Hi Is there a limit to how many fundamentals I can loop through, I keep getting an assertion error when I try 100+

import numpy as np  
import time  
import datetime  
import pandas as pd  
import talib  
import scipy.stats as stats  
import math  
import time  
import datetime  
import matplotlib as plt  
import scipy  
from scipy import stats  
from scipy.stats import pearsonr

from odo import odo  
from quantopian.pipeline import Pipeline  
from quantopian.pipeline.data.builtin import USEquityPricing  
from quantopian.pipeline.filters import QTradableStocksUS  
from quantopian.research import run_pipeline  
from quantopian.pipeline.data.builtin import USEquityPricing  
from quantopian.pipeline.factors import CustomFactor  
from quantopian.research import run_pipeline  
from quantopian.pipeline import Pipeline  
from quantopian.pipeline.filters import QTradableStocksUS  
from quantopian.research import run_pipeline  
from quantopian.pipeline import Pipeline  
from quantopian.pipeline.data import factset  
from quantopian.pipeline.data import Fundamentals

fd={'122':Fundamentals.roe.latest}  
pe=0  
def make_pipeline():  

    #ex_time= pd.Timestamp(get_datetime()).tz_convert('US/Eastern')  
    #relative_time= ((abs(9-ex_time.hour)*60)+ex_time.minute)-30  
    universe = QTradableStocksUS()  
    fund_list=[]

    start=1  
    pipe = Pipeline()  


    for column in Fundamentals.columns:  
                #print(column)  
            start=1+start  
            next = str(column)  
            pipe.add(column.latest,next)  
            if start>500:  
                break  
                #print('{}:{},').format(next,column)

    Blank=Fundamentals.cash_return.latest


    pipe.set_screen(universe)  

    return pipe  

#df2=get_pricing('SPY', start_date='2015-06-30', end_date='2016-06-30',  
#           symbol_reference_date=None, frequency='daily', fields= 'close_price', handle_missing='raise')

result=run_pipeline(make_pipeline(),start_date='2014-05-05', end_date='2014-06-05')

df=pd.DataFrame(result)


newdf=df.fillna(0, inplace=False)  
nul_val=df.isnull().sum().sort_values()  
#print(nul_val[df.isnull <100])

nul_val

#result  
#{column.name: column.latest for column in Fundamentals.columns}