TaylorTree

Sunday, July 11, 2010

Portfolio Performance for June 2010

"Time flies like an arrow; fruit flies like a banana" -- Groucho Marx

My how the months have zoomed by. As you can see from the site; I moved from blogger to wordpress. Haven't had the time to do much with the old posts. Most likely I will leave them as is and start from a clean slate. The most important posts anyway are of the portfolio performance and this post will take care of what's happening there.

You can see in the Figure 1 - VAMI; the portfolio is doing well...breaking free from the market. The main reason for this is a new trading system was added on June 1st. This trading system is a short-term mean-reversion system. It is doing as expected but taking some time to get used to. I've spent the better part of 10 years trading a long-term trend-following system. So, moving to a short-term mean-reversion system is going to take some adjustments on my part.

Again, all the charts show the portfolio breaking away from the market. June was a nasty month for the S&P 500...while the portfolio held its own. I feel I still have some work to do on the new system such as the proper risk allocation and determining a better weight for the systems in the portfolio. Also, the new system still has quite a bit of beta that I'd like to scale down. But, overall, a nice start.

On a personal note, I'm back from my yearly pilgrimage to Texas to see family & friends. Trip was good but volatile like the market.

We had to evacuate due to Hurricane Alex in the Gulf - almost lost our car.

Picture above is the day prior to the effects of Hurricane Alex.

And our transmission acted up on our stop at Broken Bow, OK - I'm guessing due to all the hill climbing.

I couldn't get over how clear the water was and the beautiful pine-lined hills surrounding the lake.

We had to cut our losses short in both of the places we would have liked to spend more time. Managing risk vs reward...being a portfolio manager is a 24x7 job.

Later Trades,

MT

Sunday, June 20, 2010

Moving to Wordpress - change your feeds

I've moved to Wordpress away from Blogger. Please change your RSS subscription feeds for this site to the following:

http://taylortree.com/feed/

And please check to see if your bookmark to the main page is working correctly. Should be: http://www.taylortree.com/

I'm not going to migrate all the Blogger posts to Wordpress. I've changed so much over the years...time for a fresh start.

Thanks,

MT

Running Simple Moving Average (SMA)

When building a platform to test trading ideas...one of the big issues to deal with is all the indicators that require a spin through the price series in order to calculate. For example, in order to calculate the 200 day simple moving average (SMA) of closing prices for Google today you would have to loop back 200 - 1 days ago and sum the closing prices and divide by 200.

When you are backtesting an idea you often need to start from day 1 of a stock's trading history and loop forward to the most current day. In essence, pretending each day is the current day at that point in time. Thus, you are looping back 200 - 1 data points for each day in the series. This isn't such a big deal with a stock such as Google whose trading history is rather limited (2004). But, take a stock like IBM with a more extensive trading history and your code is going to bog down with each call to the SMA indicator. Throw 20,000 securities into your backtest and the looping adds up.

Therefore, running calculations are the preferred method in order to spin just once through the data points. So, in order to calculate the running simple moving average for closing prices you apply the following formula:
$SMA_{today} = SMA_{yesterday} + ((Price_{today} - Price_{today - n}) /n)$
Where

$n$ = number of values included in your rolling computational window.

Straight-forward and avoids the loop. Here's the sample Python code for the Running SMA:

def cumulative_sma(bar, series, prevma):
    """
    Returns the cumulative or unweighted simple moving average.
    Avoids sum of series per call.

    Keyword arguments:
    bar     --  current index or location of the value in the series
    series  --  list or tuple of data to average
    prevma  --  previous average (n - 1) of the series.
    """
   
    if bar <= 0:
        return series[0]

    return prevma + ((series[bar] - prevma) / (bar + 1.0))

def running_sma(bar, series, period, prevma):
    """
    Returns the running simple moving average - avoids sum of series per call.

    Keyword arguments:
    bar     --  current index or location of the value in the series
    series  --  list or tuple of data to average
    period  --  number of values to include in average
    prevma  --  previous simple moving average (n - 1) of the series
    """

    if period < 1:
        raise ValueError("period must be 1 or greater")

    if bar <= 0:
        return series[0]

    elif bar < period:
        return cumulative_sma(bar, series, prevma)

    return prevma + ((series[bar] - series[bar - period]) / float(period))

And the example call and results:

prices = [10, 15, 25, 18, 13, 16]
prevsma = prices[0]   #1st day nothing to average so return itself.
for bar, close in enumerate(prices):
    currentsma = running_sma(bar, prices, 3, prevsma)
    print "Today's 3-day SMA = %.4f" % currentsma
    prevsma = currentsma

------- Results ----------------
Today's 3-day SMA = 10.0000
Today's 3-day SMA = 12.5000
Today's 3-day SMA = 16.6667
Today's 3-day SMA = 19.3333
Today's 3-day SMA = 18.6667
Today's 3-day SMA = 15.6667