symbol() and symbols() work some magic in the background, converting the symbol or symbols to security objects and adding them to the universe. So there isn't actually a need to assign the result to anything, they wind up in data and you already had that loop in place. This switches over to symbols(), plural, so you can work with more than one, and you're already set up for that too. And then this adds a couple of other things that might help.

def initialize(context):  
    symbols('AAPL', 'TSLA')

    schedule_function(order_entry_function, date_rules.every_day(), time_rules.market_open(hours=0,minutes=1))  
    schedule_function(order_exit_function, date_rules.every_day(), time_rules.market_close(hours=0,minutes=1))

def order_entry_function(context, data):  
    for stock in data:  
        cash = context.portfolio.cash / len(data)  
        current_price = data[stock].close_price  
        yc = context.yc[stock].values.tolist()[0] # Unsure about these, check them  
        od = context.od[stock].values.tolist()[0] #   in the debugger for example.  
        gap_size = (od - yc) / yc

        if gap_size < -0.01:  
            number_of_shares = int(cash / current_price)

            order(stock, +number_of_shares)  
            log.info('Buying {} {}'.format(number_of_shares, stock.symbol))

def order_exit_function(context, data):  
    for stock in data:  
        cash = context.portfolio.cash  
        record(cash = cash)  
        current_price = data[stock].close_price  
        record(**{stock.symbol: current_price})  
        yc = context.yc[stock].values.tolist()[0]  
        od = context.od[stock].values.tolist()[0]  
        gap_size = (od - yc) / yc

        if gap_size < -0.01:  
            shrs = context.portfolio.positions[stock].amount  
            if shrs:  
                order_target(stock, 0)  
                log.info('Selling {} {}'.format(shrs, stock.symbol))

def handle_data(context, data):  
    # Only need to run history once per bar/frame  
    context.yc = history(2, "1d", 'close_price')  
    context.od = history(1, "1d", 'open_price')  

Thanks Gary
That helps so much! I appreciate it.

Tim

I have one more question. I'm trying to test this strategy on a number of stocks in a csv file. I've read as much about fetcher as I can but still can't these stocks to be included in my universe for the backtest. Any help with this would be greatly appreciated.

Thanks

Tim

def initialize(context):  
    #set_do_not_order_list(security_lists.leveraged_etf_list)  
    fetch_csv("https://dl.dropboxusercontent.com/s/q7i5k3453io9xlo/SPNQ_TickersQ.csv",universe_func=my_universe,date_column='Date',symbol='Ticker')  



    schedule_function(count_triggers_function, date_rules.every_day(), time_rules.market_open(hours=0,minutes=1))  
    schedule_function(order_entry_function, date_rules.every_day(), time_rules.market_open(hours=0,minutes=1))  
    schedule_function(order_exit_function, date_rules.every_day(), time_rules.market_close(hours=0,minutes=2))

def preview(df):  
    log.info(' %s ' % df.head())  
    return df  
def my_universe(context, fetcher_data):  
    my_stocks=set(fetcher_data[symbol])  

    return my_stocks  
def count_triggers_function(context,data):  
    triggers=0  
    for stock in data:  
        print stock  
        current_price = data[stock].close_price  
        yc = context.yc[stock].values.tolist()[0] # Unsure about these, check them  
        od = context.od[stock].values.tolist()[0] #   in the debugger for example.  
        gap_size = (od - yc) / yc

        if gap_size < -0.05:  
            triggers=triggers+1  
        elif gap_size>.05:  
            triggers=triggers+1  
    return triggers


def order_entry_function(context, data):  
    for stock in data:  
        current_price = data[stock].close_price  
        yc = context.yc[stock].values.tolist()[0] # Unsure about these, check them  
        od = context.od[stock].values.tolist()[0] #   in the debugger for example.  
        gap_size = (od - yc) / yc

        if gap_size <- 0.05:  
            print gap_size  
            triggers=count_triggers_function(context,data)  
            cash = (context.portfolio.cash / triggers)  
            print cash  
            number_of_shares = int((cash *.04)/(current_price-0.9*current_price))

            order(stock, +number_of_shares)  
            log.info('Buying {} {}'.format(number_of_shares, stock.symbol))  
            log.info(triggers)  
        elif gap_size>0.05:  
            print gap_size  
            triggers=count_triggers_function(context,data)  
            cash=context.portfolio.cash/triggers  
            number_of_shares = int((cash *.04)/(current_price-0.9*current_price))  
            order(stock,-number_of_shares)  
            log.info('Shorting {}{}'.format(number_of_shares,stock.symbol))  
            log.info(triggers)  



def order_exit_function(context, data):  
    for stock in data:  
        print stock  
        #record(cash = cash)  
        current_price = data[stock].close_price  
        #record(**{stock.symbol: current_price})  
        yc = context.yc[stock].values.tolist()[0]  
        od = context.od[stock].values.tolist()[0]  
        gap_size = (od - yc) / yc

        if gap_size <-0.05:  
            shrs = context.portfolio.positions[stock].amount  
            if shrs:  
                order_target(stock, 0)  
                log.info('Selling Longs{} {}'.format(shrs, stock.symbol))  
        elif gap_size>0.05:  
            shrs = context.portfolio.positions[stock].amount  
            if shrs:  
                order_target(stock, 0)  
                log.info('Buying to Cover{} {}'.format(shrs, stock.symbol))  


def handle_data(context, data):  
    # Only need to run history once per bar/frame  
    context.yc = history(2, "1d", 'close_price')  
    context.od = history(1, "1d", 'open_price')