"""
Multi-Timeframe Analysis Engine
================================
Analyzes multiple timeframes to generate confluence signals.
"""
import pandas as pd
import numpy as np
from typing import Dict, List, Tuple, Optional
from .indicators import compute_all_indicators


TIMEFRAME_HIERARCHY = {
    '1m': 1, '3m': 3, '5m': 5, '15m': 15, '30m': 30,
    '1h': 60, '2h': 120, '4h': 240, '6h': 360, '8h': 480,
    '12h': 720, '1d': 1440, '3d': 4320, '1w': 10080, '1M': 43200
}

RESAMPLE_MAP = {
    '1m': '1min', '3m': '3min', '5m': '5min', '15m': '15min', '30m': '30min',
    '1h': '1h', '2h': '2h', '4h': '4h', '6h': '6h', '8h': '8h',
    '12h': '12h', '1d': '1D', '3d': '3D', '1w': '1W', '1M': '1ME'
}


def resample_ohlcv(df: pd.DataFrame, target_tf: str) -> pd.DataFrame:
    """Resample OHLCV data to a higher timeframe."""
    rule = RESAMPLE_MAP.get(target_tf, target_tf)
    resampled = df.resample(rule).agg({
        'open': 'first',
        'high': 'max',
        'low': 'min',
        'close': 'last',
        'volume': 'sum'
    }).dropna()
    return resampled


class TimeframeSignal:
    """Signal from a single timeframe analysis."""
    
    def __init__(self, timeframe: str):
        self.timeframe = timeframe
        self.trend = 0       # -1 bearish, 0 neutral, 1 bullish
        self.momentum = 0    # -1 to 1
        self.volatility = 0  # 0 to 1 (low to high)
        self.strength = 0    # 0 to 1
        self.signals = {}    # individual signal details
    
    @property
    def bias(self) -> str:
        score = self.trend * 0.5 + self.momentum * 0.3 + self.strength * 0.2 * np.sign(self.trend)
        if score > 0.2:
            return "BULLISH"
        elif score < -0.2:
            return "BEARISH"
        return "NEUTRAL"
    
    @property
    def confidence(self) -> float:
        return min(abs(self.trend * 0.5 + self.momentum * 0.3 + self.strength * 0.2), 1.0)


def analyze_timeframe(df: pd.DataFrame, timeframe: str) -> TimeframeSignal:
    """Analyze a single timeframe and return signals."""
    sig = TimeframeSignal(timeframe)
    
    if len(df) < 50:
        return sig
    
    df = compute_all_indicators(df)
    last = df.iloc[-1]
    prev = df.iloc[-2]
    
    # === TREND ANALYSIS ===
    trend_score = 0
    
    # EMA alignment
    if last['ema_9'] > last['ema_21'] > last['ema_50']:
        trend_score += 1
        sig.signals['ema_alignment'] = 'bullish'
    elif last['ema_9'] < last['ema_21'] < last['ema_50']:
        trend_score -= 1
        sig.signals['ema_alignment'] = 'bearish'
    
    # Price vs EMA200
    if last['close'] > last.get('ema_200', last['close']):
        trend_score += 0.5
        sig.signals['above_ema200'] = True
    else:
        trend_score -= 0.5
        sig.signals['above_ema200'] = False
    
    # Supertrend
    if last.get('supertrend_dir', 0) == 1:
        trend_score += 0.5
        sig.signals['supertrend'] = 'bullish'
    elif last.get('supertrend_dir', 0) == -1:
        trend_score -= 0.5
        sig.signals['supertrend'] = 'bearish'
    
    # Ichimoku cloud
    if not pd.isna(last.get('senkou_a')) and not pd.isna(last.get('senkou_b')):
        cloud_top = max(last['senkou_a'], last['senkou_b'])
        cloud_bottom = min(last['senkou_a'], last['senkou_b'])
        if last['close'] > cloud_top:
            trend_score += 0.5
            sig.signals['ichimoku'] = 'above_cloud'
        elif last['close'] < cloud_bottom:
            trend_score -= 0.5
            sig.signals['ichimoku'] = 'below_cloud'
    
    sig.trend = np.clip(trend_score / 2.5, -1, 1)
    
    # === MOMENTUM ANALYSIS ===
    momentum_score = 0
    
    # RSI
    rsi_val = last.get('rsi', 50)
    if rsi_val > 70:
        momentum_score -= 0.5  # overbought
        sig.signals['rsi'] = 'overbought'
    elif rsi_val < 30:
        momentum_score += 0.5  # oversold
        sig.signals['rsi'] = 'oversold'
    elif rsi_val > 55:
        momentum_score += 0.3
        sig.signals['rsi'] = 'bullish'
    elif rsi_val < 45:
        momentum_score -= 0.3
        sig.signals['rsi'] = 'bearish'
    
    # MACD
    if last.get('macd_hist', 0) > 0 and prev.get('macd_hist', 0) <= 0:
        momentum_score += 0.5
        sig.signals['macd'] = 'bullish_cross'
    elif last.get('macd_hist', 0) < 0 and prev.get('macd_hist', 0) >= 0:
        momentum_score -= 0.5
        sig.signals['macd'] = 'bearish_cross'
    elif last.get('macd_hist', 0) > 0:
        momentum_score += 0.2
        sig.signals['macd'] = 'bullish'
    elif last.get('macd_hist', 0) < 0:
        momentum_score -= 0.2
        sig.signals['macd'] = 'bearish'
    
    # Stochastic
    stoch_k = last.get('stoch_k', 50)
    stoch_d = last.get('stoch_d', 50)
    if stoch_k > 80 and stoch_d > 80:
        momentum_score -= 0.3
        sig.signals['stoch'] = 'overbought'
    elif stoch_k < 20 and stoch_d < 20:
        momentum_score += 0.3
        sig.signals['stoch'] = 'oversold'
    
    sig.momentum = np.clip(momentum_score, -1, 1)
    
    # === VOLATILITY ===
    bb_width = last.get('bb_width', 0)
    if bb_width > 0:
        bb_width_series = df['bb_width'].dropna()
        if len(bb_width_series) > 20:
            percentile = (bb_width_series < bb_width).sum() / len(bb_width_series)
            sig.volatility = percentile
    
    # === TREND STRENGTH ===
    adx_val = last.get('adx', 0)
    if not pd.isna(adx_val):
        sig.strength = np.clip(adx_val / 50, 0, 1)
    
    sig.signals['rsi_value'] = rsi_val
    sig.signals['atr'] = last.get('atr', 0)
    sig.signals['close'] = last['close']
    sig.signals['adx'] = adx_val
    
    return sig


class MultiTimeframeAnalyzer:
    """
    Multi-Timeframe confluence analyzer.
    Uses higher TFs for trend direction and lower TFs for entry timing.
    """
    
    def __init__(self, timeframes: List[str] = None):
        if timeframes is None:
            self.timeframes = ['15m', '1h', '4h', '1d']
        else:
            self.timeframes = sorted(timeframes, key=lambda x: TIMEFRAME_HIERARCHY.get(x, 0))
    
    def analyze(self, data_by_tf: Dict[str, pd.DataFrame]) -> Dict:
        """
        Analyze multiple timeframes and return confluence signal.
        
        Args:
            data_by_tf: Dict of timeframe -> OHLCV DataFrame
            
        Returns:
            Dict with overall signal, per-TF signals, and trading recommendation
        """
        signals = {}
        for tf in self.timeframes:
            if tf in data_by_tf and len(data_by_tf[tf]) > 0:
                signals[tf] = analyze_timeframe(data_by_tf[tf], tf)
        
        if not signals:
            return {
                'action': 'HOLD',
                'direction': 'NEUTRAL',
                'confidence': 0,
                'signals': {},
                'reason': 'No data available'
            }
        
        # Weight higher TFs more heavily
        weights = {}
        total_weight = 0
        for i, tf in enumerate(self.timeframes):
            if tf in signals:
                w = (i + 1) ** 1.5  # Higher TF = higher weight
                weights[tf] = w
                total_weight += w
        
        # Weighted trend
        weighted_trend = sum(signals[tf].trend * weights[tf] for tf in weights) / total_weight
        weighted_momentum = sum(signals[tf].momentum * weights[tf] for tf in weights) / total_weight
        weighted_strength = sum(signals[tf].strength * weights[tf] for tf in weights) / total_weight
        
        # Confluence check — do all TFs agree?
        biases = [signals[tf].bias for tf in signals]
        bullish_count = sum(1 for b in biases if b == 'BULLISH')
        bearish_count = sum(1 for b in biases if b == 'BEARISH')
        total_tf = len(biases)
        
        confluence = max(bullish_count, bearish_count) / total_tf
        
        # Combined score
        combined = weighted_trend * 0.45 + weighted_momentum * 0.35 + weighted_strength * 0.2 * np.sign(weighted_trend)
        
        # Decision
        confidence = min(abs(combined) * confluence * 1.5, 1.0)
        
        if combined > 0.15 and confluence >= 0.5:
            direction = 'LONG'
            action = 'ENTER_LONG'
        elif combined < -0.15 and confluence >= 0.5:
            direction = 'SHORT'
            action = 'ENTER_SHORT'
        else:
            direction = 'NEUTRAL'
            action = 'HOLD'
        
        # Higher TF must agree for high confidence
        higher_tfs = self.timeframes[len(self.timeframes)//2:]
        higher_tf_sigs = [signals[tf] for tf in higher_tfs if tf in signals]
        if higher_tf_sigs:
            higher_bias = [s.bias for s in higher_tf_sigs]
            if direction == 'LONG' and all(b == 'BEARISH' for b in higher_bias):
                action = 'HOLD'
                direction = 'NEUTRAL'
                confidence *= 0.3
            elif direction == 'SHORT' and all(b == 'BULLISH' for b in higher_bias):
                action = 'HOLD'
                direction = 'NEUTRAL'
                confidence *= 0.3
        
        reasons = []
        for tf in signals:
            s = signals[tf]
            reasons.append(f"{tf}: {s.bias} (trend={s.trend:.2f}, mom={s.momentum:.2f}, str={s.strength:.2f})")
        
        return {
            'action': action,
            'direction': direction,
            'confidence': confidence,
            'combined_score': combined,
            'confluence': confluence,
            'weighted_trend': weighted_trend,
            'weighted_momentum': weighted_momentum,
            'weighted_strength': weighted_strength,
            'signals': {tf: {
                'bias': signals[tf].bias,
                'trend': signals[tf].trend,
                'momentum': signals[tf].momentum,
                'volatility': signals[tf].volatility,
                'strength': signals[tf].strength,
                'confidence': signals[tf].confidence,
                'details': signals[tf].signals
            } for tf in signals},
            'reason': ' | '.join(reasons)
        }