data_treatment.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Tratamento de Dados das Ações**\n",
    "\n",
    "- Diogo Nunes Batista\n",
    "- Rafael de Oliveira Évora\n",
    "- Vinicius Gabriel \n",
    "\n",
    "### Carregamento dos dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(            ABERTURA  FECHAMENTO  VARIAÇÃO  MÍNIMO  MÁXIMO   VOLUME\n",
       " DATA                                                               \n",
       " 14/11/2024      1.40        1.35     -4.26    1.35    1.43  105,41M\n",
       " 13/11/2024      1.47        1.41      0.71    1.36    1.42   95,29M\n",
       " 12/11/2024      1.36        1.40     -5.41    1.40    1.49  105,82M\n",
       " 11/11/2024      1.36        1.48      8.82    1.35    1.49   60,08M\n",
       " 08/11/2024      1.53        1.36     -4.23    1.34    1.50  208,62M\n",
       " ...              ...         ...       ...     ...     ...      ...\n",
       " 08/01/2024      3.31        3.37      0.90    3.30    3.42   52,68M\n",
       " 05/01/2024      3.26        3.34      2.77    3.21    3.40   80,45M\n",
       " 04/01/2024      3.36        3.25     -3.56    3.22    3.36   88,91M\n",
       " 03/01/2024      3.35        3.37      0.30    3.29    3.39   63,97M\n",
       " 02/01/2024      3.48        3.36     -3.72    3.36    3.48   82,02M\n",
       " \n",
       " [222 rows x 6 columns],\n",
       "                valor\n",
       " data                \n",
       " 03/01/2022  0.034749\n",
       " 04/01/2022  0.034749\n",
       " 05/01/2022  0.034749\n",
       " 06/01/2022  0.034749\n",
       " 07/01/2022  0.034749\n",
       " ...              ...\n",
       " 08/11/2024  0.041957\n",
       " 11/11/2024  0.041957\n",
       " 12/11/2024  0.041957\n",
       " 13/11/2024  0.041957\n",
       " 14/11/2024  0.041957\n",
       " \n",
       " [723 rows x 1 columns])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas\n",
    "import numpy as np\n",
    "\n",
    "df_stocks_types = {\n",
    "    'ABERTURA': np.float64,\n",
    "    'FECHAMENTO': np.float64,\n",
    "    'VARIAÇÃO': np.float64,\n",
    "    'MÍNIMO': np.float64,\n",
    "    'MÁXIMO': np.float64,\n",
    "    'VOLUME': str\n",
    "}\n",
    "\n",
    "df_selic_types = {\n",
    "    'valor': np.float64,\n",
    "}\n",
    "\n",
    "STOCK_NAME = 'COGN3'\n",
    "\n",
    "df_stocks = pandas.read_csv(f'data/raw/{STOCK_NAME}.csv', index_col=\"DATA\" , dtype=df_stocks_types, decimal=',')\n",
    "\n",
    "df_selic = pandas.read_csv('data/raw/selic.csv', sep=';', index_col=\"data\",  dtype=df_selic_types , decimal=',')\n",
    "\n",
    "df_stocks, df_selic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tratamento dos dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>ABERTURA</th>\n",
       "      <th>FECHAMENTO</th>\n",
       "      <th>VARIAÇÃO</th>\n",
       "      <th>MÍNIMO</th>\n",
       "      <th>MÁXIMO</th>\n",
       "      <th>VOLUME</th>\n",
       "      <th>SELIC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>02/01/2024</th>\n",
       "      <td>3.48</td>\n",
       "      <td>3.36</td>\n",
       "      <td>-3.72</td>\n",
       "      <td>3.36</td>\n",
       "      <td>3.48</td>\n",
       "      <td>82.02</td>\n",
       "      <td>0.043739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>03/01/2024</th>\n",
       "      <td>3.35</td>\n",
       "      <td>3.37</td>\n",
       "      <td>0.30</td>\n",
       "      <td>3.29</td>\n",
       "      <td>3.39</td>\n",
       "      <td>63.97</td>\n",
       "      <td>0.043739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>04/01/2024</th>\n",
       "      <td>3.36</td>\n",
       "      <td>3.25</td>\n",
       "      <td>-3.56</td>\n",
       "      <td>3.22</td>\n",
       "      <td>3.36</td>\n",
       "      <td>88.91</td>\n",
       "      <td>0.043739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05/01/2024</th>\n",
       "      <td>3.26</td>\n",
       "      <td>3.34</td>\n",
       "      <td>2.77</td>\n",
       "      <td>3.21</td>\n",
       "      <td>3.40</td>\n",
       "      <td>80.45</td>\n",
       "      <td>0.043739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08/01/2024</th>\n",
       "      <td>3.31</td>\n",
       "      <td>3.37</td>\n",
       "      <td>0.90</td>\n",
       "      <td>3.30</td>\n",
       "      <td>3.42</td>\n",
       "      <td>52.68</td>\n",
       "      <td>0.043739</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>08/11/2024</th>\n",
       "      <td>1.53</td>\n",
       "      <td>1.36</td>\n",
       "      <td>-4.23</td>\n",
       "      <td>1.34</td>\n",
       "      <td>1.50</td>\n",
       "      <td>208.62</td>\n",
       "      <td>0.041957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11/11/2024</th>\n",
       "      <td>1.36</td>\n",
       "      <td>1.48</td>\n",
       "      <td>8.82</td>\n",
       "      <td>1.35</td>\n",
       "      <td>1.49</td>\n",
       "      <td>60.08</td>\n",
       "      <td>0.041957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12/11/2024</th>\n",
       "      <td>1.36</td>\n",
       "      <td>1.40</td>\n",
       "      <td>-5.41</td>\n",
       "      <td>1.40</td>\n",
       "      <td>1.49</td>\n",
       "      <td>105.82</td>\n",
       "      <td>0.041957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13/11/2024</th>\n",
       "      <td>1.47</td>\n",
       "      <td>1.41</td>\n",
       "      <td>0.71</td>\n",
       "      <td>1.36</td>\n",
       "      <td>1.42</td>\n",
       "      <td>95.29</td>\n",
       "      <td>0.041957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14/11/2024</th>\n",
       "      <td>1.40</td>\n",
       "      <td>1.35</td>\n",
       "      <td>-4.26</td>\n",
       "      <td>1.35</td>\n",
       "      <td>1.43</td>\n",
       "      <td>105.41</td>\n",
       "      <td>0.041957</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>222 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            ABERTURA  FECHAMENTO  VARIAÇÃO  MÍNIMO  MÁXIMO  VOLUME     SELIC\n",
       "02/01/2024      3.48        3.36     -3.72    3.36    3.48   82.02  0.043739\n",
       "03/01/2024      3.35        3.37      0.30    3.29    3.39   63.97  0.043739\n",
       "04/01/2024      3.36        3.25     -3.56    3.22    3.36   88.91  0.043739\n",
       "05/01/2024      3.26        3.34      2.77    3.21    3.40   80.45  0.043739\n",
       "08/01/2024      3.31        3.37      0.90    3.30    3.42   52.68  0.043739\n",
       "...              ...         ...       ...     ...     ...     ...       ...\n",
       "08/11/2024      1.53        1.36     -4.23    1.34    1.50  208.62  0.041957\n",
       "11/11/2024      1.36        1.48      8.82    1.35    1.49   60.08  0.041957\n",
       "12/11/2024      1.36        1.40     -5.41    1.40    1.49  105.82  0.041957\n",
       "13/11/2024      1.47        1.41      0.71    1.36    1.42   95.29  0.041957\n",
       "14/11/2024      1.40        1.35     -4.26    1.35    1.43  105.41  0.041957\n",
       "\n",
       "[222 rows x 7 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_selic.rename(columns={'valor': 'SELIC'}, inplace=True)\n",
    "df_selic['SELIC'] = df_selic['SELIC']\n",
    "\n",
    "df_stocks['VOLUME'] = df_stocks['VOLUME'].str.replace('M', '').str.replace(',', '.').astype(np.float64) # Remove 'M' e subtitui ',' por '.' o que possibilita a conversão para float\n",
    "\n",
    "df = df_stocks.join(df_selic, how='inner') # https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.join.html\n",
    "\n",
    "df = df.iloc[::-1]\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotando"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vinic\\AppData\\Local\\Temp\\ipykernel_17396\\2736737018.py:2: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
      "  ax.get_figure().show()\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df.plot(y='FECHAMENTO', xlabel='Data', ylabel='Valor', fontsize=8)\n",
    "ax.get_figure().show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Normalização"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[['ABERTURA', 'FECHAMENTO', 'MÍNIMO', 'MÁXIMO']] = df[['ABERTURA', 'FECHAMENTO', 'MÍNIMO', 'MÁXIMO']].div(100.0)\n",
    "df['VOLUME'] = df['VOLUME'].div(1000.0)\n",
    "df['VARIAÇÃO'] = df['VARIAÇÃO'].add(100.0).div(1000.0)\n",
    "\n",
    "df.to_csv(f'data/final/{STOCK_NAME}.csv', index_label='DATA', sep=';', decimal='.')"
   ]
  }
 ],
 "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": "3.13.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}