Stock_Price_Prediction_LSTM.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "source": [
    "# !pip install pandas_datareader"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Collecting pandas_datareader\n",
      "  Downloading pandas_datareader-0.10.0-py3-none-any.whl (109 kB)\n",
      "\u001b[K     |████████████████████████████████| 109 kB 3.7 MB/s \n",
      "\u001b[?25hRequirement already satisfied: requests>=2.19.0 in /home/nisarg/anaconda3/envs/ml/lib/python3.6/site-packages (from pandas_datareader) (2.25.1)\n",
      "Requirement already satisfied: pandas>=0.23 in /home/nisarg/anaconda3/envs/ml/lib/python3.6/site-packages (from pandas_datareader) (1.1.5)\n",
      "Requirement already satisfied: pytz>=2017.2 in /home/nisarg/anaconda3/envs/ml/lib/python3.6/site-packages (from pandas>=0.23->pandas_datareader) (2020.5)\n",
      "Requirement already satisfied: numpy>=1.15.4 in /home/nisarg/anaconda3/envs/ml/lib/python3.6/site-packages (from pandas>=0.23->pandas_datareader) (1.19.2)\n",
      "Requirement already satisfied: python-dateutil>=2.7.3 in /home/nisarg/anaconda3/envs/ml/lib/python3.6/site-packages (from pandas>=0.23->pandas_datareader) (2.8.1)\n",
      "Requirement already satisfied: six>=1.5 in /home/nisarg/anaconda3/envs/ml/lib/python3.6/site-packages (from python-dateutil>=2.7.3->pandas>=0.23->pandas_datareader) (1.15.0)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /home/nisarg/anaconda3/envs/ml/lib/python3.6/site-packages (from requests>=2.19.0->pandas_datareader) (1.26.2)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /home/nisarg/anaconda3/envs/ml/lib/python3.6/site-packages (from requests>=2.19.0->pandas_datareader) (2020.12.5)\n",
      "Requirement already satisfied: chardet<5,>=3.0.2 in /home/nisarg/anaconda3/envs/ml/lib/python3.6/site-packages (from requests>=2.19.0->pandas_datareader) (4.0.0)\n",
      "Requirement already satisfied: idna<3,>=2.5 in /home/nisarg/anaconda3/envs/ml/lib/python3.6/site-packages (from requests>=2.19.0->pandas_datareader) (2.10)\n",
      "Collecting lxml\n",
      "  Downloading lxml-4.6.3-cp36-cp36m-manylinux2014_x86_64.whl (6.3 MB)\n",
      "\u001b[K     |████████████████████████████████| 6.3 MB 11.0 MB/s \n",
      "\u001b[?25hInstalling collected packages: lxml, pandas-datareader\n",
      "Successfully installed lxml-4.6.3 pandas-datareader-0.10.0\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "source": [
    "import pandas_datareader as pdr"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "source": [
    "df = pdr.get_data_tiingo('AAPL', api_key = \"4498328d77d2ee1d6e02e16fb9d5f2aeace6af2e\")"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "source": [
    "import pandas as pd"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "source": [
    "df.to_csv('AAPL.csv')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "source": [
    "df2 = df.reset_index()['close']"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "source": [
    "df"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
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       "                                   close      high     low    open    volume  \\\n",
       "symbol date                                                                    \n",
       "AAPL   2016-08-16 00:00:00+00:00  109.38  110.2300  109.21  109.63  33794448   \n",
       "       2016-08-17 00:00:00+00:00  109.22  109.3700  108.34  109.10  25355976   \n",
       "       2016-08-18 00:00:00+00:00  109.08  109.6000  109.02  109.23  21984703   \n",
       "       2016-08-19 00:00:00+00:00  109.36  109.6900  108.36  108.77  25368072   \n",
       "       2016-08-22 00:00:00+00:00  108.51  109.1000  107.85  108.86  25820230   \n",
       "...                                  ...       ...     ...     ...       ...   \n",
       "       2021-08-09 00:00:00+00:00  146.09  146.7000  145.52  146.20  48908689   \n",
       "       2021-08-10 00:00:00+00:00  145.60  147.7100  145.30  146.44  69023081   \n",
       "       2021-08-11 00:00:00+00:00  145.86  146.7200  145.53  146.05  48493463   \n",
       "       2021-08-12 00:00:00+00:00  148.89  149.0500  145.84  146.19  73779113   \n",
       "       2021-08-13 00:00:00+00:00  149.10  149.4444  148.27  148.97  58846293   \n",
       "\n",
       "                                    adjClose     adjHigh      adjLow  \\\n",
       "symbol date                                                            \n",
       "AAPL   2016-08-16 00:00:00+00:00   25.640617   25.839872   25.600766   \n",
       "       2016-08-17 00:00:00+00:00   25.603110   25.638273   25.396822   \n",
       "       2016-08-18 00:00:00+00:00   25.570292   25.692189   25.556227   \n",
       "       2016-08-19 00:00:00+00:00   25.635929   25.713286   25.401511   \n",
       "       2016-08-22 00:00:00+00:00   25.436673   25.574980   25.281958   \n",
       "...                                      ...         ...         ...   \n",
       "       2021-08-09 00:00:00+00:00  146.090000  146.700000  145.520000   \n",
       "       2021-08-10 00:00:00+00:00  145.600000  147.710000  145.300000   \n",
       "       2021-08-11 00:00:00+00:00  145.860000  146.720000  145.530000   \n",
       "       2021-08-12 00:00:00+00:00  148.890000  149.050000  145.840000   \n",
       "       2021-08-13 00:00:00+00:00  149.100000  149.444400  148.270000   \n",
       "\n",
       "                                     adjOpen  adjVolume  divCash  splitFactor  \n",
       "symbol date                                                                    \n",
       "AAPL   2016-08-16 00:00:00+00:00   25.699221  135177792      0.0          1.0  \n",
       "       2016-08-17 00:00:00+00:00   25.574980  101423904      0.0          1.0  \n",
       "       2016-08-18 00:00:00+00:00   25.605454   87938812      0.0          1.0  \n",
       "       2016-08-19 00:00:00+00:00   25.497622  101472288      0.0          1.0  \n",
       "       2016-08-22 00:00:00+00:00   25.518720  103280920      0.0          1.0  \n",
       "...                                      ...        ...      ...          ...  \n",
       "       2021-08-09 00:00:00+00:00  146.200000   48908689      0.0          1.0  \n",
       "       2021-08-10 00:00:00+00:00  146.440000   69023081      0.0          1.0  \n",
       "       2021-08-11 00:00:00+00:00  146.050000   48493463      0.0          1.0  \n",
       "       2021-08-12 00:00:00+00:00  146.190000   73779113      0.0          1.0  \n",
       "       2021-08-13 00:00:00+00:00  148.970000   58846293      0.0          1.0  \n",
       "\n",
       "[1258 rows x 12 columns]"
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       "      <th></th>\n",
       "      <th>close</th>\n",
       "      <th>high</th>\n",
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       "      <th rowspan=\"11\" valign=\"top\">AAPL</th>\n",
       "      <th>2016-08-16 00:00:00+00:00</th>\n",
       "      <td>109.38</td>\n",
       "      <td>110.2300</td>\n",
       "      <td>109.21</td>\n",
       "      <td>109.63</td>\n",
       "      <td>33794448</td>\n",
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       "      <td>25.600766</td>\n",
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       "    <tr>\n",
       "      <th>2016-08-17 00:00:00+00:00</th>\n",
       "      <td>109.22</td>\n",
       "      <td>109.3700</td>\n",
       "      <td>108.34</td>\n",
       "      <td>109.10</td>\n",
       "      <td>25355976</td>\n",
       "      <td>25.603110</td>\n",
       "      <td>25.638273</td>\n",
       "      <td>25.396822</td>\n",
       "      <td>25.574980</td>\n",
       "      <td>101423904</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>2016-08-18 00:00:00+00:00</th>\n",
       "      <td>109.08</td>\n",
       "      <td>109.6000</td>\n",
       "      <td>109.02</td>\n",
       "      <td>109.23</td>\n",
       "      <td>21984703</td>\n",
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       "    <tr>\n",
       "      <th>2016-08-19 00:00:00+00:00</th>\n",
       "      <td>109.36</td>\n",
       "      <td>109.6900</td>\n",
       "      <td>108.36</td>\n",
       "      <td>108.77</td>\n",
       "      <td>25368072</td>\n",
       "      <td>25.635929</td>\n",
       "      <td>25.713286</td>\n",
       "      <td>25.401511</td>\n",
       "      <td>25.497622</td>\n",
       "      <td>101472288</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>2016-08-22 00:00:00+00:00</th>\n",
       "      <td>108.51</td>\n",
       "      <td>109.1000</td>\n",
       "      <td>107.85</td>\n",
       "      <td>108.86</td>\n",
       "      <td>25820230</td>\n",
       "      <td>25.436673</td>\n",
       "      <td>25.574980</td>\n",
       "      <td>25.281958</td>\n",
       "      <td>25.518720</td>\n",
       "      <td>103280920</td>\n",
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       "    <tr>\n",
       "      <th>2021-08-09 00:00:00+00:00</th>\n",
       "      <td>146.09</td>\n",
       "      <td>146.7000</td>\n",
       "      <td>145.52</td>\n",
       "      <td>146.20</td>\n",
       "      <td>48908689</td>\n",
       "      <td>146.090000</td>\n",
       "      <td>146.700000</td>\n",
       "      <td>145.520000</td>\n",
       "      <td>146.200000</td>\n",
       "      <td>48908689</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>2021-08-10 00:00:00+00:00</th>\n",
       "      <td>145.60</td>\n",
       "      <td>147.7100</td>\n",
       "      <td>145.30</td>\n",
       "      <td>146.44</td>\n",
       "      <td>69023081</td>\n",
       "      <td>145.600000</td>\n",
       "      <td>147.710000</td>\n",
       "      <td>145.300000</td>\n",
       "      <td>146.440000</td>\n",
       "      <td>69023081</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>2021-08-11 00:00:00+00:00</th>\n",
       "      <td>145.86</td>\n",
       "      <td>146.7200</td>\n",
       "      <td>145.53</td>\n",
       "      <td>146.05</td>\n",
       "      <td>48493463</td>\n",
       "      <td>145.860000</td>\n",
       "      <td>146.720000</td>\n",
       "      <td>145.530000</td>\n",
       "      <td>146.050000</td>\n",
       "      <td>48493463</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>2021-08-12 00:00:00+00:00</th>\n",
       "      <td>148.89</td>\n",
       "      <td>149.0500</td>\n",
       "      <td>145.84</td>\n",
       "      <td>146.19</td>\n",
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       "      <th>2021-08-13 00:00:00+00:00</th>\n",
       "      <td>149.10</td>\n",
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       "      <td>148.27</td>\n",
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       "      <td>58846293</td>\n",
       "      <td>0.0</td>\n",
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      ]
     },
     "metadata": {},
     "execution_count": 6
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "source": [
    "df2"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0       109.38\n",
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     "metadata": {},
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  {
   "cell_type": "code",
   "execution_count": 9,
   "source": [
    "df2.shape"
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   "outputs": [
    {
     "output_type": "execute_result",
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     "metadata": {},
     "execution_count": 9
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "source": [
    "import matplotlib.pyplot as plt"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "source": [
    "plt.plot(df2)"
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   "outputs": [
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     "output_type": "execute_result",
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     "metadata": {},
     "execution_count": 11
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"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "source": [
    "import numpy as np"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "df1 = scaler.fit_transform(np.array(df2).reshape(-1, 1))"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "source": [
    "df1"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([[0.01551022],\n",
       "       [0.01511316],\n",
       "       [0.01476573],\n",
       "       ...,\n",
       "       [0.1060403 ],\n",
       "       [0.11355966],\n",
       "       [0.1140808 ]])"
      ]
     },
     "metadata": {},
     "execution_count": 16
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [
    "training_size = int(len(df1) * 0.65)"
   ],
   "outputs": [],
   "metadata": {}
  }
 ],
 "metadata": {
  "orig_nbformat": 4,
  "language_info": {
   "name": "python",
   "version": "3.6.12",
   "mimetype": "text/x-python",
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "pygments_lexer": "ipython3",
   "nbconvert_exporter": "python",
   "file_extension": ".py"
  },
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3.6.12 64-bit ('ml': conda)"
  },
  "interpreter": {
   "hash": "5f34a96552bf512dc8653c22d8bfcf62fb183c6fa458549f5ae291add63418ca"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}