LSTM.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modelo LSTM para prever dados de ações"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, LSTM, Dropout\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import math\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('SeriesTemporais/Download/Dados/all-stocks-5yr.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        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>date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-02-08</td>\n",
       "      <td>15.07</td>\n",
       "      <td>15.12</td>\n",
       "      <td>14.63</td>\n",
       "      <td>14.75</td>\n",
       "      <td>8407500</td>\n",
       "      <td>AAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-02-11</td>\n",
       "      <td>14.89</td>\n",
       "      <td>15.01</td>\n",
       "      <td>14.26</td>\n",
       "      <td>14.46</td>\n",
       "      <td>8882000</td>\n",
       "      <td>AAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-02-12</td>\n",
       "      <td>14.45</td>\n",
       "      <td>14.51</td>\n",
       "      <td>14.10</td>\n",
       "      <td>14.27</td>\n",
       "      <td>8126000</td>\n",
       "      <td>AAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-02-13</td>\n",
       "      <td>14.30</td>\n",
       "      <td>14.94</td>\n",
       "      <td>14.25</td>\n",
       "      <td>14.66</td>\n",
       "      <td>10259500</td>\n",
       "      <td>AAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-02-14</td>\n",
       "      <td>14.94</td>\n",
       "      <td>14.96</td>\n",
       "      <td>13.16</td>\n",
       "      <td>13.99</td>\n",
       "      <td>31879900</td>\n",
       "      <td>AAL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date   open   high    low  close    volume Name\n",
       "0  2013-02-08  15.07  15.12  14.63  14.75   8407500  AAL\n",
       "1  2013-02-11  14.89  15.01  14.26  14.46   8882000  AAL\n",
       "2  2013-02-12  14.45  14.51  14.10  14.27   8126000  AAL\n",
       "3  2013-02-13  14.30  14.94  14.25  14.66  10259500  AAL\n",
       "4  2013-02-14  14.94  14.96  13.16  13.99  31879900  AAL"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['AAL', 'AAPL', 'AAP', 'ABBV', 'ABC', 'ABT', 'ACN', 'ADBE', 'ADI',\n",
       "       'ADM', 'ADP', 'ADSK', 'ADS', 'AEE', 'AEP', 'AES', 'AET', 'AFL',\n",
       "       'AGN', 'AIG', 'AIV', 'AIZ', 'AJG', 'AKAM', 'ALB', 'ALGN', 'ALK',\n",
       "       'ALLE', 'ALL', 'ALXN', 'AMAT', 'AMD', 'AME', 'AMGN', 'AMG', 'AMP',\n",
       "       'AMT', 'AMZN', 'ANDV', 'ANSS', 'ANTM', 'AON', 'AOS', 'APA', 'APC',\n",
       "       'APD', 'APH', 'APTV', 'ARE', 'ARNC', 'ATVI', 'AVB', 'AVGO', 'AVY',\n",
       "       'AWK', 'AXP', 'AYI', 'AZO', 'A', 'BAC', 'BAX', 'BA', 'BBT', 'BBY',\n",
       "       'BDX', 'BEN', 'BF.B', 'BHF', 'BHGE', 'BIIB', 'BK', 'BLK', 'BLL',\n",
       "       'BMY', 'BRK.B', 'BSX', 'BWA', 'BXP', 'CAG', 'CAH', 'CAT', 'CA',\n",
       "       'CBG', 'CBOE', 'CBS', 'CB', 'CCI', 'CCL', 'CDNS', 'CELG', 'CERN',\n",
       "       'CFG', 'CF', 'CHD', 'CHK', 'CHRW', 'CHTR', 'CINF', 'CI', 'CLX',\n",
       "       'CL', 'CMA', 'CMCSA', 'CME', 'CMG', 'CMI', 'CMS', 'CNC', 'CNP',\n",
       "       'COF', 'COG', 'COL', 'COO', 'COP', 'COST', 'COTY', 'CPB', 'CRM',\n",
       "       'CSCO', 'CSRA', 'CSX', 'CTAS', 'CTL', 'CTSH', 'CTXS', 'CVS', 'CVX',\n",
       "       'CXO', 'C', 'DAL', 'DE', 'DFS', 'DGX', 'DG', 'DHI', 'DHR', 'DISCA',\n",
       "       'DISCK', 'DISH', 'DIS', 'DLR', 'DLTR', 'DOV', 'DPS', 'DRE', 'DRI',\n",
       "       'DTE', 'DUK', 'DVA', 'DVN', 'DWDP', 'DXC', 'D', 'EA', 'EBAY',\n",
       "       'ECL', 'ED', 'EFX', 'EIX', 'EL', 'EMN', 'EMR', 'EOG', 'EQIX',\n",
       "       'EQR', 'EQT', 'ESRX', 'ESS', 'ES', 'ETFC', 'ETN', 'ETR', 'EVHC',\n",
       "       'EW', 'EXC', 'EXPD', 'EXPE', 'EXR', 'FAST', 'FBHS', 'FB', 'FCX',\n",
       "       'FDX', 'FE', 'FFIV', 'FISV', 'FIS', 'FITB', 'FLIR', 'FLR', 'FLS',\n",
       "       'FL', 'FMC', 'FOXA', 'FOX', 'FRT', 'FTI', 'FTV', 'F', 'GD', 'GE',\n",
       "       'GGP', 'GILD', 'GIS', 'GLW', 'GM', 'GOOGL', 'GOOG', 'GPC', 'GPN',\n",
       "       'GPS', 'GRMN', 'GS', 'GT', 'GWW', 'HAL', 'HAS', 'HBAN', 'HBI',\n",
       "       'HCA', 'HCN', 'HCP', 'HD', 'HES', 'HIG', 'HII', 'HLT', 'HOG',\n",
       "       'HOLX', 'HON', 'HPE', 'HPQ', 'HP', 'HRB', 'HRL', 'HRS', 'HSIC',\n",
       "       'HST', 'HSY', 'HUM', 'IBM', 'ICE', 'IDXX', 'IFF', 'ILMN', 'INCY',\n",
       "       'INFO', 'INTC', 'INTU', 'IPG', 'IP', 'IQV', 'IRM', 'IR', 'ISRG',\n",
       "       'ITW', 'IT', 'IVZ', 'JBHT', 'JCI', 'JEC', 'JNJ', 'JNPR', 'JPM',\n",
       "       'JWN', 'KEY', 'KHC', 'KIM', 'KLAC', 'KMB', 'KMI', 'KMX', 'KORS',\n",
       "       'KO', 'KR', 'KSS', 'KSU', 'K', 'LB', 'LEG', 'LEN', 'LH', 'LKQ',\n",
       "       'LLL', 'LLY', 'LMT', 'LNC', 'LNT', 'LOW', 'LRCX', 'LUK', 'LUV',\n",
       "       'LYB', 'L', 'MAA', 'MAC', 'MAR', 'MAS', 'MAT', 'MA', 'MCD', 'MCHP',\n",
       "       'MCK', 'MCO', 'MDLZ', 'MDT', 'MET', 'MGM', 'MHK', 'MKC', 'MLM',\n",
       "       'MMC', 'MMM', 'MNST', 'MON', 'MOS', 'MO', 'MPC', 'MRK', 'MRO',\n",
       "       'MSFT', 'MSI', 'MS', 'MTB', 'MTD', 'MU', 'MYL', 'M', 'NAVI', 'NBL',\n",
       "       'NCLH', 'NDAQ', 'NEE', 'NEM', 'NFLX', 'NFX', 'NI', 'NKE', 'NLSN',\n",
       "       'NOC', 'NOV', 'NRG', 'NSC', 'NTAP', 'NTRS', 'NUE', 'NVDA', 'NWL',\n",
       "       'NWSA', 'NWS', 'OKE', 'OMC', 'ORCL', 'ORLY', 'OXY', 'O', 'PAYX',\n",
       "       'PBCT', 'PCAR', 'PCG', 'PCLN', 'PDCO', 'PEG', 'PEP', 'PFE', 'PFG',\n",
       "       'PGR', 'PG', 'PHM', 'PH', 'PKG', 'PKI', 'PLD', 'PM', 'PNC', 'PNR',\n",
       "       'PNW', 'PPG', 'PPL', 'PRGO', 'PRU', 'PSA', 'PSX', 'PVH', 'PWR',\n",
       "       'PXD', 'PX', 'PYPL', 'QCOM', 'QRVO', 'RCL', 'REGN', 'REG', 'RE',\n",
       "       'RF', 'RHI', 'RHT', 'RJF', 'RL', 'RMD', 'ROK', 'ROP', 'ROST',\n",
       "       'RRC', 'RSG', 'RTN', 'SBAC', 'SBUX', 'SCG', 'SCHW', 'SEE', 'SHW',\n",
       "       'SIG', 'SJM', 'SLB', 'SLG', 'SNA', 'SNI', 'SNPS', 'SO', 'SPGI',\n",
       "       'SPG', 'SRCL', 'SRE', 'STI', 'STT', 'STX', 'STZ', 'SWKS', 'SWK',\n",
       "       'SYF', 'SYK', 'SYMC', 'SYY', 'TAP', 'TDG', 'TEL', 'TGT', 'TIF',\n",
       "       'TJX', 'TMK', 'TMO', 'TPR', 'TRIP', 'TROW', 'TRV', 'TSCO', 'TSN',\n",
       "       'TSS', 'TWX', 'TXN', 'TXT', 'T', 'UAA', 'UAL', 'UA', 'UDR', 'UHS',\n",
       "       'ULTA', 'UNH', 'UNM', 'UNP', 'UPS', 'URI', 'USB', 'UTX', 'VAR',\n",
       "       'VFC', 'VIAB', 'VLO', 'VMC', 'VNO', 'VRSK', 'VRSN', 'VRTX', 'VTR',\n",
       "       'VZ', 'V', 'WAT', 'WBA', 'WDC', 'WEC', 'WFC', 'WHR', 'WLTW', 'WMB',\n",
       "       'WMT', 'WM', 'WRK', 'WU', 'WYNN', 'WYN', 'WY', 'XEC', 'XEL',\n",
       "       'XLNX', 'XL', 'XOM', 'XRAY', 'XRX', 'XYL', 'YUM', 'ZBH', 'ZION',\n",
       "       'ZTS'], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "companies = df.Name.unique()\n",
    "companies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1259 entries, 617781 to 619039\n",
      "Data columns (total 7 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   date    1259 non-null   object \n",
      " 1   open    1259 non-null   float64\n",
      " 2   high    1259 non-null   float64\n",
      " 3   low     1259 non-null   float64\n",
      " 4   close   1259 non-null   float64\n",
      " 5   volume  1259 non-null   int64  \n",
      " 6   Name    1259 non-null   object \n",
      "dtypes: float64(4), int64(1), object(2)\n",
      "memory usage: 78.7+ KB\n"
     ]
    }
   ],
   "source": [
    "z = df.loc[df['Name'] == 'ZTS']\n",
    "z.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>617781</th>\n",
       "      <td>2013-02-08</td>\n",
       "      <td>32.31</td>\n",
       "      <td>33.48</td>\n",
       "      <td>32.30</td>\n",
       "      <td>33.05</td>\n",
       "      <td>2599232</td>\n",
       "      <td>ZTS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>617782</th>\n",
       "      <td>2013-02-11</td>\n",
       "      <td>33.06</td>\n",
       "      <td>33.50</td>\n",
       "      <td>32.88</td>\n",
       "      <td>33.26</td>\n",
       "      <td>1486115</td>\n",
       "      <td>ZTS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>617783</th>\n",
       "      <td>2013-02-12</td>\n",
       "      <td>33.40</td>\n",
       "      <td>34.00</td>\n",
       "      <td>33.15</td>\n",
       "      <td>33.74</td>\n",
       "      <td>1637279</td>\n",
       "      <td>ZTS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>617784</th>\n",
       "      <td>2013-02-13</td>\n",
       "      <td>33.80</td>\n",
       "      <td>33.80</td>\n",
       "      <td>33.00</td>\n",
       "      <td>33.55</td>\n",
       "      <td>1269625</td>\n",
       "      <td>ZTS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>617785</th>\n",
       "      <td>2013-02-14</td>\n",
       "      <td>33.39</td>\n",
       "      <td>33.45</td>\n",
       "      <td>32.77</td>\n",
       "      <td>33.27</td>\n",
       "      <td>2954324</td>\n",
       "      <td>ZTS</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              date   open   high    low  close   volume Name\n",
       "617781  2013-02-08  32.31  33.48  32.30  33.05  2599232  ZTS\n",
       "617782  2013-02-11  33.06  33.50  32.88  33.26  1486115  ZTS\n",
       "617783  2013-02-12  33.40  34.00  33.15  33.74  1637279  ZTS\n",
       "617784  2013-02-13  33.80  33.80  33.00  33.55  1269625  ZTS\n",
       "617785  2013-02-14  33.39  33.45  32.77  33.27  2954324  ZTS"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainingd = z.iloc[:, 4:5].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc = MinMaxScaler()\n",
    "training_set_scaled = sc.fit_transform(trainingd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = []\n",
    "y_train = []\n",
    "timestamp = 45\n",
    "length = len(trainingd)\n",
    "for i in range(timestamp, length):\n",
    "    x_train.append(training_set_scaled[i-timestamp:i, 0])\n",
    "    y_train.append(training_set_scaled[i, 0])\n",
    "\n",
    "x_train = np.array(x_train)\n",
    "y_train = np.array(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.08995937 0.09402205 0.10330818 0.09963242 0.09421552 0.10795125\n",
      " 0.10524279 0.08357516 0.08047978 0.08106017 0.07100019 0.07041981\n",
      " 0.07583672 0.09769781 0.11994583 0.11510931 0.11394854 0.10543625\n",
      " 0.11917199 0.11007932 0.11182047 0.10485587 0.10833817 0.12071967\n",
      " 0.09750435 0.08589669 0.08512285 0.09885858 0.09808474 0.0913136\n",
      " 0.07854517 0.07951248 0.08396208 0.09673051 0.08106017 0.08028632\n",
      " 0.0798994  0.06655059 0.06887212 0.08744438 0.09750435 0.09189398\n",
      " 0.09208744 0.09344167 0.07428903]\n",
      "\n",
      "\n",
      "0.0930547494679822\n"
     ]
    }
   ],
   "source": [
    "print (x_train[0])\n",
    "print ('\\n')\n",
    "print (y_train[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1214, 45, 1)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))\n",
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential() \n",
    "\n",
    "model.add(LSTM(units = 120, return_sequences = True, input_shape = (x_train.shape[1], 1))) \n",
    "model.add(Dropout(0.2))\n",
    "model.add(LSTM(units = 120, return_sequences = True))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(LSTM(units = 120, return_sequences = True))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(LSTM(units = 120, return_sequences = False)) \n",
    "model.add(Dropout(0.2))\n",
    "model.add(Dense(units = 1)) \n",
    "model.compile(optimizer = 'adam', loss = 'mean_squared_error')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/25\n",
      "38/38 [==============================] - 5s 130ms/step - loss: 0.0215\n",
      "Epoch 2/25\n",
      "38/38 [==============================] - 4s 115ms/step - loss: 0.0025\n",
      "Epoch 3/25\n",
      "38/38 [==============================] - 4s 112ms/step - loss: 0.0024\n",
      "Epoch 4/25\n",
      "38/38 [==============================] - 5s 127ms/step - loss: 0.0019\n",
      "Epoch 5/25\n",
      "38/38 [==============================] - 5s 124ms/step - loss: 0.0019\n",
      "Epoch 6/25\n",
      "38/38 [==============================] - 5s 121ms/step - loss: 0.0022\n",
      "Epoch 7/25\n",
      "38/38 [==============================] - 5s 121ms/step - loss: 0.0020\n",
      "Epoch 8/25\n",
      "38/38 [==============================] - 4s 113ms/step - loss: 0.0024\n",
      "Epoch 9/25\n",
      "38/38 [==============================] - 4s 114ms/step - loss: 0.0017\n",
      "Epoch 10/25\n",
      "38/38 [==============================] - 4s 116ms/step - loss: 0.0021\n",
      "Epoch 11/25\n",
      "38/38 [==============================] - 5s 122ms/step - loss: 0.0015\n",
      "Epoch 12/25\n",
      "38/38 [==============================] - 4s 113ms/step - loss: 0.0016\n",
      "Epoch 13/25\n",
      "38/38 [==============================] - 4s 113ms/step - loss: 0.0016\n",
      "Epoch 14/25\n",
      "38/38 [==============================] - 5s 120ms/step - loss: 0.0015\n",
      "Epoch 15/25\n",
      "38/38 [==============================] - 4s 111ms/step - loss: 0.0019\n",
      "Epoch 16/25\n",
      "38/38 [==============================] - 4s 110ms/step - loss: 0.0019\n",
      "Epoch 17/25\n",
      "38/38 [==============================] - 4s 109ms/step - loss: 0.0013\n",
      "Epoch 18/25\n",
      "38/38 [==============================] - 4s 113ms/step - loss: 0.0014\n",
      "Epoch 19/25\n",
      "38/38 [==============================] - 4s 118ms/step - loss: 0.0015\n",
      "Epoch 20/25\n",
      "38/38 [==============================] - 5s 122ms/step - loss: 0.0014\n",
      "Epoch 21/25\n",
      "38/38 [==============================] - 5s 121ms/step - loss: 0.0012\n",
      "Epoch 22/25\n",
      "38/38 [==============================] - 5s 123ms/step - loss: 0.0013\n",
      "Epoch 23/25\n",
      "38/38 [==============================] - 5s 121ms/step - loss: 0.0012\n",
      "Epoch 24/25\n",
      "38/38 [==============================] - 5s 120ms/step - loss: 0.0012\n",
      "Epoch 25/25\n",
      "38/38 [==============================] - 5s 120ms/step - loss: 0.0012\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x26702921610>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train, y_train, epochs = 25, batch_size = 32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_set = df.loc[df['Name'] == 'BA']\n",
    "test_set = test_set.loc[:, test_set.columns=='close']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test = test_set.iloc[timestamp:,0:].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "closing_price = test_set.iloc[:,0:].values\n",
    "closing_price_scaled = sc.transform(closing_price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1214, 45)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test = []\n",
    "length = len(test_set)\n",
    "\n",
    "for i in range(timestamp, length):\n",
    "    x_test.append(closing_price_scaled[i-timestamp:i,0])\n",
    "\n",
    "x_test = np.array(x_test)\n",
    "x_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1214, 45, 1)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1],1))\n",
    "x_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(x_test)\n",
    "predicted_price = sc.inverse_transform(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(y_test, color='blue', label='Preço Atual das Ações')\n",
    "plt.plot(predicted_price, color='red', label='Preço Previsto das Ações')\n",
    "plt.title('Previsão de Preço de Ações')\n",
    "plt.xlabel('Tempo')\n",
    "plt.ylabel('Preço das Ações')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 85.88541],\n",
       "       [ 86.24627],\n",
       "       [ 86.51383],\n",
       "       ...,\n",
       "       [167.71556],\n",
       "       [167.72542],\n",
       "       [167.7181 ]], dtype=float32)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted_price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 86.89],\n",
       "       [ 86.69],\n",
       "       [ 86.12],\n",
       "       ...,\n",
       "       [328.88],\n",
       "       [340.91],\n",
       "       [348.12]])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  }
 ],
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   "name": "python3"
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  "language_info": {
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