acoes.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importações.\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import plotly.express as px\n",
    "import yfinance as yf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n"
     ]
    }
   ],
   "source": [
    "# Carga dos dados.\n",
    "petr3 = yf.download('PETR3.SA', start = '2015-01-01')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></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>2015-01-02</th>\n",
       "      <td>9.58</td>\n",
       "      <td>9.67</td>\n",
       "      <td>9.00</td>\n",
       "      <td>9.00</td>\n",
       "      <td>4.747639</td>\n",
       "      <td>12710900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>8.64</td>\n",
       "      <td>8.96</td>\n",
       "      <td>8.25</td>\n",
       "      <td>8.27</td>\n",
       "      <td>4.362553</td>\n",
       "      <td>25730600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-06</th>\n",
       "      <td>8.28</td>\n",
       "      <td>8.53</td>\n",
       "      <td>7.78</td>\n",
       "      <td>8.06</td>\n",
       "      <td>4.251774</td>\n",
       "      <td>29754600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-07</th>\n",
       "      <td>8.30</td>\n",
       "      <td>8.51</td>\n",
       "      <td>8.08</td>\n",
       "      <td>8.45</td>\n",
       "      <td>4.457504</td>\n",
       "      <td>34128300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08</th>\n",
       "      <td>8.60</td>\n",
       "      <td>9.26</td>\n",
       "      <td>8.52</td>\n",
       "      <td>9.02</td>\n",
       "      <td>4.758188</td>\n",
       "      <td>37487300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Open  High   Low  Close  Adj Close    Volume\n",
       "Date                                                    \n",
       "2015-01-02  9.58  9.67  9.00   9.00   4.747639  12710900\n",
       "2015-01-05  8.64  8.96  8.25   8.27   4.362553  25730600\n",
       "2015-01-06  8.28  8.53  7.78   8.06   4.251774  29754600\n",
       "2015-01-07  8.30  8.51  8.08   8.45   4.457504  34128300\n",
       "2015-01-08  8.60  9.26  8.52   9.02   4.758188  37487300"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verificando os 5 primeiros elementos da base de dados.\n",
    "petr3.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Nossa base de dados começa em **`2015-01-02`**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "    <tr>\n",
       "      <th>2022-09-13</th>\n",
       "      <td>34.799999</td>\n",
       "      <td>35.310001</td>\n",
       "      <td>34.090000</td>\n",
       "      <td>34.259998</td>\n",
       "      <td>34.259998</td>\n",
       "      <td>25361200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-14</th>\n",
       "      <td>34.320000</td>\n",
       "      <td>35.189999</td>\n",
       "      <td>34.200001</td>\n",
       "      <td>34.680000</td>\n",
       "      <td>34.680000</td>\n",
       "      <td>11926000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-15</th>\n",
       "      <td>34.360001</td>\n",
       "      <td>34.880001</td>\n",
       "      <td>34.349998</td>\n",
       "      <td>34.599998</td>\n",
       "      <td>34.599998</td>\n",
       "      <td>9164700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-16</th>\n",
       "      <td>34.169998</td>\n",
       "      <td>34.580002</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>34.410000</td>\n",
       "      <td>34.410000</td>\n",
       "      <td>34954800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-19</th>\n",
       "      <td>33.900002</td>\n",
       "      <td>35.029999</td>\n",
       "      <td>33.820000</td>\n",
       "      <td>34.820000</td>\n",
       "      <td>34.820000</td>\n",
       "      <td>24596800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                 Open       High        Low      Close  Adj Close    Volume\n",
       "Date                                                                       \n",
       "2022-09-13  34.799999  35.310001  34.090000  34.259998  34.259998  25361200\n",
       "2022-09-14  34.320000  35.189999  34.200001  34.680000  34.680000  11926000\n",
       "2022-09-15  34.360001  34.880001  34.349998  34.599998  34.599998   9164700\n",
       "2022-09-16  34.169998  34.580002  34.000000  34.410000  34.410000  34954800\n",
       "2022-09-19  33.900002  35.029999  33.820000  34.820000  34.820000  24596800"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verificando os 5 últimos elementos de nossa base de dados.\n",
    "petr3.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Nossa base de dados vai até **`2022-09-16`**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1918, 6)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verificando a quantidade de observações e atributos.\n",
    "petr3.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Há um total de **1917 registros** e **6 atributos**.\n",
    "* Os registros correspondem aos dias de funcionamento da bolsa de valores de SP, ou seja, so há dados de dias da semana."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 1918 entries, 2015-01-02 to 2022-09-19\n",
      "Data columns (total 6 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   Open       1918 non-null   float64\n",
      " 1   High       1918 non-null   float64\n",
      " 2   Low        1918 non-null   float64\n",
      " 3   Close      1918 non-null   float64\n",
      " 4   Adj Close  1918 non-null   float64\n",
      " 5   Volume     1918 non-null   int64  \n",
      "dtypes: float64(5), int64(1)\n",
      "memory usage: 104.9 KB\n"
     ]
    }
   ],
   "source": [
    "# Metadados dos dados.\n",
    "petr3.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Todos os atributos são do tipo numérico, especificamente do tipo **`contínuo`**, ou seja, valores reais."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verificando a existência de valores ausentes.\n",
    "petr3.isnull().sum().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **Não há valores ausentes** em nossa base de dados."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1918.000000</td>\n",
       "      <td>1918.000000</td>\n",
       "      <td>1918.000000</td>\n",
       "      <td>1918.000000</td>\n",
       "      <td>1918.000000</td>\n",
       "      <td>1.918000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>21.707810</td>\n",
       "      <td>22.071992</td>\n",
       "      <td>21.341361</td>\n",
       "      <td>21.696236</td>\n",
       "      <td>12.644111</td>\n",
       "      <td>1.515007e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.317639</td>\n",
       "      <td>8.396702</td>\n",
       "      <td>8.233218</td>\n",
       "      <td>8.329368</td>\n",
       "      <td>6.411426</td>\n",
       "      <td>1.132672e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>5.890000</td>\n",
       "      <td>5.990000</td>\n",
       "      <td>5.670000</td>\n",
       "      <td>5.910000</td>\n",
       "      <td>3.117616</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>14.450000</td>\n",
       "      <td>14.702500</td>\n",
       "      <td>14.172500</td>\n",
       "      <td>14.422500</td>\n",
       "      <td>7.623916</td>\n",
       "      <td>8.281625e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>22.095000</td>\n",
       "      <td>22.485000</td>\n",
       "      <td>21.755000</td>\n",
       "      <td>22.115000</td>\n",
       "      <td>12.021601</td>\n",
       "      <td>1.258215e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>29.200001</td>\n",
       "      <td>29.600000</td>\n",
       "      <td>28.767500</td>\n",
       "      <td>29.185000</td>\n",
       "      <td>16.178091</td>\n",
       "      <td>1.846928e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>40.250000</td>\n",
       "      <td>40.509998</td>\n",
       "      <td>39.439999</td>\n",
       "      <td>39.849998</td>\n",
       "      <td>38.270000</td>\n",
       "      <td>1.374106e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Open         High          Low        Close    Adj Close  \\\n",
       "count  1918.000000  1918.000000  1918.000000  1918.000000  1918.000000   \n",
       "mean     21.707810    22.071992    21.341361    21.696236    12.644111   \n",
       "std       8.317639     8.396702     8.233218     8.329368     6.411426   \n",
       "min       5.890000     5.990000     5.670000     5.910000     3.117616   \n",
       "25%      14.450000    14.702500    14.172500    14.422500     7.623916   \n",
       "50%      22.095000    22.485000    21.755000    22.115000    12.021601   \n",
       "75%      29.200001    29.600000    28.767500    29.185000    16.178091   \n",
       "max      40.250000    40.509998    39.439999    39.849998    38.270000   \n",
       "\n",
       "             Volume  \n",
       "count  1.918000e+03  \n",
       "mean   1.515007e+07  \n",
       "std    1.132672e+07  \n",
       "min    0.000000e+00  \n",
       "25%    8.281625e+06  \n",
       "50%    1.258215e+07  \n",
       "75%    1.846928e+07  \n",
       "max    1.374106e+08  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Resumo estatístico.\n",
    "petr3.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* O valor de fechamento mais alto registrado foi de **R$ 39,84**, e o mínimo **R$ 5,91**.\n",
    "* O valor de abertura mais alto registrado foi de **R$ 40,25**, e o mínimo **R$ 5,89**.\n",
    "* O valor de cotação mais alto que a ação chegou a ter em um dia foi de **R$ 40,50**, e o mais baixo de **R$ 5,99**.\n",
    "* O valor de cotação mais baixo que a ação chegou a ter em um dia foi de **R$ 39,43**, e o mais baixo de **5,67**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
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       "    <tr>\n",
       "      <th>2022-08-09</th>\n",
       "      <td>39.740002</td>\n",
       "      <td>40.099998</td>\n",
       "      <td>39.439999</td>\n",
       "      <td>39.849998</td>\n",
       "      <td>32.964211</td>\n",
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      "text/plain": [
       "                 Open       High        Low      Close  Adj Close    Volume\n",
       "Date                                                                       \n",
       "2022-08-09  39.740002  40.099998  39.439999  39.849998  32.964211  16911500"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verificando a data na qual a ação teve o preço mais alto em seu fechamento.\n",
    "petr3[(petr3['Close'] >= 39.84) & (petr3['Close'] <= 40)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **`2022/08/09`** foi o momento em que a ação alcançou o preço mais alto em seu fechamento."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-20</th>\n",
       "      <td>5.93</td>\n",
       "      <td>5.99</td>\n",
       "      <td>5.67</td>\n",
       "      <td>5.93</td>\n",
       "      <td>3.128166</td>\n",
       "      <td>12696100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-26</th>\n",
       "      <td>6.10</td>\n",
       "      <td>6.24</td>\n",
       "      <td>5.86</td>\n",
       "      <td>5.96</td>\n",
       "      <td>3.143991</td>\n",
       "      <td>13040400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-11</th>\n",
       "      <td>5.89</td>\n",
       "      <td>6.05</td>\n",
       "      <td>5.78</td>\n",
       "      <td>5.91</td>\n",
       "      <td>3.117616</td>\n",
       "      <td>12812600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Open  High   Low  Close  Adj Close    Volume\n",
       "Date                                                    \n",
       "2016-01-20  5.93  5.99  5.67   5.93   3.128166  12696100\n",
       "2016-01-26  6.10  6.24  5.86   5.96   3.143991  13040400\n",
       "2016-02-11  5.89  6.05  5.78   5.91   3.117616  12812600"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verificando a data na qual a ação teve o preço mais baixo em seu fechamento.\n",
    "petr3[(petr3['Close'] >= 5.85) & (petr3['Close'] < 6.00)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **`2016/02/11`** foi o momento em que a ação alcançou o preço mais baixo em seu fechamento."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>price_var</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></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>2015-01-02</th>\n",
       "      <td>9.58</td>\n",
       "      <td>9.67</td>\n",
       "      <td>9.00</td>\n",
       "      <td>9.00</td>\n",
       "      <td>4.747639</td>\n",
       "      <td>12710900</td>\n",
       "      <td>0.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>8.64</td>\n",
       "      <td>8.96</td>\n",
       "      <td>8.25</td>\n",
       "      <td>8.27</td>\n",
       "      <td>4.362553</td>\n",
       "      <td>25730600</td>\n",
       "      <td>0.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-06</th>\n",
       "      <td>8.28</td>\n",
       "      <td>8.53</td>\n",
       "      <td>7.78</td>\n",
       "      <td>8.06</td>\n",
       "      <td>4.251774</td>\n",
       "      <td>29754600</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-07</th>\n",
       "      <td>8.30</td>\n",
       "      <td>8.51</td>\n",
       "      <td>8.08</td>\n",
       "      <td>8.45</td>\n",
       "      <td>4.457504</td>\n",
       "      <td>34128300</td>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08</th>\n",
       "      <td>8.60</td>\n",
       "      <td>9.26</td>\n",
       "      <td>8.52</td>\n",
       "      <td>9.02</td>\n",
       "      <td>4.758188</td>\n",
       "      <td>37487300</td>\n",
       "      <td>0.74</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Open  High   Low  Close  Adj Close    Volume  price_var\n",
       "Date                                                               \n",
       "2015-01-02  9.58  9.67  9.00   9.00   4.747639  12710900       0.67\n",
       "2015-01-05  8.64  8.96  8.25   8.27   4.362553  25730600       0.71\n",
       "2015-01-06  8.28  8.53  7.78   8.06   4.251774  29754600       0.75\n",
       "2015-01-07  8.30  8.51  8.08   8.45   4.457504  34128300       0.43\n",
       "2015-01-08  8.60  9.26  8.52   9.02   4.758188  37487300       0.74"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Criando o atributo `price_var`, para noção da variação do preço no dia em questão.\n",
    "petr3['price_var'] = petr3['High'] - petr3['Low']\n",
    "petr3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7306308604629246"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculando o valor médio da variação.\n",
    "petr3['price_var'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Em média a ação sofria uma volatilidade/variação de **R$ 0,73** em seu preço por dia."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Caso mude de ideia, execute a célula novamente !\n"
     ]
    }
   ],
   "source": [
    "# Criando uma função para salavar o dataframe em formato csv.\n",
    "opcao = input('Deseja salvar o DataFrame em formato csv? [s/n]: ')\n",
    "\n",
    "if opcao == 's':\n",
    "    petr3.to_csv('petr3.csv')\n",
    "else:\n",
    "    print('Caso mude de ideia, execute a célula novamente !')    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BsFadKFURBafpDYQxc3QDFk9pwUAwEuOSzgSWsSwO0gnCyob3W+aFAXX+FwAtYEJI6TGguX9rfB70B9XliFJYIesbCmNSSx0mttRh16EBLLj1Oazf15Nw/64Be4Eu9LiJPUqeBHhSS23ceaMWcPHMAVOACSFp0T0YAqAWve/VXMH9BU5D6guE0VDtg98bfbje+fLWhPv/9c1dtuvDFOCiICKdD8ICAK8QcS9ZtIAJISVLz5AqwD6Px5iLHQhmFgiVK32BMBpqfEZDdgB4YcPBhPv/5Jn38NrWjrj1FODiIBzJjwWsT40opu+5N6D+/nIOmBBScugW8N6uQTyu5doW0gIORRR0D4bQXFsFvzf66Oq3vAScfeRozBnXhHHDagAAH/vDm3HnirCTkuscffMz2HigNy8CrFvVESlxsGcIHX0BfOnv7wAAPHmwuLOFaUiEkLToGYwX28E0UoHe3d2F17d14rOnTcvp+ts7+hFRJKaNqseG/b0J9/vj1YsAqHWrAWBkQ1XcPnqPWeIe+jRGPgL59HNGFInjf/iC4+d3ClrAhJC00C1gM+kEE1//wFu4/amN2H0ovjRgJmw6qIrujFGNCdOfWhur49Y11vjj1pViEFZnXwDhSPm9OOQjlU23qhWbX9AiMoApwIS4xRvbOjHlpqXYczg3YSoU+hywGbsHnJUxTaor+M3th3K6fteAev1RTdU4f+5Y233snuV2D9xSmwNWFImFtz2PL2pu1FKlPxCOy9/OVxAWUPwvWhRgQlzigeVqhO6KHbkJU6Gwm+9NxwLWo04D4Qg2HezFn1/fkdX19YepVwgcPWGY7T5283seIRAIR2Iip4v9wWxFT6FZuma/yyPJnv3dg5jzvWew+IcvxORh59MFbZ5pmDJC7ap0vKmLlttQgAlxCV0DiikoxMqX//GO0XDBLuJZIrWQ6UU7FEXiA799Dd95bF1WrtQfPbUBgBqFnQi7gB6PAN7a2YVQROKiY1TLudQs4FIbrx0PrdxjLJt/l/LigtZOGYhErzN9VCOOGtuEEQ3x0xRuQQEmxCV0922xCvDavd145O29+NlzmwDYB1ylYwHr4hFWJPq0Ah5dNvPJqRjS8ji93sQ/L3N0tI5HCBzoGQSgzh8DQKTEgrDMLyylWsXL3M7SHE+QDwtYfxH7vw1txrrBUNi2h7CbUIAJcQk9R7FYBfiiX8c2Lxi0s4DTEWDtwRtRJOq0nq6HsywhCSS3mDr7AnHrNh7oxeF+9YHfokVEl5pFGTKlTXX0Zf+zc5PVe7qNZbMA58MC1kX9pn+tMdZtb+9HHQWYEAKYLWCXB5ImIRu3sYTE717ail02zc+jx6n3GVEkaqvUzMdsazgDUevm0S+chFsvnRuzrWfIPi954wG1XGWt9gIQLrE8YPOc9f7uQRdHkj0vb2o3lvWAOiA/L6B2gV37uoeM779YoAAT4hL6MzVR+7RiI2QjWvu7h/C/T2/Ef92fuMeunnMbkdKwQA4nqNGs8/auw7ZR10D04Tp/YjOuOmFyzLZzj4o2br/jYwuMZd360qsglZ4FHH35KbWx2/HRP7xhLOezEpaVYrOAWYiDEJfQXdCF7qmbLXYW8JA2L6zPz9qhW5uRSFSAD/UnngMORRRc9tvXsPiIFjz4mRPjtlsfri9/7XR4PQIt9VWoMs0BX3jMWPz1zRF4bWsnNh5Qc4hrNAuo5OaATaKrlIEAm8lPEJb9OTkHTAgBUFou6FBEsRVgfT4yWX3dkCZ2YUUaNZyTlbAMhNX939nVldbYJo+ox4Thdair8sFnCcL62nmzYj7X+NXtdtZ8MWMOwipV/W2oTmDv5eH3P9FLba2/uGxOCjAhLlEKaUg6/YEwguHEVuOCScMTbtO70ChSQmhPW72dof3+WrCXAz8Wa+s53UIutTxg8wtDqY1dJ6woti+br2yKb5aRK6XigqYAE+ISugWcT/3dfWgA3//3upwf2r97eWvCACcAeHDV7oTbdDd1MKygQ4tStouo1tEtYOuPZXidH1eeMCnNEatU+2MfcbqFbGfNFzPm2tWlmoYUUSSG18XX5f7Y8Zl9p+mQyAVd4y8uySsue5yQCkJ/jubreSqlxCk/fhEA8MGFEzBnnH31KCvhiGLk/ur8/uVtKa6VeJsuqBsP9GJ/t1qGMJkFrFva1mdoRJFJi3DYYXWN69WwSi0KOsYCLjEBDkcURKREKCIxoqEKnZYIeGsgnRPYpIMDsM8TdxMKMCEuoVvA6dRTzga9ZSAQDT5Kh5fea8fvXlKb3C+Y1Iy30piLTdTkXEppCPDOzn5j/UAgtQU8FFLw9NoDWDJ3DK65Zzl6hsIZB+xYXdD+UrWAS3gO+KN/eAMrdhwGAIyorwbQBwB44aunYWtbX16umWhaJ9HvqVsU12gIqSB0t3C+5vQOdEeL3mciW5390WIWp8xojdkmBHDazFbrIWixcS0CUTEFgPbe6HmTzgGHo+L82b+swoHuIbz0nppDmqwKlh1WF7RuAYdKTMVioqBLzALWxRcARphaQ05rbcC5c8bk5ZqJgrCsL2Ruk1KAhRA1QojlQoh3hRDrhBDf19a3CCGeE0Js1v5PHIVBCIlDf47mSwvMbfgyyR09bCqS4PeKmBZ/fq8H9127GHPGNcUcc6BnCGv3dhvzvToBU3pSvzbvW+3z4Jl1B2Ms4phjLMFeO0z7ZVpIweyC9nqE4cIutbZ+Zos9n2lIZ/3sJfzhleTTDYm49I5l+MTdy5PuM354bVbnzpREQVj+DF/g8k06FnAAwJlSynkA5gNYIoQ4AcBNAF6QUs4A8IL2mRCSIWaLz0nMD5tM5jzNVYq8Hk+M29evLS+98RScfaRa9GLexGYAaunKD/zutZhzPWQTnDWuWX0IX/irV+O2AbGiDQDbO6ICPFprbZgu5rzgiCkNqtTmgM3jzafxvrW9Hz94ckNWx76zuwuvbGrHq5sTRzUfq/2u5JtEQVi+UhNgqaI76v3aPwngEgD3aevvA3BpPgZISLkytlkVk1wb1SfC7IYLZ1B4wlyn2e8VMQ+tc0yVpgY0N/JRY6PW8Lp9PTHn0nsAm5+Hej5oX4JcYOsLifnn05phJxtrlTFdkIMlZgGbv798TVk4FV296WBvwm1zxg3DDy6biydvPMWRayUikQu62FL+0poDFkJ4hRDvAGgD8JyU8k0Ao6WU+wFA+39U3kZJSBmiP+/s2vw5wVcefNdYzsQF3W5qaOD1CPhNkcffOH+2sawLwdhhia3S3YcG0Fjti4mS/sCC8QCAueObbI/51H0rYz6b60Y31fqtu2eEnoZUei7o6A8wX2lITpW4TFbZbVRTNT5+/GQcNc7+u3eKngTdtkpSgKWUESnlfAATACwWQsxNcYiBEOI6IcRKIcTK9vb21AcQUiHo86V2bf6cJhOracAUIOXzxFrANaYgFj0YqKnGPpmiZyiEjQd60WuxdK856QjMm9iMlvrk1uw91xyHI0bWx1jKR4ysT/s+rDz/lVOjLuiSC8KKvjB05NDIIhn3LtthLFtF/msPvYspNy1NeOzS1fuN5WS/a1UFSgMyl5z81MlHGC97JSnAOlLKLgAvAVgC4KAQYiwAaP+3JTjmLinlIinlotbW+OhJQiqVIS3YKFlRimzo6AvgU/fGNkfIJO3GXPHK5/XE5N6a0zj056zf50G9TYWhD935urF86yVzYrb5PcLWCjUHGM0a0wivR0CREs11fnzkuIkxAWGZMn1Uo2HNl14pyuh4v/Po2rxcwzz3a215+NCqPUmP/cLf3jKWk0VpF6rxyGkzWzGxRY01aKj2YbwWd1BkacBpRUG3CiGateVaAGcD2AjgcQBXa7tdDeCxPI2RkLIkkAcL+I1tnVh02/N4YWPs+3AmFrA5CtlrsYDNhQz0B61XCDTYWMF6AwQAuPTY8THbfF5hGwhltparfB54hUBEkQiEFDQmsLRT8aPLj8afP7VYG7+WhlRiLmg7i33Zlg6s3duNf6zYha4U3aUyZW+XfctDa5Q7EP+7VQxpUkIIHDelBYD6e6R7W+oT1aN2iXRGMxbAfUIIL1TBflBK+YQQ4nUADwohPgVgF4Ar8jhOQsoO3QJ2cg746bUHbNdn4nI1W8B+r4iNgjaJsX7KdLo52RXE6AvHB2F1myKw66q88HgEugZCGAxFMCzL+d+PLo6WOtTHWmpzwObxfubUqQCAj//xTWPdc+vb8MerF2V17i1tvdjWHpsSlkjQuwdDcUVdrDXCf/jkRvQHIvjyOTOzGo9T6N4Ov1fgfy48EkePH4aTp490dUxWUgqwlHI1gGNt1ncCOCsfgyKkEggYrfzyPwecbtrN4f5gTNCT1+OJ6TBkdiHq84Rej8DBnmjglh3W/EuvR9ha5d2DZgH2wecRWL5DjaReMDn3UgNCCPi9ouQKccQEYdlsb+sdslmbHmf//JW4dYms2K6BUFwqmF2Tjv/3wmbXBVj33Pg8HtRX+/JSczpXiswjTkjloAtvvqKgzaTb//bYW5+LqdXr84iEXZAUkwB/9rRpCc9Z5fUYwv3+eeO083ps52G7BmMtL49HGBHUs0Y3pnUPqfB5PCVnAfdqjTCqvB7bF5dsZ1YHElQkS/TCdtjGMg5E0vv9veHM6ekPzAGKre6zHcU/QkLKFL2JvdNBWHak44K2mxf1eQTe3dNlu7+u6V6PwDXvmxK3fYxmKV11olpsf/0t5+GXH54PQLWI7URQLwLyxA0nq+c2KYtTzdT9XlFyQVgHeoYwor4KNX5VgK1RytUZVgjTOZQgotpsAZu9EuYiLTrWwimJKLQFqk+dZJIDX2gowIS4xFDY+SCsRDmi6bigb/n3+rh1Pq9I2OnIHIRl7ToEwChX+bXzZgFQXcr6HKzP68HW9j789c2dMcfoD/tRWrSzeX7ZqTq+fq+n5IKwDvYMYXRTjeG639kZW7wlYbP7FCT6vTD/eOZ9/1nT+vj9e4bsc26tFLoOs99X/BHvFGBCXCLqgk7cmMAp0rGAH3l7b9w6n8dja90C0UIiXo9Ac53ftF7iln+vx/LthzB/YrNtJya/R0CRwLcfWRsjhroA6wU39Mpafq9IK9grHRJFYBczB7qHMGZYDQZDEfz5jZ1GX2WdbKN7rS8iv/jwPACJWx72B8IxTTWAqHs8FXUOeTDSRS+bWswvWxRgQlzA3KavEGKQzhywXWnIiJS4+eI5NnsDnz9jGqa11mPm6EYIIfCNJWqVrO7BEO5eth29gTD2HLZPZzGnNpnTnroGgqjxewzRHjAaODj38PZ5PAgVsVvSDt0C1qct7l62PWZ7Q3V2Px+rdTipRS10kqjhw9cfXo3jfvB8zDpdgL9pqpJmZvKIOswc3ZBRS0wniFY9K96XLQowIS4QjCiGBelkVaZEZ9IftG29Q7j9qY1p5wV3agUZFk4eHteJ6JL54/HCV0/HFK06lS4C5iAuq6Wm4zUV9zBHgfcOhWO6OOkMr8+tBKWZKp+nqB/KZh5cuRu7OgfQ2R805tSBaPyATrYBR89vOBjzebjmyUj1+xH7nalei0StBf1eD2aMciaALhN8RuvJ4n3ZogAT4gK6G6++ypvX9nI6+gP1mw+vwZ0vb8Wb2zpjtptLCQLA5VrhjJmjGwAA//zsidhw65Kk19DdoG9Yzm2HuVbvotuiFlUgrKDGH/9YMlfjyhWfRxS1W1KndyiEr/9zNS741X8AAGOGRauAWef6s6198fPnNsV81iudpRLgfaZCHboFnKhQSkSRjk0fZMKZs9X2BOflqeewE1CACXGBnkH1oTWioTqvdYmnj1IFVBcc/WFp7pc6FIrElBIEgHPnjMaGW5bgmAnNANIrIVhXpT6Av/1I6lKJB3vs81YD4UiMu/n2y48GkLhzUjb4vPYpUMWG7prX732kqROUdfROVZ/ShfI9U0cj/SXMzP7u6Penj88qwHqUu1sCPHtME3bcfiEWTCreVvUUYEJcQH9g+r0iYcBLNlhPpbsmdYtGb8NndlkGbPJ8w4rMOO0nk0jcRK7pQEiJiagerXVa6rZJf8kWv1cUdWqKjjU9bXh9Ff7wCbXalTX1x6nfIL2P7p9ejc4x1/q9cSJs/v56hkKo8npQ7fPCrLM/fHIjAPcEuBQorsKYhFQIulD6ExRWcAq/pfuP/uD843+2YeHkhQDso0QTFd9IRl0GgUBtlkjaYFhBlc+DQDhWgEdqNXyd7N/r95bGHLA5Pa2uyovZYxqNOfnXLW7+bFsU1vg9uOqEyZg7fhiG1fpjhHzdvm7MGTcMErFz9kBsYFMgpKBamzYwH//a1g4AmgAXWReiYoEWMCEuoFvAPq+wLazgFEYxAu2BqUclP2WqGe2UAFtLFAJqC0A7bv/AMTGf+zU3ptUFPaKhKuNxpMLnEY4Ker4wW8BL5oxBXZXPEDor2Rr0wbCCap8Xl8wfj9NnjYr5Dr/1rzXquaWM6yJkfmkMRqIvTeZfY3PrRw8tYFsowIS4gP6c0oOL8mUE6+fX05BGaoK2YFKzsc+Ta6JirLdwmz0284bpess3M62N8aIMABdrJSl1+gwBVmJEJh8CrFrAxS/A5hKlp81SW7kmSsfKdg5YkUgojk21fry2tQO7OgfiLGBzZHEwrNj2+fUav9sypqEHiUIBJsQF9AdmlTe9qNNsOW1WqzbnqZ5/1hg1HcScMvKMyRq++sQpePe752L+xGZHrt+YZF74vDmjjeW+QBjr9nVj9Z7umPnpfFRP8nkFBoIRdCaYhy4W/r5il7FcrwW42VUcA7KbA9aj7xNp47hhtfjYH95Ez1AYll4aMb+voYhiVJ0yU+UVeGvXYRzqD3IOOAEUYEJcQJpc0IBzAixNj+I3v3UWPnfaNNT4vYaLV3drmq93/FS1b+r1Z0zHJ086AsPqss+5tVpCyVyPX18SLdzQFwjjxgfeBhA/PwwAo5uq49Zli8/jwcYDvVh42/Opd3aJnqEQHntnn/FZFzg7SxPIzgKOmEqJpsKaBmaeA37snX1xpTH1Yy7/7WvqNSjAtjAIixAX0PVPr9bjVCS0fpqRDVXGfN7E4XX49+r9uOTY8ejR0pDMucd6+shnT5+W84PS5xUIRoCfXjEP589Nnn9pdkv2DYWN8pOH+mMFePm3z3K0ilKVr/jF4FxLi0A9mC7RC002vz66aFvPec37puDe13bExAZYfy/SeWE014imANtDC5gQFzCioLUHU8ShqFz9wfj49Scb606aPgKH+oO4/LevYUtbn7qf6YndH1CtYmulq1yu31DtS1mf2OxqXr+/x6j01GepLTyqsQZNNtWxssXJoh754oAlTzqR5auTjQWsT+N6LBbwzRfPwdSR9QgkEWB9DliviGWOKdAx5wpTgO0p/t9EQsqQaB6wVq/WobzUsCIxvrkW40wBUXZlAPd3RR+OA8Ewav1eRx6Sek5xOsFT45prcf0Zao/YnzzznhGZ/fMPzc95HMkw16HOV/S50/hSCnDm5zRc0Dan9ns9eHZdNDbAaiWHwuqxy7cfAgC8tasLQLSSFhDb6pBpSPZQgAlxAcU6B+yQEIQjSpyQ2rkt/7Fyt7HcH4ygPsti/oloqU8vevnzZ0yL+TyyoRpnaCUE84XfZAHnMwfbSfzWKCgL2bxIGC5oG3H0+2J7JlujmA8PqOJqbaW59IaTYQctYHsowIS4gakQB+CcEIQUGWPhAfYWjpmBQNgoI+kUI9IU4LoqX4xYJ4rydRK/aQ64RPTXiIJORFZzwEoSAbb80mxu64353KUJsF7T+5gJwwAAM0bbN12gANtDASbEBRRDgJ2Ngo5E4nMu7R6wZgaCEcd7tWYyZzusNrpvIQTYjFM1lJ3Ezpo1fz+zx0RF7sazZmDyiLrs5oBN/ZytWAV496HYtpJ6IZN2LZXrz586PmZMVijA9lCACXGBqAvaWQs4rChxQUaFFOATp45Qr5nBA9f8cK4qgACbU2iKUYDNzTn09oANpkYHd19znLH8lXNmosbnzS4NKUkecLKgr6Yan1GL+lBfELV+b8xL1FfOmWk0AdGhANvDNCRCXMAIwvI4awGHFRk3X5hKgAdDkYwbLyTi3muPi+tVmwqzxV5dgKbt5jKUxTgHbH5B+PaFR+Gs2aNipgjGWSqOCeFsGlKidTojGqrx7p4uhCIKgpFE7SNF0s9EhQJMiAsYpSidtoAj8Z1nUgV4BcIRNNc6k+ZT7fNmXL0qpvJVlo3lMyEUYwHn/XIZYy7zOBSKYHiK+XSPEFndR7IgrGRBXds7+gGoPaRDEWkboW11YduVKSV0QRPiCtKShuRYFLSixD0QrXWPT5kxEk0ml6a5m40bmIPGCjEO889DKUIFTsdFfueVC/Hw504EAHg82UVB6y99dilC+nXN8806p85U61IPhiIIRezrQEtLcUyrS5qoUIAJcQHdyDHaBTpUiCNsE4Rl7XZUV+XFkKnbUUDriOMW5vGmKjjhBOafR1HOAWvja6j24YqFE233WTJ3DBZOVkuICoic5oCtUfNmPn78pLh1t19+tHZddax2x+u/z3o0/OQR9RmPrxKgC5oQF9Afl7r7zykdCCkSdRYRC1nEvbm2CsGwAkVrE6e2AHTRAjYFjRXCAg6afh5OeR6cJKQJ4/9ceGRac/MekZ0rXX8RsbqLAWB4nSqc5hezZ750Kjr7AobgBrU5YLv5Xb2T09eXzMKHFk2EYCEOWyjAhLiAYlQhcvbBFFEUI7Arui726TxhuDofFwgrqK3yYiikuCvA3gJbwCbrvwj11yhLmqr6lY4QIqtuSMFw7DSImVFaG8lDA9FqVmonrUYc1ipcffexdQDs3dR6nvCoxhqKbxLogibEBaQRAKN9zuoRGo9dEJbVBa2ntOh1fAPhiKPNDjLFLAB2YuA0oSKOgt7fPYj/fuhdAKmrX+l4RHZzwPrPwa45hf6SNhiMxG3zWsZl953pTT9aG53rYlWOUIAJcQH9ua+nezhhiUkpsfFAL55dfzBmvdUFrYvtUDgCKaU2B+zeo8AchVuIfNHvXHSUsVxsc8A/enIjlu9Q6yun2zRCjYLO/D70aGY7Af34CZPw+dOn4b9OOSJum98yrmQvChTg5FCACXEB/XlpzAE7cM5A2D7/ds64ppjPet7mYDCCUERCysLk3yairTfaGKI3EE6ypzPMm9iMn3zwGADF54I21+ROFhxlRohoUF8mfOkf7wCwj4Ku9nnx9SWz0ahVNJs7Pvo7ZH1JsnOVT2xRLeh0a4JXKpwDJsQFjDlgIwgrdyXo08Tr/fPGxaw/dWYr3vjmWVCkWid61Y7DAIDNbX0YUa9aKG66oA+b5hk7+wJJ9nQO4XDwm1OYv4d058PVOeDMb2TGqAZsbuvDnPHDku73znfPiRmX1eK1G+fvPr4Qe7sGCzKlUMpQgAlxAWsVIid0YEDr63u6lqdpZsywGmNZf5h+5s+r8Oo3zgAANNa49yjo7IsKcIdpOZ84PffuFOZSnOmKV7ZR0GOba1FX7YspI2lHc12sFWsNqrKz1OeOH4a5KYSd0AVNiKsYQuCADuglFv0p5nPNLeR0q7mx2j0BNr8cLD6ipSDX1DWkyGKwYiqBpVsX2yNEVh4UKSXS9HInhVZu9tACJsQF4tOQclcCq1s7EbUmd2KfFq3a4KIFfN8nF2PZlg6cPGOkkf6SbzwOuv6dJNYCzmAOOIvbiCgyZZ3wdEh3nCQeCjAhLqAHzTjxANQxSgumMEhOn9WKkQ2qW1EPempw0QKe2FKHjyyOr7hUCIrNAs4mJSvbKGhFyoy6ViUi3WhtEg9/coS4gLUQvhOGWCRJg3UzQghce/IR6OgL4kC3GoHs5hywG0TnMYtLgWMaU6TpghZCZNcNSbFvRZgpdEFnD39yhLhAtBRl7OdciFrAqZ+qeneaLW19AICGame6IZUKTs69O4l5OJkEYWXiSpdSYv2+HigyvmhLNtAFnT0UYEJcQFqjoJ2wgJP0d7WiW1d6WUE354DdQED9GRWbC9ospJkEYWVyHw8s340LfvUfrNx52KE5YMpItvAnR4gL6A9MJ/OAlSTt5azoD81DA0EIAdS5mAfsBsWahmQujZmusAlkVtFr2ZaO6LEOCHC6BUNIPBRgQlwgmgesfi60C1qvXnR4IISGKp8jwTilhJGGlEUFqXxi7s6UUSGODH6BOkzFTrJ98dt02/n4yHFqq8RCNNAoV/iTI8QF4kpROumCTssCVvc53B+sOPczYKqEVWQWsPn3IH0XdGYWcPdgyFj+z+aOJHsmpsrnMSx0WsDZQwEmxAVkXB5w7ujWXDrn9BsWcLDiIqABwIiBLi79tbig0+2GlJkFnK6wp0IXfXPPYJIZFGBCXECxWsAOWGIRQ9RT76s3Ue8dCruaA+wWxVoLWm8R+NJ/n55BP+DMLGCn6n7rBV0qbPbCUSjAhLiAtR+wE55QJc08YCA2wMfNRgxuUaxBWH2BMBqrfZgysj7tYzItxOGUXo7VUtkO9Ayl2JMkIqUACyEmCiFeFEJsEEKsE0J8UVt/sxBirxDiHe3fBfkfLiHlQbwFnDuZBGGZBbgQPXiLjWKtBd03FM54Tl6IzH5/nOqBPKmlDgAwGCyySLYSIp1vOgzgq1LKt4QQjQBWCSGe07b9Qkr50/wNj5DyxFoL2q0gLCDqjq4kRJHWgu4LZD4lkOkcsHme+W+fPj6ja5k5c/YofPGsGfj4Ce6UES0HUn7TUsr9APZry71CiA0Axud7YISUM0YUtMe5OWAlawu48mai9J9Q0VnAgcwt4EyjoCMSOGpsE/7+mRPQVJN9BTSvR+DL58zM+niS4RywEGIKgGMBvKmtul4IsVoIcbcQYniCY64TQqwUQqxsb2/PbbSElAm64OYjDSkdizZWgHO/dqnhKdJa0NkExYkM54AVRWLMsJqcxJc4Q9p/ekKIBgAPA/iSlLIHwO8ATAMwH6qF/DO746SUd0kpF0kpF7W2xjcKJ6QSiauE5cA5jWYMaRXiMLugK0+Bdf0tMg+0GoSVzRxwhi5oJ7twkexJ6y9PCOGHKr5/lVL+CwCklAellBEppQLgDwAW52+YhJQX6/b1AHA2hSPdfsAA4PdUeBBWkdaC7svCAs5mDrgSvR7FSDpR0ALAnwBskFL+3LR+rGm3ywCsdX54hJQfq/d04Zm1BwAA1X71T9CJYKBIJoU4fNF9KlGAo92QikuB1SCszFzDmc8BO9MFieROOu9BJwG4CsCZlpSjHwsh1gghVgM4A8CX8zlQQsqFi3+zDMGIgtbGalNJxNzROxul44I2zwE/8vZeB65eYhRhGlJEkVkFYQmkPwcciijY0taHjt5gNkMkDpNOFPSrsM/dftL54RBSvkgpcf/rO43PPYOh6B9WjkIQUSR+8OQGAOm5oCsx9ciMkxXInKI/GAYANGbqgvak/yLxqtYJafmOQxldg+SHyqtBR4gLvLO7C5fesSxmndcjHGsKEDa19Uknpsrchu7DiybmdO1SxKkXHyfpG9IEOOMgrPTngIvN5V7pcCqekALw2Dvxbl4B55oCmNvqpWMBm/nBZXNzu3gJorvpi8kF3RdQBTibPOB0hVU4VoiSOAEFmBCX8AjhWDqMuY9spgE26Rb9LyeiWcC5K/DS1fsx5aalaMuxJvLOzgEAwIj66oyOS1UL+lB/EGEtQi8QjgAALjh6TJajJE5SeX95hBQJQkQtklxlQM8BrvV7Maw2vSjaKxZOwPcvnpPjlUsTJ2tBP7B8FwBg44FeAMBND6/GlJuWZuzubetVBfyIDBoxAOrLRKL7GAxGsODW5/D9f68HAOw6pIr8jy47JqNrkPxAASbEYQaDEZz505fwtJZqBNi7/tKJVk4XvQzl15fMipnfTcZPrpiHq983xbExlBJO1oLWf9zX3rsCUkr8fcVuANGAp3TJbQ44/j42HexFR18AAPD0OvV3cWfnAJrr/BhWxypYxQCDsAhxmC1tfdjW0Y/P/mUVLp43DnsOD+DYSfGVWuv8XpMLOjchiFiaO5DkODX3biasSHT2R9N7+rU53XTpC4QhBFBXlVl7SLtCHN2DIZz7i1eMz1XaNMPerkFMGF6b0flJ/qAFTIjDvP83rxrLj7+7D2/t6orbZ/aYRtz/qWjxuFx1IJNGDMTZNCSzx2HRbc8by5mW+OwLhNFQ5UvbgxG9PtAbCONAd3QOejAYidlnb9cghkIRBMMKaiuw/3OxQgEmxAW+cs5MTB/V6HgQVqYR0JWKk7WgE/3EM3kZklJiz+HBjCOgAWDjAbWs6Zf/8Q4AoHcohMFQJG6/p9buR1hhFaxigi5oQgrAY+/si/msP/eFKR43FzJpxECiFrATQViJippce98KbLhlCWrSsDifW38Qz60/mNX1dWs3GFHQPRjCvO8/a7vfof4QAqEIGtkFqWigBUxIAdCDYXR0y8spS0zPA6YFnBlOBGEdNa4pwbmB2d95Oq1zvLQp+1atYe0tosrrQddA4hKTtz6xHu/u6Y7phEXchQJMSIE5afoInDZTbc1pCHCO52QQVmZkm4YUjigIRZTYdSlOsltL/UmGP4fvTfd++LwiJtq+OUGks55vTNyHAkyIg0TSeKLf+8nFqNUiXTOpTLT5YC/uWbbd9pp/1mpM07pJj2g/3MwU+NxfvoI533vG+LzpYC9+99LWpMec8uMX8c9Ve5Luk2nglRndBS5lbFDZuGG1uMYmzWx7R3/W1yLOQgEmxEG2tPWl3MduzjAdT+g5v3gF3//3enQPhmLW37NsO+7WhHnsMKaYpEO2rv9t7f0IhqMW8HceTa8L66MpOk7lMnOgez0iisQ9y3YY60c2VuPmCi20UipQgAlxkPN+qeZeDk9S6MBs7VT71D/BIZuo1US098bOJ7eb5pcntdSlfZ5KxqkgrDe3R7sKnXPUaGP5a+fNitlv7LCapOfJpUazMO5F4t/vRoP9guH0f6eIO1CACckDH1k8Ka39htdXAQAOJwmesWK2wABgeF2VsdzamFkd4UrFyVrQOt9YMttYvnjeuJhtD63aE/e9xYxHG1A2c/j6EYqURkMHABgIUoCLHQowIXkg3XrM9dpccCYPy2DEKsDqtZbMYYH9dBFZWMDJBBSIVpsCgNoqL248a0bM9kdtOmLp6LqbS5/mUEQiYBpjonnls2aPyvoaxFkowITkgXTnFs3zd8nPF90esLir9Qftty88MoMRVjbZlABN5aXw+6KC5xUCTZaiGsmupX/9/iw6U+lntf4OJdLy+mqWfygW+E0Q4hBmC8nOFfyvz78PM0c3xqwTWkvCZO3kgNiH6382d+D4qSOMz+GIlgfq4/t0umRTC9pa3tHMyIbqGPG0q8/sSRJppbcLzCWK3ZoOleh6qX7XSOHgXywhDqEXQXjftBG4/NjxxvorFk4AAExrbUCDjfXh84iUFnCyXNOIVoWDOcDpk00t6LASfcHa3tEfI8iXLxgfU2PZ40Fcx6Fk34+ex13jy75Oc0SJdZFbi7LoAX8U4OKBFjAhDtGvPZCvWDQhpiTkT66Yh59cMS/hcR6RmQDXVXttt+Uyf1hpZJOGFAxHdz7jpy/FbLv+zOkxrl2PELh43jh8/Z+rjXXJrjVxuBq9/qdrFqU/IAshzRNyzIRhWL2nGzVafMGvPnospJSo8nrwub++lVauOikMFGBCHEKvkKS7Ild8++y00ou86VjApsCrroGQZRurYGVKNmlIYcU+COuk6SPQZKmv7BEirgb035bvwtlHjbYN0NOHMXVkQ/oD0tC/dT02YNHkFrT3BvDJk6YAiEZkv7ZV7U88qjF5ShQpHHRBE+IQVgFubazGxDTycr1CGC7IRJgjn+96ZVuMIPcMheD1CNRX8X06UzIJwtItTCs3vz++2IVdJ8JVOw/je4/ZF+7Q3cLZFOTQR6VHQLc2VuP1b56FM2bFRjufOHUEfvnh+fjWBQzWKxYowIQ4hP6ArsowkrU3EMaTa/Yn3ScQirW+DmmN36WU6OgLoKW+ip2QMsBjKt+YLuGIvQU8aUT0JevsI1XR0+dfjz+iJWbfLksVMx19HMkCtVKhe1sSTUUIIXDpseONMqjEffjKTEgOSCmxek835k1sjrOAM+FgTwAbD/Rg9pj4zjptPUM43TLn2N4XwI+feQ+1fi8eWL4bo1iAIyOyKcSRKBCu2hQ4dcfHF+BgdwA+7XfgL/91PNbv68EldyyLua4VRW8nmcM7lG4Bsx546UABJiQH/vzGTnz3sXW4/9rFhuXhz/IBeKjPPs/0wZW74+aIH39nX0yB/zZLeUqSnGyCsMxVphJR7fPGWMR+rwcj03g5UhywgBmMV3rQBU1IDrx3oBcAsPPQAEJGW7js/qwSaYHZ8rrqhMlJ9yXpkU0QlrUGd7qMTkuAs58DtpLt7x8pPPymCMkBc0WlkOYCzHQOOBVm63f6KDVKVmEqSU5k44Lu6IsX4HQE0yyIiaYnpCHAuSswo+FLBwowITlgWFKKjM4B+7J7ACZyh5oLPkzW3JvWXcel6LZDYsmmFrQ1/QsAnvnSqWkdO75ZbRM5qsneGg4rMuupCytOnYfkHwowIVnSPRjC/a/vBKAK4tp93QCyC8JKhl7gY2RDNXxafou1McBXzp0VdxxJjGFoZjAJbJ2H33H7hXGlRRPxr8+/DwAwELDPCw9FFOO7zRWnzkPyD4OwCMmSA91DxvJPn3nPEMpsXdCJ3KG6e3L6qHrDvVhnSSWhzZMZ0RZ+6R+TKlc7GaObanDk2Cb0DNmnIYUi0rHoZQZhlQ58VSIkS8wWUb8DvVcTPd+btX6/d1650BDggMUCPmXGyJyvX0kYtaAzsYATFOJIl6YaH3oG7SOpw4qStefkpOmx3z2DsEoHflOEZEmi8pHZPkgTPd6fXX8Ax00Zjua6KkOA731tBwDgomPGYtsPL8CoJs4BZ4IRPJfBMckaYqRDU60/oQUcjsisLdcvnTUDd5tqSNMCLh0owIRkidUlqVuhYxwOiNpzeBALJg8HANRbGjF8+LiJrICVBdkEYVm7DWVKU40fnf32ud7BSPYWsMcjME4L8gKcj0Eg+YPfFCFZYrWAA2EFCyY1Z30+O3eolBLBsIJq7aFqftAC8S3nSHqY08fSJaxIo6XfESPrM77myIYqtPcG0GtjBYcjzkVBMwardGAQFiFZYu2r2t4bwNQsHsw6dlpg1JfWHvx1lg47J04bkfX1KpnoHHD6xyhSYlJLHa47dSrOPWpMxtecPEL93egPRNBo6Z4UVpSc5m6FKQyP7X5LB74rEZIlYUtQTlvPUFwT9kywa5R+/+s7AACHtRxU60PaicINlUhWtaAjEl6PwBWLJmb1PesvUdYUMkCLgs5hKsF8HxTg0oECTEiWWAWzPxhBc21VDueLX/ePFbsBAHsPD8ZtG56D2Fc62dSCHgxFckoV0gW4N2Dngs5+DhhATI/hWWPSy00m7kMBJiRL7KKgmx22gPXIW6/Ng/9/P3BM1teqdDKtBd0fCGPFjkOYP7E562tu3N8DAPj+v9cb655csx+9QyGEcpwDHjusFktvPBnv3bYEreyMVTJwDpiQLLErzJCLAJsDghRF4vkNBzEQVPNGzXO/Z8xqxYvvtbPtnAOk64Le1zWIoZCCxUdkP+euv7Bt7+gHAGxp68Pn//oWlswZo1bCyjF6ec64YTkdTwoPLWBCsmTPoYG4dY012b/Tmvu9/33Fblz351U42KM2AGgyuRj1huo5ZsVUNJkGYS3fcQgAUOPL/pF541kzAADvP2YcugdDCITV4i3bO/odrQVNSgdawIRkiV31q7k5WCFmF/SBnqGYbTecOd1YvuWSuRjTVIvTZ7Vmfa1KJ9M0pG8/shaAfUOGdKmv9mFUYzU2t/Vi3vefxYcXTQQAhBQF4YgCXzUfx5UGLWBCskQXTN2yAYAaS5pQNucDAGtArDnIZmRDNb77/qNYcjAHMp0DvnjeOADAJceOy+m6LfVVWLtXbdrx5Nr9AIBASMl5DpiUJvwLJiRLdL08/ogWY11tVfYCbDbGPJb0IqYbOUsWzZBwxMh6VPuy/34BNUZATynT09jaeocQyjEKmpQmKb9xIcREIcSLQogNQoh1QogvautbhBDPCSE2a/8Pz/9wCSke9KAas9U7oj6XNKSoGpjl9pL5uVldJJ5oLej0FLgvEEaDAy7iFtPvh94/OhSRCCuSHo0KJJ1vPAzgq1LKIwGcAOALQoijANwE4AUp5QwAL2ifCakYogIc/TPKxVJN5A4968jRWZ+T2JNpLei+oXBcHe5s0DtbAbHNHUIRBX7W9K44UgqwlHK/lPItbbkXwAYA4wFcAuA+bbf7AFyapzESUpQoUkKIaBnAU2fmFhRltoDN7QbbLAFZxBmEQNo+aNUCzr3wSX2CKYpgWGFaWQWSkc9DCDEFwLEA3gQwWkq5H1BFGsAox0dHSBGjSAmPEDhybCO+c9FR+PVHj83pfD979j1jeWprtKb0hOG1druTHPEIkZYFHI4oWL+/xxELONE8b1tvIKPOTKQ8SFuAhRANAB4G8CUpZU8Gx10nhFgphFjZ3t6ezRgJKUoiitqNSAiBT518REykcjboOb/qudWn8V1XLcSSuWNzOi+xRyC9OeCfP7cJALBq5+Gcr7luX+JH5z9X7cn5/KS0SEuAhRB+qOL7Vynlv7TVB4UQY7XtYwG02R0rpbxLSrlISrmotZV5i6R8UKTMW+s3XYDnjmd1o3zhESItD/Smg70AYLQizIX23kDCbR85bmLO5yelRTpR0ALAnwBskFL+3LTpcQBXa8tXA3jM+eERUrwoinSkH+8dH1sQt04v8pFLXjFJgUgvCEsPlqp3IAp6KBRfvEXnyhMm53x+Ulqk8xt1EoCrAKwRQryjrfsWgNsBPCiE+BSAXQCuyMsICSlSItoccK6Ye/pKKSGEwJa2PjTX+dnxKI+k44Le0dGPAe1lqNaBl6FkIj6iIfsUNlKapBRgKeWriE1LNHOWs8MhpDToHgjhnmU7HDmXOfskFJGo8glsPtiLmaMaWYAjj6RyQb93oBfn/fIV4/P45tyD4X75kfk462cv225rySGHnJQmzPwmGTMQDOOhlbtt2/FVCj9/7r3UO6WJWWT14gxb2vswfXSDY9cg8QiRvBZ0Z3/sfO0tl87N+ZrJRDzXKluk9GD1b5Ixn75/JZZt6cTU1gYsnFyZBdDWaPV8ncBsAfcOhVFX5UXXQAgjaRHllVRpSE01se5/Jyph+Vhsg5igBUwy5vWtnQCAz/x5lcsjcY/jpqj1n+0CqDLFPI98sGcIQc0KrnIg6pYkJoM6HI7hNQnwZceOx0x6OSoa/oWTjNGtho6+xCkV5Y7XI+DzCFx4TO45umYBHghGENKK9FOA84sQyYOwzFMsp+VY5Sx6TfW7/vjxk/CLD8/H5QsmOHJeUprQBU1IFjjZvcYcZzUYCuNQX1C7RuXOsRcCkSQI61B/EE+tPWB8drJT0ZYfnG9YwnRJVzZ8xSYZEY4oqXeqAEIR6VjtXrMFPBhUcPey7QCAe1/b4cj5iT3JgrA+8+eVuPPlrcbnKp9zQunzegxLuNAucFJcUIBJRugFInRCFSrIYcU5C9g8LxgIR3DU2CYAwGdOnerI+Yk9yYKw9h4ejPmcr169egOO6/hdVyQUYJIRA8EwAGDOOFUkDvcH3RyOa4TCEn6HLGCvR+DBz5wIQJ13rNU65jg170jsSVaIwzr/nj8B1sZCT3RFQgEmGdEfUC3gSS11AICOvqgA/335Llz+22UYDCYut1cuDIYijlRG0pnYouaHRhRptCJkXmh+STYHXDgBVgfgREU1UnpQgElG6Bbw6KaamM8AcNO/1uCtXV34xsOrXRlbIRkIRlBb5VwMo15T+tUtHfjvh94FwCjofCOS1IK2lox0ohGDHVcePxnnzRmNT59CF3QlwihokhF9AVVw9dZ7dpG6ax0sUlGs9AfCqPU791DW54GfWReNvM3XQ5+oqD9yewVuqauy7JsfC3VYnR+/v2pRXs5Nih/+hZOM6BtSBbhZaxLw+rbOuEjSbR396BkKGZ+fWrMfT5tSOkqdroEgVu08jKMdbBXo0/oaml9oaAHnl4M9ATywfLftNmtdZnqIST7gXzjJiMMD6pzvqEbVBf2rFzbjVy9sidvvwRXRB9vn/voWPvuXVeg1iXIps+vQAIIRBSfPcC5IymsT0EUL2D0aLWUoqb8kH/AvnGTEoX5VREc1VRvrfvH8prj9vDYFBp5ddzB/AysgOzsHADhTG1jHriCDL0+BPyQ1qdoUEuIE/AsnGXGoP4AavweNNcnFJxiO5geP0Nx5eyy5lcXIwlufw3X3r0y6zw0PvA0AqK92LkrZ7oWFuIdSwZ2+SOGgAJOM6OwPYkR9tTFnCQCLbDoiDYWiAqyLyy+e34Q9hwfyP8gc6OwP4tn16VnqyZqrZ4qXk4xFRYQlqkgBoACTjDjcH8Twej+qTO7Rsc21RiDWhUePhd8rMBSO5gIPhqLLf08Q9FIMmK32RHQPROex6x1MQ/J4BG5+/1GOnY/khrXXNd+PSD6gAJOM6A9GUF/lQ7UpBcfvFbjst68BAGaObkS1z4uAZgGv2dON3qForvBo09xxsZFOd6fdJgu+xsE0JAD4yOJJjp6PZE9EkRjdVI2vnTfL7aGQMoYCTDIiEIqgxu9FjakKlFcIvLO7C4CaW1nj9xgW8Pt/8yoA4KoTJgNQC1gUK229qQVYF+kpI+riGrbnivlnOqqxeF9UKoGIoqaGNWmxDh7O0ZM8wEIcJCMCYQU1fk+M9TdgcjFvautDtc+LoVCs0E4eUQchooU8ipG2niFjOVG7QT0N6+5rjsvrQ/mak6bk7dwkNYqU8HiAc44agze3H2KlKpIXaAGTtOkZCmHjgV54PSJmDrhnMDov2t47hBq/B4GQgk6TS7e2yov6Kl9RC/D+7qgAP/L2Xtt9OrXa1yPq82uhfvbUaXk9P4lnzZ5u3PrEekgpEVEkvEJgzLAa/OZjCzCygR4J4jwUYJI2//vURgDAK5s6jH6mANAzFCuqNf54C9grBBqqfegvUgE+3B/E9x5fZ3x+as1++/0GgvB6RMo0rFyhy7PwXPH71/CnV7djMBRBREp+ByTv0AVN0uaQ1npweH3s3KfZAlYk0FTjR89QKCaV49BAEPXV3qK1gFftPBzz2euxfzc91B/E8LoqPpzLEKHVu/raQ6uxdM1+lgIleYe/YSRtdAEerZWh1DELMKRaJ/rwQAiKltUzsqEaV54wGQ3VPvQFopZx90AI4Ujq1J9CMGix2K31rXX2HB7EuOYa222ktNHfqZZq3o900tIIyQUKMEmbA1qQUoPF/WpuvKBIiea6KnQNRC3gr503E001fjTU+NCn7RuOKJh3y7Mxbl830dsqvvy10zF7TGPCQgx7Dg9iotYLOR/cd+1ifO50zv+6gWCyLykwFGCSNs1ai7aLjhkHAHjhq6fBI2I7+KgC7EfXQBDb2vsARN259VU+9GsW8LaOfgCx7ffcpF1LQRrZUI0av9e2T2xEkdhzeAATh+dPgE+b2YpvLJmdt/OTxBTr9AgpXyjAJG2G1foxY1QDPrhwAgBgWmsD5lpa8ikSGF7nR1iRuOpPywEAesB0Q000CvrcX7wCAGhtLA537v7uIbTUV6G+2gePsHdBv3egF6GIxKQ8WsCEkMqBAkzSZjAYjkvHsLrtJIDmWvtm5uoccKyVUSyVsfoDYaO7kUeIuFKEALB6TxcAYPqohkIOjRBSplCASdoEw0pMCUoACFkCVaTmgjajN2Oo19KQzMEtbudXLt9+CP/Z3I6+QNhoruDxCCg2FvDuwwPwegQWTGou8ChJPvjEiZMx3PS7+oEFE2K2M9Cd5BumIZG0CYSVuCbxQUsU88XzxqGlPtYC7taipBuqfQgrEp39qUs+FoLH392HG7XWggCweEoLAPXBu6NjAN96ZA1ufv8cVPk82Nbehzte3IqmGh/79JYJAoiZ67f2ZK5zsNkGIXbwN4ykjSrAsT1wQyYBfu+2JajyehCwWMV7tT7Auot3X1e04tSgi7WhzeILAB3ai4GiqBHff3tzF5bMGYNTZ7biL2/sAgDmhpYRQoiYuX5r5HtdlXP9ngmxg08TkpCIImMeUIFQJM4CNrugq31eCCFimgqMbKjG5ZprT3fx6nOpfq/A0jX7sWF/T75uISPGN9cCAN7aFS3Ksb9bfXmYPaYRAPCHTywq/MBI3jBLrrV6m5P9ngmxgwJMEjLtW0/iepOVGAgrcRag1QVtZeX/nG0ELekW8Pf/vR5CAMdpLt/z/99/nBx2Sm7593pMuWmp8fmzp03DEzecjDuvXAgACJv8kpsOqqlUIa2qyDhNpEnp4xEiRoGtnbpoAZN8w1c8Yotu+S5dvR93fExdZ+eCHtdci46+IP7yqeNj1n/rgtlxFkSD6fOI+uqM3blSSkeKJdy9bHvM5y+dPSPGajfzrtZmMazlOlvnCUnpIgRigu30Yiw6FGCSbyjAxBa7vr12UdD3XHMcfv/KNiyY3Byz/jqbbj5+b6x47esaTHs8gXAEp/74RVx5/GTccNaMtI9LxaLJwxOK75mzR2HF9kP474fexT9X7QEABmCVEQKxLmhrPEItg7BInuHThNhibU6gKBLBSHwU9IiGanzrgiPTihid2hrNn+3oCxjBWSMsUdN2bO/ox8GeAH723KZ0hp+Uo03FQ1qTNL7vGgiiNxA2xBeIf4kgpYvHI2COu+q3CHBDNS1gkl8owMSWX72w2Vhes6fbmOu1uqAzwSp2+gOvU2vykIytbWrpSifaAI4ZFq2+ZWfRfvGsGZg3sRkHe+LTpfy0gMsGNQ0pqsCDwQg+sGAC1t9yHj60aAJuvniOe4MjFQGfJsSW900faSz3BkIIhHQBdu5X5qdXzDOWX3yvLem+uw4NAIAjZSDNqVOKTcWrL58zE4994SQcObYxbhvngMsHRUoEwopRGGYgGEZdlRd1VT78+IPzMKpIyqSS8oUCTGzp7Itaf43VfgTCqrVqnQPOlD9drabxNNX48MGFE3D8EWok9PLth5Iep6eIOPECYBbgoycMS7jf7R84Bp89LXYumx1zygd9auH3L28FoHpk6uh2JgWk4gV43b5ubGnrc3sYRUdHX6z7VS+ukYsLGgDOmDUK8yY242cfmg8A+MdnTkSN3xNniQ6FIjE5yGHFmd6sb27rxLItnThvzmj84sPz8F8nH5Fw35EN1bjp/Nn49UePdeTapLjQAw0PD4QQUSSCYQV1fgZekcJR8QJ84a9exdk/f9ntYRQdnX3RednuwRAOar2Ac7VAPR6Bx75wEs45arSxzitETO5te28As7/zNO5ZtsNYp7c8tGsTmAkfvusNAMCiyS247NgJaUU16y8f7583LreLk6JCr1GuSGmkINXTAiYFhK97xBZzOckr//SmsTxjtPOdgLye2O5Da/d2AwBe2HgQ12oWqj5PZ9cmMF2eWrPfWL58wfi0j9OjpvU2jKQ8MAuwnoJUy9xfUkAowMSWcAJTMx+VoKwC/Ml7VwCIloYE1JQgIDcL+At/e8tYHpFBF6ZZYxqx7YcXwMMArLJCD6iLKBJPrT0AADjUlzoinxCnqHgXNLEnHFEwrNYft74xD/VxvR6PUQi/ZyhkrG+pj4rko+/sAwDbNoHpoov3+CxeIii+5Yc+/aBIiaWrVe/IZsaDkAKSUoCFEHcLIdqEEGtN624WQuwVQryj/bsgv8MkheKtXYexs7Mfm9v6DBedmXxEAfs8AhFtjvfVzR3G+v5AOG7fXOeAAeDzZ8RX6SKVh1dELeAPLFSnJKxR74Tkk3TMmXsB/AbA/Zb1v5BS/tTxERFXufy3rxnLh9IokOEEXo8wLODP/1V1E09trceewwPGPrNGN+K9g705zQG31FfhUH8QZ84elduASVmgv2CGlWiNcScKvRCSLiktYCnlKwCSJ2mWAVNuWoq9GdQmLkdCKTob5QvrHDAANNf6jchnINp1KQf9xdzxwzBvYjPGDmNHIxItKxpRpJEGZ+f1ISRf5DIHfL0QYrXmoh6eaCchxHVCiJVCiJXt7e05XM55rNbUW5b6x5VG92Ao6fbvXnRUXq4bDCt45O292NLWiwnDa3H5sePh83piXggCWiGOXOaAwxEFfj5giYbHFISle2AowKSQZCvAvwMwDcB8APsB/CzRjlLKu6SUi6SUi1pbW7O8XH7os8wxDpoacvcMhbBsS4f1kLKmJ4UA58s9d0DLMb572Q615aHfA783mhu8r2sQ+7rVfTa39WHVzuwcMqGIAh+bKRANPQo6HIlawB5WOiMFJCsBllIelFJGpJQKgD8AWOzssPJPIBzBc+sPxqzrHYoK8qfvW4mP//FNI/2lErBawHq/3qmt9bbbneIsbU720bf3IhhWUOX1wOfxIBxR0B8I47al62P2/8DvXs/qOp39QbSk0XmJVAZej/r7HVakMQVCC5gUkqwEWAgx1vTxMgBrE+1brPzoyY34yoPvxqxr0ywxAHhTq008/5bnYuoilzM9Q7Eegae/eAp23H4hPqUVw8hXUJbelGEgGEEgHEGVT3U/v7unG3O+9wyeXKPmaI5syF48O/oC2NHRj+mtzhcSIaWJbgGHIgre3t0FIBoZTUghSCcN6QEArwOYJYTYI4T4FIAfCyHWCCFWAzgDwJfzPE7H2XSwN26d1SWt85iWg1rumC3c2y8/2ujfe8XCibjhzOn4zKn5SdEwVx8aCiloqPbjta2dMfv4vQKnzIhOYby5LXZ7Kt7Y1glFAmceOTr1zqQi0K3dlze1G3/jgpURSAFJOaknpfyozeo/5WEsBcWuBvB/NtvP+ep1Yssd8xzwzDHRVnxVPg++eu6svF3X2mO3ocaHy48dj3+9vddYF4pEywUCwLKtnTh+6oi0zv/alg5c/7e3AQDD6+KLi5DKxK61JC1gUkgq9n3PHA37+PUnAVB7zupt98Y0RXuB9pse/OWMbgHf/P6jcOzE5oJd1zrv1lLvx88+NC9uv6fXHTCWA6H0v5Mn10ZrQKfTfIFUBnbVzTgHTApJxT6NzNGwM0dHrb2hoNZ2z9T31q4iUznSMxhClc+Da046wtW+t9NaG1JefyCDlyJzu0k/o6BJEhgFTQpJBQtw9NZr/NE5yIFQGM+tP4idndEqTH1DFSLAQyE01bjjoj1tZnR+d1qCQKlHv3ASvn3BkRjVWB2TMpaKnsHo91dFC5gkwc4tTUi+qNinUaKCDH1DYTxqmnsEgENlnooUDCsIRRR0D4YwrNadUnxrtBaEAFCfoOHD/InN+PSpU9FY40spwG29Q1i+/RAO9wexfn+Psd4630wqF7snAJtukEJSsYVPE80F7jo0gPbeAE6Y2oK2ngC2dfTHuDDLkZn/8xSmj2rAmKYaNNl0QCoEdvnWFx0zFo01fhw7sRnr9kUFusbvxdLV+3Hi1J248oTJtudb/IMXbNezEAchpFioWAFONBf4+Lv7sHLnIZwyoxX3fPI4nPaTl1xzyxaCnZ39ANR50roqL4bXuVOowq7L0W8+tsD0aaKxpOcj/8+jazG1tR7vmzbS2HawZygmWtoKXdCEkGKhYp9GPo/9rT/2zj4oUk29mTyiHufPHWM0Aig3nlqzH6f95CXj8+o93aiv9iY+II/ceOb0tPfd3x0tmPKxP7wZs+34H76A03/6ku1x37noKFeDywghxEzFCnCq57BuKXk8Atvay9MF/apNresN++MLlBSCr2h5xpkGwTQmmC+28vj1JxkVvQghpBioWAHWW91NaqkDADz8uffFbNeDfJau3g9FqhHCpcCjb+9Nu2RkXVW8tZuoGlgh+PEHj8HTXzolo2Mmat8fAHzzX2titpndzUeNbcptcKTsoXOEFJqKFeCw5lZ+4LoTAAALJ8d2VLTOhd767/W46eHVhRlcluztGsSX/vEOrv/bW2ntb5fzaCfKheJDiyZi+qjGlPvN0vK2T5kxEuv39xiF9B9Yvitmvy+ePcNYZgEOkgrmAJNCU5FPpU/cvRwPrdqDSS11GN9s35z9lkvmAAA+d7pa//ihVXvw9xW7CzbGbNBfKnYfHkixp0pHXxAjG6pj1pXCQ+ifnzsRr910Js7W6jrrEdLml6glc8bgw8dNxNfOm4VNt53vyjhJcWP9VWcZSlJoKlKAX9nUDiBx2bnjj2gxclGPGFlfsHHlit4/d/ehwZT7DoUiePitPeiwdHoqhTTIxho/xjXX4vyjxwAA3tx2CDc+8DZW7Txs7HPSjJEY2VCNL5wx3WirSIgZaYm8Z6tKUmgq+sk0qjHW+vv1R48FAIwZFq0DbU1XKubWhEOm4hQRu7weE3e9ss1YXjCp2VgupVq4oxprUO3z4PkNB/H4u2o3m5b6Kswe04iLjxnn8uhIqdFqeR4Qkm8qNg8YUIN+zCyZOwZ3X7MI8ydGXZnWykl68FYxEghH06WCYSWmzZ8V84vEwsnD8dauLgDAydNbExxRnDRU+4zezQCweEoL7rxqoYsjIqWC1eOcS79pQrKhogV4kimCFlDF9szZsf1irfnCoSLOCTZbwKkEWBfrUY3Vhov2gwsn4FsXzM7vIB2m0xLxbW6iQUgmTBhel3onQhykIp9WiyYPx/RRqTvuAEC1r3QE+Ln1B43lebc8mzSlSE/fufbkIzBvQjMA4OwjR5d8tLD1+yIkXb5xfmm9fJLSpyKfVh4h0nY3HWuaHwWigU7FyD3LdsR8vv/1Hbb7AdF0ow8tmohz54zBi/99OpbMHZPH0RWGap97aVSktPjBZUcby9NHNaAhzaIuhDhFRQpwRMqEpSitNNdV4Z5rjsOXz54JQHXtFiOKzYtB90Di4iHdg+q2pprSi/Y2o8eMffa0ae4OhJQc01obcPmx4wGURvQ/KT8q8pUvrMiM2o6dMXsUJKRxbDEyYNOeL1nLvq6BEBprfCXvcn77O+ciIiX+b2MbAGB0EyNZSfro01ClkP9Oyo+KFGBFkRnXHNZdm0MZNIIvJAM28733v74T3794ju1cd/dgCM11pd/laZh2DxfPG4dZoxsxZxxLTpL00R8DFGDiBqVt/mRJMKxk/Aenzw/1u1grORmJAq50V7OVroEghrnU+zcfVPk8OHrCMDZUJxmxt0stWhNWinNqiZQ3FSfAUkps7+jHhOH2JSgT0aDNlbrZrCAZb2t5vFasla50ugZDaK5l3iOpbF7b2gkA2HSwPDuekeKm4gR4IBhBMKJgrKnaVTrobe96h4pTgA8PqPmwen1knYEEzem7B0NlZQETQkipUXEC3KW5ZDOd/yx2C7jGr85R//DyuTHrBxMI8EAg4mrnI0IIqXQqLgirS7MUh2Xofq31e+ERxTsHrBcIqfJ6UOXzGOlSg6EIFEXi7mXb8ZHFk/DUmv3YdLAXB3qGKMCEEOIiFSfAem5sphawEAIN1b6idUE/sXo/ALWc5mNfOAm3LV2PZVs6MRRS8Mrmdty2dAPW7u3Go+/sM44p0owqQgipCCrWBZ3N/GdjjT9hUJPb6K34/F4PjhzbhNsuVav8DIUiULS+a2bxJYQQ4i4VJ8A9egWoLAS4tbEaT6zej/f96AWnh5UTL2pFKIBo+8RabU54MBRJWJ7R52XKDiGEuEXFCXBQmyvNpmj/+GY1dWlf9xCktZu3i2w80Gss60U3DAEORhI2pK8q8SpYhBBSylTcE1jv5+tPsxa0GbNodyWps1xoFJuXAb0tX7JylNZex4QQQgpHxT2B9Whhvy9z96vZkvz2o2scG1OuRGyiqap9HggBBEIR2+0ABZgQnaU3nuz2EEgFUnFP4LAmwOl2QzJjtoCfXHMA29qLo3rOOM01bu4IJIRArd+LwWQCnMVLCCHlyLTWBreHQCqQihPgoO6CziIAyRq41R8ojsYMNZq7+fIF42PW1/q96A9GEnZwOmJEabYgJMRp2IyBuEHFCXA4osDvFbYdglKhW5o6WRjReSGsvVRYOzyNba7B3sODiNgUmv/qOTOxZO6YgoyPkGLHyyYexAWKREIKRyiiZOV+BuJzh7M9j9MY89qWOd0R9dV4eVM7egbji4ecOrM1q5cQQsoR6i9xg+JQkAISisis3M8A0FQTK8Dn/fIVJ4aUM7qL2ZrXu7OzHwDw73fjC3DotaMJqWSOHKv2j+bLKHGDChRgJevo36ba+MqdShHUc9QDy6xutJ99aD4A+w5O+rwxIZXMA58+Hg9/7n1uD4NUKBVXC/qvb+7K+li78pWBsIJal5sa6BawNbf5iJFqkNXyHYfijuGcFyFAc10VFk5mX2ziDjSDMsDqggYSF7rYfLAXJ/7oBcMNnA8UReK59QeNOWCrC9qu21G9tq6lng8dQghxk4oSYL0VYbY01sQ7DAaCYby8qR0rLFbm4+/uw/7uITz81t6crpmMnz77Hj59/0r8Y8VuAPFBWHoxDjOfPOkI7Lj9QtRVVZzzgxBCioqKEuA//md7Tsf7bOaOh0IRXH33clxx5+sx6/Ugp0BYtZDveHELzvn5yzld34yUEjsPDQAAtrarVrY1DUkIAWuVygablwhCCCGFp6IEuGdIrd9855ULcj7XHz6xCAAwELR3QetVswIh1T38k2few+Y2tXLWy5va8b3H1mZ97YgiMet/nsZSrQewTrJ53VGN1QBY8YcQQoqFijKHgmEFoxqrsWTu2KzPMXlEHeZNaDbmV/d1Ddrup1fW0bsvmbn67uUAgO9fMjerMazf12N7XrtUirOPHI3nNxzEdadOxewxTTh5xsisrkkIIcRZUgqwEOJuABcBaJNSztXWtQD4B4ApAHYA+JCU8nD+hukMwXD2KUg6L3/tDADAbs39+/yGaC9eRZHweGKFV7eAnaS2Kv4evnjWDNt9WxurtGO8FF9CCCki0lGjewEssay7CcALUsoZAF7QPhc9gYiSVR9gOya21GHC8Fo88nY0yGqLqTmDLrxWS9WcN5xtT2G71ONEPX/1yO0RjHomhJCiIqUaSSlfAWBNJL0EwH3a8n0ALnV2WPkhFFYSClU2TBxeF9NpyCzGevBVfyC2CEbIVJc5UZeiZEgpce4v4itwJXqx+NLZM3HbpXNx3hzWfSaEkGIiWzUaLaXcDwDa/6OcG1LuvLGtE29s64xbH4w4K8AtDbFW5e9e2mosB8Kq0PZqgV86euMEAAm7FCXj2ntX2K5P5FqvrfLiyhMms9QeIYQUGXkPwhJCXAfgOgCYNGlSXq91xZ2vwe/14LWtqvjuuP3CmO3BsIIqB5vQJ3Pr6hawtRHC/u4hYzlTAVYUiRffa7fd5pRrnRBCSGHI9ql9UAgxFgC0/9sS7SilvEtKuUhKuai1tTXLyyXn0/evxJSblmLFjsOG+AJAZ1/AWA5HFAwEI85awBYBNotgULOA3zvYiyk3LTXWt/dGxzT3e89gS1tv2tezCvatl0ajqHssljYhhJDiJls1ehzA1dry1QAec2Y42fHc+oO268/46UvG8rm/fAXv7O7KmwB/dPHEmKIXugvaymAo1iJ+eu2BtK+nWIK2ZoyK5vSObqpJ+zyEEELcJ6UaCSEeAPA6gFlCiD1CiE8BuB3AOUKIzQDO0T4XHT2mLkDbtGpRQwlqN2dDQ3XUg19X5UMwohiBVYnSj8wuaACo9qXfyMFqAfu9wrC633/MuLTPQwghxH3SiYL+qJRyrJTSL6WcIKX8k5SyU0p5lpRyhvZ/fLudIuHnz22K+TzkYF6u2ZrWmxz86dVtAKJzwFbuXbYj5vPKnen/6CKRWAH2eTx44oaT8cPLjjbyjwkhhJQGZRG5M3N04vKKv3phc8znq9832bHrmgO66jRr+IdPbkR/IIxAWMGiycPx5I2nxByjl6PUeWbdQbx3IL154LAS+/Lg9QjMGN2Ijx2f3+A2QgghzlPSAry1vQ9//M+2ONesXd9enbHDah27frU/6j6uN7X+6xkK4UD3EEY2VOOocU1xx41sqI753GEKFktGRFpd0CX99RFCSEVT0rWgP3rXG2jrjRev7sHYiOCBYHQueJyDAmy2gGtN7f06+4LY0dmPi45Ra077PCLmJWHC8NoY0U3krrZiLdxBrzMhhJQuJW1CWatMmfndx6Mdj259YgMA4OwjR2Fii3MCXF8dtXprTdbwpoO9UCQwTYtSfv2bZ+GJG07GR46bCACQAJ644WRj/3TbJIYtc8DZFbIkhBBSDJSsAL+w4SD6E7QCBIDzjx6LL589EwDwwPJdAIBffHi+oxWhzGlINf7oj/LwgGqB12tWcWtjNeaOH4azjhwNAPAK4IiR9cb+r23tTMsKtlrAWZaSJoQQUgSUrACv3duTcp/jjhge8zmTlJ90GFEfncs1n7tPS3/yemPFXp8n9noE6qtjvf8vbrSvcGXGOgcsaQMTQkjJUrIC3FiTevp64vC6mM9+r7OTprWmwKtqkwV8oEfN9fV77H+8Hhsr/LN/WYWVO5KnJOkW8DXvm4J5E4bFWNGEEEJKi5IV4AaLAM8e0wgAaKrx4e5rFgEAxg6LrQ6Vz4YE01qjqVBbtbaEXkuU1Lhmdf75zNn2vSue39BmlLC0Q58DPmHqCDx2/cmOW/SEEEIKR8kKcJNFgHWx++zp03DmbHWu1ef1ONp8wY7xzbU4ceoItNRX4Y+fUIV/q5bra7W4p4ysx+vfPBPXnTrV9lx3vrwVl9yxLOG1dAvYKuyEEEJKj5JNQ2qsic311atSeS1WbrXPg2BEwQVH56cf7rKbzjSWzz5qNE6Y2oI3tqmuZLvqVKnykDfsTzy3rRfi8FGACSGk5ClZC9g8BzyqsdooStFkKcLRq6UqDa9L3DrQScyR0d0DyTsUPfy5E3HDmdONz+Oba2MKeljRmzHQAiaEkNKnhAU4KrSvfuNMo/tQIqEt1Hxps+n6qYRy4eQWfPXcWcbnyxeMx2AoApkgv0ifA6YFTAghpU8JC3DUAq7yeTCgWbrWHr065jzdfLJsS4exfMqMkWkdc9RYtVxlbZUXikzcylCfA2bjBUIIKX3KQoABYEArytFSH+uC1stBTmyJTUnKF1ceH232kG7U9WPXn4SNty5BnVZNayBBgRG9nCUtYEIIKX1KNgjL6lLu1+o9N1tc0P91ylQEwgounT++IONaNGV46p0s+L0e+L3Rjkr9gbCtJU8LmBBCyoeStYAB4POnT8OfP7UYAPD182ZDCKDZEoQ1f2Iz/vCJRTFFM/KJtdNRJjRoAtyXoMZ1MKK6pvOdWkUIIST/lKwFDABfXzLbWP7Y8ZOKoi9ua2P2AqyXp1y/rwdHjo1vYxjSBLjaRwEmhJBSh09yh6nxZ29pj25SxfuRt/fabterZLEPMCGElD58khcRs8c0ocbvSRjJrVvAVbSACSGk5ClpF3Sx8vnTp8GXpZV63JQWbO/ot91GC5gQQsoHCnAeMM9NZ8rophqjlrQVPT2prkABZYQQQvIHTakio8bvwZBNIY77X9+BHz21EdU+DwWYEELKAApwkVHj82IoFF+I47uPrQMAjKivymtbRUIIIYWBAlxkHOgZwkAwghU7DtluH54gQIsQQkhpQQEuMlbtPAwAeHDFbtvterEOQgghpQ0FuMjQi2zoVa+s2LmnCSGElB4U4CJDz/EdDEZw5R/fxEvvtQEAhtepJTa3tdunKBFCCCktKMBFhi7AHX0BvLqlA/9130pIKdE9GAIAfOHM6W4OjxBCiENQgIuMT58yFQAwrbUBgNqC8B8rdkORagT0Z0+b5ubwCCGEOAQFuMg4b84YAIDX1HLwJ8+8BwDo7A+6MiZCCCHOQwEuMmr8XjRU+7B8ezQNicJLCCHlBwW4CBnRUIVtCepBE0IIKQ8owEXIyAb7nsIzRjUUeCSEEELyBQW4CDk8YO9yvvfaxQUeCSGEkHxBAS5CgjbNGC48eizGN9e6MBpCCCH5gAJchIxuqolb11TLEpSEEFJOUICLkHqbes9DIfvSlIQQQkoTCnAR8oEF4wEACyY1455rjgMAzBjNACxCCCknhJSyYBdbtGiRXLlyZcGuV8oEwhFU+7wAgNV7ujBn3LCY4hyEEEKKHyHEKinlIrttnFgsUnTxBYBjJjS7NxBCCCF5gS5oQgghxAUowIQQQogLUIAJIYQQF6AAE0IIIS6QUxCWEGIHgF4AEQDhRJFehBBCCInFiSjoM6SUHQ6chxBCCKkY6IImhBBCXCBXAZYAnhVCrBJCXGe3gxDiOiHESiHEyvb29hwvRwghhJQHuQrwSVLKBQDOB/AFIcSp1h2klHdJKRdJKRe1trbmeDlCCCGkPMhJgKWU+7T/2wA8AoANawkhhJA0yFqAhRD1QohGfRnAuQDWOjUwQgghpJzJJQp6NIBHhBD6ef4mpXzakVERQgghZU7WAiyl3AZgnoNjIYQQQioGpiERQgghLkABJoQQQlxASCkLdzEh2gHsdPCUIwGUWxWucruncrsfgPdUCpTb/QDld0/ldj+A/T1NllLa5uAWVICdRgixstzqT5fbPZXb/QC8p1Kg3O4HKL97Krf7ATK/J7qgCSGEEBegABNCCCEuUOoCfJfbA8gD5XZP5XY/AO+pFCi3+wHK757K7X6ADO+ppOeACSGEkFKl1C1gQgghpCShABNCCCEuQAEmpAIQWtH2cqIc76ncKMfvyMl7KnoBFkJMcXsMTiKEWCSEGOX2OJxECHG2EGKh2+NwEiHEMNNyOTxEcmm8Uqz43R5APhBCeN0eg4MUvcZkgWO/d0X7wxFCLBBCPA/glnL4hRRCzBFCvAbgewCaXR6OIwghjhVCPAW1F/R0t8fjBEKI44UQjwH4oxDiWiFEtSzhSEUhxAlCiL9C/TuaUSZ/SycKIR4C8FMhxFFldE+3AICUMuL2eHJFCLFYCPEXAD8SQhwthCharUkXzXh6CMBPhBAnO/F7V3Q/FKHybQAPAPi7lPIT+i9kiVsiXwTwiJTy/VLKTUDp3o8QwiuEuAvAHwD8HsDfABypbSu636l0EUIcA+AOAP8E8BCAM1HCLxZCiLkAfg3gCQAHAVwH4BPatlL93RsF4DcAnoRa8u+LAK7VtpXqPV0N4D4A/yOE+JC2riQ9FkIIjxDiewD+COApqJ6XL6CEO+dpmnQ7gDsR/Vu6HsCkXM9ddA9LzdrwA3hVSvlHwLC0fKVoiWhi1QJAQn1wQAhxmRBiAoBa7XNJPTi0F6KnAZwipXwUwMMAzhBC1EgpFVcHlxsLAWyRUv4ZwHMAagDs0jeW2vcE4AQAG6WUD0B9WRoA8HEhxBQppSzB+wHUB/kmKeU9AH4G4F8ALhFCzCzhe9oL9WVvCdR7gpQyXIr3ov397wFwjZTyrwB+AGAygJL1Umi68x8A50gp7wNwD9TneXuu5y4KARZCnCaEON606icAxgshfiqEWAHgVgD3CSE+6M4IM8N8P5pYDQA4FcCZmlvmMwBuA/BLbZ+if7GwfkdSyn9JKQe1h4QCYBOAOtcGmAU2v3dLAVwmhPgBgDUAJgD4lRDiG0Dxf08297MCwEQhxDQpZT/U76kbwKeB4r8fABBCXCqE+JYQ4kJt1TsAFpnuaQWAlVD/pkrtni7SVr0I4KCU8lkAO4UQt2rrS8IKtvmOHgDwrjZ90wmgF8BY90aYOdbvSEq5VEp5WAhxCoA3AEwBcJsQ4pxcruOqAAshGoUQ/4I6h/gZIcRwAND+sP4MYD6Ar0opLwLwCoAlQoiZbo03FUnuZwjqW9MdAJ6RUi4B8G0Ac4UQ57s24DRIdE+aW0ZoD7yNAM6CajEWvaWY5Htqg2ph+QB8S0p5AoB7AZwshDjRrfGmwuZ+WrRNWwEsB3CPEOJRAIugutZ9QogaVwabJkKIVm3MXwFwCOo9fFBK2Q7V43KDtmsXgOcB1Akhivohb3NPdwshLtNe0vW/mc8AuFEIMVpKGXJpqGmR4Du6TEo5IKWMSCkDQgg/1BfZ99wca7ok+o5MuxyCat2fCOBdAB8TQszO9npuW8BBAP8H4EoA+wBcoW/Q3BcfklK+oq16HkArgL5CDzIDEt4PgN9CdTm3AoCUci+AV6FaJcWM7T1JDSGER0q5B8CbAD6ob3NrsGmS7PduI4DZAHZrq1YBaAMQKPAYMyHRd9Qnpfw61Pmqe6WU7wewBcAx2kthMTMNwDIp5alSyjsBfBXAl7VtDwCYLYQ4S3N5dgIYD9W6L2bs7ulrACClDAohvFLKdVBfkm4HgCJ/QU94PyaOhGrdb9JeFBcXfJSZkfSepJTrpJQvah9fhhpQm7UmFVyAhRCf0FxlzVLKANTJ+uehujAX6RauZl0dMh16DlS/e1EJcLr3I6Xsg/rWfrUQYr4Q4nMAzgaww6WhJySD78gjpVSEGjCyGUC/e6NOTrr3pPEsgJs1S/4jAOZAfcgXDSnuZ6H5fqSUq7W5ekCda3yjGL0U2j2dLoSog/ric7+23gtgvfYPUKcH/g7g/wkhpkP1vggAVYUfdXLSuKc12mcB9fkGKeV/QX1OHAYwTxRRYGMG96O7z1sADAghrgHwGoCji+13L917suFcqBram+21CzLHoP3Ax0CNllWgusauE0J8UUrZoe3zOoCZAD4E4DbdugJwMoD/BzUY5htSyp5CjDkZ2dwPAEgpH9SO/RDUh/pVUsqicM1k+R0pmgiHhRCNUOdFioYM7+nDUGMNAOBuqAFMT0MNHrlWSrmzwMOPI9vfO239QqgBPhEA1xWLlyLBPX0awBellAc1qzAihDgSwDDACPS5V6gR0TdB9Vh8WkrZ5cY9WMnwnvTpDwlACiEmA/gF1KCfL0gp17pyEyayvJ+wdvh5AD4K1YP0cSnl6oLfgA3Z3JN2XBXUeJ7/hRo893UpZfaeFyllXv8B8Gr/zwTwF23ZBzU94mHLvpdBddVOhzqfKKC6BC7O9zgLcD/1APzaeuH2fTj4HdWX0T3NAFBn2neM2/fhwHdUq60bAeA0t+8jg3v6l2Wf+6FOScH8vQCocvs+HLqnVu3/ZgCL3b4PB+5ntPb/SQA+7PZ9OHRPI7X/jwFwkRNjyZsFrLkgbgHgFUI8CaAJ6ts3pGox3QhgnxDiNCnly9r6R7Q3jqcBNAA4U0q5Hurbias4dD9nANggtW/RbXhPcff0FIAGIcQZUsoNAA64cxdRnPiOhBD639HL7txFLNncE9Spp+1CLVZxuRBiiZRyj5Qy6MY9WHHoni6QUu6CGjjnKg7dz/lSymVujN8OB+9pNQBHLPm8zC0IIU6D6ksfDjXo41YAIai5oosBw+VyC4CbTcddATU6+EWogSLrUQQ4eD8bCjvyxPCeiv+eyu3vCMjunrS5uGuhFkhpAnCGVAP/igIH72lX3MldwMH72R13cpco2nvKk4l/CtT5Tf3zbwF8DsA1AFZp6zxQffAPAjjCdNwp+RgT74f3VGr3VG73k+U9TYY6DfVLAAvcHn8l3FO53U8x31O+brYOQDWifvSPA/iRtvwOgBu05UUAHnD7y6m0++E9lcY9ldv9ZHFPf3d7vJV4T+V2P8V8T3lxQUs1ETsgo0XFz0G0bNcnARwphHgCaj7fW/kYg5OU2/0AvCeUwD2V2/0AGd/TKqD4C7uU2z2V2/0AxXtPeU1D0nzoEsBoAI9rq3sBfAvAXADbpVqQoiQot/sBeE+lQLndD5DZPUnNNCl2yu2eyu1+gOK7p3wneCtQGyt0ADhGe8P4DgBFSvlqqT00UH73A/CeSoFyux+A91QKlNv9AMV2TwXwvZ+g3fSrAD5VKN8674f3VE73VG73w3sqjX/ldj/Fdk9CG1DeEGrbvasA/FyqJfNKmnK7H4D3VAqU2/0AvKdSoNzuByiue8q7ABNCCCEknqIp8k0IIYRUEhRgQgghxAUowIQQQogLUIAJIYQQF6AAE1KiCCEiQoh3hBDrhBDvCiG+IlI0bxdCTBFCfKxQYySEJIYCTEjpMiilnC+lnAO1tN4FAL6X4pgpACjAhBQBTEMipEQRQvRJKRtMn6cCWAFgJNRuLn8GUK9tvl5K+ZoQ4g0ARwLYDuA+AL8CcDuA06EWq79DSvn7gt0EIRUMBZiQEsUqwNq6wwBmQ61vq0gph4QQM6B2S1okhDgdwH9LKS/S9r8OwCgp5W1CiGoAywBcIaXcXsh7IaQSyWszBkJIwdE7uPgB/EYIMR9ABMDMBPufC7Um7ge1z8MAzIBqIRNC8ggFmJAyQXNBRwC0QZ0LPghgHtRYj6FEh0HthfpMQQZJCDFgEBYhZYAQohXAnQB+I9V5pWEA9kspFah1b73arr0AGk2HPgPgc0IIv3aemUKIehBC8g4tYEJKl1ohxDtQ3c1hqEFXP9e2/RbAw0KIKwC8CKBfW78aQFgI8S6AewH8P6iR0W9pDcjbAVxamOETUtkwCIsQQghxAbqgCSGEEBegABNCCCEuQAEmhBBCXIACTAghhLgABZgQQghxAQowIYQQ4gIUYEIIIcQFKMCEEEKIC/x/GQ+FHUDC/jsAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotando o histórico de fechamento da ação.\n",
    "plt.figure(figsize = (8, 8));\n",
    "petr3['Close'].plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Em 2020, provavelmente devido a pândemia, a ação teve uma grande desvalorização."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotando a variação de preço histórica.\n",
    "plt.figure(figsize = (8, 8));\n",
    "petr3['price_var'].plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* O preço da ação **não apresenta** uma estabilidade em seu preço, havendo certos momentos em que há picos de volatilidade/variação.\n",
    "* Oque é perfeitamente compreensivo ! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Criando uma lista com conjunto de diferentes ações.\n",
    "acoes = ['ABEV3.SA','BBAS3.SA','ITSA3.SA','VALE3.SA',]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As ações escolhidas correspondem a:\n",
    "* **Ambev, Banco do Brasil, Itaúsa e Vale**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  4 of 4 completed\n"
     ]
    }
   ],
   "source": [
    "# Carga de dados.\n",
    "acoes = yf.download(acoes, start = '2015-01-01')['Close']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* A busca será feita pelo **preço de fechamento** das ações."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>ABEV3.SA</th>\n",
       "      <th>BBAS3.SA</th>\n",
       "      <th>ITSA3.SA</th>\n",
       "      <th>VALE3.SA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>16.020000</td>\n",
       "      <td>22.650000</td>\n",
       "      <td>6.630748</td>\n",
       "      <td>21.280001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>15.730000</td>\n",
       "      <td>22.180000</td>\n",
       "      <td>6.525498</td>\n",
       "      <td>20.959999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-06</th>\n",
       "      <td>16.330000</td>\n",
       "      <td>22.490000</td>\n",
       "      <td>6.658815</td>\n",
       "      <td>21.799999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-07</th>\n",
       "      <td>16.559999</td>\n",
       "      <td>23.480000</td>\n",
       "      <td>6.834232</td>\n",
       "      <td>22.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08</th>\n",
       "      <td>16.540001</td>\n",
       "      <td>23.559999</td>\n",
       "      <td>6.876332</td>\n",
       "      <td>22.840000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             ABEV3.SA   BBAS3.SA  ITSA3.SA   VALE3.SA\n",
       "Date                                                 \n",
       "2015-01-02  16.020000  22.650000  6.630748  21.280001\n",
       "2015-01-05  15.730000  22.180000  6.525498  20.959999\n",
       "2015-01-06  16.330000  22.490000  6.658815  21.799999\n",
       "2015-01-07  16.559999  23.480000  6.834232  22.600000\n",
       "2015-01-08  16.540001  23.559999  6.876332  22.840000"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualizando os 5 primeiros registros.\n",
    "acoes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>ABEV3.SA</th>\n",
       "      <th>BBAS3.SA</th>\n",
       "      <th>ITSA3.SA</th>\n",
       "      <th>VALE3.SA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-09-13</th>\n",
       "      <td>15.59</td>\n",
       "      <td>40.220001</td>\n",
       "      <td>9.47</td>\n",
       "      <td>68.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-14</th>\n",
       "      <td>15.38</td>\n",
       "      <td>40.259998</td>\n",
       "      <td>9.47</td>\n",
       "      <td>67.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-15</th>\n",
       "      <td>15.30</td>\n",
       "      <td>39.849998</td>\n",
       "      <td>9.45</td>\n",
       "      <td>68.349998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-16</th>\n",
       "      <td>15.27</td>\n",
       "      <td>39.400002</td>\n",
       "      <td>9.39</td>\n",
       "      <td>68.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-19</th>\n",
       "      <td>15.47</td>\n",
       "      <td>40.180000</td>\n",
       "      <td>9.67</td>\n",
       "      <td>70.459999</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            ABEV3.SA   BBAS3.SA  ITSA3.SA   VALE3.SA\n",
       "Date                                                \n",
       "2022-09-13     15.59  40.220001      9.47  68.250000\n",
       "2022-09-14     15.38  40.259998      9.47  67.000000\n",
       "2022-09-15     15.30  39.849998      9.45  68.349998\n",
       "2022-09-16     15.27  39.400002      9.39  68.250000\n",
       "2022-09-19     15.47  40.180000      9.67  70.459999"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualizando os 5 últimos registros.\n",
    "acoes.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1919, 4)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verificando a quantidade de observações e atributos.\n",
    "acoes.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Há um total de **1918 registros** e **4 atributos**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verificando a existência de valores ausentes na base de dados.\n",
    "acoes.isnull().sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Removendo os valores nulos/ausentes.\n",
    "acoes.dropna(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Caso mude de ideia, execute a célula novamente !\n"
     ]
    }
   ],
   "source": [
    "# Criando uma função para salavar o dataframe em formato csv.\n",
    "opcao = input('Deseja salvar o DataFrame em formato csv? [s/n]: ')\n",
    "\n",
    "if opcao == 's':\n",
    "    acoes.to_csv('acoes.csv')\n",
    "else:\n",
    "    print('Caso mude de ideia, execute a célula novamente !')    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>ABEV3.SA</th>\n",
       "      <th>BBAS3.SA</th>\n",
       "      <th>ITSA3.SA</th>\n",
       "      <th>VALE3.SA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1918.000000</td>\n",
       "      <td>1918.000000</td>\n",
       "      <td>1918.000000</td>\n",
       "      <td>1918.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>17.506387</td>\n",
       "      <td>32.447565</td>\n",
       "      <td>9.533849</td>\n",
       "      <td>48.923707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.480207</td>\n",
       "      <td>9.723704</td>\n",
       "      <td>2.351622</td>\n",
       "      <td>27.223960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>11.030000</td>\n",
       "      <td>12.700000</td>\n",
       "      <td>4.897239</td>\n",
       "      <td>8.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>15.652500</td>\n",
       "      <td>26.620001</td>\n",
       "      <td>7.314560</td>\n",
       "      <td>26.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>17.969999</td>\n",
       "      <td>31.865001</td>\n",
       "      <td>9.723809</td>\n",
       "      <td>47.850000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>19.097500</td>\n",
       "      <td>37.037499</td>\n",
       "      <td>11.148097</td>\n",
       "      <td>62.222500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>24.270000</td>\n",
       "      <td>55.180000</td>\n",
       "      <td>15.780952</td>\n",
       "      <td>118.720001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ABEV3.SA     BBAS3.SA     ITSA3.SA     VALE3.SA\n",
       "count  1918.000000  1918.000000  1918.000000  1918.000000\n",
       "mean     17.506387    32.447565     9.533849    48.923707\n",
       "std       2.480207     9.723704     2.351622    27.223960\n",
       "min      11.030000    12.700000     4.897239     8.600000\n",
       "25%      15.652500    26.620001     7.314560    26.100000\n",
       "50%      17.969999    31.865001     9.723809    47.850000\n",
       "75%      19.097500    37.037499    11.148097    62.222500\n",
       "max      24.270000    55.180000    15.780952   118.720001"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Resumo estatístico.\n",
    "acoes.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* As empresas `ABEV3` e `ITSA3` são aquelas que possuem a **menor variação** histórica de preço.\n",
    "* `VALE3` é aquela que tem a **maior variação** em seu preço histórico, bem como a compania com o preço mais caro por ação **R$ 118,72**.\n",
    "*  `ITSA3` teve o menor preço histórico registrado no valor de **R$ 4,89**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fAKAV8QMAtCJ+AIBWxA8A0Ir4AQBaET8AQCviBwBoRfwAAK2IHwCgFfEDALQifgCAVsQPANCK+AEAWhE/AEAr4gcAaEX8AACtiB8AoBXxAwC0In4AgFbEDwDQivgBAFoRPwBAK+IHAGhF/AAArYgfAKCVGmPs+cZVFyX5wsoNhxV2UJKL5z0IVoz3d+3y3q5d3tuVdY8xxp2XLrxR8cOtW1VtH2McMe9xsDK8v2uX93bt8t7Oh8NeAEAr4gcAaEX89PKKeQ+AFeX9Xbu8t2uX93YOnPMDALRizw8A0Ir4AQBaET9rVFXdrao+VFXnVNWnqmrrtPyAqjqtqj4zfb7TvMfKjVNV+1TVv1TVJ6b39sXTcu/tGlFV66rq41X17um+93aNqKrzquqsqjqzqrZPy7y/q0z8rF1XJ/n1McZhSR6e5Feq6v5JXpjkg2OM+yT54HSfW5crkzx6jPHgJA9JclRVPTze27Vka5JzFt333q4tjxpjPGTR3/fx/q4y8bNGjTG+Msb42HT7W5l9I92Q5Ogkr5k2e02Sp81lgNxkY+ay6e5e08eI93ZNqKpDkzw5ySsXLfberm3e31Umfhqoqo1JfiTJR5McPMb4SjILpCR3mePQuImmwyJnJvlqktPGGN7btePPk/xWkmsXLfPerh0jyQeq6oyqeu60zPu7ytbPewCsrKraP8lbk/zaGOObVTXvIbEMxhjXJHlIVd0xydur6oFzHhLLoKqekuSrY4wzqurIOQ+HlfGIMcaXq+ouSU6rqnPnPaCO7PlZw6pqr8zC53VjjLdNiy+sqkOm9YdktueAW6kxxteTfDjJUfHergWPSPLUqjovyRuTPLqqXhvv7Zoxxvjy9PmrSd6e5Efj/V114meNqtkunr9Ocs4Y408XrXpXks3T7c1J3rnaY+Pmqao7T3t8UlX7JnlsknPjvb3VG2O8aIxx6BhjY5Jjkvz9GONZ8d6uCVV1u6q6/c7bSR6f5Ox4f1edv/C8RlXVI5P8Q5Kz8r1zB347s/N+3pzk7km+mOTpY4xL5zJIbpKqelBmJ0Wuy+wfMG8eY/x+VR0Y7+2aMR32+o0xxlO8t2tDVd0rs709yey0k9ePMf4f7+/qEz8AQCsOewEArYgfAKAV8QMAtCJ+AIBWxA8A0Ir4AW5QVV0zXYX6E1X1sar6sWn5xqq6YtG6f6qq+y557ElVtaOqbrNo2cFV9e7pMZ+uqvdOy+8x/dn/M6cr1v/ybsZz36r68LTdOVX1iiXrX1BV36mqH1r+2QBu7fyqO3CDquqyMcb+0+0nJPntMcZPTteNe/cY44HTul9K8mNjjM3T/dskOS/Jl5O8cIzx4Wn5/0zy6THGSdP9B40xPllVt83s+9KV06VZzp6e78tLxvP+JC8bY7xzun/4GOOsRev/JcmVSf56jHHyikwKcKtlzw9wY90hydf2cN2jMguYlyc5dtHyQ5Kcv/POGOOT0+fvjjGunBbvnd1/j1r6+MXhc+8k+yc5fslrAiRxYVNgz+w7XUV+n8zC49GL1t17Wnf7JPsl+b8WrTs2yRsy+3P9f1BVe40xrkryl0neVFW/muTvkrx6596dqrpbkvck2ZTkN5fu9Zn8WZK/r6p/SvKB6fFfX/Ka/5DkvlV1l+k6SgBJ7PkB9swVY4yHjDHul9lFVE+Zrh+XJJ+d1t07ya8leUWSTIewnpTkHWOMb2Z2aZXHJ8kY4/1J7pXk/01yvyQfr6o7T+u+NMZ4UGbxs7mqDl46mDHGq5McluRvkhyZ5PSq2ntafUySN44xrk3ytiRPX9aZAG71nPMD3KDF5/xM9y9Mcnhme3oWn/Ozb5JLxhj7VdVTM9sDc9H0sP2SfGC6UOfS5393Zntv3rpk+auTvGeM8ZYbGN/ZmV0Q8qok/5rkK9Oq2yb53BjjkTf2awbWLnt+gBulqu6X2UVVL9nF6kcm+ex0+9gkzxljbJyuUn7PJI+vqv2q6tFVtd/0fLdPcu8kX6yqQ6eASlXdKckjkvzbLsZwVFXtNd3+4SQHJtkxveYJO19zjHHXJBuq6h7L9fUDt37O+QH2xM5zfpKkkmweY1wzHfnaec5PJflukudMYfOEJL+08wnGGN+uqn9M8lOZXb36L6rq6sz+EfbKMca/VtXjkvyPqhrT871k58nMVfX7SbaPMd6V2eGzk6rqO9PT/+YY44KqOibJE5eM/e2ZHQr7o2WcD+BWzGEvAKAVh70AgFbEDwDQivgBAFoRPwBAK+IHAGhF/AAArYgfAKAV8QMAtCJ+AIBWxA8A0Ir4AQBaET8AQCviBwBoRfwAAK2IHwCgFfEDALQifgCAVsQPANCK+AEAWhE/AEAr4gcAaEX8ALtVVedV1X+rqsumj+9U1TWL7n9q2u7oqjqzqr5ZVRdX1QerauOS57pnVV1bVS/bxet8qKoumh7/iao6+geM6dCqeuv0Ot+oqrOq6tm72O7DVfW1qtr75s8EsJaIH+CG/NMYY/8xxv5JfjnJP++8P8Z4QFVtSnJKkl9P8kNJ7pnkZUmuXfI8xyX5WpJjdhEkW5McMsa4Q5LnJnltVR2ym/H8ryRfSnKPJAdOz3vh4g2m8PrxJCPJU2/C1wysYeIHuLkekuTzY4wPjplvjTHeOsb44pLtjktyfJKrkvzU4hVjjE+OMa7eeTfJXknutpvXe1iSk8cY3x5jXD3G+PgY4327eK3Tk5ycZPNN/cKAtUn8ADfXx5Lcr6r+rKoeVVX7L92gqn48yaFJ3pjkzZnFydJt3l1V30ny0SQfTrJ9N693epK/rKpjquruu9nmuCSvmz6eUFUH38ivCVjDxA9ws4wxPpfkyCQbMgubi6vq5CURtDnJ+8YYX0vy+iRPrKq7LHmepyS5fZInJXn/GGPpYbOdnp7kH5L8bpLPT+caPWznyqp6ZGaHxN48xjgjyWeT/MzN/0qBtUL8ADfbGOP0McYzxhh3zuxcm59I8jtJUlX7ZhYsr5u2/eckX8wugmSMcdV0COsJVbXLc3XGGF8bY7xwjPGAJAcnOTPJO6qqpk02J/nAGOPi6f7r49AXsIj4AZbVGONfk7wtyQOnRf93kjskeVlVXVBVF2S2l+h6h74WWZ/k3nvwWhcneUmSuyY5YAqtZyT5yUWv9YIkD66qB9/UrwlYW8QPcLNU1SOr6hd3Hsaqqvtl9htWp0+bbE7yqiSHZ3Zy9EOSPCLJQ6rq8Kq6X1U9sar2raq9qupZme05+shuXu+PquqBVbW+qm6f5HlJFsYYlyR5WpJrktx/0Wsdltlhsh8UW0Aj4ge4ub6eWeycVVWXJTk1yduT/HFVbUjymCR/Psa4YNHHGdN2m5NUkhOSfDXJRZn92vszxxgfS2YnS0/Pu9N+0/N/PcnnMju/Z+chss1JXj3G+OLi10vyF0l+tqrWr9QkALceNcaY9xgAAFaNPT8AQCviBwBoRfwAAK2IHwCgFfEDALRyo37t86CDDhobN25coaEAACyfM8444+LpL89/nxsVPxs3bsz27bu71iAAwC1HVX1hV8sd9gIAWhE/AEAr4gcAaEX8AACtiB8AoBXxAwC0In4AgFbEDwDQivgBAFoRPwBAK+IHAGhF/AAArYgfAKAV8QMAtCJ+AIBWxA8A0Ir4AQBaET8AQCviBwBoRfwAAK2IHwCgFfEDALQifgCAVsQPANCK+AEAWhE/AEAr4gcAaEX8AACtiB8AoBXxAwC0sn7eA2Bt2bZtWxYWFuY9DG6iHTt2JEk2bNgw55HM36ZNm7Jly5Z5DwNYAeKHZbWwsJAzzz4n1+x3wLyHwk2w7vJvJEkuuLL3t4Z1l1867yEAK6j3dzhWxDX7HZAr7vekeQ+Dm2Dfc9+bJO3fv53zAKxNzvkBAFoRPwBAK+IHAGhF/AAArYgfAKAV8QMAtCJ+AIBWxA8A0Ir4AQBaET8AQCviBwBoRfwAAK2IHwCgFfEDALQifgCAVsQPANCK+AEAWhE/AEAr4gcAaEX8AACtiB8AoBXxAwC0In4AgFbEDwDQivgBAFoRPwBAK+IHAGhF/AAArYgfAKAV8QMAtCJ+AIBWxA8A0Ir4AQBaET8AQCviBwBoRfwAAK2IHwCgFfEDALQifgCAVsQPANCK+AEAWhE/AEAr4gcAaEX8AACtiB8AoBXxAwC0In4AgFbEDwDQivgBAFoRPwBAK+IHAGhF/AAArYgfAKAV8QMAtCJ+AIBWxA8A0Ir4AQBaET8AQCviBwBoRfwAAK2IHwCgFfEDALQifgCAVm5R8bNt27Zs27Zt3sMAAFbILeFn/fq5vvoSCwsL8x4CALCCbgk/629Re34AAFaa+AEAWhE/AEAr4gcAaEX8AACtiB8AoBXxAwC0In4AgFbEDwDQivgBAFoRPwBAK+IHAGhF/AAArYgfAKAV8QMAtCJ+AIBWxA8A0Ir4AQBaET8AQCviBwBoRfwAAK2IHwCgFfEDALQifgCAVsQPANCK+AEAWhE/AEAr4gcAaEX8AACtiB8AoBXxAwC0In4AgFbEDwDQivgBAFoRPwBAK+IHAGhF/AAArYgfAKAV8QMAtCJ+AIBWxA8A0Ir4AQBaET8AQCviBwBoRfwAAK2IHwCgFfEDALQifgCAVsQPANCK+AEAWhE/AEAr4gcAaEX8AACtiB8AoBXxAwC0In4AgFbEDwDQivgBAFoRPwBAK+IHAGhF/AAArYgfAKAV8QMAtCJ+AIBW1s97AIvt2LEjV1xxRbZu3TrvoXATLSws5DbfHfMeBtwst/nON7Ow8C3fi2AFLCwsZN99953rGG5wz09VPbeqtlfV9osuumg1xgQAsGJucM/PGOMVSV6RJEccccSK/pN+w4YNSZKTTjppJV+GFbR169ac8bkL5z0MuFmu3ecO2XSvg30vghVwS9ij6pwfAKAV8QMAtCJ+AIBWxA8A0Ir4AQBaET8AQCviBwBoRfwAAK2IHwCgFfEDALQifgCAVsQPANCK+AEAWhE/AEAr4gcAaEX8AACtiB8AoBXxAwC0In4AgFbEDwDQivgBAFoRPwBAK+IHAGhF/AAArYgfAKAV8QMAtCJ+AIBWxA8A0Ir4AQBaET8AQCviBwBoRfwAAK2IHwCgFfEDALQifgCAVsQPANCK+AEAWhE/AEAr4gcAaEX8AACtiB8AoBXxAwC0In4AgFbEDwDQivgBAFoRPwBAK+IHAGhF/AAArYgfAKAV8QMAtCJ+AIBWxA8A0Ir4AQBaET8AQCviBwBoRfwAAK2IHwCgFfEDALQifgCAVsQPANCK+AEAWhE/AEAr4gcAaEX8AACtiB8AoJX18x7AYps2bZr3EACAFXRL+Fl/i4qfLVu2zHsIAMAKuiX8rHfYCwBoRfwAAK2IHwCgFfEDALQifgCAVsQPANCK+AEAWhE/AEAr4gcAaEX8AACtiB8AoBXxAwC0In4AgFbEDwDQivgBAFoRPwBAK+IHAGhF/AAArYgfAKAV8QMAtCJ+AIBWxA8A0Ir4AQBaET8AQCviBwBoRfwAAK2IHwCgFfEDALQifgCAVsQPANCK+AEAWhE/AEAr4gcAaEX8AACtiB8AoBXxAwC0In4AgFbEDwDQivgBAFoRPwBAK+IHAGhF/AAArYgfAKAV8QMAtCJ+AIBWxA8A0Ir4AQBaET8AQCviBwBoRfwAAK2IHwCgFfEDALQifgCAVsQPANCK+AEAWhE/AEAr4gcAaEX8AACtiB8AoBXxAwC0In4AgFbEDwDQivgBAFpZP+8BsPasu/zS7Hvue+c9DG6CdZdfkiTt3791l1+a5OB5DwNYIeKHZbVp06Z5D4GbYceOq5MkGzZ0/8F/sP+WYQ0TPyyrLVu2zHsIAPADOecHAGhF/AAArYgfAKAV8QMAtCJ+AIBWxA8A0Ir4AQBaET8AQCviBwBoRfwAAK2IHwCgFfEDALQifgCAVsQPANCK+AEAWhE/AEAr4gcAaEX8AACtiB8AoBXxAwC0In4AgFbEDwDQivgBAFoRPwBAK+IHAGhF/AAArYgfAKAV8QMAtCJ+AIBWxA8A0Ir4AQBaqTHGnm9cdVGSL6zccNacg5JcPO9BNGTeV585X33mfPWZ8/m4OfN+jzHGnZcuvFHxw41TVdvHGEfMexzdmPfVZ85XnzlffeZ8PlZi3h32AgBaET8AQCviZ2W9Yt4DaMq8rz5zvvrM+eoz5/Ox7PPunB8AoBV7fgCAVsTPCqmqO1bVW6rq3Ko6p6r+w7zHtNZV1Quq6lNVdXZVvaGq9pn3mNaiqnpVVX21qs5etOyAqjqtqj4zfb7TPMe41uxmzv9k+v7yyap6e1XdcY5DXHN2NeeL1v1GVY2qOmgeY1urdjfnVbWlqv5t+v7+x8vxWuJn5ZyU5NQxxv2SPDjJOXMez5pWVRuSPD/JEWOMByZZl+SY+Y5qzTo5yVFLlr0wyQfHGPdJ8sHpPsvn5Fx/zk9L8sAxxoOS/J8kL1rtQa1xJ+f6c56quluSxyX54moPqIGTs2TOq+pRSY5O8qAxxgOSvGQ5Xkj8rICqukOSn0jy10kyxvjuGOPrcx1UD+uT7FtV65Psl+TLcx7PmjTG+N9JLl2y+Ogkr5luvybJ01ZzTGvdruZ8jPGBMcbV093Tkxy66gNbw3bz33mS/FmS30rihNlltps5f16S/z7GuHLa5qvL8VriZ2XcK8lFSV5dVR+vqldW1e3mPai1bIyxI7N/EXwxyVeSfGOM8YH5jqqVg8cYX0mS6fNd5jyebn4hyfvmPYi1rqqemmTHGOMT8x5LI/8uyY9X1Uer6iNV9bDleFLxszLWJ3lokpePMX4kybfjMMCKms4xOTrJPZPcNcntqupZ8x0VrLyq+p0kVyd53bzHspZV1X5JfifJf5v3WJpZn+ROSR6e5DeTvLmq6uY+qfhZGecnOX+M8dHp/lsyiyFWzmOTfH6McdEY46okb0vyY3MeUycXVtUhSTJ9XpZd0/xgVbU5yVOS/Ozwd0tW2r0z+8fVJ6rqvMwOM36sqn54rqNa+85P8rYx8y9Jrs3sWl83i/hZAWOMC5J8qaruOy16TJJPz3FIHXwxycOrar/pXwWPiZPMV9O7kmyebm9O8s45jqWFqjoqyX9N8tQxxuXzHs9aN8Y4a4xxlzHGxjHGxsx+KD90+n7PynlHkkcnSVX9uyS3zTJcXFb8rJwtSV5XVZ9M8pAkfzDf4axt0162tyT5WJKzMvtv219jXQFV9YYk/5zkvlV1flX95yT/Pcnjquozmf0mzH+f5xjXmt3M+V8kuX2S06rqzKr6q7kOco3ZzZyzgnYz569Kcq/p19/fmGTzcuzl9BeeAYBW7PkBAFoRPwBAK+IHAGhF/AAArYgfAKAV8QPssaq6rKoOn361+syqurSqPj/d/ruquk1VvbSqzq6qs6rqX6vqnosef+equqqqfmnJ855aVZ+Yrtr8V1W1bhevvV9VvW563rOr6h+rav9F639kutL2E1Z2FoBbu/XzHgBw6zLGOCuzv12Vqjo5ybvHGG+Z7h+b2eVFHjTGuLaqDs3s8i47PT2zi3Aem+R/Llr+jDHGN6c/UPmWabs3LnnprUkuHGMcPr3WfZNctWj9sUn+cfr8/pv/lQJrlT0/wHI6JMlXxhjXJskY4/wxxtcWrT82ya8nObSqNuxcOMb45nRzfWZ/wXVXf4DskCQ7Fj3m33Ze6XmKpv+U5NlJHl9V+yzbVwSsOeIHWE5vTvJT02Gw/1FVP7JzRVXdLckPT9fneXOSZy5+YFW9P7Nrgn0rs70/S70qyX+tqn+uqhOr6j6L1j0is2u7fTbJh5M8aTm/KGBtET/AshljnJ/kvklelNkFCD9YVY+ZVh+TWfQks0Naxy557BMy27uzd6Zr+SxZf2aSeyX5kyQHJPnXqjpsWn1svneY7HrPDbCYy1sAe6yqLhtjLD7J+OQsOudnF9v/RpJ7jDG2VNXHkhyc752nc9ckDxhjfGbJYzYnedgY41dvYCx/keTzSf48s8NhVyW5JkklOTDJIWOMb93oLxJY8+z5AZZNVT20qu463b5Nkgcl+cJ0cvLtxhgbFl0V+w+THFNV+1fVIdNj1md2yOrcXTz3I6rqTtPt2ya5f5IvJHlskk+MMe42Pfc9krw1ydNW+MsFbqXED7Cc7pLkb6crMH8yydWZXX382CRvX7LtW6flt0vyrqr6ZJJPZHbez18lSVU9tap+f9r+3kk+UlVnJfl4ku2LnmNXz/0zy/ulAWuFw14AQCv2/AAArYgfAKAV8QMAtCJ+AIBWxA8A0Ir4AQBaET8AQCviBwBoRfwAAK2IHwCgFfEDALQifgCAVsQPANCK+AEAWhE/AEAr4gcAaEX8AACtiB8AoBXxAwC0In4AgFbEDwDQivgBAFoRP8D3qar3V9Xv72L50VV1QVWtr6ojq2pU1W8t2WbjtHz9Lh5/QlVdVVWXLfr4+qL1H6qqi6rqm1X1iao6+geM8dCqemtVXVxV36iqs6rq2bvY7sNV9bWq2vvGzgOwdokfYKmTk/xcVdWS5T+X5HVjjKuTbE5y6fT5xnjTGGP/RR93XLRua5JDxhh3SPLcJK+tqkN28zz/K8mXktwjyYFJjkty4eINqmpjkh9PMpI89UaOE1jDxA+w1DuSHJBZOCRJqupOSZ6S5JSq2i/Jf0ryK0nuU1VHLMeLjjE+OYVVMguWvZLcbTebPyzJyWOMb48xrh5jfHyM8b4l2xyX5PTMYu7GRhqwhokf4PuMMa5I8ubM4mGnZyQ5d4zxiSQ/neSyJH+T5P1LtrtZqurdVfWdJB9N8uEk23ez6elJ/rKqjqmqu+9mm+OSvG76eEJVHbxc4wRu3cQPsCuvSfL0qtp3un/ctCyZ7UV50xjjmiSvT3JsVe21h8/7jKr6+qKPDy1eOcZ4SpLbJ3lSkvePMa7dzfM8Pck/JPndJJ+vqjOr6mE7V1bVIzM7JPbmMcYZST6b5Gf2cIzAGid+gOsZY/xjkouSHF1V98rsMNPrq+puSR6V2d6UJHlnkn2SPHkPn/rNY4w7Lvp41C5e+6rpENYTqmqX5+qMMb42xnjhGOMBSQ5OcmaSdyw6T2lzkg+MMS6e7r8+Dn0Bk+v9RgbA5JTM9vjcN7OQuLCqfjuzfzT97aLzofeZtnvHMr/++iT3vqGNxhgXV9VLMoubA6rq8swO062rqgumzfZOcseqevB06A5ozJ4fYHdOSfLYJL+Y7x3yOi7Ji5M8ZNHHTyd5clUduOixe1fVPos+fuD3mqq6X1U9sar2raq9qupZSX4iyUd2s/0fVdUDp1+7v32S5yVZGGNckuRpSa5Jcv9FYzwss8Nky3Z+EnDrJX6AXRpjnJfkn5LcLsm7qurhSTYm+csxxgWLPt6VZCHJsYseflmSKxZ9PHpa/swlf+fnsqq6S5JKckKSr2Z2uG1rkmeOMT6WJFX141V12aLn3y/J25N8PcnnMju/Z+chss1JXj3G+OLicSb5iyQ/u6u/QQT0UmOMeY8BAGDV2PMDALQifgCAVsQPANCK+AEAWrlRv/Vw0EEHjY0bN67QUAAAls8ZZ5xx8RjjzkuX36j42bhxY7Zv392ldgAAbjmq6gu7Wu6wFwDQivgBAFoRPwBAK+IHAGhF/AAArYgfAKAV8QMAtCJ+AIBWxA8A0Ir4AQBaET8AQCviBwBoRfwAAK2IHwCgFfEDALQifgCAVsQPANCK+AEAWhE/AEAr4gcAaEX8AACtiB8AoBXxAwC0In4AgFbEDwDQivgBAFoRPwBAK+IHAGhF/AAArYgfAKCV9fMeAMtr27ZtWVhYmPcwmOzYsSNJsmHDhjmPZP42bdqULVu2zHsYAOJnrVlYWMiZZ5+Ta/Y7YN5DIcm6y7+RJLngyt7/q627/NJ5DwHgOr2/I69R1+x3QK6435PmPQyS7Hvue5Ok/fuxcx4Abgmc8wMAtCJ+AIBWxA8A0Ir4AQBaET8AQCviBwBoRfwAAK2IHwCgFfEDALQifgCAVsQPANCK+AEAWhE/AEAr4gcAaEX8AACtiB8AoBXxAwC0In4AgFbEDwDQivgBAFoRPwBAK+IHAGhF/AAArYgfAKAV8QMAtCJ+AIBWxA8A0Ir4AQBaET8AQCviBwBoRfwAAK2IHwCgFfEDALQifgCAVsQPANCK+AEAWhE/AEAr4gcAaEX8AACtiB8AoBXxAwC0In4AgFbEDwDQivgBAFoRPwBAK+IHAGhF/AAArYgfAKAV8QMAtCJ+AIBWxA8A0Ir4AQBaET8AQCviBwBoRfwAAK2IHwCgFfEDALQifgCAVsQPANCK+AEAWhE/AEAr4gcAaEX8AACtiB8AoJVbVPxs27Yt27Ztm/cwAIA9cGv9ub1+3gNYbGFhYd5DAAD20K315/Ytas8PAMBKEz8AQCviBwBoRfwAAK2IHwCgFfEDALQifgCAVsQPANCK+AEAWhE/AEAr4gcAaEX8AACtiB8AoBXxAwC0In4AgFbEDwDQivgBAFoRPwBAK+IHAGhF/AAArYgfAKAV8QMAtCJ+AIBWxA8A0Ir4AQBaET8AQCviBwBoRfwAAK2IHwCgFfEDALQifgCAVsQPANCK+AEAWhE/AEAr4gcAaEX8AACtiB8AoBXxAwC0In4AgFbEDwDQivgBAFoRPwBAK+IHAGhF/AAArYgfAKAV8QMAtCJ+AIBWxA8A0Ir4AQBaET8AQCviBwBoRfwAAK2IHwCgFfEDALQifgCAVsQPANCK+AEAWhE/AEAr4gcAaEX8AACtiB8AoBXxAwAsi0suuSTPf/7zc8kll3zf7d1tMy/iBwBYFq95zWty1lln5ZRTTvm+27vbZl7EDwBws11yySU59dRTM8bI+973vutun3rqqdft5Vm8zeLlq239XF51N3bs2JErrrgiW7dunfdQbrUWFhZym++OeQ8Dvs9tvvPNLCx8y//bsMYsLCxk3333TTLbo3PttdcmSa666qrrtrnmmmtyyimn5AUveMH3bbN4+Wq7wT0/VfXcqtpeVdsvuuii1RgTAHAr83d/93e5+uqrkyRjjIwx+4f41VdfndNOO+162yxevtpucM/PGOMVSV6RJEccccSK7lLYsGFDkuSkk05ayZdZ07Zu3ZozPnfhvIcB3+fafe6QTfc62P/bsMYs3pv72Mc+Nu9973tz9dVXp6qSzCJo/fr1edzjHne9bRYvX23O+QEAbrbNmzfnNreZZcVee+2VvfbaK0mybt26HHfccdfbZvHy1SZ+AICb7cADD8xRRx2VqsoTn/jE624fddRROfDAA6+3zeLlq+0WdcIzAHDrtXnz5px33nnX7dFZfHt328yD+AEAlsWBBx6Yl770pdfdX3x7d9vMg8NeAEAr4gcAaEX8AACtiB8AoBXxAwC0In4AgFbEDwDQivgBAFoRPwBAK+IHAGhF/AAArYgfAKAV8QMAtCJ+AIBWxA8A0Ir4AQBaET8AQCviBwBoRfwAAK2IHwCgFfEDALQifgCAVsQPANCK+AEAWhE/AEAr4gcAaEX8AACtiB8AoBXxAwC0In4AgFbEDwDQivgBAFoRPwBAK+IHAGhF/AAArYgfAKAV8QMAtCJ+AIBWxA8A0Ir4AQBaET8AQCviBwBoRfwAAK2IHwCgFfEDALQifgCAVsQPANCK+AEAWhE/AEAr4gcAaEX8AACtiB8AoBXxAwC0In4AgFbEDwDQivgBAFoRPwBAK+IHAGhF/AAArYgfAKCV9fMewGKbNm2a9xAAgD10a/25fYuKny1btsx7CADAHrq1/tx22AsAaEX8AACtiB8AoBXxAwC0In4AgFbEDwDQivgBAFoRPwBAK+IHAGhF/AAArYgfAKAV8QMAtCJ+AIBWxA8A0Ir4AQBaET8AQCviBwBoRfwAAK2IHwCgFfEDALQifgCAVsQPANCK+AEAWhE/AEAr4gcAaEX8AACtiB8AoBXxAwC0In4AgFbEDwDQivgBAFoRPwBAK+IHAGhF/AAArYgfAKAV8QMAtCJ+AIBWxA8A0Ir4AQBaET8AQCviBwBoRfwAAK2IHwCgFfEDALQifgCAVsQPANCK+AEAWhE/AEAr4gcAaEX8AACtiB8AoBXxAwC0In4AgFbEDwDQivgBAFoRPwBAK+IHAGhF/AAArYgfAKAV8QMAtCJ+AIBWxA8A0Ir4AQBaET8AQCvr5z0Alt+6yy/Nvue+d97DIMm6yy9Jkvbvx7rLL01y8LyHAZBE/Kw5mzZtmvcQWGTHjquTJBs2dP/Bf7D/NoFbDPGzxmzZsmXeQwCAWzTn/AAArYgfAKAV8QMAtCJ+AIBWxA8A0Ir4AQBaET8AQCviBwBoRfwAAK2IHwCgFfEDALQifgCAVsQPANCK+AEAWhE/AEAr4gcAaEX8AACtiB8AoBXxAwC0In4AgFbEDwDQivgBAFoRPwBAK+IHAGhF/AAArYgfAKAV8QMAtCJ+AIBWxA8A0Ir4AQBaET8AQCs1xtjzjasuSvKFlRvOmnVQkovnPYhGzPfqMt+ry3yvLvO9upZ7vu8xxrjz0oU3Kn64aapq+xjjiHmPowvzvbrM9+oy36vLfK+u1Zpvh70AgFbEDwDQivhZHa+Y9wCaMd+ry3yvLvO9usz36lqV+XbODwDQij0/AEAr4mcZVdXdqupDVXVOVX2qqrZOyw+oqtOq6jPT5zvNe6xrSVWtq6qPV9W7p/vme4VU1R2r6i1Vde703/l/MN8rp6peMH0vObuq3lBV+5jv5VVVr6qqr1bV2YuW7XaOq+pFVbVQVf9WVU+Yz6hvvXYz338yfU/5ZFW9varuuGjdisy3+FleVyf59THGYUkenuRXqur+SV6Y5INjjPsk+eB0n+WzNck5i+6b75VzUpJTxxj3S/LgzObdfK+AqtqQ5PlJjhhjPDDJuiTHxHwvt5OTHLVk2S7nePp+fkySB0yPeVlVrVu9oa4JJ+f6831akgeOMR6U5P8keVGysvMtfpbRGOMrY4yPTbe/ldkPhg1Jjk7ymmmz1yR52lwGuAZV1aFJnpzklYsWm+8VUFV3SPITSf46ScYY3x1jfD3meyWtT7JvVa1Psl+SL8d8L6sxxv9OcumSxbub46OTvHGMceUY4/NJFpL86GqMc63Y1XyPMT4wxrh6unt6kkOn2ys23+JnhVTVxiQ/kuSjSQ4eY3wlmQVSkrvMcWhrzZ8n+a0k1y5aZr5Xxr2SXJTk1dNhxldW1e1ivlfEGGNHkpck+WKSryT5xhjjAzHfq2F3c7whyZcWbXf+tIzl8wtJ3jfdXrH5Fj8roKr2T/LWJL82xvjmvMezVlXVU5J8dYxxxrzH0sT6JA9N8vIxxo8k+XYcclkx03kmRye5Z5K7JrldVT1rvqNqr3axzK9ML5Oq+p3MTh953c5Fu9hsWeZb/Cyzqtors/B53RjjbdPiC6vqkGn9IUm+Oq/xrTGPSPLUqjovyRuTPLqqXhvzvVLOT3L+GOOj0/23ZBZD5ntlPDbJ58cYF40xrkrytiQ/FvO9GnY3x+cnudui7Q7N7FAkN1NVbU7ylCQ/O773N3hWbL7FzzKqqsrsfIhzxhh/umjVu5Jsnm5vTvLO1R7bWjTGeNEY49AxxsbMTor7+zHGs2K+V8QY44IkX6qq+06LHpPk0zHfK+WLSR5eVftN31sek9l5hOZ75e1ujt+V5Jiq2ruq7pnkPkn+ZQ7jW1Oq6qgk/zXJU8cYly9atWLz7Y8cLqOqemSSf0hyVr53DspvZ3bez5uT3D2zb2hPH2MsPcGOm6GqjkzyG2OMp1TVgTHfK6KqHpLZyeW3TfK5JD+f2T+izPcKqKoXJ3lmZocCPp7kOUn2j/leNlX1hiRHZnY18QuT/F6Sd2Q3czwdmvmFzN6TXxtjvO/6z8ru7Ga+X5Rk7ySXTJudPsb45Wn7FZlv8QMAtOKwFwDQivgBAFoRPwBAK+IHAGhF/AAArYgf4AZV1YeXXlG5qn6tql5WVXeuqquq6peWrD+vqg5asuzZVXVRVZ256OP+VXWPqjpjuv+pqvrl3YzjvtNYzpyuKv+KJetfUFXfqaofWq6vHVh7/Ko7cIOmsHn4GOPnFy07PclvJjk8ybFJrhljHLlo/XmZXZH84kXLnj0t+9Ulz3/bzL4fXTldHubsJD82xvjyku3en+RlY4x3TvcPH2OctWj9vyS5MslfjzFOXoYvHViD7PkB9sRbkjylqvZOrrtw712T/GNm4fPrSQ6tqpt00cHpCvFXTnf3zu6/Nx2S2Z+83/m4xeFz78z+AODx05gAdkn8ADdojHFJZn9W/qhp0TFJ3pTZtXZ+eIzxL5n9Rdxn7sHTPXPJYa99k6Sq7lZVn8zsKs5/tHSvz+TPkvx9Vb1vOsR1x0Xrjk3yhsz+yvp9q8rVzoFdEj/AnnpDZtGT6fPO+2+elr0xe7bH5U1jjIcs+rgiScYYXxpjPCjJpiSbq+rgpQ8cY7w6yWFJ/iazP5F/+s69UdNY3jjGuDazi4A+/SZ8jUAD4gfYU+9I8piqemiSfccYH8ssdp49nd/zriQPrqr73JwXmfb4fCrJj+9u/RjjVWOMozO73s8Dq+pBmV308LRpLMfEoS9gN8QPsEfGGJcl+XCSVyV5w3R199uNMTaMMTaOMTYm+cN8b+/QHquqQxcd/rpTkkck+bddbHdUVe013f7hJAcm2ZFZ6JywcxxjjLsm2VBV97gJXyqwxokf4MZ4Q5IH53uHuN6+ZP1b8/17XD5ZVedPH386LVt6zs+PZXYo66NV9YkkH0nykp0nM1fV71fVU6fHPj7J2dN270/ym2OMCzILrqVjeXtuQogBa59fdQcAWrHnBwBoRfwAAK2IHwCgFfEDALQifgCAVsQPANCK+AEAWhE/AEAr/z8GYiZAdur3yQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 720x3600 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotando o gráfico de caixa para cada ação.\n",
    "plt.figure(figsize = (10, 50));\n",
    "for i in range(1, len(acoes.columns)):\n",
    "    plt.subplot(7, 1, i + 1);\n",
    "    sns.boxplot(x = acoes[acoes.columns[i]]);\n",
    "    plt.title(acoes.columns[i]);\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normalizando os valores para compararmos as ações que apresentaram maior rentabilidade.\n",
    "acoesn = acoes.copy()\n",
    "for acao in acoes.columns:\n",
    "  acoesn[acao] = acoesn[acao] / acoesn[acao][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>ABEV3.SA</th>\n",
       "      <th>BBAS3.SA</th>\n",
       "      <th>ITSA3.SA</th>\n",
       "      <th>VALE3.SA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>0.981898</td>\n",
       "      <td>0.979249</td>\n",
       "      <td>0.984127</td>\n",
       "      <td>0.984962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-06</th>\n",
       "      <td>1.019351</td>\n",
       "      <td>0.992936</td>\n",
       "      <td>1.004233</td>\n",
       "      <td>1.024436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-07</th>\n",
       "      <td>1.033708</td>\n",
       "      <td>1.036645</td>\n",
       "      <td>1.030688</td>\n",
       "      <td>1.062030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08</th>\n",
       "      <td>1.032459</td>\n",
       "      <td>1.040177</td>\n",
       "      <td>1.037037</td>\n",
       "      <td>1.073308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-13</th>\n",
       "      <td>0.973159</td>\n",
       "      <td>1.775718</td>\n",
       "      <td>1.428195</td>\n",
       "      <td>3.207237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-14</th>\n",
       "      <td>0.960050</td>\n",
       "      <td>1.777483</td>\n",
       "      <td>1.428195</td>\n",
       "      <td>3.148496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-15</th>\n",
       "      <td>0.955056</td>\n",
       "      <td>1.759382</td>\n",
       "      <td>1.425179</td>\n",
       "      <td>3.211936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-16</th>\n",
       "      <td>0.953184</td>\n",
       "      <td>1.739514</td>\n",
       "      <td>1.416130</td>\n",
       "      <td>3.207237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-19</th>\n",
       "      <td>0.965668</td>\n",
       "      <td>1.773951</td>\n",
       "      <td>1.458357</td>\n",
       "      <td>3.311090</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1918 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            ABEV3.SA  BBAS3.SA  ITSA3.SA  VALE3.SA\n",
       "Date                                              \n",
       "2015-01-02  1.000000  1.000000  1.000000  1.000000\n",
       "2015-01-05  0.981898  0.979249  0.984127  0.984962\n",
       "2015-01-06  1.019351  0.992936  1.004233  1.024436\n",
       "2015-01-07  1.033708  1.036645  1.030688  1.062030\n",
       "2015-01-08  1.032459  1.040177  1.037037  1.073308\n",
       "...              ...       ...       ...       ...\n",
       "2022-09-13  0.973159  1.775718  1.428195  3.207237\n",
       "2022-09-14  0.960050  1.777483  1.428195  3.148496\n",
       "2022-09-15  0.955056  1.759382  1.425179  3.211936\n",
       "2022-09-16  0.953184  1.739514  1.416130  3.207237\n",
       "2022-09-19  0.965668  1.773951  1.458357  3.311090\n",
       "\n",
       "[1918 rows x 4 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualizando os dados.\n",
    "acoesn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Resetando o indíce do DataFrame para que consiguamos plotar e utilizar o atributo Date como eixo.\n",
    "acoesn.reset_index(inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Se fizer a gravação e importação dos dados em csv, não tera de resetar o indíce !"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "hovertemplate": "<extra></extra>",
         "legendgroup": "",
         "line": {
          "color": "#636efa",
          "dash": "solid"
         },
         "marker": {
          "symbol": "circle"
         },
         "mode": "lines",
         "name": "",
         "orientation": "v",
         "showlegend": false,
         "type": "scatter",
         "xaxis": "x",
         "yaxis": "y"
        },
        {
         "name": "ABEV3.SA",
         "type": "scatter",
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          "hoverlabel": {
           "align": "left"
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          "hovermode": "closest",
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           "style": "light"
          },
          "paper_bgcolor": "white",
          "plot_bgcolor": "#E5ECF6",
          "polar": {
           "angularaxis": {
            "gridcolor": "white",
            "linecolor": "white",
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           },
           "bgcolor": "#E5ECF6",
           "radialaxis": {
            "gridcolor": "white",
            "linecolor": "white",
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          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
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           },
           "yaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
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           },
           "zaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
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           }
          },
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           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "title": {
         "text": "Preço histórico das ações"
        },
        "xaxis": {
         "anchor": "y",
         "domain": [
          0,
          1
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        },
        "yaxis": {
         "anchor": "x",
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          1
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        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "# Plotando a evolução de rentabilidade histórica.\n",
    "fig = px.line(title = 'Preço histórico das ações')\n",
    "for i in acoesn.columns[1:]:\n",
    "  fig.add_scatter(x = acoesn['Date'], y = acoesn[i], name = i)\n",
    "fig.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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