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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/rsquared/Rsquared.md
+7 -7
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@@ -11,7 +11,7 @@ R² is computed as 1 minus the ratio of residual sum of squares (RSS) to total s
### Interpretation Guide
| R² Value | Interpretation |
|:---------|:---------------|
| :------- | :------------- |
| **R² = 1** | Perfect predictions (all variance explained) |
| **R² > 0.9** | Excellent model |
| **R² > 0.7** | Good model |
@@ -42,7 +42,7 @@ $$R^2 = 1 - \text{RSE}$$
## 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 |
@@ -89,7 +89,7 @@ Rsquared.Batch(actualSpan, predictedSpan, outputSpan, 14);
## R² Quick Reference
| R² Value | Quality | Description |
|:---------|:--------|:------------|
| :------- | :------ | :---------- |
| 1.00 | Perfect | Model explains all variance |
| 0.95 | Excellent | Model explains 95% of variance |
| 0.80 | Good | Model explains 80% of variance |
@@ -100,7 +100,7 @@ Rsquared.Batch(actualSpan, predictedSpan, outputSpan, 14);
## Comparison with RSE
| Property | R² | RSE |
|:---------|:---|:----|
| :------- | :- | :-- |
| **Range** | (-∞, 1] | [0, +∞) |
| **Perfect score** | 1 | 0 |
| **Mean predictor** | 0 | 1 |
@@ -109,6 +109,6 @@ Rsquared.Batch(actualSpan, predictedSpan, outputSpan, 14);
## When to Use R²
- **Use R²** when you want an intuitive measure of model quality (0-1 scale for good models)
- **Use RSE** when you want to compare error magnitudes directly
- **Use both** to get complementary perspectives on model performance
* **Use R²** when you want an intuitive measure of model quality (0-1 scale for good models)
* **Use RSE** when you want to compare error magnitudes directly
* **Use both** to get complementary perspectives on model performance