// === Config ===
#define INPUT_SIZE 5       // Number of past MMI points as ML features
#define TRAIN_BARS 500     // Training period for ML model

// === Globals ===
int PeriodMax = 0;

// === Helper Functions ===
var clamp(var value, var minv, var maxv)
{
    if (value < minv) return minv;
    if (value > maxv) return maxv;
    return value;
}

int adaptiveWinLength()
{
    var p = priceClose();
    if (p == 0) return 20; // safety
    var atr_val = ATR(14);
    int w = round(50 * (atr_val / p));
    return max(10, w);
}

var sineWave()
{
    return sin(2*PI*Bar/20.); // SINE_LEN = 20
}

var spectralEnergy(int baseWin)
{
    static var* lowSeries;
    static var* highSeries;

    var lowBand  = EMA(priceClose(),34) - EMA(priceClose(),89);
    var highBand = priceClose() - EMA(priceClose(),8);

    lowSeries  = series(lowBand);
    highSeries = series(highBand);

    var eLow  = Variance(lowSeries, baseWin);
    var eHigh = Variance(highSeries, baseWin);

    var denom = eHigh + eLow + 1e-6;
    var spectralRatio = eHigh / denom;
    return clamp(spectralRatio,0,1);
}

var predictabilityMI(int window)
{
    static var* priceSeries;
    priceSeries = series(priceClose());

    var x1 = priceClose(1);
    var x2 = priceClose(2);
    var y  = priceClose();

    var s_x1   = EMA(x1, window);
    var s_x2   = EMA(x2, window);
    var s_y    = EMA(y,  window);

    var s_x1x1 = EMA(x1*x1, window);
    var s_x2x2 = EMA(x2*x2, window);
    var s_x1x2 = EMA(x1*x2, window);
    var s_x1y  = EMA(x1*y,  window);
    var s_x2y  = EMA(x2*y,  window);

    var denom = s_x1x1*s_x2x2 - s_x1x2*s_x1x2;
    var a = ifelse(denom != 0, (s_x2x2*s_x1y - s_x1x2*s_x2y)/denom, 0);
    var b = ifelse(denom != 0, (s_x1x1*s_x2y - s_x1x2*s_x1y)/denom, 0);

    var y_hat = a*x1 + b*x2;
    var residual  = y - y_hat;
    var mse_pred  = EMA(residual*residual, window);
    var var_price = Variance(priceSeries, 50);

    var ratio = ifelse(var_price > 0, mse_pred/var_price, 0);
    return clamp(ratio,0,1);
}

var sineMI(int window)
{
    static var* priceSeries;
    priceSeries = series(priceClose());

    var s       = sineWave();
    var price   = priceClose();
    var s_dev   = s     - EMA(s,     window);
    var p_dev   = price - EMA(price, window);
    var mse_sin = EMA((p_dev - s_dev)*(p_dev - s_dev), window);
    var var_pr  = Variance(priceSeries, 50);

    var ratio = ifelse(var_pr > 0, mse_sin/var_pr, 0);
    return clamp(ratio,0,1);
}

// === Main Indicator (Adaptive MMI) ===
function run()
{
    set(PLOTNOW);

    // Ensure enough history for all components and ML
    LookBack = max( max(90, 50), INPUT_SIZE + 6 ); // EMA(89) & Variance(50) & features depth
    BarPeriod = 60; // example; set to your actual bar size

    if (Bar < max(LookBack, TRAIN_BARS)) return;

    int win = adaptiveWinLength();

    // --- Base MMI Components ---
    var mi_sine = sineMI(win);
    var mi_pred = predictabilityMI(win);
    var mi_spec = spectralEnergy(50);
    var cmi_raw = (mi_sine + mi_pred + mi_spec)/3;

    // --- Store MMI history for ML ---
    static var* mmiSeries;
    mmiSeries = series(EMA(cmi_raw,5));

    // Make sure series depth is sufficient
    if (mmiSeries[INPUT_SIZE] == 0 && Bar < LookBack + INPUT_SIZE + 2) return;

    // --- Build ML Features (past INPUT_SIZE values) ---
    var features[INPUT_SIZE];
    int i;
    for(i=0; i<INPUT_SIZE; i++)
        features[i] = mmiSeries[i+1]; // strictly past values

    // --- Predict the next change in MMI using ML ---
    // Train once, then reuse model each bar (Retrain=0). You can switch to Retrain=1 for rolling retrain.
    var predicted_delta = adviseLong(TRAIN_BARS, 0, features, INPUT_SIZE);

    // --- Normalize and control adaptivity ---
    var norm_delta = clamp(predicted_delta, -1, 1);    // keep it bounded
    var adaptFactor = clamp(1 - fabs(norm_delta), 0.4, 0.9);

    // Integer, bounded smoothing period for EMA
    int adaptPeriod = (int)round(5./adaptFactor);
    adaptPeriod = max(2, min(50, adaptPeriod));        // guard rails

    // --- Compute the adaptive MMI (bounded 0-1) ---
    var adaptiveMMI = clamp(EMA(cmi_raw, adaptPeriod), 0, 1);

    // --- Plot the indicator ---
    plot("Adaptive MMI", adaptiveMMI, LINE, RED);
    plot("Predicted ?",  norm_delta, LINE, BLUE);
    plot("Low",  0.3, LINE, GREEN);
    plot("High", 0.7, LINE, ORANGE);
}