ml-finance-python

python scripts for finance machine learning
git clone https://9o.is/git/ml-finance-python.git
lab_118.ipynb

(337045B)
      1 {
      2  "cells": [
      3   {
      4    "cell_type": "markdown",
      5    "metadata": {},
      6    "source": [
      7     "# Limits of Diversification\n",
      8     "\n",
      9     "Why doesnt diversification help you in market crashes?\n",
     10     "\n",
     11     "One reason is that correlations across assets increases as the market drops. Let's see if we can see this in the data.\n",
     12     "\n",
     13     "Let's load up the industry data that we've used in the past."
     14    ]
     15   },
     16   {
     17    "cell_type": "code",
     18    "execution_count": 1,
     19    "metadata": {},
     20    "outputs": [],
     21    "source": [
     22     "%load_ext autoreload\n",
     23     "%autoreload 2\n",
     24     "%matplotlib inline\n",
     25     "import edhec_risk_kit_118 as erk\n",
     26     "\n",
     27     "ind_return = erk.get_ind_returns()"
     28    ]
     29   },
     30   {
     31    "cell_type": "markdown",
     32    "metadata": {},
     33    "source": [
     34     "# Implementing a Cap-Weighted Portfolio\n",
     35     "\n",
     36     "Most market indices are constructed by taking a set of assets and weighting them by their market equity aka market capitalization.\n",
     37     "\n",
     38     "Let's construct a market cap weighted index from our 30 industry portfolios.\n",
     39     "\n",
     40     "For each of these industries, we also have data on the number of stocks in each industry as well as the average market cap in each industry. Thus, we can multiply the average market cap by the number of companies to get the total market capitalization of each industry. We can then weight each industry portfolio proportional to the market capitalization of that industry.\n",
     41     "\n",
     42     "Let's load the industry size and average number of firms:\n",
     43     "\n",
     44     "```python\n",
     45     "def get_ind_nfirms():\n",
     46     "    \"\"\"\n",
     47     "    Load and format the Ken French 30 Industry Portfolios Average number of Firms\n",
     48     "    \"\"\"\n",
     49     "    ind = pd.read_csv(\"data/ind30_m_nfirms.csv\", header=0, index_col=0)\n",
     50     "    ind.index = pd.to_datetime(ind.index, format=\"%Y%m\").to_period('M')\n",
     51     "    ind.columns = ind.columns.str.strip()\n",
     52     "    return ind\n",
     53     "\n",
     54     "def get_ind_size():\n",
     55     "    \"\"\"\n",
     56     "    Load and format the Ken French 30 Industry Portfolios Average size (market cap)\n",
     57     "    \"\"\"\n",
     58     "    ind = pd.read_csv(\"data/ind30_m_size.csv\", header=0, index_col=0)\n",
     59     "    ind.index = pd.to_datetime(ind.index, format=\"%Y%m\").to_period('M')\n",
     60     "    ind.columns = ind.columns.str.strip()\n",
     61     "    return ind\n",
     62     "```\n",
     63     "\n",
     64     "We can clearly refactor the code to be less repetitive, but for now, this will do fine:"
     65    ]
     66   },
     67   {
     68    "cell_type": "code",
     69    "execution_count": 2,
     70    "metadata": {},
     71    "outputs": [],
     72    "source": [
     73     "import pandas as pd\n",
     74     "\n",
     75     "def get_ind_nfirms():\n",
     76     "    \"\"\"\n",
     77     "    Load and format the Ken French 30 Industry Portfolios Average number of Firms\n",
     78     "    \"\"\"\n",
     79     "    ind = pd.read_csv(\"data/ind30_m_nfirms.csv\", header=0, index_col=0)\n",
     80     "    ind.index = pd.to_datetime(ind.index, format=\"%Y%m\").to_period('M')\n",
     81     "    ind.columns = ind.columns.str.strip()\n",
     82     "    return ind\n",
     83     "\n",
     84     "def get_ind_size():\n",
     85     "    \"\"\"\n",
     86     "    Load and format the Ken French 30 Industry Portfolios Average size (market cap)\n",
     87     "    \"\"\"\n",
     88     "    ind = pd.read_csv(\"data/ind30_m_size.csv\", header=0, index_col=0)\n",
     89     "    ind.index = pd.to_datetime(ind.index, format=\"%Y%m\").to_period('M')\n",
     90     "    ind.columns = ind.columns.str.strip()\n",
     91     "    return ind\n",
     92     "\n",
     93     "ind_nfirms = get_ind_nfirms()\n",
     94     "ind_size = get_ind_size()\n"
     95    ]
     96   },
     97   {
     98    "cell_type": "code",
     99    "execution_count": 3,
    100    "metadata": {},
    101    "outputs": [
    102     {
    103      "data": {
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    105        "<div>\n",
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    108        "        vertical-align: middle;\n",
    109        "    }\n",
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    117        "    }\n",
    118        "</style>\n",
    119        "<table border=\"1\" class=\"dataframe\">\n",
    120        "  <thead>\n",
    121        "    <tr style=\"text-align: right;\">\n",
    122        "      <th></th>\n",
    123        "      <th>Food</th>\n",
    124        "      <th>Beer</th>\n",
    125        "      <th>Smoke</th>\n",
    126        "      <th>Games</th>\n",
    127        "      <th>Books</th>\n",
    128        "      <th>Hshld</th>\n",
    129        "      <th>Clths</th>\n",
    130        "      <th>Hlth</th>\n",
    131        "      <th>Chems</th>\n",
    132        "      <th>Txtls</th>\n",
    133        "      <th>...</th>\n",
    134        "      <th>Telcm</th>\n",
    135        "      <th>Servs</th>\n",
    136        "      <th>BusEq</th>\n",
    137        "      <th>Paper</th>\n",
    138        "      <th>Trans</th>\n",
    139        "      <th>Whlsl</th>\n",
    140        "      <th>Rtail</th>\n",
    141        "      <th>Meals</th>\n",
    142        "      <th>Fin</th>\n",
    143        "      <th>Other</th>\n",
    144        "    </tr>\n",
    145        "  </thead>\n",
    146        "  <tbody>\n",
    147        "    <tr>\n",
    148        "      <th>1926-07</th>\n",
    149        "      <td>43</td>\n",
    150        "      <td>3</td>\n",
    151        "      <td>16</td>\n",
    152        "      <td>7</td>\n",
    153        "      <td>2</td>\n",
    154        "      <td>8</td>\n",
    155        "      <td>12</td>\n",
    156        "      <td>7</td>\n",
    157        "      <td>17</td>\n",
    158        "      <td>13</td>\n",
    159        "      <td>...</td>\n",
    160        "      <td>5</td>\n",
    161        "      <td>3</td>\n",
    162        "      <td>7</td>\n",
    163        "      <td>6</td>\n",
    164        "      <td>74</td>\n",
    165        "      <td>2</td>\n",
    166        "      <td>33</td>\n",
    167        "      <td>6</td>\n",
    168        "      <td>12</td>\n",
    169        "      <td>4</td>\n",
    170        "    </tr>\n",
    171        "    <tr>\n",
    172        "      <th>1926-08</th>\n",
    173        "      <td>43</td>\n",
    174        "      <td>3</td>\n",
    175        "      <td>16</td>\n",
    176        "      <td>7</td>\n",
    177        "      <td>2</td>\n",
    178        "      <td>8</td>\n",
    179        "      <td>12</td>\n",
    180        "      <td>7</td>\n",
    181        "      <td>17</td>\n",
    182        "      <td>13</td>\n",
    183        "      <td>...</td>\n",
    184        "      <td>5</td>\n",
    185        "      <td>3</td>\n",
    186        "      <td>7</td>\n",
    187        "      <td>6</td>\n",
    188        "      <td>74</td>\n",
    189        "      <td>2</td>\n",
    190        "      <td>33</td>\n",
    191        "      <td>6</td>\n",
    192        "      <td>12</td>\n",
    193        "      <td>4</td>\n",
    194        "    </tr>\n",
    195        "    <tr>\n",
    196        "      <th>1926-09</th>\n",
    197        "      <td>43</td>\n",
    198        "      <td>3</td>\n",
    199        "      <td>16</td>\n",
    200        "      <td>7</td>\n",
    201        "      <td>2</td>\n",
    202        "      <td>8</td>\n",
    203        "      <td>12</td>\n",
    204        "      <td>7</td>\n",
    205        "      <td>17</td>\n",
    206        "      <td>13</td>\n",
    207        "      <td>...</td>\n",
    208        "      <td>5</td>\n",
    209        "      <td>3</td>\n",
    210        "      <td>7</td>\n",
    211        "      <td>6</td>\n",
    212        "      <td>74</td>\n",
    213        "      <td>2</td>\n",
    214        "      <td>33</td>\n",
    215        "      <td>6</td>\n",
    216        "      <td>12</td>\n",
    217        "      <td>4</td>\n",
    218        "    </tr>\n",
    219        "    <tr>\n",
    220        "      <th>1926-10</th>\n",
    221        "      <td>43</td>\n",
    222        "      <td>3</td>\n",
    223        "      <td>16</td>\n",
    224        "      <td>7</td>\n",
    225        "      <td>2</td>\n",
    226        "      <td>8</td>\n",
    227        "      <td>12</td>\n",
    228        "      <td>7</td>\n",
    229        "      <td>17</td>\n",
    230        "      <td>13</td>\n",
    231        "      <td>...</td>\n",
    232        "      <td>5</td>\n",
    233        "      <td>3</td>\n",
    234        "      <td>7</td>\n",
    235        "      <td>6</td>\n",
    236        "      <td>74</td>\n",
    237        "      <td>2</td>\n",
    238        "      <td>33</td>\n",
    239        "      <td>6</td>\n",
    240        "      <td>12</td>\n",
    241        "      <td>4</td>\n",
    242        "    </tr>\n",
    243        "    <tr>\n",
    244        "      <th>1926-11</th>\n",
    245        "      <td>43</td>\n",
    246        "      <td>3</td>\n",
    247        "      <td>16</td>\n",
    248        "      <td>7</td>\n",
    249        "      <td>2</td>\n",
    250        "      <td>8</td>\n",
    251        "      <td>12</td>\n",
    252        "      <td>7</td>\n",
    253        "      <td>17</td>\n",
    254        "      <td>13</td>\n",
    255        "      <td>...</td>\n",
    256        "      <td>5</td>\n",
    257        "      <td>3</td>\n",
    258        "      <td>7</td>\n",
    259        "      <td>6</td>\n",
    260        "      <td>74</td>\n",
    261        "      <td>2</td>\n",
    262        "      <td>33</td>\n",
    263        "      <td>6</td>\n",
    264        "      <td>12</td>\n",
    265        "      <td>4</td>\n",
    266        "    </tr>\n",
    267        "  </tbody>\n",
    268        "</table>\n",
    269        "<p>5 rows × 30 columns</p>\n",
    270        "</div>"
    271       ],
    272       "text/plain": [
    273        "         Food  Beer  Smoke  Games  Books  Hshld  Clths  Hlth  Chems  Txtls  \\\n",
    274        "1926-07    43     3     16      7      2      8     12     7     17     13   \n",
    275        "1926-08    43     3     16      7      2      8     12     7     17     13   \n",
    276        "1926-09    43     3     16      7      2      8     12     7     17     13   \n",
    277        "1926-10    43     3     16      7      2      8     12     7     17     13   \n",
    278        "1926-11    43     3     16      7      2      8     12     7     17     13   \n",
    279        "\n",
    280        "         ...  Telcm  Servs  BusEq  Paper  Trans  Whlsl  Rtail  Meals  Fin  \\\n",
    281        "1926-07  ...      5      3      7      6     74      2     33      6   12   \n",
    282        "1926-08  ...      5      3      7      6     74      2     33      6   12   \n",
    283        "1926-09  ...      5      3      7      6     74      2     33      6   12   \n",
    284        "1926-10  ...      5      3      7      6     74      2     33      6   12   \n",
    285        "1926-11  ...      5      3      7      6     74      2     33      6   12   \n",
    286        "\n",
    287        "         Other  \n",
    288        "1926-07      4  \n",
    289        "1926-08      4  \n",
    290        "1926-09      4  \n",
    291        "1926-10      4  \n",
    292        "1926-11      4  \n",
    293        "\n",
    294        "[5 rows x 30 columns]"
    295       ]
    296      },
    297      "execution_count": 3,
    298      "metadata": {},
    299      "output_type": "execute_result"
    300     }
    301    ],
    302    "source": [
    303     "ind_nfirms.head()"
    304    ]
    305   },
    306   {
    307    "cell_type": "code",
    308    "execution_count": 4,
    309    "metadata": {},
    310    "outputs": [
    311     {
    312      "data": {
    313       "text/html": [
    314        "<div>\n",
    315        "<style scoped>\n",
    316        "    .dataframe tbody tr th:only-of-type {\n",
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    318        "    }\n",
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    327        "</style>\n",
    328        "<table border=\"1\" class=\"dataframe\">\n",
    329        "  <thead>\n",
    330        "    <tr style=\"text-align: right;\">\n",
    331        "      <th></th>\n",
    332        "      <th>Food</th>\n",
    333        "      <th>Beer</th>\n",
    334        "      <th>Smoke</th>\n",
    335        "      <th>Games</th>\n",
    336        "      <th>Books</th>\n",
    337        "      <th>Hshld</th>\n",
    338        "      <th>Clths</th>\n",
    339        "      <th>Hlth</th>\n",
    340        "      <th>Chems</th>\n",
    341        "      <th>Txtls</th>\n",
    342        "      <th>...</th>\n",
    343        "      <th>Telcm</th>\n",
    344        "      <th>Servs</th>\n",
    345        "      <th>BusEq</th>\n",
    346        "      <th>Paper</th>\n",
    347        "      <th>Trans</th>\n",
    348        "      <th>Whlsl</th>\n",
    349        "      <th>Rtail</th>\n",
    350        "      <th>Meals</th>\n",
    351        "      <th>Fin</th>\n",
    352        "      <th>Other</th>\n",
    353        "    </tr>\n",
    354        "  </thead>\n",
    355        "  <tbody>\n",
    356        "    <tr>\n",
    357        "      <th>1926-07</th>\n",
    358        "      <td>35.98</td>\n",
    359        "      <td>7.12</td>\n",
    360        "      <td>59.72</td>\n",
    361        "      <td>26.41</td>\n",
    362        "      <td>12.02</td>\n",
    363        "      <td>22.27</td>\n",
    364        "      <td>18.36</td>\n",
    365        "      <td>25.52</td>\n",
    366        "      <td>57.59</td>\n",
    367        "      <td>6.18</td>\n",
    368        "      <td>...</td>\n",
    369        "      <td>350.36</td>\n",
    370        "      <td>13.60</td>\n",
    371        "      <td>56.70</td>\n",
    372        "      <td>35.35</td>\n",
    373        "      <td>66.91</td>\n",
    374        "      <td>1.19</td>\n",
    375        "      <td>46.65</td>\n",
    376        "      <td>10.82</td>\n",
    377        "      <td>18.83</td>\n",
    378        "      <td>24.25</td>\n",
    379        "    </tr>\n",
    380        "    <tr>\n",
    381        "      <th>1926-08</th>\n",
    382        "      <td>36.10</td>\n",
    383        "      <td>6.75</td>\n",
    384        "      <td>60.47</td>\n",
    385        "      <td>27.17</td>\n",
    386        "      <td>13.33</td>\n",
    387        "      <td>22.13</td>\n",
    388        "      <td>19.83</td>\n",
    389        "      <td>25.80</td>\n",
    390        "      <td>62.13</td>\n",
    391        "      <td>6.20</td>\n",
    392        "      <td>...</td>\n",
    393        "      <td>353.27</td>\n",
    394        "      <td>14.75</td>\n",
    395        "      <td>57.74</td>\n",
    396        "      <td>37.86</td>\n",
    397        "      <td>67.99</td>\n",
    398        "      <td>0.90</td>\n",
    399        "      <td>46.57</td>\n",
    400        "      <td>11.00</td>\n",
    401        "      <td>18.88</td>\n",
    402        "      <td>25.51</td>\n",
    403        "    </tr>\n",
    404        "    <tr>\n",
    405        "      <th>1926-09</th>\n",
    406        "      <td>37.00</td>\n",
    407        "      <td>8.58</td>\n",
    408        "      <td>64.03</td>\n",
    409        "      <td>27.30</td>\n",
    410        "      <td>14.67</td>\n",
    411        "      <td>21.18</td>\n",
    412        "      <td>19.29</td>\n",
    413        "      <td>26.73</td>\n",
    414        "      <td>65.53</td>\n",
    415        "      <td>6.71</td>\n",
    416        "      <td>...</td>\n",
    417        "      <td>360.96</td>\n",
    418        "      <td>15.05</td>\n",
    419        "      <td>59.61</td>\n",
    420        "      <td>36.82</td>\n",
    421        "      <td>71.02</td>\n",
    422        "      <td>0.95</td>\n",
    423        "      <td>46.11</td>\n",
    424        "      <td>10.94</td>\n",
    425        "      <td>19.67</td>\n",
    426        "      <td>27.21</td>\n",
    427        "    </tr>\n",
    428        "    <tr>\n",
    429        "      <th>1926-10</th>\n",
    430        "      <td>37.14</td>\n",
    431        "      <td>8.92</td>\n",
    432        "      <td>64.42</td>\n",
    433        "      <td>28.76</td>\n",
    434        "      <td>14.42</td>\n",
    435        "      <td>21.23</td>\n",
    436        "      <td>19.03</td>\n",
    437        "      <td>26.87</td>\n",
    438        "      <td>68.47</td>\n",
    439        "      <td>6.82</td>\n",
    440        "      <td>...</td>\n",
    441        "      <td>364.16</td>\n",
    442        "      <td>15.30</td>\n",
    443        "      <td>59.52</td>\n",
    444        "      <td>34.77</td>\n",
    445        "      <td>70.83</td>\n",
    446        "      <td>0.88</td>\n",
    447        "      <td>46.15</td>\n",
    448        "      <td>10.80</td>\n",
    449        "      <td>19.36</td>\n",
    450        "      <td>26.16</td>\n",
    451        "    </tr>\n",
    452        "    <tr>\n",
    453        "      <th>1926-11</th>\n",
    454        "      <td>35.88</td>\n",
    455        "      <td>8.62</td>\n",
    456        "      <td>65.08</td>\n",
    457        "      <td>27.38</td>\n",
    458        "      <td>15.79</td>\n",
    459        "      <td>20.14</td>\n",
    460        "      <td>19.03</td>\n",
    461        "      <td>26.54</td>\n",
    462        "      <td>65.06</td>\n",
    463        "      <td>6.84</td>\n",
    464        "      <td>...</td>\n",
    465        "      <td>363.74</td>\n",
    466        "      <td>14.89</td>\n",
    467        "      <td>58.74</td>\n",
    468        "      <td>32.80</td>\n",
    469        "      <td>68.75</td>\n",
    470        "      <td>0.74</td>\n",
    471        "      <td>45.03</td>\n",
    472        "      <td>10.33</td>\n",
    473        "      <td>18.35</td>\n",
    474        "      <td>23.94</td>\n",
    475        "    </tr>\n",
    476        "  </tbody>\n",
    477        "</table>\n",
    478        "<p>5 rows × 30 columns</p>\n",
    479        "</div>"
    480       ],
    481       "text/plain": [
    482        "          Food  Beer  Smoke  Games  Books  Hshld  Clths   Hlth  Chems  Txtls  \\\n",
    483        "1926-07  35.98  7.12  59.72  26.41  12.02  22.27  18.36  25.52  57.59   6.18   \n",
    484        "1926-08  36.10  6.75  60.47  27.17  13.33  22.13  19.83  25.80  62.13   6.20   \n",
    485        "1926-09  37.00  8.58  64.03  27.30  14.67  21.18  19.29  26.73  65.53   6.71   \n",
    486        "1926-10  37.14  8.92  64.42  28.76  14.42  21.23  19.03  26.87  68.47   6.82   \n",
    487        "1926-11  35.88  8.62  65.08  27.38  15.79  20.14  19.03  26.54  65.06   6.84   \n",
    488        "\n",
    489        "         ...   Telcm  Servs  BusEq  Paper  Trans  Whlsl  Rtail  Meals    Fin  \\\n",
    490        "1926-07  ...  350.36  13.60  56.70  35.35  66.91   1.19  46.65  10.82  18.83   \n",
    491        "1926-08  ...  353.27  14.75  57.74  37.86  67.99   0.90  46.57  11.00  18.88   \n",
    492        "1926-09  ...  360.96  15.05  59.61  36.82  71.02   0.95  46.11  10.94  19.67   \n",
    493        "1926-10  ...  364.16  15.30  59.52  34.77  70.83   0.88  46.15  10.80  19.36   \n",
    494        "1926-11  ...  363.74  14.89  58.74  32.80  68.75   0.74  45.03  10.33  18.35   \n",
    495        "\n",
    496        "         Other  \n",
    497        "1926-07  24.25  \n",
    498        "1926-08  25.51  \n",
    499        "1926-09  27.21  \n",
    500        "1926-10  26.16  \n",
    501        "1926-11  23.94  \n",
    502        "\n",
    503        "[5 rows x 30 columns]"
    504       ]
    505      },
    506      "execution_count": 4,
    507      "metadata": {},
    508      "output_type": "execute_result"
    509     }
    510    ],
    511    "source": [
    512     "ind_size.head()"
    513    ]
    514   },
    515   {
    516    "cell_type": "code",
    517    "execution_count": 5,
    518    "metadata": {},
    519    "outputs": [
    520     {
    521      "data": {
    522       "text/plain": [
    523        "(1110, 30)"
    524       ]
    525      },
    526      "execution_count": 5,
    527      "metadata": {},
    528      "output_type": "execute_result"
    529     }
    530    ],
    531    "source": [
    532     "ind_return.shape"
    533    ]
    534   },
    535   {
    536    "cell_type": "code",
    537    "execution_count": 6,
    538    "metadata": {},
    539    "outputs": [
    540     {
    541      "data": {
    542       "text/plain": [
    543        "(1110, 30)"
    544       ]
    545      },
    546      "execution_count": 6,
    547      "metadata": {},
    548      "output_type": "execute_result"
    549     }
    550    ],
    551    "source": [
    552     "ind_size.shape"
    553    ]
    554   },
    555   {
    556    "cell_type": "code",
    557    "execution_count": 7,
    558    "metadata": {},
    559    "outputs": [
    560     {
    561      "data": {
    562       "text/plain": [
    563        "(1110, 30)"
    564       ]
    565      },
    566      "execution_count": 7,
    567      "metadata": {},
    568      "output_type": "execute_result"
    569     }
    570    ],
    571    "source": [
    572     "ind_nfirms.shape"
    573    ]
    574   },
    575   {
    576    "cell_type": "code",
    577    "execution_count": 8,
    578    "metadata": {},
    579    "outputs": [],
    580    "source": [
    581     "ind_mktcap = ind_nfirms * ind_size"
    582    ]
    583   },
    584   {
    585    "cell_type": "code",
    586    "execution_count": 9,
    587    "metadata": {},
    588    "outputs": [
    589     {
    590      "data": {
    591       "text/plain": [
    592        "(1110, 30)"
    593       ]
    594      },
    595      "execution_count": 9,
    596      "metadata": {},
    597      "output_type": "execute_result"
    598     }
    599    ],
    600    "source": [
    601     "ind_mktcap.shape"
    602    ]
    603   },
    604   {
    605    "cell_type": "markdown",
    606    "metadata": {},
    607    "source": [
    608     "Now that we have the market caps for each month, we can compute the total market capitalization over time by summing over all industries:"
    609    ]
    610   },
    611   {
    612    "cell_type": "code",
    613    "execution_count": 10,
    614    "metadata": {},
    615    "outputs": [],
    616    "source": [
    617     "total_mktcap = ind_mktcap.sum(axis=1)"
    618    ]
    619   },
    620   {
    621    "cell_type": "code",
    622    "execution_count": 11,
    623    "metadata": {},
    624    "outputs": [
    625     {
    626      "data": {
    627       "text/plain": [
    628        "<matplotlib.axes._subplots.AxesSubplot at 0x1a2373e4e0>"
    629       ]
    630      },
    631      "execution_count": 11,
    632      "metadata": {},
    633      "output_type": "execute_result"
    634     },
    635     {
    636      "data": {
    637       "image/png": 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\n",
    638       "text/plain": [
    639        "<Figure size 432x288 with 1 Axes>"
    640       ]
    641      },
    642      "metadata": {
    643       "needs_background": "light"
    644      },
    645      "output_type": "display_data"
    646     }
    647    ],
    648    "source": [
    649     "total_mktcap.plot()"
    650    ]
    651   },
    652   {
    653    "cell_type": "markdown",
    654    "metadata": {},
    655    "source": [
    656     "Clearly, the market has grown over time. We can now compute the capweight of each industry as follows:"
    657    ]
    658   },
    659   {
    660    "cell_type": "code",
    661    "execution_count": 12,
    662    "metadata": {},
    663    "outputs": [],
    664    "source": [
    665     "ind_capweight = ind_mktcap.divide(total_mktcap, axis=\"rows\")"
    666    ]
    667   },
    668   {
    669    "cell_type": "markdown",
    670    "metadata": {},
    671    "source": [
    672     "Let's verify that the sum of the cap weights of all the columns should always sum to 1.\n",
    673     "\n",
    674     "Because these are floating point numbers, it is not a good idea to test if they sum to 1 by using `== 1.0`. Instead, we make sure they are very close to 1 as follows"
    675    ]
    676   },
    677   {
    678    "cell_type": "code",
    679    "execution_count": 13,
    680    "metadata": {},
    681    "outputs": [
    682     {
    683      "data": {
    684       "text/plain": [
    685        "True"
    686       ]
    687      },
    688      "execution_count": 13,
    689      "metadata": {},
    690      "output_type": "execute_result"
    691     }
    692    ],
    693    "source": [
    694     "all(abs(ind_capweight.sum(axis=\"columns\") - 1) < 1E-10)"
    695    ]
    696   },
    697   {
    698    "cell_type": "markdown",
    699    "metadata": {},
    700    "source": [
    701     "Let's look at the fraction of the market occupied by the Steel industry over time and compare it with the Finance industry."
    702    ]
    703   },
    704   {
    705    "cell_type": "code",
    706    "execution_count": 14,
    707    "metadata": {},
    708    "outputs": [
    709     {
    710      "data": {
    711       "text/plain": [
    712        "<matplotlib.axes._subplots.AxesSubplot at 0x1a22edb438>"
    713       ]
    714      },
    715      "execution_count": 14,
    716      "metadata": {},
    717      "output_type": "execute_result"
    718     },
    719     {
    720      "data": {
    721       "image/png": 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\n",
    722       "text/plain": [
    723        "<Figure size 432x288 with 1 Axes>"
    724       ]
    725      },
    726      "metadata": {
    727       "needs_background": "light"
    728      },
    729      "output_type": "display_data"
    730     }
    731    ],
    732    "source": [
    733     "ind_capweight[[\"Steel\", \"Fin\"]].plot()"
    734    ]
    735   },
    736   {
    737    "cell_type": "markdown",
    738    "metadata": {},
    739    "source": [
    740     "# Constructing a Cap Weighted Market Index\n",
    741     "\n",
    742     "One way to construct a market index is to build a portfolio whose weights are rebalanced back to the target weights every period. In reality, the weights may drift over time but this simplification is close enough."
    743    ]
    744   },
    745   {
    746    "cell_type": "code",
    747    "execution_count": 15,
    748    "metadata": {},
    749    "outputs": [
    750     {
    751      "data": {
    752       "text/plain": [
    753        "<matplotlib.axes._subplots.AxesSubplot at 0x1a22aaac88>"
    754       ]
    755      },
    756      "execution_count": 15,
    757      "metadata": {},
    758      "output_type": "execute_result"
    759     },
    760     {
    761      "data": {
    762       "image/png": 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aMDHGGANAyIKJMcaYvurL2OcWTIwxxgAQjrR3TJ8yfVRSZS2YGGOMAaA1EEzmWTAxxhiTinDk4DO5yopykyprwcQYYwzQ+chkVLqDiYgcKSJrA6/9IvIFERklIstFZJN/L/P5RURuFZEqEXlZROYE1rXI598kIosC6SeJyCu+zK1+rHlSqcMYY0xqgsGkrDDNwURVX1fVE1T1BOAkoBn4E3Ad8JiqzgQe8/MAC4CZ/rUYuA1cYACWAHOBU4AlseDg8ywOlJvv05OqwxhjTOoaDhy84/3YSaVJlU32NNfZwGZVfQtYCNzj0+8BLvTTC4F71XkeGCkiE4APAMtVtVZV9wHLgfl+WYmqPqduQPp7u6wrmTqMMcakqLapjZGFOTx//dmUFuQkVTbZYHIpcL+fHqeqOwD8+1ifPgnYFihT7dN6Sq+Ok55KHZ2IyGIRWSUiq2pqapLYTGOMGX5qm1qpKCtgfGk+oZCQzD2MCQcTEckFLgB+11vWOGmaQnoqdXROUL1dVStVtbK8vLyXVRpjzPBW29zGqKK8jvnsQzSeyQJgjaru8vO7YqeW/Ptun14NTA6UqwC295JeESc9lTqMMcakaF9TmFGFB09vJfOsrmSCyWUcPMUFsBSI9chaBDwUSL/C97iaB9T7U1TLgHNEpMxfeD8HWOaXNYjIPN+L64ou60qmDmOMMSna1xTudH9JVhLBJDuRTCJSCLwfuDqQfCPwoIhcBWwFLvHpDwPnAlW4nl+fBFDVWhH5FrDS5/umqtb66WuAu4EC4BH/SroOY4wxqWmNRGlojTA6EExykjjNlVAwUdVmYHSXtL243l1d8ypwbTfruQu4K076KuCYOOlJ12GMMSZ5sYGwUj0ysTvgjTHGUNsUBmBU4GbFHAsmxhhjkhELJsEjk4+/a1rC5S2YGGOM6QgmwWsm15x5WMLlLZgYY4xhX/M7j0ySYcHEGGMMextdMBmZ5GNUYiyYGGOMYV9zmNKCnKTueg+yYGKMMYaahtZO10uSZcHEGGOGuUi0nSdfr+GIcSNSXocFE2OMGeb2NoVpaYvynpljUl6HBRNjjBnmdu9vBWDsiLxecnbPgokxxgxzu/YfAGBsSX7K67BgYowxw9zG3Q0ATB9TlPI6LJgYY8wwt2FHA5NGFiQ9VG+QBRNjjBnmNuzYz9ETSvq0DgsmxhgzjB1oi7KlppFZE1LvFgwWTIwxZlhbtm4n7QqzJvbDkYmIjBSR34vIayKyQUTeJSKjRGS5iGzy72U+r4jIrSJSJSIvi8icwHoW+fybRGRRIP0kEXnFl7nVD99LKnUYY4xJ3MvV9QCcffS4Pq0n0SOTHwGPqupRwPHABuA64DFVnQk85ucBFgAz/WsxcBu4wAAsAeYCpwBLYsHB51kcKDffpydVhzHGmOQ0HogwriQvqSF64+m1tIiUAKcDdwKoalhV64CFwD0+2z3AhX56IXCvOs8DI0VkAvABYLmq1qrqPmA5MN8vK1HV5/xwvPd2WVcydRhjjElCYzhCcV5CI7j3KJFQNAOoAX4pIi+KyC9EpAgYp6o7APz7WJ9/ErAtUL7ap/WUXh0nnRTq6EREFovIKhFZVVNTk8CmGmPM8NJ4IEJxfupdgmMSCSbZwBzgNlU9EWji4OmmeOINGqwppPckoTKqeruqVqpqZXl5eS+rNMaY4aexNcKIfjoyqQaqVXWFn/89Lrjsip1a8u+7A/knB8pXANt7Sa+Ik04KdRhjjElCfUsbI/L7IZio6k5gm4gc6ZPOBtYDS4FYj6xFwEN+eilwhe9xNQ+o96eolgHniEiZv/B+DrDML2sQkXm+F9cVXdaVTB3GGGOSsKv+AOP68EyumETD0b8BvxaRXGAL8ElcIHpQRK4CtgKX+LwPA+cCVUCzz4uq1orIt4CVPt83VbXWT18D3A0UAI/4F8CNydRhjDEmcY2tERpaI4wv7adgoqprgco4i86Ok1eBa7tZz13AXXHSVwHHxEnfm2wdxhhjErOz3j0teEIagondAW+MMcNULJiMT8NpLgsmxhgzTO2obwFIy2kuCybGGDNM1TW3ATCqKLfP67JgYowxw1RTOAJAYW7/3GdijDFmCGpqjZCfEyIrFO8+8OT0PRwZY4wZdN7a28Qdz7yRtvXZkYkxxgxD9z33VlrXZ8HEGGOGoep9LWldnwUTY4wZhhpbI2ldnwUTY4wZhhoOtFE5tYyXv3FOWtZnwcQYY4aBF7fu446nt9De7kbraDgQYVxpPiVpGMsErDeXMcYMeVtqGrnop88CMLYkj9NmlrNlTxMnTxuVtjosmBhjzBB31vef6pj+yRNV7Nrvnsk1vbwobXXYaS5jjBlGNu5q5LsPvwbA5XOnpG29FkyMMWYYmjSyIG3XS8CCiTHGDGm/et7dnPiuGaM7pZ8wZWRa60komIjImyLyioisFZFVPm2UiCwXkU3+vcyni4jcKiJVIvKyiMwJrGeRz79JRBYF0k/y66/yZSXVOowxxhz0zT+vB9xY70FFuVlprSeZI5P3quoJqhobcfE64DFVnQk85ucBFgAz/WsxcBu4wAAsAeYCpwBLYsHB51kcKDc/lTqMMcZ0Fo62AyBdnuVYkJO5YNLVQuAeP30PcGEg/V51ngdGisgE4APAclWtVdV9wHJgvl9WoqrP+eF47+2yrmTqMMYYE0fU318Sk5+hYKLA30RktYgs9mnjVHUHgH8f69MnAdsCZat9Wk/p1XHSU6mjExFZLCKrRGRVTU1NgptqjDFDw+1Pb+6YjgyQYHKqqs7BnV66VkRO7yFvvAfjawrpPUmojKrerqqVqlpZXl7eyyqNMWZoiXUBBvjJ5Z0vLWckmKjqdv++G/gT7prHrtipJf++22evBiYHilcA23tJr4iTTgp1GGOM6eJr5x3NkeNHdErLz0lvZ95e1yYiRSIyIjYNnAO8CiwFYj2yFgEP+emlwBW+x9U8oN6foloGnCMiZf7C+znAMr+sQUTm+V5cV3RZVzJ1GGOMAV6pru+Y/sDs8e9Ynp2V3mCSyONUxgF/8r11s4HfqOqjIrISeFBErgK2Apf4/A8D5wJVQDPwSQBVrRWRbwErfb5vqmqtn74GuBsoAB7xL4Abk6nDGGOMs2l3Q8f05FGFAHzwuAn85WX3u3tiaX5a6xPXgWroq6ys1FWrVmW6GcYY0y/ue+5N/uuhdQC8eeN5HenRdmVPYyvjShILJiKyOnBLSLfsDnhjjBmCtte7hzne9YnOcSArJAkHkmRYMDHGmCFoR10LFWUFnHXUuH6pz4KJMcYMMXXNYf5v7XYmlhb0W50WTIwxZoh5bIO7i+IjJ0/uJWf6WDAxxpghRFX50u9eAuCc2f1zigssmBhjzJDSFI52TBfl9t9guhZMjDFmCKltDHdMh0Lxnjx1aFgwMcaYIWRvU2tG6rVgYowxQ8jewJFJf7JgYowxQ0htkwsmP/vYSf1arwUTY4wZQvb6YHL6EWP6tV4LJsYYM4TsbWylICeLwn7syQUWTIwxZkipbQozqii33+u1YGKMMUPI3qYwo4stmBhjjOmDvU2tjLYjE2OMMalqi7azeXcTo4ry+r3uhIOJiGSJyIsi8hc/P11EVojIJhH5rYjk+vQ8P1/ll08LrON6n/66iHwgkD7fp1WJyHWB9KTrMMaY4eprf3qVlrYoIwtz+r3uZI5MPg9sCMzfBNyiqjOBfcBVPv0qYJ+qHg7c4vMhIrOAS4HZwHzgpz5AZQE/ARYAs4DLfN6k6zDGmOHst6u2AdCegRF0EwomIlIBnAf8ws8LcBbwe5/lHuBCP73Qz+OXn+3zLwQeUNVWVX0DN377Kf5VpapbVDUMPAAsTLEOY4wZ9sKR9n6vM9Ejkx8C/w+ItXA0UKeqET9fDUzy05OAbQB+eb3P35HepUx36anU0YmILBaRVSKyqqamJsFNNcaYwemw8iIAzpk9vt/r7jWYiMgHgd2qujqYHCer9rIsXem91X8wQfV2Va1U1cry8vI4RYwxZmhoCUfZXNMEwBlH9P/+LpFbJE8FLhCRc4F8oAR3pDJSRLL9kUEFsN3nrwYmA9Uikg2UArWB9JhgmXjpe1KowxhjhqXNNY0Zrb/XIxNVvV5VK1R1Gu4C+uOq+lHgCeDDPtsi4CE/vdTP45c/rqrq0y/1PbGmAzOBF4CVwEzfcyvX17HUl0m2DmOMGZZqGtyj5y87ZUpG6u/Lw1v+A3hARL4NvAjc6dPvBO4TkSrc0cKlAKq6TkQeBNYDEeBaVY0CiMhngWVAFnCXqq5LpQ5jjBmu3q5rAeDfzjo8I/XLcPlBX1lZqatWrcp0M4wx5pC4/o8v8/ArO1n79feTzs6tIrJaVSt7y2d3wBtjzBDwxp4mZo4tTmsgSYYFE2OMGQJqM/SAxxgLJsYYMwTsbQwzurj/n8kVY8HEGGMGuXCknX3NYcZk4GnBMRZMjDFmkHvl7TraFWZNLM1YGyyYGGPMIFe9z3ULPnxsccbaYMHEGGMGuf0H3CMMSwv6/9HzMRZMjDFmkNvf0gbAiPy+3IfeNxZMjDFmkNt/oI287BD5OVkZa4MFE2OMGcRWbNnLz5/aQkkGT3GBBRNjjBnUPnbnCgAqygoy2o7MnWAzxhiTsrrmMPc99xaRdvd8xcsz9LTgGAsmxhgzCD2wchvfX74RgJOnlXFJ5eReShxadprLGGMGoeZwtGP61MPHZLAljgUTY4zJAFVl/fb9SZUJR9ppDrt7Snb48UsAZk0oSWvbUmHBxBhjMuCnT27m3Fuf4dW36xMuc9U9K5n19WUA7Gls7Ug/48j+H/O9q16DiYjki8gLIvKSiKwTkRt8+nQRWSEim0Tkt37IXfywvL8VkSq/fFpgXdf79NdF5AOB9Pk+rUpErgukJ12HMcYMdF944EW+t+x1APY2hRMu98ymPQBsq21mT+PBcnnZmbu/JCaRI5NW4CxVPR44AZgvIvOAm4BbVHUmsA+4yue/CtinqocDt/h8iMgs3PC6s4H5wE9FJEtEsoCfAAuAWcBlPi/J1mGMMYPB/63d3jFd15xYMGmNHLxG8o+qPewNHJkMBL0GE3Ua/WyOfylwFvB7n34PcKGfXujn8cvPFjf010LgAVVtVdU3gCrgFP+qUtUtqhoGHgAW+jLJ1mGMMQNaMCgA7Kg/kFC5FVtqO6bXb9/PnqYwc6aM5NefmpvW9qUqoWsm/ghiLbAbWA5sBupUNeKzVAOT/PQkYBuAX14PjA6mdynTXfroFOro2u7FIrJKRFbV1NQksqnGGHNI7d7f+Yji8Q27EyoXezLwuJI8Xq6uIxxpZ8ExEwZETy5IMJioalRVTwAqcEcSR8fL5t/jHSFoGtN7qqNzgurtqlqpqpXl5Zm/QGWMMTv3dz4S2dOU2Omq7XUthMT13Kra7U4WZXKY3q6S6s2lqnXAk8A8YKSIxG56rABiJwGrgckAfnkpUBtM71Kmu/Q9KdRhjDED2o8frwLgxg8dy+Vzp7ClpokzvvcEW/c291jutZ0NTBtTRFlRLk3+HpNxJfmHvL2JSqQ3V7mIjPTTBcD7gA3AE8CHfbZFwEN+eqmfxy9/XFXVp1/qe2JNB2YCLwArgZm+51Yu7iL9Ul8m2TqMMWbAemtvE09vdKfcFxwzoWP8kbf2NvPQ2rd7LLtxVwOzJ5ZSkn/wgY6nTB916BqbpEQepzIBuMf3ugoBD6rqX0RkPfCAiHwbeBG40+e/E7hPRKpwRwuXAqjqOhF5EFgPRIBrVTUKICKfBZYBWcBdqrrOr+s/kqnDGGMGsuc27+2YLi3M6RQYejvKqGsOM7oot+PpwJeePJmcrIFzq2CvwURVXwZOjJO+BXf9pGv6AeCSbtb1HeA7cdIfBh5ORx3GGDNQPfbabiaU5vPkV84EYOyIvI5lB7r08gpqb1caWiOUFORQ4gfAah9gJ2MGTlgzxpgh7tW365k7fVTHTYbTxhR2LGtsjXRXjIbWCKpQkp/dcTTTFrVgYowxw05LOMqO+gMcPra4I+2IcSM6pv+wupruLv3WNLgeYKUFOeTluN12ONp+CFubPAsmxhjTD6r3ud5ak0cdPBoZkZ/DU/6U1+aaJtZs3depTMOBNm5+9DUefmUnACdOGdlxnST03+kyAAAZF0lEQVQywIKJjWdijDEBB9qi/G51NZefMoWsUHoerPF/L77NU74XVzCYAEwdXURedojWSDvhSOcjk5sefY1fPb+VEfnZlBXmcPjYEbyxxwWlgXaay4KJMcZ4q9/ax53/2MLDr+ykKDeLD82p6PM6W8JRvvDbtQCEBI4e/87Hxf/m0/O4+LZnaWnrfN1k4053c2LDgQizJ7pyR413p8Y+eNyEPrctnSyYGGOMd/Ftz3ZM70nTgxRvf3pLx3RBThYFue98wm9pgdsVN7Z27tEVaT94KmtMsev5NXlUIVXfWUD2AOoWDHbNxBhj4mptS881iWc37+mY/sjJ8cdpL8x1waS5S4+u9sCZrOL8g7/9B1ogATsyMcYYAHbUt3SaT8eRSV1zmBVv1PLhkyq48UPHdnsNpijP7YqbAkPxvrStjrXb6jrm0xXcDhULJsYYA7zrvx/vNB/ckaeitinM1fetAlwvrJ6OJgr9qa+mwJHJZXc83ynPlppGBrKBd6xkjDEDQF1LW5/K3/jIBla+6br6XnD8xB7z5mSFyApJp7FOmsPRd+QZyAZ264wxpp/l54Q444hy3trbzK79iQ1cFdQWbaepNcL+loNHGSMCz+DqTm5WiHDEncqKd/PiuNKB84TgeCyYGGNMwPob5nPEOHeX+tzvPpZ0+X/99RpmL1nGo+t2cuKUkaz5r/cnVC43+2Aw+eMa9wTh84492P33ln85Pum29Ce7ZmKMGfZip5f+/X1HEApJxwXxVCxfv6tj+vzjJjKqKLEBrGI3LgLc/8JWAPYfaOPXn5rLnsZWRhfn9VQ84yyYGGOGtc01jdz33FvAwQcv1jWnfr0kOyREfJ/e2I2GiQgemcRGULzhgtnMKC/uqdiAYcHEGDOsfewXK9hR766NHOZ33GNLUjsKiLYrInD42GLe2NPEUXHudu9ObnaIVv+8ra21Lbz3yPJBE0jArpkYY4axcKS9I5DAwQGqPn3aDM6ZNQ6AC/73H90+zTdm6Uvbef8PnmLOt5bTFlWues90Nn/3XEoLe7/wHpOXnUVrWzuqylt7m5g2piiFLcqcRIbtnSwiT4jIBhFZJyKf9+mjRGS5iGzy72U+XUTkVhGpEpGXRWROYF2LfP5NIrIokH6SiLziy9wqIpJqHcYYk6h12+s7zceub+RkhTqGxH25up4Xe7nn5HP3v8im3Y3U++7ER44f0WP+eHKzQ4Sj7TSHozSHo4wfQOO7JyKRI5MI8CVVPRqYB1wrIrOA64DHVHUm8JifB1iAG999JrAYuA1cYACWAHNxoycuiQUHn2dxoNx8n55UHcYYk4xf/vPNTvPBO9Rj47MDPPHa7m7X8caepneknVAxMum25GWFCEei1DaFAShL8ML9QJHIsL07gB1+ukFENgCTgIXAmT7bPcCTuDHbFwL3qjsufF5ERorIBJ93uarWAojIcmC+iDwJlKjqcz79XuBC4JFk6/BtNcaYXtU2hVn60nYAHv/SGTQc6PxcrNzsg7+1t9Q0cc+zbxKOtPPp02d0yvf6zv0AfO/Dx/HPqj1ccMJEQik8uj4vJ0Rja4R9zS6YjCocYsEkSESm4caDXwGMi+28VXWHiIz12SYB2wLFqn1aT+nVcdJJoQ4LJsaYhGzc1dAxHe9Cd3CM9b++soO/vuJ2L588dVqnR6O8udeNL3LO7PFcUjk55fYU5maxa/8B/lHlHgxZPmJgdwXuKuEL8CJSDPwB+IKq7u8pa5w0TSG9x+YkUkZEFovIKhFZVVNT08sqjTHDydZaFwROnlYWd/mcKS49J6vz7qamywMgH9+wm8PHFnc6LZaKssJc9jW3sWFHAyMLcziuorRP6+tvCQUTEcnBBZJfq+offfIuf/oK/x47qVgNBMNzBbC9l/SKOOmp1NGJqt6uqpWqWlleXp7IphpjhomaBhcU7rtqbtzlU0cX8eaN57HwhEmd0jft6vzAxS17GrsNSMkoLcyhpqGV13fu57DyYnw/pEEjkd5cAtwJbFDVHwQWLQViPbIWAQ8F0q/wPa7mAfX+VNUy4BwRKfMX3s8BlvllDSIyz9d1RZd1JVOHMcb06o09TeysP0BJfjb5Oe8crCpopD/iuOyUyeRlh3ji9YMX41+urmNPY5iyNFzf2LzbBamNuxoZUzy4rpdAYtdMTgU+DrwiImt92leBG4EHReQqYCtwiV/2MHAuUAU0A58EUNVaEfkWsNLn+2bsYjxwDXA3UIC78P6IT0+qDmOM6c13/rqeO555A4CZY3u/KTB2IX5cST4TSvPZ0xjuWHbjI68BMCEND2H8wvuO4O8bXKAa6I9OiSeR3lz/IP41CoCz4+RX4Npu1nUXcFec9FXAMXHS9yZbhzHGdKc5HOkIJADjEwgC2+vcoFnlI/IYU5zHXn/NRFV5u66FSSMLuPSU+CMoJuOYSaUU52XT2BrpGKJ3MLE74I0xw8bb+zqPppjIEcXi0w/j+IpSPnjsREYX57LXH5ms2VrHW3ub+exZh6dtrJHYnfaD8TSXBRNjzLCxvb7z+CTjSwt6LTNrYgkPffY9lBbmMLo4r2M43w07XKfWM49MX+eeaEcwGXxHJvagR2PMsBE7ZTUiL5uG1kjSjywZU5TL3qYwc7/7dw4fW0xIoDyNO/52P8z76EF29ztYMDHGDCPb61rICgn5uVk0tEaSvnAee3bXrv2t7NrfyriSvB7Hdk9WxEeTMYPshkWw01zGmGFCVXn8td2ML8mnqdU9OiWRC/BBR3Z5pHy6H8boh0FhTJEFE2OMGXAOtEWZfv3DrNu+n0Xvnkpz2I2smOyRybsOG91pfmyag8kP/uV4ZowpoqRg8J00smBijBny/vNPr3ZMX3RiRcdYJX19BEpxH4b3jedDcyp4/MtnDrq738GumRhjhoFHXz34gIzyEXn8+PIT2d8S6dNO+/K5U7jmjMPS0bwhwYKJMWbIO3L8CNZsrWPt198PuFENy0f0/BiV7lx56nTaou1868J33Gc9rFkwMcYMeW/XtfDhkyoYmYZnaH39/FlpaNHQY9dMjDFDWks4yq79rUwbXZjppgxpFkyMMUNabNySKaOLMtySoc2CiTFmyNpR38IHfvg0gB2ZHGIWTIwxQ9Y3lq7rmD4sztC8Jn0smBhjhiRVZdm6XQAU5GRRlOZ7QkxnFkyMMUNSbFjeC46fyIu+S7A5dCyYGGOGnGc37+HSO54H4FOnTe91aF7Td4mMAX+XiOwWkVcDaaNEZLmIbPLvZT5dRORWEakSkZdFZE6gzCKff5OILAqknyQir/gyt/px4FOqwxhjwpF2Lr9jBVtqmgA4rmJkhls0PCRyZHI3ML9L2nXAY6o6E3jMzwMsAGb612LgNnCBAVgCzAVOAZbEgoPPszhQbn4qdRhjDMD/PlHVMT17YkkPOU069RpMVPVpoLZL8kLgHj99D3BhIP1edZ4HRorIBOADwHJVrVXVfcByYL5fVqKqz/lx3e/tsq5k6jDGDHPbapv5+VOb+eBxE/jaeUdz56KTM92kYSPV7g3jVHUHgKruEJGxPn0SsC2Qr9qn9ZReHSc9lTp20IWILMYdvTBlypQkN9EYM5hEou2cdvMTAFx/7tFMGtn7kLwmfdJ9AT7eIzg1hfRU6nhnourtqlqpqpXl5ekbp9kYM/A8tHY7ACdNLbNAkgGpBpNdsVNL/n23T68GJgfyVQDbe0mviJOeSh3GmGFKVfnJE1XMGFPEr66am+nmDEupBpOlQKxH1iLgoUD6Fb7H1Tyg3p+qWgacIyJl/sL7OcAyv6xBROb5XlxXdFlXMnUYY4apZzbtYcueJj5zxmEU5Fo34Ezo9ZqJiNwPnAmMEZFqXK+sG4EHReQqYCtwic/+MHAuUAU0A58EUNVaEfkWsNLn+6aqxi7qX4PrMVYAPOJfJFuHMWZ4UlV+/PgmJpTms/DEiZluzrAlrhPV0FdZWamrVq3KdDOMMX10oC3KHU9v4UMnVfDgym08u3kPK9/cxzfOn8UnTp2e6eYNOSKyWlUre8tnD6sxxgwaS1/azufufxGA7y/f2JEeErj0FOuxmUkWTIwxA97TG2u44q4X4i4799jxXPvew+2RKRlmwcQYM2CFI+38cU011/3xFQBOnlbGTy6fw5jiPBpaI7RF2xlTnJfhVhqwYGKMGYAeXLWN/354A/ua2wCYOrqQhz93WqfHyJcW5GSqeSYOCybGmAGjel8zP1i+kT+uebsjbUxxHvd/ep6NRzLA2V/HGDMgvPBGLf/y8+c65q8+YwbXLzg6gy0yybBgYozpVw0H2vjR3zexfsd+xhTn8fyWvfzneUdzxzNbADj9iHJu/NCxdi1kkLFgYow5JA60Rd/Rw6q9Xbn4tmfZuKuxU/rnH1gLwJLzZ/FJu1dkULKRFo0xaXfbk5s5ZskyVmzZ25HW2BrhyntWsnFXI2ccUc4NF8zmtJljWHDMeACmjCrkMrtXZNCyIxNjTNpsq23myrtXsmm3O/JY+tJ2Ntc0cdc/36DKp5111FjuXFSJiLDo3dNoi7Zz+9NbePdho+1ekUHMgokxpk8aDrTxzT+v53erDw5NtOhdU/nDmrf59YqtHWkTS/NZfPoMFr17Gn50bgByskJc+97D+7XNJv0smBhjUtbUGuHKu1ey8s19HWkfqZzMDQuPYcueJp7ZtIdPvHsanzt7JqUFOWSF4g1HZIYCCybGmKQcaIvy5Ou7eWpjDb9fXU1bVLn69Bmcf/xEivOymTKqEIAfX3YiL1XXc8YRNjDdcGDBxBjzDqrKYxt28+K2feRkhSjKzSY3O8Tqt/bxz6o97G0Kk+2PMr5/yfFcfFLFO9YxsjDXAskwYsHEmGGorjlMVkiItisiwoi8bEI+ODzx2m5+/Pgm1myti1v2xCkj+eI5R/DB4yaSnxMiL9sumhsLJsYMeS3hKOt31LOzvpV9zWF21Ldw33Nvsf9ApFO+qaMLibYr1ftamFiaz3cuOoaL51SQkxVia20zVbsbmVCazzGTSjO0JWYgG7TBRETmAz8CsoBfqOqNGW6SGUbaou3sb2kjqsr+ljbqmttoaYuSJUJudoiC3CzyskOUj8inJD+buuY2Nu5qYF9zmILcbBoPRNi2r5lwpJ1IuyJAUV4W2aEQ+TlZVJQVMHNcMRNKCwhH2qne10xdSxv1zW2s37GfA21RwtF22iJKpL2dtmg77e2ubc1tUdZtr6e1zaXXNLYSHAMvJHDS1DJmjhvBjDFFbK1tprE1wt7GMHXNYc48spzrFhxNceBZWNPHFDF9TFH/fshmUBmUwUREsoCfAO8HqoGVIrJUVddntmXDl6qiCgq0d0xrx04sOK8+f3s7RNrbiaoSbVciUfcef76dkAhZIaEt2k5rpJ22qBKOtLtXNEpbRF3dQLRdaQ5HaDwQoTkcJdKuFOdlU5yfTWtbOy1tUQ60RWkJR2lpi9Ic9vM+7UBblFBIaG6N0NwWJS87REFOFpF2ZW9jmJa2aEKfS0igKDebhtZI75njKMrNctsZbe+ULgK5WSFys0JkZwk5WSHaVWlXKMjJYkZ5EdF2ZeyIPCaVFTBz7AiK87IJR9s5edooykfYo0pMeg3KYAKcAlSp6hYAEXkAWAh0G0xe39XAe//nyR5XmugQxokOdJzoiMia8BoPEqSjrCqBnbb6nXXXnbfPrXTscF05X3tgJx+vPHGCwcH1DlwhcTvXUEhoDkeJtrsG5/rgUJCTRUFuFvk5WRTkhCjMzaasMJf8HLdzLsrNpiA3i3DEBSDBPcW2pCCH0oIcRKAwN5vyEXkU+nwNByK0q9IWbadqdyO1TWGmjynisLHFlBfn0dIWJTcrxOFji8nNDnVcyN7fEqE1EmVvU5j9LW288nY967bvpyQ/m+MqRjKqKJe8nBBHjBthz60yA85gDSaTgG2B+WpgbtdMIrIYWAxQMnEGxyZwrlcS7AafaG95SXCFyfS+j+2/Vd3FU/ErEAQRty4JzvsMIm7n2jmf+HbGKZ/Iun3hYHpIDpbvdt24o4zYKzvUdT7UaVkoJLSrEo0qOdnuF3lutpCblUVudojc7BA5WdJxH0OWCEV52RTmZnW0I9qutEai5GVnDcj7HUoLc4AcxpbkAzB3xujMNsiYJAzWYBJvT/CO38iqejtwO0BlZaXeetmJh7pdZgDLCgmFuYP1K2/MwDZYH/RYDUwOzFcA2zPUFmOMGfYGazBZCcwUkekikgtcCizNcJuMMWbYGpTH/KoaEZHPAstwXYPvUtV1GW6WMcYMW4MymACo6sPAw5luhzHGmMF7mssYY8wAYsHEGGNMn1kwMcYY02cWTIwxxvSZJPoIkcFORGqAt3rJNgXY2kueRJQC9WlYz0BrDwy8Nll7ejZU2wMDr01DtT1TVbX3gWncA/rs5YNqTZrWc/tQbM9AbJO1Z3i2ZyC2aai2J9GXnebqLP5oQMn7c5rWM9DaAwOvTdaeng3V9sDAa9NQbU9CLJh0lpZTQaqarj/iQGsPDLw2WXt6NiTbAwOvTUO4PQmxYNLZ7ZluQBcDrT0w8Npk7emZtad3A61NA609CRk2F+CNMcYcOnZkYowxps8smBhjjOmzIR9MROQuEdktIq8G0o4XkedE5BUR+bOIlPj094vIap++WkTOCpT5iIi8LCLrROTmfmrPKSKy1r9eEpGLAmU+LyKv+vZ8oT/aE1g+RUQaReTLmWyPiEwTkZbAZ/SzQJl+/3v5Zcf5Zev88vx0tifZNonIRwOfz1oRaReRE9LZpiTbkyMi9/j0DSJyfaBMJr5DuSLyS5/+koicGSiTrs9nsog84bd3nYh83qePEpHlIrLJv5f5dBGRW0Wkytc/J7Cum/xn9KqIfCTVNh0S/dkPORMv4HRgDvBqIG0lcIafvhL4lp8+EZjop48B3vbTo3E3EZX7+XuAs/uhPYVAtp+eAOzGPen5GODV2HLg78DMQ92ewPI/AL8Dvhz4rPq9PcC0YL5A/kz9vbKBl4HjA+3ISmd7Uv2b+fRjgS0Z/owuBx4IfL/f9H/HTH2HrgV+6afHAqtxP7LT+flMAOb46RHARmAWcDNwnU+/DrjJT58LPIIbUXYesMKnnwcs959PEbAKKEn1e5Tu15A/MlHVp4HaLslHAk/76eXAxT7vi6oaG7FxHZAvInnADGCjqtb4ZX+PlTnE7WlW1YhPz+fg0MRHA88Hlj8FXEQKkmkPgIhcCGzBfT4xGWtPNzLy9wLOAV5W1Zd82b2qGk1ne1JoU9BlwP1+OlOfkQJFIpINFABhYD+Z+w7NAh7z5Xbj7vGoJL2fzw5VXeOnG4ANwCRgIS5I4d8v9NMLgXvVeR4YKSITfFufUtWIqjYBLwHzU2nToTDkg0k3XgUu8NOX0HkI4JiLgRdVtRWoAo7yp1WycX/0eGXS3h4RmSsi64BXgM/4f7RXgdNFZLSIFOJ+yRzy9ohIEfAfwA1x8vd7e7zpIvKiiDwlIqf5tEz9vY4AVESWicgaEfl//dSentoU9BEOBpNMfUa/B5qAHbhf/v+jqrVk7jv0ErBQRLJFZDpwkl92SD4fEZmGOwOyAhinqjvABRzckRG4QLMtUKzap70ELBCRQhEZA7w3HW1Kl+EaTK4ErhWR1bjDznBwoYjMBm4CrgZQ1X3ANcBvgWdwh+YR0qfb9qjqClWdDZwMXC8i+aq6wbdvOfAo7kvWH+25AbhFVRuDmTPYnh3AFFU9Efgi8BsRKcng3ysbeA/wUf9+kYic3Q/t6alNgPtRAjSr6quQ0e/0KUAUmAhMB74kIjMy+B26C7ezXgX8EHgWiByKz0dEinGniL+gqvt7yhonTVX1b7gBAZ/F/Sh4rq9tSqtMn2frjxfdnFv3y44AXgjMV+DOaZ7aw/oWAzf3R3u6LHsCqIyT/l3gXw91ezj4T/Um7nRALfDZTLUnzrInu/l8+uXvBVwK3B1Y9l/AV9LdnlQ+I+AW4Ks9rK+/PqOfAB8PLLsL+JcB9B16Fph1CD6fHNww418MpL0OTPDTE4DX/fTPgcvi5euyzt8A5/ble5TOV8Yb0C8b2eWLBYz17yHgXuBKPz8S94vo4jjriJUpA9YCR/RDe6Zz8AL8VGA7MKZLmSnAa0DZoW5PlzLfwF+Az1R7gHIgy0/PAN4GRmXw71UGrKHzReTz0t2eZP9mPq0amDEAvtP/AfwS9+u7CFgPHJfB71AhUOSn3w88ne7Px2/rvcAPu6R/j84X4G/20+fR+QJ8LBBnAaP99HG4U3fZffkepfOV8QYc8g10h4M7gDb/D3UV8Hnc0cdG4EYOPgnga7jzuWsDr7GB9az3r0v7qT0fx13oXovbSV0YWM8zvi0v0beeQQm3p0u5b9A5mPR7e3DXtdb5OtcA53dZT7/+vXz+j/k2vUrgl2y62pNim87EXdyOt57+/k4X43oCrvP1fiWwnkx8h6bhfvlvwAX/qYfg83kPruPByxzcr5yL6zH2GLDJv8d+CAnuCG4z7lpppU/PD7TneeCEvnyP0v2yx6kYY4zps+F6Ad4YY0waWTAxxhjTZxZMjDHG9JkFE2OMMX1mwcQYY0yfWTAxxhjTZxZMjDHG9Nn/B8BZxYbVKSQ8AAAAAElFTkSuQmCC\n",
    763       "text/plain": [
    764        "<Figure size 432x288 with 1 Axes>"
    765       ]
    766      },
    767      "metadata": {
    768       "needs_background": "light"
    769      },
    770      "output_type": "display_data"
    771     }
    772    ],
    773    "source": [
    774     "total_market_return = (ind_capweight * ind_return).sum(axis=\"columns\")\n",
    775     "total_market_index = erk.drawdown(total_market_return).Wealth\n",
    776     "total_market_index.plot(title=\"Total Market Cap Weighted Index 1926-2018\")"
    777    ]
    778   },
    779   {
    780    "cell_type": "markdown",
    781    "metadata": {},
    782    "source": [
    783     "Putting it all together we have:\n",
    784     "\n",
    785     "```python\n",
    786     "def get_total_market_index_returns():\n",
    787     "    \"\"\"\n",
    788     "    Load the 30 industry portfolio data and derive the returns of a capweighted total market index\n",
    789     "    \"\"\"\n",
    790     "    ind_nfirms = get_ind_nfirms()\n",
    791     "    ind_size = get_ind_size()\n",
    792     "    ind_return = get_ind_returns()\n",
    793     "    ind_mktcap = ind_nfirms * ind_size\n",
    794     "    total_mktcap = ind_mktcap.sum(axis=1)\n",
    795     "    ind_capweight = ind_mktcap.divide(total_mktcap, axis=\"rows\")\n",
    796     "    total_market_return = (ind_capweight * ind_return).sum(axis=\"columns\")\n",
    797     "    return total_market_return\n",
    798     "```\n"
    799    ]
    800   },
    801   {
    802    "cell_type": "code",
    803    "execution_count": 16,
    804    "metadata": {},
    805    "outputs": [
    806     {
    807      "data": {
    808       "text/plain": [
    809        "<matplotlib.axes._subplots.AxesSubplot at 0x1a22fa6668>"
    810       ]
    811      },
    812      "execution_count": 16,
    813      "metadata": {},
    814      "output_type": "execute_result"
    815     },
    816     {
    817      "data": {
    818       "image/png": 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\n",
    819       "text/plain": [
    820        "<Figure size 432x288 with 1 Axes>"
    821       ]
    822      },
    823      "metadata": {
    824       "needs_background": "light"
    825      },
    826      "output_type": "display_data"
    827     }
    828    ],
    829    "source": [
    830     "total_market_return = erk.get_total_market_index_returns()\n",
    831     "total_market_index = erk.drawdown(total_market_return).Wealth\n",
    832     "total_market_index.plot(title=\"Total Market Cap Weighted Index 1926-2018\")"
    833    ]
    834   },
    835   {
    836    "cell_type": "markdown",
    837    "metadata": {},
    838    "source": [
    839     "# Rolling Windows\n",
    840     "\n",
    841     "Let's construct a rolling window of returns over a trailing 36 month period. Pandas contains direct support for rolling windows, and allows you to aggregate the returns over a window. In our case, lets compute the average return over a trailing 36 month window."
    842    ]
    843   },
    844   {
    845    "cell_type": "code",
    846    "execution_count": 17,
    847    "metadata": {},
    848    "outputs": [
    849     {
    850      "data": {
    851       "text/plain": [
    852        "<matplotlib.axes._subplots.AxesSubplot at 0x1a24e10f98>"
    853       ]
    854      },
    855      "execution_count": 17,
    856      "metadata": {},
    857      "output_type": "execute_result"
    858     },
    859     {
    860      "data": {
    861       "image/png": 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\n",
    862       "text/plain": [
    863        "<Figure size 864x432 with 1 Axes>"
    864       ]
    865      },
    866      "metadata": {
    867       "needs_background": "light"
    868      },
    869      "output_type": "display_data"
    870     }
    871    ],
    872    "source": [
    873     "total_market_index[\"1980\":].plot(figsize=(12, 6))\n",
    874     "total_market_index[\"1980\":].rolling(window=36).mean().plot()"
    875    ]
    876   },
    877   {
    878    "cell_type": "markdown",
    879    "metadata": {},
    880    "source": [
    881     "We dont really want the mean return, we want to look at the trailing 3 year compounded return. We need to apply our own function, instead of `mean()`.\n",
    882     "\n",
    883     "Each time the window is advanced, a new DataFrame (with just the slice of the DataFrame in the window) is used to generate a new value. You can either use one of the built-in DataFrame methods like `.mean` or you can use the `.aggregate` method to apply your own function to each column for all the rows that fall in that window.\n",
    884     "\n",
    885     "Let's create a time series of the annualized returns over the trailing 36 months and the average correlation across stocks over that same 36 months."
    886    ]
    887   },
    888   {
    889    "cell_type": "code",
    890    "execution_count": 18,
    891    "metadata": {},
    892    "outputs": [
    893     {
    894      "data": {
    895       "text/plain": [
    896        "<matplotlib.axes._subplots.AxesSubplot at 0x1a23557588>"
    897       ]
    898      },
    899      "execution_count": 18,
    900      "metadata": {},
    901      "output_type": "execute_result"
    902     },
    903     {
    904      "data": {
    905       "image/png": 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\n",
    906       "text/plain": [
    907        "<Figure size 864x360 with 1 Axes>"
    908       ]
    909      },
    910      "metadata": {
    911       "needs_background": "light"
    912      },
    913      "output_type": "display_data"
    914     }
    915    ],
    916    "source": [
    917     "tmi_tr36rets = total_market_return.rolling(window=36).aggregate(erk.annualize_rets, periods_per_year=12)\n",
    918     "tmi_tr36rets.plot(figsize=(12,5), label=\"Tr 36 mo Returns\", legend=True)\n",
    919     "total_market_return.plot(label=\"Returns\", legend=True)"
    920    ]
    921   },
    922   {
    923    "cell_type": "markdown",
    924    "metadata": {},
    925    "source": [
    926     "# Rolling Correlations: Multi Indexes and `.groupby`\n",
    927     "\n",
    928     "Next we want to look at average correlations between all the industries over that same trailing 3 year window.\n",
    929     "\n",
    930     "Let's start by contructing the time series of correlations over time over a 36 month window."
    931    ]
    932   },
    933   {
    934    "cell_type": "code",
    935    "execution_count": 19,
    936    "metadata": {},
    937    "outputs": [
    938     {
    939      "data": {
    940       "text/html": [
    941        "<div>\n",
    942        "<style scoped>\n",
    943        "    .dataframe tbody tr th:only-of-type {\n",
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    945        "    }\n",
    946        "\n",
    947        "    .dataframe tbody tr th {\n",
    948        "        vertical-align: top;\n",
    949        "    }\n",
    950        "\n",
    951        "    .dataframe thead th {\n",
    952        "        text-align: right;\n",
    953        "    }\n",
    954        "</style>\n",
    955        "<table border=\"1\" class=\"dataframe\">\n",
    956        "  <thead>\n",
    957        "    <tr style=\"text-align: right;\">\n",
    958        "      <th></th>\n",
    959        "      <th></th>\n",
    960        "      <th>Food</th>\n",
    961        "      <th>Beer</th>\n",
    962        "      <th>Smoke</th>\n",
    963        "      <th>Games</th>\n",
    964        "      <th>Books</th>\n",
    965        "      <th>Hshld</th>\n",
    966        "      <th>Clths</th>\n",
    967        "      <th>Hlth</th>\n",
    968        "      <th>Chems</th>\n",
    969        "      <th>Txtls</th>\n",
    970        "      <th>...</th>\n",
    971        "      <th>Telcm</th>\n",
    972        "      <th>Servs</th>\n",
    973        "      <th>BusEq</th>\n",
    974        "      <th>Paper</th>\n",
    975        "      <th>Trans</th>\n",
    976        "      <th>Whlsl</th>\n",
    977        "      <th>Rtail</th>\n",
    978        "      <th>Meals</th>\n",
    979        "      <th>Fin</th>\n",
    980        "      <th>Other</th>\n",
    981        "    </tr>\n",
    982        "  </thead>\n",
    983        "  <tbody>\n",
    984        "    <tr>\n",
    985        "      <th rowspan=\"5\" valign=\"top\">2018-12</th>\n",
    986        "      <th>Whlsl</th>\n",
    987        "      <td>0.474948</td>\n",
    988        "      <td>0.356983</td>\n",
    989        "      <td>0.122672</td>\n",
    990        "      <td>0.510425</td>\n",
    991        "      <td>0.803362</td>\n",
    992        "      <td>0.419280</td>\n",
    993        "      <td>0.570071</td>\n",
    994        "      <td>0.739764</td>\n",
    995        "      <td>0.785796</td>\n",
    996        "      <td>0.634197</td>\n",
    997        "      <td>...</td>\n",
    998        "      <td>0.648092</td>\n",
    999        "      <td>0.567395</td>\n",
   1000        "      <td>0.543362</td>\n",
   1001        "      <td>0.764252</td>\n",
   1002        "      <td>0.829185</td>\n",
   1003        "      <td>1.000000</td>\n",
   1004        "      <td>0.744842</td>\n",
   1005        "      <td>0.643879</td>\n",
   1006        "      <td>0.746480</td>\n",
   1007        "      <td>0.767652</td>\n",
   1008        "    </tr>\n",
   1009        "    <tr>\n",
   1010        "      <th>Rtail</th>\n",
   1011        "      <td>0.517856</td>\n",
   1012        "      <td>0.406107</td>\n",
   1013        "      <td>0.030283</td>\n",
   1014        "      <td>0.676464</td>\n",
   1015        "      <td>0.636320</td>\n",
   1016        "      <td>0.358336</td>\n",
   1017        "      <td>0.676598</td>\n",
   1018        "      <td>0.714933</td>\n",
   1019        "      <td>0.626034</td>\n",
   1020        "      <td>0.634202</td>\n",
   1021        "      <td>...</td>\n",
   1022        "      <td>0.562238</td>\n",
   1023        "      <td>0.762616</td>\n",
   1024        "      <td>0.628246</td>\n",
   1025        "      <td>0.656510</td>\n",
   1026        "      <td>0.630615</td>\n",
   1027        "      <td>0.744842</td>\n",
   1028        "      <td>1.000000</td>\n",
   1029        "      <td>0.616947</td>\n",
   1030        "      <td>0.611883</td>\n",
   1031        "      <td>0.619918</td>\n",
   1032        "    </tr>\n",
   1033        "    <tr>\n",
   1034        "      <th>Meals</th>\n",
   1035        "      <td>0.370187</td>\n",
   1036        "      <td>0.385483</td>\n",
   1037        "      <td>0.122007</td>\n",
   1038        "      <td>0.301516</td>\n",
   1039        "      <td>0.520649</td>\n",
   1040        "      <td>0.308216</td>\n",
   1041        "      <td>0.302176</td>\n",
   1042        "      <td>0.416193</td>\n",
   1043        "      <td>0.520023</td>\n",
   1044        "      <td>0.491726</td>\n",
   1045        "      <td>...</td>\n",
   1046        "      <td>0.406184</td>\n",
   1047        "      <td>0.444629</td>\n",
   1048        "      <td>0.399438</td>\n",
   1049        "      <td>0.627113</td>\n",
   1050        "      <td>0.663358</td>\n",
   1051        "      <td>0.643879</td>\n",
   1052        "      <td>0.616947</td>\n",
   1053        "      <td>1.000000</td>\n",
   1054        "      <td>0.502563</td>\n",
   1055        "      <td>0.605226</td>\n",
   1056        "    </tr>\n",
   1057        "    <tr>\n",
   1058        "      <th>Fin</th>\n",
   1059        "      <td>0.298823</td>\n",
   1060        "      <td>0.192706</td>\n",
   1061        "      <td>0.027593</td>\n",
   1062        "      <td>0.480276</td>\n",
   1063        "      <td>0.694812</td>\n",
   1064        "      <td>0.162690</td>\n",
   1065        "      <td>0.425899</td>\n",
   1066        "      <td>0.658468</td>\n",
   1067        "      <td>0.760151</td>\n",
   1068        "      <td>0.577090</td>\n",
   1069        "      <td>...</td>\n",
   1070        "      <td>0.420863</td>\n",
   1071        "      <td>0.585418</td>\n",
   1072        "      <td>0.517947</td>\n",
   1073        "      <td>0.670936</td>\n",
   1074        "      <td>0.760730</td>\n",
   1075        "      <td>0.746480</td>\n",
   1076        "      <td>0.611883</td>\n",
   1077        "      <td>0.502563</td>\n",
   1078        "      <td>1.000000</td>\n",
   1079        "      <td>0.734837</td>\n",
   1080        "    </tr>\n",
   1081        "    <tr>\n",
   1082        "      <th>Other</th>\n",
   1083        "      <td>0.436952</td>\n",
   1084        "      <td>0.376565</td>\n",
   1085        "      <td>0.224010</td>\n",
   1086        "      <td>0.331829</td>\n",
   1087        "      <td>0.558072</td>\n",
   1088        "      <td>0.390610</td>\n",
   1089        "      <td>0.467099</td>\n",
   1090        "      <td>0.645035</td>\n",
   1091        "      <td>0.712511</td>\n",
   1092        "      <td>0.520953</td>\n",
   1093        "      <td>...</td>\n",
   1094        "      <td>0.607868</td>\n",
   1095        "      <td>0.460322</td>\n",
   1096        "      <td>0.434487</td>\n",
   1097        "      <td>0.773798</td>\n",
   1098        "      <td>0.756961</td>\n",
   1099        "      <td>0.767652</td>\n",
   1100        "      <td>0.619918</td>\n",
   1101        "      <td>0.605226</td>\n",
   1102        "      <td>0.734837</td>\n",
   1103        "      <td>1.000000</td>\n",
   1104        "    </tr>\n",
   1105        "  </tbody>\n",
   1106        "</table>\n",
   1107        "<p>5 rows × 30 columns</p>\n",
   1108        "</div>"
   1109       ],
   1110       "text/plain": [
   1111        "                   Food      Beer     Smoke     Games     Books     Hshld  \\\n",
   1112        "2018-12 Whlsl  0.474948  0.356983  0.122672  0.510425  0.803362  0.419280   \n",
   1113        "        Rtail  0.517856  0.406107  0.030283  0.676464  0.636320  0.358336   \n",
   1114        "        Meals  0.370187  0.385483  0.122007  0.301516  0.520649  0.308216   \n",
   1115        "        Fin    0.298823  0.192706  0.027593  0.480276  0.694812  0.162690   \n",
   1116        "        Other  0.436952  0.376565  0.224010  0.331829  0.558072  0.390610   \n",
   1117        "\n",
   1118        "                  Clths      Hlth     Chems     Txtls  ...     Telcm  \\\n",
   1119        "2018-12 Whlsl  0.570071  0.739764  0.785796  0.634197  ...  0.648092   \n",
   1120        "        Rtail  0.676598  0.714933  0.626034  0.634202  ...  0.562238   \n",
   1121        "        Meals  0.302176  0.416193  0.520023  0.491726  ...  0.406184   \n",
   1122        "        Fin    0.425899  0.658468  0.760151  0.577090  ...  0.420863   \n",
   1123        "        Other  0.467099  0.645035  0.712511  0.520953  ...  0.607868   \n",
   1124        "\n",
   1125        "                  Servs     BusEq     Paper     Trans     Whlsl     Rtail  \\\n",
   1126        "2018-12 Whlsl  0.567395  0.543362  0.764252  0.829185  1.000000  0.744842   \n",
   1127        "        Rtail  0.762616  0.628246  0.656510  0.630615  0.744842  1.000000   \n",
   1128        "        Meals  0.444629  0.399438  0.627113  0.663358  0.643879  0.616947   \n",
   1129        "        Fin    0.585418  0.517947  0.670936  0.760730  0.746480  0.611883   \n",
   1130        "        Other  0.460322  0.434487  0.773798  0.756961  0.767652  0.619918   \n",
   1131        "\n",
   1132        "                  Meals       Fin     Other  \n",
   1133        "2018-12 Whlsl  0.643879  0.746480  0.767652  \n",
   1134        "        Rtail  0.616947  0.611883  0.619918  \n",
   1135        "        Meals  1.000000  0.502563  0.605226  \n",
   1136        "        Fin    0.502563  1.000000  0.734837  \n",
   1137        "        Other  0.605226  0.734837  1.000000  \n",
   1138        "\n",
   1139        "[5 rows x 30 columns]"
   1140       ]
   1141      },
   1142      "execution_count": 19,
   1143      "metadata": {},
   1144      "output_type": "execute_result"
   1145     }
   1146    ],
   1147    "source": [
   1148     "ts_corr = ind_return.rolling(window=36).corr()\n",
   1149     "ts_corr.tail()"
   1150    ]
   1151   },
   1152   {
   1153    "cell_type": "markdown",
   1154    "metadata": {},
   1155    "source": [
   1156     "What is created is a DataFrame with a MultiLevel Index. The first level is the date and the second level is industry name. We can make this easier to see if we give names to the levels of the index:"
   1157    ]
   1158   },
   1159   {
   1160    "cell_type": "code",
   1161    "execution_count": 20,
   1162    "metadata": {},
   1163    "outputs": [
   1164     {
   1165      "data": {
   1166       "text/html": [
   1167        "<div>\n",
   1168        "<style scoped>\n",
   1169        "    .dataframe tbody tr th:only-of-type {\n",
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   1171        "    }\n",
   1172        "\n",
   1173        "    .dataframe tbody tr th {\n",
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   1176        "\n",
   1177        "    .dataframe thead th {\n",
   1178        "        text-align: right;\n",
   1179        "    }\n",
   1180        "</style>\n",
   1181        "<table border=\"1\" class=\"dataframe\">\n",
   1182        "  <thead>\n",
   1183        "    <tr style=\"text-align: right;\">\n",
   1184        "      <th></th>\n",
   1185        "      <th></th>\n",
   1186        "      <th>Food</th>\n",
   1187        "      <th>Beer</th>\n",
   1188        "      <th>Smoke</th>\n",
   1189        "      <th>Games</th>\n",
   1190        "      <th>Books</th>\n",
   1191        "      <th>Hshld</th>\n",
   1192        "      <th>Clths</th>\n",
   1193        "      <th>Hlth</th>\n",
   1194        "      <th>Chems</th>\n",
   1195        "      <th>Txtls</th>\n",
   1196        "      <th>...</th>\n",
   1197        "      <th>Telcm</th>\n",
   1198        "      <th>Servs</th>\n",
   1199        "      <th>BusEq</th>\n",
   1200        "      <th>Paper</th>\n",
   1201        "      <th>Trans</th>\n",
   1202        "      <th>Whlsl</th>\n",
   1203        "      <th>Rtail</th>\n",
   1204        "      <th>Meals</th>\n",
   1205        "      <th>Fin</th>\n",
   1206        "      <th>Other</th>\n",
   1207        "    </tr>\n",
   1208        "    <tr>\n",
   1209        "      <th>date</th>\n",
   1210        "      <th>industry</th>\n",
   1211        "      <th></th>\n",
   1212        "      <th></th>\n",
   1213        "      <th></th>\n",
   1214        "      <th></th>\n",
   1215        "      <th></th>\n",
   1216        "      <th></th>\n",
   1217        "      <th></th>\n",
   1218        "      <th></th>\n",
   1219        "      <th></th>\n",
   1220        "      <th></th>\n",
   1221        "      <th></th>\n",
   1222        "      <th></th>\n",
   1223        "      <th></th>\n",
   1224        "      <th></th>\n",
   1225        "      <th></th>\n",
   1226        "      <th></th>\n",
   1227        "      <th></th>\n",
   1228        "      <th></th>\n",
   1229        "      <th></th>\n",
   1230        "      <th></th>\n",
   1231        "      <th></th>\n",
   1232        "    </tr>\n",
   1233        "  </thead>\n",
   1234        "  <tbody>\n",
   1235        "    <tr>\n",
   1236        "      <th rowspan=\"5\" valign=\"top\">2018-12</th>\n",
   1237        "      <th>Whlsl</th>\n",
   1238        "      <td>0.474948</td>\n",
   1239        "      <td>0.356983</td>\n",
   1240        "      <td>0.122672</td>\n",
   1241        "      <td>0.510425</td>\n",
   1242        "      <td>0.803362</td>\n",
   1243        "      <td>0.419280</td>\n",
   1244        "      <td>0.570071</td>\n",
   1245        "      <td>0.739764</td>\n",
   1246        "      <td>0.785796</td>\n",
   1247        "      <td>0.634197</td>\n",
   1248        "      <td>...</td>\n",
   1249        "      <td>0.648092</td>\n",
   1250        "      <td>0.567395</td>\n",
   1251        "      <td>0.543362</td>\n",
   1252        "      <td>0.764252</td>\n",
   1253        "      <td>0.829185</td>\n",
   1254        "      <td>1.000000</td>\n",
   1255        "      <td>0.744842</td>\n",
   1256        "      <td>0.643879</td>\n",
   1257        "      <td>0.746480</td>\n",
   1258        "      <td>0.767652</td>\n",
   1259        "    </tr>\n",
   1260        "    <tr>\n",
   1261        "      <th>Rtail</th>\n",
   1262        "      <td>0.517856</td>\n",
   1263        "      <td>0.406107</td>\n",
   1264        "      <td>0.030283</td>\n",
   1265        "      <td>0.676464</td>\n",
   1266        "      <td>0.636320</td>\n",
   1267        "      <td>0.358336</td>\n",
   1268        "      <td>0.676598</td>\n",
   1269        "      <td>0.714933</td>\n",
   1270        "      <td>0.626034</td>\n",
   1271        "      <td>0.634202</td>\n",
   1272        "      <td>...</td>\n",
   1273        "      <td>0.562238</td>\n",
   1274        "      <td>0.762616</td>\n",
   1275        "      <td>0.628246</td>\n",
   1276        "      <td>0.656510</td>\n",
   1277        "      <td>0.630615</td>\n",
   1278        "      <td>0.744842</td>\n",
   1279        "      <td>1.000000</td>\n",
   1280        "      <td>0.616947</td>\n",
   1281        "      <td>0.611883</td>\n",
   1282        "      <td>0.619918</td>\n",
   1283        "    </tr>\n",
   1284        "    <tr>\n",
   1285        "      <th>Meals</th>\n",
   1286        "      <td>0.370187</td>\n",
   1287        "      <td>0.385483</td>\n",
   1288        "      <td>0.122007</td>\n",
   1289        "      <td>0.301516</td>\n",
   1290        "      <td>0.520649</td>\n",
   1291        "      <td>0.308216</td>\n",
   1292        "      <td>0.302176</td>\n",
   1293        "      <td>0.416193</td>\n",
   1294        "      <td>0.520023</td>\n",
   1295        "      <td>0.491726</td>\n",
   1296        "      <td>...</td>\n",
   1297        "      <td>0.406184</td>\n",
   1298        "      <td>0.444629</td>\n",
   1299        "      <td>0.399438</td>\n",
   1300        "      <td>0.627113</td>\n",
   1301        "      <td>0.663358</td>\n",
   1302        "      <td>0.643879</td>\n",
   1303        "      <td>0.616947</td>\n",
   1304        "      <td>1.000000</td>\n",
   1305        "      <td>0.502563</td>\n",
   1306        "      <td>0.605226</td>\n",
   1307        "    </tr>\n",
   1308        "    <tr>\n",
   1309        "      <th>Fin</th>\n",
   1310        "      <td>0.298823</td>\n",
   1311        "      <td>0.192706</td>\n",
   1312        "      <td>0.027593</td>\n",
   1313        "      <td>0.480276</td>\n",
   1314        "      <td>0.694812</td>\n",
   1315        "      <td>0.162690</td>\n",
   1316        "      <td>0.425899</td>\n",
   1317        "      <td>0.658468</td>\n",
   1318        "      <td>0.760151</td>\n",
   1319        "      <td>0.577090</td>\n",
   1320        "      <td>...</td>\n",
   1321        "      <td>0.420863</td>\n",
   1322        "      <td>0.585418</td>\n",
   1323        "      <td>0.517947</td>\n",
   1324        "      <td>0.670936</td>\n",
   1325        "      <td>0.760730</td>\n",
   1326        "      <td>0.746480</td>\n",
   1327        "      <td>0.611883</td>\n",
   1328        "      <td>0.502563</td>\n",
   1329        "      <td>1.000000</td>\n",
   1330        "      <td>0.734837</td>\n",
   1331        "    </tr>\n",
   1332        "    <tr>\n",
   1333        "      <th>Other</th>\n",
   1334        "      <td>0.436952</td>\n",
   1335        "      <td>0.376565</td>\n",
   1336        "      <td>0.224010</td>\n",
   1337        "      <td>0.331829</td>\n",
   1338        "      <td>0.558072</td>\n",
   1339        "      <td>0.390610</td>\n",
   1340        "      <td>0.467099</td>\n",
   1341        "      <td>0.645035</td>\n",
   1342        "      <td>0.712511</td>\n",
   1343        "      <td>0.520953</td>\n",
   1344        "      <td>...</td>\n",
   1345        "      <td>0.607868</td>\n",
   1346        "      <td>0.460322</td>\n",
   1347        "      <td>0.434487</td>\n",
   1348        "      <td>0.773798</td>\n",
   1349        "      <td>0.756961</td>\n",
   1350        "      <td>0.767652</td>\n",
   1351        "      <td>0.619918</td>\n",
   1352        "      <td>0.605226</td>\n",
   1353        "      <td>0.734837</td>\n",
   1354        "      <td>1.000000</td>\n",
   1355        "    </tr>\n",
   1356        "  </tbody>\n",
   1357        "</table>\n",
   1358        "<p>5 rows × 30 columns</p>\n",
   1359        "</div>"
   1360       ],
   1361       "text/plain": [
   1362        "                      Food      Beer     Smoke     Games     Books     Hshld  \\\n",
   1363        "date    industry                                                               \n",
   1364        "2018-12 Whlsl     0.474948  0.356983  0.122672  0.510425  0.803362  0.419280   \n",
   1365        "        Rtail     0.517856  0.406107  0.030283  0.676464  0.636320  0.358336   \n",
   1366        "        Meals     0.370187  0.385483  0.122007  0.301516  0.520649  0.308216   \n",
   1367        "        Fin       0.298823  0.192706  0.027593  0.480276  0.694812  0.162690   \n",
   1368        "        Other     0.436952  0.376565  0.224010  0.331829  0.558072  0.390610   \n",
   1369        "\n",
   1370        "                     Clths      Hlth     Chems     Txtls  ...     Telcm  \\\n",
   1371        "date    industry                                          ...             \n",
   1372        "2018-12 Whlsl     0.570071  0.739764  0.785796  0.634197  ...  0.648092   \n",
   1373        "        Rtail     0.676598  0.714933  0.626034  0.634202  ...  0.562238   \n",
   1374        "        Meals     0.302176  0.416193  0.520023  0.491726  ...  0.406184   \n",
   1375        "        Fin       0.425899  0.658468  0.760151  0.577090  ...  0.420863   \n",
   1376        "        Other     0.467099  0.645035  0.712511  0.520953  ...  0.607868   \n",
   1377        "\n",
   1378        "                     Servs     BusEq     Paper     Trans     Whlsl     Rtail  \\\n",
   1379        "date    industry                                                               \n",
   1380        "2018-12 Whlsl     0.567395  0.543362  0.764252  0.829185  1.000000  0.744842   \n",
   1381        "        Rtail     0.762616  0.628246  0.656510  0.630615  0.744842  1.000000   \n",
   1382        "        Meals     0.444629  0.399438  0.627113  0.663358  0.643879  0.616947   \n",
   1383        "        Fin       0.585418  0.517947  0.670936  0.760730  0.746480  0.611883   \n",
   1384        "        Other     0.460322  0.434487  0.773798  0.756961  0.767652  0.619918   \n",
   1385        "\n",
   1386        "                     Meals       Fin     Other  \n",
   1387        "date    industry                                \n",
   1388        "2018-12 Whlsl     0.643879  0.746480  0.767652  \n",
   1389        "        Rtail     0.616947  0.611883  0.619918  \n",
   1390        "        Meals     1.000000  0.502563  0.605226  \n",
   1391        "        Fin       0.502563  1.000000  0.734837  \n",
   1392        "        Other     0.605226  0.734837  1.000000  \n",
   1393        "\n",
   1394        "[5 rows x 30 columns]"
   1395       ]
   1396      },
   1397      "execution_count": 20,
   1398      "metadata": {},
   1399      "output_type": "execute_result"
   1400     }
   1401    ],
   1402    "source": [
   1403     "ts_corr.index.names = ['date', 'industry']\n",
   1404     "ts_corr.tail()"
   1405    ]
   1406   },
   1407   {
   1408    "cell_type": "markdown",
   1409    "metadata": {},
   1410    "source": [
   1411     "To compute the rolling correlations, we need to group all the rows for the same date, and then compute the average across all the entries in the dataframe. \n",
   1412     "\n",
   1413     "We need to compute the mean of all the values of the dataframe, not the mean of each column. We could compute the means of the means, but it's simpler to just call our own function on the correlation matrix.\n",
   1414     "\n",
   1415     "Although we could construct a function to compute this, it's often easier to provide a `lambda` function which is a simple one-time-use function and therefore has no name. In every other way, it's a standard pandas function.\n",
   1416     "\n",
   1417     "We do so as follows:"
   1418    ]
   1419   },
   1420   {
   1421    "cell_type": "code",
   1422    "execution_count": 21,
   1423    "metadata": {},
   1424    "outputs": [],
   1425    "source": [
   1426     "\n",
   1427     "ind_tr36corr = ts_corr.groupby(level='date').apply(lambda cormat: cormat.values.mean())"
   1428    ]
   1429   },
   1430   {
   1431    "cell_type": "markdown",
   1432    "metadata": {},
   1433    "source": [
   1434     "Now, we can plot the trailing 36 month returns against the average correlations in the trailing 36 months."
   1435    ]
   1436   },
   1437   {
   1438    "cell_type": "code",
   1439    "execution_count": 22,
   1440    "metadata": {},
   1441    "outputs": [
   1442     {
   1443      "data": {
   1444       "text/plain": [
   1445        "<matplotlib.axes._subplots.AxesSubplot at 0x1a2379f240>"
   1446       ]
   1447      },
   1448      "execution_count": 22,
   1449      "metadata": {},
   1450      "output_type": "execute_result"
   1451     },
   1452     {
   1453      "data": {
   1454       "image/png": 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\n",
   1455       "text/plain": [
   1456        "<Figure size 864x432 with 2 Axes>"
   1457       ]
   1458      },
   1459      "metadata": {
   1460       "needs_background": "light"
   1461      },
   1462      "output_type": "display_data"
   1463     }
   1464    ],
   1465    "source": [
   1466     "tmi_tr36rets.plot(secondary_y=True, legend=True, label=\"Tr 36 mo return\", figsize=(12,6))\n",
   1467     "ind_tr36corr.plot(legend=True, label=\"Tr 36 mo Avg Correlation\")"
   1468    ]
   1469   },
   1470   {
   1471    "cell_type": "code",
   1472    "execution_count": 23,
   1473    "metadata": {},
   1474    "outputs": [
   1475     {
   1476      "data": {
   1477       "text/plain": [
   1478        "-0.28010065062884126"
   1479       ]
   1480      },
   1481      "execution_count": 23,
   1482      "metadata": {},
   1483      "output_type": "execute_result"
   1484     }
   1485    ],
   1486    "source": [
   1487     "tmi_tr36rets.corr(ind_tr36corr)"
   1488    ]
   1489   },
   1490   {
   1491    "cell_type": "markdown",
   1492    "metadata": {},
   1493    "source": [
   1494     "Clearly, these two series are negatively correlated, which explains why diversification fails you when you need it most. When markets fall, correlations rise, making diversification much less valuable.\n",
   1495     "\n",
   1496     "Instead, we'll look at how to use Insurance to protect the downside."
   1497    ]
   1498   },
   1499   {
   1500    "cell_type": "code",
   1501    "execution_count": null,
   1502    "metadata": {},
   1503    "outputs": [],
   1504    "source": []
   1505   }
   1506  ],
   1507  "metadata": {
   1508   "kernelspec": {
   1509    "display_name": "Python 3",
   1510    "language": "python",
   1511    "name": "python3"
   1512   },
   1513   "language_info": {
   1514    "codemirror_mode": {
   1515     "name": "ipython",
   1516     "version": 3
   1517    },
   1518    "file_extension": ".py",
   1519    "mimetype": "text/x-python",
   1520    "name": "python",
   1521    "nbconvert_exporter": "python",
   1522    "pygments_lexer": "ipython3",
   1523    "version": "3.8.8"
   1524   }
   1525  },
   1526  "nbformat": 4,
   1527  "nbformat_minor": 2
   1528 }