LSTM Model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bb80d870",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas_datareader as data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "795537c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>27.860001</td>\n",
       "      <td>26.837500</td>\n",
       "      <td>27.847500</td>\n",
       "      <td>27.332500</td>\n",
       "      <td>212818400.0</td>\n",
       "      <td>24.714512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>27.162500</td>\n",
       "      <td>26.352501</td>\n",
       "      <td>27.072500</td>\n",
       "      <td>26.562500</td>\n",
       "      <td>257142000.0</td>\n",
       "      <td>24.018259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-06</th>\n",
       "      <td>26.857500</td>\n",
       "      <td>26.157499</td>\n",
       "      <td>26.635000</td>\n",
       "      <td>26.565001</td>\n",
       "      <td>263188400.0</td>\n",
       "      <td>24.020521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-07</th>\n",
       "      <td>27.049999</td>\n",
       "      <td>26.674999</td>\n",
       "      <td>26.799999</td>\n",
       "      <td>26.937500</td>\n",
       "      <td>160423600.0</td>\n",
       "      <td>24.357344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08</th>\n",
       "      <td>28.037500</td>\n",
       "      <td>27.174999</td>\n",
       "      <td>27.307501</td>\n",
       "      <td>27.972500</td>\n",
       "      <td>237458000.0</td>\n",
       "      <td>25.293207</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 High        Low       Open      Close       Volume  Adj Close\n",
       "Date                                                                          \n",
       "2015-01-02  27.860001  26.837500  27.847500  27.332500  212818400.0  24.714512\n",
       "2015-01-05  27.162500  26.352501  27.072500  26.562500  257142000.0  24.018259\n",
       "2015-01-06  26.857500  26.157499  26.635000  26.565001  263188400.0  24.020521\n",
       "2015-01-07  27.049999  26.674999  26.799999  26.937500  160423600.0  24.357344\n",
       "2015-01-08  28.037500  27.174999  27.307501  27.972500  237458000.0  25.293207"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start = '2015-01-01'\n",
    "end = '2022-04-11'\n",
    "df = data.DataReader('AAPL','yahoo',start,end)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ac572340",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-04-05</th>\n",
       "      <td>178.300003</td>\n",
       "      <td>174.419998</td>\n",
       "      <td>177.500000</td>\n",
       "      <td>175.059998</td>\n",
       "      <td>73401800.0</td>\n",
       "      <td>175.059998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-06</th>\n",
       "      <td>173.630005</td>\n",
       "      <td>170.130005</td>\n",
       "      <td>172.360001</td>\n",
       "      <td>171.830002</td>\n",
       "      <td>89058800.0</td>\n",
       "      <td>171.830002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-07</th>\n",
       "      <td>173.360001</td>\n",
       "      <td>169.850006</td>\n",
       "      <td>171.160004</td>\n",
       "      <td>172.139999</td>\n",
       "      <td>77594700.0</td>\n",
       "      <td>172.139999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-08</th>\n",
       "      <td>171.779999</td>\n",
       "      <td>169.199997</td>\n",
       "      <td>171.779999</td>\n",
       "      <td>170.089996</td>\n",
       "      <td>76515900.0</td>\n",
       "      <td>170.089996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-04-11</th>\n",
       "      <td>169.020004</td>\n",
       "      <td>165.729996</td>\n",
       "      <td>168.710007</td>\n",
       "      <td>165.789993</td>\n",
       "      <td>58090621.0</td>\n",
       "      <td>165.789993</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  High         Low        Open       Close      Volume  \\\n",
       "Date                                                                     \n",
       "2022-04-05  178.300003  174.419998  177.500000  175.059998  73401800.0   \n",
       "2022-04-06  173.630005  170.130005  172.360001  171.830002  89058800.0   \n",
       "2022-04-07  173.360001  169.850006  171.160004  172.139999  77594700.0   \n",
       "2022-04-08  171.779999  169.199997  171.779999  170.089996  76515900.0   \n",
       "2022-04-11  169.020004  165.729996  168.710007  165.789993  58090621.0   \n",
       "\n",
       "             Adj Close  \n",
       "Date                    \n",
       "2022-04-05  175.059998  \n",
       "2022-04-06  171.830002  \n",
       "2022-04-07  172.139999  \n",
       "2022-04-08  170.089996  \n",
       "2022-04-11  165.789993  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cdaba109",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cb0f46d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(['Date','Adj Close'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b8745f6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x196ac6e07f0>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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5AGMzUxjZt7vrnDgfN1XX3zOz9YFHKC3LKKU6hPvki+K2aNvit3aQtfANn68586HVQNM97fTucV5tJZW1HotY+7qpmhwfGYtcB0KTu1KqQ1TVNtwcrXebFfKvH+wFvNdEzXd78KipdUt99cr3FZXTwy25O3v/P5w62I+oI5eWZZRSHcJ9WgD33nSUgN1AZW29x7j0hS9vdm27fzC483VDFSDFLbk7e/cDeibw2o1n00mnb/fSYnIXkaeAi4ECY8wYq+0R4BKgBtgLXGOM+do6dgdwLVAP/NQY805wQldKRZJaaxhknC3KI7nHREdRXWensqbeo8d93G3OmMLShnHs7nz13AGP9/nGuAF0j7Nxwei+rpusXUFryjJPA7Mata0ExhhjTgV2AXcAiMgoYC4w2nrNX0QkshYeVEoFhXMmyOo6O8s3HXG1O0sua/cf8zi/X0o8AL27x/G9SVk+37OpnntVbUOJJypKuPCUjC6V2KEVyd0Y8yFQ3KhthTHGOZvPGsA5NmkOsNQYU22M2Q/sASa0Y7xKqQhV41aW6e72dOg5I9IB2FvgGHv+8e4isha+wb7Cck4fmMK6u86jR6LvG6DJCTZSfByrauLhp66kPW6ofh94y9ruD7gvsZJrtXkRkQUikiMiOYWF/q9grpSKDM6yzMxRfeiV1LACUqI1T0xxRQ1FZdVc/eRaAHYcLW1xVIuI8Mnt53q1d/ZJwVojoBuqInIXUAc872zycZrPiZuNMUuAJQDZ2dn+LaiolIoYzrJMQky0x81V52iW4xW13PXqlx6vcR/S2BT3OWIOLL6oPULtFPxO7iIyH8eN1hmmYbXbXCDT7bQBwJHGr1VKdT1VtXZEINYWxeGvG1ZLcvbo39ic57V4RnK8Dujzl19XTkRmAbcD5xhjKtwOLQf+LSKPAf2AYcC6gKNUSkW8suo6kmJtvLQ+F4CNh76mvLqO1zfnuc5xT/rQup678q01QyFfAKYBaSKSC9yHY3RMHLBSHING1xhjrjfGbBWRZcA2HOWaG4wxemdDKUV5dR1J8TZKrZWS9hWWccuyTc2+ple32GaPu3NOA6wcWkzuxph5PpqfbOb8RcCiQIJSSnUulTX1/Gd9LiP7difPWge1pcQOcHJGcqve/62bprTpg6Ar0IKWUiro3rPWLfU1z4svibHRVNTUk9kzsVXnt/ZDoCvR32OUUkF1+OtKfvz8BgAemDPa5zlThqV57PdNdjzA1LdHfHCD68S0566UCqqth0+4tkdn9PB5zoSsVD7aXeTa/9cPzmR7XonPNVBV6+iVU0oFlfvCHAmx0Tz/gzM9jk/ISuXH04e69m1RQr+UBGac7D1/u2o9Te5KqaByn9Ml1hbFWUN6eRx/6Ioxnh8AMTodVXvQ5K6UCipjPG+iigiLLh/j2o+3kvmtM4cDdLkJvoJFk7tSKqiKy2u92rrFNtzuc/bUJ1s3VXW8evvQq6iUCqovDh4H8Ji90f1GaUKsI7nHRDnahvb2XtBatZ2OllFKBVV+SRUnZyTz1k1TXG3ulZp4a0724X2TuPS0fsw/K6uDI+ycNLkrpYKqrLqOpDjPm6R2t+zurLHH2aL5w7zTOzS2zkzLMkqpoCqvrveYlhcaSi9Jcdq/DBa9skqpoKqqrfca3nhyRjI5d5+n88EEkSZ3pVRQ1RvjMY7dKS0pLgTRdB1allFKBVW93XdyV8GlyV0pFVT1dkO0aHLvaJrclVJBZdeee0i0mNxF5CkRKRCRLW5tqSKyUkR2W197uh27Q0T2iMhOEbkgWIErpSJDnSb3kGhNz/1pYFajtoXAamPMMGC1tY+IjALmAqOt1/xFRHQWIKW6MLsxOl9MCLSY3I0xHwLFjZrnAM9Y288Al7m1LzXGVBtj9gN7gAntE6pSKhLV2Q02Te4dzt+aex9jTB6A9bW31d4fOOR2Xq7V5kVEFohIjojkFBYW+hmGUirc1dsNUXpDtcO19w1VXz9Bn4smGmOWGGOyjTHZ6enp7RyGUirU8k5UcuO/N1BaVac19xDw9yGmfBHJMMbkiUgGUGC15wKZbucNAI4EEqBSKjJN+vW7rm0ty3Q8f3vuy4H51vZ84H9u7XNFJE5EBgHDgHWBhaiUinR6Q7XjtdhzF5EXgGlAmojkAvcBi4FlInItcBC4CsAYs1VElgHbgDrgBmNMfZBiV0qFqZ1HSz32tefe8VpM7saYeU0cmtHE+YuARYEEpZSKbPf+b4vHfkqiThDW0fQJVaVUu+sWZyPObbWlHgkxzZytgkGTu1Kq3b27o8BjmJxWZTqeJnelVLuqq7cDUFNnd7Vln5QaqnC6LJ3PXSnVbmrr7byyIReAO2ePJDY6iqMl1QzslRjiyLoeTe5KqXbzxPt7eWzlLsBRZ//W+IEhjqjr0rKMUqrd7Dha4to+Q0sxIaXJXSnVbvomJ7i2nYtgq9DQ5K6Uahdl1XXo/GDhQ2vuSql2Mea+d1zbU4alhTASBdpzV0oFwXPXnhnqELo8Te5KqXb1+NyxoQ5BocldKdXOhqTrjdRwoMldKRWw6rqGyV8ze+oDS+FAk7tSKmBlVXWu7R6JOklYONDkrpQKWFFZDQCXn+5zyWQVAprclVIeauvt5B6vaNNr9haWAXDdlMHBCEn5IaDkLiI3i8hWEdkiIi+ISLyIpIrIShHZbX3t2V7BKqWC69M9RQy76y3O/s17bDr0datf9+4OxzLKWpIJH34ndxHpD/wUyDbGjAGigbnAQmC1MWYYsNraV0qFuYLSKr79j7Wu/Tl//qTVr31pvWMmyO7x+lxkuAi0LGMDEkTEBiQCR4A5wDPW8WeAywL8HkqpICuvrmPCotVtfl1VbT1ZC99w7XeP0+QeLvxO7saYw8CjOBbIzgNOGGNWAH2MMXnWOXlAb1+vF5EFIpIjIjmFhYX+hqGUagej3aYOaK2aOju/eGmza//pa8YjOrlM2AikLNMTRy99ENAP6CYiV7f29caYJcaYbGNMdnp6ur9hKKUCVFVb79X2iwtGNHnMacmHe3lt0xEALhzTl2kjfPbjVIgEUpY5D9hvjCk0xtQCrwBnAfkikgFgfS0IPEylVLAc+brSq81ZOy+rrvM65pR3osq1/Yd5p7d/YCoggST3g8BEEUkUx+9iM4DtwHJgvnXOfOB/gYWolAomZ5L+v7OyAJg4OJWEmGgAKmua7rm7L4AdE62jqsNNIDX3tcBLwAbgS+u9lgCLgZkishuYae0rpcLUYavn/v3Jgziw+CKWLphEYqyj5/7Aa9vIO+HdswfIs1636pZzOiZQ1SYB3do2xtwH3NeouRpHL14pFeaqauu5zbop2qdHnKs9MdbRc1+1PZ+SqlqW/XCS12tzj1dy/qg+uuJSmNLfpZTqwg4VNzyJGmeLdm0nxDZs78ov9XqdMYbDX1cyQCcJC1ua3JXqwo5X1AJw1+yTPdoT3ZL719Y57l7ecJiKmnoyesQHN0DlN03uSnVhz635CoBLTuvn0R4fE+3rdJef/2cTALZoHdcerjS5K9WF7bZKLn0b9cATmkjunx8oJmvhG8THOFLH2UN1rdRwpcldqS6quq6ePQVlzB2f6XUsM9Wzln7CKs3MW7IGgKpaOwDD+nQPcpTKX5rcleqifvnaNurshlMHpLR47teVjvnanWPhVfjT5K5UF/X82oMATBiU2uK5i9/aAYDdtHCiChua3JXqgsqtaQVuPm94q8apv7XlKACVbnPN/PXqccEJTrULTe5KdUHX/2s9ACdnNF0z91WCKalqGBZ5wei+7R6Xaj+a3JXqIux2w+9W7iJr4Rt8tLsIgDMH9Wry/PsvHe3Vlu82WZhO7xvedGZ9pbqIR1bs5In393q0tWVZPGMMOV8dZ+rwdG6cPrS9w1PtTHvuSnUBxhivxP7ctRNafN2/rzvTtf3ml0dd2625CatCS5O7Ul2A80nUkX270yfZMUHYsN4tj1F3n8r3hn9vACBVF8GOCFqWUaoL+NsH+wBYdv0kusXayDtR6fVUamv9aJqWZCKBJneluoDqOjtzx2eSHO/odbd2Nkdft0ydUw+o8BbQT0lEUkTkJRHZISLbRWSSiKSKyEoR2W197dlewSql2u7e/22hqKyajB4JbX6trwExsTZN7pEg0J/S48DbxpiRwGk4ltlbCKw2xgwDVlv7SqkQefYzR73dWWtvi7GZ3n0zXVIvMvj9UxKRZGAq8CSAMabGGPM1MAd4xjrtGeCywEJUSvnLffHrgaltX1gjOsq76x4Tpck9EgTyUxoMFAL/FJEvROQfItIN6GOMyQOwvvZuhziVUn74tzV/zK0zhzNpSNMPLDXnpzOGeex3j9dbdZEgkORuA8YBTxhjTgfKaUMJRkQWiEiOiOQUFhYGEIZSypfV2/P503t7ALjx3KF+P1E68+Q+ACTF2Tiw+CKifPTmVfgJJLnnArnGmLXW/ks4kn2+iGQAWF8LfL3YGLPEGJNtjMlOT08PIAylOk5VbT23LNvIvsKyUIfSoq1HSgC4/pwhAU0V4BwdYzc6JWQk8Tu5G2OOAodEZITVNAPYBiwH5ltt84H/BRShUiG2/qtiXv0iF4DP9h7jlQ2HWfjKlyGOqmXrvzpOQkw0Cy8cGdD7OBfOrtP5fiNKoMWznwDPi0gssA+4BscHxjIRuRY4CFwV4PdQKqSufOIzAG5+cRPfnXgSAOv2F1NVW9/iWqMdray6jm6x0SzLOcQHu9qn3JmaFAtATZ29Xd5PdYyAkrsxZiOQ7ePQjEDeV6lwUVFT57HvfIwfYOQ9bzN1eDozRvZmfhisUFRZU8+Y+97xaBvRDsvgJcU50sS8Cd7L8anwpbe9lWpCTZ2d1zfnNXvOh7sK+XBXYVgk98LSaq+2537Q8uRgrbF70YXY9EZqRNEBq0o14eG3d3DbS5u92m9qNDQwXBSVeyb30wb0oHd3/+aPaSwmOkrnb48w2nNXqgk7jpYCcM3kLO67ZDTl1XVU1NRTXF7D46t3e5xrjGm35LdyWz6nD0whLan5J0rveGUzpw1IYfygVH63chelVY4S0vIbJ9M3OZ7eye2T2FVk0uSuVCPGGO5fvpWP9xQxbmAK913iWJGoW5yNbnE20rvHsf7u84iLiXbVuJ/+9ADXTB4U8Pd+8fOD3P7yl/RPSeCThec2ed5za77ihXWHeGHdIY/2jB7xnDogJeA4VOTTsoxSjWzKPcEz1nwsV4wb4POcXklxJMXZuO+SUQA88Nq2gL/vB7sKuf1lxxDL6rp6n+cUllbz+uYj3PPfLT6P64gW5aQ9d6Uacc7HMmdsP75z5sBmz62o8Z2E/fG820icorIar+PHy2sYv2hVs+/x+7lj2y0eFdm0565UIyWVtQDcPmtki3X0URnJAX8/u92QtfANVmzL92gvKmu4QfrqF7mc/quVHscvOa2fx/6Dl41hyjB92ls5aHJXqpF9ReUAJCe0vJzc9JG9+VZ2pt9znO8tLGPLkROu/QcvG8P15wwBYEee44ZuaVUtN7+4yXXOPReP4sDii0iwpgU4dUAP9v96NldbD1gpBVqWUV1UXb2db/9jLScqannzpilERwm19Xb+/N4elnzoWJKuW2zrnj7NTE2gps7OPz/Z36abqrX1dmb89gPX/ke3TSczNZE9BWX89YO9FFc4SjM/fn6D65z5k07i2rMd36Ok0jE65qYZw3SYovKiyV11Kb9dsZOBqYnsKSxj3f5iAH724kZuu2AEUx5+z+Pc1ibM4dZToA+8tq1Nyf14RUNdferwdDKt+da7xTk+VH76whdcfEoGH+0uAuD1n5zNmP49XK+5c/bJ9EqK1VKM8kmTu+oyDhVX8Md393i1v7bpCK9tOuLanz4ineys1Fa/bw+38k1Nnb3VJZpfvb7dtX3luP6u7cTYhv+WL21wTFh2x4UjPRI7wMBeiSy6/JRWx6m6Fk3uqtMpr64j1hbltRxc46kEfnjOYAandXMNPwRYe+cM+rTx4Z+xA1Nc28PvfovfXnUaV57hewilu493Oyb2unBMXy51uznqnMsFcD0hOyitW5tiUkpvqKqI9emeIg4VV7j2y6vr2F9Uzuj73uGSP37sardbU9XuKSgjo0c8BxZfxIHFF3HHhSfzrfEDibN62n+Yd3qbEzs4psSdP6nhZubrm480c7bDsbJqSqvq+OHUwTxx9RkeJaDoKGHdXZ5z743sG/ioHNW1aM9dha1d+aUUl9cwbmBPYm1R1NXbeeHzQ1x6Wj925Zfy7X841onZ9eCFxNqieGzlLp78eD/gmDpgc+7XnDoghVv/s4k9BWUkxEaT2i3W6/usv2cmguMJVH+5j6xJaMWN2OfXHqTObjhnuO96ee/u8cybMJAX1jmWyctI0akEVNtocldhyW43zFuyhmPljpuOV4zrT2x0FEs/P+T1dObWIyfokxzvSuxO3386h2smZ/HqF4cBx4pC5470XtI3KYCk7tQvJcG17Vzcwl1pVS3XPZvDD84ezA+ezXG1ZzVTbrl91ghXcm9cYlKqJZrcVVjallfiSuwAr2w47HXOvAmZvLDuEH9+by+JVm85NjqKR646lZuWbqSorJpH3tnpOr+q1s6w3oHPb+7LxMENi0+/+sVhLj+9P1PdeuVbDpewZl8xa/YVe7yuuTJQSmIs/3dWluuhKqXaIuDugIhEi8gXIvK6tZ8qIitFZLf1tWfgYaqu5mK3mrm7y8Y6bjwOTuvGQ9ZIkVXb8zlwzPHg0apbzmHOWEcv3xdfZZn2MCitG+/9fBoJ1spM33tqncfxLw4d99jfdO/5bHngAqJbmCP9/ktH89i3xrZrrKpraI+e+03AdsB5x2chsNoYs1hEFlr7t7fD91GdzKpt+WzLK+En5w5tckz5pwvPpXu8jeo6O/klVYzu14P7Lx1NnC0aEWHB1MEs+XAfm3NPcM7wdAb2cowVP3Nwqmt8+IsLJvKtJWuA5ssggRqU1o3XfnI25z3meDCpsqbeVX9fuS2fOFsUa+6YQc8gfcAo5S6g5C4iA4CLgEXALVbzHGCatf0M8D6a3JVlf1E50x99n5hoobbeMYrlsZW72HjvTFISY8kvqeJla2z3T2cMc9Wyu4NrfvOUxIbk+ONpQ1xPlI7NTHG1/3He6by15Shzx3suDTe0d1Kw/mqu9z/jpJ6s/+o4R0uqXEMYi8qqmTWmryZ21WECLcv8HrgNcJ9ntI8xJg/A+up9BwsQkQUikiMiOYWF7bOQrwqduno7R09UNXuO3W6Y/uj7AK7E7jT2l45Jsb799zU8/LajTt6vR8sjRNwfIBpoPeEJjg+AeRMGIiKICOvvPo//XD+J/m43PoPl1vOHAzD90fdZs+8Y/8k5xKHiSqJ0igDVgfzuuYvIxUCBMWa9iExr6+uNMUuAJQDZ2dmmhdNVmLv4jx+z42gpsbYo15zi+x6aTZRbTbnxaJZbZg5nbGaKqz6dtfANj+OnDPB8ItMX93JOSmLTE331SoqjVwsrG7WXjB4NHyBzrXIQONZbVaqjBFKWmQxcKiKzgXggWUT+BeSLSIYxJk9EMoCC9ghUha8TlbWuJencF4soKqt2LfX24Ovb+IeV3CcN7sU/5me7xpU/8/0JzHe7Abnsh5PolxLPgJ4NPfHmfHTbdF7fnMe0ET5/SexwGU38xvHE1Wd0cCSqK/O7LGOMucMYM8AYkwXMBd41xlwNLAfmW6fNB/4XcJQqrD2+qmE90UtO68eiy8cA8MneIkqravl0T5ErsQP8+opTPB4YOnNQKlm9ErnvklHs//VsJgxKbXViB8hMTeRH04a0OPKko8THeI9z/+8Nk5kwqPXz1SgVqGCMc18MLBORa4GDwFVB+B4qhL7MPcEjK3bynTMHkpIQw1OfOBL3pnvPJznBxtYjJQAec5AnxdlYumAiO46Weo1YiY+J5v1fTO+4v0AH2/LABe3yoJRSbdEu/+KMMe/jGBWDMeYYMKO581XkOV5egwjMfvwjjlg3Tt1ryL+cM5oeVs3b1+pEPztvGGP69/Ca2bCz2vGrWYy8522gfZ6AVaqt9F+datGh4gqvuc5vmD6Epz85QHlNPYuvOIVvuQ05jIoSPrptOh/tLmLaiHQ+3l3EN1oxS2JnEh8TzZPzsz1G8yjVkcSY0A9Uyc7ONjk5OS2f2AZ2u/EYqaH88+meItcEXQCzRvfllvOHuxao0OusVOiIyHpjTLavY51yNqK/f7iPwXe+SVl1ndexA0XlTF78LvsKy8g7UUnWwjcYdtebVNd5rmK/YutRFr+1o6NCDkuHiitciX3xFaew76HZ/PW7Z7gSO6CJXakw1el67rX1dobd9ZZr/+FvnMrRE1V8MzuTw19X8N0n11FRU+/ztbYo4arsTH41ZzRDrff44p6ZXfKpwrp6O+f//kP2FZbz7PcneEyCpZQKD8313Dtdzd35KLqTcyWbx1buavI1g9O7sa+wnDq74YV1B13TrAKs3V/MrDF9gxNsGDt0vJJ9heWcP6qPJnalIlCnK8u8t6OA0zJT2PvQ7CbPOeMkx0SVPRNj2Hz/+bx76zTe+/k0rp440Ovc6/+1nvyS5h+rB8cDO9/++xre3ZHvf/Bhwm43PLrCMQXAvAne10QpFf46TVmmoqaOUfe+A8D3Jp3EL+eM4b0dBTzyzk6umzqIvBNV/OicIS2uaG+3Gwbf+SYAv7nyFNf6mv/4Xjbnjerj8zWFpdWMX7TKtZ8YG01FTT0XjO7DI1edRnJ86EdMHC+vYe3+YgpKq5g7fqDXIs519XaKymp4a0seD7y2zdX+0W3TyUxt/QNFSqmO0yXKMh/sbBhzPf+sLACmj+zNdB8r7zQnKkp486dTiI4ShvdJciX3Hzybw9PXjOfsoWl898l1fLbvGC8umMiZg3t5lXycNf13tuZTVPY5L//orAD+Zv575tMDHCur5qrsTOYuWcPhrysBSIiJ5qrshqGLa/cd4+YXN7rGrwNcN2UQP542tEveb1CqM4j4nvu+wjLe3VHAg29sB2DFzVM9RnMEat6SNXy271iTx79xxgBeWu+YovaH5wzmbx84av5Th6e7HvJxrvHZGlsOn2DNvmMM6JnArDEZfsdtjGHQHW96tA3tncSegjIA3r31HN7YnIfdwO9WOT6cusfZyM7qyUNXnOIx+ZVSKjx12p77zqOlXPD7Dz3aTurVviWEZ74/gfd3FnD/8q0ePVsnZ2KfMiyN2y4YSWVNPQUl1fz1u2fwp3d38+iKXQy/+y1XL785q7ble6yvuf/Xs1ssIzmVVNVyy4sbOXCsgrOG9HLdV3C6bsog7px9Mv9ae5B7/ruFc3/7gcfx/zsri/svHd2q76WUCn8R3XM/UFTONGt+cHAk2OeuPbMdI2vwzb99xrr9jvUv54ztx60zRzD1EcdTm1edMYCHv3GqVyKurqtnxN1vN8S7+CKf722M4e7/buH5tQc92n923jB+dt7wFmM7Xl7D6b9a6fPYc9dOYMqwhtEuxhiG3/2Waz718Vk96ZEQy5LvnqFj1pWKMJ225z4wNZG54zMpr6kn3hbFI1edFrTvNSAlgXXAg5eN4eqJJwFNJ2unOFs0t84czm+tmvzeQkdJZHBaN9cHwYGicq7622cUllYDsGDqYG6ZOZyzf/Muv1+1m6uyM1tcYOLOVx33BRJjo1l/90weXbHTNXf6qf1TPM4VEXY9eCGz//Ax3514Et8+U0fDKNUZRXTPvSPtPFrK82u/4q6LTibO5j2la3OOnqhi4q9Xu/ZH90vm9Z+czcHiCm5/eTNr9hUjAuvvnknPxBhExPXY/79/cCZnDU3jwde38fSnB5g2Ip37LhlNZmoi9XbDrcs28t+NRwDP2v66/cV8fqCYG6YPbb+LoJQKK522596RRvTtzi/njPHrtX0bLd6w9UiJx83O/ikJvPjDiaS6jUzJsHrrq7YXcFJaN9d86Ku2F7BqewETslJZd6DYdX5aUqzHTdsJg1J1/nClujBN7h3k5+cP59EVvp+S/fN3xnktTuFczeepT/a75kv/zZWnsDu/jH98vN+V2M8emsbEwamcOiAleMErpSKOJvcOcuO5w7jx3GGs2HqUx1fvJiEmmsvH9WfK0HQG+hjhEx8TzT0Xj+JXrzseKEqOt/Gt8Y76+ORhaVzzz8+5a/bJXDd1cIf+PZRSkcHvmruIZALPAn0BO7DEGPO4iKQCLwJZwAHgm8aY4829VyTU3EMl70Qlf3lvLz87b5jHAs+lVbV0D4MnX5VSoROsKX/rgFuNMScDE4EbRGQUsBBYbYwZBqy29pWfMnok8KvLxngkdkATu1KqWYEskJ1njNlgbZcC24H+wBzgGeu0Z4DLAoxRKaVUG7XLrJAikgWcDqwF+hhj8sDxAQD4nNxFRBaISI6I5BQWFvo6RSmllJ8CTu4ikgS8DPzMGFPS2tcZY5YYY7KNMdnp6TpfuFJKtaeAkruIxOBI7M8bY16xmvNFJMM6ngEUBBaiUkqptvI7uYvj+fknge3GmMfcDi0H5lvb84H/+R+eUkopfwQyzn0y8F3gSxHZaLXdCSwGlonItcBB4KqAIlRKKdVmfid3Y8zHQFPTCM7w932VUkoFrtOtoaqUUipMZoUUkULgqwDeIg0oaqdwgiUSYoTIiDMSYoTIiDMSYoTIiDMUMZ5kjPE53DAsknugRCSnqUdww0UkxAiREWckxAiREWckxAiREWe4xahlGaWU6oQ0uSulVCfUWZL7klAH0AqRECNERpyRECNERpyRECNERpxhFWOnqLkrpZTy1Fl67koppdxocldKqU4oopO7iMwSkZ0iskdEQrYoiIhkish7IrJdRLaKyE1W+/0iclhENlp/Zru95g4r7p0ickEHxnpARL604smx2lJFZKWI7La+9gxVnCIywu16bRSREhH5WThcSxF5SkQKRGSLW1ubr52InGH9DPaIyB+seZqCHecjIrJDRDaLyKsikmK1Z4lIpdt1/WtHxNlEjG3+GYfoWr7oFuMB5/QrobqWTTLGROQfIBrYCwwGYoFNwKgQxZIBjLO2uwO7gFHA/cDPfZw/yoo3Dhhk/T2iOyjWA0Bao7aHgYXW9kLgN6GO0+1nfBQ4KRyuJTAVGAdsCeTaAeuASTim73gLuLAD4jwfsFnbv3GLM8v9vEbvE7Q4m4ixzT/jUFzLRsd/C9wbymvZ1J9I7rlPAPYYY/YZY2qApThWgepwpulVqZoyB1hqjKk2xuwH9uD4+4RKU6tnhTrOGcBeY0xzTy93WIzGmA+BYh/fv9XXThzTYCcbYz4zjv/1z9LOq5X5itMYs8IYU2ftrgEGNPcewY6ziWvZlLC6lk5W7/ubwAvNvUdHxOlLJCf3/sAht/1cmk+oHUI8V6UCuNH6Vfgpt1/ZQxm7AVaIyHoRWWC1NbV6Vqiv8Vw8/+OE27WEtl+7/tZ24/aO9H0cvUenQSLyhYh8ICJTrLZQxdmWn3Gor+UUIN8Ys9utLWyuZSQnd181q5CO6xTvVameAIYAY4E8HL/CQWhjn2yMGQdciGNR86nNnBuyOEUkFrgU+I/VFI7XsjlNxRXSeEXkLhyL2z9vNeUBA40xpwO3AP8WkWRCE2dbf8ah/tnPw7PzEU7XMqKTey6Q6bY/ADgSolh8rkpljMk3xtQbY+zA32koF4QsdmPMEetrAfCqFVNTq2eF8hpfCGwwxuRb8YbdtbS09drl4lkS6bB4RWQ+cDHwHas8gFXqOGZtr8dRzx4eijj9+BmH8lragCuAF51t4XQtIbKT++fAMBEZZPXy5uJYBarDWbU3r1WpnP/pLZcDzjvuy4G5IhInIoOAYThuuAQ7zm4i0t25jeMm2xaaXj0rJHFaPHpF4XYt3bTp2lmlm1IRmWj9u/keHbBamYjMAm4HLjXGVLi1p4tItLU92IpzXyjibOvPOFTX0nIesMMY4yq3hNO1BCJ3tIzV8ZiNY2TKXuCuEMZxNo5fszYDG60/s4HngC+t9uVAhttr7rLi3kkH3Dm3vudgHKMONgFbndcM6AWsBnZbX1NDHGcicAzo4dYW8muJ48MmD6jF0Ru71p9rB2TjSFx7gT9hPSke5Dj34KhbO/99/tU690rr38ImYANwSUfE2USMbf4Zh+JaWu1PA9c3Ojck17KpPzr9gFJKdUKRXJZRSinVBE3uSinVCWlyV0qpTkiTu1JKdUKa3JVSqhPS5K6UUp2QJnellOqE/h8A6vh7PQAAjQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df.Close)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5243819d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0              NaN\n",
       "1              NaN\n",
       "2              NaN\n",
       "3              NaN\n",
       "4              NaN\n",
       "           ...    \n",
       "1827    167.610401\n",
       "1828    167.851401\n",
       "1829    168.100401\n",
       "1830    168.317801\n",
       "1831    168.227000\n",
       "Name: Close, Length: 1832, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ma50 = df.Close.rolling(50).mean()\n",
    "ma50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b071b7ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x196ac360490>]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ERGqx7HynRFpuQfmXb7ncHvLdntJncSdPhvBwOPHEcp+/UPHAOKC0yhdHKAXGIiIiIjVAarZTTWLfGsKHIscbVJcaGE+aBAMHQmhouc9fqCYtuCtOgbGIiIhIDZCS7exYl5SZV+5z+ALjkH0C4y1bYM2aMsu0HaqIfc9fQygwFhEREakB/tmeBkBugafc6RQ5+WXMGBeWaaukwLgmLbgrToGxiIiIyBFudkIS3y3a7nteGOAeqmzv60rM6E6ZAo0aQZcu5R5jcZoxFhEREZHD4uL35wFQP8rJ/811HVxgPHt9Eit2pPmeF+Yp14kILupkLfzxBwweXKEybcVFKMdYRERERA6nHs3qAE46xcG4+L15DH99pu95qjdPOTY8pKjT8uWQmOgExpWkRA5zDaHAWERERKSGOLlDPHDoJdu+WbgNl9tDao4zYxxbfMZ46lTn+6BBlTJGUCqFiIiIiFSS3AI3V3/0N6t2pgMQEhRA75Z1aREX4Tt+KO7+einHP/sHyVnOjHHdiGIzxr/8Ah07QsuWlTN4IDiwZoaYNXPUIiIiIkextbszmLIqkZs+WwSAAfq0quvbRe5gUimstX7P92TkMX7mRqLDgggL9oaAqakwfTqMGFGJo6+5amZmtIiIiMhRLCPX2cQjMT2PXWm55Lmc3epCvWXQDmbxXWnB896sfPq2qospXGT3+uvgcsGoUZU3eK/XL+pJ+wZRlX7ew0kzxiIiIiLVILfAXWZKROEmHpl5Lvo/4+QAhwcH+mZ6X528dr/nnrJyN/96e7bv+WmdG/oeH9PUWcBHZia8/DKMHAm9e5f3bZTpnB5N6Nw4ptLPezgpMBYRERGpBoNf+pM+T00hLaeAi9+by9Ktqb5ji7eklugfHhLoy91dui2NSSt2lXpel9vD1R8v8OUnA9SPKsopPrdnU+fBxx9DWhrce2/F38xRQoGxiIiISDXYnppDZp6Lz+ZtYfb6vXw+f4vv2LyNySX6hwcH0jY+igCPm0sX/ULe6Ivhscfgp58guah/wp5Mv9ed1L4+d57ewfc8LjLECYiffx769oX+/Q/Du6uZlGMsIiIiUo2We7d6jgotCssycgtK9GtVP5JAA98sHE+vP34kJSoWnvoTPB4ICYFXXoHrrmNPhpOG8fx53RnSpSF1I0MgMZHOiRtYFd+aehEhcOkY2LYNPv200jb1OBooMBYRERGpRsu9O9PtTM/lwnfncPFxLXyL74rr0CAaPviAXn/8yFdnjGHsaZcz5cbjYMECeOIJuOkmeOop6p91IRdss5yx6zti3PlOneKdO5kI7G3ehvD1PeHbb50Z4xNOqOJ3e2Qz+5byqA59+vSxCxYsqO5hiIiIiFSZVvf/4ve8QXQoid7Z3uJuP609Q7s2onN8BHToAPHx3H/Xu0xYsZsG0aEM6dKQB4a0gx9/hA8/dOoSA7ZOHYzLBWee6aRMREXB++/D4sVwzjnw/fcQUPuyao0xC621fUo7phljERERkSpW4C5ZSq20oBigZ4u6TnWHr7+GjRvh5ZeJDg4hM89FZp6Ld2ds4IFhneH88+H883lz/GQmzt/AhP9eDYH77EB3ww2waRO0aFErg+IDUWAsIiIiUsWy8kqmSuzrriEdaBIbzsnt6zsNL78M7drB2WcTM32Dr19ggH+O8PrIeNJaB2L2DYrBySdu3bpCYz+aKTAWERERqWIfzd5c5rE3LurJuzPWc/FxLagXFeo0zpkDc+fCm29CYCDRYUUhnNtjKXB7fKXckjLzqF/4OjkkmkMXERERqUJbk7N5ZYr/Bh1PjOgKwMKHT+PsHk2YcMtJRUExOLPFdevC5ZcDEBMe7Pf64jPQu9NziY9WYFweCoxFREREqtBdXy8t0XbZ8a3Y8J9h/sFwoU2b4Lvv4NprITISgJgw/8C4sIqFy+1hU1I2bepHVvq4awMFxiIiInLUstZyJFTgKi7Puw10zxaxfu0BAWXUE379dWeh3M03+5qKp1IAZOW7SEzPZcWOdPLdHtrGR1XqmGsL5RiLiIjIUeu6TxaS7/bw4RX9qnsoPlHeoLZXi7qlbv3sJy3NKbF24YXQrJmvOTLUP4RbtTOde75ehsvj/BJQPzoEOXSaMRYREZGjksvtYdLK3Uxfswe358iZNS7c4e6+MzoduPO4cZCRAXfc4dfcJj6Sfq3ieHh4ZwDu+HKpLygGqLNPDrIcHM0Yi4iIyFEp25uyALA9JYcW9SKqcTRFUrIL6Nc6jpCgAJ4Y0ZWgsuoJu1zw2mswcCD07u13KCIkiK+uP55FW1JKfem+OchycA44Y2yMGW+MSTTGLC/l2N3GGGuMqV+s7QFjTIIxZo0xZmhlD1hERETkYGTnFQXGaTkF1TgSf6nZ+dSNcALXy45vxcXHtSi94/ffw5YtJWaLi+vSOKbU9n2rVsjBOZhUig+BM/ZtNMY0B4YAW4q1dQFGA129r3nLGFNKdWkRERGRwysrv6iEWa7LvZ+eVSslu4C6EQeRA/z++84OdWedVWaXsOBA/rr31BLt+y7Ok4NzwMDYWjsDSC7l0CvAvUDxpJ0RwBfW2jxr7UYgAThyst1FRESk1sjJLwqGcwuOjMDYWktqdj6xBwqMt26FyZOdusWl7WBXTPO4ohSRSXeczD1DOxIRosC4PMp114wx5wDbrbVLjfErLdIUmFvs+TZvm4iIiEiVKr7pRW6BpxpHUiQ7302B2xIbcYBUh08+AWthzJhDOn+HhtF0aBhdgRHWboccGBtjIoCHgNNLO1xKW6nLQI0x1wLXArRoUUZujYiIiEg5ZR+BM8Yp2fkAvhzjUnk8MH68s+iuTZsqGplA+cq1tQVaA0uNMZuAZsAiY0wjnBni5sX6NgN2lHYSa+1Ya20fa22f+Pj4cgxDREREpGx+OcZHSGCcmu0sAtxvKsXUqbB+PVxzzUGfd0iXhlx2fMuKDq/WO+QZY2vtP0CDwufe4LiPtTbJGPMT8Jkx5mWgCdAemF9JYxURERE5aMWrUuS6joxUiqIZ4/0Exu++C/XqwXnnHfR537usT0WHJhxcubbPgTlAR2PMNmPMVWX1tdauAL4CVgK/ATdZa4+MX9FERESkVskslmOck+9i9a70ahyNI8U7Y1xmKsXmzU6ZtiuvhLCwKhyZwMFVpbjIWtvYWhtsrW1mrR23z/FW1tqkYs+ftta2tdZ2tNZOPByDFhERETmQ7GKpFJ/M3cwZr/7FuJkbq3FEsGFPJgBxkWXMGP/3v2AM3HJLFY5KCmlLaBEREanxjnnsd96anuDXlpXvJjjQEGBgR2ouAE9OWIm11bc99PeLt3Nc6zjqRYWWPJiZCWPHwvnnQ/PmJY/LYafAWERERGo0t8eSmefi+d/W+LVn57mIDA0iLDgQT7FgeHtqTlUPEYDEjFw2783m9K6NSu/w4YeQlga3316Vw5JiFBiLiIhIjVZWxYmsfDeRIU5gXHyS+Ib/LcLjqfpZ4+0pTkDeql5EyYNuN7zyCvTv73xJtdC2KCIiIlKj5ZQRGGfnu4gI8Z8tBvhnexqrd2XQpUnMIV3n703J5BV4iA4LomOjaMKC978j3b52pTnpHI3qlLKo7vvvYcMGeOGFQzqnVC4FxiIiInJYbErKom5ECHUOtMtbBRWfMbbWUrgrb2aem8jQIDYkZfmOv3RBD+76einJWfkHfX6PxzLs9b9YvSvD13ZG10a883+9D2mcO72BcZM64SUPvvwytG0LI0Yc0jmlcimVQkRERA6LU16czrDX/zrs1ykeGO8tFvA6OcaBuIulTfRoXgeA5OyDD4yTs/P9gmKA31bsOqRFfE9OWMkTE1YSGhRQcjvoJUtgzhy4+WYIPLRZaKlcCoxFRETksKmKhW45+UWbd6zbnel7nJXvJiKk6I/jT47o6ttYI+UQZowzcl2ltv+2fNdBn6OwTFyey+Ob0fZ57z2nZvGYMQd9Pjk8FBiLiIhIpavKkmjFc4wTEotmdrPzXUSGFM3AXtSvBXXCgzGGQ0qlOPXF6aW2/7xsx0GfIyrUCdCPb1PP/0B2Nnz2mbPLXd26B30+OTwUGIuIiEily3dX3RbMxVMptiRnM2/DXgCy8txEhBbNGAcFBhAUGECd8GBSM3JgyxZYuBCyskqc82D8+s8udqYdeEY8t8BNZp6LWwa144Mr+voffPNNSE2FG28s1xikcikwFhERkUqXm191gfFl4+f7Hr/310YuHDuXhZuTycpzERUaRHSYf62BU3eu5M6rBkPLltCnD7RrB3/+Wa5rZ+WVXhGjuPu+XQZAi7gI/0oWaWnw7LMwbBgMGFCu60vlUlUKERERqXS5rgMHjJWhoNjMdP2oEJIynRSJDXuyyClwExESyF/3nlqUbjF/Ps+/fy+J9ZtQZ+wzEBMDjz8OZ50FS5dCmzZlXuvza/rTJj6SAreHE5+bBpRdQ7m4H5c4KRcNY/Yp0/bSS5CSAk89dQjvWA4nzRiLiIhIpcvJr5rAOLvYjG1hUAxwzzfOLG1kSBCxESE0rhMO1sKNN5Jepx633fQGXHMNXHgh/P67Uw1i1CjIzfU7f/Fc6eNax9EwJoxmdSN465JeAOQd4BeANcWqWcRHF9sGessWZ0OPCy6Anj0P/Y3LYaHAWERERCpdWZtuVLasfKdixONndyn1eERosdSFGTNg4ULmXXQdCzIDyMzzVpto0QI++cTJN77xRvAUzUJneQP8B4d1IiCgqJpE/SgnyD3v7Tn8tW5PmeMb+uoM3+Mmsd76xXl5cPHFzuNnnjm4NypVQoGxiIiIVLoqC4y9wW29qNBSj0cWK9fGSy9B/fpkXeAEpW/8sa7o2Nlnw0MPwQcfwOjRkO/MPqd66x3Hhof4nTe8WK7wh7M2HXCcH13Zjzrhwc5CuxEjYNYsp0xb27YHfK1UHQXGIiIiUukOJve2MhRu6BEVGkSHhlEljkcWVqVYswZ+/hluvJFzBjjBaF7BPgsEn3wSnnsOvv4azj8fcnNJzS4AKLF7X3hIUQjVol5EmePrmLSZcd/8m5NP6QHR0U5JtkmTYNw4JwCXI4oW34mIiEilKx4YezzWLw2hsrg9ltFj5wIQERLIO5f2ZtBL/tUlfHWMX3gBQkPhppsIDQokLjIEl2efwNgYuPdeJ4C98UYYNozMl8cDEBvuHxgXry5RUFppusxMePJJJnzwEgURkZgLzoPYWGex39lnQ+9D205aqoYCYxEREal0xXejc3ksIYchMP524Tbf46iwIL9d7gpFhAbBP/84KRK33AINGjjtIYFk57mZlZBEl8Yx1I0slipxww1OcHz55XS69FxiB99bYsa4eGCcWbgzXm4ufP45TJkCEydCSgrf9TidpAf/zU2j+lfiO5fDRakUIiIiUumK5xiXmJmtJNPWJPoeN4oJ88v7LVQ/Mhhuuw3q1IFHH/W1R4YEkZpTwCXvz+P/xs8refJLL4Xvvyd63Upe/fkl4sL9g+464cF0a1oHgEVbUnn/tufIbN0OrrzSCYyHDSN50h/cd8atBDWMr6R3LIebAmMRERGpdLl+gfHh2R7aU6yUWlxkCGHF8n4L842bzZ4G06bBf/4DcXG+4xGhgaR4F9Yt355e+gXOPpup19zHKRsXEn/nLc4s8K5dAAQHBvDz6I68Pe9D3nnpKq5+/X62eEK45MKnGPfNHOwnn9BrarZvbFIzKJVCREREKtU/29J4+Iflvudu9+EJjPNcRTPRxhhCAp3A+NSO8bx2UU8nOB9zMTRsCFdf7ffayJAgdqQeeDvniSeeS9CffzJo/HgYP97JQ+7bF9xuWLSIM61lXb3mvDZgNK+fcBHugEB2zd/CoM4NfedoXT+ykt6xHG4KjEVERKRSnf3mTL/nBYcplSLf5aFH81i+vNbJ3zXGMOv+QdSLDCEsOJCYrHSYMAFuvhmC9kmFiAhmZkKSX5vbY1m9K52uTer4nv+VkETa7c8y6KR6kJgIU6fC9OnOCx5/nK+a9OLeBP/86T0ZeTz/22rf81YKjGsMBcYiIiJSaTylpE24D1MqRZ7LQ1RooN9CuKaFm2iAs2lHQQFcdlmJ114xoBW/LNvp1/beXxt4duJqfrjpBI5tHktqdj5Jmfmc1KkRdGgNHTrAiSfCY4/5XhOyeDskLPE7T3qui4nLdxFgYMHDQ5RKUYMox1hEREQqzZ7MvBJtrv2kUrg9lnzXoc8ou9we8l0eX/pECbm58PrrTiB77LElDvduWbdE27rdmQAs3ZoKOAEuOAvtyhId5j/HOLpvc9/jvq3iFBTXMAqMRUREpNIU5u22rBdBv9bOYrf9Lb67+qO/6fDwxEO6xm/Ld9HuoYms2Z1BaFDJShSAs1nHxo1+lSiKM6Zk+bjgQKct2btpSEaus7lHTFjZgXFUaFFgPKhTA+4/s5PveVVtciKVR4GxiIiIVJrsfCcYfP687vxf/5YAuPeTYzxtzZ5DvsbrU52tnPNdHkKCSgllUlOd2eLRo2HIkIM6p8dj2ZPhzHYnJGZS4PaQnuPMGMfsd8bYORYbEcz4y/sSGxHCPUM7AkX3QmoOBcYiIiJSafJcTjAYGhxIkHdTj4KDqEpxsHnI09YksnJnUXm10NIC43ffdXaeu+++gzonQGa+i3TvDPEv/+zk9i+W+J7vmy5RXOGxoICicVx5Qmuiw4K42xsgS82hxXciIiJSaQrzhUMCAwj0BsYHE/Rm5bv2m7JQ6KoP//Z7nr1vukJSEjz7LJx5Zqm5xWVJyy7wzRCDExwf2zwWgPjo0DJfF+lNpejdMtbXFh4SyD+PDz3oa8uRQ4GxiIiIVJrC2sKhwQEEexfGHcwGH5m5Bw6MPR7Lvqdati3Vv+HhhyEjA1588aDHDLAxKYs1uzP8z709jaax4dSPKjswjosM4Zvrj6dz45hDup4cmQ6YSmGMGW+MSTTGLC/W9oIxZrUxZpkx5ntjTGyxYw8YYxKMMWuMMfp1SUREpBbJK2XG2OU+cNWJwl3o9qdwdvjcnk19bf1a1SvqMHOmk0Zx223QpcuhDJvLxs8v0fbz0h00qxteSm9/fVrF+WaOpWY7mBzjD4Ez9mmbDBxjre0OrAUeADDGdAFGA129r3nLGFPGclERERE52hSfMS7MMf5z7YEX2L0yed0B+2TlOakOfVrVZf5Dg5l536n851/HOAfdbrj1VmjeHJ544qDGWliFYn/2Zh04YJejxwEDY2vtDCB5n7ZJ1trCRJy5QDPv4xHAF9baPGvtRiAB6FeJ4xUREZEjWGGOcWhgIO0aRAGwfT9bLxfm8S7dNyXCK8/l5rN5W3B7rC8wjgwJokF0GM3qRhSVaxs/HhYvhhdegMiD22nuzYt7cd3ANvvtk6PKErVKZVSluBIoLEDYFNha7Ng2b5uIiIjUAkVVKQJoEBNG09hwAkqpGVzIWidpOCkzj4JSUi7e/2sjD37/D98v3k5WnnPuEmkLqanw0ENw0kkwatRBj3Vo10bcNcS/ckS/VnF+z/czdDkKVSghxhjzEOACPi1sKqVbqRn3xphrgWsBWrRoUZFhiIiIyBGieFUKgKBAU2rA6+vvLeVmLSRm5Pm2dHZ7LMu3p/HC72sA2Jqc7cv3jQwplqWZl+cEwykp8NprhxzJhgQF0CY+kg17shjWrRH/ObcbX/y9lR7NYpm2JpGRx2p+rzYpd2BsjBkDnAUMtoW/7jkzxM2LdWsG7Cjt9dbascBYgD59+hyeTdRFRESkSuW5PAQHGgK8+cXBgQH73RI63+UmIiSQ7Hw3u9NzfYHxIz8u57N5W3z9xs/aSLemdYB9ZoxvuAEmT3ZSKXr2LNeYW9dzAuMzj2lMbEQI1w9sC8Dxbesd4JVytClXKoUx5gzgPuAca212sUM/AaONMaHGmNZAe6DkMk8RERE5KuW7PL7ZYnAC4/z9zBgXuK2vHFpaToGv/ZuF2/z6ZeS6+GmpM9cWG+Et6/bDD/DBB/Dgg3DFFRUee6mbhUitcjDl2j4H5gAdjTHbjDFXAW8C0cBkY8wSY8w7ANbaFcBXwErgN+Ama62y1kVERGqJrDwXEcVmdIMPkEpR4PYQFxkCQMKKTbBpE2RmUt/bBhDj3V1uzS6nznBseAj8+KOz5XOvXvDYYxUac+F8tlFCca13MFUpLrLWNrbWBltrm1lrx1lr21lrm1trj/V+XV+s/9PW2rbW2o7W2on7O7eIiIgcXVKzC4gNL9qoY0tyNtPX7GHtPptnFCpwe2gWmM/908Zz2agToHVriIvjtNWzfX3+vOdUANbsziDAQPQ/i+GCC6BHD5g0CUJCSj33wSrKCJXaTn8zEBERkUqTllNQlOqAEygD/LTEf8nR8c9MZcR/Z+HKyeXuV+/k2vnf83PngXxz4+PktmnH3d+8SP2sFADqRoZQ13vOZuQScOEoaNwYJk6EepWXB6z5YlFgLCIiIpUmNaeAOuElZ3BtsSJVyVn57EzLZenWVK6c9imt1izh1nPu4e7hd3B3dB9GnnwrYXk5fPj14zTMSAK8C+6s5YVfXoUdO+CrryAursR1yqOHt5Zy49iwSjmf1FwKjEVERKRSpOUUkJCY4ZvdLe6/09bz9yZnv7DCtIrovCwuX/gzW047i+nHDvL1XR3XnIcv+zetUnbw0cQXISeHQUun8eKvr3LcPzOdTTyOO67Sxn3LoPb8eutJdG1Sp9LOKTWTAmMRERGpsJx8N0NfmUGB2zKsW+NS+7zwm1OTeOyMDQCMWjaZmPxsdl13C8v/PZQ6xXKTv2rQnV9ueIROG5dDRARPfP4U5y+fyraLr3C2fq5EgQGGLk1iKvWcUjNVaIMPEREREYC/NyWzKz0XKLv+7+4M5/gfqxMJ8LgZs/Bn5jfrQquhJwPw7L+6ccOni3z91ww6B5oGwZIlXJfdijktuzPx8XO0HZ0cNgqMRUREpEJ+XLKdxVtSAbjp1LaEBQeW2bewdNvg9X/TIm03z5xyBW9HO7m9Z+4z0xwRGgT33APA7/f/AkCjGOUBy+GjwFhEREQq5LYvlvgeX+fdNa40Bkj3buJxxYKf2BYTz6QOx5fZP7zY1s83nNKWqat2Exig2WI5fJRjLCIiIpUiONAQHbr/ObfUnAK67N7AgC3L+KTXcI5pUXZlifBiM8/3ndGJSXcMrLSxipRGgbGIiIiUm9tTVIatwG1L7B735IiuvscujyUtp4A7Zv6PvKhoOjx0J59f27/Mc0eElJ2SIXI4KDAWERGRcsspcO/3+P8d34oo7yzy9tQc3n72M4YkzGfPdbdw3mndiAgpe4Y5XIGxVDEFxiIiIlJu2XmuA/aZetdATuvcgKjcLO7/8ln2RMSScd2NB3xd+H4W8YkcDgqMRUREpNyy8vc/YwzQMCaM07s24s6//kfr5O3cPOI+wurGltr35lPb+R7vbzZZ5HBQYCwiIiLllp1/4BljgLYbV3DFwp/5uNdw5rXoVmb+8C2DiwJjpVJIVVNgLCIiIuW2bFvaQfVr89E7pIVG8vzAMUDZC+tCAotCkwbRoRUfoMghUGAsIiIi5TZl5W5a1YsAoHlceOmdNm0iduLPfH7sGWSHOH3KSpMoXtWioTbzkCqm5B0REREpt+U70jixXTyfXHUcMeHBpXd64w0who96neVrOpiNOkKCNH8nVUuBsYiIiJRbSnYB9aNCaB4XUXqH3FwYN46Cc//Fzpj4gzrn77effMAycCKHgwJjERERKZfcAjf5Lk/ZM8UAEyZAWhqBV10F0w9uoV7HRtGVNEKRQ6O/UYiIiEi5pOUUAFBnf4HxF19A48YEDjmtikYlUn4KjEVERKRcDhgY5+TAxIkwciQEBhIdGkT7BlH8ec8pVTZGkUOhVAoREREpl4xcJzUiKqyMcGLKFMjOhnPPBeCffw+tqqGJlItmjEVERKRcXG4P4F972M8vv0BMDAwcWIWjEik/BcYiIiJSLi6PBSCorNJrs2bBgAEQElKFoxIpPwXGIiIiUi6+wDiwlMA4LQ1WrIDjj6/iUYmUnwJjERERKZfCVIqggFLCifnzwVpnxlikhlBgLCIiIuVSOGNc6i52s2eDMdCvXxWPSqT8FBiLiIhIubjcTmAcXNriuzlzoFs3Z/GdSA2hwFhERETKxeXxplLsm2Ps8cDcucovlhpHgbGIiIiUS+GMcYmqFKtWOYvvlF8sNcwBA2NjzHhjTKIxZnmxtjhjzGRjzDrv97rFjj1gjEkwxqwxxqiSt4iIyFGqaMZ4n3Bi9mznu2aMpYY5mBnjD4Ez9mm7H5hqrW0PTPU+xxjTBRgNdPW+5i1jTGCljVZERESOGGXWMZ4zB+rXh3btqmFUIuV3wMDYWjsDSN6neQTwkffxR8DIYu1fWGvzrLUbgQRAy1FFRESOQmWmUsye7cwWmzI2/hA5QpU3x7ihtXYngPd7A297U2BrsX7bvG0lGGOuNcYsMMYs2LNnTzmHISIiItWlaMa4WDixdy+sWaP8YqmRKnvxXWm/GtrSOlprx1pr+1hr+8THx1fyMERERORw823wUbwqRWF+sQJjqYHKGxjvNsY0BvB+T/S2bwOaF+vXDNhR/uGJiIjIkSQn380jPywnJSu/9A0+pk2DsDBt7CE1UlA5X/cTMAZ41vv9x2LtnxljXgaaAO2B+RUdpIiIiFQ/l9vDBe/OZvn2dAIDDHUjQoB9NviYNs3JLw4Lq6ZRipTfwZRr+xyYA3Q0xmwzxlyFExAPMcasA4Z4n2OtXQF8BawEfgNusta6D9fgRUREpOp8Pn8Ly7enA/Dh7E24veXafBPGu3fDkiUweHD1DFCkgg44Y2ytvaiMQ6V+6q21TwNPV2RQIiIicuRZuzvT73mBxxIcaDCF1ScmT3a+n7FvlVeRmkE734mIiMhB2bQ3y+95vsvjX5Hit98gPh569qzikYlUDgXGIiIickBf/r2Fv9YlAdCuQRQA21KyqRMe7HTweGDSJDj9dAhQeCE1kz65IiIickDv/bURgBHHNmHM8S0BWLYtjbYNIp0OixfDnj0wdGh1DVGkwhQYi4iIyAHtTM1hdN/mvHRBD8JDnCVKO9NyaVnPGxj//rvzXYGx1GAKjEVERGS/cgvcZOW7aVY3nKDAAMKDA33HokK96/h/+83JLW7QoIyziBz5FBiLiIjIfs3dsBeAelGhAISHFIUPbo+F9HSYM0ezxVLjKTAWERGR/Xrm19UAxIQ5C+3Cg4uqvXZtEgN//AEul8q0SY2nwFhERET2q0OjaACGdGkIQJNYZ1e76NAgzu3Z1MkvjopydrwTqcHKuyW0iIiIHOX2ZuZRLyqU9JwCujerQ0iQM5/Wsl4kH13Zj+Z1wzHg5BcPGgQhIdU6XpGK0oyxiIiIlLB0ayq9n5rCnV8t4c+1e4iN8A96B3aIp018FKxbB5s2KY1CjgoKjEVERKSENbsyAPhu0XYA6keWMRusMm1yFFFgLCIiIiUZ/6e3n9ah9H4TJkCHDtCmzeEfk8hhpsBYRERESkjNzvc9PqNrI1rUiyilU6pTkWLkyCobl8jhpMBYREREStibWRQYBweVES5MnOiUaRsxoopGJXJ4KTAWERGREpKKB8aBpmQHa+Htt6FpUzjuuCocmcjho3JtIiIiAkBaTgHXf7KQ0f2a8+2ibb72Xi3qluw8YQL89Re89RYEBpY8LlIDKTAWERERACYs28GcDXuZ490CGmDibSfRybvBh09BAdxzD3TsCFdfXcWjFDl8FBiLiIgIAHkFHr/nI49tQufGMSU7jh8Pa9bAjz9CcHAVjU7k8FNgLCIiIgDkuty+x3ec1oFbBrUr2cnjgRdfhH794Oyzq3B0IoefAmMREREB/GeMG9UJJSCglEV3v/wCCQnwxRdgSjkuUoOpKoWIiIgA/jPGcZGhpXd67TVo1gz+9a8qGpVI1VFgLCIiIgCkZRf4HreNjyzZYcMGmDoVrr9eucVyVFJgLCIiIgCkFNvtrmW9UgLjjz920icuu6wKRyVSdRQYi4iICAApxWaMA/fNL/Z4nMB48GBo3ryKRyZSNRQYi4iICADJWfmEBgUw4ZYTSx786y/YuBEuv7zKxyVSVRQYi4iICLvTc0lIzOSmU9txTNM6JTt8+CFER8O551b52ESqigJjERERYVNSFgA9W8SWPJiZCV9/DaNGQURE1Q5MpAopMBYRETmCzUpI4ty3ZpFb4D5w5wooXHgXFxlS8uB330FWFowZc1jHIFLdKrTBhzHmDuBqwAL/AFcAEcCXQCtgEzDKWptSoVGKiIjUIrMSksjMc/H1gm1MWbUbgGs/WciHl/ctfdONCrLWcv3/FgFlBMYffABt2sCJpeQeixxFyj1jbIxpCtwK9LHWHgMEAqOB+4Gp1tr2wFTvcxERETlIl7w/j+s+WegLigFmrN3Dm9MSDsv1Fm1J9T2uG7FPYLxyJUyfDlddpZ3u5KhX0VSKICDcGBOEM1O8AxgBfOQ9/hEwsoLXEBERqTWSs/LLPPbDku2Veq303AJy8t38smwnAKd1bkBYcKB/pzfegNBQuOaaSr22yJGo3KkU1trtxpgXgS1ADjDJWjvJGNPQWrvT22enMaZBaa83xlwLXAvQokWL8g5DRESkxrPWsiczj35PT91vv8qer+3++CTf42OaxvD+mL7+HVJSnNrFl1wC8fGVfHWRI09FUinq4swOtwaaAJHGmEsP9vXW2rHW2j7W2j7x+mETEZFa7J0/N5QZFK9+8gyWPnY6DWNCcXtspVxva3I2+S6PX9v5vZqV7DhuHGRnwy23VMp1RY50FVl8dxqw0Vq7B8AY8x0wANhtjGnsnS1uDCRWwjhFRESOWt8v3lbmsbDgQMKCAzmrexM+m7cFay2mArm+K3ekM+z1v3zPQwID+OXWE2nfMNq/o9sN//0vnHQSHHtsua8nUpNUJMd4C9DfGBNhnJ/QwcAq4CegsJ7LGODHig1RRETk6FYnPNj3uGW90usEx0eHklPgJiu/YmXbNu/N8nv+3pg+JYNigN9+g02bNFsstUq5A2Nr7TzgG2ARTqm2AGAs8CwwxBizDhjifS4iIiKlKHB72J6SA8BVJ7bG5S49XSImzAmeM3NdFbrervRc3+PIkEAGdigjnfGtt6BRIxg5skLXE6lJKlTH2Fr7GPDYPs15OLPHIiIicgCb92axIy2Xf5/TlTEDWtE0NpwnJqwE4JSORUFrdFgQEfk5uCZNgoWz4fff4eST4eGHD2lh3NbkHN/jMmefN26EiRPhkUcgOLj0PiJHoQoFxiIiIlIxO9OcGdxOjZx0hitPbM2VJ7YmLaeAwGKbeTTasIrJ799I01f2QECAk/f7xhvwww/w99/QoNQiUCVsS8kGoHGdMF44v0fpnd5/36lZrBJtUssoMBYREalGhYFx4zrhfu3F847JzKTbHdeQjGXlK2PJ69WHwDat6Z6wBIYOheuuc7ZtPohFeVuSsxnUqQHjL+9begePB/73P+e8zUqpVCFyFKvoBh8iIiJSTut2Z/D9ou0EBhga1Qkru+MDDxC6ZRO3n303GwYN59xfd3DOm7PglFPgiSecWePffjvg9bLyXKxLzKRrk5iyO/35J2zZ4tQuFqllFBiLiIhUkyGvzGDOhr24PZaQoDL+lzxnDvz3v2Rdcz3zmx/Dos2p/sfvuAPatYMHHgC7/zrHK3ak4/ZYeraILbvTe+9BnTpw7rmH9F5EjgYKjEVERKrZgLb1Sj+Qnw/XXgtNm2KfegqA8bM2+vcJCYGHHoKlS+Hnn/d7nZU70gDo2qRO6R327oVvv4VLL4WI0svGiRzNFBiLiIhUA5fb2Xmua5MYPrumf+mdXnwRli+Ht94isl7dslOIL7kEOnaEe++FgoJSu6TlFPDF31uJiwyhQXRo6ef55BMnGNeiO6mlFBiLiIhUg4Q9mQD8X/+WpXdYu9bJHz7/fDj7bAICTIlMiTyXt9xacLATRK9ZA2PHlnq6mz5dxOpdGbSuH1n6znnWOmkU/fpBjzKqVYgc5RQYi4iIVDFrLbd/sYQAA6d1aVhaB6fSRFgYvP56mefZsKfYLnbDh8PAgU4wnZlZou+s9UkARIQEln6yOXNg5UrNFkutpsBYRESkim1Jzmb1rgw6NYqhflQpaQ0ffgjTp8Pzz0PjxmWeZ+3ujKInxsBzz0FiIrz6KvkuDzvTijbzCA50/pffv00Z+cxjx0JUFIweXY53JHJ0UGAsIiJSxbZ5t4B+5KwuJQ8mJsJdd8FJJ8HVV/sd6t7Mf9HcpJW7/V973HFwzjnw4ou88uVcjn/mD6788G/A2dCjdf1IbhjYtuQ1k5Phyy+dRXdRUeV/YyI1nAJjERGRKvbTkh0ANKsbXvLg7bdDVha8+66zw10x1+8T1P6ybCfpufsstnvyScjIYMCLD4O1/LE6kTW7Mti8N5uBHeIJCCglv/ittyA3F266qSJvS6TGU2AsIiJShdKyC5i7cS/tGkSVDIwnToTPP4cHH4TOnUu8Njy4ZH5wes4+gXH37vDMM5y0ZBpXLfgRgKGvzgAgtLRaydnZ8NprMGwYHHNM+d6UyFFCgbGIiEgVmbN+Lz2emMTmvdkM7drQvzpEVhbccIMTEN9/f6mvDw0u+b/t1OxSyrPdcw/L+pzKQ3+M4/IFP+2/7wcfQFIS3HffIb8fkaONAmMREZEqMn1NIgC9WsRyTo+m/gcffRQ2b3YWwYWWXme4tBnjs96YSYG3JrKPMbxzzb+Z3P44Hp36Hqeud/KM+7aO8++XmAhPPw39+zs5zSK1nAJjERGRKrJmdwadGkXz3Y0n0LFRdNGBlSvh1VedXe5OPLHM14eXUWotp8Bdom2vDeLpSx5mVYPWvDLhJR7oHMZ5vYoF4243XHwxpKQ4OcZl7h4iUnsoMBYREaki63Zn+gfEhR58ECIjndnb/YgIDgIgLjLEr916SvbNynfRqGEcN4x8gABrufyZmzF79zoHf/8dzj4bpk51guKePcv1fkSONgqMRUREDrPtqTnc+OlCtqfm0KHhPoHxd9/Bjz86wXH9+vs9T7O64dxxWgdevfBYv/YCT8nIeFdaHq3rR7KlbmOu/ddDhG7eBIMHw403whlnwPz5Tt3jK66o4LsTOXoEVfcAREREjlZuj2Xt7gyenLCS2eud2drzezcr6pCS4pRIO/ZYp3bxAQQEGG47rX2Jdpfbf6/orDwXSZl5tKgXAcDcFt2d4PvCC2HZMhgzxsllDgkpcS6R2kyBsYiISCV78Pt/SM7MZ8nWVHal5/raG8aE0jAmrKjjPffAnj3wyy8QHFzu6+27+G7ptlQA2jWI4rTODWhVLxJO7wIbN0JGBjRvXu5riRzNFBiLiIhUotenruOzeVtKPfbN9QOKnkydCuPGOWXSevU65Ov0bxPH3A3JQMnAeHbCXgIDDCe3j2do10ZFB2JjnS8RKZVyjEVERCrJxqQsXp68ttRjV57QmuZxTmoDWVlOBYr27eGxx8p1rc+v6e977PIUpVLkFrh5c1oCbo8ts4qFiJROM8YiIiKV5NUpTlDcp2Vd2jeM5s81iTSICWPJ1lQ6Fa9G8eijsGEDTJ8O4aVsC30Qim8OUnzGeM2ujHKdT0QUGIuIiFSYtZY3/kjgxyU7uHxAKx4/p6vv2HeLtrFkayoDO8Y7DfPnOzWLr7sOBg6slOu73JYfl2znuYmrefOSQ0/LEBGHAmMREZEKWrQl1ZdCcf3Atn7H/tWrGef0aEJQYADk58NVV0Hjxk6ptEri8ni4/9t/yClws2FPFgCfXNWv0s4vUlsoMBYREamgRZtTAJh610Aa1QkrcTwo0Luk57nnYPly+OknqFOn0q6/PTWXwAAnteLur5cCpW8fLSL7p8V3IiIiFbQjLYfIkEDa1I8su9OCBfDUUzB6tLPrXCW69fPFvsC4UJgCY5FDpsBYRESkAuZu2MtXf2+lcWy434I4P8nJcP750KgRvPHGYRmHx+O/yUdokP4XL3KolEohIiJSAaPHzgWgcSkpFAB4PHDppbBjB8ycecBtn8srI8/l91wzxiKHrkK/ThpjYo0x3xhjVhtjVhljjjfGxBljJhtj1nm/162swYqIiBxJEovtatekThll155+GiZOhNdeg36VuyDu/cv6lHksNFgzxiKHqqI/Na8Bv1lrOwE9gFXA/cBUa217YKr3uYiIyFHn/ZkbfY9jI0vZ0nnSJGcDj0svheuvr/Trx4T7X7NXi1jf49AgzRiLHKpyB8bGmBjgZGAcgLU231qbCowAPvJ2+wgYWbEhioiIHHmSs/IZO2MDjeuEMeb4llzUt4V/h4QEZ6Fd167wzjtQVv5xBTSP85+lHtC2KE0jTDPGIoesIj81bYA9wAfGmMXGmPeNMZFAQ2vtTgDv9walvdgYc60xZoExZsGePXsqMAwREZGq9+XfWwG44ZS2/HvEMbQqXpEiM9OpPBEQAD/+CJH7qVZRAY3rhLPi30N9z/u1jmN4t8Y8OfIYzRiLlENFFt8FAb2AW6y184wxr3EIaRPW2rHAWIA+ffrYA3QXERE5IiRn5fO/uZuZs34vMWFB/F//liU73X03rFkDU6ZAmzaHdTwRIUUBcGRoEP/Vznci5VaRGeNtwDZr7Tzv829wAuXdxpjGAN7viRUbooiIyJHjpyXbeXnyWuZs2Muwbo1Llmj75Rd4910nOB406LCPp/j1VaJNpGLK/RNkrd0FbDXGdPQ2DQZWAj8BY7xtY4AfKzRCEREpt68WbGXqqt3VPYyjyo40pxLF+b2b8cCwzv4Hk5KcLZ+7dYMnn6zysalEm0jFVLSO8S3Ap8aYEGADcAVOsP2VMeYqYAtwQQWvISIi5eBye7j3m2UAbHp2eDWP5uixPTWH1vUjefGCHv4HrIVrr4WUFKcaRWholY9NM8YiFVOhwNhauwQorYji4IqcV0REDk6B28MN/1tIm/goHtxn9nLuhmTf463J2TSPi6jq4R11Fm1J4ZdlOxnSpWHJgx99BN9/D88/D927V/3gUO1ikYrST5CISA02KyGJKasSGTtjA+/N2ECr+38hJ9+NtZbxs4pq7J70/DQK3J5qHGnNtz01h3+9NRuA/m3q+R+cPx9uuAEGDoQ776yG0TlUiUKkYhQYi4jUYJNXFuUPP/3rKgD+3pTMmt0Z/LE6kcGdiipmpmYX4PFYlmxNxeX24PZYrFVRoNJM/GcnL09ey+ItKWTluVi0JYUTnv3Dd7xfq7iizps3wznnQOPG8PXXEFj1wWlP78Yeql0sUjEVzTEWEZFqNDMhiV4tYlm0JdXXtmZXBpeNnw/Anad3YHtqDqt3ZdD36Sm02buNwQnzCWtoWblyC2EtmjHsxfuhU6dqegdHpge//4eU7AJen7qOPi3rsiU523fsjtM60K1ZHedJerpTrzgnB/74A+Ljq2W8H13ZjzW7MjRjLFJBCoxFRGqoxIxcNu/N5qJ+LfwC48KZY4A29aO4/7S2zLz1Uc5dMZ2uiRsAyAwJJzYknHorpsMvH8HVVzu7s1XDbOeRKCW7wPd4weYUv2OX9PfucJebC+efDytXwsSJ0KVLVQ7RT0xYMH2Lz2KLSLkoMBYRqWGmrU7kxUlrfAvATuvcgM17s/h8/laaxoazPTUHgG9vGEC4dXHsXddyyrRfWdSkI08MuoZfOp3A7mhn6+D6WSksCF4Er7ziBHoffljrg2NrLSGBAeS7PYQGBZDnKsrNHtWnGfWjQiE/3wmKJ0+G8eNhyJBqHLGIVBYFxiIiNcxTv6xk/Z4sVuxIp3+bONo1iObJEcdwx5AOJGXks3JnOkEBht7xoXDOOcROmsSjp13Hx73PLnGu9Jh68PTLEBcHjzziNH70kbOVcS21NTmHfLeHJ0Z0ZWTPpjzz6yqGd2vCie2dXybIyHBqFf/yizPLfsUV1TtgEak0CoxFRGqYuhEhQBYjj23iK9EWFBhAg+gwGkSH0aVJjJP7esYZMGsWjB/P/LS2XNWuPqP6NGdmQhJPTlgJQGxEsHPShx926vA++ih07gwPPlhN785fTr6b7HwX9aIqpybwpqQsnpiwktdGH0t0mPPeE9Nz+e+0BK44oTXN6oZz0XtzARjYIZ6YsGCe+Ze39JrHA1984exot3MnvPACXHddpYxLRI4MCoxFRGqQ9NwClm5L5fIBrXj8nK6ld1q/3qmSsHYtfP45jBrFb8UOd2wUzZ9r9zBj7R5SswtYuDmF3i3rOsHx6tXO9169nMC6GqXnFtD98UkAbHxmWMmtl8vhti+XsHRrKtPW7OGcHk0AuOXzxczbmExEaBDn9mzK9tQcLujdjJb1Ip0XZWbCuHHw1lvOPe3TB374Afr1q/B4ROTIUnv/ViYiUoN8Om8zre7/he6PT6LAbTm9aykbTABMnQp9+8KuXfD77zBqVKndPrqiL1ee0Jp8t4fz3p7N+j2ZYAy8956znfFFFzkBdjXZsCeTP1Yl+p4X5k2X1/LtafT/z1SWbk0FIDkzjyVbU2n9wC/M2+hshDIrIYnTX5kBwFUntXZ2sHvySWjVCm6/3Uk3+fRTmDtXQbHIUUozxiIiR7g9GXk89P1y3/PrTm7DgLb1S3acPBnOOgvat4effoI2bco8pzGG6LCi/wXMXJdE2/goiIhwdm/r0wfOPRfmzIHIyEp9PwcyKyGJS96fR0yx8a1LzKRZ3fLt3JeUmccVH/7Nnow8X1vCnkzmbUymeBnnZdvSAGdBYvuXn4Z33nbyic86y0ktOf748r0hEakxFBiLiBzhNiZlAXBhn+aM7tecY5vHluw0aRKMGOHkB//xhzO7eQCN64T5Hq/elVF0oE0bJwXjzDPhmmvgs88q+hYOmrWWbxduAyA91+VrX5+YyakdG5T1shI+m7eFKat2065BFGNnbPC1f3Ftf577bTVrdmWwIzXX194yZQeD1v/NSRsXc8KWZQS6C5zZ9gcegB49KuGdiUhNoMBYROQItz3V2VzimpPb0K5BVMkO8+bByJHQsSNMmXJQQTFAfHTRgrbE9Fz/g0OHwr//7SzGu+QSGD68vMM/aNZa5mzYy3eLtzO0a0N+X+Hs6hccaHjql1VcdWLrg8ozTssu4MHv/wHgj9VF6RidGkXTv009mteN4KelOwCIyc3k7hmfcOniXwnAkt2qDSHXXwu33AIdOhyGdykiRzIFxiIiR7jd6U4KQPEZXp9165w/9Tdu7Mwa1y8lxaIMPZrHEhkSSFa+m9ScgpId7rvPmS2+7TYYPBjCSrl+Jfll2U5u+myR7/nz5/Xg9xXOwrtLjmvJh7M3sWJHOsc0rVPmOVbvSmfBphQe/mG5X/sz/+oGwEnecmuxEcEY6+H8f6Zw//QPqZubSeqV17J5zHX0PLlnZb81EalBFBiLiBzh0nIKCA40RITss/HGunVFlSN+/x0aHHyqAUD9qFBWPHEGN326iNW70kt2CAmB11+H00+Hl18+rCXc3pyW4HvcqVE0dSKCmX3/IPZk5BEXGcKHszexdFtqmYHx8u1pnPXGTL+2q09szeDODTm+bT2/9vbb1vLdJw/Sc+caMnofR8C4d4nr0QPtGyciCoxFRI5gi7eksDstl5iw4KI0goICePttZ0OOoCBnO+J27cp9jZjwYNJyXKUfHDLEWYT39NNw+eXQpEm5r1Pc1uRstiRnc0K7+szdsJe1uzO4fEArRhzbhM6NYwBoEhtOk9hwrLXEBrgJ/v47eHYGJCRAixbOLPawYawLjOaPRVtokLEXT0AAaWFRPDCiB1ee2Nr/oklJ8PDDXDp2LEnhdfj2tqc575UHnGocIiIoMBYRqVKfzN3MezM28ML53TmuTb399l24OZnz3p4DQJv63soQU6Y4qQ0rVzpB69ixTjmxCqgbEUxSZh43frqQR87qQuM64f4dXnwRJkxw8o3ff79C1yp09pszSc0uYP1/hvHgd//QMDqUG09pS4OYkukaZsYMfn/rahqm7YGmTaF3b2e2/K674K67aA+0B24t/qJ3oqBuXSffOi4OoqNh+nTIymL2sIu5od3Z3DXqOAXFIuJHgbGIyGFgrSU7301kaNE/s2nZBTzz6yqy891cOHYuZ3RtxNuX9vJbUDZzXRI/LtnO197KDIXaZex2Ftj9+KNTNeLHH+HssyslsDu+bT3emr6eX//ZRedGMdwyuL1/hzZtnMVor7wCt94K3btX+Jqp2U5O859rE9mQlMWTI48pNSjmu+/gwgspqNuYy89/nAdfu40OTWKdY5s2sXT81/w0cw0FgUEc36kxZ3aq79QfTkmB5GTnKykJ9uyBYcPgkUdo1rAlvX9awbm9mlb4fYjI0UWBsYjIYfDdou3c9fVSXht9LIM7N+TXZTuZmZBEdr6bG09py1vT1/Pbil18+fdWRvdrAUBCYgaXjptHWEEup2z5hw5JWzjVpBC4bi09d62D0BB45hm44w4IrZwtkgG6N431PQ4NLmPfp4cegg8+gHvvhd9+K73PQcpzuX2Pr/xwASGBAQzuVEp+9CefwBVXwHHHcfPwB1mSDtNfn8Xyfw8lKjQIWrXik26n80fwscy+fxBhwYElz1GKlsAHV2iDDhEpSYGxiMhh8MnczQDc9sWSEsfuGdqR609py7H/nsT93/3D6V0bsSU5m5FvzuTCZZO498+PqJfjLIazDRqQ0qwVBWddS/AjD1Zajm9xdSKCfY/3ZuaX3ikuzslpvvNOZ6Hf0KHlvt7Nny32e379wDY0id0nfePNN51Z6sGD4YcfWPLUn75Dr0xeyyNndQEgITGTTo2iDzooFhHZH20JLSJSyeas38uSram0iS/aMW54t8ZcclwL7j+zE8YYYsKCudWbstDriUnc/9j/+N+XD/Pcb28Q0u0Yp/RaSgpm927iFs4j4u03D0tQXOiKE1oBkOQNjD+Zu5npaxL9O914o5NWcc894PGU6zr5Lg+TVzr1iS8f0IqzujfmjiHF6gVb6yz0u+UWJ3VkwgSIivK7l+NmbsTjsVhr2ZiURav6Vbszn4gcvTRjLCKyHx6PZUNSVukba5Th3RnraVwnjF9uOYnwkEDyXR5CgkrOQ1wbl02PBR/SY/Yk4nLSyQgJ55cbH2P4G49CQNXOWzx2dlcWbk5hb5ZTM/kRby3gTc8W29gjNBSeegouvhi+/RYuuOCQrzMrIQmAcWP6MLhzQ/+D1jpB90svwWWXwbhxTtUN4Itr+pOYkecrybbBuxtgWk4B3fZT21hE5FBoxlhEahVrLTn57gN39Hr7z/Wc9vKfLNqSctCv2ZaSQ49msYR76w6XCIoTE+Hii4no3ZNTZv7M7Da9WPDIC2T/s4Jhbz5W5UFxoXqRIezNzOfOL5eU3WnUKOjUCZ544pBnja21vPHHOmIjgjmpffy+B51qGy+95MwWf/CBLygGaBATxjFN63DNSU4JtmXbUpmz3gmyB7Tdf3UPEZGDpcBYRGqVh39YTvd//87Ef3aW2eelSWsY+d9ZbE3OZsIyp9+/3prNJu8sZXHWWl6fuo75G5N9bbvTc2lU2i511sIXX0CXLs6M68MPY7Zt46zl0+jzxN007HBwWx4fLvWiQknKzOO7xdvL7hQYCA8/DMuXw9dfH9L51+7OZNGWVG4b3L7kLwtPPQVvvOHkML/2Wpm/HNx/ZmdiI4K586ulPPLjChrGhNIiLuKQxiEiUhYFxiJSo1hree631Xz191YA3B5LanZ+iT73fbOMn5buIDvf2bgiJ9/NtNWJfDpvCwVuyw2fLmJrcnap13jvrw0s2ZrKSc9PY9XOdKK9Jdf+8+uqEn3/2Z7Gy5PXMupdp97w8u1pZOS6igJjtxsWLXKqSQwYABddBG3bwuLF8OSTh7SF8+FWPyqUnWm5fm1ujy3ZcfRop2TbAw9AXt4Bz+vxWH5YvJ0PZ28CKLETHW+95dRIvuwyeOGF/ZagCwwwvD66aNvmJrHh1frLhIgcXRQYi0iN8tn8Lbw9fT33fruM2euTePC7f+j91BTfgi6A1bsy+HLBVm79fDEXvjuX7HwXJz0/jSs+/JtQVz731s9g4IaFrP7uN9iyxQlegex8FwmJGeQW+KcIjL+iL/1a1mXyip1sSsrCWkuB28PmvVlc+eECp5O1eBL3sHjOchpmJDFq/Wy45BKIj3c2pHjwQcjPd2ZDZ892Zo2PMCOOLVrcd1pnp3za35uSS3YMDHQ2/di40akeUcwzv67ioe//8T2fumo3bR78ldu/XMLn87cA0LR4BYrPP4ebb4ZzznFyig8ijeTkDvE8PLwzADtTcw/QW0Tk4BlrS5kNqGJ9+vSxCxYsqO5hiEgNcMKzfxAREkhyVj6RoUFsKTbr+8W1/Zm2OpF3Z2zwe03d0AD6LJ/N1Ut/pd/mZRjXPtsfN2oEN9/MCwGtmbNyBw0yk7mpQzhNsvbCtu3US9qJa+kyAjIz2VanAVl9+vF7TFtmRjSmflYq5+9YTL8lM4jJ32cGul49OOssZ4e6QYOgcePDdVsqTav7fwFgwi0nctYbM3ns7C4M6dKQZnVLSVcYNgzmzHF2oatfn/TcAro/PgmAH246geXb03jYu4ivON+Cvl9/hREjnJn0336D8PASfcuSne+iy6O/c93ANjxwZudDf6MiUmsZYxZaa/uUekyBsYjUFH+u3cOY8fN5eHhnlm9P44clOwBoGBPK7nT/P+k/elYXGnuyWfnQM4xeOommGXuwzZphLr4Y+vblob+T2b5hJ4/3jKHVjN+d2rz7CglxSqQ1a4ar6zG8tSyFdnu30mf7KhpkFS3Gc0VG8V2b/pzwr8H8tS2D3Unp3PbApc5McVDNKv7z45LtNIwJo2+rONo++KuvfcN/hhEQsE/KwooV0LMnDB8O333H7A17ufi9eSXO+fYlvejTKo4lW1OJiwyhd8u6zqz5aac5C/mmTYM6h15ZIivPRVhwIIH7jktEZD/2FxjXrH+xRaTWysxzMWb8fADaN4ymU6MYfliyg06NonnuvO5cOHYO1kKey8OrJ8Yz8vs3se++y5lZWfzZuhehr79K/UtH+QLVGwZnc+Jz0zglHb59dwy9XSnc+cAHdGvXiAtH9ieidUtnxtf7p/0g4L3HficjzwXW0jxtN62Tt3PDv/oR3bcX9773N2Mv6M1Xf64nODCA2447rrpuVYWMOLb0bZJ3Z+TSuM4+M7pdu8Kzz8Jdd8Hbb7Osq7PpxyXHteDTeVsYc3xLrjqxDS3qObPNQ7p4y7OtX++kTjRr5swUlyMoBvy22xYRqQwV/lfFGBMILAC2W2vPMsbEAV8CrYBNwChr7cHXORIRKcXc9Xt9j/u1iiM8JJDVT57h2/Fs9ZNn4lmXgOvZZwk57xNwuzGjR8N99zGwW7cS52tWN4LBnRowdXUivyzbRaOTWvNdm+PpPrwLEf1alzqGJrHhrNmdAcawNbYRD98ynP5dGrInw5mtnpWQxJKtqVx5Qumvr2l+ufVEPp69mS8XbOX4Z/5g4zPDSi50u/12mDwZe+ed/HrpS0Q3a8dTI4/h6XNL3nMAFiyACy90KnT8+is0KGUraBGRalIZi+9uA4ov1b4fmGqtbQ9M9T4XEQFgza4MRvx3FoNfml56xYMyLN+RBsCiR4b46gOHBQc6AdacOXDJJQR06kjIp/+DK6+EtWvhf/+DUoLiQmMv60OzuuGMn7WR/3m3cO7TKq7M/ref5uxU99roY1n8yBCGdm2EMYb46FB6tYjlozmb8Vjo0Tz2oN/Xkaxrkzrcd2Yn3/O9WaVsFx0QAB99REGdWD769AHeNqsxSUnOf5fisrKc2sfHH+8sQvz1V2jX7jC/AxGRQ1OhwNgY0wwYDrxfrHkE8JH38UfAyIpcQ0RqrtTsfJ6csJLLP5jPy5PWkJ5bwL3fLmPp1lTW78mi7YO/8t9pCeQWlL7hRp7LKbHmcntYl5hJq3oRxEWGgMsF06fDfffBMcc4i7d++smpgbtxI7z9trN18QEEBjhbMwO8PX09repF0LVJTJn9z+zWmE3PDmfEsU2pGxniazfGcO8ZRQHksUdJYAwQFxni2y56Yyl1nAFo0IC5H3zP1tiGnPj47c4scHQ0DBzolGA79VRo2BAeewzOOw+WLYMammoiIke3iqZSvArcC0QXa2tord0JYK3daYzR38lEaqlrPl7A35ucTKrpa/bw+h8JADw58himr05k6upEXvh9DS/8voafbj6B7s1inRe6XLB3Lx9+PYc/Z64grV9LwnbmcG7WXrjyE/j5Z0hKguBgJyh+7z3nz/PR0WWM5OC0jY8qd03c/m3qseE/w9icnE3zo2zDiSsGtOaDWZvYuCeLLo1jyHN5nF9QitlUtzGP/99LzO/jov6ubZCQAPPmOb/ANGsGY8bApZc6M8YiIkeocgfGxpizgERr7UJjzCnleP21wLUALVq0KO8wRKQSpecWsHJHOn1bxfmt9LfWHnLAeOeXS3xBcXGX9m/B6L7NOb5NPeZtTCZ87x7OXz6FwAlPgicdEhOxyckYa7kOuA7gy2J/eoqNhTPPdGYehw6FqKjyvVmvmPCifwYbx5ayW90hCAgwtK4fWaFzHIma1g0nONBw77fLuPfbZTSMCWXeg6cBTl71F39v5eelOyAgkNBzzgTvLLyISE1TkRnjE4BzjDHDgDAgxhjzP2C3Maaxd7a4MZBY2outtWOBseCUa6vAOESkEqTlFHDxe3NZsSOdC/s05/i29WhUJ4zHf1rB6l0ZnNa5Ie+PKbW6TQn5Lk+JbYWfP787eQVu/u/4VgC027WB5QkfY7/6CuNysbJRW3Z17UhCs2NYkB1EUkQseyPq4K5Xn/ScfCLyc7nvwuPoOHKIM1NcSe4c0tG3a13T2KNrpreyBAYYWtaLJCExE4Dd6XmkZRewLjGDS973L88WpUoRIlKDVUodY++M8d3eqhQvAHuttc8aY+4H4qy19+7v9apjLFL9Ln5vLrOLVX4ozfJ/D91v4JPv8nDRe3PZsCeTlOwChnVrxAV9mtO8bgTtGkQ5C7KmTXN2TZs40ZntveYa/jz1X4yZleZ3rpHHNuHOIR1pEBPK3qx8/93SKllKVj6fzd/C1Se1JjQo8LBdpya7+qMFTFlVtLtgfHSorxpHoVF9mvH8+T2qemgiIoekqusYPwt8ZYy5CtgCXHAYriEilcjtsczbmExsRDBZeS4K3JaL+rUgJjyIzFwXretH8tQvq9iRmkOHhtFOgJuSAomJToUBY1i4NZX7vluOxwQQHBJOeGgk5zQwnJq0DmatcXJOf/0Vli51Fmc9/TTccAPUrctxBW6Y9RsA9wztSK8WdenfJs6XvnE4g2KAupEh3HSqKiTsT4OYUACax4WzNTnHLyi+tH8LggICePycrtU1PBGRSlEpgbG1djow3ft4LzC4Ms4rIoeftZbP5m3G7bE8OKwz9aNC+PLvrTw98hhnp7PFi0l89T+c+OsUWo7Phbwcp/SW27+SRG9gyr4nf6XY46Agp3TauHFw8cUQVpTPGxYcyLgxfVi2LU0B6hGqnnexXfE/MtaNCOaqE1tz86D21TQqEZHKpWQwkVrE5fYQFOhfpfHrhdt45McVABzfph7N4yIY1DAE3n7LCWIXLyY+NJTlTbuxNLIuBRGRuMIiyK8bx+kDj6FJ4zjemLqWxLRcHjurExGBhh1bE9m7fQ/dOjWFli2hY0do1Wq/2yMP7tyQwZ0bHs63LxXQroGzyLFRTBjbUnIAp6Z0eat4iIgciRQYixzlCtweHvlhOTPW7iEr381tg9tz2fEtMcZwzccL+GO1sz72qRFdab5sPrz/Pnz7LeTmQo8e8MYbmIsv5srn55Q49392AbuAuGO5e3QHIrwzh028X3L0OKdHEwIDDNbCgs0pDOnSUEGxiBx1FBiLHKUSM3J5dco6EhIzmb8x2df+xISVPDFhpfPEWjrt2cSjgZsZcO39sHw51KkDV1wBV10FvXqBN/hZ+9SZdHh4IgA/3HQCI/87y3fOWwe105/Tj3LGGM7q3sQpy8bhz/sWEakOlVKVoqJUlUKk4hZuTua8t+dwepeGBAYYJi7f5TsWFRrE3AcHExUaxJ2fL2LbhMkMXTuHszfOp8HenU6nPn3gxhudjTIiSi9blpCYwaLNqYzq2xxw8pNnJiQxoG19v7rHcvQqcHt4a9p6rj6pNZEqzSYiNdD+qlIoMBap4TJyC/hi/la+XbSN1bsy/I5FhgRy+QmtOK9XM9rER8E//8A118C8eXhCQzGDT8OcOxKGDYMmSn4QEZGjX1WXaxORKjJh2Q6e+XU121OdxVDn9mzKM//qxraUbJrVjSAs2FuTNzcXHn4YnnvO2Tlu3DgCLrigwlsoi4iIHE0UGB+kmeuS2JmWwwV9mh/yayev3E2/VnHUifDfrSshMZPU7HyObR5bolJAWbLyXBgDESH6T1fb/bMtjZs/W0xsRDBPjujKie3jaVUvAmMM7RoUC3j//BOuvRbWroUxY+Cll6BeveobuIiIyBFK0dVBuubjBeQUuIkICWJ498YH/bqExAyu+XgBJ3eI5+Mr+/naZ69P4uL3nK1UQ4MC+PK64zm2eex+z2Wt5ew3Z+LxWKbdfYpWhNdiPy7Zzm1fLAHg4yv70b1ZbMlOKSlw771OlYk2bWDSJBgypErHKSIiUpMoMD4IvyzbSU6Bs5nBTZ8t4vvFDXnn0l4EBQbw2/Kd9GxRl4YxYSVe53J7ePiH5QDMWLuHp39ZyR1DOvDxnM08O3E1AIEeN3WTk3jk4Q/pHpzLSXEBDKgfhMflIis7j6bRIc6fvjt1YmVQLHkJG7EG9qxaT4M2zfw2SZDa4dd/dnLP18vo2iSGp8/tVjIotha++QZuuQWSkuCee+Dxx8tcUCciIiIOLb47gCkrd3PNJwvY9zaFe3fquvh9Z9b3tsHtiQ4L4uM5m9mVlsMp6ZtpvnIRnRM30jtlM6HpqeQEh7KtTkMyQ5wyRycHZhCxfi2BebnlG1xoKJxyihMAnXkmBBxcOobUbKe8MI1Ne7P5/faT6dhonxzhrVvhppvg55+dUmvvvw89e1bPQEVERI5AWnxXAS9OWkP7BlF8c8MAYsKCyS1w0/3xSeQUuH1BMcBrU9fRLG03ly/4iTPXzKZpxh4AdkfFEdm3F78mQWR+Ds3TdtM4PYmG0cFEd2gLw06H9u1xN2jIjFTDwqwApuzIY0eWC7cJwB0QQFxOOh32bCE+K4UujaJZtTOdehFB3NXcTdC338JZZzl/Kr/xRrj6aqcOrRy10nIKuLR/C/+g2Fp4911ndtjthhdfhNtu2+9OcyIiIuJP/9fcj01JWazelcEjZ3UhJsxZOBcWHMjqJ8+gzYO/AnDxcS1ot2sjdd98hbNXzYCAADIHnY7r4gt4ybbirKE9adikDizYSlK+m0RrObVjA2LqRfg2TgAIBE71ft3tbft7UzJvT19PbEQw3y1qAMDqJ8/gud9W8/asTbxrYNLcR2k343d4+224+27nT+ZXXw333QeNGpXrfb8xdR0hQQFcN7Bt+W6cHBbJWfl8+fdWUrILiI8qlkKzdavz33zSJDjtNBg7Flq3rr6BioiI1FAKjEthrWXz3mwueNfZAvf0Lg39jgcEGO4Z2pGMP/7kvnHvYH6ZQEF4BKnX3ED9Rx8gtmlTAO4r9ppR5ahm0bdVHH0vj2Pyyt18t2g77RtEERYcyIPDOuP2WCdXecp67hwynJDBZ9Nu21p45RV44w146y0491y48koYPBgCAw94vaw8F7d+vpip3i2Cp6zazVfXHU+ey0NKdj6N64RjrSXP5SkqAyaH1fbUHL5duI2kzDxW7khnweYUAFrUC3dmiT/+2JkZLihw/ptff73fL1wiIiJy8JRjvI8Ct4f2D030PW/fIIrJdw4svfPw4TBvnhOY3HQTxMUdljFl5bkYM34+DwzrRO+WRde4/9tlTFy+i7ScAgD+uvdUmsSGE7hhPbz+Onz6KSQnQ/PmcPnlMHo0dO5cauCUkJjBaS/P8D0/tnksS7amMvv+QVzxwd+s2Z1BnfBg6kWGsCEpizcu6snZPbQhxL6mr0lkza4MGtUJ4881e7j6pDZ0aRJz0K9PyswjOSufiJBAcgvcjHhzFln5zsLPqNAgzuremIv6taDrxn8Iuu9emDsXTjwRPvwQ2mqGX0RE5EC0890hWLkjnWGv/wXA42d3YXj3JsRHh5beeds2qFsXIiOrcIRFvpi/hfu/+8ev7YLezXjhgh7Ok7w8+Oknkl5/m7hZ0wmwloKmzTHXXkPQHbf7be7w+fwtPOA911fXHU9ggOG8t2fTv3kMW5cn0DBzL/GZKTTM3EvDzGQaZe5leKsowuJincC7Uycn6O7eHYL96zUfbbLzXUxYtpNzejQhNCjAVzbvs3lbePB7//8e5/VqxkujehzwnJv3ZvHqlHX8sGS730LPqNAgHh7emb6t42gbE+ykS4wbBz/+CI0bw5NPOr/0HMRfBERERESB8UErcHu4++ul/LhkB59dfRwD2tWv7iHt19rdGZz+yowS7XMeGMSshL2c2jGeAGM44bk/iN67m1M2LGT46pmcvGmxM7s9ahRccAE5nboy6P3F7Mx2Y6yHVaeGEvrRByTOmEu9nVsIsh6/8xcEBLInsi6h9eOo586F7dudBV/gLAJ87jk477yj9k/642du5IkJKwHnLZ7cPp6eLWJ5dco6IkMCGX95XxIz8njql5UkZ+bx7r86M6hBkDN7v3ev85Wa6pRPq1ePjTaMa75bTURBLic2DCUoMJDdOS4CAwyjjonn2Py9MGuWU2kiLc35Zez22+Guu6rtlzIREZGaSoHxfnz59xa+WrCNcWP68PLktXw8ZzMAq544g/CQI38WbsKyHexKy+WdP9fz8qhjuWz8fLo0jmHlznS/foM7NfDlDvfZvY6xyTOJ/G0Cofl5vj6uwCACPW6MtVC/Pkk9+/FFZhTbYhrwzK1nYho1IqteA4IbN+Kct2azelcG394wgN6NIyEhAZYsgWeegeXL4aST4L33oGPHcr+3nWk5TFqxm2OaxvDIDyu48sTWnN+7WbnPV1nu+XopXy/cVqK9QXQon17Wk/arFsKMGRTM/5uM2fOJy0qt+EXr13eqj4wa5eSMh4RU/JwiIiK1kALjUmTnu3j+tzV8OHuTry0sOIDcAmd2dNOzw6t0PJUhO99Fl0d/L/XYxmeGYYzh56U7uOXzxQBE5OfQb+tyWqfsoHmwmyu618eEhkKXLjByJJ6QUL5ZuI2zejQusQX1tNWJXPHh38RGBPPN9QNIzsqnT8u6BFiP86f++++HnBx44AG44w6/tI2DMX1NIpd/8LdfW+fGMUy87aRDOs+hstYyZ8NeNu/NZsmWVHJdbi45riX9Wju53dn5Li54Zw5xkSGMv7wvf29MZl7CHtqumM+ZS6YS/OMPkJHhpDZ07Upih2MYvyeElPBoUsOjadWhOdP2uEkPjSLclUfdnHRiczK4qHNdhhzXDqKinGlolws8HmcDl6ZNnSoTR+kMvIiISFVSHeNShAUF8s0+s365BR4aRIeW3DShhogICSI6LIiMXJdf+7c3DPDlwR7XxgnwGkSHcmrH5jSM6UbbBlGc3b0JJsA/8AoARvUtvZrGqZ0a0LdVXf7elMJpL/8JwD1DO3LTqe3g2mvh7LPh5pvhscfgzTfh4YfhhhsOmH+c7/IwY+0eHvtphV97WHAAq3amM27mRq468fCUInO5PYyftZH//Lrar33Csp1ce3Ibpq1OZPWuDACuO7kNwVs2M+D9dxnw8cewc6dTP/qCC5xqIIMGQUQEDYDYP9fzzsSic0Y3D2LibSexZW82l4ybx2mdGzLo0t4QoMBXRESkOtXaGWOAyz+Yz/Q1exj7f7259pOFQNHMak016KXpbNiTxZnHNKJ7s1iuH9jmsL2fwlnj4mbfP4gmseFFDfPmwYMPwh9/QLduTkmxE08s9Xwv/r6GN6cl+J7fe0ZHLju+FbvScokKDWLgC9Nw5+XzSPMCLhvQCtO8OcTHV8qOf1uTsznp+WkAxEYEc9nxrRhzfEu2puTw759XsHhLqq9vxz2b+CjpTxpN+NaZxR0+HC67zPlexhbduQVuOj3yGwCvX9STc1TRQ0REpFoolaIMiRm5rNudyYC29Vi7O5OM3AL6tDo8JdeqypUf/s0fqxP5z7nduPi4Fof1Wh6P5bcVuxjcuQG/r9jNrZ8v5u7TO3DzoPb+Ha2FH35wytpt3epUUXj+eYiPJy27gHdmrGd4t8Zc8M4c3NZyXq+m7E7P49l/daNBTJiTUvDHH7jHvkfBzxMIy80uOndwMLRoAf37w4AB0KcP9OjhbJd9CIa99pcvL3v+g4Od6xYO3+Nh7oSZtJzzB2njPqLznk3Y8HDMjTc6i+CaHVze8+70XH5csp2rTmxDoGaHRUREqoUC41pk6dZUPpi1kX+fcwx1Iqq2bNqI/84Ca3l1dE9a1Yvgti+WEBUWxH/O7eZ0yMpyyou99BJER1Pw5FPcHNKD39enAhAYYPjuhgH0aB4L+fkwezb8/jt88QVs2gR165J61rk8lBbP5ad0oG9IjlMyb906mDkTdu8GwBMcTEC3brh79yGg/3GYkSPLrDG9dncGwYEBnPridM48phEvjerh5FOnpMCUKc71f//duQ6Q1K03q04dzkmP3OIsiBMREZEaRYGxVImXJq3hjT8SSrR/ff3x9PXOxHs8lvm//sVxLz2GmT6dtNBIEpq2J7VVO1r27Ua7euFOabKpUyEzE4KCYOBAuOoqOPdc0mwgPf49iYeGdeaak9sUXcRa1vy9glee+Yzuu9ZxYuomWm1cRUxeFjY4GHP22U66w5lnQkgI1lqu+XghU1Y5wXRoQR7jusKJm5c6gfDffzsz1XXqONssn346DB0KLVtWyb0UERGRw0OL76RKlLVo8YJ35tA8Lpxnzu1OQmIGj8/MgH53cXL8qZy5ZhajwlIJXDgZ/vjGeUGrVnDppU4gOmgQxBTtHBdjLVGhQWxOzsJa68uftsBT/2Qxr+tJLOh9Ks9n5mOshy6JGzl3+R+MnjqdqO++wx0Xx+Yh5/CeaUajvXt5NW8PLRL+4Zhd6wnxuJx85X79nMWCQ4c6j4P0YyIiIlIbaMZYKk1SZh43/m8R3ZvVYeGWFBLT89iemrPf11x3chseGNbZyUNOSXHKnMXE7Lc02aXvz2NmQhLHNI3hvxf3wlo4582ZpOe6eOSsLozq04wdqbm0iIvgtanreOfP9QR63Jy0cRHnLf+D09fNJdTtbKNtIyLI7NqdWQ06cMpV/yJs4EmHbWtvERERqX5KpZBq0+XR38jOd/u13TmkA9ZCj+Z1OKVjg0M+Z/FazH7XahzDTzefQFCgf5WKv9bt4bEfV7AhKQuAq7vFcXPbYGJbNHG2Va7BVUhERETk0CgwlmqzKSmLuhEhvoWAezLyqB8VUuEScttTc1i6NZUbP10EQNv4SKbedUqZ/XML3Fz6/jxG9W3OqD6l12YWERGRo58CYzlqJabn8sSElVx1Ymt6tqhb3cMRERGRI9z+AuNy74xgjGlujJlmjFlljFlhjLnN2x5njJlsjFnn/a5oRQ6bBjFhvHlxLwXFIiIiUmEV2TLMBdxlre0M9AduMsZ0Ae4Hplpr2wNTvc9FRERERI5o5Q6MrbU7rbWLvI8zgFVAU2AE8JG320fAyAqOUURERETksKvIjLGPMaYV0BOYBzS01u4EJ3gGDr3sgIiIiIhIFatwYGyMiQK+BW631qYfwuuuNcYsMMYs2LNnT0WHISIiIiJSIRUKjI0xwThB8afW2u+8zbuNMY29xxsDiaW91lo71lrbx1rbJz4+viLDEBERERGpsIpUpTDAOGCVtfblYod+AsZ4H48Bfiz/8EREREREqkZQBV57AvB/wD/GmCXetgeBZ4GvjDFXAVuACyo0QhERERGRKlDuwNhaOxMoa/uyweU9r4iIiIhIdaiUqhQiIiIiIjWdAmMRERERERQYi4iIiIgACoxFRERERAAFxiIiIiIiABhrbXWPAWPMHmBzNV2+PpBUTdc+2uheVh7dy8qje1l5dC8rj+5l5dG9rFy14X62tNaWurvcEREYVydjzAJrbZ/qHsfRQPey8uheVh7dy8qje1l5dC8rj+5l5art91OpFCIiIiIiKDAWEREREQEUGAOMre4BHEV0LyuP7mXl0b2sPLqXlUf3svLoXlauWn0/a32OsYiIiIgIaMZYRERERASoxYGxMeYMY8waY0yCMeb+6h7Pkc4Y09wYM80Ys8oYs8IYc5u3/XFjzHZjzBLv17Bir3nAe3/XGGOGVt/ojzzGmE3GmH+892yBty3OGDPZGLPO+71usf66l6UwxnQs9tlbYoxJN8bcrs/lwTPGjDfGJBpjlhdrO+TPojGmt/cznWCMed0YY6r6vVS3Mu7lC8aY1caYZcaY740xsd72VsaYnGKf0XeKvUb3svR7ecg/17qXZd7LL4vdx03GmCXedn0urbW17gsIBNYDbYAQYCnQpbrHdSR/AY2BXt7H0cBaoAvwOHB3Kf27eO9rKNDae78Dq/t9HClfwCag/j5tzwP3ex/fDzyne3lI9zQQ2AW01OfykO7byUAvYHmxtkP+LALzgeMBA0wEzqzu93aE3MvTgSDv4+eK3ctWxfvtcx7dy9Lv5SH/XOteln4v9zn+EvCo93Gt/1zW1hnjfkCCtXaDtTYf+AIYUc1jOqJZa3daaxd5H2cAq4Cm+3nJCOALa22etXYjkIBz36VsI4CPvI8/AkYWa9e9PLDBwHpr7f42C9K93Ie1dgaQvE/zIX0WjTGNgRhr7Rzr/B/042KvqTVKu5fW2knWWpf36Vyg2f7OoXvpKONzWRZ9Lvdjf/fSO+s7Cvh8f+eoTfeytgbGTYGtxZ5vY/9BnhRjjGkF9ATmeZtu9v6ZcHyxP7nqHu+fBSYZYxYaY671tjW01u4E5xcRoIG3Xffy4IzG/x93fS7L71A/i029j/dtF39X4sy0FWptjFlsjPnTGHOSt033cv8O5eda9/LATgJ2W2vXFWur1Z/L2hoYl5YXo/IcB8EYEwV8C9xurU0H3gbaAscCO3H+JAO6xwdygrW2F3AmcJMx5uT99NW9PABjTAhwDvC1t0mfy8OjrPun+3oAxpiHABfwqbdpJ9DCWtsTuBP4zBgTg+7l/hzqz7Xu5YFdhP+EQq3/XNbWwHgb0LzY82bAjmoaS41hjAnGCYo/tdZ+B2Ct3W2tdVtrPcB7FP1ZWvd4P6y1O7zfE4Hvce7bbu+fqwr/bJXo7a57eWBnAoustbtBn8tKcKifxW34pwjovhZjjBkDnAVc4v0zNN4/++/1Pl6IkxfbAd3LMpXj51r3cj+MMUHAv4AvC9v0uay9gfHfQHtjTGvvTNNo4KdqHtMRzZuHNA5YZa19uVh742LdzgUKV73+BIw2xoQaY1oD7XES92s9Y0ykMSa68DHO4pzlOPdsjLfbGOBH72PdywPzm/XQ57LCDumz6E23yDDG9Pf+W3FZsdfUasaYM4D7gHOstdnF2uONMYHex21w7uUG3cuyHerPte7lAZ0GrLbW+lIk9Lmkdlal8P7CPgynssJ64KHqHs+R/gWciPNnk2XAEu/XMOAT4B9v+09A42Kvech7f9dwlK5eLee9bIOzgnopsKLw8wfUA6YC67zf43QvD+p+RgB7gTrF2vS5PPj79znOn08LcGaFrirPZxHogxOorAfexLuBVG36KuNeJuDkvxb+u/mOt+953p//pcAi4GzdywPey0P+uda9LP1eets/BK7fp2+t/1xq5zsREREREWpvKoWIiIiIiB8FxiIiIiIiKDAWEREREQEUGIuIiIiIAAqMRUREREQABcYiIiIiIoACYxERERERQIGxiIiIiAgA/w9pOV00f1eiGQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12,6))\n",
    "plt.plot(df.Close)\n",
    "plt.plot(ma50,'r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "768baf1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0              NaN\n",
       "1              NaN\n",
       "2              NaN\n",
       "3              NaN\n",
       "4              NaN\n",
       "           ...    \n",
       "1827    168.096001\n",
       "1828    168.335601\n",
       "1829    168.557101\n",
       "1830    168.758000\n",
       "1831    168.905900\n",
       "Name: Close, Length: 1832, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ma100 = df.Close.rolling(100).mean()\n",
    "ma100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "90bed0f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x196ac3ca130>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12,6))\n",
    "plt.plot(df.Close)\n",
    "plt.plot(ma50,'r')\n",
    "plt.plot(ma100,'g')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ce85aa2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1832, 5)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6da106aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1282, 1)\n",
      "(550, 1)\n"
     ]
    }
   ],
   "source": [
    "data_training = pd.DataFrame(df['Close'][0:int(len(df)*0.70)])\n",
    "data_testing = pd.DataFrame(df['Close'][int(len(df)*0.70):int(len(df))])\n",
    "print(data_training.shape)\n",
    "print(data_testing.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "efb882f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler(feature_range=(0,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4698d199",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.08115387],\n",
       "       [0.06799147],\n",
       "       [0.06803421],\n",
       "       ...,\n",
       "       [0.93299148],\n",
       "       [0.9765385 ],\n",
       "       [0.98764964]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_training_array = scaler.fit_transform(data_training)\n",
    "data_training_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "88064f74",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = []\n",
    "y_train = []\n",
    "for i in range(100, data_training_array.shape[0]):\n",
    "    x_train.append(data_training_array[i-100:i])\n",
    "    y_train.append(data_training_array[i,0])\n",
    "x_train, y_train = np.array(x_train), np.array(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "34f50ada",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.layers import Dense, Dropout, LSTM\n",
    "from keras.models import Sequential"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "54f2cadf",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(LSTM(units=50,activation = 'relu',return_sequences=True,\n",
    "              input_shape = (x_train.shape[1],1)))\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "model.add(LSTM(units=60,activation = 'relu',return_sequences=True))\n",
    "model.add(Dropout(0.3))\n",
    "\n",
    "model.add(LSTM(units=80,activation = 'relu',return_sequences=True))\n",
    "model.add(Dropout(0.4))\n",
    "\n",
    "model.add(LSTM(units=120,activation = 'relu'))\n",
    "model.add(Dropout(0.5))\n",
    "\n",
    "model.add(Dense(units = 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ccba13f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " lstm (LSTM)                 (None, 100, 50)           10400     \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 100, 50)           0         \n",
      "                                                                 \n",
      " lstm_1 (LSTM)               (None, 100, 60)           26640     \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 100, 60)           0         \n",
      "                                                                 \n",
      " lstm_2 (LSTM)               (None, 100, 80)           45120     \n",
      "                                                                 \n",
      " dropout_2 (Dropout)         (None, 100, 80)           0         \n",
      "                                                                 \n",
      " lstm_3 (LSTM)               (None, 120)               96480     \n",
      "                                                                 \n",
      " dropout_3 (Dropout)         (None, 120)               0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 1)                 121       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 178,761\n",
      "Trainable params: 178,761\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e9da8823",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "37/37 [==============================] - 7s 119ms/step - loss: 0.0329\n",
      "Epoch 2/50\n",
      "37/37 [==============================] - 4s 119ms/step - loss: 0.0062\n",
      "Epoch 3/50\n",
      "37/37 [==============================] - 4s 111ms/step - loss: 0.0050\n",
      "Epoch 4/50\n",
      "37/37 [==============================] - 4s 112ms/step - loss: 0.0051\n",
      "Epoch 5/50\n",
      "37/37 [==============================] - 4s 113ms/step - loss: 0.0049\n",
      "Epoch 6/50\n",
      "37/37 [==============================] - 4s 109ms/step - loss: 0.0046\n",
      "Epoch 7/50\n",
      "37/37 [==============================] - 4s 109ms/step - loss: 0.0039\n",
      "Epoch 8/50\n",
      "37/37 [==============================] - 4s 111ms/step - loss: 0.0045\n",
      "Epoch 9/50\n",
      "37/37 [==============================] - 4s 111ms/step - loss: 0.0046\n",
      "Epoch 10/50\n",
      "37/37 [==============================] - 4s 111ms/step - loss: 0.0037\n",
      "Epoch 11/50\n",
      "37/37 [==============================] - 4s 112ms/step - loss: 0.0043\n",
      "Epoch 12/50\n",
      "37/37 [==============================] - 4s 111ms/step - loss: 0.0047\n",
      "Epoch 13/50\n",
      "37/37 [==============================] - 4s 112ms/step - loss: 0.0042\n",
      "Epoch 14/50\n",
      "37/37 [==============================] - 4s 115ms/step - loss: 0.0031\n",
      "Epoch 15/50\n",
      "37/37 [==============================] - 4s 110ms/step - loss: 0.0036\n",
      "Epoch 16/50\n",
      "37/37 [==============================] - 4s 111ms/step - loss: 0.0036\n",
      "Epoch 17/50\n",
      "37/37 [==============================] - 4s 110ms/step - loss: 0.0035\n",
      "Epoch 18/50\n",
      "37/37 [==============================] - 4s 111ms/step - loss: 0.0027\n",
      "Epoch 19/50\n",
      "37/37 [==============================] - 4s 111ms/step - loss: 0.0026\n",
      "Epoch 20/50\n",
      "37/37 [==============================] - 4s 111ms/step - loss: 0.0030\n",
      "Epoch 21/50\n",
      "37/37 [==============================] - 4s 109ms/step - loss: 0.0028\n",
      "Epoch 22/50\n",
      "37/37 [==============================] - 4s 112ms/step - loss: 0.0025\n",
      "Epoch 23/50\n",
      "37/37 [==============================] - 4s 113ms/step - loss: 0.0026\n",
      "Epoch 24/50\n",
      "37/37 [==============================] - 4s 114ms/step - loss: 0.0024\n",
      "Epoch 25/50\n",
      "37/37 [==============================] - 4s 110ms/step - loss: 0.0024\n",
      "Epoch 26/50\n",
      "37/37 [==============================] - 4s 110ms/step - loss: 0.0032\n",
      "Epoch 27/50\n",
      "37/37 [==============================] - 4s 111ms/step - loss: 0.0030\n",
      "Epoch 28/50\n",
      "37/37 [==============================] - 4s 110ms/step - loss: 0.0034\n",
      "Epoch 29/50\n",
      "37/37 [==============================] - 4s 113ms/step - loss: 0.0023\n",
      "Epoch 30/50\n",
      "37/37 [==============================] - 4s 116ms/step - loss: 0.0029\n",
      "Epoch 31/50\n",
      "37/37 [==============================] - 4s 112ms/step - loss: 0.0023\n",
      "Epoch 32/50\n",
      "37/37 [==============================] - 4s 111ms/step - loss: 0.0024\n",
      "Epoch 33/50\n",
      "37/37 [==============================] - 4s 111ms/step - loss: 0.0025\n",
      "Epoch 34/50\n",
      "37/37 [==============================] - 4s 110ms/step - loss: 0.0022\n",
      "Epoch 35/50\n",
      "37/37 [==============================] - 4s 111ms/step - loss: 0.0022\n",
      "Epoch 36/50\n",
      "37/37 [==============================] - 4s 114ms/step - loss: 0.0020\n",
      "Epoch 37/50\n",
      "37/37 [==============================] - 4s 119ms/step - loss: 0.0023\n",
      "Epoch 38/50\n",
      "37/37 [==============================] - 4s 114ms/step - loss: 0.0021\n",
      "Epoch 39/50\n",
      "37/37 [==============================] - 4s 118ms/step - loss: 0.0020\n",
      "Epoch 40/50\n",
      "37/37 [==============================] - 4s 113ms/step - loss: 0.0020\n",
      "Epoch 41/50\n",
      "37/37 [==============================] - 4s 111ms/step - loss: 0.0021\n",
      "Epoch 42/50\n",
      "37/37 [==============================] - 4s 113ms/step - loss: 0.0025\n",
      "Epoch 43/50\n",
      "37/37 [==============================] - 4s 113ms/step - loss: 0.0023\n",
      "Epoch 44/50\n",
      "37/37 [==============================] - 4s 112ms/step - loss: 0.0019\n",
      "Epoch 45/50\n",
      "37/37 [==============================] - 4s 115ms/step - loss: 0.0021\n",
      "Epoch 46/50\n",
      "37/37 [==============================] - 4s 113ms/step - loss: 0.0020\n",
      "Epoch 47/50\n",
      "37/37 [==============================] - 4s 110ms/step - loss: 0.0019\n",
      "Epoch 48/50\n",
      "37/37 [==============================] - 4s 110ms/step - loss: 0.0018\n",
      "Epoch 49/50\n",
      "37/37 [==============================] - 4s 111ms/step - loss: 0.0023\n",
      "Epoch 50/50\n",
      "37/37 [==============================] - 4s 112ms/step - loss: 0.0015\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x196b5e2b220>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.compile(optimizer='adam',loss='mean_squared_error')\n",
    "model.fit(x_train,y_train,epochs=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3b372de4",
   "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>Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1182</th>\n",
       "      <td>54.687500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1183</th>\n",
       "      <td>54.974998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1184</th>\n",
       "      <td>55.174999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1185</th>\n",
       "      <td>55.692501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1186</th>\n",
       "      <td>55.240002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1277</th>\n",
       "      <td>80.967499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1278</th>\n",
       "      <td>77.377502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1279</th>\n",
       "      <td>77.165001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1280</th>\n",
       "      <td>79.712502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1281</th>\n",
       "      <td>80.362503</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Close\n",
       "1182  54.687500\n",
       "1183  54.974998\n",
       "1184  55.174999\n",
       "1185  55.692501\n",
       "1186  55.240002\n",
       "...         ...\n",
       "1277  80.967499\n",
       "1278  77.377502\n",
       "1279  77.165001\n",
       "1280  79.712502\n",
       "1281  80.362503\n",
       "\n",
       "[100 rows x 1 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_training.tail(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8dbe9724",
   "metadata": {},
   "outputs": [],
   "source": [
    "past_100_days = data_training.tail(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "65844911",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df = past_100_days.append(data_testing, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "0c0dca81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2.09657370e-03],\n",
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       "       [6.42686365e-03],\n",
       "       [9.79760428e-05],\n",
       "       [2.03779406e-03],\n",
       "       [0.00000000e+00],\n",
       "       [6.56400619e-03],\n",
       "       [4.33028995e-03],\n",
       "       [2.23374614e-03],\n",
       "       [1.23246507e-02],\n",
       "       [1.35394699e-02],\n",
       "       [2.50806112e-03],\n",
       "       [6.15254867e-03],\n",
       "       [1.82812174e-02],\n",
       "       [1.83791934e-02],\n",
       "       [1.31671789e-02],\n",
       "       [1.83204138e-02],\n",
       "       [2.43161769e-02],\n",
       "       [3.63077329e-02],\n",
       "       [3.56415138e-02],\n",
       "       [3.45638670e-02],\n",
       "       [3.27024119e-02],\n",
       "       [3.44854742e-02],\n",
       "       [3.66996072e-02],\n",
       "       [4.47331344e-02],\n",
       "       [4.36554877e-02],\n",
       "       [4.99647322e-02],\n",
       "       [5.07485107e-02],\n",
       "       [5.66267144e-02],\n",
       "       [6.14664380e-02],\n",
       "       [5.01802675e-02],\n",
       "       [5.01214879e-02],\n",
       "       [6.08981948e-02],\n",
       "       [7.47315929e-02],\n",
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       "       [8.67622855e-02],\n",
       "       [9.16804018e-02],\n",
       "       [8.80947238e-02],\n",
       "       [9.42080462e-02],\n",
       "       [9.68336367e-02],\n",
       "       [9.52465265e-02],\n",
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       "       [9.82835745e-02],\n",
       "       [9.71275350e-02],\n",
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       "       [1.83517529e-01],\n",
       "       [1.91159182e-01],\n",
       "       [1.97997534e-01],\n",
       "       [1.93765220e-01],\n",
       "       [1.95979353e-01],\n",
       "       [1.98977234e-01],\n",
       "       [1.97174559e-01],\n",
       "       [1.78834592e-01],\n",
       "       [1.95959739e-01],\n",
       "       [2.08989746e-01],\n",
       "       [2.08068825e-01],\n",
       "       [1.79931851e-01],\n",
       "       [1.78266348e-01],\n",
       "       [1.98232652e-01],\n",
       "       [2.03327107e-01],\n",
       "       [2.10694415e-01],\n",
       "       [2.00544731e-01],\n",
       "       [2.03523000e-01],\n",
       "       [1.99721756e-01],\n",
       "       [2.14593665e-01],\n",
       "       [2.10028226e-01],\n",
       "       [2.10185012e-01],\n",
       "       [1.98526550e-01],\n",
       "       [2.07578975e-01],\n",
       "       [2.01073748e-01],\n",
       "       [1.86868089e-01],\n",
       "       [1.57731802e-01],\n",
       "       [1.37941837e-01],\n",
       "       [1.46896315e-01],\n",
       "       [1.09412959e-01],\n",
       "       [1.09099448e-01],\n",
       "       [1.58966234e-01],\n",
       "       [1.40371535e-01],\n",
       "       [1.66666667e-01],\n",
       "       [1.47425392e-01],\n",
       "       [1.39803292e-01],\n",
       "       [9.50114079e-02],\n",
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       "       [5.72145408e-02],\n",
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       "       [7.17336814e-02],\n",
       "       [4.55169129e-02],\n",
       "       [5.33936844e-02],\n",
       "       [4.64966135e-02],\n",
       "       [8.77615993e-02],\n",
       "       [8.18050027e-02],\n",
       "       [9.48154558e-02],\n",
       "       [9.85774728e-02],\n",
       "       [1.08883943e-01],\n",
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       "       [8.61352867e-01],\n",
       "       [8.28043046e-01],\n",
       "       [8.48969437e-01],\n",
       "       [8.65506788e-01],\n",
       "       [8.67622855e-01],\n",
       "       [8.52574668e-01],\n",
       "       [8.78909026e-01],\n",
       "       [8.76322602e-01],\n",
       "       [8.52339550e-01],\n",
       "       [8.22008056e-01],\n",
       "       [8.07430106e-01],\n",
       "       [8.50615267e-01],\n",
       "       [8.15894734e-01],\n",
       "       [7.86190144e-01],\n",
       "       [7.53977582e-01],\n",
       "       [7.89011686e-01],\n",
       "       [8.24280909e-01],\n",
       "       [8.32353633e-01],\n",
       "       [8.58687990e-01],\n",
       "       [8.69660709e-01],\n",
       "       [8.96622090e-01],\n",
       "       [9.07516356e-01],\n",
       "       [9.37769516e-01],\n",
       "       [9.42863912e-01],\n",
       "       [9.49761043e-01],\n",
       "       [9.76095400e-01],\n",
       "       [9.66768631e-01],\n",
       "       [9.42001770e-01],\n",
       "       [9.39650465e-01],\n",
       "       [9.72019812e-01],\n",
       "       [9.45528669e-01],\n",
       "       [9.20213238e-01],\n",
       "       [9.22642876e-01],\n",
       "       [9.06575762e-01],\n",
       "       [8.72874036e-01]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_data = scaler.fit_transform(final_df)\n",
    "input_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "7f274ac2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(650, 1)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c8feb4c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test = []\n",
    "y_test = []\n",
    "for i in range(100, input_data.shape[0]):\n",
    "    x_test.append(input_data[i-100:i])\n",
    "    y_test.append(input_data[i,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f8cf034e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(550, 100, 1)\n",
      "(550,)\n"
     ]
    }
   ],
   "source": [
    "x_test,y_test = np.array(x_test),np.array(y_test)\n",
    "print(x_test.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "ce1f3dae",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predicted = model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "02b45918",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(550, 1)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predicted.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "0759a331",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.21069442, 0.20054473, 0.203523  , 0.19972176, 0.21459366,\n",
       "       0.21002823, 0.21018501, 0.19852655, 0.20757897, 0.20107375,\n",
       "       0.18686809, 0.1577318 , 0.13794184, 0.14689632, 0.10941296,\n",
       "       0.10909945, 0.15896623, 0.14037154, 0.16666667, 0.14742539,\n",
       "       0.13980329, 0.09501141, 0.1325731 , 0.11315542, 0.05985971,\n",
       "       0.11813232, 0.04806414, 0.06893175, 0.05680305, 0.05309979,\n",
       "       0.0226507 , 0.0131084 , 0.05721454, 0.05454975, 0.07986521,\n",
       "       0.05889963, 0.07275258, 0.07173368, 0.04551691, 0.05339368,\n",
       "       0.04649661, 0.0877616 , 0.081805  , 0.09481546, 0.09857747,\n",
       "       0.10888394, 0.13592366, 0.13079003, 0.1352183 , 0.1275962 ,\n",
       "       0.11609452, 0.09932205, 0.11446825, 0.11237167, 0.12792933,\n",
       "       0.12832123, 0.11932752, 0.13725609, 0.14914961, 0.13988168,\n",
       "       0.14789563, 0.15651698, 0.16253236, 0.16862607, 0.18114667,\n",
       "       0.19070856, 0.1836547 , 0.17628733, 0.17999063, 0.17640489,\n",
       "       0.19061055, 0.18704449, 0.19897723, 0.19431385, 0.19831104,\n",
       "       0.19407873, 0.19678265, 0.197057  , 0.19644959, 0.20411086,\n",
       "       0.20703034, 0.21051808, 0.20503178, 0.22301907, 0.22685948,\n",
       "       0.24749197, 0.26483268, 0.23164042, 0.23732267, 0.24553257,\n",
       "       0.26334352, 0.26238343, 0.26265778, 0.25871936, 0.27664787,\n",
       "       0.29165689, 0.27897956, 0.2883455 , 0.26638063, 0.28234973,\n",
       "       0.2882671 , 0.28691511, 0.28691511, 0.30599972, 0.30372681,\n",
       "       0.3207344 , 0.32394784, 0.32526061, 0.32179249, 0.33417592,\n",
       "       0.33940749, 0.32998277, 0.32845444, 0.34436477, 0.33372524,\n",
       "       0.33586098, 0.30116   , 0.29935732, 0.31656086, 0.30435383,\n",
       "       0.31836354, 0.3273768 , 0.40630148, 0.42728665, 0.43298851,\n",
       "       0.43610395, 0.46620033, 0.44433346, 0.45699118, 0.4307156 ,\n",
       "       0.45920531, 0.47488052, 0.47407716, 0.47172585, 0.47921078,\n",
       "       0.48034721, 0.5004703 , 0.54824051, 0.55989891, 0.55180657,\n",
       "       0.56511093, 0.55325657, 0.55166946, 0.58484205, 0.62512734,\n",
       "       0.6033388 , 0.52088722, 0.52151425, 0.45771615, 0.49298537,\n",
       "       0.46296733, 0.45128931, 0.47762367, 0.47903444, 0.45230818,\n",
       "       0.43827886, 0.41084724, 0.43624113, 0.44980015, 0.41304182,\n",
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       "       0.48883145, 0.45928364, 0.48655854, 0.46038096, 0.47542915,\n",
       "       0.47456701, 0.49024222, 0.54847563, 0.52261151, 0.52331692,\n",
       "       0.51955485, 0.50630927, 0.48248301, 0.49447453, 0.48945847,\n",
       "       0.48068033, 0.47511564, 0.47519403, 0.48734228, 0.44501921,\n",
       "       0.47731016, 0.42667924, 0.42597382, 0.43906267, 0.47441022,\n",
       "       0.50638767, 0.50372291, 0.48514776, 0.48240461, 0.50999296,\n",
       "       0.50779844, 0.50819034, 0.51634146, 0.50920921, 0.49855006,\n",
       "       0.50333101, 0.4931421 , 0.46578887, 0.47613451, 0.48287485,\n",
       "       0.48726389, 0.50654445, 0.53530845, 0.53812999, 0.53703273,\n",
       "       0.53162477, 0.54338117, 0.54831884, 0.52794108, 0.53938398,\n",
       "       0.53287881, 0.52794108, 0.57575046, 0.57520183, 0.58217729,\n",
       "       0.56618863, 0.57849361, 0.60710094, 0.59989036, 0.6078063 ,\n",
       "       0.6447998 , 0.6305353 , 0.6215221 , 0.61344938, 0.58774205,\n",
       "       0.60028214, 0.56571834, 0.59957679, 0.60843332, 0.58437181,\n",
       "       0.5829611 , 0.59934167, 0.58382324, 0.56995065, 0.57535862,\n",
       "       0.60827653, 0.64621051, 0.66345334, 0.69362805, 0.69550912,\n",
       "       0.6868877 , 0.6479348 , 0.60772796, 0.62481388, 0.6314759 ,\n",
       "       0.62324639, 0.6502861 , 0.64534837, 0.64652408, 0.63947017,\n",
       "       0.63461089, 0.63257316, 0.63445411, 0.61736818, 0.59894976,\n",
       "       0.59009335, 0.59134728, 0.56101578, 0.55991852, 0.55592133,\n",
       "       0.52174937, 0.52386555, 0.5750451 , 0.55411871, 0.5301356 ,\n",
       "       0.51500902, 0.52511954, 0.48546128, 0.52253311, 0.51383343,\n",
       "       0.52935185, 0.52206288, 0.54526218, 0.55764561, 0.55129717,\n",
       "       0.51814407, 0.51391176, 0.54055963, 0.53389768, 0.51469551,\n",
       "       0.51861431, 0.52347365, 0.52488442, 0.51320641, 0.53084102,\n",
       "       0.53750297, 0.56023204, 0.56266167, 0.57590725, 0.59518775,\n",
       "       0.61587902, 0.60208488, 0.62708674, 0.60827653, 0.62763543,\n",
       "       0.62497067, 0.63030018, 0.61674116, 0.61979782, 0.60757118,\n",
       "       0.62622472, 0.62935971, 0.62677329, 0.62042484, 0.61964104,\n",
       "       0.60380916, 0.61227367, 0.57551534, 0.5774748 , 0.59032847,\n",
       "       0.59401215, 0.56767774, 0.56031043, 0.53570029, 0.55294306,\n",
       "       0.57238029, 0.56313191, 0.55200253, 0.55074854, 0.57128303,\n",
       "       0.55654835, 0.56963714, 0.56806964, 0.56767774, 0.5553727 ,\n",
       "       0.55012152, 0.54753509, 0.55364842, 0.54173528, 0.56015364,\n",
       "       0.56023204, 0.5668156 , 0.56987226, 0.56187793, 0.57159654,\n",
       "       0.59612822, 0.58954466, 0.5935418 , 0.60639546, 0.59597156,\n",
       "       0.61039272, 0.62355984, 0.62136532, 0.61909247, 0.61674116,\n",
       "       0.62982995, 0.64197826, 0.64691599, 0.64934563, 0.6704288 ,\n",
       "       0.68657425, 0.70656017, 0.69613614, 0.71079242, 0.70601148,\n",
       "       0.71494634, 0.74245629, 0.73720511, 0.72082455, 0.68994436,\n",
       "       0.71894348, 0.71306527, 0.72403799, 0.73783213, 0.74120237,\n",
       "       0.72380287, 0.70977349, 0.71494634, 0.71667063, 0.71400587,\n",
       "       0.72842703, 0.72521359, 0.72607573, 0.71886515, 0.71847324,\n",
       "       0.71463289, 0.71667063, 0.74041856, 0.74206451, 0.75789638,\n",
       "       0.75060747, 0.72058943, 0.72325418, 0.73493226, 0.74684545,\n",
       "       0.74613998, 0.73626464, 0.72983774, 0.73814571, 0.77357159,\n",
       "       0.76346114, 0.76879065, 0.77772552, 0.78282003, 0.8015519 ,\n",
       "       0.78916847, 0.78101742, 0.74104558, 0.74559141, 0.73438357,\n",
       "       0.74151582, 0.73963475, 0.71823812, 0.69378483, 0.69762518,\n",
       "       0.71659229, 0.72427311, 0.72497847, 0.71283016, 0.68571211,\n",
       "       0.69292269, 0.68249866, 0.69151186, 0.66400191, 0.679442  ,\n",
       "       0.68641746, 0.69652792, 0.69347126, 0.69276591, 0.682577  ,\n",
       "       0.6778745 , 0.70021161, 0.70867623, 0.72207859, 0.73939963,\n",
       "       0.74331843, 0.74504272, 0.73885106, 0.73845916, 0.74378879,\n",
       "       0.74010511, 0.76926101, 0.74755081, 0.74096725, 0.74927509,\n",
       "       0.76079638, 0.75664246, 0.75915043, 0.75256687, 0.75546675,\n",
       "       0.73281607, 0.73242417, 0.74903997, 0.74911831, 0.75695591,\n",
       "       0.77647159, 0.81080022, 0.83180506, 0.83548875, 0.83854541,\n",
       "       0.84269933, 0.80249238, 0.82937542, 0.86903369, 0.86487977,\n",
       "       0.85696371, 0.84191552, 0.86919047, 0.91511872, 0.94568545,\n",
       "       0.94160987, 0.97993575, 0.9508583 , 0.93980725, 0.97876016,\n",
       "       0.92358335, 0.91480527, 0.90391101, 0.92930489, 0.95007449,\n",
       "       0.95509056, 0.98683288, 0.9786817 , 0.97938718, 0.97013874,\n",
       "       0.96520113, 1.        , 0.98189515, 0.94443141, 0.92154562,\n",
       "       0.92287799, 0.92303478, 0.94568545, 0.94921235, 0.92303478,\n",
       "       0.92993191, 0.90430291, 0.8763226 , 0.86284191, 0.84638301,\n",
       "       0.84019124, 0.82577007, 0.82506472, 0.82138103, 0.90845683,\n",
       "       0.94333415, 0.94200177, 0.95164199, 0.92859941, 0.92460228,\n",
       "       0.91888086, 0.94372605, 0.95509056, 0.92248609, 0.89521126,\n",
       "       0.89709233, 0.92773727, 0.92585632, 0.89709233, 0.8847089 ,\n",
       "       0.86135287, 0.82804305, 0.84896944, 0.86550679, 0.86762286,\n",
       "       0.85257467, 0.87890903, 0.8763226 , 0.85233955, 0.82200806,\n",
       "       0.80743011, 0.85061527, 0.81589473, 0.78619014, 0.75397758,\n",
       "       0.78901169, 0.82428091, 0.83235363, 0.85868799, 0.86966071,\n",
       "       0.89662209, 0.90751636, 0.93776952, 0.94286391, 0.94976104,\n",
       "       0.9760954 , 0.96676863, 0.94200177, 0.93965047, 0.97201981,\n",
       "       0.94552867, 0.92021324, 0.92264288, 0.90657576, 0.87287404])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "95b1ccb0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.21353918],\n",
       "       [0.21338956],\n",
       "       [0.21346998],\n",
       "       [0.2138339 ],\n",
       "       [0.21444437],\n",
       "       [0.21532917],\n",
       "       [0.21644959],\n",
       "       [0.21774557],\n",
       "       [0.21906064],\n",
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       "       [0.22126934],\n",
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       "       [0.2214753 ],\n",
       "       [0.21979056],\n",
       "       [0.21668749],\n",
       "       [0.21165757],\n",
       "       [0.20437899],\n",
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       "       [0.18585478],\n",
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       "       [0.14076261],\n",
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       "       [0.12819918],\n",
       "       [0.12024723],\n",
       "       [0.11129479],\n",
       "       [0.10144819],\n",
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       "       [0.3343863 ],\n",
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       "       [0.31658977],\n",
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       "       [0.3202032 ],\n",
       "       [0.3297254 ],\n",
       "       [0.34388262],\n",
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       "       [0.4538774 ],\n",
       "       [0.46064115],\n",
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       "       [0.4689153 ],\n",
       "       [0.471814  ],\n",
       "       [0.47602597],\n",
       "       [0.48264953],\n",
       "       [0.49173665],\n",
       "       [0.5029243 ],\n",
       "       [0.5151707 ],\n",
       "       [0.5271894 ],\n",
       "       [0.5386586 ],\n",
       "       [0.5501939 ],\n",
       "       [0.56179905],\n",
       "       [0.57071614],\n",
       "       [0.57442725],\n",
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      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predicted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "924b811b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.00783761])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "scaler.scale_"
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  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "a178b397",
   "metadata": {},
   "outputs": [],
   "source": [
    "scale_factor = 1/0.00783761\n",
    "y_predicted = y_predicted*scale_factor\n",
    "y_test = y_test*scale_factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "ff17113a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "plt.plot(y_test,'b',label='original price')\n",
    "plt.plot(y_predicted,'r',label='predicted price')\n",
    "plt.xlabel('time')\n",
    "plt.ylabel('price')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}