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_IIR/nlma/nlma.pine

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+// The MIT License (MIT)
+// © mihakralj
+//@version=6
+indicator("Non-Lag Moving Average (NLMA)", "NLMA", overlay=true)
+
+//@function Calculates NLMA using damped cosine (fading sinusoid) FIR kernel
+//@param source Series to calculate NLMA from
+//@param period Lookback period - defines the cycle length of the cosine kernel
+//@returns NLMA value, calculates from first bar using available data
+//@description NonLagMA by Igorad (TrendLaboratory). Uses a damped cosine kernel
+//  (fading sinusoid) derived from FATL/SATL digital filter coefficient analysis.
+//  The cosine creates negative weights that subtract lagged price components,
+//  reducing lag while maintaining smoothness. Weight formula:
+//    w[i] = cos(2*PI*i / cycle) * (1 - i/cycle)
+//  where cycle = period, creating one full cosine oscillation with linear decay.
+//  Negative weights in the mid-section cancel lag (analogous to DEMA's 2*EMA-EMA2).
+//  Normalization by signed weight sum preserves DC gain = 1.
+nlma(series float source, simple int period) =>
+    if period <= 0
+        runtime.error("Period must be greater than 0")
+    int p = math.min(bar_index + 1, period)
+    var int prev_p = 0
+    var array<float> cos_weights = array.new_float(period, 0.0)
+    // Recompute weights when effective period changes (warmup)
+    if p != prev_p
+        cos_weights := array.new_float(p, 0.0)
+        for j = 0 to p - 1
+            // Damped cosine: cosine oscillation × linear decay envelope
+            float angle = 2.0 * math.pi * j / p
+            float decay = 1.0 - float(j) / float(p)
+            array.set(cos_weights, j, math.cos(angle) * decay)
+        prev_p := p
+    float sum_wv = 0.0
+    float sum_w = 0.0
+    for i = 0 to p - 1
+        float price = source[i]
+        if not na(price)
+            float w = array.get(cos_weights, i)
+            sum_wv += price * w
+            sum_w += w
+    nz(sum_wv / sum_w, source)
+
+// ---------- Main loop ----------
+
+// Inputs
+i_period = input.int(14, "Period", minval=1)
+i_source = input.source(close, "Source")
+
+// Calculation
+nlma_value = nlma(i_source, i_period)
+
+// Plot
+plot(nlma_value, "NLMA", color=color.yellow, linewidth=2)