Chapter 8/MC Predicting Stock Prices - Part II - Lecture_CSV.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np  \n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt  \n",
    "from scipy.stats import norm\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('MSFT_2000.csv', index_col = 'Date')\n",
    "\n",
    "log_returns = np.log(1 + data.pct_change())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MSFT</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-10-12</th>\n",
       "      <td>0.009118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-13</th>\n",
       "      <td>0.004786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-16</th>\n",
       "      <td>0.002063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-17</th>\n",
       "      <td>-0.000773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-18</th>\n",
       "      <td>0.000258</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                MSFT\n",
       "Date                \n",
       "2017-10-12  0.009118\n",
       "2017-10-13  0.004786\n",
       "2017-10-16  0.002063\n",
       "2017-10-17 -0.000773\n",
       "2017-10-18  0.000258"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_returns.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot(figsize=(10, 6));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b1d70149b0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sfY16di4G/+N7v4thmtv7alFJGdb9cdjdlRKDMq/c//lv2Lj7CPYV6gcwbkh0YHHHh4sx/JnZmq8dKipBYbiG7Lb39ZcLGjZHkbILfL5oK7btP2r6fQzsKVm4va/e+eESDPvXDygs5hzIbmJQRqYs3LQPbe+fhMm/7Yh6/oZ3FqCoJBSInfH/5tha9x8Hi3DPJ0sTnvKD11NSqC9YJ0+cifJyqfs62bfvSDFyx03CrN//8LsoZMBocMu89aHuOyWlPDjc5EpQJoQYKYRYLYRYJ4QYp/F6VSHER+HXfxFC5IafzxVCHBVCLAn/+48b5fHKrNWhE0mpiY7zqVD5ou5f8/rcDZoXpmkrd2L2ml0AgIJ95msYFLPX7MJ1by/AJwsLMHWF9bQdbkiBn4ocit21l287YOp9qXCcJ9LK8KCaV2Zv8Lkk6cdoX334qxXo8rcpptfHUMwbjoMyIUQmgBcAnAGgK4BLhBBdYxa7DsA+KWUHAM8AeFL12nopZe/wv5uclsdLc8KjCg8f06+uTcUdVUDErSlw8pmvfGM+ftt6ILydxGLtByli9714+8ahohJTy2mRUuKxb1Zi5bb0HPHLGNY/RvvqW/PycbSkorXC9L5t8Ufdd6QYxaXe9qdOZm7UlPUDsE5KuUFKWQzgQwBnxyxzNoC3w39/CuA0kQIdeb5csg1rbHZm97qTv1Xl5RJb9hZqvrZZ53miVGF0/VHXGpc7COYPHC3Ba3M34pJXf7a/EhMGPz0LD3+1wtNtOME5Q9NXn0en4fYPFvtdjMByIyhrAUA9V1BB+DnNZaSUpQAOAGgQfq2tEGKxEOIHIcRAF8rjmh/W7ELuuEmRFBhaYvtGmI00x3/p7gnTaYj77My1GPjULGz0cYg3T9OULJQBLUG9tczfU4i35uX7XYzKAvp9pYMg7atTVuwwXihNuRGUaf3UsddXvWW2A2gtpewD4C4A7wshamtuRIixQogFQogFu3btclRgs6YsD+04Cyzk+jIbWCj9sPQU7Cu0NY3RoaISW8OUf1q/BwCw/YD1fmHJLkgnK/KHYcb+mJf32swxqCSZ5gwBFHSm91Duyq7KcmEdBQBaqR63BLBNZ5kCIUQWgDoA9srQmekYAEgpFwoh1gM4DkClOYGklK8AeAUA8vLyArMb6F3Qjc65peXxmy9PeXKWqe3HbufiV37Gim0HkT9xlKn3KzKEcrHQX3eqSaUmlFmr/0B2RgZO6djQ76KkjO9W7ED1Klnufqfh80Xq7Hn2pPq5hcguN2rKfgXQUQjRVghRBcDFAL6KWeYrAFeF/z4fwEwppRRCNAoPFIAQoh2AjgACNCzH+ZnDbtBmdzsrbHYgVt5vp1zJfoL1e4osN1zzZmgmBbJHq4vr2HcXevedJvkxY1cqHGvpwnRtLn9SVzmuKZNSlgohbgMwFUAmgDeklCuEEBMALJBSfgXgdQDvCiHWAdiLUOAGAIMATBBClAIoA3CTlNL5vEAe2LTHoK9VwHZMq3mAIjVlaXS1SPZgktwT2xwZO7jFrV1FsKYMAD8/kR43mi8hpZwMYHLMc+NVfxcBuEDjfZ8B+MyNMnjtka9Xaj4vAtoecc1bv1paXrlY6I0sS+UAJp37lP2+4yByG9RATnam30UJlD97NDos1Xa1Scu2o1PTWujQuKap5Y2Otdxxk3DvyE64ZXAHF0pHTrBPmT+Y0d+k8oBGJW7XbPnZAZmdnxNr35FijPz3HNz76TLc9v4iPPTlcr+LZNrqHYfwwqx1fhdD0wfzN2P3Ye1BOsoenir7+q3vL8Kwf/1g/Y1xPv5TU1bbLxDpcn2XS41dOHAYlJlkuENb7Dvm1v6srN9pX42K5ktbpXC0bfLHkfCcdQvy9+KbZdvx9k+bfC5RtF2HjkUlalU798Uf8fTU1YZJKAv2FeLb37Y7KscPa3aZvqBt2nME93/+G27576K4y7lxxMzfuBcF+5Irh2Cq1RQCSPj0cIlidp93s2LgWGkZdhwocm19yYhBmY5t+49i9ppQBn8BobvbCRFKCPnDau0UF8nSR6uio39Fec2W/Jtl23HNm/PdL5THUqSywrag528+4fHpGPKP7wGE9svnZqyNpIk5Fg7GtD6ClBKvzt6AfUeK8afn5uLm9+IHSEauesP8vl0SnoJt9xGdmjIZ/b8TF778k+lR2kH0v8Vbo/rqJmvt4WWvJscAG68Odzd/tj+/vxj9n5jh3gqTkCt9ylLRqGfnYF+hejoV/T3vtvcXRaZgiizvWcm8oRyvdg6wb5Y5q4lIJ8pE1xkZwQmIgrKvah1juw+HOuAv2rwf/5y2Br9u2od3ru0XKXNJWTmyMgS27D2K5nVzkJWZgUWb9+Hxyavwy8a9UcdwILj0Zes1jyYLCYk7P1qCmlWzsPyREX4Xx5EFm/b5XQRfWdmljQLv71bujCwX9JtGr7CmTIeVk/mGXXFGZtroOG8naazT/VdpvnQyhUw8m/cU4pb3FqKoxFxVv5QSl732M6av9G6Scj9qMfv9fQZODMidYDKd8srCO+bR4uh5Z7uOn4oxL/yIQU/Pwt8n/w4AKA3XVh04ai/Bqxaz+0rkODTMRWt/35u2cifyHptu+/1+ir3QqucRTtKKMlO6jZ+CSw2m1iorl/jjoPmmOyklnp+5Flv3e5Pw2+w+6mYNp9GAs3TAoMwEIfRPGIePlWoeFCbPzZpmrTafzsK9ofpKUBZa4+jn5uDRb7RHnFq1cNM+DHp6Fib/tgM/bdhj6j3lEvhx3R7c8G6lPMKYu3Y3csdN0p2r06pE3pHtPnzMVtCdDuJOeh/zovrx0oLQhPbz1odqqzMzvL3BiEeZ01ZvYJByoXNyHbMyw4gXylz4YrU+vx/X4QX5e3HlG/NR6vFcxEeKyzBvffxz398nr0K/v88wPVvEpj2F+Md3azD2ncrnyESw83sZ7ffKmTioA+sSgUGZSXp3Df+evjbq8acLC6L7ZdnYt/yowYhNHrt860HMVOU6c3Jnf95L8yy/R/kOtb6/TxaGplpd6LDZII2P+6RlZkCLEmSrg4dzX/wRb/240fZ2ze4rz84InQ/y92jfMCT7Pvf10m1o/9fJ2LDL+lRuQPwafT/6lN3x4RLMXrML2wPQuXzGqlCrwP5Cc0FZmVRqj90daPDpwgIAxvuqMsjGzV8ttnIgHTEoM8nKPrJye0VWfb1gZvfhY+j3d+1mrPVxmkNHPDM7plzu7LwVXZw8PhhMj+ipcKCwJHICeHbGWny5JDSL1zfLtps+gQWN13fmVljdhbbsLcSP63brvl5cWo4vl2y1vG+ql84dN8n0spHnwk8qNWXq7S/avB8P6+QadCL2I+4+ZG5/TNZLjjIf8Gn/+gE9Hppqez1B+Py3vb/Is6Y/O4SjEfDuefB/vxkuc6y0DMXhc5iVw9xsEoM0jskYlJllZSdR36Hb2bn+88N63ddW7zwU9VhZvdPaNaUGQq9lwu07yf2FxThyrNR4QQC9JnyH4c/8gGvenI9X51TMwjV91U7c+r6zkXV+eWzSKs3nn5i8Cvd8sjQhZbDbajvkH9/jstf0R5w9P3Mt7vhwCaau8K4/YDzKDUaZzsHnpMbMkMF3Kiv9YWcT/vUGzIgEvMAhk8evWrySO70O/+WjJXhyyu+Rx6Vl5Xji21W6zYHqAUpBCAKCEpAYTMsMACgqrlhIQqK4tNyVCoIMjVrudMOgzCQrzXdRk3p7UBa9bRk5VlqGxZtDTX4LN+3F8q0HIq8ZzX25rOCA9gsWKd9j7wnTMPSf3+svF1OOTXsKMWv1Lhwqir4QbN3n/E7Xj0ucXr/Bl2dvwCfh5oNEsdo0XWpwwtwR7qxstaN9vJN67Cvx9vvIoBWdi4udGjP38gr6e7E5UFiCyQ7ythkdK7PX7MJdHy2xtW6nX80Xi7fipe8rbminr/oDL/+wAY98vcLZihMlICNvrDYd7i8swXEPfouXZxtPW220/1d09GdQRnEcLS6z1GlYvUNJKXGstAx3f7IUOy2MrIln4+5Q82ZRSVkkcaGZzuqdHpyCc16ch9fnbsR5L/2E0c/Njbzm1dyX8Zqhdh6M7vAeHcyaHPljo0wLN+1Fpwe/Nd2hNlUcOVYaNbrLqxoXT8+n8fokhfeGjDj9UuwGRWZHxcUWb8W2A5o1wn7lL7ztg0W45b1FtpPOGmVyufKN+fh88VbN144Wl0XOXU6DUyklXpi1Lu6gGaW2pcREV4Fg5ZN0ryxrdh6yPLBILyBavHkfpmmMhleua58vqnwzWVpWjvUW+h9y9CWDMlMmfLPS0nEy8dvfoxb/bsVOfLqwILQeFygXiO4PTcWoZ+caLF2Z5qjKgB0MprNJ2yjvS99vwLHSclwZTgqaLulwxrzwo2Y/Rq+CKKtBn9NirNl5GAeLSlSjL7WCMnvrPqYzc4CExN4jxZp9BA8VlWDUs3Nx47sLVcs7KwfgbH9Vapb1Po8Rvfx6V8Yk2FUHXcdKy1BeLnHr+4twz6fLQq9rrMNKYLS04ACenroad32sXytnt3XDL140X57+zGzLedSUa0BsOc55cR5uiDPSU6vcT3z7O0775w+RmwDjPmWV+4OmGwZlJs3PNz8M/ZeNFct6sW/9tvUA1u48ZNiMZMab4f41FScEbw+GeDnd7Fxs7N3h+n/A+3HOWftH9B1rabmSvsHd7bj90VbvOISLX4mf40mxbf/Rij5lGh/MbrOI3q556tPf4/hHp0VuuNT78O87Qv0/f1WdO5Q0Lm5/R/sLiw2nnIrd7pTl2/Hxgi2WtqMXaM9eEz2jifqr7/TgFNz32TLMVSXY1kyJEfPc0i37MeQf3+MnjVQSSu1XoUsjD/0/I9jv6L9h9xHcFw52/aRV7vnha+Gew+ZaJZRj94VZ60ztz6mIQVkSemzSKgyPGYW5cfeRSLZ4Kx75eiWWFeyPjKryml4HdyCm+dLDmrLY9yRLTVlZuTTVFGOW0v/Gs+zwFr9Xvd9S3QfKzCr3hJultVZnNwA16h7wbfj4UQctygVGvclzXgylh3H75qf3hGm46b8LNV9bu/MQrnj9l6jEzQLATf9dhHstXszNTkQRGxB/srAAGaqrjZlP/+SU37Fx9xFc8urP+E2nT6tWcT5fVICDRSWWampjf48py3cgd9wkrPvDeuqP0rJy00my1eLVlB05Vhp3YNRHFoNrM+Ld8P6fzcFIhnnKwsfZq3M24u15+ba2kewYlHksaif0+HasxMywGQ1nPf9jpNYtKB0szWeTtrPu5DTy37PR8YFvo57bdegYttkc1p8sU/VYDZojtWoaP7TtmjKTZdBcTqtWyFYp4lPnFVR76KsVmLN2t+O8fkBFfz0tB1WTxxdr3DxEvdfi77B+12Hc8eFi7DPRD/Suj5fivk+XWWu+jHms3Ais2GZ9gNPVb/6Kzn+bYjktTGSwlUa5uz00Fd0MUpAkMr3H9FWV+5ZpfVatG5N41LvIkWLro3tTAYMyjwWrA6kxKYHvLcwoYMeBo8ZTWJk9l23dfxRTlmuPJttfWIw/DvmfFNItsc2PQGjS7pMnzrS1PqfNluqT8Lx1u6NG8+o5Wlyme6HSqwlwc0CC3XsOoxoizVhM6ZtjcA6Ys3YXXptjPHINAP72v+VRIwzNMBpZbUVGnCtG4bGK2qGHvqw84lEdlGn2KYtTvjs/WoIvl2zDOS/+aLgsAPxhsXP7soL9UY+V4L20TOJki9OizQ3n8LvjwyX4eYP5bi8V/aksbS5igM3zgB6z5bji9fn6L8YMujE6FqL2keS6dLqGQZnHonYsj5vJ3Lh4lcv4edLc0OuR73RfKyopw9y1uy2Fsg9/VTFw4d5Pl0byfPV5dBr6Pa7VsT2YR3uiE+HG+xr+t3grBj41M+53pW6iuvS1XzD6ubkojHN3e6CwBF3GT8GzM9Zpvt7n0Wmaz9ttXtYq+Ye/bra3MoNj649Dx/Dlkq2atZZG/aeueH1+3GZ9tXd/3mRqObXIxR7S0r6/ac8RfBuTPiNeM666D9BniwrQ8YHJWLmtIpG2OrDVHIRh4qiPnSlBrzhWd5lxn0UnTFVKsvvwMWxzkKPxcMyNxtHiMuw4UKTZLGomeB701CzT+R0TTavYVgcvRFWmOi1QkmJQ5pHWe6lcAAAgAElEQVS9Gv1aJi2znx8oUcqltHR356Y7P1qCq96Yj8tf/wXdbWYL/3hBQSTPl+kTgQ8JgrQuQL0naAclbtt16FhkKhU993y6FFv2HkVJWZygTOMLfmbaGt3vffeRUO3Fl0tCKRMmfL3SMHM/4O69zCMeZPVX3PHhEs3plSpGXHpzmTHzHYa2X/G3mRQ6w/81Gze/F52cOV6NYXFZdD+qkjKJM5+dU/HejMTVgqhrXMwc38dKy/H63I0VT+iUT69WXk/sli9+9Wf0f2IGhv3rB933xAtON+8txCrVjDFWvDF3o+npsRZv3heZMswsrUFcFT9DuKbMqE+Z+oGUyB03Cde//SuA0E3gSU/MiJw/9GzeU4jccZMwdUVi+km7jUFZAiSqI/nvO+wdrFF8vj1Rj1w1a4fF/G9mP2LuuEkY91l0R+h9R4ojI4qS1dh3F+DuT5Zix0H9Pihm5qDT6sL46pyN+EwjX5GWN0xm1i9R1cht2lOomQ9Ji5ITyw12ajFjg7EvdPJ3ec3q+edocSiFhdIv7H+qcscLcIzSbBg1TbkaqNk45z76zUos2RJqxoxMHB+zzE3/tTaDSOx3v3RLdDPpj+t242WLLRPxfs94AduCTfsw9J8/RD5jPOe8OM9UMthYL36/LirwU4q6esdh5I6bhBMenx73/UKjiXv6qlB3msLiUmw/UIS/fh5/Gqjfwt0ojIK3oGJQ5rFENpX9uK7y0HGr7v3M/6HVdvxxsCiq9mfTHvMXZPVJ7uMFW6IO5g9/jR7VdOlrv+DCl3+yX9Awq7vFf200W+lZHU7VoDfkvKSsPPJavKBMbxojRey1I7K4xQum+o59x8Ei3PWx/siv+z4znrfPjts/WGz7veUSeHzSSs1ZMcz0w3OisLgUc8KpKMzsckUlZegyfgomqqYrMpsOyCiFgVGfsp83mDt/SWncDGv3PnjMCz/i8LHSyMwh2n0FZaVcj3qjNI0C4ste+wVPfPt7eFmzfcr0V3rG/5uj+5pizAs/4m//W46ikjI88MVv+Nv/luMzl2YReWrKagz9Z0UtoPKbKwMnYmdkiaWuiVV/D8/OWBvZZ+INNpFSGp6Xgi7L7wKkukTuHsk2qMBNo5+bG9W5NzZliFq8Y1ZJEdCnVb3Ic3d8uBj/7+I+ACruRKWUppqA3PL3yeb6HJmh5HbS6uj/xeICPDezos+XOhfensPHUDOn4pRhNJIx9vtRaq427DpiusnNqtiaCLccsZEPS52f7NU5G3Hpia2jXo/9Ds56fi7W7jyMqXcOwp0fLcab1/RDnWrZKCuX+PDXzbgwr5XhNr9cshVn924ReXz/59p9pYwGW6hvcASAp6b8bphmwih9hHq0r3r7T0xehfP7tsR1b0cnJtXbvR79ZhW6Na8dLpt2eeJduI2ou058p5HBvmDf0eimTgAbdh1Gh8Y1Ky1rpWuE2SV/2bgHfdvUi7uMlDJSY6Tl3Z83oX2jGnjvl82Rx2a9MEu7T2isMlVtq9lRz+pzxvOq7fxr2hpcdVJueCH999/32TJ8vMBa95WgYVDmsUTuGMm6E7ohdrSVlcSDyjGunqh60NOzIn9/uWRbJChTPDV1NVZuO4gf1uzC61fl4bQuTawXWsVKXqOFm/aib5v6jrYHaJ8o//JRdC2UOvdd38em45QODaNeO3C0xHDKni17C1FYXKabRytVPT8z+uL1/i/xBxkoNWnPzVyLRZv3Y+ryHbjwhFb4ZMEWPPDFclOjlr9bsRMt61WLPP5yybbI3+pASO9Uoff8i+ERn1ee1EZ32/fYTGD68uwNlprKzDR7/7b1ADbvtTeVlJpWGpFMjY51et9bvNjw39PXaC6rlURX7akpq3HL4A66692w6zDmb9yLcQbNfHZHXz89dbWp5e76eElknza6Ni3evA/z1u8xmLA+tJJ4AbcSkAHBSe9kFYMyzyVux0iGgQR+WL71ALo1r62bMVt53spE1eq0BNe9vQD5E0cZvkfdBBZ7vhj1bPxmB/XyZrNjG4k3u4KirFyirFxiT7iTvjLcX3ntmrfmG05WP/CpWXFfD+poWKfsZpuPnbtTGcG3+5Dx7y4hcd5L2s3r6m/Zynfuxa9jtHkpJX4y2Zyp5fCxUjw1JRQ8bD9wFAOfmonTOjfBw2d1s71ORZZWUKbzeYQQ2KOTD/Df09fGLBtel+obn/n7Tlz7VuWpjb5YXICiEu0bz6H//AHXDMjVLpBNdmq21TcEyhzNWqSUkaTKTWpX1V1OCSK1YrKjxWX4etm2qOeU36SopAxVszIq1dwfPlaKN+duxC1DOmgG2n5hnzKPJfJ6s9LmqJxUN/q5ufhgfkXfsNgLkhvH4wX/mWe4zFdLt+m+tj4mQIrN3K8+Uce7U9x16FilKW+01mdWmZR4YvIqzdQiZVLGDcjMXvhHPzcXbe+fbKt8qUjJBaZchJQgoMxmcmjFg18sj/ytriWRUmL7AXOJR926dBnVYtzynrkO9cdM7NeLNu/Hlr1H8da8fLzzUz5mOc3DqPEl3PTfhRjzwo+ai/Z9LH7n9lAZ90WaOtW/zcbd2rV9f/loaaWmaTUzh14iO8JrJRNWqFPkGPUXA7T3wQnfrKw0O0W5BP44VITOf5sSaW5Wn5OenvI7/jltDb5Zpn9e9gODMo9J+JNygaK981M+Rj07R7OZ0Ez/E6Omp1/znWdLV4sd4ak+ycZL4HnRyz/hyjfmo6SsHKVl5ZAy1MQYOxOAWeXl2tm7ldfiecnkqLIV23gzES26piwzM/SDl5hob4p3Md66/2jkdXVQdMF/fsJJT8yMGnRwUKep1K1+lEaf5FuT074dLjJu0lUb/+UKXPPmr5beE0vrBgUAlmzZX6lGyezXde6L8yLLKr9Nebm0Nc2TWUsNarid+DrmBjRe7PyMqhk33tel7P4ZQuC7FTtw6/sVgbv2bAYS2/eHRuZ/uWQbnpuxFl3HV/QXPBSugQ7aHJtsvkyAdO6AHxTK5NBaJ7kMIaKmiNHy8FcrKnXU1rOsYD8W5O/Dtae0tV5QVZkUh2LKFi932IZwZ/qOD3yLtg1roG+beoY5yeIpkzIqv5SaUW3Hhl1H4jZbkDbl6z4abv7MDj9RaqJWyGww84+pFRfCBeF+U+t3HUbTOjmhbekEgG+5NB+hnbkhk5Gdjv5Kbc4rczbgg/n2kh3Hq5lKhD/HjFY2m1stXtAfqSkTwNh3Q/1TX7g09Fqxxnlm6/4i/OO7UBP2b1sPRAY+jHnhR3x+88mRO4NEDtgygzVlGsxW5ZuRot1lkpaUlX8TIYDRz86N/0YLx+1Zz/+ICeEh8+Xl2k1DW/cfrZTtW600XA21duch9Hj4u6gcUHd9tMRUOTbuPuIoIAPCnfkLtQPWx02MCOX+b53Sb+/xyatQXi4jc1qq++g4pVf7qfV7eTGitWCfO+fY9buO4ClV+o6gsXK9V2qufis4gNxxkzDxW/ufS68fW9DF+77KIkFZxUIXvvwTHvzfb5q19qu2H4ykg1FbsmU/2v11cuSmMkDdyQAwKNP0x0H3dmgJyebLAPlpw+6ozupAqMO60Uit4tLyqJGIWrT6UL34/Tqc9MRMzbxp575YuQ+KQplPboNGAlQ76RnsKi2X2KMzCTQHlnhjk2pWgCvfmB9Jy2CUnNUpKbX7Hga9efnF79fHvcHx09IC6wGtlQFHeqau0A66gy5eUKacfnepRtrP37gX//3ZXm1iJG1iwC7PDMo0zPjdvQm5V+84ZDnjPHnn75Mr330u0Bj2rsVokmP1YAKFcqc2TSPf0ZqdoabUfTpBDwBUy840VTavlNkdNx/W+W9TXCpJelqxzd1+P/FuPiSk7cnt/WZ3WjavKSNAqbLpGufEeDXrRjfFVm0N19YGrdKEQZmGSS6OxrjjwyWVsj9Tcur/hHYHX8W89ZWrypW7sHgTTutNxC2l1B2EEG/ibzcl6/xxqWKfTtMxOXM0gbXNpO36dyqn+ojXrB03tY6NuEq5Gb8vYLPYMCjTkJ3Jr4WsC/VXi023Ef9sEa//Ytv7J+tWrXd/aCr+OOR9Dex6D0d/UbCUxhlAkmq6jGcNbipxMh+x190CrGL0oSFIieQoeaz94xAWba7oQ7K/sBjz1sdPgHnSE/Gbi/QmaC+XwI3vep8h/3OfJtGmxLObkZ+I3MOgjMgla3Yejprfz2wCzHjijaJyc0AKEVGipepsHk4wKNPA/YTsUtdeGdWSmRGv+TNo1e5ERFYY9dNNR64EZUKIkUKI1UKIdUKIcRqvVxVCfBR+/RchRK7qtfvDz68WQoxwozxOMSajoIifeJh7KhElr52s7a/EcVAmhMgE8AKAMwB0BXCJEKJrzGLXAdgnpewA4BkAT4bf2xXAxQC6ARgJ4MXw+ogIQOEx/VFiu12amJyIKJ0FqRnVjZqyfgDWSSk3SCmLAXwI4OyYZc4G8Hb4708BnCZCaXnPBvChlPKYlHIjgHXh9fkqSD8QpTd2tCci8laQcom6EZS1AKDOmlkQfk5zGSllKYADABqYfC8RERGRJ7yc+N0qN4IyrZ7IsVVNesuYeW9oBUKMFUIsEEIs2LVrl8UiWqNMXk1ERESprUeLOn4XIcKNoKwAQCvV45YAYlPiR5YRQmQBqANgr8n3AgCklK9IKfOklHmNGjVyodhERESUzvInjkLd6lX8LkaEG0HZrwA6CiHaCiGqINRx/6uYZb4CcFX47/MBzJShjltfAbg4PDqzLYCOAOa7UCYiIiKipOI4KAv3EbsNwFQAqwB8LKVcIYSYIIQ4K7zY6wAaCCHWAbgLwLjwe1cA+BjASgBTANwqpeSkZEQB8twlffwuAhFRWshyYyVSyskAJsc8N171dxGAC3Te+ziAx90oBxG5b1BHdhcgouA6v29LHDxagu9W7rT0vtb1q3tUIvuY0Z+IKjmlQ8PI31WyeJogIndd0q+1qeUu72+83D8u6IVXrsyzXIbjmtSy/B6v8WxLKeu+kZ0Tur1GtapiWJfGCd3mn3o1R+NaVT1bf69WdRmUpZg+reuiRd1qfhfDtkv6tTJeiALv2gG5+PSmkwyXa66xr3ZyLZgKXk5Snm0p6Sx/xHg2ril3DjSYokhbtWz7E0o8OKoLRJy5Kq1S11ap/alX88jfGQI4sV2DqNf7tK7reNtVsjKw4MFh+PCG/sjMcO8zkTVO9sd4Jt8x0JP1WtG3TT08/KfYyV+M3TioveNtf3azcTDgp5PbN8A71/qeR91TEkBebv1Kz9eqGt2rSmhkzrJzbk8WDMoo6Zi5UHVuWtvyev88tAPm3Dck7jI/3T806vHtp3XEs5f0wTMX9cJZvZpXOn3oVb1flGd8t//f60/UfP65S/rg/13cGwBQLivPQPHh2P6G69bzzZ9PAQD8qVczNKxZFdWqhL7rL28dEHnNSHam9SBuQIcGxgulIS8uPlICdaplx13mtM7e1/h+dvPJuHpAW8vvy21Yw/G2+7apj14tg5ObKtYrV+Zh0HGNIsd5KmpVT7s/14c3ap+/aqqCNbcm3WlaJ8edFbmIQRklHa8qbmpUzULDmvGbArMyog+Zu4Yfh7N6Ncc5fVpWqiWbdfdgPDamh+Z6JCROCtdwPXBmF5itYHv9qlC/iZb1QlX6XZrVqnTZrpplv3alS7PaWPbw6TinT8uo53u1qovuLeqgXvVswwBq7eNnWt7uW9ckd63APSM6ebLexrX8uWhkBLx2tGPjmo7X4Wddy1Pn94z7uhKAnN07NSe46dmyTuSGz0hm+JSr7qah/HYdG9e0NTpcSRarVQvnNwZllHTMNhEa3U3lTxwV9Xhkt6aG66xRNf6JRF20tjF39LcN6RBVtpzs0OHXrlENbHxilGGw+eCoLhgarsHo26Y+vrptAG4yaMqJ/YxGMgRQO0e/FmXx+NPx3vX2a+L0ZGdmRN0JJxs7tYNm3tOlmT8dkWvlJO63mHiu9o1LPO0aOa8tU84PfvSZVP/yD5zZJfL3nHuH4PlL9YOMfm3rY+ygdra3O7BjQ9N9UDs39W7f0zo3K9vLiDm/t6hbHbcOaY/Xrz5B9f7QCl687Pio7hx61DWOI7o1wXnHh4JdF3ubuIZBmce83LFJ22s6o3DiNRnOvW+IqWaR6lWysCJOnzblzusJ1YXm8v6t8dJlx6Nhzeis0T1bhvp+NamtXxsyqmezyN/XD2wXFZD2bFk3VKOhOsF9fKOzvjJu9Yl76bLj8eR51i62sc2wbmsa53v2w4ltjZtsvbiTV77lybcPxFk6F7QL81rhwVFdNF9z4m+ju2LcGZ3xrapP28X9WuPGU60FGsqxc9mJ5kbwRb83VEuiNA2/fEVf/Psib5sJz+2jXeN13vEtccOgdph65yB8ctNJaFW/Okb31A8y/nlBL4zo1iTutmKbpn+4Z3Dk78fGdMf8B4ZV6relJUMIR10h4tFqllcOf61T0D0jOkfd5NYM3zjG1ujqXW9jaxyVrQcwJmNQ5rX+7RqgnQt9INLF+zdo96My68nzemBYV+2T1pOqJoPYvhotdfo3aKkR54SmnGzqqk6Mj43pgTN6NEPDmOr320/riMm3D0T3FnUiz8V64dLjTZfruUv6oF/byh1n/XBGj2a46ITWgUo8e2FeS+OFPKLVD9JME6G6tdzKDd7k24078ndtXhsTzu6m+VpmhsD1A7UDpVuHGHe0P6O7dq3zdae0xU2ntkeXZtF9PmNrR4zcfGp7fHvHQDx+jn7gr/V9fXzjSfhobOjGpbw89FyDGlUwpk8L3DzY+QCCWC3qVsPCB4fh6Qt6aY7MVs4XnZrWwgkand5jZWQItG0Yv+n2gZhguk2DGqgebipUgvwbTNS2XTMgF/3bNcDKCSPw28OnGy5vhdb9119HdUHjWlXRpn709VIrgHv58r64/4zOlq6t6hHHOeHjsXoAa+cZlHksiNWjQXZye+0Rh2ZlZ5rbpb3qqxHvbm9Uj2YY2LHi82VmCHRtrj0g4S/DjvOieAlnZuRmbA2iV7JM7hte0AoIY7+a0apaUYVSc1m/RhVcdXKuqW399zrtG5s7Tuto6v1uUPo8mmX1NJmRISoFdmq/PXw6/nfrgErP92tbP9KXqaK2JLT1cg9qaod3bYIGNasiM0NEBSL9w/1JLzQx4EdNILQv5E8chSGdQkmdX7syDw+pRrFqfZdK0Kucl8x83xeEy1a9ShZqxenSEMvMoJ3Tu1YO2k89rhHmPzCsUl8zrZ+laZ0c3Hhqe0s1++qa3/P7tsT/DT8Otw9N3DFhFoMyjwkI1+tIhQDOPT41O4Daoe7/or7jrm6yI6mbKs4flX90IUTc/g92rwnKnaTZ81P3FrVx9+mJCfrM1IDMuGuw9wWBuQDRK1oXj6rhvkwX5rXE29f2wxndNYIyACsnjMC8cUMrvabn+DbaKVEGhy/i6h3NTvOogMAJufXiLmO15svq8mqf33Jypedq5WRHakP0KM3lyqavtTES1K5W9asjf+KoSHBmlvp7ygxXo8YGk7F9WQFVMBbzvxeM+pwuGT8cfx7aIe4yanWrVwSEX946QPemw8iJ7RqgepVM3Hhqe2RnZuDPp3U0PdggkRiUeUwIuD7Mp1bVLPRpHf+kmE7U5xf1yeayE9tERsU5PQmZ6YMBAJnhDRld/73oPmX2AturZV3c5tEdYuwF0kwcVCd80vV6NFzQ8q0pTZontW+AU49rpPldZQiB6lWykJOdaTp80gtwNGsVVE/NunuwqfVLSOPRsjGbevaSPujdSj9/npOf5niDc2H9GlUw594hmHKndpOu8rU0qZ2DjzT6UC18cFil52b+36mRv9VBQk+P02yof0Llb4no84lW7i9l3xci+n89Wp/ZrtiRsnWrVzE9uvely46PytfYq1VdnNLRXmtK/RpVsHLCSMP9xW/Ba1BNMQLuV4sLIby5qqcA9cmmSlYGbh3SAQX7jmKQhQP56fN74u+TV2FfYUnkuXn3D0W5ia98wphuaFSrKobo5HnyIixwY1d45Yq++GHNLsfriT3hxQZC94zohKenrna8HTv0kvF65YaBbVFYXIb3ftms3Xl5ZGccLCqNNOWo991mdXKw82ARrhmQa3m7GUKYvgmJHS3cpkF1bNpTWGmf6te2PuZv3Asg1P8ytl9lpya1sGnvERSVlEfKoHZWr+a6gwpC5fAmYP7s5pPRql41NNYY5KF8RnVZYxMxA0ADjTQ5yn7dqn61qCAhdn9Xfyw3ztjq9SmbMjNAJiPmZrFrnKZfQPszT7r9FIx6dq65goa9ckVf9GtbH70nTLP0PsUZPSrXHltxSb/WuGt4cnUFYVDmMSGAUjNXc4vrTFda/UTUJ3StGzD1SMh544ZGmo30XJDXCid3aIgBE2eieZ0czLv/NNPla1wrB4+O6W64nJWkoDcMbIvDx0r11xWnH5v2tkOuPjkXb83LBwCc3q0pTjeREkRxRf82ePfnTYbLxd4Rl5SVR/5u16gGPr2pctOTV5QBFYlSLTszMrJWXYs59c5BKCwuRYu61fCGapi/+vdrVKsqfrKw36kJUbGuTk1qYfXOQ1Gvq/e82F2mca2q2LSnsNI6s8K/402ntsfFJ1TuBxW7P1vuIxYucLfmtbFi20GL79bXt41+rYhSZjtNp8p7ysujn483wteN0cUZUec6EV6vmfeF/lf2wyGdG+OHewbj6jd/xcbdR9CuUQ1s2HUk7jrsNHUP7tTY12nanrCRbsVvbL70WL+2DVDudlAGpE1k9n/Dj8N3fxkUeRyvCQQwPsE2r1tN8y4wUQZ2DPXruaJ/G9PveWBUVzxxbvxkk0D0hfCr2wbonpCUJlZlOiYzeX5iPTqmu6kcaI1ivmt1UNaoZlXUr5GYTv5B0qlpLc3uB0ZHtNEh//71J+L1q/KQnZmBdg1rYkCHBlEjjp2cMZQL/8CODTVrtaSMvmhb71MW+n9Ip8TNHWv1ZkZNqRGLbQV5Mk5SWFdqytR/hx+US+N1a/0ebRpU9D175KyKUbh6I2cbGeQ3e+3KPNw5LLpbhJmA7F8X9sLXt5mbLcRIKjQgMSjz0OK/Dcfwrk1Q5vKeEq8ZwMiNDhIPWjWmt/1yKto1qmmpw77bfTrcPsab1slB/sRRcfsE1qmWbWkgxwOjumB41yYYrLqg9WxZF5f0087hpDQ9KZn/axokxHWie4s6+OCGin46xaUVQVnHJtF9TVLhhBrLyoXfKJAxauJrWKsqTusSSgdTJSsD713f3/AmJnadsTNWKHq2Ch1XeolH1a0Bn918suU+YkPD6SKG66Sz8YKTXFXKb1UWc8Mdm3hZHaja3b/n3Duk0naBit9Oq3tMr5jfPVKrpnNGU69Xr5xGQdmwrk1wp41R4+ce3xI9XDpvK5/v6pNzMcph06dfGJR5qF64FqCs3GBBi8b/STu30HWnGI8eMpsywg3/vrgP/nO5+TxbTrVrVMNSvrF4/KyHXPjgMLSqb/5ztKpfHa9emWc4kkgZfTcyfCd8etcmuG9kZzwwyvqk0Fac1D7UT6dvm3q49pS2OL51XUw8twf+Ntr97V7Q159cZJpNO0JUjIw1sw6DhYxuxozms+zWvDbO6dMC/7qwV8U2Y5b554W9cO2AtpWa/e45vRMm3X4KOjbRzpV2+Fhp5LM2rlUVQ7tYC666Na+D/ImjKgUTXiqPGX1pReNaVXFhXku8flWo+Tleeg6n1OeC6D5lSqBV2Zcx3TwyVLVqWtRfQVAm+67tYFaJS/q1xguXJe7a4yYGZQngdkf/zAyB7hr5rczcnd6ikfjxapP5j+yI99HN5E0yc8JU+mo0r2MtNxIALH3odCwd725iRKe86vQ8rEsT5E8cFak9ycgQuHlwe0fTG711zQnGCwFYNWEkPhzbH83qVMPntwzAxf1aV5qj042LwdMX9Ir7utkpZsy63uBGyEpNmdEy8VI8TL/r1LgzQwChPG3PXNQbHRpXBFax22xetxrG/6lrpQ7rWZkZ6NZcvzbj1pjEq+oausdM9LFUe//6EzHtL4NsTV1lSWR3s9GnLEPgqfN7RWp4vrptAFY/NrLScu539FfVlCnrNXF9idSqmYjKglBj/c2fT8F01QhXs4JQdqcYlCWAF0kJ+7SuhyXjh0c9Z+ZiXr1K5Qvwecd7V7ug98lPPa4Rbjy1neEoIHM1DPZP3nWqZUdSMmjx4yD36lLkxUdpp8ouHi/Te7UqmbZqabWawBvXqmo74Wwi+ywBFUlCzWzXyZRKHVyYoNuJq1U5vmIPx8st9J8EgJM7NETHJrWw9KHTMf8Be4MdzIg0X5r42q86KfQZerasg7WPn1Hp9ezMjEo3GZW250pH/8p/m7m+6PWBq1iXqvnSfvF0Kd+fWd1b1EHjWvanRUvmLtcMyhIgtt+BW+pWj74w2d0P/diBG9eqiupVsgybsJL54LIrGT9zy3rVcM+Izq6v998XV56mqUPjmljw4HDcMrh9VCZzMx47x1qtjRGlD5TSRKsmEOrbs/GJM3GyiXQc6t99hIWRsGboDahwEgi2qq9dM+0kEaxa9SpZaFwrB4+N6Y5PbnI2p6uWSPJYE8ueFb45yMwQlm4u1OtWahqN+vnFXZ/O6EujgE/Jsq83RVxU86UHl6tHznb3uNOjzInqpPbfbwzKXDT1zkGaz5sdfWnUFAIYtLPbPBcmKgi4vH9Fx3OjjqfJQD2XmhuUGkuvmi+95GWR9b7ne0d2xjUD2lZq5oo3SMLtPpUntmuA/ImjNNNtVGRPN/flqJe7xcV5GL+45WTDxKl29NBJMeL2vnB5/zY4Ibc+pt91KqbfpX2OtaOipsx8ga1+NPWq7z79OHxxy8maaX3MUteUVXT0N37fI2d1x6y7B6NhzGhoZXXRqWv0V/jNn0/RbN1w8pnc9Pg53fHVbQPQ3OVzcyIxKHPJlSe1QSedCYPdGn35/g0nYupf9E9KRne9qyZU7gSIp90AABiSSURBVPOgFi/Hjp7zDTpWqz+6el5L8+dBodmX5pe/VjRruNEsUGmrBuVb89gZ+P6ewa5u88nzerg+8W8UD9ti3Vi1so7Pbo6uFWnTIP6gh7WPnxn1+F8X9jaVrsMrl52oPerVSHS6A+0d8D+X97W83j6t69luChp0XKNKaQ4Uer+5k9q3eDo0rhnVH05t+l2n4k2T/RsVFcljzS9rxvjRXdEt3OdX/TtmZWY4nolF/d1WpMQwLlyVrAzN6ZeUd6q/g3ir696iDm48tfIIfie1f27Kyc6M1JYlq+St40siZpsvjQIBo8m6jd6vNzqvQY3Q3dP1A9visUmr4q8kxrAujfHpwgJTy2rl2DFSp1p2pbs7AFGdmv2oWfIiIWJWZgZq+ThpdlDEdiiP/XmDUJHYs2Ud3X5iDWzmXjPT7DdSJ4eUXUabfOdag+mUVKwEOW4LBWzW+tVVjIy1UFNm4je69pS2KC4rdzUJbsX2K/6OfM+u3GuZ71OWjDX5yYRBmUuU3fSDG/rjkld/jnrNfFDmbGe3++5qVTIjNQtWgzKjMisnvjN7xF5M9N/37CV9sPvQMTSuXTXSV+ep83u6PnLOjGRuXk0kN87Tr1yZh9fnbjScccGtPktOfDT2JMMUJFZrjAI2Nad9Pn+Ofm0rz/2oRW9k7Bndm+KUjg3xwBfLK5a1WRa3vwr1vn9cOD1Js7o5OLijRO8tcSlrixolalDzliq7aVAxKHNZ+0aVq4g96udfid2LlZNrnNm3CkTPx9elmXYzBKCdj+nCvMpTu3hJGaV6YtvKHbiTlZe7oRvNl6ce1winHhfKpVarahYOhaeW6tK0Nn5ct8f5Bjx2z4hO6NqsNhZv3qf5+ouXHR8/bYUPVzsvmhq9ar40Y+WEEab7Dertsy+Fm4ijgjIlgHNUOufU59BrB7RFn9b10LdNPazecUj/TSZYGX0ZgHuilMagTMO8cUNx8sSZlt4TW2NkJ8eOX/u6+oCslp2JoyVllZbp3aou/nN5X/R/YkbU80Y1ZcqceaHaj4plrUwz5Ic61bIx/a5BriWjDYLY3FNu8OoEPfe+oZH98L4zOmNk96Y4/z8/hbfp/1VBqwi3DukAAFikE5SdaZBhXMmmn5OduCZsRzdkOu/1s8ZPK+WPnsjoSwvlNT2/rEd3QFFzX2aIuHN7fn/34KhpzeKvt+Jvo7IbJSkmZ9iBRUPzutVwYZ7N3F3hnbtOtYp+JcpwZC/kqQ5KuydY9dsWx+Q+UzStnYOmdax3Fh7WpQluGdwe4//UNap8bl5YlU61bjc1dmhcK27CzmRzQV/3axu9uvjUqZ4d2d+yMzOQl1vRJOV/SGaO1V08r0093DqkPWbfM8SbAmlw87tUOpwrwf+tQ9qjXpwcgH6zMvrS6mCiuuHPXc/luV2tBLy5DWvozsAQS1joU3aKKr1LhoDudG5kD4Myz1Ts2q9emWd6yPDAjsb5jNTevOYENAtfvOyeYM00ez55nvZEu7Enif8bHj33WVZmBu4d2blSTjU33Xiqe+kDUpkXgxP8YPZC38vleVDVjPq92ZGRIXDPiM5obGMUtF1KQGLnHumiE6Ivxmf3DqUiUWpS7hnRGYsDNluGmp0mSbNNsxfmtcKT5/UwlebICrdriQeEA6y6qmPKsE+ZEJH9f8UjI/HEuT1cLVO6S42ztAfcrAGoXiULbRtU7mtWiQDeve5ES+uulZNdkYrDhT5leqtQst4/NqY7hnZurLt89apZmH7XqZpBqFc1HEpg6GdflnSVyJbElvVCuYeUZkIj793QP5LctUltdwaJPH5Od/z7ot5xL47JONWLnZ+xY8xox4nnhlK6BKF52YzI6EszKTGUP0x+tMwMgYtOaI2sgI+mHv+nrph9zxDDycZjWZkNgaxhn7IAsRtUKAMJ7PblsHJgXd6/DcrKJWb+/kfovRpl1hua7vbJ+sXLjsdK1bBzjpRMvEQGIMruY7bGr2bVLLQJT+Z8/SmVcyvZ0blp7bj9eNSS4XqlnDP+Muy4+AuakGwpXSpSeJhpvgz9H/Tf9ESTI08V2ZkZaN2gOo6VVvQjNrV/81TrmeQ5gpKEk9qaZjb6bAHW+ztM+8sgfH3bKZHHUQkJTZQ/KviLzSEV531Ga7aa/PHMHs1w94hOlt5Dyc/KMebHtSNRNwduNKEKIZA/cRT+fJp2gtj473W8eV9Fant8LYU5r12Zh9PDtb5alEuA1swSZqgD09uHGu8Lfwl3U7E6Q8ZJ7VJnNLtXWFPmEjdOUE5HJMa7WKn7qsV2/oyeusN4O+opOdw6ofVvVz/hk0WTc0r/IXWTdhD5EUB4tc1544birXn5GDeyM3pN+I6j4WySFqIyv2vhh3VtgmFxgjKF3V1O/b4ME00uNw9uj5stTgW2asJIZNnISpBuGJQFiJmDQYtycmlaR7tfwPS7BqFdQ/1s13rNip/cdBIuCKchiFo+aqoPK9mwTS9qCfuS+adO9Wz8fP9paFjTu4EcTiRj/y4jzetWw1/P7AIAWOpjR/pkP+4qJiT3/xzmt0QkZDZKtkwhbL5MEC93SOUurkXd6njz6spNgM3rVosb8EXVlKmePyHXuH9C7Fr9OGn5fReb7prWyQl8h2a3mOoUnsDdMSND2L6ZS3eWOqtH+pQF87t2eg5M1WAzGTk6kwoh6gshpgkh1ob/1+whKIS4KrzMWiHEVarnvxdCrBZCLAn/C0wbiNVd3OhgrZKV4Wiy6QdHddF9TT1diJ1cYlY74JsZrWn0PqJE8DNgT5ZRiOnqqfN6olOTWqhrovk31Ucbcl8NDqe3t+MAzJBSdgQwI/w4ihCiPoCHAJwIoB+Ah2KCt8uklL3D//5wWJ6U1bq+fmZ5o5FBbk+4a3vdAb3LpNTHi477kv0rHda1Cab+ZZCpWt7O4bRDV5+c63GpKN05DcrOBvB2+O+3AYzRWGYEgGlSyr1Syn0ApgEY6XC7ScnJhWF4nE6eF4RnH2ivk4rCCqsl7NO6btTj3IZx8rEl+Umc/GWnWdCNpsSf7h+KphYSuqZLY3o6DTBoULMq8ieOwundmvpdlLiSPVAm5x39m0gptwOAlHK7TvNjCwBbVI8Lws8p3hRClAH4DMBj0mp+h4AwczA4OV7iBXTnHt8S5x4fCsz2HinWeK+DDWuVRfV3japZuKJ/GyzctA9PntcTPUxkUR8UnnQaAOqHpyHpZHI6EO3y8EyUTuzsz072kGZ1qqFJnRzsOFhkavnkPINZl0pTkBEFhWFQJoSYDkDr9uABk9vQOh8qp63LpJRbhRC1EArKrgDwjk45xgIYCwCtWwdnrq2bTm2P//ywvtKHjHdizsnOQFGJuYliE83OBe/RMd1NLaeM8FHH3Z2b1sbHN56E3q3q6r3NEDv6kxHWIFAqS5cbgXRg2HwppRwmpeyu8e9LADuFEM0AIPy/Vp+wAgDqmZBbAtgWXvfW8P+HALyPUJ8zvXK8IqXMk1LmNWrUSG+xhGtgYcJZ5cIgJfD5LSfHXXZwp4rPaDbocYOZJlY3Jj5X69e2fsrMy0jBkqQV70SUppxeCb8CoIymvArAlxrLTAVwuhCiXriD/+kApgohsoQQDQFACJENYDSA5Q7L4xqz53IrtTTqZrbjW+tPZbH0odPxyhV5kcdWksq6VSMwqkczd1akwe3rJJsvyUgi9xArcyoSEak57VM2EcDHQojrAGwGcAEACCHyANwkpbxeSrlXCPEogF/D75kQfq4GQsFZNoBMANMBvOqwPL5RTsBunIiddKB1I+BZ+OAw1MoJleGRs7o5alpU40WKEs2t+L91/epYumU/alZlvm0KLo4yTn6OzjBSyj0ATtN4fgGA61WP3wDwRswyRwD0dbL9ZBNpvgw/PqVDQ8xdt9u38jSsWRW7Dx+r9HyDmhUzA1ylMQTcac0U+4BRojm9WE08twdG92yG4xwMSCEiMsLbPh1mA4egdVmpUcX8Tzr59lOQv6fQw9JEU4K5oH1nlBzcSokxsltTTFmxw9J6alTNwgiz6RACnv3dTe9c2y+tUmMEFU+pqYO9q10SlGrj1g2qo0XdaqaWbVw7B/3aGk+lVIndjv6qgQ5EiaQ+PJ+/tE/Ct5mqBh3XCL1c6t5AzqXBLpfyGJQ55CS+8KoZTz1yM0h4wqBE0zrGjDK4/+fytOpVQRRxYTgROfmHzZcGHjmrGxrWrIp2jWrgUFEpLnz5J83lghRwBP0OnX3KKNGsHBIjuzvL2s69m5JR/sRRfheBwKDMUI2qWRjVM5QewrWcR0l+1rYd87H5khLMz30t4PdGlEJ4Tk0dbL50KIgHg1KmDuG5MLNNTLibCJGO/j6Xg9JH52a1AQBtGsSZk5WIKCCCcbUOIquRQ8xtcRACj6tOaoP8iaOQmeHuPbvdQQ1Bb1al1HP5ia3xzZ9PiZpv1YxlD59ue5ucRYCI7GLzpQVawcjFJ7TC7DW7cN2AtobvV+Z+bFQrlAfMq3N34IMfXrPIZbcOaa+ZQ0wIge4t6lheX+0c52keAn8cUurhPpf0GJQ5VK9GFXwwtr+pZatkZeCZi3qhX9sGnpYpqDfqyvnCq47+Qf3c5C6tYOeeEZ0drbNNg+rYlMCcfUREWhiUGbB642G0/Dl9Ejjk2KNbdbtrVWoaGTxR0Li5T3L/pkTjiPbUwT5lqSxgVwc255ATHZuEBq5Uzcp0fd1eXNTSIaM/EbmLNWVkmdPgyu3LH4O99PDcJX2wdMuBSJ9MN7laU+beqogozbCmTIfTE6uZOMHziqyARStelSZgFYLkkVo52TilY0NP1n1Jv9aurzNghx+lMOUcyNrZ5MegzGX1a1TB2EHt8P4N5jr/eypg0UrF3JfBKhfRLYPb+10EIiI2X1p1XJOaWLPzsO7rQgj89cwuCSxR4tmtAahbvQoAoFPTyqkLiPxkN/eeFt5zEJFdDMoMJHUTRMAK375RTXw0tj96tarrd1GIiFJOwE75ZAODMh1j+rTAF4u34vjW9TzbRrIOY3bSb+HEdu7naOOJiIgoOCac3U0zmTMZY1Cm49TjGiF/4ii/i+FMmrSjpMnHpCSRrDdbRG658qRcv4uQtNjRn2wb6NFIOKJkFhkJxypcIrKIQVlAnGpxwmRTvMroH15tvXDHfb/x2kdBxN2SiKxiUJbKPG7XYyMNEVFw8EYg+TEo8xH7QhEREZGCHf1TGdv1iOJ6dEx37D9S7Mm6efgRkVUMysi2oGTm796iDqplZ+K2oR38LgolmSv6t4n8ffEJrfDLxr0+lobInqCci8k5BmUWJcO+375RTQBAi7o5nqw/aKPK6lTLxqpHR/pdDEpyE8/r6cp6eIEkvwTs1Ew2MChLQVefnIseLevghNz6nm6Hlx6iyjIzMsL/8wpJRNYwKEtBGRnC04CMlxoifXcO74iy8nJcmNfK76JQmmDlbOpgUGaRm9XDRsfRvSM7Ydv+o+5tkIg8VzsnG4+c3d3vYhBREmJQZlEi70huGRzwjuu8OyMiInIN85SRZexMSkRE5D4GZX5K8pomTrxMyeqM7k2Rncm7CyIKFjZfkmWCXf0pyb10eV+/i0DkGuX2mOfm5MeaMiIiIqIAYFBGtnEYNhERkXscBWVCiPpCiGlCiLXh/+vpLDdFCLFfCPFNzPNthRC/hN//kRCiipPyUGKwoz8RUfDw3Jz8nNaUjQMwQ0rZEcCM8GMtTwO4QuP5JwE8E37/PgDXOSyP51g5VIE1ZURE/hvWpQkAYES3pj6XhJxyGpSdDeDt8N9vAxijtZCUcgaAQ+rnRGgCxaEAPjV6f6pK1tGLvBkjIgqOrs1rI3/iKHRvUcfvopBDToOyJlLK7QAQ/r+xhfc2ALBfSlkaflwAoIXD8lACKAf+mD78uYiIiNximBJDCDEdgFad6AMOt61V4aJbdSSEGAtgLAC0bt3a4abtYy0R0Kp+deRPHOV3MYiIiFKKYVAmpRym95oQYqcQopmUcrsQohmAPyxsezeAukKIrHBtWUsA2+KU4xUArwBAXl6eb+1+ydngSEREREHntPnyKwBXhf++CsCXZt8opZQAZgE43877iYiIiFKJ06BsIoDhQoi1AIaHH0MIkSeEeE1ZSAgxB8AnAE4TQhQIIUaEX7oPwF1CiHUI9TF73WF5kgpHLxIREZHC0TRLUso9AE7TeH4BgOtVjwfqvH8DgH5OykBERESUCpjRn4iIiCgAGJQRERERBQCDMiIiIqIAYFBGREREFAAMynzEwZdERESkYFBGREREFAAMyoiIiIgCgEGZjySzxxIREVEYgzKLGEgRERGRFxiUEREREQUAgzIiIiKiAGBQZpEQwu8iEBERUQpiUGaRV33KRnRr6sl6ibq3qO13EYiIyIQsvwtAwH+vOxEDOjTwuxiUor64ZQBKyzhAhciKn+8/DQeOlvhdDEozDMp8pFwmq1XJYLMoeSY7MwPZmX6Xgii5NK2Tg6Z1cvwuBqUZNl8GAgMyIiKidMeaMh8d17gWFm/ejzrV+DMQBc2ce4egsLjM72IQURphNOCjR87uhjF9WqBD41p+F4WIYrSqX93vIhBRmmHzpY9ysjNxUnt28CciIiIGZZZxDBsRERF5gUEZERERUQAwKCMiIiIKAAZlFjF5BREREXmBQZlF7FNGREREXmBQRkRERBQADMqIiIiIAoBBGREREVEAMCgjIiIiCgAGZUREREQBwKCMiIiIKAAYlBEREREFAIMyIiIiogBgUEZEREQUAI6CMiFEfSHENCHE2vD/9XSWmyKE2C+E+Cbm+beEEBuFEEvC/3o7KQ8RERFRsnJaUzYOwAwpZUcAM8KPtTwN4Aqd1+6RUvYO/1visDxERERESclpUHY2gLfDf78NYIzWQlLKGQAOOdxWMHDySyIiIvKA06CsiZRyOwCE/29sYx2PCyGWCSGeEUJUdVgeIiIioqSUZbSAEGI6gKYaLz3gwvbvB7ADQBUArwC4D8AEnXKMBTAWAFq3bu3CpomIiIiCwzAok1IO03tNCLFTCNFMSrldCNEMwB9WNq7UsgE4JoR4E8DdcZZ9BaHADXl5eWxEJCIiopTitPnyKwBXhf++CsCXVt4cDuQghBAI9Udb7rA8REREREnJaVA2EcBwIcRaAMPDjyGEyBNCvKYsJISYA+ATAKcJIQqEECPCL70nhPgNwG8AGgJ4zGF5iIiIiJKSYfNlPFLKPQBO03h+AYDrVY8H6rx/qJPtExEREaUKZvS3qEPjmn4XgYiIiFIQgzKLnrmoN967/kS/i0FEREQphkGZRTWqZmFAh4Z+F4OIiIhSDIMyIiIiogBgUEZEREQUAAzKiIiIiAKAQRkRERFRADAoIyIiIgoABmVEREREAcCgjIiIiCgAGJQRERERBQCDMiIiIqIAcDQheTp7/JzuaFYnx+9iEBERUYpgUGbTZSe28bsIRERElELYfElEREQUAAzKiIiIiAKAQRkRERFRADAoIyIiIgoABmVEREREAcCgjIiIiCgAGJQRERERBQCDMiIiIqIAYFBGREREFAAMyoiIiIgCgEEZERERUQAwKCMiIiIKAAZlRERERAEgpJR+l8EyIcQuAJs83kxDALs93gZ5i79hauDvmPz4GyY//obOtJFSNjJaKCmDskQQQiyQUub5XQ6yj79hauDvmPz4GyY//oaJweZLIiIiogBgUEZEREQUAAzK9L3idwHo/7d3x6xRBWEUht+DhiBRUmlrEBIUQVP4A9JoIRYWphFsBUEbsbexsBKUKNopFlb+gFRWiiCERLBOKWphEcUUST6Le4sgRAvDZrL7Ps1y751ZZhhYDjPf7v4313A4uI77n2u4/7mGA2BNmSRJUgPcKZMkSWqAoUySJKkBhjJJkqQGGMokSZIaYCiTJElqgKFM0lBLsplkOcmnJCtJbif562dfkqkkVwc1RkkCQ5mk4ferqmar6jRwHrgI3P1HnynAUCZpoPydMklDLcmPqjq87foE8IHuD5aPAy+Bif7xzap6l+Q9cApYBV4Aj4D7wBwwDjyuqmcDm4SkkWAokzTU/gxl/b3vwElgDdiqqvUk08CrqjqXZA64U1WX+vbXgWNVdS/JOPAWmK+q1YFORtJQO7jXA5CkPZD+dQxYSDILbAIzO7S/AJxJcqW/ngSm6XbSJGlXGMokjZT++HIT+EpXW/YFOEtXY7u+UzfgVlUtDmSQkkaShf6SRkaSo8BTYKG62o1J4HNVbQHXgAN90zXgyLaui8CNJGP9+8wkmUCSdpE7ZZKG3aEky3RHlRt0hf0P+mdPgNdJ5oE3wM/+/kdgI8kK8Bx4SPeNzKUkAb4Blwc1AUmjwUJ/SZKkBnh8KUmS1ABDmSRJUgMMZZIkSQ0wlEmSJDXAUCZJktQAQ5kkSVIDDGWSJEkNMJRJkiQ14DfsxIy84yw1PQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "log_returns.plot(figsize = (10, 6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MSFT    0.000154\n",
       "dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "u = log_returns.mean()\n",
    "u"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MSFT    0.000376\n",
       "dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "var = log_returns.var()\n",
    "var"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MSFT   -0.000034\n",
       "dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drift = u - (0.5 * var)\n",
    "drift"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MSFT    0.019397\n",
       "dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stdev = log_returns.std()\n",
    "stdev"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "******"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(drift)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(stdev)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-3.42521946e-05])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(drift)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-3.42521946e-05])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drift.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.01939682])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stdev.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.6448536269514722"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "norm.ppf(0.95)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.56555911, 0.30563333],\n",
       "       [0.2558629 , 0.3623618 ],\n",
       "       [0.02093475, 0.78152537],\n",
       "       [0.51250654, 0.20669873],\n",
       "       [0.11159959, 0.92041264],\n",
       "       [0.57354439, 0.90275911],\n",
       "       [0.50732264, 0.58218642],\n",
       "       [0.13884827, 0.73142924],\n",
       "       [0.10923014, 0.48959636],\n",
       "       [0.74980671, 0.54223763]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.random.rand(10, 2)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.16507903, -0.50826622],\n",
       "       [-0.65615283, -0.35215289],\n",
       "       [-2.03481488,  0.77735517],\n",
       "       [ 0.03135437, -0.81792947],\n",
       "       [-1.21806524,  1.40785262],\n",
       "       [ 0.1854052 ,  1.29743435],\n",
       "       [ 0.01835617,  0.20749006],\n",
       "       [-1.0855084 ,  0.6171413 ],\n",
       "       [-1.2306327 , -0.02608103],\n",
       "       [ 0.6738816 ,  0.10607262]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "norm.ppf(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.45015915,  0.19631405],\n",
       "       [-1.0100133 , -0.35284644],\n",
       "       [-1.40043117,  0.25628267],\n",
       "       [-0.77801174,  0.80260848],\n",
       "       [ 0.53589663, -0.27688877],\n",
       "       [-0.44906061,  1.57629762],\n",
       "       [-0.28410626, -0.48493221],\n",
       "       [ 0.04759264,  0.92631139],\n",
       "       [ 0.45262652,  0.70276892],\n",
       "       [ 0.59550801,  0.04143592]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Z = norm.ppf(np.random.rand(10,2))\n",
    "Z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "t_intervals = 1000\n",
    "iterations = 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\n",
    "daily\\_returns = e^{r}\n",
    "$$\n",
    "\n",
    "$$\n",
    "r = drift + stdev \\cdot z\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "daily_returns = np.exp(drift.values + stdev.values * norm.ppf(np.random.rand(t_intervals, iterations)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.01591081, 0.9794886 , 1.00048003, ..., 1.02416   , 1.02649606,\n",
       "        1.00107475],\n",
       "       [1.02952747, 0.98716138, 1.00885144, ..., 1.00744178, 0.99612322,\n",
       "        0.9957195 ],\n",
       "       [1.03545845, 1.00564673, 1.00229303, ..., 1.05751804, 0.99713746,\n",
       "        0.99991494],\n",
       "       ...,\n",
       "       [0.99215031, 0.9734628 , 1.01616243, ..., 1.01639533, 0.9727786 ,\n",
       "        1.01131246],\n",
       "       [1.00229691, 0.98945243, 1.00550888, ..., 0.99096372, 0.98795269,\n",
       "        1.00527073],\n",
       "       [0.98754594, 0.98971277, 1.02871545, ..., 1.01032874, 0.97908139,\n",
       "        1.02108459]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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