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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.
39 lines
1.5 KiB
Plaintext
39 lines
1.5 KiB
Plaintext
// The MIT License (MIT)
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// © mihakralj
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//@version=6
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indicator("Pseudo-Huber Loss", "PseudoHuber", overlay=false)
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//@function Calculates Pseudo-Huber Loss (Charbonnier 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 delta Scale parameter controlling transition smoothness (default 1.0)
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//@returns Mean Pseudo-Huber loss over the window
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pseudohuber(series float actual, series float predicted, simple int length, simple float delta = 1.0) =>
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float deltaSquared = delta * delta
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// Compute Pseudo-Huber loss for current bar: δ² * (√(1 + (error/δ)²) - 1)
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float diff = nz(actual, 0.0) - nz(predicted, 0.0)
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float ratio = diff / delta
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float sqrtTerm = math.sqrt(1.0 + ratio * ratio)
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float loss = deltaSquared * (sqrtTerm - 1.0)
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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_delta = input.float(1.0, "Delta (transition scale)", minval=0.001, step=0.1, tooltip="Controls transition between quadratic and linear behavior")
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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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pseudohuber_value = pseudohuber(i_actual, i_predicted, i_length, i_delta)
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// Plot
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plot(pseudohuber_value, "Pseudo-Huber", color=color.yellow, linewidth=2)
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hline(0, "Zero", color=color.gray, linestyle=hline.style_dotted)
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