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.

2026-08-25 22:08:05 +00:00 · 2026-02-20 18:44:56 -08:00
parent 3dd05f23e4
commit cbeefc9d64
283 changed files with 23963 additions and 3838 deletions
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+// The MIT License (MIT)
+// © mihakralj
+//@version=6
+indicator("Cubic Regression Moving Average (CRMA)", "CRMA", overlay=true)
+
+//@function Computes Cubic Regression Moving Average — fits a degree-3 polynomial
+//          y = a0 + a1*x + a2*x² + a3*x³ to the most recent `period` bars via
+//          normal equations with Gaussian elimination, returns the fitted endpoint.
+//@param source Series to analyze
+//@param period Lookback window for the cubic regression
+//@returns Fitted value at the most recent bar (x = 0)
+//@reference Polynomial least-squares regression (degree 3), evaluated at endpoint
+//@optimized O(period) per bar for accumulating sums; O(1) for 4×4 solve
+crma(series float source, simple int period) =>
+    if period < 4
+        runtime.error("Period must be at least 4 for cubic regression")
+
+    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
+
+    int p = math.min(bar_index + 1, period)
+    if p < 4
+        price
+    else
+        // --- Accumulate power sums and cross-products ---
+        // Normal equations for degree-3 polynomial: M * a = rhs
+        // M[i][j] = Σ x^(i+j),  rhs[i] = Σ x^i * y   for i,j = 0..3
+        // x = 0 (newest) to p-1 (oldest), so a0 = fitted value at newest bar
+        float s0 = 0.0,  s1 = 0.0,  s2 = 0.0,  s3 = 0.0
+        float s4 = 0.0,  s5 = 0.0,  s6 = 0.0
+        float r0 = 0.0,  r1 = 0.0,  r2 = 0.0,  r3 = 0.0
+
+        int idx = (head - 1 + period) % period
+        for i = 0 to p - 1
+            float val = array.get(buffer, idx)
+            float v = na(val) ? price : val
+            float x  = float(i)
+            float x2 = x * x
+            float x3 = x2 * x
+
+            s0 += 1.0       // Σ x^0 = count
+            s1 += x         // Σ x^1
+            s2 += x2        // Σ x^2
+            s3 += x3        // Σ x^3
+            s4 += x2 * x2   // Σ x^4
+            s5 += x2 * x3   // Σ x^5
+            s6 += x3 * x3   // Σ x^6
+
+            r0 += v          // Σ y
+            r1 += x * v      // Σ x*y
+            r2 += x2 * v     // Σ x²*y
+            r3 += x3 * v     // Σ x³*y
+
+            idx := (idx - 1 + period) % period
+
+        // --- Build 4×4 augmented matrix (row-major, 4 rows × 5 cols) ---
+        var matrix<float> m = matrix.new<float>(4, 5, 0.0)
+
+        // Row 0: [s0, s1, s2, s3 | r0]
+        matrix.set(m, 0, 0, s0), matrix.set(m, 0, 1, s1), matrix.set(m, 0, 2, s2), matrix.set(m, 0, 3, s3), matrix.set(m, 0, 4, r0)
+        // Row 1: [s1, s2, s3, s4 | r1]
+        matrix.set(m, 1, 0, s1), matrix.set(m, 1, 1, s2), matrix.set(m, 1, 2, s3), matrix.set(m, 1, 3, s4), matrix.set(m, 1, 4, r1)
+        // Row 2: [s2, s3, s4, s5 | r2]
+        matrix.set(m, 2, 0, s2), matrix.set(m, 2, 1, s3), matrix.set(m, 2, 2, s4), matrix.set(m, 2, 3, s5), matrix.set(m, 2, 4, r2)
+        // Row 3: [s3, s4, s5, s6 | r3]
+        matrix.set(m, 3, 0, s3), matrix.set(m, 3, 1, s4), matrix.set(m, 3, 2, s5), matrix.set(m, 3, 3, s6), matrix.set(m, 3, 4, r3)
+
+        // --- Gaussian elimination with partial pivoting ---
+        bool singular = false
+        for col = 0 to 3
+            // Find pivot row
+            int pivot_row = col
+            float pivot_max = math.abs(matrix.get(m, col, col))
+            for row = col + 1 to 3
+                float absval = math.abs(matrix.get(m, row, col))
+                if absval > pivot_max
+                    pivot_max := absval
+                    pivot_row := row
+
+            if pivot_max < 1e-12
+                singular := true
+                break
+
+            // Swap rows if needed
+            if pivot_row != col
+                for k = col to 4
+                    float tmp = matrix.get(m, col, k)
+                    matrix.set(m, col, k, matrix.get(m, pivot_row, k))
+                    matrix.set(m, pivot_row, k, tmp)
+
+            // Eliminate below
+            float diag = matrix.get(m, col, col)
+            for row = col + 1 to 3
+                float factor = matrix.get(m, row, col) / diag
+                for k = col to 4
+                    matrix.set(m, row, k, matrix.get(m, row, k) - factor * matrix.get(m, col, k))
+
+        float result = price
+        if not singular
+            // Back-substitution
+            var array<float> a = array.new_float(4, 0.0)
+            for row = 3 to 0
+                float val = matrix.get(m, row, 4)
+                for k = row + 1 to 3
+                    val -= matrix.get(m, row, k) * array.get(a, k)
+                array.set(a, row, val / matrix.get(m, row, row))
+
+            // a[0] is the fitted value at x = 0 (most recent bar)
+            result := array.get(a, 0)
+
+        result
+
+// ---------- Main loop ----------
+
+// Inputs
+i_period = input.int(14, "Period", minval=4)
+i_source = input.source(close, "Source")
+
+// Calculation
+crma_value = crma(i_source, i_period)
+
+// Plot
+plot(crma_value, "CRMA", color=color.yellow, linewidth=2)