Hello,

I was following the pipeline tutorial and tried my hand at making my own algorithm using a pipeline. I tried making it similar to the tutorial but i also wanted to tweak a few things in order to learn. Anyways, I keep getting an error: "TimeoutException: Call to before_trading_start timed out
There was a runtime error on line 59." I have tried messing with it for a few hours but I can't figure out what i'm doing wrong. Can someone help point me in the right direction? The line that causes the error is in "before_trading_start."

from quantopian.pipeline import Pipeline  
from quantopian.algorithm import attach_pipeline, pipeline_output  
from quantopian.pipeline.factors import RollingLinearRegressionOfReturns

import pandas as pd  
import numpy as np  
from scipy import stats

def initialize(context):  
    schedule_function(balance, date_rules.week_start(), time_rules.market_open(hours=1))  
    schedule_function(my_record_vars, date_rules.every_day(), time_rules.market_close())

    my_pipe = make_pipeline()  
    attach_pipeline(my_pipe, name='my_pipeline')  
def make_pipeline():  
    rolling_correlations = RollingLinearRegressionOfReturns(  
        target=sid(8554),  
        returns_length=70,  
        regression_length = 35,  
    )  
    beta = rolling_correlations.beta  
    r = rolling_correlations.r_value  
    rsquared = r**2  
    shouldbuy_beta =  beta > 1.1  
    shouldbuy_r = rsquared > 0.9  
    securities_to_trade = (shouldbuy_beta & shouldbuy_r)  
    return Pipeline(  
        columns={  
            'beta':beta,  
            'rsquared':rsquared,  
            'should buy beta':shouldbuy_beta,  
            'shouldbuy r':shouldbuy_r,  
            'securities_to_trade':securities_to_trade  
        },  
        screen = (securities_to_trade)  
    )

def compute_weights(context):  
    if len(context.longs) != 0:  
        long_weight = 1/len(context.longs)  
    else:  
        long_weight = 0  
    print long_weight  
    return long_weight

def before_trading_start(context, data):  
    context.output = pipeline_output('my_pipeline') ## <<<<<------THE LINE THAT TIMES OUT  
    context.longs = context.output[context.output['securities_to_trade']].index.tolist()  
    context.long_weight = compute_weights(context)  
def balance(context, data):  
    for security in context.portfolio.positions:  
        if security not in context.longs and data.can_trade(security):  
            order_target_percent(security, 0)  
    for security in context.longs:  
        if data.can_trade(security):  
            order_target_percent(security, context.long_weight)

def my_record_vars(context, data):  
    """  
    Record variables at the end of each day.  
    """  
    longs = 0  
    for position in context.portfolio.positions.itervalues():  
        if position.amount > 0:  
            longs += 1  
    record(leverage=context.account.leverage, long_count=longs)