Sean/ARM64
1
0
Fork 0
Code Issues Pull Requests Actions 1 Packages Projects Releases Wiki Activity

Implement new CNN Client code

Browse Source
This commit is contained in:
Sean
2026-03-10 21:25:14 -04:00
parent f3734184b4
commit 7beb895ceb
4 changed files with 205 additions and 10 deletions
Download Patch File Download Diff File Expand all files Collapse all files

51
MarketData/MarketDataLib/Generator/CMMomentum/CMMomentumGenerator.cs
View File

@@ -129,19 +129,31 @@ namespace MarketData.Generator.CMMomentum
}
return true;
}
// This method is made public in order that it can be tested
/// <summary>
/// PredictCandidate - 2026 convnext version - Be sure that the model parameters has 90 days in UseCNNDayCount
/// If we need to revert back to CNNClient.Model.resnet50_20241024_270 then uncomment the below method and update cmParams.UseCNNDayCount=270
/// </summary>
/// <param name="cmCandidate"></param>
/// <param name="cmParams"></param>
/// <returns></returns>
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;
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);
if(90!=cmParams.UseCNNDayCount)
{
throw new InvalidDataException("CNNClient.Model.convnext must be used with cmParams.UseCNNDayCount=90.");
}
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)
@@ -157,5 +169,34 @@ namespace MarketData.Generator.CMMomentum
return false;
}
}
// Keep this until happy with the new model
// 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;
// }
// }
}
}