Hidden_Markov_Model.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Hidden Markov Model Clean Final.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "SvdfZf9TFyiQ",
        "T6fqpiT7GE1N",
        "fkhLgRzCEVfL",
        "-Q99Lk_3Ejt3",
        "CvC23QlFi0Ua",
        "2zH-DRT4nRcA",
        "0S0IVt6Hbn3g"
      ]
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Setup Libraries"
      ],
      "metadata": {
        "id": "SvdfZf9TFyiQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Needed in google colab"
      ],
      "metadata": {
        "id": "JQwxG5G0F5xj"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RM149AzLYri6",
        "outputId": "6af6c49b-3928-407a-ef15-df4c79881ba1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting yfinance\n",
            "  Downloading yfinance-0.1.70-py2.py3-none-any.whl (26 kB)\n",
            "Requirement already satisfied: numpy>=1.15 in /usr/local/lib/python3.7/dist-packages (from yfinance) (1.21.6)\n",
            "Collecting lxml>=4.5.1\n",
            "  Downloading lxml-4.8.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_24_x86_64.whl (6.4 MB)\n",
            "\u001b[K     |████████████████████████████████| 6.4 MB 7.3 MB/s \n",
            "\u001b[?25hCollecting requests>=2.26\n",
            "  Downloading requests-2.27.1-py2.py3-none-any.whl (63 kB)\n",
            "\u001b[K     |████████████████████████████████| 63 kB 597 kB/s \n",
            "\u001b[?25hRequirement already satisfied: multitasking>=0.0.7 in /usr/local/lib/python3.7/dist-packages (from yfinance) (0.0.10)\n",
            "Requirement already satisfied: pandas>=0.24.0 in /usr/local/lib/python3.7/dist-packages (from yfinance) (1.3.5)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24.0->yfinance) (2022.1)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24.0->yfinance) (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>=0.24.0->yfinance) (1.15.0)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.26->yfinance) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.26->yfinance) (2021.10.8)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.7/dist-packages (from requests>=2.26->yfinance) (2.0.12)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.26->yfinance) (2.10)\n",
            "Installing collected packages: requests, lxml, yfinance\n",
            "  Attempting uninstall: requests\n",
            "    Found existing installation: requests 2.23.0\n",
            "    Uninstalling requests-2.23.0:\n",
            "      Successfully uninstalled requests-2.23.0\n",
            "  Attempting uninstall: lxml\n",
            "    Found existing installation: lxml 4.2.6\n",
            "    Uninstalling lxml-4.2.6:\n",
            "      Successfully uninstalled lxml-4.2.6\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "google-colab 1.0.0 requires requests~=2.23.0, but you have requests 2.27.1 which is incompatible.\n",
            "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\u001b[0m\n",
            "Successfully installed lxml-4.8.0 requests-2.27.1 yfinance-0.1.70\n"
          ]
        }
      ],
      "source": [
        "pip install yfinance"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Provides ways to work with large multidimensional arrays\n",
        "import numpy as np \n",
        "# Allows for further data manipulation and analysis\n",
        "import pandas as pd \n",
        "import matplotlib.pyplot as plt # Plotting\n",
        "import matplotlib.dates as mdates # Styling dates\n",
        "#import matplotlib.mlab as mlab \n",
        "%matplotlib inline\n",
        "\n",
        "# statistical calculation\n",
        "#from scipy.stats import norm\n",
        "\n",
        "import datetime as dt # For defining dates\n",
        "\n",
        "import time\n",
        "\n",
        "import yfinance as yf"
      ],
      "metadata": {
        "id": "Ca_QWCWpF-yU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Get Stock Data"
      ],
      "metadata": {
        "id": "T6fqpiT7GE1N"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "all_stocks = [\"^GSPC\", \"^FTLC\", \"^RUT\", \"IEUS\", \"GOVT\", \"IGOV\", \"SIVR\", \"SGOL\"]"
      ],
      "metadata": {
        "id": "EM5tszECGHM9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_all_closes_of_stocks(tickers):\n",
        "  i = 0\n",
        "  stock_list = pd.DataFrame()\n",
        "  for ticker in tickers:\n",
        "    stock = yf.Ticker(ticker)        \n",
        "    data = stock.history(start=\"2012-02-24\",end =\"2022-02-25\")\n",
        "    data.drop(data.columns[[0,1,2,4,5,6]], axis=1, inplace=True) # Just gets the adjacent close for each asset\n",
        "    data.columns = [ticker]\n",
        "    stock_list[ticker] = data.iloc[:,0]\n",
        "  return stock_list.dropna()\n",
        "stock_data = get_all_closes_of_stocks(all_stocks)\n",
        "print(stock_data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6pHUhYdCGLFq",
        "outputId": "2ff22ab1-d033-4a82-c0d7-7e65979b0dcd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                  ^GSPC        ^FTLC         ^RUT       IEUS       GOVT  \\\n",
            "Date                                                                      \n",
            "2012-02-24  1365.739990  3144.399902   826.919983  27.285173  21.551908   \n",
            "2012-02-27  1367.589966  3133.399902   826.659973  27.201170  21.551908   \n",
            "2012-02-28  1372.180054  3141.199951   823.799988  27.445536  21.551908   \n",
            "2012-02-29  1365.680054  3112.399902   810.940002  27.155359  21.551908   \n",
            "2012-03-01  1374.089966  3142.899902   815.219971  27.117172  21.551908   \n",
            "...                 ...          ...          ...        ...        ...   \n",
            "2022-02-17  4380.259766  4240.200195  2028.089966  63.590000  25.511515   \n",
            "2022-02-18  4348.870117  4222.799805  2009.329956  62.840000  25.571404   \n",
            "2022-02-22  4304.759766  4201.799805  1980.170044  61.560001  25.581383   \n",
            "2022-02-23  4225.500000  4198.799805  1944.089966  60.889999  25.461611   \n",
            "2022-02-24  4288.700195  4042.899902  1996.010010  60.000000  25.471592   \n",
            "\n",
            "                 IGOV       SIVR       SGOL  \n",
            "Date                                         \n",
            "2012-02-24  47.279083  35.110001  17.559000  \n",
            "2012-02-27  47.222900  35.099998  17.504000  \n",
            "2012-02-28  47.592926  36.610001  17.688000  \n",
            "2012-02-29  47.358727  34.240002  16.750000  \n",
            "2012-03-01  46.416683  35.180000  16.987000  \n",
            "...               ...        ...        ...  \n",
            "2022-02-17  48.439999  22.959999  18.219999  \n",
            "2022-02-18  48.380001  23.040001  18.200001  \n",
            "2022-02-22  48.330002  23.260000  18.240000  \n",
            "2022-02-23  48.130001  23.680000  18.330000  \n",
            "2022-02-24  47.959999  23.270000  18.219999  \n",
            "\n",
            "[2401 rows x 8 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Hidden Markov Model"
      ],
      "metadata": {
        "id": "fkhLgRzCEVfL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Setup libaries"
      ],
      "metadata": {
        "id": "-Q99Lk_3Ejt3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pip install hmmlearn"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-GZxJWGIE-jN",
        "outputId": "e6bf668b-127b-4cb6-f41e-2cb3ed129e41"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: hmmlearn in /usr/local/lib/python3.7/dist-packages (0.2.7)\n",
            "Requirement already satisfied: scikit-learn>=0.16 in /usr/local/lib/python3.7/dist-packages (from hmmlearn) (1.0.2)\n",
            "Requirement already satisfied: numpy>=1.10 in /usr/local/lib/python3.7/dist-packages (from hmmlearn) (1.21.6)\n",
            "Requirement already satisfied: scipy>=0.19 in /usr/local/lib/python3.7/dist-packages (from hmmlearn) (1.4.1)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.16->hmmlearn) (1.1.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.16->hmmlearn) (3.1.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import itertools\n",
        "from tqdm import tqdm\n",
        "from hmmlearn.hmm import MultinomialHMM\n",
        "from hmmlearn import hmm\n",
        "from hmmlearn.hmm import GaussianHMM\n",
        "from sklearn.metrics import r2_score"
      ],
      "metadata": {
        "id": "z7r1BPkDEVAm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model setup"
      ],
      "metadata": {
        "id": "Ahv0rTeMEmOs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "stock = yf.Ticker(\"^GSPC\")\n",
        "data = stock.history(start=\"2012-02-24\",end=\"2022-02-25\")\n",
        "test_data = data.iloc[-252:]\n",
        "data = data.iloc[:-252]"
      ],
      "metadata": {
        "id": "zR_btRRww2gi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.drop(data.columns[[4,5,6]], axis=1, inplace=True)\n",
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 519
        },
        "id": "6a7a-Rh10Jvr",
        "outputId": "5ea2a060-059b-4eef-8529-f4fe523ddf52"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   Open         High          Low        Close\n",
              "Date                                                          \n",
              "2012-02-24  1363.459961  1368.920044  1363.459961  1365.739990\n",
              "2012-02-27  1365.199951  1371.939941  1354.920044  1367.589966\n",
              "2012-02-28  1367.560059  1373.089966  1365.969971  1372.180054\n",
              "2012-02-29  1372.199951  1378.040039  1363.810059  1365.680054\n",
              "2012-03-01  1365.900024  1376.170044  1365.900024  1374.089966\n",
              "...                 ...          ...          ...          ...\n",
              "2021-02-19  3921.159912  3930.409912  3903.070068  3906.709961\n",
              "2021-02-22  3885.550049  3902.919922  3874.709961  3876.500000\n",
              "2021-02-23  3857.070068  3895.979980  3805.590088  3881.370117\n",
              "2021-02-24  3873.709961  3928.649902  3859.600098  3925.429932\n",
              "2021-02-25  3915.800049  3925.020020  3814.040039  3829.340088\n",
              "\n",
              "[2266 rows x 4 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8a2cd013-f0d2-4cdf-ba68-82ec5afce97e\">\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>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2012-02-24</th>\n",
              "      <td>1363.459961</td>\n",
              "      <td>1368.920044</td>\n",
              "      <td>1363.459961</td>\n",
              "      <td>1365.739990</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-02-27</th>\n",
              "      <td>1365.199951</td>\n",
              "      <td>1371.939941</td>\n",
              "      <td>1354.920044</td>\n",
              "      <td>1367.589966</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-02-28</th>\n",
              "      <td>1367.560059</td>\n",
              "      <td>1373.089966</td>\n",
              "      <td>1365.969971</td>\n",
              "      <td>1372.180054</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-02-29</th>\n",
              "      <td>1372.199951</td>\n",
              "      <td>1378.040039</td>\n",
              "      <td>1363.810059</td>\n",
              "      <td>1365.680054</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-03-01</th>\n",
              "      <td>1365.900024</td>\n",
              "      <td>1376.170044</td>\n",
              "      <td>1365.900024</td>\n",
              "      <td>1374.089966</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-02-19</th>\n",
              "      <td>3921.159912</td>\n",
              "      <td>3930.409912</td>\n",
              "      <td>3903.070068</td>\n",
              "      <td>3906.709961</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-02-22</th>\n",
              "      <td>3885.550049</td>\n",
              "      <td>3902.919922</td>\n",
              "      <td>3874.709961</td>\n",
              "      <td>3876.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-02-23</th>\n",
              "      <td>3857.070068</td>\n",
              "      <td>3895.979980</td>\n",
              "      <td>3805.590088</td>\n",
              "      <td>3881.370117</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-02-24</th>\n",
              "      <td>3873.709961</td>\n",
              "      <td>3928.649902</td>\n",
              "      <td>3859.600098</td>\n",
              "      <td>3925.429932</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-02-25</th>\n",
              "      <td>3915.800049</td>\n",
              "      <td>3925.020020</td>\n",
              "      <td>3814.040039</td>\n",
              "      <td>3829.340088</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2266 rows × 4 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8a2cd013-f0d2-4cdf-ba68-82ec5afce97e')\"\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-8a2cd013-f0d2-4cdf-ba68-82ec5afce97e 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-8a2cd013-f0d2-4cdf-ba68-82ec5afce97e');\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": 141
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "daily_stock_data = data.pct_change().dropna()"
      ],
      "metadata": {
        "id": "gR_ZTNjh0im2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "daily_stock_data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f7SYDE_UC45-",
        "outputId": "6aa58366-b70b-4473-b1ed-6c14c6824324"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2265, 4)"
            ]
          },
          "metadata": {},
          "execution_count": 143
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Make Predictions for Individual Stocks"
      ],
      "metadata": {
        "id": "_4f6vgBkErID"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test_dates = training_data = stock_data.iloc[(-253):, 0:1].pct_change().dropna()"
      ],
      "metadata": {
        "id": "LF6UrPsbEboH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_dates"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 519
        },
        "id": "PtZ0TX3yEiw3",
        "outputId": "e20d3016-8f23-449a-c543-8dd25a3eb3eb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               ^GSPC\n",
              "Date                \n",
              "2021-02-04  0.010853\n",
              "2021-02-05  0.003897\n",
              "2021-02-08  0.007399\n",
              "2021-02-09 -0.001114\n",
              "2021-02-10 -0.000345\n",
              "...              ...\n",
              "2022-02-17 -0.021173\n",
              "2022-02-18 -0.007166\n",
              "2022-02-22 -0.010143\n",
              "2022-02-23 -0.018412\n",
              "2022-02-24  0.014957\n",
              "\n",
              "[252 rows x 1 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-81b44e18-80e1-4567-a635-2143f8213f3a\">\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>^GSPC</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2021-02-04</th>\n",
              "      <td>0.010853</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-02-05</th>\n",
              "      <td>0.003897</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-02-08</th>\n",
              "      <td>0.007399</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-02-09</th>\n",
              "      <td>-0.001114</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-02-10</th>\n",
              "      <td>-0.000345</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-02-17</th>\n",
              "      <td>-0.021173</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-02-18</th>\n",
              "      <td>-0.007166</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-02-22</th>\n",
              "      <td>-0.010143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-02-23</th>\n",
              "      <td>-0.018412</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-02-24</th>\n",
              "      <td>0.014957</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>252 rows × 1 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-81b44e18-80e1-4567-a635-2143f8213f3a')\"\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-81b44e18-80e1-4567-a635-2143f8213f3a 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-81b44e18-80e1-4567-a635-2143f8213f3a');\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": 145
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "training_data = stock_data.iloc[:-252].pct_change().dropna()"
      ],
      "metadata": {
        "id": "WmTNtkGO_zyg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "training_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 519
        },
        "id": "o4iHjMwc475W",
        "outputId": "58f92201-bc92-4a33-e0aa-e50b20460452"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               ^GSPC     ^FTLC      ^RUT      IEUS      GOVT      IGOV  \\\n",
              "Date                                                                     \n",
              "2012-02-27  0.001355 -0.003498 -0.000314 -0.003079  0.000000 -0.001189   \n",
              "2012-02-28  0.003356  0.002489 -0.003460  0.008984  0.000000  0.007836   \n",
              "2012-02-29 -0.004737 -0.009168 -0.015611 -0.010573  0.000000 -0.004921   \n",
              "2012-03-01  0.006158  0.009800  0.005278 -0.001406  0.000000 -0.019891   \n",
              "2012-03-02 -0.003246 -0.002895 -0.015701 -0.002253  0.000000 -0.007479   \n",
              "...              ...       ...       ...       ...       ...       ...   \n",
              "2021-01-27 -0.025678 -0.012199 -0.019146 -0.026092  0.001103 -0.003997   \n",
              "2021-01-29 -0.009739 -0.020476 -0.016626  0.003389 -0.003307 -0.001277   \n",
              "2021-02-01  0.016052  0.009006  0.025327  0.012064  0.000775 -0.006210   \n",
              "2021-02-02  0.013898  0.008979  0.011890  0.014463 -0.002213 -0.001838   \n",
              "2021-02-03  0.001009 -0.000563  0.003839  0.001253 -0.002587 -0.001289   \n",
              "\n",
              "                SIVR      SGOL  \n",
              "Date                            \n",
              "2012-02-27 -0.000285 -0.003132  \n",
              "2012-02-28  0.043020  0.010512  \n",
              "2012-02-29 -0.064736 -0.053030  \n",
              "2012-03-01  0.027453  0.014149  \n",
              "2012-03-02 -0.020182 -0.001766  \n",
              "...              ...       ...  \n",
              "2021-01-27 -0.009756 -0.006180  \n",
              "2021-01-29  0.066092  0.000565  \n",
              "2021-02-01  0.072006  0.008475  \n",
              "2021-02-02 -0.083333 -0.011205  \n",
              "2021-02-03  0.015674 -0.001700  \n",
              "\n",
              "[2148 rows x 8 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f0850d8f-4196-41d6-a252-274aca2f3cd8\">\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>^GSPC</th>\n",
              "      <th>^FTLC</th>\n",
              "      <th>^RUT</th>\n",
              "      <th>IEUS</th>\n",
              "      <th>GOVT</th>\n",
              "      <th>IGOV</th>\n",
              "      <th>SIVR</th>\n",
              "      <th>SGOL</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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2012-02-27</th>\n",
              "      <td>0.001355</td>\n",
              "      <td>-0.003498</td>\n",
              "      <td>-0.000314</td>\n",
              "      <td>-0.003079</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-0.001189</td>\n",
              "      <td>-0.000285</td>\n",
              "      <td>-0.003132</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-02-28</th>\n",
              "      <td>0.003356</td>\n",
              "      <td>0.002489</td>\n",
              "      <td>-0.003460</td>\n",
              "      <td>0.008984</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.007836</td>\n",
              "      <td>0.043020</td>\n",
              "      <td>0.010512</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-02-29</th>\n",
              "      <td>-0.004737</td>\n",
              "      <td>-0.009168</td>\n",
              "      <td>-0.015611</td>\n",
              "      <td>-0.010573</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-0.004921</td>\n",
              "      <td>-0.064736</td>\n",
              "      <td>-0.053030</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-03-01</th>\n",
              "      <td>0.006158</td>\n",
              "      <td>0.009800</td>\n",
              "      <td>0.005278</td>\n",
              "      <td>-0.001406</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-0.019891</td>\n",
              "      <td>0.027453</td>\n",
              "      <td>0.014149</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-03-02</th>\n",
              "      <td>-0.003246</td>\n",
              "      <td>-0.002895</td>\n",
              "      <td>-0.015701</td>\n",
              "      <td>-0.002253</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-0.007479</td>\n",
              "      <td>-0.020182</td>\n",
              "      <td>-0.001766</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-01-27</th>\n",
              "      <td>-0.025678</td>\n",
              "      <td>-0.012199</td>\n",
              "      <td>-0.019146</td>\n",
              "      <td>-0.026092</td>\n",
              "      <td>0.001103</td>\n",
              "      <td>-0.003997</td>\n",
              "      <td>-0.009756</td>\n",
              "      <td>-0.006180</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-01-29</th>\n",
              "      <td>-0.009739</td>\n",
              "      <td>-0.020476</td>\n",
              "      <td>-0.016626</td>\n",
              "      <td>0.003389</td>\n",
              "      <td>-0.003307</td>\n",
              "      <td>-0.001277</td>\n",
              "      <td>0.066092</td>\n",
              "      <td>0.000565</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-02-01</th>\n",
              "      <td>0.016052</td>\n",
              "      <td>0.009006</td>\n",
              "      <td>0.025327</td>\n",
              "      <td>0.012064</td>\n",
              "      <td>0.000775</td>\n",
              "      <td>-0.006210</td>\n",
              "      <td>0.072006</td>\n",
              "      <td>0.008475</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-02-02</th>\n",
              "      <td>0.013898</td>\n",
              "      <td>0.008979</td>\n",
              "      <td>0.011890</td>\n",
              "      <td>0.014463</td>\n",
              "      <td>-0.002213</td>\n",
              "      <td>-0.001838</td>\n",
              "      <td>-0.083333</td>\n",
              "      <td>-0.011205</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-02-03</th>\n",
              "      <td>0.001009</td>\n",
              "      <td>-0.000563</td>\n",
              "      <td>0.003839</td>\n",
              "      <td>0.001253</td>\n",
              "      <td>-0.002587</td>\n",
              "      <td>-0.001289</td>\n",
              "      <td>0.015674</td>\n",
              "      <td>-0.001700</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2148 rows × 8 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f0850d8f-4196-41d6-a252-274aca2f3cd8')\"\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-f0850d8f-4196-41d6-a252-274aca2f3cd8 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-f0850d8f-4196-41d6-a252-274aca2f3cd8');\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": 147
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "days = np.array(test_dates.reset_index()['Date'], dtype=\"datetime64[ms]\")\n",
        "days"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gd4lYip-G94q",
        "outputId": "4c6c705b-0b95-4282-ec9a-c9541dfc238b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['2021-02-04T00:00:00.000', '2021-02-05T00:00:00.000',\n",
              "       '2021-02-08T00:00:00.000', '2021-02-09T00:00:00.000',\n",
              "       '2021-02-10T00:00:00.000', '2021-02-11T00:00:00.000',\n",
              "       '2021-02-12T00:00:00.000', '2021-02-16T00:00:00.000',\n",
              "       '2021-02-18T00:00:00.000', '2021-02-19T00:00:00.000',\n",
              "       '2021-02-22T00:00:00.000', '2021-02-23T00:00:00.000',\n",
              "       '2021-02-24T00:00:00.000', '2021-02-25T00:00:00.000',\n",
              "       '2021-02-26T00:00:00.000', '2021-03-01T00:00:00.000',\n",
              "       '2021-03-02T00:00:00.000', '2021-03-04T00:00:00.000',\n",
              "       '2021-03-05T00:00:00.000', '2021-03-08T00:00:00.000',\n",
              "       '2021-03-09T00:00:00.000', '2021-03-10T00:00:00.000',\n",
              "       '2021-03-11T00:00:00.000', '2021-03-15T00:00:00.000',\n",
              "       '2021-03-16T00:00:00.000', '2021-03-17T00:00:00.000',\n",
              "       '2021-03-18T00:00:00.000', '2021-03-19T00:00:00.000',\n",
              "       '2021-03-22T00:00:00.000', '2021-03-23T00:00:00.000',\n",
              "       '2021-03-24T00:00:00.000', '2021-03-25T00:00:00.000',\n",
              "       '2021-03-26T00:00:00.000', '2021-03-29T00:00:00.000',\n",
              "       '2021-03-30T00:00:00.000', '2021-03-31T00:00:00.000',\n",
              "       '2021-04-01T00:00:00.000', '2021-04-06T00:00:00.000',\n",
              "       '2021-04-07T00:00:00.000', '2021-04-08T00:00:00.000',\n",
              "       '2021-04-09T00:00:00.000', '2021-04-12T00:00:00.000',\n",
              "       '2021-04-13T00:00:00.000', '2021-04-14T00:00:00.000',\n",
              "       '2021-04-15T00:00:00.000', '2021-04-16T00:00:00.000',\n",
              "       '2021-04-19T00:00:00.000', '2021-04-20T00:00:00.000',\n",
              "       '2021-04-21T00:00:00.000', '2021-04-22T00:00:00.000',\n",
              "       '2021-04-23T00:00:00.000', '2021-04-26T00:00:00.000',\n",
              "       '2021-04-27T00:00:00.000', '2021-04-28T00:00:00.000',\n",
              "       '2021-04-29T00:00:00.000', '2021-04-30T00:00:00.000',\n",
              "       '2021-05-04T00:00:00.000', '2021-05-06T00:00:00.000',\n",
              "       '2021-05-07T00:00:00.000', '2021-05-10T00:00:00.000',\n",
              "       '2021-05-12T00:00:00.000', '2021-05-13T00:00:00.000',\n",
              "       '2021-05-14T00:00:00.000', '2021-05-17T00:00:00.000',\n",
              "       '2021-05-18T00:00:00.000', '2021-05-19T00:00:00.000',\n",
              "       '2021-05-20T00:00:00.000', '2021-05-21T00:00:00.000',\n",
              "       '2021-05-24T00:00:00.000', '2021-05-26T00:00:00.000',\n",
              "       '2021-05-27T00:00:00.000', '2021-05-28T00:00:00.000',\n",
              "       '2021-06-01T00:00:00.000', '2021-06-02T00:00:00.000',\n",
              "       '2021-06-03T00:00:00.000', '2021-06-04T00:00:00.000',\n",
              "       '2021-06-07T00:00:00.000', '2021-06-08T00:00:00.000',\n",
              "       '2021-06-09T00:00:00.000', '2021-06-11T00:00:00.000',\n",
              "       '2021-06-14T00:00:00.000', '2021-06-15T00:00:00.000',\n",
              "       '2021-06-16T00:00:00.000', '2021-06-17T00:00:00.000',\n",
              "       '2021-06-18T00:00:00.000', '2021-06-21T00:00:00.000',\n",
              "       '2021-06-22T00:00:00.000', '2021-06-23T00:00:00.000',\n",
              "       '2021-06-24T00:00:00.000', '2021-06-25T00:00:00.000',\n",
              "       '2021-06-28T00:00:00.000', '2021-06-29T00:00:00.000',\n",
              "       '2021-06-30T00:00:00.000', '2021-07-01T00:00:00.000',\n",
              "       '2021-07-02T00:00:00.000', '2021-07-06T00:00:00.000',\n",
              "       '2021-07-07T00:00:00.000', '2021-07-08T00:00:00.000',\n",
              "       '2021-07-09T00:00:00.000', '2021-07-12T00:00:00.000',\n",
              "       '2021-07-13T00:00:00.000', '2021-07-14T00:00:00.000',\n",
              "       '2021-07-15T00:00:00.000', '2021-07-16T00:00:00.000',\n",
              "       '2021-07-19T00:00:00.000', '2021-07-20T00:00:00.000',\n",
              "       '2021-07-21T00:00:00.000', '2021-07-22T00:00:00.000',\n",
              "       '2021-07-23T00:00:00.000', '2021-07-27T00:00:00.000',\n",
              "       '2021-07-28T00:00:00.000', '2021-07-29T00:00:00.000',\n",
              "       '2021-07-30T00:00:00.000', '2021-08-02T00:00:00.000',\n",
              "       '2021-08-03T00:00:00.000', '2021-08-04T00:00:00.000',\n",
              "       '2021-08-05T00:00:00.000', '2021-08-06T00:00:00.000',\n",
              "       '2021-08-10T00:00:00.000', '2021-08-11T00:00:00.000',\n",
              "       '2021-08-12T00:00:00.000', '2021-08-13T00:00:00.000',\n",
              "       '2021-08-16T00:00:00.000', '2021-08-17T00:00:00.000',\n",
              "       '2021-08-18T00:00:00.000', '2021-08-19T00:00:00.000',\n",
              "       '2021-08-20T00:00:00.000', '2021-08-23T00:00:00.000',\n",
              "       '2021-08-24T00:00:00.000', '2021-08-25T00:00:00.000',\n",
              "       '2021-08-26T00:00:00.000', '2021-08-27T00:00:00.000',\n",
              "       '2021-08-31T00:00:00.000', '2021-09-01T00:00:00.000',\n",
              "       '2021-09-02T00:00:00.000', '2021-09-03T00:00:00.000',\n",
              "       '2021-09-07T00:00:00.000', '2021-09-08T00:00:00.000',\n",
              "       '2021-09-09T00:00:00.000', '2021-09-10T00:00:00.000',\n",
              "       '2021-09-13T00:00:00.000', '2021-09-14T00:00:00.000',\n",
              "       '2021-09-15T00:00:00.000', '2021-09-16T00:00:00.000',\n",
              "       '2021-09-17T00:00:00.000', '2021-09-20T00:00:00.000',\n",
              "       '2021-09-21T00:00:00.000', '2021-09-22T00:00:00.000',\n",
              "       '2021-09-23T00:00:00.000', '2021-09-24T00:00:00.000',\n",
              "       '2021-09-27T00:00:00.000', '2021-09-28T00:00:00.000',\n",
              "       '2021-09-29T00:00:00.000', '2021-09-30T00:00:00.000',\n",
              "       '2021-10-01T00:00:00.000', '2021-10-04T00:00:00.000',\n",
              "       '2021-10-05T00:00:00.000', '2021-10-06T00:00:00.000',\n",
              "       '2021-10-07T00:00:00.000', '2021-10-08T00:00:00.000',\n",
              "       '2021-10-11T00:00:00.000', '2021-10-12T00:00:00.000',\n",
              "       '2021-10-13T00:00:00.000', '2021-10-14T00:00:00.000',\n",
              "       '2021-10-15T00:00:00.000', '2021-10-18T00:00:00.000',\n",
              "       '2021-10-19T00:00:00.000', '2021-10-20T00:00:00.000',\n",
              "       '2021-10-21T00:00:00.000', '2021-10-22T00:00:00.000',\n",
              "       '2021-10-25T00:00:00.000', '2021-10-26T00:00:00.000',\n",
              "       '2021-10-27T00:00:00.000', '2021-10-28T00:00:00.000',\n",
              "       '2021-10-29T00:00:00.000', '2021-11-01T00:00:00.000',\n",
              "       '2021-11-02T00:00:00.000', '2021-11-03T00:00:00.000',\n",
              "       '2021-11-04T00:00:00.000', '2021-11-05T00:00:00.000',\n",
              "       '2021-11-08T00:00:00.000', '2021-11-09T00:00:00.000',\n",
              "       '2021-11-10T00:00:00.000', '2021-11-11T00:00:00.000',\n",
              "       '2021-11-12T00:00:00.000', '2021-11-15T00:00:00.000',\n",
              "       '2021-11-16T00:00:00.000', '2021-11-17T00:00:00.000',\n",
              "       '2021-11-18T00:00:00.000', '2021-11-19T00:00:00.000',\n",
              "       '2021-11-22T00:00:00.000', '2021-11-23T00:00:00.000',\n",
              "       '2021-11-24T00:00:00.000', '2021-11-26T00:00:00.000',\n",
              "       '2021-11-29T00:00:00.000', '2021-11-30T00:00:00.000',\n",
              "       '2021-12-01T00:00:00.000', '2021-12-02T00:00:00.000',\n",
              "       '2021-12-03T00:00:00.000', '2021-12-06T00:00:00.000',\n",
              "       '2021-12-07T00:00:00.000', '2021-12-08T00:00:00.000',\n",
              "       '2021-12-09T00:00:00.000', '2021-12-10T00:00:00.000',\n",
              "       '2021-12-13T00:00:00.000', '2021-12-14T00:00:00.000',\n",
              "       '2021-12-15T00:00:00.000', '2021-12-16T00:00:00.000',\n",
              "       '2021-12-17T00:00:00.000', '2021-12-20T00:00:00.000',\n",
              "       '2021-12-21T00:00:00.000', '2021-12-22T00:00:00.000',\n",
              "       '2021-12-23T00:00:00.000', '2021-12-29T00:00:00.000',\n",
              "       '2021-12-30T00:00:00.000', '2021-12-31T00:00:00.000',\n",
              "       '2022-01-04T00:00:00.000', '2022-01-05T00:00:00.000',\n",
              "       '2022-01-06T00:00:00.000', '2022-01-07T00:00:00.000',\n",
              "       '2022-01-10T00:00:00.000', '2022-01-11T00:00:00.000',\n",
              "       '2022-01-12T00:00:00.000', '2022-01-13T00:00:00.000',\n",
              "       '2022-01-14T00:00:00.000', '2022-01-18T00:00:00.000',\n",
              "       '2022-01-19T00:00:00.000', '2022-01-20T00:00:00.000',\n",
              "       '2022-01-21T00:00:00.000', '2022-01-24T00:00:00.000',\n",
              "       '2022-01-25T00:00:00.000', '2022-01-26T00:00:00.000',\n",
              "       '2022-01-27T00:00:00.000', '2022-01-28T00:00:00.000',\n",
              "       '2022-01-31T00:00:00.000', '2022-02-01T00:00:00.000',\n",
              "       '2022-02-02T00:00:00.000', '2022-02-03T00:00:00.000',\n",
              "       '2022-02-04T00:00:00.000', '2022-02-07T00:00:00.000',\n",
              "       '2022-02-08T00:00:00.000', '2022-02-09T00:00:00.000',\n",
              "       '2022-02-10T00:00:00.000', '2022-02-11T00:00:00.000',\n",
              "       '2022-02-14T00:00:00.000', '2022-02-15T00:00:00.000',\n",
              "       '2022-02-16T00:00:00.000', '2022-02-17T00:00:00.000',\n",
              "       '2022-02-18T00:00:00.000', '2022-02-22T00:00:00.000',\n",
              "       '2022-02-23T00:00:00.000', '2022-02-24T00:00:00.000'],\n",
              "      dtype='datetime64[ms]')"
            ]
          },
          "metadata": {},
          "execution_count": 148
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "all_samples = []\n",
        "for close in training_data:\n",
        "  print(close)\n",
        "  hmm = GaussianHMM(n_components=4)\n",
        "  #print(pd.DataFrame(training_data[close]))\n",
        "  hmm.fit(pd.DataFrame(training_data[close]))\n",
        "  samples, _ = hmm.sample(252)\n",
        "  if len(all_samples)==0:\n",
        "    test_dates[close] = samples\n",
        "    all_samples = pd.DataFrame( test_dates[close], index=days)\n",
        "  else:\n",
        "    all_samples[close] = samples\n",
        "print(all_samples)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FWQd11vkAd7L",
        "outputId": "d30e1702-d597-4f2c-bf66-c294f62ea097"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "^GSPC\n",
            "^FTLC\n",
            "^RUT\n",
            "IEUS\n",
            "GOVT\n",
            "IGOV\n",
            "SIVR\n",
            "SGOL\n",
            "               ^GSPC     ^FTLC      ^RUT      IEUS      GOVT      IGOV  \\\n",
            "2021-02-04 -0.010307  0.007558  0.013225 -0.011663  0.000767  0.003214   \n",
            "2021-02-05  0.007197  0.009218 -0.001597 -0.003947 -0.001431  0.002831   \n",
            "2021-02-08  0.002876  0.009048  0.006321 -0.011854 -0.000963 -0.003745   \n",
            "2021-02-09  0.000985  0.004728 -0.011199 -0.004659  0.003468  0.003281   \n",
            "2021-02-10  0.001214  0.069803  0.000681  0.009745  0.004562 -0.002624   \n",
            "...              ...       ...       ...       ...       ...       ...   \n",
            "2022-02-17  0.009361  0.006727 -0.005631  0.007611  0.003904 -0.000497   \n",
            "2022-02-18  0.001717  0.014321 -0.015088  0.011493  0.007274 -0.001274   \n",
            "2022-02-22 -0.004819  0.006589 -0.027943  0.000711 -0.008312 -0.007245   \n",
            "2022-02-23 -0.001432  0.003649  0.005201  0.012505  0.003869 -0.012937   \n",
            "2022-02-24  0.000785  0.001777 -0.014113  0.001266  0.017552  0.004139   \n",
            "\n",
            "                SIVR      SGOL  \n",
            "2021-02-04  0.004775 -0.006643  \n",
            "2021-02-05 -0.006994 -0.011162  \n",
            "2021-02-08 -0.012379  0.000228  \n",
            "2021-02-09 -0.003643 -0.004354  \n",
            "2021-02-10  0.004535  0.003847  \n",
            "...              ...       ...  \n",
            "2022-02-17  0.005502 -0.018500  \n",
            "2022-02-18 -0.020951 -0.009638  \n",
            "2022-02-22 -0.015007  0.001138  \n",
            "2022-02-23  0.010813  0.002934  \n",
            "2022-02-24  0.028842 -0.001296  \n",
            "\n",
            "[252 rows x 8 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "all_samples.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "LzaQy5fnGgzM",
        "outputId": "c354eb58-797c-4db5-afdf-4f0976908d4c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff0cc7b5f90>"
            ]
          },
          "metadata": {},
          "execution_count": 150
        },
        {
          "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": [
        "## Calculating the resulting stock price change using cumilative product"
      ],
      "metadata": {
        "id": "Sgg1tg7ubE-V"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "stock_data.iloc[-253:-252,:] #Gets the stock prices for the last day of the training data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 176
        },
        "id": "EthGFgBoOPrs",
        "outputId": "c5dd008a-739c-4078-b6f7-7799239f2eab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  ^GSPC   ^FTLC         ^RUT       IEUS       GOVT       IGOV  \\\n",
              "Date                                                                            \n",
              "2021-02-03  3830.169922  3728.5  2159.699951  62.261761  26.677723  54.029949   \n",
              "\n",
              "             SIVR       SGOL  \n",
              "Date                          \n",
              "2021-02-03  25.92  17.620001  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5e3bc641-71b6-4197-bd5c-561fd374b776\">\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>^GSPC</th>\n",
              "      <th>^FTLC</th>\n",
              "      <th>^RUT</th>\n",
              "      <th>IEUS</th>\n",
              "      <th>GOVT</th>\n",
              "      <th>IGOV</th>\n",
              "      <th>SIVR</th>\n",
              "      <th>SGOL</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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2021-02-03</th>\n",
              "      <td>3830.169922</td>\n",
              "      <td>3728.5</td>\n",
              "      <td>2159.699951</td>\n",
              "      <td>62.261761</td>\n",
              "      <td>26.677723</td>\n",
              "      <td>54.029949</td>\n",
              "      <td>25.92</td>\n",
              "      <td>17.620001</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5e3bc641-71b6-4197-bd5c-561fd374b776')\"\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-5e3bc641-71b6-4197-bd5c-561fd374b776 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-5e3bc641-71b6-4197-bd5c-561fd374b776');\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": 151
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "all_samples"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "xOE1nnOYOgKT",
        "outputId": "55fc895b-6cdf-43c6-866e-c676ea0835d1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               ^GSPC     ^FTLC      ^RUT      IEUS      GOVT      IGOV  \\\n",
              "2021-02-04 -0.010307  0.007558  0.013225 -0.011663  0.000767  0.003214   \n",
              "2021-02-05  0.007197  0.009218 -0.001597 -0.003947 -0.001431  0.002831   \n",
              "2021-02-08  0.002876  0.009048  0.006321 -0.011854 -0.000963 -0.003745   \n",
              "2021-02-09  0.000985  0.004728 -0.011199 -0.004659  0.003468  0.003281   \n",
              "2021-02-10  0.001214  0.069803  0.000681  0.009745  0.004562 -0.002624   \n",
              "...              ...       ...       ...       ...       ...       ...   \n",
              "2022-02-17  0.009361  0.006727 -0.005631  0.007611  0.003904 -0.000497   \n",
              "2022-02-18  0.001717  0.014321 -0.015088  0.011493  0.007274 -0.001274   \n",
              "2022-02-22 -0.004819  0.006589 -0.027943  0.000711 -0.008312 -0.007245   \n",
              "2022-02-23 -0.001432  0.003649  0.005201  0.012505  0.003869 -0.012937   \n",
              "2022-02-24  0.000785  0.001777 -0.014113  0.001266  0.017552  0.004139   \n",
              "\n",
              "                SIVR      SGOL  \n",
              "2021-02-04  0.004775 -0.006643  \n",
              "2021-02-05 -0.006994 -0.011162  \n",
              "2021-02-08 -0.012379  0.000228  \n",
              "2021-02-09 -0.003643 -0.004354  \n",
              "2021-02-10  0.004535  0.003847  \n",
              "...              ...       ...  \n",
              "2022-02-17  0.005502 -0.018500  \n",
              "2022-02-18 -0.020951 -0.009638  \n",
              "2022-02-22 -0.015007  0.001138  \n",
              "2022-02-23  0.010813  0.002934  \n",
              "2022-02-24  0.028842 -0.001296  \n",
              "\n",
              "[252 rows x 8 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-50670e62-f217-4b02-bdf2-519562887020\">\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>^GSPC</th>\n",
              "      <th>^FTLC</th>\n",
              "      <th>^RUT</th>\n",
              "      <th>IEUS</th>\n",
              "      <th>GOVT</th>\n",
              "      <th>IGOV</th>\n",
              "      <th>SIVR</th>\n",
              "      <th>SGOL</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2021-02-04</th>\n",
              "      <td>-0.010307</td>\n",
              "      <td>0.007558</td>\n",
              "      <td>0.013225</td>\n",
              "      <td>-0.011663</td>\n",
              "      <td>0.000767</td>\n",
              "      <td>0.003214</td>\n",
              "      <td>0.004775</td>\n",
              "      <td>-0.006643</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-02-05</th>\n",
              "      <td>0.007197</td>\n",
              "      <td>0.009218</td>\n",
              "      <td>-0.001597</td>\n",
              "      <td>-0.003947</td>\n",
              "      <td>-0.001431</td>\n",
              "      <td>0.002831</td>\n",
              "      <td>-0.006994</td>\n",
              "      <td>-0.011162</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-02-08</th>\n",
              "      <td>0.002876</td>\n",
              "      <td>0.009048</td>\n",
              "      <td>0.006321</td>\n",
              "      <td>-0.011854</td>\n",
              "      <td>-0.000963</td>\n",
              "      <td>-0.003745</td>\n",
              "      <td>-0.012379</td>\n",
              "      <td>0.000228</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-02-09</th>\n",
              "      <td>0.000985</td>\n",
              "      <td>0.004728</td>\n",
              "      <td>-0.011199</td>\n",
              "      <td>-0.004659</td>\n",
              "      <td>0.003468</td>\n",
              "      <td>0.003281</td>\n",
              "      <td>-0.003643</td>\n",
              "      <td>-0.004354</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-02-10</th>\n",
              "      <td>0.001214</td>\n",
              "      <td>0.069803</td>\n",
              "      <td>0.000681</td>\n",
              "      <td>0.009745</td>\n",
              "      <td>0.004562</td>\n",
              "      <td>-0.002624</td>\n",
              "      <td>0.004535</td>\n",
              "      <td>0.003847</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-02-17</th>\n",
              "      <td>0.009361</td>\n",
              "      <td>0.006727</td>\n",
              "      <td>-0.005631</td>\n",
              "      <td>0.007611</td>\n",
              "      <td>0.003904</td>\n",
              "      <td>-0.000497</td>\n",
              "      <td>0.005502</td>\n",
              "      <td>-0.018500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-02-18</th>\n",
              "      <td>0.001717</td>\n",
              "      <td>0.014321</td>\n",
              "      <td>-0.015088</td>\n",
              "      <td>0.011493</td>\n",
              "      <td>0.007274</td>\n",
              "      <td>-0.001274</td>\n",
              "      <td>-0.020951</td>\n",
              "      <td>-0.009638</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-02-22</th>\n",
              "      <td>-0.004819</td>\n",
              "      <td>0.006589</td>\n",
              "      <td>-0.027943</td>\n",
              "      <td>0.000711</td>\n",
              "      <td>-0.008312</td>\n",
              "      <td>-0.007245</td>\n",
              "      <td>-0.015007</td>\n",
              "      <td>0.001138</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-02-23</th>\n",
              "      <td>-0.001432</td>\n",
              "      <td>0.003649</td>\n",
              "      <td>0.005201</td>\n",
              "      <td>0.012505</td>\n",
              "      <td>0.003869</td>\n",
              "      <td>-0.012937</td>\n",
              "      <td>0.010813</td>\n",
              "      <td>0.002934</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-02-24</th>\n",
              "      <td>0.000785</td>\n",
              "      <td>0.001777</td>\n",
              "      <td>-0.014113</td>\n",
              "      <td>0.001266</td>\n",
              "      <td>0.017552</td>\n",
              "      <td>0.004139</td>\n",
              "      <td>0.028842</td>\n",
              "      <td>-0.001296</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>252 rows × 8 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-50670e62-f217-4b02-bdf2-519562887020')\"\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-50670e62-f217-4b02-bdf2-519562887020 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-50670e62-f217-4b02-bdf2-519562887020');\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": 152
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plus_samples = 1 +all_samples"
      ],
      "metadata": {
        "id": "t4uTRcmvPcFH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plus_samples.iloc[0:1,:].reset_index()\n",
        "plus_samples"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "xNGgG3EwRu-Y",
        "outputId": "765a5408-ed65-49b6-f98e-ed46635b0806"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               ^GSPC     ^FTLC      ^RUT      IEUS      GOVT      IGOV  \\\n",
              "2021-02-04  0.989693  1.007558  1.013225  0.988337  1.000767  1.003214   \n",
              "2021-02-05  1.007197  1.009218  0.998403  0.996053  0.998569  1.002831   \n",
              "2021-02-08  1.002876  1.009048  1.006321  0.988146  0.999037  0.996255   \n",
              "2021-02-09  1.000985  1.004728  0.988801  0.995341  1.003468  1.003281   \n",
              "2021-02-10  1.001214  1.069803  1.000681  1.009745  1.004562  0.997376   \n",
              "...              ...       ...       ...       ...       ...       ...   \n",
              "2022-02-17  1.009361  1.006727  0.994369  1.007611  1.003904  0.999503   \n",
              "2022-02-18  1.001717  1.014321  0.984912  1.011493  1.007274  0.998726   \n",
              "2022-02-22  0.995181  1.006589  0.972057  1.000711  0.991688  0.992755   \n",
              "2022-02-23  0.998568  1.003649  1.005201  1.012505  1.003869  0.987063   \n",
              "2022-02-24  1.000785  1.001777  0.985887  1.001266  1.017552  1.004139   \n",
              "\n",
              "                SIVR      SGOL  \n",
              "2021-02-04  1.004775  0.993357  \n",
              "2021-02-05  0.993006  0.988838  \n",
              "2021-02-08  0.987621  1.000228  \n",
              "2021-02-09  0.996357  0.995646  \n",
              "2021-02-10  1.004535  1.003847  \n",
              "...              ...       ...  \n",
              "2022-02-17  1.005502  0.981500  \n",
              "2022-02-18  0.979049  0.990362  \n",
              "2022-02-22  0.984993  1.001138  \n",
              "2022-02-23  1.010813  1.002934  \n",
              "2022-02-24  1.028842  0.998704  \n",
              "\n",
              "[252 rows x 8 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-50682f4e-182d-46e7-b6b6-5a956f4a49e9\">\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>^GSPC</th>\n",
              "      <th>^FTLC</th>\n",
              "      <th>^RUT</th>\n",
              "      <th>IEUS</th>\n",
              "      <th>GOVT</th>\n",
              "      <th>IGOV</th>\n",
              "      <th>SIVR</th>\n",
              "      <th>SGOL</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2021-02-04</th>\n",
              "      <td>0.989693</td>\n",
              "      <td>1.007558</td>\n",
              "      <td>1.013225</td>\n",
              "      <td>0.988337</td>\n",
              "      <td>1.000767</td>\n",
              "      <td>1.003214</td>\n",
              "      <td>1.004775</td>\n",
              "      <td>0.993357</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-02-05</th>\n",
              "      <td>1.007197</td>\n",
              "      <td>1.009218</td>\n",
              "      <td>0.998403</td>\n",
              "      <td>0.996053</td>\n",
              "      <td>0.998569</td>\n",
              "      <td>1.002831</td>\n",
              "      <td>0.993006</td>\n",
              "      <td>0.988838</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-02-08</th>\n",
              "      <td>1.002876</td>\n",
              "      <td>1.009048</td>\n",
              "      <td>1.006321</td>\n",
              "      <td>0.988146</td>\n",
              "      <td>0.999037</td>\n",
              "      <td>0.996255</td>\n",
              "      <td>0.987621</td>\n",
              "      <td>1.000228</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-02-09</th>\n",
              "      <td>1.000985</td>\n",
              "      <td>1.004728</td>\n",
              "      <td>0.988801</td>\n",
              "      <td>0.995341</td>\n",
              "      <td>1.003468</td>\n",
              "      <td>1.003281</td>\n",
              "      <td>0.996357</td>\n",
              "      <td>0.995646</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-02-10</th>\n",
              "      <td>1.001214</td>\n",
              "      <td>1.069803</td>\n",
              "      <td>1.000681</td>\n",
              "      <td>1.009745</td>\n",
              "      <td>1.004562</td>\n",
              "      <td>0.997376</td>\n",
              "      <td>1.004535</td>\n",
              "      <td>1.003847</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-02-17</th>\n",
              "      <td>1.009361</td>\n",
              "      <td>1.006727</td>\n",
              "      <td>0.994369</td>\n",
              "      <td>1.007611</td>\n",
              "      <td>1.003904</td>\n",
              "      <td>0.999503</td>\n",
              "      <td>1.005502</td>\n",
              "      <td>0.981500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-02-18</th>\n",
              "      <td>1.001717</td>\n",
              "      <td>1.014321</td>\n",
              "      <td>0.984912</td>\n",
              "      <td>1.011493</td>\n",
              "      <td>1.007274</td>\n",
              "      <td>0.998726</td>\n",
              "      <td>0.979049</td>\n",
              "      <td>0.990362</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-02-22</th>\n",
              "      <td>0.995181</td>\n",
              "      <td>1.006589</td>\n",
              "      <td>0.972057</td>\n",
              "      <td>1.000711</td>\n",
              "      <td>0.991688</td>\n",
              "      <td>0.992755</td>\n",
              "      <td>0.984993</td>\n",
              "      <td>1.001138</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-02-23</th>\n",
              "      <td>0.998568</td>\n",
              "      <td>1.003649</td>\n",
              "      <td>1.005201</td>\n",
              "      <td>1.012505</td>\n",
              "      <td>1.003869</td>\n",
              "      <td>0.987063</td>\n",
              "      <td>1.010813</td>\n",
              "      <td>1.002934</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-02-24</th>\n",
              "      <td>1.000785</td>\n",
              "      <td>1.001777</td>\n",
              "      <td>0.985887</td>\n",
              "      <td>1.001266</td>\n",
              "      <td>1.017552</td>\n",
              "      <td>1.004139</td>\n",
              "      <td>1.028842</td>\n",
              "      <td>0.998704</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>252 rows × 8 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-50682f4e-182d-46e7-b6b6-5a956f4a49e9')\"\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-50682f4e-182d-46e7-b6b6-5a956f4a49e9 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-50682f4e-182d-46e7-b6b6-5a956f4a49e9');\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": 154
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "last_stock_price = stock_data.iloc[-253:-252,:].reset_index().drop(\"Date\", axis=1)\n",
        "last_stock_price"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145
        },
        "id": "CAzXS_Z4SQM_",
        "outputId": "6350fb47-0fb0-47ac-d68f-c10bf71c7748"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         ^GSPC   ^FTLC         ^RUT       IEUS       GOVT       IGOV   SIVR  \\\n",
              "0  3830.169922  3728.5  2159.699951  62.261761  26.677723  54.029949  25.92   \n",
              "\n",
              "        SGOL  \n",
              "0  17.620001  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9ba66d00-f51c-4b7e-b2d1-386835801aea\">\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>^GSPC</th>\n",
              "      <th>^FTLC</th>\n",
              "      <th>^RUT</th>\n",
              "      <th>IEUS</th>\n",
              "      <th>GOVT</th>\n",
              "      <th>IGOV</th>\n",
              "      <th>SIVR</th>\n",
              "      <th>SGOL</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3830.169922</td>\n",
              "      <td>3728.5</td>\n",
              "      <td>2159.699951</td>\n",
              "      <td>62.261761</td>\n",
              "      <td>26.677723</td>\n",
              "      <td>54.029949</td>\n",
              "      <td>25.92</td>\n",
              "      <td>17.620001</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9ba66d00-f51c-4b7e-b2d1-386835801aea')\"\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-9ba66d00-f51c-4b7e-b2d1-386835801aea 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-9ba66d00-f51c-4b7e-b2d1-386835801aea');\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": 155
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Cumulative_returns_daily = (plus_samples).cumprod().dropna()\n",
        "print(Cumulative_returns_daily.dropna())\n",
        "#print(plus_samples.cumprod())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p5O7CpbxTyV7",
        "outputId": "388cbb70-2352-4b58-d8c8-d75acafbdd72"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "               ^GSPC     ^FTLC      ^RUT      IEUS      GOVT      IGOV  \\\n",
            "2021-02-04  0.989693  1.007558  1.013225  0.988337  1.000767  1.003214   \n",
            "2021-02-05  0.996816  1.016845  1.011607  0.984436  0.999335  1.006054   \n",
            "2021-02-08  0.999682  1.026045  1.018001  0.972767  0.998373  1.002286   \n",
            "2021-02-09  1.000667  1.030896  1.006600  0.968234  1.001835  1.005575   \n",
            "2021-02-10  1.001882  1.102856  1.007286  0.977670  1.006405  1.002937   \n",
            "...              ...       ...       ...       ...       ...       ...   \n",
            "2022-02-17  1.182365  1.029796  0.996858  1.388157  1.026781  0.923769   \n",
            "2022-02-18  1.184395  1.044543  0.981818  1.404111  1.034251  0.922593   \n",
            "2022-02-22  1.178687  1.051425  0.954383  1.405109  1.025654  0.915909   \n",
            "2022-02-23  1.177000  1.055262  0.959347  1.422679  1.029622  0.904060   \n",
            "2022-02-24  1.177924  1.057138  0.945808  1.424480  1.047693  0.907802   \n",
            "\n",
            "                SIVR      SGOL  \n",
            "2021-02-04  1.004775  0.993357  \n",
            "2021-02-05  0.997747  0.982270  \n",
            "2021-02-08  0.985396  0.982494  \n",
            "2021-02-09  0.981806  0.978216  \n",
            "2021-02-10  0.986259  0.981980  \n",
            "...              ...       ...  \n",
            "2022-02-17  0.885813  0.940021  \n",
            "2022-02-18  0.867254  0.930961  \n",
            "2022-02-22  0.854239  0.932021  \n",
            "2022-02-23  0.863476  0.934755  \n",
            "2022-02-24  0.888380  0.933544  \n",
            "\n",
            "[252 rows x 8 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "last_stock_price"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145
        },
        "id": "nZPFoQDCUhfU",
        "outputId": "5658dbc1-c21c-411a-ef84-f26bce7939ae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         ^GSPC   ^FTLC         ^RUT       IEUS       GOVT       IGOV   SIVR  \\\n",
              "0  3830.169922  3728.5  2159.699951  62.261761  26.677723  54.029949  25.92   \n",
              "\n",
              "        SGOL  \n",
              "0  17.620001  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9e257f15-014f-4b55-ada4-167e8f33fad6\">\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>^GSPC</th>\n",
              "      <th>^FTLC</th>\n",
              "      <th>^RUT</th>\n",
              "      <th>IEUS</th>\n",
              "      <th>GOVT</th>\n",
              "      <th>IGOV</th>\n",
              "      <th>SIVR</th>\n",
              "      <th>SGOL</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3830.169922</td>\n",
              "      <td>3728.5</td>\n",
              "      <td>2159.699951</td>\n",
              "      <td>62.261761</td>\n",
              "      <td>26.677723</td>\n",
              "      <td>54.029949</td>\n",
              "      <td>25.92</td>\n",
              "      <td>17.620001</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9e257f15-014f-4b55-ada4-167e8f33fad6')\"\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-9e257f15-014f-4b55-ada4-167e8f33fad6 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-9e257f15-014f-4b55-ada4-167e8f33fad6');\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": 157
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stock_prices = Cumulative_returns_daily * last_stock_price.iloc[0,:]\n",
        "stock_prices"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "w6yI2a2pURT8",
        "outputId": "ff6756b0-afb3-48a1-bc22-9b77cd8df933"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  ^GSPC        ^FTLC         ^RUT       IEUS       GOVT  \\\n",
              "2021-02-04  3790.694239  3756.678437  2188.261628  61.535610  26.698188   \n",
              "2021-02-05  3817.974001  3791.306064  2184.767742  61.292744  26.659971   \n",
              "2021-02-08  3828.953029  3825.608316  2198.577272  60.566185  26.634307   \n",
              "2021-02-09  3832.723807  3843.696412  2173.954691  60.283981  26.726673   \n",
              "2021-02-10  3837.377580  4111.997516  2175.435051  60.871454  26.848596   \n",
              "...                 ...          ...          ...        ...        ...   \n",
              "2022-02-17  4528.657177  3839.594451  2152.915089  86.429069  27.392191   \n",
              "2022-02-18  4536.433338  3894.579985  2120.432616  87.422395  27.591451   \n",
              "2022-02-22  4514.572591  3920.239647  2061.181538  87.484551  27.362102   \n",
              "2022-02-23  4508.109098  3934.544249  2071.900790  88.578521  27.467962   \n",
              "2022-02-24  4511.649386  3941.537230  2042.660535  88.690647  27.950071   \n",
              "\n",
              "                 IGOV       SIVR       SGOL  \n",
              "2021-02-04  54.203625  26.043756  17.502960  \n",
              "2021-02-05  54.357052  25.861614  17.307597  \n",
              "2021-02-08  54.153486  25.541471  17.311550  \n",
              "2021-02-09  54.331180  25.448421  17.236175  \n",
              "2021-02-10  54.188611  25.563830  17.302485  \n",
              "...               ...        ...        ...  \n",
              "2022-02-17  49.911218  22.960268  16.563178  \n",
              "2022-02-18  49.847639  22.479216  16.403535  \n",
              "2022-02-22  49.486496  22.141870  16.422210  \n",
              "2022-02-23  48.846292  22.381287  16.470390  \n",
              "2022-02-24  49.048483  23.026814  16.449046  \n",
              "\n",
              "[252 rows x 8 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-80a27ec0-5dcc-425c-944e-8998bf496179\">\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>^GSPC</th>\n",
              "      <th>^FTLC</th>\n",
              "      <th>^RUT</th>\n",
              "      <th>IEUS</th>\n",
              "      <th>GOVT</th>\n",
              "      <th>IGOV</th>\n",
              "      <th>SIVR</th>\n",
              "      <th>SGOL</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2021-02-04</th>\n",
              "      <td>3790.694239</td>\n",
              "      <td>3756.678437</td>\n",
              "      <td>2188.261628</td>\n",
              "      <td>61.535610</td>\n",
              "      <td>26.698188</td>\n",
              "      <td>54.203625</td>\n",
              "      <td>26.043756</td>\n",
              "      <td>17.502960</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-02-05</th>\n",
              "      <td>3817.974001</td>\n",
              "      <td>3791.306064</td>\n",
              "      <td>2184.767742</td>\n",
              "      <td>61.292744</td>\n",
              "      <td>26.659971</td>\n",
              "      <td>54.357052</td>\n",
              "      <td>25.861614</td>\n",
              "      <td>17.307597</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-02-08</th>\n",
              "      <td>3828.953029</td>\n",
              "      <td>3825.608316</td>\n",
              "      <td>2198.577272</td>\n",
              "      <td>60.566185</td>\n",
              "      <td>26.634307</td>\n",
              "      <td>54.153486</td>\n",
              "      <td>25.541471</td>\n",
              "      <td>17.311550</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-02-09</th>\n",
              "      <td>3832.723807</td>\n",
              "      <td>3843.696412</td>\n",
              "      <td>2173.954691</td>\n",
              "      <td>60.283981</td>\n",
              "      <td>26.726673</td>\n",
              "      <td>54.331180</td>\n",
              "      <td>25.448421</td>\n",
              "      <td>17.236175</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-02-10</th>\n",
              "      <td>3837.377580</td>\n",
              "      <td>4111.997516</td>\n",
              "      <td>2175.435051</td>\n",
              "      <td>60.871454</td>\n",
              "      <td>26.848596</td>\n",
              "      <td>54.188611</td>\n",
              "      <td>25.563830</td>\n",
              "      <td>17.302485</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-02-17</th>\n",
              "      <td>4528.657177</td>\n",
              "      <td>3839.594451</td>\n",
              "      <td>2152.915089</td>\n",
              "      <td>86.429069</td>\n",
              "      <td>27.392191</td>\n",
              "      <td>49.911218</td>\n",
              "      <td>22.960268</td>\n",
              "      <td>16.563178</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-02-18</th>\n",
              "      <td>4536.433338</td>\n",
              "      <td>3894.579985</td>\n",
              "      <td>2120.432616</td>\n",
              "      <td>87.422395</td>\n",
              "      <td>27.591451</td>\n",
              "      <td>49.847639</td>\n",
              "      <td>22.479216</td>\n",
              "      <td>16.403535</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-02-22</th>\n",
              "      <td>4514.572591</td>\n",
              "      <td>3920.239647</td>\n",
              "      <td>2061.181538</td>\n",
              "      <td>87.484551</td>\n",
              "      <td>27.362102</td>\n",
              "      <td>49.486496</td>\n",
              "      <td>22.141870</td>\n",
              "      <td>16.422210</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-02-23</th>\n",
              "      <td>4508.109098</td>\n",
              "      <td>3934.544249</td>\n",
              "      <td>2071.900790</td>\n",
              "      <td>88.578521</td>\n",
              "      <td>27.467962</td>\n",
              "      <td>48.846292</td>\n",
              "      <td>22.381287</td>\n",
              "      <td>16.470390</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-02-24</th>\n",
              "      <td>4511.649386</td>\n",
              "      <td>3941.537230</td>\n",
              "      <td>2042.660535</td>\n",
              "      <td>88.690647</td>\n",
              "      <td>27.950071</td>\n",
              "      <td>49.048483</td>\n",
              "      <td>23.026814</td>\n",
              "      <td>16.449046</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>252 rows × 8 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-80a27ec0-5dcc-425c-944e-8998bf496179')\"\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-80a27ec0-5dcc-425c-944e-8998bf496179 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-80a27ec0-5dcc-425c-944e-8998bf496179');\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": 158
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stock_prices[\"^GSPC\"].plot()\n",
        "stock_data.iloc[-253:,0:1].plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 91
        },
        "id": "tEKAEHR0Urgw",
        "outputId": "65188267-55b1-46d2-f7b4-461f44c4cef0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff0cc6029d0>"
            ]
          },
          "metadata": {},
          "execution_count": 159
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "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": [
        "## Calculating stock price individually for plotting purposes"
      ],
      "metadata": {
        "id": "GNvkxsrubKjW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "open_price = test_data.iloc[-1,0]\n",
        "#print(last_close_value)\n",
        "print(samples[1,:])\n",
        "print(open_price * (1 + all_samples.iloc[0,0])) # =closing value\n",
        "closing_samples = []\n",
        "closing_samples = np.array(closing_samples)\n",
        "i = 0\n",
        "for sample in all_samples.iloc[:,0]:\n",
        "  if i == 0:\n",
        "    closing_samples = np.append(closing_samples, (open_price * (1 + sample)))\n",
        "  else:\n",
        "    closing_samples = np.append(closing_samples, (closing_samples[i-1] * (1 + sample)))\n",
        "  i += 1\n",
        "days = np.array(test_data.reset_index()['Date'], dtype=\"datetime64[ms]\")\n",
        "print(r2_score(test_data[\"Close\"],closing_samples))\n",
        "fig = plt.figure()\n",
        "axes = fig.add_subplot(111)\n",
        "axes.plot(days, closing_samples, c=\"black\", label=\"predicted\")\n",
        "axes.plot(days, test_data[\"Close\"], c=\"blue\", label=\"actual\")\n",
        "fig.autofmt_xdate()\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "CuUQq-lT54hQ",
        "outputId": "ab546cef-b990-4d9f-b815-cc7fe0329e9c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[-0.01116167]\n",
            "4112.9385359720545\n",
            "-0.31220730661010476\n"
          ]
        },
        {
          "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": [
        ""
      ],
      "metadata": {
        "id": "2SSqICAG5rZd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Creating the Mean-Variance Portfolio Using the HMM Prediction"
      ],
      "metadata": {
        "id": "CvC23QlFi0Ua"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pip install PyPortfolioOpt"
      ],
      "metadata": {
        "id": "RE4HbEP9egEj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "28796b17-d74f-4b72-ba8c-3427ff9758a2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: PyPortfolioOpt in /usr/local/lib/python3.7/dist-packages (1.5.2)\n",
            "Requirement already satisfied: pandas>=0.19 in /usr/local/lib/python3.7/dist-packages (from PyPortfolioOpt) (1.3.5)\n",
            "Requirement already satisfied: numpy<2.0,>=1.12 in /usr/local/lib/python3.7/dist-packages (from PyPortfolioOpt) (1.21.6)\n",
            "Requirement already satisfied: scipy<2.0,>=1.3 in /usr/local/lib/python3.7/dist-packages (from PyPortfolioOpt) (1.4.1)\n",
            "Requirement already satisfied: cvxpy<2.0.0,>=1.1.10 in /usr/local/lib/python3.7/dist-packages (from PyPortfolioOpt) (1.2.0)\n",
            "Requirement already satisfied: ecos>=2 in /usr/local/lib/python3.7/dist-packages (from cvxpy<2.0.0,>=1.1.10->PyPortfolioOpt) (2.0.10)\n",
            "Requirement already satisfied: osqp>=0.4.1 in /usr/local/lib/python3.7/dist-packages (from cvxpy<2.0.0,>=1.1.10->PyPortfolioOpt) (0.6.2.post0)\n",
            "Requirement already satisfied: scs>=1.1.6 in /usr/local/lib/python3.7/dist-packages (from cvxpy<2.0.0,>=1.1.10->PyPortfolioOpt) (3.2.0)\n",
            "Requirement already satisfied: qdldl in /usr/local/lib/python3.7/dist-packages (from osqp>=0.4.1->cvxpy<2.0.0,>=1.1.10->PyPortfolioOpt) (0.1.5.post2)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.19->PyPortfolioOpt) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.19->PyPortfolioOpt) (2022.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas>=0.19->PyPortfolioOpt) (1.15.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from pypfopt.expected_returns import mean_historical_return\n",
        "from pypfopt.risk_models import CovarianceShrinkage\n",
        "\n",
        "\n",
        "mu = mean_historical_return(stock_prices)\n",
        "S = CovarianceShrinkage(stock_prices).ledoit_wolf()\n",
        "\n",
        "from pypfopt.efficient_frontier import EfficientFrontier \n",
        "\n",
        "ef = EfficientFrontier(mu, S)\n",
        "weights = ef.max_sharpe() # This gets rid of most of the assets\n",
        "\n",
        "cleaned_weights = ef.clean_weights()\n",
        "print(dict(cleaned_weights))\n",
        "ef.portfolio_performance(verbose=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bfGgshnVesfn",
        "outputId": "ee257f51-8755-4674-e007-0ad7bac3749c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'^GSPC': 0.37874, '^FTLC': 0.04216, '^RUT': 0.0, 'IEUS': 0.5363, 'GOVT': 0.0428, 'IGOV': 0.0, 'SIVR': 0.0, 'SGOL': 0.0}\n",
            "Expected annual return: 31.4%\n",
            "Annual volatility: 11.2%\n",
            "Sharpe Ratio: 2.62\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.3142327785500421, 0.11227031704998751, 2.620753074198919)"
            ]
          },
          "metadata": {},
          "execution_count": 162
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Format the Weights for Further Use"
      ],
      "metadata": {
        "id": "h5_XRd7GnEH7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(dict(cleaned_weights))"
      ],
      "metadata": {
        "id": "q34TSoA5nRBF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c5769983-fd27-42f4-d6c5-f4f354acb322"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'^GSPC': 0.37874, '^FTLC': 0.04216, '^RUT': 0.0, 'IEUS': 0.5363, 'GOVT': 0.0428, 'IGOV': 0.0, 'SIVR': 0.0, 'SGOL': 0.0}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tmp_list = []\n",
        "for value, key in dict(cleaned_weights).items():\n",
        "  tmp_list.append(key)\n",
        "\n",
        "portfolio_weights = pd.Series(tmp_list, index=all_stocks)\n",
        "portfolio_weights"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n0pdvoj1chqA",
        "outputId": "1f20a222-40a0-4b95-9b3c-30e5725acf31"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "^GSPC    0.37874\n",
              "^FTLC    0.04216\n",
              "^RUT     0.00000\n",
              "IEUS     0.53630\n",
              "GOVT     0.04280\n",
              "IGOV     0.00000\n",
              "SIVR     0.00000\n",
              "SGOL     0.00000\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 164
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Test Year"
      ],
      "metadata": {
        "id": "2zH-DRT4nRcA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "initial_investment = 10000 # 10'000 dollar invesment. Can Change to 100'000 later \n",
        "trading_year = 252 # Number of trading days in a year\n",
        "stock_returns = stock_data.pct_change() "
      ],
      "metadata": {
        "id": "mvk66HGWnYQL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "weighted_portfolio = portfolio_weights * initial_investment # Calculate the dollars invested in each asset in the portfolio\n",
        "num_of_shares = weighted_portfolio / stock_data.iloc[(-trading_year),:] # Calculate the number of shares owned of each asset in the portfolio\n",
        "portfolio_test_values = num_of_shares * stock_data.iloc[(-trading_year):,:] # share * share price = position value and it is done for each day in the trading year for all assets\n",
        "portfolio_test_values['Total'] = portfolio_test_values.sum(axis=1)\n",
        "portfolio_test_values "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "GGzI4KFTdW01",
        "outputId": "9963cca4-af49-4a48-9f51-449bfd3fb3c3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  ^GSPC       ^FTLC  ^RUT         IEUS        GOVT  IGOV  \\\n",
              "Date                                                                       \n",
              "2021-02-04  3787.400000  421.600000   0.0  5363.000000  428.000000   0.0   \n",
              "2021-02-05  3802.161373  421.780931   0.0  5419.099807  427.365248   0.0   \n",
              "2021-02-08  3830.294890  423.669277   0.0  5448.405764  427.523997   0.0   \n",
              "2021-02-09  3826.029761  424.189406   0.0  5473.525530  427.841312   0.0   \n",
              "2021-02-10  3824.709073  423.375271   0.0  5448.405764  428.634814   0.0   \n",
              "...                 ...         ...   ...          ...         ...   ...   \n",
              "2022-02-17  4284.842442  479.460481   0.0  5465.436753  409.593617   0.0   \n",
              "2022-02-18  4254.136570  477.492932   0.0  5400.975713  410.555147   0.0   \n",
              "2022-02-22  4210.987096  475.118358   0.0  5290.962309  410.715366   0.0   \n",
              "2022-02-23  4133.453884  474.779133   0.0  5233.376942  408.792398   0.0   \n",
              "2022-02-24  4195.277359  457.150757   0.0  5156.883227  408.952648   0.0   \n",
              "\n",
              "            SIVR  SGOL         Total  \n",
              "Date                                  \n",
              "2021-02-04   0.0   0.0  10000.000000  \n",
              "2021-02-05   0.0   0.0  10070.407358  \n",
              "2021-02-08   0.0   0.0  10129.893928  \n",
              "2021-02-09   0.0   0.0  10151.586008  \n",
              "2021-02-10   0.0   0.0  10125.124922  \n",
              "...          ...   ...           ...  \n",
              "2022-02-17   0.0   0.0  10639.333293  \n",
              "2022-02-18   0.0   0.0  10543.160362  \n",
              "2022-02-22   0.0   0.0  10387.783128  \n",
              "2022-02-23   0.0   0.0  10250.402357  \n",
              "2022-02-24   0.0   0.0  10218.263991  \n",
              "\n",
              "[252 rows x 9 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8e7c2864-1e88-483f-95c2-1be8adea8c88\">\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>^GSPC</th>\n",
              "      <th>^FTLC</th>\n",
              "      <th>^RUT</th>\n",
              "      <th>IEUS</th>\n",
              "      <th>GOVT</th>\n",
              "      <th>IGOV</th>\n",
              "      <th>SIVR</th>\n",
              "      <th>SGOL</th>\n",
              "      <th>Total</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>2021-02-04</th>\n",
              "      <td>3787.400000</td>\n",
              "      <td>421.600000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5363.000000</td>\n",
              "      <td>428.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-02-05</th>\n",
              "      <td>3802.161373</td>\n",
              "      <td>421.780931</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5419.099807</td>\n",
              "      <td>427.365248</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10070.407358</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-02-08</th>\n",
              "      <td>3830.294890</td>\n",
              "      <td>423.669277</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5448.405764</td>\n",
              "      <td>427.523997</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10129.893928</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-02-09</th>\n",
              "      <td>3826.029761</td>\n",
              "      <td>424.189406</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5473.525530</td>\n",
              "      <td>427.841312</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10151.586008</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-02-10</th>\n",
              "      <td>3824.709073</td>\n",
              "      <td>423.375271</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5448.405764</td>\n",
              "      <td>428.634814</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10125.124922</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-02-17</th>\n",
              "      <td>4284.842442</td>\n",
              "      <td>479.460481</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5465.436753</td>\n",
              "      <td>409.593617</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10639.333293</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-02-18</th>\n",
              "      <td>4254.136570</td>\n",
              "      <td>477.492932</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5400.975713</td>\n",
              "      <td>410.555147</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10543.160362</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-02-22</th>\n",
              "      <td>4210.987096</td>\n",
              "      <td>475.118358</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5290.962309</td>\n",
              "      <td>410.715366</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10387.783128</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-02-23</th>\n",
              "      <td>4133.453884</td>\n",
              "      <td>474.779133</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5233.376942</td>\n",
              "      <td>408.792398</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10250.402357</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-02-24</th>\n",
              "      <td>4195.277359</td>\n",
              "      <td>457.150757</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5156.883227</td>\n",
              "      <td>408.952648</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10218.263991</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>252 rows × 9 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8e7c2864-1e88-483f-95c2-1be8adea8c88')\"\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-8e7c2864-1e88-483f-95c2-1be8adea8c88 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-8e7c2864-1e88-483f-95c2-1be8adea8c88');\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": 166
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Plot the portfolio"
      ],
      "metadata": {
        "id": "gE0p1eIthATB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "portfolio_test_values['Total'].plot() # Need to pretty this up for the dssertation or not use it at all"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "QAz2ZtdrhEDW",
        "outputId": "a4bba42a-ddd3-4fc0-fb23-f3b2af110dca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff0cc652950>"
            ]
          },
          "metadata": {},
          "execution_count": 167
        },
        {
          "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": [
        "### Plot the Individual Assets"
      ],
      "metadata": {
        "id": "1YhvXTIDhCtZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Plotted on the same graph to see general trend\n",
        "figure = plt.figure()\n",
        "axis = plt.plot(portfolio_test_values.iloc[:,0:1], label='^GSPC')\n",
        "#axis = plt.plot(portfolio_test_values.iloc[:,1:2], label='^FTLC')\n",
        "axis = plt.plot(portfolio_test_values.iloc[:,2:3], label='^RUT')\n",
        "#axis = plt.plot(portfolio_test_values.iloc[:,3:4], label='IEUS')\n",
        "\n",
        "axis = plt.plot(portfolio_test_values.iloc[:,4:5], label='GOVT')\n",
        "#axis = plt.plot(portfolio_test_values.iloc[:,5:6], label='IGOV')\n",
        "#axis = plt.plot(portfolio_test_values.iloc[:,6:7], label='SIVR')\n",
        "axis = plt.plot(portfolio_test_values.iloc[:,7:8], label='SGOL')\n",
        "plt.tight_layout()\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "id": "ASQAbUAHe3xl",
        "outputId": "e8ba8c18-6cc7-4850-9d2c-fd69a9047ab2"
      },
      "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": [
        "# Analyse the Test Year"
      ],
      "metadata": {
        "id": "1oIDwjllnZo5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Sharpe Ratio"
      ],
      "metadata": {
        "id": "YiV1QxPpnnaz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#daily_stock_returns = portfolio_position_values.pct_change().dropna() # Analysing holding such portfolio position from the start\n",
        "daily_stock_returns = portfolio_test_values.pct_change()\n",
        "daily_stock_returns.drop(daily_stock_returns.columns[[2,5,6,7]], axis=1, inplace=True)\n",
        "daily_stock_returns = daily_stock_returns.dropna()\n",
        "#print(daily_stock_returns)\n",
        "# To calculate sharpie ratio for individual stocks \n",
        "def sharpe_ratio(return_series, N, rf):\n",
        "    mean = return_series.mean() * N -rf\n",
        "    sigma = return_series.std() * np.sqrt(N)\n",
        "    return mean / sigma\n",
        "\n",
        "N = 252 #252 trading days in a year\n",
        "rf = 0 #I assume this to be 0 in my dissertation due to there not being a trully risk free asset\n",
        "sharpes = daily_stock_returns.apply(sharpe_ratio, args=(N,rf,),axis=0)\n",
        "sharpes.plot.bar()\n",
        "sharpes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "id": "5j0NlsCDoEpV",
        "outputId": "32b00f56-5005-45b0-d6fe-d30389162e89"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "^GSPC    0.775173\n",
              "^FTLC    0.693307\n",
              "IEUS    -0.162606\n",
              "GOVT    -0.831363\n",
              "Total    0.227708\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "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": [
        "## Volatility"
      ],
      "metadata": {
        "id": "a3ol81j1onsk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Individual Stock Volatility"
      ],
      "metadata": {
        "id": "2Og6XfRLvrqr"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Rolling Volatilty for Each stock"
      ],
      "metadata": {
        "id": "mY4OONPS1Uz_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#data = portfolio_position_values.pct_change().dropna() # Analysing holding such portfolio position from the start\n",
        "rolling_stock = pd.DataFrame()\n",
        "daily_stock_returns = portfolio_test_values.pct_change()\n",
        "daily_stock_returns.drop(daily_stock_returns.columns[[2,5,6,7]], axis=1, inplace=True)\n",
        "data = daily_stock_returns.dropna()\n",
        "#print(data)\n",
        "for stock in [\"^GSPC\",\"^FTLC\",\"GOVT\",\"IEUS\", \"Total\"]:\n",
        "  rolling_stock[stock] = data[stock].rolling(7).std()*(252**0.5)\n",
        "f1 = plt.figure()\n",
        "for i in range(0,3):\n",
        "  rolling_stock.iloc[:,i].plot()\n",
        "  plt.tight_layout()\n",
        "  #plt.show() # To display in separate graphs instead of 1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "9Zc8OPdqx421",
        "outputId": "5d893f94-3c47-4bed-de8d-cdf9cbf1c144"
      },
      "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": [
        "#### Anualised volatility for Each Stock"
      ],
      "metadata": {
        "id": "MDOiJi5Q03bn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#data = portfolio_position_values.pct_change().dropna() # Analysing holding such portfolio position from the start\n",
        "stock_volatility = pd.DataFrame()\n",
        "daily_stock_returns = portfolio_test_values.pct_change()\n",
        "daily_stock_returns.drop(daily_stock_returns.columns[[2,5,6,7]], axis=1, inplace=True)\n",
        "data = daily_stock_returns.dropna()\n",
        "tmp = pd.DataFrame()\n",
        "i = 0 \n",
        "for stock in [\"^GSPC\",\"^RUT\",\"GOVT\",\"SGOL\", \"Total\"]:\n",
        "  tmp[stock] = data.iloc[:,i:(i+1)]\n",
        "  stock_volatility = tmp.std()*(252**0.5)\n",
        "  i = i + 1\n",
        "print(stock_volatility)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qZTilRKUzSFO",
        "outputId": "9a4dc662-b03e-4969-e342-ec406acca56d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "^GSPC    0.146273\n",
            "^RUT     0.129323\n",
            "GOVT     0.161712\n",
            "SGOL     0.053270\n",
            "Total    0.135348\n",
            "dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Portfolio Volatility"
      ],
      "metadata": {
        "id": "K22vlVhovwjH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Just the Anualised Volatility for the Portfolio"
      ],
      "metadata": {
        "id": "MreARAfmy5Tx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "total_portfolio_change = portfolio_test_values['Total'].pct_change().dropna()\n",
        "portfolio_vol = total_portfolio_change.std()*(252**0.5)\n",
        "portfolio_vol"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p-TrycZSrEAF",
        "outputId": "3251e5cf-955d-4727-b83d-c77175c882ab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.13534837315954762"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Maximum Drawdown"
      ],
      "metadata": {
        "id": "3OpjwwfunpmH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def max_drawdown(return_series):\n",
        "    comp_ret = (return_series+1).cumprod()\n",
        "    peak = comp_ret.expanding(min_periods=1).max()\n",
        "    dd = (comp_ret/peak)-1\n",
        "    return dd.min()\n",
        "\n",
        "daily_stock_returns = portfolio_test_values.pct_change()\n",
        "daily_stock_returns.drop(daily_stock_returns.columns[[2,5,6,7]], axis=1, inplace=True)\n",
        "daily_stock_returns = daily_stock_returns.dropna()\n",
        "max_drawdowns = daily_stock_returns.apply(max_drawdown,axis=0)\n",
        "max_drawdowns.plot.bar()\n",
        "plt.ylabel('Max Drawdown')\n",
        "max_drawdowns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "id": "m0-mPR9VoPTE",
        "outputId": "42d30257-776b-441f-b6a5-cc7a74d2ba92"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "^GSPC   -0.118501\n",
              "^FTLC   -0.065139\n",
              "IEUS    -0.187752\n",
              "GOVT    -0.053062\n",
              "Total   -0.125477\n",
              "dtype: float64"
            ]
          },
          "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": [
        "## Compound Annual Growth Rate (CARG)"
      ],
      "metadata": {
        "id": "gehqsfUbnvUx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "start_value = portfolio_test_values.iloc[0,:]\n",
        "end_value = portfolio_test_values.iloc[-1,:]\n",
        "number_of_years =  round(len(portfolio_test_values)/252)\n",
        "CARG = (((end_value / start_value) ** (1/number_of_years)) -1)# Shows % of values\n",
        "CARG"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cbyG1U8PtjiW",
        "outputId": "3b67d482-f241-4ce9-f1fa-ec848ac52843"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "^GSPC    0.107693\n",
              "^FTLC    0.084323\n",
              "^RUT          NaN\n",
              "IEUS    -0.038433\n",
              "GOVT    -0.044503\n",
              "IGOV          NaN\n",
              "SIVR          NaN\n",
              "SGOL          NaN\n",
              "Total    0.021826\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Calmar Ratio"
      ],
      "metadata": {
        "id": "k9vlY18Jn09e"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#daily_stock_returns = portfolio_position_values.pct_change().dropna() # Analysing holding such portfolio position from the start\n",
        "daily_stock_returns = portfolio_test_values.pct_change()\n",
        "daily_stock_returns.drop(daily_stock_returns.columns[[2,5,6,7]], axis=1, inplace=True)\n",
        "daily_stock_returns = daily_stock_returns.dropna()\n",
        "calmars = daily_stock_returns.mean()*252/abs(max_drawdowns)\n",
        "\n",
        "calmars.plot.bar()\n",
        "plt.ylabel('Calmar ratio')\n",
        "calmars"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "id": "VoRp1tp9owR7",
        "outputId": "7b0d5d33-2f55-4f05-a85a-78e58da8cd81"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "^GSPC    0.956844\n",
              "^FTLC    1.376445\n",
              "IEUS    -0.140054\n",
              "GOVT    -0.834610\n",
              "Total    0.245622\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "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": [
        "# Export Test Year to CSV"
      ],
      "metadata": {
        "id": "0S0IVt6Hbn3g"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uBIyR-8pZ3_y",
        "outputId": "aacefe65-92d9-4666-ba90-6a36f5d68f4a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "portfolio_test_values.to_csv('HiddenMarkovModelData.csv')"
      ],
      "metadata": {
        "id": "abcVtYS6XXZ7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "portfolio_weights.to_csv('HiddenMarkovModelWeights.csv')"
      ],
      "metadata": {
        "id": "ZKxhXGQSenY8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!cp HiddenMarkovModelWeights.csv \"drive/My Drive/DisData\""
      ],
      "metadata": {
        "id": "ffx_dWWJZlhj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!cp HiddenMarkovModelData.csv \"drive/My Drive/DisData\""
      ],
      "metadata": {
        "id": "M4m82sfEF_-y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Recreating\n"
      ],
      "metadata": {
        "id": "FTCjQetUgLJS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Unfortunetly due to the unreliability of the Hidden Markov Model predictions we can't recreate the exact same results due to it outputting completetly different predictions each time. The weights of the model are saved so we know what output we were working with."
      ],
      "metadata": {
        "id": "FK3XzXEZgN5W"
      }
    }
  ]
}