Stock_Correlations.ipynb

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      "name": "Stock_Correlations.ipynb",
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      "name": "python3",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/tylee33/DLforFinance/blob/master/Stock_Correlations.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CB5nefB14pST",
        "colab_type": "text"
      },
      "source": [
        "**Stock Covariance & Correlations**\n",
        "\n",
        "Covariance measures the directional relationship between the returns on two assets. A positive covariance means that asset returns move together while a negative covariance means they move inversely. Covariance is calculated by analyzing at-return surprises (standard deviations from the expected return) or by multiplying the correlation between the two variables by the standard deviation of each variable. (https://www.investopedia.com/terms/c/covariance.asp)\n",
        "Stock correlation explained the relationship that exists between two stocks and their respective price movements which has a value that must fall between -1.0 and +1.0.\n",
        "A perfect positive correlation means that the correlation coefficient is exactly 1. This implies that as one security moves, either up or down, the other security moves in lockstep, in the same direction. A perfect negative correlation means that two assets move in opposite directions, while a zero correlation implies no relationship at all. (https://www.investopedia.com/terms/c/correlation.asp)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SGlycSid4tdP",
        "colab_type": "code",
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      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import math\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "# fix_yahoo_finance is used to fetch data \n",
        "import fix_yahoo_finance as yf\n",
        "yf.pdr_override()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pmb4ZuW14wRW",
        "colab_type": "text"
      },
      "source": [
        "**Two Securities Correlation**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4KIdqQDa4oth",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 252
        },
        "outputId": "5090c907-dbd0-4f6a-9b76-9decb1a594fe"
      },
      "source": [
        "# input\n",
        "symbols = ['AMD','INTC']\n",
        "start = '2012-01-01'\n",
        "end = '2019-01-01'\n",
        "\n",
        "# Read data \n",
        "dataset = yf.download(symbols,start,end)['Adj Close']\n",
        "\n",
        "# View Columns\n",
        "dataset.head()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[*********************100%***********************]  2 of 2 downloaded\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>AMD</th>\n",
              "      <th>INTC</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2012-01-03</th>\n",
              "      <td>5.48</td>\n",
              "      <td>19.226799</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-01-04</th>\n",
              "      <td>5.46</td>\n",
              "      <td>19.673389</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-01-05</th>\n",
              "      <td>5.46</td>\n",
              "      <td>19.900604</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-01-06</th>\n",
              "      <td>5.43</td>\n",
              "      <td>19.783070</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-01-09</th>\n",
              "      <td>5.59</td>\n",
              "      <td>19.955444</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             AMD       INTC\n",
              "Date                       \n",
              "2012-01-03  5.48  19.226799\n",
              "2012-01-04  5.46  19.673389\n",
              "2012-01-05  5.46  19.900604\n",
              "2012-01-06  5.43  19.783070\n",
              "2012-01-09  5.59  19.955444"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TZs3dJzL44Uc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "stocks_returns = np.log(dataset / dataset.shift(1))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pvVNjGaG47jU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "7cfb5298-53e8-4278-993e-af72e127b5ff"
      },
      "source": [
        "AMD = stocks_returns['AMD'].var() \n",
        "AMD"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0013675618444054449"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IQktyWMW493Q",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "31d673e0-78cd-485c-9f2e-bab1823647b3"
      },
      "source": [
        "INTC = stocks_returns['INTC'].var() \n",
        "INTC"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.00022174926369329965"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kl-k0_-75AQ1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        },
        "outputId": "fe3fa0c9-3135-4853-ad10-e70924f3647b"
      },
      "source": [
        "cov_matrix = stocks_returns.cov()\n",
        "cov_matrix"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>AMD</th>\n",
              "      <th>INTC</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>AMD</th>\n",
              "      <td>0.001368</td>\n",
              "      <td>0.000172</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>INTC</th>\n",
              "      <td>0.000172</td>\n",
              "      <td>0.000222</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           AMD      INTC\n",
              "AMD   0.001368  0.000172\n",
              "INTC  0.000172  0.000222"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FwMx1Dz15FZu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 128
        },
        "outputId": "7c1bf55a-8c89-4613-a6e9-7664146e0c86"
      },
      "source": [
        "print('Covariance Matrix')\n",
        "cov_matrix = stocks_returns.cov()*250\n",
        "cov_matrix"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Covariance Matrix\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>AMD</th>\n",
              "      <th>INTC</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>AMD</th>\n",
              "      <td>0.341890</td>\n",
              "      <td>0.043085</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>INTC</th>\n",
              "      <td>0.043085</td>\n",
              "      <td>0.055437</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           AMD      INTC\n",
              "AMD   0.341890  0.043085\n",
              "INTC  0.043085  0.055437"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KPdrftZc5NGk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 128
        },
        "outputId": "ba2d4007-84a5-41bf-ddc8-56288ef4ccee"
      },
      "source": [
        "print('Correlation Matrix')\n",
        "corr_matrix = stocks_returns.corr()*250\n",
        "corr_matrix"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Correlation Matrix\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>AMD</th>\n",
              "      <th>INTC</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>AMD</th>\n",
              "      <td>250.000000</td>\n",
              "      <td>78.239575</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>INTC</th>\n",
              "      <td>78.239575</td>\n",
              "      <td>250.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             AMD        INTC\n",
              "AMD   250.000000   78.239575\n",
              "INTC   78.239575  250.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zfEI4MqZ5Qut",
        "colab_type": "text"
      },
      "source": [
        "**Four Securities Correlation**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rShCnz-b5TCM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 252
        },
        "outputId": "f5e1307e-f5ac-451d-8f0e-c1dd356dedf8"
      },
      "source": [
        "# input\n",
        "symbols = ['AAPL','MSFT','AMD','NVDA']\n",
        "start = '2012-01-01'\n",
        "end = '2019-01-01'\n",
        "\n",
        "# Read data \n",
        "dataset = yf.download(symbols,start,end)['Adj Close']\n",
        "\n",
        "# View Columns\n",
        "dataset.head()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[*********************100%***********************]  4 of 4 downloaded\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>AAPL</th>\n",
              "      <th>AMD</th>\n",
              "      <th>MSFT</th>\n",
              "      <th>NVDA</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>2012-01-03</th>\n",
              "      <td>51.115936</td>\n",
              "      <td>5.48</td>\n",
              "      <td>22.080940</td>\n",
              "      <td>12.929856</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-01-04</th>\n",
              "      <td>51.390648</td>\n",
              "      <td>5.46</td>\n",
              "      <td>22.600586</td>\n",
              "      <td>13.077204</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-01-05</th>\n",
              "      <td>51.961189</td>\n",
              "      <td>5.46</td>\n",
              "      <td>22.831539</td>\n",
              "      <td>13.546881</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-01-06</th>\n",
              "      <td>52.504375</td>\n",
              "      <td>5.43</td>\n",
              "      <td>23.186216</td>\n",
              "      <td>13.390319</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012-01-09</th>\n",
              "      <td>52.421093</td>\n",
              "      <td>5.59</td>\n",
              "      <td>22.881027</td>\n",
              "      <td>13.390319</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 AAPL   AMD       MSFT       NVDA\n",
              "Date                                             \n",
              "2012-01-03  51.115936  5.48  22.080940  12.929856\n",
              "2012-01-04  51.390648  5.46  22.600586  13.077204\n",
              "2012-01-05  51.961189  5.46  22.831539  13.546881\n",
              "2012-01-06  52.504375  5.43  23.186216  13.390319\n",
              "2012-01-09  52.421093  5.59  22.881027  13.390319"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pXLZBpee5WKL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "stocks_returns = np.log(dataset / dataset.shift(1))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E5nN79X95Z90",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d4055a5b-16b4-4bae-ddde-138e01de3a97"
      },
      "source": [
        "AAPL = stocks_returns['AAPL'].var() \n",
        "AAPL"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.00025807535873387195"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xa-oacxE5cbj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "5485810d-9582-4d9e-d01c-8063ac9ffb19"
      },
      "source": [
        "MSFT = stocks_returns['MSFT'].var() \n",
        "MSFT"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.00021096766928776573"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wY6HVdq65e1r",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "7301b1be-31dc-49aa-8270-ea666bd584d0"
      },
      "source": [
        "AMD = stocks_returns['AMD'].var() \n",
        "AMD"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0013675618444054449"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L4bdYGdN5hBN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "eddb6408-6277-43b9-a432-4484caefd2c3"
      },
      "source": [
        "NVDA = stocks_returns['NVDA'].var() \n",
        "NVDA"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0005325809979459117"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C2peSWim5jT1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "8021efab-ea9a-4ca7-b064-068cf99aa3d0"
      },
      "source": [
        "AAPL = stocks_returns['AAPL'].var() * 250\n",
        "AAPL"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.06451883968346799"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xNTFfbpK5lYC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "34c788cc-3af0-4c2a-d4e8-1de987502956"
      },
      "source": [
        "MSFT = stocks_returns['MSFT'].var() * 250 \n",
        "MSFT"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.05274191732194143"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VhIYRW7N5nbk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "6fe33877-b5c8-42ed-b595-6bcdb1290c23"
      },
      "source": [
        "AMD = stocks_returns['AMD'].var() * 250 \n",
        "AMD"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.34189046110136123"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ST3ZQzbx5qoc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "cd565c10-1ce8-45b0-8d86-bff1b67630e0"
      },
      "source": [
        "NVDA = stocks_returns['NVDA'].var() * 250 \n",
        "NVDA"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.13314524948647793"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U-2lgyOf5qqM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "95c0ec13-60c8-451a-f29e-ba6dd6c95a03"
      },
      "source": [
        "cov_matrix = stocks_returns.cov()\n",
        "cov_matrix"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>AAPL</th>\n",
              "      <th>AMD</th>\n",
              "      <th>MSFT</th>\n",
              "      <th>NVDA</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>AAPL</th>\n",
              "      <td>0.000258</td>\n",
              "      <td>0.000133</td>\n",
              "      <td>0.000094</td>\n",
              "      <td>0.000121</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AMD</th>\n",
              "      <td>0.000133</td>\n",
              "      <td>0.001368</td>\n",
              "      <td>0.000136</td>\n",
              "      <td>0.000348</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MSFT</th>\n",
              "      <td>0.000094</td>\n",
              "      <td>0.000136</td>\n",
              "      <td>0.000211</td>\n",
              "      <td>0.000143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NVDA</th>\n",
              "      <td>0.000121</td>\n",
              "      <td>0.000348</td>\n",
              "      <td>0.000143</td>\n",
              "      <td>0.000533</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          AAPL       AMD      MSFT      NVDA\n",
              "AAPL  0.000258  0.000133  0.000094  0.000121\n",
              "AMD   0.000133  0.001368  0.000136  0.000348\n",
              "MSFT  0.000094  0.000136  0.000211  0.000143\n",
              "NVDA  0.000121  0.000348  0.000143  0.000533"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5wOo-srt5uxD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 190
        },
        "outputId": "134db864-9ad0-480b-cb1a-23185ec0c874"
      },
      "source": [
        "print('Covariance Matrix')\n",
        "cov_matrix = stocks_returns.cov()*250\n",
        "cov_matrix"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Covariance Matrix\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>AAPL</th>\n",
              "      <th>AMD</th>\n",
              "      <th>MSFT</th>\n",
              "      <th>NVDA</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>AAPL</th>\n",
              "      <td>0.064519</td>\n",
              "      <td>0.033303</td>\n",
              "      <td>0.023565</td>\n",
              "      <td>0.030209</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AMD</th>\n",
              "      <td>0.033303</td>\n",
              "      <td>0.341890</td>\n",
              "      <td>0.034009</td>\n",
              "      <td>0.086893</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MSFT</th>\n",
              "      <td>0.023565</td>\n",
              "      <td>0.034009</td>\n",
              "      <td>0.052742</td>\n",
              "      <td>0.035644</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NVDA</th>\n",
              "      <td>0.030209</td>\n",
              "      <td>0.086893</td>\n",
              "      <td>0.035644</td>\n",
              "      <td>0.133145</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          AAPL       AMD      MSFT      NVDA\n",
              "AAPL  0.064519  0.033303  0.023565  0.030209\n",
              "AMD   0.033303  0.341890  0.034009  0.086893\n",
              "MSFT  0.023565  0.034009  0.052742  0.035644\n",
              "NVDA  0.030209  0.086893  0.035644  0.133145"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dx-pxgwN5x2t",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 190
        },
        "outputId": "ad499f69-fe9b-4c22-97fd-2752ba5e3959"
      },
      "source": [
        "print('Correlation Matrix')\n",
        "corr_matrix = stocks_returns.corr()*250\n",
        "corr_matrix"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Correlation Matrix\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>AAPL</th>\n",
              "      <th>AMD</th>\n",
              "      <th>MSFT</th>\n",
              "      <th>NVDA</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>AAPL</th>\n",
              "      <td>250.000000</td>\n",
              "      <td>56.057063</td>\n",
              "      <td>100.993903</td>\n",
              "      <td>81.482620</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AMD</th>\n",
              "      <td>56.057063</td>\n",
              "      <td>250.000000</td>\n",
              "      <td>63.316328</td>\n",
              "      <td>101.816603</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MSFT</th>\n",
              "      <td>100.993903</td>\n",
              "      <td>63.316328</td>\n",
              "      <td>250.000000</td>\n",
              "      <td>106.337983</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NVDA</th>\n",
              "      <td>81.482620</td>\n",
              "      <td>101.816603</td>\n",
              "      <td>106.337983</td>\n",
              "      <td>250.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            AAPL         AMD        MSFT        NVDA\n",
              "AAPL  250.000000   56.057063  100.993903   81.482620\n",
              "AMD    56.057063  250.000000   63.316328  101.816603\n",
              "MSFT  100.993903   63.316328  250.000000  106.337983\n",
              "NVDA   81.482620  101.816603  106.337983  250.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    }
  ]
}