#!/usr/bin/env python3
"""
AlligatorIllusionDetector.py - Multi-timeframe Alligator Pattern Illusion Detection

Detects false-positive trade entries when lower timeframes contradict broader market structure.
Building on successful FDB scanning activation.

Author: JGT Platform
Created: 2025-01-15
"""

import pandas as pd
from pathlib import Path
import logging

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

class AlligatorIllusionDetector:
    """
    Multi-timeframe Alligator Illusion Detection System
    
    Analyzes alligator patterns across multiple timeframes to detect:
    - False-positive trade entries
    - Timeframe contradictions
    - Premature entry signals
    """
    
    def __init__(self, data_path="/src/jgtml/cache/fdb_scanners"):
        self.data_path = Path(data_path)
    
    def load_market_data(self, instrument, timeframe):
        """Load CDS market data for specified instrument and timeframe"""
        filename = f"{instrument}_{timeframe}.csv"
        filepath = self.data_path / filename
        
        if not filepath.exists():
            logger.warning(f"Data file not found: {filepath}")
            return None
            
        try:
            df = pd.read_csv(filepath)
            logger.info(f"Loaded {len(df)} records for {instrument} {timeframe}")
            return df
        except Exception as e:
            logger.error(f"Error loading data: {e}")
            return None
    
    def analyze_alligator_patterns(self, df, timeframe):
        """Analyze alligator patterns in the data"""
        if df is None or len(df) == 0:
            return None
        
        # Get latest alligator readings
        latest = df.iloc[-1]
        
        # Extract alligator indicators
        jaw = latest.get('alligator_jaw', 0)
        teeth = latest.get('alligator_teeth', 0)
        lips = latest.get('alligator_lips', 0)
        
        # Determine trend direction
        if lips > teeth > jaw:
            trend = "bullish"
        elif lips < teeth < jaw:
            trend = "bearish"
        else:
            trend = "sideways"
        
        # Calculate mouth openness
        mouth_open = abs(lips - jaw) > abs(teeth - jaw) * 0.1
        
        return {
            'timeframe': timeframe,
            'jaw': jaw,
            'teeth': teeth,
            'lips': lips,
            'trend': trend,
            'mouth_open': mouth_open,
            'timestamp': latest.get('timestamp', '')
        }
    
    def detect_illusions(self, readings):
        """Detect illusion patterns from multi-timeframe readings"""
        illusions = []
        
        if len(readings) < 2:
            return illusions
        
        # Check for timeframe contradictions
        timeframes = list(readings.keys())
        for i in range(len(timeframes)):
            for j in range(i+1, len(timeframes)):
                tf1, tf2 = timeframes[i], timeframes[j]
                r1, r2 = readings[tf1], readings[tf2]
                
                # Check for trend contradiction
                if (r1['trend'] == 'bullish' and r2['trend'] == 'bearish') or \
                   (r1['trend'] == 'bearish' and r2['trend'] == 'bullish'):
                    illusions.append({
                        'type': 'timeframe_contradiction',
                        'description': f"{tf1} shows {r1['trend']} while {tf2} shows {r2['trend']}",
                        'recommendation': 'Wait for timeframe alignment before entry',
                        'confidence': 0.8
                    })
        
        return illusions
    
    def scan_instrument(self, instrument, timeframes=None):
        """Complete illusion detection scan for an instrument"""
        if timeframes is None:
            timeframes = ["D1", "H1"]
        
        logger.info(f"🐊 Starting Alligator Illusion Detection scan for {instrument}")
        
        # Load and analyze data for each timeframe
        readings = {}
        for tf in timeframes:
            df = self.load_market_data(instrument, tf)
            if df is not None:
                analysis = self.analyze_alligator_patterns(df, tf)
                if analysis:
                    readings[tf] = analysis
        
        if not readings:
            return {
                "instrument": instrument,
                "status": "error",
                "message": "No data available for analysis"
            }
        
        # Detect illusions
        illusions = self.detect_illusions(readings)
        
        # Generate report
        report = f"""
🐊 ALLIGATOR ILLUSION DETECTION REPORT
Instrument: {instrument}
Timestamp: {pd.Timestamp.now()}

{"="*50}

📊 TIMEFRAME ANALYSIS:
"""
        
        for tf, reading in readings.items():
            report += f"""
{tf}: {reading['trend'].upper()} trend
  Jaw: {reading['jaw']:.5f}
  Teeth: {reading['teeth']:.5f} 
  Lips: {reading['lips']:.5f}
  Mouth Open: {'Yes' if reading['mouth_open'] else 'No'}
"""
        
        if illusions:
            report += f"\n⚠️  {len(illusions)} ILLUSION(S) DETECTED:\n"
            for i, illusion in enumerate(illusions, 1):
                report += f"""
{i}. {illusion['type'].upper()}
   Description: {illusion['description']}
   Recommendation: {illusion['recommendation']}
   Confidence: {illusion['confidence']:.2f}
"""
        else:
            report += "\n✅ NO ILLUSIONS DETECTED - Clear signal environment\n"
        
        report += f"\n{'='*50}\n"
        
        return {
            "instrument": instrument,
            "status": "success",
            "readings": readings,
            "illusions": illusions,
            "report": report,
            "recommendation": "PROCEED" if not illusions else "CAUTION"
        }

def main():
    """CLI interface for Alligator Illusion Detection"""
    import argparse
    
    parser = argparse.ArgumentParser(description="Alligator Illusion Detection Scanner")
    parser.add_argument("-i", "--instrument", required=True, 
                       help="Instrument to analyze (e.g., SPX500, EUR-USD)")
    parser.add_argument("-t", "--timeframes", nargs="+", default=["D1", "H1"],
                       help="Timeframes to analyze (D1, H1, W1)")
    parser.add_argument("--data-path", default="/src/jgtml/cds", 
                       help="Path to CDS data files")
    
    args = parser.parse_args()
    
    # Initialize detector
    detector = AlligatorIllusionDetector(args.data_path)
    
    # Perform scan
    result = detector.scan_instrument(args.instrument, args.timeframes)
    
    # Output results
    print(result["report"])
    
    if result["status"] == "success":
        print(f"🎯 RECOMMENDATION: {result['recommendation']}")
        if result["illusions"]:
            print(f"⚠️  {len(result['illusions'])} illusion(s) detected - proceed with caution")
        else:
            print("✅ Clear signal environment - safe to proceed")
    else:
        print(f"❌ Error: {result['message']}")

if __name__ == "__main__":
    main()