ipynb/patrick_david-kernel.ipynb

{
    "cells": [
        {
            "cell_type": "markdown",
            "metadata": {
                "_uuid": "9067c258e929458d68d6023a972ab0820de15405"
            },
            "source": [
                "# <center><span style=\"color:blue; font-size: 2em\"> Stocks, Significance Testing & p-Hacking: How <span style=\"color:green\"> volatile </span> <span style=\"color:red\"> is volatile? </span></span></center>\n",
                "\n",
                "\n",
                ">## ** October is historically the most volatile month for stocks, but is this a persistent signal or just noise in the data?**\n",
                "\n",
                "\n",
                "![](https://cdn-images-1.medium.com/max/600/1*D0_bUZNtw_3aq_CyMqj0dw.gif)\n"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "_uuid": "fbe8484df715722a859878d765e8b29e95143267"
            },
            "source": [
                "### Stocks, Significance Testing & p-Hacking.\n",
                "\n",
                " <center><span style=\" font-size: 20px\"> Over the past 32 years, October has been the most volatile month on average for the S&P500 and December the least, in this article we will use simulation to assess the statistical significance of this observation and to what extent this observation could occur by chance.</span> </center>"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "_uuid": "66ac331a65002956f2b851f3bf8c4d54b7a7ed6f"
            },
            "source": [
                "### Our goal:\n",
                "\n",
                "*  [**Demonstrate how to use Pandas to analyze Time Series**](#Wrangle-with-Pandas:)\n",
                "* [**Understand how to construct a hypothesis test**](#Hypothesis-Testing:-What’s-the-question?)\n",
                "* [**Use simulation to perform hypothesis testing**](#Simulation)\n",
                "* [**Show the importance of accounting for multiple comparison bias**](#p-Hacking)"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "_uuid": "d225e7ac8c50277d8db279cbdfce5ebd43ed7acf"
            },
            "source": [
                "### Our data:\n",
                "\n",
                "We will be using Daily S&P500 data for this analysis, in particular we will use the raw daily closing prices from 1986 to 2018 (which is surprisingly hard to find so I’ve made it [publicly available](https://raw.githubusercontent.com/Patrick-David/Stocks_Significance_PHacking/master/spx.csv)).\n",
                "\n",
                "The inspiration for this post came from [Winton](https://www.winton.com/research/seasonal-volatility-and-the-multiplicity-effect), which we will be reproducing here, albeit with 32 years of data vs their 87 years.\n",
                "\n",
                "The Code is also available on [Kaggle](https://www.kaggle.com/pdquant/stocks-significance-testing-p-hacking)\n",
                "\n"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "_uuid": "b416cc7c609d2f4deab1945215aa261c551628cd"
            },
            "source": [
                "### Wrangle with Pandas:\n",
                "\n",
                "To answer the question of whether the extreme volatility seen in certain months really is significant, we need to transform our 32yrs of price data into a format that shows the phenomena we are investigating.\n",
                "\n",
                "Our format of choice will be **average monthly volatility rankings (AMVR)**.\n",
                "\n",
                "The following code shows how we get our raw price data into this format:"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 4,
            "metadata": {
                "_kg_hide-input": false,
                "_uuid": "65a35723b3f4aac44891c76f55afab6f36465df1"
            },
            "outputs": [],
            "source": [
                "#standard imports\n",
                "import numpy as np\n",
                "import pandas as pd\n",
                "import matplotlib.pyplot as plt\n",
                "import matplotlib.patches as mpatches\n",
                "%matplotlib inline\n",
                "import pandas_datareader.data as web\n",
                "import seaborn as sns"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "_uuid": "e2370e32f877d548d2e134e95fd6d5557c5d0ca2"
            },
            "source": [
                "A nice trick to make charts display full width in Jupyter notebooks:"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 5,
            "metadata": {
                "_kg_hide-input": false,
                "_uuid": "c74e890c703f19aa18b61ccac399378a888f00c8"
            },
            "outputs": [],
            "source": [
                "#resize charts to fit screen if using Jupyter Notebook\n",
                "plt.rcParams['figure.figsize']=[20,5]"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "_uuid": "dae9a80961eabcf64c2d2826969437b7aa26173c"
            },
            "source": [
                "import our data and convert into daily returns using pct_change"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 6,
            "metadata": {
                "_kg_hide-input": false,
                "_uuid": "7d15bfd1de13f275432ed69201b87c9655e7d8d4"
            },
            "outputs": [
                {
                    "data": {
                        "text/html": [
                            "<div>\n",
                            "<style scoped>\n",
                            "    .dataframe tbody tr th:only-of-type {\n",
                            "        vertical-align: middle;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe tbody tr th {\n",
                            "        vertical-align: top;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe thead th {\n",
                            "        text-align: right;\n",
                            "    }\n",
                            "</style>\n",
                            "<table border=\"1\" class=\"dataframe\">\n",
                            "  <thead>\n",
                            "    <tr style=\"text-align: right;\">\n",
                            "      <th></th>\n",
                            "      <th>close</th>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>date</th>\n",
                            "      <th></th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th>1986-01-02</th>\n",
                            "      <td>209.59</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>1986-01-03</th>\n",
                            "      <td>210.88</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>1986-01-06</th>\n",
                            "      <td>210.65</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>1986-01-07</th>\n",
                            "      <td>213.80</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>1986-01-08</th>\n",
                            "      <td>207.97</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "</div>"
                        ],
                        "text/plain": [
                            "             close\n",
                            "date              \n",
                            "1986-01-02  209.59\n",
                            "1986-01-03  210.88\n",
                            "1986-01-06  210.65\n",
                            "1986-01-07  213.80\n",
                            "1986-01-08  207.97"
                        ]
                    },
                    "execution_count": 6,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "#Daily S&P500 data from 1986==>\n",
                "url = \"https://raw.githubusercontent.com/Patrick-David/Stocks_Significance_PHacking/master/spx.csv\"\n",
                "df = pd.read_csv(url,index_col='date', parse_dates=True)\n",
                "\n",
                "#view raw S&P500 data\n",
                "df.head()"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "_uuid": "4159594fd9681d19bb79f43883f0f000416907b8"
            },
            "source": [
                "Now we use one of the more powerful tools in Pandas: [Resample](https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=2ahUKEwiJl6ShnJXeAhXHK8AKHQBCCAsQFjAAegQIBxAB&url=https%3A%2F%2Fpandas.pydata.org%2Fpandas-docs%2Fstable%2Fgenerated%2Fpandas.DataFrame.resample.html&usg=AOvVaw1le9agxvLanaQp9zlNYG9Y). This allows us to change the frequency of our data from daily to monthly and to use standard deviation as a measure of volatility. These are the design choices we have when constructing an analysis."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 7,
            "metadata": {
                "_uuid": "89b629a5599e1d9c0c81d628190e666ee437c665"
            },
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "date\n",
                            "1986-01-03    0.006155\n",
                            "1986-01-06   -0.001091\n",
                            "1986-01-07    0.014954\n",
                            "1986-01-08   -0.027268\n",
                            "1986-01-09   -0.008944\n",
                            "Name: close, dtype: float64"
                        ]
                    },
                    "execution_count": 7,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "#To model returns we will use daily % change\n",
                "daily_ret = df['close'].pct_change()\n",
                "#drop the 1st value - nan\n",
                "daily_ret.dropna(inplace=True)\n",
                "#daily %change\n",
                "daily_ret.head()"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "_uuid": "a568179298b1af9b065dffe22b19a9ca30757ef7"
            },
            "source": [
                "A quick look at the annualized monthly vol, shows major market events clearly, such as Black Monday and the 2008 Financial Crisis."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 8,
            "metadata": {
                "_uuid": "04b40f3d51286c8b9ee21f0794c0a400d53e1e71"
            },
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "date\n",
                        "1986-01-31    0.033317\n",
                        "1986-02-28    0.023585\n",
                        "1986-03-31    0.027961\n",
                        "1986-04-30    0.037426\n",
                        "1986-05-31    0.027412\n",
                        "Freq: M, Name: close, dtype: float64\n"
                    ]
                },
                {
                    "data": {
                        "text/plain": [
                            "<matplotlib.legend.Legend at 0x18aae9e6b00>"
                        ]
                    },
                    "execution_count": 8,
                    "metadata": {},
                    "output_type": "execute_result"
                },
                {
                    "data": {
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\n",
                        "text/plain": [
                            "<Figure size 1440x360 with 1 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "#use pandas to resample returns per month and take Standard Dev as measure of Volatility\n",
                "#then annualize by multiplying by sqrt of number of periods (12)\n",
                "mnthly_annu = daily_ret.resample('M').std()* np.sqrt(12)\n",
                "\n",
                "print(mnthly_annu.head())\n",
                "#we can see major market events show up in the volatility\n",
                "plt.plot(mnthly_annu)\n",
                "plt.axvspan('1987','1989',color='r',alpha=.5)\n",
                "plt.axvspan('2008','2010',color='r',alpha=.5)\n",
                "plt.title('Monthly Annualized vol - Black Monday and 2008 Financial Crisis highlighted')\n",
                "labs = mpatches.Patch(color='red',alpha=.5, label=\"Black Monday & '08 Crash\")\n",
                "plt.legend(handles=[labs])\n"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "_uuid": "c7befb308efaaa41ef27980a723c0a2c519b33c4"
            },
            "source": [
                "Here’s where we can use the power of pandas to group our volatility by year and create a ranking for each of the 12 months over all 32 years of data"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 9,
            "metadata": {
                "_uuid": "e96cee742cbbed47922ff81d9431347205813af3"
            },
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "count    12.000000\n",
                            "mean      6.450521\n",
                            "std       0.627458\n",
                            "min       5.218750\n",
                            "25%       6.031013\n",
                            "50%       6.491004\n",
                            "75%       6.704545\n",
                            "max       7.531250\n",
                            "Name: close, dtype: float64"
                        ]
                    },
                    "execution_count": 9,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "#for each year rank each month based on volatility lowest=1 Highest=12\n",
                "ranked = mnthly_annu.groupby(mnthly_annu.index.year).rank()\n",
                "\n",
                "#average the ranks over all years for each month\n",
                "final = ranked.groupby(ranked.index.month).mean()\n",
                "\n",
                "final.describe()"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "_uuid": "155f7dbf044a3516877c8b7f92b5b6aa427e7644"
            },
            "source": [
                "this gives our final** Average Monthly Volatility Rankings**. Numerically we can see that **month 10 (October) is the highest and 12 (December) is the lowest**."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 10,
            "metadata": {
                "_uuid": "13e44e882a7f9e3e5721f447d43b83cb57148a94"
            },
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "date\n",
                            "1     6.818182\n",
                            "2     6.666667\n",
                            "3     6.575758\n",
                            "4     7.303030\n",
                            "5     6.606061\n",
                            "6     6.030303\n",
                            "7     6.031250\n",
                            "8     5.875000\n",
                            "9     6.406250\n",
                            "10    7.531250\n",
                            "11    6.343750\n",
                            "12    5.218750\n",
                            "Name: close, dtype: float64"
                        ]
                    },
                    "execution_count": 10,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "#the final average results over 32 years \n",
                "final"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "_uuid": "2bea95e7660a465c2caf3e1d797f5d2e832be44c"
            },
            "source": [
                "By plotting our AMVR we can clearly see the most volatile month has been October and the lowest, December."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 11,
            "metadata": {
                "_uuid": "bc752ce6088167295b1f3c9f8810623490f644fa",
                "scrolled": true
            },
            "outputs": [
                {
                    "data": {
                        "image/png": 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qfs7bcjLZwsXL8ho7Wpi6TSmlR8gWDf8R2Wiye3lnNNonycKbv+b1TCcLadqyDVko1jgKaRUwu0Wtr5Ot3XMQcEjK1ghrHDH2dbLLzC8nC7fOTyndnrfPzo/t4ryOY4FjUvsLeUuSVJLofAq5JEn9Q0R8F9gspXRKpztvpCJia7JFZTdLKS0rdz09ocjHGBGfBk5MKbU5CkWSJAkc2SNJ6sciYqd8Ckbk03ROIxv1UEj51J3/AH5btBCkUdGOMSI2z6dODYjsCm9nU+A+KkmSukdl57tIklRYw8mmbm1BNgXjB8D/lbWiHhIRQ8nWuJlLNv2mcAp6jFVkV4ralmya1m+Bn5W1IkmS1Oc5jUuSJEmSJKlAnMYlSZIkSZJUIIY9kiRJkiRJBdIja/aMHTs2TZgwoSceWpIkSZIkqV967LHHFqSUajrbr0fCngkTJjBjxoyeeGhJkiRJkqR+KSLmlrKf07gkSZIkSZIKxLBHkiRJkiSpQAx7JEmSJEmSCqRH1uyRJEmSJEnqbmvWrGHevHnU19eXu5QeNXjwYMaPH8/AgQPX6/6GPZIkSZIkaaMwb948hg8fzoQJE4iIcpfTI1JKLFy4kHnz5rHtttuu12M4jUuSJEmSJG0U6uvrGTNmTGGDHoCIYMyYMRs0esmwR5IkSZIkbTSKHPQ02tBjNOyRJEmSJEnqgiVLljB16lR22mknJk2axIMPPtjmfo8++igVFRVMnz797W0VFRVMnjyZyZMnc8wxx/RIfa7ZI0mSJEmS1AVnnnkmhx12GNOnT2f16tXU1dW12mft2rV86Utf4tBDD222fciQIcycObNH63NkjyRJkiRJUomWLVvGfffdx2mnnQZAVVUVo0aNarXfRRddxPHHH8+mm27a2yUa9kiSJEmSJJXqxRdfpKamhlNPPZXdd9+df/7nf2blypXN9nn11Ve5/vrr+dSnPtXq/vX19UyZMoW99tqLG264oUdqdBqXJEmSJHVgs+9vxhsr3yh3GX3KuKHjmP/5+eUuQ+KAAw5ote2EE07gM5/5DHV1dRxxxBGt2qdNm8a0adNYsGABU6dObdZ2zz33dPqcDQ0NPP7441x00UXsueeenHnmmVxwwQV861vfenufs846i+9+97tUVFS0uv/LL7/MFltswYsvvsiBBx7Irrvuyvbbb9/5wXaBYY8kSZIkdcCgpzXPifqz8ePHM378ePbcc08Apk6dygUXXNBsnxkzZnDiiScCsGDBAm655RYqKys57rjj2GKLLQDYbrvtOOCAA3jiiScMeyRJkiRJkqDjkTjV1dUdto8dO7akkTwtbbbZZmy11VbMnj2biRMnctddd7Hzzjs32+ell156++tp06Zx1FFHcdxxx7F48WKqq6sZNGgQCxYs4IEHHuCLX/xil2vojGGPJEmSJElSF1x00UWcdNJJrF69mu22245f//rXXHzxxQBtrtPTaNasWZxxxhkMGDCAdevWcc4557QKirpDpJS6/UGnTJmSZsyY0e2PK0mSJEm9Lb4R5S6hT0rndf97Sakzs2bNYtKkSeUuo1e0dawR8VhKaUpn9/VqXJIkSZIkSQVi2CNJkiRJklQghj2SJEmSJEkF4gLNktSBCefcXO4S+pw5FxxZ7hIkSZLUj6WUiCj2Wlobur6yI3skSZIkSdJGYfDgwSxcuHCDw5C+LKXEwoULGTx48Ho/hiN7JEmSJEnSRmH8+PHMmzeP2tracpfSowYPHsz48ePX+/6GPZIkSZIkaaMwcOBAtt1223KX0ec5jUuSJEmSJKlADHskSZIkSZIKpNOwJyImRsTMJh/LIuKs3ihOkiRJkiRJXdPpmj0ppdnAZICIqABeBa7v4bokSZIkSZK0Hro6jesg4IWU0tyeKEaSJEmSJEkbpqthz4nA1T1RiCRJkiRJkjZcyWFPRFQBxwC/b6f99IiYEREzin69e0mSJEmSpL6qKyN7DgceTym90VZjSumSlNKUlNKUmpqa7qlOkiRJkiRJXdKVsOdjOIVLkiRJkiSpTysp7ImIauAQ4LqeLUeSJEmSJEkbotNLrwOklOqAMT1ciyRJkiRJkjZQV6/GJUmSJEmSpD6spJE9/dWEc24udwl9zpwLjix3CZIkSZIkqQOO7JEkSZIkSSoQwx5JkiRJkqQCMeyRJEmSJEkqEMMeSZIkSZKkAjHskSRJkiRJKhCvxqVe51XOWvMqZ5IkSZKk7uLIHkmSJEmSpAJxZI8kqdc5wq81R/hJkiSpuxj2SAXhm+fWfPMsSZIkqT9yGpckSZIkSVKBGPZIkiRJkiQVSI9M45o9ezYHHHBAs20nnHACn/nMZ6irq+OII45odZ9p06Yxbdo0FixYwNSpU1u1f/rTn+ajH/0or7zyCieffHKr9rPPPpujjz6a2bNnc8YZZ7Rq/+pXv8rBBx/MzJkzOeuss1q1n3/++ey999785S9/4dxzzwVg/osL324ffdDpVI3bjlVzZrL0L79tdf8xh36WgWPGU/f3h1n2yPWt2scedTaVI2pYOes+lj9xS6v2muO+TEX1SFY8fScrnr6zVfumH/k6AwYOZvnjN7PyuT+3at/s4xcAsPTh61j1wiPN2qJyEONO+AYASx64mvq5TzZrrxgygpoPZce8+N7LeOvV55q1Vw4fy9ijPw/AWWedxcyZM5u177jjjlxyySUAnH766Tz//PPN2idPnsyFF14IwCc+8Qnm39f8+QdtuROb7D8NgNrrz2ftqmXN2gdvsxuj3v8xAN645jxSw1vN2ods/15G7vlhAOb/5hxaGrrTvgzf40jWrannzd9/vVX7sF0PZtiuB7O2bim1N3ynVfvw3Y9g6KT9aFhWy4KbftCqfcR7P0T1DnuyZuE8Fv7xJ63aR+59IkMmTGb1Gy+y6K5LWrWP2u8U4Mhmfa+pCy+8kMmTJ3PnnXfy7W9/u1X7L37xCyZOnFj4vrfozktY/eaLzdoHjt6SMYd9DoCFt13EmkWvNms/q/6OZn1v3rx5zdrf97738Z3vZN/z448/noULFzZrP+igg4A9gOL2vcHjJ1E/bxZL7ru8VXt7r3sHPPTfwDt978Ybb+QHP2hd3//+7/+y1VZb8bvf/Y6f//znzdrmv7iw0H2vatPtGH3w6QAsuPH7NCxf0Ky9rde9xvMKWd/7z//8TwAOP/xwVq1a1ez+Rx11FJ//fFZfy9+3sHH+zm2q1Ne99el7ANOnT2fs2LFcdtllXHbZZa3ab7nlFqqrq/nZz37GNddc06r9nnvuAeD73/8+N910U7O2IUOGcOuttwLwrW99i7vuuqtZ+5gxY7j22msB+PKXv8yDDz7YrH38+PFceeWVQPf8zl2f1z37nn3Pvte677EFMBFYANzYupn9gO2B14Hb2mg/CNgaeBm4q432w4DNgReA+9poPxoYC8wG/tJG+4eBkcAzwKNttJ8ADAWeAGa20X4SUAU8AjzbRvup+ecHgCbf+gP+dIB9z9c9X/coT9/rjCN7JEmSJEmSCiRSSt3+oFOmTEkzZszo9sftbS5421p3LHjreW3N89ozPK89w/PaM1xQXJL6rvhGlLuEPimd1/3vJSV1LCIeSylN6Ww/r8YlSZIkFYRhemuG6ZL6I6dxSZIkSZIkFYgjeyRJkiRJvc6RaK05Ek3dxZE9kiRJkiRJBWLYI0mSJEmSVCCGPZIkSZIkSQVi2CNJkiRJklQghj2SJEmSJEkFYtgjSZIkSZJUIIY9kiRJkiRJBVJS2BMRoyJiekQ8FxGzIuJ9PV2YJEmSJEmSuq6yxP1+DNyWUpoaEVVAdQ/WJEmSJEmSpPXUadgTESOA/YBpACml1cDqni1LkiSpb5hwzs3lLqHPmXPBkeUuQZIkdaCUkT3bAbXAryNiN+Ax4MyU0soerUySJHWJoURrhhKSJKk/KmXNnkpgD+DnKaXdgZXAOS13iojTI2JGRMyora3t5jIlSZIkSZJUilLCnnnAvJTSw/nt6WThTzMppUtSSlNSSlNqamq6s0ZJkiRJkiSVqNOwJ6U0H3glIibmmw4C/tqjVUmSJEmSJGm9lHo1rs8BV+VX4noROLXnSpIkSZIkSdL6KinsSSnNBKb0cC2SJEnqJ1xQvDUXFJckdZdS1uyRJEmSJEnSRsKwR5IkSZIkqUAMeyRJkiRJkgrEsEeSJEmSJKlADHskSZIkSZIKxLBHkiRJkiSpQAx7JEmSJEmSCsSwR5IkSZIkqUAMeyRJkiRJkgrEsEeSJEmSJKlADHskSZIkSZIKxLBHkiRJkiSpQAx7JEmSJEmSCsSwR5IkSZIkqUAMeyRJkiRJkgrEsEeSJEmSJKlADHskSZIkSZIKxLBHkiRJkiSpQAx7JEmSJEmSCsSwR5IkSZIkqUAMeyRJkiRJkgqkstwFSJIkSZKkbrLZZvDGG+Wuom8ZNw7mzy93Fb3KkT2SJEmSJBWFQU9r/fCcGPZIkiRJkiQViGGPJEmSJElSgZS0Zk9EzAGWA2uBhpTSlJ4sSpIkSZIkSeunKws0fyCltKDHKpEkSZIkSdIGcxqXJEmSJElSgZQa9iTg9oh4LCJO78mCJEmSJEmStP5Kncb1/pTSaxGxKXBHRDyXUrqv6Q55CHQ6wNZbb93NZUqSJEmSJKkUJY3sSSm9ln9+E7geeG8b+1ySUpqSUppSU1PTvVVKkiRJkiSpJJ2GPRExNCKGN34NfBB4pqcLkyRJkiRJUteVMo1rHHB9RDTu/5uU0m09WpUkSZIkSZLWS6dhT0rpRWC3XqhFkiRJkiRJG8hLr0uSJEmSJBWIYY8kSZIkSVKBGPZIkiRJkiQViGGPJEmSJElSgRj2SJIkSZIkFYhhjyRJkiRJUoEY9kiSJEmSJBWIYY8kSZIkSVKBGPZIkiRJkiQViGGPJEmSJElSgRj2SJIkSZIkFYhhjyRJkiRJUoEY9kiSJEmSJBWIYY8kSZIkSVKBGPZIkiRJkiQViGGPJEmSJElSgRj2SJIkSZIkFYhhjyRJkiRJUoEY9kiSJEmSJBWIYY8kSZIkSVKBGPZIkiRJkiQViGGPJEmSJElSgRj2SJIkSZIkFYhhjyRJkiRJUoEY9kiSJEmSJBWIYY8kSZIkSVKBlBz2RERFRDwRETf1ZEGSJEmSJElaf10Z2XMmMKunCpEkSZIkSdKGKynsiYjxwJHAL3u2HEmSJEmSJG2IUkf2XAh8EVjX3g4RcXpEzIiIGbW1td1SnCRJkiRJkrqm07AnIo4C3kwpPdbRfimlS1JKU1JKU2pqarqtQEmSJEmSJJWulJE97weOiYg5wG+BAyPiyh6tSpIkSZIkSeul07AnpfTllNL4lNIE4ETg7pTSJ3q8MkmSJEmSJHVZV67GJUmSJEmSpD6usis7p5TuAe7pkUokSZIkSZK0wRzZI0mSJEmSVCCGPZIkSZIkSQVi2CNJkiRJklQghj2SJEmSJEkFYtgjSZIkSZJUIIY9kiRJkiRJBWLYI0mSJEmSVCCGPZIkSZIkSQVi2CNJkiRJklQghj2SJEmSJEkFYtgjSZIkSZJUIIY9kiRJkiRJBWLYI0mSJEmSVCCGPZIkSZIkSQVi2CNJkiRJklQghj2SJEmSJEkFYtgjSZIkSZJUIIY9kiRJkiRJBWLYI0mSJEmSVCCGPZIkSZIkSQVi2CNJkiRJklQghj2SJEmSJEkFYtgjSZIkSZJUIIY9kiRJkiRJBWLYI0mSJEmSVCCdhj0RMTgiHomIJyPi2Yj4Rm8UJkmSJEmSpK6rLGGft4ADU0orImIgcH9E3JpSeqiHa5MkSZIkSVIXdRr2pJQSsCK/OTD/SD1ZlCRJkiRJktZPSWv2RERFRMwE3gTuSCk93LNlSZIkSZIkaX2UFPaklNamlCYD44H3RsQuLfeJiNMjYkZEzKitre3uOiVJkiRJklSCLl2NK6W0BLgHOKyNtktSSlNSSlNqamq6qTxJkiRJkiR1RSlX46qJiFH510OAg4HnerowSZIkSZIkdV0pV+PaHLg8IirIwqFrUko39WxZkiRJkiRJWh+lXI3rKWD3XqhFkiRJkiRJG6hLa/ZIkiRJkiSpbzPskSRJkiRJKhDDHkmSJEmSpAIx7JEkSZIkSSoQwx5JkiRJkqQCMeyRJEnPTcj3AAAWNklEQVSSJEkqEMMeSZIkSZKkAjHskSRJkiRJKhDDHkmSJEmSpAIx7JEkSZIkSSoQwx5JkiRJkqQCMeyRJEmSJEkqEMMeSZIkSZKkAjHskSRJkiRJKhDDHkmSJEmSpAIx7JEkSZIkSSoQwx5JkiRJkqQCMeyRJEmSJEkqEMMeSZIkSZKkAjHskSRJkiRJKhDDHkmSJEmSpAIx7JEkSZIkSSoQwx5JkiRJkqQCMeyRJEmSJEkqEMMeSZIkSZKkAuk07ImIrSLiTxExKyKejYgze6MwSZIkSZIkdV1lCfs0AGenlB6PiOHAYxFxR0rprz1cmyRJkiRJkrqo05E9KaXXU0qP518vB2YBW/Z0YZIkSZIkSeq6Lq3ZExETgN2Bh3uiGEmSJEmSJG2YksOeiBgGXAuclVJa1kb76RExIyJm1NbWdmeNkiRJkiRJKlFJYU9EDCQLeq5KKV3X1j4ppUtSSlNSSlNqamq6s0ZJkiRJkiSVqJSrcQVwKTArpfTDni9JkiRJkiRJ66uUkT3vB04GDoyImfnHET1clyRJkiRJktZDp5deTyndD0Qv1CJJkiRJkqQN1KWrcUmSJEmSJKlvM+yRJEmSJEkqEMMeSZIkSZKkAjHskSRJkiRJKhDDHkmSJEmSpAIx7JEkSZIkSSoQwx5JkiRJkqQCMeyRJEmSJEkqEMMeSZIkSZKkAjHskSRJkiRJKhDDHkmSJEmSpAIx7JEkSZIkSSoQwx5JkiRJkqQCMeyRJEmSJEkqEMMeSZIkSZKkAjHskSRJkiRJKhDDHkmSJEmSpAIx7JEkSZIkSSoQwx5JkiRJkqQCMeyRJEmSJEkqEMMeSZIkSZKkAjHskSRJkiRJKpDKchfQX6yrX8GiO39B3d8fgXVrqRq3PZud9N1m+6SUWHLf5ax85m7WrlpO5chxjNrn4wydtB9vvTqLxXdfyuqFrwAwZJvdGH3ov1JRPbIch9NnlHJeARqW1bLojoupnzsTooIhO7yHmqO/wJL7r2LpA1e32n+bL93UG+X3WaWe17nfParZ7SHv2otNP/zVDvtyf7Xi6TtZeMuFrbZv+alLqRw57u3bq2vnUHvDBTQsfYOorGLwlpMYfdhnqRw+tjfL3ahs6OsAQO3151M/90nWvbWS4XscyehDPt3bh9HnlHpe655/kMV/+hUNyxcwaIuJjDniLAaO2sy+3A7Pa8+Y9/N/Yu2yN9++PXDTbdni1Iua7dPR7yZ/b7Wt1P4KsLZuKa/98tOsW7WMUQf8EyP3/DAAi+78BStn/Zl1dUsYsv172HTqeb15CH3PE8D/tbH9TGCTJrcXAz9usc+ewOHAG8Dv830qga2Bo4ER3V3sxqWU/rq2bilv/O6rNCx+DWIAVeO2Z/Qhn6KqZsLb+6xZ+Aqv/fpzsLaBsceew9Cd9unlI+lbJgBzm9zeDZjZYp8HgbOBv+a3DwIuBmqAPwBfB/4GDAKOAX4KDOmpgvU2w55esuDWH7Pqbw8zfMoxDByzFW+9OqvVPvVzZrLsoelUjdueEXt9hCX3XcHCWy6kese9WbPoNQZUj2CTXadR//Iz1M26l6iqZuyRZ5XhaPqOUs5rSona6/+LNQteYcSeH6Zi2GjWLMhCs+qJ72fg6PEArKtfzqI7LqZq3Pa9egx9USnntVH1jntTPfH9AFSMyN5sdNSXo6J/vuwM2moXxubBQkrrWHjrjxkweBgVw8Y03zEGMHTSflSOGkf9y0+z8uk7WfrA1Yw57HNlqHrjsKGvAwBUDmTIju9j5dN39mLlfVsp53XtisXU/uF7VI3dmk2m/FP2s37zj7I/ru3LbfK89pxBW+3C8MmHAzBg8LBW7R39bqp/+Wl/b7WhK38PLLrzElLDW222DZ20L8sfu7Gnyty4TACOz79eR/ZOeDDtBzVTgG3yrxsz3QB2AUYBc8jeed9D9i66Hyu1vw7Z7h8ZOPo41iyYy7JHrmPx3Zcy7qPfArK/FxbedhExoIK0tqE3y+/T9gMa/w22SRvtz5N1z+8C9wJXk3XpXwNPAjvn95+eb9sG6Oexb6/ov7+9etGaJfNZ9fyDDN35ADbZ/xSIAQzf7dDWO6Z1AFSO2pwhEyaz7KHppHUNEMHQnfdj2K4HATB00v7UzbqXNQvntn6MfqTU81r/8lOsnv93Rrzvo4zc6yNQMZCIAKCqZsLbSf7Sh68DYFj+h2J/VXJ/zQ0cuzVDdtiTAVWD39nYQV/urwaO2oyBozYDYOVz98PaBobtekirNxFVY7dm4Ps+wrr6laS1DVn40I/PW2e643UAoOboL1D/8lOGPblSz+vKWffC2jWM2OsjDN1pH1a//jwrn/0Taxa/bl9ug+e1Z1WOHMeQ7d/DgEHVbe/Q0e8mf2+10pW/B1a9MINVLzzCiD2nsvT+q5q1jT74DBqWvmHY02gT3nm3/CywFtgdqGhn/y2AiUBVk22bAqOB+vz+M8kCoH6s1P5aUT2SUfuezLr6FVQMHcWyR65r9nO+4olbaFj6JsN2O4zlM9oagtU/bQscCQxvp/1jwCn51x8nC3uezW9/iXe6717APzRpU88y7OkFaxa8DMBb8//Gyz+cCjGAEVOOYZMDTm223+Bt92D4Hkey/PGbqZt9P1FZRc3xXyMGVND0N8Cqlx7P9h+/S68dQ19U6nlt3K9u9gMse/Aaomowo/Y9mRFT3vn3R0qJFU/eRlRVM3Tn/XvvIPqgUs9ro6V/+R1L//JbKkbUMPqQT1O9w3s76ctaMfO27I+QyYe12b7qhRnUXv9fQBamjdrnpN4sb6PSna8Dekep57Vh6RsAb49Qq8inEjUsmc/ATTa3L7fgee1ZK5+5m5XP3MWA6pGM2u8Uhu/2wWbtHf1u8vdWa6X213WrV7Hw9p8yar9PMqDKiRld8hhZSPOPHezzh/yjhmzkzlb59r8Bv8u/rgE+0EM1biS68vfrmtq5vH7ZvwHZ6+zog/4FgIblC1h87+WMPfrzrJ7/t94rfiNwBXA5WVf7DnBai/amWeQf88/7ldCmntXpAs0R8auIeDMinumNgooorV2TfV5dT80xX2Lw+Ekse/haVs1pPtuxYdE8Vjx7D4Mn7E7Nh85lQPUoFt78I9atrn97n/p5f2XhrT+marMdGLnPx3v1OPqaUs8r+X5RUUnNh79C5chxLL7r/7Fm0atv71L/8lM0LH6Noe/+QL//Q6Xk8wqM2HMqNR86l9GHfpZ19StYcON/s25NfUl9ub9as/h16uc+yZBt92i2Vk9Tg8bvzKYf+QbD//EY1ix4meUzb+vlKjce3fk6oHd05XWgxT2zT/l/Se3LzXlee87w3Q5l7LFfYsxRZxMDKln0x5+wZsn8Zvt09LvJ31utldpflz00nagcxJBt92Bt3RIA1tUvY239il6veaOyCHgR2IG258VUAQcAJwIfBBYC1zVp3xo4iWwdn1pgRg/WuhHoyutr5Sabs+kJ32Tkvp9g7YpFLH34WgCW3Hs5gzbfgYFjxrMu779rVy5m3epVvXcgfdC/ANcA/0vWLc8AXmpn3weAfyLLL7/eou1a4FzgCN6ZEqaeVcrVuC4D2v73s0rS+IZu0Fbvpnri3lTvtC8ADUteJzWsfvvFqe7vD5PeWsnQXQ6kese9GTJhMmtXLGTNwiyprn/lGd78/XlUjspeoPp7KFHqea3I9xuy/RSq37UXQ7afAiQamvwRuOKJWwEYvnv/nsIFpZ9XgE0OmEb1ju9j+OTDGDJhd9LqVaxdtqDTvtyfrZh5K5AYtvsRQDaqLDuv78wLr6geyZDt/pFNDjwNYgB1z/25TNX2fd35OqB3lHpeG/dbu3xB/nlhs+325eY8rz1n5N4fZehO+zDs3R+getK+kNbRsOjVkv/O8vdWa6X214blC2hYNI/X/t8ZLLnnMiALgJY/3r8vdtGpxnBmSv45AWvIpmUBDCULe3YC9gbGkS3IvKZJ+7uAQ8lGB/XzeTFd+ft1QNUQhmy7B6P2PpGKEWOpe+7+bN9ltdTPfYrXLjn97WmHi+/8BXV/e6iXj6Zv+QowFfgE8FGyLvo82SzC1U32u48sNNiebARP05XTfkeWW36ALPTpv2Mme1en07hSSvdFxISeL6W4qsZtz8CaCdTPfZLlM29jxVN3Qgxg0JaTePkHH2bg2K3Z4rSfUTlqcyCbK5rWvEXdC49ARSWVI8fx1vy/8+bvz4OU/feq/qUniKrBVO+wZ5mPrnxKPa9DtpvCgOpR1M3+C5WjtqBu9gNE1ZC3F2Jeu3IJdX97iEFb7txsJf7+qtTzuuqFR7P/gm69K+vqV7DqxccYUD2SylHjOuzL/Vlau4YVz9xFxYiaPGyAtcve5NWLT3v7CiVLH7yGdavrGDh6PPVzn4S0joFjti5z5X1Xd70OrJx1H6vn/x2A1QteYfmTf2TI9u+hctjoch5e2ZR6Xqsn7cfiey9j2cPTWVuXv5aO35mBm2xuX26D57VnrK6dw5J7L2fIdlNI69ay8pm7icpBDKyZUPLfWf7eaq3U/jp8j6MYsv17AHjrladZ/vjNDN3lQIbmF2+oe+FR1tRm60w2LKtl+ZN/ZPBWuzBw9JZlO7ayayBbZ2ckWWADsITs6lvvIhux8xjwKjCeLOSZTxb4DAT+DLwFjCEbYpHI5tf0Y6X21xVP3cHqN1+katPtWF07h7XLaqnaPPsmjNrnJNbWLQWg7rn7qZt9PyPe8yEGb/Xuch5aWT1NNhrncLJuewXZVbR2zT+/G3gGeDzfJ5GNBLqDLI88GriZrEtvQra2zw1ky04d2IvH0V+VMrKnJBFxekTMiIgZtbW13fWwhRARjD36C1SO2oxFd/6CdfXLGXvUf7QKFqp33JsRex5Pw9I3WHTnL6gYPJyxR32eiuqRrKmdQ1rzFqnhLRbd8XMW3PjfLLrjF+U5oD6i1PM6YOAgao47h6gYyKI7fk5UDqLmuHOpGDoKgBVP3wHrGhjmqB6g9PNaMWJT1q5cxOJ7fs3SB39H1WY7sOnU84iKgR325f6sbvZfWFe3lGG7HUpE2y+/A6pHsvKv97Lwtp+w6qUnqJ60P6MP+VQvV7rx6K7XgcX3XJYt0gi89fJTLLrtIhoWzevtw+kzSj2vlcNGU3P0F1n31koW/+lSqjbdjjFH/DtgX26L57VnDBgygpTWseT+q1hy7+VUjtyUmg+dS+Xw5lc77Oh3k7+3Wiu1vw7a/F0M3Wkfhu60D1WbZW+aB46dwMAx2eIyyx6+liX3XgbAmto5LLrtog6v6tUvzALqgD1o/93YGLJLrN8GPEo23euEvK2a7F34jcALZFfmOqIH690IlPz3QPVIVr04g4W3/5SVz9zNkO3fw9ijPg/A4K13fbsvDxyb9d+qLSZSOWLT3j6cPmMs2UierwHnkF1F63qydcObeoqsS68C/pUs1Gm8RuSj+WMsAE7N277Z04ULgEgpdb5TNrLnppRSSSsCT5kyJc2YsfFPHJ1wzs3lLqHPmXPBkRv8GJ7X1jyvPcPz2jM8rz3D89ozPK89w/PaMzyvPaM7zmt8o59f7qod6bzO30t2xP7aWnf01/58JcEOlZB9bAwi4rGU0pTO9uu2kT2SJEmSJEkqP8MeSZIkSZKkAinl0utXAw8CEyNiXkSc1vNlSZIkSZIkaX2UcjWuj/VGIZIkSZIkSdpwTuOSJEmSJEkqEMMeSZIkSZKkAjHskSRJkiRJKhDDHkmSJEmSpAIx7JEkSZIkSSoQwx5JkiRJkqQCMeyRJEmSJEkqEMMeSZIkSZKkAjHskSRJkiRJKhDDHkmSJEmSpAIx7JEkSZIkSSoQwx5JkiRJkqQCMeyRJEmSJEkqEMMeSZIkSZKkAjHskSRJkiRJKhDDHkmSJEmSpAIx7JEkSZIkSSoQwx5JkiRJkqQCMeyRJEmSJEkqEMMeSZIkSZKkAjHskSRJkiRJKhDDHkmSJEmSpAIx7JEkSZIkSSoQwx5JkiRJkqQCMeyRJEmSJEkqkJLCnog4LCJmR8TfI+Kcni5KkiRJkiRJ66fTsCciKoCfAocDOwMfi4ide7owSZIkSZIkdV0pI3veC/w9pfRiSmk18Fvg2J4tS5IkSZIkSeujlLBnS+CVJrfn5dskSZIkSZLUx0RKqeMdIj4CHJpS+uf89snAe1NKn2ux3+nA6fnNicDs7i9X6nZjgQXlLkIqkf1VGxP7qzYm9ldtLOyr2pjYX3vGNimlms52qizhgeYBWzW5PR54reVOKaVLgEtKLk/qAyJiRkppSrnrkEphf9XGxP6qjYn9VRsL+6o2JvbX8iplGtejwLsiYtuIqAJOBP7Qs2VJkiRJkiRpfXQ6siel1BARnwX+CFQAv0opPdvjlUmSJEmSJKnLSpnGRUrpFuCWHq5FKgenHmpjYn/VxsT+qo2J/VUbC/uqNib21zLqdIFmSZIkSZIkbTxKWbNHkiRJkiRJGwnDHvU7EbFVRPwpImZFxLMRcWa5a5I6ExEVEfFERNxU7lqkjkTEqIiYHhHP5a+z7yt3TVJ7IuLf878FnomIqyNicLlrkhpFxK8i4s2IeKbJttERcUdE/C3/vEk5a5QatdNf/zv/e+CpiLg+IkaVs8b+xrBH/VEDcHZKaRKwF/CvEbFzmWuSOnMmMKvcRUgl+DFwW0ppJ2A37LfqoyJiS+DfgCkppV3ILkRyYnmrkpq5DDisxbZzgLtSSu8C7spvS33BZbTur3cAu6SU/gF4HvhybxfVnxn2qN9JKb2eUno8/3o52RuRLctbldS+iBgPHAn8sty1SB2JiBHAfsClACml1SmlJeWtSupQJTAkIiqBauC1MtcjvS2ldB+wqMXmY4HL868vB47r1aKkdrTVX1NKt6eUGvKbDwHje72wfsywR/1aREwAdgceLm8lUocuBL4IrCt3IVIntgNqgV/n0w5/GRFDy12U1JaU0qvA94GXgdeBpSml28tbldSpcSml1yH7ByawaZnrkUr1T8Ct5S6iPzHsUb8VEcOAa4GzUkrLyl2P1JaIOAp4M6X0WLlrkUpQCewB/DyltDuwEqcYqI/K1zo5FtgW2AIYGhGfKG9VklQ8EfEVsqU0rip3Lf2JYY/6pYgYSBb0XJVSuq7c9UgdeD9wTETMAX4LHBgRV5a3JKld84B5KaXG0ZLTycIfqS86GHgppVSbUloDXAfsXeaapM68ERGbA+Sf3yxzPVKHIuIU4CjgpJRSKnc9/Ylhj/qdiAiy9SRmpZR+WO56pI6klL6cUhqfUppAtnDo3Skl//OsPimlNB94JSIm5psOAv5axpKkjrwM7BUR1fnfBgfhguLq+/4AnJJ/fQrwf2WsRepQRBwGfAk4JqVUV+56+hvDHvVH7wdOJhshMTP/OKLcRUlSQXwOuCoingImA+eXuR6pTfkItOnA48DTZH8XX1LWoqQmIuJq4EFgYkTMi4jTgAuAQyLib8Ah+W2p7Nrprz8BhgN35O+5Li5rkf1MOJJKkiRJkiSpOBzZI0mSJEmSVCCGPZIkSZIkSQVi2CNJkiRJklQghj2SJEmSJEkFYtgjSZIkSZJUIIY9kiRJkiRJBWLYI0mSJEmSVCCGPZIkSZIkSQXy/wGaTYNyE3VOEgAAAABJRU5ErkJggg==\n",
                        "text/plain": [
                            "<Figure size 1440x360 with 1 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "#plot results for ranked s&p 500 volatility\n",
                "#clearly October has the highest AMVR\n",
                "#and December has the lowest\n",
                "#mean of 6.45 is plotted\n",
                "\n",
                "b_plot = plt.bar(x=final.index,height=final)\n",
                "b_plot[9].set_color('g')\n",
                "b_plot[11].set_color('r')\n",
                "for i,v in enumerate(round(final,2)):\n",
                "    plt.text(i+.8,1,str(v), color='black', fontweight='bold')\n",
                "plt.axhline(final.mean(),ls='--',color='k',label=round(final.mean(),2))\n",
                "plt.title('Average Monthly Volatility Ranking S&P500 since 1986')\n",
                "\n",
                "plt.legend()\n",
                "plt.show()\n",
                "\n",
                "\n"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "_uuid": "e247a6beba8bbeb4186d58fff0c8c6fd82e3e368"
            },
            "source": [
                "So that’s our data, now onto Hypothesis testing…\n",
                "\n",
                "### Hypothesis Testing: What’s the question?\n",
                "\n",
                "Hypothesis testing is one of the most fundamental techniques of data science, yet it is one on the most intimidating and misunderstood. The basis for this fear, is the way it is taught in Stats 101, where we are told to:\n",
                "\n",
                "*\"perform a t-test, is it a one-sided or two-sided test?, choose a suitable test-statistic such as Welch’s t-test, calculate degrees of freedom, calculate the t score, look up the critical value in a table, compare critical value to t statistic ……\"*"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "_uuid": "b73d278bc75d978e5a268d578bda7af0de61ca3d"
            },
            "source": [
                "![](https://cdn-images-1.medium.com/max/800/1*_yxe_kGGtT9m4qd6ahGohQ.png)"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "_uuid": "66db53c53f89c4864adb113ea659b455c3797805"
            },
            "source": [
                "Understandably, this is leads to confusion over what test to conduct and how to conduct it. However, all of these classical statistical techniques for performing a hypothesis test, were developed at a time when we had very little computing power and were simply closed-form analytical solutions for calculating a p-value,that's it! But with the added complication of needing to pick the right formula for the given situation, due to their restrictive and sometimes opaque assumptions.\n",
                "\n",
                "But rejoice!\n",
                "\n",
                "There is a better way. Simulation.\n",
                "\n",
                "To understand how simulation can help us, lets remind ourselves what a hypothesis test is:\n",
                "\n",
                "We wish to test ***“whether the observed effect in our data is real or whether it could happen simply by chance”***\n",
                "and to perform this test we do the following:\n",
                "   *  Choose an appropriate ‘test statistic’: this is simply a number that measures the observed effect. In our case we will choose the **absolute deviation in AMVR from the mean**.\n",
                "   * Construct a Null Hypothesis: this is simply a version of the data where the observed effect in not present. In our case we will shuffle the labels of the data repeatedly **permutation**. The justification for this is detailed below.\n",
                "   * Compute a p-value: this is the probability of seeing the observed effect amongst the null data, in other words, by chance. **We do this through repeated simulation** of the null data. In our case, we shuffle the ‘date’ labels of the data many times and simply count the occurrence of our test statistic as it appears through multiple simulations.\n",
                "   \n",
                ">    That’s hypothesis testing in 3 steps! No matter what phenomena we are testing, the question is always the same: “**is the observed effect real, or is it due to chance**”\n",
                "\n",
                "[There is only one test! This great blog by Allen Downey has more details on hypothesis testing](http://allendowney.blogspot.com/2011/05/there-is-only-one-test.html)\n",
                "\n",
                "**The real power of simulation is that we have to make explicit what our model assumptions are through code**. Whereas classical techniques can be a ‘black-box’ when it comes to there assumptions."
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "_uuid": "491ff91ad965eb3842fa0cccb0bbf4fb4426f0fc"
            },
            "source": [
                "Example below: The left plot shows the true data and the observed effect with a certain probability (green). The right plot is our simulated null data with a recording of when the observed effect was seen by chance (red). This is the basis of hypothesis testing, what is the probability of seeing the observed effect in our null data."
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "_uuid": "ac91039d65aeae32ea77596e6c35becdfc8113bf"
            },
            "source": [
                "![](https://cdn-images-1.medium.com/max/1000/1*UW9OJHyVcl0qWWikSv0kpQ.png)"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "_uuid": "35aad08a82c4caa1d610719fe3c1bf3df4290837"
            },
            "source": [
                "The most important part of hypothesis testing is being clear what question we are trying to answer. In our case we are asking:\n",
                "\n",
                "“**Could the most extreme value happen by chance?**”\n",
                "\n",
                "The most extreme value we define as the **greatest absolute AMVR deviation from the mean**. This question forms our null hypothesis.\n",
                "\n",
                "In our data the most extreme value is the December value (1.23) not the October value (1.08), because we are looking at the biggest absolute deviation from the mean not simply the highest volatility:"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 12,
            "metadata": {
                "_uuid": "ea1d4b9b5ba8b873edc7a0413880d1349dcdaa19"
            },
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "date\n",
                        "9     0.044271\n",
                        "11    0.106771\n",
                        "3     0.125237\n",
                        "5     0.155540\n",
                        "2     0.216146\n",
                        "1     0.367661\n",
                        "7     0.419271\n",
                        "6     0.420218\n",
                        "8     0.575521\n",
                        "4     0.852509\n",
                        "10    1.080729\n",
                        "12    1.231771\n",
                        "Name: close, dtype: float64\n",
                        "Extreme Dec value: 1.231770833333333\n",
                        "Extreme Oct value: 1.080729166666667\n"
                    ]
                }
            ],
            "source": [
                "#take abs value move from the mean\n",
                "#we see Dec and Oct are the biggest abs moves\n",
                "\n",
                "fin = abs(final - final.mean())\n",
                "print(fin.sort_values())\n",
                "Oct_value = fin[10]\n",
                "Dec_value = fin[12]\n",
                "print('Extreme Dec value:', Dec_value)\n",
                "print('Extreme Oct value:', Oct_value)\n"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "_uuid": "2c8eeb1ecc13bd270ceff2ae6aad4777cc658d4a"
            },
            "source": [
                "### Simulation\n",
                "\n",
                "Now we know what question we are asking, we need to construct our ‘Null Model’.\n",
                "\n",
                "There are a number of options here:\n",
                "\n",
                "   * **Parametric models**. If we had a good idea of the data distribution or simply made assumption on it, we could use ‘classical’ hypothesis testing techniques, t-test, X², one-way ANOVA etc. These models can be restrictive and something of a blackbox if their assumptions aren’t fully understood by the researcher.\n",
                "   * **Direct Simulation**. We could make assumptions about the [data generating process](http://www.rimini.unibo.it/fanelli/econometric_models2_2012.pdf) and simulate directly. For example we could specify an ARMA time series model for the financial data we are modeling and deliberately engineer it to have no seasonality. This could be a reasonable choice for our problem, however if we knew the data generating process for the S&P500 we would be rich already!\n",
                "   * **Simulation through Resampling**. This is the approach we will take. By repeatedly sampling at random from the existing dataset and shuffling the labels, we can make the observed effect equally likely amongst all labels in our data (in our case the labels are the dates), thus giving the desired null dataset.\n",
                "\n",
                "Sampling is a big topic but we will focus on one particular technique, **[permutation](https://speakerdeck.com/jakevdp/statistics-for-hackers)** or shuffling.\n",
                "\n",
                "To get the desired null model, we need to construct a dataset that has **no seasonality** present. If the null is true, *that there is no seasonality in the data and the observed effect was simply by chance*, then the labels for each month (Jan,Feb etc) are meaningless and therefore we can shuffle the data repeatedly to build up what classical statistics would call the ‘*[sampling distribution of the test statistic under the null hypothesis](https://www.youtube.com/watch?time_continue=441&v=5Dnw46eC-0o)*’. This has the desired effect of making the observed phenomena (the extreme December value) **equally likely** for all months, which is exactly what our null model requires.\n",
                "\n",
                "To prove how powerful simulation techniques are with modern computing power, the code in this example will actually permute the daily price data, which requires lots more processing power, yet still completes in seconds on a modern CPU. \n",
                "\n",
                "Note: *shuffling either the daily or the monthly labels will give us the desired null dataset in our case.*"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "_uuid": "d882201d7aa9b434ad0306ea9da8ba5e7cb20c36"
            },
            "source": [
                "                              Shuffle 'date' label to create null dataset\n",
                "![](https://cdn-images-1.medium.com/max/1000/1*-rzHR7JrlTjcXAYp4zKCUA.gif)"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "_uuid": "d9e8009cef1054e28fea4694d51b4825222070af"
            },
            "source": [
                "A great resource for learning about sampling is by [Julian Simon](http://www.resample.com/intro-text-online/).\n",
                "\n",
                "Note: *The way our test is constructed is the equivalent of a two sided test using ‘classical’ methods, such as Welch’s t-test or ANOVA etc, because we are interested in the most extreme value, either above or below the mean.*\n",
                "\n",
                "These decisions are design choices and we have this freedom because **the Null Model is just that, it’s a Model!** This means we can specify its parameters as we choose, the key is to really be clear what question we are trying to answer.\n",
                "\n",
                "Below is the code to simulate 1000 sets of 12 AMVR, permuting the date labels each time to build up the sampling distribution. The output from this code is included below in the p-hacking section…"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 13,
            "metadata": {
                "_uuid": "9761db73d11d0ba79de2d345a75d7fd23e090612"
            },
            "outputs": [],
            "source": [
                "#as our Null is that no seasonality exists or alternatively that the month does not matter in terms of AMVR,\n",
                "#we can shuffle 'date' labels\n",
                "#for simplicity, we will shuffle the 'daily' return data, which has the same effect as shuffling 'month' labels\n",
                "\n",
                "#generate null data \n",
                "\n",
                "new_df_sim = pd.DataFrame()\n",
                "highest_only = []\n",
                "\n",
                "count=0\n",
                "n=1000\n",
                "for i in range(n):\n",
                "    #sample same size as dataset, drop timestamp\n",
                "    daily_ret_shuffle = daily_ret.sample(8191).reset_index(drop=True)\n",
                "    #add new timestamp to shuffled data\n",
                "    daily_ret_shuffle.index = (pd.bdate_range(start='1986-1-3',periods=8191))\n",
                "    \n",
                "    #then follow same data wrangling as before...\n",
                "    mnthly_annu = daily_ret_shuffle.resample('M').std()* np.sqrt(12)\n",
                "    \n",
                "    ranked = mnthly_annu.groupby(mnthly_annu.index.year).rank()\n",
                "    sim_final = ranked.groupby(ranked.index.month).mean()\n",
                "    #add each of 1000 sims into df\n",
                "    new_df_sim = pd.concat([new_df_sim,sim_final],axis=1)\n",
                "    \n",
                "    #also record just highest AMVR for each year (we will use this later for p-hacking explanation)\n",
                "    maxi_month = max(sim_final)\n",
                "    highest_only.append(maxi_month)\n",
                "    \n",
                "\n",
                "    \n",
                "#calculate absolute deviation in AMVR from the mean\n",
                "all_months = new_df_sim.values.flatten()\n",
                "mu_all_months = all_months.mean()\n",
                "abs_all_months = abs(all_months-mu_all_months)    \n",
                "\n",
                "#calculate absolute deviation in highest only AMVR from the mean\n",
                "mu_highest = np.mean(highest_only)\n",
                "abs_highest = [abs(x - mu_all_months) for x in highest_only]\n",
                "\n"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "_uuid": "121b43f22027b91685bad15666f751c20f8a290a"
            },
            "source": [
                "### p-Hacking\n",
                "\n",
                "Here’s the interesting bit. We’ve constructed a hypothesis to test, we’ve generated simulated data by shuffling the ‘date’ labels of the data , now we need to perform our hypothesis test to find the probability of observing a result as significant as the December result given that the null hypothesis (no seasonality) is true.\n",
                "\n",
                "Before we perform the test lets set our expectations.\n",
                "\n",
                "**Whats the probability of seeing *at least one* significant result given a 5% significance level?**\n",
                "\n",
                "= 1-p(not significant)\n",
                "\n",
                "= 1-(1–0.05)¹²\n",
                "\n",
                "= 0.46\n",
                "\n",
                "so there’s a **46%** chance of seeing **at least one** month with a significant result, given our null is true.\n",
                "\n",
                "Now lets ask, **for each individual test** (*comparing each of the 12 months absolute AMVR to the mean*) **how many significant values should we expect to see amongst our random, non-seasonal data?**\n",
                "\n",
                "12 x 0.05 = 0.6\n",
                "\n",
                "So with a 0.05 significance level we should expect a false positive rate of **0.6**. In other words, for each test (with the null data) comparing all 12 months AMVR to the mean, 0.6 months will have show a significant result.(*obviously we cant have less than 1 month showing a result, but under repeat testing the math should tend towards this number*).\n",
                "\n",
                "All the way through this work, we have stressed the importance of being really clear with the question we are trying to answer. The problem with the expectations we’ve just calculated is **we have assumed we are testing for a significant result against all 12 months!** That’s why the probability of seeing at least one false positive is so high at 46%.\n",
                "\n",
                "This is an example of [multiple comparison bias](https://www.quantopian.com/lectures/p-hacking-and-multiple-comparisons-bias). Where we have expanded our search space and increased the likelihood of finding a significant result. This a problem because we could abuse this phenomena to cherry pick the parameters of our model which give us the ‘desired’ p-value.\n",
                "\n",
                "> This is the essence of p-Hacking\n",
                "\n",
                "This xkcd nicely highlights the issue with multiple comparison bias and it’s subsequent abuse, p-hacking.\n"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "_uuid": "6bcae847fc7f92fe938464659bb1cc4edabcdeec"
            },
            "source": [
                "                               Whoa! Green Jelly beans are significant\n",
                "![](https://cdn-images-1.medium.com/max/800/0*zZgRnBSbwUN3b6D5.png)"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "_uuid": "6182b3dd8ce8558e4c93df772ee9808da37cfee8"
            },
            "source": [
                "To illustrate the effect of p-hacking and how to reduce multiplicity, we need to understand the subtle but significant difference between the following 2 question:\n",
                "\n",
                "*     **“Whats the probability that December would appear this extreme by chance.”**\n",
                "*     **“Whats the probability any month would appear this extreme by chance.\"**\n",
                "\n",
                "The beauty of simulation lies in its simplicity. The following code is all we need to compute the p-value to answer the 1st question. We simply count how many values in our dataset using all 12000 AMVR deviations (12 months x 1000 trials) are greater than the observed December value. We get a **p-value of 4.4%**, close to our somewhat arbitrary 5% cut off, but **significant** none the less.\n",
                "\n"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 14,
            "metadata": {
                "_uuid": "9dbd22b42be3b777b1725380e9eb5a83aed63236"
            },
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "p-value: 0.041833333333333333\n"
                    ]
                }
            ],
            "source": [
                "#count number of months in sim data where ave-vol-rank is >= Dec\n",
                "#Note: we are using Dec not Oct, as Dec has highest absolute deviation from the mean\n",
                "count=0\n",
                "for i in abs_all_months:\n",
                "    if i> Dec_value:\n",
                "        count+=1\n",
                "ans = count/len(abs_all_months)        \n",
                "print('p-value:', ans )"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "_uuid": "c35620d6867b1c1b806d86287aaeb2f9c9fdcaab"
            },
            "source": [
                "To answer the 2nd question and to avoid multiplicity, instead of comparing our result to the distribution made with all 12000 AMVR deviations, we only consider the highest value from each of the absolute AMVR 1000 trials. This gives a **p-value of 23%**, very much **not significant!**"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 15,
            "metadata": {
                "_uuid": "6a33f406e90ea536010bf60923e26c4a15ead93e"
            },
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "p-value: 0.231\n"
                    ]
                }
            ],
            "source": [
                "#same again but just considering highest AMVR for each of 100 trials\n",
                "count=0\n",
                "for i in abs_highest:\n",
                "    if i> Dec_value:\n",
                "        count+=1\n",
                "ans = count/len(abs_highest)        \n",
                "print('p-value:', ans )"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "_uuid": "a8d88a6d6a1c4daee9ec61fdf78f08b667fba35e"
            },
            "source": [
                "Now lets plot these distributions:"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 16,
            "metadata": {
                "_uuid": "846df48cb7bd569b2e929f54444a8b1adc01b9a2"
            },
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "<matplotlib.legend.Legend at 0x18aaee81710>"
                        ]
                    },
                    "execution_count": 16,
                    "metadata": {},
                    "output_type": "execute_result"
                },
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": [
                            "<Figure size 1440x1440 with 4 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "abs_all_months_95 = np.quantile(abs_all_months,.95)\n",
                "abs_highest_95 = np.quantile(abs_highest,.95)\n",
                "\n",
                "fig, ((ax1,ax2),(ax3,ax4)) = plt.subplots(2,2,sharex='col',figsize=(20,20))\n",
                "\n",
                "#need to normalize!\n",
                "\n",
                "#plot 1\n",
                "ax1.hist(abs_all_months,histtype='bar',color='#42a5f5')\n",
                "ax1.set_title('AMVR all months',fontsize=30)\n",
                "ax1.set_ylabel('Frequency',fontsize=20)\n",
                "n,bins,patches = ax3.hist(abs_all_months,density=1,histtype='bar',cumulative=True,bins=30,color='#42a5f5')\n",
                "ax3.set_ylabel('Cumulative probability',fontsize=20)\n",
                "ax1.axvline(Dec_value,color='b',label='Dec Result',lw=10)\n",
                "ax3.axvline(Dec_value,color='b',lw=10)\n",
                "ax3.axvline(abs_all_months_95,color='r',ls='--',label='5% Sig level',lw=10)\n",
                "\n",
                "\n",
                "#plot2\n",
                "ax2.hist(abs_highest,histtype='bar',color='g')\n",
                "ax2.set_title('AMVR highest only',fontsize=30)\n",
                "ax2.axvline(Dec_value,color='b',lw=10)\n",
                "n,bins,patches = ax4.hist(abs_highest,density=1,histtype='bar',cumulative=True,bins=30,color='g')\n",
                "ax4.axvline(Dec_value,color='b',lw=10)\n",
                "ax4.axvline(abs_highest_95,color='r',ls='--',lw=10)\n",
                "\n",
                "ax1.legend(fontsize=15)\n",
                "ax3.legend(fontsize=15)\n"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "_uuid": "5d9b5588d49addfbf196b9891f34d867ce5e6961"
            },
            "source": [
                "The left column is the data that answers question 1 and the right column, question 2. The top row are the probability distributions and the bottom row are the CDF. The red dashed line is the 5% significance level that we arbitrarily decided upon. The blue line is the original extreme December AMVR value of 1.23."
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "_uuid": "9acd6d477dc4aca70d651c177df957506b3e95b4"
            },
            "source": [
                "The left side plot shows that the original December value is significant at a 5% level, but only just! However, when we account for multiple comparison bias, in the right hand plot the threshold for significance moves up from around 1.2 (abs AMVR) up to around 1.6 (see the redline).\n",
                "\n",
                ">    **By accounting for multiple comparison bias our December value of 1.23 is no longer significant!**\n",
                "\n",
                "By taking into consideration the specific question we are trying to answer and avoiding multiple comparison bias, we have avoided p-hacking our model and avoided showing a significant result when there isn’t one.\n",
                "\n",
                "To further explore p-hacking and how it can be abused to tell a particular story about our data, see this great interactive app from [FiveThirtyEight](https://fivethirtyeight.com/features/science-isnt-broken/#part2)\n",
                "\n",
                "### Conclusions\n",
                "\n",
                "* **We have learnt that hypothesis testing is not the big scary beast we thought it was. Simply follow the 3 steps above to construct your model for any kind of data or test statistic.**\n",
                "\n",
                "* **We’ve show that asking the right question is vital for scientific analysis. A slight change in the wording can lead to a very different model with very different results.**\n",
                "\n",
                "* **We discussed the importance of recognizing and correcting for multiple comparison bias and avoiding the pitfalls of p-hacking and showed how a seemingly significant result can become non-significant.**\n",
                "\n",
                "* **With more and more ‘big data’ along with academic pressure to produce a research paper with ‘novel’ findings or political pressure to show a result as being ‘significant’, the temptation for p-hacking is ever increasing. By learning to recongise when we are guilty of it and correcting for it accordingly we can become better researchers and ultimately produce more accurate and therefore actionable scientific results!**\n",
                "\n",
                "Authors Notes: *Our results differ slightly from the original [Winton research](https://www.winton.com/research/seasonal-volatility-and-the-multiplicity-effect), this due in part to having a slightly different data set (32yrs vs 87yrs) and they have October being the month of interest whereas we have December. Also they used an undisclosed method for their ‘simulated data’ whereas we have made explicit, through code our methodology for creating that data. We have made certain modeling assumptions through out this work, again, these have been made explicit and can be seen in the code. These design and modeling choices are part of the scientific process, so long as they are made explicit, the analysis has merit.*\n",
                "\n",
                "Hope you found this useful and interesting, follow to be notified with my latest posts!\n",
                "I have also written this in [blog form on medium](https://medium.com/@pdquant/stocks-significance-testing-p-hacking-how-volatile-is-volatile-1a0da3064b8a) for those who prefer it.\n",
                "\n",
                "Follow [twitter.com/pdquant](http://www.twitter.com/pdquant) for more!\n",
                "\n"
            ]
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