High Gains - Low Returns. Is this right?

I have made a recent algorithm that is generating negative returns but my overall cash gain is high. Can anyone explain this?
def initialize(context):  
      
    #set_slippage()  
        #Come back to this later  
    #set_slippage(MyCustomSlippage(slippage-param))  
      
    #window = 255  
    #refresh_rate = 10  
      
    context.jblu = sid(23599)  
    context.dal = sid(33729)  
    #context.aal = sid(45971)  
    context.luv = sid(4589)  
    context.ual = sid(28051)  
    context.current_stock = sid(23599)  
    #context.current_stock = sid(33729)  
    #context.current_stock = sid(45971)  
    #context.current_stock = sid(4589)  
    #context.current_stock = sid(28051)  
    #context.orders_placed = 0  
      
    context.max_notional = 100000.1  
    context.min_notional = -100000.0

def handle_data(context, data):  
      
    record(Cash=context.portfolio.cash)  
    #record(amount=context.portfolio.amount)  
        #^This is incorrect in its current state - try to illustrate number of shares held for current sid  
      
    pavg = ((((data[context.jblu].close_price)-(data[context.jblu].open_price))/(data[context.jblu].open_price))*100)  
    pavg2 = ((((data[context.dal].close_price)-(data[context.dal].open_price))/(data[context.dal].open_price))*100)  
    pavg3 = ((((data[context.luv].close_price)-(data[context.luv].open_price))/(data[context.luv].open_price))*100)  
    pavg4 = ((((data[context.ual].close_price)-(data[context.ual].open_price))/(data[context.ual].open_price))*100)  
      
    price = ((((data[context.jblu].close_price)-(data[context.jblu].open_price))/data[context.jblu].open_price)*100)  
    #price2 = data[context.dal].price  
    #price3 = data[context.aal].price  
    #price3 = data[context.luv].price  
    #price4 = data[context.ual].price  
      
    average = ((pavg * pavg2 * pavg3 * pavg4)/4)  
    #This is the average of all four stocks whcih JetBlue will be bought in comparison to  
      
    #returns = data[context.jblu].returns  
      
    notional = context.portfolio.positions[context.jblu].amount * price

    message = average  
    log.info(message)  
    message = price  
    log.info(message)  
      
    if price < (average) and notional > context.min_notional:  
        #message = context.portfolio.positions[context.jblu].amount  
        #log.info(message)  
        #message = context.portfolio.cash  
        #log.info(message)  
        message = 'Buy'  
        log.info(message)  
        order(context.jblu,+1)  
    elif price >= average and context.portfolio.positions[context.jblu].amount > 1 and notional < context.max_notional:  
        #message = context.portfolio.positions[context.jblu].amount  
        #log.info(message)  
        #message = context.portfolio.cash  
        #log.info(message)  
        message = 'Sell'  
        log.info(message)  
        order_target_percent(context.jblu,-1)  
        return()  
          
    #if context.orders_placed < 1:  
     #   order(context.current_stock, -1000)  
      #  context_orders_placed=1