Why are quants afraid of Mark Jurik?

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Quants are afraid of Mark Jurik because he writes books and sells add-ins for Tradestation.

They also fear him because of his large and growing portfolio of US Patents. Have a look at the list, which you can find at the US Patent and Trademark Office website: http://bit.ly/d5WKOn
 
Quote from Random.Capital:

On ET pimping the JMA, obviously.

HMA supersedes it, anyway, and is non-proprietary. You can_do it in Excel, if you like.

http://en.wikipedia.org/wiki/Arnaud_Legoux_Moving_Average
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Thanks for the link but if you think the HMA outperforms the JMA, you haven't been paying attention.

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Quote from kut2k2:

Thanks for the link but if you think the HMA outperforms the JMA, you haven't been paying attention.
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I'll keep that in mind next time I see prices moving in a periodic square wave pattern.
 
Quote from Random.Capital:

"Searching US Patents Text Collection...
Results of Search in US Patents Text Collection db for:
IN/"Jurik, Mark": 0 patents."
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I don't think you can patent an algorithm, but even if you could, why would you reveal said algorithm in a patent knowing every programming-savvy trader in the world would just co-opt it illegally to keep from paying you for it? Best to keep it a trade secret with encrypted software.
 
Quote from Random.Capital:

I'll keep that in mind next time I see prices moving in a periodic square wave pattern.
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You can't have it both ways. Either it outperforms the JMA or it doesn't. Fact: the HMA underperforms the JMA.

Next.
 
Quote from dtrader98:

What metric do you propose to demonstrate better results?
I can whip up a version of the reverse engineered JMA that I've seen in the past.
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What metric were you using when you said this:

"I have personally seen reverse engineered JMA , and RSX that are identical to the real thing and they were mediocre."

I can't do RSX but I have a custom ama that is superior to the KAMA and it isn't mediocre. And it isn't the JMA either. No PHASE parameter, for one thing.
 
Quote from kut2k2:

What metric were you using when you said this:

"I have personally seen reverse engineered JMA , and RSX that are identical to the real thing and they were mediocre."

I can't do RSX but I have a custom ama that is superior to the KAMA and it isn't mediocre.
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That was another poster's quote, but I ask because it's hard to really say what's better or not, without some type of metric for comparison.
As another poster pointed out, what good is a diagram of the response to a perfect edged step input? It is meaningless and arbitrary with regards to trading. I hope you don't rely on that diagram as the arbiter of superiority over filters, because it doesn't really mean anything by itself, other than one response has less overshoot... the obvious trade-off is that it also responds slower, which may or may not be good depending on the data it's filtering.

You could pick out several filters; Hull, Jurik, KAMA, etc that perform well based upon a small snippet of market data, which is cherry picking and a general problem with TA to begin with (one of the reasons you don't see quants discussing it much). In order to properly compare them or ascribe any usefulness, there must be either a metric or system to demonstrate their usefulness towards some specific trading application. And the fact that they rely on parameters means that you need to validate them out of sample at a minimum, which you won't see on his website. Secondly, if it is proprietary and you don't have control over the parameters, then you are pretty much at the mercy of his in sample optimized values, which don't necessarily translate well OOS.
 
Quote from dtrader98:

That was another poster's quote, but I ask because it's hard to really say what's better or not, without some type of metric for comparison.
As another poster pointed out, what good is a diagram of the response to a perfect edged step input? It is meaningless and arbitrary with regards to trading.

You could pick out several filters; Hull, Jurik, KAMA, etc that perform well based upon a small snippet of market data, which is cherry picking and a general problem with TA to begin with (one of the reasons you don't see quants discussing it much). In order to properly compare them or ascribe any usefulness, there must be either a metric or system to demonstrate their usefulness.
And the fact that they rely on parameters means that you need to validate them out of sample at a minimum, which you won't see on his website.
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Sorry for the mix-up, dtrader98. You're absolutely right, an objective trade-free metric is needed. I'll give it some thought.
 
The Jurik smoothing includes 3 stages:


1st stage - preliminary smoothing by adaptive EMA:
MA1 = (1-alpha)*Price + alpha*MA1[1];
2nd stage - one more preliminary smoothing by Kalman filter:
Det0 = (Price - MA1)*(1-beta) + beta*Det0[1];
MA2 = MA1 + PR*Det0;
3rd stage - final smoothing by unique Jurik adaptive filter:
Det1 = (MA2 - JMA[1]) * (1-alpha)^2 + alpha^2 * Det1[1];
JMA = JMA[1] + Det1;
where:
- Price - Price Series
- alpha - dynamic factor(will be described below)
- beta - periodic ratio = 0.45*(Length-1)/(0.45*(Length-1)+2)
- PR - Phase Ratio: PR = Phase/100 + 1.5 (if Phase < -100 then PR=0.5, if Phase > 100 then PR=2.5).



The Dynamic Factor is periodic factor (beta) raised to a power

(pow):
alpha = beta ^ Pow,
where:
- pow = rVolty ^ pow1
- rVolty - relative price volatility
- pow1 - power of relative volatility with following formula:
pow1 = len1 - 2 (if pow1 < 0.5 then pow1 = 0.5),
where len1 - additional periodic factor:
len1 = Log(SquareRoot(len))/Log(2.0) + 2 (if len1 < 0 then len1 = 0).
Thus you can see that the Dynamic factor is based on the relative price volatility giving the required
adaptability for this kind of the price filter.
The formula for relative price volatility is
rVolty = Volty/AvgVolty
(if rVolty > len1^(1/pow1) then rVolty = len1^(1/pow1), if rVolty < 1 then rVolty = 1),
 
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