I'd like to implement a Kalman crossover strategy, referencing Vladimir's  pykalman example as a foundation.

The idea is to use a more responsive filter crossing over/under a less responsive filter. Looking at the wikipedia page for Kalman filter, I need to adjust the 'gain' to adjust the responsiveness.

Does anyone know how to do this? Looking at Pykalman docs there is no easy way to do so, and I can't quite parse the information presented in the article, even though it gives some clues (see excerpt below).

Thoughts?  Vladimir?
 

From Wikipedia:
https://en.wikipedia.org/wiki/Kalman_filter#Kalman_gain_derivation

It is common to discuss the filter's response in terms of the Kalman filter's gain. The Kalman-gain is the weight given to the measurements and current-state estimate, and can be "tuned" to achieve a particular performance. With a high-gain, the filter places more weight on the most recent measurements, and thus conforms to them more responsively. With a low gain, the filter conforms to the model predictions more closely. At the extremes, a high gain close to one will result in a more jumpy estimated trajectory, while a low gain close to zero will smooth out noise but decrease the responsiveness.

When performing the actual calculations for the filter (as discussed below), the state estimate and covariances are coded into matrices because of the multiple dimensions involved in a single set of calculations. This allows for a representation of linear relationships between different state variables (such as position, velocity, and acceleration) in any of the transition models or covariances.