Remove multiple Pine Script indicators: SSFDSP, STARCHANNEL, STBANDS, STC, UBANDS, UCHANNEL, VWAPBANDS, and VWAPSD. These indicators were deleted to streamline the library and remove unused or redundant code.

lib/trends_FIR/lanczos/lanczos.pine

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+// The MIT License (MIT)
+// © mihakralj
+//@version=6
+indicator("Lanczos (Sinc) Window Moving Average (LANCZOS)", "LANCZOS", overlay=true)
+
+//@function Computes Lanczos Window Moving Average — a symmetric FIR filter using the
+//          normalized sinc function as the window shape. The Lanczos window is the
+//          central lobe of a sinc function, providing excellent frequency-domain
+//          characteristics with minimal Gibbs phenomenon ringing.
+//@param source Series to smooth
+//@param period Lookback window (>= 2)
+//@returns Lanczos-windowed moving average
+//@reference Lanczos, C. (1956). "Applied Analysis." Prentice-Hall.
+//@reference The Lanczos window w(k) = sinc(2k/(N-1) - 1) where sinc(x) = sin(πx)/(πx).
+//           This is the simplest sinc-based window; higher-order Lanczos kernels use
+//           sinc(x) * sinc(x/a) for Lanczos-a resampling (a=2 or 3 typical).
+//@optimized O(period) per bar for convolution; weights precomputed once
+lanczos(series float source, simple int period) =>
+    if period < 2
+        runtime.error("Period must be at least 2")
+
+    float price = nz(source)
+
+    // --- Circular buffer for rolling window ---
+    var array<float> buffer = array.new_float(period, na)
+    var int head = 0
+    array.set(buffer, head, price)
+    head := (head + 1) % period
+
+    // --- Precompute Lanczos (sinc) window weights once ---
+    // Lanczos window: w(k) = sinc(2k/(N-1) - 1)
+    // sinc(x) = sin(π·x) / (π·x) for x != 0, sinc(0) = 1
+    var array<float> weights = array.new_float(0)
+    if barstate.isfirst
+        float N = period - 1
+        float wsum = 0.0
+        for k = 0 to period - 1
+            float x = N > 0 ? (2.0 * k / N) - 1.0 : 0.0
+            float w = 0.0
+            if math.abs(x) < 1e-10
+                w := 1.0  // sinc(0) = 1
+            else
+                float pi_x = math.pi * x
+                w := math.sin(pi_x) / pi_x
+            // Clamp negative weights to 0 for pure Lanczos window
+            // (sinc sidelobes are negative but we keep them for fidelity)
+            array.push(weights, w)
+            wsum += w
+
+        // Normalize weights to sum exactly 1.0
+        if wsum > 0
+            for j = 0 to period - 1
+                array.set(weights, j, array.get(weights, j) / wsum)
+
+    int count = math.min(bar_index + 1, period)
+    if count < period
+        price
+    else
+        // --- Apply Lanczos convolution via circular buffer ---
+        // Buffer: head points to next-write = oldest entry
+        // Weight[0] = oldest bar, Weight[period-1] = newest
+        float result = 0.0
+        for j = 0 to period - 1
+            int idx = (head + j) % period
+            float val = nz(array.get(buffer, idx))
+            result += val * array.get(weights, j)
+        result
+
+// ── Inputs ──────────────────────────────────────────────────────────────
+src = input.source(close, "Source")
+per = input.int(14, "Period", minval=2)
+
+// ── Plot ────────────────────────────────────────────────────────────────
+plot(lanczos(src, per), "LANCZOS", color.new(color.yellow, 0), 2)