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Enhance and add some useful comments.

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Sean
2026-03-11 20:56:13 -04:00
parent aae501f0ab
commit 013af819cd
1 changed files with 53 additions and 22 deletions
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75
Program.cs
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@@ -211,7 +211,7 @@ namespace CNNImageProcessor
List<Holding> holdings = new List<Holding>();
DateGenerator dateGenerator = new DateGenerator();
DateTime startDate = DateTime.Parse("10/31/2019");
DateTime endDate = DateTime.Parse("02/01/2026");
DateTime endDate = DateTime.Parse("12/31/2025");
DateTime actualEndDate = endDate;
DateTime analysisDate = DateTime.Now;
@@ -282,7 +282,6 @@ namespace CNNImageProcessor
}
}
public static List<Holding> ReadHoldings(String strPathFileName)
{
String strLine;
@@ -301,6 +300,20 @@ namespace CNNImageProcessor
return universe;
}
public static void WriteHoldings(List<Holding> holdings,String strPathFileName)
{
if(File.Exists(strPathFileName))File.Delete(strPathFileName);
StreamWriter outStream = new StreamWriter(strPathFileName);
outStream.WriteLine(Holding.Heading);
foreach(Holding holding in holdings)
{
outStream.WriteLine(holding);
}
outStream.Flush();
outStream.Close();
outStream.Dispose();
}
public static (List<Holding> avoid, List<Holding> good) GenerateCodeTestCases(List<Holding> universe)
{
double validationPercent=0.05;
@@ -335,38 +348,56 @@ namespace CNNImageProcessor
public static void GenerateTrainingImages(List<Holding> avoid, List<Holding> good)
{
String cnnClientUrl="http://127.0.0.1:5000";
int imageSize=224;
int dayCount=90; // 90
Console.WriteLine($"Generate training into {@"C:\Data"}");
CNNProcessor.GenerateTraining(avoid, good, imageSize,dayCount, TestCase.GenerateType.BollingerBandWithVIX,@"C:\Data");
ClearFolderPath(@"C:\boneyard\DeepLearning\ModelInputData\0");
ClearFolderPath(@"C:\boneyard\DeepLearning\ModelInputData\1");
if(!ProcessImages(@"C:\Data\0",@"C:\boneyard\DeepLearning\ModelInputData\0",imageSize)) // Process through PIL and put in C:\boneyard\DeepLearning\ModelInputData\0
int dayCount=90;
Console.WriteLine($"Generate training into {@"C:\boneyard\DeepLearning\ModelInputData"}");
CNNProcessor.GenerateTraining(avoid, good, imageSize,dayCount, TestCase.GenerateType.BollingerBandWithVIX,@"C:\boneyard\DeepLearning\ModelInputData");
ClearFolderPath(@"C:\boneyard\DeepLearning\Data\0");
ClearFolderPath(@"C:\boneyard\DeepLearning\Data\1");
CNNClient cnnClient = new CNNClient(cnnClientUrl);
if(!cnnClient.Ping())
{
Console.WriteLine($"Process image failed, is the server running?");
Console.WriteLine($"CNN Server @ {cnnClientUrl} is not responding.");
return;
}
if(!ProcessImages(@"C:\Data\1",@"C:\boneyard\DeepLearning\ModelInputData\1",imageSize)) // Process through PIL and put in C:\boneyard\DeepLearning\ModelInputData\1
{
Console.WriteLine($"Process image failed, is the server running?");
}
Console.WriteLine("Please copy these files into the training folder.");
ProcessImages(@"C:\boneyard\DeepLearning\ModelInputData\0",@"C:\boneyard\DeepLearning\Data\0",imageSize,cnnClientUrl); // Process through PIL and put in C:\boneyard\DeepLearning\Data\0
ProcessImages(@"C:\boneyard\DeepLearning\ModelInputData\1",@"C:\boneyard\DeepLearning\Data\1",imageSize,cnnClientUrl); // Process through PIL and put in C:\boneyard\DeepLearning\Data\1
Console.WriteLine("Done.");
}
/// <summary>
/// This will generate images into C:\boneyard\DeepLearning\ModelInputData\0 and C:\boneyard\DeepLearning\ModelInputData\1
/// You should then copy the generated images into C:\boneyard\DeepLearning\Data folder and then proceed to train tbe latest model
/// which at the time of writing this is model_sk_convnext_v1.py. After running the model you shoukd then run
/// verify_model_sk_convnext_v1.py. This will produce a validation score which at the time of writing is 99%. It will also produce
/// some output images including the confusion matrix.
/// This will generate images into C:\boneyard\DeepLearning\Data\0 and C:\boneyard\DeepLearning\Data\1
/// You should then proceed to train tbe latest model which at the time of writing this is model_sk_convnext_v1.py.
/// After running the model you should then run verify_model_sk_convnext_v1.py. This will produce a validation score
/// which at the time of writing is 99%. It will also produce some output images including the confusion matrix.
///
/// I am using WSL2 to perform the training because WSL2 is the only option for tensorflow with GPU.
/// To launch WSL2 at a command prompt type "wsl ~". If the enviroment is not set up then you can use the setup_tf_gpu.sh
/// script in CNN/Scripts folder to re-create the full Python3 environment and Tensorflow. The script will create the
/// virtual environment and install everything. It was used to create the current WSL enviroment.
/// To start the environment "source tf_gpu/bin/activate"
/// then type "code ." This will run VSCODE and attach to the WSL environment.
/// Train the model on EUPORIE laptop using the GPU card with WSL2. (Windows Subsystem For Linux). I am running Ubuntu1 22.04.2
/// To launch WSL open up a command prompt, run powershell and type "wsl ~".
/// The folder structure will be /home/pi/CNN /home/pi/DeepLearning
/// You can access the folder structure through windows explorer. type "\\wsl$" in explorer and navigate to the folder.
/// drop in the Data and Model and run the model.
/// There is a shell script in the Scripts folder of the CNN project. setup_tf_gpu.sh
/// Copy the script to the CNN folder and run it from the CNN folder.
/// It will create the venv enviroment and install python 3.10 and tensorflow (gpu)
///
/// ******************************************************************************************************************** ///
/// </summary>
/// <param name="args"></param>
static void Main(string[] args)
{
// The modified flow
//List<Holding> holdings = GenerateTrades(); // generate a holding set from the CMMomentum monthly candidates
List<Holding> holdings = ReadHoldings("holdings.csv"); // read a holding set that was previously generated
// List<Holding> holdings = GenerateTrades(); // generate a new holding set from the CMMomentum monthly candidates
// WriteHoldings(holdings,"holdings.csv"); // save the list. The saved list can be read back in to save time in case reruns are necessary
List<Holding> holdings = ReadHoldings("holdings.csv"); // read a holding set that was previously generated. You'll want to create a new set of holdings for retraiing
(List<Holding> avoid, List<Holding> good)=GenerateCodeTestCases(holdings); // split the dataset into avoid and good
GenerateTrainingImages(avoid, good);
GenerateTrainingImages(avoid, good); // Generate the training images
// Clear cache at the end
GBPriceCache.GetInstance().Dispose();
}