port_construction_exercise1.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNN2QrpbxP7pxza+F9DelPf",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/TheresaZhu21/learning/blob/main/port_construction_exercise1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Portfolio construction exercise1"
      ],
      "metadata": {
        "id": "kNQZJuAhMDu3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1. Univariate returns"
      ],
      "metadata": {
        "id": "IbICP6u38n_Y"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EOkn-Fop8hsb"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from scipy.stats import skew\n",
        "from scipy.stats import kurtosis\n",
        "from scipy.stats import norm\n",
        "from scipy import stats"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Returns\n",
        "df = pd.read_csv('https://assets.datacamp.com/production/repositories/1546/datasets/0f1a004a8aa693163fa55f277513309f710b700d/MSFTPrices.csv', \n",
        "                 parse_dates=['Date'])\n",
        "df = df.sort_values(by='Date')\n",
        "df.set_index('Date', inplace=True)\n",
        "df['Return'] = df['Adjusted'].pct_change()\n",
        "df['Return'].plot() # plot returns over time\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "wqclLjZ98xNE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "aa93c75c-8b21-4b12-b93c-4784fa8660e9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize return distributions\n",
        "plt.hist(df['Return'].dropna(), bins=75, density=False) # density=True: probability density plot\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "kaGxgjTn9OA8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "4edcbbb6-04aa-4460-d9a4-86691836ac79"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Average annualized return\n",
        "mean_rtn_daily = np.mean(df['Return'])\n",
        "annualized_rtn = ((1+mean_rtn_daily)**252)-1\n",
        "annualized_rtn"
      ],
      "metadata": {
        "id": "bTrLlANWEYSI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "87071e13-6936-4096-fe19-1e19d77f9bb1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.09985839482852632"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Volatility and variance\n",
        "sigma = np.std(df['Return'])\n",
        "var = sigma**2\n",
        "sigma_annualized = sigma*np.sqrt(252)\n",
        "var_annualized = sigma_annualized**2\n",
        "print(f\"\"\"vol:{sigma}\\nvar:{var}\\nannualized vol: {sigma_annualized}\\nannualized variance: {var_annualized}\"\"\")"
      ],
      "metadata": {
        "id": "X395dId2d4wX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9ba73bc5-4873-4569-f375-baf28e179ced"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "vol:0.019341100408708317\n",
            "var:0.00037407816501973704\n",
            "annualized vol: 0.3070304505826315\n",
            "annualized variance: 0.09426769758497373\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Volatility in time series\n",
        "windowed = df['Return'].rolling(30)\n",
        "vol_30_annualized = windowed.std()*np.sqrt(252)\n",
        "vol_30_annualized.plot().set_ylabel(\"Volatility - 30 days\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 271
        },
        "id": "ia23HG9byLuz",
        "outputId": "a6a0e37e-1bea-4f6a-d2f1-0174f39a7f26"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Skewness\n",
        "skew(df['Return'].dropna())"
      ],
      "metadata": {
        "id": "akeNUnL_RopQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "22388186-341a-427c-86d1-7bbfaf19db80"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.21935459193067805"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Kurtosis\n",
        "excess_kurtosis = kurtosis(df['Return'].dropna()) # the kurtosis function actually returns excess kutosis\n",
        "kurtosis = excess_kurtosis+3\n",
        "print(f\"Fourth moment of the returns distribution: {kurtosis}\")"
      ],
      "metadata": {
        "id": "dnVRCU2cXOFF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "527daef1-1350-4eae-951a-05b627bf03d2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fourth moment of the returns distribution: 13.314572618025519\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Statistical test for normality\n",
        "p_value = stats.shapiro(df['Return'].dropna())[1]\n",
        "\n",
        "if p_value<=0.05:\n",
        "  print(\"Normality rejected\")\n",
        "else:\n",
        "  print(\"Normality accepted\")"
      ],
      "metadata": {
        "id": "jzcgj8z_ZSmz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f1c20709-c113-4c9a-925d-389ac2ac515b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Normality rejected\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2. Portfolio composition and backtesting"
      ],
      "metadata": {
        "id": "HMI5pdCAMHbE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Portfolio return:\\\n",
        "$R_p = R_{a_1}w_{a_1}+R_{a2}w_{a_2}+...+R_{a_n}w_{a_n}$\n",
        ">$R_{a_n}$: return for asset n\\\n",
        ">$w_{a_n}$: weight for asset n"
      ],
      "metadata": {
        "id": "AAa0x4h0Mbu7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_port = pd.read_csv('https://assets.datacamp.com/production/repositories/1546/datasets/fb7165b7270a3721f69abf9ff09b85938d9d1068/Big9Returns2017.csv',\n",
        "                            parse_dates=['Date'])\n",
        "df_port = df_port.sort_values(by=['Date'])\n",
        "df_port.set_index('Date', inplace=True)\n",
        "df_port.head(2)"
      ],
      "metadata": {
        "id": "UKA83yKPbJkz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "outputId": "bbf9515a-d1f7-4bae-9fd0-93e473f37c8b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                AAPL      MSFT       XOM       JNJ       JPM      AMZN  \\\n",
              "Date                                                                     \n",
              "2017-01-03  0.002849  0.007081  0.006980  0.005468  0.010894  0.005068   \n",
              "2017-01-04 -0.001119 -0.004474 -0.011002 -0.001640  0.001844  0.004657   \n",
              "\n",
              "                  GE        FB         T  \n",
              "Date                                      \n",
              "2017-01-03  0.002848  0.015732  0.011521  \n",
              "2017-01-04  0.000316  0.015660 -0.005811  "
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              "\n",
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              "      <th>AAPL</th>\n",
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              "      <th>XOM</th>\n",
              "      <th>JNJ</th>\n",
              "      <th>JPM</th>\n",
              "      <th>AMZN</th>\n",
              "      <th>GE</th>\n",
              "      <th>FB</th>\n",
              "      <th>T</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2017-01-03</th>\n",
              "      <td>0.002849</td>\n",
              "      <td>0.007081</td>\n",
              "      <td>0.006980</td>\n",
              "      <td>0.005468</td>\n",
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              "      <td>0.015732</td>\n",
              "      <td>0.011521</td>\n",
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              "    <tr>\n",
              "      <th>2017-01-04</th>\n",
              "      <td>-0.001119</td>\n",
              "      <td>-0.004474</td>\n",
              "      <td>-0.011002</td>\n",
              "      <td>-0.001640</td>\n",
              "      <td>0.001844</td>\n",
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              "      <td>0.000316</td>\n",
              "      <td>0.015660</td>\n",
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              "          const element = document.querySelector('#df-a0faee73-2132-45ae-8b03-bb6c5c53759e');\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": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Returns for assigned weighted portfolio\n",
        "port_weights = np.array([0.20, 0.20, 0.05, 0.05, 0.20, 0.20, 0.02, 0.05, 0.03])\n",
        "\n",
        "df_port_copy = df_port.copy()\n",
        "\n",
        "# Method 1\n",
        "df_port['port_rtn'] = df_port.mul(port_weights, axis=1).sum(axis=1) # .mul(): get multiplication of dataframe and other\n",
        "\n",
        "# Method 2\n",
        "df_port_copy['port_rtn'] = df_port_copy.dot(port_weights) # .dot(): returns the dot product of vectors"
      ],
      "metadata": {
        "id": "ZaVb_tXSOV-1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Returns for equally weighted portfolio\n",
        "n_ticker = 5\n",
        "port_weights_ew = np.repeat(1/n_ticker, n_ticker)\n",
        "df_port['port_rtn_ew'] = df_port.iloc[:,:n_ticker].mul(port_weights_ew, axis=1).sum(axis=1)"
      ],
      "metadata": {
        "id": "r74hHAYYOvAW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Market-cap weighted portfolios:\\\n",
        "$w_{mcap_n}=\\frac{mcap_n}{\\sum_{i=1}^{n}mcap_i}$"
      ],
      "metadata": {
        "id": "MrJtbQ6UbRr4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Returns for market-cap weighted portfolio\n",
        "mkt_cap = np.array([601.51, 469.25, 349.5, 310.48, 299.77])\n",
        "mcap_weights = mkt_cap/sum(mkt_cap)\n",
        "df_port['port_rtn_mcap'] = df_port.iloc[:,:n_ticker].mul(mcap_weights,axis=1).sum(axis=1)\n",
        "df_port.head(2)"
      ],
      "metadata": {
        "id": "z48P0H0OcsuL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 242
        },
        "outputId": "711bfa33-eb87-4181-e717-5506fb70fcf2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                AAPL      MSFT       XOM       JNJ       JPM      AMZN  \\\n",
              "Date                                                                     \n",
              "2017-01-03  0.002849  0.007081  0.006980  0.005468  0.010894  0.005068   \n",
              "2017-01-04 -0.001119 -0.004474 -0.011002 -0.001640  0.001844  0.004657   \n",
              "\n",
              "                  GE        FB         T  port_rtn  port_rtn_ew  port_rtn_mcap  \n",
              "Date                                                                            \n",
              "2017-01-03  0.002848  0.015732  0.011521  0.006990     0.006654       0.006126  \n",
              "2017-01-04  0.000316  0.015660 -0.005811  0.000164    -0.003278      -0.003238  "
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              "\n",
              "  <div id=\"df-36a9cda8-8c29-4f7a-99c7-066730e2e569\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
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              "  <thead>\n",
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              "      <th>AAPL</th>\n",
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              "      <th>Date</th>\n",
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              "      <th>2017-01-03</th>\n",
              "      <td>0.002849</td>\n",
              "      <td>0.007081</td>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
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              "\n",
              "      <script>\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-36a9cda8-8c29-4f7a-99c7-066730e2e569');\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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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot cumulative returns for portfolio\n",
        "cum_rtn = ((1+df_port).cumprod()-1)\n",
        "cum_rtn[['port_rtn', 'port_rtn_ew', 'port_rtn_mcap']].plot()"
      ],
      "metadata": {
        "id": "IXPnYy3_SdLp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "67e7f948-38d1-4e86-b3c1-30f409bb35e7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fba14a23850>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot cumulative returns for single tickers\n",
        "cum_rtn.iloc[:,:n_ticker].plot()"
      ],
      "metadata": {
        "id": "Oae2i26FVAcS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "outputId": "e9a8082f-accc-44b7-86c3-667f96e1ed7d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fba15099580>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3. Correlation and co-variance"
      ],
      "metadata": {
        "id": "K_lA6Ttre9l1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Correlation matrix\n",
        "corr_mat = df_port.iloc[:,:n_ticker].corr()\n",
        "corr_mat"
      ],
      "metadata": {
        "id": "QpFTN9wGcdg_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "d9990206-d292-4522-a28e-8f8ba7731e97"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          AAPL      MSFT       XOM       JNJ       JPM\n",
              "AAPL  1.000000  0.437430  0.055505  0.038432  0.199914\n",
              "MSFT  0.437430  1.000000  0.084108  0.230215  0.191248\n",
              "XOM   0.055505  0.084108  1.000000  0.121012  0.305926\n",
              "JNJ   0.038432  0.230215  0.121012  1.000000  0.070328\n",
              "JPM   0.199914  0.191248  0.305926  0.070328  1.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-96117995-052e-4df2-bdda-29301a5c4f17\">\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></th>\n",
              "      <th>AAPL</th>\n",
              "      <th>MSFT</th>\n",
              "      <th>XOM</th>\n",
              "      <th>JNJ</th>\n",
              "      <th>JPM</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>AAPL</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.437430</td>\n",
              "      <td>0.055505</td>\n",
              "      <td>0.038432</td>\n",
              "      <td>0.199914</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MSFT</th>\n",
              "      <td>0.437430</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.084108</td>\n",
              "      <td>0.230215</td>\n",
              "      <td>0.191248</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>XOM</th>\n",
              "      <td>0.055505</td>\n",
              "      <td>0.084108</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.121012</td>\n",
              "      <td>0.305926</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>JNJ</th>\n",
              "      <td>0.038432</td>\n",
              "      <td>0.230215</td>\n",
              "      <td>0.121012</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.070328</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>JPM</th>\n",
              "      <td>0.199914</td>\n",
              "      <td>0.191248</td>\n",
              "      <td>0.305926</td>\n",
              "      <td>0.070328</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-96117995-052e-4df2-bdda-29301a5c4f17')\"\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",
              "      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-96117995-052e-4df2-bdda-29301a5c4f17 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-96117995-052e-4df2-bdda-29301a5c4f17');\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": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Heatmap\n",
        "sns.heatmap(corr_mat, annot=True, fmt=\".2f\", cmap='crest')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "Avrr_IQ_KtPn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "935e402f-5d22-47f1-c8e6-0827029e9253"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Co-variance matrix\n",
        "cov_mat = df_port.iloc[:,:n_ticker].cov()\n",
        "cov_mat"
      ],
      "metadata": {
        "id": "uKdCbvOVHITx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "0ee69b17-67cb-4449-e77a-41fc5b55ac73"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          AAPL      MSFT       XOM       JNJ       JPM\n",
              "AAPL  0.000123  0.000045  0.000004  0.000003  0.000023\n",
              "MSFT  0.000045  0.000087  0.000006  0.000015  0.000018\n",
              "XOM   0.000004  0.000006  0.000050  0.000006  0.000022\n",
              "JNJ   0.000003  0.000015  0.000006  0.000052  0.000005\n",
              "JPM   0.000023  0.000018  0.000022  0.000005  0.000105"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8b7fb91b-c69d-4814-9a75-b2eacae6bc22\">\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></th>\n",
              "      <th>AAPL</th>\n",
              "      <th>MSFT</th>\n",
              "      <th>XOM</th>\n",
              "      <th>JNJ</th>\n",
              "      <th>JPM</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>AAPL</th>\n",
              "      <td>0.000123</td>\n",
              "      <td>0.000045</td>\n",
              "      <td>0.000004</td>\n",
              "      <td>0.000003</td>\n",
              "      <td>0.000023</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MSFT</th>\n",
              "      <td>0.000045</td>\n",
              "      <td>0.000087</td>\n",
              "      <td>0.000006</td>\n",
              "      <td>0.000015</td>\n",
              "      <td>0.000018</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>XOM</th>\n",
              "      <td>0.000004</td>\n",
              "      <td>0.000006</td>\n",
              "      <td>0.000050</td>\n",
              "      <td>0.000006</td>\n",
              "      <td>0.000022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>JNJ</th>\n",
              "      <td>0.000003</td>\n",
              "      <td>0.000015</td>\n",
              "      <td>0.000006</td>\n",
              "      <td>0.000052</td>\n",
              "      <td>0.000005</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>JPM</th>\n",
              "      <td>0.000023</td>\n",
              "      <td>0.000018</td>\n",
              "      <td>0.000022</td>\n",
              "      <td>0.000005</td>\n",
              "      <td>0.000105</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8b7fb91b-c69d-4814-9a75-b2eacae6bc22')\"\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",
              "      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-8b7fb91b-c69d-4814-9a75-b2eacae6bc22 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-8b7fb91b-c69d-4814-9a75-b2eacae6bc22');\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": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Annualized co-variance matrix\n",
        "cov_mat_annual = cov_mat*252\n",
        "cov_mat_annual"
      ],
      "metadata": {
        "id": "IO-AGDVeNxy1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "6c582159-6164-4741-ba98-28be961b4949"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          AAPL      MSFT       XOM       JNJ       JPM\n",
              "AAPL  0.030996  0.011400  0.001093  0.000774  0.005716\n",
              "MSFT  0.011400  0.021912  0.001392  0.003899  0.004597\n",
              "XOM   0.001093  0.001392  0.012500  0.001548  0.005554\n",
              "JNJ   0.000774  0.003899  0.001548  0.013092  0.001307\n",
              "JPM   0.005716  0.004597  0.005554  0.001307  0.026371"
            ],
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              "\n",
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              "      <th></th>\n",
              "      <th>AAPL</th>\n",
              "      <th>MSFT</th>\n",
              "      <th>XOM</th>\n",
              "      <th>JNJ</th>\n",
              "      <th>JPM</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>AAPL</th>\n",
              "      <td>0.030996</td>\n",
              "      <td>0.011400</td>\n",
              "      <td>0.001093</td>\n",
              "      <td>0.000774</td>\n",
              "      <td>0.005716</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MSFT</th>\n",
              "      <td>0.011400</td>\n",
              "      <td>0.021912</td>\n",
              "      <td>0.001392</td>\n",
              "      <td>0.003899</td>\n",
              "      <td>0.004597</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>XOM</th>\n",
              "      <td>0.001093</td>\n",
              "      <td>0.001392</td>\n",
              "      <td>0.012500</td>\n",
              "      <td>0.001548</td>\n",
              "      <td>0.005554</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>JNJ</th>\n",
              "      <td>0.000774</td>\n",
              "      <td>0.003899</td>\n",
              "      <td>0.001548</td>\n",
              "      <td>0.013092</td>\n",
              "      <td>0.001307</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>JPM</th>\n",
              "      <td>0.005716</td>\n",
              "      <td>0.004597</td>\n",
              "      <td>0.005554</td>\n",
              "      <td>0.001307</td>\n",
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              "          const element = document.querySelector('#df-90414cbf-f6d0-4132-8476-b735995fb02b');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
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              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3.1 Portfolio standard deviation"
      ],
      "metadata": {
        "id": "o1wjFeiSb9Ri"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 3.1.1 Two asset portfolio\n",
        "$\\sigma_p=\\sqrt{w_1^2 \\sigma_1^2 + w_2^2 \\sigma_2^2 + 2w_1w_2 \\rho_{1,2} \\sigma_1 \\sigma_2}$\n",
        ">$\\sigma_p$: portfolio standard deviation\\\n",
        ">$w$: asset weight\\\n",
        ">$\\sigma$: asset volatility\\\n",
        ">$\\rho_{1,2}$: correlation between assets 1 and 2"
      ],
      "metadata": {
        "id": "S0nWr1jUH_pL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 3.1.2 Multi-asset portfolio\n",
        "$\\sigma_{Portfolio}=\\sqrt{w_T \\cdot \\Sigma \\cdot w}$\n",
        ">$\\sigma_{Portfolio}$: portfolio volatility\\\n",
        ">$\\Sigma$: covariance matrix of returns\\\n",
        ">$w$: portfolio weights\\\n",
        ">$w_T$: transposed portfolio weights\\\n",
        ">$\\cdot$: **dot product operator**:\n",
        "$a\\cdot b=\\Sigma_{i=1}^{n}a_ib_i=a_1b_1+a_2b_2+...+a_nb_n$"
      ],
      "metadata": {
        "id": "PxxwqKjjOrKc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Matrix multiplication\n",
        "## Method 1: np.dot()\n",
        "port_vol = np.sqrt(np.dot(port_weights[:n_ticker].T, np.dot(cov_mat, port_weights[:n_ticker]))) # .T: transpose\n",
        "print(port_vol)\n",
        "\n",
        "## Method 2: @\n",
        "port_vol_copy = np.sqrt(np.transpose(port_weights[:n_ticker]) @cov_mat @port_weights[:n_ticker]) # np.transpose(): transpose\n",
        "print(port_vol_copy)"
      ],
      "metadata": {
        "id": "Q0yAzMjrOCy1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7190aa1b-53c1-4668-e139-4c549fc82fbb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.004568831703557459\n",
            "0.004568831703557459\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3.2 Sharpe ratio\n",
        "$S = \\frac{R_a-r_f}{\\sigma_a}$\n",
        ">$R_a$: asset return\\\n",
        ">$r_f$: risk-free rate of return\\\n",
        ">$\\sigma_a$: asset volatility"
      ],
      "metadata": {
        "id": "rqJWGmq3bQKx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3.3 Markovitz portfolios"
      ],
      "metadata": {
        "id": "wqmAq5eohIIV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rfr = 0\n",
        "df_port2 = pd.read_csv(\"https://assets.datacamp.com/production/repositories/1546/datasets/85e2663a50d3445cbc2c2d30ac81abbaae6a7f56/EfficientFrontierPortfoliosSlim.csv\")\n",
        "df_port2[\"Sharpe\"] = (df_port2['Returns']-rfr)/df_port2['Volatility']\n",
        "df_port2.head(2)"
      ],
      "metadata": {
        "id": "Z6uNDTjhhy1i",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "734685bd-8ab2-4da8-88af-96e070993d96"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   AAPL weight  MSFT weight  XOM weight  JNJ weight  JPM weight  AMZN weight  \\\n",
              "0     0.000053     0.263110    0.007022    0.040087    0.000027     0.024316   \n",
              "1     0.042360     0.034922    0.079355    0.043057    0.001435     0.202032   \n",
              "\n",
              "   GE weight  FB weight  T weight   Returns  Volatility    Sharpe  \n",
              "0   0.355899   0.299400  0.010087  0.127453    0.171565  0.742884  \n",
              "1   0.225924   0.362324  0.008590  0.138614    0.182723  0.758601  "
            ],
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              "\n",
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              "      <th></th>\n",
              "      <th>AAPL weight</th>\n",
              "      <th>MSFT weight</th>\n",
              "      <th>XOM weight</th>\n",
              "      <th>JNJ weight</th>\n",
              "      <th>JPM weight</th>\n",
              "      <th>AMZN weight</th>\n",
              "      <th>GE weight</th>\n",
              "      <th>FB weight</th>\n",
              "      <th>T weight</th>\n",
              "      <th>Returns</th>\n",
              "      <th>Volatility</th>\n",
              "      <th>Sharpe</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.000053</td>\n",
              "      <td>0.263110</td>\n",
              "      <td>0.007022</td>\n",
              "      <td>0.040087</td>\n",
              "      <td>0.000027</td>\n",
              "      <td>0.024316</td>\n",
              "      <td>0.355899</td>\n",
              "      <td>0.299400</td>\n",
              "      <td>0.010087</td>\n",
              "      <td>0.127453</td>\n",
              "      <td>0.171565</td>\n",
              "      <td>0.742884</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.042360</td>\n",
              "      <td>0.034922</td>\n",
              "      <td>0.079355</td>\n",
              "      <td>0.043057</td>\n",
              "      <td>0.001435</td>\n",
              "      <td>0.202032</td>\n",
              "      <td>0.225924</td>\n",
              "      <td>0.362324</td>\n",
              "      <td>0.008590</td>\n",
              "      <td>0.138614</td>\n",
              "      <td>0.182723</td>\n",
              "      <td>0.758601</td>\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-2d1c0e2c-27f1-44c2-85af-13757e060964 button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-2d1c0e2c-27f1-44c2-85af-13757e060964');\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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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Maximum Sharpe Ratio (MSR) Returns portfolio\n",
        "MSR_weights = np.array(df_port2.sort_values(by=['Sharpe'], ascending=False).iloc[0, :n_ticker])\n",
        "MSR_weights"
      ],
      "metadata": {
        "id": "8lsuUi5fiUvV",
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          "base_uri": "https://localhost:8080/"
        },
        "outputId": "22452dd9-aa24-42ae-ac00-f766f151b37e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.00755369, 0.00573255, 0.00724446, 0.16672379, 0.38235578])"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Global Minimum Volatility (GMV) Returns portfolio\n",
        "GMV_weights = np.array(df_port2.sort_values(by=['Volatility'], ascending=True).iloc[0, :n_ticker])\n",
        "GMV_weights"
      ],
      "metadata": {
        "id": "8LL23ZobijB7",
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          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e80dae4d-838c-4f1a-e7ff-282564cfc042"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.08658622, 0.01522472, 0.04522291, 0.34099938, 0.02868646])"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot cumulative returns for portfolio\n",
        "df_port['port_rtn_msr'] = df_port.iloc[:, :n_ticker].mul(MSR_weights, axis=1).sum(axis=1)\n",
        "df_port['port_rtn_gmv'] = df_port.iloc[:, :n_ticker].mul(GMV_weights, axis=1).sum(axis=1)\n",
        "cum_rtn = ((1+df_port).cumprod()-1)\n",
        "cum_rtn[['port_rtn', 'port_rtn_ew', 'port_rtn_mcap', 'port_rtn_msr', 'port_rtn_gmv']].plot()"
      ],
      "metadata": {
        "id": "_-FUNJfWjQ18",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "7cf71d7d-53e3-4e4e-e375-5a90376affda"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fba14fbcbb0>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Model 1: CAPM model\n",
        "$E(R_P)-RF=\\beta_P(E(R_M-RF))$\n",
        ">$E(R_P)-RF$: the excess expected return of portfolio P\\\n",
        ">$E(R_M)-RF$: the excess expected return of the broad market portfolio B\\\n",
        ">$RF$: the regional risk-free rate\\\n",
        ">$\\beta_P$: portfolio beta, or exposure, to the broad market portfolio B\n",
        ">>$\\beta_P=\\frac{Cov(R_P,R_B)}{Var(R_B)}$\n",
        ">>>$\\beta_P$: portfolio beta\\\n",
        ">>>$Cov(R_P,R_B)$: co-variance between portfolio P and the benchmark index (B)\\\n",
        ">>>$Var(R_B)$: variance of the benchmark market index"
      ],
      "metadata": {
        "id": "XoCwbrhn9rUT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Regression: \\\n",
        "$y = \\beta X + \\epsilon$"
      ],
      "metadata": {
        "id": "Ek-krhoPTfTK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_port3 = pd.read_csv('https://assets.datacamp.com/production/repositories/1546/datasets/3d9b734fea954b629d2477ef48c36525dfecf6e0/FamaFrenchFactors.csv',\n",
        "                       parse_dates=['Date'])\n",
        "df_port3 = df_port3.sort_values(by=['Date'], ascending=True)\n",
        "df_port3.set_index('Date', inplace=True)\n",
        "df_port3.head()"
      ],
      "metadata": {
        "id": "FQhL7CJrni3D",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "a6cde84d-5f05-4b82-cec1-5d1a188ce785"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Portfolio  Market_Excess     SMB     HML     RMW     CMA   RF\n",
              "Date                                                                     \n",
              "2013-01-03  -0.005066        -0.0014  0.0014  0.0004  0.0020  0.0023  0.0\n",
              "2013-01-04   0.004024         0.0055  0.0019  0.0043 -0.0037  0.0027  0.0\n",
              "2013-01-07   0.004421        -0.0031 -0.0009 -0.0037 -0.0013 -0.0012  0.0\n",
              "2013-01-08  -0.004659        -0.0027  0.0004 -0.0007 -0.0012  0.0009  0.0\n",
              "2013-01-09   0.004636         0.0034  0.0024 -0.0041 -0.0007 -0.0015  0.0"
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          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_port3['Portfolio_Excess'] = df_port3['Portfolio']-df_port3['RF']\n",
        "\n",
        "# Plot cumulative return vs cumulative excess return\n",
        "cum_rtn = (1+df_port3[['Portfolio','Portfolio_Excess']]).cumprod()-1\n",
        "cum_rtn.plot()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "K3yOW-z3YADJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "3e2bcc62-e870-41da-b26a-46de31534567"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Co-variance\n",
        "covariance_matrix = df_port3[['Portfolio_Excess','Market_Excess']].cov()\n",
        "covariance_coefficient = covariance_matrix.iloc[0,1]\n",
        "benchmark_variance = df_port3['Market_Excess'].var()\n",
        "portfolio_beta = covariance_coefficient/benchmark_variance\n",
        "portfolio_beta"
      ],
      "metadata": {
        "id": "A43DbhN-YvEp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "16469fbd-6aba-4e1a-9a5e-ba88642d57de"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9737755165745455"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "covariance_matrix"
      ],
      "metadata": {
        "id": "wlrTSAZgaHLl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "outputId": "c07b3050-f80b-4b98-f7ef-19736290a801"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  Portfolio_Excess  Market_Excess\n",
              "Portfolio_Excess          0.000070       0.000057\n",
              "Market_Excess             0.000057       0.000059"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-baf13af4-160c-4a39-bd2a-1f345bda79f4\">\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></th>\n",
              "      <th>Portfolio_Excess</th>\n",
              "      <th>Market_Excess</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Portfolio_Excess</th>\n",
              "      <td>0.000070</td>\n",
              "      <td>0.000057</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Market_Excess</th>\n",
              "      <td>0.000057</td>\n",
              "      <td>0.000059</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-baf13af4-160c-4a39-bd2a-1f345bda79f4')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "\n",
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              "            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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate beta with linear regression\n",
        "import statsmodels.formula.api as smf\n",
        "\n",
        "CAPM_model = smf.ols(formula='Portfolio_Excess~Market_Excess', data=df_port3)\n",
        "CAPM_fit = CAPM_model.fit()\n",
        "regression_beta = CAPM_fit.params['Market_Excess']\n",
        "\n",
        "print(regression_beta)\n",
        "print(CAPM_fit.rsquared) # R-squared\n",
        "print(CAPM_fit.rsquared_adj) # adjusted R-squared: avoid overfitting\n"
      ],
      "metadata": {
        "id": "uSM3P3iOaK2H",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5f45092e-4e4a-4925-d646-a202e1373c14"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.973775516574546\n",
            "0.7944263892587435\n",
            "0.7942627160017839\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Model 2: Fama-French 3 Factor Model\n",
        "$R_P=RF+\\beta_M(R_M-RF)+b_{SMB}\\cdot SMB + b_{HML} \\cdot HML + \\alpha $\n",
        ">$SMB$: small minus big factor\\\n",
        ">$b_{SMB}$: exposure to the SMB factor\\\n",
        ">$HML$: high minus low factor\\\n",
        ">$b_{HML}$: exposure to the HML factor\\\n",
        ">$\\alpha$: performance which is unexplained by any other factors\\\n",
        ">$\\beta_M$: beta to the braod market portfolio B"
      ],
      "metadata": {
        "id": "bK6C7BzDyqHQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define a regression model that explains Portfolio_Excess as a function of Market_Excess, SMB, and HML\n",
        "FamaFrench_model = smf.ols(formula='Portfolio_Excess~Market_Excess+SMB+HML', data=df_port3)\n",
        "FamaFrench_fit = FamaFrench_model.fit()\n",
        "FamaFrench_adj_r_squared = FamaFrench_fit.rsquared_adj\n",
        "FamaFrench_adj_r_squared # higher than CAPM model"
      ],
      "metadata": {
        "id": "GH4ZrBkWcYJo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9cd7448d-39b3-45b0-860f-574dfbd26333"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8193910088585152"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# p-value of HML factor\n",
        "pvalue_HML = FamaFrench_fit.pvalues['HML']\n",
        "print(pvalue_HML)\n",
        "\n",
        "# Test if p-value is statistically significant\n",
        "print(pvalue_HML<0.05)\n",
        "\n",
        "# Extract coefficients\n",
        "print(FamaFrench_fit.params['HML'])\n",
        "print(FamaFrench_fit.params['SMB'])\n",
        "\n",
        "# Alpha and the efficient market hypothesis\n",
        "portfolio_alpha = FamaFrench_fit.params['Intercept']\n",
        "portfolio_alpha_annualized = ((1+portfolio_alpha)**252)-1\n",
        "print(portfolio_alpha_annualized) # which is efficient"
      ],
      "metadata": {
        "id": "jCDG-_YB0kI-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d798c0be-f12a-40fb-f06e-022c3f0ce7f2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4.699308183443772e-07\n",
            "True\n",
            "-0.10865715035429256\n",
            "-0.26215152743192643\n",
            "0.04726181730280854\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Model 3: Fama-French 5 Factor Model"
      ],
      "metadata": {
        "id": "BE3yjJ289Zu2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Expanded 5 factor model\n",
        "FamaFrench5_model = smf.ols(formula='Portfolio_Excess~Market_Excess+SMB+HML+RMW+CMA', data=df_port3)\n",
        "FamaFrench5_fit = FamaFrench5_model.fit()\n",
        "FamaFrench5_adj_r_squared = FamaFrench5_fit.rsquared_adj\n",
        "FamaFrench5_adj_r_squared # higher adjusted R-squared"
      ],
      "metadata": {
        "id": "WxjR5gv207dK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d83a7870-d138-4d84-ec9e-8ca68ea4de66"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8367245019225793"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Alpha\n",
        "portfolio5_alpha = FamaFrench5_fit.params['Intercept']\n",
        "portfolio5_alpha_annualied = ((1+portfolio5_alpha)**252)-1\n",
        "portfolio5_alpha_annualied"
      ],
      "metadata": {
        "id": "9niAXFr79LkS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1197c330-bdc5-43a5-d7b4-df6f203df31e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.044902389879467375"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 4. Value at risk"
      ],
      "metadata": {
        "id": "GGWjbZ5PZFZ9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 4.1 Drawdown"
      ],
      "metadata": {
        "id": "H0NsDAD5idYg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "$Drawdown=\\frac{r_t}{RM}-1$\n",
        ">$r_t$: cumulative return at time t\\\n",
        ">$RM$: running maximum\n"
      ],
      "metadata": {
        "id": "1EpPaE1yYlJJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_port4 = pd.read_csv('https://assets.datacamp.com/production/repositories/1546/datasets/dfe9da08c986709d59943d1d5c0106537a8c608a/USO.csv',\n",
        "                       parse_dates=['Date'])\n",
        "df_port4 = df_port4.sort_values(by=['Date'])\n",
        "df_port4.set_index('Date', inplace=True)\n",
        "df_port4.columns = ['Return']\n",
        "df_port4['cum_rtn'] = (1+df_port4).cumprod()-1\n",
        "df_port4.head()"
      ],
      "metadata": {
        "id": "M5kAdGIWZj0J",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "80875a61-58b6-4c83-e1ea-40988fe1e016"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Return   cum_rtn\n",
              "Date                          \n",
              "2007-01-03 -0.042636 -0.042636\n",
              "2007-01-04 -0.040688 -0.081589\n",
              "2007-01-05  0.009285 -0.073062\n",
              "2007-01-08 -0.007109 -0.079651\n",
              "2007-01-09 -0.004001 -0.083333"
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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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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate drawdown\n",
        "df_cum_rtn = pd.DataFrame(df_port4['cum_rtn'])\n",
        "running_max = np.maximum.accumulate(df_cum_rtn) # calculate the running maximum\n",
        "running_max[running_max<1] = 1 # ensure the value never drops below 1\n",
        "drawdown = df_cum_rtn/running_max -1 # calculate the percentage drawdown\n",
        "drawdown.columns = ['Drawdown']\n",
        "drawdown.head(2)"
      ],
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        "id": "MdYek9NTdd4l",
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          "base_uri": "https://localhost:8080/",
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      "execution_count": null,
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        {
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          "data": {
            "text/plain": [
              "            Drawdown\n",
              "Date                \n",
              "2007-01-03 -1.042636\n",
              "2007-01-04 -1.081589"
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              "    <tr>\n",
              "      <th>2007-01-03</th>\n",
              "      <td>-1.042636</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2007-01-04</th>\n",
              "      <td>-1.081589</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-469ed6f5-0c52-44a7-8082-a7e8031a4d51')\"\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",
              "      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-469ed6f5-0c52-44a7-8082-a7e8031a4d51 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-469ed6f5-0c52-44a7-8082-a7e8031a4d51');\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": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot drawdown\n",
        "drawdown.plot()\n",
        "plt.fill_between(drawdown.index, drawdown['Drawdown'])\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "v9qKRAPBe9zV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "b78eb4aa-a95b-40fe-d99a-342b52a07898"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 4.2 VaR & CVaR"
      ],
      "metadata": {
        "id": "624iz_fEigq_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# VaR(95)\n",
        "var_level = 95\n",
        "return_pct = df_port4['Return']*100\n",
        "var_95 = np.percentile(return_pct, 100-var_level)\n",
        "var_95 # 95% certain that losses will not exceed -3.61% in a given day based on historical values"
      ],
      "metadata": {
        "id": "fINF3yh3ilIu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ee504c82-7d80-413f-c8db-9a3288526c6d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-3.608233764037496"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# CVaR(95): \n",
        "cvar_95 = (df_port4[return_pct<=var_95]['Return']*100).mean()\n",
        "cvar_95 # in the worst 5% of cases, losses were on average exceed -5.05% historically"
      ],
      "metadata": {
        "id": "odUfFPfhfYir",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8939d5f1-c39b-46eb-c5b9-b53631b44185"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-5.054143158346777"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist(return_pct, density=True)\n",
        "\n",
        "plt.axvline(var_95, color='r')\n",
        "plt.text(var_95*0.9, 0.15, 'VaR(95)={:.3f}'.format(var_95), color='r')\n",
        "\n",
        "plt.axvline(cvar_95, color='b')\n",
        "plt.text(cvar_95*2.2, 0.1, 'CVaR(95)={:.3f}'.format(cvar_95), color='b')\n",
        "\n",
        "plt.xlabel('Return(%)')\n",
        "plt.ylabel('Probability')\n",
        "plt.title('Distribution of Returns')\n",
        "plt.grid(False)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "5BrZn6SghJ4S",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "b56437ac-e6dd-44af-d236-67c9629b4be6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Parametric VaR(95)\n",
        "mu = np.mean(df_port4['Return'])\n",
        "vol = np.std(df_port4['Return'])\n",
        "confidence_level = 0.05\n",
        "parametric_var_95 = norm.ppf(confidence_level, mu, vol) # probability density function\n",
        "print(f\"Mean: {mu}\\nVolatility: {vol}\\nVaR(95): {parametric_var_95}\")"
      ],
      "metadata": {
        "id": "3Wzqy6E7FKt2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "06ec29ed-ab69-4b30-8144-35850bf35e2e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean: -0.00028638956240214787\n",
            "Volatility: 0.021888087129708852\n",
            "VaR(95): -0.03628908906473361\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "$VaR(95)_{T\\;days} = VaR(95)_{daily}\\sqrt{T}$"
      ],
      "metadata": {
        "id": "nbwuuECFfoI0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Scaling risk estimates\n",
        "forecast_days = 5\n",
        "parametric_var_95_5d = parametric_var_95*np.sqrt(forecast_days)\n",
        "parametric_var_95_5d"
      ],
      "metadata": {
        "id": "sU7TslDLbDdk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "79d8bc48-acf7-472b-8ebf-03ba33944f29"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-0.08114486999028862"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 4.3 Random walks"
      ],
      "metadata": {
        "id": "fqyYKA9z3uHR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# One random simulation\n",
        "T = 252\n",
        "S0 = 10 # starting stock value\n",
        "random_tests = np.random.normal(mu, vol, T) + 1\n",
        "forecasted_values = S0*(random_tests.cumprod()) # simulate the stock value over 1 yr"
      ],
      "metadata": {
        "id": "FIZWBKJis-JZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Monte Carlo simulation\n",
        "simulated_rtn = []\n",
        "\n",
        "for i in range(100):\n",
        "  random_rtn = np.random.normal(mu, vol, T)\n",
        "  simulated_rtn.append(random_rtn)\n",
        "  # Plot the Monte Carlo path\n",
        "  forecasted_values = S0*((1+random_rtn).cumprod())\n",
        "  plt.plot(range(T), forecasted_values)\n",
        "\n",
        "var_95 = np.percentile(simulated_rtn, 5)\n",
        "var_95"
      ],
      "metadata": {
        "id": "RywEXfu04SBw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "3ac2bd9b-6689-4285-b160-fc299205f4dd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-0.03669209506844224"
            ]
          },
          "metadata": {},
          "execution_count": 42
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}