Refactor tests and improve random number generation handling; update Dema, Ema, Sma, Tema, Wma, and GBM classes for consistency and clarity

lib/averages/tema/Tema.Tests.cs

@@ -1,5 +1,6 @@
 namespace QuanTAlib.Tests;
 
+#pragma warning disable S2245 // Random is acceptable for simulation/testing purposes
 public class TemaTests
 {
     [Fact]
@@ -253,7 +254,7 @@ public class TemaTests
     {
         double[] source = new double[10000];
         double[] output = new double[10000];
-        var rng = new Random(42);
+        var rng = new Random(42); // nosemgrep
         for (int i = 0; i < source.Length; i++)
             source[i] = rng.NextDouble() * 100;
 

lib/averages/tema/Tema.cs

@@ -19,7 +19,9 @@ namespace QuanTAlib;
 /// Uses three EMA instances, each with O(1) update complexity.
 ///
 /// IsHot:
-/// Becomes true when the third EMA converges (approx. 3x EMA convergence time).
+/// Becomes true when the TEMA step response converges to within 5% error.
+/// This happens when the third EMA's error factor drops below ~9% (approx 2.43/alpha steps),
+/// which is faster than the standard EMA convergence (3/alpha steps).
 /// </remarks>
 [SkipLocalsInit]
 public sealed class Tema
@@ -48,7 +50,7 @@ public sealed class Tema
 
     public string Name { get; }
     public TValue Value { get; private set; }
-    public bool IsHot => _state3.IsHot;
+    public bool IsHot => _state3.E <= 0.09;
 
     public Tema(int period)
     {