#define PAIRS 28 // Number of currency pairs

string CurrencyPairs[PAIRS] = {
    "EURUSD", "GBPUSD", "USDJPY", "GBPJPY", "USDCAD", "EURAUD", "EURJPY",
    "AUDCAD", "AUDJPY", "AUDNZD", "AUDUSD", "CADJPY", "EURCAD", "EURCHF",
    "EURGBP", "EURNZD", "GBPCAD", "GBPCHF", "NZDCAD", "NZDJPY", "NZDUSD",
    "USDCHF", "CHFJPY", "AUDCHF", "GBPNZD", "NZDCHF", "CADCHF", "GBPAUD"
};

vars volatilities[PAIRS];             // Volatility for each pair
vars correlations[PAIRS];             // Correlations for each pair
vars kernelMatrix1[PAIRS][PAIRS];     // Kernel matrix for PCA-1
vars kernelMatrix2[PAIRS][PAIRS];     // Kernel matrix for PCA-2
vars ReducedMatrix1[PAIRS][PAIRS];    // Reduced matrix for PCA-1
vars ReducedMatrix2[PAIRS][PAIRS];    // Reduced matrix for PCA-2
vars FeedbackMatrix1[PAIRS];          // Feedback-influenced matrix for PCA-1
vars FeedbackMatrix2[PAIRS];          // Feedback-influenced matrix for PCA-2
vars Threshold1[PAIRS];               // Thresholds from NN-1
vars Threshold2[PAIRS];               // Thresholds from NN-2
vars MetaFeatures[PAIRS];             // Meta-features for the meta neural network
vars FinalSignals[PAIRS];             // Final signals from the meta neural network
int lookback = 50;                    // Lookback period
int NN1, NN2, MetaNN;                 // Neural network handles
var lambda = 0.5;                     // Cross-influence weight
var sigma1 = 0.5;                     // Initial kernel width for PCA-1
var sigma2 = 0.5;                     // Initial kernel width for PCA-2

// Step 1: Calculate volatilities and correlations
function calculateFeatures() {
    for (int i = 0; i < PAIRS; i++) {
        volatilities[i] = StdDev(series(price(CurrencyPairs[i])), lookback);
        correlations[i] = priceClose(CurrencyPairs[i]) - priceOpen(CurrencyPairs[i]); // Correlation proxy
    }
}

// Step 2: Calculate kernel matrices with initial parameters
function calculateKernelMatrices() {
    for (int i = 0; i < PAIRS; i++) {
        for (int j = 0; j < PAIRS; j++) {
            kernelMatrix1[i][j] = exp(-(pow(volatilities[i] - volatilities[j], 2)) / (2 * pow(sigma1, 2)));
            kernelMatrix2[i][j] = exp(-(pow(correlations[i] - correlations[j], 2)) / (2 * pow(sigma2, 2)));
        }
    }
}

// Step 3: Perform Kernel PCA
function performKernelPCA(vars kernelMatrix[PAIRS][PAIRS], vars eigenvalues, vars eigenvectors) {
    eigenDecomposition(kernelMatrix, eigenvalues, eigenvectors);
}

// Step 4: Reduce kernel data
function reduceKernelData(vars kernelMatrix[PAIRS][PAIRS], vars eigenvectors[PAIRS][PAIRS], vars ReducedMatrix[PAIRS][PAIRS], int components) {
    for (int i = 0; i < PAIRS; i++) {
        for (int j = 0; j < components; j++) {
            ReducedMatrix[i][j] = dotProduct(kernelMatrix[i], eigenvectors[j]);
        }
    }
}

// Step 5: Apply feedback adjustments
function applyFeedbackAdjustments() {
    for (int i = 0; i < PAIRS; i++) {
        FeedbackMatrix1[i] = dotProduct(ReducedMatrix2[i], ReducedMatrix1[i]); // PCA-1 influenced by PCA-2
        FeedbackMatrix2[i] = dotProduct(ReducedMatrix1[i], ReducedMatrix2[i]); // PCA-2 influenced by PCA-1
    }
}

// Step 6: Train and predict neural networks
function trainNeuralNetworks() {
    // Train NN-1
    LayerPerceptron(10);
    LayerNormalize();
    LayerPerceptron(1);
    NN1 = Train("NN1", ReducedMatrix1, Threshold1, PAIRS);

    // Train NN-2
    LayerPerceptron(10);
    LayerNormalize();
    LayerPerceptron(1);
    NN2 = Train("NN2", ReducedMatrix2, Threshold2, PAIRS);

    // Train Meta Neural Network
    LayerPerceptron(5);
    LayerNormalize();
    LayerPerceptron(1);
    MetaNN = Train("MetaNN", [Threshold1, Threshold2, FeedbackMatrix1, FeedbackMatrix2], FinalSignals, PAIRS);
}

function predictSignalsAndThresholds() {
    for (int i = 0; i < PAIRS; i++) {
        Threshold1[i] = Predict(NN1, FeedbackMatrix1[i]);
        Threshold2[i] = Predict(NN2, FeedbackMatrix2[i]);

        // Meta Neural Network combines predictions
        MetaFeatures[i] = [Threshold1[i], Threshold2[i], FeedbackMatrix1[i], FeedbackMatrix2[i]];
        FinalSignals[i] = Predict(MetaNN, MetaFeatures[i]);
    }
}

// Step 7: Execute trades
function executeTrades() {
    for (int i = 0; i < PAIRS; i++) {
        if (FinalSignals[i] > 0.5) {
            enterLong(CurrencyPairs[i]);
        } else if (FinalSignals[i] < -0.5) {
            enterShort(CurrencyPairs[i]);
        }
    }
}

// Main function
function run() {
    set(PLOTNOW);

    // Step 1: Calculate features
    calculateFeatures();

    // Step 2: Compute initial kernel matrices
    calculateKernelMatrices();

    // Step 3: Perform Kernel PCA
    vars eigenvalues1, eigenvectors1;
    vars eigenvalues2, eigenvectors2;
    performKernelPCA(kernelMatrix1, eigenvalues1, eigenvectors1);
    performKernelPCA(kernelMatrix2, eigenvalues2, eigenvectors2);

    // Step 4: Reduce data
    reduceKernelData(kernelMatrix1, eigenvectors1, ReducedMatrix1, 3); // Top 3 components
    reduceKernelData(kernelMatrix2, eigenvectors2, ReducedMatrix2, 3);

    for (int iter = 0; iter < 5; iter++) { // Recursive iterations
        // Step 5: Apply feedback adjustments
        applyFeedbackAdjustments();

        // Step 6: Recompute kernel matrices with feedback
        calculateKernelMatrices();

        // Step 7: Perform Kernel PCA again
        performKernelPCA(kernelMatrix1, eigenvalues1, eigenvectors1);
        performKernelPCA(kernelMatrix2, eigenvalues2, eigenvectors2);

        // Step 8: Predict thresholds and signals
        predictSignalsAndThresholds();

        // Step 9: Execute trades
        executeTrades();
    }
}