#define PAIRS 28 // Number of currency pairs
#define COMBINATIONS 378 // Total number of unique pair combinations

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 volatilityRatios[COMBINATIONS]; // Volatility ratios for pair combinations
vars kernelMatrix1[COMBINATIONS][COMBINATIONS]; // Kernel matrix for PCA-1
vars kernelMatrix2[COMBINATIONS][COMBINATIONS]; // Kernel matrix for PCA-2
vars ReducedMatrix1[COMBINATIONS][COMBINATIONS]; // Reduced matrix for PCA-1
vars ReducedMatrix2[COMBINATIONS][COMBINATIONS]; // Reduced matrix for PCA-2
vars profitabilityMatrix[COMBINATIONS]; // Profitability matrix for pair combinations
vars Threshold1[COMBINATIONS];        // Thresholds from NN-1
vars Threshold2[COMBINATIONS];        // Thresholds from NN-2
vars MetaFeatures[COMBINATIONS];      // Meta-features for MetaNN
vars FinalSignals[COMBINATIONS];      // Final trade signals
int combinationIndex[COMBINATIONS][2]; // Index mapping for combinations
int NN1, NN2, MetaNN;                 // Neural network handles
int lookback = 50;                    // Lookback period

// Step 1: Calculate volatilities for all currency pairs
function calculateVolatilities() {
    for (int i = 0; i < PAIRS; i++) {
        volatilities[i] = StdDev(series(price(CurrencyPairs[i])), lookback);
    }
}

// Step 2: Generate all pair combinations
function generateCombinations() {
    int index = 0;
    for (int i = 0; i < PAIRS - 1; i++) {
        for (int j = i + 1; j < PAIRS; j++) {
            combinationIndex[index][0] = i; // First pair in the combination
            combinationIndex[index][1] = j; // Second pair in the combination
            index++;
        }
    }
}

// Step 3: Calculate volatility ratios for all combinations
function calculateVolatilityRatios() {
    for (int k = 0; k < COMBINATIONS; k++) {
        int i = combinationIndex[k][0];
        int j = combinationIndex[k][1];
        volatilityRatios[k] = abs(volatilities[i] - volatilities[j]); // Volatility difference
    }
}

// Step 4: Compute kernel matrices
function calculateKernelMatrices(var sigma1, var sigma2) {
    for (int i = 0; i < COMBINATIONS; i++) {
        for (int j = 0; j < COMBINATIONS; j++) {
            kernelMatrix1[i][j] = exp(-pow(volatilityRatios[i] - volatilityRatios[j], 2) / (2 * pow(sigma1, 2)));
            kernelMatrix2[i][j] = exp(-pow(profitabilityMatrix[i] - profitabilityMatrix[j], 2) / (2 * pow(sigma2, 2)));
        }
    }
}

// Step 5: Perform Kernel PCA
function performKernelPCA(vars kernelMatrix[COMBINATIONS][COMBINATIONS], vars eigenvalues, vars eigenvectors) {
    eigenDecomposition(kernelMatrix, eigenvalues, eigenvectors);
}

// Step 6: Reduce kernel data
function reduceKernelData(vars kernelMatrix[COMBINATIONS][COMBINATIONS], vars eigenvectors[COMBINATIONS][COMBINATIONS], vars ReducedMatrix[COMBINATIONS][COMBINATIONS], int components) {
    for (int i = 0; i < COMBINATIONS; i++) {
        for (int j = 0; j < components; j++) {
            ReducedMatrix[i][j] = dotProduct(kernelMatrix[i], eigenvectors[j]);
        }
    }
}

// Step 7: Train neural networks
function trainNeuralNetworks() {
    // Train NN-1
    LayerPerceptron(10);
    LayerNormalize();
    LayerPerceptron(1);
    NN1 = Train("NN1", ReducedMatrix1, Threshold1, COMBINATIONS);

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

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

// Step 8: Predict thresholds and signals
function predictThresholdsAndSignals() {
    for (int i = 0; i < COMBINATIONS; i++) {
        Threshold1[i] = Predict(NN1, ReducedMatrix1[i]);
        Threshold2[i] = Predict(NN2, ReducedMatrix2[i]);

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

// Step 9: Execute trades
function executeTrades() {
    for (int i = 0; i < COMBINATIONS; i++) {
        int pair1 = combinationIndex[i][0];
        int pair2 = combinationIndex[i][1];

        // Example trade logic based on meta signal
        if (FinalSignals[i] > 0.5) {
            enterLong(CurrencyPairs[pair1]);
            enterShort(CurrencyPairs[pair2]);
        } else if (FinalSignals[i] < -0.5) {
            enterShort(CurrencyPairs[pair1]);
            enterLong(CurrencyPairs[pair2]);
        }
    }
}

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

    // Step 1: Calculate volatilities
    calculateVolatilities();

    // Step 2: Generate pair combinations
    generateCombinations();

    // Step 3: Calculate volatility ratios
    calculateVolatilityRatios();

    // Step 4: Compute kernel matrices
    calculateKernelMatrices(0.5, 0.5); // Example sigma values

    // Step 5: Perform Kernel PCA
    vars eigenvalues1, eigenvectors1;
    vars eigenvalues2, eigenvectors2;
    performKernelPCA(kernelMatrix1, eigenvalues1, eigenvectors1);
    performKernelPCA(kernelMatrix2, eigenvalues2, eigenvectors2);

    // Step 6: Reduce kernel data
    reduceKernelData(kernelMatrix1, eigenvectors1, ReducedMatrix1, 3); // Top 3 components
    reduceKernelData(kernelMatrix2, eigenvectors2, ReducedMatrix2, 3);

    // Step 7: Train neural networks
    trainNeuralNetworks();

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

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