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;
}
