#include <default.c>

// Variables
var alpha, beta;
var filt, filttr, hband, lband;
vars PriceClose, GaussianFiltered, GaussianFilteredTR;
vars UpperBand, LowerBand;

// Initialization
void init() {
    BarPeriod = 60; // 1-hour bars
    LookBack = 150; // Lookback period for the Gaussian filter

    PriceClose = series(priceClose());
    GaussianFiltered = series(0);
    GaussianFilteredTR = series(0);
    UpperBand = series(0);
    LowerBand = series(0);

    alpha = calculateAlpha(144, 4); // Sampling Period and Poles
    beta = (1 - cos(2 * PI / 144)) / (pow(1.414, 2 / 4) - 1);

    adviseLong(DTREE, 0, NULL, 0); // Initialize the Decision Tree model
}

// Calculate Alpha
var calculateAlpha(int period, int poles) {
    var beta = (1 - cos(2 * PI / period)) / (pow(1.414, 2 / poles) - 1);
    return -beta + sqrt(beta * beta + 2 * beta);
}

// Modified Gaussian Filter with Bands
var filt9x_with_bands(var a, var s, int poles, var deviationMultiplier, var* upperBand, var* lowerBand) {
    static vars f[10]; // Array to store previous filter values
    var x = 1 - a;
    var filt = s * a;

    // Precompute powers of x to avoid redundant calculations
    var x2 = x * x;
    var x3 = x2 * x;
    var x4 = x3 * x;
    var x5 = x4 * x;
    var x6 = x5 * x;
    var x7 = x6 * x;
    var x8 = x7 * x;
    var x9 = x8 * x;

    // Calculate the filter value iteratively
    for (int i = 1; i <= poles; i++) {
        switch (i) {
            case 1:
                filt += x * f[i];
                break;
            case 2:
                filt -= 36 * x2 * f[i];
                break;
            case 3:
                filt += 84 * x3 * f[i];
                break;
            case 4:
                filt -= 126 * x4 * f[i];
                break;
            case 5:
                filt += 126 * x5 * f[i];
                break;
            case 6:
                filt -= 84 * x6 * f[i];
                break;
            case 7:
                filt += 36 * x7 * f[i];
                break;
            case 8:
                filt -= 9 * x8 * f[i];
                break;
            case 9:
                filt += x9 * f[i];
                break;
        }
    }

    // Shift the previous values in the array
    for (int j = 9; j > 0; j--) {
        f[j] = f[j - 1];
    }
    f[0] = filt;

    // Calculate standard deviation of filter values
    var sum = 0, mean = 0, count = 10;
    for (int j = 0; j < count; j++) {
        sum += f[j];
    }
    mean = sum / count;

    var varianceSum = 0;
    for (int j = 0; j < count; j++) {
        varianceSum += (f[j] - mean) * (f[j] - mean);
    }
    var stddev = sqrt(varianceSum / count);

    // Calculate upper and lower bands
    *upperBand = filt + deviationMultiplier * stddev;
    *lowerBand = filt - deviationMultiplier * stddev;

    return filt;
}

// Gaussian Filter Calculation
void calculateGaussianFilter(vars src, int poles, int period) {
    vars filtSeries = series(0);
    vars filtSeriesTR = series(0);
    var lag = (period - 1) / (2 * poles);
    vars srcData = series(0);
    vars trData = series(0);
    
    for (int i = 0; i < Bar; i++) {
        if (i > lag) {
            srcData[i] = src[i] + (src[i] - src[i - lag]);
            trData[i] = TR() + (TR() - TR(lag));
        } else {
            srcData[i] = src[i];
            trData[i] = TR();
        }
    }
    
    for (int i = 0; i < Bar; i++) {
        var upper, lower;
        filtSeries[i] = filt9x_with_bands(alpha, srcData[i], poles, 1.414, &upper, &lower);
        filtSeriesTR[i] = filt9x_with_bands(alpha, trData[i], poles, 1.414, &upper, &lower);
        UpperBand[i] = upper;
        LowerBand[i] = lower;
    }
    
    GaussianFiltered[0] = filtSeries[0];
    GaussianFilteredTR[0] = filtSeriesTR[0];
    
    hband = UpperBand[0];
    lband = LowerBand[0];
}

// Main Trading Logic
void run() {
    BarPeriod = 60; // 1-hour bars
    LookBack = 150; // Lookback period
    TradesPerBar = 2;

    if (Train) {
        Hedge = 2; // Hedge during training
    }

    set(RULES | TESTNOW); // Set rules and test mode

    if (is(INITRUN)) {
        init();
    }

    calculateGaussianFilter(PriceClose, 4, 144);

    // Generate some signals from GaussianFiltered in the -100..100 range
    var Signals[2]; 
    Signals[0] = (GaussianFiltered[0] - GaussianFilteredTR[0]) / PIP;
    Signals[1] = 100 * FisherN(priceClose(), 100);

    // Train and trade the signals using the decision tree model
    if (adviseLong(DTREE, 0, Signals, 2) > 0)
        enterLong();
    if (adviseShort(DTREE, 0, Signals, 2) > 0)
        enterShort();
}