New article Creating Non-Lagging Digital Filters has been published:
The article describes one of the approaches to determining a useful signal (trend) in stream data. Small filtering (smoothing) tests applied to market quotes demonstrate the potential for creating non-lagging digital filters (indicators) that are not redrawn on the last bars.
Cluster filter is a set of digital filters approximating the initial sequence. Cluster filters should not be confused with cluster indicators.
Cluster filters are convenient when analyzing non-stationary time
series in real time, in other words, stream data. It means that these
filters are of principal interest not for smoothing the already known
time series values, but for getting the most probable smoothed values of
the new data received in real time.
Unlike various decomposition methods or simply filters of desired
frequency, cluster filters create a composition or a fan of probable
values of initial series which are further analyzed for approximation of
the initial sequence. The input sequence acts more as a reference than
the target of the analysis. The main analysis concerns values calculated
by a set of filters after processing the data received.
Figure 1. The diagram of a simple cluster filter
In the general case, every filter included in the cluster has its own
individual characteristics and is not related to others in any way.
These filters are sometimes customized for the analysis of a stationary
time series of their own which describes individual properties of the
initial non-stationary time series. In the simplest case, if the initial
non-stationary series changes its parameters, the filters "switch"
over. Thus, a cluster filter tracks real time changes in
Author: Konstantin Gruzdev