Real-time chart analysis needs deterministic updates per bar and explicit handling of warm-up periods. WMA addresses this by implementing Calculates WMA using circular buffer with O(1) complexity 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.
Calculates WMA using circular buffer with O(1) complexity
| Parameter | Purpose |
|---|---|
source |
Series to calculate WMA from |
period |
Lookback period - FIR window size |
- WMA value, calculates from first bar using available data
| Input variable | Type | Configuration |
|---|---|---|
i_period |
input.int |
default: 10, label: "Period" |
i_source |
input.source |
default: close, label: "Source" |
- Declared optimization: Uses dual running sums with cached denominator for O(1) complexity per bar
- Streaming model: single-pass update on each new bar.
- Warm-up behavior: outputs can be unstable until enough samples satisfy
period. - 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/wma.pine - Documentation file:
indicators/trends_FIR/wma.md - GitHub source view: https://github.com/mihakralj/QuanTAlib/blob/main/indicators/trends_FIR/wma.pine
- GitHub documentation view: https://github.com/mihakralj/QuanTAlib/blob/main/indicators/trends_FIR/wma.md