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/feeds/gbm/GBM.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 QuanTAlib;
+
+// 1. Initialize GBM Generator
+// GBM simulates price movements using Geometric Brownian Motion
+// Parameters: Start Price, Drift (mu), Volatility (sigma)
+
+var gbm = new GBM(startPrice: 100.0, mu: 0.05, sigma: 0.2);
+Console.WriteLine("GBM Generator initialized (Start=100, Drift=5%, Vol=20%)");
+
+#!csharp
+
+// 2. Batch Generation
+// Generate a sequence of bars at once
+// Useful for backtesting or initializing indicators
+
+long startTime = DateTime.UtcNow.Ticks;
+var interval = TimeSpan.FromMinutes(1);
+
+var history = gbm.Fetch(10, startTime, interval);
+
+Console.WriteLine($"Generated {history.Count} bars:");
+for (int i = 0; i < history.Count; i++)
+{
+    Console.WriteLine($"  Bar {i}: Time={history[i].AsDateTime:HH:mm}, Close={history[i].Close:F2}");
+}
+
+#!csharp
+
+// 3. Streaming Generation
+// Simulate real-time data feed bar by bar
+
+Console.WriteLine("\nStreaming new bars:");
+for (int i = 0; i < 3; i++)
+{
+    var bar = gbm.Next(isNew: true);
+    Console.WriteLine($"  New Bar: {bar.Close:F2}");
+}
+
+#!csharp
+
+// 4. Intra-bar Updates
+// Simulate real-time price ticks within a single bar
+// The High/Low will expand, and Close will update
+
+Console.WriteLine("\nSimulating intra-bar updates:");
+
+// Start a new bar
+var liveBar = gbm.Next(isNew: true);
+Console.WriteLine($"  Open: {liveBar.Open:F2}, Close: {liveBar.Close:F2}");
+
+// Simulate 5 ticks
+for (int i = 1; i <= 5; i++)
+{
+    liveBar = gbm.Next(isNew: false);
+    Console.WriteLine($"  Tick {i}: Close={liveBar.Close:F2}, High={liveBar.High:F2}, Low={liveBar.Low:F2}");
+}
+
+// Finalize bar
+liveBar = gbm.Next(isNew: true);
+Console.WriteLine($"  Finalized Previous, Started New: {liveBar.Open:F2}");

lib/feeds/gbm/GBM.md

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+# GBM Class
+
+`GBM` (Geometric Brownian Motion) is a synthetic data generator that simulates realistic financial price movements. It is useful for testing indicators, strategies, and system performance without relying on external data files.
+
+## Key Features
+
+- **Geometric Brownian Motion**: Uses the standard mathematical model for asset price dynamics.
+- **Configurable Parameters**: Control drift (trend) and volatility (noise).
+- **Stateless Design**: Minimal memory footprint; only maintains state needed for continuity.
+- **Dual Modes**: Supports both streaming (bar-by-bar) and batch generation.
+- **Intra-bar Updates**: Can simulate real-time price updates within a single bar.
+
+## Mathematical Model
+
+The price evolution follows the stochastic differential equation:
+
+$$ dS_t = \mu S_t dt + \sigma S_t dW_t $$
+
+Where:
+- $S_t$: Asset price at time $t$
+- $\mu$: Drift (expected return)
+- $\sigma$: Volatility (standard deviation of returns)
+- $W_t$: Wiener process (Brownian motion)
+
+## Class Definition
+
+```csharp
+public class GBM : IFeed
+{
+    public GBM(double startPrice = 100.0, double mu = 0.05, double sigma = 0.2, TimeSpan? defaultTimeframe = null);
+    
+    public TBar Next(bool isNew = true);
+    public TBarSeries Fetch(int count, long startTime, TimeSpan interval);
+}
+```
+
+## Usage
+
+### 1. Initialization
+```csharp
+// Default: Start at 100, 5% drift, 20% volatility
+var gbm = new GBM();
+
+// Custom: Start at 50, 10% drift, 50% volatility
+var volatileGbm = new GBM(startPrice: 50.0, mu: 0.10, sigma: 0.50);
+```
+
+### 2. Streaming Generation
+```csharp
+// Generate a new bar
+var bar = gbm.Next(isNew: true);
+
+// Simulate intra-bar updates (e.g., real-time ticks)
+for (int i = 0; i < 5; i++)
+{
+    var updatedBar = gbm.Next(isNew: false);
+    Console.WriteLine($"Update: {updatedBar.Close}");
+}
+```
+
+### 3. Batch Generation
+```csharp
+long startTime = DateTime.UtcNow.Ticks;
+var interval = TimeSpan.FromMinutes(1);
+
+// Generate 1000 bars
+var history = gbm.Fetch(1000, startTime, interval);