import pandas as pd
import ta
import logging

logger = logging.getLogger(__name__)

class TechnicalAgent:
    MIN_CANDLES = 30

    def analyze(self, df: pd.DataFrame) -> float:
        if len(df) < self.MIN_CANDLES:
            return 50.0

        try:
            signals = []

            # RSI (14)
            rsi = ta.momentum.RSIIndicator(df["close"], window=14).rsi().iloc[-1]
            if rsi < 30:
                signals.append(65)
            elif rsi < 45:
                signals.append(70)
            elif rsi < 55:
                signals.append(50)
            elif rsi < 70:
                signals.append(35)
            else:
                signals.append(40)

            # MACD
            macd_ind = ta.trend.MACD(df["close"])
            macd_diff = macd_ind.macd_diff().iloc[-1]
            prev_diff = macd_ind.macd_diff().iloc[-2]
            if macd_diff > 0 and prev_diff <= 0:
                signals.append(85)
            elif macd_diff < 0 and prev_diff >= 0:
                signals.append(15)
            elif macd_diff > 0:
                signals.append(65)
            else:
                signals.append(35)

            # Bollinger Bands
            bb = ta.volatility.BollingerBands(df["close"])
            bb_high = bb.bollinger_hband().iloc[-1]
            bb_low = bb.bollinger_lband().iloc[-1]
            price = df["close"].iloc[-1]
            bb_pct = (price - bb_low) / (bb_high - bb_low) if (bb_high - bb_low) > 0 else 0.5
            if bb_pct < 0.2:
                signals.append(55)
            elif bb_pct > 0.8:
                signals.append(40)
            else:
                signals.append(50)

            # SMA trend (20 vs 50)
            sma20 = df["close"].rolling(20).mean().iloc[-1]
            sma50 = df["close"].rolling(50).mean().iloc[-1] if len(df) >= 50 else sma20
            if price > sma20 > sma50:
                signals.append(80)
            elif price > sma20:
                signals.append(65)
            elif price < sma20 < sma50:
                signals.append(20)
            else:
                signals.append(40)

            # SMA 200 (long-term trend)
            if len(df) >= 200:
                sma200 = df["close"].rolling(200).mean().iloc[-1]
                if price > sma200:
                    signals.append(70)
                else:
                    signals.append(30)

            # Volume trend
            vol_avg = df["volume"].rolling(20).mean().iloc[-1]
            vol_current = df["volume"].iloc[-1]
            vol_ratio = vol_current / vol_avg if vol_avg > 0 else 1.0
            if vol_ratio > 2.0 and price > sma20:
                signals.append(80)
            elif vol_ratio > 2.0:
                signals.append(40)
            else:
                signals.append(50)

            # OBV trend
            obv = ta.volume.OnBalanceVolumeIndicator(df["close"], df["volume"]).on_balance_volume()
            obv_sma = obv.rolling(20).mean()
            if obv.iloc[-1] > obv_sma.iloc[-1]:
                signals.append(65)
            else:
                signals.append(35)

            # Candlestick patterns (simplified)
            last = df.iloc[-1]
            prev = df.iloc[-2]
            body = last["close"] - last["open"]
            prev_body = prev["close"] - prev["open"]
            if prev_body < 0 and body > 0 and body > abs(prev_body):
                signals.append(80)  # bullish engulfing
            elif prev_body > 0 and body < 0 and abs(body) > prev_body:
                signals.append(20)  # bearish engulfing
            elif abs(body) < (last["high"] - last["low"]) * 0.1:
                signals.append(50)  # doji
            else:
                signals.append(50)

            return round(sum(signals) / len(signals), 1)

        except Exception as e:
            logger.error(f"Technical analysis error: {e}")
            return 50.0