Real-time chart analysis needs deterministic updates per bar and explicit handling of warm-up periods. PWMA addresses this by implementing Calculates PWMA using Pascal's triangle coefficients as weights with compensator 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 PWMA using Pascal's triangle coefficients as weights with compensator
| Parameter | Purpose |
|---|---|
source |
Series to calculate PWMA from |
period |
Lookback period - FIR window size |
- PWMA 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 Pascal's triangle weighting with O(n) complexity per bar due to lookback loop
- 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/pwma.pine - Documentation file:
indicators/trends_FIR/pwma.md - GitHub source view: https://github.com/mihakralj/QuanTAlib/blob/main/indicators/trends_FIR/pwma.pine
- GitHub documentation view: https://github.com/mihakralj/QuanTAlib/blob/main/indicators/trends_FIR/pwma.md