feat: Implement SIMD-optimized Multi-Period Simple Moving Average (SMA) with RingBuffer

- Added SmaVector class for calculating multiple SMAs in parallel using SIMD.
- Introduced RingBuffer class for efficient circular buffer management with running sum.
- Implemented unit tests for RingBuffer to ensure correctness and performance.
- Enhanced Add method in RingBuffer to support bar correction semantics.
- Added methods for calculating Min and Max using SIMD acceleration.
- Improved performance with pinned memory and direct span access for SIMD compatibility.

.editorconfig

@@ -0,0 +1,15 @@
+# EditorConfig for QuanTAlib
+root = true
+
+[*]
+indent_style = space
+indent_size = 4
+end_of_line = lf
+charset = utf-8
+trim_trailing_whitespace = true
+insert_final_newline = true
+
+[*.cs]
+# Suppress SonarQube S3776 - Cognitive Complexity
+# High-performance SIMD code intentionally has complex control flow
+dotnet_diagnostic.S3776.severity = none