Linear_Correlation_Analysis.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Linear Correlation Analysis",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/TheHouseOfVermeulens/Financial-Engineering/blob/master/Linear_Correlation_Analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2lrXnUoo8kST",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Useful Functions\n",
        "def find_most_correlated(data):\n",
        "    n = data.shape[1]\n",
        "    keys = data.keys()\n",
        "    pair = []\n",
        "    max_value = 0\n",
        "    for i in range(n):\n",
        "        for j in range(i+1, n):\n",
        "            S1 = data[keys[i]]\n",
        "            S2 = data[keys[j]]\n",
        "            result = np.corrcoef(S1, S2)[0,1]\n",
        "            if result > max_value:\n",
        "                pair = (keys[i], keys[j])\n",
        "                max_value = result\n",
        "    return pair, max_value"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fcqwoY-y81t_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Useful Libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zoJRTA9S9AfQ",
        "colab_type": "text"
      },
      "source": [
        "#Dependence of Artificial Variables\n",
        "##a. Finding Variance, Covariance, and Correlation I\n",
        "\n",
        "By reading the matrix output from the `np.cov()` and `np.corrcoef() `functions, find the variance of the variables A\n",
        "and B and the covariance and correlation of their relationship."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2SRwnjhV855u",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 243
        },
        "outputId": "1d9cce3c-36f1-425e-cdbc-02e15cd9a0ba"
      },
      "source": [
        "A = np.random.rand(100)\n",
        "B = -3 * A + np.random.exponential(0.05, 100)\n",
        "\n",
        "covm = np.cov(A, B)\n",
        "corrm = np.corrcoef(A, B)\n",
        "\n",
        "print ('Covariance matrix: \\n' + str(covm) + '\\n')\n",
        "print ('Correlation matrix: \\n' + str(corrm) + '\\n')\n",
        "\n",
        "print ('Variance of A: ' + str(covm[0,0]))\n",
        "print ('Variance of B: ' + str(covm[1,1]) + '\\n')\n",
        "\n",
        "print ('Covariance of A and B: ' + str(covm[1,0]))\n",
        "print ('Correlation of A and B: ' + str(corrm[1,0]))"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Covariance matrix: \n",
            "[[ 0.074023   -0.22181858]\n",
            " [-0.22181858  0.66999476]]\n",
            "\n",
            "Correlation matrix: \n",
            "[[ 1.        -0.9960448]\n",
            " [-0.9960448  1.       ]]\n",
            "\n",
            "Variance of A: 0.07402299831418033\n",
            "Variance of B: 0.6699947585802903\n",
            "\n",
            "Covariance of A and B: -0.2218185754711069\n",
            "Correlation of A and B: -0.9960448014911548\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jLXh9KAu91D6",
        "colab_type": "text"
      },
      "source": [
        "# Finding Variance, Covariance, and Correlation II\n",
        "\n",
        "By reading the matrix output from the `np.cov()` and `np.corrcoef()` functions, find the variance of the variables C\n",
        "and D and the covariance and correlation of their relationship."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "182wDH0U9exI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 243
        },
        "outputId": "be688a14-271f-404e-c6cf-35dcd8753ae9"
      },
      "source": [
        "C = np.random.rand(100)\n",
        "D = np.random.normal(0, 0.5, 100)\n",
        "\n",
        "covm = np.cov(C, D)\n",
        "corrm = np.corrcoef(C, D)\n",
        "\n",
        "print ('Covariance matrix: \\n' + str(covm) + '\\n')\n",
        "print ('Correlation matrix: \\n' + str(corrm) + '\\n')\n",
        "\n",
        "print ('Variance of C: ' + str(covm[0,0]))\n",
        "print ('Variance of D: ' + str(covm[1,1]) + '\\n')\n",
        "\n",
        "print ('Covariance of C and D: ' + str(covm[1,0]))\n",
        "print ('Correlation of C and D: ' + str(corrm[1,0]))"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Covariance matrix: \n",
            "[[0.0887617  0.00525044]\n",
            " [0.00525044 0.29778715]]\n",
            "\n",
            "Correlation matrix: \n",
            "[[1.         0.03229458]\n",
            " [0.03229458 1.        ]]\n",
            "\n",
            "Variance of C: 0.08876170167100861\n",
            "Variance of D: 0.2977871468845068\n",
            "\n",
            "Covariance of C and D: 0.005250436161312912\n",
            "Correlation of C and D: 0.03229457683204365\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bbBhExle-NnD",
        "colab_type": "text"
      },
      "source": [
        "#Constructing Example Relationships\n",
        "##a. Positive Correlation Example\n",
        "\n",
        "Construct a variable Y\n",
        "which has a strong, but not perfect, positive correlation with X (0.9<Corr(X,Y)<1), and plot their relationship."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mW-6yNSp-JwC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "93ad7711-cb30-4672-b53b-2973b3136510"
      },
      "source": [
        "X = np.random.rand(100)\n",
        "\n",
        "Y = 2*X + np.random.normal(0, 0.1, 100)\n",
        "\n",
        "plt.scatter(X,Y)\n",
        "plt.xlabel('X Value')\n",
        "plt.ylabel('Y Value')\n",
        "\n",
        "print ('Correlation of X and Y: ' + str(np.corrcoef(X, Y)[0,1]))"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Correlation of X and Y: 0.9843628012992833\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jyJdQ6af-ldz",
        "colab_type": "text"
      },
      "source": [
        "#Negative Correlation Example\n",
        "\n",
        "Construct a variable W\n",
        "which has a weak, negative correlation with Z (−0.3<Corr(Z,W)<0), and plot their relationship."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "izjQYVvq-aCd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "46749877-38ce-4371-bc40-5cefc9ecf2e5"
      },
      "source": [
        "Z = np.random.rand(100)\n",
        "\n",
        "W = -4*Z + np.random.normal(0, 10, 100)\n",
        "\n",
        "plt.scatter(Z,W)\n",
        "plt.xlabel('Z Value')\n",
        "plt.ylabel('W Value')\n",
        "\n",
        "print ('Correlation of Z and W: ' + str(np.corrcoef(Z, W)[0,1]))\n"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Correlation of Z and W: 0.021294675951341775\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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sCZIx2a01mJTO8n+9F9jq7gfd/XHgEZJgUVVZzvkKYDOAu/8V8BKSxenqLPct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\ngg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DjmBxXNwAWOB",
        "colab_type": "text"
      },
      "source": [
        "#Correlation of Real Assets\n",
        "##a. Finding Correlation of Real Assets\n",
        "\n",
        "Find the correlation between the stocks OKE and LAKE. Also check how they correlate with the provided benchmark."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o_l8KwUTAQpf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "OKE = get_pricing('OKE', fields='price', start_date='2013-01-01', end_date='2015-01-01')\n",
        "LAKE = get_pricing('LAKE', fields='price', start_date='2013-01-01', end_date='2015-01-01')\n",
        "benchmark = get_pricing('SPY', fields='price', start_date='2013-01-01', end_date='2015-01-01')\n",
        "\n",
        "print (\"Correlation coefficient of OKE and LAKE: \", np.corrcoef(OKE, LAKE)[0,1])\n",
        "print (\"Correlation coefficient of OKE and benchmark: \", np.corrcoef(OKE, benchmark)[0,1])\n",
        "print (\"Correlation coefficient of LAKE and benchmark: \", np.corrcoef(LAKE, benchmark)[0,1])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hse3hjR7BGpg",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "```\n",
        "Correlation coefficient of OKE and LAKE:  0.446484870844\n",
        "Correlation coefficient of OKE and benchmark:  0.853638499475\n",
        "Correlation coefficient of LAKE and benchmark:  0.625923407419\n",
        "```\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ES2iXFP0Axh8",
        "colab_type": "text"
      },
      "source": [
        "#Finding Correlated Pairs\n",
        "\n",
        "Find the most correlated pair of stocks in the following portfolio using 2015 pricing data and the `find_most_correlated` function defined in the Helper Functions section above."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vr6ScjPxAqn6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "symbol_list = ['GSK', 'SNOW', 'FB', 'AZO', 'XEC', 'AMZN']\n",
        "data = get_pricing(symbol_list, fields=['price']\n",
        "                               , start_date='2015-01-01', end_date='2016-01-01')['price']\n",
        "data.columns = symbol_list\n",
        "\n",
        "find_most_correlated(data)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2bpzRHxDBAhI",
        "colab_type": "text"
      },
      "source": [
        "`(('FB', 'AMZN'), 0.95751089566007552)`"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "buOKaZFPBaF6",
        "colab_type": "text"
      },
      "source": [
        "#Limitations of Correlation\n",
        "##a. Out of Sample Tests\n",
        "\n",
        "Using pricing data from the first half of 2016, find the correlation coefficient between FB and AMZN and compare it to the strong positive relationship predicted from the 2015 correlation coefficient to see if that result holds."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iaIxkdM9A81H",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "FB_15 = get_pricing('FB', fields='price', start_date='2015-01-01', end_date='2016-01-01')\n",
        "AMZN_15 = get_pricing('AMZN', fields='price', start_date='2015-01-01', end_date='2016-01-01')\n",
        "FB_16 = get_pricing('FB', fields='price', start_date='2016-01-01', end_date='2016-07-01')\n",
        "AMZN_16 = get_pricing('AMZN', fields='price', start_date='2016-01-01', end_date='2016-07-01')\n",
        "\n",
        "print (\"2015 correlation coefficient: \", np.corrcoef(FB_15, AMZN_15)[0,1])\n",
        "print (\"2016 correlation coefficient: \", np.corrcoef(FB_16, AMZN_16)[0,1])\n",
        "print (\"The strong correlation from 2015 did not hold outside of the 2015 pricing sample.\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1VeF72w3Bqve",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "```\n",
        "2015 correlation coefficient:  0.95751089566\n",
        "2016 correlation coefficient:  0.651288727673\n",
        "The strong correlation from 2015 did not hold outside of the 2015 pricing sample.\n",
        "```\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9HfgF7ryBxwR",
        "colab_type": "text"
      },
      "source": [
        "#Rolling Correlation\n",
        "\n",
        "Plot the 60-day rolling correlation coefficient between FB and AMZN to make a conclusion about the stability of their relationship."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZrMy6uirBpNx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "FB = get_pricing('FB', fields='price', start_date='2015-01-01', end_date='2017-01-01')\n",
        "AMZN = get_pricing('AMZN', fields='price', start_date='2015-01-01', end_date='2017-01-01')\n",
        "\n",
        "rolling_correlation = FB.rolling(window=60).corr(AMZN)\n",
        "plt.plot(rolling_correlation)\n",
        "plt.xlabel('Day')\n",
        "plt.ylabel('60-day Rolling Correlation')\n",
        "print (\"Upon further investigation, FB and AMZN do not consistently have the strong correlation suggested by our result from question 3b.\")\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lofrxCW6B_hj",
        "colab_type": "text"
      },
      "source": [
        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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FZ6XqiZxCCL-",
        "colab_type": "text"
      },
      "source": [
        "Upon further investigation, FB and AMZN do not consistently have the strong correlation suggested by our result from question 3b."
      ]
    }
  ]
}