AAPL stock price Prediction using RNN.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading  Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "training_set = pd.read_csv('C:/Users/prade/Downloads/aapl_train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "training_set['Date'] = pd.to_datetime(training_set.Date)\n",
    "training_set.sort_values('Date',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2516</th>\n",
       "      <td>2007-01-03</td>\n",
       "      <td>12.33</td>\n",
       "      <td>12.37</td>\n",
       "      <td>11.70</td>\n",
       "      <td>11.97</td>\n",
       "      <td>311433248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2515</th>\n",
       "      <td>2007-01-04</td>\n",
       "      <td>12.01</td>\n",
       "      <td>12.28</td>\n",
       "      <td>11.97</td>\n",
       "      <td>12.24</td>\n",
       "      <td>214031636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2514</th>\n",
       "      <td>2007-01-05</td>\n",
       "      <td>12.25</td>\n",
       "      <td>12.31</td>\n",
       "      <td>12.06</td>\n",
       "      <td>12.15</td>\n",
       "      <td>208817119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2513</th>\n",
       "      <td>2007-01-08</td>\n",
       "      <td>12.28</td>\n",
       "      <td>12.36</td>\n",
       "      <td>12.18</td>\n",
       "      <td>12.21</td>\n",
       "      <td>199431337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2512</th>\n",
       "      <td>2007-01-09</td>\n",
       "      <td>12.35</td>\n",
       "      <td>13.28</td>\n",
       "      <td>12.16</td>\n",
       "      <td>13.22</td>\n",
       "      <td>838036682</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date   Open   High    Low  Close     Volume\n",
       "2516 2007-01-03  12.33  12.37  11.70  11.97  311433248\n",
       "2515 2007-01-04  12.01  12.28  11.97  12.24  214031636\n",
       "2514 2007-01-05  12.25  12.31  12.06  12.15  208817119\n",
       "2513 2007-01-08  12.28  12.36  12.18  12.21  199431337\n",
       "2512 2007-01-09  12.35  13.28  12.16  13.22  838036682"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_set.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Taking out open price and modifying as tensor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_set = training_set['Open'].values\n",
    "type(training_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "training_set = training_set.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sc = MinMaxScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "scaled_training_set = sc.fit_transform(training_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Inputs and outputs for the Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** timesteps = 60**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#empty list\n",
    "X_train = []\n",
    "y_train = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2517, 1)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#shape of training set\n",
    "scaled_training_set.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for i in range(60,2516):\n",
    "    X_train.append(scaled_training_set[i-60:i,0])\n",
    "    y_train.append(scaled_training_set[i,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, y_train = np.array(X_train),np.array(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2456, 60)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#reshaping as 3d tensor\n",
    "X_train = np.reshape(X_train, (2456,60,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2456, 60, 1)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating RNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rnnregressor = Sequential()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rnnregressor.add(LSTM(units=3, return_sequences = True, input_shape=(None,1))) #1st layer\n",
    "rnnregressor.add(LSTM(units=3, return_sequences = True))                       #2nd layer\n",
    "rnnregressor.add(LSTM(units=3, return_sequences = True))                       #3nd layer\n",
    "rnnregressor.add(LSTM(units=3))                                                #4nd layer\n",
    "rnnregressor.add(Dense(units=1))                                              #output layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#compile\n",
    "rnnregressor.compile(optimizer='rmsprop', loss = 'mean_squared_error')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "2456/2456 [==============================] - 11s - loss: 4.3388e-04    - ETA: 1s - loss: 4.\n",
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     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x170cab58a90>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Fitting\n",
    "rnnregressor.fit(X_train,y_train, epochs=100, batch_size= 32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test_set = pd.read_csv('C:/Users/prade/Downloads/AAPL_test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test_set['Date'] = pd.to_datetime(test_set.Date)\n",
    "test_set.sort_values('Date',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>2017-01-03</td>\n",
       "      <td>115.80</td>\n",
       "      <td>116.33</td>\n",
       "      <td>114.76</td>\n",
       "      <td>116.15</td>\n",
       "      <td>28781865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>2017-01-04</td>\n",
       "      <td>115.85</td>\n",
       "      <td>116.51</td>\n",
       "      <td>115.75</td>\n",
       "      <td>116.02</td>\n",
       "      <td>21118116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>2017-01-05</td>\n",
       "      <td>115.92</td>\n",
       "      <td>116.86</td>\n",
       "      <td>115.81</td>\n",
       "      <td>116.61</td>\n",
       "      <td>22193587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>2017-01-06</td>\n",
       "      <td>116.78</td>\n",
       "      <td>118.16</td>\n",
       "      <td>116.47</td>\n",
       "      <td>117.91</td>\n",
       "      <td>31751900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>2017-01-09</td>\n",
       "      <td>117.95</td>\n",
       "      <td>119.43</td>\n",
       "      <td>117.94</td>\n",
       "      <td>118.99</td>\n",
       "      <td>33561948</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date    Open    High     Low   Close    Volume\n",
       "102 2017-01-03  115.80  116.33  114.76  116.15  28781865\n",
       "101 2017-01-04  115.85  116.51  115.75  116.02  21118116\n",
       "100 2017-01-05  115.92  116.86  115.81  116.61  22193587\n",
       "99  2017-01-06  116.78  118.16  116.47  117.91  31751900\n",
       "98  2017-01-09  117.95  119.43  117.94  118.99  33561948"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_set.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(103, 6)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_set.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "real_price = test_set.iloc[:,1:2].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "real_price = np.concatenate((training_set[0:2456], real_price),axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scaling test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "scaled_real_price = sc.fit_transform(real_price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#preparing inputs\n",
    "inputs= []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for i in range(2456, 2559):\n",
    "    inputs.append(scaled_real_price[i-60:i,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "inputs = np.array(inputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inputs.shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "inputs = np.reshape(inputs, (inputs.shape[0], inputs.shape[1],1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predict = rnnregressor.predict(inputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predict = sc.inverse_transform(predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x170ccadc128>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PHgoWLAhA7969464PCAjA2dn5toAH/23XTEjp0qXj/Xzp0qXb7nHhwgXOnj3LG2+8AUBY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\nAADAh/wfDzg9H33MY/UAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x170cad4c438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('white')\n",
    "plt.figure(figsize=(15,10))\n",
    "plt.plot(real_price[2457:], color = 'red', label = 'Real Apple Stock Price')\n",
    "plt.plot(predict, color = 'blue', label = 'Predicted Apple Stock Price')\n",
    "plt.title('Apple Stock Price Prediction')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Apple Stock Price')\n",
    "plt.legend()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}