5_libs_para_coletar_dados_de_acoes.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5 bibliotecas para coletar dados do mercado financeiro"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Yahoo Finance\n",
    "- Dados da cotação de ações brasileiras e estrangeiras\n",
    "- Balanço patrimonial das empresas e o fluxo de caixa\n",
    "- Informações estatísticas e distribuição acionárias\n",
    "- Cotação do dolar e indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import yfinance as yf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Definir um ticker\n",
    "tickerSymbol = 'TAEE11.SA'\n",
    "tickerData = yf.Ticker(tickerSymbol)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "yfinance.Ticker object <TAEE11.SA>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tickerData"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# consultar os dados históricos\n",
    "tickerDf = tickerData.history(interval='1d',\n",
    "                              start='2010-01-01',\n",
    "                              end='2021-01-01')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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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>Volume</th>\n",
       "      <th>Dividends</th>\n",
       "      <th>Stock Splits</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>2010-01-04</th>\n",
       "      <td>4.440037</td>\n",
       "      <td>4.442407</td>\n",
       "      <td>4.432925</td>\n",
       "      <td>4.441222</td>\n",
       "      <td>133500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>4.432927</td>\n",
       "      <td>4.442409</td>\n",
       "      <td>4.432927</td>\n",
       "      <td>4.442409</td>\n",
       "      <td>343800</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>4.442409</td>\n",
       "      <td>4.442409</td>\n",
       "      <td>4.432927</td>\n",
       "      <td>4.442409</td>\n",
       "      <td>489900</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>4.442409</td>\n",
       "      <td>4.442409</td>\n",
       "      <td>4.436483</td>\n",
       "      <td>4.442409</td>\n",
       "      <td>130500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-08</th>\n",
       "      <td>4.441223</td>\n",
       "      <td>4.441223</td>\n",
       "      <td>4.427000</td>\n",
       "      <td>4.432926</td>\n",
       "      <td>1853400</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-22</th>\n",
       "      <td>31.589835</td>\n",
       "      <td>31.762878</td>\n",
       "      <td>31.436020</td>\n",
       "      <td>31.685968</td>\n",
       "      <td>1230800</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-23</th>\n",
       "      <td>31.724424</td>\n",
       "      <td>31.887851</td>\n",
       "      <td>31.464860</td>\n",
       "      <td>31.676357</td>\n",
       "      <td>997100</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-28</th>\n",
       "      <td>31.974370</td>\n",
       "      <td>32.349298</td>\n",
       "      <td>31.878237</td>\n",
       "      <td>32.330070</td>\n",
       "      <td>738500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-29</th>\n",
       "      <td>32.387749</td>\n",
       "      <td>32.387749</td>\n",
       "      <td>32.080119</td>\n",
       "      <td>32.272388</td>\n",
       "      <td>967700</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-30</th>\n",
       "      <td>32.320457</td>\n",
       "      <td>32.358911</td>\n",
       "      <td>31.926305</td>\n",
       "      <td>32.108963</td>\n",
       "      <td>1131600</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2725 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Open       High        Low      Close   Volume  Dividends  \\\n",
       "Date                                                                         \n",
       "2010-01-04   4.440037   4.442407   4.432925   4.441222   133500        0.0   \n",
       "2010-01-05   4.432927   4.442409   4.432927   4.442409   343800        0.0   \n",
       "2010-01-06   4.442409   4.442409   4.432927   4.442409   489900        0.0   \n",
       "2010-01-07   4.442409   4.442409   4.436483   4.442409   130500        0.0   \n",
       "2010-01-08   4.441223   4.441223   4.427000   4.432926  1853400        0.0   \n",
       "...               ...        ...        ...        ...      ...        ...   \n",
       "2020-12-22  31.589835  31.762878  31.436020  31.685968  1230800        0.0   \n",
       "2020-12-23  31.724424  31.887851  31.464860  31.676357   997100        0.0   \n",
       "2020-12-28  31.974370  32.349298  31.878237  32.330070   738500        0.0   \n",
       "2020-12-29  32.387749  32.387749  32.080119  32.272388   967700        0.0   \n",
       "2020-12-30  32.320457  32.358911  31.926305  32.108963  1131600        0.0   \n",
       "\n",
       "            Stock Splits  \n",
       "Date                      \n",
       "2010-01-04           0.0  \n",
       "2010-01-05           0.0  \n",
       "2010-01-06           0.0  \n",
       "2010-01-07           0.0  \n",
       "2010-01-08           0.0  \n",
       "...                  ...  \n",
       "2020-12-22           0.0  \n",
       "2020-12-23           0.0  \n",
       "2020-12-28           0.0  \n",
       "2020-12-29           0.0  \n",
       "2020-12-30           0.0  \n",
       "\n",
       "[2725 rows x 7 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tickerDf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Investpy\n",
    "- dados do investing.com\n",
    "    - 39952 ações (nacionais e internacionais)\n",
    "    - 82221 fundos\n",
    "    - 11403 ETFs\n",
    "    - 7797 indíces\n",
    "    - 2029 cruzamento de moedas "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import investpy as iv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = iv.get_stock_historical_data(stock='ITSA4',\n",
    "                                  country='brazil',\n",
    "                                  from_date='01/01/2010', \n",
    "                                  to_date='01/01/2021',\n",
    "                                  interval='Daily')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Currency</th>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <th></th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-04</th>\n",
       "      <td>3.93</td>\n",
       "      <td>4.09</td>\n",
       "      <td>3.93</td>\n",
       "      <td>4.08</td>\n",
       "      <td>9705803</td>\n",
       "      <td>BRL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>4.11</td>\n",
       "      <td>4.11</td>\n",
       "      <td>4.03</td>\n",
       "      <td>4.07</td>\n",
       "      <td>9236729</td>\n",
       "      <td>BRL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>4.06</td>\n",
       "      <td>4.09</td>\n",
       "      <td>4.01</td>\n",
       "      <td>4.02</td>\n",
       "      <td>9304605</td>\n",
       "      <td>BRL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>4.01</td>\n",
       "      <td>4.02</td>\n",
       "      <td>3.96</td>\n",
       "      <td>4.00</td>\n",
       "      <td>7777444</td>\n",
       "      <td>BRL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-08</th>\n",
       "      <td>3.98</td>\n",
       "      <td>4.01</td>\n",
       "      <td>3.95</td>\n",
       "      <td>3.97</td>\n",
       "      <td>8196523</td>\n",
       "      <td>BRL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-22</th>\n",
       "      <td>11.41</td>\n",
       "      <td>11.59</td>\n",
       "      <td>11.35</td>\n",
       "      <td>11.40</td>\n",
       "      <td>17251600</td>\n",
       "      <td>BRL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-23</th>\n",
       "      <td>11.46</td>\n",
       "      <td>11.72</td>\n",
       "      <td>11.40</td>\n",
       "      <td>11.67</td>\n",
       "      <td>27595400</td>\n",
       "      <td>BRL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-28</th>\n",
       "      <td>11.74</td>\n",
       "      <td>11.80</td>\n",
       "      <td>11.67</td>\n",
       "      <td>11.80</td>\n",
       "      <td>12864200</td>\n",
       "      <td>BRL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-29</th>\n",
       "      <td>11.81</td>\n",
       "      <td>11.88</td>\n",
       "      <td>11.74</td>\n",
       "      <td>11.87</td>\n",
       "      <td>16012900</td>\n",
       "      <td>BRL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-30</th>\n",
       "      <td>11.89</td>\n",
       "      <td>11.94</td>\n",
       "      <td>11.70</td>\n",
       "      <td>11.73</td>\n",
       "      <td>23948900</td>\n",
       "      <td>BRL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2721 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Open   High    Low  Close    Volume Currency\n",
       "Date                                                     \n",
       "2010-01-04   3.93   4.09   3.93   4.08   9705803      BRL\n",
       "2010-01-05   4.11   4.11   4.03   4.07   9236729      BRL\n",
       "2010-01-06   4.06   4.09   4.01   4.02   9304605      BRL\n",
       "2010-01-07   4.01   4.02   3.96   4.00   7777444      BRL\n",
       "2010-01-08   3.98   4.01   3.95   3.97   8196523      BRL\n",
       "...           ...    ...    ...    ...       ...      ...\n",
       "2020-12-22  11.41  11.59  11.35  11.40  17251600      BRL\n",
       "2020-12-23  11.46  11.72  11.40  11.67  27595400      BRL\n",
       "2020-12-28  11.74  11.80  11.67  11.80  12864200      BRL\n",
       "2020-12-29  11.81  11.88  11.74  11.87  16012900      BRL\n",
       "2020-12-30  11.89  11.94  11.70  11.73  23948900      BRL\n",
       "\n",
       "[2721 rows x 6 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Quandl\n",
    "- Dados financeiros, econômicos e alternativos \n",
    "- **link:** https://www.quandl.com/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import quandl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = open('quandl_api.txt', 'r')\n",
    "api_key = f.read()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>WAP</th>\n",
       "      <th>No. of Shares</th>\n",
       "      <th>No. of Trades</th>\n",
       "      <th>Total Turnover</th>\n",
       "      <th>Deliverable Quantity</th>\n",
       "      <th>% Deli. Qty to Traded Qty</th>\n",
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       "      <th>Spread C-O</th>\n",
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       "    <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",
       "      <th></th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1995-05-26</th>\n",
       "      <td>42.00</td>\n",
       "      <td>45.00</td>\n",
       "      <td>38.50</td>\n",
       "      <td>40.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.50</td>\n",
       "      <td>-2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-05-29</th>\n",
       "      <td>39.00</td>\n",
       "      <td>40.25</td>\n",
       "      <td>38.75</td>\n",
       "      <td>39.25</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-05-30</th>\n",
       "      <td>39.00</td>\n",
       "      <td>40.00</td>\n",
       "      <td>39.00</td>\n",
       "      <td>39.50</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-05-31</th>\n",
       "      <td>39.75</td>\n",
       "      <td>40.25</td>\n",
       "      <td>39.50</td>\n",
       "      <td>40.25</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996-04-02</th>\n",
       "      <td>33.00</td>\n",
       "      <td>35.00</td>\n",
       "      <td>33.00</td>\n",
       "      <td>34.75</td>\n",
       "      <td>33.878745</td>\n",
       "      <td>70100.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>2374900.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-07</th>\n",
       "      <td>1407.00</td>\n",
       "      <td>1424.50</td>\n",
       "      <td>1407.00</td>\n",
       "      <td>1414.60</td>\n",
       "      <td>1419.260000</td>\n",
       "      <td>421320.0</td>\n",
       "      <td>18874.0</td>\n",
       "      <td>597963233.0</td>\n",
       "      <td>252494.0</td>\n",
       "      <td>59.93</td>\n",
       "      <td>17.50</td>\n",
       "      <td>7.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-10</th>\n",
       "      <td>1426.00</td>\n",
       "      <td>1429.15</td>\n",
       "      <td>1413.05</td>\n",
       "      <td>1419.30</td>\n",
       "      <td>1419.420000</td>\n",
       "      <td>114149.0</td>\n",
       "      <td>4651.0</td>\n",
       "      <td>162025844.0</td>\n",
       "      <td>26112.0</td>\n",
       "      <td>22.88</td>\n",
       "      <td>16.10</td>\n",
       "      <td>-6.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-11</th>\n",
       "      <td>1397.90</td>\n",
       "      <td>1424.00</td>\n",
       "      <td>1396.95</td>\n",
       "      <td>1403.70</td>\n",
       "      <td>1408.440000</td>\n",
       "      <td>132214.0</td>\n",
       "      <td>5147.0</td>\n",
       "      <td>186215421.0</td>\n",
       "      <td>33226.0</td>\n",
       "      <td>25.13</td>\n",
       "      <td>27.05</td>\n",
       "      <td>5.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-12</th>\n",
       "      <td>1403.00</td>\n",
       "      <td>1408.90</td>\n",
       "      <td>1389.05</td>\n",
       "      <td>1399.50</td>\n",
       "      <td>1397.980000</td>\n",
       "      <td>255383.0</td>\n",
       "      <td>14652.0</td>\n",
       "      <td>357019991.0</td>\n",
       "      <td>74987.0</td>\n",
       "      <td>29.36</td>\n",
       "      <td>19.85</td>\n",
       "      <td>-3.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-14</th>\n",
       "      <td>1398.70</td>\n",
       "      <td>1398.70</td>\n",
       "      <td>1382.80</td>\n",
       "      <td>1387.15</td>\n",
       "      <td>1388.540000</td>\n",
       "      <td>218465.0</td>\n",
       "      <td>19767.0</td>\n",
       "      <td>303347980.0</td>\n",
       "      <td>103340.0</td>\n",
       "      <td>47.30</td>\n",
       "      <td>15.90</td>\n",
       "      <td>-11.55</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6235 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Open     High      Low    Close          WAP  No. of Shares  \\\n",
       "Date                                                                         \n",
       "1995-05-26    42.00    45.00    38.50    40.00     0.000000            0.0   \n",
       "1995-05-29    39.00    40.25    38.75    39.25     0.000000            0.0   \n",
       "1995-05-30    39.00    40.00    39.00    39.50     0.000000            0.0   \n",
       "1995-05-31    39.75    40.25    39.50    40.25     0.000000            0.0   \n",
       "1996-04-02    33.00    35.00    33.00    34.75    33.878745        70100.0   \n",
       "...             ...      ...      ...      ...          ...            ...   \n",
       "2021-05-07  1407.00  1424.50  1407.00  1414.60  1419.260000       421320.0   \n",
       "2021-05-10  1426.00  1429.15  1413.05  1419.30  1419.420000       114149.0   \n",
       "2021-05-11  1397.90  1424.00  1396.95  1403.70  1408.440000       132214.0   \n",
       "2021-05-12  1403.00  1408.90  1389.05  1399.50  1397.980000       255383.0   \n",
       "2021-05-14  1398.70  1398.70  1382.80  1387.15  1388.540000       218465.0   \n",
       "\n",
       "            No. of Trades  Total Turnover  Deliverable Quantity  \\\n",
       "Date                                                              \n",
       "1995-05-26            0.0             0.0                   NaN   \n",
       "1995-05-29            0.0             0.0                   NaN   \n",
       "1995-05-30            0.0             0.0                   NaN   \n",
       "1995-05-31            0.0             0.0                   NaN   \n",
       "1996-04-02          208.0       2374900.0                   NaN   \n",
       "...                   ...             ...                   ...   \n",
       "2021-05-07        18874.0     597963233.0              252494.0   \n",
       "2021-05-10         4651.0     162025844.0               26112.0   \n",
       "2021-05-11         5147.0     186215421.0               33226.0   \n",
       "2021-05-12        14652.0     357019991.0               74987.0   \n",
       "2021-05-14        19767.0     303347980.0              103340.0   \n",
       "\n",
       "            % Deli. Qty to Traded Qty  Spread H-L  Spread C-O  \n",
       "Date                                                           \n",
       "1995-05-26                        NaN        6.50       -2.00  \n",
       "1995-05-29                        NaN        1.50        0.25  \n",
       "1995-05-30                        NaN        1.00        0.50  \n",
       "1995-05-31                        NaN        0.75        0.50  \n",
       "1996-04-02                        NaN        2.00        1.75  \n",
       "...                               ...         ...         ...  \n",
       "2021-05-07                      59.93       17.50        7.60  \n",
       "2021-05-10                      22.88       16.10       -6.70  \n",
       "2021-05-11                      25.13       27.05        5.80  \n",
       "2021-05-12                      29.36       19.85       -3.50  \n",
       "2021-05-14                      47.30       15.90      -11.55  \n",
       "\n",
       "[6235 rows x 12 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = quandl.get(\"BSE/BOM500180\", authtoken=api_key)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = quandl.get_table('WIKI/PRICES', ticker = ['AAPL', 'MSFT'], \n",
    "                        qopts = { 'columns': ['ticker', 'date', 'adj_close'] }, \n",
    "                        date = { 'gte': '2015-12-31', 'lte': '2016-12-31' }, \n",
    "                        paginate=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>ticker</th>\n",
       "      <th>date</th>\n",
       "      <th>adj_close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>None</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>2016-12-30</td>\n",
       "      <td>60.788710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>2016-12-29</td>\n",
       "      <td>61.532183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>2016-12-28</td>\n",
       "      <td>61.620226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>2016-12-27</td>\n",
       "      <td>61.903920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>2016-12-23</td>\n",
       "      <td>61.864790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>AAPL</td>\n",
       "      <td>2016-01-07</td>\n",
       "      <td>93.185040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>AAPL</td>\n",
       "      <td>2016-01-06</td>\n",
       "      <td>97.291172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>AAPL</td>\n",
       "      <td>2016-01-05</td>\n",
       "      <td>99.233131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>AAPL</td>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>101.783763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>AAPL</td>\n",
       "      <td>2015-12-31</td>\n",
       "      <td>101.696810</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>506 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     ticker       date   adj_close\n",
       "None                              \n",
       "0      MSFT 2016-12-30   60.788710\n",
       "1      MSFT 2016-12-29   61.532183\n",
       "2      MSFT 2016-12-28   61.620226\n",
       "3      MSFT 2016-12-27   61.903920\n",
       "4      MSFT 2016-12-23   61.864790\n",
       "...     ...        ...         ...\n",
       "501    AAPL 2016-01-07   93.185040\n",
       "502    AAPL 2016-01-06   97.291172\n",
       "503    AAPL 2016-01-05   99.233131\n",
       "504    AAPL 2016-01-04  101.783763\n",
       "505    AAPL 2015-12-31  101.696810\n",
       "\n",
       "[506 rows x 3 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Pandas Reader\n",
    "- Extrair recursos da internet para um pandas dataframe\n",
    "- **link**: https://pandas-datareader.readthedocs.io/en/latest/remote_data.html\n",
    "- Resources:\n",
    "    - Yahoo Finance\n",
    "    - IEX\n",
    "    - Alpha Vantage\n",
    "    - Stooq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas_datareader.famafrench import get_available_datasets\n",
    "from pandas_datareader import data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "datasets = get_available_datasets()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['F-F_Research_Data_Factors',\n",
       " 'F-F_Research_Data_Factors_weekly',\n",
       " 'F-F_Research_Data_Factors_daily',\n",
       " 'F-F_Research_Data_5_Factors_2x3',\n",
       " 'F-F_Research_Data_5_Factors_2x3_daily',\n",
       " 'Portfolios_Formed_on_ME',\n",
       " 'Portfolios_Formed_on_ME_Wout_Div',\n",
       " 'Portfolios_Formed_on_ME_Daily',\n",
       " 'Portfolios_Formed_on_BE-ME',\n",
       " 'Portfolios_Formed_on_BE-ME_Wout_Div',\n",
       " 'Portfolios_Formed_on_BE-ME_Daily',\n",
       " 'Portfolios_Formed_on_OP',\n",
       " 'Portfolios_Formed_on_OP_Wout_Div',\n",
       " 'Portfolios_Formed_on_OP_Daily',\n",
       " 'Portfolios_Formed_on_INV',\n",
       " 'Portfolios_Formed_on_INV_Wout_Div',\n",
       " 'Portfolios_Formed_on_INV_Daily',\n",
       " '6_Portfolios_2x3',\n",
       " '6_Portfolios_2x3_Wout_Div',\n",
       " '6_Portfolios_2x3_weekly',\n",
       " '6_Portfolios_2x3_daily',\n",
       " '25_Portfolios_5x5',\n",
       " '25_Portfolios_5x5_Wout_Div',\n",
       " '25_Portfolios_5x5_Daily',\n",
       " '100_Portfolios_10x10',\n",
       " '100_Portfolios_10x10_Wout_Div',\n",
       " '100_Portfolios_10x10_Daily',\n",
       " '6_Portfolios_ME_OP_2x3',\n",
       " '6_Portfolios_ME_OP_2x3_Wout_Div',\n",
       " '6_Portfolios_ME_OP_2x3_daily',\n",
       " '25_Portfolios_ME_OP_5x5',\n",
       " '25_Portfolios_ME_OP_5x5_Wout_Div',\n",
       " '25_Portfolios_ME_OP_5x5_daily',\n",
       " '100_Portfolios_ME_OP_10x10',\n",
       " '100_Portfolios_10x10_ME_OP_Wout_Div',\n",
       " '100_Portfolios_ME_OP_10x10_daily',\n",
       " '6_Portfolios_ME_INV_2x3',\n",
       " '6_Portfolios_ME_INV_2x3_Wout_Div',\n",
       " '6_Portfolios_ME_INV_2x3_daily',\n",
       " '25_Portfolios_ME_INV_5x5',\n",
       " '25_Portfolios_ME_INV_5x5_Wout_Div',\n",
       " '25_Portfolios_ME_INV_5x5_daily',\n",
       " '100_Portfolios_ME_INV_10x10',\n",
       " '100_Portfolios_10x10_ME_INV_Wout_Div',\n",
       " '100_Portfolios_ME_INV_10x10_daily',\n",
       " '25_Portfolios_BEME_OP_5x5',\n",
       " '25_Portfolios_BEME_OP_5x5_Wout_Div',\n",
       " '25_Portfolios_BEME_OP_5x5_daily',\n",
       " '25_Portfolios_BEME_INV_5x5',\n",
       " '25_Portfolios_BEME_INV_5x5_Wout_Div',\n",
       " '25_Portfolios_BEME_INV_5x5_daily',\n",
       " '25_Portfolios_OP_INV_5x5',\n",
       " '25_Portfolios_OP_INV_5x5_Wout_Div',\n",
       " '25_Portfolios_OP_INV_5x5_daily',\n",
       " '32_Portfolios_ME_BEME_OP_2x4x4',\n",
       " '32_Portfolios_ME_BEME_OP_2x4x4_Wout_Div',\n",
       " '32_Portfolios_ME_BEME_INV_2x4x4',\n",
       " '32_Portfolios_ME_BEME_INV_2x4x4_Wout_Div',\n",
       " '32_Portfolios_ME_OP_INV_2x4x4',\n",
       " '32_Portfolios_ME_OP_INV_2x4x4_Wout_Div',\n",
       " 'Portfolios_Formed_on_E-P',\n",
       " 'Portfolios_Formed_on_E-P_Wout_Div',\n",
       " 'Portfolios_Formed_on_CF-P',\n",
       " 'Portfolios_Formed_on_CF-P_Wout_Div',\n",
       " 'Portfolios_Formed_on_D-P',\n",
       " 'Portfolios_Formed_on_D-P_Wout_Div',\n",
       " '6_Portfolios_ME_EP_2x3',\n",
       " '6_Portfolios_ME_EP_2x3_Wout_Div',\n",
       " '6_Portfolios_ME_CFP_2x3',\n",
       " '6_Portfolios_ME_CFP_2x3_Wout_Div',\n",
       " '6_Portfolios_ME_DP_2x3',\n",
       " '6_Portfolios_ME_DP_2x3_Wout_Div',\n",
       " 'F-F_Momentum_Factor',\n",
       " 'F-F_Momentum_Factor_daily',\n",
       " '6_Portfolios_ME_Prior_12_2',\n",
       " '6_Portfolios_ME_Prior_12_2_Daily',\n",
       " '25_Portfolios_ME_Prior_12_2',\n",
       " '25_Portfolios_ME_Prior_12_2_Daily',\n",
       " '10_Portfolios_Prior_12_2',\n",
       " '10_Portfolios_Prior_12_2_Daily',\n",
       " 'F-F_ST_Reversal_Factor',\n",
       " 'F-F_ST_Reversal_Factor_daily',\n",
       " '6_Portfolios_ME_Prior_1_0',\n",
       " '6_Portfolios_ME_Prior_1_0_Daily',\n",
       " '25_Portfolios_ME_Prior_1_0',\n",
       " '25_Portfolios_ME_Prior_1_0_Daily',\n",
       " '10_Portfolios_Prior_1_0',\n",
       " '10_Portfolios_Prior_1_0_Daily',\n",
       " 'F-F_LT_Reversal_Factor',\n",
       " 'F-F_LT_Reversal_Factor_daily',\n",
       " '6_Portfolios_ME_Prior_60_13',\n",
       " '6_Portfolios_ME_Prior_60_13_Daily',\n",
       " '25_Portfolios_ME_Prior_60_13',\n",
       " '25_Portfolios_ME_Prior_60_13_Daily',\n",
       " '10_Portfolios_Prior_60_13',\n",
       " '10_Portfolios_Prior_60_13_Daily',\n",
       " 'Portfolios_Formed_on_AC',\n",
       " '25_Portfolios_ME_AC_5x5',\n",
       " 'Portfolios_Formed_on_BETA',\n",
       " '25_Portfolios_ME_BETA_5x5',\n",
       " 'Portfolios_Formed_on_NI',\n",
       " '25_Portfolios_ME_NI_5x5',\n",
       " 'Portfolios_Formed_on_VAR',\n",
       " '25_Portfolios_ME_VAR_5x5',\n",
       " 'Portfolios_Formed_on_RESVAR',\n",
       " '25_Portfolios_ME_RESVAR_5x5',\n",
       " '5_Industry_Portfolios',\n",
       " '5_Industry_Portfolios_Wout_Div',\n",
       " '5_Industry_Portfolios_daily',\n",
       " '10_Industry_Portfolios',\n",
       " '10_Industry_Portfolios_Wout_Div',\n",
       " '10_Industry_Portfolios_daily',\n",
       " '12_Industry_Portfolios',\n",
       " '12_Industry_Portfolios_Wout_Div',\n",
       " '12_Industry_Portfolios_daily',\n",
       " '17_Industry_Portfolios',\n",
       " '17_Industry_Portfolios_Wout_Div',\n",
       " '17_Industry_Portfolios_daily',\n",
       " '30_Industry_Portfolios',\n",
       " '30_Industry_Portfolios_Wout_Div',\n",
       " '30_Industry_Portfolios_daily',\n",
       " '38_Industry_Portfolios',\n",
       " '38_Industry_Portfolios_Wout_Div',\n",
       " '38_Industry_Portfolios_daily',\n",
       " '48_Industry_Portfolios',\n",
       " '48_Industry_Portfolios_Wout_Div',\n",
       " '48_Industry_Portfolios_daily',\n",
       " '49_Industry_Portfolios',\n",
       " '49_Industry_Portfolios_Wout_Div',\n",
       " '49_Industry_Portfolios_daily',\n",
       " 'ME_Breakpoints',\n",
       " 'BE-ME_Breakpoints',\n",
       " 'OP_Breakpoints',\n",
       " 'INV_Breakpoints',\n",
       " 'E-P_Breakpoints',\n",
       " 'CF-P_Breakpoints',\n",
       " 'D-P_Breakpoints',\n",
       " 'Prior_2-12_Breakpoints',\n",
       " 'Developed_3_Factors',\n",
       " 'Developed_3_Factors_Daily',\n",
       " 'Developed_ex_US_3_Factors',\n",
       " 'Developed_ex_US_3_Factors_Daily',\n",
       " 'Europe_3_Factors',\n",
       " 'Europe_3_Factors_Daily',\n",
       " 'Japan_3_Factors',\n",
       " 'Japan_3_Factors_Daily',\n",
       " 'Asia_Pacific_ex_Japan_3_Factors',\n",
       " 'Asia_Pacific_ex_Japan_3_Factors_Daily',\n",
       " 'North_America_3_Factors',\n",
       " 'North_America_3_Factors_Daily',\n",
       " 'Developed_5_Factors',\n",
       " 'Developed_5_Factors_Daily',\n",
       " 'Developed_ex_US_5_Factors',\n",
       " 'Developed_ex_US_5_Factors_Daily',\n",
       " 'Europe_5_Factors',\n",
       " 'Europe_5_Factors_Daily',\n",
       " 'Japan_5_Factors',\n",
       " 'Japan_5_Factors_Daily',\n",
       " 'Asia_Pacific_ex_Japan_5_Factors',\n",
       " 'Asia_Pacific_ex_Japan_5_Factors_Daily',\n",
       " 'North_America_5_Factors',\n",
       " 'North_America_5_Factors_Daily',\n",
       " 'Developed_Mom_Factor',\n",
       " 'Developed_Mom_Factor_Daily',\n",
       " 'Developed_ex_US_Mom_Factor',\n",
       " 'Developed_ex_US_Mom_Factor_Daily',\n",
       " 'Europe_Mom_Factor',\n",
       " 'Europe_Mom_Factor_Daily',\n",
       " 'Japan_Mom_Factor',\n",
       " 'Japan_Mom_Factor_Daily',\n",
       " 'Asia_Pacific_ex_Japan_MOM_Factor',\n",
       " 'Asia_Pacific_ex_Japan_MOM_Factor_Daily',\n",
       " 'North_America_Mom_Factor',\n",
       " 'North_America_Mom_Factor_Daily',\n",
       " 'Developed_6_Portfolios_ME_BE-ME',\n",
       " 'Developed_6_Portfolios_ME_BE-ME_daily',\n",
       " 'Developed_ex_US_6_Portfolios_ME_BE-ME',\n",
       " 'Developed_ex_US_6_Portfolios_ME_BE-ME_daily',\n",
       " 'Europe_6_Portfolios_ME_BE-ME',\n",
       " 'Europe_6_Portfolios_ME_BE-ME_daily',\n",
       " 'Japan_6_Portfolios_ME_BE-ME',\n",
       " 'Japan_6_Portfolios_ME_BE-ME_daily',\n",
       " 'Asia_Pacific_ex_Japan_6_Portfolios_ME_BE-ME',\n",
       " 'Asia_Pacific_ex_Japan_6_Portfolios_ME_BE-ME_daily',\n",
       " 'North_America_6_Portfolios_ME_BE-ME',\n",
       " 'North_America_6_Portfolios_ME_BE-ME_daily',\n",
       " 'Developed_25_Portfolios_ME_BE-ME',\n",
       " 'Developed_25_Portfolios_ME_BE-ME_daily',\n",
       " 'Developed_ex_US_25_Portfolios_ME_BE-ME',\n",
       " 'Developed_ex_US_25_Portfolios_ME_BE-ME_daily',\n",
       " 'Europe_25_Portfolios_ME_BE-ME',\n",
       " 'Europe_25_Portfolios_ME_BE-ME_daily',\n",
       " 'Japan_25_Portfolios_ME_BE-ME',\n",
       " 'Japan_25_Portfolios_ME_BE-ME_daily',\n",
       " 'Asia_Pacific_ex_Japan_25_Portfolios_ME_BE-ME',\n",
       " 'Asia_Pacific_ex_Japan_25_Portfolios_ME_BE-ME_daily',\n",
       " 'North_America_25_Portfolios_ME_BE-ME',\n",
       " 'North_America_25_Portfolios_ME_BE-ME_daily',\n",
       " 'Developed_6_Portfolios_ME_OP',\n",
       " 'Developed_6_Portfolios_ME_OP_Daily',\n",
       " 'Developed_ex_US_6_Portfolios_ME_OP',\n",
       " 'Developed_ex_US_6_Portfolios_ME_OP_Daily',\n",
       " 'Europe_6_Portfolios_ME_OP',\n",
       " 'Europe_6_Portfolios_ME_OP_Daily',\n",
       " 'Japan_6_Portfolios_ME_OP',\n",
       " 'Japan_6_Portfolios_ME_OP_Daily',\n",
       " 'Asia_Pacific_ex_Japan_6_Portfolios_ME_OP',\n",
       " 'Asia_Pacific_ex_Japan_6_Portfolios_ME_OP_Daily',\n",
       " 'North_America_6_Portfolios_ME_OP',\n",
       " 'North_America_6_Portfolios_ME_OP_Daily',\n",
       " 'Developed_25_Portfolios_ME_OP',\n",
       " 'Developed_25_Portfolios_ME_OP_Daily',\n",
       " 'Developed_ex_US_25_Portfolios_ME_OP',\n",
       " 'Developed_ex_US_25_Portfolios_ME_OP_Daily',\n",
       " 'Europe_25_Portfolios_ME_OP',\n",
       " 'Europe_25_Portfolios_ME_OP_Daily',\n",
       " 'Japan_25_Portfolios_ME_OP',\n",
       " 'Japan_25_Portfolios_ME_OP_Daily',\n",
       " 'Asia_Pacific_ex_Japan_25_Portfolios_ME_OP',\n",
       " 'Asia_Pacific_ex_Japan_25_Portfolios_ME_OP_Daily',\n",
       " 'North_America_25_Portfolios_ME_OP',\n",
       " 'North_America_25_Portfolios_ME_OP_Daily',\n",
       " 'Developed_6_Portfolios_ME_INV',\n",
       " 'Developed_6_Portfolios_ME_INV_Daily',\n",
       " 'Developed_ex_US_6_Portfolios_ME_INV',\n",
       " 'Developed_ex_US_6_Portfolios_ME_INV_Daily',\n",
       " 'Europe_6_Portfolios_ME_INV',\n",
       " 'Europe_6_Portfolios_ME_INV_Daily',\n",
       " 'Japan_6_Portfolios_ME_INV',\n",
       " 'Japan_6_Portfolios_ME_INV_Daily',\n",
       " 'Asia_Pacific_ex_Japan_6_Portfolios_ME_INV',\n",
       " 'Asia_Pacific_ex_Japan_6_Portfolios_ME_INV_Daily',\n",
       " 'North_America_6_Portfolios_ME_INV',\n",
       " 'North_America_6_Portfolios_ME_INV_Daily',\n",
       " 'Developed_25_Portfolios_ME_INV',\n",
       " 'Developed_25_Portfolios_ME_INV_Daily',\n",
       " 'Developed_ex_US_25_Portfolios_ME_INV',\n",
       " 'Developed_ex_US_25_Portfolios_ME_INV_Daily',\n",
       " 'Europe_25_Portfolios_ME_INV',\n",
       " 'Europe_25_Portfolios_ME_INV_Daily',\n",
       " 'Japan_25_Portfolios_ME_INV',\n",
       " 'Japan_25_Portfolios_ME_INV_Daily',\n",
       " 'Asia_Pacific_ex_Japan_25_Portfolios_ME_INV',\n",
       " 'Asia_Pacific_ex_Japan_25_Portfolios_ME_INV_Daily',\n",
       " 'North_America_25_Portfolios_ME_INV',\n",
       " 'North_America_25_Portfolios_ME_INV_Daily',\n",
       " 'Developed_6_Portfolios_ME_Prior_12_2',\n",
       " 'Developed_6_Portfolios_ME_Prior_250_20_daily',\n",
       " 'Developed_ex_US_6_Portfolios_ME_Prior_12_2',\n",
       " 'Developed_ex_US_6_Portfolios_ME_Prior_250_20_daily',\n",
       " 'Europe_6_Portfolios_ME_Prior_12_2',\n",
       " 'Europe_6_Portfolios_ME_Prior_250_20_daily',\n",
       " 'Japan_6_Portfolios_ME_Prior_12_2',\n",
       " 'Japan_6_Portfolios_ME_Prior_250_20_daily',\n",
       " 'Asia_Pacific_ex_Japan_6_Portfolios_ME_Prior_12_2',\n",
       " 'Asia_Pacific_ex_Japan_6_Portfolios_ME_Prior_250_20_daily',\n",
       " 'North_America_6_Portfolios_ME_Prior_12_2',\n",
       " 'North_America_6_Portfolios_ME_Prior_250_20_daily',\n",
       " 'Developed_25_Portfolios_ME_Prior_12_2',\n",
       " 'Developed_25_Portfolios_ME_Prior_250_20_daily',\n",
       " 'Developed_ex_US_25_Portfolios_ME_Prior_12_2',\n",
       " 'Developed_ex_US_25_Portfolios_ME_Prior_250_20_daily',\n",
       " 'Europe_25_Portfolios_ME_Prior_12_2',\n",
       " 'Europe_25_Portfolios_ME_Prior_250_20_daily',\n",
       " 'Japan_25_Portfolios_ME_Prior_12_2',\n",
       " 'Japan_25_Portfolios_ME_Prior_250_20_daily',\n",
       " 'Asia_Pacific_ex_Japan_25_Portfolios_ME_Prior_12_2',\n",
       " 'Asia_Pacific_ex_Japan_25_Portfolios_ME_Prior_250_20_daily',\n",
       " 'North_America_25_Portfolios_ME_Prior_12_2',\n",
       " 'North_America_25_Portfolios_ME_Prior_250_20_daily',\n",
       " 'Developed_32_Portfolios_ME_BE-ME_OP_2x4x4',\n",
       " 'Developed_ex_US_32_Portfolios_ME_BE-ME_OP_2x4x4',\n",
       " 'Europe_32_Portfolios_ME_BE-ME_OP_2x4x4',\n",
       " 'Japan_32_Portfolios_ME_BE-ME_OP_2x4x4',\n",
       " 'Asia_Pacific_ex_Japan_32_Portfolios_ME_BE-ME_OP_2x4x4',\n",
       " 'North_America_32_Portfolios_ME_BE-ME_OP_2x4x4',\n",
       " 'Developed_32_Portfolios_ME_BE-ME_INV(TA)_2x4x4',\n",
       " 'Developed_ex_US_32_Portfolios_ME_BE-ME_INV(TA)_2x4x4',\n",
       " 'Europe_32_Portfolios_ME_BE-ME_INV(TA)_2x4x4',\n",
       " 'Japan_32_Portfolios_ME_BE-ME_INV(TA)_2x4x4',\n",
       " 'Asia_Pacific_ex_Japan_32_Portfolios_ME_BE-ME_INV(TA)_2x4x4',\n",
       " 'North_America_32_Portfolios_ME_BE-ME_INV(TA)_2x4x4',\n",
       " 'Developed_32_Portfolios_ME_INV(TA)_OP_2x4x4',\n",
       " 'Developed_ex_US_32_Portfolios_ME_INV(TA)_OP_2x4x4',\n",
       " 'Europe_32_Portfolios_ME_INV(TA)_OP_2x4x4',\n",
       " 'Japan_32_Portfolios_ME_INV(TA)_OP_2x4x4',\n",
       " 'Asia_Pacific_ex_Japan_32_Portfolios_ME_INV(TA)_OP_2x4x4',\n",
       " 'North_America_32_Portfolios_ME_INV(TA)_OP_2x4x4',\n",
       " 'Emerging_5_Factors',\n",
       " 'Emerging_MOM_Factor',\n",
       " 'Emerging_Markets_6_Portfolios_ME_BE-ME',\n",
       " 'Emerging_Markets_6_Portfolios_ME_OP',\n",
       " 'Emerging_Markets_6_Portfolios_ME_INV',\n",
       " 'Emerging_Markets_6_Portfolios_ME_Prior_12_2',\n",
       " 'Emerging_Markets_4_Portfolios_BE-ME_OP',\n",
       " 'Emerging_Markets_4_Portfolios_OP_INV',\n",
       " 'Emerging_Markets_4_Portfolios_BE-ME_INV']"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "tickers = 'AAPL'\n",
    "\n",
    "start_date = '2010-01-01'\n",
    "end_date = '2021-01-01'\n",
    "\n",
    "dados = data.DataReader(tickers, 'yahoo', start_date, end_date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</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>2010-01-04</th>\n",
       "      <td>7.660714</td>\n",
       "      <td>7.585000</td>\n",
       "      <td>7.622500</td>\n",
       "      <td>7.643214</td>\n",
       "      <td>493729600.0</td>\n",
       "      <td>6.572422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>7.699643</td>\n",
       "      <td>7.616071</td>\n",
       "      <td>7.664286</td>\n",
       "      <td>7.656429</td>\n",
       "      <td>601904800.0</td>\n",
       "      <td>6.583784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>7.686786</td>\n",
       "      <td>7.526786</td>\n",
       "      <td>7.656429</td>\n",
       "      <td>7.534643</td>\n",
       "      <td>552160000.0</td>\n",
       "      <td>6.479061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>7.571429</td>\n",
       "      <td>7.466071</td>\n",
       "      <td>7.562500</td>\n",
       "      <td>7.520714</td>\n",
       "      <td>477131200.0</td>\n",
       "      <td>6.467082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-08</th>\n",
       "      <td>7.571429</td>\n",
       "      <td>7.466429</td>\n",
       "      <td>7.510714</td>\n",
       "      <td>7.570714</td>\n",
       "      <td>447610800.0</td>\n",
       "      <td>6.510078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-24</th>\n",
       "      <td>133.460007</td>\n",
       "      <td>131.100006</td>\n",
       "      <td>131.320007</td>\n",
       "      <td>131.970001</td>\n",
       "      <td>54930100.0</td>\n",
       "      <td>131.549637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-28</th>\n",
       "      <td>137.339996</td>\n",
       "      <td>133.509995</td>\n",
       "      <td>133.990005</td>\n",
       "      <td>136.690002</td>\n",
       "      <td>124486200.0</td>\n",
       "      <td>136.254608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-29</th>\n",
       "      <td>138.789993</td>\n",
       "      <td>134.339996</td>\n",
       "      <td>138.050003</td>\n",
       "      <td>134.869995</td>\n",
       "      <td>121047300.0</td>\n",
       "      <td>134.440399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-30</th>\n",
       "      <td>135.990005</td>\n",
       "      <td>133.399994</td>\n",
       "      <td>135.580002</td>\n",
       "      <td>133.720001</td>\n",
       "      <td>96452100.0</td>\n",
       "      <td>133.294067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-31</th>\n",
       "      <td>134.740005</td>\n",
       "      <td>131.720001</td>\n",
       "      <td>134.080002</td>\n",
       "      <td>132.690002</td>\n",
       "      <td>99116600.0</td>\n",
       "      <td>132.267349</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2769 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  High         Low        Open       Close       Volume  \\\n",
       "Date                                                                      \n",
       "2010-01-04    7.660714    7.585000    7.622500    7.643214  493729600.0   \n",
       "2010-01-05    7.699643    7.616071    7.664286    7.656429  601904800.0   \n",
       "2010-01-06    7.686786    7.526786    7.656429    7.534643  552160000.0   \n",
       "2010-01-07    7.571429    7.466071    7.562500    7.520714  477131200.0   \n",
       "2010-01-08    7.571429    7.466429    7.510714    7.570714  447610800.0   \n",
       "...                ...         ...         ...         ...          ...   \n",
       "2020-12-24  133.460007  131.100006  131.320007  131.970001   54930100.0   \n",
       "2020-12-28  137.339996  133.509995  133.990005  136.690002  124486200.0   \n",
       "2020-12-29  138.789993  134.339996  138.050003  134.869995  121047300.0   \n",
       "2020-12-30  135.990005  133.399994  135.580002  133.720001   96452100.0   \n",
       "2020-12-31  134.740005  131.720001  134.080002  132.690002   99116600.0   \n",
       "\n",
       "             Adj Close  \n",
       "Date                    \n",
       "2010-01-04    6.572422  \n",
       "2010-01-05    6.583784  \n",
       "2010-01-06    6.479061  \n",
       "2010-01-07    6.467082  \n",
       "2010-01-08    6.510078  \n",
       "...                ...  \n",
       "2020-12-24  131.549637  \n",
       "2020-12-28  136.254608  \n",
       "2020-12-29  134.440399  \n",
       "2020-12-30  133.294067  \n",
       "2020-12-31  132.267349  \n",
       "\n",
       "[2769 rows x 6 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Pandas Finance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas_finance import Equity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "stock = Equity('PETR4.SA')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas_finance.api.Equity at 0x7fe9f76ed190>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.792834"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock.annual_dividend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "        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>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</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>2000-01-03</th>\n",
       "      <td>5.875000</td>\n",
       "      <td>5.875000</td>\n",
       "      <td>5.875000</td>\n",
       "      <td>5.875000</td>\n",
       "      <td>3.538944e+10</td>\n",
       "      <td>3.812799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-04</th>\n",
       "      <td>5.550000</td>\n",
       "      <td>5.550000</td>\n",
       "      <td>5.550000</td>\n",
       "      <td>5.550000</td>\n",
       "      <td>2.886144e+10</td>\n",
       "      <td>3.601876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-05</th>\n",
       "      <td>5.494000</td>\n",
       "      <td>5.494000</td>\n",
       "      <td>5.494000</td>\n",
       "      <td>5.494000</td>\n",
       "      <td>4.303360e+10</td>\n",
       "      <td>3.565535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-06</th>\n",
       "      <td>5.475000</td>\n",
       "      <td>5.475000</td>\n",
       "      <td>5.475000</td>\n",
       "      <td>5.475000</td>\n",
       "      <td>3.405568e+10</td>\n",
       "      <td>3.553203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-07</th>\n",
       "      <td>5.500000</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>2.091264e+10</td>\n",
       "      <td>3.569429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-10</th>\n",
       "      <td>24.980000</td>\n",
       "      <td>24.580000</td>\n",
       "      <td>24.799999</td>\n",
       "      <td>24.700001</td>\n",
       "      <td>7.380450e+07</td>\n",
       "      <td>24.700001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-11</th>\n",
       "      <td>25.170000</td>\n",
       "      <td>24.320000</td>\n",
       "      <td>24.440001</td>\n",
       "      <td>25.150000</td>\n",
       "      <td>7.372990e+07</td>\n",
       "      <td>25.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-12</th>\n",
       "      <td>25.530001</td>\n",
       "      <td>24.690001</td>\n",
       "      <td>24.950001</td>\n",
       "      <td>24.780001</td>\n",
       "      <td>8.998070e+07</td>\n",
       "      <td>24.780001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-13</th>\n",
       "      <td>25.100000</td>\n",
       "      <td>24.430000</td>\n",
       "      <td>24.760000</td>\n",
       "      <td>24.990000</td>\n",
       "      <td>7.728210e+07</td>\n",
       "      <td>24.990000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-14</th>\n",
       "      <td>26.379999</td>\n",
       "      <td>25.549999</td>\n",
       "      <td>25.799999</td>\n",
       "      <td>26.280001</td>\n",
       "      <td>1.958086e+08</td>\n",
       "      <td>26.280001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5367 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 High        Low       Open      Close        Volume  \\\n",
       "Date                                                                   \n",
       "2000-01-03   5.875000   5.875000   5.875000   5.875000  3.538944e+10   \n",
       "2000-01-04   5.550000   5.550000   5.550000   5.550000  2.886144e+10   \n",
       "2000-01-05   5.494000   5.494000   5.494000   5.494000  4.303360e+10   \n",
       "2000-01-06   5.475000   5.475000   5.475000   5.475000  3.405568e+10   \n",
       "2000-01-07   5.500000   5.500000   5.500000   5.500000  2.091264e+10   \n",
       "...               ...        ...        ...        ...           ...   \n",
       "2021-05-10  24.980000  24.580000  24.799999  24.700001  7.380450e+07   \n",
       "2021-05-11  25.170000  24.320000  24.440001  25.150000  7.372990e+07   \n",
       "2021-05-12  25.530001  24.690001  24.950001  24.780001  8.998070e+07   \n",
       "2021-05-13  25.100000  24.430000  24.760000  24.990000  7.728210e+07   \n",
       "2021-05-14  26.379999  25.549999  25.799999  26.280001  1.958086e+08   \n",
       "\n",
       "            Adj Close  \n",
       "Date                   \n",
       "2000-01-03   3.812799  \n",
       "2000-01-04   3.601876  \n",
       "2000-01-05   3.565535  \n",
       "2000-01-06   3.553203  \n",
       "2000-01-07   3.569429  \n",
       "...               ...  \n",
       "2021-05-10  24.700001  \n",
       "2021-05-11  25.150000  \n",
       "2021-05-12  24.780001  \n",
       "2021-05-13  24.990000  \n",
       "2021-05-14  26.280001  \n",
       "\n",
       "[5367 rows x 6 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock.trading_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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