ml-finance-python

python scripts for finance machine learning

git clone https://9o.is/git/ml-finance-python.git

notebook.ipynb

(308655B)


      1 {
      2  "cells": [
      3   {
      4    "cell_type": "markdown",
      5    "metadata": {
      6     "deletable": true,
      7     "editable": true
      8    },
      9    "source": [
     10     "# Exercises: Instability of Parameter Estimates - Answer Key\n",
     11     "\n",
     12     "## Lecture Link\n",
     13     "\n",
     14     "This exercise notebook refers to this lecture. Please use the lecture for explanations and sample code.\n",
     15     "\n",
     16     "https://www.quantopian.com/lectures#Instability-of-Estimates\n",
     17     "\n",
     18     "Part of the Quantopian Lecture Series:\n",
     19     "\n",
     20     "* [www.quantopian.com/lectures](https://www.quantopian.com/lectures)\n",
     21     "* [github.com/quantopian/research_public](https://github.com/quantopian/research_public)"
     22    ]
     23   },
     24   {
     25    "cell_type": "code",
     26    "execution_count": 1,
     27    "metadata": {
     28     "collapsed": true,
     29     "deletable": true,
     30     "editable": true
     31    },
     32    "outputs": [],
     33    "source": [
     34     "import numpy as np\n",
     35     "import matplotlib.pyplot as plt\n",
     36     "import pandas as pd\n",
     37     "from statsmodels.stats.stattools import jarque_bera\n",
     38     "\n",
     39     "# Set a seed so we can play with the data without generating new random numbers every time\n",
     40     "np.random.seed(321)\n"
     41    ]
     42   },
     43   {
     44    "cell_type": "markdown",
     45    "metadata": {
     46     "deletable": true,
     47     "editable": true
     48    },
     49    "source": [
     50     "# Exercise 1: Sample Size vs. Standard Deviation\n",
     51     "\n",
     52     "Using the below normal distribution with mean 100 and standard deviation 50, find the means and standard deviations of  samples of size 5, 25, 100, and 500."
     53    ]
     54   },
     55   {
     56    "cell_type": "code",
     57    "execution_count": 2,
     58    "metadata": {
     59     "collapsed": false,
     60     "deletable": true,
     61     "editable": true,
     62     "scrolled": true
     63    },
     64    "outputs": [
     65     {
     66      "name": "stdout",
     67      "output_type": "stream",
     68      "text": [
     69       "Mean 1: 99.089982     Std 1: 24.4641171659\n",
     70       "Mean 2: 94.706474     Std 2: 20.1246310051\n",
     71       "Mean 3: 100.107503    Std 3: 24.6748889015\n",
     72       "Mean 4: 100.661661    Std 4: 24.6649605309\n",
     73       "\n",
     74       "As sample size increases, the mean and standard deviation approach those of the population. However, even at the 500 sample level the sample mean is not the same as the population mean.\n"
     75      ]
     76     }
     77    ],
     78    "source": [
     79     "POPULATION_MU = 100\n",
     80     "POPULATION_SIGMA = 25\n",
     81     "sample_sizes = [5, 25, 100, 500]\n",
     82     "\n",
     83     "#Your code goes here\n",
     84     "\n",
     85     "for i in range(len(sample_sizes)):\n",
     86     "    sample = np.random.normal(POPULATION_MU, POPULATION_SIGMA, sample_sizes[i])\n",
     87     "    row = 'Mean',(i+1),':', np.mean(sample),'Std',(i+1),':',np.std(sample)\n",
     88     "    print (\"{} {}{} {:<10f}    {} {}{} {}\").format(*row)\n",
     89     "    \n",
     90     "print \"\\nAs sample size increases, the mean and standard deviation approach those of the population. However, even at the 500 sample level the sample mean is not the same as the population mean.\"\n"
     91    ]
     92   },
     93   {
     94    "cell_type": "markdown",
     95    "metadata": {
     96     "deletable": true,
     97     "editable": true
     98    },
     99    "source": [
    100     "# Exercise 2: Instability of Predictions on Mean Alone\n",
    101     "\n",
    102     "## a. Finding Means\n",
    103     "\n",
    104     "Find the means of the following three data sets $X$, $Y$, and $Z$."
    105    ]
    106   },
    107   {
    108    "cell_type": "code",
    109    "execution_count": 3,
    110    "metadata": {
    111     "collapsed": false,
    112     "deletable": true,
    113     "editable": true,
    114     "scrolled": true
    115    },
    116    "outputs": [
    117     {
    118      "name": "stdout",
    119      "output_type": "stream",
    120      "text": [
    121       "Mean X: 25.00\n",
    122       "Mean Y: 24.69\n",
    123       "Mean Z: 25.61\n"
    124      ]
    125     }
    126    ],
    127    "source": [
    128     "X = [ 31.,   6.,  21.,  32.,  41.,   4.,  48.,  38.,  43.,  36.,  50., 20.,  46.,  33.,   8.,  27.,  17.,  44.,  16.,  39.,   3.,  37.,\n",
    129     "        35.,  13.,  49.,   2.,  18.,  42.,  22.,  25.,  15.,  24.,  11., 19.,   5.,  40.,  12.,  10.,   1.,  45.,  26.,  29.,   7.,  30.,\n",
    130     "        14.,  23.,  28.,   0.,  34.,   9.,  47.]\n",
    131     "Y = [ 15.,  41.,  33.,  29.,   3.,  28.,  28.,   8.,  15.,  22.,  39., 38.,  22.,  10.,  39.,  40.,  24.,  15.,  21.,  25.,  17.,  33.,\n",
    132     "        40.,  32.,  42.,   5.,  39.,   8.,  15.,  25.,  37.,  33.,  14., 25.,   1.,  31.,  45.,   5.,   6.,  19.,  13.,  39.,  18.,  49.,\n",
    133     "        13.,  38.,   8.,  25.,  32.,  40.,  17.]\n",
    134     "Z = [ 38.,  23.,  16.,  35.,  48.,  18.,  48.,  38.,  24.,  27.,  24., 35.,  37.,  28.,  11.,  12.,  31.,  -1.,   9.,  19.,  20.,   0.,\n",
    135     "        23.,  33.,  34.,  24.,  14.,  28.,  12.,  25.,  53.,  19.,  42., 21.,  15.,  36.,  47.,  20.,  26.,  41.,  33.,  50.,  26.,  22.,\n",
    136     "        -1.,  35.,  10.,  25.,  23.,  24.,   6.]\n",
    137     "\n",
    138     "#Your code goes here\n",
    139     "\n",
    140     "print \"Mean X: %.2f\"% np.mean(X)\n",
    141     "print \"Mean Y: %.2f\"% np.mean(Y)\n",
    142     "print \"Mean Z: %.2f\"% np.mean(Z)\n"
    143    ]
    144   },
    145   {
    146    "cell_type": "markdown",
    147    "metadata": {
    148     "deletable": true,
    149     "editable": true
    150    },
    151    "source": [
    152     "## b. Checking for Normality\n",
    153     "\n",
    154     "Use the `jarque_bera` function to conduct a Jarque-Bera test on $X$, $Y$, and $Z$ to determine whether their distributions are normal. "
    155    ]
    156   },
    157   {
    158    "cell_type": "code",
    159    "execution_count": 4,
    160    "metadata": {
    161     "collapsed": false,
    162     "deletable": true,
    163     "editable": true,
    164     "scrolled": true
    165    },
    166    "outputs": [
    167     {
    168      "name": "stdout",
    169      "output_type": "stream",
    170      "text": [
    171       "0.216026379492 0.25028131217 0.866907001763\n",
    172       "The distribution of X is likely not normal.\n",
    173       "The distribution of Y is likely not normal.\n",
    174       "The distribution of Z is likely not normal.\n"
    175      ]
    176     }
    177    ],
    178    "source": [
    179     "#Your code goes here\n",
    180     "\n",
    181     "Xp = jarque_bera(X)[1]\n",
    182     "Yp = jarque_bera(Y)[1]\n",
    183     "Zp = jarque_bera(Z)[1]\n",
    184     "\n",
    185     "print Xp, Yp, Zp\n",
    186     "\n",
    187     "if Xp < 0.05:\n",
    188     "    print 'The distribution of X is likely normal.'\n",
    189     "else:\n",
    190     "    print 'The distribution of X is likely not normal.'\n",
    191     "    \n",
    192     "if Yp < 0.05:\n",
    193     "    print 'The distribution of Y is likely normal.'\n",
    194     "else:\n",
    195     "    print 'The distribution of Y is likely not normal.'\n",
    196     "    \n",
    197     "if Zp < 0.05:\n",
    198     "    print 'The distribution of Z is likely normal.'\n",
    199     "else:\n",
    200     "    print 'The distribution of Z is likely not normal.'\n"
    201    ]
    202   },
    203   {
    204    "cell_type": "markdown",
    205    "metadata": {
    206     "deletable": true,
    207     "editable": true
    208    },
    209    "source": [
    210     "## c. Instability of Estimates\n",
    211     "\n",
    212     "Create a histogram of the sample distributions of $X$, $Y$, and $Z$ along with the best estimate/mean based on the sample."
    213    ]
    214   },
    215   {
    216    "cell_type": "code",
    217    "execution_count": 5,
    218    "metadata": {
    219     "collapsed": false,
    220     "deletable": true,
    221     "editable": true,
    222     "scrolled": false
    223    },
    224    "outputs": [
    225     {
    226      "name": "stdout",
    227      "output_type": "stream",
    228      "text": [
    229       "All three datasets have a similar mean, but have very different distributions. Mean alone is very non-informative about what is going on in data, and should not be used alone as an estimator.\n"
    230      ]
    231     },
    232     {
    233      "data": {
    234       "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0EAAAHiCAYAAAAu6sewAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAjPL/AStN1m8jLSV/AAAAAElFTkSuQmCC\n",
    235       "text/plain": [
    236        "<matplotlib.figure.Figure at 0x7f5862305710>"
    237       ]
    238      },
    239      "metadata": {},
    240      "output_type": "display_data"
    241     }
    242    ],
    243    "source": [
    244     "#Your code goes here\n",
    245     "\n",
    246     "plt.hist([X, Y, Z], normed=1, histtype='bar', stacked=False, alpha = 0.7);\n",
    247     "plt.axvline(np.mean(X));\n",
    248     "plt.axvline(np.mean(Y), c='r');\n",
    249     "plt.axvline(np.mean(Z), c='g');\n",
    250     "\n",
    251     "print \"All three datasets have a similar mean, but have very different distributions. Mean alone is very non-informative about what is going on in data, and should not be used alone as an estimator.\" \n"
    252    ]
    253   },
    254   {
    255    "cell_type": "markdown",
    256    "metadata": {
    257     "deletable": true,
    258     "editable": true
    259    },
    260    "source": [
    261     "# Exercise 3: Sharpe Ratio Window Adjustment\n",
    262     "\n",
    263     "## a. Effect on Variability\n",
    264     "\n",
    265     "Just as in the lecture, find the mean and standard deviation of the running sharpe ratio for THO, this time testing for multiple window lengths: 300, 150, and 50. Restrict your mean and standard deviation calculation to pricing data up to 200 days away from the end."
    266    ]
    267   },
    268   {
    269    "cell_type": "code",
    270    "execution_count": 6,
    271    "metadata": {
    272     "collapsed": false,
    273     "deletable": true,
    274     "editable": true
    275    },
    276    "outputs": [
    277     {
    278      "name": "stdout",
    279      "output_type": "stream",
    280      "text": [
    281       "Sharpe Mean  50: 0.041578         Std  50: 0.152672986316\n",
    282       "Sharpe Mean 150: 0.045063         Std 150: 0.0708117281461\n",
    283       "Sharpe Mean 300: 0.042417         Std 300: 0.041788831424\n",
    284       "As we increase the length of the window, the variability of the running sharpe ratio decreases.\n"
    285      ]
    286     }
    287    ],
    288    "source": [
    289     "def sharpe_ratio(asset, riskfree):\n",
    290     "    return np.mean(asset - riskfree)/np.std(asset - riskfree)\n",
    291     "\n",
    292     "start = '2010-01-01'\n",
    293     "end = '2015-01-01'\n",
    294     "\n",
    295     "treasury_ret = get_pricing('BIL', fields='price', start_date=start, end_date=end).pct_change()[1:]\n",
    296     "pricing = get_pricing('THO', fields='price', start_date=start, end_date=end)\n",
    297     "returns = pricing.pct_change()[1:]\n",
    298     "\n",
    299     "#Your code goes here\n",
    300     "\n",
    301     "for window in [50, 150, 300]:\n",
    302     "    running_sharpe = [sharpe_ratio(returns[i-window+10:i], treasury_ret[i-window+10:i]) for i in range(window-10, len(returns))]\n",
    303     "    mean_rs = np.mean(running_sharpe[:-200])\n",
    304     "    std_rs = np.std(running_sharpe[:-200])\n",
    305     "    \n",
    306     "    row = 'Sharpe Mean',(window),':', mean_rs,'Std', window,':',std_rs\n",
    307     "    print (\"{} {:>3}{} {:<11f}    {:>5} {:>3}{} {}\").format(*row)\n",
    308     "    \n",
    309     "print \"As we increase the length of the window, the variability of the running sharpe ratio decreases.\" \n"
    310    ]
    311   },
    312   {
    313    "cell_type": "markdown",
    314    "metadata": {
    315     "deletable": true,
    316     "editable": true
    317    },
    318    "source": [
    319     "## b. Out-of-Sample Instability\n",
    320     "\n",
    321     "Plot the running sharpe ratio of all three window lengths, as well as their in-sample mean and standard deviation bars."
    322    ]
    323   },
    324   {
    325    "cell_type": "code",
    326    "execution_count": 7,
    327    "metadata": {
    328     "collapsed": false,
    329     "deletable": true,
    330     "editable": true,
    331     "scrolled": false
    332    },
    333    "outputs": [
    334     {
    335      "name": "stderr",
    336      "output_type": "stream",
    337      "text": [
    338       "/usr/local/lib/python2.7/dist-packages/pandas/tseries/base.py:192: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
    339       "  val = getitem(key)\n"
    340      ]
    341     },
    342     {
    343      "name": "stdout",
    344      "output_type": "stream",
    345      "text": [
    346       "Despite the longer window Sharpe ratios having less variability, they are still unpredictable with repect to just the mean. But within the context of the standard deviation the mean has more predictive value, as we see that even in the out-of-sample periods the ratios of all window lengths stay mainly within 1 standard deviation of the mean.\n"
    347      ]
    348     },
    349     {
    350      "data": {
    351       "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0UAAAIACAYAAABNZcfeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n",
    352       "text/plain": [
    353        "<matplotlib.figure.Figure at 0x7f586c54b950>"
    354       ]
    355      },
    356      "metadata": {},
    357      "output_type": "display_data"
    358     },
    359     {
    360      "data": {
    361       "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0UAAAIACAYAAABNZcfeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEQAAAABD+3/jlBrNKrVplgAAAABJRU5ErkJggg==\n",
    362       "text/plain": [
    363        "<matplotlib.figure.Figure at 0x7f58484aa4d0>"
    364       ]
    365      },
    366      "metadata": {},
    367      "output_type": "display_data"
    368     },
    369     {
    370      "data": {
    371       "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0sAAAIACAYAAABTrPdtAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nREREREREGhgsERERERERafj/B/9nxlwVb84AAAAASUVORK5CYII=\n",
    372       "text/plain": [
    373        "<matplotlib.figure.Figure at 0x7f5848cb2790>"
    374       ]
    375      },
    376      "metadata": {},
    377      "output_type": "display_data"
    378     }
    379    ],
    380    "source": [
    381     "#Your code goes here\n",
    382     "\n",
    383     "for window in [50, 150, 300]:\n",
    384     "    running_sharpe = [sharpe_ratio(returns[i-window+10:i], treasury_ret[i-window+10:i]) for i in range(window-10, len(returns))]\n",
    385     "    mean_rs = np.mean(running_sharpe[:-200])\n",
    386     "    std_rs = np.std(running_sharpe[:-200])\n",
    387     "    \n",
    388     "    _, ax2 = plt.subplots()\n",
    389     "    \n",
    390     "    ax2.plot(range(window-10, len(returns)), running_sharpe)\n",
    391     "    ticks = ax2.get_xticks()\n",
    392     "\n",
    393     "    ax2.set_xticklabels([pricing.index[i].date() for i in ticks[:-1]])\n",
    394     "    \n",
    395     "    ax2.axhline(mean_rs)\n",
    396     "    ax2.axhline(mean_rs + std_rs, linestyle='--')\n",
    397     "    ax2.axhline(mean_rs - std_rs, linestyle='--')\n",
    398     "    \n",
    399     "    ax2.axvline(len(returns) - 200, color='pink');\n",
    400     "    plt.title(window, fontsize = 20)\n",
    401     "    plt.xlabel('Date')\n",
    402     "    plt.ylabel('Sharpe Ratio')\n",
    403     "    plt.legend(['Sharpe Ratio', 'Mean', '+/- 1 Standard Deviation'])\n",
    404     "\n",
    405     "    \n",
    406     "print \"Despite the longer window Sharpe ratios having less variability, they are still unpredictable with repect to just the mean. But within the context of the standard deviation the mean has more predictive value, as we see that even in the out-of-sample periods the ratios of all window lengths stay mainly within 1 standard deviation of the mean.\"\n"
    407    ]
    408   },
    409   {
    410    "cell_type": "markdown",
    411    "metadata": {
    412     "deletable": true,
    413     "editable": true
    414    },
    415    "source": [
    416     "# Exercise 4: Weather\n",
    417     "\n",
    418     "## a. Temperature in Boston\n",
    419     "\n",
    420     "Find the mean and standard deviation of Boston weekly average temperature data for the year of 2015 stored in `b15_df`. "
    421    ]
    422   },
    423   {
    424    "cell_type": "code",
    425    "execution_count": 8,
    426    "metadata": {
    427     "collapsed": false,
    428     "deletable": true,
    429     "editable": true,
    430     "scrolled": true
    431    },
    432    "outputs": [
    433     {
    434      "name": "stdout",
    435      "output_type": "stream",
    436      "text": [
    437       "Boston Weekly Temp Mean:  49.0769230769\n",
    438       "Boston Weekly Temp Std:   22.983979499\n"
    439      ]
    440     }
    441    ],
    442    "source": [
    443     "b15_df = pd.DataFrame([ 29.,  22.,  19.,  17.,  19.,  19.,  15.,  16.,  18.,  25.,  21.,\n",
    444     "        25.,  29.,  27.,  36.,  38.,  40.,  44.,  49.,  50.,  58.,  61.,\n",
    445     "        67.,  69.,  74.,  72.,  76.,  81.,  81.,  80.,  83.,  82.,  80.,\n",
    446     "        79.,  79.,  80.,  74.,  72.,  68.,  68.,  65.,  61.,  57.,  50.,\n",
    447     "        46.,  42.,  41.,  35.,  30.,  27.,  28.,  28.],\n",
    448     "        columns = ['Weekly Avg Temp'],\n",
    449     "        index = pd.date_range('1/1/2012', periods=52, freq='W')          )\n",
    450     "\n",
    451     "#Your code goes here\n",
    452     "\n",
    453     "b15_mean = np.mean(b15_df['Weekly Avg Temp'])\n",
    454     "b15_std = np.std(b15_df['Weekly Avg Temp'])\n",
    455     "\n",
    456     "print \"Boston Weekly Temp Mean: \", b15_mean\n",
    457     "print \"Boston Weekly Temp Std:  \", b15_std\n"
    458    ]
    459   },
    460   {
    461    "cell_type": "markdown",
    462    "metadata": {
    463     "deletable": true,
    464     "editable": true
    465    },
    466    "source": [
    467     "## b. Temperature in Palo Alto\n",
    468     "\n",
    469     "Find the mean and standard deviation of Palo Alto weekly average temperature data for the year of 2015 stored in `p15_df`."
    470    ]
    471   },
    472   {
    473    "cell_type": "code",
    474    "execution_count": 9,
    475    "metadata": {
    476     "collapsed": false,
    477     "deletable": true,
    478     "editable": true,
    479     "scrolled": true
    480    },
    481    "outputs": [
    482     {
    483      "name": "stdout",
    484      "output_type": "stream",
    485      "text": [
    486       "Palo Alto Weekly Temp Mean:  59.7884615385\n",
    487       "Palo Alto Weekly Temp Std:   7.97432548018\n"
    488      ]
    489     }
    490    ],
    491    "source": [
    492     "p15_df = pd.DataFrame([ 49.,  53.,  51.,  47.,  50.,  46.,  49.,  51.,  49.,  45.,  52.,\n",
    493     "        54.,  54.,  55.,  55.,  57.,  56.,  56.,  57.,  63.,  63.,  65.,\n",
    494     "        65.,  69.,  67.,  70.,  67.,  67.,  68.,  68.,  70.,  72.,  72.,\n",
    495     "        70.,  72.,  70.,  66.,  66.,  68.,  68.,  65.,  66.,  62.,  61.,\n",
    496     "        63.,  57.,  55.,  55.,  55.,  55.,  55.,  48.],\n",
    497     "        columns = ['Weekly Avg Temp'],\n",
    498     "        index = pd.date_range('1/1/2012', periods=52, freq='W'))\n",
    499     "\n",
    500     "#Your code goes here\n",
    501     "\n",
    502     "p15_mean = np.mean(p15_df['Weekly Avg Temp'])\n",
    503     "p15_std = np.std(p15_df['Weekly Avg Temp'])\n",
    504     "\n",
    505     "print \"Palo Alto Weekly Temp Mean: \", p15_mean\n",
    506     "print \"Palo Alto Weekly Temp Std:  \", p15_std\n"
    507    ]
    508   },
    509   {
    510    "cell_type": "markdown",
    511    "metadata": {
    512     "deletable": true,
    513     "editable": true
    514    },
    515    "source": [
    516     "## c. Predicting 2016 Temperatures\n",
    517     "\n",
    518     "Use the means you found in parts a and b to attempt to predict  2016 temperature data for both cities. Do this by creating two histograms for the 2016 temperature data in `b16_df` and `p16_df` with a vertical line where the 2015 means were to represent your prediction."
    519    ]
    520   },
    521   {
    522    "cell_type": "code",
    523    "execution_count": 10,
    524    "metadata": {
    525     "collapsed": false,
    526     "deletable": true,
    527     "editable": true
    528    },
    529    "outputs": [
    530     {
    531      "name": "stdout",
    532      "output_type": "stream",
    533      "text": [
    534       "Avg of Absolute Value of Prediction Error in Boston: 20.8106508876\n",
    535       "Avg of Absolute Value of Prediction Error in Palo Alto: 7.20857988166\n",
    536       "\n",
    537       "We know from parts a and b that the weather in Boston is much more variable than that of Palo Alto. As a result, we can predict that an estimate based on a sample mean in Boston will be less accurate than an estimate based on a sample from Palo Alto, which is confirmed by this test. The Palo Alto predictions had a much lower error than those of Boston. With mean alone we would not have been able to make any conclusions about the accuracy of our predictions.\n"
    538      ]
    539     },
    540     {
    541      "data": {
    542       "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0IAAAHrCAYAAADi0i17AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n",
    543       "text/plain": [
    544        "<matplotlib.figure.Figure at 0x7f5848cb2e50>"
    545       ]
    546      },
    547      "metadata": {},
    548      "output_type": "display_data"
    549     },
    550     {
    551      "data": {
    552       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAzwAAAHrCAYAAAAKUphQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK8Pu5lCUikAAAAASUVORK5CYII=\n",
    553       "text/plain": [
    554        "<matplotlib.figure.Figure at 0x7f5840407710>"
    555       ]
    556      },
    557      "metadata": {},
    558      "output_type": "display_data"
    559     }
    560    ],
    561    "source": [
    562     "b16_df = pd.DataFrame([ 26.,  22.,  20.,  19.,  18.,  19.,  17.,  17.,  19.,  20.,  23., 22.,  28.,  28.,  35.,  38.,  42.,  47.,  49.,  56.,  59.,  61.,\n",
    563     "        61.,  70.,  73.,  73.,  73.,  77.,  78.,  82.,  80.,  80.,  81., 78.,  82.,  78.,  76.,  71.,  69.,  66.,  60.,  63.,  56.,  50.,\n",
    564     "        44.,  43.,  34.,  33.,  31.,  28.,  27.,  20.],\n",
    565     "        columns = ['Weekly Avg Temp'],\n",
    566     "        index = pd.date_range('1/1/2012', periods=52, freq='W'))\n",
    567     "\n",
    568     "p16_df = pd.DataFrame([ 50.,  50.,  51.,  48.,  48.,  49.,  50.,  45.,  52.,  50.,  51., 52.,  50.,  56.,  58.,  55.,  61.,  56.,  61.,  62.,  62.,  64.,\n",
    569     "        64.,  69.,  71.,  66.,  69.,  70.,  68.,  71.,  70.,  69.,  72., 71.,  66.,  69.,  70.,  70.,  66.,  67.,  64.,  64.,  65.,  61.,\n",
    570     "        61.,  59.,  56.,  53.,  55.,  52.,  52.,  51.],\n",
    571     "        columns = ['Weekly Avg Temp'],\n",
    572     "        index = pd.date_range('1/1/2012', periods=52, freq='W'))\n",
    573     "\n",
    574     "#Your code goes here\n",
    575     "\n",
    576     "b16_df.plot.hist(title = \"Boston 2016 Temperature vs. Prediction\");\n",
    577     "plt.axvline(b15_mean);\n",
    578     "\n",
    579     "p16_df.plot.hist(title = \"Palo Alto 2016 Temperature vs. Prediction\");\n",
    580     "plt.axvline(p15_mean);\n",
    581     "\n",
    582     "b_avg_error = np.mean(abs(b16_df['Weekly Avg Temp'] - b15_mean))\n",
    583     "p_avg_error = np.mean(abs(p16_df['Weekly Avg Temp'] - p15_mean))\n",
    584     "\n",
    585     "print \"Avg of Absolute Value of Prediction Error in Boston:\", b_avg_error\n",
    586     "print \"Avg of Absolute Value of Prediction Error in Palo Alto:\", p_avg_error\n",
    587     "\n",
    588     "print \"\\nWe know from parts a and b that the weather in Boston is much more variable than that of Palo Alto. As a result, we can predict that an estimate based on a sample mean in Boston will be less accurate than an estimate based on a sample from Palo Alto, which is confirmed by this test. The Palo Alto predictions had a much lower error than those of Boston. With mean alone we would not have been able to make any conclusions about the accuracy of our predictions.\"\n"
    589    ]
    590   },
    591   {
    592    "cell_type": "markdown",
    593    "metadata": {
    594     "deletable": true,
    595     "editable": true
    596    },
    597    "source": [
    598     "---\n",
    599     "\n",
    600     "Congratulations on completing the instability of parameter estimates exercises!\n",
    601     "\n",
    602     "As you learn more about writing trading models and the Quantopian platform, enter a daily [Quantopian Contest](https://www.quantopian.com/contest). Your strategy will be evaluated for a cash prize every day.\n",
    603     "\n",
    604     "Start by going through the [Writing a Contest Algorithm](https://www.quantopian.com/tutorials/contest) tutorial."
    605    ]
    606   },
    607   {
    608    "cell_type": "markdown",
    609    "metadata": {
    610     "deletable": true,
    611     "editable": true
    612    },
    613    "source": [
    614     "*This presentation is for informational purposes only and does not constitute an offer to sell, a solic\n",
    615     "itation 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.*"
    616    ]
    617   }
    618  ],
    619  "metadata": {
    620   "kernelspec": {
    621    "display_name": "Python 2",
    622    "language": "python",
    623    "name": "python2"
    624   },
    625   "language_info": {
    626    "codemirror_mode": {
    627     "name": "ipython",
    628     "version": 2
    629    },
    630    "file_extension": ".py",
    631    "mimetype": "text/x-python",
    632    "name": "python",
    633    "nbconvert_exporter": "python",
    634    "pygments_lexer": "ipython2",
    635    "version": "2.7.12"
    636   }
    637  },
    638  "nbformat": 4,
    639  "nbformat_minor": 1
    640 }