Summary
EMA computes an exponential moving average over one scalar stream. The object
stores only the latest EMA value, a sample counter, and the fixed smoothing
multiplier derived from window.
Update API
value = rtta.EMA(window=30.0, fillna=False).update(value)
The same scalar stream can be supplied through update(...), advance(...),
or batch(...).
Theory Of Operation
An exponential moving average gives the newest sample a fixed weight and carries forward the prior average with the complementary weight. RTTA seeds the state from the first input sample, so the first internal EMA value equals the first input value.
Recurrence
Let \(x_t\) be the new sample and \(n\) be window. RTTA clamps window to at
least 1 and uses:
Initialization:
For each later update:
With fillna=False, the state updates immediately, but returned values are
NaN until the internal counter reaches window.
Implementation Notes
The recurrence is implemented in src/rtta/indicator.cpp in class EMA.