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docs: add PineScript links to all indicator .md files, docsify .pine renderer with comprehensive Prism v6 syntax highlighting

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- Added PineScript row to property tables in 375 .md files linking to companion .pine files
- Docsify plugin intercepts .pine link clicks, fetches and renders content as syntax-highlighted code blocks
- Comprehensive Prism.languages.pine grammar covering 18 token categories: annotations, types, qualifiers, namespaces, OHLCV builtins, functions, keywords, operators
- Custom CSS tokens using GitHub dark palette for Pine-specific visual differentiation
This commit is contained in:
Miha Kralj
2026-03-11 15:36:23 -07:00
parent 567fa89465
commit 19f956521d
396 changed files with 606 additions and 816 deletions
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lib/errors/huber/Huber.md
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| **Outputs** | Single series (Huber) |
| **Output range** | $\geq 0$ |
| **Warmup** | `period` bars |
### TL;DR
| **PineScript** | [huber.pine](huber.pine) |
- Huber Loss is a hybrid loss function that combines the best properties of Mean Squared Error (MSE) and Mean Absolute Error (MAE).
- Parameterized by `period`, `delta` (default 1.345).
lib/errors/logcosh/LogCosh.md
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| **Outputs** | Single series (LogCosh) |
| **Output range** | $\geq 0$ |
| **Warmup** | `period` bars |
### TL;DR
| **PineScript** | [logcosh.pine](logcosh.pine) |
- Log-Cosh Loss combines the best properties of L1 (absolute) and L2 (squared) error metrics through the logarithm of the hyperbolic cosine function.
- Parameterized by `period`.
lib/errors/maape/Maape.md
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| **Outputs** | Single series (MAAPE) |
| **Output range** | $[0, \pi/2]$ |
| **Warmup** | `period` bars |
### TL;DR
| **PineScript** | [maape.pine](maape.pine) |
- Mean Arctangent Absolute Percentage Error (MAAPE) transforms percentage errors through the arctangent function, naturally bounding the metric betwe...
- Parameterized by `period`.
lib/errors/mae/Mae.md
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| **Outputs** | Single series (MAE) |
| **Output range** | $\geq 0$ |
| **Warmup** | `period` bars |
### TL;DR
| **PineScript** | [mae.pine](mae.pine) |
- Mean Absolute Error (MAE) measures the average magnitude of errors in a set of predictions, without considering their direction.
- Parameterized by `period`.
lib/errors/mapd/Mapd.md
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| **Outputs** | Single series (MAPD) |
| **Output range** | $\geq 0$ |
| **Warmup** | `period` bars |
### TL;DR
| **PineScript** | [mapd.pine](mapd.pine) |
- Mean Absolute Percentage Deviation (MAPD) measures the average absolute percentage difference between actual and predicted values, using the predic...
- Parameterized by `period`.
lib/errors/mape/Mape.md
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| **Outputs** | Single series (MAPE) |
| **Output range** | $\geq 0$ |
| **Warmup** | `period` bars |
### TL;DR
| **PineScript** | [mape.pine](mape.pine) |
- Mean Absolute Percentage Error (MAPE) measures the average absolute percentage difference between actual and predicted values.
- Parameterized by `period`.
lib/errors/mase/Mase.md
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| **Outputs** | Single series (Mase) |
| **Output range** | $\geq 0$ |
| **Warmup** | `period + 1` bars |
### TL;DR
| **PineScript** | [mase.pine](mase.pine) |
- Mean Absolute Scaled Error (MASE) normalizes forecast errors by the average error of a naive "random walk" forecast (using the previous value as th...
- Parameterized by `period`.
lib/errors/mdae/Mdae.md
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| **Outputs** | Single series (Mdae) |
| **Output range** | $\geq 0$ |
| **Warmup** | `period` bars |
### TL;DR
| **PineScript** | [mdae.pine](mdae.pine) |
- Median Absolute Error (MdAE) measures the middle value of all absolute errors.
- Parameterized by `period`.
lib/errors/mdape/Mdape.md
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| **Outputs** | Single series (Mdape) |
| **Output range** | $\geq 0$ |
| **Warmup** | `period` bars |
### TL;DR
| **PineScript** | [mdape.pine](mdape.pine) |
- Median Absolute Percentage Error (MdAPE) combines the scale-independence of percentage errors with the robustness of median statistics.
- Parameterized by `period`.
lib/errors/me/Me.md
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| **Outputs** | Single series (ME) |
| **Output range** | Any (positive or negative) |
| **Warmup** | `period` bars |
### TL;DR
| **PineScript** | [me.pine](me.pine) |
- Mean Error (ME), also known as Mean Bias Error, measures the average error between actual and predicted values while preserving the sign.
- Parameterized by `period`.
lib/errors/mpe/Mpe.md
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| **Outputs** | Single series (MPE) |
| **Output range** | Any (positive or negative) |
| **Warmup** | `period` bars |
### TL;DR
| **PineScript** | [mpe.pine](mpe.pine) |
- Mean Percentage Error measures the average percentage difference between actual and predicted values while preserving the sign.
- Parameterized by `period`.
lib/errors/mrae/Mrae.md
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| **Outputs** | Single series (MRAE) |
| **Output range** | $\geq 0$ |
| **Warmup** | `period` bars |
### TL;DR
| **PineScript** | [mrae.pine](mrae.pine) |
- Mean Relative Absolute Error (MRAE) measures the average magnitude of errors relative to the actual values.
- Parameterized by `period`.
lib/errors/mse/Mse.md
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| **Outputs** | Single series (MSE) |
| **Output range** | $\geq 0$ |
| **Warmup** | `period` bars |
### TL;DR
| **PineScript** | [mse.pine](mse.pine) |
- Mean Squared Error (MSE) measures the average of the squares of the errors between actual and predicted values.
- Parameterized by `period`.
lib/errors/msle/Msle.md
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| **Outputs** | Single series (MSLE) |
| **Output range** | $\geq 0$ |
| **Warmup** | `period` bars |
### TL;DR
| **PineScript** | [msle.pine](msle.pine) |
- Mean Squared Logarithmic Error transforms both actual and predicted values through logarithms before computing squared error.
- Parameterized by `period`.
lib/errors/pseudohuber/PseudoHuber.md
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| **Outputs** | Single series (PseudoHuber) |
| **Output range** | $\geq 0$ |
| **Warmup** | `period` bars |
### TL;DR
| **PineScript** | [pseudohuber.pine](pseudohuber.pine) |
- Pseudo-Huber Loss (also called Charbonnier Loss) is a smooth approximation to the Huber loss function.
- Parameterized by `period`, `delta` (default 1.0).
lib/errors/quantileloss/QuantileLoss.md
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| **Outputs** | Single series (QuantileLoss) |
| **Output range** | $\geq 0$ |
| **Warmup** | `period` bars |
### TL;DR
| **PineScript** | [quantileloss.pine](quantileloss.pine) |
- Quantile Loss (also called Pinball Loss) measures prediction accuracy with asymmetric penalties for over-prediction versus under-prediction.
- Parameterized by `period`, `quantile` (default 0.5).
lib/errors/rae/Rae.md
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| **Outputs** | Single series (Rae) |
| **Output range** | $\geq 0$ |
| **Warmup** | `period` bars |
### TL;DR
| **PineScript** | [rae.pine](rae.pine) |
- Relative Absolute Error (RAE) measures the total absolute error of predictions relative to the total absolute error of a simple baseline predictor ...
- Parameterized by `period`.
lib/errors/rmse/Rmse.md
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| **Outputs** | Single series (RMSE) |
| **Output range** | $\geq 0$ |
| **Warmup** | `period` bars |
### TL;DR
| **PineScript** | [rmse.pine](rmse.pine) |
- Root Mean Squared Error (RMSE) is the square root of MSE, providing an error metric in the same units as the original data while retaining sensitiv...
- Parameterized by `period`.
lib/errors/rmsle/Rmsle.md
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| **Outputs** | Single series (RMSLE) |
| **Output range** | $\geq 0$ |
| **Warmup** | `period` bars |
### TL;DR
| **PineScript** | [rmsle.pine](rmsle.pine) |
- Root Mean Squared Logarithmic Error is the square root of MSLE, providing an error metric in log-scale units.
- Parameterized by `period`.
lib/errors/rse/Rse.md
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| **Outputs** | Single series (Rse) |
| **Output range** | $\geq 0$ |
| **Warmup** | `period` bars |
### TL;DR
| **PineScript** | [rse.pine](rse.pine) |
- Relative Squared Error (RSE) measures the total squared error of predictions relative to the total squared error of a simple baseline predictor tha...
- Parameterized by `period`.
lib/errors/rsquared/Rsquared.md
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| **Outputs** | Single series (R²) |
| **Output range** | $(-\infty, 1]$ |
| **Warmup** | `period` bars |
### TL;DR
| **PineScript** | [rsquared.pine](rsquared.pine) |
- The Coefficient of Determination (R²) measures the proportion of variance in the actual values that is predictable from the predicted values.
- Parameterized by `period`.
lib/errors/smape/Smape.md
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| **Outputs** | Single series (SMAPE) |
| **Output range** | $\geq 0$ |
| **Warmup** | `period` bars |
### TL;DR
| **PineScript** | [smape.pine](smape.pine) |
- Symmetric Mean Absolute Percentage Error addresses a fundamental asymmetry in MAPE: the fact that over-predictions and under-predictions of the sam...
- Parameterized by `period`.
lib/errors/theilu/TheilU.md
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| **Outputs** | Single series (TheilU) |
| **Output range** | $\geq 0$ |
| **Warmup** | `period` bars |
### TL;DR
| **PineScript** | [theilu.pine](theilu.pine) |
- Theil's U Statistic measures forecast accuracy relative to a naive no-change forecast.
- Parameterized by `period`.
lib/errors/tukeybiweight/TukeyBiweight.md
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| **Outputs** | Single series (TukeyBiweight) |
| **Output range** | $\geq 0$ |
| **Warmup** | `period` bars |
### TL;DR
| **PineScript** | [tukeybiweight.pine](tukeybiweight.pine) |
- Tukey's Biweight (also called Bisquare) is a redescending M-estimator that completely ignores errors beyond a threshold.
- Parameterized by `period`, `c` (default defaultc).
lib/errors/wmape/Wmape.md
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| **Outputs** | Single series (Wmape) |
| **Output range** | $\geq 0$ |
| **Warmup** | `period` bars |
### TL;DR
| **PineScript** | [wmape.pine](wmape.pine) |
- Weighted Mean Absolute Percentage Error (WMAPE) adjusts MAPE by weighting each error by the magnitude of the actual value.
- Parameterized by `period`.
lib/errors/wrmse/Wrmse.md
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| **Outputs** | Single series (Wrmse) |
| **Output range** | $\geq 0$ |
| **Warmup** | `period` bars |
### TL;DR
| **PineScript** | [wrmse.pine](wrmse.pine) |
- WRMSE extends the classic RMSE by incorporating weights for each observation, enabling analysts to emphasize critical data points such as recent ob...
- Parameterized by `period`.