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https://github.com/mihakralj/QuanTAlib.git
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Miha Kralj
37 lines
1.4 KiB
Plaintext
37 lines
1.4 KiB
Plaintext
// The MIT License (MIT)
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// © mihakralj
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//@version=6
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indicator("Median Absolute Percentage Error", "MdAPE", overlay=false, format=format.percent)
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//@function Calculates Median Absolute Percentage Error
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//@doc Median of absolute percentage errors, robust to outliers.
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//@doc Scale-independent (expressed as percentage), handles zero actual with epsilon.
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//@param actual Series of actual values
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//@param predicted Series of predicted/forecast values
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//@param length Rolling window for median calculation
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//@returns MdAPE value as percentage
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mdape(series float actual, series float predicted, simple int length) =>
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float epsilon = 1e-10
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// Compute absolute percentage error for current bar
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float absActual = math.abs(nz(actual, 1.0))
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float absError = math.abs(nz(actual, 0.0) - nz(predicted, 0.0))
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float pctError = absActual > epsilon ? (absError / absActual) * 100.0 : 0.0
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// Use ta.median for rolling median of percentage errors
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float result = ta.median(pctError, length)
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result
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// ---------- Main loop ----------
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// Inputs
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i_length = input.int(14, "Length", minval=1)
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i_actual = input.source(close, "Actual")
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i_predicted = input.source(open, "Predicted")
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// Calculation
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mdape_value = mdape(i_actual, i_predicted, i_length)
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// Plot
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plot(mdape_value, "MdAPE", color=color.yellow, linewidth=2)
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hline(0, "Zero", color=color.gray, linestyle=hline.style_dotted) |