ml-finance-python

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

(151942B)
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      9     "# CBOE SKEW Index\n",
     10     "\n",
     11     "In this notebook, we'll take a look at the CBOE SKEW Index dataset, available on the [Quantopian Store](https://www.quantopian.com/store). This dataset spans 02 Jan 2002 through the current day. This data has a daily frequency. The CBOE SKEW Index is derived from the price of S&P 500 tail risk.\n",
     12     "\n",
     13     "## Notebook Contents\n",
     14     "\n",
     15     "There are two ways to access the data and you'll find both of them listed below. Just click on the section you'd like to read through.\n",
     16     "\n",
     17     "- <a href='#interactive'><strong>Interactive overview</strong></a>: This is only available on Research and uses blaze to give you access to large amounts of data. Recommended for exploration and plotting.\n",
     18     "- <a href='#pipeline'><strong>Pipeline overview</strong></a>: Data is made available through pipeline which is available on both the Research & Backtesting environment. Recommended for custom factor development and moving back & forth between research/backtesting.\n",
     19     "\n",
     20     "\n",
     21     "### Limits\n",
     22     "One key caveat: 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",
     23     "\n",
     24     "With preamble in place, let's get started:\n",
     25     "\n",
     26     "<a id='interactive'></a>\n",
     27     "#Interactive Overview\n",
     28     "### Accessing the data with Blaze and Interactive on Research\n",
     29     "Partner datasets are available on Quantopian Research through an API service known as [Blaze](http://blaze.pydata.org). Blaze provides the Quantopian user with a convenient interface to access very large datasets, in an interactive, generic manner.\n",
     30     "\n",
     31     "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",
     32     "\n",
     33     "It is common to use Blaze to reduce your dataset in size, convert it over to Pandas and then to use Pandas for further computation, manipulation and visualization.\n",
     34     "\n",
     35     "Helpful links:\n",
     36     "* [Query building for Blaze](http://blaze.readthedocs.io/en/latest/queries.html)\n",
     37     "* [Pandas-to-Blaze dictionary](http://blaze.readthedocs.io/en/latest/rosetta-pandas.html)\n",
     38     "* [SQL-to-Blaze dictionary](http://blaze.readthedocs.io/en/latest/rosetta-sql.html).\n",
     39     "\n",
     40     "\n",
     41     "Once you've limited the size of your Blaze object, you can convert it to a Pandas DataFrames using:\n",
     42     "> `from odo import odo`  \n",
     43     "> `odo(expr, pandas.DataFrame)`\n",
     44     "\n",
     45     "\n",
     46     "###To see how this data can be used in your algorithm, search for the `Pipeline Overview` section of this notebook or head straight to <a href='#pipeline'>Pipeline Overview</a>"
     47    ]
     48   },
     49   {
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     56    "source": [
     57     "# For use in Quantopian Research, exploring interactively\n",
     58     "from quantopian.interactive.data.quandl import cboe_skew as dataset\n",
     59     "\n",
     60     "# import data operations\n",
     61     "from odo import odo\n",
     62     "# import other libraries we will use\n",
     63     "import pandas as pd"
     64    ]
     65   },
     66   {
     67    "cell_type": "code",
     68    "execution_count": 2,
     69    "metadata": {
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     73     {
     74      "data": {
     75       "text/plain": [
     76        "dshape(\"var * {skew: float64, asof_date: datetime, timestamp: datetime}\")"
     77       ]
     78      },
     79      "execution_count": 2,
     80      "metadata": {},
     81      "output_type": "execute_result"
     82     }
     83    ],
     84    "source": [
     85     "# Let's use blaze to understand the data a bit using Blaze dshape()\n",
     86     "dataset.dshape"
     87    ]
     88   },
     89   {
     90    "cell_type": "code",
     91    "execution_count": 3,
     92    "metadata": {
     93     "collapsed": false
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    110    "source": [
    111     "# And how many rows are there?\n",
    112     "# N.B. we're using a Blaze function to do this, not len()\n",
    113     "dataset.count()"
    114    ]
    115   },
    116   {
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    118    "execution_count": 4,
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    126        "<table border=\"1\" class=\"dataframe\">\n",
    127        "  <thead>\n",
    128        "    <tr style=\"text-align: right;\">\n",
    129        "      <th></th>\n",
    130        "      <th>skew</th>\n",
    131        "      <th>asof_date</th>\n",
    132        "      <th>timestamp</th>\n",
    133        "    </tr>\n",
    134        "  </thead>\n",
    135        "  <tbody>\n",
    136        "    <tr>\n",
    137        "      <th>0</th>\n",
    138        "      <td>119.67</td>\n",
    139        "      <td>2016-02-23</td>\n",
    140        "      <td>2016-02-24 12:04:15.368225</td>\n",
    141        "    </tr>\n",
    142        "    <tr>\n",
    143        "      <th>1</th>\n",
    144        "      <td>119.46</td>\n",
    145        "      <td>2016-02-24</td>\n",
    146        "      <td>2016-02-25 12:03:11.401964</td>\n",
    147        "    </tr>\n",
    148        "    <tr>\n",
    149        "      <th>2</th>\n",
    150        "      <td>122.02</td>\n",
    151        "      <td>2016-02-25</td>\n",
    152        "      <td>2016-02-26 12:01:59.815538</td>\n",
    153        "    </tr>\n",
    154        "  </tbody>\n",
    155        "</table>"
    156       ],
    157       "text/plain": [
    158        "     skew  asof_date                  timestamp\n",
    159        "0  119.67 2016-02-23 2016-02-24 12:04:15.368225\n",
    160        "1  119.46 2016-02-24 2016-02-25 12:03:11.401964\n",
    161        "2  122.02 2016-02-25 2016-02-26 12:01:59.815538"
    162       ]
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    164      "execution_count": 4,
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    169    "source": [
    170     "# Let's see what the data looks like. We'll grab the first three rows.\n",
    171     "dataset[:3]"
    172    ]
    173   },
    174   {
    175    "cell_type": "markdown",
    176    "metadata": {},
    177    "source": [
    178     "Let's go over the columns:\n",
    179     "- **skew**: value of the SKEW index for a single day\n",
    180     "- **asof_date**: the timeframe to which this data applies\n",
    181     "- **timestamp**: this is our timestamp on when we registered the data.\n",
    182     "\n",
    183     "We've done much of the data processing for you. Fields like `timestamp` are standardized across all our Store Datasets, so the datasets are easy to combine.\n",
    184     "\n",
    185     "We can select columns and rows with ease. Below, we'll do a simple plot."
    186    ]
    187   },
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    199        "<table border=\"1\" class=\"dataframe\">\n",
    200        "  <thead>\n",
    201        "    <tr style=\"text-align: right;\">\n",
    202        "      <th></th>\n",
    203        "      <th>skew</th>\n",
    204        "      <th>asof_date</th>\n",
    205        "      <th>timestamp</th>\n",
    206        "    </tr>\n",
    207        "  </thead>\n",
    208        "  <tbody>\n",
    209        "    <tr>\n",
    210        "      <th>0</th>\n",
    211        "      <td>119.67</td>\n",
    212        "      <td>2016-02-23</td>\n",
    213        "      <td>2016-02-24 12:04:15.368225</td>\n",
    214        "    </tr>\n",
    215        "    <tr>\n",
    216        "      <th>1</th>\n",
    217        "      <td>119.46</td>\n",
    218        "      <td>2016-02-24</td>\n",
    219        "      <td>2016-02-25 12:03:11.401964</td>\n",
    220        "    </tr>\n",
    221        "    <tr>\n",
    222        "      <th>2</th>\n",
    223        "      <td>122.02</td>\n",
    224        "      <td>2016-02-25</td>\n",
    225        "      <td>2016-02-26 12:01:59.815538</td>\n",
    226        "    </tr>\n",
    227        "    <tr>\n",
    228        "      <th>3</th>\n",
    229        "      <td>118.65</td>\n",
    230        "      <td>2016-02-26</td>\n",
    231        "      <td>2016-02-29 12:03:49.297200</td>\n",
    232        "    </tr>\n",
    233        "    <tr>\n",
    234        "      <th>4</th>\n",
    235        "      <td>117.68</td>\n",
    236        "      <td>2016-02-29</td>\n",
    237        "      <td>2016-03-01 12:03:06.496775</td>\n",
    238        "    </tr>\n",
    239        "  </tbody>\n",
    240        "</table>\n",
    241        "</div>"
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    243       "text/plain": [
    244        "     skew  asof_date                  timestamp\n",
    245        "0  119.67 2016-02-23 2016-02-24 12:04:15.368225\n",
    246        "1  119.46 2016-02-24 2016-02-25 12:03:11.401964\n",
    247        "2  122.02 2016-02-25 2016-02-26 12:01:59.815538\n",
    248        "3  118.65 2016-02-26 2016-02-29 12:03:49.297200\n",
    249        "4  117.68 2016-02-29 2016-03-01 12:03:06.496775"
    250       ]
    251      },
    252      "execution_count": 5,
    253      "metadata": {},
    254      "output_type": "execute_result"
    255     }
    256    ],
    257    "source": [
    258     "# Plotting this DataFrame\n",
    259     "df = odo(dataset, pd.DataFrame)\n",
    260     "df.head(5)"
    261    ]
    262   },
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    264    "cell_type": "code",
    265    "execution_count": 6,
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    273        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
    274        "<table border=\"1\" class=\"dataframe\">\n",
    275        "  <thead>\n",
    276        "    <tr style=\"text-align: right;\">\n",
    277        "      <th></th>\n",
    278        "      <th>skew</th>\n",
    279        "      <th>timestamp</th>\n",
    280        "    </tr>\n",
    281        "    <tr>\n",
    282        "      <th>asof_date</th>\n",
    283        "      <th></th>\n",
    284        "      <th></th>\n",
    285        "    </tr>\n",
    286        "  </thead>\n",
    287        "  <tbody>\n",
    288        "    <tr>\n",
    289        "      <th>2016-02-23</th>\n",
    290        "      <td>119.67</td>\n",
    291        "      <td>2016-02-24 12:04:15.368225</td>\n",
    292        "    </tr>\n",
    293        "    <tr>\n",
    294        "      <th>2016-02-24</th>\n",
    295        "      <td>119.46</td>\n",
    296        "      <td>2016-02-25 12:03:11.401964</td>\n",
    297        "    </tr>\n",
    298        "    <tr>\n",
    299        "      <th>2016-02-25</th>\n",
    300        "      <td>122.02</td>\n",
    301        "      <td>2016-02-26 12:01:59.815538</td>\n",
    302        "    </tr>\n",
    303        "    <tr>\n",
    304        "      <th>2016-02-26</th>\n",
    305        "      <td>118.65</td>\n",
    306        "      <td>2016-02-29 12:03:49.297200</td>\n",
    307        "    </tr>\n",
    308        "    <tr>\n",
    309        "      <th>2016-02-29</th>\n",
    310        "      <td>117.68</td>\n",
    311        "      <td>2016-03-01 12:03:06.496775</td>\n",
    312        "    </tr>\n",
    313        "  </tbody>\n",
    314        "</table>\n",
    315        "</div>"
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    318        "              skew                  timestamp\n",
    319        "asof_date                                    \n",
    320        "2016-02-23  119.67 2016-02-24 12:04:15.368225\n",
    321        "2016-02-24  119.46 2016-02-25 12:03:11.401964\n",
    322        "2016-02-25  122.02 2016-02-26 12:01:59.815538\n",
    323        "2016-02-26  118.65 2016-02-29 12:03:49.297200\n",
    324        "2016-02-29  117.68 2016-03-01 12:03:06.496775"
    325       ]
    326      },
    327      "execution_count": 6,
    328      "metadata": {},
    329      "output_type": "execute_result"
    330     }
    331    ],
    332    "source": [
    333     "# So we can plot it, we'll set the index as the `asof_date`\n",
    334     "df['asof_date'] = pd.to_datetime(df['asof_date'])\n",
    335     "df = df.set_index(['asof_date'])\n",
    336     "df.head(5)"
    337    ]
    338   },
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    341    "execution_count": 8,
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irbfewltvvQUAWLZsGe666y40a9ZMaCJ2++23AzA3N3v44YcxevRojBs3Tth2ZmYmjh49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\niIiIKKIwCCIiIiIioojy/wD8RZ5yb+4XewAAAABJRU5ErkJggg==\n",
    359       "text/plain": [
    360        "<matplotlib.figure.Figure at 0x7fdb80b35990>"
    361       ]
    362      },
    363      "metadata": {},
    364      "output_type": "display_data"
    365     }
    366    ],
    367    "source": [
    368     "import matplotlib.pyplot as plt\n",
    369     "df['skew'].plot(label=str(dataset))\n",
    370     "plt.ylabel(str(dataset))\n",
    371     "plt.legend()\n",
    372     "plt.title(\"Graphing %s since %s\" % (str(dataset), min(df.index)))"
    373    ]
    374   },
    375   {
    376    "cell_type": "markdown",
    377    "metadata": {},
    378    "source": [
    379     "<a id='pipeline'></a>\n",
    380     "\n",
    381     "#Pipeline Overview\n",
    382     "\n",
    383     "### Accessing the data in your algorithms & research\n",
    384     "The only method for accessing partner data within algorithms running on Quantopian is via the pipeline API. Different data sets work differently but in the case of this data, you can add this data to your pipeline as follows:\n",
    385     "\n",
    386     "Import the data set here\n",
    387     "> `from quantopian.pipeline.data.quandl import cboe_skew`\n",
    388     "\n",
    389     "Then in intialize() you could do something simple like adding the raw value of one of the fields to your pipeline:\n",
    390     "> `pipe.add(cboe_skew.open_.latest, 'open')`\n",
    391     "\n",
    392     "Pipeline usage is very similar between the backtester and Research so let's go over how to import this data through pipeline and view its outputs."
    393    ]
    394   },
    395   {
    396    "cell_type": "code",
    397    "execution_count": 1,
    398    "metadata": {
    399     "collapsed": true
    400    },
    401    "outputs": [],
    402    "source": [
    403     "# Import necessary Pipeline modules\n",
    404     "from quantopian.pipeline import Pipeline\n",
    405     "from quantopian.research import run_pipeline\n",
    406     "from quantopian.pipeline.factors import AverageDollarVolume"
    407    ]
    408   },
    409   {
    410    "cell_type": "code",
    411    "execution_count": 2,
    412    "metadata": {
    413     "collapsed": false
    414    },
    415    "outputs": [],
    416    "source": [
    417     "# Import the datasets available\n",
    418     "from quantopian.pipeline.data.quandl import cboe_skew"
    419    ]
    420   },
    421   {
    422    "cell_type": "markdown",
    423    "metadata": {},
    424    "source": [
    425     "Now that we've imported the data, let's take a look at which fields are available for each dataset.\n",
    426     "\n",
    427     "You'll find the dataset, the available fields, and the datatypes for each of those fields."
    428    ]
    429   },
    430   {
    431    "cell_type": "code",
    432    "execution_count": 3,
    433    "metadata": {
    434     "collapsed": false
    435    },
    436    "outputs": [
    437     {
    438      "name": "stdout",
    439      "output_type": "stream",
    440      "text": [
    441       "Here are the list of available fields per dataset:\n",
    442       "---------------------------------------------------\n",
    443       "\n",
    444       "Dataset: cboe_skew\n",
    445       "\n",
    446       "Fields:\n",
    447       "skew - float64\n",
    448       "\n",
    449       "\n",
    450       "---------------------------------------------------\n",
    451       "\n"
    452      ]
    453     }
    454    ],
    455    "source": [
    456     "print \"Here are the list of available fields per dataset:\"\n",
    457     "print \"---------------------------------------------------\\n\"\n",
    458     "\n",
    459     "def _print_fields(dataset):\n",
    460     "    print \"Dataset: %s\\n\" % dataset.__name__\n",
    461     "    print \"Fields:\"\n",
    462     "    for field in list(dataset.columns):\n",
    463     "        print \"%s - %s\" % (field.name, field.dtype)\n",
    464     "    print \"\\n\"\n",
    465     "\n",
    466     "_print_fields(cboe_skew)\n",
    467     "\n",
    468     "\n",
    469     "print \"---------------------------------------------------\\n\""
    470    ]
    471   },
    472   {
    473    "cell_type": "markdown",
    474    "metadata": {},
    475    "source": [
    476     "Now that we know what fields we have access to, let's see what this data looks like when we run it through Pipeline.\n",
    477     "\n",
    478     "\n",
    479     "This is constructed the same way as you would in the backtester. For more information on using Pipeline in Research view this thread:\n",
    480     "https://www.quantopian.com/posts/pipeline-in-research-build-test-and-visualize-your-factors-and-filters"
    481    ]
    482   },
    483   {
    484    "cell_type": "code",
    485    "execution_count": 4,
    486    "metadata": {
    487     "collapsed": false
    488    },
    489    "outputs": [],
    490    "source": [
    491     "pipe = Pipeline()\n",
    492     "       \n",
    493     "pipe.add(cboe_skew.skew.latest, 'skew')"
    494    ]
    495   },
    496   {
    497    "cell_type": "code",
    498    "execution_count": 5,
    499    "metadata": {
    500     "collapsed": false
    501    },
    502    "outputs": [],
    503    "source": [
    504     "# Setting some basic liquidity strings (just for good habit)\n",
    505     "dollar_volume = AverageDollarVolume(window_length=20)\n",
    506     "top_1000_most_liquid = dollar_volume.rank(ascending=False) < 1000\n",
    507     "\n",
    508     "pipe.set_screen(top_1000_most_liquid & cboe_skew.skew.latest.notnan())"
    509    ]
    510   },
    511   {
    512    "cell_type": "code",
    513    "execution_count": 6,
    514    "metadata": {
    515     "collapsed": false
    516    },
    517    "outputs": [
    518     {
    519      "data": {
    520       "image/png": 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wrRGp0qtXL8vNfu7fv69evXo5uSIAAOBsBEwgE6pevbpKlSolKfbusUxrRGo8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\n5YkfXQAAAABJRU5ErkJggg==\n",
    521       "text/plain": [
    522        "<IPython.core.display.Image object>"
    523       ]
    524      },
    525      "execution_count": 6,
    526      "metadata": {},
    527      "output_type": "execute_result"
    528     }
    529    ],
    530    "source": [
    531     "# The show_graph() method of pipeline objects produces a graph to show how it is being calculated.\n",
    532     "pipe.show_graph(format='png')"
    533    ]
    534   },
    535   {
    536    "cell_type": "code",
    537    "execution_count": 7,
    538    "metadata": {
    539     "collapsed": false
    540    },
    541    "outputs": [
    542     {
    543      "data": {
    544       "text/html": [
    545        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
    546        "<table border=\"1\" class=\"dataframe\">\n",
    547        "  <thead>\n",
    548        "    <tr style=\"text-align: right;\">\n",
    549        "      <th></th>\n",
    550        "      <th></th>\n",
    551        "      <th>skew</th>\n",
    552        "    </tr>\n",
    553        "  </thead>\n",
    554        "  <tbody>\n",
    555        "    <tr>\n",
    556        "      <th rowspan=\"30\" valign=\"top\">2013-11-01 00:00:00+00:00</th>\n",
    557        "      <th>Equity(21 [AAME])</th>\n",
    558        "      <td>127.95</td>\n",
    559        "    </tr>\n",
    560        "    <tr>\n",
    561        "      <th>Equity(25 [AA_PR])</th>\n",
    562        "      <td>127.95</td>\n",
    563        "    </tr>\n",
    564        "    <tr>\n",
    565        "      <th>Equity(117 [AEY])</th>\n",
    566        "      <td>127.95</td>\n",
    567        "    </tr>\n",
    568        "    <tr>\n",
    569        "      <th>Equity(225 [AHPI])</th>\n",
    570        "      <td>127.95</td>\n",
    571        "    </tr>\n",
    572        "    <tr>\n",
    573        "      <th>Equity(312 [ALOT])</th>\n",
    574        "      <td>127.95</td>\n",
    575        "    </tr>\n",
    576        "    <tr>\n",
    577        "      <th>Equity(392 [AMS])</th>\n",
    578        "      <td>127.95</td>\n",
    579        "    </tr>\n",
    580        "    <tr>\n",
    581        "      <th>Equity(468 [API])</th>\n",
    582        "      <td>127.95</td>\n",
    583        "    </tr>\n",
    584        "    <tr>\n",
    585        "      <th>Equity(548 [ASBI])</th>\n",
    586        "      <td>127.95</td>\n",
    587        "    </tr>\n",
    588        "    <tr>\n",
    589        "      <th>Equity(717 [BAMM])</th>\n",
    590        "      <td>127.95</td>\n",
    591        "    </tr>\n",
    592        "    <tr>\n",
    593        "      <th>Equity(790 [BDL])</th>\n",
    594        "      <td>127.95</td>\n",
    595        "    </tr>\n",
    596        "    <tr>\n",
    597        "      <th>Equity(880 [BIO_B])</th>\n",
    598        "      <td>127.95</td>\n",
    599        "    </tr>\n",
    600        "    <tr>\n",
    601        "      <th>Equity(925 [BKSC])</th>\n",
    602        "      <td>127.95</td>\n",
    603        "    </tr>\n",
    604        "    <tr>\n",
    605        "      <th>Equity(1088 [BRID])</th>\n",
    606        "      <td>127.95</td>\n",
    607        "    </tr>\n",
    608        "    <tr>\n",
    609        "      <th>Equity(1095 [BRN])</th>\n",
    610        "      <td>127.95</td>\n",
    611        "    </tr>\n",
    612        "    <tr>\n",
    613        "      <th>Equity(1157 [BTUI])</th>\n",
    614        "      <td>127.95</td>\n",
    615        "    </tr>\n",
    616        "    <tr>\n",
    617        "      <th>Equity(1190 [BWIN_A])</th>\n",
    618        "      <td>127.95</td>\n",
    619        "    </tr>\n",
    620        "    <tr>\n",
    621        "      <th>Equity(1193 [BWL_A])</th>\n",
    622        "      <td>127.95</td>\n",
    623        "    </tr>\n",
    624        "    <tr>\n",
    625        "      <th>Equity(1323 [CAW])</th>\n",
    626        "      <td>127.95</td>\n",
    627        "    </tr>\n",
    628        "    <tr>\n",
    629        "      <th>Equity(1653 [MOC])</th>\n",
    630        "      <td>127.95</td>\n",
    631        "    </tr>\n",
    632        "    <tr>\n",
    633        "      <th>Equity(1668 [CMS_PRB])</th>\n",
    634        "      <td>127.95</td>\n",
    635        "    </tr>\n",
    636        "    <tr>\n",
    637        "      <th>Equity(1988 [CUO])</th>\n",
    638        "      <td>127.95</td>\n",
    639        "    </tr>\n",
    640        "    <tr>\n",
    641        "      <th>Equity(2078 [DAIO])</th>\n",
    642        "      <td>127.95</td>\n",
    643        "    </tr>\n",
    644        "    <tr>\n",
    645        "      <th>Equity(2103 [ESCR])</th>\n",
    646        "      <td>127.95</td>\n",
    647        "    </tr>\n",
    648        "    <tr>\n",
    649        "      <th>Equity(2124 [DD_PRA])</th>\n",
    650        "      <td>127.95</td>\n",
    651        "    </tr>\n",
    652        "    <tr>\n",
    653        "      <th>Equity(2209 [DGSE])</th>\n",
    654        "      <td>127.95</td>\n",
    655        "    </tr>\n",
    656        "    <tr>\n",
    657        "      <th>Equity(2292 [DRCO])</th>\n",
    658        "      <td>127.95</td>\n",
    659        "    </tr>\n",
    660        "    <tr>\n",
    661        "      <th>Equity(2344 [DRAM])</th>\n",
    662        "      <td>127.95</td>\n",
    663        "    </tr>\n",
    664        "    <tr>\n",
    665        "      <th>Equity(2382 [DXR])</th>\n",
    666        "      <td>127.95</td>\n",
    667        "    </tr>\n",
    668        "    <tr>\n",
    669        "      <th>Equity(2389 [COBR])</th>\n",
    670        "      <td>127.95</td>\n",
    671        "    </tr>\n",
    672        "    <tr>\n",
    673        "      <th>Equity(2391 [DYNT])</th>\n",
    674        "      <td>127.95</td>\n",
    675        "    </tr>\n",
    676        "    <tr>\n",
    677        "      <th>...</th>\n",
    678        "      <th>...</th>\n",
    679        "      <td>...</td>\n",
    680        "    </tr>\n",
    681        "    <tr>\n",
    682        "      <th rowspan=\"30\" valign=\"top\">2013-11-25 00:00:00+00:00</th>\n",
    683        "      <th>Equity(45179 [ERW])</th>\n",
    684        "      <td>133.76</td>\n",
    685        "    </tr>\n",
    686        "    <tr>\n",
    687        "      <th>Equity(45195 [LGL_WS])</th>\n",
    688        "      <td>133.76</td>\n",
    689        "    </tr>\n",
    690        "    <tr>\n",
    691        "      <th>Equity(45203 [NASH])</th>\n",
    692        "      <td>133.76</td>\n",
    693        "    </tr>\n",
    694        "    <tr>\n",
    695        "      <th>Equity(45222 [QPAC_U])</th>\n",
    696        "      <td>133.76</td>\n",
    697        "    </tr>\n",
    698        "    <tr>\n",
    699        "      <th>Equity(45240 [INTL_L])</th>\n",
    700        "      <td>133.76</td>\n",
    701        "    </tr>\n",
    702        "    <tr>\n",
    703        "      <th>Equity(45270 [TIPT])</th>\n",
    704        "      <td>133.76</td>\n",
    705        "    </tr>\n",
    706        "    <tr>\n",
    707        "      <th>Equity(45288 [EMHD])</th>\n",
    708        "      <td>133.76</td>\n",
    709        "    </tr>\n",
    710        "    <tr>\n",
    711        "      <th>Equity(45301 [TRC_WS])</th>\n",
    712        "      <td>133.76</td>\n",
    713        "    </tr>\n",
    714        "    <tr>\n",
    715        "      <th>Equity(45390 [CPXX])</th>\n",
    716        "      <td>133.76</td>\n",
    717        "    </tr>\n",
    718        "    <tr>\n",
    719        "      <th>Equity(45412 [EAGL])</th>\n",
    720        "      <td>133.76</td>\n",
    721        "    </tr>\n",
    722        "    <tr>\n",
    723        "      <th>Equity(45414 [EAGL_W])</th>\n",
    724        "      <td>133.76</td>\n",
    725        "    </tr>\n",
    726        "    <tr>\n",
    727        "      <th>Equity(45420 [ROIQ_U])</th>\n",
    728        "      <td>133.76</td>\n",
    729        "    </tr>\n",
    730        "    <tr>\n",
    731        "      <th>Equity(45432 [SPCB])</th>\n",
    732        "      <td>133.76</td>\n",
    733        "    </tr>\n",
    734        "    <tr>\n",
    735        "      <th>Equity(45510 [MLPC])</th>\n",
    736        "      <td>133.76</td>\n",
    737        "    </tr>\n",
    738        "    <tr>\n",
    739        "      <th>Equity(45524 [NVEE])</th>\n",
    740        "      <td>133.76</td>\n",
    741        "    </tr>\n",
    742        "    <tr>\n",
    743        "      <th>Equity(45525 [NVEE_W])</th>\n",
    744        "      <td>133.76</td>\n",
    745        "    </tr>\n",
    746        "    <tr>\n",
    747        "      <th>Equity(45527 [JASN])</th>\n",
    748        "      <td>133.76</td>\n",
    749        "    </tr>\n",
    750        "    <tr>\n",
    751        "      <th>Equity(45536 [JASN_W])</th>\n",
    752        "      <td>133.76</td>\n",
    753        "    </tr>\n",
    754        "    <tr>\n",
    755        "      <th>Equity(45562 [ESBA])</th>\n",
    756        "      <td>133.76</td>\n",
    757        "    </tr>\n",
    758        "    <tr>\n",
    759        "      <th>Equity(45563 [OGCP])</th>\n",
    760        "      <td>133.76</td>\n",
    761        "    </tr>\n",
    762        "    <tr>\n",
    763        "      <th>Equity(45564 [FISK])</th>\n",
    764        "      <td>133.76</td>\n",
    765        "    </tr>\n",
    766        "    <tr>\n",
    767        "      <th>Equity(45646 [CHNA])</th>\n",
    768        "      <td>133.76</td>\n",
    769        "    </tr>\n",
    770        "    <tr>\n",
    771        "      <th>Equity(45678 [SLQD])</th>\n",
    772        "      <td>133.76</td>\n",
    773        "    </tr>\n",
    774        "    <tr>\n",
    775        "      <th>Equity(45680 [ADXS_W])</th>\n",
    776        "      <td>133.76</td>\n",
    777        "    </tr>\n",
    778        "    <tr>\n",
    779        "      <th>Equity(45717 [FTGC])</th>\n",
    780        "      <td>133.76</td>\n",
    781        "    </tr>\n",
    782        "    <tr>\n",
    783        "      <th>Equity(45768 [KODK_WS])</th>\n",
    784        "      <td>133.76</td>\n",
    785        "    </tr>\n",
    786        "    <tr>\n",
    787        "      <th>Equity(45792 [FTSD])</th>\n",
    788        "      <td>133.76</td>\n",
    789        "    </tr>\n",
    790        "    <tr>\n",
    791        "      <th>Equity(45824 [ROIQ_W])</th>\n",
    792        "      <td>133.76</td>\n",
    793        "    </tr>\n",
    794        "    <tr>\n",
    795        "      <th>Equity(45854 [PGAL])</th>\n",
    796        "      <td>133.76</td>\n",
    797        "    </tr>\n",
    798        "    <tr>\n",
    799        "      <th>Equity(45895 [EMSH])</th>\n",
    800        "      <td>133.76</td>\n",
    801        "    </tr>\n",
    802        "  </tbody>\n",
    803        "</table>\n",
    804        "<p>16983 rows × 1 columns</p>\n",
    805        "</div>"
    806       ],
    807       "text/plain": [
    808        "                                                     skew\n",
    809        "2013-11-01 00:00:00+00:00 Equity(21 [AAME])        127.95\n",
    810        "                          Equity(25 [AA_PR])       127.95\n",
    811        "                          Equity(117 [AEY])        127.95\n",
    812        "                          Equity(225 [AHPI])       127.95\n",
    813        "                          Equity(312 [ALOT])       127.95\n",
    814        "                          Equity(392 [AMS])        127.95\n",
    815        "                          Equity(468 [API])        127.95\n",
    816        "                          Equity(548 [ASBI])       127.95\n",
    817        "                          Equity(717 [BAMM])       127.95\n",
    818        "                          Equity(790 [BDL])        127.95\n",
    819        "                          Equity(880 [BIO_B])      127.95\n",
    820        "                          Equity(925 [BKSC])       127.95\n",
    821        "                          Equity(1088 [BRID])      127.95\n",
    822        "                          Equity(1095 [BRN])       127.95\n",
    823        "                          Equity(1157 [BTUI])      127.95\n",
    824        "                          Equity(1190 [BWIN_A])    127.95\n",
    825        "                          Equity(1193 [BWL_A])     127.95\n",
    826        "                          Equity(1323 [CAW])       127.95\n",
    827        "                          Equity(1653 [MOC])       127.95\n",
    828        "                          Equity(1668 [CMS_PRB])   127.95\n",
    829        "                          Equity(1988 [CUO])       127.95\n",
    830        "                          Equity(2078 [DAIO])      127.95\n",
    831        "                          Equity(2103 [ESCR])      127.95\n",
    832        "                          Equity(2124 [DD_PRA])    127.95\n",
    833        "                          Equity(2209 [DGSE])      127.95\n",
    834        "                          Equity(2292 [DRCO])      127.95\n",
    835        "                          Equity(2344 [DRAM])      127.95\n",
    836        "                          Equity(2382 [DXR])       127.95\n",
    837        "                          Equity(2389 [COBR])      127.95\n",
    838        "                          Equity(2391 [DYNT])      127.95\n",
    839        "...                                                   ...\n",
    840        "2013-11-25 00:00:00+00:00 Equity(45179 [ERW])      133.76\n",
    841        "                          Equity(45195 [LGL_WS])   133.76\n",
    842        "                          Equity(45203 [NASH])     133.76\n",
    843        "                          Equity(45222 [QPAC_U])   133.76\n",
    844        "                          Equity(45240 [INTL_L])   133.76\n",
    845        "                          Equity(45270 [TIPT])     133.76\n",
    846        "                          Equity(45288 [EMHD])     133.76\n",
    847        "                          Equity(45301 [TRC_WS])   133.76\n",
    848        "                          Equity(45390 [CPXX])     133.76\n",
    849        "                          Equity(45412 [EAGL])     133.76\n",
    850        "                          Equity(45414 [EAGL_W])   133.76\n",
    851        "                          Equity(45420 [ROIQ_U])   133.76\n",
    852        "                          Equity(45432 [SPCB])     133.76\n",
    853        "                          Equity(45510 [MLPC])     133.76\n",
    854        "                          Equity(45524 [NVEE])     133.76\n",
    855        "                          Equity(45525 [NVEE_W])   133.76\n",
    856        "                          Equity(45527 [JASN])     133.76\n",
    857        "                          Equity(45536 [JASN_W])   133.76\n",
    858        "                          Equity(45562 [ESBA])     133.76\n",
    859        "                          Equity(45563 [OGCP])     133.76\n",
    860        "                          Equity(45564 [FISK])     133.76\n",
    861        "                          Equity(45646 [CHNA])     133.76\n",
    862        "                          Equity(45678 [SLQD])     133.76\n",
    863        "                          Equity(45680 [ADXS_W])   133.76\n",
    864        "                          Equity(45717 [FTGC])     133.76\n",
    865        "                          Equity(45768 [KODK_WS])  133.76\n",
    866        "                          Equity(45792 [FTSD])     133.76\n",
    867        "                          Equity(45824 [ROIQ_W])   133.76\n",
    868        "                          Equity(45854 [PGAL])     133.76\n",
    869        "                          Equity(45895 [EMSH])     133.76\n",
    870        "\n",
    871        "[16983 rows x 1 columns]"
    872       ]
    873      },
    874      "execution_count": 7,
    875      "metadata": {},
    876      "output_type": "execute_result"
    877     }
    878    ],
    879    "source": [
    880     "# run_pipeline will show the output of your pipeline\n",
    881     "pipe_output = run_pipeline(pipe, start_date='2013-11-01', end_date='2013-11-25')\n",
    882     "pipe_output"
    883    ]
    884   },
    885   {
    886    "cell_type": "markdown",
    887    "metadata": {},
    888    "source": [
    889     "Here, you'll notice that each security is mapped to the corresponding value, so you could grab any security to get what you need.\n",
    890     "\n",
    891     "Taking what we've seen from above, let's see how we'd move that into the backtester."
    892    ]
    893   },
    894   {
    895    "cell_type": "code",
    896    "execution_count": 17,
    897    "metadata": {
    898     "collapsed": false
    899    },
    900    "outputs": [],
    901    "source": [
    902     "# This section is only importable in the backtester\n",
    903     "from quantopian.algorithm import attach_pipeline, pipeline_output\n",
    904     "\n",
    905     "# General pipeline imports\n",
    906     "from quantopian.pipeline import Pipeline\n",
    907     "from quantopian.pipeline.factors import AverageDollarVolume\n",
    908     "\n",
    909     "# For use in your algorithms via the pipeline API\n",
    910     "from quantopian.pipeline.data.quandl import cboe_skew\n",
    911     "\n",
    912     "def make_pipeline():\n",
    913     "    # Create our pipeline\n",
    914     "    pipe = Pipeline()\n",
    915     "    \n",
    916     "    # Screen out penny stocks and low liquidity securities.\n",
    917     "    dollar_volume = AverageDollarVolume(window_length=20)\n",
    918     "    is_liquid = dollar_volume.rank(ascending=False) < 1000\n",
    919     "    \n",
    920     "    # Create the mask that we will use for our percentile methods.\n",
    921     "    base_universe = (is_liquid)\n",
    922     "\n",
    923     "    # Add the datasets available\n",
    924     "    pipe.add(cboe_skew.skew.latest, 'skew')\n",
    925     "\n",
    926     "    # Set our pipeline screens\n",
    927     "    pipe.set_screen(is_liquid)\n",
    928     "    return pipe\n",
    929     "\n",
    930     "def initialize(context):\n",
    931     "    attach_pipeline(make_pipeline(), \"pipeline\")\n",
    932     "    \n",
    933     "def before_trading_start(context, data):\n",
    934     "    results = pipeline_output('pipeline')"
    935    ]
    936   },
    937   {
    938    "cell_type": "markdown",
    939    "metadata": {},
    940    "source": [
    941     "Now you can take that and begin to use it as a building block for your algorithms, for more examples on how to do that you can visit our <a href='https://www.quantopian.com/posts/pipeline-factor-library-for-data'>data pipeline factor library</a>"
    942    ]
    943   }
    944  ],
    945  "metadata": {
    946   "kernelspec": {
    947    "display_name": "Python 2",
    948    "language": "python",
    949    "name": "python2"
    950   },
    951   "language_info": {
    952    "codemirror_mode": {
    953     "name": "ipython",
    954     "version": 2
    955    },
    956    "file_extension": ".py",
    957    "mimetype": "text/x-python",
    958    "name": "python",
    959    "nbconvert_exporter": "python",
    960    "pygments_lexer": "ipython2",
    961    "version": "2.7.12"
    962   }
    963  },
    964  "nbformat": 4,
    965  "nbformat_minor": 0
    966 }