ml-finance-python

python scripts for finance machine learning
git clone https://9o.is/git/ml-finance-python.git
notebook.ipynb

(78420B)
      1 {
      2  "cells": [
      3   {
      4    "cell_type": "markdown",
      5    "metadata": {
      6     "collapsed": true
      7    },
      8    "source": [
      9     "# Exercises: Comparing ETFs - Answer Key\n",
     10     "By Christopher van Hoecke, Maxwell Margenot, and Delaney Mackenzie\n",
     11     "\n",
     12     "\n",
     13     "## Lecture Link :\n",
     14     "https://www.quantopian.com/lectures/statistical-moments\n",
     15     "\n",
     16     "https://www.quantopian.com/lectures/hypothesis-testing\n",
     17     "\n",
     18     "###IMPORTANT NOTE: \n",
     19     "This lecture corresponds to the statistical moments and hypothesis testing lecture, which is part of the Quantopian lecture series. This homework expects you to rely heavily on the code presented in the corresponding lecture. Please copy and paste regularly from that lecture when starting to work on the problems, as trying to do them from scratch will likely be too difficult.\n",
     20     "\n",
     21     "When you feel comfortable with the topics presented here, see if you can create an algorithm that qualifies for the Quantopian Contest. Participants are evaluated on their ability to produce risk-constrained alpha and the top 10 contest participants are awarded cash prizes on a daily basis.\n",
     22     "\n",
     23     "https://www.quantopian.com/contest\n",
     24     "\n",
     25     "Part of the Quantopian Lecture Series:\n",
     26     "\n",
     27     "* [www.quantopian.com/lectures](https://www.quantopian.com/lectures)\n",
     28     "* [github.com/quantopian/research_public](https://github.com/quantopian/research_public)\n",
     29     "\n",
     30     "----"
     31    ]
     32   },
     33   {
     34    "cell_type": "markdown",
     35    "metadata": {},
     36    "source": [
     37     "## Key Concepts\n",
     38     "t-statistic formula for unequal variances : $ t = \\frac{\\bar{X}_1 - \\bar{X}_2}{(\\frac{s_1^2}{n_1} + \\frac{s_2^2}{n_2})^{1/2}}$\n",
     39     "\n",
     40     "Where $s_1$ and $s_2$ are the standard deviation of set 1 and set 2; and $n_1$ and $n_2$ are the number of observations we have."
     41    ]
     42   },
     43   {
     44    "cell_type": "code",
     45    "execution_count": 1,
     46    "metadata": {
     47     "collapsed": true
     48    },
     49    "outputs": [],
     50    "source": [
     51     "# Useful functions\n",
     52     "def normal_test(X):\n",
     53     "    z, pval = stats.normaltest(X)\n",
     54     "    if pval < 0.05:\n",
     55     "        print 'Values are not normally distributed.'\n",
     56     "    else: \n",
     57     "        print 'Values are normally distributed.'\n",
     58     "    return"
     59    ]
     60   },
     61   {
     62    "cell_type": "code",
     63    "execution_count": 2,
     64    "metadata": {
     65     "collapsed": true
     66    },
     67    "outputs": [],
     68    "source": [
     69     "# Useful Libraries\n",
     70     "import numpy as np\n",
     71     "import matplotlib.pyplot as plt\n",
     72     "from scipy import stats\n",
     73     "import seaborn as sns"
     74    ]
     75   },
     76   {
     77    "cell_type": "markdown",
     78    "metadata": {},
     79    "source": [
     80     "#### Data:"
     81    ]
     82   },
     83   {
     84    "cell_type": "code",
     85    "execution_count": 3,
     86    "metadata": {
     87     "collapsed": true
     88    },
     89    "outputs": [],
     90    "source": [
     91     "# Get pricing data for an energy (XLE) and industrial (XLI) ETF\n",
     92     "xle = get_pricing('XLE', fields = 'price', start_date = '2016-01-01', end_date = '2017-01-01')\n",
     93     "xli = get_pricing('XLI', fields = 'price', start_date = '2016-01-01', end_date = '2017-01-01')\n",
     94     "\n",
     95     "# Compute returns\n",
     96     "xle_returns = xle.pct_change()[1:]\n",
     97     "xli_returns = xli.pct_change()[1:]"
     98    ]
     99   },
    100   {
    101    "cell_type": "markdown",
    102    "metadata": {},
    103    "source": [
    104     "## Exercise 1 : Hypothesis Testing on Variances. \n",
    105     "- Plot the histogram of the returns of XLE and XLI\n",
    106     "- Check to see if each return stream is normally distributed\n",
    107     "- If the assets are normally distributed, use the F-test to perform a hypothesis test and decide whether they have the two assets have the same variance.\n",
    108     "- If the assets are **not** normally distributed, use the Levene test (in the scipy library) to perform a hypothesis test on variance. "
    109    ]
    110   },
    111   {
    112    "cell_type": "code",
    113    "execution_count": 4,
    114    "metadata": {},
    115    "outputs": [
    116     {
    117      "data": {
    118       "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0YAAAH6CAYAAAAjs1w/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEREREREJeGqMREREREQk4P0FiH0dJUqLk+8AAAAASUVORK5CYII=\n",
    119       "text/plain": [
    120        "<matplotlib.figure.Figure at 0x7f200ad63c50>"
    121       ]
    122      },
    123      "metadata": {},
    124      "output_type": "display_data"
    125     }
    126    ],
    127    "source": [
    128     "xle = plt.hist(xle_returns, bins=30)\n",
    129     "xli = plt.hist(xli_returns, bins=30, color='r')\n",
    130     "\n",
    131     "plt.xlabel('returns')\n",
    132     "plt.ylabel('Frequency')\n",
    133     "plt.title('Histogram of the returns of XLE and XLI')\n",
    134     "plt.legend(['XLE returns', 'XLI returns']);"
    135    ]
    136   },
    137   {
    138    "cell_type": "code",
    139    "execution_count": 5,
    140    "metadata": {},
    141    "outputs": [
    142     {
    143      "name": "stdout",
    144      "output_type": "stream",
    145      "text": [
    146       "XLE\n",
    147       "Values are normally distributed.\n",
    148       "XLI\n",
    149       "Values are not normally distributed.\n"
    150      ]
    151     }
    152    ],
    153    "source": [
    154     "# Checking for normality using function above. \n",
    155     "\n",
    156     "print 'XLE'\n",
    157     "normal_test(xle_returns)\n",
    158     "print 'XLI'\n",
    159     "normal_test(xli_returns)"
    160    ]
    161   },
    162   {
    163    "cell_type": "code",
    164    "execution_count": 6,
    165    "metadata": {},
    166    "outputs": [
    167     {
    168      "data": {
    169       "text/plain": [
    170        "LeveneResult(statistic=42.17712538196367, pvalue=2.0149310189859354e-10)"
    171       ]
    172      },
    173      "execution_count": 6,
    174      "metadata": {},
    175      "output_type": "execute_result"
    176     }
    177    ],
    178    "source": [
    179     "# Because the data is not normally distributed, we must use the levene and not the F-test of variance. \n",
    180     "\n",
    181     "stats.levene(xle_returns, xli_returns)"
    182    ]
    183   },
    184   {
    185    "cell_type": "markdown",
    186    "metadata": {},
    187    "source": [
    188     "Since we find a pvalue for the Levene test of less than our $\\alpha$ level (0.05), we can reject the null hypothesis that the variability of the two groups are equal thus implying that **the variances are unequal.**"
    189    ]
    190   },
    191   {
    192    "cell_type": "markdown",
    193    "metadata": {},
    194    "source": [
    195     "-----"
    196    ]
    197   },
    198   {
    199    "cell_type": "markdown",
    200    "metadata": {},
    201    "source": [
    202     "## Exercise 2 : Hypothesis Testing on Means.\n",
    203     "\n",
    204     "Since we know that the variances are not equal, we must use Welch's t-test. \n",
    205     "- Calculate the mean returns of XLE and XLI.\n",
    206     "    - Find the difference between the two means.\n",
    207     "- Calculate the standard deviation of the returns of XLE and XLI\n",
    208     "- Using the formula given above, calculate the t-test statistic (Using $\\alpha = 0.05$) for Welch's t-test to test whether the mean returns of XLE and XLI are different.\n",
    209     "- Consult the [Hypothesis Testing Lecture](https://www.quantopian.com/lectures#Hypothesis-Testing) to calculate the p-value for this test. Are the mean returns of XLE and XLI the same?\n",
    210     "\n",
    211     "- Now use the t-test function for two independent samples from the scipy library. Compare the results."
    212    ]
    213   },
    214   {
    215    "cell_type": "code",
    216    "execution_count": 7,
    217    "metadata": {},
    218    "outputs": [
    219     {
    220      "name": "stdout",
    221      "output_type": "stream",
    222      "text": [
    223       "t-test statistic: 0.246212554505\n"
    224      ]
    225     }
    226    ],
    227    "source": [
    228     "# Manually calculating the t-statistic\n",
    229     "\n",
    230     "N1 = len(xle_returns)\n",
    231     "N2 = len(xli_returns)\n",
    232     "\n",
    233     "m1 = xle_returns.mean()\n",
    234     "m2 = xli_returns.mean()\n",
    235     "\n",
    236     "s1 = xle_returns.std()\n",
    237     "s2 = xli_returns.std()\n",
    238     "\n",
    239     "test_statistic = (m1 - m2) / (s1**2 / N1 + s2**2 / N2)**0.5\n",
    240     "print 't-test statistic:', test_statistic"
    241    ]
    242   },
    243   {
    244    "cell_type": "code",
    245    "execution_count": 8,
    246    "metadata": {},
    247    "outputs": [
    248     {
    249      "data": {
    250       "text/plain": [
    251        "Ttest_indResult(statistic=0.24621255450523835, pvalue=0.80564256071472806)"
    252       ]
    253      },
    254      "execution_count": 8,
    255      "metadata": {},
    256      "output_type": "execute_result"
    257     }
    258    ],
    259    "source": [
    260     "# Alternative form, using the scipy library on python. \n",
    261     "\n",
    262     "stats.ttest_ind(xle_returns, xli_returns, equal_var=False)"
    263    ]
    264   },
    265   {
    266    "cell_type": "markdown",
    267    "metadata": {},
    268    "source": [
    269     "----"
    270    ]
    271   },
    272   {
    273    "cell_type": "markdown",
    274    "metadata": {
    275     "collapsed": true
    276    },
    277    "source": [
    278     "## Exercise 3 : Skewness\n",
    279     "- Calculate the mean and median of the two assets\n",
    280     "- Calculate the skewness using the scipy library"
    281    ]
    282   },
    283   {
    284    "cell_type": "code",
    285    "execution_count": 9,
    286    "metadata": {},
    287    "outputs": [
    288     {
    289      "name": "stdout",
    290      "output_type": "stream",
    291      "text": [
    292       "Mean of XLE returns =  0.0011018423017 ; median =  0.000303393455803\n",
    293       "Mean of XLI returns =  0.000822235474081 ; median =  0.000546099936288\n"
    294      ]
    295     }
    296    ],
    297    "source": [
    298     "# Calculate the mean and median of xle and xli using the numpy library\n",
    299     "\n",
    300     "xle_mean = np.mean(xle_returns)\n",
    301     "xle_median = np.median(xle_returns)\n",
    302     "print 'Mean of XLE returns = ', xle_mean, '; median = ', xle_median\n",
    303     "\n",
    304     "xli_mean = np.mean(xli_returns)\n",
    305     "xli_median = np.median(xli_returns)\n",
    306     "print 'Mean of XLI returns = ', xli_mean, '; median = ', xli_median"
    307    ]
    308   },
    309   {
    310    "cell_type": "code",
    311    "execution_count": 10,
    312    "metadata": {},
    313    "outputs": [
    314     {
    315      "name": "stdout",
    316      "output_type": "stream",
    317      "text": [
    318       "Skew of XLE returns: 0.090984051661\n",
    319       "Skew of XLI returns: -0.332324357079\n"
    320      ]
    321     }
    322    ],
    323    "source": [
    324     "# Print values of Skewness for xle and xli returns \n",
    325     "\n",
    326     "print 'Skew of XLE returns:', stats.skew(xle_returns)\n",
    327     "print 'Skew of XLI returns:',  stats.skew(xli_returns)"
    328    ]
    329   },
    330   {
    331    "cell_type": "markdown",
    332    "metadata": {},
    333    "source": [
    334     "And the skewness of XLE returns of values > 0 means that there is more weight in the right tail of the distribution. The skewness of XLI returns of value > 0 means that there is more weight in the left tail of the distribution.\n",
    335     "\n",
    336     "----"
    337    ]
    338   },
    339   {
    340    "cell_type": "markdown",
    341    "metadata": {},
    342    "source": [
    343     "## Exercise 4 :  Kurtosis\n",
    344     "- Check the kurtosis of the two assets, using the scipy library. \n",
    345     "- Using the seaborn library, plot the distribution of XLE and XLI returns. \n",
    346     "\n",
    347     "Recall: \n",
    348     "- Kurtosis > 3 is leptokurtic, a highly peaked, narrow deviation from the mean\n",
    349     "- Kurtosis = 3 is mesokurtic. The most significant mesokurtic distribution is the normal distribution family. \n",
    350     "- Kurtosis < 3 is platykurtic, a lower-peaked, broad deviation from the mean "
    351    ]
    352   },
    353   {
    354    "cell_type": "code",
    355    "execution_count": 11,
    356    "metadata": {
    357     "scrolled": true
    358    },
    359    "outputs": [
    360     {
    361      "name": "stdout",
    362      "output_type": "stream",
    363      "text": [
    364       "kurtosis: 0.854981014172\n",
    365       "kurtosis: 2.17804780091\n"
    366      ]
    367     }
    368    ],
    369    "source": [
    370     "# Print value of Kurtosis for xle and xli returns \n",
    371     "\n",
    372     "print 'kurtosis:', stats.kurtosis(xle_returns)\n",
    373     "print 'kurtosis:', stats.kurtosis(xli_returns)"
    374    ]
    375   },
    376   {
    377    "cell_type": "code",
    378    "execution_count": 12,
    379    "metadata": {
    380     "scrolled": false
    381    },
    382    "outputs": [
    383     {
    384      "data": {
    385       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAzcAAAHxCAYAAABQ/0JaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nkOBGCCGEEEII0Sf8f2cyb8rlfPsOAAAAAElFTkSuQmCC\n",
    386       "text/plain": [
    387        "<matplotlib.figure.Figure at 0x7f200bc9ab90>"
    388       ]
    389      },
    390      "metadata": {},
    391      "output_type": "display_data"
    392     }
    393    ],
    394    "source": [
    395     "# Distribution plot of XLE returns in red (for Kurtosis of 1.6). \n",
    396     "# Distribution plot of XLI returns in blue (for Kurtosis of 2.0).\n",
    397     "\n",
    398     "xle = sns.distplot(xle_returns, color = 'r', axlabel  = 'xle')\n",
    399     "xli = sns.distplot(xli_returns, axlabel = 'xli');"
    400    ]
    401   },
    402   {
    403    "cell_type": "markdown",
    404    "metadata": {},
    405    "source": [
    406     "We can clearly see from the two graphs that as our kurtosis gets lower, the distribution gets more flat."
    407    ]
    408   },
    409   {
    410    "cell_type": "markdown",
    411    "metadata": {},
    412    "source": [
    413     "*This presentation is for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation for any security; nor does it constitute an offer to provide investment advisory or other services by Quantopian, Inc. (\"Quantopian\"). Nothing contained herein constitutes investment advice or offers any opinion with respect to the suitability of any security, and any views expressed herein should not be taken as advice to buy, sell, or hold any security or as an endorsement of any security or company.  In preparing the information contained herein, Quantopian, Inc. has not taken into account the investment needs, objectives, and financial circumstances of any particular investor. Any views expressed and data illustrated herein were prepared based upon information, believed to be reliable, available to Quantopian, Inc. at the time of publication. Quantopian makes no guarantees as to their accuracy or completeness. All information is subject to change and may quickly become unreliable for various reasons, including changes in market conditions or economic circumstances.*"
    414    ]
    415   }
    416  ],
    417  "metadata": {
    418   "kernelspec": {
    419    "display_name": "Python 2",
    420    "language": "python",
    421    "name": "python2"
    422   },
    423   "language_info": {
    424    "codemirror_mode": {
    425     "name": "ipython",
    426     "version": 2
    427    },
    428    "file_extension": ".py",
    429    "mimetype": "text/x-python",
    430    "name": "python",
    431    "nbconvert_exporter": "python",
    432    "pygments_lexer": "ipython2",
    433    "version": "2.7.10"
    434   }
    435  },
    436  "nbformat": 4,
    437  "nbformat_minor": 2
    438 }