ml-finance-python

notebook.ipynb

(146813B)

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     "# PsychSignal: StockTwits Trader Mood (All Fields)\n",
     "\n",
     "In this notebook, we'll take a look at PsychSignal's *StockTwits Trader Mood (All Fields)* dataset, available on the [Quantopian Store](https://www.quantopian.com/store). This dataset spans 2009 through the current day, and documents the mood of traders based on their messages.\n",
     "\n",
     "## Notebook Contents\n",
     "\n",
     "There are two ways to access the data and you'll find both of them listed below. Just click on the section you'd like to read through.\n",
     "\n",
     "- <a href='#interactive'><strong>Interactive overview</strong></a>: This is only available on Research and uses blaze to give you access to large amounts of data. Recommended for exploration and plotting.\n",
     "- <a href='#pipeline'><strong>Pipeline overview</strong></a>: Data is made available through pipeline which is available on both the Research & Backtesting environment. Recommended for custom factor development and moving back & forth between research/backtesting.\n",
     "\n",
     "### Free samples and limits\n",
     "One key caveat: we limit the number of results returned from any given expression to 10,000 to protect against runaway memory usage. To be clear, you have access to all the data server side. We are limiting the size of the responses back from Blaze.\n",
     "\n",
     "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",
     "\n",
     "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",
     "\n",
     "With preamble in place, let's get started:\n",
     "\n",
     "<a id='interactive'></a>\n",
     "#Interactive Overview\n",
     "### Accessing the data with Blaze and Interactive on Research\n",
     "Partner datasets are available on Quantopian Research through an API service known as [Blaze](http://blaze.pydata.org). Blaze provides the Quantopian user with a convenient interface to access very large datasets, in an interactive, generic manner.\n",
     "\n",
     "Blaze provides an important function for accessing these datasets. Some of these sets are many millions of records. Bringing that data directly into Quantopian Research directly just is not viable. So Blaze allows us to provide a simple querying interface and shift the burden over to the server side.\n",
     "\n",
     "It is common to use Blaze to reduce your dataset in size, convert it over to Pandas and then to use Pandas for further computation, manipulation and visualization.\n",
     "\n",
     "Helpful links:\n",
     "* [Query building for Blaze](http://blaze.readthedocs.io/en/latest/queries.html)\n",
     "* [Pandas-to-Blaze dictionary](http://blaze.readthedocs.io/en/latest/rosetta-pandas.html)\n",
     "* [SQL-to-Blaze dictionary](http://blaze.readthedocs.io/en/latest/rosetta-sql.html).\n",
     "\n",
     "Once you've limited the size of your Blaze object, you can convert it to a Pandas DataFrames using:\n",
     "> `from odo import odo`  \n",
     "> `odo(expr, pandas.DataFrame)`\n",
     "\n",
     "\n",
     "###To see how this data can be used in your algorithm, search for the `Pipeline Overview` section of this notebook or head straight to <a href='#pipeline'>Pipeline Overview</a>"
    ]
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   {
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    "source": [
     "# import the free sample of the dataset\n",
     "from quantopian.interactive.data.psychsignal import stocktwits_free  as dataset\n",
     "\n",
     "# or if you want to import the full dataset, use:\n",
     "# from quantopian.interactive.data.psychsignal import stocktwits\n",
     "\n",
     "# import data operations\n",
     "from odo import odo\n",
     "# import other libraries we will use\n",
     "import pandas as pd\n",
     "import matplotlib.pyplot as plt"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {
     "collapsed": false
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    "outputs": [
     {
      "data": {
       "text/plain": [
        "dshape(\"\"\"var * {\n",
        "  source: ?string,\n",
        "  symbol: string,\n",
        "  bullish_intensity: float64,\n",
        "  bearish_intensity: float64,\n",
        "  bull_minus_bear: float64,\n",
        "  bull_scored_messages: float64,\n",
        "  bear_scored_messages: float64,\n",
        "  bull_bear_msg_ratio: float64,\n",
        "  total_scanned_messages: float64,\n",
        "  sid: int64,\n",
        "  asof_date: datetime,\n",
        "  timestamp: datetime\n",
        "  }\"\"\")"
       ]
      },
      "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "# Let's use blaze to understand the data a bit using Blaze dshape()\n",
     "dataset.dshape"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {
     "collapsed": false
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    "outputs": [
     {
      "data": {
       "text/html": [
        "2993191"
       ],
       "text/plain": [
        "2993191"
       ]
      },
      "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "# And how many rows are there?\n",
     "# N.B. we're using a Blaze function to do this, not len()\n",
     "dataset.count()"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 4,
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     {
      "data": {
       "text/html": [
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>source</th>\n",
        "      <th>symbol</th>\n",
        "      <th>bullish_intensity</th>\n",
        "      <th>bearish_intensity</th>\n",
        "      <th>bull_minus_bear</th>\n",
        "      <th>bull_scored_messages</th>\n",
        "      <th>bear_scored_messages</th>\n",
        "      <th>bull_bear_msg_ratio</th>\n",
        "      <th>total_scanned_messages</th>\n",
        "      <th>sid</th>\n",
        "      <th>asof_date</th>\n",
        "      <th>timestamp</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>stocktwits</td>\n",
        "      <td>AA</td>\n",
        "      <td>1.19</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.19</td>\n",
        "      <td>1</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>2</td>\n",
        "      <td>2</td>\n",
        "      <td>2009-08-24 04:00:00</td>\n",
        "      <td>2009-08-25 04:00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>stocktwits</td>\n",
        "      <td>AA</td>\n",
        "      <td>1.33</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.33</td>\n",
        "      <td>1</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>2</td>\n",
        "      <td>2</td>\n",
        "      <td>2009-09-03 04:00:00</td>\n",
        "      <td>2009-09-04 04:00:00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>stocktwits</td>\n",
        "      <td>AA</td>\n",
        "      <td>2.50</td>\n",
        "      <td>2.3</td>\n",
        "      <td>0.20</td>\n",
        "      <td>1</td>\n",
        "      <td>1</td>\n",
        "      <td>1</td>\n",
        "      <td>2</td>\n",
        "      <td>2</td>\n",
        "      <td>2009-09-10 04:00:00</td>\n",
        "      <td>2009-09-11 04:00:00</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>"
       ],
       "text/plain": [
        "       source symbol  bullish_intensity  bearish_intensity  bull_minus_bear  \\\n",
        "0  stocktwits     AA               1.19                0.0             1.19   \n",
        "1  stocktwits     AA               1.33                0.0             1.33   \n",
        "2  stocktwits     AA               2.50                2.3             0.20   \n",
        "\n",
        "   bull_scored_messages  bear_scored_messages  bull_bear_msg_ratio  \\\n",
        "0                     1                     0                    0   \n",
        "1                     1                     0                    0   \n",
        "2                     1                     1                    1   \n",
        "\n",
        "   total_scanned_messages  sid           asof_date           timestamp  \n",
        "0                       2    2 2009-08-24 04:00:00 2009-08-25 04:00:00  \n",
        "1                       2    2 2009-09-03 04:00:00 2009-09-04 04:00:00  \n",
        "2                       2    2 2009-09-10 04:00:00 2009-09-11 04:00:00  "
       ]
      },
      "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "# Let's see what the data looks like. We'll grab the first three rows.\n",
     "dataset[:3]"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "There are two versions of each data set from PsychSignal. A simple version with fewer fields and full version with more fields. This is an basic data set with fewer fields.\n",
     "\n",
     "Let's go over the columns:\n",
     "- **asof_date**: The date to which this data applies.\n",
     "- **symbol**: stock ticker symbol of the affected company.\n",
     "- **source**: the same value for all records in this data set\n",
     "- **bull_scored_messages**: total count of bullish sentiment messages scored by PsychSignal's algorithm\n",
     "- **bear_scored_messages**: total count of bearish sentiment messages scored by PsychSignal's algorithm\n",
     "- **bullish_intensity**: score for each message's language for the stength of the bullishness present in the messages on a 0-4 scale. 0 indicates no bullish sentiment measured, 4 indicates strongest bullish sentiment measured. 4 is rare\n",
     "- **bearish_intensity**: score for each message's language for the stength of the bearish present in the messages on a 0-4 scale. 0 indicates no bearish sentiment measured, 4 indicates strongest bearish sentiment measured. 4 is rare\n",
     "- **total_scanned_messages**: number of messages coming through PsuchSignal's feeds and attributable to a symbol regardless of whether the PsychSignal sentiment engine can score them for bullish or bearish intensity- **timestamp**: this is our timestamp on when we registered the data.\n",
     "- **bull_minus_bear**: subtracts the bearish intesity from the bullish intensity [BULL - BEAR] to rpovide an immediate net score.\n",
     "- **bull_bear_msg_ratio**: the ratio between bull scored messages and bear scored messages.\n",
     "- **sid**: the equity's unique identifier. Use this instead of the symbol.\n",
     "\n",
     "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",
     "\n",
     "We can select columns and rows with ease. Below, we'll fetch all rows for Apple (sid 24) and explore the scores a bit with a chart."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
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     {
      "data": {
       "text/plain": [
        "<matplotlib.legend.Legend at 0x7fb5059cee90>"
       ]
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      "execution_count": 5,
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IksqXL697771XklS1alVNnDhRV69e1enTp9WmTRudP39eLVu2lCR17NhRHh4e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7epc1A6CXjhSInvmMTijZ41Pea2UIgWKhxRKBRKskKswpW//Bbw0SbMPL8fzhvVFVnpetx9ZfhN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\ne6rGeYTt7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qBnyVAX9aLErVyBoYxNWVnWZ6JpsR5Ke1QEKYDiYvfM8TzNq6ftO8CUTH5U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\nTkSuQmCC\n",
       "text/plain": [
        "<matplotlib.figure.Figure at 0x7fb5059d3c90>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "# Filtering for AAPL\n",
     "aapl = dataset[dataset.sid == 24]\n",
     "aapl_df = odo(aapl.sort('asof_date'), pd.DataFrame)\n",
     "plt.plot(aapl_df.asof_date, aapl_df.bull_scored_messages, marker='.', linestyle='None', color='r')\n",
     "plt.plot(aapl_df.asof_date, pd.rolling_mean(aapl_df.bull_scored_messages, 30))\n",
     "plt.xlabel(\"As Of Date (asof_date)\")\n",
     "plt.ylabel(\"Count of Bull Messages\")\n",
     "plt.title(\"Count of Bullish Messages for AAPL\")\n",
     "plt.legend([\"Bull Messages - Single Day\", \"30 Day Rolling Average\"], loc=2)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "<a id='pipeline'></a>\n",
     "\n",
     "#Pipeline Overview\n",
     "\n",
     "### Accessing the data in your algorithms & research\n",
     "The only method for accessing partner data within algorithms running on Quantopian is via the pipeline API. Different data sets work differently but in the case of this data, you can add this data to your pipeline as follows:\n",
     "\n",
     "Import the data set here\n",
     "> `from quantopian.pipeline.data.psychsignal import (`\n",
     "> `stocktwits_free `\n",
     "> `)`\n",
     "\n",
     "Then in intialize() you could do something simple like adding the raw value of one of the fields to your pipeline:\n",
     "> `pipe.add(stocktwits_free.total_scanned_messages.latest, 'total_scanned_messages')`"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 9,
    "metadata": {
     "collapsed": true
    },
    "outputs": [],
    "source": [
     "# Import necessary Pipeline modules\n",
     "from quantopian.pipeline import Pipeline\n",
     "from quantopian.research import run_pipeline\n",
     "from quantopian.pipeline.factors import AverageDollarVolume"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
     "# For use in your algorithms\n",
     "# Using the full paid dataset in your pipeline algo\n",
     "# from quantopian.pipeline.data.psychsignal import stocktwits\n",
     "\n",
     "# Using the free sample in your pipeline algo\n",
     "from quantopian.pipeline.data.psychsignal import stocktwits_free "
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Now that we've imported the data, let's take a look at which fields are available for each dataset.\n",
     "\n",
     "You'll find the dataset, the available fields, and the datatypes for each of those fields."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 7,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "Here are the list of available fields per dataset:\n",
       "---------------------------------------------------\n",
       "\n",
       "Dataset: stocktwits_free\n",
       "\n",
       "Fields:\n",
       "bull_minus_bear - float64\n",
       "bullish_intensity - float64\n",
       "bull_bear_msg_ratio - float64\n",
       "bearish_intensity - float64\n",
       "total_scanned_messages - float64\n",
       "bull_scored_messages - float64\n",
       "bear_scored_messages - float64\n",
       "\n",
       "\n",
       "---------------------------------------------------\n",
       "\n"
      ]
     }
    ],
    "source": [
     "print \"Here are the list of available fields per dataset:\"\n",
     "print \"---------------------------------------------------\\n\"\n",
     "\n",
     "def _print_fields(dataset):\n",
     "    print \"Dataset: %s\\n\" % dataset.__name__\n",
     "    print \"Fields:\"\n",
     "    for field in list(dataset.columns):\n",
     "        print \"%s - %s\" % (field.name, field.dtype)\n",
     "    print \"\\n\"\n",
     "\n",
     "for data in (stocktwits_free ,):\n",
     "    _print_fields(data)\n",
     "\n",
     "\n",
     "print \"---------------------------------------------------\\n\""
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Now that we know what fields we have access to, let's see what this data looks like when we run it through Pipeline.\n",
     "\n",
     "\n",
     "This is constructed the same way as you would in the backtester. For more information on using Pipeline in Research view this thread:\n",
     "https://www.quantopian.com/posts/pipeline-in-research-build-test-and-visualize-your-factors-and-filters"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 10,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
     "# Let's see what this data looks like when we run it through Pipeline\n",
     "# This is constructed the same way as you would in the backtester. For more information\n",
     "# on using Pipeline in Research view this thread:\n",
     "# https://www.quantopian.com/posts/pipeline-in-research-build-test-and-visualize-your-factors-and-filters\n",
     "pipe = Pipeline()\n",
     "       \n",
     "pipe.add(stocktwits_free.total_scanned_messages.latest,\n",
     "         'total_scanned_messages')\n",
     "pipe.add(stocktwits_free.bear_scored_messages .latest,\n",
     "         'bear_scored_messages ')\n",
     "pipe.add(stocktwits_free.bull_scored_messages .latest,\n",
     "         'bull_scored_messages ')\n",
     "pipe.add(stocktwits_free.bull_bear_msg_ratio .latest,\n",
     "         'bull_bear_msg_ratio ')"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 12,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
     "# Setting some basic liquidity strings (just for good habit)\n",
     "dollar_volume = AverageDollarVolume(window_length=20)\n",
     "top_1000_most_liquid = dollar_volume.rank(ascending=False) < 1000\n",
     "\n",
     "pipe.set_screen(top_1000_most_liquid &\n",
     "                (stocktwits_free.total_scanned_messages.latest>20))"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 13,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
       "image/png": 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       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
      },
      "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "# The show_graph() method of pipeline objects produces a graph to show how it is being calculated.\n",
     "pipe.show_graph(format='png')"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 14,
    "metadata": {
     "collapsed": false,
     "scrolled": true
    },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th>bear_scored_messages</th>\n",
        "      <th>bull_bear_msg_ratio</th>\n",
        "      <th>bull_scored_messages</th>\n",
        "      <th>total_scanned_messages</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2013-11-01 00:00:00+00:00</th>\n",
        "      <th>Equity(21 [AAME])</th>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>6</td>\n",
        "      <td>30</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2013-11-04 00:00:00+00:00</th>\n",
        "      <th>Equity(21 [AAME])</th>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>6</td>\n",
        "      <td>30</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2013-11-05 00:00:00+00:00</th>\n",
        "      <th>Equity(21 [AAME])</th>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>6</td>\n",
        "      <td>30</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2013-11-06 00:00:00+00:00</th>\n",
        "      <th>Equity(21 [AAME])</th>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>6</td>\n",
        "      <td>30</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2013-11-07 00:00:00+00:00</th>\n",
        "      <th>Equity(21 [AAME])</th>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>6</td>\n",
        "      <td>30</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2013-11-08 00:00:00+00:00</th>\n",
        "      <th>Equity(21 [AAME])</th>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>6</td>\n",
        "      <td>30</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2013-11-11 00:00:00+00:00</th>\n",
        "      <th>Equity(21 [AAME])</th>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>6</td>\n",
        "      <td>30</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2013-11-12 00:00:00+00:00</th>\n",
        "      <th>Equity(21 [AAME])</th>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>6</td>\n",
        "      <td>30</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
        "                                             bear_scored_messages   \\\n",
        "2013-11-01 00:00:00+00:00 Equity(21 [AAME])                      0   \n",
        "2013-11-04 00:00:00+00:00 Equity(21 [AAME])                      0   \n",
        "2013-11-05 00:00:00+00:00 Equity(21 [AAME])                      0   \n",
        "2013-11-06 00:00:00+00:00 Equity(21 [AAME])                      0   \n",
        "2013-11-07 00:00:00+00:00 Equity(21 [AAME])                      0   \n",
        "2013-11-08 00:00:00+00:00 Equity(21 [AAME])                      0   \n",
        "2013-11-11 00:00:00+00:00 Equity(21 [AAME])                      0   \n",
        "2013-11-12 00:00:00+00:00 Equity(21 [AAME])                      0   \n",
        "\n",
        "                                             bull_bear_msg_ratio   \\\n",
        "2013-11-01 00:00:00+00:00 Equity(21 [AAME])                     0   \n",
        "2013-11-04 00:00:00+00:00 Equity(21 [AAME])                     0   \n",
        "2013-11-05 00:00:00+00:00 Equity(21 [AAME])                     0   \n",
        "2013-11-06 00:00:00+00:00 Equity(21 [AAME])                     0   \n",
        "2013-11-07 00:00:00+00:00 Equity(21 [AAME])                     0   \n",
        "2013-11-08 00:00:00+00:00 Equity(21 [AAME])                     0   \n",
        "2013-11-11 00:00:00+00:00 Equity(21 [AAME])                     0   \n",
        "2013-11-12 00:00:00+00:00 Equity(21 [AAME])                     0   \n",
        "\n",
        "                                             bull_scored_messages   \\\n",
        "2013-11-01 00:00:00+00:00 Equity(21 [AAME])                      6   \n",
        "2013-11-04 00:00:00+00:00 Equity(21 [AAME])                      6   \n",
        "2013-11-05 00:00:00+00:00 Equity(21 [AAME])                      6   \n",
        "2013-11-06 00:00:00+00:00 Equity(21 [AAME])                      6   \n",
        "2013-11-07 00:00:00+00:00 Equity(21 [AAME])                      6   \n",
        "2013-11-08 00:00:00+00:00 Equity(21 [AAME])                      6   \n",
        "2013-11-11 00:00:00+00:00 Equity(21 [AAME])                      6   \n",
        "2013-11-12 00:00:00+00:00 Equity(21 [AAME])                      6   \n",
        "\n",
        "                                             total_scanned_messages  \n",
        "2013-11-01 00:00:00+00:00 Equity(21 [AAME])                      30  \n",
        "2013-11-04 00:00:00+00:00 Equity(21 [AAME])                      30  \n",
        "2013-11-05 00:00:00+00:00 Equity(21 [AAME])                      30  \n",
        "2013-11-06 00:00:00+00:00 Equity(21 [AAME])                      30  \n",
        "2013-11-07 00:00:00+00:00 Equity(21 [AAME])                      30  \n",
        "2013-11-08 00:00:00+00:00 Equity(21 [AAME])                      30  \n",
        "2013-11-11 00:00:00+00:00 Equity(21 [AAME])                      30  \n",
        "2013-11-12 00:00:00+00:00 Equity(21 [AAME])                      30  "
       ]
      },
      "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "# run_pipeline will show the output of your pipeline\n",
     "pipe_output = run_pipeline(pipe, start_date='2013-11-01', end_date='2013-11-25')\n",
     "pipe_output"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Taking what we've seen from above, let's see how we'd move that into the backtester."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 9,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
    "source": [
     "# This section is only importable in the backtester\n",
     "from quantopian.algorithm import attach_pipeline, pipeline_output\n",
     "\n",
     "# General pipeline imports\n",
     "from quantopian.pipeline import Pipeline\n",
     "from quantopian.pipeline.factors import AverageDollarVolume\n",
     "\n",
     "# Import the datasets available\n",
     "# For use in your algorithms\n",
     "# Using the full paid dataset in your pipeline algo\n",
     "# from quantopian.pipeline.data.psychsignal import stocktwits\n",
     "\n",
     "# Using the free sample in your pipeline algo\n",
     "from quantopian.pipeline.data.psychsignal import stocktwits_free\n",
     "\n",
     "def make_pipeline():\n",
     "    # Create our pipeline\n",
     "    pipe = Pipeline()\n",
     "    \n",
     "    # Screen out penny stocks and low liquidity securities.\n",
     "    dollar_volume = AverageDollarVolume(window_length=20)\n",
     "    is_liquid = dollar_volume.rank(ascending=False) < 1000\n",
     "    \n",
     "    # Create the mask that we will use for our percentile methods.\n",
     "    base_universe = (is_liquid)\n",
     "\n",
     "    # Add pipeline factors\n",
     "    pipe.add(stocktwits_free.total_scanned_messages.latest,\n",
     "             'total_scanned_messages')\n",
     "    pipe.add(stocktwits_free.bear_scored_messages .latest,\n",
     "             'bear_scored_messages ')\n",
     "    pipe.add(stocktwits_free.bull_scored_messages .latest,\n",
     "             'bull_scored_messages ')\n",
     "    pipe.add(stocktwits_free.bull_bear_msg_ratio .latest,\n",
     "             'bull_bear_msg_ratio ')\n",
     "\n",
     "    # Set our pipeline screens\n",
     "    pipe.set_screen(is_liquid)\n",
     "    return pipe\n",
     "\n",
     "def initialize(context):\n",
     "    attach_pipeline(make_pipeline(), \"pipeline\")\n",
     "    \n",
     "def before_trading_start(context, data):\n",
     "    results = pipeline_output('pipeline')"
    ]
   },
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    "metadata": {},
    "source": [
     "Now you can take that and begin to use it as a building block for your algorithms, for more examples on how to do that you can visit our <a href='https://www.quantopian.com/posts/pipeline-factor-library-for-data'>data pipeline factor library</a>"
    ]
   }
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