Risk(4_6_2023).ipynb

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      "source": [
        "!pip install yfinance"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from pandas_datareader import data as pdr\n",
        "import yfinance as yf\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from datetime import datetime\n",
        "import matplotlib as plt\n",
        "yf.pdr_override()"
      ],
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        "id": "CwMT5icznGyg"
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      "source": [
        "tickers = ['PG', 'AAPL']\n",
        "\n",
        "sec_data = pd.DataFrame()\n",
        "\n",
        "for t in tickers:\n",
        "  sec_data[t] = pdr.get_data_yahoo(t, start = '2007-1-1')['Adj Close']"
      ],
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            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n"
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        "sec_data.tail()"
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              "                    PG        AAPL\n",
              "Date                              \n",
              "2023-03-31  148.690002  164.899994\n",
              "2023-04-03  149.509995  166.169998\n",
              "2023-04-04  150.229996  165.630005\n",
              "2023-04-05  151.259995  163.759995\n",
              "2023-04-06  152.220001  164.660004"
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        "sec_returns = (sec_data / sec_data.shift(1)) - 1"
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    {
      "cell_type": "markdown",
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        "Let's check which one is more volatile"
      ],
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              "                  PG      AAPL\n",
              "Date                          \n",
              "2007-01-03       NaN       NaN\n",
              "2007-01-04 -0.007592  0.022196\n",
              "2007-01-05 -0.008587 -0.007122\n",
              "2007-01-08  0.002205  0.004939\n",
              "2007-01-09 -0.002514  0.083070\n",
              "...              ...       ...\n",
              "2023-03-31  0.008410  0.015644\n",
              "2023-04-03  0.005515  0.007702\n",
              "2023-04-04  0.004816 -0.003250\n",
              "2023-04-05  0.006856 -0.011290\n",
              "2023-04-06  0.006347  0.005496\n",
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              "      <td>-0.003250</td>\n",
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              "      <td>0.006856</td>\n",
              "      <td>-0.011290</td>\n",
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          "metadata": {},
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    {
      "cell_type": "markdown",
      "source": [
        "For PG"
      ],
      "metadata": {
        "id": "vWSxHEABRw_N"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# daily average return\n",
        "sec_returns['PG'].mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kUB2qNA3QG3B",
        "outputId": "f24e5395-afb3-4f1e-ca1d-7ada7611ae66"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.00039452527047951434"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# average annual return (mean)\n",
        "sec_returns['PG'].mean() * 252"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rwD23OTVQkCu",
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            "text/plain": [
              "0.09942036816083762"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
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    {
      "cell_type": "code",
      "source": [
        "# calculates standard deviation\n",
        "sec_returns['PG'].std()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7jndiR0_QkMW",
        "outputId": "9a2ec8b6-622b-4bff-b989-71d7ea725cd0"
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          "data": {
            "text/plain": [
              "0.011832284207292414"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
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    {
      "cell_type": "code",
      "source": [
        "# annualizing standard deviation\n",
        "# This will equal the product of the square root of the variance (which is obtained by the .std() method), and the square root of the number of 250 (written as 250**0.5 in our code).\n",
        "sec_returns['PG'].std() * 252 ** 0.5"
      ],
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              "0.18783168872599654"
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      "cell_type": "markdown",
      "source": [
        "For AAPL"
      ],
      "metadata": {
        "id": "RiwINPvSR2PG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sec_returns['AAPL'].mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w_WzkbCmR3Bo",
        "outputId": "c0cff50c-5a8a-4f4b-fce3-b37d07a1b3d8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0012255643649470523"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sec_returns['AAPL'].mean() * 252"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bLbqfQxoR537",
        "outputId": "20c41e1c-0db8-49a5-bbf7-4d85124a7c1d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.30884221996665717"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sec_returns['AAPL'].std()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HLC9lTZaR8Nr",
        "outputId": "2f8f6572-c555-4f36-d03a-eef35cbf8809"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.020301936435359796"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sec_returns['AAPL'].std() * 252 ** 0.5"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e9EoUnBER-k9",
        "outputId": "51085f4b-57f2-4b3a-dee9-78f82f371689"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.322283249646019"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Comparing the two"
      ],
      "metadata": {
        "id": "s5DqfH6cSToH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(sec_returns['PG'].mean() * 252)\n",
        "print(sec_returns['AAPL'].mean() * 252)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3PwFYWqjSVKB",
        "outputId": "10dcc3dd-3598-42e0-c533-c169d6072abe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.09942036816083762\n",
            "0.30884221996665717\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sec_returns[['PG', 'AAPL']].mean() * 252"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g3Z4J-p7tRQB",
        "outputId": "0a6c1180-ba98-41b9-a411-f484585036c8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PG      0.099420\n",
              "AAPL    0.308842\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# higher standard deviation means that the bell curve is more spread out\n",
        "sec_returns[['PG', 'AAPL']].std() * 252 ** 0.5"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pu_C_Bhdthis",
        "outputId": "82eff5a2-ebac-46ee-ada4-fafe2ec721e1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PG      0.187832\n",
              "AAPL    0.322283\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Covariance and Correlation"
      ],
      "metadata": {
        "id": "fIU3xIIbNAu0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "AAPL_var = sec_returns['AAPL'].var()\n",
        "AAPL_var"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lV4O_IuMNFk9",
        "outputId": "8ed22b21-f207-4971-c45d-4e8d878dc3a0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0004121686230253896"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "PG_var = sec_returns['PG'].var()\n",
        "PG_var"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m4A-A9FtOqCE",
        "outputId": "8eef5f88-38d1-4712-dbef-3a772bde62a9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.00014000294956214147"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# annualizing AAPL varaince\n",
        "\n",
        "AAPL_var_ann = sec_returns['AAPL'].var() * 252\n",
        "AAPL_var_ann"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iJcF_6YROxkn",
        "outputId": "5e7fe038-761d-4b50-dcc7-f6c78fe3fba1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.10386649300239818"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# annualizing PG varaince\n",
        "\n",
        "PG_var_ann = sec_returns['PG'].var() * 252\n",
        "PG_var_ann"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sK2d2FMPO1Ll",
        "outputId": "d58eff33-3dd4-4fba-9685-d07d746b6aba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.035280743289659654"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cov_matrix = sec_returns.cov()\n",
        "cov_matrix"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "kEjqhdQKPK2h",
        "outputId": "28817873-3358-4314-8c10-dc8b33d4c2fd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            PG      AAPL\n",
              "PG    0.000140  0.000088\n",
              "AAPL  0.000088  0.000412"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-792cc958-529f-43c4-9b12-f2bd0fb36d66\">\n",
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
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              "    .dataframe tbody tr th {\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PG</th>\n",
              "      <th>AAPL</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>PG</th>\n",
              "      <td>0.000140</td>\n",
              "      <td>0.000088</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AAPL</th>\n",
              "      <td>0.000088</td>\n",
              "      <td>0.000412</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-792cc958-529f-43c4-9b12-f2bd0fb36d66')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "    .colab-df-convert:hover {\n",
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              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\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",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
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              "          document.querySelector('#df-792cc958-529f-43c4-9b12-f2bd0fb36d66 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-792cc958-529f-43c4-9b12-f2bd0fb36d66');\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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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# annualizing the covariance matrix\n",
        "\n",
        "cov_matrix_ann = sec_returns.cov() * 252\n",
        "cov_matrix_ann"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "0fwa-htaPYzL",
        "outputId": "c4fba314-1615-4f80-927c-9d52156a9be7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            PG      AAPL\n",
              "PG    0.035281  0.022211\n",
              "AAPL  0.022211  0.103866"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0d28e9b5-a5c1-48a4-a835-b2c7f6faadce\">\n",
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              "      <th>AAPL</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>PG</th>\n",
              "      <td>0.035281</td>\n",
              "      <td>0.022211</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AAPL</th>\n",
              "      <td>0.022211</td>\n",
              "      <td>0.103866</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "      \n",
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              "      gap: 12px;\n",
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              "\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-0d28e9b5-a5c1-48a4-a835-b2c7f6faadce 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-0d28e9b5-a5c1-48a4-a835-b2c7f6faadce');\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": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# This is NOT the price correlation between the price of the two equitiies, this is correlation between returns\n",
        "# This reflects the dependence between prices at different times and focuses on the returns of your portfolo\n",
        "# Correlation of price focuses on stock price levels\n",
        "\n",
        "# as an investor return is what we care about, not the nominal price of the stock\n",
        "# We also dont need to annualize the correlation table because it doesn't contain average daily values, \n",
        "# it shows us the relation that exists between two variables\n",
        "\n",
        "corr_matrix = sec_returns.corr()\n",
        "corr_matrix"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "vsiDrgl7PkxA",
        "outputId": "9489dac9-605f-432d-c3fb-eab920f64e03"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            PG      AAPL\n",
              "PG    1.000000  0.366909\n",
              "AAPL  0.366909  1.000000"
            ],
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              "\n",
              "  <div id=\"df-a32bf26e-d0ba-4767-800d-79ad4a6b1c88\">\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",
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              "\n",
              "    .dataframe thead th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PG</th>\n",
              "      <th>AAPL</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>PG</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.366909</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AAPL</th>\n",
              "      <td>0.366909</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a32bf26e-d0ba-4767-800d-79ad4a6b1c88')\"\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",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a32bf26e-d0ba-4767-800d-79ad4a6b1c88 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-a32bf26e-d0ba-4767-800d-79ad4a6b1c88');\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
        }
      ]
    }
  ]
}