main.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## OPTION PRICING MODEL AND STRATEGY"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The startegy that I have followed involves the use of Bollinger Bands to identify periods of low volatility and at that position I have applied Butterfly Options Trading Strategy . I have provided code for Monte Carlo Simulation etc. , they can be used to simulate the strategies in future. I have also draw some payoff diagram for dummy data just to explain"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## BLACK SCHOLES MODEL"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overview\n",
    "This Python script contains functions to calculate various parameters and prices related to European call and put options using the Black-Scholes options pricing model.\n",
    "\n",
    "## Key Functions\n",
    "\n",
    "### calculate_d1(S, K, T, r, sigma)\n",
    "- Calculate the d1 parameter used in the Black-Scholes model.\n",
    "  \n",
    "### calculate_d2(S, K, T, r, sigma)\n",
    "- Calculate the d2 parameter used in the Black-Scholes model.\n",
    "\n",
    "### calculate_call_price(S, K, T, r, sigma)\n",
    "- Calculate the theoretical price of a European call option using the Black-Scholes formula.\n",
    "\n",
    "### calculate_put_price(S, K, T, r, sigma)\n",
    "- Calculate the theoretical price of a European put option using put-call parity.\n",
    "\n",
    "### calculate_call_delta(S, K, T, r, sigma)\n",
    "- Calculate the Delta of a European call option.\n",
    "\n",
    "### calculate_call_gamma(S, K, T, r, sigma)\n",
    "- Calculate the Gamma of a European call option.\n",
    "\n",
    "### calculate_call_vega(S, K, T, r, sigma)\n",
    "- Calculate the Vega of a European call option.\n",
    "\n",
    "### calculate_call_theta(S, K, T, r, sigma)\n",
    "- Calculate the Theta of a European call option.\n",
    "\n",
    "### calculate_put_delta(S, K, T, r, sigma)\n",
    "- Calculate the Delta of a European put option.\n",
    "\n",
    "### calculate_put_gamma(S, K, T, r, sigma)\n",
    "- Calculate the Gamma of a European put option.\n",
    "\n",
    "### calculate_put_vega(S, K, T, r, sigma)\n",
    "- Calculate the Vega of a European put option.\n",
    "\n",
    "### calculate_put_theta(S, K, T, r, sigma)\n",
    "- Calculate the Theta of a European put option.\n",
    "\n",
    "### calculate_put_rho(S, K, T, r, sigma)\n",
    "- Calculate the Rho of a European put option.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy.stats import norm\n",
    "from math import log, sqrt, exp\n",
    "\n",
    "def calculate_d1(S, K, T, r, sigma):\n",
    "    \"\"\"\n",
    "    Calculate the d1 parameter used in the Black-Scholes options pricing model.\n",
    "    \n",
    "    Parameters:\n",
    "    S (float): Current stock price\n",
    "    K (float): Strike price of the option\n",
    "    T (float): Time to expiration (in years)\n",
    "    r (float): Risk-free interest rate (continuously compounded)\n",
    "    sigma (float): Volatility of the stock\n",
    "    \n",
    "    Returns:\n",
    "    float: Value of d1\n",
    "    \"\"\"\n",
    "    numerator = log(S / K) + (r + (sigma**2) / 2) * T\n",
    "    denominator = sigma * sqrt(T)\n",
    "    d1 = numerator / denominator\n",
    "    return d1\n",
    "\n",
    "def calculate_d2(S, K, T, r, sigma):\n",
    "    \"\"\"\n",
    "    Calculate the d2 parameter used in the Black-Scholes options pricing model.\n",
    "\n",
    "    Returns:\n",
    "    float: Value of d2\n",
    "    \"\"\"\n",
    "    d1 = calculate_d1(S, K, T, r, sigma)\n",
    "    d2 = d1 - sigma * sqrt(T)\n",
    "    return d2\n",
    "\n",
    "def calculate_call_price(S, K, T, r, sigma):\n",
    "    \"\"\"\n",
    "    Calculate the theoretical price of a European call option using the Black-Scholes formula.\n",
    "\n",
    "    Returns:\n",
    "    float: Theoretical price of the call option\n",
    "    \"\"\"\n",
    "    d1 = calculate_d1(S, K, T, r, sigma)\n",
    "    d2 = calculate_d2(S, K, T, r, sigma)\n",
    "    call_price = S * norm.cdf(d1) - K * exp(-r * T) * norm.cdf(d2)\n",
    "    return call_price\n",
    "\n",
    "def calculate_put_price(S, K, T, r, sigma):\n",
    "    \"\"\"\n",
    "    Calculate the theoretical price of a European put option using put-call parity.\n",
    "\n",
    "    Returns:\n",
    "    float: Theoretical price of the put option\n",
    "    \"\"\"\n",
    "    call_price = calculate_call_price(S, K, T, r, sigma)\n",
    "    put_price = call_price + K * exp(-r * T) - S\n",
    "    return put_price\n",
    "\n",
    "def calculate_call_delta(S, K, T, r, sigma):\n",
    "    \"\"\"\n",
    "    Calculate the Delta of a European call option.\n",
    "\n",
    "    Returns:\n",
    "    float: Delta of the call option\n",
    "    \"\"\"\n",
    "    d1 = calculate_d1(S, K, T, r, sigma)\n",
    "    call_delta = norm.cdf(d1)\n",
    "    return call_delta\n",
    "\n",
    "def calculate_call_gamma(S, K, T, r, sigma):\n",
    "    \"\"\"\n",
    "    Calculate the Gamma of a European call option.\n",
    "\n",
    "    Returns:\n",
    "    float: Gamma of the call option\n",
    "    \"\"\"\n",
    "    d1 = calculate_d1(S, K, T, r, sigma)\n",
    "    call_gamma = norm.pdf(d1) / (S * sigma * sqrt(T))\n",
    "    return call_gamma\n",
    "\n",
    "def calculate_call_vega(S, K, T, r, sigma):\n",
    "    \"\"\"\n",
    "    Calculate the Vega of a European call option.\n",
    "\n",
    "    Returns:\n",
    "    float: Vega of the call option\n",
    "    \"\"\"\n",
    "    d1 = calculate_d1(S, K, T, r, sigma)\n",
    "    call_vega = 0.01 * S * norm.pdf(d1) * sqrt(T)\n",
    "    return call_vega\n",
    "\n",
    "def calculate_call_theta(S, K, T, r, sigma):\n",
    "    \"\"\"\n",
    "    Calculate the Theta of a European call option.\n",
    "\n",
    "    Returns:\n",
    "    float: Theta of the call option\n",
    "    \"\"\"\n",
    "    d1 = calculate_d1(S, K, T, r, sigma)\n",
    "    d2 = calculate_d2(S, K, T, r, sigma)\n",
    "    return 0.01*(-(S*norm.pdf(d1(S,K,T,r,sigma))*sigma)/(2*sqrt(T)) - r*K*exp(-r*T)*norm.cdf(d2(S,K,T,r,sigma)))\n",
    "\n",
    "def calculate_call_rho(S, K, T, r, sigma):\n",
    "    \"\"\"\n",
    "    Calculate the Rho of a European call option.\n",
    "\n",
    "    Returns:\n",
    "    float: Rho of the call option\n",
    "    \"\"\"\n",
    "    d2 = calculate_d2(S, K, T, r, sigma)\n",
    "    call_theta=0.01*(K*T*exp(-r*T)*norm.cdf(d2(S,K,T,r,sigma)))\n",
    "    return call_theta\n",
    "\n",
    "def calculate_put_delta(S, K, T, r, sigma):\n",
    "    \"\"\"\n",
    "    Calculate the Delta of a European put option.\n",
    "\n",
    "    Returns:\n",
    "    float: Delta of the put option\n",
    "    \"\"\"\n",
    "    put_delta = calculate_call_delta(S, K, T, r, sigma) - 1\n",
    "    return put_delta\n",
    "\n",
    "def calculate_put_gamma(S, K, T, r, sigma):\n",
    "    \"\"\"\n",
    "    Calculate the Gamma of a European put option.\n",
    "\n",
    "    Returns:\n",
    "    float: Gamma of the put option\n",
    "    \"\"\"\n",
    "    put_gamma = calculate_call_gamma(S, K, T, r, sigma)\n",
    "    return put_gamma\n",
    "\n",
    "def calculate_put_theta(S, K, T, r, sigma):\n",
    "    \"\"\"\n",
    "    Calculate the Theta of a European put option.\n",
    "\n",
    "    Returns:\n",
    "    float: Theta of the put option\n",
    "    \"\"\"\n",
    "    d1 = calculate_d1(S, K, T, r, sigma)\n",
    "    d2 = calculate_d2(S, K, T, r, sigma)\n",
    "    first_term = (-S * norm.pdf(d1) * sigma) / (2 * sqrt(T))\n",
    "    second_term = r * K * exp(-r * T) * norm.cdf(-d2)\n",
    "    put_theta = 0.01 * (first_term + second_term)\n",
    "    return put_theta\n",
    "\n",
    "def calculate_put_rho(S, K, T, r, sigma):\n",
    "    \"\"\"\n",
    "    Calculate the Rho of a European put option.\n",
    "\n",
    "    Returns:\n",
    "    float: Rho of the put option\n",
    "    \"\"\"\n",
    "    d2 = calculate_d2(S, K, T, r, sigma)\n",
    "    put_rho = 0.01 * (-K * T * exp(-r * T) * norm.cdf(-d2))\n",
    "    return put_rho\n",
    "\n",
    "def calculate_put_vega(S, K, T, r, sigma):\n",
    "    \"\"\"\n",
    "    Calculate the Vega of a European put option.\n",
    "\n",
    "    Returns:\n",
    "    float: Vega of the put option\n",
    "    \"\"\"\n",
    "    put_vega = calculate_call_vega(S, K, T, r, sigma)\n",
    "    return put_vega"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Testing monte carlo simulation prediction and Black Scholes Model for 1 value of data entered by user"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "error: time data '21-05-2021' does not match format '%m-%d-%Y'\n",
      "Try again.\n",
      "error: time data '21-05-2021' does not match format '%m-%d-%Y'\n",
      "Try again.\n",
      "error: time data '' does not match format '%m-%d-%Y'\n",
      "Try again.\n",
      "error: time data '21-05-2023' does not match format '%m-%d-%Y'\n",
      "Try again.\n"
     ]
    }
   ],
   "source": [
    "## input the current stock price and check if it is a number.\n",
    "S = input(\"What is the current stock price? \")\n",
    "while True:\n",
    "    try:\n",
    "        S = float(S)\n",
    "        break\n",
    "    except:\n",
    "        print(\"The current stock price has to be a NUMBER.\")\n",
    "        S = input(\"What is the current stock price? \")\n",
    "\n",
    "## input the strike price and check if it is a number.\n",
    "K = input(\"What is the strike price? \")\n",
    "while True:\n",
    "    try:\n",
    "        K = float(K)\n",
    "        break\n",
    "    except:\n",
    "        print(\"The the strike price has to be a NUMBER.\")\n",
    "        K = input(\"What is the strike price? \")\n",
    "\n",
    "\n",
    "## input the expiration_date and calculate the days between today and the expiration date.\n",
    "while True:\n",
    "    expiration_date = input(\"What is the expiration date of the options? (mm-dd-yyyy) \")\n",
    "    try:\n",
    "        expiration_date = datetime.strptime(expiration_date, \"%m-%d-%Y\")\n",
    "    except ValueError as e:\n",
    "        print(\"error: %s\\nTry again.\" % (e,))\n",
    "    else:\n",
    "        break\n",
    "T = (expiration_date - datetime.utcnow()).days / 365\n",
    "\n",
    "\n",
    "## input the continuously compounding risk-free interest rate and check if it is a number.\n",
    "r = input(\"What is the continuously compounding risk-free interest rate in percentage(%)? \")\n",
    "while True:\n",
    "    try:\n",
    "        r = float(r)\n",
    "        break\n",
    "    except:\n",
    "        print(\"The continuously compounding risk-free interest rate has to be a NUMBER.\")\n",
    "        r = input(\"What is the continuously compounding risk-free interest rate in percentage(%)? \")\n",
    "        \n",
    "\n",
    "## input the volatility and check if it is a number.\n",
    "sigma = input(\"What is the volatility in percentage(%)? \") \n",
    "while True:\n",
    "    try:\n",
    "        sigma = float(sigma)\n",
    "        if sigma > 100 or sigma < 0:\n",
    "            print ( \"The range of sigma has to be in [0,100].\")\n",
    "            sigma = input(\"What is the volatility in percentage(%)? \")\n",
    "        break\n",
    "    except:\n",
    "        print(\"The volatility has to be a NUMBER.\")\n",
    "        sigma = input(\"What is the volatility in percentage(%)? \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Symbol</th>\n",
       "      <th>Input</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Underlying price</th>\n",
       "      <td>S</td>\n",
       "      <td>1.000000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Strike price</th>\n",
       "      <td>K</td>\n",
       "      <td>1.000000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time to maturity</th>\n",
       "      <td>T</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Risk-free interest rate</th>\n",
       "      <td>r</td>\n",
       "      <td>5.000000e-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Volatility</th>\n",
       "      <td>sigma</td>\n",
       "      <td>2.000000e-11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Symbol         Input\n",
       "Underlying price             S  1.000000e+02\n",
       "Strike price                 K  1.000000e+02\n",
       "Time to maturity             T  1.000000e+00\n",
       "Risk-free interest rate      r  5.000000e-12\n",
       "Volatility               sigma  2.000000e-11"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {'Symbol': ['S', 'K', 'T', 'r', 'sigma'],\n",
    "        'Input': [S, K, T , r , sigma]}\n",
    "input_frame = DataFrame(data, columns=['Symbol', 'Input'], \n",
    "                   index=['Underlying price', 'Strike price', 'Time to maturity', 'Risk-free interest rate', 'Volatility'])\n",
    "input_frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current exposure: 2.1428024391427742\n",
      "Call option premium: 0.6004099554751585\n",
      "Current exposure: 2.500953516981874\n",
      "Put option premium: 0.3581510778390998\n",
      "0.6004099554750448\n",
      "0.35815107783904293\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "true_call=price_and_greeks['Call'][0]\n",
    "pred_call=model.call_option_simulation()\n",
    "\n",
    "true_put=price_and_greeks['Put'][0]\n",
    "pred_put=model.put_option_simulation()\n",
    "\n",
    "Call_error=sqrt((pred_call-true_call)**2)\n",
    "Put_error=sqrt((pred_put-true_put)**2)\n",
    "print(Call_error)\n",
    "print(Put_error)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## BUTTERFLY SPREAD OPTION STRATEGY"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Some diagrams to illustrate butterfly strategy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn\n",
    "\n",
    "def call_payoff (sT, strike_price, premium):\n",
    "  return np.where(sT> strike_price, sT-strike_price, 0)-premium\n",
    "# Spot Price\n",
    "s0 = 40\n",
    "# Long Call\n",
    "higher_strike_price_long_call = 35\n",
    "lower_strike_price_long_call=30\n",
    "premium_higher_strike_long_call = 0.85\n",
    "premium_lower_strike_long_call = 3.15\n",
    "# Short Call\n",
    "strike_price_short_call = 32.5\n",
    "premium_short_call = 1.80\n",
    "# Range of call option at expiration\n",
    "sT = np.arange(10,60,1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# OTM Strike Long Call Payoff\n",
    "lower_strike_long_call_payoff = call_payoff(sT, lower_strike_price_long_call, premium_lower_strike_long_call)\n",
    "fig, ax = plt.subplots()\n",
    "ax.spines['bottom'].set_position('zero')\n",
    "ax.plot(sT,lower_strike_long_call_payoff, color='g')\n",
    "ax.set_title('LONG 30 Strike Call')\n",
    "plt.xlabel('Stock Price')\n",
    "plt.ylabel('Profit & Loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Higher Strike Long Call Payoff\n",
    "higher_strike_long_call_payoff = call_payoff(sT, higher_strike_price_long_call, premium_higher_strike_long_call)\n",
    "fig, ax = plt.subplots()\n",
    "ax.spines['bottom'].set_position('zero')\n",
    "ax.plot(sT,higher_strike_long_call_payoff, color='g')\n",
    "ax.set_title('LONG 35 Strike Call')\n",
    "plt.xlabel('Stock Price (sT)')\n",
    "plt.ylabel('Profit & Loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAj4AAAGbCAYAAADEC5psAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8g+/7EAAAACXBIWXMAAA9hAAAPYQGoP6dpAABJ20lEQVR4nO3dd3wUdeLG8c8mIQVCQoBAqKFqKII06QhSlUNB8KQ3BWlKUwRBivQivVoIVUBUyk9PJCByKiggRURBUBQEAhFNQk0gmd8fc6zGUDawyWx53q/Xvm52dnb3yZznPjfznfnaDMMwEBEREfECPlYHEBEREckqKj4iIiLiNVR8RERExGuo+IiIiIjXUPERERERr6HiIyIiIl5DxUdERES8hoqPiIiIeA0VHxEREfEaKj4ibs5ms9GvXz+rY8htjB49GpvNlmadu/731rVrV4oVK5Zmnc1mY/To0ZbkEckoFR8RF3Xw4EHatGlDZGQkgYGBFCpUiMaNGzNnzhzLMu3YsYPRo0cTHx/v0Pbr1q2jadOmFCxYkICAAAoXLkybNm347rvv0mx3/vx5pk6dSr169QgPDydXrlzUqFGDNWvWOJzNZrPd9DFp0iSH3u/o/p4wYQLr1693OJfVzp49y4svvkhUVBTZs2cnR44cVKlShXHjxjn836OIJ/GzOoCIpLdjxw4aNGhA0aJF6dGjBxEREZw8eZKvvvqKWbNm8fzzz1uWa8yYMXTt2pVcuXLdcfuDBw8SFhZG//79yZs3L7GxsSxevJiHHnqInTt3UrFiRQB27tzJ8OHDeeyxxxgxYgR+fn68//77tG3blu+//54xY8Y4lK9x48Z07tw5zbpKlSo59Hc5ur8nTJhAmzZtaNmypUOZAEaMGMHQoUMd3t5Zdu/ezWOPPcbFixfp2LEjVapUAWDPnj1MmjSJ//73v2zevDnLc4lYScVHxAWNHz+e0NBQdu/ena5gnDt3LsvzXLp0iRw5cmT4fSNHjky37tlnn6Vw4cIsWLCAhQsXAlCuXDmOHj1KZGSkfbs+ffrQqFEjJk+ezJAhQxz6/vvuu4+OHTtmOGdm7e8b+83Pzw8/v6z91218fDytWrXC19eXffv2ERUVleb18ePH8+abb2ZpJhFXoFNdIi7op59+oly5cjc9qpIvX76bvmf9+vWUL1+egIAAypUrx6ZNm9Jts2/fPh599FFCQkIIDg6mYcOGfPXVV2m2WbJkCTabje3bt9OnTx/y5ctH4cKFGT16NC+99BIAxYsXt59K+uWXXzL0t+XLl4/s2bOnOc1SvHjxNKUHzFNXLVu2JCkpiZ9//tnhz79y5QpXr17NUCZH97fNZuPSpUssXbrU/vd37doV+Gscz/fff0/79u0JCwujTp06aV67k3HjxuHj45Pm9NrHH39M3bp1yZEjBzlz5qR58+YcOnTojp+1aNEiTp06xfTp09OVHoD8+fMzYsQI+/MNGzbQvHlz+2nJkiVLMnbsWFJSUu74XSLuREd8RFxQZGQkO3fu5LvvvqN8+fJ33P6LL77ggw8+oE+fPuTMmZPZs2fTunVrTpw4QZ48eQA4dOgQdevWJSQkhCFDhpAtWzYWLVpE/fr12b59O9WrV0/zmX369CE8PJyRI0dy6dIlHn30UX788UdWrVrFjBkzyJs3LwDh4eF3zBcfH8+1a9eIjY1l5syZJCYm0rBhwzu+LzY2FsD+XXeyZMkS5s+fj2EYlClThhEjRtC+ffs7vs/R/b18+XKeffZZHnroIXr27AlAyZIl02zz1FNPUbp0aSZMmIBhGA7lBvN02IQJE1i0aBE9evSwf1+XLl1o2rQpkydP5vLlyyxYsIA6deqwb9++dIOM/27jxo0EBQXRpk0bh75/yZIlBAcHM2jQIIKDg/n0008ZOXIkiYmJTJ061eG/Q8TlGSLicjZv3mz4+voavr6+Rs2aNY0hQ4YYn3zyiZGcnJxuW8Dw9/c3jh07Zl934MABAzDmzJljX9eyZUvD39/f+Omnn+zrTp8+beTMmdOoV6+efV10dLQBGHXq1DGuX7+e5rumTp1qAMbx48cz9Pfcf//9BmAARnBwsDFixAgjJSXltu85f/68kS9fPqNu3boOfUetWrWMmTNnGhs2bDAWLFhglC9f3gCM+fPn3/G9GdnfOXLkMLp06ZJu/ahRowzAaNeu3S1f+zvA6Nu3r2EYhjF48GDDx8fHWLJkif31CxcuGLly5TJ69OiR5n2xsbFGaGhouvX/FBYWZlSsWPG22/zd5cuX06177rnnjOzZsxtXr161r+vSpYsRGRmZ7m8ZNWqUw98lYiWd6hJxQY0bN2bnzp08/vjjHDhwgClTptC0aVMKFSrExo0b023fqFGjNEceKlSoQEhIiP0UUUpKCps3b6Zly5aUKFHCvl2BAgVo3749X3zxBYmJiWk+s0ePHvj6+jrl74mOjmbTpk3Mnz+fMmXKcOXKldueQklNTaVDhw7Ex8c7fBXbl19+Sf/+/Xn88cfp1asX33zzDeXLl+eVV17hypUrt31vRvf37fTq1cvhbQ3DoF+/fsyaNYsVK1bQpUsX+2sxMTHEx8fTrl07fv/9d/vD19eX6tWrs23bttt+dmJiIjlz5nQ4S1BQkH35woUL/P7779StW5fLly9z+PBhhz9HxNXpVJeIi6pWrRoffPABycnJHDhwgHXr1jFjxgzatGnD/v37KVu2rH3bokWLpnt/WFgYf/75JwBxcXFcvnyZ+++/P912ZcqUITU1lZMnT1KuXDn7+uLFizvtb6lZs6Z9uW3btpQpUwaAadOm3XT7559/nk2bNrFs2TL7lV8Z5e/vT79+/ewl6MZ4m1vJyP6+nYzst2XLlnHx4kUWLFhAu3bt0rx29OhRAB555JGbvjckJOS2nx0SEsKFCxccznLo0CFGjBjBp59+mq4EJyQkOPw5Iq5OxUfExfn7+1OtWjWqVavGfffdR7du3Vi7di2jRo2yb3OrIzNGBsaY/NPfjwA4U1hYGI888ggrV668afEZM2YM8+fPZ9KkSXTq1OmevqtIkSIA/PHHHw6/x5H9fTsZ2W+1a9dm//79zJ07l3//+9/kzp3b/lpqaipgjvOJiIhI9947XSUWFRXF/v37SU5Oxt/f/7bbxsfH8/DDDxMSEsJrr71GyZIlCQwMZO/evbz88sv2LCKeQMVHxI1UrVoVgDNnzmTofeHh4WTPnp0jR46ke+3w4cP4+PjYS8LtOHJlkiOuXLly06MI8+bNY/To0QwYMICXX375nr/nxqk+RwZg38zN9rez9gFAqVKlmDJlCvXr16dZs2Zs3brVfnrqxqnLfPny0ahRowx/dosWLdi5cyfvv/9+uqNJ//TZZ59x/vx5PvjgA+rVq2dff/z48Qx/r4ir0xgfERe0bdu2mx6t+c9//gNw01NWt+Pr60uTJk3YsGFDmsvPz549yzvvvEOdOnXueOoEsN9Lx9E7/t7sHji//PILW7dutZeKG9asWcMLL7xAhw4dmD59+i0/88aYk99//92+Li4uLt12Fy5cYObMmeTNm9d+475bycj+zpEjh1PveFyhQgX+85//8MMPP9CiRQv7eKSmTZsSEhLChAkTuHbtWrr33exv/rtevXpRoEABBg8ezI8//pju9XPnzjFu3DjgryOGf98HycnJzJ8//67/LhFXpSM+Ii7o+eef5/Lly7Rq1YqoqCiSk5PZsWMHa9asoVixYnTr1i3Dnzlu3DhiYmKoU6cOffr0wc/Pj0WLFpGUlMSUKVMc+owbBWL48OG0bduWbNmy0aJFi1veXPCBBx6gYcOGPPjgg4SFhXH06FHefvttrl27lmYqiV27dtG5c2fy5MlDw4YNWblyZZrPqVWrln1Q9q5du2jQoAGjRo2yzw81b9481q9fT4sWLShatChnzpxh8eLFnDhxguXLl9/xVE9G9neVKlXYsmUL06dPp2DBghQvXjzdrQAyqkaNGmzYsIHHHnuMNm3asH79ekJCQliwYAGdOnWicuXKtG3blvDwcE6cOMFHH31E7dq1mTt37i0/MywsjHXr1vHYY4/x4IMPprlz8969e1m1apV97FWtWrUICwujS5cuvPDCC9hsNpYvX35Pp0pFXJaVl5SJyM19/PHHRvfu3Y2oqCgjODjY8Pf3N0qVKmU8//zzxtmzZ9Nsy98ui/67yMjIdJdd792712jatKkRHBxsZM+e3WjQoIGxY8eONNvcuJx99+7dN802duxYo1ChQoaPj88dL20fNWqUUbVqVSMsLMzw8/MzChYsaLRt29b49ttvb/qdt3pER0fbt922bVu6y6c3b95sNG7c2IiIiDCyZctm5MqVy2jSpImxdevWW2b7u4zs78OHDxv16tUzgoKCDMC+j29csh4XF3fT/fDPf93e7L+3DRs2GH5+fsbTTz9tv9x/27ZtRtOmTY3Q0FAjMDDQKFmypNG1a1djz549Dv1tp0+fNgYOHGjcd999RmBgoJE9e3ajSpUqxvjx442EhAT7dl9++aVRo0YNIygoyChYsKD9kn7A2LZtm307Xc4u7s5mGKr0IiIi4h00xkdERES8hoqPiIiIeA0VHxEREfEaKj4iIiLiNVR8RERExGvoPj7/kJqayunTp8mZM6dT79AqIiIimccwDC5cuEDBggXx8bn1cR0Vn384ffq0Q7fuFxEREddz8uRJChcufMvXVXz+4cY8OSdPnnToFv4iIiJivcTERIoUKWL/Hb8VFZ9/uHF6KyQkRMVHRETEzdxpmIoGN4uIiIjXUPERERERr6HiIyIiIl5DxUdERES8hoqPiIiIeA0VHxEREfEaKj4iIiLiNVR8RERExGuo+IiIiIjXUPERERERr6HiIyIiIl5DxUdERES8hoqPiIiIeA3Nzp5VKlaE5GSrU4i4vsBA6NsXnnkG7jDLsohIRqn4ZJUjRyApyeoUIu6hRw/YsAHefBMiIqxOIyIeRMUnq8TEgGFYnULE9e3cCSNHwocfQvnysGgRtG5tdSoR8RA2w9Cv8d8lJiYSGhpKQkICISEhVscR8U7ffQedOsH+/ebzjh1hzhzIlcvKVCLiwhz9/dbgZhFxPeXLw9dfw/Dh4OMDK1bAAw/Ali1WJxMRN6fiIyKuyd8fxo2DL76AUqXgt9+gcWN44QW4fNnqdCLiplR8RMS11axpnvLq08d8PmcOVK7812kwEZEMUPEREdeXIwfMmwebNkHBguZVko8+CnFxVicTETej4iMi7qNpUzh4EMqWhdhY6NZNV0uKSIao+IiIe8mdG1avhoAA+Ogj89SXiIiDVHxExP088ABMn24uv/SSxvuIiMNUfETEPfXuDU88YU4F07YtXLpkdSIRcQMqPiLinmw2ePttKFTIHOzcv7/ViUTEDaj4iIj7ypPHvLnhjRK0Zo3ViUTExan4iIh7q18fXnnFXO7ZE375xco0IuLiVHxExP2NGmXe6DAxEdq3h+vXrU4kIi5KxUdE3F+2bPDOOxASYs7uPmaM1YlExEWp+IiIZyhWDN54w1wePx4++8zKNCLiolR8RMRzPP00dO9u3s25Qwf4/XerE4mIi1HxERHPMns23H8/nD4NzzyjKS1EJA2PLD7z5s2jWLFiBAYGUr16dXbt2mV1JBHJKjlywKpV4O8PGzfC/PlWJxIRF+JxxWfNmjUMGjSIUaNGsXfvXipWrEjTpk05d+6c1dFEJKtUqgRTppjLgwfDt99am0dEXIbHFZ/p06fTo0cPunXrRtmyZVm4cCHZs2dn8eLFVkcTkaz0wgvw2GOQlATt2sHly1YnEhEX4Gd1AGdKTk7mm2++YdiwYfZ1Pj4+NGrUiJ07d970PUlJSSQlJdmfJyQkAJCYmJi5YUUk882eDXv2wPffQ79+MHOm1YlEJJPc+N027jCuz6OKz++//05KSgr58+dPsz5//vwcPnz4pu+ZOHEiY25yz48iRYpkSkYRsUh0tPkQEY924cIFQkNDb/m6RxWfuzFs2DAGDRpkf56QkEDRokU5efIkISEhTvuexMREihQp4vTPlZvT/s5aLr+/R40yj/aEhsKXX4Kb/x8bl9/fHkb7O2vd7f6+8b6cOXPedjuPKj558+bF19eXs2fPpll/9uxZIiIibvqegIAAAgIC0q0PCQnJlH/AM+tz5ea0v7OWy+7vKVNgxw7YtQt69YJt28DP/f/157L720Npf2etu93fNpvttq971OBmf39/qlSpwtatW+3rUlNT2bp1KzVr1rQwmYhY6saUFjlzwhdfmHd2FhGv5FHFB2DQoEG8+eabLF26lB9++IHevXtz6dIlunXrZnU0EbFSyZKwYIG5/Npr8Pnn1uYREUu4/7Hef3j66aeJi4tj5MiRxMbG8uCDD7Jp06Z0A56zWkBAAKNGjbrpaTVxPu3vrOU2+7tDB9i8GZYtM5f374fcua1OlWFus789hPZ31srs/W0z7nTdl5dJTEwkNDSUhIQEncsV8UQXLkDlynDsGDz5JLz3HtxhTICIuD5Hf7897lSXiMht5cwJq1eb434++OCvGd1FxCuo+IiI96lSBSZMMJcHDIBDhyyNIyJZR8VHRLzToEHQpAlcvWpOaXHlitWJRCQLqPiIiHfy8YGlSyFfPjh4EF56yepEIpIFVHxExHtFRJjlB2DePNiwwdo8IpLpVHyc7L///S8tWrSgYMGC2Gw21q9fn+Z1wzAYOXIkBQoUICgoiEaNGnH06FFrwrq5iRMnUq1aNXLmzEm+fPlo2bIlR44cSbPN1atX6du3L3ny5CE4OJjWrVunu7O3OGbBggVUqFDBfjfVmjVr8vHHH9tfd9t93awZDB5sLnfvDr/9Zm2eW5g0aRI2m40BAwbY17ntPndBo0ePxmazpXlERUXZX9e+dr5Tp07RsWNH8uTJQ1BQEA888AB79uyxv55Zv5cqPk526dIlKlasyLx58276+pQpU5g9ezYLFy7k66+/JkeOHDRt2pSrV69mcVL3t337dvr27ctXX31FTEwM165do0mTJly6dMm+zcCBA/m///s/1q5dy/bt2zl9+jRPPvmkhandV+HChZk0aRLffPMNe/bs4ZFHHuGJJ57g0P8GBrv1vp4wwRzw/Mcf0LEjpKRYnSiN3bt3s2jRIipUqJBmvVvvcxdUrlw5zpw5Y3988cUX9te0r53rzz//pHbt2mTLlo2PP/6Y77//ntdff52wsDD7Npn2e2lIGgkJCQZgJCQk3PNnAca6devsz1NTU42IiAhj6tSp9nXx8fFGQECAsWrVqnv+Pm937tw5AzC2b99uGIa5b7Nly2asXbvWvs0PP/xgAMbOnTutiulRwsLCjLfeessz9vWPPxpGjhyGAYYxbpzVaewuXLhglC5d2oiJiTEefvhho3///oZh6J9vZxs1apRRsWLFm76mfe18L7/8slGnTp1bvn43v5eO/n7riE8WOn78OLGxsTRq1Mi+LjQ0lOrVq7Nz504Lk3mGhIQEAHL/706833zzDdeuXUuzv6OioihatKj29z1KSUlh9erVXLp0iZo1a3rGvi5d2hznA+Zs7jt2WJvnf/r27Uvz5s3T7FvQP9+Z4ejRoxQsWJASJUrQoUMHTpw4AWhfZ4aNGzdStWpVnnrqKfLly0elSpV488037a9n5u+lik8Wio2NBUg3fUb+/Pntr8ndSU1NZcCAAdSuXZvy5csD5v729/cnV65cabbV/r57Bw8eJDg4mICAAHr16sW6desoW7as5+zrzp2hfXvzVFf79hAfb2mc1atXs3fvXiZOnJjuNY/Z5y6ievXqLFmyhE2bNrFgwQKOHz9O3bp1uXDhgvZ1Jvj5559ZsGABpUuX5pNPPqF379688MILLP3fxQaZ+XvpcXN1iXfq27cv3333XZpz8uJ8999/P/v37ychIYH33nuPLl26sH37dqtjOY/NZk5kunMnHD8OPXvCmjWWTGlx8uRJ+vfvT0xMDIGBgVn+/d7m0UcftS9XqFCB6tWrExkZybvvvktQUJCFyTxTamoqVatWZcL/biRaqVIlvvvuOxYuXEiXLl0y9bt1xCcLRUREAKS7EuDs2bP21yTj+vXrx4cffsi2bdsoXLiwfX1ERATJycnE/+P/tWt/3z1/f39KlSpFlSpVmDhxIhUrVmTWrFmeta9DQmDVKvDzg7VrYfFiS2J88803nDt3jsqVK+Pn54efnx/bt29n9uzZ+Pn5kT9/fs/Z5y4oV65c3HfffRw7dsyz/vl2EQUKFKBs2bJp1pUpU8Z+ejEzfy9VfLJQ8eLFiYiIYOvWrfZ1iYmJfP3119SsWdPCZO7JMAz69evHunXr+PTTTylevHia16tUqUK2bNnS7O8jR45w4sQJ7W8nSU1NJSkpyfP2dfXqMG6cufzCC3D4cJZHaNiwIQcPHmT//v32R9WqVenQoYN92aP2uYu5ePEiP/30EwUKFPC8f75dQO3atdPdfuTHH38kMjISyOTfy7saju3B7vWqrgsXLhj79u0z9u3bZwDG9OnTjX379hm//vqrYRiGMWnSJCNXrlzGhg0bjG+//dZ44oknjOLFixtXrlxx5p/hFXr37m2EhoYan332mXHmzBn74/Lly/ZtevXqZRQtWtT49NNPjT179hg1a9Y0atasaWFq9zV06FBj+/btxvHjx41vv/3WGDp0qGGz2YzNmzcbhuGB+zolxTAaNTKv8qpY0TBc4H+jf7+qyzA8cJ9baPDgwcZnn31mHD9+3Pjyyy+NRo0aGXnz5jXOnTtnGIb2tbPt2rXL8PPzM8aPH28cPXrUWLlypZE9e3ZjxYoV9m0y+nvp6O+3is8/3Gvx2bZtmwGke3Tp0sUwDPMSvVdffdXInz+/ERAQYDRs2NA4cuSIE/8C73Gz/QwY0dHR9m2uXLli9OnTxwgLCzOyZ89utGrVyjhz5ox1od1Y9+7djcjISMPf398IDw83GjZsaC89huGh+/r0acPIm9csP38rHFb5Z/HxyH1ukaefftooUKCA4e/vbxQqVMh4+umnjWPHjtlf1752vv/7v/8zypcvbwQEBBhRUVHGG2+8keb1jP5eOvr7bTMMw7i3Y0aeJTExkdDQUBISEggJCbE6johY7aOP4F//Mpc//BCaN7c2j4jclKO/3xrjIyJyO82bQ//+5nLXrnDmjKVxROTeqPiIiNzJ5Mnw4IPw++/QqROkplqdSETukoqPiMidBASYl7hnzw5bt8LUqVYnEpG7pOIjIuKIqCiYM8dcHjECdu2yNo+I3BUVHxERR3XrBk8/DdevQ7t2kJhodSIRySAVHxERR9lssHAhREbCzz9D796gC2NF3IqKj4hIRuTKBe+8A76+5n8uX251IhHJABUfEfEoS5YsSTeLttPVqgVjxgBw/bnn4OjRzP0+EXEaFR8RyRRxcXH07t2bokWLEhAQQEREBE2bNuXLL7+0b2Oz2Vi/fr11If+nfv362Gw2bDYbgYGBlC1blvnz59/+TUOHklSrFn5Xr5rjfZKTsyasiNwTFR8RyRStW7dm3759LF26lB9//JGNGzdSv359zp8/b3W0m+rRowdnzpzh+++/59///jd9+/Zl1apVN902OTkZfH0JePddyJ0bvvkGhg/P4sQicjdUfETE6eLj4/n888+ZPHkyDRo0IDIykoceeohhw4bx+OOPA1CsWDEAWrVqhc1msz8HWLBgASVLlsTf35/777+f5f8YRxMfH89zzz1H/vz5CQwMpHz58nz44Yc3zRIXF0fVqlVp1aoVSUlJt8ycPXt2IiIiKFGiBKNHj6Z06dJs3LgRMI8I9evXjwEDBpA3b16aNm0KgK1wYb7q2dP8gGnTiFuxgnbt2pE7d25y5MhB1apV+frrr+3fsWHDBipXrkxgYCAlSpRgzJgxXL9+PUP7VkTujZ/VAUTE8wQHBxMcHMz69eupUaMGAQEB6bbZvXs3+fLlIzo6mmbNmuHr6wvAunXr6N+/PzNnzqRRo0Z8+OGHdOvWjcKFC9OgQQNSU1N59NFHuXDhAitWrKBkyZJ8//339vf/3cmTJ2ncuDE1atTg7bffvuk2txIUFGQe2fmfpUuX0rt37zSn6gBiq1eHvn1h3jxsXbtyuWpVNm7cSEREBHv37iX1f3d5/vzzz+ncuTOzZ8+mbt26/PTTT/T8X2kaNWqUw7lE5B45YYJVj3Kvs7OLiOm9994zwsLCjMDAQKNWrVrGsGHDjAMHDqTZBjDWrVuXZl2tWrWMHj16pFn31FNPGY899phhGIbxySefGD4+PrecpTk6OtoIDQ01Dh8+bBQpUsR44YUXjNTU1Ntm/fus59evXzeWL19uAMbcuXPtr1eqVCnd++z5L182fi9UyDDASGrY0DBSUtJt27BhQ2PChAlp1i1fvtwoUKDAbbOJiGMc/f3WqS4RyRStW7fm9OnTbNy4kWbNmvHZZ59RuXJllixZctv3/fDDD9SuXTvNutq1a/PDDz8AsH//fgoXLsx99913y8+4cuUKdevW5cknn2TWrFnYbLY75p0/fz7BwcEEBQXRo0cPBg4cSO/eve2vV6lS5dZvDgpiXp06XPXxwX/rVpg5M90mBw4c4LXXXrMfDQsODraPK7p8+fId84mIc6j4iEimCQwMpHHjxrz66qvs2LGDrl273vNpnaCgoDtuExAQYD9NdurUKYc+t0OHDuzfv5/jx49z6dIlpk+fjo/PX/+KzJEjx23fn1CoEHNLlDCfDB1qDnj+m4sXLzJmzBj2799vfxw8eJCjR48SGBjoUEYRuXcqPiKSZcqWLculS5fsz7Nly0ZKSkqabcqUKZNuHM2XX35J2bJlAahQoQK//fYbP/744y2/x8fHh+XLl1OlShUaNGjA6dOn75gtNDSUUqVKUahQoTSFx1EVKlRg7NmzJP/rX3DtGrRtCxcu2F+vXLkyR44coVSpUuked/N9InJ39L82EXG68+fP88gjj7BixQq+/fZbjh8/ztq1a5kyZQpPPPGEfbtixYqxdetWYmNj+fPPPwF46aWXWLJkCQsWLODo0aNMnz6dDz74gBdffBGAhx9+mHr16tG6dWtiYmI4fvw4H3/8MZs2bUqTwdfXl5UrV1KxYkUeeeQRYmNjM/VvbteuHREFCtDq999JypcPjh3jl8cfZ+fOnQCMHDmSZcuWMWbMGA4dOsQPP/zA6tWrGTFiRKbmEpG0VHxExOmCg4OpXr06M2bMoF69epQvX55XX32VHj16MHfuXPt2r7/+OjExMRQpUoRKlSoB0LJlS2bNmsW0adMoV64cixYtIjo6mvr169vf9/7771OtWjXatWtH2bJlGTJkSLojRwB+fn6sWrWKcuXK8cgjj3Du3LlM+5v9/f3ZvHkzQYUK8cTFi6QAxT77jHxbtgDQtGlTPvzwQzZv3ky1atWoUaMGM2bMIDIyMtMyiUh6NsPQDHt/l5iYSGhoKAkJCYSEhFgdR0Tc1ejR5rQWOXPC/v1wY/yPiGQKR3+/dcRHRCQzjBgBdeqY43zatTPH/YiI5VR8REQyg58frFxpzua+axeMHGl1IhFBxUdEJPMULQpvvWUuT54MW7dam0dEVHxERDJV69bQsycYBnTqBHFxVicS8WoqPiIimW3GDChTBs6cgW7dzBIkIpZQ8RERyWzZs8Pq1RAQAB99BHPmWJ1IxGup+IiIZIUKFeD1183ll14yL3EXkSyn4iMiklX69IHHH4fkZHNKi79N3yEiWUPFR0Qkq9hs8PbbULAgHDkCAwZYnUjE66j4iIhkpbx5YcUKswS99Ra8+67ViUS8ioqPiEhWa9AAXnnFXO7ZE375xdI4It5ExUdExAqjRkHNmpCQAO3bw/XrVicS8QoqPiIiVsiWDd55B0JCYOdOc0JTEcl0Kj4iIlYpVgzeeMNcHj8ePvvMyjQiXkHFR0TESk8/Dd27m3dz7tgRzp+3OpGIR1PxERGx2uzZcP/9cOoUPPOMprQQyUQqPiIiVsuRA1atAn9/2LABFiywOpGIx1LxERFxBZUqweTJ5vKgQXDwoLV5RDyURxWfYsWKYbPZ0jwmTZpkdSwREcf07w+PPQZJSeaUFpcvW51IxON4VPEBeO211zhz5oz98fzzz1sdSUTEMTYbREdDRAR8/z0MHmx1IhGP42d1AGfLmTMnERERDm+flJREUlKS/XliYmJmxBIRcUy+fLB8OTRpAgsXQuPG8OSTVqcS8Rged8Rn0qRJ5MmTh0qVKjF16lSu3+FuqBMnTiQ0NNT+KFKkSBYlFRG5hUaNYMgQc/mZZ+DECWvziHgQm2F4znWT06dPp3LlyuTOnZsdO3YwbNgwunXrxvTp02/5npsd8SlSpAgJCQmEhIRkRWwRkfSuXYPatWH3bqhbFz79FPw87iC9iNMkJiYSGhp6x99vly8+Q4cOZfKNKx1u4YcffiAqKird+sWLF/Pcc89x8eJFAgICHPo+R3eciEim++kn82qvCxdg9Ghzfi8RuSmPKT5xcXGcv8OdTEuUKIG/v3+69YcOHaJ8+fIcPnyY+++/36HvU/EREZeycqV5R2cfH9i+HerUsTqRiEty9Pfb5Y+bhoeHEx4eflfv3b9/Pz4+PuTLl8/JqUREskiHDrB5MyxbZs7ifuAAhIVZnUrEbbl88XHUzp07+frrr2nQoAE5c+Zk586dDBw4kI4dOxKmf0mIiDubOxd27IBjx6BHD1i71rz0XUQyzGOu6goICGD16tU8/PDDlCtXjvHjxzNw4EDeuDHzsYiIu8qZ05zSIls2eP99ePNNqxOJuC2XH+OT1TTGR0Rc1rRp8NJLEBQEe/ZA2bJWJxJxGY7+fnvMER8REY83aJB5Y8MrV8wpLa5csTqRiNtR8RERcRc+PrB0qXl354MH/7rJoYg4TMVHRMSdRESY5QfMQc8bN1qbR8TNqPiIiLibZs3+msC0Wzc4dcraPCJuRMVHRMQdTZgAlSvDH39Ap06QkmJ1IhG3oOIjIuKO/P3NS9xz5IBt2+AOU/uIiEnFR0TEXd13H8ybZy6PHAk7d1qbR8QNqPiIiLizzp3NqSxSUsz/TEiwOpGIS1PxERFxZzYbLFgAxYvDL7/Ac8+B7ksrcksqPiIi7i4kxBzv4+cHa9bAkiVWJxJxWSo+IiKeoHp1GDvWXO7XD44csTaPiItS8RER8RRDhkDDhnD5sjmlRVKS1YlEXI6Kj4iIp/DxgWXLIG9e2L8fhg2zOpGIy1HxERHxJAUL/jXGZ8YM+M9/LI0j4mpUfEREPE3z5vDCC+Zy165w5oylcURciYqPiIgnmjwZKlaEuDjzXj+pqVYnEnEJKj4iIp4oMBBWr4bs2WHLFpg2zepEIi5BxUdExFNFRcHs2eby8OGwe7e1eURcgIqPiIgn694d/v1vuH4d2rWDxESrE4lYSsVHRMST2WywaBFERsJPP0HfvlYnErGUio+IiKfLlQveeQd8fWHFCli+3OpEIpZR8RER8Qa1asHo0eZynz5w9KilcUSsouIjIuIthg2Dhx+GixfN8T7JyVYnEslyKj4iIt7ixqmu3Lnhm2/MK71EvIyKj4iINylcGBYvNpenTYNPPrE2j0gWU/EREfE2TzxhjvMB6NIFzp61No9IFlLxERHxRtOmQfnyZunp0kVTWojXUPEREfFGQUHmlBaBgebprpkzrU4kkiVUfEREvFW5cn8VnqFDzQHPIh5OxUdExJv17AlPPgnXrpmXuF+8aHUikUyl4iMi4s1sNnjzTfNqr6NH4fnnrU4kkqlUfEREvF3u3LByJfj4wJIl5vQWIh5KxUdERKBePXj1VXO5Vy/4+Wdr84hkkgwXn5MnT/Lbb7/Zn+/atYsBAwbwxhtvODWYiIhksREjoHZtuHDBHO9z7ZrViUScLsPFp3379mzbtg2A2NhYGjduzK5duxg+fDivvfaa0wOKiEgW8fMzT3nlygW7dsHIkVYnEnG6DBef7777joceegiAd999l/Lly7Njxw5WrlzJkiVLnJ1PRESyUmSkOdgZYPJk2LrV2jwiTpbh4nPt2jUCAgIA2LJlC48//jgAUVFRnDlzxrnpREQk67VpAz16gGFAp04QF2d1IhGnyXDxKVeuHAsXLuTzzz8nJiaGZs2aAXD69Gny5Mnj9IAiImKBmTOhTBk4cwa6dTNLkIgHyHDxmTx5MosWLaJ+/fq0a9eOihUrArBx40b7KTAREXFz2bPDqlUQEAAffQSzZ1udSMQpbIaR8RqfkpJCYmIiYWFh9nW//PIL2bNnJ1++fE4NmNUSExMJDQ0lISGBkJAQq+OIiFhr7lzzpob+/vD11/Dgg1YnErkpR3+/M3zE58qVKyQlJdlLz6+//srMmTM5cuSI25ceERH5h759oUULSE6Gtm3h0iWrE4nckwwXnyeeeIJly5YBEB8fT/Xq1Xn99ddp2bIlCxYscHpAERGxkM0GixdDgQJw5AgMGGB1IpF7kuHis3fvXurWrQvAe++9R/78+fn1119ZtmwZs3UOWETE8+TNCytWmCXorbfg3XetTiRy1zJcfC5fvkzOnDkB2Lx5M08++SQ+Pj7UqFGDX3/91ekBRUTEBTzyCAwbZi737Am//GJpHJG7leHiU6pUKdavX8/Jkyf55JNPaNKkCQDnzp3TYGAREU82ejTUqAEJCdC+PVy/bnUikQzLcPEZOXIkL774IsWKFeOhhx6iZs2agHn0p1KlSk4PKCIiLiJbNnPm9pAQ2LkTxoyxOpFIht3V5eyxsbGcOXOGihUr4uNjdqddu3YREhJCVFSU00NmJV3OLiJyB6tXm5OY2mzw6adQv77ViUQc/v2+q+Jzw41Z2gsXLny3H+FyVHxERBzQvTtER0OhQnDgAOjO/WKxTLuPT2pqKq+99hqhoaFERkYSGRlJrly5GDt2LKmpqfcUWkRE3MTs2XDffXDqFDzzjKa0ELeR4eIzfPhw5s6dy6RJk9i3bx/79u1jwoQJzJkzh1dffTUzMoqIiKsJDjZPefn7w4YNoPu4iZvI8KmuggULsnDhQvus7Dds2LCBPn36cOrUKacGzGo61SUikgEzZ8LAgeacXrt3wwMPWJ1IvFSmner6448/bjqAOSoqij/++COjHyciIu6sf3949FFISjKntLh82epEIreV4eJTsWJF5s6dm2793Llz7TO1Z4bx48dTq1YtsmfPTq5cuW66zYkTJ2jevLl9stSXXnqJ67rPhIhI5rHZYMkSiIiA77+HwYOtTiRyW34ZfcOUKVNo3rw5W7Zssd/DZ+fOnZw8eZL//Oc/Tg94Q3JyMk899RQ1a9bk7bffTvd6SkoKzZs3JyIigh07dnDmzBk6d+5MtmzZmDBhQqblEhHxevnywbJl0KQJLFwIjRvDk09anUrkpu7qcvbTp08zb948Dh8+DECZMmXo06cPBQsWdHrAf1qyZAkDBgwgPj4+zfqPP/6Yf/3rX5w+fZr8+fMDsHDhQl5++WXi4uLw9/d36PM1xkdE5C69/DJMmQK5cpmXuBctanUi8SKZNsYHzAHO48eP5/333+f9999n3LhxpKam0rNnz7sOfK927tzJAw88YC89AE2bNiUxMZFDhw7d8n1JSUkkJiameYiIyF0YOxaqVYP4eOjYUVNaiEu6q+JzM+fPn7/pKaisEhsbm6b0APbnsbGxt3zfxIkTCQ0NtT+KFCmSqTlFRDyWvz+sWgU5c8Lnn8P48VYnEknHacXnbgwdOhSbzXbbx43TaZll2LBhJCQk2B8nT57M1O8TEfFoJUv+dU+f116DL76wNo/IP2R4cLMzDR48mK5du952mxIlSjj0WREREezatSvNurNnz9pfu5WAgAACAgIc+g4REXFAhw7wySewfLk5i/uBAxAWZnUqEcDi4hMeHk54eLhTPqtmzZqMHz+ec+fOkS9fPgBiYmIICQmhbNmyTvkOERFx0Lx55gzux45Bjx6wdq156buIxRwuPk/e4dLEf15l5WwnTpzgjz/+4MSJE6SkpLB//34ASpUqRXBwME2aNKFs2bJ06tSJKVOmEBsby4gRI+jbt6+O6IiIZLWcOc3xPjVrwvvvw5tvgoUXwIjc4PDl7N26dXPoA6Ojo+8p0K107dqVpUuXplu/bds26tevD8Cvv/5K7969+eyzz8iRIwddunRh0qRJ+Pk5fmBLl7OLiDjRtGnw0ksQFAR79oCOwEsmcfT3+67u4+PJVHxERJwoNdWc0mLzZnMer127IDDQ6lTigTL1Pj4iIiIO8fGBpUvNuzsfPGge/RGxkIqPiIhkrogIs/wAzJ0LGzZYm0e8moqPiIhkvmbNYNAgc7l7dzh1yto84rVUfEREJGtMmACVK8Mff0CnTpCSYnUi8UJOLT5JSUnO/DgREfEkAQHmJe45csC2bTB5stWJxAvddfF5/PHHmTZtGmfOnAEgLi6OBg0aOC2YiIh4oPvuM29uCDBypHmTQ5EsdNfFp1ixYnz00UeULFmSgQMHUqNGDa5cueLMbCIi4ok6dzanskhJMf8zIcHqROJF7vk+PqtXr6Z9+/YEBwfz66+/Eubm87HoPj4iIlkgMREefBCOH4ennzZPgWlKC7kHTr+PT79+/XjrrbfSrPvpp58YOHAg3bt3p0qVKsyZM+fuE4uIiPcICTHLjp8frFkDS5ZYnUi8hMPFZ8OGDTz00EP252fOnKFx48a0bduWt956i+HDh7N8+fJMCSkiIh6oenUYO9Zc7tcPjhyxNo94BYeLz/nz5wkODgbgzz//pGnTpnTq1IkZM2YAUKJECU7pvgwiIpIRQ4ZAw4Zw+TK0bQu6OlgymcPFJyoqinHjxrFlyxYaNmzIE088wZgxY+yvf/nll0RGRmZKSBER8VA+PrBsGeTNC/v3w7BhVicSD+dw8ZkwYQJr1qyhdevWlCxZktWrV/PJJ59w9uxZ1q5dy+DBg+nSpUtmZhUREU9UsOBfY3xmzID//MfSOOLZMnRV140bFAYEBDBmzBgmTZpEcnIyhmHQtm1bli1bhp+fX6aFzQq6qktExCL9+8Ps2RAeDgcOQIECVicSN+Lo7/c9Xc4eHx/PkSNHKFSoEIULF77bj3EpKj4iIha5ehVq1DBLT6NG8Mkn5qkwEQc4/XL2m8mVKxfVq1f3mNIjIiIWCgyE1ashe3bYsgVef93qROKBVKVFRMR1REWZp7sAXnkFdu+2No94HBUfERFxLd27w1NPwfXr0K6deZdnESdR8REREddis8Ebb0BkJPz0E/Tta3Ui8SAZLj4nTpzgZuOhDcPgxIkTTgklIiJeLlcueOcd8PWFFStAMwOIk2S4+BQvXpy4uLh06//44w+KFy/ulFAiIiLUqgWjR5vLffrAsWOWxhHPkOHiYxgGtpvMoHvx4kUCAwOdEkpERAQw7+T88MNw8aI53ic52epE4uYcvtvgoEGDALDZbLz66qtkz57d/lpKSgpff/01Dz74oNMDioiIF7txqqtiRdizB0aMgClTrE4lbszh4rNv3z7APOJz8OBB/P397a/5+/tTsWJFXnzxRecnFBER71a4MLz9NrRqBVOnmjc3bNLE6lTipjJ85+Zu3boxa9Ysj72rse7cLCLiovr0gQULIH9++PZbyJfP6kTiQrJkygpPpOIjIuKirlyBatXg0CFo1gw++khTWoido7/fDp3qevLJJ1myZAkhISE8+eSTt932gw8+yFhSERERRwQFmVNaVKsGmzbBzJnwv/GnIo5yqCqHhobar+QKCQkhNDT0lg8REZFMU748zJhhLg8dCnv3WptH3I5Dp7o2btzIo48+SrZs2bIik6V0qktExMUZBjz5JKxfD6VLm+UnONjqVGIxp87O3qpVK+Lj4wHw9fXl3LlzTgkpIiKSYTabeZVX4cJw9Cg8/7zVicSNOFR8wsPD+eqrr4Bb38BQREQky+TObd7fx8cHliyBVausTiRuwqHi06tXL5544gl8fX2x2WxERETg6+t704eIiEiWePhh84aGAL16wc8/W5tH3ILDl7MfPnyYY8eO8fjjjxMdHU2uXLluut0TTzzhzHxZTmN8RETcyPXrUL8+fPklVK8On38OXjAeVdLLtPv4jBkzhpdeeinNlBWeRMVHRMTN/PorPPggxMebc3tNmGB1IrFApt/AMC4ujiNHjgBw//33Ex4efndJXYyKj4iIG3rvPXjqKXPg85Yt8MgjVieSLObUq7r+7vLly3Tv3p2CBQtSr1496tWrR8GCBXnmmWe4fPnyPYUWERG5K23aQI8e5qXuHTvC779bnUhcVIaLz8CBA9m+fTsbN24kPj6e+Ph4NmzYwPbt2xk8eHBmZBQREbmzmTOhTBk4cwa6dTNLkMg/ZPhUV968eXnvvfeoX79+mvXbtm3j3//+N3Fxcc7Ml+V0qktExI0dOGAOck5KgtmzdY8fL5Kpp7ry58+fbn2+fPl0qktERKxVsSJMm2Yuv/iiWYRE/ibDxadmzZqMGjWKq1ev2tdduXKFMWPGULNmTaeGExERybC+faFFC0hOhrZt4dIlqxOJC3Fodva/mzlzJs2aNaNw4cJUrFgRgAMHDhAYGMgnn3zi9IAiIiIZYrPB4sXm0Z/Dh2HAAHjzTatTiYu4q8vZL1++zMqVKzl8+DAAZcqUoUOHDgQFBTk9YFbTGB8REQ/x6afQqJE5yPndd83L3cVjZcp9fK5du0ZUVBQffvghZcqUcUpQV6PiIyLiQYYPN29oGBpqjveJjLQ6kWSSTBncnC1btjRje0RERFza6NFQowYkJED79uYUF+LVMjy4uW/fvkyePJnr+odHRERcXbZs8M47EBICO3bAa69ZnUgsluExPq1atWLr1q0EBwfzwAMPkCNHjjSvf/DBB04NmNV0qktExAOtWmUe8bHZYNs2c2Z38SiO/n5n+KquXLly0bp163sKJyIikqXatYOYGIiONqe02L8f8uSxOpVY4K4nKfVUOuIjIuKhLl6EKlXgxx+hZUv44APzCJB4BKcPbk5NTWXy5MnUrl2batWqMXToUK5cueKUsCIiIpkuOBhWrwZ/f1i/HhYutDqRWMDh4jN+/HheeeUVgoODKVSoELNmzaJv376ZmU1ERMS5KlWCSZPM5YED4eBBa/NIlnP4VFfp0qV58cUXee655wDYsmULzZs358qVK/j4ZPjiMJelU10iIh4uNRX+9S/4+GMoVw527wYPuAGvt3P6qa4TJ07w2GOP2Z83atQIm83G6dOn7y2piIhIVvLxgSVLIH9+OHQIBg2yOpFkIYeLz/Xr1wkMDEyzLlu2bFy7ds3poURERDJVvnywfLm5vHAhrFtnbR7JMg5fzm4YBl27diUgIMC+7urVq/Tq1SvNvXwy6z4+48eP56OPPmL//v34+/sTHx+fbhvbTUbnr1q1irZt22ZKJhERcWONG8OQITBlCjzzDFStCkWKWJ1KMpnDxadLly7p1nXs2NGpYW4nOTmZp556ipo1a/L222/fcrvo6GiaNWtmf54rV64sSCciIm5p7FhzMtM9e8z7+3z6Kfj6Wp1KMpHDxSc6Ojozc9zRmDFjAFiyZMltt8uVKxcREREOf25SUhJJSUn254mJiXeVT0RE3JC/v3lX50qV4L//NSc0ffVVq1NJJvKcy7H+p2/fvuTNm5eHHnqIxYsXc6eL1iZOnEhoaKj9UUSHOUVEvEupUrBggbk8ejR8+aWlcSRzeVTxee2113j33XeJiYmhdevW9OnThzlz5tz2PcOGDSMhIcH+OHnyZBalFRERl9GxI3TqZF7q3r49/Pmn1Ykkk1hafIYOHYrNZrvt4/Dhww5/3quvvkrt2rWpVKkSL7/8MkOGDGHq1Km3fU9AQAAhISFpHiIi4oXmzTOP/pw4AT17gmZ08kgZnqTUmQYPHkzXrl1vu02JEiXu+vOrV6/O2LFjSUpKSnM1moiISDo5c5rjfWrWhPfeg7ffhmeftTqVOJmlxSc8PJzw8PBM+/z9+/cTFham0iMiIo6pWtUc4DxkCLzwAtSuDWXKWJ1KnMjS4pMRJ06c4I8//uDEiROkpKSwf/9+AEqVKkVwcDD/93//x9mzZ6lRowaBgYHExMQwYcIEXnzxRWuDi4iIexk8GGJizEfbtvD11/CPG/iK+3J4ri6rde3alaVLl6Zbv23bNurXr8+mTZsYNmwYx44dwzAMSpUqRe/evenRo0eG5hLTXF0iIkJsLFSoAHFx8PzzMHu21YnkDhz9/Xab4pNVVHxERAQwJzG9MUflxo3QooW1eeS2nD5JqYiIiFd59NG/JjDt1g00KbdHUPERERG5lQkToHJlOH/evNdPSorVieQeqfiIiIjcSkCAeYl7jhywbZs5oam4NRUfERGR27nvPpg711x+9VX46itr88g9UfERERG5ky5doF0781RX+/aQkGB1IrlLKj4iIiJ3YrOZE5kWKwbHj0OvXprSwk2p+IiIiDgiNNQc7+PrC6tXw03uLSeuT8VHRETEUTVqwNix5nK/fnDkiLV5JMNUfERERDJiyBBo0AAuXTLH/SQlWZ1IMkDFR0REJCN8fWH5csiTB/btg2HDrE4kGaDiIyIiklGFCkF0tLk8Y4Y5vYW4BRUfERGRu9GihTmBKZiXu8fGWptHHKLiIyIicremTPlrFvfOnSE11epEcgcqPiIiIncrMNC8xD0oCGJiYNo0qxPJHaj4iIiI3IuyZWHWLHN5+HDYvdvaPHJbKj4iIiL36tlnoU0buH7dvMQ9MdHqRHILKj4iIiL3ymaDN96AokXhp5+gb1+rE8ktqPiIiIg4Q1gYvPMO+PjAihXmvX7E5aj4iIiIOEvt2jBqlLncpw8cO2ZtHklHxUdERMSZhg+HevXg4kVzvE9ystWJ5G9UfERERJzJ19c81RUWBnv2wIgRVieSv1HxERERcbYiReDtt83lqVNh82Zr84idio+IiEhmaNUKevUylzt3hnPnrM0jgIqPiIhI5pk+HcqVg7Nnzfm8NKWF5VR8REREMktQEKxebU5tsWnTX3d4Fsuo+IiIiGSm8uXNIz8AL78Me/dam8fLqfiIiIhktl69oGVLuHYN2rY1L3UXS6j4iIiIZDabDd56CwoVgqNH4fnnrU7ktVR8REREskKePLBypVmCliyBVausTuSVVHxERESyysMP/3VDw1694Oefrc3jhVR8REREstLIkVCrFiQmQvv25rgfyTIqPiIiIlnJz8+cxT00FL7++q9JTSVLqPiIiIhktchIePNNc3nSJPj0U2vzeBEVHxERESs89RQ8+ywYBnTsCL//bnUir6DiIyIiYpWZMyEqCs6cgW7dzBIkmUrFR0RExCo5cphTWvj7w4cfwty5VifyeCo+IiIiVqpYEaZNM5dffBEOHLA2j4dT8REREbFav37wr39BcrI5pcWlS1Yn8lgqPiIiIlaz2SA6GgoUgMOHYcAAqxN5LBUfERERV5A3L6xY8de8XmvXWp3II6n4iIiIuIpHHoGhQ83lHj3g11+tzeOBVHxERERcyZgxUL06JCSYU1pcv251Io+i4iMiIuJKsmUzZ24PCYEdO2DsWKsTeRQVHxEREVdTvDgsXGgujxsH27dbm8eDqPiIiIi4onbtoGtXSE01p7Q4f97qRB5BxUdERMRVzZkDpUvDb7/9Na+X3BMVHxEREVcVHGxOaZEtG6xfD4sWWZ3I7an4iIiIuLLKlWHyZHN54ED47jtr87g5FR8RERFX178/PPooXL1qTmlx5YrVidyWio+IiIir8/GBJUsgf344dMiczFTuioqPiIiIO8iXD5YvN5fnzzfH/EiGqfiIiIi4i8aNYcgQc7l7dzh50to8bsgtis8vv/zCM888Q/HixQkKCqJkyZKMGjWK5OTkNNt9++231K1bl8DAQIoUKcKUKVMsSiwiIpJJxo6FqlXhzz/N+/ukpFidyK24RfE5fPgwqampLFq0iEOHDjFjxgwWLlzIK6+8Yt8mMTGRJk2aEBkZyTfffMPUqVMZPXo0b7zxhoXJRUREnMzf35zSIjgY/vtfmDDB6kRuxWYY7nk3pKlTp7JgwQJ+/vlnABYsWMDw4cOJjY3F398fgKFDh7J+/XoOHz7s8OcmJiYSGhpKQkICISEhmZJdRETknq1YAZ06ga+vOaVF7dpWJ7KUo7/fbnHE52YSEhLInTu3/fnOnTupV6+evfQANG3alCNHjvDnn3/e8nOSkpJITExM8xAREXF5HTuaxSclxZzFPT7e6kRuwS2Lz7Fjx5gzZw7PPfecfV1sbCz58+dPs92N57Gxsbf8rIkTJxIaGmp/FClSJHNCi4iIONu8eVCqFJw4AT17akoLB1hafIYOHYrNZrvt45+nqU6dOkWzZs146qmn6NGjxz1nGDZsGAkJCfbHSY2QFxERd5EzJ7zzDvj5wdq18PbbVidyeX5WfvngwYPp2rXrbbcpUaKEffn06dM0aNCAWrVqpRu0HBERwdmzZ9Osu/E8IiLilp8fEBBAQEBABpOLiIi4iGrVzAHOQ4bACy+YY33KlLE6lcuytPiEh4cTHh7u0LanTp2iQYMGVKlShejoaHx80h6sqlmzJsOHD+fatWtky5YNgJiYGO6//37CwsKcnl1ERMRlDB4MMTHmo107+OorCAy0OpVLcosxPqdOnaJ+/foULVqUadOmERcXR2xsbJqxO+3bt8ff359nnnmGQ4cOsWbNGmbNmsWgQYMsTC4iIpIFfHxg2TIID4cDB+Dll61O5LIsPeLjqJiYGI4dO8axY8coXLhwmtduXI0fGhrK5s2b6du3L1WqVCFv3ryMHDmSnj17WhFZREQka0VEwNKl8NhjMHs2NGoELVpYncrluO19fDKL7uMjIiJubdAgmDED8uSBb7+FggWtTpQlPP4+PiIiInITEydCpUpw/rymtLgJFR8RERFPEhAAq1dDjhywbRtMnWp1Ipei4iMiIuJp7rsP5s41l0eMMK/yEkDFR0RExDN16WJe2n5jSouEBKsTuQQVHxEREU9ks8GCBVCsGBw/Dr16aUoLVHxEREQ8V2gorFplzuC+erV5ubuXU/ERERHxZDVqwNix5nK/fvDjj9bmsZiKj4iIiKcbMgQaNIBLl6BtW0hKsjqRZVR8REREPJ2vLyxfbt7UcN8+eOUVqxNZRsVHRETEGxQqBNHR5vL06fDxx9bmsYiKj4iIiLdo0QKef95c7tIF/jbZt7dQ8REREfEmU6ZAhQoQFwedO0NqqtWJspSKj4iIiDcJDDQvcQ8KgpgY87SXF1HxERER8TZly8KsWebysGGwe7e1ebKQio+IiIg3evZZaNMGrl83p7a4cMHqRFlCxUdERMQb2WzwxhtQtCj89BP07Wt1oiyh4iMiIuKtwsJg5Urw8THv87NihdWJMp2Kj4iIiDerUwdGjTKXe/c2j/54MBUfERERbzd8ONSrBxcvmuN9kpOtTpRpVHxERES8na+veZorLMy8wuvVV61OlGlUfERERASKFIG33zaXp0yBzZutzZNJVHxERETE1KoV9OplLnfuDOfOWZsnE6j4iIiIyF+mT4dy5eDsWeja1eOmtFDxERERkb8EBcHq1ebUFh9/DLNnW53IqVR8REREJK3y5f+aw2vIENi719o8TqTiIyIiIun16gUtW8K1a+Yl7hcvWp3IKVR8REREJD2bDd56CwoVgh9/hBdesDqRU6j4iIiIyM3lyWNOaWGzQXS0OfbHzan4iIiIyK09/DCMGGEuP/ccHD9ubZ57pOIjIiIitzdyJNSqBYmJ0L69Oe7HTan4iIiIyO35+cE770BoKHz1FYwebXWiu6biIyIiIncWGQlvvmkuT5wIn35qbZ67pOIjIiIijnnqKXj2WTAM6NgRfv/d6kQZpuIjIiIijps5E6Ki4MwZ6N7dLEFuRMVHREREHJcjh3lZu78//N//wbx5VifKEBUfERERyZiKFWHaNHP5xRfhwAFr82SAio+IiIhkXL9+8K9/QVIStG0Lly5ZncghKj4iIiKScTYbLF4MBQrA4cMwcKDViRyi4iMiIiJ3Jzwcli83S9Cbb8J771md6I5UfEREROTuNWwIQ4eayz16wK+/WpvnDlR8RERE5N6MGQPVq0N8PHToANevW53ollR8RERE5N5ky2ZOaZEzJ3z5JYwda3WiW1LxERERkXtXogQsWmQujxsH//2vtXluQcVHREREnKNdO+jaFVJTzVNef/xhdaJ0VHxERETEeebMgdKl4bff/prXy4Wo+IiIiIjzBAebU1pkywbr1v11+stFqPiIiIiIc1WuDJMmmcsDB8J331mb529UfERERMT5BgyAZs3g6lVzSosrV6xOBKj4iIiISGbw8YElSyB/fjh0yJzM1AWo+IiIiEjmyJ8fli0zl+fPh/XrLY0DKj4iIiKSmZo0+etoT/fucPKkpXFUfERERCRzjR8PVarAn39Cx46QkmJZFLcoPr/88gvPPPMMxYsXJygoiJIlSzJq1CiSk5PTbGOz2dI9vvrqKwuTi4iICP7+sGqVean7nj3w7beWRfGz7Jsz4PDhw6SmprJo0SJKlSrFd999R48ePbh06RLTpk1Ls+2WLVsoV66c/XmePHmyOq6IiIj8U+nS5nxepUtDVJRlMdyi+DRr1oxmzZrZn5coUYIjR46wYMGCdMUnT548REREZHVEERERuZMWLaxO4B6num4mISGB3Llzp1v/+OOPky9fPurUqcPGjRvv+DlJSUkkJiameYiIiIhncsvic+zYMebMmcNzzz1nXxccHMzrr7/O2rVr+eijj6hTpw4tW7a8Y/mZOHEioaGh9keRIkUyO76IiIhYxGYY1s0eNnToUCZPnnzbbX744Qei/nYu8NSpUzz88MPUr1+ft95667bv7dy5M8ePH+fzzz+/5TZJSUkkJSXZnycmJlKkSBESEhIICQlx8C8RERERKyUmJhIaGnrH329Lx/gMHjyYrl273nabEiVK2JdPnz5NgwYNqFWrFm+88cYdP7969erExMTcdpuAgAACAgIcyisiIiLuzdLiEx4eTnh4uEPbnjp1igYNGlClShWio6Px8bnzWbr9+/dToECBe40pIiIiHsItruo6deoU9evXJzIykmnTphEXF2d/7cYVXEuXLsXf359KlSoB8MEHH7B48eI7ng4TERER7+EWxScmJoZjx45x7NgxChcunOa1vw9RGjt2LL/++it+fn5ERUWxZs0a2rRpk9VxRURExEVZOrjZFTk6OEpERERch6O/3255ObuIiIjI3VDxEREREa+h4iMiIiJeQ8VHREREvIaKj4iIiHgNFR8RERHxGio+IiIi4jV0H59/MAyDCxcukDNnTmw2m9VxRERExAGO/n6r+IiIiIjX0KkuERER8RoqPiIiIuI1VHxERETEa6j4iIiIiNdQ8RERERGvoeIjIiIiXkPFR0RERLzG/wNszIvbcXdNFwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Short Call Payoff\n",
    "Short_call_payoff = call_payoff(sT, strike_price_short_call, premium_short_call)*-1.0\n",
    "fig, ax = plt.subplots()\n",
    "ax.spines['bottom'].set_position('zero')\n",
    "ax.plot(sT, Short_call_payoff, color='r')\n",
    "ax.set_title('Short 32.5 Strike Call')\n",
    "plt.xlabel('Stock Price')\n",
    "plt.ylabel('Profit & Loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Butterfly_spread_payoff = lower_strike_long_call_payoff + higher_strike_long_call_payoff + 2 *Short_call_payoff\n",
    "fig, ax = plt.subplots()\n",
    "ax.spines['bottom'].set_position('zero')\n",
    "ax.plot(sT,Butterfly_spread_payoff ,color='b', label= 'Butterfly Spread')\n",
    "ax.plot(sT, lower_strike_long_call_payoff,'--', color='g',label='Lower Strike Long Call')\n",
    "ax.plot(sT, higher_strike_long_call_payoff,'--', color='g', label='Higher Strike Long Call')\n",
    "ax.plot(sT, Short_call_payoff, '--', color='r', label='Short call')\n",
    "plt.legend()\n",
    "plt.xlabel('Stock Price')\n",
    "plt.ylabel('Profit & Loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "Butterfly_spread_payoff = lower_strike_long_call_payoff + higher_strike_long_call_payoff + 2 *Short_call_payoff\n",
    "fig, ax = plt.subplots()\n",
    "ax.spines['bottom'].set_position('zero')\n",
    "ax.plot(sT,Butterfly_spread_payoff ,color='b', label= 'Butterfly Spread')\n",
    "plt.legend()\n",
    "plt.xlabel('Stock Price')\n",
    "plt.ylabel('Profit & Loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.60\n",
      "-0.40\n"
     ]
    }
   ],
   "source": [
    "profit = max(Butterfly_spread_payoff)\n",
    "loss = min(Butterfly_spread_payoff)\n",
    "print (\"%.2f\" %profit)\n",
    "print (\"%.2f\" %loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[*********************100%%**********************]  1 of 1 completed"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum Profit: $-0.40\n",
      "Maximum Loss: $-0.40\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import yfinance as yf\n",
    "\n",
    "# Function to fetch stock data\n",
    "def fetch_stock_data(symbol, start_date, end_date):\n",
    "    stock_data = yf.download(symbol, start=start_date, end=end_date)\n",
    "    return stock_data\n",
    "\n",
    "# Function to calculate the payoff for a call option\n",
    "def call_payoff(sT, strike_price, premium):\n",
    "    return np.where(sT > strike_price, sT - strike_price, 0) - premium\n",
    "\n",
    "# Function to calculate the payoff for a butterfly spread strategy\n",
    "def butterfly_spread_payoff(sT, lower_strike, higher_strike, short_strike, premiums):\n",
    "    lower_strike_payoff = call_payoff(sT, lower_strike, premiums[0])\n",
    "    higher_strike_payoff = call_payoff(sT, higher_strike, premiums[1])\n",
    "    short_call_payoff = -1 * call_payoff(sT, short_strike, premiums[2])\n",
    "\n",
    "    butterfly_payoff = lower_strike_payoff + higher_strike_payoff + 2 * short_call_payoff\n",
    "    return butterfly_payoff\n",
    "\n",
    "# Define inputs for the option strategy\n",
    "symbol = 'SBIN.NS'  # Example stock symbol\n",
    "start_date = '2023-01-01'\n",
    "end_date = '2023-12-31'\n",
    "s0 = 40\n",
    "lower_strike_price_long_call = 30\n",
    "higher_strike_price_long_call = 35\n",
    "strike_price_short_call = 32.5\n",
    "premium_lower_strike_long_call = 3.15\n",
    "premium_higher_strike_long_call = 0.85\n",
    "premium_short_call = 1.80\n",
    "\n",
    "# Fetch stock data\n",
    "stock_data = fetch_stock_data(symbol, start_date, end_date)\n",
    "sT = stock_data['Adj Close'].values  # Using adjusted closing prices for stock price at expiration\n",
    "\n",
    "# Calculate butterfly spread payoff\n",
    "butterfly_payoff = butterfly_spread_payoff(sT, lower_strike_price_long_call, higher_strike_price_long_call,\n",
    "                                           strike_price_short_call, [premium_lower_strike_long_call, premium_higher_strike_long_call, premium_short_call])\n",
    "\n",
    "# Plot butterfly spread payoff\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(sT, butterfly_payoff, color='b', label='Butterfly Spread')\n",
    "plt.legend()\n",
    "plt.xlabel('Stock Price')\n",
    "plt.ylabel('Profit & Loss')\n",
    "plt.title('Butterfly Spread Payoff')\n",
    "plt.show()\n",
    "\n",
    "# Calculate and print profit and loss\n",
    "profit = max(butterfly_payoff)\n",
    "loss = min(butterfly_payoff)\n",
    "print(f\"Maximum Profit: ${profit:.2f}\")\n",
    "print(f\"Maximum Loss: ${loss:.2f}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Function to calculate the payoff for a call option\n",
    "def call_payoff(sT, strike_price, premium):\n",
    "    return np.where(sT > strike_price, sT - strike_price, 0) - premium\n",
    "\n",
    "# Function to calculate the payoff for a butterfly spread strategy\n",
    "def butterfly_spread_payoff(sT, lower_strike, higher_strike, short_strike, premiums):\n",
    "    lower_strike_payoff = call_payoff(sT, lower_strike, premiums[0])\n",
    "    higher_strike_payoff = call_payoff(sT, higher_strike, premiums[1])\n",
    "    short_call_payoff = -1 * call_payoff(sT, short_strike, premiums[2])\n",
    "\n",
    "    butterfly_payoff = lower_strike_payoff + higher_strike_payoff + 2 * short_call_payoff\n",
    "    return butterfly_payoff\n",
    "\n",
    "# Read the dataset\n",
    "data = pd.read_csv('your_dataset.csv')\n",
    "\n",
    "# Example data points from your dataset to use in the strategy\n",
    "# Assuming you want to pick specific options for the strategy:\n",
    "lower_strike_price_long_call = data.loc[0, 'Strike']\n",
    "higher_strike_price_long_call = data.loc[1, 'Strike']\n",
    "strike_price_short_call = data.loc[2, 'Strike']\n",
    "\n",
    "premium_lower_strike_long_call = data.loc[0, 'Close']\n",
    "premium_higher_strike_long_call = data.loc[1, 'Close']\n",
    "premium_short_call = data.loc[2, 'Close']\n",
    "\n",
    "# Stock price series (for simplicity, using the 'Close' prices as a proxy for underlying stock prices)\n",
    "sT = data['Close'].values\n",
    "\n",
    "# Calculate butterfly spread payoff\n",
    "butterfly_payoff = butterfly_spread_payoff(sT, lower_strike_price_long_call, higher_strike_price_long_call,\n",
    "                                           strike_price_short_call, [premium_lower_strike_long_call, premium_higher_strike_long_call, premium_short_call])\n",
    "\n",
    "# Plot butterfly spread payoff\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(sT, butterfly_payoff, color='b', label='Butterfly Spread')\n",
    "plt.legend()\n",
    "plt.xlabel('Stock Price')\n",
    "plt.ylabel('Profit & Loss')\n",
    "plt.title('Butterfly Spread Payoff')\n",
    "plt.show()\n",
    "\n",
    "# Calculate and print profit and loss\n",
    "profit = max(butterfly_payoff)\n",
    "loss = min(butterfly_payoff)\n",
    "print(f\"Maximum Profit: ${profit:.2f}\")\n",
    "print(f\"Maximum Loss: ${loss:.2f}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import yfinance as yf\n",
    "\n",
    "# Fetch the latest stock data\n",
    "ticker = \"SBIN.NS\"  # Replace with the desired ticker symbol\n",
    "stock_data = yf.Ticker(ticker)\n",
    "latest_data = stock_data.history(period=\"1d\").iloc[-1]\n",
    "\n",
    "# Get the current stock price\n",
    "S = latest_data[\"Close\"]\n",
    "\n",
    "# Get the strike price (you may need to fetch option data or provide a fixed value)\n",
    "K = 500  # Replace with the desired strike price\n",
    "\n",
    "expiration_date = \"2023-06-16\"  # Replace with the desired expiration date (YYYY-MM-DD)\n",
    "expiration_datetime = datetime.strptime(expiration_date, \"%Y-%m-%d\")\n",
    "today = datetime.now()\n",
    "T = (expiration_datetime - today).days / 365\n",
    "\n",
    "# Get the risk-free interest rate (you may need to fetch this data or provide a fixed value)\n",
    "r = 0.05  # Replace with the desired risk-free interest rate (e.g., 0.05 for 5%)\n",
    "\n",
    "# Get the volatility (you may need to fetch this data or provide a fixed value)\n",
    "sigma = 0.2  # Replace with the desired volatility (e.g., 0.2 for 20%)\n",
    "\n",
    "\n",
    "def call_payoff(sT, strike_price, premium):\n",
    "    return np.where(sT > strike_price, sT - strike_price, 0) - premium\n",
    "\n",
    "# Input parameters\n",
    "s0 = 40\n",
    "higher_strike_price_long_call = 35\n",
    "lower_strike_price_long_call = 30\n",
    "premium_higher_strike_long_call = 0.85\n",
    "premium_lower_strike_long_call = 3.15\n",
    "strike_price_short_call = 32.5\n",
    "premium_short_call = 1.80\n",
    "\n",
    "# Range of call option at expiration\n",
    "sT = np.arange(10, 60, 1)\n",
    "\n",
    "# Calculate payoffs for individual options\n",
    "lower_strike_long_call_payoff = call_payoff(sT, lower_strike_price_long_call, premium_lower_strike_long_call)\n",
    "higher_strike_long_call_payoff = call_payoff(sT, higher_strike_price_long_call, premium_higher_strike_long_call)\n",
    "\n",
    "# plotting the payoffs\n",
    "fig, ax = plt.subplots()\n",
    "ax.spines['bottom'].set_position('zero')\n",
    "ax.plot(sT, lower_strike_long_call_payoff, color='g', label='Lower Strike Long Call')\n",
    "ax.plot(sT, higher_strike_long_call_payoff, color='b', label='Higher Strike Long Call')\n",
    "\n",
    "plt.xlabel('Stock Price')\n",
    "plt.ylabel('Profit & Loss')\n",
    "plt.title('Long Call Payoff')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Backtesting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "df=pd.read_csv('BANKNIFTY_FNO_Data_2017/BANKNIFTY_2017_OPTIONS.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Profit: $-121.30\n",
      "Average Profit per Trade: $-40.43\n",
      "Maximum Profit: $-4.00\n",
      "Maximum Loss: $-98.30\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Function to calculate the payoff for a call option\n",
    "def call_payoff(sT, strike_price, premium):\n",
    "    return np.where(sT > strike_price, sT - strike_price, 0) - premium\n",
    "\n",
    "# Function to calculate the payoff for a butterfly spread strategy\n",
    "def butterfly_spread_payoff(sT, lower_strike, higher_strike, short_strike, premiums):\n",
    "    lower_strike_payoff = call_payoff(sT, lower_strike, premiums[0])\n",
    "    higher_strike_payoff = call_payoff(sT, higher_strike, premiums[1])\n",
    "    short_call_payoff = -1 * call_payoff(sT, short_strike, premiums[2])\n",
    "\n",
    "    butterfly_payoff = lower_strike_payoff + higher_strike_payoff + 2 * short_call_payoff\n",
    "    return butterfly_payoff\n",
    "\n",
    "# Read the dataset\n",
    "data = df\n",
    "\n",
    "# Convert date columns to datetime\n",
    "data['Date'] = pd.to_datetime(data['Date'])\n",
    "data['Expiry'] = pd.to_datetime(data['Expiry'])\n",
    "\n",
    "# Calculate Bollinger Bands\n",
    "window = 20  # Define the rolling window size\n",
    "data['Rolling Mean'] = data['Close'].rolling(window).mean()\n",
    "data['Rolling Std'] = data['Close'].rolling(window).std()\n",
    "data['Upper Band'] = data['Rolling Mean'] + 2 * data['Rolling Std']\n",
    "data['Lower Band'] = data['Rolling Mean'] - 2 * data['Rolling Std']\n",
    "\n",
    "# Identify low volatility periods\n",
    "data['Low Volatility'] = (data['Close'] > data['Lower Band']) & (data['Close'] < data['Upper Band'])\n",
    "\n",
    "results = []\n",
    "\n",
    "# Loop through each expiration date to backtest the strategy\n",
    "expiration_dates = data['Expiry'].unique()\n",
    "\n",
    "for expiry in expiration_dates:\n",
    "    # Filter data for the current expiry\n",
    "    expiry_data = data[data['Expiry'] == expiry]\n",
    "    \n",
    "    if len(expiry_data) < 3:\n",
    "        # Skip if there are not enough options to form a butterfly spread\n",
    "        continue\n",
    "\n",
    "    # Check for low volatility periods within the expiry date range\n",
    "    low_volatility_data = expiry_data[expiry_data['Low Volatility']]\n",
    "    \n",
    "    if low_volatility_data.empty:\n",
    "        # Skip if there are no low volatility periods\n",
    "        continue\n",
    "\n",
    "    # Choose specific options for the strategy (this can be adjusted as needed)\n",
    "    lower_strike_option = expiry_data.iloc[0]\n",
    "    higher_strike_option = expiry_data.iloc[1]\n",
    "    short_call_option = expiry_data.iloc[2]\n",
    "\n",
    "    lower_strike_price_long_call = lower_strike_option['Strike']\n",
    "    higher_strike_price_long_call = higher_strike_option['Strike']\n",
    "    strike_price_short_call = short_call_option['Strike']\n",
    "\n",
    "    premium_lower_strike_long_call = lower_strike_option['Close']\n",
    "    premium_higher_strike_long_call = higher_strike_option['Close']\n",
    "    premium_short_call = short_call_option['Close']\n",
    "\n",
    "    # Stock price at expiration\n",
    "    sT = expiry_data['Close'].values\n",
    "\n",
    "    # Calculate butterfly spread payoff\n",
    "    butterfly_payoff = butterfly_spread_payoff(sT, lower_strike_price_long_call, higher_strike_price_long_call,\n",
    "                                               strike_price_short_call, [premium_lower_strike_long_call, premium_higher_strike_long_call, premium_short_call])\n",
    "    if max(butterfly_payoff)>500:\n",
    "        break\n",
    "    if min(butterfly_payoff)<-1000: \n",
    "        break\n",
    "    # Store the results\n",
    "    results.append({\n",
    "        'Expiry': expiry,\n",
    "        'Max Profit': max(butterfly_payoff),\n",
    "        'Max Loss': min(butterfly_payoff),\n",
    "        'Final Payoff': butterfly_payoff[-1]  # Payoff at the last stock price point\n",
    "    })\n",
    "\n",
    "# Convert results to DataFrame for analysis\n",
    "results_df = pd.DataFrame(results)\n",
    "\n",
    "# Calculate and print overall profit and loss statistics\n",
    "total_profit = results_df['Final Payoff'].sum()\n",
    "average_profit = results_df['Final Payoff'].mean()\n",
    "max_profit = results_df['Max Profit'].max()\n",
    "max_loss = results_df['Max Loss'].min()\n",
    "\n",
    "print(f\"Total Profit: ${total_profit:.2f}\")\n",
    "print(f\"Average Profit per Trade: ${average_profit:.2f}\")\n",
    "print(f\"Maximum Profit: ${max_profit:.2f}\")\n",
    "print(f\"Maximum Loss: ${max_loss:.2f}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Risk Management"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "class RiskManagement:\n",
    "    def __init__(self, initial_capital, max_loss_pct, max_exposure_pct):\n",
    "        self.initial_capital = initial_capital\n",
    "        self.max_loss_pct = max_loss_pct\n",
    "        self.max_exposure_pct = max_exposure_pct\n",
    "        self.current_capital = initial_capital\n",
    "        self.current_exposure = 0\n",
    "\n",
    "    def check_capital_exposure(self, option_premium):\n",
    "        # Check if the option premium exceeds the maximum exposure limit\n",
    "        if option_premium > self.initial_capital * self.max_exposure_pct:\n",
    "            print(f\"Warning: Option premium {option_premium} exceeds the maximum exposure limit of {self.initial_capital * self.max_exposure_pct}\")\n",
    "            return False\n",
    "        return True\n",
    "\n",
    "    def check_capital_loss(self, potential_loss):\n",
    "        # Check if the potential loss exceeds the maximum loss limit\n",
    "        if potential_loss > self.initial_capital * self.max_loss_pct:\n",
    "            print(f\"Warning: Potential loss {potential_loss} exceeds the maximum loss limit of {self.initial_capital * self.max_loss_pct}\")\n",
    "            return False\n",
    "        return True\n",
    "\n",
    "    def update_capital(self, pnl):\n",
    "        self.current_capital += pnl\n",
    "        print(f\"Current capital: {self.current_capital}\")\n",
    "\n",
    "    def update_exposure(self, option_premium):\n",
    "        self.current_exposure += option_premium\n",
    "        print(f\"Current exposure: {self.current_exposure}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Overview\n",
    "This Python script performs Monte Carlo simulations to price call and put options based on the Black-Scholes model. It utilizes a Risk Management module to control exposure and potential loss during trading.\n",
    "\n",
    "# Key Components\n",
    "\n",
    "## OptionPricing Class\n",
    "- This class handles the option pricing simulations.\n",
    "- It inherits from a RiskManagement class to manage trading risk.\n",
    "\n",
    "## Attributes\n",
    "- `S0`: Initial stock price.\n",
    "- `E`: Strike price of the option.\n",
    "- `T`: Time to maturity (in years).\n",
    "- `rf`: Risk-free rate.\n",
    "- `sigma`: Volatility of the stock.\n",
    "- `iterations`: Number of iterations for the Monte Carlo simulation.\n",
    "- `initial_capital`: Initial capital available for trading.\n",
    "- `max_loss_pct`: Maximum allowable loss percentage.\n",
    "- `max_exposure_pct`: Maximum allowable exposure percentage.\n",
    "\n",
    "## Methods\n",
    "- `call_option_simulation()`: Simulates a call option using Monte Carlo simulation.\n",
    "- `put_option_simulation()`: Simulates a put option using Monte Carlo simulation.\n",
    "- `calculate_metrics(portfolio_value)`: Calculates Sharpe Ratio and Maximum Drawdown based on portfolio value.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current exposure: 0.6512597302941787\n",
      "Call option premium: 0.6512597302941787\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from scipy.stats import norm\n",
    "\n",
    "class OptionPricing(RiskManagement):\n",
    "    def __init__(self, S0, E, T, rf, sigma, iterations, initial_capital, max_loss_pct, max_exposure_pct):\n",
    "        \"\"\"\n",
    "        Initialize the OptionPricing class.\n",
    "\n",
    "        Parameters:\n",
    "        S0 (float): Current stock price.\n",
    "        E (float): Strike price of the option.\n",
    "        T (float): Time to maturity (in years).\n",
    "        rf (float): Risk-free rate.\n",
    "        sigma (float): Volatility of the stock.\n",
    "        iterations (int): Number of iterations for Monte Carlo simulation.\n",
    "        initial_capital (float): Initial capital for trading.\n",
    "        max_loss_pct (float): Maximum allowable loss percentage.\n",
    "        max_exposure_pct (float): Maximum allowable exposure percentage.\n",
    "        \"\"\"\n",
    "        super().__init__(initial_capital, max_loss_pct, max_exposure_pct)\n",
    "        self.S0 = S0\n",
    "        self.E = E\n",
    "        self.T = T\n",
    "        self.rf = rf\n",
    "        self.sigma = sigma\n",
    "        self.iterations = iterations\n",
    "\n",
    "    def call_option_simulation(self):\n",
    "        \"\"\"\n",
    "        Perform Monte Carlo simulation for a call option.\n",
    "\n",
    "        Returns:\n",
    "        float: Premium of the call option.\n",
    "        \"\"\"\n",
    "        # Generate random numbers for stock price simulation\n",
    "        rand = np.random.normal(0, 1, [1, self.iterations])\n",
    "        stock_price = self.S0 * np.exp(self.T * (self.rf - 0.5 * self.sigma ** 2) +\n",
    "                                       self.sigma * np.sqrt(self.T) * rand)\n",
    "\n",
    "        # Calculate option payoff and premium\n",
    "        option_data = stock_price - self.E\n",
    "        average = np.sum(np.amax(option_data, axis=1)) / float(self.iterations)\n",
    "        call_option_premium = np.exp(-1.0 * self.rf * self.T) * average\n",
    "\n",
    "        # Check exposure and potential loss\n",
    "        if self.check_capital_exposure(call_option_premium):\n",
    "            self.update_exposure(call_option_premium)\n",
    "        else:\n",
    "            print(\"Call option trade rejected due to excessive exposure.\")\n",
    "            return None\n",
    "\n",
    "        potential_loss = call_option_premium\n",
    "        if self.check_capital_loss(potential_loss):\n",
    "            print(f\"Call option premium: {call_option_premium}\")\n",
    "            return call_option_premium\n",
    "        else:\n",
    "            print(\"Call option trade rejected due to excessive potential loss.\")\n",
    "            return None\n",
    "\n",
    "    def put_option_simulation(self):\n",
    "        \"\"\"\n",
    "        Perform Monte Carlo simulation for a put option.\n",
    "\n",
    "        Returns:\n",
    "        float: Premium of the put option.\n",
    "        \"\"\"\n",
    "        # Generate random numbers for stock price simulation\n",
    "        rand = np.random.normal(0, 1, [1, self.iterations])\n",
    "        stock_price = self.S0 * np.exp(self.T * (self.rf - 0.5 * self.sigma ** 2) +\n",
    "                                       self.sigma * np.sqrt(self.T) * rand)\n",
    "\n",
    "        # Calculate option payoff and premium\n",
    "        option_data = self.E - stock_price\n",
    "        average = np.sum(np.amax(option_data, axis=1)) / float(self.iterations)\n",
    "        put_option_premium = np.exp(-1.0 * self.rf * self.T) * average\n",
    "\n",
    "        # Check exposure and potential loss\n",
    "        if self.check_capital_exposure(put_option_premium):\n",
    "            self.update_exposure(put_option_premium)\n",
    "        else:\n",
    "            print(\"Put option trade rejected due to excessive exposure.\")\n",
    "            return None\n",
    "\n",
    "        potential_loss = put_option_premium\n",
    "        if self.check_capital_loss(potential_loss):\n",
    "            print(f\"Put option premium: {put_option_premium}\")\n",
    "            return put_option_premium\n",
    "        else:\n",
    "            print(\"Put option trade rejected due to excessive potential loss.\")\n",
    "            return None\n",
    "\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    # Define option parameters\n",
    "    S0 = 100\n",
    "    E = 100\n",
    "    T = 1\n",
    "    rf = 0.05\n",
    "    sigma = 0.2\n",
    "    iterations = 100\n",
    "    initial_capital = 100000\n",
    "    max_loss_pct = 0.1  # 10% maximum loss\n",
    "    max_exposure_pct = 0.5  # 50% maximum exposure\n",
    "\n",
    "    # Create an instance of OptionPricing class and perform call option simulation\n",
    "    model = OptionPricing(S0, E, T, rf, sigma, iterations,\n",
    "                          initial_capital, max_loss_pct, max_exposure_pct)\n",
    "    call_option_premium = model.call_option_simulation()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Thank You"
   ]
  }
 ],
 "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": "3.11.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}