private static void ExportFeaturesAndLabel(string code)
{
string filePath = (Environment.OSVersion.Platform == PlatformID.Unix || Environment.OSVersion.Platform == PlatformID.MacOSX) ? Environment.GetEnvironmentVariable("HOME") : Environment.ExpandEnvironmentVariables("%HOMEDRIVE%%HOMEPATH%");
filePath += "\\indicatorOutput.txt";
DateTime targetDate = IndicatorService.LastDate(code);
int numberOfData = 4000;
var data = IndicatorService.GetData(code, targetDate, new string[] { "Tarih", "Kapanis" }, numberOfData + 1);
double[] sma = MovingAverage.Simple(code, targetDate, 14, numberOfData);
double[] wma = MovingAverage.Weighted(code, targetDate, 14, numberOfData);
double[] ema = MovingAverage.Exponential(code, targetDate, 14, numberOfData);
MovingAverageConvergenceDivergence macd = new MovingAverageConvergenceDivergence(code, targetDate, 12, 26, 9, numberOfData);
double[] rsi = RelativeStrengthIndex.Rsi(code, targetDate, 14, numberOfData);
double[] williams = WilliamsR.Wsr(code, targetDate, 14, numberOfData);
Stochastics stochastics = new Stochastics(code, targetDate, 14, 3, 3, numberOfData);
double[] closesOut = IndicatorDataPreprocessor.GetClosesOut(numberOfData, data);
double[] smaOut = IndicatorDataPreprocessor.GetSMAOut(sma);
double[] wmaOut = IndicatorDataPreprocessor.GetWMAOut(wma);
double[] emaOut = IndicatorDataPreprocessor.GetEMAOut(ema);
double[] macdOut = IndicatorDataPreprocessor.GetMACDOut(macd);
double[] rsiOut = IndicatorDataPreprocessor.GetRSIOut(rsi);
double[] williamsROut = IndicatorDataPreprocessor.GetWilliamsROut(williams);
double[] stochasticsOut = IndicatorDataPreprocessor.GetStochasticsOut(stochastics);
int minRowCount;
minRowCount = smaOut.Length;
minRowCount = minRowCount < wmaOut.Length ? minRowCount : wmaOut.Length;
minRowCount = minRowCount < emaOut.Length ? minRowCount : emaOut.Length;
minRowCount = minRowCount < macdOut.Length ? minRowCount : macdOut.Length;
minRowCount = minRowCount < rsiOut.Length ? minRowCount : rsiOut.Length;
minRowCount = minRowCount < williamsROut.Length ? minRowCount : williamsROut.Length;
minRowCount = minRowCount < stochasticsOut.Length ? minRowCount : stochasticsOut.Length;
minRowCount = minRowCount < closesOut.Length ? minRowCount : closesOut.Length;
FeatureVector vector = new FeatureVector();
vector.AddColumn("SMA", smaOut.Select(p => (object)p.ToString(CultureInfo.InvariantCulture)).Take(minRowCount).ToArray());
vector.AddColumn("WMA", wmaOut.Select(p => (object)p.ToString(CultureInfo.InvariantCulture)).Take(minRowCount).ToArray());
vector.AddColumn("EMA", emaOut.Select(p => (object)p.ToString(CultureInfo.InvariantCulture)).Take(minRowCount).ToArray());
vector.AddColumn("MACD", macdOut.Select(p => (object)p.ToString(CultureInfo.InvariantCulture)).Take(minRowCount).ToArray());
vector.AddColumn("RSI", rsiOut.Select(p => (object)p.ToString(CultureInfo.InvariantCulture)).Take(minRowCount).ToArray());
vector.AddColumn("WilliamsR", williamsROut.Select(p => (object)p.ToString(CultureInfo.InvariantCulture)).Take(minRowCount).ToArray());
vector.AddColumn("Stochastics", stochasticsOut.Select(p => (object)p.ToString(CultureInfo.InvariantCulture)).Take(minRowCount).ToArray());
vector.AddColumn("label", closesOut.Select(p => (object)string.Format("{0:0.0}", p).ToString(CultureInfo.InvariantCulture)).Take(minRowCount).ToArray());
new CSVExporter(vector).Export(filePath);
Console.WriteLine("Operations completed.");
}
private void PreprocessIndicators()
{
var data = IndicatorService.GetData(code, targetDate, new string[] { "Tarih", "Kapanis" }, numberOfData + 1);
double[] sma = null;
double[] wma = null;
double[] ema = null;
MovingAverageConvergenceDivergence macd = null;
double[] rsi = null;
double[] williamsR = null;
Stochastics stochastics = null;
if (isSMA)
{
sma = MovingAverage.Simple(code, targetDate, 14, numberOfData);
}
if (isWMA)
{
wma = MovingAverage.Weighted(code, targetDate, 14, numberOfData);
}
if (isEMA)
{
ema = MovingAverage.Exponential(code, targetDate, 14, numberOfData);
}
if (isMACD)
{
macd = new MovingAverageConvergenceDivergence(code, targetDate, 12, 26, 9, numberOfData);
}
if (isRSI)
{
rsi = RelativeStrengthIndex.Rsi(code, targetDate, 14, numberOfData);
}
if (isWilliamsR)
{
williamsR = WilliamsR.Wsr(code, targetDate, 14, numberOfData);
}
if (isStochastics)
{
stochastics = new Stochastics(code, targetDate, 14, 3, 3, numberOfData);
}
double[] closesOut = IndicatorDataPreprocessor.GetClosesOut(numberOfData, data);
double[] smaOut = null;
double[] wmaOut = null;
double[] emaOut = null;
double[] macdOut = null;
double[] rsiOut = null;
double[] williamsROut = null;
double[] stochasticsOut = null;
if (isSMA)
{
smaOut = IndicatorDataPreprocessor.GetSMAOut(sma);
}
if (isWMA)
{
wmaOut = IndicatorDataPreprocessor.GetWMAOut(wma);
}
if (isEMA)
{
emaOut = IndicatorDataPreprocessor.GetEMAOut(ema);
}
if (isMACD)
{
macdOut = IndicatorDataPreprocessor.GetMACDOut(macd);
}
if (isRSI)
{
rsiOut = IndicatorDataPreprocessor.GetRSIOut(rsi, false);
}
if (isWilliamsR)
{
williamsROut = IndicatorDataPreprocessor.GetWilliamsROut(williamsR, false);
}
if (isStochastics)
{
stochasticsOut = IndicatorDataPreprocessor.GetStochasticsOut(stochastics, false);
}
minRowCount = closesOut.Length;
if (isSMA)
{
minRowCount = minRowCount < smaOut.Length ? minRowCount : smaOut.Length;
}
if (isWMA)
{
minRowCount = minRowCount < wmaOut.Length ? minRowCount : wmaOut.Length;
}
if (isEMA)
{
minRowCount = minRowCount < emaOut.Length ? minRowCount : emaOut.Length;
}
if (isMACD)
{
minRowCount = minRowCount < macdOut.Length ? minRowCount : macdOut.Length;
}
if (isRSI)
{
minRowCount = minRowCount < rsiOut.Length ? minRowCount : rsiOut.Length;
}
if (isWilliamsR)
{
minRowCount = minRowCount < williamsROut.Length ? minRowCount : williamsROut.Length;
}
if (isStochastics)
{
minRowCount = minRowCount < stochasticsOut.Length ? minRowCount : stochasticsOut.Length;
}
FeatureVector featureVector = new FeatureVector();
if (isSMA)
{
featureVector.AddColumn("SMA", smaOut.Select(p => (object)p.ToString(CultureInfo.InvariantCulture)).Take(minRowCount).ToArray());
}
if (isWMA)
{
featureVector.AddColumn("WMA", wmaOut.Select(p => (object)p.ToString(CultureInfo.InvariantCulture)).Take(minRowCount).ToArray());
}
if (isEMA)
{
featureVector.AddColumn("EMA", emaOut.Select(p => (object)p.ToString(CultureInfo.InvariantCulture)).Take(minRowCount).ToArray());
}
if (isMACD)
{
featureVector.AddColumn("MACD", macdOut.Select(p => (object)p.ToString(CultureInfo.InvariantCulture)).Take(minRowCount).ToArray());
}
if (isRSI)
{
featureVector.AddColumn("RSI", rsiOut.Select(p => (object)p.ToString(CultureInfo.InvariantCulture)).Take(minRowCount).ToArray());
}
if (isWilliamsR)
{
featureVector.AddColumn("WilliamsR", williamsROut.Select(p => (object)p.ToString(CultureInfo.InvariantCulture)).Take(minRowCount).ToArray());
}
if (isStochastics)
{
featureVector.AddColumn("Stochastics", stochasticsOut.Select(p => (object)p.ToString(CultureInfo.InvariantCulture)).Take(minRowCount).ToArray());
}
featureVector.AddColumn("label", closesOut.Select(p => (object)string.Format("{0:0.0}", p).ToString(CultureInfo.InvariantCulture)).Take(minRowCount).ToArray());
int count = featureVector.Values[0].Length;
training = new FeatureVector();
test = new FeatureVector();
for (int i = 0; i < featureVector.ColumnName.Count; i++)
{
training.AddColumn(featureVector.ColumnName[i], featureVector.Values[i].Take((int)(count * trainingPercentage / 100.0)).ToArray());
test.AddColumn(featureVector.ColumnName[i], featureVector.Values[i].Skip((int)(count * trainingPercentage / 100.0)).Take(count).ToArray()); // Take(count) means take the rest of all elements, number of the rest of the elements is smaller than count.
}
}
private void mavParameterNext_Click(object sender, EventArgs e)
{
mavResultTitle.Text = Code + " için Moving Average sonucu";
MAVPeriod = (int)mavPeriod.Value;
mavResultChart.Series.Clear();
var closeSeries = new Series
{
Name = "Kapanış",
Color = System.Drawing.Color.Black,
ChartType = SeriesChartType.Line
};
var smaSeries = new Series
{
Name = "Simple Moving Average",
Color = System.Drawing.Color.Red,
ChartType = SeriesChartType.Line
};
var wmaSeries = new Series
{
Name = "Weighted Moving Average",
Color = System.Drawing.Color.Green,
ChartType = SeriesChartType.Line
};
var emaSeries = new Series
{
Name = "Exponential Moving Average",
Color = System.Drawing.Color.Blue,
ChartType = SeriesChartType.Line
};
var closeData = IndicatorService.GetData(Code, TargetDate, new string[] { "Tarih", "Kapanis" }, NumberOfData);
double[] smaData, wmaData, emaData;
if (MapReduceAllowed)
{
smaData = MovingAverage.SimpleMR(Code, TargetDate, MAVPeriod, NumberOfData);
wmaData = MovingAverage.WeightedMR(Code, TargetDate, MAVPeriod, NumberOfData);
emaData = MovingAverage.ExponentialMR(Code, TargetDate, MAVPeriod, NumberOfData);
}
else
{
smaData = MovingAverage.Simple(Code, TargetDate, MAVPeriod, NumberOfData);
wmaData = MovingAverage.Weighted(Code, TargetDate, MAVPeriod, NumberOfData);
emaData = MovingAverage.Exponential(Code, TargetDate, MAVPeriod, NumberOfData);
}
DateTime date;
for (int i = 0; i < smaData.Length; i++)
{
var close = closeData.ElementAt(i);
date = close.GetElement("Tarih").Value.AsBsonDateTime.ToLocalTime();
closeSeries.Points.AddXY(date, close.GetElement("Kapanis").Value.ToDouble());
smaSeries.Points.AddXY(date, smaData[i]);
wmaSeries.Points.AddXY(date, wmaData[i]);
emaSeries.Points.AddXY(date, emaData[i]);
}
mavResultChart.ChartAreas[0].AxisY.Minimum = Math.Floor(new double[] { closeData.Select(p => p.GetElement("Kapanis").Value.ToDouble()).Min(), smaData.Min(), wmaData.Min(), emaData.Min() }.Min());
mavResultChart.ChartAreas[0].AxisY.Maximum = Math.Ceiling(new double[] { closeData.Select(p => p.GetElement("Kapanis").Value.ToDouble()).Max(), smaData.Max(), wmaData.Max(), emaData.Max() }.Max());
mavResultChart.Series.Add(closeSeries);
mavResultChart.Series.Add(smaSeries);
mavResultChart.Series.Add(wmaSeries);
mavResultChart.Series.Add(emaSeries);
mavResultChart.Invalidate();
mavResultPanel.BringToFront();
}
private void buttonForDataSplitNext_Click(object sender, EventArgs e)
{
trainingSetPercentage = (double)numericUpDownForTrainingSetPercent.Value / 100.0;
numFolds = (int)numericUpDownForNumFolds.Value;
double[] smaOut = null;
double[] wmaOut = null;
double[] emaOut = null;
double[] macdOut = null;
double[] stochasticsOut = null;
double[] williamsROut = null;
double[] rsiOut = null;
double[] closesOut = null;
var data = IndicatorService.GetData(code, targetDate, new string[] { "Tarih", "Kapanis" }, numberOfData + 1);
if (isSMAChecked)
{
smaOut = IndicatorDataPreprocessor.GetSMAOut(MovingAverage.Simple(code, targetDate, smaPeriod, numberOfData));
}
if (isWMAChecked)
{
wmaOut = IndicatorDataPreprocessor.GetWMAOut(MovingAverage.Weighted(code, targetDate, wmaPeriod, numberOfData));
}
if (isEMAChecked)
{
emaOut = IndicatorDataPreprocessor.GetEMAOut(MovingAverage.Exponential(code, targetDate, emaPeriod, numberOfData));
}
if (isMACDChecked)
{
macdOut = IndicatorDataPreprocessor.GetMACDOut(new MovingAverageConvergenceDivergence(code, targetDate, firstPeriod, secondPeriod, triggerPeriod, numberOfData));
}
if (isStochasticsChecked)
{
stochasticsOut = IndicatorDataPreprocessor.GetStochasticsOut(new Stochastics(code, targetDate, fastKPeriod, fastDPeriod, slowDPeriod, numberOfData));
}
if (isWilliamsRChecked)
{
williamsROut = IndicatorDataPreprocessor.GetWilliamsROut(WilliamsR.Wsr(code, targetDate, williamsRPeriod, numberOfData));
}
if (isRSIChecked)
{
rsiOut = IndicatorDataPreprocessor.GetRSIOut(RelativeStrengthIndex.Rsi(code, targetDate, rsiPeriod, numberOfData));
}
closesOut = IndicatorDataPreprocessor.GetClosesOut(numberOfData, data);
int minRowCount = 1000000;
if (smaOut != null)
{
minRowCount = smaOut.Length;
}
if (wmaOut != null)
{
minRowCount = minRowCount < wmaOut.Length ? minRowCount : wmaOut.Length;
}
if (emaOut != null)
{
minRowCount = minRowCount < emaOut.Length ? minRowCount : emaOut.Length;
}
if (macdOut != null)
{
minRowCount = minRowCount < macdOut.Length ? minRowCount : macdOut.Length;
}
if (rsiOut != null)
{
minRowCount = minRowCount < rsiOut.Length ? minRowCount : rsiOut.Length;
}
if (williamsROut != null)
{
minRowCount = minRowCount < williamsROut.Length ? minRowCount : williamsROut.Length;
}
if (stochasticsOut != null)
{
minRowCount = minRowCount < stochasticsOut.Length ? minRowCount : stochasticsOut.Length;
}
if (closesOut != null)
{
minRowCount = minRowCount < closesOut.Length ? minRowCount : closesOut.Length;
}
var fv = new FeatureVector();
if (isSMAChecked)
{
fv.AddColumn("SMA", smaOut.Select(p => (object)p.ToString(CultureInfo.InvariantCulture)).Take(minRowCount).ToArray());
}
if (isWMAChecked)
{
fv.AddColumn("WMA", wmaOut.Select(p => (object)p.ToString(CultureInfo.InvariantCulture)).Take(minRowCount).ToArray());
}
if (isEMAChecked)
{
fv.AddColumn("EMA", emaOut.Select(p => (object)p.ToString(CultureInfo.InvariantCulture)).Take(minRowCount).ToArray());
}
if (isMACDChecked)
{
fv.AddColumn("MACD", macdOut.Select(p => (object)p.ToString(CultureInfo.InvariantCulture)).Take(minRowCount).ToArray());
}
if (isRSIChecked)
{
fv.AddColumn("RSI", rsiOut.Select(p => (object)p.ToString(CultureInfo.InvariantCulture)).Take(minRowCount).ToArray());
}
if (isWilliamsRChecked)
{
fv.AddColumn("WilliamsR", williamsROut.Select(p => (object)p.ToString(CultureInfo.InvariantCulture)).Take(minRowCount).ToArray());
}
if (isStochasticsChecked)
{
fv.AddColumn("Stochastics", stochasticsOut.Select(p => (object)p.ToString(CultureInfo.InvariantCulture)).Take(minRowCount).ToArray());
}
fv.AddColumn("label", closesOut.Select(p => (object)string.Format("{0:0.0}", p).ToString(CultureInfo.InvariantCulture)).Take(minRowCount).ToArray());
var training = new FeatureVector();
var test = new FeatureVector();
int count = fv.Values[0].Length;
for (int i = 0; i < fv.ColumnName.Count; i++)
{
training.AddColumn(fv.ColumnName[i], fv.Values[i].Take((int)(count * trainingSetPercentage)).ToArray());
}
for (int i = 0; i < fv.ColumnName.Count; i++)
{
test.AddColumn(fv.ColumnName[i], fv.Values[i].Skip((int)(count * trainingSetPercentage)).Take(count).ToArray()); // Take(count) means take the rest of all elements, number of the rest of the elements is smaller than count.
}
if (numFolds > 0)
{
BinaryClassificationEvaluator bce1 = new BinaryClassificationEvaluator();
LinearRegression linearRegression = new LinearRegression();
CrossValidator cvLinReg = new CrossValidator(linearRegression, bce1, numFolds);
CrossValidatorModel cvLinRegModel = (CrossValidatorModel)cvLinReg.Fit(training);
FeatureVector linRegPredictions = cvLinRegModel.transform(test);
bce1.evaluate(linRegPredictions);
linRegAcc = bce1.Accuracy;
BinaryClassificationEvaluator bce2 = new BinaryClassificationEvaluator();
LogisticRegression logisticRegression = new LogisticRegression();
CrossValidator cvLogReg = new CrossValidator(logisticRegression, bce2, numFolds);
CrossValidatorModel cvLogRegModel = (CrossValidatorModel)cvLogReg.Fit(training);
FeatureVector logRegPredictions = cvLogRegModel.transform(test);
bce2.evaluate(logRegPredictions);
logRegAcc = bce2.Accuracy;
BinaryClassificationEvaluator bce3 = new BinaryClassificationEvaluator();
NaiveBayes naiveBayes = new NaiveBayes();
CrossValidator cvNaiBay = new CrossValidator(naiveBayes, bce3, numFolds);
CrossValidatorModel cvNaiBayModel = (CrossValidatorModel)cvNaiBay.Fit(training);
FeatureVector naiBayPredictions = cvNaiBayModel.transform(test);
bce3.evaluate(naiBayPredictions);
naiBayAcc = bce3.Accuracy;
}
else
{
BinaryClassificationEvaluator bce1 = new BinaryClassificationEvaluator();
LinearRegression linearRegression = new LinearRegression();
LinearRegressionModel linearRegressionModel = (LinearRegressionModel)linearRegression.Fit(training);
FeatureVector linRegPredictions = linearRegressionModel.transform(test);
bce1.evaluate(linRegPredictions);
linRegAcc = bce1.Accuracy;
BinaryClassificationEvaluator bce2 = new BinaryClassificationEvaluator();
LogisticRegression logicticRegression = new LogisticRegression();
LogisticRegressionModel logisticRegressionModel = (LogisticRegressionModel)logicticRegression.Fit(training);
FeatureVector logRegPredictions = logisticRegressionModel.transform(test);
bce2.evaluate(logRegPredictions);
logRegAcc = bce2.Accuracy;
BinaryClassificationEvaluator bce3 = new BinaryClassificationEvaluator();
NaiveBayes naiveBayes = new NaiveBayes();
NaiveBayesModel naiveBayesModel = (NaiveBayesModel)naiveBayes.Fit(training);
FeatureVector naiBayPredictions = naiveBayesModel.transform(test);
bce3.evaluate(naiBayPredictions);
naiBayAcc = bce3.Accuracy;
}
labelForLinRegAcc.Text = linRegAcc.ToString();
labelForLogRegAcc.Text = logRegAcc.ToString();
labelForNaiBayAcc.Text = naiBayAcc.ToString();
panelForResults.BringToFront();
}
private static void RunConsoleApplication()
{
string filePath = (Environment.OSVersion.Platform == PlatformID.Unix || Environment.OSVersion.Platform == PlatformID.MacOSX) ? Environment.GetEnvironmentVariable("HOME") : Environment.ExpandEnvironmentVariables("%HOMEDRIVE%%HOMEPATH%");
filePath += "\\indicatorOutput.txt";
string code = "AKBNK";
DateTime targetDate = new DateTime(2018, 11, 1).ToLocalTime();
int numberOfData = 1000;
var data = IndicatorService.GetData(code, targetDate, new string[] { "Tarih", "Kapanis" }, numberOfData + 1);
double[] sma = MovingAverage.Simple(code, targetDate, 14, numberOfData);
double[] wma = MovingAverage.Weighted(code, targetDate, 14, numberOfData);
double[] ema = MovingAverage.Exponential(code, targetDate, 14, numberOfData);
MovingAverageConvergenceDivergence macd = new MovingAverageConvergenceDivergence(code, targetDate, 12, 26, 9, numberOfData);
double[] rsi = RelativeStrengthIndex.Rsi(code, targetDate, 14, numberOfData);
double[] williams = WilliamsR.Wsr(code, targetDate, 14, numberOfData);
Stochastics stochastics = new Stochastics(code, targetDate, 14, 3, 3, numberOfData);
double[] closesOut = IndicatorDataPreprocessor.GetClosesOut(numberOfData, data);
double[] smaOut = IndicatorDataPreprocessor.GetSMAOut(sma);
double[] wmaOut = IndicatorDataPreprocessor.GetWMAOut(wma);
double[] emaOut = IndicatorDataPreprocessor.GetEMAOut(ema);
double[] macdOut = IndicatorDataPreprocessor.GetMACDOut(macd);
double[] rsiOut = IndicatorDataPreprocessor.GetRSIOut(rsi);
double[] williamsROut = IndicatorDataPreprocessor.GetWilliamsROut(williams);
double[] stochasticsOut = IndicatorDataPreprocessor.GetStochasticsOut(stochastics);
int minRowCount;
minRowCount = smaOut.Length;
minRowCount = minRowCount < wmaOut.Length ? minRowCount : wmaOut.Length;
minRowCount = minRowCount < emaOut.Length ? minRowCount : emaOut.Length;
minRowCount = minRowCount < macdOut.Length ? minRowCount : macdOut.Length;
minRowCount = minRowCount < rsiOut.Length ? minRowCount : rsiOut.Length;
minRowCount = minRowCount < williamsROut.Length ? minRowCount : williamsROut.Length;
minRowCount = minRowCount < stochasticsOut.Length ? minRowCount : stochasticsOut.Length;
minRowCount = minRowCount < closesOut.Length ? minRowCount : closesOut.Length;
FeatureVector vector = new FeatureVector();
vector.AddColumn("SMA", smaOut.Select(p => (object)p.ToString(CultureInfo.InvariantCulture)).Take(minRowCount).ToArray());
vector.AddColumn("WMA", wmaOut.Select(p => (object)p.ToString(CultureInfo.InvariantCulture)).Take(minRowCount).ToArray());
vector.AddColumn("EMA", emaOut.Select(p => (object)p.ToString(CultureInfo.InvariantCulture)).Take(minRowCount).ToArray());
vector.AddColumn("MACD", macdOut.Select(p => (object)p.ToString(CultureInfo.InvariantCulture)).Take(minRowCount).ToArray());
vector.AddColumn("RSI", rsiOut.Select(p => (object)p.ToString(CultureInfo.InvariantCulture)).Take(minRowCount).ToArray());
vector.AddColumn("WilliamsR", williamsROut.Select(p => (object)p.ToString(CultureInfo.InvariantCulture)).Take(minRowCount).ToArray());
vector.AddColumn("Stochastics", stochasticsOut.Select(p => (object)p.ToString(CultureInfo.InvariantCulture)).Take(minRowCount).ToArray());
vector.AddColumn("label", closesOut.Select(p => (object)string.Format("{0:0.0}", p).ToString(CultureInfo.InvariantCulture)).Take(minRowCount).ToArray());
new LabeledPointExporter(vector).Export(filePath);
int count = vector.Values[0].Length;
FeatureVector training = new FeatureVector();
for (int i = 0; i < vector.ColumnName.Count; i++)
{
training.AddColumn(vector.ColumnName[i], vector.Values[i].Take(count / 2).ToArray());
}
FeatureVector test = new FeatureVector();
for (int i = 0; i < vector.ColumnName.Count; i++)
{
test.AddColumn(vector.ColumnName[i], vector.Values[i].Skip(count / 2).Take(count / 2).ToArray());
}
//TestNaiveBayes(training, test);
//TestNaiveBayesUsingCrossValidation(training, test);
//TestLinearRegression(training, test);
//TestLinearRegressionUsingCrossValidation(training, test);
//TestLogisticRegression(training, test);
//TestLogisticRegressionUsingCrossValidation(training, test);
}
private static void RunFeatureSelector(int mlAlgorithm, bool isCrossValidationEnabled, double trainingSetPercentage)
{
string code = "AKBNK";
DateTime targetDate = new DateTime(2018, 11, 1).ToLocalTime();
int numberOfData = 4000;
var data = IndicatorService.GetData(code, targetDate, new string[] { "Tarih", "Kapanis" }, numberOfData + 1);
double[] sma = MovingAverage.Simple(code, targetDate, 14, numberOfData);
double[] wma = MovingAverage.Weighted(code, targetDate, 14, numberOfData);
double[] ema = MovingAverage.Exponential(code, targetDate, 14, numberOfData);
MovingAverageConvergenceDivergence macd = new MovingAverageConvergenceDivergence(code, targetDate, 12, 26, 9, numberOfData);
double[] rsi = RelativeStrengthIndex.Rsi(code, targetDate, 14, numberOfData);
double[] williams = WilliamsR.Wsr(code, targetDate, 14, numberOfData);
Stochastics stochastics = new Stochastics(code, targetDate, 14, 3, 3, numberOfData);
double[] closesOut = IndicatorDataPreprocessor.GetClosesOut(numberOfData, data);
double[] smaOut = IndicatorDataPreprocessor.GetSMAOut(sma);
double[] wmaOut = IndicatorDataPreprocessor.GetWMAOut(wma);
double[] emaOut = IndicatorDataPreprocessor.GetEMAOut(ema);
double[] macdOut = IndicatorDataPreprocessor.GetMACDOut(macd);
double[] rsiOut = IndicatorDataPreprocessor.GetRSIOut(rsi, false);
double[] williamsROut = IndicatorDataPreprocessor.GetWilliamsROut(williams, false);
double[] stochasticsOut = IndicatorDataPreprocessor.GetStochasticsOut(stochastics, false);
int minRowCount;
minRowCount = smaOut.Length;
minRowCount = minRowCount < wmaOut.Length ? minRowCount : wmaOut.Length;
minRowCount = minRowCount < emaOut.Length ? minRowCount : emaOut.Length;
minRowCount = minRowCount < macdOut.Length ? minRowCount : macdOut.Length;
minRowCount = minRowCount < rsiOut.Length ? minRowCount : rsiOut.Length;
minRowCount = minRowCount < williamsROut.Length ? minRowCount : williamsROut.Length;
minRowCount = minRowCount < stochasticsOut.Length ? minRowCount : stochasticsOut.Length;
minRowCount = minRowCount < closesOut.Length ? minRowCount : closesOut.Length;
int numberOfIndicators = IndicatorService.indicators.Length;
int numberOfCombinations = (int)Math.Pow(2, numberOfIndicators) - 1;
for (int i = 1; i <= numberOfCombinations; i++)
{
int tmp = i;
List <int> indicators = new List <int>();
for (int j = 0; j < numberOfIndicators; j++)
{
if ((tmp & 1) == 1)
{
indicators.Add(IndicatorService.indicators[j]);
}
tmp >>= 1;
}
double accuracy = CalculateAccuracy(indicators, mlAlgorithm, isCrossValidationEnabled, minRowCount, trainingSetPercentage, smaOut, wmaOut, emaOut, macdOut, rsiOut, williamsROut, stochasticsOut, closesOut);
if (indicators.Contains(IndicatorService.SMA))
{
Console.Write("SMA ");
}
if (indicators.Contains(IndicatorService.WMA))
{
Console.Write("WMA ");
}
if (indicators.Contains(IndicatorService.EMA))
{
Console.Write("EMA ");
}
if (indicators.Contains(IndicatorService.MACD))
{
Console.Write("MACD ");
}
if (indicators.Contains(IndicatorService.RSI))
{
Console.Write("RSI ");
}
if (indicators.Contains(IndicatorService.WilliamsR))
{
Console.Write("WilliamsR ");
}
if (indicators.Contains(IndicatorService.Stochastics))
{
Console.Write("Stochastics ");
}
Console.WriteLine("=>\t" + accuracy);
}
}
