The update_universe() method is probably what you're looking for: https://www.quantopian.com/help/#api-update-universe

Note, you need to pass update_universe() a list of sids, not tickers. See the fundamentals algo example for a template: https://www.quantopian.com/help/#sample-fundamentals

If you pass their ticker symbols as string constants to symbol or symbols, they will be placed in your initial universe. Typically, you would write in initialize:

    context.stocks=symbols('BA', 'AAPL', 'NAV', 'AGU', 'XLNX', 'WY', 'TAP')  

The assignment is not required, but useful. There is no way to find out later what your universe is if you don't save this information yourself. See https://www.quantopian.com/posts/search?q=get_universe .

You should include them in any subsequent calls to update_universe, otherwise they will be removed.

I appreciate the help guys, unfortunately I am still running into issues. I decided to use the fundamentals.valuation.sid in my query of fundamentals data, then feed that list of SID's into the update_universe method. Now, when I try to query the price of a stock in that list I have created, I get hit with this error:

TypeError: 'zipline.assets._assets.Equity' object has no attribute '__getitem__'
USER ALGORITHM:72, in handle_data
log.info(stock[data].price)

Here is a sample of my code. Thanks for all the help in advance!

import pandas as pd  
import numpy as np

def initialize(context):  
    # Dictionary of stocks  
    context.stocks = []  
    context.fun_stocks = []  
    # Count of days before rebalancing  
    context.days = 0  
    set_benchmark(sid(8554))  
    # Sector mappings  
    context.sector_mappings = {  
       101.0: "Basic Materials",  
       102.0: "Consumer Cyclical",  
       103.0: "Financial Services",  
       104.0: "Real Estate",  
       205.0: "Consumer Defensive",  
       206.0: "Healthcare",  
       207.0: "Utilites",  
       308.0: "Communication Services",  
       309.0: "Energy",  
       310.0: "Industrials",  
       311.0: "Technology"  
    }  
    context.sectors = context.sector_mappings.keys()

def before_trading_start(context, data):  
    num_stocks = 100  
    fundamental_df = get_fundamentals(  
        query(  
            fundamentals.valuation.sid, #get stock ID  
            fundamentals.company_reference.primary_symbol,  
            fundamentals.valuation_ratios.pe_ratio,     # Price to Earnings ratio  
            fundamentals.asset_classification.morningstar_sector_code  
        )  
        .filter(fundamentals.valuation_ratios.pe_ratio != None)  
        .filter(fundamentals.asset_classification.morningstar_sector_code != None)  
        .filter(fundamentals.asset_classification.morningstar_sector_code != 309.0)    # filter out utilities  
        .filter(fundamentals.asset_classification.morningstar_sector_code != 103.0)    # filter out finance  
        .filter(fundamentals.asset_classification.morningstar_sector_code != 206.0)    # filter out healthcare  
        .filter(fundamentals.valuation.market_cap >= 50000000)  
        .filter(fundamentals.valuation.shares_outstanding != None)

        .order_by(fundamentals.valuation.market_cap.desc())  
        .limit(num_stocks)  
    )  
    sectors = [101.0, 102.0, 104.0, 205.0, 207.0, 308.0, 310.0, 311.0]  
    context.fun_stocks = [stock for stock in fundamental_df if fundamental_df[stock]['morningstar_sector_code'] in sectors]  
    for stock in context.fun_stocks:  
        stock = fundamental_df[stock]['sid']  
        context.stocks.append(stock) #iterate through list of SIDs and append them to context.stocks  
    update_universe(context.stocks)

def handle_data(context, data):  
    for stock in context.stocks:  #loop through context.stocks and log their price  
        log.info(stock[data].price)

The last line should have data[stock].price. But that is only a number, indistinguishable from others. You'd better write something like

        log.info("{}: {} ${}".format(get_datetime('US/Eastern').time(), stock.symbol, data[stock].price))