mirror of
https://github.com/mihakralj/QuanTAlib.git
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Miha Kralj
47 lines
1.6 KiB
Plaintext
47 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("Tukey's Biweight Loss", "TukeyBiweight", overlay=false)
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//@function Calculates Tukey's Biweight (Bisquare) Loss
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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 averaging
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//@param c Threshold for outlier rejection (default 4.685)
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//@returns Mean Tukey biweight loss over the window
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tukey_biweight(series float actual, series float predicted, simple int length, simple float c = 4.685) =>
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float cSquaredOver6 = (c * c) / 6.0
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// Compute Tukey biweight loss for current bar
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float error = nz(actual, 0.0) - nz(predicted, 0.0)
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float absError = math.abs(error)
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float loss = 0.0
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if absError > c
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loss := cSquaredOver6
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else
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float ratio = error / c
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float ratioSq = ratio * ratio
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float oneMinusRatioSq = 1.0 - ratioSq
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float cubed = oneMinusRatioSq * oneMinusRatioSq * oneMinusRatioSq
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loss := cSquaredOver6 * (1.0 - cubed)
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// Rolling mean of losses
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float result = ta.sma(loss, 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_c = input.float(4.685, "Threshold c", minval=0.1, step=0.1, tooltip="4.685=95% efficiency for normal; 6.0=more permissive")
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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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tukey_value = tukey_biweight(i_actual, i_predicted, i_length, i_c)
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// Plot
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plot(tukey_value, "Tukey Biweight", color=color.yellow, linewidth=2)
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hline(0, "Zero", color=color.gray, linestyle=hline.style_dotted)
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