Real-time chart analysis needs deterministic updates per bar and explicit handling of warm-up periods. RWMA addresses this by implementing Computes indicator values from streaming bar data. with parameterized inputs and direct state progression.
This implementation favors streaming execution over batch recomputation. The trade-off is more attention to state initialization, but latency stays predictable when charts scale.
| Input variable | Type | Configuration |
|---|---|---|
p |
input.int |
default: 14, label: "Period" |
- Declared optimization: not explicitly annotated in source comments.
- Streaming model: single-pass update on each new bar.
- Warm-up behavior: outputs can be unstable until enough samples satisfy
lookback parameter. - Memory model: state is kept in Pine series context rather than external buffers.
Streaming logic keeps incremental cost stable, but initialization and edge-case handling become first-class concerns. That is a deliberate choice: predictable execution beats opaque recalculation spikes in live charts.
- Open the script in TradingView and confirm it compiles under Pine Script v6.
- Validate warm-up behavior on sparse data and short histories.
- Compare output against a trusted reference implementation for the same parameters.
- Confirm parameter bounds reject invalid values without silent fallback.
- Source code:
indicators/trends_FIR/rwma.pine - Documentation file:
indicators/trends_FIR/rwma.md - GitHub source view: https://github.com/mihakralj/QuanTAlib/blob/main/indicators/trends_FIR/rwma.pine
- GitHub documentation view: https://github.com/mihakralj/QuanTAlib/blob/main/indicators/trends_FIR/rwma.md