4dy5c64IKAq8LzGzL4DOwCRgbGCxF4Fc4AdhCFFEREQk4uzfX87s2QV8/vkuqquuomjRFnz548hmE5cQR9EFD8K/Z4Y7zNPKnHPhjgEAM+sO/BMYCGxxzrWp17bfORfyJ4OZ3QXcBZCenj5kxowZLRMscOjQIZKTk1tsf2cC5SSUctI45SWUchJKOQmlnDTubM9LaamPL79M5v33PyV/eS6pe5YzoOpLcnBkE0MOKbRjHwA1GPvSO7Pz8kvZ863bWizGcePGLXXODW3OfURE0WpmycAnwC+dc2+b2YGmFK31DR061C1ZsqS5Qw3Kzc1l7NixLba/M4FyEko5aZzyEko5CaWchFJOGnc25WXt2j28/XYe//znMtauXELbHUsYWP1DclhJDm+TyXZS8Gq3al805Rf0I3n0CGzIEMjJgUGDIDGxxXNiZs1etIa1ewCAmcUAbwGvOufeDszeaWYdnXNFZtYR2BW+CEVEREROr9pax9Kl25gzZxl+/0VsXJXA/n8+Sq+S35ID/AwYDMQBcDfV8UlUD8wkftRUyM6GnByiMzJIiY0N52G0qLAWrWZmwJ+BL5xzT9RrmgPcBvwm8Do7DOGJiIiInDLnYONG+Mc/tvDyy0+zfc1izt+/hGwOkg3kcD4ZbCWaGgDKE1Jw2dnEXTTicIHaqxfRPl94DyTMwn2l9SLgm0CBmS0PzPsRXrH6hplNA7YA14cpPhEREZEmq6io5v331/L++3ksXryMgxsWklFxMQOr2pLDJ7zI+/Sut3xp63ZED8sgeuQ3vdv7OTkkdOsGZmE7hkgV7qcHLACO9q8yviVjERERETkRxcWVzJmzkjVrjL17sin8bB1R+YPJwc+VwI8xuuKAzwCo6NQD39ApMDzQ/zQ7m6TzzgvrMZxJwn2lVURERCTiHToEK1bAk08+w9LP/4+YwkUM8K8jm1pG054h5khzewCoxSjt0oPEi0fA0ECBmpVFfOrZ9+zUlqSiVURERKSeL7/cx9tvL+OTT5bxRcES0vYcoG/ZzWSTx7d5jixKaF03gj/KR/kFHUi+eAQM8a6eRmVmkpKUFOajOPuoaBUREZFz1vLlRfz97ytxbgIrl1RQ9MG/0K98JtnAQ8BgjAQcMJea2AT8/QcSP2posP9p9IABpMTFhfkozg0qWkVEROSs5xxs3gxvvrmM2bPfYtuaxXTdt5RMt48cIIcB/CdriaEagIr4JGoHZ5Fw8eER/L4+fUiIVukULsq8iIiInFX8/ho++GA9776bx6JFeexdv4iBVd+iX/lecniTZ1lMHyAqsHxZSirRwzsTM/za4BXU+B49NII/wqhoFRERkTPWoUN+PvpoG59/vpXNmzrzxcdzSFx7I9lUcinwfaA7AAsAqEjvStTQa4gafvgWf2LHjipQzwAqWkVEROSMUFoKixeX8cc/vsCyvDx8WxbSv3IN2dQwkD7cZgfo4A5/iWZJp/NJuHA4DBsafMRUfLt2YTwCORUqWkVERCTibN58gJkzlzNvXh6r8pfSqaQj3fdnk+U+5y6mkwO0qRvBb1FU9PSRdPFEyPH6n5KZSUpKSngPQk4rFa0iIiISVitX7uKTT4o4cCCTgqV+Nv19GAOr8skG/hPIxEgKFKg1sfFU9MokcfSI4O39T/ftY8xll4X1GKT5qWgVERGRFuEcfPUVvPzyJ3zwwccUrllMpz1LyHR7yKEVV3IB32clsVQBUBmXSPWgTJIuGh4sUH39+pF0xAj+2tzcMByNtDQVrSIiInLaVVXV8uGHG3j33TwWLsxj/8Z8Miq/T5/S5eTwFE+zkb6AL7B8eRJED08jZvi/Bfufxl1wAXFRUcfajZxDVLSKiIjIKSktreLvf/+C/fv7sHJlPIv/9l+02/Iw2VRwCfAA0BOAuQCUp3XCN2QivpHDgs9ATejSRSP45ZhUtIqIiEiTlZbCRx8V8dprs1m+LA+2LKRfxRfkUE0Oo5hkm+noioLLl5zXlYRRw2H44RH8Ce3bh/EI5EylolVEREQatXlzMTNnLic3dxkr85fStWIsnXYkkMXfuZPXyMZoFxggVWtGRY/9JFx4KQzxilOyskhp3TrMRyFnCxWtIiIi5zjnoKBgN0uXVrB1a1c+/7SIbR+MZHDNFrLxHtCfhZHMywDURMdSfsEgkupG8GdnEzV4MImJiWE9Djm7qWgVERE5h9TWwsaN8Oc/v8OCBUv4as3ndN67mEy3mxz6cCUp/MAKiHV+APyx8VQNHEzSRSOC/U99/fuTHBMT5iORc42KVhERkbNUZWUtH364kXffXcbixXmUFVXRbd819C1fRg4/5V8oJoPDxUBF0g6ih3Uleth9wQI1tndvYjWCXyKAilYREZGzQHFxNf/4x2b27OnFsmUw/+276brnFbIpC47g7wXA4wCUp3bAN3QU0SOGBZ+BGt+tm0bwS8RS0SoiInKG2bcP3nlnA3PmzGPF8qW4woVkVK4OjOCfyDW2gs5ua3D5Q+mdiR81HIYN9a6gZmeTcN55YTwCkROnolVERCRCOQfr1h1i1qwVfPJJHnlLPqW3L46OOwrJ4Q/cSS45GO3rjeCv6vklsSPGwJAhwRH8yampYT4SkVOnolVERCQC1NTA55/vY9WqaNata8UnH+VSvuxbZNZuJhv4AZCN0YoZ3vK+GCouyCDxopFegZqTQ9TgwcQlJYX1OESai4pWERGRFlZeDkuWlPPGGx+zaFEeheuX0OXAYrLZQQ5juM4qeJQVxLtKAKpiYqnuP5C93bvS6pprIDsb34ABJMXFhflIRFqOilYREZFmtGeP4/33N/PBB3nk5eVRta8X6Tt6k1k7nxx+xJ1Af6DuAVL+xGVEDx9KVM49wWegxvTtS4zPx4bcXLqMHRu+gxEJIxWtIiIip4Fz8OWXNcyfv5fNmzuwbBks/cdlZFR+SjalXAH8COgN1D1AqqJ1Kr7hQ4kZdngEf2z37hrBL9IIFa0iIiInqLISVq+Gd95ZRW7uIr5YvRTfzoUMrl1FDu3JYSh3x+TRqWpLcJ3yDucRO2IYUcOGBUfwx3fqpAJVpIlUtIqIiBzDnj2wcGEpc+fms2jRMjZt3Ejqge8yuCaPbH7Ev7OeHIz0wAh+Z9twvZOIyrkQcr53+BFT7dqF+UhEzmwqWkVERPBG769fD59+eoD161uxYkUUiz79M+kHf0U2m8jBMRVvBH+bwAP6a33R+Hv3I27EiOAjpiwrC0tODu/BiJyFVLSKiMiTaTy6AAAXkUlEQVQ558AByM+HhQsP8vHHn7FyZR57ti8lwy0ih20MsW9wY8Jm+lbkkYA3gr86JpaaAf2JrVegRg0cSHx8fJiPRuTcoKJVRETOWtXV3tXTFSscCxZsYdGiZaxbl0d18UQyqSWbl7ieP/IrYCAQG1ivNulvROXkQM53vNv7OTlE9+tHdLR+bYqEi/73iYjIWWHnTigogOXLa1m+vJwvvkhi5crtJPr/hSzyyKGE+4EcoC+/ICrQB9XfujU2ZMjhEfzZ2URdcAFERR1zfyLSslS0iojIGaW0FFat8grU/HzHZ5/ls2bNMkpK8khnEdnkMyJmCHennkdG/FLS/JuD61Z26IBv2DCihg49/Iipzp01gl/kDKCiVUREIlJVlbFyJcFpxYpy8vIK2L49D4jmfC5lZGwek6q+ycOujByMToGrp1T9H7TqBZcMg5y7g1dQ49q3D+sxicjJU9EqIiJhVV0NGzZ4V09XrfIK1IKCCtavH01NDUTxE3rzBtls4H4cOcCQKB9tamvADy4qCn+v3sTUDZDKyYHMTGjdOtyHJiKnkYpWERFpEVVVXnG6erVXnNa9rl27l6qqpUAe0SxlsG8RF7GP3/W8jmz7klYbFxNfXQVATXQ01f36ETtqVPD2vg0aRFxCQngPTkSanYpWERE5rUpLYe1a+OKLw9Pq1bB+vaOmZhuQByyjX7fvMrHrRr7Z+WHabP4HOcAgIK7G20514RtEDx0Kl387WKD6MjLwxcSE7+BEJGxUtIqIyAlzDrZv94rTtWthzZrD05YtALVALT5fNJ06/R+x5Q/xNV8eA2oOkgNkAwO2/gzbUgtAVUoK5OQQM3x4sEBdsHUrY7/2tfAdpIhEFBWtIiJyVAcPes85Xb8e1q07XKSuWweHDtUtVU1CwhrS05eRmJjHkK6L6LxrBf9x6RRGxvvxL/yUhD3bgtusTEvDN3QoVu8RUzHduoWO4N++vcWOU0Qin4pWEZFzXHExfPmlV5hu2OBNdUXqrl2HlzODrl0r6NhxJcOGLSNzcG+uG3EBqZv+zl9//F1yNsMQoHPdCu+8Cj17kjBqVPAB/WRnE5ee3vIHKSJnPBWtIiJnOedgxw7YuNErTuumus/1C1OAjh2hVy+46qoa+vXzccEFtbz2yp34v/iM5PXryNxSSw4w6rN4Uv6nAoAMM6p69PBG8A8d6hWpWVmQmtryBywiZyUVrSIiZzjnYO9e2LwZvvrKe920yStKN23yPldUHF7eu2IKF1wA117rFajp6Xvx+5exe/cyVucvoWTxYgaXtOH+VpfA3/O44tNPSarxRkjV+nxU9etH7IgRh79BKjOTuKSkcBy+iJwjVLSKiEQ4vx+2bfMGOG3ZAoWFh9/XFallZQ3Xad0aevaE/v3hqqugRw/vc8+ejtjYIlatymP79u3cdeutUFDAf/3Lv5CyYQMTgH83I94FHtK/bh1kZpJ0993e1dMhQ4gaMIC42NiWToOInONUtIqIhFFJiTfeKC+vDVu3esXp1q3eVFjove7cGbpeWpp3tbRPH7jsMujeHc4///Braio457DA4KbXX3+dZ554luqlS+m+fz85wEgz3He/i9XU8H2gKjkZsrK8W/x1fVD79AGfrwUzIiLSOBWtIiKnWVWV1090587D044dUFQU+np4BH5WcP02baBLF2/Kzj78vls3b+raFRITG+6zpqaGdevWkZeXxyuvLGPDwoXY8uW89MADpKxfz4SPP+b63buJCizvb9uWqCFDDo/gz8khpnv30BH8IiIRQkWriMgxOOcVlnv2eP1G9+49/H737tBp1y7Yt6/xbaWkeIOczjvPqxM7doROnbzX3buXc9VVWXTuDMfrGur3+1m2bBV5eXlMuPRSukVHk/vYY3zyu9+RA9wLdKtb+Be/gPPPp+1FFwVv75OTQ2zHjqcvSSIiLSCii1YzuwL4H8AHPOuc+02YQxKRM4xzXn/P4uLD08GD3nTgQMNp//7D0759h99XVTW+7agoaNcO2rf3poEDoUMHSE/3pvrvzzuv8WJ04cKF/M///A95eXksXpzDfffdx8iRI+vF793iLyws5JGf/pRdixaRtHYtg2tqyAbat2oFxcWMB75mRuX55xM7fLg3gj8nxxvB365ds+RWRKQlRWzRamY+4PfABGAr8LmZzXHOrQ5vZCJyOjnnFYUVFVBefvi1biorC51KS73p0KGG7w8d8vqIlpQ0fB8Y9H5UZt7ApTZtoG1brz9oly7ea2qq13+0XbvDU93n1NRT6+7505/+lMcee4zy8nKcc6xfv56ZM2cyZswY0tPSOLBoEXcNG8Y1nTuTvnAhT3z2Ga0DA6RqfT6qevf2RvAHrp5aZibxycknH5CISASL2KIVGA5scM5tBDCzGcAkIOxF68GtB9m3aQ+FaVsazI+Pjyc2No7a2hoOHe6oFpSQkEBMTCw1NdWUlpaecHtiYiLR0TFUV1dRduRQYSApKQmfL5qqKj/l5eUh7cnJyURF+fD7K6mo//ybpranpBBlUVRWVlBZWRnSXr6jjINbDlJRUY7f7w9pb9WqtbdceRlV9S5d1Q1SPlo7gJmRktIKgLKyUqqrq49ojyI5JQWA0kOHqK1tWKVERflICvwyLykuwbna0PakQHtJCbX12x1ERUWTFLhMVnKoGFfrGsQe5YshMSER56Ck5CDOBb7m8ov9rEnYQkxMLHFxCQAUFx8Mrls31bXX1jpKSopxgf06B7W1EB0dR2xsPNXVtZSWlgTng/caExNPdHQcVVU1lJcforbWK9Rqa71t+HwJ+Hyx+P3VlJWVNmivqfHazWLxV1VTXlZKTQ3BqboafL5EnIuhoqKKiooyqqsJTt4/VRLV1dFUVvqpqCinqorgVFkJtbXJ+P0+Kioq8fsrOHSoK85toaICKv0AyXg3VCqB0HMPUoCoQNvhcy8xARISICWlFUlJRkJCOR0S/fRs513VTEqC5GRIa9+a1q0gNraMxMQqkpOhVavDU6dOrYmKgrKyxs+9Vq28c6+0tN65VwaHyiAqKoqUwLl36NAhao6okH0+H8mBc2/Xrl3BfeTl5fGH3/yGaL+fVkBPINs5cioryfngAzKBRPCeWxUfT+zgwcTcdVewQI0aMIC4+PhGciUicnaK5KK1M1BY7/NWYESYYmlgX49splZvOuYyx3ucdttTbI/Em31dA6+tj7Pc8drbnGL78XJ/qv82acdpP6/e+77HWVZOUXlgOkof0hOVeJz24z2F9HjXODvUe98buLGRZYqBZUBunz5M/PGPvVv8/fpBdDQaIiUi57JILlob+/nsGixgdhdwF0B6ejq5ubktEBYUjfsms9fOJyqq4X3BVq26kpzSkeqqcnbtKghZr3Xr80lKTqfKf4jdu0MvGKem9iQhMY3KimL27l0T0t62bW/iE1KpKN/Pvn3rQ9rbtetHXHwrysv2sH//xpD29u37ExObTOmhnRw8+FVIe4cOg4iOSeBQSRHFxYUh7eedl0mUL46S4q2UlIR+J3j79pnExMZx8OBXlB4KfUZPp87DATiwfxNlZbsbtFlUFJ06DgVg374NlJc3rEJ8vhjOOy8bgL1711JRcbBBe3R0HOnpgwHYs+cLKitLGrTHxCTRocMAAHbtWkV1dcMr2bFxKbRPywBg5858qqsrGpyB8XFtSEvrA0BR0TJqag9fjTMgIbEt7dr1AmD7tiXUOu9qm6utJSoqiuTkDqSmdgcchYWfhwzQTml1HqltuuJcNVu3LqPuVK9brnWbTrRN7Ux1tZ+tW1c0WN8M2rXrQps26fj95WzdusprNzAcGHRo3402qe3xV5ZSWLgGcA2W6dy5O23apFJWVsKWLesxo8HUo0cPWrduRUnJQTZtDv2DrdcFF5CcnMz+/fv5asuWkPY+vXuTmJjInj172LptGzXV1fiiD//4yejXj7i4OHbt2sX2oqKQ9Qf0709MTAw7duxgRyPPfxo0cCA+n49t27axe8+ekPaszEwACrduZe/evQ3afFFRDBo0CICvvvqK/QcONGiPiY5mwADv3Nm4aRPFxcUN2uPj4ujXrx8AGzZs4NARd0kSEhLo26dPcH1/ZSVRPh+LFi6kuOTweboNyAM24v3r9wESu3XzRn0tWBByTOeCQ4cOtdjP9TOFctI45SXU2ZgTc84df6kwMLNRwCPOucsDn38I4Jz7dWPLDx061C1ZsqTF4svNzWXs2LEttr8zgXISSjlpnPIC3/jGN3jjjTeora0NaYuKiuLGG2/ktddeC0NkkUPnSSjlpHHKS6iWzomZLXXODW3OfUQdf5Gw+RzobWY9zCwWuAmYE+aYREROi/vuu4/4o/RJjY+P5957723hiEREIlvEFq3OuWrge8Bc4AvgDefcqvBGJSJyeowcOZIHH3yQxMREoqK8H8VRUVEkJiby4IMPNnjslYiIRHafVpxz7wLvhjsOEZHm8Oijj3LllVcGn9OakxP6nFYREfFEdNEqInK2GzlyJCNHjlSfPBGR44jY7gEiIiIiInVUtIqIiIhIxFPRKiIiIiIRT0WriIiIiEQ8Fa0iIiIiEvFUtIqIiIhIxFPRKiIiIiIRT0WriIiIiEQ8Fa0iIiIiEvFUtIqIiIhIxDPnXLhjOC3MbDfwVQvuMg3Y04L7OxMoJ6GUk8YpL6GUk1DKSSjlpHHKS6iWzsn5zrn2zbmDs6ZobWlmtsQ5NzTccUQS5SSUctI45SWUchJKOQmlnDROeQl1NuZE3QNEREREJOKpaBURERGRiKei9eT9KdwBRCDlJJRy0jjlJZRyEko5CaWcNE55CXXW5UR9WkVEREQk4ulKq4iIiIhEPBWtIiIiIhLxVLQ2gZk9YGarzGylmf3FzOLNrIeZLTKz9Wb2upnFhjvOlnSUnLxgZpvMbHlgygp3nC3NzO4L5GSVmd0fmNfWzD4InCsfmFlquONsSUfJySNmtq3euTIx3HE2NzN7zsx2mdnKevMaPTfMM93MNphZvpnlhC/y5nOCORlrZgfrnTMPhy/y5nOUnFwf+P9Ta2ZDj1j+h4HzZK2ZXd7yETe/E8mJmXU3s/J658nT4Ym6eR0lJ/9lZmsCPzNmmlmbem1nxXmiovU4zKwzcC8w1Dk3EPABNwG/Bf7bOdcb2A9MC1+ULesYOQH4vnMuKzAtD1uQYWBmA4H/DxgOZAJXm1lv4D+BjwLnykeBz+eEY+QEvP8/defKu2ELsuW8AFxxxLyjnRtXAr0D013AH1ooxpb2Ak3PCcD8eufMz1ooxpb2AqE5WQlMBf5Zf6aZ9cf72TsgsM5TZuZrgRhb2gs0MScBX9Y7T77d3MGFyQuE5uQDYKBzbjCwDvghnF3niYrWpokGEswsGkgEioCvAW8G2l8EJocptnA5MifbwxxPJMgAFjrnypxz1cAnwBRgEt45AufeuXK0nJxznHP/BPYdMfto58Yk4CXnWQi0MbOOLRNpyznBnJwTGsuJc+4L59zaRhafBMxwzlU65zYBG/D+QDyrnGBOzglHyck/Aj9nARYCXQLvz5rzREXrcTjntgGPAVvwitWDwFLgQL2TYyvQOTwRtrzGcuKc+0eg+ZeBWxP/bWZxYQsyPFYCY8ysnZklAhOBrkC6c64IIPDaIYwxtrSj5QTge4Fz5blzrctEPUc7NzoDhfWWO5d+xhzr/8soM1thZu+Z2YDwhBdRzuXz5Fh6mNkyM/vEzEaHO5gwuQN4L/D+rDlPVLQeR+CX6SSgB9AJSMK7dXekc+bZYY3lxMxuwbsV0Q8YBrQFfhC2IMPAOfcFXreRD4D3gRVA9TFXOssdIyd/AC4AsvD+8Hk8XDFGKGtk3jnzM+Yo8vC+2zwT+F9gVpjjiQQ6T0IVAd2cc9nAvwGvmVmrMMfUoszsx3g/Z1+tm9XIYmfkeaKi9fguBTY553Y756qAt4EL8W7XRQeW6cK5dXu80Zw454oCtzMrgec5Q28/nArn3J+dcznOuTF4t27WAzvrbu0GXneFM8aW1lhOnHM7nXM1zrla4BnOwXMl4GjnxlYOX5GGc+tnTKM5cc4VO+cOBd6/C8SYWVr4wowI5/J50qjALfC9gfdLgS+BPuGNquWY2W3A1cDN7vCD+M+a80RF6/FtAUaaWaKZGTAeWA3MA74eWOY2YHaY4guHxnLyRb1fNIbXD23lMbZxVjKzDoHXbniDBP4CzME7R+DcO1cazckR/TOncA6eKwFHOzfmALcGniIwEq8LTlE4AgyDRnNiZucFfrZgZsPxfn/tDUuEkWMOcJOZxZlZD7yBe4vDHFNYmVn7ukFGZtYTLycbwxtVyzCzK/DucF7rnCur13T2nCfOOU3HmYBHgTV4v1hfBuKAnnj/6BuAvwJx4Y4zAnLyMVAQmPcKkBzuOMOQl/l4f9SsAMYH5rXDGwW9PvDaNtxxRkBOXg6cK/l4P1A7hjvOFsjDX/BuXVbhXfmYdrRzA+923u/xrhIV4D2pI+zHEOacfA9YFTiPFuLd3Qn7MbRQTqYE3lcCO4G59Zb/ceA8WQtcGe74w50T4Lp650kecE2442/BnGzA67u6PDA9fbadJ/oaVxERERGJeOoeICIiIiIRT0WriIiIiEQ8Fa0iIiIiEvFUtIqIiIhIxFPRKiIiIiIRT0WriIiIiEQ8Fa0iIiIiEvFUtIqInCQze8HMfnEK6282s0ubsNwqMxt7svsRETkbqGgVkXOGmTkz63XEvEfM7JVwxdQUzrkBzrnc5tq+md1sZkvN7ICZ7TOzXDPr3Fz7ExE5GdHhDkBE5FxjZtHOuepwxwFgZrcDPwFuxPvay3bAZGB/GMMSEQmhK60iIgGB2/UPmlm+mR00s9fNLL5ee7aZ5ZlZiZm9DsQfsX4nM3vLzHab2SYzu/eIbf/AzPKBUjOLrtf2fTN764ht/a+Z/a7eupcesa1G4zSzHDNbFojxr4G2Y3VhmAb80Tm31Hn2OOeedc6VnVwWRUSah4pWEZGGbgCuAHoAg4HbAcwsFpgFvAy0Bf4KXFe3kplFAX8DVgCdgfHA/WZ2eb1tfwO4CmhzxJXWV4ArzKxNYFvReFc+Xz6ROAMxzgReCMT4F2DKcY63HLjDzG4ws7TjLCsiEjYqWkVEGprunNvunNuHV4RmBeaPBGKA3znnqpxzbwKf11tvGNDeOfcz55zfObcReAa46YhtFzrnyuvv0DlXBPwTuD4w6wpgj3Nu6QnGORKv29f0QIxvA4uPc7y3AnOBx4CdZvY3M+sAYGbjzKz7cdYXEWkRKlpF5FxSg1d41hcDVNX7vKPe+zIgOfC+E7DNOefqtX9V7/35QKfAYKYDZnYA+BGQXm+ZwmPE9iJwS+D9LRz7KuvR4mwsxmPtE+fcDufc/c65bsBwvKu2Pwg03wHYceIQEWkRKlpF5FyyBeh+xLweNCw+j6YI6Gxm9Yu4bvXeFwKbnHNt6k0pzrmJ9ZapX0weaRYw2MwGAlcDrzYhpqbE2LWpKweu7BYASWZ2LXAN8LyZ3XoSsYiInFYqWkXkXPI68BMz62JmUYHBTdcAbzZh3c+AauBeM4s2s6l4VybrLAaKA4OtEszMZ2YDzWxYUwJzzlUE4ngNWOyc23IiB1Yvxhrge4EYJx0RYwNm9p9mdpGZxQWm24GxwPPA34FlzrmxzrmXTiIWEZHTSkWriJxLfgZ8CizAe6TT/wNuds6tPN6Kzjk/MBVvYNZ+vIFSb9drr8ErgLOATcAe4Fmg9QnE9yIwiON3DThejNOAA3jdDP4OVB5llVZ4BepevKvQNwLjnXOLgF7A2pOJQ0SkOVjDrk8iIhIuZtYNWAOc55wrPk3bXAQ87Zx7/gTXmwx0d8797nTEISJyqnSlVUQkAgQemfVvwIxTKVjN7BIzOy/QPeA2vIFV75/EptYBd9Y9K1ZEJNz0jVgiImFmZknATrwBYVec4ub6Am/gPU3gS+DrgUdqnRDn3Gpg4CnGIiJy2qh7gIiIiIhEPHUPEBEREZGIp6JVRERERCKeilYRERERiXgqWkVEREQk4qloFREREZGIp6JVRERERCKeilYRERERiXj/P3XpZnUcyUB6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_single_tau(Vanilla_Call, S_strip, Vanilla_Call.get_tau())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now a plain-vanilla put option"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calling the EuropeanOption constructor!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "PlainVanillaOption('put', S_t=90.0, K=100.0, t=19-04-2020, T=31-12-2020, tau=0.70y, r=5.0%, sigma=20.0%)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Vanilla_Put = PlainVanillaOption(option_type='put', S_t=90.0, K=100.0, t=\"19-04-2020\", T=\"31-12-2020\", r=0.05, sigma=0.2)\n",
    "Vanilla_Put"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "which is equivalent to"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calling the EuropeanOption constructor!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "PlainVanillaOption('put', S_t=90.0, K=100.0, t=19-04-2020, T=31-12-2020, tau=0.70y, r=5.0%, sigma=20.0%)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Vanilla_Put = PlainVanillaOption(option_type='put')\n",
    "Vanilla_Put"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "__main__.PlainVanillaOption"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(Vanilla_Put)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "isinstance(Vanilla_Put, PlainVanillaOption)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100.0"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Vanilla_Put.get_K()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.041330587465126"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Vanilla_Put.price()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.0"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Vanilla_Put.payoff()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_multi_tau(Vanilla_Put, S_strip, tau_strip)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_single_tau(Vanilla_Put, S_strip, Vanilla_Put.get_tau())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now a plain-vanilla digital CON option"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_multi_tau(CON_Call, S_strip, tau_strip)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_single_tau(CON_Call, S_strip, CON_Call.get_tau())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and digital CON put option "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calling the EuropeanOption constructor!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "DigitalOption('put', cash=1.0, S_t=90.0, K=100.0, t=19-04-2020, T=31-12-2020, tau=0.70y, r=5.0%, sigma=20.0%)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CON_Put = DigitalOption(option_type='put', cash_amount = 1.0, S_t=90.0, K=100.0, t=\"19-04-2020\", T=\"31-12-2020\", r=0.05, sigma=0.2)\n",
    "CON_Put"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "equivalent to"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calling the EuropeanOption constructor!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "DigitalOption('put', cash=1.0, S_t=90.0, K=100.0, t=19-04-2020, T=31-12-2020, tau=0.70y, r=5.0%, sigma=20.0%)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CON_Put = DigitalOption(option_type='put')\n",
    "CON_Put"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "__main__.DigitalOption"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(CON_Put)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "isinstance(CON_Put, DigitalOption)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100.0"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CON_Put.get_K()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CON_Put.get_Q()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6687932243156424"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CON_Put.price()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CON_Put.payoff()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_multi_tau(CON_Put, S_strip, tau_strip)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_single_tau(CON_Put, S_strip, CON_Put.get_tau())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TAKE-HOME MESSAGE**: key points are that\n",
    "\n",
    "- all sub-classes derive attributes and methods defined in the base class. So that you define common attributes and methods once-for-all in the base class, and then you are able to access and use them from any of the sub-classes. \n",
    "\n",
    "- attributes and/or methods which are specific to one sub-class only are defined within that sub-class only."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}