Efficient_CVar_Allocation.ipynb

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      "source": [
        "<a href=\"https://colab.research.google.com/github/MarcelDelhez/Albatross/blob/master/Efficient_CVar_Allocation.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WzHr8McwAbNd"
      },
      "source": [
        "# Portfolio Management Tool.\n",
        "This proof of concept aims to introduce the discussion about an interactive way to build portfolio's."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Initialization\n",
        "!pip install pandas numpy matplotlib yfinance PyPortfolioOpt\n",
        "import os\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import yfinance as yf\n",
        "\n",
        "from pypfopt import EfficientCVaR, EfficientCDaR, expected_returns, objective_functions\n",
        "from pypfopt.exceptions import OptimizationError\n",
        "\n",
        "from ipywidgets import widgets"
      ],
      "metadata": {
        "id": "sXdbiHc0AnfS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
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        "cellView": "form"
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      "execution_count": 5,
      "outputs": [
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          "name": "stdout",
          "text": [
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      "cell_type": "markdown",
      "source": [
        "## First step: chosing the building blocks.\n",
        "Into this example, the building blocks are just shares and not building blocks as such.  It is for the ease of building a proof of concept as it eliminates the development related to building blocks management."
      ],
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(Checkbox(value=True, description='AAPL - Apple Inc.'), Checkbox(value=True, description='ADBE -…"
            ],
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              "model_id": "435dfed814cf4f8fab58f6b746d94cc8"
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      ],
      "source": [
        "# @title Chosing building blocks.\n",
        "# --- Here, the instruments present into the Kern.\n",
        "\n",
        "tickers = [\"XOM\", \"SHEL\", \"CRH\", \"LIN\", \"SGO.PA\", \"DG.PA\", \"SIE.DE\", \"EMR\", \"OTIS\", \"SBUX\", \"ADS.DE\", \"BKNG\", \"MBG.DE\", \"AMZN\", \"RI.PA\", \"OR.PA\", \"NSRGY\", \"PEP\", \"UCB.BR\", \"RHHBY\", \"UEEC\", \"JNJ\", \"BMY\", \"MDT\", \"AZN\", \"KBC.BR\", \"BNP.PA\", \"STAN.L\", \"JPM\", \"AXP\", \"AGS.BR\", \"MET\", \"ZURN.SW\", \"DB1.DE\", \"NXPI\", \"QCOM\", \"MSFT\", \"ADBE\", \"ORCL\", \"AAPL\", \"ASML\", \"META\", \"NFLX\", \"ELI.BR\", \"ENEL.MI\"]\n",
        "tickers.sort()\n",
        "\n",
        "checkboxes = []\n",
        "for item in tickers:\n",
        "    checkbox = widgets.Checkbox(description=item + \" - \" + yf.Ticker(item).info['shortName'], value=True)\n",
        "    checkboxes.append(checkbox)\n",
        "\n",
        "widgets.VBox(checkboxes)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Step 2: Retrieve expected returns.\n",
        "Here as well, for the proof of concept, I made it easy into the sense I calculated the expected returns based on the mean historical returns during last two years.\n",
        "Ideally, those expected returns must be provided by Investment Office and would never be showed to customers.  This step is only for illustrating the concept.\n",
        "\n",
        "It is also at this step that the risk is estimated.\n",
        "For that, we can use diverse risk metrics like Volatility, VaR, CVaR ... Here, I opted for the *Expected Shortfall* (CDaR)."
      ],
      "metadata": {
        "id": "GA7GC69DXAxj"
      }
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[*********************100%***********************]  20 of 20 completed\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# @title Historical data to retrieve { run: \"auto\" }\n",
        "HistData = \"1y\" # @param [\"1y\", \"2y\", \"5y\", \"10y\"]\n",
        "\n",
        "selection = []\n",
        "for i in range(0, len(checkboxes)):\n",
        "    if checkboxes[i].value == True:\n",
        "        selection.append(checkboxes[i].description.split(\" \", 1)[0])\n",
        "tickers = selection\n",
        "\n",
        "ohlc = yf.download(tickers, period=HistData)\n",
        "prices = ohlc[\"Close\"]\n",
        "prices = prices.ffill()\n",
        "\n",
        "beta = 0.99\n",
        "df = prices.resample(\"ME\").first()\n",
        "mu = expected_returns.mean_historical_return(df, frequency=12)\n",
        "historical_rets = expected_returns.returns_from_prices(df).dropna()\n",
        "\n",
        "# --- Si les expected returns sont donnés par IIO ---\n",
        "#IIO_Ret = {'AMZN': .34, 'KO': .03, 'COST': .19, 'DPZ': -0.01, 'XOM': 0.21, 'TSLA': -0.10, 'MCD': .11, 'MSFT': .14, 'NAT': .27, 'SBUX': 0.01}\n",
        "#mu = pd.Series(data=IIO_Ret, index=IIO_Ret.keys(), dtype=\"float64\")\n",
        "#mu.index.name = \"Ticker\"\n",
        "mu=mu.sort_index()\n",
        "\n",
        "plt.figure(figsize=(8,10))\n",
        "sns.barplot(x=mu*100, y=mu.keys(), orient='h')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Step 3: Select the maximum allocation percentage.\n",
        "This represent the percentage of allocation that is allowed for the position that has the most weight into the portfolio.\n",
        "How smaller this number how more diversificated the portfolio will be.\n",
        "If set to 100%, the portfolio could be composed with just one building block."
      ],
      "metadata": {
        "id": "Ngbv-Ty3Ybol"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @title A view on Risk / Reward { run: \"auto\" }\n",
        "MaxPctAlloc = 8 # @param {type:\"slider\", min:3, max:100, step:1}\n",
        "\n",
        "def _plot_ef_cdar(ef, ef_param, ef_param_range, ax, show_assets, show_tickers):\n",
        "    \"\"\"\n",
        "    Helper function to plot the efficient frontier from an EfficientFrontier object\n",
        "    \"\"\"\n",
        "    mus, sigmas = [], []\n",
        "\n",
        "    # Create a portfolio for each value of ef_param_range\n",
        "    for param_value in ef_param_range:\n",
        "        try:\n",
        "            if ef_param == \"utility\":\n",
        "                ef.max_quadratic_utility(param_value)\n",
        "            elif ef_param == \"risk\":\n",
        "                ef.efficient_risk(param_value)\n",
        "            elif ef_param == \"return\":\n",
        "                ef.efficient_return(param_value)\n",
        "            else:\n",
        "                raise NotImplementedError(\n",
        "                    \"ef_param should be one of {'utility', 'risk', 'return'}\"\n",
        "                )\n",
        "        except:\n",
        "        # except exceptions.OptimizationError:\n",
        "            continue\n",
        "        #except ValueError:\n",
        "        #    warnings.warn(\n",
        "        #        \"Could not construct portfolio for parameter value {:.3f}\".format(\n",
        "        #            param_value\n",
        "        #        )\n",
        "        #    )\n",
        "\n",
        "        ret, sigma = ef.portfolio_performance()\n",
        "        mus.append(ret)\n",
        "        sigmas.append(sigma)\n",
        "\n",
        "    ax.plot(sigmas, mus, label=\"Risk/Return\")\n",
        "\n",
        "    asset_mu = ef.expected_returns\n",
        "    asset_sigma = np.sqrt(np.diag(ef.cov_matrix))\n",
        "    if show_assets:\n",
        "        ax.scatter(\n",
        "            asset_sigma,\n",
        "            asset_mu,\n",
        "            s=30,\n",
        "            color=\"k\",\n",
        "            label=\"assets\",\n",
        "        )\n",
        "        if show_tickers:\n",
        "            for i, label in enumerate(ef.tickers):\n",
        "                ax.annotate(label, (asset_sigma[i], asset_mu[i]))\n",
        "    return ax\n",
        "\n",
        "def plot_ef_cdar(\n",
        "    opt,\n",
        "    ef_param=\"return\",\n",
        "    ef_param_range=None,\n",
        "    points=100,\n",
        "    ax=None,\n",
        "    show_assets=True,\n",
        "    show_tickers=False,\n",
        "    **kwargs\n",
        "):\n",
        "    ax = ax or plt.gca()\n",
        "\n",
        "    if ef_param_range is None:\n",
        "        ef_param_range = _ef_default_returns_range(opt, points)\n",
        "    ax = _plot_ef_cdar(\n",
        "        opt,\n",
        "        ef_param,\n",
        "        ef_param_range,\n",
        "        ax=ax,\n",
        "        show_assets=show_assets,\n",
        "        show_tickers=show_tickers,\n",
        "    )\n",
        "\n",
        "    ax.legend()\n",
        "    ax.set_xlabel(\"Risk\")\n",
        "    ax.set_ylabel(\"Return\")\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.show()\n",
        "    return ax\n",
        "\n",
        "cv = EfficientCDaR(mu, historical_rets, beta=beta, weight_bounds=(0, MaxPctAlloc/100))\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "risk_range = np.linspace(0.00, 0.50, 50)\n",
        "plot_ef_cdar(cv, ax=ax, ef_param=\"return\", ef_param_range=risk_range, show_assets=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 504
        },
        "id": "QZzgccdJ1ThX",
        "outputId": "1d7313fe-3fd2-4b91-b6b8-d7963a868ebb",
        "cellView": "form"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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rbjSwcVJCx8NgR0REZMIam5SgC3A36m7Aq9bc/vZO5j4poaNgsCMiIuqgDDkpwc1OP6xxUkLHxFFqJc45MV8ceyJqLzUaLfJKqnRH17KLfj89+ntwyymuRIX6zicluNmpYKFo2X1jyTgx2LVQ/RMKKioqmnyKAZk2tbruf7/mNCOLiNrGrSYl5BRX4mpLJiXUnx7tYE9KIMNisGuh+gfQX7t2DQCE33rDnEiShOrqasjlcmF9rtVqkZ+fDxsbG6N5LiARGSdOSiAR+MnUCm5ubgCgC3fUPiRJQk1NDSwtxf7PUy6Xw8vLi4GeyMzdPCmhwRE3TkogQRjsWkEmk6Fbt25wdXVFTU2N6HLMhkajwenTp9GnTx+hp0GVSmWLn2FMRB1LU5MSsooqcD6nEEU796GgnJMSyPjwp+kOKBQKXmfVjupvTKlSqdjvRHRHOCmBTBWDHRERmZyy6lrdadHsm2//UXRnkxK62Vuhuugqhg/sj+6OnTgpgYwOgx0REXUoIicl1D2zsxj+7nY8c0BGicGOiIiMSlWNBrk3qhq/DQgnJRDdEoMdERG1m6YmJdx8ndv1surbbufPkxIaO+LGSQlkjvhTT0REBlOr0eIqJyUQCcNgR0RELZZ7oxL7T+fjSlGFQSYl8EkJRIbBYEdERM1SWlWD705exZbfsnEkswBNPTaZT0ogEofBjoiImlSj0eKHs/nYkpKN79PzUF37x6SFIT0d0L+bHSclEBkRBjsiItIjSRKOZ91AYko2th/P0XvCQm+XTogc5Imxwe7wdLARWCURNYbBjoiIAABXCiuwJSUbiSnZyLherlvu3FmJxwZ4YNxADwR42PHaNyIjxmBHRGTGiivU2JGaiy2/ZePopSLdcpWlHA/7u2HcQA8M7+PMGahEHQSDHRGRmamu1WD/6XxsScnC/tP5upv9ymTAsN7OGDfQAw8HuPE+cEQdEH9riYjMgCRJSL5UhG9SsrHjRC5uVNboXvN1s0XkIA88NsADbvYqgVUS0Z1isCMiMmEZ+WVITMnGlmPZuFJYqVve1c4Kjwd74PGBHvDrZiewQiIyJAY7IiITU1BWje3Hc7DlWA6OXynWLe+kVGBUQDdEDvLAXd5OUPCWJEQmh8GOiMgEVNVo8H16HhJTsnHwbD5qf3/8g0Iuw4i+znh8oAce6u8GayVvDExkyhjsiIg6KK1WwpHMAmz5LRvfnbyKsupa3WtBnvYYN9ADjwa5w8XWSmCVRNSeGOyIiDqYs3ml2Hr8KrYey0bujSrdco8u1hg3sO66uT6unQVWSESiMNgREXUA10qqkJiShf9Luo7M4qu65bYqCzwa1A3jBnoipIcDH+VFZOYY7IiIjFR5dS12p1/FN79l46fz1/H7ZXOwVMhwn48rIgd64H5fV6gsed0cEdVhsCMiMiK1Gi1+ulCAxJRs/C/tKirUGt1rA726IMRZi2mPhMDZ1lpglURkrBjsiIgEkyQJaTklSEzJxtbjOcgvrda91tPJBo8PrHtOq2cXFY4dOwYHG6XAaonImDHYEREJklNcicRj2UhMycbZvDLdcgcbS4wZ4I7HB3pgYPcukMnqrpvTaDRNbYqICABgFE913rRpE0aOHInAwEBERUXhxIkTzVpvx44d8PHxwYwZM5pss2DBAvj4+GD9+vUGqpaIqPVKq2rw5a9X8OTqJAxbug/v7jqDs3llUFrIMTqwG9Y8F4Kf54Xj7bEBGOTloAt1RETNIfyI3c6dO7F48WLEx8djwIABSEhIQExMDHbt2gUnJ6cm18vKysLSpUsREhLSZJvvv/8ex48fh6ura1uUTkTULDUaLX44m49vUrKxJz0P1bVa3WuhvRwxbqAHHgnsBntrS4FVEpEpEB7s1q1bh4kTJ2L8+PEAgPj4eBw4cACbN2/G1KlTG11Ho9Fgzpw5eOmll5CcnIySkpIGbfLy8rBo0SJ8/vnnmDZtWpvuAxHRn0mShONZN7DltyxsP5GLwnK17rU+rp0xbqAHxga7w9PBRmCVRGRqhAY7tVqNtLQ0veAll8sRFhaGlJSUJtf75JNP4OTkhKioKCQnJzd4XavV4rXXXkNMTAz69u3b6vpuvp6l/mte4yIOx0A8jsHtXS6swNZjOdh6PAeZ1yt0y507KzEmqBseD3aHv7tdq66bY/+LxzEQy1z7vyX7KzTYFRUVQaPRNDjl6uTkhIyMjEbXOXr0KL7++mskJiY2ud01a9bAwsICzz333B3Vl5qa2qxl1L44BuJxDPSVqrVIulKFg5cqcbqgRrdcqQBCPVS4t4c1glyVUMirUZufieP5d/Z+7H/xOAZisf+bJvxUbEuUlZUhNjYWixYtgqOjY6NtTp48iQ0bNuCbb76544uOAwMDoVDU3fhTo9EgNTVVbxm1L46BeByDP9RqtNh7Oh+Jx3Jw4Ew+1Jq6uwfLZUBYbyeMDXbHQ/27orOV4f6ZZf+LxzEQy1z7v36/m0NosHNwcIBCoUBBQYHe8oKCAjg7Ozdof+XKFWRnZ2P69Om6ZVpt3UXI/fv3x65du3D06FEUFBTg/vvv17XRaDRYunQpNmzYgH379jW7PoVC0eAHp7Fl1L44BuKZ+xgcyShA3LY0nL5aqlvm180O4wa6Y2ywB7raqdr0/c29/40Bx0As9n/ThAY7pVIJf39/JCUlITw8HEBdUEtKSkJ0dHSD9t7e3ti+fbvesg8//BDl5eV488034ebmhrFjxyIsLEyvTUxMDMaOHYvIyMi22xkiMnm5Nyrx9x2n8O2JXABAFxtLPBHSHeMGecDXzU5wdURERnAqdsqUKZg7dy4CAgIQFBSEhIQEVFZW6kJYbGwsunbtitmzZ8PKygr9+vXTW9/Oru4f0/rlSqUSDg4Oem0sLS3h7OwMb2/vdtgjIjI11bUarD2UiY/3nUdljQYyGfD0UC/MecgHDp34FAgiMh7Cg11ERAQKCwuxcuVK5Ofnw8/PD2vXrtWdis3NzYVcbhT3USYiM7T/9DXEb0/DxYK6Ga6Dezgg/jF/BHjYC66MiKgh4cEOAKKjoxs99QoAGzduvOW6S5Ysue32W3JdHRERAFwqKMfb29Ox9/Q1AICLrRXeeMQX4wZ68GkQRGS0jCLYEREZiwp1Lf65/wJW/5ABtUYLC7kMfxneCy+N7ANbFZ8MQUTGjcGOiAh1T4rYmXoVf9+RjpwbVQCAe/o6Y+GY/ujjaiu4OiKi5mGwIyKzdzavFAu3piEpo+7WSx5drDH/0f542L8rT7sSUYfCYEdEZqukqgYffn8OCUkXodFKsLKQ44V7e+OFe3vDWsl7ZBFRx8NgR0RmR6uVsPm3LCzddRrXy9QAgIf6d8X8R/uju6ON4OqIiFqPwY6IzMqJrGIs3JaGlMvFAABv505Y+Jg/7u3nIrYwIiIDYLAjIrNQWK7Gsv+dxn9/vQJJAjopFXj5gb6YMqwXlBa8VyYRmQYGOyIyabUaLf7zy2W8978zKKmqBQA8HuyONyL82vyZrkRE7Y3BjohM1i+ZhViw9SROXy0FAPh1s8PbY/0xpKej4MqIiNoGgx0RmZyrN6qw+LtT2HosBwBgb22JOQ/1w9OhPaCQ8/YlRGS6GOyIyGSoa7X410+ZWLn3HCrUGshkwFNDvTDnIR84dlKKLo+IqM0x2BGRSThw5hre3p6OjOvlAIBBXl0Q/1gAAj3tBVdGRNR+GOyIqEO7XFCBRTvS8X16HgDAubMV3njEF+MGekDO065EZGYY7IioQ6pUa/DpgfP47IcMqGu1sJDLMDmsJ14O7ws7laXo8oiIhGCwI6IORZIk7Dp5Fe/sOIXs4koAwLA+Togb44++XW0FV0dEJBaDHRF1GOfyShG3PQ0/nS8AAHh0scZbo/0wKsANMhlPuxIRMdgRkdErrarBij3nsP7wRdRqJSgt5HhhhDem39cH1kqF6PKIiIwGgx0RGS2tVsKWlGws/u40rpdVAwDC/bpiwaP94eVkI7g6IiLjw2BHREbpZPYNLNh6Er9dLgYA9HLuhIVj+uM+H1exhRERGTEGOyIyKkXlaizbfQb/98tlSBJgo1TgpZF98ZfhPWFlwdOuRES3wmBHREZBo5Xwn18u473/ncGNyhoAwNhgd7zxiB/c7FWCqyMi6hgY7IhIuF8vFmLh1jSk55YAAHzdbBH/mD9CvZ0EV0ZE1LEw2BGRMNdKqrD4u9PYkpINALBTWWDOwz54eqgXLBRywdUREXU8DHZE1O7UtVqs+ykTK/eeQ7laA5kMeHJId8x5yAdOna1El0dE1GEx2BFRu/rhbD7itqchI78cABDcvQveHuuPIM8uYgsjIjIBDHZE1C6uFFZg0bfp2J2eBwBw7qzE3FG+GD/IE3I5nxpBRGQIDHZE1KaqajT49MAFfHbwAqprtVDIZZh0d0+88mBf2KksRZdHRGRSGOyIqE1IkoT/peVh0bfpyC6uBADc7e2E+LH+6NfVVnB1RESmicGOiAzu/LUyxG9Pw6Fz1wEA7vYqvDm6PyIC3SCT8bQrEVFbYbAjIoMprarBR/vO418/ZqJWK0GpkGPqCG/MuL83bJT854aIqK3xX1oiumOSJCHxWDb+sfM08kurAQDhfq6Y/2h/9HDqJLg6IiLzwWBHRHfkZPYNxG1Lw9FLRQCAnk42WDjGH/f7ugqujIjI/DDYEVGrFJWr8f73Z/Cfny9DKwHWlgq89EAfxAzvBSsLhejyiIjMEoMdEbWIRpLwn58v4/0951BcUQMAGDPAHfMifNHN3lpwdURE5s0oHsa4adMmjBw5EoGBgYiKisKJEyeatd6OHTvg4+ODGTNm6JbV1NRg2bJlGDNmDIKDgzF8+HDExsYiLy+vrconMhvJl4owd08B5m9LR3FFDXy62uL/nr8LHz01kKGOiMgICA92O3fuxOLFi/Hiiy9iy5Yt8PX1RUxMDAoKCm65XlZWFpYuXYqQkBC95VVVVUhPT8f06dPxzTff4OOPP0ZmZiamT5/elrtBZNKulVTh1S+OYeLqn5FZXAtblQXixvTHjpeH4+7eTqLLIyKi3wk/Fbtu3TpMnDgR48ePBwDEx8fjwIED2Lx5M6ZOndroOhqNBnPmzMFLL72E5ORklJSU6F6ztbXFunXr9NrPnz8fUVFRyMnJgbu7e9vtDJGJqdFosf6ni1ix9xzKqmshkwEje1rjH0/eha72NqLLIyKiPxEa7NRqNdLS0jBt2jTdMrlcjrCwMKSkpDS53ieffAInJydERUUhOTn5tu9TVlYGmUwGOzu7FtWn0WgafH3zMmpfHIP29eP563j721O4kF8OAAjysMf80T6QF12Gg7UFx0EA/g6IxzEQy1z7vyX7KzTYFRUVQaPRwMlJ/1SOk5MTMjIyGl3n6NGj+Prrr5GYmNis96iursZ7772H0aNHo3Pnzi2qLzU1tVnLqH1xDNrWtXIN1h8vwc/Zdfejs7OSIzqwM+7vqYK86DIAjoFo7H/xOAZisf+bJvxUbEuUlZUhNjYWixYtgqOj423b19TUYObMmZAkCfHx8S1+v8DAQCgUdbdt0Gg0SE1N1VtG7Ytj0LaqajRYcygTnx7MQHWtFgq5DNGhXnjlgT6ws7YEwDEQjf0vHsdALHPt//r9bg6hwc7BwQEKhaLBRImCggI4Ozs3aH/lyhVkZ2frTYTQarUAgP79+2PXrl3w8vICUBfqXnnlFeTk5CAhIaHFR+sAQKFQNPjBaWwZtS+OgWFJkoTv0/Pw9rfpyCqqBADc5e2IuMf84evW+OULHAOx2P/icQzEYv83TWiwUyqV8Pf3R1JSEsLDwwHUBbWkpCRER0c3aO/t7Y3t27frLfvwww9RXl6ON998E25ubgD+CHWXLl3Chg0b4ODg0PY7Q9QBlVTVYM6Xx7E7ve52QN3sVZgX4YdHg7pBJpMJro6IiFpK+KnYKVOmYO7cuQgICEBQUBASEhJQWVmJyMhIAEBsbCy6du2K2bNnw8rKCv369dNbv35CRP3ympoavPzyy0hPT8eqVaug0WiQn58PALC3t4dSqWzHvSMyXuevlWLqhmRkXC+HUiHHX+/phRfv74NOVsL/WSAiolYS/i94REQECgsLsXLlSuTn58PPzw9r167VnYrNzc2FXN782+3l5eVh3759AICxY8fqvbZhwwaEhoYarniiDmp32lW8+uVxlFXXwt1ehVXPhiDQ0150WUREdIeEBzsAiI6ObvTUKwBs3LjxlusuWbJE73tPT0+cOXPGYLURmRKtVsKKveewYu85AEBoL0d88swgOHe2ElwZEREZglEEOyJqe6VVNZj1xXHsOVV3Pd3ksJ54c7QfLBXCH0BDREQGwmBHZAbOXyvD1I1HkZFfDqWFHH9/PABRId1Fl0VERAbGYEdk4vak5+GVL46hrLoW3exV+Cx6MAZ07yK6LCIiagMMdkQmSquV8NG+8/hgz1kAwNCeddfTudjyejoiIlPFYEdkgkqrajD7pvvTPXd3D7w1uj+UFryejojIlDHYEZmYjPwyTN2YjPPXyqBUyPHOuABM5PV0RERmgcGOyITsO52Hmf93DKXVtehqZ4XPogdjoBefvEJEZC4Y7IhMgFYr4ZP957F8z1lIEhDSwwH/jB4EV1uV6NKIiKgdMdgRdXBl1bWY8+Vx7Eq7CgCIvssLCx715/V0RERmiMGOqAPLvF6OqRuO4tzv19O9PdYfTw71El0WEREJwmBH1EHtP3MNL/9fCkqrauFqa4XPnh2MQbyejojIrDHYEXUwkiThnwcu4L3dZyBJwOAeDvj0mUFwteP1dERE5o7BjqgDKa+uxZyvjuO7k3XX0z0d6oW4MbyejoiI6jDYEXUQlwrKMXVDMs7klcJSIUP8YwF4OpTX0xER0R8Y7Ig6gAO/X09XUlULF1srfBY9CIN7OIoui4iIjAyDHZERkyQJnx68gGX/q7uebqBXF3wWPRhdeT0dERE1gsGOyEhVqGvx2tcnsONELgDgySHdET/WH1YWCsGVERGRsWKwIzJClwsqMHXjUZy+Wnc93cIx/ngm1AsymUx0aUREZMQY7IiMzA9n8/HS/6XgRmUNnDvXXU8X0pPX0xER0e0x2BEZkY1HLmHh1pPQSsCA7l2wKnow3Ox5PR0RETUPgx2Rkdh7Kg8Ltp6EJAETQzzx9tgAqCx5PR0RETUfgx2RETh9tQQv/18KJAl4aqgX/jEugNfTERFRi/F29USCFZRV468JR1Gu1uAub0e8PdafoY6IiFqFwY5IoOpaDV74dzKyiirRw8kGnz4zGJYK/loSEVHr8BOESBBJkvDmlpP49WIRbFUW+HzSEDh0Uooui4iIOjAGOyJB1hzKwNfJWZDLgE+eHoQ+rp1Fl0RERB0cgx2RAHvS87D4u9MAgPmP9seIfi6CKyIiIlPAYEfUzk5fLcHM/9bNgH061AuTw3qKLomIiEwEgx1RO7p+0wzYu72dEP8YZ8ASEZHhMNgRtZPqWg1e2PjHDNh/PjOIM2CJiMig+KlC1A7qZ8AevcQZsERE1HYY7IjaAWfAEhFRe2CwI2pjN8+AXcAZsERE1IYY7Ija0J9nwE7iDFgiImpDRhHsNm3ahJEjRyIwMBBRUVE4ceJEs9bbsWMHfHx8MGPGDL3lkiRhxYoVGD58OIKCgjB58mRcvHixDSonatr1smrErOcMWCIiaj/Cg93OnTuxePFivPjii9iyZQt8fX0RExODgoKCW66XlZWFpUuXIiQkpMFra9aswcaNGxEXF4cvv/wS1tbWiImJQXV1dVvtBpGe+hmw2cWV6MkZsERE1E6Ef9KsW7cOEydOxPjx49GnTx/Ex8dDpVJh8+bNTa6j0WgwZ84cvPTSS+jevbvea5IkYcOGDZg+fTrCw8Ph6+uLd999F9euXcOePXvaeneIGsyAXcsZsERE1E6EBju1Wo20tDSEhYXplsnlcoSFhSElJaXJ9T755BM4OTkhKiqqwWtZWVnIz8/X26atrS0GDBhwy20SGcrqHzgDloiIxLAQ+eZFRUXQaDRwcnLSW+7k5ISMjIxG1zl69Ci+/vprJCYmNvp6fn6+bht/3ub169dbVJ9Go2nw9c3LqH11hDHYe+oaluyqmwH71mg/DOvtaNT1tlRHGANTxv4Xj2Mglrn2f0v2V2iwa6mysjLExsZi0aJFcHR0bPP3S01NbdYyal/GOgaXbtRg3r5CSBLwkLc1glSFOHasSHRZbcJYx8BcsP/F4xiIxf5vmtBg5+DgAIVC0WCiREFBAZydnRu0v3LlCrKzszF9+nTdMq1WCwDo378/du3aBRcXF902XF1d9bbp6+vbovoCAwOhUCgA1KXl1NRUvWXUvox5DK6XVePlT5NQVSvhbm9HrJwcYpKTJYx5DMwB+188joFY5tr/9fvdHEKDnVKphL+/P5KSkhAeHg6gLqglJSUhOjq6QXtvb29s375db9mHH36I8vJyvPnmm3Bzc4OlpSVcXFyQlJQEPz8/AHVH+o4fP46nnnqqRfUpFIoGPziNLaP2ZWxjUF2rwYv/OYbs4ir0dLLBp9GDoVJaii6rTRnbGJgb9r94HAOx2P9NE34qdsqUKZg7dy4CAgIQFBSEhIQEVFZWIjIyEgAQGxuLrl27Yvbs2bCyskK/fv301rezswMAveXPPfccPv30U/To0QOenp5YsWIFXF1ddeGRyJD+PAO2iw1nwBIRkRjCg11ERAQKCwuxcuVK5Ofnw8/PD2vXrtWdis3NzYVc3rJTWs8//zwqKyuxYMEClJSUYPDgwVi7di2srKzaYhfIjH2fnoevk7OgkMs4A5aIiIQTHuwAIDo6utFTrwCwcePGW667ZMmSBstkMhlmzpyJmTNnGqQ+osZUqjWI25YGAPjrPb34DFgiIhLO9K7uJmonH+8/h+ziSrjbqzDzgb6iyyEiImKwI2qNC/llWP1D3b0WF4zxh43SKA5+ExGRmWOwI2ohSZKwYOtJ1Ggk3O/jgof9u4ouiYiICACDHVGLbT+Ri5/OF8DKQo74xwIgk8lEl0RERASAwY6oRUqravDOt+kAgBn39YGXk43gioiIiP7AYEfUAh98fw7XSqvR08kG0+71Fl0OERGRHgY7omZKzynB+sOZAIC3xwZAZcm7nhMRkXFhsCNqBq1WwvytJ6GVgNGB3XjPOiIiMkoMdkTN8HVyFpIvFcFGqcBbj/qJLoeIiKhRDHZEt1FUrsbi704BAGaF90M3e2vBFRERETWOwY7oNt7932kUVdTAp6stJg/rKbocIiKiJjHYEd3Cb5eL8N9frwAAFj0eAEsFf2WIiMh48VOKqAm1Gi3mJ56EJAHjB3liaC9H0SURERHdEoMdURP+feQS0nJKYKeywBsRvqLLISIiui0GO6JGXCutwvu7zwIAXhvlC+fOVoIrIiIiuj0GO6JG/GPHKZRW1yLI0x5PD/USXQ4REVGzMNgR/cnhC9eReCwHMhnwzuMBUMhloksiIiJqFgY7opuoa7VYsDUNAPBMqBeCPLuILYiIiKgFGOyIbvL5j5k4f60MTp2UeO0hTpggIqKOhcGO6HfZxZVYufccAGBehB/sbSwFV0RERNQyDHZEv3t7exoqazQY2tMRkYM8RJdDRETUYgx2RAD2n76G/6XlQSGXYdHjAZDJOGGCiIg6HgY7MntVNRos2HYSAPCXYT3h42YruCIiIqLWYbAjs/fPAxdwpbASbnYqvBLeT3Q5RERErcZgR2Yt83o5PjtwAQCwYEx/dLKyEFwRERFR67XqU6yiogKrV6/GkSNHUFBQAK1Wq/f63r17DVIcUVuSJAkLtp6EWqPFiH4ueCTATXRJREREd6RVwe6tt97CL7/8grFjx8LFxYUXmlOHtDP1Kg6duw6lhRxvP+bPn2MiIurwWhXsfvjhB6xatQqDBw82dD1E7aKsuhaLvk0HAEy/tzd6OncSXBEREdGda9U1dnZ2dujSpYuBSyFqPyv2nMXVkip4Odpg+n29RZdDRERkEK0KdjNnzsSKFStQWVlp6HqI2tzpqyX4108XAQDxY/2hslSILYiIiMhAWnUqdt26dbh8+TLCwsLg6ekJCwv9zWzZssUgxREZmiRJmJ94EhqthFH+brjfx1V0SURERAbTqmAXHh5u6DqI2sXm37Lx68Ui2CgVWDCmv+hyiIiIDKrFwa62thYAMGHCBLi58fYQ1HEUV6ixeOcpAMDLD/SFexdrwRUREREZVouvsbOwsMDnn3+uC3hEHcWy/51BQbkafV074y/Deokuh4iIyOBaNXnirrvuwq+//mroWojazPErxfjPL5cBAIseD4DSgg9dISIi09Oqa+xGjBiB999/H2fPnoW/vz+srfVPaT3wwAPN3tamTZvw+eefIz8/H76+vpg/fz6CgoIabbt792589tlnuHz5Mmpra9GjRw9MmTIFjz/+uK5NeXk53n//fezZswfFxcXw9PTEs88+i6eeeqo1u0omQKOV8FbiSUgSEDnQA3d5O4kuiYiIqE20KtjFx8cDqJsd+2cymQynTp1q1nZ27tyJxYsXIz4+HgMGDEBCQgJiYmKwa9cuODk1/PC1t7fH9OnT4e3tDUtLS+zfvx/z5s2Dk5MT7rnnHgDAkiVLcOTIESxbtgweHh746aefEB8fD1dX1xYFTjId//n5ElKzb8BWZYE3IvxEl0NERNRmWhXsTp8+bZA3X7duHSZOnIjx48cDqAuMBw4cwObNmzF16tQG7UNDQ/W+nzRpEhITE5GcnKwLdikpKXj88cd1bZ944gl88cUXOHHiBIOdGcovrca7/zsDAHjtYR+42FoJroiIiKjttCrYGYJarUZaWhqmTZumWyaXyxEWFoaUlJTbri9JEo4cOYLMzEzMmTNHt3zgwIHYt28fJkyYAFdXV/z888/IzMzEG2+80eIaNRpNg69vXkbtqzVj8I+d6SitqoW/ux2eDPHk+N0h/h6Ixf4Xj2Mglrn2f0v2t1XB7uOPP77l63/7299uu42ioiJoNJoGp1ydnJyQkZHR5HqlpaUYMWIE1Go15HI5Fi5ciGHDhulenz9/PubPn48RI0bAwsICMpkM77zzDoYMGXLbmv4sNTW1WcuofTV3DNLy1diSUggZgGf9LJB64njbFmZG+HsgFvtfPI6BWOz/prUq2O3Zs0fv+9raWmRlZUGhUMDLy6tZwa61OnXqhMTERFRUVCApKQlLlixB9+7ddadeN27ciGPHjuHTTz+Fu7s7jh49qrvGLiwsrEXvFRgYCIWi7nFTGo0GqampesuofbVkDGo0Wrz+8WEAwJNDuiNqpH97lGjy+HsgFvtfPI6BWOba//X73RytCnaJiYkNlpWVleH1119v9lMpHBwcoFAoUFBQoLe8oKAAzs7OTa4nl8vRo0cPAICfnx8uXLiA1atXIzQ0FFVVVfjggw/w8ccf47777gMA+Pr64tSpU/j8889bHOwUCkWDH5zGllH7as4YfP7TRZy7VgbHTkrMfcSXY2Zg/D0Qi/0vHsdALPZ/0wx2M6/OnTvjpZdewsqVK5vVXqlUwt/fH0lJSbplWq0WSUlJGDhwYLPfV6vVQq1WA6g7clhTUwOZTKbXRqFQQJKkZm+TOrac4kp8uOccAOCNR3zRxUYpuCIiIqL2YdDJE6WlpSgtLW12+ylTpmDu3LkICAhAUFAQEhISUFlZicjISABAbGwsunbtitmzZwMAVq1ahYCAAHh5eUGtVuPgwYPYtm0b4uLiANSFy6FDh2LZsmVQqVRwd3fHr7/+isTERLz++uuG3FUyYou+TUeFWoOQHg4YP8hTdDlERETtplXBbsOGDXrfS5KE/Px8bN26FSNGjGj2diIiIlBYWIiVK1ciPz8ffn5+WLt2re5UbG5uLuTyPw4qVlRUID4+HlevXoVKpYK3tzeWLVuGiIgIXZvly5dj+fLlmDNnDm7cuAF3d3fMmjWLNyg2Ez+czcd3J69CIZdh0eMBkMtlt1+JiIjIRLQq2K1fv17ve7lcDkdHR4wbN67R+8/dSnR0NKKjoxt9bePGjXrfz5o1C7Nmzbrl9lxcXLB48eIW1UCmQV2rRdz2NADApLt7wq+bneCKiIiI2lergt2+ffsMXQfRHUs4fBEZ+eVw7qzEKw/2FV0OERFRu2vV5Ik33ngDZWVlDZZXVFS06kbARHfqWmkVVuytmzAR+7Av7FSWgisiIiJqf60KdomJiaiurm6wvKqqClu3br3jooha6t1dZ1BWXYsBnvaYMJgTJoiIyDy16FRsWVkZJEmCJEkoLy+HldUfz93UaDT44Ycf4OjoaPAiiW7lt8tF+Do5CwAQ95g/J0wQEZHZalGwCwkJgUwmg0wmw8MPP9zgdZlMhpdeeslgxRHdjlYrIW5b3YSJCYM9MdDLQXBFRERE4rQo2G3YsAGSJGHSpEn46KOPYG9vr3vN0tIS7u7u6Nq1q8GLJGrK18lZOJF1A7ZWFpg7yld0OUREREK1KNgNHToUALB37164u7s3eMIDUXu6UVmDpbtOAwBmhveFi63VbdYgIiIyba2aPOHh4YHk5GTMmTMHTz75JPLy8gDUTao4evSoQQskasqKPedQUK5Gb5dOeO7unqLLISIiEq5Vwe5///sfYmJioFKpkJaWpntWa1lZGVatWmXQAokacy6vFAlJFwHUTZhQWhjsscdEREQdVqs+DT/99FPEx8fjnXfegYXFH2dzBw0ahPT0dIMVR9QYSZIQtz0NGq2Eh/p3xT19XUSXREREZBRaFewyMzMREhLSYLmtrS1KSkruuCiiW9mdnoefzhdAaSHHW6P7iy6HiIjIaLQq2Dk7O+Py5csNlicnJ6N79+53XBRRU6o1Ev6+s27CxAsjvOHlZCO4IiIiIuPRqmA3ceJE/P3vf8fx48chk8mQl5eHbdu2YcmSJXjqqacMXSORztbT5cguroK7vQrT7+sjuhwiIiKj0qLbndSbOnUqtFotJk+ejMrKSkRHR0OpVCImJgZRUVGGrpEIAJBdVIktp+ueUTxvtB+slQrBFRERERmXVh2xk8lkmD59On7++Wd8++23+PLLL5GUlARbW1s88MADhq6RCACweNdpqLVAaC9HjA7sJrocIiIio9OiI3ZqtRofffQRfvrpJyiVSvz1r39FeHg4Nm/ejBdffBEKhQKTJk1qq1rJjP10/jq+O5kHOYCFj/rx5thERESNaFGwW7FiBb744guEhYXht99+w8yZMxEZGYljx47h9ddfx6hRo6BQ8PQYGVaNRov47XXPg324jw183GwFV0RERGScWhTsdu3ahaVLl+KBBx7A2bNn8dhjj6G2thbbtm3jERRqM/8+cgln88rgYGOJJ/07iy6HiIjIaLXoGru8vDwEBAQAAPr16welUonJkycz1FGbuV5WjeXfnwUAzH6wHzor+YQJIiKiprToU1Kj0cDS0lL3vUKhgI0N7yNGbee9/51BaVUtAjzsMDHEU3Q5RERERq1Fp2IlScLrr78OpVIJoG4yRVxcHKytrfXaffzxx4arkMzWiaxifHH0CgAgbow/FHIeGSYiIrqVFgW7cePG6X3/2GOPGbQYonqSJCFuWxokCRg30AMhPR2h0WhEl0VERGTUWhTsFi9e3FZ1EOnZfiIXv10uho1Sgdcf8RVdDhERUYfAK9HJ6FTVaLD0u9+fB3tvb3S1UwmuiIiIqGNgsCOj8/mPmcgurkQ3exWev8dbdDlEREQdBoMdGZVrpVX45/7zAIC5o3z5PFgiIqIWYLAjo/LB92dRrtZggKc9HhvgLrocIiKiDoXBjozGqdwSfPFr3e1N5j/aH3Le3oSIiKhFGOzIKEiShHd2pEMrAaODuiGkp6PokoiIiDocBjsyCvtOX8NP5wugVMjx+ije3oSIiKg1GOxIuBqNFn/feQoA8JfhvdDdkY+pIyIiag0GOxLu30cuISO/HE6dlHjx/t6iyyEiIuqwGOxIqOIKNT7ccw4A8OpD/WCrshRcERERUcfFYEdCrdx7Hjcqa+DT1RZPhHQXXQ4REVGHJjzYbdq0CSNHjkRgYCCioqJw4sSJJtvu3r0bkZGRCAkJQXBwMMaOHYvExMQG7S5cuIAXXngBgwcPRnBwMMaPH4+cnJw23AtqjYz8MmxIuggAeOtRP1gohP84EhERdWgWIt98586dWLx4MeLj4zFgwAAkJCQgJiYGu3btgpOTU4P29vb2mD59Ory9vWFpaYn9+/dj3rx5cHJywj333AMAuHz5Mp5++mmMHz8eL7/8Mjp37oxz587BysqqvXePbmPxd6dRq5Uw0tcV9/R1EV0OERFRhyc02K1btw4TJ07E+PHjAQDx8fE4cOAANm/ejKlTpzZoHxoaqvf9pEmTkJiYiOTkZF2w++CDDzBixAjExsbq2nl5ebXhXlBrHL5wHd+n50Ehl2FeBG9vQkREZAjCzn2p1WqkpaUhLCzsj2LkcoSFhSElJeW260uShKSkJGRmZmLIkCEAAK1WiwMHDqBnz56IiYnB3XffjaioKOzZs6fN9oNaTqOVsOjbutubRId6oY+rreCKiIiITIOwI3ZFRUXQaDQNTrk6OTkhIyOjyfVKS0sxYsQIqNVqyOVyLFy4EMOGDQMAFBQUoKKiAmvWrMErr7yCOXPm4NChQ/jb3/6GDRs2YOjQoS2qUaPRNPj65mXUOl8dzcKp3BLYqSzw0sjeze5TjoF4HAOx2P/icQzEMtf+b8n+Cj0V2xqdOnVCYmIiKioqkJSUhCVLlqB79+4IDQ2FVqsFADzwwAOYPHkyAMDPzw+//fYb/vvf/7Y42KWmpjZrGTVfZY0WS3ZdBwCM87HGpbPpuNTCbXAMxOMYiMX+F49jIBb7v2nCgp2DgwMUCgUKCgr0lhcUFMDZ2bnJ9eRyOXr06AGgLrRduHABq1evRmhoKBwcHGBhYYHevfVvctu7d28kJye3uMbAwEAoFAoAdWk5NTVVbxm13Pvfn0Vx1TX0cLLB65F3Q2nR/KsBOAbicQzEYv+LxzEQy1z7v36/m0NYsFMqlfD390dSUhLCw8MB1F0jl5SUhOjo6GZvR6vVQq1W67YZGBiIzMxMvTYXL16Eh4dHi2tUKBQNfnAaW0bNk11cic9/vAgAmBfhB2ur1t2MmGMgHsdALPa/eBwDsdj/TRN6KnbKlCmYO3cuAgICEBQUhISEBFRWViIyMhIAEBsbi65du2L27NkAgFWrViEgIABeXl5Qq9U4ePAgtm3bhri4ON02Y2JiMGvWLAwZMgShoaE4dOgQ9u/fjw0bNojYRbrJu7tOo7pWi9Bejniof1fR5RAREZkcocEuIiIChYWFWLlyJfLz8+Hn54e1a9fqTsXm5uZCLv/jVF1FRQXi4+Nx9epVqFQqeHt7Y9myZYiIiNC1efDBBxEXF4fVq1fjnXfeQa9evbBy5UqEhIS0+/7RH1IuF2HrsRzIZMD8R/tDJpOJLomIiMjkCJ88ER0d3eSp140bN+p9P2vWLMyaNeu225wwYQImTJhgkProzkmShEXfpgMAJgzyRICHveCKiIiITBOf4URt7tsTufjtcjGsLRWY87CP6HKIiIhMFoMdtamqGg2WfHcaADD9vt7oaqcSXBEREZHpYrCjNvWvnzKRXVyJbvYqPH+Pt+hyiIiITBqDHbWZ/NJq/HP/BQBA7CgfWCs5NZ2IiKgtMdhRm1n+/RmUVddigKc9xg5o+X0EiYiIqGUY7KhNnMotwRe/XgFQd3sTuZy3NyEiImprDHZkcJIk4Z0d6dBKwOjAbgjp6Si6JCIiIrPAYEcGt+/0Nfx0vgBKhRyvP+IruhwiIiKzwWBHBlWj0eLvO08BAP4yvBe6O9oIroiIiMh8MNiRQW06cgkZ+eVw6qTEi/f3Fl0OERGRWWGwI4O5UVGDD/eeAwC8+lA/2KosBVdERERkXhjsyGBW7D2H4ooa+HS1xRMh3UWXQ0REZHYY7MggMvLLsCHpIgDgzdF+sFDwR4uIiKi98dOXDGLxd6dRq5Vwv48LRvRzEV0OERGRWWKwozt2+MJ1fJ+eB4VchjdH+4kuh4iIyGwx2NEd0WglLPq27vYmz4R6oY+rreCKiIiIzBeDHd2RzclZOJVbAluVBV4J7ye6HCIiIrPGYEetVlZdi2W7zwAAZj7QF46dlIIrIiIiMm8MdtRqnx24gPzSavRwssGzd/cQXQ4REZHZY7CjVskursSaQxkAgDce8YOVhUJwRURERMRgR63y7q7TqK7VIrSXIx727yq6HCIiIgKDHbVCyuUibD2WA5kMmP9of8hkMtElERERERjsqIUkScKib9MBAOMHeSLAw15wRURERFSPwY5aZEdqLn67XAxrSwVee9hHdDlERER0EwY7araqGg2WfHcaADDtXm90tVMJroiIiIhuxmBHzZZw+CKyiirR1c4KU0d4iy6HiIiI/oTBjpqloKwaH+87DwCY85APbJQWgisiIiKiP2Owo2ZZsfccSqtr0b+bHcYP8hRdDhERETWCwY5u6/y1Mmz6+TIA4K3RfpDLeXsTIiIiY8RgR7e15LtT0GglhPu5IqyPs+hyiIiIqAkMdnRLh89fx55T16CQy/D6I36iyyEiIqJbYLCjJmm0Et7ZcQoA8EyoF/q4dhZcEREREd0Kgx016ZvfspCeWwJblQVmPtBXdDlERER0Gwx21KgKdS3e230GAPC3+/vAqbOV4IqIiIjodowi2G3atAkjR45EYGAgoqKicOLEiSbb7t69G5GRkQgJCUFwcDDGjh2LxMTEJtsvWLAAPj4+WL9+veELN2Grf8hAXkk1PB2sMSmsp+hyiIiIqBmE32V2586dWLx4MeLj4zFgwAAkJCQgJiYGu3btgpOTU4P29vb2mD59Ory9vWFpaYn9+/dj3rx5cHJywj333KPX9vvvv8fx48fh6uraXrtjEvJKqrDqYAYA4PVHfKGyVAiuiIiIiJpD+BG7devWYeLEiRg/fjz69OmD+Ph4qFQqbN68udH2oaGhePDBB9G7d294eXlh0qRJ8PHxQXJysl67vLw8LFq0CO+99x4sLS3bY1dMxvu7z6CyRoNBXl0wOrCb6HKIiIiomYQGO7VajbS0NISFhemWyeVyhIWFISUl5bbrS5KEpKQkZGZmYsiQIbrlWq0Wr732GmJiYtC3Ly/6b4m0nBv4KjkLAPDm6P6QyXgzYiIioo5C6KnYoqIiaDSaBqdcnZyckJGR0eR6paWlGDFiBNRqNeRyORYuXIhhw4bpXl+zZg0sLCzw3HPP3VF9Go2mwdc3LzM1kiTh7ztOQZKA0YFuCPa0M6r9NYcxMHYcA7HY/+JxDMQy1/5vyf4Kv8auNTp16oTExERUVFQgKSkJS5YsQffu3REaGoqTJ09iw4YN+Oabb+74aFNqamqzlpmK5NwqHL5QDAs5MLp7LY4dOya6pEaZ8hh0FBwDsdj/4nEMxGL/N01osHNwcIBCoUBBQYHe8oKCAjg7N/3oKrlcjh49egAA/Pz8cOHCBaxevRqhoaE4evQoCgoKcP/99+vaazQaLF26FBs2bMC+ffuaXV9gYCAUCoVuG6mpqXrLTEmNRovYAz8BAKYM64WHh/kIrqghUx+DjoBjIBb7XzyOgVjm2v/1+90cQoOdUqmEv78/kpKSEB4eDqDu+rikpCRER0c3eztarRZqtRoAMHbsWL1r9gAgJiYGY8eORWRkZIvqUygUDX5wGltmCv7zyxVcyC+HYycl/jayr1Hvo6mOQUfCMRCL/S8ex0As9n/ThJ+KnTJlCubOnYuAgAAEBQUhISEBlZWVuhAWGxuLrl27Yvbs2QCAVatWISAgAF5eXlCr1Th48CC2bduGuLg4AHVHAR0cHPTew9LSEs7OzvD29m7XfesoSqpq8MGecwCAV8L7wt6as4iJiIg6IuHBLiIiAoWFhVi5ciXy8/Ph5+eHtWvX6k7F5ubmQi7/Y/JuRUUF4uPjcfXqVahUKnh7e2PZsmWIiIgQtQsd3j/3X0BhuRreLp3w1FAv0eUQERFRKwkPdgAQHR3d5KnXjRs36n0/a9YszJo1q0Xbb8l1debmSmEF/vVjJgDgzQg/WCqE39qQiIiIWomf4mbu3f+dgVqjRVhvJ4z05RM6iIiIOjIGOzOWeb0c357IAQC8OdqPNyMmIiLq4BjszFjC4YuQJGCkryv83e1Fl0NERER3iMHOTN2orMGXR68AAP4yrJfgaoiIiMgQGOzM1FdHr6BCrUG/rp0xrI/T7VcgIiIio8dgZ4ZqNVqs++kigLqjdby2joiIyDQw2JmhPafykF1cCQcbSzw+0EN0OURERGQgDHZm6F+/H617OtQLKks+koWIiMhUMNiZmZPZN/BLZiEs5DI8e1dP0eUQERGRATHYmZl//VT3lInRQd3gZq8SXA0REREZEoOdGblWWoXtx+tuSDyFtzghIiIyOQx2ZuTfRy6jRiNhcA8HBHfvIrocIiIiMjAGOzNRVaPBpiOXAPCGxERERKaKwc5MbD+eg4JyNdztVXjYv6vocoiIiKgNMNiZAUmSdLc4eS6sJywUHHYiIiJTxE94M3AkoxCncktgbanAk0O6iy6HiIiI2giDnRmov8XJ+MEe6GKjFFwNERERtRUGOxN3qaAce07lAQAmh3HSBBERkSljsDNx6w9fhCQB9/ZzQR/XzqLLISIiojbEYGfCSqtq8NXRLADAX4bzaB0REZGpY7AzYV8dzUJZdS36uHbGiL7OosshIiKiNsZgZ6I0WgnrD18EAEwZ1hMymUxsQURERNTmGOxM1N5TebhcWAF7a0tEDvQUXQ4RERG1AwY7E1V/i5OnhnrBWqkQXA0RERG1BwY7E3QqtwRHMgqhkMvw3N09RJdDRERE7YTBzgRtSLoEABjl7wb3LtaCqyEiIqL2wmBnYm5U1CAxJRsAeLSOiIjIzDDYmZivkq+gskYDXzdbDO3lKLocIiIiakcMdiZEq5V0p2EnhfEWJ0REROaGwc6EHDybj8uFFbBTWWBssLvocoiIiKidMdiZkISkiwCAiSHdYaO0EFsMERERtTsGOxOReb0cB87kQyYDou/ipAkiIiJzxGBnIjb+fm3dff1c0NO5k+BqiIiISAQGOxNQXl2Lr5KvAKibNEFERETmicHOBCQey0ZpVS16OtlgRF8X0eUQERGRIEYR7DZt2oSRI0ciMDAQUVFROHHiRJNtd+/ejcjISISEhCA4OBhjx45FYmKi7vWamhosW7YMY8aMQXBwMIYPH47Y2Fjk5eW1w560P0mSsOFw3WnYZ+/uCbmctzghIiIyV8KD3c6dO7F48WK8+OKL2LJlC3x9fRETE4OCgoJG29vb22P69On44osvsG3bNkRGRmLevHk4dOgQAKCqqgrp6emYPn06vvnmG3z88cfIzMzE9OnT23O32s2RjEKcySuFtaUCEwZ7ii6HiIiIBBIe7NatW4eJEydi/Pjx6NOnD+Lj46FSqbB58+ZG24eGhuLBBx9E79694eXlhUmTJsHHxwfJyckAAFtbW6xbtw4RERHw9vZGcHAw5s+fj7S0NOTk5LTnrrWLDb/f4mTcIA/YW1uKLYaIiIiEEnqzM7VajbS0NEybNk23TC6XIywsDCkpKbddX5IkHDlyBJmZmZgzZ06T7crKyiCTyWBnZ9ei+jQaTYOvb14mWk5xJXan151ijg7tblS1tQVjHANzwzEQi/0vHsdALHPt/5bsr9BgV1RUBI1GAycnJ73lTk5OyMjIaHK90tJSjBgxAmq1GnK5HAsXLsSwYcMabVtdXY333nsPo0ePRufOnVtUX2pqarOWifKfk6XQaCX4uyhRmXsBx3JFV9Q+jGkMzBXHQCz2v3gcA7HY/03rkI8n6NSpExITE1FRUYGkpCQsWbIE3bt3R2hoqF67mpoazJw5E5IkIT4+vsXvExgYCIVCAaAuLaempuotE6m6Vov9Ow8AAGaE90dwgJvYgtqBsY2BOeIYiMX+F49jIJa59n/9fjeH0GDn4OAAhULRYKJEQUEBnJ2dm1xPLpejR4+6pyv4+fnhwoULWL16tV6wq6mpwSuvvIKcnBwkJCS0+GgdACgUigY/OI0tE2HX8VwUlqvRzV6FhwO6QaEQfrlkuzGWMTBnHAOx2P/icQzEYv83TWgaUCqV8Pf3R1JSkm6ZVqtFUlISBg4c2OztaLVaqNVq3ff1oe7SpUtYv349HBwcDFq3MUj4/UkTz4R6wcKMQh0RERE1Tfip2ClTpmDu3LkICAhAUFAQEhISUFlZicjISABAbGwsunbtitmzZwMAVq1ahYCAAHh5eUGtVuPgwYPYtm0b4uLiANSFupdffhnp6elYtWoVNBoN8vPzAdTdKkWpVArZT0M6dqUYx68UQ6mQ48mhXqLLISIiIiMhPNhFRESgsLAQK1euRH5+Pvz8/LB27Vrdqdjc3FzI5X8ckaqoqEB8fDyuXr0KlUoFb29vLFu2DBEREQCAvLw87Nu3DwAwduxYvffasGFDg+vwOqL6W5w8GtQNzp2txBZDRERERkN4sAOA6OhoREdHN/raxo0b9b6fNWsWZs2a1eS2PD09cebMGYPWZ0wKyqrx7fG66a/P8bmwREREdBNenNXB/PfXK1BrtBjgaY/g7l1El0NERERGhMGuA9FoJfzn58sAgOfu7im2GCIiIjI6DHYdyOEL15FdXAk7lQVGB3UTXQ4REREZGQa7DuSro1kAgLHBHlBZ8v49REREpI/BroO4UVGDXWlXAQATQ7oLroaIiIiMEYNdB7HtRA7UtVr4utkiwMNOdDlERERkhBjsOoivjl4BAESFdIdMJhNcDRERERkjBrsO4PTVEpzIugELuQyPB7uLLoeIiIiMFINdB1A/aeIBP1c48UkTRERE1AQGOyOnrtUiMSUbACdNEBER0a0x2Bm5faevoaBcDRdbK9zbz0V0OURERGTEGOyM3NfJdZMmIgd5wELB4SIiIqKmMSkYsWulVdh/Jh8AEDWYp2GJiIjo1hjsjNiW37Kh0UoY5NUFfVw7iy6HiIiIjByDnZGSJAlf3nTvOiIiIqLbYbAzUilXinEhvxwqSzkeDeomuhwiIiLqABjsjFT9kyYiArrBVmUpuBoiIiLqCBjsjFClWoPtx3MB8DQsERERNR+DnRH67mQuyqpr0d3RGqG9HEWXQ0RERB0Eg50Rqn+EWNTg7pDLZYKrISIioo6Cwc7IXC6oQFJGAWQyYPxgT9HlEBERUQfCYGdkvv6t7mjd8D7O8OhiLbgaIiIi6kgY7IyIVithc3JdsJvAo3VERETUQgx2RuTwhQJkF1fCVmWBh/3dRJdDREREHQyDnRH5JqXuaN3YYHeoLBWCqyEiIqKOhsHOSEiShKQLBQCARwL4pAkiIiJqOQY7I5FVVIncG1WwkMsw0KuL6HKIiIioA2KwMxJHLxUCAPw97GGjtBBcDREREXVEDHZG4pfMIgDA0J4OgishIiKijorBzkj8erHuiN2QnnyEGBEREbUOg50RKCxX4/y1MgAMdkRERNR6DHZGoP5oXV/XznDopBRcDREREXVUDHZG4OjvwS6ER+uIiIjoDjDYGYFfLv4+caIXJ04QERFR6xlFsNu0aRNGjhyJwMBAREVF4cSJE0223b17NyIjIxESEoLg4GCMHTsWiYmJem0kScKKFSswfPhwBAUFYfLkybh48WLb7kQrVahrkZZ9AwCvryMiIqI7IzzY7dy5E4sXL8aLL76ILVu2wNfXFzExMSgoKGi0vb29PaZPn44vvvgC27ZtQ2RkJObNm4dDhw7p2qxZswYbN25EXFwcvvzyS1hbWyMmJgbV1dXttVvNlnK5GLVaCe72Kng62Iguh4iIiDow4cFu3bp1mDhxIsaPH48+ffogPj4eKpUKmzdvbrR9aGgoHnzwQfTu3RteXl6YNGkSfHx8kJycDKDuaN2GDRswffp0hIeHw9fXF++++y6uXbuGPXv2tOeuNcsvmb/f5qQXj9YRERHRnREa7NRqNdLS0hAWFqZbJpfLERYWhpSUlNuuL0kSkpKSkJmZiSFDhgAAsrKykJ+fr7dNW1tbDBgwoFnbbG/1T5zgxAkiIiK6U0KfXVVUVASNRgMnJye95U5OTsjIyGhyvdLSUowYMQJqtRpyuRwLFy7EsGHDAAD5+fm6bfx5m9evX29RfRqNpsHXNy+7UzUaLX67VAwAGOxlb9Btm6K2GANqGY6BWOx/8TgGYplr/7dkfzvkQ0k7deqExMREVFRUICkpCUuWLEH37t0RGhpq0PdJTU1t1rLWOleoRmWNBp0tZSjPOY9juTKDbduUGXIMqHU4BmKx/8XjGIjF/m+a0GDn4OAAhULRYKJEQUEBnJ2dm1xPLpejR48eAAA/Pz9cuHABq1evRmhoKFxcXHTbcHV11dumr69vi+oLDAyEQqEAUJeWU1NT9ZbdqaM/ZgIoRGhvZwwaONAg2zRlbTEG1DIcA7HY/+JxDMQy1/6v3+/mEBrslEol/P39kZSUhPDwcACAVqtFUlISoqOjm70drVYLtVoNAPD09ISLiwuSkpLg5+cHACgrK8Px48fx1FNPtag+hULR4AensWWtdfT307BDezmZ1Q/onTLkGFDrcAzEYv+LxzEQi/3fNOGnYqdMmYK5c+ciICAAQUFBSEhIQGVlJSIjIwEAsbGx6Nq1K2bPng0AWLVqFQICAuDl5QW1Wo2DBw9i27ZtiIuLAwDIZDI899xz+PTTT9GjRw94enpixYoVcHV11YVHYyBJEp84QURERAYlPNhFRESgsLAQK1euRH5+Pvz8/LB27Vrdqdjc3FzI5X9M3q2oqEB8fDyuXr0KlUoFb29vLFu2DBEREbo2zz//PCorK7FgwQKUlJRg8ODBWLt2LaysrNp9/5pyIb8MRRU1UFnKEehhL7ocIiIiMgHCgx0AREdHN3nqdePGjXrfz5o1C7Nmzbrl9mQyGWbOnImZM2carEZD+yWz7jFiwd27QGkh/HaCREREZAKYKAT59ffTsEN5GpaIiIgMhMFOkC0p2QD4xAkiIiIyHAY7ASRJ0n09oHsXcYUQERGRSTGKa+zMjUwmw55XR0Amk8FOZSm6HCIiIjIRDHaC9HG1FV0CERERmRieiiUiIiIyEQx2RERERCaCwY6IiIjIRDDYEREREZkIBjsiIiIiE8FgR0RERGQiGOyIiIiITASDHREREZGJYLAjIiIiMhEMdkREREQmgsGOiIiIyEQw2BERERGZCAY7IiIiIhPBYEdERERkIhjsiIiIiEyEhegCjJEkSQAAjUajW1b/9c3LqH1xDMTjGIjF/hePYyCWufZ//f7W55NbkUnNaWVm1Go1UlNTRZdBREREpBMYGAilUnnLNgx2jdBqtaitrYVcLodMJhNdDhEREZkxSZKg1WphYWEBufzWV9Ex2BERERGZCE6eICIiIjIRDHZEREREJoLBjoiIiMhEMNgRERERmQgGOyIiIiITwWBHREREZCIY7IiIiIhMhNkEu02bNmHkyJEIDAxEVFQUTpw4ccv23333HUaNGoXAwECMGTMGBw8e1HtdkiSsWLECw4cPR1BQECZPnoyLFy/qtSkuLsbs2bMxaNAghISEYN68eSgvLzf0rnUYIsZg5MiR8PHx0fuzevVqQ+9ah2HoMdi9ezf+8pe/IDQ0FD4+Pjh16lSDbVRXVyM+Ph6hoaEYOHAgXnrpJVy/ft2g+9VRiOj/Z599tsHvwIIFCwy6Xx2JIcegpqYGy5Ytw5gxYxAcHIzhw4cjNjYWeXl5etvgZ4E+EWNgVp8FkhnYsWOH5O/vL3399dfSuXPnpLfeeksKCQmRrl+/3mj75ORkyc/PT1qzZo10/vx56YMPPpD8/f2lM2fO6NqsWrVKGjx4sPT9999Lp06dkl544QVp5MiRUlVVla5NTEyM9Nhjj0nHjh2Tfv31V+nBBx+UXn311TbfX2Mkagzuv/9+6eOPP5auXbum+1NeXt7m+2uM2mIMtmzZIn300UfSl19+KfXr109KT09vsJ0FCxZI9957r3T48GEpNTVVmjhxovTEE0+02X4aK1H9Hx0dLb311lt6vwOlpaVttp/GzNBjUFJSIk2ePFnasWOHdOHCBSklJUWaMGGCNG7cOL3t8LPgD6LGwJw+C8wi2E2YMEGKj4/Xfa/RaKThw4dLq1atarT9zJkzpalTp+oti4qKkubPny9JkiRptVpp2LBh0tq1a3Wvl5SUSAEBAdK3334rSZIknT9/XurXr5904sQJXZuDBw9KPj4+0tWrVw22bx2FiDGQpLpf5nXr1hlwTzouQ4/Bza5cudJosCgpKZH8/f2l7777Tres/ncjJSXlDvam4xHR/5JUF+zeeeedO6zeNLTlGNQ7fvy41K9fPyk7O1uSJH4W/JmIMZAk8/osMPlTsWq1GmlpaQgLC9Mtk8vlCAsLQ0pKSqPrHDt2DHfffbfesuHDh+PYsWMAgKysLOTn5+tt09bWFgMGDNBtMyUlBXZ2dggMDNS1CQsLg1wuv+1hZ1MjagzqrVmzBqGhoXj88cexdu1a1NbWGmjPOo62GIPmOHnyJGpqavTet3fv3nB3d2/Rdjo6Uf1fb/v27QgNDcWjjz6K999/H5WVlS3eRkfXXmNQVlYGmUwGOzs7APwsuJmoMahnLp8FFqILaGtFRUXQaDRwcnLSW+7k5ISMjIxG17l+/TqcnZ0btK+/Lig/P1+3rKk2169fh6Ojo97rFhYWsLe3161vLkSNAVB3fVH//v1hb2+PlJQULF++HPn5+XjjjTfueL86krYYg+a4fv06LC0tG/wD6+TkZFa/B6L6HwAeffRRuLu7w9XVFWfOnMF7772HzMxMfPzxxy3biQ6uPcaguroa7733HkaPHo3OnTvrtsHPgjqixgAwr88Ckw92ZN6mTJmi+9rX1xeWlpZYuHAhZs+eDaVSKbAyovbxxBNP6L728fGBi4sLJk+ejMuXL8PLy0tgZaalpqYGM2fOhCRJiI+PF12OWbrVGJjTZ4HJn4p1cHCAQqFAQUGB3vKCgoIG/wuo5+zs3OB/Aze3d3Fx0S1rqo2zszMKCwv1Xq+trcWNGzd065sLUWPQmAEDBqC2thZZWVkt3o+OrC3GoDmcnZ1RU1ODkpKSBtsxp98DUf3fmAEDBgAALl26dEfb6WjacgxqamrwyiuvICcnB//617/0jhTxs+APosagMab8WWDywU6pVMLf3x9JSUm6ZVqtFklJSRg4cGCj6wQHB+PIkSN6yw4fPozg4GAAgKenJ1xcXPS2WVZWhuPHj+u2OXDgQJSUlODkyZO6NkeOHIFWq0VQUJChdq9DEDUGjTl16hTkcnmDUwGmri3GoDkCAgJgaWmp974ZGRnIyclp0XY6OlH935j6W6KYW6hoqzGoDxSXLl3C+vXr4eDgoNeenwV/EDUGjTHpzwLRszfaw44dO6SAgADpm2++kc6fPy/Nnz9fCgkJkfLz8yVJkqTXXntNeu+993Ttk5OTpf79+0uff/65dP78eWnlypWN3mojJCRE2rNnj3T69Glp+vTpjd7u5PHHH5eOHz8uHT16VHrooYfMeop7e4/Bb7/9Jq1bt046deqUdPnyZWnr1q3SXXfdJcXGxrbvzhuJthiDoqIiKT09XTpw4IDUr18/aceOHVJ6erp07do1XZsFCxZI9913n5SUlCSlpqZKTzzxhNne7qS9+//SpUvSxx9/LKWmpkpXrlyR9uzZIz3wwAPSM8880747byQMPQZqtVp64YUXpBEjRkinTp3Su5VGdXW1bjv8LPiDiDEwt88Cswh2kiRJGzdulO677z7J399fmjBhgnTs2DHda9HR0dLcuXP12u/cuVN66KGHJH9/f2n06NHSgQMH9F7XarXShx9+KIWFhUkBAQHSpEmTpIyMDL02RUVF0quvvioFBwdLgwYNkl5//XWprKys7XbSyLX3GJw8eVKKioqSBg8eLAUGBkqPPPKI9Nlnn+n9g2tuDD0Gmzdvlvr169fgz8qVK3VtqqqqpLi4OGnIkCHSgAEDpBdffFEv+JmT9u7/nJwc6ZlnnpGGDh0qBQQESA8++KC0dOlSs72PnSQZdgzqbzPT2J8jR47o2vGzQF97j4G5fRbIJEmSRB81JCIiIqI7Z/LX2BERERGZCwY7IiIiIhPBYEdERERkIhjsiIiIiEwEgx0RERGRiWCwIyIiIjIRDHZEREREJoLBjoiIiMhEMNgRERlIVlYWfHx8dM9jNVRbIqLmshBdABFRR/H6669jy5YtAAALCwt07doVo0aNwsyZM2FlZYVu3brhxx9/bNZDyImI2gKDHRFRC9xzzz1YvHgxamtrkZaWhrlz50Imk+G1116DQqGAi4uL6BKJyIzxVCwRUQsolUq4uLigW7duCA8PR1hYGA4fPgyg4enVGzduYPbs2bjrrrsQFBSEhx56CJs3b250uxqNBm+88QZGjRqFnJycdtsfIjItPGJHRNRKZ8+eRUpKCtzd3Rt9fcWKFbhw4QLWrFkDBwcHXL58GVVVVQ3aqdVqvPrqq8jOzsZ//vMfODo6tnXpRGSiGOyIiFrgwIEDGDhwIGpra6FWqyGXyzF//vxG2+bk5MDPzw+BgYEAAE9PzwZtysvLMXXqVKjVamzYsAG2trZtWj8RmTYGOyKiFggNDUVcXBwqKyuxfv16KBQKPPzww422feqpp/Dyyy8jPT0dw4YNQ3h4OAYNGqTXZvbs2XBzc0NCQgJUKlV77AIRmTBeY0dE1ALW1tbo0aMHfH198Y9//AMnTpzAV1991Wjbe++9F/v378fkyZNx7do1TJ48GUuXLm3Q5syZM0hJSWmP8onIxDHYERG1klwux7Rp07BixYpGr50DAEdHR4wbNw7vvfce5s2bhy+++ELv9aeeegqzZ8/GjBkz8Msvv7RH2URkwhjsiIjuwKhRoyCXy7Fp06YGr61YsQJ79uzBpUuXcO7cORw4cAC9e/du0O7ZZ5/FzJkzMW3aNBw9erQ9yiYiE8Vr7IiI7oCFhQWio6Oxdu1ajBgxQu81S0tLLF++HNnZ2VCpVBg8eDCWL1/e6HYmT54MSZIwdepUrF27tsG1eEREzSGTJEkSXQQRERER3TmeiiUiIiIyEQx2RERERCaCwY6IiIjIRDDYEREREZkIBjsiIiIiE8FgR0RERGQiGOyIiIiITASDHREREZGJYLAjIiIiMhEMdkREREQmgsGOiIiIyEQw2BERERGZiP8H+uCAvpglrfMAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Risk', ylabel='Return'>"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Step 4: Customer select the accepted risk.\n",
        "Based on the curve representing the return that can be expected when taking a given level of risk, customer express its risk appetite.\n",
        "How more risk he accept to take, how higher the potential return.\n",
        "\n",
        "When selecting a level of risk, customer receive a portfolio with allocations that gives best expected return for the accepted level of risk."
      ],
      "metadata": {
        "id": "zHujIOKYaX4p"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Select accepted risk level { run: \"auto\" }\n",
        "RiskMaxi = 2 # @param {type:\"slider\", min:0, max:25, step:1}\n",
        "# MaxPctAlloc = 100 # @param {type:\"integer\"}\n",
        "cv = EfficientCDaR(mu, historical_rets, beta=beta, weight_bounds=(0, MaxPctAlloc/100))\n",
        "try:\n",
        "    Orig_weights = cv.efficient_risk(RiskMaxi/100)\n",
        "except:\n",
        "    print(\"Sorry ... vous êtes trop gourmand!\")\n",
        "else:\n",
        "    weights = {k:v for k,v in cv.clean_weights(cutoff=0.01).items() if v != 0}\n",
        "    ret, risk = cv.portfolio_performance(verbose=True)\n",
        "\n",
        "    labels = list(weights.keys())\n",
        "    values = [j if j>=0 else 0 for j in list(weights.values())]\n",
        "\n",
        "    sns.set_style(\"whitegrid\")\n",
        "    plt.figure(figsize=(8,8))\n",
        "    plt.pie(values, labels=labels, autopct='%1.1f%%')\n",
        "    #plt.title(f\"Proposed allocation\")\n",
        "    plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 688
        },
        "cellView": "form",
        "id": "_4L1gweY8Dea",
        "outputId": "62dd70d4-6bbe-4a61-ff43-1b274c21fc88"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Expected annual return: 45.3%\n",
            "Conditional Drawdown at Risk: 2.00%\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Manually change allocation\n",
        "def handle_weights_change(change):\n",
        "    values = [bBloc.value for bBloc in bBlocs]\n",
        "    caption.value = (\n",
        "        f'The slider {change.owner.description} has a value of {change.new}. '\n",
        "        f'The values of all sliders are {values}'\n",
        "    )\n",
        "\n",
        "n_bBlocs = len(weights)\n",
        "bBlocs = [\n",
        "    widgets.FloatSlider(min=0, max=MaxPctAlloc/100, step=0.01, value=weights[labels[i]], description=f'{labels[i]}', orientation='vertical', disabled=False)\n",
        "    for i in range(n_bBlocs)\n",
        "]\n",
        "for widget in bBlocs:\n",
        "    widget.observe(handle_weights_change, names='value')\n",
        "ui = widgets.HBox(bBlocs)\n",
        "caption = widgets.Label(value='No change was made yet.')\n",
        "display(caption, ui)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253,
          "referenced_widgets": [
            "138562f8dde64fefbd0b42d08b962e84",
            "702c87dadebf4f6c9569a3bae7c84398",
            "170a84c4b95342ba8da3f28b3e0a4826",
            "eabaecf2c41f42fdb5f8424958d417ae",
            "f2398d1529c148fd86fe60e6c981c6af",
            "7bb22df8a9e2479fa9892215813e4186",
            "d6eb22b5d998407f943487717aa53fcc",
            "44a7fae21a37465da7139d9530962bb5",
            "d42fdca879d74ee5ab36ed90fb68e7a6",
            "243b35a3c4294931b3e0eb19cceb76a7",
            "d5671fdf05ed475e97039d182b0f7c49",
            "4fbe59b2644149e2ae10176d11cfe63b",
            "a65693274ee44766b98cd2320734a199",
            "ea6b3cd3b4ee450f93c4d1c55bc0397c",
            "1ba4031ba36e42df938565dd87978f88",
            "7c56c296f8974021a0b97358e7e35543",
            "f36ed6f751ff4338b306ab6dc51a82b3",
            "ae6e8c4d5ea040fbb5988485151074e6",
            "740bf20245ef447cabc0d443d762e17a",
            "d31b72bad81043aaa2a67f50753bdd00",
            "982374713f464802b0c493df6f52e8da",
            "d26b78007cd143d493ffefd2837166f2",
            "c4fc24c611c74f738d2b454d2c8db075",
            "76654f54e2724360b1504c1e646d2c80",
            "f626a152743b402380e9771271199147",
            "7175da5e01c54cf5988273dff96cdb1c",
            "3a9584d0c30b451fb372782776095e53",
            "8b0acbe5d0ce447e98d6fc3cf478e03e",
            "b40c5ec1ea40498caa0715cfaa456c86",
            "4dd0c2a087d643de90e785d6258b8887",
            "176ac6d24d6140118d5ffcec9c57cb77",
            "1fc1bf878c364728881e534aa465b0d6",
            "e83fbe1ee7d145fe945cd6161371ded6",
            "a6938acc2c024828937c44a5b4cb8696",
            "5a14d07136714452b43ed4c281e53845",
            "0b052fbc466c41f4b2d312a74a73c0b8",
            "80a0e86e94ae40ac9fb71131be02165b",
            "adb141bf7ae8411b981fbf46b69f9f5b",
            "f05c565f4c5440e6b8f4acbdb253c7ef",
            "363c283069b24799b1d7c4f0c22910dd",
            "5eae631ee4f944caafa750b9a4ba6012",
            "2bdaadd0569f44bcbc1ae2c5dcde3dfe",
            "81c0d326d79d475ebaa9f468dad0e152",
            "ce7bc545959f4db7a3fe01dc114b170a"
          ]
        },
        "id": "TBJRgPxFtPsy",
        "outputId": "37c95cec-ca3c-4b1d-a859-84b53f91fe78",
        "cellView": "form"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Label(value='No change was made yet.')"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "138562f8dde64fefbd0b42d08b962e84"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "HBox(children=(FloatSlider(value=0.08, description='AAPL', max=0.08, orientation='vertical', step=0.01), Float…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "eabaecf2c41f42fdb5f8424958d417ae"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Convert allocation in number of shares\n",
        "availableCash = 321654 # @param {type:\"number\"}\n",
        "#availableCash = 2000000\n",
        "from pypfopt import DiscreteAllocation\n",
        "da = DiscreteAllocation(cv.clean_weights(cutoff=0.01), prices.iloc[-1], total_portfolio_value=availableCash)\n",
        "alloc, cashLeft = da.greedy_portfolio()\n",
        "for k in alloc:\n",
        "    print(f\"{k:8s} : {alloc[k]:6d} shares\")\n",
        "\n",
        "print(f\"Remaining cash : {cashLeft:7.2f}.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "cellView": "form",
        "id": "i2RMsSPZheae",
        "outputId": "94434bf4-3b95-4de2-dd3f-80e289c3b2f1"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "AAPL     :    111 shares\n",
            "AMZN     :    113 shares\n",
            "AXP      :     82 shares\n",
            "BKNG     :      5 shares\n",
            "BNP.PA   :    408 shares\n",
            "CRH      :    263 shares\n",
            "EMR      :    206 shares\n",
            "ENEL.MI  :   3802 shares\n",
            "JPM      :     99 shares\n",
            "KBC.BR   :    348 shares\n",
            "NFLX     :     30 shares\n",
            "UCB.BR   :    140 shares\n",
            "BMY      :    200 shares\n",
            "Remaining cash : 3489.92.\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "widgets": {
      "application/vnd.jupyter.widget-state+json": {
        "435dfed814cf4f8fab58f6b746d94cc8": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "VBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "VBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "VBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_b6cd01fb6bd44e249c5f618513a9c8b5",
              "IPY_MODEL_7a189d4d7ef9427b8b4cdfab1b84510f",
              "IPY_MODEL_9f47d35690cd4beb92f451dbe42e06cc",
              "IPY_MODEL_7738b009f728427ba12615655ee1e022",
              "IPY_MODEL_07e10fea1ba24099a58c5d5ef8f27295",
              "IPY_MODEL_eb79ca9f0fdf456c9da6e68da97d7856",
              "IPY_MODEL_55a7b159104741caacdebda2715db91d",
              "IPY_MODEL_19ce34d68879424595364df30f3ee2ba",
              "IPY_MODEL_4aa4db8fa020489c83913e36333b53d5",
              "IPY_MODEL_85dceab59e0a42d6aef34e350548c453",
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