ml-finance-python

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

(154698B)
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      9     "# CBOE VVIX Index\n",
     10     "\n",
     11     "In this notebook, we'll take a look at the CBOE VVIX Index dataset, available on the [Quantopian Store](https://www.quantopian.com/store). This dataset spans 03 Jan 2007 through the current day, and documents the expected volatility of the 30-day forward price of the VIX. Calculated by CBOE, provided by Quandl.\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   {
     50    "cell_type": "code",
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     56    "source": [
     57     "# For use in Quantopian Research, exploring interactively\n",
     58     "from quantopian.interactive.data.quandl import cboe_vvix 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": {
     70     "collapsed": false
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     72    "outputs": [
     73     {
     74      "data": {
     75       "text/plain": [
     76        "dshape(\"var * {vvix: 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   {
    117    "cell_type": "code",
    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>vvix</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>89.67</td>\n",
    139        "      <td>2016-02-23</td>\n",
    140        "      <td>2016-02-24 12:00:26.399143</td>\n",
    141        "    </tr>\n",
    142        "    <tr>\n",
    143        "      <th>1</th>\n",
    144        "      <td>86.74</td>\n",
    145        "      <td>2016-02-24</td>\n",
    146        "      <td>2016-02-25 12:00:47.967020</td>\n",
    147        "    </tr>\n",
    148        "    <tr>\n",
    149        "      <th>2</th>\n",
    150        "      <td>83.51</td>\n",
    151        "      <td>2016-02-25</td>\n",
    152        "      <td>2016-02-26 12:01:40.620973</td>\n",
    153        "    </tr>\n",
    154        "  </tbody>\n",
    155        "</table>"
    156       ],
    157       "text/plain": [
    158        "    vvix  asof_date                  timestamp\n",
    159        "0  89.67 2016-02-23 2016-02-24 12:00:26.399143\n",
    160        "1  86.74 2016-02-24 2016-02-25 12:00:47.967020\n",
    161        "2  83.51 2016-02-25 2016-02-26 12:01:40.620973"
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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     "- **vvix**: volatility of VIX\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 of VVIX since 2007."
    186    ]
    187   },
    188   {
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    190    "execution_count": 7,
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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>vvix</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>89.67</td>\n",
    212        "      <td>2016-02-23</td>\n",
    213        "      <td>2016-02-24 12:00:26.399143</td>\n",
    214        "    </tr>\n",
    215        "    <tr>\n",
    216        "      <th>1</th>\n",
    217        "      <td>86.74</td>\n",
    218        "      <td>2016-02-24</td>\n",
    219        "      <td>2016-02-25 12:00:47.967020</td>\n",
    220        "    </tr>\n",
    221        "    <tr>\n",
    222        "      <th>2</th>\n",
    223        "      <td>83.51</td>\n",
    224        "      <td>2016-02-25</td>\n",
    225        "      <td>2016-02-26 12:01:40.620973</td>\n",
    226        "    </tr>\n",
    227        "    <tr>\n",
    228        "      <th>3</th>\n",
    229        "      <td>83.72</td>\n",
    230        "      <td>2016-02-26</td>\n",
    231        "      <td>2016-02-29 12:00:32.099177</td>\n",
    232        "    </tr>\n",
    233        "    <tr>\n",
    234        "      <th>4</th>\n",
    235        "      <td>85.06</td>\n",
    236        "      <td>2016-02-29</td>\n",
    237        "      <td>2016-03-01 12:01:19.224513</td>\n",
    238        "    </tr>\n",
    239        "  </tbody>\n",
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    243       "text/plain": [
    244        "    vvix  asof_date                  timestamp\n",
    245        "0  89.67 2016-02-23 2016-02-24 12:00:26.399143\n",
    246        "1  86.74 2016-02-24 2016-02-25 12:00:47.967020\n",
    247        "2  83.51 2016-02-25 2016-02-26 12:01:40.620973\n",
    248        "3  83.72 2016-02-26 2016-02-29 12:00:32.099177\n",
    249        "4  85.06 2016-02-29 2016-03-01 12:01:19.224513"
    250       ]
    251      },
    252      "execution_count": 7,
    253      "metadata": {},
    254      "output_type": "execute_result"
    255     }
    256    ],
    257    "source": [
    258     "# Plotting this DataFrame since 2007\n",
    259     "df = odo(dataset, pd.DataFrame)\n",
    260     "df.head(5)"
    261    ]
    262   },
    263   {
    264    "cell_type": "code",
    265    "execution_count": 8,
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    275        "  <thead>\n",
    276        "    <tr style=\"text-align: right;\">\n",
    277        "      <th></th>\n",
    278        "      <th>vvix</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>89.67</td>\n",
    291        "      <td>2016-02-24 12:00:26.399143</td>\n",
    292        "    </tr>\n",
    293        "    <tr>\n",
    294        "      <th>2016-02-24</th>\n",
    295        "      <td>86.74</td>\n",
    296        "      <td>2016-02-25 12:00:47.967020</td>\n",
    297        "    </tr>\n",
    298        "    <tr>\n",
    299        "      <th>2016-02-25</th>\n",
    300        "      <td>83.51</td>\n",
    301        "      <td>2016-02-26 12:01:40.620973</td>\n",
    302        "    </tr>\n",
    303        "    <tr>\n",
    304        "      <th>2016-02-26</th>\n",
    305        "      <td>83.72</td>\n",
    306        "      <td>2016-02-29 12:00:32.099177</td>\n",
    307        "    </tr>\n",
    308        "    <tr>\n",
    309        "      <th>2016-02-29</th>\n",
    310        "      <td>85.06</td>\n",
    311        "      <td>2016-03-01 12:01:19.224513</td>\n",
    312        "    </tr>\n",
    313        "  </tbody>\n",
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    317       "text/plain": [
    318        "             vvix                  timestamp\n",
    319        "asof_date                                   \n",
    320        "2016-02-23  89.67 2016-02-24 12:00:26.399143\n",
    321        "2016-02-24  86.74 2016-02-25 12:00:47.967020\n",
    322        "2016-02-25  83.51 2016-02-26 12:01:40.620973\n",
    323        "2016-02-26  83.72 2016-02-29 12:00:32.099177\n",
    324        "2016-02-29  85.06 2016-03-01 12:01:19.224513"
    325       ]
    326      },
    327      "execution_count": 8,
    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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    340    "cell_type": "code",
    341    "execution_count": 16,
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zq+m9zt/P2Lx5c83X3XTTTWjZsqWifadPn0bjxo0VnQxEFN4YBBFRwMXHx+O5557Dq6++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\nv2HDhoA1ykhmunj1SodTtS0u1gHIMuAEk+1okUz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ZOuoEiZmgnkpIUHhmgpgDAxj1QYsW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\nRU5ErkJggg==\n",
    359       "text/plain": [
    360        "<matplotlib.figure.Figure at 0x7f03a4284f10>"
    361       ]
    362      },
    363      "metadata": {},
    364      "output_type": "display_data"
    365     }
    366    ],
    367    "source": [
    368     "# Plotting the VVIX\n",
    369     "import matplotlib.pyplot as plt\n",
    370     "df.vvix.plot(label=str(dataset))\n",
    371     "plt.ylabel(str(dataset))\n",
    372     "plt.legend()\n",
    373     "plt.title(\"Graphing %s since %s\" % (str(dataset), min(df.index)))"
    374    ]
    375   },
    376   {
    377    "cell_type": "markdown",
    378    "metadata": {},
    379    "source": [
    380     "<a id='pipeline'></a>\n",
    381     "\n",
    382     "#Pipeline Overview\n",
    383     "\n",
    384     "### Accessing the data in your algorithms & research\n",
    385     "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",
    386     "\n",
    387     "Import the data set here\n",
    388     "> `from quantopian.pipeline.data.quandl import cboe_vvix`\n",
    389     "\n",
    390     "Then in intialize() you could do something simple like adding the raw value of one of the fields to your pipeline:\n",
    391     "> `pipe.add(cboe_vvix.vvix.latest, 'vvix')`\n",
    392     "\n",
    393     "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."
    394    ]
    395   },
    396   {
    397    "cell_type": "code",
    398    "execution_count": 20,
    399    "metadata": {
    400     "collapsed": true
    401    },
    402    "outputs": [],
    403    "source": [
    404     "# Import necessary Pipeline modules\n",
    405     "from quantopian.pipeline import Pipeline\n",
    406     "from quantopian.research import run_pipeline\n",
    407     "from quantopian.pipeline.factors import AverageDollarVolume"
    408    ]
    409   },
    410   {
    411    "cell_type": "code",
    412    "execution_count": 17,
    413    "metadata": {
    414     "collapsed": false
    415    },
    416    "outputs": [],
    417    "source": [
    418     "# Import the datasets available\n",
    419     "from quantopian.pipeline.data.quandl import cboe_vvix"
    420    ]
    421   },
    422   {
    423    "cell_type": "markdown",
    424    "metadata": {},
    425    "source": [
    426     "Now that we've imported the data, let's take a look at which fields are available for each dataset.\n",
    427     "\n",
    428     "You'll find the dataset, the available fields, and the datatypes for each of those fields."
    429    ]
    430   },
    431   {
    432    "cell_type": "code",
    433    "execution_count": 18,
    434    "metadata": {
    435     "collapsed": false
    436    },
    437    "outputs": [
    438     {
    439      "name": "stdout",
    440      "output_type": "stream",
    441      "text": [
    442       "Here are the list of available fields per dataset:\n",
    443       "---------------------------------------------------\n",
    444       "\n",
    445       "Dataset: cboe_vvix\n",
    446       "\n",
    447       "Fields:\n",
    448       "vvix - float64\n",
    449       "\n",
    450       "\n",
    451       "---------------------------------------------------\n",
    452       "\n"
    453      ]
    454     }
    455    ],
    456    "source": [
    457     "print \"Here are the list of available fields per dataset:\"\n",
    458     "print \"---------------------------------------------------\\n\"\n",
    459     "\n",
    460     "def _print_fields(dataset):\n",
    461     "    print \"Dataset: %s\\n\" % dataset.__name__\n",
    462     "    print \"Fields:\"\n",
    463     "    for field in list(dataset.columns):\n",
    464     "        print \"%s - %s\" % (field.name, field.dtype)\n",
    465     "    print \"\\n\"\n",
    466     "\n",
    467     "_print_fields(cboe_vvix)\n",
    468     "\n",
    469     "\n",
    470     "print \"---------------------------------------------------\\n\""
    471    ]
    472   },
    473   {
    474    "cell_type": "markdown",
    475    "metadata": {},
    476    "source": [
    477     "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",
    478     "\n",
    479     "\n",
    480     "This is constructed the same way as you would in the backtester. For more information on using Pipeline in Research view this thread:\n",
    481     "https://www.quantopian.com/posts/pipeline-in-research-build-test-and-visualize-your-factors-and-filters"
    482    ]
    483   },
    484   {
    485    "cell_type": "code",
    486    "execution_count": 21,
    487    "metadata": {
    488     "collapsed": false
    489    },
    490    "outputs": [],
    491    "source": [
    492     "pipe = Pipeline()\n",
    493     "       \n",
    494     "pipe.add(cboe_vvix.vvix.latest, 'vvix')"
    495    ]
    496   },
    497   {
    498    "cell_type": "code",
    499    "execution_count": 22,
    500    "metadata": {
    501     "collapsed": false
    502    },
    503    "outputs": [],
    504    "source": [
    505     "# Setting some basic liquidity strings (just for good habit)\n",
    506     "dollar_volume = AverageDollarVolume(window_length=20)\n",
    507     "top_1000_most_liquid = dollar_volume.rank(ascending=False) < 1000\n",
    508     "\n",
    509     "pipe.set_screen(top_1000_most_liquid & cboe_vvix.vvix.latest.notnan())"
    510    ]
    511   },
    512   {
    513    "cell_type": "code",
    514    "execution_count": 23,
    515    "metadata": {
    516     "collapsed": false
    517    },
    518    "outputs": [
    519     {
    520      "data": {
    521       "image/png": 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25eOVHGN7SsT4kniML/+x1/Nkj9ptGXc61a0tSeoxZbqK9ntJL834Uit27VHOzJkt9pXa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\nfAAAAABJRU5ErkJggg==\n",
    522       "text/plain": [
    523        "<IPython.core.display.Image object>"
    524       ]
    525      },
    526      "execution_count": 23,
    527      "metadata": {},
    528      "output_type": "execute_result"
    529     }
    530    ],
    531    "source": [
    532     "# The show_graph() method of pipeline objects produces a graph to show how it is being calculated.\n",
    533     "pipe.show_graph(format='png')"
    534    ]
    535   },
    536   {
    537    "cell_type": "code",
    538    "execution_count": 24,
    539    "metadata": {
    540     "collapsed": false
    541    },
    542    "outputs": [
    543     {
    544      "data": {
    545       "text/html": [
    546        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
    547        "<table border=\"1\" class=\"dataframe\">\n",
    548        "  <thead>\n",
    549        "    <tr style=\"text-align: right;\">\n",
    550        "      <th></th>\n",
    551        "      <th></th>\n",
    552        "      <th>vvix</th>\n",
    553        "    </tr>\n",
    554        "  </thead>\n",
    555        "  <tbody>\n",
    556        "    <tr>\n",
    557        "      <th rowspan=\"30\" valign=\"top\">2013-11-01 00:00:00+00:00</th>\n",
    558        "      <th>Equity(21 [AAME])</th>\n",
    559        "      <td>69.56</td>\n",
    560        "    </tr>\n",
    561        "    <tr>\n",
    562        "      <th>Equity(25 [AA_PR])</th>\n",
    563        "      <td>69.56</td>\n",
    564        "    </tr>\n",
    565        "    <tr>\n",
    566        "      <th>Equity(117 [AEY])</th>\n",
    567        "      <td>69.56</td>\n",
    568        "    </tr>\n",
    569        "    <tr>\n",
    570        "      <th>Equity(225 [AHPI])</th>\n",
    571        "      <td>69.56</td>\n",
    572        "    </tr>\n",
    573        "    <tr>\n",
    574        "      <th>Equity(312 [ALOT])</th>\n",
    575        "      <td>69.56</td>\n",
    576        "    </tr>\n",
    577        "    <tr>\n",
    578        "      <th>Equity(392 [AMS])</th>\n",
    579        "      <td>69.56</td>\n",
    580        "    </tr>\n",
    581        "    <tr>\n",
    582        "      <th>Equity(468 [API])</th>\n",
    583        "      <td>69.56</td>\n",
    584        "    </tr>\n",
    585        "    <tr>\n",
    586        "      <th>Equity(548 [ASBI])</th>\n",
    587        "      <td>69.56</td>\n",
    588        "    </tr>\n",
    589        "    <tr>\n",
    590        "      <th>Equity(717 [BAMM])</th>\n",
    591        "      <td>69.56</td>\n",
    592        "    </tr>\n",
    593        "    <tr>\n",
    594        "      <th>Equity(790 [BDL])</th>\n",
    595        "      <td>69.56</td>\n",
    596        "    </tr>\n",
    597        "    <tr>\n",
    598        "      <th>Equity(880 [BIO_B])</th>\n",
    599        "      <td>69.56</td>\n",
    600        "    </tr>\n",
    601        "    <tr>\n",
    602        "      <th>Equity(925 [BKSC])</th>\n",
    603        "      <td>69.56</td>\n",
    604        "    </tr>\n",
    605        "    <tr>\n",
    606        "      <th>Equity(1088 [BRID])</th>\n",
    607        "      <td>69.56</td>\n",
    608        "    </tr>\n",
    609        "    <tr>\n",
    610        "      <th>Equity(1095 [BRN])</th>\n",
    611        "      <td>69.56</td>\n",
    612        "    </tr>\n",
    613        "    <tr>\n",
    614        "      <th>Equity(1157 [BTUI])</th>\n",
    615        "      <td>69.56</td>\n",
    616        "    </tr>\n",
    617        "    <tr>\n",
    618        "      <th>Equity(1190 [BWIN_A])</th>\n",
    619        "      <td>69.56</td>\n",
    620        "    </tr>\n",
    621        "    <tr>\n",
    622        "      <th>Equity(1193 [BWL_A])</th>\n",
    623        "      <td>69.56</td>\n",
    624        "    </tr>\n",
    625        "    <tr>\n",
    626        "      <th>Equity(1323 [CAW])</th>\n",
    627        "      <td>69.56</td>\n",
    628        "    </tr>\n",
    629        "    <tr>\n",
    630        "      <th>Equity(1653 [MOC])</th>\n",
    631        "      <td>69.56</td>\n",
    632        "    </tr>\n",
    633        "    <tr>\n",
    634        "      <th>Equity(1668 [CMS_PRB])</th>\n",
    635        "      <td>69.56</td>\n",
    636        "    </tr>\n",
    637        "    <tr>\n",
    638        "      <th>Equity(1988 [CUO])</th>\n",
    639        "      <td>69.56</td>\n",
    640        "    </tr>\n",
    641        "    <tr>\n",
    642        "      <th>Equity(2078 [DAIO])</th>\n",
    643        "      <td>69.56</td>\n",
    644        "    </tr>\n",
    645        "    <tr>\n",
    646        "      <th>Equity(2103 [ESCR])</th>\n",
    647        "      <td>69.56</td>\n",
    648        "    </tr>\n",
    649        "    <tr>\n",
    650        "      <th>Equity(2124 [DD_PRA])</th>\n",
    651        "      <td>69.56</td>\n",
    652        "    </tr>\n",
    653        "    <tr>\n",
    654        "      <th>Equity(2209 [DGSE])</th>\n",
    655        "      <td>69.56</td>\n",
    656        "    </tr>\n",
    657        "    <tr>\n",
    658        "      <th>Equity(2292 [DRCO])</th>\n",
    659        "      <td>69.56</td>\n",
    660        "    </tr>\n",
    661        "    <tr>\n",
    662        "      <th>Equity(2344 [DRAM])</th>\n",
    663        "      <td>69.56</td>\n",
    664        "    </tr>\n",
    665        "    <tr>\n",
    666        "      <th>Equity(2382 [DXR])</th>\n",
    667        "      <td>69.56</td>\n",
    668        "    </tr>\n",
    669        "    <tr>\n",
    670        "      <th>Equity(2389 [COBR])</th>\n",
    671        "      <td>69.56</td>\n",
    672        "    </tr>\n",
    673        "    <tr>\n",
    674        "      <th>Equity(2391 [DYNT])</th>\n",
    675        "      <td>69.56</td>\n",
    676        "    </tr>\n",
    677        "    <tr>\n",
    678        "      <th>...</th>\n",
    679        "      <th>...</th>\n",
    680        "      <td>...</td>\n",
    681        "    </tr>\n",
    682        "    <tr>\n",
    683        "      <th rowspan=\"30\" valign=\"top\">2013-11-25 00:00:00+00:00</th>\n",
    684        "      <th>Equity(45179 [ERW])</th>\n",
    685        "      <td>63.77</td>\n",
    686        "    </tr>\n",
    687        "    <tr>\n",
    688        "      <th>Equity(45195 [LGL_WS])</th>\n",
    689        "      <td>63.77</td>\n",
    690        "    </tr>\n",
    691        "    <tr>\n",
    692        "      <th>Equity(45203 [NASH])</th>\n",
    693        "      <td>63.77</td>\n",
    694        "    </tr>\n",
    695        "    <tr>\n",
    696        "      <th>Equity(45222 [QPAC_U])</th>\n",
    697        "      <td>63.77</td>\n",
    698        "    </tr>\n",
    699        "    <tr>\n",
    700        "      <th>Equity(45240 [INTL_L])</th>\n",
    701        "      <td>63.77</td>\n",
    702        "    </tr>\n",
    703        "    <tr>\n",
    704        "      <th>Equity(45270 [TIPT])</th>\n",
    705        "      <td>63.77</td>\n",
    706        "    </tr>\n",
    707        "    <tr>\n",
    708        "      <th>Equity(45288 [EMHD])</th>\n",
    709        "      <td>63.77</td>\n",
    710        "    </tr>\n",
    711        "    <tr>\n",
    712        "      <th>Equity(45301 [TRC_WS])</th>\n",
    713        "      <td>63.77</td>\n",
    714        "    </tr>\n",
    715        "    <tr>\n",
    716        "      <th>Equity(45390 [CPXX])</th>\n",
    717        "      <td>63.77</td>\n",
    718        "    </tr>\n",
    719        "    <tr>\n",
    720        "      <th>Equity(45412 [EAGL])</th>\n",
    721        "      <td>63.77</td>\n",
    722        "    </tr>\n",
    723        "    <tr>\n",
    724        "      <th>Equity(45414 [EAGL_W])</th>\n",
    725        "      <td>63.77</td>\n",
    726        "    </tr>\n",
    727        "    <tr>\n",
    728        "      <th>Equity(45420 [ROIQ_U])</th>\n",
    729        "      <td>63.77</td>\n",
    730        "    </tr>\n",
    731        "    <tr>\n",
    732        "      <th>Equity(45432 [SPCB])</th>\n",
    733        "      <td>63.77</td>\n",
    734        "    </tr>\n",
    735        "    <tr>\n",
    736        "      <th>Equity(45510 [MLPC])</th>\n",
    737        "      <td>63.77</td>\n",
    738        "    </tr>\n",
    739        "    <tr>\n",
    740        "      <th>Equity(45524 [NVEE])</th>\n",
    741        "      <td>63.77</td>\n",
    742        "    </tr>\n",
    743        "    <tr>\n",
    744        "      <th>Equity(45525 [NVEE_W])</th>\n",
    745        "      <td>63.77</td>\n",
    746        "    </tr>\n",
    747        "    <tr>\n",
    748        "      <th>Equity(45527 [JASN])</th>\n",
    749        "      <td>63.77</td>\n",
    750        "    </tr>\n",
    751        "    <tr>\n",
    752        "      <th>Equity(45536 [JASN_W])</th>\n",
    753        "      <td>63.77</td>\n",
    754        "    </tr>\n",
    755        "    <tr>\n",
    756        "      <th>Equity(45562 [ESBA])</th>\n",
    757        "      <td>63.77</td>\n",
    758        "    </tr>\n",
    759        "    <tr>\n",
    760        "      <th>Equity(45563 [OGCP])</th>\n",
    761        "      <td>63.77</td>\n",
    762        "    </tr>\n",
    763        "    <tr>\n",
    764        "      <th>Equity(45564 [FISK])</th>\n",
    765        "      <td>63.77</td>\n",
    766        "    </tr>\n",
    767        "    <tr>\n",
    768        "      <th>Equity(45646 [CHNA])</th>\n",
    769        "      <td>63.77</td>\n",
    770        "    </tr>\n",
    771        "    <tr>\n",
    772        "      <th>Equity(45678 [SLQD])</th>\n",
    773        "      <td>63.77</td>\n",
    774        "    </tr>\n",
    775        "    <tr>\n",
    776        "      <th>Equity(45680 [ADXS_W])</th>\n",
    777        "      <td>63.77</td>\n",
    778        "    </tr>\n",
    779        "    <tr>\n",
    780        "      <th>Equity(45717 [FTGC])</th>\n",
    781        "      <td>63.77</td>\n",
    782        "    </tr>\n",
    783        "    <tr>\n",
    784        "      <th>Equity(45768 [KODK_WS])</th>\n",
    785        "      <td>63.77</td>\n",
    786        "    </tr>\n",
    787        "    <tr>\n",
    788        "      <th>Equity(45792 [FTSD])</th>\n",
    789        "      <td>63.77</td>\n",
    790        "    </tr>\n",
    791        "    <tr>\n",
    792        "      <th>Equity(45824 [ROIQ_W])</th>\n",
    793        "      <td>63.77</td>\n",
    794        "    </tr>\n",
    795        "    <tr>\n",
    796        "      <th>Equity(45854 [PGAL])</th>\n",
    797        "      <td>63.77</td>\n",
    798        "    </tr>\n",
    799        "    <tr>\n",
    800        "      <th>Equity(45895 [EMSH])</th>\n",
    801        "      <td>63.77</td>\n",
    802        "    </tr>\n",
    803        "  </tbody>\n",
    804        "</table>\n",
    805        "<p>16983 rows × 1 columns</p>\n",
    806        "</div>"
    807       ],
    808       "text/plain": [
    809        "                                                    vvix\n",
    810        "2013-11-01 00:00:00+00:00 Equity(21 [AAME])        69.56\n",
    811        "                          Equity(25 [AA_PR])       69.56\n",
    812        "                          Equity(117 [AEY])        69.56\n",
    813        "                          Equity(225 [AHPI])       69.56\n",
    814        "                          Equity(312 [ALOT])       69.56\n",
    815        "                          Equity(392 [AMS])        69.56\n",
    816        "                          Equity(468 [API])        69.56\n",
    817        "                          Equity(548 [ASBI])       69.56\n",
    818        "                          Equity(717 [BAMM])       69.56\n",
    819        "                          Equity(790 [BDL])        69.56\n",
    820        "                          Equity(880 [BIO_B])      69.56\n",
    821        "                          Equity(925 [BKSC])       69.56\n",
    822        "                          Equity(1088 [BRID])      69.56\n",
    823        "                          Equity(1095 [BRN])       69.56\n",
    824        "                          Equity(1157 [BTUI])      69.56\n",
    825        "                          Equity(1190 [BWIN_A])    69.56\n",
    826        "                          Equity(1193 [BWL_A])     69.56\n",
    827        "                          Equity(1323 [CAW])       69.56\n",
    828        "                          Equity(1653 [MOC])       69.56\n",
    829        "                          Equity(1668 [CMS_PRB])   69.56\n",
    830        "                          Equity(1988 [CUO])       69.56\n",
    831        "                          Equity(2078 [DAIO])      69.56\n",
    832        "                          Equity(2103 [ESCR])      69.56\n",
    833        "                          Equity(2124 [DD_PRA])    69.56\n",
    834        "                          Equity(2209 [DGSE])      69.56\n",
    835        "                          Equity(2292 [DRCO])      69.56\n",
    836        "                          Equity(2344 [DRAM])      69.56\n",
    837        "                          Equity(2382 [DXR])       69.56\n",
    838        "                          Equity(2389 [COBR])      69.56\n",
    839        "                          Equity(2391 [DYNT])      69.56\n",
    840        "...                                                  ...\n",
    841        "2013-11-25 00:00:00+00:00 Equity(45179 [ERW])      63.77\n",
    842        "                          Equity(45195 [LGL_WS])   63.77\n",
    843        "                          Equity(45203 [NASH])     63.77\n",
    844        "                          Equity(45222 [QPAC_U])   63.77\n",
    845        "                          Equity(45240 [INTL_L])   63.77\n",
    846        "                          Equity(45270 [TIPT])     63.77\n",
    847        "                          Equity(45288 [EMHD])     63.77\n",
    848        "                          Equity(45301 [TRC_WS])   63.77\n",
    849        "                          Equity(45390 [CPXX])     63.77\n",
    850        "                          Equity(45412 [EAGL])     63.77\n",
    851        "                          Equity(45414 [EAGL_W])   63.77\n",
    852        "                          Equity(45420 [ROIQ_U])   63.77\n",
    853        "                          Equity(45432 [SPCB])     63.77\n",
    854        "                          Equity(45510 [MLPC])     63.77\n",
    855        "                          Equity(45524 [NVEE])     63.77\n",
    856        "                          Equity(45525 [NVEE_W])   63.77\n",
    857        "                          Equity(45527 [JASN])     63.77\n",
    858        "                          Equity(45536 [JASN_W])   63.77\n",
    859        "                          Equity(45562 [ESBA])     63.77\n",
    860        "                          Equity(45563 [OGCP])     63.77\n",
    861        "                          Equity(45564 [FISK])     63.77\n",
    862        "                          Equity(45646 [CHNA])     63.77\n",
    863        "                          Equity(45678 [SLQD])     63.77\n",
    864        "                          Equity(45680 [ADXS_W])   63.77\n",
    865        "                          Equity(45717 [FTGC])     63.77\n",
    866        "                          Equity(45768 [KODK_WS])  63.77\n",
    867        "                          Equity(45792 [FTSD])     63.77\n",
    868        "                          Equity(45824 [ROIQ_W])   63.77\n",
    869        "                          Equity(45854 [PGAL])     63.77\n",
    870        "                          Equity(45895 [EMSH])     63.77\n",
    871        "\n",
    872        "[16983 rows x 1 columns]"
    873       ]
    874      },
    875      "execution_count": 24,
    876      "metadata": {},
    877      "output_type": "execute_result"
    878     }
    879    ],
    880    "source": [
    881     "# run_pipeline will show the output of your pipeline\n",
    882     "pipe_output = run_pipeline(pipe, start_date='2013-11-01', end_date='2013-11-25')\n",
    883     "pipe_output"
    884    ]
    885   },
    886   {
    887    "cell_type": "markdown",
    888    "metadata": {},
    889    "source": [
    890     "Here, you'll notice that each security is mapped to VVIX. So you could grab any security to obtain the value of VVIX.\n",
    891     "\n",
    892     "Taking what we've seen from above, let's see how we'd move that into the backtester."
    893    ]
    894   },
    895   {
    896    "cell_type": "code",
    897    "execution_count": 75,
    898    "metadata": {
    899     "collapsed": false
    900    },
    901    "outputs": [],
    902    "source": [
    903     "# This section is only importable in the backtester\n",
    904     "from quantopian.algorithm import attach_pipeline, pipeline_output\n",
    905     "\n",
    906     "# General pipeline imports\n",
    907     "from quantopian.pipeline import Pipeline\n",
    908     "from quantopian.pipeline.factors import AverageDollarVolume\n",
    909     "\n",
    910     "# For use in your algorithms via the pipeline API\n",
    911     "from quantopian.pipeline.data.quandl import cboe_vvix\n",
    912     "\n",
    913     "def make_pipeline():\n",
    914     "    # Create our pipeline\n",
    915     "    pipe = Pipeline()\n",
    916     "    \n",
    917     "    # Screen out penny stocks and low liquidity securities.\n",
    918     "    dollar_volume = AverageDollarVolume(window_length=20)\n",
    919     "    is_liquid = dollar_volume.rank(ascending=False) < 1000\n",
    920     "    \n",
    921     "    # Create the mask that we will use for our percentile methods.\n",
    922     "    base_universe = (is_liquid)\n",
    923     "\n",
    924     "    # Add the datasets available\n",
    925     "    pipe.add(cboe_vvix.vvix.latest, 'vvix')\n",
    926     "\n",
    927     "    # Set our pipeline screens\n",
    928     "    pipe.set_screen(is_liquid)\n",
    929     "    return pipe\n",
    930     "\n",
    931     "def initialize(context):\n",
    932     "    attach_pipeline(make_pipeline(), \"pipeline\")\n",
    933     "    \n",
    934     "def before_trading_start(context, data):\n",
    935     "    results = pipeline_output('pipeline')"
    936    ]
    937   },
    938   {
    939    "cell_type": "markdown",
    940    "metadata": {},
    941    "source": [
    942     "Now you can take that and begin to use it as a building block for your algorithms, for examples on how to do that you can <a href='https://www.quantopian.com/data/eventvestor/_13d_filings'>clone the sample algorithm</a> or visit our <a href='https://www.quantopian.com/posts/pipeline-factor-library-for-data'>data pipeline factor library</a>"
    943    ]
    944   }
    945  ],
    946  "metadata": {
    947   "kernelspec": {
    948    "display_name": "Python 2",
    949    "language": "python",
    950    "name": "python2"
    951   },
    952   "language_info": {
    953    "codemirror_mode": {
    954     "name": "ipython",
    955     "version": 2
    956    },
    957    "file_extension": ".py",
    958    "mimetype": "text/x-python",
    959    "name": "python",
    960    "nbconvert_exporter": "python",
    961    "pygments_lexer": "ipython2",
    962    "version": "2.7.11"
    963   }
    964  },
    965  "nbformat": 4,
    966  "nbformat_minor": 0
    967 }