Add TBar, TBarSeries, TSeries, TValue, and IFeed implementations with comprehensive documentation and examples

- Introduced TBar struct for efficient OHLCV data representation.
- Implemented TBarSeries class for high-performance collection of TBar instances using Structure of Arrays (SoA) layout.
- Added TSeries class for time-series data management with zero-copy access.
- Created TValue struct for time-value pairs with implicit conversions.
- Defined IFeed interface for consistent data feed implementations.
- Developed CsvFeed class for loading historical OHLCV data from CSV files.
- Implemented GBM class for generating synthetic financial data using Geometric Brownian Motion.
- Added Quantower project files for Averages indicator with necessary dependencies and configurations.
- Included extensive usage examples and notebooks for TBar, TBarSeries, TSeries, TValue, and feed implementations.

lib/core/simd/SimdExtensions.Notebook.dib

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+#!meta
+
+{"kernelInfo":{"defaultKernelName":"csharp","items":[{"name":"csharp","languageName":"csharp"},{"name":"fsharp","languageName":"F#","aliases":["f#","fs"]},{"name":"html","languageName":"HTML"},{"name":"http","languageName":"HTTP"},{"name":"javascript","languageName":"JavaScript","aliases":["js"]},{"name":"mermaid","languageName":"Mermaid"},{"name":"pwsh","languageName":"PowerShell","aliases":["powershell"]},{"name":"value"}]}}
+
+#!csharp
+
+// Reference the library
+#r "..\..\bin\QuanTAlib.dll"
+
+using System;
+using System.Linq;
+using System.Numerics;
+using QuanTAlib;
+
+// 1. Check Hardware Support
+Console.WriteLine($"SIMD Hardware Acceleration: {Vector.IsHardwareAccelerated}");
+Console.WriteLine($"Vector<double> Count: {Vector<double>.Count}");
+
+#!csharp
+
+// 2. Basic Operations
+// Demonstrate Sum, Min, Max, Average using SIMD extensions
+
+// Define data locally in this cell
+double[] data = new double[1000];
+for (int i = 0; i < data.Length; i++) data[i] = i;
+
+// We use explicit static method calls with .AsSpan() to ensure correct overload resolution
+// and avoid creating top-level ReadOnlySpan variables (which causes CS8345).
+
+double sum = SimdExtensions.SumSIMD(data.AsSpan());
+double minVal = SimdExtensions.MinSIMD(data.AsSpan());
+double maxVal = SimdExtensions.MaxSIMD(data.AsSpan());
+double avg = SimdExtensions.AverageSIMD(data.AsSpan());
+
+Console.WriteLine($"Sum: {sum}");
+Console.WriteLine($"Min: {minVal}");
+Console.WriteLine($"Max: {maxVal}");
+Console.WriteLine($"Average: {avg}");
+
+#!csharp
+
+// 3. Advanced Statistics
+
+double[] dataStats = new double[1000];
+for (int i = 0; i < dataStats.Length; i++) dataStats[i] = i;
+
+double variance = SimdExtensions.VarianceSIMD(dataStats.AsSpan());
+double stdDev = SimdExtensions.StdDevSIMD(dataStats.AsSpan());
+
+Console.WriteLine($"Variance: {variance:F4}");
+Console.WriteLine($"Standard Deviation: {stdDev:F4}");
+
+#!csharp
+
+// 4. Combined Operations
+
+double[] dataComb = new double[1000];
+for (int i = 0; i < dataComb.Length; i++) dataComb[i] = i;
+
+var (min, max) = SimdExtensions.MinMaxSIMD(dataComb.AsSpan());
+Console.WriteLine($"Min: {min}, Max: {max}");
+
+#!csharp
+
+// 5. Performance Comparison (Simple Benchmark)
+
+int size = 1_000_000;
+double[] largeData = new double[size];
+Random rnd = new Random(42);
+for (int i = 0; i < size; i++) largeData[i] = rnd.NextDouble();
+
+// Warmup
+SimdExtensions.SumSIMD(largeData.AsSpan());
+
+// Measure SIMD
+long start = DateTime.UtcNow.Ticks;
+double sumSimd = SimdExtensions.SumSIMD(largeData.AsSpan());
+long end = DateTime.UtcNow.Ticks;
+double simdTime = (end - start) / 10000.0; // ms
+
+// Measure Scalar (LINQ Sum as proxy for scalar loop)
+start = DateTime.UtcNow.Ticks;
+double sumScalar = largeData.Sum();
+end = DateTime.UtcNow.Ticks;
+double scalarTime = (end - start) / 10000.0; // ms
+
+Console.WriteLine($"Array Size: {size:N0}");
+Console.WriteLine($"SIMD Time: {simdTime:F4} ms");
+Console.WriteLine($"Scalar Time: {scalarTime:F4} ms");
+Console.WriteLine($"Speedup: {scalarTime / simdTime:F2}x");