Very near the bottom of the equity drawdown, I decided to segregate some of my trading money into a living expense account so that I would feel less pressure from the day to day moves in the market.  I looked at the distribution of gains and losses for the prior year, and established a knee-jerk rule to add money to the living expense account whenever I experienced a “large” gain. 

In retrospect, this strategy amounts to a dynamic cash hedge, meaning that my total risk level fluctuates with the percentage of the total account that is held out in the living expense, or “hedge” account.  In truth, establishing the hedge at the equity trough was exactly the wrong thing to do, and it delayed my recovery from the drawdown.

From June to September, I blissfully ignored my hedge % as the Cable Glider posted gains in each of those months.  A losing October, however, has caused me to re-evaluate how I hedge my risk, and it’s led me to an interesting discovery that will affect how I handle money management with my systems going forward.

I have found that by picking a fixed hedge percent, say 25%, and resetting it to 25% at the end of each subsequent month, I can achieve better returns than by simply reducing my risk on each trade by 25%. 

Essentially, I’ve discovered the position sizing method that chaffcombe (http://www.futurestech.com.au/MonthlyReport_Sep2007.htm) refers to as Variable Fractional Position Sizing. (VFP).

Rather than re-invent the wheel, I’ll quote his comments on the subject below:

”

Although the drawdown was lengthy, it was extremely shallow – something that I am particularly pleased about considering the earlier major gains.   I largely attribute this to tight risk management and, specifically,  the variable fractional position sizing methodology (VFP) that I employ.  VFP increases or decreases the risk taken for each system in proportion to the realtime P/L  for the calendar month, thus allowing increased leverage during profitably months, without taking undue risks with what I consider to be ‘capital’ ie NAV booked at the end of the previous month.   VFP can make a lot of money very quickly in good trading conditions, while limiting losses very quickly in bad…

”

Armed with VFP, we can re-think the subject of drawdowns.  Traders will first and foremost consider the maximum peak to valley drawdown in their trading, but may not pay any attention to the timespan that those drawdowns cover.  Indeed, visualizing a drawdown as a lake whose volume is measured on the vertical axis by % drawdown and whose horizontal axis is measured by time, we can work on decreasing the pain of drawdowns by attacking both the vertical and the horizontal dimensions.

Most of the vertical value of a hedge is accomplished in the first down month.  If we reset the hedge to a fixed percent after a down month, we are effectively adding money to the trading account.  This is an anti-hedging technique meant to decrease the time taken to recover the drawdown.  With this methodology, we do risk a deeper drawdown if we experience consecutive losing months, but we retain the majority of the vertical value of the original hedge.

So, VFP has the potential to outperform fixed fractional in a drawdown.  What about in a run up to new equity peaks?  The beauty of VFP is its ability to lock in more of the gains from a positive outlier.  If we are happily moving along making 5% per month with no variation, VFP will reduce our total return by hedging out a portion of the gain each month.  If however, as has been the case with the Cable Glider, it is true that large positive and negative outliers are clustered together in time, VFP will outperform fixed fractional.  If we have a big gain followed by a big loss, VFP locks in more of the first month’s gain and reduces the subsequent drawdown. 

If we have a big loss followed by a big gain, the anti-hedging ability of VFP shines again by adding more capital to the system at the equity curve trough and speeding us out of the drawdown.

As with many other trading system parameters, the optimal (defined here as maximum net profit for any given level of risk) hedge percent is a moving target, and so this is not a simple topic.  For the Cable Glider in 2006, the optimal hedge % would have been in the 40′s, but this is because there were two cases of big run ups followed by drawdowns.  In 2007, the optimal hedge percent is much lower, because overall the equity curve has been much more flat.  When I combine the two years, 25% is the optimal hedge.  Over the past five months, my original dynamic hedge was moving in the 22% to 31% range.  Even though the length of this post and my long-winded explanation might suggest otherwise, the new method is simpler, cleaner, and easier to verify.

By trading in one market and having no scale in provision, I dodge a lot of the complexities associated with position sizing in an active portfolio.  In that case, many more possibilities exist with respect to accounting for unrealized gains and losses.  Without the burden of multiple open positions, I have only one possible number to use for the “equity” piece of my position sizing algorithm…my actual account equity.

I’ve tried various scale in techniques, but I’ve yet to find one that works for me, so unlike the Turtles, and unlike Jesse Livermore, when I enter, I enter my full position.

I expect that the bulk of my research going forward will focus on refinements to position sizing, since in my experience, this is where most of the variation in returns comes from.