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https://github.com/mihakralj/QuanTAlib.git
synced 2026-08-02 19:37:43 +00:00
Miha Kralj
35a6702b06
Deep review of all indicator categories verified .md headers against .cs WarmupPeriod, parameters, inputs, and outputs. Fixes include warmup corrections, parameter documentation, output type accuracy, and Pine Script alignment.
40 lines
1.6 KiB
Plaintext
40 lines
1.6 KiB
Plaintext
// Licensed under the Apache License, Version 2.0
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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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