docs: remove C# Implementation Considerations sections, clean up temp scripts, reorganize test files

- Remove 'C# Implementation Considerations' sections from 34 indicator .md files - Delete 29 temp PowerShell scripts (_fix_mojibake.ps1, _hex_scan.ps1, etc.) - Move test files into tests/ subdirectories for consistent project structure - Add trader-focused bullet points to indicator documentation
2026-08-26 06:18:05 +00:00 · 2026-03-12 12:34:16 -07:00
parent 8937b0c0fa
commit 060649192f
1149 changed files with 1780 additions and 3316 deletions
@@ -172,122 +172,6 @@ QuanTAlib validates against reference implementations that respect the Gaussian
 | **TA-Lib** | ❌ | Not included in standard C distribution. |
 | **Tulip** | ❌ | Not included. |

-## C# Implementation Considerations
-
-### Precomputed Gaussian Weights
-
-Weights are computed once in the constructor and stored in a `double[]` array:
-
-```csharp
-_weights = new double[period];
-ComputeWeights(_weights, period, offset, sigma, out _invWeightSum);
-```
-
-The inverse of the weight sum is precomputed for multiplication instead of division in the hot path.
-
-### State Record Struct with Auto Layout
-
-Minimal state for bar correction:
-
-```csharp
-[StructLayout(LayoutKind.Auto)]
-private record struct State(double LastValidValue, bool IsInitialized);
-```
-
-The `LayoutKind.Auto` lets the JIT optimize field placement for cache efficiency.
-
-### SIMD-Optimized Dot Product
-
-The weighted sum calculation delegates to a SIMD-optimized `DotProduct` extension method:
-
-```csharp
-double sum1 = internalBuf.Slice(head, part1Len).DotProduct(_weights.AsSpan(0, part1Len));
-double sum2 = internalBuf[..head].DotProduct(_weights.AsSpan(part1Len));
-return (sum1 + sum2) * _invWeightSum;
-```
-
-The dot product leverages AVX2/AVX-512/NEON intrinsics internally, achieving up to 8× speedup.
-
-### Circular Buffer Handling
-
-The RingBuffer's internal array is accessed directly to split the dot product across the wrap boundary:
-
-```csharp
-ReadOnlySpan<double> internalBuf = _buffer.InternalBuffer;
-int head = _buffer.StartIndex;
-int part1Len = _period - head;
-
-// Part 1: head..end with weights[0..part1Len]
-// Part 2: 0..head with weights[part1Len..period]
-```
-
-This avoids copying the buffer into a contiguous array.
-
-### Stackalloc/ArrayPool Allocation Strategy
-
-The static `Calculate` method uses stackalloc for small periods and ArrayPool for large:
-
-```csharp
-double[]? weightsArray = period > 256 ? ArrayPool<double>.Shared.Rent(period) : null;
-Span<double> weights = period <= 256
-    ? stackalloc double[period]
-    : weightsArray!.AsSpan(0, period);
-```
-
-The 256-element threshold balances stack safety with allocation overhead.
-
-### NaN Handling with Initialization Tracking
-
-Non-finite inputs are replaced with the last valid value, with explicit tracking for uninitialized state:
-
-```csharp
-private double GetValidValue(double input)
-{
-    if (double.IsFinite(input))
-        return input;
-    return _state.IsInitialized ? _state.LastValidValue : double.NaN;
-}
-```
-
-This prevents NaN propagation while correctly handling series that start with invalid values.
-
-### Incremental Weight Sum for Warmup
-
-During the warmup period, the weight sum is computed incrementally:
-
-```csharp
-if (count < period)
-{
-    count++;
-    currentWeightSum += weights[period - count];
-}
-```
-
-This avoids recalculating the partial sum on each bar during convergence.
-
-### Separate Internal Update Method
-
-The `Update` method has a private overload with a `publish` parameter:
-
-```csharp
-private TValue Update(TValue input, bool isNew, bool publish)
-```
-
-This allows state restoration after batch processing without firing events.
-
-### Memory Layout
-
-| Component | Size | Purpose |
-| :--- | :--- | :--- |
-| `_weights` | 8×period bytes | Precomputed Gaussian weights |
-| `_buffer` (RingBuffer) | 32 + 8×period bytes | Sliding window history |
-| `_state` | ~16 bytes | LastValidValue, IsInitialized |
-| `_p_state` | ~16 bytes | Previous state for rollback |
-| Scalars | ~40 bytes | Period, offset, sigma, invWeightSum |
-| **Total** | **~104 + 16N bytes** | Per-instance footprint |
-
-For ALMA(50), total memory is approximately 900 bytes per instance.
-
 ## Common Pitfalls

 1. **Offset Abuse**: Setting offset to `0.99` creates a filter that barely filters. It tracks price so closely you might as well use `Price[0]`. Setting it to `0.5` makes it a centered moving average (great for smoothing, terrible for trading due to repainting if used as such, but ALMA does not repaint). The magic is in the `0.85` region.