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Refactor documentation for various filters and indicators to enhance clarity and consistency

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- Updated Bessel, Bilateral, Blma, Butter, Conv, Ema, Kama, LSMA, MAMA, MGDI, SSF, USF, ATR, ADL, and ADOSC documentation to use bullet points for key concepts and features.
- Added a new Qodana configuration file for code analysis.
- Removed coverage configuration from Quantower.Tests.csproj to streamline testing setup.
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Miha Kralj
2025-12-31 23:39:47 -08:00
parent 11f4ec2497
commit d493bfd42f
175 changed files with 11977 additions and 897 deletions
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lib/errors/rae/Rae.md
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@@ -11,7 +11,7 @@ RAE computes a ratio of summed absolute errors. The numerator is the sum of abso
### Interpretation Guide
| RAE Value | Interpretation |
|:----------|:---------------|
| ------ | ------ |
| **RAE < 1** | Predictions are better than mean predictor |
| **RAE = 1** | Predictions equal mean predictor performance |
| **RAE > 1** | Predictions are worse than mean predictor |
@@ -38,7 +38,7 @@ $$\text{RAE} = \frac{\sum_{t=1}^{n} |y_t - \hat{y}_t|}{\sum_{t=1}^{n} |y_t - \ba
## Performance Profile
| Metric | Score | Notes |
|:-------|:------|:------|
| ------ | ------ | ------ |
| **Throughput** | ~40 ns/bar | Three running sums maintained |
| **Allocations** | 0 | Zero-allocation implementation |
| **Complexity** | O(1) | Constant time per update |
@@ -59,9 +59,10 @@ The baseline error is calculated against the rolling mean, which updates each ti
### Different from R²
RAE and R² (coefficient of determination) are related but distinct:
- RAE uses absolute errors (L1 norm)
- R² uses squared errors (L2 norm)
- Both use mean-predictor as baseline
* RAE uses absolute errors (L1 norm)
* R² uses squared errors (L2 norm)
* Both use mean-predictor as baseline
## Usage
@@ -84,7 +85,7 @@ Rae.Batch(actualSpan, predictedSpan, outputSpan, 14);
## Comparison with Related Metrics
| Metric | Error Type | Baseline | Range | Units |
|:-------|:-----------|:---------|:------|:------|
| ------ | ------ | ------ | ------ | ------ |
| **RAE** | Absolute | Mean predictor | [0, ∞) | Ratio |
| **RSE** | Squared | Mean predictor | [0, ∞) | Ratio |
| **R²** | Squared | Mean predictor | (-∞, 1] | Coefficient |
@@ -92,6 +93,6 @@ Rae.Batch(actualSpan, predictedSpan, outputSpan, 14);
RAE is preferable when:
- You want robustness to outliers (absolute vs squared errors)
- You need a ratio interpretation (< 1 is good, > 1 is bad)
- The mean predictor is a relevant baseline for your domain
* You want robustness to outliers (absolute vs squared errors)
* You need a ratio interpretation (< 1 is good, > 1 is bad)
* The mean predictor is a relevant baseline for your domain