Here's another test case that results in:

There was a runtime error.
MemoryError
Algorithm used too much memory. Need to optimize your code for better performance. Learn More

Note that I am pulling 20 days of minutely data across ~8000 stocks: 62.4 million price values. How many can Q2 support? If it is 8 bytes per point (see Scott's comment, Oct 12, 2015, https://www.quantopian.com/posts/introducing-the-pipeline-api), then it would only take 0.5 GB of RAM, so I don't understand the memory overflow problem.

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

class OvernightGap(CustomFactor):  
    inputs = [USEquityPricing.open,USEquityPricing.close]  
    window_length = 2  
    def compute(self, today, assets, out, open, close):  
        out[:] = 100*(open[-1,:] - close[0,:])/close[0,:]  
def make_pipeline():  
    pipe = Pipeline()  
    gap = OvernightGap()  
    pipe.add(gap, "gap")  
    return pipe

def initialize(context):  
    attach_pipeline(make_pipeline(), 'example')  
    schedule_function(place_orders,  
                      date_rules.every_day(),  
                      time_rules.market_open(hours=1))  
def before_trading_start(context,data):  
    context.output = pipeline_output('example')  
    context.security_list = context.output.index  
    prices = data.history(context.security_list, 'close', 20*390, '1m')  
def handle_data(context,data):  
    pass

def place_orders(context,data):  
    for stock in context.security_list[0:10]:  
        order(stock,1)  
    # prices = data.history(context.security_list, 'close', 20*390, '1m')