Stock Market.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.read_csv('/home/ajai/Documents/srpProjects/DataSets/stock/Google_Stock_Price_Train.csv',index_col=\"Date\",parse_dates=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-12-23</th>\n",
       "      <td>790.90</td>\n",
       "      <td>792.74</td>\n",
       "      <td>787.28</td>\n",
       "      <td>789.91</td>\n",
       "      <td>623,400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-27</th>\n",
       "      <td>790.68</td>\n",
       "      <td>797.86</td>\n",
       "      <td>787.66</td>\n",
       "      <td>791.55</td>\n",
       "      <td>789,100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-28</th>\n",
       "      <td>793.70</td>\n",
       "      <td>794.23</td>\n",
       "      <td>783.20</td>\n",
       "      <td>785.05</td>\n",
       "      <td>1,153,800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-29</th>\n",
       "      <td>783.33</td>\n",
       "      <td>785.93</td>\n",
       "      <td>778.92</td>\n",
       "      <td>782.79</td>\n",
       "      <td>744,300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-30</th>\n",
       "      <td>782.75</td>\n",
       "      <td>782.78</td>\n",
       "      <td>770.41</td>\n",
       "      <td>771.82</td>\n",
       "      <td>1,770,000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Open    High     Low   Close     Volume\n",
       "Date                                                 \n",
       "2016-12-23  790.90  792.74  787.28  789.91    623,400\n",
       "2016-12-27  790.68  797.86  787.66  791.55    789,100\n",
       "2016-12-28  793.70  794.23  783.20  785.05  1,153,800\n",
       "2016-12-29  783.33  785.93  778.92  782.79    744,300\n",
       "2016-12-30  782.75  782.78  770.41  771.82  1,770,000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.tail()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Open      False\n",
       "High      False\n",
       "Low       False\n",
       "Close     False\n",
       "Volume    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.isna().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 1258 entries, 2012-01-03 to 2016-12-30\n",
      "Data columns (total 5 columns):\n",
      "Open      1258 non-null float64\n",
      "High      1258 non-null float64\n",
      "Low       1258 non-null float64\n",
      "Close     1258 non-null object\n",
      "Volume    1258 non-null object\n",
      "dtypes: float64(3), object(2)\n",
      "memory usage: 59.0+ KB\n"
     ]
    }
   ],
   "source": [
    "dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x8880c18>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset['Open'].plot(figsize=(16,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert column \"a\" of a DataFrame\n",
    "dataset[\"Close\"] = dataset[\"Close\"].str.replace(',', '').astype(float)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset[\"Volume\"] = dataset[\"Volume\"].str.replace(',', '').astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-01-03</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-04</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-05</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-06</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-09</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-10</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-11</th>\n",
       "      <td>323.002857</td>\n",
       "      <td>325.392857</td>\n",
       "      <td>318.682857</td>\n",
       "      <td>643.132857</td>\n",
       "      <td>7.208100e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-12</th>\n",
       "      <td>321.457143</td>\n",
       "      <td>322.882857</td>\n",
       "      <td>316.841429</td>\n",
       "      <td>638.037143</td>\n",
       "      <td>6.691514e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-13</th>\n",
       "      <td>318.698571</td>\n",
       "      <td>319.801429</td>\n",
       "      <td>314.025714</td>\n",
       "      <td>631.870000</td>\n",
       "      <td>6.531857e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-17</th>\n",
       "      <td>316.552857</td>\n",
       "      <td>317.524286</td>\n",
       "      <td>311.851429</td>\n",
       "      <td>627.534286</td>\n",
       "      <td>6.137929e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-18</th>\n",
       "      <td>314.238571</td>\n",
       "      <td>315.674286</td>\n",
       "      <td>309.882857</td>\n",
       "      <td>625.097143</td>\n",
       "      <td>6.157657e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-19</th>\n",
       "      <td>313.847143</td>\n",
       "      <td>315.247143</td>\n",
       "      <td>310.610000</td>\n",
       "      <td>627.534286</td>\n",
       "      <td>6.296086e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-20</th>\n",
       "      <td>311.055714</td>\n",
       "      <td>312.201429</td>\n",
       "      <td>308.104286</td>\n",
       "      <td>622.242857</td>\n",
       "      <td>8.068629e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-23</th>\n",
       "      <td>308.387143</td>\n",
       "      <td>309.302857</td>\n",
       "      <td>305.402857</td>\n",
       "      <td>616.481429</td>\n",
       "      <td>8.359129e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-24</th>\n",
       "      <td>305.192857</td>\n",
       "      <td>306.085714</td>\n",
       "      <td>301.951429</td>\n",
       "      <td>609.541429</td>\n",
       "      <td>8.697700e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-25</th>\n",
       "      <td>301.724286</td>\n",
       "      <td>302.652857</td>\n",
       "      <td>298.060000</td>\n",
       "      <td>601.634286</td>\n",
       "      <td>9.466400e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-26</th>\n",
       "      <td>297.454286</td>\n",
       "      <td>298.561429</td>\n",
       "      <td>293.710000</td>\n",
       "      <td>593.017143</td>\n",
       "      <td>9.844071e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-27</th>\n",
       "      <td>293.480000</td>\n",
       "      <td>294.741429</td>\n",
       "      <td>289.952857</td>\n",
       "      <td>585.475714</td>\n",
       "      <td>1.008950e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-30</th>\n",
       "      <td>289.001429</td>\n",
       "      <td>290.401429</td>\n",
       "      <td>285.821429</td>\n",
       "      <td>576.660000</td>\n",
       "      <td>8.949586e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-31</th>\n",
       "      <td>288.465714</td>\n",
       "      <td>289.902857</td>\n",
       "      <td>285.355714</td>\n",
       "      <td>575.821429</td>\n",
       "      <td>6.530857e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Open        High         Low       Close        Volume\n",
       "Date                                                                    \n",
       "2012-01-03         NaN         NaN         NaN         NaN           NaN\n",
       "2012-01-04         NaN         NaN         NaN         NaN           NaN\n",
       "2012-01-05         NaN         NaN         NaN         NaN           NaN\n",
       "2012-01-06         NaN         NaN         NaN         NaN           NaN\n",
       "2012-01-09         NaN         NaN         NaN         NaN           NaN\n",
       "2012-01-10         NaN         NaN         NaN         NaN           NaN\n",
       "2012-01-11  323.002857  325.392857  318.682857  643.132857  7.208100e+06\n",
       "2012-01-12  321.457143  322.882857  316.841429  638.037143  6.691514e+06\n",
       "2012-01-13  318.698571  319.801429  314.025714  631.870000  6.531857e+06\n",
       "2012-01-17  316.552857  317.524286  311.851429  627.534286  6.137929e+06\n",
       "2012-01-18  314.238571  315.674286  309.882857  625.097143  6.157657e+06\n",
       "2012-01-19  313.847143  315.247143  310.610000  627.534286  6.296086e+06\n",
       "2012-01-20  311.055714  312.201429  308.104286  622.242857  8.068629e+06\n",
       "2012-01-23  308.387143  309.302857  305.402857  616.481429  8.359129e+06\n",
       "2012-01-24  305.192857  306.085714  301.951429  609.541429  8.697700e+06\n",
       "2012-01-25  301.724286  302.652857  298.060000  601.634286  9.466400e+06\n",
       "2012-01-26  297.454286  298.561429  293.710000  593.017143  9.844071e+06\n",
       "2012-01-27  293.480000  294.741429  289.952857  585.475714  1.008950e+07\n",
       "2012-01-30  289.001429  290.401429  285.821429  576.660000  8.949586e+06\n",
       "2012-01-31  288.465714  289.902857  285.355714  575.821429  6.530857e+06"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 7 day rolling mean\n",
    "dataset.rolling(7).mean().head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x8aa7160>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset['Open'].plot(figsize=(16,6))\n",
    "dataset.rolling(window=30).mean()['Close'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x8c5bef0>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset['Close: 30 Day Mean'] = dataset['Close'].rolling(window=30).mean()\n",
    "dataset[['Close','Close: 30 Day Mean']].plot(figsize=(16,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x8c3c898>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Optional specify a minimum number of periods\n",
    "dataset['Close'].expanding(min_periods=1).mean().plot(figsize=(16,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_set=dataset['Open']\n",
    "training_set=pd.DataFrame(training_set)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Feature Scaling\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "sc = MinMaxScaler(feature_range = (0, 1))\n",
    "training_set_scaled = sc.fit_transform(training_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a data structure with 60 timesteps and 1 output\n",
    "X_train = []\n",
    "y_train = []\n",
    "for i in range(60, 1258):\n",
    "    X_train.append(training_set_scaled[i-60:i, 0])\n",
    "    y_train.append(training_set_scaled[i, 0])\n",
    "X_train, y_train = np.array(X_train), np.array(y_train)\n",
    "\n",
    "# Reshaping\n",
    "X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Part 2 - Building the RNN\n",
    "\n",
    "# Importing the Keras libraries and packages\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import LSTM\n",
    "from keras.layers import Dropout\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialising the RNN\n",
    "regressor = Sequential()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adding the first LSTM layer and some Dropout regularisation\n",
    "regressor.add(LSTM(units = 50, return_sequences = True, input_shape = (X_train.shape[1], 1)))\n",
    "regressor.add(Dropout(0.2))\n",
    "\n",
    "# Adding a second LSTM layer and some Dropout regularisation\n",
    "regressor.add(LSTM(units = 50, return_sequences = True))\n",
    "regressor.add(Dropout(0.2))\n",
    "\n",
    "# Adding a third LSTM layer and some Dropout regularisation\n",
    "regressor.add(LSTM(units = 50, return_sequences = True))\n",
    "regressor.add(Dropout(0.2))\n",
    "\n",
    "# Adding a fourth LSTM layer and some Dropout regularisation\n",
    "regressor.add(LSTM(units = 50))\n",
    "regressor.add(Dropout(0.2))\n",
    "\n",
    "# Adding the output layer\n",
    "regressor.add(Dense(units = 1))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "1198/1198 [==============================] - 12s 10ms/step - loss: 0.0519\n",
      "Epoch 2/100\n",
      "1198/1198 [==============================] - 5s 5ms/step - loss: 0.0062\n",
      "Epoch 3/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0050\n",
      "Epoch 4/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0048\n",
      "Epoch 5/100\n",
      "1198/1198 [==============================] - 5s 5ms/step - loss: 0.0047\n",
      "Epoch 6/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0046\n",
      "Epoch 7/100\n",
      "1198/1198 [==============================] - 10s 8ms/step - loss: 0.0052\n",
      "Epoch 8/100\n",
      "1198/1198 [==============================] - 8s 6ms/step - loss: 0.0041\n",
      "Epoch 9/100\n",
      "1198/1198 [==============================] - 6s 5ms/step - loss: 0.0043\n",
      "Epoch 10/100\n",
      "1198/1198 [==============================] - 6s 5ms/step - loss: 0.0047\n",
      "Epoch 11/100\n",
      "1198/1198 [==============================] - 6s 5ms/step - loss: 0.0041\n",
      "Epoch 12/100\n",
      "1198/1198 [==============================] - 6s 5ms/step - loss: 0.0037\n",
      "Epoch 13/100\n",
      "1198/1198 [==============================] - 6s 5ms/step - loss: 0.0042\n",
      "Epoch 14/100\n",
      "1198/1198 [==============================] - 6s 5ms/step - loss: 0.0042\n",
      "Epoch 15/100\n",
      "1198/1198 [==============================] - 6s 5ms/step - loss: 0.0039\n",
      "Epoch 16/100\n",
      "1198/1198 [==============================] - 6s 5ms/step - loss: 0.0040\n",
      "Epoch 17/100\n",
      "1198/1198 [==============================] - 6s 5ms/step - loss: 0.0036\n",
      "Epoch 18/100\n",
      "1198/1198 [==============================] - 7s 5ms/step - loss: 0.0036\n",
      "Epoch 19/100\n",
      "1198/1198 [==============================] - 6s 5ms/step - loss: 0.0040\n",
      "Epoch 20/100\n",
      "1198/1198 [==============================] - 6s 5ms/step - loss: 0.0033\n",
      "Epoch 21/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0032\n",
      "Epoch 22/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0035\n",
      "Epoch 23/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0031\n",
      "Epoch 24/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0033\n",
      "Epoch 25/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0035\n",
      "Epoch 26/100\n",
      "1198/1198 [==============================] - 5s 5ms/step - loss: 0.0028\n",
      "Epoch 27/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0031\n",
      "Epoch 28/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0034\n",
      "Epoch 29/100\n",
      "1198/1198 [==============================] - 5s 5ms/step - loss: 0.0031\n",
      "Epoch 30/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0030\n",
      "Epoch 31/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0029\n",
      "Epoch 32/100\n",
      "1198/1198 [==============================] - 6s 5ms/step - loss: 0.0026\n",
      "Epoch 33/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0029\n",
      "Epoch 34/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0027\n",
      "Epoch 35/100\n",
      "1198/1198 [==============================] - 5s 5ms/step - loss: 0.0029\n",
      "Epoch 36/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0028\n",
      "Epoch 37/100\n",
      "1198/1198 [==============================] - 6s 5ms/step - loss: 0.0031\n",
      "Epoch 38/100\n",
      "1198/1198 [==============================] - 5s 5ms/step - loss: 0.0028\n",
      "Epoch 39/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0025\n",
      "Epoch 40/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0025\n",
      "Epoch 41/100\n",
      "1198/1198 [==============================] - 5s 5ms/step - loss: 0.0024\n",
      "Epoch 42/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0024\n",
      "Epoch 43/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0023\n",
      "Epoch 44/100\n",
      "1198/1198 [==============================] - 5s 5ms/step - loss: 0.0025\n",
      "Epoch 45/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0027\n",
      "Epoch 46/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0025\n",
      "Epoch 47/100\n",
      "1198/1198 [==============================] - 5s 5ms/step - loss: 0.0027\n",
      "Epoch 48/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0023\n",
      "Epoch 49/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0026\n",
      "Epoch 50/100\n",
      "1198/1198 [==============================] - 5s 5ms/step - loss: 0.0023\n",
      "Epoch 51/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0023\n",
      "Epoch 52/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0022\n",
      "Epoch 53/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0022\n",
      "Epoch 54/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0022\n",
      "Epoch 55/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0023\n",
      "Epoch 56/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0021\n",
      "Epoch 57/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0024\n",
      "Epoch 58/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0020\n",
      "Epoch 59/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0022\n",
      "Epoch 60/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0020\n",
      "Epoch 61/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0019\n",
      "Epoch 62/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0020\n",
      "Epoch 63/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0020\n",
      "Epoch 64/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0019\n",
      "Epoch 65/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0018\n",
      "Epoch 66/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0024\n",
      "Epoch 67/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0018\n",
      "Epoch 68/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0019\n",
      "Epoch 69/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0019\n",
      "Epoch 70/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0020\n",
      "Epoch 71/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0018\n",
      "Epoch 72/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0017\n",
      "Epoch 73/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0018\n",
      "Epoch 74/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0018\n",
      "Epoch 75/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0017\n",
      "Epoch 76/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0018\n",
      "Epoch 77/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0017\n",
      "Epoch 78/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0016\n",
      "Epoch 79/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0018\n",
      "Epoch 80/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0016\n",
      "Epoch 81/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0016\n",
      "Epoch 82/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0016\n",
      "Epoch 83/100\n",
      "1198/1198 [==============================] - 6s 5ms/step - loss: 0.0017\n",
      "Epoch 84/100\n",
      "1198/1198 [==============================] - 11s 9ms/step - loss: 0.0017\n",
      "Epoch 85/100\n",
      "1198/1198 [==============================] - 7s 6ms/step - loss: 0.0015\n",
      "Epoch 86/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0018\n",
      "Epoch 87/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0018\n",
      "Epoch 88/100\n",
      "1198/1198 [==============================] - 6s 5ms/step - loss: 0.0016\n",
      "Epoch 89/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0016\n",
      "Epoch 90/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0015\n",
      "Epoch 91/100\n",
      "1198/1198 [==============================] - 6s 5ms/step - loss: 0.0016\n",
      "Epoch 92/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0014\n",
      "Epoch 93/100\n",
      "1198/1198 [==============================] - 6s 5ms/step - loss: 0.0017\n",
      "Epoch 94/100\n",
      "1198/1198 [==============================] - 6s 5ms/step - loss: 0.0015\n",
      "Epoch 95/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0017\n",
      "Epoch 96/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0015\n",
      "Epoch 97/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0014\n",
      "Epoch 98/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0015\n",
      "Epoch 99/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0014\n",
      "Epoch 100/100\n",
      "1198/1198 [==============================] - 6s 5ms/step - loss: 0.0014\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x5324a58>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compiling the RNN\n",
    "regressor.compile(optimizer = 'adam', loss = 'mean_squared_error')\n",
    "\n",
    "# Fitting the RNN to the Training set\n",
    "regressor.fit(X_train, y_train, epochs = 100, batch_size = 32)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Part 3 - Making the predictions and visualising the results\n",
    "\n",
    "# Getting the real stock price of 2017\n",
    "dataset_test = pd.read_csv('Google_Stock_Price_Test.csv',index_col=\"Date\",parse_dates=True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "real_stock_price = dataset_test.iloc[:, 1:2].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-03</th>\n",
       "      <td>778.81</td>\n",
       "      <td>789.63</td>\n",
       "      <td>775.80</td>\n",
       "      <td>786.14</td>\n",
       "      <td>1,657,300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-04</th>\n",
       "      <td>788.36</td>\n",
       "      <td>791.34</td>\n",
       "      <td>783.16</td>\n",
       "      <td>786.90</td>\n",
       "      <td>1,073,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-05</th>\n",
       "      <td>786.08</td>\n",
       "      <td>794.48</td>\n",
       "      <td>785.02</td>\n",
       "      <td>794.02</td>\n",
       "      <td>1,335,200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-06</th>\n",
       "      <td>795.26</td>\n",
       "      <td>807.90</td>\n",
       "      <td>792.20</td>\n",
       "      <td>806.15</td>\n",
       "      <td>1,640,200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-09</th>\n",
       "      <td>806.40</td>\n",
       "      <td>809.97</td>\n",
       "      <td>802.83</td>\n",
       "      <td>806.65</td>\n",
       "      <td>1,272,400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Open    High     Low   Close     Volume\n",
       "Date                                                 \n",
       "2017-01-03  778.81  789.63  775.80  786.14  1,657,300\n",
       "2017-01-04  788.36  791.34  783.16  786.90  1,073,000\n",
       "2017-01-05  786.08  794.48  785.02  794.02  1,335,200\n",
       "2017-01-06  795.26  807.90  792.20  806.15  1,640,200\n",
       "2017-01-09  806.40  809.97  802.83  806.65  1,272,400"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 20 entries, 2017-01-03 to 2017-01-31\n",
      "Data columns (total 5 columns):\n",
      "Open      20 non-null float64\n",
      "High      20 non-null float64\n",
      "Low       20 non-null float64\n",
      "Close     20 non-null float64\n",
      "Volume    20 non-null object\n",
      "dtypes: float64(4), object(1)\n",
      "memory usage: 960.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "dataset_test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_test[\"Volume\"] = dataset_test[\"Volume\"].str.replace(',', '').astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_set=dataset_test['Open']\n",
    "test_set=pd.DataFrame(test_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 20 entries, 2017-01-03 to 2017-01-31\n",
      "Data columns (total 1 columns):\n",
      "Open    20 non-null float64\n",
      "dtypes: float64(1)\n",
      "memory usage: 320.0 bytes\n"
     ]
    }
   ],
   "source": [
    "test_set.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Getting the predicted stock price of 2020\n",
    "dataset_total = pd.concat((dataset['Open'], dataset_test['Open']), axis = 0)\n",
    "inputs = dataset_total[len(dataset_total) - len(dataset_test) - 60:].values\n",
    "inputs = inputs.reshape(-1,1)\n",
    "inputs = sc.transform(inputs)\n",
    "X_test = []\n",
    "for i in range(60, 80):\n",
    "    X_test.append(inputs[i-60:i, 0])\n",
    "X_test = np.array(X_test)\n",
    "X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))\n",
    "predicted_stock_price = regressor.predict(X_test)\n",
    "predicted_stock_price = sc.inverse_transform(predicted_stock_price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 20 entries, 0 to 19\n",
      "Data columns (total 1 columns):\n",
      "0    20 non-null float32\n",
      "dtypes: float32(1)\n",
      "memory usage: 160.0 bytes\n"
     ]
    }
   ],
   "source": [
    "predicted_stock_price=pd.DataFrame(predicted_stock_price)\n",
    "predicted_stock_price.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "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": [
    "\n",
    "# Visualising the results\n",
    "plt.plot(real_stock_price, color = 'red', label = 'Real Google Stock Price')\n",
    "plt.plot(predicted_stock_price, color = 'blue', label = 'Predicted Google Stock Price')\n",
    "plt.title('Google Stock Price Prediction')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Google Stock Price')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}