mpt_test_03.ipynb

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        "<a href=\"https://colab.research.google.com/github/Maulanawesome5/modern_portfolio_markowitz/blob/master/mpt_test_03.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "xU-vWxlK5qgF"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import pandas_datareader as web\n",
        "import matplotlib.pyplot as plt\n",
        "import datetime as dt\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 333
        },
        "id": "OLKz8FAq5qgn",
        "outputId": "d93a4bf2-fc24-4d66-ec0b-523a024612e7"
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              "Attributes   Adj Close           Close            High             Low  \\\n",
              "Symbols        PWON.JK ASRI.JK PWON.JK ASRI.JK PWON.JK ASRI.JK PWON.JK   \n",
              "Date                                                                     \n",
              "2020-01-02  564.864868   238.0     570     238     575     240     565   \n",
              "2020-01-03  559.909912   236.0     565     236     575     240     555   \n",
              "2020-01-06  540.090088   234.0     545     234     565     236     545   \n",
              "2020-01-07  540.090088   236.0     545     236     550     238     535   \n",
              "2020-01-08  535.135132   232.0     540     232     550     236     535   \n",
              "\n",
              "Attributes            Open            Volume            \n",
              "Symbols    ASRI.JK PWON.JK ASRI.JK   PWON.JK   ASRI.JK  \n",
              "Date                                                    \n",
              "2020-01-02     236     575     238  12286300   3323600  \n",
              "2020-01-03     236     575     238  84199700  15815400  \n",
              "2020-01-06     232     560     236  76881100   3246300  \n",
              "2020-01-07     232     545     236  56419000   3340600  \n",
              "2020-01-08     230     540     236  50358200   2501900  "
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              "      <th>Symbols</th>\n",
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              "      <th>ASRI.JK</th>\n",
              "      <th>PWON.JK</th>\n",
              "      <th>ASRI.JK</th>\n",
              "      <th>PWON.JK</th>\n",
              "      <th>ASRI.JK</th>\n",
              "      <th>PWON.JK</th>\n",
              "      <th>ASRI.JK</th>\n",
              "      <th>PWON.JK</th>\n",
              "      <th>ASRI.JK</th>\n",
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              "      <th>2020-01-02</th>\n",
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              "      <td>238</td>\n",
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              "      <th>2020-01-03</th>\n",
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              "      <td>565</td>\n",
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              "    <tr>\n",
              "      <th>2020-01-06</th>\n",
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              "      <td>545</td>\n",
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              "      <td>560</td>\n",
              "      <td>236</td>\n",
              "      <td>76881100</td>\n",
              "      <td>3246300</td>\n",
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              "      <th>2020-01-07</th>\n",
              "      <td>540.090088</td>\n",
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              "      <th>2020-01-08</th>\n",
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              "      <td>232.0</td>\n",
              "      <td>540</td>\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-91c826cb-40e3-429c-a9ca-adcba44e31a4');\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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "# inisialisasi jangka waktu\n",
        "start_date = dt.datetime(year=2020, month=1, day=1)\n",
        "end_date = dt.datetime(year=2022, month=1, day=1)\n",
        "\n",
        "# akses data harga saham dari yahoo finance\n",
        "# test = data.DataReader([\"PWON.JK\", \"ASRI.JK\"], \"yahoo\", start_date, end_date)\n",
        "test = web.DataReader([\"PWON.JK\", \"ASRI.JK\"], \"yahoo\", start_date, end_date)\n",
        "test = pd.DataFrame(test)\n",
        "test.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "sgUhQeBB5qgw",
        "outputId": "438c3746-a6f4-4bd1-c3a7-bff73346773b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Symbols     PWON.JK  ASRI.JK\n",
              "Date                        \n",
              "2020-01-02      570      238\n",
              "2020-01-03      565      236\n",
              "2020-01-06      545      234\n",
              "2020-01-07      545      236\n",
              "2020-01-08      540      232\n",
              "...             ...      ...\n",
              "2021-12-24      460      162\n",
              "2021-12-27      466      163\n",
              "2021-12-28      478      165\n",
              "2021-12-29      476      162\n",
              "2021-12-30      464      162\n",
              "\n",
              "[489 rows x 2 columns]"
            ],
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Symbols</th>\n",
              "      <th>PWON.JK</th>\n",
              "      <th>ASRI.JK</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-01-02</th>\n",
              "      <td>570</td>\n",
              "      <td>238</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-03</th>\n",
              "      <td>565</td>\n",
              "      <td>236</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-06</th>\n",
              "      <td>545</td>\n",
              "      <td>234</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-07</th>\n",
              "      <td>545</td>\n",
              "      <td>236</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-08</th>\n",
              "      <td>540</td>\n",
              "      <td>232</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-12-24</th>\n",
              "      <td>460</td>\n",
              "      <td>162</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-12-27</th>\n",
              "      <td>466</td>\n",
              "      <td>163</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-12-28</th>\n",
              "      <td>478</td>\n",
              "      <td>165</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-12-29</th>\n",
              "      <td>476</td>\n",
              "      <td>162</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-12-30</th>\n",
              "      <td>464</td>\n",
              "      <td>162</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>489 rows × 2 columns</p>\n",
              "</div>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-338165f6-619a-4ecc-9a76-145aca0bbd7a 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-338165f6-619a-4ecc-9a76-145aca0bbd7a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "# ambil kolom harga close\n",
        "test = test[\"Close\"]\n",
        "test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zsksUvsN5qg0",
        "outputId": "7e87b151-a148-466c-ecd7-cffa23526b57"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<bound method NDFrame.head of Date\n",
              "2020-01-02         NaN\n",
              "2020-01-03   -0.008811\n",
              "2020-01-06   -0.036040\n",
              "2020-01-07    0.000000\n",
              "2020-01-08   -0.009217\n",
              "                ...   \n",
              "2021-12-24   -0.004338\n",
              "2021-12-27    0.012959\n",
              "2021-12-28    0.025425\n",
              "2021-12-29   -0.004193\n",
              "2021-12-30   -0.025533\n",
              "Name: PWON.JK, Length: 489, dtype: float64>"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "# Log of percentage change\n",
        "pwon = test[\"PWON.JK\"].pct_change().apply(lambda x: np.log(1+x))\n",
        "pwon.head"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZosDVuop5qg4",
        "outputId": "c573c92b-49cd-4b27-8d6d-1d850b9c1076"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0010020343762599815"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "# variance\n",
        "var_pwon = pwon.var()\n",
        "var_pwon"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "liVb8Tw65qg7",
        "outputId": "85ae4251-0da7-4e9b-f916-c073ae50cd45"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date\n",
              "2020-01-02         NaN\n",
              "2020-01-03   -0.008439\n",
              "2020-01-06   -0.008511\n",
              "2020-01-07    0.008511\n",
              "2020-01-08   -0.017094\n",
              "                ...   \n",
              "2021-12-24    0.000000\n",
              "2021-12-27    0.006154\n",
              "2021-12-28    0.012195\n",
              "2021-12-29   -0.018349\n",
              "2021-12-30    0.000000\n",
              "Name: ASRI.JK, Length: 489, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "# Log of percentage change ASRI\n",
        "asri = test[\"ASRI.JK\"].pct_change().apply(lambda x: np.log(1+x))\n",
        "asri"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y_Swtovr5qg9",
        "outputId": "552bff44-330e-428b-91b2-2a6bd377c4f7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0013916886728823988"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "# variance of asri\n",
        "var_asri = asri.var()\n",
        "var_asri"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DbeTaenE5qhC",
        "outputId": "74be2ebf-b6c6-4d85-a357-f9f57f3898df"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.5005083356598523, 0.5898492758498561)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "# volatility\n",
        "pwon_vol = np.sqrt(var_pwon * 250)\n",
        "asri_vol = np.sqrt(var_asri * 250)\n",
        "\n",
        "pwon_vol, asri_vol"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 330
        },
        "id": "FpfPirux5qhH",
        "outputId": "ccf656ad-6df4-4bf5-818d-6563351510c2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fcf289f3b50>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAEoCAYAAABILwrfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAATFklEQVR4nO3df5Ddd13v8eeLxCCWX2IXpzZpUzWIsUCpayooP4RW0yk2VURThxFUjNcxA7T3OqQDU2eq4wgO3MtodIxYwR8YigouEowoIAKC2UKlpjWwN1SSoNdtKVyL0jTt2z/Od+Gw3eyepCd7OJ/zfMycmfP9fj/7Pa9kdl7z3c/3x0lVIUkafw8bdQBJ0nBY6JLUCAtdkhphoUtSIyx0SWqEhS5JjVg7qg8+++yza+PGjaP6eEkaSzfffPOdVTW11LaRFfrGjRuZnZ0d1cdL0lhK8i8n2+aUiyQ1YqBCT7I1yaEkc0l2nWTMjya5LcnBJG8ebkxJ0kpWnHJJsgbYDVwGHAUOJJmpqtv6xmwCrgO+p6ruTvL4MxVYkrS0QY7QtwBzVXW4qo4De4Fti8b8DLC7qu4GqKp/H25MSdJKBin0c4EjfctHu3X9ngA8IckHk3w4ydaldpRkR5LZJLPz8/Onl1iStKRhnRRdC2wCng1cDfxOkscuHlRVe6pquqqmp6aWvOpGknSaBin0Y8CGvuX13bp+R4GZqrqvqj4FfIJewUuSVskghX4A2JTkgiTrgO3AzKIxb6d3dE6Ss+lNwRweYk5J0gpWvMqlqk4k2QnsB9YAN1bVwSQ3ALNVNdNt+/4ktwH3A79QVXedyeDSpNu4652jjtCUO371ilFHeMgGulO0qvYB+xatu77vfQHXdi9J0gh4p6gkNcJCl6RGWOiS1AgLXZIaYaFLUiMsdElqhIUuSY2w0CWpERa6JDXCQpekRljoktQIC12SGmGhS1IjLHRJaoSFLkmNsNAlqREWuiQ1wkKXpEZY6JLUCAtdkhphoUtSIyx0SWqEhS5JjbDQJakRFrokNcJCl6RGDFToSbYmOZRkLsmuJba/OMl8klu610uGH1WStJy1Kw1IsgbYDVwGHAUOJJmpqtsWDX1LVe08AxklSQMY5Ah9CzBXVYer6jiwF9h2ZmNJkk7VikfowLnAkb7lo8AlS4x7fpJnAp8ArqmqI0uMGTsbd71z1BGacsevXjHqCFKzhnVS9B3Axqp6MvBu4E1LDUqyI8lsktn5+fkhfbQkCQYr9GPAhr7l9d26L6mqu6rq3m7xDcB3LrWjqtpTVdNVNT01NXU6eSVJJzFIoR8ANiW5IMk6YDsw0z8gyTl9i1cCtw8voiRpECvOoVfViSQ7gf3AGuDGqjqY5AZgtqpmgJcmuRI4AXwWePEZzCxJWsIgJ0Wpqn3AvkXrru97fx1w3XCjSZJOhXeKSlIjLHRJaoSFLkmNsNAlqREWuiQ1wkKXpEZY6JLUCAtdkhphoUtSIyx0SWqEhS5JjbDQJakRFrokNcJCl6RGWOiS1AgLXZIaYaFLUiMsdElqhIUuSY2w0CWpERa6JDXCQpekRljoktQIC12SGmGhS1IjLHRJasRAhZ5ka5JDSeaS7Fpm3POTVJLp4UWUJA1ixUJPsgbYDVwObAauTrJ5iXGPAl4GfGTYISVJKxvkCH0LMFdVh6vqOLAX2LbEuF8CXg18cYj5JEkDGqTQzwWO9C0f7dZ9SZKLgQ1V9c4hZpMknYKHfFI0ycOA1wH/c4CxO5LMJpmdn59/qB8tSeozSKEfAzb0La/v1i14FHAh8L4kdwDfDcwsdWK0qvZU1XRVTU9NTZ1+aknSgwxS6AeATUkuSLIO2A7MLGysqs9X1dlVtbGqNgIfBq6sqtkzkliStKQVC72qTgA7gf3A7cBNVXUwyQ1JrjzTASVJg1k7yKCq2gfsW7Tu+pOMffZDjyVJOlXeKSpJjbDQJakRFrokNcJCl6RGWOiS1AgLXZIaYaFLUiMsdElqhIUuSY2w0CWpERa6JDXCQpekRljoktQIC12SGmGhS1IjLHRJaoSFLkmNsNAlqREWuiQ1wkKXpEZY6JLUCAtdkhphoUtSIyx0SWqEhS5JjbDQJakRAxV6kq1JDiWZS7Jrie3/I8mtSW5J8oEkm4cfVZK0nBULPckaYDdwObAZuHqJwn5zVT2pqi4CXgO8buhJJUnLGuQIfQswV1WHq+o4sBfY1j+gqv5/3+JZQA0voiRpEGsHGHMucKRv+ShwyeJBSX4euBZYBzxnKOkkSQMb2knRqtpdVd8CvAJ41VJjkuxIMptkdn5+flgfLUlisEI/BmzoW17frTuZvcBVS22oqj1VNV1V01NTU4OnlCStaJBCPwBsSnJBknXAdmCmf0CSTX2LVwCfHF5ESdIgVpxDr6oTSXYC+4E1wI1VdTDJDcBsVc0AO5NcCtwH3A286EyGliQ92CAnRamqfcC+Reuu73v/siHnkiSdIu8UlaRGWOiS1AgLXZIaYaFLUiMsdElqhIUuSY2w0CWpERa6JDXCQpekRljoktQIC12SGmGhS1IjLHRJaoSFLkmNsNAlqREWuiQ1wkKXpEZY6JLUCAtdkhphoUtSIyx0SWqEhS5JjbDQJakRFrokNcJCl6RGWOiS1IiBCj3J1iSHkswl2bXE9muT3Jbk40n+Jsn5w48qSVrOioWeZA2wG7gc2AxcnWTzomEfA6ar6snAnwCvGXZQSdLyBjlC3wLMVdXhqjoO7AW29Q+oqvdW1X92ix8G1g83piRpJYMU+rnAkb7lo926k/lp4F0PJZQk6dStHebOkrwQmAaedZLtO4AdAOedd94wP1qSJt4gR+jHgA19y+u7dV8hyaXAK4Erq+repXZUVXuqarqqpqempk4nryTpJAYp9APApiQXJFkHbAdm+gckeSrw2/TK/N+HH1OStJIVC72qTgA7gf3A7cBNVXUwyQ1JruyG/RrwSOCtSW5JMnOS3UmSzpCB5tCrah+wb9G66/veXzrkXJKkU+SdopLUCAtdkhphoUtSIyx0SWqEhS5JjbDQJakRFrokNcJCl6RGWOiS1AgLXZIaYaFLUiMsdElqhIUuSY2w0CWpERa6JDXCQpekRljoktQIC12SGmGhS1IjLHRJaoSFLkmNsNAlqREWuiQ1wkKXpEZY6JLUCAtdkhphoUtSIwYq9CRbkxxKMpdk1xLbn5nko0lOJPmR4ceUJK1kxUJPsgbYDVwObAauTrJ50bBPAy8G3jzsgJKkwawdYMwWYK6qDgMk2QtsA25bGFBVd3TbHjgDGSVJAxhkyuVc4Ejf8tFu3SlLsiPJbJLZ+fn509mFJOkkVvWkaFXtqarpqpqemppazY+WpOYNUujHgA19y+u7dZKkryKDFPoBYFOSC5KsA7YDM2c2liTpVK1Y6FV1AtgJ7AduB26qqoNJbkhyJUCS70pyFHgB8NtJDp7J0JKkBxvkKheqah+wb9G66/veH6A3FSNJGhHvFJWkRljoktQIC12SGmGhS1IjLHRJaoSFLkmNsNAlqREWuiQ1wkKXpEZY6JLUCAtdkhphoUtSIyx0SWqEhS5JjbDQJakRFrokNcJCl6RGWOiS1AgLXZIaYaFLUiMsdElqhIUuSY2w0CWpERa6JDXCQpekRljoktSIgQo9ydYkh5LMJdm1xPaHJ3lLt/0jSTYOO6gkaXkrFnqSNcBu4HJgM3B1ks2Lhv00cHdVfSvwv4FXDzuoJGl5gxyhbwHmqupwVR0H9gLbFo3ZBrype/8nwHOTZHgxJUkrWTvAmHOBI33LR4FLTjamqk4k+TzwDcCd/YOS7AB2dIv3JDl0OqG1pLNZ9P/91Sj+7TaJ/N0crvNPtmGQQh+aqtoD7FnNz5wUSWaranrUOaTF/N1cPYNMuRwDNvQtr+/WLTkmyVrgMcBdwwgoSRrMIIV+ANiU5IIk64DtwMyiMTPAi7r3PwK8p6pqeDElSStZccqlmxPfCewH1gA3VtXBJDcAs1U1A/wu8AdJ5oDP0it9rS6nsvTVyt/NVRIPpCWpDd4pKkmNsNAlqREWuiQ1wkIfQ0kuW2bb+NweoaYk2bDMtuetZpZJZaGPp91JruhfkeRhSd4IPGU0kSTevdSD+ZL8FPD6VU8zgSz08fQDwGuT/BBAkq+ldy/A1wA/OMpgmmjXAn+VZNPCiiTXAdcAzxpZqgmyqrf+aziq6lNJLgX2J/lG4IXAgaq6ZsTRNMGqal+Se4F3JbkKeAm9h/s9s6ruHm26yeB16GMoycXd22+i95TLdwOvWdheVR8dRS4JIMkzgLcBHwJ+tKq+OOJIE8NCH0NJ3rvM5qqq56xaGKmT5D+AAgI8HLgPuL9brqp69AjjTQQLXZIa4Rz6GEryw8tsvhf4v1X1z6uVRwJI8rhlNt9bVV9YtTATykIfT8tdybIW+PYkH6qql65WIAm4mS9PuSy2tvsSs11V9UermmqCOOXSoCQPA26tqu8YdRZpQZIp4G+ravF3EmtIvA69MUkurqoHgEtHnUXqV1XzwCtGnaNlFnp7fg6gqv511EGkBUk+ClBV7xh1lpY55SJJjfCk6BhKct5y26vq06uVRdJXD4/Qx1CSW3nw1QQFTAGPr6o1IwmmidZ3Y9GDNuGNRavCI/QxVFVP6l/unnD3CnonQn9lBJEkqupRo84w6TwpOsaSbOoemfsuetcAb66qXx9tKukrJXlskleOOscksNDHUJILk/wx8KfAXwMXVtUbquq+EUfTBEuyIcmeJH+R5CVJzkryWuCTwONHnW8SOIc+hpLcDxwB3knv4UdfwTtENQrdQ+P+Fvh7YGv3ugW4pqr+bZTZJoWFPoaSvGi57VX1ptXKIi1I8o9V9ZS+5aPAed2NbloFnhQdQ/2FneSR3bp7RpdI6kny9Xz56qu7gMeke4hLVX12ZMEmhEfoYyrJzwHXAWd1q+4BXl1Vvzm6VJpkSe4AHmDph3NVVX3z6iaaPB6hj6EkrwKeDjy7qg53674ZeH2Sx1XVL480oCZSVW0cdYZJ5xH6GEpyCHjK4q/2SvII4B+r6gmjSaZJluR84HNV9flu+fuAq4A7gN1VdXyE8SaCly2Op1rqexqr6r/o/ckrjcJNdFOASS4C3gp8GrgIcCpwFTjlMp6OJXluVf1N/8okzwF8yqJG5RFV9Znu/QuBG6vqtd3z+W8ZYa6JYaGPp5cCf57kA/TuEAWYBr4H2DayVJp0/SdDn0PvpD1V9cDClS46syz0MVRVB5NcCPw4sPCtRO8HfnapqRhplbwnyU30/kr8euA9AEnOAfy9XAUW+hhK8nLgg8DvV9WJUeeROi8Hfgw4B/jevkdRfCuw3BdIa0gs9PG0Hng98MTuUbofBD4EfMibNzQq1btkbi9Akqd2Bx4vAD4F/J9RZpsUXrY4xpKsozd3/nTgad3rc34Jr0YhyROAq7vXncBbgP9VVeePNNgE8Qh9vD0CeDTwmO71GeDWkSbSJPtn4O+A51XVHECSa0YbabJY6GMoyR56J0P/A/gIvemW11XV3SMNpkn3w8B24L1J/pLe9ItXt6wibywaT+cBDwf+DTgGHAU+N9JEmnhV9faq2g48EXgvvZOkj0/yW0m+f7TpJoNz6GOqu673O+jNnz8duBD4LPD3VfWLo8wmLeievvgC4Meq6rmjztM6C33MJVlP74aipwPPA76hqh472lSSRsFCH0NJXkqvxJ8G3Ed3yWL3utUvFJAmkydFx9NGepeEvbyqfHaLJMAj9LHUPSb3Z+ndgfdxeg9B8o5RacJZ6GMoyVuA48AHgMuBf6mql402laRRs9DHUJJbq+pJ3fu1wD9U1cUjjiVpxLwOfTwtPPQIp1okLfAIfQwluR/4wsIivUcA/Gf3vqrq0aPKJml0LHRJaoRTLpLUCAtdkhphoasJSV6Z5GCSjye5JcklD3F/L07yG6f4M/c8lM+UHirvFNXYS/I0es+xubiq7k1yNrBuxLGkVecRulpwDnBnVd0LUFV30vt6vrcvDEhyWZK3de/vSfJr3RH9XyfZkuR9SQ4nubJvvxu69Z9M8ot9+7o2yT91r5cvDpPknCTv7/5S+Kckzzhj/3Kpj4WuFvwVvfL9RJLfTPIses/jfmKSqW7MTwI3du/PAt5TVQtfEvLLwGXADwE39O13C/B84MnAC5JMJ/nObl+XAN8N/EySpy7K8+PA/qq6CHgKcMtw/7nS0pxy0dirqnu6on0G8H30Hly2C/gD4IVJfo/ekyl/ovuR48Bfdu9vBe6tqvu6L9ze2Lfrd1fVXQBJ/gz4XqCAt1XVF/rWPwP4WN/PHQBuTPI1wNurykLXqrDQ1YSquh94H/C+rphfRO8BZu8Avgi8te+u2vvqyzdgPAAsTNU80D1K4Uu7XfwxA2Z5f5JnAlcAb0zyuqr6/dP4Z0mnxCkXjb0k35ZkU9+qi+g9sOwz9L44+1XA753Gri9L8rju6ZZXAR+k9yXIVyX5uiRn0Zum+btFec4H/l9V/Q7wBsDn7GhVeISuFjwS+PUkjwVOAHPAjm7bHwFTVXX7aez3H4A/BdYDf1hVswBJ3thtA3hDVX1s0c89G/iFJPcB9/DlqR7pjPLWfzWtu5b8Y1X1u6POIp1pFrqaleRmeg8xu2zhkkapZRa6JDXCk6KS1AgLXZIaYaFLUiMsdElqhIUuSY2w0CWpEf8NRo8RL/9/NIwAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Plot volatility of both stock\n",
        "test.pct_change().apply(lambda x: np.log(1+x)).std().apply(lambda x: x * np.sqrt(250)).plot(kind='bar')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b_Fw5Tx35qhK"
      },
      "source": [
        "## Covariance"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "YbQjchA55qha",
        "outputId": "62af1e9a-bd1f-4d2f-8e3d-125445264530"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Symbols      PWON.JK   ASRI.JK\n",
              "Date                          \n",
              "2020-01-02       NaN       NaN\n",
              "2020-01-03 -0.008811 -0.008439\n",
              "2020-01-06 -0.036040 -0.008511\n",
              "2020-01-07  0.000000  0.008511\n",
              "2020-01-08 -0.009217 -0.017094"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-26da37ac-e38a-4961-926f-b7068ff0f649\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Symbols</th>\n",
              "      <th>PWON.JK</th>\n",
              "      <th>ASRI.JK</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-01-02</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-03</th>\n",
              "      <td>-0.008811</td>\n",
              "      <td>-0.008439</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-06</th>\n",
              "      <td>-0.036040</td>\n",
              "      <td>-0.008511</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-07</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.008511</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-08</th>\n",
              "      <td>-0.009217</td>\n",
              "      <td>-0.017094</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-26da37ac-e38a-4961-926f-b7068ff0f649')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-26da37ac-e38a-4961-926f-b7068ff0f649 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-26da37ac-e38a-4961-926f-b7068ff0f649');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "# Log of percentage\n",
        "test1 = test.pct_change().apply(lambda x: np.log(1+x))\n",
        "test1.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SD3jUBIa5qhd",
        "outputId": "879657c9-e6e6-4e5b-9a5a-20b4c9faeb42"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0006991339493891837"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "test1[\"PWON.JK\"].cov(test1[\"ASRI.JK\"])\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VgWwC3kz5qhf"
      },
      "source": [
        "## Correlation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JDF8zkrj5qhh",
        "outputId": "7a2ceac3-b549-40e3-828a-a904db36be09"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5920358898917099"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "test1[\"PWON.JK\"].corr(test1[\"ASRI.JK\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VLNy2Usl5qhj"
      },
      "source": [
        "## Expected Return"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "oeovM4Al5qhk",
        "outputId": "7cac127c-13a8-445d-afe8-e2c70130580b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Symbols      PWON.JK   ASRI.JK\n",
              "Date                          \n",
              "2020-01-02       NaN       NaN\n",
              "2020-01-03 -0.008811 -0.008439\n",
              "2020-01-06 -0.036040 -0.008511\n",
              "2020-01-07  0.000000  0.008511\n",
              "2020-01-08 -0.009217 -0.017094"
            ],
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              "  <tbody>\n",
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              "      <th>2020-01-02</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>2020-01-03</th>\n",
              "      <td>-0.008811</td>\n",
              "      <td>-0.008439</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-06</th>\n",
              "      <td>-0.036040</td>\n",
              "      <td>-0.008511</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-07</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.008511</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-08</th>\n",
              "      <td>-0.009217</td>\n",
              "      <td>-0.017094</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-983736c5-7e4a-4fd5-bc8e-e60b1353340f')\"\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-983736c5-7e4a-4fd5-bc8e-e60b1353340f button.colab-df-convert');\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-983736c5-7e4a-4fd5-bc8e-e60b1353340f');\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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "# calculate log of percentage\n",
        "test2 = test.pct_change().apply(lambda x: np.log(1+x))\n",
        "test2.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uLqbE9ds5qhn"
      },
      "source": [
        "### Weights"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UnteZoo15qho",
        "outputId": "b5b13d22-45c3-4895-d049-ad26267d9cb8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Symbols\n",
              "PWON.JK   -0.000422\n",
              "ASRI.JK   -0.000788\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "# Define weights allocation\n",
        "w = [0.2, 0.8]\n",
        "e_r_ind = test2.mean()  # expected return individual\n",
        "e_r_ind"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "upFBudol5qhp",
        "outputId": "dee00735-64c9-4fd7-e8e0-7cd37b29da01"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-0.0007149381812945529"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "# Total expected return\n",
        "e_r = (e_r_ind * w).sum()\n",
        "e_r"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d7_u_o_a5qhr"
      },
      "source": [
        "## Build Optimal Risky Portfolio"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "2O2_MVqB5qht",
        "outputId": "5c62a71d-326c-4554-d457-42aa930e7439"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Attributes   Adj Close                                     Close          \\\n",
              "Symbols        PWON.JK ASRI.JK      BBCA.JK      BMRI.JK PWON.JK ASRI.JK   \n",
              "Date                                                                       \n",
              "2020-01-02  564.864868   238.0  6346.921387  6809.337891   570.0   238.0   \n",
              "2020-01-03  559.909912   236.0  6451.279297  6787.372559   565.0   236.0   \n",
              "2020-01-06  540.090088   234.0  6389.613281  6677.543945   545.0   234.0   \n",
              "2020-01-07  540.090088   236.0  6394.356445  6677.543945   545.0   236.0   \n",
              "2020-01-08  535.135132   232.0  6337.434082  6589.681641   540.0   232.0   \n",
              "\n",
              "Attributes                    High          ...     Low            Open  \\\n",
              "Symbols    BBCA.JK BMRI.JK PWON.JK ASRI.JK  ... BBCA.JK BMRI.JK PWON.JK   \n",
              "Date                                        ...                           \n",
              "2020-01-02  6690.0  7750.0   575.0   240.0  ...  6680.0  7650.0   575.0   \n",
              "2020-01-03  6800.0  7725.0   575.0   240.0  ...  6725.0  7625.0   575.0   \n",
              "2020-01-06  6735.0  7600.0   565.0   236.0  ...  6690.0  7525.0   560.0   \n",
              "2020-01-07  6740.0  7600.0   550.0   238.0  ...  6730.0  7575.0   545.0   \n",
              "2020-01-08  6680.0  7500.0   550.0   236.0  ...  6670.0  7375.0   540.0   \n",
              "\n",
              "Attributes                              Volume                          \\\n",
              "Symbols    ASRI.JK BBCA.JK BMRI.JK     PWON.JK     ASRI.JK     BBCA.JK   \n",
              "Date                                                                     \n",
              "2020-01-02   238.0  6695.0  7675.0  12286300.0   3323600.0  49445000.0   \n",
              "2020-01-03   238.0  6750.0  7750.0  84199700.0  15815400.0  47755500.0   \n",
              "2020-01-06   236.0  6720.0  7650.0  76881100.0   3246300.0  27300000.0   \n",
              "2020-01-07   236.0  6735.0  7725.0  56419000.0   3340600.0  45022500.0   \n",
              "2020-01-08   236.0  6670.0  7550.0  50358200.0   2501900.0  53692500.0   \n",
              "\n",
              "Attributes              \n",
              "Symbols        BMRI.JK  \n",
              "Date                    \n",
              "2020-01-02  18689900.0  \n",
              "2020-01-03  35147300.0  \n",
              "2020-01-06  30946000.0  \n",
              "2020-01-07  35447800.0  \n",
              "2020-01-08  52540300.0  \n",
              "\n",
              "[5 rows x 24 columns]"
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              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th>2020-01-02</th>\n",
              "      <td>564.864868</td>\n",
              "      <td>238.0</td>\n",
              "      <td>6346.921387</td>\n",
              "      <td>6809.337891</td>\n",
              "      <td>570.0</td>\n",
              "      <td>238.0</td>\n",
              "      <td>6690.0</td>\n",
              "      <td>7750.0</td>\n",
              "      <td>575.0</td>\n",
              "      <td>240.0</td>\n",
              "      <td>...</td>\n",
              "      <td>6680.0</td>\n",
              "      <td>7650.0</td>\n",
              "      <td>575.0</td>\n",
              "      <td>238.0</td>\n",
              "      <td>6695.0</td>\n",
              "      <td>7675.0</td>\n",
              "      <td>12286300.0</td>\n",
              "      <td>3323600.0</td>\n",
              "      <td>49445000.0</td>\n",
              "      <td>18689900.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-03</th>\n",
              "      <td>559.909912</td>\n",
              "      <td>236.0</td>\n",
              "      <td>6451.279297</td>\n",
              "      <td>6787.372559</td>\n",
              "      <td>565.0</td>\n",
              "      <td>236.0</td>\n",
              "      <td>6800.0</td>\n",
              "      <td>7725.0</td>\n",
              "      <td>575.0</td>\n",
              "      <td>240.0</td>\n",
              "      <td>...</td>\n",
              "      <td>6725.0</td>\n",
              "      <td>7625.0</td>\n",
              "      <td>575.0</td>\n",
              "      <td>238.0</td>\n",
              "      <td>6750.0</td>\n",
              "      <td>7750.0</td>\n",
              "      <td>84199700.0</td>\n",
              "      <td>15815400.0</td>\n",
              "      <td>47755500.0</td>\n",
              "      <td>35147300.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-06</th>\n",
              "      <td>540.090088</td>\n",
              "      <td>234.0</td>\n",
              "      <td>6389.613281</td>\n",
              "      <td>6677.543945</td>\n",
              "      <td>545.0</td>\n",
              "      <td>234.0</td>\n",
              "      <td>6735.0</td>\n",
              "      <td>7600.0</td>\n",
              "      <td>565.0</td>\n",
              "      <td>236.0</td>\n",
              "      <td>...</td>\n",
              "      <td>6690.0</td>\n",
              "      <td>7525.0</td>\n",
              "      <td>560.0</td>\n",
              "      <td>236.0</td>\n",
              "      <td>6720.0</td>\n",
              "      <td>7650.0</td>\n",
              "      <td>76881100.0</td>\n",
              "      <td>3246300.0</td>\n",
              "      <td>27300000.0</td>\n",
              "      <td>30946000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-07</th>\n",
              "      <td>540.090088</td>\n",
              "      <td>236.0</td>\n",
              "      <td>6394.356445</td>\n",
              "      <td>6677.543945</td>\n",
              "      <td>545.0</td>\n",
              "      <td>236.0</td>\n",
              "      <td>6740.0</td>\n",
              "      <td>7600.0</td>\n",
              "      <td>550.0</td>\n",
              "      <td>238.0</td>\n",
              "      <td>...</td>\n",
              "      <td>6730.0</td>\n",
              "      <td>7575.0</td>\n",
              "      <td>545.0</td>\n",
              "      <td>236.0</td>\n",
              "      <td>6735.0</td>\n",
              "      <td>7725.0</td>\n",
              "      <td>56419000.0</td>\n",
              "      <td>3340600.0</td>\n",
              "      <td>45022500.0</td>\n",
              "      <td>35447800.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-08</th>\n",
              "      <td>535.135132</td>\n",
              "      <td>232.0</td>\n",
              "      <td>6337.434082</td>\n",
              "      <td>6589.681641</td>\n",
              "      <td>540.0</td>\n",
              "      <td>232.0</td>\n",
              "      <td>6680.0</td>\n",
              "      <td>7500.0</td>\n",
              "      <td>550.0</td>\n",
              "      <td>236.0</td>\n",
              "      <td>...</td>\n",
              "      <td>6670.0</td>\n",
              "      <td>7375.0</td>\n",
              "      <td>540.0</td>\n",
              "      <td>236.0</td>\n",
              "      <td>6670.0</td>\n",
              "      <td>7550.0</td>\n",
              "      <td>50358200.0</td>\n",
              "      <td>2501900.0</td>\n",
              "      <td>53692500.0</td>\n",
              "      <td>52540300.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 24 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ef8c5f23-e65d-40a6-b925-d2e2eba02aa5')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      fill: #1967D2;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\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",
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              "  </style>\n",
              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-ef8c5f23-e65d-40a6-b925-d2e2eba02aa5 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-ef8c5f23-e65d-40a6-b925-d2e2eba02aa5');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "# import data\n",
        "df = web.DataReader([\"PWON.JK\", \"ASRI.JK\", \"BBCA.JK\", \"BMRI.JK\"], \"yahoo\", start_date, end_date)\n",
        "df = pd.DataFrame(df)\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "UXk5Xtg-5qhv",
        "outputId": "ae20c0b5-8638-47e9-e1ba-655ac884bd1c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Symbols     PWON.JK  ASRI.JK  BBCA.JK  BMRI.JK\n",
              "Date                                          \n",
              "2020-01-02    570.0    238.0   6690.0   7750.0\n",
              "2020-01-03    565.0    236.0   6800.0   7725.0\n",
              "2020-01-06    545.0    234.0   6735.0   7600.0\n",
              "2020-01-07    545.0    236.0   6740.0   7600.0\n",
              "2020-01-08    540.0    232.0   6680.0   7500.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1ffdb092-9c16-4a00-8828-339e087d5d5b\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Symbols</th>\n",
              "      <th>PWON.JK</th>\n",
              "      <th>ASRI.JK</th>\n",
              "      <th>BBCA.JK</th>\n",
              "      <th>BMRI.JK</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-01-02</th>\n",
              "      <td>570.0</td>\n",
              "      <td>238.0</td>\n",
              "      <td>6690.0</td>\n",
              "      <td>7750.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-03</th>\n",
              "      <td>565.0</td>\n",
              "      <td>236.0</td>\n",
              "      <td>6800.0</td>\n",
              "      <td>7725.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-06</th>\n",
              "      <td>545.0</td>\n",
              "      <td>234.0</td>\n",
              "      <td>6735.0</td>\n",
              "      <td>7600.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-07</th>\n",
              "      <td>545.0</td>\n",
              "      <td>236.0</td>\n",
              "      <td>6740.0</td>\n",
              "      <td>7600.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-08</th>\n",
              "      <td>540.0</td>\n",
              "      <td>232.0</td>\n",
              "      <td>6680.0</td>\n",
              "      <td>7500.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1ffdb092-9c16-4a00-8828-339e087d5d5b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1ffdb092-9c16-4a00-8828-339e087d5d5b 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-1ffdb092-9c16-4a00-8828-339e087d5d5b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "# Close price\n",
        "df = df[\"Close\"]\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YPl1q5Su5qhx"
      },
      "source": [
        "## Covariance and Correlation matrix"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "3F2bOVOR5qhy",
        "outputId": "23e00064-b965-41b5-f543-778a292fbacf"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Symbols   PWON.JK   ASRI.JK   BBCA.JK   BMRI.JK\n",
              "Symbols                                        \n",
              "PWON.JK  0.001002  0.000699  0.000304  0.000475\n",
              "ASRI.JK  0.000699  0.001392  0.000300  0.000468\n",
              "BBCA.JK  0.000304  0.000300  0.000387  0.000339\n",
              "BMRI.JK  0.000475  0.000468  0.000339  0.000718"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6b5ce862-9a76-4a74-b019-740cbdf273db\">\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Symbols</th>\n",
              "      <th>PWON.JK</th>\n",
              "      <th>ASRI.JK</th>\n",
              "      <th>BBCA.JK</th>\n",
              "      <th>BMRI.JK</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Symbols</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>PWON.JK</th>\n",
              "      <td>0.001002</td>\n",
              "      <td>0.000699</td>\n",
              "      <td>0.000304</td>\n",
              "      <td>0.000475</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ASRI.JK</th>\n",
              "      <td>0.000699</td>\n",
              "      <td>0.001392</td>\n",
              "      <td>0.000300</td>\n",
              "      <td>0.000468</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BBCA.JK</th>\n",
              "      <td>0.000304</td>\n",
              "      <td>0.000300</td>\n",
              "      <td>0.000387</td>\n",
              "      <td>0.000339</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BMRI.JK</th>\n",
              "      <td>0.000475</td>\n",
              "      <td>0.000468</td>\n",
              "      <td>0.000339</td>\n",
              "      <td>0.000718</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6b5ce862-9a76-4a74-b019-740cbdf273db')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      width: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
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              "          document.querySelector('#df-6b5ce862-9a76-4a74-b019-740cbdf273db 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-6b5ce862-9a76-4a74-b019-740cbdf273db');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "cov_matrix = df.pct_change().apply(lambda x: np.log(1+x)).cov()\n",
        "cov_matrix"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "zBp-VkMG5qh0",
        "outputId": "aabc33a6-1b90-4180-bdcf-e5b4b247412d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Symbols   PWON.JK   ASRI.JK   BBCA.JK   BMRI.JK\n",
              "Symbols                                        \n",
              "PWON.JK  1.000000  0.592036  0.488025  0.560538\n",
              "ASRI.JK  0.592036  1.000000  0.408273  0.468138\n",
              "BBCA.JK  0.488025  0.408273  1.000000  0.642839\n",
              "BMRI.JK  0.560538  0.468138  0.642839  1.000000"
            ],
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              "\n",
              "  <div id=\"df-d672c4d7-1f3f-482e-895c-3e897a6909ca\">\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Symbols</th>\n",
              "      <th>PWON.JK</th>\n",
              "      <th>ASRI.JK</th>\n",
              "      <th>BBCA.JK</th>\n",
              "      <th>BMRI.JK</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Symbols</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>PWON.JK</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.592036</td>\n",
              "      <td>0.488025</td>\n",
              "      <td>0.560538</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ASRI.JK</th>\n",
              "      <td>0.592036</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.408273</td>\n",
              "      <td>0.468138</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BBCA.JK</th>\n",
              "      <td>0.488025</td>\n",
              "      <td>0.408273</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.642839</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BMRI.JK</th>\n",
              "      <td>0.560538</td>\n",
              "      <td>0.468138</td>\n",
              "      <td>0.642839</td>\n",
              "      <td>1.000000</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d672c4d7-1f3f-482e-895c-3e897a6909ca')\"\n",
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              "        \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
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              "    .colab-df-container {\n",
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              "      width: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d672c4d7-1f3f-482e-895c-3e897a6909ca 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-d672c4d7-1f3f-482e-895c-3e897a6909ca');\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",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "corr_matrix = df.pct_change().apply(lambda x: np.log(1+x)).corr()\n",
        "corr_matrix"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m5HwUXaI5qh2"
      },
      "source": [
        "## Portfolio Variance"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "auYp9f_r5qh3",
        "outputId": "3295627d-385f-42db-c907-a5ed1604824e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.00043354763975707794"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "w = {\"PWON.JK\":0.1, \"ASRI.JK\":0.2, \"BBCA.JK\":0.5, \"BMRI.JK\":0.2}\n",
        "port_var = cov_matrix.mul(w, axis=0).mul(w, axis=1).sum().sum()\n",
        "port_var"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IrZFGip85qh5"
      },
      "source": [
        "## Portfolio expected return"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_QZ2IAQz5qh6",
        "outputId": "090d111d-7f10-406a-f9d2-13a883cf7834"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Symbols\n",
              "PWON.JK   -0.090196\n",
              "ASRI.JK   -0.330579\n",
              "BBCA.JK    0.078287\n",
              "BMRI.JK    0.110672\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "# Yearly return for individual companies\n",
        "ind_er = df.resample(\"Y\").last().pct_change().mean()\n",
        "ind_er"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P0HNa5HB5qh8",
        "outputId": "3d26db67-9e3c-4b88-9ad1-23f045cfa451"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-0.013857643797876202"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "# Portfolio return\n",
        "w = [0.1, 0.2, 0.5, 0.2]\n",
        "port_er = (w * ind_er).sum()\n",
        "port_er"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BuGA1ciL5qh9"
      },
      "source": [
        "### Plotting Efficient Frontier"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ltv3L0xJ5qh9",
        "outputId": "0b79f7d6-cec1-434b-f733-e28ad6cdc968"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Symbols\n",
              "PWON.JK    0.500508\n",
              "ASRI.JK    0.589849\n",
              "BBCA.JK    0.310949\n",
              "BMRI.JK    0.423657\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "# Volatility is given by the annual standard deviation. We multiply by 250 because there are 250 trading days/year.\n",
        "ann_sd = df.pct_change().apply(lambda x: np.log(1+x)).std().apply(lambda x: x*np.sqrt(250))\n",
        "ann_sd"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "8ZZr_3AI5qh_",
        "outputId": "1a7cef8f-971c-47dc-9fa1-e004d59ed8fc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          Returns  Volatility\n",
              "Symbols                      \n",
              "PWON.JK -0.090196    0.500508\n",
              "ASRI.JK -0.330579    0.589849\n",
              "BBCA.JK  0.078287    0.310949\n",
              "BMRI.JK  0.110672    0.423657"
            ],
            "text/html": [
              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Returns</th>\n",
              "      <th>Volatility</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Symbols</th>\n",
              "      <th></th>\n",
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              "  <tbody>\n",
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              "      <th>PWON.JK</th>\n",
              "      <td>-0.090196</td>\n",
              "      <td>0.500508</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ASRI.JK</th>\n",
              "      <td>-0.330579</td>\n",
              "      <td>0.589849</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BBCA.JK</th>\n",
              "      <td>0.078287</td>\n",
              "      <td>0.310949</td>\n",
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              "    <tr>\n",
              "      <th>BMRI.JK</th>\n",
              "      <td>0.110672</td>\n",
              "      <td>0.423657</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-ad45685b-e4d0-421b-ae35-55c4799ff882 button.colab-df-convert');\n",
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ad45685b-e4d0-421b-ae35-55c4799ff882');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "assets = pd.concat([ind_er, ann_sd], axis=1) # Creating a table for visualising returns and volatility of assets\n",
        "assets.columns = ['Returns', 'Volatility']\n",
        "assets"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KtI7Cr5_5qiE"
      },
      "source": [
        "# MASIH GAGAL SAMPAI DI TITIK INI\n",
        "\n",
        "### PROSES LOOPING LAMA"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "31/07/2022 01:24 -> Berhasil selesai loop dalam 1 jam 57 menit memakai google colab"
      ],
      "metadata": {
        "id": "CAIRhTxDWjJd"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "EwXaqEcH5qiF",
        "outputId": "f2e9903e-576a-4187-a8cb-3f10072c78c1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Returns  Volatility  PWON.JK weight  ASRI.JK weight  BBCA.JK weight  \\\n",
              "0 -0.058468    0.371748        0.331841        0.214538        0.241426   \n",
              "1 -0.064678    0.397651        0.337984        0.238536        0.068104   \n",
              "2 -0.071904    0.364362        0.182782        0.306838        0.323250   \n",
              "3 -0.009141    0.354656        0.288348        0.120247        0.272786   \n",
              "4 -0.040947    0.329945        0.017365        0.285224        0.687834   \n",
              "\n",
              "   BMRI.JK weight  \n",
              "0        0.212195  \n",
              "1        0.355376  \n",
              "2        0.187129  \n",
              "3        0.318619  \n",
              "4        0.009578  "
            ],
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              "      <th>Returns</th>\n",
              "      <th>Volatility</th>\n",
              "      <th>PWON.JK weight</th>\n",
              "      <th>ASRI.JK weight</th>\n",
              "      <th>BBCA.JK weight</th>\n",
              "      <th>BMRI.JK weight</th>\n",
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              "      <th>0</th>\n",
              "      <td>-0.058468</td>\n",
              "      <td>0.371748</td>\n",
              "      <td>0.331841</td>\n",
              "      <td>0.214538</td>\n",
              "      <td>0.241426</td>\n",
              "      <td>0.212195</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-0.064678</td>\n",
              "      <td>0.397651</td>\n",
              "      <td>0.337984</td>\n",
              "      <td>0.238536</td>\n",
              "      <td>0.068104</td>\n",
              "      <td>0.355376</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-0.071904</td>\n",
              "      <td>0.364362</td>\n",
              "      <td>0.182782</td>\n",
              "      <td>0.306838</td>\n",
              "      <td>0.323250</td>\n",
              "      <td>0.187129</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.009141</td>\n",
              "      <td>0.354656</td>\n",
              "      <td>0.288348</td>\n",
              "      <td>0.120247</td>\n",
              "      <td>0.272786</td>\n",
              "      <td>0.318619</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-0.040947</td>\n",
              "      <td>0.329945</td>\n",
              "      <td>0.017365</td>\n",
              "      <td>0.285224</td>\n",
              "      <td>0.687834</td>\n",
              "      <td>0.009578</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f7879499-24cc-44dd-9028-2958f6cd5936');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "p_ret = []          # Define an empty array for portfolio returns\n",
        "p_vol = []          # Define an empty array for portfolio volatility\n",
        "p_weights = []      # Define an empty array for asset weights\n",
        "\n",
        "num_assets = len(df.columns)\n",
        "# num_portfolios = 100000000  # saldo Rp. 100 juta, membuat loop menjadi lama\n",
        "num_portfolios = 5000000  # saldo Rp. 5 juta\n",
        "\n",
        "for portfolio in range(num_portfolios):\n",
        "    weights = np.random.random(num_assets)\n",
        "    weights = weights/np.sum(weights)\n",
        "    p_weights.append(weights)\n",
        "    returns = np.dot(weights, ind_er) # Returns are the product of individual expected returns of asset and its \n",
        "                                      # weights \n",
        "    p_ret.append(returns)\n",
        "    var = cov_matrix.mul(weights, axis=0).mul(weights, axis=1).sum().sum()# Portfolio Variance\n",
        "    sd = np.sqrt(var) # Daily standard deviation\n",
        "    ann_sd = sd*np.sqrt(250) # Annual standard deviation = volatility\n",
        "    p_vol.append(ann_sd)\n",
        "\n",
        "data = {'Returns':p_ret, 'Volatility':p_vol}\n",
        "\n",
        "for counter, symbol in enumerate(df.columns.tolist()):\n",
        "    #print(counter, symbol)\n",
        "    data[symbol+' weight'] = [w[counter] for w in p_weights]\n",
        "\n",
        "portfolios = pd.DataFrame(data)\n",
        "portfolios.head()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot efficient frontier\n",
        "portfolios.plot.scatter(x='Volatility', y='Returns', marker='o', s=10, alpha=0.3, grid=True, figsize=[10,10])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 623
        },
        "id": "EYHoDRjoXBiO",
        "outputId": "15a610a2-60fb-4dcc-8a21-92852471913a"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fced0cdfe50>"
            ]
          },
          "metadata": {},
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# minimum volatility portfolio\n",
        "min_vol_port = portfolios.iloc[portfolios['Volatility'].idxmin()]\n",
        "min_vol_port"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uJsb0D4YXpRa",
        "outputId": "26badbf4-3ee1-4d99-bc27-4e942e88d3b5"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Returns           0.053953\n",
              "Volatility        0.306276\n",
              "PWON.JK weight    0.072087\n",
              "ASRI.JK weight    0.034701\n",
              "BBCA.JK weight    0.831443\n",
              "BMRI.JK weight    0.061768\n",
              "Name: 3954439, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Plotting the minimum volatility portfolio"
      ],
      "metadata": {
        "id": "t6aJcmnzYFE4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.subplots(figsize=[10,10])\n",
        "plt.scatter(portfolios['Volatility'], portfolios['Returns'],marker='o', s=10, alpha=0.3)\n",
        "plt.scatter(min_vol_port[1], min_vol_port[0], color='r', marker='*', s=500)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609
        },
        "id": "2jzGHdKiYEYb",
        "outputId": "545be6ec-88af-43c5-869c-19090c438afa"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7fced07d2510>"
            ]
          },
          "metadata": {},
          "execution_count": 36
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Optimal Risky Portfolio"
      ],
      "metadata": {
        "id": "2UYMihrWY0ap"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Finding the optimal portfolio\n",
        "risk_factor = 0.1\n",
        "optimal_risky_port = portfolios.iloc[\n",
        "    ( (portfolios['Returns'] - risk_factor) / portfolios['Volatility']).idxmax()\n",
        "    ]\n",
        "optimal_risky_port"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BHX9REmTY7O5",
        "outputId": "e8cee0ef-5e7b-4e71-f1eb-71058be6c6c0"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Returns           0.108323\n",
              "Volatility        0.410120\n",
              "PWON.JK weight    0.000506\n",
              "ASRI.JK weight    0.000621\n",
              "BBCA.JK weight    0.060925\n",
              "BMRI.JK weight    0.937948\n",
              "Name: 3294956, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plotting optimal portfolio\n",
        "plt.subplots(figsize=(10, 10))\n",
        "plt.scatter(portfolios['Volatility'], portfolios['Returns'],marker='o', s=10, alpha=0.3)\n",
        "plt.scatter(min_vol_port[1], min_vol_port[0], color='r', marker='*', s=500)\n",
        "plt.scatter(optimal_risky_port[1], optimal_risky_port[0], color='g', marker='*', s=500)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609
        },
        "id": "vBijKpVeZpWI",
        "outputId": "bf138035-2ca5-4cf1-e25d-af8ad638bddd"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7fced07399d0>"
            ]
          },
          "metadata": {},
          "execution_count": 38
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ],
            "image/png": 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\n"
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      "language": "python",
      "name": "python3"
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    "language_info": {
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      "collapsed_sections": [],
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}