Refactor code formatting and improve consistency across various test files

- Removed unnecessary blank lines in multiple test files to enhance readability.
- Ensured consistent spacing and formatting in the `Trima`, `Usf`, `Vidya`, `Wma`, and `Atr` test classes.
- Updated comments for clarity and consistency in the `Atr` and `Adl` classes.
- Adjusted project files for better structure and maintainability.

lib/statistics/beta/Beta.Tests.cs

@@ -23,7 +23,7 @@ public class BetaTests
     {
         int period = 5;
         var beta = new Beta(period);
-        
+
         // We need period returns.
         // 1st update: initializes prev prices. No return.
         // 2nd update: 1st return.
@@ -45,7 +45,7 @@ public class BetaTests
     {
         // Scenario: Asset returns are exactly 2x Market returns.
         // We need variable market returns to have non-zero variance.
-        
+
         int period = 10;
         var beta = new Beta(period);
 
@@ -66,9 +66,9 @@ public class BetaTests
 
             marketPrice *= (1 + marketReturn);
             assetPrice *= (1 + assetReturn);
-            
+
             TValue result = beta.Update(assetPrice, marketPrice);
-            
+
             if (beta.IsHot)
             {
                 Assert.Equal(2.0, result.Value, precision: 6);
@@ -88,7 +88,7 @@ public class BetaTests
 
         beta.Reset();
         Assert.False(beta.IsHot);
-        
+
         // Re-initialize
         beta.Update(100, 100);
         Assert.False(beta.IsHot);

lib/statistics/beta/Beta.Validation.Tests.cs

@@ -25,13 +25,13 @@ public sealed class BetaValidationTests : IDisposable
     {
         // Generate Market Data (use existing Data)
         var marketQuotes = _data.Data;
-        
+
         // Generate Asset Data correlated to Market
         // Asset Returns = 1.5 * Market Returns + Noise
         var assetQuotes = new List<TBar>();
         double assetPrice = 100;
         double targetBeta = 1.5;
-        
+
         // Use GBM for noise generation (sigma=0.2 gives ~0.0006 per step noise which matches original random noise level)
         var noiseGbm = new GBM(startPrice: 100, mu: 0, sigma: 0.2, seed: 777);
 
@@ -40,13 +40,13 @@ public sealed class BetaValidationTests : IDisposable
         for (int i = 1; i < marketQuotes.Count; i++)
         {
             double marketReturn = (marketQuotes[i].Value - marketQuotes[i-1].Value) / marketQuotes[i-1].Value;
-            
+
             // Get noise from GBM return
             var noiseBar = noiseGbm.Next();
             double noise = (noiseBar.Close - noiseBar.Open) / noiseBar.Open;
-            
+
             double assetReturn = targetBeta * marketReturn + noise;
-            
+
             assetPrice *= (1 + assetReturn);
             assetQuotes.Add(new TBar(marketQuotes[i].Time, assetPrice, assetPrice, assetPrice, assetPrice, 1000));
         }
@@ -73,7 +73,7 @@ public sealed class BetaValidationTests : IDisposable
         // Skip warmup period. Skender Beta needs period returns, so period+1 prices?
         // Skender results align with input quotes.
         // First valid value should be at index 'period'.
-        
+
         // We verify the last 100 values
         int count = qlBeta.Count;
         int skip = period + 5; // Safety margin
@@ -82,7 +82,7 @@ public sealed class BetaValidationTests : IDisposable
         {
             double sk = (skenderBeta[i].Beta ?? 0);
             double ql = qlBeta[i];
-            
+
             // Skender might return null/0 for warmup.
             if (Math.Abs(sk) > 1e-10)
             {

lib/statistics/beta/Beta.cs

@@ -9,14 +9,14 @@ namespace QuanTAlib;
 /// <remarks>
 /// Beta is calculated as the covariance of the asset's returns and the market's returns,
 /// divided by the variance of the market's returns.
-/// 
+///
 /// Formula:
 /// Beta = Cov(Ra, Rm) / Var(Rm)
-/// 
+///
 /// Where:
 /// Ra = Return of Asset
 /// Rm = Return of Market
-/// 
+///
 /// This implementation uses the O(1) slope formula for linear regression of Ra vs Rm:
 /// Beta = (N * Sum(Ra*Rm) - Sum(Ra) * Sum(Rm)) / (N * Sum(Rm^2) - Sum(Rm)^2)
 /// </remarks>
@@ -25,7 +25,7 @@ public sealed class Beta : AbstractBase
 {
     private readonly RingBuffer _returnsAsset;
     private readonly RingBuffer _returnsMarket;
-    
+
     private double _prevAsset;
     private double _prevMarket;
     private double _p_prevAsset;