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This commit is contained in:
Sean Kessler (Europa)
2026-03-10 21:43:40 -04:00
parent f248701d17
commit 22b387a2e3
5 changed files with 343 additions and 17 deletions
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137
MarketDataLib/CNNProcessing/CNNProcessor.cs
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@@ -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;
}
}
}