mirror of
https://github.com/mihakralj/QuanTAlib.git
synced 2026-07-27 17:27:43 +00:00
Miha Kralj
59 lines
2.3 KiB
Plaintext
59 lines
2.3 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("Deviation-Scaled Moving Average (DSMA)", "DSMA", overlay=true)
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//@function Calculates DSMA using standard deviation to scale the averaging factor
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//@param source Series to calculate DSMA from
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//@param period Length of the lookback period for both average and deviation calculation
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//@param scaleFactor Combined scaling/smoothing factor (0.01-0.9)
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//@returns DSMA value that adapts to market volatility through deviation scaling
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//@optimized Uses circular buffer for RMS calculation and adaptive alpha for O(1) complexity
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dsma(series float source, simple int period, simple float scaleFactor=0.5) =>
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float a1 = math.exp(-1.414 * math.pi / (period * 0.5))
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float b1 = 2.0 * a1 * math.cos(1.414 * math.pi / (period * 0.5))
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float c1 = 1.0 - b1 + (a1 * a1)
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float c1Half = c1 * 0.5
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float periodRecip = 1.0 / period
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float scaleAdjustment = scaleFactor * 5.0 * periodRecip
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var float result = na
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var float filt = 0.0
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var float filt1 = 0.0
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var float filt2 = 0.0
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var float zeros1 = 0.0
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var float sumSquared = 0.0
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var array<float> filtSquared = array.new_float(period, 0.0)
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var int bufferIndex = 0
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if na(source)
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result
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else
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if na(result)
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result := source
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else
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float zeros = source - result
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filt := c1Half * (zeros + zeros1) + b1 * filt1 - (a1 * a1) * filt2
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float filtSq = filt * filt
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sumSquared := sumSquared + filtSq - array.get(filtSquared, bufferIndex)
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array.set(filtSquared, bufferIndex, filtSq)
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bufferIndex := (bufferIndex + 1) % period
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float rms = math.sqrt(math.max(sumSquared * periodRecip, 1e-10))
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float alpha = math.min(scaleAdjustment * math.abs(filt / rms), 1.0)
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result := alpha * source + (1 - alpha) * result
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zeros1 := zeros
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filt2 := filt1
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filt1 := filt
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result
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// ---------- Main loop ----------
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// Inputs
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i_source = input.source(close, "Source")
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i_period = input.int(25, "Period", minval=2)
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i_scale = input.float(0.9, "Scale Factor", minval=0.01, maxval=0.9, step=0.01)
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// Calculation
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dsma_value = dsma(i_source, i_period, i_scale)
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// Plot
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plot(dsma_value, "DSMA", color=color.yellow, linewidth=2)
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