ml-finance-python

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

(149478B)
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      9     "# CBOE VXXLE  Index\n",
     10     "\n",
     11     "In this notebook, we'll take a look at the CBOE VXXLE Index dataset, available on the [Quantopian Store](https://www.quantopian.com/store). This dataset spans 16 Mar 2011 through the current day. This data has a daily frequency. VXXLE is the CBOE Energy Sector ETF Volatility Index, reflecting the implied volatility of the XLE ETF\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",
     51    "execution_count": 2,
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     55    "outputs": [],
     56    "source": [
     57     "# For use in Quantopian Research, exploring interactively\n",
     58     "from quantopian.interactive.data.quandl import cboe_vxxle 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": 3,
     69    "metadata": {
     70     "collapsed": false
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     73     {
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     76        "dshape(\"\"\"var * {\n",
     77        "  open_: float64,\n",
     78        "  high: float64,\n",
     79        "  low: float64,\n",
     80        "  close: float64,\n",
     81        "  asof_date: datetime,\n",
     82        "  timestamp: datetime\n",
     83        "  }\"\"\")"
     84       ]
     85      },
     86      "execution_count": 3,
     87      "metadata": {},
     88      "output_type": "execute_result"
     89     }
     90    ],
     91    "source": [
     92     "# Let's use blaze to understand the data a bit using Blaze dshape()\n",
     93     "dataset.dshape"
     94    ]
     95   },
     96   {
     97    "cell_type": "code",
     98    "execution_count": 4,
     99    "metadata": {
    100     "collapsed": false
    101    },
    102    "outputs": [
    103     {
    104      "data": {
    105       "text/html": [
    106        "1307"
    107       ],
    108       "text/plain": [
    109        "1307"
    110       ]
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    117    "source": [
    118     "# And how many rows are there?\n",
    119     "# N.B. we're using a Blaze function to do this, not len()\n",
    120     "dataset.count()"
    121    ]
    122   },
    123   {
    124    "cell_type": "code",
    125    "execution_count": 5,
    126    "metadata": {
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    130     {
    131      "data": {
    132       "text/html": [
    133        "<table border=\"1\" class=\"dataframe\">\n",
    134        "  <thead>\n",
    135        "    <tr style=\"text-align: right;\">\n",
    136        "      <th></th>\n",
    137        "      <th>open_</th>\n",
    138        "      <th>high</th>\n",
    139        "      <th>low</th>\n",
    140        "      <th>close</th>\n",
    141        "      <th>asof_date</th>\n",
    142        "      <th>timestamp</th>\n",
    143        "    </tr>\n",
    144        "  </thead>\n",
    145        "  <tbody>\n",
    146        "    <tr>\n",
    147        "      <th>0</th>\n",
    148        "      <td>34.43</td>\n",
    149        "      <td>35.66</td>\n",
    150        "      <td>34.05</td>\n",
    151        "      <td>35.14</td>\n",
    152        "      <td>2016-02-23</td>\n",
    153        "      <td>2016-02-24 12:03:41.481355</td>\n",
    154        "    </tr>\n",
    155        "    <tr>\n",
    156        "      <th>1</th>\n",
    157        "      <td>37.26</td>\n",
    158        "      <td>37.69</td>\n",
    159        "      <td>35.63</td>\n",
    160        "      <td>35.64</td>\n",
    161        "      <td>2016-02-24</td>\n",
    162        "      <td>2016-02-25 12:02:01.622172</td>\n",
    163        "    </tr>\n",
    164        "    <tr>\n",
    165        "      <th>2</th>\n",
    166        "      <td>35.30</td>\n",
    167        "      <td>36.46</td>\n",
    168        "      <td>35.10</td>\n",
    169        "      <td>35.15</td>\n",
    170        "      <td>2016-02-25</td>\n",
    171        "      <td>2016-02-26 12:03:31.237381</td>\n",
    172        "    </tr>\n",
    173        "  </tbody>\n",
    174        "</table>"
    175       ],
    176       "text/plain": [
    177        "   open_   high    low  close  asof_date                  timestamp\n",
    178        "0  34.43  35.66  34.05  35.14 2016-02-23 2016-02-24 12:03:41.481355\n",
    179        "1  37.26  37.69  35.63  35.64 2016-02-24 2016-02-25 12:02:01.622172\n",
    180        "2  35.30  36.46  35.10  35.15 2016-02-25 2016-02-26 12:03:31.237381"
    181       ]
    182      },
    183      "execution_count": 5,
    184      "metadata": {},
    185      "output_type": "execute_result"
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    187    ],
    188    "source": [
    189     "# Let's see what the data looks like. We'll grab the first three rows.\n",
    190     "dataset[:3]"
    191    ]
    192   },
    193   {
    194    "cell_type": "markdown",
    195    "metadata": {},
    196    "source": [
    197     "Let's go over the columns:\n",
    198     "- **open**: open price for vxxle\n",
    199     "- **high**: daily high for vxxle\n",
    200     "- **low**: daily low for vxxle\n",
    201     "- **close**: close price for vxxle\n",
    202     "- **asof_date**: the timeframe to which this data applies\n",
    203     "- **timestamp**: this is our timestamp on when we registered the data.\n",
    204     "\n",
    205     "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",
    206     "\n",
    207     "We can select columns and rows with ease. Below, we'll do a simple plot."
    208    ]
    209   },
    210   {
    211    "cell_type": "code",
    212    "execution_count": 6,
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    217     {
    218      "data": {
    219       "text/html": [
    220        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
    221        "<table border=\"1\" class=\"dataframe\">\n",
    222        "  <thead>\n",
    223        "    <tr style=\"text-align: right;\">\n",
    224        "      <th></th>\n",
    225        "      <th>open_</th>\n",
    226        "      <th>high</th>\n",
    227        "      <th>low</th>\n",
    228        "      <th>close</th>\n",
    229        "      <th>asof_date</th>\n",
    230        "      <th>timestamp</th>\n",
    231        "    </tr>\n",
    232        "  </thead>\n",
    233        "  <tbody>\n",
    234        "    <tr>\n",
    235        "      <th>0</th>\n",
    236        "      <td>34.43</td>\n",
    237        "      <td>35.66</td>\n",
    238        "      <td>34.05</td>\n",
    239        "      <td>35.14</td>\n",
    240        "      <td>2016-02-23</td>\n",
    241        "      <td>2016-02-24 12:03:41.481355</td>\n",
    242        "    </tr>\n",
    243        "    <tr>\n",
    244        "      <th>1</th>\n",
    245        "      <td>37.26</td>\n",
    246        "      <td>37.69</td>\n",
    247        "      <td>35.63</td>\n",
    248        "      <td>35.64</td>\n",
    249        "      <td>2016-02-24</td>\n",
    250        "      <td>2016-02-25 12:02:01.622172</td>\n",
    251        "    </tr>\n",
    252        "    <tr>\n",
    253        "      <th>2</th>\n",
    254        "      <td>35.30</td>\n",
    255        "      <td>36.46</td>\n",
    256        "      <td>35.10</td>\n",
    257        "      <td>35.15</td>\n",
    258        "      <td>2016-02-25</td>\n",
    259        "      <td>2016-02-26 12:03:31.237381</td>\n",
    260        "    </tr>\n",
    261        "    <tr>\n",
    262        "      <th>3</th>\n",
    263        "      <td>33.62</td>\n",
    264        "      <td>34.09</td>\n",
    265        "      <td>33.49</td>\n",
    266        "      <td>33.78</td>\n",
    267        "      <td>2016-02-26</td>\n",
    268        "      <td>2016-02-29 12:02:15.245695</td>\n",
    269        "    </tr>\n",
    270        "    <tr>\n",
    271        "      <th>4</th>\n",
    272        "      <td>34.85</td>\n",
    273        "      <td>34.91</td>\n",
    274        "      <td>34.10</td>\n",
    275        "      <td>34.30</td>\n",
    276        "      <td>2016-02-29</td>\n",
    277        "      <td>2016-03-01 12:02:40.003945</td>\n",
    278        "    </tr>\n",
    279        "  </tbody>\n",
    280        "</table>\n",
    281        "</div>"
    282       ],
    283       "text/plain": [
    284        "   open_   high    low  close  asof_date                  timestamp\n",
    285        "0  34.43  35.66  34.05  35.14 2016-02-23 2016-02-24 12:03:41.481355\n",
    286        "1  37.26  37.69  35.63  35.64 2016-02-24 2016-02-25 12:02:01.622172\n",
    287        "2  35.30  36.46  35.10  35.15 2016-02-25 2016-02-26 12:03:31.237381\n",
    288        "3  33.62  34.09  33.49  33.78 2016-02-26 2016-02-29 12:02:15.245695\n",
    289        "4  34.85  34.91  34.10  34.30 2016-02-29 2016-03-01 12:02:40.003945"
    290       ]
    291      },
    292      "execution_count": 6,
    293      "metadata": {},
    294      "output_type": "execute_result"
    295     }
    296    ],
    297    "source": [
    298     "# Plotting this DataFrame\n",
    299     "df = odo(dataset, pd.DataFrame)\n",
    300     "df.head(5)"
    301    ]
    302   },
    303   {
    304    "cell_type": "code",
    305    "execution_count": 7,
    306    "metadata": {
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    310     {
    311      "data": {
    312       "text/html": [
    313        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
    314        "<table border=\"1\" class=\"dataframe\">\n",
    315        "  <thead>\n",
    316        "    <tr style=\"text-align: right;\">\n",
    317        "      <th></th>\n",
    318        "      <th>open_</th>\n",
    319        "      <th>high</th>\n",
    320        "      <th>low</th>\n",
    321        "      <th>close</th>\n",
    322        "      <th>timestamp</th>\n",
    323        "    </tr>\n",
    324        "    <tr>\n",
    325        "      <th>asof_date</th>\n",
    326        "      <th></th>\n",
    327        "      <th></th>\n",
    328        "      <th></th>\n",
    329        "      <th></th>\n",
    330        "      <th></th>\n",
    331        "    </tr>\n",
    332        "  </thead>\n",
    333        "  <tbody>\n",
    334        "    <tr>\n",
    335        "      <th>2016-02-23</th>\n",
    336        "      <td>34.43</td>\n",
    337        "      <td>35.66</td>\n",
    338        "      <td>34.05</td>\n",
    339        "      <td>35.14</td>\n",
    340        "      <td>2016-02-24 12:03:41.481355</td>\n",
    341        "    </tr>\n",
    342        "    <tr>\n",
    343        "      <th>2016-02-24</th>\n",
    344        "      <td>37.26</td>\n",
    345        "      <td>37.69</td>\n",
    346        "      <td>35.63</td>\n",
    347        "      <td>35.64</td>\n",
    348        "      <td>2016-02-25 12:02:01.622172</td>\n",
    349        "    </tr>\n",
    350        "    <tr>\n",
    351        "      <th>2016-02-25</th>\n",
    352        "      <td>35.30</td>\n",
    353        "      <td>36.46</td>\n",
    354        "      <td>35.10</td>\n",
    355        "      <td>35.15</td>\n",
    356        "      <td>2016-02-26 12:03:31.237381</td>\n",
    357        "    </tr>\n",
    358        "    <tr>\n",
    359        "      <th>2016-02-26</th>\n",
    360        "      <td>33.62</td>\n",
    361        "      <td>34.09</td>\n",
    362        "      <td>33.49</td>\n",
    363        "      <td>33.78</td>\n",
    364        "      <td>2016-02-29 12:02:15.245695</td>\n",
    365        "    </tr>\n",
    366        "    <tr>\n",
    367        "      <th>2016-02-29</th>\n",
    368        "      <td>34.85</td>\n",
    369        "      <td>34.91</td>\n",
    370        "      <td>34.10</td>\n",
    371        "      <td>34.30</td>\n",
    372        "      <td>2016-03-01 12:02:40.003945</td>\n",
    373        "    </tr>\n",
    374        "  </tbody>\n",
    375        "</table>\n",
    376        "</div>"
    377       ],
    378       "text/plain": [
    379        "            open_   high    low  close                  timestamp\n",
    380        "asof_date                                                        \n",
    381        "2016-02-23  34.43  35.66  34.05  35.14 2016-02-24 12:03:41.481355\n",
    382        "2016-02-24  37.26  37.69  35.63  35.64 2016-02-25 12:02:01.622172\n",
    383        "2016-02-25  35.30  36.46  35.10  35.15 2016-02-26 12:03:31.237381\n",
    384        "2016-02-26  33.62  34.09  33.49  33.78 2016-02-29 12:02:15.245695\n",
    385        "2016-02-29  34.85  34.91  34.10  34.30 2016-03-01 12:02:40.003945"
    386       ]
    387      },
    388      "execution_count": 7,
    389      "metadata": {},
    390      "output_type": "execute_result"
    391     }
    392    ],
    393    "source": [
    394     "# So we can plot it, we'll set the index as the `asof_date`\n",
    395     "df['asof_date'] = pd.to_datetime(df['asof_date'])\n",
    396     "df = df.set_index(['asof_date'])\n",
    397     "df.head(5)"
    398    ]
    399   },
    400   {
    401    "cell_type": "code",
    402    "execution_count": 8,
    403    "metadata": {
    404     "collapsed": false
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    406    "outputs": [
    407     {
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    409       "text/plain": [
    410        "<matplotlib.text.Text at 0x7f143b640a10>"
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S//d//4dXX30VQ4YMiWrfnTt39jveBUEwLLXr2bMn9uzZo2Xi1NfYrVs3lJaWoqamRgsw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\n8LRZ7CivMWN4ZpxP+Y1Rr0G7n252ADwyP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Z59y54KNLVYceHiUVCpVDh0vAH/+uRnWGwO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\nAAAASUVORK5CYII=\n",
    420       "text/plain": [
    421        "<matplotlib.figure.Figure at 0x7f143b68e590>"
    422       ]
    423      },
    424      "metadata": {},
    425      "output_type": "display_data"
    426     }
    427    ],
    428    "source": [
    429     "import matplotlib.pyplot as plt\n",
    430     "df['open_'].plot(label=str(dataset))\n",
    431     "plt.ylabel(str(dataset))\n",
    432     "plt.legend()\n",
    433     "plt.title(\"Graphing %s since %s\" % (str(dataset), min(df.index)))"
    434    ]
    435   },
    436   {
    437    "cell_type": "markdown",
    438    "metadata": {},
    439    "source": [
    440     "<a id='pipeline'></a>\n",
    441     "\n",
    442     "#Pipeline Overview\n",
    443     "\n",
    444     "### Accessing the data in your algorithms & research\n",
    445     "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",
    446     "\n",
    447     "Import the data set here\n",
    448     "> `from quantopian.pipeline.data.quandl import cboe_vxxle`\n",
    449     "\n",
    450     "Then in intialize() you could do something simple like adding the raw value of one of the fields to your pipeline:\n",
    451     "> `pipe.add(cboe_vxxle.open_.latest, 'open')`\n",
    452     "\n",
    453     "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."
    454    ]
    455   },
    456   {
    457    "cell_type": "code",
    458    "execution_count": 9,
    459    "metadata": {
    460     "collapsed": true
    461    },
    462    "outputs": [],
    463    "source": [
    464     "# Import necessary Pipeline modules\n",
    465     "from quantopian.pipeline import Pipeline\n",
    466     "from quantopian.research import run_pipeline\n",
    467     "from quantopian.pipeline.factors import AverageDollarVolume"
    468    ]
    469   },
    470   {
    471    "cell_type": "code",
    472    "execution_count": 10,
    473    "metadata": {
    474     "collapsed": false
    475    },
    476    "outputs": [],
    477    "source": [
    478     "# Import the datasets available\n",
    479     "from quantopian.pipeline.data.quandl import cboe_vxxle"
    480    ]
    481   },
    482   {
    483    "cell_type": "markdown",
    484    "metadata": {},
    485    "source": [
    486     "Now that we've imported the data, let's take a look at which fields are available for each dataset.\n",
    487     "\n",
    488     "You'll find the dataset, the available fields, and the datatypes for each of those fields."
    489    ]
    490   },
    491   {
    492    "cell_type": "code",
    493    "execution_count": 11,
    494    "metadata": {
    495     "collapsed": false
    496    },
    497    "outputs": [
    498     {
    499      "name": "stdout",
    500      "output_type": "stream",
    501      "text": [
    502       "Here are the list of available fields per dataset:\n",
    503       "---------------------------------------------------\n",
    504       "\n",
    505       "Dataset: cboe_vxxle\n",
    506       "\n",
    507       "Fields:\n",
    508       "high - float64\n",
    509       "low - float64\n",
    510       "close - float64\n",
    511       "open_ - float64\n",
    512       "\n",
    513       "\n",
    514       "---------------------------------------------------\n",
    515       "\n"
    516      ]
    517     }
    518    ],
    519    "source": [
    520     "print \"Here are the list of available fields per dataset:\"\n",
    521     "print \"---------------------------------------------------\\n\"\n",
    522     "\n",
    523     "def _print_fields(dataset):\n",
    524     "    print \"Dataset: %s\\n\" % dataset.__name__\n",
    525     "    print \"Fields:\"\n",
    526     "    for field in list(dataset.columns):\n",
    527     "        print \"%s - %s\" % (field.name, field.dtype)\n",
    528     "    print \"\\n\"\n",
    529     "\n",
    530     "_print_fields(cboe_vxxle)\n",
    531     "\n",
    532     "\n",
    533     "print \"---------------------------------------------------\\n\""
    534    ]
    535   },
    536   {
    537    "cell_type": "markdown",
    538    "metadata": {},
    539    "source": [
    540     "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",
    541     "\n",
    542     "\n",
    543     "This is constructed the same way as you would in the backtester. For more information on using Pipeline in Research view this thread:\n",
    544     "https://www.quantopian.com/posts/pipeline-in-research-build-test-and-visualize-your-factors-and-filters"
    545    ]
    546   },
    547   {
    548    "cell_type": "code",
    549    "execution_count": 12,
    550    "metadata": {
    551     "collapsed": false
    552    },
    553    "outputs": [],
    554    "source": [
    555     "pipe = Pipeline()\n",
    556     "       \n",
    557     "pipe.add(cboe_vxxle.open_.latest, 'open_vxxle')"
    558    ]
    559   },
    560   {
    561    "cell_type": "code",
    562    "execution_count": 15,
    563    "metadata": {
    564     "collapsed": false
    565    },
    566    "outputs": [],
    567    "source": [
    568     "# Setting some basic liquidity strings (just for good habit)\n",
    569     "dollar_volume = AverageDollarVolume(window_length=20)\n",
    570     "top_1000_most_liquid = dollar_volume.rank(ascending=False) < 1000\n",
    571     "\n",
    572     "pipe.set_screen(top_1000_most_liquid & cboe_vxxle.open_.latest.notnan())"
    573    ]
    574   },
    575   {
    576    "cell_type": "code",
    577    "execution_count": 16,
    578    "metadata": {
    579     "collapsed": false
    580    },
    581    "outputs": [
    582     {
    583      "data": {
    584       "image/png": 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6depk9U1nXFycevTo4cKKAACAOyF0AhmYyWRSr169LM9ajImJ4dRa2C1Hjhxq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\n",
    585       "text/plain": [
    586        "<IPython.core.display.Image object>"
    587       ]
    588      },
    589      "execution_count": 16,
    590      "metadata": {},
    591      "output_type": "execute_result"
    592     }
    593    ],
    594    "source": [
    595     "# The show_graph() method of pipeline objects produces a graph to show how it is being calculated.\n",
    596     "pipe.show_graph(format='png')"
    597    ]
    598   },
    599   {
    600    "cell_type": "code",
    601    "execution_count": 17,
    602    "metadata": {
    603     "collapsed": false
    604    },
    605    "outputs": [
    606     {
    607      "data": {
    608       "text/html": [
    609        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
    610        "<table border=\"1\" class=\"dataframe\">\n",
    611        "  <thead>\n",
    612        "    <tr style=\"text-align: right;\">\n",
    613        "      <th></th>\n",
    614        "      <th></th>\n",
    615        "      <th>open_vxxle</th>\n",
    616        "    </tr>\n",
    617        "  </thead>\n",
    618        "  <tbody>\n",
    619        "    <tr>\n",
    620        "      <th rowspan=\"30\" valign=\"top\">2013-11-01 00:00:00+00:00</th>\n",
    621        "      <th>Equity(21 [AAME])</th>\n",
    622        "      <td>16.42</td>\n",
    623        "    </tr>\n",
    624        "    <tr>\n",
    625        "      <th>Equity(25 [AA_PR])</th>\n",
    626        "      <td>16.42</td>\n",
    627        "    </tr>\n",
    628        "    <tr>\n",
    629        "      <th>Equity(117 [AEY])</th>\n",
    630        "      <td>16.42</td>\n",
    631        "    </tr>\n",
    632        "    <tr>\n",
    633        "      <th>Equity(225 [AHPI])</th>\n",
    634        "      <td>16.42</td>\n",
    635        "    </tr>\n",
    636        "    <tr>\n",
    637        "      <th>Equity(312 [ALOT])</th>\n",
    638        "      <td>16.42</td>\n",
    639        "    </tr>\n",
    640        "    <tr>\n",
    641        "      <th>Equity(392 [AMS])</th>\n",
    642        "      <td>16.42</td>\n",
    643        "    </tr>\n",
    644        "    <tr>\n",
    645        "      <th>Equity(468 [API])</th>\n",
    646        "      <td>16.42</td>\n",
    647        "    </tr>\n",
    648        "    <tr>\n",
    649        "      <th>Equity(548 [ASBI])</th>\n",
    650        "      <td>16.42</td>\n",
    651        "    </tr>\n",
    652        "    <tr>\n",
    653        "      <th>Equity(717 [BAMM])</th>\n",
    654        "      <td>16.42</td>\n",
    655        "    </tr>\n",
    656        "    <tr>\n",
    657        "      <th>Equity(790 [BDL])</th>\n",
    658        "      <td>16.42</td>\n",
    659        "    </tr>\n",
    660        "    <tr>\n",
    661        "      <th>Equity(880 [BIO_B])</th>\n",
    662        "      <td>16.42</td>\n",
    663        "    </tr>\n",
    664        "    <tr>\n",
    665        "      <th>Equity(925 [BKSC])</th>\n",
    666        "      <td>16.42</td>\n",
    667        "    </tr>\n",
    668        "    <tr>\n",
    669        "      <th>Equity(1088 [BRID])</th>\n",
    670        "      <td>16.42</td>\n",
    671        "    </tr>\n",
    672        "    <tr>\n",
    673        "      <th>Equity(1095 [BRN])</th>\n",
    674        "      <td>16.42</td>\n",
    675        "    </tr>\n",
    676        "    <tr>\n",
    677        "      <th>Equity(1157 [BTUI])</th>\n",
    678        "      <td>16.42</td>\n",
    679        "    </tr>\n",
    680        "    <tr>\n",
    681        "      <th>Equity(1190 [BWIN_A])</th>\n",
    682        "      <td>16.42</td>\n",
    683        "    </tr>\n",
    684        "    <tr>\n",
    685        "      <th>Equity(1193 [BWL_A])</th>\n",
    686        "      <td>16.42</td>\n",
    687        "    </tr>\n",
    688        "    <tr>\n",
    689        "      <th>Equity(1323 [CAW])</th>\n",
    690        "      <td>16.42</td>\n",
    691        "    </tr>\n",
    692        "    <tr>\n",
    693        "      <th>Equity(1653 [MOC])</th>\n",
    694        "      <td>16.42</td>\n",
    695        "    </tr>\n",
    696        "    <tr>\n",
    697        "      <th>Equity(1668 [CMS_PRB])</th>\n",
    698        "      <td>16.42</td>\n",
    699        "    </tr>\n",
    700        "    <tr>\n",
    701        "      <th>Equity(1988 [CUO])</th>\n",
    702        "      <td>16.42</td>\n",
    703        "    </tr>\n",
    704        "    <tr>\n",
    705        "      <th>Equity(2078 [DAIO])</th>\n",
    706        "      <td>16.42</td>\n",
    707        "    </tr>\n",
    708        "    <tr>\n",
    709        "      <th>Equity(2103 [ESCR])</th>\n",
    710        "      <td>16.42</td>\n",
    711        "    </tr>\n",
    712        "    <tr>\n",
    713        "      <th>Equity(2124 [DD_PRA])</th>\n",
    714        "      <td>16.42</td>\n",
    715        "    </tr>\n",
    716        "    <tr>\n",
    717        "      <th>Equity(2209 [DGSE])</th>\n",
    718        "      <td>16.42</td>\n",
    719        "    </tr>\n",
    720        "    <tr>\n",
    721        "      <th>Equity(2292 [DRCO])</th>\n",
    722        "      <td>16.42</td>\n",
    723        "    </tr>\n",
    724        "    <tr>\n",
    725        "      <th>Equity(2344 [DRAM])</th>\n",
    726        "      <td>16.42</td>\n",
    727        "    </tr>\n",
    728        "    <tr>\n",
    729        "      <th>Equity(2382 [DXR])</th>\n",
    730        "      <td>16.42</td>\n",
    731        "    </tr>\n",
    732        "    <tr>\n",
    733        "      <th>Equity(2389 [COBR])</th>\n",
    734        "      <td>16.42</td>\n",
    735        "    </tr>\n",
    736        "    <tr>\n",
    737        "      <th>Equity(2391 [DYNT])</th>\n",
    738        "      <td>16.42</td>\n",
    739        "    </tr>\n",
    740        "    <tr>\n",
    741        "      <th>...</th>\n",
    742        "      <th>...</th>\n",
    743        "      <td>...</td>\n",
    744        "    </tr>\n",
    745        "    <tr>\n",
    746        "      <th rowspan=\"30\" valign=\"top\">2013-11-25 00:00:00+00:00</th>\n",
    747        "      <th>Equity(45179 [ERW])</th>\n",
    748        "      <td>16.22</td>\n",
    749        "    </tr>\n",
    750        "    <tr>\n",
    751        "      <th>Equity(45195 [LGL_WS])</th>\n",
    752        "      <td>16.22</td>\n",
    753        "    </tr>\n",
    754        "    <tr>\n",
    755        "      <th>Equity(45203 [NASH])</th>\n",
    756        "      <td>16.22</td>\n",
    757        "    </tr>\n",
    758        "    <tr>\n",
    759        "      <th>Equity(45222 [QPAC_U])</th>\n",
    760        "      <td>16.22</td>\n",
    761        "    </tr>\n",
    762        "    <tr>\n",
    763        "      <th>Equity(45240 [INTL_L])</th>\n",
    764        "      <td>16.22</td>\n",
    765        "    </tr>\n",
    766        "    <tr>\n",
    767        "      <th>Equity(45270 [TIPT])</th>\n",
    768        "      <td>16.22</td>\n",
    769        "    </tr>\n",
    770        "    <tr>\n",
    771        "      <th>Equity(45288 [EMHD])</th>\n",
    772        "      <td>16.22</td>\n",
    773        "    </tr>\n",
    774        "    <tr>\n",
    775        "      <th>Equity(45301 [TRC_WS])</th>\n",
    776        "      <td>16.22</td>\n",
    777        "    </tr>\n",
    778        "    <tr>\n",
    779        "      <th>Equity(45390 [CPXX])</th>\n",
    780        "      <td>16.22</td>\n",
    781        "    </tr>\n",
    782        "    <tr>\n",
    783        "      <th>Equity(45412 [EAGL])</th>\n",
    784        "      <td>16.22</td>\n",
    785        "    </tr>\n",
    786        "    <tr>\n",
    787        "      <th>Equity(45414 [EAGL_W])</th>\n",
    788        "      <td>16.22</td>\n",
    789        "    </tr>\n",
    790        "    <tr>\n",
    791        "      <th>Equity(45420 [ROIQ_U])</th>\n",
    792        "      <td>16.22</td>\n",
    793        "    </tr>\n",
    794        "    <tr>\n",
    795        "      <th>Equity(45432 [SPCB])</th>\n",
    796        "      <td>16.22</td>\n",
    797        "    </tr>\n",
    798        "    <tr>\n",
    799        "      <th>Equity(45510 [MLPC])</th>\n",
    800        "      <td>16.22</td>\n",
    801        "    </tr>\n",
    802        "    <tr>\n",
    803        "      <th>Equity(45524 [NVEE])</th>\n",
    804        "      <td>16.22</td>\n",
    805        "    </tr>\n",
    806        "    <tr>\n",
    807        "      <th>Equity(45525 [NVEE_W])</th>\n",
    808        "      <td>16.22</td>\n",
    809        "    </tr>\n",
    810        "    <tr>\n",
    811        "      <th>Equity(45527 [JASN])</th>\n",
    812        "      <td>16.22</td>\n",
    813        "    </tr>\n",
    814        "    <tr>\n",
    815        "      <th>Equity(45536 [JASN_W])</th>\n",
    816        "      <td>16.22</td>\n",
    817        "    </tr>\n",
    818        "    <tr>\n",
    819        "      <th>Equity(45562 [ESBA])</th>\n",
    820        "      <td>16.22</td>\n",
    821        "    </tr>\n",
    822        "    <tr>\n",
    823        "      <th>Equity(45563 [OGCP])</th>\n",
    824        "      <td>16.22</td>\n",
    825        "    </tr>\n",
    826        "    <tr>\n",
    827        "      <th>Equity(45564 [FISK])</th>\n",
    828        "      <td>16.22</td>\n",
    829        "    </tr>\n",
    830        "    <tr>\n",
    831        "      <th>Equity(45646 [CHNA])</th>\n",
    832        "      <td>16.22</td>\n",
    833        "    </tr>\n",
    834        "    <tr>\n",
    835        "      <th>Equity(45678 [SLQD])</th>\n",
    836        "      <td>16.22</td>\n",
    837        "    </tr>\n",
    838        "    <tr>\n",
    839        "      <th>Equity(45680 [ADXS_W])</th>\n",
    840        "      <td>16.22</td>\n",
    841        "    </tr>\n",
    842        "    <tr>\n",
    843        "      <th>Equity(45717 [FTGC])</th>\n",
    844        "      <td>16.22</td>\n",
    845        "    </tr>\n",
    846        "    <tr>\n",
    847        "      <th>Equity(45768 [KODK_WS])</th>\n",
    848        "      <td>16.22</td>\n",
    849        "    </tr>\n",
    850        "    <tr>\n",
    851        "      <th>Equity(45792 [FTSD])</th>\n",
    852        "      <td>16.22</td>\n",
    853        "    </tr>\n",
    854        "    <tr>\n",
    855        "      <th>Equity(45824 [ROIQ_W])</th>\n",
    856        "      <td>16.22</td>\n",
    857        "    </tr>\n",
    858        "    <tr>\n",
    859        "      <th>Equity(45854 [PGAL])</th>\n",
    860        "      <td>16.22</td>\n",
    861        "    </tr>\n",
    862        "    <tr>\n",
    863        "      <th>Equity(45895 [EMSH])</th>\n",
    864        "      <td>16.22</td>\n",
    865        "    </tr>\n",
    866        "  </tbody>\n",
    867        "</table>\n",
    868        "<p>16983 rows × 1 columns</p>\n",
    869        "</div>"
    870       ],
    871       "text/plain": [
    872        "                                                   open_vxxle\n",
    873        "2013-11-01 00:00:00+00:00 Equity(21 [AAME])             16.42\n",
    874        "                          Equity(25 [AA_PR])            16.42\n",
    875        "                          Equity(117 [AEY])             16.42\n",
    876        "                          Equity(225 [AHPI])            16.42\n",
    877        "                          Equity(312 [ALOT])            16.42\n",
    878        "                          Equity(392 [AMS])             16.42\n",
    879        "                          Equity(468 [API])             16.42\n",
    880        "                          Equity(548 [ASBI])            16.42\n",
    881        "                          Equity(717 [BAMM])            16.42\n",
    882        "                          Equity(790 [BDL])             16.42\n",
    883        "                          Equity(880 [BIO_B])           16.42\n",
    884        "                          Equity(925 [BKSC])            16.42\n",
    885        "                          Equity(1088 [BRID])           16.42\n",
    886        "                          Equity(1095 [BRN])            16.42\n",
    887        "                          Equity(1157 [BTUI])           16.42\n",
    888        "                          Equity(1190 [BWIN_A])         16.42\n",
    889        "                          Equity(1193 [BWL_A])          16.42\n",
    890        "                          Equity(1323 [CAW])            16.42\n",
    891        "                          Equity(1653 [MOC])            16.42\n",
    892        "                          Equity(1668 [CMS_PRB])        16.42\n",
    893        "                          Equity(1988 [CUO])            16.42\n",
    894        "                          Equity(2078 [DAIO])           16.42\n",
    895        "                          Equity(2103 [ESCR])           16.42\n",
    896        "                          Equity(2124 [DD_PRA])         16.42\n",
    897        "                          Equity(2209 [DGSE])           16.42\n",
    898        "                          Equity(2292 [DRCO])           16.42\n",
    899        "                          Equity(2344 [DRAM])           16.42\n",
    900        "                          Equity(2382 [DXR])            16.42\n",
    901        "                          Equity(2389 [COBR])           16.42\n",
    902        "                          Equity(2391 [DYNT])           16.42\n",
    903        "...                                                       ...\n",
    904        "2013-11-25 00:00:00+00:00 Equity(45179 [ERW])           16.22\n",
    905        "                          Equity(45195 [LGL_WS])        16.22\n",
    906        "                          Equity(45203 [NASH])          16.22\n",
    907        "                          Equity(45222 [QPAC_U])        16.22\n",
    908        "                          Equity(45240 [INTL_L])        16.22\n",
    909        "                          Equity(45270 [TIPT])          16.22\n",
    910        "                          Equity(45288 [EMHD])          16.22\n",
    911        "                          Equity(45301 [TRC_WS])        16.22\n",
    912        "                          Equity(45390 [CPXX])          16.22\n",
    913        "                          Equity(45412 [EAGL])          16.22\n",
    914        "                          Equity(45414 [EAGL_W])        16.22\n",
    915        "                          Equity(45420 [ROIQ_U])        16.22\n",
    916        "                          Equity(45432 [SPCB])          16.22\n",
    917        "                          Equity(45510 [MLPC])          16.22\n",
    918        "                          Equity(45524 [NVEE])          16.22\n",
    919        "                          Equity(45525 [NVEE_W])        16.22\n",
    920        "                          Equity(45527 [JASN])          16.22\n",
    921        "                          Equity(45536 [JASN_W])        16.22\n",
    922        "                          Equity(45562 [ESBA])          16.22\n",
    923        "                          Equity(45563 [OGCP])          16.22\n",
    924        "                          Equity(45564 [FISK])          16.22\n",
    925        "                          Equity(45646 [CHNA])          16.22\n",
    926        "                          Equity(45678 [SLQD])          16.22\n",
    927        "                          Equity(45680 [ADXS_W])        16.22\n",
    928        "                          Equity(45717 [FTGC])          16.22\n",
    929        "                          Equity(45768 [KODK_WS])       16.22\n",
    930        "                          Equity(45792 [FTSD])          16.22\n",
    931        "                          Equity(45824 [ROIQ_W])        16.22\n",
    932        "                          Equity(45854 [PGAL])          16.22\n",
    933        "                          Equity(45895 [EMSH])          16.22\n",
    934        "\n",
    935        "[16983 rows x 1 columns]"
    936       ]
    937      },
    938      "execution_count": 17,
    939      "metadata": {},
    940      "output_type": "execute_result"
    941     }
    942    ],
    943    "source": [
    944     "# run_pipeline will show the output of your pipeline\n",
    945     "pipe_output = run_pipeline(pipe, start_date='2013-11-01', end_date='2013-11-25')\n",
    946     "pipe_output"
    947    ]
    948   },
    949   {
    950    "cell_type": "markdown",
    951    "metadata": {},
    952    "source": [
    953     "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",
    954     "\n",
    955     "Taking what we've seen from above, let's see how we'd move that into the backtester."
    956    ]
    957   },
    958   {
    959    "cell_type": "code",
    960    "execution_count": 17,
    961    "metadata": {
    962     "collapsed": false
    963    },
    964    "outputs": [],
    965    "source": [
    966     "# This section is only importable in the backtester\n",
    967     "from quantopian.algorithm import attach_pipeline, pipeline_output\n",
    968     "\n",
    969     "# General pipeline imports\n",
    970     "from quantopian.pipeline import Pipeline\n",
    971     "from quantopian.pipeline.factors import AverageDollarVolume\n",
    972     "\n",
    973     "# For use in your algorithms via the pipeline API\n",
    974     "from quantopian.pipeline.data.quandl import cboe_vxxle\n",
    975     "\n",
    976     "def make_pipeline():\n",
    977     "    # Create our pipeline\n",
    978     "    pipe = Pipeline()\n",
    979     "    \n",
    980     "    # Screen out penny stocks and low liquidity securities.\n",
    981     "    dollar_volume = AverageDollarVolume(window_length=20)\n",
    982     "    is_liquid = dollar_volume.rank(ascending=False) < 1000\n",
    983     "    \n",
    984     "    # Create the mask that we will use for our percentile methods.\n",
    985     "    base_universe = (is_liquid)\n",
    986     "\n",
    987     "    # Add the datasets available\n",
    988     "    pipe.add(cboe_vxxle.open_.latest, 'vxxle_open')\n",
    989     "\n",
    990     "    # Set our pipeline screens\n",
    991     "    pipe.set_screen(is_liquid)\n",
    992     "    return pipe\n",
    993     "\n",
    994     "def initialize(context):\n",
    995     "    attach_pipeline(make_pipeline(), \"pipeline\")\n",
    996     "    \n",
    997     "def before_trading_start(context, data):\n",
    998     "    results = pipeline_output('pipeline')"
    999    ]
   1000   },
   1001   {
   1002    "cell_type": "markdown",
   1003    "metadata": {},
   1004    "source": [
   1005     "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>"
   1006    ]
   1007   }
   1008  ],
   1009  "metadata": {
   1010   "kernelspec": {
   1011    "display_name": "Python 2",
   1012    "language": "python",
   1013    "name": "python2"
   1014   },
   1015   "language_info": {
   1016    "codemirror_mode": {
   1017     "name": "ipython",
   1018     "version": 2
   1019    },
   1020    "file_extension": ".py",
   1021    "mimetype": "text/x-python",
   1022    "name": "python",
   1023    "nbconvert_exporter": "python",
   1024    "pygments_lexer": "ipython2",
   1025    "version": "2.7.11"
   1026   }
   1027  },
   1028  "nbformat": 4,
   1029  "nbformat_minor": 0
   1030 }