feat: add new indicators (Decay, Edecay, MinusDi, MinusDm, PlusDi, PlusDm, Maxindex, Minindex, Sarext) and update pine scripts, core libs, validation tests, and python bindings
2026-03-09 13:45:46 -07:00
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// Licensed under the Apache License, Version 2.0
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2026-01-18 19:02:03 -08:00
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// © mihakralj
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//@version=6
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indicator("Recursive Gaussian Moving Average (RGMA)", "RGMA", overlay=true)
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//@function Calculates RGMA using cascaded recursive filters to approximate Gaussian smoothing
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//@param source Series to calculate RGMA from
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//@param period Effective smoothing period
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//@param passes Number of recursive passes (higher = more Gaussian-like)
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//@returns RGMA value with gaussian-like smoothing properties using recursive calculation
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//@optimized Uses cascaded exponential filters for O(1) complexity per bar
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rgma(series float source, simple int period, simple int passes=3) =>
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simple float alpha = 2.0 / (period / math.sqrt(passes) + 1.0)
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var array<float> filters = array.new_float(passes, na)
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float result = na
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if not na(source)
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if na(array.get(filters, 0))
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array.fill(filters, source)
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result := source
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else
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array.set(filters, 0, alpha * (source - array.get(filters, 0)) + array.get(filters, 0))
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if passes > 1
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for i = 1 to passes - 1
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array.set(filters, i, alpha * (array.get(filters, i - 1) - array.get(filters, i)) + array.get(filters, i))
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result := array.get(filters, passes - 1)
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result
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// ---------- Main loop ----------
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// Inputs
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i_period = input.int(10, "Period", minval=1)
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i_passes = input.int(3, "Passes", minval=1, maxval=10, tooltip="More passes create more Gaussian-like smoothing")
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i_source = input.source(close, "Source")
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// Calculation
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rgma_value = rgma(i_source, i_period, i_passes)
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// Plot
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plot(rgma_value, "RGMA", color=color.yellow, linewidth=2)
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