gavindiaz/QuanTAlib
1
0
Fork 0
mirror of https://github.com/mihakralj/QuanTAlib.git synced 2026-08-14 00:28:05 +00:00
Code Issues Packages Projects Releases Wiki Activity

doc headers

Browse Source
This commit is contained in:
Miha Kralj
2026-02-27 07:48:12 -08:00
parent 8a1ba95173
commit 4ab3a7fb53
389 changed files with 6682 additions and 468 deletions
Download Patch File Download Diff File Expand all files Collapse all files
lib/errors/huber/Huber.md
+18 -1
View File
@@ -1,4 +1,21 @@
# Huber: Huber Loss
# Huber: Huber Loss
| Property | Value |
| ---------------- | -------------------------------- |
| **Category** | Error Metric |
| **Inputs** | Source (close) |
| **Parameters** | `period`, `delta` (default 1.345) |
| **Outputs** | Single series (Huber) |
| **Output range** | $\geq 0$ |
| **Warmup** | 1 bar |
### TL;DR
- 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).
- Output range: $\geq 0$.
- Requires 1 bar of warmup before first valid output (IsHot = true).
- Validated against TA-Lib, Skender, and Tulip reference implementations where available.
> "The Goldilocks of loss functions: not too sensitive, not too robust, just right."
lib/errors/logcosh/LogCosh.md
+18 -1
View File
@@ -1,4 +1,21 @@
# Log-Cosh: Logarithm of Hyperbolic Cosine Loss
# Log-Cosh: Logarithm of Hyperbolic Cosine Loss
| Property | Value |
| ---------------- | -------------------------------- |
| **Category** | Error Metric |
| **Inputs** | Source (close) |
| **Parameters** | `period` |
| **Outputs** | Single series (UNKNOWN) |
| **Output range** | $\geq 0$ |
| **Warmup** | 1 bar |
### TL;DR
- 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`.
- Output range: $\geq 0$.
- Requires 1 bar of warmup before first valid output (IsHot = true).
- Validated against TA-Lib, Skender, and Tulip reference implementations where available.
> "The smooth operator that acts like L2 for small errors and L1 for large ones."
lib/errors/maape/Maape.md
+18 -1
View File
@@ -1,4 +1,21 @@
# MAAPE: Mean Arctangent Absolute Percentage Error
# MAAPE: Mean Arctangent Absolute Percentage Error
| Property | Value |
| ---------------- | -------------------------------- |
| **Category** | Error Metric |
| **Inputs** | Source (close) |
| **Parameters** | `period` |
| **Outputs** | Single series (MAAPE) |
| **Output range** | $\geq 0$ |
| **Warmup** | 1 bar |
### TL;DR
- Mean Arctangent Absolute Percentage Error (MAAPE) transforms percentage errors through the arctangent function, naturally bounding the metric betwe...
- Parameterized by `period`.
- Output range: $\geq 0$.
- Requires 1 bar of warmup before first valid output (IsHot = true).
- Validated against TA-Lib, Skender, and Tulip reference implementations where available.
> "When percentage errors need boundaries, arctangent provides the walls."
lib/errors/mae/Mae.md
+18 -1
View File
@@ -1,4 +1,21 @@
# MAE: Mean Absolute Error
# MAE: Mean Absolute Error
| Property | Value |
| ---------------- | -------------------------------- |
| **Category** | Error Metric |
| **Inputs** | Source (close) |
| **Parameters** | `period` |
| **Outputs** | Single series (MAE) |
| **Output range** | $\geq 0$ |
| **Warmup** | 1 bar |
### TL;DR
- Mean Absolute Error (MAE) measures the average magnitude of errors in a set of predictions, without considering their direction.
- Parameterized by `period`.
- Output range: $\geq 0$.
- Requires 1 bar of warmup before first valid output (IsHot = true).
- Validated against TA-Lib, Skender, and Tulip reference implementations where available.
> "When you need to know how wrong you are on average, without the drama of squared errors."
lib/errors/mapd/Mapd.md
+18 -1
View File
@@ -1,4 +1,21 @@
# MAPD: Mean Absolute Percentage Deviation
# MAPD: Mean Absolute Percentage Deviation
| Property | Value |
| ---------------- | -------------------------------- |
| **Category** | Error Metric |
| **Inputs** | Source (close) |
| **Parameters** | `period` |
| **Outputs** | Single series (MAPD) |
| **Output range** | $\geq 0$ |
| **Warmup** | 1 bar |
### TL;DR
- Mean Absolute Percentage Deviation (MAPD) measures the average absolute percentage difference between actual and predicted values, using the predic...
- Parameterized by `period`.
- Output range: $\geq 0$.
- Requires 1 bar of warmup before first valid output (IsHot = true).
- Validated against TA-Lib, Skender, and Tulip reference implementations where available.
> "Like MAPE, but divides by what you predicted instead of what actually happened."
lib/errors/mape/Mape.md
+18 -1
View File
@@ -1,4 +1,21 @@
# MAPE: Mean Absolute Percentage Error
# MAPE: Mean Absolute Percentage Error
| Property | Value |
| ---------------- | -------------------------------- |
| **Category** | Error Metric |
| **Inputs** | Source (close) |
| **Parameters** | `period` |
| **Outputs** | Single series (MAPE) |
| **Output range** | $\geq 0$ |
| **Warmup** | 1 bar |
### TL;DR
- Mean Absolute Percentage Error (MAPE) measures the average absolute percentage difference between actual and predicted values.
- Parameterized by `period`.
- Output range: $\geq 0$.
- Requires 1 bar of warmup before first valid output (IsHot = true).
- Validated against TA-Lib, Skender, and Tulip reference implementations where available.
> "The metric that lets you compare apples to oranges, as long as you don't have any zeros."
lib/errors/mase/Mase.md
+18 -1
View File
@@ -1,4 +1,21 @@
# MASE: Mean Absolute Scaled Error
# MASE: Mean Absolute Scaled Error
| Property | Value |
| ---------------- | -------------------------------- |
| **Category** | Error Metric |
| **Inputs** | Source (close) |
| **Parameters** | `period` |
| **Outputs** | Single series (Mase) |
| **Output range** | $\geq 0$ |
| **Warmup** | `period + 1` bars |
### TL;DR
- 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`.
- Output range: $\geq 0$.
- Requires `period + 1` bars of warmup before first valid output (IsHot = true).
- Validated against TA-Lib, Skender, and Tulip reference implementations where available.
> "A good forecast is one that's better than guessing. MASE tells you exactly how much better."
lib/errors/mdae/Mdae.md
+18 -1
View File
@@ -1,4 +1,21 @@
# MdAE: Median Absolute Error
# MdAE: Median Absolute Error
| Property | Value |
| ---------------- | -------------------------------- |
| **Category** | Error Metric |
| **Inputs** | Source (close) |
| **Parameters** | `period` |
| **Outputs** | Single series (Mdae) |
| **Output range** | $\geq 0$ |
| **Warmup** | `period` bars |
### TL;DR
- Median Absolute Error (MdAE) measures the middle value of all absolute errors.
- Parameterized by `period`.
- Output range: $\geq 0$.
- Requires `period` bars of warmup before first valid output (IsHot = true).
- Validated against TA-Lib, Skender, and Tulip reference implementations where available.
> "When outliers scream but you need to hear the whisper of typical performance."
lib/errors/mdape/Mdape.md
+18 -1
View File
@@ -1,4 +1,21 @@
# MdAPE: Median Absolute Percentage Error
# MdAPE: Median Absolute Percentage Error
| Property | Value |
| ---------------- | -------------------------------- |
| **Category** | Error Metric |
| **Inputs** | Source (close) |
| **Parameters** | `period` |
| **Outputs** | Single series (Mdape) |
| **Output range** | $\geq 0$ |
| **Warmup** | `period` bars |
### TL;DR
- Median Absolute Percentage Error (MdAPE) combines the scale-independence of percentage errors with the robustness of median statistics.
- Parameterized by `period`.
- Output range: $\geq 0$.
- Requires `period` bars of warmup before first valid output (IsHot = true).
- Validated against TA-Lib, Skender, and Tulip reference implementations where available.
> "When you need relative errors but can't trust the outliers."
lib/errors/me/Me.md
+18 -1
View File
@@ -1,4 +1,21 @@
# ME: Mean Error (Mean Bias Error)
# ME: Mean Error (Mean Bias Error)
| Property | Value |
| ---------------- | -------------------------------- |
| **Category** | Error Metric |
| **Inputs** | Source (close) |
| **Parameters** | `period` |
| **Outputs** | Single series (ME) |
| **Output range** | $\geq 0$ |
| **Warmup** | 1 bar |
### TL;DR
- 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`.
- Output range: $\geq 0$.
- Requires 1 bar of warmup before first valid output (IsHot = true).
- Validated against TA-Lib, Skender, and Tulip reference implementations where available.
> "Sometimes you need to know not just how wrong you are, but which direction you're wrong in."
lib/errors/mpe/Mpe.md
+18 -1
View File
@@ -1,4 +1,21 @@
# MPE: Mean Percentage Error
# MPE: Mean Percentage Error
| Property | Value |
| ---------------- | -------------------------------- |
| **Category** | Error Metric |
| **Inputs** | Source (close) |
| **Parameters** | `period` |
| **Outputs** | Single series (MPE) |
| **Output range** | $\geq 0$ |
| **Warmup** | 1 bar |
### TL;DR
- Mean Percentage Error measures the average percentage difference between actual and predicted values while preserving the sign.
- Parameterized by `period`.
- Output range: $\geq 0$.
- Requires 1 bar of warmup before first valid output (IsHot = true).
- Validated against TA-Lib, Skender, and Tulip reference implementations where available.
> "MAPE tells you how wrong you are; MPE tells you which direction you're wrong in."
lib/errors/mrae/Mrae.md
+18 -1
View File
@@ -1,4 +1,21 @@
# MRAE: Mean Relative Absolute Error
# MRAE: Mean Relative Absolute Error
| Property | Value |
| ---------------- | -------------------------------- |
| **Category** | Error Metric |
| **Inputs** | Source (close) |
| **Parameters** | `period` |
| **Outputs** | Single series (MRAE) |
| **Output range** | $\geq 0$ |
| **Warmup** | 1 bar |
### TL;DR
- Mean Relative Absolute Error (MRAE) measures the average magnitude of errors relative to the actual values.
- Parameterized by `period`.
- Output range: $\geq 0$.
- Requires 1 bar of warmup before first valid output (IsHot = true).
- Validated against TA-Lib, Skender, and Tulip reference implementations where available.
> "When you need to understand your error in the context of what you're predicting."
lib/errors/mse/Mse.md
+18 -1
View File
@@ -1,4 +1,21 @@
# MSE: Mean Squared Error
# MSE: Mean Squared Error
| Property | Value |
| ---------------- | -------------------------------- |
| **Category** | Error Metric |
| **Inputs** | Source (close) |
| **Parameters** | `period` |
| **Outputs** | Single series (MSE) |
| **Output range** | $\geq 0$ |
| **Warmup** | 1 bar |
### TL;DR
- Mean Squared Error (MSE) measures the average of the squares of the errors between actual and predicted values.
- Parameterized by `period`.
- Output range: $\geq 0$.
- Requires 1 bar of warmup before first valid output (IsHot = true).
- Validated against TA-Lib, Skender, and Tulip reference implementations where available.
> "The metric that makes outliers pay dearly for their transgressions."
lib/errors/msle/Msle.md
+18 -1
View File
@@ -1,4 +1,21 @@
# MSLE: Mean Squared Logarithmic Error
# MSLE: Mean Squared Logarithmic Error
| Property | Value |
| ---------------- | -------------------------------- |
| **Category** | Error Metric |
| **Inputs** | Source (close) |
| **Parameters** | `period` |
| **Outputs** | Single series (MSLE) |
| **Output range** | $\geq 0$ |
| **Warmup** | 1 bar |
### TL;DR
- Mean Squared Logarithmic Error transforms both actual and predicted values through logarithms before computing squared error.
- Parameterized by `period`.
- Output range: $\geq 0$.
- Requires 1 bar of warmup before first valid output (IsHot = true).
- Validated against TA-Lib, Skender, and Tulip reference implementations where available.
> "When your data spans orders of magnitude, MSLE keeps outliers from hijacking your loss function."
lib/errors/pseudohuber/PseudoHuber.md
+18 -1
View File
@@ -1,4 +1,21 @@
# Pseudo-Huber: Smooth Huber Approximation
# Pseudo-Huber: Smooth Huber Approximation
| Property | Value |
| ---------------- | -------------------------------- |
| **Category** | Error Metric |
| **Inputs** | Source (close) |
| **Parameters** | `period`, `delta` (default 1.0) |
| **Outputs** | Single series (UNKNOWN) |
| **Output range** | $\geq 0$ |
| **Warmup** | 1 bar |
### TL;DR
- Pseudo-Huber Loss (also called Charbonnier Loss) is a smooth approximation to the Huber loss function.
- Parameterized by `period`, `delta` (default 1.0).
- Output range: $\geq 0$.
- Requires 1 bar of warmup before first valid output (IsHot = true).
- Validated against TA-Lib, Skender, and Tulip reference implementations where available.
> "All the robustness of Huber, none of the discontinuities."
lib/errors/quantileloss/QuantileLoss.md
+18 -1
View File
@@ -1,4 +1,21 @@
# Quantile Loss: Pinball Loss Function
# Quantile Loss: Pinball Loss Function
| Property | Value |
| ---------------- | -------------------------------- |
| **Category** | Error Metric |
| **Inputs** | Source (close) |
| **Parameters** | `period`, `quantile` (default 0.5) |
| **Outputs** | Single series (Quantile) |
| **Output range** | $\geq 0$ |
| **Warmup** | 1 bar |
### TL;DR
- 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).
- Validated against TA-Lib, Skender, and Tulip reference implementations where available.
> "When over-prediction and under-prediction carry different costs, quantiles find the balance."
lib/errors/rae/Rae.md
+18 -1
View File
@@ -1,4 +1,21 @@
# RAE: Relative Absolute Error
# RAE: Relative Absolute Error
| Property | Value |
| ---------------- | -------------------------------- |
| **Category** | Error Metric |
| **Inputs** | Source (close) |
| **Parameters** | `period` |
| **Outputs** | Single series (Rae) |
| **Output range** | $\geq 0$ |
| **Warmup** | `period` bars |
### TL;DR
- 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`.
- Output range: $\geq 0$.
- Requires `period` bars of warmup before first valid output (IsHot = true).
- Validated against TA-Lib, Skender, and Tulip reference implementations where available.
> "How much better than just guessing the mean? RAE gives you the ratio."
lib/errors/rmse/Rmse.md
+18 -1
View File
@@ -1,4 +1,21 @@
# RMSE: Root Mean Squared Error
# RMSE: Root Mean Squared Error
| Property | Value |
| ---------------- | -------------------------------- |
| **Category** | Error Metric |
| **Inputs** | Source (close) |
| **Parameters** | `period` |
| **Outputs** | Single series (RMSE) |
| **Output range** | $\geq 0$ |
| **Warmup** | 1 bar |
### TL;DR
- 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`.
- Output range: $\geq 0$.
- Requires 1 bar of warmup before first valid output (IsHot = true).
- Validated against TA-Lib, Skender, and Tulip reference implementations where available.
> "MSE's more interpretable sibling that speaks the language of your data."
lib/errors/rmsle/Rmsle.md
+18 -1
View File
@@ -1,4 +1,21 @@
# RMSLE: Root Mean Squared Logarithmic Error
# RMSLE: Root Mean Squared Logarithmic Error
| Property | Value |
| ---------------- | -------------------------------- |
| **Category** | Error Metric |
| **Inputs** | Source (close) |
| **Parameters** | `period` |
| **Outputs** | Single series (RMSLE) |
| **Output range** | $\geq 0$ |
| **Warmup** | 1 bar |
### TL;DR
- Root Mean Squared Logarithmic Error is the square root of MSLE, providing an error metric in log-scale units.
- Parameterized by `period`.
- Output range: $\geq 0$.
- Requires 1 bar of warmup before first valid output (IsHot = true).
- Validated against TA-Lib, Skender, and Tulip reference implementations where available.
> "RMSLE: because sometimes your errors need to be measured in decades, not dollars."
lib/errors/rse/Rse.md
+18 -1
View File
@@ -1,4 +1,21 @@
# RSE: Relative Squared Error
# RSE: Relative Squared Error
| Property | Value |
| ---------------- | -------------------------------- |
| **Category** | Error Metric |
| **Inputs** | Source (close) |
| **Parameters** | `period` |
| **Outputs** | Single series (Rse) |
| **Output range** | $\geq 0$ |
| **Warmup** | `period` bars |
### TL;DR
- 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`.
- Output range: $\geq 0$.
- Requires `period` bars of warmup before first valid output (IsHot = true).
- Validated against TA-Lib, Skender, and Tulip reference implementations where available.
> "The squared error version of RAE. RSE and R² are two sides of the same coin: R² = 1 - RSE."
lib/errors/rsquared/Rsquared.md
+18 -1
View File
@@ -1,4 +1,21 @@
# R²: Coefficient of Determination
# R²: Coefficient of Determination
| Property | Value |
| ---------------- | -------------------------------- |
| **Category** | Error Metric |
| **Inputs** | Source (close) |
| **Parameters** | `period` |
| **Outputs** | Single series (R) |
| **Output range** | $\geq 0$ |
| **Warmup** | `period` bars |
### TL;DR
- The Coefficient of Determination (R²) measures the proportion of variance in the actual values that is predictable from the predicted values.
- Parameterized by `period`.
- Output range: $\geq 0$.
- Requires `period` bars of warmup before first valid output (IsHot = true).
- Validated against TA-Lib, Skender, and Tulip reference implementations where available.
> "R² tells you how much of the variance in actual values is explained by your predictions. It's the statistician's favorite metric for good reason."
lib/errors/smape/Smape.md
+18 -1
View File
@@ -1,4 +1,21 @@
# SMAPE: Symmetric Mean Absolute Percentage Error
# SMAPE: Symmetric Mean Absolute Percentage Error
| Property | Value |
| ---------------- | -------------------------------- |
| **Category** | Error Metric |
| **Inputs** | Source (close) |
| **Parameters** | `period` |
| **Outputs** | Single series (SMAPE) |
| **Output range** | $\geq 0$ |
| **Warmup** | 1 bar |
### TL;DR
- 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`.
- Output range: $\geq 0$.
- Requires 1 bar of warmup before first valid output (IsHot = true).
- Validated against TA-Lib, Skender, and Tulip reference implementations where available.
> "MAPE punishes based on who's right; SMAPE punishes based on how different they are."
lib/errors/theilu/TheilU.md
+18 -1
View File
@@ -1,4 +1,21 @@
# Theil's U: Theil's U Statistic
# Theil's U: Theil's U Statistic
| Property | Value |
| ---------------- | -------------------------------- |
| **Category** | Error Metric |
| **Inputs** | Source (close) |
| **Parameters** | `period` |
| **Outputs** | Single series (TheilU) |
| **Output range** | $\geq 0$ |
| **Warmup** | `period` bars |
### TL;DR
- Theil's U Statistic measures forecast accuracy relative to a naive no-change forecast.
- Parameterized by `period`.
- Output range: $\geq 0$.
- Requires `period` bars of warmup before first valid output (IsHot = true).
- Validated against TA-Lib, Skender, and Tulip reference implementations where available.
> "The forecast that matters is the one that beats a naive guess."
lib/errors/tukeybiweight/TukeyBiweight.md
+18 -1
View File
@@ -1,4 +1,21 @@
# Tukey's Biweight: Robust Loss Function
# Tukey's Biweight: Robust Loss Function
| Property | Value |
| ---------------- | -------------------------------- |
| **Category** | Error Metric |
| **Inputs** | Source (close) |
| **Parameters** | `period`, `c` (default DefaultC) |
| **Outputs** | Single series (UNKNOWN) |
| **Output range** | $\geq 0$ |
| **Warmup** | 1 bar |
### TL;DR
- Tukey's Biweight (also called Bisquare) is a redescending M-estimator that completely ignores errors beyond a threshold.
- Parameterized by `period`, `c` (default defaultc).
- Output range: $\geq 0$.
- Requires 1 bar of warmup before first valid output (IsHot = true).
- Validated against TA-Lib, Skender, and Tulip reference implementations where available.
> "When outliers need to be silenced, not just quieted."
lib/errors/wmape/Wmape.md
+18 -1
View File
@@ -1,4 +1,21 @@
# WMAPE: Weighted Mean Absolute Percentage Error
# WMAPE: Weighted Mean Absolute Percentage Error
| Property | Value |
| ---------------- | -------------------------------- |
| **Category** | Error Metric |
| **Inputs** | Source (close) |
| **Parameters** | `period` |
| **Outputs** | Single series (Wmape) |
| **Output range** | $\geq 0$ |
| **Warmup** | `period` bars |
### TL;DR
- Weighted Mean Absolute Percentage Error (WMAPE) adjusts MAPE by weighting each error by the magnitude of the actual value.
- Parameterized by `period`.
- Output range: $\geq 0$.
- Requires `period` bars of warmup before first valid output (IsHot = true).
- Validated against TA-Lib, Skender, and Tulip reference implementations where available.
> "When not all errors are created equal, weight them by what matters."
lib/errors/wrmse/Wrmse.md
+18 -1
View File
@@ -1,5 +1,22 @@
# WRMSE: Weighted Root Mean Squared Error
| Property | Value |
| ---------------- | -------------------------------- |
| **Category** | Error Metric |
| **Inputs** | Source (close) |
| **Parameters** | `period` |
| **Outputs** | Single series (Wrmse) |
| **Output range** | $\geq 0$ |
| **Warmup** | `period` bars |
### TL;DR
- 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`.
- Output range: $\geq 0$.
- Requires `period` bars of warmup before first valid output (IsHot = true).
- Validated against TA-Lib, Skender, and Tulip reference implementations where available.
> "Not all errors are created equal—WRMSE lets you decide which ones matter most."
WRMSE extends the classic RMSE by incorporating weights for each observation, enabling analysts to emphasize critical data points such as recent observations, high-volume periods, or specific market regimes. When all weights are equal, WRMSE reduces exactly to RMSE, making it a strict generalization. This implementation uses dual RingBuffers for O(1) streaming updates with periodic resync to manage floating-point drift.
@@ -169,4 +186,4 @@ WRMSE is validated by:
- Aitken, A.C. (1936). "On Least Squares and Linear Combinations of Observations." *Proceedings of the Royal Society of Edinburgh*.
- Gauss, C.F. (1809). *Theoria Motus Corporum Coelestium*. (Foundation of least squares theory)
- Greene, W.H. (2012). *Econometric Analysis*. 7th ed. Chapter 9: Generalized Least Squares.
- Greene, W.H. (2012). *Econometric Analysis*. 7th ed. Chapter 9: Generalized Least Squares.