Stock_tangency_portfolio.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyP5v02HAFKyoqRiQ5yLH3Mg",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/freddyacuna/Master-in-Finance/blob/main/Stock_tangency_portfolio.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install yfinance"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OoY-HXVmjy7j",
        "outputId": "cd779d4c-7745-4566-a51c-625bd9c6c078"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting yfinance\n",
            "  Downloading yfinance-0.1.74-py2.py3-none-any.whl (27 kB)\n",
            "Requirement already satisfied: lxml>=4.5.1 in /usr/local/lib/python3.7/dist-packages (from yfinance) (4.9.1)\n",
            "Collecting requests>=2.26\n",
            "  Downloading requests-2.28.1-py3-none-any.whl (62 kB)\n",
            "\u001b[K     |████████████████████████████████| 62 kB 1.0 MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.15 in /usr/local/lib/python3.7/dist-packages (from yfinance) (1.21.6)\n",
            "Requirement already satisfied: multitasking>=0.0.7 in /usr/local/lib/python3.7/dist-packages (from yfinance) (0.0.11)\n",
            "Requirement already satisfied: pandas>=0.24.0 in /usr/local/lib/python3.7/dist-packages (from yfinance) (1.3.5)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24.0->yfinance) (2022.2.1)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24.0->yfinance) (2.8.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas>=0.24.0->yfinance) (1.15.0)\n",
            "Requirement already satisfied: charset-normalizer<3,>=2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.26->yfinance) (2.1.1)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.26->yfinance) (2.10)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.26->yfinance) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.26->yfinance) (2022.6.15)\n",
            "Installing collected packages: requests, yfinance\n",
            "  Attempting uninstall: requests\n",
            "    Found existing installation: requests 2.23.0\n",
            "    Uninstalling requests-2.23.0:\n",
            "      Successfully uninstalled requests-2.23.0\n",
            "Successfully installed requests-2.28.1 yfinance-0.1.74\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "ytLtKEpajQcZ"
      },
      "outputs": [],
      "source": [
        "\n",
        "import pandas as pd \n",
        "import numpy as np \n",
        "import yfinance as yf\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "tickers = ['^GSPC', 'TSLA', 'AMZN', 'NVDA']\n",
        "\n",
        "stocks = yf.download(tickers, start = '2015-01-01', end =  '2021-03-01', interval = '1mo')\n",
        "stocks = stocks['Close'].dropna()\n",
        "\n",
        "returns = stocks.pct_change().dropna()\n",
        "\n",
        "fig, axes = plt.subplots(2, 2, sharex=True, figsize=(8,4))\n",
        "fig.suptitle('Density Plot')\n",
        "\n",
        "for k, ax in zip(returns.columns, np.ravel(axes)):\n",
        "    sns.kdeplot(returns[k], fill=True, color='darkorange', bw_adjust=.50, ax=ax)\n",
        "    ax.grid(True, linestyle='--')\n",
        "    ax.set_title(f'{k}: Density Plot')\n",
        "    \n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "                "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        },
        "id": "BPNrIXstjvGo",
        "outputId": "905830a6-ec27-43f6-af91-b52de0c55ad8"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  4 of 4 completed\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x288 with 4 Axes>"
            ],
            "image/png": 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95YUO07TzVbj3VLh7Jvx2DFQlXFsz83ocQTX545qeMDDGfNkYM9oYU4ZdSuMZY8wVWZaVNE7VTfU/4dcjmLDxx7D0/7Kt5hBOlZGHavInaj0ag5OAoiLfdSEjp6ioCNr2w8u3wdKbYMaHYf4PYG8tPP9FyC2Ak4KusxmSHsdQTf64pkfpwpm6aamDxy6HM75DUc5IWPp5GDAcZlydbWXulFEMqsmfqPVoC04COoOjnOCNp+Ev57Li6XvhtiGw/q/w1lthwvkgfaBwBJzxXevk1K2OTJZTZeShmvxxTU/YGGMWGmMuzLaOVHCmbl68GUa/BY47kRU78mDuN2Hh56Fxc7aVuVNGMagmf6LWow7O0UD1P+GxS2D4bOvIXHA/zLsJikYffl7xWJh+NTy+ADrasiJVUZReQPsBWPlHmPzern2DJsDk98DTn8qeLkVJAnVwElBaWpptCbY76p8fhlO/DOPOoXRgf+hX2PP5Ey6EPrnw32j6y50oo26oJn9c06N04UTdVP8LCkdB0RgASgtz7P6pl8CuV2DDP7IozpEy6oZq8idqPU6tRVVRUWFcmnSro6ODPn2y7AOu+A2svhvmfddqMoY+Iomv2bcDnvoEXPwojEwwR0AIOFFG3VBN/mRKj4gsN8ZUhJ5whnDN5oAj98qT/wN9+8OU91tNsXZnx8u2++rKFdA/O5NWO1FG3VBN/kRtd9z55g7SOXFUVnn5lzCpa2Hs59bu9b+m/1CouB4efj80b8+gOEfKqBuqyR/X9ChdZL1uTAdsfARGnn5o12F2Z+jJMOat8PB74WAAe5QBsl5GcVBN/kStR0dRucyulbBvJwybmfy1I0+Hhg3WCF3yvO22UhRF8WPnq5DbHwpH9nzOjP+B5bfAXeVwwgJo2we1VdDaAKPnw2nfgPxB0WlWlDhoC04CcnOz7BSsecAaC+mqptxkamzaFfZtbNlPwtfWqSfbZRQH1eSPa3qULrJeN5ufgaGnHLbrCLvTJwdmXw/ln4Dd62HfLhhzFky/CurXwgNvgQNNGZOY9TKKg2ryJ2o9GoPjMn+cDid/HIacmHoaTVvgmc/AR16H/MHhaVOUOGgMTi/gbxfA8ArbDZUKxsDyn0B+CbzzznC1KUocNAYnBToXBcsKjW/AvhoomXbY7qrqfcmlUzTaDi9fdUd42mL1ZLOMekA1+eOaHqWLrNaNMbB98REvVUnZHREo/yRUPwnbloQs0NPj4P2rmvyJWo86OAnoXOE0K2x6AoafapuCY2jc35F8WmXnwqq7QhJ2OFktox5QTf64pkfpIqt1U7/Wxt8UDDlsd9J2p2+Bjc35z9dCFBejx8H7VzX5E7UedXBcZeNjqQUXx+O4ctsi1PhmOOkpitI72fYClE4PJ62yc6FuFdQuDyc9RUkSdXASUFGRpVCCjjbY8hwMm3XEoYqyguTT65Nj+9TffDoEcd30ZKuMEqCa/HFNj9JFVutm+xIYPOWI3SnZnZy+MOliePGHIQjrpsfB+1c1+RO1HnVwElBbW5udjGuWQf9hNkivG7WNKS7BMGQGvPFUmsKOJGtllADV5I9repQuslo325dA6QlH7E7Z7ow/HzY9aQc7hIiL969q8idqPergJGDz5iwtKvfm03YyrThsrj+YWppDTrTGK2SyVkYJUE3+uKZH6SJrdXOwBRrWw6BJRxxK2e70K4Rxb4eqn6UprpseB+9f1eRP1Hoy7uCISI6IvCQij2Y6r17Dm0/DcSeFm2bRWNhbA/t3h5uuoii9g12vQHEZ5PQLN93J74VXb4fWPeGmqyg+RNGC81ngtQjyCZ3JkydHn2n7Qah5EYbEd3AmD03R+PTJsX3rO8IdppeVMvJBNfnjmh6li6zVTe1yGHxk6w2kYXcACkfAiNNg+U9TT6O7HgfvX9XkT9R6MurgiMho4ALgd5nMJ1Pk5eVFn+mOl2DACMgrjns4r28aVTawzC7/ECJZKSMfVJM/rulRusha3dS8GLd7CtK0OwDTroSqW6ExnC4KF+9f1eRP1HoyPW/yLcAXgKKeThCRa4FrAUaOHMnChQsBmDBhAkVFRaxYsQKwy6xPnz790GJdubm5zJs3j6qqqkNj6ysqKqitrT3Uzzd58mTy8vJYudL+qA8dOpQpU6ZQWVkJ2MKeO3cuy5Yto7m5GYA5c+awZcsWtm7dSnNzM7NmzSInJ4fVq1cDMHz4cMaPH8/ixYsBKCgoYM6cOSxdupSWlhYA5s6dy6ZNm6ipqQFg2rRptLe3s3btWgBGjRrF6NGjWbp0KQCFhYVUVFSwePFiWhs2w5AvMq/dsK6mlR1NNrhvxqh8Wg928NKbLRTm5zCmpC/DinNZVm3zLM7vw8yy/lSua6bNm7Ji/tQBrNq6n7rmdgDKB8ygqaaejV4Zl5WVUVJScmjypcGDB1NeXs6iRYswxiAinHnmmaxYsYKGhgYAZs6cSX19PdXV1QAcOHCA2bNnZ7WeAKZOnXqonpqbm5k0aVJm66m1FYB58+axbt06duzYYetpxgxaW1tZv349AGPGjGHYsGEsWbKEwsJCiouLmTlzJpWVlbS12bqdP38+q1atoq6uztZTeTlNTU1s3LgxtHrq/jy1tLRw7rnnhl5PSvqsXLmSs846K/qMa5dD+cfjHlq5dT9nHV+YetqFI2DKB+DRD8IHnoK+A+z+fTvtqNHCEUkll7UySoBq8idqPRlbqkFELgTON8Z8UkTOAm4wxlyY6BrXpk1fuHBh9DfHP94LJcdD2Tvia1rTnLqh2bkCVt8DV7yYhsBuerJRRj6oJn8ypUeXakifrNwr7Qfg5wPhoocg90hHNS2700lHu12gs24ljJwLtS/B3m12rb1x58A774mbdzxce55ANQUharuTyRacM4B3i8j5QD5QLCL3GGOuyGCeoTJ06NDoM61ZaoPyemBoURpVVjTWLoxnjJ1OPQSyUkY+qCZ/XNOjdJGVuqlbDYWjenQw0rI7nXQu0LlrFTS9CaPm2Ze5jjZY8h2o/DKcFWxhYBfvX9XkT9R6MhaDY4z5sjFmtDGmDLgEeOZocm4Apkw5csKrjNK8HQ7us4amB6YMT6MbIG+QdW5adqWeRnc9UZdRAFSTP67pUbrISt3seKnH+BtI0+50Z8h0GP9OKJ1mW29y+sGsz8PKO2BPdaAkXLx/VZM/UevReXAS0BlbEBm1y+wbTYLWlcr1e1NPXwSKx9n1ZkIi8jIKgGryxzU9ShdZqZvaKhg0ocfDadmdIOQPtt1Ur/wm0Oku3r+qyZ+o9UTi4BhjFvrF3yjYIL9BEzObR+Eo202lKIrSyY6qhC04kTD+fNuKY1JYUFhR4qAtOAmIfFRIgmGaneTlphk7M2A47N6QXhoxuDhyRjX545oepYvI68YYO31EgpertO1OEAaW2VXIAyzO6eL9q5r8iVpPIAdHRP4mIheIyDHlEM2dOzfaDHeugEGJJ0KaO2lAenkUjoT6demlEUPkZRQA1eSPa3rioXYnIhqr7bDtvIE9npK23QnKiNNgwz98T3Px/lVN/kStJ6jhuA24DFgvIv8nIlMzqMkZIh0+un837G/wnQ9i2aZ96eUzYGSoLTiuDbEF1RQE1/T0gNqdKNjxsm/XeNp2JyjDZsEb//Y9zcX7VzX5E7WeQA6OMeYpY8zlwEygGnhKRF4QkWtEpG8mBWaTzknlImHXqzbIz+dltbk1zf7pwpH2jS0kIi2jgKgmf1zTEw+1OxGx4yUY2HOAMYRgd4JSOt12lx1MHNTs4v2rmvyJWk/gpl8RKQWuBj4CvATcijU8/u624k/dKrvQXabJG2Qn9dKF75SjALU7EVC7POEIqkjJzbdr5m1bkm0lSi8gaAzO34Hngf7Au4wx7zbGPGCM+TSQ5vSW7jJnzpzoMtu1EorG+J42Z0L/9PIRsa04ezall06nnijLKCCqyR/X9MRD7U5E7FzhO7ghbbuTDCXHw/bFCU9x8f5VTf5ErSdoC87txphpxpjvG2O2A4hIHsDRNC17smzZsiW6zHa9GqgFZ0v9wfTzKhwJezamnw4Rl1FAVJM/runpAbU7maalHlp32wV+ExCK3QlKyfGw7YWEp7h4/6omf6LWE9TB+W6cfYld7F5A50KOkVC/zk7C58PW3SEYmoJhobXgRFpGAVFN/rimpwfU7mSanS/bLiGf2L9Q7E5QSo63U2YkWCfRxftXNfkTtZ6EC4yIyHBgFFAgIqcAnZMhFGObjZUwaG2EA3ug/3HR5DdgKOx+PZq8FCVJ1O5EyI6XfQOMI6f/UDvZX/NWKBqdbTXKUYzfCmrnYgP8RgOxq6A1AV/JkCZnmDo1olGpDWtt602A6T6mhrEmzIARsOW59NMhwjJKAtXkj2t6uqF2JypqlwVycEKxO0ERsa1KO17q0cFx8f5VTf5ErSehg2OMuRO4U0TeZ4z5a0SanCEnJyeajOrXBgowBsgJY8qzASNCGyoeWRklgWryxzU9sajdibBual+CMW/1PS0Uu5MMAydYB2fiu+LrcfD+VU3+RK0n4W0rIp2rf5eJyOe7bxHoyyqrV6+OJqP6tXYCvgCs3taafn4DRkDjmwn7uIMSWRklgWryxzU9sajdiahuDu6zLzoBBjeEYneSYdBE27rUAy7ev6rJn6j1+HVRdc7P3WuHZDpB/RooiXAZ+b4F0Lc/7Ku1a1Mpiluo3YmCXa/a9Z9yHJwzcdAkWH13tlUoRzl+XVS/8f5+Kxo5bjF8eEQ//rvXwdizA506fKCfTxqQwlGwe2PaDk5kZZQEqskf1/TEonYnorqpDb6CeGh2JyiFo+wL2IFm6Hekn+vi/aua/IlaT9CJ/m4WkWIR6SsiT4vIzphm5F7L+PHjM5+JMdbRCDhaYPyQfuHkO2BEKHPhRFJGSaKa/HFNTzzU7mSYmhd916DqJDS7E5Q+ObbrbNfK+HocvH9Vkz9R6wkaOvYOY0wjcCF2TZhJwP9mSpQrLF4cwZQb+3ZAn1zoVxTo9MWvh7ToXf9hoSy6GUkZJYlq8sc1PT2QtN0RkTEi8qyIrBaRVSLy2Qh0hkpkdVO7zI5WCkBodicZBo6HXa/EPeTi/aua/IlaT9B2x87zLgAeNMbsEZFE5ytBaVgfeARVqBSNhIZ10eerKMFJxe60AdcbY6pEpAhYLiL/Nsa4FW2Zbdr22+d/YLAWnKxQPM4uI6EoKRK0BedREVkDzAKeFpHjgP2Zk+UGBQUFmc9k94bAI6gACvqG5FgWjrLOVZpEUkZJopr8cU1PDyRtd4wx240xVd7nJuA17KSBRw2R1M3OV6wDkRtsfpvQ7E4yDJxgdcbBxftXNfkTtR4xAYcKi0gJsMcY0y4i/YFiY0xNmGIqKirMsmU9Dw3slVR+FZq3wfSros13fz388yNwXX20+Sq9GhFZHuY6UenYHREpA54DZnhdXZ37rwWuBRg5cuSse++9F4AJEyZQVFTEihW21aC0tJTp06fz3HN2Uszc3FzmzZtHVVUVjY02uYqKCmpra9m8eTMAkydPJi8vj5UrbezI0KFDmTJlCpWVlQDk5eUxd+5cli1bRnNzM2AXINyyZcuhaeynTp1KTk7OoSG1w4cPZ/z48Yea9wsKCpgzZw5Lly6lpaUFgLlz57Jp0yZqamzRTJs2jfb2dtauXQvAqFGjGD16NEuXLgWg8OB2KprvY/Ggj9HaZn8D5k0ewLqaVnY0tQEwY1Q+rQc7WL/jAABjSvoyrDiXZdU2z+L8Psws60/lumbaOmzZzp86gFVb91PX3A5A+Zh8mvZ3sHGnTaNsSD9KBuRQ9YZNY3D/HMrHFrBoTTMGO2X1mccXsuLNFhqaW6GxmpnvuIb6hgaqq6uPvXoqLKSiooLFixfT2mqH6s+bN49169axY8cOW08zZtDa2sr69faFdcyYMQwbNozO39Li4mJmzpxJZWUlbW22bufPn8+qVauoq6uz9VReTlNTExs32rjMsrIySkpKqKqqsvU0eDDl5eUsWrQIYwwiwplnnsmKFStoaGgAYObMmdTX12elnnqyO8k4OKcDZcR0axlj7gp0cUBcc3CWLl2a+dVPH/6AHSI+7u3BNL2+lzkTB/if6Icx8NC74aNvQEFJyslEUkZJopr8yZSeDBhb3DAAACAASURBVDg4KdkdESkEFgE3GWP+1tN5rtkciOheeXyBXRKhh4n0jtAUlt1JBmPg4ffCNa8dMdrTtecJVFMQorY7gWJwRORuYCLwMtDu7TZAqA6Oa3R63Rll9/rAQ8QBWg6mPzkfYKdDH1hm5+AZdXrKyURSRkmimvxxTU88UrU7ItIX+CtwbyLnxlUiqZuaF6HihsCnh2Z3kkHEDmPf+eoRDo6L969q8idqPUGDjCuAaSZoc48SjM4h4oVZChEoHAP1r6Xl4ChKBkna7oiNQv498Jox5id+5x+TtO6Bps32Bcd1isfZCQnLgrVwK0osQYOMVwJuzRgUAXPnzs1sBi277HwPecWBL5k7McTFlIvGWOORBhkvoxRQTf64pqcHUrE7ZwALgLNF5GVvOz98aZkj43VT8yKUTLXTUwQkVLuTDMVldsXzbrh4/6omf6LWE9TBGQKsFpF/isjDnVsmhbnApk2bMptBw/rAE/x1smnXgfDyHzTRLmiXBhkvoxRQTf64pqcHkrY7xphKY4wYY04yxpzsbY9HpDcUMl432xbD4ORWdQ7V7iTDoAmw68ih4i7ev6rJn6j1BHVwbgTeA3wP+HHM1qvpjHTPGLvXJ909VbOnLbz8B02ywzDT6HnMeBmlgGryxzU9PXAjanfCZ9t/oPT4pC4J1e4kQ/F4qF8HHYfn7+L9q5r8iVpPIAfHGLMIO5NoX+/zi0BVBnUdGzSsg/5Z7PkrKIE+fWGPW16+ooDanYxgOmD7EiidkW0lwehbYEd76aSkSgoEXYvqo8BfgN94u0YBD/lcc9RPmT5t2rTMZlC3BoqTm8V42shgE3MF5riT7BtdimS8jFJANfnjmp54pGJ3egMZrZv6NdC3CApKk7osdLuTDIMmwo7Du6lcvH9Vkz9R6wnaRfUpbPBeI4AxZj0w1OeazinTpwGnAZ8SEbdK24f29nb/k9KhYS0UJheD094RsobS6fDG0ylfnvEySgHV5I9renogFbtz1JPRutn6HxiSfOtN6HYnGYrHw87DA41dvH9Vkz9R6wnq4LQaYw5FmYlILnY+ih7pDVOmd84umRFMh13NO8kg47U1reHqGDkXNj5yRB93YD2ZLKMUUU3+uKanB5K2O72BjNbN5mftS02ShG53kmHwJDvyKwYX71/V5E/UeoKOE1wkIl8BCkTk7cAngUeCZuJNmX4KsDTOsdhp01m4cCHgxnTczc3NbN++PTPTcQ8Ulo78PrxuKMzbR8X4/izesNd32vTm/e0sXNMc4rTpx1E1/Dvw70cYPHxC0tNxHzhwgIaGBqemTW9ubmbNmjVOTZve3NzMwoULnZk2vaWlhY6OjtDrKWTSsjtKHLY8B/O+l20VyTF4Crx4sx0MoYs8K0kQaKkGEekDfBh4B3a5kH8CvwsyAVfQKdPBvWnT169fz+TJkzOTePU/4T9fh/k3J6epppXJw0P+Ial5Eapuhbf/xi5wV3J8YEOS0TJKEdXkT6b0hLlUQzp2Jyiu2RzI4L2ypxrunQ0X/jlpRyEjdicZHr0ELlsMA8dbPY49T6CaghC13QnUgmOM6RCRh4CHjDE7k8j0qJ4yffTo5LqPkqJ+TdLxNwCjS/qGr2X4bJh+DbxwIzRvgSEnwcWPQE4/fz2ZLKMUUU3+uKYnHqnanaOdjNXN5mdh6CkptYJkxO4kQ8lU+yLmOTgu3r+qyZ+o9SSMwRHLjSKyC1gLrBWRnSLyDb+Ee8OU6Z1dExlh56t2JuEkWbpxXwbEAOPeBmf+EN55Dxxstk3CQfRksoxSRDX545qeWNKxO72BjNXNG0/BkBNTujRjdicog6faAGkPF+9f1eRP1Hr8gow/hx3FMNsYU2KMKQHmAGeIyOd8rj3qp0zPKHUrD72NOEWfHDjpY7D8p9CepdlLlWOddOyOEg9j4M1nYOjMbCtJjSHTYevz2VahHGX4OTgLgEuNMYdmgjPGbASuAK5MdGFvmDK9sLAwMwkbA3Wv2XVWkqQwL+jAtzQoHmu1bXrCX0+myigNVJM/runpRsp2pzeQkbqpW21fXgpHpnR5JHYnEYOPt936B/daPQ7ev6rJn6j1+N21fY0xu7rv9PrDs9wpm3kqKkKJlTySpi02viV/UNKXVoyPaNG7UWfA+r/7npaxMkoD1eSPa3q6oXYnbN74FwyblfIopMjsTk/k5nndVHbEnov3r2ryJ2o9fg5Ooj6KXt9/0TnEOHR2vgyDUoskX7xhb8hiemD4HKh+0nedqoyVURqoJn9c09MNtTths/FR6+CkSGR2JxFDZ8KmJwE371/V5E/UevwcnHIRaYyzNQGpRasdRXTOdRI6O15KOf6mc56cjFM0CqSPXfE8kZ5MlVEaqCZ/XNPTDbU7YXJwL2xfmpaDE5ndScTwWYe6zV28f1WTP1HrSThM3BiTE5WQY4qaF2HYURDsN+Qk2LIISqZkW4lyDKF2J2TeeBpKp0HfAdlWkh4lx0Prbhu/qCgBCDTRX1S4NulWW1sbublBJ3sOiDHw6+Hw1lthQPIribe1G3JzIprNc8PD0LILzr+rZz2ZKKM0UU3+ZEpPmBP9RYFrNgcyUDdPXmOdmynvT11TlHYnES/dBqUn0Dbn6049T+DeMw7uaYra7mQ5NN5t1q1bF36iTVvsuk/9h6V0+boo14QpPQFqEs9bkJEyShPV5I9repQuQq2bjnYbfzPyjLSSidTuJGL8ufDq7axb414rjovPlGuaotajDk4COtcXCpWtlXayrRRHM3SuTRUJAydYh6x1T896MlFGaaKa/HFNj9JFqHWz7T+QXwqFI9JKJlK7k4hBE6FwFDu2uPXDDW4+U65pilqPOjhRs2WR7Q8/GuiTY1fy3fFStpUoipIKax+AUfOyrSJcZlwDTVuhpT7bShTHUQcnATNmzAg/0Tf+ndZsojNG5YcoJgCDJkPt8h4PZ6SM0kQ1+eOaHqWL0Oqmox3W/QVGz087qcjtTiJKpzEjbx08+iFoc6TrDDefKdc0Ra1HHZwEhD6kbc8m290zaELKSbQe7AhRUAAGT7JDTHvAtWGIoJqC4JoepYvQ6mbzQts9VTw27aQitzs+tI69EEw7/OUc2FuTbTmAm8+Ua5qi1qMOTgLWr088B0zSbHgIRs6188ukyPodEc9zNnhqwhac0MsoBFSTP67pUboIrW5W3wVjzgolqcjtjg/rd7bBnK/CoElwVzls+Ee2JTn5TLmmKWo96uBEyWv32SUQjiaKxto3pASBxoqiOMbBvfaFauw52VaSOfrkwPSr4LSvwdOfgn9/Atr2Z1uV4hDq4CRgzJgx4SVW9xo0VsOw2WklM6Yk4qV4fAKNQy2jkFBN/rimR+kilLpZ+yAcdxIUlKSfFlmwOz4cpmfIiXDOr6FhLdx7KuxalR1NDj5TrmmKWo86OAkYNiy1uWrisvynMP6d1mFIg2HFWZi0aeCkHrup4paRMfDfH8AdM2DhDdB+MMMCA2jKMq5pck2P0kUodbPiV1B2XvrpeGTF7iTgCD39CuG0r8O4c+H+t8Azn7UvlZ0T2ZoOONBktwxNbuviM+Wapqj1qIOTgNBmOG3YYEczTHpP2kktq24JQVCSlEyGbUviHopbRi/9Al79PZR/DLa9AE8syJhRCawpy7imyTU9Shdp182Ol6HxTRhxWjiCyJLdSUBcPSIw8QJ4x+2wbwc8eDbcWgA/Hwg/7Qe/Gg6/Gmb//+v5dqb2EO2Si8+Ua5qi1uOWW94baT8IT14Nx38I8gdnW01qlJxg44eCsLcWXvgmnP0zKBoNc6fAs5+BV26H8mszq1NRXOZgCyz7Ibz+COQVw/Rr4ITL0hp0EJeqn8GEC9JuLT5qKSi1L1flH4O2FjtzfG4B9PF+7vbvhpr/wnP/C8t+BOffC8VudeUo4aAtOAkoLi5OL4H2A/DkVbZ5dHLq68Acpik/C1VWNAZaG2DfziP1dC+jZT+CsW+1zg1Abh6c+iWo/DI0bo5AbAj1lgFc0+Sanl5PSx3cdxpsXgQnXA6jz4QXfwB/Ptu2NsSQVt3srYH1f4WJ70pT8OFkxe4kILCe3ALoV9Tl3ADkD4Kyd8DbbrMLeN47G2rSb1lw8ZlyTVPUenSxzUxRvw4evwxyB8CcL0OuQxNlpcLzX4ZTvwyT3t3zOQea4bdj4JxfHbmQ6Oq7oLkG3vtYystUKO6ji23Gof0APDDfLn1y4ke77v+Odlh1h12+5X1PQsnU9PNa9AXYvR5mfjb9tI4VtlbC8lvgfU/A8PQGgSjZQRfbTIHKysrULlx9L9w31y5wd/qNoTo3leuaQ0srKUpOgK3PH7H7sDJa8yc4rjz+KulTL4WGdbDm/gyKjKPJEVzT5JqeXk3lV0Fy4cSPHO7c98mBEz8MUz9kA2O32DpJuW727YJXb7fphUzW7E4PhKpn1DyY9f/g7xfaeMlUNTn4TLmmKWo96uAkoK0thQXmXv6V7dudfzNMvjj01oq2bE0oetyJ8OYzR+w+VEbGwEu/hAnnx78+py/MvsHG42R45tGU6i3DuKbJNT29lq0vwKo7oeKGnmNtxp8Hs/8X/nERvHxb6nWz5Dsw9uz4LxhpkjW70wOh6xk1D05YAH89L+U1rlx8plzTFLUedXDCZNMTsPhGOPNHdtXb3kTpdDvPxP7d8Y/XLoP99TAsQe9EyfEw/gJ44qpIR1UpSlZoPwD//B84+RM27iMRw2fDW2+Bqlth9wY7CioZ6l6D1+628T1Kaky80Nqvhy+2dacc9WgMTgI6Ojro0yegD9i0Fe4+GU77hm3tyJQmY+iTrRiW578Csz4HUz/QpaezjJ64CnLy4YRLE6fR0WbnqCj/OMz8dEZkJlVvEeGapkzp0RicGJZ8FzY9CWd8J3hLbvsBOtY8QJ8Nf4NTPg2zv2DneElERzvcPw+Gn2pbjTNAVu1OHDKmp6MdFn/Lrt/1zruSGuHm2jMO7mmK2u64880dZNWqgDNiGmOHgk+4MKPODcCqrVmcinzEaXaERgyrVq2yo0A2PNRz91QsfXJt0PXiG2HnqxmRGbjeIsQ1Ta7p6XXUr4VlP4FTrkuumzqnH6sGvt8G6m9fCr+fZKdY6Gjv+ZrKr9gXh0kXpa+7B7Jqd+KQMT19cqx92vkKPPUJOwI2qCYHnynXNEWtRx2ceDRvh8qvUbdpuZ3y3K+V65XboXlLJM3Ddc0JDF2mGT3PdsMdaOrSU1dnZ2ke81bIGxgsnaLRdjTJox+Eg/tCl1lXVxd6munimibX9PQqOtrgiSth2oKU4mHqmtvtdXO+DKd/y8b13XUSbHzscFtkOuCFb9nA/dO+Fv58Ot01OURG9eQWwLzv2aHjfzv/yOkt2vbD9v/Cmgdg7Z8PzZjs4jPlmqao9ehEf91542l47BIYfRbI8fCfb9ghzhfcF7+puGGDnePlzB8dPtdCbyS/BIbNglf/ALO8YagdB2Hlb+Cc25JLq+xcu77V09fBeX8IX6uiZIsXvmn/htGiUjIVzvqxHcr87Odg4edhwrvsyMzXH7HnnPXj4C8XSjD6FcL8H8Br98Kd0+G4k6FgCDS+AfWv2UWIC0cBBurXQN4gGHGTdUAd6so71tEYnFhef8R2NZ32dRh6Mg172xicb6DqFthXC+99EvoP6Tr/QBP8aS6Me3tGm4djadjbxuABWXSkGtbBf74OV74C+SU0/P1qBg/oCzOuTj6tg/vgmU/bESYnfyI8iQ0NDB7s1qzRrmnKlJ5jPgZn7Z9tjNnbfpnyQpc9PuPGwK6VsHOFfbEonQ7DKzLacuOrKUtEqufgXruA58FmKDgOBk2CvgVdx42B2mU0vPYvBvdtgXf8DkqPj0abD8e63cnokyEi54nIWhHZICJfymReabPmfnjyGph3Eww9GYCm/R12eHPFDd6Ml6fCtsX2/MY34MG3weDJMDHB5Hch07Q/y+M1B0+x3/fe2XBPBU0dhTDtitTS6tsfTv+2feNd+2BoEpuamvxPihjXNLmmJ0yyZndW3wNPfRLmfSetVbx7fMZFbIzftCtgxjUw4tRInJuEmrJEpHr6DrBlPfZsW/6xzg3Yehk+m6YTr7ct3PedDpVfy0j3e7K49pxHrSdjT4eI5AC/BN4JTAMuFZFpmcovZVob4dn/Bws/Z5skS7o87407vaGCInaSrukL4OH32kXb7jzRTmp3yqcjbZI8pCmbnHA5zP4iTLuCjYMuTK9rrmgUvOV78Mx1tjvwYPqL+m3cuDHtNMLGNU2u6QmLrNid3Rvh8SvsbN9n/tC+4aeBE894N1zT5JoegI272uwotrf/BrYvgd9NhP/ebNfny5Ymx57zqPVkso3vVGCDMWYjgIjcD1wErM5gnv4YAy/+EDY+YmNADu6FfsV22PK+Wrt1cmACbHul6/+cPDj5U3ZNpvzBkNPP3shR0l1Ttjm4F7aFMBrqhMvhv9+3k5Xll8CQE+1b6vSr0k9bOZbIvN3Ztcq+FDWsgyZvvpqBE+29une73dLBtWcc3NPkmh44XNPYs+0afktvgue/aPcVDIWBZdD/OJh6Seot30pgMungjAJiw8+3AHO6nyQi1wKdy0w3i8jaDGpCQKYPZ1peLvkA+w/S0tbReABuPuLchhb6DS7AqVcF1zSFradvDnl5ufX5sIjG/Yt2r9919espJDME2BWWppBwTVOm9IzLQJrJ4Gt30rU5pf0ZXFbCBIAOQ0fLQfYa83oH3JSG7C5ce8bBPU2u6YGeNYnQJz+X/jl9duSAXVi1cf9ju9fvWpCKbUuWY9ruZD1qzBjzW+C32dYRDxFZVtvkVsCka5pc0wNWk2uBrq5pck1PlLhsc8DdZ8olTa7pAXc1ufScR60nkxFqW4ExMf+P9vYpiqJkCrU7iqIAmXVwXgQmi8h4EekHXAI8nMH8FEVR1O4oigJksIvKGNMmItcB/wRygD8YY9yaN9ofF5uxXdPkmh5QTUFwTU8oqN3JGK5pck0PqKYgRKrHqYn+FEVRFEVRwkDXolIURVEUpdehDo6iKIqiKL0OdXAURVEURel1qIOjKIqiKEqvQx0cRVEURVF6HergKIqiKIrS61AHR1EURVGUXoc6OIqiKIqi9DrUwVEURVEUpdehDo6iKIqiKL0OdXAURVEURel1qIOjOI+I/FpEvh5BPleLSGWm81EUVxCRD4vIJ7Ot42hCRMaKSLOI5ESQV7WInJPpfHor6uA4hvfgdG4dItIS8//lIjJIRP4gIjUi0iQi60TkSzHXGxGZlCD9Qi+tJ5LUdZanp1PLFhH5s4jMTuf7BsEY83FjzHdidGxJNS0RuVFEDnrfYbeIvCAic1NIZ6GIfCRVHYqSbUTkf4FvAdeLyHd7OOcSEVkqIntFZIf3+ZMiIt7x0SLyVxHZJSJ7RGSliFztHSvz7FGnzajuZqtERD7jXbPXsykPisiJAfUvFJH9nh1sFJHlIvIlEclLu3ASYIx50xhTaIxpj9GRsi3wymivV0ZbReQnyTpP6drF3oo6OI7hPTiFxphC4E3gXTH77gV+ChQCJwADgXcDG5LI4n1AK/B2ERmepLxtnq4i4DRgDfC8iLwtyXSyzQPe9zgOqAT+1mmwFeVYQESuAj4BzPe294nIdd3OuR64FfghMBwYBnwcOAPo5512N7AZGAeUAguA2m7ZDfKet0uBb4jIed7+W4HPAp8BSoApwEPABUl8leuMMUXACOB64BLg8aPweS73yuhtwGXAR7Osp3dgjNHN0Q2oBs7ptm8l8J4E1xhgUoLjzwA3AVXADUloOQvYEmf/L4BlMf8fD/wbqAfWAh+MOXYH8EvgMaAJWApM9I4J1nnbATQCrwIzYq77LjAAaAE6gGZvGwnsA0pj8pkJ7AT6xtF7I3BPzP/TvTIbAlwNVMYcOx14Edjj/T3d238T0A7s9zT8Itv3im66Bd2wDsRqYEzMvqHAy8AHvP8HAnuB9/mk1Qyc3MOxMu/Zyo3Z9yJwAzDZe4ZOTeN7LAQ+0m3fWM8eXOj93wf4EvA6UAf8GSjppu8q7MvkLuCrMWmdCizz7FEt8JPu3yueLfBs3I+76XoY+FwP3+Mwmw082GlTiPkNAPKAW4Bt3naLty+uXcz2febCpi04Rx9LgJtE5BoRmZzMhSIyDuuo3OttV3Y7/oqIXJaknr8BM0VkgIgMwDo3f8IazEuA20RkWsz5l2CbxQdjW55u8va/A/smOQVrXD+INUiHMMbsBd6J15Lkbduwhu6DMacuAO43xhxMJNxryr4a2GyM2dXtWAnWEfsZ9s30J8BjIlJqjPkq8Dz27bHQGHMdinKUYIx5zBgzzRizOWbfDmPMycaYB71dc7E/nv/wSW4J8EuvK2tsTyd53VFnYF8oXsK2VGwxxvw3wTWXicgrAb9W5/d4E+uUvMXb9WngPcCZ2JehBqwDEss8YKqn6RsicoK3/1bgVmNMMTAR6xx1zy+eLbgTuFRE+njfYwhwDtYuJsSzlW/BllF3voptOT8ZKMc6YF9LYBePedTBOfr4NNY5uQ5YLSIbROSdAa9dALxijFkN3A9MF5FTOg8aY04yxvg+hN3Yhm19GQRcCFQbY/5ojGkzxrwE/BX4QMz5fzfG/NcY0+Z9j5O9/QexXV/HA2KMec0Ysz2ghjuBKwC8vutLsU3nPfFBEdmNbVqfBVwc55wLgPXGmLu973IftkvuXQE1KcrRzBBgl/ecAuDFq+324gLne7s/gP2B/zqwSURejhOXtwvbovs74EvGmKexLw0Jn29jzJ+MMSeloH0btssLbJfaV40xW4wxrdgW3PeLSG7M+d8yxrQYY1YAK7DOA1ibNElEhhhjmo0xS4Jk7jlte7AOE9iXuoXGmO5dd7FUiUgD8Ai2nP4Y55zLgW97zuhO7IvigiCajlXUwTnK8B7E7xljZmGNxJ+BB70WBz+uxDoVGGO2AouwzbPpMArbxLob2w8/xzOCuz0n4nJs/30nNTGf92HjiTDGPENX8+4OEfmtiBQH1PAPYJqIjAfeDuxJ9GYI/NkYM8gYM9QYc7YxZnmcc0YCb3Tb94b3fRWlt1MHDIl1BIwxpxtjBnnH+nj7GowxXzLGTMfG6LwMPNQtBmaIMWawMeYEY8zPYtIfkSHto7AOFVib9PcYe/QatktpWMz5cW0S8GFsi/IaEXlRRC5MQsOhly7vb6IXLoCZXhlNNMZ8zRjTEeec7jbpDW+f0gPq4BzFGGMage9h+2DHJzpXRE7H9nt/WewIrBpgDnBZt7eZZLkYqPKaSTcDizznoXMrNMZ8IuD3+ZnnuE3DGpb/jXdanOv2Yx29K7BvNH7GJAjbsMYxlrHA1p50KEovYjF2MMJFQS/wunl/hP3R9XvhehoYLSIVKSuMg4iMwbbKPu/t2gy8s5tNyvde8BJijFlvjLkU293+A+AvXjf8EafG2XcPcJGIlGMHhDyUyvfpRnebNNbb15OGYx51cI4yROTrIjJbRPqJSD52FMJubEBvJ/1EJD9my8G21Pwb6zyc7G0zgAJs/20yGkRERonIN4GPAF/xDj0KTBGRBSLS19tmx/RpJ0pztojMEZG+2ODG/digue7UAqUiMrDb/ruw8TTvJhwH53Hsd7lMRHJF5EPYsns0RseEEPJRFOcwxuzGdoHcJiLvF5EiEekjIidjX6gAEJEfiMgM7xkpwo7M2mCMqesh6c701wO3Afd5Q5w7bdYlEjOUPCgi0l9EzsS25v4X+/wC/BobszjOO+84EQnktInIFSJynNeastvb3ZNNOswWGGO2YAOq7wb+aoxpSfY7xeE+4GvedxgCfAPrSHVqiGcXj2nUwTn6MNj+2V1Y7/3twAXGmOaYc1Zho+o7t09gg3B/boypidk2YR/AqwBEZJWIXJ4g75Ei0hml/yJwInCWMeZfAMaYJmyw8CWethrsm0+QeSmKgduxQYBvYJuwf3jElzdmDfZB3+g1O4/09v8Ha3yqjDHdu5aSxjPQF2KHntYBX8COzOgMRr4V25ffICI/6yEZRTlqMcbcDHwee+/XettvgC8CL3in9Qf+jnUANmJbGN4dMIvP0NUtvRs70ulibBwKYuf9WuWTxi9EpMnTdgs25u+8mC6eW7EjmP7lnbcE23IdhPOAVZ7NuxW4pAdHpSdbcCfWRobxwgV2JOky4BXsKNMqb1+PdvFYR4zRli2ldyAizwB/Msb8LttaFEU5tvECse8Bxhn9oc0K6cReKIozeCM3ZpJEzICiKEom8LraPwv8Tp2b7KFdVMpRj4jcCTwF/D+vm0xRFCUreDGHu7GjxG7JspxjGu2iUhRFURSl16EtOIqiKIqi9DrUwVEURVEUpdfhVJDxkCFDTFlZWbZlKIqSIsuXL99ljDku2zqCojZHUY5+erI7Tjk4ZWVlLFu2LNsyDlFdXY1rxs81Ta7pAdUUhEzpEZG05yCKEtdsDrh3r4B7mlzTA6opCFHbHe2iSkB1dXW2JRyBa5pc0wOqKQiu6VG6cLFuXNPkmh5QTUGIWo86OC5iDLQfyLYKRVGUw+lo8z9HURxBHZwETJgQ8VJD+3fDU5+Cnw+EnxfBn06HN5/NriYfXNMDqikIrulRunCxbiZMmAD7dsBP+8LyW7Mtx90ycgzXNEWtRx2cBBQVFUWXWUsdPDAfGqvhvD/CRQ/DuHPg8cug8qvgLa0SqaYAuKYHVFMQXNOjdOFi3RQVFcG6v0DhSHj19mzLcbeMHMM1TVHrUQcnAStWrIgmI9MBj7wfSk6AihugoBRy82Ds2fC222DDw/Dvj4PpiE5TQFzTA6opCK7pUbpwsW5WrFhhW5OPvxz2bIQDzf4XZVqPY6gmf6LWow6OC1T9DFrq4aRrQeTwY/mDYf7/wfYl8OznsqNPURRlx0tQegIMHA+7VmZbjaL4og5OAkpLSzOfyb5dsPjbUHE99MmJf07fATDvJtj0BKUHX8+8piSIpIySRDX545oe9XC4ZgAAIABJREFUpQsX66a0ZBA0b4bC0XbbvT67elwsI9XkS9R61MFJwPTp0zOfyZJvw5i3QvHYxOf1K4K3fI/pG78FK36beV0BiaSMkkQ1+eOaHqULF+tm+sg86D8ccvrCgOHQkF0Hx8kyUk2+RK1HHZwEPPfcc5nNYG8trL4Lpl0e7PyCITw37hZ44euw6q7MagtIxssoBVSTP67pUbpwsW6eq1pnA4wB+g+FPdXZ1eNiGakmX6LWow5ONqm6xbbe5JcEvyanH7zlB/DcF6Dq55nTpiiK0kn7ASgYaj/3HwZNR9WE1coxijo4CcjNzeBKFgf3wYrfwJT3J3VZbh9gYBmc9RPrID1xNbTuyYTCYHoyWUYpopr8cU2P0oWLdZPbsd+O7gTofxw0b8uuHhfLSDX5ErUeMcZEmmEiKioqjGvrwmSMV/8AK/8A876behoH98Erv4Xti2HW9TD9KhgwLDyNipIkIrLcGFORbR1BOaZsTjo8fgUUjoLx77RDxB+7FD7TlG1VigL0bHe0BScBVVVVmUv8pZ/DxHclfVlV9b6uf/r2h1n/D864CTY/C7+fBHfMgMcXwNLvw/qHMv6mldEyShHV5I9repQuXKybqv3TId9rwek7AEx7VufCcbKMVJMvUetxq/3KMRobGzOTcG2VnfZ8+OykL23c33HkzsGT4NQvQvvn7fDNPZtg16uw8TGoWwXFZTD7Bjj+siPn2UmTjJVRGqgmf1zTo3ThYt00mmLIz7P/iEDBENhbA/0mZUePi2WkmnyJWo86ONngldth/LkgITeg5fSF0ml266SjHWqXw5Lvwau/h3f/1U4eqCiKEpSONsgf3vV/fol1cAZnx8FRlCBoF1UCKioyEEpwsAXW3g/jzk3p8oqyguQu6JMDI06Fs39m37runxdqUHJGyihNVJM/rulRunCubkwHFTU/gryBXfvyB8O+mqxJcq6MUE1BiFqPOjgJqK2tDT/R1x+GwVNTDgaubWxLLd8+OVD+CZv3Ix84tHhnumSkjNJENfnjmh6lC+fqpqWe2qLToE9Mg3/eINvNniWcKyNUUxCi1qMOTgI2b94cfqIr/wDj3pby5ZvrD6aetwic/CnYu90OUQ+BjJRRmqgmf1zTo3ThXN207GRz4fzD9/UbaCcqzRLOlRGqKQhR61EHJ0qat9tFM0e9JXsa+uTArM9D5Vez+gamKMpRQksdSLd18vIGZrWLSlGCoA5OAiZPnhxugq/da52b3PyUk5g8tF/6OgaOtzMoL7kp7aRCL6MQUE3+uKZH6cK5umnZxeSOlw7flz8oqy04zpURqikIUetRBycBeXl54Sa46s60uqcA8vqGVGUnXG7XwUpznpzQyygEVJM/rulRunCubvbXkZfTLWYvbxC07MyOHhwsI1RTEKLWow5OAlauXBleYjtfhf274LjytJJZuXV/OHoKSmDM2fDSL9LTE2YZhYRq8sc1PUoXztVNSx0rc844fF/eIGjZlR09OFhGqKYgRK1HHZyoWHUHjD0n/Llv0mHyxXaph4Mt2VaiKIqr7O8hBieLDo6iBCFjv7YiMkZEnhWR1SKySkQ+m6m8MsXQoUPDSaijHV77E4w7J+2khhaFODdj0WgomQrrHkxdT1hlFCKqyR/X9ChdOFc3LXUM7dttBtp+A+18Wh3tWZHkXBmhmoIQtZ5MNie0AdcbY6YBpwGfEpFpPtc4xZQpU8JJ6M2nbZdQ8bi0k5oyPOQ+zLJ3wKu/S/ny0MooRFSTP67pSRYR+YOI7BCRlTH7SkTk3yKy3vt7VE7Z7Vzd7K9jSlHd4fv65EDfQtjfkBVJzpURqikIUevJmINjjNlujKnyPjcBrwGjMpVfJqisrAwnoZV/sN1TIVC5fm8o6RxixOl2vardG1PTE1YZhYhq8sc1PSlwB3Bet31fAp42xkwGnvb+P+pwrm7211PZeMKR+/OzF4fjXBmhmoIQtZ5I1qISkTLgFGBpnGPXAtcCjBw5koULFwIwYcIEioqKWLFiBQClpaVMnz6d5557DoDc3FzmzZtHVVXVoQW8KioqqK2tPTSZ0OTJk8nLyzsU2DR06FCmTJlyqJDz8vKYO3cuy5Yto7nZrow7Z84ctmzZwtatW2lubmb79u3k5OSwevVqAIYPH8748eNZvHgxAAUFBcyZM4elS5fS0mJjWebOncumTZuoqakB08G0bW/SXnEpa9fYPEYN6svokr4s3WhXBi/M60PF+P4s3rCX1jYDwLzJA1hX08qOJjtz8YxR+bQe7KB5fzsL1zQzpqQvw4pzWVZt8yzO78PMsv5UrmumzRvwMH/qAFZt3U9ds21GLh+TT9P+DjbuPABA2ZB+lAzIoWr0zfCfpQwe3UR5eTmLFi3CGIOIcOaZZ7JixQoaGuyb2syZM6mvr6e6uhqAAwcO0NDQkNV6Apg6deqhempubmbNmjXJ1RMwbdo02tvbWbt2ra2nUaMYPXo0S5fa27awsJCKigoWL15Ma2urrad581i3bh07dtg5hWbMmEFrayvr168HYMyYMQwbNozm5mYWLlxIcXExM2fOpLKykrY2W7fz589n1apV1NXZt+Ty8nKamprYuNE6nWVlZZSUlBxaiXfw4MFJ11P356mlpYWOjo7Q6ykqjDHPeXYllouAs7zPdwILgS9GJqq3sr8BinOO3H9oJNXxkUtSlCCIMSazGYgUAouAm4wxf0t0bkVFhVm27P+3d+bhVVXn4n4/MhFIAiSMCSCjILMQxQjO81Rt623rrXWoQ221vdbW1mp/VzteO9jeax9rtdVqrVMdaq3WEUXEIoIIMohBAmoiM0EShpBh/f5YOyGEc84+OTl775Xke5/nPJxhn31e1tr7y9prr7W+xYH6tIcFCxZQVlbWsZ0sv8dOxz76R+lx+mAXZWN6p2VfLWxdAUvvgEvfa79POsoozaiTP0H5iMjbxphQEs54DZxnjDGTvNc7jDF9vecCVDe/bvO91hdVMx588EHAjYuqZiZMmJD6RRVpbqwveJJdOcMYX5x74EXVzmVMnzKR+Zv6h95YX79+PU1NTZHWU+uLKoCGhgaOOeYYZy6qFi9ezK5duxgyZIgTF1VFRUXU1NSwb9++tNdTvLiTVANHRJ4E7gGeMyb5JEYikgU8A7xgjPmN3/auNXDSwkNHw6gzoWR21CbxMU3wrwvh/Beh/6SobZROTCoNnA7ElxHEaeB4r6uNMQnH4XTJmJNuftcHzngAcgoOfH/Rr+1MzMmXReOlKB7x4k6yY3B+D/wnsEZEbhWRcUn8oGCD1nvJNG5cpMOBr3oN7FgDQ45KjxCweN3utO2rBekBQ4+F1Y+038fBPw7q5I9jPu2OL3HYJCJDALx/O2UuEqfqpqkR6mtZXBXrFlU+7I5msT+nyshDnfwJ2yepBo4x5mVjzJeB6cB64GUR+beIXOr10sRiFvAV4EQRWeo9zkyLdUg0d02mzIp7YdhJB2bh7SC1denJAn4QJcfAmifa/bUOl1EAqJM/LvmkGF9i8TRwsff8YuAf6TUNB5fqhn07Ias3tfti9PRnFUQ2yNipMvJQJ3/C9kl6FpWIFAGXAJcD7wD/hw1IL8Xa3hgz3xgjxpgpxphp3uNfaXDuHDQ1wIr7YORpUZskR9FhNqne9vejNlG6Ie2NLyLyMLAAGCcilSJyGXArcIqIrAFO9l4rHWFvNWQXxP4spyDSdA2K4kdSXQsi8ndgHPAAcI4xZoP30aMi4lYfWBqZOXNm6l9e9zz0GmATW6aRmaN6pXV/LUgPO05ozRMw88bkfTpSRgGhTv645JNKfDHGXBBndx1L9uYALtUNddWQnR877uT0gW2rwnfCsTLyUCd/wvZJtgfnj8aYCcaY/2kOPiKSAxDWjIkoqKysTP3L794NI9Lfe1O5vT7t+2yhZBasSTjR7SA6VEYBoU7+OObTLeNLPJyqm722gRMz7mQX2DQOEeBUGXmokz9h+yTbwPlpjPcWpFPERVpP22wXtRug8jWbzDLNVO0IsIEzYCpUf9CuDOMpl1GAqJM/jvl0y/gSD6fqZm81ZOXHjjvZBbBne/hOOFZGHurkT9g+CW9Richg7OrDuSJyOCDeRwVAQPdKugAr7oWhx0FWbtQm7aNHpp3xtfZpmHpV1DZKF0fjSyegrtqmZIhFTgHsjaaBoyjJ4DcG5zTswL+hQOup3jVA8gM1OinjxqUwW9U02dtTM3+QfiFgXLpzUbWl+CibfDPJBk5KZRQw6uSPIz7dOr7Ew5G6sezZDtm9Y8edrHybcNMYEDn48wBxqow81MmfsH0SNnCMMfcD94vI540x7Z9D3MnJyIix9oMf61+ErF7QL5iKzAgyPSrA4CNh8W02cOX08fdJpYwCRp38ccGnu8eXeLhQNy3s3Q5ZebHjTkYWZObYWNHzoAWjA8WpMvJQJ3/C9kn451JELvSejhCR69o+QvCLlOYluNvF0t/DyDMCu6JZ9UldIPttIauXHYtTkdyM/pTKKGDUyR8XfLp7fImHC3XTwp4tkF0QP+7k9I3kNpVTZeShTv6E7ePXH9Cc9CgPyI/xUFpTUwWV89KWOTwyisug/PGoLZSuj8YX19mz1a5YHI8IZ1Ipih9+t6ju8v5NT6bITsbgwYPb94V374bhJ9hekIAY3CeEBPDFs+z/pX6P70DpdpdRCKiTPy74dPf4Eg8X6qaFPdsgu0/8uJNTYLcJGafKyEOd/AnbJ6kRHSLySxEpEJEsEZkjIltadS93WUaObMcifU0NsPyPMOrs4ISAkf2zA90/YO+n9zsU1r/g79OeMgoJdfLHJZ/uGl/i4VLdsHc7ZBfEjzsR9eA4VUYe6uRP2D7JDlk91RizEzgbmytmDHB9UFKu0Jz2PinWPg29BkHf0cEJAQvWBpBsMxYls6D8b76btauMQkKd/HHMp1vGl3g4VTd7tkJO3/hxJysvkrVwnCojD3XyJ2yfZBs4zf2TZwGPGWM+Dcin87LkdhgdbO9NqJQcCxXP2ttUihIsGl9cpKkR6nbY21DxyM7XMTiKsyTbwHlGRFYDM4A5IjIA2Buclhvk5ia5UN+292DbSpuRO2Bys0JabyK3EArHQ8UziTdLtoxCRJ38ccynW8aXeDhTN3u32wZMj8z4cSc7P5KM4s6UUSvUyZ+wfcQYk9yGIoXAp8aYRhHpBRQYYzamU6a0tNQsXtwJc3e+/A2o3wWTLo3aJL2se8423j77dNQmSidBRN5OJX9UGPElFp025oTB1pXw1GfgtHvib/PhS7BjLZz9SHheitKGeHGnPcvGjQe+KCIXAecDp6ZLzlUWLlzov1HdTlj9UOCDi5tZuHZXKL8D2B6pyrmwe0t8n2TKKGTUyR/XfOiG8SUeztRN7SfQsxBIEHeyCyLpwXGmjFqhTv6E7ZPUnGMReQAYDSwFGr23DfCXgLycYM+eJMafrLgHBh0BvQYELwTsqU+uxy0tZOdB8WxYeT8c8d3YPsmUUciokz8u+XTX+BIPZ+pm14aWBk7cuJMdzTRxZ8qoFerkT9g+yS6qUgpMMMnez+ouNDXC2/8LR34/apPgGHkGvHM7lH4n9HwzSrdB44uL1FZCblHibbI14abiLsneoloBuLViUAiUlZUl3mDtP+wMg6IJ4QgBZaNDTrLcfxIg9l57LB+/MooAdfLHMZ9uGV/i4Uzd7KiAXgOBBHEnpw/srQ5RyuJMGbVCnfwJ2yfZBk5/YJWIvCAiTzc/ghRzgXXr1sX/0BhYeCuMPT88IWDd1n2h/h4icOjn4K1bY/skKqOIUCd/HPPplvElHs7UzacV0Nu2O+PGnaze0LAHGsONS86UUSvUyZ+wfZJt4NwCnAf8HLit1aNLs3FjgkkcVW/A7o1QMjs8IWDjpw2h/h4Aw06ys6k2HDxALGEZRYQ6+eOYzy10w/gSD2fqproc8ocBCeKOiNeLE+5tKmfKqBXq5E/YPkk1cIwxr2FXGM3yni8ClgTo5T5v/sT23vRwKx19IGRkwYQL4bXv2p4rRUkjGl8cpG6nbbT0SuLOYU7fhDMtFSUqks1FdQXwOHCX91YJ8FRQUq4wYUKcsTWb3oYty2Dk6eEKAROKc0L/TQBGnG6nja558kCfeGUUIerkj0s+3TW+xMOJutm8BPqObbmASxh3evYNfaq4E2XUBnXyJ2yfZG9RXQ3MAnYCGGPWAAODknKFxsbG2B/M/yGM+yJkhJD4sg2NTaH/pKVHBsy4FuZcfcC00LhlFCHq5I9jPt0yvsTDibr5eC70n9jyMmHcyekDe8LtwXGijNqgTv6E7ZNsA6fOGNMyikxEMrHrVMRFRO4Vkc0isqIjglHy/vvvH/xm1Ru292bUWeELAe9vrIvkdwHoPxmGnQDPXmCnyBOnjCJGnfxxzKfd8aUr40TdVDwDg2a0vEwYd7ILQr9F5UQZtUGd/AnbJ9kGzmsiciOQKyKnAI8B//T5zn1A+PdwgsQYmHsdTLgokt4bJ5h8mZ0W+so1Oh5HSRepxJe4iMh6EVkuIktFRPMwtJddm6B6DfSfktz22X1gl1uDWRUFkm/g3ABsAZYDXwP+Bfww0ReMMfOATr0CVElJyYFvrH4Y9u2EEdGtIl/SNyuy3wagRyaU3QyVr8NLV1FSPCRanxgcVG8O4JqTYz7tji9JcIIxZloqebGiJvK6+fBFGDTdTi7wSBh3evazqx6HSORlFAN18idsn6RWMjbGNInIU8BTxpi09kWKyJXAlQDFxcXMnTsXgFGjRpGfn8+yZcsAKCoqYuLEicybN8+KZ2Yye/ZslixZws6dOwEoLS1l06ZNfPzxxwCMHTuWnJwcVqywd8kGDhzIoYceyvz58wHIycmhrKyMxYsXU1tbC8DMmTOprKykqqoKYwx5eXlkZGSwauVy2LqVwSNvYGQDLFhrt8/NEmaO7s3CtbtaljMvG92LdVv3tUytnFCcQ2PT/m7ekr5ZDC3MYmHFbgDycnpQOrIXCz7YRV2D3cfssb0p31jH5hq7j0klPamrb6Jy+z6qdtQzrDCLQQWZLF5vl74u6NmD6SN6Mb+8lgbvfvmx43qzsmov22rt7aSpw3pSs7eJii32bsCI/tkU9s5gyYd2H/16ZTB1eC6vra7FAAIcNz6PZR/toXq33cf0Q3LZviub9UN+BrUbGLr8T1QXXMOy1esiqyeAcePG2XpatQpjDI2NjYwcOZIFCxbYesrNZebMmSxcuLBlufCysjLWrVvXMnVxwoQJNDY2tnSjlpSUMHTo0Jb8KXl5eZSWlrJgwQLq6mxdzp49m/LycjZv3mzradIk6urqWLNmDQDDhg1j0KBBLa4FBQVMnz6d+fPn09Bg6/bYY49l5cqVbNtmxzZNnTqVmpoaKioqbD2NGEFhYSFLltiJRf369WPq1Km89tprGGMQEY477jiWLVtGdbVddG369Ols376d9evXAwefT3369KGpqSnt9ZQKQcaXzsjQoUOjFfjolYN6b4YWJmrgFNoenxCJvIxioE7+hO2TMJu4iAhwM3AN+3t7GoHfGWN+7LtzkRHAM8aYScnIuJbZd+7cuRx//PH2xQtX2IF0M66N1ml1LcePz4vUoYWmBuYu/YjjN90CZz0MJbOiNgLa1JsjuOYUlE97sol3NL4k2O86oBo7jucuY8zdbT5vfVE148EHHwTcuKgCqK+vZ+rUqaxatQqAwYMHh9tYf+9V6DWISYf0pa6+iTWb91G7t5HDinvGvqhauZmG3dug8LDQGuvl5eVkZmZGWk+tL6oA9u7dywknnODMRVWze3FxsRMXVUVFRWzduhXxUv6ks57ixR2/Bs51wBnAlcaYdd57o4A7geeNMb+N+2W6UAOn4l/w4uVwyt02AWWUTi41cPB8Ct6Ft38Lk6+Ao2+JfHySa40JcM/JkQZOh+JLgv2WGGOqRGQg8BLwTe+W+UG4FnMg4mOlfhfc0R8++097O7rZKVHc2bUJ5n4brvokJEn3zidQp2QIO+74jcH5CnBBc/ABMMZUABcCF6VX0T3y8vKgdgO88FU44nuRN27A3s5yibycHlB8NJx8F1TOg7+WwuZl0TrlRV9PbXHNyRGfQOKLMabK+3cz8HfgyA56hkqkdbNlOfQZeUDjBnziTs9CO4uqKbwpwI4cvwegTv6E7eP31zLLGHPQCk7effKEo11F5GFgATBORCpF5LLUNaOh9PCp8M//gFFnw8BpUesAUDoy5GSbPrT45BbCrJ/AyDPhsRPhte/Zq8EonErdG1fqmpMjPinHl3iISG8RyW9+DpyKTebZaYi0brausA2cNiSMOxlZoQ80duT4PQB18idsH78GTqIMagmzqxljLjDGDDHGZBljhhpj7mm/XrQseO4vYJrgsP+MWqWFBR9E02iIxwE+InZ151P+aNcKumcsLPsDNIS7dk/zPXCXcM3JEZ+U40sCBgHzRWQZ8BbwrDHm+RT3FQmR1s3W5S35p1rjG3d6D4adHwYkdTCOHL8HoE7+hO3jN4tqqojsjPG+AD0D8HGH5fdSt3c3zLwRxJ3bQs2zrFwhpk9uIcz8AWxbBSv/Am/8N0y8GA67EAZMsQ2hIJ3qIlwMMQ6uOTnik/b44t3imtohq4iJtG62rYThJx30tm/c6T0EdqwNbaKBI8fvAaiTP2H7JGzgGGO6QSbJGGxYCPOuh9HRDyru1BRNgGN+bq/s1r8Ifz/bNm7GfNamuiguC7yxo7hLt40vLrP9fZh4afu/13sIVLu1aq6iJJxFFTZOzGjYtQkemA7TvkHD4KPJzHDrD3BDo3HKqV0+xsCnFfDJG/DRq5CRYxcNHP/FtPaSNTQ0tEwhdQXXnILyac8sKhdwIua0IbJjpX433FEIn322Jclmi5Pfef7xq7D5HTjv6YAlPR/HzidQp2QIO+64c+/FBUyTzbM0/CQomUV5lHmf4uCaU7t8RKDvaJvq4rR7YfLl8OZP4dHjYefH6XMqL0/bvtKFa06u+Sj7iaxuqsvt+JseB3es+Z7nfcfCpiUBiR2Mi8evOvkTto82cFqz6Fewd4cdLwItqwi7hGtOKfuIwOBSOPF2KBwHDx4BGxelx8lbAMslXHNyzUfZT2R1s+09yB8e8yPf8zyvBBr3wY6KAMRi+Dh4/KqTP2H7aAOnmS3vwqJfwJHfi3kFowREjww47Mtw+DfhyTPT1shRFKWdbFsVcwZVUojY9bBWPZBeJ0XpANrAAWhqgOcuhkmX2+mOHpNK3Jso5ppT2nxKZsH0a+1A5E/X+W+fyGlSUgtnh4prTq75KPuJrG62vgsFsXtwkjrPD/08vHM7fPJmmsVi+Dh4/KqTP2H7aAMHYMntduXOkWcc8HZdfVNEQvFxzSmtPiWzYNyXbCOnfk/qTo5NjQT3nFzzUfYTWd1sXRlzkT9I8jzPHwZHXA9/Pws+CHawsYvHrzr5E7aPNnBqP7EDXad/66Apy2s2p7rWWHC45pR2nzHnQd5QeOWalHfRnHTOJVxzcs1H2U8kdVO/C2qr4t6iSvo8H3IUzP4ZvHAp7PwojYJtfBw8ftXJn7B9tIEz9zqbiiHVe89KehGB6f9l181Z81TUNorSPdjybswcVClROB5GngULftTxfSlKB+jeDZyqf8PHc+GwC2J+PKwwpXQ4geKaUyA+Wb3gyO/Dy1+D3QelKvJ3GuZeY9U1J9d8lP1EUjcbF0G/Q+N+3O7zfOznoPxxqPu0g2JxfBw8ftXJn7B9um8DxzTBq9+CSV+FzNyYmwwqcGeBpGZccwrMp/8kGHoCzLm63V8dNGhQAEIdwzUn13yU/URSN5WvQ9H4uB+3+zzv2RcGTIUP/tFBsTg+Dh6/6uRP2D7dt4Gz+mFo2AuHnBx3k8XrUx/oGhSuOQXqM+kS2LAA1j7Trq+5tjItuOfkmo+yn9Drxhiomgf9p8TdJKXzvGQWrHmiA2LxcfH4VSd/wvbpng2c+t0w7/sw5WtOJdJU2pDZ004df/kqqIuVk1FRlA6z5V2bNiWvOL37HXykHQLQWJ/e/SpKknTPv+6Lfw39xsGAyQk3K+jpXvG45hS4z6DpMOBweP0HSX+loKAgQKHUcM3JNR9lP4HWjTEw/yZ48iyo3WDfK/+bXaQvkVMq53nPfjYJ58a3UhBNjIvHrzr5E7ZP90u2WVMJ90+Gk34PeUOC/S0lPeyrgRcvh3OegKGzo7ZREqDJNh3n/b/B6zfC4CNgxxr43PNw/0Q45ta4a+B0iKV32vF0Zf8v/ftWFA9NttnM3Otg9GeSatzML68NQah9uOYUik92vk3l8PxF9vain9P8+cE7tRPXnFzzUfYTWN001sPrN8C0r8OUK6FnEfzxEBhS5tu4Sfk8HzgNPpqT2ncT4OLxq07+hO3j1pScoPlwDlTNt5msk6DBrUWDAfecQvMpmW1nesz9DpxyZ2KnhhQSgNbthA1vQvUaqK+FrN5QcIidCZI/7KBFINtLSk4B4pqPsp/A6uad2yF3IAyaYV8f+X2ovdAmyvRzSvU87z8ZFv4MGuogMyfFncTwcfD4VSd/wvbpPg2c+j3w4hW2JyDTrXxOSpIc/k146Wsw6kwYfU569ln1hs0i/9Ecuw5I/jDIyrUz7HZvhu3v2wGYw0+EUWfBIadCbmF6fltRwqCpARb9Gt6+DY7/3/3vS4/gFzjNzrMXChsXwtBjg/0tRWlD9xmDM+ebsH01HHVT0l9pMoYeHbxyTzeuOYXus3Ul/Ptm+PJC6Ds6tlNTEz16+Nx93bHWHhNblsG4L8Dwk2wwbosxUFsJGxfDprft9gOm2JQSo8+FwviLo7XbKUSC8tExOB0nrXVjDPzzfJs2Yca3U54p1aHz/N27oe8YmPXj1L4fy8ex8wnUKRnCjjvu/M+DZN1zdlXN6d9q19dWVu0NSCh1XHMK3af/RJjwFXjyDNhbHdtp5cr4329qgLd+AX89wmZOPv3PMObc2I0bsLdYxSpqAAANSklEQVSm8ofB2M/C7J/COY/b1B5Vb8Ajs+HPh8G/f2QbTAlI6BQBrvko+0lr3ax5ErYut/mhOjANvEPn+aBSqHg29e/HwMXjV538Cdun6zdwqj+A5y6CmTfawartYFttY0BSqeOaUyQ+Y86FgTPgsZNjNnK2bdsW+3tbV8JDR9nG7kl3wPgvQUZ2+347MweKy2DGtXD2IzDtaruOyINHwqPHwfuP2UZUsk4R4ZqPsp+01Y0xNh/UxEvbf5y3derIeT5gCnxaATs/7JDDAT4OHr/q5E/YPl17DE7tJ/D4KTDhYt81b5ROxpQrYdkf4KGj4bx/JL5VtK/WZoxffrcN9qPO7vCgYcCOYeg/yT6mXgVVr8PCn8Nr34EZ18GUK+xg5aAwTfa22YdzYPMSuwRC0z7I6WcTHpbMhhGn2vVIlK6NMYA5cOHSynl2iYUhMyPTAmwCz+Enw9u/hbJb7Hm7fTXk9IGmeig5xl5sOHTrXekadN0xONUfwOOnwohTYHzsZJq+u9jVQL/ebrUBXXOK3GftM7DiXtuTcvjV0Hsw1dXV9OvXz447WHm/nT0yaAZMvhxy+wfvtO09KH/M9uwcfg1Mu4bqugzrlA62vw/L74H3HrQ9SgNnQL8x0GuQ/WOyr8ZeLW9dCVuW2iXzJ33VjhnyZrK0lFGa0TE4HafddbPzQ3jidNi1Cc5+1MY8gL+fY8e+jDm3404dPc/3bIdXrrbLPBTPgqIJ0LAbJAPWvwDj/sPeRkvWJ6DjtyOokz9hx51A/zKJyOnA/wEZwJ+MMbcG+XuAvZJ576/w6rdh4iUw+uyUd1Wzt4l+AV6Ap4JrTpH7jD4bBpfCew/BPWOh92Bqeh9Lv50v2T/0Q4+FY38ZzCJm8Sg6DMr+2zawyh+De0ZTM/rb9JtxDgycntqVat1Om9dn+Z/sVPZDTrZjguL9v4rL7L/1e2zP0qJfwctft+OXJn2Vmj19nQp86SSSuJNGampqkq+bxn3w1Lkw9DjbaHj2AvjCK3YW4IaFtuGfDqeOnue5hXZ5joa9B/coDj/JpmMZdkLC3IAH+LSnjEJCnfwJ2yewBo6IZAB3AKcAlcAiEXnaGLMqkB/cVwtr/wGLf2OvDI75uZ322wEqtuxjeFHH7l2nG9ecnPDpPRhKr4Pp/wU1H1HxcW+GTznBDqqMMtdYwXAo/Q5MupSK92oY/tR5kNULxn7OTjcffET8wc1NjbYbv+p120tVOQ8GHQ4jz4Cjf2R7apIhK9fephpxKtRU2avlJ06jYsAPGf7RJzDidOvRRZZOCD3uBEBFRQXDhw/339AYePVayO4D475oG87TvgF/O9F7fk3a6jUt53lmrn20pWdfKP2uHSt54eKkBkMnXUYhok7+hO0TZA/OkcAHxpgKABF5BDgX6Hig2bXJXpHWfGwHr21dDo11kNPXjq8omWVP/u3vd+x3Gopge1WHddOKa06u+QCQD/W7bE+HK2QV2SUKtr0HH70Ci2+z4w8AemRDToFtjNXvgfqa/d8rGAmDZ8DRN0OW1xjymbGVkJJZUHI0fNQb1t9nxww102c09Blh8wf1LLSD8jNzbe9A50mREVzccYX63bD097DsThv/jvs1VJfbz/JK7ErFTQ224d/RGNhM0Od5dr4dN3ZXiU2CPO1qHTepdJjAxuCIyPnA6caYy73XXwFmGmOuabPdlcCV3stxgO8Z2S+XPqOKGNP6vX2N1DUZ0rqu7vbdZBb2wqmlIF1zcs0HOo+TAFkZZPcQMlq/bwymvol96T6e4/n0gB7ZmcRdZnbXPmpWb6Y8yV0fYowZkBbJFEgm7qQSc0KmP7A13of5OfQ+dADjAfY1UNdEcMdJM2GdUz0zyQXYtovN66v5OMGmCcsoItTJn6B8YsadyEerGmPuBu6O2iMWIrL4k0/dGjDpmpNrPqBOyeCaT5i4HHPA1o1rA7VdO15cLSN1SkzYPkEOUKgCWq8DPtR7T1EUJSg07iiKAgTbwFkEjBWRkSKSDXwJeDrA31MURdG4oygKEOAtKmNMg4hcA7yAna55rzHGrXWj/XGxG9s1J9d8QJ2SwTWftKBxJzBcc3LNB9QpGUL1cWqhP0VRFEVRlHTQ9XNRKYqiKIrS7dAGjqIoiqIoXQ5t4LRCRApF5CURWeP9e9Ca0iIyTUQWiMhKEXlXRL4YkMvpIvK+iHwgIjfE+DxHRB71Pl8oIiOC8GiHz3UissorkzkickiQPsk4tdru8yJiRCTQ6YnJ+IjIF7xyWikiDwXpk4yTiAwXkVdF5B2v7s4M2kk5EFfijmsxJ0knjTsad+JjjNGH9wB+CdzgPb8B+EWMbQ4FxnrPi4ENQN80e2QAa4FRQDawDJjQZptvAH/wnn8JeDTAcknG5wSgl/f860H6JOvkbZcPzAPeBEojLqOxwDtAP+/1wKjLCDvo7+ve8wnA+iCd9BGzniKPO67FnHY4adzRuBP3oT04B3IucL/3/H7gvLYbGGPKjTFrvOefAJuBdK/c2rLcvDFmH9C83Hw818eBk0RSyeKYHh9jzKvGmN3eyzex648ESTJlBPAT4BfAXgd8rgDuMMZUAxhjNjvgZIAC73kf4JOAnZSDcSHuuBZzknLSuKNxJxHawDmQQcaYDd7zjcCgRBuLyJHYFmoHkgPFpAQOWKa80nsv5jbGmAbgU6AozR7t8WnNZcBzAbk04+skItOBYcaYZwN2ScoHexV+qIi8ISJvis16HbXTLcCFIlIJ/Av4ZsBOysG4EHdciznJOrVG447GnQOIPFVD2IjIy8DgGB/d1PqFMcaISNw59CIyBHgAuNgYE3gumM6CiFwIlALHRezRA/gNcEmUHm3IxHYXH4+90pwnIpONMTsidLoAuM8Yc5uIlAEPiMgkPabTi8adYNG4k5BuG3e6XQPHGHNyvM9EZJOIDDHGbPACScyuPBEpAJ4FbjLGvBmAZjLLzTdvUykimdhuvm0BuCTrg4icjA3Yxxlj6gJySdYpH5gEzPV60QcDT4vIZ4wxiyPwAXsls9AYUw+sE5FybOBZFIBPsk6XAacDGGMWiEhPbEK8oLuxuxWdIO64FnOSddK4o3EnPkEONupsD+BXHDjY75cxtskG5gDXBuiRCVQAI9k/SGtim22u5sABf3+L2OdwbJf52JDqytepzfZzCXawXzJldDpwv/e8P7Ybtyhip+eAS7znh2HvhUsYdaiPljqIPO64FnPa4aRxR+NOfJcwDorO8sDeT54DrAFeBgq990uBP3nPLwTqgaWtHtMCcDkTKPdO3pu8934MfMZ73hN4DPgAeAsYFXDZ+Pm8DGxqVSZPh1BfCZ3abBtooEmyjATbfb0KWA58Keoyws5geMMLQkuBU4N20sdBdeRE3HEt5iTppHFH407ch6ZqUBRFURSly6GzqBRFURRF6XJoA0dRFEVRlC6HNnAURVEURelyaANHURRFUZQuhzZwFEVRFEXpcmgDR0kZESkSkaXeY6OIVLV6fXOrzMdLRWSm95258bLrish5Xvbd8eH+TxRFcRURyRSRZ0Vkq4hMavPZdSKyWkSWi8gyEfmNiGR5n33Ve/9dEVkhIud6798nIuu8uLTEW0m3eX/f9fa3VEQWichF4f5vlXSiDRwlZYwx24wx04wx04A/AL/1nn8du7jUdGPMFOBkDsxNEo8LgPnev4qiKAB3AquxSUgfFZGhACJyFXAqcJQxZjJwBHYl3Fxvm5uA2V4MOgp4t9U+r/di1Q3AXa32dwpwpPfZSdg1ZJROSrdL1aCEwhBgq/GWTTfGbPX7gojkAbOBE4B/AjcHaqgoivOIyM3Ap8aY73qvLwceFpGzsQ2YY42XU8nYzNW3etuNAWqAWu+z2ubnbZgHjPGe3wgcb4zZ6X1nJ/uzpyudEO3BUYLgRWCYiJSLyO9FJJkEeOcCzxtjyoFtIjIjWEVFUVzHGPOj5saN93qBMeYYwAB5xph1cb66DLvC8ToR+bOInBNnu3OA5V6er3xjTEU6/ZVo0QaOkna8q6UZwJXAFmy38iU+X7sAeMR7/gh6m0pRlCQRkdO8cTPrReRoY0wj9jb5+diUAb8VkVtafeVXIrIUG6MuC99YCQO9RaUEghdg5mKz6i4HLgbui7WtiBQCJwKTRcQAGYARkeuN5hJRFKUNxpidIlIrIiONMeuMMS8AL4jIM9gEj3ix4y3gLRF5CfgzcIu3i+uNMY+33qe3v1Hai9N10B4cJe2IyDgRGdvqrWnAhwm+cj7wgDHmEGPMCGPMMGAdcEyQnoqidGr+B7hTRPoCiIhgE4IiIsUiMr3Vtn4xqHl/d3i3qxCRPJ1F1bnRHhwlCPKA33mBpwGbffjKVp8/KyL13vMFQH/gF2328QT2NtW8gF0VRemc3An0BhaKSB12EPEbwDtAX+DXIlIM7MXeKr8qif3lAYu8+FQP3BaQuxICmk1cURRFUZQuh96iUhRFURSly6ENHEVRFEVRuhzawFEURVEUpcuhDRxFURRFUboc2sBRFEVRFKXLoQ0cRVEURVG6HNrAURRFURSly/H/AeIu3tVMtyeXAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "cov = returns.cov()\n",
        "mu = returns.mean()\n",
        "\n",
        "ones = np.ones((1, len(mu)))\n",
        "t = np.linalg.inv(cov).dot(mu) / (ones.dot(np.linalg.inv(cov)).dot(mu))\n",
        "\n",
        "np.cumsum(returns.dot(t) * 100).plot(figsize=(8,3), \n",
        "                                     title='Retorno Acumulado: Media-Varianza', \n",
        "                                     ylabel='%', \n",
        "                                     xlabel='')\n",
        "\n",
        "df = returns.dot(t).to_frame()\n",
        "df.columns = ['tg']\n",
        "df.index = df.index.strftime('%Y-%m-%d')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 227
        },
        "id": "Kbt8xMvFj8KI",
        "outputId": "86543eee-27ae-4866-c588-b444fa732bd6"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x216 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.to_csv('portafolio.csv')"
      ],
      "metadata": {
        "id": "xcOzAII_l2r0"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "ifxBI6uak71z",
        "outputId": "02d8a605-61a6-46e7-c9c7-0511a12ed134"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  tg\n",
              "Date                \n",
              "2015-02-01  0.188695\n",
              "2015-03-01 -0.103104\n",
              "2015-04-01  0.406482\n",
              "2015-05-01  0.058956\n",
              "2015-06-01 -0.014978\n",
              "...              ...\n",
              "2020-10-01 -0.145900\n",
              "2020-11-01  0.140866\n",
              "2020-12-01  0.046735\n",
              "2021-01-01  0.066213\n",
              "2021-02-01 -0.136011\n",
              "\n",
              "[73 rows x 1 columns]"
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-72181f48-7238-4183-9ee0-8e131163d3e0 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-72181f48-7238-4183-9ee0-8e131163d3e0');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "returns.cov()\n",
        "returns.mean()"
      ],
      "metadata": {
        "id": "1Wy5WRhOlj-z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.cov()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "Zz9d-MxWlngJ",
        "outputId": "dee0d84d-f180-44e2-fde8-5ecb4cf43787"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          tg\n",
              "tg  0.061531"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e3698be5-35a7-4ad7-85db-321ac3c826bf\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>tg</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>tg</th>\n",
              "      <td>0.061531</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e3698be5-35a7-4ad7-85db-321ac3c826bf')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e3698be5-35a7-4ad7-85db-321ac3c826bf button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e3698be5-35a7-4ad7-85db-321ac3c826bf');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tsMZyHG2lpxq",
        "outputId": "976de938-0a1b-4bea-8591-c58207955d98"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tg    0.138641\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "import statsmodels.formula.api as smf\n",
        "\n",
        "ff = pd.read_csv('F-F_Research_Data_Factors.csv', index_col = 0)\n",
        "\n",
        "df1 = pd.concat([ff,df], axis=1).dropna()\n",
        "df1['tg_rf'] = df1.tg - df1.RF\n",
        "\n",
        "df1.columns = [i.replace('-','_') for i in df1.columns]\n",
        "\n",
        "res = smf.ols(\"tg_rf ~ Mkt_RF + SMB + HML\", data = df1).fit()\n",
        "\n",
        "print(res.summary())"
      ],
      "metadata": {
        "id": "Z5ERyujLk2cD"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}