public override void pushIndicatorStatistics(LearningIndicator li)
{
string id = li.getName();
string algo = li.getName().Split('_')[0];
double[] pp = li.getPredictivePowerArray();
double score = 0;
foreach (double d in pp)
{
if (double.IsNaN(d) == false && Math.Abs(d) < 1 && Math.Abs(d) > 0)
{
score += Math.Abs(d);
}
}
ValueAndIDPair pair = new ValueAndIDPair()
{
_id = id, _value = score
};
if (candidates.ContainsKey(algo) == false)
{
candidates.Add(algo, pair);
}
else if (candidates[algo]._value < score)
{
candidates[algo] = pair;
}
analysedIndicators++;
}
public override void pushIndicatorStatistics(LearningIndicator li)
{
string id = li.getName();
string algo = li.getName().Split('_')[0];
double[] pp = li.getPredictivePowerArray();
double buySellCodeScore = pp[(int)LearningIndicator.LearningIndicatorPredictivePowerIndecies.maxBuyCode] + pp[(int)LearningIndicator.LearningIndicatorPredictivePowerIndecies.maxSellCode];
ValueAndIDPair pair = new ValueAndIDPair()
{
_id = id, _value = buySellCodeScore
};
if (candidates.ContainsKey(algo) == false)
{
candidates.Add(algo, pair);
}
else if (candidates[algo]._value < buySellCodeScore)
{
candidates[algo] = pair;
}
analysedIndicators++;
}
public override void pushIndicatorStatistics(LearningIndicator li)
{
string id = li.getName();
string algo = li.getName().Split('_')[0];
double[] pp = li.getPredictivePowerArray();
double score = 0;
double wightCode = pp[(int)LearningIndicator.LearningIndicatorPredictivePowerIndecies.valuesOverMinPercentRatioCode];
double wightOutcome = pp[(int)LearningIndicator.LearningIndicatorPredictivePowerIndecies.valuesOverMinPercentRatioOutcome];
score = pp[(int)LearningIndicator.LearningIndicatorPredictivePowerIndecies.buySellCodeDistanceStD] * wightCode //Codeprop
+ pp[(int)LearningIndicator.LearningIndicatorPredictivePowerIndecies.buyCodeStD] * wightCode //Codeprop
+ pp[(int)LearningIndicator.LearningIndicatorPredictivePowerIndecies.sellCodeStD] * wightCode //Codeprop
+ pp[(int)LearningIndicator.LearningIndicatorPredictivePowerIndecies.minStD] * wightOutcome //Diff
+ pp[(int)LearningIndicator.LearningIndicatorPredictivePowerIndecies.maxStD] * wightOutcome //Diff
+ pp[(int)LearningIndicator.LearningIndicatorPredictivePowerIndecies.minMaxDistanceStd] * wightOutcome //Diff
+ pp[(int)LearningIndicator.LearningIndicatorPredictivePowerIndecies.actualStD] * wightOutcome; //Pred
if (double.IsNaN(score) || double.IsInfinity(score))
{
throw new Exception("Score is wired: " + score);
}
ValueAndIDPair pair = new ValueAndIDPair()
{
_id = id, _value = score
};
if (candidates.ContainsKey(algo) == false)
{
candidates.Add(algo, pair);
}
else if (candidates[algo]._value < score)
{
candidates[algo] = pair;
}
}
