diff --git a/Include/MyIncludes/MathStatistics_Calculator.mqh b/Include/MyIncludes/MathStatistics_Calculator.mqh
new file mode 100644
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+++ b/Include/MyIncludes/MathStatistics_Calculator.mqh
@@ -0,0 +1,91 @@
+//+------------------------------------------------------------------+
+//|                                    MathStatistics_Calculator.mqh |
+//|      Engine for Financial Statistics (Beta, Alpha, Correlation). |
+//|                                        Copyright 2026, xxxxxxxx  |
+//+------------------------------------------------------------------+
+#property copyright "Copyright 2026, xxxxxxxx"
+
+//+------------------------------------------------------------------+
+//|                                                                  |
+//+------------------------------------------------------------------+
+class CMathStatisticsCalculator
+  {
+public:
+                     CMathStatisticsCalculator() {};
+                    ~CMathStatisticsCalculator() {};
+
+   //--- Calculate Beta (Sensitivity to Benchmark)
+   // Beta = Covariance(Asset, Bench) / Variance(Bench)
+   double            CalculateBeta(const double &asset_returns[], const double &bench_returns[])
+     {
+      int n = MathMin(ArraySize(asset_returns), ArraySize(bench_returns));
+      if(n < 2)
+         return 0.0; // Need at least 2 points
+
+      double mean_asset = Mean(asset_returns, n);
+      double mean_bench = Mean(bench_returns, n);
+
+      double cov = Covariance(asset_returns, mean_asset, bench_returns, mean_bench, n);
+      double var = Variance(bench_returns, mean_bench, n);
+
+      if(var == 0.0)
+         return 0.0;
+      return cov / var;
+     }
+
+   //--- Calculate Alpha (Excess Return)
+   // Alpha = AssetReturn - (Beta * BenchReturn)
+   // Usually calculated over a period based on cumulative return or average return
+   // Here we calculate Period Alpha (Total Return logic)
+   double            CalculateAlpha(double asset_total_return, double bench_total_return, double beta)
+     {
+      return asset_total_return - (beta * bench_total_return);
+     }
+
+   //--- Helpers
+   double            Mean(const double &arr[], int n)
+     {
+      double sum = 0;
+      for(int i=0; i<n; i++)
+         sum += arr[i];
+      return sum / n;
+     }
+
+   double            Variance(const double &arr[], double mean, int n)
+     {
+      double sum_sq_diff = 0;
+      for(int i=0; i<n; i++)
+         sum_sq_diff += MathPow(arr[i] - mean, 2);
+      return sum_sq_diff / (n - 1); // Sample Variance
+     }
+
+   double            Covariance(const double &arr1[], double mean1, const double &arr2[], double mean2, int n)
+     {
+      double sum_prod = 0;
+      for(int i=0; i<n; i++)
+         sum_prod += (arr1[i] - mean1) * (arr2[i] - mean2);
+      return sum_prod / (n - 1);
+     }
+
+   // Helper to compute log returns from price array
+   // Returns array size will be price_size - 1
+   void              ComputeReturns(const double &prices[], double &out_returns[])
+     {
+      int total = ArraySize(prices);
+      if(total < 2)
+        {
+         ArrayResize(out_returns, 0);
+         return;
+        }
+
+      ArrayResize(out_returns, total - 1);
+      for(int i=1; i<total; i++)
+        {
+         if(prices[i-1] != 0)
+            out_returns[i-1] = MathLog(prices[i] / prices[i-1]); // Log Return
+         else
+            out_returns[i-1] = 0.0;
+        }
+     }
+  };
+//+------------------------------------------------------------------+