Sure pals, a self studying EA. Isnt it nice to be a software program developer? free to create issues past creativeness. Now lets study some variables and see how its performed.

This is a simplified instance of a self-learning Professional Advisor (EA) utilizing a easy machine studying algorithm known as Ok-Nearest Neighbors (KNN) for classification. Do you know about it? This code demonstrates the fundamental construction and idea, however take into account that real-world implementation would require extra refined strategies and in depth testing.

enter int okay = 5; 


int learningPeriod = 100; 
bool isLearningCompleted = false;
double[] options;
int[] labels;


int OnInit()
{
    
    ArrayResize(options, learningPeriod);
    ArrayResize(labels, learningPeriod);

    return INIT_SUCCEEDED;
}


void OnTick()
{
    
    if (!isLearningCompleted && Bars >= learningPeriod)
    {
        Study();
        isLearningCompleted = true;
    }

    
    if (isLearningCompleted && Bars > learningPeriod)
    {
        
        double currentFeature = CalculateFeature();

        
        int predictedLabel = Classify(currentFeature);

        
        if (predictedLabel == 1)
        {
            
            OrderSend(Image(), OP_BUY, 0.01, Ask, 3, 0, 0, "Self-Studying EA");
        }
        else if (predictedLabel == -1)
        {
            
            OrderSend(Image(), OP_SELL, 0.01, Bid, 3, 0, 0, "Self-Studying EA");
        }
    }
}


void Study()
{
    
    for (int i = 0; i < learningPeriod; i++)
    {
        options[i] = CalculateFeature();
        labels[i] = GetLabel();
        Sleep(100); 
    }
}


int Classify(double currentFeature)
{
    double[] distances;
    ArrayResize(distances, learningPeriod);

    
    for (int i = 0; i < learningPeriod; i++)
    {
        distances[i] = MathAbs(options[i] - currentFeature);
    }

    
    ArraySort(distances);

    int positiveVotes = 0;
    int negativeVotes = 0;

    
    for (int i = 0; i < okay; i++)
    {
        int nearestIndex = ArrayBsearch(distances, distances[i]);

        if (labels[nearestIndex] == 1)
            positiveVotes++;
        else if (labels[nearestIndex] == -1)
            negativeVotes++;
    }

    
    if (positiveVotes > negativeVotes)
        return 1;
    else if (negativeVotes > positiveVotes)
        return -1;
    else
        return 0; 
}


double CalculateFeature()
{
    
    
    
    return MathRand();
}


int GetLabel()
{
    
    
    
    int random = MathRand() % 2;
    if (random == 0)
        return -1; 
    else
        return 1; 
}

Now we have to have a look at features and variables used right here:

On this instance, the EA collects options and labels in the course of the preliminary studying interval ( learningPeriod ), proper. After the educational interval, it begins making predictions utilizing the KNN algorithm based mostly on the present characteristic worth. The CalculateFeature()perform represents the characteristic extraction course of, and the GetLabel()perform simulates labeling for demonstration functions.

Please notice that this can be a simplified instance, and real-world implementation of a self-learning EA requires cautious consideration of characteristic choice, knowledge preprocessing, superior machine studying algorithms, mannequin analysis, and threat administration strategies.

Be sure to totally check and validate any algorithmic buying and selling methods earlier than deploying them in dwell buying and selling.

Get pleasure from it and have enjoyable.