Statistical_Moments_and_Normality_Testing.ipynb

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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Statistical Moments and Normality Testing",
      "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/Statistical_Moments_and_Normality_Testing.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GZr-C7WV3Cjw",
        "colab_type": "text"
      },
      "source": [
        "# Libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xU5sJC5I0rdI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Useful Libraries\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import scipy.stats as stats\n",
        "from statsmodels.stats.stattools import jarque_bera"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rGci074b26gm",
        "colab_type": "text"
      },
      "source": [
        "# 1: Testing for Skew\n",
        "\n",
        "### Artificial Example\n",
        "\n",
        "Use the results from the `stats.skew` function to determine the skew of the artificial distribution named X."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TrguWXei217z",
        "colab_type": "code",
        "outputId": "b23fe335-b369-471a-cc20-a9e90d1ea90e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        }
      },
      "source": [
        "xs2 = np.linspace(stats.gamma.ppf(0.01, 0.7, loc=-1), stats.gamma.ppf(0.99, 0.7, loc=-1), 150) + 1\n",
        "\n",
        "X = stats.gamma.pdf(xs2, 1.5)\n",
        "\n",
        "skew = stats.skew(X)\n",
        "\n",
        "plt.plot(xs2, X)\n",
        "\n",
        "print ('Skew:', skew)\n",
        "if skew > 0:\n",
        "    print ('The distribution is positively skewed')\n",
        "elif skew < 0:\n",
        "    print ('The distribution is negatively skewed')\n",
        "else:\n",
        "    print ('The distribution is symmetric')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Skew: 0.26474831096388496\n",
            "The distribution is positively skewed\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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Lp3qjDaXOSUKTMP56/UV8ePcgWsZH8Ov3vmP0tK9Z/X2h1dGUD9my2Mt1xK7U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\nATiW3TpqgYgnAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mi-q3pRE31-c",
        "colab_type": "text"
      },
      "source": [
        "### Real Example\n",
        "\n",
        "Use the results from the `stats.skew` function to determine the skew of the returns of NFLX and use it to make a conclusion about the symmetry of the stock's returns."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5o1UuoNC3QP-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "start = '2015-01-01'\n",
        "end = '2016-01-01'\n",
        "pricing = get_pricing('NFLX', fields='price', start_date=start, end_date=end)\n",
        "returns = pricing.pct_change()[1:]\n",
        "\n",
        "skew = stats.skew(returns)\n",
        "\n",
        "plt.hist(returns, 30)\n",
        "\n",
        "print ('Skew:', skew)\n",
        "print ('The returns of NFLX have a strong positive skew, meaning their volatility is characterized by frequent small changes in price with interspersed large upticks.')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i74in14C4QuO",
        "colab_type": "text"
      },
      "source": [
        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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T6tdgHa-4Ylm",
        "colab_type": "text"
      },
      "source": [
        "# 2: Testing for Kurtosis\n",
        "### a. Artificial Example\n",
        "\n",
        "Use the results from the `stats.kurtosis` function to determine the excess kurtosis of the artificial distribution named Y."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3x0yY3sd4FxH",
        "colab_type": "code",
        "outputId": "ccd5bc84-4508-4cde-bcd1-64fce4f61585",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        }
      },
      "source": [
        "xs = np.linspace(-6,6, 300) + 2 \n",
        "\n",
        "Y = stats.cosine.pdf(xs)\n",
        "\n",
        "plt.plot(xs, Y)\n",
        "\n",
        "print ('Excess kurtosis of Y:', (stats.kurtosis(Y)))\n",
        "print ('Because the excess kurtosis is negative, Y is platykurtic. Platykurtic distributions cluster around the mean, so large values in either direction are less likely')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Excess kurtosis of Y: -0.6344720164161468\n",
            "Because the excess kurtosis is negative, Y is platykurtic. Platykurtic distributions cluster around the mean, so large values in either direction are less likely\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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l+ftWABsO1dHQ2sEfLC7wuhSJsDvmTyQpYKxT+0aiTDhBXwNM7fO8ILRswHXM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\nRMTn/j/yq2JdREUKOgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mzG7TQrU4y44",
        "colab_type": "text"
      },
      "source": [
        "#3: Skew and Normality\n",
        "### Artificial Example II\n",
        "\n",
        "Use the results from the `stats.skew` function to determine the skew of the artificial distribution named Z."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lWxTRH6C4pGj",
        "colab_type": "code",
        "outputId": "b1b0f4a6-80e2-4969-9eb4-2465e8d29c6e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "\n",
        "\n",
        "xs2 = np.linspace(stats.lognorm.ppf(0.01, 0.7, loc=-.1), stats.lognorm.ppf(0.99, 0.7, loc=-.1), 150)\n",
        "\n",
        "lognorm = stats.lognorm.pdf(xs2, 0.4)\n",
        "\n",
        "Z = lognorm/2 + lognorm[::-1]\n",
        "\n",
        "skew = stats.skew(Z)\n",
        "\n",
        "print ('Skew:', skew)\n",
        "if skew > 0:\n",
        "    print ('The distribution is positively skewed')\n",
        "elif skew < 0:\n",
        "    print ('The distribution is negatively skewed')\n",
        "else:\n",
        "    print ('The distribution is symmetric')\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Skew: 1.1121491036049673\n",
            "The distribution is positively skewed\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_Sd5HBL25MBU",
        "colab_type": "text"
      },
      "source": [
        "#Jarque-Bera Calibration\n",
        "\n",
        "Ensure that the `jarque-bera` function is calibrated by running it on many trials of simulated data and ensuring that the sample probability that the test returns a result under the p-value is equal to the p-value."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7hsLYoPE5Evt",
        "colab_type": "code",
        "outputId": "24b089e2-feed-40c3-9cf6-3b03c9278c70",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "N = 1000\n",
        "M = 1000\n",
        "\n",
        "pvalues = np.ndarray((N))\n",
        "\n",
        "for i in range(N):\n",
        "    # Draw M samples from a normal distribution \n",
        "    X = np.random.normal(0, 1, M);\n",
        "    _, pvalue, _, _ = jarque_bera(X)\n",
        "    pvalues[i] = pvalue\n",
        "\n",
        "num_significant = len(pvalues[pvalues < 0.05])\n",
        "\n",
        "print (float(num_significant) / N)\n",
        "print ('Our answer is around 5%, which is what we would expect for a cutoff of 5% and a correctly-calibrated Jarque-Bera test.')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.052\n",
            "Our answer is around 5%, which is what we would expect for a cutoff of 5% and a correctly-calibrated Jarque-Bera test.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TJWvslsp5ohG",
        "colab_type": "text"
      },
      "source": [
        "##Jarque-Bera Test\n",
        "\n",
        "Use the `Jarque-Bera` function to determine the normality of Z."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QMPB9Gzj5h8R",
        "colab_type": "code",
        "outputId": "5991a082-cb0b-45d0-9049-60dd3a174493",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "_, pvalue, _, _ = jarque_bera(Z)\n",
        "\n",
        "print (pvalue)\n",
        "if pvalue > 0.05:\n",
        "    print ('The returns are likely normal.')\n",
        "else:\n",
        "    print ('The returns are likely not normal.')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1.6768991741743907e-07\n",
            "The returns are likely not normal.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1_HgOi9U57WU",
        "colab_type": "text"
      },
      "source": [
        "#Skewness and Normality\n",
        "\n",
        "Plot Z and observe that skewness is not informative unless the underlying distribution is somewhat normal."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9WFuSH4o52hW",
        "colab_type": "code",
        "outputId": "0fcbeea9-8cef-4eb2-9488-f4e43cfc3fc8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        }
      },
      "source": [
        "plt.plot(Z)\n",
        "\n",
        "print ('The positive skew found in part a would have led us to believe values are concentrated below the mean and a tail extends to the right, however this is not the case. Because Z is bimodal, we can make no conclusions based on the skewness value alone. In order for skewness to be useful, the underlying distribution must be somewhat normal')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The positive skew found in part a would have led us to believe values are concentrated below the mean and a tail extends to the right, however this is not the case. Because Z is bimodal, we can make no conclusions based on the skewness value alone. In order for skewness to be useful, the underlying distribution must be somewhat normal\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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p3j+EpfUdFkcyNprQR7CprAkRWOTwhC4i/J+r5xIdHsJdz22jt19LL07xwuYK\nthxu4V8umU1cRKjV4QSs3BTv0MXShsDYU2BMCV1EVojIfhEpFpG7Rzj+XRHZIyI7RGS1iAT0jrOb\nDzUze3JcULwRJsVG8Iur57K7qo0H3jlgdTjKDxqO9PDzv+1lcW4i15yWZXU4AS02IpRJseHOaaGL\niBt4CLgUyAduEJH8YadtBQqMMfOAF4H/5+9AJ0r/gIctDliQ63hccspkrivI5pG/l/DRwSarw1En\n6T9f20Nnbz+/uHouLpcOUzxZ01KjKa13Tgv9dKDYGFNqjOkFngOuGnqCMWaNMWaw12AjELDNgn01\n7XT0DlCQGzwJHeDfrsgnOzGK7/x5G226ImPAWrOvjle2VfGN82YwY1Ks1eE4wtSUGA42OKSFDmQC\nQxfSrvA9dzS3Aq+PdEBEbheRQhEprK+vH3uUE2hTmbeF6uQRLiOJCQ/h/usWUN3axb+/sjuglgxV\nXi2dvfzwpR3MSovlG+fr5hX+Mj01mubOPpo7eq0OZVR+7RQVkRuBAuDekY4bYx4zxhQYYwpSU1P9\neWm/KTzUTGZCJBkJkVaHMuFOm5LIty7M4+Wtlfzpw8NWh6OO07+9spumjl5+ee18wkMCZ0Nzu5uW\nGg0ERsfoWBJ6JZA95HGW77lPEJHlwI+BK40xgbOA8BDGGArLmhw/XPFYvnVBHufPSuVnr+6msEzr\n6YHilW2VvLq9irsuzOPUTN0n1J8Ghy6WBEDH6FgS+iYgT0SmikgYcD2wcugJIrIQeBRvMq/zf5gT\no6K5i9q2HhYHWf18KJdLeOD6hWQmRPL1p7dQ29ZtdUhqFAcbOvjRX3ZSMCWRr5+npRZ/y0qMJNQt\nAVFHHzWhG2P6gTuBN4G9wPPGmN0i8jMRudJ32r1ADPCCiGwTkZVHeTlbKzzkbZE6bUGu4xUfGcqj\nNxXQ0dPP1/+0Wcen21hP/wB3PrOF0BAXv7phISEOXarCSiFuFzlJUQEx0iVkLCcZY1YBq4Y995Mh\n3y/3c1yW2FTWTGx4CDPTdHTArMmx3HvNfO54Zgs/fXU3P//sqbpSn80YY/j3V3azu6qN33+pICj7\nfSbKtNSYgBiLrn/Ohygsa2LRlETcOnYXgM/MS+fr503nmQ8P8/B7uhep3fzpw8M8t6mcO86fznLd\nhWhcTUuJ5lBjp+0XstOE7tN4pIcDtUeCun4+kh9cPIvPLsjg3jf389xHOvLFLjaWNvIfK3dzwexJ\nfPeiWVaH43izJsfSO+CxfR1dE7rPxlJv/fzMGSkWR2IvLpdw7+fnc+7MVH708k7dus4Giuvauf1/\nC5mSHMX91y3QT5QTIN+3LwfcldcAAA9VSURBVMKe6jaLIzk2Teg+60oaiAkPYZ4O+fqUULeLR25c\nxLysBL757FY2ljZaHVLQqmvr5uYnNhEW4uYP/3w68ZHOX2/IDqanxhDmdrGnShN6QFhf3MAZU5N0\nlMBRRIWF8OQti8lJiuK2PxZ+PKNWTZzmjl5uevwjmjp6eeKWArKToqwOKWiEul3MnByjLfRAUNnS\nRVljp5ZbRpEYHcZTt57OpLhwbnr8Q94/YM/lG5yorbuPm5/8iIONHfz+5gLmZSVYHVLQyU+PY09V\nm62XxdCEDqwrbgDgrBnJFkdif+nxkTz/1aVMTYnhtj8W8sYuramPt5ZOb8t8T1Ubj3xxEWdpw8MS\n+elxNHb0Utdu34nwmtCBDSWNpMSEMUvHn49JSkw4z31lCadmxnHHM1t4aXOF1SE5VsORHm743Yfs\nrWrjkRtP48I5OjzRKvkZ3v41O9fRgz6hG2NYV9zA0ukpOnHmOMRHhfLUrWewZFoS33thO/e/fQCP\nx74fRQNRWUMH1zyynoMNR/j9zQVcpGPNLTU73dvgs3MdPegT+t7qdurae1imH2OPW3R4CI/fvJh/\nWpTFg6uL+OqfNtOua6n7xeZDzVz9yHpau/p4+rYlnDPTnquTBpO4iFBykqK0hW5n7+ytRQTOnz3J\n6lACUkSom/s+P4+fXJ7Pu/vq+NzD6wNizQs7+/Omw9zw2EZiwkN46etnBvXqn3aTnx6nLXQ7W723\nlgXZCaTGhlsdSsASEb589lSe+vLpNB7p4aqH1vHKtkpbjwawo+6+Ae75y05++NJOzpiWxMo7z2Ja\naozVYakhTsmI42BDh2139QrqhF7b1s32ilaWa0eTX5w5I4WVd57N9NQY7npuG3c+s5WmANjlxQ72\n17Rz1W/W8exHh/naudN58pbFJESFWR2WGmbw09LmsmaLIxlZUCf0d/d5l27XhO4/2UlRvPi1pfzg\nklm8taeGi+9/n3f21Fodlm319A9w/9sHuPzXa2k40sMfv3w6d186Wye42dSiKYmEuV1ssOls6aD+\nrXlnTy3ZSZHMTNOPtf4U4nZxx/kzWHnn2aTEhHHb/xby1acKOdzYOfoPB5HCsiY+86sPeHB1EZ+Z\nm85b3zmHc7Xz09YiQt0syElgQ4kmdFvp7O3ng+IGls9J0+GK42ROehwr7zybH1wyi7VFDSz/n7/z\ni9f3Bv1ImOrWLn744g6u+e0GunoHePKfF/PA9QtJjtF+nECwdFoyu6taae2y3+9x0Cb013fW0NPv\n4bK56VaH4mhhId7W+prvn8eVCzJ49O+lnH/fe/x+bSkdPf1Whzehmjp6+a/X9nDuve/xl60V3Hb2\nVN76zjmcP0tHWAWSJdOS8RjYdNB+6xmNacciJ3phczm5yVEU6JCwCZEWF8F9n5/PzUtz+cXre/mv\nv+3lN2uKuXlpLrecmUtitHM7AGvbunlqwyH+sL6Mzt5+rl6UxV0X5uniWgFqYU4CYSEuNpY22m5j\nkaBM6OVNnWwsbeL7F8/UcssEm5sVzzNfWcLWw808/F4JD64u4ndrS7lqQSbXLc5mfla8I/5PjDFs\nOdzCH9aX8frOagaMYcUpk/nuRTPJ0yUmAlpEqJtFOQm27BgNyoT+0pYKROBzi7KsDiVoLcxJ5Hdf\nKuBAbTuPvV/Ky1srePajw8xKi+XaxdlcMT+dSbERVod53CqaO/nbjmpe2VbFnuo2YsNDuPnMXL60\ndApTkqOtDk/5ydJpKTyw+gBNHb0k2ejTpVg1+aOgoMAUFhZO+HU9HsO5961hSlI0f7rtjAm/vhpZ\nW3cfr26v4vnCCraXtyACC7MTuCh/MhflpzFjkj1HIhljKKk/wt8PNLBqZzWbD3nHJ8/Piuea07K4\nelEW0eFB2W5ytH01bax4YC3/ceUp3Hxm7oReW0Q2G2MKRjwWbAn9jV01fO1Pm3nw+gVctSBzwq+v\nRnegtp03dtXw9p5adla2ApCZEMnpU5M4fWoSi3OTmJ4abUlpxuMxlDV2sL2ihXXFjXxQ1EBNWzcA\nsyfHcsX8DC6fl66t8SBw+a/XIgivfvPsCb3usRJ6UDUdPB7DA+8cYGpKNJ/R0S22NTMtlplpsXzr\nwjyqW7t4Z08tG0obWVvUwMtbKwGIjQhhzuQ4ZqfHMmtyLDNSY8hMjGRyXIRfJuV4PIba9m7KGjop\na+ygtP4Iuyrb2FXZSrtvdE58ZChnzUjm7BmpnD0jhZxk7eQMJtcsyuKnr+5hX00bsyfHWR0OEGQJ\n/c3dNeyraef+6+brTLwAkR4fyU1Lc7lpaS7GGA42dLCprImdla3sq27nL1sqOTJk+KPbJUyOiyAt\nLpz4yFASosKIjwwlJjyEELcQ4hLcLhci3rVTuvs8dPcN0NnbT1NHL/VHemk80kN9ew89/Z6PXzfM\n7WJOeixXLcxgXmYCp2bGM2tyrG7QHMSuXJDJz1ft5cXCCv718nyrwwGCKKF7PIYHVxcxLTWaK+dr\nqSUQiQjTUmOYlhrDdYu9zxljqGju4mBDB5UtXVQ2d1HZ0kVdezf1R3oorj9CS2cfHT39jLRce6hb\niAhxExnmJjkmnJSYMKalRJMSE0ZOcjS5yVHkJkeTkRCpyVt9QlJ0GBfOTuOv2yr54aWzCbVBIzFo\nEvqj75eyr6adX92wUN+YDiIiZCdFjWlMt8djGDCGAY/BYwzhIW79XVAn5YYzcnhjdw1PfHCQr547\n3epwgmOm6KayJu57az+fmZfOFfO0dh6sXC4h1O0iItRNVFiIJnN10s7JS+Gi/DTuf+eALdYqcnxC\nP9TYwTef2UpWYiT/9+q5jpi0opSyBxHhZ1edQojLxY9e3mn5HgBjSugiskJE9otIsYjcPcLxcBH5\ns+/4hyKS6+9AT8SafXVc8esP6Oob4OEvLiI2ItTqkJRSDpMeH8kPV8zig+IGvvH0lk900k+0UWvo\nIuIGHgIuAiqATSKy0hizZ8hptwLNxpgZInI98N/AdeMR8LEMeAyVzV1sOdzM84XlrC9pJD89jkdv\nOk3XzVBKjZsbl0yhp9/DL17fx/7ffMA/n5nLhXPSSI+PmNCqwKgTi0RkKfBTY8wlvsf3ABhjfjHk\nnDd952wQkRCgBkg1x3jxE51Y9OdNh3n0/VL6Bwz9Ax76PYZ+j6FvwDv8rG/Ae8msxEhuOD2HL581\nlcgw93FfRymljtf6kgb+9a+7KK3vALyrjSZFhREaIrhFcIkgAt9ePpMr5mec0DVOdmJRJlA+5HEF\nMHzO/MfnGGP6RaQVSAYahgVyO3A7QE5OzpiCHy4pOpz89DhCXEKI20WoWwhxuQhxC+EhbqamRJGX\nFsuCrARc2umllJpAZ05P4d3vnUdx3RHWFtVT09ZN05Fe+n0jqzzGO9oqIWp8yr8TOmzRGPMY8Bh4\nW+gn8hoX5adxkc2WrFRKqaFmTIqxZP2hsXSKVgLZQx5n+Z4b8RxfySUesN/akkop5WBjSeibgDwR\nmSoiYcD1wMph56wEbvZ9fw3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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F9124T_06cDM",
        "colab_type": "text"
      },
      "source": [
        "#Out of Sample Test\n",
        "##a. Testing for Normality\n",
        "\n",
        "Plot a histogram of the historical returns of AMC to ensure it is unimodal and vaguely normal before testing it for skewness in part b."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hFaRRpIe6Hdl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "start = '2014-01-01'\n",
        "end = '2016-01-01'\n",
        "pricing = get_pricing('AMC', fields='price', start_date=start, end_date=end)\n",
        "returns = pricing.pct_change()[1:]\n",
        "\n",
        "print ('The returns of AMC from 2014 through 2016 are unimodal and vaguely normal, so a skewness measure would be relevant.')\n",
        "\n",
        "plt.hist(returns, 30);"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UG9QC4dW6xxV",
        "colab_type": "text"
      },
      "source": [
        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NZubeF7N+KFpsdG5/vrrk3Ntxu3i9jS33bt3pxlmTEkF1K+khUmZlvqlhCrhMLUrYVGmSGlgYGCKrfls2rNnT/JmYz7RRDmdgJ+908m0xvPq1xp1KiLLms1NPBTt0ksvDWeccUY477zzwty5c5MLCxw9ejTMnj07DA4Ohu7u7pq5LVu2rOYYA8opEF8g1K+ctUmTlfqlUSrnGLUrZ12myqqzszOEJ/ZPNaTubUuXLg29vb11xxHg9AJ+9k5v0wpb1K8VqnT6HGP96rnVPOcmnm/z5JNPJp/W/OQnPwnDw8Nh+fLlYdu2bcm827dvDytWrKgnB/sSIECAAAECBAgQIECgboGan9ycc8454corrwxXX311mDFjRli/fn2I7xrddttt4bHHHguLFi0Kq1evrjsRAQgQIECAAAECBAgQIFCPQM3mJgaPjU387/jb5s2bj3/oPgECBAgQIECAAAECBJoqUPOwtKZmZ3ICBAgQIECAAAECBAikFNDcpIQyjAABAgQIECBAgACBcgtobspdH9kRIECAAAECBAgQIJBSQHOTEsowAgQIECBAgAABAgTKLaC5KXd9ZEeAAAECBAgQIECAQEoBzU1KKMMIECBAgAABAgQIECi3gOam3PWRHQECBAgQIECAAAECKQVS/Z2blLEMI0CAAAECpRYYGRkJfX19heXY399fWGyBCRAgQKC2gOamtpERBAgQIFARgdjYrF33SJgzv7uQFR3c93ToOvfiQmILSoAAAQK1BTQ3tY2MIECAAIEKCcTGZt6CxYWsaPjQYCFxBSVAgACBdALOuUnnZBQBAgQIECBAgAABAiUX0NyUvEDSI0CAAAECBAgQIEAgnYDmJp2TUQQIECBAgAABAgQIlFxAc1PyAkmPAAECBAgQIECAAIF0ApqbdE5GESBAgAABAgQIECBQcgHNTckLJD0CBAgQIECAAAECBNIJaG7SORlFgAABAgQIECBAgEDJBTQ3JS+Q9AgQIECAAAECBAgQSCeguUnnZBQBAgQIECBAgAABAiUX0NyUvEDSI0CAAAECBAgQIEAgnYDmJp2TUQQIECBAgAABAgQIlFxAc1PyAkmPAAECBAgQIECAAIF0ApqbdE5GESBAgAABAgQIECBQcgHNTckLJD0CBAgQIECAAAECBNIJaG7SORlFgAABAgQIECBAgEDJBTQ3JS+Q9AgQIECAAAECBAgQSCeguUnnZBQBAgQIECBAgAABAiUX0NyUvEDSI0CAAAECBAgQIEAgnYDmJp2TUQQIECBAgAABAgQIlFxAc1PyAkmPAAECBAgQIECAAIF0ApqbdE5GESBAgAABAgQIECBQcgHNTckLJD0CBAgQIECAAAECBNIJzEw3zCgCBAgQIECg2QKjx46F/v7+QtPo6ekJHR0dhc4hOAECBIoS0NwUJSsuAQIECBDIWeDI4aGwftNzYc78vpwj/0+44UMHwoN3Xxd6e3sLiS8oAQIEihbQ3BQtLD4BAgQIEMhRYM787jBvweIcIwpFgACB6gg456Y6tbQSAgQIECBAgAABAm0toLlp6/JbPAECBAgQIECAAIHqCGhuqlNLKyFAgAABAgQIECDQ1gKam7Yuv8UTIECAAAECBAgQqI6A5qY6tbQSAgQIECBAgAABAm0toLlp6/JbPAECBAgQIECAAIHqCGhuqlNLKyFAgAABAgQIECDQ1gKam7Yuv8UTIECAAAECBAgQqI6A5qY6tbQSAgQIECBAgAABAm0toLlp6/JbPAECBAgQIECAAIHqCGhuqlNLKyFAgAABAgQIECDQ1gKam7Yuv8UTIECAAAECBAgQqI6A5qY6tbQSAgQIECBAgAABAm0toLlp6/JbPAECBAgQIECAAIHqCGhuqlNLKyFAgAABAgQIECDQ1gKam7Yuv8UTIECAAAECBAgQqI6A5qY6tbQSAgQIECBAgAABAm0toLlp6/JbPAECBAgQIECAAIHqCGhuqlNLKyFAgAABAgQIECDQ1gKam7Yuv8UTIECAAAECBAgQqI7AzFpL2blzZ/jd3/3d8PM///NhdHQ0XHTRReH9739/uPXWW5PHCxcuDBs2bAizZs2qFcp2AgQIECBAgAABAgQIFCZQs7mJM1922WVh48aNE0msW7curF27NlxxxRXh3nvvDVu3bg3XXHPNxHZ3CBAgQIAAAQIECBAg0GiBVIelxU9sjr/FT3NWrlyZPBW/7tix4/jN7hMgQIAAAQIECBAgQKDhAqk+uenr6wsf+tCHwqFDh8KHP/zh8NJLL00chtbV1RWGhoYanrgJCRAgQIAAAQIECBAgcLxAzebmggsuCDfeeGN4+9vfHn70ox+F66+/PvzsZz+biDH5U52JDZPu7N69e9IzHraSgPq1UrVOzlX9TjZplWfULt9KDQwM5BuwgtH27NkTDh8+XMGVZVuSn71sXmUbrX5lq0jj8qnZ3JxzzjlJYxNTOu+888LZZ58d4gvf0aNHw+zZs8Pg4GDo7u6umfGyZctqjjGgnALxBUL9ylmbNFmpXxqlco5Ru/zr0tnZGcIT+/MPXKGIS5cuDb29vRVaUfal+NnLblamPdSvTNXInkusXz23mufc/M3f/E3YvHlzMkc8/OzgwYPhqquuCtu2bUue2759e1ixYkU9OdiXAAECBAgQIECAAAECdQvU/ORm1apV4ZZbbglf+9rXksPRPvnJT4Y3vOEN4eMf/3h47LHHwqJFi8Lq1avrTkQAAgQIECBAgAABAgQI1CNQs7mZO3duuP/++0+aY/zTnJM2eIIAAQIECBAgQIAAAQJNEKh5WFoTcjIlAQIECBAgQIAAAQIEMgtobjKT2YEAAQIECBAgQIAAgTIKaG7KWBU5ESBAgAABAgQIECCQWUBzk5nMDgQIECBAgAABAgQIlFFAc1PGqsiJAAECBAgQIECAAIHMApqbzGR2IECAAAECBAgQIECgjAKamzJWRU4ECBAgQIAAAQIECGQW0NxkJrMDAQIECBAgQIAAAQJlFNDclLEqciJAgAABAgQIECBAILOA5iYzmR0IECBAgAABAgQIECijgOamjFWREwECBAgQIECAAAECmQU0N5nJ7ECAAAECBAgQIECAQBkFNDdlrIqcCBAgQIAAAQIECBDILKC5yUxmBwIECBAgQIAAAQIEyiiguSljVeREgAABAgQIECBAgEBmAc1NZjI7ECBAgAABAgQIECBQRgHNTRmrIicCBAgQIECAAAECBDILaG4yk9mBAAECBAgQIECAAIEyCmhuylgVOREgQIAAAQIECBAgkFlAc5OZzA4ECBAgQIAAAQIECJRRQHNTxqrIiQABAgQIECBAgACBzAKam8xkdiBAgAABAgQIECBAoIwCmpsyVkVOBAgQIECAAAECBAhkFtDcZCazAwECBAgQIECAAAECZRTQ3JSxKnIiQIAAAQIECBAgQCCzgOYmM5kdCBAgQIAAAQIECBAoo4DmpoxVkRMBAgQIECBAgAABApkFNDeZyexAgAABAgQIECBAgEAZBTQ3ZayKnAgQIECAAAECBAgQyCyguclMZgcCBAgQIECAAAECBMoooLkpY1XkRIAAAQIECBAgQIBAZgHNTWYyOxAgQIAAAQIECBAgUEYBzU0ZqyInAgQIECBAgAABAgQyC2huMpPZgQABAgQIECBAgACBMgpobspYFTkRIECAAAECBAgQIJBZQHOTmcwOBAgQIECAAAECBAiUUUBzU8aqyIkAAQIECBAgQIAAgcwCmpvMZHYgQIAAAQIECBAgQKCMApqbMlZFTgQIECBAgAABAgQIZBbQ3GQmswMBAgQIECBAgAABAmUU0NyUsSpyIkCAAAECBAgQIEAgs4DmJjOZHQgQIECAAAECBAgQKKOA5qaMVZETAQIECBAgQIAAAQKZBTQ3mcnsQIAAAQIECBAgQIBAGQU0N2WsipwIECBAgAABAgQIEMgsoLnJTGYHAgQIECBAgAABAgTKKKC5KWNV5ESAAAECBAgQIECAQGYBzU1mMjsQIECAAAECBAgQIFBGAc1NGasiJwIECBAgQIAAAQIEMgtobjKT2YEAAQIECBAgQIAAgTIKaG7KWBU5ESBAgAABAgQIECCQWUBzk5nMDgQIECBAgAABAgQIlFFAc1PGqsiJAAECBAgQIECAAIHMApqbzGR2IECAAAECBAgQIECgjAKamzJWRU4ECBAgQIAAAQIECGQWSNXcvPzyy+Hyyy8PX/rSl8L+/fvD2rVrw5o1a8LNN98cXnnllcyT2oEAAQIECBAgQIAAAQJ5C6Rqbj7/+c+H17zmNcncGzduTJqbhx56KJx//vlh69ateeckHgECBAgQIECAAAECBDIL1GxufvjDH4b431ve8pYwOjoadu3aFVauXJlMFL/u2LEj86R2IECAAAECBAgQIECAQN4CNZube+65J9x+++0T8x45ciTMmjUredzV1RWGhoYmtrlDgAABAgQIECBAgACBZgnMnGrieI7NpZdeGhYvXnzKYfGTnLS33bt3px1qXAkF1K+ERcmQkvplwCrZULXLtyADAwP5BqxgtD179oTDhw9XcGXZluRnL5tX2UarX9kq0rh8pmxuvvnNb4Z9+/aFb3zjG2FwcDD5xGbOnDnh6NGjYfbs2clz3d3dqbJdtmxZqnEGlU8gvkCoX/nqkjYj9UsrVb5xapd/TTo7O0N4Yn/+gSsUcenSpaG3t7dCK8q+FD972c3KtIf6laka2XOJ9avnNmVzc++9907E/tznPhfOPffc8NRTT4Vt27aFX/3VXw3bt28PK1asmBjjDgECBAgQIECAAAECBJolUPOcm8mJ3XTTTckloeOloF944YWwevXqyUM8JkCAAAECBAgQIECAQMMFpvzk5vhsbrzxxomHmzdvnrjvDgECBAgQIECAAAECBMogkPmTmzIkLQcCBAgQIECAAAECBAhMFtDcTBbxmAABAgQIECBAgACBlhTQ3LRk2SRNgAABAgQIECBAgMBkAc3NZBGPCRAgQIAAAQIECBBoSQHNTUuWTdIECBAgQIAAAQIECEwW0NxMFvGYAAECBAgQIECAAIGWFNDctGTZJE2AAAECBAgQIECAwGQBzc1kEY8JECBAgAABAgQIEGhJAc1NS5ZN0gQIECBAgAABAgQITBbQ3EwW8ZgAAQIECBAgQIAAgZYU0Ny0ZNkkTYAAAQIECBAgQIDAZAHNzWQRjwkQIECAAAECBAgQaEkBzU1Llk3SBAgQIECAAAECBAhMFtDcTBbxmAABAgQIECBAgACBlhTQ3LRk2SRNgAABAgQIECBAgMBkAc3NZBGPCRAgQIAAAQIECBBoSQHNTUuWTdIECBAgQIAAAQIECEwW0NxMFvGYAAECBAgQIECAAIGWFNDctGTZJE2AAAECBAgQIECAwGSBmZOf8JgAAQIECJxKYGRkJPT19Z1qUy7Pxfjx1tHRkUu8UwXp7+8/1dOe+1+B0WPHQiOMenp6Cq2zghIg0L4Cmpv2rb2VEyBAIJNAbGzWrnskzJnfnWm/tIMP7ns6nNXZVVj8mEeco+vci9Om1HbjjhweCus3PTdWg+Ka2OFDB8KDd18Xent7287XggkQKF5Ac1O8sRkIECBQGYHY2MxbsLiQ9QwfGkwam6Lix6TjHG5TCxRZ46lntpUAAQL1Czjnpn5DEQgQIECAAAECBAgQKIGA5qYERZACAQIECBAgQIAAAQL1C2hu6jcUgQABAgQIECBAgACBEghobkpQBCkQIECAAAECBAgQIFC/gOamfkMRCBAgQIAAAQIECBAogYDmpgRFkAIBAgQIECBAgAABAvULaG7qNxSBAAECBAgQIECAAIESCGhuSlAEKRAgQIAAAQIECBAgUL+A5qZ+QxEIECBAgAABAgQIECiBgOamBEWQAgECBAgQIECAAAEC9QvMrD+ECAQIECBAgACBdAKjx46F/v7+dIOnOaqnpyd0dHRMc2+7ESDQygKam1auntwJECBAgECLCRw5PBTWb3ouzJnfV0jmw4cOhAfvvi709vYWEl9QAgTKLaC5KXd9ZEeAAAECBConMGd+d5i3YHHl1mVBBAg0X8A5N82vgQwIECBAgAABAgQIEMhBQHOTA6IQBAgQIECAAAECBAg0X0Bz0/wayIAAAQIECBAgQIAAgRwENDc5IApBgAABAgQIECBAgEDzBTQ3za+BDAgQIECAAAECBAgQyEFAc5MDohAECBAgQIAAAQIECDRfQHPT/BrIgAABAgQIECBAgACBHAQ0NzkgCkGAAAECBAgQIECAQPMFNDfNr4EMCBAgQIAAAQIECBDIQUBzkwOiEAQIECBAgAABAgQINF9Ac9P8GsiAAAECBAgQIECAAIEcBDQ3OSAKQYAAAQIECBAgQIBA8wU0N82vgQwIECBAgAABAgQIEMhBQHOTA6IQBAgQIECAAAECBAg0X0Bz0/wayIAAAQIECBAgQIAAgRwENDc5IApBgAABAgQIECBAgEDzBTQ3za+BDAgQIECAAAECBAgQyEFAc5MDohAECBAgQIAAAQIECDRfQHPT/BrIgAABAgQIECBAgACBHARm1orx0ksvhdtvvz0cPHgwHD16NNxwww3hDW94Q7j11lvD6OhoWLhwYdiwYUOYNWtWrVC2EyBAgAABAgQIECBAoDCBms3N17/+9XDJJZeE973vfeHZZ58N73nPe8Kb3vSmsGbNmnDllVeGe++9N2zdujVcc801hSUpMAECBAgQIECAAAECBGoJ1Dws7R3veEfS2MRAsbl5/etfH3bt2hVWrVqVxF65cmXYsWNHrXlsJ0CAAAECBAgQIECAQKECNT+5GZ89fjJz4MCB8Kd/+qfhve9978RhaF1dXWFoaGh8mK8ECBAgQIAAAQIECBBoikDq5mbLli3he9/7XvjYxz6WnGsznm087ybNbffu3WmGGVNSAfUraWFSpqV+KaFKOKxMtRsYGCihkJQInCywZ8+ecPjw4ZM3ZHimTD97GdI29H8F1K99vxVqNjd79+4N8dOZ173udcmFBI4dOxbmzp2bXFxg9uzZYXBwMHR3d9cUXLZsWc0xBpRTIL5AqF85a5MmK/VLo1TOMWWrXWdnZwhP7C8nlqwIHCewdOnS0Nvbe9wz2e6W7WcvW/ZGq19rfw/E+tVzq3nOTTy/ZvPmzckczz33XBgeHg7Lly8P27ZtS57bvn17WLFiRT052JcAAQIECBAgQIAAAQJ1C9T85Obaa68Nd9xxR/it3/qt8PLLL4e77rorLFmyJNx2223hscceC4sWLQqrV6+uOxEBCBAgQIAAAQIECBAgUI9AzebmzDPPDJ/97GdPmmP805yTNniCAAECBAgQIECAAAECTRCoeVhaE3IyJQECBAgQIECAAAECBDILaG4yk9mBAAECBAgQIECAAIEyCmhuylgVOREgQIAAAQIECBAgkFlAc5OZzA4ECBAgQIAAAQIECJRRQHNTxqrIiQABAgQIECBAgACBzAKam8xkdiBAgAABAgQIECBAoIwCmpsyVkVOBAgQIECAAAECBAhkFtDcZCazAwECBAgQIECAAAECZRTQ3JSxKnIiQIAAAQIECBAgQCCzgOYmM5kdCBAgQIAAAQIECBAoo4DmpoxVkRMBAgQIECBAgAABApkFNDeZyexAgAABAgQIECBAgEAZBTQ3ZayKnAgQIECAAAECBAgQyCyguclMZgcCBAgQIECAAAECBMooMLOMScmJAAECBLILjIyMhL6+vuw7ptyjv78/5UjDCBAgQIBAcwQ0N81xNysBAgRyF4iNzdp1j4Q587tzjx0DHtz3dOg69+JCYgtKgAABAgTyENDc5KEoBgECBEoiEBubeQsWF5LN8KHBQuIKSoAAAQIE8hJwzk1ekuIQIECAAAECBAgQINBUAc1NU/lNToAAAQIECBAgQIBAXgKam7wkxSFAgAABAgQIECBAoKkCmpum8pucAAECBAgQIECAAIG8BDQ3eUmKQ4AAAQIECBAgQIBAUwU0N03lNzkBAgQIECBAgAABAnkJaG7ykhSHAAECBAgQIECAAIGmCmhumspvcgIECBAgQIAAAQIE8hLQ3OQlKQ4BAgQIECBAgAABAk0VmNnU2U1OgAABAgQIEMhRYPTYsdDf319XxIGBgdDZ2XnaGD09PaGjo+O0220gQKB5Apqb5tmbmQABAgQIEMhZ4MjhobB+03Nhzvy++iI/sf+U+w8fOhAevPu60Nvbe8rtniRAoLkCmpvm+pudAAECBAgQyFlgzvzuMG/B4pyjCkeAQCsIOOemFaokRwIECBAgQIAAAQIEagpobmoSGUCAAAECBAgQIECAQCsIaG5aoUpyJECAAAECBAgQIECgpoDmpiaRAQQIECBAgAABAgQItIKA5qYVqiRHAgQIECBAgAABAgRqCmhuahIZQIAAAQIECBAgQIBAKwhoblqhSnIkQIAAAQIECBAgQKCmgOamJpEBBAgQIECAAAECBAi0goDmphWqJEcCBAgQIECAAAECBGoKaG5qEhlAgAABAgQIECBAgEArCMxshSTlSIAAAQIECBAog8DosWOhv7+/0FR6enpCR0dHoXMITqCqApqbqlbWuggQIECAAIHcBY4cHgrrNz0X5szvyz12DDh86EB48O7rQm9vbyHxBSVQdQHNTdUrbH0ECBAgQIBArgJz5neHeQsW5xpTMAIE8hFwzk0+jqIQIECAAAECBAgQINBkAc1NkwtgegIECBAgQIAAAQIE8hHQ3OTjKAoBAgQIECBAgAABAk0W0Nw0uQCmJ0CAAAECBAgQIEAgHwHNTT6OohAgQIAAAQIECBAg0GQBzU2TC2B6AgQIECBAgAABAgTyEdDc5OMoCgECBAgQIECAAAECTRbQ3DS5AKYnQIAAAQIECBAgQCAfAc1NPo6iECBAgAABAgQIECDQZAHNTZMLYHoCBAgQIECAAAECBPIR0Nzk4ygKAQIECBAgQIAAAQJNFtDcNLkApidAgAABAgQIECBAIB8BzU0+jqIQIECAAAECBAgQINBkgZlp5t+wYUN46qmnwsjISPjgBz8YLrnkknDrrbeG0dHRsHDhwhC3z5o1K00oYwgQIECAAAECBAgQIFCIQM3m5sknnwx9fX1hy5Yt4fnnnw+rV68Ov/RLvxTWrFkTrrzyynDvvfeGrVu3hmuuuaaQBAUlQIAAAQIECBAgQIBAGoGah6VddtllYePGjUmsV7/61WF4eDjs2rUrrFq1Knlu5cqVYceOHWnmMoYAAQIECBAgQIAAAQKFCdRsbmbMmBFe9apXJQk8/vjj4a1vfWs4cuTIxGFoXV1dYWhoqLAEBSZAgAABAgQIECBAgEAagZqHpY0H+epXv5ocfvbAAw+EK664Yvzp5LybiQdT3Nm9e/cUW20qu4D6lb1CU+enflP7lHlrltoNDAyUeSlyI0AgpcCePXvC4cOHU4427FQCWV47T7W/51pXIFVz861vfSts2rQpxMZm3rx5Ye7cueHo0aNh9uzZYXBwMHR3d9cUWLZsWc0xBpRTIL5AqF85a5MmK/VLo1TOMVlr19nZGcIT+8u5GFkRIJBaYOnSpaG3tzf1eANPFMj62nni3h41WyDWr55bzcPSXnzxxfCZz3wm3H///SH5h3NstuXLl4ft27cn88avK1asqCcH+xIgQIAAAQIECBAgQKBugZqf3Pzd3/1dcpW0j370o8khaPEcnHvuuSfceeed4dFHHw2LFi1KrqBWdyYCECBAgAABAgQIECBAoA6Bms3N1VdfHeJ/k2+bN2+e/JTHBAgQIECAAAECBAgQaJpAzcPSmpaZiQkQIECAAAECBAgQIJBBQHOTActQAgQIECBAgAABAgTKK6C5KW9tZEaAAAECBAgQIECAQAYBzU0GLEMJECBAgAABAgQIECivgOamvLWRGQECBAgQIECAAAECGQQ0NxmwDCVAgAABAgQIECBAoLwCmpvy1kZmBAgQIECAAAECBAhkENDcZMAylAABAgQIECBAgACB8gpobspbG5kRIECAAAECBAgQIJBBQHOTActQAgQIECBAgAABAgTKK6C5KW9tZEaAAAECBAgQIECAQAYBzU0GLEMJECBAgAABAgQIECivgOamvLWRGQECBAgQIECAAAECGQQ0NxmwDCVAgAABAgQIECBAoLwCmpvy1kZmBAgQIECAAAECBAhkENDcZMAylAABAgQIECBAgACB8gpobspbG5kRIECAAAECBAgQIJBBQHOTActQAgQIECBAgAABAgTKK6C5KW9tZEaAAAECBAgQIECAQAYBzU0GLEMJECBAgAABAgQIECivwMzypiYzAgQIVEtgZGQk9PX1pV7UwMBA6OzsTD2+v78/9VgDCRAgQIBAFQU0N1WsqjURIFBKgdjYrF33SJgzvzt9fk/sTz324L6nQ9e5F6cebyABAgQIEKiagOamahW1HgIESi0QG5t5CxYXkuPwocFC4gpKgAABAgRaRcA5N61SKXkSIECAAAECBAgQIDClgOZmSh4bCRAgQIAAAQIECBBoFQHNTatUSp4ECBAgQIAAAQIECEwpoLmZksdGAgQIECBAgAABAgRaRUBz0yqVkicBAgQIECBAgAABAlMKaG6m5LGRAAECBAgQIECAAIFWEdDctEql5EmAAAECBAgQIECAwJQCmpspeWwkQIAAAQIECBAgQKBVBDQ3rVIpeRIgQIAAAQIECBAgMKWA5mZKHhsJECBAgAABAgQIEGgVgZmtkqg8CRBob4GRkZHQ19dXOEJPT0/o6OgofB4TECBAgAABAvkLaG7yNxWRAIECBGJjs3bdI2HO/O4Cov9PyOFDB8KDd18Xent7C5tDYAIECBAgQKA4Ac1NcbYiEyCQs0BsbOYtWJxzVOEIECBAgACBqgg456YqlbQOAgQIECBAgAABAm0uoLlp828AyydAgAABAgQIECBQFQHNTVUqaR0ECBAgQIAAAQIE2lzAOTdt/g1g+QTyEij6amb9/f15pSoOAQIECBAgUFEBzU1FC2tZBBotUPTVzA7uezp0nXtxo5dlPgIECBAgQKCFBDQ3LVQsqRIou0CRVzMbPjRY9uXLjwABAgQIEGiygHNumlwA0xMgQIAAAQIECBAgkI+A5iYfR1EIECBAgAABAgQIEGiygOamyQUwPQECBAgQIECAAAEC+QhobvJxFIUAAQIECBAgQIAAgSYLaG6aXADTEyBAgAABAgQIECCQj4DmJh9HUQgQIECAAAECBAgQaLKA5qbJBTA9AQIECBAgQIAAAQL5CGhu8nEUhQABAgQIECBAgACBJgtobppcANMTIECAAAECBAgQIJCPgOYmH0dRCBAgQIAAAQIECBBossDMJs9vegIECJRGYPTYsdDf319YPkXGLixpgQkQIECAQAsJaG5aqFhSJUCgWIEjh4fC+k3PhTnz+wqZ6OC+p0PXuRcXEltQAgQIECBAIATNje8CAgQIHCcwZ353mLdg8XHP5Hd3+NBgfsFEIkCAAAECBE4SSHXOzfe///1w+eWXh4cffjgJsH///rB27dqwZs2acPPNN4dXXnnlpMCeIECAAAECBAgQIECAQCMFajY3R44cCX/4h38Yli9fPpHXxo0bk+bmoYceCueff37YunXrxDZ3CBAgQIAAAQIECBAg0AyBms3NmWeeGb7whS+E7u7uifx27twZVq5cmTyOX3fs2DGxzR0CBAgQIECAAAECBAg0Q6Bmc3PGGWeE2bNnn5Bb/DRn1qxZyXNdXV1haGjohO0eECBAgAABAgQIECBAoNECdV9QYHR0NFXOu3fvTjXOoHIKqF8565I2q0bUb2BgIG06xhEgQIDAFAJ79uwJhw8fnmKETbUEGvHvXq0cbG+OwLSam7lz54ajR48mn+gMDg6ecMja6ZaxbNmy023yfMkF4guE+pW8SFOk16j6dXZ2hvDE/ikysYkAAQIE0ggsXbo09Pb2phlqzCkEGvXv3imm9lQOAvU2pjUPSztVjvHiAtu3b082xa8rVqw41TDPESBAgAABAgQIECBAoGECNT+52bt3b/j0pz8dnn322TBz5sykqfmjP/qjcPvtt4dHH300LFq0KKxevbphCZuIAAECBAgQIECAAAECpxKo2dwsWbIkPPjggyftu3nz5pOe8wQBAgQIECBAgAABAgSaJVCzuWlWYuYlQIAAAQIECLSbwOixY6G/v7/QZff09ISOjo5C5xCcQLMENDfNkjcvAQIECBAgQGCSwJHDQ2H9pufCnPl9k7bk83D40IHw4N3XuWBBPpyilFBAc1PCokiJAAECBAgQaF+BOfO7w7wFi9sXwMoJ1CEwraul1TGfXQkQIECAAAECBAgQIFCIgOamEFZBCRAgQIAAAQIECBBotIDmptHi5iNAgAABAgQIECBAoBABzU0hrIISIECAAAECBAgQINBoAc1No8XNR4AAAQIECBAgQIBAIQKam0JYBSVAgAABAgQIECBAoNECmptGi5uPAAECBAgQIECAAIFCBDQ3hbAKSoAAAQIECBAgQIBAowU0N40WNx8BAgQIECBAgAABAoUIaG4KYRWUAAECBAgQIECAAIFGC8xs9ITmI0CgOQIjIyOhr6+vsMn7+/sLiy0wAQIECBAgQCCNgOYmjZIxBCogEBubteseCXPmdxeymoP7ng5d515cSGxBCRAgQIAAAQJpBDQ3aZSMIVARgdjYzFuwuJDVDB8aLCSuoAQIECBAgACBtALOuUkrZRwBAgQIECBAgAABAqUW0NyUujySI0CAAAECBAgQIEAgrYDmJq2UcQQIECBAgAABAgQIlFpAc1Pq8kiOAAECBAgQIECAAIG0Ai4okFbKOAIECBAgQIAAgZoCRf/pgZhAT09P6OjoqJmLAe0noLlpv5pbMQECBAgQIECgMIGi//TA8KED4cG7rwu9vb2FrUHg1hXQ3LRu7WROgAABAgQIECilQJF/eqCUC5ZUaQScc1OaUkiEAAECBAgQIECAAIF6BDQ39ejZlwABAgQIECBAgACB0ghobkpTCokQIECAAAECBAgQIFCPgHNu6tGzLwECBAgQIECghQRGjx0L/f39hWZcdPxCkxe85QU0Ny1fQgsgQIAAAQIECKQTOHJ4KKzf9FyYM78v3Q7TGHVw39Oh69yLp7GnXQjUL6C5qd9QBAIECBAgQIBAywgUfSWz4UODLWMh0eoJOOemejW1IgIECBAgQIAAAQJtKaC5acuyWzQBAgQIECBAgACB6globqpXUysiQIAAAQIECBAg0JYCzrlpy7JbNAECBAgQIECgNQVqXfFtYGAgdHZ2TntxIyMjyb4dHR3TjjHVjkXHH5+7p6cnFLWG8TnK+FVzU8aqyIkAAQIECBAgQOCUAqmu+PbE/lPum+bJeLW3szq7xq4o151meOYxRcePCQ0fOhAevPu60Nvbmzm/Vt9Bc9PqFZQ/AQIECBAgQKDNBIq84lu82lsrx2+zb4WTluucm5NIPEGAAAECBAgQIECAQCsKaG5asWpyJkCAAAECBAgQIEDgJAHNzUkkniBAgAABAgQIECBAoBUFNDetWDU5EyBAgAABAgQIECBwkoALCpxE4gkCJwvEyzb29fWdvCHnZ9r1so05MwpHgAABAgTaWqDW5bLzwCnr7yyamzyqK0blBWJjs3bdI4VdFjICtvNlGyv/DWSBBAgQIECggQKpLpddRz5l/p1Fc1NHYe3aXgJFXhayvSStlgABAgQIEChaoF1/b3HOTdHfWeITIECAAAECBAgQINAQAc1NQ5hNQoAAAQIECBAgQIBA0QKam6KFxSdAgAABAgQIECBAoCECzrlpCLNJir7aWIwfbx0dHYVg9/f3FxL3+KBFXdlkYGAgdHZ2hkas4fj1uE+AAAECBAgQaLSA5qbR4m06X9FXGzu47+lwVmdXYVczi/G7zr240OoVemWTJ/aHRqyhUCDBCRAgQIAAAQI1BDQ3NYBszk+gyKt2DB8aTBqbeQsW55fwcZFi/EbcijZqxBrMQYAAAQIECBBoloBzbpolb14CBAgQIECAAAECBHIV0NzkyikYAQIECBAgQIAAAQLNEtDcNEvevAQIECBAgAABAgQI5CqgucmVUzACBAgQIECAAAECBJol0BYXFHjxxRfDR9Z9Npw1b0Fhziv+z4Xh2ne/s7D4AhMgQIAAAQIECBAgMLVAWzQ3L7/8cnjmxXnhzNkXTK1Rx9Zn9/+4jr3tSoAAAQIECBAgQIBAvQIOS6tX0P4ECBAgQIAAAQIECJRCYNqf3Nx9993hO9/5TpgxY0a44447wiWXXFKKBUmCAAECBAgQIECAAIH2FJhWc7Nr164wMDAQtmzZEuJfnr/zzjuT++1JaNUECBAgQIAAAQIECJRBYFqHpX37298Ob3vb25L8e3p6wgsvvBB++tOflmE9ciBAgAABAgQIECBAoE0FpvXJzXPPPReWLl06QbZgwYIQn5s7d+7Ec2W609HREY4d/r9hRsfLhaV1cKgjfP/73y8sfjMDx0/pOjs760qhv78/DB86UFeMqXY+cjhe0GHGVEPq2lZ0/Jhc0XOIX/tbgNHURq3u4+ds6vo2wqcRc7T692mr56/G7fFzVuTvdLUFpx4xY3TsNvWQk7euX78+vPWtbw2rVq1KNl533XUhnoNzwQWnvhrZ7t27Tw7iGQIECBAgQIAAAQIECEwSWLZs2aRn0j+c1ic33d3dySc149McOHAgLFy4cPzhSV/rSfCkYJ4gQIAAAQIECBAgQIDAKQSmdc7Nm9/85rB9+/Yk3N69e8M555wT5syZc4rwniJAgAABAgQIECBAgEBjBKb1yc2ll14alixZEq655poQz2eJh6m5ESBAgAABAgQIECBAoJkC0zrnppkJm5sAAQIECBAgQIAAAQKnEpjWYWmnCuQ5AgQIECBAgAABAgQINFNAc9NMfXMTIECAAAECBAgQIJCbgOYmN0qBCBAgQIAAAQIECBBopkBuzc3Pfvaz8LGPfSzEv3mzdu3asG/fvpPW9eUvfzn8+q//evjN3/zN8Pjjjyfb42Wk3//+94frr78+2e8///M/T9rPE8ULTLd+MbMHHnggvOtd7wq/8Ru/Efbs2VN8smY4QaCe2sVA8Q/wXnbZZWHXrl0nxPWgMQLTrd/IyEi4/fbbk9fceHGXp556qjEJmyURiH/bLbpfe+214bvf/e4JKjt27EheD+P2z3/+8xPbptpnYpA7DRGYqhanq9+GDRuSmsd/6/7hH/6hIXma5NQC06lfjPTyyy+Hyy+/PHzpS186dWDPFi4wndrF/uHXfu3Xwrvf/e7wzW9+s3aO8Y945nH7q7/6q9FPfepTSah//ud/Hv3oRz96Qtjh4eHRK6+8cvTFF18cfemll0bf+c53jh46dGj005/+9Oijjz6ajB37x3n0fe973wn7edAYgenW7wc/+MHo2Dfb6LFjx0bHGtPRP/mTP2lMwmaZEJhu7cYD3HbbbaNXXXXV6M6dO8ef8rWBAtOt39atW0c/+clPJpnGn8OxN44amHV7TxV/Vn7nd34nQfiv//qv0bE37E4Aecc73jG6f//+5HVx7A2/0Tim1j4nBPCgUIFatThV/f71X/919IMf/GCS109+8pPRsT9kXmiOgp9eYDr1G4/2x3/8x8lrZXzddWu8wHRqF3/errjiitHYRwwNDY3+wR/8Qc3Ec/vk5tvf/nZ429velnRTv/zLv3zSu4jf+c53wi/8wi+EuXPnhjPPPDO86U1vCrt37w6vfe1rw/PPP5/sN9bsJI9rt2RG5C0w3fp94xvfCG9/+9vDjBkzwsUXXxxuvPHGvFMTr4bAdGo3/i7/2D/YYd68eaG3t7fGLDYXJTDd+sV3seInN/EWX0fj66dbYwSOr1lPT0944YUXwk9/+tNk8h/96EfhNa95TfL33+Lr4lve8pYQx0+1T2OyNsu4wFS1OFX94utk/HR748aNSYhXv/rV4ciRI2HsN6zxkL42UGA69Yvp9fX1hR/+8IfJz2QD0zXVcQLTqV38JDX+fc2zzjornH322WHsg5TjIp76bm7NTTy0Jf4DG2/xBf2MM84I8XCL8dvx2+NzcWx87rd/+7fD3/7t3ya/IMe/l3PTTTeN7+JrAwWOr0/a+o110OGZZ54Jzz77bHJo4Xve857wve99r4FZmyoKTLd2r7zySrjvvvvCzTffDLKJAtOtX/wbY7Nnz04y/4u/+Isw9ml4E1fRXlMfX7O48gULFiQ/h/H+5G3x37r4Wjn5+eP3ifu5NU5gqlpM3hbrFw+fj/8uvupVr0qS/OIXv5j8ghyfc2u8wOQaHf+zNHnbeP1ilvGwwvE3hBqftRmjwOT6pKld/D0zvplwww03hDVr1iRvFNXSnNYf8Yw/2PGcmfEf7PjuxX/8x3+cMNfYYUonPJ78YPwdj3i+xthHwGHsI/7kOLp77rknjB3aNHm4xzkK5FW/WP9Yx1jrL3zhC8kncb//+78/cT5VjikL9b8CedUuhtu0aVO4+uqrk09u4uPxn8l4360YgTzrN57hww8/HOK5ivfff//4U742WGCqn53TbTvd8w1O3XRjAlPVYvK2r371q+Ev//Ivk3NN4ZVDYHKNjs9qfFs8xyb+AfrFixcnm8efP36s+40XmKoO49vi13iEVzx/MZ7PH8/Rj0cNTXWbVnMTT6aL/x1/W7duXdKRXXTRRROf2Myc+f/Dd3d3J+9eje8zODiYfKN95StfmXjnePny5eGuu+4aH+JrQQJ51m/hwoXhwgsvTDJdtmxZ8ilOQWkLOyaQZ+3GjjkO3/rWt8JDDz0U/vu//zs5KToedhEPs3ErRiDP+sUMY7P0j//4j8mLfvwkx60xAvHfs/gO5PgtvrMfXwvj7VT/1sXnZs2addp9xuP42hiB6dQvZhZfL+ObQvFN2Xg4r1tzBKZTv3/6p38K8ZDD+Evx2PlwyekRr3vd60L8vdOtcQLTqd2cOXOSfiG+oX7eeeclp7f8+Mc/njha7FTZ53ZYWjwebtu2bckcX//618Mv/uIvnjDfG9/4xuRKWmMXFEiOTf63f/u3EH8ZvuCCC8K///u/J2Pjpz8/93M/d8J+HjRGYLr1W7FiRfKCH7OMx7PGFwu3xgpMt3aPPPJI2LJlSxi7oEcYOzk2fOITn9DYNLZ0yWzTrV/8hzrW7nOf+1zyi3MTUm/bKWPNtm/fnqx/7969yfk18R/geIvvDMfzb+LhuvHQ7Nh8/sqv/EpyzPjp9kl29L+GCUynfvF3l8985jPJJ6SdnZ0Ny9VEJwtMp35jFxJI3gyKr5nxTaYPfehDGpuTaQt/Zjq1i+fxP/nkk8knrGMXFwhjFxaYsrGJi/j/H63UuaR4aNm//Mu/JJcljRcMGLsKWhIxvssRG53Y3Nxyyy3hve99b3I+zkc+8pHknY94ONqdd94Z/v7v/z45zC0e1uTWeIHp1i/WNb4jEi95Gm/xF2S3xgpMt3aNzdJspxOYbv3+/M//PLmIwAc+8IHkRT++q7V58+Zw/Cfmp5vT8/UJxMNblixZkrzuxU/M4vmi8ZPQ+EtvvLBOfB38vd/7vWSSeC5UfBMv/jd5n/qysPd0BaZTv8ceeyw5NGbsSrATP2/xHA5v6E23CtPfbzr1m/5s9sxTYLq1G7vacnIYffx3Lr7e1rrNGDuWzeU+ainZToAAAQIECBAgQIBA6QVyOyyt9CuVIAECBAgQIECAAAEClRbQ3FS6vBZHgAABAgQIECBAoH0ENDftU2srJUCAAAECBAgQIFBpAc1NpctrcQQIECBAgAABAgTaR0Bz0z61tlICBAgQIECAAAEClRbQ3FS6vBZHgAABAgQIECBAoH0ENDftU2srJUCAAAECBAgQIFBpgf8HIrXvai+CdLgAAAAASUVORK5CYII=)\n",
        "\n",
        "The returns of AMC from 2014 through 2016 are unimodal and vaguely normal, so a skewness measure would be relevant."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6Siq7tSt7oG7",
        "colab_type": "text"
      },
      "source": [
        "##Test for Skew\n",
        "\n",
        "Find the skew of the historical returns of AMC between 2014 to 2016."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tyXSHnc66sCW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "start = '2014-01-01'\n",
        "end = '2016-01-01'\n",
        "pricing = get_pricing('AMC', fields='price', start_date=start, end_date=end)\n",
        "returns = pricing.pct_change()[1:]\n",
        "\n",
        "print ('Skew of AMC:', stats.skew(returns))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MRDWjzbv7wsk",
        "colab_type": "text"
      },
      "source": [
        "##Out of Sample Test\n",
        "\n",
        "Find the skew of the historical retunrs of AMC from the first half of 2016 to determine if the skew from part b holds outside of the original sample."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mSaYu1HY7gzo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "start = '2016-01-01'\n",
        "end = '2016-07-01'\n",
        "out_pricing = get_pricing('AMC', fields='price', start_date=start, end_date=end)\n",
        "out_returns = out_pricing.pct_change()[1:]\n",
        "\n",
        "print ('Skew of AMC:', stats.skew(out_returns))\n",
        "print ('The negative skew of AMC between 2014 and 2016 did not hold outside of the orignal sample, meaning the skew of AMC might be volatile and not reliable enough for predictions about future behavior.')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8T2cuwTI8Aui",
        "colab_type": "text"
      },
      "source": [
        "#Rolling Skew\n",
        "\n",
        "Plot the rolling skew of AMC using the `pd.rolling_skew` function."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EERZP1Jv77sF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "AMC = get_pricing('AMC', fields='price', start_date='2015-01-01', end_date='2017-01-01')\n",
        "\n",
        "rolling_skew = AMC.rolling(window=60,center=False).skew()\n",
        "plt.plot(rolling_skew)\n",
        "plt.xlabel('Day')\n",
        "plt.ylabel('60-day Rolling Skew')\n",
        "print (\"This confirms our result from part c, that the skew is too volatile to use it to make predictions outside of the sample.\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k961UCYD8SKY",
        "colab_type": "text"
      },
      "source": [
        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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zQH0_LKw8YEf",
        "colab_type": "text"
      },
      "source": [
        "This confirms our result from part c, that the skew is too volatile to use it to make predictions outside of the sample."
      ]
    }
  ]
}