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Long only Need help with Screen totally stuck!

Hello, I need a little help with my algo. Im trying to run a screen on my pipeline results but it doesn't seem to be working. Im new to python and quantopian so I wouldn't be surprised if I made a coding error somewhere along the line. I still have more to add to this but I have been stuck since yesterday on trying to get the screen function to work properly. Any help and other observations would be much appreciated!!!

Clone Algorithm
Total Returns
Max Drawdown
Benchmark Returns
Returns 1 Month 3 Month 6 Month 12 Month
Alpha 1 Month 3 Month 6 Month 12 Month
Beta 1 Month 3 Month 6 Month 12 Month
Sharpe 1 Month 3 Month 6 Month 12 Month
Sortino 1 Month 3 Month 6 Month 12 Month
Volatility 1 Month 3 Month 6 Month 12 Month
Max Drawdown 1 Month 3 Month 6 Month 12 Month
from quantopian.algorithm import attach_pipeline, pipeline_output  
from quantopian.pipeline import Pipeline  
from import USEquityPricing  
from import morningstar
from quantopian.pipeline.factors import SimpleMovingAverage, AverageDollarVolume

from import (

def initialize(context):  
    attach_pipeline(my_pipeline(context), 'my_pipeline')  
                      time_rules.market_open(hours = 1, minutes = 0))
    context.my_leverage = 0.99
def my_pipeline(context):
    return Pipeline(  
            'close': USEquityPricing.close.latest,
            'dollar_volume': AverageDollarVolume(window_length=20), 
            'rev_growth' :morningstar.operation_ratios.revenue_growth.latest,
            'eps_growth' :morningstar.earnings_ratios.diluted_eps_growth.latest,
            'roe'  : morningstar.operation_ratios.roe.latest,
            'pe' : morningstar.valuation_ratios.pe_ratio.latest,
            'mkt_cap' : morningstar.valuation.market_cap.latest,
            'roe_pe' : morningstar.operation_ratios.roe.latest /                                                                                        morningstar.valuation_ratios.pe_ratio.latest,
            '50d' : SimpleMovingAverage(inputs=[USEquityPricing.close], window_length=50),
            'roe_pe_rank' :  (((morningstar.operation_ratios.roe.latest /                                                                                morningstar.valuation_ratios.pe_ratio.latest)*100)*4).rank(ascending=False)  
            screen= (morningstar.valuation.market_cap.latest > 2000000000 and USEquityPricing.close.latest > 5 and 'dollar_volume' > 10**7                     and consensus_estimize_eps.estimize_eps_final.latest > 1.00 and USEquityPricing.close.latest >                                             SimpleMovingAverage(inputs= [USEquityPricing.close], window_length=50) and                                                                 morningstar.earnings_ratios.diluted_eps_growth.latest > 20 and                                                                             morningstar.operation_ratios.revenue_growth.latest > 20 and (((morningstar.operation_ratios.roe.latest /                                   morningstar.valuation_ratios.pe_ratio.latest)*100)*4)>2)

def before_trading_start(context, data): = pipeline_output('my_pipeline').dropna()  
    context.data_sort =['rev_growth'], ascending=False).iloc[:200]
    context.long_secs = context.data_sort

    context.final_longs_sort = context.data_sort.sort(['eps_growth'], ascending=False).iloc[:20]
    context.long_final = context.final_longs_sort
    context.security_list = context.long_final.index
def compute_weights(context):
    long_weight = context.my_leverage / len(context.long_final)
    return long_weight

def rebalance(context, data):
    long_weight = compute_weights(context)
    for stock in context.security_list:
        if data.can_trade(stock):
            if stock in context.long_final.index:
                order_target_percent(stock, long_weight)
    for stock in context.portfolio.positions:
        if stock not in context.security_list and data.can_trade(stock):
            order_target_percent(stock, 0)"This week's longs: "+", ".join([long_.symbol for long_ in context.long_final.index]))

def record_vars(context, data):
    This function is called at the end of each day and plots certain variables.

    longs = shorts = 0
    for position in context.portfolio.positions.itervalues():
        if position.amount > 0:
            longs += 1
        if position.amount < 0:
            shorts += 1

    record(leverage = context.account.leverage, long_count=longs, short_count=shorts)
There was a runtime error.
4 responses

Hi Santiago,

When you're defining factors using inequalities and boolean operators, you need to be very explicit with parentheses, because our order of operations in that area is not very good. What that means is that the definition for the screen should have parentheses around each individual inequality, like this:

(morningstar.valuation.market_cap.latest > 2000000000) & (USEquityPricing.close.latest > 5) & ('dollar_volume' > 10**7)

Note that I'm also using & instead of and, as prescribed by the documentation here.


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Nathan Thanks so much for your help! I made the change and Im getting There was a runtime error.
ZeroDivisionError: float division by zero
... USER ALGORITHM:73, in compute_weights
long_weight = context.my_leverage / len(context.long_final)

Any idea why?

You're getting a divide by 0 because context.long_final is empty. It's possible that your screen is returning nothing, but if I were to take a wild guess, one of he columns that's being returned in your dataframe is filled with nan's, and the call to dropna() is removing all the rows.

I would start by printing the contents of your pipeline out to the log and see what's in it.

Thank you for your response Tar. I will give it a go and see what happens. I will also post the algo giving the error here if Im unsuccessful to see if you can spot the error! Thanks again!