src/analysis/fibonacci_analysis.py

import numpy as np
import pandas as pd
from typing import Dict, List, Tuple, Optional, Any
from dataclasses import dataclass, asdict
import logging
from src.core.log_parser import FibonacciAnalysis, BotAnalysis

# Configurar logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

@dataclass
class FibonacciLevel:
    """Estrutura para um nível de Fibonacci"""
    level: float
    price: float
    type: str  # 'retracement', 'extension', 'projection'
    ratio: float
    distance_from_current: float
    strength: float

@dataclass
class SwingPoint:
    """Estrutura para pontos de swing"""
    price: float
    timestamp: str
    type: str  # 'high' ou 'low'
    strength: float

@dataclass
class ConfluenceZone:
    """Estrutura para zonas de confluência"""
    price_range: Tuple[float, float]
    levels_count: int
    strength: float
    types: List[str]

@dataclass
class HarmonicPattern:
    """Estrutura para padrões harmônicos"""
    name: str
    completion_point: float
    confidence: float
    target_levels: List[float]
    stop_loss: float

@dataclass
class AdvancedFibonacciAnalysis:
    """Análise avançada de Fibonacci completa"""
    swing_high: float
    swing_low: float
    current_price: float
    swing_range: float
    retracement_levels: List[FibonacciLevel]
    extension_levels: List[FibonacciLevel]
    projection_levels: List[FibonacciLevel]
    confluence_zones: List[ConfluenceZone]
    harmonic_patterns: List[HarmonicPattern]
    key_support: float
    key_resistance: float
    trend_direction: str
    fibonacci_zone: str
    overall_strength: float
    trading_signal: str
    alerts_count: int

class AdvancedFibonacciEngine:
    """Engine para análise avançada de Fibonacci"""
    
    def __init__(self):
        # Ratios de Fibonacci padrão
        self.retracement_ratios = [0.236, 0.382, 0.5, 0.618, 0.786]
        self.extension_ratios = [1.272, 1.414, 1.618, 2.0, 2.618]
        self.projection_ratios = [0.618, 1.0, 1.272, 1.618]
        
        # Configurações de confluência
        self.confluence_threshold = 0.001  # 0.1% de tolerância
        self.min_confluence_levels = 2
        
        # Padrões harmônicos
        self.harmonic_patterns = {
            'Gartley': {'XA': 0.618, 'AB': 0.618, 'BC': 0.786, 'CD': 1.272},
            'Butterfly': {'XA': 0.786, 'AB': 0.618, 'BC': 0.886, 'CD': 1.618},
            'Bat': {'XA': 0.382, 'AB': 0.618, 'BC': 0.886, 'CD': 2.618},
            'Crab': {'XA': 0.618, 'AB': 0.618, 'BC': 0.886, 'CD': 3.618}
        }
    
    def analyze_from_bot_data(self, bot_analysis: BotAnalysis) -> AdvancedFibonacciAnalysis:
        """Analisa dados do bot externo"""
        fib_data = bot_analysis.fibonacci_analysis
        market_data = bot_analysis.market_data
        
        return self.perform_advanced_analysis(
            swing_high=fib_data.swing_high,
            swing_low=fib_data.swing_low,
            current_price=fib_data.current_price,
            historical_data=None,  # Pode ser expandido futuramente
            bot_fibonacci_data=fib_data
        )
    
    def perform_advanced_analysis(
        self, 
        swing_high: float, 
        swing_low: float, 
        current_price: float,
        historical_data: Optional[pd.DataFrame] = None,
        bot_fibonacci_data: Optional[FibonacciAnalysis] = None
    ) -> AdvancedFibonacciAnalysis:
        """Realiza análise avançada de Fibonacci"""
        
        try:
            swing_range = swing_high - swing_low
            
            # Calcular níveis de retracement
            retracement_levels = self._calculate_retracement_levels(
                swing_high, swing_low, current_price
            )
            
            # Calcular níveis de extensão
            extension_levels = self._calculate_extension_levels(
                swing_high, swing_low, current_price
            )
            
            # Calcular níveis de projeção
            projection_levels = self._calculate_projection_levels(
                swing_high, swing_low, current_price
            )
            
            # Identificar zonas de confluência
            all_levels = retracement_levels + extension_levels + projection_levels
            confluence_zones = self._identify_confluence_zones(all_levels)
            
            # Detectar padrões harmônicos
            harmonic_patterns = self._detect_harmonic_patterns(
                swing_high, swing_low, current_price, historical_data
            )
            
            # Determinar suporte e resistência chave
            key_support, key_resistance = self._determine_key_levels(
                all_levels, current_price
            )
            
            # Determinar direção da tendência
            trend_direction = self._determine_trend_direction(
                swing_high, swing_low, current_price
            )
            
            # Determinar zona de Fibonacci
            fibonacci_zone = self._determine_fibonacci_zone(
                swing_high, swing_low, current_price
            )
            
            # Calcular força geral da análise
            overall_strength = self._calculate_overall_strength(
                retracement_levels, extension_levels, confluence_zones, harmonic_patterns
            )
            
            # Gerar sinal de trading
            trading_signal = self._generate_trading_signal(
                current_price, key_support, key_resistance, trend_direction, overall_strength
            )
            
            # Contar alertas (baseado nos dados do bot se disponível)
            alerts_count = bot_fibonacci_data.alerts if bot_fibonacci_data else len(confluence_zones)
            
            return AdvancedFibonacciAnalysis(
                swing_high=swing_high,
                swing_low=swing_low,
                current_price=current_price,
                swing_range=swing_range,
                retracement_levels=retracement_levels,
                extension_levels=extension_levels,
                projection_levels=projection_levels,
                confluence_zones=confluence_zones,
                harmonic_patterns=harmonic_patterns,
                key_support=key_support,
                key_resistance=key_resistance,
                trend_direction=trend_direction,
                fibonacci_zone=fibonacci_zone,
                overall_strength=overall_strength,
                trading_signal=trading_signal,
                alerts_count=alerts_count
            )
            
        except Exception as e:
            logger.error(f"Erro na análise avançada de Fibonacci: {e}")
            raise
    
    def _calculate_retracement_levels(
        self, swing_high: float, swing_low: float, current_price: float
    ) -> List[FibonacciLevel]:
        """Calcula níveis de retracement de Fibonacci"""
        levels = []
        swing_range = swing_high - swing_low
        
        for ratio in self.retracement_ratios:
            price = swing_high - (swing_range * ratio)
            distance = abs(current_price - price)
            strength = self._calculate_level_strength(price, current_price, 'retracement')
            
            levels.append(FibonacciLevel(
                level=ratio,
                price=price,
                type='retracement',
                ratio=ratio,
                distance_from_current=distance,
                strength=strength
            ))
        
        return sorted(levels, key=lambda x: x.distance_from_current)
    
    def _calculate_extension_levels(
        self, swing_high: float, swing_low: float, current_price: float
    ) -> List[FibonacciLevel]:
        """Calcula níveis de extensão de Fibonacci"""
        levels = []
        swing_range = swing_high - swing_low
        
        for ratio in self.extension_ratios:
            # Extensão para cima
            price_up = swing_high + (swing_range * (ratio - 1))
            distance_up = abs(current_price - price_up)
            strength_up = self._calculate_level_strength(price_up, current_price, 'extension')
            
            levels.append(FibonacciLevel(
                level=ratio,
                price=price_up,
                type='extension_up',
                ratio=ratio,
                distance_from_current=distance_up,
                strength=strength_up
            ))
            
            # Extensão para baixo
            price_down = swing_low - (swing_range * (ratio - 1))
            distance_down = abs(current_price - price_down)
            strength_down = self._calculate_level_strength(price_down, current_price, 'extension')
            
            levels.append(FibonacciLevel(
                level=ratio,
                price=price_down,
                type='extension_down',
                ratio=ratio,
                distance_from_current=distance_down,
                strength=strength_down
            ))
        
        return sorted(levels, key=lambda x: x.distance_from_current)
    
    def _calculate_projection_levels(
        self, swing_high: float, swing_low: float, current_price: float
    ) -> List[FibonacciLevel]:
        """Calcula níveis de projeção de Fibonacci"""
        levels = []
        swing_range = swing_high - swing_low
        
        for ratio in self.projection_ratios:
            # Projeção baseada no movimento atual
            if current_price > (swing_high + swing_low) / 2:  # Tendência de alta
                price = current_price + (swing_range * ratio)
                direction = 'projection_up'
            else:  # Tendência de baixa
                price = current_price - (swing_range * ratio)
                direction = 'projection_down'
            
            distance = abs(current_price - price)
            strength = self._calculate_level_strength(price, current_price, 'projection')
            
            levels.append(FibonacciLevel(
                level=ratio,
                price=price,
                type=direction,
                ratio=ratio,
                distance_from_current=distance,
                strength=strength
            ))
        
        return sorted(levels, key=lambda x: x.distance_from_current)
    
    def _calculate_level_strength(
        self, level_price: float, current_price: float, level_type: str
    ) -> float:
        """Calcula a força de um nível de Fibonacci"""
        distance_factor = 1 / (1 + abs(level_price - current_price) / current_price)
        
        type_weights = {
            'retracement': 1.0,
            'extension': 0.8,
            'projection': 0.6
        }
        
        base_strength = type_weights.get(level_type, 0.5)
        return base_strength * distance_factor
    
    def _identify_confluence_zones(
        self, all_levels: List[FibonacciLevel]
    ) -> List[ConfluenceZone]:
        """Identifica zonas de confluência entre níveis"""
        confluence_zones = []
        
        # Agrupar níveis próximos
        sorted_levels = sorted(all_levels, key=lambda x: x.price)
        
        i = 0
        while i < len(sorted_levels):
            current_level = sorted_levels[i]
            zone_levels = [current_level]
            zone_types = [current_level.type]
            
            # Procurar níveis próximos
            j = i + 1
            while j < len(sorted_levels):
                next_level = sorted_levels[j]
                price_diff = abs(next_level.price - current_level.price) / current_level.price
                
                if price_diff <= self.confluence_threshold:
                    zone_levels.append(next_level)
                    zone_types.append(next_level.type)
                    j += 1
                else:
                    break
            
            # Criar zona de confluência se houver níveis suficientes
            if len(zone_levels) >= self.min_confluence_levels:
                min_price = min(level.price for level in zone_levels)
                max_price = max(level.price for level in zone_levels)
                avg_strength = sum(level.strength for level in zone_levels) / len(zone_levels)
                
                confluence_zones.append(ConfluenceZone(
                    price_range=(min_price, max_price),
                    levels_count=len(zone_levels),
                    strength=avg_strength * len(zone_levels),  # Força multiplicada pelo número de níveis
                    types=list(set(zone_types))
                ))
            
            i = j if j > i + 1 else i + 1
        
        return sorted(confluence_zones, key=lambda x: x.strength, reverse=True)
    
    def _detect_harmonic_patterns(
        self, swing_high: float, swing_low: float, current_price: float,
        historical_data: Optional[pd.DataFrame] = None
    ) -> List[HarmonicPattern]:
        """Detecta padrões harmônicos (implementação básica)"""
        patterns = []
        
        # Implementação simplificada - pode ser expandida com dados históricos
        swing_range = swing_high - swing_low
        
        for pattern_name, ratios in self.harmonic_patterns.items():
            # Verificar se o preço atual está em uma posição válida para o padrão
            completion_point = swing_low + (swing_range * ratios['CD'])
            
            if abs(current_price - completion_point) / current_price < 0.02:  # 2% de tolerância
                confidence = 0.7  # Confiança básica
                
                # Calcular alvos baseados no padrão
                target_levels = [
                    completion_point + (swing_range * 0.382),
                    completion_point + (swing_range * 0.618),
                    completion_point + (swing_range * 1.0)
                ]
                
                stop_loss = completion_point - (swing_range * 0.236)
                
                patterns.append(HarmonicPattern(
                    name=pattern_name,
                    completion_point=completion_point,
                    confidence=confidence,
                    target_levels=target_levels,
                    stop_loss=stop_loss
                ))
        
        return sorted(patterns, key=lambda x: x.confidence, reverse=True)
    
    def _determine_key_levels(
        self, all_levels: List[FibonacciLevel], current_price: float
    ) -> Tuple[float, float]:
        """Determina níveis chave de suporte e resistência"""
        support_levels = [level for level in all_levels if level.price < current_price]
        resistance_levels = [level for level in all_levels if level.price > current_price]
        
        # Suporte mais próximo e forte
        key_support = current_price
        if support_levels:
            key_support = max(support_levels, key=lambda x: x.strength).price
        
        # Resistência mais próxima e forte
        key_resistance = current_price
        if resistance_levels:
            key_resistance = min(resistance_levels, key=lambda x: x.strength).price
        
        return key_support, key_resistance
    
    def _determine_trend_direction(
        self, swing_high: float, swing_low: float, current_price: float
    ) -> str:
        """Determina direção da tendência"""
        mid_point = (swing_high + swing_low) / 2
        
        if current_price > mid_point + (swing_high - swing_low) * 0.1:
            return 'ALTA'
        elif current_price < mid_point - (swing_high - swing_low) * 0.1:
            return 'BAIXA'
        else:
            return 'LATERAL'
    
    def _determine_fibonacci_zone(
        self, swing_high: float, swing_low: float, current_price: float
    ) -> str:
        """Determina zona de Fibonacci atual"""
        swing_range = swing_high - swing_low
        position = (current_price - swing_low) / swing_range
        
        if position >= 0.786:
            return 'ZONA_ALTA'
        elif position >= 0.618:
            return 'ZONA_MEDIA_ALTA'
        elif position >= 0.382:
            return 'ZONA_MEDIA'
        elif position >= 0.236:
            return 'ZONA_MEDIA_BAIXA'
        else:
            return 'ZONA_BAIXA'
    
    def _calculate_overall_strength(
        self, retracement_levels: List[FibonacciLevel], 
        extension_levels: List[FibonacciLevel],
        confluence_zones: List[ConfluenceZone],
        harmonic_patterns: List[HarmonicPattern]
    ) -> float:
        """Calcula força geral da análise"""
        # Força baseada em níveis próximos
        level_strength = sum(level.strength for level in retracement_levels[:3])  # Top 3
        level_strength += sum(level.strength for level in extension_levels[:3])   # Top 3
        
        # Força das zonas de confluência
        confluence_strength = sum(zone.strength for zone in confluence_zones)
        
        # Força dos padrões harmônicos
        harmonic_strength = sum(pattern.confidence for pattern in harmonic_patterns)
        
        # Normalizar para 0-1
        total_strength = (level_strength + confluence_strength + harmonic_strength) / 10
        return min(total_strength, 1.0)
    
    def _generate_trading_signal(
        self, current_price: float, key_support: float, key_resistance: float,
        trend_direction: str, overall_strength: float
    ) -> str:
        """Gera sinal de trading baseado na análise"""
        support_distance = abs(current_price - key_support) / current_price
        resistance_distance = abs(current_price - key_resistance) / current_price
        
        if overall_strength < 0.3:
            return 'HOLD'
        
        if trend_direction == 'ALTA' and support_distance < 0.02:
            return 'BUY'
        elif trend_direction == 'BAIXA' and resistance_distance < 0.02:
            return 'SELL'
        elif support_distance < resistance_distance and overall_strength > 0.6:
            return 'BUY'
        elif resistance_distance < support_distance and overall_strength > 0.6:
            return 'SELL'
        else:
            return 'HOLD'
    
    def format_analysis_report(self, analysis: AdvancedFibonacciAnalysis) -> str:
        """Formata relatório da análise"""
        report = f"""
🔮 ANÁLISE AVANÇADA DE FIBONACCI
{'='*50}

📊 DADOS BÁSICOS:
Swing Alto: {analysis.swing_high:,.2f}
Swing Baixo: {analysis.swing_low:,.2f}
Preço Atual: {analysis.current_price:,.2f}
Range: {analysis.swing_range:,.2f}

📈 NÍVEIS DE RETRACEMENT ({len(analysis.retracement_levels)}):
"""
        
        for level in analysis.retracement_levels[:5]:  # Top 5
            report += f"  {level.ratio:.1%}: {level.price:,.2f} (Força: {level.strength:.2f})\n"
        
        report += f"\n📊 NÍVEIS DE EXTENSÃO ({len(analysis.extension_levels)}):\n"
        for level in analysis.extension_levels[:5]:  # Top 5
            report += f"  {level.ratio:.1%}: {level.price:,.2f} ({level.type})\n"
        
        if analysis.confluence_zones:
            report += f"\n🎯 ZONAS DE CONFLUÊNCIA ({len(analysis.confluence_zones)}):\n"
            for zone in analysis.confluence_zones[:3]:  # Top 3
                report += f"  {zone.price_range[0]:,.2f} - {zone.price_range[1]:,.2f} ({zone.levels_count} níveis)\n"
        
        if analysis.harmonic_patterns:
            report += f"\n🎼 PADRÕES HARMÔNICOS ({len(analysis.harmonic_patterns)}):\n"
            for pattern in analysis.harmonic_patterns:
                report += f"  {pattern.name}: {pattern.confidence:.1%} confiança\n"
        
        report += f"""

🎯 NÍVEIS CHAVE:
Suporte: {analysis.key_support:,.2f}
Resistência: {analysis.key_resistance:,.2f}

📊 ANÁLISE GERAL:
Tendência: {analysis.trend_direction}
Zona Fibonacci: {analysis.fibonacci_zone}
Força da Análise: {analysis.overall_strength:.1%}
Sinal: {analysis.trading_signal}
Alertas: {analysis.alerts_count}
        """
        
        return report

# Exemplo de uso
if __name__ == "__main__":
    engine = AdvancedFibonacciEngine()
    
    # Exemplo com dados do bot
    analysis = engine.perform_advanced_analysis(
        swing_high=140570.0,
        swing_low=139540.0,
        current_price=140135.0
    )
    
    print(engine.format_analysis_report(analysis))