ml-finance-python

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

(196631B)
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      9     "# Accern: Alpha One\n",
     10     "\n",
     11     "In this notebook, we'll take a look at Accern's *Alpha One* dataset, available on [Quantopian](https://www.quantopian.com/store). This dataset spans August 26, 2012 through the current day. It contains comprehensive sentiment analysis related to equities from 20 million+ sources.\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     "### Free samples and limits\n",
     21     "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",
     22     "\n",
     23     "There is a *free* version of this dataset as well as a paid one. The free sample includes data until 2 months prior to the current date.\n",
     24     "\n",
     25     "To access the most up-to-date values for this data set for trading a live algorithm (as with other partner sets), you need to purchase acess to the full set.\n",
     26     "\n",
     27     "With preamble in place, let's get started:\n",
     28     "\n",
     29     "<a id='interactive'></a>\n",
     30     "#Interactive Overview\n",
     31     "### Accessing the data with Blaze and Interactive on Research\n",
     32     "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",
     33     "\n",
     34     "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",
     35     "\n",
     36     "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",
     37     "\n",
     38     "Helpful links:\n",
     39     "* [Query building for Blaze](http://blaze.readthedocs.io/en/latest/queries.html)\n",
     40     "* [Pandas-to-Blaze dictionary](http://blaze.readthedocs.io/en/latest/rosetta-pandas.html)\n",
     41     "* [SQL-to-Blaze dictionary](http://blaze.readthedocs.io/en/latest/rosetta-sql.html).\n",
     42     "\n",
     43     "Once you've limited the size of your Blaze object, you can convert it to a Pandas DataFrames using:\n",
     44     "> `from odo import odo`  \n",
     45     "> `odo(expr, pandas.DataFrame)`\n",
     46     "\n",
     47     "\n",
     48     "###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>"
     49    ]
     50   },
     51   {
     52    "cell_type": "code",
     53    "execution_count": 15,
     54    "metadata": {
     55     "collapsed": false
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     57    "outputs": [],
     58    "source": [
     59     "# import the dataset\n",
     60     "from quantopian.interactive.data.accern import alphaone_free as dataset\n",
     61     "\n",
     62     "# or if you want to import the paid dataset, use:\n",
     63     "# from quantopian.interactive.data.accern import alphaone\n",
     64     "\n",
     65     "# import data operations\n",
     66     "from odo import odo\n",
     67     "# import other libraries we will use\n",
     68     "import pandas as pd\n",
     69     "import matplotlib.pyplot as plt"
     70    ]
     71   },
     72   {
     73    "cell_type": "code",
     74    "execution_count": 3,
     75    "metadata": {
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     78    "outputs": [
     79     {
     80      "data": {
     81       "text/plain": [
     82        "dshape(\"\"\"var * {\n",
     83        "  symbol: string,\n",
     84        "  name: ?string,\n",
     85        "  article_sentiment: float64,\n",
     86        "  impact_score: float64,\n",
     87        "  sid: int64,\n",
     88        "  asof_date: datetime,\n",
     89        "  timestamp: datetime\n",
     90        "  }\"\"\")"
     91       ]
     92      },
     93      "execution_count": 3,
     94      "metadata": {},
     95      "output_type": "execute_result"
     96     }
     97    ],
     98    "source": [
     99     "# Let's use blaze to understand the data a bit using Blaze dshape()\n",
    100     "dataset.dshape"
    101    ]
    102   },
    103   {
    104    "cell_type": "code",
    105    "execution_count": 4,
    106    "metadata": {
    107     "collapsed": false
    108    },
    109    "outputs": [
    110     {
    111      "data": {
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    113        "591697"
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    116        "591697"
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    119      "execution_count": 4,
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    122     }
    123    ],
    124    "source": [
    125     "# And how many rows are there?\n",
    126     "# N.B. we're using a Blaze function to do this, not len()\n",
    127     "dataset.count()"
    128    ]
    129   },
    130   {
    131    "cell_type": "code",
    132    "execution_count": 5,
    133    "metadata": {
    134     "collapsed": false
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    137     {
    138      "data": {
    139       "text/html": [
    140        "<table border=\"1\" class=\"dataframe\">\n",
    141        "  <thead>\n",
    142        "    <tr style=\"text-align: right;\">\n",
    143        "      <th></th>\n",
    144        "      <th>symbol</th>\n",
    145        "      <th>name</th>\n",
    146        "      <th>article_sentiment</th>\n",
    147        "      <th>impact_score</th>\n",
    148        "      <th>sid</th>\n",
    149        "      <th>asof_date</th>\n",
    150        "      <th>timestamp</th>\n",
    151        "    </tr>\n",
    152        "  </thead>\n",
    153        "  <tbody>\n",
    154        "    <tr>\n",
    155        "      <th>0</th>\n",
    156        "      <td>AAPL</td>\n",
    157        "      <td>Apple Inc.</td>\n",
    158        "      <td>-0.194</td>\n",
    159        "      <td>70.495</td>\n",
    160        "      <td>24</td>\n",
    161        "      <td>2012-08-26</td>\n",
    162        "      <td>2012-08-26</td>\n",
    163        "    </tr>\n",
    164        "    <tr>\n",
    165        "      <th>1</th>\n",
    166        "      <td>GRPN</td>\n",
    167        "      <td>Groupon, Inc.</td>\n",
    168        "      <td>-0.167</td>\n",
    169        "      <td>79.000</td>\n",
    170        "      <td>42118</td>\n",
    171        "      <td>2012-08-26</td>\n",
    172        "      <td>2012-08-26</td>\n",
    173        "    </tr>\n",
    174        "    <tr>\n",
    175        "      <th>2</th>\n",
    176        "      <td>TWTR</td>\n",
    177        "      <td>Twitter, Inc.</td>\n",
    178        "      <td>-0.473</td>\n",
    179        "      <td>29.362</td>\n",
    180        "      <td>19171</td>\n",
    181        "      <td>2012-08-26</td>\n",
    182        "      <td>2012-08-26</td>\n",
    183        "    </tr>\n",
    184        "  </tbody>\n",
    185        "</table>"
    186       ],
    187       "text/plain": [
    188        "  symbol           name  article_sentiment  impact_score    sid  asof_date  \\\n",
    189        "0   AAPL     Apple Inc.             -0.194        70.495     24 2012-08-26   \n",
    190        "1   GRPN  Groupon, Inc.             -0.167        79.000  42118 2012-08-26   \n",
    191        "2   TWTR  Twitter, Inc.             -0.473        29.362  19171 2012-08-26   \n",
    192        "\n",
    193        "   timestamp  \n",
    194        "0 2012-08-26  \n",
    195        "1 2012-08-26  \n",
    196        "2 2012-08-26  "
    197       ]
    198      },
    199      "execution_count": 5,
    200      "metadata": {},
    201      "output_type": "execute_result"
    202     }
    203    ],
    204    "source": [
    205     "# Let's see what the data looks like. We'll grab the first three rows.\n",
    206     "dataset[:3]"
    207    ]
    208   },
    209   {
    210    "cell_type": "markdown",
    211    "metadata": {},
    212    "source": [
    213     "Let's go over the columns:\n",
    214     "- **symbol**: the ticker symbol of the company.\n",
    215     "- **name**: the name of the company.\n",
    216     "- **asof_date**: Alpha One's timestamp of delivery of **article_sentiment**.\n",
    217     "- **article_sentiment**: a score in [-1,1] reflecting the sentiment of articles written about the company in the last day. The higher score, the more positive the outlook.\n",
    218     "- **timestamp**: this is our timestamp on when we registered the data.\n",
    219     "- **impact_score**: on [0,100], this is the probability that the stock price will change by more than 1% (given by: close - open / open) on the next trading day.\n",
    220     "- **sid**: the equity's unique identifier. Use this instead of ticker or name. \n",
    221     "\n",
    222     "We've done much of the data processing for you. Fields like `timestamp` and `sid` are standardized across all our Store Datasets, so the datasets are easy to combine. We have standardized the `sid` across all our equity databases.\n",
    223     "\n",
    224     "We can select columns and rows with ease. Below, we'll fetch all entries for the sid 24, which identifies AAPL. We're really only interested in the timestamp, article sentiment, and the impact score, so we'll display only those."
    225    ]
    226   },
    227   {
    228    "cell_type": "code",
    229    "execution_count": 7,
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    234     {
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    237        "<table border=\"1\" class=\"dataframe\">\n",
    238        "  <thead>\n",
    239        "    <tr style=\"text-align: right;\">\n",
    240        "      <th></th>\n",
    241        "      <th>asof_date</th>\n",
    242        "      <th>article_sentiment</th>\n",
    243        "      <th>impact_score</th>\n",
    244        "    </tr>\n",
    245        "  </thead>\n",
    246        "  <tbody>\n",
    247        "    <tr>\n",
    248        "      <th>0</th>\n",
    249        "      <td>2012-08-26</td>\n",
    250        "      <td>-0.194</td>\n",
    251        "      <td>70.495</td>\n",
    252        "    </tr>\n",
    253        "    <tr>\n",
    254        "      <th>1</th>\n",
    255        "      <td>2012-08-27</td>\n",
    256        "      <td>-0.126</td>\n",
    257        "      <td>70.086</td>\n",
    258        "    </tr>\n",
    259        "    <tr>\n",
    260        "      <th>2</th>\n",
    261        "      <td>2012-08-28</td>\n",
    262        "      <td>-0.367</td>\n",
    263        "      <td>70.100</td>\n",
    264        "    </tr>\n",
    265        "    <tr>\n",
    266        "      <th>3</th>\n",
    267        "      <td>2012-08-29</td>\n",
    268        "      <td>-0.219</td>\n",
    269        "      <td>70.436</td>\n",
    270        "    </tr>\n",
    271        "    <tr>\n",
    272        "      <th>4</th>\n",
    273        "      <td>2012-08-30</td>\n",
    274        "      <td>-0.071</td>\n",
    275        "      <td>70.738</td>\n",
    276        "    </tr>\n",
    277        "    <tr>\n",
    278        "      <th>5</th>\n",
    279        "      <td>2012-08-31</td>\n",
    280        "      <td>-0.167</td>\n",
    281        "      <td>70.417</td>\n",
    282        "    </tr>\n",
    283        "    <tr>\n",
    284        "      <th>6</th>\n",
    285        "      <td>2012-09-01</td>\n",
    286        "      <td>-0.392</td>\n",
    287        "      <td>71.379</td>\n",
    288        "    </tr>\n",
    289        "    <tr>\n",
    290        "      <th>7</th>\n",
    291        "      <td>2012-09-02</td>\n",
    292        "      <td>-0.270</td>\n",
    293        "      <td>70.664</td>\n",
    294        "    </tr>\n",
    295        "    <tr>\n",
    296        "      <th>8</th>\n",
    297        "      <td>2012-09-03</td>\n",
    298        "      <td>-0.320</td>\n",
    299        "      <td>70.570</td>\n",
    300        "    </tr>\n",
    301        "    <tr>\n",
    302        "      <th>9</th>\n",
    303        "      <td>2012-09-04</td>\n",
    304        "      <td>-0.361</td>\n",
    305        "      <td>70.746</td>\n",
    306        "    </tr>\n",
    307        "    <tr>\n",
    308        "      <th>10</th>\n",
    309        "      <td>2012-09-05</td>\n",
    310        "      <td>-0.295</td>\n",
    311        "      <td>70.631</td>\n",
    312        "    </tr>\n",
    313        "  </tbody>\n",
    314        "</table>"
    315       ],
    316       "text/plain": [
    317        "    asof_date  article_sentiment  impact_score\n",
    318        "0  2012-08-26             -0.194        70.495\n",
    319        "1  2012-08-27             -0.126        70.086\n",
    320        "2  2012-08-28             -0.367        70.100\n",
    321        "3  2012-08-29             -0.219        70.436\n",
    322        "4  2012-08-30             -0.071        70.738\n",
    323        "5  2012-08-31             -0.167        70.417\n",
    324        "6  2012-09-01             -0.392        71.379\n",
    325        "7  2012-09-02             -0.270        70.664\n",
    326        "8  2012-09-03             -0.320        70.570\n",
    327        "9  2012-09-04             -0.361        70.746\n",
    328        "..."
    329       ]
    330      },
    331      "execution_count": 7,
    332      "metadata": {},
    333      "output_type": "execute_result"
    334     }
    335    ],
    336    "source": [
    337     "aapl = dataset[dataset.sid==24][['asof_date','article_sentiment', 'impact_score']].sort('asof_date')\n",
    338     "# Whenever you print a Blaze Data Object, it will be truncated to ten rows.\n",
    339     "aapl"
    340    ]
    341   },
    342   {
    343    "cell_type": "markdown",
    344    "metadata": {},
    345    "source": [
    346     "You can see that Accern collects data for every day, not just for every business day.\n",
    347     "\n",
    348     "Let's convert `aapl` to a DataFrame."
    349    ]
    350   },
    351   {
    352    "cell_type": "code",
    353    "execution_count": 10,
    354    "metadata": {
    355     "collapsed": false
    356    },
    357    "outputs": [
    358     {
    359      "data": {
    360       "text/html": [
    361        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
    362        "<table border=\"1\" class=\"dataframe\">\n",
    363        "  <thead>\n",
    364        "    <tr style=\"text-align: right;\">\n",
    365        "      <th></th>\n",
    366        "      <th>article_sentiment</th>\n",
    367        "      <th>impact_score</th>\n",
    368        "    </tr>\n",
    369        "  </thead>\n",
    370        "  <tbody>\n",
    371        "    <tr>\n",
    372        "      <th>2012-08-26</th>\n",
    373        "      <td>-0.194</td>\n",
    374        "      <td>70.495</td>\n",
    375        "    </tr>\n",
    376        "    <tr>\n",
    377        "      <th>2012-08-27</th>\n",
    378        "      <td>-0.126</td>\n",
    379        "      <td>70.086</td>\n",
    380        "    </tr>\n",
    381        "    <tr>\n",
    382        "      <th>2012-08-28</th>\n",
    383        "      <td>-0.367</td>\n",
    384        "      <td>70.100</td>\n",
    385        "    </tr>\n",
    386        "  </tbody>\n",
    387        "</table>\n",
    388        "</div>"
    389       ],
    390       "text/plain": [
    391        "            article_sentiment  impact_score\n",
    392        "2012-08-26             -0.194        70.495\n",
    393        "2012-08-27             -0.126        70.086\n",
    394        "2012-08-28             -0.367        70.100"
    395       ]
    396      },
    397      "execution_count": 10,
    398      "metadata": {},
    399      "output_type": "execute_result"
    400     }
    401    ],
    402    "source": [
    403     "aapl_sentiment = odo(aapl, pd.DataFrame)\n",
    404     "# let's say we only want one year of data.\n",
    405     "aapl_sentiment = aapl_sentiment[aapl_sentiment['asof_date'] <= '2013-08-26']\n",
    406     "# suppose we want the rows to be indexed by timestamp.\n",
    407     "aapl_sentiment.index = list(aapl_sentiment['asof_date'])\n",
    408     "aapl_sentiment.drop('asof_date',1,inplace=True)\n",
    409     "# display the first three rows. DataFrames, when printed, display 60 rows at a time.\n",
    410     "aapl_sentiment[:3]"
    411    ]
    412   },
    413   {
    414    "cell_type": "markdown",
    415    "metadata": {},
    416    "source": [
    417     "Let's take a look at the sentiment in a chart"
    418    ]
    419   },
    420   {
    421    "cell_type": "code",
    422    "execution_count": 11,
    423    "metadata": {
    424     "collapsed": false
    425    },
    426    "outputs": [
    427     {
    428      "data": {
    429       "text/plain": [
    430        "<matplotlib.axes._subplots.AxesSubplot at 0x7f4a905f0650>"
    431       ]
    432      },
    433      "execution_count": 11,
    434      "metadata": {},
    435      "output_type": "execute_result"
    436     },
    437     {
    438      "data": {
    439       "image/png": 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SOYN1zlnghYOLvp93TU7C0DtzbdbSmNBSUHPZZZfhU5/6FK666irs3bsXW7duxcjICADg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gFQSjrwdIJ4ChWZKAu6nqqaEJ2lM5AlS1WSedESE8g6g2rRoB6orYarL2p8c8KmWhoPauThOhan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\n",
    440       "text/plain": [
    441        "<matplotlib.figure.Figure at 0x7f4a905f0a50>"
    442       ]
    443      },
    444      "metadata": {},
    445      "output_type": "display_data"
    446     }
    447    ],
    448    "source": [
    449     "aapl_sentiment.plot(y='article_sentiment')"
    450    ]
    451   },
    452   {
    453    "cell_type": "markdown",
    454    "metadata": {},
    455    "source": [
    456     "<a id='pipeline'></a>\n",
    457     "\n",
    458     "#Pipeline Overview\n",
    459     "\n",
    460     "### Accessing the data in your algorithms & research\n",
    461     "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 PsychSignal data, you can add this data to your pipeline as follows:\n",
    462     "\n",
    463     "Import the data set here\n",
    464     "> `from quantopian.pipeline.data.accern import alphaone`\n",
    465     "\n",
    466     "Then in intialize() you could do something simple like adding the raw value of one of the fields to your pipeline:\n",
    467     "> `pipe.add(alphaone.impact_score.latest, 'impact_score')`"
    468    ]
    469   },
    470   {
    471    "cell_type": "code",
    472    "execution_count": 1,
    473    "metadata": {
    474     "collapsed": true
    475    },
    476    "outputs": [],
    477    "source": [
    478     "# Import necessary Pipeline modules\n",
    479     "from quantopian.pipeline import Pipeline\n",
    480     "from quantopian.research import run_pipeline\n",
    481     "from quantopian.pipeline.factors import AverageDollarVolume"
    482    ]
    483   },
    484   {
    485    "cell_type": "code",
    486    "execution_count": 2,
    487    "metadata": {
    488     "collapsed": false
    489    },
    490    "outputs": [],
    491    "source": [
    492     "# For use in your algorithms\n",
    493     "# Using the full paid dataset in your pipeline algo\n",
    494     "# from quantopian.pipeline.data.accern import alphaone\n",
    495     "\n",
    496     "# Using the free sample in your pipeline algo\n",
    497     "from quantopian.pipeline.data.accern import alphaone_free"
    498    ]
    499   },
    500   {
    501    "cell_type": "markdown",
    502    "metadata": {},
    503    "source": [
    504     "Now that we've imported the data, let's take a look at which fields are available for each dataset.\n",
    505     "\n",
    506     "You'll find the dataset, the available fields, and the datatypes for each of those fields."
    507    ]
    508   },
    509   {
    510    "cell_type": "code",
    511    "execution_count": 3,
    512    "metadata": {
    513     "collapsed": false
    514    },
    515    "outputs": [
    516     {
    517      "name": "stdout",
    518      "output_type": "stream",
    519      "text": [
    520       "Here are the list of available fields per dataset:\n",
    521       "---------------------------------------------------\n",
    522       "\n",
    523       "Dataset: alphaone_free\n",
    524       "\n",
    525       "Fields:\n",
    526       "article_sentiment - float64\n",
    527       "impact_score - float64\n",
    528       "\n",
    529       "\n",
    530       "---------------------------------------------------\n",
    531       "\n"
    532      ]
    533     }
    534    ],
    535    "source": [
    536     "print \"Here are the list of available fields per dataset:\"\n",
    537     "print \"---------------------------------------------------\\n\"\n",
    538     "\n",
    539     "def _print_fields(dataset):\n",
    540     "    print \"Dataset: %s\\n\" % dataset.__name__\n",
    541     "    print \"Fields:\"\n",
    542     "    for field in list(dataset.columns):\n",
    543     "        print \"%s - %s\" % (field.name, field.dtype)\n",
    544     "    print \"\\n\"\n",
    545     "\n",
    546     "for data in (alphaone_free,):\n",
    547     "    _print_fields(data)\n",
    548     "\n",
    549     "\n",
    550     "print \"---------------------------------------------------\\n\""
    551    ]
    552   },
    553   {
    554    "cell_type": "markdown",
    555    "metadata": {},
    556    "source": [
    557     "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",
    558     "\n",
    559     "\n",
    560     "This is constructed the same way as you would in the backtester. For more information on using Pipeline in Research view this thread:\n",
    561     "https://www.quantopian.com/posts/pipeline-in-research-build-test-and-visualize-your-factors-and-filters"
    562    ]
    563   },
    564   {
    565    "cell_type": "code",
    566    "execution_count": 4,
    567    "metadata": {
    568     "collapsed": false
    569    },
    570    "outputs": [],
    571    "source": [
    572     "# Let's see what this data looks like when we run it through Pipeline\n",
    573     "# This is constructed the same way as you would in the backtester. For more information\n",
    574     "# on using Pipeline in Research view this thread:\n",
    575     "# https://www.quantopian.com/posts/pipeline-in-research-build-test-and-visualize-your-factors-and-filters\n",
    576     "pipe = Pipeline()\n",
    577     "\n",
    578     "pipe.add(alphaone_free.impact_score.latest, 'impact_score')\n",
    579     "pipe.add(alphaone_free.article_sentiment.latest, 'article_sentiment')"
    580    ]
    581   },
    582   {
    583    "cell_type": "code",
    584    "execution_count": 5,
    585    "metadata": {
    586     "collapsed": false
    587    },
    588    "outputs": [],
    589    "source": [
    590     "# Setting some basic liquidity strings (just for good habit)\n",
    591     "dollar_volume = AverageDollarVolume(window_length=20)\n",
    592     "top_1000_most_liquid = dollar_volume.rank(ascending=False) < 1000\n",
    593     "\n",
    594     "pipe.set_screen(top_1000_most_liquid & alphaone_free.article_sentiment.latest.notnan())"
    595    ]
    596   },
    597   {
    598    "cell_type": "code",
    599    "execution_count": 6,
    600    "metadata": {
    601     "collapsed": false
    602    },
    603    "outputs": [
    604     {
    605      "data": {
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    607       "text/plain": [
    608        "<IPython.core.display.Image object>"
    609       ]
    610      },
    611      "execution_count": 6,
    612      "metadata": {},
    613      "output_type": "execute_result"
    614     }
    615    ],
    616    "source": [
    617     "# The show_graph() method of pipeline objects produces a graph to show how it is being calculated.\n",
    618     "pipe.show_graph(format='png')"
    619    ]
    620   },
    621   {
    622    "cell_type": "code",
    623    "execution_count": 7,
    624    "metadata": {
    625     "collapsed": false,
    626     "scrolled": true
    627    },
    628    "outputs": [
    629     {
    630      "data": {
    631       "text/html": [
    632        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
    633        "<table border=\"1\" class=\"dataframe\">\n",
    634        "  <thead>\n",
    635        "    <tr style=\"text-align: right;\">\n",
    636        "      <th></th>\n",
    637        "      <th></th>\n",
    638        "      <th>article_sentiment</th>\n",
    639        "      <th>impact_score</th>\n",
    640        "    </tr>\n",
    641        "  </thead>\n",
    642        "  <tbody>\n",
    643        "    <tr>\n",
    644        "      <th rowspan=\"30\" valign=\"top\">2013-11-01 00:00:00+00:00</th>\n",
    645        "      <th>Equity(2 [AA])</th>\n",
    646        "      <td>0.000</td>\n",
    647        "      <td>76.000</td>\n",
    648        "    </tr>\n",
    649        "    <tr>\n",
    650        "      <th>Equity(24 [AAPL])</th>\n",
    651        "      <td>0.001</td>\n",
    652        "      <td>73.065</td>\n",
    653        "    </tr>\n",
    654        "    <tr>\n",
    655        "      <th>Equity(62 [ABT])</th>\n",
    656        "      <td>-0.392</td>\n",
    657        "      <td>77.271</td>\n",
    658        "    </tr>\n",
    659        "    <tr>\n",
    660        "      <th>Equity(64 [ABX])</th>\n",
    661        "      <td>0.000</td>\n",
    662        "      <td>96.000</td>\n",
    663        "    </tr>\n",
    664        "    <tr>\n",
    665        "      <th>Equity(67 [ADSK])</th>\n",
    666        "      <td>1.000</td>\n",
    667        "      <td>88.333</td>\n",
    668        "    </tr>\n",
    669        "    <tr>\n",
    670        "      <th>Equity(76 [TAP])</th>\n",
    671        "      <td>-1.000</td>\n",
    672        "      <td>80.000</td>\n",
    673        "    </tr>\n",
    674        "    <tr>\n",
    675        "      <th>Equity(88 [ACI])</th>\n",
    676        "      <td>-0.190</td>\n",
    677        "      <td>84.143</td>\n",
    678        "    </tr>\n",
    679        "    <tr>\n",
    680        "      <th>Equity(114 [ADBE])</th>\n",
    681        "      <td>-0.341</td>\n",
    682        "      <td>76.354</td>\n",
    683        "    </tr>\n",
    684        "    <tr>\n",
    685        "      <th>Equity(122 [ADI])</th>\n",
    686        "      <td>0.000</td>\n",
    687        "      <td>88.000</td>\n",
    688        "    </tr>\n",
    689        "    <tr>\n",
    690        "      <th>Equity(128 [ADM])</th>\n",
    691        "      <td>-0.333</td>\n",
    692        "      <td>88.667</td>\n",
    693        "    </tr>\n",
    694        "    <tr>\n",
    695        "      <th>Equity(154 [AEM])</th>\n",
    696        "      <td>0.000</td>\n",
    697        "      <td>100.000</td>\n",
    698        "    </tr>\n",
    699        "    <tr>\n",
    700        "      <th>Equity(161 [AEP])</th>\n",
    701        "      <td>0.000</td>\n",
    702        "      <td>86.000</td>\n",
    703        "    </tr>\n",
    704        "    <tr>\n",
    705        "      <th>Equity(166 [AES])</th>\n",
    706        "      <td>0.000</td>\n",
    707        "      <td>70.000</td>\n",
    708        "    </tr>\n",
    709        "    <tr>\n",
    710        "      <th>Equity(168 [AET])</th>\n",
    711        "      <td>0.708</td>\n",
    712        "      <td>84.500</td>\n",
    713        "    </tr>\n",
    714        "    <tr>\n",
    715        "      <th>Equity(185 [AFL])</th>\n",
    716        "      <td>-0.405</td>\n",
    717        "      <td>86.000</td>\n",
    718        "    </tr>\n",
    719        "    <tr>\n",
    720        "      <th>Equity(197 [AGCO])</th>\n",
    721        "      <td>1.000</td>\n",
    722        "      <td>84.000</td>\n",
    723        "    </tr>\n",
    724        "    <tr>\n",
    725        "      <th>Equity(216 [HES])</th>\n",
    726        "      <td>0.500</td>\n",
    727        "      <td>87.200</td>\n",
    728        "    </tr>\n",
    729        "    <tr>\n",
    730        "      <th>Equity(239 [AIG])</th>\n",
    731        "      <td>0.083</td>\n",
    732        "      <td>81.667</td>\n",
    733        "    </tr>\n",
    734        "    <tr>\n",
    735        "      <th>Equity(273 [ALU])</th>\n",
    736        "      <td>0.000</td>\n",
    737        "      <td>92.000</td>\n",
    738        "    </tr>\n",
    739        "    <tr>\n",
    740        "      <th>Equity(300 [ALK])</th>\n",
    741        "      <td>0.000</td>\n",
    742        "      <td>69.000</td>\n",
    743        "    </tr>\n",
    744        "    <tr>\n",
    745        "      <th>Equity(328 [ALTR])</th>\n",
    746        "      <td>0.000</td>\n",
    747        "      <td>82.000</td>\n",
    748        "    </tr>\n",
    749        "    <tr>\n",
    750        "      <th>Equity(337 [AMAT])</th>\n",
    751        "      <td>1.000</td>\n",
    752        "      <td>100.000</td>\n",
    753        "    </tr>\n",
    754        "    <tr>\n",
    755        "      <th>Equity(351 [AMD])</th>\n",
    756        "      <td>-0.061</td>\n",
    757        "      <td>78.727</td>\n",
    758        "    </tr>\n",
    759        "    <tr>\n",
    760        "      <th>Equity(353 [AME])</th>\n",
    761        "      <td>0.000</td>\n",
    762        "      <td>89.875</td>\n",
    763        "    </tr>\n",
    764        "    <tr>\n",
    765        "      <th>Equity(357 [TWX])</th>\n",
    766        "      <td>0.000</td>\n",
    767        "      <td>92.727</td>\n",
    768        "    </tr>\n",
    769        "    <tr>\n",
    770        "      <th>Equity(368 [AMGN])</th>\n",
    771        "      <td>0.000</td>\n",
    772        "      <td>79.000</td>\n",
    773        "    </tr>\n",
    774        "    <tr>\n",
    775        "      <th>Equity(410 [AN])</th>\n",
    776        "      <td>0.000</td>\n",
    777        "      <td>77.000</td>\n",
    778        "    </tr>\n",
    779        "    <tr>\n",
    780        "      <th>Equity(438 [AON])</th>\n",
    781        "      <td>0.000</td>\n",
    782        "      <td>92.333</td>\n",
    783        "    </tr>\n",
    784        "    <tr>\n",
    785        "      <th>Equity(448 [APA])</th>\n",
    786        "      <td>0.000</td>\n",
    787        "      <td>85.000</td>\n",
    788        "    </tr>\n",
    789        "    <tr>\n",
    790        "      <th>Equity(455 [APC])</th>\n",
    791        "      <td>0.714</td>\n",
    792        "      <td>88.571</td>\n",
    793        "    </tr>\n",
    794        "    <tr>\n",
    795        "      <th>...</th>\n",
    796        "      <th>...</th>\n",
    797        "      <td>...</td>\n",
    798        "      <td>...</td>\n",
    799        "    </tr>\n",
    800        "    <tr>\n",
    801        "      <th rowspan=\"30\" valign=\"top\">2013-11-25 00:00:00+00:00</th>\n",
    802        "      <th>Equity(42950 [FB])</th>\n",
    803        "      <td>0.358</td>\n",
    804        "      <td>69.945</td>\n",
    805        "    </tr>\n",
    806        "    <tr>\n",
    807        "      <th>Equity(43127 [NOW])</th>\n",
    808        "      <td>1.000</td>\n",
    809        "      <td>94.000</td>\n",
    810        "    </tr>\n",
    811        "    <tr>\n",
    812        "      <th>Equity(43399 [ADT])</th>\n",
    813        "      <td>-1.000</td>\n",
    814        "      <td>70.000</td>\n",
    815        "    </tr>\n",
    816        "    <tr>\n",
    817        "      <th>Equity(43405 [KRFT])</th>\n",
    818        "      <td>0.000</td>\n",
    819        "      <td>63.000</td>\n",
    820        "    </tr>\n",
    821        "    <tr>\n",
    822        "      <th>Equity(43413 [TRLA])</th>\n",
    823        "      <td>1.000</td>\n",
    824        "      <td>84.000</td>\n",
    825        "    </tr>\n",
    826        "    <tr>\n",
    827        "      <th>Equity(43414 [SRC])</th>\n",
    828        "      <td>0.000</td>\n",
    829        "      <td>84.667</td>\n",
    830        "    </tr>\n",
    831        "    <tr>\n",
    832        "      <th>Equity(43500 [RLGY])</th>\n",
    833        "      <td>0.000</td>\n",
    834        "      <td>100.000</td>\n",
    835        "    </tr>\n",
    836        "    <tr>\n",
    837        "      <th>Equity(43510 [WDAY])</th>\n",
    838        "      <td>0.000</td>\n",
    839        "      <td>90.000</td>\n",
    840        "    </tr>\n",
    841        "    <tr>\n",
    842        "      <th>Equity(43512 [FANG])</th>\n",
    843        "      <td>-1.000</td>\n",
    844        "      <td>53.000</td>\n",
    845        "    </tr>\n",
    846        "    <tr>\n",
    847        "      <th>Equity(43647 [YY])</th>\n",
    848        "      <td>-1.000</td>\n",
    849        "      <td>88.000</td>\n",
    850        "    </tr>\n",
    851        "    <tr>\n",
    852        "      <th>Equity(43694 [ABBV])</th>\n",
    853        "      <td>0.000</td>\n",
    854        "      <td>71.000</td>\n",
    855        "    </tr>\n",
    856        "    <tr>\n",
    857        "      <th>Equity(43721 [SCTY])</th>\n",
    858        "      <td>-0.500</td>\n",
    859        "      <td>100.000</td>\n",
    860        "    </tr>\n",
    861        "    <tr>\n",
    862        "      <th>Equity(43919 [LMCA])</th>\n",
    863        "      <td>-1.000</td>\n",
    864        "      <td>66.000</td>\n",
    865        "    </tr>\n",
    866        "    <tr>\n",
    867        "      <th>Equity(44060 [ZTS])</th>\n",
    868        "      <td>0.000</td>\n",
    869        "      <td>84.000</td>\n",
    870        "    </tr>\n",
    871        "    <tr>\n",
    872        "      <th>Equity(44102 [XONE])</th>\n",
    873        "      <td>0.000</td>\n",
    874        "      <td>91.000</td>\n",
    875        "    </tr>\n",
    876        "    <tr>\n",
    877        "      <th>Equity(44645 [VOYA])</th>\n",
    878        "      <td>1.000</td>\n",
    879        "      <td>70.000</td>\n",
    880        "    </tr>\n",
    881        "    <tr>\n",
    882        "      <th>Equity(44747 [DATA])</th>\n",
    883        "      <td>0.000</td>\n",
    884        "      <td>55.000</td>\n",
    885        "    </tr>\n",
    886        "    <tr>\n",
    887        "      <th>Equity(44931 [NWSA])</th>\n",
    888        "      <td>0.000</td>\n",
    889        "      <td>37.000</td>\n",
    890        "    </tr>\n",
    891        "    <tr>\n",
    892        "      <th>Equity(44965 [GOGO])</th>\n",
    893        "      <td>-1.000</td>\n",
    894        "      <td>88.000</td>\n",
    895        "    </tr>\n",
    896        "    <tr>\n",
    897        "      <th>Equity(45656 [GLPI])</th>\n",
    898        "      <td>0.000</td>\n",
    899        "      <td>63.000</td>\n",
    900        "    </tr>\n",
    901        "    <tr>\n",
    902        "      <th>Equity(45689 [VJET])</th>\n",
    903        "      <td>0.000</td>\n",
    904        "      <td>100.000</td>\n",
    905        "    </tr>\n",
    906        "    <tr>\n",
    907        "      <th>Equity(45755 [BRX])</th>\n",
    908        "      <td>0.000</td>\n",
    909        "      <td>100.000</td>\n",
    910        "    </tr>\n",
    911        "    <tr>\n",
    912        "      <th>Equity(45769 [WUBA])</th>\n",
    913        "      <td>0.000</td>\n",
    914        "      <td>83.000</td>\n",
    915        "    </tr>\n",
    916        "    <tr>\n",
    917        "      <th>Equity(45780 [TCS])</th>\n",
    918        "      <td>1.000</td>\n",
    919        "      <td>66.000</td>\n",
    920        "    </tr>\n",
    921        "    <tr>\n",
    922        "      <th>Equity(45781 [QUNR])</th>\n",
    923        "      <td>-1.000</td>\n",
    924        "      <td>83.000</td>\n",
    925        "    </tr>\n",
    926        "    <tr>\n",
    927        "      <th>Equity(45815 [TWTR])</th>\n",
    928        "      <td>-0.043</td>\n",
    929        "      <td>30.354</td>\n",
    930        "    </tr>\n",
    931        "    <tr>\n",
    932        "      <th>Equity(45848 [STAY])</th>\n",
    933        "      <td>0.000</td>\n",
    934        "      <td>90.000</td>\n",
    935        "    </tr>\n",
    936        "    <tr>\n",
    937        "      <th>Equity(45866 [ZU])</th>\n",
    938        "      <td>-1.000</td>\n",
    939        "      <td>28.000</td>\n",
    940        "    </tr>\n",
    941        "    <tr>\n",
    942        "      <th>Equity(45902 [WBAI])</th>\n",
    943        "      <td>0.000</td>\n",
    944        "      <td>81.000</td>\n",
    945        "    </tr>\n",
    946        "    <tr>\n",
    947        "      <th>Equity(45906 [VNCE])</th>\n",
    948        "      <td>0.167</td>\n",
    949        "      <td>66.667</td>\n",
    950        "    </tr>\n",
    951        "  </tbody>\n",
    952        "</table>\n",
    953        "<p>14482 rows × 2 columns</p>\n",
    954        "</div>"
    955       ],
    956       "text/plain": [
    957        "                                                article_sentiment  \\\n",
    958        "2013-11-01 00:00:00+00:00 Equity(2 [AA])                    0.000   \n",
    959        "                          Equity(24 [AAPL])                 0.001   \n",
    960        "                          Equity(62 [ABT])                 -0.392   \n",
    961        "                          Equity(64 [ABX])                  0.000   \n",
    962        "                          Equity(67 [ADSK])                 1.000   \n",
    963        "                          Equity(76 [TAP])                 -1.000   \n",
    964        "                          Equity(88 [ACI])                 -0.190   \n",
    965        "                          Equity(114 [ADBE])               -0.341   \n",
    966        "                          Equity(122 [ADI])                 0.000   \n",
    967        "                          Equity(128 [ADM])                -0.333   \n",
    968        "                          Equity(154 [AEM])                 0.000   \n",
    969        "                          Equity(161 [AEP])                 0.000   \n",
    970        "                          Equity(166 [AES])                 0.000   \n",
    971        "                          Equity(168 [AET])                 0.708   \n",
    972        "                          Equity(185 [AFL])                -0.405   \n",
    973        "                          Equity(197 [AGCO])                1.000   \n",
    974        "                          Equity(216 [HES])                 0.500   \n",
    975        "                          Equity(239 [AIG])                 0.083   \n",
    976        "                          Equity(273 [ALU])                 0.000   \n",
    977        "                          Equity(300 [ALK])                 0.000   \n",
    978        "                          Equity(328 [ALTR])                0.000   \n",
    979        "                          Equity(337 [AMAT])                1.000   \n",
    980        "                          Equity(351 [AMD])                -0.061   \n",
    981        "                          Equity(353 [AME])                 0.000   \n",
    982        "                          Equity(357 [TWX])                 0.000   \n",
    983        "                          Equity(368 [AMGN])                0.000   \n",
    984        "                          Equity(410 [AN])                  0.000   \n",
    985        "                          Equity(438 [AON])                 0.000   \n",
    986        "                          Equity(448 [APA])                 0.000   \n",
    987        "                          Equity(455 [APC])                 0.714   \n",
    988        "...                                                           ...   \n",
    989        "2013-11-25 00:00:00+00:00 Equity(42950 [FB])                0.358   \n",
    990        "                          Equity(43127 [NOW])               1.000   \n",
    991        "                          Equity(43399 [ADT])              -1.000   \n",
    992        "                          Equity(43405 [KRFT])              0.000   \n",
    993        "                          Equity(43413 [TRLA])              1.000   \n",
    994        "                          Equity(43414 [SRC])               0.000   \n",
    995        "                          Equity(43500 [RLGY])              0.000   \n",
    996        "                          Equity(43510 [WDAY])              0.000   \n",
    997        "                          Equity(43512 [FANG])             -1.000   \n",
    998        "                          Equity(43647 [YY])               -1.000   \n",
    999        "                          Equity(43694 [ABBV])              0.000   \n",
   1000        "                          Equity(43721 [SCTY])             -0.500   \n",
   1001        "                          Equity(43919 [LMCA])             -1.000   \n",
   1002        "                          Equity(44060 [ZTS])               0.000   \n",
   1003        "                          Equity(44102 [XONE])              0.000   \n",
   1004        "                          Equity(44645 [VOYA])              1.000   \n",
   1005        "                          Equity(44747 [DATA])              0.000   \n",
   1006        "                          Equity(44931 [NWSA])              0.000   \n",
   1007        "                          Equity(44965 [GOGO])             -1.000   \n",
   1008        "                          Equity(45656 [GLPI])              0.000   \n",
   1009        "                          Equity(45689 [VJET])              0.000   \n",
   1010        "                          Equity(45755 [BRX])               0.000   \n",
   1011        "                          Equity(45769 [WUBA])              0.000   \n",
   1012        "                          Equity(45780 [TCS])               1.000   \n",
   1013        "                          Equity(45781 [QUNR])             -1.000   \n",
   1014        "                          Equity(45815 [TWTR])             -0.043   \n",
   1015        "                          Equity(45848 [STAY])              0.000   \n",
   1016        "                          Equity(45866 [ZU])               -1.000   \n",
   1017        "                          Equity(45902 [WBAI])              0.000   \n",
   1018        "                          Equity(45906 [VNCE])              0.167   \n",
   1019        "\n",
   1020        "                                                impact_score  \n",
   1021        "2013-11-01 00:00:00+00:00 Equity(2 [AA])              76.000  \n",
   1022        "                          Equity(24 [AAPL])           73.065  \n",
   1023        "                          Equity(62 [ABT])            77.271  \n",
   1024        "                          Equity(64 [ABX])            96.000  \n",
   1025        "                          Equity(67 [ADSK])           88.333  \n",
   1026        "                          Equity(76 [TAP])            80.000  \n",
   1027        "                          Equity(88 [ACI])            84.143  \n",
   1028        "                          Equity(114 [ADBE])          76.354  \n",
   1029        "                          Equity(122 [ADI])           88.000  \n",
   1030        "                          Equity(128 [ADM])           88.667  \n",
   1031        "                          Equity(154 [AEM])          100.000  \n",
   1032        "                          Equity(161 [AEP])           86.000  \n",
   1033        "                          Equity(166 [AES])           70.000  \n",
   1034        "                          Equity(168 [AET])           84.500  \n",
   1035        "                          Equity(185 [AFL])           86.000  \n",
   1036        "                          Equity(197 [AGCO])          84.000  \n",
   1037        "                          Equity(216 [HES])           87.200  \n",
   1038        "                          Equity(239 [AIG])           81.667  \n",
   1039        "                          Equity(273 [ALU])           92.000  \n",
   1040        "                          Equity(300 [ALK])           69.000  \n",
   1041        "                          Equity(328 [ALTR])          82.000  \n",
   1042        "                          Equity(337 [AMAT])         100.000  \n",
   1043        "                          Equity(351 [AMD])           78.727  \n",
   1044        "                          Equity(353 [AME])           89.875  \n",
   1045        "                          Equity(357 [TWX])           92.727  \n",
   1046        "                          Equity(368 [AMGN])          79.000  \n",
   1047        "                          Equity(410 [AN])            77.000  \n",
   1048        "                          Equity(438 [AON])           92.333  \n",
   1049        "                          Equity(448 [APA])           85.000  \n",
   1050        "                          Equity(455 [APC])           88.571  \n",
   1051        "...                                                      ...  \n",
   1052        "2013-11-25 00:00:00+00:00 Equity(42950 [FB])          69.945  \n",
   1053        "                          Equity(43127 [NOW])         94.000  \n",
   1054        "                          Equity(43399 [ADT])         70.000  \n",
   1055        "                          Equity(43405 [KRFT])        63.000  \n",
   1056        "                          Equity(43413 [TRLA])        84.000  \n",
   1057        "                          Equity(43414 [SRC])         84.667  \n",
   1058        "                          Equity(43500 [RLGY])       100.000  \n",
   1059        "                          Equity(43510 [WDAY])        90.000  \n",
   1060        "                          Equity(43512 [FANG])        53.000  \n",
   1061        "                          Equity(43647 [YY])          88.000  \n",
   1062        "                          Equity(43694 [ABBV])        71.000  \n",
   1063        "                          Equity(43721 [SCTY])       100.000  \n",
   1064        "                          Equity(43919 [LMCA])        66.000  \n",
   1065        "                          Equity(44060 [ZTS])         84.000  \n",
   1066        "                          Equity(44102 [XONE])        91.000  \n",
   1067        "                          Equity(44645 [VOYA])        70.000  \n",
   1068        "                          Equity(44747 [DATA])        55.000  \n",
   1069        "                          Equity(44931 [NWSA])        37.000  \n",
   1070        "                          Equity(44965 [GOGO])        88.000  \n",
   1071        "                          Equity(45656 [GLPI])        63.000  \n",
   1072        "                          Equity(45689 [VJET])       100.000  \n",
   1073        "                          Equity(45755 [BRX])        100.000  \n",
   1074        "                          Equity(45769 [WUBA])        83.000  \n",
   1075        "                          Equity(45780 [TCS])         66.000  \n",
   1076        "                          Equity(45781 [QUNR])        83.000  \n",
   1077        "                          Equity(45815 [TWTR])        30.354  \n",
   1078        "                          Equity(45848 [STAY])        90.000  \n",
   1079        "                          Equity(45866 [ZU])          28.000  \n",
   1080        "                          Equity(45902 [WBAI])        81.000  \n",
   1081        "                          Equity(45906 [VNCE])        66.667  \n",
   1082        "\n",
   1083        "[14482 rows x 2 columns]"
   1084       ]
   1085      },
   1086      "execution_count": 7,
   1087      "metadata": {},
   1088      "output_type": "execute_result"
   1089     }
   1090    ],
   1091    "source": [
   1092     "# run_pipeline will show the output of your pipeline\n",
   1093     "pipe_output = run_pipeline(pipe, start_date='2013-11-01', end_date='2013-11-25')\n",
   1094     "pipe_output"
   1095    ]
   1096   },
   1097   {
   1098    "cell_type": "markdown",
   1099    "metadata": {},
   1100    "source": [
   1101     "Taking what we've seen from above, let's see how we'd move that into the backtester."
   1102    ]
   1103   },
   1104   {
   1105    "cell_type": "code",
   1106    "execution_count": 11,
   1107    "metadata": {
   1108     "collapsed": false
   1109    },
   1110    "outputs": [],
   1111    "source": [
   1112     "# This section is only importable in the backtester\n",
   1113     "from quantopian.algorithm import attach_pipeline, pipeline_output\n",
   1114     "\n",
   1115     "# General pipeline imports\n",
   1116     "from quantopian.pipeline import Pipeline\n",
   1117     "from quantopian.pipeline.factors import AverageDollarVolume\n",
   1118     "\n",
   1119     "# Import the datasets available\n",
   1120     "# For use in your algorithms\n",
   1121     "# Using the full paid dataset in your pipeline algo\n",
   1122     "# from quantopian.pipeline.data.accern import alphaone\n",
   1123     "\n",
   1124     "# Using the free sample in your pipeline algo\n",
   1125     "from quantopian.pipeline.data.accern import alphaone_free\n",
   1126     "\n",
   1127     "def make_pipeline():\n",
   1128     "    # Create our pipeline\n",
   1129     "    pipe = Pipeline()\n",
   1130     "    \n",
   1131     "    # Screen out penny stocks and low liquidity securities.\n",
   1132     "    dollar_volume = AverageDollarVolume(window_length=20)\n",
   1133     "    is_liquid = dollar_volume.rank(ascending=False) < 1000\n",
   1134     "    \n",
   1135     "    # Create the mask that we will use for our percentile methods.\n",
   1136     "    base_universe = (is_liquid)\n",
   1137     "\n",
   1138     "    # Add pipeline factors\n",
   1139     "    pipe.add(alphaone_free.impact_score.latest, 'impact_score')\n",
   1140     "    pipe.add(alphaone_free.article_sentiment.latest, 'article_sentiment')\n",
   1141     "\n",
   1142     "    # Set our pipeline screens\n",
   1143     "    pipe.set_screen(is_liquid)\n",
   1144     "    return pipe\n",
   1145     "\n",
   1146     "def initialize(context):\n",
   1147     "    attach_pipeline(make_pipeline(), \"pipeline\")\n",
   1148     "    \n",
   1149     "def before_trading_start(context, data):\n",
   1150     "    results = pipeline_output('pipeline')"
   1151    ]
   1152   },
   1153   {
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   1157     "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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   1159   }
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