> 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. -- You received this message because you are subscribed to the Google Groups "JBookTrader" group. To post to this group, send email to [email protected]. To unsubscribe from this group, send email to [email protected]. For more options, visit this group at http://groups.google.com/group/jbooktrader?hl=en.
