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https://github.com/mihakralj/QuanTAlib.git
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Miha Kralj
24e86d762a
- Updated BBWN, BBWP, CCV, CV, CVI, EWMA, GKV, HLV, HV, Jvolty, JVOLTYN, MASSI, NATR, RSV, RV, RVI, TR, UI, VOV, VR, YZV indicators with documentation links. - Added documentation links for Aberration, Acceleration Bands, Andrews' Pitchfork, Adaptive Price Zone, ATR Bands, Bollinger Bands, Center of Gravity, Donchian Channels, Decay Min-Max Channel, Detrended Synthetic Price, EACP, EBSW, HOMOD, Jurik Volatility Bands, Keltner Channel, MA Envelope, Min-Max Channel, Price Channel, Regression Channels, Standard Deviation Channel, Stoller Average Range Channel, Super Trend Bands, Ultimate Bands, Ultimate Channel, VWAP Bands, and VWAP with Standard Deviation Bands.
47 lines
1.6 KiB
Plaintext
47 lines
1.6 KiB
Plaintext
// The MIT License (MIT)
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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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