From 2cfafadc9cad6f610ace949a9e73629b08227f3b Mon Sep 17 00:00:00 2001
From: Ambrose Ikpele <68953964+ambroseikpele@users.noreply.github.com>
Date: Tue, 1 Oct 2024 15:26:46 +0100
Subject: [PATCH 1/2] Added file : Created Notebook for Portfolio Optimization
 Using Modern Portfolio Theory

---
 ...timizationUsingModernPortfolioTheory.ipynb | 2248 +++++++++++++++++
 1 file changed, 2248 insertions(+)
 create mode 100644 examples/portfolioOptimizationUsingModernPortfolioTheory.ipynb

diff --git a/examples/portfolioOptimizationUsingModernPortfolioTheory.ipynb b/examples/portfolioOptimizationUsingModernPortfolioTheory.ipynb
new file mode 100644
index 000000000000..08732cb16501
--- /dev/null
+++ b/examples/portfolioOptimizationUsingModernPortfolioTheory.ipynb
@@ -0,0 +1,2248 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "HRMC-0N0sSjJ"
+      },
+      "source": [
+        "## Portfolio Optimization Using Modern Portfolio Theory\n",
+        "\n",
+        "#### Description\n",
+        "This notebook utilizes OpenBB’s data for portfolio optimization based on MPT principles. We would be optimizing a portfolio of top 10 crypto assets, using the daily close data from 1st october 2023 to 1st october 2024.\n",
+        "\n",
+        "The portfolio optimization would be done using the mean-variance approach. The mean-variance approach helps determine the optimal allocation of assets in a portfolio to minimize overall risk while maximizing expected returns.\n",
+        "\n",
+        "#### Author\n",
+        "[Ambrose Ikpele](https://github.com/ambroseikpele)\n",
+        "\n",
+        "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/OpenBB-Finance/OpenBB/blob/develop/examples/[Notebook_Name].ipynb)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Install external packages"
+      ],
+      "metadata": {
+        "id": "_rMPfEH2KJjA"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install openbb\n",
+        "!pip install PyPortfolioOpt"
+      ],
+      "metadata": {
+        "id": "iexQsZ1XvYa8"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Import necessary packages"
+      ],
+      "metadata": {
+        "id": "CH4i_WQGRmG-"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "B8m_9BassSjK"
+      },
+      "outputs": [],
+      "source": [
+        "from openbb import obb\n",
+        "\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "from pypfopt import EfficientFrontier\n",
+        "from pypfopt import CovarianceShrinkage, CLA, expected_returns"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Fetch the daily data of the top crypto currencies for a period of one year using openbb"
+      ],
+      "metadata": {
+        "id": "w3XB7egmzlmI"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "top_crypto= ['ADA-USD', 'BNB-USD', 'BTC-USD', 'DOT-USD', 'ETH-USD', 'LTC-USD','MATIC-USD', 'SOL-USD', 'TRX-USD', 'XRP-USD']\n",
+        "\n",
+        "ohlc_data= obb.crypto.price.historical(top_crypto, provider=\"yfinance\", interval='1d', start_date='2023-10-01', end_date='2024-10-01').to_df()\n",
+        "ohlc_data"
+      ],
+      "metadata": {
+        "id": "C3UwCtY8vDaQ",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 455
+        },
+        "outputId": "8ab3c023-43e8-4d81-f5ef-22451d3604bb"
+      },
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                    open          high           low         close  \\\n",
+              "date                                                                 \n",
+              "2023-10-01      0.254043      0.267471      0.254019      0.265895   \n",
+              "2023-10-01    214.800323    219.133835    213.506516    218.047134   \n",
+              "2023-10-01  26967.396484  28047.238281  26965.093750  27983.750000   \n",
+              "2023-10-01      4.105477      4.279937      4.103880      4.261009   \n",
+              "2023-10-01   1671.161499   1750.595703   1670.082153   1733.810425   \n",
+              "...                  ...           ...           ...           ...   \n",
+              "2024-10-01     66.820854     68.483223     66.786285     67.686295   \n",
+              "2024-10-01      0.395941      0.404462      0.394638      0.399719   \n",
+              "2024-10-01    152.604584    157.199478    151.796982    155.045334   \n",
+              "2024-10-01      0.155929      0.156981      0.155710      0.156684   \n",
+              "2024-10-01      0.611519      0.633387      0.611519      0.627602   \n",
+              "\n",
+              "                  volume     symbol  \n",
+              "date                                 \n",
+              "2023-10-01  1.650882e+08    ADA-USD  \n",
+              "2023-10-01  3.874081e+08    BNB-USD  \n",
+              "2023-10-01  9.503917e+09    BTC-USD  \n",
+              "2023-10-01  8.294334e+07    DOT-USD  \n",
+              "2023-10-01  5.054880e+09    ETH-USD  \n",
+              "...                  ...        ...  \n",
+              "2024-10-01  2.475363e+08    LTC-USD  \n",
+              "2024-10-01  3.507427e+07  MATIC-USD  \n",
+              "2024-10-01  1.949461e+09    SOL-USD  \n",
+              "2024-10-01  3.093434e+08    TRX-USD  \n",
+              "2024-10-01  1.424177e+09    XRP-USD  \n",
+              "\n",
+              "[3670 rows x 6 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-acd753f8-3dd8-4cab-9540-7b8c2b61c6ea\" 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>open</th>\n",
+              "      <th>high</th>\n",
+              "      <th>low</th>\n",
+              "      <th>close</th>\n",
+              "      <th>volume</th>\n",
+              "      <th>symbol</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>date</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>2023-10-01</th>\n",
+              "      <td>0.254043</td>\n",
+              "      <td>0.267471</td>\n",
+              "      <td>0.254019</td>\n",
+              "      <td>0.265895</td>\n",
+              "      <td>1.650882e+08</td>\n",
+              "      <td>ADA-USD</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2023-10-01</th>\n",
+              "      <td>214.800323</td>\n",
+              "      <td>219.133835</td>\n",
+              "      <td>213.506516</td>\n",
+              "      <td>218.047134</td>\n",
+              "      <td>3.874081e+08</td>\n",
+              "      <td>BNB-USD</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2023-10-01</th>\n",
+              "      <td>26967.396484</td>\n",
+              "      <td>28047.238281</td>\n",
+              "      <td>26965.093750</td>\n",
+              "      <td>27983.750000</td>\n",
+              "      <td>9.503917e+09</td>\n",
+              "      <td>BTC-USD</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2023-10-01</th>\n",
+              "      <td>4.105477</td>\n",
+              "      <td>4.279937</td>\n",
+              "      <td>4.103880</td>\n",
+              "      <td>4.261009</td>\n",
+              "      <td>8.294334e+07</td>\n",
+              "      <td>DOT-USD</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2023-10-01</th>\n",
+              "      <td>1671.161499</td>\n",
+              "      <td>1750.595703</td>\n",
+              "      <td>1670.082153</td>\n",
+              "      <td>1733.810425</td>\n",
+              "      <td>5.054880e+09</td>\n",
+              "      <td>ETH-USD</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2024-10-01</th>\n",
+              "      <td>66.820854</td>\n",
+              "      <td>68.483223</td>\n",
+              "      <td>66.786285</td>\n",
+              "      <td>67.686295</td>\n",
+              "      <td>2.475363e+08</td>\n",
+              "      <td>LTC-USD</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2024-10-01</th>\n",
+              "      <td>0.395941</td>\n",
+              "      <td>0.404462</td>\n",
+              "      <td>0.394638</td>\n",
+              "      <td>0.399719</td>\n",
+              "      <td>3.507427e+07</td>\n",
+              "      <td>MATIC-USD</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2024-10-01</th>\n",
+              "      <td>152.604584</td>\n",
+              "      <td>157.199478</td>\n",
+              "      <td>151.796982</td>\n",
+              "      <td>155.045334</td>\n",
+              "      <td>1.949461e+09</td>\n",
+              "      <td>SOL-USD</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2024-10-01</th>\n",
+              "      <td>0.155929</td>\n",
+              "      <td>0.156981</td>\n",
+              "      <td>0.155710</td>\n",
+              "      <td>0.156684</td>\n",
+              "      <td>3.093434e+08</td>\n",
+              "      <td>TRX-USD</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2024-10-01</th>\n",
+              "      <td>0.611519</td>\n",
+              "      <td>0.633387</td>\n",
+              "      <td>0.611519</td>\n",
+              "      <td>0.627602</td>\n",
+              "      <td>1.424177e+09</td>\n",
+              "      <td>XRP-USD</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>3670 rows × 6 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-acd753f8-3dd8-4cab-9540-7b8c2b61c6ea')\"\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 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\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",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\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-acd753f8-3dd8-4cab-9540-7b8c2b61c6ea 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-acd753f8-3dd8-4cab-9540-7b8c2b61c6ea');\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",
+              "\n",
+              "\n",
+              "<div id=\"df-d9d1f0ab-15bd-4cd2-9aa5-6799bf1ddf1e\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d9d1f0ab-15bd-4cd2-9aa5-6799bf1ddf1e')\"\n",
+              "            title=\"Suggest charts\"\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",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-d9d1f0ab-15bd-4cd2-9aa5-6799bf1ddf1e button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "  <div id=\"id_0cd69b5f-c354-4c95-b378-96848db116ca\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\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-generate: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-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate: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",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('ohlc_data')\"\n",
+              "            title=\"Generate code using this dataframe.\"\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",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_0cd69b5f-c354-4c95-b378-96848db116ca button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('ohlc_data');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "ohlc_data",
+              "summary": "{\n  \"name\": \"ohlc_data\",\n  \"rows\": 3670,\n  \"fields\": [\n    {\n      \"column\": \"date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2023-10-01\",\n        \"max\": \"2024-10-01\",\n        \"num_unique_values\": 367,\n        \"samples\": [\n          \"2024-04-11\",\n          \"2023-11-03\",\n          \"2023-10-16\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16646.479249400243,\n        \"min\": 0.08486499637365341,\n        \"max\": 73079.375,\n        \"num_unique_values\": 3669,\n        \"samples\": [\n          0.49170398712158203,\n          0.4794990122318268,\n          2683.719970703125\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"high\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16979.65920014512,\n        \"min\": 0.08571500331163406,\n        \"max\": 73750.0703125,\n        \"num_unique_values\": 3670,\n        \"samples\": [\n          0.4922049939632416,\n          0.4812979996204376,\n          63872.44140625\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16307.879682680239,\n        \"min\": 0.0847959965467453,\n        \"max\": 71334.09375,\n        \"num_unique_values\": 3665,\n        \"samples\": [\n          6.224710941314697,\n          0.3214839994907379,\n          0.4614889919757843\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16670.124390346416,\n        \"min\": 0.08486700057983398,\n        \"max\": 73083.5,\n        \"num_unique_values\": 3665,\n        \"samples\": [\n          65738.7265625,\n          519.870361328125,\n          139.4636993408203\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10083345827.063368,\n        \"min\": 33682820.0,\n        \"max\": 108991085584.0,\n        \"num_unique_values\": 3670,\n        \"samples\": [\n          734621817.0,\n          1322556294.0,\n          42710252573.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"symbol\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"TRX-USD\",\n          \"BNB-USD\",\n          \"LTC-USD\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 2
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Select the close data from ohlc_data for each crypto currency"
+      ],
+      "metadata": {
+        "id": "diAUqkPi5BYe"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "close_symbol= ohlc_data[['close', 'symbol']]\n",
+        "\n",
+        "# Setting the symbol as the second index level\n",
+        "close_symbol = close_symbol.set_index('symbol', append= True)\n",
+        "\n",
+        "# Unstack 'symbol' to make each unique symbol a separate column\n",
+        "close_symbol_unstacked= close_symbol.unstack(level='symbol')\n",
+        "\n",
+        "# Flatten the column headers\n",
+        "close_symbol_unstacked.columns = close_symbol_unstacked.columns.get_level_values(1)\n",
+        "\n",
+        "prices= close_symbol_unstacked\n",
+        "\n",
+        "prices"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 455
+        },
+        "id": "PXNs59lSzMqv",
+        "outputId": "265b039d-f797-40be-8817-6c96a7aaf431"
+      },
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "symbol       ADA-USD     BNB-USD       BTC-USD   DOT-USD      ETH-USD  \\\n",
+              "date                                                                    \n",
+              "2023-10-01  0.265895  218.047134  27983.750000  4.261009  1733.810425   \n",
+              "2023-10-02  0.259513  214.757935  27530.785156  4.123762  1663.627563   \n",
+              "2023-10-03  0.261028  213.435944  27429.978516  4.074247  1656.685669   \n",
+              "2023-10-04  0.259315  213.413086  27799.394531  4.047316  1647.838135   \n",
+              "2023-10-05  0.260149  210.679672  27415.912109  4.022738  1611.476440   \n",
+              "...              ...         ...           ...       ...          ...   \n",
+              "2024-09-27  0.402328  607.867004  65790.664062  4.892055  2695.900635   \n",
+              "2024-09-28  0.401052  601.567200  65887.648438  4.805896  2677.539062   \n",
+              "2024-09-29  0.397597  596.411194  65635.304688  4.768428  2659.346924   \n",
+              "2024-09-30  0.373214  567.260071  63329.500000  4.437086  2603.062744   \n",
+              "2024-10-01  0.380300  574.313965  63394.039062  4.461926  2610.701416   \n",
+              "\n",
+              "symbol        LTC-USD  MATIC-USD     SOL-USD   TRX-USD   XRP-USD  \n",
+              "date                                                              \n",
+              "2023-10-01  68.233315   0.568532   23.836487  0.090118  0.524204  \n",
+              "2023-10-02  66.011124   0.547513   23.371700  0.087566  0.512832  \n",
+              "2023-10-03  65.493515   0.566308   23.552694  0.090858  0.538387  \n",
+              "2023-10-04  64.452065   0.563369   23.144787  0.088999  0.532931  \n",
+              "2023-10-05  64.858765   0.546018   22.694141  0.088276  0.523366  \n",
+              "...               ...        ...         ...       ...       ...  \n",
+              "2024-09-27  71.188202   0.433774  157.749939  0.155170  0.588927  \n",
+              "2024-09-28  70.003967   0.423559  156.912430  0.155068  0.614801  \n",
+              "2024-09-29  69.314423   0.421419  158.629166  0.156474  0.641947  \n",
+              "2024-09-30  66.820450   0.395917  152.618469  0.155915  0.611492  \n",
+              "2024-10-01  67.686295   0.399719  155.045334  0.156684  0.627602  \n",
+              "\n",
+              "[367 rows x 10 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-96abb763-e128-49ed-91a8-7b869f966576\" 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>symbol</th>\n",
+              "      <th>ADA-USD</th>\n",
+              "      <th>BNB-USD</th>\n",
+              "      <th>BTC-USD</th>\n",
+              "      <th>DOT-USD</th>\n",
+              "      <th>ETH-USD</th>\n",
+              "      <th>LTC-USD</th>\n",
+              "      <th>MATIC-USD</th>\n",
+              "      <th>SOL-USD</th>\n",
+              "      <th>TRX-USD</th>\n",
+              "      <th>XRP-USD</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>date</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>2023-10-01</th>\n",
+              "      <td>0.265895</td>\n",
+              "      <td>218.047134</td>\n",
+              "      <td>27983.750000</td>\n",
+              "      <td>4.261009</td>\n",
+              "      <td>1733.810425</td>\n",
+              "      <td>68.233315</td>\n",
+              "      <td>0.568532</td>\n",
+              "      <td>23.836487</td>\n",
+              "      <td>0.090118</td>\n",
+              "      <td>0.524204</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2023-10-02</th>\n",
+              "      <td>0.259513</td>\n",
+              "      <td>214.757935</td>\n",
+              "      <td>27530.785156</td>\n",
+              "      <td>4.123762</td>\n",
+              "      <td>1663.627563</td>\n",
+              "      <td>66.011124</td>\n",
+              "      <td>0.547513</td>\n",
+              "      <td>23.371700</td>\n",
+              "      <td>0.087566</td>\n",
+              "      <td>0.512832</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2023-10-03</th>\n",
+              "      <td>0.261028</td>\n",
+              "      <td>213.435944</td>\n",
+              "      <td>27429.978516</td>\n",
+              "      <td>4.074247</td>\n",
+              "      <td>1656.685669</td>\n",
+              "      <td>65.493515</td>\n",
+              "      <td>0.566308</td>\n",
+              "      <td>23.552694</td>\n",
+              "      <td>0.090858</td>\n",
+              "      <td>0.538387</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2023-10-04</th>\n",
+              "      <td>0.259315</td>\n",
+              "      <td>213.413086</td>\n",
+              "      <td>27799.394531</td>\n",
+              "      <td>4.047316</td>\n",
+              "      <td>1647.838135</td>\n",
+              "      <td>64.452065</td>\n",
+              "      <td>0.563369</td>\n",
+              "      <td>23.144787</td>\n",
+              "      <td>0.088999</td>\n",
+              "      <td>0.532931</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2023-10-05</th>\n",
+              "      <td>0.260149</td>\n",
+              "      <td>210.679672</td>\n",
+              "      <td>27415.912109</td>\n",
+              "      <td>4.022738</td>\n",
+              "      <td>1611.476440</td>\n",
+              "      <td>64.858765</td>\n",
+              "      <td>0.546018</td>\n",
+              "      <td>22.694141</td>\n",
+              "      <td>0.088276</td>\n",
+              "      <td>0.523366</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2024-09-27</th>\n",
+              "      <td>0.402328</td>\n",
+              "      <td>607.867004</td>\n",
+              "      <td>65790.664062</td>\n",
+              "      <td>4.892055</td>\n",
+              "      <td>2695.900635</td>\n",
+              "      <td>71.188202</td>\n",
+              "      <td>0.433774</td>\n",
+              "      <td>157.749939</td>\n",
+              "      <td>0.155170</td>\n",
+              "      <td>0.588927</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2024-09-28</th>\n",
+              "      <td>0.401052</td>\n",
+              "      <td>601.567200</td>\n",
+              "      <td>65887.648438</td>\n",
+              "      <td>4.805896</td>\n",
+              "      <td>2677.539062</td>\n",
+              "      <td>70.003967</td>\n",
+              "      <td>0.423559</td>\n",
+              "      <td>156.912430</td>\n",
+              "      <td>0.155068</td>\n",
+              "      <td>0.614801</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2024-09-29</th>\n",
+              "      <td>0.397597</td>\n",
+              "      <td>596.411194</td>\n",
+              "      <td>65635.304688</td>\n",
+              "      <td>4.768428</td>\n",
+              "      <td>2659.346924</td>\n",
+              "      <td>69.314423</td>\n",
+              "      <td>0.421419</td>\n",
+              "      <td>158.629166</td>\n",
+              "      <td>0.156474</td>\n",
+              "      <td>0.641947</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2024-09-30</th>\n",
+              "      <td>0.373214</td>\n",
+              "      <td>567.260071</td>\n",
+              "      <td>63329.500000</td>\n",
+              "      <td>4.437086</td>\n",
+              "      <td>2603.062744</td>\n",
+              "      <td>66.820450</td>\n",
+              "      <td>0.395917</td>\n",
+              "      <td>152.618469</td>\n",
+              "      <td>0.155915</td>\n",
+              "      <td>0.611492</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2024-10-01</th>\n",
+              "      <td>0.380300</td>\n",
+              "      <td>574.313965</td>\n",
+              "      <td>63394.039062</td>\n",
+              "      <td>4.461926</td>\n",
+              "      <td>2610.701416</td>\n",
+              "      <td>67.686295</td>\n",
+              "      <td>0.399719</td>\n",
+              "      <td>155.045334</td>\n",
+              "      <td>0.156684</td>\n",
+              "      <td>0.627602</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>367 rows × 10 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-96abb763-e128-49ed-91a8-7b869f966576')\"\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 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\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",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\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-96abb763-e128-49ed-91a8-7b869f966576 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-96abb763-e128-49ed-91a8-7b869f966576');\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",
+              "\n",
+              "\n",
+              "<div id=\"df-bad89d7a-81d2-4408-88c3-d7d19d4c74b0\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-bad89d7a-81d2-4408-88c3-d7d19d4c74b0')\"\n",
+              "            title=\"Suggest charts\"\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",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-bad89d7a-81d2-4408-88c3-d7d19d4c74b0 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "  <div id=\"id_9856cf46-24ab-49eb-a611-9397bb203c9f\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\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-generate: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-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate: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",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('close_symbol_unstacked')\"\n",
+              "            title=\"Generate code using this dataframe.\"\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",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_9856cf46-24ab-49eb-a611-9397bb203c9f button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('close_symbol_unstacked');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "close_symbol_unstacked",
+              "summary": "{\n  \"name\": \"close_symbol_unstacked\",\n  \"rows\": 367,\n  \"fields\": [\n    {\n      \"column\": \"date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2023-10-01\",\n        \"max\": \"2024-10-01\",\n        \"num_unique_values\": 367,\n        \"samples\": [\n          \"2024-04-11\",\n          \"2023-11-03\",\n          \"2023-10-16\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ADA-USD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.12231550584081373,\n        \"min\": 0.2434529960155487,\n        \"max\": 0.7741900086402893,\n        \"num_unique_values\": 366,\n        \"samples\": [\n          0.5035750269889832,\n          0.32902100682258606,\n          0.25156301259994507\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BNB-USD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 152.46468320666116,\n        \"min\": 205.2294158935547,\n        \"max\": 710.4640502929688,\n        \"num_unique_values\": 367,\n        \"samples\": [\n          604.893798828125,\n          230.60597229003906,\n          214.82395935058594\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BTC-USD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13044.480615583347,\n        \"min\": 26756.798828125,\n        \"max\": 73083.5,\n        \"num_unique_values\": 367,\n        \"samples\": [\n          70060.609375,\n          34732.32421875,\n          28519.466796875\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DOT-USD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.7193378737799296,\n        \"min\": 3.6488780975341797,\n        \"max\": 11.542901992797852,\n        \"num_unique_values\": 367,\n        \"samples\": [\n          8.379441261291504,\n          4.6154937744140625,\n          3.7862110137939453\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ETH-USD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 644.9463072659804,\n        \"min\": 1539.6124267578125,\n        \"max\": 4066.445068359375,\n        \"num_unique_values\": 367,\n        \"samples\": [\n          3505.247802734375,\n          1832.795166015625,\n          1600.5343017578125\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LTC-USD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.815022518838514,\n        \"min\": 55.983909606933594,\n        \"max\": 109.25897216796875,\n        \"num_unique_values\": 367,\n        \"samples\": [\n          98.68910217285156,\n          69.49114227294922,\n          63.337162017822266\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MATIC-USD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.20676285473019815,\n        \"min\": 0.3659299910068512,\n        \"max\": 1.2714049816131592,\n        \"num_unique_values\": 367,\n        \"samples\": [\n          0.8783450126647949,\n          0.6719430088996887,\n          0.5341209769248962\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SOL-USD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 46.954204670567634,\n        \"min\": 21.300268173217773,\n        \"max\": 202.87413024902344,\n        \"num_unique_values\": 367,\n        \"samples\": [\n          172.5763702392578,\n          39.51976013183594,\n          23.98295783996582\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TRX-USD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.018127298632971522,\n        \"min\": 0.08486700057983398,\n        \"max\": 0.16655699908733368,\n        \"num_unique_values\": 363,\n        \"samples\": [\n          0.13199299573898315,\n          0.09731300175189972,\n          0.08891399949789047\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"XRP-USD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.056190198963527865,\n        \"min\": 0.4198229908943176,\n        \"max\": 0.7180359959602356,\n        \"num_unique_values\": 367,\n        \"samples\": [\n          0.6088799834251404,\n          0.6130020022392273,\n          0.49797698855400085\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 3
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "calculate the expected returns of each asset and covariance matrix of crypto assets"
+      ],
+      "metadata": {
+        "id": "pFLyyT7v4E5J"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Expected returns of crypto assets using the mean historical return.\n",
+        "portfolio_expected_returns = expected_returns.mean_historical_return(prices, frequency=365, compounding=False)\n",
+        "\n",
+        "# Covariance matrix of crypto assets using the Ledoit-Wolf shrinkage method.\n",
+        "covariance = CovarianceShrinkage(prices).ledoit_wolf()"
+      ],
+      "metadata": {
+        "id": "S_sH-1sq6bnI"
+      },
+      "execution_count": 4,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "portfolio_expected_returns"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 429
+        },
+        "id": "8q1RvkuV7ouF",
+        "outputId": "7cdc9e09-c38c-4443-8d66-2de154614d74"
+      },
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "symbol\n",
+              "ADA-USD      0.621306\n",
+              "BNB-USD      1.130899\n",
+              "BTC-USD      0.950751\n",
+              "DOT-USD      0.324462\n",
+              "ETH-USD      0.592597\n",
+              "LTC-USD      0.186095\n",
+              "MATIC-USD   -0.048860\n",
+              "SOL-USD      2.297820\n",
+              "TRX-USD      0.619805\n",
+              "XRP-USD      0.370310\n",
+              "dtype: float64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>0</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>symbol</th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>ADA-USD</th>\n",
+              "      <td>0.621306</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>BNB-USD</th>\n",
+              "      <td>1.130899</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>BTC-USD</th>\n",
+              "      <td>0.950751</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>DOT-USD</th>\n",
+              "      <td>0.324462</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>ETH-USD</th>\n",
+              "      <td>0.592597</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>LTC-USD</th>\n",
+              "      <td>0.186095</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>MATIC-USD</th>\n",
+              "      <td>-0.048860</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>SOL-USD</th>\n",
+              "      <td>2.297820</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>TRX-USD</th>\n",
+              "      <td>0.619805</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>XRP-USD</th>\n",
+              "      <td>0.370310</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> float64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "covariance"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 394
+        },
+        "id": "OGZqN0i47sBC",
+        "outputId": "f4e4ac0a-f2c0-4f58-b999-e7d056d59b18"
+      },
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "symbol      ADA-USD   BNB-USD   BTC-USD   DOT-USD   ETH-USD   LTC-USD  \\\n",
+              "symbol                                                                  \n",
+              "ADA-USD    0.365908  0.149985  0.179671  0.303813  0.203350  0.204707   \n",
+              "BNB-USD    0.149985  0.230834  0.122378  0.150464  0.138874  0.108916   \n",
+              "BTC-USD    0.179671  0.122378  0.189577  0.175989  0.172308  0.127100   \n",
+              "DOT-USD    0.303813  0.150464  0.175989  0.385419  0.206734  0.192864   \n",
+              "ETH-USD    0.203350  0.138874  0.172308  0.206734  0.259265  0.160001   \n",
+              "LTC-USD    0.204707  0.108916  0.127100  0.192864  0.160001  0.270176   \n",
+              "MATIC-USD  0.278248  0.173514  0.170163  0.287835  0.224481  0.190943   \n",
+              "SOL-USD    0.296429  0.171700  0.221531  0.333077  0.232270  0.191517   \n",
+              "TRX-USD    0.083626  0.046899  0.054940  0.085921  0.063282  0.055659   \n",
+              "XRP-USD    0.194114  0.093489  0.120379  0.192047  0.133110  0.158148   \n",
+              "\n",
+              "symbol     MATIC-USD   SOL-USD   TRX-USD   XRP-USD  \n",
+              "symbol                                              \n",
+              "ADA-USD     0.278248  0.296429  0.083626  0.194114  \n",
+              "BNB-USD     0.173514  0.171700  0.046899  0.093489  \n",
+              "BTC-USD     0.170163  0.221531  0.054940  0.120379  \n",
+              "DOT-USD     0.287835  0.333077  0.085921  0.192047  \n",
+              "ETH-USD     0.224481  0.232270  0.063282  0.133110  \n",
+              "LTC-USD     0.190943  0.191517  0.055659  0.158148  \n",
+              "MATIC-USD   0.414685  0.290214  0.075435  0.180736  \n",
+              "SOL-USD     0.290214  0.593740  0.095372  0.184846  \n",
+              "TRX-USD     0.075435  0.095372  0.098433  0.057513  \n",
+              "XRP-USD     0.180736  0.184846  0.057513  0.267617  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-568f972d-ae84-4827-9205-28a3a1e18f22\" 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>symbol</th>\n",
+              "      <th>ADA-USD</th>\n",
+              "      <th>BNB-USD</th>\n",
+              "      <th>BTC-USD</th>\n",
+              "      <th>DOT-USD</th>\n",
+              "      <th>ETH-USD</th>\n",
+              "      <th>LTC-USD</th>\n",
+              "      <th>MATIC-USD</th>\n",
+              "      <th>SOL-USD</th>\n",
+              "      <th>TRX-USD</th>\n",
+              "      <th>XRP-USD</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>symbol</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>ADA-USD</th>\n",
+              "      <td>0.365908</td>\n",
+              "      <td>0.149985</td>\n",
+              "      <td>0.179671</td>\n",
+              "      <td>0.303813</td>\n",
+              "      <td>0.203350</td>\n",
+              "      <td>0.204707</td>\n",
+              "      <td>0.278248</td>\n",
+              "      <td>0.296429</td>\n",
+              "      <td>0.083626</td>\n",
+              "      <td>0.194114</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>BNB-USD</th>\n",
+              "      <td>0.149985</td>\n",
+              "      <td>0.230834</td>\n",
+              "      <td>0.122378</td>\n",
+              "      <td>0.150464</td>\n",
+              "      <td>0.138874</td>\n",
+              "      <td>0.108916</td>\n",
+              "      <td>0.173514</td>\n",
+              "      <td>0.171700</td>\n",
+              "      <td>0.046899</td>\n",
+              "      <td>0.093489</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>BTC-USD</th>\n",
+              "      <td>0.179671</td>\n",
+              "      <td>0.122378</td>\n",
+              "      <td>0.189577</td>\n",
+              "      <td>0.175989</td>\n",
+              "      <td>0.172308</td>\n",
+              "      <td>0.127100</td>\n",
+              "      <td>0.170163</td>\n",
+              "      <td>0.221531</td>\n",
+              "      <td>0.054940</td>\n",
+              "      <td>0.120379</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>DOT-USD</th>\n",
+              "      <td>0.303813</td>\n",
+              "      <td>0.150464</td>\n",
+              "      <td>0.175989</td>\n",
+              "      <td>0.385419</td>\n",
+              "      <td>0.206734</td>\n",
+              "      <td>0.192864</td>\n",
+              "      <td>0.287835</td>\n",
+              "      <td>0.333077</td>\n",
+              "      <td>0.085921</td>\n",
+              "      <td>0.192047</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>ETH-USD</th>\n",
+              "      <td>0.203350</td>\n",
+              "      <td>0.138874</td>\n",
+              "      <td>0.172308</td>\n",
+              "      <td>0.206734</td>\n",
+              "      <td>0.259265</td>\n",
+              "      <td>0.160001</td>\n",
+              "      <td>0.224481</td>\n",
+              "      <td>0.232270</td>\n",
+              "      <td>0.063282</td>\n",
+              "      <td>0.133110</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>LTC-USD</th>\n",
+              "      <td>0.204707</td>\n",
+              "      <td>0.108916</td>\n",
+              "      <td>0.127100</td>\n",
+              "      <td>0.192864</td>\n",
+              "      <td>0.160001</td>\n",
+              "      <td>0.270176</td>\n",
+              "      <td>0.190943</td>\n",
+              "      <td>0.191517</td>\n",
+              "      <td>0.055659</td>\n",
+              "      <td>0.158148</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>MATIC-USD</th>\n",
+              "      <td>0.278248</td>\n",
+              "      <td>0.173514</td>\n",
+              "      <td>0.170163</td>\n",
+              "      <td>0.287835</td>\n",
+              "      <td>0.224481</td>\n",
+              "      <td>0.190943</td>\n",
+              "      <td>0.414685</td>\n",
+              "      <td>0.290214</td>\n",
+              "      <td>0.075435</td>\n",
+              "      <td>0.180736</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>SOL-USD</th>\n",
+              "      <td>0.296429</td>\n",
+              "      <td>0.171700</td>\n",
+              "      <td>0.221531</td>\n",
+              "      <td>0.333077</td>\n",
+              "      <td>0.232270</td>\n",
+              "      <td>0.191517</td>\n",
+              "      <td>0.290214</td>\n",
+              "      <td>0.593740</td>\n",
+              "      <td>0.095372</td>\n",
+              "      <td>0.184846</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>TRX-USD</th>\n",
+              "      <td>0.083626</td>\n",
+              "      <td>0.046899</td>\n",
+              "      <td>0.054940</td>\n",
+              "      <td>0.085921</td>\n",
+              "      <td>0.063282</td>\n",
+              "      <td>0.055659</td>\n",
+              "      <td>0.075435</td>\n",
+              "      <td>0.095372</td>\n",
+              "      <td>0.098433</td>\n",
+              "      <td>0.057513</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>XRP-USD</th>\n",
+              "      <td>0.194114</td>\n",
+              "      <td>0.093489</td>\n",
+              "      <td>0.120379</td>\n",
+              "      <td>0.192047</td>\n",
+              "      <td>0.133110</td>\n",
+              "      <td>0.158148</td>\n",
+              "      <td>0.180736</td>\n",
+              "      <td>0.184846</td>\n",
+              "      <td>0.057513</td>\n",
+              "      <td>0.267617</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-568f972d-ae84-4827-9205-28a3a1e18f22')\"\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 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\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",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\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-568f972d-ae84-4827-9205-28a3a1e18f22 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-568f972d-ae84-4827-9205-28a3a1e18f22');\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",
+              "\n",
+              "\n",
+              "<div id=\"df-0aaf3632-7f9e-46e3-86d0-137050a540d1\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0aaf3632-7f9e-46e3-86d0-137050a540d1')\"\n",
+              "            title=\"Suggest charts\"\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",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-0aaf3632-7f9e-46e3-86d0-137050a540d1 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "  <div id=\"id_cd43f4fb-70df-4849-801f-88f17fed014b\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\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-generate: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-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate: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",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('covariance')\"\n",
+              "            title=\"Generate code using this dataframe.\"\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",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_cd43f4fb-70df-4849-801f-88f17fed014b button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('covariance');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "covariance",
+              "summary": "{\n  \"name\": \"covariance\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"symbol\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"TRX-USD\",\n          \"BNB-USD\",\n          \"LTC-USD\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ADA-USD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.08399565915696695,\n        \"min\": 0.08362553819611086,\n        \"max\": 0.3659083337012825,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          0.08362553819611086,\n          0.14998516163196035,\n          0.2047069631024427\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BNB-USD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.05020004647894999,\n        \"min\": 0.046898875545741514,\n        \"max\": 0.2308336523892049,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          0.046898875545741514,\n          0.2308336523892049,\n          0.10891574953326724\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BTC-USD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04740882944655909,\n        \"min\": 0.05494028218787688,\n        \"max\": 0.2215314271485503,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          0.05494028218787688,\n          0.122377933753199,\n          0.1271004200389094\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DOT-USD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.09244576902400627,\n        \"min\": 0.08592093186029039,\n        \"max\": 0.38541925039220976,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          0.08592093186029039,\n          0.15046379153531964,\n          0.1928641592350875\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ETH-USD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.05794804124952129,\n        \"min\": 0.06328236381571051,\n        \"max\": 0.2592647482722747,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          0.06328236381571051,\n          0.13887388492595099,\n          0.16000095558799182\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LTC-USD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.05905788884342094,\n        \"min\": 0.055658596533961685,\n        \"max\": 0.2701762101903239,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          0.055658596533961685,\n          0.10891574953326724,\n          0.2701762101903239\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MATIC-USD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0931143715716287,\n        \"min\": 0.07543475955857806,\n        \"max\": 0.41468483972877146,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          0.07543475955857806,\n          0.17351366843631816,\n          0.19094304224077283\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SOL-USD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.13598906639151898,\n        \"min\": 0.09537234646176183,\n        \"max\": 0.5937402669570883,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          0.09537234646176183,\n          0.1717001397491843,\n          0.1915168174066942\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TRX-USD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.018435705985375535,\n        \"min\": 0.046898875545741514,\n        \"max\": 0.09843309328469316,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          0.09843309328469316,\n          0.046898875545741514,\n          0.055658596533961685\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"XRP-USD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.05963845253060871,\n        \"min\": 0.05751300817149977,\n        \"max\": 0.2676166222951364,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          0.05751300817149977,\n          0.09348869771452553,\n          0.1581484240854997\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 6
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Calculate the efficient frontier using the Critical Line Algorithm"
+      ],
+      "metadata": {
+        "id": "3IsFJL2x9qHV"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Create a Critical Line Algorithm (CLA) object using the calculated expected returns and covariance matrix.\n",
+        "cla = CLA(portfolio_expected_returns, covariance)\n",
+        "\n",
+        "# Calculate the efficient frontier, obtaining the returns, volatility, and weights for various portfolios.\n",
+        "(returns, volatility, weights) = cla.efficient_frontier()"
+      ],
+      "metadata": {
+        "id": "NarHAjA25b61"
+      },
+      "execution_count": 7,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Visualize the efficient frontier"
+      ],
+      "metadata": {
+        "id": "AaQTTEhZ70e7"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plt.figure(figsize=(10,5))\n",
+        "plt.scatter(volatility, returns, label='Portfolios on efficient frontier')\n",
+        "plt.legend()\n",
+        "plt.ylabel('Expected Reward')\n",
+        "plt.xlabel('Volatiity')\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 465
+        },
+        "id": "Kt1n-3HQ1gj7",
+        "outputId": "cea126b2-e715-4995-bbdf-e201a35d0cfd"
+      },
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x500 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Get the weights (in perentage) of the portfolio with the lowest volatility"
+      ],
+      "metadata": {
+        "id": "v4jE07br-DEJ"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "optimized_weight=weights[volatility.index(min(volatility))]\n",
+        "pie_df=pd.DataFrame(optimized_weight*100, index=prices.columns, columns=['weights'])\n",
+        "pie_df= pie_df.sort_values(by=['weights'], ascending=False)\n",
+        "pie_df"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 394
+        },
+        "id": "FzvioR9k11Tx",
+        "outputId": "16dd12f8-ee60-44ab-eaa0-1c4dc2482a01"
+      },
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "             weights\n",
+              "symbol              \n",
+              "TRX-USD    67.715856\n",
+              "BNB-USD    13.161715\n",
+              "BTC-USD     9.982129\n",
+              "XRP-USD     5.275661\n",
+              "LTC-USD     3.864639\n",
+              "ADA-USD     0.000000\n",
+              "DOT-USD     0.000000\n",
+              "ETH-USD     0.000000\n",
+              "MATIC-USD   0.000000\n",
+              "SOL-USD     0.000000"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-21f41f4b-1262-434e-93c8-198879e2ff12\" 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>weights</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>symbol</th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>TRX-USD</th>\n",
+              "      <td>67.715856</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>BNB-USD</th>\n",
+              "      <td>13.161715</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>BTC-USD</th>\n",
+              "      <td>9.982129</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>XRP-USD</th>\n",
+              "      <td>5.275661</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>LTC-USD</th>\n",
+              "      <td>3.864639</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>ADA-USD</th>\n",
+              "      <td>0.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>DOT-USD</th>\n",
+              "      <td>0.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>ETH-USD</th>\n",
+              "      <td>0.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>MATIC-USD</th>\n",
+              "      <td>0.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>SOL-USD</th>\n",
+              "      <td>0.000000</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-21f41f4b-1262-434e-93c8-198879e2ff12')\"\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 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\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",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\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-21f41f4b-1262-434e-93c8-198879e2ff12 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-21f41f4b-1262-434e-93c8-198879e2ff12');\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",
+              "\n",
+              "\n",
+              "<div id=\"df-df605205-cb64-4e00-9ad2-ff496c6c4740\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-df605205-cb64-4e00-9ad2-ff496c6c4740')\"\n",
+              "            title=\"Suggest charts\"\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",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-df605205-cb64-4e00-9ad2-ff496c6c4740 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "  <div id=\"id_a9b26081-d038-46c6-8ede-de59ec2c422f\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\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-generate: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-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate: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",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('pie_df')\"\n",
+              "            title=\"Generate code using this dataframe.\"\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",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_a9b26081-d038-46c6-8ede-de59ec2c422f button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('pie_df');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "pie_df",
+              "summary": "{\n  \"name\": \"pie_df\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"symbol\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"MATIC-USD\",\n          \"BNB-USD\",\n          \"ADA-USD\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"weights\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 20.819539048319967,\n        \"min\": 0.0,\n        \"max\": 67.71585619449834,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          67.71585619449834,\n          13.161715147317723,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 9
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Display Pie Chart of the weights"
+      ],
+      "metadata": {
+        "id": "WSHUOb_O_12C"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "pie_df= pie_df.query('weights != 0.000000')\n",
+        "\n",
+        "fig, ax = plt.subplots()\n",
+        "ax.pie(pie_df.weights, labels=pie_df.index.values.tolist(), autopct='%1.1f%%', radius=2)\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 664
+        },
+        "id": "Ad1Ncn3N2iRj",
+        "outputId": "3f635343-2f6d-4966-e15f-13ac9e15bb40"
+      },
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAArgAAAKHCAYAAABq7DtXAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAACK+klEQVR4nOzdd3SU1doF8P1Oz2TSeyO9UEPvvYsUAcUOWFCwot5rv1Y+27W3q6iI2BsCYqMqKL2E3kJICOm9J1O/P4LRSICUmTlT9m+tLGCYzOxQkp0z5z2PZLFYLCAiIiIichEy0QGIiIiIiKyJBZeIiIiIXAoLLhERERG5FBZcIiIiInIpLLhERERE5FJYcImIiIjIpbDgEhEREZFLYcElIiIiIpeiEB2AiNyPxWJBea0BJTV61OlNqDOYUP/nm9H8188NJtQbzGgwmmA0W2AyWWCyWGA2W2A0W2C2WGA8e5vJbIEEwEMlh4dSAa1Kfvbn8n/8XAEP1dnb/vZ7WpUCcpkk+o+GiIisgAWXiKymotaAouoGFFU1oLiFH4ur9SiqakBJTQMMJscboqiSy/4qvyo5/LUqhPhoEOatQaiPBmE+Hmd/1CDEW8NCTETkoCSO6iWiizGZLcgqqcHJohrklNU2FdXG0nq2wNbooTeaRUe1G7lMQqBOhVAfj78V4MYfQ73/KsMqBXeCERHZGwsuETWp05twsqgaJ4uqkV7414+ZxbXQm9ynvFqLJAH+WhVCfTSI8PVAUogXUsK8kBLqhdhAHVeAiYhshAWXyA2V1eiRfra8/vl2sqgaOeV14GcE+1ApZEgI0jUV3uRQb6SEeiHEWyM6GhGR02PBJXJhlfUG7Msux/GCsyW2sBrpRdUordGLjkbn4adVIjnUCymh3md/9EJyqBe0Kl4yQUTUWiy4RC4ks7gGu7LKsDurDHuyynCisApm/g93epIERPlpkRzqhc6hXkgJ80bvTn4I9eFqLxFRS1hwiZxUg9GEgzkV2JV5ttCeLkNxNVdm3Ul0gBYDYv0xIDYAA+L8EemnFR2JiMghsOASOYni6gbsymwssruzynAgp8KtTi2gi4vw9cCAOH8MPFt4owM8RUciIhKCBZfIAZnNFhwvrMLus9sNdmeVIaukVnQscjKh3hoMiPtrhTc+SCc6EhGRXbDgEjmI8lo9fj1WhHVHCrD5RDEq6gyiI5GLCfJSo3+sf9O2hqQQHSSJR5URkethwSUSKL2wCuuOFGLDkULsPl0GE68IIzvy91Shf4w/RqcEY1yXEPh5qkRHIiKyChZcIjsymMzYcaoU644UYMPRQm47IIchl0noF+OHCV1DMaFrKMJ9PURHIiJqNxZcIhsrrdFj49FCbDhaiE3Hi1DVYBQdieiiekT6NJXdhGDu3SUi58KCS2QDxwuqGldpjxRiz+kynkVLTi0+yLOp7KZG+YqOQ0R0UTLRARydJEkXfHviiSeQmZnZ7DZ/f3+MGDECmzdvbvZYV155Jfr37w+TydR0m8FgQJ8+fXDttddeMMOKFSvOuX3u3Lm47LLLmn596tQpXHPNNQgPD4dGo0FkZCSmTZuGo0ePtvjxeHp6IjExEXPnzsXu3bvb/4dEMJst2JJejCdWHcKwFzZg/Cub8MLPx7Ari+WWnN/Johq8/etJTHvrDwx+dj2eWHUIW04Wc884ETkszn68iLy8vKaff/nll3jsscdw7Nixptt0Oh2Ki4sBAOvWrUPXrl1RXFyM//u//8PkyZNx/PhxhISEAADefvttdO3aFc899xweeeQRAMDTTz+NvLw8rFu3rkM5DQYDxo0bh+TkZCxfvhxhYWE4c+YMfvrpJ5SXlze774cffoiJEyeivr4ex48fx+LFizFgwAAsWbIEs2fP7lAOd3OquAbf7M7Gd3tykFtRLzoOkc3lVtRj6ZZMLN2SCX9PFcakBGNC11AMSwqEWiEXHY+ICAAL7kWFhoY2/dzHxweSJDW7DUBTwQ0ICEBoaChCQ0Px8MMP44svvsD27dsxderUpt9fvHgxrrjiCkyZMgV6vR7PPvssVq5cCT8/vw7lPHToEE6ePIn169cjOjoaABAdHY0hQ4acc19fX9+mjyEmJgbjx4/HnDlzcMcdd2DKlCkdzuLqKusN+H5fLr7dfQZ7TpeLjkMkTGmNHl/vPoOvd5+Bp0qO0Z1DMKtvJIYmBPL4MSISigXXBurq6rBs2TIAgErV/NidqVOn4qqrrsLs2bNhMBgwZ84cTJo0qcPPGRQUBJlMhm+++QYLFy6EXN62lZR77rkHy5Ytw9q1azFr1qwO53E1JrMFm08U4ZvdZ7D2cAEaOEGMqJkavQnf78vF9/tyEenngVl9ozCrbxRCfTSioxGRG2LBtaLBgwdDJpOhtrYWFosFffr0wZgxY86536uvvoqIiAh4e3vj5ZdftspzR0RE4PXXX8f999+PJ598En379sWoUaNw7bXXIi4u7qLvn5KSAgDIzMy0Sh5XcaKgCt/sOYMVe3NQUNkgOg6RUzhTVoeX1x7Ha+tPYERSEK7sF4UxKcFQyHnZBxHZBz/bWNGXX36JvXv34ttvv0VCQgKWLl0KpVJ5zv0+//xzSJKE4uLiZheAPfPMM9DpdE1vp0+fbtPz33777cjPz8enn36KQYMG4euvv0bXrl2xdu3ai77vn4dp8GXFxoliy7ZmYuqbv2PcK5vw7m8ZLLdE7WAyW7DhaCFu/Xg3Bj+3Ac//fBRZJTWiYxGRG+AKrhVFRUUhMTERiYmJMBqNmD59Og4ePAi1Wt10n4yMDNx///343//+h40bN2Lu3LnYu3cv1Go15s+f32x7QHh4OADAy8sLFRUV5zxfeXk5fHx8mt3m5eWFKVOmYMqUKVi0aBEmTJiARYsWYdy4cRfMfuTIEQBAbGxsuz9+Z2Y0mfHrsSJ8u+cM1h8phN7ELQhE1lRY1YD//XoS7/x2EgNjA3BV/yhM7BbKC9OIyCa4gmsjl19+ORQKBd5+++2m28xmM+bOnYsxY8Zg9uzZePXVV1FVVYXHHnsMAODv74+EhISmN4Wi8fuP5OTkc47xMplM2LdvH5KSks6bQZIkpKSkoKbm4ismr776Kry9vTF27Nj2fLhO63RJLRatPoyBz27Azct24aeD+Sy3RDZksQBbM0pw9xdpGPBM45Fjx/KrRMciIhfDFVwbkSQJd911F5544gnceuut0Gq1eO2113Do0CEcOnQIQOOpDO+//z4mT56MmTNnon///i0+1r333oubbroJKSkpGDduHGpqavDGG2+grKwMN998MwAgLS0Njz/+OK6//np06dIFKpUKv/32G5YsWYIHHnig2eOVl5cjPz8fDQ0NOH78ON59912sWLECy5Ytg6+vr03/XBzF7qwyvL85A78cyuc5tUSClNcamo4c6xnli6v6RWFKajg81fzSREQdw0lmbbB06VIsXLjwnHNlMzMzERsbi71796Jnz55Nt9fW1iIyMhIPPvggLrvsMvTs2RPvv/8+rrnmmmbvf8stt+D3339v2qrQks8++wwvv/wyjh8/Dq1Wiz59+uDZZ59Fjx49ADQeVfb0009jw4YNTYMnYmJiMGfOHNxzzz2QyRoX6/++x1aj0SAiIgJDhw7FXXfdhd69e1vhT8lxmc0WrDmcj8WbMni8F5GD0qkVmJIahpuGxiIh2Et0HCJyUiy45PLq9CZ8vTsbH/x+ClkltaLjEFErSBIwrnMIbhuVgJ4cD0xEbcSCSy6rsKoeH23JxKfbT6O81iA6DhG10+D4ACwYGY9hiUGioxCRk2DBJZdzvKAK723KwMp9udBzIAORy+gR6YP5I+IxsWsoZDIeaUhE58eCSy7j9xPFeG9zBn47XiQ6ChHZUFyQJ+YPj8f03hFQcngEEbWABZecmsFkxvf7cvHe5lM4klcpOg4R2VGYjwY3DY3FNQM6QaviyQtE9BcWXHJKdXoTlm3NxId/ZCK/sl50HCISyFerxJxBMbhhSAx8tSrRcYjIAbDgklMxmMz4YsdpvL4hHUVVHJ9LRH/RquS4ql8nzBseizAfD9FxiEggFlxyChaLBav25eLltcd51BcRXZBKLsO0nuGYPzIe8UE60XGISAAWXHJ4vx4rxH9/OYZDudxjS0StJ5dJuLx3JO4Zl4RQH43oOERkRyy45LD2ni7D8z8fxbaMUtFRiMiJaZQy3DAkFgtGxsNboxQdh4jsgAWXHE56YRVe+PkY1hwuEB2FiFyIn1aJ20cl4PpB0VAr5KLjEJENseCSw8gtr8Mra49j+d4cmMz8Z0lEthHh64H7xifhsp4RHBhB5KJYcEm48lo93tqYjmVbs9DAyWNEZCedw7zx8KQUjgAmckEsuCRMrd6IDzafwuLNGaiqN4qOQ0RuanRKMB65tDNPXCByISy4ZHcGkxmf7ziNN3iWLRE5CKVcwnUDo7FwTBJ8tLwQjcjZseCSXW3LKMF/VhzEicJq0VGIiM7hp1Vi4dgkXDugExRymeg4RNROLLhkF0VVDXjmxyP4bm+O6ChERBeVGKzDo5O7YEQS9+cSOSMWXLIpk9mCj7dm4qW1x7nPloiczqjkIDw5tRs6BWhFRyGiNmDBJZvZe7oMj644yAlkROTUPJRy3Dc+CTcMiYWcx4oROQUWXLK6sho9nv/5KL7clQ3+6yIiV5Ea5YsXZvZAcqiX6ChEdBEsuGRV3+09g6dXH0FpjV50FCIiq1PKJSwYEY87RidCpeBFaESOigWXrCK7tBYPf3cAm08Ui45CRGRzicE6PDezB/pE+4mOQkQtYMGlDjGZLfjwj1N4ee1x1OpNouMQEdmNTAJmD4rB/ROToVUpRMchor9hwaV2O5RbgYeWH8D+MxWioxARCRPp54FnpnfHcB4pRuQwWHCpzeoNJry67gTe35wBo5n/fIiIAGBG7wg8NrkLfLUq0VGI3B4LLrXJntNluPfLNGSW1IqOQkTkcAJ1ajwxtQsm9wgXHYXIrbHgUquYzRa8uTEdr68/wVVbIqKLGNclBIsu64YQb43oKERuiQWXLiq3vA4Lv0zDjlOloqMQETkNL40CD0/qjKv6RUGSOCCCyJ5YcOmCfjyQh4eWH0BFnUF0FCIipzQsMRAvz+qJIC+16ChEboMFl1pUqzfiyVWH8eWubNFRiIicXpCXGq9d2RODEwJFRyFyCyy4dI6DORW464u9yCiqER2FiMhlyCTgjtGJWDgmETIZtywQ2RILLjWxWCx4b3MGXvzlOPQms+g4REQuaWCcP16/qheCeQEakc2w4BIAoLCyHvd9vY+jdomI7CBQp8LLs3pyOASRjbDgEtYdLsD93+5HaY1edBQiIrchScCCEfG4b3wy5NyyQGRVLLhurN5gwv/9cAQfb8sSHYWIyG31i/HD61f3QpiPh+goRC6DBddNHcuvwl2f78WxgirRUYiI3J6fVomXZ/XEqJRg0VGIXAILrhtatjUT//fDETQYeSEZEZGjkCRg3rA4/HtCMpRymeg4RE6NBdeNNBhNeGj5ASzfkyM6ChERnUevTr544+peiPTTio5C5LRYcN1EYVU9bv14N/aeLhcdhYiILsLHQ4n/Xt4D47uGio5C5JRYcN3AwZwKzFu2C3kV9aKjEBFRG8wdHIOHJ3WGSsEtC0RtwYLr4lbvz8W/v96POoNJdBQiImqH/rH+WHx9H/hqVaKjEDkNFlwXZbFY8Mra43h9Q7roKERE1EGxgZ5YMrcfYgM9RUchcgosuC6oVm/EvV/uw8+H8kVHISIiK/HTKvHOdX0wIC5AdBQih8eC62Jyyutw80e7cCSvUnQUIiKyMpVchudmdseM3pGioxA5NBZcF7IrsxTzP9mN4mqO3CUicmV3jk7AveOSIEkc8UvUEhZcF/HVzmw8uuIg9CYObyAicgdTUsPx4hU9oFbIRUchcjgsuE7OZLbg/344giV/nBIdhYiI7KxPtB8WX98HATq16ChEDoUF14lV1Blw5+d7sel4kegoREQkSCd/LZbM7YeEYJ3oKEQOgwXXSWUUVePmj3Yho7hGdBQiIhLMW6PAO9f1weCEQNFRiBwCC64T2nGqFDd/tBOV9UbRUYiIyEEo5RL+77LumNUvSnQUIuFYcJ3Mb8eLcOvHu1Bv4MVkRER0rvkj4vHAxGSesEBujQXXifx0IA93f5HGkxKIiOiCJnUPxcuzekKj5AkL5J5YcJ3E17uy8eDyAzCZ+ddFREQXlxrliyVz+vKEBXJLLLhOYOkfp/Dk6sPg3xQREbVFYrAOn84bgGAvjegoRHbFguvg3lh/Ai+tPS46BhEROam4IE98Pm8gQrxZcsl9sOA6sGd/PIJ3N2WIjkFERE4uOkCLz+YNRISvh+goRHbBguuAzGYLHl15EJ9tPy06ChERuYhIPw98Pm8govy1oqMQ2RwLroMxmsy47+t9WJmWKzoKERG5mHAfDT6bNxAxgZ6ioxDZFAuuA2kwmnD7p3ux7kiB6ChEROSiQrzV+PTmgRztSy6NBddB1DQYMW/ZLmw5WSI6ChERubhAnRqfzRuApBAv0VGIbIIF1wFU1Bowd+kO7D1dLjoKERG5CX9PFT65aQC6hHuLjkJkdSy4ghVXN+D6D3bgSF6l6ChERORmfLVKfHzjAHSP9BEdhciqWHAFyquow7XvbUdGcY3oKERE5Ka8NAosu7E/enXyEx2FyGpkogO4q5LqBlz7PsstERGJVVVvxPUf7MCuzFLRUYishgVXgKp6A+Z8uAMZRSy3REQkXnWDEbOX7MBWXuhMLoIF187qDSbc9NEuHMzhnlsiInIctXoTbli6A5tPFImOQtRhLLh2ZDSZccdne7DjFF8GIiIix1NvMOPmj3ZhS3qx6ChEHcKCaycWiwX//mY/1h0pFB2FiIjovBqMZtz68W4cyq0QHYWo3Vhw7eTJ7w/ju705omMQERFdVFWDEXM/3Ins0lrRUYjahQXXDl5ZexxLt2SKjkFERNRqRVUNmLNkB0pr9KKjELUZC66NLf3jFF5bf0J0DCIiojbLKK7BjUt3ok5vEh2FqE1YcG3ou71n8OTqw6JjEBERtVtadjlu/2wPjCaz6ChErcaCayPrDhfg31/vB+fEERGRs9twtBAPLT8gOgZRq7Hg2sC2jJLG73bNbLdEROQavt59Bi/+ckx0DKJWYcG1soM5FZj30S40GPlSDhERuZY3N6bj462ZomMQXRQLrhWdLKrGnCU7UNVgFB2FiIjIJh5fdQg/H8wTHYPoglhwrSS3vA7Xv78dJTxOhYiIXJjZAtz9RRqncpJDY8G1guoGI+Z+uAO5FfWioxAREdlcg9GMmz/aiWP5VaKjELWIBbeDLBYL7vkyDccLqkVHISIispvK+rOLO+V1oqMQnYMFt4NeWXscaw8XiI5BRERkd3kV9ZizZAcqag2ioxA1w4LbAT8dyMMbG9NFxyAiIhLmRGE1bl62E/UGTjsjx8GC205H8ytx39f7OMiBiIjc3s7MMjz8HQdBkONgwW2Hsho95i3bhVrO5iYiIgIALN+TwzNyyWGw4LaR0WTG7Z/tQXYpN9UTERH93VOrD2N3VpnoGEQsuG216Icj2HKyRHQMIiIih2MwWXD7p3tQVNUgOgq5ORbcNvhqVzaWbskUHYOIiMhh5VfW447P9sBo4sh6EocFt5X2nC7DoysOio5BRETk8LafKsVzPx0VHYPcGAtuKxRU1mP+x7uhN/K7USIiotZ4//dT+H5frugY5KZYcC+iwWjCLR/vRiH3ExEREbXJA9/ux/ECjvMl+2PBvYiHlx/Evuxy0TGIiIicTq3ehPkf70ZVPSedkX2x4F7AB7+fwrd7zoiOQURE5LQyimtw31f7YOFkJLIjFtzz+CO9GM/8eER0DCIiIqe35nAB3v71pOgY5EZYcFuQW16H2z/bA5OZ320SERFZw0trjmHziSLRMchNsOD+g9lswb1fpaG8lvuFiIiIrMVsAe76fC/OlNWKjkJugAX3HxZvzsC2jFLRMYiIiFxOWa0BCz7ZgwajSXQUcnEsuH9zMKcCL685LjoGERGRyzqQU4HHVhwSHYNcHAvuWfUGE+7+Yi/0HC1IRERkU1/uysZXu7JFxyAXxoJ71qIfDuNkUY3oGERERG7hqe8Pcz8u2QwLLoD1RwrwybbTomMQERG5jeoGI/799X6ej0s24fYFt7i6AQ98u190DCIiIrezNaMES7dkio5BLsjtC+793+xHcbVedAwiIiK39PzPR3GyqFp0DHIxbl1wP96aiQ1HC0XHICIiclv1BjPu+2ofhyuRVbltwU0vrML/cRQvERGRcGnZ5XjnN47yJetxy4KrN5px9xdpqDfwSDAiIiJH8Nq6EzicWyk6BrkItyy4L609hkP8T0REROQw9CYz7v0qDXojF5+o49yu4G49WYL3NmWIjkFERET/cDS/Cq+u40RR6ji3KrgVtQbc+1UauI+diIjIMb27KQN7TpeJjkFOzq0K7sMrDiCvol50DCIiIjoPk9mCf321D3V6k+go5MTcpuCuTMvBD/vzRMcgIiKii8gorsHzPx8VHYOcmFsU3Io6A55ezSPBiIiInMVHWzOxJb1YdAxyUm5RcF/85RiKqxtExyAiIqJWsliAf3+zH1X1BtFRyAm5fMHdf6Ycn27PEh2DiIiI2iinvA5Prz4sOgY5IZcuuGazBY98d5CnJhARETmpr3adwYajBaJjkJNx6YL7yfYsHMipEB2DiIiIOuA/Kw6h3sBTFaj1XLbgFlU14L+/HBMdg4iIiDoop7wOb25IFx2DnIhCdABb+b8fDqOq3ig6Bjk4Y1Uxyn9dirqM3bAYG6DwDUPApIVQhyUCALKen9zi+/mOvAE+A2a2+Htn/ncjTJWF59yu63UpAsYvAACUrn8PNQfXQ1Jq4DtiDnRdRzXdr+bo76g5uB7Blz/e0Q+PiMhlLN6UgZl9IhEb6Ck6CjkBlyy4W04WY0VarugY5OBM9dXI/+R+aDr1QPAVT0Cm9YGxLBcyja7pPpG3f9zsfeoydqHkp9ehTR5y3scNm/MKYP5rlrq+OAuFXz4Kz5TG96lN346aI78heNbTMJblouSn1+AR2xtyrQ/MDTUo37QMIVctsvJHS0Tk3PQmMx5beRAf3zRAdBRyAi5XcPVGM/6z4qDoGOQEKrd9A4V3IAIvXdh0m9I3tNl95Dq/Zr+uTd8OTXT3c+7X7H20Ps1+Xbftayh8w6CO6g4AMJRkQxPVHeqwRKjDElG6/j0YKwog1/qgbOOH8Oo1CQrv4A5+dERErmfziWL8eCAPk7qHiY5CDs7l9uC+tzkDJ4tqRMcgJ1CXvh2q0EQUrXgW2W9ci9wP70JV2s/nvb+ppgx1J3dC12N8q5/DYjKg5vCv0PUYB0mSAACqoFjo89Nhqq9GQ35649YIv3DUnzkEfcFJePWZ0uGPjYjIVT29+jBq9dyCSBfmUiu42aW1eGPDCdExyEkYyvNh2PsjvPtdhpBBs9CQdwJl6xdDkiuh6z7mnPtXH1wPmcoD2qTBrX6O2uPbYK6vhme3vx7PI64PPLuORP5H90BSqBB46T2QKdUo/eVtBFx6D6r2/oiqPash9/CG/4Q7oAqKtsrHS0TkCvIq6vHa+hN46JLOoqOQA3OpgvvEqkOoN5gvfkciALBYoA5NgN+IOQAAVUg8DMVZqEr7seWCu38dPLuMhKRQtfopqvevgUdcHyi8Aprd7jv0WvgOvbbp1+W/fwZNTE9IMjkqtn6J8BvfQl36DpT88DLC5r7Wzg+QiMg1Lfn9FK7oE4mEYC/RUchBucwWhV8O5WP90XOvXCc6H7nOD8rATs1uUwZEwVRZdM5967MPwlh6BrrU1m9PMFYUoj5rH3SpEy54P0NJNmoOb4TvsOtQf/oANJHdINf6QJsyDPqCkzA31Lb6OYmI3IHBZMETqzjhjM7PJQpurd6Ip77nP3RqG3VEFxhKzzS7zVCa0+IFXtX710IVmgBVcFyrH7/6wFrItT7wiO933vtYLBaU/PIW/EbfDJnKA7CYYTGf3Vv2548WvipBRPRPv6cXY+1hTjijlrlEwX1t/QnklNeJjkFOxrvfNDTkHkPF1q9gKMtFzeFfUb3vZ+h6X9rsfuaGWtQe+/28F5cVfPEwKnd/3+w2i8WM6gPr4NltDCSZ/LwZqvf9ArmHN7QJjcfeqCM6oz5rPxpyjqJy50ooAzo1O7aMiIj+8n8/HIbeyEUAOpfTF9zjBVVY8vsp0THICanDkhA0/RHUHP4NuR/cjvI/voDf6HnNhi4AQM2RTYAF8OwyosXHMZTlw1xX2ey2+sw0mCqLoOsx7rzPb6opQ8XWr+A39ta/MoUnw7v/dBR+8yRqjm5GwKS7O/AREhG5tsySWizdwg5A55IsFotFdIiOuPb9bfgjvUR0DCIiIhLAS6PAr/8aiQCdWnQUciBOvYL72/EillsiIiI3VlVvxItrjouOQQ7GaQuuxWLB8z8dFR2DiIiIBPty52kczq28+B3JbThtwV21LxeH8/iPmYiIyN2ZLcBTqw+JjkEOxCkLrt5oxotrjomOQURERA5iW0Ypfj6YLzoGOQinLLifbs9CdimPBSMiIqK/vLz2GJz82nmyEqcruNUNRry5IV10DCIiInIwxwuq8cOBPNExyAE4XcFdvCkDJTV60TGIiIjIAb2+/gTMZq7iujunKrjF1Q34YHOG6BhERETkoLiKS4CTFdy3NqajRm8SHYOIiIgcGFdxyWkKbn5FPT7bflp0DCIiInJwJwqrsZqruG7NaQruWxvT0WA0i45BREREToCruO7NKQpuTnkdvtyZLToGEREROYn0wmp8vz9XdAwSxCkK7psbTkBv4uotERERtR5Xcd2Xwxfc7NJafLP7jOgYRERE5GROFtVwFddNOXzBfW39CRhM/O6LiIiI2u41ruK6JYcuuKeKa/Dd3hzRMYiIiMhJZRTVYNU+ruK6G4cuuK+vPwETv+siIiKiDnh9A/uEu3HYgptbXofv+R0XERERdVDjKi5fEXYnDltwP9qaCSO/2yIiIiIreGN9Oldx3YhDFtxavRGfc2oZERERWUlGcQ1WpnEV1104ZMH9ZvcZVNYbRccgIiIiF/LGBq7iuguHK7gWiwUf/pEpOgYRERG5mFPFNVjNc3HdgsMV3PVHCnGquEZ0DCIiInJBH23JFB2B7MDhCu4Hv58SHYGIiIhc1J7T5TiYUyE6BtmYQxXcw7mV2JpRIjoGERERuTCu4ro+hyq4XL0lIiIiW/t+fy7Ka/WiY5ANOUzBLayq52AHIiIisrl6gxlf7swWHYNsyGEK7idbs6A3mUXHICIiIjfwyfYsmHlkmMtyiIJbbzDhUw52ICIiIjvJLq3DhqOFomOQjThEwV2xNwclNdwLQ0RERPbz0dZM0RHIRhyi4C75gxeXERERkX39nl7Ms/ddlPCCu+l4EY4XVIuOQURERG7GYgGWcRXXJQkvuDwajIiIiET5ZvcZ1OqNomOQlQktuOmFVdh0okhkBCIiInJjVfVGLN+TIzoGWZnQgrvkj0xYeEIHERERCfTx1izREcjKhBXceoMJ36dxsAMRERGJdaygCtsySkTHICsSVnDXHC5AVQP3vBAREZF4vNjMtQgruN/tOSPqqYmIiIiaWXOoAPkV9aJjkJUIKbjF1Q3YfKJYxFMTERERncNotuDT7dyL6yqEFNxVabkwcv4zEREROZAvdmbDxH7iEoQU3O/28jgOIiIicixFVQ3YcpKvMLsCuxfcEwVVOJBTYe+nJSIiIrqo7/fxhCdXYPeCu5yrt0REROSgfj6YD73RLDoGdZBdC67FYsFKFlwiIiJyUJX1Rvx2nFNWnZ1dC+7WjBLk8ggOIiIicmDcpuD87Fpwv+OsZyIiInJw644UoE5vEh2DOsBuBbfeYMJPB/Pt9XRERERE7VKrN2H90QLRMagD7FZwfzmUj2qO5iUiIiInsCqN2xScmd0KLs++JSIiImfx6/EiVNUbRMegdrJLwS2q4mheIiIich56oxm/HOI2BWdll4K7al8uR98RERGRU1nF0xScll0K7vI9Z+zxNERERERWsyW9GKU1etExqB1sXnCPF1ThUG6lrZ+GiIiIyKqMZgt+PJAnOga1g80L7ur9/IdBREREzonbFJyTzQvuBp4jR0RERE5qV2Yp8jmF1enYtOAWVtVzewIRERE5LbMFWL2fq7jOxqYF99djRbDw8AQiIiJyYt9zu6XTsWnB3Xi00JYPT0RERGRz+7LLcbqkVnQMagObFVyjyYzfOdyBiIiIXMDaI7ymyJnYrODuzCxDVYPRVg9PREREZDe/nygSHYHawGYFd+Mxbk8gIiIi17D9VCn0RrPoGNRKtiu43H9LRERELqJWb8KurFLRMaiVbFJws0trcaKw2hYPTURERCTEZl5b5DRsUnB/5fYEIiIicjG8eN552KTgbjzGjdhERETkWg7lVqC0Ri86BrWC1QtuvcGELSf5HQ4RERG5FrMF+D2dHccZWL3gbs0oQb2BVxkSERGR6+FxYc7B6gWXpycQERGRq+I+XOdg/YLLC8yIiIjIReVW1CO9sEp0DLoIqxbc9MJqZJfWWfMhiYiIiBzKpuNcxXV0Vi243J5AREREro4Xmjk+6xZcbk8gIiIiF7cto4Rjex2c1Qqu3mjGrqwyaz0cERERkUOq1Zuwm53HoVmt4B7KreB3M0REROQWNvO4MIdmtYK793S5tR6KiIiIyKFxH65js1rB3XOaS/VERETkHg7mVKCMY3sdFldwiYiIiNrIbAH+OMlVXEdllYJbWFWPnHKef0tERETuY1cmX712VFYpuHuyyq3xMERERERO40BOhegIdB5WKbh7s/kdDBEREbmXw7mVMJktomNQC6xTcLn/loiIiNxMncGE9MJq0TGoBR0uuEaTGQfOcImeiIiI3M/+M+WiI1ALOlxwj+ZXoc5gskYWIiIiIqfCfbiOqcMFdy/PvyUiIiI3tZ+vYjukDhfcPdx/S0RERG7qSF4ljCaz6Bj0D1zBJSIiImqnBqMZxwqqRMegf+hQwS2r0SOzpNZaWYiIiIiczkHuw3U4HSq4PP+WiIiI3B334TqeDhVcTjAjIiIid8eTFBwPV3CJiIiIOuBoXhX0Rl5o5kjaXXDNZgv2ZfM7FiIiInJvepMZx/J5oZkjaXfBzSypQXWD0ZpZiIiIiJzS/pxy0RHob9pdcDOKaqyZg4iIiMhp8SQFx9L+gltcbc0cRERERE6LJyk4Fq7gEhEREXXQ8YIqNBhNomPQWR1YwWXBJSIiIgIAg8mCI3m80MxRcAWXiIiIyAqO8yQFh9GugltZb0BxdYO1sxARERE5rdOltaIj0FntKrhcvSUiIiJqLosF12G0q+Ce4gkKRERERM1wBddxcAWXiIiIyAqyWXAdBgsuERERkRWU1uhRVW8QHYPQ3oLLI8KIiIiIzsFtCo6hzQXXYrEgkwWXiIiI6BzcpuAY2lxwcyvqUWfgpA4iIiKif8oqYcF1BG0uuBlFPEGBiIiIqCXcouAY2lxwT3F7AhEREVGLWHAdQztWcFlwiYiIiFrCgusY2lxwT3KLAhEREVGLcsvrYDJbRMdwe1zBJSIiIrISg8mC3PI60THcXpsKrt5oRl4F/9KIiIiIzodHhYnXpoJbXN0ArroTERERnV8WC65wbSq4pTV6W+UgIiIicgm80Ey8Nq/gEhEREdH5seCKxxVcIiIiIis6zWlmwrWp4JZUs+ASERERXciZMhZc0dpWcLmCS0RERHRBFXUGWCy8Kl+kNq7gcg8uERER0YWYLUBlnVF0DLfGPbhEREREVlZRZxAdwa217RQFFlwiIiKii2LBFauNK7jcokBERER0MSy4YvEUBSIiIiIrY8EVq9UFt95gQq3eZMssRERERC6BBVesVhdcTjEjIiIiah0WXLFaXXB5ggIRERFR67DgitXqgsv9t0REREStw4IrVusLLldwiYiIiFqlkgVXqDas4HIPLhEREVFrlNdxYVAk7sElIiIisjJuURCrDacosOASERERtQYLrlitLrjVDfyLIiIiImqNilr2JpFaXXCNJostcxARERG5jKoGI8xmdidRWl1wDfxLIiIiImoViwWoqjeKjuG2Wl1wTWazLXMQERERuRTuwxWn9Su43KJARERE1GosuOK0YQWXBZeIiIiotaobuEVBlNZfZMaCS0RERNRqZgu7kyhtOEWBe3CJiIiIWouvfovDLQpERERENsAVXHHacJEZV3CJiIiIWosFVxyu4BIRERHZANcGxeExYUREREQ2wMVBcbiCS0RERGQD3KIgDo8JIyIiIrIBLg6K04aCy40kRERERK3FFVxxFK29o4l7cInIAb2YsgtSuQf8yzqhTBmGsgoZzEZ+viIi8ZINctER3FarC66BK7hE5GBiPOoxM/ddzOkyALGK7bh6ZTlMpdWoTx2NysheKJFCUFxihomFl4gEkEMSHcFttX4Fl/tIiMjBvBi5GVJ2DabrZXjM6zjWXqPG00e7Inz199BuWYFQAGaNJ+p7jkFlVG+UyENQXAKYDPyGnYhsT5Kx4IoiWSyt2yAS+9AP4FYSInIUkZoGbFbfBamhCrUqT4yOjkKNsRYAMKYuBreuNgLpmee8n0Xt0bjCG90HpYowFBUDRhZeIrKBibd2Q3yvYNEx3FKrVnDNZgvLLRE5lBej/oCUXQUA0OprMNErHt+WHQAArPfIxO+Xy/F4Rn8krkiDRa9vej+poQ4eO36Ax44fEAIgRaVBfeooVEX3RYkiDEWlEox6Fl4i6jhJ4gquKK1ewY176AdwlwIROYIwjR5b1HdDaqhoum1fVCquU5Sdc9/+DRG4d60asgPHW/XYZpUaDd1HoCq2P0qV4SgqlWBoYOElorabdFsPxPYIFB3DLbV6D65GKUet3mTLLERErfJi1FZI2RXNbkvN3of47oNxsvpMs9t3qHNw9aXAQ6n90Gv5IVhqay/42DJ9Azx2r4HH7jUIBpCkUEHfY3hj4VVFopCFl4haiQu44rS64Hqw4BKRAwhWGzCo6KsWf2+65IMXceac2y0S8EzUXnS5LQgP/xYH1c6DrX4+mVEPzZ510OxZhyAASXIFGroPR1XcAJSqI1FUJoe+np8biehcvMhMnDat4BIRifbfTtsgyz53KwIATDm5E6+GesNoNrb4+4eVRbhubBHu6t4bw5afhKW8osX7XYhkMkKTtgGatA2NhVcmh77bUFTFD0SpRycUlcnRUMfCS0RcwRWpDQW31UPPiIhsIkBlwLDilldvAcC/phijfPphbdmhCz7O6yH7sXKeL57Y3hOem9I6lEkym6De/xvU+39DIIBEmRz6roNRFT8QZdpoFJYr0FDLwkvkjhRcHBSm9VsUVPxLIiKx/hu9A7LskgveZ3pFOda24rGyFOW4YUg55nZJxaXLc2ApLLZKRslsgvrAZqgPbEYggARJgr7zIFQnDW4svBUq1Ne0vMJMRK5F5dHqmkVW1upTFK54Zwt2Zrb8siARka35KY3Y7XUvZLUXLqJmSYbxnXuioK71hTXYpMNTafHwX7sHtj4T0SJJMKT0R3XSEJR5xqCwQo06Fl4ilzT7mcHw8teIjuGWuAeXiJzC89G7IDtz8dIqs5hxmSoM77ah4BbKqzG/zz7MSOqMq1eWw5Kd25GoFyRZLFAd2Q7/I9vhDyAegD65H6qTh6BMF4eiSjVqq1l4iVwBV3DFYcElIofnpTBiTNkXrb7/ZacPYLGPBAvathq7vGncb3+Er94NmOyzd1Z1bCf8j+38q/Am9EJN5+Eo84pDYZUGtVUsvETORpIAlYbdSZRWF1wt9+ASkSAvxOyB/Exhq+8fWXoa/aPHYnt564Y7/F2VrAELu+zBmNjY8477tTVV+l6o0vfCD0AcAH18D9R0Ho5y7wQUVnugppKFl8jRqTwUnGQmUKsLrqeay+xEZH+eChPGl3/Z5vebUWfC9g4874XG/dqb6uR+qE7uhx+AWACG2G6o6TICZT4JKKrRorqChZfI0ag07E0itfpP31ujtGUOIqIWPRuzF/IzeW1+vzHpW+EdH49KfVW7n7tBMuHh+D3of0fbxv3amvLUQfieOghfNBZeY6fOqO42EuW+iSiq1aGq3CA4ofvYfGgVNh9ehdKqAgBAqF80LulzPbp2GtDi/U0mI9akfYbtx9egvKYYIT5RmDZgHrp06t90n50n1mHl9vfRYKjDwOQJmDn4tqbfK6nKx5s/3I/7Z/wPHipP235w1CEqLQuuSK0vuNwoTUR25ik3Y1JF21dvAUBtrMel2lh8rt/f4RxtHfdrb4rTR+B7+gh8AcQAMEYlo7rrSFT4J6GoVofKcq7w2oqvZyCmDZiHIJ8IWCwWbD++Bot/eQwPznwXYf4x59z/+51LsPPEOlwz4j6E+EbhSPYuvLfmcdx72euICkxEdV0FPvvtJVw38n4Eeofhfz89gqSIXugePQgA8OXm1zCt/zyWWyegZm8SqtV/+l5cwSUiO3s6Zh8UOTntfv8Z+Rn43Eon9HRk3K+9KbKPwTf7GHwBRAMwRiSgpttolAcko7jeCxWlXOG1lu4xg5v9emr/m/D74e9xqvBwiwV3x4l1mNDrmqYV3mFdp+Jozh5s2Pc15ox5GMVVedCoPNEnYRQAICm8JwrKTqN79CDsSt8AuUyBnnHDbP5xUcfxBAWx2rBFgX9RRGQ/apkZU6taf3JCS1LyDqNzj+E4UpVpnVCwzrhfe1PkpMMnJx0+aCy8prBY1HQfjfKAzihq8GbhtRKz2YQ9Gb9Bb6hHbEiXFu9jNOmhVKia3aaSq3Ayv/EbpmCfCBiMDcguPgF/XQiyio5hYMpE1DZUYfXOD3H3lJds/nGQdai5RUGoNmxR4AouEdnP07EHoMjJ7vDjzDBr8H9WyPNP1hz3a2/yvFPwzvsA3gA6ATCGRKO2x2hUBHZGkd4H5SXc0tAWOSUZeGnFnTCa9FArPTBvwpMI84tp8b6do/phw/5vkBDWA4He4TiWswdpmb/DYjYDALRqL1w/6gEs2/g8DMYG9E8ahy5R/fDpr//FiK6XoaQyH+/+/B+YzEZM6jsbveJG2PEjpbbgCq5YrZ5ktjurFDP/t9XWeYiIoJaZcTDwESgrszr8WJUePhgdEYwGU4MVkrVsbllXq477Fc0UFIna1DEoD+qCYoMfykoMaOORwm7FaDKgrLoQdfoa7M3YhK1Hf8TdU19useRW1ZXj800v4UDWNkgAAr3DkRzRG9uO/YxXbv6pxcc/kbsP3217FwunvIwnvpiNG8Y8Am+tP/773e14/KqP4OXhZ9sPkNql76QYDJgaJzqG2+IpCkTkcB6POQRlbsfLLQB411VgrPcQ/FBmuz2zS/0O4ce5Ojy1ry/81+y2+bhfW5MXnYHXuo/gBSAKgCkgDLU9x6IiuCuKDX4oLTGy8P6NQq5EkE8EAKBTUBJOFx3DrweW4+rh955zXy8PX9wy4WkYjHrUNFTARxuIldvfQ4B3WIuPbTDp8eXvr2HOqAdRVJkDs9mExPBUAECwTyQyC46csw+YHANXcMXiRWZE5FCUMgsur23fyQnnM7O0CD/Y+Lz1Qnk15vdOw4xE24/7tTd5SR681n8MLwCRAEz+IahNHYvKkG4oNvmjtNjo7J3eqiwWM4ymC+9rVipU8FUEwWQyIu3UZvQ+z1aDX/Z8ii5R/RAVlITs4hMwW/6armcyG2G2mK2anayHe3DFavWfvq+WBZeIbO8/0Yehysuw6mP2zdyFqK79kF2bb9XHbYmocb/2JC8tgNfGT+EFIAKAyS8YdaljUBHSHSWWAJQUG+EuvWvl9vfRNao//LyCUa+vxa70DTiRuw+3XfocAGDZhufg4xmIaQNuBgBkFhxBeU0xIgPjUV5TjB93LYPFYsHYnled89h5ZZnYfXIjHpz5LgAgxLcTJEnClqM/wtvDHwXlpxEdnGy/D5bahIMexGr1n75GKYePhxIVdbzalohsQy6ZcVX9V1Z/XAkWTFcE4nXYvuAC/xj3+4MROJFpl+cVRV5WCN2vn0OHzxEBwOwdgNpeY1EZ2gPFlkCUFptgNrvmEm91XRmWbXwOlbWl0Kg8EREQh9sufQ6dI/sCAEqrC5uNazWY9Fi9cwmKq/KgVnqga9QAzB79ILRqXbPHtVgs+HzTK5gxaAHUSg8AgEqhxnUj78dXv78Oo8mAWUPuhK9nkP0+WGoTruCK1eqLzADgktc240hepS3zEJEb+0/sEdyU97RNHrvQJwzjAzQwWey7oqqyyPFERqrwcb8imXW+qOs1DpXhqShGIEqKzTCbXLPwEv3piof6IjjaW3QMt9WmgnvzRzux7kihLfMQkZuSJAuOhj0Fdekxmz3H7b0mYFP5EZs9/oX0b3Cscb8imXU+qE8dg4rInihBMIpZeMkFzX5mMLz8rTRphtqsTevn4b4etspBRG7uwU4noC6wXbkFgBnVtdhk02c4P0cf92tPsuoKaP9YDi2WIwyA2dMb9amjURnZCyVSCIpLzDAZWXjJeUkS4OmjuvgdyWZYcIlIOEmyYI7R+ntv/2n4yW3wT+qC0oYymz9XS5xp3K89yWoqod2yAlqsQCgAs8YT9T3HoDKqN0rkISguAUwGN7lqjVyC1lsFmVwmOoZba1PBDfPhUjsRWd99USehKTxs8+dRmg2Y6hGFpYIK7p+ccdyvPcnqa6DdtgrabasQCsCi9mhc4Y3ug1JFGIqKASMLLzkwHbcmCNemPbi7Mktx+TucZkZE1nUk8hl4FNtnNTMjOAHTPB3nYq9ooy+e2B7pdON+RbKoNKhPHYWq6L4oUYShqFSCUc/CS44jvncQJt7SXXQMt8YtCkQk1D2dMuBRaL+X6uMK09EzdRTSKk/a7TkvJEtRjhuGlGNul1SXGvdrS5K+Hh47f4LHzp8QDCBZpUZD9xGoiu2PUmU4ikolGBpYeEkcnS9XcEVrU8EN8dZAIZNgdNHzDInI/m62fGP355xhkCPN7s96Ya427teeZPoGeOxeA4/daxAMIEmhgr7H8MbCq4pEIQsv2ZnOXy06gttr0w5ouUxCiDe/KyEi67g9KhOeRWl2f94J6VvhqdDa/Xkv5s9xv1/c3hlSVLjoOE5LZtRDs2cdgr59Bsmf34ah6+7E4PJv0d3/DCLCAJVGLjoiuThPX3EFd+7cubjssssAAJmZmZAk6YJvS5cuBQBs3LgRkyZNQkBAALRaLbp06YL77rsPOTk5532uJ554Aj179jzn9j+fNy0trem29957D6mpqdDpdPD19UWvXr3w7LPPNnusPzMpFAoEBgZi+PDhePXVV9HQ0NDmP4c2X+IXzmV3IrKSBbD/6i0AaPU1mOgVL+S5W2O513HceE0Fcqf1B+QsYx0lmYzQpG1A0PJnkfz57Riy5nYMKf0KPfxOIzIcUHvwz5isy1HOv42KikJeXl7T23333YeuXbs2u+3KK6/Eu+++i7FjxyI0NBTffvstDh8+jHfeeQcVFRV46aWXOpxjyZIlWLhwIe666y6kpaXhjz/+wP3334/q6upm9/sz2+nTp7Fx40ZcccUVePbZZzF48GBUVVW16TnbPEeucR+u2CuQicj53RKZBV3RHmHPP73oDL514Ema7jbu154kswnq/b9Bvf83BAJIlMmh7zoYVfEDUaaNRmG5Ag219p14R67FK8AxCq5cLkdoaGjTr3U6HRQKRbPbzpw5g7vuugt33XUXXnnllabbY2JiMHz4cJSXl3c4x6pVqzBr1izcdNNNTbd17dr1nPv9PVt4eDi6d++OcePGITU1Fc8//zwWLVrU6uds8wpumA8vNCOijrtDtlzo86dm70O8LlJohtZY75GJ62bmIX1Wf0gqHhxvC5LZBPWBzQhc8V8kfnYHBv+0AEMKP0Wq7yl0CjdD4+nA3wmRw1EoZdB6O8//1a+//hp6vR73339/i7/v6+vb4ecIDQ3Ftm3bkJWV1eb3TUlJwSWXXILly9v2NaPNBTeCWxSIqINuCM+Gd+FO0TEwXfIRHaFV9JIJD8fvwX/vCIO5e5LoOC5PsligPrwFASteRMJnd2LQj/MxtGAZevqcRHS4CR4svHQBXgEaSJIkOkarnThxAt7e3ggLC7PZczz++OPw9fVFTEwMkpOTMXfuXHz11Vcwm1t38WdKSgoyMzPb9Jzt2IPLFVwi6piFyu9ERwAATDm5EwqZ85SVxnG/GUi7rh8kreNdJOeqJIsFqiPb4b/yZcR/dhcG/XArhuYtRU/vE4gON0Grc55/Q2R73kHO1ZMsFkurCrlOp2t6mz9/fpueIywsDFu3bsWBAwdw9913w2g0Ys6cOZg4cWKrSm5rM/5dO/fgEhG1z/XhOfAp2CY6BgDAv6YYo3z6YW3ZIdFRWo3jfh2D6thO+B/bCX8A8QD0Cb1Q03k4yrziUFilQW2VUXREEsQ70Ll6UlJSEioqKpCXl3fBVdy/n4jg7e3d9GNFxbmTGP/ct+vj0/xVsm7duqFbt2647bbbMH/+fAwbNgy//fYbRo0adcGMR44cQWxsbCs/okZcwSUiu7pXtUJ0hGamV5SLjtAujeN+j+L3G3tD8nWOrRauTJW+F37fv4a4z+7GwO9vxdDsd9FLdwSx4QZ4enOF1534OFnBvfzyy6FSqfDCCy+0+Pt/ltWEhISmt+DgYABAcnIyzpw5g4KCgmbvs2fPHmg0GnTq1Om8z9ulSxcAQE1NzQXzHT16FD///DNmzpzZ2g8JQDtWcH08lNCpFahu4HenRNQ2V4XlwS//D9ExmhmSsR0hnXuioM45J4i9HrIfK+f54ontPTnu14GoTu6H6uR++AGIBWCI7YbqLiNQ7pOAohotqiv4NdRVeQeKv1apoqKi2YorAAQEBLR436ioKLzyyiu44447UFlZidmzZyMmJgZnzpzBsmXLoNPpzntU2IQJE5CcnIyrr74aixYtQmhoKPbs2YNHH30Ud999N+RnjzlcsGABwsPDMXr0aERGRiIvLw+LFi1CUFAQBg0a1PR4RqMR+fn5MJvNKCkpwa+//opFixahZ8+e+Pe//92mP4N2fVsZ5qPBicLqi9+RiOhv/q1ZKTrCOWQWM6apwrDYSQsuwHG/zkB56iD8Th1sKrzGTp1R3W0kyn0TUVSrQ1W5QXREshJH2KLw66+/olevXs1uu+mmmxAZ2fLJMbfddhuSkpLw4osvYvr06airq0NMTAwmT56Me++997zPo1AosGbNGjz88MO4+uqrUVRUhNjYWNx9993N3m/s2LFYsmQJ/ve//6GkpASBgYEYNGgQ1q9f36x4Hzp0CGFhYZDL5fDx8UGXLl3w0EMPYcGCBVCr2zY8Q7JY2j4P8pZlu7DmcMHF70hEdNbMkAK8VHGP6BgtOuPfCZN8JFjg/ONxg006PLUvgeN+nYwxKhnVXUeiwj8JRbU6VJZzhddZ3fLaCCjVHB4iWrtWcDuHebPgElGbPKhdBZx7LYJDiCw9jf6dxmJ7xXHRUTrsz3G/MxI74+qV5bBk54qORK2gyD4G3+xj8AUQDcAYkYCabqNRHpCM4novVJRyhdcZeAVoWG4dRLsLLhFRa00JLkJQ3kbRMS5oRr0J20WHsKLlXsex9ho1njraDxGr9wAmTuZyJoqcdPjkpMMHjYXXFBaLmu6jUR7QGUUN3iy8DiowUic6Ap3Vri0Kp0tqMfy/jv3Fiogcx/a4JQjJXSc6xgU1KDQYHR+PSn3b5p07gzF1MRz362KMIdGo7TEaFYGdUaT3QXkJtzQ4gr6XxmDAlDjRMQjtXMGN8veAl1qBKp6kQEQXcUlQMYJz14uOcVFqYz0u1cbic/1+0VGsbr1HJjbPlOOJjP5IXJEGi14vOhJ1kKIgC95rP4Q3gCgApqBI1KaOQXlQFxQb/FBWYoALbCl3OkGRXqIj0FntWsEFgCve2YKdmWXWzkNELmZr/EcIy/lFdIxWORrWBVdoXPuEmP4NEbh3rRqyA86/35jOzxQQhtqeY1ER3BXFBj+UlhhZeO3guqcHwcfJJpm5qnYX3MdWHsSyrVnWzkNELmRcYCkWV98JyYm+ss7qMRxHqjJFx7ApyQI8dKYXei0/BEttreg4ZAcm/xDUpo5FZUg3FJv8UVps5CEbVqbUyDHvleFtHilLttHu8SpdeKEZEV3E4z4/Qqp2rq+iM8wa/J/oEDbGcb/uR15aAK+Nn8ILQAQAk18w6lLHoCKkO0osASgpNsJiFp3SuQVG6FhuHUi7V3D3ZZdj2luONZGIiBzHSP8yfFh3JyQn+6pZ6eGD0RHBaDA1iI5iN3cV9MCw5SdhKXfQc9zI5szeAajtNRaVoT1QbAlEabEJZrNzfXMqWrcRERhxdbLoGHRWuwtuvcGEro//AhP/AxBRCzYlfIZOZ1aLjtEuD/aehB/K3GtVM9roiye2R3LcLwEAzDpf1PUah8rwVBQjECXFZphN/Hp/ISOvTUbXYRGiY9BZ7d6ioFHKERvoiXSO7CWifxjqX4GonJ9Ex2i3GaVF+MHNXmnkuF/6O1l1OTw3fw1PfI0wAGadD+pTx6AisidKEIxiFt5zBPIEBYfS7hVcALjz8734fh+n5BBRc78mfImYMytFx2g3CyRM6toPZ2rzRUcRguN+6WLMnt6oTx2NysheKJFCUFxihsnovv9WJAmY99oIKFWcYuYo2r2CCwCdw7zw/T5rRSEiVzDAtxLRuT+IjtEhEiyYrgjEG3DPgstxv3QxsppKaLesgBYrEArArPFEfc8xqIzqjRJ5CIpLAJPBufbfd4RPsJbl1sF0aAX312OFmPvhTmvmISIntz7hG8SfWS46RocV+oRhfIAGJot7j7j1Mqvx1NEuHPdLbWJRezSu8Eb3QakiDEXFgNGFC29Cn2BMmNdNdAz6mw4V3MLKevR/xvEnFBGRffT2qcK3hjsgmQ2io1jF7b0mYFP5EdExHALH/VJHWFQa1KeOQlV0X5QowlBUKsGod53CO2BaHPpeEiM6Bv1Nh7YoBHtrEKhTobiaYx+JCHgueB2kbNcotwAwo7oWm0SHcBAc90sdIenr4bHzJ3js/AnBAJJVajR0H4Gq2P4oVYajqFSCocF5C29gpE50BPqHDq3gAsD1H2zH5hO82pbI3fXwrsZK0x2QTK5TfAwyJcYmdUFpA8eS/x3H/ZK1mRUq6HsMbyy8qkgUOlnhnfPsEOj81KJj0N90aAUXADqHebPgEhFeCFkPKdt1yi0AKM0GTPWIwlIW3GZ2qHNw9aXAQ6n9OO6XrEJm1EOzZx00e9YhCECSXIGG7sNRFTcApepIFJXJoa93zD3gGk8ly60D6vAK7nd7z+CeL3mUApE76+pVg9XmOyC54PSvjOAETPN0reJuTV0MQXh4UwBUO9xrMAbZl0Umh77bUFTFD0SpRycUlcnRUOcYhTcyxQ/TFvYSHYP+ocMFN72wCmNf5i41Inf2Q+JqdM3+THQMm7k+dRTSKk+KjuHQOO6X7Mkik0PfdTCq4geiTBuNwnIFGmrFFN5+k2PRf3KskOem8+vwFoWEYC9eaEbkxlJ0teiS953oGDY1wyBHmugQDu71kP1YOc8XT2zvyXG/ZHOS2QT1gc1QH9iMQAAJkgR950GoThrcWHgrVKivMdolS1iCj12eh9qmwyu4AHD7Z3vww/48a+QhIiezKvFH9Mj+RHQMm6pV6zCqUyRqjdxr2hpzy7o65bjfN4uL8HZJSbPbYlUq/BAb1+L911ZVYXFJCU4b9DBaLOikUuEGP39M9fmr8CwpLcGS0lIAwE3+/rjBP6Dp9/bV1eHpgnx8ER0DheRms6FtzCJJMKT0R3XSEJR5xqCwQo06GxRemUzCza8Mh1LNIQ+OpsMruAAwKC6ABZfIDcVr69A93/mHOlyMtqEaE73isbzsgOgoTmGp3yH8OFeHp/b1dbpxvwkqFT6I6tT06wt9kfSRy3BrQABiVSooJQm/1VTjkfw8+CvkGOqpw7H6erxZXIy3IyJhAXBbzhkM8fREkloDo8WCJwvy8WRIKMutDUgWC1RHtsP/yHb4A4gHoE/uh+rkISjTxaGoUo3a6o4X3sAoHcutg7JOwY0PuPidiMjlvBixCVK2e6xqTi/KwXKrfMZ0D8467lcuSQhStO4vur/Ws9mvr1f5Y0VFBfbU1WGopw4Zej2S1GoM9Gy8X5JaffY2DZaUlqKvhxbdPTys/jFQy1THdsL/2M6/Cm9CL9R0Ho4yrzgUVmlQW9X2whuW6GvtmGQlVvl0HR+kQ4i3GgWVrncFNRG1LMajHj0LvhEdw256ZqchvvtgnKw+IzqKU1nudRxrr1HjqaP9nGLc72m9HiPS06GWSUjVeOCeoCCEK5UXfT+LxYJttbXI1Otxn4cWQGOhzdTrkWswwAIgS69HokqN03o9vqsoxzcxMbb9YOiCVOl7oUrfCz8AcQD08T1Q03k4yr0TUFjtgZrKixfe8HhfW8ekdrLKHlwAWPjFXqxIc47v0Imo475JXIu+2R+KjmFXH/W4BC9WHRIdw2k5+rjfTdXVqLWYEatUochkxNvFJSgwGrAqNhaespZfhq4ymTDyZDoMFgtkkoT/hIRgpo9v0+9/UV6GZaWN5yjP9vfDVb5+uDH7NK7x9YMJFrxVXAyFJOHh4BD01Wrt8WFSKxliu6G6ywiU+ySgqEaL6opzC++N/x0KDy+VgHR0MVYruF/uPI0HvuX+NCJ3EKlpwGb1XZAaqkRHsatSz0CMCfWG0Wyfq7NdkcoixxMZqU4x7rfSZMLYjJN4ICgYM319W7yP2WJBtsGAWrMZ22pr8E5JCd6IiDhn+8KfVlRUYH11FR4PCcWlpzLwZXQMCowG3J+bh7VxcVDJZDb8iKgjjJ06o7rbSJT7JqKoVgeFRolrnhgoOhadh9V2lA2KC7TWQxGRg3sx6g9I2e5VbgHAv6YYo3z6YW0ZV3HbSy+Z8HD8HvS/w/HH/XrL5YhRqZBlOH8Rl0kSolWNK3idNRpk6PV4r6S0xYJbZjTi7ZJiLIvqhP31dYhRqZrejLAg09C4P5cck+L0EfiePgJfADEAvObeDIAF11FZ7VvFTgFaRPhyszyRqwvT6DGg8GvRMYSZXlEuOoJLaBz3m4G06/pBctCX5mvMZpzW61t90RkAmC2A3mJu8feeKyrEbD8/hCqVMFsAw99eQDVZLDA5z2ETBMCre4roCHQBVn0tZGAcT1MgcnUvRm2F1OC+06qGZGxHiAdfsbIGiwQ8E7UXj9/mA33/bqLj4IXCQuysrUWOQY+9dbW4K+cM5JKES728AQAP5uXi5aLCpvsvLinBlpoaZOv1ONnQgA9LS/B9ZQWmeJ978P+Wmhpk6vW4xtcPANBNo8EpvR6bqqvxVXk5ZJKEWBX3cjoTzwEDREegC7DqoTeD4gPw7R5eYUzkqoLVBgwq+kp0DKFkFjOmqcKwuM65hhg4ssPKIlw3pgh3destdNxvgdGAf+Xmotxsgr9cjt4eHvi8UzT8z67g5hkMzVaF6sxmPFWQjwKjEWpJQpxKjefDwnGJt3ezx603m7GooAAvhYdDdvbM21ClEo8Eh+CR/DyoJAnPhoZBw/23TkOVEA9FIL/RdWRWu8gMAHLK6zDkuQ3WejgicjDLEjdjePb/RMcQ7ox/J0zykWABX1O2tmijL57YHslxv+TQ/K65BqGP/Ud0DLoAq367GOHrgU7+jrmXiog6JkhlwNDiL0XHcAiRpafR3ydRdAyXlKUoxw1DDuKH+amQgrlCRo5JO5DbExyd1V8PGcR9uEQu6fnoHZDVlYqO4TCm1zv2wAJn95HfISy4wYCSCX0AjrIlRyKXc/+tE7B+weXYXiKX46c0YmQJV2//bmz6VngpdaJjuLRiWQ0W9N6HL27vDCkqXHQcIgCAR6+ekPuceyEhORarF9zBLLhELuf56F2Q1fKiqr9TG+txqWes6BhuYbnXcdx4TQVypvUD5C1PFCOyF93wEaIjUCtYveAGe2sQF9TyBBcicj5eCiPGlH0hOoZDmpl/SnQEt1Ela8A9Xfbi3TtjgcQY0XHIjelGsOA6A5ucScJ9uESu44WYPZDXFF78jm4oJe8wOnvFiI7hVtZ7ZOK6mXlIn9UfEs+NJTtThIVBk5wkOga1gk0K7tAEXvlK5Ao8FSaML+fe2wuZYeZoVXv7c9zvf+8Ig7k7ywbZj27EcNERqJVsUnBHJAdBo+SB1UTO7tmYvZBX54mO4dAmndwOtVwtOoZbcoZxv+RauD3BedikhWpVCoxICrLFQxORnXjKzZhUwdXbi/Guq8BYb56JK4qjjfsl1yWp1fAcOFB0DGolmy2zTuoeZquHJiI7eDpmHxRVOaJjOIUZpUWiI7i9xnG/R/H7jb0h+fIIJ7I+bf/+kHl4iI5BrWSzgjs6JRgqBbcpEDkjD7kJUys/Fx3DafTL3IVIbajoGATg9ZD9+Nc8JWqG9xQdhVwMtyc4F5s1UC+NEsN4sRmRU3oq+gAUVWdEx3AaEiyYruDnO0fBcb9kC7qRLLjOxKZLrBO7cUWDyNmoZWZcVs29t201LTMNcolDCBwJx/2Staji46GKjBQdg9rApgV3fJdQKOX8pELkTB6POQRlZZboGE4npCIXg314ZJWj4bhfsgavcWNFR6A2smnB9dEqMZBDH4ichlJmweW1XL1trxk1taIj0Hlw3C91hPclk0RHoDay+VVgPE2ByHn8J/owVOUZomM4rRHp2+Cv9hMdg86D436pPVTx8Zxe5oRsXnDHdwmBXMZtCkSOTi6ZcVX9V6JjODWl2YCpHlGiY9BFcNwvtYX3hAmiI1A72LzgBujU6B/jb+unIaIOejjmGFRlJ0THcHrTc46KjkCtwHG/1Freky4RHYHawS4H1V7SnacpEDkySbLgugau3lpDXGE6enrHi45BrcRxv3Qh6sQEqBMSRMegdrBLwZ3YNRTcpUDkuB6KPgF16THRMVzGDAMvYnImHPdL5+N1CVdvnZVdCm6wtwZ9onnhBZEjkiQLZhu4emtNE05ug1bB1UBn8+e438039eG4XwIAeE9kwXVWdpulO7EbT1MgckT/6pQOTclh0TFcirahGhO9uE3BWb0RvA/33cJxv+5OnZwMdVys6BjUTnYruJd0C+UgGSIHdKPpa9ERXNL0ohzREagDTss57tfdeXN7glOzW8EN9/VAj0hfez0dEbXCPZ0y4FF8UHQMl9QzOw1xOo72dHZ/jfvty3G/bsb7komiI1AH2K3gAsCkbjxNgciR3Gz5RnQElzZD8hYdgaygcdxvGj7nuF+3oenaFaroaNExqAPsWnCn9gzn0AciB3F7VCY8i9JEx3BpU07ugkKmEB2DrOQ7jvt1Gz4zZ4iOQB1k14Ib5uOBkUlB9nxKIjqPBeDqra351xRjpA+HCLgSjvt1fZJaDZ/Jk0XHoA6ya8EFgGsGdLL3UxLRP9wSmQVd0R7RMdzCjIoK0RHIBjju13V5jRsHuTe3Fzk7uxfckcnBCPfR2Ptpiehv7pAtFx3BbQzJ2I4QD16F74o47tc1+XJ7gkuwe8GVyyRc2Y+ruESi3BCeDe/CnaJjuA2ZxYxpKp4D7so47td1KCMjoR04UHQMsgK7F1wAuLJfFC82IxJkofI70RHczvTTByCBn/NcGcf9ugaf6ZdB4nFwLkFIwQ310WBUcrCIpyZya7PDc+FTsE10DLcTWXoa/X0SRccgO+C4Xycmk8F3BrcnuAohBRcAruXFZkR2d4+Kq7eiTK83iY5AdsRxv87Hc9AgKMO4nchVCCu4I5KCEOHrIerpidzOVWF58Mv/Q3QMtzU2fSu8lDrRMciOOO7XufhePlN0BLIiYQVXJpNwZb8oUU9P5Hb+rVkpOoJbUxvrcakuTnQMEoDjfh2f3NcXXmPGiI5BViSs4AKNF5speLEZkc3NDClAQN4m0THc3oy8DNERSBCO+3Vs3lOm8DxjFyO04IZ4azAqhRebEdnag9pVoiMQgM55h9HZi/Pt3RnH/Tomv6uuFB2BrExowQU42YzI1qYEFyEob6PoGHTWdDOvPXB3HPfrWLSDBkIdHy86BlmZ8II7IpEXmxHZ0qO670VHoL+59OR2qOVq0THIAXDcr2Pwv+460RHIBoQXXJlMwtX9ebEZkS1cElSM4Nz1omPQ33jXVWCsN8/EpUYc9yuWMjwculGjRMcgGxBecAFgVl9ebEZkC495/wAJFtEx6B9mlBaJjkAOptm4X09P0XHcht81V0OSOUQVIitziL/VYG8NxnTmxWZE1jQusBShOWtEx6AW9MvchUhtqOgY5GCaxv0u8Oa4XzuQNBr4zOTZt67KIQouAFwzgFcWE1nTEz5cvXVUEiyYruDB/9Qyjvu1D5+pU6Hw8xMdg2zEYQru8MRAxAXyZRkiaxjpX4bw3F9Ex6ALmJaZBrnEY6Lo/Dju14YkCf5zZotOQTbkMAVXkiTMH8ljOois4Sn/nyBZzKJj0AWEVORisA8vKqIL47hf2/AcOpRHg7k4hym4ADC9VwSPDCPqoKH+FYjK+Ul0DGqFGTW1oiOQk+C4X+vynzNHdASyMYcquEq5DPNHcFY7UUcs8v8ZksUkOga1woj0bfBXcw8gtQ7H/VqHOjEBuqFDRMcgG3OoggsAV/SNQpAXD0Enao8BvpWIzv1BdAxqJaXZgCkePAec2objfjvGf+4NoiOQHThcwdUo5Zg3LFZ0DCKn9EzgGkhmo+gY1AYzco6JjkBOiON+20cZHg6faVOFPb/JZMLgwYMxY8aMZrdXVFQgKioKjzzyCDIzMyFJUtObv78/RowYgc2bNzd7nyeeeKLpPgqFAjExMbjnnntQXV193uefO3cuLrvssnNu//XXXyFJEsrLy5tyPvfcc0hJSYGHhwf8/f0xYMAAvP/++80e68/nVyqVCAkJwbhx47BkyRKYzeKvAXG4ggsA1w6Ihq9WKToGkVPp7VOFuFyO5XU2cYUnkOrNi12ofTjut238b74JkkIh7PnlcjmWLl2Kn3/+GZ9++mnT7XfeeSf8/f3x+OOPN922bt065OXlYdOmTQgPD8fkyZNRUFDQ7PG6du2KvLw8ZGZm4vnnn8fixYtx3333dTjnk08+iVdeeQVPP/00Dh8+jI0bN+KWW25pKsB/mjhxYtPz//TTTxg1ahTuvvtuTJ48GUaj2MUWcX/LF+CpVuCGwbF4Zd1x0VGInMZzwesgZRtEx6B2mGmQY5/oEOS0/hz32/+OCNy7Vg3ZAX7tbIkiKAi+l18uOgaSkpLw3HPP4c4778To0aOxY8cOfPHFF9i5cydUf/smJSAgAKGhoQgNDcXDDz+ML774Atu3b8fUqX+tQCsUCoSGNg6NufLKK7F+/XqsWrUK7777bocyrlq1CrfddhuuuOKKpttSU1PPuZ9arW56/oiICPTu3RsDBw7EmDFjsHTpUtx8880dytERDrmCCwBzB8dAp3bI/k3kcHp4VyMxd6XoGNROE05ug1ahFR2DnBzH/V6Y/403QuYgq9x33nknUlNTcf311+OWW27BY4891mKBBIC6ujosW7YMAJoV4JZ4eHhAr9d3OF9oaCg2bNiAoqK2jxUfPXo0UlNTsXz58g7n6AiHLbg+WiWuG8jpZkSt8ULIekimjn9SIzG0DdWY6MVtCtRxHPfbMrmfH/yunCU6RhNJkvC///0P69evR0hICB588MFz7jN48GDodDp4enrixRdfRJ8+fTBmzJjzPubu3bvx2WefYfTo0R3O9/LLL6OoqAihoaHo0aMH5s+fj59+av3xkykpKcjMzOxwjo5w2IILADcPi4VG6dARiYTr6lWD5NwVomNQB00vyhEdgVwIx/025z9nNmRax3qVZMmSJdBqtTh16hTOnDlzzu9/+eWX2Lt3L7799lskJCRg6dKlUCqbX5904MAB6HQ6eHh4oH///hg0aBDefPNNnD59GjqdruntmWeeaVO2Ll264ODBg9i2bRtuvPFGFBYWYsqUKa3ecmCxWCAJPq/ZodtjoE6Nq/p1Eh2DyKG9ELoBkqlBdAzqoJ7ZaYjTRYqOQS7mz3G/1SN6iY4ijMzLC37XXis6RjNbtmzBK6+8gtWrV6N///646aabYLFYmt0nKioKiYmJmD59Op555hlMnz4dDQ3NP9cnJycjLS0NR44cQV1dHVatWoWQkBCEh4cjLS2t6W3+/PkAAG9vb1RUVJyTp7y8HHK5HJ5/29oik8nQr18/LFy4EMuXL8fSpUvxwQcf4NSpUxf9+I4cOYLYWLEnYjl0wQWAW4bHQSnn1BailqToatElb4XoGGQlMyRv0RHIBZ2Wl+PGwQfcdtyv37XXQO7lJTpGk9raWsydOxcLFizAqFGj8MEHH2DHjh145513zvs+l19+ORQKBd5+++1mt6tUKiQkJCAmJqbZ/lyFQoGEhISmN39/fwCNhfjQoUPnFOU9e/YgNjb2nBXiv+vSpQsAoKam5oIf34YNG3DgwAHMnDnzgvezNYcvuOG+HpjRi6saRC15IexXSMY60THISqac3AWFzLEvrq05VoOsV7JwdOFRHJx7EJW7K5v9vsViQcHyAhy9+ygOzTuEUy+cQkP+xV9hKFlXgmP3HcOhmw/h5FMnUZvRfIxx3ud5OHL7ERy99yjKt5Q3+72KHRXIeiWrwx+bq3PHcb+SVutwY3kfeughWCwWPPfccwCAmJgYvPjii7j//vvPu29VkiTcddddeO6551Bb2/4R39deey0kScLs2bOxe/dupKenY8mSJXj11VebHS92+eWX45VXXsH27duRlZWFX3/9FbfffjuSkpKQkpLSdL+Ghgbk5+cjJycHe/bswTPPPINp06Zh8uTJmD17drtzWoPDF1wAWDAyHnKZe/xnJGqteG0duueLvUqVrMu/phgjfZJFx7ggc4MZmk4ahF/f8qjY4h+LUbK2BOFzwhH/WDxkahkyX8qEWX/+g98rtlcg/4t8BF8WjPgn46GJ0iDzxUwYKxvP0azcW4mKrRWI+VcMQmeFIufDHBirGn/PVGtCwbcFCJsdZv0P1gU1H/cbITqOzfldeSUUfo4zDvu3337DW2+9hQ8//BDav+0JvvXWWzF48OAWtyr8ac6cOTAYDHjzzTfb/fy+vr7YvHkzDAYDpk6dip49e+L111/Hyy+/jFtvvbXpfhMmTMD333+PKVOmICkpCXPmzEFKSgrWrFkDxd/OEf75558RFhaGmJgYTJw4ERs3bsTrr7+OlStXQi54yp5kOd+fpIO5+4u9WJmWKzoGkcP4LvEX9Mr+SHQMsrJN8YNxu/ncC04c0cG5B9Hpzk7w7tO4tcJiseDYwmMInBiIwEsaXwo31Zpw9K6jiLg5Ar4DfVt8nJNPnYRHrEdTabaYLTh27zEEjA1A0OQgFP1YhPrMekTd1jjW+MhdRxC9MBraOC1yluZAHaZG4AT3e+m9o7zMajx1tAsiVu8BTCbRcaxO0mqRsOYXKAL5b8MdOcUKLgDcPirBXV5RIbqoGI969Cz4RnQMsoEhGdsRrHHOL8iGIgOMFUZ4dvnrQhW5Vg6PeA/UnWx5K43ZaEZdZh10XXRNt0kyCbquOtSebHwpVhOlQV1mHUw1JtRl1sGit0AdokbN8RrUZ9UjYFyAbT8wF+Xq437958xmuXVjTlNwk0K8MK5ziOgYRA7hxcjNkPQX3uhPzkluMWGa2jlfbjdWNG4bUPg030es8FbAUNHylD1TlQkwt/w+fz6eV3cv+AzywcknT+LM+2cQOS8SklpC7rJchM8JR+mGUhx/8DgyFmWgPqfeBh+Za3PFcb9yPz8E3HST6BgkkNMUXAC4b3wy9+KS24vUNKBPIVdvXdmM0wcggZ/r/i5kegiSXkhC4qJEePfxRvHqYui66CDJJRStKkLcw3HwG+GHM4udY3uHo/lz3O9/7wiDuXuS6DgdFnDrLZDrdBe/I7kspyq4yaFeuLJflOgYREK9GPUHpIYq0THIhiJLT6O/T6LoGG325yrsnyuvfzJWGqH0afn4IbmXHJC1/D7/XNX9U0NuA8q3liN4RjBqjtZAm6yFwlsBn/4+qM+qh6nO9faT2osrjPtVhIfB75prRMcgwZyq4ALAveOS4KV27GN0iGwlTKPHgMKvRccgO5he73wlTRmkhMJHgZrDf22fMdWZUHeyDh7xHi2+j0whg0eMB6oPVzfdZjFbUH24Gtr4cydPWSwW5HyUg9CrQiHXyGExW2AxNV4rbTGevWb6/Ac2UCs4+7jfoDvvgsxFtlpQ+zldwQ3UqXHbqATRMYiEeClqC6SGc6fQkOsZm74VXkrHe4nVVG9CXVYd6rIaLxrTF+tRl1UHfYkekiQhYHwACr8vROXeStRn1+PM4jNQ+Cng3fuvIRannj+FknUlTb8OnBCIst/KUPZ7Gepz65G7LBfmBjP8hp17vFPZb2VQeCng3avx8bSJWtQcqUFtei2K1xRDHa6G3FPs8USuwhnH/aoTE+EzbaroGOQAnHIp9MahMfhsRxayS3nAPbmPYLUBA4u+Eh2D7ERtrMelujh8UbZfdJRm6k7VIfP5zKZf53+eDwDwHeKLyHmRCJwUCHODGbkf5sJUa4I2SYuY+2IgU/21nqIv1DedYwsAPgN8YKwyovC7QhgrjNB00iDmvphztigYK4wo+r4IcY/GNd2mjdMicGIgsl7JgsJbgYh5rn+2q729EbwPK2/xxRPbe0H3217RcS4o6J57IMmcbu2ObMBpzsH9px/25+H2z/aIjkFkN8sSN2N49v9ExyA7OhLWBbM01Re/I5GdzCnrisnLc2ApLBYd5Rweffog5tNPRMcgB+G03+Zc2iMM/WIcZzoJkS0FqQwYWvyl6BhkZ53zDqOzV7ToGERNHHncb/DfRs0SOW3BBYBHL+3iaP+/iGzi+egdkNWVio5BAkw3n3uhFZFIjjjuVzd6NLS9e4mOQQ7EqQtuapQvLuvpGP+5iGzFT2nEyBKu3rqrS09ug1quFh2D6BzfeR3HjdeUI2daP0Au8MI+pRLB//qXuOcnh+TUBRcA7p+YDA8lr5gl1/V89C7Iah1vvxvZh3ddBcZ4O9+ZuOQeHGHcr//s66GOixXy3OS4nL7ghvl4YN4w/sMm1+SlMGJM2ReiY5BgM8qKREcguqA/x/2esPO4X0VQEAIX3Ga35yPn4fQFFwDmj4xHiDdfwiPX80LMHshrCkXHIMH6n9qFSG2o6BhEF6SXTHgkfg9euCPUbuN+g//9L8h1zjlxjWzLJQquVqXAv8Yni45BZFWeChPGl3PvLQESLJiuCBQdg6hVdqpz7TLu16N3b/hM5VAHaplLFFwAuLxPJLpFeF/8jkRO4rmYNMir80THIAcxLTMNconXG5BzsPm4X5kMoY8+Yv3HJZfhMgVXkiQ8emkX0TGIrMJTbsYlFdx7S38JqcjFYB/7vOxLZC1/jfvtbdVxv76zroCmC7/m0/m5TMEFgIFxAZjQNUR0DKIOWxSTBkVVjugY5GBm1NSKjkDULm8E78d9tyhRPaLjZ9XKfXwQdPfdVkhFrsylCi4APDypM1Ryl/uwyI14yE2YUsnVWzrXiPRt8FdzgiM5p9Pyctw4+AB+mJ8KKbj9e8qDFt4NhR//H9CFuVwTjA7wxC3D40THIGq3p6IPQFF1RnQMckBKswFTPKJExyDqkI6M+1V37gzfK6+0UTJyJS5XcAHgzjEJiAvisSHkfNQyMy6r5skJdH4zco6JjkDUYe0a9ytJCP3Po5BkLlldyMpc8l+JWiHHs9O7t/UbQyLhHo85BGVllugY5MDiCk8g1TtedAwiq2jLuF/fWbOg7d3bTsnI2blkwQWAAXEBuKpfJ9ExiFpNKbPg8lqu3tLFzTDwuDByHa0Z96sIDkbwv+6zbzByai5bcAHgoUkpnHBGTuM/0YehKs8QHYOcwMST26BVaEXHILKqC437DXn0Eci9vAQlI2fk0gXXW6PEU9NscMA0kZXJJTOuqv9KdAxyEtqGakzw4jYFcj0tjfvVjR0D7/HjBScjZ+PSBRcAJnQNxcSunOFOju2RmGNQlZ0QHYOcyIwinpNMruvPcb+H5w5F6H/+IzoOOSGXL7gA8NS0rvDWKETHIGqRJFlwLVdvqY16ZqchThcpOgaRzVgkwDJjApQhHOBEbecWBTfYW8MxvuSwHoo+DnUZj36itpsheYuOQGQzA8MG4vKky0XHICflFgUXAGb1i8LI5CDRMYiakSQLZhu+Fh2DnNSUk7ugkPHVKXI9HgoPPDH4CdExyIm5TcEFgOdm9OBWBXIo/+qUDk3JYdExyEn51xRjpE+y6BhEVnd377sRoWvlAAiiFrhVwQ310eCxKV1FxyBqcqOJq7fUMdMrKkRHILKq3sG9cU3KNaJjkJNzq4ILAJf3icTYzsGiYxDhnk4Z8Cg+KDoGObkhGdsRrAkUHYPIKnRKHZ4Z9gwkjiKlDnK7ggsAz8zoDl+tUnQMcnM3W74RHYFcgNxiwjR1mOgYRFbx8ICHuTWBrMItC26wlwZPTuVWBRLn9qhMeBaliY5BLmJ69kFI4IoXObeJMRMxJX6K6BjkItyy4ALAtJ4RHABBwiwAV2/JeqJKstDPJ1F0DKJ2C9GG4NGBj4qOQS7EbQsuACya3g2BOtXF70hkRbdEZkFXtEd0DHIx0+tNoiMQtYsECc8MfQY+ah/RUciFuHXBDdSp8eqVvSDjK3tkR3fIlouOQC5oXPpWeCl1omMQtdnsLrPRP6y/6BjkYty64ALA0MRA3DmaL+2RfdwYkQ3vwp2iY9jUpiwjpnxei/CXqiA9WYkVRw3Nfv+JX+uR8mY1PJ+phN/zlRi7rAbbzxgv+JjPbm5Av/eq4fVsJYL/W4XLvqjFseLmK5b3/lIP/+crEfVKFT7d3/w5vz5kwJTPa63zATootbEel+riRMcgapNkv2Tc3ftu0THIBbl9wQWAu8ckYmgCj9kh27tb4fqrtzV6C1JDZHhrkqbF308KkOPNSRocWKDD7zd4IsZXhvGf1KKoxnzex/wty4jb+6mw7SZPrL1eC4MZGP9JLWr0FgDA98cM+OyAAWuu98QLYzW4+fs6FNc2Pl5FvQWPbGg4bx5XMiMvQ3QEolZTy9V4bthzUMp5qhFZHwsuAJlMwqtX9USwl1p0FHJhs8Nz4VOwXXQMm7skUYlFozWY3rnlL1rXdFdibJwCcX4ydA2W4+UJGlQ2APsLzl9wf77OE3N7qtA1WI7UUDmWTtPgdIUFu/MaV3GPFJsxMkaOvuFyXN1dCW+1hFNljeX3/rX1WNBXiU4+rv/prnPeYXT2ihYdg6hVFvZeiAS/BNExyEW5/mf8VgrUqfHG1b0g54ZcspF7VN+JjuBw9CYLFu/Ww0cNpIa2/tNRRUPjj/4ejf9fU0Pk2JVrQlmdBbtzTagzWJDgL8Pvp43Yk2/CXQPc52LS6Wat6AhEFzUyciSu7Xyt6Bjkwlhw/2ZAXADuHZckOga5oKvC8uCX/4foGA5j9XEDdM9UQrOoCq9s02Pt9Z4I1Lbu05HZYsHCn+sxJEqObsFyAMCEBAWu66FEv/eqMXdlHT66zAOeKmDBD/V451IP/G+XAclvVmPIkhocKnTt0wYuPbkNajlfjSLHFaGLwKKhizitjGyKBfcfbhsZj1HJQaJjkIv5t2al6AgOZVSMAmnzddhykxYT4xWY9U0tCi+wB/fvbv+hHgcLTfjico9mtz8xUoP0u7xwYIEO0zsr8exmPcbGKqCUA4s2NeD3G7S4uZcSs1fU2eJDchjedRUY480LZ8kxqWQqvDTyJR4JRjbHgvsPkiThlSt7IsLX4+J3JmqFmSEFCMjbJDqGQ/FUSUjwl2FgpAIfTPOAQibhgz2Gi77fHT/WYfUJIzbO8USk9/k/fR0tNuGTAwY8PVqNXzONGB4tR5CnDLO6KrEnz4yqBos1PxyHM6OsSHQEohY90P8BdA3gJFGyPRbcFvhqVXjjml5QyvnyCXXcg9pVoiM4PLPFggbT+UunxWLBHT/W4bujRmyYrUWs3/k/dVksFty6uh4vj1dDp5JgMgOGs4vDf/54gadyCf1P7UKklpMaybFcGncpZiXPEh2D3AQL7nn07uSHByamiI5BTm5KcBGC8jaKjmFX1XoL0vJNSMtv3Ot6qsyMtHwTTleYUaO34OH19dh2xoiscjN255pw48o65FRacEWXv05dGLOsBm/u0Df9+vYf6/HJfgM+m+EBL7WE/Goz8qvNqDOc21Tf32NAkFbClOTGxxvSSYENp4zYdsaIV7Y2oEuQDL4a1/7mVYIFlyl49CE5jnifeDw28DHRMciNKEQHcGQ3D4vDjlOlWHO4QHQUclKP6r4HKkWnsK9duSaM+uivoQr3rmkA0IA5qUq8M1mDo8VmfLSvDsW1FgR4SOgXIcfmGzzR9ewFYwBwstTcdI4tAPxvV+P2hZEfNR/W8OE0Deb2/OuEhIJqM/5vcwO23OTZdFv/CDnuG6TGpZ/VIdhTwkeXucf2o2lZ+/C2vxJmS+v2NhPZiofCAy+PfBlaJU/4IPuRLBaLi79Y1zEVdQZMeeN3nC517SlIZH2XBBXj7aq7IYH/xUiMBb3G4/fyo6JjkJt7ftjzmBQ3SXQMcjPconARPh5KvHVNb6gU/KOitnnMezXLLQk1s8a1T4wgx3dl8pUstyQEW1srdI/0wX8u7Sw6BjmRcYGlCM1ZKzoGubkR6dvgr/YTHYPcVGpQKh7o94DoGOSmWHBb6fpBMZiSGi46BjmJJ3x+4OotCac0GzDFI0p0DHJDoZ6heHXUq1DKWx7ZTWRrLLht8NyM7kgJ9RIdgxzcSP8yhOf+IjoGEQBgRs4x0RHIzXgoPPDG6DcQ6MGTPEgcFtw28FQrsGRuP4R4cwwmnd9T/j9B4pXr5CDiCk8g1TtedAxyExIkPDP0GaT485hNEosFt43CfT3wwZx+8FTJL35ncjtD/SsQlfOT6BhEzcww8PMV2cdtPW/D2OixomMQseC2R7cIH7x5TW/IZa59WDy13SL/nyFZTKJjEDUz8eQ2aBU8g5Rs65KYSzA/db7oGEQAWHDbbVRKMJ6Yynna9JcBvpWIzv1BdAyic2gbqjHBi9sUyHa6BnTFU0OeEh2DqAkLbgdcPzAatwyPEx2DHMQzgWsgmY2iYxC1aEZRrugI5KKCPYLx+ujXoVFoREchasKC20EPXZKCSd1DRccgwXr7VCEu93vRMYjOq2f2XsR6RoiOQS5GLVfjtdGvIVgbLDoKUTMsuB0kSRJentUTvTv5io5CAj0fvA6S2SA6BtEFzZD5iI5ALkQmyfDM0GfQLbCb6ChE52DBtQKNUo735/RDdAAv4nBHPb2rkZC7UnQMoouaenIXFDKF6BjkIh7o9wDGx4wXHYOoRSy4VuLvqcLSG/rDT8upLe7muZD1kEx60TGILsq/phgjfZJFxyAXcFO3m3BN52tExyA6LxZcK4oN9MTi2X2hUvCP1V109apBcu4K0TGIWm16RYXoCOTkpsZPxcI+C0XHILogNjEr6xfjj5euSIXEI3LdwguhGyCZGkTHIGq1IRnbEazhCFVqnyERQ/Dk4CdFxyC6KBZcG5iSGo5/T+DLgK4uRVeLLnkrRMcgahO5xYRp6jDRMcgJdQvohpdHvMx93OQUWHBt5LaRCbi6fyfRMciGXgj7FZKxTnQMojabnn0QEvgyE7VeJ69OeGvsW9AqeTE1OQcWXBt6elpXjEgKEh2DbCBeW4fu+ctFxyBql6iSLPTzSRQdg5xEgCYA74x7B/4af9FRiFqNBdeGFHIZ3r62N/pE+4mOQlb2YsQmSIZa0TGI2m16vVl0BHICnkpPvDX2LUR5RYmOQtQmLLg25qlW4KMb+6MXB0G4jBiPevQs+EZ0DKIOGZe+BV5KnegY5MA8FB54a8xb6BrQVXQUojZjwbUDnVqBZTf2R88oX9FRyApejNwMSV8jOgZRh6iN9ZikixUdgxyUWq7G66NfR5+QPqKjELULC66deGmUWHZTf6RGclSmM4vUNKBPIVdvyTXMyM8UHYEckFKmxCsjX8HAsIGioxC1GwuuHXlrlFh20wB0j2DJdVYvRf0BqaFKdAwiq+iSewidvaJFxyAHopAp8NKIlzAscpjoKEQdwoJrZz4eSnxy0wB0i/AWHYXaKEyjR//Cr0THILKq6WYe+0SN5JIczw17DqM6jRIdhajDWHAF8NE2ltyu4Sy5zuSlqC2QGipFxyCyqktPboNarhYdgwSTSTIsGroIE2ImiI5CZBUsuIL4alX49OYB6BzGkusMgtUGDCzi6i25Hu+6Cozx5pm47kyChCcGPYHJcZNFRyGyGhZcgXy1Knx28wCkhHqJjkIX8WKnbZDVl4uOQWQTM8qKREcggR4Z8AimJ04XHYPIqlhwBfPzVOGzeQNZch1YkMqAocVfio5BZDP9T+1CpDZUdAyyMwkSHh3wKK5MuVJ0FCKrY8F1AP6ejdsVkkJ46Lojej56B2R1paJjENmMBAsuUwSKjkF2JJfkWDR0EcstuSwWXAcRoFPjs3kDkRjMkutI/JRGjCzh6i25vmlZ+yCT+CXBHShkCrww/AVMjZ8qOgqRzfCzmQMJPFtyE1hyHcbz0bsgqy0WHYPI5kLLczDYJ0l0DLIxtVyN10a9hvEx40VHIbIpFlwHE+SlxmfzBiAuyFN0FLfnpTBiTNkXomMQ2c2MmnrREciGtAot3h7zNoZHDhcdhcjmWHAdULCXBl/eMogTzwR7IWYP5DWFomMQ2c3I9K3wV/uKjkE24KXywuLxi9E/rL/oKER2wYLroIK81PjiloEYmRwkOopb8lSYMJ6rt+RmlGYDJntEiY5BVuav8ceSCUuQGpQqOgqR3bDgOjBPtQLvz+6LK/vyC469PRe9F/KafNExiOxuZs5x0RHIioI9gvHhhA+R4p8iOgqRXbHgOjiFXIbnL++BhWM5achePOVmXFLJkxPIPcUVnkCqd7zoGGQFMd4x+OiSjxDnGyc6CpHdseA6iYVjk/DCzB5QyCTRUVzeopg0KKpyRMcgEmaGQS46AnVQ7+De+GTSJ4j0ihQdhUgIFlwnMqtfFN6f0xeeKn7xsRUPuQlTKrn3ltzbxJPboFVoRcegdhoXPQ6Lxy+Gj5oXKpP7YsF1MiOTg/HlrYMQ5KUWHcUlPRV9AIqqM6JjEAmlbajGBC9uU3BG13W+Di+OeBFqOb9GkHtjwXVC3SJ8sHzBYMTzrFyrUsvMuKyae2+JAGBGUa7oCNQGMkmG+/vdjwf6P8CJdERgwXVaUf5afLtgMPpG+4mO4jIejzkEZWWW6BhEDqFn9l7EekaIjkGtoJar8d/h/8X1Xa4XHYXIYbDgOjFfrQqf3DwAl3QLFR3F6SllFlxey9Vbor+bIfMVHYEuwkftg/fGv8fRu0T/wILr5DRKOd66pjduGBIjOopTeyz6MFTlGaJjEDmUKRm7oJApRMeg84jQReDjSz5Gr+BeoqMQORwWXBcgk0l4fEpXPHppZ0g8RazN5JIZV9Zx9ZbonwKqizDCJ0l0DGpB35C++PzSzxHrEys6CpFDYsF1ITcPi8MbV/eCSsG/1rZ4NPoYVOXpomMQOaQZFZWiI9A/XJF0BRaPXww/Da/BIDofyWKxWESHIOvac7oMt3+6B3kV9aKjODxJsuBo6FNQlx0THYXIIZkkOcanpKKwvlh0FLenkBS4v//9uDrlatFRiBwel/pcUO9OfvjhrmEYlhgoOorDeyj6OMst0QXILSZMU4eJjuH2fNW++N+4/7HcErUSV3BdmNlswSvrjuPNjeng3/K5JMmCI+H/B03JYdFRiBxadkA0LvUGLOAnEhGS/ZLx2ujXEKHjsW1ErcUVXBcmk0m4b3wylszpB1+tUnQch/OvTukst0StEFWShX4+iaJjuKWJMRPx8aSPWW6J2ogF1w2MSgnG93cMRfcIziX/uxtNX4uOQOQ0ptebRUdwKzJJhnv63IP/jvgvPBQeouMQOR0WXDcR5a/FNwsG4er+nURHcQj3dMqAR/FB0TGInMa49C3wUupEx3ALAZoAvDvuXdzY7UbRUYicFguuG1Er5Hh2Rne8eEUqNEr3/qu/2fKN6AhETkVtrMckHc9ctbX+of3xzdRvMDBsoOgoRE7NvVuOm7q8TyS+u20IYgK0oqMIcXtUJjyL0kTHIHI6M/IzRUdwWTJJhvmp8/He+PcQ6METcIg6igXXTXUO88aqO4difJcQ0VHsbgG4ekvUHl1yDyHFK1p0DJfz55aE23veDpnEL8tE1sD/SW7MW6PE4tl98eAlKZDL3GPG7/zILOiK9oiOQeS0plvc85UfW+GWBCLb4Dm4BADYllGCOz/fi6KqBtFRbOpAp5fhVbhLdAwip1Xh4YsxEUFoMLn25wpbk0ky3NLjFixIXcBVWyIb4P8qAgAMjAvAD3cORf8Yf9FRbObGiGyWW6IO8qkrx2hvnonbEf4af7wz9h1uSSCyIf7PoibB3hp8Nm8A/jU+CSq56/3TuFuxXHQEIpcws6xIdASnNTJyJL6d+i0GhQ8SHYXIpXGLArXoaH4l7vtqHw7lVoqOYhWzw3PxVOm/RMcgcgkWSJjUtR/O1OaLjuI0PJWeuL/f/ZiROEN0FCK34HrLdGQVKaHeWHn7ECwcmwil3PkvQLtH9Z3oCEQuQ4IFlyl4lFVr9Q7ujW+mfMNyS2RHXMGlizqUW4F/fb0fR/KcczX3qrA8PFd2n+gYRC4l3zcCE/yVMFs4wvd8lDIl7ux1J+Z0ncO9tkR2xv9xdFFdw32w6o4huGt0AhROeJzYvzUrRUcgcjmh5TkY7JMkOobDSvJLwheTv8AN3W5guSUSgP/rqFWUchnuHZ+MFbcPQXKIl+g4rTYzpAABeZtExyBySTNq6kVHcDgySYYbu92ILy79Akl+/AaASBRuUaA20xvNeG39cbzzWwZMZsf+57Mz9j0E5W0UHYPIJRlkSoxN6ozShnLRURxCJ69OWDR0EXoF9xIdhcjtcQWX2kylkOHfE1Lw3W2DkRisEx3nvKaFFLLcEtmQ0mzAZI9OomMIp5ApcHP3m7F82nKnKrdz586FJElNbwEBAZg4cSL279+PpUuXNvu9lt4yMzNhsViwePFiDBgwADqdDr6+vujbty9effVV1NbWnve5R44ciYULF55z+9KlS+Hr69v069raWjz00EOIj4+HRqNBUFAQRowYgZUrVzZ7rD8zqdVqREREYMqUKVi+nEdDujMWXGq3HpG+WH3XUMwfEe+Qo34f9vxedAQilzcj57joCEL1COyBLyd/ibt73w21XC06TptNnDgReXl5yMvLw/r166FQKDB58mRceeWVTbfn5eVh0KBBmDdvXrPboqKicP3112PhwoWYNm0aNm7ciLS0NPznP//BypUrsWbNmg7nmz9/PpYvX4433ngDR48exc8//4zLL78cJSUlze73Z7aTJ0/i22+/RZcuXXDVVVfhlltu6XAGck4K0QHIuakVcjx4SQomdA3Bv77eh5NFNaIjAQAmBRUjOHeD6BhELi++8Dh6pI7C/sqToqPYlafSE3f2uhNXp1zt1BeRqdVqhIaGAgBCQ0Px4IMPYtiwYaiurm66HQBUKhW0Wm2z27766it8+umnWLFiBaZNm9Z0e0xMDKZOnYrKyo6fvLNq1Sq89tprmDRpUtNj9+nT55z7/T1bZGQkBg4ciJSUFNx4442YNWsWxo4d2+Es5Fyc938lOZRenfzww13DcMvwODjCYu5/vFdDgmPvDyZyFTMMctER7Gpk5EismLYC13a+1qnL7T9VV1fjk08+QUJCAgICAi56/08//RTJycnNyu2fJEmCj49PhzOFhobixx9/RFVVVZvfd86cOfDz8+NWBTflOv8zSTiNUo6HJ3XGqjuGon+Mv7Ac4wJLEZqzVtjzE7mbS05ug1ahFR3D5gI9AvHSiJfwxpg3EOoZevF3cAKrV6+GTqeDTqeDl5cXVq1ahS+//BIy2cXrwYkTJ5CcnGzTfIsXL8aWLVsQEBCAfv364Z577sEff/zRqveVyWRISkpCZmamTTOSY2LBJavrFuGDr+YPwhtX90KEr4fdn/8Jnx+4ektkR9qGakzwihcdw2YkSLg86XKsvGwlxseMFx3HqkaNGoW0tDSkpaVhx44dmDBhAi655BJkZWVd9H1bcwjT5s2bmwq0TqfDp59+2qZ8w4cPR0ZGBtavX4/LL78chw4dwrBhw/D000+36v0tFgskyQFeViS7Y8Elm5mSGo71943AwrGJ0Cjt809tpH8ZwnN/sctzEdFfZhTlio5gE6lBqfj80s/x+KDH4a3yFh3H6jw9PZGQkICEhAT069cP77//PmpqavDee+9d9H2TkpJw9OjRC96nb9++TQU6LS0NU6dOBQB4e3ujoqLinPuXl5efs7VBqVRi2LBheOCBB7BmzRo89dRTePrpp6HX6y/43CaTCSdOnEBsbOxFPxZyPSy4ZFMapRwLxyZhw30jMblHmM2f7yn/nyBxdCiR3fXM3otYzwjRMawmWBuMZ4c9i48v+RhdA7uKjmM3kiRBJpOhrq7uove95pprcPz48WZHdv3JYrGgoqICHh4eTQU6ISEBXl6Ng4KSk5OxZ8+ec95vz549SEq68ICMLl26wGg0or7+woNGPvroI5SVlWHmzJkX/VjI9bDgkl2E+3rgzWt64+v5g9AtwjarIEP9KxCV85NNHpuILm6GzFd0hA5TyVSY130evr/se0yOm+zyL283NDQgPz8f+fn5OHLkCO68805UV1djypQpF33fWbNm4corr8TVV1+NZ555Brt27UJWVhZWr16NsWPHYuPG859DvmDBAhw/fhx33XUX9u/fj2PHjuHll1/G559/jvvuu6/pfiNHjsS7776L3bt3IzMzEz/++CMefvhhjBo1Ct7ef30tqa2tRX5+Ps6cOYNt27bhgQcewPz587FgwQKMGjWqY39I5JQ4yYzszmy24Ktd2XhxzTEUV1/4Jaa2+DXhS8ScOXclgYjso0QXhLEhXjCajaKjtMvoqNH4V79/IcorSnQUu5g7dy4++uijpl97eXkhJSUFDzzwwDmrniNHjkTPnj3x6quvNrvdbDZj8eLFWLJkCQ4dOgSFQoHExETMnj0b8+bNg4fH+a/D2LlzJx555BGkpaVBr9cjJSUFDz74IC677LKm+zz77LP4/vvvcezYMdTW1iI8PByTJ0/GY4891nTSw8iRI/Hbb78BaDzOLCAgAH369MGNN96I6dOnd/BPiZwVCy4JU1VvwOvrT2DplkwYTB37ZzjAtxJf6O+A5KRfWIlcxcLeE7G+7LDoGG2S4JuAB/o/gIFhA0VHISIrYcEl4TKKqrHohyPYcLSw3Y+xIfEbxGXzrEMi0TbFD8bt5jOiY7SKn9oP81Pn48rkKyGXuddZvkSujgWXHMavxwrx9OrDbZ6G1tenCl8b7oBkNtgoGRG1lkmSY3xKDxTWl1z8zoJ4Kj0xu8tszOk6B55KT9FxiMgGOKqXHMbI5GAMSQjER1sy8fr6E6isb912g2eD10LKZrklcgRyiwnT1OF4zwELrkqmwqzkWZjXYx78NeKG0RCR7XEFlxxSRZ0BH/x+Ch/+fgpVDecvuj29q/Gd6Q5IJutdrEZEHZMdEINLvS2wOMjAFbkkx5T4Kbgt9TaE6Wx/XCERiceCSw6totaA9zZnYOmWTFS3UHR/TlyJlOwvBSQjogu5secY7Kw4IToGxnYaizt73Yk43zjRUYjIjlhwySmU1ejx7qYMLNuaiVq9CQDQ1asGq813QDI1CE5HRP+0OmU0HmpIF/b8A0IH4O7ed6N7UHdhGYhIHBZcciol1Q14d1MGPt6ahW9iVqBr9ueiIxFRCxoUGoyKi0OVodquzzsobBDm9ZiHfqH97Pq8RORYWHDJKRVVNcBv+wtQ7HgH0Nv3CygRtc6i3pfiy7IDNn8eCRJGRo3ELT1uQbfAbjZ/PiJyfCy45NxqS4Ft/wN2vAvUV4hOQ0R/czi8K65UV9ns8eWSHBNiJuDm7jcj0S/RZs9DRM6HBZdcQ30lsPN9YOtbQG2x6DREdNYVPYbhaFWWVR9TKVNiavxU3NjtRnTy7mTVxyYi18CCS65FXwvs+QjY+jZQcVp0GiK391n3CXi2+ohVHksj12Bm0kzM7ToXoZ6hVnlMInJNLLjkmswm4NiPwPZ3gczNotMQua0KD1+MiQhCQwdOOwn2CMas5Fm4IvkKDmggolZhwSXXV3AI2P4OsP9rwFgnOg2R27m/9yT8VHawze/XI7AHru18LcbFjINSprRBMiJyVSy45D5qSxu3L+z8AKjIFp2GyG1si+2Pechv1X0VMgXGRY/DdZ2vQ4+gHjZORkSuigWX3I/ZBBxd3bh9IesP0WmIXJ4FEi7p2hc5tQXnvY+/xh8zE2fiqpSrEKwNtmM6InJFLLjk3vL2Nxbdg98AxnrRaYhc1jupk/BW5bnbFDr7d8bVKVdjUtwkqOVqAcmIyBWx4BIBQE0JsGcpsPsjoNy6RxoREZDvG4EJ/kqYLWbolDpMip2EmUkz0SWgi+hoROSCWHCJ/s5iAbK2APs+Aw6vAhoqRScichkvD78ZsVFDMSFmArRKreg4ROTCWHCJzsdQBxxZ3Vh2M34FLGbRiYicjy4USL0K6HktEJQkOg0RuQkWXKLWqMwD9n8J7PscKDoqOg2RY5OrgeRLgF7XAfGjAZlcdCIicjMsuERtlbsXSPu88cK02hLRaYgcgyQDogYC3WYA3WYCWg5kICJxWHCJ2stkAE6sAdI+a/zRpBediMi+JDkQPRjoMg3oPAXw4vhcInIMLLhE1lBfARxf03i+bvp6QF8lOhGRbcgUQMzQxlKbMgXQBYlORER0DhZcImszNgAZvwHHfgCO/QRUn/9weyKnIFMCcSPOltrJ3H5ARA6PBZfIliwW4MzOxpXdoz8AJemiExG1jlwFxI06W2onAR5+ohMREbUaCy6RPRUd+6vs5uwBwP9+5EC8woH4UY0nHySOAzQ+ohMREbULCy6RKJV5wLEfG/fsZv3euI+XyJ6UWiB6SGOhjR8NBKeITkREZBUsuESOwGwG8vcBpzYBpzYDp7cC+mrRqcjlSEBY6l+rtFEDAYVKdCgiIqtjwSVyRCYjkLMbyNzUWHqzdwDGetGpyBl5RzQW2rizb54BohMREdkcCy6RMzA2NJbcU5uAzM3AmV2A2SA6FTkcCQhMBCL6ApF9G4/zCkoWHYqIyO5YcImckb4GOL2tcZU3N61xulpVruhUZG8efmfLbD8gsk/jzz18RaciIhKOBZfIVVQVAHlpjWU3d29j8a3OF52KrEWmBEK6Nq7MRvZrfAuIF52KiMghseASubLKvMay+/fiW1MkOhVdjFILBCQAQSlAWI/GldnwnoDSQ3QyIiKnwIJL5G4qchoLb9ExoORk4/CJknSgtlh0Mvej8gKCkhqLbFDyXz/6RgOSJDodEZHTYsElokZ1Zc0Lb9NbBmCoEZ3OuWl8zi2xQSmAT6ToZERELokFl4gurjL3b4X3JFCZ07jnt/rsmzuf2avwALzDG998Is/+PKL5z7X+olMSEbkVFlwi6riG6r/KbnXB2fKbD1QXAlX5f91eUwynGE+s0ABq78aVV4134891IYBPxNnSGvlXoWV5JSJyOCy4RGQ/JiNQXw40VDW+6av/+vmfvzbUNb4Z6wFDLWCoB4x1jT+a9I17UyVZC28t3I6/3SaTA2qvs8X1bHn98+dNZfbsbZzuRUTk1FhwiYiIiMilyEQHICIiIiKyJhZcIiIiInIpLLhERERE5FJYcImIiIjIpbDgEhEREZFLYcElIiIiIpfCgktERERELoUFl4iIiIhcCgsuEREREbkUFlwiIiIiciksuERERETkUlhwiYiIiMilsOASERERkUthwSUiIiIil8KCS0REREQuhQWXiIiIiFwKCy4RERERuRQWXCIiIiJyKSy4RERERORSWHCJiIiIyKWw4BIRERGRS2HBJSIiIiKXwoJLRERERC6FBZfIwcydOxeSJDW9BQQEYOLEidi/f3/TfSRJgkajQVZWVrP3veyyyzB37tw2PVZLfv31V0iShPLy8nN+LyYmBq+++mrTr3/77TeMHj0a/v7+0Gq1SExMxJw5c6DX65s9liRJkMlk8PHxQa9evXD//fcjLy+v7X9AREREF8GCS+SAJk6ciLy8POTl5WH9+vVQKBSYPHlys/tIkoTHHnvMKo/VXocPH8bEiRPRt29fbNq0CQcOHMAbb7wBlUoFk8nU7L7Hjh1Dbm4udu7ciQceeADr1q1Dt27dcODAAatkISIi+hMLLpEDUqvVCA0NRWhoKHr27IkHH3wQ2dnZKCoqarrPHXfcgU8++QQHDx7s8GO115o1axAaGooXXngB3bp1Q3x8PCZOnIj33nsPHh4eze4bHByM0NBQJCUl4aqrrsIff/yBoKAgLFiwoMM5iIj+v527h2WujeM4/vOyiCLILW0akVA0GjbSEEoEkTAYxMRkMJgYSjeDl3QTHdAmErFJpEMjrF4WCenS2LyEICKh0aY1eQbRPFIPN72T29N8P+N1rvPP/5zpd65c1wH+jYAL/HCRSERra2uyWCwqLi5OjDc1Namnp0cTExMp1/ouo9Go6+tr7ezsfPnenJwcjYyMaH9/X7e3tyn3AgDAq+y/3QCAZIFAQAaDQZIUjUZlMpkUCASUmfn2m3R2dlZ1dXXa3d1Vc3NzSrW+o7+/X9vb23I4HDIajbLb7Wpvb9fQ0JDy8/M/vd9qtUqSzs7OVFJSknI/AABIrOACP1JbW5uCwaCCwaAODg7U1dWl7u7upENlNTU1Ghoa+nAV97Na3d3dMhgMMhgMstlsX+ozKytLKysrury8lNvtltls1szMjGw2228dIHt+fpb0sp8YAIA/hYAL/EC5ubmyWCyyWCyqr6+Xz+dTNBqV1+tNmjs1NaWjoyP5/f5v1fL5fIkAvLm5KUmJ1ddwOJxU7+HhQQUFBW/GzGazBgcH5fF4FAqFFI/Htbi4+OlzHh8fS3r5MwMAAH8KWxSA/4HXX2zFYrGka6WlpRodHZXL5VJFRcWXa5nN5qQ5lZWVyszM1OHhocrKyhLjJycnCofDqqqq+s/6hYWFMplMikajH/YRi8W0vLyslpYW/fr169O+AQD4XQRc4Ad6enrSzc2NJOn+/l4ej0eRSES9vb3vzp+cnJTX69Xp6akGBgZSqiVJeXl5Gh4e1vj4uLKzs1VbW6uLiws5nU7Z7XY1NjZKkpaWlhQMBtXX16eKigrF43Gtrq4qFAppYWHhTc3b21vF43E9Pj7q8PBQbrdbd3d32tjY+PZ7AgDgPQRc4Afa2tqSyWSS9BI2rVar1tfX1dra+u78oqIiOZ1OuVyulGu9mp+f19zcnJxOp87Pz2U0GtXR0aHp6enEntmGhgbt7e1pZGREV1dXiX28fr9fDofjTb3q6mplZGTIYDCovLxcnZ2dGhsbk9Fo/OLbAQDgYxnPr6c8AAAAgDTAITMAAACkFQIuAAAA0goBFwAAAGmFgAsAAIC0QsAFAABAWiHgAgAAIK0QcAEAAJBWCLgAAABIKwRcAAAApBUCLgAAANIKARcAAABp5R8MTTUgLADkWAAAAABJRU5ErkJggg==\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "FgllhXez_sr3"
+      },
+      "execution_count": 10,
+      "outputs": []
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "obb",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python",
+      "version": "3.9.19"
+    },
+    "colab": {
+      "provenance": []
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file

From bd8e31e358c7a8af09fad0e59fe26de4cf48cea2 Mon Sep 17 00:00:00 2001
From: Ambrose Ikpele <68953964+ambroseikpele@users.noreply.github.com>
Date: Wed, 2 Oct 2024 01:42:15 +0100
Subject: [PATCH 2/2] Updated file : Updated Notebook for Portfolio
 Optimization Using Modern Portfolio Theory (Hacktoberfest)

Addressed review comments
---
 ...timizationUsingModernPortfolioTheory.ipynb | 1159 ++++++++++++-----
 1 file changed, 844 insertions(+), 315 deletions(-)

diff --git a/examples/portfolioOptimizationUsingModernPortfolioTheory.ipynb b/examples/portfolioOptimizationUsingModernPortfolioTheory.ipynb
index 08732cb16501..ae2e21fd1502 100644
--- a/examples/portfolioOptimizationUsingModernPortfolioTheory.ipynb
+++ b/examples/portfolioOptimizationUsingModernPortfolioTheory.ipynb
@@ -19,6 +19,24 @@
         "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/OpenBB-Finance/OpenBB/blob/develop/examples/[Notebook_Name].ipynb)"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Introduction"
+      ],
+      "metadata": {
+        "id": "-Ih2c6xRxklu"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Modern Portfolio Theory (MPT) is a mathematical framework for constructing a portfolio of assets to maximize expected return based on a given level of risk. In this notebook, we will implement MPT to construct an optimal portfolio with minimum volatility using a selection of the top cryptocurrencies as our assets. The notebook will fetch historical price data for these assets, calculate the portfolio's expected return and risk, and visualize the optimal portfolio based on risk-return trade-offs."
+      ],
+      "metadata": {
+        "id": "mWd0TC0DxZm-"
+      }
+    },
     {
       "cell_type": "markdown",
       "source": [
@@ -37,7 +55,7 @@
       "metadata": {
         "id": "iexQsZ1XvYa8"
       },
-      "execution_count": null,
+      "execution_count": 17,
       "outputs": []
     },
     {
@@ -70,12 +88,21 @@
     {
       "cell_type": "markdown",
       "source": [
-        "Fetch the daily data of the top crypto currencies for a period of one year using openbb"
+        "### Define the assets and fetch the data"
       ],
       "metadata": {
         "id": "w3XB7egmzlmI"
       }
     },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Fetch the daily data of the top crypto currencies for a period of one year using openbb"
+      ],
+      "metadata": {
+        "id": "SyF2ROKDyKdJ"
+      }
+    },
     {
       "cell_type": "code",
       "source": [
@@ -90,7 +117,7 @@
           "base_uri": "https://localhost:8080/",
           "height": 455
         },
-        "outputId": "8ab3c023-43e8-4d81-f5ef-22451d3604bb"
+        "outputId": "fd4a3544-32f9-49c1-d7ad-bca9efa88baa"
       },
       "execution_count": 2,
       "outputs": [
@@ -106,11 +133,11 @@
               "2023-10-01      4.105477      4.279937      4.103880      4.261009   \n",
               "2023-10-01   1671.161499   1750.595703   1670.082153   1733.810425   \n",
               "...                  ...           ...           ...           ...   \n",
-              "2024-10-01     66.820854     68.483223     66.786285     67.686295   \n",
-              "2024-10-01      0.395941      0.404462      0.394638      0.399719   \n",
-              "2024-10-01    152.604584    157.199478    151.796982    155.045334   \n",
-              "2024-10-01      0.155929      0.156981      0.155710      0.156684   \n",
-              "2024-10-01      0.611519      0.633387      0.611519      0.627602   \n",
+              "2024-09-30     69.314308     69.321297     66.454277     66.820450   \n",
+              "2024-09-30      0.421419      0.421627      0.394318      0.395917   \n",
+              "2024-09-30    158.632416    159.508926    152.019836    152.618469   \n",
+              "2024-09-30      0.156474      0.156746      0.154867      0.155915   \n",
+              "2024-09-30      0.641945      0.652411      0.610951      0.611492   \n",
               "\n",
               "                  volume     symbol  \n",
               "date                                 \n",
@@ -120,17 +147,17 @@
               "2023-10-01  8.294334e+07    DOT-USD  \n",
               "2023-10-01  5.054880e+09    ETH-USD  \n",
               "...                  ...        ...  \n",
-              "2024-10-01  2.475363e+08    LTC-USD  \n",
-              "2024-10-01  3.507427e+07  MATIC-USD  \n",
-              "2024-10-01  1.949461e+09    SOL-USD  \n",
-              "2024-10-01  3.093434e+08    TRX-USD  \n",
-              "2024-10-01  1.424177e+09    XRP-USD  \n",
+              "2024-09-30  3.003743e+08    LTC-USD  \n",
+              "2024-09-30  3.730373e+07  MATIC-USD  \n",
+              "2024-09-30  2.376781e+09    SOL-USD  \n",
+              "2024-09-30  3.565544e+08    TRX-USD  \n",
+              "2024-09-30  2.051369e+09    XRP-USD  \n",
               "\n",
-              "[3670 rows x 6 columns]"
+              "[3660 rows x 6 columns]"
             ],
             "text/html": [
               "\n",
-              "  <div id=\"df-acd753f8-3dd8-4cab-9540-7b8c2b61c6ea\" class=\"colab-df-container\">\n",
+              "  <div id=\"df-879083e7-465e-4e83-b820-f0b469f7dbb7\" class=\"colab-df-container\">\n",
               "    <div>\n",
               "<style scoped>\n",
               "    .dataframe tbody tr th:only-of-type {\n",
@@ -222,58 +249,58 @@
               "      <td>...</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>2024-10-01</th>\n",
-              "      <td>66.820854</td>\n",
-              "      <td>68.483223</td>\n",
-              "      <td>66.786285</td>\n",
-              "      <td>67.686295</td>\n",
-              "      <td>2.475363e+08</td>\n",
+              "      <th>2024-09-30</th>\n",
+              "      <td>69.314308</td>\n",
+              "      <td>69.321297</td>\n",
+              "      <td>66.454277</td>\n",
+              "      <td>66.820450</td>\n",
+              "      <td>3.003743e+08</td>\n",
               "      <td>LTC-USD</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>2024-10-01</th>\n",
-              "      <td>0.395941</td>\n",
-              "      <td>0.404462</td>\n",
-              "      <td>0.394638</td>\n",
-              "      <td>0.399719</td>\n",
-              "      <td>3.507427e+07</td>\n",
+              "      <th>2024-09-30</th>\n",
+              "      <td>0.421419</td>\n",
+              "      <td>0.421627</td>\n",
+              "      <td>0.394318</td>\n",
+              "      <td>0.395917</td>\n",
+              "      <td>3.730373e+07</td>\n",
               "      <td>MATIC-USD</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>2024-10-01</th>\n",
-              "      <td>152.604584</td>\n",
-              "      <td>157.199478</td>\n",
-              "      <td>151.796982</td>\n",
-              "      <td>155.045334</td>\n",
-              "      <td>1.949461e+09</td>\n",
+              "      <th>2024-09-30</th>\n",
+              "      <td>158.632416</td>\n",
+              "      <td>159.508926</td>\n",
+              "      <td>152.019836</td>\n",
+              "      <td>152.618469</td>\n",
+              "      <td>2.376781e+09</td>\n",
               "      <td>SOL-USD</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>2024-10-01</th>\n",
-              "      <td>0.155929</td>\n",
-              "      <td>0.156981</td>\n",
-              "      <td>0.155710</td>\n",
-              "      <td>0.156684</td>\n",
-              "      <td>3.093434e+08</td>\n",
+              "      <th>2024-09-30</th>\n",
+              "      <td>0.156474</td>\n",
+              "      <td>0.156746</td>\n",
+              "      <td>0.154867</td>\n",
+              "      <td>0.155915</td>\n",
+              "      <td>3.565544e+08</td>\n",
               "      <td>TRX-USD</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>2024-10-01</th>\n",
-              "      <td>0.611519</td>\n",
-              "      <td>0.633387</td>\n",
-              "      <td>0.611519</td>\n",
-              "      <td>0.627602</td>\n",
-              "      <td>1.424177e+09</td>\n",
+              "      <th>2024-09-30</th>\n",
+              "      <td>0.641945</td>\n",
+              "      <td>0.652411</td>\n",
+              "      <td>0.610951</td>\n",
+              "      <td>0.611492</td>\n",
+              "      <td>2.051369e+09</td>\n",
               "      <td>XRP-USD</td>\n",
               "    </tr>\n",
               "  </tbody>\n",
               "</table>\n",
-              "<p>3670 rows × 6 columns</p>\n",
+              "<p>3660 rows × 6 columns</p>\n",
               "</div>\n",
               "    <div class=\"colab-df-buttons\">\n",
               "\n",
               "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-acd753f8-3dd8-4cab-9540-7b8c2b61c6ea')\"\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-879083e7-465e-4e83-b820-f0b469f7dbb7')\"\n",
               "            title=\"Convert this dataframe to an interactive table.\"\n",
               "            style=\"display:none;\">\n",
               "\n",
@@ -325,12 +352,12 @@
               "\n",
               "    <script>\n",
               "      const buttonEl =\n",
-              "        document.querySelector('#df-acd753f8-3dd8-4cab-9540-7b8c2b61c6ea button.colab-df-convert');\n",
+              "        document.querySelector('#df-879083e7-465e-4e83-b820-f0b469f7dbb7 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-acd753f8-3dd8-4cab-9540-7b8c2b61c6ea');\n",
+              "        const element = document.querySelector('#df-879083e7-465e-4e83-b820-f0b469f7dbb7');\n",
               "        const dataTable =\n",
               "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
               "                                                    [key], {});\n",
@@ -350,8 +377,8 @@
               "  </div>\n",
               "\n",
               "\n",
-              "<div id=\"df-d9d1f0ab-15bd-4cd2-9aa5-6799bf1ddf1e\">\n",
-              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d9d1f0ab-15bd-4cd2-9aa5-6799bf1ddf1e')\"\n",
+              "<div id=\"df-77723f97-51e0-4904-a713-496c5851eefa\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-77723f97-51e0-4904-a713-496c5851eefa')\"\n",
               "            title=\"Suggest charts\"\n",
               "            style=\"display:none;\">\n",
               "\n",
@@ -470,14 +497,14 @@
               "    }\n",
               "    (() => {\n",
               "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-d9d1f0ab-15bd-4cd2-9aa5-6799bf1ddf1e button');\n",
+              "        document.querySelector('#df-77723f97-51e0-4904-a713-496c5851eefa button');\n",
               "      quickchartButtonEl.style.display =\n",
               "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
               "    })();\n",
               "  </script>\n",
               "</div>\n",
               "\n",
-              "  <div id=\"id_0cd69b5f-c354-4c95-b378-96848db116ca\">\n",
+              "  <div id=\"id_1a79b784-8bf7-4605-b63a-f0373ecdb69c\">\n",
               "    <style>\n",
               "      .colab-df-generate {\n",
               "        background-color: #E8F0FE;\n",
@@ -521,7 +548,7 @@
               "    <script>\n",
               "      (() => {\n",
               "      const buttonEl =\n",
-              "        document.querySelector('#id_0cd69b5f-c354-4c95-b378-96848db116ca button.colab-df-generate');\n",
+              "        document.querySelector('#id_1a79b784-8bf7-4605-b63a-f0373ecdb69c button.colab-df-generate');\n",
               "      buttonEl.style.display =\n",
               "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
               "\n",
@@ -538,7 +565,7 @@
             "application/vnd.google.colaboratory.intrinsic+json": {
               "type": "dataframe",
               "variable_name": "ohlc_data",
-              "summary": "{\n  \"name\": \"ohlc_data\",\n  \"rows\": 3670,\n  \"fields\": [\n    {\n      \"column\": \"date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2023-10-01\",\n        \"max\": \"2024-10-01\",\n        \"num_unique_values\": 367,\n        \"samples\": [\n          \"2024-04-11\",\n          \"2023-11-03\",\n          \"2023-10-16\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16646.479249400243,\n        \"min\": 0.08486499637365341,\n        \"max\": 73079.375,\n        \"num_unique_values\": 3669,\n        \"samples\": [\n          0.49170398712158203,\n          0.4794990122318268,\n          2683.719970703125\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"high\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16979.65920014512,\n        \"min\": 0.08571500331163406,\n        \"max\": 73750.0703125,\n        \"num_unique_values\": 3670,\n        \"samples\": [\n          0.4922049939632416,\n          0.4812979996204376,\n          63872.44140625\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16307.879682680239,\n        \"min\": 0.0847959965467453,\n        \"max\": 71334.09375,\n        \"num_unique_values\": 3665,\n        \"samples\": [\n          6.224710941314697,\n          0.3214839994907379,\n          0.4614889919757843\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16670.124390346416,\n        \"min\": 0.08486700057983398,\n        \"max\": 73083.5,\n        \"num_unique_values\": 3665,\n        \"samples\": [\n          65738.7265625,\n          519.870361328125,\n          139.4636993408203\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10083345827.063368,\n        \"min\": 33682820.0,\n        \"max\": 108991085584.0,\n        \"num_unique_values\": 3670,\n        \"samples\": [\n          734621817.0,\n          1322556294.0,\n          42710252573.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"symbol\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"TRX-USD\",\n          \"BNB-USD\",\n          \"LTC-USD\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+              "summary": "{\n  \"name\": \"ohlc_data\",\n  \"rows\": 3660,\n  \"fields\": [\n    {\n      \"column\": \"date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2023-10-01\",\n        \"max\": \"2024-09-30\",\n        \"num_unique_values\": 366,\n        \"samples\": [\n          \"2024-04-11\",\n          \"2023-11-03\",\n          \"2023-10-16\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16639.836555374353,\n        \"min\": 0.08486499637365341,\n        \"max\": 73079.375,\n        \"num_unique_values\": 3659,\n        \"samples\": [\n          227.13491821289062,\n          0.28950101137161255,\n          0.4867730140686035\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"high\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16973.343411209262,\n        \"min\": 0.08571500331163406,\n        \"max\": 73750.0703125,\n        \"num_unique_values\": 3660,\n        \"samples\": [\n          229.19149780273438,\n          0.29728201031684875,\n          0.4899919927120209\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16300.377275687548,\n        \"min\": 0.0847959965467453,\n        \"max\": 71334.09375,\n        \"num_unique_values\": 3655,\n        \"samples\": [\n          2419.36279296875,\n          177.46127319335938,\n          0.5218260288238525\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16663.48894012965,\n        \"min\": 0.08486700057983398,\n        \"max\": 73083.5,\n        \"num_unique_values\": 3655,\n        \"samples\": [\n          2487.515625,\n          1.0434010028839111,\n          0.5468699932098389\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10083764369.937248,\n        \"min\": 33682820.0,\n        \"max\": 108991085584.0,\n        \"num_unique_values\": 3660,\n        \"samples\": [\n          282899321.0,\n          146280893.0,\n          385469444.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"symbol\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"TRX-USD\",\n          \"BNB-USD\",\n          \"LTC-USD\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
             }
           },
           "metadata": {},
@@ -579,7 +606,7 @@
           "height": 455
         },
         "id": "PXNs59lSzMqv",
-        "outputId": "265b039d-f797-40be-8817-6c96a7aaf431"
+        "outputId": "1bced258-fac0-4be7-c433-e6c08e40afd6"
       },
       "execution_count": 3,
       "outputs": [
@@ -595,11 +622,11 @@
               "2023-10-04  0.259315  213.413086  27799.394531  4.047316  1647.838135   \n",
               "2023-10-05  0.260149  210.679672  27415.912109  4.022738  1611.476440   \n",
               "...              ...         ...           ...       ...          ...   \n",
+              "2024-09-26  0.401882  596.776917  65181.019531  4.816284  2632.199951   \n",
               "2024-09-27  0.402328  607.867004  65790.664062  4.892055  2695.900635   \n",
               "2024-09-28  0.401052  601.567200  65887.648438  4.805896  2677.539062   \n",
               "2024-09-29  0.397597  596.411194  65635.304688  4.768428  2659.346924   \n",
               "2024-09-30  0.373214  567.260071  63329.500000  4.437086  2603.062744   \n",
-              "2024-10-01  0.380300  574.313965  63394.039062  4.461926  2610.701416   \n",
               "\n",
               "symbol        LTC-USD  MATIC-USD     SOL-USD   TRX-USD   XRP-USD  \n",
               "date                                                              \n",
@@ -609,17 +636,17 @@
               "2023-10-04  64.452065   0.563369   23.144787  0.088999  0.532931  \n",
               "2023-10-05  64.858765   0.546018   22.694141  0.088276  0.523366  \n",
               "...               ...        ...         ...       ...       ...  \n",
+              "2024-09-26  68.518311   0.424973  155.576096  0.153201  0.590421  \n",
               "2024-09-27  71.188202   0.433774  157.749939  0.155170  0.588927  \n",
               "2024-09-28  70.003967   0.423559  156.912430  0.155068  0.614801  \n",
               "2024-09-29  69.314423   0.421419  158.629166  0.156474  0.641947  \n",
               "2024-09-30  66.820450   0.395917  152.618469  0.155915  0.611492  \n",
-              "2024-10-01  67.686295   0.399719  155.045334  0.156684  0.627602  \n",
               "\n",
-              "[367 rows x 10 columns]"
+              "[366 rows x 10 columns]"
             ],
             "text/html": [
               "\n",
-              "  <div id=\"df-96abb763-e128-49ed-91a8-7b869f966576\" class=\"colab-df-container\">\n",
+              "  <div id=\"df-55e65dcb-ed1c-4498-9030-f9d3ef8f4ff4\" class=\"colab-df-container\">\n",
               "    <div>\n",
               "<style scoped>\n",
               "    .dataframe tbody tr th:only-of-type {\n",
@@ -743,6 +770,19 @@
               "      <td>...</td>\n",
               "    </tr>\n",
               "    <tr>\n",
+              "      <th>2024-09-26</th>\n",
+              "      <td>0.401882</td>\n",
+              "      <td>596.776917</td>\n",
+              "      <td>65181.019531</td>\n",
+              "      <td>4.816284</td>\n",
+              "      <td>2632.199951</td>\n",
+              "      <td>68.518311</td>\n",
+              "      <td>0.424973</td>\n",
+              "      <td>155.576096</td>\n",
+              "      <td>0.153201</td>\n",
+              "      <td>0.590421</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
               "      <th>2024-09-27</th>\n",
               "      <td>0.402328</td>\n",
               "      <td>607.867004</td>\n",
@@ -794,27 +834,14 @@
               "      <td>0.155915</td>\n",
               "      <td>0.611492</td>\n",
               "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2024-10-01</th>\n",
-              "      <td>0.380300</td>\n",
-              "      <td>574.313965</td>\n",
-              "      <td>63394.039062</td>\n",
-              "      <td>4.461926</td>\n",
-              "      <td>2610.701416</td>\n",
-              "      <td>67.686295</td>\n",
-              "      <td>0.399719</td>\n",
-              "      <td>155.045334</td>\n",
-              "      <td>0.156684</td>\n",
-              "      <td>0.627602</td>\n",
-              "    </tr>\n",
               "  </tbody>\n",
               "</table>\n",
-              "<p>367 rows × 10 columns</p>\n",
+              "<p>366 rows × 10 columns</p>\n",
               "</div>\n",
               "    <div class=\"colab-df-buttons\">\n",
               "\n",
               "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-96abb763-e128-49ed-91a8-7b869f966576')\"\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-55e65dcb-ed1c-4498-9030-f9d3ef8f4ff4')\"\n",
               "            title=\"Convert this dataframe to an interactive table.\"\n",
               "            style=\"display:none;\">\n",
               "\n",
@@ -866,12 +893,12 @@
               "\n",
               "    <script>\n",
               "      const buttonEl =\n",
-              "        document.querySelector('#df-96abb763-e128-49ed-91a8-7b869f966576 button.colab-df-convert');\n",
+              "        document.querySelector('#df-55e65dcb-ed1c-4498-9030-f9d3ef8f4ff4 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-96abb763-e128-49ed-91a8-7b869f966576');\n",
+              "        const element = document.querySelector('#df-55e65dcb-ed1c-4498-9030-f9d3ef8f4ff4');\n",
               "        const dataTable =\n",
               "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
               "                                                    [key], {});\n",
@@ -891,8 +918,8 @@
               "  </div>\n",
               "\n",
               "\n",
-              "<div id=\"df-bad89d7a-81d2-4408-88c3-d7d19d4c74b0\">\n",
-              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-bad89d7a-81d2-4408-88c3-d7d19d4c74b0')\"\n",
+              "<div id=\"df-bd05183d-4f63-495d-9ee7-ed7d9c0b8354\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-bd05183d-4f63-495d-9ee7-ed7d9c0b8354')\"\n",
               "            title=\"Suggest charts\"\n",
               "            style=\"display:none;\">\n",
               "\n",
@@ -1011,14 +1038,14 @@
               "    }\n",
               "    (() => {\n",
               "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-bad89d7a-81d2-4408-88c3-d7d19d4c74b0 button');\n",
+              "        document.querySelector('#df-bd05183d-4f63-495d-9ee7-ed7d9c0b8354 button');\n",
               "      quickchartButtonEl.style.display =\n",
               "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
               "    })();\n",
               "  </script>\n",
               "</div>\n",
               "\n",
-              "  <div id=\"id_9856cf46-24ab-49eb-a611-9397bb203c9f\">\n",
+              "  <div id=\"id_3fba15ae-1b60-4e81-ae38-43bb9af2ff04\">\n",
               "    <style>\n",
               "      .colab-df-generate {\n",
               "        background-color: #E8F0FE;\n",
@@ -1062,7 +1089,7 @@
               "    <script>\n",
               "      (() => {\n",
               "      const buttonEl =\n",
-              "        document.querySelector('#id_9856cf46-24ab-49eb-a611-9397bb203c9f button.colab-df-generate');\n",
+              "        document.querySelector('#id_3fba15ae-1b60-4e81-ae38-43bb9af2ff04 button.colab-df-generate');\n",
               "      buttonEl.style.display =\n",
               "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
               "\n",
@@ -1079,7 +1106,7 @@
             "application/vnd.google.colaboratory.intrinsic+json": {
               "type": "dataframe",
               "variable_name": "close_symbol_unstacked",
-              "summary": "{\n  \"name\": \"close_symbol_unstacked\",\n  \"rows\": 367,\n  \"fields\": [\n    {\n      \"column\": \"date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2023-10-01\",\n        \"max\": \"2024-10-01\",\n        \"num_unique_values\": 367,\n        \"samples\": [\n          \"2024-04-11\",\n          \"2023-11-03\",\n          \"2023-10-16\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ADA-USD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.12231550584081373,\n        \"min\": 0.2434529960155487,\n        \"max\": 0.7741900086402893,\n        \"num_unique_values\": 366,\n        \"samples\": [\n          0.5035750269889832,\n          0.32902100682258606,\n          0.25156301259994507\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BNB-USD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 152.46468320666116,\n        \"min\": 205.2294158935547,\n        \"max\": 710.4640502929688,\n        \"num_unique_values\": 367,\n        \"samples\": [\n          604.893798828125,\n          230.60597229003906,\n          214.82395935058594\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BTC-USD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13044.480615583347,\n        \"min\": 26756.798828125,\n        \"max\": 73083.5,\n        \"num_unique_values\": 367,\n        \"samples\": [\n          70060.609375,\n          34732.32421875,\n          28519.466796875\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DOT-USD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.7193378737799296,\n        \"min\": 3.6488780975341797,\n        \"max\": 11.542901992797852,\n        \"num_unique_values\": 367,\n        \"samples\": [\n          8.379441261291504,\n          4.6154937744140625,\n          3.7862110137939453\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ETH-USD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 644.9463072659804,\n        \"min\": 1539.6124267578125,\n        \"max\": 4066.445068359375,\n        \"num_unique_values\": 367,\n        \"samples\": [\n          3505.247802734375,\n          1832.795166015625,\n          1600.5343017578125\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LTC-USD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.815022518838514,\n        \"min\": 55.983909606933594,\n        \"max\": 109.25897216796875,\n        \"num_unique_values\": 367,\n        \"samples\": [\n          98.68910217285156,\n          69.49114227294922,\n          63.337162017822266\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MATIC-USD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.20676285473019815,\n        \"min\": 0.3659299910068512,\n        \"max\": 1.2714049816131592,\n        \"num_unique_values\": 367,\n        \"samples\": [\n          0.8783450126647949,\n          0.6719430088996887,\n          0.5341209769248962\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SOL-USD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 46.954204670567634,\n        \"min\": 21.300268173217773,\n        \"max\": 202.87413024902344,\n        \"num_unique_values\": 367,\n        \"samples\": [\n          172.5763702392578,\n          39.51976013183594,\n          23.98295783996582\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TRX-USD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.018127298632971522,\n        \"min\": 0.08486700057983398,\n        \"max\": 0.16655699908733368,\n        \"num_unique_values\": 363,\n        \"samples\": [\n          0.13199299573898315,\n          0.09731300175189972,\n          0.08891399949789047\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"XRP-USD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.056190198963527865,\n        \"min\": 0.4198229908943176,\n        \"max\": 0.7180359959602356,\n        \"num_unique_values\": 367,\n        \"samples\": [\n          0.6088799834251404,\n          0.6130020022392273,\n          0.49797698855400085\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+              "summary": "{\n  \"name\": \"close_symbol_unstacked\",\n  \"rows\": 366,\n  \"fields\": [\n    {\n      \"column\": \"date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2023-10-01\",\n        \"max\": \"2024-09-30\",\n        \"num_unique_values\": 366,\n        \"samples\": [\n          \"2024-04-11\",\n          \"2023-11-03\",\n          \"2023-10-16\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ADA-USD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.12241864696655712,\n        \"min\": 0.2434529960155487,\n        \"max\": 0.7741900086402893,\n        \"num_unique_values\": 365,\n        \"samples\": [\n          0.5035750269889832,\n          0.32902100682258606,\n          0.25156301259994507\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BNB-USD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 152.5214407182818,\n        \"min\": 205.2294158935547,\n        \"max\": 710.4640502929688,\n        \"num_unique_values\": 366,\n        \"samples\": [\n          604.893798828125,\n          230.60597229003906,\n          214.82395935058594\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BTC-USD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13053.33386869586,\n        \"min\": 26756.798828125,\n        \"max\": 73083.5,\n        \"num_unique_values\": 366,\n        \"samples\": [\n          70060.609375,\n          34732.32421875,\n          28519.466796875\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DOT-USD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.71868220447839,\n        \"min\": 3.6488780975341797,\n        \"max\": 11.542901992797852,\n        \"num_unique_values\": 366,\n        \"samples\": [\n          8.379441261291504,\n          4.6154937744140625,\n          3.7862110137939453\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ETH-USD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 645.7801836761455,\n        \"min\": 1539.6124267578125,\n        \"max\": 4066.445068359375,\n        \"num_unique_values\": 366,\n        \"samples\": [\n          3505.247802734375,\n          1832.795166015625,\n          1600.5343017578125\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LTC-USD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.82303289832029,\n        \"min\": 55.983909606933594,\n        \"max\": 109.25897216796875,\n        \"num_unique_values\": 366,\n        \"samples\": [\n          98.68910217285156,\n          69.49114227294922,\n          63.337162017822266\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MATIC-USD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.20641913692628033,\n        \"min\": 0.3659299910068512,\n        \"max\": 1.2714049816131592,\n        \"num_unique_values\": 366,\n        \"samples\": [\n          0.8783450126647949,\n          0.6719430088996887,\n          0.5341209769248962\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SOL-USD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 46.98292860601651,\n        \"min\": 21.300268173217773,\n        \"max\": 202.87413024902344,\n        \"num_unique_values\": 366,\n        \"samples\": [\n          172.5763702392578,\n          39.51976013183594,\n          23.98295783996582\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TRX-USD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.018051530692952913,\n        \"min\": 0.08486700057983398,\n        \"max\": 0.16655699908733368,\n        \"num_unique_values\": 362,\n        \"samples\": [\n          0.13199299573898315,\n          0.09731300175189972,\n          0.08891399949789047\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"XRP-USD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.05616593942864053,\n        \"min\": 0.4198229908943176,\n        \"max\": 0.7180359959602356,\n        \"num_unique_values\": 366,\n        \"samples\": [\n          0.6088799834251404,\n          0.6130020022392273,\n          0.49797698855400085\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
             }
           },
           "metadata": {},
@@ -1090,57 +1117,64 @@
     {
       "cell_type": "markdown",
       "source": [
-        "calculate the expected returns of each asset and covariance matrix of crypto assets"
+        "### Calculate Portfolio's Expected Returns"
       ],
       "metadata": {
-        "id": "pFLyyT7v4E5J"
+        "id": "PbL5KdtRyn9C"
       }
     },
     {
-      "cell_type": "code",
+      "cell_type": "markdown",
       "source": [
-        "# Expected returns of crypto assets using the mean historical return.\n",
-        "portfolio_expected_returns = expected_returns.mean_historical_return(prices, frequency=365, compounding=False)\n",
+        "The expected returns serve as the basis for evaluating different asset combinations and determining the optimal portfolio allocation. In the next steps, these returns will be used along with the covariance matrix to analyze risk-return profiles."
+      ],
+      "metadata": {
+        "id": "i-IM_cAUywwL"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "$$return=\\frac{Σ\\space r_{i}}{N}⋅365$$\n",
         "\n",
-        "# Covariance matrix of crypto assets using the Ledoit-Wolf shrinkage method.\n",
-        "covariance = CovarianceShrinkage(prices).ledoit_wolf()"
+        "where $r_i$ is the daily return of a particular asset and $N$ is the number of days in the data, we multiply by 365 so as to annualize the result"
       ],
       "metadata": {
-        "id": "S_sH-1sq6bnI"
-      },
-      "execution_count": 4,
-      "outputs": []
+        "id": "Ktrk_3fHbmS2"
+      }
     },
     {
       "cell_type": "code",
       "source": [
-        "portfolio_expected_returns"
+        "# Expected returns of crypto assets using the mean historical return.\n",
+        "assets_expected_returns = expected_returns.mean_historical_return(prices, frequency=365, compounding=False)\n",
+        "assets_expected_returns"
       ],
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 429
         },
-        "id": "8q1RvkuV7ouF",
-        "outputId": "7cdc9e09-c38c-4443-8d66-2de154614d74"
+        "id": "Mx_MLbe4mxfa",
+        "outputId": "310d6922-1a13-458e-b5a3-a066f4ddc7f6"
       },
-      "execution_count": 5,
+      "execution_count": 4,
       "outputs": [
         {
           "output_type": "execute_result",
           "data": {
             "text/plain": [
               "symbol\n",
-              "ADA-USD      0.621306\n",
-              "BNB-USD      1.130899\n",
-              "BTC-USD      0.950751\n",
-              "DOT-USD      0.324462\n",
-              "ETH-USD      0.592597\n",
-              "LTC-USD      0.186095\n",
-              "MATIC-USD   -0.048860\n",
-              "SOL-USD      2.297820\n",
-              "TRX-USD      0.619805\n",
-              "XRP-USD      0.370310\n",
+              "ADA-USD      0.604022\n",
+              "BNB-USD      1.121563\n",
+              "BTC-USD      0.952337\n",
+              "DOT-USD      0.319752\n",
+              "ETH-USD      0.591286\n",
+              "LTC-USD      0.173647\n",
+              "MATIC-USD   -0.058596\n",
+              "SOL-USD      2.288214\n",
+              "TRX-USD      0.616572\n",
+              "XRP-USD      0.344980\n",
               "dtype: float64"
             ],
             "text/html": [
@@ -1172,43 +1206,43 @@
               "  <tbody>\n",
               "    <tr>\n",
               "      <th>ADA-USD</th>\n",
-              "      <td>0.621306</td>\n",
+              "      <td>0.604022</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>BNB-USD</th>\n",
-              "      <td>1.130899</td>\n",
+              "      <td>1.121563</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>BTC-USD</th>\n",
-              "      <td>0.950751</td>\n",
+              "      <td>0.952337</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>DOT-USD</th>\n",
-              "      <td>0.324462</td>\n",
+              "      <td>0.319752</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>ETH-USD</th>\n",
-              "      <td>0.592597</td>\n",
+              "      <td>0.591286</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>LTC-USD</th>\n",
-              "      <td>0.186095</td>\n",
+              "      <td>0.173647</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>MATIC-USD</th>\n",
-              "      <td>-0.048860</td>\n",
+              "      <td>-0.058596</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>SOL-USD</th>\n",
-              "      <td>2.297820</td>\n",
+              "      <td>2.288214</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>TRX-USD</th>\n",
-              "      <td>0.619805</td>\n",
+              "      <td>0.616572</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>XRP-USD</th>\n",
-              "      <td>0.370310</td>\n",
+              "      <td>0.344980</td>\n",
               "    </tr>\n",
               "  </tbody>\n",
               "</table>\n",
@@ -1216,24 +1250,68 @@
             ]
           },
           "metadata": {},
-          "execution_count": 5
+          "execution_count": 4
         }
       ]
     },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "The frequency parameter indicates the number of trading periods in a year. For crypto daily data, it is set to 365, since crypto can be traded everyday.\n",
+        "\n",
+        "The compounding parameter calculate returns using simple or compounded growth. Setting compounding=False results in simple annualized returns. If compounding=True, the function would compute geometric (compounded) returns, which consider reinvested returns over time."
+      ],
+      "metadata": {
+        "id": "eBDVKkQZ2Wx3"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Calculate the Covariance Matrix Using the Ledoit-Wolf Shrinkage Estimator"
+      ],
+      "metadata": {
+        "id": "1gvka-on5JDC"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "The covariance matrix represents the relationship between the returns of the assets. It measures how returns of one asset(e.g btc) vary in relation to another(e.g eth), which is essential for understanding the overall risk of a portfolio. Assets with high positive covariance tend to move in the same direction, while those with negative covariance move in opposite directions."
+      ],
+      "metadata": {
+        "id": "fT9f1AWN5l1L"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "The formula below uses an example of btc and eth to express how covariance is calculated:\n",
+        "\n",
+        "$$Cov({r_{btc}, r_{eth} })= \\frac{Σ(r_{btc}-\\bar r_{btc})(r_{eth}-\\bar r_{eth})}{N}$$\n",
+        "\n",
+        "Where $r$ is the daily returns of the assets and $\\bar r$ is the average daily returns of the assets."
+      ],
+      "metadata": {
+        "id": "69Uyh-rPUWD3"
+      }
+    },
     {
       "cell_type": "code",
       "source": [
+        "# Covariance matrix of crypto assets using the Ledoit-Wolf shrinkage method.\n",
+        "covariance = CovarianceShrinkage(prices).ledoit_wolf()\n",
         "covariance"
       ],
       "metadata": {
+        "id": "S_sH-1sq6bnI",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 394
         },
-        "id": "OGZqN0i47sBC",
-        "outputId": "f4e4ac0a-f2c0-4f58-b999-e7d056d59b18"
+        "outputId": "c1e9855e-c6c7-4dbd-8f66-50ec26743425"
       },
-      "execution_count": 6,
+      "execution_count": 5,
       "outputs": [
         {
           "output_type": "execute_result",
@@ -1241,33 +1319,33 @@
             "text/plain": [
               "symbol      ADA-USD   BNB-USD   BTC-USD   DOT-USD   ETH-USD   LTC-USD  \\\n",
               "symbol                                                                  \n",
-              "ADA-USD    0.365908  0.149985  0.179671  0.303813  0.203350  0.204707   \n",
-              "BNB-USD    0.149985  0.230834  0.122378  0.150464  0.138874  0.108916   \n",
-              "BTC-USD    0.179671  0.122378  0.189577  0.175989  0.172308  0.127100   \n",
-              "DOT-USD    0.303813  0.150464  0.175989  0.385419  0.206734  0.192864   \n",
-              "ETH-USD    0.203350  0.138874  0.172308  0.206734  0.259265  0.160001   \n",
-              "LTC-USD    0.204707  0.108916  0.127100  0.192864  0.160001  0.270176   \n",
-              "MATIC-USD  0.278248  0.173514  0.170163  0.287835  0.224481  0.190943   \n",
-              "SOL-USD    0.296429  0.171700  0.221531  0.333077  0.232270  0.191517   \n",
-              "TRX-USD    0.083626  0.046899  0.054940  0.085921  0.063282  0.055659   \n",
-              "XRP-USD    0.194114  0.093489  0.120379  0.192047  0.133110  0.158148   \n",
+              "ADA-USD    0.366706  0.150286  0.180182  0.304589  0.203891  0.205121   \n",
+              "BNB-USD    0.150286  0.231405  0.122723  0.150846  0.139246  0.109135   \n",
+              "BTC-USD    0.180182  0.122723  0.190093  0.176476  0.172781  0.127462   \n",
+              "DOT-USD    0.304589  0.150846  0.176476  0.386458  0.207296  0.193352   \n",
+              "ETH-USD    0.203891  0.139246  0.172781  0.207296  0.259972  0.160428   \n",
+              "LTC-USD    0.205121  0.109135  0.127462  0.193352  0.160428  0.270809   \n",
+              "MATIC-USD  0.278896  0.173927  0.170640  0.288592  0.225087  0.191384   \n",
+              "SOL-USD    0.297128  0.172109  0.222148  0.333958  0.232898  0.191960   \n",
+              "TRX-USD    0.083817  0.047007  0.055094  0.086146  0.063453  0.055784   \n",
+              "XRP-USD    0.194349  0.093584  0.120736  0.192491  0.133452  0.158367   \n",
               "\n",
               "symbol     MATIC-USD   SOL-USD   TRX-USD   XRP-USD  \n",
               "symbol                                              \n",
-              "ADA-USD     0.278248  0.296429  0.083626  0.194114  \n",
-              "BNB-USD     0.173514  0.171700  0.046899  0.093489  \n",
-              "BTC-USD     0.170163  0.221531  0.054940  0.120379  \n",
-              "DOT-USD     0.287835  0.333077  0.085921  0.192047  \n",
-              "ETH-USD     0.224481  0.232270  0.063282  0.133110  \n",
-              "LTC-USD     0.190943  0.191517  0.055659  0.158148  \n",
-              "MATIC-USD   0.414685  0.290214  0.075435  0.180736  \n",
-              "SOL-USD     0.290214  0.593740  0.095372  0.184846  \n",
-              "TRX-USD     0.075435  0.095372  0.098433  0.057513  \n",
-              "XRP-USD     0.180736  0.184846  0.057513  0.267617  "
+              "ADA-USD     0.278896  0.297128  0.083817  0.194349  \n",
+              "BNB-USD     0.173927  0.172109  0.047007  0.093584  \n",
+              "BTC-USD     0.170640  0.222148  0.055094  0.120736  \n",
+              "DOT-USD     0.288592  0.333958  0.086146  0.192491  \n",
+              "ETH-USD     0.225087  0.232898  0.063453  0.133452  \n",
+              "LTC-USD     0.191384  0.191960  0.055784  0.158367  \n",
+              "MATIC-USD   0.415755  0.290945  0.075620  0.181063  \n",
+              "SOL-USD     0.290945  0.595302  0.095612  0.185187  \n",
+              "TRX-USD     0.075620  0.095612  0.098694  0.057615  \n",
+              "XRP-USD     0.181063  0.185187  0.057615  0.267912  "
             ],
             "text/html": [
               "\n",
-              "  <div id=\"df-568f972d-ae84-4827-9205-28a3a1e18f22\" class=\"colab-df-container\">\n",
+              "  <div id=\"df-5f529475-f8af-468d-a026-fa8af71ee37b\" class=\"colab-df-container\">\n",
               "    <div>\n",
               "<style scoped>\n",
               "    .dataframe tbody tr th:only-of-type {\n",
@@ -1314,133 +1392,133 @@
               "  <tbody>\n",
               "    <tr>\n",
               "      <th>ADA-USD</th>\n",
-              "      <td>0.365908</td>\n",
-              "      <td>0.149985</td>\n",
-              "      <td>0.179671</td>\n",
-              "      <td>0.303813</td>\n",
-              "      <td>0.203350</td>\n",
-              "      <td>0.204707</td>\n",
-              "      <td>0.278248</td>\n",
-              "      <td>0.296429</td>\n",
-              "      <td>0.083626</td>\n",
-              "      <td>0.194114</td>\n",
+              "      <td>0.366706</td>\n",
+              "      <td>0.150286</td>\n",
+              "      <td>0.180182</td>\n",
+              "      <td>0.304589</td>\n",
+              "      <td>0.203891</td>\n",
+              "      <td>0.205121</td>\n",
+              "      <td>0.278896</td>\n",
+              "      <td>0.297128</td>\n",
+              "      <td>0.083817</td>\n",
+              "      <td>0.194349</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>BNB-USD</th>\n",
-              "      <td>0.149985</td>\n",
-              "      <td>0.230834</td>\n",
-              "      <td>0.122378</td>\n",
-              "      <td>0.150464</td>\n",
-              "      <td>0.138874</td>\n",
-              "      <td>0.108916</td>\n",
-              "      <td>0.173514</td>\n",
-              "      <td>0.171700</td>\n",
-              "      <td>0.046899</td>\n",
-              "      <td>0.093489</td>\n",
+              "      <td>0.150286</td>\n",
+              "      <td>0.231405</td>\n",
+              "      <td>0.122723</td>\n",
+              "      <td>0.150846</td>\n",
+              "      <td>0.139246</td>\n",
+              "      <td>0.109135</td>\n",
+              "      <td>0.173927</td>\n",
+              "      <td>0.172109</td>\n",
+              "      <td>0.047007</td>\n",
+              "      <td>0.093584</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>BTC-USD</th>\n",
-              "      <td>0.179671</td>\n",
-              "      <td>0.122378</td>\n",
-              "      <td>0.189577</td>\n",
-              "      <td>0.175989</td>\n",
-              "      <td>0.172308</td>\n",
-              "      <td>0.127100</td>\n",
-              "      <td>0.170163</td>\n",
-              "      <td>0.221531</td>\n",
-              "      <td>0.054940</td>\n",
-              "      <td>0.120379</td>\n",
+              "      <td>0.180182</td>\n",
+              "      <td>0.122723</td>\n",
+              "      <td>0.190093</td>\n",
+              "      <td>0.176476</td>\n",
+              "      <td>0.172781</td>\n",
+              "      <td>0.127462</td>\n",
+              "      <td>0.170640</td>\n",
+              "      <td>0.222148</td>\n",
+              "      <td>0.055094</td>\n",
+              "      <td>0.120736</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>DOT-USD</th>\n",
-              "      <td>0.303813</td>\n",
-              "      <td>0.150464</td>\n",
-              "      <td>0.175989</td>\n",
-              "      <td>0.385419</td>\n",
-              "      <td>0.206734</td>\n",
-              "      <td>0.192864</td>\n",
-              "      <td>0.287835</td>\n",
-              "      <td>0.333077</td>\n",
-              "      <td>0.085921</td>\n",
-              "      <td>0.192047</td>\n",
+              "      <td>0.304589</td>\n",
+              "      <td>0.150846</td>\n",
+              "      <td>0.176476</td>\n",
+              "      <td>0.386458</td>\n",
+              "      <td>0.207296</td>\n",
+              "      <td>0.193352</td>\n",
+              "      <td>0.288592</td>\n",
+              "      <td>0.333958</td>\n",
+              "      <td>0.086146</td>\n",
+              "      <td>0.192491</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>ETH-USD</th>\n",
-              "      <td>0.203350</td>\n",
-              "      <td>0.138874</td>\n",
-              "      <td>0.172308</td>\n",
-              "      <td>0.206734</td>\n",
-              "      <td>0.259265</td>\n",
-              "      <td>0.160001</td>\n",
-              "      <td>0.224481</td>\n",
-              "      <td>0.232270</td>\n",
-              "      <td>0.063282</td>\n",
-              "      <td>0.133110</td>\n",
+              "      <td>0.203891</td>\n",
+              "      <td>0.139246</td>\n",
+              "      <td>0.172781</td>\n",
+              "      <td>0.207296</td>\n",
+              "      <td>0.259972</td>\n",
+              "      <td>0.160428</td>\n",
+              "      <td>0.225087</td>\n",
+              "      <td>0.232898</td>\n",
+              "      <td>0.063453</td>\n",
+              "      <td>0.133452</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>LTC-USD</th>\n",
-              "      <td>0.204707</td>\n",
-              "      <td>0.108916</td>\n",
-              "      <td>0.127100</td>\n",
-              "      <td>0.192864</td>\n",
-              "      <td>0.160001</td>\n",
-              "      <td>0.270176</td>\n",
-              "      <td>0.190943</td>\n",
-              "      <td>0.191517</td>\n",
-              "      <td>0.055659</td>\n",
-              "      <td>0.158148</td>\n",
+              "      <td>0.205121</td>\n",
+              "      <td>0.109135</td>\n",
+              "      <td>0.127462</td>\n",
+              "      <td>0.193352</td>\n",
+              "      <td>0.160428</td>\n",
+              "      <td>0.270809</td>\n",
+              "      <td>0.191384</td>\n",
+              "      <td>0.191960</td>\n",
+              "      <td>0.055784</td>\n",
+              "      <td>0.158367</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>MATIC-USD</th>\n",
-              "      <td>0.278248</td>\n",
-              "      <td>0.173514</td>\n",
-              "      <td>0.170163</td>\n",
-              "      <td>0.287835</td>\n",
-              "      <td>0.224481</td>\n",
-              "      <td>0.190943</td>\n",
-              "      <td>0.414685</td>\n",
-              "      <td>0.290214</td>\n",
-              "      <td>0.075435</td>\n",
-              "      <td>0.180736</td>\n",
+              "      <td>0.278896</td>\n",
+              "      <td>0.173927</td>\n",
+              "      <td>0.170640</td>\n",
+              "      <td>0.288592</td>\n",
+              "      <td>0.225087</td>\n",
+              "      <td>0.191384</td>\n",
+              "      <td>0.415755</td>\n",
+              "      <td>0.290945</td>\n",
+              "      <td>0.075620</td>\n",
+              "      <td>0.181063</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>SOL-USD</th>\n",
-              "      <td>0.296429</td>\n",
-              "      <td>0.171700</td>\n",
-              "      <td>0.221531</td>\n",
-              "      <td>0.333077</td>\n",
-              "      <td>0.232270</td>\n",
-              "      <td>0.191517</td>\n",
-              "      <td>0.290214</td>\n",
-              "      <td>0.593740</td>\n",
-              "      <td>0.095372</td>\n",
-              "      <td>0.184846</td>\n",
+              "      <td>0.297128</td>\n",
+              "      <td>0.172109</td>\n",
+              "      <td>0.222148</td>\n",
+              "      <td>0.333958</td>\n",
+              "      <td>0.232898</td>\n",
+              "      <td>0.191960</td>\n",
+              "      <td>0.290945</td>\n",
+              "      <td>0.595302</td>\n",
+              "      <td>0.095612</td>\n",
+              "      <td>0.185187</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>TRX-USD</th>\n",
-              "      <td>0.083626</td>\n",
-              "      <td>0.046899</td>\n",
-              "      <td>0.054940</td>\n",
-              "      <td>0.085921</td>\n",
-              "      <td>0.063282</td>\n",
-              "      <td>0.055659</td>\n",
-              "      <td>0.075435</td>\n",
-              "      <td>0.095372</td>\n",
-              "      <td>0.098433</td>\n",
-              "      <td>0.057513</td>\n",
+              "      <td>0.083817</td>\n",
+              "      <td>0.047007</td>\n",
+              "      <td>0.055094</td>\n",
+              "      <td>0.086146</td>\n",
+              "      <td>0.063453</td>\n",
+              "      <td>0.055784</td>\n",
+              "      <td>0.075620</td>\n",
+              "      <td>0.095612</td>\n",
+              "      <td>0.098694</td>\n",
+              "      <td>0.057615</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>XRP-USD</th>\n",
-              "      <td>0.194114</td>\n",
-              "      <td>0.093489</td>\n",
-              "      <td>0.120379</td>\n",
-              "      <td>0.192047</td>\n",
-              "      <td>0.133110</td>\n",
-              "      <td>0.158148</td>\n",
-              "      <td>0.180736</td>\n",
-              "      <td>0.184846</td>\n",
-              "      <td>0.057513</td>\n",
-              "      <td>0.267617</td>\n",
+              "      <td>0.194349</td>\n",
+              "      <td>0.093584</td>\n",
+              "      <td>0.120736</td>\n",
+              "      <td>0.192491</td>\n",
+              "      <td>0.133452</td>\n",
+              "      <td>0.158367</td>\n",
+              "      <td>0.181063</td>\n",
+              "      <td>0.185187</td>\n",
+              "      <td>0.057615</td>\n",
+              "      <td>0.267912</td>\n",
               "    </tr>\n",
               "  </tbody>\n",
               "</table>\n",
@@ -1448,7 +1526,7 @@
               "    <div class=\"colab-df-buttons\">\n",
               "\n",
               "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-568f972d-ae84-4827-9205-28a3a1e18f22')\"\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5f529475-f8af-468d-a026-fa8af71ee37b')\"\n",
               "            title=\"Convert this dataframe to an interactive table.\"\n",
               "            style=\"display:none;\">\n",
               "\n",
@@ -1500,12 +1578,12 @@
               "\n",
               "    <script>\n",
               "      const buttonEl =\n",
-              "        document.querySelector('#df-568f972d-ae84-4827-9205-28a3a1e18f22 button.colab-df-convert');\n",
+              "        document.querySelector('#df-5f529475-f8af-468d-a026-fa8af71ee37b 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-568f972d-ae84-4827-9205-28a3a1e18f22');\n",
+              "        const element = document.querySelector('#df-5f529475-f8af-468d-a026-fa8af71ee37b');\n",
               "        const dataTable =\n",
               "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
               "                                                    [key], {});\n",
@@ -1525,8 +1603,8 @@
               "  </div>\n",
               "\n",
               "\n",
-              "<div id=\"df-0aaf3632-7f9e-46e3-86d0-137050a540d1\">\n",
-              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0aaf3632-7f9e-46e3-86d0-137050a540d1')\"\n",
+              "<div id=\"df-5bce963a-ca3c-40af-967f-704174cef78a\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-5bce963a-ca3c-40af-967f-704174cef78a')\"\n",
               "            title=\"Suggest charts\"\n",
               "            style=\"display:none;\">\n",
               "\n",
@@ -1645,14 +1723,14 @@
               "    }\n",
               "    (() => {\n",
               "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-0aaf3632-7f9e-46e3-86d0-137050a540d1 button');\n",
+              "        document.querySelector('#df-5bce963a-ca3c-40af-967f-704174cef78a button');\n",
               "      quickchartButtonEl.style.display =\n",
               "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
               "    })();\n",
               "  </script>\n",
               "</div>\n",
               "\n",
-              "  <div id=\"id_cd43f4fb-70df-4849-801f-88f17fed014b\">\n",
+              "  <div id=\"id_3138c98b-6be9-498c-a3ac-ffe3a42a51b9\">\n",
               "    <style>\n",
               "      .colab-df-generate {\n",
               "        background-color: #E8F0FE;\n",
@@ -1696,7 +1774,7 @@
               "    <script>\n",
               "      (() => {\n",
               "      const buttonEl =\n",
-              "        document.querySelector('#id_cd43f4fb-70df-4849-801f-88f17fed014b button.colab-df-generate');\n",
+              "        document.querySelector('#id_3138c98b-6be9-498c-a3ac-ffe3a42a51b9 button.colab-df-generate');\n",
               "      buttonEl.style.display =\n",
               "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
               "\n",
@@ -1713,42 +1791,492 @@
             "application/vnd.google.colaboratory.intrinsic+json": {
               "type": "dataframe",
               "variable_name": "covariance",
-              "summary": "{\n  \"name\": \"covariance\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"symbol\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"TRX-USD\",\n          \"BNB-USD\",\n          \"LTC-USD\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ADA-USD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.08399565915696695,\n        \"min\": 0.08362553819611086,\n        \"max\": 0.3659083337012825,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          0.08362553819611086,\n          0.14998516163196035,\n          0.2047069631024427\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BNB-USD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.05020004647894999,\n        \"min\": 0.046898875545741514,\n        \"max\": 0.2308336523892049,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          0.046898875545741514,\n          0.2308336523892049,\n          0.10891574953326724\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BTC-USD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04740882944655909,\n        \"min\": 0.05494028218787688,\n        \"max\": 0.2215314271485503,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          0.05494028218787688,\n          0.122377933753199,\n          0.1271004200389094\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DOT-USD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.09244576902400627,\n        \"min\": 0.08592093186029039,\n        \"max\": 0.38541925039220976,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          0.08592093186029039,\n          0.15046379153531964,\n          0.1928641592350875\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ETH-USD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.05794804124952129,\n        \"min\": 0.06328236381571051,\n        \"max\": 0.2592647482722747,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          0.06328236381571051,\n          0.13887388492595099,\n          0.16000095558799182\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LTC-USD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.05905788884342094,\n        \"min\": 0.055658596533961685,\n        \"max\": 0.2701762101903239,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          0.055658596533961685,\n          0.10891574953326724,\n          0.2701762101903239\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MATIC-USD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0931143715716287,\n        \"min\": 0.07543475955857806,\n        \"max\": 0.41468483972877146,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          0.07543475955857806,\n          0.17351366843631816,\n          0.19094304224077283\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SOL-USD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.13598906639151898,\n        \"min\": 0.09537234646176183,\n        \"max\": 0.5937402669570883,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          0.09537234646176183,\n          0.1717001397491843,\n          0.1915168174066942\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TRX-USD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.018435705985375535,\n        \"min\": 0.046898875545741514,\n        \"max\": 0.09843309328469316,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          0.09843309328469316,\n          0.046898875545741514,\n          0.055658596533961685\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"XRP-USD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.05963845253060871,\n        \"min\": 0.05751300817149977,\n        \"max\": 0.2676166222951364,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          0.05751300817149977,\n          0.09348869771452553,\n          0.1581484240854997\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+              "summary": "{\n  \"name\": \"covariance\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"symbol\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"TRX-USD\",\n          \"BNB-USD\",\n          \"LTC-USD\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ADA-USD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.08419792941723192,\n        \"min\": 0.0838165711742476,\n        \"max\": 0.36670604147106206,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          0.0838165711742476,\n          0.15028626508263157,\n          0.20512138740953018\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BNB-USD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.05033753143602621,\n        \"min\": 0.047006792196747846,\n        \"max\": 0.2314051405350048,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          0.047006792196747846,\n          0.2314051405350048,\n          0.1091350550550174\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BTC-USD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04753744415710665,\n        \"min\": 0.05509418774013776,\n        \"max\": 0.22214831696745932,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          0.05509418774013776,\n          0.12272305327668202,\n          0.1274618190134865\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DOT-USD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.09269535390671813,\n        \"min\": 0.08614584593812935,\n        \"max\": 0.3864581165922623,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          0.08614584593812935,\n          0.15084590749143012,\n          0.19335241933484426\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ETH-USD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.05810745048757783,\n        \"min\": 0.06345275246548801,\n        \"max\": 0.25997209569152796,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          0.06345275246548801,\n          0.139245809134133,\n          0.16042795625973533\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LTC-USD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.05919478941890474,\n        \"min\": 0.05578366873261023,\n        \"max\": 0.27080940309104257,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          0.05578366873261023,\n          0.1091350550550174,\n          0.27080940309104257\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MATIC-USD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.09336165890150132,\n        \"min\": 0.07561993114423143,\n        \"max\": 0.4157545421078801,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          0.07561993114423143,\n          0.17392703381920863,\n          0.1913835647642417\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SOL-USD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1363595110476115,\n        \"min\": 0.09561239112443014,\n        \"max\": 0.5953019208397278,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          0.09561239112443014,\n          0.17210936103624314,\n          0.1919600059080584\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TRX-USD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.01848799121692438,\n        \"min\": 0.047006792196747846,\n        \"max\": 0.09869415860549288,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          0.09869415860549288,\n          0.047006792196747846,\n          0.05578366873261023\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"XRP-USD\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.05970145542480502,\n        \"min\": 0.057614883985101284,\n        \"max\": 0.2679123979532293,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          0.057614883985101284,\n          0.09358410593039336,\n          0.15836736522902278\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
             }
           },
           "metadata": {},
-          "execution_count": 6
+          "execution_count": 5
         }
       ]
     },
     {
       "cell_type": "markdown",
       "source": [
-        "Calculate the efficient frontier using the Critical Line Algorithm"
+        "**Ledoit-Wolf Shrinkage**:is a technique used to improve the estimation of covariance matrices, especially when dealing with high-dimensional data (like multiple crypto assets) relative to the number of observations."
       ],
       "metadata": {
-        "id": "3IsFJL2x9qHV"
+        "id": "91CYiL0a6XaY"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Portfolio Optimization using Critical Line Algorithm (CLA)"
+      ],
+      "metadata": {
+        "id": "apy1KgWNPBew"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Portfolio risk, also known as portfolio volatility, is determined by calculating the variance of the returns of the assets. The variance is calculated using the below equation:\n",
+        "\n",
+        "$$ \\sigma^2= W⋅Cov⋅W^T $$\n",
+        "\n",
+        "Where $W$ is the weights of the asstes and $Cov$ is the covariance of the returns of the assets in the portfolio."
+      ],
+      "metadata": {
+        "id": "rBxNCT2b6oCH"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**The Critical Line Algorithm (CLA)** optimizes asset weights in a portfolio by calculating the risk and expected return for various combinations of these weights. It systematically varies the weights assigned to each asset, assessing how each combination affects overall portfolio performance. This process helps identify efficient portfolios that maximize expected returns for a given level of risk or minimize risk for a desired return."
+      ],
+      "metadata": {
+        "id": "xgfgtZldOGE6"
       }
     },
     {
       "cell_type": "code",
       "source": [
         "# Create a Critical Line Algorithm (CLA) object using the calculated expected returns and covariance matrix.\n",
-        "cla = CLA(portfolio_expected_returns, covariance)\n",
-        "\n",
-        "# Calculate the efficient frontier, obtaining the returns, volatility, and weights for various portfolios.\n",
-        "(returns, volatility, weights) = cla.efficient_frontier()"
+        "cla = CLA(assets_expected_returns, covariance)"
       ],
       "metadata": {
         "id": "NarHAjA25b61"
       },
-      "execution_count": 7,
+      "execution_count": 6,
       "outputs": []
     },
     {
       "cell_type": "markdown",
       "source": [
-        "Visualize the efficient frontier"
+        "**The Efficient Frontier** is a curve showing optimal portfolios with the best risk-return tradeoffs. It's important because it helps us identify portfolios that maximize return for a given risk level or minimize risk for a desired return."
+      ],
+      "metadata": {
+        "id": "Cgz_DmDcPXpN"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Calculate the efficient frontier, obtaining the returns, volatility, and weights for various portfolios.\n",
+        "(returns, volatility, weights) = cla.efficient_frontier()\n",
+        "\n",
+        "efficient_frontier_portfolios= pd.DataFrame([returns, volatility, weights]).T\n",
+        "efficient_frontier_portfolios.columns=['returns', 'volatility', 'weights']\n",
+        "\n",
+        "efficient_frontier_portfolios"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 424
+        },
+        "id": "VMD3zD-p9AfE",
+        "outputId": "c43b28ee-757d-447a-f128-6cda567b1b1b"
+      },
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "     returns volatility                                            weights\n",
+              "0   2.288214   0.771558  [[0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0....\n",
+              "1   2.288214   0.771558  [[0.0], [8.540177112501205e-18], [0.0], [0.0],...\n",
+              "2   2.288214   0.771558  [[0.0], [1.708035422500241e-17], [0.0], [0.0],...\n",
+              "3   2.288214   0.771558  [[0.0], [2.5620531337503614e-17], [0.0], [0.0]...\n",
+              "4   2.288214   0.771558  [[0.0], [3.416070845000482e-17], [0.0], [0.0],...\n",
+              "..       ...        ...                                                ...\n",
+              "74  0.699686   0.289411  [[0.0], [0.14064237710852637], [0.110488877515...\n",
+              "75  0.695988   0.289372  [[0.0], [0.1384244599412712], [0.1077026598376...\n",
+              "76  0.692291   0.289344  [[0.0], [0.136206542774016], [0.10491644215937...\n",
+              "77  0.688593   0.289328  [[0.0], [0.1339886256067608], [0.1021302244811...\n",
+              "78  0.684895   0.289322  [[0.0], [0.1317707084395056], [0.0993440068028...\n",
+              "\n",
+              "[79 rows x 3 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-3f1accf5-9e79-486d-8406-613a71d5af36\" 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>returns</th>\n",
+              "      <th>volatility</th>\n",
+              "      <th>weights</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>2.288214</td>\n",
+              "      <td>0.771558</td>\n",
+              "      <td>[[0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0....</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2.288214</td>\n",
+              "      <td>0.771558</td>\n",
+              "      <td>[[0.0], [8.540177112501205e-18], [0.0], [0.0],...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>2.288214</td>\n",
+              "      <td>0.771558</td>\n",
+              "      <td>[[0.0], [1.708035422500241e-17], [0.0], [0.0],...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>2.288214</td>\n",
+              "      <td>0.771558</td>\n",
+              "      <td>[[0.0], [2.5620531337503614e-17], [0.0], [0.0]...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>2.288214</td>\n",
+              "      <td>0.771558</td>\n",
+              "      <td>[[0.0], [3.416070845000482e-17], [0.0], [0.0],...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>74</th>\n",
+              "      <td>0.699686</td>\n",
+              "      <td>0.289411</td>\n",
+              "      <td>[[0.0], [0.14064237710852637], [0.110488877515...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75</th>\n",
+              "      <td>0.695988</td>\n",
+              "      <td>0.289372</td>\n",
+              "      <td>[[0.0], [0.1384244599412712], [0.1077026598376...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>76</th>\n",
+              "      <td>0.692291</td>\n",
+              "      <td>0.289344</td>\n",
+              "      <td>[[0.0], [0.136206542774016], [0.10491644215937...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>77</th>\n",
+              "      <td>0.688593</td>\n",
+              "      <td>0.289328</td>\n",
+              "      <td>[[0.0], [0.1339886256067608], [0.1021302244811...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>78</th>\n",
+              "      <td>0.684895</td>\n",
+              "      <td>0.289322</td>\n",
+              "      <td>[[0.0], [0.1317707084395056], [0.0993440068028...</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>79 rows × 3 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3f1accf5-9e79-486d-8406-613a71d5af36')\"\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 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\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",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\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-3f1accf5-9e79-486d-8406-613a71d5af36 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-3f1accf5-9e79-486d-8406-613a71d5af36');\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",
+              "\n",
+              "\n",
+              "<div id=\"df-efe59416-aa05-440d-bc9d-21fd3845840b\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-efe59416-aa05-440d-bc9d-21fd3845840b')\"\n",
+              "            title=\"Suggest charts\"\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",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-efe59416-aa05-440d-bc9d-21fd3845840b button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "  <div id=\"id_63d7d79a-e36f-4670-be85-bb78fa882def\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\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-generate: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-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate: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",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('efficient_frontier_portfolios')\"\n",
+              "            title=\"Generate code using this dataframe.\"\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",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_63d7d79a-e36f-4670-be85-bb78fa882def button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('efficient_frontier_portfolios');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "efficient_frontier_portfolios",
+              "summary": "{\n  \"name\": \"efficient_frontier_portfolios\",\n  \"rows\": 79,\n  \"fields\": [\n    {\n      \"column\": \"returns\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 0.6848950166508636,\n        \"max\": 2.2882141236911924,\n        \"num_unique_values\": 66,\n        \"samples\": [\n          0.72557090559056,\n          0.6959884409071444,\n          2.2882141236911924\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"volatility\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 0.2893221746578987,\n        \"max\": 0.7715581124191021,\n        \"num_unique_values\": 66,\n        \"samples\": [\n          0.28999332370675096,\n          0.2893721484198184,\n          0.7715581124191021\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"weights\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 7
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Visualize the efficient frontier"
       ],
       "metadata": {
         "id": "AaQTTEhZ70e7"
@@ -1770,7 +2298,7 @@
           "height": 465
         },
         "id": "Kt1n-3HQ1gj7",
-        "outputId": "cea126b2-e715-4995-bbdf-e201a35d0cfd"
+        "outputId": "686d5698-8806-4c7e-ba07-46b26d76add0"
       },
       "execution_count": 8,
       "outputs": [
@@ -1780,7 +2308,7 @@
             "text/plain": [
               "<Figure size 1000x500 with 1 Axes>"
             ],
-            "image/png": "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\n"
+            "image/png": "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\n"
           },
           "metadata": {}
         }
@@ -1798,8 +2326,9 @@
     {
       "cell_type": "code",
       "source": [
+        "optimized_weight=np.array(list(cla.max_sharpe().values()))\n",
+        "optimized_weight= np.round(optimized_weight, 4)\n",
         "\n",
-        "optimized_weight=weights[volatility.index(min(volatility))]\n",
         "pie_df=pd.DataFrame(optimized_weight*100, index=prices.columns, columns=['weights'])\n",
         "pie_df= pie_df.sort_values(by=['weights'], ascending=False)\n",
         "pie_df"
@@ -1810,7 +2339,7 @@
           "height": 394
         },
         "id": "FzvioR9k11Tx",
-        "outputId": "16dd12f8-ee60-44ab-eaa0-1c4dc2482a01"
+        "outputId": "9eae848f-52ba-47bc-c17f-4cf753c82504"
       },
       "execution_count": 9,
       "outputs": [
@@ -1818,22 +2347,22 @@
           "output_type": "execute_result",
           "data": {
             "text/plain": [
-              "             weights\n",
-              "symbol              \n",
-              "TRX-USD    67.715856\n",
-              "BNB-USD    13.161715\n",
-              "BTC-USD     9.982129\n",
-              "XRP-USD     5.275661\n",
-              "LTC-USD     3.864639\n",
-              "ADA-USD     0.000000\n",
-              "DOT-USD     0.000000\n",
-              "ETH-USD     0.000000\n",
-              "MATIC-USD   0.000000\n",
-              "SOL-USD     0.000000"
+              "           weights\n",
+              "symbol            \n",
+              "SOL-USD      37.11\n",
+              "TRX-USD      32.71\n",
+              "BNB-USD      30.17\n",
+              "ADA-USD       0.00\n",
+              "BTC-USD      -0.00\n",
+              "DOT-USD       0.00\n",
+              "ETH-USD       0.00\n",
+              "LTC-USD       0.00\n",
+              "MATIC-USD     0.00\n",
+              "XRP-USD       0.00"
             ],
             "text/html": [
               "\n",
-              "  <div id=\"df-21f41f4b-1262-434e-93c8-198879e2ff12\" class=\"colab-df-container\">\n",
+              "  <div id=\"df-64fc90f9-a1f2-4bd3-9bdc-417d5bbc22e9\" class=\"colab-df-container\">\n",
               "    <div>\n",
               "<style scoped>\n",
               "    .dataframe tbody tr th:only-of-type {\n",
@@ -1861,44 +2390,44 @@
               "  </thead>\n",
               "  <tbody>\n",
               "    <tr>\n",
-              "      <th>TRX-USD</th>\n",
-              "      <td>67.715856</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>BNB-USD</th>\n",
-              "      <td>13.161715</td>\n",
+              "      <th>SOL-USD</th>\n",
+              "      <td>37.11</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>BTC-USD</th>\n",
-              "      <td>9.982129</td>\n",
+              "      <th>TRX-USD</th>\n",
+              "      <td>32.71</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>XRP-USD</th>\n",
-              "      <td>5.275661</td>\n",
+              "      <th>BNB-USD</th>\n",
+              "      <td>30.17</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>LTC-USD</th>\n",
-              "      <td>3.864639</td>\n",
+              "      <th>ADA-USD</th>\n",
+              "      <td>0.00</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>ADA-USD</th>\n",
-              "      <td>0.000000</td>\n",
+              "      <th>BTC-USD</th>\n",
+              "      <td>-0.00</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>DOT-USD</th>\n",
-              "      <td>0.000000</td>\n",
+              "      <td>0.00</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>ETH-USD</th>\n",
-              "      <td>0.000000</td>\n",
+              "      <td>0.00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>LTC-USD</th>\n",
+              "      <td>0.00</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>MATIC-USD</th>\n",
-              "      <td>0.000000</td>\n",
+              "      <td>0.00</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>SOL-USD</th>\n",
-              "      <td>0.000000</td>\n",
+              "      <th>XRP-USD</th>\n",
+              "      <td>0.00</td>\n",
               "    </tr>\n",
               "  </tbody>\n",
               "</table>\n",
@@ -1906,7 +2435,7 @@
               "    <div class=\"colab-df-buttons\">\n",
               "\n",
               "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-21f41f4b-1262-434e-93c8-198879e2ff12')\"\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-64fc90f9-a1f2-4bd3-9bdc-417d5bbc22e9')\"\n",
               "            title=\"Convert this dataframe to an interactive table.\"\n",
               "            style=\"display:none;\">\n",
               "\n",
@@ -1958,12 +2487,12 @@
               "\n",
               "    <script>\n",
               "      const buttonEl =\n",
-              "        document.querySelector('#df-21f41f4b-1262-434e-93c8-198879e2ff12 button.colab-df-convert');\n",
+              "        document.querySelector('#df-64fc90f9-a1f2-4bd3-9bdc-417d5bbc22e9 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-21f41f4b-1262-434e-93c8-198879e2ff12');\n",
+              "        const element = document.querySelector('#df-64fc90f9-a1f2-4bd3-9bdc-417d5bbc22e9');\n",
               "        const dataTable =\n",
               "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
               "                                                    [key], {});\n",
@@ -1983,8 +2512,8 @@
               "  </div>\n",
               "\n",
               "\n",
-              "<div id=\"df-df605205-cb64-4e00-9ad2-ff496c6c4740\">\n",
-              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-df605205-cb64-4e00-9ad2-ff496c6c4740')\"\n",
+              "<div id=\"df-186b4ecf-aac1-46a3-a78b-119b061ba683\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-186b4ecf-aac1-46a3-a78b-119b061ba683')\"\n",
               "            title=\"Suggest charts\"\n",
               "            style=\"display:none;\">\n",
               "\n",
@@ -2103,14 +2632,14 @@
               "    }\n",
               "    (() => {\n",
               "      let quickchartButtonEl =\n",
-              "        document.querySelector('#df-df605205-cb64-4e00-9ad2-ff496c6c4740 button');\n",
+              "        document.querySelector('#df-186b4ecf-aac1-46a3-a78b-119b061ba683 button');\n",
               "      quickchartButtonEl.style.display =\n",
               "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
               "    })();\n",
               "  </script>\n",
               "</div>\n",
               "\n",
-              "  <div id=\"id_a9b26081-d038-46c6-8ede-de59ec2c422f\">\n",
+              "  <div id=\"id_72584294-6040-4d1c-828e-f1d131bc9ad1\">\n",
               "    <style>\n",
               "      .colab-df-generate {\n",
               "        background-color: #E8F0FE;\n",
@@ -2154,7 +2683,7 @@
               "    <script>\n",
               "      (() => {\n",
               "      const buttonEl =\n",
-              "        document.querySelector('#id_a9b26081-d038-46c6-8ede-de59ec2c422f button.colab-df-generate');\n",
+              "        document.querySelector('#id_72584294-6040-4d1c-828e-f1d131bc9ad1 button.colab-df-generate');\n",
               "      buttonEl.style.display =\n",
               "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
               "\n",
@@ -2171,7 +2700,7 @@
             "application/vnd.google.colaboratory.intrinsic+json": {
               "type": "dataframe",
               "variable_name": "pie_df",
-              "summary": "{\n  \"name\": \"pie_df\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"symbol\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"MATIC-USD\",\n          \"BNB-USD\",\n          \"ADA-USD\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"weights\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 20.819539048319967,\n        \"min\": 0.0,\n        \"max\": 67.71585619449834,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          67.71585619449834,\n          13.161715147317723,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+              "summary": "{\n  \"name\": \"pie_df\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"symbol\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"MATIC-USD\",\n          \"TRX-USD\",\n          \"DOT-USD\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"weights\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16.184783972059133,\n        \"min\": 0.0,\n        \"max\": 37.11,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          32.71,\n          0.0,\n          37.11\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
             }
           },
           "metadata": {},
@@ -2200,10 +2729,10 @@
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 664
+          "height": 638
         },
         "id": "Ad1Ncn3N2iRj",
-        "outputId": "3f635343-2f6d-4966-e15f-13ac9e15bb40"
+        "outputId": "a6919539-1140-48d5-c244-0ea49c1e5c8c"
       },
       "execution_count": 10,
       "outputs": [
@@ -2213,20 +2742,20 @@
             "text/plain": [
               "<Figure size 640x480 with 1 Axes>"
             ],
-            "image/png": "iVBORw0KGgoAAAANSUhEUgAAArgAAAKHCAYAAABq7DtXAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAACK+klEQVR4nOzdd3SU1doF8P1Oz2TSeyO9UEPvvYsUAcUOWFCwot5rv1Y+27W3q6iI2BsCYqMqKL2E3kJICOm9J1O/P4LRSICUmTlT9m+tLGCYzOxQkp0z5z2PZLFYLCAiIiIichEy0QGIiIiIiKyJBZeIiIiIXAoLLhERERG5FBZcIiIiInIpLLhERERE5FJYcImIiIjIpbDgEhEREZFLYcElIiIiIpeiEB2AiNyPxWJBea0BJTV61OlNqDOYUP/nm9H8188NJtQbzGgwmmA0W2AyWWCyWGA2W2A0W2C2WGA8e5vJbIEEwEMlh4dSAa1Kfvbn8n/8XAEP1dnb/vZ7WpUCcpkk+o+GiIisgAWXiKymotaAouoGFFU1oLiFH4ur9SiqakBJTQMMJscboqiSy/4qvyo5/LUqhPhoEOatQaiPBmE+Hmd/1CDEW8NCTETkoCSO6iWiizGZLcgqqcHJohrklNU2FdXG0nq2wNbooTeaRUe1G7lMQqBOhVAfj78V4MYfQ73/KsMqBXeCERHZGwsuETWp05twsqgaJ4uqkV7414+ZxbXQm9ynvFqLJAH+WhVCfTSI8PVAUogXUsK8kBLqhdhAHVeAiYhshAWXyA2V1eiRfra8/vl2sqgaOeV14GcE+1ApZEgI0jUV3uRQb6SEeiHEWyM6GhGR02PBJXJhlfUG7Msux/GCsyW2sBrpRdUordGLjkbn4adVIjnUCymh3md/9EJyqBe0Kl4yQUTUWiy4RC4ks7gGu7LKsDurDHuyynCisApm/g93epIERPlpkRzqhc6hXkgJ80bvTn4I9eFqLxFRS1hwiZxUg9GEgzkV2JV5ttCeLkNxNVdm3Ul0gBYDYv0xIDYAA+L8EemnFR2JiMghsOASOYni6gbsymwssruzynAgp8KtTi2gi4vw9cCAOH8MPFt4owM8RUciIhKCBZfIAZnNFhwvrMLus9sNdmeVIaukVnQscjKh3hoMiPtrhTc+SCc6EhGRXbDgEjmI8lo9fj1WhHVHCrD5RDEq6gyiI5GLCfJSo3+sf9O2hqQQHSSJR5URkethwSUSKL2wCuuOFGLDkULsPl0GE68IIzvy91Shf4w/RqcEY1yXEPh5qkRHIiKyChZcIjsymMzYcaoU644UYMPRQm47IIchl0noF+OHCV1DMaFrKMJ9PURHIiJqNxZcIhsrrdFj49FCbDhaiE3Hi1DVYBQdieiiekT6NJXdhGDu3SUi58KCS2QDxwuqGldpjxRiz+kynkVLTi0+yLOp7KZG+YqOQ0R0UTLRARydJEkXfHviiSeQmZnZ7DZ/f3+MGDECmzdvbvZYV155Jfr37w+TydR0m8FgQJ8+fXDttddeMMOKFSvOuX3u3Lm47LLLmn596tQpXHPNNQgPD4dGo0FkZCSmTZuGo0ePtvjxeHp6IjExEXPnzsXu3bvb/4dEMJst2JJejCdWHcKwFzZg/Cub8MLPx7Ari+WWnN/Johq8/etJTHvrDwx+dj2eWHUIW04Wc884ETkszn68iLy8vKaff/nll3jsscdw7Nixptt0Oh2Ki4sBAOvWrUPXrl1RXFyM//u//8PkyZNx/PhxhISEAADefvttdO3aFc899xweeeQRAMDTTz+NvLw8rFu3rkM5DQYDxo0bh+TkZCxfvhxhYWE4c+YMfvrpJ5SXlze774cffoiJEyeivr4ex48fx+LFizFgwAAsWbIEs2fP7lAOd3OquAbf7M7Gd3tykFtRLzoOkc3lVtRj6ZZMLN2SCX9PFcakBGNC11AMSwqEWiEXHY+ICAAL7kWFhoY2/dzHxweSJDW7DUBTwQ0ICEBoaChCQ0Px8MMP44svvsD27dsxderUpt9fvHgxrrjiCkyZMgV6vR7PPvssVq5cCT8/vw7lPHToEE6ePIn169cjOjoaABAdHY0hQ4acc19fX9+mjyEmJgbjx4/HnDlzcMcdd2DKlCkdzuLqKusN+H5fLr7dfQZ7TpeLjkMkTGmNHl/vPoOvd5+Bp0qO0Z1DMKtvJIYmBPL4MSISigXXBurq6rBs2TIAgErV/NidqVOn4qqrrsLs2bNhMBgwZ84cTJo0qcPPGRQUBJlMhm+++QYLFy6EXN62lZR77rkHy5Ytw9q1azFr1qwO53E1JrMFm08U4ZvdZ7D2cAEaOEGMqJkavQnf78vF9/tyEenngVl9ozCrbxRCfTSioxGRG2LBtaLBgwdDJpOhtrYWFosFffr0wZgxY86536uvvoqIiAh4e3vj5ZdftspzR0RE4PXXX8f999+PJ598En379sWoUaNw7bXXIi4u7qLvn5KSAgDIzMy0Sh5XcaKgCt/sOYMVe3NQUNkgOg6RUzhTVoeX1x7Ha+tPYERSEK7sF4UxKcFQyHnZBxHZBz/bWNGXX36JvXv34ttvv0VCQgKWLl0KpVJ5zv0+//xzSJKE4uLiZheAPfPMM9DpdE1vp0+fbtPz33777cjPz8enn36KQYMG4euvv0bXrl2xdu3ai77vn4dp8GXFxoliy7ZmYuqbv2PcK5vw7m8ZLLdE7WAyW7DhaCFu/Xg3Bj+3Ac//fBRZJTWiYxGRG+AKrhVFRUUhMTERiYmJMBqNmD59Og4ePAi1Wt10n4yMDNx///343//+h40bN2Lu3LnYu3cv1Go15s+f32x7QHh4OADAy8sLFRUV5zxfeXk5fHx8mt3m5eWFKVOmYMqUKVi0aBEmTJiARYsWYdy4cRfMfuTIEQBAbGxsuz9+Z2Y0mfHrsSJ8u+cM1h8phN7ELQhE1lRY1YD//XoS7/x2EgNjA3BV/yhM7BbKC9OIyCa4gmsjl19+ORQKBd5+++2m28xmM+bOnYsxY8Zg9uzZePXVV1FVVYXHHnsMAODv74+EhISmN4Wi8fuP5OTkc47xMplM2LdvH5KSks6bQZIkpKSkoKbm4ismr776Kry9vTF27Nj2fLhO63RJLRatPoyBz27Azct24aeD+Sy3RDZksQBbM0pw9xdpGPBM45Fjx/KrRMciIhfDFVwbkSQJd911F5544gnceuut0Gq1eO2113Do0CEcOnQIQOOpDO+//z4mT56MmTNnon///i0+1r333oubbroJKSkpGDduHGpqavDGG2+grKwMN998MwAgLS0Njz/+OK6//np06dIFKpUKv/32G5YsWYIHHnig2eOVl5cjPz8fDQ0NOH78ON59912sWLECy5Ytg6+vr03/XBzF7qwyvL85A78cyuc5tUSClNcamo4c6xnli6v6RWFKajg81fzSREQdw0lmbbB06VIsXLjwnHNlMzMzERsbi71796Jnz55Nt9fW1iIyMhIPPvggLrvsMvTs2RPvv/8+rrnmmmbvf8stt+D3339v2qrQks8++wwvv/wyjh8/Dq1Wiz59+uDZZ59Fjx49ADQeVfb0009jw4YNTYMnYmJiMGfOHNxzzz2QyRoX6/++x1aj0SAiIgJDhw7FXXfdhd69e1vhT8lxmc0WrDmcj8WbMni8F5GD0qkVmJIahpuGxiIh2Et0HCJyUiy45PLq9CZ8vTsbH/x+ClkltaLjEFErSBIwrnMIbhuVgJ4cD0xEbcSCSy6rsKoeH23JxKfbT6O81iA6DhG10+D4ACwYGY9hiUGioxCRk2DBJZdzvKAK723KwMp9udBzIAORy+gR6YP5I+IxsWsoZDIeaUhE58eCSy7j9xPFeG9zBn47XiQ6ChHZUFyQJ+YPj8f03hFQcngEEbWABZecmsFkxvf7cvHe5lM4klcpOg4R2VGYjwY3DY3FNQM6QaviyQtE9BcWXHJKdXoTlm3NxId/ZCK/sl50HCISyFerxJxBMbhhSAx8tSrRcYjIAbDgklMxmMz4YsdpvL4hHUVVHJ9LRH/RquS4ql8nzBseizAfD9FxiEggFlxyChaLBav25eLltcd51BcRXZBKLsO0nuGYPzIe8UE60XGISAAWXHJ4vx4rxH9/OYZDudxjS0StJ5dJuLx3JO4Zl4RQH43oOERkRyy45LD2ni7D8z8fxbaMUtFRiMiJaZQy3DAkFgtGxsNboxQdh4jsgAWXHE56YRVe+PkY1hwuEB2FiFyIn1aJ20cl4PpB0VAr5KLjEJENseCSw8gtr8Mra49j+d4cmMz8Z0lEthHh64H7xifhsp4RHBhB5KJYcEm48lo93tqYjmVbs9DAyWNEZCedw7zx8KQUjgAmckEsuCRMrd6IDzafwuLNGaiqN4qOQ0RuanRKMB65tDNPXCByISy4ZHcGkxmf7ziNN3iWLRE5CKVcwnUDo7FwTBJ8tLwQjcjZseCSXW3LKMF/VhzEicJq0VGIiM7hp1Vi4dgkXDugExRymeg4RNROLLhkF0VVDXjmxyP4bm+O6ChERBeVGKzDo5O7YEQS9+cSOSMWXLIpk9mCj7dm4qW1x7nPloiczqjkIDw5tRs6BWhFRyGiNmDBJZvZe7oMj644yAlkROTUPJRy3Dc+CTcMiYWcx4oROQUWXLK6sho9nv/5KL7clQ3+6yIiV5Ea5YsXZvZAcqiX6ChEdBEsuGRV3+09g6dXH0FpjV50FCIiq1PKJSwYEY87RidCpeBFaESOigWXrCK7tBYPf3cAm08Ui45CRGRzicE6PDezB/pE+4mOQkQtYMGlDjGZLfjwj1N4ee1x1OpNouMQEdmNTAJmD4rB/ROToVUpRMchor9hwaV2O5RbgYeWH8D+MxWioxARCRPp54FnpnfHcB4pRuQwWHCpzeoNJry67gTe35wBo5n/fIiIAGBG7wg8NrkLfLUq0VGI3B4LLrXJntNluPfLNGSW1IqOQkTkcAJ1ajwxtQsm9wgXHYXIrbHgUquYzRa8uTEdr68/wVVbIqKLGNclBIsu64YQb43oKERuiQWXLiq3vA4Lv0zDjlOloqMQETkNL40CD0/qjKv6RUGSOCCCyJ5YcOmCfjyQh4eWH0BFnUF0FCIipzQsMRAvz+qJIC+16ChEboMFl1pUqzfiyVWH8eWubNFRiIicXpCXGq9d2RODEwJFRyFyCyy4dI6DORW464u9yCiqER2FiMhlyCTgjtGJWDgmETIZtywQ2RILLjWxWCx4b3MGXvzlOPQms+g4REQuaWCcP16/qheCeQEakc2w4BIAoLCyHvd9vY+jdomI7CBQp8LLs3pyOASRjbDgEtYdLsD93+5HaY1edBQiIrchScCCEfG4b3wy5NyyQGRVLLhurN5gwv/9cAQfb8sSHYWIyG31i/HD61f3QpiPh+goRC6DBddNHcuvwl2f78WxgirRUYiI3J6fVomXZ/XEqJRg0VGIXAILrhtatjUT//fDETQYeSEZEZGjkCRg3rA4/HtCMpRymeg4RE6NBdeNNBhNeGj5ASzfkyM6ChERnUevTr544+peiPTTio5C5LRYcN1EYVU9bv14N/aeLhcdhYiILsLHQ4n/Xt4D47uGio5C5JRYcN3AwZwKzFu2C3kV9aKjEBFRG8wdHIOHJ3WGSsEtC0RtwYLr4lbvz8W/v96POoNJdBQiImqH/rH+WHx9H/hqVaKjEDkNFlwXZbFY8Mra43h9Q7roKERE1EGxgZ5YMrcfYgM9RUchcgosuC6oVm/EvV/uw8+H8kVHISIiK/HTKvHOdX0wIC5AdBQih8eC62Jyyutw80e7cCSvUnQUIiKyMpVchudmdseM3pGioxA5NBZcF7IrsxTzP9mN4mqO3CUicmV3jk7AveOSIEkc8UvUEhZcF/HVzmw8uuIg9CYObyAicgdTUsPx4hU9oFbIRUchcjgsuE7OZLbg/344giV/nBIdhYiI7KxPtB8WX98HATq16ChEDoUF14lV1Blw5+d7sel4kegoREQkSCd/LZbM7YeEYJ3oKEQOgwXXSWUUVePmj3Yho7hGdBQiIhLMW6PAO9f1weCEQNFRiBwCC64T2nGqFDd/tBOV9UbRUYiIyEEo5RL+77LumNUvSnQUIuFYcJ3Mb8eLcOvHu1Bv4MVkRER0rvkj4vHAxGSesEBujQXXifx0IA93f5HGkxKIiOiCJnUPxcuzekKj5AkL5J5YcJ3E17uy8eDyAzCZ+ddFREQXlxrliyVz+vKEBXJLLLhOYOkfp/Dk6sPg3xQREbVFYrAOn84bgGAvjegoRHbFguvg3lh/Ai+tPS46BhEROam4IE98Pm8gQrxZcsl9sOA6sGd/PIJ3N2WIjkFERE4uOkCLz+YNRISvh+goRHbBguuAzGYLHl15EJ9tPy06ChERuYhIPw98Pm8govy1oqMQ2RwLroMxmsy47+t9WJmWKzoKERG5mHAfDT6bNxAxgZ6ioxDZFAuuA2kwmnD7p3ux7kiB6ChEROSiQrzV+PTmgRztSy6NBddB1DQYMW/ZLmw5WSI6ChERubhAnRqfzRuApBAv0VGIbIIF1wFU1Bowd+kO7D1dLjoKERG5CX9PFT65aQC6hHuLjkJkdSy4ghVXN+D6D3bgSF6l6ChERORmfLVKfHzjAHSP9BEdhciqWHAFyquow7XvbUdGcY3oKERE5Ka8NAosu7E/enXyEx2FyGpkogO4q5LqBlz7PsstERGJVVVvxPUf7MCuzFLRUYishgVXgKp6A+Z8uAMZRSy3REQkXnWDEbOX7MBWXuhMLoIF187qDSbc9NEuHMzhnlsiInIctXoTbli6A5tPFImOQtRhLLh2ZDSZccdne7DjFF8GIiIix1NvMOPmj3ZhS3qx6ChEHcKCaycWiwX//mY/1h0pFB2FiIjovBqMZtz68W4cyq0QHYWo3Vhw7eTJ7w/ju705omMQERFdVFWDEXM/3Ins0lrRUYjahQXXDl5ZexxLt2SKjkFERNRqRVUNmLNkB0pr9KKjELUZC66NLf3jFF5bf0J0DCIiojbLKK7BjUt3ok5vEh2FqE1YcG3ou71n8OTqw6JjEBERtVtadjlu/2wPjCaz6ChErcaCayPrDhfg31/vB+fEERGRs9twtBAPLT8gOgZRq7Hg2sC2jJLG73bNbLdEROQavt59Bi/+ckx0DKJWYcG1soM5FZj30S40GPlSDhERuZY3N6bj462ZomMQXRQLrhWdLKrGnCU7UNVgFB2FiIjIJh5fdQg/H8wTHYPoglhwrSS3vA7Xv78dJTxOhYiIXJjZAtz9RRqncpJDY8G1guoGI+Z+uAO5FfWioxAREdlcg9GMmz/aiWP5VaKjELWIBbeDLBYL7vkyDccLqkVHISIispvK+rOLO+V1oqMQnYMFt4NeWXscaw8XiI5BRERkd3kV9ZizZAcqag2ioxA1w4LbAT8dyMMbG9NFxyAiIhLmRGE1bl62E/UGTjsjx8GC205H8ytx39f7OMiBiIjc3s7MMjz8HQdBkONgwW2Hsho95i3bhVrO5iYiIgIALN+TwzNyyWGw4LaR0WTG7Z/tQXYpN9UTERH93VOrD2N3VpnoGEQsuG216Icj2HKyRHQMIiIih2MwWXD7p3tQVNUgOgq5ORbcNvhqVzaWbskUHYOIiMhh5VfW447P9sBo4sh6EocFt5X2nC7DoysOio5BRETk8LafKsVzPx0VHYPcGAtuKxRU1mP+x7uhN/K7USIiotZ4//dT+H5frugY5KZYcC+iwWjCLR/vRiH3ExEREbXJA9/ux/ECjvMl+2PBvYiHlx/Evuxy0TGIiIicTq3ehPkf70ZVPSedkX2x4F7AB7+fwrd7zoiOQURE5LQyimtw31f7YOFkJLIjFtzz+CO9GM/8eER0DCIiIqe35nAB3v71pOgY5EZYcFuQW16H2z/bA5OZ320SERFZw0trjmHziSLRMchNsOD+g9lswb1fpaG8lvuFiIiIrMVsAe76fC/OlNWKjkJugAX3HxZvzsC2jFLRMYiIiFxOWa0BCz7ZgwajSXQUcnEsuH9zMKcCL685LjoGERGRyzqQU4HHVhwSHYNcHAvuWfUGE+7+Yi/0HC1IRERkU1/uysZXu7JFxyAXxoJ71qIfDuNkUY3oGERERG7hqe8Pcz8u2QwLLoD1RwrwybbTomMQERG5jeoGI/799X6ej0s24fYFt7i6AQ98u190DCIiIrezNaMES7dkio5BLsjtC+793+xHcbVedAwiIiK39PzPR3GyqFp0DHIxbl1wP96aiQ1HC0XHICIiclv1BjPu+2ofhyuRVbltwU0vrML/cRQvERGRcGnZ5XjnN47yJetxy4KrN5px9xdpqDfwSDAiIiJH8Nq6EzicWyk6BrkItyy4L609hkP8T0REROQw9CYz7v0qDXojF5+o49yu4G49WYL3NmWIjkFERET/cDS/Cq+u40RR6ji3KrgVtQbc+1UauI+diIjIMb27KQN7TpeJjkFOzq0K7sMrDiCvol50DCIiIjoPk9mCf321D3V6k+go5MTcpuCuTMvBD/vzRMcgIiKii8gorsHzPx8VHYOcmFsU3Io6A55ezSPBiIiInMVHWzOxJb1YdAxyUm5RcF/85RiKqxtExyAiIqJWsliAf3+zH1X1BtFRyAm5fMHdf6Ycn27PEh2DiIiI2iinvA5Prz4sOgY5IZcuuGazBY98d5CnJhARETmpr3adwYajBaJjkJNx6YL7yfYsHMipEB2DiIiIOuA/Kw6h3sBTFaj1XLbgFlU14L+/HBMdg4iIiDoop7wOb25IFx2DnIhCdABb+b8fDqOq3ig6Bjk4Y1Uxyn9dirqM3bAYG6DwDUPApIVQhyUCALKen9zi+/mOvAE+A2a2+Htn/ncjTJWF59yu63UpAsYvAACUrn8PNQfXQ1Jq4DtiDnRdRzXdr+bo76g5uB7Blz/e0Q+PiMhlLN6UgZl9IhEb6Ck6CjkBlyy4W04WY0VarugY5OBM9dXI/+R+aDr1QPAVT0Cm9YGxLBcyja7pPpG3f9zsfeoydqHkp9ehTR5y3scNm/MKYP5rlrq+OAuFXz4Kz5TG96lN346aI78heNbTMJblouSn1+AR2xtyrQ/MDTUo37QMIVctsvJHS0Tk3PQmMx5beRAf3zRAdBRyAi5XcPVGM/6z4qDoGOQEKrd9A4V3IAIvXdh0m9I3tNl95Dq/Zr+uTd8OTXT3c+7X7H20Ps1+Xbftayh8w6CO6g4AMJRkQxPVHeqwRKjDElG6/j0YKwog1/qgbOOH8Oo1CQrv4A5+dERErmfziWL8eCAPk7qHiY5CDs7l9uC+tzkDJ4tqRMcgJ1CXvh2q0EQUrXgW2W9ci9wP70JV2s/nvb+ppgx1J3dC12N8q5/DYjKg5vCv0PUYB0mSAACqoFjo89Nhqq9GQ35649YIv3DUnzkEfcFJePWZ0uGPjYjIVT29+jBq9dyCSBfmUiu42aW1eGPDCdExyEkYyvNh2PsjvPtdhpBBs9CQdwJl6xdDkiuh6z7mnPtXH1wPmcoD2qTBrX6O2uPbYK6vhme3vx7PI64PPLuORP5H90BSqBB46T2QKdUo/eVtBFx6D6r2/oiqPash9/CG/4Q7oAqKtsrHS0TkCvIq6vHa+hN46JLOoqOQA3OpgvvEqkOoN5gvfkciALBYoA5NgN+IOQAAVUg8DMVZqEr7seWCu38dPLuMhKRQtfopqvevgUdcHyi8Aprd7jv0WvgOvbbp1+W/fwZNTE9IMjkqtn6J8BvfQl36DpT88DLC5r7Wzg+QiMg1Lfn9FK7oE4mEYC/RUchBucwWhV8O5WP90XOvXCc6H7nOD8rATs1uUwZEwVRZdM5967MPwlh6BrrU1m9PMFYUoj5rH3SpEy54P0NJNmoOb4TvsOtQf/oANJHdINf6QJsyDPqCkzA31Lb6OYmI3IHBZMETqzjhjM7PJQpurd6Ip77nP3RqG3VEFxhKzzS7zVCa0+IFXtX710IVmgBVcFyrH7/6wFrItT7wiO933vtYLBaU/PIW/EbfDJnKA7CYYTGf3Vv2548WvipBRPRPv6cXY+1hTjijlrlEwX1t/QnklNeJjkFOxrvfNDTkHkPF1q9gKMtFzeFfUb3vZ+h6X9rsfuaGWtQe+/28F5cVfPEwKnd/3+w2i8WM6gPr4NltDCSZ/LwZqvf9ArmHN7QJjcfeqCM6oz5rPxpyjqJy50ooAzo1O7aMiIj+8n8/HIbeyEUAOpfTF9zjBVVY8vsp0THICanDkhA0/RHUHP4NuR/cjvI/voDf6HnNhi4AQM2RTYAF8OwyosXHMZTlw1xX2ey2+sw0mCqLoOsx7rzPb6opQ8XWr+A39ta/MoUnw7v/dBR+8yRqjm5GwKS7O/AREhG5tsySWizdwg5A55IsFotFdIiOuPb9bfgjvUR0DCIiIhLAS6PAr/8aiQCdWnQUciBOvYL72/EillsiIiI3VlVvxItrjouOQQ7GaQuuxWLB8z8dFR2DiIiIBPty52kczq28+B3JbThtwV21LxeH8/iPmYiIyN2ZLcBTqw+JjkEOxCkLrt5oxotrjomOQURERA5iW0Ypfj6YLzoGOQinLLifbs9CdimPBSMiIqK/vLz2GJz82nmyEqcruNUNRry5IV10DCIiInIwxwuq8cOBPNExyAE4XcFdvCkDJTV60TGIiIjIAb2+/gTMZq7iujunKrjF1Q34YHOG6BhERETkoLiKS4CTFdy3NqajRm8SHYOIiIgcGFdxyWkKbn5FPT7bflp0DCIiInJwJwqrsZqruG7NaQruWxvT0WA0i45BREREToCruO7NKQpuTnkdvtyZLToGEREROYn0wmp8vz9XdAwSxCkK7psbTkBv4uotERERtR5Xcd2Xwxfc7NJafLP7jOgYRERE5GROFtVwFddNOXzBfW39CRhM/O6LiIiI2u41ruK6JYcuuKeKa/Dd3hzRMYiIiMhJZRTVYNU+ruK6G4cuuK+vPwETv+siIiKiDnh9A/uEu3HYgptbXofv+R0XERERdVDjKi5fEXYnDltwP9qaCSO/2yIiIiIreGN9Oldx3YhDFtxavRGfc2oZERERWUlGcQ1WpnEV1104ZMH9ZvcZVNYbRccgIiIiF/LGBq7iuguHK7gWiwUf/pEpOgYRERG5mFPFNVjNc3HdgsMV3PVHCnGquEZ0DCIiInJBH23JFB2B7MDhCu4Hv58SHYGIiIhc1J7T5TiYUyE6BtmYQxXcw7mV2JpRIjoGERERuTCu4ro+hyq4XL0lIiIiW/t+fy7Ka/WiY5ANOUzBLayq52AHIiIisrl6gxlf7swWHYNsyGEK7idbs6A3mUXHICIiIjfwyfYsmHlkmMtyiIJbbzDhUw52ICIiIjvJLq3DhqOFomOQjThEwV2xNwclNdwLQ0RERPbz0dZM0RHIRhyi4C75gxeXERERkX39nl7Ms/ddlPCCu+l4EY4XVIuOQURERG7GYgGWcRXXJQkvuDwajIiIiET5ZvcZ1OqNomOQlQktuOmFVdh0okhkBCIiInJjVfVGLN+TIzoGWZnQgrvkj0xYeEIHERERCfTx1izREcjKhBXceoMJ36dxsAMRERGJdaygCtsySkTHICsSVnDXHC5AVQP3vBAREZF4vNjMtQgruN/tOSPqqYmIiIiaWXOoAPkV9aJjkJUIKbjF1Q3YfKJYxFMTERERncNotuDT7dyL6yqEFNxVabkwcv4zEREROZAvdmbDxH7iEoQU3O/28jgOIiIicixFVQ3YcpKvMLsCuxfcEwVVOJBTYe+nJSIiIrqo7/fxhCdXYPeCu5yrt0REROSgfj6YD73RLDoGdZBdC67FYsFKFlwiIiJyUJX1Rvx2nFNWnZ1dC+7WjBLk8ggOIiIicmDcpuD87Fpwv+OsZyIiInJw644UoE5vEh2DOsBuBbfeYMJPB/Pt9XRERERE7VKrN2H90QLRMagD7FZwfzmUj2qO5iUiIiInsCqN2xScmd0KLs++JSIiImfx6/EiVNUbRMegdrJLwS2q4mheIiIich56oxm/HOI2BWdll4K7al8uR98RERGRU1nF0xScll0K7vI9Z+zxNERERERWsyW9GKU1etExqB1sXnCPF1ThUG6lrZ+GiIiIyKqMZgt+PJAnOga1g80L7ur9/IdBREREzonbFJyTzQvuBp4jR0RERE5qV2Yp8jmF1enYtOAWVtVzewIRERE5LbMFWL2fq7jOxqYF99djRbDw8AQiIiJyYt9zu6XTsWnB3Xi00JYPT0RERGRz+7LLcbqkVnQMagObFVyjyYzfOdyBiIiIXMDaI7ymyJnYrODuzCxDVYPRVg9PREREZDe/nygSHYHawGYFd+Mxbk8gIiIi17D9VCn0RrPoGNRKtiu43H9LRERELqJWb8KurFLRMaiVbFJws0trcaKw2hYPTURERCTEZl5b5DRsUnB/5fYEIiIicjG8eN552KTgbjzGjdhERETkWg7lVqC0Ri86BrWC1QtuvcGELSf5HQ4RERG5FrMF+D2dHccZWL3gbs0oQb2BVxkSERGR6+FxYc7B6gWXpycQERGRq+I+XOdg/YLLC8yIiIjIReVW1CO9sEp0DLoIqxbc9MJqZJfWWfMhiYiIiBzKpuNcxXV0Vi243J5AREREro4Xmjk+6xZcbk8gIiIiF7cto4Rjex2c1Qqu3mjGrqwyaz0cERERkUOq1Zuwm53HoVmt4B7KreB3M0REROQWNvO4MIdmtYK793S5tR6KiIiIyKFxH65js1rB3XOaS/VERETkHg7mVKCMY3sdFldwiYiIiNrIbAH+OMlVXEdllYJbWFWPnHKef0tERETuY1cmX712VFYpuHuyyq3xMERERERO40BOhegIdB5WKbh7s/kdDBEREbmXw7mVMJktomNQC6xTcLn/loiIiNxMncGE9MJq0TGoBR0uuEaTGQfOcImeiIiI3M/+M+WiI1ALOlxwj+ZXoc5gskYWIiIiIqfCfbiOqcMFdy/PvyUiIiI3tZ+vYjukDhfcPdx/S0RERG7qSF4ljCaz6Bj0D1zBJSIiImqnBqMZxwqqRMegf+hQwS2r0SOzpNZaWYiIiIiczkHuw3U4HSq4PP+WiIiI3B334TqeDhVcTjAjIiIid8eTFBwPV3CJiIiIOuBoXhX0Rl5o5kjaXXDNZgv2ZfM7FiIiInJvepMZx/J5oZkjaXfBzSypQXWD0ZpZiIiIiJzS/pxy0RHob9pdcDOKaqyZg4iIiMhp8SQFx9L+gltcbc0cRERERE6LJyk4Fq7gEhEREXXQ8YIqNBhNomPQWR1YwWXBJSIiIgIAg8mCI3m80MxRcAWXiIiIyAqO8yQFh9GugltZb0BxdYO1sxARERE5rdOltaIj0FntKrhcvSUiIiJqLosF12G0q+Ce4gkKRERERM1wBddxcAWXiIiIyAqyWXAdBgsuERERkRWU1uhRVW8QHYPQ3oLLI8KIiIiIzsFtCo6hzQXXYrEgkwWXiIiI6BzcpuAY2lxwcyvqUWfgpA4iIiKif8oqYcF1BG0uuBlFPEGBiIiIqCXcouAY2lxwT3F7AhEREVGLWHAdQztWcFlwiYiIiFrCgusY2lxwT3KLAhEREVGLcsvrYDJbRMdwe1zBJSIiIrISg8mC3PI60THcXpsKrt5oRl4F/9KIiIiIzodHhYnXpoJbXN0ArroTERERnV8WC65wbSq4pTV6W+UgIiIicgm80Ey8Nq/gEhEREdH5seCKxxVcIiIiIis6zWlmwrWp4JZUs+ASERERXciZMhZc0dpWcLmCS0RERHRBFXUGWCy8Kl+kNq7gcg8uERER0YWYLUBlnVF0DLfGPbhEREREVlZRZxAdwa217RQFFlwiIiKii2LBFauNK7jcokBERER0MSy4YvEUBSIiIiIrY8EVq9UFt95gQq3eZMssRERERC6BBVesVhdcTjEjIiIiah0WXLFaXXB5ggIRERFR67DgitXqgsv9t0REREStw4IrVusLLldwiYiIiFqlkgVXqDas4HIPLhEREVFrlNdxYVAk7sElIiIisjJuURCrDacosOASERERtQYLrlitLrjVDfyLIiIiImqNilr2JpFaXXCNJostcxARERG5jKoGI8xmdidRWl1wDfxLIiIiImoViwWoqjeKjuG2Wl1wTWazLXMQERERuRTuwxWn9Su43KJARERE1GosuOK0YQWXBZeIiIiotaobuEVBlNZfZMaCS0RERNRqZgu7kyhtOEWBe3CJiIiIWouvfovDLQpERERENsAVXHHacJEZV3CJiIiIWosFVxyu4BIRERHZANcGxeExYUREREQ2wMVBcbiCS0RERGQD3KIgDo8JIyIiIrIBLg6K04aCy40kRERERK3FFVxxFK29o4l7cInIAb2YsgtSuQf8yzqhTBmGsgoZzEZ+viIi8ZINctER3FarC66BK7hE5GBiPOoxM/ddzOkyALGK7bh6ZTlMpdWoTx2NysheKJFCUFxihomFl4gEkEMSHcFttX4Fl/tIiMjBvBi5GVJ2DabrZXjM6zjWXqPG00e7Inz199BuWYFQAGaNJ+p7jkFlVG+UyENQXAKYDPyGnYhsT5Kx4IoiWSyt2yAS+9AP4FYSInIUkZoGbFbfBamhCrUqT4yOjkKNsRYAMKYuBreuNgLpmee8n0Xt0bjCG90HpYowFBUDRhZeIrKBibd2Q3yvYNEx3FKrVnDNZgvLLRE5lBej/oCUXQUA0OprMNErHt+WHQAArPfIxO+Xy/F4Rn8krkiDRa9vej+poQ4eO36Ax44fEAIgRaVBfeooVEX3RYkiDEWlEox6Fl4i6jhJ4gquKK1ewY176AdwlwIROYIwjR5b1HdDaqhoum1fVCquU5Sdc9/+DRG4d60asgPHW/XYZpUaDd1HoCq2P0qV4SgqlWBoYOElorabdFsPxPYIFB3DLbV6D65GKUet3mTLLERErfJi1FZI2RXNbkvN3of47oNxsvpMs9t3qHNw9aXAQ6n90Gv5IVhqay/42DJ9Azx2r4HH7jUIBpCkUEHfY3hj4VVFopCFl4haiQu44rS64Hqw4BKRAwhWGzCo6KsWf2+65IMXceac2y0S8EzUXnS5LQgP/xYH1c6DrX4+mVEPzZ510OxZhyAASXIFGroPR1XcAJSqI1FUJoe+np8biehcvMhMnDat4BIRifbfTtsgyz53KwIATDm5E6+GesNoNrb4+4eVRbhubBHu6t4bw5afhKW8osX7XYhkMkKTtgGatA2NhVcmh77bUFTFD0SpRycUlcnRUMfCS0RcwRWpDQW31UPPiIhsIkBlwLDilldvAcC/phijfPphbdmhCz7O6yH7sXKeL57Y3hOem9I6lEkym6De/xvU+39DIIBEmRz6roNRFT8QZdpoFJYr0FDLwkvkjhRcHBSm9VsUVPxLIiKx/hu9A7LskgveZ3pFOda24rGyFOW4YUg55nZJxaXLc2ApLLZKRslsgvrAZqgPbEYggARJgr7zIFQnDW4svBUq1Ne0vMJMRK5F5dHqmkVW1upTFK54Zwt2Zrb8siARka35KY3Y7XUvZLUXLqJmSYbxnXuioK71hTXYpMNTafHwX7sHtj4T0SJJMKT0R3XSEJR5xqCwQo06Fl4ilzT7mcHw8teIjuGWuAeXiJzC89G7IDtz8dIqs5hxmSoM77ah4BbKqzG/zz7MSOqMq1eWw5Kd25GoFyRZLFAd2Q7/I9vhDyAegD65H6qTh6BMF4eiSjVqq1l4iVwBV3DFYcElIofnpTBiTNkXrb7/ZacPYLGPBAvathq7vGncb3+Er94NmOyzd1Z1bCf8j+38q/Am9EJN5+Eo84pDYZUGtVUsvETORpIAlYbdSZRWF1wt9+ASkSAvxOyB/Exhq+8fWXoa/aPHYnt564Y7/F2VrAELu+zBmNjY8477tTVV+l6o0vfCD0AcAH18D9R0Ho5y7wQUVnugppKFl8jRqTwUnGQmUKsLrqeay+xEZH+eChPGl3/Z5vebUWfC9g4874XG/dqb6uR+qE7uhx+AWACG2G6o6TICZT4JKKrRorqChZfI0ag07E0itfpP31ujtGUOIqIWPRuzF/IzeW1+vzHpW+EdH49KfVW7n7tBMuHh+D3of0fbxv3amvLUQfieOghfNBZeY6fOqO42EuW+iSiq1aGq3CA4ofvYfGgVNh9ehdKqAgBAqF80LulzPbp2GtDi/U0mI9akfYbtx9egvKYYIT5RmDZgHrp06t90n50n1mHl9vfRYKjDwOQJmDn4tqbfK6nKx5s/3I/7Z/wPHipP235w1CEqLQuuSK0vuNwoTUR25ik3Y1JF21dvAUBtrMel2lh8rt/f4RxtHfdrb4rTR+B7+gh8AcQAMEYlo7rrSFT4J6GoVofKcq7w2oqvZyCmDZiHIJ8IWCwWbD++Bot/eQwPznwXYf4x59z/+51LsPPEOlwz4j6E+EbhSPYuvLfmcdx72euICkxEdV0FPvvtJVw38n4Eeofhfz89gqSIXugePQgA8OXm1zCt/zyWWyegZm8SqtV/+l5cwSUiO3s6Zh8UOTntfv8Z+Rn43Eon9HRk3K+9KbKPwTf7GHwBRAMwRiSgpttolAcko7jeCxWlXOG1lu4xg5v9emr/m/D74e9xqvBwiwV3x4l1mNDrmqYV3mFdp+Jozh5s2Pc15ox5GMVVedCoPNEnYRQAICm8JwrKTqN79CDsSt8AuUyBnnHDbP5xUcfxBAWx2rBFgX9RRGQ/apkZU6taf3JCS1LyDqNzj+E4UpVpnVCwzrhfe1PkpMMnJx0+aCy8prBY1HQfjfKAzihq8GbhtRKz2YQ9Gb9Bb6hHbEiXFu9jNOmhVKia3aaSq3Ayv/EbpmCfCBiMDcguPgF/XQiyio5hYMpE1DZUYfXOD3H3lJds/nGQdai5RUGoNmxR4AouEdnP07EHoMjJ7vDjzDBr8H9WyPNP1hz3a2/yvFPwzvsA3gA6ATCGRKO2x2hUBHZGkd4H5SXc0tAWOSUZeGnFnTCa9FArPTBvwpMI84tp8b6do/phw/5vkBDWA4He4TiWswdpmb/DYjYDALRqL1w/6gEs2/g8DMYG9E8ahy5R/fDpr//FiK6XoaQyH+/+/B+YzEZM6jsbveJG2PEjpbbgCq5YrZ5ktjurFDP/t9XWeYiIoJaZcTDwESgrszr8WJUePhgdEYwGU4MVkrVsbllXq477Fc0UFIna1DEoD+qCYoMfykoMaOORwm7FaDKgrLoQdfoa7M3YhK1Hf8TdU19useRW1ZXj800v4UDWNkgAAr3DkRzRG9uO/YxXbv6pxcc/kbsP3217FwunvIwnvpiNG8Y8Am+tP/773e14/KqP4OXhZ9sPkNql76QYDJgaJzqG2+IpCkTkcB6POQRlbsfLLQB411VgrPcQ/FBmuz2zS/0O4ce5Ojy1ry/81+y2+bhfW5MXnYHXuo/gBSAKgCkgDLU9x6IiuCuKDX4oLTGy8P6NQq5EkE8EAKBTUBJOFx3DrweW4+rh955zXy8PX9wy4WkYjHrUNFTARxuIldvfQ4B3WIuPbTDp8eXvr2HOqAdRVJkDs9mExPBUAECwTyQyC46csw+YHANXcMXiRWZE5FCUMgsur23fyQnnM7O0CD/Y+Lz1Qnk15vdOw4xE24/7tTd5SR681n8MLwCRAEz+IahNHYvKkG4oNvmjtNjo7J3eqiwWM4ymC+9rVipU8FUEwWQyIu3UZvQ+z1aDX/Z8ii5R/RAVlITs4hMwW/6armcyG2G2mK2anayHe3DFavWfvq+WBZeIbO8/0Yehysuw6mP2zdyFqK79kF2bb9XHbYmocb/2JC8tgNfGT+EFIAKAyS8YdaljUBHSHSWWAJQUG+EuvWvl9vfRNao//LyCUa+vxa70DTiRuw+3XfocAGDZhufg4xmIaQNuBgBkFhxBeU0xIgPjUV5TjB93LYPFYsHYnled89h5ZZnYfXIjHpz5LgAgxLcTJEnClqM/wtvDHwXlpxEdnGy/D5bahIMexGr1n75GKYePhxIVdbzalohsQy6ZcVX9V1Z/XAkWTFcE4nXYvuAC/xj3+4MROJFpl+cVRV5WCN2vn0OHzxEBwOwdgNpeY1EZ2gPFlkCUFptgNrvmEm91XRmWbXwOlbWl0Kg8EREQh9sufQ6dI/sCAEqrC5uNazWY9Fi9cwmKq/KgVnqga9QAzB79ILRqXbPHtVgs+HzTK5gxaAHUSg8AgEqhxnUj78dXv78Oo8mAWUPuhK9nkP0+WGoTruCK1eqLzADgktc240hepS3zEJEb+0/sEdyU97RNHrvQJwzjAzQwWey7oqqyyPFERqrwcb8imXW+qOs1DpXhqShGIEqKzTCbXLPwEv3piof6IjjaW3QMt9WmgnvzRzux7kihLfMQkZuSJAuOhj0Fdekxmz3H7b0mYFP5EZs9/oX0b3Cscb8imXU+qE8dg4rInihBMIpZeMkFzX5mMLz8rTRphtqsTevn4b4etspBRG7uwU4noC6wXbkFgBnVtdhk02c4P0cf92tPsuoKaP9YDi2WIwyA2dMb9amjURnZCyVSCIpLzDAZWXjJeUkS4OmjuvgdyWZYcIlIOEmyYI7R+ntv/2n4yW3wT+qC0oYymz9XS5xp3K89yWoqod2yAlqsQCgAs8YT9T3HoDKqN0rkISguAUwGN7lqjVyC1lsFmVwmOoZba1PBDfPhUjsRWd99USehKTxs8+dRmg2Y6hGFpYIK7p+ccdyvPcnqa6DdtgrabasQCsCi9mhc4Y3ug1JFGIqKASMLLzkwHbcmCNemPbi7Mktx+TucZkZE1nUk8hl4FNtnNTMjOAHTPB3nYq9ooy+e2B7pdON+RbKoNKhPHYWq6L4oUYShqFSCUc/CS44jvncQJt7SXXQMt8YtCkQk1D2dMuBRaL+X6uMK09EzdRTSKk/a7TkvJEtRjhuGlGNul1SXGvdrS5K+Hh47f4LHzp8QDCBZpUZD9xGoiu2PUmU4ikolGBpYeEkcnS9XcEVrU8EN8dZAIZNgdNHzDInI/m62fGP355xhkCPN7s96Ya427teeZPoGeOxeA4/daxAMIEmhgr7H8MbCq4pEIQsv2ZnOXy06gttr0w5ouUxCiDe/KyEi67g9KhOeRWl2f94J6VvhqdDa/Xkv5s9xv1/c3hlSVLjoOE5LZtRDs2cdgr59Bsmf34ah6+7E4PJv0d3/DCLCAJVGLjoiuThPX3EFd+7cubjssssAAJmZmZAk6YJvS5cuBQBs3LgRkyZNQkBAALRaLbp06YL77rsPOTk5532uJ554Aj179jzn9j+fNy0trem29957D6mpqdDpdPD19UWvXr3w7LPPNnusPzMpFAoEBgZi+PDhePXVV9HQ0NDmP4c2X+IXzmV3IrKSBbD/6i0AaPU1mOgVL+S5W2O513HceE0Fcqf1B+QsYx0lmYzQpG1A0PJnkfz57Riy5nYMKf0KPfxOIzIcUHvwz5isy1HOv42KikJeXl7T23333YeuXbs2u+3KK6/Eu+++i7FjxyI0NBTffvstDh8+jHfeeQcVFRV46aWXOpxjyZIlWLhwIe666y6kpaXhjz/+wP3334/q6upm9/sz2+nTp7Fx40ZcccUVePbZZzF48GBUVVW16TnbPEeucR+u2CuQicj53RKZBV3RHmHPP73oDL514Ema7jbu154kswnq/b9Bvf83BAJIlMmh7zoYVfEDUaaNRmG5Ag219p14R67FK8AxCq5cLkdoaGjTr3U6HRQKRbPbzpw5g7vuugt33XUXXnnllabbY2JiMHz4cJSXl3c4x6pVqzBr1izcdNNNTbd17dr1nPv9PVt4eDi6d++OcePGITU1Fc8//zwWLVrU6uds8wpumA8vNCOijrtDtlzo86dm70O8LlJohtZY75GJ62bmIX1Wf0gqHhxvC5LZBPWBzQhc8V8kfnYHBv+0AEMKP0Wq7yl0CjdD4+nA3wmRw1EoZdB6O8//1a+//hp6vR73339/i7/v6+vb4ecIDQ3Ftm3bkJWV1eb3TUlJwSWXXILly9v2NaPNBTeCWxSIqINuCM+Gd+FO0TEwXfIRHaFV9JIJD8fvwX/vCIO5e5LoOC5PsligPrwFASteRMJnd2LQj/MxtGAZevqcRHS4CR4svHQBXgEaSJIkOkarnThxAt7e3ggLC7PZczz++OPw9fVFTEwMkpOTMXfuXHz11Vcwm1t38WdKSgoyMzPb9Jzt2IPLFVwi6piFyu9ERwAATDm5EwqZ85SVxnG/GUi7rh8kreNdJOeqJIsFqiPb4b/yZcR/dhcG/XArhuYtRU/vE4gON0Grc55/Q2R73kHO1ZMsFkurCrlOp2t6mz9/fpueIywsDFu3bsWBAwdw9913w2g0Ys6cOZg4cWKrSm5rM/5dO/fgEhG1z/XhOfAp2CY6BgDAv6YYo3z6YW3ZIdFRWo3jfh2D6thO+B/bCX8A8QD0Cb1Q03k4yrziUFilQW2VUXREEsQ70Ll6UlJSEioqKpCXl3fBVdy/n4jg7e3d9GNFxbmTGP/ct+vj0/xVsm7duqFbt2647bbbMH/+fAwbNgy//fYbRo0adcGMR44cQWxsbCs/okZcwSUiu7pXtUJ0hGamV5SLjtAujeN+j+L3G3tD8nWOrRauTJW+F37fv4a4z+7GwO9vxdDsd9FLdwSx4QZ4enOF1534OFnBvfzyy6FSqfDCCy+0+Pt/ltWEhISmt+DgYABAcnIyzpw5g4KCgmbvs2fPHmg0GnTq1Om8z9ulSxcAQE1NzQXzHT16FD///DNmzpzZ2g8JQDtWcH08lNCpFahu4HenRNQ2V4XlwS//D9ExmhmSsR0hnXuioM45J4i9HrIfK+f54ontPTnu14GoTu6H6uR++AGIBWCI7YbqLiNQ7pOAohotqiv4NdRVeQeKv1apoqKi2YorAAQEBLR436ioKLzyyiu44447UFlZidmzZyMmJgZnzpzBsmXLoNPpzntU2IQJE5CcnIyrr74aixYtQmhoKPbs2YNHH30Ud999N+RnjzlcsGABwsPDMXr0aERGRiIvLw+LFi1CUFAQBg0a1PR4RqMR+fn5MJvNKCkpwa+//opFixahZ8+e+Pe//92mP4N2fVsZ5qPBicLqi9+RiOhv/q1ZKTrCOWQWM6apwrDYSQsuwHG/zkB56iD8Th1sKrzGTp1R3W0kyn0TUVSrQ1W5QXREshJH2KLw66+/olevXs1uu+mmmxAZ2fLJMbfddhuSkpLw4osvYvr06airq0NMTAwmT56Me++997zPo1AosGbNGjz88MO4+uqrUVRUhNjYWNx9993N3m/s2LFYsmQJ/ve//6GkpASBgYEYNGgQ1q9f36x4Hzp0CGFhYZDL5fDx8UGXLl3w0EMPYcGCBVCr2zY8Q7JY2j4P8pZlu7DmcMHF70hEdNbMkAK8VHGP6BgtOuPfCZN8JFjg/ONxg006PLUvgeN+nYwxKhnVXUeiwj8JRbU6VJZzhddZ3fLaCCjVHB4iWrtWcDuHebPgElGbPKhdBZx7LYJDiCw9jf6dxmJ7xXHRUTrsz3G/MxI74+qV5bBk54qORK2gyD4G3+xj8AUQDcAYkYCabqNRHpCM4novVJRyhdcZeAVoWG4dRLsLLhFRa00JLkJQ3kbRMS5oRr0J20WHsKLlXsex9ho1njraDxGr9wAmTuZyJoqcdPjkpMMHjYXXFBaLmu6jUR7QGUUN3iy8DiowUic6Ap3Vri0Kp0tqMfy/jv3Fiogcx/a4JQjJXSc6xgU1KDQYHR+PSn3b5p07gzF1MRz362KMIdGo7TEaFYGdUaT3QXkJtzQ4gr6XxmDAlDjRMQjtXMGN8veAl1qBKp6kQEQXcUlQMYJz14uOcVFqYz0u1cbic/1+0VGsbr1HJjbPlOOJjP5IXJEGi14vOhJ1kKIgC95rP4Q3gCgApqBI1KaOQXlQFxQb/FBWYoALbCl3OkGRXqIj0FntWsEFgCve2YKdmWXWzkNELmZr/EcIy/lFdIxWORrWBVdoXPuEmP4NEbh3rRqyA86/35jOzxQQhtqeY1ER3BXFBj+UlhhZeO3guqcHwcfJJpm5qnYX3MdWHsSyrVnWzkNELmRcYCkWV98JyYm+ss7qMRxHqjJFx7ApyQI8dKYXei0/BEttreg4ZAcm/xDUpo5FZUg3FJv8UVps5CEbVqbUyDHvleFtHilLttHu8SpdeKEZEV3E4z4/Qqp2rq+iM8wa/J/oEDbGcb/uR15aAK+Nn8ILQAQAk18w6lLHoCKkO0osASgpNsJiFp3SuQVG6FhuHUi7V3D3ZZdj2luONZGIiBzHSP8yfFh3JyQn+6pZ6eGD0RHBaDA1iI5iN3cV9MCw5SdhKXfQc9zI5szeAajtNRaVoT1QbAlEabEJZrNzfXMqWrcRERhxdbLoGHRWuwtuvcGEro//AhP/AxBRCzYlfIZOZ1aLjtEuD/aehB/K3GtVM9roiye2R3LcLwEAzDpf1PUah8rwVBQjECXFZphN/Hp/ISOvTUbXYRGiY9BZ7d6ioFHKERvoiXSO7CWifxjqX4GonJ9Ex2i3GaVF+MHNXmnkuF/6O1l1OTw3fw1PfI0wAGadD+pTx6AisidKEIxiFt5zBPIEBYfS7hVcALjz8734fh+n5BBRc78mfImYMytFx2g3CyRM6toPZ2rzRUcRguN+6WLMnt6oTx2NysheKJFCUFxihsnovv9WJAmY99oIKFWcYuYo2r2CCwCdw7zw/T5rRSEiVzDAtxLRuT+IjtEhEiyYrgjEG3DPgstxv3QxsppKaLesgBYrEArArPFEfc8xqIzqjRJ5CIpLAJPBufbfd4RPsJbl1sF0aAX312OFmPvhTmvmISIntz7hG8SfWS46RocV+oRhfIAGJot7j7j1Mqvx1NEuHPdLbWJRezSu8Eb3QakiDEXFgNGFC29Cn2BMmNdNdAz6mw4V3MLKevR/xvEnFBGRffT2qcK3hjsgmQ2io1jF7b0mYFP5EdExHALH/VJHWFQa1KeOQlV0X5QowlBUKsGod53CO2BaHPpeEiM6Bv1Nh7YoBHtrEKhTobiaYx+JCHgueB2kbNcotwAwo7oWm0SHcBAc90sdIenr4bHzJ3js/AnBAJJVajR0H4Gq2P4oVYajqFSCocF5C29gpE50BPqHDq3gAsD1H2zH5hO82pbI3fXwrsZK0x2QTK5TfAwyJcYmdUFpA8eS/x3H/ZK1mRUq6HsMbyy8qkgUOlnhnfPsEOj81KJj0N90aAUXADqHebPgEhFeCFkPKdt1yi0AKM0GTPWIwlIW3GZ2qHNw9aXAQ6n9OO6XrEJm1EOzZx00e9YhCECSXIGG7sNRFTcApepIFJXJoa93zD3gGk8ly60D6vAK7nd7z+CeL3mUApE76+pVg9XmOyC54PSvjOAETPN0reJuTV0MQXh4UwBUO9xrMAbZl0Umh77bUFTFD0SpRycUlcnRUOcYhTcyxQ/TFvYSHYP+ocMFN72wCmNf5i41Inf2Q+JqdM3+THQMm7k+dRTSKk+KjuHQOO6X7Mkik0PfdTCq4geiTBuNwnIFGmrFFN5+k2PRf3KskOem8+vwFoWEYC9eaEbkxlJ0teiS953oGDY1wyBHmugQDu71kP1YOc8XT2zvyXG/ZHOS2QT1gc1QH9iMQAAJkgR950GoThrcWHgrVKivMdolS1iCj12eh9qmwyu4AHD7Z3vww/48a+QhIiezKvFH9Mj+RHQMm6pV6zCqUyRqjdxr2hpzy7o65bjfN4uL8HZJSbPbYlUq/BAb1+L911ZVYXFJCU4b9DBaLOikUuEGP39M9fmr8CwpLcGS0lIAwE3+/rjBP6Dp9/bV1eHpgnx8ER0DheRms6FtzCJJMKT0R3XSEJR5xqCwQo06GxRemUzCza8Mh1LNIQ+OpsMruAAwKC6ABZfIDcVr69A93/mHOlyMtqEaE73isbzsgOgoTmGp3yH8OFeHp/b1dbpxvwkqFT6I6tT06wt9kfSRy3BrQABiVSooJQm/1VTjkfw8+CvkGOqpw7H6erxZXIy3IyJhAXBbzhkM8fREkloDo8WCJwvy8WRIKMutDUgWC1RHtsP/yHb4A4gHoE/uh+rkISjTxaGoUo3a6o4X3sAoHcutg7JOwY0PuPidiMjlvBixCVK2e6xqTi/KwXKrfMZ0D8467lcuSQhStO4vur/Ws9mvr1f5Y0VFBfbU1WGopw4Zej2S1GoM9Gy8X5JaffY2DZaUlqKvhxbdPTys/jFQy1THdsL/2M6/Cm9CL9R0Ho4yrzgUVmlQW9X2whuW6GvtmGQlVvl0HR+kQ4i3GgWVrncFNRG1LMajHj0LvhEdw256ZqchvvtgnKw+IzqKU1nudRxrr1HjqaP9nGLc72m9HiPS06GWSUjVeOCeoCCEK5UXfT+LxYJttbXI1Otxn4cWQGOhzdTrkWswwAIgS69HokqN03o9vqsoxzcxMbb9YOiCVOl7oUrfCz8AcQD08T1Q03k4yr0TUFjtgZrKixfe8HhfW8ekdrLKHlwAWPjFXqxIc47v0Imo475JXIu+2R+KjmFXH/W4BC9WHRIdw2k5+rjfTdXVqLWYEatUochkxNvFJSgwGrAqNhaespZfhq4ymTDyZDoMFgtkkoT/hIRgpo9v0+9/UV6GZaWN5yjP9vfDVb5+uDH7NK7x9YMJFrxVXAyFJOHh4BD01Wrt8WFSKxliu6G6ywiU+ySgqEaL6opzC++N/x0KDy+VgHR0MVYruF/uPI0HvuX+NCJ3EKlpwGb1XZAaqkRHsatSz0CMCfWG0Wyfq7NdkcoixxMZqU4x7rfSZMLYjJN4ICgYM319W7yP2WJBtsGAWrMZ22pr8E5JCd6IiDhn+8KfVlRUYH11FR4PCcWlpzLwZXQMCowG3J+bh7VxcVDJZDb8iKgjjJ06o7rbSJT7JqKoVgeFRolrnhgoOhadh9V2lA2KC7TWQxGRg3sx6g9I2e5VbgHAv6YYo3z6YW0ZV3HbSy+Z8HD8HvS/w/HH/XrL5YhRqZBlOH8Rl0kSolWNK3idNRpk6PV4r6S0xYJbZjTi7ZJiLIvqhP31dYhRqZrejLAg09C4P5cck+L0EfiePgJfADEAvObeDIAF11FZ7VvFTgFaRPhyszyRqwvT6DGg8GvRMYSZXlEuOoJLaBz3m4G06/pBctCX5mvMZpzW61t90RkAmC2A3mJu8feeKyrEbD8/hCqVMFsAw99eQDVZLDA5z2ETBMCre4roCHQBVn0tZGAcT1MgcnUvRm2F1OC+06qGZGxHiAdfsbIGiwQ8E7UXj9/mA33/bqLj4IXCQuysrUWOQY+9dbW4K+cM5JKES728AQAP5uXi5aLCpvsvLinBlpoaZOv1ONnQgA9LS/B9ZQWmeJ978P+Wmhpk6vW4xtcPANBNo8EpvR6bqqvxVXk5ZJKEWBX3cjoTzwEDREegC7DqoTeD4gPw7R5eYUzkqoLVBgwq+kp0DKFkFjOmqcKwuM65hhg4ssPKIlw3pgh3destdNxvgdGAf+Xmotxsgr9cjt4eHvi8UzT8z67g5hkMzVaF6sxmPFWQjwKjEWpJQpxKjefDwnGJt3ezx603m7GooAAvhYdDdvbM21ClEo8Eh+CR/DyoJAnPhoZBw/23TkOVEA9FIL/RdWRWu8gMAHLK6zDkuQ3WejgicjDLEjdjePb/RMcQ7ox/J0zykWABX1O2tmijL57YHslxv+TQ/K65BqGP/Ud0DLoAq367GOHrgU7+jrmXiog6JkhlwNDiL0XHcAiRpafR3ydRdAyXlKUoxw1DDuKH+amQgrlCRo5JO5DbExyd1V8PGcR9uEQu6fnoHZDVlYqO4TCm1zv2wAJn95HfISy4wYCSCX0AjrIlRyKXc/+tE7B+weXYXiKX46c0YmQJV2//bmz6VngpdaJjuLRiWQ0W9N6HL27vDCkqXHQcIgCAR6+ekPuceyEhORarF9zBLLhELuf56F2Q1fKiqr9TG+txqWes6BhuYbnXcdx4TQVypvUD5C1PFCOyF93wEaIjUCtYveAGe2sQF9TyBBcicj5eCiPGlH0hOoZDmpl/SnQEt1Ela8A9Xfbi3TtjgcQY0XHIjelGsOA6A5ucScJ9uESu44WYPZDXFF78jm4oJe8wOnvFiI7hVtZ7ZOK6mXlIn9UfEs+NJTtThIVBk5wkOga1gk0K7tAEXvlK5Ao8FSaML+fe2wuZYeZoVXv7c9zvf+8Ig7k7ywbZj27EcNERqJVsUnBHJAdBo+SB1UTO7tmYvZBX54mO4dAmndwOtVwtOoZbcoZxv+RauD3BedikhWpVCoxICrLFQxORnXjKzZhUwdXbi/Guq8BYb56JK4qjjfsl1yWp1fAcOFB0DGolmy2zTuoeZquHJiI7eDpmHxRVOaJjOIUZpUWiI7i9xnG/R/H7jb0h+fIIJ7I+bf/+kHl4iI5BrWSzgjs6JRgqBbcpEDkjD7kJUys/Fx3DafTL3IVIbajoGATg9ZD9+Nc8JWqG9xQdhVwMtyc4F5s1UC+NEsN4sRmRU3oq+gAUVWdEx3AaEiyYruDnO0fBcb9kC7qRLLjOxKZLrBO7cUWDyNmoZWZcVs29t201LTMNcolDCBwJx/2Staji46GKjBQdg9rApgV3fJdQKOX8pELkTB6POQRlZZboGE4npCIXg314ZJWj4bhfsgavcWNFR6A2smnB9dEqMZBDH4ichlJmweW1XL1trxk1taIj0Hlw3C91hPclk0RHoDay+VVgPE2ByHn8J/owVOUZomM4rRHp2+Cv9hMdg86D436pPVTx8Zxe5oRsXnDHdwmBXMZtCkSOTi6ZcVX9V6JjODWl2YCpHlGiY9BFcNwvtYX3hAmiI1A72LzgBujU6B/jb+unIaIOejjmGFRlJ0THcHrTc46KjkCtwHG/1Freky4RHYHawS4H1V7SnacpEDkySbLgugau3lpDXGE6enrHi45BrcRxv3Qh6sQEqBMSRMegdrBLwZ3YNRTcpUDkuB6KPgF16THRMVzGDAMvYnImHPdL5+N1CVdvnZVdCm6wtwZ9onnhBZEjkiQLZhu4emtNE05ug1bB1UBn8+e438039eG4XwIAeE9kwXVWdpulO7EbT1MgckT/6pQOTclh0TFcirahGhO9uE3BWb0RvA/33cJxv+5OnZwMdVys6BjUTnYruJd0C+UgGSIHdKPpa9ERXNL0ohzREagDTss57tfdeXN7glOzW8EN9/VAj0hfez0dEbXCPZ0y4FF8UHQMl9QzOw1xOo72dHZ/jfvty3G/bsb7komiI1AH2K3gAsCkbjxNgciR3Gz5RnQElzZD8hYdgaygcdxvGj7nuF+3oenaFaroaNExqAPsWnCn9gzn0AciB3F7VCY8i9JEx3BpU07ugkKmEB2DrOQ7jvt1Gz4zZ4iOQB1k14Ib5uOBkUlB9nxKIjqPBeDqra351xRjpA+HCLgSjvt1fZJaDZ/Jk0XHoA6ya8EFgGsGdLL3UxLRP9wSmQVd0R7RMdzCjIoK0RHIBjju13V5jRsHuTe3Fzk7uxfckcnBCPfR2Ptpiehv7pAtFx3BbQzJ2I4QD16F74o47tc1+XJ7gkuwe8GVyyRc2Y+ruESi3BCeDe/CnaJjuA2ZxYxpKp4D7so47td1KCMjoR04UHQMsgK7F1wAuLJfFC82IxJkofI70RHczvTTByCBn/NcGcf9ugaf6ZdB4nFwLkFIwQ310WBUcrCIpyZya7PDc+FTsE10DLcTWXoa/X0SRccgO+C4Xycmk8F3BrcnuAohBRcAruXFZkR2d4+Kq7eiTK83iY5AdsRxv87Hc9AgKMO4nchVCCu4I5KCEOHrIerpidzOVWF58Mv/Q3QMtzU2fSu8lDrRMciOOO7XufhePlN0BLIiYQVXJpNwZb8oUU9P5Hb+rVkpOoJbUxvrcakuTnQMEoDjfh2f3NcXXmPGiI5BViSs4AKNF5speLEZkc3NDClAQN4m0THc3oy8DNERSBCO+3Vs3lOm8DxjFyO04IZ4azAqhRebEdnag9pVoiMQgM55h9HZi/Pt3RnH/Tomv6uuFB2BrExowQU42YzI1qYEFyEob6PoGHTWdDOvPXB3HPfrWLSDBkIdHy86BlmZ8II7IpEXmxHZ0qO670VHoL+59OR2qOVq0THIAXDcr2Pwv+460RHIBoQXXJlMwtX9ebEZkS1cElSM4Nz1omPQ33jXVWCsN8/EpUYc9yuWMjwculGjRMcgGxBecAFgVl9ebEZkC495/wAJFtEx6B9mlBaJjkAOptm4X09P0XHcht81V0OSOUQVIitziL/VYG8NxnTmxWZE1jQusBShOWtEx6AW9MvchUhtqOgY5GCaxv0u8Oa4XzuQNBr4zOTZt67KIQouAFwzgFcWE1nTEz5cvXVUEiyYruDB/9Qyjvu1D5+pU6Hw8xMdg2zEYQru8MRAxAXyZRkiaxjpX4bw3F9Ex6ALmJaZBrnEY6Lo/Dju14YkCf5zZotOQTbkMAVXkiTMH8ljOois4Sn/nyBZzKJj0AWEVORisA8vKqIL47hf2/AcOpRHg7k4hym4ADC9VwSPDCPqoKH+FYjK+Ul0DGqFGTW1oiOQk+C4X+vynzNHdASyMYcquEq5DPNHcFY7UUcs8v8ZksUkOga1woj0bfBXcw8gtQ7H/VqHOjEBuqFDRMcgG3OoggsAV/SNQpAXD0Enao8BvpWIzv1BdAxqJaXZgCkePAec2objfjvGf+4NoiOQHThcwdUo5Zg3LFZ0DCKn9EzgGkhmo+gY1AYzco6JjkBOiON+20cZHg6faVOFPb/JZMLgwYMxY8aMZrdXVFQgKioKjzzyCDIzMyFJUtObv78/RowYgc2bNzd7nyeeeKLpPgqFAjExMbjnnntQXV193uefO3cuLrvssnNu//XXXyFJEsrLy5tyPvfcc0hJSYGHhwf8/f0xYMAAvP/++80e68/nVyqVCAkJwbhx47BkyRKYzeKvAXG4ggsA1w6Ihq9WKToGkVPp7VOFuFyO5XU2cYUnkOrNi12ofTjut238b74JkkIh7PnlcjmWLl2Kn3/+GZ9++mnT7XfeeSf8/f3x+OOPN922bt065OXlYdOmTQgPD8fkyZNRUFDQ7PG6du2KvLw8ZGZm4vnnn8fixYtx3333dTjnk08+iVdeeQVPP/00Dh8+jI0bN+KWW25pKsB/mjhxYtPz//TTTxg1ahTuvvtuTJ48GUaj2MUWcX/LF+CpVuCGwbF4Zd1x0VGInMZzwesgZRtEx6B2mGmQY5/oEOS0/hz32/+OCNy7Vg3ZAX7tbIkiKAi+l18uOgaSkpLw3HPP4c4778To0aOxY8cOfPHFF9i5cydUf/smJSAgAKGhoQgNDcXDDz+ML774Atu3b8fUqX+tQCsUCoSGNg6NufLKK7F+/XqsWrUK7777bocyrlq1CrfddhuuuOKKpttSU1PPuZ9arW56/oiICPTu3RsDBw7EmDFjsHTpUtx8880dytERDrmCCwBzB8dAp3bI/k3kcHp4VyMxd6XoGNROE05ug1ahFR2DnBzH/V6Y/403QuYgq9x33nknUlNTcf311+OWW27BY4891mKBBIC6ujosW7YMAJoV4JZ4eHhAr9d3OF9oaCg2bNiAoqK2jxUfPXo0UlNTsXz58g7n6AiHLbg+WiWuG8jpZkSt8ULIekimjn9SIzG0DdWY6MVtCtRxHPfbMrmfH/yunCU6RhNJkvC///0P69evR0hICB588MFz7jN48GDodDp4enrixRdfRJ8+fTBmzJjzPubu3bvx2WefYfTo0R3O9/LLL6OoqAihoaHo0aMH5s+fj59+av3xkykpKcjMzOxwjo5w2IILADcPi4VG6dARiYTr6lWD5NwVomNQB00vyhEdgVwIx/025z9nNmRax3qVZMmSJdBqtTh16hTOnDlzzu9/+eWX2Lt3L7799lskJCRg6dKlUCqbX5904MAB6HQ6eHh4oH///hg0aBDefPNNnD59GjqdruntmWeeaVO2Ll264ODBg9i2bRtuvPFGFBYWYsqUKa3ecmCxWCAJPq/ZodtjoE6Nq/p1Eh2DyKG9ELoBkqlBdAzqoJ7ZaYjTRYqOQS7mz3G/1SN6iY4ijMzLC37XXis6RjNbtmzBK6+8gtWrV6N///646aabYLFYmt0nKioKiYmJmD59Op555hlMnz4dDQ3NP9cnJycjLS0NR44cQV1dHVatWoWQkBCEh4cjLS2t6W3+/PkAAG9vb1RUVJyTp7y8HHK5HJ5/29oik8nQr18/LFy4EMuXL8fSpUvxwQcf4NSpUxf9+I4cOYLYWLEnYjl0wQWAW4bHQSnn1BailqToatElb4XoGGQlMyRv0RHIBZ2Wl+PGwQfcdtyv37XXQO7lJTpGk9raWsydOxcLFizAqFGj8MEHH2DHjh145513zvs+l19+ORQKBd5+++1mt6tUKiQkJCAmJqbZ/lyFQoGEhISmN39/fwCNhfjQoUPnFOU9e/YgNjb2nBXiv+vSpQsAoKam5oIf34YNG3DgwAHMnDnzgvezNYcvuOG+HpjRi6saRC15IexXSMY60THISqac3AWFzLEvrq05VoOsV7JwdOFRHJx7EJW7K5v9vsViQcHyAhy9+ygOzTuEUy+cQkP+xV9hKFlXgmP3HcOhmw/h5FMnUZvRfIxx3ud5OHL7ERy99yjKt5Q3+72KHRXIeiWrwx+bq3PHcb+SVutwY3kfeughWCwWPPfccwCAmJgYvPjii7j//vvPu29VkiTcddddeO6551Bb2/4R39deey0kScLs2bOxe/dupKenY8mSJXj11VebHS92+eWX45VXXsH27duRlZWFX3/9FbfffjuSkpKQkpLSdL+Ghgbk5+cjJycHe/bswTPPPINp06Zh8uTJmD17drtzWoPDF1wAWDAyHnKZe/xnJGqteG0duueLvUqVrMu/phgjfZJFx7ggc4MZmk4ahF/f8qjY4h+LUbK2BOFzwhH/WDxkahkyX8qEWX/+g98rtlcg/4t8BF8WjPgn46GJ0iDzxUwYKxvP0azcW4mKrRWI+VcMQmeFIufDHBirGn/PVGtCwbcFCJsdZv0P1gU1H/cbITqOzfldeSUUfo4zDvu3337DW2+9hQ8//BDav+0JvvXWWzF48OAWtyr8ac6cOTAYDHjzzTfb/fy+vr7YvHkzDAYDpk6dip49e+L111/Hyy+/jFtvvbXpfhMmTMD333+PKVOmICkpCXPmzEFKSgrWrFkDxd/OEf75558RFhaGmJgYTJw4ERs3bsTrr7+OlStXQi54yp5kOd+fpIO5+4u9WJmWKzoGkcP4LvEX9Mr+SHQMsrJN8YNxu/ncC04c0cG5B9Hpzk7w7tO4tcJiseDYwmMInBiIwEsaXwo31Zpw9K6jiLg5Ar4DfVt8nJNPnYRHrEdTabaYLTh27zEEjA1A0OQgFP1YhPrMekTd1jjW+MhdRxC9MBraOC1yluZAHaZG4AT3e+m9o7zMajx1tAsiVu8BTCbRcaxO0mqRsOYXKAL5b8MdOcUKLgDcPirBXV5RIbqoGI969Cz4RnQMsoEhGdsRrHHOL8iGIgOMFUZ4dvnrQhW5Vg6PeA/UnWx5K43ZaEZdZh10XXRNt0kyCbquOtSebHwpVhOlQV1mHUw1JtRl1sGit0AdokbN8RrUZ9UjYFyAbT8wF+Xq437958xmuXVjTlNwk0K8MK5ziOgYRA7hxcjNkPQX3uhPzkluMWGa2jlfbjdWNG4bUPg030es8FbAUNHylD1TlQkwt/w+fz6eV3cv+AzywcknT+LM+2cQOS8SklpC7rJchM8JR+mGUhx/8DgyFmWgPqfeBh+Za3PFcb9yPz8E3HST6BgkkNMUXAC4b3wy9+KS24vUNKBPIVdvXdmM0wcggZ/r/i5kegiSXkhC4qJEePfxRvHqYui66CDJJRStKkLcw3HwG+GHM4udY3uHo/lz3O9/7wiDuXuS6DgdFnDrLZDrdBe/I7kspyq4yaFeuLJflOgYREK9GPUHpIYq0THIhiJLT6O/T6LoGG325yrsnyuvfzJWGqH0afn4IbmXHJC1/D7/XNX9U0NuA8q3liN4RjBqjtZAm6yFwlsBn/4+qM+qh6nO9faT2osrjPtVhIfB75prRMcgwZyq4ALAveOS4KV27GN0iGwlTKPHgMKvRccgO5he73wlTRmkhMJHgZrDf22fMdWZUHeyDh7xHi2+j0whg0eMB6oPVzfdZjFbUH24Gtr4cydPWSwW5HyUg9CrQiHXyGExW2AxNV4rbTGevWb6/Ac2UCs4+7jfoDvvgsxFtlpQ+zldwQ3UqXHbqATRMYiEeClqC6SGc6fQkOsZm74VXkrHe4nVVG9CXVYd6rIaLxrTF+tRl1UHfYkekiQhYHwACr8vROXeStRn1+PM4jNQ+Cng3fuvIRannj+FknUlTb8OnBCIst/KUPZ7Gepz65G7LBfmBjP8hp17vFPZb2VQeCng3avx8bSJWtQcqUFtei2K1xRDHa6G3FPs8USuwhnH/aoTE+EzbaroGOQAnHIp9MahMfhsRxayS3nAPbmPYLUBA4u+Eh2D7ERtrMelujh8UbZfdJRm6k7VIfP5zKZf53+eDwDwHeKLyHmRCJwUCHODGbkf5sJUa4I2SYuY+2IgU/21nqIv1DedYwsAPgN8YKwyovC7QhgrjNB00iDmvphztigYK4wo+r4IcY/GNd2mjdMicGIgsl7JgsJbgYh5rn+2q729EbwPK2/xxRPbe0H3217RcS4o6J57IMmcbu2ObMBpzsH9px/25+H2z/aIjkFkN8sSN2N49v9ExyA7OhLWBbM01Re/I5GdzCnrisnLc2ApLBYd5Rweffog5tNPRMcgB+G03+Zc2iMM/WIcZzoJkS0FqQwYWvyl6BhkZ53zDqOzV7ToGERNHHncb/DfRs0SOW3BBYBHL+3iaP+/iGzi+egdkNWVio5BAkw3n3uhFZFIjjjuVzd6NLS9e4mOQQ7EqQtuapQvLuvpGP+5iGzFT2nEyBKu3rqrS09ug1quFh2D6BzfeR3HjdeUI2daP0Au8MI+pRLB//qXuOcnh+TUBRcA7p+YDA8lr5gl1/V89C7Iah1vvxvZh3ddBcZ4O9+ZuOQeHGHcr//s66GOixXy3OS4nL7ghvl4YN4w/sMm1+SlMGJM2ReiY5BgM8qKREcguqA/x/2esPO4X0VQEAIX3Ga35yPn4fQFFwDmj4xHiDdfwiPX80LMHshrCkXHIMH6n9qFSG2o6BhEF6SXTHgkfg9euCPUbuN+g//9L8h1zjlxjWzLJQquVqXAv8Yni45BZFWeChPGl3PvLQESLJiuCBQdg6hVdqpz7TLu16N3b/hM5VAHaplLFFwAuLxPJLpFeF/8jkRO4rmYNMir80THIAcxLTMNconXG5BzsPm4X5kMoY8+Yv3HJZfhMgVXkiQ8emkX0TGIrMJTbsYlFdx7S38JqcjFYB/7vOxLZC1/jfvtbdVxv76zroCmC7/m0/m5TMEFgIFxAZjQNUR0DKIOWxSTBkVVjugY5GBm1NSKjkDULm8E78d9tyhRPaLjZ9XKfXwQdPfdVkhFrsylCi4APDypM1Ryl/uwyI14yE2YUsnVWzrXiPRt8FdzgiM5p9Pyctw4+AB+mJ8KKbj9e8qDFt4NhR//H9CFuVwTjA7wxC3D40THIGq3p6IPQFF1RnQMckBKswFTPKJExyDqkI6M+1V37gzfK6+0UTJyJS5XcAHgzjEJiAvisSHkfNQyMy6r5skJdH4zco6JjkDUYe0a9ytJCP3Po5BkLlldyMpc8l+JWiHHs9O7t/UbQyLhHo85BGVllugY5MDiCk8g1TtedAwiq2jLuF/fWbOg7d3bTsnI2blkwQWAAXEBuKpfJ9ExiFpNKbPg8lqu3tLFzTDwuDByHa0Z96sIDkbwv+6zbzByai5bcAHgoUkpnHBGTuM/0YehKs8QHYOcwMST26BVaEXHILKqC437DXn0Eci9vAQlI2fk0gXXW6PEU9NscMA0kZXJJTOuqv9KdAxyEtqGakzw4jYFcj0tjfvVjR0D7/HjBScjZ+PSBRcAJnQNxcSunOFOju2RmGNQlZ0QHYOcyIwinpNMruvPcb+H5w5F6H/+IzoOOSGXL7gA8NS0rvDWKETHIGqRJFlwLVdvqY16ZqchThcpOgaRzVgkwDJjApQhHOBEbecWBTfYW8MxvuSwHoo+DnUZj36itpsheYuOQGQzA8MG4vKky0XHICflFgUXAGb1i8LI5CDRMYiakSQLZhu+Fh2DnNSUk7ugkPHVKXI9HgoPPDH4CdExyIm5TcEFgOdm9OBWBXIo/+qUDk3JYdExyEn51xRjpE+y6BhEVnd377sRoWvlAAiiFrhVwQ310eCxKV1FxyBqcqOJq7fUMdMrKkRHILKq3sG9cU3KNaJjkJNzq4ILAJf3icTYzsGiYxDhnk4Z8Cg+KDoGObkhGdsRrAkUHYPIKnRKHZ4Z9gwkjiKlDnK7ggsAz8zoDl+tUnQMcnM3W74RHYFcgNxiwjR1mOgYRFbx8ICHuTWBrMItC26wlwZPTuVWBRLn9qhMeBaliY5BLmJ69kFI4IoXObeJMRMxJX6K6BjkItyy4ALAtJ4RHABBwiwAV2/JeqJKstDPJ1F0DKJ2C9GG4NGBj4qOQS7EbQsuACya3g2BOtXF70hkRbdEZkFXtEd0DHIx0+tNoiMQtYsECc8MfQY+ah/RUciFuHXBDdSp8eqVvSDjK3tkR3fIlouOQC5oXPpWeCl1omMQtdnsLrPRP6y/6BjkYty64ALA0MRA3DmaL+2RfdwYkQ3vwp2iY9jUpiwjpnxei/CXqiA9WYkVRw3Nfv+JX+uR8mY1PJ+phN/zlRi7rAbbzxgv+JjPbm5Av/eq4fVsJYL/W4XLvqjFseLmK5b3/lIP/+crEfVKFT7d3/w5vz5kwJTPa63zATootbEel+riRMcgapNkv2Tc3ftu0THIBbl9wQWAu8ckYmgCj9kh27tb4fqrtzV6C1JDZHhrkqbF308KkOPNSRocWKDD7zd4IsZXhvGf1KKoxnzex/wty4jb+6mw7SZPrL1eC4MZGP9JLWr0FgDA98cM+OyAAWuu98QLYzW4+fs6FNc2Pl5FvQWPbGg4bx5XMiMvQ3QEolZTy9V4bthzUMp5qhFZHwsuAJlMwqtX9USwl1p0FHJhs8Nz4VOwXXQMm7skUYlFozWY3rnlL1rXdFdibJwCcX4ydA2W4+UJGlQ2APsLzl9wf77OE3N7qtA1WI7UUDmWTtPgdIUFu/MaV3GPFJsxMkaOvuFyXN1dCW+1hFNljeX3/rX1WNBXiU4+rv/prnPeYXT2ihYdg6hVFvZeiAS/BNExyEW5/mf8VgrUqfHG1b0g54ZcspF7VN+JjuBw9CYLFu/Ww0cNpIa2/tNRRUPjj/4ejf9fU0Pk2JVrQlmdBbtzTagzWJDgL8Pvp43Yk2/CXQPc52LS6Wat6AhEFzUyciSu7Xyt6Bjkwlhw/2ZAXADuHZckOga5oKvC8uCX/4foGA5j9XEDdM9UQrOoCq9s02Pt9Z4I1Lbu05HZYsHCn+sxJEqObsFyAMCEBAWu66FEv/eqMXdlHT66zAOeKmDBD/V451IP/G+XAclvVmPIkhocKnTt0wYuPbkNajlfjSLHFaGLwKKhizitjGyKBfcfbhsZj1HJQaJjkIv5t2al6AgOZVSMAmnzddhykxYT4xWY9U0tCi+wB/fvbv+hHgcLTfjico9mtz8xUoP0u7xwYIEO0zsr8exmPcbGKqCUA4s2NeD3G7S4uZcSs1fU2eJDchjedRUY480LZ8kxqWQqvDTyJR4JRjbHgvsPkiThlSt7IsLX4+J3JmqFmSEFCMjbJDqGQ/FUSUjwl2FgpAIfTPOAQibhgz2Gi77fHT/WYfUJIzbO8USk9/k/fR0tNuGTAwY8PVqNXzONGB4tR5CnDLO6KrEnz4yqBos1PxyHM6OsSHQEohY90P8BdA3gJFGyPRbcFvhqVXjjml5QyvnyCXXcg9pVoiM4PLPFggbT+UunxWLBHT/W4bujRmyYrUWs3/k/dVksFty6uh4vj1dDp5JgMgOGs4vDf/54gadyCf1P7UKklpMaybFcGncpZiXPEh2D3AQL7nn07uSHByamiI5BTm5KcBGC8jaKjmFX1XoL0vJNSMtv3Ot6qsyMtHwTTleYUaO34OH19dh2xoiscjN255pw48o65FRacEWXv05dGLOsBm/u0Df9+vYf6/HJfgM+m+EBL7WE/Goz8qvNqDOc21Tf32NAkFbClOTGxxvSSYENp4zYdsaIV7Y2oEuQDL4a1/7mVYIFlyl49CE5jnifeDw28DHRMciNKEQHcGQ3D4vDjlOlWHO4QHQUclKP6r4HKkWnsK9duSaM+uivoQr3rmkA0IA5qUq8M1mDo8VmfLSvDsW1FgR4SOgXIcfmGzzR9ewFYwBwstTcdI4tAPxvV+P2hZEfNR/W8OE0Deb2/OuEhIJqM/5vcwO23OTZdFv/CDnuG6TGpZ/VIdhTwkeXucf2o2lZ+/C2vxJmS+v2NhPZiofCAy+PfBlaJU/4IPuRLBaLi79Y1zEVdQZMeeN3nC517SlIZH2XBBXj7aq7IYH/xUiMBb3G4/fyo6JjkJt7ftjzmBQ3SXQMcjPconARPh5KvHVNb6gU/KOitnnMezXLLQk1s8a1T4wgx3dl8pUstyQEW1srdI/0wX8u7Sw6BjmRcYGlCM1ZKzoGubkR6dvgr/YTHYPcVGpQKh7o94DoGOSmWHBb6fpBMZiSGi46BjmJJ3x+4OotCac0GzDFI0p0DHJDoZ6heHXUq1DKWx7ZTWRrLLht8NyM7kgJ9RIdgxzcSP8yhOf+IjoGEQBgRs4x0RHIzXgoPPDG6DcQ6MGTPEgcFtw28FQrsGRuP4R4cwwmnd9T/j9B4pXr5CDiCk8g1TtedAxyExIkPDP0GaT485hNEosFt43CfT3wwZx+8FTJL35ncjtD/SsQlfOT6BhEzcww8PMV2cdtPW/D2OixomMQseC2R7cIH7x5TW/IZa59WDy13SL/nyFZTKJjEDUz8eQ2aBU8g5Rs65KYSzA/db7oGEQAWHDbbVRKMJ6Yynna9JcBvpWIzv1BdAyic2gbqjHBi9sUyHa6BnTFU0OeEh2DqAkLbgdcPzAatwyPEx2DHMQzgWsgmY2iYxC1aEZRrugI5KKCPYLx+ujXoVFoREchasKC20EPXZKCSd1DRccgwXr7VCEu93vRMYjOq2f2XsR6RoiOQS5GLVfjtdGvIVgbLDoKUTMsuB0kSRJentUTvTv5io5CAj0fvA6S2SA6BtEFzZD5iI5ALkQmyfDM0GfQLbCb6ChE52DBtQKNUo735/RDdAAv4nBHPb2rkZC7UnQMoouaenIXFDKF6BjkIh7o9wDGx4wXHYOoRSy4VuLvqcLSG/rDT8upLe7muZD1kEx60TGILsq/phgjfZJFxyAXcFO3m3BN52tExyA6LxZcK4oN9MTi2X2hUvCP1V109apBcu4K0TGIWm16RYXoCOTkpsZPxcI+C0XHILogNjEr6xfjj5euSIXEI3LdwguhGyCZGkTHIGq1IRnbEazhCFVqnyERQ/Dk4CdFxyC6KBZcG5iSGo5/T+DLgK4uRVeLLnkrRMcgahO5xYRp6jDRMcgJdQvohpdHvMx93OQUWHBt5LaRCbi6fyfRMciGXgj7FZKxTnQMojabnn0QEvgyE7VeJ69OeGvsW9AqeTE1OQcWXBt6elpXjEgKEh2DbCBeW4fu+ctFxyBql6iSLPTzSRQdg5xEgCYA74x7B/4af9FRiFqNBdeGFHIZ3r62N/pE+4mOQlb2YsQmSIZa0TGI2m16vVl0BHICnkpPvDX2LUR5RYmOQtQmLLg25qlW4KMb+6MXB0G4jBiPevQs+EZ0DKIOGZe+BV5KnegY5MA8FB54a8xb6BrQVXQUojZjwbUDnVqBZTf2R88oX9FRyApejNwMSV8jOgZRh6iN9ZikixUdgxyUWq7G66NfR5+QPqKjELULC66deGmUWHZTf6RGclSmM4vUNKBPIVdvyTXMyM8UHYEckFKmxCsjX8HAsIGioxC1GwuuHXlrlFh20wB0j2DJdVYvRf0BqaFKdAwiq+iSewidvaJFxyAHopAp8NKIlzAscpjoKEQdwoJrZz4eSnxy0wB0i/AWHYXaKEyjR//Cr0THILKq6WYe+0SN5JIczw17DqM6jRIdhajDWHAF8NE2ltyu4Sy5zuSlqC2QGipFxyCyqktPboNarhYdgwSTSTIsGroIE2ImiI5CZBUsuIL4alX49OYB6BzGkusMgtUGDCzi6i25Hu+6Cozx5pm47kyChCcGPYHJcZNFRyGyGhZcgXy1Knx28wCkhHqJjkIX8WKnbZDVl4uOQWQTM8qKREcggR4Z8AimJ04XHYPIqlhwBfPzVOGzeQNZch1YkMqAocVfio5BZDP9T+1CpDZUdAyyMwkSHh3wKK5MuVJ0FCKrY8F1AP6ejdsVkkJ46Lojej56B2R1paJjENmMBAsuUwSKjkF2JJfkWDR0EcstuSwWXAcRoFPjs3kDkRjMkutI/JRGjCzh6i25vmlZ+yCT+CXBHShkCrww/AVMjZ8qOgqRzfCzmQMJPFtyE1hyHcbz0bsgqy0WHYPI5kLLczDYJ0l0DLIxtVyN10a9hvEx40VHIbIpFlwHE+SlxmfzBiAuyFN0FLfnpTBiTNkXomMQ2c2MmnrREciGtAot3h7zNoZHDhcdhcjmWHAdULCXBl/eMogTzwR7IWYP5DWFomMQ2c3I9K3wV/uKjkE24KXywuLxi9E/rL/oKER2wYLroIK81PjiloEYmRwkOopb8lSYMJ6rt+RmlGYDJntEiY5BVuav8ceSCUuQGpQqOgqR3bDgOjBPtQLvz+6LK/vyC469PRe9F/KafNExiOxuZs5x0RHIioI9gvHhhA+R4p8iOgqRXbHgOjiFXIbnL++BhWM5achePOVmXFLJkxPIPcUVnkCqd7zoGGQFMd4x+OiSjxDnGyc6CpHdseA6iYVjk/DCzB5QyCTRUVzeopg0KKpyRMcgEmaGQS46AnVQ7+De+GTSJ4j0ihQdhUgIFlwnMqtfFN6f0xeeKn7xsRUPuQlTKrn3ltzbxJPboFVoRcegdhoXPQ6Lxy+Gj5oXKpP7YsF1MiOTg/HlrYMQ5KUWHcUlPRV9AIqqM6JjEAmlbajGBC9uU3BG13W+Di+OeBFqOb9GkHtjwXVC3SJ8sHzBYMTzrFyrUsvMuKyae2+JAGBGUa7oCNQGMkmG+/vdjwf6P8CJdERgwXVaUf5afLtgMPpG+4mO4jIejzkEZWWW6BhEDqFn9l7EekaIjkGtoJar8d/h/8X1Xa4XHYXIYbDgOjFfrQqf3DwAl3QLFR3F6SllFlxey9Vbor+bIfMVHYEuwkftg/fGv8fRu0T/wILr5DRKOd66pjduGBIjOopTeyz6MFTlGaJjEDmUKRm7oJApRMeg84jQReDjSz5Gr+BeoqMQORwWXBcgk0l4fEpXPHppZ0g8RazN5JIZV9Zx9ZbonwKqizDCJ0l0DGpB35C++PzSzxHrEys6CpFDYsF1ITcPi8MbV/eCSsG/1rZ4NPoYVOXpomMQOaQZFZWiI9A/XJF0BRaPXww/Da/BIDofyWKxWESHIOvac7oMt3+6B3kV9aKjODxJsuBo6FNQlx0THYXIIZkkOcanpKKwvlh0FLenkBS4v//9uDrlatFRiBwel/pcUO9OfvjhrmEYlhgoOorDeyj6OMst0QXILSZMU4eJjuH2fNW++N+4/7HcErUSV3BdmNlswSvrjuPNjeng3/K5JMmCI+H/B03JYdFRiBxadkA0LvUGLOAnEhGS/ZLx2ujXEKHjsW1ErcUVXBcmk0m4b3wylszpB1+tUnQch/OvTukst0StEFWShX4+iaJjuKWJMRPx8aSPWW6J2ogF1w2MSgnG93cMRfcIziX/uxtNX4uOQOQ0ptebRUdwKzJJhnv63IP/jvgvPBQeouMQOR0WXDcR5a/FNwsG4er+nURHcQj3dMqAR/FB0TGInMa49C3wUupEx3ALAZoAvDvuXdzY7UbRUYicFguuG1Er5Hh2Rne8eEUqNEr3/qu/2fKN6AhETkVtrMckHc9ctbX+of3xzdRvMDBsoOgoRE7NvVuOm7q8TyS+u20IYgK0oqMIcXtUJjyL0kTHIHI6M/IzRUdwWTJJhvmp8/He+PcQ6METcIg6igXXTXUO88aqO4difJcQ0VHsbgG4ekvUHl1yDyHFK1p0DJfz55aE23veDpnEL8tE1sD/SW7MW6PE4tl98eAlKZDL3GPG7/zILOiK9oiOQeS0plvc85UfW+GWBCLb4Dm4BADYllGCOz/fi6KqBtFRbOpAp5fhVbhLdAwip1Xh4YsxEUFoMLn25wpbk0ky3NLjFixIXcBVWyIb4P8qAgAMjAvAD3cORf8Yf9FRbObGiGyWW6IO8qkrx2hvnonbEf4af7wz9h1uSSCyIf7PoibB3hp8Nm8A/jU+CSq56/3TuFuxXHQEIpcws6xIdASnNTJyJL6d+i0GhQ8SHYXIpXGLArXoaH4l7vtqHw7lVoqOYhWzw3PxVOm/RMcgcgkWSJjUtR/O1OaLjuI0PJWeuL/f/ZiROEN0FCK34HrLdGQVKaHeWHn7ECwcmwil3PkvQLtH9Z3oCEQuQ4IFlyl4lFVr9Q7ujW+mfMNyS2RHXMGlizqUW4F/fb0fR/KcczX3qrA8PFd2n+gYRC4l3zcCE/yVMFs4wvd8lDIl7ux1J+Z0ncO9tkR2xv9xdFFdw32w6o4huGt0AhROeJzYvzUrRUcgcjmh5TkY7JMkOobDSvJLwheTv8AN3W5guSUSgP/rqFWUchnuHZ+MFbcPQXKIl+g4rTYzpAABeZtExyBySTNq6kVHcDgySYYbu92ILy79Akl+/AaASBRuUaA20xvNeG39cbzzWwZMZsf+57Mz9j0E5W0UHYPIJRlkSoxN6ozShnLRURxCJ69OWDR0EXoF9xIdhcjtcQWX2kylkOHfE1Lw3W2DkRisEx3nvKaFFLLcEtmQ0mzAZI9OomMIp5ApcHP3m7F82nKnKrdz586FJElNbwEBAZg4cSL279+PpUuXNvu9lt4yMzNhsViwePFiDBgwADqdDr6+vujbty9effVV1NbWnve5R44ciYULF55z+9KlS+Hr69v069raWjz00EOIj4+HRqNBUFAQRowYgZUrVzZ7rD8zqdVqREREYMqUKVi+nEdDujMWXGq3HpG+WH3XUMwfEe+Qo34f9vxedAQilzcj57joCEL1COyBLyd/ibt73w21XC06TptNnDgReXl5yMvLw/r166FQKDB58mRceeWVTbfn5eVh0KBBmDdvXrPboqKicP3112PhwoWYNm0aNm7ciLS0NPznP//BypUrsWbNmg7nmz9/PpYvX4433ngDR48exc8//4zLL78cJSUlze73Z7aTJ0/i22+/RZcuXXDVVVfhlltu6XAGck4K0QHIuakVcjx4SQomdA3Bv77eh5NFNaIjAQAmBRUjOHeD6BhELi++8Dh6pI7C/sqToqPYlafSE3f2uhNXp1zt1BeRqdVqhIaGAgBCQ0Px4IMPYtiwYaiurm66HQBUKhW0Wm2z27766it8+umnWLFiBaZNm9Z0e0xMDKZOnYrKyo6fvLNq1Sq89tprmDRpUtNj9+nT55z7/T1bZGQkBg4ciJSUFNx4442YNWsWxo4d2+Es5Fyc938lOZRenfzww13DcMvwODjCYu5/vFdDgmPvDyZyFTMMctER7Gpk5EismLYC13a+1qnL7T9VV1fjk08+QUJCAgICAi56/08//RTJycnNyu2fJEmCj49PhzOFhobixx9/RFVVVZvfd86cOfDz8+NWBTflOv8zSTiNUo6HJ3XGqjuGon+Mv7Ac4wJLEZqzVtjzE7mbS05ug1ahFR3D5gI9AvHSiJfwxpg3EOoZevF3cAKrV6+GTqeDTqeDl5cXVq1ahS+//BIy2cXrwYkTJ5CcnGzTfIsXL8aWLVsQEBCAfv364Z577sEff/zRqveVyWRISkpCZmamTTOSY2LBJavrFuGDr+YPwhtX90KEr4fdn/8Jnx+4ektkR9qGakzwihcdw2YkSLg86XKsvGwlxseMFx3HqkaNGoW0tDSkpaVhx44dmDBhAi655BJkZWVd9H1bcwjT5s2bmwq0TqfDp59+2qZ8w4cPR0ZGBtavX4/LL78chw4dwrBhw/D000+36v0tFgskyQFeViS7Y8Elm5mSGo71943AwrGJ0Cjt809tpH8ZwnN/sctzEdFfZhTlio5gE6lBqfj80s/x+KDH4a3yFh3H6jw9PZGQkICEhAT069cP77//PmpqavDee+9d9H2TkpJw9OjRC96nb9++TQU6LS0NU6dOBQB4e3ujoqLinPuXl5efs7VBqVRi2LBheOCBB7BmzRo89dRTePrpp6HX6y/43CaTCSdOnEBsbOxFPxZyPSy4ZFMapRwLxyZhw30jMblHmM2f7yn/nyBxdCiR3fXM3otYzwjRMawmWBuMZ4c9i48v+RhdA7uKjmM3kiRBJpOhrq7uove95pprcPz48WZHdv3JYrGgoqICHh4eTQU6ISEBXl6Ng4KSk5OxZ8+ec95vz549SEq68ICMLl26wGg0or7+woNGPvroI5SVlWHmzJkX/VjI9bDgkl2E+3rgzWt64+v5g9AtwjarIEP9KxCV85NNHpuILm6GzFd0hA5TyVSY130evr/se0yOm+zyL283NDQgPz8f+fn5OHLkCO68805UV1djypQpF33fWbNm4corr8TVV1+NZ555Brt27UJWVhZWr16NsWPHYuPG859DvmDBAhw/fhx33XUX9u/fj2PHjuHll1/G559/jvvuu6/pfiNHjsS7776L3bt3IzMzEz/++CMefvhhjBo1Ct7ef30tqa2tRX5+Ps6cOYNt27bhgQcewPz587FgwQKMGjWqY39I5JQ4yYzszmy24Ktd2XhxzTEUV1/4Jaa2+DXhS8ScOXclgYjso0QXhLEhXjCajaKjtMvoqNH4V79/IcorSnQUu5g7dy4++uijpl97eXkhJSUFDzzwwDmrniNHjkTPnj3x6quvNrvdbDZj8eLFWLJkCQ4dOgSFQoHExETMnj0b8+bNg4fH+a/D2LlzJx555BGkpaVBr9cjJSUFDz74IC677LKm+zz77LP4/vvvcezYMdTW1iI8PByTJ0/GY4891nTSw8iRI/Hbb78BaDzOLCAgAH369MGNN96I6dOnd/BPiZwVCy4JU1VvwOvrT2DplkwYTB37ZzjAtxJf6O+A5KRfWIlcxcLeE7G+7LDoGG2S4JuAB/o/gIFhA0VHISIrYcEl4TKKqrHohyPYcLSw3Y+xIfEbxGXzrEMi0TbFD8bt5jOiY7SKn9oP81Pn48rkKyGXuddZvkSujgWXHMavxwrx9OrDbZ6G1tenCl8b7oBkNtgoGRG1lkmSY3xKDxTWl1z8zoJ4Kj0xu8tszOk6B55KT9FxiMgGOKqXHMbI5GAMSQjER1sy8fr6E6isb912g2eD10LKZrklcgRyiwnT1OF4zwELrkqmwqzkWZjXYx78NeKG0RCR7XEFlxxSRZ0BH/x+Ch/+fgpVDecvuj29q/Gd6Q5IJutdrEZEHZMdEINLvS2wOMjAFbkkx5T4Kbgt9TaE6Wx/XCERiceCSw6totaA9zZnYOmWTFS3UHR/TlyJlOwvBSQjogu5secY7Kw4IToGxnYaizt73Yk43zjRUYjIjlhwySmU1ejx7qYMLNuaiVq9CQDQ1asGq813QDI1CE5HRP+0OmU0HmpIF/b8A0IH4O7ed6N7UHdhGYhIHBZcciol1Q14d1MGPt6ahW9iVqBr9ueiIxFRCxoUGoyKi0OVodquzzsobBDm9ZiHfqH97Pq8RORYWHDJKRVVNcBv+wtQ7HgH0Nv3CygRtc6i3pfiy7IDNn8eCRJGRo3ELT1uQbfAbjZ/PiJyfCy45NxqS4Ft/wN2vAvUV4hOQ0R/czi8K65UV9ns8eWSHBNiJuDm7jcj0S/RZs9DRM6HBZdcQ30lsPN9YOtbQG2x6DREdNYVPYbhaFWWVR9TKVNiavxU3NjtRnTy7mTVxyYi18CCS65FXwvs+QjY+jZQcVp0GiK391n3CXi2+ohVHksj12Bm0kzM7ToXoZ6hVnlMInJNLLjkmswm4NiPwPZ3gczNotMQua0KD1+MiQhCQwdOOwn2CMas5Fm4IvkKDmggolZhwSXXV3AI2P4OsP9rwFgnOg2R27m/9yT8VHawze/XI7AHru18LcbFjINSprRBMiJyVSy45D5qSxu3L+z8AKjIFp2GyG1si+2Pechv1X0VMgXGRY/DdZ2vQ4+gHjZORkSuigWX3I/ZBBxd3bh9IesP0WmIXJ4FEi7p2hc5tQXnvY+/xh8zE2fiqpSrEKwNtmM6InJFLLjk3vL2Nxbdg98AxnrRaYhc1jupk/BW5bnbFDr7d8bVKVdjUtwkqOVqAcmIyBWx4BIBQE0JsGcpsPsjoNy6RxoREZDvG4EJ/kqYLWbolDpMip2EmUkz0SWgi+hoROSCWHCJ/s5iAbK2APs+Aw6vAhoqRScichkvD78ZsVFDMSFmArRKreg4ROTCWHCJzsdQBxxZ3Vh2M34FLGbRiYicjy4USL0K6HktEJQkOg0RuQkWXKLWqMwD9n8J7PscKDoqOg2RY5OrgeRLgF7XAfGjAZlcdCIicjMsuERtlbsXSPu88cK02hLRaYgcgyQDogYC3WYA3WYCWg5kICJxWHCJ2stkAE6sAdI+a/zRpBediMi+JDkQPRjoMg3oPAXw4vhcInIMLLhE1lBfARxf03i+bvp6QF8lOhGRbcgUQMzQxlKbMgXQBYlORER0DhZcImszNgAZvwHHfgCO/QRUn/9weyKnIFMCcSPOltrJ3H5ARA6PBZfIliwW4MzOxpXdoz8AJemiExG1jlwFxI06W2onAR5+ohMREbUaCy6RPRUd+6vs5uwBwP9+5EC8woH4UY0nHySOAzQ+ohMREbULCy6RKJV5wLEfG/fsZv3euI+XyJ6UWiB6SGOhjR8NBKeITkREZBUsuESOwGwG8vcBpzYBpzYDp7cC+mrRqcjlSEBY6l+rtFEDAYVKdCgiIqtjwSVyRCYjkLMbyNzUWHqzdwDGetGpyBl5RzQW2rizb54BohMREdkcCy6RMzA2NJbcU5uAzM3AmV2A2SA6FTkcCQhMBCL6ApF9G4/zCkoWHYqIyO5YcImckb4GOL2tcZU3N61xulpVruhUZG8efmfLbD8gsk/jzz18RaciIhKOBZfIVVQVAHlpjWU3d29j8a3OF52KrEWmBEK6Nq7MRvZrfAuIF52KiMghseASubLKvMay+/fiW1MkOhVdjFILBCQAQSlAWI/GldnwnoDSQ3QyIiKnwIJL5G4qchoLb9ExoORk4/CJknSgtlh0Mvej8gKCkhqLbFDyXz/6RgOSJDodEZHTYsElokZ1Zc0Lb9NbBmCoEZ3OuWl8zi2xQSmAT6ToZERELokFl4gurjL3b4X3JFCZ07jnt/rsmzuf2avwALzDG998Is/+PKL5z7X+olMSEbkVFlwi6riG6r/KbnXB2fKbD1QXAlX5f91eUwynGE+s0ABq78aVV4134891IYBPxNnSGvlXoWV5JSJyOCy4RGQ/JiNQXw40VDW+6av/+vmfvzbUNb4Z6wFDLWCoB4x1jT+a9I17UyVZC28t3I6/3SaTA2qvs8X1bHn98+dNZfbsbZzuRUTk1FhwiYiIiMilyEQHICIiIiKyJhZcIiIiInIpLLhERERE5FJYcImIiIjIpbDgEhEREZFLYcElIiIiIpfCgktERERELoUFl4iIiIhcCgsuEREREbkUFlwiIiIiciksuERERETkUlhwiYiIiMilsOASERERkUthwSUiIiIil8KCS0REREQuhQWXiIiIiFwKCy4RERERuRQWXCIiIiJyKSy4RERERORSWHCJiIiIyKWw4BIRERGRS2HBJSIiIiKXwoJLRERERC6FBZfIwcydOxeSJDW9BQQEYOLEidi/f3/TfSRJgkajQVZWVrP3veyyyzB37tw2PVZLfv31V0iShPLy8nN+LyYmBq+++mrTr3/77TeMHj0a/v7+0Gq1SExMxJw5c6DX65s9liRJkMlk8PHxQa9evXD//fcjLy+v7X9AREREF8GCS+SAJk6ciLy8POTl5WH9+vVQKBSYPHlys/tIkoTHHnvMKo/VXocPH8bEiRPRt29fbNq0CQcOHMAbb7wBlUoFk8nU7L7Hjh1Dbm4udu7ciQceeADr1q1Dt27dcODAAatkISIi+hMLLpEDUqvVCA0NRWhoKHr27IkHH3wQ2dnZKCoqarrPHXfcgU8++QQHDx7s8GO115o1axAaGooXXngB3bp1Q3x8PCZOnIj33nsPHh4eze4bHByM0NBQJCUl4aqrrsIff/yBoKAgLFiwoMM5iIj+v527h2WujeM4/vOyiCLILW0akVA0GjbSEEoEkTAYxMRkMJgYSjeDl3QTHdAmErFJpEMjrF4WCenS2LyEICKh0aY1eQbRPFIPN72T29N8P+N1rvPP/5zpd65c1wH+jYAL/HCRSERra2uyWCwqLi5OjDc1Namnp0cTExMp1/ouo9Go6+tr7ezsfPnenJwcjYyMaH9/X7e3tyn3AgDAq+y/3QCAZIFAQAaDQZIUjUZlMpkUCASUmfn2m3R2dlZ1dXXa3d1Vc3NzSrW+o7+/X9vb23I4HDIajbLb7Wpvb9fQ0JDy8/M/vd9qtUqSzs7OVFJSknI/AABIrOACP1JbW5uCwaCCwaAODg7U1dWl7u7upENlNTU1Ghoa+nAV97Na3d3dMhgMMhgMstlsX+ozKytLKysrury8lNvtltls1szMjGw2228dIHt+fpb0sp8YAIA/hYAL/EC5ubmyWCyyWCyqr6+Xz+dTNBqV1+tNmjs1NaWjoyP5/f5v1fL5fIkAvLm5KUmJ1ddwOJxU7+HhQQUFBW/GzGazBgcH5fF4FAqFFI/Htbi4+OlzHh8fS3r5MwMAAH8KWxSA/4HXX2zFYrGka6WlpRodHZXL5VJFRcWXa5nN5qQ5lZWVyszM1OHhocrKyhLjJycnCofDqqqq+s/6hYWFMplMikajH/YRi8W0vLyslpYW/fr169O+AQD4XQRc4Ad6enrSzc2NJOn+/l4ej0eRSES9vb3vzp+cnJTX69Xp6akGBgZSqiVJeXl5Gh4e1vj4uLKzs1VbW6uLiws5nU7Z7XY1NjZKkpaWlhQMBtXX16eKigrF43Gtrq4qFAppYWHhTc3b21vF43E9Pj7q8PBQbrdbd3d32tjY+PZ7AgDgPQRc4Afa2tqSyWSS9BI2rVar1tfX1dra+u78oqIiOZ1OuVyulGu9mp+f19zcnJxOp87Pz2U0GtXR0aHp6enEntmGhgbt7e1pZGREV1dXiX28fr9fDofjTb3q6mplZGTIYDCovLxcnZ2dGhsbk9Fo/OLbAQDgYxnPr6c8AAAAgDTAITMAAACkFQIuAAAA0goBFwAAAGmFgAsAAIC0QsAFAABAWiHgAgAAIK0QcAEAAJBWCLgAAABIKwRcAAAApBUCLgAAANIKARcAAABp5R8MTTUgLADkWAAAAABJRU5ErkJggg==\n"
+            "image/png": "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\n"
           },
           "metadata": {}
         }
       ]
     },
     {
-      "cell_type": "code",
-      "source": [],
+      "cell_type": "markdown",
+      "source": [
+        "The above weights forms a portfolio with maximum sharpe ratio"
+      ],
       "metadata": {
-        "id": "FgllhXez_sr3"
-      },
-      "execution_count": 10,
-      "outputs": []
+        "id": "GAT1yTIEXGh4"
+      }
     }
   ],
   "metadata": {