public void getMinMax_NAN_Test()
{
double[] inputs = new double[200];
int v = 0;
for (int i = 0; i < inputs.Length; i += 2)
{
inputs[i] = v++;
}
for (int i = 1; i < inputs.Length; i += 2)
{
inputs[i] = double.NaN;
}
Assert.AreEqual(inputs[0], 0);
Assert.AreEqual(inputs[2], 1);
Assert.AreEqual(inputs[4], 2);
Assert.AreEqual(inputs[6], 3);
Assert.AreEqual(inputs[1], double.NaN);
Assert.AreEqual(inputs[3], double.NaN);
Assert.AreEqual(inputs[5], double.NaN);
Assert.AreEqual(inputs[7], double.NaN);
double min, max;
DistributionHelper.getMinMax(inputs, 10, out min, out max);
Assert.AreEqual(5, min, 1);
Assert.AreEqual(95, max, 1);
}
public static Image visualizeArray(double[] input, int width, int height, int lineSize = 3)
{
Bitmap bmp = new Bitmap(width, height);
Graphics g = Graphics.FromImage(bmp);
g.Clear(Color.LightGray);
double stepSize = Convert.ToDouble(input.Length) / Convert.ToDouble(width);
double min, max;
DistributionHelper.getMinMax(input, out min, out max);
int oldX = -1, oldY = -1;
Color cUp = Color.Green;
Color cDown = Color.Blue;
for (int x = 0; x < width; x++)
{
int index = Convert.ToInt32(stepSize * x);
if (index < input.Length && double.IsNaN(input[index]) == false)
{
int y = height - Convert.ToInt32((input[index] - min) / (max - min) * height);
if (oldX != -1)
{
for (int yOffset = -(lineSize / 2); yOffset < (lineSize / 2); yOffset++)
{
if (y + yOffset > 0 && y + yOffset < height)
{
bmp.SetPixel(x, y + yOffset, y > oldY ? cDown : cUp);
}
}
}
oldX = x;
oldY = y;
}
else
{
for (int y = 0; y < height; y++)
{
bmp.SetPixel(x, y, Color.Yellow);
}
}
}
return(bmp);
}
public void getMinMax_Test_SmallValues()
{
double[] inputs = new double[100];
for (int i = 0; i < inputs.Length; i++)
{
inputs[i] = i * 0.001;
}
double min, max;
DistributionHelper.getMinMax(inputs, 10, out min, out max);
Assert.AreEqual(5 * 0.001, min, 1 * 0.001);
Assert.AreEqual(95 * 0.001, max, 1 * 0.001);
}
public void getMinMax_Test_MoreComplex()
{
double[] inputs = new double[200];
for (int i = 0; i < 100; i++)
{
inputs[i] = i;
}
for (int i = 100; i < 200; i++)
{
inputs[i] = 100;
}
double min, max;
DistributionHelper.getMinMax(inputs, 10, out min, out max);
Assert.AreEqual(10, min, 1);
Assert.AreEqual(100, max, 1);
}
public LearningIndicator(WalkerIndicator indicator, double[][] prices, bool[][] outcomeCodes, double[][] outcomes, long timeframe, double targetPercent, double minPercentThreshold, int steps, bool createStatistics)
{
this.targetPercent = targetPercent;
this.timeframe = timeframe;
double validRatio;
double[] values = IndicatorRunner.getIndicatorValues(prices, indicator.Clone(), out validRatio);
if (validRatio < 0.5)
{
throw new TooLittleValidDataException("Not enough valid values: " + validRatio);
}
//May be does not work properly... todo:
double min, max, usedValuesRatio;
//DistributionHelper.getMinMax(values, 4, out min, out max);
DistributionHelper.getMinMax(values, out min, out max);
outcomeCodeSamplingTable = IndicatorSampler.sampleValuesOutcomeCode(values, outcomeCodes, min, max, steps, out usedValuesRatio);
if (usedValuesRatio < 0.5)
{
throw new TooLittleValidDataException("Not enough sampling for outcomeCode: " + usedValuesRatio);
}
outcomeSamplingTable = IndicatorSampler.sampleValuesOutcome(values, prices, outcomes, min, max, out usedValuesRatio, 40);
if (usedValuesRatio < 0.5)
{
throw new TooLittleValidDataException("Not enough sampling for outcome: " + usedValuesRatio);
}
this.usedValues = usedValuesRatio;
if (createStatistics)
{
//Predictive power calculation
predictivePower = new double[33];
IndicatorSampler.getStatisticsOutcomeCodes(values, outcomeCodes, out predictivePower[0], out predictivePower[1], out predictivePower[2], out predictivePower[3]);
IndicatorSampler.getStatisticsOutcomes(values, prices, outcomes, out predictivePower[4], out predictivePower[5], out predictivePower[6], out predictivePower[7], out predictivePower[8], out predictivePower[9]);
DistributionHelper.getSampleOutcomeCodesBuyMaxSellMax(outcomeCodeSamplingTable, minPercentThreshold, out predictivePower[10], out predictivePower[11], out predictivePower[12], out predictivePower[13]);
DistributionHelper.getSampleOutcomesMinMax(outcomeSamplingTable, minPercentThreshold, out predictivePower[14], out predictivePower[15], out predictivePower[16], out predictivePower[17], out predictivePower[18], out predictivePower[19], out predictivePower[20], out predictivePower[21], out predictivePower[22], out predictivePower[23]);
//Outcome Code
List <double> buyCodesDist = new List <double>(),
sellCodesDist = new List <double>(),
buySellDistanceDist = new List <double>(),
minMaxDistanceDist = new List <double>(),
minDist = new List <double>(),
maxDist = new List <double>(),
actualDist = new List <double>();
double totalCodeSamples = 0;
foreach (double[] row in outcomeCodeSamplingTable)
{
totalCodeSamples += row[(int)SampleValuesOutcomeCodesIndices.SamplesCount];
}
int regardedStates = 0;
foreach (double[] row in outcomeCodeSamplingTable)
{
if ((row[(int)SampleValuesOutcomeCodesIndices.SamplesCount] / totalCodeSamples) * 100 >= minPercentThreshold) //minPercentThreshold
{
buyCodesDist.Add(row[(int)SampleValuesOutcomeCodesIndices.BuyRatio]);
sellCodesDist.Add(row[(int)SampleValuesOutcomeCodesIndices.SellRatio]);
buySellDistanceDist.Add(Math.Abs(row[(int)SampleValuesOutcomeCodesIndices.BuyRatio] - row[(int)SampleValuesOutcomeCodesIndices.SellRatio]));
regardedStates++;
}
}
predictivePower[(int)LearningIndicatorPredictivePowerIndecies.valuesOverMinPercentRatioCode] = Convert.ToDouble(regardedStates) / outcomeCodeSamplingTable.Length;
if (regardedStates <= 2)
{
throw new TooLittleStatesException("Too little sates: " + regardedStates);
}
predictivePower[(int)LearningIndicatorPredictivePowerIndecies.buyCodeStD] = buyCodesDist.StandardDeviation();
predictivePower[(int)LearningIndicatorPredictivePowerIndecies.sellCodeStD] = sellCodesDist.StandardDeviation();
predictivePower[(int)LearningIndicatorPredictivePowerIndecies.buySellCodeDistanceStD] = buySellDistanceDist.StandardDeviation();
//Outcome
double totalSamples = 0;
foreach (double[] row in outcomeSamplingTable)
{
totalSamples += row[(int)SampleValuesOutcomeIndices.SamplesCount];
}
//Avgs
regardedStates = 0;
foreach (double[] row in outcomeSamplingTable)
{
if ((row[(int)SampleValuesOutcomeIndices.SamplesCount] / totalSamples) * 100 > minPercentThreshold) //minPercentThreshold
{
maxDist.Add(row[(int)SampleValuesOutcomeIndices.MaxAvg]);
minDist.Add(row[(int)SampleValuesOutcomeIndices.MinAvg]);
minMaxDistanceDist.Add(Math.Abs(row[(int)SampleValuesOutcomeIndices.MaxAvg]) + row[(int)SampleValuesOutcomeIndices.MinAvg]);
actualDist.Add(row[(int)SampleValuesOutcomeIndices.ActualAvg]);
regardedStates++;
}
}
predictivePower[(int)LearningIndicatorPredictivePowerIndecies.valuesOverMinPercentRatioOutcome] += Convert.ToDouble(regardedStates) / outcomeSamplingTable.Length;
//avg distances
predictivePower[(int)LearningIndicatorPredictivePowerIndecies.maxStD] = maxDist.StandardDeviation();
predictivePower[(int)LearningIndicatorPredictivePowerIndecies.minStD] = minDist.StandardDeviation();
predictivePower[(int)LearningIndicatorPredictivePowerIndecies.minMaxDistanceStd] = minMaxDistanceDist.StandardDeviation();
predictivePower[(int)LearningIndicatorPredictivePowerIndecies.actualStD] = actualDist.StandardDeviation();
if (double.IsNaN(predictivePower[(int)LearningIndicatorPredictivePowerIndecies.buyCodeStD]) ||
double.IsNaN(predictivePower[(int)LearningIndicatorPredictivePowerIndecies.sellCodeStD]) ||
double.IsNaN(predictivePower[(int)LearningIndicatorPredictivePowerIndecies.buySellCodeDistanceStD]) ||
double.IsNaN(predictivePower[(int)LearningIndicatorPredictivePowerIndecies.maxStD]) ||
double.IsNaN(predictivePower[(int)LearningIndicatorPredictivePowerIndecies.minStD]) ||
double.IsNaN(predictivePower[(int)LearningIndicatorPredictivePowerIndecies.minMaxDistanceStd]) ||
double.IsNaN(predictivePower[(int)LearningIndicatorPredictivePowerIndecies.actualStD]))
{
throw new Exception("Not a valid predictive power!");
}
//End predictive power calculation
}
this.indicator = indicator;
}