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NLMA - Nlma

Architectural problem

Real-time chart analysis needs deterministic updates per bar and explicit handling of warm-up periods. NLMA addresses this by implementing Calculates NLMA using the original Igorad (TrendLaboratory) kernel with parameterized inputs and direct state progression.

Design decision

This implementation favors streaming execution over batch recomputation. The trade-off is more attention to state initialization, but latency stays predictable when charts scale.

API surface

Functions

  • Calculates NLMA using the original Igorad (TrendLaboratory) kernel

Parameters

Parameter Purpose
source Series to calculate NLMA from
period Length parameter (conceptual averaging period)

Returns

  • NLMA value, returns price during warmup

Input configuration

Input variable Type Configuration
i_period input.int default: 14, label: "Period"
i_source input.source default: close, label: "Source"

Runtime profile

  • 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 period.
  • Memory model: state is kept in Pine series context rather than external buffers.

Trade-offs

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.

Verification checklist

  1. Open the script in TradingView and confirm it compiles under Pine Script v6.
  2. Validate warm-up behavior on sparse data and short histories.
  3. Compare output against a trusted reference implementation for the same parameters.
  4. Confirm parameter bounds reject invalid values without silent fallback.

References