Add documentation links for various volatility indicators and channels

- 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.

lib/errors/huber/huber.pine

@@ -4,7 +4,6 @@
 indicator("Huber Loss (HUBER)", "HUBER")
 
 //@function Calculates Huber Loss between two sources using SMA for averaging
-//@doc https://github.com/mihakralj/pinescript/blob/main/indicators/errors/huber.md
 //@param source1 First series to compare
 //@param source2 Second series to compare
 //@param period Lookback period for error averaging

lib/errors/logcosh/logcosh.pine

@@ -4,7 +4,6 @@
 indicator("Log-Cosh Loss", "LogCosh", overlay=false)
 
 //@function Computes log(cosh(x)) in a numerically stable way
-//@doc     For large |x|, cosh(x) ≈ exp(|x|)/2, so log(cosh(x)) ≈ |x| - log(2)
 //@param   x The input value
 //@returns log(cosh(x))
 stable_logcosh(float x) =>
@@ -14,9 +13,6 @@ stable_logcosh(float x) =>
     absX > 20.0 ? absX - LOG2 : math.log(math.cosh(x))
 
 //@function Calculates Log-Cosh Loss
-//@doc     Smooth approximation to absolute error, twice differentiable everywhere.
-//@doc     Approximates L1 loss for large errors, L2 for small errors.
-//@doc     Less sensitive to outliers than MSE.
 //@param   actual Series of actual values
 //@param   predicted Series of predicted/forecast values
 //@param   length Rolling window for averaging
@@ -42,4 +38,4 @@ logcosh_value = logcosh_loss(i_actual, i_predicted, i_length)
 
 // Plot
 plot(logcosh_value, "Log-Cosh Loss", color=color.yellow, linewidth=2)
-hline(0, "Zero", color=color.gray, linestyle=hline.style_dotted)
+hline(0, "Zero", color=color.gray, linestyle=hline.style_dotted)

lib/errors/maape/maape.pine

@@ -4,8 +4,6 @@
 indicator("Mean Arctangent Absolute Percentage Error", "MAAPE", overlay=false, format=format.percent)
 
 //@function Calculates Mean Arctangent Absolute Percentage Error
-//@doc     Uses arctangent to bound error between 0 and π/2, robust to outliers.
-//@doc     Handles zero actual values gracefully (approaches π/2).
 //@param   actual Series of actual values
 //@param   predicted Series of predicted/forecast values
 //@param   length Rolling window for averaging
@@ -35,4 +33,4 @@ maape_value = maape(i_actual, i_predicted, i_length)
 // Plot
 plot(maape_value, "MAAPE", color=color.yellow, linewidth=2)
 hline(0, "Zero", color=color.gray, linestyle=hline.style_dotted)
-hline(math.pi / 2.0, "Max (π/2)", color=color.red, linestyle=hline.style_dotted)
+hline(math.pi / 2.0, "Max (π/2)", color=color.red, linestyle=hline.style_dotted)

lib/errors/mae/mae.pine

@@ -4,7 +4,6 @@
 indicator("Mean Absolute Error (MAE)", "MAE")
 
 //@function Calculates Mean Absolute Error between two sources using SMA for averaging
-//@doc https://github.com/mihakralj/pinescript/blob/main/indicators/errors/mae.md
 //@param source1 First series to compare
 //@param source2 Second series to compare
 //@param period Lookback period for error averaging

lib/errors/mapd/mapd.pine

@@ -4,7 +4,6 @@
 indicator("Mean Absolute %Deviation (MAPD)", "MAPD")
 
 //@function Calculates Mean Absolute Percentage Deviation between two sources using SMA for averaging
-//@doc https://github.com/mihakralj/pinescript/blob/main/indicators/errors/mapd.md
 //@param source1 First series to compare
 //@param source2 Second series to compare
 //@param period Lookback period for error averaging

lib/errors/mape/mape.pine

@@ -4,7 +4,6 @@
 indicator("Mean Absolute %Error (MAPE)", "MAPE")
 
 //@function Calculates Mean Absolute Percentage Error between two sources using SMA for averaging
-//@doc https://github.com/mihakralj/pinescript/blob/main/indicators/errors/mape.md
 //@param source1 First series to compare (actual)
 //@param source2 Second series to compare (predicted)
 //@param period Lookback period for error averaging

lib/errors/mase/mase.pine

@@ -4,7 +4,6 @@
 indicator("Mean Absolute Scaled Error (MASE)", "MASE")
 
 //@function Calculates Mean Absolute Scaled Error between two sources using SMA for averaging
-//@doc https://github.com/mihakralj/pinescript/blob/main/indicators/errors/mase.md
 //@param source1 First series to compare
 //@param source2 Second series to compare
 //@param period Lookback period for error averaging

lib/errors/mdae/mdae.pine

@@ -4,8 +4,6 @@
 indicator("Median Absolute Error", "MdAE", overlay=false)
 
 //@function Calculates Median Absolute Error
-//@doc     Median of absolute errors, robust to outliers (50% breakdown point).
-//@doc     Same units as original data, less sensitive to extreme errors than MAE.
 //@param   actual Series of actual values
 //@param   predicted Series of predicted/forecast values
 //@param   length Rolling window for median calculation
@@ -30,4 +28,4 @@ mdae_value = mdae(i_actual, i_predicted, i_length)
 
 // Plot
 plot(mdae_value, "MdAE", color=color.yellow, linewidth=2)
-hline(0, "Zero", color=color.gray, linestyle=hline.style_dotted)
+hline(0, "Zero", color=color.gray, linestyle=hline.style_dotted)

lib/errors/mdape/mdape.pine

@@ -4,8 +4,6 @@
 indicator("Median Absolute Percentage Error", "MdAPE", overlay=false, format=format.percent)
 
 //@function Calculates Median Absolute Percentage Error
-//@doc     Median of absolute percentage errors, robust to outliers.
-//@doc     Scale-independent (expressed as percentage), handles zero actual with epsilon.
 //@param   actual Series of actual values
 //@param   predicted Series of predicted/forecast values
 //@param   length Rolling window for median calculation
@@ -34,4 +32,4 @@ mdape_value = mdape(i_actual, i_predicted, i_length)
 
 // Plot
 plot(mdape_value, "MdAPE", color=color.yellow, linewidth=2)
-hline(0, "Zero", color=color.gray, linestyle=hline.style_dotted)
+hline(0, "Zero", color=color.gray, linestyle=hline.style_dotted)

lib/errors/me/me.pine

@@ -4,7 +4,6 @@
 indicator("Mean Error (ME)", "ME")
 
 //@function Calculates Mean Error between two sources using SMA for averaging
-//@doc https://github.com/mihakralj/pinescript/blob/main/indicators/errors/me.md
 //@param source1 First series to compare (actual)
 //@param source2 Second series to compare (predicted)
 //@param period Lookback period for error averaging

lib/errors/mpe/mpe.pine

@@ -4,7 +4,6 @@
 indicator("Mean %Error (MPE)", "MPE")
 
 //@function Calculates Mean Percentage Error between two sources using SMA for averaging
-//@doc https://github.com/mihakralj/pinescript/blob/main/indicators/errors/mpe.md
 //@param source1 First series to compare (actual)
 //@param source2 Second series to compare (predicted)
 //@param period Lookback period for error averaging

lib/errors/mrae/mrae.pine

@@ -4,8 +4,6 @@
 indicator("Mean Relative Absolute Error", "MRAE", overlay=false)
 
 //@function Calculates Mean Relative Absolute Error
-//@doc     Average relative absolute error, normalized by actual value.
-//@doc     Similar to MAPE but expressed as ratio (0-1) instead of percentage (0-100%).
 //@param   actual Series of actual values
 //@param   predicted Series of predicted/forecast values
 //@param   length Rolling window for averaging
@@ -35,4 +33,4 @@ mrae_value = mrae(i_actual, i_predicted, i_length)
 // Plot
 plot(mrae_value, "MRAE", color=color.yellow, linewidth=2)
 hline(0, "Perfect", color=color.green, linestyle=hline.style_dotted)
-hline(1, "100% Error", color=color.red, linestyle=hline.style_dotted)
+hline(1, "100% Error", color=color.red, linestyle=hline.style_dotted)

lib/errors/mse/mse.pine

@@ -5,7 +5,6 @@ indicator("Mean Squared Error (MSE)", "MSE")
 
 
 //@function Calculates Mean Squared Error between two sources using SMA for averaging
-//@doc https://github.com/mihakralj/pinescript/blob/main/indicators/errors/mse.md
 //@param source1 First series to compare
 //@param source2 Second series to compare
 //@param period Lookback period for error averaging

lib/errors/msle/msle.pine

@@ -4,7 +4,6 @@
 indicator("Mean Squared Logarithmic Error (MSLE)", "MSLE")
 
 //@function Calculates Mean Squared Logarithmic Error between two sources using SMA for averaging
-//@doc https://github.com/mihakralj/pinescript/blob/main/indicators/errors/msle.md
 //@param source1 First series to compare (actual)
 //@param source2 Second series to compare (predicted)
 //@param period Lookback period for error averaging

lib/errors/pseudohuber/pseudohuber.pine

@@ -4,9 +4,6 @@
 indicator("Pseudo-Huber Loss", "PseudoHuber", overlay=false)
 
 //@function Calculates Pseudo-Huber Loss (Charbonnier Loss)
-//@doc     Smooth approximation to Huber loss, differentiable everywhere.
-//@doc     Approximates L2 for small errors, L1 for large errors.
-//@doc     δ (delta) controls the transition point between quadratic and linear behavior.
 //@param   actual Series of actual values
 //@param   predicted Series of predicted/forecast values
 //@param   length Rolling window for averaging
@@ -38,4 +35,4 @@ pseudohuber_value = pseudohuber(i_actual, i_predicted, i_length, i_delta)
 
 // Plot
 plot(pseudohuber_value, "Pseudo-Huber", color=color.yellow, linewidth=2)
-hline(0, "Zero", color=color.gray, linestyle=hline.style_dotted)
+hline(0, "Zero", color=color.gray, linestyle=hline.style_dotted)

lib/errors/quantile/quantile.pine

@@ -4,8 +4,6 @@
 indicator("Quantile Loss (Pinball Loss)", "QuantileLoss", overlay=false)
 
 //@function Calculates Quantile Loss (Pinball Loss)
-//@doc     Used for quantile regression, asymmetrically penalizes over/under-predictions.
-//@doc     q=0.5 gives MAE; q>0.5 penalizes under-prediction more; q<0.5 penalizes over-prediction more.
 //@param   actual Series of actual values
 //@param   predicted Series of predicted/forecast values
 //@param   length Rolling window for averaging
@@ -33,4 +31,4 @@ quantile_value = quantile_loss(i_actual, i_predicted, i_length, i_quantile)
 
 // Plot
 plot(quantile_value, "Quantile Loss", color=color.yellow, linewidth=2)
-hline(0, "Zero", color=color.gray, linestyle=hline.style_dotted)
+hline(0, "Zero", color=color.gray, linestyle=hline.style_dotted)

lib/errors/rae/rae.pine

@@ -4,7 +4,6 @@
 indicator("Relative Absolute Error (RAE)", "RAE")
 
 //@function Calculates Relative Absolute Error between two sources using SMA for averaging
-//@doc https://github.com/mihakralj/pinescript/blob/main/indicators/errors/rae.md
 //@param source1 First series to compare (actual)
 //@param source2 Second series to compare (predicted)
 //@param period Lookback period for error averaging

lib/errors/rmse/rmse.pine

@@ -4,7 +4,6 @@
 indicator("Root Mean Squared Error (RMSE)", "RMSE")
 
 //@function Calculates Root Mean Squared Error between two sources using SMA for averaging
-//@doc https://github.com/mihakralj/pinescript/blob/main/indicators/errors/rmse.md
 //@param source1 First series to compare
 //@param source2 Second series to compare
 //@param period Lookback period for error averaging

lib/errors/rmsle/rmsle.pine

@@ -4,7 +4,6 @@
 indicator("Root Mean Squared Logarithmic Error (RMSLE)", "RMSLE")
 
 //@function Calculates Root Mean Squared Logarithmic Error between two sources using SMA for averaging
-//@doc https://github.com/mihakralj/pinescript/blob/main/indicators/errors/rmsle.md
 //@param source1 First series to compare (actual)
 //@param source2 Second series to compare (predicted)
 //@param period Lookback period for error averaging

lib/errors/rse/rse.pine

@@ -4,7 +4,6 @@
 indicator("Relative Squared Error (RSE)", "RSE")
 
 //@function Calculates Relative Squared Error between two sources using SMA for averaging
-//@doc https://github.com/mihakralj/pinescript/blob/main/indicators/errors/rse.md
 //@param source1 First series to compare (actual)
 //@param source2 Second series to compare (predicted)
 //@param period Lookback period for error averaging

lib/errors/rsquared/rsquared.pine

@@ -4,7 +4,6 @@
 indicator("R² Coefficient of Determination (RSQUARED)", "RSQUARED")
 
 //@function Calculates the R-squared (Coefficient of Determination) between two sources
-//@doc https://github.com/mihakralj/pinescript/blob/main/indicators/errors/rsquared.md
 //@param source1 First series to compare (actual)
 //@param source2 Second series to compare (predicted)
 //@param period Lookback period for averaging

lib/errors/smape/smape.pine

@@ -4,7 +4,6 @@
 indicator("Symmetric Mean Absolute %Error (SMAPE)", "SMAPE")
 
 //@function Calculates Symmetric Mean Absolute Percentage Error between two sources using SMA for averaging
-//@doc https://github.com/mihakralj/pinescript/blob/main/indicators/errors/smape.md
 //@param source1 First series to compare (actual)
 //@param source2 Second series to compare (predicted)
 //@param period Lookback period for error averaging

lib/errors/theilu/theilu.pine

@@ -4,8 +4,6 @@
 indicator("Theil's U Statistic", "TheilU", overlay=false)
 
 //@function Calculates Theil's U Statistic (U1)
-//@doc     Relative forecast accuracy measure, normalized RMSE.
-//@doc     U=0: perfect; U=1: naive forecast; U>1: worse than naive.
 //@param   actual Series of actual values
 //@param   predicted Series of predicted/forecast values
 //@param   length Rolling window for calculation
@@ -42,4 +40,4 @@ theilu_value = theil_u(i_actual, i_predicted, i_length)
 // Plot
 plot(theilu_value, "Theil's U", color=color.yellow, linewidth=2)
 hline(0, "Perfect", color=color.green, linestyle=hline.style_dotted)
-hline(1, "Naive", color=color.red, linestyle=hline.style_dotted)
+hline(1, "Naive", color=color.red, linestyle=hline.style_dotted)

lib/errors/tukey/tukey.pine

@@ -4,9 +4,6 @@
 indicator("Tukey's Biweight Loss", "TukeyBiweight", overlay=false)
 
 //@function Calculates Tukey's Biweight (Bisquare) Loss
-//@doc     Robust loss that completely rejects outliers beyond threshold c.
-//@doc     ρ(x) = (c²/6) * (1 - (1 - (x/c)²)³) for |x| ≤ c; ρ(x) = c²/6 for |x| > c
-//@doc     Common c values: 4.685 (95% efficiency), 6.0 (more permissive)
 //@param   actual Series of actual values
 //@param   predicted Series of predicted/forecast values
 //@param   length Rolling window for averaging
@@ -46,4 +43,4 @@ tukey_value = tukey_biweight(i_actual, i_predicted, i_length, i_c)
 
 // Plot
 plot(tukey_value, "Tukey Biweight", color=color.yellow, linewidth=2)
-hline(0, "Zero", color=color.gray, linestyle=hline.style_dotted)
+hline(0, "Zero", color=color.gray, linestyle=hline.style_dotted)

lib/errors/wmape/wmape.pine

@@ -4,9 +4,6 @@
 indicator("Weighted Mean Absolute Percentage Error", "WMAPE", overlay=false, format=format.percent)
 
 //@function Calculates Weighted Mean Absolute Percentage Error
-//@doc     Weights errors by actual value magnitude, industry standard for demand forecasting.
-//@doc     WMAPE = (Σ|actual - predicted| / Σ|actual|) * 100
-//@doc     More stable than MAPE for intermittent data with zero/low values.
 //@param   actual Series of actual values
 //@param   predicted Series of predicted/forecast values
 //@param   length Rolling window for calculation
@@ -38,4 +35,4 @@ wmape_value = wmape(i_actual, i_predicted, i_length)
 
 // Plot
 plot(wmape_value, "WMAPE", color=color.yellow, linewidth=2)
-hline(0, "Perfect", color=color.green, linestyle=hline.style_dotted)
+hline(0, "Perfect", color=color.green, linestyle=hline.style_dotted)

lib/errors/wrmse/wrmse.pine

@@ -4,9 +4,6 @@
 indicator("Weighted Root Mean Squared Error", "WRMSE", overlay=false)
 
 //@function Calculates Weighted Root Mean Squared Error
-//@doc     WRMSE extends RMSE by weighting each error differently.
-//@doc     WRMSE = √(Σ(w * (actual - predicted)²) / Σ(w))
-//@doc     Reduces to RMSE when all weights are equal.
 //@param   actual Series of actual values
 //@param   predicted Series of predicted/forecast values
 //@param   weight Series of weights for each observation
@@ -50,4 +47,4 @@ wrmse_value = wrmse(i_actual, i_predicted, weight, i_length)
 
 // Plot
 plot(wrmse_value, "WRMSE", color=color.yellow, linewidth=2)
-hline(0, "Perfect", color=color.green, linestyle=hline.style_dotted)
+hline(0, "Perfect", color=color.green, linestyle=hline.style_dotted)