Yangzijiang EPOCH100 GRU Final Testing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <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>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>3174</td>\n",
       "      <td>12/16/2019</td>\n",
       "      <td>1.13</td>\n",
       "      <td>1.13</td>\n",
       "      <td>1.09</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1.10</td>\n",
       "      <td>27908700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3175</td>\n",
       "      <td>12/17/2019</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1.11</td>\n",
       "      <td>1.07</td>\n",
       "      <td>1.07</td>\n",
       "      <td>1.07</td>\n",
       "      <td>32804600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3176</td>\n",
       "      <td>12/18/2019</td>\n",
       "      <td>1.08</td>\n",
       "      <td>1.11</td>\n",
       "      <td>1.08</td>\n",
       "      <td>1.09</td>\n",
       "      <td>1.09</td>\n",
       "      <td>38128700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3177</td>\n",
       "      <td>12/19/2019</td>\n",
       "      <td>1.09</td>\n",
       "      <td>1.09</td>\n",
       "      <td>1.07</td>\n",
       "      <td>1.08</td>\n",
       "      <td>1.08</td>\n",
       "      <td>20343100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3178</td>\n",
       "      <td>12/20/2019</td>\n",
       "      <td>1.08</td>\n",
       "      <td>1.09</td>\n",
       "      <td>1.07</td>\n",
       "      <td>1.07</td>\n",
       "      <td>1.07</td>\n",
       "      <td>33596100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date  Open  High   Low  Close  Adj Close    Volume\n",
       "3174  12/16/2019  1.13  1.13  1.09   1.10       1.10  27908700\n",
       "3175  12/17/2019  1.10  1.11  1.07   1.07       1.07  32804600\n",
       "3176  12/18/2019  1.08  1.11  1.08   1.09       1.09  38128700\n",
       "3177  12/19/2019  1.09  1.09  1.07   1.08       1.08  20343100\n",
       "3178  12/20/2019  1.08  1.09  1.07   1.07       1.07  33596100"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd \n",
    "\n",
    "\n",
    "df_Yzj = pd.read_csv('BS6.SI.CSV')\n",
    "df_Yzj.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>4/18/2007</td>\n",
       "      <td>1.35</td>\n",
       "      <td>1.39</td>\n",
       "      <td>1.33</td>\n",
       "      <td>1.34</td>\n",
       "      <td>0.842029</td>\n",
       "      <td>413736000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>4/19/2007</td>\n",
       "      <td>1.35</td>\n",
       "      <td>1.35</td>\n",
       "      <td>1.26</td>\n",
       "      <td>1.27</td>\n",
       "      <td>0.798042</td>\n",
       "      <td>95804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>4/20/2007</td>\n",
       "      <td>1.28</td>\n",
       "      <td>1.31</td>\n",
       "      <td>1.28</td>\n",
       "      <td>1.30</td>\n",
       "      <td>0.816894</td>\n",
       "      <td>70064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4/23/2007</td>\n",
       "      <td>1.31</td>\n",
       "      <td>1.31</td>\n",
       "      <td>1.26</td>\n",
       "      <td>1.27</td>\n",
       "      <td>0.798042</td>\n",
       "      <td>36099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>4/24/2007</td>\n",
       "      <td>1.26</td>\n",
       "      <td>1.27</td>\n",
       "      <td>1.26</td>\n",
       "      <td>1.27</td>\n",
       "      <td>0.798042</td>\n",
       "      <td>14422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3174</td>\n",
       "      <td>12/16/2019</td>\n",
       "      <td>1.13</td>\n",
       "      <td>1.13</td>\n",
       "      <td>1.09</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>27908700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3175</td>\n",
       "      <td>12/17/2019</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1.11</td>\n",
       "      <td>1.07</td>\n",
       "      <td>1.07</td>\n",
       "      <td>1.070000</td>\n",
       "      <td>32804600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3176</td>\n",
       "      <td>12/18/2019</td>\n",
       "      <td>1.08</td>\n",
       "      <td>1.11</td>\n",
       "      <td>1.08</td>\n",
       "      <td>1.09</td>\n",
       "      <td>1.090000</td>\n",
       "      <td>38128700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3177</td>\n",
       "      <td>12/19/2019</td>\n",
       "      <td>1.09</td>\n",
       "      <td>1.09</td>\n",
       "      <td>1.07</td>\n",
       "      <td>1.08</td>\n",
       "      <td>1.080000</td>\n",
       "      <td>20343100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3178</td>\n",
       "      <td>12/20/2019</td>\n",
       "      <td>1.08</td>\n",
       "      <td>1.09</td>\n",
       "      <td>1.07</td>\n",
       "      <td>1.07</td>\n",
       "      <td>1.070000</td>\n",
       "      <td>33596100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3179 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date  Open  High   Low  Close  Adj Close     Volume\n",
       "0      4/18/2007  1.35  1.39  1.33   1.34   0.842029  413736000\n",
       "1      4/19/2007  1.35  1.35  1.26   1.27   0.798042      95804\n",
       "2      4/20/2007  1.28  1.31  1.28   1.30   0.816894      70064\n",
       "3      4/23/2007  1.31  1.31  1.26   1.27   0.798042      36099\n",
       "4      4/24/2007  1.26  1.27  1.26   1.27   0.798042      14422\n",
       "...          ...   ...   ...   ...    ...        ...        ...\n",
       "3174  12/16/2019  1.13  1.13  1.09   1.10   1.100000   27908700\n",
       "3175  12/17/2019  1.10  1.11  1.07   1.07   1.070000   32804600\n",
       "3176  12/18/2019  1.08  1.11  1.08   1.09   1.090000   38128700\n",
       "3177  12/19/2019  1.09  1.09  1.07   1.08   1.080000   20343100\n",
       "3178  12/20/2019  1.08  1.09  1.07   1.07   1.070000   33596100\n",
       "\n",
       "[3179 rows x 7 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Yzj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "plt.figure()\n",
    "plt.plot(df_Yzj[\"Open\"])\n",
    "plt.plot(df_Yzj[\"High\"])\n",
    "plt.plot(df_Yzj[\"Low\"])\n",
    "plt.plot(df_Yzj[\"Close\"])\n",
    "plt.title('Yangzijiang Stock price history')\n",
    "plt.ylabel('Price (SGD)')\n",
    "plt.xlabel('Days')\n",
    "plt.legend(['Open','High','Low','Close'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Aa4GFxmhnRKWUqiriqYNfByzK+OSe2dEppVSli6cE/3dgpIhMAAoDE40xL3sWVYK0Dl4pf5uycisiwhltG6c7lColngT/HLAHqAPU8jYcpZQKd9N70wHIHtAzzZFULfEk+EONMRd5HolSSqmUiqcOfoyIZHyC1yp4pZQKFu9YNKNFZF+mdpMUvSurUkqFiedCJx0aWCmlqqCYCV5EznWbboyZGON9RwCfAIcBpcBAY8yryQSplFIqcfE0sj7meFwHOBWYDXSL8b5i4FFjzBz7BiGzReRnY8xvyYUaXaZ301dKqcoWTxXNFc7ndsn8xTjetwnYZD/OE5ElwOFAyhO89oNXSqlwydyTdT1wfCJvEJHWwEnAdJfX7hGRWSIyKzc3N4lwlFJKuYmnDv51ynshVgNOBObHuwIRqQd8DTxkjAnrfWOMGQgMBOjatavWsyilVIrEUwc/y/G4GBhqjJkcz8JFpCZWcv/MGPNNEvHFTX8ZlFIqWDx18B8ns2AREeB9YInX49ZoFbxSSoWLmOBFZCHuBWMBjDGmc4xlnwXcCiwUkXn2tD7GmJFJRaqUUioh0Urwl1dkwcaYSWjhWiml0iZigjfGrAk8FpHmwCn20xnGmByvA0uUdoNXSqlg8dyy7w/ADOB64A/AdBG5zuvAEiHaEV4ppcLE04vmCeCUQKldRJoCY4CvvAxMKaVUxcRzoVO1kCqZbXG+TymlVBrFU4IfLSI/AkPt538EMq4njFbBK6VUsGjdJN8AhhhjHhORa4GzsXrFDDTGfFtZAcZDa+CVUipctBL8CuA/ItIC+Bz4xBgzL8r8SimlMkjEunRjzKvGmDOA84DtwIciskRE+opI+0qLUCmlVFJiNpYaY9YYY/5ljDkJuAm4BljieWQJ0vHglVIqWDz94GuKyBUi8hkwClgO/N7zyBKhlfBKKRUmWiPrhcCNQE+sC52GAfcYY/IrKTallFIVEK2RtQ8wBPibMWZ7JcWjlFIqRaKNRXNBZQZSUVoDr5RSwXxxRapWwSulVDhfJHillFLhNMErpZRPaYJXSimf8k+C11ZWpZQK4osErzf8UEqpcL5I8EoppcJpgq/i+n23mE+mZqc7DKVUBornhh9VgjlAK+E/mpINwJ/OaJ3WOJRSmccXJXitgVdKqXC+SPBKKaXCaYJXSimf8k2C1/t9KKVUMF8keO0Gr5RS4XyR4JVSKlONWLCJaVnb0rJu33STVEqpTNRryBwAsgf0rPR1+6YEr3XwSikVzBcJXrQnvFJKhfFFgvcDYwx5BUXpDkPFMGLBJpZs2p3uMJSKiyb4DDF0xjo69fuJrNw96Q5FRdFryBwuffXXdIehVFx8k+C9HIvmy1nraN17BAVFJZ6tY+ySLQBk5eZ7tg6l1IHFFwne637wr4xZAUBuXqFn6yixW4m9+Jl6f9Jq5q7d4cGSlVKZzBcJ3g/GL8t1nZ6zu4BBE7MwFegm9OwPv3HNW1OSfr9SB5IJy3O57u0plJRW/a552g8+w/UaMoeZ2Ts4v0NT2jWvn+5wlPK9B4fNZefeInbvK6JR3VrpDqdCfFOC90s/+NCS+u59xQAU+6A0oZSqXL5J8F5K51g3Os6OUpXLL4VF8DDBi8gHIpIjIou8WkdlCSTZytjwkVbhp50uXfp8u5C+w6v87qhU3LwswX8EXOLh8g8oB+otCVNpyPS1fDJ1TbrDUKrSeJbgjTETge1eLT9sfZW1Io+FltRF62iUSgs/HHppr4MXkXtEZJaIzMrNde8qGMcyUhJLfmExIxduivh6ZZSiK9IdUimlnNKe4I0xA40xXY0xXZs2bZrWWJ74diF/+WwOizbsCpoebTCz3QVF5OwuSFkM89bvdJ2ueV8pfzHGeF6gS3uCzyTrduwDYF9RSdxf/gUvjefU58emLIZ3J2Sxv7i07LkPzhKVUi6Oenwkd348y9N1+CbBp+KHMJDQqwl07PsjPV6eEHMd2/L3V3zFIUq1uK7UAeGXpTmeLt/LbpJDgalABxFZLyJ3erauFC2n/FoiYV9RCavsgb/KukmmaD2xOPN7ZXbRVOpAU1pq+GHBRkp9eiGhl71objTGtDDG1DTGtDTGvO/VulIlsIlD22wru5pEu0T61/x1O9m1T8f9zxRDZqzl/iFzGTJjbdk0P3V08E0VTUqUVdGkt+bbR/uXcigtNVz15mRu/3BGukNRthx7hNite7wbKdZpd0FRpa0LfJXgK54Vy0rwkV73MPM2qVc+qJFzLeXVQ5r1q7rAFpy3zr2nlEofrw7twuLge0ic8fxYuvYf483KXPgiwaeqwF3qUoIfvWhTpVxsdFjDOmWPJziGDk7l/Waryq3mtu4pZNnmvHSHEcQYw7M//JaiZaVkMSoFAkeXV5tk/rrgLtf5+727aZAbXyT4VAkceM58ft/gOZUeR68h4etMNil8MjW77HFVudVc9/9M4OJXJqY7jCBbdhfy/qTVFVqGn+p2/cLrslu6t7kmeIdY28LLTRWp3j/aDjgnjrs0fTZtbcx5Mo02QqpM4IffY98k+FRsDLcqGnCcxnm4wZMpSFwbx12alm3JrKqOA5kP8sUBIZXbybms1r1HlD2urG6ZvkjwnleRV0anmhgfQpND1eeHEuGBxMvN9ZfPKqfq1xcJPlUCB2DkHivebfJI6b387EGzg1KecRxflXHMjV682bNlO2mCdwgk9tDtut0ejsDTKpqIGd56weszujG/beHBYXOZG0e9vkqOdnXNPIFeas4t46et5JsEn4qNEkii383fGDR9596ilK0jktgXV3m72931ySyGz9vINXHU66vk6ElY+n08JZuxS7aUPY922KVic6V7m/siwaeqr3jglGzodPeeJ+lsZE22BN+sfu3k3qiUDz393WLPR3BMlJdVQb5I8KliQv6H8nKUx90F7l0DA4k/2Vb3s45ukmRESh043A7tyip9e7keTfAOgS86UiL3ckMs37In6ut6dp9eWn9e9a3MCT7G9hQW8/ovK9IUTTkvC46+SfAVPc0xxrB6qzU8cMQEn4aDPFBHqGPEV8ze/cUVen9q7jdQ8WWo5G3YuS/o+b9/XEZRSeSNkorjPZ5l5Bd6N3yBLxJ8KvrBD52xruxxSYTqkHQcoBW9yCpT7ghVWhrfHbK2O26gksq6yT0FFUzwKYhBzwIyyz7HuDAFRSW07j2CQROzKv10+YR//uTZsn2R4FNhueOKz+IMHPw/6VwXIcMXFpcwPWtb0vEkqk2f+G5P1uebhWWPM6nE69cbQhyo8guL+XxWeaEuzy4APDdyCXsCZ3sp2OQbduyLPZOHNMG7iJRYYiWcr2evp2v/nyktNZSWGopLSqO/IYbSUsOctdbQsslW0UTqYfTM97/xx4HTWFGJQxnEc3uyfEdVit+qpXz2caq0dydmBT131gKUX/AYv9lrdoQNDQzw2FcLkogudXyT4Cvj2FmREz0Z9vl2IVv37KewuJTbPpzB0U+MqtD6sraWNwrFSnZuO5ebQLVHYOjgSL133Lw5biXTUlTq37K7IOYPYCYVmlPxY5NBHydjfTd/IwvX74o9YxKcRZ3Qfa8iQ4Jn5e7h929P4Z/fp2Y46VTyRYJPRT1zq0MPjjnPI1/Mj3t5v67YClR0DPbyT+ZMDt/P38ivK3KD5oy3oSZws4HyfCVx13W/9OMybhg4La55wToDKSk1PD9ySdD0XfuKOO35sTHHV8+kEnwGheILX8xaR+veI1i7bW/Q9L8OncsVb0yK+L4tuwtSsv7QCwsLi8KPn1jbPGd3AeOW5pSNfrpoY+bdb8EXCd5pZvZ2xi9L/E7l7ZrXS1kMzsT03IglUea0StQPDJ3r+ppzHyzYX8JT/1vErr1FPDB0Lre+H3zbt0hJOrRgsi0w7IL9vJp4l7x6DZlD2z4jGeg4Hd68q4Bb358OwE+/bYn0ViC1Cb6iZwMpKcHrr0SZN8etBOC3TYmV1k97fmxK1h96XHwzd0PYPLEaxa9/dyp3fDSzrPSfidvXdwn++nemcvuHM+Oef+qqbfzz+99SmuScyaBatejnF2u37+X7kKER3Pz5szl8Om0NL/201PX1RMLPKygixy4JiYhnJeVRi8IHVPr3T8tYYJ+Cx2rM/tbloEtWRT9j5h26Vdsau+Reu2b1tKw/9KisVSM8FZ7xwi9BPW1CrQk5+8jA/O6fBB/vl3tc39H830flPwA3DprGB5NXx50AIt1k490Jq8oelzqq92rESPB7Ct27763Zlu9a9bQ3wg4XGv62PYXMzN7uOu/J/cewaZed4Ek+eW3cuc81/r37ixkXoUHV+ZkidUcNeOLbRXHFcedHM7kqyml9POuKJRWlsww8/tMvji8lkXaiaIJK7SFF+JNbNXJ9zxvjYl8IFdg3MqlKMcAfCT6BBpL8/SWuvTn6fbc4rvdHusnGC6OWUlhsZXZnv9ZqImyLchf1QSGt+QGLNrjX5xUWuTdMhp5O3jBwGte/M9X1R2J/cfkyRMJ3zHiSWV5BEWcO+IUrXRLrE98u4o6P3M+inMOkujWyBtouEjF2aQ7zYzTMVfTY88OFTp9OzWbKysS/Xy/FPotbT+d+qekn7vz+XxsbnLinRug8sG+/+/HmPIYCi128cXdY21i8fvRo+GB/JPgUyA453YqloKgkrr7RY5Zs4eT+Y3h+5BI+mrw67PVIO7jBuLbsRywlhExeYV+WvT9GT5VqIhSXhCb48Pmy7at8wUruneyDLis3P3zebeHTyt9bXuIPLVUv2hCepK9+c3JKbuFX0dLVV3PWVziGdHtq+GJuem96usMAoE5NK/WUlEbfPx/+PP6ODbGUJLEPRCo7PvGt+/Uat74/g15D5rBg/c6E1nPvp7MTji0eB3SCT/a0u6CohGOeGs2A0e714W4GTsyin0s3qkjdsyL9dkRKVIXFpWVnCh86fkgiVek4vTAquCE4z6Xa5fx/jy97HBg+OZJY1VIBoT9ul78efjYwb91O/vPTsriWF00yB7cxho325e3vTgg+0ypw6XURe4GJvyVZa7blc/5L48jJS02vk1SrUc1KPcs2WwWRtdv2MmO1e5ViqpREGZYgkkh7srODwNchP/4jFmziyjcmhw2NkA6+SfDJHDtjliTe2wbKD+5hMyp+Q+tIZwGRfnwiJf7r3pnCyXYXyGccPySx6p5FYGzI93DCMz8xZVXkU3m30Kas3MrXs60dvXqcCT7eevHAeCHPfL+4rCotJ6+At8evirvbnPP7jOdm5QBfzlrPmQN+cZ3/0ld/jWsZQTE49tJU/GhF8+HkbLK37WXkgk2erieSffutS/8vfHlC1Pn+O2Y5AOe+NI4/vDvV05hqujSkxhKpBO88qxwSYXhxr/rzJ8IXCT7eGvide/cHPd+0K7lf2HnrrNOvZH5UduTvD1pvYFmhSo1x7V0TqSC6Zbd7PX9RjCoaQVwT7U2DppcNvhZqucvVrze9N51Hv7ROpwOls1gSHRLiw8nZfDQlG7D6S/9r9NKgbnOte4+gV4R7XTq/hmvfmsJolx4+oWbYjdShoxACYd/Nxp37GDBqadxDGrz+y8q45quqvplr/divyNnDnwcHVz8YY4Ia53+O0V021Oqt+WU/IF86hhsI2F9cSl5BUdjFf7WTSvDl2eW9X7No3XtEWRfPWJI6y0sxXyT4ePV39Elv/8Qo+g6Pr2E1VCLdMEOd8twYznjhl7LnkU7jSksp604YLLGkGE+jZaRqn/uHuCfLuz6JPKbM3v3FTEqgIW/c0hxu/3BG3FfiBuza5977aMRCq8RqjGHKqq1lJffQH/f7Bs9m9proJflEanX+8fUC3pmwKurZQbobWQO2Rmn0TxXnZw3tLht6/cPdUfYnNwVFJWWf4ZUxK4Kmvz1+Fe2fHEWnfj9x3dvBZwTJjCi6xtGe9MnUNYB1wV88Qhty0+GASvDO0mysxse4JHHAxltqjTRXqi/fN5iIy1y8cXfUfsBu3OrRo7njo5mMX5bL+GWRex/kFRQFVbEMnbE2ZvvJl7PXc9Og6fxvntWX/o8uV+DuyN8fNs3Ni6NjH9CBcKK1ebhFnFdQxMs/L6/wuEUAu/YWhV053e/732jde0TQD2jX/mPi6no4K3t72FXIobj3MoUAABUxSURBVIwxLNqwC2MM67bv5YGhc2OWXEfEUW2UX1hM694jXF8rKTVl1YDOwsmjX8znX452sYUbdjF8Xvm1FPcNdi+wRPPj4i08+8Nv7C8ujdilOZKsCGfAlck3CT6eBtPY9z1NcJ0VeG+s+udIpepYA3b9sMD9oqmDIlxQYkz0HiZu1ROhnEP8uvWqiceYKKfpPyzYxP2Oq30f/2Zh1JhHL9pc1mC3dlvkariC4hLeHLcyZi+dSCVeZ/IIXCjj7D4HVvVDtOqgAaOW8trYFa4XhSXq+nenRGwb6PDk6KDnzuGTN+3aR9/hixg+b0PQZ7runakMnGhVS7z3a5ZrCXjKqm1c/vokBk9bwzkvjuP7+RuZFOWscU9hcdg9j90MjdK+VVxqyurGN+0qoPfX1oBek13ajR4cNq+s3SZ028Tr/Umr+dMH04P286rCFwk+3ryd6rHRE/1Fd/pqdnjdodP+4lIOrVsz4eXeP8R92INIDZ+jF22O2ivmijcmcVzf0RFfB+jy7M/xBxhBrF4uoaW+aHfAum/wbL6yG3yjLXfMb1t46cdlPPO9e1VdrEvVHxw2L2bV0t2fzOI+uw7arRCy2060Wbn5UYfYWJmTx/XvTOHvX81n+LwNriX+WHcFcxKxBvbat7+E3l8v5JOpa3hw2DweHDaP3LzCoG6xYFVvdv9PeINpYDjcpxzVnQN/db+2A6yhKuIR7di6+s3JzHe0XQ2baR1LkXpvBdptKmJalrc9fLxSI90BVKaKjBiXankFxREbMQGe/F/sqzhrVa8Wd1VTpI/+RhwNRvkJVtMkw63hNhVeG7uCP3Rt6fraPrsqIa8CNwO57YMZDLvnjLLCw12fzKJWjWos739p0Hy5eYW45Z9Aog70Jske0NN1PS+MXMrM7B3MzN7BF7PW88HkbIb3OoviklIKikupWd0xMF0cZ7PTsrbx8OfzufX0I8N+xk55bozreza5JGe3M6kZq7dHHLwv3usRnHXrbkKrW7btKWTrnqpXwvaaL0rw8Yqz9x4AT13e0btAsHb0Cxx9y5ORyO9VvH3T0yXSlbupcPa/xrlO/3GxVS3kTIhTV22Lu5QJ7iW7QFWAs0rjlOfGuJ5NhDakG2PKqoR+XZFbtowa1YO3X6AE+4+vF3L80z8GtZXE007z35+tBPrNnPVMXB7/1ZdbdhcwdVX5VZ///mm563yBM6hQFR0yIpIvZkW/EO30FA1SVtUcEAm+uKSUjyavjmvn6tC8PgAnHtGQt27u4llMqWjjjbe/uTXvAbGpk+LcLW4cNI1LX51Y4WWu3ppPx74/Bk0LHWbCGENeSGPnU8MX0bX/GGZmb+fW92fw2JdW/bLbD+Cb41aWXWRz4j/Lq8na9hnJ5Bg9mdZut67cTvTs7IrXJ3HjIKvBOievIO4eOZ9MzQYIu2o6Vf4V46LDzY7rJeY/fVHM5Y168JwKxxRqep/uEV+b0rtbytcHPqmiEawLKyKNJ/PZ9LWuV5GGat6gtmP0R+GyTi2446zWfDg5O2WxBqRiYKJ4rlItlyF99DKQMYb8wmJusYcx3hHjSt1Q45blMDak8Tu07zfAOS8Gn0n0+25xWNfJwdOsxsXAsA2BXjFu3WmjdddbEUfjeDJy8qyEfsXrk1joMrREJH2HL2b4vI20twtQ6dTwoNhtW8e2aJDy9TZvUCds2qd3nkqnwxtyyMG1Ur4+8FEJftaaHREbU+JtDC01cNNprQBodLC1E3h1SunVciNJVx/syzu34G0Pz4RSodTATYOmMXdtecPd3LU74i5t3uFyXcTSzbHbFD6eusZ1WAgovxo5a2s+vSJcj5BOiST3gNlrdkTtHZNqlV0t2fvSYxKa//LOLTinXVPPkjv4KMFHE29VRnFJKbeefiSzn+xBm6bWDUCuO9lqoGtWv3ZKY4rU/fCdW05O6XoCDPDEZcd6suxo7j23LR0OS3+pLZoJy3PDRqO85q0pcXXnqwzx9BtX4erWTq6CItnSe7yFtm7HNCPr+ct47YaTklpPInyd4I0xTF21Le6LSG481Sq9N65Xnsw7tzyE7AE9ObzRQSmNLVLyuOT4w8KmXdYpfFoy7j63TUqWE4/DGtTh6Ss60qllQ9o0rcdHd5xS9tqrN5wY9b3ntm8KwNHN6nH3OUelLKZE2iyU9w6tG73kekwFCwa9LmgbNu2S46xj6YcHzi6bdtzvGpTNe+fZR8Vd//7Lo+fx3z+ewBltGgPWUAjTHu/OnKcujPie+X0v4p1bTqZaNYl5M6BU8EWCj9T98Z0JWdw4aFrEln6nlc9dymMXd4j4+l+7t0uoF05F/LHrEUHPX7+xC1nPX1ahZcbTdW7iYxckvfyrTvxd2eNh95zOtD7dueOs8uR8fodmPG6fwl5y/GHMjXAQtGhYh4/vOIXsAT0Z88h5PNGzIye0bFj2+i2nt0o6xpJSU+G6VecPld81qedd1QHAx3ecWva4S6tDgl77v7OO4ov7zuD2M1vT7ZhmQPkPfywrnruU7AE9uefc8AR/y+lHAnD84Q3JHtCTD+84hY//71RuPLUVjQ6uyc2nle9fH9zelZtOa8Vfzi9fTv061lnBi7/vTJum9bjmpJb8tXs7AM5r35TDGtbh0Lq1GPSnrq6xNTy4puvdo7zi6ZpE5BIRWSYiK0Wkt5frcpPIIPo1qleL2k/+gg7NyHqhJ22a1I26nBYN65SVPE4K2WljCYyR/ezVxwdNr27/2rePcN/YGX26c1mnw/j6z2dEXHYgvV95gpWIv7rvDGaEtOq3anwwlx5/GHVqVmNVlB+UJ3sey11nB5esX73hJN6+uQuTe3fjdLtEE+re89qSPaAntWtUp1HdWrxx00n0uSy43vL1G08K2w6HNbQapwbeejL9r+7E6hcu46eHz2Vhv4t4sHs7Tjwi/u95yF2n8eZNybcJnN+hWdLv9Vq0Eu/ZRzfhoR7tOLOt+7YJlT2gJ+/fZv2YPXZxB2Y+0SOu913QoTwJd2henxev6+wa17NXHcfxhzegz2XH8PIfTuCbv5xVlshrVhf6XtGRBnVq0u/K4xj0p66896euvHHTScx96sKw5Z3e5tCyxw/1aEfN6uVpbcwj5zHmkXPpfekxLOt/CWe3axISbzOa1KtNy0YHM7fvRWVVswDdjmnO89d04vjDywsYgTMAZyeJM9o2JntAT9o5GpAv7Nic7AE9I17XUFk860UjItWBN4ELgfXATBH5zhgTuztLinhxSv7L384nv7CYXfuKaFyvFv/+cRn3ndeWHXv3s2FnAWe2bUyNasKoRZvpcWxz2j85KuYy7z2vTdB445F+4QP9q1/8fWf+bl+efWbbxjRrUIe3brbq7rMH9OTzmWs54tCDmbh8K+2b1+ORL+aXNbK+cG0nenZuQdfW1kExpXc3/vH1Anp2agHA2442gK//fCbz7fHYA93pxjxyHkc3sw6Cxy7pQGkp5BVavU4utZcRr8s7Wz8289fvKqtnbtko/AKZF687gXPabeTCjs0B64wt0Bvj4Qvb8/CF7THGukHKqtw9HH7IQZSUGpZtyaNLq0YMn7eBB4fNA6BR3Vr07NyCIw49iyvfmBy2rrOObszctTvDeihN6d2trI613xUdmbN2J0c1qcurUQaU+uyu0zizbWOOenxk3N9J++b1gq5I7diiAb9tin6NwLntm/LJ/53Ke79m0X/EEqqJ1XDc/ZhmZb177j2vDee0s5JvYIyXpc9eQo1qwoqcPWVDHGQ9f1lZf/0TjjgkKEFN+scFzMzeTmmpdSObwPAKYx89j9GLNnNtl8NZvmUP45blMuaRczm6Wf2yz+Aco2jwnaeVJVpnKfv927py1OMjufm0I4M+X/VqQg9724O1Xz44bC69Ljiak1o14qvZ68uuRwhUswYE9tVALMlwViXVsH88Eh0JNV3EqzuBi8gZQD9jzMX288cBjDEvRHpP165dzaxZiY0sB3Dd21OYFTIyYJumdRMaF8XLX9rlW/K46L8Tad6gNp1bHkJuXiGXd25B/xFLeP6aTlzb5XCOeWo0jQ6uydy+Vh/dKau28vXsDfS/+ngOqmWNI3PRfyewfMseJv3jApZvyeP43zWkmUvXK6dd+4o44ZmfaN6gNtP7xFcKC/Xi6KW8Nd6656wX31NuXiGnPDeG/1x/Ar8/2f2q04rYXVDEXR/P4sXfd6a14wystNQwfP4GurRqRKO6tRCgfh2r99SufUU0PKgm2VvzWbxxNz07u/94/bZxNytz9/BXe6ycpy7vyLM/WGWYwHdVVFLK/uJS5q/byWcz1jLg2k5s2V3AY18t4L7z2nLxcYdx7FOjaVyvFpP+0Y2/fzWfL2at595z2/BQj/a89OMyPrBv4vLTw+fyw/yNFBaXcusZRzJx+daynl/GGNbv2McRjqtI7/xoJmOX5jDk7tM4s62VVJdvyaN6NaGto7T6w4KNdGnViN8dktq2JrC+53cmrqL7Mc1pVr82jaLUvReXlFK9miR01XlBUQkvjFzCQz3aR112RQR+FO8+5ygG/bqa5645PuyHKNZ7vcoxIjLbGONaJ+Rlgr8OuMQYc5f9/FbgNGPM/SHz3QPcA9CqVauT16xZk/C6Ri7cxNAZa/l1xVbaNKlLTl4h53doSkmpYdSizZzZtjFTHFfftW58MPn7S2h0cE2Wb9nDU5d35M6zU9eYl4x3J6yi+7HNy0ocbtZu28vweRu4v9vRCR0Ab41fySXHHRZ0+pmI/MJiLn5lIk9d3pGLj0tNg6/f9PtuMRd1bM6ZRzdhZvZ2VuXs4YZT428vKC4pRUSoXk3Ynr+fdyeu4m8XdSirbli8cRdHNanLwbUSO+nOzSvk4ynZPHJh+0pp1POrSSu2si2/kO7HNue1sSt49KL21K7hPoBfqMHT1tDp8IackEBVYiLSleCvBy4OSfCnGmMeiPSeZEvwSil1oIqW4L1sZF0POLuDtAQyo2OxUkodALxM8DOBdiJylIjUAm4AvvNwfUoppRw860VjjCkWkfuBH4HqwAfGmOTukaeUUiphng42ZowZCcTfR0wppVTK+OJKVqWUUuE0wSullE9pgldKKZ/SBK+UUj7l2YVOyRCRXCDxS1ktTYDo9ynLbBp/+lTl2EHjT7d0x3+kMcZ1qM2MSvAVISKzIl3NVRVo/OlTlWMHjT/dMjl+raJRSimf0gSvlFI+5acEPzDdAVSQxp8+VTl20PjTLWPj900dvFJKqWB+KsErpZRy0ASvlFI+VeUTfLpv7B0vEckWkYUiMk9EZtnTDhWRn0Vkhf2/kT1dROQ1+zMtEJHk7xKdfLwfiEiOiCxyTEs4XhG5zZ5/hYjclub4+4nIBnsbzBORyxyvPW7Hv0xELnZMT8v+JSJHiMg4EVkiIotF5EF7esZvgyixV4nvX0TqiMgMEZlvx/+MPf0oEZluf4+f28OgIyK17ecr7ddbx/pclcYYU2X/sIYhXgW0AWoB84GO6Y4rQqzZQJOQaS8Cve3HvYF/2Y8vA0YBApwOTE9DvOcCXYBFycYLHApk2f8b2Y8bpTH+fsDfXObtaO87tYGj7H2qejr3L6AF0MV+XB9YbseZ8dsgSuxV4vu3v8N69uOawHT7O/0CuMGe/g7wZ/vxX4B37Mc3AJ9H+1yVsf8E/qp6Cf5UYKUxJssYsx8YBlyV5pgScRXwsf34Y+Bqx/RPjGUacIiIuN/12SPGmInA9pDJicZ7MfCzMWa7MWYH8DNwiffRR4w/kquAYcaYQmPMamAl1r6Vtv3LGLPJGDPHfpwHLAEOpwpsgyixR5JR37/9He6xn9a0/wzQDfjKnh763Qe2yVdAdxERIn+uSlPVE/zhwDrH8/VE35HSyQA/ichssW40DtDcGLMJrIMCaGZPz9TPlWi8mfg57rerMD4IVG+Q4fHbp/wnYZUkq9Q2CIkdqsj3LyLVRWQekIP1o7gK2GmMKXaJpSxO+/VdQGMyYP+p6gne7Tbxmdrv8yxjTBfgUqCXiJwbZd6q9LkgcryZ9jneBtoCJwKbgP/Y0zM2fhGpB3wNPGSM2R1tVpdpaf0MLrFXme/fGFNijDkR617SpwLHRokl4+IPqOoJvsrc2NsYs9H+nwN8i7XTbAlUvdj/c+zZM/VzJRpvRn0OY8wW+8AtBQZRfrqckfGLSE2sBPmZMeYbe3KV2AZusVe17x/AGLMTGI9VB3+IiATugueMpSxO+/WGWNWDaY+/qif4KnFjbxGpKyL1A4+Bi4BFWLEGejXcBgy3H38H/MnuGXE6sCtwWp5micb7I3CRiDSyT8cvsqelRUg7xjVY2wCs+G+we0McBbQDZpDG/cuuw30fWGKMednxUsZvg0ixV5XvX0Saisgh9uODgB5Y7QjjgOvs2UK/+8A2uQ74xVitrJE+V+WpzBZdL/6weg8sx6ojeyLd8USIsQ1Wa/p8YHEgTqx6urHACvv/oaa8Ff9N+zMtBLqmIeahWKfRRVglkTuTiRf4P6zGpZXAHWmO/1M7vgVYB18Lx/xP2PEvAy5N9/4FnI11Or8AmGf/XVYVtkGU2KvE9w90BubacS4C+trT22Al6JXAl0Bte3od+/lK+/U2sT5XZf3pUAVKKeVTVb2KRimlVASa4JVSyqc0wSullE9pgldKKZ/SBK+UUj5VI/YsSvmPiJRgddmrCRRjjSXyirEuwlHKFzTBqwPVPmNdio6INAOGYF2B+HRao1IqhbSKRh3wjDV8xD1YA2GJiLQWkV9FZI79dyaAiHwqImWjGYrIZyJypYgcZ48fPs8eSKtduj6LUk56oZM6IInIHmNMvZBpO4BjgDyg1BhTYCfrocaYriJyHvCwMeZqEWmIdYVmO+C/wDRjzGf2JfXVjTH7KvcTKRVOq2iUKhcY/a8m8IaInAiUAO0BjDETRORNu0rnWuBrY0yxiEwFnhCRlsA3xpgV6QheqVBaRaMUICJtsJJ5DvAwsAU4AeiKdTehgE+Bm4E7gA8BjDFDgCuBfcCPItKt8iJXKjJN8OqAJyJNsW7B9oax6iwbApvsHjW3Yt06LuAj4CEAY8xi+/1tgCxjzGtYg2h1rrzolYpMq2jUgeog+449gW6SnwKBoW3fAr4WkeuxhojND7zJGLNFRJYA/3Ms64/ALSJSBGwG/lkJ8SsVkzayKpUAETkYq/98F2PMrnTHo1Q0WkWjVJxEpAewFHhdk7uqCrQEr5RSPqUleKWU8ilN8Eop5VOa4JVSyqc0wSullE9pgldKKZ/6f0uRHxZu9uJtAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(df_Yzj[\"Volume\"])\n",
    "plt.title('Yangzijiang stock volume history')\n",
    "plt.ylabel('Volume')\n",
    "plt.xlabel('Days')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking if any null values are present\n",
      " Date         0\n",
      "Open         0\n",
      "High         0\n",
      "Low          0\n",
      "Close        0\n",
      "Adj Close    0\n",
      "Volume       0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(\"checking if any null values are present\\n\", df_Yzj.isna().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_Yzj = df_Yzj.dropna() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking if any null values are present\n",
      " Date         0\n",
      "Open         0\n",
      "High         0\n",
      "Low          0\n",
      "Close        0\n",
      "Adj Close    0\n",
      "Volume       0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(\"checking if any null values are present\\n\", df_Yzj.isna().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>4/18/2007</td>\n",
       "      <td>1.35</td>\n",
       "      <td>1.39</td>\n",
       "      <td>1.33</td>\n",
       "      <td>1.34</td>\n",
       "      <td>0.842029</td>\n",
       "      <td>413736000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>4/19/2007</td>\n",
       "      <td>1.35</td>\n",
       "      <td>1.35</td>\n",
       "      <td>1.26</td>\n",
       "      <td>1.27</td>\n",
       "      <td>0.798042</td>\n",
       "      <td>95804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>4/20/2007</td>\n",
       "      <td>1.28</td>\n",
       "      <td>1.31</td>\n",
       "      <td>1.28</td>\n",
       "      <td>1.30</td>\n",
       "      <td>0.816894</td>\n",
       "      <td>70064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4/23/2007</td>\n",
       "      <td>1.31</td>\n",
       "      <td>1.31</td>\n",
       "      <td>1.26</td>\n",
       "      <td>1.27</td>\n",
       "      <td>0.798042</td>\n",
       "      <td>36099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>4/24/2007</td>\n",
       "      <td>1.26</td>\n",
       "      <td>1.27</td>\n",
       "      <td>1.26</td>\n",
       "      <td>1.27</td>\n",
       "      <td>0.798042</td>\n",
       "      <td>14422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3174</td>\n",
       "      <td>12/16/2019</td>\n",
       "      <td>1.13</td>\n",
       "      <td>1.13</td>\n",
       "      <td>1.09</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>27908700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3175</td>\n",
       "      <td>12/17/2019</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1.11</td>\n",
       "      <td>1.07</td>\n",
       "      <td>1.07</td>\n",
       "      <td>1.070000</td>\n",
       "      <td>32804600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3176</td>\n",
       "      <td>12/18/2019</td>\n",
       "      <td>1.08</td>\n",
       "      <td>1.11</td>\n",
       "      <td>1.08</td>\n",
       "      <td>1.09</td>\n",
       "      <td>1.090000</td>\n",
       "      <td>38128700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3177</td>\n",
       "      <td>12/19/2019</td>\n",
       "      <td>1.09</td>\n",
       "      <td>1.09</td>\n",
       "      <td>1.07</td>\n",
       "      <td>1.08</td>\n",
       "      <td>1.080000</td>\n",
       "      <td>20343100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3178</td>\n",
       "      <td>12/20/2019</td>\n",
       "      <td>1.08</td>\n",
       "      <td>1.09</td>\n",
       "      <td>1.07</td>\n",
       "      <td>1.07</td>\n",
       "      <td>1.070000</td>\n",
       "      <td>33596100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3179 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date  Open  High   Low  Close  Adj Close     Volume\n",
       "0      4/18/2007  1.35  1.39  1.33   1.34   0.842029  413736000\n",
       "1      4/19/2007  1.35  1.35  1.26   1.27   0.798042      95804\n",
       "2      4/20/2007  1.28  1.31  1.28   1.30   0.816894      70064\n",
       "3      4/23/2007  1.31  1.31  1.26   1.27   0.798042      36099\n",
       "4      4/24/2007  1.26  1.27  1.26   1.27   0.798042      14422\n",
       "...          ...   ...   ...   ...    ...        ...        ...\n",
       "3174  12/16/2019  1.13  1.13  1.09   1.10   1.100000   27908700\n",
       "3175  12/17/2019  1.10  1.11  1.07   1.07   1.070000   32804600\n",
       "3176  12/18/2019  1.08  1.11  1.08   1.09   1.090000   38128700\n",
       "3177  12/19/2019  1.09  1.09  1.07   1.08   1.080000   20343100\n",
       "3178  12/20/2019  1.08  1.09  1.07   1.07   1.070000   33596100\n",
       "\n",
       "[3179 rows x 7 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Yzj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train and Test size 2543 636\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "train_cols = [\"Open\",\"High\",\"Low\",\"Close\",\"Volume\"]\n",
    "df_train, df_test = train_test_split(df_Yzj, train_size=0.8, test_size=0.2, shuffle=False)\n",
    "print(\"Train and Test size\", len(df_train), len(df_test))\n",
    "# scale the feature MinMax, build array\n",
    "x = df_train.loc[:,train_cols].values\n",
    "min_max_scaler = MinMaxScaler()\n",
    "x_train = min_max_scaler.fit_transform(x)\n",
    "x_test = min_max_scaler.transform(df_test.loc[:,train_cols])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_timeseries(mat, y_col_index):\n",
    "    # y_col_index is the index of column that would act as output column\n",
    "    # total number of time-series samples would be len(mat) - TIME_STEPS\n",
    "    dim_0 = mat.shape[0] - TIME_STEPS\n",
    "    dim_1 = mat.shape[1]\n",
    "    x = np.zeros((dim_0, TIME_STEPS, dim_1))\n",
    "    y = np.zeros((dim_0,))\n",
    "    \n",
    "    for i in tqdm_notebook(range(dim_0)):\n",
    "        x[i] = mat[i:TIME_STEPS+i]\n",
    "        y[i] = mat[TIME_STEPS+i, y_col_index]\n",
    "    print(\"length of time-series i/o\",x.shape,y.shape)\n",
    "    return x, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def trim_dataset(mat, batch_size):\n",
    "    \"\"\"\n",
    "    trims dataset to a size that's divisible by BATCH_SIZE\n",
    "    \"\"\"\n",
    "    no_of_rows_drop = mat.shape[0]%batch_size\n",
    "    if(no_of_rows_drop > 0):\n",
    "        return mat[:-no_of_rows_drop]\n",
    "    else:\n",
    "        return mat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "import numpy as np\n",
    "from tqdm._tqdm_notebook import tqdm_notebook\n",
    "params = {\n",
    "    \"batch_size\": 20,  # 20<16<10, 25 was a bust\n",
    "    ##\"epochs\": 300,\n",
    "    \"epochs\":100,\n",
    "    \"lr\": 0.00010000,\n",
    "    \"time_steps\": 60\n",
    "}\n",
    "\n",
    "TIME_STEPS = params[\"time_steps\"]\n",
    "BATCH_SIZE = params[\"batch_size\"]\n",
    "stime = time.time()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e5188cff5d13413dbd9570821f9ddf6d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=2483), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "length of time-series i/o (2483, 60, 5) (2483,)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c84b82d9b8ce4ef5a051a98fe3815a50",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=576), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "length of time-series i/o (576, 60, 5) (576,)\n"
     ]
    }
   ],
   "source": [
    "x_t, y_t = build_timeseries(x_train, 3)\n",
    "x_t = trim_dataset(x_t, BATCH_SIZE)\n",
    "y_t = trim_dataset(y_t, BATCH_SIZE)\n",
    "x_temp, y_temp = build_timeseries(x_test, 3)\n",
    "x_test_t = trim_dataset(x_temp, BATCH_SIZE)\n",
    "y_test_t = trim_dataset(y_temp, BATCH_SIZE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential, load_model\n",
    "from keras.layers import Dense, Dropout\n",
    "from keras.layers import GRU\n",
    "from keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau, CSVLogger\n",
    "from keras import optimizers\n",
    "\n",
    "\n",
    "def create_model():\n",
    "    gru_model = Sequential()\n",
    "    gru_model.add(GRU(100, batch_input_shape=(BATCH_SIZE, TIME_STEPS, x_t.shape[2]), dropout=0.0, recurrent_dropout=0.0, stateful=True, kernel_initializer='random_uniform'))\n",
    "    gru_model.add(Dropout(0.5))\n",
    "    gru_model.add(Dense(20,activation='relu'))\n",
    "    gru_model.add(Dense(1,activation='sigmoid'))\n",
    "    optimizer = optimizers.RMSprop(lr=params[\"lr\"])\n",
    "    gru_model.compile(loss='mean_squared_error', optimizer=optimizer)\n",
    "    gru_model.summary()\n",
    "    return gru_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\Victor\\\\Desktop\\\\project5\\\\Stock-Price-Prediction\\\\outputyzjGRU1'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "#OUTPUT_PATH ='/home/victor/Stock-Price-Prediction/outputs/'\n",
    "OUTPUT_PATH ='C:\\\\Users\\\\Victor\\\\Desktop\\\\project5\\\\Stock-Price-Prediction\\\\outputyzjGRU1'\n",
    "OUTPUT_PATH"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\Victor\\\\Desktop\\\\project5\\\\Stock-Price-Prediction'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model not found\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e4f6988dc1c5453b9524cd76e9782c9b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=576), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "length of time-series i/o (576, 60, 5) (576,)\n",
      "Test size (560, 60, 5) (560,)\n",
      "Building model...\n",
      "checking if GPU available []\n",
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "gru_1 (GRU)                  (20, 100)                 31800     \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (20, 100)                 0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (20, 20)                  2020      \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (20, 1)                   21        \n",
      "=================================================================\n",
      "Total params: 33,841\n",
      "Trainable params: 33,841\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Train on 2480 samples, validate on 560 samples\n",
      "Epoch 1/100\n",
      " - 7s - loss: 0.0453 - val_loss: 0.0111\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 0.01106, saving model to C:\\Users\\Victor\\Desktop\\project5\\Stock-Price-Prediction\\outputyzjGRU1\\best_model.h5\n",
      "Epoch 2/100\n",
      " - 6s - loss: 0.0360 - val_loss: 0.0165\n",
      "\n",
      "Epoch 00002: val_loss did not improve from 0.01106\n",
      "Epoch 3/100\n",
      " - 6s - loss: 0.0348 - val_loss: 0.0228\n",
      "\n",
      "Epoch 00003: val_loss did not improve from 0.01106\n",
      "Epoch 4/100\n",
      " - 6s - loss: 0.0346 - val_loss: 0.0232\n",
      "\n",
      "Epoch 00004: val_loss did not improve from 0.01106\n",
      "Epoch 5/100\n",
      " - 6s - loss: 0.0329 - val_loss: 0.0231\n",
      "\n",
      "Epoch 00005: val_loss did not improve from 0.01106\n",
      "Epoch 6/100\n",
      " - 6s - loss: 0.0303 - val_loss: 0.0229\n",
      "\n",
      "Epoch 00006: val_loss did not improve from 0.01106\n",
      "Epoch 7/100\n",
      " - 6s - loss: 0.0275 - val_loss: 0.0217\n",
      "\n",
      "Epoch 00007: val_loss did not improve from 0.01106\n",
      "Epoch 8/100\n",
      " - 6s - loss: 0.0227 - val_loss: 0.0180\n",
      "\n",
      "Epoch 00008: val_loss did not improve from 0.01106\n",
      "Epoch 9/100\n",
      " - 6s - loss: 0.0134 - val_loss: 0.0038\n",
      "\n",
      "Epoch 00009: val_loss improved from 0.01106 to 0.00380, saving model to C:\\Users\\Victor\\Desktop\\project5\\Stock-Price-Prediction\\outputyzjGRU1\\best_model.h5\n",
      "Epoch 10/100\n",
      " - 6s - loss: 0.0060 - val_loss: 0.0018\n",
      "\n",
      "Epoch 00010: val_loss improved from 0.00380 to 0.00184, saving model to C:\\Users\\Victor\\Desktop\\project5\\Stock-Price-Prediction\\outputyzjGRU1\\best_model.h5\n",
      "Epoch 11/100\n",
      " - 6s - loss: 0.0041 - val_loss: 0.0015\n",
      "\n",
      "Epoch 00011: val_loss improved from 0.00184 to 0.00149, saving model to C:\\Users\\Victor\\Desktop\\project5\\Stock-Price-Prediction\\outputyzjGRU1\\best_model.h5\n",
      "Epoch 12/100\n",
      " - 6s - loss: 0.0029 - val_loss: 0.0014\n",
      "\n",
      "Epoch 00012: val_loss improved from 0.00149 to 0.00139, saving model to C:\\Users\\Victor\\Desktop\\project5\\Stock-Price-Prediction\\outputyzjGRU1\\best_model.h5\n",
      "Epoch 13/100\n",
      " - 6s - loss: 0.0026 - val_loss: 0.0013\n",
      "\n",
      "Epoch 00013: val_loss improved from 0.00139 to 0.00127, saving model to C:\\Users\\Victor\\Desktop\\project5\\Stock-Price-Prediction\\outputyzjGRU1\\best_model.h5\n",
      "Epoch 14/100\n",
      " - 6s - loss: 0.0025 - val_loss: 0.0012\n",
      "\n",
      "Epoch 00014: val_loss improved from 0.00127 to 0.00124, saving model to C:\\Users\\Victor\\Desktop\\project5\\Stock-Price-Prediction\\outputyzjGRU1\\best_model.h5\n",
      "Epoch 15/100\n",
      " - 6s - loss: 0.0023 - val_loss: 0.0011\n",
      "\n",
      "Epoch 00015: val_loss improved from 0.00124 to 0.00113, saving model to C:\\Users\\Victor\\Desktop\\project5\\Stock-Price-Prediction\\outputyzjGRU1\\best_model.h5\n",
      "Epoch 16/100\n",
      " - 6s - loss: 0.0023 - val_loss: 0.0011\n",
      "\n",
      "Epoch 00016: val_loss improved from 0.00113 to 0.00105, saving model to C:\\Users\\Victor\\Desktop\\project5\\Stock-Price-Prediction\\outputyzjGRU1\\best_model.h5\n",
      "Epoch 17/100\n",
      " - 6s - loss: 0.0021 - val_loss: 0.0010\n",
      "\n",
      "Epoch 00017: val_loss improved from 0.00105 to 0.00102, saving model to C:\\Users\\Victor\\Desktop\\project5\\Stock-Price-Prediction\\outputyzjGRU1\\best_model.h5\n",
      "Epoch 18/100\n",
      " - 6s - loss: 0.0021 - val_loss: 9.6228e-04\n",
      "\n",
      "Epoch 00018: val_loss improved from 0.00102 to 0.00096, saving model to C:\\Users\\Victor\\Desktop\\project5\\Stock-Price-Prediction\\outputyzjGRU1\\best_model.h5\n",
      "Epoch 19/100\n",
      " - 6s - loss: 0.0020 - val_loss: 9.2357e-04\n",
      "\n",
      "Epoch 00019: val_loss improved from 0.00096 to 0.00092, saving model to C:\\Users\\Victor\\Desktop\\project5\\Stock-Price-Prediction\\outputyzjGRU1\\best_model.h5\n",
      "Epoch 20/100\n",
      " - 6s - loss: 0.0019 - val_loss: 8.8512e-04\n",
      "\n",
      "Epoch 00020: val_loss improved from 0.00092 to 0.00089, saving model to C:\\Users\\Victor\\Desktop\\project5\\Stock-Price-Prediction\\outputyzjGRU1\\best_model.h5\n",
      "Epoch 21/100\n",
      " - 6s - loss: 0.0019 - val_loss: 8.7945e-04\n",
      "\n",
      "Epoch 00021: val_loss improved from 0.00089 to 0.00088, saving model to C:\\Users\\Victor\\Desktop\\project5\\Stock-Price-Prediction\\outputyzjGRU1\\best_model.h5\n",
      "Epoch 22/100\n",
      " - 6s - loss: 0.0020 - val_loss: 8.3754e-04\n",
      "\n",
      "Epoch 00022: val_loss improved from 0.00088 to 0.00084, saving model to C:\\Users\\Victor\\Desktop\\project5\\Stock-Price-Prediction\\outputyzjGRU1\\best_model.h5\n",
      "Epoch 23/100\n",
      " - 6s - loss: 0.0018 - val_loss: 7.8539e-04\n",
      "\n",
      "Epoch 00023: val_loss improved from 0.00084 to 0.00079, saving model to C:\\Users\\Victor\\Desktop\\project5\\Stock-Price-Prediction\\outputyzjGRU1\\best_model.h5\n",
      "Epoch 24/100\n",
      " - 6s - loss: 0.0018 - val_loss: 8.1384e-04\n",
      "\n",
      "Epoch 00024: val_loss did not improve from 0.00079\n",
      "Epoch 25/100\n",
      " - 6s - loss: 0.0018 - val_loss: 7.6226e-04\n",
      "\n",
      "Epoch 00025: val_loss improved from 0.00079 to 0.00076, saving model to C:\\Users\\Victor\\Desktop\\project5\\Stock-Price-Prediction\\outputyzjGRU1\\best_model.h5\n",
      "Epoch 26/100\n",
      " - 6s - loss: 0.0018 - val_loss: 7.6874e-04\n",
      "\n",
      "Epoch 00026: val_loss did not improve from 0.00076\n",
      "Epoch 27/100\n",
      " - 6s - loss: 0.0017 - val_loss: 7.3007e-04\n",
      "\n",
      "Epoch 00027: val_loss improved from 0.00076 to 0.00073, saving model to C:\\Users\\Victor\\Desktop\\project5\\Stock-Price-Prediction\\outputyzjGRU1\\best_model.h5\n",
      "Epoch 28/100\n",
      " - 6s - loss: 0.0017 - val_loss: 7.2114e-04\n",
      "\n",
      "Epoch 00028: val_loss improved from 0.00073 to 0.00072, saving model to C:\\Users\\Victor\\Desktop\\project5\\Stock-Price-Prediction\\outputyzjGRU1\\best_model.h5\n",
      "Epoch 29/100\n",
      " - 6s - loss: 0.0016 - val_loss: 6.9414e-04\n",
      "\n",
      "Epoch 00029: val_loss improved from 0.00072 to 0.00069, saving model to C:\\Users\\Victor\\Desktop\\project5\\Stock-Price-Prediction\\outputyzjGRU1\\best_model.h5\n",
      "Epoch 30/100\n",
      " - 6s - loss: 0.0016 - val_loss: 7.0654e-04\n",
      "\n",
      "Epoch 00030: val_loss did not improve from 0.00069\n",
      "Epoch 31/100\n",
      " - 6s - loss: 0.0016 - val_loss: 6.7798e-04\n",
      "\n",
      "Epoch 00031: val_loss improved from 0.00069 to 0.00068, saving model to C:\\Users\\Victor\\Desktop\\project5\\Stock-Price-Prediction\\outputyzjGRU1\\best_model.h5\n",
      "Epoch 32/100\n",
      " - 6s - loss: 0.0016 - val_loss: 6.6809e-04\n",
      "\n",
      "Epoch 00032: val_loss improved from 0.00068 to 0.00067, saving model to C:\\Users\\Victor\\Desktop\\project5\\Stock-Price-Prediction\\outputyzjGRU1\\best_model.h5\n",
      "Epoch 33/100\n",
      " - 6s - loss: 0.0015 - val_loss: 6.6588e-04\n",
      "\n",
      "Epoch 00033: val_loss improved from 0.00067 to 0.00067, saving model to C:\\Users\\Victor\\Desktop\\project5\\Stock-Price-Prediction\\outputyzjGRU1\\best_model.h5\n",
      "Epoch 34/100\n",
      " - 6s - loss: 0.0014 - val_loss: 6.3114e-04\n",
      "\n",
      "Epoch 00034: val_loss improved from 0.00067 to 0.00063, saving model to C:\\Users\\Victor\\Desktop\\project5\\Stock-Price-Prediction\\outputyzjGRU1\\best_model.h5\n",
      "Epoch 35/100\n",
      " - 6s - loss: 0.0015 - val_loss: 5.9814e-04\n",
      "\n",
      "Epoch 00035: val_loss improved from 0.00063 to 0.00060, saving model to C:\\Users\\Victor\\Desktop\\project5\\Stock-Price-Prediction\\outputyzjGRU1\\best_model.h5\n",
      "Epoch 36/100\n",
      " - 6s - loss: 0.0014 - val_loss: 6.1458e-04\n",
      "\n",
      "Epoch 00036: val_loss did not improve from 0.00060\n",
      "Epoch 37/100\n",
      " - 6s - loss: 0.0014 - val_loss: 5.8151e-04\n",
      "\n",
      "Epoch 00037: val_loss improved from 0.00060 to 0.00058, saving model to C:\\Users\\Victor\\Desktop\\project5\\Stock-Price-Prediction\\outputyzjGRU1\\best_model.h5\n",
      "Epoch 38/100\n",
      " - 6s - loss: 0.0013 - val_loss: 5.4415e-04\n",
      "\n",
      "Epoch 00038: val_loss improved from 0.00058 to 0.00054, saving model to C:\\Users\\Victor\\Desktop\\project5\\Stock-Price-Prediction\\outputyzjGRU1\\best_model.h5\n",
      "Epoch 39/100\n",
      " - 6s - loss: 0.0014 - val_loss: 5.7181e-04\n",
      "\n",
      "Epoch 00039: val_loss did not improve from 0.00054\n",
      "Epoch 40/100\n",
      " - 6s - loss: 0.0013 - val_loss: 5.4286e-04\n",
      "\n",
      "Epoch 00040: val_loss improved from 0.00054 to 0.00054, saving model to C:\\Users\\Victor\\Desktop\\project5\\Stock-Price-Prediction\\outputyzjGRU1\\best_model.h5\n",
      "Epoch 41/100\n",
      " - 6s - loss: 0.0013 - val_loss: 5.5576e-04\n",
      "\n",
      "Epoch 00041: val_loss did not improve from 0.00054\n",
      "Epoch 42/100\n",
      " - 6s - loss: 0.0013 - val_loss: 5.1891e-04\n",
      "\n",
      "Epoch 00042: val_loss improved from 0.00054 to 0.00052, saving model to C:\\Users\\Victor\\Desktop\\project5\\Stock-Price-Prediction\\outputyzjGRU1\\best_model.h5\n",
      "Epoch 43/100\n",
      " - 6s - loss: 0.0012 - val_loss: 5.4394e-04\n",
      "\n",
      "Epoch 00043: val_loss did not improve from 0.00052\n",
      "Epoch 44/100\n",
      " - 6s - loss: 0.0012 - val_loss: 4.9477e-04\n",
      "\n",
      "Epoch 00044: val_loss improved from 0.00052 to 0.00049, saving model to C:\\Users\\Victor\\Desktop\\project5\\Stock-Price-Prediction\\outputyzjGRU1\\best_model.h5\n",
      "Epoch 45/100\n",
      " - 6s - loss: 0.0012 - val_loss: 4.9193e-04\n",
      "\n",
      "Epoch 00045: val_loss improved from 0.00049 to 0.00049, saving model to C:\\Users\\Victor\\Desktop\\project5\\Stock-Price-Prediction\\outputyzjGRU1\\best_model.h5\n",
      "Epoch 46/100\n",
      " - 6s - loss: 0.0012 - val_loss: 4.8155e-04\n",
      "\n",
      "Epoch 00046: val_loss improved from 0.00049 to 0.00048, saving model to C:\\Users\\Victor\\Desktop\\project5\\Stock-Price-Prediction\\outputyzjGRU1\\best_model.h5\n",
      "Epoch 47/100\n",
      " - 7s - loss: 0.0011 - val_loss: 4.7530e-04\n",
      "\n",
      "Epoch 00047: val_loss improved from 0.00048 to 0.00048, saving model to C:\\Users\\Victor\\Desktop\\project5\\Stock-Price-Prediction\\outputyzjGRU1\\best_model.h5\n",
      "Epoch 48/100\n",
      " - 6s - loss: 0.0012 - val_loss: 4.7921e-04\n",
      "\n",
      "Epoch 00048: val_loss did not improve from 0.00048\n",
      "Epoch 49/100\n",
      " - 6s - loss: 0.0011 - val_loss: 4.5961e-04\n",
      "\n",
      "Epoch 00049: val_loss improved from 0.00048 to 0.00046, saving model to C:\\Users\\Victor\\Desktop\\project5\\Stock-Price-Prediction\\outputyzjGRU1\\best_model.h5\n",
      "Epoch 50/100\n",
      " - 6s - loss: 0.0011 - val_loss: 4.5346e-04\n",
      "\n",
      "Epoch 00050: val_loss improved from 0.00046 to 0.00045, saving model to C:\\Users\\Victor\\Desktop\\project5\\Stock-Price-Prediction\\outputyzjGRU1\\best_model.h5\n",
      "Epoch 51/100\n",
      " - 6s - loss: 0.0011 - val_loss: 4.5366e-04\n",
      "\n",
      "Epoch 00051: val_loss did not improve from 0.00045\n",
      "Epoch 52/100\n",
      " - 6s - loss: 0.0011 - val_loss: 4.7995e-04\n",
      "\n",
      "Epoch 00052: val_loss did not improve from 0.00045\n",
      "Epoch 53/100\n",
      " - 6s - loss: 0.0011 - val_loss: 4.3438e-04\n",
      "\n",
      "Epoch 00053: val_loss improved from 0.00045 to 0.00043, saving model to C:\\Users\\Victor\\Desktop\\project5\\Stock-Price-Prediction\\outputyzjGRU1\\best_model.h5\n",
      "Epoch 54/100\n",
      " - 7s - loss: 0.0011 - val_loss: 4.4312e-04\n",
      "\n",
      "Epoch 00054: val_loss did not improve from 0.00043\n",
      "Epoch 55/100\n",
      " - 7s - loss: 0.0010 - val_loss: 4.1749e-04\n",
      "\n",
      "Epoch 00055: val_loss improved from 0.00043 to 0.00042, saving model to C:\\Users\\Victor\\Desktop\\project5\\Stock-Price-Prediction\\outputyzjGRU1\\best_model.h5\n",
      "Epoch 56/100\n",
      " - 7s - loss: 0.0010 - val_loss: 3.9525e-04\n",
      "\n",
      "Epoch 00056: val_loss improved from 0.00042 to 0.00040, saving model to C:\\Users\\Victor\\Desktop\\project5\\Stock-Price-Prediction\\outputyzjGRU1\\best_model.h5\n",
      "Epoch 57/100\n",
      " - 6s - loss: 0.0010 - val_loss: 3.7809e-04\n",
      "\n",
      "Epoch 00057: val_loss improved from 0.00040 to 0.00038, saving model to C:\\Users\\Victor\\Desktop\\project5\\Stock-Price-Prediction\\outputyzjGRU1\\best_model.h5\n",
      "Epoch 58/100\n",
      " - 6s - loss: 9.6338e-04 - val_loss: 3.8138e-04\n",
      "\n",
      "Epoch 00058: val_loss did not improve from 0.00038\n",
      "Epoch 59/100\n",
      " - 6s - loss: 9.6287e-04 - val_loss: 3.7831e-04\n",
      "\n",
      "Epoch 00059: val_loss did not improve from 0.00038\n",
      "Epoch 60/100\n",
      " - 6s - loss: 9.5971e-04 - val_loss: 3.5441e-04\n",
      "\n",
      "Epoch 00060: val_loss improved from 0.00038 to 0.00035, saving model to C:\\Users\\Victor\\Desktop\\project5\\Stock-Price-Prediction\\outputyzjGRU1\\best_model.h5\n",
      "Epoch 61/100\n",
      " - 6s - loss: 9.9268e-04 - val_loss: 3.5725e-04\n",
      "\n",
      "Epoch 00061: val_loss did not improve from 0.00035\n",
      "Epoch 62/100\n",
      " - 6s - loss: 9.7383e-04 - val_loss: 3.8554e-04\n",
      "\n",
      "Epoch 00062: val_loss did not improve from 0.00035\n",
      "Epoch 63/100\n",
      " - 6s - loss: 9.1261e-04 - val_loss: 3.8483e-04\n",
      "\n",
      "Epoch 00063: val_loss did not improve from 0.00035\n",
      "Epoch 64/100\n",
      " - 6s - loss: 9.5707e-04 - val_loss: 3.2886e-04\n",
      "\n",
      "Epoch 00064: val_loss improved from 0.00035 to 0.00033, saving model to C:\\Users\\Victor\\Desktop\\project5\\Stock-Price-Prediction\\outputyzjGRU1\\best_model.h5\n",
      "Epoch 65/100\n",
      " - 6s - loss: 9.1346e-04 - val_loss: 3.3340e-04\n",
      "\n",
      "Epoch 00065: val_loss did not improve from 0.00033\n",
      "Epoch 66/100\n",
      " - 6s - loss: 8.6502e-04 - val_loss: 3.0861e-04\n",
      "\n",
      "Epoch 00066: val_loss improved from 0.00033 to 0.00031, saving model to C:\\Users\\Victor\\Desktop\\project5\\Stock-Price-Prediction\\outputyzjGRU1\\best_model.h5\n",
      "Epoch 67/100\n",
      " - 6s - loss: 8.7513e-04 - val_loss: 3.3671e-04\n",
      "\n",
      "Epoch 00067: val_loss did not improve from 0.00031\n",
      "Epoch 68/100\n",
      " - 6s - loss: 8.9216e-04 - val_loss: 3.2350e-04\n",
      "\n",
      "Epoch 00068: val_loss did not improve from 0.00031\n",
      "Epoch 69/100\n",
      " - 7s - loss: 8.1530e-04 - val_loss: 3.0814e-04\n",
      "\n",
      "Epoch 00069: val_loss improved from 0.00031 to 0.00031, saving model to C:\\Users\\Victor\\Desktop\\project5\\Stock-Price-Prediction\\outputyzjGRU1\\best_model.h5\n",
      "Epoch 70/100\n",
      " - 7s - loss: 8.0427e-04 - val_loss: 3.4178e-04\n",
      "\n",
      "Epoch 00070: val_loss did not improve from 0.00031\n",
      "Epoch 71/100\n",
      " - 6s - loss: 8.2458e-04 - val_loss: 3.0516e-04\n",
      "\n",
      "Epoch 00071: val_loss improved from 0.00031 to 0.00031, saving model to C:\\Users\\Victor\\Desktop\\project5\\Stock-Price-Prediction\\outputyzjGRU1\\best_model.h5\n",
      "Epoch 72/100\n",
      " - 6s - loss: 7.9441e-04 - val_loss: 2.9099e-04\n",
      "\n",
      "Epoch 00072: val_loss improved from 0.00031 to 0.00029, saving model to C:\\Users\\Victor\\Desktop\\project5\\Stock-Price-Prediction\\outputyzjGRU1\\best_model.h5\n",
      "Epoch 73/100\n",
      " - 6s - loss: 8.2352e-04 - val_loss: 2.9334e-04\n",
      "\n",
      "Epoch 00073: val_loss did not improve from 0.00029\n",
      "Epoch 74/100\n",
      " - 6s - loss: 7.6622e-04 - val_loss: 3.0408e-04\n",
      "\n",
      "Epoch 00074: val_loss did not improve from 0.00029\n",
      "Epoch 75/100\n",
      " - 6s - loss: 7.9261e-04 - val_loss: 3.0925e-04\n",
      "\n",
      "Epoch 00075: val_loss did not improve from 0.00029\n",
      "Epoch 76/100\n",
      " - 6s - loss: 7.7947e-04 - val_loss: 2.8975e-04\n",
      "\n",
      "Epoch 00076: val_loss improved from 0.00029 to 0.00029, saving model to C:\\Users\\Victor\\Desktop\\project5\\Stock-Price-Prediction\\outputyzjGRU1\\best_model.h5\n",
      "Epoch 77/100\n",
      " - 7s - loss: 7.6507e-04 - val_loss: 2.9152e-04\n",
      "\n",
      "Epoch 00077: val_loss did not improve from 0.00029\n",
      "Epoch 78/100\n",
      " - 7s - loss: 7.5806e-04 - val_loss: 2.8955e-04\n",
      "\n",
      "Epoch 00078: val_loss improved from 0.00029 to 0.00029, saving model to C:\\Users\\Victor\\Desktop\\project5\\Stock-Price-Prediction\\outputyzjGRU1\\best_model.h5\n",
      "Epoch 79/100\n",
      " - 6s - loss: 7.4339e-04 - val_loss: 3.2132e-04\n",
      "\n",
      "Epoch 00079: val_loss did not improve from 0.00029\n",
      "Epoch 80/100\n",
      " - 7s - loss: 7.1402e-04 - val_loss: 2.9247e-04\n",
      "\n",
      "Epoch 00080: val_loss did not improve from 0.00029\n",
      "Epoch 81/100\n",
      " - 6s - loss: 7.3980e-04 - val_loss: 3.1354e-04\n",
      "\n",
      "Epoch 00081: val_loss did not improve from 0.00029\n",
      "Epoch 82/100\n",
      " - 6s - loss: 7.6901e-04 - val_loss: 2.8181e-04\n",
      "\n",
      "Epoch 00082: val_loss improved from 0.00029 to 0.00028, saving model to C:\\Users\\Victor\\Desktop\\project5\\Stock-Price-Prediction\\outputyzjGRU1\\best_model.h5\n",
      "Epoch 83/100\n",
      " - 6s - loss: 7.6104e-04 - val_loss: 2.8123e-04\n",
      "\n",
      "Epoch 00083: val_loss improved from 0.00028 to 0.00028, saving model to C:\\Users\\Victor\\Desktop\\project5\\Stock-Price-Prediction\\outputyzjGRU1\\best_model.h5\n",
      "Epoch 84/100\n",
      " - 7s - loss: 7.3489e-04 - val_loss: 2.8797e-04\n",
      "\n",
      "Epoch 00084: val_loss did not improve from 0.00028\n",
      "Epoch 85/100\n",
      " - 7s - loss: 7.4493e-04 - val_loss: 2.7342e-04\n",
      "\n",
      "Epoch 00085: val_loss improved from 0.00028 to 0.00027, saving model to C:\\Users\\Victor\\Desktop\\project5\\Stock-Price-Prediction\\outputyzjGRU1\\best_model.h5\n",
      "Epoch 86/100\n",
      " - 7s - loss: 6.8334e-04 - val_loss: 2.8876e-04\n",
      "\n",
      "Epoch 00086: val_loss did not improve from 0.00027\n",
      "Epoch 87/100\n",
      " - 6s - loss: 7.0269e-04 - val_loss: 2.7026e-04\n",
      "\n",
      "Epoch 00087: val_loss improved from 0.00027 to 0.00027, saving model to C:\\Users\\Victor\\Desktop\\project5\\Stock-Price-Prediction\\outputyzjGRU1\\best_model.h5\n",
      "Epoch 88/100\n",
      " - 6s - loss: 7.3058e-04 - val_loss: 2.5563e-04\n",
      "\n",
      "Epoch 00088: val_loss improved from 0.00027 to 0.00026, saving model to C:\\Users\\Victor\\Desktop\\project5\\Stock-Price-Prediction\\outputyzjGRU1\\best_model.h5\n",
      "Epoch 89/100\n",
      " - 6s - loss: 6.7265e-04 - val_loss: 2.7922e-04\n",
      "\n",
      "Epoch 00089: val_loss did not improve from 0.00026\n",
      "Epoch 90/100\n",
      " - 6s - loss: 7.3159e-04 - val_loss: 2.6542e-04\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 00090: val_loss did not improve from 0.00026\n",
      "Epoch 91/100\n",
      " - 6s - loss: 6.9731e-04 - val_loss: 2.9539e-04\n",
      "\n",
      "Epoch 00091: val_loss did not improve from 0.00026\n",
      "Epoch 92/100\n",
      " - 6s - loss: 7.0681e-04 - val_loss: 2.8668e-04\n",
      "\n",
      "Epoch 00092: val_loss did not improve from 0.00026\n",
      "Epoch 93/100\n",
      " - 6s - loss: 6.6032e-04 - val_loss: 2.8118e-04\n",
      "\n",
      "Epoch 00093: val_loss did not improve from 0.00026\n",
      "Epoch 94/100\n",
      " - 7s - loss: 6.9801e-04 - val_loss: 2.6659e-04\n",
      "\n",
      "Epoch 00094: val_loss did not improve from 0.00026\n",
      "Epoch 95/100\n",
      " - 6s - loss: 6.8568e-04 - val_loss: 2.9172e-04\n",
      "\n",
      "Epoch 00095: val_loss did not improve from 0.00026\n",
      "Epoch 96/100\n",
      " - 6s - loss: 6.9091e-04 - val_loss: 2.6804e-04\n",
      "\n",
      "Epoch 00096: val_loss did not improve from 0.00026\n",
      "Epoch 97/100\n",
      " - 6s - loss: 7.4795e-04 - val_loss: 2.8111e-04\n",
      "\n",
      "Epoch 00097: val_loss did not improve from 0.00026\n",
      "Epoch 98/100\n",
      " - 6s - loss: 6.5695e-04 - val_loss: 3.2033e-04\n",
      "\n",
      "Epoch 00098: val_loss did not improve from 0.00026\n",
      "Epoch 99/100\n",
      " - 6s - loss: 6.8407e-04 - val_loss: 2.6525e-04\n",
      "\n",
      "Epoch 00099: val_loss did not improve from 0.00026\n",
      "Epoch 100/100\n",
      " - 6s - loss: 6.3890e-04 - val_loss: 2.6918e-04\n",
      "\n",
      "Epoch 00100: val_loss did not improve from 0.00026\n",
      "saving model...\n"
     ]
    }
   ],
   "source": [
    "model = None\n",
    "try:\n",
    "    model = pickle.load(open(\"GRU_model1\", 'rb'))\n",
    "    print(\"Loaded saved model...\")\n",
    "except FileNotFoundError:\n",
    "    print(\"Model not found\")\n",
    "\n",
    "\n",
    "x_temp, y_temp = build_timeseries(x_test, 3)\n",
    "x_test_t = trim_dataset(x_temp, BATCH_SIZE)\n",
    "y_test_t = trim_dataset(y_temp, BATCH_SIZE)\n",
    "\n",
    "print(\"Test size\", x_test_t.shape, y_test_t.shape)\n",
    "    \n",
    "is_update_model = True\n",
    "if model is None or is_update_model:\n",
    "    from keras import backend as K\n",
    "    print(\"Building model...\")\n",
    "    print(\"checking if GPU available\", K.tensorflow_backend._get_available_gpus())\n",
    "    model = create_model()\n",
    "    \n",
    "    es = EarlyStopping(monitor='val_loss', mode='min', verbose=1,\n",
    "                       patience=40, min_delta=0.0001)\n",
    "    \n",
    "    mcp = ModelCheckpoint(os.path.join(OUTPUT_PATH,\n",
    "                          \"best_model.h5\"), monitor='val_loss', verbose=1,\n",
    "                          save_best_only=True, save_weights_only=False, mode='min', period=1)\n",
    "\n",
    "    # Not used here. But leaving it here as a reminder for future\n",
    "    r_lr_plat = ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=30, \n",
    "                                  verbose=0, mode='auto', min_delta=0.0001, cooldown=0, min_lr=0)\n",
    "    \n",
    "   ## csv_logger = CSVLogger(os.path.join(OUTPUT_PATH, 'training_log_' + time.ctime().replace(\" \",\"_\") + '.log'), append=True)\n",
    "    csv_logger = CSVLogger(os.path.join(OUTPUT_PATH, 'training_log_' + time.strftime(\"%a %b %d %H %M %S %Y\", time.localtime()) + '.log'), append=True)\n",
    "    \n",
    "  #  csv_logger = CSVLogger(os.path.join(OUTPUT_PATH, 'training_log_'  + '.log'), append=True)\n",
    "    \n",
    "    history = model.fit(x_t, y_t, epochs=params[\"epochs\"], verbose=2, batch_size=BATCH_SIZE,\n",
    "                        shuffle=False, validation_data=(trim_dataset(x_test_t, BATCH_SIZE),\n",
    "                        trim_dataset(y_test_t, BATCH_SIZE)), callbacks=[es, mcp, csv_logger])\n",
    "    \n",
    "    print(\"saving model...\")\n",
    "    pickle.dump(model, open(\"GRU_model1\", \"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Square Error is 0.00026918179284912084 (560,) (560,)\n",
      "[0.5227742  0.46816742 0.43608707 0.42692447 0.4248958  0.42768398\n",
      " 0.43320084 0.44170624 0.45363107 0.45923474 0.45994833 0.45681363\n",
      " 0.45384917 0.45140067 0.4486427 ]\n",
      "[0.45102041 0.45918367 0.46734694 0.45510204 0.45714286 0.45714286\n",
      " 0.46530612 0.48163265 0.47142857 0.46122449 0.46734694 0.46326531\n",
      " 0.46122449 0.45102041 0.45714286]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "# model.evaluate(x_test_t, y_test_t, batch_size=BATCH_SIZE\n",
    "y_pred = model.predict(trim_dataset(x_test_t, BATCH_SIZE), batch_size=BATCH_SIZE)\n",
    "y_pred = y_pred.flatten()\n",
    "y_test_t = trim_dataset(y_test_t, BATCH_SIZE)\n",
    "error = mean_squared_error(y_test_t, y_pred)\n",
    "print(\"Mean Square Error is\", error, y_pred.shape, y_test_t.shape)\n",
    "print(y_pred[0:15])\n",
    "print(y_test_t[0:15])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.5707968 1.4370102 1.3584133 1.3359649 1.3309947 1.3378258 1.3513421\n",
      " 1.3721802 1.4013962 1.4151251 1.4168733 1.4091934 1.4019305 1.3959316\n",
      " 1.3891746]\n",
      "[1.395 1.415 1.435 1.405 1.41  1.41  1.43  1.47  1.445 1.42  1.435 1.425\n",
      " 1.42  1.395 1.41 ]\n"
     ]
    }
   ],
   "source": [
    "# convert the predicted value to range of real data\n",
    "y_pred_org = (y_pred * min_max_scaler.data_range_[3]) + min_max_scaler.data_min_[3]\n",
    "# min_max_scaler.inverse_transform(y_pred)\n",
    "y_test_t_org = (y_test_t * min_max_scaler.data_range_[3]) + min_max_scaler.data_min_[3]\n",
    "# min_max_scaler.inverse_transform(y_test_t)\n",
    "print(y_pred_org[0:15])\n",
    "print(y_test_t_org[0:15])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "def mean_absolute_percentage_error(y_test_t, y_pred): \n",
    "    y_test_t, y_pred = np.array(y_test_t), np.array(y_pred)\n",
    "    return np.mean(np.abs((y_test_t - y_pred) / y_test_t)) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.3504121102776514"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_absolute_percentage_error(y_test_t, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#score, acc = model.evaluate(trim_dataset(x_test_t,BATCH_SIZE),trim_dataset(y_test_t,BATCH_SIZE),batch_size=BATCH_SIZE)\n",
    "#print('Test score:', score)\n",
    "#print('Test accuracy:', acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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": [
    "# Visualize the training data\n",
    "from matplotlib import pyplot as plt\n",
    "plt.figure()\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('GRU Model loss')\n",
    "plt.ylabel('MSE')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Validation'], loc='upper left')\n",
    "#plt.show()\n",
    "#plt.savefig(os.path.join(OUTPUT_PATH, 'train_vis_BS_'+str(BATCH_SIZE)+\"_\"+time.ctime()+'.png'))\n",
    "plt.savefig(os.path.join(OUTPUT_PATH, 'train_vis_BS_'+str(BATCH_SIZE)+\"_\"+time.strftime(\"%a %b %d %H %M %S %Y\", time.localtime())+'.png'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<keras.engine.sequential.Sequential object at 0x0000000018552748>\n",
      "Mean Square Error is 0.00025562548831117464 (560,) (560,)\n",
      "[0.52169365 0.4713703  0.44283983 0.4336339  0.4302323  0.43119523\n",
      " 0.43512917 0.44223255 0.45273426 0.45803994 0.45898908 0.45684522\n",
      " 0.4549929  0.45337653 0.4508955 ]\n",
      "[0.45102041 0.45918367 0.46734694 0.45510204 0.45714286 0.45714286\n",
      " 0.46530612 0.48163265 0.47142857 0.46122449 0.46734694 0.46326531\n",
      " 0.46122449 0.45102041 0.45714286]\n",
      "[1.5681494 1.4448572 1.3749576 1.352403  1.3440691 1.3464283 1.3560665\n",
      " 1.3734697 1.3991989 1.4121978 1.4145232 1.4092708 1.4047326 1.4007725\n",
      " 1.394694 ]\n",
      "[1.395 1.415 1.435 1.405 1.41  1.41  1.43  1.47  1.445 1.42  1.435 1.425\n",
      " 1.42  1.395 1.41 ]\n"
     ]
    }
   ],
   "source": [
    "# load the saved best model from above\n",
    "saved_model = load_model(os.path.join(OUTPUT_PATH, 'best_model.h5')) # , \"lstm_best_7-3-19_12AM\",\n",
    "print(saved_model)\n",
    "\n",
    "y_pred = saved_model.predict(trim_dataset(x_test_t, BATCH_SIZE), batch_size=BATCH_SIZE)\n",
    "y_pred = y_pred.flatten()\n",
    "y_test_t = trim_dataset(y_test_t, BATCH_SIZE)\n",
    "error = mean_squared_error(y_test_t, y_pred)\n",
    "print(\"Mean Square Error is\", error, y_pred.shape, y_test_t.shape)\n",
    "print(y_pred[0:15])\n",
    "print(y_test_t[0:15])\n",
    "y_pred_org = (y_pred * min_max_scaler.data_range_[3]) + min_max_scaler.data_min_[3] # min_max_scaler.inverse_transform(y_pred)\n",
    "y_test_t_org = (y_test_t * min_max_scaler.data_range_[3]) + min_max_scaler.data_min_[3] # min_max_scaler.inverse_transform(y_test_t)\n",
    "print(y_pred_org[0:15])\n",
    "print(y_test_t_org[0:15])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "def mean_absolute_percentage_error(y_test_t, y_pred): \n",
    "    y_test_t, y_pred = np.array(y_test_t), np.array(y_pred)\n",
    "    return np.mean(np.abs((y_test_t - y_pred) / y_test_t)) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.1752360046911297"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_absolute_percentage_error(y_test_t, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_time(text, stime):\n",
    "    seconds = (time.time()-stime)\n",
    "    print(text, seconds//60,\"minutes : \",np.round(seconds%60),\"seconds\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "program completed  13.0 minutes :  58.0 seconds\n"
     ]
    },
    {
     "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": [
    "# Visualize the prediction\n",
    "from matplotlib import pyplot as plt\n",
    "plt.figure()\n",
    "plt.plot(y_pred_org)\n",
    "plt.plot(y_test_t_org)\n",
    "plt.title('GRU Prediction vs Real Stock Price')\n",
    "plt.ylabel('Price')\n",
    "plt.xlabel('Days')\n",
    "plt.legend(['Prediction', 'Real'], loc='upper left')\n",
    "#plt.show()\n",
    "##plt.savefig(os.path.join(OUTPUT_PATH, 'pred_vs_real_BS'+str(BATCH_SIZE)+\"_\"+time.ctime()+'.png'))\n",
    "plt.savefig(os.path.join(OUTPUT_PATH, 'pred_vs_real_BS'+str(BATCH_SIZE)+\"_\"+time.strftime(\"%a %b %d %H %M %S %Y\", time.localtime())+'.png'))\n",
    "print_time(\"program completed \", stime)"
   ]
  },
  {
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}