Hi,
Been stuck over this probably-silly issues where I get a RUNTIME error saying there's no "top" attribute ...
What am I doing wrong here?

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 AverageDollarVolume  
from quantopian.pipeline.filters.morningstar import Q500US  
from quantopian.pipeline.factors import SimpleMovingAverage

import datetime  
import pandas as pd

def initialize(context):  
    # In this algo, we're looking at sector ETFs.  vol data quoted on Nov 22, 2016 from http://etfdb.com/type/sector/all/#etfs__overview&sort_name=symbol&sort_order=desc&page=1  
    context.security_list = symbols(  
                           'XLY',  # XLY Consumer Discretionary SPDR Fund       vol 5816k  
                           'XLF',  # XLF Financial SPDR Fund                    vol 67797k  
                           'XLK',  # XLK Technology SPDR Fund                   vol 10191k  
                           'XLE',  # XLE Energy SPDR Fund                       vol 15515k  
                           'XLV',  # XLV Health Care SPRD Fund                  vol 10854k  
                           'XLI',  # XLI Industrial SPDR Fund                   vol 11941k  
                           'XLP',  # XLP Consumer Staples SPDR Fund             vol 14221k  
                           'XLB',  # XLB Materials SPDR Fund                    vol 5488k  
                           'XLU',  # XLU Utilities SPRD Fund                    vol 17645k  
                           'XRT',  # XRT SPDR Retail ETF                        vol 3809k  
                           'XOP',  # XOP Oil & Gas Exploration & Production ETF vol 17373k  
                           'XHB',  # XHB Homebuilders ETF                       vol 2517k  
                           'XBI')  # XBI Biotech ETF                            vol 6870k

    # This variable is used to manage leverage  
    context.weights = 0.99/len(context.security_list)

    # Building SMA factors  
    sma9 = SimpleMovingAverage(inputs=[USEquityPricing.close], window_length=9)  
    sma21 = SimpleMovingAverage(inputs=[USEquityPricing.close], window_length=21)

    # Combined factors to create new factors  
    sma_quotient = sma9 / sma21  
    sma_rank = sma_quotient.rank

    #filters  
    SMALongFilter = sma_rank.top(5)                  # pick top momentum  
    SMAShortFilter = sma_rank.bottom(5)              # pick bottom momentum

    #context.longs = sma_rank.top(5)  
    #context.longs = sma_rank.percentile_between(60, 100)  
    #context.shorts = sma_rank.bottom(5)


    # These are the default commission and slippage settings.  Change  
    # them to fit your brokerage fees. These settings only matter for  
    # backtesting.  When you live trade this algorithm, they are moot -  
    # the brokerage and real market takes over.  
    set_commission(commission.PerShare(cost=0.0075, min_trade_cost=1))  
    set_slippage(slippage.VolumeShareSlippage(volume_limit=0.025, price_impact=0.1))

    # Rebalance every day (or the first trading day if it's a holiday).  
    # At 11AM ET, which is 1 hour and 30 minutes after market open.  
    schedule_function(rebalance,  
                      date_rules.week_start(days_offset=0),  
                      time_rules.market_open(hours = 1, minutes = 30))  

def rebalance(context, data):

    # Do the rebalance. Loop through each of the stocks and order to  
    # the target percentage.  If already at the target, this command  
    # doesn't do anything. A future improvement could be to set rebalance  
    # thresholds.  
    for sec in context.security_list:  
        if data.can_trade(sec):  
            order_target_percent(sec, context.weights)