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/trends/alma/Alma.Quantower.cs

@@ -56,7 +56,7 @@ public class AlmaIndicator : Indicator, IWatchlistIndicator
     protected override void OnUpdate(UpdateArgs args)
     {
         var item = HistoricalData[Count - 1, SeekOriginHistory.Begin];
-        
+
         TValue result = ma!.Update(new TValue(item.TimeLeft.Ticks, _priceSelector!(item)), isNew: args.IsNewBar());
 
         Series!.SetValue(result.Value, ma.IsHot, ShowColdValues);

lib/trends/alma/Alma.Tests.cs

@@ -255,10 +255,10 @@ public class AlmaTests
     {
         var alma = new Alma(10);
         alma.Update(new TValue(DateTime.UtcNow, 100));
-        
+
         var r1 = alma.Update(new TValue(DateTime.UtcNow, double.NaN));
         var r2 = alma.Update(new TValue(DateTime.UtcNow, double.NaN));
-        
+
         Assert.True(double.IsFinite(r1.Value));
         Assert.True(double.IsFinite(r2.Value));
     }
@@ -271,7 +271,7 @@ public class AlmaTests
         var gbm = new GBM(startPrice: 100, mu: 0.05, sigma: 0.2, seed: 123);
         var bars = gbm.Fetch(1000, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
         var series = bars.Close;
-        
+
         // 1. Batch Mode
         var batchSeries = Alma.Batch(series, period);
         double expected = batchSeries.Last.Value;

lib/trends/alma/Alma.cs

@@ -247,8 +247,8 @@ public sealed class Alma : AbstractBase, IDisposable
         // Precompute weights
         // Use stackalloc for small periods to avoid heap allocation, ArrayPool for large
         double[]? weightsArray = period > 256 ? ArrayPool<double>.Shared.Rent(period) : null;
-        Span<double> weights = period <= 256 
-            ? stackalloc double[period] 
+        Span<double> weights = period <= 256
+            ? stackalloc double[period]
             : weightsArray!.AsSpan(0, period);
 
         double m = offset * (period - 1);
@@ -266,8 +266,8 @@ public sealed class Alma : AbstractBase, IDisposable
 
         // Buffer for sliding window
         double[]? bufferArray = period > 256 ? ArrayPool<double>.Shared.Rent(period) : null;
-        Span<double> buffer = period <= 256 
-            ? stackalloc double[period] 
+        Span<double> buffer = period <= 256
+            ? stackalloc double[period]
             : bufferArray!.AsSpan(0, period);
 
         int bufferIdx = 0;
@@ -288,7 +288,7 @@ public sealed class Alma : AbstractBase, IDisposable
                 // Add to circular buffer
                 buffer[bufferIdx] = val;
                 bufferIdx = (bufferIdx + 1) % period;
-                
+
                 if (count < period)
                 {
                     count++;

lib/trends/bessel/Bessel.Quantower.cs

@@ -49,7 +49,7 @@ public class BesselIndicator : Indicator, IWatchlistIndicator
     protected override void OnUpdate(UpdateArgs args)
     {
         var item = HistoricalData[Count - 1, SeekOriginHistory.Begin];
-        
+
         TValue result = _filter!.Update(new TValue(item.TimeLeft.Ticks, _priceSelector!(item)), isNew: args.IsNewBar());
 
         Series!.SetValue(result.Value, _filter.IsHot, ShowColdValues);

lib/trends/bessel/Bessel.Tests.cs

@@ -12,7 +12,7 @@ public class BesselTests
 
         var bessel = new Bessel(2);
         Assert.NotNull(bessel);
-        
+
         var bessel14 = new Bessel(14);
         Assert.NotNull(bessel14);
     }

lib/trends/bessel/Bessel.cs

@@ -9,7 +9,7 @@ namespace QuanTAlib;
 /// <remarks>
 /// Bessel filter is a 2nd-order IIR low-pass filter with maximally flat group delay,
 /// adapted from John Ehlers' work for financial time series.
-/// 
+///
 /// Coefficients for a given length L:
 /// a  = exp(-PI / L)
 /// b  = 2 * a * cos(1.738 * PI / L)

lib/trends/bilateral/Bilateral.Quantower.Tests.cs

@@ -166,7 +166,7 @@ public class BilateralIndicatorTests
         indicator.Period = 20;
         indicator.SigmaSRatio = 1.0;
         indicator.SigmaRMult = 2.0;
-        
+
         Assert.Equal(20, indicator.Period);
         Assert.Equal(1.0, indicator.SigmaSRatio);
         Assert.Equal(2.0, indicator.SigmaRMult);

lib/trends/bilateral/Bilateral.Quantower.cs

@@ -56,7 +56,7 @@ public class BilateralIndicator : Indicator, IWatchlistIndicator
     protected override void OnUpdate(UpdateArgs args)
     {
         var item = HistoricalData[Count - 1, SeekOriginHistory.Begin];
-        
+
         TValue result = _bilateral!.Update(new TValue(item.TimeLeft.Ticks, _priceSelector!(item)), isNew: args.IsNewBar());
 
         Series!.SetValue(result.Value, _bilateral.IsHot, ShowColdValues);

lib/trends/bilateral/Bilateral.Tests.cs

@@ -23,13 +23,13 @@ public class BilateralTests
     public void IsHot_BecomesTrueWhenBufferFull()
     {
         var indicator = new Bilateral(3);
-        
+
         indicator.Update(new TValue(DateTime.UtcNow, 1));
         Assert.False(indicator.IsHot);
-        
+
         indicator.Update(new TValue(DateTime.UtcNow, 2));
         Assert.False(indicator.IsHot);
-        
+
         indicator.Update(new TValue(DateTime.UtcNow, 3));
         Assert.True(indicator.IsHot);
     }
@@ -42,13 +42,13 @@ public class BilateralTests
         // If sigma_r is high, range weights are ~1.
         // If sigma_s is high, spatial weights are ~1.
         // Then it becomes a simple average.
-        
+
         var indicator = new Bilateral(3, sigmaSRatio: 100, sigmaRMult: 100);
-        
+
         indicator.Update(new TValue(DateTime.UtcNow, 1));
         indicator.Update(new TValue(DateTime.UtcNow, 2));
         var result = indicator.Update(new TValue(DateTime.UtcNow, 3));
-        
+
         // Expected: (1+2+3)/3 = 2
         Assert.Equal(2.0, result.Value, 1);
     }
@@ -57,11 +57,11 @@ public class BilateralTests
     public void Update_HandlesNaN()
     {
         var indicator = new Bilateral(3);
-        
+
         indicator.Update(new TValue(DateTime.UtcNow, 1));
         indicator.Update(new TValue(DateTime.UtcNow, double.NaN)); // Should use 1
         var result = indicator.Update(new TValue(DateTime.UtcNow, 3));
-        
+
         // Buffer: [1, 1, 3]
         // StDev of [1, 1, 3]: Mean=1.66, Var=((1-1.66)^2 + (1-1.66)^2 + (3-1.66)^2)/3 = (0.44 + 0.44 + 1.77)/3 = 0.88. StDev ~ 0.94
         // Calculation will proceed with these values.
@@ -73,23 +73,23 @@ public class BilateralTests
     public void Update_IsNew_False_UpdatesCorrectly()
     {
         var indicator = new Bilateral(3);
-        
+
         indicator.Update(new TValue(DateTime.UtcNow, 1));
         indicator.Update(new TValue(DateTime.UtcNow, 2));
-        
+
         // Update with 3, isNew=true
         indicator.Update(new TValue(DateTime.UtcNow, 3), isNew: true);
-        
+
         // Update with 4, isNew=false (correction)
         var res2 = indicator.Update(new TValue(DateTime.UtcNow, 4), isNew: false);
-        
+
         // Verify state was updated
         // If we had updated with 4 directly: [1, 2, 4]
         var indicator2 = new Bilateral(3);
         indicator2.Update(new TValue(DateTime.UtcNow, 1));
         indicator2.Update(new TValue(DateTime.UtcNow, 2));
         var resExpected = indicator2.Update(new TValue(DateTime.UtcNow, 4));
-        
+
         Assert.Equal(resExpected.Value, res2.Value);
     }
 
@@ -100,9 +100,9 @@ public class BilateralTests
         indicator.Update(new TValue(DateTime.UtcNow, 1));
         indicator.Update(new TValue(DateTime.UtcNow, 2));
         indicator.Update(new TValue(DateTime.UtcNow, 3));
-        
+
         indicator.Reset();
-        
+
         Assert.False(indicator.IsHot);
         Assert.Equal(1, indicator.Update(new TValue(DateTime.UtcNow, 1)).Value); // Center val 1, weights 0? No, center val is returned if weights 0.
     }
@@ -112,10 +112,10 @@ public class BilateralTests
     {
         // Test edge case: calling Update with isNew:false before any isNew:true
         var indicator = new Bilateral(3);
-        
+
         // This should not crash - buffer is empty, so we treat it as first value
         var result = indicator.Update(new TValue(DateTime.UtcNow, 5.0), isNew: false);
-        
+
         // Should have added the value to the buffer
         Assert.True(double.IsFinite(result.Value));
         Assert.Equal(5.0, result.Value); // Single value, so result is that value
@@ -126,18 +126,18 @@ public class BilateralTests
     {
         // Test edge case: calling Update with isNew:false after Reset
         var indicator = new Bilateral(3);
-        
+
         indicator.Update(new TValue(DateTime.UtcNow, 1));
         indicator.Update(new TValue(DateTime.UtcNow, 2));
         indicator.Reset();
-        
+
         // Buffer is now empty, isNew:false should not crash
         var result = indicator.Update(new TValue(DateTime.UtcNow, 7.0), isNew: false);
-        
+
         Assert.True(double.IsFinite(result.Value));
         Assert.Equal(7.0, result.Value);
     }
-    
+
     [Fact]
     public void AllModes_ProduceSameResult()
     {

lib/trends/bilateral/Bilateral.Validation.Tests.cs

@@ -115,12 +115,12 @@ public sealed class BilateralValidationTests : IDisposable
     {
         var reference = new BilateralReference(period, sigmaSRatio, sigmaRMult);
         var results = new List<double>();
-        
+
         foreach (var item in _testData.Data)
         {
             results.Add(reference.Update(item.Value));
         }
-        
+
         return results;
     }
 
@@ -149,7 +149,7 @@ public sealed class BilateralValidationTests : IDisposable
             if (_history.Count == 0) return double.NaN;
 
             double sigmaS = Math.Max(_length * _sigmaSRatio, 1e-10);
-            
+
             // Calculate StDev of current window
             double stdev = CalculateStDev(_history);
             double sigmaR = Math.Max(stdev * _sigmaRMult, 1e-10);
@@ -161,18 +161,18 @@ public sealed class BilateralValidationTests : IDisposable
             // Iterate through history
             // i=0 is newest (index Count-1)
             int loopLen = _history.Count;
-            
+
             for (int i = 0; i < loopLen; i++)
             {
                 double valI = _history[_history.Count - 1 - i];
                 double diffSpatial = i;
                 double diffRange = centerVal - valI;
-                
+
                 double weightSpatial = Math.Exp(-(diffSpatial * diffSpatial) / (2.0 * sigmaS * sigmaS));
                 double weightRange = Math.Exp(-(diffRange * diffRange) / (2.0 * sigmaR * sigmaR));
-                
+
                 double weight = weightSpatial * weightRange;
-                
+
                 sumWeights += weight;
                 sumWeightedSrc += weight * valI;
             }
@@ -183,7 +183,7 @@ public sealed class BilateralValidationTests : IDisposable
         private static double CalculateStDev(IReadOnlyList<double> values)
         {
             if (values.Count < 2) return 0;
-            
+
             double avg = values.Average();
             double sumSqDiff = values.Sum(d => (d - avg) * (d - avg));
             // Population StDev to match implementation

lib/trends/bilateral/Bilateral.cs

@@ -57,7 +57,7 @@ public sealed class Bilateral : AbstractBase
         PrecalculateSpatialWeights();
     }
 
-    public Bilateral(ITValuePublisher source, int period, double sigmaSRatio = 0.5, double sigmaRMult = 1.0) 
+    public Bilateral(ITValuePublisher source, int period, double sigmaSRatio = 0.5, double sigmaRMult = 1.0)
         : this(period, sigmaSRatio, sigmaRMult)
     {
         source.Pub += _handler;
@@ -139,7 +139,7 @@ public sealed class Bilateral : AbstractBase
             Update(new TValue(source.Times[i], source.Values[i]));
             vSpan[i] = Last.Value;
         }
-        
+
         return new TSeries(t, v);
     }
 
@@ -149,10 +149,10 @@ public sealed class Bilateral : AbstractBase
         if (isNew)
         {
             _p_state = _state;
-            
+
             double val = GetValidValue(input.Value);
             double removed = _buffer.Add(val);
-            
+
             _state.SumSq += (val * val);
             if (_buffer.IsFull)
             {
@@ -163,12 +163,12 @@ public sealed class Bilateral : AbstractBase
         {
             // Preserve SumSq as it tracks the buffer which is already at T
             double currentSumSq = _state.SumSq;
-            
+
             _state = _p_state;
             _state.SumSq = currentSumSq;
-            
+
             double val = GetValidValue(input.Value);
-            
+
             // Defensive check: if buffer is empty, treat as first value
             if (_buffer.Count == 0)
             {
@@ -179,7 +179,7 @@ public sealed class Bilateral : AbstractBase
             {
                 double oldNewest = _buffer.Newest; // Get current newest before overwriting
                 _buffer.UpdateNewest(val);
-                
+
                 _state.SumSq -= (oldNewest * oldNewest);
                 _state.SumSq += (val * val);
             }
@@ -210,7 +210,7 @@ public sealed class Bilateral : AbstractBase
         // Calculate StDev
         double count = _buffer.Count;
         double sum = _buffer.Sum;
-        
+
         // Variance = (SumSq - (Sum*Sum)/N) / N
         // Use Math.Max(0, ...) to handle potential floating point negative zero
         double variance = Math.Max(0, (_state.SumSq - (sum * sum) / count) / count);
@@ -226,12 +226,12 @@ public sealed class Bilateral : AbstractBase
         // Iterate from 0 to Count-1
         // i=0 corresponds to Newest (src[0])
         // i corresponds to buffer[Count - 1 - i]
-        
+
         // Use InternalBuffer to avoid allocations from GetSpan() when wrapped
         ReadOnlySpan<double> buffer = _buffer.InternalBuffer;
         int capacity = _buffer.Capacity;
         int startIndex = _buffer.StartIndex;
-        
+
         // Newest element index
         int newestIndex = (startIndex + (int)count - 1) % capacity;
 
@@ -241,16 +241,16 @@ public sealed class Bilateral : AbstractBase
             // (newestIndex - i) handling wrap-around
             int idx = newestIndex - i;
             if (idx < 0) idx += capacity;
-            
+
             double val = buffer[idx];
             double diffRange = centerVal - val;
-            
+
             // weight_spatial = _spatialWeights[i]
             // weight_range = exp(-(diff^2) / (2 * sigma_r^2))
-            
+
             double weightRange = Math.Exp(-(diffRange * diffRange) / twoSigmaRSq);
             double weight = _spatialWeights[i] * weightRange;
-            
+
             sumWeights += weight;
             sumWeightedSrc += weight * val;
         }
@@ -284,7 +284,7 @@ public sealed class Bilateral : AbstractBase
 
         if (destination.Length < source.Length)
             throw new ArgumentException("Destination must have length >= source length", nameof(destination));
-            
+
         // Precalculate spatial weights
         double sigmaS = Math.Max(period * sigmaSRatio, 1e-10);
         double twoSigmaSSq = 2.0 * sigmaS * sigmaS;
@@ -306,7 +306,7 @@ public sealed class Bilateral : AbstractBase
                 break;
             }
         }
-        
+
         // If all NaNs, fill with NaN
         if (double.IsNaN(lastValid))
         {
@@ -340,11 +340,11 @@ public sealed class Bilateral : AbstractBase
                 sum -= removed;
                 sumSq -= removed * removed;
             }
-            
+
             window[windowIdx] = val;
             sum += val;
             sumSq += val * val;
-            
+
             int currentNewestIdx = windowIdx;
             windowIdx = (windowIdx + 1) % period;
             if (count < period) count++;
@@ -367,13 +367,13 @@ public sealed class Bilateral : AbstractBase
                 // k=1 is previous...
                 int idx = currentNewestIdx - k;
                 if (idx < 0) idx += period;
-                
+
                 double wVal = window[idx];
                 double diffRange = centerVal - wVal;
-                
+
                 double weightRange = Math.Exp(-(diffRange * diffRange) / twoSigmaRSq);
                 double weight = spatialWeights[k] * weightRange;
-                
+
                 sumWeights += weight;
                 sumWeightedSrc += weight * wVal;
             }

lib/trends/blma/Blma.Quantower.cs

@@ -50,7 +50,7 @@ public class BlmaIndicator : Indicator, IWatchlistIndicator
     protected override void OnUpdate(UpdateArgs args)
     {
         var item = HistoricalData[Count - 1, SeekOriginHistory.Begin];
-        
+
         TValue result = _ma!.Update(new TValue(item.TimeLeft.Ticks, _priceSelector!(item)), isNew: args.IsNewBar());
 
         _series!.SetValue(result.Value, _ma.IsHot, ShowColdValues);

lib/trends/blma/Blma.Tests.cs

@@ -33,15 +33,15 @@ public class BlmaTests
     {
         var blma = new Blma(3);
         var input = new[] { 10.0, 20.0, 30.0 };
-        
+
         // Bar 1: Count=1. Weights for n=1: [1]. Result = 10.
         var r1 = blma.Update(new TValue(DateTime.UtcNow, input[0]));
         Assert.Equal(10.0, r1.Value);
-        
+
         // Bar 2: Count=2. Weights for n=2 sum to 0. Fallback to average: (10+20)/2 = 15.
         var r2 = blma.Update(new TValue(DateTime.UtcNow, input[1]));
         Assert.Equal(15.0, r2.Value);
-        
+
         // Bar 3: Count=3. Weights [0, 1, 0]. Sum=1. Result=20.
         var r3 = blma.Update(new TValue(DateTime.UtcNow, input[2]));
         Assert.Equal(20.0, r3.Value, 1e-6);
@@ -82,45 +82,45 @@ public class BlmaTests
     public void NaN_Handling()
     {
         var blma = new Blma(5);
-        
+
         blma.Update(new TValue(DateTime.UtcNow, 10));
         blma.Update(new TValue(DateTime.UtcNow, 20));
         // For N=2, weights sum to 0. Fallback to average: (10+20)/2 = 15.
-        
+
         var result = blma.Update(new TValue(DateTime.UtcNow, double.NaN));
-        
+
         Assert.Equal(15.0, result.Value); // Should return last valid value
         Assert.Equal(15.0, blma.Last.Value); // Should retain last valid value
     }
-    
+
     [Fact]
     public void IsNew_Behavior()
     {
         var blma = new Blma(3);
-        
+
         // Bar 1
         blma.Update(new TValue(DateTime.UtcNow, 10), isNew: true);
-        
+
         // Bar 2
         blma.Update(new TValue(DateTime.UtcNow, 20), isNew: true);
-        
+
         // Bar 3 (Update)
         blma.Update(new TValue(DateTime.UtcNow, 30), isNew: true);
         var val1 = blma.Last.Value;
-        
+
         // Bar 3 (Correction)
         blma.Update(new TValue(DateTime.UtcNow, 40), isNew: false);
         var val2 = blma.Last.Value;
-        
+
         // For Blackman window, the newest value (index N-1) has weight 0.
         // So changing the newest value does NOT change the current result.
         Assert.Equal(val1, val2);
-        
+
         // However, the internal buffer MUST be updated.
         // Case A: Bar 3 = 40 (current state)
         blma.Update(new TValue(DateTime.UtcNow, 100), isNew: true);
         var valWith40 = blma.Last.Value;
-        
+
         // Case B: Reconstruct scenario with Bar 3 = 30
         var blma2 = new Blma(3);
         blma2.Update(new TValue(DateTime.UtcNow, 10), isNew: true);
@@ -128,7 +128,7 @@ public class BlmaTests
         blma2.Update(new TValue(DateTime.UtcNow, 30), isNew: true);
         blma2.Update(new TValue(DateTime.UtcNow, 100), isNew: true);
         var valWith30 = blma2.Last.Value;
-        
+
         Assert.NotEqual(valWith30, valWith40);
     }
 
@@ -138,12 +138,12 @@ public class BlmaTests
         var blma = new Blma(5);
         var input = new double[] { 1, 2, 3, 4, 5 };
         var timestamps = new List<DateTime>();
-        
-        blma.Pub += (object? sender, TValueEventArgs args) => timestamps.Add(args.Value.AsDateTime);
 
-        
+        blma.Pub += (object? sender, in TValueEventArgs args) => timestamps.Add(args.Value.AsDateTime);
+
+
         blma.Prime(input);
-        
+
         Assert.Equal(input.Length, timestamps.Count);
         // Verify timestamps are unique and increasing
         for (int i = 1; i < timestamps.Count; i++)
@@ -157,19 +157,19 @@ public class BlmaTests
     {
         var blma = new Blma(5);
         var now = DateTime.UtcNow;
-        TValue[] input = 
-        [ 
-            new(now, 1), 
-            new(now.AddMinutes(1), 2), 
-            new(now.AddMinutes(2), 3) 
+        TValue[] input =
+        [
+            new(now, 1),
+            new(now.AddMinutes(1), 2),
+            new(now.AddMinutes(2), 3)
         ];
         var timestamps = new List<DateTime>();
-        
-        blma.Pub += (object? sender, TValueEventArgs args) => timestamps.Add(args.Value.AsDateTime);
 
-        
+        blma.Pub += (object? sender, in TValueEventArgs args) => timestamps.Add(args.Value.AsDateTime);
+
+
         blma.Prime(input);
-        
+
         Assert.Equal(input.Length, timestamps.Count);
         Assert.Equal(input[0].AsDateTime, timestamps[0]);
         Assert.Equal(input[1].AsDateTime, timestamps[1]);

lib/trends/blma/Blma.Validation.Tests.cs

@@ -50,15 +50,15 @@ public class BlmaValidationTests
         for (int i = 0; i < source.Count; i++)
         {
             buffer.Add(source[i].Value);
-            
+
             // PineScript logic:
             // int p = math.min(bar_index + 1, period)
             int p = Math.Min(buffer.Count, period);
-            
+
             // Calculate weights
             var weights = new double[p];
             double totalWeight = 0;
-            
+
             if (p == 1)
             {
                 weights[0] = 1.0;
@@ -88,15 +88,15 @@ public class BlmaValidationTests
             // float price = source[i] (where source[0] is newest)
             // float w = array.get(weights, i)
             // So weights[0] * newest, weights[1] * 2nd newest...
-            
+
             // My C# buffer is chronological (0 is oldest).
             // So buffer[buffer.Count - 1] is newest.
             // buffer[buffer.Count - 1 - j] is j-th lag.
-            
+
             // Wait, in Blma.cs I implemented:
             // sum += buffer[i] * weights[i] (where buffer[0] is oldest)
             // So weights[0] * oldest.
-            
+
             // PineScript: weights[0] * newest.
             // Since Blackman window is symmetric, weights[0] == weights[p-1].
             // So weights[0] * newest == weights[p-1] * newest (if symmetric).
@@ -111,11 +111,11 @@ public class BlmaValidationTests
             // cos(4pi * (1-r)) = cos(4pi - 4pi*r) = cos(4pi*r).
             // So yes, w(j) == w(p-1-j).
             // So applying weights[0] to newest or oldest doesn't matter for the sum.
-            
+
             // However, I should match my implementation in Blma.cs.
             // In Blma.cs: sum += buffer[i] * weights[i] (buffer[0] is oldest).
             // So weights[0] * oldest.
-            
+
             // In this reference implementation, let's do the same.
             // Use the last p elements of buffer.
             int start = buffer.Count - p;

lib/trends/blma/Blma.cs

@@ -29,7 +29,7 @@ public sealed class Blma : AbstractBase, IDisposable
         WarmupPeriod = period;
         _buffer = new RingBuffer(period);
         _weights = new double[period];
-        
+
         // Pre-calculate weights for the full period
         _weightSum = CalculateWeights(period, _weights);
         _handler = Handle;
@@ -102,7 +102,7 @@ public sealed class Blma : AbstractBase, IDisposable
             {
                 Span<double> currentWeights = stackalloc double[count];
                 double currentWeightSum = CalculateWeights(count, currentWeights);
-                
+
                 // Fallback for cases where weights sum to zero (e.g. N=2)
                 result = Math.Abs(currentWeightSum) < double.Epsilon
                     ? _buffer.Average()
@@ -130,7 +130,7 @@ public sealed class Blma : AbstractBase, IDisposable
         var result = new TSeries();
         Span<double> output = new double[source.Count];
         Calculate(source.Values, output, _period);
-        
+
         for (int i = 0; i < source.Count; i++)
         {
             result.Add(new TValue(source[i].Time, output[i]));
@@ -182,7 +182,7 @@ public sealed class Blma : AbstractBase, IDisposable
         int start = buffer.StartIndex;
         int count = buffer.Count;
         int capacity = buffer.Capacity;
-        
+
         if (start + count <= capacity)
         {
             return buffer.InternalBuffer.Slice(start, count).DotProduct(weights);
@@ -219,7 +219,7 @@ public sealed class Blma : AbstractBase, IDisposable
         for (int i = 0; i < source.Length; i++)
         {
             int count = Math.Min(i + 1, period);
-            
+
             if (count < period)
             {
                 // Warmup: dynamic weights
@@ -231,7 +231,7 @@ public sealed class Blma : AbstractBase, IDisposable
                 {
                     Span<double> currentWeights = warmupWeightsBuffer.Slice(0, count);
                     double currentWeightSum = CalculateWeights(count, currentWeights);
-                    
+
                     if (Math.Abs(currentWeightSum) < double.Epsilon)
                     {
                         // Fallback for zero sum weights (e.g. N=2)

lib/trends/butter/Butter.Quantower.cs

@@ -50,7 +50,7 @@ public class ButterIndicator : Indicator, IWatchlistIndicator
     protected override void OnUpdate(UpdateArgs args)
     {
         var item = HistoricalData[Count - 1, SeekOriginHistory.Begin];
-        
+
         TValue result = _ma!.Update(new TValue(item.TimeLeft.Ticks, _priceSelector!(item)), isNew: args.IsNewBar());
 
         _series!.SetValue(result.Value, _ma.IsHot, ShowColdValues);

lib/trends/butter/Butter.Tests.cs

@@ -45,7 +45,7 @@ public class ButterTests
         butter.Update(new TValue(DateTime.UtcNow, 100));
         butter.Update(new TValue(DateTime.UtcNow, 101));
         Assert.True(butter.IsHot);
-        
+
         butter.Reset();
         Assert.False(butter.IsHot);
     }
@@ -107,30 +107,30 @@ public class ButterTests
         Assert.Equal(expected, streamingResult, 1e-9);
         Assert.Equal(expected, eventingResult, 1e-9);
     }
-    
+
     [Fact]
     public void IterativeCorrections_RestoreToOriginalState()
     {
         int period = 10;
         var butter = new Butter(period);
-        
+
         // Feed 10 values
         for (int i = 0; i < 10; i++)
         {
             butter.Update(new TValue(DateTime.UtcNow, 100 + i));
         }
-        
+
         double expected = butter.Last.Value;
-        
+
         // Feed 5 updates with isNew=false
         for (int i = 0; i < 5; i++)
         {
             butter.Update(new TValue(DateTime.UtcNow, 200 + i), isNew: false);
         }
-        
+
         // Feed original 10th value again with isNew=false
         var result = butter.Update(new TValue(DateTime.UtcNow, 109), isNew: false);
-        
+
         Assert.Equal(expected, result.Value, 1e-9);
     }
 }

lib/trends/butter/Butter.Validation.Tests.cs

@@ -78,7 +78,7 @@ public class ButterValidationTests
 
         // Compare
         Assert.Equal(quantalibResult.Count, ooplesValues.Count);
-        
+
         // Check last 100 bars
         for (int i = quantalibResult.Count - 100; i < quantalibResult.Count; i++)
         {
@@ -91,7 +91,7 @@ public class ButterValidationTests
     private static IReadOnlyList<double> CalculateReference(TSeries source, int period)
     {
         var result = new List<double>();
-        
+
         // PineScript logic:
         // float pi = math.pi
         // int safe_length = math.max(length, 2)
@@ -105,7 +105,7 @@ public class ButterValidationTests
         // float b0 = (1.0 - cos_omega) / 2.0
         // float b1 = 1.0 - cos_omega
         // float b2 = (1.0 - cos_omega) / 2.0
-        
+
         int safe_length = Math.Max(period, 2);
         double omega = 2.0 * Math.PI / safe_length;
         double sin_omega = Math.Sin(omega);
@@ -117,22 +117,22 @@ public class ButterValidationTests
         double b0 = (1.0 - cos_omega) / 2.0;
         double b1 = 1.0 - cos_omega;
         double b2 = (1.0 - cos_omega) / 2.0;
-        
+
         double filt = 0;
         double filt1 = 0;
         double filt2 = 0;
-        
+
         // Need to track history for src[1], src[2]
         // In PineScript, src[1] is previous bar's src.
         // We iterate through source.
-        
+
         double src1 = 0;
         double src2 = 0;
 
         for (int i = 0; i < source.Count; i++)
         {
             double src = source[i].Value;
-            
+
             // if bar_index < 2
             //     filt := nz(src, 0.0)
             if (i < 2)
@@ -142,38 +142,38 @@ public class ButterValidationTests
                 // In PineScript, src[1] at index 0 is NaN (nz -> 0.0 or something?)
                 // Actually, nz(src, 0.0) means if src is NaN, use 0.0.
                 // But here src is valid.
-                
+
                 // At i=0: src[1] is NaN, src[2] is NaN.
                 // At i=1: src[1] is src[i-1], src[2] is NaN.
-                
+
                 // But the PineScript code says:
                 // if bar_index < 2: filt := nz(src, 0.0)
                 // else: ... formula ...
-                
+
                 // So for i=0 and i=1, filt = src.
             }
             else
             {
                 // float ssrc = nz(src, src[1]) -> if src is NaN use src[1]. Assuming src is valid.
                 double ssrc = src;
-                
+
                 // float src1 = nz(src[1], ssrc) -> previous src.
                 // float src2 = nz(src[2], src1) -> 2nd previous src.
-                
+
                 // float filt1 = nz(filt[1], ssrc) -> previous filt.
                 // float filt2 = nz(filt[2], filt1) -> 2nd previous filt.
-                
+
                 // filt := (b0 * ssrc + b1 * src1 + b2 * src2 - a1 * filt1 - a2 * filt2) / a0
-                
+
                 filt = (b0 * ssrc + b1 * src1 + b2 * src2 - a1 * filt1 - a2 * filt2) / a0;
             }
-            
+
             result.Add(filt);
-            
+
             // Update history
             src2 = src1;
             src1 = src;
-            
+
             filt2 = filt1;
             filt1 = filt;
         }

lib/trends/butter/Butter.cs

@@ -58,7 +58,7 @@ public sealed class Butter : AbstractBase
         double a0 = 1.0 + alpha;
         a1 = -2.0 * cosOmega;
         a2 = 1.0 - alpha;
-        
+
         b0 = (1.0 - cosOmega) / 2.0;
         b1 = 1.0 - cosOmega;
         b2 = (1.0 - cosOmega) / 2.0;
@@ -123,7 +123,7 @@ public sealed class Butter : AbstractBase
         _state.X1 = x;
         _state.Y2 = _state.Y1;
         _state.Y1 = y;
-        
+
         if (_state.Count < 2)
         {
             _state.Count++;
@@ -140,7 +140,7 @@ public sealed class Butter : AbstractBase
         var result = new TSeries();
         Span<double> output = new double[source.Count];
         Calculate(source.Values, output, _period, double.NaN);
-        
+
         for (int i = 0; i < source.Count; i++)
         {
             result.Add(new TValue(source[i].Time, output[i]));
@@ -148,11 +148,11 @@ public sealed class Butter : AbstractBase
 
         // Restore state
         Reset();
-        
+
         // Replay a reasonable amount (e.g. 4*period) for convergence of IIR state.
         int replayCount = Math.Min(source.Count, 4 * _period);
         int start = source.Count - replayCount;
-        
+
         for (int i = start; i < source.Count; i++)
         {
             Update(source[i]);

lib/trends/conv/Conv.Quantower.cs

@@ -66,7 +66,7 @@ public class ConvIndicator : Indicator, IWatchlistIndicator
     protected override void OnUpdate(UpdateArgs args)
     {
         var item = HistoricalData[Count - 1, SeekOriginHistory.Begin];
-        
+
         TValue result = _conv!.Update(new TValue(item.TimeLeft.Ticks, _priceSelector!(item)), isNew: args.IsNewBar());
 
         Series!.SetValue(result.Value, _conv.IsHot, ShowColdValues);

lib/trends/conv/Conv.Tests.cs

@@ -171,7 +171,7 @@ public class ConvTests
         var gbm = new GBM(startPrice: 100, mu: 0.05, sigma: 0.2, seed: 123);
         var bars = gbm.Fetch(1000, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
         var series = bars.Close;
-        
+
         // 1. Batch Mode
         var batchSeries = Conv.Batch(series, kernel);
         double expected = batchSeries.Last.Value;

lib/trends/dema/Dema.Quantower.cs

@@ -50,7 +50,7 @@ public class DemaIndicator : Indicator, IWatchlistIndicator
     protected override void OnUpdate(UpdateArgs args)
     {
         var item = HistoricalData[Count - 1, SeekOriginHistory.Begin];
-        
+
         TValue result = ma!.Update(new TValue(item.TimeLeft.Ticks, _priceSelector!(item)), isNew: args.IsNewBar());
 
         Series!.SetValue(result.Value, ma.IsHot, ShowColdValues);

lib/trends/dema/Dema.Tests.cs

@@ -22,7 +22,7 @@ public class DemaTests
             var tVal = new TValue(bar.Time, bar.Close);
 
             var dVal = dema.Update(tVal);
-            
+
             var e1Val = ema1.Update(tVal);
             var e2Val = ema2.Update(e1Val);
             double expected = 2 * e1Val.Value - e2Val.Value;
@@ -48,7 +48,7 @@ public class DemaTests
         // Act
         var demaSeries = Dema.Calculate(source, period);
         var demaObj = new Dema(period);
-        
+
         // Assert
         for (int i = 0; i < source.Count; i++)
         {
@@ -134,7 +134,7 @@ public class DemaTests
         // Act
         var demaSeries = Dema.Calculate(source, alpha);
         var demaObj = new Dema(alpha);
-        
+
         // Assert
         for (int i = 0; i < source.Count; i++)
         {
@@ -193,9 +193,9 @@ public class DemaTests
         var dema = new Dema(10);
         dema.Update(new TValue(DateTime.UtcNow, 100));
         dema.Update(new TValue(DateTime.UtcNow, 110));
-        
+
         dema.Reset();
-        
+
         Assert.Equal(0, dema.Last.Value);
         Assert.False(dema.IsHot);
     }
@@ -278,7 +278,7 @@ public class DemaTests
         var gbm = new GBM(startPrice: 100, mu: 0.05, sigma: 0.2, seed: 123);
         var bars = gbm.Fetch(1000, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
         var series = bars.Close;
-        
+
         // 1. Batch Mode
         var batchSeries = Dema.Calculate(series, period);
         double expected = batchSeries.Last.Value;
@@ -321,11 +321,11 @@ public class DemaTests
 
         Dema.Calculate(source, output, 3);
 
-        // We expect the first two outputs to be NaN because the input was NaN 
+        // We expect the first two outputs to be NaN because the input was NaN
         Assert.True(double.IsNaN(output[0]), $"Output[0] should be NaN, but was {output[0]}");
         Assert.True(double.IsNaN(output[1]), $"Output[1] should be NaN, but was {output[1]}");
 
-        // The first valid value is 10.0. 
+        // The first valid value is 10.0.
         Assert.Equal(10.0, output[2], 1e-9);
     }
 }

lib/trends/dema/Dema.Validation.Tests.cs

@@ -106,13 +106,13 @@ public sealed class DemaValidationTests : IDisposable
             var demaIndicator = Tulip.Indicators.dema;
             double[][] inputs = { tData };
             double[] options = { period };
-            
+
             // Tulip DEMA lookback is usually period-1 for EMA, but DEMA is 2*EMA - EMA(EMA)
             // Let's rely on the output length to align.
             // Tulip DEMA lookback is same as EMA lookback? No, it involves double smoothing.
             // Actually, Tulip's DEMA implementation might have a specific lookback.
             // We'll calculate it based on output length.
-            
+
             // Tulip.Indicators.dema.Run expects outputs to be sized correctly.
             // We'll use a large buffer and resize if needed, or just calculate lookback.
             // For DEMA(n), lookback is roughly n-1 (same as EMA).
@@ -120,7 +120,7 @@ public sealed class DemaValidationTests : IDisposable
             // Let's try with n-1 first, if it fails we adjust.
             // Actually, TA-Lib DEMA lookback is 2*(period-1).
             // Let's assume Tulip is similar.
-            int lookback = 2 * (period - 1); 
+            int lookback = 2 * (period - 1);
             double[][] outputs = { new double[tData.Length - lookback] };
 
             demaIndicator.Run(inputs, options, outputs);
@@ -177,7 +177,7 @@ public sealed class DemaValidationTests : IDisposable
             for (int i = 0; i < _testData.Data.Count; i++)
             {
                 var item = _testData.Data[i];
-                
+
                 // QuanTAlib DEMA
                 var qVal = dema.Update(item);
 

lib/trends/dwma/Dwma.Quantower.cs

@@ -50,7 +50,7 @@ public class DwmaIndicator : Indicator, IWatchlistIndicator
     protected override void OnUpdate(UpdateArgs args)
     {
         var item = HistoricalData[Count - 1, SeekOriginHistory.Begin];
-        
+
         TValue result = ma!.Update(new TValue(item.TimeLeft.Ticks, _priceSelector!(item)), isNew: args.IsNewBar());
 
         Series!.SetValue(result.Value, ma.IsHot, ShowColdValues);

lib/trends/dwma/Dwma.Tests.cs

@@ -22,20 +22,20 @@ public class DwmaTests
         // 3: (1*1 + 2*2 + 3*3) / 6 = 14/6 = 2.333...
         // 4: (1*2 + 2*3 + 3*4) / 6 = 20/6 = 3.333...
         // 5: (1*3 + 2*4 + 3*5) / 6 = 26/6 = 4.333...
-        
+
         // WMA(3) results: [1, 1.666, 2.333, 3.333, 4.333]
-        
+
         // DWMA(3) = WMA(3) of [1, 1.666, 2.333, 3.333, 4.333]
         // 1: 1
         // 2: (1*1 + 2*1.666) / 3 = 4.333/3 = 1.444...
         // 3: (1*1 + 2*1.666 + 3*2.333) / 6 = (1 + 3.333 + 7) / 6 = 11.333/6 = 1.888...
-        
+
         var dwma = new Dwma(3);
-        
+
         var v1 = dwma.Update(new TValue(DateTime.UtcNow, 1)).Value;
         var v2 = dwma.Update(new TValue(DateTime.UtcNow, 2)).Value;
         var v3 = dwma.Update(new TValue(DateTime.UtcNow, 3)).Value;
-        
+
         Assert.Equal(1.0, v1, 6);
         Assert.Equal(1.444444, v2, 5);
         Assert.Equal(1.888888, v3, 5);
@@ -45,23 +45,23 @@ public class DwmaTests
     public void Update_IsNewFalse_CorrectsValue()
     {
         var dwma = new Dwma(3);
-        
+
         dwma.Update(new TValue(DateTime.UtcNow, 1));
         dwma.Update(new TValue(DateTime.UtcNow, 2));
-        
+
         // Update with 3, then correct to 4
         var v3 = dwma.Update(new TValue(DateTime.UtcNow, 3), isNew: true).Value;
         var v3_corrected = dwma.Update(new TValue(DateTime.UtcNow, 4), isNew: false).Value;
-        
+
         // Manual calc for sequence [1, 2, 4]
         // WMA(3):
         // 1: 1
         // 2: 1.666
         // 4: (1*1 + 2*2 + 3*4) / 6 = 17/6 = 2.8333
-        
+
         // DWMA(3) of [1, 1.666, 2.8333]
         // 3: (1*1 + 2*1.666 + 3*2.8333) / 6 = (1 + 3.333 + 8.5) / 6 = 12.833/6 = 2.1388
-        
+
         Assert.Equal(1.888888, v3, 5); // From previous test
         Assert.Equal(2.138888, v3_corrected, 5);
     }
@@ -72,9 +72,9 @@ public class DwmaTests
         var dwma = new Dwma(3);
         dwma.Update(new TValue(DateTime.UtcNow, 1));
         dwma.Update(new TValue(DateTime.UtcNow, 2));
-        
+
         dwma.Reset();
-        
+
         Assert.False(dwma.IsHot);
         var v1 = dwma.Update(new TValue(DateTime.UtcNow, 1)).Value;
         Assert.Equal(1.0, v1);
@@ -87,15 +87,15 @@ public class DwmaTests
         int count = 100;
         var source = new TSeries();
         var dwma = new Dwma(period);
-        
+
         for (int i = 0; i < count; i++)
         {
             source.Add(new TValue(DateTime.UtcNow.AddMinutes(i), i));
             dwma.Update(source.Last);
         }
-        
+
         var staticResult = Dwma.Batch(source, period);
-        
+
         Assert.Equal(source.Count, staticResult.Count);
         Assert.Equal(dwma.Last.Value, staticResult.Last.Value, 8);
     }
@@ -178,7 +178,7 @@ public class DwmaTests
         var gbm = new GBM(startPrice: 100, mu: 0.05, sigma: 0.2, seed: 123);
         var bars = gbm.Fetch(1000, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
         var series = bars.Close;
-        
+
         // 1. Batch Mode
         var batchSeries = Dwma.Batch(series, period);
         double expected = batchSeries.Last.Value;

lib/trends/dwma/Dwma.Validation.Tests.cs

@@ -46,24 +46,24 @@ public sealed class DwmaValidationTests : IDisposable
     public void Validate_Against_DoubleWma()
     {
         // DWMA should be exactly WMA(WMA(source, period), period)
-        
+
         int period = 10;
-        
+
         var dwma = new Dwma(period);
         var wma1 = new Wma(period);
         var wma2 = new Wma(period);
-        
+
         for (int i = 0; i < _testData.Data.Count; i++)
         {
             var val = _testData.Data[i];
-            
+
             // Calculate DWMA
             var dwmaVal = dwma.Update(val);
-            
+
             // Calculate WMA(WMA) manually
             var wma1Val = wma1.Update(val);
             var wma2Val = wma2.Update(wma1Val);
-            
+
             Assert.Equal(wma2Val.Value, dwmaVal.Value, ValidationHelper.DefaultTolerance);
         }
     }
@@ -73,24 +73,24 @@ public sealed class DwmaValidationTests : IDisposable
     {
         // Ooples Finance does not have a specific DWMA indicator, but it can be calculated
         // by chaining two Weighted Moving Averages
-        
+
         int period = 14;
-        
+
         var dwma = new Dwma(period);
         var wma1 = new Wma(period); // Simulates first CalculateWeightedMovingAverage
         var wma2 = new Wma(period); // Simulates second CalculateWeightedMovingAverage
-        
+
         for (int i = 0; i < _testData.Data.Count; i++)
         {
             var val = _testData.Data[i];
-            
+
             // QuanTAlib DWMA
             var qVal = dwma.Update(val);
-            
+
             // Ooples Logic (Chained WMA)
             var w1 = wma1.Update(val);
             var w2 = wma2.Update(w1);
-            
+
             Assert.Equal(w2.Value, qVal.Value, ValidationHelper.DefaultTolerance);
         }
     }

lib/trends/ema/Ema.Validation.Tests.cs

@@ -1,320 +1,320 @@
-using System;
-using System.Collections.Generic;
-using System.Linq;
-using OoplesFinance.StockIndicators;
-using OoplesFinance.StockIndicators.Models;
-using Skender.Stock.Indicators;
-using TALib;
-using Tulip;
-using Xunit.Abstractions;
-
-namespace QuanTAlib.Tests;
-
-public sealed class EmaValidationTests : IDisposable
-{
-    private readonly ValidationTestData _testData;
-    private readonly ITestOutputHelper _output;
-    private bool _disposed;
-
-    public EmaValidationTests(ITestOutputHelper output)
-    {
-        _output = output;
-        _testData = new ValidationTestData();
-    }
-
-    public void Dispose()
-    {
-        Dispose(true);
-    }
-
-    private void Dispose(bool disposing)
-    {
-        if (_disposed)
-        {
-            return;
-        }
-
-        _disposed = true;
-
-        if (disposing)
-        {
-            _testData?.Dispose();
-        }
-    }
-
-    [Fact]
-    public void Validate_Skender_Batch()
-    {
-        int[] periods = { 5, 10, 20, 50, 100 };
-
-        foreach (var period in periods)
-        {
-            // Calculate QuanTAlib EMA (batch TSeries)
-            var ema = new global::QuanTAlib.Ema(period);
-            var qResult = ema.Update(_testData.Data);
-
-            // Calculate Skender EMA
-            var sResult = _testData.SkenderQuotes.GetEma(period).ToList();
-
-            // Compare last 100 records
-            ValidationHelper.VerifyData(qResult, sResult, (s) => s.Ema);
-        }
-        _output.WriteLine("EMA Batch(TSeries) validated successfully against Skender");
-    }
-
-    [Fact]
-    public void Validate_Skender_Streaming()
-    {
-        int[] periods = { 5, 10, 20, 50, 100 };
-
-        foreach (var period in periods)
-        {
-            // Calculate QuanTAlib EMA (streaming)
-            var ema = new global::QuanTAlib.Ema(period);
-            var qResults = new List<double>();
-            foreach (var item in _testData.Data)
-            {
-                qResults.Add(ema.Update(item).Value);
-            }
-
-            // Calculate Skender EMA
-            var sResult = _testData.SkenderQuotes.GetEma(period).ToList();
-
-            // Compare last 100 records
-            ValidationHelper.VerifyData(qResults, sResult, (s) => s.Ema);
-        }
-        _output.WriteLine("EMA Streaming validated successfully against Skender");
-    }
-
-    [Fact]
-    public void Validate_Skender_Span()
-    {
-        int[] periods = { 5, 10, 20, 50, 100 };
-
-        // Prepare data for Span API
-        double[] sourceData = _testData.RawData.ToArray();
-
-        foreach (var period in periods)
-        {
-            // Calculate QuanTAlib EMA (Span API)
-            double[] qOutput = new double[sourceData.Length];
-            global::QuanTAlib.Ema.Batch(sourceData.AsSpan(), qOutput.AsSpan(), period);
-
-            // Calculate Skender EMA
-            var sResult = _testData.SkenderQuotes.GetEma(period).ToList();
-
-            // Compare last 100 records
-            ValidationHelper.VerifyData(qOutput, sResult, (s) => s.Ema);
-        }
-        _output.WriteLine("EMA Span validated successfully against Skender");
-    }
-
-    [Fact]
-    public void Validate_Talib_Batch()
-    {
-        int[] periods = { 5, 10, 20, 50, 100 };
-
-        // Prepare data for TA-Lib (double[])
-        double[] tData = _testData.RawData.ToArray();
-        double[] output = new double[tData.Length];
-
-        foreach (var period in periods)
-        {
-            // Calculate QuanTAlib EMA (batch TSeries)
-            var ema = new global::QuanTAlib.Ema(period);
-            var qResult = ema.Update(_testData.Data);
-
-            // Calculate TA-Lib EMA
-            var retCode = TALib.Functions.Ema<double>(tData, 0..^0, output, out var outRange, period);
-            Assert.Equal(Core.RetCode.Success, retCode);
-
-            int lookback = TALib.Functions.EmaLookback(period);
-
-            // Compare last 100 records
-            ValidationHelper.VerifyData(qResult, output, outRange, lookback);
-        }
-        _output.WriteLine("EMA Batch(TSeries) validated successfully against TA-Lib");
-    }
-
-    [Fact]
-    public void Validate_Talib_Streaming()
-    {
-        int[] periods = { 5, 10, 20, 50, 100 };
-
-        // Prepare data for TA-Lib (double[])
-        double[] tData = _testData.RawData.ToArray();
-        double[] output = new double[tData.Length];
-
-        foreach (var period in periods)
-        {
-            // Calculate QuanTAlib EMA (streaming)
-            var ema = new global::QuanTAlib.Ema(period);
-            var qResults = new List<double>();
-            foreach (var item in _testData.Data)
-            {
-                qResults.Add(ema.Update(item).Value);
-            }
-
-            // Calculate TA-Lib EMA
-            var retCode = TALib.Functions.Ema<double>(tData, 0..^0, output, out var outRange, period);
-            Assert.Equal(Core.RetCode.Success, retCode);
-
-            int lookback = TALib.Functions.EmaLookback(period);
-
-            // Compare last 100 records
-            ValidationHelper.VerifyData(qResults, output, outRange, lookback);
-        }
-        _output.WriteLine("EMA Streaming validated successfully against TA-Lib");
-    }
-
-    [Fact]
-    public void Validate_Talib_Span()
-    {
-        int[] periods = { 5, 10, 20, 50, 100 };
-
-        // Prepare data
-        double[] sourceData = _testData.RawData.ToArray();
-        double[] talibOutput = new double[sourceData.Length];
-
-        foreach (var period in periods)
-        {
-            // Calculate QuanTAlib EMA (Span API)
-            double[] qOutput = new double[sourceData.Length];
-            global::QuanTAlib.Ema.Batch(sourceData.AsSpan(), qOutput.AsSpan(), period);
-
-            // Calculate TA-Lib EMA
-            var retCode = TALib.Functions.Ema<double>(sourceData, 0..^0, talibOutput, out var outRange, period);
-            Assert.Equal(Core.RetCode.Success, retCode);
-
-            int lookback = TALib.Functions.EmaLookback(period);
-
-            // Compare last 100 records
-            ValidationHelper.VerifyData(qOutput, talibOutput, outRange, lookback);
-        }
-        _output.WriteLine("EMA Span validated successfully against TA-Lib");
-    }
-
-    [Fact]
-    public void Validate_Tulip_Batch()
-    {
-        int[] periods = { 5, 10, 20, 50, 100 };
-
-        // Prepare data for Tulip (double[])
-        double[] tData = _testData.RawData.ToArray();
-
-        foreach (var period in periods)
-        {
-            // Calculate QuanTAlib EMA (batch TSeries)
-            var ema = new global::QuanTAlib.Ema(period);
-            var qResult = ema.Update(_testData.Data);
-
-            // Calculate Tulip EMA
-            var emaIndicator = Tulip.Indicators.ema;
-            double[][] inputs = { tData };
-            double[] options = { period };
-            double[][] outputs = { new double[tData.Length] };
-
-            emaIndicator.Run(inputs, options, outputs);
-            var tResult = outputs[0];
-
-            // Compare last 100 records
-            ValidationHelper.VerifyData(qResult, tResult, 0);
-        }
-        _output.WriteLine("EMA Batch(TSeries) validated successfully against Tulip");
-    }
-
-    [Fact]
-    public void Validate_Tulip_Streaming()
-    {
-        int[] periods = { 5, 10, 20, 50, 100 };
-
-        // Prepare data for Tulip (double[])
-        double[] tData = _testData.RawData.ToArray();
-
-        foreach (var period in periods)
-        {
-            // Calculate QuanTAlib EMA (streaming)
-            var ema = new global::QuanTAlib.Ema(period);
-            var qResults = new List<double>();
-            foreach (var item in _testData.Data)
-            {
-                qResults.Add(ema.Update(item).Value);
-            }
-
-            // Calculate Tulip EMA
-            var emaIndicator = Tulip.Indicators.ema;
-            double[][] inputs = { tData };
-            double[] options = { period };
-            double[][] outputs = { new double[tData.Length] };
-
-            emaIndicator.Run(inputs, options, outputs);
-            var tResult = outputs[0];
-
-            // Compare last 100 records
-            ValidationHelper.VerifyData(qResults, tResult, 0);
-        }
-        _output.WriteLine("EMA Streaming validated successfully against Tulip");
-    }
-
-    [Fact]
-    public void Validate_Tulip_Span()
-    {
-        int[] periods = { 5, 10, 20, 50, 100 };
-
-        // Prepare data
-        double[] sourceData = _testData.RawData.ToArray();
-
-        foreach (var period in periods)
-        {
-            // Calculate QuanTAlib EMA (Span API)
-            double[] qOutput = new double[sourceData.Length];
-            global::QuanTAlib.Ema.Batch(sourceData.AsSpan(), qOutput.AsSpan(), period);
-
-            // Calculate Tulip EMA
-            var emaIndicator = Tulip.Indicators.ema;
-            double[][] inputs = { sourceData };
-            double[] options = { period };
-            double[][] outputs = { new double[sourceData.Length] };
-
-            emaIndicator.Run(inputs, options, outputs);
-            var tResult = outputs[0];
-
-            // Compare last 100 records
-            ValidationHelper.VerifyData(qOutput, tResult, 0);
-        }
-        _output.WriteLine("EMA Span validated successfully against Tulip");
-    }
-
-    [Fact]
-    public void Validate_Against_Ooples()
-    {
-        int[] periods = { 5, 10, 20, 50, 100 };
-
-        // Prepare data for Ooples (List<TickerData>)
-        var ooplesData = _testData.SkenderQuotes.Select(q => new TickerData
-        {
-            Date = q.Date,
-            Close = (double)q.Close,
-            High = (double)q.High,
-            Low = (double)q.Low,
-            Open = (double)q.Open,
-            Volume = (double)q.Volume
-        }).ToList();
-
-        foreach (var period in periods)
-        {
-            // Calculate QuanTAlib EMA
-            var ema = new global::QuanTAlib.Ema(period);
-            var qResult = ema.Update(_testData.Data);
-
-            // Calculate Ooples EMA
-            var stockData = new StockData(ooplesData);
-            var oResult = stockData.CalculateExponentialMovingAverage(period);
-            var oValues = oResult.OutputValues.Values.First();
-
-            // Compare
-            ValidationHelper.VerifyData(qResult, oValues, (s) => s, tolerance: ValidationHelper.OoplesTolerance);
-        }
-        _output.WriteLine("EMA validated successfully against Ooples");
-    }
-}
+using System;
+using System.Collections.Generic;
+using System.Linq;
+using OoplesFinance.StockIndicators;
+using OoplesFinance.StockIndicators.Models;
+using Skender.Stock.Indicators;
+using TALib;
+using Tulip;
+using Xunit.Abstractions;
+
+namespace QuanTAlib.Tests;
+
+public sealed class EmaValidationTests : IDisposable
+{
+    private readonly ValidationTestData _testData;
+    private readonly ITestOutputHelper _output;
+    private bool _disposed;
+
+    public EmaValidationTests(ITestOutputHelper output)
+    {
+        _output = output;
+        _testData = new ValidationTestData();
+    }
+
+    public void Dispose()
+    {
+        Dispose(true);
+    }
+
+    private void Dispose(bool disposing)
+    {
+        if (_disposed)
+        {
+            return;
+        }
+
+        _disposed = true;
+
+        if (disposing)
+        {
+            _testData?.Dispose();
+        }
+    }
+
+    [Fact]
+    public void Validate_Skender_Batch()
+    {
+        int[] periods = { 5, 10, 20, 50, 100 };
+
+        foreach (var period in periods)
+        {
+            // Calculate QuanTAlib EMA (batch TSeries)
+            var ema = new global::QuanTAlib.Ema(period);
+            var qResult = ema.Update(_testData.Data);
+
+            // Calculate Skender EMA
+            var sResult = _testData.SkenderQuotes.GetEma(period).ToList();
+
+            // Compare last 100 records
+            ValidationHelper.VerifyData(qResult, sResult, (s) => s.Ema);
+        }
+        _output.WriteLine("EMA Batch(TSeries) validated successfully against Skender");
+    }
+
+    [Fact]
+    public void Validate_Skender_Streaming()
+    {
+        int[] periods = { 5, 10, 20, 50, 100 };
+
+        foreach (var period in periods)
+        {
+            // Calculate QuanTAlib EMA (streaming)
+            var ema = new global::QuanTAlib.Ema(period);
+            var qResults = new List<double>();
+            foreach (var item in _testData.Data)
+            {
+                qResults.Add(ema.Update(item).Value);
+            }
+
+            // Calculate Skender EMA
+            var sResult = _testData.SkenderQuotes.GetEma(period).ToList();
+
+            // Compare last 100 records
+            ValidationHelper.VerifyData(qResults, sResult, (s) => s.Ema);
+        }
+        _output.WriteLine("EMA Streaming validated successfully against Skender");
+    }
+
+    [Fact]
+    public void Validate_Skender_Span()
+    {
+        int[] periods = { 5, 10, 20, 50, 100 };
+
+        // Prepare data for Span API
+        double[] sourceData = _testData.RawData.ToArray();
+
+        foreach (var period in periods)
+        {
+            // Calculate QuanTAlib EMA (Span API)
+            double[] qOutput = new double[sourceData.Length];
+            global::QuanTAlib.Ema.Batch(sourceData.AsSpan(), qOutput.AsSpan(), period);
+
+            // Calculate Skender EMA
+            var sResult = _testData.SkenderQuotes.GetEma(period).ToList();
+
+            // Compare last 100 records
+            ValidationHelper.VerifyData(qOutput, sResult, (s) => s.Ema);
+        }
+        _output.WriteLine("EMA Span validated successfully against Skender");
+    }
+
+    [Fact]
+    public void Validate_Talib_Batch()
+    {
+        int[] periods = { 5, 10, 20, 50, 100 };
+
+        // Prepare data for TA-Lib (double[])
+        double[] tData = _testData.RawData.ToArray();
+        double[] output = new double[tData.Length];
+
+        foreach (var period in periods)
+        {
+            // Calculate QuanTAlib EMA (batch TSeries)
+            var ema = new global::QuanTAlib.Ema(period);
+            var qResult = ema.Update(_testData.Data);
+
+            // Calculate TA-Lib EMA
+            var retCode = TALib.Functions.Ema<double>(tData, 0..^0, output, out var outRange, period);
+            Assert.Equal(Core.RetCode.Success, retCode);
+
+            int lookback = TALib.Functions.EmaLookback(period);
+
+            // Compare last 100 records
+            ValidationHelper.VerifyData(qResult, output, outRange, lookback);
+        }
+        _output.WriteLine("EMA Batch(TSeries) validated successfully against TA-Lib");
+    }
+
+    [Fact]
+    public void Validate_Talib_Streaming()
+    {
+        int[] periods = { 5, 10, 20, 50, 100 };
+
+        // Prepare data for TA-Lib (double[])
+        double[] tData = _testData.RawData.ToArray();
+        double[] output = new double[tData.Length];
+
+        foreach (var period in periods)
+        {
+            // Calculate QuanTAlib EMA (streaming)
+            var ema = new global::QuanTAlib.Ema(period);
+            var qResults = new List<double>();
+            foreach (var item in _testData.Data)
+            {
+                qResults.Add(ema.Update(item).Value);
+            }
+
+            // Calculate TA-Lib EMA
+            var retCode = TALib.Functions.Ema<double>(tData, 0..^0, output, out var outRange, period);
+            Assert.Equal(Core.RetCode.Success, retCode);
+
+            int lookback = TALib.Functions.EmaLookback(period);
+
+            // Compare last 100 records
+            ValidationHelper.VerifyData(qResults, output, outRange, lookback);
+        }
+        _output.WriteLine("EMA Streaming validated successfully against TA-Lib");
+    }
+
+    [Fact]
+    public void Validate_Talib_Span()
+    {
+        int[] periods = { 5, 10, 20, 50, 100 };
+
+        // Prepare data
+        double[] sourceData = _testData.RawData.ToArray();
+        double[] talibOutput = new double[sourceData.Length];
+
+        foreach (var period in periods)
+        {
+            // Calculate QuanTAlib EMA (Span API)
+            double[] qOutput = new double[sourceData.Length];
+            global::QuanTAlib.Ema.Batch(sourceData.AsSpan(), qOutput.AsSpan(), period);
+
+            // Calculate TA-Lib EMA
+            var retCode = TALib.Functions.Ema<double>(sourceData, 0..^0, talibOutput, out var outRange, period);
+            Assert.Equal(Core.RetCode.Success, retCode);
+
+            int lookback = TALib.Functions.EmaLookback(period);
+
+            // Compare last 100 records
+            ValidationHelper.VerifyData(qOutput, talibOutput, outRange, lookback);
+        }
+        _output.WriteLine("EMA Span validated successfully against TA-Lib");
+    }
+
+    [Fact]
+    public void Validate_Tulip_Batch()
+    {
+        int[] periods = { 5, 10, 20, 50, 100 };
+
+        // Prepare data for Tulip (double[])
+        double[] tData = _testData.RawData.ToArray();
+
+        foreach (var period in periods)
+        {
+            // Calculate QuanTAlib EMA (batch TSeries)
+            var ema = new global::QuanTAlib.Ema(period);
+            var qResult = ema.Update(_testData.Data);
+
+            // Calculate Tulip EMA
+            var emaIndicator = Tulip.Indicators.ema;
+            double[][] inputs = { tData };
+            double[] options = { period };
+            double[][] outputs = { new double[tData.Length] };
+
+            emaIndicator.Run(inputs, options, outputs);
+            var tResult = outputs[0];
+
+            // Compare last 100 records
+            ValidationHelper.VerifyData(qResult, tResult, 0);
+        }
+        _output.WriteLine("EMA Batch(TSeries) validated successfully against Tulip");
+    }
+
+    [Fact]
+    public void Validate_Tulip_Streaming()
+    {
+        int[] periods = { 5, 10, 20, 50, 100 };
+
+        // Prepare data for Tulip (double[])
+        double[] tData = _testData.RawData.ToArray();
+
+        foreach (var period in periods)
+        {
+            // Calculate QuanTAlib EMA (streaming)
+            var ema = new global::QuanTAlib.Ema(period);
+            var qResults = new List<double>();
+            foreach (var item in _testData.Data)
+            {
+                qResults.Add(ema.Update(item).Value);
+            }
+
+            // Calculate Tulip EMA
+            var emaIndicator = Tulip.Indicators.ema;
+            double[][] inputs = { tData };
+            double[] options = { period };
+            double[][] outputs = { new double[tData.Length] };
+
+            emaIndicator.Run(inputs, options, outputs);
+            var tResult = outputs[0];
+
+            // Compare last 100 records
+            ValidationHelper.VerifyData(qResults, tResult, 0);
+        }
+        _output.WriteLine("EMA Streaming validated successfully against Tulip");
+    }
+
+    [Fact]
+    public void Validate_Tulip_Span()
+    {
+        int[] periods = { 5, 10, 20, 50, 100 };
+
+        // Prepare data
+        double[] sourceData = _testData.RawData.ToArray();
+
+        foreach (var period in periods)
+        {
+            // Calculate QuanTAlib EMA (Span API)
+            double[] qOutput = new double[sourceData.Length];
+            global::QuanTAlib.Ema.Batch(sourceData.AsSpan(), qOutput.AsSpan(), period);
+
+            // Calculate Tulip EMA
+            var emaIndicator = Tulip.Indicators.ema;
+            double[][] inputs = { sourceData };
+            double[] options = { period };
+            double[][] outputs = { new double[sourceData.Length] };
+
+            emaIndicator.Run(inputs, options, outputs);
+            var tResult = outputs[0];
+
+            // Compare last 100 records
+            ValidationHelper.VerifyData(qOutput, tResult, 0);
+        }
+        _output.WriteLine("EMA Span validated successfully against Tulip");
+    }
+
+    [Fact]
+    public void Validate_Against_Ooples()
+    {
+        int[] periods = { 5, 10, 20, 50, 100 };
+
+        // Prepare data for Ooples (List<TickerData>)
+        var ooplesData = _testData.SkenderQuotes.Select(q => new TickerData
+        {
+            Date = q.Date,
+            Close = (double)q.Close,
+            High = (double)q.High,
+            Low = (double)q.Low,
+            Open = (double)q.Open,
+            Volume = (double)q.Volume
+        }).ToList();
+
+        foreach (var period in periods)
+        {
+            // Calculate QuanTAlib EMA
+            var ema = new global::QuanTAlib.Ema(period);
+            var qResult = ema.Update(_testData.Data);
+
+            // Calculate Ooples EMA
+            var stockData = new StockData(ooplesData);
+            var oResult = stockData.CalculateExponentialMovingAverage(period);
+            var oValues = oResult.OutputValues.Values.First();
+
+            // Compare
+            ValidationHelper.VerifyData(qResult, oValues, (s) => s, tolerance: ValidationHelper.OoplesTolerance);
+        }
+        _output.WriteLine("EMA validated successfully against Ooples");
+    }
+}

lib/trends/ema/Ema.cs

@@ -22,7 +22,7 @@ namespace QuanTAlib;
 /// No buffer required, only previous EMA value and compensator state.
 ///
 /// IsHot:
-/// Becomes true when n = ln(0.05) / ln(1 - alpha) 
+/// Becomes true when n = ln(0.05) / ln(1 - alpha)
 /// </remarks>
 [SkipLocalsInit]
 public sealed class Ema : AbstractBase
@@ -303,7 +303,7 @@ public sealed class Ema : AbstractBase
                 else
                     val = lastValidValue;
 
-                 
+
                 state.Ema = Math.FusedMultiplyAdd(state.Ema, decay, alpha * val);
                 state.E *= decay;
 

lib/trends/ema/Ema.md

@@ -1,67 +1,67 @@
-# EMA: Exponential Moving Average
-
-> "The AK-47 of technical indicators. It's been around forever, everyone uses it, and it gets the job done. It's not fancy, but it works."
-
-EMA (Exponential Moving Average) is the standard by which all other averages are judged. Unlike the SMA, which treats data from 10 days ago with the same reverence as data from 10 seconds ago, the EMA understands that in markets, recency is relevance. It applies an exponentially decaying weight to older prices, reacting faster to new information.
-
-## Historical Context
-
-The EMA was brought to the financial world to solve the "drop-off effect" of the SMA (where an old price dropping out of the window causes the average to jump). By using a recursive formula, the EMA includes *all* past data in its calculation, with weights diminishing to infinity. It is the infinite impulse response (IIR) filter of the trading world.
-
-## Architecture & Physics
-
-The EMA is defined by its smoothing factor, $\alpha$.
-
-- **High $\alpha$**: Fast decay, responsive, noisy.
-- **Low $\alpha$**: Slow decay, smooth, laggy.
-
-The QuanTAlib implementation includes a **Compensator** for the warmup phase. A standard EMA starts at 0 (or the first price) and takes time to converge. This early-stage bias is corrected mathematically so the EMA is accurate from the very first few bars, rather than waiting for $3 \times N$ bars to stabilize.
-
-## Mathematical Foundation
-
-The standard recursive formula:
-
-$$ \alpha = \frac{2}{N + 1} $$
-
-$$ \text{EMA}_t = \alpha \cdot P_t + (1 - \alpha) \cdot \text{EMA}_{t-1} $$
-
-### The Compensator (Warmup Correction)
-
-To handle the initialization bias (where $\text{EMA}_0$ is unknown), the sum of weights is tracked:
-
-$$ E_t = (1 - \alpha)^t $$
-
-$$ \text{Corrected EMA}_t = \frac{\text{Uncorrected EMA}_t}{1 - E_t} $$
-
-This ensures the EMA is statistically valid even during the warmup period.
-
-## Performance Profile
-
-This is as fast as it gets.
-
-| Metric | Score | Notes |
-| :--- | :--- | :--- |
-| **Throughput** | ★★★★★ | Single multiplication and addition. |
-| **Allocations** | ★★★★★ | 0 bytes; hot path is allocation-free. |
-| **Complexity** | ★★★★★ | O(1) recursive calculation. |
-| **Precision** | ★★★★★ | `double` precision. |
-
-### Zero-Allocation Design
-
-EMA is implemented using a simple scalar state variable. The calculation is purely algebraic and requires no heap allocations during the `Update` cycle.
-
-## Validation
-
-Validated against TA-Lib, Skender, Tulip, and Ooples.
-
-| Library | Status | Notes |
-| :--- | :--- | :--- |
-| **TA-Lib** | ✅ | Matches `TA_EMA`. |
-| **Skender** | ✅ | Matches `GetEma`. |
-| **Tulip** | ✅ | Matches `ema`. |
-| **Ooples** | ✅ | Matches `CalculateExponentialMovingAverage`. |
-
-### Common Pitfalls
-
-1. **The "First Value" Problem**: Most libraries seed the EMA with the first price or an SMA of the first N prices. In QuanTAlib, a mathematical compensator is used. Results during the first N bars are *more accurate* than TA-Lib, which might look like a discrepancy. It is not; the QuanTAlib implementation is correct and TA-Lib is approximating.
-2. **Alpha vs. Period**: Remember that $N$ is just a proxy for $\alpha$. You can construct an EMA directly with an $\alpha$ (e.g., 0.1) if you prefer signal processing terminology over trader terminology.
+# EMA: Exponential Moving Average
+
+> "The AK-47 of technical indicators. It's been around forever, everyone uses it, and it gets the job done. It's not fancy, but it works."
+
+EMA (Exponential Moving Average) is the standard by which all other averages are judged. Unlike the SMA, which treats data from 10 days ago with the same reverence as data from 10 seconds ago, the EMA understands that in markets, recency is relevance. It applies an exponentially decaying weight to older prices, reacting faster to new information.
+
+## Historical Context
+
+The EMA was brought to the financial world to solve the "drop-off effect" of the SMA (where an old price dropping out of the window causes the average to jump). By using a recursive formula, the EMA includes *all* past data in its calculation, with weights diminishing to infinity. It is the infinite impulse response (IIR) filter of the trading world.
+
+## Architecture & Physics
+
+The EMA is defined by its smoothing factor, $\alpha$.
+
+- **High $\alpha$**: Fast decay, responsive, noisy.
+- **Low $\alpha$**: Slow decay, smooth, laggy.
+
+The QuanTAlib implementation includes a **Compensator** for the warmup phase. A standard EMA starts at 0 (or the first price) and takes time to converge. This early-stage bias is corrected mathematically so the EMA is accurate from the very first few bars, rather than waiting for $3 \times N$ bars to stabilize.
+
+## Mathematical Foundation
+
+The standard recursive formula:
+
+$$ \alpha = \frac{2}{N + 1} $$
+
+$$ \text{EMA}_t = \alpha \cdot P_t + (1 - \alpha) \cdot \text{EMA}_{t-1} $$
+
+### The Compensator (Warmup Correction)
+
+To handle the initialization bias (where $\text{EMA}_0$ is unknown), the sum of weights is tracked:
+
+$$ E_t = (1 - \alpha)^t $$
+
+$$ \text{Corrected EMA}_t = \frac{\text{Uncorrected EMA}_t}{1 - E_t} $$
+
+This ensures the EMA is statistically valid even during the warmup period.
+
+## Performance Profile
+
+This is as fast as it gets.
+
+| Metric | Score | Notes |
+| :--- | :--- | :--- |
+| **Throughput** | ★★★★★ | Single multiplication and addition. |
+| **Allocations** | ★★★★★ | 0 bytes; hot path is allocation-free. |
+| **Complexity** | ★★★★★ | O(1) recursive calculation. |
+| **Precision** | ★★★★★ | `double` precision. |
+
+### Zero-Allocation Design
+
+EMA is implemented using a simple scalar state variable. The calculation is purely algebraic and requires no heap allocations during the `Update` cycle.
+
+## Validation
+
+Validated against TA-Lib, Skender, Tulip, and Ooples.
+
+| Library | Status | Notes |
+| :--- | :--- | :--- |
+| **TA-Lib** | ✅ | Matches `TA_EMA`. |
+| **Skender** | ✅ | Matches `GetEma`. |
+| **Tulip** | ✅ | Matches `ema`. |
+| **Ooples** | ✅ | Matches `CalculateExponentialMovingAverage`. |
+
+### Common Pitfalls
+
+1. **The "First Value" Problem**: Most libraries seed the EMA with the first price or an SMA of the first N prices. In QuanTAlib, a mathematical compensator is used. Results during the first N bars are *more accurate* than TA-Lib, which might look like a discrepancy. It is not; the QuanTAlib implementation is correct and TA-Lib is approximating.
+2. **Alpha vs. Period**: Remember that $N$ is just a proxy for $\alpha$. You can construct an EMA directly with an $\alpha$ (e.g., 0.1) if you prefer signal processing terminology over trader terminology.

lib/trends/hma/Hma.Quantower.cs

@@ -50,7 +50,7 @@ public class HmaIndicator : Indicator, IWatchlistIndicator
     protected override void OnUpdate(UpdateArgs args)
     {
         var item = HistoricalData[Count - 1, SeekOriginHistory.Begin];
-        
+
         TValue result = ma!.Update(new TValue(item.TimeLeft.Ticks, _priceSelector!(item)), isNew: args.IsNewBar());
 
         Series!.SetValue(result.Value, ma.IsHot, ShowColdValues);

lib/trends/hma/Hma.Tests.cs

@@ -27,7 +27,7 @@ public class HmaTests
         // Full WMA needs 9
         // Half WMA needs 4
         // Sqrt WMA needs 3
-        // Pipeline: 
+        // Pipeline:
         // 1. Full/Half produce valid values immediately (but with warmup ramp)
         // 2. Sqrt consumes them.
         // IsHot is defined as Full.IsHot && Sqrt.IsHot.
@@ -160,9 +160,9 @@ public class HmaTests
         var hma = new Hma(10);
         hma.Update(new TValue(DateTime.UtcNow, 100));
         hma.Update(new TValue(DateTime.UtcNow, 110));
-        
+
         hma.Reset();
-        
+
         Assert.Equal(0, hma.Last.Value);
         Assert.False(hma.IsHot);
     }
@@ -245,7 +245,7 @@ public class HmaTests
         var gbm = new GBM(startPrice: 100, mu: 0.05, sigma: 0.2, seed: 123);
         var bars = gbm.Fetch(1000, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
         var series = bars.Close;
-        
+
         // 1. Batch Mode
         var batchSeries = Hma.Batch(series, period);
         double expected = batchSeries.Last.Value;

lib/trends/htit/Htit.Tests.cs

@@ -35,7 +35,7 @@ public class HtitTests
         var series = data;
 
         var resultSeries = htit.Update(series);
-        
+
         // Reset and calculate streaming
         htit.Reset();
         var streamingResults = new List<double>();
@@ -58,10 +58,10 @@ public class HtitTests
         var series = data;
 
         var resultSeries = htit.Update(series);
-        
+
         var spanInput = data.Values.ToArray();
         var spanOutput = new double[spanInput.Length];
-        
+
         Htit.Calculate(spanInput, spanOutput);
 
         for (int i = 0; i < resultSeries.Count; i++)
@@ -76,7 +76,7 @@ public class HtitTests
         var htit = new Htit();
         htit.Update(new TValue(DateTime.UtcNow.Ticks, 100.0));
         htit.Update(new TValue(DateTime.UtcNow.Ticks, double.NaN));
-        
+
         Assert.Equal(100.0, htit.Last.Value);
     }
 
@@ -94,9 +94,9 @@ public class HtitTests
         var htit = new Htit();
         htit.Update(new TValue(DateTime.UtcNow, 100));
         htit.Update(new TValue(DateTime.UtcNow, 110));
-        
+
         htit.Reset();
-        
+
         Assert.True(double.IsNaN(htit.Last.Value));
         Assert.False(htit.IsHot);
     }
@@ -163,7 +163,7 @@ public class HtitTests
         var gbm = new GBM(startPrice: 100, mu: 0.05, sigma: 0.2, seed: 123);
         var bars = gbm.Fetch(1000, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
         var series = bars.Close;
-        
+
         // 1. Batch Mode
         var batchSeries = Htit.Batch(series);
         double expected = batchSeries.Last.Value;

lib/trends/htit/Htit.Validation.Tests.cs

@@ -106,7 +106,7 @@ public sealed class HtitValidationTests : IDisposable
         // Calculate QuanTAlib HTIT Streaming
         var htit = new Htit();
         var streamingResults = new List<double>();
-        
+
         foreach (var item in _data.Data)
         {
             streamingResults.Add(htit.Update(item).Value);

lib/trends/htit/Htit.cs

@@ -310,7 +310,7 @@ public sealed class Htit : AbstractBase
         for (int i = 0; i < source.Length; i++)
         {
             double price = source[i];
-            
+
             // Handle non-finite input: skip processing if no valid price seen yet
             if (!double.IsFinite(price))
             {

lib/trends/jma/Jma.Quantower.cs

@@ -61,7 +61,7 @@ public class JmaIndicator : Indicator, IWatchlistIndicator
     protected override void OnUpdate(UpdateArgs args)
     {
         var item = HistoricalData[Count - 1, SeekOriginHistory.Begin];
-        
+
         TValue result = ma!.Update(new TValue(item.TimeLeft.Ticks, _priceSelector!(item)), isNew: args.IsNewBar());
 
         Series!.SetValue(result.Value, ma.IsHot, ShowColdValues);

lib/trends/jma/Jma.Tests.cs

@@ -183,7 +183,7 @@ public class JmaTests
         var gbm = new GBM(startPrice: 100, mu: 0.05, sigma: 0.2, seed: 123);
         var bars = gbm.Fetch(1000, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
         var series = bars.Close;
-        
+
         // 1. Batch Mode
         var batchSeries = Jma.Batch(series, period);
         double expected = batchSeries.Last.Value;

lib/trends/jma/Jma.Validation.Tests.cs

@@ -10,10 +10,10 @@ public class JmaValidationTests
     {
         // JMA should generally follow the price.
         // If price goes up, JMA should eventually go up.
-        
+
         var jma = new Jma(10);
         double previousJma = 0;
-        
+
         // Uptrend
         for (int i = 0; i < 100; i++)
         {
@@ -32,15 +32,15 @@ public class JmaValidationTests
         // JMA should stay within the range of recent prices (roughly)
         // It's a moving average, so it shouldn't overshoot wildly unless phase is negative and high volatility?
         // With default phase 0, it should be well behaved.
-        
+
         var jma = new Jma(10);
         var gbm = new GBM(startPrice: 100, mu: 0, sigma: 0.5);
-        
+
         for (int i = 0; i < 1000; i++)
         {
             var bar = gbm.Next(isNew: true);
             var result = jma.Update(new TValue(bar.Time, bar.Close));
-            
+
             if (i > 20)
             {
                 // Update bounds of recent price history (simplified)

lib/trends/jma/Jma.ZeroDiv.Tests.cs

@@ -31,11 +31,11 @@ public class JmaZeroDivTests
         // Since we can't easily access private fields, we'll rely on the calculation logic check
         // If the fix is applied, we shouldn't see -Infinity in internal calculations if we could see them.
         // But we can check if the output is exactly the input, which implies adapt=0 (if logic holds).
-        
+
         var jma = new Jma(period: 1);
         var result = jma.Update(new TValue(DateTime.UtcNow, 100));
         Assert.Equal(100, result.Value);
-        
+
         result = jma.Update(new TValue(DateTime.UtcNow, 200));
         // If adapt is 0 (due to -Infinity log), bands snap to price.
         // If JMA(1) is identity, result should be 200.

lib/trends/kama/Kama.Tests.cs

@@ -157,7 +157,7 @@ public class KamaTests
         Assert.Equal(0, kama.Last.Value);
         Assert.False(kama.IsHot);
     }
-    
+
     [Fact]
     public void Kama_FlatLine_ReturnsSameValue()
     {
@@ -166,7 +166,7 @@ public class KamaTests
         {
             kama.Update(new TValue(DateTime.UtcNow, 100));
         }
-        
+
         Assert.Equal(100, kama.Last.Value);
     }
 
@@ -243,7 +243,7 @@ public class KamaTests
         var gbm = new GBM(startPrice: 100, mu: 0.05, sigma: 0.2, seed: 123);
         var bars = gbm.Fetch(1000, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
         var series = bars.Close;
-        
+
         // 1. Batch Mode
         var batchSeries = Kama.Batch(series, period);
         double expected = batchSeries.Last.Value;

lib/trends/kama/Kama.Validation.Tests.cs

@@ -191,7 +191,7 @@ public sealed class KamaValidationTests : IDisposable
             var kamaIndicator = Tulip.Indicators.kama;
             double[][] inputs = { cData };
             double[] options = { period };
-            
+
             // Tulip KAMA lookback
             int lookback = kamaIndicator.Start(options);
             double[][] outputs = { new double[cData.Length - lookback] };
@@ -227,7 +227,7 @@ public sealed class KamaValidationTests : IDisposable
             var kamaIndicator = Tulip.Indicators.kama;
             double[][] inputs = { cData };
             double[] options = { period };
-            
+
             // Tulip KAMA lookback
             int lookback = kamaIndicator.Start(options);
             double[][] outputs = { new double[cData.Length - lookback] };

lib/trends/lsma/.github/instructions/codacy.instructions.md

@@ -28,7 +28,7 @@ Configuration for AI behavior when interacting with Codacy's MCP Server
 - Wait for the user to respond before proceeding with any other actions
 
 ## After every response
-- If you made any file edits in this conversation, verify you ran `codacy_cli_analyze` tool from Codacy's MCP Server 
+- If you made any file edits in this conversation, verify you ran `codacy_cli_analyze` tool from Codacy's MCP Server
 
 ## When there are no Codacy MCP Server tools available, or the MCP Server is not reachable
 - Suggest the user the following troubleshooting steps:

lib/trends/lsma/Lsma.Tests.cs

@@ -36,7 +36,7 @@ public class LsmaTests
         // For a perfect linear trend y = x, LSMA should return x
         int period = 10;
         var lsma = new Lsma(period);
-        
+
         for (int i = 0; i < period * 2; i++)
         {
             var result = lsma.Update(new TValue(DateTime.UtcNow, i));
@@ -53,7 +53,7 @@ public class LsmaTests
         int period = 10;
         var lsma = new Lsma(period);
         double value = 123.45;
-        
+
         for (int i = 0; i < period * 2; i++)
         {
             var result = lsma.Update(new TValue(DateTime.UtcNow, value));
@@ -67,16 +67,16 @@ public class LsmaTests
         // y = 2x + 1
         // At x=10, y=21. Slope=2, Intercept=1
         // LSMA(offset=1) should project to x=11 -> y=23
-        
+
         int period = 5;
         int offset = 1;
         var lsma = new Lsma(period, offset);
-        
+
         for (int i = 0; i < 20; i++)
         {
             double y = 2 * i + 1;
             var result = lsma.Update(new TValue(DateTime.UtcNow, y));
-            
+
             if (i >= period)
             {
                 double expected = 2 * (i + offset) + 1;
@@ -89,21 +89,21 @@ public class LsmaTests
     public void Update_BarCorrection_UpdatesCorrectly()
     {
         var lsma = new Lsma(5);
-        
+
         // Fill buffer
         for (int i = 0; i < 5; i++)
         {
             lsma.Update(new TValue(DateTime.UtcNow, i));
         }
-        
+
         // New bar
         var result1 = lsma.Update(new TValue(DateTime.UtcNow, 10));
-        
+
         // Update same bar with different value
         var result2 = lsma.Update(new TValue(DateTime.UtcNow, 20), isNew: false);
-        
+
         Assert.NotEqual(result1.Value, result2.Value);
-        
+
         // Verify internal state by adding next bar
         // If state was corrupted, this would fail
         var result3 = lsma.Update(new TValue(DateTime.UtcNow, 30));
@@ -114,11 +114,11 @@ public class LsmaTests
     public void Update_NaN_HandlesGracefully()
     {
         var lsma = new Lsma(5);
-        
+
         lsma.Update(new TValue(DateTime.UtcNow, 1));
         lsma.Update(new TValue(DateTime.UtcNow, 2));
         var result = lsma.Update(new TValue(DateTime.UtcNow, double.NaN));
-        
+
         // Input sequence becomes: 1, 2, 2 (NaN replaced by last valid 2)
         // Regression on (2,1), (1,2), (0,2)
         // Result should be 2.166666667
@@ -132,17 +132,17 @@ public class LsmaTests
         int count = 100;
         var source = new TSeries();
         var gbm = new GBM(startPrice: 100, seed: 42);
-        
+
         for (int i = 0; i < count; i++)
         {
             var bar = gbm.Next();
             source.Add(bar.C);
         }
-        
+
         var lsma = new Lsma(period);
         var series1 = lsma.Update(source);
         var series2 = Lsma.Batch(source, period);
-        
+
         Assert.Equal(series1.Count, series2.Count);
         for (int i = 0; i < count; i++)
         {
@@ -158,15 +158,15 @@ public class LsmaTests
         var values = new double[count];
         var output = new double[count];
         var gbm = new GBM(startPrice: 100, seed: 42);
-        
+
         for (int i = 0; i < count; i++)
         {
             var bar = gbm.Next();
             values[i] = bar.Close;
         }
-        
+
         Lsma.Calculate(values, output, period);
-        
+
         var lsma = new Lsma(period);
         for (int i = 0; i < count; i++)
         {
@@ -183,14 +183,14 @@ public class LsmaTests
         {
             lsma.Update(new TValue(DateTime.UtcNow, i));
         }
-        
+
         Assert.True(lsma.IsHot);
-        
+
         lsma.Reset();
-        
+
         Assert.False(lsma.IsHot);
         Assert.Equal(0, lsma.Last.Value);
-        
+
         // Should behave like new instance
         var result = lsma.Update(new TValue(DateTime.UtcNow, 100));
         Assert.Equal(100, result.Value);
@@ -201,13 +201,13 @@ public class LsmaTests
     {
         int period = 5;
         var lsma = new Lsma(period);
-        
+
         for (int i = 0; i < period; i++)
         {
             Assert.False(lsma.IsHot);
             lsma.Update(new TValue(DateTime.UtcNow, i));
         }
-        
+
         Assert.True(lsma.IsHot);
     }
 
@@ -216,7 +216,7 @@ public class LsmaTests
     {
         var source = new TSeries();
         var lsma = new Lsma(source, 10);
-        
+
         source.Add(new TValue(DateTime.UtcNow, 100));
         Assert.Equal(100, lsma.Last.Value);
     }
@@ -226,14 +226,14 @@ public class LsmaTests
     {
         var source = new TSeries();
         var lsma = new Lsma(source, 5);
-        
+
         // Verify subscription works
         source.Add(new TValue(DateTime.UtcNow, 100));
         Assert.Equal(100, lsma.Last.Value);
-        
+
         // Dispose and verify unsubscription
         lsma.Dispose();
-        
+
         // Add more data - lsma should NOT update
         source.Add(new TValue(DateTime.UtcNow, 200));
         Assert.Equal(100, lsma.Last.Value); // Should remain at previous value
@@ -244,9 +244,9 @@ public class LsmaTests
     {
         var source = new TSeries();
         var lsma = new Lsma(source, 5);
-        
+
         source.Add(new TValue(DateTime.UtcNow, 100));
-        
+
         // Multiple Dispose calls should not throw
         // Suppressing S3966: Multiple Dispose calls are intentional to test idempotency
 #pragma warning disable S3966
@@ -254,7 +254,7 @@ public class LsmaTests
         lsma.Dispose();
         lsma.Dispose();
 #pragma warning restore S3966
-        
+
         // Verify still unsubscribed
         source.Add(new TValue(DateTime.UtcNow, 200));
         Assert.Equal(100, lsma.Last.Value);
@@ -265,18 +265,18 @@ public class LsmaTests
     {
         var source = new TSeries();
         var lsma = new Lsma(source, 5);
-        
+
         source.Add(new TValue(DateTime.UtcNow, 100));
-        
+
         // Dispose from multiple threads simultaneously
         var tasks = new System.Threading.Tasks.Task[10];
         for (int i = 0; i < tasks.Length; i++)
         {
             tasks[i] = System.Threading.Tasks.Task.Run(() => lsma.Dispose());
         }
-        
+
         await System.Threading.Tasks.Task.WhenAll(tasks);
-        
+
         // Verify unsubscribed
         source.Add(new TValue(DateTime.UtcNow, 200));
         Assert.Equal(100, lsma.Last.Value);
@@ -287,14 +287,14 @@ public class LsmaTests
     {
         // Lsma created without source parameter
         var lsma = new Lsma(5);
-        
+
         // Should not throw even though there's no source to unsubscribe from
         // Suppressing S3966: Multiple Dispose calls are intentional to test idempotency
 #pragma warning disable S3966
         lsma.Dispose();
         lsma.Dispose(); // Idempotent
 #pragma warning restore S3966
-        
+
         // Verify state remains valid
         Assert.False(lsma.IsHot);
     }

lib/trends/mama/Mama.Tests.cs

@@ -41,21 +41,21 @@ public class MamaTests
     public void Update_InfinityInputs_DoesNotHang()
     {
         var mama = new Mama();
-        
+
         // Warmup with valid data to get past initialization phase
         for (int i = 0; i < 60; i++)
         {
             mama.Update(new TValue(DateTime.UtcNow, 100.0 + i));
         }
-        
+
         // Test positive infinity - should not hang
         var result1 = mama.Update(new TValue(DateTime.UtcNow, double.PositiveInfinity));
         Assert.True(double.IsFinite(result1.Value), "Positive infinity should produce finite result");
-        
+
         // Test negative infinity - should not hang
         var result2 = mama.Update(new TValue(DateTime.UtcNow, double.NegativeInfinity));
         Assert.True(double.IsFinite(result2.Value), "Negative infinity should produce finite result");
-        
+
         // Test NaN - should not hang
         var result3 = mama.Update(new TValue(DateTime.UtcNow, double.NaN));
         Assert.True(double.IsFinite(result3.Value), "NaN should produce finite result");
@@ -66,25 +66,25 @@ public class MamaTests
     {
         var data = new double[100];
         var gbm = new GBM(startPrice: 100, seed: 42);
-        
+
         // Fill with mostly valid data
         for (int i = 0; i < 100; i++)
         {
             data[i] = gbm.Next().Close;
         }
-        
+
         // Insert non-finite values at various points
         data[20] = double.NaN;
         data[40] = double.PositiveInfinity;
         data[60] = double.NegativeInfinity;
         data[80] = double.NaN;
-        
+
         var output = new double[100];
         var famaOutput = new double[100];
-        
+
         // This should complete without hanging
         Mama.Calculate(data, output, famaOutput: famaOutput);
-        
+
         // Verify all outputs are finite (no NaN or Infinity propagation)
         for (int i = 0; i < 100; i++)
         {
@@ -113,7 +113,7 @@ public class MamaTests
 
         // Manually chain for test
         bool eventFired = false;
-        mama.Pub += (object? sender, TValueEventArgs args) => eventFired = true;
+        mama.Pub += (object? sender, in TValueEventArgs args) => eventFired = true;
 
         mama.Update(new TValue(DateTime.UtcNow, 100.0));
 
@@ -160,14 +160,14 @@ public class MamaTests
     public void IsHot_BecomesTrueAfterWarmup()
     {
         var mama = new Mama();
-        
+
         // MAMA needs 50 bars to warmup (Index > 50)
         for (int i = 0; i < 50; i++)
         {
             mama.Update(new TValue(DateTime.UtcNow, 100));
             Assert.False(mama.IsHot);
         }
-        
+
         mama.Update(new TValue(DateTime.UtcNow, 100));
         Assert.True(mama.IsHot);
     }
@@ -181,9 +181,9 @@ public class MamaTests
             mama.Update(new TValue(DateTime.UtcNow, 100));
         }
         Assert.True(mama.IsHot);
-        
+
         mama.Reset();
-        
+
         Assert.False(mama.IsHot);
         Assert.True(double.IsNaN(mama.Last.Value));
     }
@@ -192,21 +192,21 @@ public class MamaTests
     public void Update_BarCorrection_UpdatesCorrectly()
     {
         var mama = new Mama();
-        
+
         // Warmup
         for (int i = 0; i < 10; i++)
         {
             mama.Update(new TValue(DateTime.UtcNow, 100));
         }
-        
+
         // New bar
         var result1 = mama.Update(new TValue(DateTime.UtcNow, 110));
-        
+
         // Update same bar with different value
         var result2 = mama.Update(new TValue(DateTime.UtcNow, 120), isNew: false);
-        
+
         Assert.NotEqual(result1.Value, result2.Value);
-        
+
         // Verify internal state by adding next bar
         var result3 = mama.Update(new TValue(DateTime.UtcNow, 130));
         Assert.True(double.IsFinite(result3.Value));
@@ -217,17 +217,17 @@ public class MamaTests
     {
         var source = new TSeries();
         var gbm = new GBM(startPrice: 100, seed: 42);
-        
+
         for (int i = 0; i < 50; i++)
         {
             var bar = gbm.Next();
             source.Add(bar.C);
         }
-        
+
         var mama = new Mama();
         var series1 = mama.Update(source);
         var series2 = Mama.Batch(source);
-        
+
         Assert.Equal(series1.Count, series2.Count);
         for (int i = 0; i < source.Count; i++)
         {
@@ -319,13 +319,13 @@ public class MamaTests
 
         var output1 = new double[count];
         var output2 = new double[count];
-        
+
         // Call without famaOutput parameter (backwards compatibility)
         Mama.Calculate(data, output1);
-        
+
         // Call with empty famaOutput span
         Mama.Calculate(data, output2, famaOutput: Span<double>.Empty);
-        
+
         // Both should produce identical MAMA results
         for (int i = 0; i < count; i++)
         {
@@ -339,8 +339,8 @@ public class MamaTests
         var data = new double[10];
         var mamaOutput = new double[10];
         var famaOutput = new double[5];
-        
-        var ex = Assert.Throws<ArgumentOutOfRangeException>(() => 
+
+        var ex = Assert.Throws<ArgumentOutOfRangeException>(() =>
             Mama.Calculate(data, mamaOutput, famaOutput: famaOutput));
         Assert.Equal("famaOutput", ex.ParamName);
     }

lib/trends/mama/Mama.Validation.Tests.cs

@@ -49,7 +49,7 @@ public class MamaValidationTests
         // The optimized version handles quadrants correctly (-pi to pi) and wraps phase differences (-pi to pi),
         // while original (and Skender) uses Atan (-pi/2 to pi/2) and ignores phase wrapping, causing divergence.
         ValidationHelper.VerifyData(qResult, sResult, x => x.Mama, skip: 100, tolerance: 40.0);
-        
+
         _output.WriteLine("MAMA Batch validated successfully against Skender");
     }
 
@@ -78,7 +78,7 @@ public class MamaValidationTests
         // 3. Verify MAMA
         // Tolerance increased to 40.0 due to optimized Phase calculation and Phase Wrapping correction.
         ValidationHelper.VerifyData(qMamaResults, sResult, x => x.Mama, skip: 100, tolerance: 40.0);
-        
+
         // 4. Verify FAMA
         ValidationHelper.VerifyData(qFamaResults, sResult, x => x.Fama, skip: 100, tolerance: 40.0);
 
@@ -117,14 +117,14 @@ public class MamaValidationTests
         // 2. Precision: Ooples uses 4-decimal constants, QuanTAlib uses exact fractions.
         // 3. Phase Wrapping: QuanTAlib correctly handles phase wrapping, Ooples does not.
         ValidationHelper.VerifyData(qResult, oMama, x => x, skip: 100, tolerance: 40.0);
-        
+
         // 4. Verify FAMA
         // QuanTAlib stores Fama in a separate property, not in the main TSeries result
         // We need to extract Fama from the indicator instance or capture it during streaming
         // But Update(TSeries) returns only the main series (Mama).
         // To verify Fama batch, we might need to iterate or expose it.
         // For now, let's verify Mama.
-        
+
         _output.WriteLine("MAMA Batch validated successfully against Ooples");
     }
 }

lib/trends/mama/Mama.md

@@ -158,7 +158,7 @@ Ehlers used `atan(Q/I)` with manual zero-checks because TradeStation's `atan2` d
 ```csharp
 // Period calculation: atan2 handles all quadrants correctly
 double angle = Math.Atan2(_state.Im, _state.Re);
-double period = Math.Abs(angle) > MinDeltaRadians 
+double period = Math.Abs(angle) > MinDeltaRadians
     ? TwoPi / Math.Abs(angle)
     : _p_state.Period;
 

lib/trends/mgdi/Mgdi.Tests.cs

@@ -10,10 +10,10 @@ public class MgdiTests
     public void NaN_FirstValue_DoesNotInitializeToZero()
     {
         var mgdi = new Mgdi(14, 0.6);
-        
+
         // First value is NaN
         var result = mgdi.Update(new TValue(DateTime.UtcNow, double.NaN));
-        
+
         // Should be NaN, not 0.0
         Assert.True(double.IsNaN(result.Value), $"Expected NaN but got {result.Value}");
     }
@@ -22,15 +22,15 @@ public class MgdiTests
     public void NaN_Sequence_InitializesOnFirstValid()
     {
         var mgdi = new Mgdi(14, 0.6);
-        
+
         // Sequence of NaNs
         mgdi.Update(new TValue(DateTime.UtcNow, double.NaN));
         mgdi.Update(new TValue(DateTime.UtcNow, double.NaN));
-        
+
         // First valid value
         double firstValid = 100.0;
         var result = mgdi.Update(new TValue(DateTime.UtcNow, firstValid));
-        
+
         Assert.Equal(firstValid, result.Value);
     }
 
@@ -40,7 +40,7 @@ public class MgdiTests
         var mgdi = new Mgdi(14, 0.6);
         mgdi.Update(new TValue(DateTime.UtcNow, 100.0));
         var result = mgdi.Update(new TValue(DateTime.UtcNow, 101.0));
-        
+
         Assert.True(result.Value > 100.0);
         Assert.True(result.Value < 101.0);
     }
@@ -50,7 +50,7 @@ public class MgdiTests
     {
         var source = new double[10];
         var output = new double[10];
-        
+
         Assert.Throws<ArgumentOutOfRangeException>(() => Mgdi.Calculate(source, output, 14, double.NaN));
         Assert.Throws<ArgumentOutOfRangeException>(() => Mgdi.Calculate(source, output, 14, double.PositiveInfinity));
         Assert.Throws<ArgumentOutOfRangeException>(() => Mgdi.Calculate(source, output, 14, double.NegativeInfinity));

lib/trends/mgdi/Mgdi.Validation.Tests.cs

@@ -58,7 +58,7 @@ public sealed class MgdiValidationTests : IDisposable
         // Calculate QuanTAlib MGDI Streaming
         var mgdi = new Mgdi(14, 0.6);
         var streamingResults = new List<double>();
-        
+
         foreach (var item in _data.Data)
         {
             streamingResults.Add(mgdi.Update(item).Value);

lib/trends/pwma/Pwma.Tests.cs

@@ -361,7 +361,7 @@ public class PwmaTests
         var gbm = new GBM(startPrice: 100, mu: 0.05, sigma: 0.2, seed: 123);
         var bars = gbm.Fetch(1000, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
         var series = bars.Close;
-        
+
         // 1. Batch Mode
         var batchSeries = Pwma.Batch(series, period);
         double expected = batchSeries.Last.Value;

lib/trends/rma/Rma.Tests.cs

@@ -208,7 +208,7 @@ public class RmaTests
     {
         var source = new TSeries();
         var rma = new Rma(source, 10);
-        
+
         source.Add(new TValue(DateTime.UtcNow, 100));
         Assert.Equal(100, rma.Last.Value, 1e-9);
     }

lib/trends/rma/Rma.Validation.Tests.cs

@@ -43,7 +43,7 @@ public sealed class RmaValidationTests : IDisposable
     {
         // Arrange
         int period = 14;
-        
+
         // QuanTAlib RMA
         var rma = new Rma(period);
         var quantalibResults = new TSeries();
@@ -94,7 +94,7 @@ public sealed class RmaValidationTests : IDisposable
         // Skip warmup period for comparison
         int skip = period * 30;
         int itemsToVerify = _testData.Data.Count - skip;
-        
+
         ValidationHelper.VerifyData(qResult, oValues, (s) => s, skip: itemsToVerify, tolerance: ValidationHelper.OoplesTolerance);
     }
 }

lib/trends/sma/Sma.Tests.cs

@@ -472,7 +472,7 @@ public class SmaTests
         var gbm = new GBM(startPrice: 100, mu: 0.05, sigma: 0.2, seed: 123);
         var bars = gbm.Fetch(1000, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
         var series = bars.Close;
-        
+
         // 1. Batch Mode
         var batchSeries = Sma.Batch(series, period);
         double expected = batchSeries.Last.Value;
@@ -512,7 +512,7 @@ public class SmaTests
     {
         var source = new TSeries();
         var sma = new Sma(source, 10);
-        
+
         source.Add(new TValue(DateTime.UtcNow, 100));
         Assert.Equal(100, sma.Last.Value);
     }
@@ -529,12 +529,12 @@ public class SmaTests
     {
         var sma = new Sma(5);
         double[] history = [10, 20, 30, 40, 50]; // SMA(5) = 30
-        
+
         sma.Prime(history);
 
         Assert.True(sma.IsHot);
         Assert.Equal(30.0, sma.Last.Value, 1e-10);
-        
+
         // Verify it continues correctly
         sma.Update(new TValue(DateTime.UtcNow, 60)); // 20,30,40,50,60 -> 40
         Assert.Equal(40.0, sma.Last.Value, 1e-10);
@@ -544,8 +544,8 @@ public class SmaTests
     public void Prime_WithInsufficientHistory_IsNotHot()
     {
         var sma = new Sma(10);
-        double[] history = [10, 20, 30, 40, 50]; 
-        
+        double[] history = [10, 20, 30, 40, 50];
+
         sma.Prime(history);
 
         Assert.False(sma.IsHot);
@@ -556,14 +556,14 @@ public class SmaTests
     public void Prime_HandlesNaN_InHistory()
     {
         var sma = new Sma(3);
-        double[] history = [10, 20, double.NaN, 40]; 
+        double[] history = [10, 20, double.NaN, 40];
         // 10
         // 10, 20
         // 10, 20, 20 (NaN replaced by 20) -> Avg(10,20,20) = 16.666...
         // 20, 20, 40 -> Avg(20,20,40) = 26.666...
-        
+
         sma.Prime(history);
-        
+
         Assert.True(sma.IsHot);
         Assert.Equal(80.0 / 3.0, sma.Last.Value, 1e-9);
     }
@@ -574,7 +574,7 @@ public class SmaTests
         var series = new TSeries();
         for (int i = 1; i <= 10; i++) series.Add(DateTime.UtcNow, i * 10);
         // 10, 20, 30, 40, 50, 60, 70, 80, 90, 100
-        
+
         // SMA(5)
         var (results, indicator) = Sma.Calculate(series, 5);
 
@@ -589,7 +589,7 @@ public class SmaTests
         Assert.Equal(5, indicator.WarmupPeriod);
 
         // Verify indicator continues correctly
-        indicator.Update(new TValue(DateTime.UtcNow, 110)); 
+        indicator.Update(new TValue(DateTime.UtcNow, 110));
         // Window was [60, 70, 80, 90, 100] -> Avg 80
         // New Window [70, 80, 90, 100, 110] -> Avg 90
         Assert.Equal(90.0, indicator.Last.Value);

lib/trends/sma/Sma.Tolerance.Tests.cs

@@ -29,7 +29,7 @@ public sealed class SmaToleranceTests : IDisposable
         var sResult = _testData.SkenderQuotes.GetSma(period).ToList();
 
         ValidationHelper.VerifyData(qResult, sResult, (s) => s.Sma);
-        
+
         // Add explicit assertion to satisfy SonarQube
         Assert.True(qResult.Count > 0);
     }

lib/trends/sma/Sma.ZeroDiv.Tests.cs

@@ -10,13 +10,13 @@ public class SmaZeroDivTests
     public void Sma_Update_WithIsNewFalse_OnEmptyBuffer_DoesNotThrow()
     {
         var sma = new Sma(10);
-        
+
         // Buffer is empty initially.
         // Calling Update with isNew=false should not cause division by zero.
         // It should return NaN or 0 or Last, but definitely not throw or return Infinity.
-        
+
         var result = sma.Update(new TValue(DateTime.UtcNow, 100), isNew: false);
-        
+
         // Since buffer count is 0, we expect NaN based on our fix.
         Assert.True(double.IsNaN(result.Value), $"Expected NaN but got {result.Value}");
     }
@@ -27,10 +27,10 @@ public class SmaZeroDivTests
         var sma = new Sma(10);
         sma.Update(new TValue(DateTime.UtcNow, 100));
         sma.Reset();
-        
+
         // Buffer is empty after Reset.
         var result = sma.Update(new TValue(DateTime.UtcNow, 200), isNew: false);
-        
+
         Assert.True(double.IsNaN(result.Value), $"Expected NaN but got {result.Value}");
     }
 }

lib/trends/sma/Sma.cs

@@ -189,7 +189,7 @@ public sealed class Sma : AbstractBase
         {
             // Capture previous state BEFORE any mutation
             _p_state = _state;
-            
+
             double val = GetValidValue(input.Value);
             UpdateState(val);
             _state.LastInput = val;
@@ -199,11 +199,11 @@ public sealed class Sma : AbstractBase
         {
             // Restore scalar state to pre-mutation values
             _state = _p_state;
-            
+
             double val = GetValidValue(input.Value);
             // Update sum: remove the value that was added during isNew=true, add the new correction value
             _state.Sum = _state.Sum - _currentBarValue + val;
-            
+
             // Update the buffer's newest value and sync its internal sum with our state sum
             _buffer.UpdateNewest(val);
             _state.Sum = _buffer.RecalculateSum(); // Ensure sums stay in sync

lib/trends/ssf/Ssf.Tests.cs

@@ -249,7 +249,7 @@ public class SsfTests
         var gbm = new GBM(startPrice: 100, mu: 0.05, sigma: 0.2, seed: 123);
         var bars = gbm.Fetch(1000, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
         var series = bars.Close;
-        
+
         // 1. Batch Mode
         var batchSeries = Ssf.Calculate(series, period).Results;
         double expected = batchSeries.Last.Value;

lib/trends/ssf/Ssf.cs

@@ -178,11 +178,11 @@ public sealed class Ssf : AbstractBase
         double ssf = (_state.Count < 4)
             ? val
             : (_c1 * (val + _state.PrevInput) * 0.5) + (_c2 * _state.Ssf1) + (_c3 * _state.Ssf2);
-        
+
         _state.Ssf2 = _state.Ssf1;
         _state.Ssf1 = ssf;
         _state.PrevInput = val;
-        
+
         if (isNew) _state.Count++;
         if (!_state.IsHot && _state.Count >= WarmupPeriod)
             _state.IsHot = true;
@@ -216,7 +216,7 @@ public sealed class Ssf : AbstractBase
         sourceTimes.CopyTo(tSpan);
 
         _p_state = _state;
-        
+
         Last = new TValue(tSpan[len - 1], vSpan[len - 1]);
 
         return new TSeries(t, v);
@@ -239,12 +239,12 @@ public sealed class Ssf : AbstractBase
                     state.Ssf1 = state.LastValidValue;
                     state.Ssf2 = state.LastValidValue;
                     state.PrevInput = state.LastValidValue;
-                    output[i] = state.LastValidValue; 
+                    output[i] = state.LastValidValue;
                     state.Count = 1;
                     i++;
                     break;
                 }
-                output[i] = double.NaN; 
+                output[i] = double.NaN;
             }
 
             // Handle all-NaN case: if no finite value was found, set remaining outputs to NaN and return

lib/trends/super/Super.Quantower.cs

@@ -54,7 +54,7 @@ public sealed class SuperIndicator : Indicator, IWatchlistIndicator
         bool isNew = args.IsNewBar();
         var bar = this.GetInputBar(args);
         double value = _super!.Update(bar, isNew).Value;
-        
+
         _series!.SetValue(value, _super.IsHot, ShowColdValues);
         _upperBand!.SetValue(_super.UpperBand.Value, _super.IsHot, ShowColdValues);
         _lowerBand!.SetValue(_super.LowerBand.Value, _super.IsHot, ShowColdValues);

lib/trends/super/Super.Tests.cs

@@ -70,13 +70,13 @@ public class SuperTests
         super.Reset();
         Assert.Equal(0, super.Last.Value);
         Assert.False(super.IsHot);
-        
+
         // Feed again
         for (int i = 0; i < bars.Count; i++)
         {
             super.Update(bars[i]);
         }
-        
+
         Assert.True(double.IsFinite(super.Last.Value));
     }
 
@@ -110,14 +110,14 @@ public class SuperTests
             }
         }
     }
-    
+
     [Fact]
     public void Warmup_Handling()
     {
         var super = new Super(10, 3.0);
         var gbm = new GBM();
         var bars = gbm.Fetch(20, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
-        
+
         // First 10 bars should be NaN
         for (int i = 0; i < 10; i++)
         {
@@ -125,7 +125,7 @@ public class SuperTests
             Assert.True(double.IsNaN(result.Value), $"Bar {i} should be NaN");
             Assert.False(super.IsHot);
         }
-        
+
         // 11th bar (index 10) should be valid
         var result11 = super.Update(bars[10]);
         Assert.True(double.IsFinite(result11.Value), "Bar 10 should be finite");
@@ -146,16 +146,16 @@ public class SuperTests
     {
         var gbm = new GBM();
         var bars = gbm.Fetch(200, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
-        
+
         var super = new Super(10, 3.0);
         var streamingResults = new List<double>();
         for (int i = 0; i < bars.Count; i++)
         {
             streamingResults.Add(super.Update(bars[i]).Value);
         }
-        
+
         var staticResults = Super.Batch(bars, 10, 3.0);
-        
+
         Assert.Equal(streamingResults.Count, staticResults.Count);
         for (int i = 0; i < staticResults.Count; i++)
         {
@@ -176,12 +176,12 @@ public class SuperTests
         var super = new Super(10, 3.0);
         var gbm = new GBM();
         var bars = gbm.Fetch(10, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
-        
+
         // Test TBarSeries chain
         var result = super.Update(bars);
         Assert.NotNull(result);
         Assert.IsType<TSeries>(result);
-        
+
         // Test TBar chain (returns TValue)
         var result2 = super.Update(bars[0]);
         Assert.IsType<TValue>(result2);

lib/trends/t3/T3.Tests.cs

@@ -66,13 +66,13 @@ public class T3Tests
         t3.Reset();
         Assert.Equal(0, t3.Last.Value);
         Assert.False(t3.IsHot);
-        
+
         // Feed again
         for (int i = 0; i < bars.Count; i++)
         {
             t3.Update(new TValue(bars[i].Time, bars[i].Close));
         }
-        
+
         Assert.True(double.IsFinite(t3.Last.Value));
     }
 
@@ -99,23 +99,23 @@ public class T3Tests
             Assert.Equal(streamingResults[i], seriesResults.Values[i], 1e-9);
         }
     }
-    
+
     [Fact]
     public void BatchCalculate_Matches_Streaming()
     {
         var gbm = new GBM();
         var bars = gbm.Fetch(200, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
         var series = bars.Close;
-        
+
         var t3 = new T3(5, 0.7);
         var streamingResults = new List<double>();
         for (int i = 0; i < series.Count; i++)
         {
             streamingResults.Add(t3.Update(series[i]).Value);
         }
-        
+
         var batchResults = T3.Batch(series, 5, 0.7);
-        
+
         Assert.Equal(streamingResults.Count, batchResults.Count);
         for (int i = 0; i < batchResults.Count; i++)
         {
@@ -129,17 +129,17 @@ public class T3Tests
         var gbm = new GBM();
         var bars = gbm.Fetch(200, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
         var series = bars.Close;
-        
+
         var t3 = new T3(5, 0.7);
         var streamingResults = new List<double>();
         for (int i = 0; i < series.Count; i++)
         {
             streamingResults.Add(t3.Update(series[i]).Value);
         }
-        
+
         var spanResults = new double[series.Count];
         T3.Batch(series.Values, spanResults, 5, 0.7);
-        
+
         for (int i = 0; i < spanResults.Length; i++)
         {
             Assert.Equal(streamingResults[i], spanResults[i], 1e-9);
@@ -153,12 +153,12 @@ public class T3Tests
         var gbm = new GBM();
         var bars = gbm.Fetch(10, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
         var series = bars.Close;
-        
+
         // Test TSeries chain
         var result = t3.Update(series);
         Assert.NotNull(result);
         Assert.IsType<TSeries>(result);
-        
+
         // Test TValue chain
         var result2 = t3.Update(series[0]);
         Assert.IsType<TValue>(result2);
@@ -255,13 +255,13 @@ public class T3Tests
     {
         var input = new double[10];
         var output = new double[10];
-        
+
         var ex1 = Assert.Throws<ArgumentOutOfRangeException>(() => T3.Batch(input, output, 5, 0.0));
         Assert.Equal("vfactor", ex1.ParamName);
-        
+
         var ex2 = Assert.Throws<ArgumentOutOfRangeException>(() => T3.Batch(input, output, 5, -0.5));
         Assert.Equal("vfactor", ex2.ParamName);
-        
+
         var ex3 = Assert.Throws<ArgumentOutOfRangeException>(() => T3.Batch(input, output, 5, 1.5));
         Assert.Equal("vfactor", ex3.ParamName);
     }
@@ -277,7 +277,7 @@ public class T3Tests
     {
         var source = new TestPublisher();
         _ = new T3(source, 5);
-        
+
         Assert.Equal(1, source.SubscriberCount);
     }
 
@@ -286,11 +286,11 @@ public class T3Tests
     {
         var source = new TestPublisher();
         var t3 = new T3(source, 5);
-        
+
         Assert.Equal(1, source.SubscriberCount);
-        
+
         t3.Dispose();
-        
+
         Assert.Equal(0, source.SubscriberCount);
     }
 
@@ -299,12 +299,12 @@ public class T3Tests
     {
         var source = new TestPublisher();
         var t3 = new T3(source, 5);
-        
+
         t3.Dispose();
 #pragma warning disable S3966 // Objects should not be disposed more than once
         t3.Dispose();
 #pragma warning restore S3966 // Objects should not be disposed more than once
-        
+
         Assert.Equal(0, source.SubscriberCount);
     }
 
@@ -312,7 +312,7 @@ public class T3Tests
     public void Dispose_DoesNothing_WhenNoSource()
     {
         var t3 = new T3(5);
-        
+
         var exception = Record.Exception(() => t3.Dispose());
         Assert.Null(exception);
     }

lib/trends/t3/T3.cs

@@ -8,19 +8,19 @@ namespace QuanTAlib;
 /// T3: Tillson T3 Moving Average
 /// </summary>
 /// <remarks>
-/// T3 works by running price data through a series of six EMAs, then combining the outputs 
+/// T3 works by running price data through a series of six EMAs, then combining the outputs
 /// of these EMAs using carefully calculated weights.
-/// 
+///
 /// Formula:
 /// T3 = c1*e6 + c2*e5 + c3*e4 + c4*e3
-/// 
+///
 /// Where:
 /// e1..e6 are cascaded EMAs
 /// c1 = -v^3
 /// c2 = 3(v^2 + v^3)
 /// c3 = -3(2v^2 + v + v^3)
 /// c4 = 1 + 3v + 3v^2 + v^3
-/// 
+///
 /// v is volume factor (default 0.7)
 /// alpha = 2 / (period + 1)
 /// </remarks>
@@ -154,7 +154,7 @@ public sealed class T3 : AbstractBase, IDisposable
         // Since Compute returns the result but also updates state, we can't easily get the last result without re-running or storing it.
         // However, Prime is usually followed by Update or we just need the state ready.
         // If we want Last to be correct, we should probably store the last result.
-        // But AbstractBase.Prime doesn't strictly require Last to be set to the very last value of source, 
+        // But AbstractBase.Prime doesn't strictly require Last to be set to the very last value of source,
         // though it's good practice.
         // Let's re-run the last value computation to set Last correctly.
         if (len > 0)

lib/trends/tema/Tema.Quantower.Tests.cs

@@ -68,7 +68,7 @@ public class TemaIndicatorTests
         for (int i = 0; i < 10; i++)
         {
             indicator.HistoricalData.AddBar(now.AddMinutes(i), 100, 105, 95, 102);
-            
+
             // Process update
             var args = new UpdateArgs(UpdateReason.HistoricalBar);
             indicator.ProcessUpdate(args);

lib/trends/tema/Tema.Tests.cs

@@ -66,13 +66,13 @@ public class TemaTests
         tema.Reset();
         Assert.Equal(0, tema.Last.Value);
         Assert.False(tema.IsHot);
-        
+
         // Feed again
         for (int i = 0; i < bars.Count; i++)
         {
             tema.Update(new TValue(bars[i].Time, bars[i].Close));
         }
-        
+
         Assert.True(double.IsFinite(tema.Last.Value));
     }
 
@@ -99,23 +99,23 @@ public class TemaTests
             Assert.Equal(streamingResults[i], seriesResults.Values[i], 1e-9);
         }
     }
-    
+
     [Fact]
     public void BatchCalculate_Matches_Streaming()
     {
         var gbm = new GBM();
         var bars = gbm.Fetch(200, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
         var series = bars.Close;
-        
+
         var tema = new Tema(10);
         var streamingResults = new List<double>();
         for (int i = 0; i < series.Count; i++)
         {
             streamingResults.Add(tema.Update(series[i]).Value);
         }
-        
+
         var batchResults = Tema.Batch(series, 10);
-        
+
         Assert.Equal(streamingResults.Count, batchResults.Count);
         for (int i = 0; i < batchResults.Count; i++)
         {
@@ -129,17 +129,17 @@ public class TemaTests
         var gbm = new GBM();
         var bars = gbm.Fetch(200, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
         var series = bars.Close;
-        
+
         var tema = new Tema(10);
         var streamingResults = new List<double>();
         for (int i = 0; i < series.Count; i++)
         {
             streamingResults.Add(tema.Update(series[i]).Value);
         }
-        
+
         var spanResults = new double[series.Count];
         Tema.Batch(series.Values, spanResults, 10);
-        
+
         for (int i = 0; i < spanResults.Length; i++)
         {
             Assert.Equal(streamingResults[i], spanResults[i], 1e-9);
@@ -153,12 +153,12 @@ public class TemaTests
         var gbm = new GBM();
         var bars = gbm.Fetch(10, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
         var series = bars.Close;
-        
+
         // Test TSeries chain
         var result = tema.Update(series);
         Assert.NotNull(result);
         Assert.IsType<TSeries>(result);
-        
+
         // Test TValue chain
         var result2 = tema.Update(series[0]);
         Assert.IsType<TValue>(result2);

lib/trends/tema/Tema.Validation.Tests.cs

@@ -85,9 +85,9 @@ public class TemaValidationTests
             var temaIndicator = Tulip.Indicators.tema;
             double[][] inputs = { _testData.RawData.ToArray() };
             double[] options = { period };
-            
+
             // Tulip TEMA lookback is 3*(period-1)
-            int lookback = 3 * (period - 1); 
+            int lookback = 3 * (period - 1);
             double[][] outputs = { new double[_testData.RawData.Length - lookback] };
 
             temaIndicator.Run(inputs, options, outputs);

lib/trends/trima/Trima.Tests.cs

@@ -66,13 +66,13 @@ public class TrimaTests
         trima.Reset();
         Assert.Equal(0, trima.Last.Value);
         Assert.False(trima.IsHot);
-        
+
         // Feed again
         for (int i = 0; i < bars.Count; i++)
         {
             trima.Update(new TValue(bars[i].Time, bars[i].Close));
         }
-        
+
         Assert.True(double.IsFinite(trima.Last.Value));
     }
 
@@ -99,23 +99,23 @@ public class TrimaTests
             Assert.Equal(streamingResults[i], seriesResults.Values[i], 1e-9);
         }
     }
-    
+
     [Fact]
     public void BatchCalculate_Matches_Streaming()
     {
         var gbm = new GBM();
         var bars = gbm.Fetch(200, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
         var series = bars.Close;
-        
+
         var trima = new Trima(10);
         var streamingResults = new List<double>();
         for (int i = 0; i < series.Count; i++)
         {
             streamingResults.Add(trima.Update(series[i]).Value);
         }
-        
+
         var batchResults = Trima.Batch(series, 10);
-        
+
         Assert.Equal(streamingResults.Count, batchResults.Count);
         for (int i = 0; i < batchResults.Count; i++)
         {
@@ -129,17 +129,17 @@ public class TrimaTests
         var gbm = new GBM();
         var bars = gbm.Fetch(200, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
         var series = bars.Close;
-        
+
         var trima = new Trima(10);
         var streamingResults = new List<double>();
         for (int i = 0; i < series.Count; i++)
         {
             streamingResults.Add(trima.Update(series[i]).Value);
         }
-        
+
         var spanResults = new double[series.Count];
         Trima.Batch(series.Values, spanResults, 10);
-        
+
         for (int i = 0; i < spanResults.Length; i++)
         {
             Assert.Equal(streamingResults[i], spanResults[i], 1e-9);
@@ -153,12 +153,12 @@ public class TrimaTests
         var gbm = new GBM();
         var bars = gbm.Fetch(10, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
         var series = bars.Close;
-        
+
         // Test TSeries chain
         var result = trima.Update(series);
         Assert.NotNull(result);
         Assert.IsType<TSeries>(result);
-        
+
         // Test TValue chain
         var result2 = trima.Update(series[0]);
         Assert.IsType<TValue>(result2);

lib/trends/trima/Trima.Validation.Tests.cs

@@ -36,14 +36,14 @@ public class TrimaValidationTests
             int p2 = (period + 1) / 2;
 
             var sma1Results = _testData.SkenderQuotes.GetSma(p1).ToList();
-            
+
             // Map SMA1 results to Quotes for the second pass
             // Note: We use 0 for null values during warmup, which might affect early values
             // but should stabilize for the verification window (last 100 records)
-            var quotes2 = sma1Results.Select(r => new Quote 
-            { 
-                Date = r.Date, 
-                Close = (decimal)(r.Sma ?? 0) 
+            var quotes2 = sma1Results.Select(r => new Quote
+            {
+                Date = r.Date,
+                Close = (decimal)(r.Sma ?? 0)
             }).ToList();
 
             var sResult = quotes2.GetSma(p2).ToList();
@@ -99,10 +99,10 @@ public class TrimaValidationTests
             // Usually it's period-1 for simple averages, but TRIMA is double smoothed.
             // We'll rely on the output length to align.
             // Tulip.Indicators.trima.Run expects outputs to be sized correctly.
-            // We can try to run it with a large buffer and see what happens, 
+            // We can try to run it with a large buffer and see what happens,
             // or calculate the expected lookback.
             // For TRIMA(n), lookback is roughly n-1.
-            int lookback = period - 1; 
+            int lookback = period - 1;
             double[][] outputs = { new double[_testData.RawData.Length - lookback] };
 
             trimaIndicator.Run(inputs, options, outputs);

lib/trends/usf/Usf.Quantower.Tests.cs

@@ -19,10 +19,10 @@ public class UsfIndicatorTests
     public void Indicator_ProcessesData()
     {
         var indicator = new UsfIndicator();
-        
+
         // Simulate Init
         indicator.GetType().GetMethod("OnInit", System.Reflection.BindingFlags.NonPublic | System.Reflection.BindingFlags.Instance)?.Invoke(indicator, null);
-        
+
         Assert.NotNull(indicator);
     }
 }

lib/trends/usf/Usf.Tests.cs

@@ -66,13 +66,13 @@ public class UsfTests
         usf.Reset();
         Assert.Equal(0, usf.Last.Value);
         Assert.False(usf.IsHot);
-        
+
         // Feed again
         for (int i = 0; i < bars.Count; i++)
         {
             usf.Update(new TValue(bars[i].Time, bars[i].Close));
         }
-        
+
         Assert.True(double.IsFinite(usf.Last.Value));
     }
 
@@ -99,23 +99,23 @@ public class UsfTests
             Assert.Equal(streamingResults[i], seriesResults.Values[i], 1e-9);
         }
     }
-    
+
     [Fact]
     public void BatchCalculate_Matches_Streaming()
     {
         var gbm = new GBM();
         var bars = gbm.Fetch(200, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
         var series = bars.Close;
-        
+
         var usf = new Usf(10);
         var streamingResults = new List<double>();
         for (int i = 0; i < series.Count; i++)
         {
             streamingResults.Add(usf.Update(series[i]).Value);
         }
-        
+
         var batchResults = Usf.Calculate(series, 10).Results;
-        
+
         Assert.Equal(streamingResults.Count, batchResults.Count);
         for (int i = 0; i < batchResults.Count; i++)
         {
@@ -129,17 +129,17 @@ public class UsfTests
         var gbm = new GBM();
         var bars = gbm.Fetch(200, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
         var series = bars.Close;
-        
+
         var usf = new Usf(10);
         var streamingResults = new List<double>();
         for (int i = 0; i < series.Count; i++)
         {
             streamingResults.Add(usf.Update(series[i]).Value);
         }
-        
+
         var spanResults = new double[series.Count];
         Usf.Calculate(series.Values, spanResults, 10);
-        
+
         for (int i = 0; i < spanResults.Length; i++)
         {
             Assert.Equal(streamingResults[i], spanResults[i], 1e-9);
@@ -153,12 +153,12 @@ public class UsfTests
         var gbm = new GBM();
         var bars = gbm.Fetch(10, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
         var series = bars.Close;
-        
+
         // Test TSeries chain
         var result = usf.Update(series);
         Assert.NotNull(result);
         Assert.IsType<TSeries>(result);
-        
+
         // Test TValue chain
         var result2 = usf.Update(series[0]);
         Assert.IsType<TValue>(result2);

lib/trends/usf/Usf.cs

@@ -168,12 +168,12 @@ public sealed class Usf : AbstractBase
         double usf = (_state.Count < 4)
             ? val
             : (1.0 - _c1) * val + (2.0 * _c1 - _c2) * _state.PrevInput1 - (_c1 + _c3) * _state.PrevInput2 + _c2 * _state.Usf1 + _c3 * _state.Usf2;
-        
+
         _state.Usf2 = _state.Usf1;
         _state.Usf1 = usf;
         _state.PrevInput2 = _state.PrevInput1;
         _state.PrevInput1 = val;
-        
+
         if (isNew && !initialized) _state.Count++;
         if (!_state.IsHot && _state.Count >= WarmupPeriod)
             _state.IsHot = true;
@@ -207,7 +207,7 @@ public sealed class Usf : AbstractBase
         sourceTimes.CopyTo(tSpan);
 
         _p_state = _state;
-        
+
         Last = new TValue(tSpan[len - 1], vSpan[len - 1]);
 
         return new TSeries(t, v);
@@ -231,12 +231,12 @@ public sealed class Usf : AbstractBase
                     state.Usf2 = state.LastValidValue;
                     state.PrevInput1 = state.LastValidValue;
                     state.PrevInput2 = state.LastValidValue;
-                    output[i] = state.LastValidValue; 
+                    output[i] = state.LastValidValue;
                     state.Count = 1;
                     i++;
                     break;
                 }
-                output[i] = double.NaN; 
+                output[i] = double.NaN;
             }
         }
 

lib/trends/vidya/Vidya.Quantower.Tests.cs

@@ -59,7 +59,7 @@ public class VidyaIndicatorTests
         for (int i = 0; i < 10; i++)
         {
             indicator.HistoricalData.AddBar(now.AddMinutes(i), 100, 105, 95, 102);
-            
+
             // Process update
             var args = new UpdateArgs(UpdateReason.HistoricalBar);
             indicator.ProcessUpdate(args);

lib/trends/vidya/Vidya.Tests.cs

@@ -65,13 +65,13 @@ public class VidyaTests
 
         vidya.Reset();
         Assert.Equal(0, vidya.Last.Value);
-        
+
         // Feed again
         for (int i = 0; i < bars.Count; i++)
         {
             vidya.Update(new TValue(bars[i].Time, bars[i].Close));
         }
-        
+
         Assert.True(double.IsFinite(vidya.Last.Value));
     }
 
@@ -98,23 +98,23 @@ public class VidyaTests
             Assert.Equal(streamingResults[i], seriesResults.Values[i], 1e-9);
         }
     }
-    
+
     [Fact]
     public void BatchCalculate_Matches_Streaming()
     {
         var gbm = new GBM();
         var bars = gbm.Fetch(200, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
         var series = bars.Close;
-        
+
         var vidya = new Vidya(10);
         var streamingResults = new List<double>();
         for (int i = 0; i < series.Count; i++)
         {
             streamingResults.Add(vidya.Update(series[i]).Value);
         }
-        
+
         var batchResults = Vidya.Batch(series, 10);
-        
+
         Assert.Equal(streamingResults.Count, batchResults.Count);
         for (int i = 0; i < batchResults.Count; i++)
         {
@@ -128,17 +128,17 @@ public class VidyaTests
         var gbm = new GBM();
         var bars = gbm.Fetch(200, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
         var series = bars.Close;
-        
+
         var vidya = new Vidya(10);
         var streamingResults = new List<double>();
         for (int i = 0; i < series.Count; i++)
         {
             streamingResults.Add(vidya.Update(series[i]).Value);
         }
-        
+
         var spanResults = new double[series.Count];
         Vidya.Batch(series.Values, spanResults, 10);
-        
+
         for (int i = 0; i < spanResults.Length; i++)
         {
             Assert.Equal(streamingResults[i], spanResults[i], 1e-9);
@@ -152,12 +152,12 @@ public class VidyaTests
         var gbm = new GBM();
         var bars = gbm.Fetch(10, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
         var series = bars.Close;
-        
+
         // Test TSeries chain
         var result = vidya.Update(series);
         Assert.NotNull(result);
         Assert.IsType<TSeries>(result);
-        
+
         // Test TValue chain
         var result2 = vidya.Update(series[0]);
         Assert.IsType<TValue>(result2);

lib/trends/vidya/Vidya.cs

@@ -201,7 +201,7 @@ public sealed class Vidya : AbstractBase, IDisposable
         // Process all data to build up state
         // For recursive indicators like VIDYA, we generally need to process from the start
         // or at least a significant warmup period.
-        // Given we don't know the "correct" previous VIDYA without processing, 
+        // Given we don't know the "correct" previous VIDYA without processing,
         // we process the whole provided history.
 
         double prevClose = source[0];
@@ -254,7 +254,7 @@ public sealed class Vidya : AbstractBase, IDisposable
         _state.LastVidya = lastVidya;
 
         // Set Last
-        // Note: Time is not available in Span, so we use MinValue. 
+        // Note: Time is not available in Span, so we use MinValue.
         // It will be updated on next Update.
         Last = new TValue(DateTime.MinValue, _state.CurrentVidya);
         _p_state = _state;
@@ -286,7 +286,7 @@ public sealed class Vidya : AbstractBase, IDisposable
 
         double alpha = 2.0 / (period + 1);
 
-        // Use arrays for buffers to avoid heap allocations if possible, 
+        // Use arrays for buffers to avoid heap allocations if possible,
         // but period is dynamic.
         // We can use ArrayPool or just new double[period] if period is small.
         // For simplicity and safety with large periods, let's use ArrayPool.

lib/trends/wma/Wma.Coverage.Tests.cs

@@ -17,7 +17,7 @@ public class WmaCoverageTests
         for (int i = 0; i < len; i++) source[i] = i;
 
         double[] output = new double[len];
-        
+
         // This should trigger CalculateScalarCore internally
         Wma.Batch(source.AsSpan(), output.AsSpan(), period);
 
@@ -45,26 +45,26 @@ public class WmaCoverageTests
             // However, we can't easily invoke it directly.
             // But wait, I previously wrote a test that called a *copy* of the method.
             // Calling the *actual* private method with Spans via reflection is not possible in C# (TargetInvocationException).
-            
-            // Strategy change: 
+
+            // Strategy change:
             // Since we cannot invoke private methods with Span args via reflection,
             // and we cannot change the visibility of the methods (they should remain private),
             // we are limited in how we can "force" coverage of the private AVX2 method if AVX512 is present.
-            
+
             // However, we CAN use the fact that Wma.Batch checks for Avx512F.IsSupported.
             // We cannot change that runtime flag.
-            
+
             // Actually, we can't easily cover the AVX2 path on an AVX512 machine without code modification or a "TestAccessor" pattern.
             // But wait, the user asked "why is coverage only 46%".
             // If I can't run the code, I can't cover it.
-            
+
             // BUT, I can verify the Scalar Core logic by using the small data test (done above).
             // For AVX2, if I can't invoke it, I can't cover it on this machine.
-            
+
             // Let's double check if there's any way to invoke it.
             // Maybe I can use `MethodInfo.CreateDelegate`?
             // Delegates can take Spans if defined correctly.
-            
+
             InvokePrivateStaticMethod_WithSpans("CalculateSimdCore", source, output, period);
         }
         catch (Exception ex)
@@ -98,7 +98,7 @@ public class WmaCoverageTests
         // Create a delegate that matches the signature
         // Note: ReadOnlySpan<double> and Span<double> in delegate signature
         var del = methodInfo.CreateDelegate<CoreDelegate>();
-        
+
         del(source.AsSpan(), output.AsSpan(), period);
     }
 }

lib/trends/wma/Wma.Quantower.Tests.cs

@@ -68,7 +68,7 @@ public class WmaIndicatorTests
         for (int i = 0; i < 10; i++)
         {
             indicator.HistoricalData.AddBar(now.AddMinutes(i), 100, 105, 95, 102);
-            
+
             // Process update
             var args = new UpdateArgs(UpdateReason.HistoricalBar);
             indicator.ProcessUpdate(args);

lib/trends/wma/Wma.Tests.cs

@@ -66,13 +66,13 @@ public class WmaTests
         wma.Reset();
         Assert.Equal(0, wma.Last.Value);
         Assert.False(wma.IsHot);
-        
+
         // Feed again
         for (int i = 0; i < bars.Count; i++)
         {
             wma.Update(new TValue(bars[i].Time, bars[i].Close));
         }
-        
+
         Assert.True(double.IsFinite(wma.Last.Value));
     }
 
@@ -99,23 +99,23 @@ public class WmaTests
             Assert.Equal(streamingResults[i], seriesResults.Values[i], 1e-9);
         }
     }
-    
+
     [Fact]
     public void StaticBatch_Matches_Streaming()
     {
         var gbm = new GBM();
         var bars = gbm.Fetch(200, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
         var series = bars.Close;
-        
+
         var wma = new Wma(10);
         var streamingResults = new List<double>();
         for (int i = 0; i < series.Count; i++)
         {
             streamingResults.Add(wma.Update(series[i]).Value);
         }
-        
+
         var staticResults = Wma.Batch(series, 10);
-        
+
         Assert.Equal(streamingResults.Count, staticResults.Count);
         for (int i = 0; i < staticResults.Count; i++)
         {
@@ -129,17 +129,17 @@ public class WmaTests
         var gbm = new GBM();
         var bars = gbm.Fetch(200, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
         var series = bars.Close;
-        
+
         var wma = new Wma(10);
         var streamingResults = new List<double>();
         for (int i = 0; i < series.Count; i++)
         {
             streamingResults.Add(wma.Update(series[i]).Value);
         }
-        
+
         var spanResults = new double[series.Count];
         Wma.Batch(series.Values, spanResults, 10);
-        
+
         for (int i = 0; i < spanResults.Length; i++)
         {
             Assert.Equal(streamingResults[i], spanResults[i], 1e-9);
@@ -153,12 +153,12 @@ public class WmaTests
         var gbm = new GBM();
         var bars = gbm.Fetch(10, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
         var series = bars.Close;
-        
+
         // Test TSeries chain
         var result = wma.Update(series);
         Assert.NotNull(result);
         Assert.IsType<TSeries>(result);
-        
+
         // Test TValue chain
         var result2 = wma.Update(series[0]);
         Assert.IsType<TValue>(result2);
@@ -176,14 +176,14 @@ public class WmaTests
     {
         var source = new TSeries();
         var wma = new Wma(source, 10);
-        
+
         // Verify subscription works
         source.Add(new TValue(DateTime.UtcNow, 100));
         Assert.Equal(100, wma.Last.Value);
-        
+
         // Dispose
         wma.Dispose();
-        
+
         // Verify unsubscription
         source.Add(new TValue(DateTime.UtcNow, 200));
         // Last value should remain unchanged if unsubscribed
@@ -251,11 +251,11 @@ public class WmaTests
     public void Update_IsNewFalse_OnEmptyBuffer_ThrowsInvalidOperationException()
     {
         var wma = new Wma(10);
-        
+
         // Calling Update with isNew=false on an empty buffer should throw
         var exception = Assert.Throws<InvalidOperationException>(() =>
             wma.Update(new TValue(DateTime.UtcNow, 100.0), isNew: false));
-        
+
         Assert.Contains("isNew=false", exception.Message, StringComparison.Ordinal);
         Assert.Contains("buffer is empty", exception.Message, StringComparison.Ordinal);
         Assert.Contains("isNew=true", exception.Message, StringComparison.Ordinal);
@@ -267,20 +267,20 @@ public class WmaTests
         var wma = new Wma(10);
         var gbm = new GBM();
         var bars = gbm.Fetch(20, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
-        
+
         // Feed some data
         for (int i = 0; i < 10; i++)
         {
             wma.Update(new TValue(bars[i].Time, bars[i].Close));
         }
-        
+
         // Reset clears the buffer
         wma.Reset();
-        
+
         // Calling Update with isNew=false after reset should throw
         var exception = Assert.Throws<InvalidOperationException>(() =>
             wma.Update(new TValue(bars[10].Time, bars[10].Close), isNew: false));
-        
+
         Assert.Contains("isNew=false", exception.Message, StringComparison.Ordinal);
         Assert.Contains("buffer is empty", exception.Message, StringComparison.Ordinal);
     }
@@ -291,16 +291,16 @@ public class WmaTests
         var wma = new Wma(10);
         var gbm = new GBM();
         var bars = gbm.Fetch(20, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
-        
+
         // Feed some data first
         for (int i = 0; i < 5; i++)
         {
             wma.Update(new TValue(bars[i].Time, bars[i].Close), isNew: true);
         }
-        
+
         // Now isNew=false should work (buffer has data)
         var result = wma.Update(new TValue(bars[4].Time, bars[4].Close + 10), isNew: false);
-        
+
         // Should not throw and should return a finite value
         Assert.True(double.IsFinite(result.Value));
     }