Markowitz_Portfolio.ipynb

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            "Requirement already satisfied: finance-datareader in /usr/local/lib/python3.7/dist-packages (0.9.33)\n",
            "Requirement already satisfied: requests>=2.3.0 in /usr/local/lib/python3.7/dist-packages (from finance-datareader) (2.23.0)\n",
            "Requirement already satisfied: requests-file in /usr/local/lib/python3.7/dist-packages (from finance-datareader) (1.5.1)\n",
            "Requirement already satisfied: lxml in /usr/local/lib/python3.7/dist-packages (from finance-datareader) (4.2.6)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from finance-datareader) (4.64.0)\n",
            "Requirement already satisfied: pandas>=0.19.2 in /usr/local/lib/python3.7/dist-packages (from finance-datareader) (1.3.5)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.19.2->finance-datareader) (2022.1)\n",
            "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.19.2->finance-datareader) (1.21.6)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.19.2->finance-datareader) (2.8.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas>=0.19.2->finance-datareader) (1.15.0)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.3.0->finance-datareader) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.3.0->finance-datareader) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.3.0->finance-datareader) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.3.0->finance-datareader) (2021.10.8)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import FinanceDataReader as fdr\n",
        "fdr.__version__"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
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          },
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "%cd /content/drive/My Drive/Colab Notebooks"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XALXQ1cIW-HJ",
        "outputId": "1533c063-701e-44bb-f0c1-1e66c0ee5a42"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
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          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n",
            "/content/drive/My Drive/Colab Notebooks\n"
          ]
        }
      ]
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        "import numpy as np\n",
        "import pandas as pd\n",
        "import re\n",
        "from itertools import product\n",
        "from tqdm.auto import tqdm\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import scipy.optimize as sco"
      ],
      "metadata": {
        "id": "EWXW0fQl50TS"
      },
      "execution_count": null,
      "outputs": []
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              "          종목명    종목코드    상장시장\n",
              "0        삼성전자  005930   KOSPI\n",
              "1      SK하이닉스  000660   KOSPI\n",
              "2       NAVER  035420   KOSPI\n",
              "3         카카오  035720   KOSPI\n",
              "4    삼성바이오로직스  207940   KOSPI\n",
              "..        ...     ...     ...\n",
              "365      맘스터치  220630  KOSDAQ\n",
              "366        다날  064260  KOSDAQ\n",
              "367   제이시스메디칼  287410  KOSDAQ\n",
              "368   크리스에프앤씨  110790  KOSDAQ\n",
              "369     쎄트렉아이  099320  KOSDAQ\n",
              "\n",
              "[370 rows x 3 columns]"
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              "<p>370 rows × 3 columns</p>\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "#You must have stock_list.csv file before running the code!\n",
        "stock_list = pd.read_csv('./stock_list.csv')\n",
        "stock_list['종목코드'] = stock_list['종목코드'].apply(lambda x : str(x).zfill(6))\n",
        "stock_list.set_index('종목코드', inplace=True)\n",
        "stock_list"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ticker=  stock_list['종목코드']\n",
        "datetime = ['2022-01-01', '2022-05-22']\n",
        "data = pd.DataFrame()\n",
        "#data = pd.read_csv('./data.csv') \n",
        "for code in tqdm(list(ticker)):\n",
        "    df = pd.DataFrame()\n",
        "    stock = fdr.DataReader(code, start = datetime[0], end = datetime[1])\n",
        "    if not stock.empty:\n",
        "      df[code] = stock['Close']\n",
        "\n",
        "      df.reset_index(inplace=True, drop=True)\n",
        "\n",
        "      data = pd.concat([data, df], axis=1)\n",
        "data = data.interpolate(method='linear', limit_direction='both')\n",
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "f29271d7f3614ea49503dd6409352d15",
            "4004887de0694fee93e20a4a4004cec9",
            "363ce81e51f04907818b9d48907651b3",
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            "275861a495b9450381338c7454cbc7a5",
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            "2b9c6744107c48cdb647ad0f989e4d7d",
            "e22dba15032c4b04b2b7d1360fa7050a",
            "94442c02044348feaa8f4a67b102e3c5",
            "7a7caf1d8fa04ffbad890758c52f8a6c",
            "02dfc0fe74a4453396ca99467e7931a4"
          ]
        },
        "id": "Hv9-UU6f_LmJ",
        "outputId": "00a17f3b-35eb-46ce-adcb-a347e90a15d0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/370 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f29271d7f3614ea49503dd6409352d15"
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    },
    {
      "cell_type": "code",
      "source": [
        "data.to_csv('./data.csv', index=0)"
      ],
      "metadata": {
        "id": "MT6eOgh-g7za"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Efficient Frontiner from Modern Portfolio Theory implemented in Python (Markowitz Portfolio Theory)"
      ],
      "metadata": {
        "id": "6gprBaoXiNSv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv('./data.csv')"
      ],
      "metadata": {
        "id": "LVfdJ60aLO9v"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "num_assets = 200\n",
        "table = data.copy().iloc[:, :num_assets]\n",
        "returns = table.pct_change()\n",
        "mean_returns = returns.mean()\n",
        "cov_matrix = returns.cov()\n",
        "risk_free_rate = 0.0178"
      ],
      "metadata": {
        "id": "hpDzIKbHDq8G"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "an_vol = pd.DataFrame(np.std(returns) * np.sqrt(94), columns=['vol'])\n",
        "an_rt = pd.DataFrame(mean_returns * 94, columns=['rt'])"
      ],
      "metadata": {
        "id": "iAJBFh4aDq8U"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "an_rt.describe()"
      ],
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               rt\n",
              "count  200.000000\n",
              "mean    -0.058588\n",
              "std      0.189259\n",
              "min     -0.717570\n",
              "25%     -0.162273\n",
              "50%     -0.054317\n",
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              "                                                     [key], {});\n",
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              "\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",
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            "text/plain": [
              "              vol\n",
              "count  200.000000\n",
              "mean     0.221283\n",
              "std      0.083073\n",
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              "          const element = document.querySelector('#df-2495c4c6-dcd4-487a-bc5b-754a7e0bc0ad');\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": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "an_rt[an_rt['rt']>0.1].index"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "16bbb7c4-30dc-4304-b1a4-81d51c508c6f",
        "id": "g3tEhnlIDq8V"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['055550', '011200', '086790', '010950', '010130', '030200', '316140',\n",
              "       '086280', '035250', '267250', '078930', '028670', '004990', '005830',\n",
              "       '008560', '010620', '036460', '047810', '023530', '010060', '002380',\n",
              "       '000060', '030000', '282330', '000080', '006260', '001450', '001440',\n",
              "       '252670', '001230', '069620', '004000', '111770', '016380', '031430',\n",
              "       '005300', '247540', '028300', '066970'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "an_vol[an_vol['vol']<0.3].index"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "eda2bf49-54d2-4b7f-acfc-9d5da94158c6",
        "id": "lwPLMphfDq8V"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['005930', '000660', '035420', '035720', '207940', '005935', '006400',\n",
              "       '005380', '068270', '000270',\n",
              "       ...\n",
              "       '161890', '005300', '004490', '214320', '035760', '145020', '253450',\n",
              "       '058470', '357780', '240810'],\n",
              "      dtype='object', length=173)"
            ]
          },
          "metadata": {},
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "table = table.loc[:, an_rt[an_rt['rt']>0.1].index & an_vol[an_vol['vol']<0.3].index]\n",
        "table"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 480
        },
        "outputId": "679c082f-f1b3-43ea-97ca-bf3abc55bdca",
        "id": "pE91SH4GDq8V"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: FutureWarning: Index.__and__ operating as a set operation is deprecated, in the future this will be a logical operation matching Series.__and__.  Use index.intersection(other) instead\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
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              "91   46000  ...   36800   59400   31050    2655   17650  167500   82900   \n",
              "92   45450  ...   36100   60500   30850    2735   16800  162000   82000   \n",
              "93   45750  ...   35550   60700   30250    2630   17200  163500   84000   \n",
              "\n",
              "    111770  031430  005300  \n",
              "0    44150   29100  131000  \n",
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              "2    46050   28400  131000  \n",
              "3    47750   28000  131000  \n",
              "4    47300   28100  136000  \n",
              "..     ...     ...     ...  \n",
              "89   48250   33650  186500  \n",
              "90   48550   34400  189500  \n",
              "91   50400   34350  182500  \n",
              "92   46250   34200  182500  \n",
              "93   47250   34250  182500  \n",
              "\n",
              "[94 rows x 31 columns]"
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              "      <td>17900</td>\n",
              "      <td>164500</td>\n",
              "      <td>83300</td>\n",
              "      <td>48550</td>\n",
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              "      <td>189500</td>\n",
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              "      <td>42050</td>\n",
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              "      <td>557000</td>\n",
              "      <td>36250</td>\n",
              "      <td>14900</td>\n",
              "      <td>209500</td>\n",
              "      <td>27200</td>\n",
              "      <td>61400</td>\n",
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              "      <td>...</td>\n",
              "      <td>36800</td>\n",
              "      <td>59400</td>\n",
              "      <td>31050</td>\n",
              "      <td>2655</td>\n",
              "      <td>17650</td>\n",
              "      <td>167500</td>\n",
              "      <td>82900</td>\n",
              "      <td>50400</td>\n",
              "      <td>34350</td>\n",
              "      <td>182500</td>\n",
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              "    <tr>\n",
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              "      <td>40700</td>\n",
              "      <td>45300</td>\n",
              "      <td>107000</td>\n",
              "      <td>561000</td>\n",
              "      <td>36350</td>\n",
              "      <td>14200</td>\n",
              "      <td>201500</td>\n",
              "      <td>26650</td>\n",
              "      <td>60400</td>\n",
              "      <td>45450</td>\n",
              "      <td>...</td>\n",
              "      <td>36100</td>\n",
              "      <td>60500</td>\n",
              "      <td>30850</td>\n",
              "      <td>2735</td>\n",
              "      <td>16800</td>\n",
              "      <td>162000</td>\n",
              "      <td>82000</td>\n",
              "      <td>46250</td>\n",
              "      <td>34200</td>\n",
              "      <td>182500</td>\n",
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              "    <tr>\n",
              "      <th>93</th>\n",
              "      <td>41700</td>\n",
              "      <td>46950</td>\n",
              "      <td>106000</td>\n",
              "      <td>573000</td>\n",
              "      <td>35950</td>\n",
              "      <td>14600</td>\n",
              "      <td>206000</td>\n",
              "      <td>26850</td>\n",
              "      <td>60000</td>\n",
              "      <td>45750</td>\n",
              "      <td>...</td>\n",
              "      <td>35550</td>\n",
              "      <td>60700</td>\n",
              "      <td>30250</td>\n",
              "      <td>2630</td>\n",
              "      <td>17200</td>\n",
              "      <td>163500</td>\n",
              "      <td>84000</td>\n",
              "      <td>47250</td>\n",
              "      <td>34250</td>\n",
              "      <td>182500</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>94 rows × 31 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5dfea5fb-f8b6-42fc-bb0e-bb633e1362a1')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "    .colab-df-container {\n",
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              "      background-color: #E8F0FE;\n",
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              "      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-5dfea5fb-f8b6-42fc-bb0e-bb633e1362a1 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-5dfea5fb-f8b6-42fc-bb0e-bb633e1362a1');\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": 107
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "num_assets = len(table.columns)"
      ],
      "metadata": {
        "id": "PRjgVG52FfDx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "an_rt = an_rt.loc[table.columns]\n",
        "an_vol = an_vol.loc[table.columns]"
      ],
      "metadata": {
        "id": "3bM6Oa41Dq8V"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Making a dataframe for log return\n",
        "df_logret = np.log(table / table.shift(1))\n",
        "df_logret = df_logret.dropna()"
      ],
      "metadata": {
        "id": "VJDN6f_lCFxg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "18d95b6d"
      },
      "outputs": [],
      "source": [
        "# calculate mu \n",
        "\n",
        "mu_df = df_logret.mean() * len(df_logret)\n",
        "mu = mu_df.to_numpy()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1c931276",
        "outputId": "8121bef9-2504-45d7-c27c-f67fe306ffa4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.11284918, 0.10311478, 0.21025527, 0.11647499, 0.16933257,\n",
              "       0.13157636, 0.17747893, 0.10391319, 0.11279549, 0.14942598,\n",
              "       0.35325798, 0.1360408 , 0.15573672, 0.14695441, 0.11460338,\n",
              "       0.12600641, 0.36303764, 0.10848064, 0.06544391, 0.12345856,\n",
              "       0.25941164, 0.16144397, 0.107743  , 0.25462569, 0.201516  ,\n",
              "       0.06609011, 0.07622737, 0.14035736, 0.06785973, 0.16294839,\n",
              "       0.33155285])"
            ]
          },
          "metadata": {},
          "execution_count": 111
        }
      ],
      "source": [
        "mu"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0eab0742"
      },
      "outputs": [],
      "source": [
        "# calculate Sigma\n",
        "\n",
        "Sigma_df = df_logret.cov() * len(df_logret)\n",
        "Sigma = Sigma_df.to_numpy()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "id": "d384dd4f"
      },
      "outputs": [],
      "source": [
        "# # Import cvxpy for solving optimization problem\n",
        "import cvxpy as cp"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "66647025",
        "outputId": "1513b1fb-193e-4346-d348-b7b93426a40e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "> Expected return of the portfolio is  0.1621\n",
            "> Standard deviation of the portfolio is  0.0411\n"
          ]
        }
      ],
      "source": [
        "# Minimum volatility portfolio optmization\n",
        "w_1 = cp.Variable(num_assets)\n",
        "\n",
        "risk_1 = cp.quad_form(w_1, Sigma)\n",
        "\n",
        "prob = cp.Problem(cp.Minimize(risk_1),\n",
        "                 [cp.sum(w_1) == 1,\n",
        "                  w_1 >= 0])\n",
        "\n",
        "prob.solve()\n",
        "\n",
        "print('> Expected return of the portfolio is ', round(mu.dot(w_1.value), 4))\n",
        "print('> Standard deviation of the portfolio is ', round(np.sqrt(w_1.value.dot(Sigma).dot(w_1.value)), 4))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "46354bf5",
        "outputId": "a4480136-6f72-47cd-e769-e29fbcd47846"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.16210592348153052\n"
          ]
        }
      ],
      "source": [
        "min_vol = np.sqrt(w_1.value.dot(Sigma).dot(w_1.value))\n",
        "min_vol_ret = mu.dot(w_1.value)\n",
        "print(min_vol_ret)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "551d3f79",
        "outputId": "af466432-f779-484c-edd6-d0610e8bfac2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "> Expected return of the portfolio is  0.363\n",
            "> Standard deviation of the portfolio is  0.2631\n"
          ]
        }
      ],
      "source": [
        "# Maximize return potfolio optimization\n",
        "w_2 = cp.Variable(num_assets)\n",
        "\n",
        "ret_2 = mu.T@w_2\n",
        "\n",
        "prob = cp.Problem(cp.Maximize(ret_2),\n",
        "                 [cp.sum(w_2) == 1,\n",
        "                  w_2 >= 0])\n",
        "\n",
        "prob.solve()\n",
        "\n",
        "print('> Expected return of the portfolio is ', round(mu.dot(w_2.value), 4))\n",
        "print('> Standard deviation of the portfolio is ', round(np.sqrt(w_2.value.dot(Sigma).dot(w_2.value)), 4))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fbc4307d",
        "outputId": "c7954677-2ded-42cc-a709-cf428c76d496"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.36303763726705995\n"
          ]
        }
      ],
      "source": [
        "max_ret = mu.dot(w_2.value)\n",
        "max_ret_vol = np.sqrt(w_2.value.dot(Sigma).dot(w_2.value))\n",
        "print(max_ret)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cd0727cc"
      },
      "outputs": [],
      "source": [
        "# Making a feasible set\n",
        "num_simulation = 10000\n",
        "all_weights = np.zeros((num_simulation, len(table.columns)))\n",
        "ret_ = np.zeros(num_simulation)\n",
        "vol_ = np.zeros(num_simulation)\n",
        "sharpe_ = np.zeros(num_simulation)\n",
        "\n",
        "for x in range(num_simulation):\n",
        "    # Weights\n",
        "    weights = np.array(np.random.random(len(table.columns)))\n",
        "    weights = weights / np.sum(weights) #Normalization for weight_sum = 1\n",
        "    \n",
        "    # Save weights\n",
        "    all_weights[x,:] = weights\n",
        "    \n",
        "    # Expected return\n",
        "    ret_[x] = mu.dot(weights)\n",
        "    \n",
        "    # Expected volatility\n",
        "    vol_[x] = np.sqrt(weights.dot(Sigma).dot(weights))\n",
        "    \n",
        "    # Sharpe Ratio\n",
        "    sharpe_[x] = ret_[x] / vol_[x]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "03a862b5"
      },
      "outputs": [],
      "source": [
        "# Making an efficient frontier\n",
        "x_ef = []\n",
        "y_ef = np.linspace(min_vol_ret, max_ret, 1000)\n",
        "\n",
        "for possible_return in y_ef:\n",
        "    w = cp.Variable(num_assets)\n",
        "\n",
        "    lambda0 = possible_return\n",
        "    ret = mu.T@w\n",
        "    risk = cp.quad_form(w, Sigma)\n",
        "\n",
        "    prob = cp.Problem(cp.Minimize(risk),\n",
        "                     [cp.sum(w) == 1,\n",
        "                      ret == lambda0,\n",
        "                      w >= 0])\n",
        "\n",
        "    prob.solve()\n",
        "    x_ef.append(np.sqrt(w.value.dot(Sigma).dot(w.value)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 551
        },
        "id": "e412293b",
        "outputId": "83430b9d-3bf3-4532-a204-dcb4d9ccdc96"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x648 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Drawing a feasible region and an efficient frontier\n",
        "\n",
        "plt.figure(figsize=(16,9))\n",
        "plt.scatter(vol_, ret_, c=sharpe_, linestyle='-.', cmap='twilight')\n",
        "plt.colorbar(label='Sharpe Ratio')\n",
        "plt.xlabel('Volatility')\n",
        "plt.ylabel('Return')\n",
        "plt.plot(x_ef, y_ef, 'r--', linewidth=7, zorder = 0)\n",
        "plt.scatter(max_ret_vol, max_ret, marker='*',color='b',s=300, label='Maximum Return', zorder = 1)\n",
        "plt.scatter(min_vol, min_vol_ret,marker='*',color='g',s=300, label='Minimum Volatility', zorder = 2)\n",
        "plt.scatter(an_vol,an_rt, marker='o',s=200)\n",
        "for i, txt in enumerate(table.columns):\n",
        "    plt.annotate(txt, (an_vol.loc[txt],an_rt.loc[txt]), xytext=(10,0), textcoords='offset points')\n",
        "plt.legend(labelspacing=1.4)\n",
        "plt.savefig('answer.png')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "weight = pd.DataFrame([np.round(w_1.value*100, 1), np.round(w_2.value*100, 1)]).T\n",
        "weight.index = table.columns"
      ],
      "metadata": {
        "id": "XAalSXzrI7Pp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "w1 = pd.DataFrame()\n",
        "w1['weight'] = weight[0]\n",
        "w1['filter'] = weight[0]>0\n",
        "w1 = pd.DataFrame(w1['weight'].loc[w1['filter']==True])\n",
        "w1 = pd.concat([w1, stock_list['종목명'].loc[w1.index.values]], axis=1)\n",
        "w1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 582
        },
        "id": "Ne0zu9Y6JVPO",
        "outputId": "ac7a6a0b-2096-4f65-bb69-2d2fab8822c3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        weight               종목명\n",
              "055550     9.1              신한지주\n",
              "030200     4.3                KT\n",
              "086280     8.0            현대글로비스\n",
              "035250     0.1              강원랜드\n",
              "267250     2.8           현대중공업지주\n",
              "028670     0.3               팬오션\n",
              "004990    14.8              롯데지주\n",
              "008560     6.0             메리츠증권\n",
              "047810     1.5            한국항공우주\n",
              "010060     5.8               OCI\n",
              "030000     2.0              제일기획\n",
              "282330     6.7            BGF리테일\n",
              "006260     4.0                LS\n",
              "252670    27.6  KODEX 200선물인버스2X\n",
              "069620     3.1              대웅제약\n",
              "004000     3.2            롯데정밀화학\n",
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              "                                                     [key], {});\n",
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              "\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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 223
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tmp = pd.DataFrame()\n",
        "tmp['매수'] = w1['weight']*20000 #for 20million won\n",
        "tmp['종목명'] = w1['종목명']\n",
        "tmp.set_index('종목명', inplace=True)\n",
        "tmp"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 614
        },
        "id": "ou6YZeXDW4L8",
        "outputId": "129e5bca-ffc6-4dfa-f4da-b4afcc7de43f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                        매수\n",
              "종목명                       \n",
              "신한지주              182000.0\n",
              "KT                 86000.0\n",
              "현대글로비스            160000.0\n",
              "강원랜드                2000.0\n",
              "현대중공업지주            56000.0\n",
              "팬오션                 6000.0\n",
              "롯데지주              296000.0\n",
              "메리츠증권             120000.0\n",
              "한국항공우주             30000.0\n",
              "OCI               116000.0\n",
              "제일기획               40000.0\n",
              "BGF리테일            134000.0\n",
              "LS                 80000.0\n",
              "KODEX 200선물인버스2X  552000.0\n",
              "대웅제약               62000.0\n",
              "롯데정밀화학             64000.0\n",
              "신세계인터내셔날           14000.0"
            ],
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              "\n",
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              "      <th></th>\n",
              "      <th>매수</th>\n",
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              "  <tbody>\n",
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              "      <td>182000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KT</th>\n",
              "      <td>86000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>현대글로비스</th>\n",
              "      <td>160000.0</td>\n",
              "    </tr>\n",
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              "      <td>2000.0</td>\n",
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              "    <tr>\n",
              "      <th>현대중공업지주</th>\n",
              "      <td>56000.0</td>\n",
              "    </tr>\n",
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              "      <th>팬오션</th>\n",
              "      <td>6000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>롯데지주</th>\n",
              "      <td>296000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>메리츠증권</th>\n",
              "      <td>120000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>한국항공우주</th>\n",
              "      <td>30000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>OCI</th>\n",
              "      <td>116000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>제일기획</th>\n",
              "      <td>40000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BGF리테일</th>\n",
              "      <td>134000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LS</th>\n",
              "      <td>80000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KODEX 200선물인버스2X</th>\n",
              "      <td>552000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>대웅제약</th>\n",
              "      <td>62000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>롯데정밀화학</th>\n",
              "      <td>64000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>신세계인터내셔날</th>\n",
              "      <td>14000.0</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "      </button>\n",
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              "\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-49741d94-2273-4d50-8d0a-e3f6511ae49c');\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": 228
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,10))\n",
        "plt.pie(w1['weight'], labels=w1.index, autopct='%.1f%%')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 575
        },
        "id": "mj5gFKhGGm0i",
        "outputId": "fe68092d-1885-40e3-8e47-05ee8126bb32"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "w2 = pd.DataFrame()\n",
        "w2['weight'] = weight[1]\n",
        "w2['filter'] = weight[1]>0\n",
        "w2 = pd.DataFrame(w2['weight'].loc[w2['filter']==True])\n",
        "w2 = pd.concat([w2, stock_list['종목명'].loc[w2.index.values]], axis=1)\n",
        "w2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "sSfUslkAVVX9",
        "outputId": "5c78430f-a676-4d88-d8de-602bb337a962"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        weight     종목명\n",
              "047810   100.0  한국항공우주"
            ],
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              "      <th>weight</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>047810</th>\n",
              "      <td>100.0</td>\n",
              "      <td>한국항공우주</td>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4b5b05c6-2ff9-4aa6-a344-a51c1ed7ad1f')\"\n",
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              "\n",
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              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\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",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-4b5b05c6-2ff9-4aa6-a344-a51c1ed7ad1f 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-4b5b05c6-2ff9-4aa6-a344-a51c1ed7ad1f');\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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 222
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "pXUdQWZzVvRX"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}