ml-finance-python

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

(139204B)
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      9     "# CBOE VXV Index\n",
     10     "\n",
     11     "In this notebook, we'll take a look at the CBOE VXV Index dataset, available on the [Quantopian Store](https://www.quantopian.com/store). This dataset spans 04 Dec 2007 through the current day. This data has a daily frequency. CBOE VXV is a constant measure of 3-month implied volatility of the S&P 500 Index options\n",
     12     "\n",
     13     "## Notebook Contents\n",
     14     "\n",
     15     "There are two ways to access the data and you'll find both of them listed below. Just click on the section you'd like to read through.\n",
     16     "\n",
     17     "- <a href='#interactive'><strong>Interactive overview</strong></a>: This is only available on Research and uses blaze to give you access to large amounts of data. Recommended for exploration and plotting.\n",
     18     "- <a href='#pipeline'><strong>Pipeline overview</strong></a>: Data is made available through pipeline which is available on both the Research & Backtesting environment. Recommended for custom factor development and moving back & forth between research/backtesting.\n",
     19     "\n",
     20     "\n",
     21     "### Limits\n",
     22     "One key caveat: we limit the number of results returned from any given expression to 10,000 to protect against runaway memory usage. To be clear, you have access to all the data server side. We are limiting the size of the responses back from Blaze.\n",
     23     "\n",
     24     "With preamble in place, let's get started:\n",
     25     "\n",
     26     "<a id='interactive'></a>\n",
     27     "#Interactive Overview\n",
     28     "### Accessing the data with Blaze and Interactive on Research\n",
     29     "Partner datasets are available on Quantopian Research through an API service known as [Blaze](http://blaze.pydata.org). Blaze provides the Quantopian user with a convenient interface to access very large datasets, in an interactive, generic manner.\n",
     30     "\n",
     31     "Blaze provides an important function for accessing these datasets. Some of these sets are many millions of records. Bringing that data directly into Quantopian Research directly just is not viable. So Blaze allows us to provide a simple querying interface and shift the burden over to the server side.\n",
     32     "\n",
     33     "It is common to use Blaze to reduce your dataset in size, convert it over to Pandas and then to use Pandas for further computation, manipulation and visualization.\n",
     34     "\n",
     35     "Helpful links:\n",
     36     "* [Query building for Blaze](http://blaze.readthedocs.io/en/latest/queries.html)\n",
     37     "* [Pandas-to-Blaze dictionary](http://blaze.readthedocs.io/en/latest/rosetta-pandas.html)\n",
     38     "* [SQL-to-Blaze dictionary](http://blaze.readthedocs.io/en/latest/rosetta-sql.html).\n",
     39     "\n",
     40     "\n",
     41     "Once you've limited the size of your Blaze object, you can convert it to a Pandas DataFrames using:\n",
     42     "> `from odo import odo`  \n",
     43     "> `odo(expr, pandas.DataFrame)`\n",
     44     "\n",
     45     "\n",
     46     "###To see how this data can be used in your algorithm, search for the `Pipeline Overview` section of this notebook or head straight to <a href='#pipeline'>Pipeline Overview</a>"
     47    ]
     48   },
     49   {
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     51    "execution_count": 1,
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     56    "source": [
     57     "# For use in Quantopian Research, exploring interactively\n",
     58     "from quantopian.interactive.data.quandl import cboe_vxv as dataset\n",
     59     "\n",
     60     "# import data operations\n",
     61     "from odo import odo\n",
     62     "# import other libraries we will use\n",
     63     "import pandas as pd"
     64    ]
     65   },
     66   {
     67    "cell_type": "code",
     68    "execution_count": 2,
     69    "metadata": {
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     73     {
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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": 2,
     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": 3,
     99    "metadata": {
    100     "collapsed": false
    101    },
    102    "outputs": [
    103     {
    104      "data": {
    105       "text/html": [
    106        "2133"
    107       ],
    108       "text/plain": [
    109        "2133"
    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": 4,
    126    "metadata": {
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    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>21.82</td>\n",
    149        "      <td>22.75</td>\n",
    150        "      <td>21.47</td>\n",
    151        "      <td>22.44</td>\n",
    152        "      <td>2016-02-23</td>\n",
    153        "      <td>2016-02-24 12:01:25.608342</td>\n",
    154        "    </tr>\n",
    155        "    <tr>\n",
    156        "      <th>1</th>\n",
    157        "      <td>23.62</td>\n",
    158        "      <td>24.07</td>\n",
    159        "      <td>22.07</td>\n",
    160        "      <td>22.34</td>\n",
    161        "      <td>2016-02-24</td>\n",
    162        "      <td>2016-02-25 12:03:16.851758</td>\n",
    163        "    </tr>\n",
    164        "    <tr>\n",
    165        "      <th>2</th>\n",
    166        "      <td>22.19</td>\n",
    167        "      <td>22.69</td>\n",
    168        "      <td>20.63</td>\n",
    169        "      <td>21.24</td>\n",
    170        "      <td>2016-02-25</td>\n",
    171        "      <td>2016-02-26 12:01:06.410442</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  21.82  22.75  21.47  22.44 2016-02-23 2016-02-24 12:01:25.608342\n",
    179        "1  23.62  24.07  22.07  22.34 2016-02-24 2016-02-25 12:03:16.851758\n",
    180        "2  22.19  22.69  20.63  21.24 2016-02-25 2016-02-26 12:01:06.410442"
    181       ]
    182      },
    183      "execution_count": 4,
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    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 VXV\n",
    199     "- **high**: daily high for VXV\n",
    200     "- **low**: daily low for VXV\n",
    201     "- **close**: close price for VXV\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": 5,
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    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>21.82</td>\n",
    237        "      <td>22.75</td>\n",
    238        "      <td>21.47</td>\n",
    239        "      <td>22.44</td>\n",
    240        "      <td>2016-02-23</td>\n",
    241        "      <td>2016-02-24 12:01:25.608342</td>\n",
    242        "    </tr>\n",
    243        "    <tr>\n",
    244        "      <th>1</th>\n",
    245        "      <td>23.62</td>\n",
    246        "      <td>24.07</td>\n",
    247        "      <td>22.07</td>\n",
    248        "      <td>22.34</td>\n",
    249        "      <td>2016-02-24</td>\n",
    250        "      <td>2016-02-25 12:03:16.851758</td>\n",
    251        "    </tr>\n",
    252        "    <tr>\n",
    253        "      <th>2</th>\n",
    254        "      <td>22.19</td>\n",
    255        "      <td>22.69</td>\n",
    256        "      <td>20.63</td>\n",
    257        "      <td>21.24</td>\n",
    258        "      <td>2016-02-25</td>\n",
    259        "      <td>2016-02-26 12:01:06.410442</td>\n",
    260        "    </tr>\n",
    261        "    <tr>\n",
    262        "      <th>3</th>\n",
    263        "      <td>20.99</td>\n",
    264        "      <td>22.00</td>\n",
    265        "      <td>20.66</td>\n",
    266        "      <td>21.86</td>\n",
    267        "      <td>2016-02-26</td>\n",
    268        "      <td>2016-02-29 12:03:16.033453</td>\n",
    269        "    </tr>\n",
    270        "    <tr>\n",
    271        "      <th>4</th>\n",
    272        "      <td>22.08</td>\n",
    273        "      <td>22.51</td>\n",
    274        "      <td>21.38</td>\n",
    275        "      <td>22.39</td>\n",
    276        "      <td>2016-02-29</td>\n",
    277        "      <td>2016-03-01 12:02:04.332504</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  21.82  22.75  21.47  22.44 2016-02-23 2016-02-24 12:01:25.608342\n",
    286        "1  23.62  24.07  22.07  22.34 2016-02-24 2016-02-25 12:03:16.851758\n",
    287        "2  22.19  22.69  20.63  21.24 2016-02-25 2016-02-26 12:01:06.410442\n",
    288        "3  20.99  22.00  20.66  21.86 2016-02-26 2016-02-29 12:03:16.033453\n",
    289        "4  22.08  22.51  21.38  22.39 2016-02-29 2016-03-01 12:02:04.332504"
    290       ]
    291      },
    292      "execution_count": 5,
    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   },
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    304    "cell_type": "code",
    305    "execution_count": 6,
    306    "metadata": {
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    311      "data": {
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    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>21.82</td>\n",
    337        "      <td>22.75</td>\n",
    338        "      <td>21.47</td>\n",
    339        "      <td>22.44</td>\n",
    340        "      <td>2016-02-24 12:01:25.608342</td>\n",
    341        "    </tr>\n",
    342        "    <tr>\n",
    343        "      <th>2016-02-24</th>\n",
    344        "      <td>23.62</td>\n",
    345        "      <td>24.07</td>\n",
    346        "      <td>22.07</td>\n",
    347        "      <td>22.34</td>\n",
    348        "      <td>2016-02-25 12:03:16.851758</td>\n",
    349        "    </tr>\n",
    350        "    <tr>\n",
    351        "      <th>2016-02-25</th>\n",
    352        "      <td>22.19</td>\n",
    353        "      <td>22.69</td>\n",
    354        "      <td>20.63</td>\n",
    355        "      <td>21.24</td>\n",
    356        "      <td>2016-02-26 12:01:06.410442</td>\n",
    357        "    </tr>\n",
    358        "    <tr>\n",
    359        "      <th>2016-02-26</th>\n",
    360        "      <td>20.99</td>\n",
    361        "      <td>22.00</td>\n",
    362        "      <td>20.66</td>\n",
    363        "      <td>21.86</td>\n",
    364        "      <td>2016-02-29 12:03:16.033453</td>\n",
    365        "    </tr>\n",
    366        "    <tr>\n",
    367        "      <th>2016-02-29</th>\n",
    368        "      <td>22.08</td>\n",
    369        "      <td>22.51</td>\n",
    370        "      <td>21.38</td>\n",
    371        "      <td>22.39</td>\n",
    372        "      <td>2016-03-01 12:02:04.332504</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  21.82  22.75  21.47  22.44 2016-02-24 12:01:25.608342\n",
    382        "2016-02-24  23.62  24.07  22.07  22.34 2016-02-25 12:03:16.851758\n",
    383        "2016-02-25  22.19  22.69  20.63  21.24 2016-02-26 12:01:06.410442\n",
    384        "2016-02-26  20.99  22.00  20.66  21.86 2016-02-29 12:03:16.033453\n",
    385        "2016-02-29  22.08  22.51  21.38  22.39 2016-03-01 12:02:04.332504"
    386       ]
    387      },
    388      "execution_count": 6,
    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": 7,
    403    "metadata": {
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    406    "outputs": [
    407     {
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    410        "<matplotlib.text.Text at 0x7f960a9ac9d0>"
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4MjIykJmZiTvvvBMAMHfuXFx22WWYPXs23nvvPbRu3RqjR48O1elJSS5aUNOn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\n0UYEIbUAAAAASUVORK5CYII=\n",
    420       "text/plain": [
    421        "<matplotlib.figure.Figure at 0x7f960a9f8e90>"
    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_vxv`\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_vxv.open_.latest, 'open_vxv')`\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": 12,
    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": 9,
    473    "metadata": {
    474     "collapsed": false
    475    },
    476    "outputs": [],
    477    "source": [
    478     "# Import the datasets available\n",
    479     "from quantopian.pipeline.data.quandl import cboe_vxv"
    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": 10,
    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_vxv\n",
    506       "\n",
    507       "Fields:\n",
    508       "high - float64\n",
    509       "close - float64\n",
    510       "low - 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_vxv)\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": 16,
    550    "metadata": {
    551     "collapsed": false
    552    },
    553    "outputs": [],
    554    "source": [
    555     "pipe = Pipeline()\n",
    556     "       \n",
    557     "pipe.add(cboe_vxv.open_.latest, 'open_vxv')"
    558    ]
    559   },
    560   {
    561    "cell_type": "code",
    562    "execution_count": 17,
    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_vxv.open_.latest.notnan())"
    573    ]
    574   },
    575   {
    576    "cell_type": "code",
    577    "execution_count": 18,
    578    "metadata": {
    579     "collapsed": false
    580    },
    581    "outputs": [
    582     {
    583      "data": {
    584       "image/png": 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VO1cEAAAcHWEScHK+vr7q0KGDJKljx47y9fW1c0VwRlWqVFG5cuUkSX369LFv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\nQZo58QAAAABJRU5ErkJggg==\n",
    585       "text/plain": [
    586        "<IPython.core.display.Image object>"
    587       ]
    588      },
    589      "execution_count": 18,
    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": 19,
    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_vxv</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>15.01</td>\n",
    623        "    </tr>\n",
    624        "    <tr>\n",
    625        "      <th>Equity(25 [AA_PR])</th>\n",
    626        "      <td>15.01</td>\n",
    627        "    </tr>\n",
    628        "    <tr>\n",
    629        "      <th>Equity(117 [AEY])</th>\n",
    630        "      <td>15.01</td>\n",
    631        "    </tr>\n",
    632        "    <tr>\n",
    633        "      <th>Equity(225 [AHPI])</th>\n",
    634        "      <td>15.01</td>\n",
    635        "    </tr>\n",
    636        "    <tr>\n",
    637        "      <th>Equity(312 [ALOT])</th>\n",
    638        "      <td>15.01</td>\n",
    639        "    </tr>\n",
    640        "    <tr>\n",
    641        "      <th>Equity(392 [AMS])</th>\n",
    642        "      <td>15.01</td>\n",
    643        "    </tr>\n",
    644        "    <tr>\n",
    645        "      <th>Equity(468 [API])</th>\n",
    646        "      <td>15.01</td>\n",
    647        "    </tr>\n",
    648        "    <tr>\n",
    649        "      <th>Equity(548 [ASBI])</th>\n",
    650        "      <td>15.01</td>\n",
    651        "    </tr>\n",
    652        "    <tr>\n",
    653        "      <th>Equity(717 [BAMM])</th>\n",
    654        "      <td>15.01</td>\n",
    655        "    </tr>\n",
    656        "    <tr>\n",
    657        "      <th>Equity(790 [BDL])</th>\n",
    658        "      <td>15.01</td>\n",
    659        "    </tr>\n",
    660        "    <tr>\n",
    661        "      <th>Equity(880 [BIO_B])</th>\n",
    662        "      <td>15.01</td>\n",
    663        "    </tr>\n",
    664        "    <tr>\n",
    665        "      <th>Equity(925 [BKSC])</th>\n",
    666        "      <td>15.01</td>\n",
    667        "    </tr>\n",
    668        "    <tr>\n",
    669        "      <th>Equity(1088 [BRID])</th>\n",
    670        "      <td>15.01</td>\n",
    671        "    </tr>\n",
    672        "    <tr>\n",
    673        "      <th>Equity(1095 [BRN])</th>\n",
    674        "      <td>15.01</td>\n",
    675        "    </tr>\n",
    676        "    <tr>\n",
    677        "      <th>Equity(1157 [BTUI])</th>\n",
    678        "      <td>15.01</td>\n",
    679        "    </tr>\n",
    680        "    <tr>\n",
    681        "      <th>Equity(1190 [BWIN_A])</th>\n",
    682        "      <td>15.01</td>\n",
    683        "    </tr>\n",
    684        "    <tr>\n",
    685        "      <th>Equity(1193 [BWL_A])</th>\n",
    686        "      <td>15.01</td>\n",
    687        "    </tr>\n",
    688        "    <tr>\n",
    689        "      <th>Equity(1323 [CAW])</th>\n",
    690        "      <td>15.01</td>\n",
    691        "    </tr>\n",
    692        "    <tr>\n",
    693        "      <th>Equity(1653 [MOC])</th>\n",
    694        "      <td>15.01</td>\n",
    695        "    </tr>\n",
    696        "    <tr>\n",
    697        "      <th>Equity(1668 [CMS_PRB])</th>\n",
    698        "      <td>15.01</td>\n",
    699        "    </tr>\n",
    700        "    <tr>\n",
    701        "      <th>Equity(1988 [CUO])</th>\n",
    702        "      <td>15.01</td>\n",
    703        "    </tr>\n",
    704        "    <tr>\n",
    705        "      <th>Equity(2078 [DAIO])</th>\n",
    706        "      <td>15.01</td>\n",
    707        "    </tr>\n",
    708        "    <tr>\n",
    709        "      <th>Equity(2103 [ESCR])</th>\n",
    710        "      <td>15.01</td>\n",
    711        "    </tr>\n",
    712        "    <tr>\n",
    713        "      <th>Equity(2124 [DD_PRA])</th>\n",
    714        "      <td>15.01</td>\n",
    715        "    </tr>\n",
    716        "    <tr>\n",
    717        "      <th>Equity(2209 [DGSE])</th>\n",
    718        "      <td>15.01</td>\n",
    719        "    </tr>\n",
    720        "    <tr>\n",
    721        "      <th>Equity(2292 [DRCO])</th>\n",
    722        "      <td>15.01</td>\n",
    723        "    </tr>\n",
    724        "    <tr>\n",
    725        "      <th>Equity(2344 [DRAM])</th>\n",
    726        "      <td>15.01</td>\n",
    727        "    </tr>\n",
    728        "    <tr>\n",
    729        "      <th>Equity(2382 [DXR])</th>\n",
    730        "      <td>15.01</td>\n",
    731        "    </tr>\n",
    732        "    <tr>\n",
    733        "      <th>Equity(2389 [COBR])</th>\n",
    734        "      <td>15.01</td>\n",
    735        "    </tr>\n",
    736        "    <tr>\n",
    737        "      <th>Equity(2391 [DYNT])</th>\n",
    738        "      <td>15.01</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>14.49</td>\n",
    749        "    </tr>\n",
    750        "    <tr>\n",
    751        "      <th>Equity(45195 [LGL_WS])</th>\n",
    752        "      <td>14.49</td>\n",
    753        "    </tr>\n",
    754        "    <tr>\n",
    755        "      <th>Equity(45203 [NASH])</th>\n",
    756        "      <td>14.49</td>\n",
    757        "    </tr>\n",
    758        "    <tr>\n",
    759        "      <th>Equity(45222 [QPAC_U])</th>\n",
    760        "      <td>14.49</td>\n",
    761        "    </tr>\n",
    762        "    <tr>\n",
    763        "      <th>Equity(45240 [INTL_L])</th>\n",
    764        "      <td>14.49</td>\n",
    765        "    </tr>\n",
    766        "    <tr>\n",
    767        "      <th>Equity(45270 [TIPT])</th>\n",
    768        "      <td>14.49</td>\n",
    769        "    </tr>\n",
    770        "    <tr>\n",
    771        "      <th>Equity(45288 [EMHD])</th>\n",
    772        "      <td>14.49</td>\n",
    773        "    </tr>\n",
    774        "    <tr>\n",
    775        "      <th>Equity(45301 [TRC_WS])</th>\n",
    776        "      <td>14.49</td>\n",
    777        "    </tr>\n",
    778        "    <tr>\n",
    779        "      <th>Equity(45390 [CPXX])</th>\n",
    780        "      <td>14.49</td>\n",
    781        "    </tr>\n",
    782        "    <tr>\n",
    783        "      <th>Equity(45412 [EAGL])</th>\n",
    784        "      <td>14.49</td>\n",
    785        "    </tr>\n",
    786        "    <tr>\n",
    787        "      <th>Equity(45414 [EAGL_W])</th>\n",
    788        "      <td>14.49</td>\n",
    789        "    </tr>\n",
    790        "    <tr>\n",
    791        "      <th>Equity(45420 [ROIQ_U])</th>\n",
    792        "      <td>14.49</td>\n",
    793        "    </tr>\n",
    794        "    <tr>\n",
    795        "      <th>Equity(45432 [SPCB])</th>\n",
    796        "      <td>14.49</td>\n",
    797        "    </tr>\n",
    798        "    <tr>\n",
    799        "      <th>Equity(45510 [MLPC])</th>\n",
    800        "      <td>14.49</td>\n",
    801        "    </tr>\n",
    802        "    <tr>\n",
    803        "      <th>Equity(45524 [NVEE])</th>\n",
    804        "      <td>14.49</td>\n",
    805        "    </tr>\n",
    806        "    <tr>\n",
    807        "      <th>Equity(45525 [NVEE_W])</th>\n",
    808        "      <td>14.49</td>\n",
    809        "    </tr>\n",
    810        "    <tr>\n",
    811        "      <th>Equity(45527 [JASN])</th>\n",
    812        "      <td>14.49</td>\n",
    813        "    </tr>\n",
    814        "    <tr>\n",
    815        "      <th>Equity(45536 [JASN_W])</th>\n",
    816        "      <td>14.49</td>\n",
    817        "    </tr>\n",
    818        "    <tr>\n",
    819        "      <th>Equity(45562 [ESBA])</th>\n",
    820        "      <td>14.49</td>\n",
    821        "    </tr>\n",
    822        "    <tr>\n",
    823        "      <th>Equity(45563 [OGCP])</th>\n",
    824        "      <td>14.49</td>\n",
    825        "    </tr>\n",
    826        "    <tr>\n",
    827        "      <th>Equity(45564 [FISK])</th>\n",
    828        "      <td>14.49</td>\n",
    829        "    </tr>\n",
    830        "    <tr>\n",
    831        "      <th>Equity(45646 [CHNA])</th>\n",
    832        "      <td>14.49</td>\n",
    833        "    </tr>\n",
    834        "    <tr>\n",
    835        "      <th>Equity(45678 [SLQD])</th>\n",
    836        "      <td>14.49</td>\n",
    837        "    </tr>\n",
    838        "    <tr>\n",
    839        "      <th>Equity(45680 [ADXS_W])</th>\n",
    840        "      <td>14.49</td>\n",
    841        "    </tr>\n",
    842        "    <tr>\n",
    843        "      <th>Equity(45717 [FTGC])</th>\n",
    844        "      <td>14.49</td>\n",
    845        "    </tr>\n",
    846        "    <tr>\n",
    847        "      <th>Equity(45768 [KODK_WS])</th>\n",
    848        "      <td>14.49</td>\n",
    849        "    </tr>\n",
    850        "    <tr>\n",
    851        "      <th>Equity(45792 [FTSD])</th>\n",
    852        "      <td>14.49</td>\n",
    853        "    </tr>\n",
    854        "    <tr>\n",
    855        "      <th>Equity(45824 [ROIQ_W])</th>\n",
    856        "      <td>14.49</td>\n",
    857        "    </tr>\n",
    858        "    <tr>\n",
    859        "      <th>Equity(45854 [PGAL])</th>\n",
    860        "      <td>14.49</td>\n",
    861        "    </tr>\n",
    862        "    <tr>\n",
    863        "      <th>Equity(45895 [EMSH])</th>\n",
    864        "      <td>14.49</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_vxv\n",
    873        "2013-11-01 00:00:00+00:00 Equity(21 [AAME])           15.01\n",
    874        "                          Equity(25 [AA_PR])          15.01\n",
    875        "                          Equity(117 [AEY])           15.01\n",
    876        "                          Equity(225 [AHPI])          15.01\n",
    877        "                          Equity(312 [ALOT])          15.01\n",
    878        "                          Equity(392 [AMS])           15.01\n",
    879        "                          Equity(468 [API])           15.01\n",
    880        "                          Equity(548 [ASBI])          15.01\n",
    881        "                          Equity(717 [BAMM])          15.01\n",
    882        "                          Equity(790 [BDL])           15.01\n",
    883        "                          Equity(880 [BIO_B])         15.01\n",
    884        "                          Equity(925 [BKSC])          15.01\n",
    885        "                          Equity(1088 [BRID])         15.01\n",
    886        "                          Equity(1095 [BRN])          15.01\n",
    887        "                          Equity(1157 [BTUI])         15.01\n",
    888        "                          Equity(1190 [BWIN_A])       15.01\n",
    889        "                          Equity(1193 [BWL_A])        15.01\n",
    890        "                          Equity(1323 [CAW])          15.01\n",
    891        "                          Equity(1653 [MOC])          15.01\n",
    892        "                          Equity(1668 [CMS_PRB])      15.01\n",
    893        "                          Equity(1988 [CUO])          15.01\n",
    894        "                          Equity(2078 [DAIO])         15.01\n",
    895        "                          Equity(2103 [ESCR])         15.01\n",
    896        "                          Equity(2124 [DD_PRA])       15.01\n",
    897        "                          Equity(2209 [DGSE])         15.01\n",
    898        "                          Equity(2292 [DRCO])         15.01\n",
    899        "                          Equity(2344 [DRAM])         15.01\n",
    900        "                          Equity(2382 [DXR])          15.01\n",
    901        "                          Equity(2389 [COBR])         15.01\n",
    902        "                          Equity(2391 [DYNT])         15.01\n",
    903        "...                                                     ...\n",
    904        "2013-11-25 00:00:00+00:00 Equity(45179 [ERW])         14.49\n",
    905        "                          Equity(45195 [LGL_WS])      14.49\n",
    906        "                          Equity(45203 [NASH])        14.49\n",
    907        "                          Equity(45222 [QPAC_U])      14.49\n",
    908        "                          Equity(45240 [INTL_L])      14.49\n",
    909        "                          Equity(45270 [TIPT])        14.49\n",
    910        "                          Equity(45288 [EMHD])        14.49\n",
    911        "                          Equity(45301 [TRC_WS])      14.49\n",
    912        "                          Equity(45390 [CPXX])        14.49\n",
    913        "                          Equity(45412 [EAGL])        14.49\n",
    914        "                          Equity(45414 [EAGL_W])      14.49\n",
    915        "                          Equity(45420 [ROIQ_U])      14.49\n",
    916        "                          Equity(45432 [SPCB])        14.49\n",
    917        "                          Equity(45510 [MLPC])        14.49\n",
    918        "                          Equity(45524 [NVEE])        14.49\n",
    919        "                          Equity(45525 [NVEE_W])      14.49\n",
    920        "                          Equity(45527 [JASN])        14.49\n",
    921        "                          Equity(45536 [JASN_W])      14.49\n",
    922        "                          Equity(45562 [ESBA])        14.49\n",
    923        "                          Equity(45563 [OGCP])        14.49\n",
    924        "                          Equity(45564 [FISK])        14.49\n",
    925        "                          Equity(45646 [CHNA])        14.49\n",
    926        "                          Equity(45678 [SLQD])        14.49\n",
    927        "                          Equity(45680 [ADXS_W])      14.49\n",
    928        "                          Equity(45717 [FTGC])        14.49\n",
    929        "                          Equity(45768 [KODK_WS])     14.49\n",
    930        "                          Equity(45792 [FTSD])        14.49\n",
    931        "                          Equity(45824 [ROIQ_W])      14.49\n",
    932        "                          Equity(45854 [PGAL])        14.49\n",
    933        "                          Equity(45895 [EMSH])        14.49\n",
    934        "\n",
    935        "[16983 rows x 1 columns]"
    936       ]
    937      },
    938      "execution_count": 19,
    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": 21,
    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_vxv\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_vxv.open_.latest, 'vxv_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 }