Maybe this is an effect of concentrating risk so I ran a backtest with the highest price stock.

This may be susceptible to the lookahead bias introduced by split-adjusted prices. For instance GOOG and AAPL would have had split-adjusted prices of just pennies ten years ago. Someone mentioned this in another thread, but I don't recall who in order to give credit...

Here's the recent relevant post by Alexis P.

https://www.quantopian.com/posts/split-adjusted-prices-introduce-a-look-ahead-bias

1) I agree that there is a lookahead bias...I believe there was a lot of leverage used originally. What could one put in place of 'APPL' to make sure this algo runs across all securities not leveraged? I think it would be a lot more efficient and more realistic.

2) However, even with a do_not_order_list for leveraged etfs, it appears to try to trade one anyway. How does one go about fixing this problem?
def initialize(context):
context.formation = 2
context.port_size = 20
context.rebalance_int = 6
context.month_count = context.rebalance_int

set_slippage(slippage.VolumeShareSlippage(volume_limit=0.25, price_impact=0.1))  
set_universe(universe.DollarVolumeUniverse(floor_percentile=95.0, ceiling_percentile=100.0))  
set_do_not_order_list(security_lists.leveraged_etf_list)  



def handle_data(context,data):  

# the point in time lists allow for in/not in checks and iteration
# the list is point-in-time and allows for checks and iterations
if symbol('AAPL') not in security_lists.leveraged_etf_list:
order_target(symbol('AAPL'), 100)

# view the contents of the list
for etf in security_lists.leveraged_etf_list:
print '{s} is a leveraged etf'.format(s=etf)

The algo stops running in 2014 due to attempt at trading a leveraged ETF...

any suggestions?

Also see https://www.quantopian.com/posts/historical-data-issues

@Dan
...You have two handle_data calls....