FIN5615_Project_6_Neil_Yejjey_TUK12930.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "<h2>FIN5615 Project #6\n",
    "    <p class=\"text-primary\">S&P 100 Return Analytics</p>\n",
    "</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "<div class = \"panel panel-success\">\n",
    "    <div class = \"panel-heading\"> \n",
    "    <span style=\"color:black\"><b>Project Introduction</b>\n",
    "    </div>\n",
    "    <div class = \"panel-body\"> \n",
    "    In this project we will examine the historical return performance of the 100 component stocks that make up the S&P 100 index. We will also explore their correlations and identify pairs of stocks that are most highly correlated and relatively uncorrelated.\n",
    "    </div>\n",
    "</div>\n",
    "\n",
    "<div class = \"panel panel-success\">\n",
    "    <div class = \"panel-heading\"> \n",
    "    <span style=\"color:black\"><b>Data</b>\n",
    "    </div>\n",
    "    <div class = \"panel-body\"> \n",
    "    The data for this project includes the historical daily prices, from the stock's inception to June 30th 2020, for each of the 100 component stocks in the S&P 100 index. In the folder \"SP100\", you will find one CSV file for each of the component stocks.\n",
    "    <br><br>For this project, we will be focusing only on the historical closing prices listed under column \"Adj Close\" (adjusted for stock splits). The ticker symbols and company names of each of the stocks are included in a separate file \"_SP100.csv\", which you can also find the folder \"SP100\".\n",
    "    <br><br>Download and place the entire \"SP100\" folder in the \"Data\" folder of your working directory. Remember to read in the datasets starting with the relative file path: <code>'../../Data/SP100'</code>.\n",
    "    </div>\n",
    "</div>\n",
    "\n",
    "<div class = \"panel panel-info\">\n",
    "    <div class = \"panel-heading\"> \n",
    "    <span style=\"color:black\"><b>Instructions</b>\n",
    "    </div>\n",
    "    <div class = \"panel-body\"> \n",
    "    Please code you solutions directly in this Notebook under each task in the cells provided below. Include all code used to produce your results, and support your answers with textual explanations if neccessary. You may feel free to add additional Code and/or Markdown cells as needed. Code must be written using <b>Python 3</b>.\n",
    "    <br><br>Please order the cells in your Notebook in the order in which they are intended to run! It is expected that your Notebook is able to run in sequence from start to finish without any errors (before submitting check by: <code>Kernel > Restart & Run All</code>).\n",
    "    <br><br>Once ready, please submit your assignment as an <b>.ipynb file</b> (<code>File > Download as > Notebook</code>). You must name your files in the following format: <b><code>FIN5615_Assignmnet_1_Richard_Lee_tuc12345.ipynb</code></b>.\n",
    "    </div>\n",
    "</div>\n",
    "\n",
    "---\n",
    "<div class = \"alert alert-block alert-warning\">\n",
    "    <b>Note</b>: Add any additional libraries that you plan on using to the import statements in the code block below.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "<div class = \"panel panel-primary\">\n",
    "    <div class = \"panel-heading\"> \n",
    "    <span style=\"color:white\"><b>Task 1</b>\n",
    "    </div>\n",
    "    <div class = \"panel-body\"> \n",
    "    Read in each of the CSV files for the S&P 100 stocks in a dictionary for us to easily reference and manipulate the data.\n",
    "    <br>\n",
    "    <ul>\n",
    "        <li>Start by reading in the companies and their stock ticker symbols into a dataframe called <code>sp100</code>, with the \"Ticker\" column as the index.</li>\n",
    "        <li>Next, create a dictionary called <code>stock_frames</code>. This will serve as a container to store all the historical price data for the component stocks.</li>\n",
    "        <li>Read in the historical price data for all stocks in <code>sp100</code> as dataframes and store them in <code>stock_frames</code> with the stock's ticker as the key. </li>\n",
    "        <li>For this project, we will only need the columns \"Date\" and \"Adj Close\" so limit to just these two columns when reading in the CSV file, with the \"Date\" column as the dataframe's index. </li>\n",
    "    </ul>\n",
    "    <p style=\"text-align:right;\">[10pts]</p>\n",
    "    </div>\n",
    "</div>\n",
    "\n",
    "<div class = \"alert alert-block alert-info\">\n",
    "    <b>Hint</b>: When reading in the CSV files, you may want to use a loop to iterate the index of *sp100* that was created earlier.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "sp100 = pd.read_csv(f'../Data Science in Finance/Data/SP100/_SP100.csv', usecols = ['Ticker'], squeeze = True).tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ticker</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AAPL</th>\n",
       "      <td>Apple Inc.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABBV</th>\n",
       "      <td>AbbVie Inc.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABT</th>\n",
       "      <td>Abbott Laboratories</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACN</th>\n",
       "      <td>Accenture</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADBE</th>\n",
       "      <td>Adobe Inc.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VZ</th>\n",
       "      <td>Verizon Communications</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WBA</th>\n",
       "      <td>Walgreens Boots Alliance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WFC</th>\n",
       "      <td>Wells Fargo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WMT</th>\n",
       "      <td>Walmart</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XOM</th>\n",
       "      <td>Exxon Mobil Corp.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>99 rows × 1 columns</p>\n",
       "</div>"
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      "text/plain": [
       "                         Company\n",
       "Ticker                          \n",
       "AAPL                  Apple Inc.\n",
       "ABBV                 AbbVie Inc.\n",
       "ABT          Abbott Laboratories\n",
       "ACN                    Accenture\n",
       "ADBE                  Adobe Inc.\n",
       "...                          ...\n",
       "VZ        Verizon Communications\n",
       "WBA     Walgreens Boots Alliance\n",
       "WFC                  Wells Fargo\n",
       "WMT                      Walmart\n",
       "XOM            Exxon Mobil Corp.\n",
       "\n",
       "[99 rows x 1 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Sp100 = pd.read_csv(f'../Data Science in Finance/Data/SP100/_SP100.csv', index_col = 'Ticker')\n",
    "Sp100\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['AAPL',\n",
       " 'ABBV',\n",
       " 'ABT',\n",
       " 'ACN',\n",
       " 'ADBE',\n",
       " 'AIG',\n",
       " 'ALL',\n",
       " 'AMGN',\n",
       " 'AMT',\n",
       " 'AMZN',\n",
       " 'AXP',\n",
       " 'BA',\n",
       " 'BAC',\n",
       " 'BIIB',\n",
       " 'BK',\n",
       " 'BKNG',\n",
       " 'BLK',\n",
       " 'BMY',\n",
       " 'BRK.B',\n",
       " 'C',\n",
       " 'CAT',\n",
       " 'CHTR',\n",
       " 'CL',\n",
       " 'CMCSA',\n",
       " 'COF',\n",
       " 'COP',\n",
       " 'COST',\n",
       " 'CRM',\n",
       " 'CSCO',\n",
       " 'CVS',\n",
       " 'CVX',\n",
       " 'DD',\n",
       " 'DHR',\n",
       " 'DIS',\n",
       " 'DUK',\n",
       " 'EMR',\n",
       " 'EXC',\n",
       " 'F',\n",
       " 'FB',\n",
       " 'FDX',\n",
       " 'GD',\n",
       " 'GE',\n",
       " 'GILD',\n",
       " 'GM',\n",
       " 'GOOG',\n",
       " 'GS',\n",
       " 'HD',\n",
       " 'HON',\n",
       " 'IBM',\n",
       " 'INTC',\n",
       " 'JNJ',\n",
       " 'JPM',\n",
       " 'KHC',\n",
       " 'KMI',\n",
       " 'KO',\n",
       " 'LLY',\n",
       " 'LMT',\n",
       " 'LOW',\n",
       " 'MA',\n",
       " 'MCD',\n",
       " 'MDLZ',\n",
       " 'MDT',\n",
       " 'MET',\n",
       " 'MMM',\n",
       " 'MO',\n",
       " 'MRK',\n",
       " 'MS',\n",
       " 'MSFT',\n",
       " 'NEE',\n",
       " 'NFLX',\n",
       " 'NKE',\n",
       " 'NVDA',\n",
       " 'ORCL',\n",
       " 'OXY',\n",
       " 'PEP',\n",
       " 'PFE',\n",
       " 'PG',\n",
       " 'PM',\n",
       " 'PYPL',\n",
       " 'QCOM',\n",
       " 'RTX',\n",
       " 'SBUX',\n",
       " 'SLB',\n",
       " 'SO',\n",
       " 'SPG',\n",
       " 'T',\n",
       " 'TGT',\n",
       " 'TMO',\n",
       " 'TXN',\n",
       " 'UNH',\n",
       " 'UNP',\n",
       " 'UPS',\n",
       " 'USB',\n",
       " 'V',\n",
       " 'VZ',\n",
       " 'WBA',\n",
       " 'WFC',\n",
       " 'WMT',\n",
       " 'XOM']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'AAPL':            Adj Close\n",
       " Date                \n",
       " 19801212    0.405683\n",
       " 19801215    0.384517\n",
       " 19801216    0.356296\n",
       " 19801217    0.365115\n",
       " 19801218    0.375698\n",
       " ...              ...\n",
       " 20200624  360.059998\n",
       " 20200625  364.839996\n",
       " 20200626  353.630005\n",
       " 20200629  361.779999\n",
       " 20200630  364.799988\n",
       " \n",
       " [9971 rows x 1 columns],\n",
       " 'ABBV':           Adj Close\n",
       " Date               \n",
       " 20130102  25.678120\n",
       " 20130103  25.466085\n",
       " 20130104  25.144379\n",
       " 20130107  25.195559\n",
       " 20130108  24.647196\n",
       " ...             ...\n",
       " 20200624  95.139999\n",
       " 20200625  96.989998\n",
       " 20200626  96.129997\n",
       " 20200629  96.440002\n",
       " 20200630  98.180000\n",
       " \n",
       " [1887 rows x 1 columns],\n",
       " 'ABT':           Adj Close\n",
       " Date               \n",
       " 19800317   0.009814\n",
       " 19800318   0.009883\n",
       " 19800319   0.010020\n",
       " 19800320   0.009780\n",
       " 19800321   0.009883\n",
       " ...             ...\n",
       " 20200624  87.300003\n",
       " 20200625  89.620003\n",
       " 20200626  88.709999\n",
       " 20200629  89.010002\n",
       " 20200630  91.430000\n",
       " \n",
       " [10160 rows x 1 columns],\n",
       " 'ACN':            Adj Close\n",
       " Date                \n",
       " 20010719   11.353168\n",
       " 20010720   11.233422\n",
       " 20010723   11.225938\n",
       " 20010724   11.121161\n",
       " 20010725   11.188517\n",
       " ...              ...\n",
       " 20200624  201.839996\n",
       " 20200625  217.320007\n",
       " 20200626  212.470001\n",
       " 20200629  212.720001\n",
       " 20200630  214.720001\n",
       " \n",
       " [4767 rows x 1 columns],\n",
       " 'ADBE':            Adj Close\n",
       " Date                \n",
       " 19860813    0.153170\n",
       " 19860814    0.161679\n",
       " 19860815    0.158843\n",
       " 19860818    0.153170\n",
       " 19860819    0.156006\n",
       " ...              ...\n",
       " 20200624  431.679993\n",
       " 20200625  436.950012\n",
       " 20200626  426.920013\n",
       " 20200629  424.200012\n",
       " 20200630  435.309998\n",
       " \n",
       " [8540 rows x 1 columns],\n",
       " 'AIG':           Adj Close\n",
       " Date               \n",
       " 19730102  17.236408\n",
       " 19730103  17.311666\n",
       " 19730104  17.110947\n",
       " 19730105  17.211294\n",
       " 19730108  17.161123\n",
       " ...             ...\n",
       " 20200624  30.299999\n",
       " 20200625  31.260000\n",
       " 20200626  29.709999\n",
       " 20200629  30.680000\n",
       " 20200630  31.180000\n",
       " \n",
       " [11980 rows x 1 columns],\n",
       " 'ALL':           Adj Close\n",
       " Date               \n",
       " 19930603   6.927579\n",
       " 19930604   6.750704\n",
       " 19930607   6.632787\n",
       " 19930608   6.544352\n",
       " 19930609   6.662268\n",
       " ...             ...\n",
       " 20200624  92.949997\n",
       " 20200625  95.830002\n",
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       " 20200630  96.989998\n",
       " \n",
       " [6819 rows x 1 columns],\n",
       " 'AMGN':            Adj Close\n",
       " Date                \n",
       " 19830617    0.277749\n",
       " 19830620    0.244082\n",
       " 19830621    0.244082\n",
       " 19830622    0.252499\n",
       " 19830623    0.252499\n",
       " ...              ...\n",
       " 20200624  232.509995\n",
       " 20200625  235.110001\n",
       " 20200626  232.839996\n",
       " 20200629  231.559998\n",
       " 20200630  235.860001\n",
       " \n",
       " [9337 rows x 1 columns],\n",
       " 'AMT':            Adj Close\n",
       " Date                \n",
       " 19980227   14.866860\n",
       " 19980302   14.759904\n",
       " 19980303   14.652946\n",
       " 19980304   13.476433\n",
       " 19980305   13.797302\n",
       " ...              ...\n",
       " 20200624  254.179993\n",
       " 20200625  254.360001\n",
       " 20200626  246.479996\n",
       " 20200629  253.800003\n",
       " 20200630  258.540009\n",
       " \n",
       " [5622 rows x 1 columns],\n",
       " 'AMZN':             Adj Close\n",
       " Date                 \n",
       " 19970515     1.958333\n",
       " 19970516     1.729167\n",
       " 19970519     1.708333\n",
       " 19970520     1.635417\n",
       " 19970521     1.427083\n",
       " ...               ...\n",
       " 20200624  2734.399902\n",
       " 20200625  2754.580078\n",
       " 20200626  2692.870117\n",
       " 20200629  2680.379883\n",
       " 20200630  2758.820068\n",
       " \n",
       " [5820 rows x 1 columns],\n",
       " 'AXP':           Adj Close\n",
       " Date               \n",
       " 19720601   0.406085\n",
       " 19720602   0.404316\n",
       " 19720605   0.396353\n",
       " 19720606   0.375120\n",
       " 19720607   0.370696\n",
       " ...             ...\n",
       " 20200624  95.480003\n",
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       " 20200626  93.419998\n",
       " 20200629  94.519997\n",
       " 20200630  95.199997\n",
       " \n",
       " [12126 rows x 1 columns],\n",
       " 'BA':            Adj Close\n",
       " Date                \n",
       " 19700102    0.221810\n",
       " 19700105    0.230198\n",
       " 19700106    0.232994\n",
       " 19700107    0.233926\n",
       " 19700108    0.231130\n",
       " ...              ...\n",
       " 20200624  176.690002\n",
       " 20200625  174.880005\n",
       " 20200626  170.009995\n",
       " 20200629  194.490005\n",
       " 20200630  183.300003\n",
       " \n",
       " [12738 rows x 1 columns],\n",
       " 'BAC':           Adj Close\n",
       " Date               \n",
       " 19730221   0.353686\n",
       " 19730222   0.354880\n",
       " 19730223   0.353686\n",
       " 19730226   0.353686\n",
       " 19730227   0.353686\n",
       " ...             ...\n",
       " 20200624  23.809999\n",
       " 20200625  24.719999\n",
       " 20200626  23.150000\n",
       " 20200629  23.389999\n",
       " 20200630  23.750000\n",
       " \n",
       " [11946 rows x 1 columns],\n",
       " 'BIIB':            Adj Close\n",
       " Date                \n",
       " 19910917    3.125000\n",
       " 19910918    3.125000\n",
       " 19910919    3.145833\n",
       " 19910920    3.083333\n",
       " 19910923    2.958333\n",
       " ...              ...\n",
       " 20200624  263.040009\n",
       " 20200625  262.630005\n",
       " 20200626  258.660004\n",
       " 20200629  260.350006\n",
       " 20200630  267.549988\n",
       " \n",
       " [7252 rows x 1 columns],\n",
       " 'BK':           Adj Close\n",
       " Date               \n",
       " 19730503   0.016740\n",
       " 19730504   0.016740\n",
       " 19730507   0.016740\n",
       " 19730508   0.016616\n",
       " 19730509   0.016864\n",
       " ...             ...\n",
       " 20200624  37.230000\n",
       " 20200625  38.150002\n",
       " 20200626  36.320000\n",
       " 20200629  36.950001\n",
       " 20200630  38.650002\n",
       " \n",
       " [11896 rows x 1 columns],\n",
       " 'BKNG':             Adj Close\n",
       " Date                 \n",
       " 19990331   497.250000\n",
       " 19990401   483.000000\n",
       " 19990405   450.750000\n",
       " 19990406   474.000000\n",
       " 19990407   447.000000\n",
       " ...               ...\n",
       " 20200624  1610.329956\n",
       " 20200625  1615.390015\n",
       " 20200626  1541.250000\n",
       " 20200629  1593.219971\n",
       " 20200630  1592.339966\n",
       " \n",
       " [5348 rows x 1 columns],\n",
       " 'BLK':            Adj Close\n",
       " Date                \n",
       " 19991001    9.548270\n",
       " 19991004    9.463775\n",
       " 19991005    9.548270\n",
       " 19991006   10.055260\n",
       " 19991007    9.548270\n",
       " ...              ...\n",
       " 20200624  538.570007\n",
       " 20200625  546.969971\n",
       " 20200626  534.849976\n",
       " 20200629  532.869995\n",
       " 20200630  544.090027\n",
       " \n",
       " [5220 rows x 1 columns],\n",
       " 'BMY':           Adj Close\n",
       " Date               \n",
       " 19720601   0.066262\n",
       " 19720602   0.066922\n",
       " 19720605   0.067054\n",
       " 19720606   0.066658\n",
       " 19720607   0.066526\n",
       " ...             ...\n",
       " 20200624  57.759998\n",
       " 20200625  58.180000\n",
       " 20200626  57.540001\n",
       " 20200629  57.799999\n",
       " 20200630  58.799999\n",
       " \n",
       " [12126 rows x 1 columns],\n",
       " 'BRK.B':            Adj Close\n",
       " Date                \n",
       " 19960509   23.200001\n",
       " 19960510   24.000000\n",
       " 19960513   23.900000\n",
       " 19960514   23.600000\n",
       " 19960515   23.200001\n",
       " ...              ...\n",
       " 20200624  177.529999\n",
       " 20200625  178.850006\n",
       " 20200626  175.369995\n",
       " 20200629  177.190002\n",
       " 20200630  178.509995\n",
       " \n",
       " [6077 rows x 1 columns],\n",
       " 'C':           Adj Close\n",
       " Date               \n",
       " 19770103  11.170399\n",
       " 19770104  11.206079\n",
       " 19770105  11.134703\n",
       " 19770106  11.063326\n",
       " 19770107  11.099014\n",
       " ...             ...\n",
       " 20200624  50.810001\n",
       " 20200625  52.680000\n",
       " 20200626  49.580002\n",
       " 20200629  50.290001\n",
       " 20200630  51.099998\n",
       " \n",
       " [10969 rows x 1 columns],\n",
       " 'CAT':            Adj Close\n",
       " Date                \n",
       " 19700102    0.359904\n",
       " 19700105    0.358820\n",
       " 19700106    0.350148\n",
       " 19700107    0.339307\n",
       " 19700108    0.340391\n",
       " ...              ...\n",
       " 20200624  122.059998\n",
       " 20200625  124.709999\n",
       " 20200626  122.389999\n",
       " 20200629  125.250000\n",
       " 20200630  126.500000\n",
       " \n",
       " [12738 rows x 1 columns],\n",
       " 'CHTR':            Adj Close\n",
       " Date                \n",
       " 20100105   35.000000\n",
       " 20100106   35.000000\n",
       " 20100107   35.000000\n",
       " 20100108   33.500000\n",
       " 20100111   33.500000\n",
       " ...              ...\n",
       " 20200624  507.989990\n",
       " 20200625  510.019989\n",
       " 20200626  502.269989\n",
       " 20200629  509.220001\n",
       " 20200630  510.040009\n",
       " \n",
       " [2640 rows x 1 columns],\n",
       " 'CL':           Adj Close\n",
       " Date               \n",
       " 19730502   0.007986\n",
       " 19730503   0.008007\n",
       " 19730504   0.007986\n",
       " 19730507   0.007945\n",
       " 19730508   0.007934\n",
       " ...             ...\n",
       " 20200624  72.029999\n",
       " 20200625  72.639999\n",
       " 20200626  71.519997\n",
       " 20200629  72.430000\n",
       " 20200630  73.260002\n",
       " \n",
       " [11897 rows x 1 columns],\n",
       " 'CMCSA':           Adj Close\n",
       " Date               \n",
       " 19800317   0.065384\n",
       " 19800318   0.065384\n",
       " 19800319   0.064657\n",
       " 19800320   0.069743\n",
       " 19800321   0.071922\n",
       " ...             ...\n",
       " 20200624  38.332245\n",
       " 20200625  38.342186\n",
       " 20200626  38.421715\n",
       " 20200629  38.709999\n",
       " 20200630  38.980000\n",
       " \n",
       " [10160 rows x 1 columns],\n",
       " 'COF':           Adj Close\n",
       " Date               \n",
       " 19941116   3.693004\n",
       " 19941117   3.750707\n",
       " 19941118   3.808411\n",
       " 19941121   3.721854\n",
       " 19941122   3.548746\n",
       " ...             ...\n",
       " 20200624  65.459999\n",
       " 20200625  67.040001\n",
       " 20200626  61.160000\n",
       " 20200629  62.380001\n",
       " 20200630  62.590000\n",
       " \n",
       " [6450 rows x 1 columns],\n",
       " 'COP':           Adj Close\n",
       " Date               \n",
       " 19811231   0.368889\n",
       " 19820104   0.363197\n",
       " 19820105   0.349534\n",
       " 19820106   0.342703\n",
       " 19820107   0.339287\n",
       " ...             ...\n",
       " 20200624  40.990002\n",
       " 20200625  42.060001\n",
       " 20200626  40.349998\n",
       " 20200629  41.299999\n",
       " 20200630  42.020000\n",
       " \n",
       " [9707 rows x 1 columns],\n",
       " 'COST':            Adj Close\n",
       " Date                \n",
       " 19860709    8.002857\n",
       " 19860710    7.957383\n",
       " 19860711    7.684556\n",
       " 19860714    7.457206\n",
       " 19860715    7.229854\n",
       " ...              ...\n",
       " 20200624  298.019989\n",
       " 20200625  300.529999\n",
       " 20200626  296.559998\n",
       " 20200629  301.589996\n",
       " 20200630  303.209991\n",
       " \n",
       " [8565 rows x 1 columns],\n",
       " 'CRM':            Adj Close\n",
       " Date                \n",
       " 20040623    4.300000\n",
       " 20040624    4.190000\n",
       " 20040625    3.950000\n",
       " 20040628    4.000000\n",
       " 20040629    4.100000\n",
       " ...              ...\n",
       " 20200624  185.550003\n",
       " 20200625  188.339996\n",
       " 20200626  183.169998\n",
       " 20200629  183.330002\n",
       " 20200630  187.330002\n",
       " \n",
       " [4034 rows x 1 columns],\n",
       " 'CSCO':           Adj Close\n",
       " Date               \n",
       " 19900216   0.059244\n",
       " 19900220   0.061241\n",
       " 19900221   0.059910\n",
       " 19900222   0.060575\n",
       " 19900223   0.060243\n",
       " ...             ...\n",
       " 20200624  44.750000\n",
       " 20200625  45.220001\n",
       " 20200626  46.310001\n",
       " 20200629  46.150002\n",
       " 20200630  46.639999\n",
       " \n",
       " [7651 rows x 1 columns],\n",
       " 'CVS':           Adj Close\n",
       " Date               \n",
       " 19730221   0.130583\n",
       " 19730222   0.133094\n",
       " 19730223   0.136861\n",
       " 19730226   0.134350\n",
       " 19730227   0.124305\n",
       " ...             ...\n",
       " 20200624  63.180000\n",
       " 20200625  64.019997\n",
       " 20200626  63.450001\n",
       " 20200629  64.419998\n",
       " 20200630  64.970001\n",
       " \n",
       " [11946 rows x 1 columns],\n",
       " 'CVX':           Adj Close\n",
       " Date               \n",
       " 19700102   0.067323\n",
       " 19700105   0.067645\n",
       " 19700106   0.066678\n",
       " 19700107   0.066517\n",
       " 19700108   0.067806\n",
       " ...             ...\n",
       " 20200624  87.639999\n",
       " 20200625  89.209999\n",
       " 20200626  86.459999\n",
       " 20200629  87.669998\n",
       " 20200630  89.230003\n",
       " \n",
       " [12738 rows x 1 columns],\n",
       " 'DD':           Adj Close\n",
       " Date               \n",
       " 19720601   0.711983\n",
       " 19720602   0.719722\n",
       " 19720605   0.717787\n",
       " 19720606   0.715853\n",
       " 19720607   0.703277\n",
       " ...             ...\n",
       " 20200624  51.540001\n",
       " 20200625  53.090000\n",
       " 20200626  51.419998\n",
       " 20200629  52.470001\n",
       " 20200630  53.130001\n",
       " \n",
       " [12126 rows x 1 columns],\n",
       " 'DHR':            Adj Close\n",
       " Date                \n",
       " 19781229    0.040891\n",
       " 19790102    0.040891\n",
       " 19790103    0.042301\n",
       " 19790104    0.043711\n",
       " 19790105    0.046531\n",
       " ...              ...\n",
       " 20200624  171.279999\n",
       " 20200625  172.149994\n",
       " 20200626  171.710007\n",
       " 20200629  172.380005\n",
       " 20200630  176.830002\n",
       " \n",
       " [10466 rows x 1 columns],\n",
       " 'DIS':            Adj Close\n",
       " Date                \n",
       " 19700102    0.276762\n",
       " 19700105    0.268438\n",
       " 19700106    0.263236\n",
       " 19700107    0.269999\n",
       " 19700108    0.276762\n",
       " ...              ...\n",
       " 20200624  112.070000\n",
       " 20200625  111.360001\n",
       " 20200626  109.099998\n",
       " 20200629  111.519997\n",
       " 20200630  111.510002\n",
       " \n",
       " [12738 rows x 1 columns],\n",
       " 'DUK':           Adj Close\n",
       " Date               \n",
       " 19800317   0.801692\n",
       " 19800318   0.862838\n",
       " 19800319   0.835662\n",
       " 19800320   0.842456\n",
       " 19800321   0.835662\n",
       " ...             ...\n",
       " 20200624  80.470001\n",
       " 20200625  79.449997\n",
       " 20200626  78.930000\n",
       " 20200629  80.250000\n",
       " 20200630  79.889999\n",
       " \n",
       " [10160 rows x 1 columns],\n",
       " 'EMR':           Adj Close\n",
       " Date               \n",
       " 19720601   0.248800\n",
       " 19720602   0.248800\n",
       " 19720605   0.247706\n",
       " 19720606   0.248070\n",
       " 19720607   0.243693\n",
       " ...             ...\n",
       " 20200624  59.630001\n",
       " 20200625  60.349998\n",
       " 20200626  59.869999\n",
       " 20200629  61.380001\n",
       " 20200630  62.029999\n",
       " \n",
       " [12126 rows x 1 columns],\n",
       " 'EXC':           Adj Close\n",
       " Date               \n",
       " 19730502   0.380934\n",
       " 19730503   0.380934\n",
       " 19730504   0.383062\n",
       " 19730507   0.383062\n",
       " 19730508   0.383062\n",
       " ...             ...\n",
       " 20200624  36.470001\n",
       " 20200625  35.419998\n",
       " 20200626  34.709999\n",
       " 20200629  35.990002\n",
       " 20200630  36.290001\n",
       " \n",
       " [11897 rows x 1 columns],\n",
       " 'F':           Adj Close\n",
       " Date               \n",
       " 19720601   0.002859\n",
       " 19720602   0.002853\n",
       " 19720605   0.002853\n",
       " 19720606   0.002821\n",
       " 19720607   0.002805\n",
       " ...             ...\n",
       " 20200624   5.950000\n",
       " 20200625   6.030000\n",
       " 20200626   5.910000\n",
       " 20200629   6.010000\n",
       " 20200630   6.080000\n",
       " \n",
       " [12126 rows x 1 columns],\n",
       " 'FB':            Adj Close\n",
       " Date                \n",
       " 20120518   38.230000\n",
       " 20120521   34.029999\n",
       " 20120522   31.000000\n",
       " 20120523   32.000000\n",
       " 20120524   33.029999\n",
       " ...              ...\n",
       " 20200624  234.020004\n",
       " 20200625  235.679993\n",
       " 20200626  216.080002\n",
       " 20200629  220.639999\n",
       " 20200630  227.070007\n",
       " \n",
       " [2042 rows x 1 columns],\n",
       " 'FDX':            Adj Close\n",
       " Date                \n",
       " 19780412    0.840976\n",
       " 19780413    0.875585\n",
       " 19780414    0.930957\n",
       " 19780417    0.903271\n",
       " 19780418    0.896349\n",
       " ...              ...\n",
       " 20200624  134.444870\n",
       " 20200625  135.460007\n",
       " 20200626  130.080002\n",
       " 20200629  134.550003\n",
       " 20200630  140.220001\n",
       " \n",
       " [10648 rows x 1 columns],\n",
       " 'GD':            Adj Close\n",
       " Date                \n",
       " 19770103    0.043551\n",
       " 19770104    0.042952\n",
       " 19770105    0.042353\n",
       " 19770106    0.042453\n",
       " 19770107    0.042053\n",
       " ...              ...\n",
       " 20200624  146.449997\n",
       " 20200625  146.960007\n",
       " 20200626  146.050003\n",
       " 20200629  149.339996\n",
       " 20200630  149.460007\n",
       " \n",
       " [10969 rows x 1 columns],\n",
       " 'GE':           Adj Close\n",
       " Date               \n",
       " 19700102   0.002236\n",
       " 19700105   0.002225\n",
       " 19700106   0.002160\n",
       " 19700107   0.002171\n",
       " 19700108   0.002189\n",
       " ...             ...\n",
       " 20200624   6.520225\n",
       " 20200625   6.670000\n",
       " 20200626   6.480000\n",
       " 20200629   6.800000\n",
       " 20200630   6.830000\n",
       " \n",
       " [12738 rows x 1 columns],\n",
       " 'GILD':           Adj Close\n",
       " Date               \n",
       " 19920122   0.544369\n",
       " 19920123   0.591413\n",
       " 19920124   0.598134\n",
       " 19920127   0.645178\n",
       " 19920128   0.631737\n",
       " ...             ...\n",
       " 20200624  75.930000\n",
       " 20200625  75.489998\n",
       " 20200626  74.570000\n",
       " 20200629  74.559998\n",
       " 20200630  76.940002\n",
       " \n",
       " [7164 rows x 1 columns],\n",
       " 'GM':           Adj Close\n",
       " Date               \n",
       " 20101118  26.155519\n",
       " 20101119  26.209070\n",
       " 20101122  26.071363\n",
       " 20101123  25.436413\n",
       " 20101124  25.612371\n",
       " ...             ...\n",
       " 20200624  25.280001\n",
       " 20200625  25.230000\n",
       " 20200626  24.459999\n",
       " 20200629  25.299999\n",
       " 20200630  25.299999\n",
       " \n",
       " [2419 rows x 1 columns],\n",
       " 'GOOG':             Adj Close\n",
       " Date                 \n",
       " 20040819    49.982655\n",
       " 20040820    53.952770\n",
       " 20040823    54.495735\n",
       " 20040824    52.239193\n",
       " 20040825    52.802086\n",
       " ...               ...\n",
       " 20200624  1431.969971\n",
       " 20200625  1441.329956\n",
       " 20200626  1359.900024\n",
       " 20200629  1394.969971\n",
       " 20200630  1413.609985\n",
       " \n",
       " [3994 rows x 1 columns],\n",
       " 'GS':            Adj Close\n",
       " Date                \n",
       " 19990504   55.471413\n",
       " 19990505   54.486107\n",
       " 19990506   53.550110\n",
       " 19990507   58.427258\n",
       " 19990510   55.717724\n",
       " ...              ...\n",
       " 20200624  198.020004\n",
       " 20200625  207.100006\n",
       " 20200626  189.190002\n",
       " 20200629  193.470001\n",
       " 20200630  197.619995\n",
       " \n",
       " [5325 rows x 1 columns],\n",
       " 'HD':            Adj Close\n",
       " Date                \n",
       " 19810922    0.016539\n",
       " 19810923    0.016539\n",
       " 19810924    0.016539\n",
       " 19810925    0.016539\n",
       " 19810928    0.016539\n",
       " ...              ...\n",
       " 20200624  246.130005\n",
       " 20200625  245.380005\n",
       " 20200626  241.009995\n",
       " 20200629  246.119995\n",
       " 20200630  250.509995\n",
       " \n",
       " [9777 rows x 1 columns],\n",
       " 'HON':            Adj Close\n",
       " Date                \n",
       " 19700102    0.240474\n",
       " 19700105    0.248849\n",
       " 19700106    0.248849\n",
       " 19700107    0.247652\n",
       " 19700108    0.253634\n",
       " ...              ...\n",
       " 20200624  138.350006\n",
       " 20200625  142.460007\n",
       " 20200626  138.210007\n",
       " 20200629  143.240005\n",
       " 20200630  144.589996\n",
       " \n",
       " [12738 rows x 1 columns],\n",
       " 'IBM':            Adj Close\n",
       " Date                \n",
       " 19700102    1.495215\n",
       " 19700105    1.509563\n",
       " 19700106    1.510588\n",
       " 19700107    1.511614\n",
       " 19700108    1.514687\n",
       " ...              ...\n",
       " 20200624  116.419998\n",
       " 20200625  119.029999\n",
       " 20200626  117.190002\n",
       " 20200629  119.750000\n",
       " 20200630  120.769997\n",
       " \n",
       " [12738 rows x 1 columns],\n",
       " 'INTC':           Adj Close\n",
       " Date               \n",
       " 19800317   0.203600\n",
       " 19800318   0.201971\n",
       " 19800319   0.206857\n",
       " 19800320   0.206043\n",
       " 19800321   0.198713\n",
       " ...             ...\n",
       " 20200624  59.090000\n",
       " 20200625  58.509998\n",
       " 20200626  57.500000\n",
       " 20200629  58.270000\n",
       " 20200630  59.830002\n",
       " \n",
       " [10160 rows x 1 columns],\n",
       " 'JNJ':            Adj Close\n",
       " Date                \n",
       " 19700102    0.000004\n",
       " 19700105    0.000004\n",
       " 19700106    0.000004\n",
       " 19700107    0.000004\n",
       " 19700108    0.000004\n",
       " ...              ...\n",
       " 20200624  139.820007\n",
       " 20200625  139.669998\n",
       " 20200626  137.809998\n",
       " 20200629  139.039993\n",
       " 20200630  140.630005\n",
       " \n",
       " [12738 rows x 1 columns],\n",
       " 'JPM':           Adj Close\n",
       " Date               \n",
       " 19800317   0.290579\n",
       " 19800318   0.292716\n",
       " 19800319   0.296989\n",
       " 19800320   0.294852\n",
       " 19800321   0.301262\n",
       " ...             ...\n",
       " 20200624  94.660004\n",
       " 20200625  97.959999\n",
       " 20200626  92.589996\n",
       " 20200629  93.000000\n",
       " 20200630  94.059998\n",
       " \n",
       " [10160 rows x 1 columns],\n",
       " 'KHC':           Adj Close\n",
       " Date               \n",
       " 20150706  59.350010\n",
       " 20150707  60.708500\n",
       " 20150708  61.334843\n",
       " 20150709  60.846783\n",
       " 20150710  62.888550\n",
       " ...             ...\n",
       " 20200624  32.130001\n",
       " 20200625  31.879999\n",
       " 20200626  30.900000\n",
       " 20200629  31.639999\n",
       " 20200630  31.889999\n",
       " \n",
       " [1257 rows x 1 columns],\n",
       " 'KMI':           Adj Close\n",
       " Date               \n",
       " 20110211  20.853304\n",
       " 20110214  20.618244\n",
       " 20110215  20.779428\n",
       " 20110216  20.631676\n",
       " 20110217  20.517498\n",
       " ...             ...\n",
       " 20200624  14.810000\n",
       " 20200625  14.960000\n",
       " 20200626  14.470000\n",
       " 20200629  14.660000\n",
       " 20200630  15.170000\n",
       " \n",
       " [2361 rows x 1 columns],\n",
       " 'KO':           Adj Close\n",
       " Date               \n",
       " 19700102   0.016325\n",
       " 19700105   0.016077\n",
       " 19700106   0.016226\n",
       " 19700107   0.016325\n",
       " 19700108   0.016350\n",
       " ...             ...\n",
       " 20200624  44.650002\n",
       " 20200625  44.930000\n",
       " 20200626  43.570000\n",
       " 20200629  44.360001\n",
       " 20200630  44.680000\n",
       " \n",
       " [12738 rows x 1 columns],\n",
       " 'LLY':            Adj Close\n",
       " Date                \n",
       " 19720601    0.248969\n",
       " 19720602    0.251853\n",
       " 19720605    0.248488\n",
       " 19720606    0.246085\n",
       " 19720607    0.248969\n",
       " ...              ...\n",
       " 20200624  157.029999\n",
       " 20200625  162.860001\n",
       " 20200626  162.830002\n",
       " 20200629  162.720001\n",
       " 20200630  164.179993\n",
       " \n",
       " [12126 rows x 1 columns],\n",
       " 'LMT':            Adj Close\n",
       " Date                \n",
       " 19770103    0.330531\n",
       " 19770104    0.330531\n",
       " 19770105    0.339345\n",
       " 19770106    0.356973\n",
       " 19770107    0.361380\n",
       " ...              ...\n",
       " 20200624  359.820007\n",
       " 20200625  362.820007\n",
       " 20200626  356.760010\n",
       " 20200629  367.500000\n",
       " 20200630  364.920013\n",
       " \n",
       " [10969 rows x 1 columns],\n",
       " 'LOW':            Adj Close\n",
       " Date                \n",
       " 19800317    0.003737\n",
       " 19800318    0.003643\n",
       " 19800319    0.003643\n",
       " 19800320    0.003643\n",
       " 19800321    0.003580\n",
       " ...              ...\n",
       " 20200624  131.979996\n",
       " 20200625  131.770004\n",
       " 20200626  129.410004\n",
       " 20200629  133.520004\n",
       " 20200630  135.119995\n",
       " \n",
       " [10160 rows x 1 columns],\n",
       " 'MA':            Adj Close\n",
       " Date                \n",
       " 20060525    3.448337\n",
       " 20060526    3.368126\n",
       " 20060530    3.298409\n",
       " 20060531    3.368875\n",
       " 20060601    3.561532\n",
       " ...              ...\n",
       " 20200624  294.010010\n",
       " 20200625  298.010010\n",
       " 20200626  289.339996\n",
       " 20200629  292.929993\n",
       " 20200630  295.700012\n",
       " \n",
       " [3549 rows x 1 columns],\n",
       " 'MCD':            Adj Close\n",
       " Date                \n",
       " 19700102    0.003997\n",
       " 19700105    0.004064\n",
       " 19700106    0.004087\n",
       " 19700107    0.004064\n",
       " 19700108    0.004064\n",
       " ...              ...\n",
       " 20200624  184.289993\n",
       " 20200625  182.759995\n",
       " 20200626  179.740005\n",
       " 20200629  182.800003\n",
       " 20200630  184.470001\n",
       " \n",
       " [12738 rows x 1 columns],\n",
       " 'MDLZ':           Adj Close\n",
       " Date               \n",
       " 20010613  12.843981\n",
       " 20010614  12.391870\n",
       " 20010615  12.576829\n",
       " 20010618  12.461741\n",
       " 20010619  12.494623\n",
       " ...             ...\n",
       " 20200624  50.351814\n",
       " 20200625  50.540733\n",
       " 20200626  49.795002\n",
       " 20200629  50.580002\n",
       " 20200630  51.130001\n",
       " \n",
       " [4792 rows x 1 columns],\n",
       " 'MDT':           Adj Close\n",
       " Date               \n",
       " 19730502   0.002374\n",
       " 19730503   0.002374\n",
       " 19730504   0.002546\n",
       " 19730507   0.002506\n",
       " 19730508   0.002533\n",
       " ...             ...\n",
       " 20200624  88.349998\n",
       " 20200625  88.860001\n",
       " 20200626  88.769997\n",
       " 20200629  90.260002\n",
       " 20200630  91.699997\n",
       " \n",
       " [11897 rows x 1 columns],\n",
       " 'MET':           Adj Close\n",
       " Date               \n",
       " 20000405   8.754044\n",
       " 20000406   8.789344\n",
       " 20000407   8.648149\n",
       " 20000410   8.718744\n",
       " 20000411   8.542254\n",
       " ...             ...\n",
       " 20200624  35.529999\n",
       " 20200625  36.340000\n",
       " 20200626  35.070000\n",
       " 20200629  35.570000\n",
       " 20200630  36.520000\n",
       " \n",
       " [5091 rows x 1 columns],\n",
       " 'MMM':            Adj Close\n",
       " Date                \n",
       " 19700102    0.413633\n",
       " 19700105    0.415991\n",
       " 19700106    0.420236\n",
       " 19700107    0.422595\n",
       " 19700108    0.428254\n",
       " ...              ...\n",
       " 20200624  152.220001\n",
       " 20200625  154.669998\n",
       " 20200626  152.520004\n",
       " 20200629  155.570007\n",
       " 20200630  155.990005\n",
       " \n",
       " [12738 rows x 1 columns],\n",
       " 'MO':           Adj Close\n",
       " Date               \n",
       " 19700102   0.000622\n",
       " 19700105   0.000635\n",
       " 19700106   0.000648\n",
       " 19700107   0.000652\n",
       " 19700108   0.000631\n",
       " ...             ...\n",
       " 20200624  39.430000\n",
       " 20200625  39.660000\n",
       " 20200626  38.189999\n",
       " 20200629  39.230000\n",
       " 20200630  39.250000\n",
       " \n",
       " [12738 rows x 1 columns],\n",
       " 'MRK':           Adj Close\n",
       " Date               \n",
       " 19700102   0.253344\n",
       " 19700105   0.251921\n",
       " 19700106   0.253913\n",
       " 19700107   0.251636\n",
       " 19700108   0.253344\n",
       " ...             ...\n",
       " 20200624  75.870003\n",
       " 20200625  76.570000\n",
       " 20200626  75.190002\n",
       " 20200629  76.120003\n",
       " 20200630  77.330002\n",
       " \n",
       " [12738 rows x 1 columns],\n",
       " 'MS':           Adj Close\n",
       " Date               \n",
       " 19930223   3.290337\n",
       " 19930224   3.383209\n",
       " 19930225   3.462815\n",
       " 19930226   3.515884\n",
       " 19930301   3.396477\n",
       " ...             ...\n",
       " 20200624  46.950001\n",
       " 20200625  48.790001\n",
       " 20200626  47.049999\n",
       " 20200629  47.240002\n",
       " 20200630  48.299999\n",
       " \n",
       " [6889 rows x 1 columns],\n",
       " 'MSFT':            Adj Close\n",
       " Date                \n",
       " 19860313    0.062205\n",
       " 19860314    0.064427\n",
       " 19860317    0.065537\n",
       " 19860318    0.063871\n",
       " 19860319    0.062760\n",
       " ...              ...\n",
       " 20200624  197.839996\n",
       " 20200625  200.339996\n",
       " 20200626  196.330002\n",
       " 20200629  198.440002\n",
       " 20200630  203.509995\n",
       " \n",
       " [8646 rows x 1 columns],\n",
       " 'NEE':            Adj Close\n",
       " Date                \n",
       " 19730221    0.564986\n",
       " 19730222    0.572779\n",
       " 19730223    0.580572\n",
       " 19730226    0.578624\n",
       " 19730227    0.584469\n",
       " ...              ...\n",
       " 20200624  241.669998\n",
       " 20200625  239.160004\n",
       " 20200626  236.220001\n",
       " 20200629  238.240005\n",
       " 20200630  240.169998\n",
       " \n",
       " [11946 rows x 1 columns],\n",
       " 'NFLX':            Adj Close\n",
       " Date                \n",
       " 20020523    1.196429\n",
       " 20020524    1.210000\n",
       " 20020528    1.157143\n",
       " 20020529    1.103571\n",
       " 20020530    1.071429\n",
       " ...              ...\n",
       " 20200624  457.850006\n",
       " 20200625  465.910004\n",
       " 20200626  443.399994\n",
       " 20200629  447.239990\n",
       " 20200630  455.040009\n",
       " \n",
       " [4558 rows x 1 columns],\n",
       " 'NKE':            Adj Close\n",
       " Date                \n",
       " 19801202    0.000004\n",
       " 19801203    0.000004\n",
       " 19801204    0.000004\n",
       " 19801205    0.000004\n",
       " 19801208    0.000003\n",
       " ...              ...\n",
       " 20200624  100.080002\n",
       " 20200625  101.400002\n",
       " 20200626   93.669998\n",
       " 20200629   95.870003\n",
       " 20200630   98.050003\n",
       " \n",
       " [9980 rows x 1 columns],\n",
       " 'NVDA':            Adj Close\n",
       " Date                \n",
       " 19990122    1.509309\n",
       " 19990125    1.667427\n",
       " 19990126    1.538057\n",
       " 19990127    1.533266\n",
       " 19990128    1.528475\n",
       " ...              ...\n",
       " 20200624  369.420013\n",
       " 20200625  379.600006\n",
       " 20200626  366.200012\n",
       " 20200629  368.000000\n",
       " 20200630  379.910004\n",
       " \n",
       " [5395 rows x 1 columns],\n",
       " 'ORCL':           Adj Close\n",
       " Date               \n",
       " 19860312   0.054830\n",
       " 19860313   0.056167\n",
       " 19860314   0.058173\n",
       " 19860317   0.056836\n",
       " 19860318   0.055499\n",
       " ...             ...\n",
       " 20200624  54.439999\n",
       " 20200625  54.529999\n",
       " 20200626  54.180000\n",
       " 20200629  54.759998\n",
       " 20200630  55.270000\n",
       " \n",
       " [8647 rows x 1 columns],\n",
       " 'OXY':           Adj Close\n",
       " Date               \n",
       " 19811231   0.481122\n",
       " 19820104   0.481122\n",
       " 19820105   0.473605\n",
       " 19820106   0.476110\n",
       " 19820107   0.476110\n",
       " ...             ...\n",
       " 20200624  18.000000\n",
       " 20200625  18.540001\n",
       " 20200626  17.690001\n",
       " 20200629  17.860001\n",
       " 20200630  18.299999\n",
       " \n",
       " [9707 rows x 1 columns],\n",
       " 'PEP':            Adj Close\n",
       " Date                \n",
       " 19720601    0.443978\n",
       " 19720602    0.442695\n",
       " 19720605    0.437547\n",
       " 19720606    0.433687\n",
       " 19720607    0.431113\n",
       " ...              ...\n",
       " 20200624  129.559998\n",
       " 20200625  131.410004\n",
       " 20200626  128.929993\n",
       " 20200629  131.080002\n",
       " 20200630  132.259995\n",
       " \n",
       " [12126 rows x 1 columns],\n",
       " 'PFE':           Adj Close\n",
       " Date               \n",
       " 19720601   0.000589\n",
       " 19720602   0.000581\n",
       " 19720605   0.000580\n",
       " 19720606   0.000592\n",
       " 19720607   0.000592\n",
       " ...             ...\n",
       " 20200624  32.189999\n",
       " 20200625  32.320000\n",
       " 20200626  32.040001\n",
       " 20200629  32.639999\n",
       " 20200630  32.700001\n",
       " \n",
       " [12126 rows x 1 columns],\n",
       " 'PG':            Adj Close\n",
       " Date                \n",
       " 19700102    0.019657\n",
       " 19700105    0.019747\n",
       " 19700106    0.019657\n",
       " 19700107    0.019970\n",
       " 19700108    0.020193\n",
       " ...              ...\n",
       " 20200624  116.419998\n",
       " 20200625  117.889999\n",
       " 20200626  115.230003\n",
       " 20200629  117.660004\n",
       " 20200630  119.570000\n",
       " \n",
       " [12738 rows x 1 columns],\n",
       " 'PM':           Adj Close\n",
       " Date               \n",
       " 20080317  27.687485\n",
       " 20080318  27.805216\n",
       " 20080319  28.085512\n",
       " 20080320  27.552954\n",
       " 20080324  28.590044\n",
       " ...             ...\n",
       " 20200624  70.089996\n",
       " 20200625  70.669998\n",
       " 20200626  68.870003\n",
       " 20200629  70.510002\n",
       " 20200630  70.059998\n",
       " \n",
       " [3095 rows x 1 columns],\n",
       " 'PYPL':            Adj Close\n",
       " Date                \n",
       " 20150706   36.709999\n",
       " 20150707   36.619999\n",
       " 20150708   34.700001\n",
       " 20150709   34.500000\n",
       " 20150710   34.689999\n",
       " ...              ...\n",
       " 20200624  168.000000\n",
       " 20200625  172.500000\n",
       " 20200626  170.869995\n",
       " 20200629  168.350006\n",
       " 20200630  174.229996\n",
       " \n",
       " [1257 rows x 1 columns],\n",
       " 'QCOM':           Adj Close\n",
       " Date               \n",
       " 19911213   0.383237\n",
       " 19911216   0.377915\n",
       " 19911217   0.377915\n",
       " 19911218   0.388560\n",
       " 19911219   0.372592\n",
       " ...             ...\n",
       " 20200624  89.139999\n",
       " 20200625  88.959999\n",
       " 20200626  88.019997\n",
       " 20200629  89.010002\n",
       " 20200630  91.209999\n",
       " \n",
       " [7190 rows x 1 columns],\n",
       " 'RTX':           Adj Close\n",
       " Date               \n",
       " 19700102   0.163260\n",
       " 19700105   0.162753\n",
       " 19700106   0.162246\n",
       " 19700107   0.160218\n",
       " 19700108   0.159204\n",
       " ...             ...\n",
       " 20200624  61.720001\n",
       " 20200625  61.060001\n",
       " 20200626  59.099998\n",
       " 20200629  61.919998\n",
       " 20200630  61.619999\n",
       " \n",
       " [12738 rows x 1 columns],\n",
       " 'SBUX':           Adj Close\n",
       " Date               \n",
       " 19920626   0.283643\n",
       " 19920629   0.303432\n",
       " 19920630   0.293537\n",
       " 19920701   0.300134\n",
       " 19920702   0.300134\n",
       " ...             ...\n",
       " 20200624  73.639999\n",
       " 20200625  73.849998\n",
       " 20200626  71.570000\n",
       " 20200629  73.480003\n",
       " 20200630  73.589996\n",
       " \n",
       " [7055 rows x 1 columns],\n",
       " 'SLB':           Adj Close\n",
       " Date               \n",
       " 19811231   1.809793\n",
       " 19820104   1.773355\n",
       " 19820105   1.684282\n",
       " 19820106   1.668086\n",
       " 19820107   1.635697\n",
       " ...             ...\n",
       " 20200624  17.860001\n",
       " 20200625  18.420000\n",
       " 20200626  17.440001\n",
       " 20200629  18.010000\n",
       " 20200630  18.389999\n",
       " \n",
       " [9707 rows x 1 columns],\n",
       " 'SO':           Adj Close\n",
       " Date               \n",
       " 19811231   0.048629\n",
       " 19820104   0.049642\n",
       " 19820105   0.049136\n",
       " 19820106   0.049136\n",
       " 19820107   0.048629\n",
       " ...             ...\n",
       " 20200624  52.380001\n",
       " 20200625  51.250000\n",
       " 20200626  50.919998\n",
       " 20200629  51.560001\n",
       " 20200630  51.849998\n",
       " \n",
       " [9707 rows x 1 columns],\n",
       " 'SPG':           Adj Close\n",
       " Date               \n",
       " 19931214   4.881142\n",
       " 19931215   4.881142\n",
       " 19931216   4.881142\n",
       " 19931217   4.854022\n",
       " 19931220   4.881142\n",
       " ...             ...\n",
       " 20200624  65.110001\n",
       " 20200625  64.940002\n",
       " 20200626  61.900002\n",
       " 20200629  68.129997\n",
       " 20200630  68.379997\n",
       " \n",
       " [6684 rows x 1 columns],\n",
       " 'T':           Adj Close\n",
       " Date               \n",
       " 19831121   0.212488\n",
       " 19831122   0.210764\n",
       " 19831123   0.209902\n",
       " 19831125   0.210333\n",
       " 19831128   0.208178\n",
       " ...             ...\n",
       " 20200624  29.420000\n",
       " 20200625  29.719999\n",
       " 20200626  29.080000\n",
       " 20200629  29.910000\n",
       " 20200630  30.230000\n",
       " \n",
       " [9228 rows x 1 columns],\n",
       " 'TGT':            Adj Close\n",
       " Date                \n",
       " 19730221    0.013649\n",
       " 19730222    0.013066\n",
       " 19730223    0.012900\n",
       " 19730226    0.012650\n",
       " 19730227    0.012484\n",
       " ...              ...\n",
       " 20200624  119.720001\n",
       " 20200625  119.809998\n",
       " 20200626  117.019997\n",
       " 20200629  118.570000\n",
       " 20200630  119.930000\n",
       " \n",
       " [11946 rows x 1 columns],\n",
       " 'TMO':            Adj Close\n",
       " Date                \n",
       " 19800317    1.699968\n",
       " 19800318    1.663799\n",
       " 19800319    1.736138\n",
       " 19800320    1.699968\n",
       " 19800321    1.699968\n",
       " ...              ...\n",
       " 20200624  347.959991\n",
       " 20200625  351.760010\n",
       " 20200626  350.950012\n",
       " 20200629  350.799988\n",
       " 20200630  362.339996\n",
       " \n",
       " [10160 rows x 1 columns],\n",
       " 'TXN':            Adj Close\n",
       " Date                \n",
       " 19720601    0.762761\n",
       " 19720602    0.776767\n",
       " 19720605    0.777934\n",
       " 19720606    0.745253\n",
       " 19720607    0.742335\n",
       " ...              ...\n",
       " 20200624  123.989998\n",
       " 20200625  125.059998\n",
       " 20200626  123.370003\n",
       " 20200629  124.769997\n",
       " 20200630  126.970001\n",
       " \n",
       " [12126 rows x 1 columns],\n",
       " 'UNH':            Adj Close\n",
       " Date                \n",
       " 19841017    0.105308\n",
       " 19841018    0.108154\n",
       " 19841019    0.105308\n",
       " 19841022    0.105308\n",
       " 19841023    0.102462\n",
       " ...              ...\n",
       " 20200624  289.179993\n",
       " 20200625  296.220001\n",
       " 20200626  286.880005\n",
       " 20200629  289.760010\n",
       " 20200630  294.950012\n",
       " \n",
       " [8999 rows x 1 columns],\n",
       " 'UNP':            Adj Close\n",
       " Date                \n",
       " 19800102    0.016816\n",
       " 19800103    0.016581\n",
       " 19800104    0.016874\n",
       " 19800107    0.016786\n",
       " 19800108    0.016171\n",
       " ...              ...\n",
       " 20200624  165.550003\n",
       " 20200625  167.020004\n",
       " 20200626  162.639999\n",
       " 20200629  165.509995\n",
       " 20200630  169.070007\n",
       " \n",
       " [10212 rows x 1 columns],\n",
       " 'UPS':            Adj Close\n",
       " Date                \n",
       " 19991110   40.782734\n",
       " 19991111   44.816196\n",
       " 19991112   42.127232\n",
       " 19991115   41.230892\n",
       " 19991116   39.438244\n",
       " ...              ...\n",
       " 20200624  109.500000\n",
       " 20200625  110.519997\n",
       " 20200626  107.320000\n",
       " 20200629  109.480003\n",
       " 20200630  111.180000\n",
       " \n",
       " [5192 rows x 1 columns],\n",
       " 'USB':           Adj Close\n",
       " Date               \n",
       " 19730503   0.000077\n",
       " 19730504   0.000078\n",
       " 19730507   0.000077\n",
       " 19730508   0.000078\n",
       " 19730509   0.000078\n",
       " ...             ...\n",
       " 20200624  36.440567\n",
       " 20200625  37.606823\n",
       " 20200626  35.640003\n",
       " 20200629  36.349998\n",
       " 20200630  36.820000\n",
       " \n",
       " [11896 rows x 1 columns],\n",
       " 'V':            Adj Close\n",
       " Date                \n",
       " 20080319   11.146158\n",
       " 20080320   12.694785\n",
       " 20080324   11.783365\n",
       " 20080325   12.477777\n",
       " 20080326   12.617845\n",
       " ...              ...\n",
       " 20200624  191.410004\n",
       " 20200625  193.979996\n",
       " 20200626  189.270004\n",
       " 20200629  191.380005\n",
       " 20200630  193.169998\n",
       " \n",
       " [3093 rows x 1 columns],\n",
       " 'VZ':           Adj Close\n",
       " Date               \n",
       " 19831121   0.298241\n",
       " 19831122   0.297180\n",
       " 19831123   0.295588\n",
       " 19831125   0.292404\n",
       " 19831128   0.284444\n",
       " ...             ...\n",
       " 20200624  53.930000\n",
       " 20200625  54.279999\n",
       " 20200626  53.160000\n",
       " 20200629  54.689999\n",
       " 20200630  55.130001\n",
       " \n",
       " [9228 rows x 1 columns],\n",
       " 'WBA':           Adj Close\n",
       " Date               \n",
       " 19800317   0.099875\n",
       " 19800318   0.099409\n",
       " 19800319   0.102209\n",
       " 19800320   0.101742\n",
       " 19800321   0.102209\n",
       " ...             ...\n",
       " 20200624  41.169998\n",
       " 20200625  42.040001\n",
       " 20200626  41.169998\n",
       " 20200629  42.320000\n",
       " 20200630  42.389999\n",
       " \n",
       " [10160 rows x 1 columns],\n",
       " 'WFC':           Adj Close\n",
       " Date               \n",
       " 19720601   0.002097\n",
       " 19720602   0.002103\n",
       " 19720605   0.002134\n",
       " 19720606   0.002134\n",
       " 19720607   0.002115\n",
       " ...             ...\n",
       " 20200624  26.120001\n",
       " 20200625  27.370001\n",
       " 20200626  25.340000\n",
       " 20200629  25.700001\n",
       " 20200630  25.600000\n",
       " \n",
       " [12126 rows x 1 columns],\n",
       " 'WMT':            Adj Close\n",
       " Date                \n",
       " 19720825    0.023218\n",
       " 19720828    0.023130\n",
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       " \n",
       " [12066 rows x 1 columns],\n",
       " 'XOM':           Adj Close\n",
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       " ...             ...\n",
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       " \n",
       " [12738 rows x 1 columns]}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock_frames = {}\n",
    "for ticker in sp100:\n",
    "    stock_frames[ticker] = pd.read_csv(f'../Data Science in Finance/Data/SP100/{ticker}.csv', index_col='Date', usecols = ['Date', 'Adj Close'])\n",
    "stock_frames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "\n",
       "           Adj Close   Adj Close  Adj Close   Adj Close  Adj Close  Adj Close  \\\n",
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       "20200630  169.070007  111.180000  36.820000  193.169998  55.130001  42.389999   \n",
       "\n",
       "          Adj Close   Adj Close  Adj Close  \n",
       "Date                                        \n",
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       "...             ...         ...        ...  \n",
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       "20200626  25.340000  118.320000  43.619999  \n",
       "20200629  25.700001  119.059998  44.320000  \n",
       "20200630  25.600000  119.779999  44.720001  \n",
       "\n",
       "[12738 rows x 99 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock_prices = pd.concat(stock_frames.values(), axis = 1)\n",
    "stock_prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
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   "source": [
    "<div class = \"panel panel-primary\">\n",
    "    <div class = \"panel-heading\"> \n",
    "    <span style=\"color:white\"><b>Task 2</b>\n",
    "    </div>\n",
    "    <div class = \"panel-body\"> \n",
    "    An issue with our data is that component stocks of the S&P 100 do not all have the same number of historical observations.\n",
    "    <br><br>To help remedy this, limit the historical price observations to the period ranging from 7/6/2015 to 6/30/2020, which spans 1257 trading days for each stock. Reorganize the data by creating an 2-d array or dataframe called <code>stock_prices</code> to store each stock's historical prices, where axis-0 of the array (rows) is each day of the historical period and axis-1 of the array (columns) are the S&P 100 stocks. \n",
    "    <br><br>There are many ways to achieve this, but the easiest way would be to use the <code>pd.concat</code> function to concatenate all the dataframes in <code>stock_frames.values()</code> with the paramters <code>axis = 1</code> and <code>join = 'inner'</code>.\n",
    "    <br><br>Confirm that at the end of your code, the shape of <code>stock_prices</code> should be <code>(1257, 99)</code>. And the first few rows should look like this:\n",
    "    ![Task_2_Output.JPG](attachment:Task_2_Output.JPG)\n",
    "    <p style=\"text-align:right;\">[10pts]</p>\n",
    "    </div>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "      <th>V</th>\n",
       "      <th>VZ</th>\n",
       "      <th>WBA</th>\n",
       "      <th>WFC</th>\n",
       "      <th>WMT</th>\n",
       "      <th>XOM</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",
       "      <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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20150706</th>\n",
       "      <td>116.122704</td>\n",
       "      <td>54.396431</td>\n",
       "      <td>44.941250</td>\n",
       "      <td>89.012009</td>\n",
       "      <td>80.500000</td>\n",
       "      <td>54.500534</td>\n",
       "      <td>58.867424</td>\n",
       "      <td>134.846283</td>\n",
       "      <td>86.097084</td>\n",
       "      <td>436.040009</td>\n",
       "      <td>...</td>\n",
       "      <td>112.806862</td>\n",
       "      <td>85.749390</td>\n",
       "      <td>82.283913</td>\n",
       "      <td>37.943161</td>\n",
       "      <td>65.693474</td>\n",
       "      <td>37.562855</td>\n",
       "      <td>75.650414</td>\n",
       "      <td>47.528847</td>\n",
       "      <td>63.890312</td>\n",
       "      <td>66.836525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20150707</th>\n",
       "      <td>115.837006</td>\n",
       "      <td>54.667538</td>\n",
       "      <td>45.365906</td>\n",
       "      <td>90.237366</td>\n",
       "      <td>80.589996</td>\n",
       "      <td>54.817768</td>\n",
       "      <td>59.712139</td>\n",
       "      <td>135.623062</td>\n",
       "      <td>86.686302</td>\n",
       "      <td>436.720001</td>\n",
       "      <td>...</td>\n",
       "      <td>111.458641</td>\n",
       "      <td>87.085892</td>\n",
       "      <td>82.998978</td>\n",
       "      <td>37.830032</td>\n",
       "      <td>65.355919</td>\n",
       "      <td>37.730549</td>\n",
       "      <td>77.298264</td>\n",
       "      <td>47.368855</td>\n",
       "      <td>65.000229</td>\n",
       "      <td>67.111877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20150708</th>\n",
       "      <td>112.961594</td>\n",
       "      <td>53.949913</td>\n",
       "      <td>44.498520</td>\n",
       "      <td>89.268074</td>\n",
       "      <td>79.989998</td>\n",
       "      <td>53.892525</td>\n",
       "      <td>58.967331</td>\n",
       "      <td>132.934891</td>\n",
       "      <td>85.643852</td>\n",
       "      <td>429.700012</td>\n",
       "      <td>...</td>\n",
       "      <td>109.685631</td>\n",
       "      <td>85.401917</td>\n",
       "      <td>81.287910</td>\n",
       "      <td>37.133812</td>\n",
       "      <td>64.352989</td>\n",
       "      <td>37.633602</td>\n",
       "      <td>76.111099</td>\n",
       "      <td>46.526745</td>\n",
       "      <td>64.357178</td>\n",
       "      <td>66.383011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20150709</th>\n",
       "      <td>110.657570</td>\n",
       "      <td>54.197105</td>\n",
       "      <td>44.444317</td>\n",
       "      <td>89.386940</td>\n",
       "      <td>80.470001</td>\n",
       "      <td>54.421230</td>\n",
       "      <td>59.412411</td>\n",
       "      <td>132.062073</td>\n",
       "      <td>85.562263</td>\n",
       "      <td>434.390015</td>\n",
       "      <td>...</td>\n",
       "      <td>110.202759</td>\n",
       "      <td>85.758308</td>\n",
       "      <td>81.509254</td>\n",
       "      <td>37.386200</td>\n",
       "      <td>64.661598</td>\n",
       "      <td>37.375053</td>\n",
       "      <td>79.335915</td>\n",
       "      <td>46.737270</td>\n",
       "      <td>64.110527</td>\n",
       "      <td>66.083359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20150710</th>\n",
       "      <td>113.615921</td>\n",
       "      <td>55.201782</td>\n",
       "      <td>45.112915</td>\n",
       "      <td>90.328812</td>\n",
       "      <td>80.589996</td>\n",
       "      <td>55.179031</td>\n",
       "      <td>60.120884</td>\n",
       "      <td>134.497192</td>\n",
       "      <td>86.495934</td>\n",
       "      <td>443.510010</td>\n",
       "      <td>...</td>\n",
       "      <td>112.797638</td>\n",
       "      <td>87.157173</td>\n",
       "      <td>82.539276</td>\n",
       "      <td>37.803921</td>\n",
       "      <td>65.982765</td>\n",
       "      <td>37.972931</td>\n",
       "      <td>82.489861</td>\n",
       "      <td>47.225693</td>\n",
       "      <td>64.410027</td>\n",
       "      <td>66.585472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20200624</th>\n",
       "      <td>360.059998</td>\n",
       "      <td>95.139999</td>\n",
       "      <td>87.300003</td>\n",
       "      <td>201.839996</td>\n",
       "      <td>431.679993</td>\n",
       "      <td>30.299999</td>\n",
       "      <td>92.949997</td>\n",
       "      <td>232.509995</td>\n",
       "      <td>254.179993</td>\n",
       "      <td>2734.399902</td>\n",
       "      <td>...</td>\n",
       "      <td>289.179993</td>\n",
       "      <td>165.550003</td>\n",
       "      <td>109.500000</td>\n",
       "      <td>36.440567</td>\n",
       "      <td>191.410004</td>\n",
       "      <td>53.930000</td>\n",
       "      <td>41.169998</td>\n",
       "      <td>26.120001</td>\n",
       "      <td>120.300003</td>\n",
       "      <td>44.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20200625</th>\n",
       "      <td>364.839996</td>\n",
       "      <td>96.989998</td>\n",
       "      <td>89.620003</td>\n",
       "      <td>217.320007</td>\n",
       "      <td>436.950012</td>\n",
       "      <td>31.260000</td>\n",
       "      <td>95.830002</td>\n",
       "      <td>235.110001</td>\n",
       "      <td>254.360001</td>\n",
       "      <td>2754.580078</td>\n",
       "      <td>...</td>\n",
       "      <td>296.220001</td>\n",
       "      <td>167.020004</td>\n",
       "      <td>110.519997</td>\n",
       "      <td>37.606823</td>\n",
       "      <td>193.979996</td>\n",
       "      <td>54.279999</td>\n",
       "      <td>42.040001</td>\n",
       "      <td>27.370001</td>\n",
       "      <td>119.709999</td>\n",
       "      <td>45.169998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20200626</th>\n",
       "      <td>353.630005</td>\n",
       "      <td>96.129997</td>\n",
       "      <td>88.709999</td>\n",
       "      <td>212.470001</td>\n",
       "      <td>426.920013</td>\n",
       "      <td>29.709999</td>\n",
       "      <td>94.580002</td>\n",
       "      <td>232.839996</td>\n",
       "      <td>246.479996</td>\n",
       "      <td>2692.870117</td>\n",
       "      <td>...</td>\n",
       "      <td>286.880005</td>\n",
       "      <td>162.639999</td>\n",
       "      <td>107.320000</td>\n",
       "      <td>35.640003</td>\n",
       "      <td>189.270004</td>\n",
       "      <td>53.160000</td>\n",
       "      <td>41.169998</td>\n",
       "      <td>25.340000</td>\n",
       "      <td>118.320000</td>\n",
       "      <td>43.619999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20200629</th>\n",
       "      <td>361.779999</td>\n",
       "      <td>96.440002</td>\n",
       "      <td>89.010002</td>\n",
       "      <td>212.720001</td>\n",
       "      <td>424.200012</td>\n",
       "      <td>30.680000</td>\n",
       "      <td>95.440002</td>\n",
       "      <td>231.559998</td>\n",
       "      <td>253.800003</td>\n",
       "      <td>2680.379883</td>\n",
       "      <td>...</td>\n",
       "      <td>289.760010</td>\n",
       "      <td>165.509995</td>\n",
       "      <td>109.480003</td>\n",
       "      <td>36.349998</td>\n",
       "      <td>191.380005</td>\n",
       "      <td>54.689999</td>\n",
       "      <td>42.320000</td>\n",
       "      <td>25.700001</td>\n",
       "      <td>119.059998</td>\n",
       "      <td>44.320000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20200630</th>\n",
       "      <td>364.799988</td>\n",
       "      <td>98.180000</td>\n",
       "      <td>91.430000</td>\n",
       "      <td>214.720001</td>\n",
       "      <td>435.309998</td>\n",
       "      <td>31.180000</td>\n",
       "      <td>96.989998</td>\n",
       "      <td>235.860001</td>\n",
       "      <td>258.540009</td>\n",
       "      <td>2758.820068</td>\n",
       "      <td>...</td>\n",
       "      <td>294.950012</td>\n",
       "      <td>169.070007</td>\n",
       "      <td>111.180000</td>\n",
       "      <td>36.820000</td>\n",
       "      <td>193.169998</td>\n",
       "      <td>55.130001</td>\n",
       "      <td>42.389999</td>\n",
       "      <td>25.600000</td>\n",
       "      <td>119.779999</td>\n",
       "      <td>44.720001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1257 rows × 99 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                AAPL       ABBV        ABT         ACN        ADBE        AIG  \\\n",
       "Date                                                                            \n",
       "20150706  116.122704  54.396431  44.941250   89.012009   80.500000  54.500534   \n",
       "20150707  115.837006  54.667538  45.365906   90.237366   80.589996  54.817768   \n",
       "20150708  112.961594  53.949913  44.498520   89.268074   79.989998  53.892525   \n",
       "20150709  110.657570  54.197105  44.444317   89.386940   80.470001  54.421230   \n",
       "20150710  113.615921  55.201782  45.112915   90.328812   80.589996  55.179031   \n",
       "...              ...        ...        ...         ...         ...        ...   \n",
       "20200624  360.059998  95.139999  87.300003  201.839996  431.679993  30.299999   \n",
       "20200625  364.839996  96.989998  89.620003  217.320007  436.950012  31.260000   \n",
       "20200626  353.630005  96.129997  88.709999  212.470001  426.920013  29.709999   \n",
       "20200629  361.779999  96.440002  89.010002  212.720001  424.200012  30.680000   \n",
       "20200630  364.799988  98.180000  91.430000  214.720001  435.309998  31.180000   \n",
       "\n",
       "                ALL        AMGN         AMT         AMZN  ...         UNH  \\\n",
       "Date                                                      ...               \n",
       "20150706  58.867424  134.846283   86.097084   436.040009  ...  112.806862   \n",
       "20150707  59.712139  135.623062   86.686302   436.720001  ...  111.458641   \n",
       "20150708  58.967331  132.934891   85.643852   429.700012  ...  109.685631   \n",
       "20150709  59.412411  132.062073   85.562263   434.390015  ...  110.202759   \n",
       "20150710  60.120884  134.497192   86.495934   443.510010  ...  112.797638   \n",
       "...             ...         ...         ...          ...  ...         ...   \n",
       "20200624  92.949997  232.509995  254.179993  2734.399902  ...  289.179993   \n",
       "20200625  95.830002  235.110001  254.360001  2754.580078  ...  296.220001   \n",
       "20200626  94.580002  232.839996  246.479996  2692.870117  ...  286.880005   \n",
       "20200629  95.440002  231.559998  253.800003  2680.379883  ...  289.760010   \n",
       "20200630  96.989998  235.860001  258.540009  2758.820068  ...  294.950012   \n",
       "\n",
       "                 UNP         UPS        USB           V         VZ        WBA  \\\n",
       "Date                                                                            \n",
       "20150706   85.749390   82.283913  37.943161   65.693474  37.562855  75.650414   \n",
       "20150707   87.085892   82.998978  37.830032   65.355919  37.730549  77.298264   \n",
       "20150708   85.401917   81.287910  37.133812   64.352989  37.633602  76.111099   \n",
       "20150709   85.758308   81.509254  37.386200   64.661598  37.375053  79.335915   \n",
       "20150710   87.157173   82.539276  37.803921   65.982765  37.972931  82.489861   \n",
       "...              ...         ...        ...         ...        ...        ...   \n",
       "20200624  165.550003  109.500000  36.440567  191.410004  53.930000  41.169998   \n",
       "20200625  167.020004  110.519997  37.606823  193.979996  54.279999  42.040001   \n",
       "20200626  162.639999  107.320000  35.640003  189.270004  53.160000  41.169998   \n",
       "20200629  165.509995  109.480003  36.349998  191.380005  54.689999  42.320000   \n",
       "20200630  169.070007  111.180000  36.820000  193.169998  55.130001  42.389999   \n",
       "\n",
       "                WFC         WMT        XOM  \n",
       "Date                                        \n",
       "20150706  47.528847   63.890312  66.836525  \n",
       "20150707  47.368855   65.000229  67.111877  \n",
       "20150708  46.526745   64.357178  66.383011  \n",
       "20150709  46.737270   64.110527  66.083359  \n",
       "20150710  47.225693   64.410027  66.585472  \n",
       "...             ...         ...        ...  \n",
       "20200624  26.120001  120.300003  44.500000  \n",
       "20200625  27.370001  119.709999  45.169998  \n",
       "20200626  25.340000  118.320000  43.619999  \n",
       "20200629  25.700001  119.059998  44.320000  \n",
       "20200630  25.600000  119.779999  44.720001  \n",
       "\n",
       "[1257 rows x 99 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock_price = pd.concat(stock_frames.values(), axis = 1, join = 'inner')\n",
    "stock_price.columns = sp100\n",
    "stock_price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "<div class = \"panel panel-primary\">\n",
    "    <div class = \"panel-heading\"> \n",
    "    <span style=\"color:white\"><b>Task 3</b>\n",
    "    </div>\n",
    "    <div class = \"panel-body\"> \n",
    "        Using the either the array or dataframe <code>stock_prices</code>, calculate the compounding daily returns (expressed as a percentage) and store them in another array called <code>stock_returns</code>. Recall that:\n",
    "    <br><br>\n",
    "    $$r_t = \\ln\\left(\\frac{P_t}{P_{t-1}}\\right) * 100$$\n",
    "    <br>Confirm that after the calculation, the shape of <code>stock_returns</code> should be <code>(1256, 99)</code>, and the first few rows should look like this:\n",
    "    ![Task_3_Output.JPG](attachment:Task_3_Output.JPG)\n",
    "    <p style=\"text-align:right;\">[10pts]</p>\n",
    "    </div>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1256, 99)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock_returns = (np.log(1+ stock_price.pct_change().dropna()))\n",
    "stock_returns.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAPL</th>\n",
       "      <th>ABBV</th>\n",
       "      <th>ABT</th>\n",
       "      <th>ACN</th>\n",
       "      <th>ADBE</th>\n",
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       "      <th>UPS</th>\n",
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       "      <th>V</th>\n",
       "      <th>VZ</th>\n",
       "      <th>WBA</th>\n",
       "      <th>WFC</th>\n",
       "      <th>WMT</th>\n",
       "      <th>XOM</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20150707</th>\n",
       "      <td>-0.002463</td>\n",
       "      <td>0.004972</td>\n",
       "      <td>0.009405</td>\n",
       "      <td>0.013672</td>\n",
       "      <td>0.001117</td>\n",
       "      <td>0.005804</td>\n",
       "      <td>0.014247</td>\n",
       "      <td>0.005744</td>\n",
       "      <td>0.006820</td>\n",
       "      <td>0.001558</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.012024</td>\n",
       "      <td>0.015466</td>\n",
       "      <td>0.008653</td>\n",
       "      <td>-0.002986</td>\n",
       "      <td>-0.005152</td>\n",
       "      <td>0.004454</td>\n",
       "      <td>0.021549</td>\n",
       "      <td>-0.003372</td>\n",
       "      <td>0.017223</td>\n",
       "      <td>0.004111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20150708</th>\n",
       "      <td>-0.025136</td>\n",
       "      <td>-0.013214</td>\n",
       "      <td>-0.019305</td>\n",
       "      <td>-0.010800</td>\n",
       "      <td>-0.007473</td>\n",
       "      <td>-0.017023</td>\n",
       "      <td>-0.012552</td>\n",
       "      <td>-0.020020</td>\n",
       "      <td>-0.012098</td>\n",
       "      <td>-0.016205</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.016035</td>\n",
       "      <td>-0.019526</td>\n",
       "      <td>-0.020831</td>\n",
       "      <td>-0.018575</td>\n",
       "      <td>-0.015465</td>\n",
       "      <td>-0.002573</td>\n",
       "      <td>-0.015477</td>\n",
       "      <td>-0.017938</td>\n",
       "      <td>-0.009942</td>\n",
       "      <td>-0.010920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20150709</th>\n",
       "      <td>-0.020607</td>\n",
       "      <td>0.004571</td>\n",
       "      <td>-0.001219</td>\n",
       "      <td>0.001331</td>\n",
       "      <td>0.005983</td>\n",
       "      <td>0.009763</td>\n",
       "      <td>0.007520</td>\n",
       "      <td>-0.006587</td>\n",
       "      <td>-0.000953</td>\n",
       "      <td>0.010855</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004704</td>\n",
       "      <td>0.004164</td>\n",
       "      <td>0.002719</td>\n",
       "      <td>0.006774</td>\n",
       "      <td>0.004784</td>\n",
       "      <td>-0.006894</td>\n",
       "      <td>0.041497</td>\n",
       "      <td>0.004515</td>\n",
       "      <td>-0.003840</td>\n",
       "      <td>-0.004524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20150710</th>\n",
       "      <td>0.026383</td>\n",
       "      <td>0.018368</td>\n",
       "      <td>0.014931</td>\n",
       "      <td>0.010482</td>\n",
       "      <td>0.001490</td>\n",
       "      <td>0.013829</td>\n",
       "      <td>0.011854</td>\n",
       "      <td>0.018271</td>\n",
       "      <td>0.010853</td>\n",
       "      <td>0.020778</td>\n",
       "      <td>...</td>\n",
       "      <td>0.023273</td>\n",
       "      <td>0.016180</td>\n",
       "      <td>0.012558</td>\n",
       "      <td>0.011111</td>\n",
       "      <td>0.020226</td>\n",
       "      <td>0.015870</td>\n",
       "      <td>0.038984</td>\n",
       "      <td>0.010396</td>\n",
       "      <td>0.004661</td>\n",
       "      <td>0.007569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20150713</th>\n",
       "      <td>0.019122</td>\n",
       "      <td>0.012293</td>\n",
       "      <td>0.000604</td>\n",
       "      <td>0.017760</td>\n",
       "      <td>0.010492</td>\n",
       "      <td>0.012064</td>\n",
       "      <td>0.015888</td>\n",
       "      <td>0.010715</td>\n",
       "      <td>0.002930</td>\n",
       "      <td>0.026829</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002453</td>\n",
       "      <td>-0.007594</td>\n",
       "      <td>0.008217</td>\n",
       "      <td>0.008938</td>\n",
       "      <td>0.015949</td>\n",
       "      <td>0.004246</td>\n",
       "      <td>0.014607</td>\n",
       "      <td>0.011701</td>\n",
       "      <td>0.010340</td>\n",
       "      <td>0.002430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20200624</th>\n",
       "      <td>-0.017810</td>\n",
       "      <td>-0.022552</td>\n",
       "      <td>-0.044800</td>\n",
       "      <td>-0.027174</td>\n",
       "      <td>-0.020339</td>\n",
       "      <td>-0.056149</td>\n",
       "      <td>-0.039138</td>\n",
       "      <td>-0.013839</td>\n",
       "      <td>-0.039799</td>\n",
       "      <td>-0.010915</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.028701</td>\n",
       "      <td>-0.022753</td>\n",
       "      <td>-0.010086</td>\n",
       "      <td>-0.045854</td>\n",
       "      <td>-0.033698</td>\n",
       "      <td>-0.018555</td>\n",
       "      <td>-0.039062</td>\n",
       "      <td>-0.041618</td>\n",
       "      <td>-0.006380</td>\n",
       "      <td>-0.048255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20200625</th>\n",
       "      <td>0.013188</td>\n",
       "      <td>0.019258</td>\n",
       "      <td>0.026228</td>\n",
       "      <td>0.073896</td>\n",
       "      <td>0.012134</td>\n",
       "      <td>0.031192</td>\n",
       "      <td>0.030514</td>\n",
       "      <td>0.011120</td>\n",
       "      <td>0.000708</td>\n",
       "      <td>0.007353</td>\n",
       "      <td>...</td>\n",
       "      <td>0.024053</td>\n",
       "      <td>0.008840</td>\n",
       "      <td>0.009272</td>\n",
       "      <td>0.031503</td>\n",
       "      <td>0.013337</td>\n",
       "      <td>0.006469</td>\n",
       "      <td>0.020912</td>\n",
       "      <td>0.046746</td>\n",
       "      <td>-0.004917</td>\n",
       "      <td>0.014944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20200626</th>\n",
       "      <td>-0.031208</td>\n",
       "      <td>-0.008906</td>\n",
       "      <td>-0.010206</td>\n",
       "      <td>-0.022570</td>\n",
       "      <td>-0.023222</td>\n",
       "      <td>-0.050856</td>\n",
       "      <td>-0.013130</td>\n",
       "      <td>-0.009702</td>\n",
       "      <td>-0.031470</td>\n",
       "      <td>-0.022657</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.032038</td>\n",
       "      <td>-0.026574</td>\n",
       "      <td>-0.029381</td>\n",
       "      <td>-0.053717</td>\n",
       "      <td>-0.024580</td>\n",
       "      <td>-0.020850</td>\n",
       "      <td>-0.020912</td>\n",
       "      <td>-0.077063</td>\n",
       "      <td>-0.011679</td>\n",
       "      <td>-0.034917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20200629</th>\n",
       "      <td>0.022785</td>\n",
       "      <td>0.003220</td>\n",
       "      <td>0.003376</td>\n",
       "      <td>0.001176</td>\n",
       "      <td>-0.006392</td>\n",
       "      <td>0.032127</td>\n",
       "      <td>0.009052</td>\n",
       "      <td>-0.005512</td>\n",
       "      <td>0.029266</td>\n",
       "      <td>-0.004649</td>\n",
       "      <td>...</td>\n",
       "      <td>0.009989</td>\n",
       "      <td>0.017492</td>\n",
       "      <td>0.019927</td>\n",
       "      <td>0.019725</td>\n",
       "      <td>0.011086</td>\n",
       "      <td>0.028375</td>\n",
       "      <td>0.027550</td>\n",
       "      <td>0.014107</td>\n",
       "      <td>0.006235</td>\n",
       "      <td>0.015920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20200630</th>\n",
       "      <td>0.008313</td>\n",
       "      <td>0.017881</td>\n",
       "      <td>0.026825</td>\n",
       "      <td>0.009358</td>\n",
       "      <td>0.025853</td>\n",
       "      <td>0.016166</td>\n",
       "      <td>0.016110</td>\n",
       "      <td>0.018399</td>\n",
       "      <td>0.018504</td>\n",
       "      <td>0.028845</td>\n",
       "      <td>...</td>\n",
       "      <td>0.017753</td>\n",
       "      <td>0.021281</td>\n",
       "      <td>0.015409</td>\n",
       "      <td>0.012847</td>\n",
       "      <td>0.009310</td>\n",
       "      <td>0.008013</td>\n",
       "      <td>0.001653</td>\n",
       "      <td>-0.003899</td>\n",
       "      <td>0.006029</td>\n",
       "      <td>0.008985</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1256 rows × 99 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              AAPL      ABBV       ABT       ACN      ADBE       AIG  \\\n",
       "Date                                                                   \n",
       "20150707 -0.002463  0.004972  0.009405  0.013672  0.001117  0.005804   \n",
       "20150708 -0.025136 -0.013214 -0.019305 -0.010800 -0.007473 -0.017023   \n",
       "20150709 -0.020607  0.004571 -0.001219  0.001331  0.005983  0.009763   \n",
       "20150710  0.026383  0.018368  0.014931  0.010482  0.001490  0.013829   \n",
       "20150713  0.019122  0.012293  0.000604  0.017760  0.010492  0.012064   \n",
       "...            ...       ...       ...       ...       ...       ...   \n",
       "20200624 -0.017810 -0.022552 -0.044800 -0.027174 -0.020339 -0.056149   \n",
       "20200625  0.013188  0.019258  0.026228  0.073896  0.012134  0.031192   \n",
       "20200626 -0.031208 -0.008906 -0.010206 -0.022570 -0.023222 -0.050856   \n",
       "20200629  0.022785  0.003220  0.003376  0.001176 -0.006392  0.032127   \n",
       "20200630  0.008313  0.017881  0.026825  0.009358  0.025853  0.016166   \n",
       "\n",
       "               ALL      AMGN       AMT      AMZN  ...       UNH       UNP  \\\n",
       "Date                                              ...                       \n",
       "20150707  0.014247  0.005744  0.006820  0.001558  ... -0.012024  0.015466   \n",
       "20150708 -0.012552 -0.020020 -0.012098 -0.016205  ... -0.016035 -0.019526   \n",
       "20150709  0.007520 -0.006587 -0.000953  0.010855  ...  0.004704  0.004164   \n",
       "20150710  0.011854  0.018271  0.010853  0.020778  ...  0.023273  0.016180   \n",
       "20150713  0.015888  0.010715  0.002930  0.026829  ...  0.002453 -0.007594   \n",
       "...            ...       ...       ...       ...  ...       ...       ...   \n",
       "20200624 -0.039138 -0.013839 -0.039799 -0.010915  ... -0.028701 -0.022753   \n",
       "20200625  0.030514  0.011120  0.000708  0.007353  ...  0.024053  0.008840   \n",
       "20200626 -0.013130 -0.009702 -0.031470 -0.022657  ... -0.032038 -0.026574   \n",
       "20200629  0.009052 -0.005512  0.029266 -0.004649  ...  0.009989  0.017492   \n",
       "20200630  0.016110  0.018399  0.018504  0.028845  ...  0.017753  0.021281   \n",
       "\n",
       "               UPS       USB         V        VZ       WBA       WFC  \\\n",
       "Date                                                                   \n",
       "20150707  0.008653 -0.002986 -0.005152  0.004454  0.021549 -0.003372   \n",
       "20150708 -0.020831 -0.018575 -0.015465 -0.002573 -0.015477 -0.017938   \n",
       "20150709  0.002719  0.006774  0.004784 -0.006894  0.041497  0.004515   \n",
       "20150710  0.012558  0.011111  0.020226  0.015870  0.038984  0.010396   \n",
       "20150713  0.008217  0.008938  0.015949  0.004246  0.014607  0.011701   \n",
       "...            ...       ...       ...       ...       ...       ...   \n",
       "20200624 -0.010086 -0.045854 -0.033698 -0.018555 -0.039062 -0.041618   \n",
       "20200625  0.009272  0.031503  0.013337  0.006469  0.020912  0.046746   \n",
       "20200626 -0.029381 -0.053717 -0.024580 -0.020850 -0.020912 -0.077063   \n",
       "20200629  0.019927  0.019725  0.011086  0.028375  0.027550  0.014107   \n",
       "20200630  0.015409  0.012847  0.009310  0.008013  0.001653 -0.003899   \n",
       "\n",
       "               WMT       XOM  \n",
       "Date                          \n",
       "20150707  0.017223  0.004111  \n",
       "20150708 -0.009942 -0.010920  \n",
       "20150709 -0.003840 -0.004524  \n",
       "20150710  0.004661  0.007569  \n",
       "20150713  0.010340  0.002430  \n",
       "...            ...       ...  \n",
       "20200624 -0.006380 -0.048255  \n",
       "20200625 -0.004917  0.014944  \n",
       "20200626 -0.011679 -0.034917  \n",
       "20200629  0.006235  0.015920  \n",
       "20200630  0.006029  0.008985  \n",
       "\n",
       "[1256 rows x 99 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock_returns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "<div class = \"panel panel-primary\">\n",
    "    <div class = \"panel-heading\"> \n",
    "    <span style=\"color:white\"><b>Task 4</b>\n",
    "    </div>\n",
    "    <div class = \"panel-body\"> \n",
    "    Using <code>stock_returns</code>, calculate the average observed daily returns across the S&P 100 stocks for each day in our historical period. Store this result in a <b>numpy array</b> called <code>daily_returns</code>. Check that the shape of <code>daily_returns</code> is a 1-d array with length 1256.\n",
    "    <br><br>Plot <code>daily_returns</code> in a line plot. Remember to provide a title, axis labels, and adjust the figure size as neccessary. Your final plot should look like this:\n",
    "    ![Task_4_Plot.JPG](attachment:Task_4_Plot.JPG)\n",
    "    <br><br>Comment on any noteworthy observations from the plot, and any possible connections to real-world events.\n",
    "    <p style=\"text-align:right;\">[10pts]</p>\n",
    "    </div>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1256,)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_returns = np.array((np.sum(stock_returns, axis = 1)))\n",
    "daily_returns.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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REZWYjJN3MurLqUIO+iSASUKIT4QQl5u/FEJcLoSYIYSYsWHDhjxsHhERERERdQQ10QunccmNQg76DpFS7gvgRABXCCEO138ppbxfSjleSjm+d+/e+dlCIiIiIiLKmsyUyjMXZ2fqr10KNuiTUq62/7sewPMAJuR3i4iIiIiIqDMwxMutggz6hBDVQoha9W8AxwGYm9+tIiIiIiKiXMgU1CWY2cupQl2yoS+A54U1R2sEwBNSytfyu0lERERERNRe590/FXVNsbTPcRZn56C+nCjIoE9KuRjAuHxvBxERERER5dZHizdnfI6Z52Pir30KsryTiIiIaGc3ZdFGfLmuLt+bQZQXzuLsnL8zJwoy00dERES0s7vwwWkAgKW3nZznLSHqfMzs5RYzfUREREREVNAYA7YPgz4iIiIiIioonMgltxj0ERERERFRQZFGbo/lnu3DoI+IiIiIiAoKg7zcYtBHREREREQdZltDK579ZGVWr2HMl1ucvZOIiIiIiDrMVf+ZhTfnr8eeg7piVN/aQK+RTPXlFDN9RERERETUYdZsawIANLcmAr8mZXH2dub+vvXwdOx50+vteo9ixkwfERERERF1mLbMxJl8TW6m73x34YacvE+xYqaPiIiIiIg63Cn3fIBPl28J+Gwr6mOZZ24w6CMiIiIiog6jh22PT1se7DUy/c+UHQZ9RERERERUUFSMx2AvNxj0FZl3Fq7HL1+Ym+/NICIiIiIKpC0lmuol7Z3AhSwM+orMJQ9/jEc/WpbvzSAiIiIi6jAq2EvYMd+/P16BVVsb87hFxY1BX5HioFYiIiIiKlVOps/+b2NrHBc+8BEAYOOOZjwweTHbw1lg0FekYgke5ERERERUmrzKOzfuaAEA/PTfs3DrK/Mxb/X2fGxaUWLQV6RaYsEXtyQiIiIiKgRBV92TzpINyccS9g/bG1sBMAmSDQZ9RaqZQR8RERERFYG2VGEmyzuTL47bQR5Dvewx6CsyIbt7hJk+IiIiIip1esBojvMLmjUkBn1FJxK2dllzLJ7nLSEiIqLOkkhIPPvJSsTi7PSl4tOWZReSY/qSVHmnej/BqC8wBn1FpswO+pjpIyIi2nk8O3MlrvrPbPzjgyX53hSiTpFuTF8y08eoLygGfUUmErYObo7pIyIi2nlsbbAmrthQ15znLSHKXrvG9Gm5PnPellxm+mav2IrPS3g20Ei+N4CyE3XKOxn0ERER7SxU45aTFVKx8wvUzDX31E9ex3xHLM93+l+mAACW3nZy7t+8ADDTV2Si9kwuLO8kIiLaeYTslnKCi1FTEWrLUSvNWVva+X47OwZ9RYYTuRAREe18wiEGfVTazENbGv/1wolcgmPQV2SiYWb6iIiISp1Z6hZyyjsZ9FFpMo/sNIm+lPODMmPQV2Q4po+IiKj0mW1a4ZR35mFjiNopSJCW+hzrZ3Z05AaDviIT5ZINREREJc9s6Koxfcxw0M7Ca52+lN/xdAiMQV+R4ZINREREpc9syzrlnbz9UxEKEpuZz9m4w1qexLO8k1nArDHoKzLRkMr0cSIXIiKiUuWX6YuzkUslyjy0v/fYTLy7cD0Wra/zfQ3LnYPjOn1FJhqxJ3KJ77xdfc2xOBIJoLIsnO9NISIqOBvqmtGjusyZ7ZGKk9kADnH2Tipm2mEr4H1tkh75wG89/LH3c+2ndsT5IKV0xtCWEmb6ikzEzvQ1t+68Qd/Rd7yHMb96Ld+bQURUcLY1tGL/W9/ErS/Pz/emUI6pGJ4xH5G2nEMbTohMr4mVaPqQQV+RURd9PdOXSEjES/QA9bJyS2O+N4GIqCBtb2oFAEz6fG2et4Tay6+8k5k+KkaBxvRlcWirwK0tp0OmJnOpzpvBoK/IqONUPyCPufM9jPklM1/pvPDpqrQ14UREpYRxQfFLXbLB+u9O1MdLJSTXs86qd9PPhzc/X4ef/Wd2xtdm6jhpbi3NeTMY9BUZdXC3apm+xRvrd+oxfkH8379n4Zg7J+d7M4iIiALRG6ZSSmb6qOQFPbQ/WbbZifr08+HSf83AM5+szPj6TOdQU4lm+jiRS5FRPSWxOC/6VHpWbG5AZVkYvWrK870pRER5pd/l9TZqgqk+KkK5PGq//repzr/b0gmS6SWlmulj0Fdk1IFaqoNMaed22B/eQUgAi393cr43hYgor/SGaVxKrZyN938qTV6zd2Z8TRtOh4xBX4lm+ljeWWTUxT7O1VlT1DW1Yu22pnxvBrUT+zOIqJSd8KfJOPfvUzM+Tx8DFU9I52deI6lUtW1SluxfpF7TogV3+vnGoI8KQoLlnb7O+MsUHPi7t1Iez/XgYSIiorZasLYO05Zszvg8V0mnlM79f2e8p513/1Rc/q8Z+d4M6mBtObLV6bB4ww7tMevBpRvrcco972NLfYvrNQkpsWRjPUbd8Cr+M2OF/Vjy900lWt7JoK/IJFje6eurDfUAUsc77EzLWRDRzm0njAdKlp7BSEhAFfjsjLe0jxZvxqTP1+V7M6gdOurapM6To+54T3vM+u89by/C3FXb8YZx7CQk8Pf3vgIAzFqxFQCwfHOD83tm+qggSKe8M/uz58VZqzB7xVYc8Ns3sXBtxy5fEE9I/PKFuVihnUSdZVtjq3tb2Aoiop3EzpwN6mjbGlpdM2d3NH0PxhPJTB/H9FGpattC66mPqXNEZewqy8Ipn1PfYv2uZ005pJSYePu7zu9LdSIXBn1FxmvJhiDiCYmfPDULp/9lCtZtb8YjU5fmfuM0s1ZswaMfLcNP/z2rQz/Hy4Ydza6fS23448otDWiOleYFiYjah51cHWfczZNwxeMz0z4nkZB4e8G6nATd5oyd6uedMdNHxU+fpEWtOZn6nOx5dYKoxEijCvqiYdc5KSUQs9vR8UQiJbPXag+henvBOvw8wLp/xYJBX5FJtDHTZwaJXSujns9bs60R33p4OrY3tXr+Plu5aoB8ua4Oc1dtS/ucntVlAIDj7prsqseOlVDUt6M5hkN//w5uemlevjeFiAoQM3wdK1OJ4WPTluHb/5yBl2avbvdnuSZykdJpNHMfUzEKcti25dD+YNFG/PnNLz3fp9HO5pVHQyljZFudoA9oaHF3pKu267f/OQP/8Vj3b+WWBidoLCYM+oqMPqbvxVmrcNbfPgz0uqBB3+wV2/Duwg34ct0Oz98HZ3Xj5OredOxdk3HKPR+4HjNvfD3soA8ANmmDdkso5sPnq7cDsMY3EBGZmAXqGEHXxlu+yRrSsG57+2eS1j/Rmsgl+W+iktSGQ/vhKUtx15tfuB5TQVuDnQCQ0p2ESMhkNi+eSDgZQef3CYkj//iO5+dtqW/Bob9/Bzf/7/PsNzbPGPQVGWdx9kQCC9bWYcayLYFe12rM9tmlwjvoUxnE9s5cpFL35vmbyxmRzGynHvTpN+hSKnf6bOVWAMCg7pX53RAiyquWWMKzzNsZ09fZG1TivO4j89dsx5yV23Dry5/j1TlrACQnWQuH2t+8ck3kkkj+XEodmbTzyKYpdsbeA9r1Wc6YPjuDp4+JtbZFOlVgsYREY0vM9fp4QmLppgbX85UWO4nyv8/WtGsb84FBX5HRl2zIprfPzPT51VOrk6CxJXNwtmj9DtQ3xzx/57y9to2Tv9iA3X75Gj4JGKh+ua4OX6zzn3DGnMG0pjzi/Fv/e71KYeMJiZtemodlm+oDbUuhUBnYSMj6hrc1tOKxj5ax3IeoiLXEEr7ncDwhPa9hE377Jva8cVLK4wwIOobXPjjxz+/j1Hs/wAPvL8H37bF+6nnqGt0e5uLszPR1ngm3volfvTjX+blUp/AvNKqEOeTXSA0oYYzpiyWk69r4s2c+Q3OrKu+UvuWdzs/a+a+2bGuDexmIYsCgr8iogzamDeoOosUYpOq35INa/68hwwVOSolj7nwP33nk48Db8Pq8tQCAeavTj81Tjr1rMo67a7Lv782bsH6S6r/zukHOX7Md//xwKX705KeBtqVQqB6mLQ3WmMtrn/8MN7wwF7NXBvtOC1nQ8imiUrK9qRWjbngVf3lnkefvx9/yBo67672Ux7c2tDrXA51fQCClxHcfnYH3vtjQvg3eSQWdPE3dh8K5CPq0fye0xdkZ83W89XXN+NfUZQCAGUs3Y7dfvoYpizbmeatKnzq2RXuDPjWmr1Vl+hKua+PkLzZg9dZGACrTl1reqYu52pTu/xYTBn1Fxsn0+fT++jFvWOYAVHXAq0xfU4ZMn/rojxZvxjf/MS3l9+qE1bdQLaXgN54wCL033Axc9e/ji3U78Nb8dZ7P0xVbj6n6G1UP08Yd1n8bWrwzrsWEa0/SzmhrvXVdfOrjFZ6/39LQ6qxBGoTfNS2WkHh93jp86+Hp2W8kBb7fxuPpg75s7tsJo/NS/VxKQxaKwRx7ErlX5xZfOV9n+NObX+DOSQuzek2m2Tvb22fizN7Zksz0meeN6jSLx2VKosM8TfXztpjPPwZ9RUYda2avhfLf2atx/+SvUh43x/TpB/ALn67CmF+9hkXr65znmYNaTfrr3/8ytfdLna/6Jqqgz288YRB6YGDePPXv44onZuI7j8ywHk9zkxVof29sZ/hiXR1uemme8zerTJ+6MHbGNWh7UyuGXvMynv80dSarXGjL2pNExU4N/WqJJfD0jBXtnhHO7zRSU5KH29mDbpr8xQZMW7wpp+9ZiIJ2SmXK9LV1nb94IjnhfbF1VrZXvqtAetWUAwA21DVneObOJ5GQ+NObX+Lut70rFXTZDENp72VKfVYy0ycx3ZgAT10T4zI102cGdq5MXxG3VRj05ZGU0pnpKyh9TJ/X+fOjJz/Fb19ZkPJ4SqZPO2jftDNi81ZvT1nbxE+mBnpyMoHk85xF09txMi/dmOzxNpdi8NqmX74wt+BmWJJS4rGPlgUaN6l848Fp+OeHS7F6m1WOsL2pFfGEdBoWndEGUMfq/ZOXdMj7t2VpjXhC4revzMcqu0yDqNhE7KhvfV0zrn7mM9z3XmqnHQDf8dOmhE8JoFpsOJSDskPdRQ9Nx7n3f5TT9yxEgTN99vP8vuXdfvla4M90TeTiGtMX+C3arCWWKJgxS63avSEf49fVfTZd0Jftdv139mrc89aXmZ9Y4Bas9Z93oS3U99jeMX0qaFPnYywucem/Zriek1yyIXN5p37+F3OfC4O+PJq5fCsO/+M7aScrMenlndn09pljP/Te5LKwdRi0xpPrlpiDWk2ZGuheDY/tdtAXj7f9jDlWG+OXkunz2KRHP1qGNzzWVeqMrNKSjfUYes3LWLTevX/fXbgBN7wwF797dX7g91Lbqy6EUlrfp/q5M8oNcpwgSNGWfTJ75VbcP3kxrnp6Vu43KAtSSjzy4dLADXMixYzBpi3ZjBWbG1IamGu2BevY0Bsr89dsx0UPTcfr89aiye7VDjLBSJ2d1X9uZsdk9YtR4Exfwt3Y9PpdUO51xaCN6ZPG8yRenbPGczZXXWNLHAvX1uHX/52H0+/9IO1zL390Bva++Y2strej6JVKnXHv1r9ffcI6NZzC9NT05dj12leyCpJ/9OSnuOONLzI/scB9taG9y3u5qW8+V2P6FK/jRmX6YonU8k7z+Xqb1z2rbnFFgAz6OthnK7fiwfcXe/5u0w7rpp5NliJZ3pk+6DMnbmlNM5FLJGydXLF4IvCSDZmSMl4VLCrTl6sAJWaWrEqJaDjYhaKtJTbZeGmWtTjvi7Pci/Sqz161Jfh+92pwNLTGnQujub87gnDWXuyYi5z+N/plO0x6L14+Tf5yI258aR5+U2BZZSp85qm9YG0dDvvDOzjxz++7Hl+9Ndi6b+r91m5vwol/fh+Tv9iA7z76iZPpC1LeuWab9Vl/fTfYeVjqEgnpex83qWtSq8c1O9v7jv4O+j3fvPe/98UGfP/xmbhbyxwt21SP7U2trudd+fQsHP+nyXh4ytKMk3+9u7BwJvzR2y9ekxfl/PO0+8kxd76Ha5+bAwDYuMPdEbOhrhlb6ltwjf37nbH8c0cWHZ1B7tLq0G5vQUK6iVjMz4onEinzWJjnmN42/scHyWqn5k5oe+USg74Odv3zc3HLy/PxybLUxbTVhWVbQ2vK7/yoA7E1nkhb4mH2OJlj+vRGckRl+hLSOTEylR5myvSpE0Q/b7baQV8iIbG5vgVXPDHTCXzT8etJ8TopVdYyk7beOFZsbggcpPuN7agqs5aWaMrQK+t6Lye4SW53Y0vcuTB2xnTSXm3F+Wu256y0Uj8mb3s1tUTZizo22lsK0l5qjZ8tBVIORW3X1BpPadzl0qYdzbju+TlOVsa8jqmGo7kNarKmbQ2taTte/DoD65qs1wcp71TXLI6ztUz6fB0enrI00HOdtb887jHZTlal70sr6LMfN956pd2BuLk+2ZY44o/v4sQ/uTsOPmrD2Mv2jjHNBT1Ybo117DEppcQfX3fff9R+M/ff/re+iQm/fdP5uSMDgFg8kRwiU0B22NeVXN+C2/t+qe1D/30Ti3ss2WAGjXb75N63F+HRj6xZXe85fx9UloXbt6GdjEFfBxvSswoA8OqctSm/UxeybEoCEk7PhEwJhvSGwBYjkEw3pi8aSmb6YtpELmN/9Rp+8tSnntsRfEyfvn32a6XErS/Px8ufrcHDU5bitlcXpG3E+AVo5gVYSomySLBDWgXB2VxYNu1oxmF/eAffeDB1tlIv6iJj9qyrH7MZ06caEnrw3tQad4KdzlxDSN9VJ/75fRxy29s5ed+2jOlTh0CeY76irvEvJA0tsU7Jwqdz0UPTMf6WNzM/sY1ufWU+npi2HK/Nte4JfkGavu4oYF3vFq2vw7ibJ+HpGd4zfaZ7v+WbrTG5QZYS0CtKCBnLJnXq8PX67rINoNzlnf6ZPpVtqa1wHzNmh1xbOsea0gQy2xpb8f6XHZ8R1NsAXu2BRet34J9TcjPWfP6aOjzwvvd7eZ06+j05m+NEd997X+GSDLPqXvPcHIz79aSCW5O3zj72glQQBNn0XK3Tl1qe6f/h8YREQ6uxOLtPpk8PvPPd2dwWDPo6iVlmAWhBn9F7M+K6V3DW3z70fJ90Y/r0Y3pzvTuQNC+Ueq+HyvTF4tK1OHtDSxwvzlqNode8jKemL3e/Ps3Z+6+pS/Hp8i0AvEsB4wnp9Dje+84i3PfeV/hinX9duFpA0+t9zG0qjwTrdcmmHHLTjmZsa2h1yl2WbAw2fbr6ys2edbUvGn3+Lu/3Uvs9+ZrmWDLT1xklBur6JgMVaWSvLQ1MdZPN97XXGYfQAbPBnn3fhzjsD7kJrDtbPCGxvi5YWSIAjP3V67jk4eBrf3aE6UtSqzJySV17hDY+18ug7pWuICGekM518o3P1/u+v9/7qTGBQRoqahuLJeibNG8t9v3NGx3W+ZVN4y7u0UGnmI9lGg+k3z+tMX3q30bQZ2dbVEeB/jo9EDHvRUECiHTf6aWPfIxv/mN6ViV+baFXgXgFfWf8ZQpu+u/nOTleQ2laxer6PnfVNrz8WeryDX5tlUxue3UB3slQTvv8p6sAANub2v5dr97aiLcXpM5x0B7q2Mt2GTFfTnlnx4/pc34nZWp5p0f70pTjObE6BYO+DqYOpI07WvDtf36MReuTwY26AWw1snKxhMSMZVs8308ddzGP8k79oE4t73RfjNZub8a9b3+JREI6A/tbEwnfJRvMAcfpxlD96sV5uH2S+Xx348XsgUx3fvv1nqWM6UsA5dGgmb7gF+f9bnkT426e5JRjdq8KtuSEk+kzrgxqfEI2DRS1X2KuTF/CaTgGfS8pJT5bubVNvYXJMX1ZvzQQsycuSK+4+rsLpcetIzbj46VbsGJzcc5O+ttX5mPCrW9lVcL+QYEsgNxR42TVdTqSYeZdIYSrgRuLJxtV6a5ffo0bNSbQrwL+uufnOJUd6v1jiQSe+WQl7n072CyDc1dtw9xV6ceKdYTfvboAm+tbOmwW3yCT3wDAf2ascBrv3uWd7sf+lmHssjmmTzqZPvfz6uxOZRX06cHl0o3JGcLNP8MrMDV53Vuueno2Hp+2DB8vtdopHT2m3F3emfpZKuhsa6ZNF0kT9TW2xvHf2atxyj0f4IonZqb8PuiQDb/79dSv/Mtvq+0ywi312Q8hOO/+qXhu5kqces8H+PY/Z2R+QRZ2NCev7ZmOA3eHsfc5NeG3b1m/zXF5Z6ZMX53RcREkaGzvZDP5wKCvg6k64bcXrMfbC9bjppfmOb9TF7Js6rTTZ/qkc9HPVN7539mrcfukL/DVhh2Iapm+uJbpS/f6oD06ahPrtN4pr0xZuntqU8BMXyIhUZ6hvFPdNNXfk80pq3rxgk/dbf03EhLY3tTqzADmZPqyKO9UWhMJ5+Lf1Bp3SiqCZvpemr0ap907BS/PyX6RWXW8bdjRjG//82PPhvy7C9fjL+9kXq/Hi/m9Zlo2BEgeG0GCvjc+X4cPv8ouoFiysR5/fTfI+kNZvW1G81Zvw+ert7seK4SxNdlSJYyq0uH4uyZnLGMqFG05P718uGgjbn89uXCxOROvXzlmSyzuakTpE3no12OzA8fv/VbbAVFYCEgpceW/Z+HdhcmM4RPTljuTTqn3b41L/Ow/s1M68bw8PWMFTrnnA5xyT/pZIXXxhMQdkxbi2U/aN0uouu53VPARdJmLG16Y6/xbTeSyaP0OfGh3ZJgdlX98fWHaQDW1vNP6d31zzJWJUA1WNZGZHnzUNfmXowUJUrzuv8/OXInrn0/+rdl+782xONZvD14BoHd+vDp3LVZuSQayevuprZk2XaYA/0dPeg95yebz9bHf+n3v/Ac+wmKf2TBV225zluPGpZT4aPFmXPn0bGyyA8Zc3kv0LG8ugm6lvVUzCekeApWu3RaLy5T2jN+YPl2QUvlCw6Cvg5kNV/2G3JYxfclMX+o6fQkpUWFnuvTeF8B/8LMeLDTH4r6ZPvOi7lfe6ZdB0stbZ63YmvL7dHGUb6bPXKcvwJg+1duj/z1NrXFnkoR01HeVroG/aH2dMxGD2tchIbDXTZNw6O+tEr1Wp7wz+wtkLC5RZV/8m1oTTs9Z0Ezfl3Z52Ffrg5Wo6tTfs7WhFW8vWI8nP16e8pxvPfwx/qg1cLNhXlSDNLrVdxikw+2yf83ABQ8EG4+pXPjAR/jDawszZqrUfshVx9/Jd3+Ak+52T8KwNotGUnv8d/bqNk344EVdD9QNdOG6Ot8ypnwFtXdOWoh/TV2a8vjRd76bVdnavNXb8Nrc1M6UCx6chnu1jhB1DVINBr8grak14Q76pHTOEf1cSR3b7L19ao3PcFhgzbYmPPfpKvzg8dRsBZC8PprDBNK5+pnPAj9XeXjKEtzz9iJc9Z/Z+Hz1dsxfsz3zizyo6766DrbEErj99YU5m/gi6IL2+v1UHc/H3PkeLrDHgXuVJr4+N3W8v6IfG7e+PB8L7eWd1mxrcnWu6SV2gDv4qNeuoylBX4D7xjF3vucKsrxk29j/0ROfYsJv33KuD8s21WPcryfhsn/N8FzCSs9I/v61BTjrb1Odn/f7TXJZCTOIff/LDVkfA+1Z+D5o56te3aVXfwHuDnKduu9nm+nzCtrTjdPMlr69fh30SjZfbXvjqXhCutqp6arT4gmZcpz4zd6Zy23MBwZ9HcxsuOrHkVPe2YZMn9eSDfpi3Y0t7pPPbzKUpta4s/BpY0tyyQazsZMS9Hlk2QCPBojdGN7emHw/r6UK0pUr+V1IvTN96cf0qRPfuYkIgbPu+9C1HtErc9Zg6DUvp3wH6saWbjzjMXdOdsZfOWVcdu+ryr6qALwt409a49LJ9DW2JjMBnTGRiznPit6wyMXntyXTp84vIQQ+XLQRrwTIYGZT2qrGT7RmXJfS+q9X7+TUrzblJGuUzRIfuqUb6/GDxz/x3UfPzVzpakj86MlPcZ6x2HYsnkjJPGYjUEYhT1Nf3/32IvzqxXkpj2/c0YJPPMrsL3zwI+x10+spj59z31R877GZWLi2Dus8AvTX5q7BK3PWONdIv1I9pTmWcAcR2lpSZtmnzj/TZ5d3CoFPl28FAAzrXe35XK/7hV5aGnRccyZztFLQk+5+P2WZCgCY/MWGjNcXlemrb7ae9+8ZK3DvO4tw/+TMS0488uFSJxPnpy09+l7lZF4Nz3SdOfqunLViq2sc2RvzrbFZ//54ufM9ei251KDdx8zKxaCZqUznfrpMX1NrHG/Nd48jm2Svnau+o5dmrca2xla88fk6/NCjbDJ1eEryO9O/Z/3v2Vzfgm/+Yzp+nCYz56U9w9KC3ge3a22+4/802fU7v6qVapXpM4K+BWu346aX5vne17w6rvy28/lPg5dye71/puA/m682aHbd97Oku02RdvbORCKlHZ5unT5nG1neScrLn63Bob9/G0s3uW+OXpm+7JZssP4bS6SO6UskkiWFZoPZL6hqbI27ZuxUB7bXOEPXz8bNq9UZvO7+HPXn6pk+ryDOfD+9590/02cEvTJzeadqzLSo6dITEnNXbUdLLOE0zFU5352TvsAt2rprarubYwmc/pcprrVadKq3SwWH5mWhOZ58n2zFEonkkg+tyczsA+8vyVlDzI8Z7OoN+fXbm33LUpRlm+pxwQMfeU5qBKReVM0plL2o47ypNY4LHpzmm7nQbUrTU/rKnDWuBanV+ep1k3xTKxf1GmcCAMs3NeD8Bz7C9c/PcT3+7Ccr8btX5mfc1qc/Ts7SuDrg4tx1xvc7bckmvDJnbUqPMmDtkyufno0f+8zSq9zxxhc46e73sWh9ai98OuqIydQDbD2n82agDcpr3c8pizZ5TqZQHrU6Y47/02Qc8Nu3nGBb+d5jM/GDx2d6dJD5VGG0xl3XiHg84ZSI6+eKGaD5NVpVY3HppgZnPNIuPb2DPq+xXqqhesv/PsfE29/NaoIeP5mCjncWrsdFD033vdYqqrNPNUCX2/ddda1M58aX5jmZOD9taYCm3gul5304XdlZuqzTrr2qkUhI/OLZOc66irGENWZbn4yovZk+INlxCXi3JdLdy259eT6+88gMfLZyq+/ruleXOY+t3NKIode8jHmrkx0C5vXVLwjX70kqc7N4o/d9af32Jgy95mW8s9A9KVJ7M33n/n1qysR3pnTjy2KJBD74cmNKx1FNuT2mz6gM+/bDH+OfHy7FervCqL455g76PaqY/Dohf/rvYKXcuh1NetCXu4679sZTcW0JMiDzmD4z0xeXEqP71rqeY25XEcZ8DPo6SmNrHCu3NKacBPph5zV7Z6YSJ7132HxuQrupmBdzv6CvoSXuvI8eRGzPkH00b1Sqp8/s8VPP0t/PqxfZNVA7nnD1vAeevTPAmD71t6q/83OtnEj9u2d1OQDgoSlL8KDW2NBntJu9YmvGhbjjcbNxZwX4j3y4NO3rTHr5bywuUV2eHNOnf98Tb3/Xs7xM156LlHkz1I+xG16ci6PueM/52ashc+cbX+DDrzbhjXnrsL6uKeVmZL5ma0MrfvO/z7GtsRXrtzd5rumoFpzOZsZFr+AHsM6tHzw+E1c+Pdt5LF3Qd6lWLup3ftXZZdafG2VrV/1nNv4+eXHGbb362WTJnJ4tNzW1xrG9qRVLN9Zj75vfwMdLk9+HKr/xWjhYZUbWb0+/Lp1qsK0KuEC4og6ZxpZ4xpkKs2kwSClx7t+nYtI8/9K4jmZe6/Ya1NX18weLNuIVj6V6YkYHWbpMX4uZ6VMTVmjXRPM+kM0Men73G6/Mjeqsef9Lq6Mj0z0iiKZYHL1qylyP6Z18av/qf9OI617BT/89y/n5N//73Jn8RwV9q+0gyFz2wpTpmGwPcz80xxKeDc+2BBn9ulRgW2NryjkTT0icdu8UXPWf5DVsR7oxfQEzfaqC4YrHZ+Lku1Ozsfp2SClxxB/fwTP2OM0v7Y6idBmn7lXJY0B19v13dvJeZrYZqnzWRlPnxeINO3DavdbY0gqf6p8v7fvAJQ9/jKHXvIy19jHTnqCvoSWOaUs2O4u1+9GPDfNvaWpN4Bv/mOZsv6ImmNHXYgSSY0fVObv7ja87GfOtDS044o/vpnz+/z5bk7Hi5VcvzsXQa17G0GteTgmIFqzdjuPvmoztTa3Y0RxDTztoz9SJkM1X294xfXEp8SdtAsK0Y/q8yjsTEhISXSujznNMHNNHDq8eYsBdWuYszt6YXGw3U8PHK1OoxLWgr6El5vQK659lsso7pfMadWJkWrzczPo4QZ9H7ybgzvSp5+rni/46s4fL7zsxT8JEIosxffFkAKc4QZ/RCEluR3aZCPUd6VnMq5+dbczgmtwOvaGuzFu9DftoYxb0TF9zLOFkDZXvPZY506VsbWhxHSOZmA0kfb9MX7LJ+F3qd6W2u6Elhgm3vpVaQmi8/xPTl+MfHyzBn9/8EhN++xb281g7rS3jIjft8M70zfMoYVKbpBpHi9bXeY5vcc5Fn3tAexoSSrrxZWf+9UPsddMkLFq/w1oSRZsFzgn6PIJmFYBEfK5XAPDBlxudBke2EzY4Y05jcac00Y/eYMj0OTuaY5i2ZLNvZrctWcMt9S0p11T1PjuaYykZavM6ZTYq/MbmxI1Gmu9ELvGEK3MRT0jssIN0vYHitV5pUF7Z9ETCOyOlPtPJT6b5mKDBVHNrAgO7Vboe0zMHqqwwGg7hhU9X4dGpSxFLSGf6egCuLKAKcNbYk6P4VQuoTie/qgNTkL9nqL0mr2Lec/UO1qDv7fUdR8MCo/rVYkt9S8px7lU+etN/P8cT06zMk9lG1Y+v1ngCf3htgecYOPU5L89Z47m8kn69b2iJY9mmBvz8GSvwVMf7X95ZhGHXvmy8zvo+vBrP+kyP5nfpF/Sp7Tz3/o+c868i6v1cs62i5hpoz7IDQbPf+mc0xxKuv1/dk9dtb8Zrc9fgooemQ0rpXC/MOSDUMaIf66rqx29s9u9fW+B03vj519Rlzr8/X73dWfIFAO564wssXFeHKV9uRGNr3AmM6pvjuOih6Tjq9nfTvncQ7Y2npi/Z7O60T9OmrW+OeQxhsr5b1Zb3HtPHoI9s+klcqV103GP6kg3+5JTD7gNvc32L66agH3ctHmv+qIv+2ws2YPcbX8cUuwfUrxHV2BJ3SicaWxOBlzIw66NbjAyaSc9StMQSEMJ9MdZvVqpURfELtu579ytXT39CAmUZxvQlZ6VL/TtVJsSvhylTKZLZ2EqurZd83My2qAv8fe99hbPvm5oyZfOqLY2uYyYWl6iMhiEEsGJzA2Z7TIqTfhvt/0Ji75vf8OwFVNtlBjfmRU9vcAzoWmn8LvW7UmMRVbnRZyvdU7ubDRbV+GiJe+//ptY4/v2x/yLVfpZtrvc8H7zKY6WW6YsnJI65czIO/f07KeU76rw1jxz1OX4N5GwaGOkCdDUBxjq70aEHsOra4pXpU9vtN2Pd5C824Bv/mIb3vrAmX7nsXzOymhZf/d3NrXFXYx6wOoKOvuNdfN1ek1Q/nswSVV1DS8y5Rng16J79ZCV2++VrWL4p/eQTpn1+8wauedbdQ68aUlc/MxvfNtYPTOmcMo55czItRV0P1LUy3XBRPXCMJaRzDOjVIeaxnE2bVX3n+vXQzDAq2xpbMXfVNu2c8N/woJ0xTbE4ulRGnUYj4O7cUH9/Q0sM//fvWfilx9hLnbq2qAy23wRdE259C996+GPPbPukeWvxd2MphSDnqRpzNax3Nfp3rUgJ8OqbY573R/XWUsqUKgSvDgEhBHpURbG5oSXle/Ybt/TH1xfYn+F+XD/n/vfZavz13a9cM80qmUrt9eNFXbfV5Dfqu5uyaBMS0r1PmlvjWLi2Dne94VFS6NFW0m3c0YwZRkdpcyyBuqZW17VOtb9embMGo294FQ0tMTS2xFOCJ7Wd7emfW7vNP+g76vZ3ndnb9TZBPJEcpw+4O3R+8tQsTP5iAxZvrHfaWH6dSfUtsZSAvabcf3mpddub8NWGHc547nSdRec/8BEO+t3bzs9qbedWu82pjv3zH/jI2d72am9AZZbBputI9BrykZBWt4Pq8PTK9BVhzMegr6Poa7306VLu/NsvU6fG0OkBzmtzrQVnf67Niqa/vsUIhi56aLpzYdho9+qrnp605Z32wdyoZfoySRnTF3P3XivqWa5e/HgCYSFcpZj69q02GpV+geTUxZtw+aPJMTNBxvSp7fa6AGyut74zv8Zapiys2ZPkBH3a4+a6LqqB86U9Y5neoJZSot5otMTsyXoqImE85RPwfLmuDk9MW44Vmz0yUgn3eMKNHtkfwDqWDv39O67HzENDD0LMTNGjU5elLF6remf9SgnNMX3mYqkA8NAHS5x1wP7xwRLPsVUXPzQds1dsTRmrofzhtYW48ulZKY97NVTV37yjOYYR17/iPG6W76hj1Ny/6j39sjlmIJROkJkk19mNjrmrt+HRj5bhuZkrnc/w2teqMRfxWbzNq9f/yn/Pwvcf+yTQ5DTqr25sjaecVwvX1uGrDfX4ZNkWrNjcgDVa6Wi6BYjH/up1HHeXNflBhce6nI/Y44HXBBwDqXtx1irXz+pvXLCmDp8s3+Ka8CZT55TfvlXnjbrmyTTTG+jBbyIhscO+Hphln7pssspq/+uN+sbWuGev+HMzV+GUez7AUjuYTjfjsXndMr2zcD22NbSiuTWB8kgIvWuT90i9UasaYyqIy8QJElvVMd+Sck9TZeLTl2zG395Nnejl8kc/we9eXeB6zPxOvRrI1XYlQ1k4hEhYpOyX+paY52QQqiLksWnLccyd77kmD/LakyFhjYFbsbkRL81e7fqd37glNYmYmRnUA3c1+ZvXvfGq/8z2HJOnbGtsxdBrXsbTH69wrhlqHKT5/evX/+ZYAl/76xRnVlI/6lw5fve+2HNgV2xrbMXZ903FWfdNdT2vqTWOaYvdgaBau/dXL85FcyyB1VsbMeZXr+Gn/57tep7aD+3J9JltF93ijfX454dL0dASS+kQ0MuQ9WBl9wFdAFjVFur64pedbmiOp0z2pScfzI69hWvrcPQd7+H6F6x7mX5/8QuQ1PwTZSroiyUQSyScISc6KSXeWbC+TWsCA+3P9JmdpOmCPq9A2loiRTptG6+MPDN95NBPsL5dKpx/64eNHsxsa2zFRQ9Nd/WmqMyDPnmClMn3NoOhBWv9L5zpJnJRv9vS0BposVbAo7wzntpjbG2w9R+zZyscEq5STP1zzd6yoNnHIOWdb85fh7mrtuGvxs2+X5cKZ6IDvwaGV8bRryRt9dbkDVn/280LmVoXS90g9QvLXW9+mXJjUs/VG7v9u1a4fn/in9/Hdc/PwWF/eAczl29x/U4FUpka7Krhsa2xFef8fSpmLt+S0vDRJ/sxL5p3vfmF5+K1ALDCZ/pv82arGm6PfZTMqt38v89xxl+mWL/3aVi+98UGnP6XKbhEy8yYN9n/fZY69lHfl2pfq795hZZx/eaBu6S81u+GorI/fve9cTdPSpvV0gUJ+pbbgf7KLY345QtzceXTs53XbdTKWhes3Y77J3/ljBELexx/ADxv5tOWbMarc9di6uKNGPfrSbj5v/5jW9Xf3dSacEoTFb2BdNgf3sGl/0ouGuw1fhNI3Y/lkTCmL9ns2m71HaSbZTe5fe7nVBqZw8ZWayziyq3W/p+6eJPTQDMzp82xBMb07+L8vNFnwiA1JscJ+tJspl4hoWf6dF5ju4Nq9Lge6PcEnT6xhnqeX2lig8c1dNXWRjz98QrUNbXikoc/xrcf+RhNsTjKo2FXo3eHFhSrAMLrXPdqnK/a2oglG+udz39y+nJc+5x7KYmvNgTLQrgrbIxOTo/7ZJV9rkTDIURDoZTvsL45OWnaKXv1dx5Xx+Bcu/Jhwdpklt5rX4aEcN7nNiM4TZeRazFmgwXc1zxniSH71mK2Z0+7d4rve7+9wOpge2jKkpRMnxmIvvF5cibPpta4a6IZnVdb6YaTx+L43fuiqdV7BtnmWCKlTF91BKvrn195v9rf7SnFn21Ur3gZ+6vXcaeR2azWjn+9o62HPVbupdmrMXeV9XfVNcXw0AdLMO7Xk1znQH2Luzrn0kc+dnXemAGKqpJZurEBG+qaXdczv3v0EnuCJFXy2Bq3Znz3Gjv7nxkrcck/P8Z/ZuhrcEqff3toZ0BltuP8yjv1j9GTBvGEBCSS61hzTB+lo2c+BmiNcv24MTN9k79wr1+1tdFeSNO4+aiDMEgwpK5f6cb0qRvIum1Nnj2RzmcnJKSU1vonxgnw1HTrAuKX6TOfHwkJ1/IK+t+y3mhMpVtfRReXMu3CqpXRMG55eT4ueih1ceiB3SudoK/Op3H9+rx1KY+t396MxpY4zrt/qmvR1u9qGcjWNJm+P76+ELNWbHW2+5W5a5yGnd9042GjNHYXYyyJfrx8oXUEtMQSTtlF0CUEZi7fgulLNuPih6Z7TrSi+JWc6NSU/H6zfJoXVb/3VM/LZlk3s7HjdT/RvxN1w1Dnj7qZ3v/N/TCwu7uUVZ+VT28wPDV9OT6yxzqma0gEXQtNb/Bva2zFr/87L6Ux/NWG+pS/rc4p70x2ppz9t6n47SsLnOyfOv70G+Obn6/zbSAB9oK2ja14aMqSNFutqgjc5Z1SyrRloqt9yqSWGiWbq7Y24py/T8UfJyVL0tRxqe/POSu34Zb/fZ4S5JnHXLkZ9LXEsXFHs3Ndm7Joo9PgqWuKucqrW2IJjOhTg9+csYe1bT5LbKiyMqe8M82xoXcIxBPSM5hKnb0zi6CvNbUMsrHFPb6lS0UEkZBImUDi+ufn+i4UfeTt76Z81xc88BGufvYzbLHf55NlW5xMn97YUseJnlX1CgxUqZzuv7NXY+Lt77oyjU9rjc7WeMJ3LUBze/XGt3mt8bpPqoZ7SFj3f/O+ZZV3Wq8b3rtGe2/reZVqKR7tb/XalQkpMXZAl9RfIP21ZLPHGMCm1jiklM69HUjeo4JkMVRjf5J9b9ylZ5XTjmlsjePz1dtTvodbtVmLg07epL63aDjkKgU2NbXGsaWhBbUVySCkJS7x9oLkvdvM0Ce3JX1VRi59aZTxVvtk+lTFg5793d7UipvtCc6syZSs7W1oibk60t6cv951HpvXiTqnM7AZ+9/6Jq5/fq7zO68MOAAssWdCdZV3JqTnLLkr7W3RA1H9q82wClK7M31mJ6lfe1mf6OfR7xyAq08Yjd615Vp5p8pYp76+CGM+Bn0dRS/vHKANVG/WLrquoK8x9WKtZkfTG3tW0Gc30AJcMNUFzK+Xo7EluUxDXXMs7Yxsdc0xPDRlKYZf90rKzeXBD5ZYg419JnKJJaRrcptwyL+80yyzm5WmrATQ1s9LyLQ3qqe/exCA1BtjZTSMXjVlWqYveMnd4o078O7C9fho8Wa8qy06rV989b/Ha+tenbvGaZi8u3ADfv1fqzHjd++xMn3JC9WWev991qo1am/677zkzHtaY1ItO+BlqR0k1jXFUjIn+jHrl4V6Zc4ap8xUNWb8etpV4+fu8/cBACzzGZOlspxeY9S8bG1oSe2MkMAJf5rs6lXX95N5DKzcbO3PXrXl6GY0Ouas2uZ8F2p8bDwhcc1zc/D396wxQ+kqhszvzq8cZsqiTU7J4p/f/BIPT1nqZIqVpRvrMW5QN9djqmxZz/SpG/6SjdZ3HPYI+i791wxcZyw1oVthBDVfrKvzLRe++X+f412t3HZTfQtWbG7w7aTxKpOau2qb7xpSz9ozBerHhB4ofP/xT/DgB0twwwtz3Q0h47gwy0UbWuLO31lTHsEHizY6geJj05bh9L9MwWtz16KuqRXbm1pRHgmhi93gnOUz3jY5pk91FHg+DYD7PI0lZMrETYAVfD8weTHuecv6bjI1psy/T/8vYJX56dfjaDiEMf27pOzbVVsbccek1PFfysotja5gUjW29eN91dZGVETDrmD7t6/Ox9XPzHZ1CjR4XF8e/WhZymOKXrao32dOu3cKbvQIFoHULNmm+mY88uFSXPavGSnXvs9WbkvpBFPjsqyGYshjCZqYs+8rtTFc6m3UdrrvwakHR1NrAueOH4wbTh6T8rt0Qd+GuuaUtTCfnbkSlz4yA2f+7UPn71EZuiDt2W5V1rVQXTtfn7fOVUVz0t3vp+1INoM+PaBzT3qngj6BnjXl8NMcS2BLQ4trNtCmljj++WHyWPEL+lRHX64mdM2mrFGvqNArW7z2p94Rp3dM7GiOp0zWFWTyOdXJPlWb9EXNvGpS9+RoKNkGldIdtJqf7fd9pitrB9pfOmmW1/u1l/Vr/pj+tfjBkSMQFgJxuyMkomX66ppanTVOgdRO/GLAoK+D6GnfflqmT7+xxOLS6ZHa4rFWnzqhd7iCvuRkJUGCPgmrxOW1uWvRpSKC+7+5n+v3q7Y2urKAq9NMyb69sdW5GHg1yBu0SWF0//tsNR6esgTRcMjJQpjlnbG4xPJNDWhsiaf8XWrmMT9qjEAiIdOm22srIujbJfWGUV0eRo/qMqcnO13QV2aMffps5baMQaneczvNY2kBFRgoKzanH4sUFkbQ59PbDlhreynvaUGp3oC74IFp+GrDDqze2oh/f7wcX2mZOP3fG40gy1zL0csPHp/pOVGHF9UgGtu/C47erY/v8xLSagxv3NGMvQZ1TTst+3MzV2Lvm9/ANGN2UcAqh7737UVYtqkeM5dvcTVWzCyjKnfpXVPuNHSU0+6d4tyoVePEDFr0G5zZGNjeGMPm+hb8a+pSJ5PuZUdzDKfcbU3jrXpP4wnpypzWNccwoFsFbj59dxw5ujcAOLPtbdzRjPlrtrtmdFvqlOuEEE9I/OIZdylcunJvfdKJ1Vsbcdxdk3HW3z7Efr95Axc++FHK3/LWgmTQN/6WN/Hk9BUY2bcWtR7776npy7HPzZNw5b9nOd/pWfd9iBeMIFfxarj8+MlP8ZOnPsWEW990gsHHpy3H/ZO/wl/eWWRlzoyGftQ4v62ld6zv+tRx/bFsU0PKEhhfbdiBPW+ahI07WlAeSZ+J0CXLO1O/Yz2bqMQT3hOsxBIJ3PrKfNxhl4y1JdOnd3hYY/qS71HXFMO4wV09X59uGZF3v9iAsb963ckaqPK5udqC7IAV7FRo94LFG+rx9IyVrgyduZ/KwqGUGbJH9631DIT0mR79snyAdX7pJembdrTgxpfm4Y3P16WUsZ53/0e4x+iAUNkOKa1Mn3nu1Dcny2b1v1e9t8pO6guP+zWYQyGB/Yf2SHn8gzSLzK/Z1phybflo8Wa8tWA9Zq/Y6nT4hIR1TKZb10wxr4Ve0o2RazbuCVUewTCQbOtEIyGM6FMD07De1nqTVqavFd2rophyzVHo37UCTbE4Fq7djuPG9gUArPUZ66s6KNozpk/XlGZZFVO1z3qSXssL6YHe1sbW5OydzTFsrHO3BYLco7Oh3k8FQmrZKv2ap/aPGkriuvdp75XpMtXeLJo59tEvq6zaUhXRkNOWCIeENXsn3LN3HvaHdzBHu34V45i+zCuX5okQ4gQAfwYQBvCglPK2PG9SVvQbUm+tZ2r55ga8NX8djh7TFy3xBPp1qcCO5h2eWQun3NAoiyqz3ztIaYSUwLX2pBODulfiuN37uX7/6lz3OlJrt/sHfWu3Nzk3La/MztJN9SmlkxLAD5+wyh5rKyIIC4GYlAiH3CU9LfEEDv/jOzh0RC+njj2oddubMLhHFeIyfdAXDgkM7FaJdcZEIlVlEfSoLsOWBmum1LSTaxhvb9bme8l2WQHV8+R3TQyHhCvblO7C3tiawFcbduCKx2e6ev0+XrrF9byj73gPuw/ognmrt2Nwj2Rm+qv1yazccmNimKClOevrmnHqPR+4LpZA6rImqnwiEhI4ddwAV5Cga4klsNdNkzC2fxf071rhudSCotar8ssa/u+zNZ7j+8yJKNRMo71qytG10v/4VA29r4wSVtXZXd8cSym/3N7Uil++MBcvz1mDcYO6YWTf1EaNoia2UP+94YW5Kc+JhEK46KChGNG7Bu8u3KBN9d3qrN+kqHExAta03Ob1QHfUbn2wemujM3ZYH2us1p5U5ZdTFm2yy8mSx4jXuTm8dzW21Le4Sqq7VESc93nu01XY2tiKU8f1d95rv126o1tlNOX4uPXlz1MmDzIzoQCcxYdfmbMm5dhR38dVx47Cgx8sQWOLtd4qAJy132A8OT118iR9rFt5JOzZ6+2lxSfT9/C39kc8IXHpv2akLM3gVaLUEku+wczlWzwbU2WRkGfA2BJLpAS/jcb6ny3xBIb18j4m010Dnp9pBW2/f20Bvn/kcCeI0NeeBJCS6VO+sCf3GNKjynU+lkVC+NHEEbjjjS9c25mQ0tXBqmRaoP2qY0fhjje+QF1Tq+v40Wf088qY6CV3gHsyjkhIpAQPyzbV4+63FwGwghd9uwFgmx1A65modA1jc2mhsnAopdKme1XU6VBe6VNurKjfCyEyLtmkdNOuhTXlEc92QbqOIzPzqI+pXb21EY99tAxj+tc6kzOVhUMY2rMakZB7opxbTt8DFzw4Dc2xBLbamb6B3Sqx9+BumLFsCzbUNWO/XbrjnYXr/TN9Lbkp75w4ujfeWbgByzc3oKElhn2GdM94rwx6zTD97pX5TmdqfUtqpm9LwKEDQW1taMG/P16ech3qUpncftV+a2hJn+nLFFu3NYv29X0H4dmZK1PaRf6ZPuuY611bnixtDtmzd2pj+jbuaEl5z3ARBn0FmekTQoQB/AXAiQDGAjhfCDE2v1uVHb2BU2Wc0H+0p0RujSdQVRZG75pyz96nj+xZqPQptK1MX8h53Is+/a/ey5JuvFsvLTA1JzNQzr5vqtPz5DX5xF1vfJmyTfoFNBISzoQlESPTp173waKNWa8F9uFXm3DO36eiqTWRtuclHBIY2L0q5XG1D+IJiY31zc4MeV56aQHpHgO9x1UA7kDPq+GZjjPWMU0vrx4Y//joEZ69n4DV4LrppXlYsLbOVd7nRTWA9ann9eDFK3Dab5fuaY8rRQV8+ppcZlZFjf2w9pN73JyXjTua0aumHN84YIjvc7zWMApiR5P3TLaVZeG0mRx1MzQnGZBSoqElht1vfN3pHVUmzVuHl+dYjc3T/zIFUxZ5r62kPPbRspQAWqfGEw/Rxnr6ndNqO1viiZSJOkxdK6Ou70TvOPCa+n7DjmbXeeCV6T14eC9nAgxl3OBuAIDLDtsVp40bgLcXrHcmNBrQtQJ//+Z++Me39sc3DnTv9wfeX4LV25owzlgk3U+6zoKuVVHUlEfQYAd9PavLsM/gbp6zA+sLsJdFQthvSHfPyX4AK4BR1IzHZqZv94Fd0L3aOsZUKVc0LJBISM9ro14C9rW/fug5u1+5z+ysgHWtanSN6UudyMVrQh/AOxgqj4QwYWgPzNTKoNJlT8xMn6KyciP61Lj+xmhIoLt9/dOn3U9I6epgVVQppf49f+fQXZ1/q0Bxm5G11KshvK6d5t+kjmMJqyTM/A5f1Gba1JcFUjGRKuvUG5bpAhC9hHHyzyd6dpZOueYozPrVsQCAp2e4OywOG9nL9bO61rfGk+2N609KzZzq9EzfQcN7ej5HL9M175lmpk8ve3117lrc8MJc/PaVBU71SzQcQlkkhF17Vbte16OmDELo5Z3WdlVEw06H+si+NehWVebq2NabC6rCx68a1azyMV180C749Wm745JDrGPr+D9Nxpl//RBNranVSyav82tsf//2hTJtyWYnWG1ojmHjjmbXeEbVcaGynKYBHp0k6bwwazV+8ewcPD/TPcux/t30sjsjVAWSXv2ln4OZyjv9mnIPXjTe9zWHjeyFbx081PX5il9Hhvq++tQmvwtV3pnQ5onwKtcvwpivMIM+ABMALJJSLpZStgB4CsDped6mrOgNWjOjoW78sbh1c+jftSJlggKTc0GS0rXItZcuWqNUH0QdStM4H9a72glU9YuGH69a8zfnp050ot+zwqGQ0zMSNiZy0RsdQXsZlT+9+QWm22WT6a7L4ZDwvMhVl0cw2N4n0xZvhpRwTSGu08ue9AacKdsgQ6fGj/hdFMNCOI3CSw/dFT88aiSe+d5BmLBrarkPgIyLsJr03vL1dcmbyDKPJSCOHdvXd/FbL0IA7/38SAztWYWGlrgzFgtINqIiYeEaB+tn445m9O1SjhtP3R3nT/AP/ADgz295jwXzs6M55puhTVfSNG3JZkxbvCkl6GuJJzPIZrbo2Znu8RP3GWuEmW54YS6iIeG7QLG6AesB9vih3dO+Z3NrwneJC2VrQ4vTgDMnDwKACUa52aWPzHD9bK5rBwATd+ud8nf84ay98PPjR+Oyw4Y5ZVvKdw4b5nRQ+fWO+x0715y4W8o2+hFCoLIsjLXbG7FySwMGda9EKCRcDW0v5ZEQQiGBq08Y7fn7R78zAXecPQ7dqqLOWCczHgpp5dsq8KitiDqZPrPTwZwYyWvMbDTNrMYfG0sXfO+xT/CPD5a4GnJ+2TKv69yj3zkAPz56pOsxr+n+VVmvlelL3b6Gljh615ajR3WZKwCLhENOgKNn4/yu202tcfxnxgr84HGrdPP6k8bgxD2SFS/qemdOLKSX3XtNaGSWP1Zr5Z1Re8mG5z9Nntt+d9+ElJj61SZnLUw9WEyXdNLPm4poyLlf6O2NqrIIulWVobY8ggVr6zCga4VTOm8Giaqzqak1GfTp7+XVwNWzm7v1q/XfWFuPavf+MTuJva5pqq0REsnO9FHGZw3tWY3ySAjNrXFsrW9FN/s81e9NPavL0aOqzDWEpb82s3pyTJ/3l+61PIzu0sOG4eKDh+Kwkb1w6IhkQP3o1GUZ2zRe1zLz2qczg17Aqk7ZWNeM48b2w42nWjkS9d357ZvDRvZOu13K3oO7YXTf5HuY1xO947envY/fnG/dT3xnnc5Y3ul9xuzp06m3x8AueOCi8c7ss+a1SZ1XZ+83yPW4up721M6HUEikZPr+Ozu1874YyzsLNegbCEBvGa20H3MIIS4XQswQQszYsME962Uh0DN9egA4fpfuTrlmSzyBaFigX9cKJ2jxs6M5BmkfhOpC6xdY6EGb3tthZmTUmB/Ayg6qC0m2Qd/lhw/zfZ5+/YyGhbMN5pg+fdKFbDN9u/VL9oilS7eHhHBdXNVTq8rCTgD3sD0Tod+YMj041XuGvFx17Kj0G+7Da200nVXeaV2gVM9ot6oy30A1W+ai6cN616BndZnnQvBDelRlvBnqGlvi2KVnNc4ePxiAVX55yj3v409vfuE0osIhgb4ef8ufz9vblWlJSKBfV6sx/osTRrsy3O312ry1OMsei6h8dO3RAFKDviNGuW+c597/EZZsrHdtT2NLLKWB812f88ZvuQLdJYfsipF9vW/kKtOnl8foU8Tf9419Me26ozF+l2QgOH3pZs/ZaXVfrNvhXHNeuuJQ5/GfH28FOGYnhVlOZpZ5vXjFIejftRJVUff1pndNOa6YOAJ9ulRgQFd3ADdYywD7jYMZ5JMl7t+1AucfMNjJAqSzcnMDFq3fgSmLNuH9LzdikH19qMxwjKngpbYiit99bU/8+KgRrt/v0rMaX99vkFWKF0tg0fodKWOIQ0I4QdaGumbUlEcQDVs9z63xBIYbjUE1jbqil90q6bIUl/zzY8+p5vXj3K+DwWtYQms8kRJQnPnXD1Oe17+bdf20Zu/0fv/B3StRXRZ2lQlHw8ngW5/pOSGl5zVw5ZZG/PyZz5zS5R7VZeivdQyo1yy3v8dzxg/CrWfu4QoCN9Wn/p3mOK1qV3lnCLF4wrXkjl+5mpQSv9BKXmMJiaHXvIwfP/lp2myI/n7l0bAzw6hqxOoBW1d7X/bpUoEKe1/6DaN4duZKfGZXEpRp+8Wrw0Nv2+jVJnqQMaZ/F6eztYdx7pnXxEqPc1plQPW1REf1cV/7KqJhlEfC2NEcQ11zzPnb9HtT18ooBnSrcHUg9O2qB332mD7foC/9ua9fd/WA7dZX5uOA376V9rU1Hn+3apPsO6Rbyu/0TLWytaEVG3e0oFdtmZMlVO202gr3uayycTuaY9jH4/11ZZEQXrjiEFf1jXnNCmv7xiw7dg1P0h7PVEbrl6PwC7T6dalERTTs+3vVrjTXFFbni369CwnhbJ/5fNe2FGoElUahbrLXt+w6QqSU90spx0spx/fuHay3ojPpF9xoOIRjxvTBMWP6oGtlFFMXb8KLs1ahNZ5ANBxyzfTpp64p5gRQfqU2ih5M6cGZeTLce8G+TgAZCYcw2r5Q11RkbhSp933/6on4v2NGZni2JWyUd+oNeH2ykyAzTun02cHS1YGHQ8JV5nb18bsBsC6Wg+yyz5nLt2JQ90rXlNq6htY43rzycKe3Pp2hvapTsry6py4/0PPxbQ2tSCT8B9KHtIXt9RKjK44c4fl86zXJf9917jjn30EC00sP3dU3sO9TW+7bYPOixubo+2Huqu3405tfOo2oSCjkuVj4sF41OHXcANdjquHYraoMfzhrXMprTF43S9P5Ewbj5c/WYMHaOs8JmarKIrj6hNGY9NPDMf26o3HHOamfu3hDPQ4anuztrW+Jp0yycPCIXp6Nca+s/5/O3Rsn2ONxjxnTF/93zEj08Qny9evJOeOtXk29R7dHdTn6dqnA948c7nqdWe6lHzNn7zcIfzx7Lzxw0Xict/9gpxEJAMfv3g9VZWH8YKL38ed3DqiGmTnWT9/3av8qB2olZKqRrXdS3XP+PrjqOO8sW6+acpy5zyC8ddWRnr/XHTCsh6vkXQWSZplsOCSwx8AuOMvuPdbPhfMnDMFwn7LraDiEpz5egWPufA+3GzNghkQyyNpU34Ka8og9G6REcyyRcm1ab4xR9pr8IRpJ3QddMnTuRcMhHDKiJ35+/GjfTJ9X9rZXTXmgcdl6Q9TsOFI/D+pelZIFiYRCTsNyjRaYJaQ78/TwJft7lsj1qClznTtqO1T5+sl7DUjp0PPK9PnO3mln+sxODn0P6LepeEK6shfqOvjS7NXOPf/CA4b4lvAD1nGpGqnV2mQU+u8B63tV9450E2CpNU7LIiE89p0D8PAl+zuBgi7iM3fB945IXlu6VEQQtp9nZvrMku9Kjw5E1XEd1f6eUR7jnmvKI05Hk2rI6+dr18oodunp7jDpp2f67HuT36ybXtlonR4A62szB1EeDeFO4z5y4DDrWnfmPgNx3zf2BQAcM6YPnrzsQFxoDGk4cFgPvDp3LVriCew9qBtq7HM7GfQl9/XnN5+Av3/TKpGsLg/jme8djD+ft3fGbdTfwwzW3Zk+93Hit5h8ppGTfsGb33AS9bDfvA5O0Ge0t5NBX3K7XbN3ppsnopQzfUKIPkKIM4UQVwghvi2EmCCE6KigcSWAwdrPgwBkNzAqz8LagRUJCTx48f548OL9ndLLnzw1C7G4tebeAcP8S47+coF1ste3xFIu6n708QJqQU8gtceiIhJyAshoWGCMHfQFyZioi0k4lOyV/to+A9O9BJGQcE7IcEi4eur1nqNsM336IpyZJnKp0P620/e2Aoh4QqKyLOz0+PapLXcCYJOUwIg+tfj6foNcDZsDPEora8ojaQexq4u67tzxg7G1sQVbGlp8S3vCoWRgr39XYwd0wVtXHeH5mmPtmv7DRvbCmfsMcm78u3iUiSjvXz0R068/GqeOG+A7uUjv2vKMN0Od6rH3ulbqmT4A+Nlxo3D+hCH4+fGjcfUJoz3HUOoL05elKWFTTtijn5OZ8rPPkGQG7NWfHOb5nB8cOQKj+taiT5cK9PDoAV+1tdG1veWRkGvSlSNH98b+Q7unlP380CdwqoiGsUsvq2Ni3KCuiIRDuMQeu2DSg6zffW0vLPjNCa7vSR23R4/pi2nXHY1jxlhZ7d0HuMtm9Eb5H88eh4OH98KxY/vitq/v5Xre4B6V+PzmEzBxdB/894eH4rHvHOD6/S9O2M35t97IUg13dSxcd9JumHrtUa7X6s+f9NPD0UXbpqF2ielBWuPo1HEDfHvk1fltLrmh/OqUsfjzeXvjy1tPxFG79cX/fpTMZu5qNxbNjNd939gP//vRYU7j0oxv/Tqh9H1kBhTCmJ23ujyMUAhOpq9HtTVJhSppNgMvr5mgvToX/IJjpTwSwuOXHogrJo7wzfTpjb+Jo3vj9f87HKP71Qaa1VEFnc2xhBMsX374MEy//mgnsB3So8p17AJWAKsali/MSo4tkpCu73tw90pnfKiuZ3WZq4Gugh9Vvt6zuiwlG+y1FElKeaf9PhLeSzbo39UxY/piwq49UBkN452FGzDHzrSO6d/Fdc9Q94Az9hmIK9N00KlMMJDMgEe1Nog6niqjYefarx8TC35zguf7lkVCOHRkL0wc3cczi6o3iGu0oOCMfQY6bZculVHn7+hR7f5eJ81zTxzl1bmgvmZ93+4xsCtCAjht3ABM+unhAKxjRY1LrtFKh5XaikhKWboenM1fU4fmWNzJmJplgBUZOje9gr4g490Bq734tX3dn7ffLt3xwS8m4hsH7uJMdNQSlzhoeE/Xd/HYdw7AyXtalRz9ulTg+N37obbc+p4317cgEhIYY3d+DLPv9/sO6Ybbzx6H608ei3BIBJpxOFPbSjGX1HBPRAjPf3vxu3b6DVNSQaLfZjb7ZPrUtVv/DkKu2Tv92xUluWSDEGKiEOJ1AC/DmlilP6zJVW4AMEcI8WshROYRp9n5GMBIIcSuQogyAOcBeCnHn9Gh9JNdb4zqB1arXd75jQN2wZtXHpESbE0Y2sNJqdc1tToXv3Q9dCb9ZhU2ejgi4ZBzo4qGQxhtl0l6zW5l9tarhoU62Rf85gT88ezUjIeewg+HhHNihkMCJ2jjKhpa9UxfdkGfPrYxY9Cn7YsB3Srx5GUH4m92L5pqYHarKsPho3rjP987KOU9/nBWssGreoYOGtYTpxgZKMB9E/Rz+eHDcMkhQ7X3jGJrQyse/GCJ0xg2x+qFtNJYM2gY3rsG71890fn5xSsOwXePGIY9B1oNevX9DLMbVXoZ5XUn7eaUAd5w8hgM7lHl9Hj7ZaP71FZklelTvMrCtjW2IiSSvfw/PGokfve1PXHFxBH4wZEjIIRIKanqr5X/6efZzafv7vm55ZGQc55dfJD3ZBt642Z47xo8cNF4/PeHh3o+F/C/Cen77arjRrnWIfznJRNQVRZxleqVhUOeHQGAdR6piT9Uj+vBI3ph/s0n4Hdf2xMPXjTeOUf1m1TYXtNRvznpnRV9u1Q456TZQ5upzFtlCvX9v+egrjjUyBjqnVQDtMydutmqz+9SEXXtTwAY3KMKVWVh3HP+PhhllLMePaYvplxzFO44Zxzm3HQc/niWOxg1qUyE1/664+xx+Pahu+L0vQc6358+tlVlCMzyTlXGpo49s5NH7wl+4rJkMKzvI/Mc1jN9gFV5EQmF7KDP6iiccs1RuGKi9f37zUaoKzPO0ce+cwAOH5W+QkbfRr265BmP6yJg3V9UZ1mQcb7qXtjcmnC+Pykl+tRWOJ2KI/rUpIzRjIZC6FlTjpP36u9MdgYkJ+BQ2b3ySNh13/r58aNxzvhBKceRup+qIRbdq8tcvf6A90Qu5qLj6juSUqIiGkoZfqHfp7pVRfH0dw/C8D7VaIklsHxzA3rXlmPcoK7uMX12PkQg/b1NCOEs/ZCs3vHK9IWd81Ufl+W3v/RscC+PSXL0NkWtUSE0YdceqK2I4IqJI5wA02y7mGXF6Y4bPQM3uEcV3v3ZRPzp3L2d/Tmsd7Wzn9Q1R8/0RcKhlKBPP8c37mjGOws2OG0WsxMxc6ZPD3ys48dvnL1JtRf1c6siGsag7lUQIjlbt95+fOmHh2DKNUfh0JG9cMIe/XHMmD544rIDEAoJV6avLBLCuMHd8PKPD8X99iQoQgictd8g5/2CBH36BG/p/nazU81r0j+g7eWdfueBOhS9MoTl2uzFZhDXZFeW6cdm2J69M6Gt05fNNhayIF30JwG4TEq5v5TycinlDVLKn0kpTwMwDsCnAI7N5UZJKWMAfgjgdQDzATwtpfReUbVARYzyTkW/ebbEE4iErYH/I/rU4B/f2t/1HtFI8mTfUt8aONPnPwFI6mNqMH0kFHIaMEs21uMnxkD8+y8a7zl4WJ2AFdGw58moB3CRUMiZaCUcEjhydG/nc/TyzmwnctEzfWln77QnZ9AdNLyn06BTF2r1natAyXnusJ44Z3wyAa2el5DSc1Y/vzFHAJzA7LqTxuDGU3fHsN7VuObE3dC1KormWAJTv9qEfYd0x6JbT8RTlx3oKgUNC+FkZfYyFuIG4ExKA1gzIV574hinEaMaFA99azxuPHWsa1zY5YcPd44xs1fXr8eysizsO6bv3gv28f37zaxSj+oyzFu9HSP61KQNIvXF5of0qHI1SlTDon/XCpy7/2BUlYVTZngsi4Qwsq+VifjpsaM8S2z10q9wSODYsX19B497KY+E8MEvJuLg4cnyzT0HdnN+r2evnrr8IMz8pXX5PGh4TwzvkzzHelaX4e7z98FBw3ri8FG98f0jh+Nr+w50xkMC1vd//oQhOGZsX+c6k+4mBaTe4FVZU3cj6MvUufSLE3bD0ttO9vzdb87Yw/m3fr1SJWrjd+nuBKLqlPUaL1cRDePzm09IKetVBnarRG1FFLUV0az+7kNH9HJ1tuyaZtIEIDlxggrGrjp2FK46dpRT9unXCXPM2D44f8IQTL/uaByslfu6gr6YGfQJRLV16GrKrWurWl5BfZY6R30nStCUGRf/cEhkHNuoN3L1DMz4oT3w8CX749nvu4O/bBtAquqlORZ3gmN1eqvspVfQp+6tE0e7x12roODhS/bHz48fjUHdK537yq9P2x1XTByBP5w1zgksvnvEMJw/YXDKfatXTVlKearXpGlmJk+/bnWvLkuZ3l0PAtX1VL9fRUMCkbB7KQI9y+V1DdYna0q2D6zt0M8HVeFSoWX6TLdo56yinzNeQV80LPCtg4fixlPHpnQS9a4tx5ybjsfeg7vhljP3QFk4hN0Hpr+O+mWUgdRywCE9q1wdOMO0smd17TKPnUHG7N39jDLMjTuaneDZDPq8Mn36LtHPadWuMq+pftQxOF6baEo/Lvce3A2/+9qeuOX05D7aa1A3Z//3ri3Hgxfv73wH6hhoiSc7VHYf0NW3RLiLR9CnSnT3svdZuqWn9OB/cI8q7KXdL81ZcZW2lnf6lVQ6yy34BX32tdk831X7Ub9PJcs73WXFQbexkGUM+qSUP5dSLvf5XUxK+YKU8tlcb5iU8hUp5Sgp5XAp5a25fv+Oph9Y+sVaH5C+vTHmKrEwx4tEwyH0sAORzfUt2pi+9I2x/bTyNJ3K1uiTlCQzfcJpwAzpUYWfHjsKS353kvO8iaP7pIwBMv82L3ovTzgknG2IhASEEPjpsaPQvSrarvJOvaEVDgHTrjsaj196QMrzQqH0JRpq1ik1Xsm8OZrJLtX4kPDuofTLlIwb3M0VmAHA21cdie8dMdyZoGXR+h3o16XC6RTQLy7hkMCEXXtg8s8n4uv7+pfU6hN1qJu3yhb171qJSw7Z1QmaVKmr+n7MG3y6Xma/RsTovrXOLGbHje2L/l0rnFkNT9qzH6ZddzQ+ueEYDOxWCQFrvbM9BqRvFKhM370X7IN3fnakK4NVFkle9MsjYXx243G45Yw9Xa8vC4dwyIhemPWr49CtqgwHDuuJpbed7CpvasuEOK//3+HOvydfPdFpXMz45TH49JfHujp79O9LzU746k8Ow18u3NfVCLn7/H1w2rgBePLyA1FTHkHv2nLcec7evue/uhn63aRe/clhuOWMPVL25Q77ptezusz1uy4Bxvb6OUFbD7RG+9tH9qnFR9cejae/mwwY1LHdloyxn99/fc+Ux/QG4mOXHoAbT01mg/0mOlGZb9URoIKf/t0q8aOjRzrHn3q9WaVQHgnjd1/bE326mCWK6YM+IHlNscb0CafRpRqX5ZFw4GPVbMBGwgJdKqJpAzX9+mU2xieO7oP9djEqELJsAKlxkGfsM9AJ/FXgooKd4b1Tgz51nzRLBdVr+napwBUTrcoA9Z15lZtee+IY/O5r7uzwneeMQ3kkdVkWr/URzTF9+t/fs7osJRjXjw2vBmo4bAX7XlPcC+F9DX7lx4fhnZ8daW2P/Vw1Jl+/DqgKl4po2Am8hhpZr294LDOiZzy9jrVwSOAme5mCdJ1EBw/vhYW3nOC5oLwum0nBTLv2Sv496nprzoBpjoPu08X9c0NLzDmOgmT69DGCeltov1264/qTxuCW0/dwTeb09HcP8pxJM1M7SgiB8ycMcY2lTqc8kuyMzbTUBJB6ra+IhnDNibvhme8d5CQj0mXm9O3vUhnFSz881Jkp2Rqqkro8jd/YSSXrTJ9WRWYqj4a1MX3m/c86T/Xj15m9E+kncknXLipUWa8IKYQ4EMBvAZQDuF1K+XzOt6oE6PX0rlkqtRvBxh3NrgPQDBwioRBq7ZnbNtW3OCed35pbZZEQPrnhGLz/5UY8MnVZyu/VJv39m/s5vYl6KYgQAv/70aHOhdGsV/YKbNItAwG4y50iYeFsgzm7qd6LlG3Q59oeIdC3S4Uz45fOK9OnM0sdzL/fbNSo2bWOG9vXO9PncRPsWV2GRy7ZP+VxRTVOdjTHXDdZ/WtW2zHEY9p85ZMbjnF9vnrfZqPxEgmHMO26o52Z2W44ZSxG96t1xknpz9NdfNAuuPQwa3IXv5KcimiyvGq3/l2c0hLA+m7VuIfT9h7gTBmfqSdYNbSi4VDKBbcsHLbf23ubAf8yVfU3nLRnPyf4z4Y+BlSf5U7dTPWp5b2+rzEeE04EGaPokubvVp/h9TnqmtSjusy1qHSQWXz96A0kvaOlPBpKWUBb7cZ0kx5l69z9h+Cs/QajqTVurS9qLGuQsr0+3/Wd5+yNW87Yw7nO7T6gC57/dFXKDHXq/DIzan7055nZQX1W4bqmGKrLIwi7gr7kawd0q/QslTaZJU0hYU2q1bUy6jkGEIAzLsjalszHgnlqjehTgy31LRg3uBvmrtrmmmkTAEb1rXUyxR/Yy8qoduATlx2ADxdtQmVZGJWwjp+ySAgXTBiCb9vroJklmF6NUjVRSNBlZdRM0Oa1xWtstt94bSmDZ3hcmaJQCNFwyDXUQf1JAt7Xrq5VUScQUIlH1ckS1ss7nUxfCKeNG4DhvatTqi28ZMr0pWu/mPzGP4W1a47wXdgi8xgwfZIY1a4xq5PMYF5dq2vLI6hrjqG+Oe4Ez+balmanVEU0hCcvOxAH/s6amVP/+4QQuMye/OzK40bj7rcXOa/xuq7rx9uknx6OhWtTZ+DNVq+acqzc0hjoPmJe69Wxpmcef3P6Hvjbe1/hvYUbsGprI4b1rsZie3kYa46GMOpb4k779OnvHYQH31+MW16ej+1NMXStjLqye5n2p98ieP5Bn//L9EyfeR6ppZRqfDJ95rCoAJtY0DJeyYUQ/aSU+mjbKwGcBus69CEABn0e9AuufmE8YY9+eOrj5GoUeg9qaqbPCsR6VJdhc32zc8JE7FkozXR7WAjUVkRTbvBqJjEnyxYOQV2/VI+zuqDtYTS6p19/NBrsTIDXArpBByoD1oUw7NEbEw2HXMFwtmP6dOp9vYJRNb7Jj5rhzm/grnnTGtS9Cp/+8lh0q4pissdaeF6zrO4/tEdKY0Wn18PrQZ/+2UF6l8zB1MlMX+p3qw9m71Fdhu9qM68p5n4e0K3SyVaaDeYLDhiCvQe5s5l+jWrA3cu4f4b15FTDwysLpW5u6TIO6XqSv7jlRES0GWbTrcPo5eKDdsEjU5d53mT1G0rQ3ux0A8i9qL872+BJ3fR62BNcqPOvXUGf9h3oAaDXcaC2O2MjIEvhkLVEy39/dKhvL7W6Nvo1jMoiIZRFkufrtw/ZFbsP6JqyEPWFB+yCuqaY0xGSSboZm9X3YQVazahVmT67PFD/Dvt3qcDsAJ9nHksqSO1eVeYb9OljkoM0HM0G+6T/OxwS1n74ZNlmfP1vU12/1/8OdXlR++ng4b1c5bAv/dBa3kO/JppLCHhNdqw6GPxmujUN9Fnuw4u5rp+6LkfCImV8rB9XRZC9pJGeQVRDNUJCZLzuq2DFayIXfUwfkCyvv+1re7qCuW8dPBT//HCp87NePu+1xmWmkup0etWUY+OOZlRFw6iz7/+qTbNbv1osMAKfTIt56xNqqQ5P836v30croiEM6FqJh741HiP71OK4uyajoSXmZKBSMn3GzyfvOSClAyuTskjIORfLwlogol2zR/WtTRl32hY9swj6zO/J61gb3KMKvz1zT5zwp8kA3MdXNCzw3A8OwZvz17k+T52jW+pbrDZIFhO5+B3u/o/7Z/oqomHn88zMnZPp04715JINMm01REmWdwK4TwjxSyGEOrq3ArgAwLkAtnfUhhU71wmhnQRHju7jGkdUnaYxqC4OParLsVnL9AnhXfuuDnbzJFcDrL1Ohrn2ejz6mn26PrUVGNrLexIDr/f0KgFVBJLBmDvoE+4xfTkI+ryCUWtWPP9DXr3GHKCveJ383avLIITwbMx6latlunHr5Rv6FNj6y9pSUqAu6l5lSkGYF0r97zU7K2orIjhn/8G+zzfpwYXXFOu6G04Zi99/fU8c6DHjrQp2/K7D/778wJQyO12ZvbA2YI25TDd5i5dfn76H7xi3Kld5Z/oecdVgDFKWo1N/d7bB4uAeVkO3W1XUtZ+rspgwyqRvu8rAAt5/u2qI+a2P1V7hkPD9TpJrmgX7zkIhkRLwAdax8+OjRwbOKKVbLF2d3uq9qssjCHmUdwJwyv93H5D+vFH3hCNH98YTlx7gzI6ZLiOVbdBvnnehUDJQSbff1XMB/xKyvQZ1SykvTJ01N/W11580Fn+7cF/XjLzp6Jmgv1ywLx6+ZH/X92DOLqsb2acGlx8+DH+9cF/PNe28uDvzQinHoZPpE+nLzIBkFYRqvHrNK2Beq8+zxwMrN522uzPbbjgkXEHdkJ5VrrG6gFXl0lZqCQi9XXHFxBG47xv74rS9U8fwZro8dNfKffV21YtXHIK3PWa1nvfrE9C1KoqjduuLwT2qUF1uZdbfXWit+2zuC/PcbktlX1k45LQzIq7EQNuDZz+9a9p2HwHStzHU/tK3X03idIUx+7QaG6uW3dDP78wTuXhvg++snmnG9OnHvdkpqsb961nhUMjKnCek+7pmjvUtyaBPSnkGgFkA/ieE+CaA/wOQAFAF4IyO27Tipp805knX3aNHCki9MaqTqldNGTbVt0Da7fWQT5mi+kjz81TGyetEvv6kMThpz344ROtV9ePVoDEH1f7ihN1cU7TrpPZ8/SIXMUpasp3IRZeutwfwL40FoC14rm2b9j7pTvB0QY1r+zLcKfzGUOif3ZYLjRoXc5nPenuZmDelcu17NNckck3LbP83XW+jXoqTqee4pjyCc/cf4nnhV5/l9/14Td/uZ3CPqsDjJ4Ko0r6vTJk+1fOe6aZoUn91psah6b5v7IeHv7W/NRlKyPvYz5Z74Wit1NPjbx/T3+rV9iof62jHjrXGHgY9f3OlJs1aq+r4VaWJPWvKXZk+vTH6o6NG4L5v7IeLfGaiVdQ9obo8goNHJK/1fgvZA8nJKIKoKgv7XvcB7zF1OnW8+CxN6skMSg/yuIdVloVxoj2dfTon79XfNSmKemzi6D7uEsY0HTahkMB1J43BLj2rXY3DEX1qPJf0Adz3z2hYpDRI1fcRJNOnOEM2tHNZfUyQKgO1r8wxi9Z7Cuc5S2872TV5CmCN0wy6bq/quNY7sHtUl+GEPfp7HnuZDg29mkKfCX3c4G4p2wmkthGqyiJ46uMVeP5TaxmQTJk+9fpfnLBb4HuLnunT9317rrV+1PU06LXthpPHOJOvmctV6NQ5oG+z3/arY2nOqm0Yft0rqNc69zNP5BJkq1Of7xn0aceDXq554QFDcO7+Q7D0tpNdHT7hkEBcWuv06RUMvz7NPSt4MS7OHuiqLqX8rxDiFQA/APAcgFullO936JYVuXQnhD5rmn6h6lIZwVXHjsL2plY88P4S50bdo7oMyzc3OI3AkPAOXpKZPvfn1ZRHATR63jSO270fjtMmXUjH64bn9Z6+5WVSutbpU8rCIddaLl43m6AyBX3peuIvOngXtMQTuOigoc5jXSqjzvTh6csGg/XwZ2qP6+WdeumIeyKXQB/lUlMe8c1CBWEGEnrHgtlwTHjsv3Q3HnUT1GdGbQt1E/WblbCzG/Y610x6GY6Vs/YbhFtfmR9orTOdajhHs7wT9awpx0R7cqdvHbwLbp/0BYDcDVLXjxWvjM9Pjh6JQ0b0wn7axEOd5ebTd8d3Dt01pRy6o6WbJEed6mvtpRiG9a5GOCScIFBvjPbvWon+XSvx/Kcr036eeo05yY85SYqu3OM4NUue1Xish761f8rkVLpB3avw4EXjcem/Znj+Xm1WpskdXK/R/pY3rzwibQCbiVpTzos6D4TIPG2/ogd93zxwF7y7cL3z8/Trjnb+rZ+qZmYNcH8fQQMDdX3xug8HyWjvmyYr6gyf8LkX3u6xbJMfdS2o9Bgv6rncURbruqXrPNx7cDfMWrE15XGzespsY6Vk+uzv4vtHDk9b4aTTlxEJuzJluQ/61LjjoFUMqjT9yNF9XJ2UpmSmL/m+ftuvzoMnp69IaddlXKcvzfhOL+nW6XMt3WE/oTIaxq1npk74pd4rnrAKivVD3TwHizHTF2RM32kArgYQB3ATgEcB/EoI8QMAN0gpv+rQLSxS+g3JPCH0DIKe6RNC4EdHj8Qjdk29Olm7VkaxrTG5ZEMoJNKXd4bdv1M9ou3tTTJ7CcMh4Zlx0T/npD374ZU51pBQNb5D31bACho+X5ObSmF9SQhFDdIG0mf6yiPhlPKELhURJ+hLd36bAUUXn9KoTJk+fb+O1ur69c/Ox4XGPHb0xs8go7GnX9vVhT1dpu+EPfrh/m/uh2PGtL1UCAB26VmFX50yFifv5d2zXygLqWYqt7n0sF3x9f0GpZSSZKJ2UbaZPt0VE0cgGg7hd68uyLpM1E+mMX2RNOsTdrRoOOQ7jXlHSrculjpOVTnnsF7ViIRCnuWdSqZ9pRqa5vXHXPj89rPH4Wf/mQ0g9ZyffeNxKceuWhQ8yL3lsFH+1SRqvNiJe2TOyumO370vRvfr0qH70LlnCRG4VE6/r0fD7sk79Onx3Us2pJZ3JrTyTr+p6k3J8sG2nb9qUiqvbJt671xcSdV1wastU1Oeen5kW/ng56nLD3Q6UHTmxGtm6bPZjgu6P3RlkZCzX1yJgRxOYqWoTF+2QzoyLdWj2lDBMn1q9vnUyaYydfBk+/V6TRKouNdrTHbi+AmH7Nk7pXWsf//I4dh/aPeUyZtKMugDcAuAgwBUAnhFSjkBwJVCiJEAboW1cDqlYTY29d5urxNMBVeq0acWltTX7PEu71SZPvfFXn1GpoAjk5TBvn6zK4XVhBrAXy/cD49PW4brn59r/c4j6BvVtxaTPl/Xrm1T1Hekl7ZMuvJwLLFnmUo3e6eXk/bsj7/aM0um+/rM8kev9fOAzDcKdaz071rh2l/mkg2dzWxA6MfwAGNBbbMhCaR2ROjCIRE425yOEALfPnTXdr9PR8t0HqrJm7LXvsae+uxz9x+MDxZtxKWH7or7Jy9u83sp5cbsneS9Lpafgd0qEQ4JZ503s5IDyBz0+U1yZC4efdZ+gzBv9TY8PGVpynHkFahGwyE0tSYCXZP0gMnMYo/UZvLMxt+/OT7zk9pJXdtDRiauZ3WZMyuv+ffrnRuRsECZdg7o+yok3A3/1OxcciKXoA1MZ/ZibZuyvWN8dO3Rnve7ZNaz7fegV358GGKJBP76jnVf9Qr6vCZBy9WI34po2LPawtwO8xpcbfy+Lffh8kjIOQ/c9/TcXxePGdMXc1dtd5ZjypVkJtk9RMf7udbjXpPztXVMn5/kuq/pyztVJUymNZ2t2TslhBBO6fqbRjs1D02xdgsS9G2DFdhVAnBqFKSUX4IBX7t5BX1f33cQ6ppi+KY9TqPMDvqkVt7pdZNXFyHzxpGs8c9t0Oc7I579OdL52dpWa/pbkbItozzWrUnHnGFMp5ai0IMrVQIFZF/i97PjRqN7VRlufWV+2ou8KqU4bGQvvP/lRhw8wp25uPXMPXD983MD9eh98IuJKQ0s/Z5QCJk+vWNBlVVdfvgw7DukO47VBvcHGdPX0Z647ABnaulSpg6LoMsG+OlWVYZHv2Otc/nEZQc4swG2lb7vc7kWXzELEvT9/ut74v0vN1qzLbvK4VO/w0wZKGc5E+Px3fp1wdtXHYGj7njPeUyVYQW5X5Q5WYvM57feGJv1q+MyPr9QOBkE47rrKicM+f+uLJxs6Jvr7Zn/Nr9HfSKXoEGGWjvuSGPxev39MvGbldIJgNtxiRlrTzqkrgte1Te1Hpm+IKW/Bw7rga/aeK03r3Nm0FdbEcXkn0/En9/6Es/OXJnVffjHR4/E3W99aU3kEk5tA3XEmL7BPapwxznBy22DUoGceyIanwSA/R15TY6XaRRPtnGw2gTv2TvdnTBA+o6QUMi7vNN87/YmUvIhyN38TADnA2iFNWsn5ZBXj1YkHHJN/V0eCSOWkGhNJHv9vII+30yfXWrYlnIEnfm+MZ+zVvX6qGt02AkCpdOjpZ88w4y1dNLZfUAX3HTa7v5Bn13KEPZp+GbbQxkKCecGmO611faYudZ4As9+stJZePic8YPw7sINWS1ArRb2dm2Hx0x3nSmlvFM7FqrLI5j9q+NQUxFJuSiqG3VHlK8EZU7/XqrUN5zLmeBy8b3px0p7Fl8uJX7l37pz97cmGQDc57zXWK1MnSpqORqvS5g5rk/tryBVEeq8LsZFioOKeNyzAGONvTRBt17eaY63Nd/DbyIXARE4MBjVtxYfXXs0+nbJ/ThVfXxjrt7Laz1brzF9QeLVpy4/qM3bY2b6zI7XcEhgSM8q53nZFFRceewoXHnsKADJY6Wjx/R1lGqPiYL8tl897lViminTl8sxfXrCIsgxHBYCUsIu7/Rve5VqeWeTlPKedE8QQtRIKdOvfkueMtVPA8kbuqpDDwnvnpXkmD731UhNwNLeC0tNeQTdqqIY1qsaM5dv9X2eeeNyTe3rMdFKuvEt2VI9SplukEEaXUpyAp3M3180HMJ5E4Y4P//hLKunrak1ji/W1eH/jhkV+HN1+ie3N3hvi5TZO41GZqaZLovv0lh81PGZzwDbi34uMtNnyXY5BP079FruIVN5p1po2qshZZ7LPz56JMojYZy5z8CM2+U0YIuo0Zqt5KyA5uPu0kw/0XBySR/zeWbZvrkfW+LJe37Q77gsEkrJUqlJedp7bVDHYS4au+otvDoX9M5wteh3B63o4lhX1+T62WxDmENT2vodOBO5GDO3FgtVmh1LJAM5v/aWsGed9UoQZJzIJcuvJOiSDaotk64TPxRCcvZO7WmpE7lkt42FIMid50UhxCwALwL4REpZDwBCiGEAJgI4B8ADAJ7pqI0sZV69XCYVxKkpu4UQnhdvv3X6nHKCdl5YwiGBWb86Ds9+sjJt0GfenPQbmdeYvvYsAm2KJTIHaC9ecUhWi6rqs6a2VUU0jBtP3T3zE32413Nq+3a0VcrsnVmWaxbKJCqlrK3r9HU01/INeSzzLSTZBr/plgACMjca0x0T5rlZWxHFz44fHWi7kmMFAz29KOlj+nTuxmCwTF+62f8EUq+zza0J57OC3r+9joUfHTUC8YRs/wzJAUrjglIdEF7lnXqpZa/actRvasjBJ6ZXZZR3mudFStDXxoNezxyHhJXN7YgxfR1FLTm2Q5txPV2HRDgkPNdezv2YPv/XeU3kkm73hYRAQpV3ptmmksz0SSmPFkKcBOC7AA4RQvSAVeq5EMDLAC6WUq7t2M0sXUGCvnJnMKzq9Uut/Qe0MT0+68nk6gDNVPZjLkyrbnR+Y/qCZDsV9ScI4d1TpMo702X6slmvDbAW6QTye4Lrf04+tiN1ooJgjdaO7p0tJu/87EjUNbV22PurPVRoQZ+OmT5Ltp0mrjF9bcj0Zfq8nx8/GvsP7ZH2OV5UuaJfqb+XfMyW2h767J0618ybaTN9yTF95n7Sr6vS4/equkdlTILw6hSorYjil6eMDfT6dJzZO3N4D/KeyCWCP5+3N/74+kIM6VGFZZ0Q9P3+63vhqN3W4trn5gDwmK3TaLu0teImqo2DjYRCaIknOmRMX0dRSyLtaE4GfemuP5GQQIvH47kO+tIt1+WayCWc+RhOrtOXYUxfKQZ9ACClfAXAKx28LTuVcYO6YvbKbSkzQnlJZvqs6CPk0+unLkLmCRjxCLTaQ/Wa+C3ea87eqLZVymTvmN6z1ZbZBgW8a/ydcY85vIjuPaQbAODkAIv8dpR8z95pHlPZZmyK79KYe7tmMXa1LdRNrNDKO3WcvdOyW79aVEbDGN6nGnNXbc/4/EwNrEyND/Uav6eZS9UEpcYKBp0WfvaNxxVdttcvs6P3u2Yq71RBt9nQNPeHGTw22RkSgeBjdTuyqiKXY/qSE095/12n7z0Q44f2QCyewBF/fLf9H5hBj+oyV8eHeZs1j4O2tjGi2jjYcEgA8eIqj1ad+nVZZPq8JDJcMrIv73T/V6dfc1TbM93bO7N3QqZdI7mIdpsjq6uvEGKgEOJgIcTh6n8dtWGl7pFvT8Az3zsoUMCj6vGTY/qE5wLMXlm06dcdnax1ztERqs6BkX29e2z7G1P4uwb8CrWtwT4j5XF499KokzpmND5O2rP9SwEM712DpbedjGPGtm8dufbId9BnfmTQhtvZ460JbUb1zW6GVsqeU95ZwKVCxdbg7yi1FVHM/80JOHZMsOvTwnV1zr+9lkSRGaa56KiOgOPGWtvfK+Di9l0ro57T5ReyIJm+dAFZRCvvNGegdGX6ZGpAnyzv9B7SoRuQxZCFtooEmO4+KPUO6dolA7tVprQnOpLedgoJgYe+lVwSRO1/Z5bIdmb6wiHhOZNnoVPrF27XqlbSbb/f7zJn+rLbLnVMCiGce2H3qij6dalwVTqot00/ps8q70wY9Z07RXmnIoT4PYBzAXwOa6F2wEq2TO6A7Sp53arKMD5gOY2T6XNKPZK19RFtkKzXGjp9ulTk/IJSW2Gl908d573+i1n+6WT6oAemGSYeiFjrP/kx/6Laigiad7S4yoxm33icZ+lIMRJpLjyd8/ltK+88fe+BOH3vzBNCUPsV2kQuvz1zT3yhBSsAx3aagu6rFZsbAQCf3HCMZ9CUqYw62SDKbvsy+eHEEThvwmD0qe34gCNfnHK+NKVd6TriysIhp7Mjbuwo81pu3hebYuknb9O99KNDsXprY9rntFdyqEj730v96Zk6MVVmLBcduJno52NICBy1W18M712NrzbUa1P9t+9c0seUec1xUOiyz/R5t/UyFYS3ZaZ159/CKs/85kFDceWxo/DczJXa+6rn+L9X2H49jNk7zfOzGG9n2cygcQaA0VLK5g7aFvKhSqL0+n61Fl55JIRYSxx7DOyCe8/f1/P1fssXtNV+u3TH6/93OEb5ZPpMqmdLShn4IlceCbuCvu8eMQx/f2+xMVg3edn49qG74q3563HJIUOdx3I5K2i+6Re0QrhB5HPdPfKWnMgl/8cHAFxwwJDMT9rJZVpfT7li4nA88P4S9PTJqGW61iUbJ7k9NkIhUdIBH6CX81k///vyAxEKCfzi2c+c53gFZGqSjmhEOGP3m42OTHejUaacu87kbcg8pq9XTXngjGtb5XJMn2pMZ8qYCSHw8fXHdMr93GsZAtXKMJcEyJSp8uO0h6APvSme+2k3j5m601Ws+XVWxDOMA85+TF/qY15DnoJ0gFnr9FkVFK4Od+PPLMZOzGyOtMUASqcVXUTKjUxfJCSck0w1vm85Y08M9Rkz1BFT/I/uV5v2gD94eE/st0t36/M9ApbMQV/y0NxrUFectIcxns54+bBe1Xj2+wd3ailIZ8r3RC4mBn2FxymXKoDjg4IJGqD//Pjd8MUtJ/r+fnCPKrz0w0Nw0UG75GrTyGZO3HHAsJ7Yf2gP1y3Ia5ylashHwyEnO6Lu4Yp5rprv8/lqa7ynEIURGORyTJ8S5L1615Z3yj3Hnemz/2HHJk7wYm9wWycpU22bllgi2R4qkI66INQx+oMjhzuPpctC+7X1zFJnU7om4uOXHuDxfL1U2r0+sH5eqeOoX5q2YjgEbNzRjNa4dC+XVQAd7u2VTaavAcAsIcRbAJxsn5TyxznfKnJRB2lDiz57p8r0hQG0pg3s8nGgPnHZgc6/9TFGqjQpY9CnTfigP1N4PAYAXSvLUMryPaZPt/S2k/P6+eRNdcIU4oSpv/vanpixdEu+N6PgeK2511Z7DeqGl+es8fydal+xPyB7vhO56GP6/JZQilv3vx7VwfrLzaBj+tLNAKz9VgjtzUgOl2xQzYJ83890evBiXk/V/jdiwaypNRS3N7YWRCDfFqoN8Nd3vwKQfh/6lbBnLu/0/90hI3p5PF8L+uz/qnO0LJL8Xd/aCtx5zjgcNrK37/tvbUiOV3S1vUrgAprNEfcSgN8A+BDAJ9r/yMfbVx2ByT+f2O73UeOnVNAX0QYA9661yjnSlT7kOwWtn/RqEgJzi47erY/rZ9eYMSEwqLvVK3O2vc6Q2UPqVXJQStzTBudvO6hwqWOkEJfJOH/CENxxzrh8b0bByfWkO8nFs4E/fH0v5/ECPCSKht+SR/rPvzhht5TXqX0RjQh0q/LulNTPVXMil4OG93T+bU1Okf8GpyrJzEU1wYUHWFnpY8bkb4I0k1eZopM1ylFwqoK+bY2tTtvInICu2LQl05epvNPreH/mewf5Pl//GGlkZ/XzSgjga/sOctrOXk7T5qtIt2RDMQqU6RNChAF8U0p5TAdvT0kZ1js36xGpTF9jizVwNhxOrtN30PCeuPeCfTCkZ5XrNY9fekCH1/cHFdLKIVRKfUuDe+WWBy4aj6827MCxd1nzApW5arCBnjXlrgyTeT0opfF7XvSbLMv3yMtPjh6Jnzw1y3N2RypM0Uhuz2U1aUL/rpU4Z//BuFqNO7NbQbxyZM9vXTaV+bnn/H1w4LCeKa9Tv4+EQuhe5T3xlTnrqt44HaCVnxVKW1Ntby5uQXsM7Iqlt52cscyvM3kFLymZPifV17btVu2y+pY47jtjD/zmf5+jX5Ffs9Nm+nx+t2mH1+p9SV6JgXSTH3q1i0IeQV+QwO243fuhpjyCHc2xnbO8U0oZF0I0CCG6Sim3dfRGkZuqAa+3M31hIZyxIImExC49U8fyeaW/C0Efu3dl/Xb3fEChkHDN+ulX3un3WKln+oJOD047L86UWnwyLaqe9fupySd8GqTsL8qe35JHyXXBvL9UvbHr2ykp3f/UX9O/WzLoEwUSrqu11XLZ8VgIGUzFM+gzskZqX7S3vBMADhvZG5N+ekQb36lwpF/o3Psat3Z7U1bv6fcR4ZC1pp6+69Ss9qrILNugz/V5Jdbhns0dpwnAHCHEP4QQd6v/ddSGUZLK9DVp5Z3qRMqQIXfJV4eaU3YGmQz66lIngdVLOss8Zltyv6f7scoiW/spW/pfWyhT8hNR++S6A0dNCJHNfYHSiziLabsfzzT1+21f3wvDelWjW1U0cEOzZ00yIBjYLZn9KZS2ZjLTVyAblGNe+0n9zeY6fW1tT+lBX7H7w1l7Yc+BXdM+p61lsear/I45tV/03zvnrJrwMEN7Mt3n75SZPtvL9v+ok5UbE7mEQ1qmr4BKI/zo1RCDultlqMN6p2YnVXZPiNQabL/3TD6n+E/GdNyZvtL+W4l2FmU5Lu9U1wbzvlD4d4nCpTpY/RZn97v3HDu2L44dm368mrlfqsqSTbIBeqavQC75PautTttDR6SWs5YCr32pTiVz5tK2tr281tksVueMH4xz7HkW/OQqUPJ7l0hYoCXuVQ2VSC7ZoF1ng2f6jHLeLF5byAIHfVLKRzpyQ8hfmVPeaY3pi4STs3dmGgyry9eNQ13k+nWtQO/acrx4xSEY0Sd1vGOZ1iujb6tXacvofrWYsWwLTtyjH2av2Noh211IhNbLnG5NHCIqHjnP9IWSa4DpnNk7C6RMsJiowgqzvFM1CoM2BG8/exy6G8MQ9DJc9e+v7TMQz326Cv26aJm+Atlv/bpW4P2rJ+5U44ZTg772lXcqteXZ5FyKV5szfcbL/DJ0FdEwGlrirox7cmkw6+eoMUdENgpp5vRcCHzUCSGWwOM4l1IOy+kWUQpV9tioLdkQ9unRTSdfScHBPapw17njcOQoa4bOcYO7eT5PZTTLIiFXj5vXuf7gxeMxd9V2HDqyMMcu5hozfUSlJ9fnspPpMzoDVUBRKBmjYuKf6XP/N5Oz9huU8pj0+PftZ4/DLWfu4VrIvZAu+YN7VGV+UgnKZYP//asnoqqsdDJ+6bT1ezM7OvyuXWpoj1cbSZ27rvLOLMf0uco7S+ACmk1Xw3jt3xUAzgbgP5UO5Ywq5Uwu2ZAMioqhvBMAztwn9YZnEkKgLBxyL9cA75O9W1XZThPwAe6bfq4nfyCi/Mh11l6NZfErISv+Jkvnc5bBMBqLyTFebf9WzSUb1OdUlUUQDSeDvlIfvlDIVIeJGby0p+m1MwXObZ2DwDzkfYM+O3j2KsNUl9cybT3UoIGbxzwugQPGQhb4jiOl3KT9b5WU8k8Ajuq4TSNFCIGKaMgp7wyHtExfcS/vkqI8EkJ5JORqnJTCjEntVWolBkSU+3NZBSi1Fe7+3BP26I8e1WX45kG75PTzdgaqoWfuquSYvra/d7q4wTWuve0fQe2k9pE5ps9cboO8+c3emS2/jg+V6dOvpercUeeoPntu9mP6SqvtlU15577ajyFYmb/anG8ReaqIhlHfrAV99oEYL5JMX1Dl0ZCrVwZg0Ae4GxZRzt5JVBJyfS6rBlaNPV7o2hN3w2ertqFf1wrM/OWxOf2snUWyVMxMPVj/ydX9qW8X/3V1eQ/Mn5QxfXCiPgogZ7N3+jzPq7xT7Ss1ZroiGsbAbpVYtbUxeNDn8VgpDK3JprzzDu3fMQBLAJyT280hP5XRsLOgeTgksppBqpgO07JwKGUiFy5RYNSrs7yTqCR0VKavxs70ffeI4Tl9/51R2Mn0eY/pa88+VKWDR47ujZtO293/ifZHPPadA0pyTdpzxg/CnoO65XszPKmMXsTI+DLmC6bN50dKeadPpq9MBX3JxyJGeScAvPZ/h2H+mrrAw2OER6dOKXS+ZBP0fUdKuVh/QAixa463h3xURsNYYw/sjrjKOzNfeorp4lQeDVsTudg/D+1Zhd9/fa+8blMh0C820RLobSKi3I/PVZeJ6p1kZsDOoO615R1QgaLuzWftN8i1XINJXfJLdRz7H84al+9N8OWMtTTKeYO0vag9mT7vThaTyvR5lWHqpaW1FVFM2DX7aUh22iUbADwDYF+Px/bL3eaQH31gvj6mL15i153ySMg1e+cvTxmLvl12numh/bh6sfKU6bvupN2wtaE1L59NVIpy3Yiob7Ym+9pZpoPvDE721PhOVRCQzbJJKQIupcGJXPJH7V1VKqj2VYk1vTpMR6/TV1XmX97ZvqaS0P4f9me05/0KQ8Y7gxBiNwC7A+gqhPia9qsusGbxpE5QWeYO+pzZOwPccIrpOC2LhFzT65bYkMU2K4TBxJcfzlIxolzK9RiRuiarU8YMUKjt1EQuZva0vYt0A8EnAymme3ipUSW4KmnE+Ds7uVqnz6/jo9xOiOjzW5gTubTn811rRpfAzg9yZxgN4BQA3QCcqj1eB+CyDtgm8lCpZfoiIeH0OBTLkg1BXXjAEJRHwhjZtwbLNtVjwjCuCmLiRC5EpSHXWfsd9mRftRWlN+4rX8xxkorqfGvPLVi9NlNbshTGEhUrtY9Upm9EnxoAwJj+XfK1SUWlrbN3Bp3IRZVdt8SSU9mbE7m05/MzZeGLTcagT0r5IoAXhRAHSSmndsI2kYeKqLbOiDZ7Z5Cg7+v7DcIHizbiB0cWfqbm3P2HOP9+66oj87chBaw9FzIiKhy5zvQdMao37nl7EY4e0yen77szU/fYamPMnTB+3xZO0JfheYz58kftXXXbPXJ0H0z66eEYaQd/lF7bM33u1/mdZWq299Z4MuhLrq3Zpo+2P9/931KRTQ3IJiHEWwD6Sin3EELsBeA0KeUtHbRtpDHH9KmSk3iAdfq6Vkbx0Lf276hNo07GTB9Rach10Dd+aA8sve3knL7nzq6xxbrJVpW5F7xX2bd2ZfrspqzvwtPRMBpb4xzmkEfO4uzaThrVl6uVBRXOUXvFr3NFDQfyyvTlYuBlKZR06rKJgx8AcC2AVgCQUn4G4LyO2ChKVWlO5OLccHg32NmU2kWIaGfFrH3ha2y1JsfRO16B5HU4F5k+v1zfcz84GN87Yrir0oc6l9pFvO+2TbiN35v5Mr/pK8o8yjvVMl+xdkyyJDwmcikF2WT6qqSU040DP5bj7SEf+kQukVDISVuX2uLsREQ7C65BWvia7aCvMmpm+qz/5mLmfr928Zj+XTh2LM+CluCSt7ZOPGe+yi/BoSZtcZd3Wo+1Z2bdTOWdX956YpvfO5+y6T7aKIQYDrvjQwhxFoA1HbJVlMLM9OVkumgiIsqbUlj3qdQ1tNhBn095Z/tm76RCN7x3NQB20LSVWcJ++eHDAr3ODLb8TjMn0+cxpq99mT73f025XmO1s2ST6bsCwP0AdhNCrAKwBMCFHbJVlKLcJ+hjoo+IqDgVa8NhZ9Lol+mzd11nTORCneeSQ4Zic32L8/M/Lt4fs1Zu5Yy4bWSO6bvwgCG4f/LirN/HPM/2GNgFc1dtR5n9/l5j+uKJAJNeZBDy6Jgb2K2y3e+bL4GDPinlYgDHCCGqYWUIGwGcC2BZB20bafQbTkgAu/ayep+OHds3X5tERETtwERf4WvyGdN3+MjeeGXOWgzv3Z5ZHNVELjwQCsWNp+7u+rl7dRkmjuZsuG1lZvqCLz9izN5p9K08872D0dyawKtzrYJDPegb1L0KAFBV1v71Ss2t/eAXE9Glsng7AIIszt4FVpZvIIAXAbxp//wzALMBPN6RG0iWSnsgd8RemH1wjyrMuek4LsJLRFSk2NgvfPsP7YFpSzZjUHd37/65+w/GsWP7omdNebs/g0cBlSpznb6gJe2pE7m4o76KaBgV0TD23aU7ALiWqbn6hNHYc1AXHDayVxu2WH2+96A+FVAWqyARw6MAtgCYCmsx9qsBlAE4Q0o5q+M2jXRqPIGeama5ARERUcf5v2NG4pzxgzG4h7uxJ4Rod8AXdHF2omJlZvoCB33Gz37D80b1rcWiW09ERCuVr4iGceY+g7LZzMDbUeyCBH3DpJR7AoAQ4kEAGwEMkVLWdeiWkYsqLcn1uk5ERETkLRIOYUjPjundTy4H0CFvT5R3ZpAXPNOXMn+n73MjHTA2ulQXZw/yTbWqf0gp4wCWMODrfGpMH2d7IyIiKn5qQogurNqhEpWS6WtjFNXZE9U7QV+J5fqCZPrGCSG22/8WACrtnwUAKaXkIjKdgJk+IiKi0nH9yWNw0PCeGD+0R743hahD1De7l/P2mg3TS2p5Z+dGfSrYK7Umd8agT0oZzvQc6nhqTB8zfURERMWvIhrGSXv2z/dmEHWYo8b0xedr6vDm/HUA2j6RS76WJ9sZyzupALC8k4iIiIiKxd6Du+HBi8c7PwetVjPLKjs901ei5Z0M+opEsryTu4yIiIiIiktICLz7syOzfl1nZ/pEyj9KAyOIIpFcsiHPG0JERERElKVwSGBor2pUlaUfOZZS3plm9s6OoGYPLbGYj0FfsaiIqMXZucuIiIiIqLiEnLLJ9KrLIyiPhFBTbk09kkh07HaZ1PaFSmxQHyOIIsGJXIiIiIioWKWuv+ettiKChbeciHvO3wdA52f6lBKL+Rj0FYuKCJdsICIiIqLilin4Uxk+tcRDZ6/Tp1J9pRb0BVmnjwpAKCRQHgmVXKqZiIiAN356OLpUcpFuIip9mVqy5faQJrWYu+z0dfrUf0urzc2gr4hUloURCZfWAUjBPfv9g9CVjUKikjSyb22+N4GIqCCoTKCaxqLTZ+9UE7mUWJObQV8RqYyGOaZvJ7bfLj3yvQlEREREnUJl+jp9nT713xKL+jimr4hURMPOCUBEREREVHQCNmXD+RrTZyu1FjeDviJSwUwfERERERWxoC1ZNZFLp8/jwolcKN96VEdRGU2/oCURERERUbHL30QuwvXfUsGgr4j8/ut7cfZOIqIS8sqPD0NdU2u+N4OIqMMcNrIX3v9yo/Nz0LFy6mmdPabP/PxSwaCviAzqXpXvTSAiohwaO6BLvjeBiKhDPXLJhMAlmt2rkrOUh5xMXwdsVBoq2Cu1EVUM+oiIiIiIqEOEjOjJL4NWFgnhzSuPSHleviZyKbWpXDiRCxERERERdQq/UGp031r0rCnXnpefMX3O55dWzMegj4iIiIiICkveF2fv3I/tcAz6iIiIiIioUwSdyCXExdlzikEfERERERF1Cr9Qyoyx1I+dHvSpdfo69VM7HoM+IiIiIiIqKCLfs3eWWJRUYn8OEREREREVKr+qSfPhcCg/5Z1KqS3OzqCPiIiIiIg6ScDF2e3/dvaSDU6wV1oxX+EFfUKIm4QQq4QQs+z/nZTvbSIiIiIiog5kpACdxdkDL+2e280osZivYBdnv0tKeXu+N4KIiIiIiHIn6KSYvWrLAACXHzasA7cmVanO3lmoQR8REREREZUY39k7jZ+ryiJYetvJHb05HhtibUmotGK+wivvtP1QCPGZEOIhIUR3rycIIS4XQswQQszYsGFDZ28fERERERHlSKEl1jiRSw4IId4UQsz1+N/pAP4GYDiAvQGsAXCH13tIKe+XUo6XUo7v3bt35208ERERERGVpGR5Z143I+fyUt4ppTwmyPOEEA8A+F8Hbw4REREREXWCoEs25EupTuRScOWdQoj+2o9nApibr20hIiIiIqLcKfSySZHyj9JQiBO5/EEIsTcACWApgO/mdWuIiIiIiKhDFcpsmcKZyKUwtidXCi7ok1J+M9/bQEREREREuVcssVSRbGZgBVfeSUREREREpanQg6lSXaePQR8REREREeVVaYVYhYdBHxERERERdQq/DFqhJNbUdkgp87shOcagj4iIiIiI8qpQZvVU21FaIR+DPiIiIiIiIkthxJ45x6CPiIiIiIg6hW8ZZ4EFWyVW3cmgj4iIiIiICEjGnrLECjwZ9BERERERUafwy/QVSqJPJKO+ksKgj4iIiIiIOkWhTNjihxO5EBERERERdYB+XSvyvQkACmfpiFxj0EdERERERJ3CK6g6aFhP3Hrmnp2/MWlwIhciIiIiIqI28EqknbHPANSURzp9W7w4i7OXWIEngz4iIiIiIsqbQhrn54zpK62Yj0EfERERERF1DlHgg+aSmb7SwqCPiIiIiIg6hWfIV9hxYElg0EdERERERKSRJVbfyaCPiIiIiIjyppASfar8tLRCPgZ9RERERETUWQopwvPgbF6JRX0M+oiIiIiIqFN4xXyFNLkLl2wgIiIiIiIqYSHBJRuIiIiIiIjazCurVzh5PuDWM/fA+ROG4PBRvfO9KTkVyfcGEBERERHRzsG7vLPTN8NX/66V+N3X9sz3ZuQcM31EREREREQljEEfERERERF1ikLK6u1MGPQRERERERGVMAZ9RERERETUKURBTduy82DQR0REREREnYLlnfnBoI+IiIiIiPKGgWDHY9BHRERERERUwhj0ERERERFRp/BanJ06HoM+IiIiIiKiEsagj4iIiIiIOgXzfPnBoI+IiIiIiDoFqzvzg0EfERERERFRCWPQR0REREREnYKZvvxg0EdERERERJ1CcFRfXjDoIyIiIiKivGEg2PEY9BEREREREZUwBn1ERERERNQpOKYvPxj0ERERERFRp2DMlx8M+oiIiIiIiEoYgz4iIiIiIuocrO/MCwZ9RERERETUKRjy5QeDPiIiIiIi6hRM9OUHgz4iIiIiIqISxqCPiIiIiIg6hVeij9m/jsegj4iIiIiIOoVghJcXDPqIiIiIiIhKGIM+IiIiIiLqFMzz5QeDPiIiIiIi6hSs7swPBn1EREREREQljEEfERERERFRCWPQR0REREREnUJwVF9eMOgjIiIiIqLOwZgvLxj0ERERERERlTAGfURERERE1CmY6MsPBn1EREREREQljEEfERERERF1Cq7Tlx8M+oiIiIiIqFNw9s78YNBHRERERERUwhj0ERERERFRp/jRUSMQDTPb19kY9BERERERUac4eEQvfHnrSfnejJ0Ogz4iIiIiIqISxqCPiIiIiIiohDHoIyIiIiIiKmEM+oiIiIiIiEoYgz4iIiIiIsobwRXbOxyDPiIiIiIiohKWl6BPCHG2EGKeECIhhBhv/O5aIcQiIcRCIcTx+dg+IiIiIiKiUhHJ0+fOBfA1AH/XHxRCjAVwHoDdAQwA8KYQYpSUMt75m0hERERERFT88pLpk1LOl1Iu9PjV6QCeklI2SymXAFgEYELnbh0REREREVHpKLQxfQMBrNB+Xmk/RkRERERERG3QYeWdQog3AfTz+NX1UsoX/V7m8Zj0ef/LAVwOAEOGDGnTNhIREREREZW6Dgv6pJTHtOFlKwEM1n4eBGC1z/vfD+B+ABg/frxnYEhERERERLSzK7TyzpcAnCeEKBdC7ApgJIDped4mIiIiIiKiopWvJRvOFEKsBHAQgJeFEK8DgJRyHoCnAXwO4DUAV3DmTiIiIiKi0sWl2TteXpZskFI+D+B5n9/dCuDWzt0iIiIiIiKi0lRo5Z1ERERERESUQwz6iIiIiIioU91+9rh8b8JOhUEfERERERF1qrP2G4ST9+yf783YaTDoIyIiIiIiKmEM+oiIiIiIiEoYgz4iIiIiIqISxqCPiIiIiIiohDHoIyIiIiKivBFcnb3DMegjIiIiIiIqYQz6iIiIiIio00nIfG/CToNBHxERERERUQlj0EdERERERFTCGPQRERERERGVMAZ9REREREREJYxBHxERERERUQlj0EdERERERFTCGPQREREREVHeCHB19o7GoI+IiIiIiKiEMegjIiIiIiIqYQz6iIiIiIiIShiDPiIiIiIi6nRS5nsLdh4M+oiIiIiIiEoYgz4iIiIiIqISxqCPiIiIiIiohDHoIyIiIiIiKmEM+oiIiIiIKG8E12bvcAz6iIiIiIiIShiDPiIiIiIiohLGoI+IiIiIiKiEMegjIiIiIiIqYQz6iIiIiIiIShiDPiIiIiIi6nRS5nsLdh4M+oiIiIiIKG+4YkPHY9BHRERERERUwhj0ERERERERlTAGfURERERERCWMQR8REREREXW6/XftAQAY0rMqz1tS+iL53gAiIiIiItr5fPuQoThubF8M7sGgr6Mx00dERERERJ1OCMGAr5Mw6CMiIiIiIiphDPqIiIiIiIhKGIM+IiIiIiKiEsagj4iIiIiIqIQx6CMiIiIiIiphDPqIiIiIiIhKGIM+IiIiIiKiEsagj4iIiIiIqIQx6CMiIiIiIiphDPqIiIiIiIhKGIM+IiIiIiKiEsagj4iIiIiIqIQx6CMiIiIiIiphDPqIiIiIiIhKGIM+IiIiIiKiEiaklPnehnYTQmwAsCzf2+GhF4CN+d4I6nDcz6WP+7j0cR+XPu7jnQP3c+njPva3i5Syt9cvSiLoK1RCiBlSyvH53g7qWNzPpY/7uPRxH5c+7uOdA/dz6eM+bhuWdxIREREREZUwBn1EREREREQljEFfx7o/3xtAnYL7ufRxH5c+7uPSx328c+B+Ln3cx23AMX1EREREREQljJk+IiIiIiKiEsagj4iIiIiIqIQx6OsgQogThBALhRCLhBDX5Ht7qG2EEIOFEO8IIeYLIeYJIX5iP95DCPGGEOJL+7/dtddca+/3hUKI4/O39ZQNIURYCPGpEOJ/9s/cxyVECNFNCPGMEGKBfT4fxH1cWoQQP7Wv03OFEE8KISq4j4ufEOIhIcR6IcRc7bGs96sQYj8hxBz7d3cLIURn/y3kzWcf/9G+Xn8mhHheCNFN+x33cRsw6OsAQogwgL8AOBHAWADnCyHG5nerqI1iAK6SUo4BcCCAK+x9eQ2At6SUIwG8Zf8M+3fnAdgdwAkA/mofD1T4fgJgvvYz93Fp+TOA16SUuwEYB2tfcx+XCCHEQAA/BjBeSrkHgDCsfch9XPz+CWsf6dqyX/8G4HIAI+3/me9J+fNPpO6PNwDsIaXcC8AXAK4FuI/bg0Ffx5gAYJGUcrGUsgXAUwBOz/M2URtIKddIKWfa/66D1VAcCGt/PmI/7REAZ9j/Ph3AU1LKZinlEgCLYB0PVMCEEIMAnAzgQe1h7uMSIYToAuBwAP8AAClli5RyK7iPS00EQKUQIgKgCsBqcB8XPSnlZACbjYez2q9CiP4Aukgpp0prBsN/aa+hPPPax1LKSVLKmP3jRwAG2f/mPm4jBn0dYyCAFdrPK+3HqIgJIYYC2AfANAB9pZRrACswBNDHfhr3fXH6E4CrASS0x7iPS8cwABsAPGyX8D4ohKgG93HJkFKuAnA7gOUA1gDYJqWcBO7jUpXtfh1o/9t8nIrDtwG8av+b+7iNGPR1DK8aYq6NUcSEEDUAngXwf1LK7eme6vEY930BE0KcAmC9lPKToC/xeIz7uLBFAOwL4G9Syn0A1MMuB/PBfVxk7DFdpwPYFcAAANVCiG+ke4nHY9zHxc9vv3J/FykhxPWwhto8rh7yeBr3cQAM+jrGSgCDtZ8HwSozoSIkhIjCCvgel1I+Zz+8zi4lgP3f9fbj3PfF5xAApwkhlsIqxT5KCPEYuI9LyUoAK6WU0+yfn4EVBHIfl45jACyRUm6QUrYCeA7AweA+LlXZ7teVSJYH6o9TARNCXAzgFAAXyuTC4tzHbcSgr2N8DGCkEGJXIUQZrAGnL+V5m6gN7Jmf/gFgvpTyTu1XLwG42P73xQBe1B4/TwhRLoTYFdZA4umdtb2UPSnltVLKQVLKobDO1bellN8A93HJkFKuBbBCCDHafuhoAJ+D+7iULAdwoBCiyr5uHw1rDDb3cWnKar/aJaB1QogD7ePjIu01VICEECcA+AWA06SUDdqvuI/bKJLvDShFUsqYEOKHAF6HNYPYQ1LKeXneLGqbQwB8E8AcIcQs+7HrANwG4GkhxHdgNTbOBgAp5TwhxNOwGpQxAFdIKeOdvtWUC9zHpeVHAB63O+IWA7gEVscn93EJkFJOE0I8A2AmrH32KYD7AdSA+7ioCSGeBHAkgF5CiJUAbkTbrs/fhzVLZCWs8WGvggqCzz6+FkA5gDfslRc+klJ+j/u47UQyW0pERERERESlhuWdREREREREJYxBHxERERERUQlj0EdERERERFTCGPQRERERERGVMAZ9REREREREJYxLNhAREWUghIgDmAMgCmua8EcA/ElKmcjrhhEREQXAoI+IiCizRinl3gAghOgD4AkAXWGtJ0VERFTQWN5JRESUBSnlegCXA/ihsAwVQrwvhJhp/+9gABBCPCqEOF29TgjxuBDitHxtNxER7by4ODsREVEGQogdUsoa47EtAHYDUAcgIaVsEkKMBPCklHK8EOIIAD+VUp4hhOgKYBaAkVLKWGdvPxER7dxY3klERNQ2wv5vFMC9Qoi9AcQBjAIAKeV7Qoi/2OWgXwPwLAM+IiLKBwZ9REREWRJCDIMV4K2HNa5vHYBxsIZNNGlPfRTAhQDOA/DtTt5MIiIiAAz6iIiIsiKE6A3gPgD3SimlXbq5UkqZEEJcDCCsPf2fAKYDWCulnNf5W0tERMSgj4iIKIhKIcQsJJdseBTAnfbv/grgWSHE2QDeAVCvXiSlXCeEmA/ghU7dWiIiIg0nciEiIuogQogqWOv77Sul3Jbv7SEiop0Tl2wgIiLqAEKIYwAsAHAPAz4iIsonZvqIiIiIiIhKGDN9REREREREJYxBHxERERERUQlj0EdERERERFTCGPQRERERERGVMAZ9REREREREJez/ASdwSApjzFdTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (15,7))\n",
    "plt.plot(daily_returns)\n",
    "plt.title('Avg Stock Returns Over Time')\n",
    "plt.xlabel('Day')\n",
    "plt.ylabel('Return(%)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Your written response here*\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "<div class = \"panel panel-primary\">\n",
    "    <div class = \"panel-heading\"> \n",
    "    <span style=\"color:white\"><b>Task 5</b>\n",
    "    </div>\n",
    "    <div class = \"panel-body\"> \n",
    "    Using <code>stock_returns</code>, calculate the average daily returns over the historical period for the S&P 100 stocks. Store this result in another array called <code>avg_returns</code>. Check that the shape of <code>avg_returns</code> is a 1-d array with length 99.\n",
    "    <br><br>Plot the average returns in <code>avg_returns</code> on a bar plot, sorted by highest to lowest returns. Provide an appropriate title, axis labels, and adjust the figure size for the plot as needed. Your final plot should look like this:\n",
    "    ![Task_5_Plot.JPG](attachment:Task_5_Plot.JPG)\n",
    "    <br><br>Which stock experienced the highest average daily returns over the historical period? Which stock had the lowest? Provide some comments on whether those observations make sense and why.\n",
    "    <p style=\"text-align:right;\">[10pts]</p>\n",
    "    </div>\n",
    "</div>\n",
    "<div class = \"alert alert-block alert-info\">\n",
    "    <b>Hint</b>: The matplotlib function <b>plt.barh<b> gives a bar plot oriented horizontally.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 9.11386784e-04,  4.70146484e-04,  5.65459849e-04,  7.01085641e-04,\n",
       "        1.34379079e-03, -4.44612768e-04,  3.97547767e-04,  4.45145643e-04,\n",
       "        8.75457733e-04,  1.46880920e-03,  2.24158691e-04,  3.14393224e-04,\n",
       "        3.41689654e-04, -3.29352438e-04,  2.44005449e-05,  2.65065845e-04,\n",
       "        4.72179794e-04, -4.49856536e-06,  2.11359011e-04,  1.39492668e-05,\n",
       "        4.69140148e-04,  8.55916890e-04,  1.76737727e-04,  2.57589751e-04,\n",
       "       -1.79015761e-04, -1.66547808e-04,  7.12869286e-04,  7.93351162e-04,\n",
       "        5.49675973e-04, -2.79730450e-04,  1.20083201e-04, -1.01792178e-04,\n",
       "        1.12726922e-03,  2.20720652e-05,  2.55920194e-04,  2.29100630e-04,\n",
       "        2.51027753e-04, -5.04917219e-04,  7.58796400e-04, -1.02068911e-04,\n",
       "        1.15534655e-04, -9.35176120e-04, -2.07561845e-04, -3.21551580e-05,\n",
       "        7.91869643e-04,  2.37514103e-05,  7.37471591e-04,  3.99861332e-04,\n",
       "       -7.40589819e-05,  6.56593209e-04,  3.94458037e-04,  3.73419133e-04,\n",
       "       -4.94554013e-04, -5.50948728e-04,  2.30516091e-04,  6.13590436e-04,\n",
       "        6.32661272e-04,  6.41425169e-04,  9.34102099e-04,  6.29708666e-04,\n",
       "        2.49872470e-04,  2.62802661e-04, -1.00187133e-04,  1.17190572e-04,\n",
       "        1.51003882e-05,  3.56530222e-04,  2.67151698e-04,  1.29179508e-03,\n",
       "        8.12718727e-04,  1.25082004e-03,  5.13073287e-04,  2.36060571e-03,\n",
       "        3.16002409e-04, -9.11116517e-04,  3.86056563e-04,  1.32554375e-04,\n",
       "        4.42989437e-04,  8.94228651e-05,  1.23991009e-03,  4.38605802e-04,\n",
       "        1.04392613e-05,  3.14363462e-04, -1.05794415e-03,  3.32032731e-04,\n",
       "       -5.87174180e-04,  8.85750093e-05,  4.24167692e-04,  8.27183181e-04,\n",
       "        8.26110424e-04,  7.65229873e-04,  5.40512659e-04,  2.39629688e-04,\n",
       "       -2.39236473e-05,  8.58734896e-04,  3.05476423e-04, -4.61154818e-04,\n",
       "       -4.92630956e-04,  5.00389316e-04, -3.19927437e-04])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg_returns = np.array(stock_returns.mean())\n",
    "avg_returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(99,)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg_returns.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "avg_returns = stock_returns.mean()\n",
    "plt.figure(figsize = (10, 20))\n",
    "plt.barh(avg_returns.sort_values(ascending = True).index, avg_returns.sort_values(ascending = True))\n",
    "plt.title('Average Daily Returns')\n",
    "plt.xlabel('Returns(%)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'AAPL'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp100[np.argmax(avg_returns.mean())]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'AAPL'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp100[np.argmin(avg_returns.mean())]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AAPL    82\n",
       "ABBV    41\n",
       "ABT     73\n",
       "ACN     84\n",
       "ADBE    53\n",
       "        ..\n",
       "VZ      69\n",
       "WBA     67\n",
       "WFC      4\n",
       "WMT      9\n",
       "XOM     71\n",
       "Length: 99, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argsort(stock_returns.mean(), axis = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Your written response here*\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "<div class = \"panel panel-primary\">\n",
    "    <div class = \"panel-heading\"> \n",
    "    <span style=\"color:white\"><b>Task 6</b>\n",
    "    </div>\n",
    "    <div class = \"panel-body\"> \n",
    "    Using stock_returns, calculate the standard deviation of daily returns over the historical period for the S&P 100 stocks. Store this result in another array called <code>std_returns</code>. Check that the shape of <code>std_returns</code> is a 1-d array with length 99.\n",
    "    <br><br>Which stock experienced the highest volatility over the historical period? Which stock had the lowest? Provide some comments on whether those observations make sense and why.\n",
    "    <p style=\"text-align:right;\">[10pts]</p>\n",
    "    </div>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "std_returns = stock_returns.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AAPL    0.018319\n",
       "ABBV    0.019272\n",
       "ABT     0.016069\n",
       "ACN     0.015726\n",
       "ADBE    0.019675\n",
       "          ...   \n",
       "VZ      0.012239\n",
       "WBA     0.018826\n",
       "WFC     0.020104\n",
       "WMT     0.014062\n",
       "XOM     0.017025\n",
       "Length: 99, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "std_returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(99,)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "std_returns.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'OXY'"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp100[np.argmax(std_returns)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'VZ'"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp100[np.argmin(std_returns)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Your written response here*\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "<div class = \"panel panel-primary\">\n",
    "    <div class = \"panel-heading\"> \n",
    "    <span style=\"color:white\"><b>Task 7</b>\n",
    "    </div>\n",
    "    <div class = \"panel-body\"> \n",
    "    Plot a scatter plot of the standard deviation of returns on the horizontal axis (X) and the average returns on the vertical axis (Y). Provide the plot with a title and axis labels. Your final plot should look like this:\n",
    "    ![Task_7_Plot.JPG](attachment:Task_7_Plot.JPG)\n",
    "    <br><br>What can we conclude from the plot? Are there any noteworthy patterns or observations?\n",
    "    <p style=\"text-align:right;\">[10pts]</p>\n",
    "    </div>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12,5))\n",
    "plt.scatter(std_returns*100, avg_returns*100)\n",
    "plt.title('Return Std vs Mean')\n",
    "plt.ylabel('Mean')\n",
    "plt.xlabel('Std')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Your written response here*\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "<div class = \"panel panel-primary\">\n",
    "    <div class = \"panel-heading\"> \n",
    "    <span style=\"color:white\"><b>Task 8</b>\n",
    "    </div>\n",
    "    <div class = \"panel-body\"> \n",
    "    Calculate the correlation matrix for daily returns of the S&P 100 component stocks in <code>stock_returns</code>. Verify that the shape of the calculated matrix is <code>(99, 99)</code>. Store the correlation matrix in a dataframe called <code>corr_returns</code>, and set the stock tickers as both the index and columns of the dataframe. The first few rows will look like this:\n",
    "    ![Task_8_Output.JPG](attachment:Task_8_Output.JPG)\n",
    "    <p style=\"text-align:right;\">[10pts]</p>\n",
    "    </div>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>AAPL</th>\n",
       "      <th>ABBV</th>\n",
       "      <th>ABT</th>\n",
       "      <th>ACN</th>\n",
       "      <th>ADBE</th>\n",
       "      <th>AIG</th>\n",
       "      <th>ALL</th>\n",
       "      <th>AMGN</th>\n",
       "      <th>AMT</th>\n",
       "      <th>AMZN</th>\n",
       "      <th>...</th>\n",
       "      <th>UNH</th>\n",
       "      <th>UNP</th>\n",
       "      <th>UPS</th>\n",
       "      <th>USB</th>\n",
       "      <th>V</th>\n",
       "      <th>VZ</th>\n",
       "      <th>WBA</th>\n",
       "      <th>WFC</th>\n",
       "      <th>WMT</th>\n",
       "      <th>XOM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AAPL</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.390847</td>\n",
       "      <td>0.557528</td>\n",
       "      <td>0.605230</td>\n",
       "      <td>0.625153</td>\n",
       "      <td>0.456664</td>\n",
       "      <td>0.511396</td>\n",
       "      <td>0.504762</td>\n",
       "      <td>0.435745</td>\n",
       "      <td>0.568603</td>\n",
       "      <td>...</td>\n",
       "      <td>0.524008</td>\n",
       "      <td>0.528295</td>\n",
       "      <td>0.487162</td>\n",
       "      <td>0.513159</td>\n",
       "      <td>0.664860</td>\n",
       "      <td>0.325173</td>\n",
       "      <td>0.404758</td>\n",
       "      <td>0.493070</td>\n",
       "      <td>0.352223</td>\n",
       "      <td>0.493823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABBV</th>\n",
       "      <td>0.390847</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.497696</td>\n",
       "      <td>0.431948</td>\n",
       "      <td>0.424513</td>\n",
       "      <td>0.369918</td>\n",
       "      <td>0.362506</td>\n",
       "      <td>0.541958</td>\n",
       "      <td>0.310967</td>\n",
       "      <td>0.342906</td>\n",
       "      <td>...</td>\n",
       "      <td>0.456695</td>\n",
       "      <td>0.349475</td>\n",
       "      <td>0.369810</td>\n",
       "      <td>0.391536</td>\n",
       "      <td>0.441915</td>\n",
       "      <td>0.313113</td>\n",
       "      <td>0.315802</td>\n",
       "      <td>0.372785</td>\n",
       "      <td>0.253245</td>\n",
       "      <td>0.362452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABT</th>\n",
       "      <td>0.557528</td>\n",
       "      <td>0.497696</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.617022</td>\n",
       "      <td>0.567810</td>\n",
       "      <td>0.429558</td>\n",
       "      <td>0.511913</td>\n",
       "      <td>0.627004</td>\n",
       "      <td>0.510068</td>\n",
       "      <td>0.441379</td>\n",
       "      <td>...</td>\n",
       "      <td>0.589273</td>\n",
       "      <td>0.463521</td>\n",
       "      <td>0.513599</td>\n",
       "      <td>0.513855</td>\n",
       "      <td>0.636263</td>\n",
       "      <td>0.436736</td>\n",
       "      <td>0.446255</td>\n",
       "      <td>0.491718</td>\n",
       "      <td>0.396957</td>\n",
       "      <td>0.448810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACN</th>\n",
       "      <td>0.605230</td>\n",
       "      <td>0.431948</td>\n",
       "      <td>0.617022</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.650124</td>\n",
       "      <td>0.594821</td>\n",
       "      <td>0.615049</td>\n",
       "      <td>0.517164</td>\n",
       "      <td>0.557219</td>\n",
       "      <td>0.504978</td>\n",
       "      <td>...</td>\n",
       "      <td>0.544213</td>\n",
       "      <td>0.597211</td>\n",
       "      <td>0.534125</td>\n",
       "      <td>0.661682</td>\n",
       "      <td>0.738841</td>\n",
       "      <td>0.441336</td>\n",
       "      <td>0.450654</td>\n",
       "      <td>0.620419</td>\n",
       "      <td>0.386750</td>\n",
       "      <td>0.573793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADBE</th>\n",
       "      <td>0.625153</td>\n",
       "      <td>0.424513</td>\n",
       "      <td>0.567810</td>\n",
       "      <td>0.650124</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.448636</td>\n",
       "      <td>0.502674</td>\n",
       "      <td>0.491707</td>\n",
       "      <td>0.452774</td>\n",
       "      <td>0.619418</td>\n",
       "      <td>...</td>\n",
       "      <td>0.511701</td>\n",
       "      <td>0.480401</td>\n",
       "      <td>0.484257</td>\n",
       "      <td>0.468921</td>\n",
       "      <td>0.722728</td>\n",
       "      <td>0.302190</td>\n",
       "      <td>0.385484</td>\n",
       "      <td>0.447240</td>\n",
       "      <td>0.351663</td>\n",
       "      <td>0.423721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VZ</th>\n",
       "      <td>0.325173</td>\n",
       "      <td>0.313113</td>\n",
       "      <td>0.436736</td>\n",
       "      <td>0.441336</td>\n",
       "      <td>0.302190</td>\n",
       "      <td>0.335947</td>\n",
       "      <td>0.444302</td>\n",
       "      <td>0.390989</td>\n",
       "      <td>0.440905</td>\n",
       "      <td>0.232091</td>\n",
       "      <td>...</td>\n",
       "      <td>0.383126</td>\n",
       "      <td>0.349599</td>\n",
       "      <td>0.390248</td>\n",
       "      <td>0.444478</td>\n",
       "      <td>0.378576</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.389948</td>\n",
       "      <td>0.405611</td>\n",
       "      <td>0.427803</td>\n",
       "      <td>0.402168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WBA</th>\n",
       "      <td>0.404758</td>\n",
       "      <td>0.315802</td>\n",
       "      <td>0.446255</td>\n",
       "      <td>0.450654</td>\n",
       "      <td>0.385484</td>\n",
       "      <td>0.361457</td>\n",
       "      <td>0.392975</td>\n",
       "      <td>0.458341</td>\n",
       "      <td>0.308733</td>\n",
       "      <td>0.320213</td>\n",
       "      <td>...</td>\n",
       "      <td>0.413886</td>\n",
       "      <td>0.368323</td>\n",
       "      <td>0.457442</td>\n",
       "      <td>0.500878</td>\n",
       "      <td>0.421464</td>\n",
       "      <td>0.389948</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.474730</td>\n",
       "      <td>0.407872</td>\n",
       "      <td>0.383292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WFC</th>\n",
       "      <td>0.493070</td>\n",
       "      <td>0.372785</td>\n",
       "      <td>0.491718</td>\n",
       "      <td>0.620419</td>\n",
       "      <td>0.447240</td>\n",
       "      <td>0.709998</td>\n",
       "      <td>0.640920</td>\n",
       "      <td>0.470637</td>\n",
       "      <td>0.439404</td>\n",
       "      <td>0.333845</td>\n",
       "      <td>...</td>\n",
       "      <td>0.522645</td>\n",
       "      <td>0.608224</td>\n",
       "      <td>0.541172</td>\n",
       "      <td>0.870003</td>\n",
       "      <td>0.605357</td>\n",
       "      <td>0.405611</td>\n",
       "      <td>0.474730</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.325054</td>\n",
       "      <td>0.655385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WMT</th>\n",
       "      <td>0.352223</td>\n",
       "      <td>0.253245</td>\n",
       "      <td>0.396957</td>\n",
       "      <td>0.386750</td>\n",
       "      <td>0.351663</td>\n",
       "      <td>0.219279</td>\n",
       "      <td>0.343658</td>\n",
       "      <td>0.395012</td>\n",
       "      <td>0.361745</td>\n",
       "      <td>0.285986</td>\n",
       "      <td>...</td>\n",
       "      <td>0.357512</td>\n",
       "      <td>0.277499</td>\n",
       "      <td>0.377739</td>\n",
       "      <td>0.319510</td>\n",
       "      <td>0.353870</td>\n",
       "      <td>0.427803</td>\n",
       "      <td>0.407872</td>\n",
       "      <td>0.325054</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.283622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XOM</th>\n",
       "      <td>0.493823</td>\n",
       "      <td>0.362452</td>\n",
       "      <td>0.448810</td>\n",
       "      <td>0.573793</td>\n",
       "      <td>0.423721</td>\n",
       "      <td>0.627119</td>\n",
       "      <td>0.566363</td>\n",
       "      <td>0.433839</td>\n",
       "      <td>0.413733</td>\n",
       "      <td>0.331070</td>\n",
       "      <td>...</td>\n",
       "      <td>0.501635</td>\n",
       "      <td>0.607369</td>\n",
       "      <td>0.494975</td>\n",
       "      <td>0.652862</td>\n",
       "      <td>0.586972</td>\n",
       "      <td>0.402168</td>\n",
       "      <td>0.383292</td>\n",
       "      <td>0.655385</td>\n",
       "      <td>0.283622</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>99 rows × 99 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          AAPL      ABBV       ABT       ACN      ADBE       AIG       ALL  \\\n",
       "AAPL  1.000000  0.390847  0.557528  0.605230  0.625153  0.456664  0.511396   \n",
       "ABBV  0.390847  1.000000  0.497696  0.431948  0.424513  0.369918  0.362506   \n",
       "ABT   0.557528  0.497696  1.000000  0.617022  0.567810  0.429558  0.511913   \n",
       "ACN   0.605230  0.431948  0.617022  1.000000  0.650124  0.594821  0.615049   \n",
       "ADBE  0.625153  0.424513  0.567810  0.650124  1.000000  0.448636  0.502674   \n",
       "...        ...       ...       ...       ...       ...       ...       ...   \n",
       "VZ    0.325173  0.313113  0.436736  0.441336  0.302190  0.335947  0.444302   \n",
       "WBA   0.404758  0.315802  0.446255  0.450654  0.385484  0.361457  0.392975   \n",
       "WFC   0.493070  0.372785  0.491718  0.620419  0.447240  0.709998  0.640920   \n",
       "WMT   0.352223  0.253245  0.396957  0.386750  0.351663  0.219279  0.343658   \n",
       "XOM   0.493823  0.362452  0.448810  0.573793  0.423721  0.627119  0.566363   \n",
       "\n",
       "          AMGN       AMT      AMZN  ...       UNH       UNP       UPS  \\\n",
       "AAPL  0.504762  0.435745  0.568603  ...  0.524008  0.528295  0.487162   \n",
       "ABBV  0.541958  0.310967  0.342906  ...  0.456695  0.349475  0.369810   \n",
       "ABT   0.627004  0.510068  0.441379  ...  0.589273  0.463521  0.513599   \n",
       "ACN   0.517164  0.557219  0.504978  ...  0.544213  0.597211  0.534125   \n",
       "ADBE  0.491707  0.452774  0.619418  ...  0.511701  0.480401  0.484257   \n",
       "...        ...       ...       ...  ...       ...       ...       ...   \n",
       "VZ    0.390989  0.440905  0.232091  ...  0.383126  0.349599  0.390248   \n",
       "WBA   0.458341  0.308733  0.320213  ...  0.413886  0.368323  0.457442   \n",
       "WFC   0.470637  0.439404  0.333845  ...  0.522645  0.608224  0.541172   \n",
       "WMT   0.395012  0.361745  0.285986  ...  0.357512  0.277499  0.377739   \n",
       "XOM   0.433839  0.413733  0.331070  ...  0.501635  0.607369  0.494975   \n",
       "\n",
       "           USB         V        VZ       WBA       WFC       WMT       XOM  \n",
       "AAPL  0.513159  0.664860  0.325173  0.404758  0.493070  0.352223  0.493823  \n",
       "ABBV  0.391536  0.441915  0.313113  0.315802  0.372785  0.253245  0.362452  \n",
       "ABT   0.513855  0.636263  0.436736  0.446255  0.491718  0.396957  0.448810  \n",
       "ACN   0.661682  0.738841  0.441336  0.450654  0.620419  0.386750  0.573793  \n",
       "ADBE  0.468921  0.722728  0.302190  0.385484  0.447240  0.351663  0.423721  \n",
       "...        ...       ...       ...       ...       ...       ...       ...  \n",
       "VZ    0.444478  0.378576  1.000000  0.389948  0.405611  0.427803  0.402168  \n",
       "WBA   0.500878  0.421464  0.389948  1.000000  0.474730  0.407872  0.383292  \n",
       "WFC   0.870003  0.605357  0.405611  0.474730  1.000000  0.325054  0.655385  \n",
       "WMT   0.319510  0.353870  0.427803  0.407872  0.325054  1.000000  0.283622  \n",
       "XOM   0.652862  0.586972  0.402168  0.383292  0.655385  0.283622  1.000000  \n",
       "\n",
       "[99 rows x 99 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_returns = stock_returns.corr()\n",
    "corr_returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(99, 99)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_returns.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "<div class = \"panel panel-primary\">\n",
    "    <div class = \"panel-heading\"> \n",
    "    <span style=\"color:white\"><b>Task 9</b>\n",
    "    </div>\n",
    "    <div class = \"panel-body\"> \n",
    "    A heatmap is a graphical representation of data that uses a system of color-coding to represent the relative magnitude of a set of data. We can create heatmaps using another external library called <b>seaborn</b>, and using the code snippet below\n",
    "    <br>\n",
    "    <code>\n",
    "    plt.imshow(corr_returns, cmap = 'coolwarm')\n",
    "    plt.colorbar()\n",
    "    plt.show()\n",
    "    </code>\n",
    "    <br>Plot the heatmap for <code>corr_returns</code>. Rremember to provide a title to the plot and adjust the figure size as neccessary. Your final plot should look like this:\n",
    "    ![Task_9_Plot.JPG](attachment:Task_9_Plot.JPG)\n",
    "    <p style=\"text-align:right;\">[5pts]</p>\n",
    "    </div>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure (figsize = (15,6))\n",
    "plt.imshow(corr_returns, cmap = 'coolwarm')\n",
    "plt.colorbar()\n",
    "plt.title ('S&P 100 Correlation Matrix')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "<div class = \"panel panel-primary\">\n",
    "    <div class = \"panel-heading\"> \n",
    "    <span style=\"color:white\"><b>Task 10</b>\n",
    "    </div>\n",
    "    <div class = \"panel-body\"> \n",
    "    From the correlation matrix <code>corr_returns</code>, identify the pair of stocks that are <i>most</i> positively correlated (BAC and JPM) and the pair that are <i>most</i> uncorrelated (DUK and NFLX). \n",
    "    <br><br>Comment on whether it is reasonable for the identified pairs of stocks to be positively correlated or uncorrelated, and propose a reason as to why for each of the two pairs of stocks.\n",
    "    <p style=\"text-align:right;\">[15pts]</p>\n",
    "    </div>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.023193572612440438"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_returns ['BAC'].cov(corr_returns['JPM'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.008049451725256484"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_returns ['DUK'].cov(corr_returns['NFLX'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I would say that it is reasonable for the two pairs of stocks (BAC and JPM) to be positively correlated, as the corr_returns function provides a positive result.  The DUK and NFLX pair are uncorrelated as the corr_returns function outputs a negative result, thus showing that their correlation is not feasible. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Your written response here*\n",
    "\n",
    "\n"
   ]
  }
 ],
 "metadata": {
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