Python_for_Finance_Tutorial_For_Beginners.ipynb

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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/tylee33/DLforFinance/blob/master/Python_for_Finance_Tutorial_For_Beginners.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ov-q5YSGi_n_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import datetime\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "03xWbIvqjoD7",
        "colab_type": "text"
      },
      "source": [
        "# **Python Basics For Finance: Pandas**\n",
        "\n",
        "**Importing Data**\n",
        "\n",
        "At this moment, there is a lot going on in the open-source community because of the changes to the Yahoo! Finance API. That's why you don't only use the pandas_datareader package, but also a custom fix fix_yahoo_finance to get your data:\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PZ9y7tHqjmvb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "f1e4406b-179a-498c-ca6a-7550628fb4ed"
      },
      "source": [
        "from pandas_datareader import data as pdr\n",
        "import fix_yahoo_finance\n",
        "\n",
        "aapl = pdr.get_data_yahoo('AAPL', \n",
        "                          start=datetime.datetime(2006, 10, 1), \n",
        "                          end=datetime.datetime(2012, 1, 1))\n",
        "aapl.head()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Open</th>\n",
              "      <th>Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Adj Close</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2006-10-02</th>\n",
              "      <td>10.838572</td>\n",
              "      <td>10.614285</td>\n",
              "      <td>10.728572</td>\n",
              "      <td>10.694285</td>\n",
              "      <td>178159800</td>\n",
              "      <td>7.103626</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2006-10-03</th>\n",
              "      <td>10.707143</td>\n",
              "      <td>10.455714</td>\n",
              "      <td>10.635715</td>\n",
              "      <td>10.582857</td>\n",
              "      <td>197677200</td>\n",
              "      <td>7.029610</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2006-10-04</th>\n",
              "      <td>10.780000</td>\n",
              "      <td>10.451428</td>\n",
              "      <td>10.585714</td>\n",
              "      <td>10.768572</td>\n",
              "      <td>207270700</td>\n",
              "      <td>7.152968</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2006-10-05</th>\n",
              "      <td>10.880000</td>\n",
              "      <td>10.590000</td>\n",
              "      <td>10.647142</td>\n",
              "      <td>10.690000</td>\n",
              "      <td>170970800</td>\n",
              "      <td>7.100781</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2006-10-06</th>\n",
              "      <td>10.720000</td>\n",
              "      <td>10.544286</td>\n",
              "      <td>10.631429</td>\n",
              "      <td>10.602858</td>\n",
              "      <td>116739700</td>\n",
              "      <td>7.042896</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 High        Low       Open      Close     Volume  Adj Close\n",
              "Date                                                                        \n",
              "2006-10-02  10.838572  10.614285  10.728572  10.694285  178159800   7.103626\n",
              "2006-10-03  10.707143  10.455714  10.635715  10.582857  197677200   7.029610\n",
              "2006-10-04  10.780000  10.451428  10.585714  10.768572  207270700   7.152968\n",
              "2006-10-05  10.880000  10.590000  10.647142  10.690000  170970800   7.100781\n",
              "2006-10-06  10.720000  10.544286  10.631429  10.602858  116739700   7.042896"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BfShtCX3jx4g",
        "colab_type": "text"
      },
      "source": [
        "The pandas_datareader offers a lot of possibilities to get financial data. If you don't want to make use of this package, however, you can also use Quandl to retrieve data:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D3Cw-Ee8j3Rg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "outputId": "7e396055-dd57-4453-ce65-8f64dbae980d"
      },
      "source": [
        "!pip install quandl"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: quandl in /usr/local/lib/python3.6/dist-packages (3.4.8)\n",
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            "Requirement already satisfied: pandas>=0.14 in /usr/local/lib/python3.6/dist-packages (from quandl) (0.24.2)\n",
            "Requirement already satisfied: numpy>=1.8 in /usr/local/lib/python3.6/dist-packages (from quandl) (1.16.3)\n",
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            "Requirement already satisfied: more-itertools<=5.0.0 in /usr/local/lib/python3.6/dist-packages (from quandl) (5.0.0)\n",
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            "Requirement already satisfied: requests>=2.7.0 in /usr/local/lib/python3.6/dist-packages (from quandl) (2.21.0)\n",
            "Requirement already satisfied: pytz>=2011k in /usr/local/lib/python3.6/dist-packages (from pandas>=0.14->quandl) (2018.9)\n",
            "Requirement already satisfied: cryptography>=2.3 in /usr/local/lib/python3.6/dist-packages (from pyOpenSSL->quandl) (2.6.1)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests>=2.7.0->quandl) (2019.3.9)\n",
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            "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests>=2.7.0->quandl) (3.0.4)\n",
            "Requirement already satisfied: asn1crypto>=0.21.0 in /usr/local/lib/python3.6/dist-packages (from cryptography>=2.3->pyOpenSSL->quandl) (0.24.0)\n",
            "Requirement already satisfied: cffi!=1.11.3,>=1.8 in /usr/local/lib/python3.6/dist-packages (from cryptography>=2.3->pyOpenSSL->quandl) (1.12.3)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.6/dist-packages (from cffi!=1.11.3,>=1.8->cryptography>=2.3->pyOpenSSL->quandl) (2.19)\n"
          ],
          "name": "stdout"
        }
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    {
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          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "1fa28556-8837-40bd-b15c-e2358857664b"
      },
      "source": [
        "import quandl \n",
        "aapl = quandl.get(\"WIKI/AAPL\", start_date=\"2006-10-01\", end_date=\"2018-01-01\")\n",
        "aapl.tail()"
      ],
      "execution_count": 7,
      "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>Volume</th>\n",
              "      <th>Ex-Dividend</th>\n",
              "      <th>Split Ratio</th>\n",
              "      <th>Adj. Open</th>\n",
              "      <th>Adj. High</th>\n",
              "      <th>Adj. Low</th>\n",
              "      <th>Adj. Close</th>\n",
              "      <th>Adj. Volume</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2017-12-22</th>\n",
              "      <td>174.68</td>\n",
              "      <td>175.424</td>\n",
              "      <td>174.500</td>\n",
              "      <td>175.01</td>\n",
              "      <td>16052615.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>174.68</td>\n",
              "      <td>175.424</td>\n",
              "      <td>174.500</td>\n",
              "      <td>175.01</td>\n",
              "      <td>16052615.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-26</th>\n",
              "      <td>170.80</td>\n",
              "      <td>171.470</td>\n",
              "      <td>169.679</td>\n",
              "      <td>170.57</td>\n",
              "      <td>32968167.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>170.80</td>\n",
              "      <td>171.470</td>\n",
              "      <td>169.679</td>\n",
              "      <td>170.57</td>\n",
              "      <td>32968167.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-27</th>\n",
              "      <td>170.10</td>\n",
              "      <td>170.780</td>\n",
              "      <td>169.710</td>\n",
              "      <td>170.60</td>\n",
              "      <td>21672062.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>170.10</td>\n",
              "      <td>170.780</td>\n",
              "      <td>169.710</td>\n",
              "      <td>170.60</td>\n",
              "      <td>21672062.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-28</th>\n",
              "      <td>171.00</td>\n",
              "      <td>171.850</td>\n",
              "      <td>170.480</td>\n",
              "      <td>171.08</td>\n",
              "      <td>15997739.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>171.00</td>\n",
              "      <td>171.850</td>\n",
              "      <td>170.480</td>\n",
              "      <td>171.08</td>\n",
              "      <td>15997739.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-29</th>\n",
              "      <td>170.52</td>\n",
              "      <td>170.590</td>\n",
              "      <td>169.220</td>\n",
              "      <td>169.23</td>\n",
              "      <td>25643711.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>170.52</td>\n",
              "      <td>170.590</td>\n",
              "      <td>169.220</td>\n",
              "      <td>169.23</td>\n",
              "      <td>25643711.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              Open     High      Low  ...  Adj. Low  Adj. Close  Adj. Volume\n",
              "Date                                  ...                                   \n",
              "2017-12-22  174.68  175.424  174.500  ...   174.500      175.01   16052615.0\n",
              "2017-12-26  170.80  171.470  169.679  ...   169.679      170.57   32968167.0\n",
              "2017-12-27  170.10  170.780  169.710  ...   169.710      170.60   21672062.0\n",
              "2017-12-28  171.00  171.850  170.480  ...   170.480      171.08   15997739.0\n",
              "2017-12-29  170.52  170.590  169.220  ...   169.220      169.23   25643711.0\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZUg3Bk36kA6g",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Alternatively, you can load in a data set that has been retrieved for you already from Yahoo! Finance: \n",
        "aapl = pd.read_csv(\"https://s3.amazonaws.com/assets.datacamp.com/blog_assets/aapl.csv\", header=0, index_col= 0, names=['Open', 'High', 'Low', 'Close', 'Volume', 'Adj Close'], parse_dates=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t7G0fDPckE5P",
        "colab_type": "text"
      },
      "source": [
        "# **Working With Time Series Data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "STHHslA1kD0f",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "32f88dff-c1cf-429d-9bf3-d789dad9ba18"
      },
      "source": [
        "# Inspect the index \n",
        "aapl.index\n",
        "\n",
        "# Inspect the columns\n",
        "aapl.columns\n",
        "\n",
        "# Select only the last 10 observations of `Close`\n",
        "ts = aapl['Close'][-10:]\n",
        "\n",
        "# Check the type of `ts` \n",
        "type(ts)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.series.Series"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ObpCQGdnkJ2F",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 646
        },
        "outputId": "7f8228d2-a937-4e7d-d1ad-ffa85210a901"
      },
      "source": [
        "# Inspect the first rows of November-December 2006\n",
        "print(aapl.loc[pd.Timestamp('2006-11-01'):pd.Timestamp('2006-12-31')].head())\n",
        "\n",
        "# Inspect the first rows of 2007 \n",
        "print(aapl.loc['2007'].head())\n",
        "\n",
        "# Inspect November 2006\n",
        "print(aapl.iloc[22:43])\n",
        "\n",
        "# Inspect the 'Open' and 'Close' values at 2006-11-01 and 2006-12-01\n",
        "print(aapl.iloc[[22,43], [0, 3]])"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "             Open   High    Low      Close       Volume  Adj Close\n",
            "2006-11-01  81.10  81.38  78.36  79.160004  152798100.0  11.308572\n",
            "2006-11-02  78.92  79.32  78.50  78.979996  116370800.0  11.282857\n",
            "2006-11-03  79.36  79.53  77.79  78.290001  107972200.0  11.184286\n",
            "2006-11-06  78.95  80.06  78.43  79.709999  108644200.0  11.387143\n",
            "2006-11-07  80.45  81.00  80.13  80.510002  131483100.0  11.501429\n",
            "             Open   High    Low      Close       Volume  Adj Close\n",
            "2007-01-03  86.29  86.58  81.90  83.800003  309579900.0  11.971429\n",
            "2007-01-04  84.05  85.95  83.82  85.659996  211815100.0  12.237143\n",
            "2007-01-05  85.77  86.20  84.40  85.049995  208685400.0  12.150000\n",
            "2007-01-08  85.96  86.53  85.28  85.470001  199276700.0  12.210000\n",
            "2007-01-09  86.45  92.98  85.15  92.570000  837324600.0  13.224286\n",
            "             Open   High    Low      Close       Volume  Adj Close\n",
            "2006-11-01  81.10  81.38  78.36  79.160004  152798100.0  11.308572\n",
            "2006-11-02  78.92  79.32  78.50  78.979996  116370800.0  11.282857\n",
            "2006-11-03  79.36  79.53  77.79  78.290001  107972200.0  11.184286\n",
            "2006-11-06  78.95  80.06  78.43  79.709999  108644200.0  11.387143\n",
            "2006-11-07  80.45  81.00  80.13  80.510002  131483100.0  11.501429\n",
            "2006-11-08  80.02  82.69  79.89  82.449997  172729200.0  11.778571\n",
            "2006-11-09  82.90  84.69  82.12  83.339996  230763400.0  11.905714\n",
            "2006-11-10  83.55  83.60  82.50  83.120003   93466100.0  11.874286\n",
            "2006-11-13  83.22  84.45  82.64  84.349998  112668500.0  12.050000\n",
            "2006-11-14  84.80  85.00  83.90  85.000000  147238700.0  12.142858\n",
            "2006-11-15  85.05  85.90  84.00  84.050003  163830800.0  12.007143\n",
            "2006-11-16  84.87  86.30  84.62  85.610001  173485200.0  12.230000\n",
            "2006-11-17  85.14  85.94  85.00  85.850006  116606000.0  12.264286\n",
            "2006-11-20  85.40  87.00  85.20  86.470001  142698500.0  12.352858\n",
            "2006-11-21  87.42  88.60  87.11  88.599998  155666700.0  12.657143\n",
            "2006-11-22  88.99  90.75  87.85  90.309998  167985300.0  12.901428\n",
            "2006-11-24  89.53  93.08  89.50  91.630005  129669400.0  13.090000\n",
            "2006-11-27  92.51  93.16  89.50  89.540001  268709000.0  12.791429\n",
            "2006-11-28  90.36  91.97  89.91  91.809998  259043400.0  13.115714\n",
            "2006-11-29  93.00  93.15  90.25  91.799995  289270800.0  13.114285\n",
            "2006-11-30  92.21  92.68  91.06  91.660004  217621600.0  13.094286\n",
            "            Open      Close\n",
            "2006-11-01  81.1  79.160004\n",
            "2006-12-01  91.8  91.320000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GaVJqBZkkOQW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        },
        "outputId": "00b33f56-4c9d-4ce9-e622-dc9f93dd822c"
      },
      "source": [
        "# Sample 20 rows\n",
        "sample = aapl.sample(20)\n",
        "\n",
        "# Print `sample`\n",
        "print(sample)\n",
        "\n",
        "# Resample to monthly level \n",
        "monthly_aapl = aapl.resample('M')\n",
        "\n",
        "# Print `monthly_aapl`\n",
        "print(monthly_aapl)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              Open    High     Low       Close       Volume  Adj Close\n",
            "2010-12-27  322.85  325.44  321.52  324.679993   62454000.0  46.382858\n",
            "2009-09-15  174.04  175.65  173.59  175.160004  106617700.0  25.022858\n",
            "2010-12-17  321.63  321.79  320.23  320.610016   96732300.0  45.801430\n",
            "2009-11-12  203.14  204.87  201.43  201.990005   90932800.0  28.855715\n",
            "2010-06-18  272.25  275.00  271.42  274.070007  196155400.0  39.152859\n",
            "2009-04-17  121.18  124.25  120.25  123.419998  124373900.0  17.631428\n",
            "2008-08-12  173.52  179.29  173.51  176.729996  209069700.0  25.247143\n",
            "2010-07-26  260.00  260.10  257.71  259.279999  105137900.0  37.040001\n",
            "2010-12-23  325.00  325.15  323.17  323.600006   55789300.0  46.228573\n",
            "2010-12-08  319.63  321.02  317.11  321.009979   80483900.0  45.858570\n",
            "2008-06-17  178.10  181.99  177.41  181.429993  224914200.0  25.918571\n",
            "2011-03-25  348.07  352.06  347.02  351.540009  112227500.0  50.220001\n",
            "2009-09-25  182.01  185.50  181.44  182.369995  111309800.0  26.052856\n",
            "2011-08-29  388.18  391.50  388.00  389.970001  101317300.0  55.709999\n",
            "2007-11-28  176.82  180.60  175.35  180.220001  287728000.0  25.745714\n",
            "2009-04-08  115.43  116.79  114.58  116.320007  113907500.0  16.617144\n",
            "2010-11-26  313.74  317.70  312.94  315.000000   59396400.0  45.000000\n",
            "2011-04-20  343.51  345.75  341.50  342.410004  175166600.0  48.915714\n",
            "2007-04-17   92.00   92.30   89.70   90.349998  187980100.0  12.907143\n",
            "2008-04-14  146.77  149.25  144.54  147.779999  211271900.0  21.111429\n",
            "DatetimeIndexResampler [freq=<MonthEnd>, axis=0, closed=right, label=right, convention=start, base=0]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q0EV9RWXkSeF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Add a column `diff` to `aapl` \n",
        "aapl['diff'] = aapl.Open - aapl.Close\n",
        "\n",
        "# Delete the new `diff` column\n",
        "del aapl['diff']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4BpvQX1QkVFf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 259
        },
        "outputId": "a02a1917-ab48-4d4b-b1b4-cb39bc56167f"
      },
      "source": [
        "# Import Matplotlib's `pyplot` module as `plt`\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Plot the closing prices for `aapl`\n",
        "aapl['Close'].plot(grid=True)\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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r9xXy5NJdPLN8DwAj+yZ0ZfFVEGtTH72IDAemAV8BacaYXPvUIcC9esEg4IDX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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "79j-WR1LkaiU",
        "colab_type": "text"
      },
      "source": [
        "# **Common Financial Analysis**\n",
        "# Returns"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l6F2gU90kdur",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 2193
        },
        "outputId": "d1f003f8-773e-4ea9-f3ad-4cf3f38ae310"
      },
      "source": [
        "# Assign `Adj Close` to `daily_close`\n",
        "daily_close = aapl[['Adj Close']]\n",
        "\n",
        "# Daily returns\n",
        "daily_pct_c = daily_close.pct_change()\n",
        "\n",
        "# Replace NA values with 0\n",
        "daily_pct_c.fillna(0, inplace=True)\n",
        "\n",
        "# Inspect daily returns\n",
        "print(daily_pct_c)\n",
        "\n",
        "# Daily log returns\n",
        "daily_log_returns = np.log(daily_close.pct_change()+1)\n",
        "\n",
        "# Print daily log returns\n",
        "print(daily_log_returns)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "            Adj Close\n",
            "2006-10-02   0.000000\n",
            "2006-10-03  -0.010419\n",
            "2006-10-04   0.017549\n",
            "2006-10-05  -0.007296\n",
            "2006-10-06  -0.008152\n",
            "2006-10-09   0.005524\n",
            "2006-10-10  -0.010987\n",
            "2006-10-11  -0.007858\n",
            "2006-10-12   0.027721\n",
            "2006-10-13  -0.003189\n",
            "2006-10-16   0.005065\n",
            "2006-10-17  -0.014721\n",
            "2006-10-18   0.003231\n",
            "2006-10-19   0.059842\n",
            "2006-10-20   0.012153\n",
            "2006-10-23   0.018887\n",
            "2006-10-24  -0.005033\n",
            "2006-10-25   0.007773\n",
            "2006-10-26   0.006244\n",
            "2006-10-27  -0.021657\n",
            "2006-10-30   0.000124\n",
            "2006-10-31   0.008207\n",
            "2006-11-01  -0.023680\n",
            "2006-11-02  -0.002274\n",
            "2006-11-03  -0.008736\n",
            "2006-11-06   0.018138\n",
            "2006-11-07   0.010036\n",
            "2006-11-08   0.024096\n",
            "2006-11-09   0.010794\n",
            "2006-11-10  -0.002640\n",
            "...               ...\n",
            "2011-11-17  -0.019128\n",
            "2011-11-18  -0.006545\n",
            "2011-11-21  -0.015816\n",
            "2011-11-22   0.020325\n",
            "2011-11-23  -0.025285\n",
            "2011-11-25  -0.009319\n",
            "2011-11-28   0.034519\n",
            "2011-11-29  -0.007764\n",
            "2011-11-30   0.024116\n",
            "2011-12-01   0.014992\n",
            "2011-12-02   0.004563\n",
            "2011-12-05   0.008494\n",
            "2011-12-06  -0.005242\n",
            "2011-12-07  -0.004758\n",
            "2011-12-08   0.004035\n",
            "2011-12-09   0.007577\n",
            "2011-12-12  -0.004522\n",
            "2011-12-13  -0.007733\n",
            "2011-12-14  -0.022170\n",
            "2011-12-15  -0.003288\n",
            "2011-12-16   0.005489\n",
            "2011-12-19   0.003123\n",
            "2011-12-20   0.035949\n",
            "2011-12-21   0.001263\n",
            "2011-12-22   0.005297\n",
            "2011-12-23   0.011993\n",
            "2011-12-27   0.007934\n",
            "2011-12-28  -0.009569\n",
            "2011-12-29   0.006159\n",
            "2011-12-30  -0.000296\n",
            "\n",
            "[1323 rows x 1 columns]\n",
            "            Adj Close\n",
            "2006-10-02        NaN\n",
            "2006-10-03  -0.010474\n",
            "2006-10-04   0.017396\n",
            "2006-10-05  -0.007323\n",
            "2006-10-06  -0.008185\n",
            "2006-10-09   0.005509\n",
            "2006-10-10  -0.011048\n",
            "2006-10-11  -0.007889\n",
            "2006-10-12   0.027344\n",
            "2006-10-13  -0.003194\n",
            "2006-10-16   0.005052\n",
            "2006-10-17  -0.014831\n",
            "2006-10-18   0.003225\n",
            "2006-10-19   0.058120\n",
            "2006-10-20   0.012080\n",
            "2006-10-23   0.018711\n",
            "2006-10-24  -0.005046\n",
            "2006-10-25   0.007743\n",
            "2006-10-26   0.006224\n",
            "2006-10-27  -0.021895\n",
            "2006-10-30   0.000124\n",
            "2006-10-31   0.008173\n",
            "2006-11-01  -0.023965\n",
            "2006-11-02  -0.002277\n",
            "2006-11-03  -0.008775\n",
            "2006-11-06   0.017975\n",
            "2006-11-07   0.009986\n",
            "2006-11-08   0.023811\n",
            "2006-11-09   0.010737\n",
            "2006-11-10  -0.002643\n",
            "...               ...\n",
            "2011-11-17  -0.019314\n",
            "2011-11-18  -0.006566\n",
            "2011-11-21  -0.015942\n",
            "2011-11-22   0.020121\n",
            "2011-11-23  -0.025610\n",
            "2011-11-25  -0.009363\n",
            "2011-11-28   0.033936\n",
            "2011-11-29  -0.007794\n",
            "2011-11-30   0.023830\n",
            "2011-12-01   0.014881\n",
            "2011-12-02   0.004552\n",
            "2011-12-05   0.008458\n",
            "2011-12-06  -0.005255\n",
            "2011-12-07  -0.004769\n",
            "2011-12-08   0.004027\n",
            "2011-12-09   0.007548\n",
            "2011-12-12  -0.004532\n",
            "2011-12-13  -0.007763\n",
            "2011-12-14  -0.022420\n",
            "2011-12-15  -0.003293\n",
            "2011-12-16   0.005474\n",
            "2011-12-19   0.003118\n",
            "2011-12-20   0.035318\n",
            "2011-12-21   0.001262\n",
            "2011-12-22   0.005283\n",
            "2011-12-23   0.011922\n",
            "2011-12-27   0.007903\n",
            "2011-12-28  -0.009615\n",
            "2011-12-29   0.006140\n",
            "2011-12-30  -0.000296\n",
            "\n",
            "[1323 rows x 1 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VhWwTE_xklEc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 576
        },
        "outputId": "ccd96488-ba8e-486f-ac1a-24bfda648378"
      },
      "source": [
        "# Resample `aapl` to business months, take last observation as value \n",
        "monthly = aapl.resample('BM').apply(lambda x: x[-1])\n",
        "\n",
        "# Calculate the monthly percentage change\n",
        "monthly.pct_change()\n",
        "\n",
        "# Resample `aapl` to quarters, take the mean as value per quarter\n",
        "quarter = aapl.resample(\"4M\").mean()\n",
        "\n",
        "# Calculate the quarterly percentage change\n",
        "quarter.pct_change()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Adj Close</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2006-10-31</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2007-02-28</th>\n",
              "      <td>0.125777</td>\n",
              "      <td>0.126451</td>\n",
              "      <td>0.121460</td>\n",
              "      <td>0.122860</td>\n",
              "      <td>0.369611</td>\n",
              "      <td>0.122860</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2007-06-30</th>\n",
              "      <td>0.196030</td>\n",
              "      <td>0.195413</td>\n",
              "      <td>0.198331</td>\n",
              "      <td>0.197735</td>\n",
              "      <td>-0.080296</td>\n",
              "      <td>0.197735</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2007-10-31</th>\n",
              "      <td>0.400961</td>\n",
              "      <td>0.404613</td>\n",
              "      <td>0.392010</td>\n",
              "      <td>0.400796</td>\n",
              "      <td>0.306882</td>\n",
              "      <td>0.400796</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2008-02-29</th>\n",
              "      <td>0.127066</td>\n",
              "      <td>0.130711</td>\n",
              "      <td>0.117746</td>\n",
              "      <td>0.120394</td>\n",
              "      <td>0.172293</td>\n",
              "      <td>0.120394</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2008-06-30</th>\n",
              "      <td>-0.003550</td>\n",
              "      <td>-0.004290</td>\n",
              "      <td>0.005927</td>\n",
              "      <td>0.003673</td>\n",
              "      <td>-0.209762</td>\n",
              "      <td>0.003673</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2008-10-31</th>\n",
              "      <td>-0.114267</td>\n",
              "      <td>-0.108879</td>\n",
              "      <td>-0.124618</td>\n",
              "      <td>-0.118803</td>\n",
              "      <td>0.133219</td>\n",
              "      <td>-0.118803</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-02-28</th>\n",
              "      <td>-0.363639</td>\n",
              "      <td>-0.363388</td>\n",
              "      <td>-0.359305</td>\n",
              "      <td>-0.360865</td>\n",
              "      <td>-0.161601</td>\n",
              "      <td>-0.360865</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-06-30</th>\n",
              "      <td>0.312134</td>\n",
              "      <td>0.304359</td>\n",
              "      <td>0.324702</td>\n",
              "      <td>0.316588</td>\n",
              "      <td>-0.386935</td>\n",
              "      <td>0.316588</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-10-31</th>\n",
              "      <td>0.421239</td>\n",
              "      <td>0.411193</td>\n",
              "      <td>0.425117</td>\n",
              "      <td>0.415901</td>\n",
              "      <td>-0.158270</td>\n",
              "      <td>0.415901</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-02-28</th>\n",
              "      <td>0.175612</td>\n",
              "      <td>0.176085</td>\n",
              "      <td>0.172864</td>\n",
              "      <td>0.173693</td>\n",
              "      <td>0.170633</td>\n",
              "      <td>0.173693</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-06-30</th>\n",
              "      <td>0.223045</td>\n",
              "      <td>0.223317</td>\n",
              "      <td>0.219063</td>\n",
              "      <td>0.223611</td>\n",
              "      <td>0.203680</td>\n",
              "      <td>0.223611</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-10-31</th>\n",
              "      <td>0.094380</td>\n",
              "      <td>0.094268</td>\n",
              "      <td>0.099356</td>\n",
              "      <td>0.096125</td>\n",
              "      <td>-0.157478</td>\n",
              "      <td>0.096125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-02-28</th>\n",
              "      <td>0.221393</td>\n",
              "      <td>0.218082</td>\n",
              "      <td>0.225622</td>\n",
              "      <td>0.221858</td>\n",
              "      <td>-0.230726</td>\n",
              "      <td>0.221858</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-06-30</th>\n",
              "      <td>0.034143</td>\n",
              "      <td>0.033863</td>\n",
              "      <td>0.031616</td>\n",
              "      <td>0.031222</td>\n",
              "      <td>-0.043455</td>\n",
              "      <td>0.031222</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-10-31</th>\n",
              "      <td>0.127202</td>\n",
              "      <td>0.133189</td>\n",
              "      <td>0.124572</td>\n",
              "      <td>0.130452</td>\n",
              "      <td>0.429821</td>\n",
              "      <td>0.130452</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-02-29</th>\n",
              "      <td>0.013755</td>\n",
              "      <td>0.008387</td>\n",
              "      <td>0.016799</td>\n",
              "      <td>0.011002</td>\n",
              "      <td>-0.404723</td>\n",
              "      <td>0.011002</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                Open      High       Low     Close    Volume  Adj Close\n",
              "2006-10-31       NaN       NaN       NaN       NaN       NaN        NaN\n",
              "2007-02-28  0.125777  0.126451  0.121460  0.122860  0.369611   0.122860\n",
              "2007-06-30  0.196030  0.195413  0.198331  0.197735 -0.080296   0.197735\n",
              "2007-10-31  0.400961  0.404613  0.392010  0.400796  0.306882   0.400796\n",
              "2008-02-29  0.127066  0.130711  0.117746  0.120394  0.172293   0.120394\n",
              "2008-06-30 -0.003550 -0.004290  0.005927  0.003673 -0.209762   0.003673\n",
              "2008-10-31 -0.114267 -0.108879 -0.124618 -0.118803  0.133219  -0.118803\n",
              "2009-02-28 -0.363639 -0.363388 -0.359305 -0.360865 -0.161601  -0.360865\n",
              "2009-06-30  0.312134  0.304359  0.324702  0.316588 -0.386935   0.316588\n",
              "2009-10-31  0.421239  0.411193  0.425117  0.415901 -0.158270   0.415901\n",
              "2010-02-28  0.175612  0.176085  0.172864  0.173693  0.170633   0.173693\n",
              "2010-06-30  0.223045  0.223317  0.219063  0.223611  0.203680   0.223611\n",
              "2010-10-31  0.094380  0.094268  0.099356  0.096125 -0.157478   0.096125\n",
              "2011-02-28  0.221393  0.218082  0.225622  0.221858 -0.230726   0.221858\n",
              "2011-06-30  0.034143  0.033863  0.031616  0.031222 -0.043455   0.031222\n",
              "2011-10-31  0.127202  0.133189  0.124572  0.130452  0.429821   0.130452\n",
              "2012-02-29  0.013755  0.008387  0.016799  0.011002 -0.404723   0.011002"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g8-juPtsko5y",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1105
        },
        "outputId": "1a269e27-6e05-427f-b21c-349bff80304b"
      },
      "source": [
        "# Daily returns\n",
        "daily_pct_c = daily_close / daily_close.shift(1) - 1\n",
        "\n",
        "# Print `daily_pct_c`\n",
        "print(daily_pct_c)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "            Adj Close\n",
            "2006-10-02        NaN\n",
            "2006-10-03  -0.010419\n",
            "2006-10-04   0.017549\n",
            "2006-10-05  -0.007296\n",
            "2006-10-06  -0.008152\n",
            "2006-10-09   0.005524\n",
            "2006-10-10  -0.010987\n",
            "2006-10-11  -0.007858\n",
            "2006-10-12   0.027721\n",
            "2006-10-13  -0.003189\n",
            "2006-10-16   0.005065\n",
            "2006-10-17  -0.014721\n",
            "2006-10-18   0.003231\n",
            "2006-10-19   0.059842\n",
            "2006-10-20   0.012153\n",
            "2006-10-23   0.018887\n",
            "2006-10-24  -0.005033\n",
            "2006-10-25   0.007773\n",
            "2006-10-26   0.006244\n",
            "2006-10-27  -0.021657\n",
            "2006-10-30   0.000124\n",
            "2006-10-31   0.008207\n",
            "2006-11-01  -0.023680\n",
            "2006-11-02  -0.002274\n",
            "2006-11-03  -0.008736\n",
            "2006-11-06   0.018138\n",
            "2006-11-07   0.010036\n",
            "2006-11-08   0.024096\n",
            "2006-11-09   0.010794\n",
            "2006-11-10  -0.002640\n",
            "...               ...\n",
            "2011-11-17  -0.019128\n",
            "2011-11-18  -0.006545\n",
            "2011-11-21  -0.015816\n",
            "2011-11-22   0.020325\n",
            "2011-11-23  -0.025285\n",
            "2011-11-25  -0.009319\n",
            "2011-11-28   0.034519\n",
            "2011-11-29  -0.007764\n",
            "2011-11-30   0.024116\n",
            "2011-12-01   0.014992\n",
            "2011-12-02   0.004563\n",
            "2011-12-05   0.008494\n",
            "2011-12-06  -0.005242\n",
            "2011-12-07  -0.004758\n",
            "2011-12-08   0.004035\n",
            "2011-12-09   0.007577\n",
            "2011-12-12  -0.004522\n",
            "2011-12-13  -0.007733\n",
            "2011-12-14  -0.022170\n",
            "2011-12-15  -0.003288\n",
            "2011-12-16   0.005489\n",
            "2011-12-19   0.003123\n",
            "2011-12-20   0.035949\n",
            "2011-12-21   0.001263\n",
            "2011-12-22   0.005297\n",
            "2011-12-23   0.011993\n",
            "2011-12-27   0.007934\n",
            "2011-12-28  -0.009569\n",
            "2011-12-29   0.006159\n",
            "2011-12-30  -0.000296\n",
            "\n",
            "[1323 rows x 1 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jlqPL2kXkrlS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 434
        },
        "outputId": "52a472db-db89-4c96-f89f-6239c4b50216"
      },
      "source": [
        "# Import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Plot the distribution of `daily_pct_c`\n",
        "daily_pct_c.hist(bins=50)\n",
        "\n",
        "# Show the plot\n",
        "plt.show()\n",
        "\n",
        "# Pull up summary statistics\n",
        "print(daily_pct_c.describe())"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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MzAqQa5ND0oHAq4ELqoY/LmkBld0728dNMzOzDspV+hHxOHDYuLE350pkZmYt40/kmpkl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\nSUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "         Adj Close\n",
            "count  1322.000000\n",
            "mean      0.001566\n",
            "std       0.023992\n",
            "min      -0.179195\n",
            "25%      -0.010672\n",
            "50%       0.001677\n",
            "75%       0.014306\n",
            "max       0.139050\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QEMWY_aHkuBx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1105
        },
        "outputId": "bf42e276-6ba5-490c-870f-b30f93b7914c"
      },
      "source": [
        "# Calculate the cumulative daily returns\n",
        "cum_daily_return = (1 + daily_pct_c).cumprod()\n",
        "\n",
        "# Print `cum_daily_return`\n",
        "print(cum_daily_return)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "            Adj Close\n",
            "2006-10-02        NaN\n",
            "2006-10-03   0.989581\n",
            "2006-10-04   1.006946\n",
            "2006-10-05   0.999599\n",
            "2006-10-06   0.991451\n",
            "2006-10-09   0.996928\n",
            "2006-10-10   0.985974\n",
            "2006-10-11   0.978226\n",
            "2006-10-12   1.005343\n",
            "2006-10-13   1.002137\n",
            "2006-10-16   1.007213\n",
            "2006-10-17   0.992386\n",
            "2006-10-18   0.995592\n",
            "2006-10-19   1.055170\n",
            "2006-10-20   1.067994\n",
            "2006-10-23   1.088165\n",
            "2006-10-24   1.082688\n",
            "2006-10-25   1.091103\n",
            "2006-10-26   1.097916\n",
            "2006-10-27   1.074138\n",
            "2006-10-30   1.074272\n",
            "2006-10-31   1.083088\n",
            "2006-11-01   1.057441\n",
            "2006-11-02   1.055036\n",
            "2006-11-03   1.045819\n",
            "2006-11-06   1.064788\n",
            "2006-11-07   1.075474\n",
            "2006-11-08   1.101389\n",
            "2006-11-09   1.113278\n",
            "2006-11-10   1.110339\n",
            "...               ...\n",
            "2011-11-17   5.041545\n",
            "2011-11-18   5.008550\n",
            "2011-11-21   4.929335\n",
            "2011-11-22   5.029522\n",
            "2011-11-23   4.902351\n",
            "2011-11-25   4.856666\n",
            "2011-11-28   5.024313\n",
            "2011-11-29   4.985306\n",
            "2011-11-30   5.105530\n",
            "2011-12-01   5.182074\n",
            "2011-12-02   5.205718\n",
            "2011-12-05   5.249934\n",
            "2011-12-06   5.222415\n",
            "2011-12-07   5.197569\n",
            "2011-12-08   5.218542\n",
            "2011-12-09   5.258082\n",
            "2011-12-12   5.234304\n",
            "2011-12-13   5.193829\n",
            "2011-12-14   5.078681\n",
            "2011-12-15   5.061983\n",
            "2011-12-16   5.089768\n",
            "2011-12-19   5.105664\n",
            "2011-12-20   5.289207\n",
            "2011-12-21   5.295886\n",
            "2011-12-22   5.323938\n",
            "2011-12-23   5.387791\n",
            "2011-12-27   5.430537\n",
            "2011-12-28   5.378574\n",
            "2011-12-29   5.411702\n",
            "2011-12-30   5.410099\n",
            "\n",
            "[1323 rows x 1 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JlyiEJWNkxsJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 454
        },
        "outputId": "45845c9a-88b9-483d-8c5d-ddeefcd39518"
      },
      "source": [
        "# Import matplotlib\n",
        "import matplotlib.pyplot as plt \n",
        "\n",
        "# Plot the cumulative daily returns\n",
        "cum_daily_return.plot(figsize=(12,8))\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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vyy0N8miyrkg/sFatQl1JHppt8e0HLl9oxMprWeTI4U88sBNbjnRjd0sfOge9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\nQmCC\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ASruGsJ7k0T5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1105
        },
        "outputId": "f3187da9-8850-4629-883d-df875fd9c9b0"
      },
      "source": [
        "# Resample the cumulative daily return to cumulative monthly return \n",
        "cum_monthly_return = cum_daily_return.resample(\"M\").mean()\n",
        "\n",
        "# Print the `cum_monthly_return`\n",
        "print(cum_monthly_return)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "            Adj Close\n",
            "2006-10-31   1.031710\n",
            "2006-11-30   1.140058\n",
            "2006-12-31   1.155110\n",
            "2007-01-31   1.187303\n",
            "2007-02-28   1.145176\n",
            "2007-03-31   1.210302\n",
            "2007-04-30   1.251737\n",
            "2007-05-31   1.453453\n",
            "2007-06-30   1.625638\n",
            "2007-07-31   1.818073\n",
            "2007-08-31   1.734484\n",
            "2007-09-30   1.897943\n",
            "2007-10-31   2.295090\n",
            "2007-11-30   2.333130\n",
            "2007-12-31   2.544817\n",
            "2008-01-31   2.142374\n",
            "2008-02-29   1.671828\n",
            "2008-03-31   1.747569\n",
            "2008-04-30   2.113108\n",
            "2008-05-31   2.468068\n",
            "2008-06-30   2.384260\n",
            "2008-07-31   2.240832\n",
            "2008-08-31   2.285193\n",
            "2008-09-30   1.886767\n",
            "2008-10-31   1.322521\n",
            "2008-11-30   1.254883\n",
            "2008-12-31   1.222865\n",
            "2009-01-31   1.185880\n",
            "2009-02-28   1.256669\n",
            "2009-03-31   1.302498\n",
            "...               ...\n",
            "2009-07-31   1.994633\n",
            "2009-08-31   2.221219\n",
            "2009-09-30   2.374922\n",
            "2009-10-31   2.575893\n",
            "2009-11-30   2.675929\n",
            "2009-12-31   2.657688\n",
            "2010-01-31   2.774716\n",
            "2010-02-28   2.655568\n",
            "2010-03-31   2.984383\n",
            "2010-04-30   3.354910\n",
            "2010-05-31   3.359090\n",
            "2010-06-30   3.488451\n",
            "2010-07-31   3.405691\n",
            "2010-08-31   3.357243\n",
            "2010-09-30   3.658175\n",
            "2010-10-31   4.019961\n",
            "2010-11-30   4.162278\n",
            "2010-12-31   4.294495\n",
            "2011-01-31   4.520171\n",
            "2011-02-28   4.691192\n",
            "2011-03-31   4.641877\n",
            "2011-04-30   4.547362\n",
            "2011-05-31   4.565653\n",
            "2011-06-30   4.422674\n",
            "2011-07-31   4.972456\n",
            "2011-08-31   5.032897\n",
            "2011-09-30   5.243032\n",
            "2011-10-31   5.306305\n",
            "2011-11-30   5.140466\n",
            "2011-12-31   5.248871\n",
            "\n",
            "[63 rows x 1 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gmFfotmvk3tC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "b41e1d18-4f2f-4cc1-99cf-12bfe73a4713"
      },
      "source": [
        "from pandas_datareader import data as pdr\n",
        "import fix_yahoo_finance\n",
        "\n",
        "def get(tickers, startdate, enddate):\n",
        "    def data(ticker):\n",
        "        return (pdr.get_data_yahoo(ticker, start=startdate, end=enddate))\n",
        "    datas = map (data, tickers)\n",
        "    return(pd.concat(datas, keys=tickers, names=['Ticker', 'Date']))\n",
        "\n",
        "tickers = ['AAPL', 'MSFT', 'IBM', 'GOOG']\n",
        "all_data = get(tickers, datetime.datetime(2006, 10, 1), datetime.datetime(2012, 1, 1))\n",
        "all_data.head()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Open</th>\n",
              "      <th>Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Adj Close</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ticker</th>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">AAPL</th>\n",
              "      <th>2006-10-02</th>\n",
              "      <td>10.838572</td>\n",
              "      <td>10.614285</td>\n",
              "      <td>10.728572</td>\n",
              "      <td>10.694285</td>\n",
              "      <td>178159800.0</td>\n",
              "      <td>7.103626</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2006-10-03</th>\n",
              "      <td>10.707143</td>\n",
              "      <td>10.455714</td>\n",
              "      <td>10.635715</td>\n",
              "      <td>10.582857</td>\n",
              "      <td>197677200.0</td>\n",
              "      <td>7.029610</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2006-10-04</th>\n",
              "      <td>10.780000</td>\n",
              "      <td>10.451428</td>\n",
              "      <td>10.585714</td>\n",
              "      <td>10.768572</td>\n",
              "      <td>207270700.0</td>\n",
              "      <td>7.152968</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2006-10-05</th>\n",
              "      <td>10.880000</td>\n",
              "      <td>10.590000</td>\n",
              "      <td>10.647142</td>\n",
              "      <td>10.690000</td>\n",
              "      <td>170970800.0</td>\n",
              "      <td>7.100781</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2006-10-06</th>\n",
              "      <td>10.720000</td>\n",
              "      <td>10.544286</td>\n",
              "      <td>10.631429</td>\n",
              "      <td>10.602858</td>\n",
              "      <td>116739700.0</td>\n",
              "      <td>7.042896</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        High        Low  ...       Volume  Adj Close\n",
              "Ticker Date                              ...                        \n",
              "AAPL   2006-10-02  10.838572  10.614285  ...  178159800.0   7.103626\n",
              "       2006-10-03  10.707143  10.455714  ...  197677200.0   7.029610\n",
              "       2006-10-04  10.780000  10.451428  ...  207270700.0   7.152968\n",
              "       2006-10-05  10.880000  10.590000  ...  170970800.0   7.100781\n",
              "       2006-10-06  10.720000  10.544286  ...  116739700.0   7.042896\n",
              "\n",
              "[5 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ehAXVknwk6Uk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#all_data = pd.read_csv(\"https://s3.amazonaws.com/assets.datacamp.com/blog_assets/all_stock_data.csv\", index_col= [0,1], header=0, parse_dates=[1])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W0eibe0fk9Ti",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        },
        "outputId": "e2c52abc-6a14-4729-b49f-5e5affc253dc"
      },
      "source": [
        "daily_close_px = all_data[['Adj Close']].reset_index().pivot('Date', 'Ticker', 'Adj Close')\n",
        "\n",
        "# Calculate the daily percentage change for `daily_close_px`\n",
        "daily_pct_change = daily_close_px.pct_change()\n",
        "\n",
        "# Plot the distributions\n",
        "daily_pct_change.hist(bins=50, sharex=True, figsize=(12,8))\n",
        "\n",
        "# Show the resulting plot\n",
        "plt.show()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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tuiTJf6VzpvlzwNHArwC7q+rLSZ45nqiaJhbJWmt+JMmBZvlrwE3Ar/XasKra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\nAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 864x576 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CaO8MlytlART",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 732
        },
        "outputId": "926eaa5a-d424-4581-e8f6-a0fcbf889993"
      },
      "source": [
        "# Plot a scatter matrix with the `daily_pct_change` data \n",
        "pd.plotting.scatter_matrix(daily_pct_change, diagonal='kde', alpha=0.1,figsize=(12,12))\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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W1+GnvW4ursN1a+kfc51/0Qh/gUDglNme+EzJkySBAW50gcviPYHnC+f8LljyiMWRkoKi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\nlVFJYaGXKyIdwr7mlcU4T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bPg9FwbV1yblMRahlyAe9SXHMa4MMt09/Xu6QknPGksziUI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\nlpSLEjMzMzMzS8pFiZmZmZmZJfX/Ku8ZS6DVQ14AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 864x864 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4jJwORfPlE3T",
        "colab_type": "text"
      },
      "source": [
        "# **Moving Windows**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jMnFRvqvlDqz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "dadec9ae-32db-47a9-ae0f-c9514a544879"
      },
      "source": [
        "# Isolate the adjusted closing prices \n",
        "adj_close_px = aapl['Adj Close']\n",
        "\n",
        "# Calculate the moving average\n",
        "moving_avg = adj_close_px.rolling(window=40).mean()\n",
        "\n",
        "# Inspect the result\n",
        "moving_avg[-10:]"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2011-12-16    55.529679\n",
              "2011-12-19    55.491607\n",
              "2011-12-20    55.456536\n",
              "2011-12-21    55.451822\n",
              "2011-12-22    55.444500\n",
              "2011-12-23    55.439643\n",
              "2011-12-27    55.445286\n",
              "2011-12-28    55.437643\n",
              "2011-12-29    55.468393\n",
              "2011-12-30    55.495500\n",
              "Name: Adj Close, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3XSN_ZNKlK1r",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 259
        },
        "outputId": "8a89c1f3-9d30-4a21-d7a2-0e672a5d427f"
      },
      "source": [
        "# Short moving window rolling mean\n",
        "aapl['42'] = adj_close_px.rolling(window=40).mean()\n",
        "\n",
        "# Long moving window rolling mean\n",
        "aapl['252'] = adj_close_px.rolling(window=252).mean()\n",
        "\n",
        "# Plot the adjusted closing price, the short and long windows of rolling means\n",
        "aapl[['Adj Close', '42', '252']].plot()\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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UuvtW4/TknrSv68qe23sYtnUY049Pp5ZdLRb3WFzocYQPHTAzM2Pu3Lm0bNmS5ORkWrVq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\nGAyGMoIx6AaDwVBG+H9ymgtUxWqmAwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "07S_o3KWlOFS",
        "colab_type": "text"
      },
      "source": [
        "# **Volatility Calculation**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NwSZDbwzlQj-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 468
        },
        "outputId": "27fa833e-fdf7-4eb3-c9e8-a161d7d9192b"
      },
      "source": [
        "# Define the minumum of periods to consider \n",
        "min_periods = 75 \n",
        "\n",
        "# Calculate the volatility\n",
        "vol = daily_pct_change.rolling(min_periods).std() * np.sqrt(min_periods) \n",
        "\n",
        "# Plot the volatility\n",
        "vol.plot(figsize=(10, 8))\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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oEskSyZquVDJdwVpni0QXsOGZjLy/cbkxHqdHV6yxMSP3FBHZnyjoEskS6Swv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\nunAX9CzT9U7lOzy68VFK/CU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ej2K9+J0psgdasYzoaUFXVpxQ6e76/i4KG8Ka\npiMyeXAKn8pTkRQPuzZEF6wBxM+ciWK1snfGCSjuGM7VVFFRUfGiBl0qEVEUhXfW5APw2GeB3geP\nrLRo71BSa+ey19ew5JdC5m84zMl/XwlAmsXQ4vMleo1TfWN6wq9rI8Go69zOxfpicNSBsx4WXC3+\nAzBYwg7VaqQunL3YNg5XiRTh4CTvy9sXQEVhGdHTgq5JWZP8nZSnf3w683bNa/WcQWkWZsw+C5ei\nJW3FfeBquUnDh3niRP9j+67d7VuwioqKSguoQZdKRLYW1vrrqNYeqKLW6sIjKwy7fxnPfNH8B9KF\nr6xm7YEqbv3gF+7+eItfoWrNHyvJLNzBfQOpg9l8uIZPthQzKD2uvT9OdDR4bSvOeA6O/b14HJcO\n/SaFHarVSLg9PbOm60i1CDLSjd7zvEXx0TjSl1vFRIh4fTSO/Z2PRW/hyROe9H//1ra3ojrvD3PG\ns9kynQxXIZ6q/KjO0WdlMmTpEgBsmza2dakqKioqraIGXSoRedYbWD1+/lEAHK62+hWUV1fu9x+n\nKArz1h9iwcbDbDlSQ503aLpiykCuPX6w/7i4VgZMJ7YQdC36uZBGh5vnLp7Q/h8oGnZ6nU4GTIEz\nn4N78uG2LRHTixqpC8xR21nTdaTaSnq8AaPiTdX6g66WC+n31+znsXXC4T03oeeMTw1W3Uy66NPL\nDaMuBqC4InqneVNeHvr+/alesACl02v2VFRUfm10kcukSm/jSLWNoekWpgwRnYL/b/E2jhmYAgjh\nRVEUJEliY0E19y0UrfkmvYZ4o56zJvTjifPHAfDRpiPU292Y9C0HXXqthjiD1h+0BePyyCSYdIzK\n7mR/rp/eFV+zxoqv5pRmD9VpNbh7qE/X4Sob/VPiwO11cvcFKh65WcsIl+zilm9vAeCx4x9DI/Wc\n+7HgoMvhcbRwZChJSWKMkLUhwsDsFrCcMIOaDz6k/quvSTzt1Dadq6KiotISPeedVaVH0ehwc9yQ\nVIZnxvPcxRPYfLiG/6466N9/7VsbmL/xMBe/ugaAoRkW7C6ZOrsrRNXyaTWtKV0AiSY9O0vqwjoY\nXR4ZfSv+UjHB2QjT/9xqsTmApisK6f20XenKTTEHapmC0ovNKV3FDcUcrj/Mg9Me5Nzh53ZotbHG\nrA0EXZW2yqhHRpnjRIrUYWvbUPXM224DwF3aiimrioqKShtRgy6ViDQ63Fi8ResXTurv3/7mNccC\nsGJ3OXd/tAWAf18xiWu9hqVOtxwSYPkGS8cZWhdVE806Vu2r5NQXvscdFNG4PErnB10eN8guMRw6\nCnQaCbmzB163Q+mSZYXCGpsIutze9KJP6XK7my2kr7KLFFx2XHa7l9tZBBu12j12Gl3RBVEmizfo\nsobanvx8qJp/frO32fMkvUh1q+70KioqsUYNulTCkGWFRqfHH3RJksTHf5zOfXNHMXNkBt/dOZP0\neNGN+IcTh3Lm+BxS4wLdicGpRI03cNBpW+/E21Ma+HAsbwikkZweGX0U53cIl6hXwxBdsb5WI+Hu\n7KCrHTVdlY1OXB6FfknmgOeYT+mS5WYL6Wscwkg11ZTa7tV2FgmGBABGp44GYEdldJPELBbhT+ay\nhwZdV/1nPc9/tYfiZkZPBYIu1TZCRUUltqhBl0oYNpcHAEuQYnXMoBRuOGkYkiQxJN3CCSMyALhk\nsii4DjY/DVa63rr2WC6ZnBsSlDVHcGBVUhvw63K5uyC96Au69NEFXRpNFxTSt0PpanCIQCHRrAO3\nDSQtaEUQgduNFCF1euu3t3LnyjsBSDYld2zNnYBRa2Tr1Vv518n/IsmYxO++/B3j3h7HtoptLZ5n\niRdKl8seqow1OsXvaPW+ysgnqkqXikrfQ5ah6gA4rd26DDXoUgnDZ/OQYNI3e8xTF47jh7tnMTxT\nqBADUwPBis/+AWB8bjLPXDTBn2ZsiaU3z+C+uaMAqGhw+re7PDIGXSf+qe5aBuVeG4wInlyR0Gk0\nuLwp0E0FVQy+97MWZ0e2j7YrXY3eoMti0ImaLm8QqcgyissV5kjv9Dj59vC3/gL1nphe9JEZl8mf\nj/6z//vH1z7u9xU7XHcYlxwaJJkt4m/T4wj8u7g9sn925raiyKa/kiSBTqcGXSoqfYlP/gz/PBo+\nu6Nbl6EGXSohKIrCqyv3Y9BpmDUqo9njjDotA4ICrYx4I+NzkzhjXDZnjGvZk6s5RuckcsoY4UDu\nCx7Al17spD/V4s3wweXwzjni+yiVrsFpcXy5vZS1Byr5aofw91r405HYrq0DSpfF6Au6RD2Xfds2\nFJcLY15eyPEP/PiA//GwpGFoo2gi6E4uyrsIg0aoptsqt3Hc/45j0ruTOGPRGUx6dxKL9y32Hyvp\nRQAtO60UVDZid3k4WNGIy+uvVm9vPn0o6fWqK72KSl/Cd2O9+X34ZR5U7gdH5LFznYlqGaESwkWv\nrmFTQTXXzxhCTlL0ruQajcTSm2d0+Pnjvc709UFBl8utdF5NV2lQfVDmWMgeF9VpL1w6kWlPfsvy\nbSVYjCJQ0cU8MOyA0mXUiUJ6r91C9fz5AMQdd1zI8XtrREH5h2d9SI6lfcFyV6KRNKy9Yi0L9y70\ne4oFK1x/XfVXzht+nvhGq8OJDltjPSc9u4LJg1I4/aiAkldvb17JkvR6VelSUelLOBuF0bW9Bhbf\nKLYNnQVXLW75vBijBl0qIWwqqAbgplnDu+X5fWN+lm8r5rdTBwFC6Uo0NJ/q7BA1BYAED5SCrvn5\nkE1JjjOQFm9g2dZiyupFas7pjrGHRDuUrkanqMeLN2pDlC7bxk0A6LKyQo5PNiZzTNYxjEkbE4MF\ndw16rZ65Q+fy7s53eWjaQ/SL74fT4+TxtY+zrmQdLtmFXiP+XpySkfIq0SSwsaCajQXV6DQSY/sl\ntqx06XQoLmez+1VUVHoZzgZxU33CHfBv0YXPgRVdvgw1vagSQoJRx7XHDya1lVmJnYXZ2/m4al8l\nn28tBrw1XZ2ldFUXQEJOmwIuH/V2tz/gArA5Y52OioXSJYIu2WYj6YILRL1SEB7Fg07qffdeiYZE\nPj3/U47NPpb+8f0ZkjSEkwacBMAT654gvzYfAJfGhJlQQ9XhmfFYjDpW76/kiWU7I15fTS+qqPQx\nnI2iZjcjDy5+C5IHQtKALl+GGnSp+FEUhUanWxRhdxPBQcGzX+5GURS2F9X5i59jTk0BpAxq16m+\nkUXf3nES2Ykmf9dnzGiP0hVW0+W1i7Db0ZjD08UexdPj67ii5dKRl5JkTOKjPR9x9uKzaXA24NGa\niZNE0GXSi7e7K6cO4rghwhrj9e8P8POh6rBrSXo9qOlFFZW+we7lYK0INEqNPV+kFj1dr2arQZeK\nH7tLRlbw+3N1F9/ccRK3zRnBgfJG7l8kbAGSzZ2kvFUXQHL7gq7zj+5PvyQTQzPiiTNosTo9Ubul\nR0fblS5fIX2c3pte9Clddjsac/jcQo/s6VEjfzqCQWvg64u+JtEgxkVd98V1ePRmztGuoR8VvPe7\nKdx5ah6/mTKQ2+bk8dGN0wA4VBXeQi6p3YsqKr2f8j1QVwTzLhXfByv9WoMadKl0Lz7/Il9heHcx\nLCOeCyflYtBqmLf+EBMGJPPg2R2oOWouEHI7oa6w3UrXC5dOZPV9JwNC9fp0SzHnvby6vasMp51K\nl0mvEUX9bmEZocgyisOBZApXumRF7pXpxeYw6Ux8fuHnzMydyc6qnVyS4aFOI/En3RImD07l5tkj\n/Gqqz+akwRGeRhSF9Gp6UUWl1+K0itqt54WpMul5MPaCwH416FLpbkI8nrqZAalxfHLLDOb9fiqL\nb5reMfXt6cHwv0vCt9ceBpR2K13BTBwgTEU3H66hujFWL+R21HQ5Pf5mBFx20JtQHCK9pjGF1625\nFXefUbp8JBoS+efsfzI1ZypVkpsf9BkkSeEeav5O2QgF9ZJej+JUC+lVVHotB78P/f7GVdBvYuB7\nnRp0qXQzNVaRTkmO66ROwTYyMjuBacPSwoq/24TLLlqE934Rvq+mQHxtp9IVzAuXTeSVKycBsL2o\nzr9dlpX2z2j0K13RnxI8M9NnGSHbhdltRKVLlvtMTVcwkiTx6PGPArBbZyGR8BSiWa9Fq5EiWkfo\nc3NpWLmSgmuuRVEUbNu2o3hiXLOnoqLSeexZHnh87O9FkBWMT+nq7MkiTVCDLhU/VVYR9ad0U+di\np1C2PfJ2RYH5V4vHqUM7/DSJJj1Th6YBsKM44HQ+9P5l3LFgczuv2naly+7yYPK5znstIxSbcG2P\npHR5FA9aqe8FXQBxXqNbSePkRO1WsNWE7JckiQSTjh/3VoSdm3zxxQBY166l/suvyL/oIipefqXz\nF62iotJxrFWw6U0YfQ48XAtnPhd+jG88mty1ZQRq0KXix5cWi2ZOYo+joRy2L4bvnwWPG2SPGO+z\nJ4LCBWCrBkcd9J8Mif1isoQUi4E4g5bSOpHO8ylci34ubN8F21HT5fIo6HXe87xKl6ukBABtenrY\n8X2pe7EpvmAyRfIqj5veCjvmpLwMthXVYW1i9xF/wgxyXxVBVt2nnwDgrggPzlRUVHoQigLL74dn\nhojvj7qw+WO13s+5Lk4xdn/xjkqPYe2BSrQaifSEtntWdTsfXQv5P4jHBWvE4+AXk9YgXpCSJEZA\nJHrd16fcGNNlpMUbqGwQQVewq77bI7fDsb7tSpfLI2OUZNiyQJgB6k049gjXeVOTEUAguhf7qtIl\neX9/nszRULde/D6acPb4fiz5pYhthXV+GwkfpjGieaP+q68BIlpuqKio9CBKt8Haf4vpIsdeB2PP\na/5Yrfdzzu2IeuZuLFCVLhUAthXWMn/jEX43Y0igELu3IMtQsCrw/f5vAgGXOQXGXya+dzaK/xbf\nCO+cK/bHNz9fsj2kWYz+Yd11tkCtUH5lY9TX2F1Sz3trC4JiruiDLrdH4be2d2Dh9WKDzoxj7140\nFgu6nPAxP27F3WeDLp+Cp4y7SLzBRpizNnGgaIBYsbssbJ8+M5OsBx4g+VLRbq4W1quo9HBqDouv\n574Ex17f8rG+9KKna61h1KBLBYBvd4kPnRtPGtbNK2kHRT+DIsMFb0DaCLFt0lXwUA3ckw+DvTMh\nrZXgCFI7tAbIim7WYrSMz01izYFKNhVU+81TIbS4HmDdgUrszZipnvbi9zyweBuy0nalS+O2cmbj\nksCG1CE49u7FOHx4xIYEWembhfSAvyvTAxCfCfa6sGPS48Xd7ssr9kf890j9zZXk/O1hdBkZ6lgg\nFZWegNsJ616H2iPi+93LYdld8NIx8MHlYls0TvPdlF5Ugy4VZFnhvbUFTMhN6rbxPx2ixFuoPnAa\njD5LPB5zXqAmKs6bNrJVhaaYxl4AlrSYLuXO00aSnWji8c92UFZv92/fWRxQWfaVNXDp62t57LMd\nkS7hx+byznJsg9I1wrEVPS4RfM75G+Xfl2Fdvx7D4Mgdmn05vej7uWRkMCaKGr4IjOufBAS6dyMh\nGQyq0qWi0t0cWAlvnQGf3wXzLoPP7xHGp+tfF2r28FPguBvETVZrqDVdKt3F/vIGyuod3HXayO5e\nSvvwycP6OJj1/2DcJZA5OrDfVyhfsQ8ygn7GE++M+VISTXounpzLi1/v5bq3NgJCTdlVEvjAr/DW\nfAUHYj7qguwLGp0eRKVB9EFXmqtUPLh6KST2o+a+EwFIOv/8iMf35e5FX02XrMhgTIBdn8K3j8Pw\nOZB7LGjEPecfThzKLfN+pt7uIjsp3LUfRNAlq0GXikr3sugGqBczeSnZKv4D+PPPbe9C16lKl0o3\n4eu28zl09zp8QZdWL/7LGhM67iF7AsRnwc6loqYL4LeLIH1EpyxnfG6S//GQdAsTByRRUhtQvRq8\nZpyR5kluPRKwm3jha1EAX1pri/q59bJ3uLN35qJst5Py299imTo14vEepe+MAWqKJEloJA0e2QOT\nrwVjEnz/DPz3VPjPKf7jEs2itqMugl+X/1oGA4pTHQukotJtKAo0lMGUP8JfdooSkovfFiUk7bH9\nUdOLKt1FZaP4oE6L74WpRQA5KOiKhEYDo86CfV+LoacAhvhOW860ocKa4eZZw/nuzplkJZrYVVLP\n7L+vwCMrfqXL5vSE1RF9uqXY/7jAOxPwuwhF3s2h9QVduqBB1xH8uXzIioxO03cFb42kEUrXhMvg\n5g1wxXyYcAUUbhTWIkCC15m+LoIzvQ81vaii0s3Ya0HxQFKuyF6c85LoTjSntO96/qBLLaRX6UJK\nau3c9dEWAFItvdAqAgIvGk0LTvqDpoPLCoWbxPfGhE5bjtmgZdejp/OXU4RFQ3aiSFkdKG9kwcbD\n/qBra2EtJ/99Zcjsv31l9UwZksqc0Zko3vRYnD769J9etiMjgc6I4vGguFxIxsgpMwC33He7FwE0\naERNF0BCFuSdBtne5gmnSO+meH3prn1zQ7PNDZLRQOMPP1D65FOdvmYVFZUIlHlrYONiVIfr715U\nlS6VLuSbXaU43TITcpNINveM8T9txh90tRA8+Irpdy8HQ0Kgy7GTMOm1aLzpw7H9E/3b71u01V/L\ndeepeRTW2Phg/SH//soGJxkJRu4+fRRHDxB3cLW26N8UDIoDt2QESWpx5qKPvpxeBGEbIcty6EaT\n99/D283YPzngv3Xje5vwRBjbpDGIwKzq7bdRunhsiIrKrx5HPbx/KZiSYWDkUok24/PpUoMula6k\nqMaGViOx8Kbj/UFCr0N2Cam4pRmNPgm6fCcMmgbarkupTRki7szSLAYUBT7bWszQdAuXHTcQgMc+\n2+lXvyoaHKRZDORlJXD36aMAaHBEL3/rZAcujXgzaWnmoqIoWF3WX0V60aM0Ua+M3qDL281o0AXe\nBlfsLudur/IbjKcx4LOm2O3IjY1Uz5uHfUfLHagqKioxoGSbeL2e/yqkDonNNX3pRbcadKl0IcU1\ndrISjBGLunsNHlfLqUUQd0g+fL5dXYTFqGPNfbNZefcs5ozOQq+V+MOJQ0mPN/LMReMBmPzY1wy+\n9zPq7G6GZnjrzbxBpM0R/Wwwg+LE7Q26FG/QFUnpemfHO0x5fwpA3w660KA07f40eRsd6or8m3Y9\nejq7Hj2dYwal8PFPR8LmMSadfY7/sdzQQMMPP1Dyt0c4eMGFVM+b12nrV1FRAWoKxNf08Kka7UZN\nL6p0B0W1Nvol9/LxJh5X68pVsG9LS/O4OomcJDPxRh0vXzmJVffO9qtcFx+TG3bsnDFZ3kci6NpY\nUMXO4jr/bMzm2F/egMZjp1EWbyayXahnkWq6NpVuCjzfwDlt/nl6CxqNt3sxmP6TxF3uwe/9m0x6\nLSa9lvd+N4Wh6Rae/HxnyCkpV15B9sMPAeBpaPCnbgHKXnhRTTmqqHQmvmkSsazFVbsXVbqDoho7\nOb096PKlF1vCYIG80+GYa0T3Szdh0GnITAgEQZIkhUwB6Jdkop/PK8qrdEkozP3HDxz96FdszK9q\n9tqb8qsx4URn9NpFeFNimrjwf99sSzYAz574LEOT29Fu3UvQSlrRvRiMMQHMqRHNUs0GLTNHZnKg\nvDEkkJIkCV2WCIbdxcUobhHIpV53HXJdHe7S0s77IVRUfu34rH5iOSNR14O7FyVJOl2SpN2SJO2T\nJOneCPv/IknSDkmStkiS9I0kSYOC9l0tSdJe739Xx3LxKh1DlhVKau30S26+u61X4HG2nl4EuOJD\nOPsfnb+eNnLv3FH+xyvvnhU0rkcK+r/gk81FNEdxrR0TTtKTRfpMrhdBhTYxMezYRlcj/eP7c/qQ\n0zu2+B6OhBToXgzGEAdOa8RzclPM2Fweqps41BsGDQagcc0aFLdI+ZrHi05I+45QZUxFRSWGOBsB\nSRhgxwq/0uVo+bgY02rQJUmSFvg3MBcYA1wuSdKYJof9DExWFGU88BHwjPfcVOAhYApwHPCQJEnt\nNNVQiTWVjU6cHpl+Sb1c6fK4u7QwvjN4+sJxPHbeUei1QS9Jb/B1/sR+fHX7iRw7OIW31xRQUNnI\n/vIGZFnB4faQXyHuAg9VWUnUudEYxBuTp04EXZrEJJrS4GwgLpZvYD2UiEoXiDtmZ+Qh5P1TxOvh\nSHVoUGYcOgRdVhbuyioUr8eXaexYkCTsO9WCehWVTsPZILwVW2qWais9eAzQccA+RVEOAEiS9AFw\nLuB/l1EU5bug49cCv/E+Pg34SlGUKu+5XwGnA2rlaTezr6yeFbvLAXpvTZfbKQZdy1EU0vdwLj12\nYISt4g3mwkn9ICuBG04cxob8jZz07AoAnrpgHBUNDp77cg+jshPYVVLPTfFuvxu9p9ardCVFULrc\njcTrO88gtqcQsaYLxBu4K3LQlesNugqrbYzPTQ7Zp01OxlNbC970ojYhAcOgQdh3qkqXikqnkf+j\nUKdjib+QvuelF/sDh4O+P+Ld1hy/Az5vy7mSJP1BkqSNkiRtLC8vj2JJKh3lvoVbeewz8UGR08y8\nuR7P6zPh8Sw4sKL1mq7eiO+uzltaNGdMFi9fOQmTXrxs7124lee+3APArhJRaGrRukAn/j09dWKk\nkDYhvPi03FpOqim1M1ffI4jYvQig0YlC+k9uCxso7jOzLa2zh52mTUrCU1vjTy+i02EaMxqHml5U\nUekcFEUYo8b6g506GAAAIABJREFUxrov+HRJkvQbYDLwbFvOUxTldUVRJiuKMjkjIyOWS1JpBoc7\nkHLp31uVrrLt4qu1EszJLR/bK/FJ6YGg4IxxOWx9+DRumT2cBKOO44eHujObcQbmLtbVg06HFBd6\nh+j0ODlcf5iBiZHUtb5FRJ8uANkbNG16Ewp/CtllMYoEgM0VnpbUJiXhqanxpxclnQ7jqNG4ior8\n6VwVFZUY0lAqXq8zbovtdXuwT1chMCDo+1zvthAkSZoD/D/gHEVRHG05V6XrMemEe/uo7ASS43pp\nas7nLzX2AjjlEVxFRcjWyMXRvRK/0hWqxOi1Gu44dSRb/3Ya/7t+KtOHpZFJNddpP8csNwYpXXVo\nExODCvPB7rZzy7e34JJdTM2JkbNzDyaiIz3AWS/A3GfE46oDIbuMXrNUW4SRQNrkJJFe9Ih9klaL\nYaAIXl1FzTc5qKiotIPdn8PfR4rHWWNje22NFpB6pNK1ARghSdIQSZIMwGXA0uADJEk6GngNEXAF\nT+f9AjhVkqQUbwH9qd5tKt2Mw+1h5sgMlt92YsiHcq9BUUTQNf3PcPGbeFLGsG/2yRTdd393ryyG\neP9dNs+Dz+6EqoMRj3rgzDFcqP2BB/XvYnDV+jt8PHW1YZ2LWyu2srpoNZfkXcL0ftM7dfU9AQkp\nstKVOTrg12arDj1HkjDrtRHnMGqTkpBralFcgfSivl8OoAZdKioxZ/MH4usl74r5ubFEkoTa1dOC\nLkVR3MDNiGBpJzBfUZTtkiQ9IkmSz6b5WSAeWCBJ0i+SJC31nlsFPIoI3DYAj/iK6lW6F7tL9t/R\n90rcDnDb/WlF68aNANR/8QVlL77oH4HTu/EqXNsXwoY34O1zIh41pl8id5+UFdjg/Z3ItXVomhTR\nV9nFy++yUZfFfrk9EK2kjVzTBYEpBbbwtySzQYvNGUnpSkZxuZAb6kGrRZIk9Dm+oKs4ZutWUVFB\nvDYHTIUxkd/7OozO2OWF9FH12SuKsgxY1mTbg0GPm7W0VhTlv8B/27tAlc7B4fZg0rcwILqn4/YG\nVToztm3bOXLjH/27Kl99DSSJzFtv7abFxYi04YHH4y+FLR+CtSowvDsIqXJv4JuhMwFvejE5tNbN\nF3SlmH4dzi3Ndi+CsBkxJonfaRPMem0z6UXx+3SVlSFpxetHmybq6moXLSLpnLMj+qKpqKi0A3sd\nWDqxzlur73lKl0rfpPcrXb6gy4hty2YAki+/jFHbt6FNT8ddVtbCyb2E4OaA8ZeKrwdWhB+3ZT7s\nXgb9J8PdB2GgqNXy1NeFBQA19hoAko19sfEgHA2ayD5dPlIGQtX+sM0mvSZi0KX31m859+1D0ol7\nVkmjQdcvB/v27ew5foZaUK+iEisc9WDqxJsYraHnmaOq9D32lzdQUmdvWp/du/AFXXozilPcqWTe\nfjuSVotk0IOnhQ/aXoTznEVYxz8p1Kv0PFj5dGi3TclWYXuQNQ7OeyVEBYuUXmxwNWDWmfv0kOtg\nNJImsiO9j+zxcGQjuGwhm80GLdYIg8aNI0YA4Ni7D3SB3+GgN98UipfLhVxfH5vFq8SMPaX1PLx0\nOy9+vYefD1W3foJKz8BRF9t5i03R6nukT5dKC8iywoKNh1l/sIpZz63gm52BGWwfbTrChxsOdePq\nwjlUaWXuiz8AsPlITTevpgO4fEqXCcUpXjSSQbQASxotSnMppV7G/iv+RMH9L4lOm9l/hfJdIs0I\nIvja/IEw+bziQ8jIw1VURMkjj+AqK8NTX4+2iRu9zW3DrOulFiHtQCtpaXA2UGmrjKx4Tbgc7DWw\nPHS6WUqcIWwMEIAuJcWfTvSlFwEMgwaReeedACiRuiVVugW7y8OWIzVc+PJq3lqdz4tf7+Wq/6zH\nI/fmO84+iOwJpPllD7w0GXZ+KpQuY2cqXUbRqLRjCTyWBa/PgvVvhHWMx5Jfx+1uJ1Fe7+CWeT+x\n9kCgJuTehVtZf38mGwuquXOBSHtFdhvvHpZtK8bpkZk1MoObZg1v/YSeiturTOhMfqXLH3Rptb1e\n6VIUBSXI/sJVXIx+9NliUPOXD8DSmwMHZ46FJOE5XLNoEdXvz6P+m2/B40GXGVoP8WsLutLj0llx\neAUz58/k3uPu5crRV6IoCh7FI9S+wTMACTa9Bac+Dkbh0p8ebyS/MrJjvXHYMKyVlf70og/JN8LJ\n0zcC/t6OLItB8Qe9Y7IeOHM0tTYXL327D7vL4/djU+kBLLgGdi6Fi96EH1+Ayr2w7C6R0ejMoMtX\nCzv/KvG16Cfxn8ECE6/olKdUla4mFNbY+HRLEaV19ogt4z4UReHat9azMb+a8blCTRiVnUB5vYOL\nX13D797a4D/W96LvCVQ3OjHqNPz3mmM5dnAvdiR3e/Pwem/QpdMhabx/zlotSi/+4LNu2MCu0WPY\nc/wM/7YD55yL7HLBiXeBOagIftJVcLK/p0WkvQB3qVBcE2bNCr22y/qrCrqeO+k5njvpOQDWFq9l\nT/Ue7vvxPk5ecLI4QJLg5L+Kx/Za/3lpFgMV9U6UCHe8hkHiJsrddHqGRihfqtLV/WwqqOKnQ9WU\nVlRyQ+Jq1p6wmesHlpAeL1zIW3pvV+lCqg7A04NFwAWw+I9QskU89vlydVJNlzv4xnzUWWBORZn+\nZwCU757olOcEVekK464Fm1m9vxKArEQjH/5hGjnJJgxaTYif1Z7SBrYV1nH0wGQW3XQ8jQ43Oq3E\nXz7czGdbRev4hZNy+finI8x6bgWf3jKDo/qHDx7ual77XhhB9kpvrmBcoUqXT+UCr+LQS4MuV2kZ\nBddeB4Ditb2IO+44rOvXIzc2opl2E0y7CbYvFkWgo84IOV+urxfzAWtE6liXnR2y3+a2Eafr+4Ou\nfRi1Rk4bfBpPrHuCFYdXsOLwCv8+v+qXMlhscNThm1I2Iisem8vD9qK6sNdtwqmnUrPgo7DnUpWu\n7ufhpdtZd7CKncV1gMKHhmeY4twlDIsKv8Q48R0A7G41MO4R1BwSPnkZo6F8p1C2Zt4n1C7vZI3O\nqOl66Zu9PP/1Hu5L/xMXTx1OyvHiPfftVQeZJi8kpd5BZuV+SBsW8+dWla4mPHLuWO46bSR3nz6S\n6kYX5/57FSMfWM4/vtkbclydXdR7/PlkUVhrMeow6rTceZpwz508KIVnLhrPfXNHAbCvrKELf4rI\n7C7pQwW+3i48dEYUpxONPshVX6PttWpD/fLPwe0m8557kEwmtOnpJM49HQDFEdRlM/a8sIALQLbZ\nMObl+b/3q39efm3pRR8XjLiAdHN6yLZah1fZMnqDKkfg9XHKmGy0Gsl/AxWMZWozTv6q0tWteGSF\nt1bnewMuGCEVMkWzCwZMgSEnQtHPnLrmN5hw4FCVrp6BT0k+6wW45Sc4/3U47g/C+NpnWhzj9GKd\n3cW/vtvHqOxE/ll3EjO/zmVTQTVznl/Jw5/sYL/Sj0y5DF6aFNPn9aEGXU0YnpnAn2YN56aZwzkx\nL51amwiulvwS6jbd6O1sSjSFjtAZnBbHI+eO5aUrjkarkbhq2mBApC27G1+a87Y5I7p5JR3EWgXf\nPAoJ/SBjFIorVOmiNytdRUVIcXGkXnM1I3/axIhvv0FjsQBNgq5mkK1WNPHx5P7rJQbNez/02rKL\nQ/WHSDOnNXN23+XWSbfy3SXf8dopr3HThJsAeHv722Kn707aEbB6SLUYmD4sjffXHeJgRSObD9f4\nX/OSXk/SueeQ89STIc/hV7rUoKtbqLaK2s4Lju7P7sdOZ+GVg8SOOQ+Lej2NntTqzeRIVdgjzNVU\n6QZ8zS2SRqhKEy4VHdgaXaCwPoZKl9sjM/u5lTjcMk9dMI55v59Krc3Fha+sZl9ZA2NyEkkzdW6T\nhRp0tcDLVx7DyrtmAnDCiNC7ZKvXrdpiDDUYlSQRaOUkCTXBbNCSZjFwpLr7ZwK6vDnss8bndPNK\nOoDTCs8OF95KZ78IBkuE9KKu07oXG9esoXrePPafeVanuN67iorQ98tBkiQkjQbJYEAyiDoU2R5l\n0BUXR8KcOcQdfXTIvoLaAqrsVczoP6OZs/s+0/tN5/QhQjn85tA3YqOvZsQe6q/14FljqLW5uPfj\nLZz771X858fAGKZ+Tz9N8nnnhV7cqyoqvbyJozdysKKRf3qzESePzsKo05LgqhA7E7IhZzxc+AYA\netzY3b3zpqzP4VO6mpa7aHRQ5x3THJ8Zs6dbe6CKigYH6fFGxucmMS43id9OHYQkiXKihTdN54ch\nt9GgmLBqO6eWTK3pagGDTsOgNAvp8QZcnkD0+7dPtlNYLZQri6H1X2FuipklvxQxMiuBy6cMxKjr\nHid4X9Cl1/biWLu+GBQPjD4b8k4DQG4adGk0EOM3VUWWcezZwyFvvRWA89AhTEGpvFjgKipGn9Mv\nZJtkFD+b4ow+6IrEoXphXzIkaUgHV9m7GZI0hLyUPOL18eys3Mlov9IVmn4fkZVA/2Qz6w6KO+5W\nb5x8qdw+YlfSW7C7PFz93/UcqrKSlWhkbL9EUSv05QMQly4UcQCNyEro8bApvxqdRmJ8boxMghVF\n+D3pDK0fqxKEL+hq8pmk0YHVFzTHTiSo95YFvfbbY/x1zY+edxR3nirKgkx6LXljJ/Hejjlc00lj\nonvxp2/XYdRp/TUAiqLw5qp8vtwhusPiDK0HUEcPTMHq9PDwJzt4/ss9nbrWlnC6+0DQ1eD1QZt8\nHdYNGyi49lrqP1+OZDYFjtHGvqYr/5JLOXje+SHbaj6cH9PnAJ/SFRp0aYxC6YoqvdjY2HzQVSeC\nrgEJAzq4yt5PZlwmP5X9xCWfXsIBhzeN4Qh3kh+RFe9/bG5lbJbPt6s3d872RjbkV3Goysprvz2G\ndffPYXC6BQo3iZqgS94Bvfe9QSsCIh1uHl+2k3P+tSp2i/j0dtGFV1/a6qEqQfi985ooXVpv2Y7O\nDKbYNaD5pkykWUKD46Q4PUlx4jnPmdCP4f0zMUmuTrmB6sWfvl2Hwy2z8OdC3lmTz8aCgJuxRiIq\nr5eHzh7DqntnA6J78Pq3N3D7h7+Etqx2AT6ly9Cbx//4gi5LJuUvv4x13XoSTjuN7Ace8B8iaWJf\n02Xfts3/OPeVlwGw/vxTTJ/DU1eHp7oafW7/kO2SN+iSWwm6PLW1KDYbuszIcnxBfQHJxmSSjN3f\nRdvdnDPsHOL1IqDaUVcgNjrCG03+dcUkPr/1BCyGyLMYQ9CoNV0d4ZPNRaw7UNnm83x1t4PTLIGN\nvqkNCUHdu1rxXp0Y9Hm7ck8T24/28MPzsOlNYVJcsrXj1/s10Wx60XuDk5gTvq8D+Gr5Wp077Ouc\ndMW+LKgXf/p2HRUN4sPuwSXbufKNdQBcOnkAi/90fFRDoyVJon+ymeOGCF+sr3eWsejnQnYW19MQ\nYdRIZ+H0pkh7tdJVKXyoSBmMp7KK+FmzyP3Hi8RNCuo0aaJ0uTwu3HL7f8++a6VefRUjN/9CwqxZ\npP/5Fhw7d+E4eJDapUtxFRW1cpWWsf70M/kXXwIQlrKUjOJOvTWly3nkCAD6/v0j7j9Ud4iBiT3H\nqLc7mTtkLssvXA5ApaMKDAmwaxls+L+Q4+KNOkbnJJKVaMLWSvG136FeDbraxS3zfubS19dGfXxV\no5N9ZfX+BoeQ+lrfEGNtcFezeHzW2EB97vNf7o7oxdYmti+EZO/rauN/1H//tuAvpI9Q0wUxTS1C\nwJ/NpG/lM1DvbV5yqkFXt3LrySNwemR0GonfThvU5nqA+TdMI/+pM5n3e9FyvujnQo566IsumwXm\nV7p6a9Ale2D3cuGrZIzHU1uLNjlctZG0WnCLN2Kry8qk9ybxxLr2m935/LJ0GRn+VJ9l6lRQFA7M\nPYOiu++h8s232n19AOumjTgLCki97jrimlgSaExepcvacgds/VdfgSRhPmps2L782nzWl6wnx9KL\nmyhiTKIhEZ1Gxxf5Xwgn+tKt8NkdUHsk7FijXovNGZ3SpRbSdz7rDlQy6dGvOOMfP9LgEP8u8cFZ\nB3/QZQxs86YX544RQdeUIalsPlLLL4c7OA5NUcQMz5n3i8HzP/69Y9f7VdFcTZc3WI5x0GXzB12t\niCUGUaLhtMXe6qmXfvp2D3+cOYz/u2oy6+4/uUNGp743h482HQZgbxd5eLn8NV291Bj183ugcCPM\nvB9PfT3u0tKw2YJAiNJV2CA6YBbsWdDup5VtItiRzAF/K/O4cUimQB2ZbO3Y1AHF4R3afecd/sDO\nhz5HvPG4jhxu9nxXYSFV//kv8TNnRlS6PtojzDxPG3xah9bZl5AkiaFJQ9lZuZPaUx4WXk4QSGEH\nYdZr+HpnKZe9vqb56/mVLrWmq63IbZiFWGtzcZ134ofTI1PnTS9aIgZdQUqXL72oVzj45Bm8+ptj\nkKQYpBhlj1BqTrobRp4J3z4GjW1Pk/4qCbaMCMavdIWaO3eE6kYnS34RnwfGVkpsNEahdNmtsfe2\nVIOuKHj0vKO4fU4eJr2WOWOySIs3tn5SC/hk8Dq7UGNS47qm48XlkZEk0Gp6YdBVWwgb3oCMUTD+\nEir/7z8AGIcNDTs0uKarxtHxod6+oEtjDhSoS3o9Qz/9lEHvvoN+wAD/0O2mVLzyCntPmknd8uUt\nPofisCPp9WFmpgAaiwX9gAFUvvF/uEojF+rWLluG4nKR9offR9xf56wj05zJKYNOaXEdvzYeO/4x\n3IqbpToXzPmb2Fi+O+w4X/fy2gNV1Hj9oMKQVKWrvTQ4o0v/3/juJib87UsanR7OnSgaTv7xzV50\nGim0bMIXdOmC3qt96onsQpIkUiwGxvdPYtW+io4tXpFB0orAa6SwI+HNuR275q+F5grpS721ccEj\nzzrI7fN/YU+pEDham8ii9SpdDlXp6h5+O3UQt8bQULRp8X1XFbY7PHLYOKNeQ9V+8fXUx5Dtdmo+\n+ID42bNJvuii8GO9SleFrYKiho7VWoGwYQDQxIU6uRty+xN37LFIBoN/6HZTGr7/AXdpKWXPv4C7\nooLq+fPxNIS/kGWHw18wH4ms++5DbmykdomYUSY7nVS8+hqOgweRbTYaf/gRTUJCmDeXj0ZXI3H6\nX8/4n2gZlTqKHEsOz2x4Bpd3aDhbw0f8nDMh0FG6MT9yOUDAHFVVutqKT61qicoGB8u3lwBw2tgs\nLjom17/v8uOa1Cr6Cum1wabJ3sc/vQtb5sPKZ3ja+QRzil/vWF2XIgeUmtHniK8V4YG7SgSUZtKL\nPgZMicnTWJ1u1njH+7WmcgFoTaLJxtUJQZfq09UNxDcJujxtkNY7gsut9N56rup8AOT4gew+bgq4\nXKRde03EQxWtBsXjZtb8WRH3txXrGpFS0qWnR9wvGQ3NFrn7Cuxdhw6xd8YJ4vHhI2Te8ZfAej0e\nqt95t8U1JMyehXnSJMqffx7DgFxKn3kWd3Ex5S++6D/GOHJks+c3uhux6C3N7v+1IkkSJw88mfd2\nvkeDzkDKpKtF0OW0+us6AH5/4lCumDKQsQ99wfXvbOS7O2cyJL3J71MdA9RuaoOCrnUHKpkyNHxq\nwg97hSL13u+mMGNEOoqisP7+k8lIMIbfSHqcgBRIU0Eg1bj3C/EfMAoYJcHn255k7rh21g8pnkDQ\nYE4WQ+j3fNm+a/3aaK6Q3seQE2LyNO/d+hhjHElMv3Su35OrJfSdGHT10k/g3o3FqOP2OXlcP0OY\nVLq7KOiyOt3oe6NdhLUKNn8AkgZHcQO4XJiPOQbz5MkAFNQV8NdVf2V14WoA9tTu5UDVvpBLdGTe\noGP/ATQWC+Zjjom4X2MwRlS6FFnGXVFBwmlN6qg0GmqXLsVTJ3yhfF9bw6fqFd7+F9zFYiagMS8P\nY14emffcQ87fHm72XKvLqgZdzZCXIrpF7W47jLtItP7vXhZ2XLBCPX9jeH1dXxwDVFIb+6kLkaiz\nBdKLkToYbU4PT36+E4DROcLMVpIkMhNNkZV7j1MoW8H7ggOwP66GW35Cnvw7GjGxb+OXUF/SvsUr\ncsDiAISvlL22fdf61dGK0hUDGteu44TvPuSJ1a9z2bEDm830+Pz18i+9DPPizwFw2Rtg+yJoiIG1\niJde+AncN7h1zggu9Mrjni54k75j/mY+2HCYYwbFLkfeZax6EXnfavYvy+TQ9X8AnY7+zz/vf/G8\nseUNFu9bzA1f38D7O98nv/4QGhluP+Z2ll2wjBsn3IjNbQtLITgOHqTogQdweQMYV2kZnvrQwklP\nXR018+ejH9j8i1UyGJAjuMV7amvB4yHumEmM3rWTUdtEnYJ95w6K7r6HkocfBkCOMuhKOv88zJNF\n4Jd82aX0f+F5hnz8EUMWLSTt2mswT5zY7LlqerF5TDrREGHz2GDQDNEx9fHvwBXeLfrIuaIz9ONN\nR8JTUn3MHHXJL4VMffIb1rbDO6ut1NlD04tNrXTeWp1PaZ2DN685NrqaWo8ztJ4LAulFfRxkjYW0\nYWiS+mPBzi0Ff0Z578L2LT44vQhigLrbJhzqVVrG/xrqnJIXZ0EBh665xv99bkrozffKPct57L3r\ncRw+zK6xR1F4193YNm9G/6m46dLVHIAF18BH18ZsTWrQ1Y3ovAXtwSOGOouPfxJt8M9cOL7Tnyvm\n1Jdgr9XhrNUgW630f/7v6LMy2VCygbMXnc2S/UuYkDEBgCfXP4msgSxTOtcddR0DEgZg9LaNO+VQ\nNap20WJqP/qYA+ecS8MPP3DwogvZe3xgLqGiKORfdjkA5okTml2eZDT6uw+D8VSL2h9tivBnk3Q6\ntOnpOPOFGae7TNw9eWqjuyuWJIkBr77GsOWfk/PwwyTOnSuK77Wte8U1utT0YnOYtCLosrvtwvZh\nwHFiR114PeBV0wbz4FljKKt3hHUdS33MHPXL7aJpY2dxdDcFHcGXXhyVLVSsvy7exotfB6Z37Ciu\nY1BaHLNGRTmHz14X2rkYTOqwwONRZ/sfyjXhViFRITcJupqZ5akSgeZqus57BU5/uoOXVtjw6jsA\nrMsaDYDcGPDd2l+zn/Kbb+fCx1bRuGsHAHWffBJyjcwDi8WDQ9H7x7WGGnR1IzpvOqIrarpGZSdw\nypgsUiy9cDaYox6nNBiAoZ8vI/HUU/HIHm5fcTv5dflcOvJSXpr9Eq/NeY2L8y5mxoATMUmBnzPk\nQzUI+/bt6HNzkevrqf/6GzzlFShOJ43r1gPgqajAeeAAGXf8hZyHHmp2eZJOh31ruBO1u1zUoOjS\nA/Up+pwcXIfEOB7fvEhPbfRvztp4C4bBg6M+3ofVZcWiU4OuSPiULv/fx1FexcMdObWWFi/+3U59\n4fvQHX6frr6hdPnEh8NVLfvDxQJfIf1T3pvCRT8X8uLXe/n9OxuZ+sQ3fLK5KKwWtvmLFcMv74Eh\nPnR7Yg6c/hRcGTS+KyOPnb8RkyXKM9pZtN1U6fKNrbF3vHO6z9NcTdfEK2DqjW2/XJD6/Pr3B9i3\n+if2J/Xjx/7i78pTKd6TXbKLD++7mLHirRjboXz/ebqsLAAOb07B0uidViG7xFipGKAW0ncjPqWr\nK2q6nG45qq6NHom9DrfDADSizxa+LVa3lVpHLb8Z/RvuOe4eAKb3n870/tMp/uJhakp/xF1VhS41\nFaM3zWB320NG4Nh37CD+5NlYN2hwFRb6txc/+FcGvfOuMBsFzOObV7kAGr77DgBnfr4/IKpZtJji\n++4DwDAw0FllmXG8P0DTJIi7ek+dULqyH3qw7b+bKFGVrubx1fv5gy5vENZc0DUtQpE3EDQGqGtq\nNDub8nqRMs+v7JgHXTTU2d1IEoztl4heK/nV/6+8M25H5yRy9bTB0V2syDue6+QIr6epfwzblJzR\nj4NyFnhaV4wjEpZe9CpdEWZ5qjShtUL6CGwqqOLl7/Zz06xhbD5cy8KfjzAwNQ6dRsOP+yqoanQS\n57Zj0xr4t62Gkrg0iiyiCari5VfYYT/IU4O384/vAjdHdc+KhqThK77DsX8/h393PQ0FZphQTYmS\nQrZUTcnhA2TnRa7rbQu99FO4b+Dzy+qKmi6HW8aoa+ebSney/1vkAz9iK7SisVjQeA1KbW5x9z0k\naUjYKSmXXQYeD9UffAAElC6HJ1B3JVuteKqrMQwahDYpGadXfYqfORNXwSH2nXQSpY89hi4nh7hj\nJ7e4xOSLLwbAVVbm31bzkdd2QJLQ5QS6ojJuuYXh36/EMHQorqIiFEXxpxcT5syJ/vfSBlyyC6fs\nVGu6miGkpgsCtUDuyDYgmYkmbvNayLiCPLmCzVG3HKlp3cG+hyLLCk8v38X6g2IQeH5FFwRdNhcJ\nRh16rYasRFPIvnvnjuLzW0/gkmOjGNReVwwrnhSPh58c1XOnWYw4MOBxtlPRC+5ehCClSy2mb522\nFdLbnB4uf2Md3+wq48JX1vDIpzvYVljHsq0lLN1cRG29jb9s+oB5yx5mydL7GFxfSlF8OnuTB6DL\nzqZ2yRL6f7GFl17zoAGqzg90R5pv+yOLar/nu+wqEi+9GMkb+C/zCAX04MG9MfmJ1aCrG+nKmi6H\n29P6vKmewNpX4Ilc2PMF7FgC8y6nYnsCDfsdIY7wvqArUleiadQoLCecQPW8eShOp7+my+4RyoWz\noIC65aJlXJeahjYhwZ/yS7/xBizTp/uvFX/CCRENS4NJ+c2VAHiqAvKz69Ahks47j5GbNoacL2k0\n6DMzSb3mauxbt7JrzFjKnhK1C5qkzhlEbfUObVWVrshkxok6odu+u02kJ1pRugB/YHDT/34KpDS8\nHWy1jQ7O+dcq/t/i3jn8eM2BSl5ZsZ8Zw9M5c1wOh6utuDvZ8LXO5iLRLGqwmo5oyWiLGfXmeWLo\n9GlPRm2sadBpcEl6FHfLs02bJax7McqaLtmjero1Z47aDP9bV4DTHfhbfODM0dw2ZwT/uEw0EQ2s\nL+WUwxsxyG6MKUlYLr+SeXlzOPe4wQxduoRvbj+e92ZpsA3MJPnii7D+9iyWTpHQLHydu/v/yKNr\nH+X+H++yAKKWAAAgAElEQVRnbd1WFJdY06ATxft7WWF+TH5kNb3YjQSUri4Iuly9QOlyO2H5veLx\n+2L4M4YEbKYTgF/IeeRv/kN9QVecLrJ6k/qbKzl8w400/LgKU55X6fK+qeZfcSWeStGRpU1NwTR2\nLI1r15J1/32YJ04k99VXqPv0MwwDB2A66qhWl61NEW/utYsXo8/NxThsKO7ycgyDBqKJi7y+5Isu\novyFF/FUV/vtJjSGzqm3q7SJn1UNuiKTakrFrDNjc9vYX7Of4X6lq/kP4ZPyMshKNPLVjlK2FtYy\nPjfZbxlRWWcDLGwr7J1KR0WD+LkfPmcsPxVU89nWYg5VWRmaEd/Kme2nzu4iyRt0+W5G/zRrGFWN\nLk4amRH9hfJ/EIXy025q0/O7JAMWuZ1Bl9wOpauuGF6ZDiPnwnkvt+95+wKtmaM24Y0fDqDTSCy8\naTr7yho4/+j+SJJErdXF9GFpPNjfjPIdDHz7bSxTREPMkrIGBqXFodVq2JcXT37WCB4/TxTI7z3y\nPe/N1jLGWMPOqp2MTx/PlootVGtsIMPhYx/i5Nmnw2poKD8Ukx+5F0gffRedVwHpipouu9uDsScr\nXR437PUaCo46C854DmXoHMq4DutPv4BWS8Ls2f7DfepNc/5bpjFjAHCVFPuVrk8OfIK7scEfcAEY\nBg8m8y+3M2rbVlKvFHc0GoOB5AvOJ27yZDQmU/jFm6DLyMA8aRINK1aQf9llFD8oiu71AwY2e46k\n0fgVMt86OgNFUfjXL//CoDEwJSc27s59kU/P/xSAbw59E5XS1S/ZzDvXid/nX5dsF2qXV+0orxU3\nBHtKGyir6xqfq1ji6yRMMusZ7DWAnf33lfzfDwc69TkTTaHdhkcPSOHJC8aRHq3SpSiQvwpGtH3U\nlRMDWtkpAqWqg0FWBlE+rxR0QxtNTdeez8FWBb/8D1y9728kZrSxpqvB7ubq6YMZn5vMBZNy/TY+\nSXF63v/9VPppxA2sNjnZf87wzHj/iKhaR21IXa/vs+H+H+8H4KaJN3F8v+Op1wsFMmf6taAz0KhL\nQdtY0qYZoc3Rgz+F+z46bdfUdHlkBZdH6dGF9O7lT9L40rXUHDCz97UiDjz2Kbue2UPlO2JQdbDK\nBVDvFH5azdUpab2pOk9FBdmvLGUmI5m3ax4FhaI1WJuayqB338E4RNSEdWQ0kiRJDHr3HYZ9sRxj\n3gjqPvkESa/HPH5ci+el//GPDFmyhFFbtzB4wfwWj20vm0o38VXBV1w55kr6x4cPwlYRZMZlMi59\nHD8W/hhU09Wy8jEyO4Grpw1i8+Ealm4uQpuchKTXY8svYFjNEd7//GE++s+SLlh9bKm1BoKuYNf9\nxz7b6S9sjzV1NjeJZpF4GdNPBC3jBzSTbi/eAi8dAwWrA15qsgyOevA4ILHtf+cuSY/W44D/zoV/\nToT1b0R/csRCeqllpSs49Zj/Q5vX23eIXulSFAWry4PF0HzGxlMjOkaDg65gap2hQZcm6HlNWhNH\nZx5NvCGefLcwypUbRT2jw5xFhlLFpkMd72DsuZ/CPQlnY9vufKJE20Xdi76ZUzZXD6ofKPoZ/jER\n197NHLrhBvbe8T6Hvk2neH0K7uISHPv3g9tN8uWXMWrnDpIvDBgXripcxW0rbkNCIs0cuZNMMhjQ\nxMVR8eprWOcv5A9LxQfomr1fA5D91weIO/bYmP04klaLYdAghnz8MYM/mMfQZZ+FdC1GPEejwTQy\nD0mvR+vtZIw1ZVZR3H/e8PM65fp9iSFJQyixloDWF3S1rkA8dPZYRuck8uSyXdR7NBhHjyZty3r+\nteJFUhwNHPufp3j00x2dvPLYUmtzYdZrMeg0ZCQYefu64/jh7lkkmHSs3FPW+gXaiN3l4WBFoz+9\n+LdzxvLFbSeSmdCMylywCir3iaHSj2fDsrvgkRT45hGxPz5KL68g3JKBXPseKNsuNqz6R/QnK54m\nzvcaMCa0XNPl9Hq86eNg12dtXm+foQ01XXaXjKJAXDPWIYrLFRR0hQbsVpeVlYdXsq96H8OTh/u3\nj00byxlDzmDB2Qv46qKviNPH4fQ4aTCIdcneObn6lH5kS1V8s7Pjf/9q0NUaq/8FT/SDH56L+aX9\nlhGdXEhf4k1xTMyNHP13KS47LLgWXp8J1Qexffo6jSu/R2MIqH2DFyxgxI8/MHjBfLIffDBEhdpd\ntZsbv74Rt+zm43M+ZkBCCx1Ner3fqFJ3sBCzQ2HxL+8DoFg6p5NPkiTMEydiGBBFp1UX0OgWd2qq\nR1frpJvTqbBVoGijU7oANBqJe+eOoqTOzpLNhRiHDSOtMmCqGu+2k/jPpyi6//+xc9Roaj/t+R+w\ntbZAfRWI+rUBqXGMyIxnT0nsZ9Hdv3ArTo/svwlNMOkZmd3MTYizUShcEEjprX9dfN3gVaeScsPP\nawXZd634LEgaAPXFwpcpmkL3poX0IOq6agpC153/Y+B6jgYwJIhU6KY3YffyNq+5T+BPL7YeijQ6\nxZSCpkpX9bx5HDj7HHaNG0/9V18jmc1ojIGUdL2znguWXsDN396MUWvkuqOu8++L08fx9IlPMyp1\nFMkm8fk4LHkYNm95rU/pSsgYSLZUHdKt3F7UoKsl3E748QXxeFf4LLaO0lVKly8PPX5ADwi6vn8G\nti/0f+vZJB4PPb2M5GmDMOblYR53FLqUFMzjxoWl/V7fIt5gx6WPY0TKiBafKvcfL5L+51tIv+km\nsNp4+3kPc34WL5p/7vm/WP5UPRa1czF60s3puGU3db6C6iiULoATRwgPoAeXbMc9TnRRVQ8eSeLZ\nZ2PXmzi+ZBu1C8XfedGdd4aPD+phNA26fEwamMIvh2twuGOrmPvGDI3KTmz94P9dDDuXQtpw+Gu5\n+Orjwv/ANZ/BwOnNn98M/zX9lh+TzoJbt8BF/xXq1dOD4ZFU2PdNyyc3TS8C9D8G9n8XmE6w4il4\n60zY9SmseRlqD4MxHuY+I/Yf2dDmNfcJ2lBI70ttmw0BpavijTco+dsjOPYKOwf79u0hqUW37Oby\nzy6nsEH4MF419irim5rmNuGPE/7I1GGiftjl9VAkoR9pUh0Ou7WFM6NDDbpa4uuHwVoBGr1wpI0x\nkiQRZ9BSb+/cGV1Ob3Su13bOfKs2cWSDeEP68y+QdzqyS/wJau/dTs5/P2fIksUtnl5mLSMvJY9X\n5rzS6lNZpk4l46abSL/lZlK987dO2qZQlAqfuX+mtLFz6lN6Eq01HKgESDd7DRRdXjUnSgsBSZL8\nhqkPOwbzlxNupuTRf9D/2WfYNft8LE38n2oXLordojuB5oKuIRkWnB6ZGmvo+9XPh6r55XD73Ncd\nbg9mg5YpQ1K5atqglg9WFDiyUczGvPwDoS5NvCKwf+wFMHhGwKS2DZTrcngv/S+gN4n3p2CKfhZW\nNhX7Ip/ctHsRxDrcNmgsh8r9wsoCYP5V8MV9IvgyxENCtqhBK1jV5jX3CfxBV8ufTdWNTu5bKCxY\n0uINKG43h677HeV/fx6ApHPP8R/r9s7SBcivzaegroDZA2bz1UVfceOE1l3uDVoDuVkimK+v9Q66\nThRei/oYfGaoQVdLHPbOWxpxaqf5qQxMjev0MRs+jx2Dtvv+uT0NDTS8+Qiu/H0oif0pfWMBdRnX\nU75b1F9IKTkgSa0WtJfbyhmRMiKkGLI1JEki+ZKL/d+nLvofjWaJ5zc9j6x0vjFtd9LoasSsM6Nt\nmv5QCcMfdDlrROoqSqUL4P3fT2HO6Cy+3V3OkX7DmZYn/q6Ljz+Ffcki3TVk8SJRZ/hyz7UIcLpl\n1udX+T2zgvF1F/pG9rg9Mte/vZHzX17Npa+tadfz3fvxVvaXNzJndFbrzSyV+0Wh/Il3QbpX5R53\nMYw8E466qF3Blg+dRhPIOGi0cOtmuCdfDK/+5X/CyubD30Q+WZFDuxcBkr31nHs+F8PT3U4wNck0\nDJwqvqYOhUNr4IMr+8zczuiJTukqqBI3jzecOJRpCR52HTWOxtWrSfv99eStX0e/p58mb+MGMu74\nC9mPitq+LeVbuHPlnQD8YcIfyLZko9c0M4+zCYOyxazG/CJvPWam6IbPqw0Ex+1VrFWfrpaw/3/2\nzju+qbJ949+T1TTdG0qhpWXvvbeAE1yoKK8TceHgdfE6XhfqCw5EUcGJgkwBRQRU9t57rwKlpXQ3\nHWn2+f3xJOlukw4o/Hp9PnyanJzznCchOed67vu6rzsH2t4pHtdSx/jGwTou1HKbDaf5quoqkq6U\nN15C//cmVN42fDunk71tFsya5XrdnepBWZZJM6QR5u2Bb48DmqgodL16CfPTiC480eEJvj30La2C\nW/Fou5rrIF/XYLAa6qNcbsJZlHEp75KwjfCAdEmSxIvDWpBjtPDs4GY0DBCfuToklOcGTeDE6wPR\n+vsS/MjDpH89A3tBgau7Ql2B1WZn4pJDyDL0bBpc6nUnEfvo75OsPpbCkqd7s+a4WPmbrHYMZis6\njWe3lD0XMrmpbQPGDYitfOfFjt9pk96F2wKbwP3zPDpnUdiNRiQvL1RKCWtRwhMUI/6GxIpIF0Da\ncVE52bBD4X6yDMilSUPTARDWGpa/IJ5LSng7UxDHlCOQexm6jRWv3Tsbtn8Jmz8VurSeT1b5/Vxz\ncHPRm5glSNcdnRuR8ekkAJRhoYQ9/zySWnwvlb6+hI4b5zrm1U2vklGQwbDoYbQKauXRtNrGdOcc\noM90RM2iulEgeWPNiCcjz4TBbKP/R+vpERPMdw9V3LGkJOojXRXBlCPKfxVqsFtr5RTRwToSMg21\nqvOoC+lF06lTAFgLlGRvO+/aHnD77TRbv86tMXLMOZjt5iqRLkmjIfqnWfj0EqvLMa2FR9bUvVP5\n6/xfbErcxL6Ufddd5MtkM7m8aOpRMaL9oon0ieT3M78jq7zcTi860SbSn0VP9mZAi8Lvp69WkJAC\npVDmencUfTwTHhuLNat4+bklJZX8Xbuwm0qf13T2LLbcXI/m4ym2nEnnt/1JjB8cVyYJ8ne8F6e2\n5u0/RKXfyI6RAPy45ZxH57Pa7CRnG4kLd+gNzfnlf+an18DlQ9BpDES0cfscst2ONTOz1Pb0b77l\nwiOPcqpHT1I++BCVQirbpHrkdPAJK/Rum3tP8Ur28oTgam8Y+zfcO0c8v+G/4m9IHLS5XRArpYOg\n6oJh8JuCTK56FdZ/WLjIN+oh9bjb7/eag5uarixHSjtINpOzYgXBjz5Ki82bXYSrJL7Y9wVJeUk8\n0+kZpg6a6nGk38svEJsEVn1h2jxPGYivPYfBn2xgvyOdvut8Jh3f+8ejsetJV1nY+zMcXCAiXV5+\noFDVXnoxRIfRYnc1l60NOCsu1NUIv1cHqZ9Nw3juMgFNjTSeOZOAu+4ies5sWu7dQ+SUyaiL9Cas\nCOkFokN8mM5z0lUSwdpgl2/VKxtfYfza8Tz818NM3DTRRYAv5lzkjt/v4ELOhYqGqtOw2Cz1pMtN\nKBVKHmv3GPtS9zHP19ujSFd58HGUt+eZHJVXAwbQ4N13Kdi/H/2SJa79jKdOcWbgQBIeepj06dOL\njWHYt5/4W28jderUas+nIjhNUe/qUnb1X8meiEeShCXCvd0a06ahPz9uPU+2oex+lWVhxeFkrHaZ\nFhGOSsUPI+HbwWXvvOYdkYa77TO3xwdI/+prTvfpS8ZPP2FOTOLSa69z9sabSPvsMww7diCbzWT9\n8gvNkk6WXZnWoD28dBJejYduj0HeZVj6BKSegOSDhaSrrGurNgDajIR39NDv3xVPVKGA/i+DdzBs\nnAKLHxP6rykx8HUvYdh6PcJNc1RnSlubdAFkGV238htPZxoz+fHIj3Rv0L3KVjmSJFGgU2DXF9p+\nhFmTuVO5lc9s/2PifJFOf/PW1h77X7q1tyRJN0mSdFKSpDOSJP2njNcHSJK0T5IkqyRJo0q8ZpMk\n6YDj3x8eze5qQJZh+fPw25NCCKkNEDn+WhDSg9B0QWHOujZgtckoFRIKxZWPdNlNJjK++QYASeuD\n76CBRH74Abru3VH4eFZRl1YgRI1O7U11seyOZXx1w1d8POBjvh8uqhn/Ov8XB9MOArD49GLO6s8y\n59icGjnf1YDJZkKtdE/HUA+4t+W99GrYix90SuQacAr3K0G6JEki6L570TSLI2fVX9jy8jAnJmE+\nd951jGH/gWJjpE4RvTnzNmys9nwqgrNBt7e67KhAZKA3q/89gB2v3eDqdQcQ7u/FpDvakplvptN7\nqzlwMZtTKZVH5f5xRMz6Nivye3b6ZBWFMUek5Do+UGhc6yacVW2pk6dwduhQ9L/9BgoFAXfdRYs9\nu2m2bi0olfTfu6r8dmwKJWh84KbJQvx+eBF83RO+GVCYAXGzjU2FaD5UkLuWjirHY8sKSUnS3tL7\ny7LoUfte6LVrOeGmZcTZtDyUCgn9F5+DWo22XfnG02surMEm25jYfSJBWvf6b5YFk06NnJvH29ve\nZuGJhRAjmmPfoNzPce1j3Ot7gMf6NuX4ezd5NG6lCXhJkpTAV8AwIBHYLUnSH7IsF3X8SwAeAV4u\nY4gCWZY7lbG9biKvqPmZJMSOOZdqLb0YEyKIx46zGXSPKa2jqAlYbParllo0HhNfE02ohqDu1Xt/\naQZBuqqSXiwLXkovBkQNcD1/v+/7vLn1TRaeXIjBahDu5MDZ7LM1cr6rAZPdhJeiPtLlLiRJYlDj\nQexI3kG6NY/qftOcka58U/Hrh65zF7J//ZVT3Yob9GrbtKFg717SZ84kcNQolMHBrvSYNTkZc0JC\npaa7VYXRUjHpAmjuiErd1iGSxKwC8k1W4sJ8USok7u/RmPm7LnLHV0JsvObFgTQLL7s8f/HeRFYc\nSqZ3bIho81NeJiE/HfbPAWRo1Nnj92RNSUHbti22nBwsFy8S8fprBD/0kOt1pa8voc88DdO/xDez\nEuNLlReM+FwI45342iGGLymkryokSWjU7HYw54LZAFNbiSrIkkg+WNij9tACaOnZzb9uoOL0Ymqu\nkefm7WfnuUyQZQoOHCBg5AjUEWUb4BqtRj7e/TEx/jG0CGpRrZlZfbTY9HqWnl7KUpZy4z1rCFT7\nYtv4MYqdM/jI+hHsDvVYg+eO6rEHcEaW5XgASZIWALcDLtIly/J5x2vXviBGnyj+jp4PUd2Eu/Hx\n5bWn6QrRMbhlGF9tOMPTg+JqRexuttldvaeuFGx5+Rh27cJ8ToTFm9zljzqiehGqmkwvloWRcSM5\nkn6EBScX8Ge86MUnIXEw7SBGqxGtqvI+jHUNFpsFjbJ2Gmlfr2jiJ0jNJbO+2qTLqenKK0G6wl99\nBZCxpqahDAlx+XhZHX1B06Z9Ttq0zwm4/XbsxsLqZltO7em6Cizi8q2tgHQ5oVRIjB/crNi2t0e0\nRadRcSY1j42n0hg6dSO/j+9LpxL+gJn5Zl7+VUSTXVm5ghLtVf58EWL6wtYvIPmA6BIQ2cXt92LL\nySF9xkwKjh4l6IH7iXjtNcznzqNpGlNqX/8bbyR9+pc0STpV+cAljVezzjvmX1o3Vi0oFCLLonHI\nW44shdjBEN5KRLUuHwZbkVRug4pbjtVZVOJI/8eBS+yOT+cGXxNDfM3IFgvaFuWTqWMZxzDajAxu\nMrhard0AbH7eaNP0OGnSRUMqgWERKIe9A/1eEKnfVa9Cixs9Gtcd0tUIuFjkeSLgSedcrSRJewAr\nMFmW5VJGTJIkPQE8AdCkllZxbmObo/1DQFRhOwmFSjRkrgVIksStHSJZfzKNC5kG4sIqNm6rDLIs\nY7LaSdYb8VYraRCgdUS6rizpyvj2WzK+FUamqoYNUauyhT6uGjiVdYpgbXCtGX1KksQbvd7AZDNx\nPPM404dM51TWKcavHc/UvVN5vefrtXLe2oTJZqqvXvQQET4RAKRc3g9//htu/FAIo6sAX0ek65FZ\nu5l+f2dGOETnSj8/Gk6a5NpP0zQGa1oastlM9oKFru36ZcuQNIWk2Z6fj/H4cTTR0Sh0NdtVwdkm\nrKo9WrVqJf+9TYjc311+lFlbz7P9bEYp0nUwUYiQW0T48ng/h2A/P734YHt+EP+UXkJ4fvNHQnDu\nJrKXLiVz1iy0HTsQNn48kiThFdu0zH01MTFYlSrC05MwmK20eetv3ru9LQ/1jim9c8OOwp7CUgCD\nX4PTq2Htu6XnX1NQKCCsFSTugpn94MlNMP++0vvVccPdclGJkN60excrlhXvu6tqUFoDLMsyq86t\nYukZsXi5p8U9pfbxFEr/AHwvXkaj0GC2m0nMS6R9mIPcegfBPT/Dksdh1q0ejXslLCOiZVlOkiQp\nFlgnSdJhWZaL5WtkWf4W+BagW7duV/fbExgNLW+BsJaF2xTKWot0ATR3hOBPp+RVm3SNn7ePlYcv\nu54fe+9GTJYrn140nz/vehxw261gmlkt0iXLMjuSd9CjQY8amF3FeK/ve67H/hrhkr03pQxNxTUA\ns81MoFcd6ERwDcGZvk5Te8GeHyGqh7AJCIwWLuIeICbEB2+1kgKLjX8vPOAiXSXhLHWXzWZCn36a\nhMfGYj571rXNp08f8rdtI3/bNjK++YaQceMIf+nFarzL4sjIM/HFWqF/qgnt51u3tWHujgSXOL8o\nUvRCK/fToz2IDHSQWUMR0lJUS2czic/fr4Hb587bvIXUyVNQhoUSs2BBpREPSaUiNyAMbeolpi0/\nxPiDS/jVnMZDvR8pvbPaG0b9UPg8XXxmmGqxsvTxNbBlGmycDDN6i4r6Gz8Ei0FYZswffQ2TroqF\n9NKpE8WeB40Zg9/gQaX223ZpGxM3TwSgf6P+riKp6iCucQdyDyWyefQmes7rSWJuYvEd2t4hrEXK\n828rB+4saZKAoo3kohzb3IIsy0mOv/HABsDzxPyVxPBJcP/84oLNWrSMAFy6h9NuiE8rgt0us+Z4\nqtBIOPDorN38ujcRRTVDrZ7CfO4cvoMG0XzbVsJefFFclKpBulZfWE16QTr9GvWrwVlWDp1ax4Co\nAShrSrNxhWG2mevTix7CTyO+p7mDXhUbTq6EGX1ECysPoVEp+PN58Z212uVKq5QljQZ1RARNvv+O\niNdfRxkmUvKqMEEEnUUplsuXyx2jKnCK2msKkiTh761GX1C6mtFkFTfaYhE1Q0bhY2MJd3vfCLfP\na7l8maQXXkDSaGj89Qy3U0w6rPRJPkLjLyZx27ntPHzQzZovL0frInPN96R0Qe0NXR4SGReAe2ZB\nlweFlqj5cLHtWrW6qSTSZcnMQi6Segx74flikV8ntiRtwVvlzb5/7eProV+jqIHCBm1IOFKeAa1N\nQYhXMIl5iaV3iuwEEw57NK47M9sNNJckqakkSRpgNODWN1KSpCBJkrwcj0OBvhTRgl0zUKhE9WIt\nrSZ8vFSE+npxSV+9aqmUXCNmq50JQ5vz7YOipHbnOaE1eHtE22rP011YUlMxX7iAJjYWVXAwktXo\nqAStWsRFb9Kz/OxytEott8XeVsOzrRx+Gj9yzbXrkVRbMNqM9aTLQ6gUKrxV3uRaDeAdhP3cRg54\naTBW0S8pLsyXoa0Fceg3ZR1L9pZx8S4BdcOGBD/0ID49hVBb1bB4pMfm7AlXQ4hPE6RhZDmRuKog\nUKfmdEppMmJ2kC5NUdJVND2X5bBouWkyDH3XbYG4LMucuWEodoOBBm+/hXf7dm7PNex+kbLrliIi\nK2GZyeRm5XBx/LOYzpTT/gcKI5+mWiRdAAGNxM396W3QbGjhdhe5uEYjXa55l02OFTnZGP2DUEdG\n4tWyJUr/0v05J++azG9nfiMuIK5GK7W17dqCLHO6T1+mfJaFYs+Rsnf0MKBRKemSZdkKPAv8DRwH\nFsmyfFSSpPckSRopzil1lyQpEbgH+EaSJGfdb2tgjyRJB4H1CE3XtUm6oFZXE6G+GjLyqufVdSFD\n2E5Eh+gY3rYBn97TkUCdmjdvbc1N7dwPz1cX5+8bjWyx4NvfEZXKdbj6+lftgv7NoW/YkLiBIU2G\nXJV2Nv4af3LMOZXvWMeQnJdMUl4SDX3c80GrRyH81H7kWfJ4O9ifjg0DeDCyAXOVVW/X9dZtbXh7\nRBuigrz5an0FN/ESUPqLqJsqOJimy36n1eFD+PTpXcw/yBMYLTamrj7F0n3Fid/BRD2dGgfyxf01\nl4jo2TSYPReyaP3fv1h5uLAfntOs2UtV5LdcNNL1oyN6E9UD+k1wO0JuSboENhvq6CYEjBjh0Vwj\nn3kK9d338mfT3qyM6UV4QTbxn35O3tq1pH/zbfkHhjjaEXUc7dH5qgT/SIgosXh23vCv2UhX+ZYR\ndruMzmTA6uNH3D9/EzNvbql9DBYDc4/PpXlgc17sVnPpdgBdVxG4sBsM+Ost3PzziUqOcA9uabpk\nWV4JrCyx7a0ij3cj0o4lj9sGXKNlFUXgdA62W4W+qxYQ4qshI989Y8GUHCPvLj/KlLs74KctZPYJ\nTtIVLITmd3eN4u6uZRsd1iZsmZkEP/wwPr0d7TpyHBdcD3QZRZGYm0hjv8ZMGTClhmboGQK9Askx\n55BpzCRYWzu2HrWBoxli7TO0ydBK9qxHSfhp/MgyZnFarSDQZiZbqSS/GtGEJiE6Hu3blKOXcth2\nxn3Rta57d7LmzUfduDHalkJnKmm9sWd53mB6yd5Epq4+RVJ2AWqlxG0dItGoFNjsMkeT9Iyq4WvF\nWyPaEBfmyzebzvLM3H0cffdGfLxUmByC/WI6U2ekK6qHWKRF94Wo8g0wy4L+N9FIPGr69DJTUBVB\nUipp9sG7DD6fiTojFcbciWaxaC+kblTBYtEnRJifXk1IimuYdJXf8Npss6O2WZC9NUgqFZJKVeJ1\nM1/s/wKAR9o+QvcG3UuNUR0UjaqldmhE+KEkrJmZqIKrdw+od6R3B4oipKuWEOzj5Xak68OVx1l5\n+LKr75kTqbkiPdkg4CpbG9jtxdszOCNdfp5HugwWA+svrifaP7qGJuc5nF5eq86tuqbaBDn9xZoG\nlJ/dIX8AACAASURBVF21VY/y0TqkNesvridRsnG3MgQvux1rDRgkq5UKLOWZcJYB/5tvpuWB/fgN\nLnRqV2i9kAuKR92OJOl58IedjJu9h/m7Eoq9ZrfLbDqVxku/HsRis9O/eSgWm8y2s4LobD6dRr7Z\nRoeomi248FIpeaxfU551WEusOyF8sEw2OxqVolBvlZMMu74B/yh4fDX8+wjc9Y1H58qcN4/0r77C\nb/jwCi0FKkO3mGA6dm3FmaBCGbPRWDvG2DUH6boU0pssdjR2K5Qg0AaLgd9O/8aI30Yw9/hcukZ0\npV9U7Wh9G74/CW379qQPFKnqjDmzqz1mPelyB07SVUtNrwFCfCqPdFlsdiw2O8sOXAIEUSuKfLMN\njVJRXCtxFSDb7cXbYrjSi56nuZzu8G1DrpwmrSSaBYqbxuRdk5m2d9pVm4enOJV1ika+jdCpa9Za\n4P8D/tPjP+hU4nO7ecT3qCQFFrsFshPgj+cgM75K46qVUtntZiqAQlt8ESV5aYv1Z7Ta7Nw2fQub\nT6ez+lgKry09TL7JSnxaHgt2JRD7+koe+nEXAENahfP9w93w9VLxyKzdjPl+B4/M2g1Ax8a1U+X6\nQM9oGvhreW7+fm75fDN5RiteRS1sFj8m/lbh+gBQcOQoKe8J+42w55+r7nQB8Lr3ftfjCwmVmKZe\nbVzLka6ymoU7YLLa0NisoCm8z1ntVu778z7e2vYWl/Iv8WynZ/lu+He11uoscNQomv66iPz+wt/d\nmFr9ApZ60uUOnJWMlnI0HSnHBCFb/VahWZ6HCPXVkGu0YrKW7cysL7Bw42eb6PnhWtc2e4nVjcFk\nxVtTB6rs7HakohfVnGRQ6worfTzAsQwhAXywzYM1NTuPoVVpuTnmZgAWnFyAtRYjnjWF9Qnr+efC\nP0jlCFTrUTECvAJYd+861oxaQ8vglqiRsFiNsG067JsNX/cGq/t9Bp1QKxVYbVWLSlhsdsxWO5LW\nC9lYWHTz6Wph6tk9JoghrYS3YLK+gFu+2Mx/lhavrNKqlXiplHRsHADA1jOFWqq4sNrxv1MqJH55\nXFg7HkvOIT4tHy91keuDxnHeh5ZVaXxLotCnRc+bi1ezZpXs7R6GPn0/pqcmYEMidN2fjJ70Ozvj\nMyo/8GrgWiZdsp3yRPQmqx2N3QJehZGuledWcj7nPK2DWzM8ejijW41Graj9Nmd+3gHkacGwZBn5\nO3YAMO/4PLYlbfN4rHrS5Q78HVoHfRlVR1nnhXfKrFtg6+ew/IUqnSLEYfOQWU60a8pfJ4hPzy/2\nurMKyAmD2YbPVSZdsiyLULdUItLl19DtKo8NFzfw+b7PSS9IZ1PiJhr5NiLAK6CWZuwePhr4ERO7\nT6TAWkC+Jf+qzqUyJOcl8/z65wHq/FzrMnzUPi6jVBUS1tzLsMshqrYa4X3P/epVSsklJPcUI7/c\nSv+P1qHQersiXXa7zIwNIo08f1wvbm0vokVn0/IxWuz0aBrM9w914/A7w5l0e1ueGCDMSP97Wxva\nNfJny8TBnJh0EwffGl5tB++K0Czcl3kO4pWYbUBTdFGmVENE+0Ly5SFsuaKoQN2g5oqFFFotHV94\ngj+aDwQg4Og+vttcR5tOS9K1TbrKiXQZLTbUNitSkUjXwpPCOHj6kOl8OujTK3Zf8FH7sK6j+H0k\nPjMeu9XK/3b9jyfXPMmhtEMejXUlzFGvfQQ59ETZF6BxCbFenqMnVqII3xO/0UE6ilzAVv1HeM8o\nVMIHzLt0E84QH8HmM/LMNAwodL/OzDdzNi2PZfuTuLldA1YdKQxvOknXufR8Np5MJc9kRed1lf9L\n7Y4fv7IE6fKgcnHKrikk5iXy/WHRhPrlbmW19LzycLYBMlqNV50ElgdZlnnkr0dcz2cMm3H1JnMd\nQS0psZbFSfb/Ap3dN0fUKBUepxedOJ4syIU+QMJeUMBrSw6R72hS/dRA0UJM51h0peaISNhzQ5rR\nv7kghw8WcVhv1cCfP5/r73ruTuuf6iLcX0t/xSEG5ByikVcBrN8JHe8XHldVJFwA9lxh16Aow06g\nOpAkie/a3EL/i/t44sgfrOnXrUbHrzFICq5Zy4iS98oicEa6FF6FpCs+O54HWj3gWgxdKXQI68BL\nQ5REt+9Fx++3kHX+pOu1Fzd4VjVZT7rcQaCjNVH2hdKvOXtuNeoGSXsAGU79BS1FOgqLEXYWufG1\nurXwtSJwRrrSi4jpDWYrwz/bSHqeiG41CSmuzXGSrpkbzrJwj+jU1DHqKpMBm7gJSMU0XZehUcWV\nSKmGVKbtnUa70HYk5iXip/EjNiCWXg178VCbhyo89krBSboKrFW3DqhtpBhSuJR/yfX8amrhrieo\nfMKw+MdyNGcjL8e0IMJiYta500jLxkOLm8DHvb6iaqUCWQabXUbpofO7RqnAbLPz057LPCjLLN5x\nDoujsrpzE6HHcsoLUh0mrL5XexFWBLGhPnzmv4BQ43nyZV/YuEF4cpnzq+zhZ0lJJe1z0bqtptsi\nAciSgjf6PME36z7B69BeYGSNn6PakBTXtpC+Ek2XQivujRabhTxL3lWpIG/g04Bo/2h+TdxOR+CD\nRc9AI+jZoCc7L+/0aKy684usy9D4gC600LTPCVmGM2vE47u+hbwUmHUznF0HSg1Edi5sD9HvRdgy\nFQrKLvUO9RWRrk2n0hnUMpy0XBP/HLtMep6Zzk0C2Z+QTYvw4n41Jqsdm10mI7+QqIX51Y6g0F3I\nzh9/UWsNi6HM9imvbnqVnck7yTQWNotdHr8cgGmDptGjYe23/PEEzh6GRlv1TGxrE3qTKF/3Vnnz\ncNuHr/Jsrh+oVF5YA6PZN2IKifs+IxHIG/UjfosfgzNroWMZ/fDKGsdhk2Cx2T32nPPVqsjMN2OQ\nxGX7jWGxKP39WX7wkot06TTitQW7xSLMT1t3LvEKhUSoTkl+1Ejhqr74fji0QCxq/T1r2yJbrUgq\nFSnvT0I2mVBHNym+0Ksh/DK2JzvPpiGvl8hIyeToJT1tI+tYlPta1nTpL5Y7d6PFjtpudUW61l9c\nD0CQtnSm6EqgaUBT9gSfB2DIP6k8oFcQ9nx3vmvegCOUY5xaBurOL7KuIyhaVC4VxaGFhRoP33AI\niYMGHcS2XSUM9UIdZcwlW1w4EOEvoihrjqcwtE04D3xXyJ6n3ScqJ5oE62gQoCUj38zz8/fz/opj\nvP6bEMoObBHGuP6xdGhcVyJdRVbxVpNoXFsECTkJrDq3qljVSavgVtzR7A56R/YmNiD2ikzXE3gr\nBemqy5GubJP4fn055Ms6R1qvZagVaix2C5mWws4E54Ib08EnHA7Od5t0ObVMFpvdo5SevsBCZr6Z\ngS3CMJ0TwuGb4gJoENeEB3sV2qk404tprkhX7YuMPYLdio9OB14q0cLm9D/iutp0oNtD5O/aRcJD\nDxP88EPkrl5D4Oj7iPjPf2pluv2ah9KveSjH3/TBx2Lk4MU6SLquRcuItJPw062QnwYNyrbyzMw3\n09hmQavTkmfO46WNLwEi6nQ18G6fd9kbNxI+f564ywB2DFOm8eb6tXzAB26PU0+63EVgE0g+WPg8\nOwF+e1I8fm5foWtyoy5w+RCovKHTA3B0KXgHQ4zDR8RYtpGeVq3k/Tva8ebvR1yE69G+MYT7aYkO\nKdQ79G0WSp5JVM8ZHHqOZwbFMX5wM3zqQCpBtpcR6bKaiveypLCB9Af9PqBHgx6upsy1KeatLq6F\n9KIz0lVXNWfXKlQKFXqTnq1JW13bxqwey+HWt8HhxaW1KbmXYf2H0PcFsRhzjSP2MVvtnEnNJS7M\n1/WdP3gxm8SsAga3CnNFrJyY9Keo4k3LNdHQ0eokuAw+FeJbWOnVJy6ECP+rG/kuBbut0IKn21hI\nOSKqmns+6fYQzkbgmT/PRt2kCWEvvFDKVqOmofT2ZkDSQQ7u3oPco3Hduk5da0J6Yw585VgQNugA\nd84sc7fM3AKa2a14+/mQZcoC4O7md9M3su+VmmkxBGuDGRY9jD0fv0xwmpGQkCguvfEm5wbf4NE4\nV/8ufa0gtCUcWwZ5qSKq5bSGaHd3sYsqkV1g709i5XvbVPHPCV0IXNgmLsZeftD2TggodIEe2SmS\n7fEZJGQYeG5IM4a3LZvRF20U26qBH6/c2LLuXATsDssLZ6RLlkWll6r4RdHZVqdvZF98NaVTj3UR\nzvTihPUTeK/Pe9zU1L2ecFcSenM96aoNqBVq9qXuK7VdDo5DMuWAIVO4kzuxfw7s+xkSd8MTG1yL\nDrXjt/vt5ni+2RhP/+ah/KtXNN1jgrn3m+2YrHaeGhjHf25uVew8KQ5hvFat4KFBLWAf2I2l09wR\nfoW/s3njelXzXdcC7NZCDz+FAkZ87vEQskX4JWrbtaPxNzNRBdV+usmWlkYQMOjLN8gKNbO8SS8y\n8ky8OLxlrZ+7UlxL6UVZhj+eLXw+bn1hx5cSyM4UUWVdgB+pjvvFwKiBV6UVXFF0GzHW9VjduAkX\nHnjAo+PrLSPcRfNh4ot90VGl6OwV1r9EZV37e6DrI9D98dJjdH8czm2EjVPgnzfhs7aQWtjPyV+r\n5qsHurD8uX7lEi4Qq+V2jfx5amAcf00YUHcIF7iqFyXnD8NmAWQuymbiswsNJfUmPUpJiY+6dryB\nagPR/tGE68IpsBbw/s7366RflzPS5Ywc1qNmULITwUtdRaojL8zRe2/u3ZBfxMfJ4NApph6DxD2u\nzWpHenHzKacbfDpPztlLl0mrMTkKY9aW6DQBhfKDyXd3oEtzcW2QTaU7WCgUEg/3jmbK3XW0+5pc\nJNJVRdgNot1ZzLy5qEJCKtm7ZhA1cwYXHpsAwLez1/L2H0f5Yt0Zev9vLZNXncDuQZeBGockcUWq\nF405kHOp8v0qwunVInjRsJOIdJZDuAD02YJ0qXx05JgE6fKvgtdjbULXpTNNf//No2PqSZe7CHGY\n7i0cA190gb9eF891JX70Gp1YvZWVp+7zHDQbVnxbwnaPpyJJEn8+17/UarguQHZaRjhXs1axGr8l\ncSm3L7sdgG2XtvHbmd8I8AqoW4SxEujUOpbdvoy3er+F3qTnYu7Fqz2lUtCb9HgpvVyp0HrUDA6n\nFzcZDdcJE9IzAeEw4gu4tB9O/CleNOphx9eFO6/+r+uhs5rwWHIOg1uGsWViYXsfgLu7RHE6NQ+j\noz/hLzsu8O7yo+yIz6BrdBAtIvxQeIuIq72g7DT3u7e3477uTar+ZmsTdmsNkK4CUKk87q9YHfgN\nGkTP8Y9ywS+C2+O3uDRURouNmRvPkpR9FSUHtRHp2vktTO8Gc+8V2YsVL8GUaLHNaZNUFSRsF///\nj/1dPAtUBvKyBdGSvL1dmRF/Td0iXQDaVp7dh+tJl7vwDhSpxMBoyDwLuZdEytHNUnFApBT/tRie\n2AgdHMLbgsyKj7nW4Ix0OX26bMXNXucen8uTq58kvSD9mmzE7KvxpUWQKIq4kFOGhchVht6kr08t\n1gKGRRculryUXvSP6o+/xp+XN7xMQevbxAsFQnfC/rnFD07a63o4pFU4/ZuLa0bbyACignRsfrWQ\neHWNFqmyy3qxWHnz9yPM2nqexKwCuseIUnnJUc1VVqSrzsNeM5Gu2rCHqAxBPhpiY4Q/lM5q5J6u\nUUy6Q/Tk++PgJV5cdKDcjiK1itogXYcXQcZpOP03vBcMu78X9y9LPlzcUfVxz66DsNagrnxRaNA7\n/Ne8dRgsIrp5LWVGykO9pssTjPpR/D28WKQZh75TXDDuLiI7CYuJ48tFGuLCNojqLpyZr3HITuNH\nSSEusNbiupPJuyYDMP/W+bQLbXelp1cjiNCJC29Rq4u6gmxTdj3pqgV8POBjbLKNFfEr6BDWAT+N\nHw+0foCZB2eyMnE9dys1YgFltzv8+hzo/Szs/MZBNpRo1UrmjO1JWq7JZYjcOFjHdw91Y9vZdGIc\nXnwJmQZiQn3oEBXAoUQ9sx/rQc9YQbqcovGyNF11HnZrub5M7sCSnEzW/PmoQj1Y7NYgwkbdxeXD\nh9BazQR4q9GqxPX/47+FWebj/WJpE1lxNKYqHm0VojZIl90GjXuK+9Lu76HZULhtGnzSDPRJ5R9X\ngdkpqScg+QC0G+XWFApyRDcNhc6bAqtIx18PEfz6SFdV0H4U3PKRSCVWB7pQ2P6l8PaafUeVernV\nOTh//Be2iBVSyVU/8EbPN65ZwgWgVdbdKka9SU+App501TSUCiUapYY7m99JXKAonHm649PoVDq+\nO/w9mbJVtAGb3BiOLIGIdqKqOaQZ2C1Cy2LMEaRMlgnz80JR5MY7rE0Eb49oS5tIf/y8VMzZcQG7\nXSbfZOWW9g0Y0CIML8cNXvIS3z/Z+P8v0pU5ew7YbOi6dnFr/w0XN3Dzkpt5bu1zrIhfwcyDM7mU\nd4nL+Ze5lOe5PskZYfO2mYkN8y3V6zbXaKnw+KOX9MS9vpKtZ9I9Pnf5qAXLCNkG2gC48QN49Rzc\n85PI6qh1sOZt2DEDEnbA/Pvh0gHIT4cNk2FKDGz7Ei5sLz2nFIeXVbdH3ZqCRS/0qQofH5c3orOY\n6VpGfaTrauKGt0Sl44Ut4t/fr8Gtn17tWVUPTp+uk39CE+DY7wB4SSpMspUFty245l3S67J1RI45\nhxj/mKs9jf8XUEgKpg2exvi147k9ugm9CoxMsPnSKO0MxA4SVc16h+5vvkNOoFBDdG94eHmZYwbq\nNIwbEMvU1ad46s130ckh+DQZUPy83s5IV937/lWKamq6shcvRhUeTqOpFeuBACx2C+/veJ8UQwqJ\neYlsSNwAwPwT811R6i7hXWgZ3JKXur1UzDOwPDj1dH/cEoFvqIUjquJxi1xjxcU1v+wQkoQtZ9Lp\n26yGonW14Uhvt4PkIJRFgwu3ToXfn4K/iviinVxZ/Nh/3hB/R82CuCFwfrNIvW/6RIwZWTlh1hdY\nCMhOBUAdFUXBJaF9duf/qK6jPtJ1NdHhHnh0BXQfJ57v/h4mRxdqQ65ByCf/Fg9sjhtC7mW+DAzA\nJFt5pdsr1zzhAvHDl5AwWuteeifTmFmfXryC6B3Zm5e6vUQ2dv7y1nCTr5mpkbGighlExEvr+P8I\nay18/M5tgtzSFYpOPNo3Bh+Nkm81n7Hc681SN3JJe41GumS5ytWLptOnSRj3BPbcXLxauWfTcCrz\nFCmGFJ7o8AQ/3/Sza7uTcKkkFftS9zH/xHw+2f2JW2NKDtKV+uKLxN96G9qSpMtUfqRr65l0lh0Q\n0TVnC7caQW2kF2WRDj+VksuaYyn8fVT0/JU7ji4eGNCFiDRkt7Ew+E14fr/QLINIJe75ERb+C/54\nTkhp7p3tVoboi7WnaZSXjtVLiyosjAJLAd4qbxTVSE3XFdRHuuoCbv0Ehr0HHzYUjvUH5kHv8Vd7\nVlXDTuHEL7W8CYLOQMphfg+NpEdAc+5r5Z5rd12HJEloVdo6Fen6387/sSlxE5nGTKL8oio/oB41\nhjGtx4i+bCd/Zd3FdczyshJ8eStjgmJQ+4TCxAuFOpdjy+DiTvi0Bdz8EXR9FFTFq/D8tGq+/ldX\nmCeeR3gVJ1cKl5C+7pH+CuHy8PNcB5u1cBH527fjf9ttRPxnolvHJOUJ7dHw6OG0DG7JltFbyDBm\nsCJ+BQ+2fpBAbSAp+Sncvux2lp5eyr/a/Ito/+gKx1R4FycM6g2rAR1eKgUmq51/LzxIst7IM4Oa\nlTp2wsIDGMw2fDRKV6FEjaA2LCNkOwaLzPDPNhXbHObnxe/jx9DooWaCcEW0KzKHIvCNgG3Ti5PB\npzZDcFO3Tq9VK2iYl4qmaVMkSSIxL/G6IFxQH+mqO9Do4HZHmbn22o1UyE5/ovajXLYZZkkixify\nuggNO+Gt8q5Tka55J+aRmJeIhMSo5u4JVetRc+jXqB+fD/mcyf1Focinez/l3e3vYrKZit+QWo2A\nR/8CtQ+sehXm3CG0Xk7IMqQcY2CYwbXpDZ8/ip1LcgnpayfSJcsy+du3k/nLXNJnzuRUv/6kfPxx\nDQxcddJlz81FHR5Oo08+dktEn16QzvT909Eqta5FSIBXALEBsTzX+TkCHQ22I3wiWDpyKWa7mYUn\nF1Y6rjKguEjeNuktPtv4BY39NYzsGEm4nxcf/XWS5+bvZ9OpQmuFXKOFtFwTL9zQnA5RgeyIz6Dn\nh2v49J+TnnwMZaM2HOntNtadEr5zj/drip+XiugQHWm5JjaeTBPp8wbtxbnLEs7npRSf08TzbhMu\nAKtdJiovHd+4WFLyU1ibsJZ8S3613lJdQX2kqy6h+XDx11J3Iigew5gHBAhHep2otjJLEprriHCB\naAuz6NQiRrcaTfOg5ld7Oi408W/iuqHU48pjWPQw0gvS2XV5F8vOLiPUO5QJXScU7qBQCE3X01vh\nzBqhjZl1Cwx+Xfz+138AW4rrlTTxq4HJrueSQoGk0SDXkqarYO9eEh59rNg242H3G/qWC6eZcBXS\ni7b8PBS+7nWukGWZecfncT7nPDOGzqjUZiDSN5JIn0iyy+mLWxSa2FjCX3kFbetWSFotFx4YQ6us\nBKb1C6Fdn07M2HCWKX+dYPnBS5xIzmH1iwM5eTmXiUsOAdC6oT8XMw1sjxeEZvq6M/xzNIXfxvcp\n1frJbdRCelFvMGJzxGRGdYvi9VtaIwNd31/N9HWnuatLo4p7h97zs+irePkQ2KxYNQEs3X2R7AIz\n4/rHVurPaMwzEGbIQhMTwz8JawB4ttOzFR5zraA+0lWXoHFcHMx5ohrkWoPVLPosApJS6fIwM0sS\nmuug1LcoOoZ1BGDBiQVXeSZiVe+En9rvKs6kHhqlhofbPsxXN3zFiNgRzD42u2wT3eCm0GOcsJRI\nOQwL7odJIYJwNe5ZuF9Ya9dvqigkrbbWIl3WtOLml7revZDNNVBZXQ3SZc/Pd4t0vbjhRTrO7sh3\nh79DKSnd7tMnSRLL45cjVyJIlySJkLGP4dOnD7ouXdC2FRrVZsHi+ua09egdG8Lp1DwW7b7I8/P3\nc+BiNoNbhjG0dThD20QQF+bDiI6RNAr05mRKLjvjq2E/U8NCen2BhRyDiVA/LRteHkSrBv4oFBJK\nhcTUezuSrDfy+drTFY6R2uQm8f0eOR3unMH0dWd4dckhPlx5ghkbz1Y6h/jlf6NARtu6FZuTNhPj\nH8OTHd3vz1mXUU+66hLU3oAkdFEfxwkfr2sFB+bD++GFv31JAbpQZMAiSWiug1Lfopg6aCptQtqw\n6NQi3tv+HjMOzLhqc1l1bhUAIdoQ/tv7v5XsXY8rhRe6vIBKoeKtrW9hs5djmnnDW/BkEd1Mmzvg\nru+g34vEd76f9OhewuG+BBReXjWv6bJZ4Pif2OL3FtssaTQ1RLocn4HkWXrRXlCAYfsOLEkV+EMB\nNruN1RdWIyMTog3h6xu+drvjhVP/dTa7ckJQFGETRBTTmpaONS2NLk2COP3BzTzcR2jDXl1yiJMp\nubSM8OPHR7qjUiq4pX1D1r40iOn3d+aff4vK1GPJOeWeo3LUTHrRZpdZfSyFAR+tR8JOm0ZBxIQW\njxIOaRXB0NbhrDiUXOp4u13mdEoury4+SI8P1hKflud6be+FLFpE+OLnpWLXufIJ5trjKczefp6G\neWIheaK5N1uTttI7sne1319dQT3pqkuQJND4Crd7gON/Xt35eIITfwIy+Av9hKRUgC4EZy3P9Rbp\nAujdUFwIfj31K18f/Jpz+nNXfA4F1gI+2v0RbULasOG+DbQJaXPF51CPshHhE8ELXV5gT8oeOs3p\nxIr4FaV3UiihYUfo9C/x/O4fICgahr7N7dlbGZyxFky5rk4PzlWNKjwc84UEAKyZmS5SZNi7l6QX\nX0K2ee6Mbju4gtQ3nsL8zzcAeHfrSuD9o1FoNK4m09WCK9LlGekqOCxaMFlTyq/4BMgyiarvN3q+\nwYb7NtCnUR+Pp2iTPfvcFDqxmLw4bhyn+wsCpVYqGN6mARNvasUt7UWfzEyDuUwC6OOlwl+rcjU0\nrxKqkV6UZRlZlll+8BI3fLqBcbP3oNMoCdGpCPIpe6HcqXEgCZkGCszFP6vf9icx7LNNLNqTCMDh\nJL3rHPsTsogJ8WFgyzDOpZevzRr78x7eWnYUX0sBVqWafdkirT261egqvb+6iHrSVdfgH1n4OKPi\nEG6dgaVAlMG3uAn5XkfJlUIBPqGYHRea6y3SBTCh6wR2jdnFtEHTADiTfYYfj/yIxVb1G1R6QToT\n1k/g6TVPu7X/z0dFKfzIuJFVPmc9ag/3tbyPgVEDAZh5cGb5O46YJsTGZTQA/irQny92f4Ttu8Hw\n440A+PTti2HXLi4+/Qyn+/TlZK/eZC1YwIUx/yJn5UqMJ054PNf8PQfJOOZH5kmRxoueM4eGb7+N\npK6hSNdeh22DB+nF5LfeJuGhhwGIWVS+0D29IJ11CesACPX23P/qvT7vAZ577zl9u5ywZmUJEpyj\n5+lBcXw9pisvD2/BVw+U703VIEDL8eQcbFVtmi0pqEr1oizLjPxyKy3eXMVz8/ejUSn48M72/DVh\nAN5KCvvnlkCIr9DnTli4n0l/HnNt//2AiBb++Eg3ABIyRDHIkaQc8s02MvLNBOk05BRUfn28q0UA\nXkEBXDZcJlgbTGxArMfvr66iXkhf1zD0bVjwgHiccabitgp1BfEbwJQjUiNykYbXuhAX6VKrrz/S\nBaKKMTZQXBBe3PAiAD8c/oHXe77OrbG3uj2O3qRnwYkFzDo6y1Wlk2XMIkgbVOFxl/OFf87oltfP\nSvB6gkqhYvqQ6XT7pRvNAkvbCLigVIN32f/XM4MC4MRc/PKyeFQvol4hYx8ja8EC8tavB0A2GLj8\nzruuYxIeG0vExIkE3nWn23O15RWPQDgjM5JGg91SA6Rr/fuOgd1f6+euWYMqIoKQxx9H2759mfvo\nTXpG/DaCPEse0f7RdI3o6vHUGvs1BgTpmnlwJsNjhrt1o5dKkK4LDz6I+YxIUbY6fgxJknh2efeZ\nZAAAIABJREFUSNmFNunffoc5Pp7RQ8bw3roLxL2+klFdo/jkno6eTb6Kka5DiXoOJ+np2TSY7jHB\nPDUoztWQHdlWbho4SCfa1f19VEQeDWYbnRoHsO1sBs8MimNIqwi81Ur0DnKVlC3I1+jujTmTlke+\nuXQ00Wy1k5Ap9nvhhub4zV6I1c+PjIKMKpHouox60lXX0KrIjdqoF4ZyPiFXbz7u4NwmUGmh7Z1w\nVKywJYUCfMIKI12a61fgHa4LL/Y8x5zDvOPz6BvZl6VnlvJAqwcq7Rk2acck/j7/Ny2DWtIutB1L\nTi9h3D/jWDxycbnHGK1GlpxeQougFiir0gO0HlcEkiTRMbwjZ/VnkWXZbZ1RSRzXOPy8Uo+ibNCe\nFju2k/nzbFTBgqxdmvgfombOIPWjjzHHx5P2xRcVkq68jRsxHj9O3uYtqEJDyf37b9dr3m0KCaLQ\ndHkYvTUbxELMkAFBMVA00h3ufgrcbjAQNOYBgh/8V7n7HEg9QJ4lj7HtxjK+83jUCs972Ho7FoVH\n0o/w1YGvWHluJX/c8UclR4GyhLjfSbgATnboSNzqf1A3aFDmsVlz5mBNS6P377+zCtBrdHzg/V/A\nU9LlWRsgWZb54+Al/vv7EdRKia/GdCHUt0R1uaNXaFkI0hX3lZu/K4H5u8TjWzs0BCDAW42+wILd\nLpPs8CTrHRfCZb0Rs9WOxWZHrSwk3wM/Xk+y3oi3QuaGggQMu3fj3bEjZpvZ1XbtekE96brKyFqw\nAGSZgLvucpkeMuEwnPwLVr0CmWfrPumK3ygqrtRaZEeI3C7B3HPL6dHvBUhYjEZ1fVlGFIWP2od3\ner+D2W7m7uZ38/LGl1l/cT39F/YHwF/jz6gW5Xtn5Vvy+fu8uOEtHrkYi93CktNLOJl1ssKb9PJ4\nUWhxY8yNNfyO6lHT6NGgB18d+Ip5J+Zxf6v73TJ6VClUWO1W7mk+irOnV7BJZ0cGpJn9YOxqpMY9\nCHn0Edf+fjffjEKjQdetGymTJpG7Zm2Z49rNZrIXLiLlgw/KfL3lqGTQFUD6aQhtjqRWe5ZeTNoL\n3w8r9OUqits+g8bd3RpGttmQjUZXv8Py4HSYv6flPVUiXADeSkG6ZhwUBTHpBveqx1VhYQQ//BCW\n5Mto4mJRhYbi07sP8bfcgmyxoF/2B6FPPlHmsXaDAYWfH/bcXAACzAaGbVnC/vi+dI4Nc3/yHvp0\nLdmXxMu/HgREKrAU4QJHpKvs72izcEE0e8eGEO7vRcsGfhgtdlL0Rto0FD5mBRYbv+5N5Ne9ia7j\nArzV6ByRNIPJRoBOjC/LhcRsufcRLK+KNHTw2Mcw2uahURYnedc66knXVYRss7lSApLGi8C77xIv\nBDaBuMHi8cpX4MmNV2mGbqAgG1KPihYQ4KpQ2nxpK5Pj5zEidgQAGsX19cMpibtb3O16PCBqAOsv\nrnc9ryjK9e2hb5l9bLYYo7kYQ61Q83K3l/lkzyfoTXp8Nb7sTN5JjwY9UCvFTcUu25l9dDZtQtow\nrv242nhL9ahBPNL2EbYmbWXyrsmc15/njV5vVLi/XbZjtVt5uuPTPNPpGV40ZZOfsIaLKhVNrFZY\nMhbG73JUPAsoHJEwpa8vXs2bo1/2BwljH8d0Lh5t6zaEjnsc706dSH7zTXL+WI6k0aCKiEAZEEDA\nHXeQ8r5I/ylUMpiz4Mtu0PEBJE3TyoX0dhvsmw05SXBpv5jXgFdEyjQvBXIvQ0w/EQ13E/YCoa9S\n6Cr22XIK6IO8Kk7FVwTnb9Rit7j+Wu1WVG7ozyJee63UtphFCzl/732YLyYI8mi1Fi6qAbvJhD0/\nn7AJLxD61FPIdjs7n3iBYVvWkD5qJNb1f6EKcNMk20PLiDOpoqrwPze3YkiriLJ3Ktp7sQRCfL1Y\n/e8BhPh6EexT9nW9pMgewNdLha+XGPO5Bfv58M52RAXp+OuIkEh8eGd7FFN+ACB6zmx03btjXvET\n/l7+pca6llFPuq4i7PmFGgprRkbxFwMd7SiSDxRu2/q5SOXd+imY8yHiKvcxzE6AaQ6dRUMREjce\nE8LKRLPI95vtYoV8PXSHdxejWowiwCuAYG0wj/z1SIXO9QtPLERv0qNT6Xi2c6H5n9MHbPHpxSTl\nJbH41GJ6NujJ9zd+D4g0yPmc83zY78Mqp6vqceWgVWn5+eafeWHdC6xLWFcp6XLe/J2r/JHNbmd1\nwhpyn1wP8x6CrHPw/VB4cnOZgmf/ESMx7N6DOSkR66Vk8i4lY9i+nebbt5G3fgMBd9xBw/8Vfndk\nu91Fuhj0OiTuhjOr4eA8pIB3K4502Syw8mXY+1Phts4PQr8J5R7iDuz5QuNTVqQr25hNoDaQHw7/\nwLR9opClOtcYP40fSkmJTbYR7h1OakEqZ7PP0jLYvT6PJeHdoQPa9u3RL16CNS2N/E2bCX/1VQLv\nvIP83bvxbifa5yiDhK+XpFDQ9b3X2PiqhUZ7NnK6Zy/UkZHELFpYqQN/nsmGl7cVd2N8JqsNP62K\npwbGlb+TbK+wyrR5RMVykd/H98Vml2kfFcCRJD0HLmYjSZIrqrbpVBofrDjO/T2a8N3meBoHe3N3\n10YkpKUTcPdd6LqLaKjJZsJLcX1lSepJ11WEM6wMYMsu4Yas0ogO7WfXweapcHA+pJ8Sr33eEZQa\neOWMRy2Dzmaf5cnVTzKo8SDe7OWITOVnwNbP4NQ/INvIvelDTvuFcCLrFLfG3lpx8+QtnxU+btIT\nw+7dpHz4PwDm6teAn+RaKf5/Il0gnMlzzMJ7p7z2FQaLgdSCVMa2G8v9re4vJhjtFN6JvpF9+Xzf\n565tOy/v5HDaYXLMOTy15imxX1inWnwX9ahJKCQFzYKasSVpC3bZXmGK0WwTJMeZLnO6qudaDTBu\nHWz/EjZ/CkcWQ4d7Sx2vjgin8TeiWvJ4q9aASGed7Ci+Lz59+xQj61JR4tbnWWHU/H4EWI1IuRfB\nZiP/383R3vUqyv4lIqtr3hGEK3YQRPcF/UXo/5IHn0zZsBvE76Yo6Xp548uuVHxsQCzx+niifKMY\n235stRYffho/Fo1YhMVuQavUcseyOziacbTKpAvAq2ULjIcPk79R+LClTplCzqpVGA8dotE0QRRV\nIcGu/dWRkfi8PYndY8fRJTMeLl3CePwEvv37lTm+zS7z07bzdE03oDXn0srNeZmsdrxUlWhAK0gv\nuoM2kYXRqXaNAmjXSNxHBrUM57dn+vC/VSdYdeQyqxxRrneGxZL9ySdY09JQhRWmVs0283XVPg7q\nSddVhS2v0Dwu88cfsefl4dOnD/43OTQ6Ud0F6Vr7bvEDG3aE5IOQcky0FHETp7JOkWJIYeHJhSTm\nJvJou0fpeXi5aEzqwNj1z3PcS6yuM4wZPB39L5RBgaxJWEOwNriwMshugxMrxeM7v0XW+JHw1FOu\ncTq2GsSGpI2u1hr/30gXgE4lbhYGi4G1CWs5nHa4WEuYtALh/B0XGEeET+kw//v93mfe8Xn4a/zp\n3qA7o1eM5oGVorJVKSlp4NOASN/IUsfVo+4iRBuCVbaSa84td0FjsplKkS4/RyFKvjlftNca/IYw\nJN79g1icAVw+DJnxENIMYge6xoucMhmFnx+yzYbx0CFM8efw7d+/1HmjX7kF1fFZ4NRFdbgX9s1G\ndWoeEEjCKhWqTdNovrcE6cqMF3/vnQPamksF2Q2OSJdPIelae6FQp5ZlzKJtSFumDppaI7+DFkEt\nxHllO35qP97e9jZLTy/lv73+WyXy1fCddwi8624Me/ag8PYm5YMPMB4S7YCSHKaqyuDgYsf0ahbK\nh/e8zJwjR/hi4+c88eN2uhjCeHZIs1Jtd+ZsP8+kP4/xm0aBJJWfXlyyNxGD2cqDvWMAMFpsaNWV\nEKoKhPTVgVIh0blJEP2ahRYzSe3z80dk7tqB5O2NT69eru0mm6le01UP92HYvx9JqSRn1V+Y4+Np\nMOk9Mr79Dp9+fbFevuyKCjmRvWgR2YsWYXrmacKef76whLzrI3DLp4UePhd3ww9DRbsgD5BrLoys\nbb20lUifhvQ8vFj45vR8CkPT/hzf/gpNLBb0JhUNJs7gdMLX5N/QmXfaHqL9eZkWb2zEz8cf/nkD\n8i7DyC+h432YsjKQ8w3MGaIg6sGxfNz9GbrP7e4Suf5/JF3OKN/XB792bYvyi2L+ifmkF6TzYJsH\nAXEjLguh3qE83+V51/OeDXuyM3kn7ULa8cWQLwjTeSC2rUedgDOamWJIKZd03bf8PuL1gsg4bziu\nSJfF8RtWKKHH47D2PfiisxBTO53rJQW8lgQaQVYCbr/dNbb/sGHlzk0XGwoXbcK+AqDHE+ATjqa1\nP+wRUTNrvp30GV8TOPp+VEGO61N+OjQdWG3CJZvNoFYXpjwNpdOLGqWG0c1HM7HHxGqdqyIoJAX9\nGvVj1flVHEw7yI7kHVUiXZJKha5LZ3RdOgNgPHEc/ZKlxfZRBhXXoUmSxNdjurBvgw02QtMADV+u\nP0O7RgHc1M5htJpvZs72C3zxz3HuO70Be0tQlRHkm7czgV92XHC53Y/pGY1CITkiXZWQrgosI2oC\nz9/QnKcHxXH3jG2k6wuwHzmIJjaWuJXFDYRNNlN9pKselcNuNpO/ZSuJzzxTbPvZocOQzWayfvlF\nbFCrUfj7492xI/mbNxPz668kvfQSGT/8iE/v3nh3GI2Uegx6P1fcNNHLkU83edY6wpnmGtd+HGtO\n/o60+jfMOaloHvuRrYFhrpTVC5nZ7DkeTLsEsXryWbufmetBZYfEP/oTds9AQhTzsVsllKFidfj5\nxg8YCbSI6cZDvSYgIa4CTpGr93Xq01UZnun0DD8d+QmDVdxA3t3+LoFegWSbsvnn/D8AhHi7V536\n5ZAvOZpxtEo+RPWoG2jsL/ygLuZcdEVWisIu2zmrL7QdyCgQWk8nQSvWlLn/S5B1XgjYfcKg0xgh\nN9jxtbg2aCqu+it9cotYgDnTdA3aQ4P2eOfnE3AgBZWcSsbvW0n7fDqW5BS8O7RDOjgPf+0xpJbV\nq6CVzWZOdOhI4D2jaDhpkphOEdJ1MfcimxI3YbAaCPSq/Ybu7/R5h1tib+HVTa+SnF+65U1V0PCd\nd/C/6WbUUY0o2LuX/F270ERFldqvcbCO8M5NiAee6duYuXvhTGoum08r6R4TTJdJqwFok5XAI8dX\nIZ+TsI2xFhsj32Tl9d+Ek/9E1XwuyBFsPduD/s3DMFlsFacXZblSTVdNQK1UsOTpPpiSL3NxTgHB\nDz1Yah+zzVwf6apH5Uj694vkrS1dri2bzeh690IVGoYqNJTwf08QxoNmM3a9HlVYGI1nziD+1tu4\n8OBD+A69gdAnn0ItB3Bu4CC8WrbA/+Zb8O/XUbQSOLcJIjtDsHtuvbnmXBSSguc6P4dq7iwGrzAR\nr4yAiXG8u+kltEotH52JJXLJARpbZC7FWckfmI/vCR+sGXDIT82te2TSft1IXmgoBekaYu6Q8W4C\nmUliZT683Z0urYpCUrjMO/8/RroAnu74NA+3eZjLhss8sOIB8i35PNruUX468hPHM4+jUWhcxoyV\nQavS1hOuaxxN/ZvirfJm5qGZ9GjYw5U2BEjOS2Zz0mYAmgU240z2GVoFC6WOn9oPX7Wvq0egC4Ne\nF+nF5sOFDuvQr2K7KRf8yvaHKg82q5mtOh96lbjRKXx8iPzfh3B6DUG2pVzY0tgVlQe4JPkR815z\nqvMLz1ok5p396+IySdfkXZPZlCi0USV98WoDOrWOQY0H0TywOavOrWJc+3FuL47Kg6RWu/RZXk2b\nEjiqfBsZSS0+f51CLHw/+edUqX2i/cTtWzLK2H65jPyiDUkpiNLBxGxApnW4D0/nCGuZAT935u0x\nwzBZ7RWnF52VkNXQdJUHu8GAPT/fpdtSKxXYC0QwQOlfPFKaa87FYDXgr7m+qhfr2wDVAkwnTuDV\nsiXR8+bid+ON+PTtS/TcX2i2YT2NZ86k0ccfETHxVSRHibdCo3F9Cb1iY4mZPw/vrl3J27iJ8/fc\nw+n+A7CmpJC/aTPJr73Gyf63cHxBJNats/+vvfuOj7LKGjj+OzOZ9EJCEkpCCx1UkN6LoKIiukgT\n666KZXUB18oKrsruq+uivvaCWHcBRUUQVOAVESmCFEVAegkBAiGF9GSS+/7xTCYJJJCEZNLO9/Px\nY+Zpcx9uMnOeW86FT24rU5le3fIq72x7hwADMqsDzeKsbgqTB3cu+ROnc07zyrBXiF52kPxcGxG9\nbQx+9/8YNWgGlzU/QWDf03xwuZ3M8db1MhOssh+85Y9kbNrE8GXW+KQGEVHu9wz3LRwYHugonkSw\nPvF3+BMTEsPDPR4mxCeEwdGD6RzemQY+DZjcbTL+jnK2SKhay9/hz6zBs/g98Xf+8KWVPiE1J5UH\nvnuAKz67gmfWWwHH/V3vZ9PNmxjczBqbJSJEBUadHXQFN7HSMHi70iq4W8FTKZfkw/ywfwl/jghh\n+KfDOZFxAnNmGoLASBwBeTTqfNy9yTcsB4yQsOLQ2ceXw+klVreSXzdruZzkzz7nxL9nAXCKDHfA\nNefKOeVa6eFCTek+hcSsRLae3Hr+gyuReLu6eHNz6d+meLDn7UoqGl00E022IXvXLhI/+hhjDJsO\nJPKN92N8ffp69yEr7JP56KN3OHgq/TwtXRVbmPx88tLS2NWtOwcn3Fh8u2tCmS2oeHC19cRW8k0+\nPRr3qNRyVLcytXSJyAjgfwE7MNsY8+wZ+wcBLwGXABOMMQuK7LsNcE2VY6Yx5oPKKHhNlRsfT25c\nHA0nTcK/Wzf8u5W+5lZp/Lp2peV/PubIAw+QunwFwddeS+DgwSTNm0vmz5vcx2UmeBPk+ys4c6zZ\njqXI3jqXt361FrEdk5wMack0TCocDyQZmXx0/Re0CWjB70mphF/Xm/CnXwWfQMi1yh/kWnB3y7V3\nM6HJAX7p1oe1O1ZyzbOrOfLC8zTab3Uj+l5yifu6rw57lbuW30WnsE51rom4Im5odwN/aGu1BL4+\n7HXyTb5mkq+HBkYPJNI/kviMeAbMG0BKdop73+i2o/H38mdws8Fn5YhqEtCEo+lHz31xH9fDTXmC\nru0L4dPbiA8KBP8wkrKTuHLBlQR6B/JYr8cKg5zITjBgKkHJh2kTsYTUQ3ZCB7Rm3wJv0latYt/w\ny/Hr3o2of/2r7O+NlRwze7/VUp6XkkJ+ejrH/laYUiMu3+piveviu+jZuGyJVStL6wZWWoUpK6ew\n4NoFFzSbsTwKcq6ZnBw+vqM3+06mcyI1i29/O06zMH9mLtlJ8wDrsyOvawD2rekcGD8BcnMJHDqU\n+L2b6WCLBe9AGPQQeAfivfQh3vd+np1pzdjiMxLoU/Kbu3Itlrb2YkVlbNwIQG5cHPnp6YiPD+Ll\n5Z7Fbw8q/mBeMK6xbYOSl1Gqrc4bdImIHXgNuBw4AmwUkUXGmB1FDjsM3A48dMa5YcCTQA+sFTk3\nuc5Nqpzi1yy5cXHE3v8AAP69el3w9SKmTsUeHk7jxx9HvL3J3r2bzJ834dO+Pdm7diGBIUAWHFwN\nbYaVeI2Uvct54ce/QVAAk31acGfSakzrYTRcso2CHMZv9J1F29C25Bw+DMbg1eOawg/vqB5w3etE\nBoTCukd5afu7rI/pw/qd/wQM1wB5m37BASREBdKxSALAjg07snr86gv+d6hLCrpeRQR7FQ5UVTXb\nvGvm8djqx0jMSqRtg7YMaTaEViGtGBQ9qNRzIv0j2XJyy7kvXNDS9fENMKMMWdXjd8CnVmt5oqtr\nqqBrMzk7maUHlhYGXXYvGP53ABw91xI2byJc9xKtbmlHysIvif/HP8iNi6Px9OnYg4JwJiWR9NHH\n2BuEkLV7N5GTJxdLB1DAZGSQn2IFnjn79rH/+sIEqiYogPcPzAOqZ1H3UJ9Q/Lz8yHRmMmbxGP45\n4J9c2/raKn9fcVgtXSY3FxGhTWQgbSID6dc6nEzX4tFXJKWTCEhoCJAOrgS2uSdO0OC463P3/o0Q\n7JrZudT6eu5oi6Vj0htw7EZ3fsViqqilK3vvXvfPu7pbrVfBI0eSc+gQALag4rm/DqQcIMw3jAa+\nVT+Gz5PK0tLVC9hrjNkPICLzgOsAd9BljDno2nfmWgRXAsuNMYmu/cuBEcDcCy55DXRk6oNk79xJ\n46eeInBA/wu+nk9MDE2efNL9OuL+P+Po1AGvpk04Mu5GTPd7YN9TkJtR6jWe3vwCywP9ubHtGP7Y\n5wmw2Yl/Zib5qTtxNG9O7uHDNE8Q8k6f5tj0GQD4XVLkD9Fmg0tvohHwZaMO/HnFn1l/bD0AN3ac\nyMfD59LiaB7xoRAysCdnTkTXxJ1KnS3CP4J3r3y3XOc0CmhESnYKWc6s0lc5aHSRNcYzcT9kpxU+\nPJUkMxkWutK8RHZmc+uOhCXtZljzYexNtr4gC1JXnKVFP3j0IGB1f4TdcjM+bVpz+I9/4tScOXiF\nh5M45z1y4wq7Q33atKHh7beTGx+PV2ioe3iFMzGx2KVzY2MBSLr5Sp5v/Tt7j6+lU8NORAVG4Wki\nwtLRSxn+6XDyTB7TfpzmmaCrSEtX4sf/IWPDBsLvuZtTs2fjTErirqGXuY/1bt4EJ4UtoJvvfoC+\nA52kRLUhJLhIKo3Lnybvx1ewZ54kL0ewJR9BigZdxsCBVRDiGmNaya3w2Xv2WOWNiSHH1bJ5+quv\nAPDt3BlHVPH63Z+yn1YhrSq1DDVBWYKuKCC2yOsjQO8yXr+kc8/6yxGRScAkgObNm5fx0jVP7tGj\n+PfuTej4s5MVVgbj8GKKmUfCiq38G8i0BxAE1odrKfbnpDAkK5dp/azgLff4cZL+8x8AGt55B8dn\nPEncXyZjCwwkPy2NiKlT8WlXcnNuTEgMS0cvZVvCNpz5TjqEdeBtL39+Td5LdFA0N3Q6e/aJUqpy\nNPK3crmdyDhB8+BSPidtdrj8GZh/E5zcBdHnmHjx3TNWvr/w9nDPj/w2rx/Dmg9j0iWTmNhxIo+s\neqT0oKsE/n36EDBgAKfeeNO9LeSG0TS84072X301J559jqS5c8k9HEvw1VcTNevfADgTrBa58Pvu\nRRwOUpYsIWfvPhYdW87eZjb3UkjVJdwvnDHtxjB/13wA8vLz2HxiM/uS9zG+/fiqebD0smaR5ufk\ncNK1UkDqsmXu3SYjE1wtk15t2tBsxNeM9nqKJ7e+R/iRUwRty8Q5wBqof/KVV/Fu2YLAoXewf/qX\nSFYQuYlZhNuWEDGzyPi4da9ZqYAKhLYsVqQLWawdrJaugAEDaD77Hfe6mpnbt5N7JK7EhdkPpBxg\neIvhFX6/mqpGzF40xrwNvA3Qo0ePio/GrEYmL4+8xET8xtxw/oMraPWR1Ww+sZkIu/VPFJd6kkiA\nH56Hw2th2N/PWhw70zgJpPAPJeXLRQC0XrEcR+PGHJ9hBWP5aWngcNDwrjvP+YclIlwSUThuq2iy\nT6VU1SmYtRefEV960AUQaWWg58SOcwddJ36H5n3hT9+QkZtBem46MSExeNu9CbOH4W33LswLVgYi\nQvif7yP9xx9xNGtGi48+xCsiArHbCb76Kk4v/ZrcQ4cBa+C8d4sWBF52GUkfWw+Bwddcg0/r1pxe\nZqVEyHaNJW8S0KTMZagq03pPo2VwS57b+Bzzds3j2Q3WsOZdSbuY0WdGpQdeIgLGFAtgAQL698fR\ntCnJn1qzPb2aNMEnpie+250s5W8wAI5vCiZpTyBZb2zhxLSOpb5H0vINhD+Z6+7KZN93Be8O/SdD\nx8IWvfj/+R9Sv1tJy//+p8Qu4vMxeXnk7NtPQC+rvUbsdiQggIBevay+tDPsT95PcnYyMSFlm5lf\nm5Ql6IoDis5pj3ZtK4s4YMgZ535fxnNrDeN0knPwIOTn49Xw3OtkXYiCDOZPDvkHvPEY2TlZpBzw\nI3VNIl5+C4mI7I29z83FzskyTnzFTu6JE6R9/z1J8+fh3bKlOz9Muw0/kZeSwumvv8GnXdviy4Eo\npWqMglUL4jPiycjN4NPdn7Lh+AaGNBvC2HZjCw8MbQlevnDy93NfMP2EO0AryEVVdCkqH7tPuVq6\nAPwvvZRWn3+Gd4sW2AIKF6qOeuEFIh58EJOTS+6RWGIn3U3C66+T8LqVODhoxAh8WluD1gvSHtj8\n/BgQ1ZOhzYaWqwxVwSY2BjcbzHMbn3MHXF7ixYLdC7io4UXFFryvLIGDB5O+bp173cvQiROJ+MsD\n5B49StbvvxN8zdWETpiAzdvBsfjHiQzywe4bhP2mYXDt1eTGnnvSRV5KOmmrVxN0maurMivFSj9y\n02dnDaJP/vwL8lNT2TNwEIHDhhF2y81WPYng3+P8swtzY2Mx2dn4tC3boPg1R9cA0L/phQ/TqWnK\nEnRtBNqKSCusIGoCMLGM1/8W+KeIFKTdvQI4e0n2Wsw4nRy6/Xb3rEKftm3KfO7x9OMcPn2YXk3K\nNui+ILlpo7DmGCA7I4NTuwLJTraeVPw2bCHkrKArD1/sJLz+OsnzrObx6Ndede+3BwdjDw4mfNIZ\nS3sopWqUgu7Fo2lHmbhkojuJ6g9HfqBVcKvCqfU2O4S3gx2L4JLx0KhzyeNz0k+y3bsrX6yfyYpD\nK/C1+7oXWgcr+3t2Xna5y+nbqVOJ2wse9BxNGuPfqxfB147k+PQZBPTvT9PnrEDmSOoRcm3W0OD+\nrYcxevhz5X7/qtIsqBkP93jYvZpEmG8Ydy2/i5c2v8SprFN8e/Bb5o+cf9as0wq/31tvYnJyyPz1\nV7xbt3avAGBv0IBWn35S7NgmVz/m/jkCCF66BEfjxojDgTgc5KWlY7Iyyfh5E8f+9jdCouJJ2h1I\n9p69RYKuZGjQzB1wmdxcEt58i5xDh4qtE5y+bm1hHkqbjY47tp/zPnLj4zky2eoRKev3Y5YzC7BW\n8KhrzvvbYYxxisj9WAGUHZhjjNkuIk8DPxtjFolIT+ALIBS4VkSeMsZ0NsYkisgzWIHmBlspAAAe\nV0lEQVQbwNMFg+rrgoR33iHpo49xnjgBgCMqCt+LLi7z+Q+teohfTv7C4usXk0/+eZtS03LTEIRG\noc04Dvhs+Z3sZAe2AH/y0zPI37/RGqPhGhxpjCGLfHxtDvcMEb/u3Qm87LJzvItSqiYKcAQQ4hPC\nK1ustVLv63If7cLaMWXlFO5YdgcTO0wkwBFAWm4aD/e8E/tXU+CtgeAIgDuXW8EX8OYvb3IgeT+p\nwV6sTlkPKeuJ9I/kjeFv0DKkpfv9fOw+FQq6zsfm50eLD63MQYGDB+PVsCFit3Mk9QhXf3410zIM\nXQB7g5KXSapOt3a+tdjrUa1HMX3NdHed7EveV6lpJcTbu0wtSWfyiSn+XWIPDIDAAIJHXElg/57I\nv1uRtC/YGlYCkH4KTu2FloVTodJWrSLhtdfcr4PH3sCINl9yR9NRjHrjV7J37MQecu46OnjTzWRu\nKkxz5NO+bP82WXlZCOJee7QuKVNIboxZCiw9Y9uMIj9vxOo6LOncOcCcCyhjjZQTG8vJWS/g06kj\nEVOnEnzF5Yi/f7n69p351tIN1y60+s5XjltZrHkfYHP8ZpYdWoYgHEk9QoAjgGD/UFbE2Llo6wEA\ngq++muRPF5AXtw/eGgQT5kKHq3HmOwlPNLQ4aMg9epTgq6+i6axZOqNQqVrqqX5PMWXlFLpFduOe\nLvcgIqwav4qn1z3Nxzs/dh93rNllbGzbgVbiQ0baMbp8MwkvvwYcssH6dGtcFf5+jG5wEQ9d9Xax\nzPgFvO3e5e5eLC9HZGF2+UOnD2Ew/HegYW+k0PGimp+f6cwliQrWmq3Jcn18OObwwu6VS95v38Bv\nLeHLP1s7AwvrI/uo1T25va0Pnfdk81P2LgA+SVjG5AU/Ejd5Mlk7z+7Czo2LwxYcjHE63QFX1Esv\n4Yhqis2nbOso5uTl4GP3qZPfVTViIH1tlJdkpRqL+MtfCBoypELXCPO1VphvGdySg6cPcvmCy3nt\nstfoF9UPgLScNO5dca973T6A6MBo7DY7Ld+ezbf/mEK/dSnc3eJrXvdz4PSOJl12cWThHTS46wdW\nn/qFaZ/k0SQpj1wOEzT0sjr5S6xUfTGs+TC23LKl2EyyMN8wXhzyIvuS9xHmF8Yjqx7hu1hrULQt\nog3+xrAkOwFJTyYkP59b0zMwQILdzt97TkRKCLjAaunKcmaRlJVEqG9oicdUpoIA70Bj4UBjO8+W\nd+3IanBmsJqUlcT7v73P6ZzTxRarr0n+u/sTXmjWlPd9csjffIjDH91PUmNfBg5/mYC2hWtoJq39\ngTyBhSNC2NIymRWtdwLWRCqx2bCHNSQ/M7PYtRPeepuTL74IDgf+Xawel+hXXyFoePlmIWY5s/Dx\nqlsLXRfQoKuC8rOsPmebbyn5csogNSeV3k16M/uK2aw+spr7/u8+dibupF9UP05mnOTJtU+S4czg\n3SvepXFAY46mHyXUx/rw6xndh/2Tp3JHn5lANrFBTnLjAnmhdQe+bpIOX40mINPwXpE0tN4tW1zI\nLSulaoCSxgyJCG1CrfEys6+cjTPfSXJ2stVybgwc/xWTdBgcfkjacdjwDhzbCpEdSn2fUN9QsvKy\nGDR/EF+M+sJ9/aqSk28FXReHX8y2hG3uxb5rsjODrkdXP+r++eZON7sfrGuSExnWcBj/0zYMEPhl\nCDnBMDVjFqM3/S8Bdn/Cth/Blm841hAeHPMi3Rp1Y2pmAuMXjyfPlbHe5ufnXh/TGEN+SgonX3nF\nnf8x4+efCRw2jMDBg8tdxuy8bHxsGnSpIky29QEh3hX/xUjNSSXC35p+OyBqADaxuQfLT145mR2n\ndnB5i8u5NPJSHHbHWdPEr465mpk/WTlcjje00XJnEn/8BAaNs3OqQTKdYlsBrqzUXl40GD++wmVV\nStUeXjavwqEKItCkS/FEmB1HQUoshJfehXdD2xv45cQvfH/ke5YeWMpfQqu25aagpeveLvfyxJon\n3OtO1mRnLsbcOqS1e4LD0v1LubnTzSWdBlx43quKynRm4pefz7ZLHEScsBHWpBXhW35n6kcFy1FZ\nwe6aa1vhHHUZQyKsccrhfuG0DWvLmrg1bD2xlSh/P0xmJmlr1nDsiemIlxc4nTR6/DGydu4kLymZ\nRo8/VqEZ8dl52drSpYoz2QUtXRcWdBU8KYkIAV4BZDgzMMawJ2kPN3W8iYd7Plzq+UHeQYxrN44h\nzYZwutkO1n39JX0XH6DtV15cGu7F6cMJIIb2n86CmKHataiUsvgGg2/ncx4S6hvKK8Ne4Zalt7Dx\n+EZOZZ4iOTvZvR5hZSsIutqGtmXV+FVV8h6VLdI/klCfUJKyk7ip40080vMRNhzfwKyfZ7Fo36JS\ng67UnFRGfjESh83B5S0u55Gej3js8znTmUl4SAvGzl0MNhsiwtEnniBlwWcEDruMBnf+iYB2HehY\nJOVHgUg/a8zXLV/fwpc51sLVsXfcWewYn1atCBp6YWk+YlNjL2gB9ZpMg64Kys+2ZvXIhXQv5qYS\n5ChsnvZ3+JOem85Px38iKy/LnQzxXKb3nW79ED0QLrub+LBnSfzgA04f9gPAK7IRtouuOccVlFKq\ndO3D2jN/13yGfjIUu83O9+O+J8Sn8mcW/nT8J8AawF9b2MTGc4OeY1fiLq5qdRU2sdGnSR8GRg1k\nzm9zzmrNMsaw/NBy8sknMSsRQfh458e0CmnFuPZVs5LJmTKdmfh5+bnzoQE0nTmTyMmTsYeGWi1W\npXik5yM4850s3r+YW/z/y5NRPnTsPIi0Zcvx69qVhlMnc6RBPpkJ22kX1q5Csw8zcjPYlrCNCL/y\nJ2GtDTQTZgWZLCvoKutsjDPl5ueS6cwsNibA18uXhXsXctcyK2dWRXKUhN93b7HXIaOuq1D5lFIK\n4NrW19K/aX9GtByBM9/JXcvu4pl1z1T6+3x94GsAvG21J+gC6Nu0L7dfdLs7eS1YrYR5Jo/TOaeL\nHbszcSd/XfVXHl5l9WAsHb2UqMAonln/zFnHVrbj6ccZt3gcK2NX4ufld9Z+r4iIcwZcAIHegYxo\nNQKA0wHCX2/N453Rgewe1JKcafcw8uCjXPfldUxYMoG5O8u3xHJ2XjbT10xn6CdWK9mfLvpTuc6v\nLTToqqB8V/eiFAm6vtjzBc9ueLZMfzxpOVZ+lKJBV0Hyw+taX8dbw99iSPSQcpfLHhKCIzoavLzo\nsHMHkX99sNzXUEqpAl0iuvDm5W8yrfc0ogKj2Jm4k092f8KOUzsAq/Xm8OnD5Jv8Ml3vm4PfMHvb\n7GLbMp2Fs+BqU0tXaQpSSfwY9yM/H/+Zv6/9O/3m9mP8V9a42t6NezMyZiTRQdHupdSW7l9a6vVK\nk5yVzLMbnuWHIz+c99jfEn5jZ+JOgAvquosOtBoDbuxgdS9+cWgxT/Q/wu2/PU5iViKj244GYM5v\nc7j4g4uZuGSie/B9aV7Z8gpD5g9h4d6FRPpHMrX7VCZ2LGsO9tpFuxcrID8jg+S5VhQvvr4YY8h0\nZvL0uqdxGid+Xn786aI/lZj7BmDd0XX846d/ABRrpn9+8PMkZSVd8JiJmMWLID9fx3AppSpNA98G\nfHPDNyRkJjB28VjGfzWe5wc/j5d4MfX7qdjERvOg5nx67accTT/KiYwTXBp5KT5268E03+QTnx7v\nbuXJcmbx1q9v0a9pP9YeXet+n7qQEDPQEQjAY6sfIyowiri0wpXzejTqwTtXvOP+fB7RcgTvbnuX\nz/d8zoQOE8r8HsYYZqydwcrYlSzau4hVE1ad89+uYDZoy+CWDGsxrCK3BUBMgxiWj1lO44DG9G/a\nn5ScFOxiZ+7vc+kf1Z+7L7mbJgFNeG2rlVh1W8I2VsetZkizISVeLyU7hbd/fZt2oe14tOejZV6h\npbbSoKsCTi9dSvaevXi3bk2yLYshH/Z17wvyDmL2ttn8Z+d/mD9yPq1CWp11/tzf53Lo9CHu63Jf\nsXXFwnzDKmWKsc3v7KZjpZSqDOF+4TzY/UGm/TjNHUCBFVQdPH2Qnv/p6d52RYsriPSPZFTrUfxy\n8hf3wybAW7++BVAs4ALqxMPiwOiBBDmCSM1NJS4tjokdJnLo9CGm9Z5G08CmZ93j6Laj+edP/+SD\n7R9wW+fbyvQen+7+lJWxK2nk34j4jHjSc9Jp4Nug1ONPZVlB1+fXfX7BgW3jgMYAxWaYXhNTOHb4\nni73cMfFd7Bk/xKmr5nOJ7s+KTXoKljy58YON9b5gAs06KqQ4089DQ4HMYsX8fOJwiUOpnSbQt+m\nfZm+Zjq7k3bz7cFvuafLPcXOTclO4deTv3Jlyyu5t+u9Z15aKaVqvJExI4lpEMOBlANsT9hO7ya9\nGRw9mE93f8pLm19ibLuxrDi0gmWHlgEUy5YPMKH9BPYk72F48+F0b9SdcV9Zg8ivanmVx++lKnjZ\nvPhhwg8cSDmAj93nrHQ/ZxrddjQvb36Zz/Z8Vqag65eTv/DM+mfw8/Ljlk638O+f/01WXtY5z9l+\najvRgdEea0l02Bxc3+Z6jqUf4/Wtr7Px+EZ6Nu551nF5xup6tEsJ64PWQRp0nWFt3FoQ6Ne031n7\nnAkJJL7/PiY3F//evUGEdUfXATD7itn0btIbgM9Gfcb4r8az7ui6YkFX7OlYJi2fREpOCmPajfHM\nDSmlVCUTETo37Eznhp0ZGTPSvX1c+3GMaTcGm9i4v+v97E7aTXJ2MvN2zSMtJ41Q31Cm95leaob7\nfw3+l6duocp52bxoG1q2pYx87D6MbT+W9357j28OfOMerF6aZQeX4bA5mDdyHr+fspbiKbpyyZm+\n2PMF646uc4/D8qQ/dv4jH27/kCX7l5QcdLnGe1XWQuE1Xf24y3J4e9vbbI7fzFd/+IqU7BQaBTRy\np25IXb6cU7PfBSDywamsjF3JO9veoXlQcy6NvLTYdfo06cOH2z/k15O/8u+f/01j/8bupTneu/I9\nukZ29eyNKaWUB9jEmp/lsDvoHG7lAusf1f+c54xuO5qTGServGw12aCoQbz323tM+3Eaw5oPw2Ev\nuUVq5vqZzN81n16NexETEsPBlINAYTfdmfYn72fG2hm0C21XLUGXr5cvPRr1YPOJzSXuzzW5gLZ0\n1VuP93qcMYvHcM0Xhf3T3SK78dLQl3CeshYzbbfhJ+zBwSxYcR/+Xv4svH7hWU22Q5sNZc5vc7hp\n6U3ubb52X6Z0n6IBl1JKFfFUv6equwjVrkfjHjw/6Hke/uFh9iTvoVPDTiUeN3/XfAD3eOGC9A+Z\nzkyMMWTlZeHn5ceGYxsI8QlhzGKrV+WloS9VKA1RZWge3Jz1x9aXmIW/oKXLbtOgq15qF9qOh3o8\nRGxqLKuPrOZo+lE2n9jMgZQDNDmVgC0kBHtwMHFpcayOW809Xe4psY+8a2RXnu73NBnODEa0HEFD\nv4bVcDdKKaVqi4JAa8Yaq2Vq5oCZ7pbDM41qPQooDLoW7VvElJVTSM5O5tZOt/Lhjg+LHd8sqFkV\nlvzcmgQ0ISsvi1NZpwqXp3IpGNOl3Yv1lIi4BzKm56bz8Y6PeXXrq2QknSR57jy8Y2KAwkVDu0aU\n3mr1h7Z/qPoCK6WUqhOaBTWjW2Q34jPiWbx/MX2b9qV7o+4cSDlAl4guBDgC8LJ5cVun27gk4hKg\nMOj6fM/nRAdGk5yd7A64Lg6/mAkdJlT7BIUejXsAMPSTobw45EWGtxju3uce0yX1IxypH3dZQQGO\nAIYeC2XQ/zgBK4Fd0BWXA9baWXD2KvNKKaVURYgIH1z1Abl5uYz8YiTTfpzm3hfmG8aMvjNw5jsJ\n9A50b28V0opRrUeRmpPK470e56XNL7H0wFImXTKJBy59oDpu4ywdwjrgbfMmJz+H17a+Vizochon\noN2L9VrywoWkrfyejM2byDuZAEBa19a0HjWR0LFjgcKgq+gvv1JKKXWhHHYH7494nzVH17Di8Aoy\nczPZfGIzU1ZaD/8BjsLFqL3t3vxjQGH+s+l9pjOhwwQuCr/I4+U+lwWjFjBq4SjSc9OLbXeP6dKB\n9PVT2o9rOPbY48W2vTfcxtc9D/Fo13wuStpO18iu7mV8gr2Dq6OYSiml6rAmgU0Y024MY9qNIS4t\njhGfjaBNgzZ42bzoEtGl1PMCvQPPmk1fE7QKacX49uNZdnBZse06pqueO/nyywAE9OtL+lorB9fa\nTtZsi+c2PgdYsxl3J+3G38tfgy6llFJVKiowip8m/oS/w7+6i3JBfOw+ZyVxdea7uhe1pat+irj/\nz8ROupu81DS8W7cmLzGRJ695irScNBKzEtl+ajurYlfRwKcBs4bMqhOLsyqllKrZanvABVbQlZ2X\nXSx1hDsjvY7pqp8C+vQhaMQIwifdhXebNmAM7Xx8ih2Tkm0t8KnjuZRSSqmy8fXyJd/k48x3upO/\n6uzFek68vYl+6cVzHhPiE+Kh0iillFJ1g4/dasDIystyB13u7sV60tJVctY1pZRSSqlK5Gv3BSA7\nL9u9zZ0yQsd0KaWUUkpVDh8vq6Xr8OnDJGQmsCZujTu5q85eVEoppZSqJEEOK5n4bd/c5t7mbbMm\no+mYLqWUUkqpSjIweiAz+89kX8o+Gvk3YsHuBexN3gsUtoLVdRp0KaWUUqrKedu9ua7Nde7X49qN\n47vY7/C2eRPpH1mNJfMcDbqUUkop5XEOu4MrW15Z3cXwKJ29qJRSSinlARp0KaWUUkp5gAZdSiml\nlFIeoEGXUkoppZQHaNCllFJKKeUBGnQppZRSSnmABl1KKaWUUh6gQZdSSimllAeUKegSkREisktE\n9orIYyXs9xGR+a79P4lIS9f2liKSKSJbXf+9WbnFV0oppZSqHc6bkV5E7MBrwOXAEWCjiCwyxuwo\nctgdQJIxpo2ITACeA8a79u0zxnSt5HIrpZRSStUqZWnp6gXsNcbsN8bkAPOA68445jrgA9fPC4Bh\nIiKVV0yllFJKqdqtLEFXFBBb5PUR17YSjzHGOIEUoKFrXysR2SIiq0RkYElvICKTRORnEfn55MmT\n5boBpZRSSqnaoKoH0h8DmhtjLgUeBP4rIsFnHmSMedsY08MY0yMiIqKKi6SUUkop5XllCbrigGZF\nXke7tpV4jIh4ASHAKWNMtjHmFIAxZhOwD2h3oYVWSimllKptyhJ0bQTaikgrEfEGJgCLzjhmEXCb\n6+cxwHfGGCMiEa6B+IhIDNAW2F85RVdKKaWUqj3OO3vRGOMUkfuBbwE7MMcYs11EngZ+NsYsAt4F\nPhKRvUAiVmAGMAh4WkRygXzgHmNM4rneb9OmTQkicqjit1Qu4UCCh95LVZzWU+2hdVU7aD3VHlpX\nNV+Lsh4oxpiqLEiNJiI/G2N6VHc51LlpPdUeWle1g9ZT7aF1VbdoRnqllFJKKQ/QoEsppZRSygPq\ne9D1dnUXQJWJ1lPtoXVVO2g91R5aV3VIvR7TpZRSSinlKfW9pUsppZRSyiM06FJKKaWU8gANupRS\nSimlPKDOB10i0k1EGp7/SFWdRMRR3WVQ5SMiUt1lUKUrshqI1lMNJyJ1/rtYWepsRYvIpSKyAviJ\nMmTeV9VDRPqIyDzgeRG5qLrLo0onIn1F5GURuR3A6CycGklE+ovIB8ATIhKm9VQziUgvEfkLgDEm\nv7rLozyjzgVdIuIjIm8C7wCvAz8A17j26RNfDSIiY4E3gK8AX+BB13atpxpGRMYAr2KtxTpMRGZq\nkFzzuNa4fR1YibU0yTMick31lkqdSUSmAF9gBcZXubbZq7dUyhPqXNAFNAE2AQOMMZ8Dy4CGIiL6\nxFfjtAUWG2M+Bl4Eq5tR66lG6gx8boz5CHgY6A2MFZEG1VssdYbuwE5jzPvAX4GtwEgRaVatpVJn\n2guMBO4FHgcwxuTpA2fdVyeCLhEZJyIPiUgvY8xBY8w7xpgs1+5AoJkxxuiTRPVy1dODItLXtWkX\nMFpEHgHWAU2B10RE1xmrZiXUVSLgKyIhxpjjQDxWS0rfUi+iqpyre75dkU0bgWgRaWaMSQLWAMnA\n6GopoAJKrKclwK+u/6cVdDMC+h1Vx9XqoEtE7CIyA3gUyAfeFZHRrn0F97YQGCUi/saYvGoqar12\nRj0BvCMio4DPgcnAIOBWY8wI4CQwRkQaV09p67dS6upKYAMQCcwWkU+wvhxSgUau8/QJ3YNEpIGI\nLAGWA+NEJNC1Kwv4ERjner0L2AGEiYiv50tav5VQTwEFu4wxea7GgVnAHSISboxxVlthlUfU6qDL\nFUS1B/5qjHkBeBK4X0Q6FhmYeBL4DuhQTcWs90qpp6lAO2PM/2F9UexyHf4lcAmQXh1lre9KqKu/\nY3VTpWJ1gywAvjHG3Ig1SeUq13naJexZAcC3wAOunwe5tp8E1gMXu1r+84A4oH+R1n/lOSXW0xkD\n57/HqrMHwBpg79kiKk+qdUGXiNwqIoOLjCWJB0JFxMs1hmsHML5IV2Ia0AYwrvP1idwDzlNPnwHb\ngRtdLVr7gDGu4y7FCsKUh5ynrhYAe4AJxphEY8x8Y8wc13HtsVqSlQcUqadgY0wc1pp8n2D9vfQS\nkShXkLUO2AK86GoB6wwcFhH/ait8PXKeeuotIk1dxwm4H3RmAo+KSArQTb+n6q5aEXSJpYmIrARu\nA27CGvsTCCQAF2ON3QJ4BfgDVlcIxphE4BRwmeu1PpFXkXLW06vA9UAe1mSHniKyHhgLTDPGpHr8\nBuqRctbVy8B1ItLEde4wEdmOFSD/6PnS1x+l1NMbrq6oLGNMBrACCKXwMy7eGPO/WC2Rc4Cbgedc\nx6oqUMF6MiJiE5E2wH+xxt8NMMa8qd9TdVeND7pExO76BQwC4owxw7BmfJzG+jJ4HegHXOIat7UL\n+B3ry7vAbcaYWR4uer1SgXr6HasFZayri/FW4C5jzHDXPlVFLuBvqmCc0EHgCWPMSGNMrMdvoJ44\nRz0lYrWeAGCMWYNVJ+1FJEREgly7HgbuMMb0dtWhqgIVqKcOrnryd3UzngZmGGOGGWO2ef4OlCfV\n2KShru7BZwC7iCwFgrFaRQqm1t4PHMMahPhfYAJWuoj5gBPrKQ/X8ac9W/r64wLrKQcrvQfGmDRA\nP3CqUCX8Ta13HbsPq0tYVYEy1NNk4KiIDDbGrHKd9g5WF9VyoIWIXGqMOYo1Fk9VgUqqp+7GmCPA\nCc/fgaoONbKlS0QGY30Zh2LlM3kGyAWGFgwydPWDPwU8b4z5EKuL6lYR2YIVTOoXeBXTeqo9tK5q\nhzLWUz7WBIe/Fzn1GuA+4BfgYlfApapIJdbTEc+VWtUEUhO7jkVkINDSlYgREXkd6wM/E3jAGNNd\nrJQQkVhjg6YaY2Jdg7L9jTH7q6vs9YnWU+2hdVU7lLOeXgYeMcYcFJHrgCRjzA/VVfb6ROtJVVSN\nbOnCeoL4RApnIK4Bmhsry7JdRB5wPUVEA7kF40qMMcf1y8GjtJ5qD62r2qE89ZRnjDkIYIz5Ur/I\nPUrrSVVIjQy6jDEZxphsU5jM9HKs/DMAfwQ6ishXwFxgc3WUUWk91SZaV7VDRepJ0wt4ntaTqqga\nO5Ae3AMVDVbW60WuzanANOAi4ICx8qCoaqT1VHtoXdUO5aknTS9QfbSeVHnVyJauIvIBB1beoEtc\nTw7TgXxjzI/65VBjaD3VHlpXtYPWU+2g9aTKpUYOpC9KRPoAa13/vWeMebeai6RKoPVUe2hd1Q5a\nT7WD1pMqj9oQdEUDtwAvGGOyq7s8qmRaT7WH1lXtoPVUO2g9qfKo8UGXUkoppVRdUNPHdCmllFJK\n1QkadCmllFJKeYAGXUoppZRSHqBBl1JKKaWUB2jQpZRSSinlARp0KaVqNRHJE5GtIrJdRH4Rkb+6\nFhs+1zktRWSip8qolFKgQZdSqvbLNMZ0NcZ0xloD7yrgyfOc0xLQoEsp5VGap0spVauJSJoxJrDI\n6xhgIxAOtAA+AgJcu+83xqwVkfVAR+AA8AHwMvAsMATwAV4zxrzlsZtQStULGnQppWq1M4Mu17Zk\noD3W4sP5xpgsEWkLzDXG9BCRIcBDxpiRruMnAZHGmJki4gOsAcYaYw549GaUUnWaV3UXQCmlqpAD\neFVEugJ5QLtSjrsCa8HiMa7XIUBbrJYwpZSqFBp0KaXqFFf3Yh5wAmtsVzzQBWsMa1ZppwEPGGO+\n9UghlVL1kg6kV0rVGSISAbwJvGqssRMhwDFjTD7WosR216GpQFCRU78F7hURh+s67UQkAKWUqkTa\n0qWUqu38RGQrVleiE2vg/Auufa8Dn4nIrcA3QLpr+69Anoj8ArwP/C/WjMbNIiLASeB6T92AUqp+\n0IH0SimllFIeoN2LSimllFIeoEGXUkoppZQHaNCllFJKKeUBGnQppZRSSnmABl1KKaWUUh6gQZdS\nSimllAdo0KWUUkop5QH/D+4tMAJm8arfAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tkzuRBvHlT3p",
        "colab_type": "text"
      },
      "source": [
        "# **Ordinary Least-Squares Regression (OLS)**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IDazf3uHldUA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "outputId": "8a794273-7c79-4249-e73c-b4bfcf1da0ce"
      },
      "source": [
        "!pip install scipy==1.1 --upgrade"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting scipy==1.1\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/a8/0b/f163da98d3a01b3e0ef1cab8dd2123c34aee2bafbb1c5bffa354cc8a1730/scipy-1.1.0-cp36-cp36m-manylinux1_x86_64.whl (31.2MB)\n",
            "\u001b[K     |████████████████████████████████| 31.2MB 1.5MB/s \n",
            "\u001b[?25hRequirement already satisfied, skipping upgrade: numpy>=1.8.2 in /usr/local/lib/python3.6/dist-packages (from scipy==1.1) (1.16.3)\n",
            "\u001b[31mERROR: albumentations 0.1.12 has requirement imgaug<0.2.7,>=0.2.5, but you'll have imgaug 0.2.9 which is incompatible.\u001b[0m\n",
            "Installing collected packages: scipy\n",
            "  Found existing installation: scipy 1.0.0\n",
            "    Uninstalling scipy-1.0.0:\n",
            "      Successfully uninstalled scipy-1.0.0\n",
            "Successfully installed scipy-1.1.0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "scipy"
                ]
              }
            }
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PMqtUgCylWbF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 501
        },
        "outputId": "6fb79b3c-ff56-42e8-abfb-4d1835cdac29"
      },
      "source": [
        "# Import the `api` model of `statsmodels` under alias `sm`\n",
        "import statsmodels.api as sm\n",
        "from pandas import tseries\n",
        "#from pandas.core import datetools\n",
        "\n",
        "# Isolate the adjusted closing price\n",
        "all_adj_close = all_data[['Adj Close']]\n",
        "\n",
        "# Calculate the returns \n",
        "all_returns = np.log(all_adj_close / all_adj_close.shift(1))\n",
        "\n",
        "# Isolate the AAPL returns \n",
        "aapl_returns = all_returns.iloc[all_returns.index.get_level_values('Ticker') == 'AAPL']\n",
        "aapl_returns.index = aapl_returns.index.droplevel('Ticker')\n",
        "\n",
        "# Isolate the MSFT returns\n",
        "msft_returns = all_returns.iloc[all_returns.index.get_level_values('Ticker') == 'MSFT']\n",
        "msft_returns.index = msft_returns.index.droplevel('Ticker')\n",
        "\n",
        "# Build up a new DataFrame with AAPL and MSFT returns\n",
        "return_data = pd.concat([aapl_returns, msft_returns], axis=1)[1:]\n",
        "return_data.columns = ['AAPL', 'MSFT']\n",
        "\n",
        "# Add a constant \n",
        "X = sm.add_constant(return_data['AAPL'])\n",
        "\n",
        "# Construct the model\n",
        "model = sm.OLS(return_data['MSFT'],X).fit()\n",
        "\n",
        "# Print the summary\n",
        "print(model.summary())"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:                   MSFT   R-squared:                       0.281\n",
            "Model:                            OLS   Adj. R-squared:                  0.280\n",
            "Method:                 Least Squares   F-statistic:                     515.5\n",
            "Date:                Thu, 30 May 2019   Prob (F-statistic):           1.32e-96\n",
            "Time:                        07:27:40   Log-Likelihood:                 3514.0\n",
            "No. Observations:                1322   AIC:                            -7024.\n",
            "Df Residuals:                    1320   BIC:                            -7014.\n",
            "Df Model:                           1                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "const         -0.0005      0.000     -1.119      0.263      -0.001       0.000\n",
            "AAPL           0.4407      0.019     22.704      0.000       0.403       0.479\n",
            "==============================================================================\n",
            "Omnibus:                      268.593   Durbin-Watson:                   2.074\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             7029.443\n",
            "Skew:                          -0.211   Prob(JB):                         0.00\n",
            "Kurtosis:                      14.289   Cond. No.                         41.6\n",
            "==============================================================================\n",
            "\n",
            "Warnings:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/numpy/core/fromnumeric.py:2389: FutureWarning: Method .ptp is deprecated and will be removed in a future version. Use numpy.ptp instead.\n",
            "  return ptp(axis=axis, out=out, **kwargs)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nrWMuC6lo2Cb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "cd0c2195-7059-434a-fae7-eb88152b720f"
      },
      "source": [
        "plt.plot(return_data['AAPL'], return_data['MSFT'], 'r.')\n",
        "\n",
        "ax = plt.axis()\n",
        "x = np.linspace(ax[0], ax[1] + 0.01)\n",
        "\n",
        "plt.plot(x, model.params[0] + model.params[1] * x, 'b', lw=2)\n",
        "\n",
        "plt.grid(True)\n",
        "plt.axis('tight')\n",
        "plt.xlabel('Apple Returns')\n",
        "plt.ylabel('Microsoft returns')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Nv20OQRvAve/WzXXgieyaOBGuvNL1vv/5DwwY0Hr7N2xws9Dq6tyMtNpa51Wqq4Of/xzu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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5W7tklnMo44D",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 273
        },
        "outputId": "5711f570-5a18-4c79-cc79-2ac85963ec6b"
      },
      "source": [
        "return_data['MSFT'].rolling(window=252).corr(return_data['AAPL']).plot()\n",
        "plt.show()"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Yc7T985B97kDgFuATwHHALSLije7AGUxrsK1xRCoLdAVDJm4xK4DHrzgeaKt7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\nYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I8wSq3w-o7Uy",
        "colab_type": "text"
      },
      "source": [
        "# **Building A Trading Strategy With Python**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f2TFOrIjo-ch",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Initialize the short and long windows\n",
        "short_window = 40\n",
        "long_window = 100\n",
        "\n",
        "# Initialize the `signals` DataFrame with the `signal` column\n",
        "signals = pd.DataFrame(index=aapl.index)\n",
        "signals['signal'] = 0.0\n",
        "\n",
        "# Create short simple moving average over the short window\n",
        "signals['short_mavg'] = aapl['Close'].rolling(window=short_window, min_periods=1, center=False).mean()\n",
        "\n",
        "# Create long simple moving average over the long window\n",
        "signals['long_mavg'] = aapl['Close'].rolling(window=long_window, min_periods=1, center=False).mean()\n",
        "\n",
        "# Create signals\n",
        "signals['signal'][short_window:] = np.where(signals['short_mavg'][short_window:] \n",
        "                                            > signals['long_mavg'][short_window:], 1.0, 0.0)   \n",
        "\n",
        "# Generate trading orders\n",
        "signals['positions'] = signals['signal'].diff()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bb4AUb1zpCCI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 259
        },
        "outputId": "d9d9622a-273a-4817-e451-99e782de9a0b"
      },
      "source": [
        "# Initialize the plot figure\n",
        "fig = plt.figure()\n",
        "\n",
        "# Add a subplot and label for y-axis\n",
        "ax1 = fig.add_subplot(111,  ylabel='Price in $')\n",
        "\n",
        "# Plot the closing price\n",
        "aapl['Close'].plot(ax=ax1, color='r', lw=2.)\n",
        "\n",
        "# Plot the short and long moving averages\n",
        "signals[['short_mavg', 'long_mavg']].plot(ax=ax1, lw=2.)\n",
        "\n",
        "# Plot the buy signals\n",
        "ax1.plot(signals.loc[signals.positions == 1.0].index, \n",
        "         signals.short_mavg[signals.positions == 1.0],\n",
        "         '^', markersize=10, color='m')\n",
        "         \n",
        "# Plot the sell signals\n",
        "ax1.plot(signals.loc[signals.positions == -1.0].index, \n",
        "         signals.short_mavg[signals.positions == -1.0],\n",
        "         'v', markersize=10, color='k')\n",
        "         \n",
        "# Show the plot\n",
        "plt.show()"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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fApuBOKAXKLsEane0RqPRZA3HlZ2BOnVUHmrA6unFoj3nAXg5ebTWqGuw6FWI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\ng8HgEqMgDAaDweCS/wc6Lz0VF6/riwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CG5vGv4gpEeI",
        "colab_type": "text"
      },
      "source": [
        "# **Backtesting A Strategy**\n",
        "# Implementation Of A Simple Backtester With Pandas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cupFQdqYpLQI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Set the initial capital\n",
        "initial_capital= float(100000.0)\n",
        "\n",
        "# Create a DataFrame `positions`\n",
        "positions = pd.DataFrame(index=signals.index).fillna(0.0)\n",
        "\n",
        "# Buy a 100 shares\n",
        "positions['AAPL'] = 100*signals['signal']   \n",
        "  \n",
        "# Initialize the portfolio with value owned   \n",
        "portfolio = positions.multiply(aapl['Adj Close'], axis=0)\n",
        "\n",
        "# Store the difference in shares owned \n",
        "pos_diff = positions.diff()\n",
        "\n",
        "# Add `holdings` to portfolio\n",
        "portfolio['holdings'] = (positions.multiply(aapl['Adj Close'], axis=0)).sum(axis=1)\n",
        "\n",
        "# Add `cash` to portfolio\n",
        "portfolio['cash'] = initial_capital - (pos_diff.multiply(aapl['Adj Close'], axis=0)).sum(axis=1).cumsum()   \n",
        "\n",
        "# Add `total` to portfolio\n",
        "portfolio['total'] = portfolio['cash'] + portfolio['holdings']\n",
        "\n",
        "# Add `returns` to portfolio\n",
        "portfolio['returns'] = portfolio['total'].pct_change()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hAsd1cuipPa3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 259
        },
        "outputId": "f0c7049f-11b1-45c1-ece9-a8aa9816a954"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "fig = plt.figure()\n",
        "\n",
        "ax1 = fig.add_subplot(111, ylabel='Portfolio value in $')\n",
        "\n",
        "# Plot the equity curve in dollars\n",
        "portfolio['total'].plot(ax=ax1, lw=2.)\n",
        "\n",
        "# Plot the \"buy\" trades against the equity curve\n",
        "ax1.plot(portfolio.loc[signals.positions == 1.0].index, \n",
        "         portfolio.total[signals.positions == 1.0],\n",
        "         '^', markersize=10, color='m')\n",
        "\n",
        "# Plot the \"sell\" trades against the equity curve\n",
        "ax1.plot(portfolio.loc[signals.positions == -1.0].index, \n",
        "         portfolio.total[signals.positions == -1.0],\n",
        "         'v', markersize=10, color='k')\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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amrjWbFR1T9Bnq6r+GTj3MJ73BnCNe3wN8DqAiPQBXgGuVtVvg9ovAga7kWde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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lnF2sRcQpSPP",
        "colab_type": "text"
      },
      "source": [
        "# **Evaluating Moving Average Crossover Strategy**\n",
        "# Sharpe Ratio"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PndPY8J9pVef",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "5682cc9f-e9c7-4ff5-b7b7-480a376a9942"
      },
      "source": [
        "# Isolate the returns of your strategy\n",
        "returns = portfolio['returns']\n",
        "\n",
        "# annualized Sharpe ratio\n",
        "sharpe_ratio = np.sqrt(252) * (returns.mean() / returns.std())\n",
        "\n",
        "# Print the Sharpe ratio\n",
        "print(sharpe_ratio)"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.7268769500133708\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s-JnuE_IpbMf",
        "colab_type": "text"
      },
      "source": [
        "# Maximum Drawdown"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ap86DLHPpaKW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 259
        },
        "outputId": "d4e29634-9ba3-46b0-b15c-25091fc9c651"
      },
      "source": [
        "# Define a trailing 252 trading day window\n",
        "window = 252\n",
        "\n",
        "# Calculate the max drawdown in the past window days for each day\n",
        "rolling_max = aapl['Adj Close'].rolling(window, min_periods=1).max()\n",
        "daily_drawdown = aapl['Adj Close']/rolling_max - 1.0\n",
        "\n",
        "# Calculate the minimum (negative) daily drawdown\n",
        "max_daily_drawdown = daily_drawdown.rolling(window, min_periods=1).min()\n",
        "\n",
        "# Plot the results\n",
        "daily_drawdown.plot()\n",
        "max_daily_drawdown.plot()\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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fy6+lW/S62JuWv5nqIV3IkzLlZjHFa0drH2+19VvGTr7++9FkiW1HsV38p1QG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\nChLJPwiCoIJE8g+CIKgg/wCNkf5yGgymcQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "enDs4LbcpiF1",
        "colab_type": "text"
      },
      "source": [
        "# Compound Annual Growth Rate (CAGR)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8Xru8OKRpk2l",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "bbfe5e0a-5eb8-4670-c712-a73e873a720e"
      },
      "source": [
        "# Get the number of days in `aapl`\n",
        "days = (aapl.index[-1] - aapl.index[0]).days\n",
        "\n",
        "# Calculate the CAGR \n",
        "cagr = ((((aapl['Adj Close'][-1]) / aapl['Adj Close'][1])) ** (365.0/days)) - 1\n",
        "\n",
        "# Print CAGR\n",
        "print(cagr)"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.3823445620598196\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}