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Miha Kralj
2026-03-13 13:47:10 -07:00
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lib/errors/quantileloss/QuantileLoss.md
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| **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).
- Output range: $\geq 0$.
- Requires 1 bar of warmup before first valid output (IsHot = true).
- **Similar:** [MAE](../mae/Mae.md), [Huber](../huber/Huber.md) | **Trading note:** Asymmetric loss for quantile regression; set tau to penalize over/under-prediction differently.
- Validated against TA-Lib, Skender, and Tulip reference implementations where available.
Quantile Loss (also called Pinball Loss) measures prediction accuracy with asymmetric penalties for over-prediction versus under-prediction. It's essential for probabilistic forecasting where different quantiles of the distribution matter.
@@ -178,4 +176,4 @@ With τ=0.9, under-predictions are penalized 9x more than over-predictions.
* [MAE](../mae/Mae.md) - Mean Absolute Error (equivalent to τ=0.5 × 2)
* [Huber](../huber/Huber.md) - Huber Loss (robust symmetric)
* [MAPE](../mape/Mape.md) - Mean Absolute Percentage Error
* [MAPE](../mape/Mape.md) - Mean Absolute Percentage Error