Summary
OnlineMarkovSwitchingVolatilityFilter is RTTA's streaming implementation of: Online two-state Markov-switching volatility filter over close-to-close moves.
Update API
result = rtta.OnlineMarkovSwitchingVolatilityFilter().update(close)
The update(...) call consumes one observation using close. advance(...)
uses the same inputs when the caller wants to update state without materializing
a Python return value.
Theory Of Operation
OnlineMarkovSwitchingVolatilityFilter maintains online probabilities for latent states or components. An update combines the previous probabilities with the new observation likelihoods and normalizes the result.
Recurrence
Let \(z_t = close_t\) denote the observation consumed by one
update(...) call and let \(\theta\) denote constructor parameters such as
window lengths, thresholds, and smoothing constants.
The return value is the current scalar indicator value.
Implementation Notes
The recurrence is implemented in src/rtta/indicator.cpp in class OnlineMarkovSwitchingVolatilityFilter.