Hi,
I'm pretty new to the programming world and was wondering if someone out there could please give me a helping hand. I'm trying to create an algorithm that relies on fundamental data. The issue that I am facing is that I am not sure whether the universe of stocks output by the get_fundamentals function have the data that I am looking for (or whether these are NA). You can see below that after the function I have used " context.fundamentals=context.fundamentals.dropna()" to try and get rid of any N/A's in the data set. Towards the end of the algo I use " print stock, cash, fundamentals.valuation_ratios.pe_ratio" to see what is happening, a sample of the produced log can be seen directly below. Every other stock is different but still indicates $1,000,000 cash remaining (which tells me that this stock has not been bought, despite meeting my criteria?) and UserInstrumentedAttribute (which I assume is the print off of fundamentals.valuation_ratios.pe_ratio). Assuming that the stocks that have been sorted into my universe don't actually have the data I require how do I ensure that I only get stocks in my universe that have these fundamental values?

Thanks in advance!

Log

2015-07-07PRINTEquity(17920 [RNWK])
2015-07-07PRINT
2015-07-07PRINT1000000.0
2015-07-07PRINT
2015-07-07PRINTUserInstrumentedAttribute

Algo

import pandas as pd
import numpy as np

def initialize(context):
set_max_order_count(50)

def before_trading_start(context):

context.fundamentals=get_fundamentals(  
        query(  
            fundamentals.balance_sheet.ordinary_shares_number,  
            fundamentals.valuation_ratios.book_value_per_share,  
            fundamentals.earnings_ratios.equity_per_share_growth,  
            fundamentals.valuation_ratios.pe_ratio,  
            fundamentals.valuation_ratios.pb_ratio,  
            fundamentals.balance_sheet.total_assets,  
            fundamentals.balance_sheet.total_liabilities,  
            fundamentals.earnings_report.normalized_basic_eps  

            # put your query in here by typing "fundamentals."  
        )  
        .filter(  
            fundamentals.valuation_ratios.pe_ratio<18  

            # put your filters here  
        )  
        .filter(  
        fundamentals.valuation_ratios.pb_ratio<2  

        )  
        .order_by(  
            fundamentals.valuation_ratios.pe_ratio.asc()  
            # sort your query  
        )  
        .limit(200)  
    )  
context.fundamentals=context.fundamentals.dropna()  


update_universe(context.fundamentals.columns.values)  

def handle_data(context, data):

    cash = context.portfolio.cash  
    #current_positions = context.portfolio.positions




    for stock in data:  
            current_position = context.portfolio.positions[stock].amount  
            stock_price = data[stock].price  
        #how much of the remaining cash you're willing to invest  
            plausible_investment = cash/50

          #  share_amount = int(plausible_investment/stock_price)  

            try:  
                if stock_price<plausible_investment:  
                    if current_position==0:  
                        #if context.fundamentals[stock]['normalized_basic_eps']>0:  
                            if context.fundamentals[stock]['pe_ratio']<14:  

                                order_target_percent(stock,5, style=MarketOrder())  


                                print stock, cash, fundamentals.valuation_ratios.pe_ratio  



            except Exception as e:  
                print(str(e))