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https://github.com/mihakralj/QuanTAlib.git
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Miha Kralj
42 lines
1.4 KiB
Plaintext
42 lines
1.4 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("Log-Cosh Loss", "LogCosh", overlay=false)
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//@function Computes log(cosh(x)) in a numerically stable way
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//@param x The input value
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//@returns log(cosh(x))
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stable_logcosh(float x) =>
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float LOG2 = 0.6931471805599453
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float absX = math.abs(x)
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// For large values, use asymptotic approximation to avoid overflow
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absX > 20.0 ? absX - LOG2 : math.log((math.exp(x) + math.exp(-x)) / 2.0)
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//@function Calculates Log-Cosh 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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//@returns Mean log-cosh loss over the window
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logcosh_loss(series float actual, series float predicted, simple int length) =>
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// Compute log-cosh loss for current bar
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float error = nz(actual, 0.0) - nz(predicted, 0.0)
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float loss = stable_logcosh(error)
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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_actual = input.source(close, "Actual")
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i_predicted = input.source(open, "Predicted")
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// Calculation
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logcosh_value = logcosh_loss(i_actual, i_predicted, i_length)
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// Plot
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plot(logcosh_value, "Log-Cosh Loss", color=color.yellow, linewidth=2)
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hline(0, "Zero", color=color.gray, linestyle=hline.style_dotted)
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