QuanTAlib/lib/trends_FIR/nlma/nlma.pine

// Licensed under the Apache License, Version 2.0
// © mihakralj
//@version=6
indicator("Non-Lag Moving Average (NLMA)", "NLMA", overlay=true)

//@function Calculates NLMA using the original Igorad (TrendLaboratory) kernel
//@param source Series to calculate NLMA from
//@param period Length parameter (conceptual averaging period)
//@returns NLMA value, returns price during warmup
//@description NonLagMA by Igorad (TrendLaboratory). Original two-phase kernel:
//  Phase 1 (focus zone, i=0..Length-2): t = i/(Length-2), beta=cos(pi*t), g=1 for t<=0.5 else 1/(Coeff*t+1)
//  Phase 2 (cycle zone, i=Length-1..Len-2): oscillating cosine with 1/(Coeff*t+1) decay
//  Coeff = 3*pi. Kernel length Len = 4*Length + (Length-1) = 5*Length-1.
//  alfa[i] = g * beta. Normalized by signed sum preserves DC gain = 1.
nlma(series float source, simple int period) =>
    if period < 2
        runtime.error("Period must be at least 2")

    float price = nz(source)

    // Original Igorad parameters
    int phase = period - 1           // Phase zone length
    int cycle = 4                     // Default cycle parameter
    int flen = period * 4 + phase     // Full kernel length = 5*period - 1

    // --- Circular buffer for rolling window ---
    var array<float> buffer = array.new_float(flen, na)
    var int head = 0
    array.set(buffer, head, price)
    head := (head + 1) % flen

    // --- Precompute Igorad kernel weights once ---
    var array<float> weights = array.new_float(0)
    var float weightSum = 0.0
    if barstate.isfirst
        float coeff = 3.0 * math.pi
        float wsum = 0.0

        for i = 0 to flen - 2
            float t = 0.0
            if i <= phase - 1
                // Phase zone: t ramps from 0 to 1
                t := phase > 1 ? float(i) / float(phase - 1) : 0.0
            else
                // Cycle zone: t continues from 1 upward
                float numer = float(i - phase + 1) * float(2 * cycle - 1)
                float denom = float(cycle * period - 1)
                t := 1.0 + (denom > 0 ? numer / denom : 0.0)

            float beta = math.cos(math.pi * t)
            float g = t <= 0.5 ? 1.0 : 1.0 / (coeff * t + 1.0)
            float w = g * beta
            array.push(weights, w)
            wsum += w

        // Last tap (i = flen-1) has weight 0 (original loop goes to Len-2)
        array.push(weights, 0.0)
        weightSum := wsum

    int count = math.min(bar_index + 1, flen)
    if count < flen
        price
    else
        // --- Convolution via circular buffer ---
        // head = next write = oldest | weights[0] maps to oldest bar
        // Original MQL4: alfa[0] = newest price, alfa[Len-1] = oldest
        // Our buffer: (head+0) = oldest, (head+flen-1) = newest
        // So we need to reverse: oldest bar × weights[flen-1], newest bar × weights[0]
        float nlma_sum = 0.0
        for j = 0 to flen - 1
            int idx = (head + j) % flen
            float val = nz(array.get(buffer, idx))
            nlma_sum += val * array.get(weights, flen - 1 - j)
        math.abs(weightSum) > 1e-15 ? nlma_sum / weightSum : price

// ---------- Main loop ----------

// Inputs
i_period = input.int(14, "Period", minval=2)
i_source = input.source(close, "Source")

// Calculation
nlma_value = nlma(i_source, i_period)

// Plot
plot(nlma_value, "NLMA", color=color.yellow, linewidth=2)