"""SFP & RSI Divergence Detection untuk BTC Scalper"""

def detect_sfp(candles, lookback=10):
    if not candles or len(candles) < lookback + 2:
        return {'type': 'NONE', 'sweep_level': 0, 'score_bonus': 0}
    try:
        recent = candles[-(lookback+2):]
        last = recent[-2]  # pakai candle sebelum terakhir (sudah close)
        prev = recent[:-2]
        # Support both list [ts,o,h,l,c,v] and dict {open,high,low,close}
        def _low(c): return float(c['low'] if isinstance(c,dict) else c[3])
        def _high(c): return float(c['high'] if isinstance(c,dict) else c[2])
        def _close(c): return float(c['close'] if isinstance(c,dict) else c[4])
        last_low = _low(last)
        last_high = _high(last)
        last_close = _close(last)
        swing_lows = [_low(c) for c in prev[-5:]]
        swing_highs = [_high(c) for c in prev[-5:]]
        recent_swing_low = min(swing_lows)
        recent_swing_high = max(swing_highs)
        # Bullish SFP
        if last_low < recent_swing_low and last_close > recent_swing_low:
            depth = (recent_swing_low - last_low) / recent_swing_low * 100
            if depth >= 0.01:
                return {'type': 'BULLISH', 'sweep_level': recent_swing_low, 'score_bonus': 15}
        # Bearish SFP
        if last_high > recent_swing_high and last_close < recent_swing_high:
            depth = (last_high - recent_swing_high) / recent_swing_high * 100
            if depth >= 0.01:
                return {'type': 'BEARISH', 'sweep_level': recent_swing_high, 'score_bonus': 15}
        return {'type': 'NONE', 'sweep_level': 0, 'score_bonus': 0}
    except:
        return {'type': 'NONE', 'sweep_level': 0, 'score_bonus': 0}

def detect_rsi_divergence(candles, lookback=14):
    if not candles or len(candles) < lookback + 6:
        return {'type': 'NONE', 'score_bonus': 0}
    try:
        def _cls(c): return float(c['close'] if isinstance(c,dict) else c[4])
        closes = [_cls(c) for c in candles[-(lookback+6):]]
        def calc_rsi(cls):
            deltas = [cls[i]-cls[i-1] for i in range(1,len(cls))]
            gains = [d if d>0 else 0 for d in deltas]
            losses = [-d if d<0 else 0 for d in deltas]
            ag = sum(gains[-lookback:])/lookback
            al = sum(losses[-lookback:])/lookback
            if al == 0: return 100
            return 100-(100/(1+ag/al))
        rsi_now = calc_rsi(closes)
        rsi_prev = calc_rsi(closes[:-5])
        price_now = closes[-1]
        price_prev = closes[-6]
        if price_now < price_prev and rsi_now > rsi_prev and rsi_now < 45:
            return {'type': 'BULLISH', 'rsi': round(rsi_now,1), 'score_bonus': 10}
        if price_now > price_prev and rsi_now < rsi_prev and rsi_now > 55:
            return {'type': 'BEARISH', 'rsi': round(rsi_now,1), 'score_bonus': 10}
        return {'type': 'NONE', 'score_bonus': 0}
    except:
        return {'type': 'NONE', 'score_bonus': 0}