{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "# CBOE VXFXI  Index\n",
    "\n",
    "In this notebook, we'll take a look at the CBOE VXFXI 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. CBOE VXFI is the China ETF Volatility Index which reflects the implied volatility of the FXI ETF\n",
    "\n",
    "## Notebook Contents\n",
    "\n",
    "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",
    "\n",
    "- <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",
    "- <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",
    "\n",
    "\n",
    "### Limits\n",
    "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",
    "\n",
    "With preamble in place, let's get started:\n",
    "\n",
    "<a id='interactive'></a>\n",
    "#Interactive Overview\n",
    "### Accessing the data with Blaze and Interactive on Research\n",
    "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",
    "\n",
    "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",
    "\n",
    "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",
    "\n",
    "Helpful links:\n",
    "* [Query building for Blaze](http://blaze.readthedocs.io/en/latest/queries.html)\n",
    "* [Pandas-to-Blaze dictionary](http://blaze.readthedocs.io/en/latest/rosetta-pandas.html)\n",
    "* [SQL-to-Blaze dictionary](http://blaze.readthedocs.io/en/latest/rosetta-sql.html).\n",
    "\n",
    "\n",
    "Once you've limited the size of your Blaze object, you can convert it to a Pandas DataFrames using:\n",
    "> `from odo import odo`  \n",
    "> `odo(expr, pandas.DataFrame)`\n",
    "\n",
    "\n",
    "###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>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# For use in Quantopian Research, exploring interactively\n",
    "from quantopian.interactive.data.quandl import cboe_vxfxi as dataset\n",
    "\n",
    "# import data operations\n",
    "from odo import odo\n",
    "# import other libraries we will use\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dshape(\"\"\"var * {\n",
       "  open_: float64,\n",
       "  high: float64,\n",
       "  low: float64,\n",
       "  close: float64,\n",
       "  asof_date: datetime,\n",
       "  timestamp: datetime\n",
       "  }\"\"\")"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's use blaze to understand the data a bit using Blaze dshape()\n",
    "dataset.dshape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "1307"
      ],
      "text/plain": [
       "1307"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# And how many rows are there?\n",
    "# N.B. we're using a Blaze function to do this, not len()\n",
    "dataset.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open_</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>asof_date</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>36.01</td>\n",
       "      <td>36.04</td>\n",
       "      <td>35.40</td>\n",
       "      <td>35.89</td>\n",
       "      <td>2016-02-23</td>\n",
       "      <td>2016-02-24 12:01:15.351703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37.98</td>\n",
       "      <td>38.83</td>\n",
       "      <td>37.26</td>\n",
       "      <td>37.49</td>\n",
       "      <td>2016-02-24</td>\n",
       "      <td>2016-02-25 12:01:06.402868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38.84</td>\n",
       "      <td>39.42</td>\n",
       "      <td>38.84</td>\n",
       "      <td>38.90</td>\n",
       "      <td>2016-02-25</td>\n",
       "      <td>2016-02-26 12:00:59.519176</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "   open_   high    low  close  asof_date                  timestamp\n",
       "0  36.01  36.04  35.40  35.89 2016-02-23 2016-02-24 12:01:15.351703\n",
       "1  37.98  38.83  37.26  37.49 2016-02-24 2016-02-25 12:01:06.402868\n",
       "2  38.84  39.42  38.84  38.90 2016-02-25 2016-02-26 12:00:59.519176"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's see what the data looks like. We'll grab the first three rows.\n",
    "dataset[:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's go over the columns:\n",
    "- **open**: open price for vxfxi\n",
    "- **high**: daily high for vxfxi\n",
    "- **low**: daily low for vxfxi\n",
    "- **close**: close price for vxfxi\n",
    "- **asof_date**: the timeframe to which this data applies\n",
    "- **timestamp**: this is our timestamp on when we registered the data.\n",
    "\n",
    "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",
    "\n",
    "We can select columns and rows with ease. Below, we'll do a simple plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open_</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>asof_date</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>36.01</td>\n",
       "      <td>36.04</td>\n",
       "      <td>35.40</td>\n",
       "      <td>35.89</td>\n",
       "      <td>2016-02-23</td>\n",
       "      <td>2016-02-24 12:01:15.351703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37.98</td>\n",
       "      <td>38.83</td>\n",
       "      <td>37.26</td>\n",
       "      <td>37.49</td>\n",
       "      <td>2016-02-24</td>\n",
       "      <td>2016-02-25 12:01:06.402868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38.84</td>\n",
       "      <td>39.42</td>\n",
       "      <td>38.84</td>\n",
       "      <td>38.90</td>\n",
       "      <td>2016-02-25</td>\n",
       "      <td>2016-02-26 12:00:59.519176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>37.94</td>\n",
       "      <td>38.10</td>\n",
       "      <td>37.05</td>\n",
       "      <td>37.35</td>\n",
       "      <td>2016-02-26</td>\n",
       "      <td>2016-02-29 12:00:59.357731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>38.68</td>\n",
       "      <td>38.68</td>\n",
       "      <td>37.27</td>\n",
       "      <td>37.58</td>\n",
       "      <td>2016-02-29</td>\n",
       "      <td>2016-03-01 12:02:36.764752</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   open_   high    low  close  asof_date                  timestamp\n",
       "0  36.01  36.04  35.40  35.89 2016-02-23 2016-02-24 12:01:15.351703\n",
       "1  37.98  38.83  37.26  37.49 2016-02-24 2016-02-25 12:01:06.402868\n",
       "2  38.84  39.42  38.84  38.90 2016-02-25 2016-02-26 12:00:59.519176\n",
       "3  37.94  38.10  37.05  37.35 2016-02-26 2016-02-29 12:00:59.357731\n",
       "4  38.68  38.68  37.27  37.58 2016-02-29 2016-03-01 12:02:36.764752"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Plotting this DataFrame\n",
    "df = odo(dataset, pd.DataFrame)\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open_</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>asof_date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-02-23</th>\n",
       "      <td>36.01</td>\n",
       "      <td>36.04</td>\n",
       "      <td>35.40</td>\n",
       "      <td>35.89</td>\n",
       "      <td>2016-02-24 12:01:15.351703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-24</th>\n",
       "      <td>37.98</td>\n",
       "      <td>38.83</td>\n",
       "      <td>37.26</td>\n",
       "      <td>37.49</td>\n",
       "      <td>2016-02-25 12:01:06.402868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-25</th>\n",
       "      <td>38.84</td>\n",
       "      <td>39.42</td>\n",
       "      <td>38.84</td>\n",
       "      <td>38.90</td>\n",
       "      <td>2016-02-26 12:00:59.519176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-26</th>\n",
       "      <td>37.94</td>\n",
       "      <td>38.10</td>\n",
       "      <td>37.05</td>\n",
       "      <td>37.35</td>\n",
       "      <td>2016-02-29 12:00:59.357731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-29</th>\n",
       "      <td>38.68</td>\n",
       "      <td>38.68</td>\n",
       "      <td>37.27</td>\n",
       "      <td>37.58</td>\n",
       "      <td>2016-03-01 12:02:36.764752</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            open_   high    low  close                  timestamp\n",
       "asof_date                                                        \n",
       "2016-02-23  36.01  36.04  35.40  35.89 2016-02-24 12:01:15.351703\n",
       "2016-02-24  37.98  38.83  37.26  37.49 2016-02-25 12:01:06.402868\n",
       "2016-02-25  38.84  39.42  38.84  38.90 2016-02-26 12:00:59.519176\n",
       "2016-02-26  37.94  38.10  37.05  37.35 2016-02-29 12:00:59.357731\n",
       "2016-02-29  38.68  38.68  37.27  37.58 2016-03-01 12:02:36.764752"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# So we can plot it, we'll set the index as the `asof_date`\n",
    "df['asof_date'] = pd.to_datetime(df['asof_date'])\n",
    "df = df.set_index(['asof_date'])\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f75fd329a50>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8rVu3RkZGBu68804AQHZ2Nn71q19hzpw5ePfdd9GuXTuMGTMmUi9PHt6SNs7h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\niIiIAtb/B/PBdGf6HcFaAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f75fd378ed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "df['open_'].plot(label=str(dataset))\n",
    "plt.ylabel(str(dataset))\n",
    "plt.legend()\n",
    "plt.title(\"Graphing %s since %s\" % (str(dataset), min(df.index)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='pipeline'></a>\n",
    "\n",
    "#Pipeline Overview\n",
    "\n",
    "### Accessing the data in your algorithms & research\n",
    "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",
    "\n",
    "Import the data set here\n",
    "> `from quantopian.pipeline.data.quandl import cboe_vxfxi`\n",
    "\n",
    "Then in intialize() you could do something simple like adding the raw value of one of the fields to your pipeline:\n",
    "> `pipe.add(cboe_vxfxi.open_.latest, 'open')`\n",
    "\n",
    "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Import necessary Pipeline modules\n",
    "from quantopian.pipeline import Pipeline\n",
    "from quantopian.research import run_pipeline\n",
    "from quantopian.pipeline.factors import AverageDollarVolume"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Import the datasets available\n",
    "from quantopian.pipeline.data.quandl import cboe_vxfxi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we've imported the data, let's take a look at which fields are available for each dataset.\n",
    "\n",
    "You'll find the dataset, the available fields, and the datatypes for each of those fields."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here are the list of available fields per dataset:\n",
      "---------------------------------------------------\n",
      "\n",
      "Dataset: cboe_vxfxi\n",
      "\n",
      "Fields:\n",
      "low - float64\n",
      "high - float64\n",
      "close - float64\n",
      "open_ - float64\n",
      "\n",
      "\n",
      "---------------------------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print \"Here are the list of available fields per dataset:\"\n",
    "print \"---------------------------------------------------\\n\"\n",
    "\n",
    "def _print_fields(dataset):\n",
    "    print \"Dataset: %s\\n\" % dataset.__name__\n",
    "    print \"Fields:\"\n",
    "    for field in list(dataset.columns):\n",
    "        print \"%s - %s\" % (field.name, field.dtype)\n",
    "    print \"\\n\"\n",
    "\n",
    "_print_fields(cboe_vxfxi)\n",
    "\n",
    "\n",
    "print \"---------------------------------------------------\\n\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "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",
    "\n",
    "\n",
    "This is constructed the same way as you would in the backtester. For more information on using Pipeline in Research view this thread:\n",
    "https://www.quantopian.com/posts/pipeline-in-research-build-test-and-visualize-your-factors-and-filters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pipe = Pipeline()\n",
    "       \n",
    "pipe.add(cboe_vxfxi.open_.latest, 'open_vxfxi')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Setting some basic liquidity strings (just for good habit)\n",
    "dollar_volume = AverageDollarVolume(window_length=20)\n",
    "top_1000_most_liquid = dollar_volume.rank(ascending=False) < 1000\n",
    "\n",
    "pipe.set_screen(top_1000_most_liquid & cboe_vxfxi.open_.latest.notnan())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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liqlU/nwW/T8f2F91y5TW9Tt3VWXI2+pct7Z6NWlk83aNWk90thzPqGqWKmG1f9TnKqHH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\ntGO/JCl3vlzyyJhB6dKnU8GiBTRgXJ+4fXcwny3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ZAXqtzXdq17KCpoxrmeCzDb2i76Wc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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The show_graph() method of pipeline objects produces a graph to show how it is being calculated.\n",
    "pipe.show_graph(format='png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>open_vxfxi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"30\" valign=\"top\">2013-11-01 00:00:00+00:00</th>\n",
       "      <th>Equity(21 [AAME])</th>\n",
       "      <td>22.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(25 [AA_PR])</th>\n",
       "      <td>22.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(117 [AEY])</th>\n",
       "      <td>22.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(225 [AHPI])</th>\n",
       "      <td>22.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(312 [ALOT])</th>\n",
       "      <td>22.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(392 [AMS])</th>\n",
       "      <td>22.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(468 [API])</th>\n",
       "      <td>22.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(548 [ASBI])</th>\n",
       "      <td>22.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(717 [BAMM])</th>\n",
       "      <td>22.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(790 [BDL])</th>\n",
       "      <td>22.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(880 [BIO_B])</th>\n",
       "      <td>22.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(925 [BKSC])</th>\n",
       "      <td>22.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(1088 [BRID])</th>\n",
       "      <td>22.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(1095 [BRN])</th>\n",
       "      <td>22.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(1157 [BTUI])</th>\n",
       "      <td>22.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(1190 [BWIN_A])</th>\n",
       "      <td>22.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(1193 [BWL_A])</th>\n",
       "      <td>22.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(1323 [CAW])</th>\n",
       "      <td>22.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(1653 [MOC])</th>\n",
       "      <td>22.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(1668 [CMS_PRB])</th>\n",
       "      <td>22.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(1988 [CUO])</th>\n",
       "      <td>22.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(2078 [DAIO])</th>\n",
       "      <td>22.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(2103 [ESCR])</th>\n",
       "      <td>22.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(2124 [DD_PRA])</th>\n",
       "      <td>22.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(2209 [DGSE])</th>\n",
       "      <td>22.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(2292 [DRCO])</th>\n",
       "      <td>22.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(2344 [DRAM])</th>\n",
       "      <td>22.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(2382 [DXR])</th>\n",
       "      <td>22.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(2389 [COBR])</th>\n",
       "      <td>22.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(2391 [DYNT])</th>\n",
       "      <td>22.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"30\" valign=\"top\">2013-11-25 00:00:00+00:00</th>\n",
       "      <th>Equity(45179 [ERW])</th>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(45195 [LGL_WS])</th>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(45203 [NASH])</th>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(45222 [QPAC_U])</th>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(45240 [INTL_L])</th>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(45270 [TIPT])</th>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(45288 [EMHD])</th>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(45301 [TRC_WS])</th>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(45390 [CPXX])</th>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(45412 [EAGL])</th>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(45414 [EAGL_W])</th>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(45420 [ROIQ_U])</th>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(45432 [SPCB])</th>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(45510 [MLPC])</th>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(45524 [NVEE])</th>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(45525 [NVEE_W])</th>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(45527 [JASN])</th>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(45536 [JASN_W])</th>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(45562 [ESBA])</th>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(45563 [OGCP])</th>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(45564 [FISK])</th>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(45646 [CHNA])</th>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(45678 [SLQD])</th>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(45680 [ADXS_W])</th>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(45717 [FTGC])</th>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(45768 [KODK_WS])</th>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(45792 [FTSD])</th>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(45824 [ROIQ_W])</th>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(45854 [PGAL])</th>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equity(45895 [EMSH])</th>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16983 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   open_vxfxi\n",
       "2013-11-01 00:00:00+00:00 Equity(21 [AAME])              22.7\n",
       "                          Equity(25 [AA_PR])             22.7\n",
       "                          Equity(117 [AEY])              22.7\n",
       "                          Equity(225 [AHPI])             22.7\n",
       "                          Equity(312 [ALOT])             22.7\n",
       "                          Equity(392 [AMS])              22.7\n",
       "                          Equity(468 [API])              22.7\n",
       "                          Equity(548 [ASBI])             22.7\n",
       "                          Equity(717 [BAMM])             22.7\n",
       "                          Equity(790 [BDL])              22.7\n",
       "                          Equity(880 [BIO_B])            22.7\n",
       "                          Equity(925 [BKSC])             22.7\n",
       "                          Equity(1088 [BRID])            22.7\n",
       "                          Equity(1095 [BRN])             22.7\n",
       "                          Equity(1157 [BTUI])            22.7\n",
       "                          Equity(1190 [BWIN_A])          22.7\n",
       "                          Equity(1193 [BWL_A])           22.7\n",
       "                          Equity(1323 [CAW])             22.7\n",
       "                          Equity(1653 [MOC])             22.7\n",
       "                          Equity(1668 [CMS_PRB])         22.7\n",
       "                          Equity(1988 [CUO])             22.7\n",
       "                          Equity(2078 [DAIO])            22.7\n",
       "                          Equity(2103 [ESCR])            22.7\n",
       "                          Equity(2124 [DD_PRA])          22.7\n",
       "                          Equity(2209 [DGSE])            22.7\n",
       "                          Equity(2292 [DRCO])            22.7\n",
       "                          Equity(2344 [DRAM])            22.7\n",
       "                          Equity(2382 [DXR])             22.7\n",
       "                          Equity(2389 [COBR])            22.7\n",
       "                          Equity(2391 [DYNT])            22.7\n",
       "...                                                       ...\n",
       "2013-11-25 00:00:00+00:00 Equity(45179 [ERW])            27.0\n",
       "                          Equity(45195 [LGL_WS])         27.0\n",
       "                          Equity(45203 [NASH])           27.0\n",
       "                          Equity(45222 [QPAC_U])         27.0\n",
       "                          Equity(45240 [INTL_L])         27.0\n",
       "                          Equity(45270 [TIPT])           27.0\n",
       "                          Equity(45288 [EMHD])           27.0\n",
       "                          Equity(45301 [TRC_WS])         27.0\n",
       "                          Equity(45390 [CPXX])           27.0\n",
       "                          Equity(45412 [EAGL])           27.0\n",
       "                          Equity(45414 [EAGL_W])         27.0\n",
       "                          Equity(45420 [ROIQ_U])         27.0\n",
       "                          Equity(45432 [SPCB])           27.0\n",
       "                          Equity(45510 [MLPC])           27.0\n",
       "                          Equity(45524 [NVEE])           27.0\n",
       "                          Equity(45525 [NVEE_W])         27.0\n",
       "                          Equity(45527 [JASN])           27.0\n",
       "                          Equity(45536 [JASN_W])         27.0\n",
       "                          Equity(45562 [ESBA])           27.0\n",
       "                          Equity(45563 [OGCP])           27.0\n",
       "                          Equity(45564 [FISK])           27.0\n",
       "                          Equity(45646 [CHNA])           27.0\n",
       "                          Equity(45678 [SLQD])           27.0\n",
       "                          Equity(45680 [ADXS_W])         27.0\n",
       "                          Equity(45717 [FTGC])           27.0\n",
       "                          Equity(45768 [KODK_WS])        27.0\n",
       "                          Equity(45792 [FTSD])           27.0\n",
       "                          Equity(45824 [ROIQ_W])         27.0\n",
       "                          Equity(45854 [PGAL])           27.0\n",
       "                          Equity(45895 [EMSH])           27.0\n",
       "\n",
       "[16983 rows x 1 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# run_pipeline will show the output of your pipeline\n",
    "pipe_output = run_pipeline(pipe, start_date='2013-11-01', end_date='2013-11-25')\n",
    "pipe_output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "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",
    "\n",
    "Taking what we've seen from above, let's see how we'd move that into the backtester."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# This section is only importable in the backtester\n",
    "from quantopian.algorithm import attach_pipeline, pipeline_output\n",
    "\n",
    "# General pipeline imports\n",
    "from quantopian.pipeline import Pipeline\n",
    "from quantopian.pipeline.factors import AverageDollarVolume\n",
    "\n",
    "# For use in your algorithms via the pipeline API\n",
    "from quantopian.pipeline.data.quandl import cboe_vxfxi\n",
    "\n",
    "def make_pipeline():\n",
    "    # Create our pipeline\n",
    "    pipe = Pipeline()\n",
    "    \n",
    "    # Screen out penny stocks and low liquidity securities.\n",
    "    dollar_volume = AverageDollarVolume(window_length=20)\n",
    "    is_liquid = dollar_volume.rank(ascending=False) < 1000\n",
    "    \n",
    "    # Create the mask that we will use for our percentile methods.\n",
    "    base_universe = (is_liquid)\n",
    "\n",
    "    # Add the datasets available\n",
    "    pipe.add(cboe_vxfxi.open_.latest, 'vxfxi_open')\n",
    "\n",
    "    # Set our pipeline screens\n",
    "    pipe.set_screen(is_liquid)\n",
    "    return pipe\n",
    "\n",
    "def initialize(context):\n",
    "    attach_pipeline(make_pipeline(), \"pipeline\")\n",
    "    \n",
    "def before_trading_start(context, data):\n",
    "    results = pipeline_output('pipeline')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "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>"
   ]
  }
 ],
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