"""
Trading Strategy using Fundamental Data

1. Look at stocks in the Q1500US.  
2. Filter stocks by low pe_ratio. Prefer book valuation but unsupported.  
3. Go long on momentum from this filter.  
4. Rebalance every week day 1 at market open. I want a minimum number of days to hold and daily rebalance, will figure out later.  
"""
from quantopian.algorithm import attach_pipeline, pipeline_output  
from quantopian.pipeline import Pipeline  
from quantopian.pipeline.data import morningstar  
from quantopian.pipeline.filters.morningstar import Q1500US  
from quantopian.pipeline.data.builtin import USEquityPricing  
from quantopian.pipeline.factors import SimpleMovingAverage


def initialize(context):  
    # Rebalance monthly on the first day of the week at market open  
    schedule_function(rebalance,  
                      date_rule=date_rules.week_start(days_offset=1),  
                      time_rule=time_rules.market_open())  
    my_pipe = make_pipeline()  
    attach_pipeline(my_pipe, 'value_momo_pipeline')

def make_pipeline():

    # Latest p/b ratio.  
    pe_ratio = morningstar.valuation_ratios.pe_ratio.latest  
    # Q1500US is a pre-defined universe of liquid securities.  
    universe = Q1500US()  
    pe_stocks = pe_ratio.bottom(250, mask = universe)  
    # Momentum of pe stocks  
    SMA_10 = SimpleMovingAverage(inputs = [USEquityPricing.close], window_length=10, mask = pe_stocks)  
    SMA_30 = SimpleMovingAverage(inputs = [USEquityPricing.close], window_length=30, mask = pe_stocks)  
    percent_diff = (SMA_10 - SMA_30) / SMA_30  
    longs = percent_diff.bottom(50)  
    # Screen to include only securities tradable for the day  
    securities_to_trade = (longs)  
    pipe = Pipeline(  
              columns={  
                'pe_ratio': pe_ratio,  
                'longs': longs,  
              },  
              screen = (securities_to_trade),  
          )

    return pipe  
"""
Runs our fundamentals pipeline before the market opens every day.  
"""
def before_trading_start(context, data): 

    # Gets our pipeline output every day.  
    context.output = pipeline_output('value_momo_pipeline')

    # The low p/e stocks that we want to long.  
    context.longs = context.output[context.output['longs']].index.tolist()


def rebalance(context, data):  
    my_positions = context.portfolio.positions  
    if (len(context.longs) > 0):

        # Equally weight all of our long positions  
        long_weight = 1/len(context.longs)  
        print(len(context.longs))  
        print(long_weight)  
        # Open long positions in our stocks.  
        for security in context.longs:  
            if data.can_trade(security) and security not in my_positions:  
                    order_target_percent(security, long_weight)  
    # Close our previous positions that are no longer in our pipeline.  
    for security in my_positions:  
        if security not in context.longs and data.can_trade(security):  
            order_target_percent(security, 0)

Above is the code that I have been basically copying from examples and trying to make up new things as I go. I am running into an issue where the length of my securities to trade is 50, but when I compute weights as 1/length it comes out as 0, not 0.02. I was feeling pretty good about everything when I finally got it to run and saw things ticking, then realized nothing was being traded. Woot. What's the fix? I've been trying now for a couple hours, true Newbington Bear here. Thanks.

P.S. The two numbers it's printing is number of longs out of pipeline and weights.