fronteira_eficiente.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "255ffe0c-d009-4749-9c88-1f7e6db606cc",
   "metadata": {},
   "source": [
    "# Fronteira eficiente em um portifólio\n",
    "\n",
    "Contribuidores:\n",
    "- Adriano Alvim\n",
    "- Carlos Cahu\n",
    "- Davi Galati\n",
    "- Edson Grimello\n",
    "- Gabriel D'Agosto\n",
    "- Gabriel Padilha"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "01259961-1d15-4c32-b924-5c845fac7f77",
   "metadata": {},
   "outputs": [],
   "source": [
    "# bibliotecas\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.linear_model import LinearRegression\n",
    "import scipy.optimize as solver\n",
    "from functions import read_infomoney,calc_return, f_obj_min_risk,f_obj_max_ret\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bb29ee96-375e-40b9-9b8a-2c080d64bffe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ABEV3': 'Ambev SA ABEV3 - Histórico InfoMoney.csv',\n",
       " 'PETR3': 'Petrobras PETR3 - Histórico InfoMoney.csv',\n",
       " 'MGLU3': 'Magazine Luiza MGLU3 - Histórico InfoMoney.csv',\n",
       " 'ITUB3': 'Itaú Unibanco ITUB3 - Histórico InfoMoney.csv',\n",
       " 'VALE3': 'Vale S.A. VALE3 - Histórico InfoMoney.csv'}"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dicionário de ativos\n",
    "assets = {\n",
    "      'ABEV3': 'Ambev SA ABEV3 - Histórico InfoMoney.csv',\n",
    "      'PETR3': 'Petrobras PETR3 - Histórico InfoMoney.csv',\n",
    "      'MGLU3': 'Magazine Luiza MGLU3 - Histórico InfoMoney.csv',\n",
    "      'ITUB3': 'Itaú Unibanco ITUB3 - Histórico InfoMoney.csv',\n",
    "#      'ALPA3': 'Alpargatas ALPA3 - Histórico InfoMoney.csv',\n",
    "      'VALE3': 'Vale S.A. VALE3 - Histórico InfoMoney.csv'\n",
    "}\n",
    "assets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5ff5a7a9-dfd5-4fc3-a76b-66d19bd18a12",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ABEV3</th>\n",
       "      <th>PETR3</th>\n",
       "      <th>MGLU3</th>\n",
       "      <th>ITUB3</th>\n",
       "      <th>VALE3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATA</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>28/12/2018</th>\n",
       "      <td>13.99</td>\n",
       "      <td>20.27</td>\n",
       "      <td>5.59</td>\n",
       "      <td>20.52</td>\n",
       "      <td>40.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27/12/2018</th>\n",
       "      <td>13.73</td>\n",
       "      <td>19.51</td>\n",
       "      <td>5.38</td>\n",
       "      <td>20.14</td>\n",
       "      <td>39.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26/12/2018</th>\n",
       "      <td>13.77</td>\n",
       "      <td>19.62</td>\n",
       "      <td>5.46</td>\n",
       "      <td>19.81</td>\n",
       "      <td>40.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21/12/2018</th>\n",
       "      <td>13.80</td>\n",
       "      <td>19.26</td>\n",
       "      <td>5.65</td>\n",
       "      <td>20.01</td>\n",
       "      <td>40.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20/12/2018</th>\n",
       "      <td>13.83</td>\n",
       "      <td>19.24</td>\n",
       "      <td>5.50</td>\n",
       "      <td>20.35</td>\n",
       "      <td>39.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08/01/2018</th>\n",
       "      <td>19.08</td>\n",
       "      <td>14.32</td>\n",
       "      <td>2.42</td>\n",
       "      <td>17.01</td>\n",
       "      <td>33.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05/01/2018</th>\n",
       "      <td>19.11</td>\n",
       "      <td>14.10</td>\n",
       "      <td>2.42</td>\n",
       "      <td>17.11</td>\n",
       "      <td>32.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>04/01/2018</th>\n",
       "      <td>19.04</td>\n",
       "      <td>13.99</td>\n",
       "      <td>2.47</td>\n",
       "      <td>16.99</td>\n",
       "      <td>31.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>03/01/2018</th>\n",
       "      <td>19.15</td>\n",
       "      <td>13.87</td>\n",
       "      <td>2.41</td>\n",
       "      <td>16.80</td>\n",
       "      <td>31.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>02/01/2018</th>\n",
       "      <td>19.10</td>\n",
       "      <td>13.70</td>\n",
       "      <td>2.44</td>\n",
       "      <td>16.67</td>\n",
       "      <td>32.04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>245 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            ABEV3  PETR3  MGLU3  ITUB3  VALE3\n",
       "DATA                                         \n",
       "28/12/2018  13.99  20.27   5.59  20.52  40.76\n",
       "27/12/2018  13.73  19.51   5.38  20.14  39.56\n",
       "26/12/2018  13.77  19.62   5.46  19.81  40.31\n",
       "21/12/2018  13.80  19.26   5.65  20.01  40.65\n",
       "20/12/2018  13.83  19.24   5.50  20.35  39.81\n",
       "...           ...    ...    ...    ...    ...\n",
       "08/01/2018  19.08  14.32   2.42  17.01  33.20\n",
       "05/01/2018  19.11  14.10   2.42  17.11  32.48\n",
       "04/01/2018  19.04  13.99   2.47  16.99  31.98\n",
       "03/01/2018  19.15  13.87   2.41  16.80  31.85\n",
       "02/01/2018  19.10  13.70   2.44  16.67  32.04\n",
       "\n",
       "[245 rows x 5 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# consolidar os dados em um único DataFrame\n",
    "df_assets = pd.DataFrame()\n",
    "for i in assets:\n",
    "    df_assets[i] = read_infomoney(assets[i])['FECHAMENTO']\n",
    "\n",
    "df_assets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4110fa67-4015-48cc-99f9-dcf8d90543ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ABEV3</th>\n",
       "      <th>PETR3</th>\n",
       "      <th>MGLU3</th>\n",
       "      <th>ITUB3</th>\n",
       "      <th>VALE3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATA</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>28/12/2018</th>\n",
       "      <td>0.018937</td>\n",
       "      <td>0.038954</td>\n",
       "      <td>0.039033</td>\n",
       "      <td>0.018868</td>\n",
       "      <td>0.030334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27/12/2018</th>\n",
       "      <td>-0.002905</td>\n",
       "      <td>-0.005607</td>\n",
       "      <td>-0.014652</td>\n",
       "      <td>0.016658</td>\n",
       "      <td>-0.018606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26/12/2018</th>\n",
       "      <td>-0.002174</td>\n",
       "      <td>0.018692</td>\n",
       "      <td>-0.033628</td>\n",
       "      <td>-0.009995</td>\n",
       "      <td>-0.008364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21/12/2018</th>\n",
       "      <td>-0.002169</td>\n",
       "      <td>0.001040</td>\n",
       "      <td>0.027273</td>\n",
       "      <td>-0.016708</td>\n",
       "      <td>0.021100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20/12/2018</th>\n",
       "      <td>-0.012848</td>\n",
       "      <td>-0.024341</td>\n",
       "      <td>0.005484</td>\n",
       "      <td>0.003947</td>\n",
       "      <td>-0.004003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>09/01/2018</th>\n",
       "      <td>-0.003145</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.024793</td>\n",
       "      <td>-0.014109</td>\n",
       "      <td>-0.003614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08/01/2018</th>\n",
       "      <td>-0.001570</td>\n",
       "      <td>0.015603</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.005845</td>\n",
       "      <td>0.022167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05/01/2018</th>\n",
       "      <td>0.003676</td>\n",
       "      <td>0.007863</td>\n",
       "      <td>-0.020243</td>\n",
       "      <td>0.007063</td>\n",
       "      <td>0.015635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>04/01/2018</th>\n",
       "      <td>-0.005744</td>\n",
       "      <td>0.008652</td>\n",
       "      <td>0.024896</td>\n",
       "      <td>0.011310</td>\n",
       "      <td>0.004082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>03/01/2018</th>\n",
       "      <td>0.002618</td>\n",
       "      <td>0.012409</td>\n",
       "      <td>-0.012295</td>\n",
       "      <td>0.007798</td>\n",
       "      <td>-0.005930</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>244 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               ABEV3     PETR3     MGLU3     ITUB3     VALE3\n",
       "DATA                                                        \n",
       "28/12/2018  0.018937  0.038954  0.039033  0.018868  0.030334\n",
       "27/12/2018 -0.002905 -0.005607 -0.014652  0.016658 -0.018606\n",
       "26/12/2018 -0.002174  0.018692 -0.033628 -0.009995 -0.008364\n",
       "21/12/2018 -0.002169  0.001040  0.027273 -0.016708  0.021100\n",
       "20/12/2018 -0.012848 -0.024341  0.005484  0.003947 -0.004003\n",
       "...              ...       ...       ...       ...       ...\n",
       "09/01/2018 -0.003145  0.000000 -0.024793 -0.014109 -0.003614\n",
       "08/01/2018 -0.001570  0.015603  0.000000 -0.005845  0.022167\n",
       "05/01/2018  0.003676  0.007863 -0.020243  0.007063  0.015635\n",
       "04/01/2018 -0.005744  0.008652  0.024896  0.011310  0.004082\n",
       "03/01/2018  0.002618  0.012409 -0.012295  0.007798 -0.005930\n",
       "\n",
       "[244 rows x 5 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_returns = calc_return(df_assets, -1) # calculando retorno diário\n",
    "df_returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "14ed8b1f-f190-4b98-88a2-90e15e8f97c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Retorno Médio\n",
      "ABEV3   -0.001183\n",
      "PETR3    0.002088\n",
      "MGLU3    0.003857\n",
      "ITUB3    0.000988\n",
      "VALE3    0.001201\n",
      "dtype: float64\n",
      "\n",
      "Desvio-Padrão dos Retornos\n",
      "ABEV3    0.013610\n",
      "PETR3    0.030809\n",
      "MGLU3    0.030383\n",
      "ITUB3    0.016562\n",
      "VALE3    0.020712\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# calcular retorno médio e desvio-padrão médio dos retornos\n",
    "mi = df_returns.mean() # média dos retornos\n",
    "sigma = df_returns.std() # desvio-padrão dos retornos\n",
    "\n",
    "print('Retorno Médio')\n",
    "print(mi)\n",
    "print()\n",
    "print('Desvio-Padrão dos Retornos')\n",
    "print(sigma)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cd39398d-6f52-4b04-8c62-19c119d3557a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matriz de Correlação dos Retornos dos Ativos\n",
      "          ABEV3     PETR3     MGLU3     ITUB3     VALE3\n",
      "ABEV3  1.000000  0.189402  0.253537  0.366211  0.275809\n",
      "PETR3  0.189402  1.000000  0.309332  0.463841  0.242256\n",
      "MGLU3  0.253537  0.309332  1.000000  0.405856  0.194290\n",
      "ITUB3  0.366211  0.463841  0.405856  1.000000  0.185306\n",
      "VALE3  0.275809  0.242256  0.194290  0.185306  1.000000\n",
      "\n",
      "Matriz de Covariância dos Retornos dos Ativos\n",
      "          ABEV3     PETR3     MGLU3     ITUB3     VALE3\n",
      "ABEV3  0.000185  0.000079  0.000105  0.000083  0.000078\n",
      "PETR3  0.000079  0.000949  0.000290  0.000237  0.000155\n",
      "MGLU3  0.000105  0.000290  0.000923  0.000204  0.000122\n",
      "ITUB3  0.000083  0.000237  0.000204  0.000274  0.000064\n",
      "VALE3  0.000078  0.000155  0.000122  0.000064  0.000429\n"
     ]
    }
   ],
   "source": [
    "# calcular matriz de correlação e de covariância dos retornos dos ativos\n",
    "correl_matrix = df_returns.corr()\n",
    "cov_matrix = df_returns.cov()\n",
    "\n",
    "print('Matriz de Correlação dos Retornos dos Ativos')\n",
    "print(correl_matrix)\n",
    "print()\n",
    "print('Matriz de Covariância dos Retornos dos Ativos')\n",
    "print(cov_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4bca3390-2de1-4cf2-b745-aa438dda4447",
   "metadata": {},
   "outputs": [
    {
     "data": {
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YFBEREfFgCoMiIiIiHkxhUERERMSDKQyKiIiIeDCFQREREREPpjAoIiIi4sEUBkVEREQ8mMKgiIiIiAdTGBQRERHxYAqDIiIiIh6sXMLgxo0bCQ8Pp1u3bqxcubJE/6FDh4iOjiYsLIxJkyZhtVoBOHHiBIMHD6Z79+6MGjWK3NxcAI4cOUJMTAxRUVEMGTKE1NRUt+6PiIiISEXl9jCYlpbGm2++yapVq0hMTGTNmjUcOXLEYZ64uDimTJnC5s2bMcaQkJAAQHx8PIMGDSIlJYW7776bhQsX2ttHjx7NRx99RHh4OHPnznX3bomIiIhUSG4Pgzt37qRt27YEBQVRtWpVwsLCSElJsfenpqaSn59Pq1atAIiOjiYlJYWioiJ2795NWFiYQzvAX/7yFzp16oTNZuPEiRMEBga6e7dEREREKiQfd28wPT2d4OBg+3RISAj79++/bH9wcDBpaWmcOXOGgIAAfHx8HNoBfHx8yM7OJjw8nPz8fJYvX37V46pVK+Bad6lSCQ6uXt5DqBBUJ9eoTs6pRq5RnVyjOjmnGpXk9jBos9mwWCz2aWOMw/Tl+n89H+AwHRgYyI4dO/j8888ZNWoUn376Kd7e3i6PKzPzHDabuZZdqjSCg6uTkZFT3sO44alOrlGdnFONXKM6uUZ1cq4y1MjLy1LqJ7Dcfpm4bt26ZGRk2KczMjIICQm5bP/p06cJCQmhZs2a5OTkUFxcXGK55ORkjLkQ5Dp16kR+fj4///yzO3ZHREREpEJzexhs3749u3btIisri7y8PLZs2UKnTp3s/fXr18ff35+9e/cCkJSURKdOnfD19SU0NJTk5GQAEhMT7cu99957bN26FYAvv/ySm2++mZo1a7p5z0REREQqHou5eErNjTZu3MiSJUsoKiqiX79+jBgxghEjRjB27FiaN2/O4cOHmTx5MufOnaNZs2bMnDkTPz8/UlNTmThxIpmZmdSrV4+5c+dSo0YNjhw5wiuvvML58+epXr06U6dO5be//e1VjUmXiSvH6XN3UJ1cozo5pxq5RnVyjerkXGWoUVlcJi6XMHgjUhisHP+TuIPq5BrVyTnVyDWqk2tUJ+cqQ40qxT2DIiIiInLjUBgUERER8WAKgyIiIiIeTGFQRERExIMpDIqIiIh4MIVBEREREQ+mMCgiIiLiwRQGRURERDyYwqCIiIiIB1MYFBEREfFgCoMiIiIiHkxhUERERMSDKQyKiIiIeDCFQREREREPpjAoIiIi4sEUBkVEREQ8mMKgiIiIiAdTGBQRERHxYAqDIiIiIh5MYVBERETEgykMioiIiHgwhUERERERD6YwKCIiIuLBFAZFREREPJjCoIiIiIgHUxgUERER8WA+5T0AERERkcri5MkTDBzYm0aNGtvbjDH07x/DvffeV6Lvoj/96a/Ex0/i+PHjABw58j2NGt2Bl5c31atXZ8GCJfTrF4mfnx9Vq1bBYrFQWFiIl5cXL7zwAp06dcIYw1tvvcWWLVsAaN68OdOmTaNKlSpXHLPCoIiIiEgp8vf35/33V9mnMzLSGTJkIG+/vbRE3y/NmDHb/t8dOoQyf/4SgoKCHOaZOnUGHTrcb59OSUnh5ZdfZseOHWzdupUdO3aQmJiIr68v48aNY9myZTz11FNXHK/CoIiIiEgZCg4O4dZbb+Xrr78s1fUaYzh+/Dg1atQAoFu3bjz00EP4+vpy7tw5srKySoTJS1EYFBERESlDBw/u5/jx4zRrdjcFBQUMGzbIob9585Y8//yLLq0rPn4yVarcxNmzZwHo0KEDixcvtvf7+vqyYsUK5s2bR506dXjkkUecrlNhUERERKQU/TLwFRdbqVEjiClTphMSUueKl4ldcfEy8X//+18ef/xxmjZtyq233uowz2OPPcbgwYOZN28eY8eOZcWKFVdcp8KgiIiISCm6XOA7efJEqW3j1ltvZfbs2cTGxtKyZUtatGjB4cOHsdls/O53v8NisdC/f3+WLVvmdF16tYyIiIhIBdS6dWt69+7NtGnTsNlsHD58mJdeeom8vDwAEhMTadu2rdP16MygiIiIiJtc6p5BgEmTpvLb3za56vU999xz9OjRg4SEBGJiYjh27Bh9+/bF29ub3/72t7z22mtO12Exxpir3nIllJl5DpvNs0sRHFydjIyc8h7GDU91co3q5Jxq5BrVyTWqk3OVoUZeXhZq1Qoo3XWW6tpEREREpEJRGBQRERG5HhbIziviWEYu2flWsJT3gK6OS/cM5ubm8t1332G1WmnRogUBAaV7elJERESkQrLAoWM/Mz/hWwqKivH39WbsgFY0va0GVJC7z5yGwf379zN69Ghq1aqFzWYjLS2NxYsX07p1a3eMT0REROSGlX2+yB4EAQqKipmf8C2zRrcnsIpvOY/ONU7D4KxZs5gzZ4790eRdu3bxxhtvkJCQUOaDExEREbmRnT1XaA+CFxUUFXM2t7DChEGn9wzm5uY6vKOmXbt29vfXXKuNGzcSHh5Ot27dWLlyZYn+Q4cOER0dTVhYGJMmTcJqtQJw4sQJBg8eTPfu3Rk1ahS5ubkAHD16lMGDB9OrVy8GDhzIoUOHrmt8IiIiIq4Iqu6Pv6+3Q5u/rzdB1fzKaURXz2kYtFgspKam2qePHz+Ot7f3FZa4srS0NN58801WrVpFYmIia9as4ciRIw7zxMXFMWXKFDZv3owxxn4WMj4+nkGDBpGSksLdd9/NwoULAZg8eTIjRowgKSmJZ599lhdfdO37fiIiIiLXI7CKD2MHtLIHwov3DAZWrRhnBcGFMDhmzBgGDhxIXFwccXFxDBgwgFGjRl3zBnfu3Enbtm0JCgqiatWqhIWFkZKSYu9PTU0lPz+fVq1aARAdHU1KSgpFRUXs3r2bsLAwh3aA/v3707FjRwCaNGnCyZMnr3l8IiIiIi4z0PS2Gswa3Z5pT7Zh1uj2FerhEXDhnsGuXbvSqFEjvvzyS2w2G08//TR33HHHNW8wPT2d4OBg+3RISAj79++/bH9wcDBpaWmcOXOGgIAAfHx8HNrhQjC8aP78+XTt2vWqx1XaL3CsqIKDq5f3ECoE1ck1qpNzqpFrVCfXqE7OlVWNgp3PcsO6bBjctWsX7dq1Y8uWLQDUrl0buHB/3tGjR+nWrds1bdBms2Gx/N8LeIwxDtOX6//1fECJ+WbPns2+fftc+ijzr+kLJJXjzezuoDq5RnVyTjVyjerkGtXJucpQo7L4Asllw+Ann3xCu3btWL58eYk+i8VyzWGwbt267Nmzxz6dkZFBSEiIQ39GRoZ9+vTp04SEhFCzZk1ycnIoLi7G29vbYTmr1cqLL75IWloay5Yto3p1/WUkIiIi4orLhsEZM2YAXDIMXo/27duzYMECsrKyqFKlClu2bGH69On2/vr16+Pv78/evXu59957SUpKolOnTvj6+hIaGkpycjKRkZEkJibSqVMn4MLrb86dO8d7772Hn1/FeXpHREREpLxZjDGXvDb60ksvXXHBmTNnXvNGN27cyJIlSygqKqJfv36MGDGCESNGMHbsWJo3b87hw4eZPHky586do1mzZsycORM/Pz9SU1OZOHEimZmZ1KtXj7lz51JcXEyHDh245ZZbqFKlin0bSUlJVzUmXSauHKfP3UF1co3q5Jxq5BrVyTWqk3OVoUZlcZn4smHwvffeA+Cf//wnJ06cICoqCm9vb5KTk7n11luZPXt2qQ6kvCkMVo7/SdxBdXKN6uScauQa1ck1qpNzlaFGbr1ncPjw4QBs3bqVlStX2s+6DRgwgNjY2FIdhIiIiIiUD6fvGczMzHS4D89isXDmzJkyHZSIiIiIuIfT9wy2a9eOJ598kp49e2KMISkpiS5durhjbCIiIiJSxpyGwVdeeYWVK1eydetWAHr06EFMTEyZD0xEREREyp7TMOjj40NYWBi33347DzzwAOnp6Xh5Ob26LCIiIiIVgNNU99lnnxETE0N8fDxZWVlERESwbds2d4xNRERERMrYZcPgyJEjycrK4p133iEhIYHAwEBCQkJYtWoV8+fPd+cYRURERKSMXDYMNm7cmAkTJlBcXOzwubimTZuW+EawiIiIiFRMl71n8IUXXqC4uJjY2FhOnDhhD4B79uzB39/fbQMUERERkbJzxQdIvL29ef755xk+fDgZGRkMHDiQH3/8kQULFrhrfCIiIiJShpw+Tdy6dWsSEhL45ptvsNlstGzZkpo1a7pjbCIiIiJSxi4bBr/77juH6dq1awNw8uRJTp48SbNmzcp2ZCIiIiJS5i4bBvv27UuNGjUICAjAGOPQZ7FY+PTTT8t8cCIiIiJSti4bBseMGUNKSgp33HEHffv2pWPHjnrZtIiIiEglc9l098wzz/DJJ58QGxvL1q1biYiIYPbs2Rw5csSd4xMRERGRMuT0AZLQ0FBCQ0MpKChg69atvPLKKxQVFbFu3Tp3jE9EREREypBL132Lior4/PPPSUlJ4dixYzRt2rSsxyUiIiIibnDFM4N79uzho48+YuvWrbRs2ZKoqCjmzp2Ln5+fu8YnIiIiImXosmHw4YcfxhhDVFQUK1asoFatWgCcP3+e8+fPExQU5K4xioiIiEgZuWwYTE1NBWDx4sUsWbLE3m6MwWKxcOjQobIfnYiIiIiUqcuGwcOHD7tzHCIiIiJSDpw+TSwicqN45pmnuP/+dgwZMsyhffXqFezb90/eeGMuTzwxhIKCfJYvT8Bisdjn6dAhlI8/3lbiFpd//nMPEyaM47bbGji016gRxFtvLaSoqIg335zN/v3fAtCmTXtGjx6Lt7d3WeyiiIjbKQyKSIXRp09/li5dWCIMbty4gWefjeO77w5SWFiAj48vX321i7Zt27u03vr16/P++6su2bd+/RrOnj3LsmVrsNlsjBkzgr/9bSuPPNL9endHROSGoE+KiEiF0anTg+Tn57Fv3zf2tm++2Ysxhvvua0Ni4jrat+9It249SEhYXSrbjIl5jFdfnYmXlxfZ2T9z7lwOgYE1SmXdIiI3ApfODObm5vLdd99htVpp0aIFAQEBZT0uEZESfHx8iIzszccfJ9Gy5T0AfPTRBvr06U9OTjaffrqVd9/9KzVqBLFkydv85z8/0LBhI6frTU1NZdiwQQ5tDz30MEOHPmHf7qJFC/jwwwSaNGlq37aISGXgNAzu37+f0aNHU6tWLWw2G2lpaSxevJjWrVu7Y3wiIg569Yrmscf6c/58Llarla+/3sXzz09k48ZEbr+9IY0aNQbgvvvasHbtal54YZLTdV7pMvFFo0Y9w4gRo5g1awZz5sxk8uT4UtkfEZHy5jQMzpo1izlz5tC2bVsAdu3axRtvvEFCQkKZD05E5Ndq1w4mNLQN27ZtIT8/jwcffJhq1aqRlLSe7Oxs+vWLBCA/P59vvtnLU0+NoUaNoGve3v793xIUdDO33dYAHx8fevToybx5fyilvRERKX9O7xnMzc21B0GAdu3akZeXV6aDEhG5kujo/mzZsolNmz4mOnoAu3d/xZkzWSQkJLFu3UbWrdtIYuImatWqTVLSh9e1rX/+cw8LFszFarVis9nYujWF1q3vK6U9EREpf07PDFosFlJTU6lfvz4Ax48f1ysVRKRctW4dyrx5fyAwsAZ33NGYd99dTFRUtMP9zD4+PsTGPs677y5h0KBYAPr3j3RYz7Rpr1OlSpVL3jMI8OabbzN48FDeeuuPDBs2CC8vCy1atOLpp39ftjsoIuJGFmOMudIM27ZtY9q0abRr1w6AL774gqlTpxIWFuaWAbpLZuY5bLYrlqLSCw6uTkZGTnkP44anOrlGdXJONXKN6uQa1cm5ylAjLy8LtWqV7oO8Ts8Mdu3alUaNGvHll19is9l4+umnueOOO0p1ECIiV2SB7PNFnD1XSFB1fwKr+IBn/+0mIlJqXHq1zOnTpwkODsYYw9GjRzl69CjdunUr67GJuKxfv0giIqL4xz/+DkBa2in8/f0JCroZgPHj49iz52t+/vkszz33osOyQ4YMYPz4F2jdOpR+/SLx9fXF3/8mjLFRXGyjf/8YevWKBi7cP7ZkyQLy8wvx9/fn2Wcn8Lvf3e3enfU0Fjh07GfmJ3xLQVEx/r7ejB3Qiqa31VAgFBEpBU7D4OTJk/n8889p0OD/PtVksVgUBuWG067dAzz++AgAXnttGg0b3sGgQUPs/Xv2fO3SeqZOncFdd/0OuBAqH320L23btqdmzVpMmfISf/nLewQH38oXX2xn+vQprF59fQ8oyJVlny+yB0GAgqJi5id8y6zR7Qms4lvOoxMRqfichsFdu3aRnJysF02LR8rJyaFKlZuoUqUqvr6+JCZuol69m0lPz+bEidTremWJuObsuUJ7ELyooKiYs7mFCoMiIqXAaRisV6+egqB4lPj4yfj730RhYQHHj/+Xxx4bRmBgIHDhCdXTp0/Tp09vfv75LPHxM8t5tJVfUHV//H29HQKhv683QdX8ynFUIiKVh9Mw2Lp1a8aPH89DDz3ETTfdZG/XZWKpaLy8Lv1aTWOMw+uSfnmZ+MSJVMaNG03Dho145JHuANSuXZvExE3861+HGTduFLff3pDbbmtwyXXL9Qus4sPYAa1K3DMYWNVX9wyKiJQCp2Hwm28ufBB+7dq19jbdMygVUY0aQfz4439KtGdlZREYWOOSy/zmN/Xp0KET+/Z9Q7t2Hdi7dzf9+kUB0KTJXTRu/Ft++OGIwmBZMtD0thrMGt2es7mFBFXzUxAUESlFTsNg9+7dGTx4sDvGIlKmQkPv5733lvLTTz/SoMHtAGza9DHVqgXYp38tLy+Pb7/dS79+MXh5eTFz5qs0bFif2267kx9+OMqxYz/paWJ3MBBYxff/7hFUEBQRKTVOw+Dq1asVBqVSuO22BrzwwiSmT5+C1WqlqKiQevV+wx//+JbDJeSL9wxaLJCXl8/DDz9CRMSFs4EzZ87h9ddfJz+/EF9fX6ZOnUFISJ3y2iUREZHr5vQLJM888ww1atQgNDSUqlWr2tsr22VifYGkcryZ3R1UJ9eoTs6pRq5RnVyjOjlXGWpULl8gOXv2LGfPnuWnn36yt13vPYMbN25k0aJFWK1Whg4dWuLM46FDh5g0aRK5ubmEhoYSHx+Pj48PJ06cIC4ujszMTBo2bMicOXOoVq2afbm1a9eyd+9e3njjjWsem4iIiIgncRoGly9fDoDVasUYg6/v9b3XKy0tjTfffJMPP/wQPz8/YmJiaNOmDY0bN7bPExcXx4wZM2jVqhUvv/wyCQkJDBo0iPj4eAYNGkRERATvvPMOCxcuJC4ujoKCAhYsWMDKlSsr3TeT5Qr0iTIREZHrdul3bfxCZmYmTz75JK1ataJFixbExsaSlpZ2zRvcuXMnbdu2JSgoiKpVqxIWFkZKSoq9PzU1lfz8fFq1agVAdHQ0KSkpFBUVsXv3bnvYu9gOsHv3bmw2G3Fxcdc8Lqlg/veJshcX7mTan7/ixXe+4NCxn8FS3gMTERGpWJyeGXz11Vdp1aoVc+fOpbi4mOXLlzNt2jQWLVp0TRtMT08nODjYPh0SEsL+/fsv2x8cHExaWhpnzpwhICAAHx8fh3aADh060KFDBz788No/C1ba198rquDg6uU9BJekpp+75CfK3nruQeqHlP2/ZUWpU3lTnZxTjVyjOrlGdXJONSrJaRj88ccfeeutt+zTY8eOJSIi4po3aLPZsFj+7/SNMcZh+nL9v54PKDF9PfQAScW6sfbU6dxLfqLsVOY5/Cxl++9YkepUnlQn51Qj16hOrlGdnKsMNSqLB0icXia2Wq0UFBTYp/Py8q4rhNWtW5eMjAz7dEZGBiEhIZftP336NCEhIdSsWZOcnByKi4svuZx4loufKPslfaJMRETk6jkNg+Hh4QwbNoy1a9eybt06hg8ffl1PErdv355du3aRlZVFXl4eW7ZsoVOnTvb++vXr4+/vz969ewFISkqiU6dO+Pr6EhoaSnJyMgCJiYkOy4lnufiJsouB0OETZSIiIuIyp5eJx4wZQ926ddm+fTs2m43o6Gj69+9/zRusU6cO48ePJzY2lqKiIvr160eLFi0YMWIEY8eOpXnz5syZM4fJkydz7tw5mjVrRmxsLABTp05l4sSJLFq0iHr16jF37txrHodUcPpEmYiISKlw+tLpefPm8eyzzzq0zZgxg8mTJ5fluNxO9wxWjnsp3EF1co3q5Jxq5BrVyTWqk3OVoUZufen0/Pnzyc7OJjk5mXPnztnbi4qK2LFjR6ULgyIiIiKe6LJhsGXLlhw4cAAvLy+CgoLs7d7e3syZM8cdYxMRERGRMnbZMNi5c2c6d+5Mp06daNGihTvHJCIiIiJu4vRp4rp16zJy5EjCwsLIzMzkiSeeID093R1jExEREZEy5jQMvvrqq3Tt2hV/f38CAwO56667dL+giIiISCXhNAympqYyYMAAvLy88PX1JS4ujpMnT7pjbCIiIiJSxpyGQYvFgs1ms0+fO3fOYVpEREREKi6nL53u1q0bEyZMICcnhw8++IC1a9fSo0cPd4xNRERERMqY0zD49NNPk5iYiM1mY+fOnQwcOJABAwa4Y2wiIiIiUsachkGA3r1707t3b/v0F198wQMPPFBWYxIRERERN7nsPYMHDx4kJiaGp59+mqysLABOnDjB73//e0aNGuW2AYqIiIhI2blsGIyPj6dbt27ccsstLFq0iG3bthEVFUVeXh5JSUnuHKOIiIiIlJHLXibOyclh+PDhFBcXExYWxqZNm4iPjyciIsKd4xMRERGRMnTZMFilShXgwreICwoKWLp0Kb/73e/cNjARERERKXuXvUxsjLH/980336wgKCIiIlIJXfbMoM1m4+eff7aHwl/+N0BQUFCZD05EREREytZlw+D3339P27Zt7QGwTZs29j6LxcKhQ4fKfnQiIiIiUqYuGwYPHz7sznGIiIiISDlw+m1iEREREam8FAZFREREPJjCoIiIiIgHUxgUERER8WAKgyIiIiIeTGFQRERExIMpDIqIiIh4MIVBEREREQ+mMCgiIiLiwRQGRURERDyYwqCIiIiIB1MYFBEREfFgCoMiIiIiHkxhUERERMSDKQyKiIiIeDCFQREREREPpjAoIiIi4sEUBkVEREQ8mMKgiIiIiAdTGBQRERHxYAqDIiIiIh5MYVBERETEg5VLGNy4cSPh4eF069aNlStXlug/dOgQ0dHRhIWFMWnSJKxWKwAnTpxg8ODBdO/enVGjRpGbmwtAdnY2I0eOpEePHgwePJiMjAy37o+IiIhIReX2MJiWlsabb77JqlWrSExMZM2aNRw5csRhnri4OKZMmcLmzZsxxpCQkABAfHw8gwYNIiUlhbvvvpuFCxcCMG/ePEJDQ9m0aRP9+/fntddec/duiYiIiFRIbg+DO3fupG3btgQFBVG1alXCwsJISUmx96emppKfn0+rVq0AiI6OJiUlhaKiInbv3k1YWJhDO8Bnn31GZGQkAD179uTzzz+nqKjIvTsmIiIiUgH5uHuD6enpBAcH26dDQkLYv3//ZfuDg4NJS0vjzJkzBAQE4OPj49D+62V8fHwICAggKyuLOnXquDyuWrUCrmu/Kovg4OrlPYQKQXVyjerknGrkGtXJNaqTc6pRSW4PgzabDYvFYp82xjhMX67/1/MBJaZ/uYyX19Wd9MzMPIfNZq5qmcomOLg6GRk55T2MG57q5BrVyTnVyDWqk2tUJ+cqQ428vCylfgLL7ZeJ69at6/CAR0ZGBiEhIZftP336NCEhIdSsWZOcnByKi4tLLBcSEsLp06cBsFqt5ObmEhQU5Ia9EREREanY3B4G27dvz65du8jKyiIvL48tW7bQqVMne3/9+vXx9/dn7969ACQlJdGpUyd8fX0JDQ0lOTkZgMTERPtynTt3JjExEYDk5GRCQ0Px9fV1746JiIiIVEBuD4N16tRh/PjxxMbG0rt3b3r27EmLFi0YMWIEBw4cAGDOnDnMnDmT7t27c/78eWJjYwGYOnUqCQkJhIeHs2fPHp599lkAxo0bx7fffktERASrVq1iypQp7t4tERERkQrJYozx7Bvl/kf3DFaOeyncQXVyjerknGrkGtXJNaqTc5WhRpXinkERERERuXEoDIqIiIh4MIVBEREREQ+mMCgiIiLiwRQGRURERDyYwqCIiIiIB1MYFBEREfFgCoMiIiIiHkxhUERERMSDKQyKiIiIeDCFQREREREPpjAoIiIi4sEUBkVEREQ8mMKgiIiIiAdTGBQRERHxYAqDIiIiIh5MYVBERETEgykMioiIiHgwhUERERERD6YwKCIiIuLBFAZFREREPJjCoIiIiIgHUxgUERER8WAKgyIiIiIeTGFQRERExIMpDIqIiIh4MIVBEREREQ+mMCgiIiLiwRQGRURERDyYwqCIiIiIB1MYFBEREfFgCoMiIiIiHkxhUERERMSDKQyKiIiIeDCFQREREREPpjAoIiIi4sEUBkVEREQ8mMKgiIiIiAdTGBQRERHxYAqDIiIiIh7Mx90bPHHiBHFxcWRmZtKwYUPmzJlDtWrVHOYpLCxk0qRJHDx4kJtuuok5c+Zwxx13YIxh9uzZ/P3vf8fLy4vp06dz77332pdLS0ujb9++7Nixw927dVWsVit9+0bQuHET/vjH+QCcPHmCgQN706hRYwBstmL8/W/imWfG06JFKwA6dAilUaM78PLydljfzJlzeP31eO6/vx1Dhgxz6Fu9egX79v2TN96Yy/r1a9iwYT0WC9SvfwsvvjiZm2+uWeb7KyIiIjcut4fB+Ph4Bg0aREREBO+88w4LFy4kLi7OYZ7ly5dTpUoVNm3axO7du3nppZdISEhg8+bNHD16lOTkZH766SeeeuopkpOT8fHx4R//+Aevv/46GRkZ7t6lq/aPf/yNxo2b8K9//T9+/PE/3H57QwD8/f15//1V9vk+/XQrr78ezwcfbLC3zZ+/hKCgoBLr7NOnP0uXLiwRBjdu3MCzz8Zx+PAhVq9ewfvvryYgIIC3357Hn/60iBdemFQm+ygiIiIVg1svExcVFbF7927CwsIAiI6OJiUlpcR8n332GVFRUQDcd999ZGVlceLECf7xj38QHh6Ol5cXDRs2pF69enzzzTcArFu3jgULFrhvZ67Dhg3r6NixE126PMLatasvO1929s/UqlXbpXV26vQg+fl57Nv3jb3tm2/2YozhvvvacNddTfnggw0EBARQUFBARkY6NWoEXe+uiIiISAXn1jODZ86cISAgAB+fC5sNDg4mLS2txHzp6ekEBwfbp4ODgzl16hTp6emEhISUaAeuOwjWqhVwXcu76siRI3z33QEWL17If//7X4YMGcJLL71AzZrVKCgo4MknHwMgOzubjIwM3nnnHYKDq9uXf+650Xh5/V+Gv+WWW3jnnXcAiIkZyLZtyXTt2gmAzZs3MmTIY4SEBNrn37ZtG5MmTcLPz48XXnjeYd1AiWm5NNXJNaqTc6qRa1Qn16hOzqlGJZVZGNy0aRMzZ850aGvQoAEWi8Wh7dfTAMYYh3ZjDF5eXthstku2l4bMzHPYbKZU1nUl7733V9q374DV6kO9eg2pW/c3/OUvy+naNQx/f3/efXeFfd7du79i3Lhx/PWvH/Cb39QHYO7chSUuE2dk5ADQtWsEjz3Wn1GjnsVqtbJ9+3Z+//sJ9n6Ali3b8PHH2/joow0MGzacNWs22GsYHFzdYV65NNXJNaqTc6qRa1Qn16hOzlWGGnl5WUr9BFaZhcEePXrQo0cPh7aioiLatGlDcXEx3t7eZGRkOJzpu6hOnTqkp6dz2223AXD69GlCQkKoW7cu6enp9vkutlcUeXl5bN6cjK+vH/36RQKQm5vL+vUJPPjgwyXmv+++NtSvfyuHDn1nD4NXUrt2MKGhbdi2bQv5+Xk8+ODDBARc+MEcP/5fMjMzadmyFQAREVHMmTOTnJxsXS4WERHxYG69Z9DX15fQ0FCSk5MBSExMpFOnTiXm69y5M0lJSQDs2bMHf39/fvOb39CpUyc2btxIcXExP/30Ez/++CPNmzd35y5cly1bNhEYWIPExE2sW7eRdes2kpCQRF7eef7+920l5j927CdOnTrBb3/bxOVtREf3Z8uWTWza9DHR0QPs7ZmZp5k27WXOnj1rH0vDhncoCIqIiHg4tz9NPHXqVCZOnMiiRYuoV68ec+fOBWD16tWkp6czbtw4hgwZwpQpU4iIiMDPz4/Zs2cD0L17d/bv329/uOS1117jpptucvcuXLPExHUMHDgYb+//ezVM9erV6dcvhoSEVRQUFDBs2CB7nzE24uImcdttDextY8c+VeLVMk89NZp27ToA0Lp1KPPm/YHAwBrccUdj+zwtW95DbOxwnnlmJN7ePtSuXZuZM+eU1a6KiIhIBWExxpT9jXIVgLvuGbyRVYZ7KdxBdXKN6uScauQa1ck1qpNzlaFGZXHPoL5AUtYskJ1XxLGMXLLzrVDyeRkRERGRcuP2y8QexQKHjv3M/IRvKSgqxt/Xm7EDWtH0thrg2SchRURE5AahM4NlKPt8kT0IAhQUFTM/4VuyzxeV88hERERELlAYLENnzxXag+BFBUXFnM0tLKcRiYiIiDhSGCxDQdX98fd1fPLX39eboGp+5TQiEREREUcKg2UosIoPYwe0sgfCi/cMBlb1LeeRiYiIiFygB0jKkoGmt9Vg1uj2nM0tJKia34UgqIdHRERE5AahMFjWDARW8SWwiq99WkRERORGocvEIiIiIh5MYVBERETEgykMioiIiHgwhUERERERD6YwKCIiIuLBFAZFREREPJheLfM/Xl6W8h7CDUF1cI3q5BrVyTnVyDWqk2tUJ+cqeo3KYvwWY4zefCciIiLioXSZWERERMSDKQyKiIiIeDCFQREREREPpjAoIiIi4sEUBkVEREQ8mMKgiIiIiAdTGBQRERHxYAqDIiIiIh5MYVBERETEgykMVjIbN24kPDycbt26sXLlyhL9hw4dIjo6mrCwMCZNmoTVanXonzdvHgsWLLBPf/3117Rp04ZevXrRq1cvXnrpJQAKCwuJi4ujR48e9OnTh6NHj5btjpWi0q5RdHS0vT5hYWH87ne/4/Tp06SmpnLPPffY+5544oky37fSdK112rt3L/369aNXr14MHTqU1NRUALKzsxk5ciQ9evRg8ODBZGRkABX7twSlX6ejR48yePBgevXqxcCBAzl06BCAfk+/qpOOTc5rpGOTY5327NlDdHQ0kZGRPP300/z8889A5T02XRUjlcapU6fMQw89ZM6cOWNyc3NNZGSk+fe//+0wT0REhPnmm2+MMca89NJLZuXKlcYYY7Kzs81LL71kWrRoYebPn2+f/89//rNZvHhxiW29++675pVXXjHGGPP111+b/v37l9Fela6yqNEvxcXFmUWLFhljjElJSbHXqKK5njo99NBD5tChQ8YYY9auXWuefvppY4wx8fHxZsmSJcYYYzZs2GDGjRtnjKm4vyVjyqZOMTEx5u9//7sxxpidO3eayMhIY4x+T8Y41knHJuc1+iUdm4zp2rWrfd4//OEP5o9//KMxpnIem66WzgxWIjt37qRt27YEBQVRtWpVwsLCSElJsfenpqaSn59Pq1atgAt/NV7s//TTT7n99tt5/PHHHdZ54MABduzYYf9L6uTJkwB89tlnREVFAXDfffeRlZXFiRMn3LCX16csanTRrl27OHz4MCNGjAAu1O7777+nV69exMbG8q9//atsd64UXWudCgsLGTduHHfddRcATZo0cfjNREZGAtCzZ08+//xzioqKKuxvCcqmTv3796djx44l2vV7KlkPHZuuXKOLdGy60J+cnEzjxo0pKioiLS2NwMBAoHIem66WwmAlkp6eTnBwsH06JCSEtLS0y/YHBwfb+3v37s3IkSPx9vZ2WGf16tUZMmQIGzdupHPnzowfP/6y6zp16lSZ7FdpKosaXTR//nzGjx9v7/f39ycqKooNGzbwxBNPMGbMGAoLC8tit0rdtdbJz8+PXr16AWCz2Xj77bfp2rVriWV8fHwICAggKyurwv6WoGzqFB0dbf8NzZ8/396u35NjnXRscl6ji3RsutDv6+vLv/71Lzp37sxXX31FREREiWUqy7HpaikMViI2mw2LxWKfNsY4TDvrv5RXX32Vbt26AfDoo49y5MgRcnJySixrjMHL68b/OZVFjQD+/e9/c+bMGR566CF72zPPPMOgQYPw8vKic+fOVK1alR9++KGU9qRsXW+dCgsLmTBhAlarlaeeeuqS27j4m6movyUouzoZY5g1axb79u3j5ZdfBvR7+nWddGxy7bekY5Njf5MmTdi5cyejR4+2/wHxa5Xh2HS1Kudeeai6devab3wFyMjIICQk5LL9p0+fduj/NZvNxqJFiyguLnZo9/b2pk6dOqSnp7u8rhtFadfoom3bthEeHu7Qtnz5cs6cOWOfNsbg4+NzPcN3m+upU25uLk8++SRWq5VFixbh6+sLXPgL/vTp0wBYrVZyc3MJCgqqsL8lKJs6Wa1WJkyYwIEDB1i2bBnVq1cH9Hv6ZZ10bHLttwQ6Nl2sU0FBAdu2bbO3R0VF2S+PV8Zj09VSGKxE2rdvz65du8jKyiIvL48tW7bQqVMne3/9+vXx9/dn7969ACQlJTn0/5qXlxdbt25l8+bNACQmJtKyZUuqVq1K586dSUpKAi48oeXv789vfvObMty70lHaNbro22+/JTQ01KFt9+7drFu3Drjw5KPNZqNRo0aluDdl53rqFBcXR4MGDZg3bx5+fn72ZTp37kxiYiJw4d6d0NBQfH19K+xvCcqmTrNmzeLcuXO899579iAI+j39sk46Nrn2WwIdmy7WycfHh/j4eA4ePAjApk2baN26NVA5j01XzT3PqYi7fPTRRyYiIsJ069bNLF261BhjzJNPPmn2799vjDHm0KFDpm/fviYsLMw899xzpqCgwGH5+fPnOzwp+/3335uBAwea8PBw89hjj5kTJ04YY4zJz883L7zwggkPDze9e/c2Bw8edNMeXr/SrpExxvTo0cMcOXLEoe3UqVNm2LBhJiIiwkRHR9uf9qsorqVO3333nbnzzjtNeHi4iYqKMlFRUebJJ580xhhz5swZ89RTT5nw8HAzcOBA89///tcYU7F/S8aUbp0yMzNN06ZNzSOPPGJvj4qKMsbo9/Tr35OOTc5rZIyOTb88hu/evdv06dPHREVFmREjRpiTJ08aYyrvselqWIwxprwDqYiIiIiUD10mFhEREfFgCoMiIiIiHkxhUERERMSDKQyKiIiIeDCFQREREREPVjHeMikilcLx48d55JFHuPPOO4ELLzavVq0asbGxJV6MWxpGjBjBiy++SOPGjV1eZsGCBaxcuZI6depgsVgoLi6mVq1aTJ06lYYNG7q8ngMHDjBu3Dj+9re/XbL/xRdf5F//+hd+fn40b96cV155xeV1X3To0CGWLVvGzJkzmThxIl988QU1a9YELtT2/PnzxMTEMGLECNLS0hg3bhwffPDBVW/nSt544w0eeugh2rRpU6rrFRH3URgUEbe66aab7C9yhQsflh82bBje3t6EhYWV6rb+9Kc/XdNy4eHhTJkyxT69fPlynn/+eT788MPSGhqzZs26ruVtNhuTJk1i0aJF9rZhw4bxxBNP2KdPnDhBeHg4Xbp04Y477ij1IAgwZswYBg0axNq1a7nppptKff0iUvZ0mVhEylX9+vUZO3Ysf/7zn4EL31h9/fXX6dOnD1FRUUycOJFz584BsGrVKqKioujbty+DBg3iyJEj7Nixg8jISPv6srOzue+++/j555/p0qULBw4cAGDNmjX07NmTqKgohg8fzn/+8x+Xx9iuXTv7/N9++y2DBw+mf//+PPjgg/ZvB18cX1hYGH379mXVqlX29gULFvDEE08QGRnJhAkTOH36NKNHj2bgwIF06dKFIUOGkJmZCVz4luyQIUOIjIwkKirK/mWEX9u0aRO33HILderUuey4T506hTGGgIAAjh8/zj333APA0aNHiYmJITo6mj59+rBy5Urgwqe4Zs6cSVhYGOHh4UyaNInCwkKKioqYPn064eHhREZGMmnSJPu/SfXq1bnnnntYs2aNy/UUkRuLwqCIlLu77rqL77//HoClS5fi7e3Nhx9+yEcffURISAhz5syhuLiY119/nXfffZf169czYMAA9u7dywMPPEBubq499H388cd07tyZGjVq2Ne/a9cu3n33XZYtW8ZHH31Ez549GTNmDK68c99qtbJu3Tr7ZdBly5YxduxY1q5dyyeffMLf/vY3Dh48yKFDh3j77bdZsWIF69evd/g+LFw4A7phwwbmzJnDJ598QqtWrVizZg2ffvqp/Wyp1Wpl1KhRDBkyhI0bN/KnP/2JuXPn8s0335QY1+bNm3nwwQcd2t5//3169erFww8/TJs2bXj33XdZsmRJicD45z//mS5duvDhhx+ydOlS9uzZg81mY9WqVXz33XckJSXx8ccfk5ubS3JyMosWLSI9PZ2kpCSSkpKw2WzMnj3bvr4OHTqwdetWp7UUkRuTLhOLSLmzWCz2S4yfffYZOTk57Ny5E4CioiJq1aqFt7c33bt3JyYmhgcffJAOHTrQuXNnLBYLffv2ZcOGDTRv3pwPP/yQF154wWH927dvJzw83H4/XXR0NK+99hrHjx/n1ltvLTGe5ORk+7dNi4qKaNasGdOnTwcu3CP3+eefs3jxYn744QcKCgo4f/48Bw8e5IEHHiA4OBiAgQMHsmPHDvs6W7VqhY/PhUPu0KFD2bNnD3/5y1/48ccf+fe//03Lli358ccfKSgooFu3bgDUqVOHbt26sX37dvtZvYt++OEHYmNjHdouXiY+f/4848ePx8/P75L38j3yyCO8+OKL7N+/n3bt2jF58mS8vLzYuXMnvXr1sv9bzJs3D4B+/foxfvx4e8AdMmQIY8aMsa/vlltuuaozrSJyY1EYFJFyd+DAAYeHSl5++WU6d+4MQG5uLgUFBQDMmTOH77//np07d7J06VKSkpJ466236NevH3369KF///7k5ORw//33O6zfZrOV2KYxBqvVSq9evextM2bMAEreM/hLjz32GE2aNKFjx4706NGDffv22c8w/vJMo7e3t8NyVatWtf/3H/7wB/bv30/fvn1p06YNVqsVYwzFxcVYLJZLjvPXLBbLZc9sVq1aldmzZxMeHs7777/P448/7tD/0EMPsXnzZnbu3MmuXbt45513+PDDD+1h9aLTp09js9mw2WwO47LZbBQVFdmnfXx88PLShSaRikr/94pIufrPf/7DwoULGT58OHDhkuPKlSspLCzEZrPxyiuvMHfuXLKysujcuTNBQUEMGzaMZ5991n5puE6dOrRo0YIpU6bQr1+/Etvo2LEjycnJZGVlAbB+/XqCgoJo0KCB/dJnUlISzZs3v+JYs7OzOXDgABMmTKBbt26cOnWKY8eOYbPZeOCBB/jiiy84deoUABs2bLjsenbs2MHQoUPp3bs3tWrVYufOnRQXF9OoUSN8fHzYsmULAGlpaWzevJn27duXWEfDhg05duzYZbdRo0YNXnzxRebPn09aWppD3/PPP09ycjIRERFMnTqVgIAAjh07Rrt27fj444/ttZ82bRqffPIJHTt2ZPXq1RQVFWGz2Vi5ciUPPPCAfX3Hjx+nUaNGV6ydiNy4dGZQRNwqPz/ffjbOy8sLf39/nnvuOfv9b6NHj2bWrFn06dOH4uJimjZtysSJEwkICGDUqFEMGzaMm266CW9vb/uZPID+/fszbtw4h6drL3rggQcYNmwYQ4cOxWazUbNmTZYsWXLVZ7MCAwMZOXIkffr0oWrVqtSpU4fWrVvz008/0a5dO+Li4hg6dCjVqlWjRYsWl13PmDFjmD17Nm+99Ra+vr60bt2aY8eO4evry8KFC5kxYwYLFiyguLiYMWPG0LZt2xLrCAsLY+vWrfTt2/ey24mKimLt2rXMmjWL5557zt4+evRoJk2axJo1a/D29qZr167cd9993HvvvaSmphIdHY0xhvvvv58hQ4ZgtVqZNWsWvXv3xmq10qJFC4dX4Wzfvp3u3btfVS1F5MZhMa7cQS0iIjeU4uJioqOjWbp06RWfKC5r586dIyYmhvXr1+Pv719u4xCRa6fLxCIiFZC3tzfTp09n7ty55TqOBQsW8PLLLysIilRgOjMoIiIi4sF0ZlBERETEgykMioiIiHgwhUERERERD6YwKCIiIuLBFAZFREREPJjCoIiIiIgH+//X4XBQ4g59zAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# gráfico de risco x retorno dos ativos\n",
    "fig, ax = plt.subplots(figsize=(10,5))\n",
    "\n",
    "sns.scatterplot(x=sigma, y=mi)\n",
    "plt.title('Risco x Retorno')\n",
    "plt.xlabel('Desvio-Padrão (Risco)')\n",
    "plt.ylabel('Retorno Médio')\n",
    "for i, name in enumerate(mi.index.values): # identificando os pontos no gráfico\n",
    "    plt.annotate(name, (sigma[i], mi[i]+1e-4))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "71dfa744-8c6f-4288-93c9-263f6670e1eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.2, 0.2, 0.2, 0.2, 0.2])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# definir pesos iguais para compor carteira de ativos\n",
    "w = np.array([1/len(mi)]*len(mi))\n",
    "w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "de5a320b-bc66-45bf-afcc-42019c85a26b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Retorno médio esperado da carteira: 0.139%\n",
      "Risco (desvio-padrão) da carteira: 1.4955%\n"
     ]
    }
   ],
   "source": [
    "# calcular risco e retorno da carteira com pesos iguais\n",
    "mi_p = np.sum(w*mi)\n",
    "sigma_p = np.sqrt(np.dot(w, np.dot(cov_matrix, w)))\n",
    "print(f'Retorno médio esperado da carteira: {round(mi_p*100, 4)}%\\nRisco (desvio-padrão) da carteira: {round(sigma_p*100, 4)}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "038bdc47-1a9b-4f9c-8dfe-ebdb20a478fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# simular carteiras com diferentes combinações de pesos dos ativos\n",
    "n = len(mi)\n",
    "\n",
    "mi_random_w = []\n",
    "sigma_random_w = []\n",
    "\n",
    "for i in range(10000):\n",
    "    # Geramdo pesos aleatórios\n",
    "    w = np.random.random(n) # gera n valores entre 0 e 1\n",
    "    w = w/np.sum(w) # divide cada valor pela soma dos n valores para que a soma de w seja igual a 1\n",
    "    \n",
    "    # Calculando risco e retorno\n",
    "    mi_random_w.append(np.sum(w*mi))\n",
    "    sigma_random_w.append(np.sqrt(np.dot(w, np.dot(cov_matrix, w))))\n",
    "    \n",
    "# Gráfico\n",
    "sns.scatterplot(x=sigma_random_w, y=mi_random_w,alpha=0.4)\n",
    "plt.title('Risco x Retorno de Diferentes Carteiras')\n",
    "plt.xlabel('Desvio-Padrão (Risco)')\n",
    "plt.ylabel('Retorno Médio')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "dcfaf4ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "     fun: 0.01900513619889989\n",
       "     jac: array([0.00481062, 0.0162258 , 0.02779627, 0.00952299, 0.01043617])\n",
       " message: 'Optimization terminated successfully'\n",
       "    nfev: 36\n",
       "     nit: 6\n",
       "    njev: 6\n",
       "  status: 0\n",
       " success: True\n",
       "       x: array([0.        , 0.09677297, 0.46995597, 0.1640386 , 0.26923246])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w_ini = np.array([1/len(mi)]*len(mi)) # definindo pesos iniciais\n",
    "\n",
    "ret = 0.0025 # definindo o retorno desejado\n",
    "\n",
    "constraints = [\n",
    "                {'type': 'eq', 'fun': lambda x: sum(x) -1}, # primeira restrição: soma dos pesos deve ser igual a 1\n",
    "                {'type': 'eq', 'fun': lambda x: sum(x*mi) -ret} # segunda restrição: retorno esperado deve ser igual ao retorno desejado\n",
    "              ]\n",
    "\n",
    "bounds = tuple((0,1) for x in range(len(w))) # definindo limites para os pesos\n",
    "\n",
    "result = solver.minimize(f_obj_min_risk, w_ini,(cov_matrix), constraints=constraints, bounds=bounds, method='SLSQP')\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f79b1b80-4273-4d63-99bb-c072645f46c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pesos (%): [0.0, 9.68, 47.0, 16.4, 26.92]\n",
      "Risco (desvio-padrão) da carteira: 1.9005%\n"
     ]
    }
   ],
   "source": [
    "# peso dos ativos e risco da carteira para o retorno desejado\n",
    "pesos = [round(w*100, 2) for w in result.x]\n",
    "print(f'Pesos (%): {pesos}\\nRisco (desvio-padrão) da carteira: {round(result.fun*100, 4)}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "37336636-122b-4e82-ab81-681c09624809",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Retorno Médio')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# fronteira eficiente: mínimo risco para diferentes níveis de retorno\n",
    "ret_range = np.arange(0,0.004,0.0001)\n",
    "sigma_w = []\n",
    "\n",
    "for ret in ret_range:\n",
    "    constraints = [\n",
    "                    {'type': 'eq', 'fun': lambda x: sum(x) -1}, # primeira restrição: soma dos pesos deve ser igual a 1\n",
    "                    {'type': 'eq', 'fun': lambda x: sum(x*mi) -ret} # segunda restrição: retorno esperado deve ser igual ao retorno desejado\n",
    "                  ]\n",
    "    \n",
    "    result = solver.minimize(f_obj_min_risk, w_ini,(cov_matrix), constraints=constraints, bounds=bounds, method='SLSQP')\n",
    "    sigma_w.append(result.fun)\n",
    "\n",
    "plt.plot(sigma_w, ret_range, '--')\n",
    "plt.title('Fronteira Eficiente:\\nmínimo risco para cada nível de retorno')\n",
    "plt.xlabel('Desvio-Padrão (Risco)')\n",
    "plt.ylabel('Retorno Médio')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c347b1cd-620d-4ab6-8ab6-27103302b622",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "     fun: -0.002499670346455768\n",
       "     jac: array([ 0.00118279, -0.00208787, -0.00385672, -0.00098787, -0.00120124])\n",
       " message: 'Optimization terminated successfully'\n",
       "    nfev: 80\n",
       "     nit: 13\n",
       "    njev: 13\n",
       "  status: 0\n",
       " success: True\n",
       "       x: array([6.58839782e-17, 9.74350545e-02, 4.69937336e-01, 1.68103022e-01,\n",
       "       2.64524588e-01])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w_ini = np.array([1/len(mi)]*len(mi)) # definindo pesos iniciais\n",
    "\n",
    "risk = 0.0190051362 # definindo o risco desejado\n",
    "\n",
    "constraints = [\n",
    "                {'type': 'eq', 'fun': lambda x: sum(x) -1}, # primeira restrição: soma dos pesos deve ser igual a 1\n",
    "                {'type': 'eq', 'fun': lambda x: np.sqrt(np.dot(x, np.dot(cov_matrix, x))) - risk} # segunda restrição: risco esperado deve ser igual ao risco desejado\n",
    "              ]\n",
    "\n",
    "bounds = tuple((0,1) for x in range(len(w))) # definindo limites para os pesos\n",
    "\n",
    "result = solver.minimize(f_obj_max_ret, w_ini,mi, constraints=constraints, bounds=bounds, method='SLSQP')\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b498f483-5c57-424c-abe2-c33d48eab075",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pesos (%): [0.0, 9.74, 46.99, 16.81, 26.45]\n",
      "Retorno médio esperado da carteira: 0.25%\n"
     ]
    }
   ],
   "source": [
    "# peso dos ativos e retorno da carteira para o risco desejado\n",
    "pesos = [round(w*100, 2) for w in result.x]\n",
    "print(f'Pesos (%): {pesos}\\nRetorno médio esperado da carteira: {round(-result.fun*100, 4)}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4224afc8-c18f-4cc4-b126-a35418c76ef8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# fronteira eficiente: máximo retorno para diferentes níveis de risco\n",
    "risk_range = np.arange(0.01,0.032,0.0005)\n",
    "ret_w = []\n",
    "\n",
    "for risk in risk_range:\n",
    "    constraints = [\n",
    "                    {'type': 'eq', 'fun': lambda x: sum(x) -1}, # primeira restrição: soma dos pesos deve ser igual a 1\n",
    "                    {'type': 'eq', 'fun': lambda x: np.sqrt(np.dot(x, np.dot(cov_matrix, x))) -risk} # segunda restrição: risco esperado deve ser igual ao risco desejado\n",
    "                  ]\n",
    "    \n",
    "    result = solver.minimize(f_obj_max_ret, w_ini,mi, constraints=constraints, bounds=bounds, method='SLSQP')\n",
    "    ret_w.append(-result.fun)\n",
    "\n",
    "sns.lineplot(x=risk_range, y=ret_w, ls='--')\n",
    "plt.title('Máximo retorno para cada nível de risco')\n",
    "plt.xlabel('Desvio-Padrão (Risco)')\n",
    "plt.ylabel('Retorno Médio');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a96163e0-1f89-4a8b-b97c-2325dacb63c8",
   "metadata": {},
   "source": [
    "Diferentemente da otimização do portfólio através da minimização do risco para cada nível de retorno, a otimização através da maximização do retorno para cada nível de risco gerou pontos em que o retorno não cresce junto com o risco, e que, portanto, não fazem parte da fronteira eficiente."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.8 ('base')",
   "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": "3.8.8"
  },
  "vscode": {
   "interpreter": {
    "hash": "f0772a89b7b7552231f8b4808588399a8029470ff763161a0caeccf5fec49431"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}