I used the example "multiple SID" algorithim with minor modifications. I'm a bit green when it comes to the stock market, so I'm wondering why I'm getting a large amount of profit after starting with 100k capital, despite having negative returns (and stocks?) Backtests I run show at least positive returns at points, but my recent live trading shows nothing but a steady decline, yet my portfolio's funds grow. Is it a bug in Quantopian?

    # This example runs the same momentum play as the first sample  
    # (https://www.quantopian.com/help#basic-algo), but this time it uses more  
    # securities during the backtest.  
    # Important note: All securities in an algorithm must be traded for the  
    # entire length of the backtest.  For instance, if you try to backtest both  
    # Google and Facebook against 2011 data you will get an error; Facebook  
    # wasn't traded until 2012.

    # First step is importing any needed libraries.

import datetime  
import pytz

def initialize(context):  
    # Here we initialize each stock.  Note that we're not storing integers; by  
    # calling sid(123) we're storing the Security object.  
    context.stocks = [sid(3951), sid(7027), sid(351), sid(1234), sid(24)]  
    context.vwap = {}  
    context.price = {}  
    # Setting our maximum position size, like previous example  
    context.max_notional = 1000000.1  
    context.min_notional = -1000000.0

    # initializing the time variables we use for logging  
    utc = pytz.timezone('UTC')  
    context.d=datetime.datetime(2000, 1, 1, 0, 0, 0, tzinfo=utc)

def handle_data(context, data):  
    # Initializing the position as zero at the start of each frame  
    notional=0  
    # This runs through each stock.  It computes  
    # our position at the start of each frame.  
    money = []  
    for stock in context.stocks:  
        price = data[stock].price  
        money.append(price*context.portfolio.positions[stock].amount) # price*amount of shares  
        notional = notional + context.portfolio.positions[stock].amount * price  
        tradeday = data[stock].datetime  
    # This runs through each stock again.  It finds the price and calculates  
    # the volume-weighted average price.  If the price is moving quickly, and  
    # we have not exceeded our position limits, it executes the order and  
    # updates our position.  
    for stock in context.stocks:  
        vwap = data[stock].vwap(3)  
        price = data[stock].price  

        if price < vwap * 0.995 and notional > context.min_notional:  
            order(stock,+100)  
            notional = notional - price*100  
        elif price > vwap * 1.005 and notional < context.max_notional:  
            order(stock,-100)  
            notional = notional + price*100

    # If this is the first trade of the day, it logs the notional.  
    if (context.d + datetime.timedelta(days=1)) < tradeday:  
        log.debug(str(notional) + ' - notional start ' + tradeday.strftime('%m/%d/%y'))  
        context.d = tradeday  
        record(Intel_money=money[0], Sonic_money=money[1], AMD_money=money[2], Cheesecake_money=money[3], Apple_money=money[4])