The_MA_Model.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.7"
    },
    "colab": {
      "name": "The MA Model.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/RegaipKURT/Machine-Learning-Python/blob/master/The_MA_Model.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9XmyPh4ES8si",
        "colab_type": "text"
      },
      "source": [
        "## Importing the relevant packages"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QcrhqkCIS8sj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import statsmodels.graphics.tsaplots as sgt\n",
        "import statsmodels.tsa.stattools as sts\n",
        "from statsmodels.tsa.arima_model import ARMA\n",
        "from scipy.stats.distributions import chi2 \n",
        "from math import sqrt\n",
        "import seaborn as sns\n",
        "sns.set()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sCpO3PuzS8sr",
        "colab_type": "text"
      },
      "source": [
        " "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Iv80gvJ-S8ss",
        "colab_type": "text"
      },
      "source": [
        "## Importing the Data and Pre-processing "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "inV2X4vnS8ss",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "raw_csv_data = pd.read_csv(\"Index2018.csv\") \n",
        "df_comp=raw_csv_data.copy()\n",
        "df_comp.date = pd.to_datetime(df_comp.date, dayfirst = True)\n",
        "df_comp.set_index(\"date\", inplace=True)\n",
        "df_comp=df_comp.asfreq('b')\n",
        "df_comp=df_comp.fillna(method='ffill')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b6OgwVbnS8sx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_comp['market_value']=df_comp.ftse"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lpPff52vS8s1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "del df_comp['spx']\n",
        "del df_comp['dax']\n",
        "del df_comp['ftse']\n",
        "del df_comp['nikkei']\n",
        "size = int(len(df_comp)*0.8)\n",
        "df, df_test = df_comp.iloc[:size], df_comp.iloc[size:]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hZ84QexqS8s9",
        "colab_type": "text"
      },
      "source": [
        " "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q2UbAfgjS8s-",
        "colab_type": "text"
      },
      "source": [
        "## The LLR Test"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ozUQrQstS8s_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def LLR_test(mod_1, mod_2, DF = 1):\n",
        "    L1 = mod_1.fit().llf\n",
        "    L2 = mod_2.fit().llf\n",
        "    LR = (2*(L2-L1))    \n",
        "    p = chi2.sf(LR, DF).round(3)\n",
        "    return p"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hfQYmAqoS8tD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L6XeAC78S8tI",
        "colab_type": "text"
      },
      "source": [
        "## Creating Returns"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "Jvot91R9S8tI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df['returns'] = df.market_value.pct_change(1)*100"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ziBYPyDcS8tN",
        "colab_type": "text"
      },
      "source": [
        "## ACF for Returns"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q7EUaxa5S8tN",
        "colab_type": "code",
        "colab": {},
        "outputId": "834bca62-92e7-4bab-b096-a615d445b546"
      },
      "source": [
        "sgt.plot_acf(df.returns[1:], zero = False, lags = 40)\n",
        "plt.title(\"ACF for Returns\", size=24)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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z8v5+CnhjmW0kp9zbgBqlSkFGx7wZk2lvaxkoQQC0t7Vw4Izk1qlLculO6gaXX6WQZXCVgjSfhfM7mT97CmR6IZtlXHjBsHB+Z71DkwakBNHgVKUg+dLpFJ9csgheeYCWrU/xkXNer9KkxFZRFZPUn6oUpFA6nSK1cy2tva+oYVoqohJEg1OVgiSJOkyMLSpBNLhclcLFl30a2jv5yKUXsXB+p6oUZNSpw8TYoxLEGJCrUki99hSLFkzTl1HqQh0mxh4lCBGpCnWYGHuUIESkKnIdJvKpw0RjU4IQkaoY6x0mmrEBXo3UIlIVY7nDRLM2wKsEISJVM1Y7TDRrA7wShIhIGc3aAK8qpjpIlb2oGvlVVyrcaCqVGsb2oyX5ai8qtkwmy/IXunlp43YOmjGZhYdWXp2RzQ6uVy6sZS6YTW9fZsgy5fcx4rDCT0R2yMRS28pksmSy2X1rZXPTM/xxVTcvv7qDA6dP4i8OCY9bxAdn6PbLB59borc/U2QbsGN3L3v7+ofOiNxgKm+vWVIFUQz/eBY+v6xw/eCP17btYdfevkFrzjigg/a2NHt79z0Tra0tTdf+HezY08dwjkuxSIYzM5PJ8uzqLWzZsYa5nR0cNUpVd02XILIEX5Ah0wd9dlLh62zE/CyZbPARzU3LZsMvYha27+4lt2ruZJPNBlvqS+1g6/a9EH5p8+cHq2TD9YbGNXAaKvLJ2tsTfNnWbtox5IQwsO0SXtvVy7bte4ZMj3Myq7YtO4fGlslk+d7PV7Bu0056+jK0t6aZ0zWRC840UqlU2f83SuH/Opz/vXtHL9u27R7xvqD8e1JixbL29PTT0p/lxfXbBk3PZLLceu/zrNu8k96+DG2taWZPm8j7TjusxAknG/FXiX2HJ9fVG/L2XbDifrv62Lp114i2G3w9KvtADuc9nbqnnz//edegaZPHtzHjgA5eWv8apFtpb2th5gEdTJnQxtoalyIymSw/eGAlG7p30duXGWj8H432j6ZLEDt297Jxy67omdmhLwpPtrnXhSf3PeEJet2rO4rue29/dsgHr1r6wqS3fWdPrPXb2tvYuau3miFVTfu4NnbtHhzbynVbWbspOMkB9PRlWLtpJ0+/uIUFc/Ybtdj6+jNDqh6SIJPNkoaB45Ozct3WgeRAOH/95p08t/bPVTtuue9If3/xs3Emm6W/gXoBpdMp3nvqAq7+ly+THt/JuYvfy/xZU0blKn7Vhm0DyQFG9yFQTdkG0deXif7pz//J0tefpT/3kwl+MuFPNpuMq+tm9epru4ec/Hr7Mrz6Wryr+Wah4xZfOp0is30NfZueZMGc/UatSjbqPRut9o+mK0HI2DB9/wm0taYHfXHaWtNM339CHaNKvkY/bplMllUbtvHqa7uZvv+EUbuKr6eo92y0bkBUgpCGNH/WFGZ1Dq4TntXZwfxZU+odWqI18nErrItva00zq7OD9566YEwnicL3bHx766jdgNiUVUzS+HJ1wj1r76Pv1d/zjhMOHvMnimpo5ONWWBff25dhQ/cuVm3YVmbNxpb/ntH9JJeO4kOglCCkYdWrTrjRNepxa+b2k9x7xmtPseiw0bsBUQlCRBpCri4+XyO1nzSiitogzOw84HKgDfiau3+jYP4i4FvAFOAh4FJ37zOzE4FrgXagG/igu79USSwiMrY1cvtJo4pdgjCzOcDVwEnAIuASM3tdwWI3AR9z98MJbl38cDj9ZuBid18U/n1d3DhEkiiTybJy3VYeefoVVq7b2hQjf9ZaI7efNKpKqpjOAO5z9y3uvhO4A3hPbqaZHQRMcPfHwklLgcVmNg643N2Xh9OXA/MqiEMkUXK9be56ZDW/+eMG7npkNT94YKWSRBU0avtJo6qkimk2sCHv9QbgjWXmz3X3vQQlC8wsDXwe+HEFcYgkSqneNqN5l7dIpSpJEGmGjn6VGe58M2sHvhvG8IWR7ryzc1LReV1dxW8gSb22i509Q8diqlRra/AkralTO0ouV25+rfdfSq1iq4ZisVXj/65U4b63reyO7G2zbU/fqMVZ7rjU8rjV+rswGu95Ej9vuX1PmzZ6T+irJEGsBU7Oez0TWF8wf1bUfDObBPwXQQP1Oe4+4kGAurt3RBbZu7oms2lT8VvQt+/urcl4SH3hyJSltj11akftxmIaxv5LqWVslSoVW6X/d6WiYpsyoTXybuUp41tHLc6+vn5aW1vqctxq/V2o9Xue1M9bX18/rS2tbN68PdYwP+l0quSFdeQ6I9/NgF8Bp5tZl5l1AOcCP8vNDHsl7Ql7LAFcANwT/n0TsBJYElY5iYwZud422f5estnswB2/6m0jjSZ2gnD3dcBngPuBZcAt7v47M7vbzI4LFzsfuNbMVgCTgOvM7BjgHOBE4A9mtszM7q7ovxBJEPW2kbGiovsg3P0W4JaCaWfl/f0UgxuuAZ4kzhNxRBpIrrdNZvsaFsz5cPkVmkRusL1tK7uZMqG1KQbba2QarE9ERkWzDrbXyDTUhoiMimYdbK+RKUGIyKho5sH2GpUShIiMCg2213iUIERkVKj7b+NRghCRUZHf/Tez+Q/q/tsA1IupATTyc3gbOXapvoHuv7vXsWDOxfUOR8pQgki4Ru4a2Mixi4iqmBKvkbsGNnLsIqIEkXiN3DWwkWMXEVUxJV6ua2DhyKCN0DWwkWMXGamx2N6mBJFwjfwc3kaOXWQkxmp7m6qYEq6RRwZt5NhFRmKstrcpQTSAej6HN5PJsnLdVh55+hVWrts64ucq6xnCY0+ln4mxaKy2t6mKSYoaq8VmiU+fiWhjtb1NJQgpaqwWmyU+fSaijdVhRJQgpKixWmyW+PSZiDZW29uUIKSoRh99U3Xl1dfon4laGovtbWqDkKIauZuq6spro5E/EzJyKkFIUY1cbFZdeW008mdCRk4JQkpq1GKz6sprp1E/EzJyFVUxmdl5wOVAG/A1d/9GwfxFwLeAKcBDwKXu3mdm84CbgOmAA+e7+45KYhHJN1a7HYqMptglCDObA1wNnAQsAi4xs9cVLHYT8DF3PxxIAR8Op18PXO/uRwBPAFfEjUMkyljtdigymiqpYjoDuM/dt7j7TuAO4D25mWZ2EDDB3R8LJy0FFptZG3BKuPzA9AriEBlCdeUilaukimk2sCHv9QbgjWXmzwWmAdvcva9g+oh8886neW3b3iHT29pb6O3pL7pefybL3t7i8+NqP+RsAG699/miy7S2pukrqBev1vaHs/9SSsVW6bbrGVvrtKMAeHzFqzy+4tVY+69FbNlslj09/fT0ZWhvTTO+vYVUqnrJq/2Qs0mlUjX7vJRafzif1VKxVbLvaqjk81bL2NoPORtSKb508x8gRo/t/aeM4zMffNOI1qkkQaQZHGYKyAxjfuF0CtYblta2FtraWyLnFZv+zDPPkM3CIYceHjn/hZUOwKELbMTzFxx2RMl4K9n2cLZfan6t913J+vU8LuW2X6vYstksr3TvZvfeXiBFOp1iXFsLMzs7BiWJSmKr5XEpt349911u/Ub+vC047AhaW9K0tUWf38ppjbFeJQliLXBy3uuZwPqC+bMi5r8K7GdmLe7eHy6Tv96wXHzWkZE3PnV1TWbTpu2R63zoZ18hk8my+INvi5x/5S+uAWDxh86KNb+UK39xDa2tLTXZdimZTJarH72d9PhOjllwYuQY9eViK6fS41Jq3Upjq2T/tYpt5bqt3LVpNalUUMObzQYl2zcc1sWCOftVJTaAqVM7+POfd40otpFsvxKlYqt03/V4T0cjNoCDDpxKR0uKbIwSRJzq1UraIH4FnG5mXWbWAZwL/Cw3091fAvaY2YnhpAuAe9y9F/g1sCScfiFwTwVxSBG5m8Xa555G6/RjueuR1fzggZW6o7jO1AVXGkXsBOHu64DPAPcDy4Bb3P13Zna3mR0XLnY+cK2ZrQAmAdeF0z9K0OvpWYJSyOVx45DicjeLpVraSKVSulksITRchTSKiu6DcPdbgFsKpp2V9/dTDG64zk1/CTi1kn1LeaWuVPOrMmR05brgFg4Doi64zSuTyZKePI/0+E5WrtuamMeVaiymMUw3iyVTrgvuWHt+scSTXxVMupW7HlmdmHHDlCDGMF2pJlc6nWLBnP1UkpNBVcEweNywen8+lCDGMF2piiRfkquClSDGOF2p1sZAnXHHtETVGUvjSXJVsEZzlcQaOAl3Hp2oB/7k1xmnp71B3YelIrmq4FzPtiRVBasEIRWpVe+LJDfc1bvOOKk9XiSeJFcFK0FIbLU8idf7JFxKPeuMk5w4Jb6kVgWrikliq+WNeEm+27ieN7rp5sfGlSv5tXYdk6gq01KUICS2Wp7Ek3y3cT3rjJOcOKW4Rh32RlVMElulvS9K1aUn+R6O/DrjbXv6mDK+ddTqjJPc40WKS3KVaSlKEBJbJSfxcnXp9TwJD0euzriSEVPjSHLilOKSfK9DKUoQElslvS+Gc0VVr5NwkiW5x4sU16glPyUIqUjc3heNekWVBEnt8SLFNWrJTwlC6qJRr6hE4mjUkp8SRJOr15ARjXpFJRJXI5b8lCCaWD1vumrUKyqRZqIE0cTq3fWuEa+oRJqJbpRrYrrpSkRKUYJoYkm+W1lE6k8JookleZhhEak/tUE0saTfrSwi9RU7QZjZPOAmYDrgwPnuvqNgmXbgRuA4YDdwnruvMLNJwLeBI4AUcLW7fz9uLBKf7lYWkWIqqWK6Hrje3Y8AngCuiFjm48BOdz8SuAxYGk7/J2CNuy8ETge+amYzKohFRESqLFaCMLM24BTgjnDSUmBxxKJnAzcDuPtDQFdY8ngQuC6c/iqwBZgZJxYREamNuFVM04Bt7t4Xvt4AzI1YbnY4j/zl3P2XuQlm9l5gHPBMzFhERKQGyiYIM1sMXFsw+Xmg8EkXGYZKFyyXyl8u3Pa/AX+dl2yGpbNzUtF5XV2TI6e3t7fS29vP1KkdkfNbW1sAYs8vpZbbrpZ67rucWsVW6rgP9z2pR2zDFXfd0fg81uq7UMl7moTvYTnTpkWf32qhbIJw99uB2/OnhVVM3WbW4u79wCxgfcTqa8N5L4SvZ+aWM7O/B/4RONPd/zjSwLu7d0Q+jamrazKbNm2PXKenp4/+TLZoY2xfXz9A7Pml9PX109raUpNtV0OSG6lrGVup4z6c96ResQ1HJbHV+vNYKrZK913Je1rue1pv+01uZ/Pm7WRjPIgunU6VvLCOXGfkuwF37wV+DSwJJ10I3BOx6N3hPMzsJGCPu68xs3cBnwBOjJMc4shksmQnzoXOoxvmebAiIvVUyX0QHwW+a2aXA2uA9wGY2aXAbHf/LPB14AYzewbYC1wQrnslMAG4y8xy27vY3Z+oIJ6iMpks19y2DGaeSio1uoPSicRR6nGsUhv1Gtk4yWInCHd/CTg1Yvo38/7eA7w/Ypmj4+43juWrulm1fhuk20jROM+DleZUz1F2m5WOebSmGGpjzcbt9PT2D5qmQekkqfJH2U2lUoMuaKQ2dMyjNUWCmDdjMu1tLYOmaVA6SSqNshtfrpqoteuYEbU16phHa4oEsXB+J/NnT2FcmCQ0KJ0kmUbZjSe/mqh1+rHc9chqfvDAymElCR3zaE0xWF86neKTSxaxfFU3K9dtZXxbixqgpKR6NhLrcazxVPIALB3zaE2RICBIEosWTOPQOfux7tUd5VeQplXvBks9jjWeUtVE5RKERjaO1jQJQmS46v0oVtDjWOPIVRPlJ4mRVBNpZOOhmjJBjGtviZyeTqUG5kfdqZib39aaDudnCX/l/d63Ypy7HaX+KrkSlfpRNVH1NV2C2G9iO/tNbB94nc07i08YFySO+XOmDJz0c7OzWRgfJpaDZ00hm82SJQVkyWSzkE0NJId9m8wCqYF1W1paOHDm5IFkAtmBZXMN6LOmTRzYxr7N7Es7ZXNOuMHsoEnlM9WUKeNoiRxOqzZGkjynTG4nnY0XW5ZsyYPW1hI0TE6dPG5g2iGzpvC7P22kJy9JtLemOWjWZCblPjvhNjvGtdLXse/zVDSGUvPjXkiUWXFcewsd41uDz+cI95e7GCrs/VeJ/OPQ2pIa0ihcuO+o+fu+OxFa4LzTD+eF9VvZ+NouZuzfwaER4y64AAAO4ElEQVSzg2qi3Cq5CqOWllTeBgv3z8irlrKFL2t1dTi6VV5NlyBK9WgYSAZ556Lc25HKe19a0qn8OcPabzoVfCEmjos+5C3hBzI/eY1UqoLPzrRpk9mc0D5t06ZNZnNLbb4Y7W3BPz2rc+LAtBn7d/CEv8qq9dvo6e2nva2F+bOncPob5g45cXR2TqK7O5ltWkFsbUXnl7pwyF0sHTonuPoefMERL57ceilg6v4d/HliQWypwRdih8yekneRtC+KbJaiH/ZsFg4ZUsrbF/D48Pt3cFiqKPxfsmSZul8H+3W0hpd/hVIRyWDw7FpWHRwwZTy7duyp2fYLNV2CGMsq/VwmuUqsVrHtKyHu20EqxUCvt5c3bufAGZNZOL8TiL7ASPK4XnFiy2SyZDrmwrhOnnx+Mwvndw5KjHFTdf45vb2thVTEST5/Ujr/RaqiPQ/5K1dyjDKpo53dO/fG3FdtTZzQpgQhUm+5Xm+LFkyrdyijKn/cMlKt3HDnM8yfPYVPLlnU9D16mlFCKxVEpB7yxy0jlWJvbz+r1m9j+arueocmdaAEISIDosYt6+nt5+WN0c9YkbFNCUJEBkSNW9be1sKBM0bvKWaSHEoQIjIgf9yyFEH36/mzpww00ktzUSO1iAzIH7csvweXGqibkxKEiAzSrD24ZChVMYlIU8g9lz57wNEsW7k50fevJIUShIiMeYPu7zjgGG648xmuuW2ZkkQZShAiMubp/o54lCBEZMzT/R3xxG6kNrN5wE3AdMCB8919R8Ey7cCNwHHAbuA8d1+RN78V+DVwg7svjRuLiEgpufs79uYlCd3fUV4lJYjrgevd/QjgCeCKiGU+Dux09yOBy4ClBfM/CxxeQQwiImXp/o54YpUgzKwNOAV4VzhpKfAg8KmCRc8mSAK4+0Nm1mVm89x9jZmdABwN3BUnBhGR4dL9HfHErWKaBmxz977w9QZgbsRys8N55C9nZn8GrgXeCXw5ZgwiIsOm+ztGrmyCMLPFBCfzfM8z9FkaUY/8ShcslwqX+wbwBXffaGbDjzZPZ+ekovO6uuLVK7a3t5Zcv9z8uNvuz2Rp2e8gsu0H8OKmnRx7xIyBBwiNprjHbTTUKrZK3tOcZjxu1VCL71m1NOJxq4WyCcLdbwduz58WVjF1m1mLu/cDs4D1EauvDee9EL6eSVCKOB04ysyuBOYBp5lZr7vfPNzAu7t3RPZh7uqazKZN8Xom9PQEBaJi65ebX27b7e2tQ9bN9c/um3YypFr5l//3RF3G36/kuNVaLWOr5D2F5j1ulSoVW6XvSaUa9biVk06nSl5YR64TZ0fu3kvQ+2hJOOlC4J6IRe8O52FmJwF73P0ld5/t7ovcfRHwX8BnR5IcxhL1zxbZR3c7J0slvZg+ClxiZs8CJwOXA5jZpWZ2VbjM14FxZvYMcB1wQSXBjkXqny0S0N3OyRP7Pgh3fwk4NWL6N/P+3gO8v8x2Loobw1ig/tkigUGlaRhUmlbDcn3oTuo6U//s+lF1RrKoNJ08Gu67ztQ/uz4GVWekWrnhzmfq0jlA9lFpOnmUIBJA/bNHn6ozkidXml61fhs9vf20qzRdd0oQ0pRKVWcoQdSHStPJowQhTUnVGcmk0nSyqJFampI6B4iUpxJEKNejhXGdLFu5WUXbMU7VGSLlKUGgHi3NStUZIqWpigkNdyEiEkUJAt2gIyISRQmCfT1a8qlHi4g0OyUI1KNFRCSKGqmpfY+WXA+pvo4u9ZASkYahBBGqVY+W/B5S/eohJSINRFVMNaYeUiLSqJQgakw9pESkUSlB1Jh6SIlIo1KCqDH1kBKRRqVG6hrL7yG1ZUcPB0xqVy8mEWkIShCjINdDqqtrMps2qe1BRBqDqphERCRS7BKEmc0DbgKmAw6c7+47CpZpB24EjgN2A+e5+wozSwGXA38DdAD/7O7fixuLiIhUXyUliOuB6939COAJ4IqIZT4O7HT3I4HLgKXh9POBtwLHA28GvmJmUyuIRUREqixWgjCzNuAU4I5w0lJgccSiZwM3A7j7Q0BXWPJYAnzF3Xvc/RXgJIIShoiIJETcKqZpwDZ37wtfbwDmRiw3O5xHwXILgCPN7DJgKvAld38+ZiwiIlIDZROEmS0Gri2Y/DyQLZiWiVg9XbBcKlyuFVgI/BUwE3jYzJ4cSZLo7JxUdF5XV21uQmtvb614+7WKrRoUWzyKLR7FFs9oxlY2Qbj77cDt+dPCKqZuM2tx935gFrA+YvW14bwXwtczw+VeAe5w917gZTN7DDiGIPEMS3f3DjKZwhxFTbuS9vQEBaa4209yN1fFFo9ii0exxVNJbOl0quSFdeQ6cXYUnth/TdCWAHAhcE/EoneH8zCzk4A97r4GuAt4r5mlzKyToLF6WZxYRESkNirpxfRR4BIzexY4maDbKmZ2qZldFS7zdWCcmT0DXAdcEE6/lqAU8TTwMHCVuz9XQSwiIlJlse+DcPeXgFMjpn8z7+89wPsjlukFPhF33yIiUnu6k1pERCIpQYiISCQlCBERiaQEISIikZQgREQkkhKEiIhEUoIQEZFIShAiIhJJCUJERCIpQYiISCQlCBERiaQEISIikZQgREQkkhKEiIhEUoIQEZFIShAiIhJJCUJERCIpQYiISCQlCBERiaQEISIikZQghimTyZKdOJfsAUezbOVmMplsvUMSEakpJYhhyGSyXHPbMph5KhxwDDfc+QzX3LZMSUJExrTWuCua2TzgJmA64MD57r6jYJl24EbgOGA3cJ67rwjnXQucCWSBq9391rix1NryVd2sWr8N0m0A7O3tZ9X6bSxf1c2iBdPqHJ2ISG1UUoK4Hrje3Y8AngCuiFjm48BOdz8SuAxYCmBmpwPHAwuB04Hrzayjglhqas3G7fT09g+a1tPbz8sbt9cpIhGR2ouVIMysDTgFuCOctBRYHLHo2cDNAO7+ENAVljxagPFAG9AB7I0Tx2iZN2My7W0tg6a1t7Vw4IzJdYpIRKT24lYxTQO2uXtf+HoDMDdiudnhPPKXc/dfmNklwDpgIvApd981kgA6OycVndfVVd0T92mdk3jwqfX4S6+xt6efce0t2EH7c9rxB9OSTo1oW9WOrZoUWzyKLR7FFs9oxlY2QZjZYuDagsnPE7Qd5MtErJ4uWC4FZMLk0AfMBDqB+83st+7+2HAD7+7eEdlI3NU1mU2bql/18/fvPorlq7p5eeN2DpwxmYXzO9nSvaP8iqMQWzUotngUWzyKLZ5KYkunUyUvrKOUTRDufjtwe/60sIqp28xa3L0fmAWsj1h9bTjvhfD1zHC5K4B/d/de4BUz+wlwMjDsBDHa0ukUixZMU6O0iDSNWG0Q4Yn918CScNKFwD0Ri94dzsPMTgL2uPsa4CngXeH0iQQN1U/EiUVERGqjkl5MHwUuMbNnCa7+Lwcws0vN7Kpwma8D48zsGeA64IJw+tVAm5n9Cfgt8D13v7+CWEREpMpi3wfh7i8Bp0ZM/2be33uA90csszNquoiIJIfupBYRkUixSxB11AJBo3ExpebVm2KLR7HFo9jiGYux5a3XUmq5fKlstuHGEzqJoIFcRERG7mTgN8NZsBETxDjgLwluuusvs6yIiARaCG47eJxhjl7RiAlCRERGgRqpRUQkkhKEiIhEUoIQEZFIShAiIhJJCUJERCIpQYiISCQlCBERidSIQ20UZWbnEYwq2wZ8zd2/UeeQBpjZ/cB0oDec9BF3/20d45kCPAK83d1Xm9kZwFeBCcBt7n55gmL7DsEd9DvDRa509x/VIa7PAe8NX/7U3f93Uo5bkdgScdzC+K4C3kPwALEb3f2rCTp2UbEl6dh9BZjm7heZ2SLgW8AU4CHg0rwne1bdmEkQZjaHYBjxYwnuEnzEzO5392frGxmYWQo4HDiolm/mCOI5HvgPgpgwswnAt4E3Ay8DPzWzt7l71DM+RjW20HHAKe6+IXqt2gtPZmcCxxCcSH5mZu8Dvkydj1uR2N5NAo5bGN+bgdOAhQQXb8+a2b0k4DNXJLafkpxjdzrByNc/DSfdBFzs7o+Z2Y3Ah4F/r9X+x1IV0xnAfe6+JRxO/A6Cq4IksPD3L8zsKTP7WF2jCT5Uf8e+pwC+EXje3V8ME9hNwOIkxGZmHcA84NtmttzMrjSzenxuNwCfdPee8IFZfyJIYkk4blGxzSMZxw13fxB4S3iMphNcmE4lAceuSGy7ScCxM7MDCC56vxC+PgiYkPdo5qXU+JiNpQQxm+CLkrMBmFunWArtD9wLvJvg6XmXmtlb6xWMu1/s7vkDHibm2EXENhO4D/gg8CaCgcY+VIe4nsl9Mc3sMILqnAwJOG5FYvsZCThueTH2mtmVwLME34UkfeYKY2sjGcfuBuAzwGvh61E/ZmMpQaQJitc5KYIvcN25+6PufqG7b3X3zcCNwFn1jitPko/dKnd/t7tvcPddBE8prNuxM7PXA78E/hFYRYKOW35sHkjMcQNw988BXcCBBKWvxBy7gthOr/exM7OLgZfd/d68yaP+PR1LCWItwUiFOTPZV4VSV2Z2UliXmJNiX2N1EiT52B1lZufmTarbsTOzEwmuMP/J3b9Lgo5bYWwJO25HhI2rhCfcHxI8jbLux65IbEsScOyWAGea2TLgKuCdwMWM8jEbM43UwK+Az5tZF0HPg3OBS+ob0oCpwFVmdgJB8fX9wKX1DWmQ3wJmZguAF4HzCBoQkyAFfM3M7gN2ELyn3x3tIMzsQODHwBJ3vy+cnIjjViS2RBy30HzgSjM7ieAK+ByC6pN/rfexKxLbg9T52Ln7QBW0mV0EnOruHzCzp83sRHd/GLgAqGmj/pgpQbj7OoL6uvuBZcAt7v67+kYVcPefEPRCeBL4PfBtd3+0vlHtEz47/CLgPwnqYVcQNPLXnbsvB74IPEwQ2zJ3v7UOofwvYDzwVTNbFl7ZXUQyjltUbCeQjOOGu9/N4M//I+7+fRJw7IrEdhUJOXYRzgeuNbMVwCTgulruTM+DEBGRSGOmBCEiItWlBCEiIpGUIEREJJIShIiIRFKCEBGRSEoQIiISSQlCREQiKUGIiEik/w9Uc9DM7DtAKQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5weDzJKfS8tT",
        "colab_type": "text"
      },
      "source": [
        "## MA(1) for Returns"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BJTVc_CUS8tU",
        "colab_type": "code",
        "colab": {},
        "outputId": "7bfeacb4-6b0f-446b-aaa0-b8d503da424d"
      },
      "source": [
        "model_ret_ma_1 = ARMA(df.returns[1:], order=(0,1))\n",
        "results_ret_ma_1 = model_ret_ma_1.fit()\n",
        "results_ret_ma_1.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<caption>ARMA Model Results</caption>\n",
              "<tr>\n",
              "  <th>Dep. Variable:</th>      <td>returns</td>     <th>  No. Observations:  </th>   <td>5020</td>   \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Model:</th>            <td>ARMA(0, 1)</td>    <th>  Log Likelihood     </th> <td>-7929.595</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Method:</th>             <td>css-mle</td>     <th>  S.D. of innovations</th>   <td>1.174</td>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Date:</th>          <td>Sat, 07 Sep 2019</td> <th>  AIC                </th> <td>15865.190</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>              <td>14:39:10</td>     <th>  BIC                </th> <td>15884.754</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Sample:</th>           <td>01-10-1994</td>    <th>  HQIC               </th> <td>15872.045</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th></th>                 <td>- 04-05-2013</td>   <th>                     </th>     <td> </td>    \n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "        <td></td>           <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>const</th>         <td>    0.0188</td> <td>    0.016</td> <td>    1.162</td> <td> 0.245</td> <td>   -0.013</td> <td>    0.050</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ma.L1.returns</th> <td>   -0.0257</td> <td>    0.015</td> <td>   -1.720</td> <td> 0.086</td> <td>   -0.055</td> <td>    0.004</td>\n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<caption>Roots</caption>\n",
              "<tr>\n",
              "    <td></td>   <th>            Real</th>  <th>         Imaginary</th> <th>         Modulus</th>  <th>        Frequency</th>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>MA.1</th> <td>          38.8547</td> <td>          +0.0000j</td> <td>          38.8547</td> <td>           0.0000</td>\n",
              "</tr>\n",
              "</table>"
            ],
            "text/plain": [
              "<class 'statsmodels.iolib.summary.Summary'>\n",
              "\"\"\"\n",
              "                              ARMA Model Results                              \n",
              "==============================================================================\n",
              "Dep. Variable:                returns   No. Observations:                 5020\n",
              "Model:                     ARMA(0, 1)   Log Likelihood               -7929.595\n",
              "Method:                       css-mle   S.D. of innovations              1.174\n",
              "Date:                Sat, 07 Sep 2019   AIC                          15865.190\n",
              "Time:                        14:39:10   BIC                          15884.754\n",
              "Sample:                    01-10-1994   HQIC                         15872.045\n",
              "                         - 04-05-2013                                         \n",
              "=================================================================================\n",
              "                    coef    std err          z      P>|z|      [0.025      0.975]\n",
              "---------------------------------------------------------------------------------\n",
              "const             0.0188      0.016      1.162      0.245      -0.013       0.050\n",
              "ma.L1.returns    -0.0257      0.015     -1.720      0.086      -0.055       0.004\n",
              "                                    Roots                                    \n",
              "=============================================================================\n",
              "                  Real          Imaginary           Modulus         Frequency\n",
              "-----------------------------------------------------------------------------\n",
              "MA.1           38.8547           +0.0000j           38.8547            0.0000\n",
              "-----------------------------------------------------------------------------\n",
              "\"\"\""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IQA3d7x6S8tY",
        "colab_type": "text"
      },
      "source": [
        "## Higher-Lag MA Models for Returns"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cRn7qfkcS8tZ",
        "colab_type": "code",
        "colab": {},
        "outputId": "ad81bca3-1125-4b14-8e0d-d6b2987cf2df"
      },
      "source": [
        "model_ret_ma_2 = ARMA(df.returns[1:], order=(0,2))\n",
        "results_ret_ma_2 = model_ret_ma_2.fit()\n",
        "print(results_ret_ma_2.summary())\n",
        "print(\"\\nLLR test p-value = \" + str(LLR_test(model_ret_ma_1, model_ret_ma_2)))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                              ARMA Model Results                              \n",
            "==============================================================================\n",
            "Dep. Variable:                returns   No. Observations:                 5020\n",
            "Model:                     ARMA(0, 2)   Log Likelihood               -7922.864\n",
            "Method:                       css-mle   S.D. of innovations              1.173\n",
            "Date:                Sat, 07 Sep 2019   AIC                          15853.728\n",
            "Time:                        14:39:10   BIC                          15879.812\n",
            "Sample:                    01-10-1994   HQIC                         15862.868\n",
            "                         - 04-05-2013                                         \n",
            "=================================================================================\n",
            "                    coef    std err          z      P>|z|      [0.025      0.975]\n",
            "---------------------------------------------------------------------------------\n",
            "const             0.0188      0.015      1.239      0.215      -0.011       0.049\n",
            "ma.L1.returns    -0.0327      0.014     -2.297      0.022      -0.061      -0.005\n",
            "ma.L2.returns    -0.0502      0.014     -3.669      0.000      -0.077      -0.023\n",
            "                                    Roots                                    \n",
            "=============================================================================\n",
            "                  Real          Imaginary           Modulus         Frequency\n",
            "-----------------------------------------------------------------------------\n",
            "MA.1            4.1499           +0.0000j            4.1499            0.0000\n",
            "MA.2           -4.8004           +0.0000j            4.8004            0.5000\n",
            "-----------------------------------------------------------------------------\n",
            "\n",
            "LLR test p-value = 0.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x53IrPWIS8td",
        "colab_type": "code",
        "colab": {},
        "outputId": "a1ca3964-f747-40ef-818f-b9c936474d1a"
      },
      "source": [
        "model_ret_ma_3 = ARMA(df.returns[1:], order=(0,3))\n",
        "results_ret_ma_3 = model_ret_ma_3.fit()\n",
        "print(results_ret_ma_3.summary())\n",
        "print(\"\\nLLR test p-value = \" + str(LLR_test(model_ret_ma_2, model_ret_ma_3)))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                              ARMA Model Results                              \n",
            "==============================================================================\n",
            "Dep. Variable:                returns   No. Observations:                 5020\n",
            "Model:                     ARMA(0, 3)   Log Likelihood               -7904.317\n",
            "Method:                       css-mle   S.D. of innovations              1.168\n",
            "Date:                Sat, 07 Sep 2019   AIC                          15818.634\n",
            "Time:                        14:39:11   BIC                          15851.240\n",
            "Sample:                    01-10-1994   HQIC                         15830.060\n",
            "                         - 04-05-2013                                         \n",
            "=================================================================================\n",
            "                    coef    std err          z      P>|z|      [0.025      0.975]\n",
            "---------------------------------------------------------------------------------\n",
            "const             0.0189      0.014      1.376      0.169      -0.008       0.046\n",
            "ma.L1.returns    -0.0203      0.014     -1.423      0.155      -0.048       0.008\n",
            "ma.L2.returns    -0.0556      0.013     -4.134      0.000      -0.082      -0.029\n",
            "ma.L3.returns    -0.0904      0.015     -6.121      0.000      -0.119      -0.061\n",
            "                                    Roots                                    \n",
            "=============================================================================\n",
            "                  Real          Imaginary           Modulus         Frequency\n",
            "-----------------------------------------------------------------------------\n",
            "MA.1            2.0105           -0.0000j            2.0105           -0.0000\n",
            "MA.2           -1.3129           -1.9441j            2.3459           -0.3445\n",
            "MA.3           -1.3129           +1.9441j            2.3459            0.3445\n",
            "-----------------------------------------------------------------------------\n",
            "\n",
            "LLR test p-value = 0.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ORKJUa_RS8tj",
        "colab_type": "code",
        "colab": {},
        "outputId": "288a143b-d94d-4974-c42f-c477cf07665b"
      },
      "source": [
        "model_ret_ma_4 = ARMA(df.returns[1:], order=[0,4])\n",
        "results_ret_ma_4 = model_ret_ma_4.fit()\n",
        "print(results_ret_ma_4.summary())\n",
        "print(\"\\nLLR test p-value = \" + str(LLR_test(model_ret_ma_3, model_ret_ma_4)))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                              ARMA Model Results                              \n",
            "==============================================================================\n",
            "Dep. Variable:                returns   No. Observations:                 5020\n",
            "Model:                     ARMA(0, 4)   Log Likelihood               -7898.773\n",
            "Method:                       css-mle   S.D. of innovations              1.167\n",
            "Date:                Sat, 07 Sep 2019   AIC                          15809.547\n",
            "Time:                        14:39:12   BIC                          15848.674\n",
            "Sample:                    01-10-1994   HQIC                         15823.257\n",
            "                         - 04-05-2013                                         \n",
            "=================================================================================\n",
            "                    coef    std err          z      P>|z|      [0.025      0.975]\n",
            "---------------------------------------------------------------------------------\n",
            "const             0.0189      0.014      1.305      0.192      -0.009       0.047\n",
            "ma.L1.returns    -0.0203      0.014     -1.443      0.149      -0.048       0.007\n",
            "ma.L2.returns    -0.0551      0.014     -3.902      0.000      -0.083      -0.027\n",
            "ma.L3.returns    -0.0922      0.015     -6.306      0.000      -0.121      -0.064\n",
            "ma.L4.returns     0.0454      0.014      3.338      0.001       0.019       0.072\n",
            "                                    Roots                                    \n",
            "=============================================================================\n",
            "                  Real          Imaginary           Modulus         Frequency\n",
            "-----------------------------------------------------------------------------\n",
            "MA.1           -1.2257           -1.4206j            1.8763           -0.3633\n",
            "MA.2           -1.2257           +1.4206j            1.8763            0.3633\n",
            "MA.3            2.2405           -1.1103j            2.5006           -0.0732\n",
            "MA.4            2.2405           +1.1103j            2.5006            0.0732\n",
            "-----------------------------------------------------------------------------\n",
            "\n",
            "LLR test p-value = 0.001\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nj0ZV3tjS8to",
        "colab_type": "code",
        "colab": {},
        "outputId": "5bcceb15-d94e-4df5-c040-69770962ced4"
      },
      "source": [
        "model_ret_ma_5 = ARMA(df.returns[1:], order=[0,5])\n",
        "results_ret_ma_5 = model_ret_ma_5.fit()\n",
        "print(results_ret_ma_5.summary())\n",
        "print(\"\\nLLR test p-value = \" + str(LLR_test(model_ret_ma_4, model_ret_ma_5)))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                              ARMA Model Results                              \n",
            "==============================================================================\n",
            "Dep. Variable:                returns   No. Observations:                 5020\n",
            "Model:                     ARMA(0, 5)   Log Likelihood               -7892.823\n",
            "Method:                       css-mle   S.D. of innovations              1.166\n",
            "Date:                Sat, 07 Sep 2019   AIC                          15799.645\n",
            "Time:                        14:39:14   BIC                          15845.294\n",
            "Sample:                    01-10-1994   HQIC                         15815.641\n",
            "                         - 04-05-2013                                         \n",
            "=================================================================================\n",
            "                    coef    std err          z      P>|z|      [0.025      0.975]\n",
            "---------------------------------------------------------------------------------\n",
            "const             0.0189      0.014      1.376      0.169      -0.008       0.046\n",
            "ma.L1.returns    -0.0262      0.014     -1.851      0.064      -0.054       0.002\n",
            "ma.L2.returns    -0.0497      0.014     -3.518      0.000      -0.077      -0.022\n",
            "ma.L3.returns    -0.0852      0.015     -5.799      0.000      -0.114      -0.056\n",
            "ma.L4.returns     0.0458      0.014      3.374      0.001       0.019       0.072\n",
            "ma.L5.returns    -0.0500      0.014     -3.454      0.001      -0.078      -0.022\n",
            "                                    Roots                                    \n",
            "=============================================================================\n",
            "                  Real          Imaginary           Modulus         Frequency\n",
            "-----------------------------------------------------------------------------\n",
            "MA.1           -1.2127           -1.0805j            1.6242           -0.3842\n",
            "MA.2           -1.2127           +1.0805j            1.6242            0.3842\n",
            "MA.3            1.7305           -0.0000j            1.7305           -0.0000\n",
            "MA.4            0.8062           -1.9326j            2.0940           -0.1871\n",
            "MA.5            0.8062           +1.9326j            2.0940            0.1871\n",
            "-----------------------------------------------------------------------------\n",
            "\n",
            "LLR test p-value = 0.001\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WWpVHM2JS8tr",
        "colab_type": "code",
        "colab": {},
        "outputId": "1d8595cb-9394-4d3d-8785-66a1a3eaef93"
      },
      "source": [
        "model_ret_ma_6 = ARMA(df.returns[1:], order=[0,6])\n",
        "results_ret_ma_6 = model_ret_ma_6.fit()\n",
        "print(results_ret_ma_6.summary())\n",
        "print(\"\\nLLR test p-value = \" + str(LLR_test(model_ret_ma_5, model_ret_ma_6)))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                              ARMA Model Results                              \n",
            "==============================================================================\n",
            "Dep. Variable:                returns   No. Observations:                 5020\n",
            "Model:                     ARMA(0, 6)   Log Likelihood               -7889.221\n",
            "Method:                       css-mle   S.D. of innovations              1.165\n",
            "Date:                Sat, 07 Sep 2019   AIC                          15794.442\n",
            "Time:                        14:39:18   BIC                          15846.612\n",
            "Sample:                    01-10-1994   HQIC                         15812.723\n",
            "                         - 04-05-2013                                         \n",
            "=================================================================================\n",
            "                    coef    std err          z      P>|z|      [0.025      0.975]\n",
            "---------------------------------------------------------------------------------\n",
            "const             0.0189      0.013      1.430      0.153      -0.007       0.045\n",
            "ma.L1.returns    -0.0237      0.014     -1.682      0.093      -0.051       0.004\n",
            "ma.L2.returns    -0.0486      0.014     -3.436      0.001      -0.076      -0.021\n",
            "ma.L3.returns    -0.0827      0.014     -5.846      0.000      -0.110      -0.055\n",
            "ma.L4.returns     0.0466      0.014      3.434      0.001       0.020       0.073\n",
            "ma.L5.returns    -0.0484      0.014     -3.371      0.001      -0.077      -0.020\n",
            "ma.L6.returns    -0.0387      0.014     -2.682      0.007      -0.067      -0.010\n",
            "                                    Roots                                    \n",
            "=============================================================================\n",
            "                  Real          Imaginary           Modulus         Frequency\n",
            "-----------------------------------------------------------------------------\n",
            "MA.1            1.5011           -0.0000j            1.5011           -0.0000\n",
            "MA.2            0.8248           -1.4775j            1.6921           -0.1690\n",
            "MA.3            0.8248           +1.4775j            1.6921            0.1690\n",
            "MA.4           -1.0066           -1.2260j            1.5863           -0.3594\n",
            "MA.5           -1.0066           +1.2260j            1.5863            0.3594\n",
            "MA.6           -2.3891           -0.0000j            2.3891           -0.5000\n",
            "-----------------------------------------------------------------------------\n",
            "\n",
            "LLR test p-value = 0.007\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WV5hG0wUS8tv",
        "colab_type": "code",
        "colab": {},
        "outputId": "44978372-705d-4cd6-bf28-d99ce2d74ada"
      },
      "source": [
        "model_ret_ma_7 = ARMA(df.returns[1:], order=[0,7])\n",
        "results_ret_ma_7 = model_ret_ma_7.fit()\n",
        "print(results_ret_ma_7.summary())\n",
        "print(\"\\nLLR test p-value = \" + str(LLR_test(model_ret_ma_6, model_ret_ma_7)))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                              ARMA Model Results                              \n",
            "==============================================================================\n",
            "Dep. Variable:                returns   No. Observations:                 5020\n",
            "Model:                     ARMA(0, 7)   Log Likelihood               -7889.046\n",
            "Method:                       css-mle   S.D. of innovations              1.165\n",
            "Date:                Sat, 07 Sep 2019   AIC                          15796.091\n",
            "Time:                        14:39:23   BIC                          15854.782\n",
            "Sample:                    01-10-1994   HQIC                         15816.657\n",
            "                         - 04-05-2013                                         \n",
            "=================================================================================\n",
            "                    coef    std err          z      P>|z|      [0.025      0.975]\n",
            "---------------------------------------------------------------------------------\n",
            "const             0.0189      0.013      1.418      0.156      -0.007       0.045\n",
            "ma.L1.returns    -0.0247      0.014     -1.739      0.082      -0.052       0.003\n",
            "ma.L2.returns    -0.0488      0.014     -3.454      0.001      -0.077      -0.021\n",
            "ma.L3.returns    -0.0821      0.014     -5.789      0.000      -0.110      -0.054\n",
            "ma.L4.returns     0.0458      0.014      3.360      0.001       0.019       0.073\n",
            "ma.L5.returns    -0.0486      0.014     -3.381      0.001      -0.077      -0.020\n",
            "ma.L6.returns    -0.0390      0.014     -2.702      0.007      -0.067      -0.011\n",
            "ma.L7.returns     0.0085      0.014      0.592      0.554      -0.020       0.037\n",
            "                                    Roots                                    \n",
            "=============================================================================\n",
            "                  Real          Imaginary           Modulus         Frequency\n",
            "-----------------------------------------------------------------------------\n",
            "MA.1           -0.9690           -1.1814j            1.5279           -0.3593\n",
            "MA.2           -0.9690           +1.1814j            1.5279            0.3593\n",
            "MA.3           -2.0397           -0.0000j            2.0397           -0.5000\n",
            "MA.4            0.7516           -1.5246j            1.6998           -0.1771\n",
            "MA.5            0.7516           +1.5246j            1.6998            0.1771\n",
            "MA.6            1.5538           -0.0000j            1.5538           -0.0000\n",
            "MA.7            5.5175           -0.0000j            5.5175           -0.0000\n",
            "-----------------------------------------------------------------------------\n",
            "\n",
            "LLR test p-value = 0.554\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Arv0kPjgS8tz",
        "colab_type": "code",
        "colab": {},
        "outputId": "833c5b92-522f-4b74-a9e3-bdef6c53de49"
      },
      "source": [
        "model_ret_ma_8 = ARMA(df.returns[1:], order=[0,8])\n",
        "results_ret_ma_8 = model_ret_ma_8.fit()\n",
        "print(results_ret_ma_8.summary())\n",
        "print(\"\\nLLR test p-value = \" + str(LLR_test(model_ret_ma_7, model_ret_ma_8)))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                              ARMA Model Results                              \n",
            "==============================================================================\n",
            "Dep. Variable:                returns   No. Observations:                 5020\n",
            "Model:                     ARMA(0, 8)   Log Likelihood               -7885.603\n",
            "Method:                       css-mle   S.D. of innovations              1.164\n",
            "Date:                Sat, 07 Sep 2019   AIC                          15791.205\n",
            "Time:                        14:39:31   BIC                          15856.417\n",
            "Sample:                    01-10-1994   HQIC                         15814.056\n",
            "                         - 04-05-2013                                         \n",
            "=================================================================================\n",
            "                    coef    std err          z      P>|z|      [0.025      0.975]\n",
            "---------------------------------------------------------------------------------\n",
            "const             0.0189      0.014      1.364      0.173      -0.008       0.046\n",
            "ma.L1.returns    -0.0238      0.014     -1.688      0.091      -0.051       0.004\n",
            "ma.L2.returns    -0.0495      0.014     -3.514      0.000      -0.077      -0.022\n",
            "ma.L3.returns    -0.0847      0.014     -5.993      0.000      -0.112      -0.057\n",
            "ma.L4.returns     0.0488      0.014      3.440      0.001       0.021       0.077\n",
            "ma.L5.returns    -0.0518      0.014     -3.634      0.000      -0.080      -0.024\n",
            "ma.L6.returns    -0.0402      0.014     -2.787      0.005      -0.068      -0.012\n",
            "ma.L7.returns     0.0084      0.014      0.587      0.557      -0.020       0.036\n",
            "ma.L8.returns     0.0366      0.014      2.626      0.009       0.009       0.064\n",
            "                                    Roots                                    \n",
            "=============================================================================\n",
            "                  Real          Imaginary           Modulus         Frequency\n",
            "-----------------------------------------------------------------------------\n",
            "MA.1            1.4716           -0.4282j            1.5326           -0.0451\n",
            "MA.2            1.4716           +0.4282j            1.5326            0.0451\n",
            "MA.3            0.5944           -1.3169j            1.4448           -0.1825\n",
            "MA.4            0.5944           +1.3169j            1.4448            0.1825\n",
            "MA.5           -0.7318           -1.2736j            1.4689           -0.3330\n",
            "MA.6           -0.7318           +1.2736j            1.4689            0.3330\n",
            "MA.7           -1.4485           -0.6983j            1.6081           -0.4285\n",
            "MA.8           -1.4485           +0.6983j            1.6081            0.4285\n",
            "-----------------------------------------------------------------------------\n",
            "\n",
            "LLR test p-value = 0.009\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bsza_g11S8t4",
        "colab_type": "code",
        "colab": {},
        "outputId": "ad3e477f-3901-4142-d3d2-d515c815c4f5"
      },
      "source": [
        "LLR_test(model_ret_ma_6, model_ret_ma_8, DF = 2)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.027"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CTprNFujS8t8",
        "colab_type": "text"
      },
      "source": [
        "## Residuals for Returns"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QQYD3CUDS8t8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df['res_ret_ma_8'] = results_ret_ma_8.resid[1:]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Csg2dONbS8uA",
        "colab_type": "code",
        "colab": {},
        "outputId": "efdcf850-a5ea-43dc-e5a7-c196cb1d2b1d"
      },
      "source": [
        "print(\"The mean of the residuals is \" + str(round(df.res_ret_ma_8.mean(),3)) + \"\\nThe variance of the residuals is \" + str(round(df.res_ret_ma_8.var(),3)))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The mean of the residuals is -0.0\n",
            "The variance of the residuals is 1.356\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "JjtT4-znS8uD",
        "colab_type": "code",
        "colab": {},
        "outputId": "82c4ab76-60db-49c4-eced-38eaa47fe2ad"
      },
      "source": [
        "round(sqrt(df.res_ret_ma_8.var()),3)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.164"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "id": "7GY51FMJS8uH",
        "colab_type": "code",
        "colab": {},
        "outputId": "55cd8c66-e618-47f1-a31e-adca38d99bd0"
      },
      "source": [
        "df.res_ret_ma_8[1:].plot(figsize = (20,5))\n",
        "plt.title(\"Residuals of Returns\", size = 24)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QGu25z2tS8uK",
        "colab_type": "code",
        "colab": {},
        "outputId": "ff61b4d4-c63b-4677-dc84-8ce4a97d3661"
      },
      "source": [
        "sts.adfuller(df.res_ret_ma_8[2:])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(-70.83581593162957,\n",
              " 0.0,\n",
              " 0,\n",
              " 5018,\n",
              " {'1%': -3.4316538358533277,\n",
              "  '5%': -2.8621161549128313,\n",
              "  '10%': -2.567076687880585},\n",
              " 15684.052185061355)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XG1AEeISS8uS",
        "colab_type": "code",
        "colab": {},
        "outputId": "9a00101a-3d2b-4ff9-e14e-c14b7faac614"
      },
      "source": [
        "sgt.plot_acf(df.res_ret_ma_8[2:], zero = False, lags = 40)\n",
        "plt.title(\"ACF Of Residuals for Returns\",size=24)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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3Z5SITI0Ij3o/9va/67T3GljgGibBGEQuc+8duMmAn08PUNX1dPvaF+N99QSuJ9aIM+VF8U/+9w95jPf0C/7bvcAf7kvv5x8M+mYatD0d98yX40yzAYH86e9f8Kx72aO9VeKY9PMl4iScK/uS9G/El33gPlu0x10mqlZB+Fbm0f7wtmxxVbUzFOcEcbN7UTdLcQmunH4pIleGW8Le1//TuA9rD9y4xb/1641k5jzcRL99gadE5B/CczlEpE5E/oHuiV9r6J8FzvJGVZ/E2Y/BufkeHQpehBssPwrnobHbdOF7PIHH2b2qGvQ2lvjfBSLyj+GPRtzCgEGlFl7eI4orcZXD+SLyxSAdrxx+gfMuSucVut2Kr/Gtf0RkuIj8B90TK8ME41izRGS3OTDk139dKG5UnmGCe79E0pZLEZF5uHJM0rfJkz3wHkjBqgE3icgnQ3kMF5Hv4RRhO9mX6O53/LcYzIa+WETCkxmvxpnZTheRq4JnA7vXUwtcSn+sPRfSDExk0+lJMBg+U0QCxYSI7IlzdOmLc0gx/AZn/j4AuNabLANZptHtkpzrfS+YqlUQuNZZDOdeGuVmmc7NuNZuLT33b/gsbtp8sH5+s4i85j1INuEqlEm4ivgjqtqnJbsLxbs4zsHNBp6K61FsEpHnRGQ5zqzxM9wknP8FjghMGwPMxXRXrD8QkVoA7yd+Mm5A+LPAWyLyrB+QewY3AWyFD8Nf8xhu4lANbomKt0TkGRFZjdt3YjTwI41YriCMuuU4voJ7P34IrBORZ3FLJcymu4cRvqaL7oUYT8E5PDyLM31cTPQijb/CmRtrgF+LyOv+mndwnkg7/D3i7zcb/41710bjxhre8Pe+HldpJYCFmsdyGzm4CqecxwK/EZG1IvK0l/mfcaals1X1uSxplIp/wimnYYQWnlTVl3HWgl24Bto7vmzW4Ly8xuCWCfl6WnpB2V/gv5t/9ek9R/dabdf6MngOZ/c/iu7JnyXFK+wzcPXSV3Dv+3Mi8jLOA+9g3Pv17cypFEc1K4jALrfE9xCyoqqv4yoZCHXj1a2KugD4a5wSWYlTCLNxL+tSXMv8iMAnf6BQ1a2qejLOJfOnuI/4ANykq7dxvaJjVfXjA+D7nUnGTXSbBA6ku9UTVPgH47x83sANtE7Fdau/ARzpbcxhvoYb73kEVynOxlWaDwCfUdX0CXmZ5Po+rtX/AG6caj/cbOej6HblTL/mBtzS6Y/jTEP74WbjBku+p8fvxHmeXIlb+mEi7vk041rqB9O9zEZWhwPv2XMqrqJ4EjeQPBv3Df8P7jn/R+YU8sMrwtNwk+AexFWus3Dv1iLgEFXNNN+gpKiq0t3D+ZiInBoKuxtXHrfgGiSzcKayZ3DmqeNVNd176Nu4b6QV982EZ4f/A84z7kWcm+0UnML/IN09jJLjPfKOwSmsNi/jFNw7+jXcN1KyRmkslcrk6WcYhmEMZaq5B2EYhmEUgSkIwzAMIxJTEIZhGEYkpiAMwzCMSAbjWkzDcLtcNeMW4DIMwzByU4PzwHyGPLeaHYwK4kMMko3WDcMwKpCjcJN7czIYFUQzwObN20gme7voNjSMpqWl5GuIFYTJVhgmW2GYbIVRrbLF4zHGjx8FfVjufDAqiC6AZDIVqSCCsErFZCsMk60wTLbCqHLZ8jbN2yC1YRiGEYkpCMMwDCMSUxCGYRhGJKYgDMMwjEgG4yC1YRhZSCZTrFjVQsvyDTSMrmPWjAbi8Uy7ZxpGZkxBGEYVkUymuOau5aza0Ep7Rxd1tTXMmFzPhfNnm5Iw+oyZmAyjilixqoVVG1rZ1dFFCtjV0cWqDa2sWBW146lhZMcUhGFUEevebqO9o6ebe3tHF2+8PSAbHRpVhikIw6gipu05hrrannvY19XWsNeeY8okkTGYMQVhGFXErBkNzJhcD8kOSKUY5scgZs1oKLdoxiDEFIRhVBHxeIwL58+Gtx6hZsvzfPFTB9kAtVEwRXkxichpwEKgFrhOVW9MC58N3IzbYP0x4FxV7RSRo4DrcBu/rwbOUtXNxchiGIYjHo8R2/YmiY63mD2zsdziGIOYgnsQIjIFuAqYA8wGzhGRA9Oi3Q6cp6r7ATHgC/78T4AzVPVg4GXgokLlMAzDMEpDMSam44CHVPVdVd0G3AN8JggUkenACFV90p9aDMzzfx+gqi+LSC0wBbDeg2EMEMlkiuUrN/HrP65m+cpNFb1yqVFeijExTabnuuLNwOE5wqcCqGqHiBwMPAh0AP9WhByGYeRJqSfSBbO4173dxrQ9x9gs7kFOMQoiDoSbHjEgmW+4qr4A7CkiXwTuAo7oS+YNDaMzhjU1Va5Ln8lWGCZb36irc592umxPv/wWq5vdRDpwE+lWN7eytmU7hx84sag8u5IpvvGjZejazexq72JYXQ0yfTxXnHMENRFKohLLLcBkcxSjIN7EbV0XMBHYkBY+KT1cRIYDf6Oqv/Lnbweu6WvmLS1bI7vGTU1j2LixMicFmWyFYbL1nfb2TurqEr1ke+HVd9jV3nMi3a72Ll589R32aRpVVJ7LV27ilTWbdyufne1dvLJmMw89tabXYHmllhtUr2zxeCxrwzrymoJycjwIHCsiTSIyEjgZeCAIVNW1wE4ROdKfOgO4H2dSulFEDvXnTyHP/VENwyiOUk6ks1nc1UfBCkJV1wOXAA8Dy4Elqvq0iCwVkcN8tNOBa0XkFWA0cL2qdgHzgR+JyHLcwPaCYm7CMIz8KOVEOpvFXX0UNQ9CVZcAS9LOHR/6+3l6DlwH558ADk0/bxhGaQkm0i244N+groEvnnt2vw0kB8rnL6vfgViCYXWJQTuLO2bj6sAQXO47VuYnX+78s1FK2YpNeqA8YVKp7nGtFBA63P13bHeoM6EkU6V1E82Yeo5sk8kUXUnnF5JKsfshpFLAtjdh25sctM8edKVSdHWlSL+NVMYMYhnzPu/Ts/inry2EYQ18fsGZHLhPA+1dKehK9ojXum0XO9LGQqJJAbGQbKnQ/z1j9ZYp4p5SwW+q53HoXGcsxpYtOyMl6XUuOuOSkaqpIRaj132ViiGnILbu6GDLtl1lybutvZPW1t4v3kCR7Z1q29XJlrYcshX5UhZ6+ZYdEbJlSCxcqeX9EaVSuyv57gokd/qkYOy2DrZs2ZFnRgPHzvYuarpSrGlui6w8d+7qBGBNc2v3yYh7LuSZ7dy8BlhD/agFvPlO2+5EwmU3bnsn723ZnjOtgaoIw7Qn4b33Ku+ZAoweM5yRNQPXyBxyCiKZStG6tb0seccTibLlnYuaRIK2CpUtUZtg67bKlK2zK0VHZzJ3xAEmmUoRh4yyBfVuV1f/18BBitkm4KX3zozKxBbrMwzDMCIxBWEYhmFEYgrCMAzDiGTIjUEY1UMymWJVcyvvbN7BhPEjmDGp3tb9MYx+xBSEMShJJlP8/JGVNLdsp6MzSW0izqSGkZwyd6YpCcPoJ8zEZAxKVjW37lYO4Lx1mlu2syrstmkYRlGYgjAGJe9s3tHLhbOjM8k7myvTf90wiiGZTLFy/RbuX7aG5a8N3B4eZmIyBiUTxo+gNhHvoSRqE3EmjB9RRqkMo/9JN6cO6+c9PLJhPQhjUDJjUj2TGkaS6uoglUrtHoOYMam+3KIZRr+Sbk7d1dHFqg2trFjVUvK8TUEYg5J4PMYpc2fS/uZDdL7zf5xwxN42QG1UJVHm1IFaRt0UhDFoicdjJNvW0bnxOWZOGWvKwahKAnNqmIFaRt0UhGEYRh4EA8XLXnyLleu3DNhAcbo5tT/38MiFDVIbhmHkoJzzbgJz6lVXf4e6UU2ce+7ZHNxPe3jkoigFISKnAQuBWuA6Vb0xLXw2cDNQDzwGnKuqnX4b0muBOqAF+JzfotQwDKPiyDbvZuaUsSXPPzCn0r6B2fs2DthKuAWbmERkCnAVMAeYDZwjIgemRbsdOE9V98Pts/IFf/4OYIGqzvZ/X1+oHIZhDB3KZeYZqvNuihmDOA54SFXfVdVtwD24/aUBEJHpwAhVfdKfWgzME5FhwEJVXeHPrwCmFSGHYRhDgMDMc9+yNTzxQjP3LVvDzx9ZOSBKImqgeCjMuylGQUwGmkPHzcDUXOGquktVbwcQkThwOfCrIuQwDGMIUM7lVYbqvJtixiDi9NyRMAYk8w0XkTrgNi/Dt/qaeUPD6IxhTU2Z3b9im7ezrb18O4CNGzeybHnnYjDKlkjUZA0fCCqx3HKVSynLLd+0+5p368qWSDNP687Ofr+PqPTOOXEWFy+8gtjwPTj1zH9kv2njB9S1OijXxsbSu7fuzrOIa98EjgodTwQ2pIVPigoXkdHAr3ED1J9S1Y6+Zt7SsjWya9nUNIaNGzNPIGnb0cF77+XeC7cUjBs3smx552KwytbZ6Ta+t2fak87OLhKJmrKUWz5pF1Ju9SMSkcur1A9P9Ot9ZJOtY8sa2LKGieMW0No6sOMPnZ1dJGoSbNrUVtAgdTwey9qwjrym79ns5kHgWBFpEpGRwMnAA0Gg90ra6T2WAM4A7vd/3w6sBOar6q4iZDAMY4gwVM085aRgBaGq64FLgIeB5cASVX1aRJaKyGE+2unAtSLyCjAauF5EDgE+BRwJ/FlElovI0qLuwjCMqseWVxl4ipoHoapLgCVp544P/f08cHjaZc/hxiMMwzD6RDAfINm2jplTvpD7AqMobKkNwzAMIxJTEIZhGEYkpiAMwzCMSExBGIZhGJGYgjAMwzAiMQVhGIZhRGL7QRiGMWAkkylWNbfSurKF+hEJZkyqt3kMFYwpCMMwBoRybrpjFIaZmAzDGBDKuRqrURimIAzDGBCG6qY7gxlTEIZhDAhDddOdwYwpCMMwBgRbjXXwYQrCMEpAufZOrmTCq7EmN/3ZVmMdBJgXk2H0M+atk5ndq7HuWM/MKQvKLY6RA+tBGEY/Y946RrVQVA9CRE4DFgK1wHWqemNa+GzgZqAeeAw4V1U7Q+HfBLpU9fJi5DCMSiKbt87MKWPLJJVh9J2CexAiMgW4CpgDzAbOEZED06LdDpynqvvhNgn6gr92rIjcAlxYaP6GUamYt45RLRRjYjoOeEhV31XVbcA9wGeCQBGZDoxQ1Sf9qcXAPP/3p4DXgGuKyN8wKhLz1jGqhWIUxGSgOXTcDEzNJ1xVf6qq3wa6isjfMCoS2zvZqBaKGYOIA2HfvRiQ7EN4UTQ0jM4Y1tQ0JmNYbPN2trX3mxh9Zty4kWXLOxeDUbZEoiZr+ECQKe/4jvWwYz2HHfSVAZYod7mUstxKnfdAPPNKfN+CvBsbM9dv/Z5nEde+CRwVOp4IbEgLn5QlvChaWrZG+pY3NY1h48a2jNe17ejgvfe295cYfWLcuJFlyzsXg1W2zk7XCa3EZ1pO2To7u0gkasoiW660c8lWbPrFUtHPtCbBpk1tpAqYVhOPx7I2rCOv6Xs2u3kQOFZEmkRkJHAy8EAQqKprgZ0icqQ/dQZwfxH5GWXAJnwZxtCl4B6Eqq4XkUuAh4E64GZVfVpElgKXqeqzwOnAj0WkHvgzcH1/CG0MDDbhyzCGNkXNg1DVJcCStHPHh/5+Hjg8y/WXF5O/UVqyTfjK158/2CDmnc07mDB+hG0QYxiDCFtqw8hIsRO+rAdiGIMbW2rDyEixE75syQnDGNyYgjAyUuyEL9sgxjAGN6YgjIwUO+HLlpwwjMGNKQgjK8HyzJ0bn2PmlLF9GjuwJScMY3BjCsIoGbbkhGEMbkxBGCWlmB6IYRjlxdxcDcMw+oFqnPNjCsIwDKNIqnXOj5mYDMMwiqRa5/yYgjAMo0/YAo69qdY5P2ZiMgwjb6rVlFIswZyfsJKohjk/1oMYBFiLzagUqtWUUizVOufHehAVjrXYjEqi2AUcq5Vgzs9VV3+H+PAGTp53SlV4MVkPosKxFptRSdjyKZmpxjk/piDypFxmnmod/DIGJ9VqSjGiKcrEJCKnAQuBWuA6Vb0xLXw2cDNQDzwGnKuqnSIyDbgdmAAocLqqbi1GllKSy8xTygky1Tr4ZQxOqtWUYkRTcA9CRKYAVwFzgNnAOSJyYFq024HzVHU/IAZ8wZ9fBCxS1f2BZ4FLC5UjX5LJFMtXbuJ3T63rcw8gm5knUB5EuoACAAAZo0lEQVT3LVvDEy80c9+yNfz8kZU90g96Hw89+0Zk3tl6J9ZiMyqNajSlGNEU04M4DnhIVd8FEJF7gM8AV/rj6cAIVX3Sx18MXCEiNwNHAyeGzj8KfL0IWbKSTKa45q7lrNrQyq6OrsiB3my9gFxmnmzbcubT+8gWnk+LrRqn+BuGUX5iqVRhtnQR+VdglKou9McLgMNV9Rx//NfAd1V1jj+eCSwFjgGeUdWp/nwC2K6qdXlmvTew+qpbn2Rz665egbV1NXS0d/U4t3VHBxtathG+1RjQMHY4I4YlSKVSbHxvJ+0dXaR8WF1tDU3jhhOLxdixq5OWLTsJl1RwfXtnktZt7b3kqB9Vx9hRdVmvHTEskTM8YM3qV93N77Nfj3xyyZ4viUSczjQlmCvvfCn2+lLKViyVKtua1a8Si8WYvve+GcOhNM80V9q5ZCsm7/6gmGdaStnWrH6VeCzG/vsfAAVU2+Prh3HJ5z4MsA+wJp9riulBxKFXvZbMIzz9PGnX5UWitobauprIsPTzHVt3ka4HU0BnMkUiEWf7zg7aO7t2C5UC2ju76OhKMnJ4LaNratm6o4NdHV2kUhCLwbDaGkaPrGXHrk7attNT+cRgxLAa96LtSPW62XDeucIDZu67f+S95pId4PWVCsD7ZkpkGrnCM+XdH9eXW7Zs4aWULZVKsWr1WojXMmniBEYMS/RS6MXIlqtcSvlMy5l3rutL/b6VUraZ++5PoiZObW10vZeLRAHXFaMg3gSOCh1PBDakhU+KCH8HGCsiNara5eOEr8uLBccfEDmO0NQ0ho0b23qcW75yEzfd+xK7Orp7FrWJOB89ZAozp4xl2Ytv8fa7Pb2CUinYd8o4jnj/RCCzGSeXiWjl+i3ct2xNr0HmIO9c4bnIR/Yrfn8NAPM+f3xkGlf8/hoSiZqM4bnIlX4x15ZatmzhpZIteGditWMgnuDd1l1MaqjhlLnv62EaLEY2gHHjRvLee9v7JFtf0i+GbLIVm3c5nulAyAYwfa9xjKyJ9Wrw5kMhZudi3FwfBI4VkSYRGQmcDDwQBKrqWmCniBzpT50B3K+qHcDjwHx//kzg/iLkyMmsGQ3MmFzPMK9B0wd68/HtjsdjzJwyliPeP7HHwFwwRnDCEXsz5+BJvTbFCQaZg/TT884VngvzSx98BE4PsZpaYrGYzW0xKpaCexCqul5ELgEeBuqAm1X1aRFZClymqs8CpwM/FpF64M/A9f7yLwG3ichCYB1wajE3kYt4PMaF82ezYlULK9dvYXhtTY+B3KCSTu8F5FtJB8ojqsUfKJBVza207uykfniiR97h8EIGmYuV3Rh4bDayMVgoah6Eqi4BlqSdOz709/PA4RHXrQXmFpN3X4nHY8ye2cj7poxl/Ttbe4UVU0nnk/fMKWMzdquzKZh80i6l7Eb/Y3NbjHSSyRTxMdOID29g5fotFfMN21pMnmIq6XIzmGUvJZX60VmvzwgTjEnVTf0oxBPct2xNxay3ZgrCqEoq+aOzXp8RJjwmBb3nUpUTW4vJqEoqfSA4k9ODMfSo5PXWTEEYVUklf3SGEaaSPRFNQVQ5gR0+0XTIkNpsqJI/OsMIU6yreymxMYgqppLt8KXGBoKNwUIlj0mZgqhiKnnwq9RU8kdnGOlUqieiKYgqZqhPyKrUj84wBgs2BlHFmB3eMIxiMAVRxVTy4JdhGJWPmZiqGLPDG4ZRDKYgqhyzwxuGUShmYjIMwzAiMQVhGIZhRGIKwjAMYwAYjKsamIIwysZg/GAMoxDCqxokJhzKfcvW8PNHVlb8O1/wILWITANuByYACpyuqlvT4tQBtwCHATuA01T1lVD4wcCdqnpQoXIYg5PBvgzI7r0mRjYO+F4TlbrPhZGZwbqqQTE9iEXAIlXdH3gWuDQizvnANlU9ALgAWBwEiMiZuD2sRxUhgzFIqfTluLMRVm7xxg8OaGtwsLZEhzqDdXXhghSEiNQCRwP3+FOLgXkRUT8J3AGgqo8BTSIyTUTGAp+ixHtRG5XLYP1goLzKbTAr1qHMYF3VoFATUyPQqqqd/rgZmBoRb7IPIxxPVdcBJ4vI3gXmT0PD6IxhTU1jMobFNm9nW3syY3ipGTduZNnyzkQiUQMULlsh18+YOo6n//I27SElUZeIM2PquB7pFCtbLrKlnymsdWVLpHJr3dnZr3JG5d/XvAfymfaVTGmX8n3MlXap7vuD9SN4/vUW3ni7jfbOJHWJOHvtOYYPHjCxz+bBxsbM9Vt/k1NBiMg84Nq0068B6X3aqFo3nhYvliFen2lp2RrZrW5qGsPGjW0Zr2vb0cF7723vDxH6zLhxI8uWdzY6O7tIJGoKlq2zswugT9dPqB/GxLTluCc2jGRC/bDd6SSTKZIjppAY2cizLzWXxNaeTfZMYfUjEtQm4j0q6tpEnPrhiX59vlH59yXvYt63Qp5pX8gmW7F5F/JMw+HFfAvZOGnOPr1WNWht7VuPeeyYOjZtaiNVgEUxHo9lbVhHkVNBqOrdwN3hc97E1CIiNaraBUwCNkRc/qYPe90fT8wQzygT5RpszbUMSCUPYpdzrwnb52LwMhhXNSjIxKSqHSLyODAfWAKcCdwfEXWpD3tCROYAO715yagAyl0JZ/tgKtnrI6zcWnd2Uj88UTGK1TD6k2LWYvoScJuILATW4QecReRcYLKqXgbcANwkIi8Bu4AzipTX6EcquRKu9L0sAuVWDrPhYGyJGoOTghWEqq4F5kac/2Ho753AWVnSWAPsXagMRnH0RyVcKp/8wOsj3dZe6V4fhlFN2EzqIUyxrnel9Mm3vSwMo/zYct9DmGIHPEtpoiqnnd8wDIcpiCFMsZVwqccJymnnNwzDFMSQp5hK2MYJDKO6sTEIo2BsnMAwqhvrQRgFYz75hlHdmIIwisJ88g2jejETk2EYhhGJKQjDMAwjElMQhmEYRiSmIAzDMAgtG9PwAdsj3WMKwjAqkKCySjQdYpXVAFDObWQrGVMQhlFh2L7TA49t5RqNKQjDqDCsshp4BvMe6aXEFIRhVBhWWQ08xa5sXK0UPFFORKYBtwMTAAVOV9WtaXHqgFuAw4AdwGmq+oqIjAZuBfbH7VN9lareWagshlFN2BpXA49t5RpNMT2IRcAiVd0feBa4NCLO+cA2VT0AuABY7M9fDKxT1VnAscD3RGTPImQxjKrB1rgaeIJlY044Ym+OO3waJxyxd0Xsf15uCupBiEgtcDRwoj+1GHgU+Hpa1E8ClwGo6mMi0uR7Ho/ieh2o6jsi8i4wEXi7EHkMo5qwNa7Kgy0v35tCTUyNQKuqdvrjZmBqRLzJPoxwPFX93+CEiJwCDANeKlAWw6g6bI0roxLIqSBEZB5wbdrp14B0n7skvYmnxYuF4/m0/wv4m5CyyYuGhtEZw5qaxmQMi23ezrb2KFEHhnHjRpYt71wMRdkSiZqM6WcLC1ON5ZbvvRdDprSLzbvan2ljY+b6rb/JqSBU9W7g7vA5b2JqEZEaVe0CJgEbIi5/04e97o8nBvFE5MvARcDHVfWFvgre0rI10i+8qWkMGze2ZbyubUdH2bqPldx1HaqydXZ2AUSmny1sIGQrlmJky+feiyGbbMXmXc3PdOyYOjZtaiNVwJSYeDyWtWEdeU3fswFV7QAeB+b7U2cC90dEXerDEJE5wE5VXSciJwL/DBxZiHIwDKN02CxuI6CY/SC+BNwmIguBdcCpACJyLjBZVS8DbgBuEpGXgF3AGf7aK4ARwH0iEqS3QFWfLUIewzCKJDyLm3iC+5atYVLDSPPoGaIUrCBUdS0wN+L8D0N/7wTOiojzgULzNQyjdIRncQM9ZnHbgPnQw2ZSG4axG5vFbYQxBWEYEQxVO7wtOWGEMQVhGGkM5dVUbRa3EaaYQWrDqEqGsh3eZnEbYUxBGEYa2ezw1a4gwGZxG92Yickw0jA7vGE4TEEYRhpmhzcMh5mYDCMNs8MbhsMUhGFEYHZ4wzATk2EYhpEBUxCGYRhGJKYgDMMwjEhMQRiGYRiRDMlB6nisPN4osVj58s5Ff8iW6rXJYP8Qw8lXCIVsrGIYhmPIKYiRwxJMK5M/+9ixw9kyorYseecip2wxiqptY/TeozZfxo4dwdiRvV/V3OLkznF4XQ0QY9rE+t3xkz7xIP3dv6R6JTmmfjjDa0qr9DPeRSq7Sq6vH05d3F/vbyK1+8/uK9PLMRWKmy2DfF6HVNBsSIXPwajhCTpH1kVeUxN3ho1RI2tdHv5ZBHebK9+goVNXW5MxrLY2nvHeauIxEjV9Ma6kshz1LzEGtoE55BRETTxGTZn82etHDWPX9vay5J2LoSpbUGGMHNa7MsmHpj1GEe8q3x7n2WhqHE3C16aF9cD6/zsJ5GhoGE1Ly9bIOE5pw/Q9M++9nMqiJUb4Z/m+KfW7K+sgepD2PpPGEgv1eVOh/8aNH8Xm9AZJhgLsLUZpu6zjxw1na+vOkuYRpmAFISLTgNuBCYACp6vq1rQ4dcAtwGHADuA0VX1FREYDtwH7Al3ARar6YKGyGIaRncI6f/1f2YXlyLQ6blD5F7p6bnrPD3qrOtdGTGuP+3PD6xJ97EH0SqRkjBhWy1YGTkEUM0i9CFikqvsDzwKXRsQ5H9imqgcAFwCL/fkLgddUdRZuq9KfFiGHYRiGUQIKUhAiUgscDdzjTy0G5kVE/SRwB4CqPgY0icg0Vb0CWOjj7ANsLkQOwzAMo3QU2oNoBFpVtdMfNwNTI+JN9mGkx1PVThH5HXAfcE2BchiGYRglIucYhIjMA65NO/0avQ2UUSN18bR4sXA8Vf2EiEwHlonIn1T1L3lJjRvkykRTU+bBrXJjshVGqWSrq0sUnf5QLLf+IJNsxTyTrmSKmrHTSdXtweqN2zh0/z17OKXkm/ZgLLdSkFNBqOrdwN3hc97E1CIiNaraBUwCNkRc/qYPe90fTwQ2iMgxwKuq2qyqa0VkGXAQkLeCaGnZGjmI1dQ0ho0b2/JNZkAx2QqjlLK1t7tOcKHpD9VyK5ZsshX6TJLJFNfctZzOxqMgluDqnz7LjMn1XDh/9u6VePNJe7CWWy7i8VjWhnXkNYVkpKodwOPAfH/qTOD+iKhLfRgiMgfYqarrcGMTF/vzk4APAc8UIothGAbAilUtrNrQCvFaiMXY1dHFqg2trFjVUm7RBi3FeDF9CThHRF4GjsIPOovIuSJypY9zAzBMRF4CrgfO8Oe/CUwSkRdwSuQCVV1bhCyGYQxx1r3dRntHV49z7R1dvPF2ZfYGBgMFz4PwFfrciPM/DP29EzgrIk4bcEqheRuGYaQzbc8x1NXWsCukJOpqa9gry4Q7Izu2WJ9hGFXBrBkNzJhcz7DaGmLAsNoaZkyuZ9aMhnKLNmgZckttGIZRncTjMS6cP5sVq1p44+029tpzDLNmNNhWsUVgCsIwjKohHo8xe2Yjs2c2lluUqsBMTIZhGEYkpiAMwzCMSExBGIZhGJGYgjAMwzAiMQVhGIZhRGIKwjAMw4jEFIRhGIYRiSkIwzCGBMlkitSoqaT2+ADLV24qeEvToYQpCMMwKoZSVeLBUuBMnAt7HMJN977ENXctNyWRA1MQhmFUBKWsxG0p8MIwBWEYRkVQykrclgIvDFMQhmFUBKWsxIOlwMPYUuC5MQVhGEZFUMpK3JYCL4yCV3MVkWnA7cAEQIHTVXVrWpw64BbgMGAHcJqqvhIKT+C2Lr1JVRcXKothGIOfoBJftaGV9o4u6vqxErelwAujmOW+FwGLVPVOEbkUuBT4elqc84FtqnqAiBwNLAY+HAq/DNivCBkMw6gSSl2J21LgfacgE5OI1AJHA/f4U4uBeRFRPwncAaCqjwFNvueBiBwBfAC4rxAZDMOoPoJK/IQj92H2zEZr4ZeZQnsQjUCrqnb642ZgakS8yT6McDwReQ+4Fvh74DuFCNDQMDpjWFNT5Q48mWyFUSrZ6uoSRac/FMutPzDZCmMgZcupIERkHq4yD/MakO6cnIy4PJ4WL+bj3Qh8S1XfFpH8pQ3R0rI10j+6qWkMGzdWpuuayVYYpZStvd21cQpNf6iWW7GYbIVRjGzxeCxrwzqKnApCVe8G7g6f8yamFhGpUdUuYBKwIeLyN33Y6/54Iq4XcSxwsIhcAUwDPioiHap6R5+kNwzDMEpGQSYmVe0QkceB+cAS4Ezg/oioS33YEyIyB9ipqmtxpicARGQx8IgpB8MwjMqimHkQXwLOEZGXgaOAhQAicq6IXOnj3AAME5GXgOuBM4oR1jAMwxg4CnZz9T2BuRHnfxj6eydwVo50zi5UBsMwDKN02ExqwzAMIxJTEIZhGEYkpiAMwzCMSExBGIZhGJGYgjAMwzAiMQVhGIZhRGIKwjAMw4jEFIRhGIYRiSkIY8iSTKZIjZpKao8PsHzlpsjFHw1jKGMKwhiSJJMprrlrOUycC3scwk33vsQ1dy03JWEYIUxBGEOSFataWLWhFeK1EIuxq6OLVRtaWbGqpdyiGUbFYArCGJKse7uN9o6uHufaO7p44+3K3AfAMMqBKQhjSDJtzzHU1db0OFdXW8Nee1buTmKGMdCYgjCGJLNmNDBjcj3DamuIAcNqa5gxuZ5ZMxrKLZphVAwFL/dtGIOZeDzGhfNns2JVC2+83cZee45h1owG4vFYuUUzjIqhYAUhItOA24EJgAKnq+rWtDh1wC3AYcAO4DRVfSXYshRYFYp+qN++1DAGhHg8xuyZjcye2VhuUQyjIinGxLQIWKSq+wPPApdGxDkf2KaqBwAXAIv9+VnAn1R1duifKQfDMIwKoiAF4XsARwP3+FOLgXkRUT8J3AGgqo8BTb7n8SH/97Mi8qSIHFOIHIZhGEbpKLQH0Qi0qmqnP24GpkbEm+zDSIuXAn4F/DXw/4C7RMT6+YZhGBVEzjEIEZkHXJt2+jVcJR8mGXF5PC1eDEiq6k2hc8+JyFPAkcC9OSX2NDSMzhjW1FS5roomW2GYbIVhshWGyebIqSBU9W7g7vC5YJBZRGr82MEkYEPE5W/6sNf98URgg4icASxT1eB8DOjIU+YagM2bt0Uui9DQMJqWlq29zlcCJlthmGyFYbIVRrXKFo/HGD9+FPg6NB8K8mJS1Q4ReRyYDywBzgTuj4i61Ic9ISJzgJ2quk5EPoAzL31JRAQ4BHg8z+wnAcGNRpKtd1FuTLbCMNkKw2QrjCqXLdxoz0oslSpscTIRmQ7chnNzXQecqqqbReRcYLKqXiYiw4GbcG6uu4AFqvpnEakHbgX2x5mgzlfVh/PMehhukLsZMM8nwzCM/KjBKYdncPVxTgpWEIZhGEZ1Y0ttGIZhGJGYgjAMwzAiMQVhGIZhRGIKwjAMw4jEFIRhGIYRiSkIwzAMIxJTEIZhGEYkVbVhkIicBiwEaoHrVPXGMou0GxF5GDepMFhS5Iuq+lQZ5akHlgF/p6prROQ44HvACOAuVV1YQbL9BJgDbPNRrlDV/ymDXN8ATvGHv1XVf6mUcssgW0WUm5fvSuAzuImxt6jq9yqo7KJkq6Sy+0+gUVXPFpHZwM1APfAYcG5o0dR+p2oUhIhMAa4CDsXNElwmIg+r6svllQxEJAbsB0wv5cPsgzx/BfwYJxMiMgI3s/0Y4A3gtyLyt6oatXzKgMrmOQw4WlWbo68qPb4y+zhuWZgU8ICInAp8hzKXWwbZTqICys3LdwzwUdw+MLXAyyLyByrgncsg22+pnLI7FjgL+K0/dTtuRYonReQW4AvAD0qVfzWZmI4DHlLVd1V1G26vis+UWaYA8b+/F5HnReS8skrjXqp/onuBxcOB11R1tVdgtxO9v8eAyyYiI4FpwK0iskJErhCRcry3zcCFqtquqh3AX3BKrBLKLUq2aVRGuaGqjwIf8WU0AdcwHUcFlF0G2XZQAWUnInvgGr3f8sfTgRGq+qSPspgSl1k1KYhMe09UAuOBPwAnAccC54rIx8oljKouUNXw4ogVU3YRsk0EHgI+B3wYOAr4fBnkein4MEVkX5w5J0kFlFsG2R6gAsotJGOHiFwBvIz7FirpnUuXrZbKKLubgEuAzf54wMusmhRE5N4TZZKlB6r6J1U9U1W3qOom3D7dx5dbrhCVXHarVPUkVW1W1e3ADZSx7ETkIOB/gYtwe6pXTLmFZVNHxZQbgKp+A2gC9sL1viqm7NJkO7bcZSciC4A3VPUPodMD/p1Wk4II9p4ImEj0HhUDjojM8bbEgL7sfzEQVHLZHSwiJ4dOla3sRORIXAvzYlW9jQoqt3TZKqzc9veDq/gK95fAXCqg7DLINr8Cym4+8HERWQ5cCfw9sIABLrOqGaQGHgQuF5EmnOfBycA55RVpN+OAK0XkCFz39Szg3PKK1IOnABGRmcBq4DTcAGIlEAOuE5GHgK24Z3rbQAshInvhtsmdr6oP+dMVUW4ZZKuIcvPMAK7we8KkgE/hzCffLXfZZZDtUcpcdqq62wQtImcDc1X1syLyoogcqap/BM4geh+efqNqehCquh5nr3sYWA4sUdWnyyuVQ1V/g/NCeA74P+BWVf1TeaXqRlV3AmcDv8DZYV/BDfKXHVVdAfwH8EecbMtV9WdlEOVrwHDgeyKy3LfszqYyyi1KtiOojHJDVZfS8/1fpqp3UgFll0G2K6mQsovgdOBaEXkFGA1cX8rMbD8IwzAMI5Kq6UEYhmEY/YspCMMwDCMSUxCGYRhGJKYgDMMwjEhMQRiGYRiRmIIwDMMwIjEFYRiGYURiCsIwDMOI5P8DVh3GIesvcqYAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2j6mGtWZS8uW",
        "colab_type": "text"
      },
      "source": [
        "## Normalized Returns"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P3IMhM18S8uX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "bench_ret = df.returns.iloc[1]\n",
        "df['norm_ret'] = df.returns.div(bench_ret).mul(100)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D_5lRrc_S8ua",
        "colab_type": "code",
        "colab": {},
        "outputId": "64d894ff-8632-4794-8eae-9a0a92614b0a"
      },
      "source": [
        "sgt.plot_acf(df.norm_ret[1:], zero = False, lags = 40)\n",
        "plt.title(\"ACF of Normalized Returns\",size=24)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qM4xYSMES8ue",
        "colab_type": "code",
        "colab": {},
        "outputId": "c346ff3f-43b6-4ffd-8b10-cb21f06bfe9b"
      },
      "source": [
        "model_norm_ret_ma_8 = ARMA(df.norm_ret[1:], order=(0,8))\n",
        "results_norm_ret_ma_8 = model_norm_ret_ma_8.fit()\n",
        "results_norm_ret_ma_8.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<caption>ARMA Model Results</caption>\n",
              "<tr>\n",
              "  <th>Dep. Variable:</th>     <td>norm_ret</td>     <th>  No. Observations:  </th>    <td>5020</td>   \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Model:</th>            <td>ARMA(0, 8)</td>    <th>  Log Likelihood     </th> <td>-40307.597</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Method:</th>             <td>css-mle</td>     <th>  S.D. of innovations</th>   <td>742.822</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Date:</th>          <td>Sat, 07 Sep 2019</td> <th>  AIC                </th>  <td>80635.194</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>              <td>16:17:35</td>     <th>  BIC                </th>  <td>80700.406</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Sample:</th>           <td>01-10-1994</td>    <th>  HQIC               </th>  <td>80658.045</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th></th>                 <td>- 04-05-2013</td>   <th>                     </th>      <td> </td>    \n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "         <td></td>           <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>const</th>          <td>  -12.0369</td> <td>    8.846</td> <td>   -1.361</td> <td> 0.174</td> <td>  -29.374</td> <td>    5.301</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ma.L1.norm_ret</th> <td>   -0.0238</td> <td>    0.014</td> <td>   -1.688</td> <td> 0.091</td> <td>   -0.051</td> <td>    0.004</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ma.L2.norm_ret</th> <td>   -0.0495</td> <td>    0.014</td> <td>   -3.514</td> <td> 0.000</td> <td>   -0.077</td> <td>   -0.022</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ma.L3.norm_ret</th> <td>   -0.0847</td> <td>    0.014</td> <td>   -5.993</td> <td> 0.000</td> <td>   -0.112</td> <td>   -0.057</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ma.L4.norm_ret</th> <td>    0.0488</td> <td>    0.014</td> <td>    3.440</td> <td> 0.001</td> <td>    0.021</td> <td>    0.077</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ma.L5.norm_ret</th> <td>   -0.0518</td> <td>    0.014</td> <td>   -3.634</td> <td> 0.000</td> <td>   -0.080</td> <td>   -0.024</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ma.L6.norm_ret</th> <td>   -0.0402</td> <td>    0.014</td> <td>   -2.787</td> <td> 0.005</td> <td>   -0.068</td> <td>   -0.012</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ma.L7.norm_ret</th> <td>    0.0084</td> <td>    0.014</td> <td>    0.587</td> <td> 0.557</td> <td>   -0.020</td> <td>    0.036</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ma.L8.norm_ret</th> <td>    0.0366</td> <td>    0.014</td> <td>    2.626</td> <td> 0.009</td> <td>    0.009</td> <td>    0.064</td>\n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<caption>Roots</caption>\n",
              "<tr>\n",
              "    <td></td>   <th>            Real</th>  <th>         Imaginary</th> <th>         Modulus</th>  <th>        Frequency</th>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>MA.1</th> <td>           1.4716</td> <td>          -0.4282j</td> <td>           1.5326</td> <td>          -0.0451</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>MA.2</th> <td>           1.4716</td> <td>          +0.4282j</td> <td>           1.5326</td> <td>           0.0451</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>MA.3</th> <td>           0.5944</td> <td>          -1.3169j</td> <td>           1.4448</td> <td>          -0.1825</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>MA.4</th> <td>           0.5944</td> <td>          +1.3169j</td> <td>           1.4448</td> <td>           0.1825</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>MA.5</th> <td>          -0.7318</td> <td>          -1.2736j</td> <td>           1.4689</td> <td>          -0.3330</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>MA.6</th> <td>          -0.7318</td> <td>          +1.2736j</td> <td>           1.4689</td> <td>           0.3330</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>MA.7</th> <td>          -1.4485</td> <td>          -0.6983j</td> <td>           1.6081</td> <td>          -0.4285</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>MA.8</th> <td>          -1.4485</td> <td>          +0.6983j</td> <td>           1.6081</td> <td>           0.4285</td>\n",
              "</tr>\n",
              "</table>"
            ],
            "text/plain": [
              "<class 'statsmodels.iolib.summary.Summary'>\n",
              "\"\"\"\n",
              "                              ARMA Model Results                              \n",
              "==============================================================================\n",
              "Dep. Variable:               norm_ret   No. Observations:                 5020\n",
              "Model:                     ARMA(0, 8)   Log Likelihood              -40307.597\n",
              "Method:                       css-mle   S.D. of innovations            742.822\n",
              "Date:                Sat, 07 Sep 2019   AIC                          80635.194\n",
              "Time:                        16:17:35   BIC                          80700.406\n",
              "Sample:                    01-10-1994   HQIC                         80658.045\n",
              "                         - 04-05-2013                                         \n",
              "==================================================================================\n",
              "                     coef    std err          z      P>|z|      [0.025      0.975]\n",
              "----------------------------------------------------------------------------------\n",
              "const            -12.0369      8.846     -1.361      0.174     -29.374       5.301\n",
              "ma.L1.norm_ret    -0.0238      0.014     -1.688      0.091      -0.051       0.004\n",
              "ma.L2.norm_ret    -0.0495      0.014     -3.514      0.000      -0.077      -0.022\n",
              "ma.L3.norm_ret    -0.0847      0.014     -5.993      0.000      -0.112      -0.057\n",
              "ma.L4.norm_ret     0.0488      0.014      3.440      0.001       0.021       0.077\n",
              "ma.L5.norm_ret    -0.0518      0.014     -3.634      0.000      -0.080      -0.024\n",
              "ma.L6.norm_ret    -0.0402      0.014     -2.787      0.005      -0.068      -0.012\n",
              "ma.L7.norm_ret     0.0084      0.014      0.587      0.557      -0.020       0.036\n",
              "ma.L8.norm_ret     0.0366      0.014      2.626      0.009       0.009       0.064\n",
              "                                    Roots                                    \n",
              "=============================================================================\n",
              "                  Real          Imaginary           Modulus         Frequency\n",
              "-----------------------------------------------------------------------------\n",
              "MA.1            1.4716           -0.4282j            1.5326           -0.0451\n",
              "MA.2            1.4716           +0.4282j            1.5326            0.0451\n",
              "MA.3            0.5944           -1.3169j            1.4448           -0.1825\n",
              "MA.4            0.5944           +1.3169j            1.4448            0.1825\n",
              "MA.5           -0.7318           -1.2736j            1.4689           -0.3330\n",
              "MA.6           -0.7318           +1.2736j            1.4689            0.3330\n",
              "MA.7           -1.4485           -0.6983j            1.6081           -0.4285\n",
              "MA.8           -1.4485           +0.6983j            1.6081            0.4285\n",
              "-----------------------------------------------------------------------------\n",
              "\"\"\""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6yrdly_fS8uh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df['res_norm_ret_ma_8'] = results_ret_ma_8.resid[1:]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FSJjNizJS8ul",
        "colab_type": "code",
        "colab": {},
        "outputId": "556a914f-3b6a-454d-e74c-f0cd382184c6"
      },
      "source": [
        "df.res_norm_ret_ma_8[1:].plot(figsize=(20,5))\n",
        "plt.title(\"Residuals of Normalized Returns\",size=24)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qDfVTgisS8up",
        "colab_type": "code",
        "colab": {},
        "outputId": "86bfd79d-ec05-4b24-e5bc-a7c7b56d3708"
      },
      "source": [
        "sgt.plot_acf(df.res_norm_ret_ma_8[2:], zero = False, lags = 40)\n",
        "plt.title(\"ACF Of Residuals for Normalized Returns\",size=24)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t5vNUi3aS8us",
        "colab_type": "text"
      },
      "source": [
        "## MA Models For Prices"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "b1gZlcFpS8ut",
        "colab_type": "code",
        "colab": {},
        "outputId": "8db14f99-98b9-4899-f415-a1f7c3f7fb43"
      },
      "source": [
        "sgt.plot_acf(df.market_value, zero = False, lags = 40)\n",
        "plt.title(\"ACF for Prices\", size=20)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RaNysrH9S8ux",
        "colab_type": "code",
        "colab": {},
        "outputId": "1c7b1698-6d7e-4eef-bb53-1d2c260a2a32"
      },
      "source": [
        "model_ma_1 = ARMA(df.market_value, order=(0,1))\n",
        "results_ma_1 = model_ma_1.fit()\n",
        "results_ma_1.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<caption>ARMA Model Results</caption>\n",
              "<tr>\n",
              "  <th>Dep. Variable:</th>   <td>market_value</td>   <th>  No. Observations:  </th>    <td>5021</td>   \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Model:</th>            <td>ARMA(0, 1)</td>    <th>  Log Likelihood     </th> <td>-38629.000</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Method:</th>             <td>css-mle</td>     <th>  S.D. of innovations</th>   <td>530.756</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Date:</th>          <td>Sat, 07 Sep 2019</td> <th>  AIC                </th>  <td>77264.000</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>              <td>16:25:01</td>     <th>  BIC                </th>  <td>77283.564</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Sample:</th>           <td>01-07-1994</td>    <th>  HQIC               </th>  <td>77270.856</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th></th>                 <td>- 04-05-2013</td>   <th>                     </th>      <td> </td>    \n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "           <td></td>             <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>const</th>              <td> 5089.6144</td> <td>   14.660</td> <td>  347.188</td> <td> 0.000</td> <td> 5060.882</td> <td> 5118.347</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ma.L1.market_value</th> <td>    0.9573</td> <td>    0.003</td> <td>  335.559</td> <td> 0.000</td> <td>    0.952</td> <td>    0.963</td>\n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<caption>Roots</caption>\n",
              "<tr>\n",
              "    <td></td>   <th>            Real</th>  <th>         Imaginary</th> <th>         Modulus</th>  <th>        Frequency</th>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>MA.1</th> <td>          -1.0446</td> <td>          +0.0000j</td> <td>           1.0446</td> <td>           0.5000</td>\n",
              "</tr>\n",
              "</table>"
            ],
            "text/plain": [
              "<class 'statsmodels.iolib.summary.Summary'>\n",
              "\"\"\"\n",
              "                              ARMA Model Results                              \n",
              "==============================================================================\n",
              "Dep. Variable:           market_value   No. Observations:                 5021\n",
              "Model:                     ARMA(0, 1)   Log Likelihood              -38629.000\n",
              "Method:                       css-mle   S.D. of innovations            530.756\n",
              "Date:                Sat, 07 Sep 2019   AIC                          77264.000\n",
              "Time:                        16:25:01   BIC                          77283.564\n",
              "Sample:                    01-07-1994   HQIC                         77270.856\n",
              "                         - 04-05-2013                                         \n",
              "======================================================================================\n",
              "                         coef    std err          z      P>|z|      [0.025      0.975]\n",
              "--------------------------------------------------------------------------------------\n",
              "const               5089.6144     14.660    347.188      0.000    5060.882    5118.347\n",
              "ma.L1.market_value     0.9573      0.003    335.559      0.000       0.952       0.963\n",
              "                                    Roots                                    \n",
              "=============================================================================\n",
              "                  Real          Imaginary           Modulus         Frequency\n",
              "-----------------------------------------------------------------------------\n",
              "MA.1           -1.0446           +0.0000j            1.0446            0.5000\n",
              "-----------------------------------------------------------------------------\n",
              "\"\"\""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    }
  ]
}