added

internal/indicators/macd.go

@@ -0,0 +1,93 @@
+package indicators
+
+// MACDResult contient MACD line, signal line et histogramme.
+type MACDResult struct {
+	MACD      float64
+	Signal    float64
+	Histogram float64
+}
+
+// MACD calcule le Moving Average Convergence Divergence (12/26/9 standard).
+// Retourne zéro-value si pas assez de données.
+func MACD(closes []float64) MACDResult {
+	return MACDCustom(closes, 12, 26, 9)
+}
+
+func MACDCustom(closes []float64, fast, slow, signal int) MACDResult {
+	if len(closes) < slow+signal {
+		return MACDResult{}
+	}
+
+	emaFast := emaSlice(closes, fast)
+	emaSlow := emaSlice(closes, slow)
+
+	// Aligner les deux séries (emaSlow est plus courte)
+	offset := len(emaFast) - len(emaSlow)
+	macdLine := make([]float64, len(emaSlow))
+	for i := range emaSlow {
+		macdLine[i] = emaFast[offset+i] - emaSlow[i]
+	}
+
+	if len(macdLine) < signal {
+		return MACDResult{}
+	}
+
+	signalLine := emaSlice(macdLine, signal)
+	last := macdLine[len(macdLine)-1]
+	sig := signalLine[len(signalLine)-1]
+
+	return MACDResult{
+		MACD:      last,
+		Signal:    sig,
+		Histogram: last - sig,
+	}
+}
+
+// SMA calcule la moyenne mobile simple sur les n dernières valeurs.
+func SMA(closes []float64, period int) float64 {
+	if len(closes) < period {
+		return 0
+	}
+	slice := closes[len(closes)-period:]
+	sum := 0.0
+	for _, v := range slice {
+		sum += v
+	}
+	return sum / float64(period)
+}
+
+// AvgVolume calcule le volume moyen sur les n dernières barres.
+func AvgVolume(volumes []int64, period int) int64 {
+	if len(volumes) < period {
+		period = len(volumes)
+	}
+	if period == 0 {
+		return 0
+	}
+	slice := volumes[len(volumes)-period:]
+	var sum int64
+	for _, v := range slice {
+		sum += v
+	}
+	return sum / int64(period)
+}
+
+func emaSlice(data []float64, period int) []float64 {
+	if len(data) < period {
+		return nil
+	}
+	k := 2.0 / float64(period+1)
+
+	// Première valeur = SMA des `period` premières
+	sum := 0.0
+	for i := 0; i < period; i++ {
+		sum += data[i]
+	}
+	ema := make([]float64, 0, len(data)-period+1)
+	ema = append(ema, sum/float64(period))
+
+	for i := period; i < len(data); i++ {
+		ema = append(ema, data[i]*k+ema[len(ema)-1]*(1-k))
+	}
+	return ema
+}

internal/indicators/rsi.go

@@ -0,0 +1,43 @@
+package indicators
+
+// RSI calcule le Relative Strength Index (Wilder's smoothing, période 14).
+// Retourne NaN si pas assez de données.
+func RSI(closes []float64, period int) float64 {
+	if period <= 0 {
+		period = 14
+	}
+	if len(closes) < period+1 {
+		return -1
+	}
+
+	var gains, losses float64
+	for i := 1; i <= period; i++ {
+		delta := closes[i] - closes[i-1]
+		if delta > 0 {
+			gains += delta
+		} else {
+			losses -= delta
+		}
+	}
+
+	avgGain := gains / float64(period)
+	avgLoss := losses / float64(period)
+
+	// Wilder's smoothing pour le reste
+	for i := period + 1; i < len(closes); i++ {
+		delta := closes[i] - closes[i-1]
+		if delta > 0 {
+			avgGain = (avgGain*float64(period-1) + delta) / float64(period)
+			avgLoss = (avgLoss * float64(period-1)) / float64(period)
+		} else {
+			avgGain = (avgGain * float64(period-1)) / float64(period)
+			avgLoss = (avgLoss*float64(period-1) - delta) / float64(period)
+		}
+	}
+
+	if avgLoss == 0 {
+		return 100
+	}
+	rs := avgGain / avgLoss
+	return 100 - (100 / (1 + rs))
+}