Hann FIR Filter (HANN)

// Licensed under the Apache License, Version 2.0
// © mihakralj
//@version=6
indicator("Hann FIR Filter (HANN)", "HANN", overlay=true)

//@function Calculates a Finite Impulse Response (FIR) filter using Hann window coefficients
//@param src Series to calculate the Hann FIR filter from
//@param len The lookback period (length) of the Hann window
//@returns Hann FIR filter value
//@optimized Uses FIR convolution with Hann window, O(n) complexity per bar
hann(series float src, simple int len) =>
    if len <= 0
        runtime.error("Length must be greater than 0")
    var float[] w = array.new_float(0)
    var float coefSum = 0.0
    var int lastLen = 0
    if len != lastLen or array.size(w) == 0
        if len > 0
            w := array.new_float(len)
            coefSum := 0.0
            for i = 0 to len - 1
                float c = 0.5 * (1.0 - math.cos(2.0 * math.pi * i / (len - 1)))
                array.set(w, i, c)
                coefSum += c
            if coefSum == 0.0
                coefSum := 1.0
        lastLen := len
    int p = math.min(bar_index + 1, len)
    float acc = 0.0
    float currentWeightSum = 0.0
    if array.size(w) > 0 and p > 0
        for i = 0 to p - 1
            float price = src[p - 1 - i]
            if not na(price)
                float weight = array.get(w, i)
                acc += price * weight
                currentWeightSum += weight
    nz(currentWeightSum == 0.0 ? src : acc / currentWeightSum, src)

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

// Inputs
i_len = input.int(10, "Length", minval=1)
i_source = input.source(close, "Source")

// Calculation
hann_val = hann(i_source, i_len)

// Plot
plot(hann_val, "HANN", color=color.yellow, linewidth=2)