In my 3 minutes of reading it looks like the Kalman filter is designed to
better approximate the actual state of a system from a series of (erroneous)
inputs.  It sounds like the system needs to have a model, for instance... a
rocket.  A rocket is a well defined system, using the basic Newton equations
of motion.  For the stock market, you would first need a model, and then
this technique would help you to better curve fit to that model.

Am I correct on this point?

What would you use as your model.. some kind of momentum trading approach?
This isn't exactly newtonian physics here...

On Thu, Oct 21, 2010 at 5:21 PM, nonlinear5 <[email protected]>wrote:

> > There are many flavors of Kalman filter. I think a plain vanilla
> > implementation of scalar Kalman will be a big imporovement over
> > Exponential Moving Average (EMA). Kalman filter has a lot of
> > similarities with EMA and can be used in a similar way but it has one
> > important advantage, - it adapts to changing conditions. In EMA the
> > averaging coefficient is constant and, therefore, contribution of the
> > new information to the average is constant.
> >
> > ,In Kalman filter the averaging coefficient it is variable and
> > adaptive.
>
> Thanks for that explanation, this is very useful. Many indicators in
> JBT use EMA for filtering prices and balances, so if Kalman filter is
> an improvement over EMA filtering, we could substitute the EMA
> components with Kalman components in the existing indicators and see
> the impact on the strategies performance. The open source project
> which I referenced above (http://jkalman.sourceforge.net/ ) is a Java
> implementation of Kalman filter, so it should not be too difficult for
> us to use it for JBT purposes.
>
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