ml-finance-python

notebook.ipynb

(146519B)

 {
  "cells": [
   {
    "cell_type": "markdown",
    "metadata": {
     "collapsed": true
    },
    "source": [
     "# Quandl: Price of Gold\n",
     "\n",
     "In this notebook, we'll take a look at data set , available on [Quantopian](https://www.quantopian.com/data). This dataset spans from 1968 through the current day. It contains the value for the price of gold, as sourced by the Deutsche Bundesbank Data Repository. We access this data via the API provided by Quandl. See [Quandl's detail page](https://www.quandl.com/data/BUNDESBANK/BBK01_WT5511-Gold-Price-USD) for this set for more information.\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",
     "### 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",
     "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": [
     "# import the dataset\n",
     "from quantopian.interactive.data.quandl import bundesbank_bbk01_wt5511 as dataset\n",
     "# Since this data is public domain and provided by Quandl for free, there is no _free version of this\n",
     "# data set, as found in the premium sets. This import gets you the entirety of this data set.\n",
     "\n",
     "# import data operations\n",
     "from odo import odo\n",
     "# import other libraries we will use\n",
     "import pandas as pd\n",
     "import matplotlib.pyplot as plt"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
        "dshape(\"var * {value: float64, asof_date: datetime, timestamp: datetime}\")"
       ]
      },
      "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": [
        "12152"
       ],
       "text/plain": [
        "12152"
       ]
      },
      "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>value</th>\n",
        "      <th>asof_date</th>\n",
        "      <th>timestamp</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>1218.75</td>\n",
        "      <td>2016-02-23</td>\n",
        "      <td>2016-02-24 05:01:08.129270</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>1232.25</td>\n",
        "      <td>2016-02-24</td>\n",
        "      <td>2016-02-25 05:03:59.157245</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>1235.40</td>\n",
        "      <td>2016-02-25</td>\n",
        "      <td>2016-02-26 05:01:02.768591</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>"
       ],
       "text/plain": [
        "     value  asof_date                  timestamp\n",
        "0  1218.75 2016-02-23 2016-02-24 05:01:08.129270\n",
        "1  1232.25 2016-02-24 2016-02-25 05:03:59.157245\n",
        "2  1235.40 2016-02-25 2016-02-26 05:01:02.768591"
       ]
      },
      "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 plot it for fun."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0YAAAHZCAYAAACiiyunAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAFyPYAQAAADA9X4B1+4KwBEAsWMAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<matplotlib.figure.Figure at 0x7f2ee1eeaad0>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "gold_df = odo(dataset, pd.DataFrame)\n",
     "\n",
     "gold_df.plot(x='asof_date', y='value')\n",
     "plt.xlabel(\"As Of Date (asof_date)\")\n",
     "plt.ylabel(\"Price of Gold\")\n",
     "plt.title(\"Gold Price\")\n",
     "plt.legend().set_visible(False)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "The data points between 2007 and 2015 are missing because the number of results is limited to 10,000. Let's narrow the timeframe to get a complete picture of recent prices."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 7,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0cAAAHZCAYAAABNSUCZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nREREBID/D9pYtLJjmzICAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<matplotlib.figure.Figure at 0x7f2ee1dc6f50>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "small_df = odo(dataset[dataset.asof_date >= '2002-01-01'], pd.DataFrame)\n",
     "\n",
     "small_df.plot(x='asof_date', y='value')\n",
     "plt.xlabel(\"As Of Date (asof_date)\")\n",
     "plt.ylabel(\"Price of Gold\")\n",
     "plt.title(\"Gold Price\")\n",
     "plt.legend().set_visible(False)"
    ]
   },
   {
    "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 bundesbank_bbk01_wt5511 as gold`\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(gold.value.latest, 'value')`"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
     "collapsed": true
    },
    "outputs": [],
    "source": [
     "# Import necessary Pipeline modules\n",
     "from quantopian.pipeline import Pipeline\n",
     "from quantopian.research import run_pipeline"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
     "# For use in your algorithms\n",
     "# Using the full dataset in your pipeline algo\n",
     "from quantopian.pipeline.data.quandl import bundesbank_bbk01_wt5511 as gold"
    ]
   },
   {
    "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": 3,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "Here are the list of available fields per dataset:\n",
       "---------------------------------------------------\n",
       "\n",
       "Dataset: bundesbank_bbk01_wt5511\n",
       "\n",
       "Fields:\n",
       "value - 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",
     "for data in (gold,):\n",
     "    _print_fields(data)\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": 4,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
     "# Let's see what this data looks like when we run it through Pipeline\n",
     "# This is constructed the same way as you would in the backtester. For more information\n",
     "# 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\n",
     "pipe = Pipeline()\n",
     "       \n",
     "pipe.add(gold.value.latest, 'value')"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZAAAAEDCAYAAAAbTVIhAAAABmJLR0QA/wD/AP+gvaeTAAAgAElE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\nE+XF6x0HHTQAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
      },
      "execution_count": 5,
      "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": 6,
    "metadata": {
     "collapsed": false,
     "scrolled": true
    },
    "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>value</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(2 [AA])</th>\n",
        "      <td>1351.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(21 [AAME])</th>\n",
        "      <td>1351.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(24 [AAPL])</th>\n",
        "      <td>1351.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(25 [AA_PR])</th>\n",
        "      <td>1351.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(31 [ABAX])</th>\n",
        "      <td>1351.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(39 [DDC])</th>\n",
        "      <td>1351.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(41 [ARCB])</th>\n",
        "      <td>1351.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(52 [ABM])</th>\n",
        "      <td>1351.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(53 [ABMD])</th>\n",
        "      <td>1351.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(62 [ABT])</th>\n",
        "      <td>1351.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(64 [ABX])</th>\n",
        "      <td>1351.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(66 [AB])</th>\n",
        "      <td>1351.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(67 [ADSK])</th>\n",
        "      <td>1351.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(69 [ACAT])</th>\n",
        "      <td>1351.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(70 [VBF])</th>\n",
        "      <td>1351.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(76 [TAP])</th>\n",
        "      <td>1351.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(84 [ACET])</th>\n",
        "      <td>1351.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(86 [ACG])</th>\n",
        "      <td>1351.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(88 [ACI])</th>\n",
        "      <td>1351.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(99 [ACO])</th>\n",
        "      <td>1351.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(100 [IEP])</th>\n",
        "      <td>1351.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(106 [ACU])</th>\n",
        "      <td>1351.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(110 [ACXM])</th>\n",
        "      <td>1351.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(112 [ACY])</th>\n",
        "      <td>1351.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(114 [ADBE])</th>\n",
        "      <td>1351.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(117 [AEY])</th>\n",
        "      <td>1351.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(122 [ADI])</th>\n",
        "      <td>1351.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(128 [ADM])</th>\n",
        "      <td>1351.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(134 [SXCL])</th>\n",
        "      <td>1351.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(149 [ADX])</th>\n",
        "      <td>1351.0</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(45864 [CDX])</th>\n",
        "      <td>1248.5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(45865 [XNCR])</th>\n",
        "      <td>1248.5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(45866 [ZU])</th>\n",
        "      <td>1248.5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(45867 [EROS])</th>\n",
        "      <td>1248.5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(45873 [IR_WI])</th>\n",
        "      <td>1248.5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(45874 [ALLE])</th>\n",
        "      <td>1248.5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(45875 [HFIN])</th>\n",
        "      <td>1248.5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(45880 [CACQ])</th>\n",
        "      <td>1248.5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(45882 [TKF_WD])</th>\n",
        "      <td>1248.5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(45883 [IIF_WD])</th>\n",
        "      <td>1248.5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(45885 [EGF_WD])</th>\n",
        "      <td>1248.5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(45891 [OXFD])</th>\n",
        "      <td>1248.5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(45892 [TLOG])</th>\n",
        "      <td>1248.5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(45893 [VTL])</th>\n",
        "      <td>1248.5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(45894 [RTGN])</th>\n",
        "      <td>1248.5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(45895 [EMSH])</th>\n",
        "      <td>1248.5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(45896 [AMZG])</th>\n",
        "      <td>1248.5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(45902 [WBAI])</th>\n",
        "      <td>1248.5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(45903 [GOMO])</th>\n",
        "      <td>1248.5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(45904 [IPWR])</th>\n",
        "      <td>1248.5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(45905 [GFIS])</th>\n",
        "      <td>1248.5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(45906 [VNCE])</th>\n",
        "      <td>1248.5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(45907 [RITT_W])</th>\n",
        "      <td>1248.5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(45914 [EVGN])</th>\n",
        "      <td>1248.5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(45915 [NVGS])</th>\n",
        "      <td>1248.5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(48504 [ERUS])</th>\n",
        "      <td>1248.5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(49010 [TBRA])</th>\n",
        "      <td>1248.5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(49131 [OESX])</th>\n",
        "      <td>1248.5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(49259 [ITUS])</th>\n",
        "      <td>1248.5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equity(49523 [TLGT])</th>\n",
        "      <td>1248.5</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>134806 rows × 1 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
        "                                                   value\n",
        "2013-11-01 00:00:00+00:00 Equity(2 [AA])          1351.0\n",
        "                          Equity(21 [AAME])       1351.0\n",
        "                          Equity(24 [AAPL])       1351.0\n",
        "                          Equity(25 [AA_PR])      1351.0\n",
        "                          Equity(31 [ABAX])       1351.0\n",
        "                          Equity(39 [DDC])        1351.0\n",
        "                          Equity(41 [ARCB])       1351.0\n",
        "                          Equity(52 [ABM])        1351.0\n",
        "                          Equity(53 [ABMD])       1351.0\n",
        "                          Equity(62 [ABT])        1351.0\n",
        "                          Equity(64 [ABX])        1351.0\n",
        "                          Equity(66 [AB])         1351.0\n",
        "                          Equity(67 [ADSK])       1351.0\n",
        "                          Equity(69 [ACAT])       1351.0\n",
        "                          Equity(70 [VBF])        1351.0\n",
        "                          Equity(76 [TAP])        1351.0\n",
        "                          Equity(84 [ACET])       1351.0\n",
        "                          Equity(86 [ACG])        1351.0\n",
        "                          Equity(88 [ACI])        1351.0\n",
        "                          Equity(99 [ACO])        1351.0\n",
        "                          Equity(100 [IEP])       1351.0\n",
        "                          Equity(106 [ACU])       1351.0\n",
        "                          Equity(110 [ACXM])      1351.0\n",
        "                          Equity(112 [ACY])       1351.0\n",
        "                          Equity(114 [ADBE])      1351.0\n",
        "                          Equity(117 [AEY])       1351.0\n",
        "                          Equity(122 [ADI])       1351.0\n",
        "                          Equity(128 [ADM])       1351.0\n",
        "                          Equity(134 [SXCL])      1351.0\n",
        "                          Equity(149 [ADX])       1351.0\n",
        "...                                                  ...\n",
        "2013-11-25 00:00:00+00:00 Equity(45864 [CDX])     1248.5\n",
        "                          Equity(45865 [XNCR])    1248.5\n",
        "                          Equity(45866 [ZU])      1248.5\n",
        "                          Equity(45867 [EROS])    1248.5\n",
        "                          Equity(45873 [IR_WI])   1248.5\n",
        "                          Equity(45874 [ALLE])    1248.5\n",
        "                          Equity(45875 [HFIN])    1248.5\n",
        "                          Equity(45880 [CACQ])    1248.5\n",
        "                          Equity(45882 [TKF_WD])  1248.5\n",
        "                          Equity(45883 [IIF_WD])  1248.5\n",
        "                          Equity(45885 [EGF_WD])  1248.5\n",
        "                          Equity(45891 [OXFD])    1248.5\n",
        "                          Equity(45892 [TLOG])    1248.5\n",
        "                          Equity(45893 [VTL])     1248.5\n",
        "                          Equity(45894 [RTGN])    1248.5\n",
        "                          Equity(45895 [EMSH])    1248.5\n",
        "                          Equity(45896 [AMZG])    1248.5\n",
        "                          Equity(45902 [WBAI])    1248.5\n",
        "                          Equity(45903 [GOMO])    1248.5\n",
        "                          Equity(45904 [IPWR])    1248.5\n",
        "                          Equity(45905 [GFIS])    1248.5\n",
        "                          Equity(45906 [VNCE])    1248.5\n",
        "                          Equity(45907 [RITT_W])  1248.5\n",
        "                          Equity(45914 [EVGN])    1248.5\n",
        "                          Equity(45915 [NVGS])    1248.5\n",
        "                          Equity(48504 [ERUS])    1248.5\n",
        "                          Equity(49010 [TBRA])    1248.5\n",
        "                          Equity(49131 [OESX])    1248.5\n",
        "                          Equity(49259 [ITUS])    1248.5\n",
        "                          Equity(49523 [TLGT])    1248.5\n",
        "\n",
        "[134806 rows x 1 columns]"
       ]
      },
      "execution_count": 6,
      "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": [
     "Taking what we've seen from above, let's see how we'd move that into the backtester."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 11,
    "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",
     "\n",
     "# Import the datasets available\n",
     "# For use in your algorithms\n",
     "# Using the full dataset in your pipeline algo\n",
     "from quantopian.pipeline.data.quandl import bundesbank_bbk01_wt5511 as gold\n",
     "\n",
     "def make_pipeline():\n",
     "    # Create our pipeline\n",
     "    pipe = Pipeline()\n",
     "\n",
     "    # Add pipeline factors\n",
     "    pipe.add(gold.value.latest, 'value')\n",
     "    \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>"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
    "display_name": "Python 2",
    "language": "python",
    "name": "python2"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
     "version": 2
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython2",
    "version": "2.7.11"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 0
 }