I suggest you try trading this only once a day by inserting something like the following, and make a call to it in handledata. It won't be as slow. Also, you may want to use a moving average on your vix test, rather than just the different between two days. If you are investing a million, it may be nice to invest the rest while you are waiting in bonds or something. Kudos for coming up with an interesting idea.

put in handle data

# only execute algorithm once per day
if not intradingwindow_check(context): return

def intradingwindow_check(context):
# Converts all time-zones into US EST to avoid confusion
if get_datetime().day == context.yesterday:
return False

    loc_dt = get_datetime().astimezone(timezone('US/Eastern'))  
    if loc_dt.hour >= 10 and loc_dt.minute >= 5:  
        context.yesterday = get_datetime().day  
        return True  
    else:  
        return False  

Hey Richard,

Thanks for your thoughts. While the algorithm executes on every bar, it only places an order once per day. I updated the algorithm to use a moving average on VIX as a signal, rather than looking for VIX simply increasing over the previous day. The result is a more conservative strategy - lower returns and lower volatility.

Ryan

The original article doesn't seem very convincing. I mean the strategy only finds 7 buy points over 14 years. Then he measures performance based on the highest point of returns. That would be great if the trade is timed perfectly. Also BRK B split in 2010. So the 1510 ratio works in hindsight.

Hey Brent - thanks for your comments. You are right about the small number of buy points; seven points in fourteen years is a small sample size to determine a systematic signal and it's not always feasible to wait for two years for a buy signal. Quantopian's engine handles stock splits (help page). The buy point strategy also outperforms a buy-and-hold strategy that holds 50% of the portfolio in Berkshire Class B stock and 50% of the portfolio in SPY (see attached back-test).