Kalman Filter

Excellent.

The state vectors would have to contain high order derivatives which are noisy. That is exactly correct. Kalman filters are good for physical dynamics because those can generally be covered by position and its first and sometimes second derivatives velocity and acceleration. Market data contains instantaneous displacements that would require third and fourth derivatives jerk and yank. Those are incredibly noisy and basically unusable.

 

DECO & 377OHMS,

Do you guys have an opinion on using the MESA (All Poles) method to find and trade the dominate cycle? Thanks.

 

I'm not familiar with that.

 

Now look what you've done!

 

See Jackoff? Jerk and yank. 377, I would address you by your resistor color code, but I have forgotten how. Let's see... "Black boys rape our young girls, but Violet gives willingly, get some now!" It's been so long since I designed anything physical that I couldn't design a voltage divider. Algorithms and software are so much better behaved. Your explanation was very eloquent, and what I meant to say. I would only add that there is no system noise and no measurement noise, so a linear Kalman would devolve to however many derivatives you needed to approximate price action.

 

All price cycle theory, of whatever stripe, is bullshit. Price action is all about generating volume for accumulation or distribution by the big dogs, and support and resistance along the way.

 

Oooops! Fortunately the tools to do it are painful to even think about, much less implement.

 

This is the metastock code for an indicator named the Kalman filter. I'm sure Art will rip the code as not even close. Anyway, the resulting indicator does not appear to have any usefulness.

{Kalman Filter}
{Advantage Systems: www.advantage.online.pl}
PRICEE:=C;
Smooth:=.13785*(2*PriceE - Ref(PriceE,-1)) +
.0007*(2*Ref(PriceE,-1) - Ref(PriceE,-2)) +
.13785*(2*Ref(PriceE,-2) -Ref(PriceE,-3)) +
1.2103*PREV - .4867*Ref(PREV,-1);
SMOOTH;

 

An odd looking beast. All odball filters are a solution to some problem, you just don't know what that problem is.

 

Here is a good reference article. As has been mentioned, there is nothing particularly magical about Kalman filters or any other filter in and of themselves.