12_SimpleStockStatistics.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Simple Stock Statistics "
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "Statistics is a mathematics that deals with data collection, organization, analysis, interpretation and presentation. In addition, statistical analysis use probabilities. \n",
        "\n\n",
        "Stock price in statistics give the probability of rising or falling.\n",
        "\nApplied stock to statistics."
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Packages Libraries"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import pandas as pd\n",
        "\n",
        "import math\n",
        "from scipy.stats import norm\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\") \n",
        "\n",
        "# fix_yahoo_finance is used to fetch data \n",
        "import fix_yahoo_finance as yf\n",
        "yf.pdr_override()"
      ],
      "outputs": [],
      "execution_count": 1,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Input Stock Symbol and Date"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "# input\n",
        "symbol = 'AAPL'\n",
        "start = '2014-01-01'\n",
        "end = '2019-01-01'\n",
        "\n",
        "# Read data \n",
        "dataset = yf.download(symbol,start,end)\n",
        "\n",
        "# View Columns\n",
        "dataset.head()"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 downloaded\n"
          ]
        },
        {
          "output_type": "execute_result",
          "execution_count": 2,
          "data": {
            "text/plain": [
              "                 Open       High        Low      Close  Adj Close     Volume\n",
              "Date                                                                        \n",
              "2014-01-02  79.382858  79.575714  78.860001  79.018570  66.964325   58671200\n",
              "2014-01-03  78.980003  79.099998  77.204285  77.282860  65.493416   98116900\n",
              "2014-01-06  76.778572  78.114288  76.228569  77.704285  65.850533  103152700\n",
              "2014-01-07  77.760002  77.994286  76.845711  77.148575  65.379593   79302300\n",
              "2014-01-08  76.972855  77.937141  76.955711  77.637146  65.793633   64632400"
            ],
            "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>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2014-01-02</th>\n",
              "      <td>79.382858</td>\n",
              "      <td>79.575714</td>\n",
              "      <td>78.860001</td>\n",
              "      <td>79.018570</td>\n",
              "      <td>66.964325</td>\n",
              "      <td>58671200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-03</th>\n",
              "      <td>78.980003</td>\n",
              "      <td>79.099998</td>\n",
              "      <td>77.204285</td>\n",
              "      <td>77.282860</td>\n",
              "      <td>65.493416</td>\n",
              "      <td>98116900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-06</th>\n",
              "      <td>76.778572</td>\n",
              "      <td>78.114288</td>\n",
              "      <td>76.228569</td>\n",
              "      <td>77.704285</td>\n",
              "      <td>65.850533</td>\n",
              "      <td>103152700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-07</th>\n",
              "      <td>77.760002</td>\n",
              "      <td>77.994286</td>\n",
              "      <td>76.845711</td>\n",
              "      <td>77.148575</td>\n",
              "      <td>65.379593</td>\n",
              "      <td>79302300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-08</th>\n",
              "      <td>76.972855</td>\n",
              "      <td>77.937141</td>\n",
              "      <td>76.955711</td>\n",
              "      <td>77.637146</td>\n",
              "      <td>65.793633</td>\n",
              "      <td>64632400</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 2,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Stock Data Analysis"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"__________________Exploratory Data Analysis__________________\")\n",
        "print(\"Stock Data\")\n",
        "print('-'*60)\n",
        "print(\"Dataset information\") \n",
        "print(dataset.info(memory_usage='deep',verbose=False))\n",
        "print('-'*60)\n",
        "print(dataset.info())\n",
        "print('-'*60)\n",
        "print(\"Data type:\")\n",
        "print(dataset.dtypes)\n",
        "print('-'*60)\n",
        "print(\"Check unique values wihtout NaN\")\n",
        "print(dataset.nunique())\n",
        "print('-'*60)\n",
        "print(\"Data shape:\")\n",
        "print(dataset.shape)\n",
        "print('-'*60)\n",
        "print(\"Data columns Names:\")\n",
        "print(dataset.columns)\n",
        "print('-'*60)\n",
        "print(\"Check for NaNs:\")\n",
        "print(dataset.isnull().values.any())\n",
        "print('-'*60)\n",
        "print(\"Data Statistics Summary:\")\n",
        "print(dataset.describe())"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "__________________Exploratory Data Analysis__________________\n",
            "Stock Data\n",
            "------------------------------------------------------------\n",
            "Dataset information\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "DatetimeIndex: 1258 entries, 2014-01-02 to 2018-12-31\n",
            "Columns: 6 entries, Open to Volume\n",
            "dtypes: float64(5), int32(1)\n",
            "memory usage: 63.9 KB\n",
            "None\n",
            "------------------------------------------------------------\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "DatetimeIndex: 1258 entries, 2014-01-02 to 2018-12-31\n",
            "Data columns (total 6 columns):\n",
            "Open         1258 non-null float64\n",
            "High         1258 non-null float64\n",
            "Low          1258 non-null float64\n",
            "Close        1258 non-null float64\n",
            "Adj Close    1258 non-null float64\n",
            "Volume       1258 non-null int32\n",
            "dtypes: float64(5), int32(1)\n",
            "memory usage: 63.9 KB\n",
            "None\n",
            "------------------------------------------------------------\n",
            "Data type:\n",
            "Open         float64\n",
            "High         float64\n",
            "Low          float64\n",
            "Close        float64\n",
            "Adj Close    float64\n",
            "Volume         int32\n",
            "dtype: object\n",
            "------------------------------------------------------------\n",
            "Check unique values wihtout NaN\n",
            "Open         1184\n",
            "High         1179\n",
            "Low          1180\n",
            "Close        1170\n",
            "Adj Close    1232\n",
            "Volume       1255\n",
            "dtype: int64\n",
            "------------------------------------------------------------\n",
            "Data shape:\n",
            "(1258, 6)\n",
            "------------------------------------------------------------\n",
            "Data columns Names:\n",
            "Index(['Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume'], dtype='object')\n",
            "------------------------------------------------------------\n",
            "Check for NaNs:\n",
            "False\n",
            "------------------------------------------------------------\n",
            "Data Statistics Summary:\n",
            "              Open         High          Low        Close    Adj Close  \\\n",
            "count  1258.000000  1258.000000  1258.000000  1258.000000  1258.000000   \n",
            "mean    131.227685   132.355248   130.079289   131.241425   125.947447   \n",
            "std      37.428791    37.771772    37.064776    37.414122    39.320418   \n",
            "min      70.739998    71.647141    70.507141    71.397141    60.505547   \n",
            "25%     102.900000   104.374998   102.440003   103.037502    97.125576   \n",
            "50%     118.940002   119.889999   118.215000   119.180001   112.998409   \n",
            "75%     158.522499   159.990002   156.707500   157.830002   155.141937   \n",
            "max     230.779999   233.470001   229.779999   232.070007   230.275482   \n",
            "\n",
            "             Volume  \n",
            "count  1.258000e+03  \n",
            "mean   4.293058e+07  \n",
            "std    2.358262e+07  \n",
            "min    1.147590e+07  \n",
            "25%    2.660325e+07  \n",
            "50%    3.694200e+07  \n",
            "75%    5.234740e+07  \n",
            "max    2.663808e+08  \n"
          ]
        }
      ],
      "execution_count": 3,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Calculate Stock Daily Returns"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "# Create Log Returns\n",
        "dataset['Log_Returns'] = np.log(dataset['Adj Close'].shift(-1)) - np.log(dataset['Adj Close'])"
      ],
      "outputs": [],
      "execution_count": 4,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Histogram of Log Returns"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "mu = dataset['Log_Returns'].mean()\n",
        "sigma = dataset['Log_Returns'].std(ddof=1)\n",
        "\n",
        "density = pd.DataFrame()\n",
        "density['x'] = np.arange(dataset['Log_Returns'].min()-0.01, dataset['Log_Returns'].max()+0.01, 0.001)\n",
        "density['pdf'] = norm.pdf(density['x'], mu, sigma)\n",
        "\n",
        "dataset['Log_Returns'].hist(bins=50, figsize=(15, 8))\n",
        "plt.plot(density['x'], density['pdf'], color='red')\n",
        "plt.show()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ],
            "image/png": [
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\n"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 5,
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "# Log return goes up and down during the period\n",
        "dataset['Log_Returns'].plot(figsize=(20, 8))\n",
        "plt.title(\"Stock Log Returns\")\n",
        "plt.axhline(0, color='red')\n",
        "plt.ylabel('Log Returns')\n",
        "plt.show()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x576 with 1 Axes>"
            ],
            "image/png": [
              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\n"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 6,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Calculate the probability of the stock price will drop over a certain percentage in a day"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "drops = [-0.01, -0.05, -0.10, -0.15, -0.20, -0.25, -0.30, -0.35, -0.40, -0.45, -0.50]\n",
        "for d in drops:\n",
        "    prob_returns = norm.cdf(d, mu, sigma)\n",
        "    print('The Probability of', d, 'is', prob_returns)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The Probability of -0.01 is 0.240442527847\n",
            "The Probability of -0.05 is 0.000410935787335\n",
            "The Probability of -0.1 is 1.50711215759e-11\n",
            "The Probability of -0.15 is 1.30700421785e-23\n",
            "The Probability of -0.2 is 2.34866144412e-40\n",
            "The Probability of -0.25 is 8.34010885145e-62\n",
            "The Probability of -0.3 is 5.72414835482e-88\n",
            "The Probability of -0.35 is 7.50222945271e-119\n",
            "The Probability of -0.4 is 1.86396725051e-154\n",
            "The Probability of -0.45 is 8.73761235682e-195\n",
            "The Probability of -0.5 is 7.70262240044e-240\n"
          ]
        }
      ],
      "execution_count": 7,
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "prob_return = norm.cdf(-0.03, mu, sigma)\n",
        "print('The Probability is ', prob_return)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The Probability is  0.0214285083264\n"
          ]
        }
      ],
      "execution_count": 8,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Calculate the probability of the stock price will drop over a certain percentage in a year"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "# There are 252 trading days in a year\n",
        "# There are exactly 252 trading days in 2019. February has the fewest (19), and October the most (23), with an average of 21 per month, or 63 per quarter. Out of a possible 365 days, 104 days are weekend days (Saturday and Sunday) when the stock exchanges are closed.\n",
        "# drop over 40% in 220 days\n",
        "mu225 = 225*mu\n",
        "sigma225 = (225**0.5) * sigma\n",
        "print('The probability of dropping over 10% in 225 days is ', norm.cdf(-0.10, mu225, sigma225))"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The probability of dropping over 10% in 225 days is  0.133152331582\n"
          ]
        }
      ],
      "execution_count": 9,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "drops = [-0.01, -0.05, -0.10, -0.15, -0.20, -0.25, -0.30, -0.35, -0.40, -0.45, -0.50]\n",
        "for d in drops:\n",
        "    prob_returns = norm.cdf(d, mu225, sigma225)\n",
        "    print('The Probability of dropping over', d,'% in 225 days is', round(prob_returns, 5))"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The Probability of dropping over -0.01 % in 225 days is 0.23714\n",
            "The Probability of dropping over -0.05 % in 225 days is 0.18631\n",
            "The Probability of dropping over -0.1 % in 225 days is 0.13315\n",
            "The Probability of dropping over -0.15 % in 225 days is 0.09149\n",
            "The Probability of dropping over -0.2 % in 225 days is 0.06037\n",
            "The Probability of dropping over -0.25 % in 225 days is 0.03822\n",
            "The Probability of dropping over -0.3 % in 225 days is 0.0232\n",
            "The Probability of dropping over -0.35 % in 225 days is 0.01349\n",
            "The Probability of dropping over -0.4 % in 225 days is 0.00751\n",
            "The Probability of dropping over -0.45 % in 225 days is 0.004\n",
            "The Probability of dropping over -0.5 % in 225 days is 0.00204\n"
          ]
        }
      ],
      "execution_count": 10,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Calculate Value at Risk (VaR)"
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "Value at risk is a measurment of risk for loss of investment. On the other hand, it estimates how much of investments might lose in a normal market or any market conditions."
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "# Value at risk(VaR)\n",
        "VaR = norm.ppf(0.05, mu, sigma)\n",
        "print('Stock value at risk ', VaR)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Stock value at risk  -0.0242396439117\n"
          ]
        }
      ],
      "execution_count": 11,
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Calculate Quantile"
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "Quantile is a probability that divides in the range of a probability distribution into continuous interval with equal probabilities. \n",
        "\n\nFor example, if we were to look at all of the closing prices for a specific stock for every day in the last year, the top 20% of those prices would represent the upper quintile of the data. The bottom 20% of those prices would represent the lower quintile of the data. There would be three quintiles in between the upper and lower quintiles. The average of all the stock prices typically falls between the second and fourth quintiles, which is the middle point of the data. https://www.investopedia.com/terms/q/quintile.asp"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "# Quatile \n",
        "# 5% quantile\n",
        "print('5% quantile ', norm.ppf(0.05, mu, sigma))\n",
        "# 95% quantile\n",
        "print('95% quantile ', norm.ppf(0.95, mu, sigma))"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "5% quantile  -0.0242396439117\n",
            "95% quantile  0.025596061538\n"
          ]
        }
      ],
      "execution_count": 12,
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "# Calcuate the 25% and 75% Quantile of the stock's returns\n",
        "# 25% quantile\n",
        "q25 = norm.ppf(0.25, mu, sigma)\n",
        "print('25% quantile ', q25)\n",
        "# 75% quantile\n",
        "q75 = norm.ppf(0.75, mu, sigma) \n",
        "print('75% quantile ', q75)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "25% quantile  -0.00953962211378\n",
            "75% quantile  0.0108960397402\n"
          ]
        }
      ],
      "execution_count": 13,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Statistical Hypothesis Tests"
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "Hypothesis test is a statistical method that is used to make statistical decisions for experimental data. In addition, is used for to compare two dataset. \n",
        "\n",
        "Statistical tests is used to check if the data has a Gaussian distribution or not\n",
        "\n",
        "Alpha or α: is the probability of rejecting the null hypothesis when it is true.\n",
        "\n",
        "P-values: the probability of obtaining an effect at least as extreme as the one in the sample data while assuming the truth of the null hypothesis.\n",
        "\n",
        "p-value <= alpha: reject H0, not normal.\n",
        "\np-value > alpha: fail to reject H0, normal."
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Calculate test Statistics"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "# https://www.statisticshowto.datasciencecentral.com/standardized-test-statistic/\n",
        "mu = dataset['Log_Returns'].mean()\n",
        "sigma = dataset['Log_Returns'].std(ddof=1)\n",
        "n = dataset['Log_Returns'].shape[0]\n",
        "\n",
        "# if sample size n is large enough, we can use z-distribution, instead of t-distribtuion\n",
        "# mu = 0 under the null hypothesis\n",
        "zhat = (mu - 0)/(sigma/n**0.5)\n",
        "print(zhat)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.5878899198671632\n"
          ]
        }
      ],
      "execution_count": 14,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Set decision criteria"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "# confidence level\n",
        "alpha = 0.05\n",
        "\n",
        "zleft = norm.ppf(alpha/2, 0, 1)\n",
        "zright = -zleft  # z-distribution is symmetric \n",
        "print(zleft, zright)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-1.95996398454 1.95996398454\n"
          ]
        }
      ],
      "execution_count": 15,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Decision to reject H0 or not"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "print('At significant level of {}, shall we reject: {}'.format(alpha, zhat>zright or zhat<zleft))"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "At significant level of 0.05, shall we reject: False\n"
          ]
        }
      ],
      "execution_count": 16,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## One Tail Test"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "# Test one tail\n",
        "mu = dataset['Log_Returns'].mean()\n",
        "sigma = dataset['Log_Returns'].std(ddof=1)\n",
        "n = dataset['Log_Returns'].shape[0]\n",
        "\n",
        "# if sample size n is large enough, we can use z-distribution, instead of t-distribtuion\n",
        "# mu = 0 under the null hypothesis\n",
        "zhat = (mu - 0)/(sigma/n**0.5)\n",
        "print(zhat)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.5878899198671632\n"
          ]
        }
      ],
      "execution_count": 17,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "alpha = 0.05\n",
        "\n",
        "zright = norm.ppf(1-alpha, 0, 1)\n",
        "print(zright)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.64485362695\n"
          ]
        }
      ],
      "execution_count": 18,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print('At significant level of {}, shall we reject: {}'.format(alpha, zhat>zright))"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "At significant level of 0.05, shall we reject: False\n"
          ]
        }
      ],
      "execution_count": 19,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Test for p-value"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "p_value = 1 - norm.cdf(zhat, 0, 1)\n",
        "print(p_value)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.056155616634\n"
          ]
        }
      ],
      "execution_count": 20,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print('At significant level of {}, shall we reject: {}'.format(alpha, p_value < alpha))"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "At significant level of 0.05, shall we reject: False\n"
          ]
        }
      ],
      "execution_count": 21,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The stock data is continuous data. Here the list of doing hypothesis test.\n",
        "\n",
        "For this part, is about comparing features and targets or two samples. Some of the hypothesis testing, we can test one samples.\n",
        "\n",
        "List of continuous:\n",
        "https://docs.scipy.org/doc/scipy/reference/tutorial/stats/continuous.html"
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Shapiro-Wilk Test"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "# Shapiro-Wilk Test\n",
        "from scipy.stats import shapiro\n",
        "import scipy as sp\n",
        "\n\nW_test, p_value = shapiro(dataset['Log_Returns'])"
      ],
      "outputs": [],
      "execution_count": 22,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# confidence level is 95%, that is, alpha=0.05\n",
        "print('Shapiro-Wilk Test')\n",
        "print('-'*40)\n",
        "\n",
        "alpha = 0.05\n",
        "if p_value < alpha:  # null hypothesis: x comes from a normal distribution\n",
        "    print(\"H0: the sample has a Gaussian distribution.\")\n",
        "    print(\"The null hypothesis can be rejected\")\n",
        "else:\n",
        "    print(\"H1: the sample does not have a Gaussian distribution.\")\n",
        "    print(\"The null hypothesis cannot be rejected\")"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shapiro-Wilk Test\n",
            "----------------------------------------\n",
            "H1: the sample does not have a Gaussian distribution.\n",
            "The null hypothesis cannot be rejected\n"
          ]
        }
      ],
      "execution_count": 23,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## D’Agostino’s K^2 Test"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "# Anderson_Darling Test\n",
        "from scipy.stats import anderson\n",
        "\n",
        "result = anderson(dataset['Log_Returns'])\n",
        "result"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 24,
          "data": {
            "text/plain": [
              "AndersonResult(statistic=nan, critical_values=array([ 0.574,  0.654,  0.785,  0.915,  1.089]), significance_level=array([ 15. ,  10. ,   5. ,   2.5,   1. ]))"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 24,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print('D’Agostino’s K^2 Test')\n",
        "print('-'*40)\n",
        "print('Statistic: %.3f' % result.statistic)\n",
        "p = 0\n",
        "for i in range(len(result.critical_values)):\n",
        "\tsl, cv = result.significance_level[i], result.critical_values[i]\n",
        "\tif result.statistic < result.critical_values[i]:\n",
        "\t\tprint('%.3f: %.3f, data looks normal (fail to reject H0)' % (sl, cv))\n",
        "\telse:\n",
        "\t\tprint('%.3f: %.3f, data does not look normal (reject H0)' % (sl, cv))"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "D’Agostino’s K^2 Test\n",
            "----------------------------------------\n",
            "Statistic: nan\n",
            "15.000: 0.574, data does not look normal (reject H0)\n",
            "10.000: 0.654, data does not look normal (reject H0)\n",
            "5.000: 0.785, data does not look normal (reject H0)\n",
            "2.500: 0.915, data does not look normal (reject H0)\n",
            "1.000: 1.089, data does not look normal (reject H0)\n"
          ]
        }
      ],
      "execution_count": 25,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The correlation is used to test out the samples or features is correlation or not.\n",
        "\nTherefore, to check two samples or features are related or not."
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "## # Pearson’s Correlation Coefficient"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.stats import pearsonr\n",
        "\ncoef, p_value = pearsonr(dataset['Open'], dataset['Adj Close'])"
      ],
      "outputs": [],
      "execution_count": 26,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print('Pearson’s Correlation Coefficient')\n",
        "print('-'*40)\n",
        "print('Correlation Test: %.3f' % coef)\n",
        "# interpret the significance\n",
        "alpha = 0.05\n",
        "if p_value > alpha:\n",
        "\tprint('H0: the two samples are independent. p=%.3f' % p_value)\n",
        "else:\n",
        "\tprint('H1: there is a dependency between the samples. p=%.3f' % p_value)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Pearson’s Correlation Coefficient\n",
            "----------------------------------------\n",
            "Correlation Test: 0.998\n",
            "H1: there is a dependency between the samples. p=0.000\n"
          ]
        }
      ],
      "execution_count": 27,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Spearman's Rank Correlation"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "# Spearman's Rank Correlation\n",
        "from scipy.stats import spearmanr\n",
        "\ncoef, p_value = spearmanr(dataset['Open'], dataset['Adj Close'])"
      ],
      "outputs": [],
      "execution_count": 28,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print('Spearman’s Rank Correlation')\n",
        "print('-'*40)\n",
        "print('Spearmans correlation coefficient: %.3f' % coef)\n",
        "# interpret the significance\n",
        "alpha = 0.05\n",
        "if p_value > alpha:\n",
        "\tprint('Samples are uncorrelated (fail to reject H0) p=%.3f' % p_value)\n",
        "else:\n",
        "\tprint('Samples are correlated (reject H0) p=%.3f' % p_value)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Spearman’s Rank Correlation\n",
            "----------------------------------------\n",
            "Spearmans correlation coefficient: 0.996\n",
            "Samples are correlated (reject H0) p=0.000\n"
          ]
        }
      ],
      "execution_count": 29,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Kendall's Rank Correlation "
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "# Kendall's Rank Correlation\n",
        "from scipy.stats import kendalltau\n",
        "\ncoef, p_value = kendalltau(dataset['Open'], dataset['Adj Close'])"
      ],
      "outputs": [],
      "execution_count": 30,
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "print('Kendall’s Rank Correlation')\n",
        "print('-'*40)\n",
        "print('Kendall correlation coefficient: %.3f' % coef)\n",
        "# interpret the significance\n",
        "alpha = 0.05\n",
        "if p > alpha:\n",
        "\tprint('Samples are uncorrelated (fail to reject H0) p=%.3f' % p_value)\n",
        "else:\n",
        "\tprint('Samples are correlated (reject H0) p=%.3f' % p_value)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Kendall’s Rank Correlation\n",
            "----------------------------------------\n",
            "Kendall correlation coefficient: 0.953\n",
            "Samples are correlated (reject H0) p=0.000\n"
          ]
        }
      ],
      "execution_count": 31,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Chi-Squared Test"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "# Chi-Squared Test\n",
        "from scipy.stats import chi2_contingency\n",
        "from scipy.stats import chi2\n",
        "\nstat, p_value, dof, expected = chi2_contingency(dataset[['Open','Low','High','Adj Close','Volume']])"
      ],
      "outputs": [],
      "execution_count": 32,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# interpret test-statistic\n",
        "print('Chi-Squared Test')\n",
        "print('-'*40)\n",
        "\n",
        "prob = 0.95\n",
        "critical = chi2.ppf(prob, dof)\n",
        "if abs(stat) >= critical:\n",
        "\tprint('Dependent (reject H0)')\n",
        "else:\n",
        "\tprint('Independent (fail to reject H0)')"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Chi-Squared Test\n",
            "----------------------------------------\n",
            "Dependent (reject H0)\n"
          ]
        }
      ],
      "execution_count": 33,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# interpret p-value\n",
        "alpha = 1.0 - prob\n",
        "if p_value <= alpha:\n",
        "\tprint('Dependent (reject H0)')\n",
        "else:\n",
        "\tprint('Independent (fail to reject H0)')"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dependent (reject H0)\n"
          ]
        }
      ],
      "execution_count": 34,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print('dof=%d' % dof)\n",
        "print(expected)\n",
        "# interpret test-statistic\n",
        "prob = 0.95\n",
        "critical = chi2.ppf(prob, dof)\n",
        "print('probability=%.3f, critical=%.3f, stat=%.3f' % (prob, critical, stat))\n",
        "if abs(stat) >= critical:\n",
        "\tprint('Dependent (reject H0)')\n",
        "else:\n",
        "\tprint('Independent (fail to reject H0)')\n",
        "# interpret p-value\n",
        "alpha = 1.0 - prob\n",
        "print('significance=%.3f, p=%.3f' % (alpha, p_value))\n",
        "if p <= alpha:\n",
        "\tprint('Dependent (reject H0)')\n",
        "else:\n",
        "\tprint('Independent (fail to reject H0)')"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "dof=5028\n",
            "[[  1.79341454e+02   1.77772007e+02   1.80882430e+02   1.72125252e+02\n",
            "    5.86707947e+07]\n",
            " [  2.99915291e+02   2.97290682e+02   3.02492288e+02   2.87847533e+02\n",
            "    9.81160132e+07]\n",
            " [  3.15308232e+02   3.12548917e+02   3.18017492e+02   3.02621105e+02\n",
            "    1.03151748e+08]\n",
            " ..., \n",
            " [  1.62365169e+02   1.60944284e+02   1.63760278e+02   1.55832046e+02\n",
            "    5.31170753e+07]\n",
            " [  1.29274227e+02   1.28142927e+02   1.30385005e+02   1.24072592e+02\n",
            "    4.22915143e+07]\n",
            " [  1.06997287e+02   1.06060936e+02   1.07916652e+02   1.02692013e+02\n",
            "    3.50037078e+07]]\n",
            "probability=0.950, critical=5194.074, stat=335688.814\n",
            "Dependent (reject H0)\n",
            "significance=0.050, p=0.000\n",
            "Dependent (reject H0)\n"
          ]
        }
      ],
      "execution_count": 35,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "This part is to compare two samples or features; as a results, to check if they both independent samples."
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Parametric Statistical Hypothesis Tests"
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "T-test for the means of two independent samples of scores\n",
        "Two-sided test for the null hypothesis that 2 independent samples have identical average (expected) values"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "# Parametric Statistical Hypothesis Tests\n",
        "# Student’s t-test\n",
        "from scipy.stats import ttest_ind\n",
        "\nstat, p_value = ttest_ind(dataset['Open'], dataset['Adj Close'])"
      ],
      "outputs": [],
      "execution_count": 36,
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "print('Paired Students t-Test')\n",
        "print('-'*40)\n",
        "print('Statistics=%.3f, p=%.3f' % (stat, p_value))\n",
        "# interpret\n",
        "alpha = 0.05\n",
        "if p_value > alpha:\n",
        "\tprint('Same distributions (fail to reject H0)')\n",
        "else:\n",
        "\tprint('Different distributions (reject H0)')"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Paired Students t-Test\n",
            "----------------------------------------\n",
            "Statistics=3.450, p=0.001\n",
            "Different distributions (reject H0)\n"
          ]
        }
      ],
      "execution_count": 37,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Paired Student’s t-test"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "# Paired Student’s t-test\n",
        "from scipy.stats import ttest_rel\n",
        "\nstat, p_value = ttest_rel(dataset['Open'], dataset['Adj Close'])"
      ],
      "outputs": [],
      "execution_count": 38,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print('Paired Students t-Test')\n",
        "print('-'*40)\n",
        "print('Statistics=%.3f, p=%.3f' % (stat, p))\n",
        "# interpret\n",
        "alpha = 0.05\n",
        "if p_value > alpha:\n",
        "\tprint('Same distributions (fail to reject H0)')\n",
        "else:\n",
        "\tprint('Different distributions (reject H0)')"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Paired Students t-Test\n",
            "----------------------------------------\n",
            "Statistics=62.959, p=0.000\n",
            "Different distributions (reject H0)\n"
          ]
        }
      ],
      "execution_count": 39,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Analysis of Variance Test (ANOVA)"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "# Analysis of Variance Test (ANOVA)\n",
        "from scipy.stats import f_oneway\n",
        "\nstat, p_value = f_oneway(dataset['Open'], dataset['Adj Close'], dataset['Volume'])"
      ],
      "outputs": [],
      "execution_count": 40,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print('Analysis of Variance Test (ANOVA)')\n",
        "print('-'*40)\n",
        "print('Statistics=%.3f, p=%.3f' % (stat, p_value))\n",
        "# interpret\n",
        "alpha = 0.05\n",
        "if p_value > alpha:\n",
        "\tprint('Same distributions (fail to reject H0)')\n",
        "else:\n",
        "\tprint('Different distributions (reject H0)')"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Analysis of Variance Test (ANOVA)\n",
            "----------------------------------------\n",
            "Statistics=4168.958, p=0.000\n",
            "Different distributions (reject H0)\n"
          ]
        }
      ],
      "execution_count": 41,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Mann-Whitney U Test"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "# Nonparametric Statistical Hypothesis Tests\n",
        "# Mann-Whitney U Test\n",
        "from scipy.stats import mannwhitneyu\n",
        "\nstat, p_value = mannwhitneyu(dataset['Open'], dataset['Adj Close'])"
      ],
      "outputs": [],
      "execution_count": 42,
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "print('Mann-Whitney U Test')\n",
        "print('-'*40)\n",
        "print('Statistics=%.3f, p=%.3f' % (stat, p_value))\n",
        "# interpret\n",
        "alpha = 0.05\n",
        "if p_value > alpha:\n",
        "\tprint('Same distribution (fail to reject H0)')\n",
        "else:\n",
        "\tprint('Different distribution (reject H0)')"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mann-Whitney U Test\n",
            "----------------------------------------\n",
            "Statistics=707543.000, p=0.000\n",
            "Different distribution (reject H0)\n"
          ]
        }
      ],
      "execution_count": 43,
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Wilcoxon Signed-Rank Test"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "# Wilcoxon Signed-Rank Test\n",
        "from scipy.stats import wilcoxon\n",
        "\nstat, p_value = wilcoxon(dataset['Open'], dataset['Adj Close'])"
      ],
      "outputs": [],
      "execution_count": 44,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print('Wilcoxon Signed-Rank Test')\n",
        "print('-'*40)\n",
        "print('Statistics=%.3f, p=%.3f' % (stat, p_value))\n",
        "# interpret\n",
        "alpha = 0.05\n",
        "if p_value > alpha:\n",
        "\tprint('Same distribution (fail to reject H0)')\n",
        "else:\n",
        "\tprint('Different distribution (reject H0)')"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Wilcoxon Signed-Rank Test\n",
            "----------------------------------------\n",
            "Statistics=7095.000, p=0.000\n",
            "Different distribution (reject H0)\n"
          ]
        }
      ],
      "execution_count": 45,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Kruskal-Wallis Test"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "# Kruskal-Wallis Test\n",
        "from scipy.stats import kruskal\n",
        "\nstat, p_value = kruskal(dataset['Open'], dataset['Adj Close'], dataset['Volume'])"
      ],
      "outputs": [],
      "execution_count": 46,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print('Kruskal-Wallis Test')\n",
        "print('-'*40)\n",
        "print('Statistics=%.3f, p=%.3f' % (stat, p_value))\n",
        "# interpret\n",
        "alpha = 0.05\n",
        "if p_value > alpha:\n",
        "\tprint('Same distributions (fail to reject H0)')\n",
        "else:\n",
        "\tprint('Different distributions (reject H0)')"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Kruskal-Wallis Test\n",
            "----------------------------------------\n",
            "Statistics=2524.724, p=0.000\n",
            "Different distributions (reject H0)\n"
          ]
        }
      ],
      "execution_count": 47,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Friedman Test"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.stats import friedmanchisquare\n",
        "\nstat, p_value = friedmanchisquare(dataset['Open'], dataset['Adj Close'], dataset['Volume'])"
      ],
      "outputs": [],
      "execution_count": 48,
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "print('Friedman Test')\n",
        "print('-'*40)\n",
        "print('Statistics=%.3f, p=%.3f' % (stat, p_value))\n",
        "# interpret\n",
        "alpha = 0.05\n",
        "if p_value > alpha:\n",
        "\tprint('Same distributions (fail to reject H0)')\n",
        "else:\n",
        "\tprint('Different distributions (reject H0)')"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Friedman Test\n",
            "----------------------------------------\n",
            "Statistics=2429.219, p=0.000\n",
            "Different distributions (reject H0)\n"
          ]
        }
      ],
      "execution_count": 49,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Levene Test"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "# Levene Test\n",
        "from scipy.stats import levene\n",
        "\nstat, p_value = levene(dataset['Open'], dataset['Adj Close'])"
      ],
      "outputs": [],
      "execution_count": 50,
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "print('Levene Test')\n",
        "print('-'*40)\n",
        "print('Statistics=%.3f, p=%.3f' % (stat, p_value))\n",
        "# interpret\n",
        "alpha = 0.05\n",
        "if p_value > alpha:\n",
        "\tprint('Same distributions (fail to reject H0)')\n",
        "else:\n",
        "\tprint('Different distributions (reject H0)')"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Levene Test\n",
            "----------------------------------------\n",
            "Statistics=1.310, p=0.252\n",
            "Same distributions (fail to reject H0)\n"
          ]
        }
      ],
      "execution_count": 51,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Mood’s test"
      ],
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.stats import mood\n",
        "\nstat, p_value = mood(dataset['Open'], dataset['Adj Close'])"
      ],
      "outputs": [],
      "execution_count": 52,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print('Mood Test')\n",
        "print('-'*40)\n",
        "print('Statistics=%.3f, p=%.3f' % (stat, p_value))\n",
        "# interpret\n",
        "alpha = 0.05\n",
        "if p_value > alpha:\n",
        "\tprint('Same distributions (fail to reject H0)')\n",
        "else:\n",
        "\tprint('Different distributions (reject H0)')"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mood Test\n",
            "----------------------------------------\n",
            "Statistics=-4.115, p=0.000\n",
            "Different distributions (reject H0)\n"
          ]
        }
      ],
      "execution_count": 53,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Mood’s median test"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.stats import median_test\n",
        "\nstat, p_value, med, tbl = median_test(dataset['Open'], dataset['Adj Close'], dataset['Volume'])"
      ],
      "outputs": [],
      "execution_count": 54,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print('Mood’s median test')\n",
        "print('-'*40)\n",
        "print('Statistics=%.3f, p=%.3f' % (stat, p_value))\n",
        "# interpret\n",
        "alpha = 0.05\n",
        "if p_value > alpha:\n",
        "\tprint('Same distributions (fail to reject H0)')\n",
        "else:\n",
        "\tprint('Different distributions (reject H0)')"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mood’s median test\n",
            "----------------------------------------\n",
            "Statistics=1888.528, p=0.000\n",
            "Different distributions (reject H0)\n"
          ]
        }
      ],
      "execution_count": 55,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "stat, p_value, med, tbl = median_test(dataset['Open'], dataset['Adj Close'], dataset['Volume'],lambda_=\"log-likelihood\")"
      ],
      "outputs": [],
      "execution_count": 56,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print('Mood’s median test with lambda')\n",
        "print('-'*40)\n",
        "print('Statistics=%.3f, p=%.3f' % (stat, p_value))\n",
        "# interpret\n",
        "alpha = 0.05\n",
        "if p_value > alpha:\n",
        "\tprint('Same distributions (fail to reject H0)')\n",
        "else:\n",
        "\tprint('Different distributions (reject H0)')"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mood’s median test with lambda\n",
            "----------------------------------------\n",
            "Statistics=2404.242, p=0.000\n",
            "Different distributions (reject H0)\n"
          ]
        }
      ],
      "execution_count": 57,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Kolmogorov-Smirnov Test"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "# Kolmogorov-Smirnov test\n",
        "from scipy.stats import ks_2samp\n",
        "\nstat, p_value = ks_2samp(dataset['Open'], dataset['Adj Close'])"
      ],
      "outputs": [],
      "execution_count": 58,
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "print('Kolmogorov-Smirnov Test')\n",
        "print('-'*40)\n",
        "print('Statistics=%.3f, p=%.3f' % (stat, p_value))\n",
        "# interpret\n",
        "alpha = 0.05\n",
        "if p_value > alpha:\n",
        "\tprint('Same distributions (fail to reject H0)')\n",
        "else:\n",
        "\tprint('Different distributions (reject H0)')"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Kolmogorov-Smirnov Test\n",
            "----------------------------------------\n",
            "Statistics=0.125, p=0.000\n",
            "Different distributions (reject H0)\n"
          ]
        }
      ],
      "execution_count": 59,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Box-Cox Power Transformation"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.stats import boxcox\n",
        "\ndataset['boxcox'], lam = boxcox(dataset['Adj Close'])"
      ],
      "outputs": [],
      "execution_count": 60,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print('Lambda: %f' % lam)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Lambda: -0.153051\n"
          ]
        }
      ],
      "execution_count": 61,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot line\n",
        "plt.title('Stock Box-Cox Power Transformation')\n",
        "plt.plot(dataset['boxcox'])\n",
        "plt.show()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": [
              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\n"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 62,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot histogram\n",
        "plt.title('Stock Box-Cox Power Transformation in Histogram')\n",
        "plt.hist(dataset['boxcox'])\n",
        "plt.grid()\n",
        "plt.show()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": [
              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\n"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 63,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    }
  ],
  "metadata": {
    "kernel_info": {
      "name": "python3"
    },
    "language_info": {
      "name": "python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "version": "3.5.5",
      "nbconvert_exporter": "python",
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