Lampiran

// Pseudocode Komponen Valuasi CIVM
function hitungNilaiWajar(saham, data_historis, currentBondYield) {
    // 1. Ekstrak data kuartalan
    income_q = data_historis.laba_bersih_kuartalan;
    dividend_cash_q = data_historis.dividen_tunai_kuartalan;
    dividend_stock_q = data_historis.dividen_saham_kuartalan;
    equity = data_historis.ekuitas_kuartalan;
    outstanding_shares = data_historis.jumlah_saham_beredar;
    
    // 2. Hitung TTM dan metrik dasar
    metrics = {};
    metrics['ttmIncome'] = sum(income_q[-4:]);
    metrics['ttmEPS'] = metrics['ttmIncome'] / outstanding_shares[-1];
    metrics['bvps'] = equity[-1] / outstanding_shares[-1];
    metrics['ttmROE'] = metrics['ttmIncome'] / equity[-1];
    
    // 3. Hitung total dividen dan DPR
    totalDividend = sum(dividend_cash_q[-4:]) + sum(dividend_stock_q[-4:]);
    metrics['dividendPayoutRatio'] = totalDividend / metrics['ttmIncome'];
    
    // 4. Hitung pertumbuhan dan CV
    bvps_historical = [equity[i] / outstanding_shares[i] for i in range(len(equity))];
    bvps_growth = [100*(bvps_historical[i]/bvps_historical[i-1]-1) for i in range(1, len(bvps_historical))];
    metrics['ttmBookValueGrowth'] = mean(bvps_growth[-4:]);
    
    // Hitung CV (Coefficient of Variation) untuk stabilitas
    if mean(bvps_growth[-4:]) != 0:
        metrics['ttmBookValueCV'] = stdev(bvps_growth[-4:]) / abs(mean(bvps_growth[-4:]));
    else:
        metrics['ttmBookValueCV'] = 999; // Nilai tinggi jika mean = 0
    
    // Metrik ROE TTM dan CV
    roe_historical = [income_q[i] / equity[i] for i in range(len(income_q))];
    roe_growth = [100*(roe_historical[i]/roe_historical[i-1]-1) for i in range(1, len(roe_historical))];
    metrics['ttmROEGrowth'] = mean(roe_growth[-4:]);
    
    if mean(roe_growth[-4:]) != 0:
        metrics['ttmROECV'] = stdev(roe_growth[-4:]) / abs(mean(roe_growth[-4:]));
    else:
        metrics['ttmROECV'] = 999;
    
    // 5. Hitung Graham Number (GN)
    metrics['grahamNumber'] = floor(sqrt(22.5 * max(0, metrics['ttmEPS']) * max(0, metrics['bvps'])));
    
    // 6. Hitung Graham Formula Termodifikasi (CanonMR)
    // Modifikasi pertumbuhan untuk mengatasi nilai negatif
    G = metrics['ttmBookValueGrowth'];
    if G >= 0:
        G_adj = G;
    else:
        G_adj = max(exp(G) - 1, -0.1); // Transformasi logaritmik dibatasi -10%
    
    metrics['grahamFormulaMod'] = floor(max(0, metrics['ttmEPS']) * (8.5 + 2*G_adj) * (4.4/currentBondYield));
    
    // 7. Kombinasi Hibrida Graham dengan Pembobotan CV
    // Bobot berdasarkan inverse dari CV (stabilitas lebih tinggi, bobot lebih tinggi)
    if metrics['ttmBookValueCV'] + metrics['ttmROECV'] == 0:
        GN_weight = 0.5; // Jika tidak ada variasi, bobot sama
    else:
        // Rumus pembobotan kompleks berdasarkan CV
        // Semakin stabil BVPS, semakin besar bobot GN
        GN_weight = metrics['ttmROECV'] / (metrics['ttmBookValueCV'] + metrics['ttmROECV']);
    
    GF_weight = 1 - GN_weight;
    
    metrics['combinedFairPriceRaw'] = floor(GN_weight * metrics['grahamNumber'] + GF_weight * metrics['grahamFormulaMod']);
    
    // 8. Penyesuaian Berdasarkan DPR
    dpr = metrics['dividendPayoutRatio'];
    
    // Rentang DPR optimal (contoh: 28%-42%)
    dpr_min = 0.28;
    dpr_max = 0.42;
    
    // Faktor penyesuaian DPR
    if dpr < dpr_min:
        // Penalti untuk DPR rendah
        dpr_adjustment = 1 - (dpr_min - dpr) * 2;
    else if dpr > dpr_max:
        // Bonus terbatas untuk DPR tinggi
        dpr_adjustment = 1 + min(0.1, (dpr - dpr_max) * 0.5);
    else:
        dpr_adjustment = 1.0; // Dalam rentang optimal
    
    // Nilai wajar final
    metrics['adjustedFairPrice'] = floor(metrics['combinedFairPriceRaw'] * dpr_adjustment);
    
    return metrics;
}
                            

// Pseudocode Pembentukan Rekomendasi Teknikal
function generateRecommendation(data, optimizedWeights=null) {
    // Menghitung indikator
    sma20 = calculateSMA(data.close, 20);
    sma50 = calculateSMA(data.close, 50);
    sma200 = calculateSMA(data.close, 200);
    
    ema9 = calculateEMA(data.close, 9);
    ema20 = calculateEMA(data.close, 20);
    ema50 = calculateEMA(data.close, 50);
    
    macd = calculateMACD(data.close, 12, 26, 9); // Hasil berisi macd_line, signal_line, histogram
    
    rsi = calculateRSI(data.close, 14);
    
    bollinger = calculateBollingerBands(data.close, 20, 2); // Upper, Middle, Lower
    
    // ...dan seterusnya untuk indikator lainnya...
    
    // Default weights jika optimized weights tidak tersedia
    weights = optimizedWeights || {
        'sma_ema': 20,
        'macd': 15,
        'rsi': 15,
        'bollinger': 10,
        'ichimoku': 15,
        'stochastic': 10,
        'candlestick': 10,
        'divergence': 5
    };
    
    // Inisialisasi kekuatan buy dan sell
    buyStrength = 0;
    sellStrength = 0;
    totalWeight = 0;
    
    // Evaluasi SMA/EMA
    if (sma20[-1] > sma50[-1] && sma50[-1] > sma200[-1]) {
        buyStrength += weights['sma_ema'];
    } else if (sma20[-1] < sma50[-1] && sma50[-1] < sma200[-1]) {
        sellStrength += weights['sma_ema'];
    }
    totalWeight += weights['sma_ema'];
    
    // Evaluasi MACD
    if (macd.histogram[-1] > 0 && macd.histogram[-1] > macd.histogram[-2]) {
        buyStrength += weights['macd'];
    } else if (macd.histogram[-1] < 0 && macd.histogram[-1] < macd.histogram[-2]) {
        sellStrength += weights['macd'];
    }
    totalWeight += weights['macd'];
    
    // Evaluasi RSI
    if (rsi[-1] < 30) {
        buyStrength += weights['rsi'];
    } else if (rsi[-1] > 70) {
        sellStrength += weights['rsi'];
    }
    totalWeight += weights['rsi'];
    
    // ...dan seterusnya untuk indikator lainnya...
    
    // Normalisasi kekuatan buy dan sell (persentase)
    buyStrength = (buyStrength / totalWeight) * 100;
    sellStrength = (sellStrength / totalWeight) * 100;
    
    // Tentukan rekomendasi
    if (buyStrength >= 60) {
        return "BUY";
    } else if (sellStrength >= 60) {
        return "SELL";
    } else {
        return "HOLD";
    }
}