Hi Guarav,

Unfortunately this isn't possible on Quantopian. You can use Fetcher to import data to a live algorithm, but input for a given day needs to be added before midnight of the previous night. Is there some type of data missing on Q that you are using to determine this list? On top of the pricing and fundamentals data, there are 42+ other datasets available to play with!

The data I am fetching is a list of top gainers during market open hours.

Have you tried using the Pipeline API to determine these? With Pipeline, you can rank securities by custom factors and pick the top gainers this way. Check out this example as a starting point.

Thanks Jamie, let me try that out as see if I am able to replicate the Yahoo top gainers list.

It works! Now I don't have to rely on my remote fetcher and can backtest my stuff.

can you share the code?

Sure (there is a problem with USEquityPricing.open in the current release, so I am using USEquityPricing.high right now):

from quantopian.algorithm import attach_pipeline, pipeline_output  
from quantopian.pipeline import Pipeline  
from quantopian.pipeline import CustomFactor  
from quantopian.pipeline.data.builtin import USEquityPricing

# Create custom factor #2 Price of current day / Price of 60 days ago.  
class PctGain(CustomFactor):  
    # Pre-declare inputs and window_length  
    inputs = [USEquityPricing.close, USEquityPricing.high]  
    window_length = 2  
    # Compute factor2 value  
    def compute(self, today, assets, out, close, high):  
        out[:] = (high[0] - close[1]) * 100 / close[1]

def initialize(context):  
    # Create and attach an empty Pipeline.  
    pipe = Pipeline()  
    pipe = attach_pipeline(pipe, name='ranked_pct_gainers')  
    pct_gain = PctGain()  
    pipe.add(pct_gain, 'pct_gain')  
    pct_gain_rank = pct_gain.rank()  
    pipe.add(pct_gain_rank, 'pct_gain_rank')  
    context.order_size = 1

def before_trading_start(context, data):  
    # Access results using the name passed to `attach_pipeline`.  
    results = pipeline_output('ranked_pct_gainers')  
    top_gainers = results.sort(['pct_gain_rank'], ascending=False)  
    # print top_gainers.head(19)

    # Define a universe with the results of a Pipeline.  
    # Take the first ten assets by Percent gain.  
    update_universe(top_gainers.index[:100])


# This function is run once per bar, REQUIRED  
def handle_data(context, data):  
    for stock in data:  
        log.info(stock.symbol)