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.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);