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Custom Factor including intraday data

Hello all,

I am trying to include both end of day and intraday data in a CustomFactor. Is there an example of this?

Even more simply, I am not clear on how any intraday data can get into the calculation. The sample below shows that creating a variable based on price_close = USEquityPricing.close.latest up in the make_pipeline() method does not get updated throughout the day.



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Max Drawdown 1 Month 3 Month 6 Month 12 Month
This is a template algorithm on Quantopian for you to adapt and fill in.
from quantopian.algorithm import attach_pipeline, pipeline_output
from quantopian.pipeline import Pipeline
from import USEquityPricing
from quantopian.pipeline.factors import AverageDollarVolume
def initialize(context):
    Called once at the start of the algorithm.
    # Rebalance every day, 1 hour after market open.
    schedule_function(my_rebalance, date_rules.every_day(), time_rules.market_open(hours=1))
    # Record tracking variables at the end of each day.
    schedule_function(my_record_vars, date_rules.every_day(), time_rules.market_close())
    # Create our dynamic stock selector.
    attach_pipeline(make_pipeline(), 'my_pipeline')
def make_pipeline():
    A function to create our dynamic stock selector (pipeline). Documentation on
    pipeline can be found here:
    # Create a dollar volume factor.
    dollar_volume = AverageDollarVolume(window_length=1)
    # Pick the top 1% of stocks ranked by dollar volume.
    high_dollar_volume = dollar_volume.percentile_between(99, 100)
    pipe = Pipeline(
        screen = high_dollar_volume,
        columns = {
            'dollar_volume': dollar_volume
    return pipe
def before_trading_start(context, data):
    Called every day before market open.
    context.output = pipeline_output('my_pipeline')
    # These are the securities that we are interested in trading each day.
    context.security_list = context.output.index
def my_assign_weights(context, data):
    Assign weights to securities that we want to order.
def my_rebalance(context,data):
    Execute orders according to our schedule_function() timing. 
def my_record_vars(context, data):
    Plot variables at the end of each day.
def handle_data(context,data):
    Called every minute.
There was a runtime error.
6 responses

USEquityPricing.close.latest refers to the close price of the previous day - so it won't change throughout the current day.

If you want to get current pricing on a minute basis you should use data.current as referenced in this tutorial

Pipelines are meant to be computed on the order of once a day for security selection (the values in pipeline only uses daily data as recent as the previous trading day)

Hopes this helps!


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Thank you for your response. So Pipeline is designed to calculate factors once a day using historical daily data? OK. How do I calculate a factor using intraday data? Is there an example?

Best regards,


Yes, the pipeline is designed for calculating things once per day using historical daily data.

So - if you scheduled a function to run every 30 minutes and wanted to trade based on some information, you should use the data object.
A quick example of getting the most recent price of APPL as shown in the tutorial would be

data.current(sid(24), 'price')

Besides price, what other kinds of intraday data are you interested in computing?

Data would be average price performance and average volume over a window of several days. But I wanted to include the intraday price performance and volume within the factor.

I had hoped that something like:

price_history = data.history(context.securities, "price", bar_count=20, frequency="1d")  
volume_history = data.history(context.securities, "volume", bar_count=20, frequency="1d")

would get me access to the data I needed, for both historical days and current day. I was looking for an example of how to use that data in a screen at 3:50 pm ET.

You could generate some data within the pipeline, then do other filtering outside of the pipeline.

For example - you could do all your regular filtering based off historical data in the pipeline.

Then, when you order stocks (at a higher frequency) you could check a condition like

if data.current(sid(24), 'price') > x:  
    do something  

in the scheduled function

Is there an algorithm implementing that strategy -- either in the Quantopian examples or forum posts -- that you could point me towards?