"""
Technical indicators module
Uses pandas_ta for efficient calculation
"""
import pandas as pd
import numpy as np
import pandas_ta as ta
from typing import Dict, List


class TechnicalIndicators:
    """Calculate various technical indicators"""

    @staticmethod
    def add_all_indicators(df: pd.DataFrame, include_advanced: bool = False) -> pd.DataFrame:
        """
        Add all common technical indicators to dataframe

        Args:
            df: OHLCV dataframe
            include_advanced: Include advanced indicators

        Returns:
            DataFrame with indicators added
        """
        df = df.copy()

        # Moving Averages
        df = TechnicalIndicators.add_moving_averages(df)

        # Momentum Indicators
        df = TechnicalIndicators.add_momentum_indicators(df)

        # Volatility Indicators
        df = TechnicalIndicators.add_volatility_indicators(df)

        # Volume Indicators
        df = TechnicalIndicators.add_volume_indicators(df)

        if include_advanced:
            df = TechnicalIndicators.add_advanced_indicators(df)

        return df

    @staticmethod
    def add_moving_averages(df: pd.DataFrame) -> pd.DataFrame:
        """Add various moving averages"""
        df['sma_20'] = ta.sma(df['close'], length=20)
        df['sma_50'] = ta.sma(df['close'], length=50)
        df['sma_200'] = ta.sma(df['close'], length=200)

        df['ema_9'] = ta.ema(df['close'], length=9)
        df['ema_21'] = ta.ema(df['close'], length=21)
        df['ema_55'] = ta.ema(df['close'], length=55)

        # VWAP
        df['vwap'] = ta.vwap(df['high'], df['low'], df['close'], df['volume'])

        return df

    @staticmethod
    def add_momentum_indicators(df: pd.DataFrame) -> pd.DataFrame:
        """Add momentum indicators"""
        # RSI
        df['rsi'] = ta.rsi(df['close'], length=14)

        # MACD
        macd = ta.macd(df['close'], fast=12, slow=26, signal=9)
        df['macd'] = macd['MACD_12_26_9']
        df['macd_signal'] = macd['MACDs_12_26_9']
        df['macd_hist'] = macd['MACDh_12_26_9']

        # Stochastic
        stoch = ta.stoch(df['high'], df['low'], df['close'])
        df['stoch_k'] = stoch['STOCHk_14_3_3']
        df['stoch_d'] = stoch['STOCHd_14_3_3']

        # CCI
        df['cci'] = ta.cci(df['high'], df['low'], df['close'], length=20)

        # Williams %R
        df['willr'] = ta.willr(df['high'], df['low'], df['close'], length=14)

        return df

    @staticmethod
    def add_volatility_indicators(df: pd.DataFrame) -> pd.DataFrame:
        """Add volatility indicators"""
        # Bollinger Bands
        bbands = ta.bbands(df['close'], length=20, std=2)
        df['bb_upper'] = bbands['BBU_20_2.0']
        df['bb_middle'] = bbands['BBM_20_2.0']
        df['bb_lower'] = bbands['BBL_20_2.0']
        df['bb_width'] = (df['bb_upper'] - df['bb_lower']) / df['bb_middle']

        # ATR
        df['atr'] = ta.atr(df['high'], df['low'], df['close'], length=14)

        # Keltner Channels
        kc = ta.kc(df['high'], df['low'], df['close'], length=20)
        df['kc_upper'] = kc['KCUe_20_2']
        df['kc_middle'] = kc['KCBe_20_2']
        df['kc_lower'] = kc['KCLe_20_2']

        return df

    @staticmethod
    def add_volume_indicators(df: pd.DataFrame) -> pd.DataFrame:
        """Add volume indicators"""
        # OBV (On Balance Volume)
        df['obv'] = ta.obv(df['close'], df['volume'])

        # Volume SMA
        df['volume_sma'] = ta.sma(df['volume'], length=20)

        # MFI (Money Flow Index)
        df['mfi'] = ta.mfi(df['high'], df['low'], df['close'], df['volume'], length=14)

        # AD (Accumulation/Distribution)
        df['ad'] = ta.ad(df['high'], df['low'], df['close'], df['volume'])

        return df

    @staticmethod
    def add_advanced_indicators(df: pd.DataFrame) -> pd.DataFrame:
        """Add advanced indicators"""
        # Ichimoku Cloud
        ichimoku = ta.ichimoku(df['high'], df['low'], df['close'])
        if ichimoku is not None and len(ichimoku[0].columns) > 0:
            df['ichimoku_a'] = ichimoku[0]['ISA_9']
            df['ichimoku_b'] = ichimoku[0]['ISB_26']

        # Supertrend
        supertrend = ta.supertrend(df['high'], df['low'], df['close'])
        if supertrend is not None:
            df['supertrend'] = supertrend['SUPERT_7_3.0']
            df['supertrend_direction'] = supertrend['SUPERTd_7_3.0']

        return df

    @staticmethod
    def get_signal_summary(df: pd.DataFrame) -> Dict:
        """
        Get trading signal summary from indicators

        Returns:
            Dict with buy/sell/neutral signal counts
        """
        if len(df) < 2:
            return {'buy': 0, 'sell': 0, 'neutral': 0, 'score': 0}

        latest = df.iloc[-1]
        prev = df.iloc[-2]

        signals = {
            'buy': 0,
            'sell': 0,
            'neutral': 0
        }

        # RSI signals
        if not pd.isna(latest['rsi']):
            if latest['rsi'] < 30:
                signals['buy'] += 1
            elif latest['rsi'] > 70:
                signals['sell'] += 1
            else:
                signals['neutral'] += 1

        # MACD signals
        if not pd.isna(latest['macd']) and not pd.isna(latest['macd_signal']):
            if latest['macd'] > latest['macd_signal'] and prev['macd'] <= prev['macd_signal']:
                signals['buy'] += 1
            elif latest['macd'] < latest['macd_signal'] and prev['macd'] >= prev['macd_signal']:
                signals['sell'] += 1
            else:
                signals['neutral'] += 1

        # MA crossover
        if not pd.isna(latest['sma_20']) and not pd.isna(latest['sma_50']):
            if latest['sma_20'] > latest['sma_50'] and prev['sma_20'] <= prev['sma_50']:
                signals['buy'] += 1
            elif latest['sma_20'] < latest['sma_50'] and prev['sma_20'] >= prev['sma_50']:
                signals['sell'] += 1
            else:
                signals['neutral'] += 1

        # Bollinger Bands
        if not pd.isna(latest['bb_lower']) and not pd.isna(latest['bb_upper']):
            if latest['close'] < latest['bb_lower']:
                signals['buy'] += 1
            elif latest['close'] > latest['bb_upper']:
                signals['sell'] += 1
            else:
                signals['neutral'] += 1

        # Stochastic
        if not pd.isna(latest['stoch_k']):
            if latest['stoch_k'] < 20:
                signals['buy'] += 1
            elif latest['stoch_k'] > 80:
                signals['sell'] += 1
            else:
                signals['neutral'] += 1

        # Calculate score (-100 to +100)
        total = signals['buy'] + signals['sell'] + signals['neutral']
        if total > 0:
            signals['score'] = int(((signals['buy'] - signals['sell']) / total) * 100)
        else:
            signals['score'] = 0

        return signals

    @staticmethod
    def detect_patterns(df: pd.DataFrame) -> List[str]:
        """Detect chart patterns"""
        patterns = []

        if len(df) < 20:
            return patterns

        latest = df.iloc[-1]
        prev = df.iloc[-2]

        # Golden Cross
        if not pd.isna(latest['sma_50']) and not pd.isna(latest['sma_200']):
            if latest['sma_50'] > latest['sma_200'] and prev['sma_50'] <= prev['sma_200']:
                patterns.append('Golden Cross (Bullish)')

        # Death Cross
        if not pd.isna(latest['sma_50']) and not pd.isna(latest['sma_200']):
            if latest['sma_50'] < latest['sma_200'] and prev['sma_50'] >= prev['sma_200']:
                patterns.append('Death Cross (Bearish)')

        # Bollinger Squeeze
        if not pd.isna(latest['bb_width']):
            if latest['bb_width'] < df['bb_width'].quantile(0.2):
                patterns.append('Bollinger Squeeze (Breakout Pending)')

        # RSI Divergence (simplified)
        if len(df) >= 10 and not pd.isna(latest['rsi']):
            # Bullish divergence: price lower low, RSI higher low
            recent_df = df.tail(10)
            price_low = recent_df['close'].min()
            rsi_low = recent_df['rsi'].min()

            if latest['close'] > price_low and latest['rsi'] > rsi_low:
                if price_low == recent_df.iloc[-5:]['close'].min():
                    patterns.append('Bullish RSI Divergence')

        # MACD Histogram increasing
        if not pd.isna(latest['macd_hist']) and not pd.isna(prev['macd_hist']):
            if latest['macd_hist'] > prev['macd_hist'] > 0:
                patterns.append('MACD Momentum Increasing')
            elif latest['macd_hist'] < prev['macd_hist'] < 0:
                patterns.append('MACD Momentum Decreasing')

        return patterns