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You kept calling different systems A and B. That would cause confusion when I put them all in one post, so I did the logical thing and relabelled some of them. Here are the last two systems you posted: [-8, -8, -8, +25, +25] [-1, +1, +1, +21, +50] As you yourself pointed out, the last...

Then use the Eckhardt criterion instead. He explained why he chose 1800 trades. But the idea of no upper limit is ridiculous. You can make a lousy system look good by simply padding your trade count when there is no upper limit to the trade count in your performance measure.

I PROVED that what I called "Bad Kelly" is not Kelly at all. Would it help you understand it better if I called it "Not Kelly"? If the SPS values I calculated based on your systems don't make sense, you've yet to point out why. Let's take another look at your systems: System E : [-8...

You've misinterpreted the count parameter, which has an upper limit by the way, unlike sqrt(N).. The point of min[1, N/1000] is to penalize SPS values that are based on an insufficient number of trades, i.e., below 1000 trades. No need for a square root version because the standard deviation...

What you posted isn't the Kelly fraction. Once again, go read the "Bad Kelly" thread in Trade Management to find out why.

If you feel that system F is only a bit more than twice better than system E, then by all means use your measure. My gut tells me that my numbers make more sense than yours do.

Here is the final version of the System Performance Score (SPS). The previous versions suffered from too much focus on the NOBF , which it turns out is entirely unnecessary. SPS = (p*(W/L) - q)*min[1, N/1000] , where p is the winrate, W is the average winning trade return (%), L is...

Fail. One key characteristic of a good performance measure should be its universality. For example, horsepower, a widely accepted measure of engine performance, is universal: its measurement does not vary from user to user or from year to year. But the Sortino ratio, the Sharpe ratio, and...

Pick one.

So what performance metric do you prefer?

In fact, system B is so out of phase with reality that higher-order polynomial approximations of the Kelly fraction get farther away from the exact value rather than the norm of getting closer. Linear approximation = 0.0245 Cubic approximation = 0.0225 Quintic approximation = 0.0217...

It's not all gambling. Gambling is adding to losers. Adding to winners can make sense if you don't use optimal trade sizing. If you use optimal trade sizing, it doesn't make sense to add to winners.

That's not trading, that's gambling. Good luck with your gambling.

Kelly is the true optimal trading fraction, "optimal f" is just some contrived crap that Ralph Vince created to try to profit off something that is available for free.

All you've proven is that the NOBF can't handle ridiculous extremes like your system B, which never occur IRL. Calculating the true Kelly fractions gives the correct order for the SPS relationships. Kelly(A) = .026 NOBF(A) = .018 = about 70% of the true Kelly NOBF(B)...

It's even easier to put them on ignore so what's your issue with that?

You've completely misinterpreted the formula. There is no absolute P&L. The NOBF is based on percent returns, not absolute returns. No need for me to "experiment" further. The formula you posted looks pretty much the same as the SQN with all the same flaws.

True. If an outsized trading loss is not offset by a similar outsized trading gain, then the linear approximation can overestimate the Kelly fraction. Always check the reciprocal of the absolute value of your worst trading return for a complete risk assessment.

I've concluded that nonlinear5 has pointed out a valid weakness in the original formula. So here is the revision : SPS = NOBF*R*min[1, N/1000] , where R is the average winning trade return (%).

I totally agree with Frank, fill your ignore list up with all the ET assclowns who provide no net value. Also check out this thread of threads: http://www.elitetrader.com/vb/showthread.php?s=&threadid=204669