PsychSignal: StockTwits Trader Mood (All Fields)

In this notebook, we'll take a look at PsychSignal's StockTwits Trader Mood (All Fields) dataset, available on the Quantopian Store. This dataset spans 2009 through the current day, and documents the mood of traders based on their messages.

Notebook Contents

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.

  • Interactive overview: This is only available on Research and uses blaze to give you access to large amounts of data. Recommended for exploration and plotting.
  • Pipeline overview: 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.

Free samples and limits

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.

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.

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.

With preamble in place, let's get started:


Interactive Overview

Accessing the data with Blaze and Interactive on Research

Partner datasets are available on Quantopian Research through an API service known as Blaze. Blaze provides the Quantopian user with a convenient interface to access very large datasets, in an interactive, generic manner.

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.

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.

Helpful links:

Once you've limited the size of your Blaze object, you can convert it to a Pandas DataFrames using: > from odo import odo
> odo(expr, pandas.DataFrame)

To see how this data can be used in your algorithm, search for the Pipeline Overview section of this notebook or head straight to Pipeline Overview

In [1]:
# import the free sample of the dataset
from import stocktwits_free  as dataset

# or if you want to import the full dataset, use:
# from import stocktwits

# import data operations
from odo import odo
# import other libraries we will use
import pandas as pd
import matplotlib.pyplot as plt
In [2]:
# Let's use blaze to understand the data a bit using Blaze dshape()
dshape("""var * {
  source: ?string,
  symbol: string,
  bullish_intensity: float64,
  bearish_intensity: float64,
  bull_minus_bear: float64,
  bull_scored_messages: float64,
  bear_scored_messages: float64,
  bull_bear_msg_ratio: float64,
  total_scanned_messages: float64,
  sid: int64,
  asof_date: datetime,
  timestamp: datetime
In [3]:
# And how many rows are there?
# N.B. we're using a Blaze function to do this, not len()
In [4]:
# Let's see what the data looks like. We'll grab the first three rows.
source symbol bullish_intensity bearish_intensity bull_minus_bear bull_scored_messages bear_scored_messages bull_bear_msg_ratio total_scanned_messages sid asof_date timestamp
0 stocktwits AA 1.19 0.0 1.19 1 0 0 2 2 2009-08-24 04:00:00 2009-08-25 04:00:00
1 stocktwits AA 1.33 0.0 1.33 1 0 0 2 2 2009-09-03 04:00:00 2009-09-04 04:00:00
2 stocktwits AA 2.50 2.3 0.20 1 1 1 2 2 2009-09-10 04:00:00 2009-09-11 04:00:00

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.

Let's go over the columns:

  • asof_date: The date to which this data applies.
  • symbol: stock ticker symbol of the affected company.
  • source: the same value for all records in this data set
  • bull_scored_messages: total count of bullish sentiment messages scored by PsychSignal's algorithm
  • bear_scored_messages: total count of bearish sentiment messages scored by PsychSignal's algorithm
  • 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
  • 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
  • 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.
  • bull_minus_bear: subtracts the bearish intesity from the bullish intensity [BULL - BEAR] to rpovide an immediate net score.
  • bull_bear_msg_ratio: the ratio between bull scored messages and bear scored messages.
  • sid: the equity's unique identifier. Use this instead of the symbol.

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.

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.

In [5]:
# Filtering for AAPL
aapl = dataset[dataset.sid == 24]
aapl_df = odo(aapl.sort('asof_date'), pd.DataFrame)
plt.plot(aapl_df.asof_date, aapl_df.bull_scored_messages, marker='.', linestyle='None', color='r')
plt.plot(aapl_df.asof_date, pd.rolling_mean(aapl_df.bull_scored_messages, 30))
plt.xlabel("As Of Date (asof_date)")
plt.ylabel("Count of Bull Messages")
plt.title("Count of Bullish Messages for AAPL")
plt.legend(["Bull Messages - Single Day", "30 Day Rolling Average"], loc=2)
<matplotlib.legend.Legend at 0x7fb5059cee90>


Pipeline Overview

Accessing the data in your algorithms & research

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:

Import the data set here > from import ( > stocktwits_free > )

Then in intialize() you could do something simple like adding the raw value of one of the fields to your pipeline: > pipe.add(stocktwits_free.total_scanned_messages.latest, 'total_scanned_messages')

In [9]:
# Import necessary Pipeline modules
from quantopian.pipeline import Pipeline
from quantopian.research import run_pipeline
from quantopian.pipeline.factors import AverageDollarVolume
In [6]:
# For use in your algorithms
# Using the full paid dataset in your pipeline algo
# from import stocktwits

# Using the free sample in your pipeline algo
from import stocktwits_free 

Now that we've imported the data, let's take a look at which fields are available for each dataset.

You'll find the dataset, the available fields, and the datatypes for each of those fields.

In [7]:
print "Here are the list of available fields per dataset:"
print "---------------------------------------------------\n"

def _print_fields(dataset):
    print "Dataset: %s\n" % dataset.__name__
    print "Fields:"
    for field in list(dataset.columns):
        print "%s - %s" % (, field.dtype)
    print "\n"

for data in (stocktwits_free ,):

print "---------------------------------------------------\n"
Here are the list of available fields per dataset:

Dataset: stocktwits_free

bull_minus_bear - float64
bullish_intensity - float64
bull_bear_msg_ratio - float64
bearish_intensity - float64
total_scanned_messages - float64
bull_scored_messages - float64
bear_scored_messages - float64


Now that we know what fields we have access to, let's see what this data looks like when we run it through Pipeline.

This is constructed the same way as you would in the backtester. For more information on using Pipeline in Research view this thread:

In [10]:
# Let's see what this data looks like when we run it through Pipeline
# This is constructed the same way as you would in the backtester. For more information
# on using Pipeline in Research view this thread:
pipe = Pipeline()
pipe.add(stocktwits_free.bear_scored_messages .latest,
         'bear_scored_messages ')
pipe.add(stocktwits_free.bull_scored_messages .latest,
         'bull_scored_messages ')
pipe.add(stocktwits_free.bull_bear_msg_ratio .latest,
         'bull_bear_msg_ratio ')
In [12]:
# Setting some basic liquidity strings (just for good habit)
dollar_volume = AverageDollarVolume(window_length=20)
top_1000_most_liquid = dollar_volume.rank(ascending=False) < 1000

pipe.set_screen(top_1000_most_liquid &
In [13]:
# The show_graph() method of pipeline objects produces a graph to show how it is being calculated.
In [14]:
# run_pipeline will show the output of your pipeline
pipe_output = run_pipeline(pipe, start_date='2013-11-01', end_date='2013-11-25')
bear_scored_messages bull_bear_msg_ratio bull_scored_messages total_scanned_messages
2013-11-01 00:00:00+00:00 Equity(21 [AAME]) 0 0 6 30
2013-11-04 00:00:00+00:00 Equity(21 [AAME]) 0 0 6 30
2013-11-05 00:00:00+00:00 Equity(21 [AAME]) 0 0 6 30
2013-11-06 00:00:00+00:00 Equity(21 [AAME]) 0 0 6 30
2013-11-07 00:00:00+00:00 Equity(21 [AAME]) 0 0 6 30
2013-11-08 00:00:00+00:00 Equity(21 [AAME]) 0 0 6 30
2013-11-11 00:00:00+00:00 Equity(21 [AAME]) 0 0 6 30
2013-11-12 00:00:00+00:00 Equity(21 [AAME]) 0 0 6 30

Taking what we've seen from above, let's see how we'd move that into the backtester.

In [9]:
# This section is only importable in the backtester
from quantopian.algorithm import attach_pipeline, pipeline_output

# General pipeline imports
from quantopian.pipeline import Pipeline
from quantopian.pipeline.factors import AverageDollarVolume

# Import the datasets available
# For use in your algorithms
# Using the full paid dataset in your pipeline algo
# from import stocktwits

# Using the free sample in your pipeline algo
from import stocktwits_free

def make_pipeline():
    # Create our pipeline
    pipe = Pipeline()
    # Screen out penny stocks and low liquidity securities.
    dollar_volume = AverageDollarVolume(window_length=20)
    is_liquid = dollar_volume.rank(ascending=False) < 1000
    # Create the mask that we will use for our percentile methods.
    base_universe = (is_liquid)

    # Add pipeline factors
    pipe.add(stocktwits_free.bear_scored_messages .latest,
             'bear_scored_messages ')
    pipe.add(stocktwits_free.bull_scored_messages .latest,
             'bull_scored_messages ')
    pipe.add(stocktwits_free.bull_bear_msg_ratio .latest,
             'bull_bear_msg_ratio ')

    # Set our pipeline screens
    return pipe

def initialize(context):
    attach_pipeline(make_pipeline(), "pipeline")
def before_trading_start(context, data):
    results = pipeline_output('pipeline')

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 data pipeline factor library