The Kalman filtering technique rapidly developed in recent decades. It's widely used in many areas such as Aerospace, Earthquake monitoring, Economic trending Control and Inertial navigation. Not like other filters, the Kalman filtering is suitable for multi-input and multi-output system. It provides the most optimal filtering and estimation based on mathematical model describing the system. A classic example of Kalman filtering application is to predict the position and velocity of the object from a set of limited observation sequences containing the noise. It can be found in many engineering applications such as Radar, Computer vision. Meanwhile, it is an important topic in control theory and control system engineering. For example, people are interested in tracking targets in radar, but the measurements of the target position, velocity and acceleration contain noise at all times. The Kalman filter removes the noise and gets a good estimate of the target location by the dynamic target informations. This estimates maybe the current target position estimates(filtering), as well as the estimates of the future(projections). It can also be estimated location of the past (interpolation or smoothing). People often confused with the complex formulas of Kalman filtering. They are eager to find a simple way to achieve the operation. Visual Kalman Filter is a nice tool for training and simulation. It meets the needs of many beginners. Visual Kalman Filter is developed for science researchers based on visual windows interface. It helps people to deal with the dynamic data, and draw predictions and graphics. Users need not to write any code(Such as Matlab, C++, etc.). Users should build the system model first, and get the matrice of the model. That is, you'd better input the matrice A,B,P0,Z,Q,R,etc. in the base equations as follow: X(k) = AX(k-1) + BU(k) + W(k) Z(k) = HX(k) + V(k) In the two formulas, X(k) is the system state, U(k) is the amount of system control. Z(k) is the measured value, and W(k) and V(k) represent process and measurement noise, which are assumed as Gaussian white noise. After inputting the system matrix parameters, click 'step 3', users will get the results such as X(k|k-1),P(k|k-1),X(k|k) and P(k|k). When users click the strings in the listbox, the results and curve will appear. Then just save the results. Visual Kalman Filter is for training and learning as well as analysis of the data. It's not a real-time tool to track the state. There is a dll file for trial, please contact the developer. So, it is easy to operate for the users. Only three steps you need to do. Have a try, maybe it's helpful for you. Article Source: http://EzineArticles.com/?expert=Steven_Jet

The Kalman filter is just that, a filter. That is it smooths your data with minimal lag. The work of John Ehlers (Rocket Science for Traders and mesasoftware.com) is good material to explore if you are interested in low pass (FIR and IIR) filters.

Jackoff, what a complete crock of shit. I don't know if YOU ever designed Kalman's, but I have. If you understood them like I do, you'd know that there is NO possible physical model for markets. The markets are like women. They can displace instantly. So you need an infinite number of derivatives to model it. Also, there is NO noise in the markets. It's all signal. Dipshit.

Is it the name of a South Park character? Regarding Calman filter, if we ignore all the theory behind it for a moment, isn't it just a weighted moving average? We know moving averages are popular and some traders make them work. So, why deny people the opportunity to see for themseves that improving simple moving average is no easy feat? I agree though that for more complex techniques a little reading is better time spent than experimenting with a grey-box algorithm.

This: http://www.amazon.com/Poor-Mans-Exp...1_fkmr0_1?ie=UTF8&qid=1294833249&sr=8-1-fkmr0 is a great reference to the Kalman filter. The original version is out of print, but I believe that some company bought the rights to the booklet and is publishing it for a small fee. In fact, here you go: http://www.taygeta.com/kalman_book.html and if you are a cheap arse: http://mildpdf.com/search-poor-man-s-explanation-of-kalman-filtering.html

Is Deco and engineer? That would explain a few things, as to why he doesn't put up with any Kalman crap as that would be bread and butter sheah for him. Plus we have enough weirdos trying to sell their (under?)wares here so I can see where he's coming from. For the rest of us trained in maths/physics/whatever the du Plessis booklet is an accessible intro. Don't expect miracles from the Kalman or any filter - if you get my drift (mu - pun intended).

I thought it was unnecessarily rude of the Opie to post crap that so offended my delicate sensibilities. I was just holding up a mirror.

I have a pretty fair background in engineering mathematics and can assure you from bitter experience that any tool developed to describe physically realizable processes is useless for trading. Price action always violates one or more of the existence conditions required by such tools. The only thing that works is weird hypothesis testing by statistical approaches.