FSP_PS2.ipynb

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  "metadata": {
    "colab": {
      "name": "FSP_PS2.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "authorship_tag": "ABX9TyMGnd7m5bOFciHvGf7uRDwi",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/yuvalofek/FinancialSignalProcessing/blob/main/FSP_PS2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CTadNZVFFyLH"
      },
      "source": [
        "imports"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RVMoENBFFuf1"
      },
      "source": [
        "import numpy as np\n",
        "import numpy.random as npr\n",
        "import pandas as pd\n",
        "from tabulate import tabulate\n",
        "import matplotlib.pyplot as plt "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dhERY-_vF33K"
      },
      "source": [
        "# Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ng-ZiHIrJP7J"
      },
      "source": [
        "##Farma-French\n",
        "\n",
        "http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jUYNLtm8F64q",
        "outputId": "abcbc2dd-8549-4eae-c0d4-c7099c22646e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 413
        }
      },
      "source": [
        "!wget -O 48_IP.zip http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/48_Industry_Portfolios_daily_CSV.zip\n",
        "\n",
        "#for indexing\n",
        "leng = 49624\n",
        "\n",
        "#Getting the column names\n",
        "cols = pd.read_csv('48_IP.zip',\n",
        "                 skiprows = 9,\n",
        "                 skipfooter = leng+4,\n",
        "                 engine = 'python',\n",
        "                 compression = 'zip') \n",
        "cols =  [col.replace(' ', '') for col in cols.columns.to_list()]\n",
        "cols[0] = 'Date'\n",
        "\n",
        "#Getting the values\n",
        "ff = pd.read_csv('48_IP.zip',\n",
        "                      names = cols,\n",
        "                      skiprows = 25000, \n",
        "                      skipfooter = 3,\n",
        "                      na_values = [' -99.99'],\n",
        "                      engine = 'python',\n",
        "                      compression = 'zip')\n",
        "\n",
        "#Formatting the date\n",
        "ff['Year'] = ff['Date'].astype(str).str[:4].astype(int)\n",
        "#ff['Month'] = ff['Date'].astype(str).str[4:6].astype(int)\n",
        "#ff['day']  = ff['Date'].astype(str).str[6:].astype(int)\n",
        "ff = ff.drop('Date', 1)\n",
        "\n",
        "#Selecting the date range we care about\n",
        "ff = ff[(ff['Year'] >=2010) & (ff['Year']<=2019)]\n",
        "ff.reset_index(drop = True, inplace = True)\n",
        "\n",
        "ff.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-10-12 19:37:27--  http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/48_Industry_Portfolios_daily_CSV.zip\n",
            "Resolving mba.tuck.dartmouth.edu (mba.tuck.dartmouth.edu)... 129.170.172.144\n",
            "Connecting to mba.tuck.dartmouth.edu (mba.tuck.dartmouth.edu)|129.170.172.144|:80... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 3850438 (3.7M) [application/x-zip-compressed]\n",
            "Saving to: ‘48_IP.zip’\n",
            "\n",
            "48_IP.zip           100%[===================>]   3.67M  1.05MB/s    in 3.6s    \n",
            "\n",
            "2020-10-12 19:37:30 (1.03 MB/s) - ‘48_IP.zip’ saved [3850438/3850438]\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Agric</th>\n",
              "      <th>Food</th>\n",
              "      <th>Soda</th>\n",
              "      <th>Beer</th>\n",
              "      <th>Smoke</th>\n",
              "      <th>Toys</th>\n",
              "      <th>Fun</th>\n",
              "      <th>Books</th>\n",
              "      <th>Hshld</th>\n",
              "      <th>Clths</th>\n",
              "      <th>Hlth</th>\n",
              "      <th>MedEq</th>\n",
              "      <th>Drugs</th>\n",
              "      <th>Chems</th>\n",
              "      <th>Rubbr</th>\n",
              "      <th>Txtls</th>\n",
              "      <th>BldMt</th>\n",
              "      <th>Cnstr</th>\n",
              "      <th>Steel</th>\n",
              "      <th>FabPr</th>\n",
              "      <th>Mach</th>\n",
              "      <th>ElcEq</th>\n",
              "      <th>Autos</th>\n",
              "      <th>Aero</th>\n",
              "      <th>Ships</th>\n",
              "      <th>Guns</th>\n",
              "      <th>Gold</th>\n",
              "      <th>Mines</th>\n",
              "      <th>Coal</th>\n",
              "      <th>Oil</th>\n",
              "      <th>Util</th>\n",
              "      <th>Telcm</th>\n",
              "      <th>PerSv</th>\n",
              "      <th>BusSv</th>\n",
              "      <th>Comps</th>\n",
              "      <th>Chips</th>\n",
              "      <th>LabEq</th>\n",
              "      <th>Paper</th>\n",
              "      <th>Boxes</th>\n",
              "      <th>Trans</th>\n",
              "      <th>Whlsl</th>\n",
              "      <th>Rtail</th>\n",
              "      <th>Meals</th>\n",
              "      <th>Banks</th>\n",
              "      <th>Insur</th>\n",
              "      <th>RlEst</th>\n",
              "      <th>Fin</th>\n",
              "      <th>Other</th>\n",
              "      <th>Year</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.61</td>\n",
              "      <td>1.65</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.71</td>\n",
              "      <td>2.03</td>\n",
              "      <td>4.24</td>\n",
              "      <td>3.37</td>\n",
              "      <td>3.33</td>\n",
              "      <td>2.29</td>\n",
              "      <td>1.43</td>\n",
              "      <td>1.71</td>\n",
              "      <td>1.08</td>\n",
              "      <td>1.96</td>\n",
              "      <td>3.19</td>\n",
              "      <td>2.25</td>\n",
              "      <td>3.10</td>\n",
              "      <td>2.80</td>\n",
              "      <td>2.57</td>\n",
              "      <td>4.59</td>\n",
              "      <td>2.99</td>\n",
              "      <td>2.92</td>\n",
              "      <td>2.27</td>\n",
              "      <td>3.02</td>\n",
              "      <td>2.18</td>\n",
              "      <td>2.45</td>\n",
              "      <td>2.96</td>\n",
              "      <td>6.00</td>\n",
              "      <td>4.96</td>\n",
              "      <td>6.51</td>\n",
              "      <td>4.45</td>\n",
              "      <td>1.03</td>\n",
              "      <td>2.52</td>\n",
              "      <td>1.56</td>\n",
              "      <td>2.29</td>\n",
              "      <td>2.20</td>\n",
              "      <td>1.79</td>\n",
              "      <td>1.22</td>\n",
              "      <td>1.91</td>\n",
              "      <td>2.22</td>\n",
              "      <td>1.15</td>\n",
              "      <td>1.86</td>\n",
              "      <td>1.19</td>\n",
              "      <td>0.81</td>\n",
              "      <td>1.49</td>\n",
              "      <td>1.90</td>\n",
              "      <td>3.21</td>\n",
              "      <td>2.18</td>\n",
              "      <td>2.14</td>\n",
              "      <td>2010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.69</td>\n",
              "      <td>0.18</td>\n",
              "      <td>0.18</td>\n",
              "      <td>-0.08</td>\n",
              "      <td>-0.21</td>\n",
              "      <td>-0.43</td>\n",
              "      <td>0.18</td>\n",
              "      <td>-0.15</td>\n",
              "      <td>-0.28</td>\n",
              "      <td>1.15</td>\n",
              "      <td>-0.41</td>\n",
              "      <td>-0.45</td>\n",
              "      <td>-0.42</td>\n",
              "      <td>1.62</td>\n",
              "      <td>-0.84</td>\n",
              "      <td>0.02</td>\n",
              "      <td>-0.76</td>\n",
              "      <td>1.24</td>\n",
              "      <td>0.34</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>1.64</td>\n",
              "      <td>2.18</td>\n",
              "      <td>0.03</td>\n",
              "      <td>-0.67</td>\n",
              "      <td>0.73</td>\n",
              "      <td>1.64</td>\n",
              "      <td>2.46</td>\n",
              "      <td>3.96</td>\n",
              "      <td>1.43</td>\n",
              "      <td>-1.14</td>\n",
              "      <td>-0.18</td>\n",
              "      <td>-1.14</td>\n",
              "      <td>-0.50</td>\n",
              "      <td>0.23</td>\n",
              "      <td>0.17</td>\n",
              "      <td>0.21</td>\n",
              "      <td>-0.07</td>\n",
              "      <td>-0.41</td>\n",
              "      <td>-0.01</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0.26</td>\n",
              "      <td>-0.31</td>\n",
              "      <td>-0.75</td>\n",
              "      <td>-0.44</td>\n",
              "      <td>-0.02</td>\n",
              "      <td>-0.62</td>\n",
              "      <td>0.27</td>\n",
              "      <td>2010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-0.54</td>\n",
              "      <td>-0.22</td>\n",
              "      <td>1.08</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.70</td>\n",
              "      <td>-1.08</td>\n",
              "      <td>0.34</td>\n",
              "      <td>1.24</td>\n",
              "      <td>-0.12</td>\n",
              "      <td>1.39</td>\n",
              "      <td>-0.07</td>\n",
              "      <td>0.46</td>\n",
              "      <td>0.45</td>\n",
              "      <td>0.19</td>\n",
              "      <td>-0.65</td>\n",
              "      <td>-1.24</td>\n",
              "      <td>-0.24</td>\n",
              "      <td>0.65</td>\n",
              "      <td>2.84</td>\n",
              "      <td>-0.09</td>\n",
              "      <td>0.62</td>\n",
              "      <td>0.49</td>\n",
              "      <td>0.73</td>\n",
              "      <td>0.77</td>\n",
              "      <td>-1.46</td>\n",
              "      <td>2.68</td>\n",
              "      <td>4.64</td>\n",
              "      <td>3.98</td>\n",
              "      <td>3.75</td>\n",
              "      <td>1.34</td>\n",
              "      <td>0.34</td>\n",
              "      <td>-0.67</td>\n",
              "      <td>0.48</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.23</td>\n",
              "      <td>-0.15</td>\n",
              "      <td>0.33</td>\n",
              "      <td>-0.36</td>\n",
              "      <td>0.16</td>\n",
              "      <td>-0.86</td>\n",
              "      <td>-0.08</td>\n",
              "      <td>0.75</td>\n",
              "      <td>-0.42</td>\n",
              "      <td>-0.29</td>\n",
              "      <td>-0.55</td>\n",
              "      <td>0.76</td>\n",
              "      <td>-0.06</td>\n",
              "      <td>0.33</td>\n",
              "      <td>2010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.49</td>\n",
              "      <td>0.24</td>\n",
              "      <td>-1.49</td>\n",
              "      <td>-0.15</td>\n",
              "      <td>0.11</td>\n",
              "      <td>1.20</td>\n",
              "      <td>1.03</td>\n",
              "      <td>2.42</td>\n",
              "      <td>2.61</td>\n",
              "      <td>0.19</td>\n",
              "      <td>1.04</td>\n",
              "      <td>0.59</td>\n",
              "      <td>1.60</td>\n",
              "      <td>0.73</td>\n",
              "      <td>1.32</td>\n",
              "      <td>1.55</td>\n",
              "      <td>1.82</td>\n",
              "      <td>3.10</td>\n",
              "      <td>0.89</td>\n",
              "      <td>0.91</td>\n",
              "      <td>1.14</td>\n",
              "      <td>1.02</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.75</td>\n",
              "      <td>1.00</td>\n",
              "      <td>-0.95</td>\n",
              "      <td>1.73</td>\n",
              "      <td>-0.75</td>\n",
              "      <td>-0.66</td>\n",
              "      <td>0.58</td>\n",
              "      <td>-0.35</td>\n",
              "      <td>0.50</td>\n",
              "      <td>1.20</td>\n",
              "      <td>0.80</td>\n",
              "      <td>0.16</td>\n",
              "      <td>0.28</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.65</td>\n",
              "      <td>-1.06</td>\n",
              "      <td>-0.07</td>\n",
              "      <td>0.44</td>\n",
              "      <td>1.26</td>\n",
              "      <td>0.82</td>\n",
              "      <td>1.94</td>\n",
              "      <td>1.33</td>\n",
              "      <td>0.54</td>\n",
              "      <td>1.05</td>\n",
              "      <td>0.49</td>\n",
              "      <td>2010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.32</td>\n",
              "      <td>-0.19</td>\n",
              "      <td>0.22</td>\n",
              "      <td>-1.37</td>\n",
              "      <td>0.31</td>\n",
              "      <td>0.19</td>\n",
              "      <td>-0.47</td>\n",
              "      <td>-0.14</td>\n",
              "      <td>0.37</td>\n",
              "      <td>0.41</td>\n",
              "      <td>0.45</td>\n",
              "      <td>0.87</td>\n",
              "      <td>1.66</td>\n",
              "      <td>1.66</td>\n",
              "      <td>-0.63</td>\n",
              "      <td>-0.46</td>\n",
              "      <td>1.22</td>\n",
              "      <td>0.42</td>\n",
              "      <td>2.35</td>\n",
              "      <td>2.42</td>\n",
              "      <td>1.61</td>\n",
              "      <td>1.27</td>\n",
              "      <td>1.75</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.22</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.35</td>\n",
              "      <td>2.14</td>\n",
              "      <td>2.78</td>\n",
              "      <td>0.85</td>\n",
              "      <td>0.12</td>\n",
              "      <td>-0.46</td>\n",
              "      <td>-0.25</td>\n",
              "      <td>0.88</td>\n",
              "      <td>0.66</td>\n",
              "      <td>0.81</td>\n",
              "      <td>1.56</td>\n",
              "      <td>0.93</td>\n",
              "      <td>0.66</td>\n",
              "      <td>0.52</td>\n",
              "      <td>0.62</td>\n",
              "      <td>0.38</td>\n",
              "      <td>0.47</td>\n",
              "      <td>1.18</td>\n",
              "      <td>0.44</td>\n",
              "      <td>0.51</td>\n",
              "      <td>0.65</td>\n",
              "      <td>0.18</td>\n",
              "      <td>2010</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Agric  Food  Soda  Beer  Smoke  Toys  ...  Banks  Insur  RlEst   Fin  Other  Year\n",
              "0   1.61  1.65  0.75  0.71   2.03  4.24  ...   1.49   1.90   3.21  2.18   2.14  2010\n",
              "1   1.69  0.18  0.18 -0.08  -0.21 -0.43  ...  -0.75  -0.44  -0.02 -0.62   0.27  2010\n",
              "2  -0.54 -0.22  1.08  0.03   0.70 -1.08  ...  -0.29  -0.55   0.76 -0.06   0.33  2010\n",
              "3   1.49  0.24 -1.49 -0.15   0.11  1.20  ...   1.94   1.33   0.54  1.05   0.49  2010\n",
              "4   0.32 -0.19  0.22 -1.37   0.31  0.19  ...   1.18   0.44   0.51  0.65   0.18  2010\n",
              "\n",
              "[5 rows x 49 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 163
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JM_y2FY0JxmO"
      },
      "source": [
        "##S&P500\n",
        "\n",
        "Found on finance.yahoo.com"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kj6WWkKSKUgX",
        "outputId": "6092b68a-25b4-45e7-af03-01f5704b9bc2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        }
      },
      "source": [
        "def extract(path, year_range = (2010,2019)):\n",
        "  yr_bg, yr_end = year_range\n",
        "\n",
        "  df = adobe = pd.read_csv(path)\n",
        "  df['Year'] = df['Date'].str[:4].astype(int)\n",
        "  df['Month'] = df['Date'].str[5:7].astype(int)\n",
        "  df = df.drop('Date', 1)\n",
        "  df =  df[(df['Year'] >=yr_bg) & (df['Year']<=yr_end)]\n",
        "  return df\n",
        "Snp = extract('^GSPC.csv')\n",
        "\n",
        "Snp.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Year</th>\n",
              "      <th>Month</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>1116.560059</td>\n",
              "      <td>1133.869995</td>\n",
              "      <td>1116.560059</td>\n",
              "      <td>1132.989990</td>\n",
              "      <td>1132.989990</td>\n",
              "      <td>3991400000</td>\n",
              "      <td>2010</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>1132.660034</td>\n",
              "      <td>1136.630005</td>\n",
              "      <td>1129.660034</td>\n",
              "      <td>1136.520020</td>\n",
              "      <td>1136.520020</td>\n",
              "      <td>2491020000</td>\n",
              "      <td>2010</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>1135.709961</td>\n",
              "      <td>1139.189941</td>\n",
              "      <td>1133.949951</td>\n",
              "      <td>1137.140015</td>\n",
              "      <td>1137.140015</td>\n",
              "      <td>4972660000</td>\n",
              "      <td>2010</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>1136.270020</td>\n",
              "      <td>1142.459961</td>\n",
              "      <td>1131.319946</td>\n",
              "      <td>1141.689941</td>\n",
              "      <td>1141.689941</td>\n",
              "      <td>5270680000</td>\n",
              "      <td>2010</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>1140.520020</td>\n",
              "      <td>1145.390015</td>\n",
              "      <td>1136.219971</td>\n",
              "      <td>1144.979980</td>\n",
              "      <td>1144.979980</td>\n",
              "      <td>4389590000</td>\n",
              "      <td>2010</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           Open         High          Low  ...      Volume  Year  Month\n",
              "23  1116.560059  1133.869995  1116.560059  ...  3991400000  2010      1\n",
              "24  1132.660034  1136.630005  1129.660034  ...  2491020000  2010      1\n",
              "25  1135.709961  1139.189941  1133.949951  ...  4972660000  2010      1\n",
              "26  1136.270020  1142.459961  1131.319946  ...  5270680000  2010      1\n",
              "27  1140.520020  1145.390015  1136.219971  ...  4389590000  2010      1\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 164
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "us-2wWxhJ2ii"
      },
      "source": [
        "### 5 Companies of interest:\n",
        "\n",
        "\n",
        "*   Adobe Inc.\n",
        "*   Apple Inc.\n",
        "*   AT&T Inc.\n",
        "*   Nike, Inc.\n",
        "*   Microsoft Corp.\n",
        "\n",
        "Found in finance.yahoo.com, downloaded as seperate files"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Czci7c3cJXeO"
      },
      "source": [
        "adobe = extract('ADBE.csv')\n",
        "apple = extract('AAPL.csv')\n",
        "att = extract('T.csv')\n",
        "nike = extract('NKE.csv')\n",
        "msft = extract('MSFT.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4peveapjNqya"
      },
      "source": [
        "##USD LIBOR rate for 1 year maturity\n",
        "\n",
        "https://www.macrotrends.net/2515/1-year-libor-rate-historical-chart"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SJTM0uKXNuXK"
      },
      "source": [
        "#Averaging rate for each of the years of interest in ascending order (2010-2019)\n",
        "LIBOR_rate = [0.92, 0.83, 1.01, 0.68, 0.56, 0.79, 1.38, 1.79, 2.76,2.37]\n",
        "LIBOR_daily = [r/360 for r in LIBOR_rate]\n",
        "LIBOR_monthly = [r/12 for r in LIBOR_rate]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qvY0kzaCQuEN"
      },
      "source": [
        "##Splitting by year"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LG314pe0PIkb"
      },
      "source": [
        "def split_year(df, name):\n",
        "  for yr in df.Year.unique().tolist():\n",
        "    (df[(df['Year'] == yr)]).drop('Year',1).to_csv(name+str(yr), index = False)\n",
        "\n",
        "split_year(ff, 'Farma-French_')\n",
        "split_year(Snp, 'S&P500_')\n",
        "split_year(adobe, 'Adobe_')\n",
        "split_year(apple, 'Apple_')\n",
        "split_year(att, 'AT&T_')\n",
        "split_year(nike, 'Nike_')\n",
        "split_year(msft, 'Microsoft_')\n",
        "#split_year(mcd, 'MCD_')\n",
        "\n",
        "#I forgo saving the USD LIBOR rate in a seperate file as it isn't a large array \n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "80t74T2TQ_wh"
      },
      "source": [
        "# Analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pvNInBr2RB01"
      },
      "source": [
        "## Basic Markowitz Portfolio Analysis:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7ysFBQjTPdC1"
      },
      "source": [
        "def get_MVP_M(df, R):\n",
        "  m = df.mean().to_numpy().reshape(-1,1)\n",
        "  C = df.cov().to_numpy()\n",
        "  C_inv = np.linalg.inv(C)\n",
        "  one = np.ones(m.shape)\n",
        "\n",
        "  sig_mvp = 1/np.sqrt(one.T@C_inv@one)\n",
        "  w_mvp = (sig_mvp**2)*C_inv@one\n",
        "  mu_mvp = m.T@w_mvp\n",
        "\n",
        "  m_ex = m-R\n",
        "  w_m = (C_inv@m_ex)/(one.T@C_inv@m_ex)\n",
        "  sig_m = np.sqrt(w_m.T@C@w_m)\n",
        "  mu_m = m.T@w_m \n",
        "\n",
        "  return (w_mvp, sig_mvp, mu_mvp), (w_m, sig_m, mu_m), m, C_inv, C\n",
        "  \n",
        "\n",
        "def bullet_sig(m, C_inv, mu_vect):\n",
        "  m_ = np.concatenate((m, np.ones(shape=m.shape)), axis = 1)\n",
        "  B = m_.T@C_inv@m_\n",
        "  B_inv = np.linalg.inv(B)\n",
        "\n",
        "  G = B_inv@m_.T@C_inv@m_@B_inv\n",
        "  a = G[0,0]\n",
        "\n",
        "  sig = np.sqrt(a*(mu_vect+G[1,0]/a)**2+np.linalg.det(G)/a)\n",
        "  return sig\n",
        "\n",
        "def w2noshort(w):\n",
        "  w2 = np.copy(w)\n",
        "  w2[w2<0] = 0  \n",
        "  return w2/sum(w2)\n",
        "\n",
        "def table_params(mu_v, mu_m, sig_v, sig_m, R):\n",
        "  mu_v = mu_v.reshape(-1)\n",
        "  N = mu_v.shape[0]\n",
        "  \n",
        "  beta = (mu_v-R)/(mu_m-R)\n",
        "  corr = beta*sig_m**2\n",
        "  sys_risk =(beta*sig_m)**2\n",
        "  div_risk = sig_v**2 - sys_risk\n",
        "\n",
        "  return corr, beta, sys_risk, div_risk\n",
        "\n",
        "\n",
        "def year_analysis(path, R, TwoPortfNames, N=100):\n",
        "\n",
        "  #48 portfolio\n",
        "  df = pd.read_csv(path)\n",
        "  (w_mvp, sig_mvp, mu_mvp), (w_m, sig_m, mu_m), m, C_inv, C = get_MVP_M(df, R)\n",
        "  mu_vals = np.linspace(mu_mvp, mu_m+0.2, N).reshape(-1)\n",
        "  sig = bullet_sig(m, C_inv, mu_vals)\n",
        "\n",
        "  print('R<mu_MVP:', R<mu_mvp)\n",
        "  #2 Portfolios only\n",
        "  portf1, portf2 = TwoPortfNames\n",
        "  two_portf = df[[portf1, portf2]]\n",
        "  (w_mvp2, sig_mvp2, mu_mvp2), (w_m2, sig_m2, mu_m2), m2, C_inv2, _ = get_MVP_M(two_portf, R)\n",
        "  mu_vals2 = np.linspace(mu_mvp2, mu_m2, N).reshape(-1)\n",
        "  sig2 = bullet_sig(m2, C_inv2, mu_vals2)\n",
        "  \n",
        "  #No Shorting\n",
        "  w_mvp_no_short = w2noshort(w_mvp)\n",
        "  w_m_no_short = w2noshort(w_m)\n",
        "  mu_mvp_no_short = m.T@w_mvp_no_short\n",
        "  mu_m_no_short = m.T@w_m_no_short\n",
        "  sigs_no_short = bullet_sig(m, C_inv, [mu_mvp_no_short, mu_m_no_short])\n",
        "\n",
        "  ###Annotated Bullet plot\n",
        "  plt.figure(figsize = (7,7))\n",
        "  plt.plot(sig, mu_vals)\n",
        "  plt.title('Markowitz Portfolio Analysis - '+path[-4:], fontsize = 18)\n",
        "  plt.xlabel(r'$\\sigma$', fontsize = 14)\n",
        "  h = plt.ylabel(r'$\\mu$', fontsize = 14)\n",
        "  h.set_rotation(0)\n",
        "  x_min, x_mx = plt.xlim()\n",
        "  plt.xlim((0,x_mx))\n",
        "\n",
        "  #labeling\n",
        "  plt.scatter(sig_mvp, mu_mvp, c = 'k')\n",
        "  plt.scatter(sig_m,  mu_m, c = 'r')\n",
        "  plt.scatter(sigs_no_short[0],  mu_mvp_no_short, c = 'y')\n",
        "  plt.scatter(sigs_no_short[1],  mu_m_no_short, c = 'm')\n",
        "\n",
        "  #(0,R) to (sig_m, mu_m) line\n",
        "  plt.plot([0, sig_m.reshape(-1)], [R, mu_m.reshape(-1)])\n",
        "  #efficient frontier for 2 portfolios\n",
        "  plt.plot(sig2, mu_vals2)\n",
        "\n",
        "  plt.legend(['48-portfolio efficient frontier',\n",
        "              '(0,R) -> $(\\sigma_M, \\mu_M)$',\n",
        "              '2-portfolio efficient frontier',\n",
        "              'MVP', \n",
        "              'Market', \n",
        "              'MVP - No Shorting',\n",
        "              'Market - No Shorting'\n",
        "              ])\n",
        "  plt.show()\n",
        "\n",
        "\n",
        "  ###corr, beta, and risk table\n",
        "  mu_values = np.array([mu_mvp,\n",
        "                        mu_mvp_no_short,\n",
        "                        mu_m_no_short,\n",
        "                        mu_mvp2,\n",
        "                        mu_m2])\n",
        "  sig_values = np.array([sig_mvp,\n",
        "                        sigs_no_short[0], \n",
        "                        sigs_no_short[1],\n",
        "                        sig_mvp2,\n",
        "                        sig_m2\n",
        "                        ])\n",
        "\n",
        "  corr, beta, sys_risk, div_risk = table_params(mu_values, mu_m, sig_values, sig_m, R)\n",
        "\n",
        "  #Formatting the table\n",
        "  corr = corr.reshape(-1).tolist()\n",
        "  beta = beta.reshape(-1).tolist()\n",
        "  sys_risk = sys_risk.reshape(-1).tolist()\n",
        "  div_risk = div_risk.reshape(-1).tolist()\n",
        "\n",
        "  corr.insert(0, 'Correlation Coefficient')\n",
        "  beta.insert(0, 'Beta')\n",
        "  sys_risk.insert(0,'Systematic Risk')\n",
        "  div_risk.insert(0, 'Diversifiable Risk')\n",
        "  inp = [corr, beta, sys_risk,div_risk]\n",
        "  header = ['{MVP}',\n",
        "            '{MVP-NoShort}',\n",
        "            '{M-NoShort}',\n",
        "            '{MVP-2portfolios}',\n",
        "            '{M-2portfolios}']\n",
        "  t = tabulate(inp, headers =header ,tablefmt='orgtbl')\n",
        "  print(t, '\\n')\n",
        "\n",
        "\n",
        "  ### Eigenvalue stuff\n",
        "  evals, _ = np.linalg.eig(C)\n",
        "  evals = np.sort(evals)\n",
        "  evals = evals[::-1]\n",
        "  print('All evals greater than zero:', all(evals>0))\n",
        "  cond_num = evals[0]/evals[-1]\n",
        "  print('Condition Number:', cond_num)\n",
        "\n",
        "  plt.figure(figsize = (7,7))\n",
        "  plt.plot(evals)\n",
        "  plt.title('Eigenvalues of the Covariance Matrix - '+path[-4:], fontsize = 18)\n",
        "  plt.xlabel(r'$\\lambda_i$', fontsize = 14)\n",
        "  plt.yscale('log')\n",
        "  h = plt.ylabel(r'log$(\\lambda)$', fontsize = 14)\n",
        "  h.set_rotation(0)\n",
        "  plt.xlim((0,48))\n",
        "  plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N4d6cqteUD5Q"
      },
      "source": [
        "###2010"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dLXFDjuFRxcx",
        "outputId": "9b9e5b50-8fc6-4c76-c2a9-6c20d5183b19",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "R = LIBOR_daily[0]\n",
        "year_analysis('Farma-French_2010', R,  ('Guns', 'Food'))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "R<mu_MVP: [[ True]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "|                         |     {MVP} |   {MVP-NoShort} |   {M-NoShort} |   {MVP-2portfolios} |   {M-2portfolios} |\n",
            "|-------------------------+-----------+-----------------+---------------+---------------------+-------------------|\n",
            "| Correlation Coefficient | 0.223358  |       0.452846  |      0.678123 |           0.332393  |         0.392233  |\n",
            "| Beta                    | 0.0557942 |       0.11312   |      0.169393 |           0.0830309 |         0.0979786 |\n",
            "| Systematic Risk         | 0.0124621 |       0.0512257 |      0.11487  |           0.0275989 |         0.0384304 |\n",
            "| Diversifiable Risk      | 0.210896  |       0.172132  |      0.108488 |           0.195759  |         0.184927  | \n",
            "\n",
            "All evals greater than zero: True\n",
            "Condition Number: 1845.9880867600216\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CtLRi5DGzBx2"
      },
      "source": [
        "We see that the line from (0,R) to ($\\sigma_M$, $\\mu_M$) is indeed tangent to the bullet curve at the Market point. \n",
        "\n",
        "We also see that there seems to be a sudden drop or threshold effect in the eigenvalue plot. This is in line with the condition number.  "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fiY0fyn2VGKl"
      },
      "source": [
        "###2012"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H95BLpdKTPOj",
        "outputId": "7ffd4b48-ffdd-4ae5-9cc9-c21d200607cc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "R = LIBOR_daily[2]\n",
        "year_analysis('Farma-French_2012', R,  ('Guns', 'Food'))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "R<mu_MVP: [[ True]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "|                         |     {MVP} |   {MVP-NoShort} |   {M-NoShort} |   {MVP-2portfolios} |   {M-2portfolios} |\n",
            "|-------------------------+-----------+-----------------+---------------+---------------------+-------------------|\n",
            "| Correlation Coefficient | 0.180408  |       0.190421  |     0.291681  |           0.212082  |         0.271932  |\n",
            "| Beta                    | 0.10487   |       0.11069   |     0.169552  |           0.123282  |         0.158072  |\n",
            "| Systematic Risk         | 0.0189193 |       0.0210778 |     0.0494549 |           0.0261458 |         0.0429848 |\n",
            "| Diversifiable Risk      | 0.161488  |       0.15933   |     0.130953  |           0.154262  |         0.137423  | \n",
            "\n",
            "All evals greater than zero: True\n",
            "Condition Number: 1155.5187061163535\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nV-5VN9UVIlw"
      },
      "source": [
        "Very close to the conclusions\\comments  above. We see that the eigenvalue drop is less steep than before, and also that the condition number is smaller. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9FRWdtuzVqvu"
      },
      "source": [
        "###2014"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uKfwXkAXVVXS",
        "outputId": "3145e79d-9b86-426d-aedd-6376348be6e6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "R = LIBOR_daily[4]\n",
        "year_analysis('Farma-French_2014', R,  ('Guns', 'Food'))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "R<mu_MVP: [[ True]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "|                         |     {MVP} |   {MVP-NoShort} |   {M-NoShort} |   {MVP-2portfolios} |   {M-2portfolios} |\n",
            "|-------------------------+-----------+-----------------+---------------+---------------------+-------------------|\n",
            "| Correlation Coefficient | 0.160342  |      0.113913   |     0.193442  |           0.208275  |         0.300386  |\n",
            "| Beta                    | 0.0663556 |      0.0471418  |     0.0800537 |           0.0861921 |         0.124311  |\n",
            "| Systematic Risk         | 0.0106396 |      0.00537008 |     0.0154857 |           0.0179516 |         0.0373413 |\n",
            "| Diversifiable Risk      | 0.149702  |      0.154972   |     0.144856  |           0.14239   |         0.123     | \n",
            "\n",
            "All evals greater than zero: True\n",
            "Condition Number: 1022.2857860461596\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zbNuyHGPVpY7"
      },
      "source": [
        "###2017"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GfT6RdzQVvLP",
        "outputId": "c4817a15-f868-418d-a77a-8bb2425bb70b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "R = LIBOR_daily[7]\n",
        "year_analysis('Farma-French_2017', R,  ('Guns', 'Food'))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "R<mu_MVP: [[ True]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "|                         |     {MVP} |   {MVP-NoShort} |   {M-NoShort} |   {MVP-2portfolios} |   {M-2portfolios} |\n",
            "|-------------------------+-----------+-----------------+---------------+---------------------+-------------------|\n",
            "| Correlation Coefficient | 0.110317  |       0.138464  |     0.231139  |           0.0362332 |        0.0708962  |\n",
            "| Beta                    | 0.104751  |       0.131477  |     0.219477  |           0.034405  |        0.067319   |\n",
            "| Systematic Risk         | 0.0115557 |       0.0182048 |     0.0507296 |           0.0012466 |        0.00477266 |\n",
            "| Diversifiable Risk      | 0.098761  |       0.092112  |     0.0595871 |           0.10907   |        0.105544   | \n",
            "\n",
            "All evals greater than zero: True\n",
            "Condition Number: 441.1522867244662\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "enXb6lNgWr3F"
      },
      "source": [
        "###Comments \n",
        "The point that maximizes $\\beta$ is the Market point. $\\beta = cov(K_V, K_M)/\\sigma^2_M$, so for a fixed market point we see that $\\beta$ is maximized when $K_V = K_M$, for which it equals $1$. This cannot really be achieved with only 2 porfolios and without short selling, as the efficient frontier generated will not necessarily include the market price. We can re-write $\\beta$ as $\\beta = (\\mu_V - R)/(\\mu_M - R)$ as the ratio of the expected returns. For beta to be maximized we see that we need $\\mu_V = \\mu_M$. This is very similar to our earlier conclusion, but it gives us more intuition as to where the point we are looking for lies. We see that we want the closest $\\mu$ value to $\\mu_M$, which can only be the market point for the two porfolios without short selling. \n",
        "\n",
        "We see that the results are comparable across the years picked. We see that the parameters that make up the Markowitz bullet move around from year to year (by this I mean the points themselves), but the relationships between all of the parameters stay the same (the line is still tangential, we are still working with a hyperbola, the 2 portfolios are always inside the bullet, etc.). One other thing to note is that I didn't encounter a year where $R>\\mu_{MVP}$. \n",
        "\n",
        "The one odd thing I saw was in year 2014. The the no-shorting approximation we made for the MVP yielded a point bellow the original MVP (by this I mean in the bottom half of the hyperbola as opposed to the top half where it should be). This is odd as the MVP should be the 'vertex' of the hyperbola and in this scenario this could not be the case (assuming we stay on the original Markowitz bullet). I believe this happened because of the approximation itself, so I don't find it too odd that something like this happened (the values was close to the MVP, but still a bit under). \n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QU-kI_IrTOxW"
      },
      "source": [
        "##S&P500 and Stock Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lTTVh0mVTJls"
      },
      "source": [
        "def returns(df):\n",
        "  ''' Get the daily returns of the df'''\n",
        "  diff = df['Adj Close'].diff().drop(0).to_numpy()\n",
        "  Sx = (df['Adj Close'].drop(df.tail(1).index)).to_numpy()\n",
        "  K = diff/Sx\n",
        "  return pd.Series(K)\n",
        "\n",
        "def returns_monthly(df):\n",
        "  '''return the monthly returns of the df'''\n",
        "  months = df.groupby('Month').first().append(df.tail(1)).drop('Month', 1)\n",
        "  diff = months['Adj Close'].diff().to_numpy()[1:]\n",
        "  Sx = (months['Adj Close'].drop(months.tail(1).index)).to_numpy()\n",
        "  K = diff/Sx\n",
        "  return pd.Series(K)\n",
        "\n",
        "def extract_year(year, returns):\n",
        "  '''\n",
        "  Read through the proper files and create a df with the daily returns of the \n",
        "  listed companies   \n",
        "  '''\n",
        "  SNP = {'S&P500': 'S&P500_'+str(year),\n",
        "        'Adobe': 'Adobe_'+str(year),\n",
        "        'Apple': 'Apple_'+str(year),\n",
        "        'AT&T': 'AT&T_'+str(year),\n",
        "        'Microsoft': 'Microsoft_'+str(year),\n",
        "        'Nike': 'Nike_'+str(year),\n",
        "        #'McDonalds': 'MCD_'+str(year)\n",
        "        }\n",
        "  for key, val in SNP.items():\n",
        "    SNP[key] = returns(pd.read_csv(val))\n",
        "  SNP = pd.DataFrame(SNP)\n",
        "  return SNP\n",
        "\n",
        "def extract_quarters(year):\n",
        "    '''\n",
        "  Read through the proper files and create a df with the monthly returns of the \n",
        "  listed companies   \n",
        "  '''\n",
        "  SNP = {'S&P500': 'S&P500_'+str(year),\n",
        "        'Adobe': 'Adobe_'+str(year),\n",
        "        'Apple': 'Apple_'+str(year),\n",
        "        'AT&T': 'AT&T_'+str(year),\n",
        "        'Microsoft': 'Microsoft_'+str(year),\n",
        "        'Nike': 'Nike_'+str(year),\n",
        "        #'McDonalds': 'MCD_'+str(year)\n",
        "        }\n",
        "  SNPK = {}\n",
        "  ks = SNP.keys()\n",
        "  for key, val in SNP.items():\n",
        "    df = pd.read_csv(val)\n",
        "    #column to denote the quarter\n",
        "    df['Split'] = np.floor((df.Month-1)/3).astype(int)\n",
        "    for i in range(4):\n",
        "      #get returns for each quarter seperately\n",
        "      SNPK[key+str(i)] = returns(df[df['Split'] == i].reset_index(drop = True))\n",
        "  SNP = pd.DataFrame(SNPK)\n",
        "\n",
        "  return SNP, ks\n",
        "\n",
        "def daily_SNP(year, R, N = 100):\n",
        "  '''\n",
        "  Perform analysis to plot the efficient frontier or the upper side of the hyperbola\n",
        "  also compute beta, and the risks and print them in a table\n",
        "  '''\n",
        "  SNP = extract_year(year, returns)\n",
        "  mu_SP = SNP.iloc[:,0].mean()\n",
        "  sig_SP = SNP.iloc[:,0].var()\n",
        "\n",
        "\n",
        "  #No market price as R>mu_mvp\n",
        "  (w_mvp, sig_mvp, mu_mvp), _, m, C_inv, C = get_MVP_M(SNP.iloc[:,1:], R)\n",
        "\n",
        "  print('R<mu_MVP:', R<mu_mvp)\n",
        "  mu_vals = np.linspace(mu_mvp, mu_mvp+0.004, N).reshape(-1)\n",
        "  sig = bullet_sig(m, C_inv, mu_vals)\n",
        "\n",
        "\n",
        "\n",
        "  ###Annotated Bullet plot\n",
        "  plt.figure(figsize = (7,7))\n",
        "  #plt.plot(sigs, mus)\n",
        "  plt.plot(sig, mu_vals)\n",
        "  plt.title('Daily Returns Analysis - '+str(year), fontsize = 18)\n",
        "  plt.xlabel(r'$\\sigma$', fontsize = 14)\n",
        "  h = plt.ylabel(r'$\\mu$', fontsize = 14)\n",
        "  h.set_rotation(0)\n",
        "\n",
        "  plt.scatter(np.sqrt(np.diag(C)), m)\n",
        "  plt.scatter(sig_SP, mu_SP)\n",
        "  plt.legend(['Efficient Frontier', \n",
        "              '($\\sigma, \\mu$) for the stocks',\n",
        "              '($\\sigma_{SP}, \\mu_{SP}$)'\n",
        "              ])\n",
        "  plt.show()\n",
        "\n",
        "  ### Table\n",
        "  sigs = np.sqrt(np.diag(C))\n",
        "  corr, beta, sys_risk, div_risk = table_params(m, mu_SP, sigs, sig_SP, R)\n",
        "\n",
        "  stock_pts =[(sigs[i], m[i][0]) for i in range(len(m))]\n",
        "  stock_pts.insert(0, (sig_SP, mu_SP))\n",
        "  #Formatting the table\n",
        "  beta = beta.reshape(-1).tolist()\n",
        "  sys_risk = sys_risk.reshape(-1).tolist()\n",
        "  div_risk = div_risk.reshape(-1).tolist()\n",
        "\n",
        "  beta.insert(0, 'Beta_Year')\n",
        "  sys_risk.insert(0,'Systematic Risk')\n",
        "  div_risk.insert(0, 'Diversifiable Risk')\n",
        "  inp = [beta, sys_risk,div_risk]\n",
        "  header = SNP.columns[1:].to_list()\n",
        "  t = tabulate(inp, headers =header ,tablefmt='orgtbl')\n",
        "  stock_pts.insert(0, 'S&P and Stock Points, Year')\n",
        "  print(t, '\\n')\n",
        "  return  beta, stock_pts\n",
        "\n",
        "\n",
        "\n",
        "def monthly_SNP(year, R, N=100):\n",
        "  '''\n",
        "  Perform analysis to plot the efficient frontier or the upper side of the hyperbola\n",
        "  also compute beta, and the risks and print them in a table\n",
        "  -Monthly returns used\n",
        "  '''\n",
        "  SNP = extract_year(year, returns_monthly)\n",
        "  mu_SP = SNP.iloc[:,0].mean()\n",
        "  sig_SP = SNP.iloc[:,0].var()\n",
        "\n",
        "\n",
        "  #No market price as R>mu_mvp\n",
        "  (w_mvp, sig_mvp, mu_mvp), _, m, C_inv, C = get_MVP_M(SNP.iloc[:,1:], R)\n",
        "  print('R<mu_MVP:', R<mu_mvp)\n",
        "\n",
        "  #Efficient Frontier\n",
        "  mu_vals = np.linspace(mu_mvp, mu_mvp+0.1, N).reshape(-1)\n",
        "  sig = bullet_sig(m, C_inv, mu_vals)\n",
        "\n",
        "\n",
        "\n",
        "  ###Annotated Bullet plot\n",
        "  plt.figure(figsize = (7,7))\n",
        "  #plt.plot(sigs, mus)\n",
        "  plt.plot(sig, mu_vals)\n",
        "  plt.title('Monthly Returns Analysis - '+str(year), fontsize = 18)\n",
        "  plt.xlabel(r'$\\sigma$', fontsize = 14)\n",
        "  h = plt.ylabel(r'$\\mu$', fontsize = 14)\n",
        "  h.set_rotation(0)\n",
        "\n",
        "  plt.scatter(np.sqrt(np.diag(C)), m)\n",
        "  plt.scatter(sig_SP, mu_SP)\n",
        "  plt.legend(['Efficient Frontier', \n",
        "              '($\\sigma, \\mu$) for the stocks',\n",
        "              '($\\sigma_{SP}, \\mu_{SP}$)'\n",
        "              ])\n",
        "  plt.show()\n",
        "\n",
        "  ### Table\n",
        "  corr, beta, sys_risk, div_risk = table_params(m, mu_SP, np.sqrt(np.diag(C)), sig_SP, R)\n",
        "  #Formatting the table\n",
        "  beta = beta.reshape(-1).tolist()\n",
        "  sys_risk = sys_risk.reshape(-1).tolist()\n",
        "  div_risk = div_risk.reshape(-1).tolist()\n",
        "\n",
        "  beta.insert(0, 'Beta')\n",
        "  sys_risk.insert(0,'Systematic Risk')\n",
        "  div_risk.insert(0, 'Diversifiable Risk')\n",
        "  inp = [beta, sys_risk,div_risk]\n",
        "  header = SNP.columns[1:].to_list()\n",
        "  t = tabulate(inp, headers =header ,tablefmt='orgtbl')\n",
        "  print(t, '\\n')\n",
        "\n",
        "\n",
        "def daily_in_4_SNP(year, R):\n",
        "  '''\n",
        "  Perform analysis for the data for a given year in quarters. \n",
        "  Present the beta and (mu,sigma) points for the data in a table\n",
        "  '''\n",
        "  quarterly, cols = extract_quarters(2010)\n",
        "  num_cols = len(quarterly.columns)\n",
        "  indexing = np.arange(num_cols)%4\n",
        "  storage = {}\n",
        "  for i in range(4):\n",
        "    SNP = quarterly.loc[:,indexing == i]\n",
        "    mu_SP = SNP.iloc[:,0].mean()\n",
        "    sig_SP = SNP.iloc[:,0].var()\n",
        "    (w_mvp, sig_mvp, mu_mvp), _, m, C_inv, C = get_MVP_M(SNP.iloc[:,1:], R)\n",
        "\n",
        "    sigs = np.sqrt(np.diag(C))\n",
        "    stock_pts =[(sigs[i], m[i][0]) for i in range(len(m))]\n",
        "    stock_pts.insert(0, (sig_SP, mu_SP))\n",
        "    \n",
        "    _, beta, _, _7 = table_params(m, mu_SP,\n",
        "                                                  np.sqrt(np.diag(C)), sig_SP, R)\n",
        "    #Formatting the table\n",
        "    beta = beta.reshape(-1).tolist()\n",
        "    beta.insert(0, '')\n",
        "    beta.insert(0, 'Beta, Quarter:' +str(i+1))\n",
        "    stock_pts.insert(0, 'S&P and Stock Points, Quarter:' +str(i+1))\n",
        "\n",
        "    storage['beta'+str(i)] = beta\n",
        "    storage['pts'+str(i)] = stock_pts\n",
        "\n",
        "  head = list(cols)\n",
        "  head.insert(0, 'S&P500')\n",
        "  inp = storage.values()\n",
        "  t = tabulate(inp, headers =head ,tablefmt='orgtbl')\n",
        "  print(t, '\\n')\n",
        "\n",
        "def SNP500_Analysis(year):\n",
        "  '''\n",
        "  Go through the analysis outlined in problem 2 of the homework for a given year\n",
        "  '''\n",
        "  R = int(str(year)[-1])\n",
        "  print('DAILY for a year')\n",
        "  beta_yr, stock_pts_yr =daily_SNP(year, LIBOR_daily[R])\n",
        "\n",
        "  print('MONTHLY')\n",
        "  monthly_SNP(year, LIBOR_monthly[R])\n",
        "\n",
        "  print('DAILY in Quarters')\n",
        "  ###Quarterly\n",
        "  daily_in_4_SNP(year, LIBOR_daily[R])\n",
        "  #reformatting (from above when we did the daily analysis for the entire year)\n",
        "  beta = beta_yr[1:]\n",
        "  beta.insert(0,'')\n",
        "  beta.insert(0,'Beta, Year')\n",
        "  print(tabulate([beta,stock_pts_yr], tablefmt='orgtbl'))\n",
        "  "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hc0zDGwIkD8S"
      },
      "source": [
        "###2010"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PqJuR0BVsWg3",
        "outputId": "7d670a23-d7e2-4327-9860-65d8c363127f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "SNP500_Analysis(2010)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "DAILY for a year\n",
            "R<mu_MVP: [[False]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 504x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "|                    |       Adobe |       Apple |        AT&T |   Microsoft |        Nike |\n",
            "|--------------------+-------------+-------------+-------------+-------------+-------------|\n",
            "| Beta_Year          | 1.44757     | 0.37509     | 1.03656     | 1.34431     | 0.550924    |\n",
            "| Systematic Risk    | 3.47971e-08 | 2.33633e-09 | 1.78424e-08 | 3.00099e-08 | 5.04019e-09 |\n",
            "| Diversifiable Risk | 0.000575841 | 0.000284513 | 8.89498e-05 | 0.000191235 | 0.00020966  | \n",
            "\n",
            "MONTHLY\n",
            "R<mu_MVP: [[False]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "|                    |       Adobe |       Apple |        AT&T |   Microsoft |        Nike |\n",
            "|--------------------+-------------+-------------+-------------+-------------+-------------|\n",
            "| Beta               | 1.32949     | 0.592699    | 1.01849     | 1.20013     | 0.707566    |\n",
            "| Systematic Risk    | 1.87337e-05 | 3.72328e-06 | 1.09943e-05 | 1.52656e-05 | 5.30628e-06 |\n",
            "| Diversifiable Risk | 0.00797859  | 0.00562639  | 0.00316802  | 0.00820425  | 0.00434359  | \n",
            "\n",
            "DAILY in Quarters\n",
            "| S&P500                          | S&P500                                           | Adobe                                         | Apple                                         | AT&T                                           | Microsoft                                      | Nike                                           |\n",
            "|---------------------------------+--------------------------------------------------+-----------------------------------------------+-----------------------------------------------+------------------------------------------------+------------------------------------------------+------------------------------------------------|\n",
            "| Beta, Quarter:1                 |                                                  | 1.6205319788571797                            | 0.42614465110061195                           | 1.9859784353027687                             | 1.673727223443647                              | 0.1313941039820193                             |\n",
            "| S&P and Stock Points, Quarter:1 | (8.002800424413999e-05, 0.0005673055658760014)   | (0.015908305734341266, -0.00066646713468262)  | (0.017285845233759613, 0.0017082734574027667) | (0.00874036224619855, -0.0013930660479389925)  | (0.011750018724154175, -0.0007722325791826651) | (0.011538452211273729, 0.0022943112296693515)  |\n",
            "| Beta, Quarter:2                 |                                                  | 1.562314462848699                             | 0.27745876338022896                           | 0.7541889291020482                             | 1.3397458766407269                             | 0.7819401275963147                             |\n",
            "| S&P and Stock Points, Quarter:2 | (0.00024887666656293954, -0.0020309392474303347) | (0.022253650582482992, -0.004609991608929695) | (0.0227387767389149, 0.0012829923792692438)   | (0.011793089853335385, -0.0009035280482404824) | (0.01753467665290823, -0.003589181944978914)   | (0.020322391202763367, -0.0010308087759110657) |\n",
            "| Beta, Quarter:3                 |                                                  | 2.8140134830112133                            | 0.4269870698831739                            | -0.3899476685417554                            | 1.8199165214072681                             | -0.42524345835786354                           |\n",
            "| S&P and Stock Points, Quarter:3 | (0.00013210669984193053, 0.001734176293989263)   | (0.0324273463052225, 0.0002441832388422139)   | (0.01397759396783362, 0.0022048372313965593)  | (0.008593946491708061, 0.00287585048359188)    | (0.013543965242800325, 0.0010607138670897579)  | (0.011978080957156903, 0.002904841713367434)   |\n",
            "| Beta, Quarter:4                 |                                                  | -0.1962906401491392                           | 0.35463102969442706                           | 1.882725222108768                              | 0.2316473525435918                             | 1.1936282529276974                             |\n",
            "| S&P and Stock Points, Quarter:4 | (5.6269785702241474e-05, 0.0015009108220919493)  | (0.02259365811124336, 0.0027625724454170453)  | (0.012425890909350047, 0.0021815458077655524) | (0.007831717546919106, 0.0005699493154994449)  | (0.011522462252650283, 0.0023112498951746692)  | (0.012601051976910367, 0.0012967018048919941)  | \n",
            "\n",
            "| Beta, Year                 |                                                | 1.447570424291157                              | 0.37509007744001277                         | 1.036561072283793                             | 1.344313751054323                               | 0.5509239573038053                            |\n",
            "| S&P and Stock Points, Year | (0.0001288640020523657, 0.0004801608624618017) | (0.023997416318642356, -0.0004487244208977847) | (0.01686759431155908, 0.001777095599404428) | (0.009432264045373567, 0.0004042822070702011) | (0.013829871252217622, -0.00023442606923554423) | (0.014479799180271296, 0.0014121708982690285) |\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cK-1_3srVfw9"
      },
      "source": [
        "###2016"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "As94ydkNVhTR",
        "outputId": "d1e9eae8-b376-4519-8b9b-8d15fdbc41b2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "SNP500_Analysis(2016)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "DAILY for a year\n",
            "R<mu_MVP: [[False]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "|                    |       Adobe |       Apple |        AT&T |   Microsoft |        Nike |\n",
            "|--------------------+-------------+-------------+-------------+-------------+-------------|\n",
            "| Beta_Year          | 0.966833    | 0.965758    | 0.81254     | 0.925092    | 1.31431     |\n",
            "| Systematic Risk    | 4.23751e-09 | 4.22809e-09 | 2.99293e-09 | 3.87951e-09 | 7.83071e-09 |\n",
            "| Diversifiable Risk | 0.000241046 | 0.000217001 | 8.37167e-05 | 0.00020448  | 0.00019826  | \n",
            "\n",
            "MONTHLY\n",
            "R<mu_MVP: [[False]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "|                    |       Adobe |       Apple |        AT&T |   Microsoft |        Nike |\n",
            "|--------------------+-------------+-------------+-------------+-------------+-------------|\n",
            "| Beta               | 0.988497    | 0.969817    | 0.861617    | 0.954136    | 1.21517     |\n",
            "| Systematic Risk    | 3.47818e-07 | 3.34797e-07 | 2.64259e-07 | 3.24058e-07 | 5.25626e-07 |\n",
            "| Diversifiable Risk | 0.00210194  | 0.00550293  | 0.00365612  | 0.00268684  | 0.00212106  | \n",
            "\n",
            "DAILY in Quarters\n",
            "| S&P500                          | S&P500                                           | Adobe                                         | Apple                                         | AT&T                                           | Microsoft                                      | Nike                                           |\n",
            "|---------------------------------+--------------------------------------------------+-----------------------------------------------+-----------------------------------------------+------------------------------------------------+------------------------------------------------+------------------------------------------------|\n",
            "| Beta, Quarter:1                 |                                                  | 1.377759403288582                             | 0.6506557896122752                            | 1.6002311533747868                             | 1.4101429137883676                             | 0.471221377539647                              |\n",
            "| S&P and Stock Points, Quarter:1 | (8.002800424413999e-05, 0.0005673055658760014)   | (0.015908305734341266, -0.00066646713468262)  | (0.017285845233759613, 0.0017082734574027667) | (0.00874036224619855, -0.0013930660479389925)  | (0.011750018724154175, -0.0007722325791826651) | (0.011538452211273729, 0.0022943112296693515)  |\n",
            "| Beta, Quarter:2                 |                                                  | 1.439790669001185                             | 0.43489468112888674                           | 0.8077491822450318                             | 1.265717985664585                              | 0.8294536180327026                             |\n",
            "| S&P and Stock Points, Quarter:2 | (0.00024887666656293954, -0.0020309392474303347) | (0.022253650582482992, -0.004609991608929695) | (0.0227387767389149, 0.0012829923792692438)   | (0.011793089853335385, -0.0009035280482404824) | (0.01753467665290823, -0.003589181944978914)   | (0.020322391202763367, -0.0010308087759110657) |\n",
            "| Beta, Quarter:3                 |                                                  | 1.709805425330461                             | 0.7757857422833095                            | 0.4561273081506282                             | 1.3208251761430598                             | 0.4423164168108297                             |\n",
            "| S&P and Stock Points, Quarter:3 | (0.00013210669984193053, 0.001734176293989263)   | (0.0324273463052225, 0.0002441832388422139)   | (0.01397759396783362, 0.0022048372313965593)  | (0.008593946491708061, 0.00287585048359188)    | (0.013543965242800325, 0.0010607138670897579)  | (0.011978080957156903, 0.002904841713367434)   |\n",
            "| Beta, Quarter:4                 |                                                  | 0.45907672505973085                           | 0.7081853813392714                            | 1.399139307782199                              | 0.6525762081367351                             | 1.0875523264827645                             |\n",
            "| S&P and Stock Points, Quarter:4 | (5.6269785702241474e-05, 0.0015009108220919493)  | (0.02259365811124336, 0.0027625724454170453)  | (0.012425890909350047, 0.0021815458077655524) | (0.007831717546919106, 0.0005699493154994449)  | (0.011522462252650283, 0.0023112498951746692)  | (0.012601051976910367, 0.0012967018048919941)  | \n",
            "\n",
            "| Beta, Year                 |                                                 | 0.9668332147838055                            | 0.9657581282632239                            | 0.8125399083345085                           | 0.925091781019013                             | 1.3143068774259221                           |\n",
            "| S&P and Stock Points, Year | (6.732925286360196e-05, 0.00045796764833499977) | (0.015525778117050039, 0.0005699176770354528) | (0.014731104704279172, 0.0005735464871854281) | (0.009149847908093105, 0.001090714009049342) | (0.014299774015110992, 0.0007108102802077642) | (0.0140807631864328, -0.0006029330002874346) |\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1j-rvpfZVd7S"
      },
      "source": [
        "###2019"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ehYmsdZAU11N",
        "outputId": "71137e1d-6105-4c7e-d171-2e169d71f949",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "SNP500_Analysis(2019)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "DAILY for a year\n",
            "R<mu_MVP: [[False]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "|                    |       Adobe |       Apple |        AT&T |   Microsoft |        Nike |\n",
            "|--------------------+-------------+-------------+-------------+-------------+-------------|\n",
            "| Beta_Year          | 0.890442    | 0.705468    | 0.930633    | 0.84288     | 0.938824    |\n",
            "| Systematic Risk    | 3.04528e-09 | 1.91148e-09 | 3.32639e-09 | 2.72865e-09 | 3.3852e-09  |\n",
            "| Diversifiable Risk | 0.000225775 | 0.00027219  | 0.000125796 | 0.000156481 | 0.000170741 | \n",
            "\n",
            "MONTHLY\n",
            "R<mu_MVP: [[False]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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dlAZ34X2bzW83m1QMv8U7hpv7rj8iRaJjdhIUvgt7++DdqqkH8JDzc6aaVAxmNgA4D+/SkI+dcwlBLknKGe3GlKAw76bKX+Ad33kL+L079fojqSDMu2lzMt6JJ3e4X+4LKxIQCjsRESn3yuxuzPr167tmzZoFuwwRESklEhIS9jnn/N0JqOyGXbNmzYiPL/JNBUREpJwwsy15DdPZmCIiUu4p7EREpNxT2ImISLlXZo/Z+ZOWlsb27dtJTs7vfqtSkVStWpUmTZoQGhp6+pFFpNwqV2G3fft2atasSbNmzTizToylPHLOsX//frZv307z5s1PP4GIlFvlajdmcnIyERERCjoBwMyIiIjQlr6IlK+wAxR0chJ9HkQEymHYiYiI5KawC7CQkBA6dOiQ/XjqqacA+Oabb2jbti0dOnQgKSmJsWPH0rZtW8aOHcvrr7/O1KlT85znzp07uemmmwpd04svvsiJE/77Fe3VqxetWrXKrnfu3LmFXk6W+fPns3r16uznjz32GJ9++mmR5ysiUlhl9t6Y0dHRLvcdVNasWcNFF10UpIo8NWrU4NixY6e033nnnVx66aUMGjQIgNq1a3PgwAFCQkKKvaasu83Ur1//lGG9evXiueeeIzo62u+0GRkZZ1zjsGHDuPbaawsd0Onp6VSuHLhzp0rD50JEip+ZJTjn/K7MtGVXAiZOnMicOXN49NFHGThwINdddx3Hjh2jc+fOzJ49m8cff5znnnsOgA0bNnD55ZfTvn17OnXqxMaNG9m8eTORkZGAFz5jx46lS5cuREVF8cYbbwDw5Zdf0qtXL2666SZat27NwIEDcc7x0ksvsXPnTnr37k3v3r0LVG+zZs148MEH6dSpE2+//TazZs2iXbt2REZG8uCDD2aPV6NGDR5++GHat29Pt27d2L17N99//z0LFixg7NixdOjQgY0bNzJs2LDsLcaEhAR69uxJ586dufLKK9m1axfghe69995LdHQ0//rXvwL23ouIQDm79CCnv/5nFat3HgnoPNs0qsVfftM233GSkpLo0KFD9vOHHnqIkSNH8u233560tVOjRg2WLl0KwOOPP549/sCBAxk3bhzXX389ycnJZGZmsmfPnuzhkyZNonbt2ixevJiUlBQuueQS+vbtC8CSJUtYtWoVjRo14pJLLuG7777j97//Pc8//zxffPGF3y27rGWGh4cD8NlnnwEQERFBYmIiO3fupFu3biQkJFC3bl369u3L/Pnz6d+/P8ePH6dbt248+eSTPPDAA0yYMIFHHnmE6667zu+WXVpaGvfccw/vvfceDRo0YPbs2Tz88MPExsYCkJqaqvudikixKLdhFyzh4eHZIXamjh49yo4dO7j++usB74Lo3P73v/+xfPny7C2lw4cP8+OPPxIWFkZMTAxNmjQBoEOHDmzevJlLL730tMudMWPGKbsxb731VgAWL15Mr169aNDAu5H4wIED+frrr+nfvz9hYWFce+21AHTu3JlPPvkk3+WsW7eOlStXcsUVVwDeVuo555xzyjJFRAKt3Ibd6bbAyirnHC+//DJXXnnlSe1ffvklVapUyX4eEhJCenp6oZdTvXr1044TGhqafWp/QZbnnKNt27b88MMPhV6miEhh6JhdKVKzZk2aNGnC/PnzAUhJSTnlLMorr7yS1157jbQ0r1Pv9evXc/z48dPO9+jRo4WqKSYmhq+++op9+/aRkZHBrFmz6NmzZ6GW16pVK/bu3ZsddmlpaaxatapQdYlI+bHopwMU98mSCrsAyzpml/UYN27cGU0/bdo0XnrpJaKiorj44ov5+eefTxo+cuRI2rRpQ6dOnYiMjGTMmDGn3aIaPXo0V111VYFPUMnpnHPO4amnnqJ37960b9+ezp07069fv3ynGTBgAM8++ywdO3Zk48aN2e1hYWHMnTuXBx98kPbt29OhQwe+//77M65JRMoH5xz/+vRHbnnjBz5YsatYl6VLD6Tc0+dCpPRxzvG3D9Yw6dufuKFTY565MYrKIUXb/srv0oNye8xORERKp/SMTB6at4K3E7Yz7OJmPHZtGypVKt5b+ynsRESkxKSkZ3DvW0v578qf+UOfltx7ecsSuYetwk5ERErEidR0xkxL4Jsf9/HINRcx8lfnl9iyFXYiIlLsDp9IY/iUxSzZepBnboriluhzS3T5CjsRESlWe4+mMHhSHBv3HuPVgZ24KvKc008UYAo7EREpNtsPnmDQxDh2H0lh0tAu9LiwQVDqUNiJiEix2LDnGIMnxXE8JZ3pI2Po3LRe0GpR2ImISMCt3HGYIbGLqGTw1ujutGlUK6j16A4qxSApKYmePXuSkZERkPmlpqbSo0ePPO+U8tJLL3HRRRcxcODAQs3/0KFDvPrqq9nPc3YpFCi5l3GmiqMmESkei346wG3jFxIeGsLbd14c9KADhV2xiI2N5YYbbghYx6xhYWH06dOH2bNn+x3+6quv8sknnzBjxowCzc85R2ZmZvbzogZRQZTEMkQk+L5Yt4fBk+JoUKsKb9/Zneb1S8cN3it02M1fsoNLnvqc5uM+4JKnPmf+kh0Bme+MGTOy7x+5bNkyevToQZs2bahUqRJmxmOPPZbntL169WLt2rUA7N+/P3trpn///n7D7M4772TTpk1cffXVvPDCCwA8//zzREZGEhkZyYsvvgh4W0atWrViyJAhREZGsm3btux5jBs3jo0bN9KhQwfGjh0LeN3vjBo1irZt29K3b1+SkpIAmD59OjExMXTo0IExY8b43Xo9fvw411xzDe3btycyMpLZs2f7XYa/OgGmTp1KVFQU7du3Z/DgwafMf9OmTXTs2JGvvvrqlOWISPD8Z9lORk2Jp8VZNXh7THca1QkPdkm/cM6VyUfnzp1dbqtXrz6lLS/vJm53rR/5r2v64PvZj9aP/Ne9m7i9wPPwJyUlxTVs2NA551xSUpJr1aqVi4uLc84598gjj7j777/fZWZm5jl948aNXUZGhnPOuc8//9wNGDDAOedcenq6q1+/vt9pmjZt6vbu3euccy4+Pt5FRka6Y8eOuaNHj7o2bdq4xMRE99NPPzkzcz/88MMp0//000+ubdu2Jz0PCQlxS5Yscc45d/PNN7tp06a51atXu2uvvdalpqY655z77W9/66ZMmXLK/ObOnetGjhyZ/fzQoUOnLCOvOleuXOlatmyZ/Xr2799/Uo1r1651HTp0cEuXLvW7HH/O5HMhIoUzM26LazbufXfTa9+5w0mpQakBiHd5ZEaF3bJ79uN1JKWdvFWSlJbBsx+vK9J89+3bR506dQD49NNP6dSpEzExMQBERUVx4MCBPG+Ns2XLFho3bkylSt6fZfny5URFRQFef3FhYWGn7arn22+/5frrr6d69erUqFGDG264gW+++QaApk2b0q1btwK9jubNm2f3uN65c2c2b97MZ599RkJCAl26dKFDhw589tlnbNq06ZRp27VrxyeffMKDDz7IN998Q+3atQtc5+eff87NN9+c3at6vXq/nL21d+9e+vXrx4wZM2jfvn2BliMixe+Nrzby0LwV9GjZgKnDu1KramiwSzpFhQ27nYeSzqi9oMLDw0lOTgZg5cqVtGvXLntYYmIinTp1ynPaZcuWZYcbQEJCwknPU1JS/PZeXlBn0jmqv45gnXMMHTqUpUuXsnTpUtatW8fjjz9+yrQXXnghiYmJtGvXjkceeYQnnnii0DXnVLt2bc477zy+/fbbYl2OiBSMc45nPlrLP/67lmuizmHCkGjCwwJzrkKgVdiwy2tfclH3MdetW5eMjAySk5OJiIhg+fLlgNfJ6rx58xgwYED2uH369GHHjl+OEy5dujQ7KH/88Ufee++97LDcv38/9evXJzQ0/29Mv/rVr5g/fz4nTpzg+PHjvPvuu/zqV7/Kd5qCdu7ap08f5s6dy549ewA4cOAAW7ZsOWW8nTt3Uq1aNQYNGsTYsWNJTEw8ZRl51XnZZZfx9ttvs3///uxlZAkLC+Pdd99l6tSpzJw50+9yRKRkZGY6HntvFa9+uZHbYs7lpQEdCatceiOlwl5nN/bKVjw0b8VJuzLDQ0MYe2WrIs+7b9++fPvtt9x2220sWLCAyMhI6tevz6xZs4iIiAAgMzOTDRs2nLSbbtmyZVStWpX27dsTFRVFmzZtmDJlCo8++ihffPEF11xzzWmX3alTJ4YNG5a963TkyJF07NiRzZs35zlNREQEl1xyCZGRkVx99dXcddddfsdr06YNf/vb3+jbty+ZmZmEhobyyiuv0LRp05PGW7FiBWPHjqVSpUqEhoby2muvnbKMZ5991m+dAA8//DA9e/YkJCSEjh07Mnny5Ox5V69enffff58rrriC6tWr8+ijj560HBEpfmkZmYx9exnzl+5kdI/zeejq1iXSc0FRVOjOW+cv2cGzH69j56EkGtUJZ+yVrejfsXGRa0tMTOSFF15g2rRpeY6zcuVKYmNjef7557PbWrZsmb0VlNsNN9zAU089xYUXXljk+ioadd4qEjjJaRncPTORT9fsYeyVrfhdrwtKTdCVSOetZnYV8C8gBJjonHsq1/AewItAFDDAOTfX194BeA2oBWQATzrnSuQc8v4dGwck3HLr1KkTvXv3JiMjI89r7SIjI08KuqNHj2JmfoMuNTWV/v37K+hEJKiOpaQzako8P2zaz//1a8vg7s2CXVKBBSTszCwEeAW4AtgOLDazBc651TlG2woMA+7PNfkJYIhz7kczawQkmNnHzrlDgagtWIYPH35G49esWZP169f7HRYWFsaQIUMCUZaISKEcPJ7KsDcXsXLnEV68tUOxbCgUp0Bt2cUAG5xzmwDM7C2gH5Adds65zb5hmTkndM6tz/H7TjPbAzQAynTYiYiUF7uPJDNoYhxbDpzg9UGduaJNw2CXdMYCdepMY2BbjufbfW1nxMxigDBgYx7DR5tZvJnF7927t1CFiohIwW3df4KbXv+enYeSmHxHlzIZdFCKLj0ws3OAacAdzrlMf+M458Y756Kdc9ENGgSnTyQRkYpi/e6j3PT69xxNTmfGqG5cfEH9YJdUaIHajbkDyNnHehNfW4GYWS3gA+Bh59zCANUkIiKFtHTbIYa9uYiwkErMHt2dVmefevJcWRKoLbvFQEsza25mYcAAYEFBJvSN/y4wNesMTRERCZ7vN+5j4ISF1Kxambl3Xlzmgw4CFHbOuXTgbuBjYA0wxzm3ysyeMLPrAMysi5ltB24G3jCzVb7JbwF6AMPMbKnv0SEQdYmIyJn5dPVuhr25mEZ1wpl758WcF1Et2CUFRMCus3POfQh8mKvtsRy/L8bbvZl7uunA9EDVISIihTN/yQ7+9PYyIhvVYvIdMdStHhbskgKmwt4uTEREfjHth808tmAV3ZpHMGFoNDWqlK94KDVnY5YnSUlJ9OzZ02/HpsGUmppKjx49SE9PD3YpIlJKOOd45YsNPPreKvq0bsibd3Qpd0EHCrtiERsbyw033JDnrcKCJSwsjD59+qhHbxEBvKB76qO1PPvxOvp3aMRrgzpRNbR0rbcCpWKH3fI58EIkPF7H+7l8TkBmO2PGDPr16wd4PRn06NGDNm3aUKlSJcyMxx577DRzyF+vXr1Yu3Yt4HX9ExkZWeBp+/fvz4wZM4q0fBEp+zIyHX9+dyVvfLWJwd2a8vwtHQgNKb+RUP62VQtq+Rz4z+8hzddZ6+Ft3nOAqFsKPdvU1FQ2bdpEs2bNSE5O5tZbb2Xq1KnExMTw6KOPkpyczF//+tcilb5hw4bsm0IvX778pA5iTycyMpLFixcXafkiUralpmfyxzlLeX/5Lu7qfQH3921VanouKC4VN+w+e+KXoMuSluS1FyHs9u3bR506dQD49NNP6dSpU3afbVFRUXz00UeYGampqfzpT38iJCSEQ4cO8eSTT9K/f3+io6NZt24d8+fPp1atWqfMf8uWLTRu3JhKlbxvYMuXLycqKsrv/Bo3bkyXLl1OmWdYWBhHjx7128OCiJRvSakZ/G5GAl+s28tDV7dmTM8Lgl1SiSi/26ync3j7mbUXUHh4eHZv4ytXrjxpqysxMZFOnToB8OWXX9KgQQNefPFFJk+eTGZmJj169M5P85EAACAASURBVOC1116jefPmHDx40O/8ly1bRlRUVPbzhIQEoqKiTplf48aN2bZtm995pqSkULVq1SK9ThEpe44kpzE0dhFfrt/L369vV2GCDipy2NU+5ZK//NsLqG7dumRkZJCcnExERATLly8HYP369cybN48BAwYAcPHFF5OSksLgwYOZPn06CQkJrF27lvvuu4+LL744u/fvPn36sGPHL3deW7p0aXaY/vjjj7z33nu0a9fulPkBfue5f/9+6tevT2hoaJFep4iULfuPpXD7hIUkbj3ISwM6cnvX84JdUomquLsx+zx28jE7gNBwr72I+vbty7fffsttt93GggULiIyMpH79+syaNYuIiAgAatSowZNPPklmZiaXXHIJl19+Oc8//zytWrXKnk9mZiYbNmygXr162W3Lli2jatWqtG/fnqioKNq0acOUKVN49NFHT5rfoEGDSEhIOGWeX3zxBddcc02RX6OIlB27DicxaGIc2w8mMWFINL1bnxXskkpcxQ27rONynz3h7bqs3cQLuiIcr8ty11138cILL3D55Zfzn//8x+84v/vd76hcuTLHjh3jz3/+M5MnT6ZFixYnjbN69WpuvPFGwsPDs9uWL19OYmLiKcfbcs8va/rc85w5cyZPPXVSJ/IiUo5t3necgRPjOJyUxtThMXQ9PyLYJQWFOeeCXUOhREdHu/j4+JPa1qxZw0UXXRSkik4WGxvL0KFDA3qt3dGjR+ncuXOePZqfTmpqKm+99VaF6/W8NH0uRErSml1HGDxpEZnOMeWOGNo1qR3skoqVmSU456L9Dau4W3bFbPjw4QGfZ82aNQsddOBdVF7Rgk6kokrcepBhsYuoFlaZ6SO70uKsin32tcJORKSc+W7DPkZNjeesmlWYNqIr59YrHz0XFIXCTkSkHPl41c/cM3MJ5zeoztQRMZxVU5cZQTkMO+dcub8TgBRcWT0mLVIY7yRs54F3lhPVpDZvDutCnWrlp4ueoipX19lVrVqV/fv3awUngBd0+/fv1wX0UiFM+X4zf3p7Gd3Or8f0EV0VdLmUqy27Jk2asH37dvbu3RvsUqSUqFq1Kk2aFO1GASKlmXOOf3++gX9+sp4r2jTk5ds6ltueC4qiXIVdaGgozZs3D3YZIiIlwjnH3z9cw4RvfuKGjo155qYoKpfjnguKolyFnYhIRZGR6Xj43RW8tXgbQ7s35S+/aUulSjpfIS8KOxGRMiY1PZP7Zi/lgxW7uLt3C/7U90KdmHcaCjsRkTIkKTWDO6cn8NX6vTz864sY1eP8YJdUJijsRETKiCPJaYyYvJj4LQd56oZ2DIipWD0XFIXCTkSkDNh/LIUhsYtYv/soL9/WkWujGgW7pDJFYSciUsplddGz41AS44dE07tVxeuip6gUdiIipdhP+44zaGIcR5LSmDq8KzHN651+IjmFwk5EpJTK2UXPrNHdiGxcvrvoKU66+lBEpBRK3HqQW9/4gcqVjDljFHRFpS07EZFS5tsf9zF6mrroCSSFnYhIKfK/VT9zt7roCTiFnYhIKTEvcTtj5y6nXePaTL5DXfQEksJORKQUmPL9Zv6yYBWXtIhg/OBoqlfR6jmQ9G6KiASRc45XvtjAc/9TFz3FSWEnIhIkzjn+8d+1jP96k7roKWYKOxGRIMjZRc+Q7k15XF30FCuFnYhICUtNz+SPc5by/nJ10VNSFHYiIiUoKTWD381I4It1e3no6taM6XlBsEuqEBR2IiIl5GhyGiOmxLN48wH+fn07bu+qLnpKisJORKQEHDieytDYRazZdYSXBnTkN+3VRU9JUtiJiBSznw8nM2hSHNsOnGDCkGh6t1YXPSVNYSciUoy27D/OwIlxHDqRxtThMXQ9PyLYJVVICjsRkWKy7uejDJoUR3pGJjNHdSWqSZ1gl1RhKexERIrB0m2HGBq7iKqhlZgzpjstG9YMdkkVmsJORCTAvt+4j1FT4omoUYUZI9VFT2mgsBMRCaBPV+/mdzMTaRZRjWkjutKwlrroKQ0UdiIiAfLe0h38cc4yIhvVYvIdMdStri56SguFnYhIAExfuIVH31tJTLN6TBrWhRrqoqdU0V9DRKSIXv1yA898tI4+rc/ilYGd1EVPKaSwExEpJOccT3+0jte/2sh17Rvxz1vaE6ouekolhZ2ISCFkZjoeW7CS6Qu3MrDreTzRL5IQddFTainsRETOUFpGJve/vYz3lu5kTM/zGXdVa3XRU8op7EREzkByWgZ3z0zk0zV7eOCqVvyuV4tglyQFoLATESmgYynpjJoSzw+b9vN//doyuHuzYJckBaSwExEpgEMnUhn25mJW7DjMC7e25/qOTYJdkpwBhZ2IyGnsOZLM4EmL+GnfcV4b2Im+bc8OdklyhhR2IiL52HbgBIMmxbH3aApv3tGFS1rUD3ZJUggKOxGRPGzYc4xBE+M4kZrO9JFd6XRe3WCXJIWksBMR8WPljsMMiV1EJTNmj+nORefUCnZJUgQBu9TfzK4ys3VmtsHMxvkZ3sPMEs0s3cxuyjVsqJn96HsMDVRNIiKFsXjzAW4bv5Dw0BDevlNBVx4EZMvOzEKAV4ArgO3AYjNb4JxbnWO0rcAw4P5c09YD/gJEAw5I8E17MBC1iYicia/W72XMtHga1Qln+oiuNKoTHuySJAACtWUXA2xwzm1yzqUCbwH9co7gnNvsnFsOZOaa9krgE+fcAV/AfQJcFaC6REQK7MMVuxg5ZTHn16/BnDHdFXTlSKDCrjGwLcfz7b62gE5rZqPNLN7M4vfu3VuoQkVE/JkTv427ZyYS1aQOs0Z3o36NKsEuSQKoTN2e2zk33jkX7ZyLbtCgQbDLEZFyIvbbn3hg7nIuaVGfaSNiqB0eGuySJMACFXY7gHNzPG/iayvuaUVECs05x78+/ZEn3l/NVW3PZuLQaKqF6ST18ihQYbcYaGlmzc0sDBgALCjgtB8Dfc2srpnVBfr62kREio1zjr99sIYXPl3PjZ2a8O/bO1KlsjpdLa8CEnbOuXTgbryQWgPMcc6tMrMnzOw6ADPrYmbbgZuBN8xslW/aA8D/4QXmYuAJX5uISLHIyHSMe2cFk779iWEXN+PZm6KorE5XyzVzzgW7hkKJjo528fHxwS5DRMqY1PRM7pu9lA9W7OL3l7XgvisuVF905YSZJTjnov0N085pEakwklIz+O2MBL5ct5eHf30Ro3qcH+ySpIQo7ESkQjianMaIyfEs3nKAf9zQjttizgt2SVKCFHYiUu4dOJ7K0NhFrNl1hJcGdOQ37RsFuyQpYQo7ESnXfj6czOBJcWw9cILxQzpzWeuGwS5JgkBhJyLl1tb9Jxg4aSEHjqUyZXgM3c6PCHZJEiQKOxEpl9bvPsqgiXGkZmQyc1Q32p9bJ9glSRAp7ESk3Fm+/RBDYxdROaQSs0d3p9XZNYNdkgSZwk5EypW4TfsZMSWeOtVCmTGyK00jqge7JCkFFHYiUm58sXYPd05P4Nx61Zg+oitn164a7JKklFDYiUi58P7yndz71lJan1OTKXfEEKEueiQHhZ2IlHmzF2/loXkr6Ny0LpOGdaFWVXXRIydT2IlImTbxm0387YM19LywAa8P6kx4mHoukFMp7ESkTHLO8eKnP/Kvz37k1+3O5sVbOxJWWT0XiH8KOxEpc5xz/N/7a4j97idu6tyEp25opy56JF8KOxEpUzIyHQ/NW86c+O0Mu7gZj13bhkqV1EWP5E9hJyJlhvqik8JS2IlImaC+6KQoFHYiUuodTU5jxJR4Fm9WX3RSOAo7ESnVDh5PZeibi1i98wj/GtCR69QXnRSCwk5ESq3dR7y+6LbsV190UjQKOxEplbYdOMHAiXHsP5bC5Dti6H6B+qKTwlPYiUips2HPUQZOjCM5LZMZo7rRQX3RSREp7ESkVFm54zBDYhcRUsmYM0Z90Ulg6JYDIlJqLN58gNvGLyQ8NIS3FXQSQNqyE5FS4av1exkzLZ5GdcKZPqIrjeqEB7skKUcUdiISdB+t3MU9s5bQ8qyaTB0RQ331RScBprATkaB6J2E7Y+cuo+N5dYkd1oXa4eqLTgJPYSciQTPl+838ZcEqLm1Rn/FDOlMtTKskKR76ZIlIiXPO8eqXG3n243X0bdOQl27rSNVQdboqxUdhJyIlyjnHUx+t5Y2vNnF9x8Y8e1OU+qKTYqewE5ESk5npePS9lcyI28qgbufxxHWR6otOSoTCTkRKRFpGJmPfXsb8pTu5s+cFPHhVK/VFJyVGYScixS45LYN7Zi3hk9W7GXtlK+7q3SLYJUkFo7ATkWJ1IjWd0VMT+HbDPp7o15Yh3ZsFuySpgBR2IlJsDielMXzyYpZsPchzN7fnps5Ngl2SVFAKOxEpFvuOpTBk0iJ+3HOUV27vxNXtzgl2SVKBKexEJOB2HU5i4MQ4dh5KYuLQLvS8sEGwS5IKTmEnIgG1Zf9xbp8Qx+GkNKYO70pM83rBLklEYScigbN+91EGTYwjLSOTWaO60a5J7WCXJAIo7EQkQJZvP8SQ2EWEhVRizpjutGyovuik9FDYiUiRxW3az4gp8dStHsqMEd04L6JasEsSOYluSCciRfLluj0MiV1Ew1pVeHvMxQo6KZW0ZScihfbhil384a0lXNiwJlOHxxChTlellFLYiUihvB2/jQffWU6n8+oySZ2uSimnsBORM6ZOV6Ws0SdURM7IK19s4NmP13FFm4a8rE5XpYxQ2IlIgTjnePqjdbz+1Ub6d2jEsze3J1SdrkoZobATkdPKzHT8ZcEqpi3cwu1dz+Nv/dTpqpQtCjsRyVd6RiYPzF3OvCU7GNPjfMZd3VqdrkqZo7ATkTylpGfwh1lL+WjVz9zf90Lu6t1CQSdlksJORPxKSs1g9LR4vvlxH49d24bhlzYPdkkihaawE5FTHElOY8TkxSRsOcgzN0ZxS5dzg12SSJEo7ETkJAePpzIkdhFrdh3hpds6cm1Uo2CXJFJkCjsRybbnSDIDJ8ax5cAJxg/pzGWtGwa7JJGAUNiJCADbDpxg0KQ49h1NYfIdXbj4gvrBLkkkYBR2IsLGvccYNDGO4ynpTB/ZlY7n1Q12SSIBpbATqeBW7zzCkNg4AGaP6c5F59QKckUigRewe/2Y2VVmts7MNpjZOD/Dq5jZbN/wODNr5msPNbMpZrbCzNaY2UOBqklE8pe49SADxv9AaEglBZ2UawEJOzMLAV4BrgbaALeZWZtco40ADjrnWgAvAE/72m8Gqjjn2gGdgTFZQSgixef7jfsYNDGOutXDmDOmOxc0qBHskkSKTaC27GKADc65Tc65VOAtoF+ucfoBU3y/zwX6mHcrBgdUN7PKQDiQChwJUF0i4sfna3dzx5uLaVI3nLfHdOfceupdXMq3QIVdY2BbjufbfW1+x3HOpQOHgQi84DsO7AK2As855w4EqC4RyeWD5bsYPTWBCxvW5K3R3TmrVtVglyRS7EpD/xwxQAbQCGgO/MnMzvc3opmNNrN4M4vfu3dvSdYoUi7Mid/GPbMS6XheHWaM6kq96mHBLkmkRAQq7HYAOe8n1MTX5ncc3y7L2sB+4HbgI+dcmnNuD/AdEO1vIc658c65aOdcdIMGDQJUukjFMPm7n3hg7nIuaVGfKcNjqFU1NNgliZSYQIXdYqClmTU3szBgALAg1zgLgKG+328CPnfOObxdl5cBmFl1oBuwNkB1iQhe7+KP/2c1fds0ZOLQaKqF6aojqVgC8ol3zqWb2d3Ax0AIEOucW2VmTwDxzrkFwCRgmpltAA7gBSJ4Z3G+aWarAAPedM4tD0RdIhWdc45nPl7Ha1+qd3Gp2MzbuCp7oqOjXXx8fLDLECm1MjMdf/3PKqb8oN7FpWIwswTnnN/DYNqXIVIOZWQ6HnxnOXMTtjPqV835868vUqerUqEVaH+GmXUzs0wzq5mjra6ZOTNrX3zliciZSk3P5PezljA3YTv3Xt5SQSdCwbfsOgA/OueO5mjriHcB+OqAVyUihZKclsFvpyfwxbq9PPzrixjVw+9VPCIVzpmEXWKuto7AaudcWmBLEpHCOJaSzqgp8Sz8aT9PXh/JwK5Ng12SSKlxJmE3L1dbJ2BpYMsRkcI4fCKNoW8uYsWOwzx/S3uu79gk2CWJlCqnPWZnZpWAdpy6ZReNwk4k6PYdS2HAhIWs3nmEV27vpKAT8aMgW3YXAtWAnVkNZtbO166wEwminw8nM3DiQnYcSmLC0Gh6Xqg7C4n4U5CzMTv4ft5tZi3N7Aq8Xg0AqhRPWSJyOtsOnODmN75n95EUpg7vqqATyUdBw+4TvPtdrgSeB54ADgL3FF9pIpKXDXuOcfPrP3AkKZ0ZI7sS07xesEsSKdUKshuzA5DgnMvdg/jsYqhHRE5j1c7DDJm0CDNj9phutD5bvYuLnE5Bt+x0r0qRUiBx60FuG7+QKpUrMUdBJ1Jg+W7ZmdnZQEMUdiJB98PG/YyYspgGNaswY2RXmtRV7+IiBZVv2DnnfsbriUBEguiLdXu4c1oC59WrxoyRXdW7uMgZ0o2gRUq5j1bu4p5ZS2h1dk2mDlfv4iKFoY6tREqxeYnb+d2MRNo1rs2Mkd0UdCKFpC07kVJqRtwWHpm/ku7nRzBhSDTVq+jfVaSw9N8jUgpN/GYTf/tgDZe1PotXB3aiamhIsEsSKdMUdiKliHOOlz/fwPOfrOeadufwwq0dCKusow0iRaWwEyklnHM89d+1vPH1Jm7s1ISnb2xH5RAFnUggKOxESoHMTMdfFqxi2sItDO7WlL9e15ZKlXTVj0igKOxEgiw9I5Nx81YwN2E7Y3qcz7irW2OmoBMJJIWdSBClpmdy3+ylfLBiF/de3pI/9GmpoBMpBgo7kSBJTsvgrhmJfLZ2Dw//+iJG9Tg/2CWJlFsKO5EgOJGazuipCXy7YR//1z+Swd2aBrskkXJNYSdSwo4kpzH8zcUkbj3Icze356bOTYJdkki5p7ATKUGHTqQyJHYRq3ce4eXbOnFN1DnBLkmkQlDYiZSQvUdTGDwpjk37jvPG4M70uahhsEsSqTAUdiIl4OfDydw+cSG7DiUTO7QLl7asH+ySRCoUhZ1IMdt24AS3T1zIweNpTB0RQ5dm9YJdkkiFo7ATKUab9h5j4MQ4TqRmMGNkV9qfWyfYJYlUSAo7kWKy7uejDJwYBzjeGt2Ni86pFeySRCoshZ1IMVix/TCDY+OoUrkSM0Z2p8VZNYJdkkiFprATCbCELQcYFruY2tVCmTmyG+dFVAt2SSIVnsJOJIC+37iPkVPiObtWVaaP7EqjOuHBLklEUNiJBMwX6/Zw57QEmkZUY/rIrpxVs2qwSxIRH4WdSAB8tPJn7pmVyIUNazJtRFfqVQ8LdkkikoPCTqSI3lu6gz/OWUZUk9pMviOG2uGhwS5JRHKpFOwCRMqyOfHbuHf2UqKb1mXaiK4KOpFSSlt2IoU09YfNPPbeKn7Vsj7jB0cTHhYS7JJEJA8KO5FCmPD1Jp78cA2XX9SQVwZ2pEplBZ1IaaawEzkDzjle/nwDz3+ynmvancOLAzoQGqKjASKlncJOpICcczz78Tpe/XIjN3RqzDM3RlFZQSdSJijsRArAOccT76/mze82c3vX8/hbv0gqVbJglyUiBaSwEzmNzEzHI++tZGbcVu64pBmPXdsGMwWdSFmisBPJR0am44G5y3kncTu/7XUBD1zZSkEnUgYp7ETykJaRyX2zl/L+8l388YoLueeyFgo6kTJKYSfiR0p6BvfMXML/Vu/moatbM6bnBcEuSUSKQGEnkktyWga/nZ7AF+v28vhv2jDskubBLklEikhhJ5LDidR0Rk9N4LuN+/j79e24vet5wS5JRAJAYSficywlneFvLiZ+ywGeu6k9N3ZuEuySRCRAFHYiwOGkNIa9uYjl2w/zrwEd+U37RsEuSUQCSGEnFd7B46kMjo1j3c9HeeX2TlwVeXawSxKRAFPYSYW271gKgybGsWnfcd4Y3JnLWjcMdkkiUgwUdlJh7TmSzO0T49h+8ASThkbzq5YNgl2SiBQThZ1USLsOJ3H7hDh2H0lm8h0xdDs/ItgliUgxUthJhbPtwAlun7iQQ8fTmDYihs5N6wW7JBEpZgo7qVA27zvOwIlxHE1OY/rIrrQ/t06wSxKREqCwkwpj495j3D5hIanpmcwc1Y3IxrWDXZKIlJCA9TxpZleZ2Toz22Bm4/wMr2Jms33D48ysWY5hUWb2g5mtMrMVZlY1UHWJAPy4+yi3vrGQ9AzHrNEKOpGKJiBhZ2YhwCvA1UAb4DYza5NrtBHAQedcC+AF4GnftJWB6cCdzrm2QC8gLRB1iQCs2XWEAeMXUslg9phutD67VrBLEpESFqgtuxhgg3Nuk3MuFXgL6JdrnH7AFN/vc4E+5vWX0hdY7pxbBuCc2++cywhQXVLBrdxxmNsmLCSsciVmj+lOi7NqBrskEQmCQIVdY2BbjufbfW1+x3HOpQOHgQjgQsCZ2cdmlmhmD+S1EDMbbWbxZha/d+/eAJUu5dXSbYe4fcJCqodVZvbo7jSvXz3YJYlIkATsmF0RVAYuBQb6fl5vZn38jeicG++ci3bORTdooAuAJW8JWw4waGIcdaqFMXtMN86LqBbskkQkiAIVdjuAc3M8b+Jr8zuO7zhdbWA/3lbg1865fc65E8CHQKcA1SUV0KKfDjBk0iIa1KzC7DHdaFJXQSdS0QUq7BYDLc2suZmFAQOABbnGWQAM9f1+E/C5c84BHwPtzKyaLwR7AqsDVJdUMN9v3MfQ2EWcXbsqs0d345za4fmOP3/JDi556nOaj/uAS576nPlLcn9HE5HyICDX2Tnn0s3sbrzgCgFinXOrzOwJIN45twCYBEwzsw3AAbxAxDl30MyexwtMB3zonPsgEHVJxfLNj3sZOSWeZhHVmT6yKw1qVsl3/PlLdvDQvBUkpXnnQ+04lMRD81YA0L9j7kPOIlKWmbdxVfZER0e7+Pj4YJchpcQX6/YwZloC59evzoyRXYmokX/QAVzy1OfsOJR0SnvjOuF8N+6y4ihTRIqRmSU456L9DdMdVKTM+2zNbn47PZGWDWswfURX6lYPK9B0O/0EXX7tIlJ2lYazMUUK7eNVP3Pn9ARan1OTmSO7FTjoABrV8X88L692ESm7FHZSZv13xS7umpFI20a1mTaiK7WrhZ7R9GOvbEV4aMhJbeGhIYy9slUgyxSRUkC7MaVMen/5Tv7w1lI6nFuHyXd0oWbVMws6+OUklGc/XsfOQ0k0qhPO2Ctb6eQUkXJIYSdlzntLd3Df7KV0blqXN++IoUaVwn+M+3dsrHATqQAUdlKmvLtkO3+as4wuzeoRO6wL1YsQdCJSceiYnZQZcxO288c5y+jaPII371DQiUjBaW0hZcKc+G08+M5yLrmgPhOGRBMeFnL6iUREfBR2UurNXryVcfNWcGkLL+iqhiroROTMaDemlGqzFm3lwXdW0KNlAwWdiBSawk5KrZlxW3lo3gp6tWrAG4M7K+hEpNAUdlIqzYjbwp/fXUHvVg14fZCCTkSKRsfspNSZtnALj85fyWWtz+K1QZ2oUllBJyJFoy07KVWygq6Pgk5EAkhhJ6VGzqB7VUEnIgGksJNSISvoLr9IQScigaewk6DLGXSvDFTQiUjgKewkqKYr6ESkBCjsJGhmxm3lEd8xOgWdiBQnhZ0ExaxFW/nzuyu4TCejiEgJUNhJiZu92LszSu9WDXR5gYiUCIWdlKi347cxbt4KelzYgNcGdVbQiUiJUNhJiXknYTsPvLOcS1vUZ7zudSkiJUhhJyVi/pId3D93WXZ/dAo6ESlJCjspdv9ZtpM/zllKt+YRCjoRCQqFnRSrD1fs4t7ZS4luVo9Jw9TDuIgEh8JOis3Hq37m97OW0OHcOsQO60K1MHWyISLBobCTYvHZmt3cPTORyMa1mXxHF2pUUdCJSPAo7CTgvl6/l99OT6T12bWYMjyGmlVDg12SiFRwCjsJqO837mPU1HguOKsG00bEUDtcQSciwaewk4BZvPkAIybH0zSiGtNHxFCnWliwSxIRARR2EiBLtx3ijjcXc06dqkwf2ZWIGlWCXZKISDaFnRTZqp2HGTIpjnrVw5g5shtn1awa7JJERE6isJMiWb/7KIMnLaJGlcrMHNWVs2sr6ESk9FHYSaH9tO84AyfGEVLJmDmqG03qVgt2SSIifinspFC2HzzBwAkLych0zBzZlWb1qwe7JBGRPCns5IztPpLMwIlxHEtJZ+rwGFo2rBnskkRE8qWwkzNy4HgqgybGse9oClOGxxDZuHawSxIROS3dw0kK7EhyGkNi49h64AST74ih43l1g12SiEiBaMtOCuREajrD31zM2l1HeX1QZ7pfEBHskkRECkxhJ6eVkp7BndMTSdx6kH8N6Ejv1mcFuyQRkTOi3ZiSr4xMx32zl/L1+r08c2MU10SdE+ySRETOmLbsJE/OOf48bwUfrviZR665iFu6nBvskkRECkVhJ3455/j7h2uYHb+N31/WgpG/Oj/YJYmIFJrCTvx67auNTPjmJ4Zd3Iz7rrgw2OWIiBSJwk5OMWvRVp75aB39OzTisWvbYGbBLklEpEgUdnKS/67YxcPvrqBXqwY8e3N7KlVS0IlI2aewk2zfb9jHH95aSsfz6vLawM6EhujjISLlg9ZmAsDKHYcZPS2BZvWrETu0C+FhIcEuSUQkYBR2wpb9xxn25iJqh4cydXhXalcLDXZJIiIBpYvKK7i9R1MYEruI9EzHW8Nj1PmqiJRL2rKrwI6npDNiymJ2H0kmdlgXWpxVI9gliYgUC4VdBZWWkcldMxNZueMw/76tE53Ug4GIlGPajVkBOed45N2VfLluL/+4oR2Xt2kY7JJERIqVtuwqoJc/3+DdBqxPS26LOS/Y5YiIFDuFXQUzL3E7z3+ynhs6lgnV9AAADdNJREFUNea+y1sGuxwRkRKh3ZgVyPcb9vHgO8u5+IIInrohqsC3AZu/ZAfPfryOnYeSaFQnnLFXtqJ/x8bFXK2ISOAEbMvOzK4ys3VmtsHMxvkZXsXMZvuGx5lZs1zDzzOzY2Z2f6Bqkl9s2HOUMdMTaBZRndcGdSascsH+9POX7OCheSvYcSgJB+w4lMRD81Ywf8mO4i1YRCSAAhJ2ZhYCvAJcDbQBbjOzNrlGGwEcdM61AF4Ans41/Hngv4GoR0524HgqwyfHU6VyJWKHdaF2eMEvGn/243UkpWWc1JaUlsGzH68LdJkiIsUmUFt2McAG59wm51wq8BbQL9c4/YApvt/nAn3Mtx/NzPoDPwGrAlSP+KSkZzB6ajw/H0lm/JBozq1X7Yym33ko6YzaRURKo0CFXWNgW47n231tfsdxzqUDh4EIM6sBPAj89XQLMbPRZhZvZvF79+4NSOHlmXOOh95ZQfyWg/zz5vaFupauUZ3wM2oXESmNSsPZmI8DLzjnjp1uROfceOdctHMuukGDBsVfWRk3/utNzFuyg3svb8lv2jcq1DzGXtmK8NCTbwodHhrC2CtbBaJEEZESEaizMXcA5+Z43sTX5m+c7WZWGagN7Ae6AjeZ2TNAHSDTzJKdc/8OUG0V0udrd/PUR//f3r0HW1Wedxz/PlwEVAKIeCmoHCIpIJIhIdjYsbmgYqV4JYY2GZnWNL2Mk7TOZMZWxzqmGdNMJplJmpkkHdOapA0QZ2qJiXVUbG2caADlUkDIATV4NHIEAuF+gLd/7HWS3eM5wj5nw7rs72dmz157rXfhs14X82Ot/e71vsi8S8/n03P6/xOD7lGXjsaUVGbNCrsVwOSIaKMWaguBP+rRZhmwCPgJsABYnlJKwBXdDSLiXmCvQTcw7dt/xae+t5pp57+DL37k3QOeafyGmeMNN0ml1pSwSykdiYjbgceAwcC3UkrrI+I+YGVKaRnwAPCdiGgHdlILRDXZnoNdfPLbqxg+dBD/dOss56WTJJr4o/KU0o+AH/VYd0/d8kHgI8f5M+5tVj2t6NixxB1L1vDznfv5109c5iASScoUYYCKmuQfn2rniY1vcPe8qVw2aWze5UhSYRh2FfH05k6+/MRmbpo5nkWXT8y7HEkqFMOuAl7ffYC/WrKa3z53JJ+78dIBD0iRpKox7Equ6+gxbv+3FzjUdZSvfew9DkiRpF4460HJffGxTax6ZRdf/cOZvHPcmXmXI0mF5JVdiT29uZNvPL2Vj112Yb+fkCJJrcCwK6k39x7ijqVrmHzOmdw9r+cEE5Kket7GLKGUEp/5/hr2HOziu5+Y7fd0knQchl0JLV6xjac2dXLv/GlMOe8deZdz0jlTuqSBMuxKZtvO/fz9Ixu4/J1jufX9E/Mu56Trnim9ewLZ7pnSAQNP0gnzO7sSOXYs8ZmH1hARfGHBDAYNqv7v6ZwpXVIzGHYl8r0VP+fZrTu5e95UJoxpbMbxsnKmdEnNYNiVxPZfHeTzj77I+yeN5aPvu+D4O1SEM6VLagbDriQ++8hGDnUd43M3Tm+px4E5U7qkZnCASgn8z886+cGa1/jrK9/FpBZ7SoozpUtqBsOu4I4cPcZnH9nAhWedzp9/cFLe5eTCmdIlDZS3MQtu8YptbH5jL3977RSGDfHH45LUH4Zdge052MWXHt/M7LazmHvJeXmXI0mlZdgV2D//+GV27jvM3fOmttSgFElqNsOuoHYf6OKBH2/lqmnnMmPC6LzLkaRSM+wK6l+eeZk9B4/w6TmT8y5FkkrPsCugA4eP8q1nXuLKqecyffyovMuRpNIz7AroP1Z3sPtAF396RVvepUhSJRh2BZNS4sGfvMKU80Yyu+2svMuRpEow7Apm1Su72Pj6HhZdPtERmJLUJIZdwTy8uoPhQwdx3bt/K+9SJKkyfFxYgRw5eoxH1/2COVPO5Yxhff+vceZuSWqMYVcgz27dyY59h/mDGef32aasM3cb0JLy5G3MAln+4naGDRnEh6ac02ebMs7c3R3QHb88QOI3Af3wCx15lyapRRh2BfLcSzt4z4VjGD607wc+l3Hm7jIGtKRqMewKYvf+Lja8voffmTT2bduVcebuMga0pGox7Ari+W27SInj/raujDN3lzGgJVWLYVcQW7bvBWDKeSPftt0NM8dz/02XMn70CAIYP3oE9990aaEHe5QxoCVVi6MxC2JL5z7GnD6UMWecdty2ZZu5u7tWR2NKyothVxAvvbmXtrPPyLuMk6ZsAS2pWryNWRA79h7mnJHD8y5DkirJsCuIfYeOvO1TUyRJ/WfYFcTeQ0c4c1jfv6+TJPWfYVcQEcHI4UPzLkOSKsn7ZgWx5u+uzrsESaosr+wkSZVn2EmSKs+wkyRVnmEnSao8w06SVHmGnSSp8gw7SVLlGXaSpMoz7CRJlWfYSZIqz7CTJFVea4bd2qXw5elw7+ja+9qleVckSTqJWu9B0GuXwg8+BV0Hap93b6t9BphxS351SZJOmta7snvyvt8EXbeuA7X1kqRKar2w2/1qY+slSaXXemE3akJj6yVJpdd6YTfnHhg64v+vGzqitl6SVElNC7uIuCYiNkVEe0Tc2cv2YRGxJNv+XERMzNZfFRGrImJd9v7hZtXUqxm3wPyvwKgLgKi9z/+Kg1MkqcKaMhozIgYDXwOuAl4FVkTEspTShrpmtwG7UkoXR8RC4B+AjwJvAvNTSq9FxHTgMWB8M+rq04xbDDdJaiHNurKbDbSnlLamlA4Di4Hre7S5HngwW34ImBMRkVJ6IaX0WrZ+PTAiIoY1qS5JkpoWduOBbXWfX+WtV2e/bpNSOgLsBsb2aHMz8HxK6VBv/5GI+GRErIyIlZ2dnU0pXJJUfYUZoBIRl1C7tflnfbVJKX0zpTQrpTRr3Lhxp644SVKpNSvsOoAL6j5PyNb12iYihgCjgB3Z5wnAvwO3ppS2NKkmSZKA5oXdCmByRLRFxGnAQmBZjzbLgEXZ8gJgeUopRcRo4IfAnSmlZ5pUjyRJv9aUsMu+g7ud2kjKjcDSlNL6iLgvIq7Lmj0AjI2IduAOoPvnCbcDFwP3RMTq7HVOM+qSJAkgUkp519Avs2bNSitXrsy7DElSQUTEqpTSrN62FWaAiiRJJ4thJ0mqPMNOklR5hp0kqfIMO0lS5ZV2NGZEdAKv9HP3s6k9gFonxv5qjP3VGPurcfZZ7y5KKfX6eK3Sht1ARMTKvoan6q3sr8bYX42xvxpnnzXO25iSpMoz7CRJldeqYffNvAsoGfurMfZXY+yvxtlnDWrJ7+wkSa2lVa/sJEktxLCTJFVe5cIuIq6JiE0R0R4Rd/ayfVhELMm2PxcRE+u2/U22flNEzD2Vdeelv/0VERMj4kDdtExfP9W15+EE+uv3IuL5iDgSEQt6bFsUET/LXot67ltFA+yvo3XnV8/5MSvpBPrrjojYEBFrI+LJiLioblvLnV8NSSlV5gUMBrYAk4DTgDXAtB5t/hL4era8EFiSLU/L2g8D2rI/Z3Dex1Tg/poI/G/ex1DA/poIzAC+DSyoW38WsDV7H5Mtj8n7mIraX9m2vXkfQwH760PA6dnyX9T9fWy586vRV9Wu7GYD7SmlrSmlw8Bi4Poeba4HHsyWHwLmRERk6xenlA6llF4C2rM/r8oG0l+t6Lj9lVJ6OaW0FjjWY9+5wOMppZ0ppV3A48A1p6LoHA2kv1rRifTXUyml/dnHZ4EJ2XIrnl8NqVrYjQe21X1+NVvXa5tUm2F9NzD2BPetmoH0F0BbRLwQEf8dEVec7GILYCDniOdX48c8PCJWRsSzEXFDc0srpEb76zbg0X7u23KG5F2ASut14MKU0o6IeC/wcERcklLak3dhqoyLUkodETEJWB4R61JKW/Iuqggi4uPALOADeddSFlW7susALqj7PCFb12ubiBgCjAJ2nOC+VdPv/spu9+4ASCmtovZdw7tOesX5Gsg54vnV4DGnlDqy963AfwEzm1lcAZ1Qf0XElcBdwHUppUON7NvKqhZ2K4DJEdEWEadRG1DRcxTXMqB7pNICYHmqfcO7DFiYjT5sAyYDPz1Fdeel3/0VEeMiYjBA9i/vydS+FK+yE+mvvjwGXB0RYyJiDHB1tq7K+t1fWT8Ny5bPBn4X2HDSKi2G4/ZXRMwEvkEt6LbXbWrF86sxeY+QafYLuBbYTO1K465s3X3UTg6A4cD3qQ1A+SkwqW7fu7L9NgG/n/exFLm/gJuB9cBq4Hlgft7HUpD+eh+170v2UbtjsL5u3z/J+rEd+OO8j6XI/QVcDqyjNiJxHXBb3sdSkP56Angj+3u3GljWyudXIy8fFyZJqryq3caUJOktDDtJUuUZdpKkyjPsJEmVZ9hJkirPsJMkVZ5hJ0mqPMNOklR5hp1UQhExOyIej4jOiEg9XhfnXZ9UNIadVDIRMZ3ag5E3Ah8EPgz8gtrj3D5O7VFTkur4uDCpZCLiSeCXKaWb69bdT22m78n5VSYVl/PZSSWSzQDwAWozU9fbB/gvV6kP3saUyuW9wGBqswHUm0VtihhJvfDKTiqXwdn7iO4V2YCUucCNuVQklYBXdlK5PAfsB74QEVMjYi7wQ2BxSuk/8y1NKi4HqEglExHXAl8CJgEdwAPA51NKR3ItTCoww06SVHnexpQkVZ5hJ0mqPMNOklR5hp0kqfIMO0lS5Rl2kqTKM+wkSZVn2EmSKu//AEinbO6YltFkAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 504x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "|                    |       Adobe |       Apple |        AT&T |   Microsoft |        Nike |\n",
            "|--------------------+-------------+-------------+-------------+-------------+-------------|\n",
            "| Beta               | 0.926465    | 0.794665    | 0.958038    | 0.896334    | 0.959111    |\n",
            "| Systematic Risk    | 1.96073e-06 | 1.44254e-06 | 2.09665e-06 | 1.83527e-06 | 2.10135e-06 |\n",
            "| Diversifiable Risk | 0.00518041  | 0.0076305   | 0.00109276  | 0.00258626  | 0.00389165  | \n",
            "\n",
            "DAILY in Quarters\n",
            "| S&P500                          | S&P500                                           | Adobe                                         | Apple                                         | AT&T                                           | Microsoft                                      | Nike                                           |\n",
            "|---------------------------------+--------------------------------------------------+-----------------------------------------------+-----------------------------------------------+------------------------------------------------+------------------------------------------------+------------------------------------------------|\n",
            "| Beta, Quarter:1                 |                                                  | 1.2050809517922279                            | 0.8103453082948462                            | 1.325858139222576                              | 1.2226615628845112                             | 0.712932564384877                              |\n",
            "| S&P and Stock Points, Quarter:1 | (8.002800424413999e-05, 0.0005673055658760014)   | (0.015908305734341266, -0.00066646713468262)  | (0.017285845233759613, 0.0017082734574027667) | (0.00874036224619855, -0.0013930660479389925)  | (0.011750018724154175, -0.0007722325791826651) | (0.011538452211273729, 0.0022943112296693515)  |\n",
            "| Beta, Quarter:2                 |                                                  | 1.2993929362368428                            | 0.6152975662623165                            | 0.8691228784996369                             | 1.180890804526926                              | 0.8838984415523793                             |\n",
            "| S&P and Stock Points, Quarter:2 | (0.00024887666656293954, -0.0020309392474303347) | (0.022253650582482992, -0.004609991608929695) | (0.0227387767389149, 0.0012829923792692438)   | (0.011793089853335385, -0.0009035280482404824) | (0.01753467665290823, -0.003589181944978914)   | (0.020322391202763367, -0.0010308087759110657) |\n",
            "| Beta, Quarter:3                 |                                                  | 1.3072684681188622                            | 0.9029396380466653                            | 0.7645623393221664                             | 1.1388823709843396                             | 0.7585837270519655                             |\n",
            "| S&P and Stock Points, Quarter:3 | (0.00013210669984193053, 0.001734176293989263)   | (0.0324273463052225, 0.0002441832388422139)   | (0.01397759396783362, 0.0022048372313965593)  | (0.008593946491708061, 0.00287585048359188)    | (0.013543965242800325, 0.0010607138670897579)  | (0.011978080957156903, 0.002904841713367434)   |\n",
            "| Beta, Quarter:4                 |                                                  | 0.7517597916083261                            | 0.8660805975559561                            | 1.1831727890653305                             | 0.8405604667281402                             | 1.040179464959531                              |\n",
            "| S&P and Stock Points, Quarter:4 | (5.6269785702241474e-05, 0.0015009108220919493)  | (0.02259365811124336, 0.0027625724454170453)  | (0.012425890909350047, 0.0021815458077655524) | (0.007831717546919106, 0.0005699493154994449)  | (0.011522462252650283, 0.0023112498951746692)  | (0.012601051976910367, 0.0012967018048919941)  | \n",
            "\n",
            "| Beta, Year                 |                                               | 0.8904417392182831                            | 0.705467665708022                            | 0.9306332099822117                            | 0.842880386331558                             | 0.9388239142572001                            |\n",
            "| S&P and Stock Points, Year | (6.19738103391029e-05, 0.0010371289979990354) | (0.015025908309518787, 0.0016447614989182793) | (0.016498234380352632, 0.002670665507345335) | (0.011216035197067788, 0.0014218513895239177) | (0.012509341072050234, 0.0019085464804929986) | (0.013066936760700702, 0.0013764240699645353) |\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dJlv_0-QVlOz"
      },
      "source": [
        "### Comments\n",
        "Comparing the daily analysis by year and the monthly analysis we see some substantial differences. For one, the sigma scale for the monthly analysis is much higher than for the daily. This also shows up in the diversifiable and systematic risk, the monthly risks are over an order of magnitude higher than the daily risk values. This makes sense as we were doing a monthly returns where the data points are further apart in time from each other, and therefore there is more variation in the data points we get. \n",
        "\n",
        "For the daily returns analyzed in the 4 quarters, I didn't see things too distinctly different from the daily return analysis performed on the entire year. We saw some fluctuations in the values of the stock points and the betas, but not by significant amounts. "
      ]
    }
  ]
}