MPT.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Libraries necessárias para o estudo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Abrindo as bases contendo os dados das ações"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Abrindo bases em CSV das ações\n",
    "ABEV3 = pd.read_csv(\"ABEV3.csv\")\n",
    "EQTL3 = pd.read_csv(\"EQTL3.csv\")\n",
    "LREN3 = pd.read_csv(\"LREN3.csv\")\n",
    "CIEL3 = pd.read_csv(\"CIEL3.csv\")\n",
    "RADL3 = pd.read_csv(\"RADL3.csv\")\n",
    "RENT3 = pd.read_csv(\"RENT3.csv\")\n",
    "MDIA3 = pd.read_csv(\"MDIA3.csv\")\n",
    "WEGE3 = pd.read_csv(\"WEGE3.csv\")\n",
    "EZTC3 = pd.read_csv(\"EZTC3.csv\")\n",
    "FLRY3 = pd.read_csv(\"FLRY3.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Vamos ver se a base está no formato correto"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>ticker</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/2/2012</td>\n",
       "      <td>WEGE3</td>\n",
       "      <td>4.158826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/3/2012</td>\n",
       "      <td>WEGE3</td>\n",
       "      <td>4.256892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/4/2012</td>\n",
       "      <td>WEGE3</td>\n",
       "      <td>4.279175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/5/2012</td>\n",
       "      <td>WEGE3</td>\n",
       "      <td>4.305927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/6/2012</td>\n",
       "      <td>WEGE3</td>\n",
       "      <td>4.256892</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Date ticker  Adj Close\n",
       "0  1/2/2012  WEGE3   4.158826\n",
       "1  1/3/2012  WEGE3   4.256892\n",
       "2  1/4/2012  WEGE3   4.279175\n",
       "3  1/5/2012  WEGE3   4.305927\n",
       "4  1/6/2012  WEGE3   4.256892"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "WEGE3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "all_dfs = [ABEV3, EQTL3, LREN3,RADL3,RENT3,WEGE3,EZTC3,FLRY3,CIEL3,MDIA3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "selected = ['ABEV3', 'EQTL3', 'EZTC3', 'FLRY3','LREN3',\n",
    "           'RADL3','RENT3','WEGE3','CIEL3','MDIA3']\n",
    "\n",
    "for df in all_dfs:\n",
    "    df.columns = ['Date','ticker','Adj Close']\n",
    "\n",
    "dfnew = pd.concat(all_dfs).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>ticker</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2/1/2012</td>\n",
       "      <td>ABEV3</td>\n",
       "      <td>7.045039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2/2/2012</td>\n",
       "      <td>ABEV3</td>\n",
       "      <td>7.058415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2/3/2012</td>\n",
       "      <td>ABEV3</td>\n",
       "      <td>7.165369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2/6/2012</td>\n",
       "      <td>ABEV3</td>\n",
       "      <td>7.049052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2/7/2012</td>\n",
       "      <td>ABEV3</td>\n",
       "      <td>6.978216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2/8/2012</td>\n",
       "      <td>ABEV3</td>\n",
       "      <td>6.947450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2/9/2012</td>\n",
       "      <td>ABEV3</td>\n",
       "      <td>7.003567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2/10/2012</td>\n",
       "      <td>ABEV3</td>\n",
       "      <td>6.971529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2/13/2012</td>\n",
       "      <td>ABEV3</td>\n",
       "      <td>7.091860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2/14/2012</td>\n",
       "      <td>ABEV3</td>\n",
       "      <td>7.105239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2/15/2012</td>\n",
       "      <td>ABEV3</td>\n",
       "      <td>7.125236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2/16/2012</td>\n",
       "      <td>ABEV3</td>\n",
       "      <td>7.270984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2/17/2012</td>\n",
       "      <td>ABEV3</td>\n",
       "      <td>7.452784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2/23/2012</td>\n",
       "      <td>ABEV3</td>\n",
       "      <td>7.365896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2/24/2012</td>\n",
       "      <td>ABEV3</td>\n",
       "      <td>7.554386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2/27/2012</td>\n",
       "      <td>ABEV3</td>\n",
       "      <td>7.619871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2/28/2012</td>\n",
       "      <td>ABEV3</td>\n",
       "      <td>7.674717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2/29/2012</td>\n",
       "      <td>ABEV3</td>\n",
       "      <td>7.769629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>3/1/2012</td>\n",
       "      <td>ABEV3</td>\n",
       "      <td>8.190977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>3/2/2012</td>\n",
       "      <td>ABEV3</td>\n",
       "      <td>8.329587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>3/5/2012</td>\n",
       "      <td>ABEV3</td>\n",
       "      <td>8.255276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>3/6/2012</td>\n",
       "      <td>ABEV3</td>\n",
       "      <td>8.248123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>3/7/2012</td>\n",
       "      <td>ABEV3</td>\n",
       "      <td>8.469705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>3/8/2012</td>\n",
       "      <td>ABEV3</td>\n",
       "      <td>8.592651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>3/9/2012</td>\n",
       "      <td>ABEV3</td>\n",
       "      <td>8.862800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>3/12/2012</td>\n",
       "      <td>ABEV3</td>\n",
       "      <td>8.934321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>3/13/2012</td>\n",
       "      <td>ABEV3</td>\n",
       "      <td>9.081518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>3/14/2012</td>\n",
       "      <td>ABEV3</td>\n",
       "      <td>9.034382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>3/15/2012</td>\n",
       "      <td>ABEV3</td>\n",
       "      <td>8.947125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>3/16/2012</td>\n",
       "      <td>ABEV3</td>\n",
       "      <td>8.837050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17049</th>\n",
       "      <td>10/4/2018</td>\n",
       "      <td>MDIA3</td>\n",
       "      <td>40.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17050</th>\n",
       "      <td>10/5/2018</td>\n",
       "      <td>MDIA3</td>\n",
       "      <td>41.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17051</th>\n",
       "      <td>10/8/2018</td>\n",
       "      <td>MDIA3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17052</th>\n",
       "      <td>10/9/2018</td>\n",
       "      <td>MDIA3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17053</th>\n",
       "      <td>10/10/2018</td>\n",
       "      <td>MDIA3</td>\n",
       "      <td>44.220001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17054</th>\n",
       "      <td>10/11/2018</td>\n",
       "      <td>MDIA3</td>\n",
       "      <td>44.200001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17055</th>\n",
       "      <td>10/15/2018</td>\n",
       "      <td>MDIA3</td>\n",
       "      <td>44.049999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17056</th>\n",
       "      <td>10/16/2018</td>\n",
       "      <td>MDIA3</td>\n",
       "      <td>45.009998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17057</th>\n",
       "      <td>10/17/2018</td>\n",
       "      <td>MDIA3</td>\n",
       "      <td>45.369999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17058</th>\n",
       "      <td>10/18/2018</td>\n",
       "      <td>MDIA3</td>\n",
       "      <td>45.349998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17059</th>\n",
       "      <td>10/19/2018</td>\n",
       "      <td>MDIA3</td>\n",
       "      <td>46.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17060</th>\n",
       "      <td>10/22/2018</td>\n",
       "      <td>MDIA3</td>\n",
       "      <td>46.990002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17061</th>\n",
       "      <td>10/23/2018</td>\n",
       "      <td>MDIA3</td>\n",
       "      <td>46.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17062</th>\n",
       "      <td>10/24/2018</td>\n",
       "      <td>MDIA3</td>\n",
       "      <td>44.970001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17063</th>\n",
       "      <td>10/25/2018</td>\n",
       "      <td>MDIA3</td>\n",
       "      <td>45.400002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17064</th>\n",
       "      <td>10/26/2018</td>\n",
       "      <td>MDIA3</td>\n",
       "      <td>43.830002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17065</th>\n",
       "      <td>10/29/2018</td>\n",
       "      <td>MDIA3</td>\n",
       "      <td>43.599998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17066</th>\n",
       "      <td>10/30/2018</td>\n",
       "      <td>MDIA3</td>\n",
       "      <td>45.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17067</th>\n",
       "      <td>10/31/2018</td>\n",
       "      <td>MDIA3</td>\n",
       "      <td>44.470001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17068</th>\n",
       "      <td>11/1/2018</td>\n",
       "      <td>MDIA3</td>\n",
       "      <td>44.990002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17069</th>\n",
       "      <td>11/5/2018</td>\n",
       "      <td>MDIA3</td>\n",
       "      <td>44.840000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17070</th>\n",
       "      <td>11/6/2018</td>\n",
       "      <td>MDIA3</td>\n",
       "      <td>45.990002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17071</th>\n",
       "      <td>11/7/2018</td>\n",
       "      <td>MDIA3</td>\n",
       "      <td>44.919998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17072</th>\n",
       "      <td>11/8/2018</td>\n",
       "      <td>MDIA3</td>\n",
       "      <td>43.669998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17073</th>\n",
       "      <td>11/9/2018</td>\n",
       "      <td>MDIA3</td>\n",
       "      <td>43.330002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17074</th>\n",
       "      <td>11/12/2018</td>\n",
       "      <td>MDIA3</td>\n",
       "      <td>43.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17075</th>\n",
       "      <td>11/13/2018</td>\n",
       "      <td>MDIA3</td>\n",
       "      <td>42.509998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17076</th>\n",
       "      <td>11/14/2018</td>\n",
       "      <td>MDIA3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17077</th>\n",
       "      <td>11/16/2018</td>\n",
       "      <td>MDIA3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17078</th>\n",
       "      <td>11/19/2018</td>\n",
       "      <td>MDIA3</td>\n",
       "      <td>43.639999</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17079 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Date ticker  Adj Close\n",
       "0        2/1/2012  ABEV3   7.045039\n",
       "1        2/2/2012  ABEV3   7.058415\n",
       "2        2/3/2012  ABEV3   7.165369\n",
       "3        2/6/2012  ABEV3   7.049052\n",
       "4        2/7/2012  ABEV3   6.978216\n",
       "5        2/8/2012  ABEV3   6.947450\n",
       "6        2/9/2012  ABEV3   7.003567\n",
       "7       2/10/2012  ABEV3   6.971529\n",
       "8       2/13/2012  ABEV3   7.091860\n",
       "9       2/14/2012  ABEV3   7.105239\n",
       "10      2/15/2012  ABEV3   7.125236\n",
       "11      2/16/2012  ABEV3   7.270984\n",
       "12      2/17/2012  ABEV3   7.452784\n",
       "13      2/23/2012  ABEV3   7.365896\n",
       "14      2/24/2012  ABEV3   7.554386\n",
       "15      2/27/2012  ABEV3   7.619871\n",
       "16      2/28/2012  ABEV3   7.674717\n",
       "17      2/29/2012  ABEV3   7.769629\n",
       "18       3/1/2012  ABEV3   8.190977\n",
       "19       3/2/2012  ABEV3   8.329587\n",
       "20       3/5/2012  ABEV3   8.255276\n",
       "21       3/6/2012  ABEV3   8.248123\n",
       "22       3/7/2012  ABEV3   8.469705\n",
       "23       3/8/2012  ABEV3   8.592651\n",
       "24       3/9/2012  ABEV3   8.862800\n",
       "25      3/12/2012  ABEV3   8.934321\n",
       "26      3/13/2012  ABEV3   9.081518\n",
       "27      3/14/2012  ABEV3   9.034382\n",
       "28      3/15/2012  ABEV3   8.947125\n",
       "29      3/16/2012  ABEV3   8.837050\n",
       "...           ...    ...        ...\n",
       "17049   10/4/2018  MDIA3  40.250000\n",
       "17050   10/5/2018  MDIA3  41.500000\n",
       "17051   10/8/2018  MDIA3        NaN\n",
       "17052   10/9/2018  MDIA3        NaN\n",
       "17053  10/10/2018  MDIA3  44.220001\n",
       "17054  10/11/2018  MDIA3  44.200001\n",
       "17055  10/15/2018  MDIA3  44.049999\n",
       "17056  10/16/2018  MDIA3  45.009998\n",
       "17057  10/17/2018  MDIA3  45.369999\n",
       "17058  10/18/2018  MDIA3  45.349998\n",
       "17059  10/19/2018  MDIA3  46.000000\n",
       "17060  10/22/2018  MDIA3  46.990002\n",
       "17061  10/23/2018  MDIA3  46.500000\n",
       "17062  10/24/2018  MDIA3  44.970001\n",
       "17063  10/25/2018  MDIA3  45.400002\n",
       "17064  10/26/2018  MDIA3  43.830002\n",
       "17065  10/29/2018  MDIA3  43.599998\n",
       "17066  10/30/2018  MDIA3  45.000000\n",
       "17067  10/31/2018  MDIA3  44.470001\n",
       "17068   11/1/2018  MDIA3  44.990002\n",
       "17069   11/5/2018  MDIA3  44.840000\n",
       "17070   11/6/2018  MDIA3  45.990002\n",
       "17071   11/7/2018  MDIA3  44.919998\n",
       "17072   11/8/2018  MDIA3  43.669998\n",
       "17073   11/9/2018  MDIA3  43.330002\n",
       "17074  11/12/2018  MDIA3  43.000000\n",
       "17075  11/13/2018  MDIA3  42.509998\n",
       "17076  11/14/2018  MDIA3        NaN\n",
       "17077  11/16/2018  MDIA3        NaN\n",
       "17078  11/19/2018  MDIA3  43.639999\n",
       "\n",
       "[17079 rows x 3 columns]"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfnew.replace('null', np.NaN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dfnew.columns = ['date', 'ticker', 'adj_close']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dfnew['date'] = pd.to_datetime(dfnew['date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clean = dfnew.set_index('date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clean['adj_close'] = pd.to_numeric(clean['adj_close'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Vamos fazer agora algumas modificações no formato da base. \n",
    "### Vamos ordenar o preço de fechamento colocando os tickers como colunas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
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       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"10\" halign=\"left\">adj_close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ticker</th>\n",
       "      <th>ABEV3</th>\n",
       "      <th>CIEL3</th>\n",
       "      <th>EQTL3</th>\n",
       "      <th>EZTC3</th>\n",
       "      <th>FLRY3</th>\n",
       "      <th>LREN3</th>\n",
       "      <th>MDIA3</th>\n",
       "      <th>RADL3</th>\n",
       "      <th>RENT3</th>\n",
       "      <th>WEGE3</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-01-02</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5.354475</td>\n",
       "      <td>11.482443</td>\n",
       "      <td>9.526285</td>\n",
       "      <td>6.750966</td>\n",
       "      <td>5.241881</td>\n",
       "      <td>13.014443</td>\n",
       "      <td>12.262033</td>\n",
       "      <td>6.113688</td>\n",
       "      <td>4.158826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-03</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5.374410</td>\n",
       "      <td>11.221079</td>\n",
       "      <td>9.822123</td>\n",
       "      <td>6.887123</td>\n",
       "      <td>5.221321</td>\n",
       "      <td>12.938909</td>\n",
       "      <td>12.447824</td>\n",
       "      <td>6.297850</td>\n",
       "      <td>4.256892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-04</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5.393235</td>\n",
       "      <td>11.401327</td>\n",
       "      <td>9.899043</td>\n",
       "      <td>6.842793</td>\n",
       "      <td>5.166850</td>\n",
       "      <td>13.193463</td>\n",
       "      <td>12.447824</td>\n",
       "      <td>6.059124</td>\n",
       "      <td>4.279175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-05</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5.315714</td>\n",
       "      <td>11.446412</td>\n",
       "      <td>9.496675</td>\n",
       "      <td>6.807961</td>\n",
       "      <td>5.342606</td>\n",
       "      <td>13.280160</td>\n",
       "      <td>12.354930</td>\n",
       "      <td>5.870412</td>\n",
       "      <td>4.305927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-06</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5.282492</td>\n",
       "      <td>11.491495</td>\n",
       "      <td>9.543985</td>\n",
       "      <td>6.839625</td>\n",
       "      <td>5.416609</td>\n",
       "      <td>13.260604</td>\n",
       "      <td>12.308481</td>\n",
       "      <td>5.911335</td>\n",
       "      <td>4.256892</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           adj_close                                                     \\\n",
       "ticker         ABEV3     CIEL3      EQTL3     EZTC3     FLRY3     LREN3   \n",
       "date                                                                      \n",
       "2012-01-02       NaN  5.354475  11.482443  9.526285  6.750966  5.241881   \n",
       "2012-01-03       NaN  5.374410  11.221079  9.822123  6.887123  5.221321   \n",
       "2012-01-04       NaN  5.393235  11.401327  9.899043  6.842793  5.166850   \n",
       "2012-01-05       NaN  5.315714  11.446412  9.496675  6.807961  5.342606   \n",
       "2012-01-06       NaN  5.282492  11.491495  9.543985  6.839625  5.416609   \n",
       "\n",
       "                                                      \n",
       "ticker          MDIA3      RADL3     RENT3     WEGE3  \n",
       "date                                                  \n",
       "2012-01-02  13.014443  12.262033  6.113688  4.158826  \n",
       "2012-01-03  12.938909  12.447824  6.297850  4.256892  \n",
       "2012-01-04  13.193463  12.447824  6.059124  4.279175  \n",
       "2012-01-05  13.280160  12.354930  5.870412  4.305927  \n",
       "2012-01-06  13.260604  12.308481  5.911335  4.256892  "
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table = clean.pivot(columns='ticker')\n",
    "\n",
    "table.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cálculo dos retornos diários e anuais médios das ações"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "returns_daily = table.pct_change()\n",
    "\n",
    "returns_annual = returns_daily.mean() * 250"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "           ticker\n",
       "adj_close  ABEV3     0.152039\n",
       "           CIEL3     0.127975\n",
       "           EQTL3     0.298371\n",
       "           EZTC3     0.187256\n",
       "           FLRY3     0.206811\n",
       "           LREN3     0.346407\n",
       "           MDIA3     0.219219\n",
       "           RADL3     0.284564\n",
       "           RENT3     0.281424\n",
       "           WEGE3     0.252510\n",
       "dtype: float64"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "returns_annual"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Retornar retornos diários e covariâncias das ações"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cov_daily = returns_daily.cov()\n",
    "\n",
    "cov_annual = cov_daily * 250"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"10\" halign=\"left\">adj_close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>ticker</th>\n",
       "      <th>ABEV3</th>\n",
       "      <th>CIEL3</th>\n",
       "      <th>EQTL3</th>\n",
       "      <th>EZTC3</th>\n",
       "      <th>FLRY3</th>\n",
       "      <th>LREN3</th>\n",
       "      <th>MDIA3</th>\n",
       "      <th>RADL3</th>\n",
       "      <th>RENT3</th>\n",
       "      <th>WEGE3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>ticker</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"10\" valign=\"top\">adj_close</th>\n",
       "      <th>ABEV3</th>\n",
       "      <td>0.051897</td>\n",
       "      <td>0.018914</td>\n",
       "      <td>0.012167</td>\n",
       "      <td>0.020311</td>\n",
       "      <td>0.008843</td>\n",
       "      <td>0.022832</td>\n",
       "      <td>0.015088</td>\n",
       "      <td>0.016512</td>\n",
       "      <td>0.022155</td>\n",
       "      <td>0.017733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CIEL3</th>\n",
       "      <td>0.018914</td>\n",
       "      <td>0.095079</td>\n",
       "      <td>0.016193</td>\n",
       "      <td>0.024688</td>\n",
       "      <td>0.011276</td>\n",
       "      <td>0.027496</td>\n",
       "      <td>0.020843</td>\n",
       "      <td>0.021892</td>\n",
       "      <td>0.025998</td>\n",
       "      <td>0.017270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EQTL3</th>\n",
       "      <td>0.012167</td>\n",
       "      <td>0.016193</td>\n",
       "      <td>0.061397</td>\n",
       "      <td>0.025315</td>\n",
       "      <td>0.012249</td>\n",
       "      <td>0.026215</td>\n",
       "      <td>0.011114</td>\n",
       "      <td>0.014620</td>\n",
       "      <td>0.025550</td>\n",
       "      <td>0.012444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EZTC3</th>\n",
       "      <td>0.020311</td>\n",
       "      <td>0.024688</td>\n",
       "      <td>0.025315</td>\n",
       "      <td>0.115110</td>\n",
       "      <td>0.019315</td>\n",
       "      <td>0.041935</td>\n",
       "      <td>0.026139</td>\n",
       "      <td>0.022070</td>\n",
       "      <td>0.045691</td>\n",
       "      <td>0.019348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FLRY3</th>\n",
       "      <td>0.008843</td>\n",
       "      <td>0.011276</td>\n",
       "      <td>0.012249</td>\n",
       "      <td>0.019315</td>\n",
       "      <td>0.086206</td>\n",
       "      <td>0.021079</td>\n",
       "      <td>0.014903</td>\n",
       "      <td>0.012604</td>\n",
       "      <td>0.019916</td>\n",
       "      <td>0.007784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LREN3</th>\n",
       "      <td>0.022832</td>\n",
       "      <td>0.027496</td>\n",
       "      <td>0.026215</td>\n",
       "      <td>0.041935</td>\n",
       "      <td>0.021079</td>\n",
       "      <td>0.098709</td>\n",
       "      <td>0.022460</td>\n",
       "      <td>0.030137</td>\n",
       "      <td>0.047214</td>\n",
       "      <td>0.022472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MDIA3</th>\n",
       "      <td>0.015088</td>\n",
       "      <td>0.020843</td>\n",
       "      <td>0.011114</td>\n",
       "      <td>0.026139</td>\n",
       "      <td>0.014903</td>\n",
       "      <td>0.022460</td>\n",
       "      <td>0.083330</td>\n",
       "      <td>0.014422</td>\n",
       "      <td>0.023715</td>\n",
       "      <td>0.016689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RADL3</th>\n",
       "      <td>0.016512</td>\n",
       "      <td>0.021892</td>\n",
       "      <td>0.014620</td>\n",
       "      <td>0.022070</td>\n",
       "      <td>0.012604</td>\n",
       "      <td>0.030137</td>\n",
       "      <td>0.014422</td>\n",
       "      <td>0.086073</td>\n",
       "      <td>0.027216</td>\n",
       "      <td>0.018336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RENT3</th>\n",
       "      <td>0.022155</td>\n",
       "      <td>0.025998</td>\n",
       "      <td>0.025550</td>\n",
       "      <td>0.045691</td>\n",
       "      <td>0.019916</td>\n",
       "      <td>0.047214</td>\n",
       "      <td>0.023715</td>\n",
       "      <td>0.027216</td>\n",
       "      <td>0.111827</td>\n",
       "      <td>0.020855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WEGE3</th>\n",
       "      <td>0.017733</td>\n",
       "      <td>0.017270</td>\n",
       "      <td>0.012444</td>\n",
       "      <td>0.019348</td>\n",
       "      <td>0.007784</td>\n",
       "      <td>0.022472</td>\n",
       "      <td>0.016689</td>\n",
       "      <td>0.018336</td>\n",
       "      <td>0.020855</td>\n",
       "      <td>0.071397</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 adj_close                                                    \\\n",
       "ticker               ABEV3     CIEL3     EQTL3     EZTC3     FLRY3     LREN3   \n",
       "          ticker                                                               \n",
       "adj_close ABEV3   0.051897  0.018914  0.012167  0.020311  0.008843  0.022832   \n",
       "          CIEL3   0.018914  0.095079  0.016193  0.024688  0.011276  0.027496   \n",
       "          EQTL3   0.012167  0.016193  0.061397  0.025315  0.012249  0.026215   \n",
       "          EZTC3   0.020311  0.024688  0.025315  0.115110  0.019315  0.041935   \n",
       "          FLRY3   0.008843  0.011276  0.012249  0.019315  0.086206  0.021079   \n",
       "          LREN3   0.022832  0.027496  0.026215  0.041935  0.021079  0.098709   \n",
       "          MDIA3   0.015088  0.020843  0.011114  0.026139  0.014903  0.022460   \n",
       "          RADL3   0.016512  0.021892  0.014620  0.022070  0.012604  0.030137   \n",
       "          RENT3   0.022155  0.025998  0.025550  0.045691  0.019916  0.047214   \n",
       "          WEGE3   0.017733  0.017270  0.012444  0.019348  0.007784  0.022472   \n",
       "\n",
       "                                                          \n",
       "ticker               MDIA3     RADL3     RENT3     WEGE3  \n",
       "          ticker                                          \n",
       "adj_close ABEV3   0.015088  0.016512  0.022155  0.017733  \n",
       "          CIEL3   0.020843  0.021892  0.025998  0.017270  \n",
       "          EQTL3   0.011114  0.014620  0.025550  0.012444  \n",
       "          EZTC3   0.026139  0.022070  0.045691  0.019348  \n",
       "          FLRY3   0.014903  0.012604  0.019916  0.007784  \n",
       "          LREN3   0.022460  0.030137  0.047214  0.022472  \n",
       "          MDIA3   0.083330  0.014422  0.023715  0.016689  \n",
       "          RADL3   0.014422  0.086073  0.027216  0.018336  \n",
       "          RENT3   0.023715  0.027216  0.111827  0.020855  \n",
       "          WEGE3   0.016689  0.018336  0.020855  0.071397  "
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cov_annual"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Esvaziando as listas para guardar os retornos, volatilidades e pesos dos portfólios imaginários"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "port_returns = []\n",
    "\n",
    "port_volatility = []\n",
    "\n",
    "stock_weights = []"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Configurar número de portfólios simulados\n",
    "\n",
    "### Portfólios serão encontrados utilizando simulação de Monte-Carlo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "num_assets = len(selected)\n",
    "\n",
    "num_portfolios = 200000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Vamos preencher cada uma das listas vazias com os retornos, riscos e pesos dos portfólios"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for single_portfolio in range(num_portfolios):\n",
    "\n",
    "    weights = np.random.random(num_assets)\n",
    "\n",
    "    weights /= np.sum(weights)\n",
    "\n",
    "    returns = np.dot(weights, returns_annual)\n",
    "\n",
    "    volatility = np.sqrt(np.dot(weights.T, np.dot(cov_annual, weights)))\n",
    "\n",
    "    port_returns.append(returns)\n",
    "\n",
    "    port_volatility.append(volatility)\n",
    "\n",
    "    stock_weights.append(weights)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Um dicionário para retornos e riscos de cada portfólio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "portfolio = {'Retornos': port_returns,\n",
    "\n",
    "             'Volatilidade': port_volatility}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ampliando o dicionário original para conter cada código de ação e peso no portfólio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for counter,symbol in enumerate(selected):\n",
    "\n",
    "    portfolio[symbol+' peso'] = [weight[counter] for weight in stock_weights]\n",
    "    \n",
    "df = pd.DataFrame(portfolio)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Organizando a tabela para visualização"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Retornos</th>\n",
       "      <th>Volatilidade</th>\n",
       "      <th>ABEV3 peso</th>\n",
       "      <th>EQTL3 peso</th>\n",
       "      <th>EZTC3 peso</th>\n",
       "      <th>FLRY3 peso</th>\n",
       "      <th>LREN3 peso</th>\n",
       "      <th>RADL3 peso</th>\n",
       "      <th>RENT3 peso</th>\n",
       "      <th>WEGE3 peso</th>\n",
       "      <th>CIEL3 peso</th>\n",
       "      <th>MDIA3 peso</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.242499</td>\n",
       "      <td>0.166850</td>\n",
       "      <td>0.169228</td>\n",
       "      <td>0.009380</td>\n",
       "      <td>0.072200</td>\n",
       "      <td>0.073226</td>\n",
       "      <td>0.142212</td>\n",
       "      <td>0.174580</td>\n",
       "      <td>0.112357</td>\n",
       "      <td>0.045092</td>\n",
       "      <td>0.070162</td>\n",
       "      <td>0.131562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.239623</td>\n",
       "      <td>0.186454</td>\n",
       "      <td>0.128558</td>\n",
       "      <td>0.095533</td>\n",
       "      <td>0.027492</td>\n",
       "      <td>0.182853</td>\n",
       "      <td>0.021966</td>\n",
       "      <td>0.176298</td>\n",
       "      <td>0.011599</td>\n",
       "      <td>0.121802</td>\n",
       "      <td>0.122079</td>\n",
       "      <td>0.111818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.229810</td>\n",
       "      <td>0.159898</td>\n",
       "      <td>0.075204</td>\n",
       "      <td>0.068327</td>\n",
       "      <td>0.128083</td>\n",
       "      <td>0.119485</td>\n",
       "      <td>0.183804</td>\n",
       "      <td>0.025998</td>\n",
       "      <td>0.111891</td>\n",
       "      <td>0.109013</td>\n",
       "      <td>0.051095</td>\n",
       "      <td>0.127100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.244091</td>\n",
       "      <td>0.186099</td>\n",
       "      <td>0.141386</td>\n",
       "      <td>0.032853</td>\n",
       "      <td>0.173230</td>\n",
       "      <td>0.183018</td>\n",
       "      <td>0.101992</td>\n",
       "      <td>0.138109</td>\n",
       "      <td>0.005388</td>\n",
       "      <td>0.018738</td>\n",
       "      <td>0.178034</td>\n",
       "      <td>0.027251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.235528</td>\n",
       "      <td>0.158617</td>\n",
       "      <td>0.089258</td>\n",
       "      <td>0.094774</td>\n",
       "      <td>0.099724</td>\n",
       "      <td>0.046855</td>\n",
       "      <td>0.118913</td>\n",
       "      <td>0.051433</td>\n",
       "      <td>0.099230</td>\n",
       "      <td>0.149310</td>\n",
       "      <td>0.048252</td>\n",
       "      <td>0.202250</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Retornos  Volatilidade  ABEV3 peso  EQTL3 peso  EZTC3 peso  FLRY3 peso  \\\n",
       "0  0.242499      0.166850    0.169228    0.009380    0.072200    0.073226   \n",
       "1  0.239623      0.186454    0.128558    0.095533    0.027492    0.182853   \n",
       "2  0.229810      0.159898    0.075204    0.068327    0.128083    0.119485   \n",
       "3  0.244091      0.186099    0.141386    0.032853    0.173230    0.183018   \n",
       "4  0.235528      0.158617    0.089258    0.094774    0.099724    0.046855   \n",
       "\n",
       "   LREN3 peso  RADL3 peso  RENT3 peso  WEGE3 peso  CIEL3 peso  MDIA3 peso  \n",
       "0    0.142212    0.174580    0.112357    0.045092    0.070162    0.131562  \n",
       "1    0.021966    0.176298    0.011599    0.121802    0.122079    0.111818  \n",
       "2    0.183804    0.025998    0.111891    0.109013    0.051095    0.127100  \n",
       "3    0.101992    0.138109    0.005388    0.018738    0.178034    0.027251  \n",
       "4    0.118913    0.051433    0.099230    0.149310    0.048252    0.202250  "
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "column_order = ['Retornos', 'Volatilidade'] + [stock+' peso' for stock in selected]\n",
    "\n",
    "df = df[column_order]\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ordenando a tabela em ordem decrescente de retorno"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Retornos</th>\n",
       "      <th>Volatilidade</th>\n",
       "      <th>ABEV3 peso</th>\n",
       "      <th>EQTL3 peso</th>\n",
       "      <th>EZTC3 peso</th>\n",
       "      <th>FLRY3 peso</th>\n",
       "      <th>LREN3 peso</th>\n",
       "      <th>RADL3 peso</th>\n",
       "      <th>RENT3 peso</th>\n",
       "      <th>WEGE3 peso</th>\n",
       "      <th>CIEL3 peso</th>\n",
       "      <th>MDIA3 peso</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>281157</th>\n",
       "      <td>0.295297</td>\n",
       "      <td>0.198030</td>\n",
       "      <td>0.020767</td>\n",
       "      <td>0.010897</td>\n",
       "      <td>0.170674</td>\n",
       "      <td>0.014670</td>\n",
       "      <td>0.007832</td>\n",
       "      <td>0.316392</td>\n",
       "      <td>0.045113</td>\n",
       "      <td>0.296442</td>\n",
       "      <td>0.069533</td>\n",
       "      <td>0.047681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278615</th>\n",
       "      <td>0.293119</td>\n",
       "      <td>0.193877</td>\n",
       "      <td>0.039275</td>\n",
       "      <td>0.011457</td>\n",
       "      <td>0.315867</td>\n",
       "      <td>0.017182</td>\n",
       "      <td>0.022699</td>\n",
       "      <td>0.312873</td>\n",
       "      <td>0.027502</td>\n",
       "      <td>0.052185</td>\n",
       "      <td>0.121717</td>\n",
       "      <td>0.079243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239438</th>\n",
       "      <td>0.292424</td>\n",
       "      <td>0.197803</td>\n",
       "      <td>0.000972</td>\n",
       "      <td>0.010305</td>\n",
       "      <td>0.275526</td>\n",
       "      <td>0.050512</td>\n",
       "      <td>0.042356</td>\n",
       "      <td>0.271343</td>\n",
       "      <td>0.023849</td>\n",
       "      <td>0.122430</td>\n",
       "      <td>0.182683</td>\n",
       "      <td>0.020022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210615</th>\n",
       "      <td>0.288401</td>\n",
       "      <td>0.195096</td>\n",
       "      <td>0.007726</td>\n",
       "      <td>0.006312</td>\n",
       "      <td>0.278966</td>\n",
       "      <td>0.060421</td>\n",
       "      <td>0.073943</td>\n",
       "      <td>0.309158</td>\n",
       "      <td>0.059380</td>\n",
       "      <td>0.034456</td>\n",
       "      <td>0.132300</td>\n",
       "      <td>0.037338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201982</th>\n",
       "      <td>0.287702</td>\n",
       "      <td>0.205506</td>\n",
       "      <td>0.029966</td>\n",
       "      <td>0.012670</td>\n",
       "      <td>0.070382</td>\n",
       "      <td>0.022993</td>\n",
       "      <td>0.016282</td>\n",
       "      <td>0.247480</td>\n",
       "      <td>0.005272</td>\n",
       "      <td>0.264827</td>\n",
       "      <td>0.250625</td>\n",
       "      <td>0.079502</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Retornos  Volatilidade  ABEV3 peso  EQTL3 peso  EZTC3 peso  \\\n",
       "281157  0.295297      0.198030    0.020767    0.010897    0.170674   \n",
       "278615  0.293119      0.193877    0.039275    0.011457    0.315867   \n",
       "239438  0.292424      0.197803    0.000972    0.010305    0.275526   \n",
       "210615  0.288401      0.195096    0.007726    0.006312    0.278966   \n",
       "201982  0.287702      0.205506    0.029966    0.012670    0.070382   \n",
       "\n",
       "        FLRY3 peso  LREN3 peso  RADL3 peso  RENT3 peso  WEGE3 peso  \\\n",
       "281157    0.014670    0.007832    0.316392    0.045113    0.296442   \n",
       "278615    0.017182    0.022699    0.312873    0.027502    0.052185   \n",
       "239438    0.050512    0.042356    0.271343    0.023849    0.122430   \n",
       "210615    0.060421    0.073943    0.309158    0.059380    0.034456   \n",
       "201982    0.022993    0.016282    0.247480    0.005272    0.264827   \n",
       "\n",
       "        CIEL3 peso  MDIA3 peso  \n",
       "281157    0.069533    0.047681  \n",
       "278615    0.121717    0.079243  \n",
       "239438    0.182683    0.020022  \n",
       "210615    0.132300    0.037338  \n",
       "201982    0.250625    0.079502  "
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "retornos = df.sort_values(by=['Retornos'], ascending=False)\n",
    "\n",
    "retornos.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotando a fronteira eficiente"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qieySYy0xtCtclZQBwmxOotXCwGG3wG6zYCyRzDhEp10EotzuKp2JrLA0GgbN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\nAnhdDtz29eUYGMgubJOpYxrItm/fDlEU8bOf/Qx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FHVd/Aj/e/iaiiVR2wLhqv6Cfx93X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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ea3269e438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('seaborn')\n",
    "\n",
    "df.plot.scatter(x='Volatilidade', y='Retornos', figsize=(10, 8), grid=True)\n",
    "\n",
    "plt.xlabel('Volatilidade (Desvio-padrão)')\n",
    "\n",
    "plt.ylabel('Retornos Esperados')\n",
    "\n",
    "plt.title('Fronteira Eficiente')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}