ml-finance-python

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

(106074B)
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      9     "# Estimize: Analyst-by-Analyst Estimates\n",
     10     "\n",
     11     "In this notebook, we'll take a look at Estimizes's *Analyst-by-Analyst Estimates* dataset, available on the [Quantopian](https://www.quantopian.com/store). This dataset spans January, 2010 through the current day.\n",
     12     "\n",
     13     "This data contains a record for every estimate made by an individual on the Estimize product. By comparison, the Estimize Revisions product provides rolled-up consensus numbers for each possible earnings announcement.\n",
     14     "\n",
     15     "In this notebook, we'll examine these detailed estimates and pull in that consensus data as well.\n",
     16     "\n",
     17     "### Blaze\n",
     18     "Before we dig into the data, we want to tell you about how  you generally access Quantopian Store data sets. These datasets are available using the [Blaze](http://blaze.pydata.org) library. Blaze provides the Quantopian user with a convenient interface to access very large datasets.\n",
     19     "\n",
     20     "Blaze provides an important function for accessing these datasets. Some of these sets are many millions of records. Bringing that data directly into Quantopian Research directly just is not viable. So Blaze allows us to provide a simple querying interface and shift the burden over to the server side.\n",
     21     "\n",
     22     "To learn more about using Blaze and generally accessing Quantopian Store data, clone [this tutorial notebook](https://www.quantopian.com/clone_notebook?id=561827d21777f45c97000054).\n",
     23     "\n",
     24     "### Free samples and limits\n",
     25     "A few key caveats:\n",
     26     "\n",
     27     "1) We limit the number of results returned from any given expression to 10,000 to protect against runaway memory usage. To be clear, you have access to all the data server side. We are limiting the size of the responses back from Blaze.\n",
     28     "\n",
     29     "2) There is a *free* version of this dataset as well as a paid one. The free one includes about three years of historical data, though not up to the current day.\n",
     30     "\n",
     31     "With preamble in place, let's get started:"
     32    ]
     33   },
     34   {
     35    "cell_type": "code",
     36    "execution_count": 1,
     37    "metadata": {
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     39    },
     40    "outputs": [],
     41    "source": [
     42     "# import the free sample of the dataset\n",
     43     "from quantopian.interactive.data.estimize import estimates_free\n",
     44     "\n",
     45     "# or if you want to import the full dataset, use:\n",
     46     "# from quantopian.interactive.data.estimize import estimates\n",
     47     "\n",
     48     "# import data operations\n",
     49     "from odo import odo\n",
     50     "# import other libraries we will use\n",
     51     "import pandas as pd\n",
     52     "import matplotlib.pyplot as plt"
     53    ]
     54   },
     55   {
     56    "cell_type": "code",
     57    "execution_count": 2,
     58    "metadata": {
     59     "collapsed": false
     60    },
     61    "outputs": [
     62     {
     63      "data": {
     64       "text/plain": [
     65        "dshape(\"\"\"var * {\n",
     66        "  analyst_id: ?string,\n",
     67        "  asof_date: datetime,\n",
     68        "  eps: ?float64,\n",
     69        "  fiscal_quarter: ?float64,\n",
     70        "  fiscal_year: ?float64,\n",
     71        "  id: ?string,\n",
     72        "  revenue: ?float64,\n",
     73        "  symbol: ?string,\n",
     74        "  username: ?string,\n",
     75        "  timestamp: datetime,\n",
     76        "  sid: ?int64\n",
     77        "  }\"\"\")"
     78       ]
     79      },
     80      "execution_count": 2,
     81      "metadata": {},
     82      "output_type": "execute_result"
     83     }
     84    ],
     85    "source": [
     86     "# Let's use blaze to understand the data a bit using Blaze dshape()\n",
     87     "estimates_free.dshape"
     88    ]
     89   },
     90   {
     91    "cell_type": "code",
     92    "execution_count": 3,
     93    "metadata": {
     94     "collapsed": false
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     96    "outputs": [
     97     {
     98      "data": {
     99       "text/html": [
    100        "120400"
    101       ],
    102       "text/plain": [
    103        "120400"
    104       ]
    105      },
    106      "execution_count": 3,
    107      "metadata": {},
    108      "output_type": "execute_result"
    109     }
    110    ],
    111    "source": [
    112     "# And how many rows are in this free sample?\n",
    113     "# N.B. we're using a Blaze function to do this, not len()\n",
    114     "estimates_free.count()"
    115    ]
    116   },
    117   {
    118    "cell_type": "code",
    119    "execution_count": 4,
    120    "metadata": {
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    124     {
    125      "data": {
    126       "text/html": [
    127        "<table border=\"1\" class=\"dataframe\">\n",
    128        "  <thead>\n",
    129        "    <tr style=\"text-align: right;\">\n",
    130        "      <th></th>\n",
    131        "      <th>analyst_id</th>\n",
    132        "      <th>asof_date</th>\n",
    133        "      <th>eps</th>\n",
    134        "      <th>fiscal_quarter</th>\n",
    135        "      <th>fiscal_year</th>\n",
    136        "      <th>id</th>\n",
    137        "      <th>revenue</th>\n",
    138        "      <th>symbol</th>\n",
    139        "      <th>username</th>\n",
    140        "      <th>timestamp</th>\n",
    141        "      <th>sid</th>\n",
    142        "    </tr>\n",
    143        "  </thead>\n",
    144        "  <tbody>\n",
    145        "    <tr>\n",
    146        "      <th>0</th>\n",
    147        "      <td>4e679bb77cb02d0b6700000f</td>\n",
    148        "      <td>2010-01-02 17:00:00</td>\n",
    149        "      <td>0.90</td>\n",
    150        "      <td>1</td>\n",
    151        "      <td>2011</td>\n",
    152        "      <td>4e6dee5a7cb02d2adc000014</td>\n",
    153        "      <td>26430</td>\n",
    154        "      <td>AAPL</td>\n",
    155        "      <td>postsateventide</td>\n",
    156        "      <td>2010-01-02 17:20:00</td>\n",
    157        "      <td>24</td>\n",
    158        "    </tr>\n",
    159        "    <tr>\n",
    160        "      <th>1</th>\n",
    161        "      <td>4e679bb77cb02d0b67000005</td>\n",
    162        "      <td>2010-09-28 16:00:00</td>\n",
    163        "      <td>0.63</td>\n",
    164        "      <td>4</td>\n",
    165        "      <td>2010</td>\n",
    166        "      <td>4e6df18f7cb02d2adc000024</td>\n",
    167        "      <td>19530</td>\n",
    168        "      <td>AAPL</td>\n",
    169        "      <td>DennisHildebrand</td>\n",
    170        "      <td>2010-09-28 16:20:00</td>\n",
    171        "      <td>24</td>\n",
    172        "    </tr>\n",
    173        "    <tr>\n",
    174        "      <th>2</th>\n",
    175        "      <td>4e679bb87cb02d0b6700001b</td>\n",
    176        "      <td>2010-09-28 16:00:00</td>\n",
    177        "      <td>0.71</td>\n",
    178        "      <td>4</td>\n",
    179        "      <td>2010</td>\n",
    180        "      <td>4e6df0977cb02d2adc00001f</td>\n",
    181        "      <td>20500</td>\n",
    182        "      <td>AAPL</td>\n",
    183        "      <td>asymco</td>\n",
    184        "      <td>2010-09-28 16:20:00</td>\n",
    185        "      <td>24</td>\n",
    186        "    </tr>\n",
    187        "  </tbody>\n",
    188        "</table>"
    189       ],
    190       "text/plain": [
    191        "                 analyst_id           asof_date   eps  fiscal_quarter  \\\n",
    192        "0  4e679bb77cb02d0b6700000f 2010-01-02 17:00:00  0.90               1   \n",
    193        "1  4e679bb77cb02d0b67000005 2010-09-28 16:00:00  0.63               4   \n",
    194        "2  4e679bb87cb02d0b6700001b 2010-09-28 16:00:00  0.71               4   \n",
    195        "\n",
    196        "   fiscal_year                        id  revenue symbol          username  \\\n",
    197        "0         2011  4e6dee5a7cb02d2adc000014    26430   AAPL   postsateventide   \n",
    198        "1         2010  4e6df18f7cb02d2adc000024    19530   AAPL  DennisHildebrand   \n",
    199        "2         2010  4e6df0977cb02d2adc00001f    20500   AAPL            asymco   \n",
    200        "\n",
    201        "            timestamp  sid  \n",
    202        "0 2010-01-02 17:20:00   24  \n",
    203        "1 2010-09-28 16:20:00   24  \n",
    204        "2 2010-09-28 16:20:00   24  "
    205       ]
    206      },
    207      "execution_count": 4,
    208      "metadata": {},
    209      "output_type": "execute_result"
    210     }
    211    ],
    212    "source": [
    213     "# Let's see what the data looks like. We'll grab the first three rows.\n",
    214     "estimates_free.head(3)"
    215    ]
    216   },
    217   {
    218    "cell_type": "markdown",
    219    "metadata": {},
    220    "source": [
    221     "Let's go over the columns:\n",
    222     "- **analyst_id**: the unique identifier assigned by Estimize for the person making the estimate.\n",
    223     "- **asof_date**: Estimize's timestamp of event capture.\n",
    224     "- **eps**: EPS estimate made by the analyst on the `asof_date`\n",
    225     "- **fiscal_quarter**: fiscal quarter for which this estimate is made, related to `fiscal_year`\n",
    226     "- **fiscal_year**: fiscal year for which this estimate is made, related to `fiscal_quarter`\n",
    227     "- **revenue**: revenue estimate made by the analyst on the `asof_date`\n",
    228     "- **symbol**: ticker symbol provided by Estimize for the company for whom these estimates have been made\n",
    229     "- **username**: Estimize username of the analyst making this estimate\n",
    230     "- **timestamp**: the datetime when Quantopian registered the data. For data loaded up via initial, historic loads, this timestamp is an estimate.\n",
    231     "- **sid**: the equity's unique identifier. Use this instead of the symbol. Derived by Quantopian using the symbol and our market data"
    232    ]
    233   },
    234   {
    235    "cell_type": "markdown",
    236    "metadata": {},
    237    "source": [
    238     "We've done much of the data processing for you. Fields like `asof_date` and `sid` are standardized across all our Store Datasets, so the datasets are easy to combine. We have standardized the `sid` across all our equity databases.\n",
    239     "\n",
    240     "We can select columns and rows with ease. Below, let's just look at the estimates made for TSLA for a particular quarter. Also, we're filtering out some spurious data:"
    241    ]
    242   },
    243   {
    244    "cell_type": "code",
    245    "execution_count": 5,
    246    "metadata": {
    247     "collapsed": false
    248    },
    249    "outputs": [
    250     {
    251      "data": {
    252       "text/html": [
    253        "<table border=\"1\" class=\"dataframe\">\n",
    254        "  <thead>\n",
    255        "    <tr style=\"text-align: right;\">\n",
    256        "      <th></th>\n",
    257        "      <th>analyst_id</th>\n",
    258        "      <th>asof_date</th>\n",
    259        "      <th>eps</th>\n",
    260        "      <th>fiscal_quarter</th>\n",
    261        "      <th>fiscal_year</th>\n",
    262        "      <th>id</th>\n",
    263        "      <th>revenue</th>\n",
    264        "      <th>symbol</th>\n",
    265        "      <th>username</th>\n",
    266        "      <th>timestamp</th>\n",
    267        "      <th>sid</th>\n",
    268        "    </tr>\n",
    269        "  </thead>\n",
    270        "  <tbody>\n",
    271        "    <tr>\n",
    272        "      <th>0</th>\n",
    273        "      <td>500ee59d810f8d1c49000091</td>\n",
    274        "      <td>2013-05-30 19:00:13</td>\n",
    275        "      <td>0.08</td>\n",
    276        "      <td>1</td>\n",
    277        "      <td>2014</td>\n",
    278        "      <td>51a7a1bd810f8d6e27000362</td>\n",
    279        "      <td>500</td>\n",
    280        "      <td>TSLA</td>\n",
    281        "      <td>hsctiger2009</td>\n",
    282        "      <td>2013-05-30 19:20:13</td>\n",
    283        "      <td>39840</td>\n",
    284        "    </tr>\n",
    285        "    <tr>\n",
    286        "      <th>1</th>\n",
    287        "      <td>4f997632810f8d1aaf0001ec</td>\n",
    288        "      <td>2013-09-18 14:11:45</td>\n",
    289        "      <td>0.35</td>\n",
    290        "      <td>1</td>\n",
    291        "      <td>2014</td>\n",
    292        "      <td>5239b4a1b7529b033a001ecc</td>\n",
    293        "      <td>700</td>\n",
    294        "      <td>TSLA</td>\n",
    295        "      <td>aarkayne</td>\n",
    296        "      <td>2013-09-18 14:31:45</td>\n",
    297        "      <td>39840</td>\n",
    298        "    </tr>\n",
    299        "    <tr>\n",
    300        "      <th>2</th>\n",
    301        "      <td>500ee59d810f8d1c49000091</td>\n",
    302        "      <td>2013-09-20 21:56:58</td>\n",
    303        "      <td>0.33</td>\n",
    304        "      <td>1</td>\n",
    305        "      <td>2014</td>\n",
    306        "      <td>523cc4aab7529b009b00b0e1</td>\n",
    307        "      <td>620</td>\n",
    308        "      <td>TSLA</td>\n",
    309        "      <td>hsctiger2009</td>\n",
    310        "      <td>2013-09-20 22:16:58</td>\n",
    311        "      <td>39840</td>\n",
    312        "    </tr>\n",
    313        "    <tr>\n",
    314        "      <th>3</th>\n",
    315        "      <td>500ee59d810f8d1c49000091</td>\n",
    316        "      <td>2013-09-26 16:40:26</td>\n",
    317        "      <td>0.35</td>\n",
    318        "      <td>1</td>\n",
    319        "      <td>2014</td>\n",
    320        "      <td>5244637ab7529bb85c033499</td>\n",
    321        "      <td>620</td>\n",
    322        "      <td>TSLA</td>\n",
    323        "      <td>hsctiger2009</td>\n",
    324        "      <td>2013-09-26 17:00:26</td>\n",
    325        "      <td>39840</td>\n",
    326        "    </tr>\n",
    327        "    <tr>\n",
    328        "      <th>4</th>\n",
    329        "      <td>524990dfb7529b150a01af9a</td>\n",
    330        "      <td>2013-09-30 15:49:41</td>\n",
    331        "      <td>0.30</td>\n",
    332        "      <td>1</td>\n",
    333        "      <td>2014</td>\n",
    334        "      <td>52499d95b7529b6c8201d1ef</td>\n",
    335        "      <td>590</td>\n",
    336        "      <td>TSLA</td>\n",
    337        "      <td>wjhughes</td>\n",
    338        "      <td>2013-09-30 16:09:41</td>\n",
    339        "      <td>39840</td>\n",
    340        "    </tr>\n",
    341        "  </tbody>\n",
    342        "</table>"
    343       ],
    344       "text/plain": [
    345        "                 analyst_id           asof_date   eps  fiscal_quarter  \\\n",
    346        "0  500ee59d810f8d1c49000091 2013-05-30 19:00:13  0.08               1   \n",
    347        "1  4f997632810f8d1aaf0001ec 2013-09-18 14:11:45  0.35               1   \n",
    348        "2  500ee59d810f8d1c49000091 2013-09-20 21:56:58  0.33               1   \n",
    349        "3  500ee59d810f8d1c49000091 2013-09-26 16:40:26  0.35               1   \n",
    350        "4  524990dfb7529b150a01af9a 2013-09-30 15:49:41  0.30               1   \n",
    351        "\n",
    352        "   fiscal_year                        id  revenue symbol      username  \\\n",
    353        "0         2014  51a7a1bd810f8d6e27000362      500   TSLA  hsctiger2009   \n",
    354        "1         2014  5239b4a1b7529b033a001ecc      700   TSLA      aarkayne   \n",
    355        "2         2014  523cc4aab7529b009b00b0e1      620   TSLA  hsctiger2009   \n",
    356        "3         2014  5244637ab7529bb85c033499      620   TSLA  hsctiger2009   \n",
    357        "4         2014  52499d95b7529b6c8201d1ef      590   TSLA      wjhughes   \n",
    358        "\n",
    359        "            timestamp    sid  \n",
    360        "0 2013-05-30 19:20:13  39840  \n",
    361        "1 2013-09-18 14:31:45  39840  \n",
    362        "2 2013-09-20 22:16:58  39840  \n",
    363        "3 2013-09-26 17:00:26  39840  \n",
    364        "4 2013-09-30 16:09:41  39840  "
    365       ]
    366      },
    367      "execution_count": 5,
    368      "metadata": {},
    369      "output_type": "execute_result"
    370     }
    371    ],
    372    "source": [
    373     "stocks = symbols('TSLA')\n",
    374     "one_quarter = estimates_free[(estimates_free.sid == stocks.sid) &\n",
    375     "                 (estimates_free.fiscal_year == '2014') &\n",
    376     "                 (estimates_free.fiscal_quarter == '1') &\n",
    377     "                 (estimates_free.eps < 100)\n",
    378     "                ]\n",
    379     "one_quarter.head(5)"
    380    ]
    381   },
    382   {
    383    "cell_type": "markdown",
    384    "metadata": {},
    385    "source": [
    386     "How many records do we have now?"
    387    ]
    388   },
    389   {
    390    "cell_type": "code",
    391    "execution_count": 6,
    392    "metadata": {
    393     "collapsed": false,
    394     "scrolled": true
    395    },
    396    "outputs": [
    397     {
    398      "data": {
    399       "text/html": [
    400        "136"
    401       ],
    402       "text/plain": [
    403        "136"
    404       ]
    405      },
    406      "execution_count": 6,
    407      "metadata": {},
    408      "output_type": "execute_result"
    409     }
    410    ],
    411    "source": [
    412     "one_quarter.count()"
    413    ]
    414   },
    415   {
    416    "cell_type": "markdown",
    417    "metadata": {},
    418    "source": [
    419     "Let's break it down by user:"
    420    ]
    421   },
    422   {
    423    "cell_type": "code",
    424    "execution_count": 7,
    425    "metadata": {
    426     "collapsed": false
    427    },
    428    "outputs": [
    429     {
    430      "data": {
    431       "text/html": [
    432        "<table border=\"1\" class=\"dataframe\">\n",
    433        "  <thead>\n",
    434        "    <tr style=\"text-align: right;\">\n",
    435        "      <th></th>\n",
    436        "      <th>username</th>\n",
    437        "      <th>count</th>\n",
    438        "    </tr>\n",
    439        "  </thead>\n",
    440        "  <tbody>\n",
    441        "    <tr>\n",
    442        "      <th>0</th>\n",
    443        "      <td>Analyst_7066456</td>\n",
    444        "      <td>3</td>\n",
    445        "    </tr>\n",
    446        "    <tr>\n",
    447        "      <th>1</th>\n",
    448        "      <td>Cwill</td>\n",
    449        "      <td>3</td>\n",
    450        "    </tr>\n",
    451        "    <tr>\n",
    452        "      <th>2</th>\n",
    453        "      <td>hsctiger2009</td>\n",
    454        "      <td>3</td>\n",
    455        "    </tr>\n",
    456        "    <tr>\n",
    457        "      <th>3</th>\n",
    458        "      <td>a76marine</td>\n",
    459        "      <td>3</td>\n",
    460        "    </tr>\n",
    461        "    <tr>\n",
    462        "      <th>4</th>\n",
    463        "      <td>wjbuckner</td>\n",
    464        "      <td>3</td>\n",
    465        "    </tr>\n",
    466        "    <tr>\n",
    467        "      <th>5</th>\n",
    468        "      <td>phi16</td>\n",
    469        "      <td>3</td>\n",
    470        "    </tr>\n",
    471        "    <tr>\n",
    472        "      <th>6</th>\n",
    473        "      <td>Mgspooner</td>\n",
    474        "      <td>3</td>\n",
    475        "    </tr>\n",
    476        "    <tr>\n",
    477        "      <th>7</th>\n",
    478        "      <td>Essential</td>\n",
    479        "      <td>2</td>\n",
    480        "    </tr>\n",
    481        "    <tr>\n",
    482        "      <th>8</th>\n",
    483        "      <td>Nils1975</td>\n",
    484        "      <td>2</td>\n",
    485        "    </tr>\n",
    486        "    <tr>\n",
    487        "      <th>9</th>\n",
    488        "      <td>Cassanova23</td>\n",
    489        "      <td>2</td>\n",
    490        "    </tr>\n",
    491        "    <tr>\n",
    492        "      <th>10</th>\n",
    493        "      <td>golfinguy224</td>\n",
    494        "      <td>2</td>\n",
    495        "    </tr>\n",
    496        "  </tbody>\n",
    497        "</table>"
    498       ],
    499       "text/plain": [
    500        "           username  count\n",
    501        "0   Analyst_7066456      3\n",
    502        "1             Cwill      3\n",
    503        "2      hsctiger2009      3\n",
    504        "3         a76marine      3\n",
    505        "4         wjbuckner      3\n",
    506        "5             phi16      3\n",
    507        "6         Mgspooner      3\n",
    508        "7         Essential      2\n",
    509        "8          Nils1975      2\n",
    510        "9       Cassanova23      2\n",
    511        "..."
    512       ]
    513      },
    514      "execution_count": 7,
    515      "metadata": {},
    516      "output_type": "execute_result"
    517     }
    518    ],
    519    "source": [
    520     "one_quarter.username.count_values()"
    521    ]
    522   },
    523   {
    524    "cell_type": "markdown",
    525    "metadata": {},
    526    "source": [
    527     "Let's convert it over to a Pandas DataFrame so we can chart it and examine it closer"
    528    ]
    529   },
    530   {
    531    "cell_type": "code",
    532    "execution_count": 31,
    533    "metadata": {
    534     "collapsed": false
    535    },
    536    "outputs": [
    537     {
    538      "data": {
    539       "text/plain": [
    540        "<matplotlib.legend.Legend at 0x7f6c66b172d0>"
    541       ]
    542      },
    543      "execution_count": 31,
    544      "metadata": {},
    545      "output_type": "execute_result"
    546     },
    547     {
    548      "data": {
    549       "image/png": 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hCQAAAABMIDwBAAAAgAmEJwAAADQ9NlvlDyKX+3scoPc7xu8WAAAAgMbEZpMc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\nZrtl1zQAAAAASUVORK5CYII=\n",
    550       "text/plain": [
    551        "<matplotlib.figure.Figure at 0x7f6c66f50310>"
    552       ]
    553      },
    554      "metadata": {},
    555      "output_type": "display_data"
    556     }
    557    ],
    558    "source": [
    559     "one_q_df = odo(one_quarter.sort('asof_date'), pd.DataFrame)\n",
    560     "plt.plot(one_q_df.asof_date, one_q_df.eps, marker='.', linestyle='None', color='r')\n",
    561     "plt.xlabel(\"As Of Date (asof_date)\")\n",
    562     "plt.ylabel(\"EPS Estimate\")\n",
    563     "plt.title(\"Analyst by Analyst EPS Estimates for TSLA\")\n",
    564     "plt.legend([\"Individual Estimate\"], loc=2)"
    565    ]
    566   },
    567   {
    568    "cell_type": "markdown",
    569    "metadata": {},
    570    "source": [
    571     "That's neat. But let's add in some data from another dataset -- the Estimize Revisions data. For the same timeframe, the revisions data provides each revision to the overall consensus estimates. So where `estimates_free` data provides every single estimate made by an individual on the Estimize site, `revisions_free` provides rolled up summaries of the estimates."
    572    ]
    573   },
    574   {
    575    "cell_type": "code",
    576    "execution_count": 32,
    577    "metadata": {
    578     "collapsed": false
    579    },
    580    "outputs": [],
    581    "source": [
    582     "from quantopian.interactive.data.estimize import revisions_free\n",
    583     "consensus = revisions_free[(revisions_free.sid == stocks.sid) &\n",
    584     "                 (revisions_free.fiscal_year == '2014') &\n",
    585     "                 (revisions_free.fiscal_quarter == '1') &\n",
    586     "                 (revisions_free.source == 'estimize') &\n",
    587     "                 (revisions_free.metric == 'eps')\n",
    588     "                ]"
    589    ]
    590   },
    591   {
    592    "cell_type": "code",
    593    "execution_count": 33,
    594    "metadata": {
    595     "collapsed": false
    596    },
    597    "outputs": [
    598     {
    599      "data": {
    600       "text/html": [
    601        "<table border=\"1\" class=\"dataframe\">\n",
    602        "  <thead>\n",
    603        "    <tr style=\"text-align: right;\">\n",
    604        "      <th></th>\n",
    605        "      <th>count</th>\n",
    606        "      <th>high</th>\n",
    607        "      <th>low</th>\n",
    608        "      <th>mean</th>\n",
    609        "      <th>metric</th>\n",
    610        "      <th>source</th>\n",
    611        "      <th>standard_deviation</th>\n",
    612        "      <th>asof_date</th>\n",
    613        "      <th>consensus_eps_estimate</th>\n",
    614        "      <th>consensus_revenue_estimate</th>\n",
    615        "      <th>eps</th>\n",
    616        "      <th>fiscal_quarter</th>\n",
    617        "      <th>fiscal_year</th>\n",
    618        "      <th>id</th>\n",
    619        "      <th>name</th>\n",
    620        "      <th>release_date</th>\n",
    621        "      <th>revenue</th>\n",
    622        "      <th>symbol</th>\n",
    623        "      <th>wallstreet_eps_estimate</th>\n",
    624        "      <th>wallstreet_revenue_estimate</th>\n",
    625        "      <th>timestamp</th>\n",
    626        "      <th>sid</th>\n",
    627        "    </tr>\n",
    628        "  </thead>\n",
    629        "  <tbody>\n",
    630        "    <tr>\n",
    631        "      <th>0</th>\n",
    632        "      <td>112</td>\n",
    633        "      <td>0.42</td>\n",
    634        "      <td>0.04</td>\n",
    635        "      <td>0.210357</td>\n",
    636        "      <td>eps</td>\n",
    637        "      <td>estimize</td>\n",
    638        "      <td>0.095823</td>\n",
    639        "      <td>2014-05-07 19:22:15.904000</td>\n",
    640        "      <td>0.210714285714286</td>\n",
    641        "      <td>719.256071428571</td>\n",
    642        "      <td>0.12</td>\n",
    643        "      <td>1</td>\n",
    644        "      <td>2014</td>\n",
    645        "      <td>510c4310810f8d63ab004f49</td>\n",
    646        "      <td>Tesla Motors, Inc.</td>\n",
    647        "      <td>2014-05-07 20:00:00</td>\n",
    648        "      <td>713.0</td>\n",
    649        "      <td>TSLA</td>\n",
    650        "      <td>0.08</td>\n",
    651        "      <td>693.425</td>\n",
    652        "      <td>2014-05-07 19:42:15.904000</td>\n",
    653        "      <td>39840</td>\n",
    654        "    </tr>\n",
    655        "    <tr>\n",
    656        "      <th>1</th>\n",
    657        "      <td>111</td>\n",
    658        "      <td>0.42</td>\n",
    659        "      <td>0.04</td>\n",
    660        "      <td>0.211622</td>\n",
    661        "      <td>eps</td>\n",
    662        "      <td>estimize</td>\n",
    663        "      <td>0.095319</td>\n",
    664        "      <td>2014-05-07 19:08:04.281000</td>\n",
    665        "      <td>0.210714285714286</td>\n",
    666        "      <td>719.256071428571</td>\n",
    667        "      <td>0.12</td>\n",
    668        "      <td>1</td>\n",
    669        "      <td>2014</td>\n",
    670        "      <td>510c4310810f8d63ab004f49</td>\n",
    671        "      <td>Tesla Motors, Inc.</td>\n",
    672        "      <td>2014-05-07 20:00:00</td>\n",
    673        "      <td>713.0</td>\n",
    674        "      <td>TSLA</td>\n",
    675        "      <td>0.08</td>\n",
    676        "      <td>693.425</td>\n",
    677        "      <td>2014-05-07 19:28:04.281000</td>\n",
    678        "      <td>39840</td>\n",
    679        "    </tr>\n",
    680        "    <tr>\n",
    681        "      <th>2</th>\n",
    682        "      <td>110</td>\n",
    683        "      <td>0.42</td>\n",
    684        "      <td>0.04</td>\n",
    685        "      <td>0.212727</td>\n",
    686        "      <td>eps</td>\n",
    687        "      <td>estimize</td>\n",
    688        "      <td>0.095040</td>\n",
    689        "      <td>2014-05-07 18:38:08.005000</td>\n",
    690        "      <td>0.210714285714286</td>\n",
    691        "      <td>719.256071428571</td>\n",
    692        "      <td>0.12</td>\n",
    693        "      <td>1</td>\n",
    694        "      <td>2014</td>\n",
    695        "      <td>510c4310810f8d63ab004f49</td>\n",
    696        "      <td>Tesla Motors, Inc.</td>\n",
    697        "      <td>2014-05-07 20:00:00</td>\n",
    698        "      <td>713.0</td>\n",
    699        "      <td>TSLA</td>\n",
    700        "      <td>0.08</td>\n",
    701        "      <td>693.425</td>\n",
    702        "      <td>2014-05-07 18:58:08.005000</td>\n",
    703        "      <td>39840</td>\n",
    704        "    </tr>\n",
    705        "  </tbody>\n",
    706        "</table>"
    707       ],
    708       "text/plain": [
    709        "   count  high   low      mean metric    source  standard_deviation  \\\n",
    710        "0    112  0.42  0.04  0.210357    eps  estimize            0.095823   \n",
    711        "1    111  0.42  0.04  0.211622    eps  estimize            0.095319   \n",
    712        "2    110  0.42  0.04  0.212727    eps  estimize            0.095040   \n",
    713        "\n",
    714        "                   asof_date consensus_eps_estimate  \\\n",
    715        "0 2014-05-07 19:22:15.904000      0.210714285714286   \n",
    716        "1 2014-05-07 19:08:04.281000      0.210714285714286   \n",
    717        "2 2014-05-07 18:38:08.005000      0.210714285714286   \n",
    718        "\n",
    719        "  consensus_revenue_estimate  ...   fiscal_year                        id  \\\n",
    720        "0           719.256071428571  ...          2014  510c4310810f8d63ab004f49   \n",
    721        "1           719.256071428571  ...          2014  510c4310810f8d63ab004f49   \n",
    722        "2           719.256071428571  ...          2014  510c4310810f8d63ab004f49   \n",
    723        "\n",
    724        "                 name        release_date revenue symbol  \\\n",
    725        "0  Tesla Motors, Inc. 2014-05-07 20:00:00   713.0   TSLA   \n",
    726        "1  Tesla Motors, Inc. 2014-05-07 20:00:00   713.0   TSLA   \n",
    727        "2  Tesla Motors, Inc. 2014-05-07 20:00:00   713.0   TSLA   \n",
    728        "\n",
    729        "  wallstreet_eps_estimate wallstreet_revenue_estimate  \\\n",
    730        "0                    0.08                     693.425   \n",
    731        "1                    0.08                     693.425   \n",
    732        "2                    0.08                     693.425   \n",
    733        "\n",
    734        "                   timestamp    sid  \n",
    735        "0 2014-05-07 19:42:15.904000  39840  \n",
    736        "1 2014-05-07 19:28:04.281000  39840  \n",
    737        "2 2014-05-07 18:58:08.005000  39840  \n",
    738        "\n",
    739        "[3 rows x 22 columns]"
    740       ]
    741      },
    742      "execution_count": 33,
    743      "metadata": {},
    744      "output_type": "execute_result"
    745     }
    746    ],
    747    "source": [
    748     "consensus.head(3)"
    749    ]
    750   },
    751   {
    752    "cell_type": "markdown",
    753    "metadata": {},
    754    "source": [
    755     "For this quick demonstration, let's just grab the consensus mean from the `revisions_free` data set and convert it over to Pandas. Note, we need to rename the `mean` column name because it causes problems otherwise:"
    756    ]
    757   },
    758   {
    759    "cell_type": "code",
    760    "execution_count": 13,
    761    "metadata": {
    762     "collapsed": true
    763    },
    764    "outputs": [],
    765    "source": [
    766     "consensus_df = odo(consensus[['asof_date', 'mean']].sort('asof_date'), pd.DataFrame)\n",
    767     "consensus_df.rename(columns={'mean':'eps_mean'}, inplace=True)"
    768    ]
    769   },
    770   {
    771    "cell_type": "markdown",
    772    "metadata": {},
    773    "source": [
    774     "Let's chart that in the same chart again so we get a trend of the mean over time, overlayed on a chart of each individual analyst estimate:"
    775    ]
    776   },
    777   {
    778    "cell_type": "code",
    779    "execution_count": 30,
    780    "metadata": {
    781     "collapsed": false
    782    },
    783    "outputs": [
    784     {
    785      "data": {
    786       "text/plain": [
    787        "<matplotlib.legend.Legend at 0x7f6c66ba1dd0>"
    788       ]
    789      },
    790      "execution_count": 30,
    791      "metadata": {},
    792      "output_type": "execute_result"
    793     },
    794     {
    795      "data": {
    796       "image/png": 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nxowZvPvuuyxcuBCALVu2lDlWcnIyixcvZt++ffz2t7/l1VdfZdasWUyfPp2NGzcycuRI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\n7NixduvefPNNp3cMAAAAAK7Woml9dQ643Jy/oW0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A8gQAAAAAFpA8AQAAAIAFJE8AAAAA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\nc0muBxKX+gAAAABJRU5ErkJggg==\n",
    797       "text/plain": [
    798        "<matplotlib.figure.Figure at 0x7f6c66bafd50>"
    799       ]
    800      },
    801      "metadata": {},
    802      "output_type": "display_data"
    803     }
    804    ],
    805    "source": [
    806     "plt.plot(consensus_df.asof_date, consensus_df.eps_mean)\n",
    807     "plt.plot(one_q_df.asof_date, one_q_df.eps, marker='.', linestyle='None', color='r')\n",
    808     "plt.xlabel(\"As Of Date (asof_date)\")\n",
    809     "plt.ylabel(\"EPS Estimate\")\n",
    810     "plt.title(\"EPS Estimates for TSLA\")\n",
    811     "plt.legend([\"Mean Estimate\", \"Individual Estimates\"], loc=2)"
    812    ]
    813   }
    814  ],
    815  "metadata": {
    816   "kernelspec": {
    817    "display_name": "Python 2",
    818    "language": "python",
    819    "name": "python2"
    820   },
    821   "language_info": {
    822    "codemirror_mode": {
    823     "name": "ipython",
    824     "version": 2
    825    },
    826    "file_extension": ".py",
    827    "mimetype": "text/x-python",
    828    "name": "python",
    829    "nbconvert_exporter": "python",
    830    "pygments_lexer": "ipython2",
    831    "version": "2.7.10"
    832   }
    833  },
    834  "nbformat": 4,
    835  "nbformat_minor": 0
    836 }