Summary
Kama is RTTA's streaming implementation of: Kaufman Adaptive Moving Average.
Update API
result = rtta.Kama().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
Kama is a causal smoother or average. It updates compact rolling or exponential state with the newest observation and returns the current smoothed estimate.
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.
\[E_t=\alpha z_t+(1-\alpha)E_{t-1}\]
\[y_t = G(E_t,E^{(2)}_t,\ldots,z_t)\]
The return value is the current scalar indicator value.
Implementation Notes
The recurrence is implemented in src/rtta/indicator.cpp in class Kama.