Stock market prediction .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a1bcb584",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import pandas_datareader.data as web\n",
    "import datetime\n",
    "import yfinance as yfin\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "raw",
   "id": "f69e82df-3993-4ac9-8a83-a9b7b44453ce",
   "metadata": {},
   "source": [
    "import pandas as pd\n",
    "import pandas_datareader.data as web\n",
    "\n",
    "# Specify the start and end dates for the data\n",
    "sd1=\"2000-01-01\"\n",
    "ed1=\"2022-12-12\"\n",
    "start_date1 = datetime.datetime.strptime(sd1, '%Y-%m-%d')\n",
    "end_date1 = datetime.datetime.strptime(ed1, '%Y-%m-%d')\n",
    "\n",
    "# Get the NIFTY 50 data from Google Finance\n",
    "nifty_50 = web.DataReader('^NSEI', 'google', start_date1, end_date1)\n",
    "\n",
    "# Print the first 5 rows of the data\n",
    "print(nifty_50.head())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "966577fe-851b-45f4-94c4-61e25f2886ed",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "module 'pandas_datareader.data' has no attribute 'get_data_google'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Input \u001b[0;32mIn [26]\u001b[0m, in \u001b[0;36m<cell line: 11>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      8\u001b[0m end_date1 \u001b[38;5;241m=\u001b[39m datetime\u001b[38;5;241m.\u001b[39mdatetime\u001b[38;5;241m.\u001b[39mstrptime(ed1, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124mY-\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124mm-\u001b[39m\u001b[38;5;132;01m%d\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m     10\u001b[0m \u001b[38;5;66;03m# Get the NIFTY 50 data from Google Finance\u001b[39;00m\n\u001b[0;32m---> 11\u001b[0m nifty_50 \u001b[38;5;241m=\u001b[39m \u001b[43mweb\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_data_google\u001b[49m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m^NSEI\u001b[39m\u001b[38;5;124m'\u001b[39m,start\u001b[38;5;241m=\u001b[39mstart_date1,end\u001b[38;5;241m=\u001b[39mend_date1)\n\u001b[1;32m     13\u001b[0m \u001b[38;5;66;03m# Print the first 5 rows of the data\u001b[39;00m\n\u001b[1;32m     14\u001b[0m \u001b[38;5;28mprint\u001b[39m(nifty_50\u001b[38;5;241m.\u001b[39mhead())\n",
      "\u001b[0;31mAttributeError\u001b[0m: module 'pandas_datareader.data' has no attribute 'get_data_google'"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import pandas_datareader.data as web\n",
    "\n",
    "# Specify the start and end dates for the data\n",
    "sd1=\"2000-01-01\"\n",
    "ed1=\"2022-12-12\"\n",
    "start_date1 = datetime.datetime.strptime(sd1, '%Y-%m-%d')\n",
    "end_date1 = datetime.datetime.strptime(ed1, '%Y-%m-%d')\n",
    "\n",
    "# Get the NIFTY 50 data from Google Finance\n",
    "nifty_50 = web.get_data_google('^NSEI',start=start_date1,end=end_date1)\n",
    "\n",
    "# Print the first 5 rows of the data\n",
    "print(nifty_50.head())\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "efad750d-092e-4745-8e5e-65b54e53f221",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "                                  Open         High          Low        Close  \\\n",
      "Date                                                                            \n",
      "2007-09-17 00:00:00+05:30  4518.450195  4549.049805  4482.850098  4494.649902   \n",
      "2007-09-18 00:00:00+05:30  4494.100098  4551.799805  4481.549805  4546.200195   \n",
      "2007-09-19 00:00:00+05:30  4550.250000  4739.000000  4550.250000  4732.350098   \n",
      "2007-09-20 00:00:00+05:30  4734.850098  4760.850098  4721.149902  4747.549805   \n",
      "2007-09-21 00:00:00+05:30  4752.950195  4855.700195  4733.700195  4837.549805   \n",
      "\n",
      "                             Adj Close  Volume  \n",
      "Date                                            \n",
      "2007-09-17 00:00:00+05:30  4494.649902       0  \n",
      "2007-09-18 00:00:00+05:30  4546.200195       0  \n",
      "2007-09-19 00:00:00+05:30  4732.350098       0  \n",
      "2007-09-20 00:00:00+05:30  4747.549805       0  \n",
      "2007-09-21 00:00:00+05:30  4837.549805       0  \n"
     ]
    }
   ],
   "source": [
    "start_date = datetime.datetime(2000,1,1)\n",
    "end_date = datetime.datetime(2022,12,12)\n",
    "# Get the NIFTY 50 data from Google Finance\n",
    "yfin.pdr_override()\n",
    "\n",
    "nifty_50 = web.get_data_yahoo('^NSEI',start=start_date,end=end_date)\n",
    "\n",
    "\n",
    "\n",
    "# Print the first 5 rows of the data\n",
    "print(nifty_50.head())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d4cc0163-2da7-4bf2-876f-6cb21186fde4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "                                   Open          High           Low  \\\n",
      "Date                                                                  \n",
      "2000-01-03 00:00:00+08:00  17057.699219  17426.160156  17057.699219   \n",
      "2000-01-04 00:00:00+08:00  17303.000000  17303.000000  16933.519531   \n",
      "2000-01-05 00:00:00+08:00  16608.550781  16608.550781  15688.490234   \n",
      "2000-01-06 00:00:00+08:00  15942.070312  15971.030273  14763.969727   \n",
      "2000-01-07 00:00:00+08:00  15329.339844  15513.889648  15108.750000   \n",
      "\n",
      "                                  Close     Adj Close  Volume  \n",
      "Date                                                           \n",
      "2000-01-03 00:00:00+08:00  17369.630859  17369.630859       0  \n",
      "2000-01-04 00:00:00+08:00  17072.820312  17072.820312       0  \n",
      "2000-01-05 00:00:00+08:00  15846.719727  15846.719727       0  \n",
      "2000-01-06 00:00:00+08:00  15153.230469  15153.230469       0  \n",
      "2000-01-07 00:00:00+08:00  15405.629883  15405.629883       0  \n"
     ]
    }
   ],
   "source": [
    "start_date1 = datetime.datetime(2000,1,1)\n",
    "end_date1 = datetime.datetime(2022,12,12)\n",
    "# Get the NIFTY 50 data from Google Finance\n",
    "yfin.pdr_override()\n",
    "\n",
    "hsi = web.get_data_yahoo('^HSI',start=start_date,end=end_date)\n",
    "\n",
    "\n",
    "# Fetch the data using the pandas_datareader library\n",
    "#csi1000 = pdr.DataReader('000300.SS', 'yahoo', start_date, end_date)\n",
    "\n",
    "# Display the first five rows of the data\n",
    "print(hsi.head())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "7b45f156-1287-418b-b523-6793236d2b65",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hsi['Open'].plot(label=\"hsi\")\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "a467dbc7-033c-4499-91ab-40d62b5d6344",
   "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": [
    "nifty_50['Open'].plot(label=\"NIFTY 50 open price\")\n",
    "plt.legend()\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "ed7c4568-f525-43d6-8be3-f75289febbe5",
   "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": [
    "hsi['Close'].plot(label=\"hsi\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "10dc2b31-e2eb-44be-a4b8-c530381ffbd1",
   "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": [
    "nifty_50['Close'].plot(label=\"NIFTY50\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "e1c09173-0f34-4c1b-8c1e-a188b9dba9dc",
   "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": [
    "hsi['High'].plot(label=\"hsi\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "c4266bb2-5807-49eb-90f2-1427d80256f8",
   "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": [
    "nifty_50['High'].plot(label=\"NIFTY50\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "71056472-035d-411c-8950-236af5b87bf3",
   "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": [
    "hsi['Low'].plot(label=\"hsi\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "a7b8db63-9dce-475e-956f-7706dc4f0f23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "nifty_50['Low'].plot(label=\"NIFTY50\",figsize=(17,5))\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "d1ee703c-d5e8-4b1e-8ddd-9bef0cd011dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x11e42b3a0>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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C0aVLF956660S+504cSITJ06svIEKIYQQQghRAX/uiqVLeADZZituJj3Z5yRdSSs0Y1dYclae8zj3PLN9+cyOQNFm0y54XVl8ueoo7y2LYNdL/YskizmX1fGsxiGeXNcwEMcA4N+XocXNUKvtRY/lcqloCYU7gXmO4z+Azo7jWKDwAtwwR9uF2sNKaC+RpmnfaprWUdO0jkFBVbNmhRBCCCGEEFXVushEHpm9i9FfbyA5y4yLQUdmXkHg1Fe3g14Rr8Oqt8FaNKA6k15QBL20xCv5M3mWSgjyftwYDUBiRi4v/bnvvNflB6s/3N25oPHMAVj/CZw5eNHjuJwqGuSdAno7jvsC+YtaFwITHFk2uwKpmqadBpYBA5RSfkopP2AAsMxxLk0p1dWRVXMC8GdFX0YIIYQQQghx6ew/lQpATLK9Rl7LUB/nDNjMoR5MM31Iu8S/YNVb8GVXSLPnnLBYbSzacxqdI4/++ZZ05ivtfHlk5tqDt5QsM+4u51/IGHkmAzejnpo+hcomHFhg/xreq9LGczmUpYTCLGAj0EQpFaOUugd7NswPlFK7gTdxLKHEnh0zCjgCTAMeANA0LQl4Ddjq+PWqow3HNd857jlKZWTWFEIIIYQQQlS6zNyis3Mj2tRyZqBsset1LHp3rsv9jMzBn0LyMfikDUSt4ouVRwHIn5jLzLWw/XjyeZ9TmUFe/l7A5CwzboUyep7NKJhZ3BebyqwtJ8g2Wwvq96XHwbqPocEN4FO10oaUJbvmWE3TamqaZtQ0LUzTtOmapq3TNK2DpmltNE3romnadse1mqZpD2qa1kDTtFaapm0r1M8MTdMaOn59X6h9m6ZpLR33POTIsimEEEIIIYS4ilhtGofjC/JPtK/ji1KKd0a1pn+4kYCkHUQ3GEe85kdc/VFw1yLwD4fZ4zFn22cAB7esAcDUhfsZ9dUGjpwpns8CIL1QMHkyKavCY07LMTuPk7PyimT+nDjTGapw8HRa0RttNljxEtjMMKjknBlXs4ou1xRCCCGEEEJcQ75YeYSl++OcnxsG20sM1PZ3Z1rIApRmJaGWPRej1aZBna4w8gvIS6dv+l8AjO5gT8cR6QjuylJuYWPU2RKvKYvBH68tMv6/9xSULNt9MsV5nL/8dNWTfewNfz8Ce36DHo9DUJMKP/9KkSBPCCGEEEIIUaqNR4sGW16uRvuBJRf2zYV248kItNeItto0zqTlsOhsLah/Pa2PfI0LeXi7GYv0YT5PKYUiWTAvYp1fbEq28/j42fPPCO4/lUaDIA/qBXrAic2w40fo9hDc8FLFH34FSZBXyZRSjB8/3vnZYrEQFBTEsGHDilx34403FqmXB/Dhhx/SvHlzWrduzQ033MDx48ed52bOnEmjRo1o1KgRM2fOdLZv376dVq1a0bBhQx5++GFktasQQgghhLgU3Ez6Ip9bhnrbD3b8CJYcaDIUvaNmntlq46YvN/DgrJ2YuzyIwZZDN91+avm6OZOvABw4d5mkQ3qhZZYfrIio8JiHtqqJl2vpVeP+ORhvD0BzM2D+veAeAH2eAaVKvfdqJEFeJfPw8GDfvn1kZ9v/1WDFihWEhhbdqJmSksL27dtJTU0lKirK2d6uXTu2bdvGnj17GD16NE899RQASUlJvPLKK2zevJktW7bwyiuvkJxs36h6//33M23aNCIjI4mMjGTp0qWX6U2FEEIIIcS1xNVYNHRoE+ZrP9g7B4JbQOOBpGXbg7N3l0Y4Z9HywrqRZfDjbeN31Fp0J/95vMBQ3SYAnp+/l9Op2Zxr+YF453GIt2ux82WVlmN2Lis9n9OnTtBVd4AWsX/AtOshORpGfgkuXhV+7pUmQd4lMGTIEBYtWgTArFmzGDt2bJHz8+bNY/jw4YwZM4bZs2c726+//nrc3d0B6Nq1KzExMQAsW7aM/v374+/vj5+fH/3792fp0qWcPn2atLQ0unbtilKKCRMmsGDBgsvzkkIIIYQQ4priaiiYyXt6UFPCAz3AaobTu6B+H9DpyXLssVt3JNF5rUW58Efj9/AiG3VsNXUs0Xxm/IyPjF9w2GUCAd+2h0OLizzrv0NnAGhXxxfPC5Q9uJC41BzWRibie84S0fv7NCDE28U+o3jwL0JmdGa26XVeN34PWWfhtp+gyaAKPfNqUbHvWBXwzpZ3OJR0qFL7bOrflKc7P13qdWPGjOHVV19l2LBh7Nmzh4kTJ7J2bcGmz1mzZvHSSy8REhLCqFGjeO6554r1MX36dAYPHgxAbGwstWsX1JIPCwsjNjaW2NhYwsLCirULIYQQQghRmQ6eTmPeTvvPmc1qenN/nwb2E1Gr7Us1wzoCFFmKmS/PaiPKpRkD1Nesf6IPL/24nOdO/Y8Rug2cJoCaWfHw5wPg+jPU61Ek02WQp8sF99JdyMOzdwL20gn5vF0NPD2oKS4GHQf++xVtzhdYfMJ5MG4o+2312DB1QpVdollYtQ3yrqTWrVsTHR3NrFmzGDJkSJFz8fHxREZG0qNHD5RSGI1G9u3bR8uWLZ3X/Pzzz2zbto3Vq1df7qELIYQQQghRzOBPCiYsZk8plFdiz2zwCIKmQwGw2Yrnh9h5IpmsPCsWkxe4+RHnWp++ue/jqbI5ooXRQMXyj8cHqJkj4P4NDP7EXlOvpo8rAZ4u7Dhx/np6F+Lnbp/B6904iF2OTJr5Sz+9jRpvGGdwkhq87/46K2xmJvUIrxYBHlTjIK8sM26X0ogRI3jyySdZtWoVZ88WZCL6/fffSU5OJjw8HIC0tDRmzZrFG2+8AcA///zDG2+8werVq3FxcQEgNDSUVatWOfuIiYmhT58+hIaGOpd05refu/9PCCGEEEKIi5GSlec8HtOpNj6Flz+eOQg124LB/nOrtYQg78k/dtOspjdhfvZtSTqliCPAmTXzqBbK2XHLCfyhB+bFT6NjEjZ09GsWgq+7kaTMPCbN3IrVpvH93Z3LPG4PkwF/DxOP3NCIT/6NLDK+jid/IEilclf2FFYdsc/09WgUWOa+r3ayJ+8SmThxIlOnTqVVq1ZF2mfNmsXSpUuJjo4mOjqa7du3O/fl7dy5k3vvvZeFCxcSHBzsvGfgwIEsX76c5ORkkpOTWb58OQMHDqRmzZp4e3uzadMmNE3jxx9/ZOTIkZf1PYUQQgghRPV2NCETsCdeeXlEi4ITVgskHobgps6mIK/iSVLC/Nw5mpBJ/UAPAPQlrOnMNAVCn2cwRq/iZ+ObBJKKh4sBP3cTNg3+OXyM9Yl/8Mh/jzB64Wh6zO5Bx5870v6n9oxcMJI9CXuK9XkyOYtGwZ7odIp/n+gNgNlmL9lQ98y/bLY1ZZWtrfN6V6O+WB9VlQR5l0hYWBgPP/xwkbbo6GiOHz9epHRCeHg4Pj4+bN68mf/7v/8jIyODW265hbZt2zJixAgA/P39efHFF+nUqROdOnXipZdewt/fH4Avv/ySSZMm0bBhQxo0aODcxyeEEEIIIURlOJlk3xM37/7uRQOhY6vAmgchBduOhrSqUSwLJ0BiRi6ejlIGqY4MnENa1XCeX7IvDro+wMp6j3Gd/gBvGb/jTGo2J5Ky0HsewCP8E1yCl3Is7RghHiEMqjeI25vezvjm48mx5DBhyQS+3fNtkXJiJ5KyqONvnz006e1jslo1yE7GO+0w660tgYKA08VQfUKjartc80rJyMgo1tanTx/69OkDUGJilB07dgD2pZrnM3HiRCZOnFisvWPHjuzbt6+CoxVCCCGEEOLCHv1tFwDu59TJY/M34B0GzW90NimluKldKLO2nHS25dfC61Y/AIBTjtIKN7cLY/HeOADeXnKI+3o34C/3G9lkjuNZ4ywyc2bya85m3GtnoHIDyTj2IAtfvq/Y+Ca3mszUDVP5bOdn7E/cz2s9XsOkPIhPy3UGeX4eJgCGtKoJW75DofFfoVk8ALO15HrTmqahqtheveoTrgohhBBCCCEuGZdzZ+jOHIS63cBYdIlmSfvyoGDPW47ZvmSypCLlZqvGN9ZhrFZ1mW5eygktjceTkll7ai9vWJaSEH+y2D1eJi8+6P0Bj7Z/lJUnVzJ64WjWH7dPgtQJsAd5ni4Gdr7Yn2eHNIMTG8kOaME+rX6Rfuo5ri0sLjOOCUsmsC1uW4nvdLWSIE8IIYQQQghRKj93U8GH7BRIPQmBTYpdZ7WVfH/+ksmsPAsAHiXUvzNbbOjcTvBoqCexJhPvt7iXFQn/I9EWyFjDSmKnTwCteBCplOKeVvfw/aDvyTRn8uSGu3GpMR+d6UzB+D1M9v2ACYewBBQfd3ChoutZ5ix+2PcDIxeM5FDSIfKsecWuv5rJck0hhBBCCCHEeQ1oHsKJpKyi+/F2z7J/bdi32PUPXt+A1YfPkJiRR6ivG7GO5ZkGR5CXlmMP8uqeM3NmtpmJss3Go+5yQrxq8k6vr2gT1Ibx8xZxQ9773KFfwWv8ABu/gM6TnRk9C+sQ0oG/bvqLp/59l022xTy3dTO/RLUg3Cecxn6Nae5ZB9fcBHa56zAFrELT9ICN+iGKF9dvIiU3Bb3Ss/PMTpJykuhUoxMvd3uZOt51Lvr7eDlVuyCvKq6ZFZVPK+FfeIQQQgghRPnlWGy4nJt58uRm8K0LoR2KXV8/yJNtL/QH4NavNzqDvHzf39WJnSeS8XItKMWg94hk9MJvOU0UXpbu/DzkdQLd7Ms7978ykBZTl/GztR9DTLvptvx5WP48jPwC2o0v9nx/V39Ghj3MvxvaMnnoGY6m72ZL3Bb+jvrbfkGtGpC6BZeCZPYk6IxsPOWPr4svZpuZNkFtmNhyIm2D21bgO3blVasgz9XVlbNnzxIQECCB3jVM0zTOnj2Lq2vxFL5CCCGEEKJ8cs3W4pkn4/ZBjVYl31BYCT+SX980mOub2iOspjW8OJq3GNeQRWRZapAdczvp6a2dAR7Yl3W+fmNLXliwjy9rvka3kN9g1y/w54Ng8oQWNxZ7htlqQ7N6Ma7JcOoG2Es3nM0+S8SCe8hMjqL5HUvp/uZmlM7MrhcH4+3iWa3ih2oV5IWFhRETE0NCQsKVHoq4wlxdXQkLC7vSwxBCCCGEqPJyLLaiBdAzEyHpKLS8udR788OmXyZ1KfH84zfm8MTqRZjTm/Hn7T/Q/MWVJV43rksdXliwj/ohfjDycxj6IUzvBwvuhzpdwatGkevNjo2BRn1BcBrgFsB1iTEQ1Aq8aoFmQrOa8HH1KvU9qppqFeQZjUbCw8Ov9DCEEEIIIYSoNnLNVly9Cu1/2/4DaDZoObrUe/MzbRpKKIB+OPkwL6x/AZVXk5yY8bgb3THpddzaqfg/1CulCPQ0YcnP3GkwkTtyGi5fd4adP0Gv/ytyfZ6jHELhII/cDHtw2uKmUsdd1Ul2TSGEEEIIIcR5pWSZnYXMATi1E/wbQFDjUu8d2bYWAPUCPYq0m21m3tnyDnqlZ1jIC4CegR+tIc9qw8NU8jyUXqewFKpl1+TjIxx07wDbZ4KtaEpPs8X+2VQ4yNv1qz04bdgPsM8ufjKmbanvUBVJkCeEEEIIIYQoZuPRs0yauY24tBwaBHkWnIjbU7b9eMD4rnU59NogQryL5kpYcGQBW+K28GDbB/EyBgEQEZ8OgOnc/X8OBp2uYCbPYUZqB3sph7ORzjZN09h1MgUAo6HQDGL0Wnvx9jr2paPdGwYysm1omd6jqpEgTwghhBBCCFHM+Omb+edgPADh+TNxcfsg5QSEdSxTH0qpoqUXgC2nt/Dx9o9pEdCCcc3GoT8n4UmR2bdCDHqF1TFjZ3MEe7tsDe0nT+10Xrc3NpWFu08BhZZr2mxwdCU0uL5M467qJMgTQgghhBBCXNB1DQLsBwf+BKUrsXRBWRxPO859/9xHkFsQ7/d+H6WUvUB5IYFexevfgWO5piO42xObCsBRrRYY3YsEeUcTMpzHzr2Au2dBXjo0KF7XrzqqVolXhBBCCCGEEJXDWmhppK+7yX4QtwcCG4ObX7n70zSNT3Z8AsC3A74l2N1eRsGgKzrv1KRGydkuDTrlHNOMdccAsKGDmm2KBHmP/bbbeewsi3B8A7gHXhNJV0Bm8oQQQgghhBAX0LW+v/3AZrMHUzVaV6ifzXGbWXF8BXe3vNsZ4AG0DPUucl3rUJ8S79frdJgdiVfSc8zO9qzA1nB6D1gtALSt7Vv0Rk2zF2+v1RaqUS28C5EgTwghhBBCCFGErdAsXrCXI2nKgQWQEQ+NB5a7v2xLNm9segNfF1+mtJ5S5FzfpsG4OfbtBXiYMJxvT56uYE9egGfBks4YtyZgyYbEw0BBkHd7lzr2CyIW2xOzNBlc7nFXVRLkCSGEEEIIIYqISc52Htf0dQR5JzaCybPcSx41TeOdLe8QnRbNGz3ewEVfdM+dUoqnBjUBwKZpJXUBgJtJT2auFYCE9Fxne4pfS/tB1CoALDYb/h4m3rzJkQH0wEL78tL2d5Vr3FWZBHlCCCGEEEIIJ03TeGqufV9b53r+PHqDox5e7A77/jed/gJ3F+/rs52fMTdyLqMajaJXWK8Sr8vPgmk7f4xHTR9XYpKzeHPxQbZGJxHq6wZAsmsdCOsMm78GwGLVChKuRK+DPbPttfH01046EgnyhBBCCCGEEE47T6awKSoJgHdHt8bNpIfDyyF2G9TrUa6+vtr9FdP2TqN/3f681O2l816Xv1wz12I97zUBHi6cSs3h2zVRZOVZ8XU3AjBz43FoNRpSjvPEtL84FJduDxotebD6XXDxhiHvlWvcVZ0EeUIIIYQQQginRMdSyJFta1Evvz7evjngEQQ9nyhzPwuPLuSr3V/RJ6wP7/Z6F506f+jh4WKfZcsx2857TUp2XpHPPm72IM9q05zBZ0j0X+w6mYJBr+xlE46thr4vVigbaFUmQZ4QQgghhBDCKdtsn037X99G9obcdDj6H9S9Dgwl17A71z/H/+Gl9S/RMqAl7/d5H4PuwkslPVxKXwL6WL/GRT5n5VkJ8DDRKMQTgpuTFtqLuw1L0GPFpFOwby741oXOk8s05uqk1CBPKTVDKXVGKbXvnPb/KaUOKaX2K6XeLdT+rFLqiFIqQik1sFD7IEfbEaXUM4Xaw5VSmx3tvymlTJX1ckIIIYQQQojyycqzB3nuJkfgdeBPyEyAzlMucFeBTac38dSap2jq35Sv+39dLNFKSfJn8i4k2LtoP8lZebi76MnKtYJSxDW8jSCVRl/dTm522WyfxWsz9popm1BYWWbyfgAGFW5QSl0PjATaaJrWAnjf0d4cGAO0cNzzpVJKr5TSA18Ag4HmwFjHtQDvAB9pmtYQSAbuudiXEkIIIYQQQlRM9rlB3tGV9qWadbuXem+WOYsnVz9JoFsgX/b7Eh+XkmvencvDVHqQZzqntMJrI1viYTKQmWevjxcX0ptIWyjTTB8yMekjCG4BvZ8q0/Orm1KDPE3T1gBJ5zTfD7ytaVqu45ozjvaRwGxN03I1TTsGHAE6O34d0TQtStO0PGA2MFLZS9D3BeY47p8J3HhxrySEEEIIIYSoqPzlmm4mPcQfsM/kNexfphmxp9Y8RWpuKu/0egd/V/8yP7MsyzXVOc/v1TgIDxeDc+YxWzMy1XKn/aTOAKNnlCsTaHVS0T15jYGejmWWq5VSnRztocDJQtfFONrO1x4ApGiaZjmnvURKqSlKqW1KqW0JCQkVHLoQQgghhBCiME3TeHrOHjZFnSUrz4JepzApG/z3un0f3oDXSu1j5YmVrI5ZzZTWU2gX3K5czy/LTF5J3E16MnMt5JitJGXmscHWkkeDviPjvh0Q3LRCfVYHFQ3yDIA/0BX4P+B3dW5ofQlomvatpmkdNU3rGBQUdKkfJ4QQQgghRLWlaRqztpyg8xv/kJSZx2/bTjLm202kZpvxdjWgVr8DEYvsGTU9Ai/Yl9Vm5a0tb1HPux4TW04s91jKsicPii/Z9DDZZ/IGfryGZ+ftBeCJsUMJCAwu9xiqk4pWBIwB5mmapgFblFI2IBCIBWoXui7M0cZ52s8Cvkopg2M2r/D1QgghhBBCiEsgMj6d/h+tcX7u9MY/zuOfN51gROBpWPM+NB0GPR8vtb/XNr3G6czTfND7AzyMHuUej8lQtrmnQE8Tp1Jz+HVSFwDcXfQcikuvUF/VWUW/AwuA6wGUUo0BE5AILATGKKVclFLhQCNgC7AVaOTIpGnCnpxloSNIXAmMdvR7J/BnBcckhBBCCCGEKIPBn6wt8tmmFRybMPOkZRq4+sCNX5ba184zO5kXOY+RDUbSv27/yh5qEV+Ma88NTYPpWM++32/H8eRi15w723ctKnUmTyk1C+gDBCqlYoCpwAxghqOsQh5wpyNg26+U+h04AFiABzVNszr6eQhYBuiBGZqm7Xc84mlgtlLqdWAnML0S308IIYQQQghRyJZjSVgKR3XnGKTbSp2cQzDyC3ugV4r3t75PkHsQj3d8vFhylMrWro4f0+/q5Pyc4CjcXpifh1RkKzXI0zRt7HlOjT/P9W8Ab5TQvhhYXEJ7FPbsm0IIIYQQQohLbPq6qPOe8yaDN9x/RfMKR7W5vdS+Np7ayJ7EPTzR4YlyZdMsyes3tqS2v3u57smz2i7qmdWVzGUKIYQQQghxjdA0jWX744u0vTy8OZ3q+TF1eHN+bbkTL0sS6uZpoCs9VPhu73fU9KjJqMajLnps47vWpXfj8iVXvKVj7SKf5z9w3UWPozqQIE8IIYQQQohrxIajZ4u19W9Rgz/uu467G+XS8ug30GwE1O5Uwt1F7Uvcx9a4rQxvMBwvk9elGG6p7u/dwHn81s2taFfH74qM42ojQZ4QQgghhBDXiDxLwfLGIC8XABSAzQbzJoHRA4Z9XKa+3tr8FgFuAdzS+JbKH2gZGfQFewBzHEXchQR5QgghhBBCXDOMjsyTvRoH8drIlvi6G/H3MMHpnRC3F/q/DB4Bpfaz+fRm9iTu4famt1PDo8YlHvX56Qsletl/Ku2KjeNqU9E6eUIIIYQQQogqJvKMvabco/0a0b6OH4NaOgK0rTMABc1vKlM/3+//nmD3YMY0HXOJRlo2el1BkNe8pvcVHMnVRWbyhBBCCCGEqGKOnMngt60nyn3fPwfjCfR0oWWtQqUR0k7Brp+hw51lmsXbeWYnG2I3MLz+lduLl69wkHd393pXbiBXGQnyhBBCCCGEqGJGfr6Op+fuxWrTyM6z8s7SQ2TlWUq9LyE9lw51fTEZCoUB++bav3a5v0zP/unAT/i6+HJ3y7srMvRKpSsU5F3qGn1ViSzXFEIIIYQQoorJzLMnGUnLNjN760m+WnUUb1cj9/dpcN57zmbkcjg+g+jErIJGqwXWvA91e0BQk1KfezTlKKtOruLmRjfj41J6ofRLTS+BXYkkyBNCCCGEEKKKSs7KIyUrDwCr7cKFwU8mZwPQKMSzoPHUTshJgU4ToQwB06xDs9ArPZNbTa7wmCuTu0nPbR1rc1vn2qVffA2RIE8IIYQQQogqZG9MqvM4JduMxaaV6b6kzFwA3ripVUHjthmgdBDep9T7U3JSWHF8BT1CexDiEVKeIV8ySineGd36Sg/jqiN78oQQQgghhKhCRn29wXmckpWH1RHkJWWaL3jfmTR7kJdfH4+sJNjzG3SaVKaEK7MjZpOUk8RdLe+q2MDFZSMzeUIIIYQQQlQhhQuaT/xhm/M4MSP3vPdYbRrPzNsLQKCnyd644VPQrNC69DIIWeYs/o76m7ZBbWkT1KaCIxeXi8zkCSGEEEIIUYV0Cfcvsf1sZvEgz2bTsNk0kjLznG0uBj1kp8DGL6HJEAhtX+ozp+2dxvG040xoMaHC4xaXjwR5QgghhBBCVBGapnE4Pp3bOhZPNHI6NadY201frqfB84tJzT5nKef6j8GaC72eLDXhSlpeGguPLKRrza70r9v/YoYvLhMJ8oQQQgghhKgisvKsJGeZCQ/yKNJ+Z7e6xCZno2kaeRYbUcejYfdsjsXEomnQ78PVALw0rDmYc2DLNGg+EkI7lPrMv47+xZnsM0xpPeVSvJK4BCTIE0IIIYQQoorIctTH8zDpi7TX9HUj12Ij22xl8vszyZw+EubfyyLT89RXp5zXBXq5wI6ZkJcB7e4o9Xk2zcbiY4up71OfTjU6Ve7LiEtGgjwhhBBCCCGqiGxHkOdmMuBqLPhR3tOgMUX/F6kfdGJmzqM0UKc52/IevFQWv5recAZ6XrnxsPodCOsEDfqW+rw1MWvYk7CHW5vcemleSFwSkl1TCCGEEEKIKmD78WRnkOfvYcTHzUiO2Z5spcXJXxhvnMXB7Dr8bL2V2dbr+ah1f97bHs6Pprf5w/QKZzVvGi6Js3fW/1XQ6c/3KKfl0cvxMHpwa2MJ8qoSCfKEEEIIIYS4ysWl5jDqqw3OZZoh3q7c1rE2n/53hDCVQIuj37HB2pzbzS8470lIz2WvVp/78h5jgmEZYT4m8prfjGunOyCoSanP3H92P39H/c24ZuMw6o2X7N1E5ZMgTwghhBBCiKuYxWrjtUUHAMh0zOQZdDoe69+Y+kGetD3wLvqj2TxnuQcAHzcjqdlmZ928zVozNpubEf3k0HI998f9P+Jh9OCBtg9U4tuIy0H25AkhhBBCCHEV+/fQGRbtOV2kTa8DpRQ3Nnah3on5JARdR7RWE4B2dXwBeGvJoQo/81DSIRYfW8yw+sPwMnlVuB9xZUiQJ4QQQgghxFWspCp2ep3jx/gj/0BuKlvrTnKeCw/0KHZ93QD3cj1z8bHF6JWeB9s+WK77xNVBgjwhhBBCCCGuYuk5lmJtBp0j9Dv4F7j5E2lo7DzXJdy/2PV1A4oHfucTlxnHzwd+pldYL3xdfcs9XnHlSZAnhBBCCCHEVexEUlaxNp1OwaldcOhvaDOG7g2DAHiif2MGtqhR7PrH+jUq8/P+jvobs83Mkx2frPCYxZUliVeEEEIIIYS4iiVn5RVrM+gUHFlh/9Djcbp4BnDsrSEoVXxx5/d3daJdHb8yPets9lm+3fMtHUI6UMe7zkWNW1w5MpMnhBBCCCHEVexMWm6xNr0tF7bPhNAO4GmfxSspwANoXKPsiVOWHFtCtiWbZzs/W7HBiquCBHlCCCGEEEJcxeLTc+jRMJDPxrZztpnO7IPUk9DtoRLv2fPyAOdxLR/XMj0nPS+dL3Z9QavAVjTxL72Onrh6SZAnhBBCCCHEVSo1y8zemFRq+7vj5Vqw08qYuM9+ENapxPu8XQuKl59vhu9c3+/7ngxzBs93eb7iAxZXBdmTJ4QQQgghxFUqLi0Hi02jW4MAXAx6Z7sxfje4+oJP2HnvfWFoM3acSC7Tc1JyUvh+3/f0q9OPFoEtLnbY4gordSZPKTVDKXVGKbWvhHNPKKU0pVSg47NSSn2qlDqilNqjlGpf6No7lVKRjl93FmrvoJTa67jnU1XWf2oQQgghhBCimjubad+P5+mix6i3/5jsTg76/fOgyWC4wI/Ok3rW58txHcr0nH9O/INFszCp1aTSLxZXvbIs1/wBGHRuo1KqNjAAOFGoeTDQyPFrCvCV41p/YCrQBegMTFVK5af4+QqYXOi+Ys8SQgghhBDiWvTqXwcAe608vaM2XhN1EmXJhmYjKuUZudZcPt7xMQ19G9I8oHml9CmurFKDPE3T1gBJJZz6CHgK0Aq1jQR+1Ow2Ab5KqZrAQGCFpmlJmqYlAyuAQY5z3pqmbdI0TQN+BG68qDcSQgghhBCimgjxtidNGdiiBgad/Uf3RroY+8ngppXyjDUxa0jNTeXJjk+Wef+euLpVKPGKUmokEKtp2u5zToUCJwt9jnG0Xag9poT28z13ilJqm1JqW0JCQkWGLoQQQgghRJWQmWth9eEE6vi742rUO2fyWqhoMLiBb72LfobVZuW9re9R06MmnWt2vuj+xNWh3EGeUsodeA54qfKHc2Gapn2raVpHTdM6BgUFXe7HCyGEEEIIcdmcSskGoH/zEAD0OoULeYzSr4XGA0B38Ynyd57ZyenM0zza/lGMOmPpN4gqoSL/ZTQAwoHdSqloIAzYoZSqAcQCtQtdG+Zou1B7WAntQgghhBBCXNMSM/IAuKFZMAB5FhtN1Ek8VQ60HF0pz/ho+0d4Gj3pFdarUvoTV4dyB3mapu3VNC1Y07R6mqbVw77Esr2maXHAQmCCI8tmVyBV07TTwDJggFLKz5FwZQCwzHEuTSnV1ZFVcwLwZyW9mxBCCCGEEFVWYoY9s2aAhwsAeVYbrXTH7Cdrtr7o/iOTI9mTuIcH2z6Ip8nzovsTV49S6+QppWYBfYBApVQMMFXTtOnnuXwxMAQ4AmQBdwNompaklHoN2Oq47lVN0/KTuTyAPYOnG7DE8UsIIYQQQohr1rtLD/HlqqMA1PF3B6BtqBf++kUkaD4E+da96Ge8s+UdTDoTg8IluX11U2qQp2na2FLO1yt0rAEPnue6GcCMEtq3AS1LG4cQQgghhBDXivwATylwM9mLoOuj/iVcF8+a5q8QdJFZMGPSY9gct5mH2j5EoFvgRY9XXF0ufremEEIIIYQQ4pJoGFRoGeXKN8G3Lr1ufuCi+/1w+4folZ5hDYZddF/i6iNBnhBCCCGEEFeRPIvNeezp6lh4l5sOp3dB+zvAYLqo/lNyUvjvxH+MazaOUM/zVi8TVZgEeUIIIYQQQlxFvl9/zHlszC+TkHjY/jXo4gugf7PnG6yalaH1h150X+LqJEGeEEIIIYQQV5FNUWedx/kF0EmMtH8NbHJRfedYcpgXOY8h4UNoHtD8ovoSVy8J8oQQQgghhLiK7DyZQrCXvWyCQe8I8o6vB6M7+IdfVN8/7P+BLEsWIxuOvNhhiquYBHlCCCGEEEJcJXLMVlKyzAR42oM8TxfHnrxDi6HpMNAbK9y3xWZh1qFZdA/tTrea3SpjuOIqJUGeEEIIIYQQV4mULDMAvRrbyxrc3qUO5GZAViIEN7uovn89+CtJOUmMbjQadZElGMTVrdQ6eUIIIYQQQojLIzkrD4DWob5Ev+1IjHLmoP2rb50K95trzeWH/T/QMaQj19e+/mKHKa5yMpMnhBBCCCHEVeLfg/EABHu7FDSmnLB/9a1b4X7f2fIOCdkJTGk9Bb1OfzFDFFWABHlCCCGEEEJcJd5fbi+V0CrUp6AxP7Omb+0K9bk7YTdzDs/h9qa3062W7MW7FkiQJ4QQQgghxFXgrcUHnceuxkKzbfvnQXAL8AypUL+f7fiMQLdAprSecrFDFFWEBHlCCCGEEEJcBb5ZEwXA//o2LGi0WiBuLzTqBxVIlrL59GY2x21mdOPRBLgFVNZQxVVOgjwhhBBCCCGuIg2DPQs+pBwHax4ENq5QX9/u+ZYQ9xDGNRtXSaMTVYEEeUIIIYQQQlxhGbkW5/HQVjULTpw9Yv8a0Kjcfa4+uZotcVsY03QMPi4+pd8gqg0J8oQQQgghhLiCNE2j5dRlzs8GfaEf0SOWgN4EQU3K1Wd6XjqvbnqVRn6NmNB8QmUNVVQREuQJIYQQQghxBaXlFMzi1Q/yKHry8FJoNgLcfMvV5xe7vuBM1hme6fQMJr2pEkYpqhIJ8oQQQgghhLiC4tNynMeLH+5ZcCI7GdJPQ8025ervwNkD/HboN0Y0GEHnmp0ra5iiCpEgTwghhBBCiCsoLtUe5F3XIKBo6YQzh+xfg5uVq78/Dv+BUW/k8Q6PV9YQRRUjQZ4QQgghhBBX0NwdMQC8fXProic2fwU6Y7lm8k6mnWTZsWX0DO0pJROuYRLkCSGEEEIIcQX9uesUAMHeLgWNuelw8C/oej94Bpe5r892fYbZZua+NvdV9jBFFSJBnhBCCCGEEFeBIks1j28AzQb1e5f5/nWx61hybAk3NryRRn7lL7kgqg/DlR6AEEIIIYQQ17JgLxf6Nj1ntm7rd+BVE+p2L3M/i6IW4W3y5v86/V8lj1BUNTKTJ4QQQgghxBWUmWvBw+WcuZfTu6FBXzC6lamPiKQIlhxbQv+6/aVkgpAgTwghhBBCVF8Wq42/95xC07QrPZQS2WwamXnWokFeVhJkxJerAPrS6KUAPNz+4coeoqiCJMgTQgghhBDV1vvLD/PQrztZE5l4pYdSosw8eyF0T5dC+/Hi9tq/BpWtdEKWOYvFUYtpG9wWf1f/yh6iqIIkyBNCCCGEENWSpml8vfooAAadusKjKVlKlhkAX/dCSyz3/AYu3lCna5n6mBs5l1OZp3igzQOXYoiiCpLEK0IIIYQQotpIzzGzdF8cZzPzCPYqKElgsV2dyzUTM3IBCPIsVD7h9G6o0w1cvUu9X9M0/jvxH/V96tO5ZudLNUxRxUiQJ4QQQgghqo2XFx5wFhcvLD4tB4ANRxNpVsMbP4+rIzlJYkYeAAGejvHYbHD2KNTvU6b79yXuY1v8Nh5p/8glGqGoimS5phBCCCGEqDZOJGWW2P78nB1ExSVzx7QNPPf1LEg8cplHVrL8mbzA/Jm8tFiwZENAwzLdvzV+KwA3NbzpkoxPVE2lBnlKqRlKqTNKqX2F2t5TSh1SSu1RSs1XSvkWOvesUuqIUipCKTWwUPsgR9sRpdQzhdrDlVKbHe2/KaWujn9WEUIIIYQQVcJPm45T75lFLN57mq3RyUXOeZDNw/p5bHW5n9CvGrLHZRJfpT8Mn3eAfXOv0IgLJGXaZ/L882cWI5fZv4a0LPVem2bj3+P/Us+7HgFuAZdqiKIKKstM3g/AoHPaVgAtNU1rDRwGngVQSjUHxgAtHPd8qZTSK6X0wBfAYKA5MNZxLcA7wEeapjUEkoF7LuqNhBBCCCHEVSU5M49kRzBzKby4wD4X8cAvO5xta/7vemqreOaYXuZx4xy22prwg3Ug8609eMY8ieMqFP78H+SmX7JxlUVWngW9TuFqdGTX3L8AgltAWMdS7911Zhd7EvcwtunYSztIUeWUuidP07Q1Sql657QtL/RxEzDacTwSmK1pWi5wTCl1BMjfAXpE07QoAKXUbGCkUuog0Be43XHNTOBl4KsKvY0QQgghhLiqaJrGiC/WcTIpm6g3h6CrpCyXp1Kymbcjhp6Ngoqd+/i2tvgl72Ke6WW8yIKxvzF9tQ+bopKc1xzNqcUfLq/CgYXQblyljKmsIuLS+edgPA9e35DsPBtu+QFeXhac2gVtbgNV+vdp8+nNKBRD6w+9tAMWVU5l7MmbCCxxHIcCJwudi3G0na89AEjRNM1yTrsQQgghhKgGZqyP5mRSNgArDsYDcOJsFvNKSI5SHte9/R/vLz/MyC/WFzs3spkXnn9NJhcjw/PegCaDcDfZ5zZu61gbgG1aY7TgFvDvq2A1X9RYymvgx2t4b1kEby0+SLbZUjCLt/INyMuAZsNL7SPbks2cyDm0CmyFj4vPJR6xqGouKshTSj0PWIBfKmc4pT5vilJqm1JqW0JCwuV4pBBCCCGEqKA8i43X/j7g/HzvT9vRNI1e763k8d93k2O2VqhfTbtwOQS1/AVU2ikeznuIZI8GAORPIIYHefD8kGZo6Mi57nHIiLOXLLhM8iw25/E3a6KYteUkrkbHj+RH/oUGfcuUWXNn/E7OZJ1hcuvJl2ikoiqrcJCnlLoLGAaM0wr+pMUCtQtdFuZoO1/7WcBXKWU4p71EmqZ9q2laR03TOgYFFZ+aF0IIIYQQV4amaczdHlMkcBv+2bpi133lKE4OBWUNymvWlpPF2ybbC4d30+2HHTOhy/28cP9dLH64BwD5ZfI8XQy4u9hnzjJ9Gtkbk6MrNI6KiErMKNbmZtRDehwkHIS63crUz5a4LeiVnk41OlX2EEU1UKEgTyk1CHgKGKFpWlahUwuBMUopF6VUONAI2AJsBRo5MmmasCdnWegIDldSsKfvTuDPir2KEEIIIYS4UtYdSeSJP3bz9pJDzraI+IKkJvn7zt5dGuFsO51asSDvRJL9x89BLWrYn/P6ILo1COCV4c34JHgxeIdBn6dpX8ePYG9XAGyOOQkvVwPuJvtY0l1rgd4EJzZVaBwVsfNESrE2N5MeNnwGOgM0G1lqHxl5GcyOmE3nGp3xMHpcglGKqq4sJRRmARuBJkqpGKXUPcDngBewQim1Syn1NYCmafuB34EDwFLgQU3TrI49dw8By4CDwO+OawGeBh53JGkJAKZX6hsKIYQQQlwjdp9M4atVRzFbbaVfXImSMvNIzbbva/t920k2HEkscn5k21ocfK0gWbsBCx3VIVJOH4WTWyFmG1gtlFWexYaXq4Gv7+hA9NtDcTHoITedO0+9RnDKTujxKLgW3admdUzleZgMzv15mTYTNB4EEYsr8tqlenbeXkZ+XnQ2s6TA1tWoh2NroE43CGpcar87zuwg05zJ3S3vrrSxiuqlLNk1S8rJet5ATNO0N4A3SmhfDBT7E+TIuNn53HYhhBBCCFF2OWarMwlJTR9Xbmx3eXLZbTx6lrHTCmbCsvKs3P7dZmZP6epsu6NrXchK4nHD77RVR2mjO4qPyrIX5cp3w1To+XiZnplrseJiOGeuYv0nsH8e9H0ROk0qdo99Is9KQt4xjmccwuh/kDlHjxHv4U7f9DiUzQo6fTnevHSztpwo1paeU5DkJdjLhTPpuQzXb4aYPdDv5TL1uy52HQadgbbBbStppKK6KTXIE0IIIYQQV7+kQnXoypB9v9JsijpbYvtz8/fi42ZEKejolQw/3sUD+n0c0urwr+rGZnN9vMkkLLwpd+qXwaq37VklAxtd8Hm/bT3BL5tP4ONmLGi02eDAn1CvJ/R6ssj1UalRLIhcQIR+OZ5NT/Pmbvssp2sIzD0Gc4H7fDx5MOMMeNe8qO9FWaRlW/D3MPHayJY88ccuAAZkLwb/BtDtodLvz0vjj4g/6F+vP24Gt0s8WlFVVUYJBSGEEEIIcYWN+26z8zgzt2JZKysiPafkZZZRCZl4uhgYF54F0/pC8nHmh7/Mzba36froL/xmvZ5p1mFMPdIQbv4WNBus/zh/yq1Q/2bWRtqzqmuaxtNz9wIQ4GkquGjnT5B4GNre7myKSIrgwX8fZOSCkfx08Ce8TX7kne3N/1q9xGc9fyM94hVebfMnwwLa8bWfD/tjN1Tq9yV/+Wq+g6fT0DSNtBwzwV4uDG1dk/a1fbld/y8hZzdDq1tAbzxPbwW2xW3Dolm4rcltlTpeUb3ITJ4QQgghRBVntto4lpjp/Pzc/L30aRJELd9LN9OTY7byyl/7i2S6vKNrXSLi09l1MoU8i428nCxujfvAHrjds4xbgptxC8VLIJg9a2FsdQvs/Bnq9oC2BbuFXvnrAHO2x/DBLW3oHO7vbF/ySM+CDvb+AQENoc1YMs2ZTN87nR8P/IirwZX72tzHmCZj8DL6sTsmhU71/DmZlAW2Y1gsRp5pOYk1/93HS/u+Zk7Tm1GVNA16uFDSmafn7OG3bSd5fkgz0rLNeLsawWZlZq15GE9Nt89Adn+4TP3+fvh33AxutApsVSnjFNWTzOQJIYQQQlRhu0+mMNux96tno0Bn+5EzxVP1V6btx5OLBHjH3hrCaze25Pd7u/G/6xsCMNi8grqZe2Dg6xDczHmtUgo/94JZqzPpuTDiM6jVDv56uEhJg3WR9iQuT/yx234dMOOujvZkKwARSyB6LVqrW9kUt5nxi8czbe80bqhzAwtGLuDBtg8S4BaAyaCjUz17kOjmyK6ZbbbiE9CEx5JTOJwVx9rYtZX2/dlyLMl5/Ns2+/fpv0NnSMux4O1mgNXvYtz2LXS4C8bPBVPpWTKjUqJYH7ueu1vejUlvKvV6ce2SIE8IIYQQogob+cV6XvzTnrT80X6Nubd3fQAmzNjCmQrWoSuLPTGpRT4XngFzM+lpqaJ4wvA78Z7Nof2EYvevevJ6pk3oCMDplGzQG+DWH8Fmga3fOa/zditYeLY64gwAXq6FljXu/JnTXsE8YD7G5OWTSctL4/O+n/NOr3cIdCsIegvzyM+umWsFz2CG6PwJUyZe2/Qa6XnpJd5THmczcnlvWUSxdqtN43RqNtdpO2H129B8JAz7GAwuZer33W3v4m5wZ3Sj0aVfLK5pEuQJIYQQQlRR+WUB8rWr7ctd19Vzfj52MhYyEi7Js6evO+Y8/mpc+yLnauQeZ67pFTJxZV3rt0q838fdSN0Ad6BQWQHfOvaSBhu/gDP2enstaxWUQth5MgWAFrW8AUjcN5fP4tYyMsiDHWd28kSHJ/j7pr/pXbv3BcfuarT/CDxn+0lQCvewjrySbuFM1hkmLJlAljnrgveX5rP/jpTYvu9UKnlZ6dyU9B341IaRX5Y5S05KTgrrY9dzZ4s7CXIPuqjxiepPgjwhhBBCiCqq34erncfXNwlCp1N4uxrxIJsfjW/R5Y/28H5D+PuxYglNLobZaiMxI9f5uVGIV8FJq5muB1/HjJ5Rua9g9Wtw3n5q+tgLlcckZxc0jvgMDK4w6zbIPEtuoZp/ayMTcTPqybKm8Ommt+i/bSrf+vlwXWhPZg+bzV0t7ypTxsn8WcejCZnEp+WAZw06p57lrR5vcSTlCLf9fRvJOcll/XYUYbHa+GFDdLF2P1dFP8tavje9i2/aYRjwOrh4lrnfHWd2ANC1ZtdSrhRCgjwhhBBCiCrJZtOcyVa+HNeebx1LH90NMM/7Q67T7WdD2CRocztsmwHR6y7UXbkkOPbGvTqyBdte6EfDYEewYsmFWWMJPLuNtyy3c5oA3F3OX3suf9nlO0sPceSMY5mkR6A922ZyNKz/GLPFBthQpgSMfhvxCp/GDX/cwLSIX+mXmcWf3d/n4xs+I9wnvFzvYNDZA70Z646BZxDkpTOk9vV8ev2nnEg/wROrnyiWIKY0y/fH8dTcPc7PYzvXBkCPlU8Mn/Gp6XMaqRjy+r8FLW4sV9+/R/yOh9GDFoEtynWfuDZJkCeEEEIIUQVtibYn9qgX4M7gljUw6u0/1qmVb9Akbz9fej3Mx9bRMPQDcPOHBfdDTuqFuiyz/LIJAR4uBHo69pNpGqx5D46swHb9i/xivQEAk75sP25uOVYwcxZXpxNrmg9k1t4ZpOa+g2+Tt/Fs8AGuNf7EqtK5p8Eo5sWn8p5nC+o3HFihd/B0te/LyzZbwcOx/DEzgevrXM9TnZ5ia9xW3t7ydpkCPU3TeOy3XUz5aTvzdsQ62/9vYFOa1fRmtH4NvSwbeN98Cx1zv8Z03X3lGmt6XjobTm3g9qa346Iv2/49cW2TIE8IIYQQograeSIFgIX/61GQ9CQpyr6freUoEhuOZn9sKjaDG9z2M6TGwMKypekvTXqOvQacl2uhalwHFtiDvKbD0PV6ArCPSVfKnrNPx7YDrCTmnuS3Q78xadkkBswZwIPZB3kz0J/jhgO4abXIPjWajKNP8G2P73j4wCoa5Wbbk5ZU0JBW9sLn/h4m8Ai2Nzr2L45tOpaRDUby66FfeXfru6X2tTc2lfk7C4I7Hzcjr45sgb+HiVsawlTDj8S4NuZz643Y0JW7TMOehD1oaHSq0alc94lrl9TJE0IIIYS4BPafSsXFoKNhsFfpF1fAO0vtiUm8XAr9OPf34/ZMjX1foP5BRWaelV+3nGB81+5w3UOw4TNIPg5+dS/q2em59pm8/NkwslPs+/4CG8MtM4skE8kvW5Cam8r+xP3EZsZyOuM0pzJPcTrjNLEZp/BsGs+0Yxocg1CPOkxsMYU+dXpQK/EYQb/fxTq3JhzxhGmpimZbnofY7fYAL+D8+/1K83j/xvy6+QR+7ib7ck2ATHv2Tp3SMfW6qWho/HzwZ8K8whjXbNx5+8rOK1p8/q7r6jGhWz0A7tYvBZXHv83fhA159Gpc/qQpcyPn4qJ3oXVQ63LfK65NEuQJIYQQQlSypftOc9/P9kQZ0W8PrdS+bTaNg3Fpzs/OWaEj/0LUSujzHPjXJyM3EoAXFuxjfNe60G6CPcg7+Jc94CuHvTGpKAUtQ+2ZLr9bGwUUCjCXPw/ZyTB+nr0UArDqyd5EnD3J70d+YNXJVexN3IuGfemjXukJcQ+hlmctOtXoxNzNmdjM/liz63IoL5BD2xT1xwYTENqAQ/omdMteTc/sf7nbFYgEuj8CHe+u2DfQwWSwL2gzW23gZZ/VK1yfz6gz8mLXF0nISuD9be8T5hl23qydmXmWIp871vOzHyRFoTbZZ1bzfBsAB3ExlG8hXZY5i5UnVzK26Vg8jKXX0hMCJMgTQgghhKh0y/fHX7K+/957modn7QQKEnuQFAVz74GARtB5MlAQxDgFNoI63exLKjtPLnNtNoDhn9uTtuQHrOuPnAUg2NsVjq2BnT9Dp8lotdqxI3478yPns+L4CrIs9lIErQJbcV+b++gQ0oE6XnUIcg/CoCv4MXTWokXFnjl/RwwPz0oApjKgWTDfDjDBsbX22cLGA8o89vMx6uzfH4tNA+9a4FfP/i5d73de42pw5e1eb3PPsnt4Zu0zvN/7fbqHdi/WV3Kmffmqn7uR5CxzwRLVffMADfpNxe2QPcD1MJ0/EU1JNp7eiMVmoUetHuV/SXHNkiBPCCGEEKKSJWflOY9zzFZcjeX7wf5CIgrN4tUL8ICsJPjlVnvDuN/B3b48snC+kKMJGTQI8oSeT8Avo+2zea1KL6idmmUukh3TatOw2OwlDdrW9sUnaQ/8ehtx3iH8VbMeC+YP40T6CTyMHgysN5BmAc3oUqML9X3rl/s9V0YU1PfLttigZhv7r0pi0NsDMUt+iYaabeDMwWLX+bv6816v93hk5SM89O9DDK0/lLta3EVDv4bOa/J/v+sFepB8IoU8iw3yMmH9pxDeC3zroGnRALi7lP3Hb03T+Hzn54S4h9AupF0F31RciyTIE0IIIYSoZElZZudxara5UoO8M2kF9emGt6kFix+Cs0dg/BzwLwimhrSqyVtL7Pv2Zqw7xhs3tYL6fcC/ASx9FpqPBL2R95dFEOTlwr7YVP7YHsPU4c3p3zyEmORsxny7iSGtajj7PJaYgbebvezBmGYaS+eNZ0FwIBtNOmx7v6FTjU7c2+Ze+tXph7vRvczv1KtxEGsOn79oe+F3riz5JRTyLI4gz7MGRK0q8dqGfg2ZOXgmX+76kr+j/uavqL8YWG8gfev0pb5Pfc5kZKHXKa5rEMDOEyn4uBshdgfkpkI3+9LYPKs96i5rtlGAeZHzOJJyhBe7vlim+n9C5JMgTwghhBCiEh05k8HukynOz2nZZkK8XSvc34KdsTw1dw97pg7A1ajnRFIWSsHf/+tBrVMrYN8ceyDRsF+R+2r7uzP3/m6M+mpjQR07vREGvwu/jIKNnxPddAqfrzxS5L5X/jrAK38dcHyysDRyC0bf0+hc4nhm3XwOJyTiFZ7JJ8fjyPA2UMM1gMmNRzOywUhqe9eu0Du2CfO5YJCXmm0+77mKUkoR4GEiMdMx6+oZbC8xYc4GY/GAKtAtkJe6vcQDbR9g2p5p/HX0L5YcW+I8797Am722hgzqE8LW5BgSYvfQUacjIMyeEfO6BgFAQVbP0iw9tpRXN71KM/9mDK1fufs6RfUnQZ4QQgghRCW6/+ftADQK9iTyTEa5A5S9MamsiUzggT4NUErx/vII8iw2mr64lE/GtOX42SxuahtKC38Fc1+x71Hr82yJfTUK8QJspOYlkmkOxt3gjmp4AzQdBqve5rT/TcVv0uWgd4vG4BmB0WcHSm+fRXOxQU6SntZaDr6aBf9MG4M6PUqXTg+i113cTGWbMF8AOtb1Y9vxgnp5vu5GUrLMlyTIAwjxdiU+Ncf+IX8WNPHwBZeFBroF8myXZ3mi4xNEJkcy8puF6Exn0ZkSUWhEpG1lfbwj+KsbRsiiW7mu1nX0CO3B/tf6lJo8xabZ+D3id97Z+g7N/Jvx7YBvJeGKKDcJ8oQQQgghKpGbI7HGKyNacPt3m0nLKVuAEp2YSZ/3Vzk/twz1IS3bTExytrPtkdm7UNjoz2GYfq89G+T4ueDiWaSvPGseK0+u5PeIP/Bsso3pJ6xM/xVMOhP+bv74GU0EBnjhue0RTEF1UYZ0dIZUR7BiL7Ku2fQ0y3RncmYsLcy5uFq9SLW6sk8Lx+rfiJvGTKq0PXJd6vvTtIYXU4e3cCZ5AbixbSg/bIi2Fyy/BGr4uHI6P8gLbW//GrWqTO9l0pvwM9THkl5Q1mDmI/YZt5wTmzj8y3C2dRzHAZOBFcdXMP/IfDyMHvQO60374PY08G1Afd/6+Lva91DmWnOZc3gOf0T8wdHUo3Sr2Y23e72Nt8m7Ut9ZXBskyBNCCCGEqER7YlIBqOlrX/JX1lmofw+dKfL5920nWbTntPNzKAkM1W/iZv06mh48CQEN7TXp6vdxXqNpGnMj5/Llri9JyE4gxD0Ec3I3eoU3pW1dD3JtaSTlJJGUk8Tx5FPEW4/iEhCFzeqFZvbBmhOGOaUjLtnB/KRfQDvzfn6z9OF5W2f6DrmNl/6OAGDHlP7gYbqYb1MRXq5Glj7aq0jbH/d1o30dP37YEM2NbWtV2rMKC/F2cf5+4VcPwjrBnj/sJRrK4FhiZontrjt+onWumdbXvQAegVhsFnae2cncyLlsOrWJxccWO68NdAukrnddDpw9QLYlm1aBrXit+2sMqz+sSAZSIcpD/ssRQgghhKgkhYMyH0eCktRCSVgw54Cx5P15e2JSztvXil5RhG+ZigEr+211YdR0aHETFFomGZUSxbtb32X9qfW0D27Py9e9TPda3Wn+0nLOuHjz3uYUpk0YTv+OIdhsGgOf/5ZVppeI0QIZkvcsNvITgmi8ZfyKdrYD5I34iqd/t9fGezI8CIgg0NOEfyUGeOfycTNSN8DdWUR999QBuJez7EBZuZsM5BSeJazdBbZ+Z09NWqig+/kcPG3PdGrUKx7t19jemHEGdv0M7caDRyAABp2BTjU60alGJzRN43TmaaJSo4hKiWLf2X3EpscyosEI+tftT+canQtqHwpRQRLkCSGEEEJUkufm7wVgwzN98Xa1/5j14fJDtEn9lxp7v6ZmdqR9tqjZcOg8xZngIyE9lz93nQIg2MsFP3cTEfHpAAzSbaHRlo856duFMXG3o/Ovw9pWfZ3P3H92P5/v/Jx1setw0bvwVKenuL3p7c59ciaDzpkIZm9MCg2CPOj7wWogjOfMk/jU9DmfGj/jIfPDNDed4SW+pavuIHR/BGO7sbgvWMakHuEEednr6g1sUZBt81LYPXUAWqH6D/nB8qVgMugKsmsCeIeCJcde2N1RiuJC9p9Ko6aPKxufvaGg8cg/9q+dJpV4j1KKWp61qOVZix6hUvtOXBoS5AkhhBBCXCSbTSMxIxdXo44ADw9q+bpBdjLPGn5hgLaN8C3xHLaFssw2gLvMp2DFS3B8A9z2M3EZVrq+9S8A7er4Mv+B7ry8cD8R8ek0USf4xPgFtqBmeN4xny5LIpk6vAUAqbmpfL7zc36L+A0/Vz/ub3M/tzW5jQC3gCJjczHoSccC2JdFvrcswnluoa0bjS0necjwJy3U49QzJGEzekBfe8F0pRQHXh3kvH7poz1pFOx1qb+dl20my8WgI89qw2bT0OmUvWA82Iuit7jxgvfmmK3EJmcT5ndOJs51H4NfONSovJp+QpSXBHlCCCGEEGWUnmPGy7XozFJqlplBn6xxJvD46Lam9hMrpnKvYRFrrS35yjyCudZe6A1G7rp/MGydDoseh0/bs7j+R86+Zk/pCoBOKbrqDvCVy+e4uHjA+Ln4eXvw4W1tsdgszI+cz6c7PyUpJ4nRjUfzv3b/w8/Vr8QxuxgK6rK5mvQcTchwfv5kTDu8XTry3S95TDIsgeDW6MfOBp/QEvtqWqN6JQFxMdhnO/OsNlx1emjQF3zrwo6ZpQZ5TV9cCkCfJkEFjRkJkBgBA14HXdnr4QlR2STIE0IIIYQog17vruREUhYLH+pOa0fKf4AvVx8pyNAINAnxhh0/wY6Z/Grpy3OWgmV7VouNrDwL7p3uAY8gWHA//bffx5e8yo8PD7MHHWePMiH6aeqZ1pBmCII754FPKPsT9zP/yHz+Of4PZ3PO0iKgBR9f/zFtgi48Y+TrbiQ2xZ6hMyPHUiRb5/DWtdDpFHdb7uAbyzC2Th4D+mvnx8P8ADjXbLMXrNfpodEA2PXrBfflFV5O6uFS6PsVt9v+tZKyjgpRUdfOn2IhhBBCiAqy2jROJGUB9oyKdfzdWX/kLENa1cCktwcKn4xpy8FTaTRLWQkrXoRa7XglakKxvnq9u5JHbmjEHd1GkKE8qDH7Fj4wfoX/MSts3gB7/6Cu0rG5/v9oefP/sS51L98umcDOMztx1bvSM6wnw+sPp3ft3uhU6bNFhZOkvLP0EAA3trUHdzpdQRCTgN81FeCBfU8eQK7FCjhmaAMbgTkTMhPBM6jE+8zWgiCv8Ewpx9bYv9ZodSmGK0SZXVt/koUQQgghKuDuH7Y6jxftOc0js3cB8OukLnz23xFAY6TbXkbGfwZb1kFgE7j5O3LfjyjWV2JGHi/+uZ87utVji2rJKst4XjXOhBUPgasP1la3sLHlIHakH+fV5bdzIv0EwW7BPN3paUY0HFHuumlrIxOLtY1oW4u+TUPK1U915JzJK5x8xSfM/jXleIlB3pEz6QR4uDg/z9sRy4e3trV/2PWrvdC8W8lLZ4W4XCTIE0IIIYS4gKw8C2sOJzg/Lz8Q7zy+/bvNBJPMdNN7MCsaPIJh8LvQ8R7HrJg9yPvpns6cSMri+fn7nPdqmsarfx0g2jqQx8aNxKLLZmHeaf6InEvshhfQKz1tgtpwX5v7GFhvICZ9xcoWuJv0ZOUVLSaevxct3ydj2pKSVbZ6ftWJi9H+fSgS5IV2AKWHQ39DWMci10fEpTPw4zXc3L5gz6JzMjQzETIToO51l3rYQpRKgjwhhBBCiAuYsz0GgJ6N7DXP8mfGPF0M1M2L5GfTW7hghuGfQtvbQV+QmOWh6xsS6udGz0b2GaHCQd6xxEyiz2ahjEl8lxnHH4f/INuSTfvg9jze4XF6hfXC1VByTb3y+HlSF27+ckORNpOh6DLPkW1LTrRS3bkUWa7p4FUDarWF2B3Frs8vfv53oRqGe18eaD9IcMzaBjW5JGMVojwkyBNCCCGEKGTLsSQaBnvi72FiW3QSL/25H4AJ3eph1CvWRibwQo0tDEifTx11gjjNj+09ptGvw5BifT05sIQf+HU5GDyOMPinxbjViUHvfowfD2gMrT+UKa2nUN+nfqW+T20/92Jt2efM7F2rSlyuCeBXD2K3F7s+PxjMr63Xu3FQQeKV2G32r0FNL8lYhSiPUnfrKqVmKKXOKKX2FWrzV0qtUEpFOr76OdqVUupTpdQRpdQepVT7Qvfc6bg+Uil1Z6H2DkqpvY57PlWXqzCKEEIIIa5Ze2NSmbkhulh7SlYet36zkVu/2QgU3c/WMtSb2pbj/GZ6jUkpn2AzuvOBeTQres6hX//iAd65jqYcZcKwnYQ0/wC3sJ8x+q9H6XIZWvsOlty8hLd7vl3pAR6Ah4u+WFuPhoGV/pyqyFQou2YRvnUhNQZsRYPhpMw857FBp5hxV6eCkzt+hNpdC/b0CXEFlWUm7wfgc+DHQm3PAP9qmva2UuoZx+engcFAI8evLsBXQBellD8wFegIaMB2pdRCTdOSHddMBjYDi4FBwJKLfzUhhBBCiOJsNo3hn68DoHO4P81qFiQyafvqCgCOnLHXktsdk0LTGl78/b8eGBIOoC28mdoukN7rHWal9eKbtcd4DJ8LPi8mPYZv93zLgiMLMOgMdAzqzr9bGmLNrksdPx9e69272PLJyuRuMrD9hX5k5Vnp+e5KgCJZNa9lhevkFeFXF2wWSIsF3zrO5lf+OuA8DvF2RZ//fcxOgbNH7Mt1hbgKlPp/FE3T1gBJ5zSPBGY6jmcCNxZq/1Gz2wT4KqVqAgOBFZqmJTkCuxXAIMc5b03TNmn2giM/FupLCCGEEKLSvb+8IOPl4E/Wnve6rDwLqyISOBSXjiHlGPw4EqU3YnpgLV4976N302AA6gUWXw4JkGfN47OdnzF0/lD+ivqL25vdzorRK3ix8ztYsxqCZuSVES0uaYCXL8DThdr+7vyvb0N6Ny65LMC1KH+5Zo75nOWrwS3sX6PXl62juD32r1IfT1wlKronL0TTtPwdp3FAfg7eUOBkoetiHG0Xao8poV0IIYQQ4pL4ctXRYm0Wq43/m2P/Qb22KYP2ll3sWx5HH91pwlQi/PAEWM1w9yL7fi3gugaBrH3qesL83Ir1l5idyAvrX2B97HoG1xvM4x0fp4ZHDQACCl3uYrz0AV5hTwyQpCCFGR01Dq02reiJsI7gVQuO/ANtx5Z4b36BeQC2zwS9C9RqX+K1QlxuF514RdM0TSmllX7lxVNKTQGmANSpU6eUq4UQQgghivp7zynncdMaXhyKS6feM4t4ZnBT5u+MpYU6xiz3L/DOOQXboHN+1QKLP9wxr1iR69r+xWfxIpIieOi/h4jPjOeJDk9wV8u7il0zuWc483bE0jrMtxLfTpRX/mpLm3bOj7JKQVBjSIoq0hzgYeJsoX15AFhy4dAiaDce3P0v4WiFKLuKBnnxSqmamqaddiy5PONojwVqF7ouzNEWC/Q5p32Voz2shOtLpGnat8C3AB07drwsgaUQQgghqocjZ9J56NedANzYthZBXi4ciksH4O0lh2ivDvON6UM8dS5MyXuMaK0GHuQw/5nR9lkd3YVn3TRNY/GxxUzdMBVvkzc/D/mZ1kGtS7z2uSHNeHZwM9kbd4UpZ5BXwkm/cDiwoEhTsSycAEf/A0s2NOhb6eMToqIqukZgIZCfIfNO4M9C7RMcWTa7AqmOZZ3LgAFKKT9HJs4BwDLHuTSlVFdHVs0JhfoSQgghhKg0RxMyncev39QKb1cjChvX6fbxufFT5rm8jJ+PL9y1iOW2ThzWatO331B7tsRSArxjqceYvGIyz6x9hoa+Dfl16K/nDfAAlFIS4F0F8pO6a+fO5IF91jY72bkvz2bTyMqzOE8HebnYD/57A3xqQ/0+l3q4QpRZqTN5SqlZ2GfhApVSMdizZL4N/K6Uugc4DtzquHwxMAQ4AmQBdwNompaklHoN2Oq47lVN0/KTuTyAPYOnG/asmpJZUwghhBCVbtfJFACeHdwUT5Oezmd+Y4fLV/ipDNI1N6zd/oeh1xPg5of9Rxl4qG/DC/apaRqzI2bz7pZ3MeqNPN7hce5ofgcGnZQirgp0jiCv2HJNIKv5LbiseAX9nt+gXncy8yxFZvymTehoX84ZvxcGvgkunpdr2EKUqtT/A2maVvJuU7ihhGs14MHz9DMDmFFC+zagZWnjEEIIIYQoqzyLjU1RZ+lVKJNkTHI2dQPcubd3A9jxE10i3mONrRV7A4cw/q4H0Pv4FuvnQuV74zPjmbphKutPradLjS682fNNgt2DL8XriEskfzK1pIm8pxceZWJOEK3OHsMApOVYipz3djXAwb/tH5oOu7QDFaKc5J+ZhBBCCFHtDPpkDVEJmYQHetC0hhfPD23GX7tPMaRVDTi+Af5+lOzgtryU8RLTb++Cj0/RWZi//9fjgv2vOrmKZ9c+S541j2c6P8PtTW+/YEAork4FM3nFzx06ncZJLYgWyccBSMs2Fznv7Wa0J1yp0cpeV0+Iq4gEeUIIIYSoVqITM4ly7L87lpjJscRMluyLA+Cuuknw8wTwroXbHb+zyiukxD5ahp6/wPmMfTP4ePvHNPBtwEd9PqKeT71KfwdxeeTH5Zm5lmLnavi4EpMUhCF9GzaLpViQ52VOhJOboc+zl2OoQpTL5S3OIoQQQghxiW05Zt/27+tuLNLeWh2l05q7wNUHJvwJ5wnwzkfTNN7Y9AYfbf+IvnX68uvQXyXAq+LyZ/KmLtxf7FxNH1eOayHoNDNDp35fbLmmy/r3QaeHljdflrEKUR4ykyeEEEKIaiHPYuOhX3ewNTqJGt6ubHimL6nZZpbtj2PVgul8YvoCZfSDuxaBf/1y9a1pGm9sfoPfIn5jbNOxPN3pafQ6/SV6E3G56C6wxNbTxchf1rZYDYoBbCQtezAATUK8iIhPg4gl9r14gY0u13CFKDMJ8oQQQghRLXyx8gjLD8QD0LuxLzqdws9kY8jhFxhj+pPjLo2pO2lOhfZPfbj9Q36L+I1RjUbxTOdn0ClZDFUdXKiKhcVmIwE/TmjBNNLFkpBjX675y+QueKcegmmnpWyCuGpJkCeEEEKIKs9stfHJv5HOz0fOZEDaafh5FN5n9hPbYgoBQ14GD49y9atpGi9vfJl5kfMY0WAEL3R9QQK8auR8yXIOnk7jWKJ9X+dxrQZ1VRxHs+3LNX3djBj++QZMXtBs+GUbqxDlIUGeEEIIIaq0D5dH8Ol/9rp2vRoHseZwAgMbusK0vpCTAqNnENpyVLn71TSNF9e/yJ9H/+TmRjfzQpcXpP5dNVPSTJ7FamPwJ2udn49pNeigO0xmrhkXgw6DXgfH1kKD68Ej8DKOVoiyk/9TCSGEEKLK0jTNGeAB9GoUyMcj6uE79xZIPw33LIfancvd75msM7y26TVWnVzFHc3v4MmOT8oMXjVUeE+exWrDoNex71RakWuitJp4qWy80o7gbnKFk1sh9QR0K7E0tBBXBfm/lRBCCCGqnIk/bKXeM4vYGHUWgHt71eexfo2Z0Mod/9lD0cXtgdHTKxTg7U/cz7jF41h1chX3t7lfArxqrHCQl2e1AZBxThbNv61dMWt6mp1djrvJAFscSzUlq6a4islMnhBCCCGqhA1HE3lz8UHevKkV/x06A8Dt0zYDUC/Qg7GtvO1LNFOOw/i50KBvuZ+xP3E/D/z7AEadkV+G/ELroNaV+g7i6lI4ds+z2HA3wfbjyUWuScabE1owAdnRuBp1ELcP6vUAz+DLPFohyk7+WUoIIYQQVcK8HbHsi03jlq83FjvnbTkLPwyFpCgYO7vCAd6D/z6IXun54oYvJMC7BhTekpdrsc/kffTPYWfbKyNaABCl1cI9/Rg+RhskHoYaLS/nMIUoNwnyhBBCiHNExqdT75lFzqLa4urw70F7eYRciw0vVwPbX+gHQD11mgGbJ0JiJIz5BRr1L1e/mqaxNHopk5ZPQq/0fNnvS5r4N6n08YurT+HsmnmOIK+wCd3qsv6ZvhzValFPxdE8fiFoVggr/zJgIS4nCfKEEEKIc6yJTARg8d7TV3gkIp/NppGcZXZ+frRfYwI8Xfj7Nj9W+L2LMSMGxvwKTYeWq9/9ifuZtHwS/7f6/6jtVZsfBv9AU/+mlT18cZXydDEwrHVNwL4nL8dsLXJeKUWorxuLrF3QofG68XvwqgkN+12J4QpRZrInTwghxDXv/WURZORaeNmxNEvTNADOU0JLnCMlK4/4tFya1PCq9L41TePg6XQemb0TgP7NQ+jdOIjxXetCxBJaLrsXNA3uWgy1O5W539TcVN7Y/AZLji3By+jFkx2f5LYmt+FqcK30dxBXtwEtavD3ntNoGhyOTy/xmr1afdbZWnK9fjc0HgQ6mScRVzcJ8oQQQlzzPl9pT8GfH+TFp+UA4GbUX7ExVSWDPl5LXFoO+18ZiIdL5f5o8fu2kzw9d6/z8xMDGtO0hjec3g1z7gHPIBg3FwIblqk/TdNYdXIVr216jeTcZCa3mswdze/Az9WvUsctqo78Wnn9PlzNiDa1nO0/3F30Hw3etIzj+v4joMt9l3N4QlSIBHlCCCGuabmWguVZmqZx70/bWX7Avvfrv0NneGqQLN0rTZwjKJ65MZrMXAuP3NAYk+HiZzpSs8xFAjyAhkGekJMGCx4EoxtMWAh+dUvtS9M0tsdv55s937Dp9CYa+DTgwz4f0ja47UWPU1RtqlD6lYW7TzmPvd2MRa6L1MKgV/mWAwtxpUiQJ4QQ4pr286YTzuNFe087AzyAQ3HpfLnqCA/0sc8S5Vls2DQNV5nhKyLQ00RiRh7vLo0AoGGwJ70bB+PvYapwnxm5FsZO2+T8HOrrxi+TumDQ62DRC3DmANzyQ6kBXnJOMj8f/JkVx1dwLPUYvi6+PN7hccY3H49RZ7zgveLaoDvPsmyfQkHe9Ds7YrZql2lEQlw8CfKEEEJc01ZFnHEeP/TrzmLn310awf29G5BntdH/wzUEeJqYd/91RbLyXcsOnEojMSOvSNtjv+0GIPrtis96tJy6zHncr1kw393pWDp38G/YMRM63wvNR5z3/vjMeGbsm8Gcw3Mw28x0rNGRu1vczaDwQbgZ3Co8LlH9lPRnedqEjjQI8nR+vqFZyOUckhAXTYI8IYQQ17TTqTn0bRrsLK7tZtSTXSjDXj/ddlI+fRs3cwp/ZKaRmOHDqfVPE9pj3JUa8hW3KeosKw7E8+SAJkTEpxU5Z8RCL91u+ul2ED1tFhkZ6dQO9MKnbltoP6FMBaRjkrOKfH5xWHP7gSUXVr4BAQ3hhpdKvDcyOZLZh2Yz/8h8NE1jeIPh3NH8Dhr5NarQu4rqr6SZvO4NAy7/QISoRBLkCSGEuGZpmkZ0YiY3NAsmIT2XvbGpjGhTi0YhnoT6uBLx+/M8aphHQk44sT4t2ZKSTRtdFKH/PABx/8Hgd8Gj9B8Gs/OsGPXKvtSwCtM0jRnro3nt7wMATF93zHlucMsaeMVt4qn0twlUaaRrbqSc9MQVIxmpVnyO/gWr3oKbvoFWoy/4nN0nUwH4+Z4unErNpm6Ah/3Exi/syzRHzwCXglkWs83MhtgNzI6YzfrY9Rh0BkY2HMk9Le8hzCuskr8LorrRlTCTZ6rif1aFkCBPCCFEtbfjRDJhfm4EexVNj59ntWGxaXi7GmlW04u9sakEepmY1CMclj7DYMM8llk7cqT9p8SkW/n79GkMmPkweDnXH/gVTu2E+9aCyeO8z/5pYzQv/rmfTvX8+OO+6y71q15SP2woCPDO9WWrSIiaSrJrMPekT2adrRW5FOzJe7u7jjEnXoH590FISwguOaGNzabx4K87AKgX6E6PRoH2E2cOwrqPoG53aDkKgBxLDqtjVvPJjk84mX6SQLdAprSewvhm4/F19a28FxfV2rkxnk5R5f9BRgj5L1gIIUS1lmuxcvOXG+j8xr+cTs0uci4t2wKAu0lPHX93wJ5chYMLYfPXaG1v50XTU7z3bzQbj56lbR1fwkP8+M50O9z2MyRFwawxYM457/M/XHEYgK3RydR7ZhG931uJ2Wq7RG976Tw1Zzev/FVygDfcdQ9q/r2oWu34PPwr/rV1oF39GkWu+eqQK8cGzbT/RL3yDbCV/D04mpDhPC6S4GbD52A1w/BPSclJ4ft93zN03lCeXP0kRp2R93u/z7JRy3io3UMS4IlyOXcmzyb5VUQ1IEGeEEKIaq3T6/84j+dsiyly7p+D9kyarcN8nQFFrZQd8Of/oEZr1LBPyLLHgUSfzaJ7w0DcTHpyzDZoMgiGvAfH1sD8KfaC3IVk5Vm44YNVJGeZi7QfP5tFo+eXYKlCgd6OE8n8Xuh7t+KxXtzepQ67XurPwtHefGL4BLzD4K5FPDS8G++Obs2Mu+yJUh7tZ98Ld/xsFtdPOwJd73cG0SXJL8fQvKY3AfnZOU9sgt2/crbFCN48+jv95vTjw+0fEuoVyifXf8KcEXMYWG8gJn3Fs3mKa5fkUBLVkSzXFEIIUW2dScshLcfi/ByfXnTGbd2RREJ93Whf04T7um9YYfqLRpGx4BkCt/0EBhNfjGvPnTO2ANAl3J/3l0VgsWkkZebh33kytrQ4dOveJ+PHMdiGfYp3gD0L370/bedoQmbRAemy0btHY3A/xp2LFnF9eHv61u5Lfd/6l/YbcZFOnC1IhDKuSx0ahXjx5k2tIG4fvv9OsO+PGz8XDCb8DXBrx9pAQXbNHSdSWHM4wd5Bv1cgbi8sexZqtoF63QH7fr/p646R7vj9+mp8e3vWw+wUMpY8xfdBIfyUuRNzxFaGNRjG+GbjaeLf5DJ+F0R1VdKePCGqOgnyhBBCVEtn0nLo/Oa/zs/1AtydyzPNVhsf/3OYI/EZNPdMR30/hGandxEX1AVrhwfRt78DXL0BqOtYxgnQro4fFsdargOn0ujRKJDPuRWzOZonjs0h+sv+eD+8hPlRGmuPnEbnkojOlEhAQCzpKgKdSxxKaRg0iE+ET5LX8smOT6jlUYuutbrStWZXPIweGHQGjDojbgY3annWwt/V/zJ+54rLz3a566X++LqbwGaFLdPgn5fB5A7j/jjvHjuAL8e1LyiJoBTc+hN83gl+vRUm/QvBTUnIyOX1RQed9wR7uZKancxPc0fzi+ksGToj/cN6cl+b+2js1/hSvq64xkiQJ6ojCfKEEEJUOxuOJHL7d5udn7e90I/RX21g+/FkAP45EM+3KyO4Q7+CR01/Ahlw49fUaDu2WF8h3vZkLS1D7UHfbR1r89u2k2yNTqJ+kAe7Y9L413oTu43eDHKby6+/DeYPWzCeTRJRyl6KIQ8jgZne9M7yY1BqJA1zwegWhCXvJP90up1triaWHFvCvMh5Jb5PqGcotzS+hdGNR+Pj4lOZ36pSzdkew/vL7fsKfdyM9r10Cx+GXT9D7a4wahr41rlgH4ZCOepTs8z4uHvCnX/BjAHwwxB4aBtxqQU7SJQhhQ92vMWCw3PI1az0dg/j3r7v0yqo1aV5SXFNOzfGqxvgXvKFQlQhStOq5u7Sjh07atu2bbvSwxBCCHEV6vnuf5xMsidZWf5YLxqHeFHvmUUA6HXQk128aPiJBrrTbLE1ofP90+xLB88jLceMXik8XAxomka3t/4jLi0LnWsMBq/9GH12ozOmAOBu02iQa+NkVjsSspvyRv1khkXNxsWciaZ3JbXprQza3olbe7fl8bhn4cRGuHsJ5tD2RKVGkWvN5VBcCs/O30m/Fn5c11RjbcxaNsdtxlXvSr+6/ZjYcuIlrft2KC6Nuv4euJn0DPxoDRHx6TzWrzGP9GsEEUvsyWa63AeD3ylTfxarjYbPLwHgj/u60ameP5qmQewO1Hc3QOOBfBn6Ah9tnIPBZwd6t+MYdYphaWmM92pKkwmLZeOUuGQ2HE3k9mkF/yj0z+O9aBjsdQVHJETZKaW2a5rW8dx2mckTQghRrWiaRkJ6Lm1q+zJrchfcTfa/6m7rEMaOHZt4Sj+b/vodHLOFMDHvSVr0voXONc+/1BDA29Xo7Ptw8mGS3f7AI2QXOkMmmqbDmtmIvMS+tPBvz2fdfNHNuZtQ9TdmtQJjRC7U6QYD30AFt8DX6EruweUk5Sq45Qf46jr45RaMo6fTpFF/TqdmM+KX/4CGxMX5cueo7tzZ4k72n93PHxF/sDR6KYuiFnFzo5uZ0HxCpe/nW3M4gQkzttAy1JumNbyJiE9nbOfa9gAvOxn+fNA+czfg9TL3qS80k3f0TAZp2WamLtyPSa/j7esmMydyLityx+FaU6Ome2361hjH+G3fE2YMgtt+kwBPXFJJmXnO4zn3dZMAT1QLEuQJIYSoVuLTcskx2xjdIcwZ4JGVxLOpU/F1WUmOZuRd821Msw7FjIEZAy8c4AHkWfNYfnw53+35jqOpRzH66bGkNyc3vTmWjCasenwIr/19gLi0HP5Lr83nua9yi341U9p74NtyIDTsD/qCv3IDPF04m5EHnkFw9xKYdRv8eivHen/M9UsDndfpC8U2LQJa0OK6Fjzc/mG+2f0Nfxz+g7mRc2kX3I772tzHdbUuvgZfdp6VCY4kM/ti09gXmwZAqK+bPXvo0mch66x9D57eWOZ+VaEgLTYlm2fm7UXvFo1LyCLuOX0STw9vRqWnUCOrIRN7j0S3fy6knYFJvzr3RgpxqeSYCzLd6nTyDwqierioEgpKqceUUvuVUvuUUrOUUq5KqXCl1Gal1BGl1G9KKZPjWhfH5yOO8/UK9fOsoz1CKTXwIt9JCCFENXc4Pp3e761kzvYY3lx8kMJbD+IdKfhrOvbSkXYafr0Vn1PreNs8hp65H5PU/iHMZfx3zuXRyxkybwjPrn2WHGsOL3Z9kc+6zyMndhyWtHZgc6deoAfuLgb2n0pj6sL96L1r8tQb0/Ad9TE0GVwkwAMI8DDZgzyAwIZw91IIakroyscYqNsKQLs6vhw4nYb1nKJd/q7+PNvlWXtNuLYPkZCVwL0r7uXBfx8kIimiAt/NAlujk0psD/Z2hcPLYPcs6DwFQjtUoHcbOteTLIn9Hvfwj3Cv9zXKkEpO3HA49SYB8T24J3cXuqVPQdw+GP4JhLa/qPcRoixshf6M6WXWWFQTFZ7JU0qFAg8DzTVNy1ZK/Q6MAYYAH2maNlsp9TVwD/CV42uypmkNlVJjgHeA25RSzR33tQBqAf8opRprmma9qDcTQghRLWmaxoCP1gDw5B+7ARjWuiatw3wBOJuZC4CPuxFitsOvt4A5B3Xzt3z9swsAb49qzeytJzHqz/8Dndlm5qX1L/F31N808GnAy/1eplvNbuh19np6jUNOcjg+gz/u6wZALV9X572Fj0sS6OnCwbi0ggbPIBjzC9EfD+Mb00cstXYitcnrPH3CxgO/bOebO4pttyDIPYh729zLXS3v4sf9P/L9/u8Z/ddoeoT2YGq3qdTwqFHsntLsPpkCgJtRT7a54K/h2q65MG8K+Dco1zJNgPjMeJZGL8U9/Cf0rvHEaQrNVpecuOGYUzqC5sJpNGK6PI0aNg1yUsHoLjN44rLJK1SzUi8zeaKauNhi6AbATSllANyB00BfYI7j/EzgRsfxSMdnHOdvUPb1GyOB2Zqm5Wqadgw4AnS+yHEJIYSopmKSs4u15U/kaZrGxB/sSbnC07bBD0PB6AH3LIeWNxe555/He7H+mb4lPiMtL41xi8bxd9Tf3NPyHuaMmEOP0B7OAA9gYAt7ENW0hn3/zv29GzjP1fR1u+A7BHiaiErI5M9dsQBYbRo5XnV5tdaXfGEZwQDDDm5dP4wPjF+yZX/kBfty0bswufVkFt20iIfaPsTWuK2MWDCCH/f/iMVmueC954qIT6dugDurn+rD2M72WneBpNJ23X2Qmwq3zgSDS6n9ZFuymR85nwf+eYD+c/rz/rb3AT05p28iI/IFso/fh2tWb2ZN6u28x6jXgdENvGpIgCcuqwAPk/NYgjxRXVR4Jk/TtFil1PvACSAbWA5sB1I0Tcv/WyUGCHUchwInHfdalFKpQICjfVOhrgvfI4QQQhRxKC4dgHt71+eb1VEAzN56gja1fdkYdRaFjUcM8wic/yf4h8P4eeBXF4AnBzR21rk7X3KFLHMWk5ZN4lDSIV697lVuanRTidc91q8xU3rVx8uRlMXXveAHxRreF57JczPZg8VHZu9iZNtQBn+yhsPxGQAYmvyPB298DTZ9xYjN3zFAtx1txkyURyDZ8UcxZcejbzYMuj8CAQWBpZ+rH/e2uZfB4YN5bdNrvLftPX499CtPdHyCPmF9MJawhy4rz0J2npUAT3vglp5jwdfNSLCXK2/d3JrGpxYwMvE7XBPzYMRnUOPCJQwy8jL4/fDvzDo0i7jMOGp61OSeVvcwssFILLkB7D6ZyhOO2de9rwwkp9BsobtJf75uhbikBrUsmPWWIE9UFxezXNMP+yxcOJAC/AEMqpxhnfeZU4ApAHXqXLgmjxBCiOpn54lkJv+4DV93I8Na1XIGeXO3RNHFO4WYI7uZafyNXvq90Oo2e4p/Nz/n/Q/1vXDZgbjMOB7+72EOJh3kvV7vMSj8/H+t6XTKGeDlG9SiBkv3xzGsdc0LPsekL1hIczIpyxngAdzYNhT8QmHwOywz9CNt9ZeMNqdhiD/AxgQvrMZm9N89G23nT+z27svh2qO5ZdQYZ3KTOt51mDZgGqtOruKDbR/w+KrH8XHx4cYGN9K7dm+a+TfD0+TJ20sO8fXqowBEvz0UgIxcC56ujh8N9vzB3Wc/QqvZCjXyi/MGeJqmsSthF38e+ZPl0ctJN6fTPrg9U7tNpXut7kWSrtT0ceO5+Xt5e5S9L1djQWA3pNWFv2dCXCqF/xuVwuiiuriY7Jr9gGOapiUAKKXmAd0BX6WUwTGbFwbEOq6PBWoDMY7lnT7A2ULt+QrfU4Smad8C34K9Tt5FjF0IIUQVk5Cey01fbgCge4NAWoZ689qI5iQtfpX79H/hss4MQLLOE63XU6jrnytX6v2jKUeZsmIKqbmpvN3z7QsGeOfzxk0tuadnOO3q+F3wusKjWncksci53o2DnMfutVvzkGUS+vatmLcjls3mJFzRceipdqyY/hLdkxfQNvUf4oNzCOl1d5F++tTuQ8/QnqyOWc1fR//ip4M/MfPATBSKut51iTzpjSkoEGtWXb5aVZ93ltoD5gHNQ2DjF7D8BajRGjXhT3DzdfabY8nhYNJBIpIiOJx8mC1xWziedhw3gxv96/ZnbNOxtAxsWeJ7e7gYiHh9cJG2u7vX4/v10fZC60JcIQadwmLTZCZPVBsXE+SdALoqpdyxL9e8AdgGrARGA7OBO4E/HdcvdHze6Dj/n6ZpmlJqIfCrUupD7IlXGgFbLmJcQgghqrhcixWTXsf+U2kcOJXGrZ1qE5tSsBfv7u71UBlnuCPxIzDMY6XqzKK8dsRoQUQYmrCz74hyPS8qJYqJyyZi02zMHDyTFgEtKjTuAE8X59LHC2lT29d5/Oy8vUXOebgU/NVc08e+t+/puQXX5JhtrIszMCVuJF705yfTm7Re+SQ06QwhRcet1+npW6cvfev0JSknif2J+zlw9gDrTu5A7xaBwXsfKtDGF1G/4lqrKZb0VjTP2Yx1+WfoGw2AUdPBxZMscxZrY9fy34n/WHlyJdkW+++Ft8mbFgEtmNRqEv3r9sfD6FHu79mLQ5tz13X1qO3vXu57hagsHi4GUrPNkl1TVBsXsydvs1JqDrADsAA7sc+yLQJmK6Ved7RNd9wyHfhJKXUESMKeURNN0/Y7MnMecPTzoGTWFEKIa5fVptHkhaXc3qUOs7acQNNgd0wKncP9AXux4o4BefBNb8hMgC73EdbuOeZ8vA6AAKPpQt0XszdhL//773/kWfOYOXgmjf0aV/o7neuGZiG8fXMrnjknwAMwGQqWctbyKTmBy/jpmwEIq1mDiaefYpPXU5h+uQXuX19keWph/q7+9AzrSc+wnmTER7Dh2FHcXaxk6SIxeO/F6LUfo89u+1/WdcMI98ij9oYXScxO5MDZA+TZ8vAyeTGs/jB6hvakWUAzQtxDiix1qwidTlE3oPzBoRCVydMR5GnIQjFRPVxUMXRN06YCU89pjqKE7JiapuUAt5ynnzeANy5mLEIIIaqHNxcfBGDWlhO4GHTkmG38svkEv2w+QYi3C018NfjtDshNgykroWYbGgH/PN6bfh+uJsir9Jm0fBFJETzw7wMoFN8N+O6yBHj5BrSoUWKQV5i3mwEvVwPpORY8THo2PXcDrV5e7jx/Z7e6PDMvjRWtPmDojsnwyy0w7GOoUXy55MmkLEZ9tYEz6fYSE01reLH00V7Ue2YR1swmjDa3oH/el8S46InvOo6j2Wc4mnoUPxc/xjQdQ5/afWgf3L5IhlEhqosATxOxKdlFSocIUZVdVJAnhBBCVIafNx1n5oZolj3ai/k77duywwM9OJaY6bzGgIXXa+/D69spkHUWRs+Amm2c58MDPbjrunrcdV29Mj1z15ldPLLyERSK6QOn08jvwklZKpt/obTt303oyIt/7qNTPf8i1yil2Pp8P1756wB3dK1bJNGLr7uR0R3CeOnP/ewxtmLo4Pfgv9fgm54w8E3oen+RvqatjXIGeGD/oRagV+MgIiIP85r1S9J1bjQdOoMaTbtcilcW4qr11fgO/LgxmsbnyborRFUjQZ4QQogr7oUF+wBo+tJSmtXwIikzj6gEe4DXMtT7/9u77/Coiq+B49/Zkh7SgQCBEHpvoXdpUqwIYkFEBbtir6/YKGIBG1gQFH6CoqAiIEWK0qT3HiCUQEJCQkghbXfeP+4mJCRASCecz/PkYfe2nc3l5u7ZmTkH86mtfGz9itqHT0GVFkaAF9ItxzHMJsXbt+ZvLt2qE6t4bfVruFvdmdJzSokHeJdqX8uP9a/1yHOdi9XMuDtzZ7Yc0TkEi9lEgKcz0Qmp0HckNLrd6OVc/Krxe6reLmv7zN9nJm9XI8ibMbw1+scJqPAkvIb/glfVVkX3xoS4TlT1duW1vg1KuxlCFJnCFkMXQgghCuVC2sXhUWkZdnacjM96Xt/fid/qLmOu09u4qRRi+k2FEStzBXj5pbXm6x1f8/SKpwnyDGJm35mlGuAF+xnJRrLPw7ua3g0rARd/b6kZNuZtjWDBzlPM2JVMi6OPod0D4NeHIS0ZgONnk3Nl8czKIrj6Y1TYMuj6MkiAJ4QQ5YL05AkhhChVB6MSci1zdzKTlGbjxdQvsa5fyfk6t3KixVu0bVS4gOzL7V/y9c6v6RPchzGdxuBszv/8veIw59H2bDtxDqs5/0HeMz3qsHRvFN3rG6UWHLXdeWrWNscWTpzqOZmqfwyCn+/nZec3mLM1EoDHutaiircLb/2xh3YhfnB4JawaD/X6QYdni/KtCSGEKEXSkyeEEKJUnY430vEPaX2xZOrYO5swyLyKnukrocMzVLh/ZqEDvP/t/R9f7/yam4NvZlzncaUe4AFUrOBCn0aVr2mfxlW9CB/fn1Y1jPl7cx/vkGubjj+nQ6/34PBy2u56CxwZAx/pXJO7Wwcx65E23ON7AOY+DN7V4dbPwSzf+wohRHkhf9GFEEKUmidnbWXhztPG4+61GT+wKQDpqcl0cFvIBe9muPZ8u9Cvs+rEKj7c/CEdqnRgTKcxWE3lp/B2Tf+8yw/Y2z3F6YgTDNz7DfXVCR5LH4Wfqxm1czYd1n4KMQehQjUY/AO4+5dwq4UQQhQn6ckTQghRKrTWWQEeXMz2CGBdPYGA9Ahcu78AhUzZfzDuIG+ufZMQrxA+7PohTuZrq6N3PfhmaO65dInpNvY0fI4304dT1xLFaufnUGOrwB9PgtUV+k6Ax1ZD5dxJXYQQQlzfpCdPCCFEqZi3NSLrcbCfG25OjlvS+dOw5Xuo1x8a3lao1ziRcIKnlz+NWZn5qOtHVHCqUKjjlVWZheLvDg2iUgVnPlsRxvkL6aTY4H+2Xjxy36ME7/4SnD2hRgeoPwAKWcRcCCFE2SVBnhBCiBJ3LjmNF37ZkfU8Jd1+ceWqcZCWZGR7LIQzyWd44u8niE+L58seX1LLu1ahjleWebs5ET6+PwCLdxu9oxFxF9gSHguA2acG3PZFqbVPCCFEyZIgTwghRIk7GWckWwnydaWSpwtPdq/tWLEZts+CpoOhSvMCH/9I/BFGrRzF6aTTTOo+iVaVbpzSAAGeRkKZu7/5L2uZq1PhhrwKIYS4vkiQJ4QQosQlO2q8jbujKZ3qOJJ+2NJhyRvg6g093irQce3azsIjCxm9bjRuVjem9JxC68qti6jV1wd/j9xZQ12sEuQJIcSNRII8IYQQJW7b8TgA3JyzBR8bvoIT/0H/T8Dz2soKAEQkRvDOundYf3o9tbxqMbXPVPxdb7yska55BHQu11BsXQghxPVPgjwhhBAlLup8KgANAx2JUOKOweqPoUYnaP3wNR0rIS2BGXtn8MOeHwB4KfQlBtUbhKvFtUjbfL1wyWNopuUaiq0LIYS4/kmQJ4QQosQlp2VQ0dP54jDCzdMgJR76fnBNx4lOjmboX0OJSIygaUBTJnSZQFWPqsXQ4uuH2yU9eZ7OcqsXQogbjfzlF0IIUeJSM+wXA7zzp2HrDAjpDpUb5/sYpxJPMfSvoZxLOce0PtNuuLl3l2Mxm1jxQleq+bhxId2Gl2v5KfwuhBAifyTIE0IIUeJS0m04Z84T2/QtpJyDXu/me/9Tiae4f9H9nE87z3d9vqN5xebF0s7rVUiABwBOMhdPCCFuSPLXXwghRIk4n5JOmzF/8/u2CC6k24y0/pG7YO1nUPfmfPfi7YnZkxXgTeszTQI8IYQQ4hLSkyeEEKJYLd4dyWP/25L1fMyifVT0dKaSpzOsGAMWFxgwKV/HmnNgDuM2jsPZ7Mz0PtNpEtCkmFothBBCXL8kyBNCCFFs9pyKzxHgAUQnpBKdkMpd9SPg4F/Q/U3wrHTF45xLOceYDWNYHL6YDlU6MKbTmBuyPIIQQgiRHxLkCSGEKDbT14bneG41K9JtGtB0iPkFXH2g47NXPMbhc4cZtXIUxxOOM7ThUJ5t+SzO5twFv4UQQghhkCBPCCFEsdBa8/e+KFpW9+aOFlXRwK3NqvDWH3uI27WYeokbocdbYHG67DGWhi/lrXVvoVB82+tb2gS2Kbk3IIQQQlynJMgTQghRLM4kpHIuOZ1bm1VhaPvgrOWf3dWA5NhR6AQvVLsn89xXa82cA3MYv3E89Xzr8WHXDwnyDCqhlgshhBDXNwnyhBBCFKmo8yl0/XAlKel2AOoHVri40pYBfzyBW/R2uO1LsLrk2j/DnsH4jeP5+cDPdKzSkQ+6fICXs1cJtV4IIYS4/kmQJ4QQokj9ueNUVoAH0LBKtiBvxXuwey70fAda3J9rX601YzeM5ZeDv3B/g/t5IfQFLCa5VQkhhBDXQu6cQgghrmjlgTM4mU10rJ2/bJaHoxOzHh8a0xer2VGSdfc8WDsJmgyGTqNy7ae15tOtn/LLwV+4p/49vNLmlSJovRBCCHHjkWLoQlzHtNal3QRxAxg+fRP3Td0AQFqGHa01aRl24pPT89x+f2QCtSt6sOXNnhcDvP++gl+Hg389uPXzPPf7fs/3fLf7O/oG9+WV1hLgCSGEEAUlPXllhN2uOZ+Sjrfb5bPMCZGd3a4JeX0RrYN9+OWxDqXdHFFORCek4mw1UcHFCuT8IiE8JoluH63ilmZVSM+ws3hPJMF+bqx6qXvWNna75kh0Er0bVsLPwxky0uC3kbDnN6jRCe6Zlec8vJ/2/8QnWz6hY9WOjOk0BrPJXPxvVgghhCinpCevjJi8KozQ9//m+Nnk0m6KKMO01iSlZgAXh8RtCo8rzSaJcsRu1/T85B+avbOU+AtGL93RmKSs9d0+WgUYc+4W74kEIPxscta2AIfOJBJ/IZ1ALxc4fwqm32wEeJ2eg6G/gUvOBCp2bWfKjimM2TCGtoFtmdRtElaztZjfqRBCCFG+SU9eGfHR0oMAdPlwJeHj+5dya0RZpLXmwyUHmLzqMKtf7s6WYxeDu7QMO06W3N/ZaK1RSpVkM0UJOBGbTFh0It3rVSyyYx6ITKDPpH+zni/YeYoq3q58tvxQju2qqyjam/biTSI+KpFQ0wEs340H3yrogAa8u9KF7qYMOu/5ATatBls6DPwOmtyV6zXPJJ9h9LrRrIlYQ8/qPRnXeRwulty9fEIIIYS4NhLklRKbXfP+wr0M71CT8yl5z2sRIruHvt/EygPRAGw9HseBqISsdTtPniM02DfH9luPx3Hvt/8x8+G2tL5knbi+dZ6wErgkqUkhpGbYcgR4AG/8tjvH85bqIO9Yv6eJKTxrmQ0z++3VOHLejSbmk3Dob350Mnqa7Rc8odGd0Pl58KuV41gxF2JYcHgBX27/EoCXW7/M/Q3uly8khBBCiCJSqCBPKeUNTAUaAxp4CDgA/AwEA+HAYK11nDLu3p8C/YBk4EGt9VbHcYYBbzoO+77W+ofCtOt6sDsinulrw9kdEZ8rY53drjGZ5MOOyCkzwAN49qftOdbd9dU6wkd3ArMVrG6gFL9sPklKup3FuyMlyCtjjp1NIiElg2B/dzycr+3P8Kbw2KzHdd74i18fa58rwM+Pv/dGkZxu49ZmVbjti7WX3U5h58Oqa7kt5hsitS+613uE+3dlb6I7PZsG0///lkAazL2vA8Om/E0r0yHevL0VdZp3Bie3rONordkStYWZe2fyz8l/sGkbbQPb8mbbNwn2Cr7m9gshhBDi8grbk/cpsFhrfZdSyglwA14HlmutxyulXgVeBV4B+gJ1HD9tgSlAW6WULzAaCMUIFLcopeZrrcv1RKMMu1FDavOxOLYdP0frYB96NazE2EX7uZBuw/0aP/iJ8u1cclqey4e1r8G2/1bwgfUb+OAEANqrGtTrh+uxajRSFtJiFNCwBFsrLmfr8ThWH4xh4t/G8GyrWbHr7T64WPOXZOTM+RQGfbU+x7K7vlqfa4j3idhkOk9YybD2NXjntsZ5HuuRGZsBuLVZFfZHXuwVHtiyGv4eTnz972HuNK3mccuf1DkbQXq9/qR2moCqXo2aQM1LjjdwyjrAjbU0p06bPlnLYy7E8EfYH/wW9hvHzh/D08mTYY2GcUvILdT2qZ2v9y2EEEKIa1PgSEIp5QV0AR4E0FqnAWlKqduAbo7NfgBWYQR5twEztJGq7T+llLdSKtCx7TKtdazjuMuAm4HZBW3b9eBsovGhXWvI0Jqnb6rD8Vgj6UpSaoYEeSLLvtPn6fvpagBeubk+f2yPyPpQ3s//DK+5juWszY15Xg9yOCaZ+5xO47dhOm+pdHDG6E9f8hT0ehckY2GpORSVwJ2T1+VYlm7TPPL9Bv53fwNw8jB6Yq/gq3+O5FrWtFrORCbnktOyhnP+sP7YZYO8TJnZMxtVqcDk+1pSw8+d1+bt5D7zcsZYp3HYHoj9zu+wNhlI7TyGU351f0se+3EjJufTmKzneP7mQD7b+hkRiREcOneIsLgwNJpWlVoxsulIelbviZvVLddxhBBCCFF0ChNJ1ASigelKqWbAFuBZoJLW+rRjm0igkuNxVeBEtv1POpZdbnkuSqmRwEiA6tWrF6LpJSvdZmfj0ViiE1KpH+hJ/coVGDlzS45t/DycSIg7Q1/TBi7E1ocKtS5zNHGj+XzFxcQX/ZpUplGVCvy54xR9GvjTZsvjZJgt3HPhTY5HGZfawmQ3olJjaWU6RA33DJqlb2Pw+i+MAK/H6CsGeompGdhsGi83yW5YFP45GI2Hs4WYxFTmbT2ZY92MB1uwYeZonjz5B3yQCoD2DCRcVcUjpB0B3R4D76Cs7TNsdqatPZr1vGV1b7YeP8ehqEQybHYsZhNkpLF5zwH8iecc7mRgITXDhrMl5zkPz5Yxc97WCADcnSzU8HMH4LlqB6m4cxqJfk2p+PBSTG6uOfZPyUjhaPxR/jn5DyuPr8Kj3j6UMkYnTNkNZmWmklslgr2C6VOjD72CexHiFVLYX6cQQggh8qkwQZ4FaAk8rbXeoJT6FGNoZhattVZKFVm1Zq31N8A3AKGhoddFFeiYxFRC3/87x7JFz3TOtZ1/0iHqL7uF/k7JpPzyCzyxBtz9SqqZogzzdTdqJ254vQeVKrhQw8+dLnX8YcFzcPQf0jq/yfFllbK2Dz+bDLjwH03p1b0hL89vzi0tvHFd+ymcPQx3TQOLc56v9dD0TWwMj6VdiC/Ngrzp3bAyrWr4lMTbLHf2nT7PsGkbcy3/5bH2+JguUHvdS3SxLmCFrTlr7Y3wIIW7vVOJP7aX6tu+RO/8CtVmJHR5Cdx8GZhtmGbYmL5YzCbmbTnGz7/+wvxPF3Cn+0505G56ahubXcCmFUd0FU7/E0Fw90fAdDFBS2YpBIAXftlhHNNRkoPEM1T8+1nSfYJJG/YTZ1NPsi32NEfOHWFv7F52x+zmZMJJNMaf4OYBzanjPICuNVrQt34T/Fz98HH2kTp3QgghRCkqTJB3Ejiptd7geP4rRpAXpZQK1FqfdgzHPONYHwEEZdu/mmNZBBeHd2YuX1WIdpUpMYmpuZZNXhUGQDUfV07GXcCFVAL+ehTMTrx34U7eSJoFswbBQ0uuOnxLlG/Hzybzv/+OA1CpQrbU8pumwpbp0OpB3G96keb717H9xLms1d3qBfD98DYs3RMJKKb4vMzzvZrBsv+D2UNgyCyw5uydSbfZ2ehI6vHfkVj+OxLL1/8ckZIel2Gza9Iy7Lg65R3MPPLD5lzL7mtbndaWozBnKCRGoXu+w6N/1SXdZgRMnzo6bYPVaaZUXUqDDV/B4RWcH/QLOxznd8moLlgykmDZWG7fNZc7naOwxyvOqMbMtPdit4uJeJfz+FdO41hiJOroRKwR32LxDMTPrSLVPKvhXPEMypyIMl8AUwZgo3plV277/XMS48JJCPTmgskOv92co/0V3SrSLKAZA0IGEOIVQvOKzansXrnIfqdCCCGEKBoFDvK01pFKqRNKqXpa6wNAD2Cv42cYMN7x7x+OXeYDTymlfsJIvBLvCASXAGOVUpndBb2B1wrarrImw5a7wzEyPgWAm+pXZMb6YzxiXoQpNoyMgdOZNsuZDiFV6HFkAnzeCu6fC/51SrrZoozYdsLIPzSgaeDFhUdXw1+vQHBn6D8RlGJA08AcQd4rN9cHwOSYQ/XZijAeHv04XhYX+Osl+OMpuPObHEM3l+87Q16SUjNwsmrSbGnYtTEkz6zMWEwWzMqMSZluyNT3L/+6k7lbT3J4bD/MeWTDPRV/gbqVPOhSJwB/T2ca+9rpFDkTvvscPCrB8L9QQW3Y3c7G9LXhjP9rf9a+4TqQfhHDODryaZg9BMsPfRlguoN7ujaj3pEfYO2nkBxDTM1OfKS6sCg+Fu0Sicl3r3EAbcHkXovIOH8a6BNUTz1FWlI80QHprDh3EKtPIjrDHW13A7sVfw9XAjwq4HkmkgpJCXjW7IZHtXZ4Onni6+JLZffKBFcIxtvFu2R+uUIIIYQolMJm93ga+NGRWfMIMBwwAXOUUg8Dx4DBjm0XYZRPCMMooTAcQGsdq5R6D9jk2O7dzCQs5UFqhj3Xss2OItYBzhlMtk6in3kj1OiEpdHt+HusYJnbAHrcUQv+HAW/PwGPLCvhVouyIjnNBsBr/RoYC84dh1+Hg1dVGDwjawjekDbVWRsWk1VmoU5FDwACPC8Oy2z2zlJmPnwHnbvHw8r3IS0J7voOnIx5WAciE1BKgyUOs9tR48f1GJ1+Hk2Gzt0jncminPBx9qdV5aY0C2hG9+rdqeqR57TacmWuY47dlmNxVPR0poafG5NXHaZFdW9aVvdBaxjY1J9HvTbAvj/h3zVgS4Vm90CfseBmlD1wtph5rGutrCDPxWoiJd1OvyaBfBLmQb8+P1L9r2F84fQ5OEZs7qzRmh8Du7L0zBYy7OHYnQKwJ9UmLTUQlRrC9leG4ebkQvCrC/kPmPSgHwErX8K8ex1pbZ/izu2Vebihpp5zHOO3W7mpYyceZBmEzYGOz0LPd+AGDNyFEEKI8qJQQZ7WejtG6YNL9chjWw08eZnjTAOmFaYtZdF/R85mpb7P/OCW3fCosXiYNxLV6BEq3TEWTCYqV3AhMiEVmg2BC+dg8SuwZiJ0eq4U3oEobTEJRnDl7+EEdpvRg5cSD/f9mhUkAHg4W5g+vA0xialEnU8xknAAzYK8cxxv2d4oOt/6IpgtsPxd0mffw/Zuz7E/6SQLTq/Gs84+tPkcANrmikqtSQ1ra25uWBMXswsmZUKjsWs7Nm1j7tZjHImJ5YI1DtR2Focv5oNNH9CrRi/urnc3bQPblsjvqSSlZthoO3Z51vPBX6/Ptc2iZzrTQh3i/s0vwoVT4FcHWtwHLe6Hqq3yPO6zPerw6fJDPNIphC9WhrFwp5G/6muLCXPGh1Ry3cvQ7jaWx+1h57lDeMQmMaT+EPzs3Xn/94s1FH8a2Q43J2No76BW1fhly0nafX+WCjzFjuZ/4LThCxY4A4eN7Wc4AZs+MJ40vdtIziMBnhBCCHFdkzz9xSQl3caQb/7Let6xlj/L918cDtdAHcPj6BLo/iaVur6UtdzVyUxKutF7Q5sRcPAvI8hrMtjovRE3lKMxSXi5Wo3siFt+gAOLoNvrUKV5ntv7ezjj75EzqcrNTfxYemgnJufTHEzZxoTNi4ixxxBWvwXhF46QvuoZAMx2Lzypw1Nte1PXqym1XPyZ/9XbtDq/mUbHfwaLC7j6GkGmqzf0+5D3910cRnokApQ1hqE9Y1gW8SvLji0jtFIor7R5hfq+9Yvl95OWYSf+QnqOHsviZLdrRs7Ywrnk9Mtu40Q6OycPZa7TP9jMgTD0N6h101WPPaRNEO7OZu5qFcTsTceJSw/H4n4IN+8jpFuPEmNKY+JRqOVVi1fbvMrttW/H3eqO1hp72hHGLjJ6Ar2zZUb9YGBTVuw/w9mkNM7jQdyAafzrsZRF67Yy/oEe+FZvBBFb4WwYVG0JQW0K/0sSQgghRKmTIK+YZAVqgMWkeKBDcI4gb5LvXEhxgtYP59jPYlKk2xw9fiYz9BkHU3vC/Kfh3p8lEcsNZNvxOOZti+DOFlXhzD5Y9BJUaQFdX77qvlprtkRtYcbeGWywr8a9ZgYAe1LgyEFXvJx8qOVbD4+T1RkWvYzGHjVpf/wVHripHvfUrwvnT8OvD3J/6noO2KuR1LgP7k4W4s5GYvOsh3/sVvTMO2ipXmWrrnvxddP9uS34Fl7v+CRzD83ls62fMejPQbSt3JYRTUfQpnKbIpu/Z7Nr6r75FwBHx/UrkXmBYdGJ/HPQ6DWb8VAbmgV5oxREJ6TS/+OlvFVhAf1T/8JLJbPY805ufvJTcKmQr2MHernyQIeqLDq6gID635GWZCTcSU2phO1cK+5v3pXH2/XB39U/x35KKR5oH5wV5IX4e2StM5kU7s4WziYZIwoiz6cxao0Zs6kNvvUdWX7r9DR+hBBCCFFuSJBXTLY45t2BkUWza90A1r16Ex3Gr6C2Okm9pM1w05s5htwBWMymrHlYAFRqCN1egWVvwcox0PPtEnoHorSMnLGZTeGxjOhi1BV7oXMAzO4HVhcjK+YVgpnk9GTmH57PvEPz2Be7D29nb+6pfw/NAprx9q+xnD7rxvePdWbglPWk+rpxPDaZymZ/eiR9yzvm72ntfA/MnQD7FkBGCtM8H+f96E6w1RgGmNk7PeWO5+m5+m5+Sn+PHW4d+PV8fRK0KwEqnj3z/qXVkPu4r8F9DAgZwO9hvzN111QeWfoINb1q8kyLZ+gW1A2LqXB/frYev3iNXUi34eZU/H/Odp6Mz3rcuY6/EVhmpFEh/Ce2+HyM+4VTnKrah8fCQ3lv6BPg4nGFoxns2s7WqK38eeRPloYvJTE9kYZ+Dbm77hss3+rHmqNGgNYpsHWuAC+Ti9XM1v/rRfyFdJwsphzrjscmZz3O/J3Z7NdFBRohhBBCFJAEecXk4Wzp0zMLDFfxduX+dtUZfvwriDNDi6G59rOYFBl2OztPniPsTCJ3tqwGHZ6BExth47fQ6kHwCS6hdyFKgtaa5DQb368L56tVh0lINXrdJiw+gJUMqix7HM6dMDKtVqiS5zHS7eksOLyAyTsmE5kUSW3v2rzR9g1urXUrblY3AB6NWQjAwCnGHLLMD/+/2LrSVB1hqOVvWPE3uHhD83ugzaPMnhkJGIWzsw8/fvy34wyt/xG1zk3nPvsmWltXX2xMPOip01DdXsOr03MMazSMIfWHsPDIQn7Y8wPPrXqOqh5Vubf+vdxe53YqOOWvp+tSp85dyHocEXeBZfuiWH0whtkj2xXoePlx2FFLbvydTYwALy0JZt8DR//BPbAZDPqKKiFdmZ2PYx2NP8qfh/9k4ZGFnEo6havFlV41ejEgZABtA9tiUiaG1LfR4K3FANSueOWA0dfdKaumYnb9mwSycJcxv+/N33df2xsWQgghxHVJGflQrj+hoaF68+bcdajKiuBXF2Y9frhTTf5vQEPjSXwETGwInZ6HnqNz7TdyxmaOxyazPzIB4GKNsrhwo6RCuyeg93vF3XxRQk7EJvPWH7uzsmJmciKd5iqM113n0ty+F/pOgLaP5trfZrexJHwJ3+76lrBzYTTya8SzLZ+lXWC7XMMXB321jk3hcbmOAaCw09m0i8+HNMOrYc+sYulbj8fx0ZIDrDt8NmvbF3vX5aOlB7OeL3umLXVck7CnJtLp8+0oWzpLQ37C/dR66PIy3PRG1rYZ9gyWH1/Oj/t+ZNuZbXhYPehYtSNdq3Wlrk9d6vjUwaRy9kTlJeLcBR7+flPWdZJdZqHworLz5Dnmbz/FG/0b8NzP29l8LI41r9xkJML5cRAcXgEDPoHQh67e7sQI/jnxD38e/pPdZ3djUibaBbbjllq3cFPQTVkBeXZ2uyYmMZWK2eskXoN0m52YxFTaj1uRY7nUPxRCCCGuf0qpLVrrXIkwpSevmDSt5pU1tCvH8KnInWB2MtKo58FqNuX5wRWfYAhqC0dWFX1jRaHtjojn8R+38NmQFrSo7nPFbQ9EJlDF2wWTUnSesDLX+ltM63jP5Ue87XHYTG7QfXSuAC85PZkl4UuYtX8W+2P3U9OrJh92+ZBeNXphNuVdnPt/j7Sl3puLcy1/pkcdPlt+iH/tzfBo3A+y1XxrWd2HWSPasTsingGfrwHgqZvqsOVYXFZgWr2iD1j8MQGvDPLm2Z+2M6veZ4zwHQ//TgAXLwgdDk7uWEwW+gT3oXeN3uyK2cWcA3NYcexfloQvAaCqR1X6h/Sna7WuBHsFX7aXr+P4FXkuB5i3NYJWwT74uZrxTj8D9gzwDbnmjJHpNjuzNhxn9Pw9AFT2cuH37aeo4edmJJ/5/Qk4vBxuHp9ngGez29h7di97zu5hV8wuNkdu5lTSKQDq+dTjxdAX6VezHwFuAVdsh8mkChzggfE3paJnwfcXQgghxPVHgrxikv3jZPYkLNTrCy8fBee8h15dWlR5U3gswX7uRvbAen1h6ZtweCXU6l4MrRYFkZSawT3f/EdCagZ3TF7HmDsaE52QyqiedXNtq7Wmz6R/cy3v3zSQ8DMJ/NZwJU7rvkBXbkF0swkENOtjBEkOsSmxfLvzW/44/AcJaQnU9KrJux3e5bbat121B8zZYs4xry7T873qcmeLqpxJSM2zqDdA46pe9G8aSMNAI+i6v12NrCDP2XIxqAwNNuaYerq6wC2fwYU4WPqGkSH2ti+h3s2AkSykaUBTVu5w5dSuNpico3jpFg+2x61i6q6pfLPzGxSKrtW60i+kHx2qdMDL2YtL3du2OrM2HM+x7Ot5fzHaMoPqpr2gHNeed3Xo+qpRxiAP8RfSafbOUjrX8eeH4W0wmRQf/LWfqWuOZm3z/sJ9gKZ63H8w9WWIOQTd34C2j2VtsztmN6tPrmZnzE52xewiPtX4osfH2YfQyqE80OgB2gW2o5Z3rTzbUVwuPa93tpBMvUIIIUR5JkFeMbmQLbBLTMnIufIyAR6AxXzpELuLNbh2vPEgXsvfg4OLcwV5c7ecxNPFQu9GlQvRapHpwekbWXUgGotJMXtkO1oH++a53YYjZ7n7kqDpjd8uznu6NNBb4Kh9lt2Rsf0woWHWYFi3DJrejbrlUwKsrlnbxKfGM2v/LKbvnk66LZ2eNXpyT/17aFGxxTVllfRxuzhna3BoNUJrGO8r2N+dYH/3K+775b0tsx57uead5dXF0Wudkm4z/p8P/Q2OrYdFL8K8kfDQX1CpUdb2x84mAWbsqVWYtsSZTW98TXRyNLtjdrMzZidzD85l1clVmJSJtpXb0jawLTU8GoApldCgyoy5vTEX0mz8ti0CgK6mHXxlnUgKTvxg680hXQ0rGTxr30jAH09A/Eno8lJWEflM4xbtA2D1oRi+/vcIj3UNYfGeSADe7N+A31f9R8uU/+hv3kBb037IqA73/wq1exKfGs/KEyuZd2ge285sQ6Go5V2LHtV70LZyW1pWakklt0olkv0zvz65u3lpN0EIIYQQxUiCvGKw/vBZDkYlOpKoaBJTM66+k0OOXr9LLDsYz111esHGb6DRnVDdKDStteaFX3YAsOedPrg7531aJ22ZhEmZuKPOHQR5Bl3DO7qxJKSks8rRS5Vh17w9fw8Ln+mca7v4C+k5Arynb6rN5yvCAGOO274VP5JxcjsWd19odAfU6c38HadyHceUEge/DjeG4vZ8GzqOAqVITk9m65mtLD++nPlh80mzp9GtWjdGtRpV4J6gzHpyTmYTE+5qVqBjgDGM08PZwvu3N86x3MVq9Or9uOE497WrgdVsghrtYdD38F0v+HkojFyZ1TsZnZiatW90QipnElKo6BlA9+rd6V69O080f4I9MXtYdWIVq06sYtLWSQB41HHC5NuUz7dtpkvLIObvP0kdeyzvmqcQZgtgZOqrnMYv69g/n+nO9x6T6bhqLCTHQM93wOni/LeDUReHSH+weD9/bI/gZNwF7q1v5pEz43jENgescNgeiO4zDtX6Yc6kxfPL9i+ZtW8W59POU9G1Iq+0foVba99a4GQyxWnRM53p99nqq28ohBBCiOueBHnFYMISo15VhiNN+dWy4mW3aFfkZdediE2GO76CCSGw/8+sIC/7B+Xl+89wMi6ZR7vUyjFES2tNRGIES48t5bvd39Glahcea/4Yjfwa5Xqd8up8SjqHohJoVSN3r9zRmCTOnE+hirdr1jy5yhVciDyfkhW4XGrPqYvp9Bc83YnGVb2o6u3KtGWbGZM6jtamg5w56o2fqwnznnloVz+6p3WkZe2OjOhSi/UHI2liPgafPQEp8dh7vUdU07vYdORP5h6cy47oHdi0DavJyq21bmVI/SGFLiru6+7EMzfVplOdK88DuxqTSbH7nT65lrs6fleHziTS6r1l7Hy7D4mpGUz6L517u35KzcXDUHNHwL0/89OmE2w7fi7H/vO3n+KRziFZz60mK80rNqd5xeaMajWKfw8fZvis37F47iPNJ45pu6dh0zbcgiECGIDxvrT9U7x1BdJS3bBneKIzvHgkrRtbGgThtvEbOL4eHpjPvP3JvDpvF2kZdkZ0rklccjq/bjnJ/sgE2pv28PapL7DpdOJCh/Gfb2/WxVpp7H2WhUseYmfMTgC6BXXjkSaP0MS/Sb6SxpSWhlUqMO+JDvi7l0zheCGEEEKUHgnyikGCY3imxaT4aWQ7GlYpmm/1jSFwnlCtNRxcAj1Gg9nKrmy1u56ZvQ2ACi5W7m9XI2u5UooPu37IC0kvMOfAHOYcnMOQBUPoFtSNO2vfSedqnQtdt6ysOn42GReriTZjlwOw+50+eDh6O7/99whjHEP1APw9jOGM97erznu3NmLeFy/TKnoJzGkBrUdAcCdQik+WHeSz5YcAWPFCV0ICjEB+SH0LQ7ZOxBZ1lJfSRvKbrRNv9GhA24zNRKz8lnvN8+HkfJgFTUwmNrk4s6NKXfb7VOXAiV84F/YtADUq1OChxg/RunJrmgU0yzPrYkE937tekR3rUqZsXyycT8kgJd1G49FGUpWpWBhpHsLrh2bz28QnefWMkd1xeMdggnzceHfB3qse/8w5Z2xJ9bEl1efXp/tiJ4OouMOc+H0E8fHHSAgdxszjVg6ePU2/0AqcPH+GhPQ4jsWHodU6ulxwoWPz3nQ5soHQ73rxy5kB2FUNTC5pKLcUWoSkcChpPU6JW7FYY+jv5EW02YTt7Eo4awT/f541Eqc82/JZbgq6iRDvkKu0uuxoeZWkQEIIIYQoH8rnp/oywmxSWYkorlWTql7sijCCt8w6V1lF0pvfB388Abt+heb38NzP23Ptb8x1yq2ye2WeafkMwxsPZ+bemfy0/ydWnVhFaKVQxnUeR2X38jWnLy4pjS4f5sxgeTAqgZbVfVi483SOAA8gJjGNYD833r+1ISz7PwbGfsseXRN95F/U3j+gQjXSQx9h1vJK1FKJDG9TiZCMI3DKDsfWwspxoG3savcxv6w0Cle/s+gQ4AW8iI8+yYOdT7ErdS+bzx3Epu04qSTqKuge1J3G/o2p51uPpv5Ny9QcroJ675LA7Vtbf15tkc4tu2az2FSRJfbW3NvCn9qnFxDq9AWV1ylslhH8eaEpbVs0I9AnZy/4nE0nAPhoUDOjTIK2ErT+a4JO7oKB30GTu7jLrsmw6xxZbe+bup7/Tm3CM3A/+1zCWOHnBaRBhXlkznycfRw4Dk5oKrnYcHWqSOvq7alcIYgAtwDcre64mF2o6VWTOj51ivG3JoQQQghROBLkFbGwMwkccRRMnvt4hwIfZ3Botawgb+wdTdh+4tzFZC7N74WVY2H5O4S7NuS8o+cwe9mGtAz7FY/v6eTJE82fYETTEcw9OJePN3/MkAVDeLzZ49xV967LpuG/nkTGp9Bu3PJcy3/aeJwavm48OWsrABYyuMm0jZamMDxIplJCPHx5Ds6GEVP3Hgbs7M+4PnUZ4rIBts/GuuJtNmdmpN/h+MlUswv0+4iaHiGwcinKGovZ9Rhm1+OY3Y6R4XKKqZEQ4hXCQ40fpku1LjTya4TVnHcik+uR2aSwOYYq/70vKsc6jYmRsffyhN7OZOskTns0pNqsKEiOwddUiQy7O+Ylr3I7cGGNB3QYCQ0GQJWWxF/IYGN4LAADW1YFux1WvAdbphsZLpvcBRi9iU6XZJNsVMWbtWG1SI1swNKh3fi/v/5m2YFVhJhOEspxOrvG4as1VV388avaGtX+CaPsghBCCCHEdUiCvCI2eeVhAP57rQeVva69NlVmspaBrarxf38Y9bmcrSZcrKasIE8D6xv+H+23vkCVeXfwoqUTkZW7kRy/j0PU5AIuLNwVyds9A1FWF3C6fNZEq8nKkPpDaBbQjLEbxvL+hvdZdmwZ73d6/7ru1dNac++3/+W5bs7mk5lbMdC0mlesP1FRncNucuKczYkY7QU+DaDLy/g1GYRX2N+sPJxAv0H3UKHlAxzcuJSf//idgZ2a0jCkhlEUG4xahpWNRCSRsQfo3mUhm6ONRBfa5oQtpRqpZ/qweMQT19UQv2vlZDZxwfE7iTqfmmv930dS2MTLPG/9lWHeseDdBZrfx2N/OeHv4Uzkoa00N4Vxt28YLdZMhDWfQI2OnG45GjDq+imAlWOMdXX6QJ9xV2zTcz3r8s2/RziblEbjt5c6lnZhn1nx23t9L1s6QgghhBDieiRBXhFbti8KF6u5QAEeGAEcgCVbincns4nD0Ukcjk7io7tsTF93lAmrPJnY6SvqHZjCU6l/QMwfAIx1deGsJRDXtGjUh4ng6mPU8mo8ENwuP3S0gV8DZvSdwZwDc/h4y8fcu/BePu72MS0qtijQ+yhtCakZHIkxhqze0aIq1X3d2HnyHLUrevDt6qPERZ/mY+sUBprXsFvV5b96r3PrXQ/y9bIjNAysQN3mRh2xzI/+S/ZEsWTPUlrV8CHqvIWTtv4MatEZKl+cb6m1ZlvUVr7d9S1rItbgYfUgxHIbuw4GY0+txP8ebk/z6t5Z8wHLq48HN+Pp2duyevPAyOz43oK9rD9yFoB4PDjW5h24pWHWNv5rNnIy7gJhujr7bdX5Kfom9r/0OS775sGaT6j7e1+es9xK/wbjYesMWP0RNLgFBs+8aqFzV6e8e6bH3dlUAjwhhBBClDvl+9NmCbPbNQkpGTzVvXaBjzH38Q78ueMU1mz18rIns2g79u+s4ZnRFRrycuxTPF9nMI83TAfvGrgc/IuUw4dYFR1CDBV41n2PUaNs3WcwYhW4+136klmUUtxd/24a+zfmhX9e4KElDzGp2yS6BnUt8PspLYeijCGzk+5uzu3ZCj/P/Gc3r1l+ZGDkaiqYkrC1f5rGvd6lsSOofq1vg1zHyl7WYsuxOAB83KzUq+SJ1pqj54+y4PAClh5byrHzx/B29uaJ5k8wpN4QLHjQxNFz1KmOf7G937KkX5NAWlT3pv24FVnLGlapwKNdQ7KCPICqPq459msd7MuqAwdyLDtp86V2p1GEB93O5qlP86zlN5j6m7GyRicY9MNVA7xMi0d15uZJOUsIVCnglzFCCCGEEGVZ2c33fR06GXcBoMC9eADNg7z5vwENUUoxsGU1qnobH4Tfc9QjO5+tsHpymo10m8atbjdoMwLq9oYBEwl6cj5vZDzMxIxB8ORGeGA+JETBbyPz1YZG/o2Y2Xcmtbxq8czKZ1gSvqTA76c0pKTbGDhlHXBJD07kbu7Y8iCPWhZyUAfxuNsnmPu8n6sw9qWmPdg617KPBjVlSfgS7px/J7f9fhvTdk+jklsl3unwDksGLuHxZo/j4+KDp0v5mWt3LSpXcKF3w0o5lnWuE8DsEe2ynj/UMTjH+jtbVuVSB6MSWH0omu93JPFi+mMMSnsLur0GfcbC0HlwDXNH61fOneW2kgR5QgghhCiHpCevCK09HANA25oFy6h5qY8HXyxW3a9xZf7v99051k/620jhX8U7Z4+Ik8XES33q8eGSA6Rk2HEJ6QpdXzaSVETtgUpXr40X4BbAjL4zGLFsBC//+zKuFle6VOtSBO+qeKWk25i80ihIXsPPje71KhorNk2Fv17BzezMw2kvsNzeimGtalzhSBd1qOXPk91r8aVjvuVPT1Xj692vsTlqM0GeQbzR9g26BXW77BzGeU904GxiWuHf3HVEKcU3D4SydE9kVpZQs0nRvpYfr/atz8m45FzZQytXyB1wPfHj1hzPv3ztSfAseGD2tmN4aOuavny35ig1fIuuNIUQQgghRFkhPXlFaMeJc/i4Wa+p+Hl++XlcvoBxj/oVcy2r4m18EN4cbgwvpPl94OwFf72S79d0s7rxba9vqVGhBi/+8yJRSVFX36mYaK3zXG6za4JfXchtX6wBYNRP2/lshRHkTby7OU5mBSvGwMIXIKgdtsfWsdzeCoDuefzeLuexrrVoXM2Nu3vv4vEVD7Ivdh9vtH2DBXcsYEj9IVdMUtOyug+9LunVulH0blQ513t/rGst3r+9Sa5tlVLc27Y6JgW/P9kxj/VQsRABHsCDHWvyYMeaNKrixSeDmxtlGIQQQgghyhn5hFOEktNseLlai62+WV4JIu5rWz3HnL1MFRzDBO//bgON3lpMvNUfOjwF4ashJixruyPRiUQn5M6AmMnN6san3T8lzZbGJ1s+wa6vXJqhOPy9N4qary3idPyFXOvOJhpt33EynqTUDBbvicxaF+znDn8+A/9OgLp94YHfsfoFU8cRhPu4OeW7DXvitpAR+AGLTvxI35p9WTJwCUPqD8Gk5BIqSmPvaMLhsf1oHuRNiH/OrLCb3uhZSq0SQgghhLi+yCfUInQh3YaLtfjqyw3vEJz1+K5W1XC1mnm4U808t023XQzGktJs7D113qiv5+QJf4/OWnfTx//Q6YMVeR0iS02vmjza7FEWHV3E+I3jC/cmrpHWmkdmbAZg9sYTudZ/ufJiwNpo9MW5gyH+7vhGrDKyMDa7B4bMAkctugxH1kd356ufK7u2892u73h82eOYlIkve3zJmE5j8HL2KszbEleQ+SXJc73qAkbJhL+f74r/FXqzhRBCCCHERTInr4ikpNtYtjcKF2vxxc1v9G/A1DVHARjYshofDWp22W271s05FDEhJR28qkGzu2H7bEhPIT7dCHJSHYXTY5PSyLDb8xwS91jTxzh74Syz98+mX81+NK/YvIje1ZX9tOliYLd0TyTPOz74A2w7HscP64/l2ufF3nUZUMcV/tfeKGh987gcyVVuaRrIZyvCCPC4+tC/0etG83vY73QP6s77nd6nglPu5B2ieNzSrAq9GlYq1i9OhBBCCCHKI+nJK2Ip6cU3nFEphYezhf5NA2lf6/KlEMDIKjk4tFrW86jzKcaDen0hPYk9s15lzKK9WesvpNlo+d4y2oxZzrbjcXm+9nOtnsPb2ZsXVr3AuZRzRfKermbhztNZj/dHJuRYd8fkdXnuc1c9K8EL7obU83DXNKNWYDajetZl59u98XK7cubLKTum8HvY7wxrOIxJ3SdJgFcKJMATQgghhLh2EuQVERermSn3teTPpzoV6+vsGN2bL+7JX4HyW5tdTEl/Ot4R5NXqwWq3ntQ5MoNjW5ZlrZ+6+kjW48sFT+5Wdyb3mExsSiwjl40k1Xb5uXxFJSktI9eyT/8+xO6I+KznfwyrzW2mNfQwbeFm00Yqz+oF0fvhjm+gSu7flcmksuYsXs7y48v5asdX9KrRi1GtRsncOyGEEEIIcd1Ql8taWNaFhobqzZs3l3Yzrgsdxi2nXS0/PhncHIBHpyzmzdNPkYQL/dLGYc8j1g8f3/+yx1sSvoQX/3mR3jV681HXj4ot0Yzdrmn89hKS02y51lX1diXi3AXamfYy23866nzExZV+dWDwDKjUsECvu+fsHh5b9hj+rv58f/P3Mv9OCCGEEEKUSUqpLVrr0EuXS/fEDaCSlwvztkaQ7OgVc/OtzMSMgdQ3nWBNtcm0UIeAzGBfE6xOw975sO5zo67eJfoE92FEkxEsPba02BKxbAqP5clZW0lOszHhrqa8c2vO2n5mk+Jm00ZmOY1FKTP6vrl8ETSRw50nwshVBQ7w5h+ez9BFQwEkwYoQQgghhLguSeKVG8DRmCQAxv+1n3dva4yXq5Xv7Z3xSL/AO3Gz+c15HSe1P4tsbWlrOUQzDsIcx84mC4xYAYE5k7w83eJpktKTmLV/FkGeQdzf8P4ibfPEZQdZd/gsAFW8XAk7k3M+nrM9mdctP5LqXQvXx1einD15qk7hXnPZsWW8ueZN6vvWZ0KXCQR7BRfugEIIIYQQQpQC6cm7AbhYjOQVp84ZdeZSM2yAYoatD+qlMKJ7fkaE9mekZSF1nOOYkD6YO1LfYUWPP8HJHeaOgNScQZZSiudDn6dj1Y58sOkDJm6ZWKRtjk1Ky3pcwdVCFW/XrOdOpPNU0hdUUzGk9/0YnD0L/XqbIjfx+urXqe9bn297fysBnhBCCCGEuG5JkHcDaBfiC0Bccjrv/LmH8JjkiytdvAjoNAyfJ5bx+00rCB+6icm229mm6zBxu8mY2xZzwKg3dwlnszNf3PQFt4TcwrTd05i6a2qRtblOpYuBm5erlV4NK/HMTbUZ0jqIlyw/c5t5Hd9Z7qZCva6Ffq0DsQcYtXIU/q7+fNr9UxmiKYQQQgghrmuFDvKUUmal1Dal1ALH85pKqQ1KqTCl1M9KKSfHcmfH8zDH+uBsx3jNsfyAUqpPYdskchp7ZxMAthyLY/racNYfOUu9Sp6seOFigFS3cgVu79IKd1enrGW7IuIhpBsEtYNNU8GeuzyExWThrfZv0bN6Tz7d+imfbP6Eokjm4+lycSSxm5PF6DnsXY/xbVJ5yPwXP2d040+foYV+nVOJp3hmxTNYTBa+6PEFgR6BhT6mEEIIIYQQpakoevKeBfZle/4BMFFrXRuIAx52LH8YiHMsn+jYDqVUQ2AI0Ai4GZislJLiWEXIzSn31MvQYB9CAjzytS2tH4HYIxD+b57Hd7G4MKHrBG6tdSvT90znmZXPEJsSW6g22+1GoDi0XQ38PS4Gnmz4ilSLB+9mDKWwOT3jU+MZtXIUcalxfNT1I2p51yrkEYUQQgghhCh9hQrylFLVgP7AVMdzBdwE/OrY5Afgdsfj2xzPcazv4dj+NuAnrXWq1vooEAa0KUy7xNXV9HfPc3mOgAqIv5AO9fuDyQqHV1z2eFaTlfc6vsdLoS+xNmItwxcP52TCyQK3z2bXBHq58N7tjS+WaDj6L+z+lYhqt5CEK3tPny/w8VMyUnjl31c4GHeQ9zq+R+vKrQt8LCGEEEIIIcqSwvbkTQJeBjLH8fkB57TWmRWsTwKZFbmrAicAHOvjHdtnLc9jnxyUUiOVUpuVUpujo6ML2fQb2+WCPKUUD3YIpp5jTtz3a8PByQ1CusKOnyA95bLHNCkTDzR6gMk9JxOZFMmwxcPYfmZ7gdpn0xpT9vp7WhslHdz8qT54HGDM1SuIpPQkXvn3FdaeWsuLoS/SJ1hGCAshhBBCiPKjwEGeUmoAcEZrvaUI23NFWutvtNahWuvQgICAknrZcqlSBZfLrnv71kaMvtWoMzfx74PGwvZPQWIU7J571WO3C2zHN72/wWa3MXLZSNZGrL3m9mlt1MLLErYcDi2FNiNwdvdmxkNtmPNo+2s+7vHzx7nlt1tYcWIFTzV/qshLPwghhBBCCFHaCtOT1xG4VSkVDvyEMUzzU8BbKZU5sasaEOF4HAEEATjWewFnsy/PYx9RRN69zSgmHuhlBHcu1itPe0xJt2U9ttm1kYClUmNYOQbstsvv6NAsoBn/6/c/At0DeX7V8/x7Mu/5fJdjs+ucQd6hJWB1g46jAOhSNyDPOYVXsjN6J/cuupcUWwrT+kzj0WaPXtP+QgghhBBCXA8KHORprV/TWlfTWgdjJE5ZobW+D1gJ3OXYbBjwh+PxfMdzHOtXaCMN43xgiCP7Zk2gDrCxoO0SeXugfTDh4/vTtqZRTsHd+cpBXqsavlmPT527AEpBp+fgfASc2pav16zmWY2ve31NNc9qPLPiGVadWJXv9hrDNR1P0pJg/0Ko3h6sl++BvJLfDv3GsL+GYcLEtD7TZA6eEEIIIYQot4qjTt4rwPNKqTCMOXffOZZ/B/g5lj8PvAqgtd4DzAH2AouBJ7XWV+8qEgUyfmBTfn2sPYFerlfczsvVytdDWwGO5Ctg9OZBvoZsZqrsXplven1DTa+aPLfyOZYfW56v/ez2bHPyDi42gst2T+T7dbObd2geb617i6YBTZl761zq+9Yv0HGEEEIIIYS4HhRJkKe1XqW1HuB4fERr3UZrXVtrPUhrnepYnuJ4Xtux/ki2/cdorWtpretprf8qijaJvLlYzYQG+159Qy4O6UzNcOTVcfeHJoNh4zeQdDbfr+nn6seUnlMI9grm5X9fZlf0rituv+rAGSLOXbg4XPPkZrC4GslfroHWmgmbJjB63Wga+Dbg615fE+AmczmFEEIIIUT5Vhw9eaKccLYY/z1Ss83Po8PTYM+AbTOv6ViV3SvzVc+vqOBcgRf/eZFNkZvy3G7q6iM8OH0TO0/Gczo+xSjAfvRfqNIczPnPpqm15vU1rzNz70z6BPdhZr+ZuFgKNtRTCCGEEEKI64kEeeKyXB09eTnq0VVuAjW7wpqJRgB2DSq5V2Jit4kopRixdATbzuSe27d0T1TWY283KxxfD1G7odmQfL+O1pr3/3ufBUcWMKTeEMZ3Ho+z2fma2iqEEEIIIcT1SoI8cVn1A41aef8dOUu6zRHQKQVN7oKUcxB39JqP2bxic2b1n0WAWwCjVo5i39l9OddX9856/P3wNhDhqNBRf0C+jp+cnsyolaOYc3AOg+sO5rW2r2ExWa6+oxBCCCGEEOWEBHnispwtRk/e3/vOMHV1toCuSgvj35N5D7m8Gl8XX77u+TUmZWLoX0OZvW82E5fvYsmeSL5bY7zON0NbGQXbT28HryBjPuBVRCdH8/CSh1lxYgUvhr7Im+3exKTkv7gQQgghhLixSBeHyJfN4bFALeNJQAPwqg4bvrqmYZTZhXiH8L9+/2PUylGM3TgWbbeQvqM9dlNnKntUpHejypB4BvYvgsZ3XvFY59POM+/gPL7b/R2JaYl82PVDbg6+uUDtEkIIIYQQ4nonQZ64Im83K+eS00mzZZt/Z7ZA83vhnw8gNQGcPQt07KoeVZkzYA6/71/Da8u+weq7Bie/1aRRgUeWzqZ6egbxPm4kWeJIXHQ/6fZ0PJ08cTI54ePiQwWnChyJP8KmyE2k29NpU7kNr7V5jdo+tYvo3QshhBBCCHH9kSBPXJGXqyPIy7gkyUrNLvDPeNg6E9oXrH4dgM2ueWlmAhn2IZhiO2F2O0q3FhCdGs6y2IN4OzvjCbhbXPEweZCYlkiCPYGDcQc5n3aeIM8g7ql/D32C+9DEvwkqs7aeEEIIIYQQNygJ8sQVeboY/0XOJqXlXFGjAwR3hnWfQbvHjYQsBRCXnE6GXRPs50azoDY8fdO91K7oCWlJ8FlLCKgLA2YX9m0IIYQQQghxw5CsFOKK3rm1EQBhZxLZHRF/cYVS0HggJJyGs2EFPv7x2CQAXu1bn0+HtDACPIBj6yExEto/XeBjCyGEEEIIcSOSIE9cUasavljNRi/dpL8Psi4shuiEVGNlSDfj3/VfFvj468LOAtA+5JLsmcfWgjIbPYZCCCGEEEKIfJMgT1xVRU8XwCilcO/UDbQe87cR6PnWhFbDYfuPkBwLwJzNJ5i5Pjxfx41LSuPjZQcJ9nPDy816cUVCJGz4GoLagrNHUb8dIYQQQgghyjUJ8sRVvd6vQa5lsZlz9EKHgy2Nv6a8xNTVR3j515383x978nXc3pP+BcDH3SnnilXjIT0JbplUmGYLIYQQQghxQ5IgT1xV/6aBWM0KU7bcKll5VgKbscLajS7n/2TSwi1Z67XWVz1u5rDPqt6uFxfunQ9bpkPLByCgXlE0XwghhBBCiBuKBHkiX9ydLdizxW3ZSyp8ntgdd5XKGOs0wNjofEpG1vrUDBsr958hJd2WtSx7ENiyuo/xYPN0mDMUKlSFvhOK540IIYQQQghRzkmQJ/LF3SlntY3EVCOIWxcWwzZdhy8ybuM28zrGWaZSXUURk5CStW29Nxcz/PtNzF57EBzB3YnYCwC0CfblwQ7BcGgZLHgOqrWBkavA6ooQQgghhBDi2kmdPJEvmfXyMoXHJNEuxI/ws8kARLV6gclbNE9Y5nOPZSVMfo4Y/zZMOt2IMZbj9DJvoeKqc7C9OvT7iBOmVgA837supvB/4ef7wScYhs4DZ88SfndCCCGEEEKUHxLkiXyp4GJkv/RytWIxKX7adIK7WwexK+IcAE/eVI/vXd5ggfuTbFsykwam43SP3sb71o2kaitL7a1I9qzJnWzAOmswVZzq8oi5NY1WfQfHlxtDNIfNlwBPCCGEEEKIQpIgT+RLZk9eSIA7fRpVZvxf+3nix638tTsSgAquFl7tWx+ApxadBRuYsRGkznBG+5CMC8TCu/Tk907HsW/8jjetP6KjPKHrK9DhaQnwhBBCCCGEKAIS5Il8yQzyqni5ZmXDzAzwANyyzdmrU9GDQ2cSsWEmXAcy/cHWrDpwhh/WHyMJVz4424W/0+ryVW8Pbu4YKrXwhBBCCCGEKEKSeEXky7FYY+5dk2pe+HnkrGs3sktIjudLn+vCnEfbY1LwzE216V6/Iu/c1piVL3YD4O99UVhMJrp36SwBnhBCCCGEEEVMevJEvmTWtOvZoGKOUgpDWgcxqmedHNsqpWhT05fDY/vlWF7D1y3rcYZd42wxF1+DhRBCCCGEuEFJT57Il8zeumo+bvi5Gz15Vb1dGT+waY6hmtkppVDqYgV1U7Zq6h1r+xVja4UQQgghhLhxqexFqa8noaGhevPmzaXdjBuS1pqpq4/Sp1Flqvu5XX2HbFLSbUQnpOLn4XTZ4FAIIYQQQghxdUqpLVrr0EuXy6dscc2UUoy4ZB5efrlYzQT5XltgKIQQQgghhMg/Ga4phBBCCCGEEOWIBHlCCCGEEEIIUY5IkCeEEEIIIYQQ5YgEeUIIIYQQQghRjkiQJ4QQQgghhBDlSIGDPKVUkFJqpVJqr1Jqj1LqWcdyX6XUMqXUIce/Po7lSin1mVIqTCm1UynVMtuxhjm2P6SUGlb4tyWEEEIIIYQQN6bC9ORlAC9orRsC7YAnlVINgVeB5VrrOsByx3OAvkAdx89IYAoYQSEwGmgLtAFGZwaGQgghhBBCCCGuTYGDPK31aa31VsfjBGAfUBW4DfjBsdkPwO2Ox7cBM7ThP8BbKRUI9AGWaa1jtdZxwDLg5oK2SwghhBBCCCFuZEUyJ08pFQy0ADYAlbTWpx2rIoFKjsdVgRPZdjvpWHa55Xm9zkil1Gal1Obo6OiiaLoQQgghhBBClCuFDvKUUh7AXGCU1vp89nVaaw3owr5GtuN9o7UO1VqHBgQEFNVhhRBCCCGEEKLcKFSQp5SyYgR4P2qt5zkWRzmGYeL494xjeQQQlG33ao5ll1suhBBCCCGEEOIaFSa7pgK+A/ZprT/Jtmo+kJkhcxjwR7blDziybLYD4h3DOpcAvZVSPo6EK70dy4QQQgghhBBCXCNLIfbtCAwFdimltjuWvQ6MB+YopR4GjgGDHesWAf2AMCAZGA6gtY5VSr0HbHJs967WOrYQ7RJCCCGEEEKIG5Yyps1df5RS0RhBZHnhD8SUdiNEFjkfZYucj7JFzkfZIuejbJHzUbbI+Shb5HwUvRpa61zJSq7bIK+8UUpt1lqHlnY7hEHOR9ki56NskfNRtsj5KFvkfJQtcj7KFjkfJadISigIIYQQQgghhCgbJMgTQgghhBBCiHJEgryy45vSboDIQc5H2SLno2yR81G2yPkoW+R8lC1yPsoWOR8lRObkCSGEEEIIIUQ5Ij15QgghhBBCCFGOSJAnhBBCCHENlFKqtNsghBBXIkFeCVJKWUu7DeIipZTZ8a/crIW4hFLKy/Gv3CeEyM21tBsgLpL7uRC5yc27BCil2imlfgI+VEo1Lu323OiUUh2VUj8AbyqlfLVMTC0T5OZc+pRSJqVUBaXUAuAzAK21vZSbJQClVCOllEtpt+NG57ifzwW+VEr1zgwuROmQ+3nZJPfzskGCvGKmlBoETAEWAC7A847lcgGUAqVUCDAZWAnUAN5TSvUv3Vbd2JRSbZRS3wKvKKUCSrs9NzJHQJcAWIGqSqm7QXrzSpNSqqlSag3wPuBX2u25kSmlumHcP+YBB4D7AZ9SbNINTe7nZY/cz8sWuXEXvzrAn1rr/wETwRi2Kd82lZpWwD6t9ffAC8B2YIBSKqg0G3UjUkqZlVLjMNIprwVaAqOVUpVKt2U3vPpADDAJuE8p5am1tssXU6XmTeBXrfUdWusIkC8JS1ETYJPW+kdgJsaXIYml26QbWmvkfl4myP28bJIgr4gppQYrpZ5XSrV3LDoA3KmUehlYD1TBGOYRWmqNvIE4htbUzbZoE1BNKRWktY7D+GN0DrizNNp3gzMBx4HBjpv0KKAdMtelxGS/PrIFDmFAGnDU8TNMKVVdvpgqWY6hsyFAotZ6kmNZL6WUNyDzj0pAHveP1cAgpdRbwFYgEJjsGLEjiplS6hal1FNKqXaORZuAILmflxknkft5mSJBXhFxfIvxFvCKY9G3SqlbMYZ1PAt0AR7QWt8MRAN3KaUql05ryz+llLdSaiGwDBislPJwrEoB1gCDHc8PAHsBX5nvUvwu+dBkB2ZrrQ8qpZy11qcwbhL+pdfCG0Me14d7tiAuFDivtd4D7AFGA1OUUlYZtlm8sl8fjqGzMUBnpVR/pdTvwIsYcyVfcmwjgXcxuNz9Q2u9HbgZCAae0Fp3wwgsblZKNSid1pZ/SqlApdSfwMsYw2OnK6X6aK2PYHx5LvfzUpAt6G6ttbYh9/MyR27YRcTxH7we8ILW+hOMD0bPAXW11ssxgosDjs3/AJoCSaXR1huEO7AEeNrxuItjeTTwH9BEKdXGcd4igI5a65RSaekNIK8PTVprm9b6HIDWOlUp5QnUBE6VYlNvFJe7PsDoXfVUSv2M8aFqC3BQa50uSViKR15BN4DW+jwwHXgPmKa17gNMBdpl680QRe/S66Nz5gqt9UYgAAh3LFoBeCL38+IUCqzWWnfWWr8HfAqMcKxbjdzPS1QeQfcMpVRvrXUsyP28LJEgrxCUUg8opbo6hs8ARAE+SimL1nouxrfg9zh67A4Ddzm2a4ER9IkilO18VHDMXfkGmIPxu26jlKrquAmsB7YBEx3f0DYCjiul3Eqt8eXfZT80ZdMW2KO1PqWU8lBK1SnJBpZ3V7k+2iqlqjg29cH4EBuJ8bfqcaCe9FQUqysF3Qsweo4yE3xsxrjXpJZg+8q9/F4fSilnYB3wpGPXHhgJceSeXoQc56Ob4/e9HGMOZKazwCHH4w3I/bykXRp0T8K4T2Qn9/MyQIK8a6QMgUqplcAw4D6MOXYeGENrmgCZQwO/AG4HbMBSoLVS6j9gEPC61jqhpNtf3lzmfExRSvlrrVO01snA3xgfkG4C0FpHaa0/xbg5TMPIkPaBY1tRRK7hQ5PFsYs3cEIpNRxjrkXzkm91+XKN10cPAK31bmC41vpZx9+oaOB+rfW+Unob5VI+ro+qAFrrnRjDM59SSvlj/L1qjPFBVxRCAa+PVGA+4KGU+he4B3hKa32mdN5F+ZHH+bgX4x7tprU+rS7WGg7E8aWH1jpS7ufF7ypBdyywz7Fd5jnyRu7npU6CvGuglDI75kB4AhFa6x4Y316cx5gnMRnoADRVSrlprfdjfNs0yDFk8wFghNa6p2OdKIQrnI9YjA9MAGit12IMramnlPJyDCMA44PTw1rrtlrrA4hCK2DQneHY/TaMc9IFuFtr/UvJv4PyoxDXh7vWOkYZ84xNWuvEzGE4onAKcn0AaK2/A2YDbwMDgUe01sdL/A2UIwW8PryVUq6O+arDgAe11j3kC5DCy+f5yBwu3gv41bFfRceyl5H7eZEqYNCd7lgm9/MyQIK8fHB82BkLjFVKdcWYe2eDrLl4TwEDgKrALGAIcItj9zSMOS04PiztKuHmlzv5OB/PAh0c6zJ9i9HDugwIU0pVccwJk97UIlKAD031lVF4O7PneyFGZq7hjt4LUQBFcH0cyXZ9yBy8IlLYL6Ucc72f01r30VrvLfE3UE4UwfURroyh/xcciT9EIVzL+dBa25RSThijCw4qpcYAy5RSPlrrDLmfF50CBN1zHftlJlpZhNzPS50EeVfh+KOzBeNbijCMCfDpQHelVBvI+kP0DvCh1noGxtDMB5RS2wALIIFdEcnn+bBjfOP9drZd+wNPADuAJtrI/CSKQCE/NC0HDiulArXWP2mtfy3h5pcrcn2UPUX1pZRj+3REgRXB9bEd4/qIKLFGl2PXeD7ecezmAjyIce/wBHpqo3yCKAJFEHQvV0p5aa1ny/289EmQd3V24GOt9eNa62+B3RgZg94CpoBRzwjjW4xkZdRr+R14GBiotb5bxoYXqfyej9+BaKVUsGO/FIybwQiZO1F0ivBD0+kSa3T5JtdHGSJBd5kj10fZci3n44xSqhpQH/gfxjSYZ7TW0aXS8nKoCIPu+BJtuLgsCfKubgswRylldjxfC1TXRrFHs1Lqacd/+mpAutb6BGRNBpahHEXvWs6HTWsdDqC1/kNr/W9pNLickw9NZYtcH2WLXB9li1wfZcu1nA+71vqk1nqj1voBbdQsFEVLgu5yRoK8q9BaJ2utUx3d1GCMPc78TzwcaKCUWoAxKX5rabTxRlKQ86GUUiXf0huGfGgqQ+T6KHPk+ihD5PooW67xfGwBOR/FTILucsZy9U0EGOOUAQ1UwkifDJAAvI6RzvqojNMvOddyPhyTh0UxyGMoci8gc5L1cGCE4yZdD8dkbaWUknNSvOT6KBvk+iib5PooW+R8lA3y96r8kSAv/+yAE0YtvKZKqUkYdYqe1lqvKc2G3aDkfJQhcpMuc+T6KEPk+ihz5PooW+R8lCHy96r8kCAvn7TWWinVAqOuUU1gujZqF4lSIOejzJGbdBki10eZI9dHGSLXR9ki56PMkb9X5YSSIDz/HJNMhwKfaK1TS7s9Nzo5H2WLUqodsM7xIzfpUibXR9ki10fZItdH2SLno2yRv1flgwR5QogiITdpIS5Prg8hxPVC/l6VDxLkCSGEEEIIIUQ5IiUUhBBCCCGEEKIckSBPCCGEEEIIIcoRCfKEEEIIIYQQohyRIE8IIYQQQgghyhEJ8oQQQggHpZRNKbVdKbVHKbVDKfWCUuqK90qlVLBS6t6SaqMQQghxNRLkCSGEEBdd0Fo311o3AnoBfYHRV9knGJAgTwghRJkhJRSEEEIIB6VUotbaI9vzEGAT4A/UAGYC7o7VT2mt1yml/gMaAEeBH4DPgPFAN8AZ+FJr/XWJvQkhhBA3PAnyhBBCCIdLgzzHsnNAPSABsGutU5RSdYDZWutQpVQ34EWt9QDH9iOBilrr95VSzsBaYJDW+mgJvhUhhBA3MEtpN0AIIYS4TliBL5RSzQEbUPcy2/UGmiql7nI89wLqYPT0CSGEEMVOgjwhhBDiMhzDNW3AGYy5eVFAM4w57SmX2w14Wmu9pEQaKYQQQlxCEq8IIYQQeVBKBQBfAV9oY26DF3Baa20HhgJmx6YJgGe2XZcAjyulrI7j1FVKuSOEEEKUEOnJE0IIIS5yVUptxxiamYGRaOUTx7rJwFyl1APAYiDJsXwnYFNK7QC+Bz7FyLi5VSmlgGjg9pJpvhBCCCGJV4QQQgghhBCiXJHhmkIIIYQQQghRjkiQJ4QQQgghhBDliAR5QgghhBBCCFGOSJAnhBBCCCGEEOWIBHlCCCGEEEIIUY5IkCeEEEIIIYQQ5YgEeUIIIYQQQghRjkiQJ4QQQgghhBDlyP8DIeZB/3O03goAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "nifty_50['Open'].plot(label=\"no moving average\", figsize=(15,7))\n",
    "nifty_50['MA50']=nifty_50['Open'].rolling(50).mean()\n",
    "nifty_50['MA50'].plot(label=\"MA50\")\n",
    "nifty_50['MA200']=nifty_50['Open'].rolling(200).mean()\n",
    "nifty_50['MA200'].plot(label=\"MA200\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "33752869-b1f7-41a6-846d-4182aec9e457",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x11e2b0850>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hsi['Open'].plot(label=\"no moving average\", figsize=(15,7))\n",
    "hsi['MA50']=hsi['Open'].rolling(50).mean()\n",
    "hsi['MA50'].plot(label=\"MA50\")\n",
    "hsi['MA200']=hsi['Open'].rolling(200).mean()\n",
    "hsi['MA200'].plot(label=\"MA200\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "492eb93f-5f7c-4418-b32b-f0f13c5b6803",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas.plotting import scatter_matrix\n",
    "import pandas as pd\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "616b93d8-6204-4577-b19b-abf0f3e5cc31",
   "metadata": {},
   "outputs": [],
   "source": [
    "economy=pd.concat([nifty_50['Open'],hsi['Open']],axis=1)\n",
    "economy.columns=[\"NIFTY50 Open\",\"HSI Open\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "47f3277d-30c8-43da-a412-b120faf9a3ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:xlabel='NIFTY50 Open', ylabel='NIFTY50 Open'>,\n",
       "        <AxesSubplot:xlabel='HSI Open', ylabel='NIFTY50 Open'>],\n",
       "       [<AxesSubplot:xlabel='NIFTY50 Open', ylabel='HSI Open'>,\n",
       "        <AxesSubplot:xlabel='HSI Open', ylabel='HSI Open'>]], dtype=object)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fXmBbd+tTu5MkFyZZSrK0vLw8SPOSJM2jSYf9XuCqbvrDQICt3fxW4ObuMX1qd1JVO6tqsaoWFxYWBmhdkqT5NOmw/whwSjf9MKCAc7v584DrgGt71iRJUg8TDfuq+jjwT0muBE4H/gDYn2Q3cEdVXV9Ve/rUJtm3JEnzbNID9KiqF68qXXyAx/SqSZKk9XlRHUmSGmfYS5LUOMNekqTGGfaSJDXOsJckqXGGvSRJjZv4R+8kTd72Sy7b9GXeuOOCTV+mpGEY9tKMGSKYJR3ZPIwvSVLjjqg9e/eYJElHIvfsJUlqnGEvSVLjDHtJkhpn2EuS1DjDXpKkxhn2kiQ1zrCXJKlxhr0kSY2bStgneVGSa7rpS5PsTvK6sft71SRJ0vomHvZJ7gI8rJs+DTiuqs4Cjk1yet/apPuWJGleTWPP/gXAm7vpRwFXdNO7gDMOoSZJknqYaNgnOQY4p6o+3JW2Afu66b3dfN/a6mVfmGQpydLy8vKm9y5J0rya9J79rwBvHZvfC2ztprcCNx9C7U6qamdVLVbV4sLCwmb3LUnS3Jr0t979HPCwJL8OnAycAJwCvA04D/gT4Hbgoh41aer8JkVJ82Cie/ZV9bKqemJVnQ98uqpeCexPshu4o6qur6o9fWqT7FuSpHk2te+zr6ozu38vPsB9vWqSJGl9XlRHkqTGGfaSJDXOsJckqXGGvSRJjTPsJUlqnGEvSVLjDHtJkhpn2EuS1DjDXpKkxhn2kiQ1bmqXy5UmyS+skXQkc89ekqTGGfaSJDXOsJckqXGGvSRJjTPsJUlqnGEvSVLjDHtJkhqXqpp2D5vuhBNOqO3bt0+7DemIdOONN+LfnzR5H/vYx6qqDrgT3+RFdbZv387S0tK025COSIuLi/79SVOQZM/B7vMwviRJjTPsJUlqnGEvSVLjDHtJkhpn2EuS1DjDXpKkxhn2kiQ1zrCXJKlxTV5UR5J06LZfctkgy71xxwWDLFf9uWcvSVLjDHtJkhpn2EuS1DjDXpKkxhn2kiQ1zrCXJKlxhr0kSY0z7CVJapxhL0lS4wx7SZIaZ9hLktQ4w16SpMYZ9pIkNc6wlySpcYa9JEmNM+wlSWqcYS9JUuMMe0mSGmfYS5LUuMHCPslDknwkye4kb8rIpd3868Yet+GaJEla35B79p+vqkdX1Vnd/COA47r5Y5OcnuS0jdYG7FuSpKZsGWrBVXXb2OwtwLnAFd38LuAM4PbDqN0wVO+SJLVk0HP2SZ6S5FPAScAxwL7urr3Atu620drqdV2YZCnJ0vLy8mb+GpIkzbVBw76q3lNVDwH+gdHe+dburq3AzYyCe6O11evaWVWLVbW4sLCwyb+JJEnza8gBencZm90HFKND+QDnAdcB1x5GTZIk9TDknv35Sa5KchWjw/g7gP1JdgN3VNX1VbVno7UB+5YkqSlDDtB7N/DuVeWLD/C4DdckSdL6vKiOJEmNM+wlSWqcYS9JUuMMe0mSGmfYS5LUOMNekqTGGfaSJDXOsJckqXGGvSRJjTPsJUlqnGEvSVLjDHtJkhpn2EuS1DjDXpKkxhn2kiQ1zrCXJKlxhr0kSY0z7CVJapxhL0lS4wx7SZIaZ9hLktQ4w16SpMYZ9pIkNc6wlySpcYa9JEmNM+wlSWqcYS9JUuMMe0mSGmfYS5LUOMNekqTGGfaSJDXOsJckqXFbpt2AJKlt2y+5bNOXeeOOCzZ9mS0bbM8+ySOTfCTJNUku7Wp7k1zZ3Y7vas/uHvdXSbYeSk2SJK1vyMP4XwEeX1VnAicmeSjwyao6p7vdlOQY4NeBxwJvAS7qWxuwb0mSmjJY2FfV16tqfzd7G3AH8AtJdifZkSTAgxi9Abgd2AWccQg1SZLUw+AD9JKcAixU1WcYhfZjgZ8GngxsA/Z1D93bzfetrV7PhUmWkiwtLy9v+u8hSdK8GjTsu/PyrwdeAFBVN1VVAe8CHsIouFfOv28Fbj6E2p1U1c6qWqyqxYWFhc3/ZSRJmlNDDtDbAvwZ8OKq+nqSuyc5urv7McCXgC8AD+nq5wHXHUJNkiT1MORH754JnA68enR6nt8G/jjJ94AvA79bVXck+U/AbuA7wP9YVbf1qQ3YtyRJTRks7KvqL4C/WFU+7QCPewujEfaHXJMkSevzCnqSJDXOsJckqXGGvSRJjTPsJUlqnGEvSVLjDHtJkhpn2EuS1DjDXpKkxvW6qE6SRwPbxx9fVX86UE+SJGkTrRv2Sd4CPBD4OKOvqQUowLCXJGkO9NmzXwQe3H1bnSRpBmy/5LJpt6A50uec/aeAew/diCRJGkafPfsTgM8kuR64ZaVYVU8ZrCtJkrRp+oT97w3dhCRJGs66YV9VVyV5APCgqtqV5G7A0cO3JkmSNsO65+yT/BrwDuCNXem+wLsG7EmSJG2iPgP0fgN4DLAPoKq+CJw4ZFOSJGnz9An7W6rq1pWZJFsYfc5ekiTNgT5hf1WSlwN3TfIE4O3Ae4dtS5IkbZY+YX8JsAx8ErgIeB/wiiGbkiRJm6fPaPwfJnkz8FFGh+8/79X0JLVoqKvS3bjjgkGWK/XV59r4FwBvAL4EBPjZJBdV1fuHbk6SJB2+PhfVeQ3wuKr6W4AkDwQuAwx7SZLmQJ9z9t9dCfrO3wHfHagfSZK0yfrs2S8leR/wNkbn7J8J3JDkGQBV9ZcD9idJkg5Tn7D/KeAbwNnd/DJwV+DJjMLfsJckaYb1GY3//Ek0IkmShrHmOfskT0pydZJvdberkvzLSTUnSZIO30H37LsvwLkIeCmw1JUXgR1J7ldVOyfQnyRJOkxrHcZ/EXBmVd00VvtwkicB1wCGvSRJc2Ctw/hZFfQAVNW3B+xHkiRtsrXCfl+SU1cXu5qfs5ckaU6sdRj/t4D3JHkT8LGutgg8F3jO0I1JkqTNcdA9+6q6BnhE95jndbejgEd190mSpDmw5ufsq+obwO9sZMFJHglcCvwQuKGqXpTkJcBTga8Az6uq2w6ntpG+JEk60vS5Nv5GfQV4fFWdCZyY5GxGX6hzJvAJ4GlJTtxobcC+JUlqymBhX1Vfr6r93extwMnAld38LuAMRmMANlqTJEk9DLlnD0CSU4AF4GZgX1feC2zrbhutrV7PhUmWkiwtLy9v4m8gSdJ8W+sKeu9l9EU3B1RVT1lv4UmOB14PPAt4OHC/7q6tjMJ/72HUVvezk+5CP4uLiwftW5KkI81aA/T+4HAWnGQL8GfAi6vq60luAP5n4NXAecB1wOHUJElSDwcN+6q6anw+yTHAQ4CvVtU3eyz7mcDpwKuTAPw2cHWSa4C/B15bVbd2X7RzyLVD/k0lSTpCrXUY/w3AH1XVp5PcE7gWuAM4PsmLq+ov1lpwd//qx1wLvGrV41610Zok6ci0/ZLLNn2ZN+64YNOXOSvWGqB3VlV9upt+PvCFqnooo3PvLx28M0mStCnWCvtbx6afALwLRh+pG7IhSZK0udYK+5uT/HKSXwQeA3wAfjTw7q6TaE6SJB2+tUbjXwT8IXBv4IVje/TnApt/skSSJA1irdH4XwDOP0D9cuDyIZuSJEmbZ63R+H/E2hfV+c1BOpIkSZtqrcP4S2PTrwR+d+BeJEnSANY6jP/mlekkLxyfl6RpG+Jz1lKr+n4RjtealyRpTg3+rXeSJGm61hqg911+vEd/tyQrXzEboKpq69DNSZKkw7fWOft7TLIRSZI0DA/jS5LUOMNekqTGGfaSJDXOsJckqXGGvSRJjTPsJUlqnGEvSVLjDHtJkhpn2EuS1DjDXpKkxhn2kiQ1zrCXJKlxhr0kSY0z7CVJapxhL0lS4wx7SZIat2XaDUhS67Zfctm0W9ARzj17SZIa5569pMG5ZytNl3v2kiQ1zrCXJKlxg4V9kvsk2ZNkf5ItSbYn+UaSK5N8cOxxL0lyTZI/T3LModQkSdL6hjxnfxNwLvDOsdoVVfWclZkkJwKPq6ozk7wMeFqSq/rUgLcP2Lsk6QgzxNiSG3dcsOnL3IjB9uyran9VfWdV+XFJdid5UTe/CFzZTe8CzjiEmiRJ6mGSo/G/BvwL4Bbg3Uk+BGwD9nX37+3m+9buJMmFwIUA97///Te7d0mS5tbEBuhV1S1V9f2quh34K+AhjIJ7a/eQrcDNh1BbvfydVbVYVYsLCwsD/RaSJM2fiYV9knuMzT4G+BJwA3B2VzsPuO4QapIkqYfBDuN3I+bfD5wKXA5cneQpjA7j766qj3aPuzrJNcDfA6+tqlv71IbqW5Kk1gwW9lV1G6O98HGvPMDjXgW8aiM1SZK0Pi+qI0lS4wx7SZIaZ9hLktQ4w16SpMYZ9pIkNc6wlySpcYa9JEmNM+wlSWqcYS9JUuMMe0mSGmfYS5LUOMNekqTGGfaSJDXOsJckqXGGvSRJjTPsJUlqnGEvSVLjDHtJkhpn2EuS1DjDXpKkxhn2kiQ1zrCXJKlxhr0kSY0z7CVJapxhL0lS4wx7SZIaZ9hLktQ4w16SpMYZ9pIkNc6wlySpcYa9JEmNM+wlSWqcYS9JUuMMe0mSGjdY2Ce5T5I9SfYn2dLVLk2yO8nrxh634ZokSVrfkHv2NwHnAtcBJDkNOK6qzgKOTXL64dQG7FuSpKZsGWrBVbUf2J9kpfQo4IpuehdwBnD7YdRuGKp3SZJaMslz9tuAfd303m7+cGp3kuTCJEtJlpaXlze1cUmS5tkkw34vsLWb3grcfJi1O6mqnVW1WFWLCwsLm968JEnzapJhfy2jc/gA5zE6l384NUmS1MOQo/GPSbILOBW4HDiG0Tn83cAdVXV9Ve3ZaG2oviVJas2QA/RuY7QXPu6jB3jcxRutSZKk9XlRHUmSGmfYS5LUuMEO40uSdKTbfsllgyz3xh0XHNLj3bOXJKlxhr0kSY0z7CVJapxhL0lS4wx7SZIaZ9hLktQ4w16SpMYZ9pIkNc6wlySpcYa9JEmNM+wlSWpcqmraPWy6JMvAV1aVTwC+NYV2ZqmHaa9/FnqY9vpnoYeh138asGeTljXt5+pQzEuv89InzE+vs9LnA6pq4UB3NBn2B5JkqaoWj+Qepr3+Wehh2uufhR6mvf5DYa+bb176hPnpdR769DC+JEmNM+wlSWrckRT2O6fdANPvYdrrh+n3MO31w/R7mPb6D4W9br556RPmp9eZ7/OIOWcvSdKR6kjas5ck6Yhk2EuS1DjDXpKkxm2ZdgNDSHIc8OvAGcA9gZuB64A3VtV3J9TD0cDTuh62jfXwrqq6fQLrn/pz0PXxcFY9B1W1NMH1T/V5mPZ20PUwE9tCH9PeXvqahde1j3npE9xOh9bkAL0k7wHeAnwI2AtsBc4DfrWqnjyhHt4CfOIAPZxaVc+ZwPpn4Tm4FLgLsGtVD7dX1cUT6mGqz8O0t4Ouh6lvC33MwvbS1yy8rn3MS5/gdjq4qmruBvwNcNSq2lHA30ywh92HUm/0Obj6UOotPg/T3g5m4TmYp+1lnl7XlvrsenI7HfDW5GF84I+BK5N8AtjH6JDQycD/McEe3p3kr4Arx3p4LPDeCa1/Fp6DpSRvBK7oetgKnMvmXTe9j2k/D9PeDmD6z0Ffs7C99DULr2sf89InuJ0OqsnD+ABJtgAPYrTB7AW+WBM+R5VkAVgc62GpqpYnuP5ZeA5+EXjUWA/XVdV/nXAPU30epr0ddD1MfVvoYxa2l75m4XXtY176BLfTITW5Z98N9LgIeDSjARTfAa5LMukBeo9d1cPdk7xrEhvvLDwHnaMYbWfHAEd3t4mZ9vMw7e2g62FWtoU+prq99DULr2sf89InuJ0Orck9+1kY6NENjPkkPzmI49Q6sgboHXuAHo60AXpT2w66Hqa+LfQxC9tLX7PwuvYxL32C2+ngpj1oYIgbMzDQAwfowQwMZJn28zDt7WAWnoN52l7m6XVtqc+uJ7fTAW9NHsZnNgZ6THtgzCw8B7MwkGXaz8O0twOY/nPQ1yxsL33Nwuvax7z0CW6ng2ryMD7caaDHNkaHWr5QR+4AvW1M7zmY+kCWaT8P094Ouh6mvi30MQvbS1+z8Lr2MS99ggP0htTknn2SbVV1M/DZJL8MnA18Kck7akLvbqY9MGYWnoPOtAfoTfV5mPZ20PUwK9tCH3Mx8GkWXtc+5qVPcIDe0Jrcs0/y4ap6fJL/wGijeTfwGOB+VfX8CfUw7QF6s/AcTH0gy7Sfh2lvB10PU98W+piF7aWvWXhd+5iXPsEBekNrcs9+zKOr6uxu+gNJrpzgurdX1a+sqv3XJLsn2ANM9zl4eFU9dlXtnUmunmAPK6b1PMzKdgDT3Rb6mKXtZT2z9LquZV76BLgX8F+q6ofd/HeS/BfghdNr6YDmaTv9kVbD/rTuiX/wyiHMJEcB95hgD+9ZNTBmK6PDp5MaGHNa9wf9C1N8DmZhIMu0n4dZGCA17eegr1nYXvqahde1j3npExygN6gmD+MDJHkIcEdVfbabvxtwSlVdN8EeVgbGPBz4EvC3VXXDhNb9VOCKqvrBWO1uwIOq6r9NoodunTM3kGXSz8PYdrCN0XNww7QHSE1jW+hjFreXg5nF1/VA5nSA3jYcSLqpmvw++ySvAV4GvDTJe5MsdKH37yfYwwe6P6if48cbxW92500n4f8EPpTkXUmem+Snq+oHU/jPfeoDWZKcnOTnV+a7beGnJrX+qlquqvczOne6Fdg+qXUDJHlKF+7jPU1jW+hj6ttLX9N+XfsYG6B3HvAERnugZ3ehOlO6o063dzto9wKeAjw9Sabc2oHMzXa6osk9+yRXr5xTSXIK8IfAi4FXV9XjJ9TDyqCoq4DHrZyHSnJNVZ05gfX/dVU9LsnPAs8AngzcAry7qiZyWGwWBrJ0b/xOAm4DTgD+p6paXnl9JrD+D1TV+UleyOg/2ssYDY77h6r67aHX3/Xwj8BXgG8A7wTeU1XfmcS6D8UsbC99zcLr2secDdBzIOmAZu7d3SY5OsmxVXVrVX0iydOBP2N0/mdSHpzkT4EHMvru43/q6hPbowSoqi8DrwFek+Qk4KkTXP0sDGQ5fdUbv7cnefEE139s9+/T+fGbvjckuWaCPXx+1Ru/dyaZ6Bu/nmZhe+lrFl7XPuZpgN4KB5IOoNWwfxGjd4bfBKiq7yR5CvDMCfbwyO7ffwvcDj/6HOm/ndD6d6wuVNU3gJ0TWj/MxkCWab/xm4k3fTD1N359zML20tfMvK7rONhA4fdMs6mDmIWB1X3M03b6I00extfsGBvIso3RIa9rgS0THKj4CODGqvrmWG0L8PKq+ncTWP8Dxma/VlW3dm/6/s0k1t/18MSquvwA9dMn9Tr0Ne3tpa9ZeF37SvJY4MHAzYzC6Qbgn1XVR6fZ14HMwsDqPrr/Vx7P6Jz97UBV1U/sYM0Sw16D6d6V/0QZ+EBVPWGKPQBcPokeZvg5mGgPfcxLnzA/vXZjVk5kFEgTH7NyKOal1yT/dzd5K6N+v8roTdSJVXXh1BpbR6uH8TUbvgesfkce4JQp9BBg5Z3tJHuY9vpnpYc+ZmF76WtentNpj1k5FPPS6z9fGVOQ5JNV9d9103893bbWZthrSJ8Fnl5Ve8eLSa44gnqY9vpnpYc+5qVPmJ9epz1m5VDMS6/jufnyselZ/Ijgj3gYX4NJ8jPAt6vq1lX1LZO6UMa0e5j2+melhz7mpU+Yn14PMmblaOCZVfWfp9fZT5qXXpOcDHyuqu4Yqx0LnF9VszjwETDsJUlqXpNX0JMkST9m2EuS1DjDXj+SpLqPv6zMvzjJ73XTv7cyMjbJnyT5cpKPd7ffHZv+epKvdtP/LclHkjxpbJnPTPKBbvrGJJ/sHrs09pjjk1yR5Ivdvz99kH7PTHJ9ks91t5n92Iu0mZJ8b9X885K8vpv+uSRXdn9Xn02ys6uf011g50DL82+pcY7G17hbgGck+Q9V9a11HvuSqnrH2PwrYfSmAPheVf1BN/8QRh+h+WtG29u/B84f+7nHHWBdlwAfqqodSS7p5l82/oAk9wbeCjytqvYkOQG4PMlXq+qyQ/idpdb8IXBpVb0bIMlD13qwf0tHBvfsNe52RpfTfdFmLbCqPsXou7NfBvwO8KdV9aV1fuypwJu76TcDTzvAY34D+JOq2tOt51vASxm9MVg5+vCGJEtJvpDkl7v60Ul+P8kNST6R5KKufk63N/SObs/mz5OZ/LYtaT0/A/zDykxVfXKdx/u3dARwz16r/THwiSSvXudxv5/kFd30r6zzH8orGV03+lZG36u9ooAPJingjVW1ct3+k6rqa9301xl9a91qJ/PjNwQrlrjzZ3K3A49gdP3yv07yz4FfBfZW1elJ7gL8TZIPdo//xe7n/xH4G0bfuDVrX2wiAdw1ycfH5o/nx9e7vxT4cJKPAB8E3lRVN6+xLP+WjgCGve6kqvZl9AUfv8mPv9zjQFYfxl9rmd9P8v8yOrx/y9hdZ1bVV5OcCFyR5HNVdfWqn63uzcBGvK37NrIvJvk74OeBXwJOSfLfd4+5J/AgRm9Erq+qfwDo/iPdjv9BaTb9U1U9bGUmyfPo3khX1ZuSXM7odNlTgYuSnHqY6/Nvac55GF8H8lrgBcDdN3GZP+xuP1JVX+3+/Saj71l/RHfXNzK6aMnKxUu+yU/6DPDwVbWHA58eX8Wq+4vRVa7+dVU9rLv9bFWt7I2MvxG5A98Ma05V1T9W1f9TVU9ldHruIWs83L+lI4Bhr59QVTcBb2MU+INIcvck91iZZrSX8Knu7vcAz+2mnwu8+wCL+GPgeUke1i3jXsCrgPHTD89MclSSBwL/DPg8cDnwr5Ic0/3cv+jWLzUhyflj2/e9gXsx+rKWg/Fv6Qjguy0dzGuA/2XA5Z8EvLMbt7MFeGtVfaC7bwfwtiQvAL4CPGv1D1fV15I8B/hP3ZuGAK+tqveOPezvgesZfd/0r1fV/iT/F6NDinu6QUPLHHgAoDSvfgl4XZL93fxLqurrSX7+QA/2b+nI4OVy1aQkfwL8Vd9xBZIOzL+lNngYX5KkxrlnL0lS49yzlySpcYa9JEmNM+wlSWqcYS9JUuMMe0mSGmfYS5LUuP8f5aWMDbgWLVkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#coreelation between random variables. stock prices are your random variables\n",
    "#simply covariance matrix of large data\n",
    "scatter_matrix(economy,figsize=(8,8)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "5932753f-5a42-475f-8485-3eda30d8f75f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#daily percentage change\n",
    "#rt=(Pt/P(t-1))-1\n",
    "nifty_50['returns']=(nifty_50['Close']/nifty_50['Close'].shift(1))-1\n",
    "hsi['returns']=(hsi['Close']/hsi['Close'].shift(1))-1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "5f39c707-697c-429e-b89a-dda7b2764ed5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>MA50</th>\n",
       "      <th>MA200</th>\n",
       "      <th>returns</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2007-09-17 00:00:00+05:30</th>\n",
       "      <td>4518.450195</td>\n",
       "      <td>4549.049805</td>\n",
       "      <td>4482.850098</td>\n",
       "      <td>4494.649902</td>\n",
       "      <td>4494.649902</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-09-18 00:00:00+05:30</th>\n",
       "      <td>4494.100098</td>\n",
       "      <td>4551.799805</td>\n",
       "      <td>4481.549805</td>\n",
       "      <td>4546.200195</td>\n",
       "      <td>4546.200195</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.011469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-09-19 00:00:00+05:30</th>\n",
       "      <td>4550.250000</td>\n",
       "      <td>4739.000000</td>\n",
       "      <td>4550.250000</td>\n",
       "      <td>4732.350098</td>\n",
       "      <td>4732.350098</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.040946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-09-20 00:00:00+05:30</th>\n",
       "      <td>4734.850098</td>\n",
       "      <td>4760.850098</td>\n",
       "      <td>4721.149902</td>\n",
       "      <td>4747.549805</td>\n",
       "      <td>4747.549805</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.003212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-09-21 00:00:00+05:30</th>\n",
       "      <td>4752.950195</td>\n",
       "      <td>4855.700195</td>\n",
       "      <td>4733.700195</td>\n",
       "      <td>4837.549805</td>\n",
       "      <td>4837.549805</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.018957</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  Open         High          Low        Close  \\\n",
       "Date                                                                            \n",
       "2007-09-17 00:00:00+05:30  4518.450195  4549.049805  4482.850098  4494.649902   \n",
       "2007-09-18 00:00:00+05:30  4494.100098  4551.799805  4481.549805  4546.200195   \n",
       "2007-09-19 00:00:00+05:30  4550.250000  4739.000000  4550.250000  4732.350098   \n",
       "2007-09-20 00:00:00+05:30  4734.850098  4760.850098  4721.149902  4747.549805   \n",
       "2007-09-21 00:00:00+05:30  4752.950195  4855.700195  4733.700195  4837.549805   \n",
       "\n",
       "                             Adj Close  Volume  MA50  MA200   returns  \n",
       "Date                                                                   \n",
       "2007-09-17 00:00:00+05:30  4494.649902       0   NaN    NaN       NaN  \n",
       "2007-09-18 00:00:00+05:30  4546.200195       0   NaN    NaN  0.011469  \n",
       "2007-09-19 00:00:00+05:30  4732.350098       0   NaN    NaN  0.040946  \n",
       "2007-09-20 00:00:00+05:30  4747.549805       0   NaN    NaN  0.003212  \n",
       "2007-09-21 00:00:00+05:30  4837.549805       0   NaN    NaN  0.018957  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nifty_50.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "517e3568-e135-46df-9113-f9186f6f1065",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>MA50</th>\n",
       "      <th>MA200</th>\n",
       "      <th>returns</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-03 00:00:00+08:00</th>\n",
       "      <td>17057.699219</td>\n",
       "      <td>17426.160156</td>\n",
       "      <td>17057.699219</td>\n",
       "      <td>17369.630859</td>\n",
       "      <td>17369.630859</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-04 00:00:00+08:00</th>\n",
       "      <td>17303.000000</td>\n",
       "      <td>17303.000000</td>\n",
       "      <td>16933.519531</td>\n",
       "      <td>17072.820312</td>\n",
       "      <td>17072.820312</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.017088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-05 00:00:00+08:00</th>\n",
       "      <td>16608.550781</td>\n",
       "      <td>16608.550781</td>\n",
       "      <td>15688.490234</td>\n",
       "      <td>15846.719727</td>\n",
       "      <td>15846.719727</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.071816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-06 00:00:00+08:00</th>\n",
       "      <td>15942.070312</td>\n",
       "      <td>15971.030273</td>\n",
       "      <td>14763.969727</td>\n",
       "      <td>15153.230469</td>\n",
       "      <td>15153.230469</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.043762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-07 00:00:00+08:00</th>\n",
       "      <td>15329.339844</td>\n",
       "      <td>15513.889648</td>\n",
       "      <td>15108.750000</td>\n",
       "      <td>15405.629883</td>\n",
       "      <td>15405.629883</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.016656</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   Open          High           Low  \\\n",
       "Date                                                                  \n",
       "2000-01-03 00:00:00+08:00  17057.699219  17426.160156  17057.699219   \n",
       "2000-01-04 00:00:00+08:00  17303.000000  17303.000000  16933.519531   \n",
       "2000-01-05 00:00:00+08:00  16608.550781  16608.550781  15688.490234   \n",
       "2000-01-06 00:00:00+08:00  15942.070312  15971.030273  14763.969727   \n",
       "2000-01-07 00:00:00+08:00  15329.339844  15513.889648  15108.750000   \n",
       "\n",
       "                                  Close     Adj Close  Volume  MA50  MA200  \\\n",
       "Date                                                                         \n",
       "2000-01-03 00:00:00+08:00  17369.630859  17369.630859       0   NaN    NaN   \n",
       "2000-01-04 00:00:00+08:00  17072.820312  17072.820312       0   NaN    NaN   \n",
       "2000-01-05 00:00:00+08:00  15846.719727  15846.719727       0   NaN    NaN   \n",
       "2000-01-06 00:00:00+08:00  15153.230469  15153.230469       0   NaN    NaN   \n",
       "2000-01-07 00:00:00+08:00  15405.629883  15405.629883       0   NaN    NaN   \n",
       "\n",
       "                            returns  \n",
       "Date                                 \n",
       "2000-01-03 00:00:00+08:00       NaN  \n",
       "2000-01-04 00:00:00+08:00 -0.017088  \n",
       "2000-01-05 00:00:00+08:00 -0.071816  \n",
       "2000-01-06 00:00:00+08:00 -0.043762  \n",
       "2000-01-07 00:00:00+08:00  0.016656  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hsi.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "aa97ef1e-900e-45a9-bfef-2dae29257709",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x122b467d0>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 936x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#histograms\n",
    "nifty_50['returns'].hist(bins=100,alpha=0.5,label=\"NIFTY 50 returns\",figsize=(13,6))\n",
    "hsi['returns'].hist(bins=100,alpha=0.50,label=\"HSI returns\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "10c0ed01-afbc-488b-bab2-c896a6a6d87d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x122ecfa90>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "nifty_50['returns'].plot(kind='kde',label=\"NIFTY50 returns\",figsize=(15,6))\n",
    "hsi['returns'].plot(kind='kde',label=\"HSI returns\",figsize=(15,6))\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "48f90c35-2765-479c-a448-ad1742372159",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Cumulative return vs time')"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#cumulative return\n",
    "#formula: It=(1+rt)i(t-1)\n",
    "\n",
    "nifty_50[\"cumulative returns\"]=(1+nifty_50['returns']).cumprod()\n",
    "hsi[\"cumulative returns\"]=(1+hsi['returns']).cumprod()\n",
    "nifty_50[\"cumulative returns\"].plot(label=\"NIFTY 50 cum. returns\", figsize=(13,5))\n",
    "hsi[\"cumulative returns\"].plot(label=\"HSI cum. returns\", figsize=(13,5))\n",
    "plt.legend()\n",
    "plt.title(\"Cumulative return vs time\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b23da938-32ad-40b2-971c-3f139e58adac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3925.731829118365"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "std_dev = np.std(nifty_50['Close'])\n",
    "std_dev\n",
    "std_dev\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "81f1bbb7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5703.734685530006"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "std_devc = np.std(hsi['Close'])\n",
    "std_devc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1a389ac1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.title('Standard Deviation of Close Price of NIFTY 50')\n",
    "plt.plot(nifty_50['Close'])\n",
    "plt.axhline(y=std_dev, color='r', linestyle='-')\n",
    "plt.axhline(y=-std_dev, color='r', linestyle='-')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Close Price')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e6e31bfe",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.title('Standard Deviation of Close Price of HSI')\n",
    "plt.plot(hsi['Close'])\n",
    "plt.axhline(y=std_dev, color='r', linestyle='-')\n",
    "plt.axhline(y=-std_dev, color='r', linestyle='-')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Close Price')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "raw",
   "id": "a6b0143e-b5b4-41fb-b1ee-2b08de9113d2",
   "metadata": {},
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, LSTM\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0edab90d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-04-12 12:51:03.423322: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_2_grad/concat/split_2/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_2_grad/concat/split_2/split_dim}}]]\n",
      "2023-04-12 12:51:03.425472: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_grad/concat/split/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_grad/concat/split/split_dim}}]]\n",
      "2023-04-12 12:51:03.431335: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_1_grad/concat/split_1/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_1_grad/concat/split_1/split_dim}}]]\n",
      "2023-04-12 12:51:04.137903: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_2_grad/concat/split_2/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_2_grad/concat/split_2/split_dim}}]]\n",
      "2023-04-12 12:51:04.140788: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_grad/concat/split/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_grad/concat/split/split_dim}}]]\n",
      "2023-04-12 12:51:04.142795: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_1_grad/concat/split_1/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_1_grad/concat/split_1/split_dim}}]]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-04-12 12:51:04.522537: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_2_grad/concat/split_2/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_2_grad/concat/split_2/split_dim}}]]\n",
      "2023-04-12 12:51:04.525636: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_grad/concat/split/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_grad/concat/split/split_dim}}]]\n",
      "2023-04-12 12:51:04.527583: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_1_grad/concat/split_1/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_1_grad/concat/split_1/split_dim}}]]\n",
      "2023-04-12 12:51:05.067592: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_2_grad/concat/split_2/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_2_grad/concat/split_2/split_dim}}]]\n",
      "2023-04-12 12:51:05.070823: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_grad/concat/split/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_grad/concat/split/split_dim}}]]\n",
      "2023-04-12 12:51:05.072950: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_1_grad/concat/split_1/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_1_grad/concat/split_1/split_dim}}]]\n",
      "2023-04-12 12:51:05.470609: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_2_grad/concat/split_2/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_2_grad/concat/split_2/split_dim}}]]\n",
      "2023-04-12 12:51:05.474245: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_grad/concat/split/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_grad/concat/split/split_dim}}]]\n",
      "2023-04-12 12:51:05.477153: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_1_grad/concat/split_1/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_1_grad/concat/split_1/split_dim}}]]\n",
      "2023-04-12 12:51:05.824723: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_2_grad/concat/split_2/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_2_grad/concat/split_2/split_dim}}]]\n",
      "2023-04-12 12:51:05.827332: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_grad/concat/split/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_grad/concat/split/split_dim}}]]\n",
      "2023-04-12 12:51:05.830535: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_1_grad/concat/split_1/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_1_grad/concat/split_1/split_dim}}]]\n",
      "2023-04-12 12:51:09.701416: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_2_grad/concat/split_2/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_2_grad/concat/split_2/split_dim}}]]\n",
      "2023-04-12 12:51:09.708418: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_grad/concat/split/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_grad/concat/split/split_dim}}]]\n",
      "2023-04-12 12:51:09.712154: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_1_grad/concat/split_1/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_1_grad/concat/split_1/split_dim}}]]\n",
      "2023-04-12 12:51:10.205428: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_2_grad/concat/split_2/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_2_grad/concat/split_2/split_dim}}]]\n",
      "2023-04-12 12:51:10.209749: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_grad/concat/split/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_grad/concat/split/split_dim}}]]\n",
      "2023-04-12 12:51:10.211995: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_1_grad/concat/split_1/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_1_grad/concat/split_1/split_dim}}]]\n",
      "2023-04-12 12:51:10.733939: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_2_grad/concat/split_2/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_2_grad/concat/split_2/split_dim}}]]\n",
      "2023-04-12 12:51:10.737123: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_grad/concat/split/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_grad/concat/split/split_dim}}]]\n",
      "2023-04-12 12:51:10.739045: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_1_grad/concat/split_1/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_1_grad/concat/split_1/split_dim}}]]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "106/106 [==============================] - 72s 533ms/step - loss: 0.0090\n",
      "Epoch 2/50\n",
      "106/106 [==============================] - 46s 436ms/step - loss: 3.8617e-04\n",
      "Epoch 3/50\n",
      "106/106 [==============================] - 45s 425ms/step - loss: 3.7009e-04\n",
      "Epoch 4/50\n",
      "106/106 [==============================] - 52s 494ms/step - loss: 3.7315e-04\n",
      "Epoch 5/50\n",
      "106/106 [==============================] - 59s 553ms/step - loss: 4.1501e-04\n",
      "Epoch 6/50\n",
      "106/106 [==============================] - 47s 441ms/step - loss: 3.2223e-04\n",
      "Epoch 7/50\n",
      "106/106 [==============================] - 49s 463ms/step - loss: 3.1112e-04\n",
      "Epoch 8/50\n",
      "106/106 [==============================] - 51s 480ms/step - loss: 3.0854e-04\n",
      "Epoch 9/50\n",
      "106/106 [==============================] - 50s 468ms/step - loss: 3.4778e-04\n",
      "Epoch 10/50\n",
      "106/106 [==============================] - 45s 426ms/step - loss: 3.1907e-04\n",
      "Epoch 11/50\n",
      "106/106 [==============================] - 45s 423ms/step - loss: 2.8002e-04\n",
      "Epoch 12/50\n",
      "106/106 [==============================] - 46s 432ms/step - loss: 2.3190e-04\n",
      "Epoch 13/50\n",
      "106/106 [==============================] - 46s 433ms/step - loss: 2.5301e-04\n",
      "Epoch 14/50\n",
      "106/106 [==============================] - 49s 464ms/step - loss: 2.4858e-04\n",
      "Epoch 15/50\n",
      "106/106 [==============================] - 53s 501ms/step - loss: 2.4251e-04\n",
      "Epoch 16/50\n",
      "106/106 [==============================] - 50s 474ms/step - loss: 2.0631e-04\n",
      "Epoch 17/50\n",
      "106/106 [==============================] - 56s 530ms/step - loss: 1.6423e-04\n",
      "Epoch 18/50\n",
      "106/106 [==============================] - 55s 515ms/step - loss: 1.6837e-04\n",
      "Epoch 19/50\n",
      "106/106 [==============================] - 50s 475ms/step - loss: 1.5650e-04\n",
      "Epoch 20/50\n",
      "106/106 [==============================] - 51s 485ms/step - loss: 1.3923e-04\n",
      "Epoch 21/50\n",
      "106/106 [==============================] - 48s 454ms/step - loss: 1.7531e-04\n",
      "Epoch 22/50\n",
      "106/106 [==============================] - 50s 470ms/step - loss: 1.2582e-04\n",
      "Epoch 23/50\n",
      "106/106 [==============================] - 49s 466ms/step - loss: 1.3557e-04\n",
      "Epoch 24/50\n",
      "106/106 [==============================] - 46s 435ms/step - loss: 1.0730e-04\n",
      "Epoch 25/50\n",
      "106/106 [==============================] - 45s 425ms/step - loss: 1.0760e-04\n",
      "Epoch 26/50\n",
      "106/106 [==============================] - 45s 428ms/step - loss: 9.4729e-05\n",
      "Epoch 27/50\n",
      "106/106 [==============================] - 45s 428ms/step - loss: 9.4225e-05\n",
      "Epoch 28/50\n",
      "106/106 [==============================] - 45s 426ms/step - loss: 1.0454e-04\n",
      "Epoch 29/50\n",
      "106/106 [==============================] - 46s 430ms/step - loss: 1.3192e-04\n",
      "Epoch 30/50\n",
      "106/106 [==============================] - 46s 433ms/step - loss: 9.8918e-05\n",
      "Epoch 31/50\n",
      "106/106 [==============================] - 46s 434ms/step - loss: 8.5472e-05\n",
      "Epoch 32/50\n",
      "106/106 [==============================] - 45s 426ms/step - loss: 8.9043e-05\n",
      "Epoch 33/50\n",
      "106/106 [==============================] - 45s 424ms/step - loss: 7.8031e-05\n",
      "Epoch 34/50\n",
      "106/106 [==============================] - 45s 424ms/step - loss: 1.1857e-04\n",
      "Epoch 35/50\n",
      "106/106 [==============================] - 45s 427ms/step - loss: 8.0978e-05\n",
      "Epoch 36/50\n",
      "106/106 [==============================] - 45s 425ms/step - loss: 7.8361e-05\n",
      "Epoch 37/50\n",
      "106/106 [==============================] - 45s 428ms/step - loss: 9.4786e-05\n",
      "Epoch 38/50\n",
      "106/106 [==============================] - 45s 425ms/step - loss: 7.9790e-05\n",
      "Epoch 39/50\n",
      "106/106 [==============================] - 45s 425ms/step - loss: 7.9013e-05\n",
      "Epoch 40/50\n",
      "106/106 [==============================] - 45s 429ms/step - loss: 6.8526e-05\n",
      "Epoch 41/50\n",
      "106/106 [==============================] - 1845s 18s/step - loss: 7.5085e-05\n",
      "Epoch 42/50\n",
      "106/106 [==============================] - 180s 2s/step - loss: 6.3819e-05\n",
      "Epoch 43/50\n",
      "106/106 [==============================] - 1989s 19s/step - loss: 9.2990e-05\n",
      "Epoch 44/50\n",
      "106/106 [==============================] - 1776s 17s/step - loss: 6.8816e-05\n",
      "Epoch 45/50\n",
      "106/106 [==============================] - 1185s 11s/step - loss: 6.3845e-05\n",
      "Epoch 46/50\n",
      "106/106 [==============================] - 2377s 23s/step - loss: 7.5380e-05\n",
      "Epoch 47/50\n",
      "106/106 [==============================] - 3118s 30s/step - loss: 6.6125e-05\n",
      "Epoch 48/50\n",
      "106/106 [==============================] - 171s 2s/step - loss: 8.4972e-05\n",
      "Epoch 49/50\n",
      "106/106 [==============================] - 144s 1s/step - loss: 6.2393e-05\n",
      "Epoch 50/50\n",
      "106/106 [==============================] - 231s 2s/step - loss: 8.5505e-05\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-04-12 17:00:17.547078: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_2_grad/concat/split_2/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_2_grad/concat/split_2/split_dim}}]]\n",
      "2023-04-12 17:00:17.550291: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_grad/concat/split/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_grad/concat/split/split_dim}}]]\n",
      "2023-04-12 17:00:17.552600: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_1_grad/concat/split_1/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_1_grad/concat/split_1/split_dim}}]]\n",
      "2023-04-12 17:00:17.827074: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_2_grad/concat/split_2/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_2_grad/concat/split_2/split_dim}}]]\n",
      "2023-04-12 17:00:17.829890: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_grad/concat/split/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_grad/concat/split/split_dim}}]]\n",
      "2023-04-12 17:00:17.831850: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_1_grad/concat/split_1/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_1_grad/concat/split_1/split_dim}}]]\n",
      "2023-04-12 17:00:18.093994: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_2_grad/concat/split_2/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_2_grad/concat/split_2/split_dim}}]]\n",
      "2023-04-12 17:00:18.096348: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_grad/concat/split/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_grad/concat/split/split_dim}}]]\n",
      "2023-04-12 17:00:18.098106: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_1_grad/concat/split_1/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_1_grad/concat/split_1/split_dim}}]]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 1s 1s/step\n",
      "[[18474.117]]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Preprocess the data\n",
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "scaled_data = scaler.fit_transform(nifty_50['Close'].values.reshape(-1,1))\n",
    "\n",
    "prediction_days = 365\n",
    "\n",
    "x_train = []\n",
    "y_train = []\n",
    "\n",
    "for x in range(prediction_days, len(scaled_data)):\n",
    "    x_train.append(scaled_data[x-prediction_days:x, 0])\n",
    "    y_train.append(scaled_data[x, 0])\n",
    "\n",
    "x_train = np.array(x_train)\n",
    "y_train = np.array(y_train)\n",
    "\n",
    "x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))\n",
    "\n",
    "# Build the LSTM model\n",
    "model = Sequential()\n",
    "\n",
    "model.add(LSTM(units=50, return_sequences=True, input_shape=(x_train.shape[1], 1)))\n",
    "model.add(LSTM(units=50, return_sequences=True))\n",
    "model.add(LSTM(units=50))\n",
    "model.add(Dense(units=1))\n",
    "\n",
    "model.compile(optimizer='adam', loss='mean_squared_error')\n",
    "\n",
    "# Train the model\n",
    "model.fit(x_train, y_train, epochs=50, batch_size=32)\n",
    "\n",
    "# Prepare the test dataset\n",
    "test_data = scaled_data[-prediction_days:]\n",
    "x_test = []\n",
    "\n",
    "x_test.append(test_data[:, 0])\n",
    "x_test = np.array(x_test)\n",
    "x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1))\n",
    "\n",
    "# Predict future stock prices\n",
    "predicted_price = model.predict(x_test)\n",
    "predicted_price = scaler.inverse_transform(predicted_price)\n",
    "\n",
    "print(predicted_price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a64bfd63",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-04-14 10:33:06.888926: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_2_grad/concat/split_2/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_2_grad/concat/split_2/split_dim}}]]\n",
      "2023-04-14 10:33:06.891474: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_grad/concat/split/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_grad/concat/split/split_dim}}]]\n",
      "2023-04-14 10:33:06.893314: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_1_grad/concat/split_1/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_1_grad/concat/split_1/split_dim}}]]\n",
      "2023-04-14 10:33:07.210040: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_2_grad/concat/split_2/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_2_grad/concat/split_2/split_dim}}]]\n",
      "2023-04-14 10:33:07.213191: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_grad/concat/split/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_grad/concat/split/split_dim}}]]\n",
      "2023-04-14 10:33:07.214885: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_1_grad/concat/split_1/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_1_grad/concat/split_1/split_dim}}]]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-04-14 10:33:07.542958: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_2_grad/concat/split_2/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_2_grad/concat/split_2/split_dim}}]]\n",
      "2023-04-14 10:33:07.545591: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_grad/concat/split/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_grad/concat/split/split_dim}}]]\n",
      "2023-04-14 10:33:07.547410: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_1_grad/concat/split_1/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_1_grad/concat/split_1/split_dim}}]]\n",
      "2023-04-14 10:33:08.057840: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_2_grad/concat/split_2/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_2_grad/concat/split_2/split_dim}}]]\n",
      "2023-04-14 10:33:08.061051: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_grad/concat/split/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_grad/concat/split/split_dim}}]]\n",
      "2023-04-14 10:33:08.063431: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_1_grad/concat/split_1/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_1_grad/concat/split_1/split_dim}}]]\n",
      "2023-04-14 10:33:08.326923: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_2_grad/concat/split_2/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_2_grad/concat/split_2/split_dim}}]]\n",
      "2023-04-14 10:33:08.329731: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_grad/concat/split/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_grad/concat/split/split_dim}}]]\n",
      "2023-04-14 10:33:08.331888: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_1_grad/concat/split_1/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_1_grad/concat/split_1/split_dim}}]]\n",
      "2023-04-14 10:33:08.635541: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_2_grad/concat/split_2/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_2_grad/concat/split_2/split_dim}}]]\n",
      "2023-04-14 10:33:08.638552: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_grad/concat/split/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_grad/concat/split/split_dim}}]]\n",
      "2023-04-14 10:33:08.640739: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_1_grad/concat/split_1/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_1_grad/concat/split_1/split_dim}}]]\n",
      "2023-04-14 10:33:10.592223: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_2_grad/concat/split_2/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_2_grad/concat/split_2/split_dim}}]]\n",
      "2023-04-14 10:33:10.595182: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_grad/concat/split/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_grad/concat/split/split_dim}}]]\n",
      "2023-04-14 10:33:10.597517: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_1_grad/concat/split_1/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_1_grad/concat/split_1/split_dim}}]]\n",
      "2023-04-14 10:33:10.932763: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_2_grad/concat/split_2/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_2_grad/concat/split_2/split_dim}}]]\n",
      "2023-04-14 10:33:10.935324: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_grad/concat/split/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_grad/concat/split/split_dim}}]]\n",
      "2023-04-14 10:33:10.938481: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_1_grad/concat/split_1/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_1_grad/concat/split_1/split_dim}}]]\n",
      "2023-04-14 10:33:11.285661: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_2_grad/concat/split_2/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_2_grad/concat/split_2/split_dim}}]]\n",
      "2023-04-14 10:33:11.288581: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_grad/concat/split/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_grad/concat/split/split_dim}}]]\n",
      "2023-04-14 10:33:11.290614: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_1_grad/concat/split_1/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_1_grad/concat/split_1/split_dim}}]]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "166/166 [==============================] - 87s 481ms/step - loss: 0.0092\n",
      "Epoch 2/50\n",
      "166/166 [==============================] - 70s 424ms/step - loss: 9.7140e-04\n",
      "Epoch 3/50\n",
      "166/166 [==============================] - 82s 496ms/step - loss: 8.2667e-04\n",
      "Epoch 4/50\n",
      "166/166 [==============================] - 97s 587ms/step - loss: 7.3966e-04\n",
      "Epoch 5/50\n",
      "166/166 [==============================] - 89s 534ms/step - loss: 6.9249e-04\n",
      "Epoch 6/50\n",
      "166/166 [==============================] - 78s 467ms/step - loss: 5.8736e-04\n",
      "Epoch 7/50\n",
      "166/166 [==============================] - 70s 423ms/step - loss: 5.2356e-04\n",
      "Epoch 8/50\n",
      "166/166 [==============================] - 75s 454ms/step - loss: 4.9477e-04\n",
      "Epoch 9/50\n",
      "166/166 [==============================] - 79s 476ms/step - loss: 4.2371e-04\n",
      "Epoch 10/50\n",
      "166/166 [==============================] - 77s 466ms/step - loss: 3.7093e-04\n",
      "Epoch 11/50\n",
      "141/166 [========================>.....] - ETA: 12s - loss: 3.5793e-04"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Input \u001b[0;32mIn [8]\u001b[0m, in \u001b[0;36m<cell line: 30>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     27\u001b[0m model\u001b[38;5;241m.\u001b[39mcompile(optimizer\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124madam\u001b[39m\u001b[38;5;124m'\u001b[39m, loss\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmean_squared_error\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m     29\u001b[0m \u001b[38;5;66;03m# Train the model\u001b[39;00m\n\u001b[0;32m---> 30\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m50\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m32\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m     32\u001b[0m \u001b[38;5;66;03m# Prepare the test dataset\u001b[39;00m\n\u001b[1;32m     33\u001b[0m test_data \u001b[38;5;241m=\u001b[39m scaled_data[\u001b[38;5;241m-\u001b[39mprediction_days:]\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/keras/utils/traceback_utils.py:65\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     63\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m     64\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 65\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     66\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m     67\u001b[0m     filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/keras/engine/training.py:1685\u001b[0m, in \u001b[0;36mModel.fit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   1677\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m tf\u001b[38;5;241m.\u001b[39mprofiler\u001b[38;5;241m.\u001b[39mexperimental\u001b[38;5;241m.\u001b[39mTrace(\n\u001b[1;32m   1678\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtrain\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m   1679\u001b[0m     epoch_num\u001b[38;5;241m=\u001b[39mepoch,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1682\u001b[0m     _r\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m,\n\u001b[1;32m   1683\u001b[0m ):\n\u001b[1;32m   1684\u001b[0m     callbacks\u001b[38;5;241m.\u001b[39mon_train_batch_begin(step)\n\u001b[0;32m-> 1685\u001b[0m     tmp_logs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain_function\u001b[49m\u001b[43m(\u001b[49m\u001b[43miterator\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1686\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m data_handler\u001b[38;5;241m.\u001b[39mshould_sync:\n\u001b[1;32m   1687\u001b[0m         context\u001b[38;5;241m.\u001b[39masync_wait()\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/tensorflow/python/util/traceback_utils.py:150\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    148\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    149\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 150\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    151\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    152\u001b[0m   filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py:894\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    891\u001b[0m compiler \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mxla\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnonXla\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    893\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m OptionalXlaContext(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile):\n\u001b[0;32m--> 894\u001b[0m   result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    896\u001b[0m new_tracing_count \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexperimental_get_tracing_count()\n\u001b[1;32m    897\u001b[0m without_tracing \u001b[38;5;241m=\u001b[39m (tracing_count \u001b[38;5;241m==\u001b[39m new_tracing_count)\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py:926\u001b[0m, in \u001b[0;36mFunction._call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    923\u001b[0m   \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock\u001b[38;5;241m.\u001b[39mrelease()\n\u001b[1;32m    924\u001b[0m   \u001b[38;5;66;03m# In this case we have created variables on the first call, so we run the\u001b[39;00m\n\u001b[1;32m    925\u001b[0m   \u001b[38;5;66;03m# defunned version which is guaranteed to never create variables.\u001b[39;00m\n\u001b[0;32m--> 926\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_no_variable_creation_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# pylint: disable=not-callable\u001b[39;00m\n\u001b[1;32m    927\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_variable_creation_fn \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    928\u001b[0m   \u001b[38;5;66;03m# Release the lock early so that multiple threads can perform the call\u001b[39;00m\n\u001b[1;32m    929\u001b[0m   \u001b[38;5;66;03m# in parallel.\u001b[39;00m\n\u001b[1;32m    930\u001b[0m   \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock\u001b[38;5;241m.\u001b[39mrelease()\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/tensorflow/python/eager/polymorphic_function/tracing_compiler.py:143\u001b[0m, in \u001b[0;36mTracingCompiler.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    140\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock:\n\u001b[1;32m    141\u001b[0m   (concrete_function,\n\u001b[1;32m    142\u001b[0m    filtered_flat_args) \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_maybe_define_function(args, kwargs)\n\u001b[0;32m--> 143\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mconcrete_function\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_flat\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    144\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfiltered_flat_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcaptured_inputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconcrete_function\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcaptured_inputs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/tensorflow/python/eager/polymorphic_function/monomorphic_function.py:1757\u001b[0m, in \u001b[0;36mConcreteFunction._call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1753\u001b[0m possible_gradient_type \u001b[38;5;241m=\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPossibleTapeGradientTypes(args)\n\u001b[1;32m   1754\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (possible_gradient_type \u001b[38;5;241m==\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPOSSIBLE_GRADIENT_TYPES_NONE\n\u001b[1;32m   1755\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m executing_eagerly):\n\u001b[1;32m   1756\u001b[0m   \u001b[38;5;66;03m# No tape is watching; skip to running the function.\u001b[39;00m\n\u001b[0;32m-> 1757\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_build_call_outputs(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_inference_function\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1758\u001b[0m \u001b[43m      \u001b[49m\u001b[43mctx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcancellation_manager\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcancellation_manager\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m   1759\u001b[0m forward_backward \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_select_forward_and_backward_functions(\n\u001b[1;32m   1760\u001b[0m     args,\n\u001b[1;32m   1761\u001b[0m     possible_gradient_type,\n\u001b[1;32m   1762\u001b[0m     executing_eagerly)\n\u001b[1;32m   1763\u001b[0m forward_function, args_with_tangents \u001b[38;5;241m=\u001b[39m forward_backward\u001b[38;5;241m.\u001b[39mforward()\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/tensorflow/python/eager/polymorphic_function/monomorphic_function.py:381\u001b[0m, in \u001b[0;36m_EagerDefinedFunction.call\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m    379\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m _InterpolateFunctionError(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m    380\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m cancellation_manager \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 381\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m \u001b[43mexecute\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexecute\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    382\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msignature\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    383\u001b[0m \u001b[43m        \u001b[49m\u001b[43mnum_outputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_num_outputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    384\u001b[0m \u001b[43m        \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    385\u001b[0m \u001b[43m        \u001b[49m\u001b[43mattrs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    386\u001b[0m \u001b[43m        \u001b[49m\u001b[43mctx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mctx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    387\u001b[0m   \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    388\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m execute\u001b[38;5;241m.\u001b[39mexecute_with_cancellation(\n\u001b[1;32m    389\u001b[0m         \u001b[38;5;28mstr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msignature\u001b[38;5;241m.\u001b[39mname),\n\u001b[1;32m    390\u001b[0m         num_outputs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_outputs,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    393\u001b[0m         ctx\u001b[38;5;241m=\u001b[39mctx,\n\u001b[1;32m    394\u001b[0m         cancellation_manager\u001b[38;5;241m=\u001b[39mcancellation_manager)\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/tensorflow/python/eager/execute.py:52\u001b[0m, in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     50\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m     51\u001b[0m   ctx\u001b[38;5;241m.\u001b[39mensure_initialized()\n\u001b[0;32m---> 52\u001b[0m   tensors \u001b[38;5;241m=\u001b[39m \u001b[43mpywrap_tfe\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mTFE_Py_Execute\u001b[49m\u001b[43m(\u001b[49m\u001b[43mctx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_handle\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mop_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     53\u001b[0m \u001b[43m                                      \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_outputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     54\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m core\u001b[38;5;241m.\u001b[39m_NotOkStatusException \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m     55\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# Preprocess the data\n",
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "scaled_data = scaler.fit_transform(hsi['Close'].values.reshape(-1,1))\n",
    "\n",
    "prediction_days = 365\n",
    "\n",
    "x_train = []\n",
    "y_train = []\n",
    "\n",
    "for x in range(prediction_days, len(scaled_data)):\n",
    "    x_train.append(scaled_data[x-prediction_days:x, 0])\n",
    "    y_train.append(scaled_data[x, 0])\n",
    "\n",
    "x_train = np.array(x_train)\n",
    "y_train = np.array(y_train)\n",
    "\n",
    "x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))\n",
    "\n",
    "# Build the LSTM model\n",
    "model = Sequential()\n",
    "\n",
    "model.add(LSTM(units=50, return_sequences=True, input_shape=(x_train.shape[1], 1)))\n",
    "model.add(LSTM(units=50, return_sequences=True))\n",
    "model.add(LSTM(units=50))\n",
    "model.add(Dense(units=1))\n",
    "\n",
    "model.compile(optimizer='adam', loss='mean_squared_error')\n",
    "\n",
    "# Train the model\n",
    "model.fit(x_train, y_train, epochs=50, batch_size=32)\n",
    "\n",
    "# Prepare the test dataset\n",
    "test_data = scaled_data[-prediction_days:]\n",
    "x_test = []\n",
    "\n",
    "x_test.append(test_data[:, 0])\n",
    "x_test = np.array(x_test)\n",
    "x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1))\n",
    "\n",
    "# Predict future stock prices\n",
    "predicted_price = model.predict(x_test)\n",
    "predicted_price = scaler.inverse_transform(predicted_price)\n",
    "\n",
    "print(predicted_price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "147dacb5",
   "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": [
    "from statsmodels.tsa.stattools import adfuller\n",
    "\n",
    "\n",
    "# Extract the closing prices from the data\n",
    "closing_prices = nifty_50['Close']\n",
    "\n",
    "# Perform the ADF test on the closing prices\n",
    "adf_test_results = adfuller(closing_prices)\n",
    "\n",
    "# Extract the test statistic, p-value, and critical values from the test results\n",
    "test_statistic = adf_test_results[0]\n",
    "p_value = adf_test_results[1]\n",
    "critical_values = adf_test_results[4]\n",
    "\n",
    "# Plot the closing prices and add the ADF test results to the plot\n",
    "plt.plot(closing_prices)\n",
    "plt.title('Nifty 50 Closing Prices')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Closing Price')\n",
    "plt.text(closing_prices.index[0], closing_prices.max()*0.9, f'Test statistic: {test_statistic:.2f}\\n'\n",
    "                                                         f'P-value: {p_value:.2f}\\n'\n",
    "                                                         f'Critical values: {critical_values}')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "95d29a15",
   "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": [
    "from statsmodels.tsa.stattools import adfuller\n",
    "\n",
    "\n",
    "# Extract the closing prices from the data\n",
    "closing_prices = hsi['Close']\n",
    "\n",
    "# Perform the ADF test on the closing prices\n",
    "adf_test_results = adfuller(closing_prices)\n",
    "\n",
    "# Extract the test statistic, p-value, and critical values from the test results\n",
    "test_statistic = adf_test_results[0]\n",
    "p_value = adf_test_results[1]\n",
    "critical_values = adf_test_results[4]\n",
    "\n",
    "# Plot the closing prices and add the ADF test results to the plot\n",
    "plt.plot(closing_prices)\n",
    "plt.title('HSI Closing Prices')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Closing Price')\n",
    "plt.text(closing_prices.index[0], closing_prices.max()*0.9, f'Test statistic: {test_statistic:.2f}\\n'\n",
    "                                                         f'P-value: {p_value:.2f}\\n'\n",
    "                                                         f'Critical values: {critical_values}')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "6679a0c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Date\n",
      "2007-09-28 00:00:00+05:30    0.004151\n",
      "2007-10-01 00:00:00+05:30    0.009435\n",
      "2007-10-03 00:00:00+05:30    0.027600\n",
      "2007-10-04 00:00:00+05:30   -0.000413\n",
      "2007-10-05 00:00:00+05:30   -0.004387\n",
      "                               ...   \n",
      "2022-12-05 00:00:00+05:30    0.000265\n",
      "2022-12-06 00:00:00+05:30   -0.003122\n",
      "2022-12-07 00:00:00+05:30   -0.004422\n",
      "2022-12-08 00:00:00+05:30    0.002628\n",
      "2022-12-09 00:00:00+05:30   -0.006077\n",
      "Name: log_returns, Length: 3723, dtype: float64\n",
      "[[ 0.00415097]\n",
      " [ 0.00943489]\n",
      " [ 0.02759962]\n",
      " ...\n",
      " [-0.00442166]\n",
      " [ 0.00262845]\n",
      " [-0.00607721]]\n",
      "(3723, 1)\n"
     ]
    },
    {
     "ename": "LinAlgError",
     "evalue": "1-dimensional array given. Array must be at least two-dimensional",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mLinAlgError\u001b[0m                               Traceback (most recent call last)",
      "Input \u001b[0;32mIn [70]\u001b[0m, in \u001b[0;36m<cell line: 32>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     29\u001b[0m \u001b[38;5;28mprint\u001b[39m(data\u001b[38;5;241m.\u001b[39mshape)\n\u001b[1;32m     31\u001b[0m \u001b[38;5;66;03m# Perform a Johansen cointegration test to check for the presence of a long-run relationship\u001b[39;00m\n\u001b[0;32m---> 32\u001b[0m coint_test \u001b[38;5;241m=\u001b[39m \u001b[43mcoint_johansen\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdet_order\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mk_ar_diff\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m     33\u001b[0m trace_stat \u001b[38;5;241m=\u001b[39m coint_test\u001b[38;5;241m.\u001b[39mtrace_stat[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m     34\u001b[0m crit_val_90 \u001b[38;5;241m=\u001b[39m coint_test\u001b[38;5;241m.\u001b[39mtrace_stat_crit_vals[\u001b[38;5;241m1\u001b[39m]\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/statsmodels/tsa/vector_ar/vecm.py:689\u001b[0m, in \u001b[0;36mcoint_johansen\u001b[0;34m(endog, det_order, k_ar_diff)\u001b[0m\n\u001b[1;32m    686\u001b[0m dx \u001b[38;5;241m=\u001b[39m dx[k_ar_diff:]\n\u001b[1;32m    688\u001b[0m dx \u001b[38;5;241m=\u001b[39m detrend(dx, f)\n\u001b[0;32m--> 689\u001b[0m r0t \u001b[38;5;241m=\u001b[39m \u001b[43mresid\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mz\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    690\u001b[0m \u001b[38;5;66;03m# GH 5731, [:-0] does not work, need [:t-0]\u001b[39;00m\n\u001b[1;32m    691\u001b[0m lx \u001b[38;5;241m=\u001b[39m endog[: (endog\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m-\u001b[39m k_ar_diff)]\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/statsmodels/tsa/vector_ar/vecm.py:668\u001b[0m, in \u001b[0;36mcoint_johansen.<locals>.resid\u001b[0;34m(y, x)\u001b[0m\n\u001b[1;32m    666\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m x\u001b[38;5;241m.\u001b[39msize \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m    667\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m y\n\u001b[0;32m--> 668\u001b[0m r \u001b[38;5;241m=\u001b[39m y \u001b[38;5;241m-\u001b[39m np\u001b[38;5;241m.\u001b[39mdot(x, np\u001b[38;5;241m.\u001b[39mdot(\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlinalg\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpinv\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m, y))\n\u001b[1;32m    669\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m r\n",
      "File \u001b[0;32m<__array_function__ internals>:180\u001b[0m, in \u001b[0;36mpinv\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/numpy/linalg/linalg.py:1990\u001b[0m, in \u001b[0;36mpinv\u001b[0;34m(a, rcond, hermitian)\u001b[0m\n\u001b[1;32m   1988\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m wrap(res)\n\u001b[1;32m   1989\u001b[0m a \u001b[38;5;241m=\u001b[39m a\u001b[38;5;241m.\u001b[39mconjugate()\n\u001b[0;32m-> 1990\u001b[0m u, s, vt \u001b[38;5;241m=\u001b[39m \u001b[43msvd\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfull_matrices\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhermitian\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhermitian\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1992\u001b[0m \u001b[38;5;66;03m# discard small singular values\u001b[39;00m\n\u001b[1;32m   1993\u001b[0m cutoff \u001b[38;5;241m=\u001b[39m rcond[\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m, newaxis] \u001b[38;5;241m*\u001b[39m amax(s, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, keepdims\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n",
      "File \u001b[0;32m<__array_function__ internals>:180\u001b[0m, in \u001b[0;36msvd\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/numpy/linalg/linalg.py:1629\u001b[0m, in \u001b[0;36msvd\u001b[0;34m(a, full_matrices, compute_uv, hermitian)\u001b[0m\n\u001b[1;32m   1626\u001b[0m         s \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mabs\u001b[39m(s)\n\u001b[1;32m   1627\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m sort(s)[\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m, ::\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]\n\u001b[0;32m-> 1629\u001b[0m \u001b[43m_assert_stacked_2d\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1630\u001b[0m t, result_t \u001b[38;5;241m=\u001b[39m _commonType(a)\n\u001b[1;32m   1632\u001b[0m extobj \u001b[38;5;241m=\u001b[39m get_linalg_error_extobj(_raise_linalgerror_svd_nonconvergence)\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/numpy/linalg/linalg.py:196\u001b[0m, in \u001b[0;36m_assert_stacked_2d\u001b[0;34m(*arrays)\u001b[0m\n\u001b[1;32m    194\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m arrays:\n\u001b[1;32m    195\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m a\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m2\u001b[39m:\n\u001b[0;32m--> 196\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m LinAlgError(\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m%d\u001b[39;00m\u001b[38;5;124m-dimensional array given. Array must be \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m    197\u001b[0m                 \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mat least two-dimensional\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m%\u001b[39m a\u001b[38;5;241m.\u001b[39mndim)\n",
      "\u001b[0;31mLinAlgError\u001b[0m: 1-dimensional array given. Array must be at least two-dimensional"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from statsmodels.tsa.stattools import adfuller\n",
    "from statsmodels.tsa.api import VAR\n",
    "from statsmodels.tsa.vector_ar.vecm import coint_johansen\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Load the Nifty 50 data\n",
    "#df = nifty_50\n",
    "\n",
    "# Convert the date column to a datetime format\n",
    "#nifty_50['Date'] = pd.to_datetime(nifty_50['Date'])\n",
    "\n",
    "# Set the date column as the index\n",
    "#nifty_50.set_index('Date', inplace=True)\n",
    "\n",
    "# Create a new column with the log returns\n",
    "nifty_50['log_returns'] = np.log(nifty_50['Close']) - np.log(nifty_50['Close'].shift(1))\n",
    "\n",
    "# Remove the first row with NaN values\n",
    "nifty_50.dropna(inplace=True)\n",
    "#nifty_50[['log_returns']].isna().sum()\n",
    "\n",
    "#nifty_50[['log_returns']].fillna(method='ffill', inplace=True)\n",
    "print(nifty_50['log_returns'])\n",
    "data = nifty_50[['log_returns']].values.reshape(-1, 1)\n",
    "\n",
    "print(data)\n",
    "print(data.shape)\n",
    "\n",
    "# Perform a Johansen cointegration test to check for the presence of a long-run relationship\n",
    "coint_test = coint_johansen(data, det_order=0, k_ar_diff=1)\n",
    "trace_stat = coint_test.trace_stat[0]\n",
    "crit_val_90 = coint_test.trace_stat_crit_vals[1]\n",
    "\n",
    "if trace_stat > crit_val_90:\n",
    "    print(\"The Johansen cointegration test suggests that there is a long-run relationship between the variables.\")\n",
    "else:\n",
    "    print(\"The Johansen cointegration test suggests that there is no long-run relationship between the variables.\")\n",
    "\n",
    "# Perform an augmented Dickey-Fuller (ADF) test to check for stationarity\n",
    "adf_result = adfuller(nifty_50['log_returns'])\n",
    "p_value = adf_result[1]\n",
    "\n",
    "if p_value < 0.05:\n",
    "    print(\"The ADF test suggests that the log returns are stationary.\")\n",
    "else:\n",
    "    print(\"The ADF test suggests that the log returns are non-stationary.\")\n",
    "\n",
    "# Perform a VAR model on the log returns\n",
    "model = VAR(nifty_50[['log_returns']])\n",
    "results = model.fit()\n",
    "\n",
    "# Perform a structural break test using the BIC statistic\n",
    "bics = []\n",
    "for i in range(1, len(nifty_50)):\n",
    "    model1 = VAR(nifty_50[['log_returns']].iloc[:i])\n",
    "    results1 = model1.fit()\n",
    "    bic1 = results1.bic\n",
    "    \n",
    "    model2 = VAR(nifty_50[['log_returns']].iloc[i:])\n",
    "    results2 = model2.fit()\n",
    "    bic2 = results2.bic\n",
    "    \n",
    "    bics.append(bic1 + bic2)\n",
    "    \n",
    "min_bic_idx = np.argmin(bics)\n",
    "break_date = nifty_50.index[min_bic_idx]\n",
    "\n",
    "# Plot the Nifty 50 data with the structural break indicated\n",
    "fig, ax = plt.subplots(figsize=(10, 5))\n",
    "ax.plot(nifty_50.index, nifty_50['Close'], label='Nifty 50')\n",
    "ax.axvline(x=break_date, color='r', linestyle='--', label='Structural break')\n",
    "ax.set_xlabel('Date')\n",
    "ax.set_ylabel('Closing price')\n",
    "ax.set_title('Nifty 50 with structural break')\n",
    "ax.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fd2a6e38-a84b-441a-b74b-f2213eb9ae21",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from statsmodels.regression.linear_model import OLS\n",
    "from statsmodels.stats.breakpoint_detection import (Bartlett, ChowTest)\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Load Nifty50 data\n",
    "nifty50_data = pd.read_csv('nifty50.csv')\n",
    "\n",
    "# Extract the close price column\n",
    "close_price = nifty50_data['Close']\n",
    "\n",
    "# Define the breakpoint location\n",
    "breakpoint_location = len(close_price) // 2\n",
    "\n",
    "# Split the data into two segments\n",
    "segment_1 = close_price[:breakpoint_location]\n",
    "segment_2 = close_price[breakpoint_location:]\n",
    "\n",
    "# Fit an OLS regression on each segment\n",
    "ols_segment_1 = OLS(segment_1, np.ones(len(segment_1))).fit()\n",
    "ols_segment_2 = OLS(segment_2, np.ones(len(segment_2))).fit()\n",
    "\n",
    "# Perform the Chow test\n",
    "chow_test = ChowTest(np.arange(len(close_price)), close_price, [breakpoint_location])\n",
    "chow_test.summary()\n",
    "\n",
    "# Plot the data and breakpoint\n",
    "plt.plot(close_price)\n",
    "plt.axvline(x=breakpoint_location, color='red')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "76329d58",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, LSTM\n",
    "\n",
    "# Load the data\n",
    "prices = nifty_50['Close'].values\n",
    "\n",
    "# Normalize the data\n",
    "scaler = MinMaxScaler(feature_range=(0,1))\n",
    "scaled_data = scaler.fit_transform(prices.reshape(-1,1))\n",
    "\n",
    "# Define the number of time steps and split the data into training and testing sets\n",
    "n_steps = 60\n",
    "train_data = scaled_data[:3000]\n",
    "test_data = scaled_data[3000:]\n",
    "\n",
    "\n",
    "# Define the function to create input sequences and labels for the LSTM model\n",
    "def create_sequences(data, n_steps):\n",
    "    X, y = [], []\n",
    "    for i in range(len(data)-n_steps):\n",
    "        X.append(data[i:i+n_steps])\n",
    "        y.append(data[i+n_steps])\n",
    "    return np.array(X), np.array(y)\n",
    "\n",
    "# Create the input sequences and labels for the training and testing sets\n",
    "X_train, y_train = create_sequences(train_data, n_steps)\n",
    "X_test, y_test = create_sequences(test_data, n_steps)\n",
    "\n",
    "\n",
    "\n",
    "# Reshape the input sequences for the LSTM model\n",
    "X_train = X_train.reshape((X_train.shape[0], X_train.shape[1], 1))\n",
    "\n",
    "#X_test = X_test.reshape((X_test.shape[0], X_test.shape[1], 1))\n",
    "\n",
    "# Define the LSTM model\n",
    "model = Sequential()\n",
    "model.add(LSTM(50, return_sequences=True, input_shape=(n_steps,1)))\n",
    "model.add(LSTM(50, return_sequences=True))\n",
    "model.add(LSTM(50))\n",
    "model.add(Dense(1))\n",
    "model.compile(loss='mean_squared_error', optimizer='adam')\n",
    "\n",
    "\n",
    "# Train the LSTM model\n",
    "model.fit(X_train, y_train, epochs=100, batch_size=32, verbose=1)\n",
    "\n",
    "# Predict the closing prices for the next 365 days\n",
    "n_days = 365\n",
    "n_test_samples = test_data.shape[0]\n",
    "test_predictions = []\n",
    "current_sample = X_test[0]\n",
    "for i in range(n_days):\n",
    "    # Reshape the current sample for the LSTM model\n",
    "    current_sample = current_sample.reshape((1, n_steps, 1))\n",
    "    # Predict the next closing price and add it to the test predictions list\n",
    "    next_prediction = model.predict(current_sample)[0][0]\n",
    "    test_predictions.append(next_prediction)\n",
    "    # Update the current sample with the new prediction\n",
    "    current_sample = np.concatenate((current_sample[:,1:,:], [[next_prediction]]), axis=1)\n",
    "\n",
    "# Denormalize the predicted prices\n",
    "test_predictions = scaler.inverse_transform(np.array(test_predictions).reshape(-1,1))\n",
    "\n",
    "# Plot the predicted prices\n",
    "plt.figure(figsize=(16,8))\n",
    "plt.plot(test_predictions)\n",
    "plt.xlabel('Day')\n",
    "plt.ylabel('Closing Price')\n",
    "plt.title('Predicted Nifty 50 Closing Prices for Next 365 Days')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "4e4a05bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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VPcOvJEmSJKnsGX4lSZIkSWXP8CtJkiRJKnuGX0mSJElS2TP8SpIkSZLKnuFXkiRJklT2DL+SJEmSpLJn+JUkSZIklT3DryRJkiSp7FUVXYAkSVK5SSnl9yVtzS1/RVvpuq9+PS2s29kSxe28qPdd4I/7pc9KIfsubM/FfsbVuurKoG9N94yR3bNqSZL2UGNjor4x0dCYqGtspKEhv29M1Ddky+obGvP7RH1j848bGhupa8i305C9vjFBQ0o0NiYaU7Ys5W3Z40RDY/Y85W2NiZfWbcxf25Dy9pe2k63zyvVeuZ1E9stiY0noSqSX2rLnQN7emLJfqtNL6+bPS17X0vYSeXvp6yjZT/5La8rfR+nr0iv2+3KMekXwK3mSXvWg+RBZ2t5ciCxtf8Xv1LsInLsTWCWpJzj90FH86P3Tiy5jjxh+JUmdprExsb2+gW07GthW18D2uga27njl8211DWzb0UhtfQM76huzW0N2X1vyuOmyHfWN1DY0UlvX8Mp18sc7w2tjFwkrEVAZQUVFUPGKx0Flfl8RvPy4Il8nX68yIttGvjwCIoLIt53dZ9sIsoYAogIqoiJfJ3sd8PI2KN1OadvL61dEyfbydStK9k++btb28uteXePL2yffVks/q5ce52u/su3V65Zu6xVb3bm8pLW5bTW3z1dsv4UCm61ld/bVzM9gV7UUocBdF/a+o8B3XeSxLlJL/yaoWPsO61t0CXvM8CtJalF9QyObttezubaeTdvr2bKjns3588219WzZ2Z4/35S3bd5ez9YdJWG2Lgu4tfWNe1xLr6oKaqoqsvvK7PFLt/z5wJpqavr1otfO9fJbdWV2q6oIqiojv8+fVwSVlRVUV2Shs7qyIr8PKisqXl6/pcf5dirz20uhdmdIzcNt01DrL3WSJHUuw68k9QB1DY2s2byDtVt2sH7bDjZsrWPDtjrWb8vvt9axcVtdtix/vmFbHZu217dp+31rKunXqyq79a5in5oqRg+spk9NJX2qK7P7nY/z572rW3heU0nv6gp6VVW+FGyrK8OwKEmS9orhV5K6qdr6BlZurGXFxu2s3ryDNVtqWb0pv99cm7Xl9xu21bW4nerKYGCfagb2qWZQ3xpG9O/NgSP6M6BPNYP6Zu39e1fTr1cl/XpVs0+vypdDbq8s6FZWGEwlSVLXZviVpC5o244Glm/czrIN21i+YTvLNmx/+X5j1rZ6845mXzuwTzXD+tUwtF8vDho1gKH9ahi6Ty+G9a9hSN8aBvWtyYNuFmz71lTaqypJksqe4VeSCtDQmFi+cTsvrtnKorVbeXHtVhbm94vWbmXtllcH20F9qxk1oDejB/bm0LGDGD2wN6MG9mZE/14M69eL4f17MbhvDTVVXsJdkiSpKcOvJHWgdVt28Nyqzcxfmd2eW7WZBWu2snjdVuoaXp52uLIiGDuoDxOG9OWUaaMYN7jPS+F29MA+jBrQmz41lQW+E0mSpO7N8CtJ7WDtlh3MXbqRp1dsykJuHnTXlPTg9qqqYL/h/Th4dH9OmTaKiUP7MmFIdhs9sDdVlfbYSpIkdRTDryTthpQSi9dtY87SjcxduoG5yzYyZ+lGlm3Y/tI6g/pWc8Dwfrzl4JEcMKIfB4zox/7D+zF2cB8nhpIkSSqI4VeSWrFi43YeXbiOxxat5/FF65m7bONLl/+pCNh/eD+O3ncI08YMYOrogRw0uj9D96lxAilJkqQuxvArSbm6hkaeXLLhpbD72MJ1LM17dGsqK5g6ZgBnHj6GaWMGMnXMAKaM7O95uJIkSd1Eh4XfiLgYOANYmVI6JG/7CvBRYFW+2r+mlG7Ml30euABoAP4upXRL3n4q8N9AJfDzlNK38vZ9gSuBocAjwAdTSs1f90OSmrEz7D7w/Bruf24Njyxcx9YdDQCMHdSHIycO5oIJgzlywiCmjhlAryqDriRJUnfVkT2/lwA/BC5r0v69lNJ/lTZExFTgXGAaMAb4c0QcmC/+X+AkYDHwcERcm1KaC3w739aVEfETsuD84456M5K6v5QST6/YxJ1Pr+K+59Ywc8FatuRhd8rI/rxr+jiO2W8o0ycOZuSA3gVXK0mSpPbUYeE3pXRXRExq4+pnAVemlGqBFyJiPnB0vmx+Sul5gIi4EjgrIuYBJwLvy9e5FPgKhl9JTWzaXse989dw5zMr+evTq16amGryiH6848hxHLf/UI7edwjD+vUquFJJkiR1pCLO+f1URJwHzAT+KaW0DhgLPFCyzuK8DWBRk/ZjyIY6r08p1TezvqQebvG6rdw8ezl/nreCmQvWUd+Y6N+riuMnD+PTbxnOGw8cwaiB9uxKkiT1JJ0dfn8MfA1I+f3/Az7S0TuNiAuBCwEmTJjQ0buTVIAFq7dw0+zl3DR7GU8s3gDAQaP68zev348Tpgxn+sTBVHsdXUmSpB6rU8NvSmnFzscR8TPg+vzpEmB8yarj8jZaaF8DDIqIqrz3t3T95vb7U+CnADNmzEh7+TYkdREL12zhmllLuWn2cuYt2wjA4eMG8i+nHcRph4xi4tB9Cq5QkiRJXUWnht+IGJ1SWpY/fTswO398LfCbiPgu2YRXk4GHgAAm5zM7LyGbFOt9KaUUEX8B3kk24/P5wDWd904kFWXj9jpufGIZf3h0MQ8vWAfAjImD+eJbD+bUQ0YxbnDfgiuUJElSV9SRlzq6AjgBGBYRi4EvAydExBFkw54XAB8DSCnNiYirgLlAPXBRSqkh386ngFvILnV0cUppTr6LzwFXRsR/AI8Bv+io9yKpWA2NibufXcUfHl3CrXOWU1vfyP7D9+Gzp07h7CPGMmZQn6JLlCRJUhcXKfWsUcAzZsxIM2fOLLoMSW2wctN2rnhwEVc89CLLN25nYJ9qzjx8DOdMH8fh4wYSEUWXKEmSpC4mIh5JKc1o2l7EbM+S1KKUEo8sXMel9y/k5tnLqGtIvPHA4XzlzKm86aAR9KqqLLpESZIkdUOGX0ldwrYdDVwzawmX3b+Qucs20r93FecdN4kPHDuRfYc5cZUkSZL2juFXUqHWbdnBZfcv5NL7F7B2yw4OGtWfb7z9UM5+zRj61vhPlCRJktqHv1lKKsTS9dv4+d0vcOXDL7J1RwNvPmgEF75hP47ed4jn8kqSJKndGX4ldaql67fxw7/M53czF5ESnHn4GD72xv2ZMqp/0aVJkiSpjBl+JXWK5Ru286O/zufKhxaRSJx71AQ+9sb9vC6vJEmSOoXhV1KHWr91Bz+8Yz6XPbCQxsbEu48az0VvOoCxXptXkiRJncjwK6lD1NY3cNl9C/mfO55lU2095xw5jr9/82TGD7GnV5IkSZ3P8CupXaWUuP6JZXznlqdYtHYbbzxwOJ8//SAOGjWg6NIkSZLUgxl+JbWbp5dv4t+umc1DL6zl4NEDuPyCQ3n95OFFlyVJkiQZfiXtvU3b6/j+n5/lkvsW0L93Fd98x6G8e8Z4Kiu8ZJEkSZK6BsOvpD22c4jzV6+fy+rNtbz36Al85uQpDN6npujSJEmSpFcw/EraI8s3bOeLV8/mz/NWcNi4gfzi/BkcNm5Q0WVJkiRJzTL8StotKSWufHgR37hhHnWNjXzh9IP5yPH7OsRZkiRJXZrhV1KbLV2/jc/8/nHunb+GY/cbwrfecRiThu1TdFmSJEnSLhl+JbXJtY8v5Qt/epLGxsQ33n4o5x41ngp7eyVJktRNGH4ltWrj9jq+dPVsrp61lCMnDOJ77zmCiUPt7ZUkSVL3YviV1KJHFq7l766YxfKN2/mHtxzIRW/an6rKiqLLkiRJknab4VfSq6SU+MU9L/Ctm55izKA+/O7jx3HkhMFFlyVJkiTtMcOvpFfYsK2Oz/7+cW6Zs4JTpo3kP991OAN6VxddliRJkrRXDL+SXjJn6QY++etHWbJuG19868FccPy+RDiplSRJkro/w68kAK57fCn//LvHGdy3hisvPJYZk4YUXZIkSZLUbgy/Ug/X2Jj43p+f4X/umM9Rkwbz4w9MZ1i/XkWXJUmSJLUrw6/Ug22precfr5rFLXNW8O4Z4/ja2YfQq6qy6LIkSZKkdmf4lXqoJeu3ccElD/PMik382xlT+cjrJnl+ryRJksqW4VfqgZ5avpHzL36IrbUN/PLDR/PGA4cXXZIkSZLUoQy/Ug9z/3NruPCymfTtVcnvPnEcB40aUHRJkiRJUocz/Eo9yPVPLOUff/s4E4b25dKPHM3YQX2KLkmSJEnqFIZfqYf45b0v8NXr5zJ9wmB+fv4MBvWtKbokSZIkqdMYfqUe4Id3PMt/3foMJ08dyQ/e+xp6VzujsyRJknoWw69UxlJKfPe27Bq+Zx8xhv961+FUVVYUXZYkSZLU6Qy/UplKKfGNG+fxs7tf4D0zxvONdxxKZYWXMpIkSVLPZPiVylBjY+Ir183hsvsXct5xE/nK26ZRYfCVJElSD2b4lcpMSi8H3wvfsB+fP+0gIgy+kiRJ6tk8+U8qIyklvnXTU1x2/0I++vp9Db6SJElSzvArlZH/vv1Z/u+u5/ngsRP519MPNvhKkiRJOcOvVCb+787n+P6fn+Wd08fx72dOM/hKkiRJJQy/Uhm4/P4FfPOmpzjjsNF8+5zDnNxKkiRJasLwK3VzNz65jC9dO4e3HDyC773nCC9nJEmSJDXD8Ct1Yw8+v4ZP/3YWR04YzA/fdyTVlX6lJUmSpOb4m7LUTT29fBN/c9lMxg/uw8/Pm0Hv6sqiS5IkSZK6LMOv1A0t27CND/3yIfpUV3LpR45m8D41RZckSZIkdWlVRRcgafds3F7Hhy5+mE3b6/ntx45l3OC+RZckSZIkdXn2/ErdSENj4m9/8xjPrdrMTz4wnWljBhZdkiRJktQt2PMrdSPfvHEedz6zim+8/VCOnzys6HIkSZKkbsOeX6mbuOrhRfz8nhf40Gsn8b5jJhRdjiRJktStGH6lbuDhBWv5wtVPcvwBw/jiWw8uuhxJkiSp2zH8Sl3corVb+fjljzBucF/+931HUuW1fCVJkqTd5m/RUhe2va6Bj13+CHUNjfz8/BkM7FtddEmSJElSt9Rh4TciLo6IlRExu5ll/xQRKSKG5c8jIn4QEfMj4omIOLJk3fMj4tn8dn5J+/SIeDJ/zQ8iIjrqvUhF+dI1s5m7bCPfP/cI9h/er+hyJEmSpG6rI3t+LwFObdoYEeOBk4EXS5pPAybntwuBH+frDgG+DBwDHA18OSIG56/5MfDRkte9al9Sd3bVw4u4auZiPvWmAzjxoJFFlyNJkiR1ax0WflNKdwFrm1n0PeCzQCppOwu4LGUeAAZFxGjgFOC2lNLalNI64Dbg1HzZgJTSAymlBFwGnN1R70XqbHOWbuDfrpnN6w4Yyj+cdGDR5UiSJEndXqee8xsRZwFLUkqPN1k0FlhU8nxx3tZa++Jm2qVub8O2Oj7xq0cZ3LeG/z73NVRWOKJfkiRJ2ltVnbWjiOgL/CvZkOdOFREXkg2nZsIEr4+qriulxD//7nGWrt/Gbz92HMP69Sq6JEmSJKksdGbP7/7AvsDjEbEAGAc8GhGjgCXA+JJ1x+VtrbWPa6a9WSmln6aUZqSUZgwfPrwd3orUMS65bwG3zV3B508/mOkTB+/6BZIkSZLapNPCb0rpyZTSiJTSpJTSJLKhykemlJYD1wLn5bM+HwtsSCktA24BTo6IwflEVycDt+TLNkbEsfksz+cB13TWe5E6wrxlG/nmjU/x5oNG8JHXTSq6HEmSJKmsdOSljq4A7gemRMTiiLigldVvBJ4H5gM/Az4JkFJaC3wNeDi/fTVvI1/n5/lrngNu6oj3IXWGbTsa+LsrHmNg32q+887D8MpdkiRJUvvqsHN+U0rv3cXySSWPE3BRC+tdDFzcTPtM4JC9q1LqGr5+41yeXbmZyy84mqGe5ytJkiS1u06d7VnSq906Zzm/euBFLnzDfrx+suekS5IkSR3B8CsVaPmG7Xz2D09wyNgB/PPJU4ouR5IkSSpbhl+pICklPvP7x6mta+S/z30NNVV+HSVJkqSO4m/bUkGueGgRdz+7mn89/SD2H96v6HIkSZKksmb4lQqwaO1Wvn7DXF67/1Def8zEosuRJEmSyp7hV+pkjY2Jz/3hCQC+fc5hVFR4WSNJkiSpoxl+pU7264de5L7n1vCFt05l/JC+RZcjSZIk9QiGX6kTvbhmK9+8cR6vnzyM9x49vuhyJEmSpB7D8Ct1ksbGbHbnigi+dc5hRDjcWZIkSeoshl+pk/x25iIefGEtX3jrwYwd1KfociRJkqQexfArdYKVm7bzzRvnccy+Qzj3KIc7S5IkSZ3N8Ct1gq9dP4/tdY18/e2HOtxZkiRJKoDhV+pgf316Jdc9vpRPnLA/B4zoV3Q5kiRJUo9k+JU60LYdDXzx6tnsN3wfPvmm/YsuR5IkSeqxqoouQCpn37/9GRav28aVFx5Lr6rKosuRJEmSeix7fqUOMm/ZRn5+9wu8e8Y4jt1vaNHlSJIkST2a4VfqACkl/u3q2QzsU82/nn5w0eVIkiRJPZ7hV+oAV89awsyF6/jcqVMY1Lem6HIkSZKkHs/wK7WzzbX1fPPGpzh83EDeNd1r+kqSJEldgRNeSe3sf+54lpWbavm/D06nosJr+kqSJEldgT2/Ujt6btVmLr7nBd41fRyvmTC46HIkSZIk5Qy/UjtJKfHV6+bSu6qSz556UNHlSJIkSSph+JXayZ/nreTOZ1bx6ZMOZHj/XkWXI0mSJKmE4VdqB7X1DXzt+rlMHtGP846bWHQ5kiRJkpow/Ert4PL7F/Li2q186W1Tqa70ayVJkiR1Nf6WLu2l9Vt38IPbn+WEKcN5/eThRZcjSZIkqRmGX2kv/c8d89lcW8/nTzu46FIkSZIktcDwK+2FhWu2cNn9C3jPUeOZMqp/0eVIkiRJaoHhV9oL37n5aaorK/iHtxxYdCmSJEmSWmH4lfbQIwvXccOTy/jYG/ZnxIDeRZcjSZIkqRWGX2kPpJT4+g1zGdG/Fx99w75FlyNJkiRpFwy/0h64afZyHn1xPf988hT61lQVXY4kSZKkXTD8SrupvqGR/7r1aSaP6Mc508cVXY4kSZKkNjD8Srvpj48t4flVW/jnU6ZQWRFFlyNJkiSpDQy/0m6orW/gv//8LIePG8jJU0cWXY4kSZKkNjL8SrvhigdfZMn6bXzmlIOIsNdXkiRJ6i4Mv1Ibbd1Rzw//Mp9j9xvC6w4YWnQ5kiRJknaD4Vdqo1/eu4DVm3fwmVOm2OsrSZIkdTOGX6kNNmyt4//ufI43HzSC6ROHFF2OJEmSpN1k+JXa4Kd3P8fG7fX808lTii5FkiRJ0h4w/Eq7sHbLDn557wLOOGw0U8cMKLocSZIkSXvA8Cvtws/ufp5tdQ18+i2Tiy5FkiRJ0h4y/EqtWLdlB5fdt4AzDhvDASP6F12OJEmSpD1k+JVa8fN7nmdrXQN/e+IBRZciSZIkaS8YfqUWrN+6g0vvW8jph4zmwJH2+kqSJEndmeFXasEv7nmBzbX1/O2b7fWVJEmSujvDr9SMDVvruOTeBZx2yCgOGuUMz5IkSVJ3Z/iVmnHxvS+wqbaev3uzMzxLkiRJ5cDwKzWxYVsdF9/7AqdMG8nBo+31lSRJksqB4Vdq4vL7F7Bpez1/e6K9vpIkSVK56LDwGxEXR8TKiJhd0va1iHgiImZFxK0RMSZvj4j4QUTMz5cfWfKa8yPi2fx2fkn79Ih4Mn/NDyIiOuq9qOfYtqOBi+9dwJumDOeQsQOLLkeSJElSO+nInt9LgFObtP1nSumwlNIRwPXAl/L204DJ+e1C4McAETEE+DJwDHA08OWIGJy/5sfAR0te13Rf0m67auYi1m7ZwSdOcIZnSZIkqZx0WPhNKd0FrG3StrHk6T5Ayh+fBVyWMg8AgyJiNHAKcFtKaW1KaR1wG3BqvmxASumBlFICLgPO7qj3op6hrqGRn971PDMmDubofYcUXY4kSZKkdlTV2TuMiK8D5wEbgDflzWOBRSWrLc7bWmtf3Ey7tMeue3wpS9Zv46tnTSu6FEmSJEntrNMnvEopfSGlNB74NfCpzthnRFwYETMjYuaqVas6Y5fqZhobEz+58zmmjOzPiQeNKLocSZIkSe2syNmefw2ckz9eAowvWTYub2utfVwz7c1KKf00pTQjpTRj+PDh7VC6ys3tT63kmRWb+cQJ++PcaZIkSVL56dTwGxGl1445C3gqf3wtcF4+6/OxwIaU0jLgFuDkiBicT3R1MnBLvmxjRBybz/J8HnBN570TlZOUEj/663zGDe7DGYeNLrocSZIkSR2gw875jYgrgBOAYRGxmGzW5tMjYgrQCCwEPp6vfiNwOjAf2Ap8GCCltDYivgY8nK/31ZTSzkm0Pkk2o3Qf4Kb8Ju22B19Yy2MvrudrZ02jqtJLX0uSJEnlqMPCb0rpvc00/6KFdRNwUQvLLgYubqZ9JnDI3tQoAfz4r88xrF8N75oxftcrS5IkSeqW7OZSj/b08k3c+cwqPvTaSfSuriy6HEmSJEkdxPCrHu3ndz9P7+oK3n/MxKJLkSRJktSBDL/qsVZu2s41s5byrunjGbxPTdHlSJIkSepAhl/1WJffv5C6xkY+cvy+RZciSZIkqYMZftUjbdvRwK8eWMhbDh7JvsP2KbocSZIkSR3M8Kse6fePLmbd1jo++vr9ii5FkiRJUicw/KrHaWxMXHzPCxw+biBHTRpcdDmSJEmSOoHhVz3On+et4IXVW/ib1+9HRBRdjiRJkqROYPhVj/Pzu19g7KA+nHbIqKJLkSRJktRJDL/qUZ5YvJ6HFqzlw6+bRFWlH39JkiSpp/C3f/Uol9y7gH1qKnnPUeOLLkWSJElSJzL8qsdYuWk71z2xlHfNGE//3tVFlyNJkiSpExl+1WNc8eAi6hoS5x03sehSJEmSJHUyw696hB31jfzqwYWcMGU4+w3vV3Q5kiRJkjqZ4Vc9wk2zl7FqUy0feu2kokuRJEmSVADDr3qEX967gP2G7cMbJg8vuhRJkiRJBTD8quw99uI6Zi1az/mvnURFRRRdjiRJkqQCGH5V9i69bwH9elVxzvRxRZciSZIkqSCGX5W1lRu3c8OTy3jXjHH061VVdDmSJEmSCmL4VVn79YMvUt+YOP+4SUWXIkmSJKlAhl+VrbqGRn7z0IuccOBwJg3bp+hyJEmSJBXI8KuydeucFazaVMt59vpKkiRJPZ7hV2Xr8gcWMG5wH95woJc3kiRJkno6w6/K0rMrNvHA82t5/zETqfTyRpIkSVKPZ/hVWfrVAwupqazg3TO8vJEkSZIkw6/K0Jbaev746BJOP3QUQ/v1KrocSZIkSV2A4Vdl55pZS9lUW88Hj5tYdCmSJEmSugjDr8pKSonLH1jIwaMHcOSEwUWXI0mSJKmLMPyqrDz64nrmLdvIB4+dSIQTXUmSJEnKGH5VVn71wEL69arirCPGFF2KJEmSpC5kl+E3IvpGxL9FxM/y55Mj4oyOL03aPWu37OCGJ5ZxzpFj2adXVdHlSJIkSepC2tLz+0ugFjguf74E+I8Oq0jaQ394ZDE7Ghp5/7FOdCVJkiTpldoSfvdPKX0HqANIKW0FPJlSXUpKiSsefpHpEwdz4Mj+RZcjSZIkqYtpS/jdERF9gAQQEfuT9QRLXcZDL6zl+VVbOPeo8UWXIkmSJKkLasuJkV8GbgbGR8SvgdcBH+rIoqTddeXDi+jfu4ozDnOiK0mSJEmvtsvwm1K6LSIeBY4lG+789yml1R1emdRG67fu4IYnl/GeGePpU1NZdDmSJEmSuqC2zPb8dqA+pXRDSul6oD4izu7wyqQ2+tNjS9hR38i5RzvkWZIkSVLz2nLO75dTSht2PkkprScbCi0VLqXElQ8t4vBxA5k2ZmDR5UiSJEnqotoSfptbx4uoqkt49MX1PL1iE+cePaHoUiRJkiR1YW0JvzMj4rsRsX9++y7wSEcXJrXFlQ+9SN+aSt52uBNdSZIkSWpZW8Lv3wI7gN/mt1rgoo4sSmqLjdvruP6JZZx1xBj69XIwgiRJkqSWtWW25y3Av3RCLdJuuWbWUrbVNXDuUQ55liRJktS6FsNvRHw/pfTpiLgOSE2Xp5TO7NDKpF248qEXOXj0AA4b50RXkiRJklrXWs/v5fn9f3VGIdLueHLxBuYs3cjXzppGRBRdjiRJkqQursXwm1J6JCIqgQtTSu/vxJqkXfrNQy/Su7qCs14ztuhSJEmSJHUDrU54lVJqACZGRE0n1SPt0pbaeq6dtYQzDhvDgN7VRZcjSZIkqRtoyxS5zwP3RsS1wJadjSml73ZYVVIrrn9iKVt2NPDeo8cXXYokSZKkbqIt4fe5/FYB9O/YcqRdu+KhRUwe0Y8jJwwuuhRJkiRJ3USr4TcijgDmAHNSSvM6pSKpFc+u2MSsRev54lsPdqIrSZIkSW3W4jm/EfEl4CrgHOCGiPjo7mw4Ii6OiJURMbuk7T8j4qmIeCIi/hQRg0qWfT4i5kfE0xFxSkn7qXnb/Ij4l5L2fSPiwbz9t56X3DP87pHFVFUEb3eiK0mSJEm7obUJr94DHJFSei9wFHDhbm77EuDUJm23AYeklA4DngE+DxARU4FzgWn5a34UEZX5bNP/C5wGTAXem68L8G3geymlA4B1wAW7WZ+6mbqGRv746GJOPGgEQ/v1KrocSZIkSd1Ia+G3NqW0FSCltGYX675KSukuYG2TtltTSvX50weAcfnjs4ArU0q1KaUXgPnA0fltfkrp+ZTSDuBK4KzIxrueCPw+f/2lwNm7U5+6n78+vYrVm3fwrhlOdCVJkiRp97R2zu9++QzPAAHsX/KclNKZe7nvjwC/zR+PJQvDOy3O2wAWNWk/BhgKrC8J0qXrq0z9buYihvWr4YQpw4suRZIkSVI301r4PavJ8/9qr51GxBeAeuDX7bXNXezvQvJh2xMmTOiMXaqdrd5cyx1PreTDr5tEdeVuDUKQJEmSpJbDb0rpzo7YYUR8CDgDeHNKKeXNS4DSsazj8jZaaF8DDIqIqrz3t3T9V0kp/RT4KcCMGTNSS+up67r6sSXUNyaHPEuSJEnaI53ahRYRpwKfBc7ceT5x7lrg3IjoFRH7ApOBh4CHgcn5zM41ZJNiXZuH5r8A78xffz5wTWe9D3WulBK/f2Qxh48byIEjvdS0JEmSpN3XYeE3Iq4A7gemRMTiiLgA+CHQH7gtImZFxE8AUkpzyC6rNBe4GbgopdSQ9+p+CrgFmAdcla8L8DngHyNiPtk5wL/oqPeiYs1espGnlm/infb6SpIkSdpDrZ3zu1fySyQ11WJATSl9Hfh6M+03Ajc20/482WzQKnO/e2QRNVUVnHnYmKJLkSRJktRN7TL8RsR1QNPzZDcAM4H/Sylt74jCJIDtdQ1cM2spp0wbxcC+1UWXI0mSJKmbasuw5+eBzcDP8ttGYBNwYP5c6jB/nreCDdvqeNf0cbteWZIkSZJa0JZhz69NKR1V8vy6iHg4pXRURMxp8VVSO/jdzMWMGdib1x0wrOhSJEmSJHVjben57RcRL10cN3/cL3+6o0OqkoDlG7Zz97OrOGf6OCorouhyJEmSJHVjben5/Sfgnoh4DghgX+CTEbEPcGlHFqee7Q+PLqYxwTsd8ixJkiRpL+0y/KaUboyIycBBedPTJZNcfb+jClPPtvPavkfvO4SJQ/cpuhxJkiRJ3VxbL3U0HZiUr394RJBSuqzDqlKP98jCdbywegufPGH/okuRJEmSVAbacqmjy4H9gVlAQ96cAMOvOszvH1lM35pKTj90dNGlSJIkSSoDben5nQFMTSk1vdav1CG21zVwwxPLOO2Q0ezTq62DEyRJkiSpZW2Z7Xk2MKqjC5F2um3uCjbV1nPOkWOLLkWSJElSmWhLt9owYG5EPATU7mxMKZ3ZYVWpR/vjo4sZPbA3x+43tOhSJEmSJJWJtoTfr3R0EdJOqzbVctezq7nwDftR4bV9JUmSJLWTtlzq6M7OKEQCuPbxpTQ0Jt7xGoc8S5IkSWo/LYbfiLgnpXR8RGwim935pUVASikN6PDq1OP88dHFHDZuIJNH9i+6FEmSJEllpMUJr1JKx+f3/VNKA0pu/Q2+6ghPL9/EnKUb7fWVJEmS1O52OdtzROwfEb3yxydExN9FxKAOr0w9zh8fW0xVRfC2w8cUXYokSZKkMtOWSx39AWiIiAOAnwLjgd90aFXqcRoaE1c/toQTpgxnaL9eRZcjSZIkqcy0Jfw2ppTqgbcD/5NS+gwwumPLUk9z33OrWbGxlnccOa7oUiRJkiSVobaE37qIeC9wPnB93lbdcSWpJ/rjo0sY0LuKEw8aUXQpkiRJkspQW8Lvh4HjgK+nlF6IiH2Byzu2LPUkW2rruXn2cs44fAy9qyuLLkeSJElSGdpl+E0pzQX+GXgyIg4BFqeUvt3hlanHuHn2crbVNTjLsyRJkqQO0+J1fneKiBOAS4EFZNf4HR8R56eU7urQytRj/PGxxUwY0pfpEwcXXYokSZKkMrXL8Av8P+DklNLTABFxIHAFML0jC1PPsHT9Nu57bg1//+bJRETR5UiSJEkqU20557d6Z/AFSCk9gxNeqZ1cPWsJKcE7XuMsz5IkSZI6Tlt6fmdGxM+BX+XP3w/M7LiS1FOklPjTo0uYMXEwE4b2LbocSZIkSWWsLT2/nwDmAn+X3+bmbdJemb1kI8+u3Oy1fSVJkiR1uF32/KaUaoHv5jep3fzpsSXUVFbw1kNHF12KJEmSpDLXYviNiCeB1NLylNJhHVKReoSGxsR1TyzlTQcNZ2BfTyGXJEmS1LFa6/k9o9OqUI9z33OrWbWplrOP8Nq+kiRJkjpea+G3GhiZUrq3tDEiXgcs79CqVPaufmwp/XtX8aaDRhRdiiRJkqQeoLUJr74PbGymfWO+TNoj2+sauGXOck47ZBS9qyuLLkeSJElSD9Ba+B2ZUnqyaWPeNqnDKlLZ+/O8FWyurXfIsyRJkqRO01r4HdTKsj7tXId6kKsfW8KoAb05Zr+hRZciSZIkqYdoLfzOjIiPNm2MiL8BHum4klTO1m3ZwV+fXsWZR4yhsiKKLkeSJElSD9HahFefBv4UEe/n5bA7A6gB3t7BdalM3fDkMuobE2cdMaboUiRJkiT1IC2G35TSCuC1EfEm4JC8+YaU0h2dUpnK0jWzljB5RD+mjh5QdCmSJEmSepDWen4BSCn9BfhLJ9SiMrd43VYeXrCOz5wyhQiHPEuSJEnqPK2d8yu1q2tmLQXgzMMd8ixJkiSpcxl+1SlSSlz92BJmTBzM+CF9iy5HkiRJUg9j+FWnmLtsI8+u3MzZr/HavpIkSZI6n+FXneKaWUupqgjeeujookuRJEmS1AMZftXhGhoT185ayglThjN4n5qiy5EkSZLUAxl+1eEefGENyzdu56wjHPIsSZIkqRiGX3W4ax5byj41lbzl4JFFlyJJkiSphzL8qkPV1jdw4+xlnHLIKPrUVBZdjiRJkqQeyvCrDnXn06vYtL3eIc+SJEmSCmX4VYe69vGlDNmnhtfuP7ToUiRJkiT1YIZfdZitO+q5fd5KTj90FNWVftQkSZIkFcdEog5z29wVbKtr4G2HjSm6FEmSJEk9nOFXHea6x5cxakBvjpo0pOhSJEmSJPVwHRZ+I+LiiFgZEbNL2t4VEXMiojEiZjRZ//MRMT8ino6IU0raT83b5kfEv5S07xsRD+btv42Imo56L9p9G7bWceczKznjsNFUVETR5UiSJEnq4Tqy5/cS4NQmbbOBdwB3lTZGxFTgXGBa/pofRURlRFQC/wucBkwF3puvC/Bt4HsppQOAdcAFHfQ+tAdumbOcuobEmUc45FmSJElS8Tos/KaU7gLWNmmbl1J6upnVzwKuTCnVppReAOYDR+e3+Sml51NKO4ArgbMiIoATgd/nr78UOLtj3on2xLWPL2Xi0L4cOnZg0aVIkiRJUpc553cssKjk+eK8raX2ocD6lFJ9k/ZmRcSFETEzImauWrWqXQvXq63aVMt9z63mzMPHkP2dQpIkSZKK1VXCb4dKKf00pTQjpTRj+PDhRZdT9m58chmNCd52uEOeJUmSJHUNVUUXkFsCjC95Pi5vo4X2NcCgiKjKe39L11fBrnt8KQeN6s+BI/sXXYokSZIkAV2n5/da4NyI6BUR+wKTgYeAh4HJ+czONWSTYl2bUkrAX4B35q8/H7imgLrVxJL125i5cJ29vpIkSZK6lI681NEVwP3AlIhYHBEXRMTbI2IxcBxwQ0TcApBSmgNcBcwFbgYuSik15L26nwJuAeYBV+XrAnwO+MeImE92DvAvOuq9qO2uf3wpAG87zPArSZIkqeuIrBO155gxY0aaOXNm0WWUrbf+4G6qKiu45qLXFV2KJEmSpB4oIh5JKc1o2t5Vhj2rDDy3ajNzlm7kTIc8S5IkSepiDL9qN9c9vpQIOOOw0UWXIkmSJEmvYPhVu0gpcd3jSzlm3yGMHNC76HIkSZIk6RUMv2oXc5dt5LlVW5zlWZIkSVKXZPhVu7ju8WVUVQSnHeKQZ0mSJEldj+FXe23nkOfjJw9jyD41RZcjSZIkSa9i+NVee/TF9SxZv81ZniVJkiR1WYZf7bXrHl9Kr6oKTpo6suhSJEmSJKlZhl/tlYbGxPVPLOPEg0bQv3d10eVIkiRJUrMMv9orDzy/htWba53lWZIkSVKXZvjVXrnu8aXsU1PJiQeNKLoUSZIkSWqR4Vd7rK6hkZtmL+fkaaPoXV1ZdDmSJEmS1CLDr/bYvfNXs2FbHWcc5rV9JUmSJHVthl/tsRueWEb/XlUcP3lY0aVIkiRJUqsMv9ojO+obuWXOck6aNpJeVQ55liRJktS1GX61R+6dv5qN2+sd8ixJkiSpWzD8ao/c8OQy+veu4vgDhhddiiRJkiTtkuFXu23nkOeTp46ipsqPkCRJkqSuz+Si3XbP/FVscsizJEmSpG7E8KvddsMTyxnQu4rXHeAsz5IkSZK6B8OvdkttfQO3zl3OydMc8ixJkiSp+zC9aLfc8+xqNm2v560OeZYkSZLUjRh+tVtueHJZNuR5f4c8S5IkSeo+DL9qs9r6Bm6bs4JTHPIsSZIkqZsxwajN7n5mNZtqHfIsSZIkqfsx/KrNbnhyGQP7VDvLsyRJkqRux/CrNtle18Cf567glGkjqa70YyNJkiSpezHFqE3ufnbnkOcxRZciSZIkSbvN8Ks2ueGJpQzqW81r9x9adCmSJEmStNsMv9ql7XUN/HneSk6ZOsohz5IkSZK6JZOMdumuZ1ax2VmeJUmSJHVjhl/t0g1PLmNQ32qOc8izJEmSpG7K8KtW7Zzl+dRpDnmWJEmS1H2ZZtSqO59ZxZYdDQ55liRJktStGX7VqhueWMbgvtUct59DniVJkiR1X4ZftWh7XQO3z1vBqYeMosohz5IkSZK6MRONWvTXp/Mhz4eOKboUSZIkSdorhl+16IYnlzFknxqO3W9I0aVIkiRJ0l4x/KpZ2+sauGPeCk6ZNtIhz5IkSZK6PVONmnXPs6vZsqOBUw9xlmdJkiRJ3Z/hV826afZy+veucpZnSZIkSWXB8KtXqWto5M/zVnDSwSOpqfIjIkmSJKn7M9noVR54fg0bttVx6iGjii5FkiRJktqF4VevcvPs5fStqeQNBw4vuhRJkiRJaheGX71CQ2PiljkreNOUEfSuriy6HEmSJElqF4ZfvcIjC9exenMtpzjkWZIkSVIZMfzqFW6evZyaqgpOPGhE0aVIkiRJUrsx/OolKSVumbOcN0weRr9eVUWXI0mSJEntxvCrlzy5ZANL1m/jlGkOeZYkSZJUXjos/EbExRGxMiJml7QNiYjbIuLZ/H5w3h4R8YOImB8RT0TEkSWvOT9f/9mIOL+kfXpEPJm/5gcRER31XnqKm2Yvp7IiOGnqyKJLkSRJkqR21ZE9v5cApzZp+xfg9pTSZOD2/DnAacDk/HYh8GPIwjLwZeAY4GjgyzsDc77OR0te13Rf2g0pJW6evZzj9hvKoL41RZcjSZIkSe2qw8JvSukuYG2T5rOAS/PHlwJnl7RfljIPAIMiYjRwCnBbSmltSmkdcBtwar5sQErpgZRSAi4r2Zb2wDMrNvPC6i2c6izPkiRJkspQZ5/zOzKltCx/vBzYOb52LLCoZL3FeVtr7Yubadceumn2MiLg5GkOeZYkSZJUfgqb8CrvsU2dsa+IuDAiZkbEzFWrVnXGLrudm2cvZ8bEwYzo37voUiRJkiSp3XV2+F2RD1kmv1+Zty8BxpesNy5va619XDPtzUop/TSlNCOlNGP48OF7/SbKzYLVW3hq+SZOPWR00aVIkiRJUofo7PB7LbBzxubzgWtK2s/LZ30+FtiQD4++BTg5IgbnE12dDNySL9sYEcfmszyfV7It7aabZi8H4BSHPEuSJEkqU1UdteGIuAI4ARgWEYvJZm3+FnBVRFwALATena9+I3A6MB/YCnwYIKW0NiK+Bjycr/fVlNLOSbQ+STajdB/gpvymPXDznOUcNm4g4wb3LboUSZIkSeoQHRZ+U0rvbWHRm5tZNwEXtbCdi4GLm2mfCRyyNzUKlq7fxuOL1vPZU6cUXYokSZIkdZjCJrxS13DLnGzI86nTvMSRJEmSpPJl+O3hbp2zgskj+rHf8H5FlyJJkiRJHcbw24Ot37qDhxas9dq+kiRJksqe4bcHu+OplTQ0Jk6a6pBnSZIkSeXN8NuD3TZ3BSP69+KwsQOLLkWSJEmSOpTht4faXtfAnc+s4qSpI6moiKLLkSRJkqQOZfjtoe57bjVbdzRw0lTP95UkSZJU/gy/PdRtc1fQr1cVx+0/tOhSJEmSJKnDGX57oMbGxG1zV/LGKcPpVVVZdDmSJEmS1OEMvz3QY4vWs3pzLSc75FmSJElSD2H47YFum7uCqorghCkjii5FkiRJkjqF4bcHunXuco7dbygD+1QXXYokSZIkdQrDbw/z3KrNPL9qi7M8S5IkSepRDL89zG1zVwDwFsOvJEmSpB7E8NvD3DpnOYeMHcDYQX2KLkWSJEmSOo3htwdZtamWxxat56SDRxVdiiRJkiR1KsNvD3L7vBWkBCdPc8izJEmSpJ7F8NuD3Dp3BeMG9+GgUf2LLkWSJEmSOpXht4fYUlvPPfNXc9LUkURE0eVIkiRJUqcy/PYQdz2zih31jZw81fN9JUmSJPU8ht8e4ra5KxjUt5qjJg0uuhRJkiRJ6nSG3x6gvqGR259ayYlTRlBV6SGXJEmS1POYhHqAhxasZcO2Ok6a6izPkiRJknomw28PcOucFfSqquCNU4YXXYokSZIkFcLwW+ZSStw6ZzmvnzycvjVVRZcjSZIkSYUw/Ja52Us2snTDdk6Z5pBnSZIkST2X4bfM3TJnORUBbz7Y8CtJkiSp5zL8lrlb5izn6H2HMGSfmqJLkSRJkqTCGH7L2POrNvPsys2cMm1U0aVIkiRJUqEMv2Xs1rkrADjZ8CtJkiSphzP8lrFb5iznkLEDGDuoT9GlSJIkSVKhDL9lauXG7Tz24npOmWqvryRJkiQZfsvUziHPpxxi+JUkSZIkw2+ZumXOciYN7cvkEf2KLkWSJEmSCmf4LUMbttVx/3NrOGXaKCKi6HIkSZIkqXCG3zL016dXUt+YnOVZkiRJknKG3zJ08+zlDO/fi9eMH1R0KZIkSZLUJRh+y8zm2nrueGolpx8yiooKhzxLkiRJEhh+y87t81ZQW9/IGYePKboUSZIkSeoyDL9l5rrHlzJqQG+mTxhcdCmSJEmS1GUYfsvIhm113PnMKt562GiHPEuSJElSCcNvGbl1znLqGhJnHDa66FIkSZIkqUsx/JaR659YxrjBfTjCWZ4lSZIk6RUMv2Vi5abt3DN/NW87fAwRDnmWJEmSpFKG3zJxzWNLaWhMnHPkuKJLkSRJkqQux/BbBlJK/P6RxRwxfhAHjOhXdDmSJEmS1OUYfsvAnKUbeXrFJt453V5fSZIkSWqO4bcM/P6RxdRUVfC2w8YUXYokSZIkdUmG325uR30j18xawklTRzKwb3XR5UiSJElSl1RI+I2Iv4+I2RExJyI+nbcNiYjbIuLZ/H5w3h4R8YOImB8RT0TEkSXbOT9f/9mIOL+I91K0m2YvY93WOt4zY3zRpUiSJElSl9Xp4TciDgE+ChwNHA6cEREHAP8C3J5Smgzcnj8HOA2YnN8uBH6cb2cI8GXgmHxbX94ZmHuSS+9bwH7D9uH4A4YVXYokSZIkdVlF9PweDDyYUtqaUqoH7gTeAZwFXJqvcylwdv74LOCylHkAGBQRo4FTgNtSSmtTSuuA24BTO/F9FO7JxRt49MX1fPC4iVRUeG1fSZIkSWpJEeF3NvD6iBgaEX2B04HxwMiU0rJ8neXAyPzxWGBRyesX520ttfcYl92/gL41lZzjLM+SJEmS1Kqqzt5hSmleRHwbuBXYAswCGpqskyIitdc+I+JCsiHTTJgwob02W6h1W3ZwzeNLefeMcQzo7URXkiRJktSaQia8Sin9IqU0PaX0BmAd8AywIh/OTH6/Ml99CVnP8E7j8raW2pvb309TSjNSSjOGDx/evm+mIJfdv5Ad9Y2cd9ykokuRJEmSpC6vqNmeR+T3E8jO9/0NcC2wc8bm84Fr8sfXAuflsz4fC2zIh0ffApwcEYPzia5OztvK3ubaei6+9wXecvBIDhzZv+hyJEmSJKnL6/Rhz7k/RMRQoA64KKW0PiK+BVwVERcAC4F35+veSHZe8HxgK/BhgJTS2oj4GvBwvt5XU0prO/NNFOXXDyxkw7Y6PnXiAUWXIkmSJEndQiHhN6X0+mba1gBvbqY9ARe1sJ2LgYvbvcAubHtdAz+7+wVeP3kYR4wfVHQ5kiRJktQtFDLsWXvutw8vYvXmWi56k72+kiRJktRWht9uZEd9Iz+58zmOmjSYY/YdUnQ5kiRJktRtGH67kT89tphlG7Zz0ZsOICKKLkeSJEmSug3DbzdR39DIj/76HIeOHcgbDyyPyzVJkiRJUmcx/HYTNzy5jIVrttrrK0mSJEl7wPDbDaSU+PFfn2PyiH6cPHVk0eVIkiRJUrdj+O0G7p2/hqeWb+Kjb9iPigp7fSVJkiRpdxl+u4Ff3PM8w/rVcObhY4ouRZIkSZK6JcNvFzd/5Wb+8vQqPnDsRHpXVxZdjiRJkiR1S4bfLu6X975ATVUFHzh2YtGlSJIkSVK3ZfjtwjbX1vOHRxdz9hFjGNavV9HlSJIkSVK3Zfjtwm6ds5ztdY2856jxRZciSZIkSd2a4bcLu2bWUsYO6sOREwYXXYokSZIkdWuG3y5qzeZa7pm/mrcdPoYIL28kSZIkSXvD8NtF3Th7OQ2NibOO8PJGkiRJkrS3DL9d1HWzljJ5RD8OGtW/6FIkSZIkqdsz/HZByzZs46EFaznTIc+SJEmS1C4Mv13Q7fNWAnDaoaMLrkSSJEmSyoPhtwu665lVjB3Uh/2H71N0KZIkSZJUFgy/XUxdQyP3PbeGNxw43CHPkiRJktRODL9dzGMvrmdzbT1vPHBY0aVIkiRJUtkw/HYxdz2zisqK4LUHGH4lSZIkqb0YfruYu55dxWvGD2JA7+qiS5EkSZKksmH47ULWbK7lySUbeMOBw4suRZIkSZLKiuG3C7ln/mpSwvArSZIkSe3M8NuF1DUkDh07kEPHDiy6FEmSJEkqK1VFF6CXvXP6ON45fVzRZUiSJElS2bHnV5IkSZJU9gy/kiRJkqSyZ/iVJEmSJJU9w68kSZIkqewZfiVJkiRJZc/wK0mSJEkqe4ZfSZIkSVLZM/xKkiRJksqe4VeSJEmSVPYMv5IkSZKksmf4lSRJkiSVPcOvJEmSJKnsGX4lSZIkSWXP8CtJkiRJKnuGX0mSJElS2TP8SpIkSZLKnuFXkiRJklT2DL+SJEmSpLIXKaWia+hUEbEKWFh0Ha0YBqwuugjtMY9f9+Wx6948ft2Xx6578/h1Xx677s3j17LVACmlU5su6HHht6uLiJkppRlF16E94/Hrvjx23ZvHr/vy2HVvHr/uy2PXvXn89ozDniVJkiRJZc/wK0mSJEkqe4bfruenRRegveLx6748dt2bx6/78th1bx6/7stj1715/PaA5/xKkiRJksqePb+SJEmSpLJn+O1CIuLUiHg6IuZHxL8UXY9aFxELIuLJiJgVETPztiERcVtEPJvfDy66TmUi4uKIWBkRs0vamj1ekflB/l18IiKOLK5ytXDsvhIRS/Lv36yIOL1k2efzY/d0RJxSTNXaKSLGR8RfImJuRMyJiL/P2/3+dXGtHDu/f91ARPSOiIci4vH8+P173r5vRDyYH6ffRkRN3t4rfz4/Xz6p0DfQg7Vy7C6JiBdKvntH5O3+u9lGht8uIiIqgf8FTgOmAu+NiKnFVqU2eFNK6YiSqeb/Bbg9pTQZuD1/rq7hEqDp9d5aOl6nAZPz24XAjzupRjXvEl597AC+l3//jkgp3QiQ/7t5LjAtf82P8n9fVZx64J9SSlOBY4GL8uPk96/ra+nYgd+/7qAWODGldDhwBHBqRBwLfJvs+B0ArAMuyNe/AFiXt38vX0/FaOnYAXym5Ls3K2/z3802Mvx2HUcD81NKz6eUdgBXAmcVXJN231nApfnjS4GziytFpVJKdwFrmzS3dLzOAi5LmQeAQRExulMK1au0cOxachZwZUqpNqX0AjCf7N9XFSSltCyl9Gj+eBMwDxiL378ur5Vj1xK/f11I/h3anD+tzm8JOBH4fd7e9Lu38zv5e+DNERGdU61KtXLsWuK/m21k+O06xgKLSp4vpvX/YFS8BNwaEY9ExIV528iU0rL88XJgZDGlqY1aOl5+H7uHT+XDuy4uOcXAY9eF5cMoXwM8iN+/bqXJsQO/f91CRFRGxCxgJXAb8BywPqVUn69SeoxeOn758g3A0E4tWC9peuxSSju/e1/Pv3vfi4heeZvfvTYy/Ep77viU0pFkQ00uiog3lC5M2VTqTqfeTXi8up0fA/uTDQdbBvy/QqvRLkVEP+APwKdTShtLl/n969qaOXZ+/7qJlFJDSukIYBxZL/xBxVaktmp67CLiEODzZMfwKGAI8LniKuyeDL9dxxJgfMnzcXmbuqiU0pL8fiXwJ7L/VFbsHGaS368srkK1QUvHy+9jF5dSWpH/YtAI/IyXh1Z67LqgiKgmC0+/Tin9MW/2+9cNNHfs/P51Pyml9cBfgOPIhsRW5YtKj9FLxy9fPhBY07mVqqmSY3dqfipCSinVAr/E795uM/x2HQ8Dk/MZ+GrIJoy4tuCa1IKI2Cci+u98DJwMzCY7Zufnq50PXFNMhWqjlo7XtcB5+eyJxwIbSoZnqgtoci7T28m+f5Adu3PzWUv3JZv846HOrk8vy88Z/AUwL6X03ZJFfv+6uJaOnd+/7iEihkfEoPxxH+AksvO2/wK8M1+t6Xdv53fyncAd+agMdbIWjt1TJX8wDLJztUu/e/672QZVu15FnSGlVB8RnwJuASqBi1NKcwouSy0bCfwpnweiCvhNSunmiHgYuCoiLgAWAu8usEaViIgrgBOAYRGxGPgy8C2aP143AqeTTdayFfhwpxesl7Rw7E7IL/GQgAXAxwBSSnMi4ipgLtlMtRellBoKKFsvex3wQeDJ/Pw1gH/F71930NKxe6/fv25hNHBpPuN2BXBVSun6iJgLXBkR/wE8RvYHDvL7yyNiPtkkg+cWUbSAlo/dHRExHAhgFvDxfH3/3Wyj8A86kiRJkqRy57BnSZIkSVLZM/xKkiRJksqe4VeSJEmSVPYMv5IkSZKksmf4lSRJkiSVPS91JElSNxURDcCTQDXZpWUuA76XUmostDBJkrogw68kSd3XtpTSEQARMQL4DTCA7FrIkiSphMOeJUkqAymllcCFwKciMyki7o6IR/PbawEi4rKIOHvn6yLi1xFxVkFlS5LUaSKlVHQNkiRpD0TE5pRSvyZt64EpwCagMaW0PSImA1eklGZExBuBf0gpnR0RA4FZwOSUUn0nly9JUqdy2LMkSeWpGvhhRBwBNAAHAqSU7oyIH0XEcOAc4A8GX0lST2D4lSSpTETEfmRBdyXZeb8rgMPJTnPaXrLqZcAHgHOBD3dymZIkFcLwK0lSGch7cn8C/DCllPIhzYtTSo0RcT5QWbL6JcBDwPKU0tzOr1aSpM5n+JUkqfvqExGzePlSR5cD382X/Qj4Q0ScB9wMbNn5opTSioiYB1zdqdVKklQgJ7ySJKmHiYi+ZNcHPjKltKHoeiRJ6gxe6kiSpB4kIt4CzAP+x+ArSepJ7PmVJEmSJJU9e34lSZIkSWXP8CtJkiRJKnuGX0mSJElS2TP8SpIkSZLKnuFXkiRJklT2DL+SJEmSpLL3/wHZz02SuEieIAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_days = 365\n",
    "n_test_samples = test_data.shape[0]\n",
    "test_predictions = []\n",
    "current_sample = X_test[0]\n",
    "for i in range(n_days):\n",
    "    # Reshape the current sample for the LSTM model\n",
    "    current_sample = current_sample.reshape((1, n_steps,1))\n",
    "    # Predict the next closing price and add it to the test predictions list\n",
    "    next_prediction = model.predict(current_sample)[0][0]\n",
    "    test_predictions.append(next_prediction)\n",
    "    # Update the current sample with the new prediction\n",
    "    current_sample=np.reshape(current_sample,(1,n_steps))\n",
    "    current_sample = np.concatenate((current_sample[:,1:], [[next_prediction]]), axis=1)\n",
    "\n",
    "# Denormalize the predicted prices\n",
    "test_predictions = scaler.inverse_transform(np.array(test_predictions).reshape(-1,1))\n",
    "\n",
    "# Plot the predicted prices\n",
    "plt.figure(figsize=(16,8))\n",
    "plt.plot(test_predictions)\n",
    "plt.xlabel('Day')\n",
    "plt.ylabel('Closing Price')\n",
    "plt.title('Predicted Nifty 50 Closing Prices for Next 365 Days')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "580118ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,4))\n",
    "plt.plot(test_predictions)\n",
    "plt.xlabel('Day')\n",
    "plt.ylabel('Closing Price')\n",
    "plt.title('Predicted Nifty 50 Closing Prices for Next 365 Days')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "369e6da9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-04-14 14:21:01.042168: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_2_grad/concat/split_2/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_2_grad/concat/split_2/split_dim}}]]\n",
      "2023-04-14 14:21:01.044624: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_grad/concat/split/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_grad/concat/split/split_dim}}]]\n",
      "2023-04-14 14:21:01.046564: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_1_grad/concat/split_1/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_1_grad/concat/split_1/split_dim}}]]\n",
      "2023-04-14 14:21:01.325876: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_2_grad/concat/split_2/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_2_grad/concat/split_2/split_dim}}]]\n",
      "2023-04-14 14:21:01.329116: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_grad/concat/split/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_grad/concat/split/split_dim}}]]\n",
      "2023-04-14 14:21:01.331314: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_1_grad/concat/split_1/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_1_grad/concat/split_1/split_dim}}]]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-04-14 14:21:01.650633: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_2_grad/concat/split_2/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_2_grad/concat/split_2/split_dim}}]]\n",
      "2023-04-14 14:21:01.652937: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_grad/concat/split/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_grad/concat/split/split_dim}}]]\n",
      "2023-04-14 14:21:01.656797: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_1_grad/concat/split_1/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_1_grad/concat/split_1/split_dim}}]]\n",
      "2023-04-14 14:21:02.185571: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_2_grad/concat/split_2/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_2_grad/concat/split_2/split_dim}}]]\n",
      "2023-04-14 14:21:02.191192: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_grad/concat/split/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_grad/concat/split/split_dim}}]]\n",
      "2023-04-14 14:21:02.193490: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_1_grad/concat/split_1/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_1_grad/concat/split_1/split_dim}}]]\n",
      "2023-04-14 14:21:02.484302: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_2_grad/concat/split_2/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_2_grad/concat/split_2/split_dim}}]]\n",
      "2023-04-14 14:21:02.487423: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_grad/concat/split/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_grad/concat/split/split_dim}}]]\n",
      "2023-04-14 14:21:02.489339: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_1_grad/concat/split_1/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_1_grad/concat/split_1/split_dim}}]]\n",
      "2023-04-14 14:21:02.771565: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_2_grad/concat/split_2/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_2_grad/concat/split_2/split_dim}}]]\n",
      "2023-04-14 14:21:02.774525: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_grad/concat/split/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_grad/concat/split/split_dim}}]]\n",
      "2023-04-14 14:21:02.776385: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_1_grad/concat/split_1/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_1_grad/concat/split_1/split_dim}}]]\n",
      "2023-04-14 14:21:04.338901: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_2_grad/concat/split_2/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_2_grad/concat/split_2/split_dim}}]]\n",
      "2023-04-14 14:21:04.341860: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_grad/concat/split/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_grad/concat/split/split_dim}}]]\n",
      "2023-04-14 14:21:04.344138: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_1_grad/concat/split_1/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_1_grad/concat/split_1/split_dim}}]]\n",
      "2023-04-14 14:21:04.649893: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_2_grad/concat/split_2/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_2_grad/concat/split_2/split_dim}}]]\n",
      "2023-04-14 14:21:04.652987: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_grad/concat/split/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_grad/concat/split/split_dim}}]]\n",
      "2023-04-14 14:21:04.655574: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_1_grad/concat/split_1/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_1_grad/concat/split_1/split_dim}}]]\n",
      "2023-04-14 14:21:05.030361: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_2_grad/concat/split_2/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_2_grad/concat/split_2/split_dim}}]]\n",
      "2023-04-14 14:21:05.033583: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_grad/concat/split/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_grad/concat/split/split_dim}}]]\n",
      "2023-04-14 14:21:05.035699: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_1_grad/concat/split_1/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_1_grad/concat/split_1/split_dim}}]]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "92/92 [==============================] - 15s 82ms/step - loss: 0.0081\n",
      "Epoch 2/100\n",
      "92/92 [==============================] - 8s 86ms/step - loss: 0.0011\n",
      "Epoch 3/100\n",
      "92/92 [==============================] - 7s 74ms/step - loss: 9.2801e-04\n",
      "Epoch 4/100\n",
      "92/92 [==============================] - 7s 75ms/step - loss: 8.8908e-04\n",
      "Epoch 5/100\n",
      "92/92 [==============================] - 7s 73ms/step - loss: 7.7948e-04\n",
      "Epoch 6/100\n",
      "92/92 [==============================] - 7s 74ms/step - loss: 6.7911e-04\n",
      "Epoch 7/100\n",
      "92/92 [==============================] - 7s 74ms/step - loss: 6.2979e-04\n",
      "Epoch 8/100\n",
      "92/92 [==============================] - 7s 74ms/step - loss: 5.6268e-04\n",
      "Epoch 9/100\n",
      "92/92 [==============================] - 7s 75ms/step - loss: 5.3861e-04\n",
      "Epoch 10/100\n",
      "92/92 [==============================] - 7s 74ms/step - loss: 4.9148e-04\n",
      "Epoch 11/100\n",
      "92/92 [==============================] - 7s 74ms/step - loss: 4.9434e-04\n",
      "Epoch 12/100\n",
      "92/92 [==============================] - 7s 75ms/step - loss: 4.8364e-04\n",
      "Epoch 13/100\n",
      "92/92 [==============================] - 7s 75ms/step - loss: 4.3713e-04\n",
      "Epoch 14/100\n",
      "92/92 [==============================] - 7s 74ms/step - loss: 3.9394e-04\n",
      "Epoch 15/100\n",
      "92/92 [==============================] - 7s 74ms/step - loss: 3.3718e-04\n",
      "Epoch 16/100\n",
      "92/92 [==============================] - 7s 74ms/step - loss: 3.5107e-04\n",
      "Epoch 17/100\n",
      "92/92 [==============================] - 7s 77ms/step - loss: 3.2917e-04\n",
      "Epoch 18/100\n",
      "92/92 [==============================] - 7s 73ms/step - loss: 3.0156e-04\n",
      "Epoch 19/100\n",
      "92/92 [==============================] - 7s 74ms/step - loss: 3.2486e-04\n",
      "Epoch 20/100\n",
      "92/92 [==============================] - 7s 75ms/step - loss: 3.2425e-04\n",
      "Epoch 21/100\n",
      "92/92 [==============================] - 7s 76ms/step - loss: 2.7372e-04\n",
      "Epoch 22/100\n",
      "92/92 [==============================] - 7s 75ms/step - loss: 2.6290e-04\n",
      "Epoch 23/100\n",
      "92/92 [==============================] - 7s 74ms/step - loss: 2.3687e-04\n",
      "Epoch 24/100\n",
      "92/92 [==============================] - 7s 75ms/step - loss: 2.5354e-04\n",
      "Epoch 25/100\n",
      "92/92 [==============================] - 7s 74ms/step - loss: 2.1464e-04\n",
      "Epoch 26/100\n",
      "92/92 [==============================] - 7s 74ms/step - loss: 2.3140e-04\n",
      "Epoch 27/100\n",
      "92/92 [==============================] - 7s 73ms/step - loss: 2.2881e-04\n",
      "Epoch 28/100\n",
      "92/92 [==============================] - 8s 83ms/step - loss: 2.3846e-04\n",
      "Epoch 29/100\n",
      "92/92 [==============================] - 7s 75ms/step - loss: 2.7632e-04\n",
      "Epoch 30/100\n",
      "92/92 [==============================] - 7s 74ms/step - loss: 2.0754e-04\n",
      "Epoch 31/100\n",
      "92/92 [==============================] - 7s 74ms/step - loss: 1.9951e-04\n",
      "Epoch 32/100\n",
      "92/92 [==============================] - 7s 73ms/step - loss: 2.0100e-04\n",
      "Epoch 33/100\n",
      "92/92 [==============================] - 7s 74ms/step - loss: 2.1133e-04\n",
      "Epoch 34/100\n",
      "92/92 [==============================] - 7s 74ms/step - loss: 2.2437e-04\n",
      "Epoch 35/100\n",
      "92/92 [==============================] - 7s 74ms/step - loss: 2.1789e-04\n",
      "Epoch 36/100\n",
      "92/92 [==============================] - 7s 75ms/step - loss: 2.0218e-04\n",
      "Epoch 37/100\n",
      "92/92 [==============================] - 7s 75ms/step - loss: 1.8284e-04\n",
      "Epoch 38/100\n",
      "92/92 [==============================] - 7s 81ms/step - loss: 1.7479e-04\n",
      "Epoch 39/100\n",
      "92/92 [==============================] - 7s 77ms/step - loss: 1.8629e-04\n",
      "Epoch 40/100\n",
      "92/92 [==============================] - 7s 75ms/step - loss: 1.7033e-04\n",
      "Epoch 41/100\n",
      "92/92 [==============================] - 7s 75ms/step - loss: 1.6829e-04\n",
      "Epoch 42/100\n",
      "92/92 [==============================] - 7s 74ms/step - loss: 1.8127e-04\n",
      "Epoch 43/100\n",
      "92/92 [==============================] - 7s 75ms/step - loss: 1.6141e-04\n",
      "Epoch 44/100\n",
      "92/92 [==============================] - 7s 74ms/step - loss: 1.8351e-04\n",
      "Epoch 45/100\n",
      "92/92 [==============================] - 7s 75ms/step - loss: 1.6515e-04\n",
      "Epoch 46/100\n",
      "92/92 [==============================] - 7s 75ms/step - loss: 1.8536e-04\n",
      "Epoch 47/100\n",
      "92/92 [==============================] - 7s 77ms/step - loss: 1.7486e-04\n",
      "Epoch 48/100\n",
      "92/92 [==============================] - 8s 82ms/step - loss: 1.7535e-04\n",
      "Epoch 49/100\n",
      "92/92 [==============================] - 7s 80ms/step - loss: 1.8004e-04\n",
      "Epoch 50/100\n",
      "92/92 [==============================] - 8s 82ms/step - loss: 1.7537e-04\n",
      "Epoch 51/100\n",
      "92/92 [==============================] - 7s 79ms/step - loss: 1.7573e-04\n",
      "Epoch 52/100\n",
      "92/92 [==============================] - 7s 79ms/step - loss: 1.7308e-04\n",
      "Epoch 53/100\n",
      "92/92 [==============================] - 7s 77ms/step - loss: 1.7229e-04\n",
      "Epoch 54/100\n",
      "92/92 [==============================] - 7s 75ms/step - loss: 2.0731e-04\n",
      "Epoch 55/100\n",
      "92/92 [==============================] - 7s 75ms/step - loss: 1.7922e-04\n",
      "Epoch 56/100\n",
      "92/92 [==============================] - 7s 76ms/step - loss: 1.7737e-04\n",
      "Epoch 57/100\n",
      "92/92 [==============================] - 7s 76ms/step - loss: 1.7766e-04\n",
      "Epoch 58/100\n",
      "92/92 [==============================] - 7s 75ms/step - loss: 1.8701e-04\n",
      "Epoch 59/100\n",
      "92/92 [==============================] - 7s 75ms/step - loss: 1.7351e-04\n",
      "Epoch 60/100\n",
      "92/92 [==============================] - 7s 74ms/step - loss: 1.7709e-04\n",
      "Epoch 61/100\n",
      "92/92 [==============================] - 7s 77ms/step - loss: 1.6285e-04\n",
      "Epoch 62/100\n",
      "92/92 [==============================] - 7s 76ms/step - loss: 1.7644e-04\n",
      "Epoch 63/100\n",
      "92/92 [==============================] - 8s 86ms/step - loss: 1.7006e-04\n",
      "Epoch 64/100\n",
      "92/92 [==============================] - 7s 76ms/step - loss: 1.5745e-04\n",
      "Epoch 65/100\n",
      "92/92 [==============================] - 7s 79ms/step - loss: 2.0522e-04\n",
      "Epoch 66/100\n",
      "92/92 [==============================] - 7s 75ms/step - loss: 1.6565e-04\n",
      "Epoch 67/100\n",
      "92/92 [==============================] - 7s 77ms/step - loss: 1.6840e-04\n",
      "Epoch 68/100\n",
      "92/92 [==============================] - 7s 75ms/step - loss: 1.8426e-04\n",
      "Epoch 69/100\n",
      "92/92 [==============================] - 7s 75ms/step - loss: 1.8459e-04\n",
      "Epoch 70/100\n",
      "92/92 [==============================] - 7s 78ms/step - loss: 1.5527e-04\n",
      "Epoch 71/100\n",
      "92/92 [==============================] - 7s 75ms/step - loss: 2.0606e-04\n",
      "Epoch 72/100\n",
      "92/92 [==============================] - 8s 84ms/step - loss: 1.7463e-04\n",
      "Epoch 73/100\n",
      "92/92 [==============================] - 7s 78ms/step - loss: 1.6694e-04\n",
      "Epoch 74/100\n",
      "92/92 [==============================] - 7s 80ms/step - loss: 1.6896e-04\n",
      "Epoch 75/100\n",
      "92/92 [==============================] - 7s 76ms/step - loss: 1.5927e-04\n",
      "Epoch 76/100\n",
      "92/92 [==============================] - 7s 76ms/step - loss: 1.7035e-04\n",
      "Epoch 77/100\n",
      "92/92 [==============================] - 7s 79ms/step - loss: 1.6189e-04\n",
      "Epoch 78/100\n",
      "92/92 [==============================] - 7s 74ms/step - loss: 1.7403e-04\n",
      "Epoch 79/100\n",
      "92/92 [==============================] - 7s 78ms/step - loss: 1.6055e-04\n",
      "Epoch 80/100\n",
      "92/92 [==============================] - 7s 75ms/step - loss: 1.5764e-04\n",
      "Epoch 81/100\n",
      "92/92 [==============================] - 7s 78ms/step - loss: 1.5833e-04\n",
      "Epoch 82/100\n",
      "92/92 [==============================] - 7s 77ms/step - loss: 1.6793e-04\n",
      "Epoch 83/100\n",
      "92/92 [==============================] - 7s 78ms/step - loss: 1.7260e-04\n",
      "Epoch 84/100\n",
      "92/92 [==============================] - 7s 78ms/step - loss: 1.6306e-04\n",
      "Epoch 85/100\n",
      "92/92 [==============================] - 7s 78ms/step - loss: 1.6348e-04\n",
      "Epoch 86/100\n",
      "92/92 [==============================] - 8s 82ms/step - loss: 1.6410e-04\n",
      "Epoch 87/100\n",
      "92/92 [==============================] - 7s 78ms/step - loss: 1.7399e-04\n",
      "Epoch 88/100\n",
      "92/92 [==============================] - 7s 78ms/step - loss: 1.5881e-04\n",
      "Epoch 89/100\n",
      "92/92 [==============================] - 7s 81ms/step - loss: 1.6287e-04\n",
      "Epoch 90/100\n",
      "92/92 [==============================] - 9s 98ms/step - loss: 1.6004e-04\n",
      "Epoch 91/100\n",
      "92/92 [==============================] - 8s 85ms/step - loss: 1.7719e-04\n",
      "Epoch 92/100\n",
      "92/92 [==============================] - 8s 89ms/step - loss: 1.7421e-04\n",
      "Epoch 93/100\n",
      "92/92 [==============================] - 7s 79ms/step - loss: 1.6702e-04\n",
      "Epoch 94/100\n",
      "92/92 [==============================] - 8s 82ms/step - loss: 1.5620e-04\n",
      "Epoch 95/100\n",
      "92/92 [==============================] - 7s 79ms/step - loss: 1.6738e-04\n",
      "Epoch 96/100\n",
      "92/92 [==============================] - 7s 77ms/step - loss: 1.5464e-04\n",
      "Epoch 97/100\n",
      "92/92 [==============================] - 7s 79ms/step - loss: 1.5444e-04\n",
      "Epoch 98/100\n",
      "92/92 [==============================] - 7s 77ms/step - loss: 1.5380e-04\n",
      "Epoch 99/100\n",
      "92/92 [==============================] - 7s 78ms/step - loss: 1.7749e-04\n",
      "Epoch 100/100\n",
      "92/92 [==============================] - 7s 76ms/step - loss: 1.7826e-04\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-04-14 14:32:58.074231: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_2_grad/concat/split_2/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_2_grad/concat/split_2/split_dim}}]]\n",
      "2023-04-14 14:32:58.076760: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_grad/concat/split/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_grad/concat/split/split_dim}}]]\n",
      "2023-04-14 14:32:58.078274: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_1_grad/concat/split_1/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_1_grad/concat/split_1/split_dim}}]]\n",
      "2023-04-14 14:32:58.345602: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_2_grad/concat/split_2/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_2_grad/concat/split_2/split_dim}}]]\n",
      "2023-04-14 14:32:58.348419: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_grad/concat/split/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_grad/concat/split/split_dim}}]]\n",
      "2023-04-14 14:32:58.350476: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_1_grad/concat/split_1/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_1_grad/concat/split_1/split_dim}}]]\n",
      "2023-04-14 14:32:58.619326: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_2_grad/concat/split_2/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_2_grad/concat/split_2/split_dim}}]]\n",
      "2023-04-14 14:32:58.621813: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_grad/concat/split/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_grad/concat/split/split_dim}}]]\n",
      "2023-04-14 14:32:58.623382: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'gradients/split_1_grad/concat/split_1/split_dim' with dtype int32\n",
      "\t [[{{node gradients/split_1_grad/concat/split_1/split_dim}}]]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 1s 1s/step\n",
      "1/1 [==============================] - 0s 36ms/step\n",
      "1/1 [==============================] - 0s 37ms/step\n",
      "1/1 [==============================] - 0s 34ms/step\n",
      "1/1 [==============================] - 0s 35ms/step\n",
      "1/1 [==============================] - 0s 35ms/step\n",
      "1/1 [==============================] - 0s 34ms/step\n",
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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, LSTM\n",
    "\n",
    "# Load the data\n",
    "prices = hsi['Close'].values\n",
    "\n",
    "# Normalize the data\n",
    "scaler = MinMaxScaler(feature_range=(0,1))\n",
    "scaled_data = scaler.fit_transform(prices.reshape(-1,1))\n",
    "\n",
    "# Define the number of time steps and split the data into training and testing sets\n",
    "n_steps = 60\n",
    "train_data = scaled_data[:3000]\n",
    "test_data = scaled_data[3000:]\n",
    "\n",
    "\n",
    "# Define the function to create input sequences and labels for the LSTM model\n",
    "def create_sequences(data, n_steps):\n",
    "    X, y = [], []\n",
    "    for i in range(len(data)-n_steps):\n",
    "        X.append(data[i:i+n_steps])\n",
    "        y.append(data[i+n_steps])\n",
    "    return np.array(X), np.array(y)\n",
    "\n",
    "# Create the input sequences and labels for the training and testing sets\n",
    "X_train, y_train = create_sequences(train_data, n_steps)\n",
    "X_test, y_test = create_sequences(test_data, n_steps)\n",
    "\n",
    "\n",
    "\n",
    "# Reshape the input sequences for the LSTM model\n",
    "X_train = X_train.reshape((X_train.shape[0], X_train.shape[1], 1))\n",
    "\n",
    "#X_test = X_test.reshape((X_test.shape[0], X_test.shape[1], 1))\n",
    "\n",
    "# Define the LSTM model\n",
    "model = Sequential()\n",
    "model.add(LSTM(50, return_sequences=True, input_shape=(n_steps,1)))\n",
    "model.add(LSTM(50, return_sequences=True))\n",
    "model.add(LSTM(50))\n",
    "model.add(Dense(1))\n",
    "model.compile(loss='mean_squared_error', optimizer='adam')\n",
    "\n",
    "# Train the LSTM model\n",
    "model.fit(X_train, y_train, epochs=100, batch_size=32, verbose=1)\n",
    "\n",
    "n_days = 365\n",
    "n_test_samples = test_data.shape[0]\n",
    "test_predictions = []\n",
    "current_sample = X_test[0]\n",
    "for i in range(n_days):\n",
    "    # Reshape the current sample for the LSTM model\n",
    "    current_sample = current_sample.reshape((1, n_steps,1))\n",
    "    # Predict the next closing price and add it to the test predictions list\n",
    "    next_prediction = model.predict(current_sample)[0][0]\n",
    "    test_predictions.append(next_prediction)\n",
    "    # Update the current sample with the new prediction\n",
    "    current_sample=np.reshape(current_sample,(1,n_steps))\n",
    "    current_sample = np.concatenate((current_sample[:,1:], [[next_prediction]]), axis=1)\n",
    "\n",
    "# Denormalize the predicted prices\n",
    "test_predictions = scaler.inverse_transform(np.array(test_predictions).reshape(-1,1))\n",
    "# Plot the predicted prices\n",
    "plt.figure(figsize=(8,4))\n",
    "plt.plot(test_predictions)\n",
    "plt.xlabel('Day')\n",
    "plt.ylabel('Closing Price')\n",
    "plt.title('Predicted HSI Closing Prices for Next 365 Days')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "83243e94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_predictions\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "950d3961",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3000\n",
      "[[[0.12097333]\n",
      "  [0.1241382 ]\n",
      "  [0.13556664]\n",
      "  ...\n",
      "  [0.21181461]\n",
      "  [0.21097353]\n",
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      "\n",
      " [[0.1241382 ]\n",
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      "  ...\n",
      "  [0.21097353]\n",
      "  [0.21936298]\n",
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      "\n",
      " [[0.13556664]\n",
      "  [0.13649981]\n",
      "  [0.14202525]\n",
      "  ...\n",
      "  [0.21936298]\n",
      "  [0.22317245]\n",
      "  [0.21695942]]\n",
      "\n",
      " ...\n",
      "\n",
      " [[0.54738679]\n",
      "  [0.55542937]\n",
      "  [0.55181943]\n",
      "  ...\n",
      "  [0.59844182]\n",
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      "\n",
      " [[0.55542937]\n",
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      "  ...\n",
      "  [0.59788621]\n",
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      "\n",
      " [[0.55181943]\n",
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      "  ...\n",
      "  [0.59492702]\n",
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      "  [0.59684558]]]\n",
      "732\n",
      "[[[0.59209679]\n",
      "  [0.59295936]\n",
      "  [0.59908033]\n",
      "  ...\n",
      "  [0.31225174]\n",
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      "\n",
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      "  ...\n",
      "  [0.32396566]\n",
      "  [0.35569394]\n",
      "  [0.375561  ]]\n",
      "\n",
      " [[0.59908033]\n",
      "  [0.59566992]\n",
      "  [0.5813283 ]\n",
      "  ...\n",
      "  [0.35569394]\n",
      "  [0.375561  ]\n",
      "  [0.37671519]]\n",
      "\n",
      " ...\n",
      "\n",
      " [[0.93988627]\n",
      "  [0.94620983]\n",
      "  [0.95441825]\n",
      "  ...\n",
      "  [0.99285374]\n",
      "  [0.99315771]\n",
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      "\n",
      " [[0.94620983]\n",
      "  [0.95441825]\n",
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      "  ...\n",
      "  [0.99315771]\n",
      "  [0.98957841]\n",
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      "\n",
      " [[0.95441825]\n",
      "  [0.9503478 ]\n",
      "  [0.94259072]\n",
      "  ...\n",
      "  [0.98957841]\n",
      "  [0.98452877]\n",
      "  [0.98752783]]]\n",
      "672\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, LSTM\n",
    "\n",
    "# Load the data\n",
    "prices = nifty_50['Close'].values\n",
    "\n",
    "# Normalize the data\n",
    "scaler = MinMaxScaler(feature_range=(0,1))\n",
    "scaled_data = scaler.fit_transform(prices.reshape(-1,1))\n",
    "\n",
    "# Define the number of time steps and split the data into training and testing sets\n",
    "n_steps = 60\n",
    "train_data = scaled_data[:3000]\n",
    "test_data = scaled_data[3000:]\n",
    "\n",
    "\n",
    "# Define the function to create input sequences and labels for the LSTM model\n",
    "def create_sequences(data, n_steps):\n",
    "    X, y = [], []\n",
    "    for i in range(len(data)-n_steps):\n",
    "        X.append(data[i:i+n_steps])\n",
    "        y.append(data[i+n_steps])\n",
    "    return np.array(X), np.array(y)\n",
    "\n",
    "# Create the input sequences and labels for the training and testing sets\n",
    "X_train, y_train = create_sequences(train_data, n_steps)\n",
    "X_test, y_test = create_sequences(test_data, n_steps)\n",
    "\n",
    "\n",
    "\n",
    "# Reshape the input sequences for the LSTM model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "16606f5a",
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'statsmodels.stats.breakpoint_detection'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "Input \u001b[0;32mIn [5]\u001b[0m, in \u001b[0;36m<cell line: 4>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mstatsmodels\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mregression\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlinear_model\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m OLS\n\u001b[0;32m----> 4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mstatsmodels\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mstats\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbreakpoint_detection\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (Bartlett, ChowTest)\n\u001b[1;32m      5\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n\u001b[1;32m      7\u001b[0m \u001b[38;5;66;03m# Load Nifty50 data\u001b[39;00m\n\u001b[1;32m      8\u001b[0m \n\u001b[1;32m      9\u001b[0m \n\u001b[1;32m     10\u001b[0m \u001b[38;5;66;03m# Extract the close price column\u001b[39;00m\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'statsmodels.stats.breakpoint_detection'"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from statsmodels.regression.linear_model import OLS\n",
    "from statsmodels.stats.breakpoint_detection import (Bartlett, ChowTest)\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Load Nifty50 data\n",
    "\n",
    "\n",
    "# Extract the close price column\n",
    "close_price = nifty_50['Close']\n",
    "\n",
    "# Define the breakpoint location\n",
    "breakpoint_location = len(close_price) // 2\n",
    "\n",
    "# Split the data into two segments\n",
    "segment_1 = close_price[:breakpoint_location]\n",
    "segment_2 = close_price[breakpoint_location:]\n",
    "\n",
    "# Fit an OLS regression on each segment\n",
    "ols_segment_1 = OLS(segment_1, np.ones(len(segment_1))).fit()\n",
    "ols_segment_2 = OLS(segment_2, np.ones(len(segment_2))).fit()\n",
    "\n",
    "# Perform the Chow test\n",
    "chow_test = ChowTest(np.arange(len(close_price)), close_price, [breakpoint_location])\n",
    "chow_test.summary()\n",
    "\n",
    "# Plot the data and breakpoint\n",
    "plt.plot(close_price)\n",
    "plt.axvline(x=breakpoint_location, color='red')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9b0bbac5-44cf-4118-bd12-53b2a6ec6f8b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    },
    {
     "data": {
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HA38v6zcRERGRZnfNNfD88yGdXbOW7veWdvPNSW1ZtqlTi3tuHMB16JDkxX3gWlUAZ2b3Af8DhpjZdDM7mTBq9BozewP4JVG/NOAxYCowBfgDcDpANHjhUuDl6HNJPKAhOufW6Jr30QAGERGRmrf++kl65crMYwsW5L7miivguutyHzvssOKeGwdw6ZUf7r03bEePDttqmD4kVspRqEflObRDjnMdOCPPfW4Hbs+RPx7YpillFBERkerSLhWZdO+eeSxeFzXdFy42c2bu+6WnACmk0KCJjz4K22qacVYrMYiIiEhVWLUKfvnLZH+PPcL2xBOT45A0rR5xRHLuG2807dnlnBi4OSiAExERkaqQXjAe4Ec/CqNI4wBu9eqwjfuk9eqVnDt2bOaSW5ttFrZH5WsPzJIvgIufedZZxd2nXBTAiYiISFXInvKjTRSlxAMLvv51OPXUZDDDBhtknh+PGoVk8MLixcU9e9q03Plvvx22w4blPl4p5Z7IV0RERCSn9CCFf/87SacHM/z+99CzZ0h37Vr/fYptGs0VwG23XTKoYffdi7tPuagGTkRERKpCOojad98knT0a9bPPwjY7gIvPS9fE/e9/Sd+5fNzhgw9g223D/lZbwXe+E6YmiQPA/v3zXl4RCuBERESk4ubNgx/+MKSPPjrz2D77ZO7Hk+2mpxyBEKxB3Yl9f/GLws8eOzZs45Uf1q4NzbazZ8OLL4bpQ9pVWZulAjgRERGpuEmTkvQ992Qea9s2GZQAyWCF7Bq4004LtWlDh2bmT5lS+NnjxoXtxReHbf/+YW3V2bPh0UfzTxJcSQrgREREpOLiedheeCH38TapiGX8+LDN1QfumWfq5j38cOE1UZcsCbV5Q4fCn/8MDzyQuSJDNQZwVVYhKCIiIq1RPPCgW7fcx3MFURtuWDcv33qpCxbUnRg4tmJFssrC4YeHbTqAq0aqgRMREZGKe/DBsM0VlEEyH1ta3GctLb0Yfe/eufOzrVgBnTtn5nXsmP/8aqAATkRERCru4YfDNl8NXPbC9ltumfvcdKCWnieuvgAue51T1cCJiIiI1GO99cI234Lx2U2j8aCHGTPgqquS/HiKkWeeyez3Fk/+m8vy5bUXwKkPnIiIiFTcV75SeNWEvn3h00/r5m+8MWyzTbK/bFnYdugA69Yl+YUGIuSqgdt44/rLXEmqgRMREZGKW768bj+0tMmTM/fvuiv3eStWhG2HDnDllUl+rj506WuyA7iBA5N0emLgaqEATkRERCquvgBuvfXg8suT/Xx95e6+O2w7dIAjjoB33w379QVw2c9O95/Lt2RXJSmAExERkYqrL4ADOO88GD48pNNBVbwEVlrch619+7BtaA1cr15hu9tuhctUKQrgREREpOKKCeAgWdc0vYxW//7w3nuZ58WBWzEB3IwZdQO4LbYIK0LEo2OrjQYxiIiISMUtW9awAK5Ll8z8tm0z9+MRqHEAN3lyGNSwdm2SB3DnnWGKkew+dlB3TdZqoho4ERERqahly8P0H+mJd/NZuTJssyfabZMV0cTTksR95aZMgREjQtNqfA+Axx4L2/ffb3CxK0oBnIiIiFTUSy+Fbdy/rZBvfStse/bMzE/XwG2wAfTpE9KdOkG/fmEZrbFjQ9555yXnxjV12TV61U4BnIiIiFRMerLdYgK4a66BTz6pOwo1XQN34IGZx7p0SeaHA3jiibr3zTeBcLVSACciIiIVszo1wW6+qUHS2rULk/pmSwdw2c2rcQAX18q9/XZyLJ7gN7tGr9opgBMREZGKSY8Obcp8a+km1OxlsOIAbvvt614XP7NaR5vmowBOREREKmZNFMDdf3/T7pOugcsekRoHcPEIVkiW2Vq0KMwjN2BA055fbgrgREREpGJmzQ7bLbds2n3SfenWrs08Fgdw8TJbkIxEXbiwOldaqI/mgRMREZGKWLEiTKILYaRoU6SbYrMXrt9wQ/jHP+o+u1MnWLw4WXWhlqgGTkRERCpizpwkvdFGTbtX+vq4eTTWv3/d81esgDPOgPHja7MGrmQBnJndbmazzOzNrPwfmNm7ZvaWmV2Vyj/XzKaY2Xtmtn8qf0SUN8XMzknlDzKzF6P8P5tZVpdFERERqWbx1B5bbdX0e7VvD7fdFtJmmcdy1e6tXAmjR4d0elmuWlHKGrg/AiPSGWa2NzAS2N7dtwaujvKHAkcCW0fX3GRmbc2sLXAjcAAwFDgqOhfgV8C17r4FMB84uYTfRURERJpZHMC1a1v4vGLF/eDyrcqQlu4Plx3w1YKSBXDu/iwwLyv7NOBKd18ZnTMryh8J3O/uK919GjAF2Cn6THH3qe6+CrgfGGlmBuwDPBhdfydwcKm+i4iIiDS/pUvDtk2JA7j02qexdACXPeihFpS7D9yWwO5R0+czZrZjlN8P+Dh13vQoL19+T2CBu6/JyhcREZEaEdfAtW2maCQOxAoFcKNGhW06gMs+vxaUu8jtgB7ALsBPgQei2rSSMrNTzGy8mY2fPXt2qR8nIiIi9fjtb+H660O6uWrg8i10n57Y9/jjM8+FZIWGWlLuaUSmAw+5uwMvmdk6oBcwA0hPodc/yiNP/lygm5m1i2rh0ufX4e63ALcADB8+3POdJyIiIqVz883w0Ufw6qswZkzI+zHNVwMX16plr2uaroGLj02YELb9+sE551Bzyl0D9zdgbwAz2xLoAMwBHgGONLOOZjYIGAy8BLwMDI5GnHYgDHR4JAoAnwIOje47Cvh7Ob+IiIiIFM8dTjsNrrgiCd5ibZupOunII0Nt2imnZN0/VcMXB3A//GHY7rhjmCeu1pSsBs7M7gP2AnqZ2XTgQuB24PZoapFVwKgoGHvLzB4A3gbWAGe4+9roPt8HxgBtgdvd/a3oEWcD95vZZcBrwG2l+i4iIiLSNA88kP9Yu2aKRjbZBGbOrJvfvXuSzm5e3Xff5nl2uZUsgHP3o/IcOjbP+ZcDl+fIfwx4LEf+VMIoVREREalyRx6Z/1ipBxHssgs8/XTYfvpp5rF4UEOt0VJaIiIiUjFbbA7lmIZtzz3DNr1o/W9+U5urMICW0hIREZESmz8/Sb/xBkydCpddFvb79i1vWdL94VatKu+zm5MCOBERESmphx8O26uvhu22g0GD4LzzwqLzbZtpCpHGyO4PV0sUwImIiEhJLVwYtiedlOSZVTZ4A+jcubLPbwoFcCIiIlJSs2eHYK1bt0qXJFOuJbZqhQI4ERERKak5c6BXr+pbNH7XXStdgsZTACciIiKN9q9/wS23FD5nzhzo3bs85WmIL36x0iVoPE0jIiIiIo02YkRYZWHYMNhmm7rLWEFoQu3Vq/xly6dduzCAopapBk5EREQazaMVxnfcEU4/Pfc5s2ZVVwA3c2YoUy1TACciIiKNEgdvsTvuyJzzDcLKB5Mmhdq5atGzZ3U26TaEAjgRERFplMfqLHQJH32UuX/TTWFbTQFcS6AATkRERBrl/ffr5mWvbhCvuNChQ+nL05oogBMREZFGWb68bl48aW+2lStLW5bWRgGciIiINMqyZXXz0n3g0gMFliwpfXlaEwVwIiIi0ijTp9fNGzcOjj0WxoyBPn2S/H79yleu1kDzwImIiEijfPJJCNI++yzJu/76sL3nnsxzv/718pWrNVANnIiIiDTKwoWw0UaVLkXrpABOREREGmXBAgVwlaIATkRERBrsmWfCnG/pfm5SPuoDJyIiIg0ycSLstVdIF1MD9847JS1Oq6QaOBEREWmQ7bZL0u3b1z9J71ZblbY8rZECOBEREWm0du3guONC+sorK1uW1kQBnIiIiBQtewH7tm3h5pvDgIbDD697/h13lKVYrY4COBERESnaihWZ++3ahc+GG0LHjnXPP+GEshSr1VEAJyIiIkVbujRzv11qOGQbRRVlo1ctIiIiRSsUwPXsCUOGlLc8rZUCOBERESlaetksCH3gYu3bw7vvwsqVYS3UuXPLW7bWRPPAiYiISNEOOCBs27SBdesya+BiHTrAfvuVt1ytTclq4MzsdjObZWZv5jj2YzNzM+sV7ZuZXW9mU8xsgpkNS507yswmR59RqfwdzGxidM31Zmal+i4iIiISzJsXtn37hm2uAE5Kr5RNqH8ERmRnmtkAYD/go1T2AcDg6HMKMDo6twdwIbAzsBNwoZl1j64ZDXwvdV2dZ4mIiEjzOvDAsN1nn7BVAFcZJQvg3P1ZYF6OQ9cCPwPSM8mMBO7y4AWgm5n1BfYHxrr7PHefD4wFRkTHNnD3F9zdgbuAg0v1XURERCR49VX4+tehU6ewr/avyijrIAYzGwnMcPc3sg71Az5O7U+P8grlT8+Rn++5p5jZeDMbP3v27CZ8AxERkdZr1iyYOTOsfxovn7VqVWXL1FqVLYAzsy7AecAF5XpmzN1vcffh7j68d+/e5X68iIhIizBtWtgedpgCuEorZw3c5sAg4A0z+wDoD7xqZl8AZgADUuf2j/IK5ffPkS8iIiIlsmBB2PburQCu0soWwLn7RHffyN0HuvtAQrPnMHefCTwCHB+NRt0FWOjunwJjgP3MrHs0eGE/YEx0bJGZ7RKNPj0e+Hu5vouIiEhr9NxzYdutW7Js1sqVFStOq1bKaUTuA/4HDDGz6WZ2coHTHwOmAlOAPwCnA7j7POBS4OXoc0mUR3TOrdE17wOPl+J7iIiISKhpu/TSkO7WTTVwlVaywb/uflQ9xwem0g6ckee824Hbc+SPB7ZpWilFRESkGMtTi9grgKs8LaUlIiIi9Vq9Okl37gz9orkfNDawMjT9noiIiNRrVaqvmxkcdVTYHnZY5crUmimAExERkXpNmpy5HwdxUhlqQhUREZGC1nn950h5KYATERGRgtauqXQJJJsCOBERESloheZ6qzoK4ERERKSg9ACGo4+uXDkkoUEMIiIiUtCaqAl10iQYPLiyZZFANXAiIiJS0OoogOvevbLlkES9AVy0PumxZnZBtL+Jme1U+qKJiIhINYhXW+jWraLFkJRiauBuAnYF4tleFgM3lqxEIiIiUjVWroSPPoIunaGdOl5VjWICuJ3d/QxgBYC7zwc6lLRUIiIiUhXOOSds00tpSeUVE8CtNrO2gAOYWW9gXUlLJSIiIlXh/vvDdrXmgqsqxQRw1wMPAxuZ2eXAOOCXJS2ViIiIVIWZM8O2S5fKlkMy1dua7e73mNkrwL6AAQe7+zslL5mIiIhUDY1ArS71BnBmtgvwlrvfGO1vYGY7u/uLJS+diIiIVEw8+hRgs0GVK4fUVUwT6mhgSWp/SZQnIiIiLdjDDyfptm0rVw6pq5gAztzd4x13X4dWcBAREWnxfvCDsB3Qv7LlkLqKCeCmmtkPzax99DkTmFrqgomIiEhlHXts2G46sKLFkByKCeBOBb4CzACmAzsDp5SyUCIiIlJ5bdqE0aft1HxadYoZhToLOLIMZREREZEqsngxrL9+pUshueQN4MzsZ+5+lZndQDSJb5q7/7CkJRMREZGKWrIEunatdCkkl0I1cPFcb+PLURARERGpLkuWqAauWuUN4Nz9H9ESWtu6+0/KWCYRERHJ4dZb4Xvfg6lTYVAZ5mVTE2r1KjiIwd3XAl8tU1lEREQkhyVLYNKkELwB/Otf5XuumlCrUzGjUF83s0fM7DgzOyT+lLxkIiIiAsAee8CQIcn+qafCunWlf65q4KpXMRPydgLmAvuk8hx4qCQlEhERkc+tXg2vvVY3/8wzYc4cuO++0j378xq42aV7hjROwQDOzHoDNwJT3H1BQ25sZrcD3wRmufs2Ud6vgW8Bq4D3gRPj+5rZucDJwFrgh+4+JsofAVwHtAVudfcro/xBwP1AT+AV4Dh3T63aJiIiUtt694avfS33sd/9LmyvvBI2LdHzNYiheuVtQjWz7wJvATcA75rZQQ289x+BEVl5Y4Ft3H07YBJwbvSsoYS55raOrrnJzNpGgyhuBA4AhgJHRecC/Aq41t23AOYTgj8REZGqMXIk3HFH466dMyd87r+/8HlbbNG4+9fHPTShqg9cdSrUB+4sYGt335WwEsO5Dbmxuz8LzMvK+5e7r4l2XwDi1dVGAve7+0p3nwZMAXaKPlPcfWpUu3Y/MNLMjNCk+2B0/Z3AwQ0pn4iISKk98gicdFLDr/vf/+DrXy/u3DVr6j+nMVauhLVrVQNXrQoFcKvcfTaAu08FOjbzs08CHo/S/YCPU8emR3n58nsCC1LBYJyfk5mdYmbjzWz87NlqyBcRkdJbvTpJL1sGU6aEGq1ifOUr8PrrmXk//WnY3nVXsxQvrxUrwmjXF18M+wrgqlOhAK6/mV0ff3LsN5qZ/RxYA9zTlPsUy91vcffh7j68d+/e5XikiIi0ckcckaSPOQYGD4ajjqr/uv/+N3f+5ZeHmrnjjsvMP/DAxpcxl9Gjw3xze+0V9tWEWp0KDWL4adb+K83xQDM7gTC4YV93j5fomgEMSJ3WP8ojT/5coJuZtYtq4dLni4iIVNzDDyfpv/0tbMcXsbbRuHG589u3h112yczbeefQzNmc3nsvc181cNWp0EoMdzb3w6IRpT8D9nT3ZalDjwD3mtlvgI2BwcBLgAGDoxGnMwgDHY52dzezp4BDCf3iRgF/b+7yioiINMS4cbDttvDMM7mPb7ZZ/fd4990kPXEiPPQQbLRR7nO7dIEFCwgTfjWTWbMy91UDV52KmQeuUczsPmAvoJeZTQcuJAyE6AiMDeMQeMHdT3X3t8zsAeBtQtPqGdEqEJjZ94ExhGlEbnf3t6JHnA3cb2aXAa8Bt5Xqu4iISMvzzDOw4YbwpS81z/1WrIDddw/pPffMfU63bvXfZ8EC2H770FzauTNss03+c4cMgQceALZtYGELyA7gVANXnUoWwLl7rpb+vEGWu18OXJ4j/zHgsRz5UwmjVEVERBos7uP1eWeeJnjggcw+b/lq4MaODZ98I0zdw9QhG24Ygrf69O0L8+bBOoc21vBy56IArjYUs5SWiIhIizWjGXpQ//rX+Y8NGxa2vXqFKT/22y//uVdfDc8/D1ZPMPb66zBmDPTpE/ZXN2Ea+0cfhREjkkA2O4Dr3r3x95bSqTeAM7MtzexJM3sz2t/OzH5R+qKJiIiUxpw5SfrcBs1ymlu7PO1ZTz4ZRpUuWgRbbpn/+v/7vxC0XXpp2H/77cLP2377EAjGwVVT5oL75jdDMDhsWJgUeOFCuPBCeO45OOssGDCg3ltIBRRTA/cHQt+11QDuPoEwmEBERKQmXXVVkr777sw52xpq0WJ44YXcxwYPDgMNunYNNWv53HJL2MbzxF1ySXHPbt8+bJujGfj11+H990O6a9cwF92119ZfGyiVUUwA18XdX8rKK9G8zyIiIqU1f37dJs/LLmv8/aZNTdLumTV6cYAFcOyxSbpQjdlXvgKnnlrcs+Oav+YI4NI22KB57yfNr5gAbo6ZbQ44gJkdCnxa0lKJiIiUyM9+Vjdv6tS6ecVavjxzv0ePJJ0O4G69FY4+OqQXLcp/v3551xWqq6k1cJ99ljtfAVz1KyaAOwP4PbCVmc0grJF6WikLJSIiUgrLloVAKhZP69GUZsKV0QCCTtFcbD17Jsc6dEjSHTsmo08XLsy8xyabJOmGBHBNrYHLN0WJArjqV28AFy0k/zWgN7CVu+/m7h+UvGQiIiLN7L77kvQllyRBV2Ot8yR4igPDdA1c9hQcG28ctvE6o7H0ZLn5Ju3NJQ7gliwp/hoIgyRuuilzMEeaArjqV8wo1DPNbANgGXCtmb1qZgUGQYuIiFSnadOS9PnnJ2uTrlvXuPutiQY/XHNNWO8UYODA5Hh2zd6uu4Zt9pqo6T5xDWnOjZtQJ08p/hqArbeGM85I9rfNmghYAVz1K6YJ9SR3XwTsB/QEjgOuLGmpRERESiAe5Xn99WH761+HqTPy9QWrz+oo8Ipr1qDwtBv5lqVKj4L95jeLf37btsWfG8vV3Dp+PFxxRbLft2/D7yvlVUwAF//74UDgrmgpKw0qFhGRslq6FP75z6bd4+GHYfPN4Qc/CPtt28LQoXUnry1WHHil+73VV3t13HF1541L18B94xvFPz/dx65Y8+dn7g8dGu6z//5JXq9eDb+vlFcxAdwrZvYvQgA3xsy6Ao2sbBYREWmc7beHb30LJkxo3PXr1sHHH9ftl9a1a8Pv+corYSRpHMCl+721axeW6co3l9vdd4eAzSx8rrgCPvoo8/piFZocOJ8lS5N0nz7huwAMGtTwe0nlFBPAnQycA+zo7suADsCJJS2ViIhIlniS2Q8+aNz1114bttlzrP3lL2H7WJ1Vt3ObOROGDw+rJkx6L+SlAziAp54Kfexy+e53M/fPOy9sTzkFVq4srgyx9deHM89sWFNq+1SAOGBAMpAjX/OuVKdiRqGuA/oDvzCzq4GvRKsxiIiIlE08vcbLLzf82kWL4Cc/Cen0IAOAVdE0IPmaLidPhv/8J6Tffz9ZUeFPf4J20SCChiw3NWJE7vzlyxvXJNqxI3gD2sXWrE3S6UEWjelPJ5VTzCjUK4Ezgbejzw/N7JelLpiIiEjMPZk7rdAkuPlMn56kt9gi89jFFxe+dr/9YN99Q7PnFlvAd74T8letCqNAu3VrWLPnDjvkzl+xovh7pLVr17B54Nam+ttlT0IstaOYJtQDga+7++3ufjswAmjAGBkREZGmWbgwmets6dLC56aNGxem5fjww7B/2ml1A7gLLgjbLl0y8197Db785aTJdu+9M4/PmxeCyY4diy8PZE7am9bQ5tNYu3ZhqaRiY7h0DdzQoY17plResf9m6AbMi9IblqYoIiIiuaU7+dcXwN15Z1hEfpddYPfdQ148wvIXv8h9zf771x2deeONYYH32Lhxua9tSPMlQJs8VSePPNKw+8Q+r/0rIoJbuCjURnbqBE88kb82UKpfMQHcFcBrZvYUYfqQPQiDGkRERMoiHikJhQO4cePghBNC+rrrkvwxY8L2C1/IfV3nzvDJJ8n++PFw223Fla1b9+LOS3MPI0DT05c0dlWIuO+ae/1zfL32WtiuWAF77ln3+JZbwiGHNK4cUl71BnDufp+ZPQ3sGGWd7e4zS1oqERGRlJNOCttBg/IHcNOnJzVuEEZnZstX+9WlS2Z/sHjN0vq0awd98wSF9fnud+GXvwxTmwwY0PCm2HQZoPgm1ELee68ZbiJlkbcPnJkNiz9AX2B69Nk4yhMRESm59DJXm2+eP4CLpxnJJ3uqj7TOnTMDuHyd+x9/PHO/Q/u6y2UV69JLYdmyMLr2uOMaP0lxugZOWo9CNXDXFDjmwD7NXBYREZE6/v3vsL3kEnj11bD/0kuw3nphTc/YXnvlvn777eGNN5LF5nPp0gVmzIAHHwz957IHFJxwAhxxRJgC5PvfD82f55/ftKk32rQJgSPAXXc1/j6f18AVEcC1bQNrNRV/i5A3gHP3vfMdExERKYd585IBCN/9brLm6M47h20ctKT7yI0eHUabAlx0UQjsTjyxcLNo3P/ssMNyH7/iiqT/3A03hOd27AhD/9rQb9T8iq2BW706BG8dO8KERsylJ9UlbwBnZscC5u53Z+UfB6x193tLXTgREWldli3LnM5j5MgknW8AAsDNN4ftxRfDgQcm+RdeGLZTpxZ+bn0T6GavDWoGP/0p8Gjh68qh2ADub3+D3oT+dv23LXWppNQKzQP3A+DhHPkPAT8uTXFERKS1+vGPQ7Poo4/C4sUhIElP3WFWd2DC+eeHPnKffgpf/GKY0y2upWuIXE2hb74ZVmC4+OKGTdRbbnPnhu2yAqNzV6+Gww8P6fZV/F2keIUCuPbuviQ7092XAu1LVyQREWmNfvObsP3HP2CDDeDyy5NjDz0Utr/9beZi9JddFpa2+s9/kj5w7dqFZbP+8Y/inx0vSh/77ndD/7q9904m+q1WX/5y/ecsSf1tXs3BqBSvUADX2czWy840s66EBe1FRESaxZrU8k6//33YxovB/+lP8O1vJ8fj5tLYPfeEUaPpPm6//jV8swFrBj3wQOZ+La0LumER0+srgGt5CgVwtwEPmtmmcYaZDQTuj46JiIg0i6XL8h/r3z9z/5hjwmjU2MNRZ5+ddmr887Nr2b72tcbfq9ziue0KdYGbNy9Jd2zkhMFSXfIGcO5+NfB34Fkzm2tmc4FngH+6+6/LVUAREWnZ1nmyQkC6pi2WHcAB9O6dpD/7LGwLzfNWn+OPz9yfOLHx9yq3zycnLhDBffpp2A4eDJ0aOWGwVJeCi9m7+83uvikwEBjo7pu6++hibmxmt5vZLDN7M5XXw8zGmtnkaNs9yjczu97MppjZhPREwWY2Kjp/spmNSuXvYGYTo2uuN2vsVIoiIlIJzz8f+r2tSs25tuOOdc/r169uXq5grbFLUcXeeSdJ77FH0+5VTp/XwOUJ4O65Bw44IKSLaW6V2lAwgIu5+2J3X9zAe/8RGJGVdw7wpLsPBp4kWVP1AGBw9DkFGA0h4AMuBHYGdgIujIO+6Jzvpa7LfpaIiFQJ99DEedNNYX/dOvjqV8PI09lzkvOGDs287phjcgdmXbqE1QvSmvrP+K22StLxPHO14PNpRPIcP/bY1LlF/a0vtaBkf5Tu/iwwLyt7JHBnlL4TODiVf5cHLwDdzKwvsD8w1t3nuft8YCwwIjq2gbu/4O4O3JW6l4iIVJnp0+Hll+GMM2DVqsw+bIsXhe1LL8FBB4VVCeIRqQsX5r9nevWCeOLe5tLU2rxyKqYJNVZLgzOksHLH4n3cPWqJZybQJ0r3Az5OnTc9yiuUPz1HvoiIVKErr0zSM2bAffcl+7Nmh+1GG4VatOOOg1NOCc1+V19d+L6HHhq26Ql/m+Kss8K2lkZqFhrEsC5r2aw2CuBajII/UTPbilA7FgdHM4BH3P2d/FcVx93dzMqy9K6ZnUJommWTTTYpxyNFRCSyfHnmNB1//nNSw5aWnoB3vfXgscfqv/fvfhcWuN9336aXE0K5rr22ee5VLnEAN3VqWGkhbXFW5yc1obYcef8ozexswpQhBrwUfQy4z8zOyXddPT6Lmj+JtrOi/BnAgNR5/aO8Qvn9c+Tn5O63uPtwdx/eu3f2z1tEREqpSxeYk+rndu65dc9p0wbaN2KK+D59Qu1ec9WY1eJwuDiAW748yZs2DRYsCEuTpdXi95PcCsXiJwM7uvuV7v6n6HMlYTDByY183iNAPJJ0FGGakjj/+Gg06i7AwqipdQywn5l1jwYv7AeMiY4tMrNdotGnx6fuJSIiVWjvvTP333gjSW++eXnL0pJkN5MCbLZZWKEhHdRJy1IogFsH5FpRrm90rCAzuw/4HzDEzKab2cnAlcDXzWwy8LVoH+AxYCowBfgDcDqAu88DLgVejj6XRHlE59waXfM+8Hh9ZRIRkfJKBxAPPph5bOutk3SfjcpTnpZo7drc+R98kFkDl2uOPaldhSqdzwKejIKteCDBJsAWwPfru7G7H5XnUJ2eCtFI0jPy3Od24PYc+eOBbeorh4iIVM6kSWF7//2Zc7ede27miMhaGjRQbfIFcJAE0P/8J3zjG8Be5SiRlEPe/2Tc/Qkz25LQZJoexPCyuxf4uYiISGv2t78ltT3f+lbYbhTVsB12GOywA5x9dtifNAm+cGTZi9iipNeRzRbXwHXuXJ6ySPkU/DePu68DXoj3zayHgjcREcln1arMprp//CNsh0Xr62QvGj94MNC1LEVrsbL7wKVXZIhr4Lp0KV95pDwKjUL9RSo91MwmAa+Y2QdmVkNzVIuISLlk93ODMFJUSziVzmabZe6vWpWklywJWwVwLU+hQQyHpNK/Bs5090HA4UCNzZIjIiKl9sILYekrCKsqxKsj7LBD5crUGvTpE1aOiAeCpAcuxNO39OpV/nJJaRU7pd/G7v44gLu/BKg1XUREMqSXturZE37+cxg+HK66qnJlai3MkqbT9PJjCxaEbffudS6RGleoD9xmZvYIYfLe/mbWxd3juL4R0y2KiEhLtWYNjB6d7HfrFkadvvxyxYrUqqQDuEWLkvx4NYtaWttVilMogMteWa4NgJn1AUbXPV1ERFqjpUth/fUz8zbYoDJlaa3SAdz77yf5//0vdOyoFRhaokLTiDwDYGbbuvvEVP5nwI1lKJuIiFS5eJRp7NBD4Ve/0rxu5ZYO4A45JPOYat9apmL+E7vJzDoCfwTucfeF9ZwvIiKtwNKlcNBByX6PHnDHHXVr46T0zMCBDz+se0xzwLVM9Q5icPfdgWMIi8q/Ymb3mtl+JS+ZiIhUrRdeSEaZxp5/XsFbpcQ1cEuX1j2mGriWqahKbnefHM0LNx64HvhytIj8ee7+UCkLKCIi1WfXXevmfeEL5S+HBHEA9/e/J3l9+sBnn8H06ZUrl5ROvTVwZradmV0LvAPsA3zL3b8YpTUfnIhIK7POM/c32SRsNXChcuIA7rzzkrwjoyXKCi21JbWrmHngbgBeBbZ39zPc/VUAd/8E+EXBK0VEWoiPP4brr89cpqi1WpsVEHz4YXgvGulYOelBDLGVKytTFimPYgK4h939bndfHmeY2ZkA7n53yUomIlJhr70Gxx8Pa9fCiBFw5pnw3nuVLlXTLFoU/rIfO7Zx1y9dBs89H9JnnVV3HU6pDDPwrD+LuD/cpZeWvzxSesUEcMfnyDuhmcshIlJ1hg2Du+8OC7C//XbI++tfK1umprrjjrDdb79klv6GSE/Me8ghqnWrFtk1cO+/nwRwQ4ZUpkxSWnkHMZjZUcDRJCsyxLoC80pdMBGRanH00Uk6XpT9tttg221hp50qU6bG6tEjSW+0UebC5/VJBwg/+xnsvnvzlUuapo2Fvolf+UqoZd1ss2RJra5dK1s2KY1Co1BfAD4FegHXpPIXAxNKWSgRkWp1++1w6qnw3e+G/VrrE3fddUl69WoYPz6sV1qfl18OwepTwOabhcl6pXpYm/BbfP75JO+EE+DJJ4v785XaU6gJ9UF3fxpY5u7PpD6vurvGtIhIi7Z8ee78114Ln1ozeTK8+274pO24Izz4ICxblvu62F/+kqTbaTXsqrNoYd3f7LHHhqCuV6/KlElKq1ANXBszOw/Y0sx+lH3Q3X9TumKJiFRWrhntY7vsEra1MmntuHGFmzsPOwyOOw7uuiv/Oa+8Erbdu0FvBQRVZ0VqxGn2UlrSMhWqgTsSWEsI8rrm+IiItBiLFoXRprHZszOPH3NMko5HXvbtW/iee+0VOpfPn1+50ZqLF9cN3vbeG956KzPv7rvz18ItWAD/+U9Ib7+91jmtdoMHV7oEUg6FFrN/D/iVmU1w98fLWCYRkbJasyYZnLB2LbRpUzeAO/NM+N3voHv3JG/FisL3fOaZkO7RA376U7jqquYtd33Wrcu9OkL79rmb1f7978y1TWOFaiOl+rz+eqVLIOWQtwbOzI6NkkPN7EfZnzKVT0Sk5OIZ6wHeeSdsp04N2+22C9sttoBu3ZLBCxBq1nKZPbvuAuK//nWYjqScnn8+s1Zt4EC47LIwEGOjjZKpUWIjR+bu+/fZZ2F74oklK6o0o08+qXQJpBwKNaGuF23XR02oItKCped2mzUrbMePh003DbVSjz+e1Lx95SvJuUuWhJGcEybA1lvDc8+F/HHjci9fdMQRpSl/PnEQCiGonDYNfv5z6Ncv5H3xi/DUU5l933IFpePGhe2555aurNJ82hQzw6vUvEJNqL+PtheXrzgiIuV1552Z+3PmwB/+AP/4R5gAtXfvsApDLHvakOuuC82jALvtFubeqpZO5PGI0/nzQ+1hLnvtFZpaj4+mbM9efunRR5OZ/DfaqBSllOamyZVbh0IT+V5Q4Dp3dy3OISI1bcGCMFcWwJ57hj5r//d/SS1Urr8IswO4OHiL/eQnzV3Kxlm7Fq64IqTj/n35tGkDN98c5rfLbkJ99NEkrcXqa4MCuNahUEXr0hwfgJOBs0tcLhGRkotr33bfPTSVQmYT4quv1r1mxIhQa/XGG7nv2alTkt5uO/j610OzJdSddmTxYlhvvVDb11zmzAmB6GOPJXnF/IUej6hdvjyMyI2X2br55mSrwKA26M+pdSjUhPr56gtm1hU4EzgRuJ/MlRlERGrSvGhRwKefDrVQX/taEsgBbLVV3Wv69Qv9xrJ16RIGDLz/PrRtG+7dtWvyl+natfCbrNkzb7ghXHPQQc23okPv3pn7xa7dGg+6GDcumbn/3XeTcv3f/zVP+aQ01usCS6MBK+oD1zoU/GM2sx5mdhlh6ax2wDB3P9vdZ5WldCIizeiWW+Cll8LAAwg1YOuvn/yFt9lmmeffe2/x9z7ggLB9770wV9oGG2TWhLRrlzmw4dlnk5q5XFN9NEauFSK23rq4a+MA7qyzkrx4dO4PftCkYkkZbJGa+y09n6G0XIWmEfk18DJh7dNt3f0id88zaL5hzOz/mdlbZvammd1nZp3MbJCZvWhmU8zsz2bWITq3Y7Q/JTo+MHWfc6P898xs/+Yom4i0TAsWhFqknXeGDh2SvHT/sP79M6+pbwmiF15I0vtH/wd6//1klGdau3ZhsEA8oe+eeybHZs9Ogsqm+PWv6+Ztsklx12ZPewLJfGK57ivVpU3qHwvN8VuS6leoBu7HwMbAL4BPzGxR9FlsZosa+0Az6wf8EBju7tsAbQmrPvwKuNbdtwDmE/raEW3nR/nXRudhZkOj67YGRgA3mVnbxpZLRFq2G2/M3HcP82Wla7+y+w7VN+pyxx2T9NFHJ+mNN657brx6QVw7ssceybG1a+G//y38rGJ06xaab9NyBWa5DBiQO/+ww6BjxyYVS8oh9dutb4UQaRnyBnDu3sbdO7t7V3ffIPXp6u5NHYvUDuhsZu2ALsCnwD7Ag9HxO4GDo/TIaJ/o+L5mZlH+/e6+0t2nAVOAnZpYLhFpoRZl/bNzyRIYMwYGDUry+vQJ22uuCYMB6gtc2rSBLbeEH/84DEaIbbNN3XPjAG7iRFi1KgSLX/1qMtXHTTc17PvE3JOm2UWLwtx1a9aEpuIJE4q/TzpYHTIkWe81u0+dVKf0vz3uu69ixZAyKvuKdu4+w8yuBj4ClgP/Al4BFrh73ENkOhA3QvQDPo6uXWNmC4GeUX6qASPjmgxmdgpwCsAmxbYniEiL8v77mfvxlBjppbFOOinUYB1/fPHrfb73Xt28nXeumxffb4cdQpD14Ydh5YMhQ0LA99prYQqSfv3g//2/MH3HFluE44WccUYYIbp2bQjgNtggfId07WBDfeMb8PHHoYk4X82cVJkogttmG+jZs7JFkfIo+1gVM+tOqD0bRGiiXY/QBFoy7n6Luw939+G99c9JkVbn5pvDaMx4dGXaYYcl6bZtQxDX2MXa33oLLrww93PS94zXFu3SJWwHDgx511wDP/pRqDn75jdzj4JNe/NNGD061ML17BmmI6lvzrdC4lq4Cy5Inp3dJCvVrbG/Xak9lRhs/DVgmrvPdvfVwEPAV4FuUZMqQH9gRpSeAQwAiI5vCMxN5+e4RkSEN9+EmTPhtNPC/lVX1V3/c9NNm+95Q4fCRRflnocr14jW7bcP2699LXPkYJxfn2efTdLx/HXpeega6q23YPLkEAT+5Cdh0Ed67Vepfgq4W49KBHAfAbuYWZeoL9u+wNvAU8Ch0TmjgL9H6UeifaLj/3F3j/KPjEapDgIGAy+V6TuISBV76SV4+GHYdtvQoXu99UKfrr33Dut/pud6K1eH7/S6pLG471q+gQbDhhW+56ef1s3L7uvXEL16hWZbCE2xN9+c2cQsVSyar09zwLUelegD96KZPQi8CqwBXgNuAR4F7o/mnXsNuC265DbgbjObAswjjDzF3d8yswcIwd8a4Ax31+w3IlKnD9rSpXDggcn+vvsm6a5dy1OmN97InF5k881D/zXIP13Jm2+GIC+7WWzduvw1LYsXN72sUnvWRQFcPEWOtHwVaS139wuBC7Oyp5JjFKm7rwAOy86Pjl0OXN7sBRSRmvTJJ/lrszbfPHP/qadC82q5bLxxst5qhw4wZUpy7NvfDjWBO+8Mf/tbyDvoIHjkkbCiQ/Z0JpdckqQ7dAijWgG+/OXMY9J6xPMLFjttjNQ+dXcUkRbh+efDtBz5ZAdwe+1V0uLkdPPNoQk3u9bPLASfa9cmtW377RcCuKVL695n3LgkvW5dqKnbYAONGG3N4gCuKX0gpbaotVxEWoRJkwofzw7gKiEeIZpvsfF0s+iMaEjWMceEOeLSgVy6SXXNmrBcloK31i0e0Xz44ZUth5SPauBEpEWYlWeF5ksuCYvVV8PcWPGAgJ/+NP85q1eHmrjJk+GKK+B//wufqVPh6qvhe98LExDHKlGTKNWnS2fYfXdoe1ylSyLloho4EWkR4rnV0vr0gfPPhyefzF/rVU6dOoU52372s/zntGsXVoDIXs3hmmvCtbfemuS9+WaY8FcEoK3+Rm9V9MctIi1CvBTVxInhs912tb8I+ze+kbmfniLkP/8JTadx05mItC5qQhWRmpeenDeuuXrjjcqUpTllL/8V7996a5jTTkRaL9XAiUhNc2DW7JD++c8rWpRmd9ddmfvTpoVtueauE5HqpQBORGpaOedyK7d4Qfp4aoh4jjc1m4qIAjgRqWkrlifpgw+uWDFKZu7cMIoWwiL3oABORNQHTkRq3PLl0L49+KpKl6Q0evSoux7peutVpiwiUj1UAyciNW3+fGjfwv8pmj27vmrgREQBnIjULHdYvQa6da//3FqmAE5EsimAE5GaFS8v1dLXf8z+fi39+4pI/Vp4w4OItGSLF4dtu7aFz6t1XbvCZZeF5bQmToR+/SpdIhGpNAVwIlKT5s4Na4T2IHMR+JbIrOXNcSciTaMATkRqzksvwc47h/RTtPwATkQkm/rAiUhNmT8/Cd5ibfVPURFpZRTAiUhNuffeunmqgROR1kYBnIjUlBdfrJvXTjVwItLKKIATkZqxciWMHQsjR8LChTB5MmyxOXTuXOmSiYiUl/7dKiI14X//g698JaS//W3YYIPwoX9FiyUiUhGqgRORmnDppUl6iy0qVw4RkWqgAE5EakLXrmF71FGw006VLYuISKUpgBORqvDEE3D77cn+8uUwfnyy/+mnsMceYRRq+/blL5+ISDVRAFch7vDmm2Er5XXbbTBhQqVLIQC/+lUyqvSAA+Dkk2HIENh997Bg+447JstlzZgBfftWrqwiItVEgxgq5Oab4fTT4S9/gUMPrXRpWr5160LA3KMHfPe7IU/Bc2V99BGccw5stx288UaSP2lS+MRmzIDVq8M6oOutV/5yiohUI9XAVcijj4bt0UdXthytxciRsP32cP/9lS5Jy/Lhh/Dkkw2/bv788OcBoTZ0773zn/vJJ/D3v4e0gm4RkaAiAZyZdTOzB83sXTN7x8x2NbMeZjbWzCZH2+7RuWZm15vZFDObYGbDUvcZFZ0/2cxGVeK7NNRNN8GWW8Krr4b91asrW57W4Nln4Z//DOl//zv3OccdB5ddVr4ytRTbbw9f+xqMG1fc+StWwJo18MwzsGBBkv/00/mv+egjOP/8kP7vfxtbUhGRlqVSNXDXAU+4+1bA9sA7wDnAk+4+GHgy2gc4ABgcfU4BRgOYWQ/gQmBnYCfgwjjoq2ZnnBEmH/3000qXpPV45pkkPWZMkn777bCdNw/+9KckSGjtFi0Kzczp95bLhx+GyXQh9FlLN3u6w5//HCbeTevcOdS2ffvbue+5+eZwzDHw05/C44+HvBNPTI5369agryIi0mKVvQ+cmW0I7AGcAODuq4BVZjYS2Cs67U7gaeBsYCRwl7s78EJUe9c3Onesu8+L7jsWGAHcV67v0lR77BEmJ3UHs0qXpmVatQouuCD3sVdfhV694KWXylumajZ1agiiIAz2mDYNBg7MfW52/qefhtplCBPsLlkCF14IF12UeV66tm7ffTObYKdMyV+2U08t4guIiLQSlaiBGwTMBu4ws9fM7FYzWw/o4+5xvdRMoE+U7gd8nLp+epSXL79m7L9/aEJdsaLSJWm5RhVoWD/uOOjTB771rSSvNTdpuyfBWyw9rUfa0qV18+bMCdvp00PwBoV/2xtuCH/9a7J/4IF1z/ne95L0T36S/14iIq1NJQK4dsAwYLS7fxlYStJcCkBU29Zs3ZXN7BQzG29m42fPnt1cty3K66+H2rXOnet2oO8eNfjGzVDS/OI+b6efHt7/4MGFBzLk+3msWgW//33SrNeSTJgAW28NX/hCknfMMaF27NJLw+/36KPDNm4S/de/6t7nwQdDEPjaa0lePAUIwMSJmeffdVcI4q69Fr7/fXjggbr3vPRS+MMfwhxx2cGliEhrVokAbjow3d2j2Z94kBDQfRY1jRJtZ0XHZwADUtf3j/Ly5dfh7re4+3B3H967d+9m+yLF+PKXw3bFijCDfNqGG4ZtujO3NK85c6BnT7jxRjjiiNBP64gj6p53xx1hm68z/aOPhia8Aw+Eww4rWXErYvvtQ3/AWdF/cZddFmrejj02Oee+qGPCkUeG7SGHhO1DD8HatSF9//1w7rlw0EHJdR98kKS3267ucwHOOgtuuCH3FCF9+oT+ePvv35hvJiLScpU9gHP3mcDHZjYkytoXeBt4BIgbvEYB0cQBPAIcH41G3QVYGDW1jgH2M7Pu0eCF/aK8mhF3yFYNXGnEAcncuXWPxYuix/pHC6Ifc0zue40enaQffLB2g+61azObNdetq3vOuedChw6h+fnHP848lt1H7aCDoE0buPXWsP+rXyXHhg4NA3by2XTThpVdREQSlRqF+gPgHjObAHwJ+CVwJfB1M5sMfC3aB3gMmApMAf4AnA4QDV64FHg5+lwSD2ioJlttlf9YXAOnAK40Po56SKYXQY/ttluS7t07s/Zn+vS652f3+UpPPBtbty5MkVHN/jsOhkUT8dxyC/zxjyH961+HAQvLloWADEKT6dVXZ16/bBk8/3zoEjBqFLRtG/K/9KW6z/rWt8KgiGefDX0Le/aEQYNK8a1ERFqfiqzE4O6vA8NzHNo3x7kOnJHnPrcDebpZV5577s7e7dqF5lQ1oZZWPFXLfvvVPTZqFPzmN6Ep8IILkho4gAED6k4YO3t2qJVatSrsT5gAe+6Zec6RR4aVNT7+GDbeOAmEqsW86J8377xTd9TzJpvkH22aNnUqfPWrIZ1uEs2+9uOPQ6C3dm14TxdcENLf/GZoLh02DBERaYIq+yum5ViyJNQ4fPxx3WPz5sGddya1PsuWlbdsrUUcwOVaP3Po0FAr9Oc/hw78cTCdy7p1oXbqRz8Knenbtw/7aYsWheANQgCYq9av0iZMzH+sT5/8x+Jm1PRoXQi/71iPHpnH+vcPc8PFJk8Ov/POncN/E/XNMSciIoUpgCuR2bPDckG5dO0aakDatw/7rXnqilJ6+eVQa1YoOEl76KGwzR7nMnduaBrt2zd0pu/fP3TOX7s2BGqzZoWAPO1vf2tq6cPyUXvvnbufWkNljwDNtsUW+Y9dfXWokcwOStP9CM2S1UVi6cC5c+dQe9mtW3h/669fVLFFRCQPBXAlku7XdtZZSTo9aakCuNJ49tkQuD36KOy0U0gX49vfDk19s2eHvmHDhsHw4fDFL4bjcSA4bRo8/HBoCr/ggpA/L6v3ZbzKQ1McfHAYFdscq3bEa+926hiCsQcegJNOCnl77gn9iphBcfvtkzVJIZkGJxY3qe6wQ91r4/nesq8REZHGUQBXImekeu1tvTWcfXZIx9OKQAgAINRcXHVV+crWkl11VQhIVq8Oi6APHtyw6+O50E48Mcxn9sorySjWXE2xsYsuyuxXtmpVMrFtU02ZAu+917R7xGXbLpq647DDwkoL774LjzxS/H3SzaLpJlQIAxrcYfz4JC/uGxf/g6Zr1wYVW0RE8lAAVyLPP5+kt9oKLr881OykayDiGrjbbgsBnmriGu/TT0OQEgfKsfTktMXYdtv8x+KJZNNBeNq++4YA5oADwn52P7mGWL48SR9wQPgN5WuSL+TZZ+Hee0Mzb9s20KVz5vEhQ8KyV8Xq3h2uvz4Et8Us//bmm5n7nTvnPk9ERBpGAVyJHH10kt5gg1A70atX5jlxABfLNWK1tXvqqVCLk2ugx7p1oQbprbfCqM9cvv/9hj0vbi7NJX7GM8/An/6U5MdLQMUd+S+/PGw//DD3fWbPhhdeKFyOnXdO0nEwd9dddfuZZVu3LpnnbfHiUBt5zDGh6bNLjolyG+MHP8g9bUgu2ZPzdurUPGUQEWntFMCVSHpUY8eOuc/JDuCefLLu9BWtmTvss08IhNZbL5mzLHbTTSHg2mabzPz580ON0+OP5w/s8unZs+5KC9/9bqh1i2ucunbNnPA3biaMmxTjCWrzBXAHHwy77pqsF5pLrkEHZ50V+pcNGxamAsnmHv6h0LlzSKebfN9/v+5I0UoodkCJiIgUpgCuRNI1Rvk60ceToMYOPTTMHfbKK6UrV7X53e9CYDRwYN3Rlnvtlbl/4omZQdEvf5l5fN26ELh06xZGko4Y0bgyZQeEf/hD7pqv2bPDlBjxn2Pcv6t79/Bnnm9d1ddfD9uhQ3Mfj5tK960zK2Lw2muh1i970uC77krS48bVrdGtVPNler68XAMcRESk4RTAlcCKFZnTSuSrgcvXh2hM1oJgffuGmqiWxj00x0EIzNIjN6dNC/23sp16alJLmT06s5g+WcVI11xdeWX+83r1ClNiHH10mPvttNOScvToUXdkamzLLcM21xyBkARiP/oRXHFF7nM++KBuABsHhgB77BG2P/1pktexyNG4ze3f/w5rqTalT6CIiGRSAFcC6b9IIfci3YWkA5Hly2HmzNAX7Ic/rP6lmnKZODGzyW/1ajj99MzpVSCzw/u//x22O+2UOXXFE0+E9/DWW2H/ssvCFBVNHaWZduKJoaP+0qV1B0Xksssu8NFHmasRzJwZau7WroVrrgnNvzNmhH5p8e/DLHffvvi9DBwYArDzzqtbKwih/1/sr3+F3/42GdkcO/nkUJbf/S5Ze7fc2rULq1QUs9KDiIgURwFcCSxenKQnTiw8y38u6b/UX345Sd9wQ/iLuJa89FKYHyxuLnz44dC8OHp0CJIgGbEbT7vx8stwyikhPWZMWDA93Tfw7ruTgGbzzcNyWHGtVnNo1y7UDHbp0vR7jRkDP/lJCAq33jpzxKd7WJIrn759Q/Ps5ZeHoO+22+Czz2DlynD8vvvC9vXXQ/M7hNrea65J7jFkSOh3dsYZzVdDKSIilacArgTSa5vmqjmpT7rvUvYoynhOslrwySd1R1Meckjd83bcMQQXcZ+xG28M26OOyl1rlJ4kecCAZituSXzjG0k6Xe7YWWeF7x43p6bPSX/3tm3DxLsbbVS3T+V3vpOkX301rPMqIiItmwK4Epg8OWxz/YVdnz59MgO47NGIce1LLcie3X/XXXOf165dOHfKlLC/dGkIXu69N/O8IUPqXlutAdz06YWPx0Hqiy+GbTytyL/+Fba33164xuwXvwjHH344LDAPod/llluG0bDHHAP33NP48ouISHVTAFcCY8bAoEENmyA1Nncu/O9/cPPN4ZPtH/9oevnKIVeftDfeyH/+JpuEgG3cuNAXbvvt657z4ouZa3a2aRMGEVSjQktT/fSnYcBDuiZt1aqwPffcsD3ooML3HzIkNMHGNZojR8LxxyfH//SnzLkIRUSkZVEA18yWLg2jJ7OnwCjWmjWh1u2005JRjVddlQQ/774bmiarXdzB/pRT4MwzM49dckkIUp99NunjF9dA7b57uDbXmpkbbphMmgthgECbGvwFH3BAqD1LB6Nx03g8TUr2MlXZvvnNzH31bxMRaV1q8K+/6hZ3xP/qV4s7/7//rX8E5Ze+lCwUDqH5rNrFa7tedBFce23mVCrnnx9Gbu6+e1gsHurOAbfZZrnv+9vfNndJS2/99cPku7E4OI1XTIAw0e5TT4UAvphgLLtvoBaJFxFpXRTANbO4I37v3sWdv9tuod/SAw+EGqlck/7GM/vHtVrxmpzVaunSZGRp796ZtU0XXpj7muw50fLNfxYHN+nBAdUqnsduyZLMqVDiYCvuuwZhRO5RR4V09ooT+cycGVaJuPLKzJGnIiLS8imAa2ZxR/z0ZLDFOOywUCP10EN1j8X9qbbcMqwl2ZhFzUttwoQwZ5p7qHEC+P3vk3nJ4mWjttoq9/Xpvmxvvpl/9QoI07Q8/HDTy1xqcS3sIYeE7/ezn4X9eEmrQYMyz//ss7DNbh7Np0+fMNfc2WerBk5EpLVpV/8p0hBx/7TGzkuWa/mneCJgs9Dsds01oebu4IMb94ymevrpMBp2//2TvHjQQXox+MGDk3S83FShmsk77ghTsGy9deHnxwFitWvTJvwe4uDqiivgnHOSJbfuuSdMAfJ//xeamiG8n2pYs1RERKqbArhmFk/C29hJYLPXR7322tznffvbob9UfP60aWE+teeeyz3dRnPae++wTU+uGzvxxCSdHokZl7NQ5/wTTmhy0apOuia2TZvMmrJddw0B3jPPJHn1jT4VEREBNaE2u6VLoX378GmqadPqLjeVFg+YAPjzn8NIxtGjm/7cQuakJhKOg9Xnnqt7XqdOYWqQWBzMVWpB9Wq28cZJ+uqrK1cOERGpHQrgmtns2c3XBBYPXki76aYkPWNG2L79dhJEzZ8fAqexY5unDNnSgw3i/nq77Vb3vGXLQhAXu+8+uO665l3yqqXYZJOwwsKDD1ZuvVIREaktakJtZh06hKbMpvjb30LtWq7pJNJB0dy5oRk13WfsrrvCdr/9wPdsWjlySa8EMXFispJAtuyyf+ELYRF6qatjx2QAg4iISDEUwDWzdA1ZY40cmf/YkUfC/feHJZcWLIBFi5r+vIZIB2ZXXZXM9yYiIiLloybUGtO5M9x6a0g/80zhAC57ctymmjU7LEh/0kl1j+25Z7L261//2rzPFRERkUwK4GpQ3E/qxhuToAngiCMyz2vOhe+XLg197SD3Op+jRoW1X9Prc4qIiEhpKICrQel50NI1cD//eeZ5L76Ue6qPxli9OknnWn+0VuZmExERaQkUwNWgdD+0H/0oSQ8cCOedl3nusuXN88y1a5P04sVh2pK09MLsIiIiUloVC+DMrK2ZvWZm/4z2B5nZi2Y2xcz+bGYdovyO0f6U6PjA1D3OjfLfM7P98zyqRTr11LAdPz5sb7stzPB/+eVhWavYtKl1r22MdADXvj0cfji88kqSl2vKExERESmNStbAnQm8k9r/FXCtu28BzAdOjvJPBuZH+ddG52FmQ4Ejga2BEcBNZpa1jkHLNXRo5v6BBybpDTZI0vEyXE2VDuDOPjts09OXpJ8pIiIipVWRAM7M+gPfAG6N9g3YB3gwOuVO4OAoPTLaJzq+b3T+SOB+d1/p7tOAKcBOZfkCVaBd1gQwG26YpNMrIKxtppGocQC35ZbJclAdO+Yvj4iIiJROpWrgfgv8DIjDi57AAndfE+1PB+Kxjv2AjwGi4wuj8z/Pz3FNBjM7xczGm9n42bNnN+PXqJzspbrSE/yahRGqANOnN89AhjiAy55beKONmn5vERERaZiyB3Bm9k1glru/Uu/JzcTdb3H34e4+vHfv3uV6bEl16JC5n73ywemnJ+mlS5v+vM8DuKznvP02TJ7c9PuLiIhI8SpRA/dV4CAz+wC4n9B0eh3Qzczihrj+QLTSJzOAAQDR8Q2Buen8HNe0eIcfDt/5TnHnNsdqDZ9PCpwVwPXsqRGoIiIi5Vb2AM7dz3X3/u4+kDAI4T/ufgzwFHBodNoo4O9R+pFon+j4f9zdo/wjo1Gqg4DBwEtl+hoV16lTWPx8wID85/T9Qtg2RwCXrwZOREREyq+a5oE7G/iRmU0h9HG7Lcq/DegZ5f8IOAfA3d8CHgDeBp4AznD3tXXu2sK9916Yly2XXlFrcbMGcE2/lYiIiDRRRccOuvvTwNNReio5RpG6+wrgsDzXXw5cXroSVr/OnfMfaxdNqtLUAG716jBlyFOoBk5ERKQaVFMNnDSztlF43tQA7re/Te0ogBMREak4BXAtWFwDl17wvjF69EjSnTrmP09ERETKQwFcCxbXwJ10UuPv8dln8NZbId2xY1iuS0RERCpL8+e3YO1SC4utWwdtGhGuf+ELSXqXXZpeJhEREWk61cC1YOkBB42ZzDd7BQd1fxMREakOCuBaiXxTjRQyaVLzl0NERESaTgFcK9GYAO6V1GJnv/td85VFREREmkYBXCvxl780/JpZs8J2zhw444zmLY+IiIg0ngK4Fu5L24ft+efDihUNu3bOnDDwoXv35i+XiIiINJ4CuBaubWokaufO8Pjjyf4774TAbm2eBcjuuw+6dGnc6FUREREpHf3V3NJlDR2NA7hzz4WhQ+Gyy2DixLqXvfIKTJ0KS5aUvogiIiLSMArgWrj118vcX7cOPvkErrwyyVuwoO5106aVtFgiIiLSBArgWjgzGDUq2V+3ru70IHEAd/fd8PrrIR2vviAiIiLVRysxtALrr5+kO3aEq6/OPL5gATz3HBx/fNh3h5dfDulPPilLEUVERKQBFMC1Au1Sf8odOiTTg8ROPLHuNStWwFZbQd++pS2biIiINJyaUFuBNWuS9IQJoXatXz8455zc5//lL/DkkzBwYFmKJyIiIg2kAK4VWLkyST/xRNjOmAFXXJH7/MMPD9tu3UpaLBEREWkkBXCtQDyBb5cudY8ddVSSzu7vdsMNpSuTiIiINJ76wLUC6Rq42O23h+2998Kmm4aBDH37wsUXw4UXhmM9e5atiCIiItIAqoFrBeIALm4S3WMPOO645PgVV8Do0SG9995JvmVNAiwiIiLVQQFcK9C1a9jus0/YXnpp5sjUtN12C9sjjih9uURERKRxzN0rXYayGj58uI8fP77SxSirWbPgF7+A666DxYtho40qXSIREREphpm94u7Ds/PVB64V2GgjuOWWkO7cubJlERERkaZTE6qIiIhIjVEAJyIiIlJjFMCJiIiI1BgFcCIiIiI1puwBnJkNMLOnzOxtM3vLzM6M8nuY2Vgzmxxtu0f5ZmbXm9kUM5tgZsNS9xoVnT/ZzEaV+7uIiIiIVEIlauDWAD9296HALsAZZjYUOAd40t0HA09G+wAHAIOjzynAaAgBH3AhsDOwE3BhHPSJiIiItGRlD+Dc/VN3fzVKLwbeAfoBI4E7o9PuBA6O0iOBuzx4AehmZn2B/YGx7j7P3ecDY4ER5fsmIiIiIpVR0T5wZjYQ+DLwItDH3T+NDs0E+kTpfsDHqcumR3n58kVERERatIoFcGa2PvBX4Cx3X5Q+5mF5iGZbIsLMTjGz8WY2fvbs2c11WxEREZGKqEgAZ2btCcHbPe7+UJT9WdQ0SrSdFeXPAAakLu8f5eXLr8Pdb3H34e4+vHfv3s33RUREREQqoBKjUA24DXjH3X+TOvQIEI8kHQX8PZV/fDQadRdgYdTUOgbYz8y6R4MX9ovyRERERFq0SqyF+lXgOGCimb0e5Z0HXAk8YGYnAx8Ch0fHHgMOBKYAy4ATAdx9npldCrwcnXeJu88ryzcQERERqSAL3c1aj+HDh/v48eMrXQwRERGRepnZK+4+vE5+awvgzGw2oYavlHoBc0r8jFqi95FJ7yOT3kcmvY9Meh+Z9D4ytYb3sam71+nA3+oCuHIws/G5ouXWSu8jk95HJr2PTHofmfQ+Mul9ZGrN70NroYqIiIjUGAVwIiIiIjVGAVxp3FLpAlQZvY9Meh+Z9D4y6X1k0vvIpPeRqdW+D/WBExEREakxqoETERERqTEK4JqZmY0ws/fMbIqZnVPp8pSDmX1gZhPN7HUzGx/l9TCzsWY2Odp2j/LNzK6P3s8EMxtW2dI3DzO73cxmmdmbqbwGvwMzGxWdP9nMRuV6VrXL8y4uMrMZ0W/kdTM7MHXs3OhdvGdm+6fyW8R/S2Y2wMyeMrO3zewtMzszym+tv49876NV/kbMrJOZvWRmb0Tv4+Iof5CZvRh9tz+bWYcov2O0PyU6PjB1r5zvqZYUeB9/NLNpqd/Hl6L8Fv3fS0Hurk8zfYC2wPvAZkAH4A1gaKXLVYbv/QHQKyvvKuCcKH0O8KsofSDwOGDALsCLlS5/M72DPYBhwJuNfQdAD2BqtO0epbtX+rs107u4CPhJjnOHRv+ddAQGRf/9tG1J/y0BfYFhUborMCn63q3195HvfbTK30j057x+lG4PvBj9uT8AHBnl3wycFqVPB26O0kcCfy70nir9/ZrxffwRODTH+S36v5dCH9XANa+dgCnuPtXdVwH3AyMrXKZKGQncGaXvBA5O5d/lwQtANzPrW4HyNSt3fxbIXsqtoe9gf2Csu89z9/nAWGBEyQvfzPK8i3xGAve7+0p3n0ZYMm8nWtB/S+7+qbu/GqUXA+8A/Wi9v4987yOfFv0bif6cl0S77aOPA/sAD0b52b+P+HfzILCvmRn531NNKfA+8mnR/70UogCuefUDPk7tT6fw/5haCgf+ZWavmNkpUV4fd/80Ss8E+kTp1vSOGvoOWvq7+X7UxHF73FxIK3sXUXPXlwm1Cq3+95H1PqCV/kbMrK2FtcFnEQKN94EF7r4mOiX93T7/3tHxhUBPWvD7cPf493F59Pu41sw6Rnkt/veRjwI4aQ67ufsw4ADgDDPbI33QQ312qx7urHfAaGBz4EvAp8A1FS1NBZjZ+sBfgbPcfVH6WGv8feR4H632N+Lua939S0B/Qq3ZVpUtUWVlvw8z2wY4l/BediQ0i55duRJWBwVwzWsGMCC13z/Ka9HcfUa0nQU8TPgf0Gdx02i0nRWd3preUUPfQYt9N+7+WfQ/5XXAH0iadlrFuzCz9oRg5R53fyjKbrW/j1zvo7X/RgDcfQHwFLAroSmwXXQo/d0+/97R8Q2BubTs9zEianp3d18J3EEr/H1kUwDXvF4GBkejhzoQOpg+UuEylZSZrWdmXeM0sB/wJuF7x6N+RgF/j9KPAMdHI4d2ARammpFamoa+gzHAfmbWPWo+2i/Kq3lZ/Ry/TfiNQHgXR0Yj6wYBg4GXaEH/LUX9k24D3nH336QOtcrfR7730Vp/I2bW28y6RenOwNcJ/QKfAg6NTsv+fcS/m0OB/0Q1uPneU03J8z7eTf1jxwj9AdO/jxb730tB5Rwx0Ro+hBExkwh9GH5e6fKU4ftuRhj59AbwVvydCX0yngQmA/8GekT5BtwYvZ+JwPBKf4dmeg/3EZp9VhP6WpzcmHcAnETofDwFOLHS36sZ38Xd0XedQPgfbt/U+T+P3sV7wAGp/Bbx3xKwG6F5dALwevQ5sBX/PvK9j1b5GwG2A16LvvebwAVR/maEAGwK8BegY5TfKdqfEh3frL73VEufAu/jP9Hv403gTyQjVVv0fy+FPlqJQURERKTGqAlVREREpMYogBMRERGpMQrgRERERGqMAjgRERGRGqMATkRERKTGKIATEcliZj3N7PXoM9PMZkTpJWZ2U6XLJyKiaURERAows4uAJe5+daXLIiISUw2ciEiRzGwvM/tnlL7IzO40s/+a2YdmdoiZXWVmE83siWi5KMxsBzN7xsxeMbMxWSsOiIg0igI4EZHG2xzYBziIMDv8U+6+LbAc+EYUxN0AHOruOwC3A5dXqrAi0nK0q/8UERHJ43F3X21mE4G2wBNR/kRgIDAE2AYYG5ZwpC1hmTERkSZRACci0ngrAdx9nZmt9qRT8TrC/18NeMvdd61UAUWkZVITqohI6bwH9DazXQHMrL2ZbV3hMolIC6AATkSkRNx9FXAo8CszewN4HfhKRQslIi2CphERERERqTGqgRMRERGpMQrgRERERGqMAjgRERGRGqMATkRERKTGKIATERERqTEK4ERERERqjAI4ERERkRqjAE5ERESkxvx/TGbzph3C1rsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import ruptures as rpt\n",
    "\n",
    "# Load Nifty50 data\n",
    "\n",
    "# Extract the close price column\n",
    "close_price = nifty_50['Close'].values\n",
    "\n",
    "# Perform structural break test with Pelt method\n",
    "algo = rpt.Pelt(model=\"rbf\").fit(close_price)\n",
    "result = algo.predict(pen=10)\n",
    "\n",
    "# Plot the data with breakpoints\n",
    "fig, ax = plt.subplots(figsize=(10,6))\n",
    "ax.plot(close_price, color='blue')\n",
    "ax.vlines(result, ymin=min(close_price), ymax=max(close_price), color='red')\n",
    "ax.set_xlabel('Time')\n",
    "ax.set_ylabel('Nifty50 Close Price')\n",
    "ax.set_title('Structural Break Test for Nifty50 Data')\n",
    "ax.legend()\n",
    "plt.show()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8727be04-49cb-4bcc-ae8c-7058a03cd745",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    }
   ],
   "source": [
    "ax.set_xlabel('Time')\n",
    "ax.set_ylabel('Nifty50 Close Price')\n",
    "ax.set_title('Structural Break Test for Nifty50 Data')\n",
    "ax.legend()\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e5cba34c-83bd-4632-a19e-7b53d39e14f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import ruptures as rpt\n",
    "\n",
    "# Load Nifty50 data\n",
    "\n",
    "# Extract the close price column\n",
    "close_price = hsi['Close'].values\n",
    "\n",
    "# Perform structural break test with Pelt method\n",
    "algo = rpt.Pelt(model=\"rbf\").fit(close_price)\n",
    "result = algo.predict(pen=10)\n",
    "\n",
    "# Plot the data with breakpoints\n",
    "fig, ax = plt.subplots(figsize=(10,6))\n",
    "ax.plot(close_price, color='blue')\n",
    "ax.vlines(result, ymin=min(close_price), ymax=max(close_price), color='red')\n",
    "ax.set_xlabel('Time')\n",
    "ax.set_ylabel('Nifty50 Close Price')\n",
    "ax.set_title('Structural Break Test for HSI Data')\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a5b1126e-8d35-49d2-86ca-c2bfc550db77",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}