Push MarketData Changes.

MarketDataLib/CNNProcessing/CNNClient.cs

@@ -7,7 +7,7 @@ namespace MarketData.CNNProcessing
 {
   public class CNNClient
 	{
-	  public enum Model{resnet50,resnet50B,resnet50_20241024_270,inception,vgg16,lenet5,ping};
+	  public enum Model{resnet50,resnet50B,resnet50_20241024_270,inception,vgg16,lenet5,convnext,ping};
 		private static readonly string Alive="Alive";
     private readonly HttpClient client = new HttpClient();
 		private string baseUrl;

MarketDataLib/CNNProcessing/CNNProcessor.cs

@@ -3,27 +3,75 @@ using System.IO;
 using System.Collections.Generic;
 using MarketData.Utils;
 using System.Text;
+using System.Globalization;
 
 namespace MarketData.CNNProcessing
 {
   public class CNNProcessor
 	{
-    private static int dayCount=270;
-    private static int width=128;
-    private static int height=128;
+    private static int dayCount=270;   //  This is the default days
+    private static int width=128;      // This is the default width
+    private static int height=128;     // THis is the defaukt height
 
 	  private CNNProcessor()
 		{
 		}
 
-	  public static void GenerateTraining()
+    /// <summary>
+    /// GenerateTraining - This is the new one.  Please refer to the CNNImageProcessor project for information on how to call this method.
+    /// </summary>
+    /// <param name="avoid">This is the collection of avoid holdings</param>
+    /// <param name="good">This is the collection of good holdings</param>
+    /// <param name="dimension">The image dimensions.  for example 224  for 224x224 or 128 for 128x128</param>
+    /// <param name="histDays">This is the number of histDays.  For example I used 90 for convnext</param>
+    /// <param name="generateType">The type. For example I used BollingerBandWithVIX which is a bollinger band with ^VIX overay for convnext</param>
+    /// <param name="rootFolder"></param>
+    public static void GenerateTraining(List<Holding> avoid, List<Holding> good, int dimension, int histDays,TestCase.GenerateType generateType=TestCase.GenerateType.BollingerBandWithVIX,String rootFolder=@"C:\boneyard\DeepLearning\ModelInputData\")
+    {                                                                                                     
+   	   TestCases testCases=new TestCases();	
+			 DataProcessor dataProcessor=new DataProcessor();
+			 dataProcessor.Width=dimension;
+			 dataProcessor.Height=dimension;
+			 dataProcessor.PenWidthArray=new float[]{.75f,1.00f,1.12f};         
+
+       if(!rootFolder.EndsWith(@"\"))rootFolder+=@"\";
+// [0] Data - The avoid data
+       foreach(Holding holding in avoid)
+       {
+         testCases.Add(new TestCase(holding.Symbol,holding.PurchaseDate,histDays,TestCase.CaseType.Training,generateType));
+       }
+			 dataProcessor.SetOutputFolderPath(rootFolder+"0");
+       dataProcessor.ClearFolderPath();
+			 dataProcessor.ProcessData(testCases);
+			 testCases.Clear();
+
+// [1] Data - The good data
+       foreach(Holding holding in good)
+       {
+         testCases.Add(new TestCase(holding.Symbol,holding.PurchaseDate,histDays,TestCase.CaseType.Training,generateType));
+       }
+			 dataProcessor.SetOutputFolderPath(rootFolder+"1");
+       dataProcessor.ClearFolderPath();
+		   dataProcessor.ProcessData(testCases);
+    }
+
+    /// <summary>
+    /// GenerateTraining - This is the old methof training the resnet model.  Please see above
+    /// </summary>
+    /// <param name="rootFolder"></param>
+	  public static void GenerateTraining(String rootFolder=@"C:\boneyard\DeepLearning\ModelInputData\")
 		{
    	   TestCases testCases=new TestCases();	
 			 DataProcessor dataProcessor=new DataProcessor();
 			 dataProcessor.Width=width;
 			 dataProcessor.Height=height;
-			 dataProcessor.PenWidthArray=new float[]{.50f,.75f,1.00f,1.12f,1.25f,1.31f,1.37f,1.50f,1.56f,1.62f,1.75f,1.87f,2.00f};
+		//	 dataProcessor.PenWidthArray=new float[]{.50f,.75f,1.00f,1.12f,1.25f,1.31f,1.37f,1.50f,1.56f,1.62f,1.75f,1.87f,2.00f};
 
+    // Testing with  20,000 images in each set so reducing this use of pens to just one. It was producing 260,000 images for each classification,
+    // takings many hours to build the datasets
+			 dataProcessor.PenWidthArray=new float[]{.75f,1.00f,1.12f};        
+
+       if(!rootFolder.EndsWith(@"\"))rootFolder+=@"\";
 // [0] Data - The avoid data
       testCases.Add(new TestCase("CENX",DateTime.Parse("03/31/2022"),270,TestCase.CaseType.Training,TestCase.GenerateType.BollingerBand));
       testCases.Add(new TestCase("ICPT",DateTime.Parse("12/31/2019"),270,TestCase.CaseType.Training,TestCase.GenerateType.BollingerBand));
@@ -56,8 +104,8 @@ namespace MarketData.CNNProcessing
       testCases.Add(new TestCase("INBX",DateTime.Parse("01/31/2024"),270,TestCase.CaseType.Training,TestCase.GenerateType.BollingerBand));
       testCases.Add(new TestCase("WYNN",DateTime.Parse("02/28/2023"),270,TestCase.CaseType.Training,TestCase.GenerateType.BollingerBand));
 
-
-			dataProcessor.SetOutputFolderPath(@"C:\boneyard\DeepLearning\ModelInputData\0");
+// ****
+			dataProcessor.SetOutputFolderPath(rootFolder+"0");
 			dataProcessor.ProcessData(testCases);
 			testCases.Clear();
 
@@ -102,7 +150,8 @@ namespace MarketData.CNNProcessing
       testCases.Add(new TestCase("DOCU",DateTime.Parse("05/30/2020"),270,TestCase.CaseType.Training,TestCase.GenerateType.BollingerBand));
       testCases.Add(new TestCase("SIG",DateTime.Parse("10/30/2020"),270,TestCase.CaseType.Training,TestCase.GenerateType.BollingerBand));
 
-			dataProcessor.SetOutputFolderPath(@"C:\boneyard\DeepLearning\ModelInputData\1");
+// ***
+			dataProcessor.SetOutputFolderPath(rootFolder+"1");
 		  dataProcessor.ProcessData(testCases);
 		}
 
@@ -204,4 +253,76 @@ namespace MarketData.CNNProcessing
       Console.WriteLine("");
     }
 	}
+
+  public class Holding
+  {
+    public String Symbol {get;set;}
+    public DateTime PurchaseDate {get; set; }
+    public double PurchasePrice {get;set;}
+    public DateTime SellDate {get; set; }
+    public double SellPrice {get;set;}
+    public double GainLoss{ get; set;}
+    public double GainLossPercent {get;set;}
+    private static readonly string[] DateFormats = { "MM/dd/yyyy", "M/dd/yyyy", "M/d/yyyy" };
+    private static readonly CultureInfo UsCulture = CultureInfo.GetCultureInfo("en-US");
+
+    public static String Heading
+    {
+      get
+      {
+        return "Symbol,Shares,Purchase Date,Purchase Price,Sell Date,Sell Price,Exposure,Beta,BetaMonths,SharpeRatio,RiskAdjustedWeight,RiskAdjustedAllocation,TargetBetaOverBeta,Score,CNN Prediction,Market Value,Gain Loss,Gain Loss (%)";
+      }
+    }
+
+    public String ToTestCase()
+    {
+      StringBuilder sb = new StringBuilder();
+      sb.Append("testCases.Add(new TestCase(").Append("\"").Append(Symbol).Append("\"").Append(",");
+      sb.Append("DateTime.Parse(").Append("\"").Append(Utility.DateTimeToStringMMSDDSYYYY(PurchaseDate)).Append("\")").Append(",");
+      sb.Append("270,TestCase.CaseType.Training,TestCase.GenerateType.BollingerBand));");
+      return sb.ToString();
+    }
+
+    public override String ToString()
+    {
+      StringBuilder sb = new StringBuilder();
+      sb.Append(Symbol).Append(",");
+      sb.Append(",");  // shares
+      sb.Append(PurchaseDate.ToShortDateString()).Append(",");
+      sb.Append(Utility.FormatNumber(PurchasePrice,3)).Append(",");
+      sb.Append(SellDate.ToShortDateString()).Append(",");
+      sb.Append(Utility.FormatNumber(SellPrice,3)).Append(",");
+      sb.Append(",");  //exposure
+      sb.Append(",");  //beta
+      sb.Append(",");  //bta months
+      sb.Append(",");  //sharpe ratio
+      sb.Append(",");  //risk adjusted weight
+      sb.Append(",");  //RiskAdjustedAllocation
+      sb.Append(",");  //TargetBetaOverBeta
+      sb.Append(",");  //Score
+      sb.Append(",");  //CNNPrediction
+      sb.Append(",");  //Market Value
+      sb.Append(Utility.FormatNumber(GainLoss,3)).Append(",");
+      sb.Append(Utility.FormatNumber(GainLossPercent,3));
+      return sb.ToString();
+    }
+
+
+    public static Holding FromString(string strLine)
+    {
+        string[] items = strLine.Split(',');
+
+        Holding holding = new Holding();
+        holding.Symbol = items[0];
+        if(string.IsNullOrEmpty(holding.Symbol))return null;
+        holding.PurchaseDate = DateTime.ParseExact(items[2], DateFormats, UsCulture, DateTimeStyles.AssumeLocal);
+        holding.PurchasePrice = double.Parse(items[3], UsCulture);
+        holding.SellDate = DateTime.ParseExact(items[4], DateFormats, UsCulture, DateTimeStyles.AssumeLocal);
+        holding.SellPrice = double.Parse(items[5], UsCulture);
+        holding.GainLoss = double.Parse(items[16], UsCulture);
+        holding.GainLossPercent = double.Parse(items[17].TrimEnd('%'), UsCulture) / 100.0;
+
+        return holding;
+    }
+  }
 }

MarketDataLib/CNNProcessing/DataProcessor.cs

@@ -24,6 +24,7 @@ namespace MarketData.CNNProcessing
 			Height=height;
 			PenWidth=2f;
 			DrawingBrush=new SolidBrush(Color.Black);
+      DrawingBrushRed=new SolidBrush(Color.Red);
 			FillBrush=new SolidBrush(Color.White);
 			DrawPrice=true;
 			UseGrayScale=false;
@@ -59,6 +60,11 @@ namespace MarketData.CNNProcessing
 		/// </summary>
 		///<param name="value">Gets/Sets the drawing brush brush</param>
 		public Brush DrawingBrush{get;set;}
+		/// <summary>
+		/// DrawingBrush
+		/// </summary>
+		///<param name="value">Gets/Sets the drawing brush brush</param>
+		public Brush DrawingBrushRed{get;set;}
 
 		/// <summary>
 		/// DrawBlack
@@ -143,6 +149,29 @@ namespace MarketData.CNNProcessing
 		  this.strFolderPath=strFolderPath;
 			if(!this.strFolderPath.EndsWith(@"\"))this.strFolderPath=this.strFolderPath+@"\";
 		}
+
+		/// <summary>
+		/// ClearFolderPath
+		/// </summary>
+		///<param name="testCases">The test cases</param>
+		public void ClearFolderPath()
+		{
+      if(String.IsNullOrEmpty(strFolderPath))throw new InvalidDataException($"{nameof(strFolderPath)} cannot be null");
+      if(!Directory.Exists(strFolderPath))
+      {
+        Directory.CreateDirectory(strFolderPath);
+      }
+      else
+      {
+        String[] pathFileNames = Directory.GetFiles(strFolderPath);
+        Console.WriteLine($"Deleting {pathFileNames.Length} files from {strFolderPath}");
+        foreach(String file in pathFileNames)
+        {
+          File.Delete(file);
+        }
+      }
+		}
+
 		public void ProcessData(TestCases testCases)
 		{
 			for(int index=0;index<testCases.Count;index++)
@@ -173,7 +202,7 @@ namespace MarketData.CNNProcessing
           }
 				}
 			}
-			else // Bollinger bands
+			else if(testCase.TypeGenerate.Equals(TestCase.GenerateType.BollingerBand))// Bollinger bands
 			{
 				if(null==MovingAverageArray)
 				{
@@ -194,7 +223,6 @@ namespace MarketData.CNNProcessing
 					for(int avgIndex=0;avgIndex<MovingAverageArray.Length;avgIndex++)
 					{
 						int movingAverage=MovingAverageArray[avgIndex];
-
 						for(int penIndex=0;penIndex<PenWidthArray.Length;penIndex++)
 						{
 						  float penWidth=PenWidthArray[penIndex];
@@ -208,7 +236,22 @@ namespace MarketData.CNNProcessing
 						}
 					}
 				}
-			}
+			}   // Bollinger Bands
+      else if(testCase.TypeGenerate.Equals(TestCase.GenerateType.BollingerBandWithVIX))
+      {
+        for (int penIndex = 0; penIndex < PenWidthArray.Length; penIndex++)
+        {
+          float penWidth = PenWidthArray[penIndex];
+          for (int noiseIndex = 0; noiseIndex < NoiseArray.Length; noiseIndex++)
+          {
+            double noise = NoiseArray[noiseIndex];
+            String strPathFileName = CreateFileName(strFolderPath, testCase.Symbol, testCase.DayCount, index, penIndex, noiseIndex, testCase.TypeCase, testCase.TypeGenerate, testCase.PurchaseDate);
+            testCase.PathFileNames.Add(strPathFileName);
+            ProcessBollingerBandDataWithVolatility(testCase, penWidth, noise);
+          }
+        }
+      } // Bollinger Bands with ~VIX
+      else throw new InvalidDataException("Unknown option");
 		}
 
     private String CreateFileName(String strFolderPath,String symbol,int dayCount,int index,int penIndex,int noiseIndex,TestCase.CaseType caseType,TestCase.GenerateType generateType,DateTime purchaseDate)
@@ -216,6 +259,132 @@ namespace MarketData.CNNProcessing
       return String.Format("{0}{1}_{2}_{3}_{4}_{5}_{6}_{7}_{8}d.jpg",strFolderPath,symbol,index,penIndex,noiseIndex,caseType.ToString(),generateType.ToString(),Utility.DateToLong(purchaseDate),dayCount);
     }
 
+/// <summary>
+/// ProcessBollingerBandData item - Draws Price, K, L and Volatility
+/// </summary>
+///<param name="testCase">Symbol</param>
+		private void ProcessBollingerBandDataWithVolatility(TestCase testCase,float penWidth,double noise)
+		{
+      String symbolVolatility="^VIX";
+		  DateGenerator dateGenerator=new DateGenerator();
+
+			int daysInPeriod=dateGenerator.DaysBetweenActual(testCase.PurchaseDate,testCase.HistDate);
+			daysInPeriod+=60;
+		  Prices prices=PricingDA.GetPrices(testCase.Symbol,testCase.PurchaseDate,daysInPeriod);
+      Prices volatilityPrices=PricingDA.GetPrices(symbolVolatility,testCase.PurchaseDate,daysInPeriod);
+			BollingerBands bollingerBands=BollingerBandGenerator.GenerateBollingerBands(prices);	 // we want to grab K, L, and Close
+			bollingerBands=new BollingerBands(bollingerBands.Where(x=>x.Date>=testCase.HistDate).ToList());
+			float[] k=new float[bollingerBands.Count];
+			float[] l=new float[bollingerBands.Count];
+			float[] close=new float[bollingerBands.Count];
+
+// Line up volatility dates with bollinger bands
+      DateTime minDate = bollingerBands.Min(x=>x.Date);
+      DateTime maxDate = bollingerBands.Max(x=>x.Date);
+      volatilityPrices = new Prices(volatilityPrices.Where(x=>x.Date<=maxDate && x.Date>=minDate).OrderBy(x=>x.Date).ToList());  // most historical date in lowest index
+      float[] v=volatilityPrices.GetPrices();
+      float minV=Numerics.Min(ref v);              // get the minimum volatility value
+      double minP=bollingerBands.Min(x=>x.Close);  // get minimum price
+      double factor=minP/minV;                     // determine scaling factor
+      for(int index=0;index<v.Length;index++)
+      {
+        double item = v[index];
+        item*=factor;
+        v[index]=(float)Math.Log(item)*1000.00f; 
+      }
+                                                                                                                      
+// populate the arrays in reverse order so that we have the most historical date in the lowest index
+			for(int index=bollingerBands.Count-1;index>=0;index--)
+			{
+				BollingerBandElement bollingerBandElement=bollingerBands[index];
+				k[bollingerBands.Count-index-1]=(float)Math.Log(bollingerBandElement.K)*1000.00f;   // put the data in log form
+				l[bollingerBands.Count-index-1]=(float)Math.Log(bollingerBandElement.L)*1000.00f;   // put the data in log form
+				close[bollingerBands.Count-index-1]=(float)Math.Log(bollingerBandElement.Close)*1000.00f;  // put the data in log form
+			}
+			Numerics.ZeroForNaNOrInfinity(ref k);
+			Numerics.ZeroForNaNOrInfinity(ref l);
+			Numerics.ZeroForNaNOrInfinity(ref close);
+      Numerics.ZeroForNaNOrInfinity(ref v);
+			float maxY=Math.Max(Math.Max(Numerics.Max(ref l),Math.Max(Numerics.Max(ref close),Numerics.Max(ref k))),Numerics.Max(ref v));
+			float minY=Math.Min(Math.Min(Numerics.Min(ref l),Math.Min(Numerics.Min(ref close),Numerics.Min(ref k))),Numerics.Min(ref v))-5f;
+			float maxX=close.Length;
+			float minX=0.00f;
+
+      Pen pen=new Pen(DrawingBrush,penWidth);
+      Pen redPen=new Pen(DrawingBrushRed,penWidth);
+      ImageHelper imageHelper=new ImageHelper();
+
+      PointMapping pointMapping=new PointMapping(Width,Height,maxX,minX,maxY,minY);
+      imageHelper.CreateImage(Width,Height,pointMapping);
+      imageHelper.Fill(FillBrush);
+
+			LineSegments lineSegments=new LineSegments();
+// draw volatility
+			for(int index=0;index<v.Length;index++)
+			{
+			  if(0==index)continue;
+				Point p1=new Point(index-1,(int)v[index-1]);
+				Point p2=new Point(index,(int)v[index]);
+	      lineSegments.Add(p1,p2);
+			}
+			imageHelper.DrawPath(redPen,lineSegments);
+
+// draw prices
+			lineSegments.Clear();
+			for(int index=0;index<close.Length && DrawPrice;index++)
+			{
+			  if(0==index)continue;
+				Point p1=new Point(index-1,(int)close[index-1]);
+				Point p2=new Point(index,(int)close[index]);
+	      lineSegments.Add(p1,p2);
+			}
+			imageHelper.DrawPath(pen,lineSegments);
+// draw k
+			lineSegments.Clear();
+			for(int index=0;index<k.Length;index++)
+			{
+			  if(0==index)continue;
+				Point p1=new Point(index-1,(int)k[index-1]);
+				Point p2=new Point(index,(int)k[index]);
+				lineSegments.Add(p1,p2);
+			}
+			imageHelper.DrawPath(pen,lineSegments);
+
+// draw l
+			lineSegments.Clear();
+			for(int index=0;index<l.Length;index++)
+			{
+			  if(0==index)continue;
+				Point p1=new Point(index-1,(int)l[index-1]);
+				Point p2=new Point(index,(int)l[index]);
+				lineSegments.Add(p1,p2);
+			}
+			imageHelper.DrawPath(pen,lineSegments);
+
+
+      if(0.00!=noise)imageHelper.AddNoise(NoiseColor,noise);
+			if(testCase.TypeOutput.Equals(TestCase.OutputType.OutputFile))
+			{
+        MDTrace.WriteLine(LogLevel.DEBUG,$"Writing {testCase.LastPathFileName}");
+				if(File.Exists(testCase.LastPathFileName))File.Delete(testCase.LastPathFileName);
+				if(UseGrayScale)imageHelper.SaveGrayScaleJPG(testCase.LastPathFileName);
+        else imageHelper.Save(testCase.LastPathFileName);
+//				else imageHelper.SaveBlackAndWhiteJPG(testCase.LastPathFileName);
+			}
+			else
+			{
+        testCase.Streams.Add(imageHelper.ToStream());
+//			  testCase.Streams.Add(imageHelper.SaveBlackAndWhiteJPG());
+			}
+		}
+
+    /// <summary>
+    /// Generate Bollinger Band Data
+    /// </summary>
+    /// <param name="testCase"></param>
+    /// <param name="movingAverageDays"></param>
+    /// <param name="penWidth"></param>
+    /// <param name="noise"></param>
 		private void ProcessBollingerBandData(TestCase testCase,int movingAverageDays,float penWidth,double noise)
 		{
 		  int bufferDays=60;
@@ -377,6 +546,7 @@ namespace MarketData.CNNProcessing
 			
 			if(testCase.TypeOutput.Equals(TestCase.OutputType.OutputFile))
 			{
+        MDTrace.WriteLine(LogLevel.DEBUG,$"Writing {testCase.LastPathFileName}");
 				if(File.Exists(testCase.LastPathFileName))File.Delete(testCase.LastPathFileName);
 				if(UseGrayScale)imageHelper.SaveGrayScaleJPG(testCase.LastPathFileName);
 				else imageHelper.SaveBlackAndWhiteJPG(testCase.LastPathFileName);
@@ -426,6 +596,7 @@ namespace MarketData.CNNProcessing
 			
 			if(testCase.TypeOutput.Equals(TestCase.OutputType.OutputFile))
 			{
+        MDTrace.WriteLine(LogLevel.DEBUG,$"Writing {testCase.LastPathFileName}");
 				if(File.Exists(testCase.LastPathFileName))File.Delete(testCase.LastPathFileName);
 				if(UseGrayScale)imageHelper.SaveGrayScaleJPG(testCase.LastPathFileName);
 				else imageHelper.SaveBlackAndWhiteJPG(testCase.LastPathFileName);

MarketDataLib/CNNProcessing/TestCase.cs

@@ -14,7 +14,7 @@ namespace MarketData.CNNProcessing
 	public class TestCase
 	{
     public enum CaseType{Training,Test,Validation};
-		public enum GenerateType{Price,BollingerBand};
+		public enum GenerateType{Price,BollingerBand,BollingerBandWithVIX};
 		public enum OutputType{OutputFile,OutputStream}
 		private readonly List<Stream> streams=new List<Stream>();
 		private readonly List<String> pathFileNames=new List<String>();

MarketDataLib/Generator/CMMomentum/CMMomentumGenerator.cs

@@ -5,6 +5,7 @@ using MarketData.MarketDataModel;
 using MarketData.Utils;
 using System;
 using System.Collections.Generic;
+using System.IO;
 using System.Linq;
 
 namespace MarketData.Generator.CMMomentum
@@ -132,6 +133,35 @@ namespace MarketData.Generator.CMMomentum
       }
       return true;
     }
+    // This method is made public in order that it can be tested
+		//public static bool PredictCandidate(CMCandidate cmCandidate,CMParams cmParams)
+		//{
+		//  try
+		//	{
+		//	  CNNClient cnnClient=new CNNClient(cmParams.UseCNNHost);
+		//		DataProcessor dataProcessor=new DataProcessor();
+		//		dataProcessor.Width=128;
+		//		dataProcessor.Height=128;
+		//		dataProcessor.PenWidth=1;
+		//		TestCase testCase=new TestCase(cmCandidate.Symbol,cmCandidate.TradeDate,cmParams.UseCNNDayCount,TestCase.CaseType.Test,TestCase.GenerateType.BollingerBand,TestCase.OutputType.OutputStream);
+  //      dataProcessor.ProcessData(testCase);
+		//		String prediction = cnnClient.Predict(CNNClient.Model.resnet50_20241024_270,testCase.LastStream);
+		//	  prediction=prediction.Substring(prediction.IndexOf("-->"));
+		//		int result=int.Parse(Utility.BetweenString(prediction,"[[","]"));
+		//		if(1==result)
+		//		{
+		//		  cmCandidate.Score*=(1.00+cmParams.UseCNNRewardPercentDecimal);   // increase the score by the percentage indicated in the params settings
+		//			cmCandidate.CNNPrediction=true;
+		//	  }
+		//		return true;
+		//	}
+		//	catch(Exception exception)
+		//	{
+		//	  MDTrace.WriteLine(LogLevel.DEBUG,String.Format("Error encountered calling convolutional model at {0}.  Exception was {1}",cmParams.UseCNNHost,exception.ToString()));
+		//		return false;
+		//	}
+		//}
+
     // This method is made public in order that it can be tested
 		public static bool PredictCandidate(CMCandidate cmCandidate,CMParams cmParams)
 		{
@@ -139,12 +169,14 @@ namespace MarketData.Generator.CMMomentum
 			{
 			  CNNClient cnnClient=new CNNClient(cmParams.UseCNNHost);
 				DataProcessor dataProcessor=new DataProcessor();
-				dataProcessor.Width=128;
-				dataProcessor.Height=128;
+        int imageDimensions=224;
+				dataProcessor.Width=imageDimensions;
+				dataProcessor.Height=imageDimensions;
 				dataProcessor.PenWidth=1;
-				TestCase testCase=new TestCase(cmCandidate.Symbol,cmCandidate.TradeDate,cmParams.UseCNNDayCount,TestCase.CaseType.Test,TestCase.GenerateType.BollingerBand,TestCase.OutputType.OutputStream);
+				TestCase testCase=new TestCase(cmCandidate.Symbol,cmCandidate.TradeDate,cmParams.UseCNNDayCount,TestCase.CaseType.Test,TestCase.GenerateType.BollingerBandWithVIX,TestCase.OutputType.OutputStream);
         dataProcessor.ProcessData(testCase);
-				String prediction = cnnClient.Predict(CNNClient.Model.resnet50_20241024_270,testCase.LastStream);
+        Stream streamResult = cnnClient.ProcessImage(testCase.LastStream);                // process the image through PIL
+				String prediction = cnnClient.Predict(CNNClient.Model.convnext,streamResult);     
 			  prediction=prediction.Substring(prediction.IndexOf("-->"));
 				int result=int.Parse(Utility.BetweenString(prediction,"[[","]"));
 				if(1==result)
@@ -160,5 +192,7 @@ namespace MarketData.Generator.CMMomentum
 				return false;
 			}
 		}
+
+
   }
 }