Checking out the VIX performance by comparing weekly changes

app.ipynb

{
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    {
      "cell_type": "code",
      "source": [
        "%matplotlib inline  \n",
        "import pandas as pd\n",
        "import pandas_datareader.data as web\n",
        "import datetime as dt\n",
        "import numpy as np \n",
        "from datetime import datetime, timedelta\n",
        "from scipy import stats\n",
        "\n\n",
        "def pad_array(r):\n",
        "    if len(r) >= 5:\n",
        "        return r\n",
        "    return np.pad(r, (0, 5-len(r)), mode='constant', constant_values=np.nan)\n",
        "\n",
        "start = dt.datetime(2005, 1, 1)\n",
        "end = dt.datetime.now()\n",
        "\n",
        "df = web.DataReader(\"TVIX\", 'yahoo', start, end)\n",
        "\n",
        "grouped = df.groupby(pd.Grouper(level='Date',freq='W-SUN'))\n",
        "\n",
        "rows = []\n",
        "indices = []\n",
        "for index, gr in grouped:\n",
        "    first_price_that_week = gr[\"Open\"].iloc[0]\n",
        "    \n",
        "    change_compared_to_week_start = (gr[\"Close\"] / first_price_that_week) - 1\n",
        "    numerical_change = gr[\"Close\"] - first_price_that_week\n",
        "    features = change_compared_to_week_start.values\n",
        "    \n",
        "    index = gr.index[0]\n",
        "    rows.append(pad_array(features))\n",
        "    \n",
        "    indices.append(index)\n",
        "\n",
        "fd = pd.DataFrame(rows)\n",
        "fd.index = indices\n",
        "\n\n",
        "todays_days_since_monday = datetime.today().weekday()\n",
        "t = fd.tail(1)[todays_days_since_monday].values[0]\n",
        "vals = fd[todays_days_since_monday].values\n",
        "\n",
        "x = vals[~np.isnan(vals)]\n",
        "ans = stats.percentileofscore(x, t)\n",
        "\n\n",
        "todays_price = df.tail(1)[\"Close\"].values[0]\n",
        "week_start_date = fd.tail(1).index\n",
        "weeks_open_price = df.loc[week_start_date][\"Open\"].values[0]\n",
        "\n",
        "print(\n",
        "    \"Todays close of\", round(todays_price, 3), \"is a change of\", \n",
        "    str(round(t, 3)), \"from the weeks open of\", str(round(weeks_open_price, 3)),\"which is\", \n",
        "    str(round(ans, 3))+\"%\", \"above all of the other rates of change on the\",\n",
        "    todays_days_since_monday, \"th day of the week. Starting on\", str(week_start_date.date[0]),\n",
        "    \"this is based on\", str(len(x)), \"samples\"\n",
        ")\n"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Todays close of 70.13 is a change of 0.094 from the weeks open of 64.12 which is 89.831% above all of the other rates of change on the 1 th day of the week. Starting on 2019-12-09 this is based on 472 samples\n"
          ]
        }
      ],
      "execution_count": 1,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# df.tail(4)"
      ],
      "outputs": [],
      "execution_count": 2,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# fd.tail(4)"
      ],
      "outputs": [],
      "execution_count": 3,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    }
  ],
  "metadata": {
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      "name": "python3"
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      "codemirror_mode": {
        "name": "ipython",
        "version": 3
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      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "kernelspec": {
      "name": "python3",
      "language": "python",
      "display_name": "Python 3"
    },
    "nteract": {
      "version": "0.12.3"
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  "nbformat": 4,
  "nbformat_minor": 4
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