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Enhance documentation and validation for various indicators

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Miha Kralj
2025-12-22 20:42:26 -08:00
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lib/trends/conv/Conv.md
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@@ -32,22 +32,28 @@ Where:
Performance depends linearly on the kernel length ($N$).
| Metric | Complexity | Notes |
| Metric | Score | Notes |
| :--- | :--- | :--- |
| **Throughput** | Moderate | Kernel convolution per bar |
| **Complexity** | O(N) | Window iteration required |
| **Accuracy** | 8/10 | Depends on kernel, generally high |
| **Timeliness** | 7/10 | Depends on kernel design |
| **Overshoot** | 8/10 | Depends on kernel design |
| **Smoothness** | 8/10 | Depends on kernel design |
| **Throughput** | ★★★☆☆ | O(N) kernel convolution per bar. |
| **Allocations** | ★★★★★ | 0 bytes; hot path is allocation-free. |
| **Complexity** | ★★★☆☆ | O(N) window iteration required. |
| **Precision** | ★★★★★ | `double` precision. |
### Zero-Allocation Design
CONV stores the kernel in a pre-allocated array. The `Update` method performs a dot product using a circular buffer for the price history, requiring no new allocations.
## Validation
Validated against standard DSP convolution implementations (e.g., SciPy `signal.convolve`).
Validation is performed by reproducing standard moving averages (SMA, WMA, TRIMA) using their equivalent kernels and comparing against external libraries.
| Provider | Error Tolerance | Notes |
| Library | Status | Notes |
| :--- | :--- | :--- |
| **SciPy** | $10^{-12}$ | Matches standard 'valid' convolution mode |
| **QuanTAlib** | ✅ | Validated against internal SMA, WMA, TRIMA. |
| **Skender** | ✅ | Validated against WMA (using WMA kernel). |
| **TA-Lib** | ✅ | Validated against WMA (using WMA kernel). |
| **Tulip** | ✅ | Validated against WMA (using WMA kernel). |
| **Ooples** | ✅ | Validated against WMA (using WMA kernel). |
### Common Pitfalls