Optimal F in a Portfolio – Does Zorro Overrisk?

Hi,
After reading Ralph Vince’s publications I believe that the implementation of Optimal F in workshop 6 is not correct and might, in case of a multicomponent portfolio, lead to excessive risk (we are far to the right of the peak of the optimal f curve).
When setting FACTORS the optimal fractions for re-investment are calculated and as the manual states, these factors are calculated independently for each asset and algo. This means that we get the optimal fraction for re-investment of each asset and algo if we only traded this one algo and asset on its own (e.g. in Workshop 6: we only trade the trend strategy on EUR/USD, results of USD/JPY and countertrend are ignored).
However, if we add additional strategies (algos) and assets to the script the optimal f from before is not optimal anymore in context of a whole portfolio due to correlations between the various components.
Ralph Vince’s uses a simple example in his paper “The Leverage Space Model” (p.19)
http://www.automated-trading-system.com/wp-content/uploads/2010/03/Vince-LeverageSpaceModel.pdf

He uses a coin-toss experiment were with tails one loses $1 and with head one wins $2. The probability of occurrence of either event obviously is 50%.
If we calculate optimal f we get a result of 0.25. This means that for maximum growth we have to invest $1 for every $4 at stake.
Now, if we extend this experiment and throw 2 (!) coins at the same time the optimal fraction to invest changes, even if there is NO correlation at all. In case of 0 correlation the optimal f would not be 0.25 anymore BUT 0.23 (if we invest the calculated 0.25 from before we already OVERRISK).

This gets worse in case that these two games are perfectly correlated (meaning that if one coin lands on head the other one will land on head too). In this case it would be the same as playing only one game. If we invested in each game the optimal f that we calculated for the isolated game (that is, optimal f = 0.25) we’d effectively invest 0.5 not 0.25 anymore (as described above, in case of a perfect correlation, the two coin tosses that we perform at the same time can be seen as 1). Looking at the optimal f curve (figure 12 in the paper) we are far to the right of the optimal f value if we invest 0.5 and consequently severely OVERRISK.

My question to the Zorro developers is whether I am missing something or whether there is a specific reason of why optimal F is implemented in such a way in workshop 6.
I specifically refer to (Margin = 0.5 * OptimalF * Capital * sqrt(1 + ProfitClosed/Capital))
As ProfitClosed calculates the profit for each component separately (and thus, effectively creates separated sub-accounts for each algo/asset component) would you suggest to split the initial capital into X parts as well? Through this procedure it would not be required to implement the Leverage Space Model which considers joint probabilities of trade results of X components.

Thanks in advance.
Cheers