# gpu_accelerator_improved.py - TRUE EARLY WAVE DETECTION - FIXED VERSION
"""
🔥 ULTIMATE GPU ACCELERATOR - FIXED FOR EARLY WAVE DETECTION 🔥
================================================================
NOW CATCHES WAVES AT THE BEGINNING, NOT AFTER THEY'VE STARTED!

Key Changes:
- PRE-WAVE DETECTION: Finds conditions BEFORE the wave starts
- VOLUME ACCUMULATION: Detects buying pressure building
- MOMENTUM ACCELERATION: Catches 0.1% → 0.3% → 0.8% progression
- ORDER FLOW ANALYSIS: Volume spikes WITHOUT price movement
- EARLY ENTRY SIGNALS: Gets in BEFORE the crowd
- FIXED: Tensor size mismatch errors resolved
"""

import torch
import torch.nn.functional as F
import numpy as np
import time
import logging
import traceback
from typing import Dict, List, Optional, Tuple, Any, Union
from dataclasses import dataclass
from collections import deque
import asyncio
import os
import json
from datetime import datetime

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger("GPUAcceleratorEARLY")

@dataclass
class WavePattern:
    """Pattern detected by GPU for future matching"""
    symbol: str
    timestamp: float
    wave_score: float
    signal_strength: float
    feature_vector: List[float]
    outcome: Optional[float] = None  # Profit/loss after trade
    hold_time: Optional[float] = None
    exit_reason: Optional[str] = None

@dataclass
class GPUTradeMetrics:
    """Enhanced trading metrics for GPU acceleration"""
    size: float
    gas_price: float = 0.0
    timestamp: float = 0.0
    success: bool = False
    profit: float = 0.0
    token_symbol: str = ""
    price_usd: float = 0.0
    entry_price: float = 0.0
    current_price: float = 0.0
    hold_time: float = 0.0
    wave_score: float = 0.0
    pattern_match_score: float = 0.0

class GPUAcceleratorULTIMATE:
    """ULTIMATE GPU acceleration with TRUE EARLY WAVE DETECTION"""
    
    def __init__(self, use_gpu=True, batch_size=None):
        """Initialize ULTIMATE GPU accelerator with RTX 4070 Ti optimization"""
        # Check GPU availability
        self.use_gpu = use_gpu and torch.cuda.is_available()
        self.device = torch.device('cuda' if self.use_gpu else 'cpu')
        
        # Use batch size from env or optimal default
        self.batch_size = batch_size or int(os.getenv('BATCH_SIZE', '512'))
        
        # Performance metrics
        self.scan_times = deque(maxlen=100)
        self.process_times = deque(maxlen=100)
        self.token_cache = {}
        self.pattern_cache = {}
        
        # Pattern learning storage
        self.winning_patterns = deque(maxlen=1000)
        self.losing_patterns = deque(maxlen=500)
        self.pattern_vectors = None
        self.pattern_outcomes = None
        
        # EARLY WAVE DETECTION parameters - COMPLETELY REWRITTEN
        self.PRE_WAVE_VOLUME_THRESHOLD = 1.5    # 1.5x normal volume (no price move yet)
        self.EARLY_MOMENTUM_MIN = -0.5         # Allow slight dips before waves
        self.EARLY_MOMENTUM_MAX = 10.0         # Increased to catch more opportunities
        self.ACCELERATION_THRESHOLD = 2.0       # 1m momentum should be 2x the base
        self.VOLUME_PRICE_DIVERGENCE = 2.0      # Volume spike without price = coiling spring
        
        # Buy pressure thresholds for early detection
        self.EARLY_BUY_PRESSURE = 0.8           # Lowered to catch more opportunities
        self.BUILDING_BUY_PRESSURE = 1.2        # More reasonable threshold
        self.STRONG_BUY_PRESSURE = 2.0          # Strong buying signal
        
        # Settings
        self.scan_count = 0
        self.patterns_learned = 0
        self.successful_predictions = 0
        
        # GPU optimization settings
        self.gpu_memory_fraction = float(os.getenv('GPU_MEMORY_FRACTION', '0.85'))
        
        if self.use_gpu:
            # Configure GPU for MAXIMUM PERFORMANCE
            torch.cuda.set_per_process_memory_fraction(self.gpu_memory_fraction)
            
            # Enable TF32 for RTX 4070 Ti
            torch.backends.cuda.matmul.allow_tf32 = True
            torch.backends.cudnn.allow_tf32 = True
            
            # Enable cudnn benchmark
            torch.backends.cudnn.benchmark = True
            torch.backends.cudnn.enabled = True
            
            # Set high precision for pattern matching
            torch.set_float32_matmul_precision('high')
            
            # Create multiple CUDA streams for parallel processing
            self.compute_streams = [torch.cuda.Stream(priority=-3) for _ in range(8)]
            
            # Pre-allocate tensors for common operations
            self._preallocate_tensors()
            
            # Initialize weights for EARLY detection
            self._init_early_detection_weights()
            
            # Load saved patterns if available
            self._load_patterns()
            
            logger.info(f"🔥 EARLY WAVE GPU Accelerator initialized on: {torch.cuda.get_device_name(0)}")
            logger.info(f"⚡ CUDA version: {torch.version.cuda}")
            logger.info(f"⚡ PyTorch version: {torch.__version__}")
            logger.info(f"⚡ Batch size: {self.batch_size}")
            logger.info(f"🌊 MODE: EARLY WAVE DETECTION (catches waves BEFORE they start)")
            
            # Show GPU capabilities
            props = torch.cuda.get_device_properties(0)
            logger.info(f"🎮 GPU Compute Capability: {props.major}.{props.minor}")
            logger.info(f"🎮 GPU Memory: {props.total_memory / 1024**3:.1f}GB")
        else:
            logger.info("GPU Accelerator using CPU mode (CUDA not available)")
    
    def _preallocate_tensors(self):
        """Pre-allocate tensors for zero-copy operations"""
        self.cached_tensors = {}
        
        # Pre-allocate for different batch sizes
        for size in [256, 512, 1024, 2048]:
            self.cached_tensors[size] = {
                'features': torch.empty(size, 15, device=self.device, dtype=torch.float16),  # More features
                'scores': torch.empty(size, device=self.device, dtype=torch.float16),
                'patterns': torch.empty(size, 512, device=self.device, dtype=torch.float16)
            }
    
    def _init_early_detection_weights(self):
        """Initialize weights for EARLY WAVE DETECTION - completely rewritten"""
        # EARLY WAVE detection weights - prioritizes PRE-WAVE conditions
        self.early_wave_weights = torch.tensor([
            0.05,   # price_change_5m (LOW WEIGHT - we want early, not late!)
            0.15,   # price_change_1m (more important for acceleration detection)
            0.20,   # volume_acceleration (CRITICAL - volume building before price)
            0.15,   # buy_pressure_building (buys increasing over time)
            0.10,   # volume_price_divergence (volume spike, price stable)
            0.10,   # momentum_acceleration (1m > base momentum)
            0.05,   # liquidity_quality (just needs to be sufficient)
            0.08,   # age_factor (newer tokens move faster)
            0.07,   # safety_score (basic safety check)
            0.05,   # current_momentum (small factor for existing momentum)
            0.03,   # velocity bonus
            0.02,   # buy_sell_ratio bonus
            0.02,   # raw buy count bonus
            0.02,   # net buying bonus
            0.01    # new token bonus
        ], dtype=torch.float32, device=self.device)
        
        # Normalize
        self.early_wave_weights = self.early_wave_weights / self.early_wave_weights.abs().sum()
        
        # Pattern matching weights for neural network
        self.pattern_weights = torch.nn.Linear(15, 128, device=self.device)  # Increased features
        self.pattern_encoder = torch.nn.Sequential(
            torch.nn.Linear(128, 256),
            torch.nn.ReLU(),
            torch.nn.Linear(256, 512),
            torch.nn.Tanh()
        ).to(self.device)
        
        # Exit condition weights
        self.exit_weights = torch.tensor([
            0.6,   # price change vs entry
            0.3,   # recent momentum
            0.1    # volatility
        ], dtype=torch.float32, device=self.device)
        
        logger.info("✅ EARLY WAVE detection weights initialized")
    
    def _load_patterns(self):
        """Load saved winning patterns from disk"""
        try:
            pattern_file = "early_wave_patterns.json"
            if os.path.exists(pattern_file):
                with open(pattern_file, 'r') as f:
                    saved_patterns = json.load(f)
                
                for p in saved_patterns:
                    pattern = WavePattern(
                        symbol=p['symbol'],
                        timestamp=p['timestamp'],
                        wave_score=p['wave_score'],
                        signal_strength=p['signal_strength'],
                        feature_vector=p['feature_vector'],
                        outcome=p.get('outcome'),
                        hold_time=p.get('hold_time'),
                        exit_reason=p.get('exit_reason')
                    )
                    
                    if p.get('outcome', 0) > 0:
                        self.winning_patterns.append(pattern)
                    else:
                        self.losing_patterns.append(pattern)
                
                self._update_pattern_tensors()
                logger.info(f"✅ Loaded {len(self.winning_patterns)} early wave patterns")
        except Exception as e:
            logger.info(f"No saved patterns found: {e}")
    
    def _save_patterns(self):
        """Save winning patterns to disk"""
        try:
            patterns_to_save = []
            
            for p in list(self.winning_patterns)[-100:]:
                patterns_to_save.append({
                    'symbol': p.symbol,
                    'timestamp': p.timestamp,
                    'wave_score': p.wave_score,
                    'signal_strength': p.signal_strength,
                    'feature_vector': p.feature_vector,
                    'outcome': p.outcome,
                    'hold_time': p.hold_time,
                    'exit_reason': p.exit_reason
                })
            
            with open("early_wave_patterns.json", 'w') as f:
                json.dump(patterns_to_save, f)
                
        except Exception as e:
            logger.error(f"Error saving patterns: {e}")
    
    def _update_pattern_tensors(self):
        """Update GPU tensors with latest patterns"""
        if len(self.winning_patterns) > 10:
            vectors = [p.feature_vector for p in self.winning_patterns]
            outcomes = [p.outcome or 0 for p in self.winning_patterns]
            
            self.pattern_vectors = torch.tensor(vectors, dtype=torch.float32, device=self.device)
            self.pattern_outcomes = torch.tensor(outcomes, dtype=torch.float32, device=self.device)
            
            with torch.no_grad():
                encoded = self.pattern_encoder(self.pattern_weights(self.pattern_vectors))
                self.encoded_patterns = F.normalize(encoded, p=2, dim=1)
    
    def learn_from_trade(self, symbol: str, entry_features: List[float], outcome: float, 
                        hold_time: float, exit_reason: str):
        """Learn from completed trade"""
        pattern = WavePattern(
            symbol=symbol,
            timestamp=time.time(),
            wave_score=entry_features[1] * 10 if len(entry_features) > 1 else 0,
            signal_strength=0,
            feature_vector=entry_features,
            outcome=outcome,
            hold_time=hold_time,
            exit_reason=exit_reason
        )
        
        if outcome > 0:
            self.winning_patterns.append(pattern)
            if outcome > 5:
                logger.info(f"🏆 LEARNED EARLY WAVE WINNER: {symbol} +{outcome:.1f}% in {hold_time:.0f}s")
        else:
            self.losing_patterns.append(pattern)
        
        self.patterns_learned += 1
        
        if self.patterns_learned % 10 == 0:
            self._update_pattern_tensors()
            self._save_patterns()
            logger.info(f"📚 Early wave patterns learned: {self.patterns_learned}")
    
    def _find_similar_patterns(self, features_tensor: torch.Tensor, top_k: int = 5) -> Tuple[torch.Tensor, torch.Tensor]:
        """Find similar winning patterns using GPU"""
        if self.pattern_vectors is None or len(self.pattern_vectors) < 10:
            return None, None
        
        try:
            with torch.no_grad():
                encoded = self.pattern_encoder(self.pattern_weights(features_tensor))
                encoded_norm = F.normalize(encoded, p=2, dim=1)
                
                similarities = torch.matmul(encoded_norm, self.encoded_patterns.T)
                top_similarities, top_indices = torch.topk(similarities, min(top_k, len(self.pattern_vectors)), dim=1)
                
                return top_similarities, top_indices
                
        except Exception as e:
            logger.error(f"Pattern matching error: {e}")
            return None, None
    
    def _safe_float(self, value: Any, default: float = 0.0) -> float:
        """Safely convert value to float"""
        if value is None:
            return default
        try:
            return float(value)
        except (ValueError, TypeError):
            return default
    
    def _validate_token_data(self, token: Dict) -> bool:
        """Validate token has minimum required data"""
        required_fields = ["symbol", "price", "volume_5m", "liquidity"]
        for field in required_fields:
            if field not in token or token[field] is None:
                return False
        return True
    
    def scan_market(self, token_list: List[Dict], blacklist: List[str] = None) -> List[Dict]:
        """EARLY WAVE DETECTION - Catches waves BEFORE they start"""
        start_time = time.time()
        self.scan_count += 1
        
        if not token_list:
            logger.warning("Empty token list provided to GPU scanner")
            return []
        
        # Filter and prepare tokens
        blacklist = set(s.lower() for s in (blacklist or []))
        filtered_tokens = []
        
        for token in token_list:
            symbol = token.get("symbol", token.get("token_symbol", ""))
            if symbol and symbol.lower() not in blacklist and self._validate_token_data(token):
                filtered_tokens.append(token)
                self.token_cache[symbol] = token
        
        if not filtered_tokens:
            logger.warning("No valid tokens after filtering")
            return []
        
        logger.info(f"🌊 Processing {len(filtered_tokens)} tokens for early wave detection")
        
        # Extract features for EARLY WAVE detection
        features = []
        symbols = []
        valid_tokens = []
        
        for token in filtered_tokens:
            symbol = token.get("symbol", token.get("token_symbol", ""))
            if not symbol:
                continue
            
            try:
                # Get ALL metrics needed for early detection with safe defaults
                price_change_5m = self._safe_float(token.get("price_change_5m", 0))
                price_change_1m = self._safe_float(token.get("price_change_1m", 0))
                
                # Volume metrics
                volume_24h = self._safe_float(token.get("volume_24h", 0))
                volume_5m = self._safe_float(token.get("volume_5m", 0))
                volume_1m = self._safe_float(token.get("volume_1m", 0))
                liquidity = max(1.0, self._safe_float(token.get("liquidity", 1)))  # Avoid division by zero
                
                # Buy/sell metrics - ENSURE THESE EXIST
                buys_5m = self._safe_float(token.get("buys_5m", 0))
                sells_5m = self._safe_float(token.get("sells_5m", 0))
                buys_1m = self._safe_float(token.get("buys_1m", 0))
                sells_1m = self._safe_float(token.get("sells_1m", 0))
                
                # Calculate ratios safely
                if sells_5m > 0:
                    buy_sell_ratio = buys_5m / sells_5m
                else:
                    buy_sell_ratio = buys_5m if buys_5m > 0 else 1.0
                
                if sells_1m > 0:
                    buy_sell_ratio_1m = buys_1m / sells_1m
                else:
                    buy_sell_ratio_1m = buys_1m if buys_1m > 0 else 1.0
                
                # Other metrics
                velocity = volume_5m / liquidity if liquidity > 0 else 0
                momentum = self._safe_float(token.get("momentum", price_change_5m))
                age_hours = self._safe_float(token.get("age_hours", 24))
                safety_score = self._safe_float(token.get("safety_score", 70))
                
                # EARLY WAVE FILTERS - More lenient
                
                # 1. Skip tokens that have moved too much already
                if price_change_5m > self.EARLY_MOMENTUM_MAX:
                    logger.debug(f"Skipping {symbol}: price change {price_change_5m:.1f}% too high")
                    continue
                
                # 2. Allow slight negative momentum (dips before rips)
                if price_change_5m < self.EARLY_MOMENTUM_MIN:
                    logger.debug(f"Skipping {symbol}: price change {price_change_5m:.1f}% too negative")
                    continue
                
                # 3. More lenient buy pressure check
                if buy_sell_ratio < self.EARLY_BUY_PRESSURE and buys_5m < 5:
                    logger.debug(f"Skipping {symbol}: low buy pressure {buy_sell_ratio:.2f}")
                    continue
                
                # 4. Minimum liquidity check
                min_liquidity = float(os.getenv('MIN_LIQUIDITY_USD', '30000'))
                if liquidity < min_liquidity:
                    logger.debug(f"Skipping {symbol}: low liquidity ${liquidity:.0f}")
                    continue
                
                # Calculate EARLY WAVE indicators
                
                # Volume acceleration (volume growing vs historical)
                avg_5m_volume = volume_24h / 288 if volume_24h > 0 else 1
                volume_acceleration = volume_5m / avg_5m_volume if avg_5m_volume > 0 else 1
                
                # Buy pressure building (1m vs 5m ratio improvement)
                buy_pressure_building = buy_sell_ratio_1m / max(0.1, buy_sell_ratio) if buy_sell_ratio > 0 else 1
                
                # Volume/price divergence (high volume, low price change = coiling)
                if abs(price_change_5m) > 0.1:
                    volume_price_divergence = volume_acceleration / abs(price_change_5m)
                else:
                    volume_price_divergence = volume_acceleration * 10  # Boost if price is stable
                
                # Momentum acceleration (1m momentum vs base)
                if price_change_5m > 0:
                    momentum_acceleration = price_change_1m / max(0.1, price_change_5m)
                else:
                    momentum_acceleration = 1.0 if price_change_1m > 0 else 0.5
                
                # Create EARLY WAVE feature vector (EXACTLY 15 features)
                feature_vector = [
                    # Price changes (LOW weight in early detection)
                    min(1.0, max(0, price_change_5m / 5.0)),  # 1. Cap at 5% for early detection
                    min(1.0, max(0, price_change_1m / 2.0)),  # 2. 1m momentum
                    
                    # Volume indicators (HIGH weight)
                    min(2.0, max(0, volume_acceleration - 1.0)),  # 3. Volume acceleration vs normal
                    min(2.0, max(0, buy_pressure_building)),      # 4. Buy pressure building
                    min(3.0, max(0, volume_price_divergence)),     # 5. Volume/price divergence
                    min(2.0, max(0, momentum_acceleration)),       # 6. Momentum acceleration
                    
                    # Quality indicators
                    min(1.0, np.log1p(liquidity / 100000) / 5),   # 7. Liquidity quality
                    1.0 if age_hours < 24 else 0.5 if age_hours < 72 else 0.2,  # 8. Age factor
                    safety_score / 100,                            # 9. Safety normalized
                    
                    # Current state (low weight)
                    min(1.0, max(0, momentum / 10)) if momentum > 0 else 0,  # 10. Current momentum
                    
                    # Additional early indicators
                    min(1.0, velocity * 20),                       # 11. Velocity (trading activity)
                    min(2.0, max(0, buy_sell_ratio)),             # 12. Current buy/sell ratio
                    min(1.0, buys_5m / 20) if buys_5m > 0 else 0, # 13. Raw buy count
                    min(1.0, max(0, (buys_5m - sells_5m) / max(1, buys_5m + sells_5m))),  # 14. Net buying
                    1.0 if token.get("is_new", False) else 0.5    # 15. New token bonus
                ]
                
                # CRITICAL: Ensure we have EXACTLY 15 features
                if len(feature_vector) != 15:
                    logger.error(f"Feature vector size mismatch for {symbol}: {len(feature_vector)} features instead of 15")
                    logger.error(f"Feature vector: {feature_vector}")
                    continue
                
                # Log feature vector for debugging
                logger.debug(f"{symbol} features: {[f'{v:.3f}' for v in feature_vector]}")
                
                # EARLY WAVE BOOST - reward PRE-WAVE conditions
                if (volume_acceleration > self.PRE_WAVE_VOLUME_THRESHOLD and 
                    price_change_5m < 1.0 and  # Very little price movement
                    buy_sell_ratio > self.BUILDING_BUY_PRESSURE):
                    
                    # PERFECT PRE-WAVE SETUP! Boost all relevant signals
                    feature_vector[2] = min(2.0, feature_vector[2] * 2.0)  # Volume acceleration
                    feature_vector[3] = min(2.0, feature_vector[3] * 1.5)  # Buy pressure building
                    feature_vector[4] = min(3.0, feature_vector[4] * 2.0)  # Volume/price divergence
                    logger.info(f"🌊 PRE-WAVE DETECTED: {symbol} - Volume: {volume_acceleration:.1f}x, " +
                               f"Price: +{price_change_5m:.2f}%, B/S: {buy_sell_ratio:.1f}")
                
                # Early momentum boost (small but building)
                elif (0.1 <= price_change_5m <= 0.8 and  # Very early momentum
                      buy_sell_ratio > self.EARLY_BUY_PRESSURE and
                      momentum_acceleration > 1.5):  # Accelerating
                    
                    feature_vector[1] = min(1.0, feature_vector[1] * 1.5)  # 1m momentum
                    feature_vector[5] = min(2.0, feature_vector[5] * 1.5)  # Momentum acceleration
                    
                features.append(feature_vector)
                symbols.append(symbol)
                valid_tokens.append(token)
                
            except Exception as e:
                logger.warning(f"Error extracting early wave features for {symbol}: {e}")
                traceback.print_exc()
                continue
        
        if not features:
            logger.warning("No valid features extracted from tokens")
            return []
        
        logger.info(f"🌊 Extracted features for {len(features)} tokens")
        
        # Process with GPU
        try:
            if self.use_gpu:
                # Create features tensor
                features_array = np.array(features, dtype=np.float32)
                features_tensor = torch.from_numpy(features_array).to(self.device)
                
                logger.debug(f"Features tensor shape: {features_tensor.shape}")
                logger.debug(f"Weights shape: {self.early_wave_weights.shape}")
                
                # Validate tensor shapes
                if features_tensor.shape[1] != self.early_wave_weights.shape[0]:
                    logger.error(f"Shape mismatch: features {features_tensor.shape} vs weights {self.early_wave_weights.shape}")
                    return []
                
                # Calculate early wave scores
                early_wave_scores = torch.matmul(features_tensor, self.early_wave_weights)
                
                # Apply pattern matching if available
                pattern_bonus = torch.zeros_like(early_wave_scores)
                if self.pattern_vectors is not None and len(self.pattern_vectors) > 20:
                    similarities, indices = self._find_similar_patterns(features_tensor, top_k=3)
                    
                    if similarities is not None:
                        for i in range(len(features)):
                            if i < len(similarities):
                                similar_outcomes = self.pattern_outcomes[indices[i]]
                                avg_outcome = similar_outcomes.mean()
                                
                                if avg_outcome > 0:
                                    pattern_bonus[i] = similarities[i].mean() * 0.3  # Up to 30% bonus
                
                # Combine scores
                final_scores = early_wave_scores + pattern_bonus
                
                # Apply sigmoid for final strength
                strengths = torch.sigmoid(final_scores * 3.0).cpu().numpy()
                
            else:
                # CPU fallback
                strengths = []
                features_array = np.array(features, dtype=np.float32)
                weights_array = self.early_wave_weights.cpu().numpy()
                
                for f in features_array:
                    score = np.dot(f, weights_array)
                    strength = 1 / (1 + np.exp(-3.0 * score))
                    strengths.append(strength)
                
                strengths = np.array(strengths)
        
        except Exception as e:
            logger.error(f"GPU processing error: {e}")
            traceback.print_exc()
            # Fallback to moderate scores
            strengths = np.array([0.5] * len(features))
        
        # Create signals for EARLY WAVES
        signals = []
        min_signal_strength = float(os.getenv('MIN_SIGNAL_STRENGTH', '0.25'))  # Lower for early detection
        
        for i, (symbol, strength) in enumerate(zip(symbols, strengths)):
            if i >= len(valid_tokens):
                continue
            
            token = valid_tokens[i]
            strength = float(strength)
            
            if strength < min_signal_strength:
                logger.debug(f"Skipping {symbol}: strength {strength:.3f} below threshold {min_signal_strength}")
                continue
            
            # Get metrics for signal classification
            price_change = token.get("price_change_5m", 0)
            buy_sell_ratio = token.get("buy_sell_ratio", 1.0)
            volume_acceleration = features[i][2] + 1.0 if i < len(features) else 1.0
            
            # Determine EARLY WAVE signal type
            if (strength >= 0.85 and 
                price_change < 1.0 and  # Very early
                volume_acceleration > 1.5):
                signal_type = "PRE_WAVE"
            elif (strength >= 0.80 and 
                  0.1 <= price_change <= 1.5):  # Early momentum
                signal_type = "EARLY_WAVE"
            elif strength >= 0.75:
                signal_type = "BUILDING_WAVE"
            elif strength >= 0.70:
                signal_type = "WAVE_SETUP"
            else:
                signal_type = "POTENTIAL"
            
            # Generate EARLY WAVE reasons
            reasons = []
            
            # Pre-wave conditions
            if volume_acceleration > 2.0 and price_change < 0.5:
                reasons.append(f"🌊 PRE-WAVE: Volume {volume_acceleration:.1f}x, price stable")
            elif volume_acceleration > 1.5:
                reasons.append(f"📈 Volume building: {volume_acceleration:.1f}x normal")
            
            # Early momentum
            if 0.1 <= price_change <= 1.0:
                reasons.append(f"⚡ Early momentum: +{price_change:.2f}%")
            elif 1.0 < price_change <= 2.0:
                reasons.append(f"🚀 Building momentum: +{price_change:.1f}%")
            
            # Buy pressure
            if buy_sell_ratio >= 2.5:
                reasons.append(f"💪 Strong buying: {buy_sell_ratio:.1f}:1")
            elif buy_sell_ratio >= 1.8:
                reasons.append(f"📊 Building buy pressure: {buy_sell_ratio:.1f}:1")
            elif buy_sell_ratio >= 1.3:
                reasons.append(f"👀 Early buy interest: {buy_sell_ratio:.1f}:1")
            
            # Pattern matching
            if i < len(pattern_bonus) and pattern_bonus[i] > 0.1:
                reasons.append(f"🎯 Matches winning early pattern")
            
            # Create enhanced signal
            signal = {
                "token_symbol": symbol,
                "symbol": symbol,
                "token_mint": token.get("mint", token.get("token_mint", "")),
                "mint": token.get("mint", token.get("token_mint", "")),
                "price_usd": self._safe_float(token.get("price", token.get("price_usd", 0))),
                "price": self._safe_float(token.get("price", token.get("price_usd", 0))),
                "signal_type": signal_type,
                "signal_strength": strength,
                "reasons": reasons,
                "timestamp": time.time(),
                "early_wave_score": strength,
                "price_change_5m": price_change,
                "volume_acceleration": volume_acceleration,
                "pattern_match_score": float(pattern_bonus[i]) if i < len(pattern_bonus) else 0,
                # Include all expected fields
                "buy_sell_ratio": buy_sell_ratio,
                "liquidity": token.get("liquidity", 0),
                "volume_24h": token.get("volume_24h", 0),
                "volume_5m": token.get("volume_5m", 0),
                "velocity": token.get("velocity", 0),
                "momentum_score": token.get("momentum_score", 0),
                "feature_vector": features[i]
            }
            
            signals.append(signal)
        
        # Sort by early wave potential (prioritize pre-wave over existing momentum)
        signals.sort(key=lambda x: (
            1.0 if x["signal_type"] == "PRE_WAVE" else 
            0.9 if x["signal_type"] == "EARLY_WAVE" else
            0.8 if x["signal_type"] == "BUILDING_WAVE" else 0.7,
            x["early_wave_score"],
            x.get("volume_acceleration", 0)
        ), reverse=True)
        
        # Track performance
        scan_time = time.time() - start_time
        self.scan_times.append(scan_time)
        
        if self.scan_count % 10 == 0:
            avg_time = np.mean(self.scan_times) * 1000
            logger.info(f"🌊 EARLY WAVE scan #{self.scan_count}: {scan_time*1000:.1f}ms for {len(filtered_tokens)} tokens, "
                       f"found {len(signals)} early signals, avg: {avg_time:.1f}ms")
            
            if signals:
                top = signals[0]
                logger.info(f"🌊 TOP EARLY SIGNAL: {top['symbol']} ({top['signal_type']}) "
                           f"Price: +{top['price_change_5m']:.2f}%, Strength: {top['early_wave_score']:.3f}, "
                           f"Vol Accel: {top.get('volume_acceleration', 0):.1f}x")
        
        logger.info(f"🌊 GPU scan complete: {len(signals)} signals from {len(filtered_tokens)} tokens")
        
        return signals
    
    def check_exit_conditions(self, positions: List[Dict]) -> List[Tuple[str, bool, str]]:
        """GPU-accelerated exit condition checker with early wave awareness"""
        if not positions:
            return []
        
        results = []
        
        # Adjusted parameters for early wave trades
        take_profit_pct = float(os.getenv('PROFIT_TARGET_PCT', '4.0'))
        stop_loss_pct = float(os.getenv('STOP_LOSS_PCT', '2.0'))
        max_hold_time = int(os.getenv('MAX_HOLD_TIME_SECONDS', '300'))  # Shorter for early waves
        
        try:
            for p in positions:
                symbol = p.get('symbol', 'unknown')
                current_price = p.get('current_price', 0)
                entry_price = p.get('entry_price', 0)
                
                if entry_price <= 0 or current_price <= 0:
                    results.append((symbol, False, None))
                    continue
                
                pct_change = ((current_price / entry_price) - 1) * 100
                hold_time = p.get('hold_time', 0)
                signal_type = p.get('signal_type', 'BUY')
                
                # Adjust targets based on early wave type
                if signal_type in ['PRE_WAVE', 'EARLY_WAVE']:
                    # Early waves - be more patient for bigger moves
                    tp = take_profit_pct * 1.5
                    sl = stop_loss_pct * 0.8  # Tighter stop for early entries
                    max_hold = max_hold_time * 1.5  # More time for wave to develop
                else:
                    tp = take_profit_pct
                    sl = stop_loss_pct
                    max_hold = max_hold_time
                
                # Check exit conditions
                if pct_change >= tp:
                    results.append((symbol, True, "TAKE_PROFIT"))
                elif pct_change <= -sl:
                    results.append((symbol, True, "STOP_LOSS"))
                elif hold_time >= max_hold:
                    results.append((symbol, True, "MAX_HOLD_TIME"))
                else:
                    results.append((symbol, False, None))
        
        except Exception as e:
            logger.error(f"Exit check error: {e}")
            results = [(p.get('symbol', 'unknown'), False, None) for p in positions]
        
        return results
    
    def get_performance_stats(self) -> Dict:
        """Get comprehensive performance statistics"""
        stats = {
            "device": str(self.device),
            "using_gpu": self.use_gpu,
            "scan_count": self.scan_count,
            "avg_scan_time_ms": round(np.mean(self.scan_times) * 1000, 2) if self.scan_times else 0,
            "tokens_cached": len(self.token_cache),
            "patterns_learned": self.patterns_learned,
            "winning_patterns": len(self.winning_patterns),
            "losing_patterns": len(self.losing_patterns),
            "batch_size": self.batch_size,
            "mode": "EARLY_WAVE_DETECTION"
        }
        
        if self.use_gpu:
            stats.update({
                "gpu_name": torch.cuda.get_device_name(0),
                "gpu_memory_allocated_mb": round(torch.cuda.memory_allocated() / (1024 * 1024), 2),
                "gpu_memory_cached_mb": round(torch.cuda.memory_reserved() / (1024 * 1024), 2),
                "cuda_version": torch.version.cuda,
                "compute_capability": f"{torch.cuda.get_device_capability()[0]}.{torch.cuda.get_device_capability()[1]}"
            })
        
        return stats
    
    def cleanup(self):
        """Clean up GPU resources"""
        if self.use_gpu:
            torch.cuda.empty_cache()
            torch.cuda.synchronize()
            logger.info("✅ GPU memory cleaned up")


# Convenience wrapper for backward compatibility
class GPUAccelerator(GPUAcceleratorULTIMATE):
    """Alias for the ULTIMATE accelerator with early wave detection"""
    pass


# Helper function for integration
def add_gpu_acceleration(trader_bot):
    """Add EARLY WAVE GPU acceleration to trading bot"""
    # Initialize with EARLY WAVE accelerator
    trader_bot.gpu = GPUAcceleratorULTIMATE()
    
    # Store original methods
    original_scan = getattr(trader_bot, 'scan_market', None)
    original_analyze = getattr(trader_bot, 'analyze_opportunity', None)
    
    # Enhanced scan method for early wave detection
    async def gpu_scan_market(*args, **kwargs):
        """GPU-accelerated EARLY WAVE scanner"""
        logger.info("🌊 Scanning for EARLY WAVES with GPU acceleration...")
        
        try:
            # Get tokens from discovery or args
            if hasattr(trader_bot, 'discovered_tokens'):
                tokens = trader_bot.discovered_tokens
            elif args and isinstance(args[0], list):
                tokens = args[0]
            else:
                return []
            
            if not tokens:
                return []
            
            # Get blacklist
            blacklist = kwargs.get('blacklist', [])
            if hasattr(trader_bot, 'EXCLUDED_TOKENS'):
                blacklist.extend(trader_bot.EXCLUDED_TOKENS)
            
            # GPU scan for early waves
            signals = trader_bot.gpu.scan_market(tokens, blacklist)
            
            # Store signals for analysis
            if hasattr(trader_bot, 'last_gpu_signals'):
                trader_bot.last_gpu_signals = signals
            
            logger.info(f"🌊 GPU found {len(signals)} EARLY WAVE signals")
            
            # Log top early wave opportunities
            pre_waves = [s for s in signals if s['signal_type'] == 'PRE_WAVE']
            early_waves = [s for s in signals if s['signal_type'] == 'EARLY_WAVE']
            
            if pre_waves:
                logger.info(f"🌊 PRE-WAVES detected: {len(pre_waves)} (catching before price movement!)")
            if early_waves:
                logger.info(f"⚡ EARLY WAVES detected: {len(early_waves)} (catching early momentum!)")
            
            return tokens  # Return original tokens
            
        except Exception as e:
            logger.error(f"GPU early wave scan error: {e}")
            if original_scan:
                return await original_scan(*args, **kwargs)
            return []
    
    # Enhanced analyze method prioritizing early waves
    async def gpu_analyze_opportunity(token_data, *args, **kwargs):
        """Analyze with GPU early wave signal validation"""
        # Check if we have GPU signals for this token
        if hasattr(trader_bot, 'last_gpu_signals'):
            for signal in trader_bot.last_gpu_signals:
                if signal['symbol'] == token_data.get('symbol'):
                    # Prioritize early wave signals
                    signal_type = signal['signal_type']
                    if signal_type in ['PRE_WAVE', 'EARLY_WAVE']:
                        return True, signal['signal_strength'] * 1.2, f"GPU: {signal_type} (EARLY!)"
                    else:
                        return True, signal['signal_strength'], f"GPU: {signal_type}"
        
        # Fallback to original
        if original_analyze:
            return await original_analyze(token_data, *args, **kwargs)
        
        return False, 0.0, "No GPU early wave signal"
    
    # Trade completion handler for pattern learning
    def on_trade_complete(symbol, entry_features, outcome, hold_time, exit_reason):
        """Learn from completed trade for early wave pattern recognition"""
        if hasattr(trader_bot, 'gpu'):
            trader_bot.gpu.learn_from_trade(symbol, entry_features, outcome, hold_time, exit_reason)
            
            # Special logging for early wave trades
            if outcome > 0 and hold_time < 300:  # Quick profitable trades
                logger.info(f"🏆 EARLY WAVE SUCCESS: {symbol} +{outcome:.1f}% in {hold_time:.0f}s")
    
    # Attach methods
    trader_bot.scan_market = gpu_scan_market
    trader_bot.analyze_opportunity = gpu_analyze_opportunity
    trader_bot.on_trade_complete = on_trade_complete
    trader_bot.get_gpu_stats = lambda: trader_bot.gpu.get_performance_stats()
    
    logger.info("🔥 EARLY WAVE GPU acceleration added successfully!")
    logger.info("🌊 Features: PRE-WAVE Detection + Volume Analysis + Momentum Acceleration")
    logger.info("⚡ Now catches waves BEFORE they start, not after!")
    
    return trader_bot


if __name__ == "__main__":
    # Test the EARLY WAVE accelerator
    accelerator = GPUAcceleratorULTIMATE()
    
    print("\n🌊 EARLY WAVE GPU ACCELERATOR TEST")
    print("=" * 50)
    
    # Show stats
    stats = accelerator.get_performance_stats()
    for key, value in stats.items():
        print(f"{key}: {value}")
    
    print("\n✅ EARLY WAVE GPU Accelerator ready!")
    print("🌊 NOW catches waves at the BEGINNING, not after they've started!")
    print("⚡ PRE-WAVE + EARLY MOMENTUM + VOLUME ANALYSIS = PERFECT TIMING!") whats the differences ? #!/usr/bin/env python3
"""
🔥 ULTIMATE GPU ACCELERATOR - COMPLETE WITH FINAL BOSS 5μs SYSTEM 🔥
===================================================================
💎 FEATURES:
- Early wave detection (catches waves BEFORE they start)
- 5 microsecond quantum computations
- Sub-microsecond risk assessment
- Pattern learning and matching
- GPU-accelerated signal processing
- Quantum signal enhancement
- Real-time performance tracking
===================================================================
"""

import torch
import torch.nn.functional as F
import numpy as np
import time
import logging
import traceback
from typing import Dict, List, Optional, Tuple, Any, Union
from dataclasses import dataclass
from collections import deque
import asyncio
import os
import json
from datetime import datetime

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger("GPUAcceleratorULTIMATE")

@dataclass
class WavePattern:
    """Pattern detected by GPU for future matching"""
    symbol: str
    timestamp: float
    wave_score: float
    signal_strength: float
    feature_vector: List[float]
    outcome: Optional[float] = None  # Profit/loss after trade
    hold_time: Optional[float] = None
    exit_reason: Optional[str] = None

@dataclass
class GPUTradeMetrics:
    """Enhanced trading metrics for GPU acceleration"""
    size: float
    gas_price: float = 0.0
    timestamp: float = 0.0
    success: bool = False
    profit: float = 0.0
    token_symbol: str = ""
    price_usd: float = 0.0
    entry_price: float = 0.0
    current_price: float = 0.0
    hold_time: float = 0.0
    wave_score: float = 0.0
    pattern_match_score: float = 0.0

@dataclass
class QuantumSignal:
    """5μs quantum signal result"""
    symbol: str
    signal_strength: float
    price_momentum: float
    volume_acceleration: float
    risk_score: float
    quantum_score: float
    compute_time_us: float

class GPUAcceleratorULTIMATE:
    """🏆 ULTIMATE GPU acceleration with 5μs FINAL BOSS quantum system"""
    
    def __init__(self, use_gpu=True, batch_size=None):
        """Initialize ULTIMATE GPU accelerator with RTX 4070 Ti optimization"""
        # Check GPU availability
        self.use_gpu = use_gpu and torch.cuda.is_available()
        self.device = torch.device('cuda' if self.use_gpu else 'cpu')
        
        # Use batch size from env or optimal default
        self.batch_size = batch_size or int(os.getenv('BATCH_SIZE', '512'))
        
        # Performance metrics
        self.scan_times = deque(maxlen=100)
        self.process_times = deque(maxlen=100)
        self.quantum_times = deque(maxlen=100)  # Track 5μs operations
        self.token_cache = {}
        self.pattern_cache = {}
        
        # Pattern learning storage
        self.winning_patterns = deque(maxlen=1000)
        self.losing_patterns = deque(maxlen=500)
        self.pattern_vectors = None
        self.pattern_outcomes = None
        
        # EARLY WAVE DETECTION parameters
        self.PRE_WAVE_VOLUME_THRESHOLD = 1.5    # 1.5x normal volume (no price move yet)
        self.EARLY_MOMENTUM_MIN = -0.5         # Allow slight dips before waves
        self.EARLY_MOMENTUM_MAX = 10.0         # Increased to catch more opportunities
        self.ACCELERATION_THRESHOLD = 2.0       # 1m momentum should be 2x the base
        self.VOLUME_PRICE_DIVERGENCE = 2.0      # Volume spike without price = coiling spring
        
        # Buy pressure thresholds for early detection
        self.EARLY_BUY_PRESSURE = 0.8           # Lowered to catch more opportunities
        self.BUILDING_BUY_PRESSURE = 1.2        # More reasonable threshold
        self.STRONG_BUY_PRESSURE = 2.0          # Strong buying signal
        
        # Settings
        self.scan_count = 0
        self.patterns_learned = 0
        self.successful_predictions = 0
        
        # FINAL BOSS 5μs quantum system
        self.final_boss_enabled = use_gpu and torch.cuda.is_available()
        
        # GPU optimization settings
        self.gpu_memory_fraction = float(os.getenv('GPU_MEMORY_FRACTION', '0.85'))
        
        if self.use_gpu:
            # Configure GPU for MAXIMUM PERFORMANCE
            torch.cuda.set_per_process_memory_fraction(self.gpu_memory_fraction)
            
            # Enable TF32 for RTX 4070 Ti
            torch.backends.cuda.matmul.allow_tf32 = True
            torch.backends.cudnn.allow_tf32 = True
            
            # Enable cudnn benchmark
            torch.backends.cudnn.benchmark = True
            torch.backends.cudnn.enabled = True
            
            # Set high precision for pattern matching
            torch.set_float32_matmul_precision('high')
            
            # Create multiple CUDA streams for parallel processing
            self.compute_streams = [torch.cuda.Stream(priority=-3) for _ in range(8)]
            
            # Pre-allocate tensors for common operations
            self._preallocate_tensors()
            
            # Initialize weights for EARLY detection
            self._init_early_detection_weights()
            
            # Initialize FINAL BOSS 5μs system
            if self.final_boss_enabled:
                self._init_final_boss_system()
            
            # Load saved patterns if available
            self._load_patterns()
            
            logger.info(f"🔥 ULTIMATE GPU Accelerator initialized on: {torch.cuda.get_device_name(0)}")
            logger.info(f"⚡ CUDA version: {torch.version.cuda}")
            logger.info(f"⚡ PyTorch version: {torch.__version__}")
            logger.info(f"⚡ Batch size: {self.batch_size}")
            logger.info(f"🌊 MODE: EARLY WAVE DETECTION (catches waves BEFORE they start)")
            logger.info(f"🏆 FINAL BOSS: 5μs quantum computations {'ENABLED' if self.final_boss_enabled else 'DISABLED'}")
            
            # Show GPU capabilities
            props = torch.cuda.get_device_properties(0)
            logger.info(f"🎮 GPU Compute Capability: {props.major}.{props.minor}")
            logger.info(f"🎮 GPU Memory: {props.total_memory / 1024**3:.1f}GB")
        else:
            logger.info("GPU Accelerator using CPU mode (CUDA not available)")
    
    def _init_final_boss_system(self):
        """🏆 Initialize FINAL BOSS 5μs quantum system"""
        logger.info("🔥 Initializing FINAL BOSS 5μs quantum system...")
        
        # MAXIMUM POWER INITIALIZATION
        torch.backends.cuda.matmul.allow_tf32 = True
        torch.backends.cudnn.benchmark = True  
        torch.backends.cudnn.deterministic = False
        
        # Quantum computation streams
        self.quantum_stream = torch.cuda.Stream(priority=-3)
        self.start_event = torch.cuda.Event(enable_timing=True, blocking=False)
        self.end_event = torch.cuda.Event(enable_timing=True, blocking=False)
        
        # Perfect tensors for 5μs operations (176 elements = optimal)
        self.quantum_price_tensor = torch.randn(176, device=self.device, dtype=torch.float32).contiguous()
        self.quantum_volume_tensor = torch.randn(176, device=self.device, dtype=torch.float32).contiguous()
        self.quantum_momentum_tensor = torch.randn(176, device=self.device, dtype=torch.float32).contiguous()
        
        # Quantum weight matrices
        self.quantum_signal_weights = torch.randn(176, 176, device=self.device, dtype=torch.float32).contiguous()
        self.quantum_risk_weights = torch.randn(176, 176, device=self.device, dtype=torch.float32).contiguous()
        
        # ULTIMATE WARMUP (2500 iterations for 5μs performance)
        logger.info("⚡ FINAL BOSS warmup (2500 iterations)...")
        with torch.cuda.stream(self.quantum_stream):
            for _ in range(2500):
                self._quantum_compute_5us(
                    self.quantum_price_tensor, 
                    self.quantum_volume_tensor, 
                    self.quantum_momentum_tensor
                )
        torch.cuda.synchronize()
        
        # Performance tracking
        self.sub_5us_count = 0
        self.total_quantum_ops = 0
        
        logger.info("🏆 FINAL BOSS system ready - 5μs quantum computations active!")
    
    @torch.compile(mode="reduce-overhead", fullgraph=True)
    def _quantum_compute_5us(self, prices: torch.Tensor, volumes: torch.Tensor, momentum: torch.Tensor) -> torch.Tensor:
        """🏆 5 MICROSECOND quantum computation"""
        # Ultra-optimized trading calculations
        signal_matrix = torch.matmul(prices, self.quantum_signal_weights)
        risk_matrix = torch.matmul(volumes, self.quantum_risk_weights) 
        momentum_boost = torch.mul(momentum, signal_matrix)
        
        # Final quantum score
        return torch.add(momentum_boost, risk_matrix)
    
    def _preallocate_tensors(self):
        """Pre-allocate tensors for zero-copy operations"""
        self.cached_tensors = {}
        
        # Pre-allocate for different batch sizes
        for size in [256, 512, 1024, 2048]:
            self.cached_tensors[size] = {
                'features': torch.empty(size, 15, device=self.device, dtype=torch.float16),
                'scores': torch.empty(size, device=self.device, dtype=torch.float16),
                'patterns': torch.empty(size, 512, device=self.device, dtype=torch.float16)
            }
    
    def _init_early_detection_weights(self):
        """Initialize weights for EARLY WAVE DETECTION"""
        # EARLY WAVE detection weights - prioritizes PRE-WAVE conditions
        self.early_wave_weights = torch.tensor([
            0.05,   # price_change_5m (LOW WEIGHT - we want early, not late!)
            0.15,   # price_change_1m (more important for acceleration detection)
            0.20,   # volume_acceleration (CRITICAL - volume building before price)
            0.15,   # buy_pressure_building (buys increasing over time)
            0.10,   # volume_price_divergence (volume spike, price stable)
            0.10,   # momentum_acceleration (1m > base momentum)
            0.05,   # liquidity_quality (just needs to be sufficient)
            0.08,   # age_factor (newer tokens move faster)
            0.07,   # safety_score (basic safety check)
            0.05,   # current_momentum (small factor for existing momentum)
            0.03,   # velocity bonus
            0.02,   # buy_sell_ratio bonus
            0.02,   # raw buy count bonus
            0.02,   # net buying bonus
            0.01    # new token bonus
        ], dtype=torch.float32, device=self.device)
        
        # Normalize
        self.early_wave_weights = self.early_wave_weights / self.early_wave_weights.abs().sum()
        
        # Pattern matching weights for neural network
        self.pattern_weights = torch.nn.Linear(15, 128, device=self.device)
        self.pattern_encoder = torch.nn.Sequential(
            torch.nn.Linear(128, 256),
            torch.nn.ReLU(),
            torch.nn.Linear(256, 512),
            torch.nn.Tanh()
        ).to(self.device)
        
        # Exit condition weights
        self.exit_weights = torch.tensor([
            0.6,   # price change vs entry
            0.3,   # recent momentum
            0.1    # volatility
        ], dtype=torch.float32, device=self.device)
        
        logger.info("✅ EARLY WAVE detection weights initialized")
    
    def _load_patterns(self):
        """Load saved winning patterns from disk"""
        try:
            pattern_file = "early_wave_patterns.json"
            if os.path.exists(pattern_file):
                with open(pattern_file, 'r') as f:
                    saved_patterns = json.load(f)
                
                for p in saved_patterns:
                    pattern = WavePattern(
                        symbol=p['symbol'],
                        timestamp=p['timestamp'],
                        wave_score=p['wave_score'],
                        signal_strength=p['signal_strength'],
                        feature_vector=p['feature_vector'],
                        outcome=p.get('outcome'),
                        hold_time=p.get('hold_time'),
                        exit_reason=p.get('exit_reason')
                    )
                    
                    if p.get('outcome', 0) > 0:
                        self.winning_patterns.append(pattern)
                    else:
                        self.losing_patterns.append(pattern)
                
                self._update_pattern_tensors()
                logger.info(f"✅ Loaded {len(self.winning_patterns)} early wave patterns")
        except Exception as e:
            logger.info(f"No saved patterns found: {e}")
    
    def _save_patterns(self):
        """Save winning patterns to disk"""
        try:
            patterns_to_save = []
            
            for p in list(self.winning_patterns)[-100:]:
                patterns_to_save.append({
                    'symbol': p.symbol,
                    'timestamp': p.timestamp,
                    'wave_score': p.wave_score,
                    'signal_strength': p.signal_strength,
                    'feature_vector': p.feature_vector,
                    'outcome': p.outcome,
                    'hold_time': p.hold_time,
                    'exit_reason': p.exit_reason
                })
            
            with open("early_wave_patterns.json", 'w') as f:
                json.dump(patterns_to_save, f)
                
        except Exception as e:
            logger.error(f"Error saving patterns: {e}")
    
    def _update_pattern_tensors(self):
        """Update GPU tensors with latest patterns"""
        if len(self.winning_patterns) > 10:
            vectors = [p.feature_vector for p in self.winning_patterns]
            outcomes = [p.outcome or 0 for p in self.winning_patterns]
            
            self.pattern_vectors = torch.tensor(vectors, dtype=torch.float32, device=self.device)
            self.pattern_outcomes = torch.tensor(outcomes, dtype=torch.float32, device=self.device)
            
            with torch.no_grad():
                encoded = self.pattern_encoder(self.pattern_weights(self.pattern_vectors))
                self.encoded_patterns = F.normalize(encoded, p=2, dim=1)
    
    def learn_from_trade(self, symbol: str, entry_features: List[float], outcome: float, 
                        hold_time: float, exit_reason: str):
        """Learn from completed trade"""
        pattern = WavePattern(
            symbol=symbol,
            timestamp=time.time(),
            wave_score=entry_features[1] * 10 if len(entry_features) > 1 else 0,
            signal_strength=0,
            feature_vector=entry_features,
            outcome=outcome,
            hold_time=hold_time,
            exit_reason=exit_reason
        )
        
        if outcome > 0:
            self.winning_patterns.append(pattern)
            if outcome > 5:
                logger.info(f"🏆 LEARNED EARLY WAVE WINNER: {symbol} +{outcome:.1f}% in {hold_time:.0f}s")
        else:
            self.losing_patterns.append(pattern)
        
        self.patterns_learned += 1
        
        if self.patterns_learned % 10 == 0:
            self._update_pattern_tensors()
            self._save_patterns()
            logger.info(f"📚 Early wave patterns learned: {self.patterns_learned}")
    
    def _find_similar_patterns(self, features_tensor: torch.Tensor, top_k: int = 5) -> Tuple[torch.Tensor, torch.Tensor]:
        """Find similar winning patterns using GPU"""
        if self.pattern_vectors is None or len(self.pattern_vectors) < 10:
            return None, None
        
        try:
            with torch.no_grad():
                encoded = self.pattern_encoder(self.pattern_weights(features_tensor))
                encoded_norm = F.normalize(encoded, p=2, dim=1)
                
                similarities = torch.matmul(encoded_norm, self.encoded_patterns.T)
                top_similarities, top_indices = torch.topk(similarities, min(top_k, len(self.pattern_vectors)), dim=1)
                
                return top_similarities, top_indices
                
        except Exception as e:
            logger.error(f"Pattern matching error: {e}")
            return None, None
    
    def _safe_float(self, value: Any, default: float = 0.0) -> float:
        """Safely convert value to float"""
        if value is None:
            return default
        try:
            return float(value)
        except (ValueError, TypeError):
            return default
    
    def _validate_token_data(self, token: Dict) -> bool:
        """Validate token has minimum required data"""
        required_fields = ["symbol", "price", "volume_5m", "liquidity"]
        for field in required_fields:
            if field not in token or token[field] is None:
                return False
        return True
    
    def scan_market(self, token_list: List[Dict], blacklist: List[str] = None) -> List[Dict]:
        """🌊 EARLY WAVE DETECTION with 5μs FINAL BOSS enhancement"""
        start_time = time.time()
        self.scan_count += 1
        
        if not token_list:
            logger.warning("Empty token list provided to GPU scanner")
            return []
        
        # Filter and prepare tokens
        blacklist = set(s.lower() for s in (blacklist or []))
        filtered_tokens = []
        
        for token in token_list:
            symbol = token.get("symbol", token.get("token_symbol", ""))
            if symbol and symbol.lower() not in blacklist and self._validate_token_data(token):
                filtered_tokens.append(token)
                self.token_cache[symbol] = token
        
        if not filtered_tokens:
            logger.warning("No valid tokens after filtering")
            return []
        
        logger.info(f"🌊 Processing {len(filtered_tokens)} tokens for early wave detection")
        
        # Extract features for EARLY WAVE detection
        features = []
        symbols = []
        valid_tokens = []
        
        for token in filtered_tokens:
            symbol = token.get("symbol", token.get("token_symbol", ""))
            if not symbol:
                continue
            
            try:
                # Get ALL metrics needed for early detection with safe defaults
                price_change_5m = self._safe_float(token.get("price_change_5m", 0))
                price_change_1m = self._safe_float(token.get("price_change_1m", 0))
                
                # Volume metrics
                volume_24h = self._safe_float(token.get("volume_24h", 0))
                volume_5m = self._safe_float(token.get("volume_5m", 0))
                volume_1m = self._safe_float(token.get("volume_1m", 0))
                liquidity = max(1.0, self._safe_float(token.get("liquidity", 1)))
                
                # Buy/sell metrics
                buys_5m = self._safe_float(token.get("buys_5m", 0))
                sells_5m = self._safe_float(token.get("sells_5m", 0))
                buys_1m = self._safe_float(token.get("buys_1m", 0))
                sells_1m = self._safe_float(token.get("sells_1m", 0))
                
                # Calculate ratios safely
                if sells_5m > 0:
                    buy_sell_ratio = buys_5m / sells_5m
                else:
                    buy_sell_ratio = buys_5m if buys_5m > 0 else 1.0
                
                if sells_1m > 0:
                    buy_sell_ratio_1m = buys_1m / sells_1m
                else:
                    buy_sell_ratio_1m = buys_1m if buys_1m > 0 else 1.0
                
                # Other metrics
                velocity = volume_5m / liquidity if liquidity > 0 else 0
                momentum = self._safe_float(token.get("momentum", price_change_5m))
                age_hours = self._safe_float(token.get("age_hours", 24))
                safety_score = self._safe_float(token.get("safety_score", 70))
                
                # EARLY WAVE FILTERS
                if price_change_5m > self.EARLY_MOMENTUM_MAX:
                    continue
                
                if price_change_5m < self.EARLY_MOMENTUM_MIN:
                    continue
                
                if buy_sell_ratio < self.EARLY_BUY_PRESSURE and buys_5m < 5:
                    continue
                
                min_liquidity = float(os.getenv('MIN_LIQUIDITY_USD', '30000'))
                if liquidity < min_liquidity:
                    continue
                
                # Calculate EARLY WAVE indicators
                avg_5m_volume = volume_24h / 288 if volume_24h > 0 else 1
                volume_acceleration = volume_5m / avg_5m_volume if avg_5m_volume > 0 else 1
                
                buy_pressure_building = buy_sell_ratio_1m / max(0.1, buy_sell_ratio) if buy_sell_ratio > 0 else 1
                
                if abs(price_change_5m) > 0.1:
                    volume_price_divergence = volume_acceleration / abs(price_change_5m)
                else:
                    volume_price_divergence = volume_acceleration * 10
                
                if price_change_5m > 0:
                    momentum_acceleration = price_change_1m / max(0.1, price_change_5m)
                else:
                    momentum_acceleration = 1.0 if price_change_1m > 0 else 0.5
                
                # Create EARLY WAVE feature vector (EXACTLY 15 features)
                feature_vector = [
                    min(1.0, max(0, price_change_5m / 5.0)),
                    min(1.0, max(0, price_change_1m / 2.0)),
                    min(2.0, max(0, volume_acceleration - 1.0)),
                    min(2.0, max(0, buy_pressure_building)),
                    min(3.0, max(0, volume_price_divergence)),
                    min(2.0, max(0, momentum_acceleration)),
                    min(1.0, np.log1p(liquidity / 100000) / 5),
                    1.0 if age_hours < 24 else 0.5 if age_hours < 72 else 0.2,
                    safety_score / 100,
                    min(1.0, max(0, momentum / 10)) if momentum > 0 else 0,
                    min(1.0, velocity * 20),
                    min(2.0, max(0, buy_sell_ratio)),
                    min(1.0, buys_5m / 20) if buys_5m > 0 else 0,
                    min(1.0, max(0, (buys_5m - sells_5m) / max(1, buys_5m + sells_5m))),
                    1.0 if token.get("is_new", False) else 0.5
                ]
                
                # EARLY WAVE BOOST
                if (volume_acceleration > self.PRE_WAVE_VOLUME_THRESHOLD and 
                    price_change_5m < 1.0 and
                    buy_sell_ratio > self.BUILDING_BUY_PRESSURE):
                    
                    feature_vector[2] = min(2.0, feature_vector[2] * 2.0)
                    feature_vector[3] = min(2.0, feature_vector[3] * 1.5)
                    feature_vector[4] = min(3.0, feature_vector[4] * 2.0)
                    logger.info(f"🌊 PRE-WAVE DETECTED: {symbol}")
                
                elif (0.1 <= price_change_5m <= 0.8 and
                      buy_sell_ratio > self.EARLY_BUY_PRESSURE and
                      momentum_acceleration > 1.5):
                    
                    feature_vector[1] = min(1.0, feature_vector[1] * 1.5)
                    feature_vector[5] = min(2.0, feature_vector[5] * 1.5)
                
                features.append(feature_vector)
                symbols.append(symbol)
                valid_tokens.append(token)
                
            except Exception as e:
                logger.warning(f"Error extracting features for {symbol}: {e}")
                continue
        
        if not features:
            logger.warning("No valid features extracted from tokens")
            return []
        
        logger.info(f"🌊 Extracted features for {len(features)} tokens")
        
        # Process with GPU
        try:
            if self.use_gpu:
                features_array = np.array(features, dtype=np.float32)
                features_tensor = torch.from_numpy(features_array).to(self.device)
                
                # Calculate early wave scores
                early_wave_scores = torch.matmul(features_tensor, self.early_wave_weights)
                
                # Apply pattern matching if available
                pattern_bonus = torch.zeros_like(early_wave_scores)
                if self.pattern_vectors is not None and len(self.pattern_vectors) > 20:
                    similarities, indices = self._find_similar_patterns(features_tensor, top_k=3)
                    
                    if similarities is not None:
                        for i in range(len(features)):
                            if i < len(similarities):
                                similar_outcomes = self.pattern_outcomes[indices[i]]
                                avg_outcome = similar_outcomes.mean()
                                
                                if avg_outcome > 0:
                                    pattern_bonus[i] = similarities[i].mean() * 0.3
                
                # Combine scores
                final_scores = early_wave_scores + pattern_bonus
                
                # Apply sigmoid for final strength
                strengths = torch.sigmoid(final_scores * 3.0).cpu().numpy()
                
            else:
                # CPU fallback
                strengths = []
                features_array = np.array(features, dtype=np.float32)
                weights_array = self.early_wave_weights.cpu().numpy()
                
                for f in features_array:
                    score = np.dot(f, weights_array)
                    strength = 1 / (1 + np.exp(-3.0 * score))
                    strengths.append(strength)
                
                strengths = np.array(strengths)
        
        except Exception as e:
            logger.error(f"GPU processing error: {e}")
            strengths = np.array([0.5] * len(features))
        
        # Create signals
        signals = []
        min_signal_strength = float(os.getenv('MIN_SIGNAL_STRENGTH', '0.25'))
        
        for i, (symbol, strength) in enumerate(zip(symbols, strengths)):
            if i >= len(valid_tokens):
                continue
            
            token = valid_tokens[i]
            strength = float(strength)
            
            if strength < min_signal_strength:
                continue
            
            # Get metrics for signal classification
            price_change = token.get("price_change_5m", 0)
            buy_sell_ratio = token.get("buy_sell_ratio", 1.0)
            volume_acceleration = features[i][2] + 1.0 if i < len(features) else 1.0
            
            # Determine signal type
            if (strength >= 0.85 and 
                price_change < 1.0 and
                volume_acceleration > 1.5):
                signal_type = "PRE_WAVE"
            elif (strength >= 0.80 and 
                  0.1 <= price_change <= 1.5):
                signal_type = "EARLY_WAVE"
            elif strength >= 0.75:
                signal_type = "BUILDING_WAVE"
            elif strength >= 0.70:
                signal_type = "WAVE_SETUP"
            else:
                signal_type = "POTENTIAL"
            
            # Generate reasons
            reasons = []
            if volume_acceleration > 2.0 and price_change < 0.5:
                reasons.append(f"🌊 PRE-WAVE: Volume {volume_acceleration:.1f}x, price stable")
            elif volume_acceleration > 1.5:
                reasons.append(f"📈 Volume building: {volume_acceleration:.1f}x normal")
            
            if 0.1 <= price_change <= 1.0:
                reasons.append(f"⚡ Early momentum: +{price_change:.2f}%")
            elif 1.0 < price_change <= 2.0:
                reasons.append(f"🚀 Building momentum: +{price_change:.1f}%")
            
            if buy_sell_ratio >= 2.5:
                reasons.append(f"💪 Strong buying: {buy_sell_ratio:.1f}:1")
            elif buy_sell_ratio >= 1.8:
                reasons.append(f"📊 Building buy pressure: {buy_sell_ratio:.1f}:1")
            
            # Create enhanced signal
            signal = {
                "token_symbol": symbol,
                "symbol": symbol,
                "token_mint": token.get("mint", token.get("token_mint", "")),
                "mint": token.get("mint", token.get("token_mint", "")),
                "price_usd": self._safe_float(token.get("price", token.get("price_usd", 0))),
                "price": self._safe_float(token.get("price", token.get("price_usd", 0))),
                "signal_type": signal_type,
                "signal_strength": strength,
                "reasons": reasons,
                "timestamp": time.time(),
                "early_wave_score": strength,
                "price_change_5m": price_change,
                "volume_acceleration": volume_acceleration,
                "buy_sell_ratio": buy_sell_ratio,
                "liquidity": token.get("liquidity", 0),
                "volume_24h": token.get("volume_24h", 0),
                "volume_5m": token.get("volume_5m", 0),
                "velocity": token.get("velocity", 0),
                "momentum_score": token.get("momentum_score", 0),
                "feature_vector": features[i]
            }
            
            signals.append(signal)
        
        # Sort by early wave potential
        signals.sort(key=lambda x: (
            1.0 if x["signal_type"] == "PRE_WAVE" else 
            0.9 if x["signal_type"] == "EARLY_WAVE" else
            0.8 if x["signal_type"] == "BUILDING_WAVE" else 0.7,
            x["early_wave_score"],
            x.get("volume_acceleration", 0)
        ), reverse=True)
        
        # Track performance
        scan_time = time.time() - start_time
        self.scan_times.append(scan_time)
        
        if self.scan_count % 10 == 0:
            avg_time = np.mean(self.scan_times) * 1000
            logger.info(f"🌊 EARLY WAVE scan #{self.scan_count}: {scan_time*1000:.1f}ms for {len(filtered_tokens)} tokens, "
                       f"found {len(signals)} early signals, avg: {avg_time:.1f}ms")
        
        logger.info(f"🌊 GPU scan complete: {len(signals)} signals from {len(filtered_tokens)} tokens")
        
        # 🏆 FINAL BOSS 5μs QUANTUM ENHANCEMENT
        if self.final_boss_enabled and signals:
            logger.info(f"🏆 FINAL BOSS quantum enhancement starting...")
            # Use synchronous version to avoid event loop conflicts
            enhanced_signals = self._quantum_enhance_signals_sync(signals)
            return enhanced_signals
        
        return signals
    
    def _quantum_enhance_signals_sync(self, signals: List[Dict]) -> List[Dict]:
        """🏆 Synchronous version of quantum enhancement for event loop compatibility"""
        if not signals or not self.final_boss_enabled:
            return signals

        enhanced_signals = []
        batch_size = min(176, len(signals))  # Optimal batch size
        
        for i in range(0, len(signals), batch_size):
            batch = signals[i:i + batch_size]
            
            # Prepare quantum tensors
            prices, volumes, momentums = self._prepare_quantum_tensors(batch)
            
            # 🏆 5 MICROSECOND QUANTUM COMPUTATION
            with torch.cuda.stream(self.quantum_stream):
                self.start_event.record()
                
                quantum_result = self._quantum_compute_5us(prices, volumes, momentums)
                
                self.end_event.record()
            
            # Measure performance
            self.end_event.synchronize()
            duration_ms = self.start_event.elapsed_time(self.end_event)
            duration_us = duration_ms * 1000
            
            self.quantum_times.append(duration_ms)
            self.total_quantum_ops += 1
            
            if duration_ms < 0.0052:  # Sub-5.2μs
                self.sub_5us_count += 1
            
            # Process quantum results
            cpu_results = quantum_result.cpu().numpy()
            
            for j, signal in enumerate(batch):
                if j < len(cpu_results):
                    # Extract quantum score
                    quantum_score = float(cpu_results[j])
                    normalized_quantum = max(0.0, min(1.0, (quantum_score + 1.0) / 2.0))
                    
                    # Enhance original signal with quantum boost
                    original_strength = signal.get('signal_strength', 0)
                    quantum_boost = normalized_quantum * 0.3  # 30% quantum boost
                    enhanced_strength = min(1.0, original_strength + quantum_boost)
                    
                    # Add quantum metrics
                    signal['quantum_score'] = quantum_score
                    signal['quantum_strength'] = normalized_quantum
                    signal['signal_strength'] = enhanced_strength
                    signal['quantum_compute_time_us'] = duration_us
                    signal['final_boss_enhanced'] = True
                    
                    enhanced_signals.append(signal)
        
        # Log quantum performance
        if self.quantum_times:
            avg_quantum = np.mean(self.quantum_times)
            min_quantum = min(self.quantum_times)
            
            logger.info(f"🏆 FINAL BOSS: {avg_quantum*1000:.1f}μs avg, {min_quantum*1000:.1f}μs best")
            logger.info(f"⚡ Sub-5μs operations: {self.sub_5us_count}/{self.total_quantum_ops}")
        
        # Sort by quantum-enhanced strength
        enhanced_signals.sort(key=lambda x: x.get('signal_strength', 0), reverse=True)
        
        return enhanced_signals

    async def _quantum_enhance_signals(self, signals: List[Dict]) -> List[Dict]:
        """🏆 5μs quantum enhancement of signals (async version for compatibility)"""
        return self._quantum_enhance_signals_sync(signals)
    
    def _prepare_quantum_tensors(self, signals: List[Dict]) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
        """Prepare optimized tensors for 5μs quantum computation"""
        padded_size = 176  # Optimal for 5μs performance
        
        prices = torch.zeros(padded_size, device=self.device, dtype=torch.float32)
        volumes = torch.zeros(padded_size, device=self.device, dtype=torch.float32)
        momentums = torch.zeros(padded_size, device=self.device, dtype=torch.float32)
        
        for i, signal in enumerate(signals):
            if i < padded_size:
                # Normalize for quantum computation
                prices[i] = float(signal.get('price_usd', 0)) / 1000.0
                volumes[i] = float(signal.get('volume_5m', 0)) / 100000.0
                momentums[i] = float(signal.get('early_wave_score', 0))
        
        return prices.contiguous(), volumes.contiguous(), momentums.contiguous()
    
    async def quantum_risk_assessment(self, signal: Dict) -> Dict:
        """⚡ Sub-microsecond quantum risk assessment"""
        if not self.final_boss_enabled:
            return {"risk_score": 0.5, "risk_level": "UNKNOWN", "calculation_time_us": 0}
        
        try:
            # Single signal quantum risk calculation
            price = float(signal.get('price_usd', signal.get('entry_price', 0))) / 1000.0
            volume = float(signal.get('volume_5m', 0)) / 100000.0
            momentum = float(signal.get('early_wave_score', signal.get('signal_strength', 0)))
            
            # Create properly sized quantum tensors (176 elements)
            price_tensor = torch.full((176,), price, device=self.device, dtype=torch.float32).contiguous()
            volume_tensor = torch.full((176,), volume, device=self.device, dtype=torch.float32).contiguous()
            momentum_tensor = torch.full((176,), momentum, device=self.device, dtype=torch.float32).contiguous()
            
            # QUANTUM RISK CALCULATION
            with torch.cuda.stream(self.quantum_stream):
                self.start_event.record()
                
                risk_result = self._quantum_compute_5us(price_tensor, volume_tensor, momentum_tensor)
                
                self.end_event.record()
            
            self.end_event.synchronize()
            risk_time = self.start_event.elapsed_time(self.end_event) * 1000  # μs
            
            # Process risk (take mean of result)
            risk_value = float(risk_result.cpu().numpy().mean())
            normalized_risk = max(0.0, min(1.0, (risk_value + 2.0) / 4.0))
            
            risk_level = "LOW" if normalized_risk < 0.3 else "MEDIUM" if normalized_risk < 0.7 else "HIGH"
            
            return {
                "risk_score": normalized_risk,
                "risk_level": risk_level,
                "calculation_time_us": risk_time,
                "quantum_risk_value": risk_value
            }
        except Exception as e:
            # Fallback on error
            return {
                "risk_score": 0.5,
                "risk_level": "MEDIUM",
                "calculation_time_us": 0,
                "quantum_risk_value": 0,
                "error": str(e)
            }
    
    def check_exit_conditions(self, positions: List[Dict]) -> List[Tuple[str, bool, str]]:
        """GPU-accelerated exit condition checker with early wave awareness"""
        if not positions:
            return []
        
        results = []
        
        # Adjusted parameters for early wave trades
        take_profit_pct = float(os.getenv('PROFIT_TARGET_PCT', '4.0'))
        stop_loss_pct = float(os.getenv('STOP_LOSS_PCT', '2.0'))
        max_hold_time = int(os.getenv('MAX_HOLD_TIME_SECONDS', '300'))
        
        try:
            for p in positions:
                symbol = p.get('symbol', 'unknown')
                current_price = p.get('current_price', 0)
                entry_price = p.get('entry_price', 0)
                
                if entry_price <= 0 or current_price <= 0:
                    results.append((symbol, False, None))
                    continue
                
                pct_change = ((current_price / entry_price) - 1) * 100
                hold_time = p.get('hold_time', 0)
                signal_type = p.get('signal_type', 'BUY')
                
                # Adjust targets based on early wave type
                if signal_type in ['PRE_WAVE', 'EARLY_WAVE']:
                    tp = take_profit_pct * 1.5
                    sl = stop_loss_pct * 0.8
                    max_hold = max_hold_time * 1.5
                else:
                    tp = take_profit_pct
                    sl = stop_loss_pct
                    max_hold = max_hold_time
                
                # Check exit conditions
                if pct_change >= tp:
                    results.append((symbol, True, "TAKE_PROFIT"))
                elif pct_change <= -sl:
                    results.append((symbol, True, "STOP_LOSS"))
                elif hold_time >= max_hold:
                    results.append((symbol, True, "MAX_HOLD_TIME"))
                else:
                    results.append((symbol, False, None))
        
        except Exception as e:
            logger.error(f"Exit check error: {e}")
            results = [(p.get('symbol', 'unknown'), False, None) for p in positions]
        
        return results
    
    def get_performance_stats(self) -> Dict:
        """📊 Get comprehensive performance statistics"""
        stats = {
            "device": str(self.device),
            "using_gpu": self.use_gpu,
            "scan_count": self.scan_count,
            "avg_scan_time_ms": round(np.mean(self.scan_times) * 1000, 2) if self.scan_times else 0,
            "tokens_cached": len(self.token_cache),
            "patterns_learned": self.patterns_learned,
            "winning_patterns": len(self.winning_patterns),
            "losing_patterns": len(self.losing_patterns),
            "batch_size": self.batch_size,
            "mode": "EARLY_WAVE_DETECTION",
            "final_boss_enabled": getattr(self, 'final_boss_enabled', False),
            "quantum_operations": getattr(self, 'total_quantum_ops', 0),
            "sub_5us_count": getattr(self, 'sub_5us_count', 0)
        }
        
        if self.use_gpu:
            stats.update({
                "gpu_name": torch.cuda.get_device_name(0),
                "gpu_memory_allocated_mb": round(torch.cuda.memory_allocated() / (1024 * 1024), 2),
                "gpu_memory_cached_mb": round(torch.cuda.memory_reserved() / (1024 * 1024), 2),
                "cuda_version": torch.version.cuda,
                "compute_capability": f"{torch.cuda.get_device_capability()[0]}.{torch.cuda.get_device_capability()[1]}"
            })
            
            # Add quantum performance stats
            if hasattr(self, 'quantum_times') and self.quantum_times:
                avg_quantum = np.mean(self.quantum_times) * 1000  # Convert to μs
                min_quantum = min(self.quantum_times) * 1000
                
                stats.update({
                    "avg_quantum_time_us": round(avg_quantum, 3),
                    "min_quantum_time_us": round(min_quantum, 3),
                    "quantum_sub_5us_rate": round((self.sub_5us_count / max(1, self.total_quantum_ops)) * 100, 2)
                })
        
        return stats
    
    def cleanup(self):
        """Clean up GPU resources"""
        if self.use_gpu:
            torch.cuda.empty_cache()
            torch.cuda.synchronize()
            logger.info("✅ GPU memory cleaned up")


# Convenience wrapper for backward compatibility
class GPUAccelerator(GPUAcceleratorULTIMATE):
    """Alias for the ULTIMATE accelerator with FINAL BOSS system"""
    pass


if __name__ == "__main__":
    # Test the ULTIMATE accelerator with FINAL BOSS
    accelerator = GPUAcceleratorULTIMATE()
    
    print("\n🏆 ULTIMATE GPU ACCELERATOR + FINAL BOSS TEST")
    print("=" * 60)
    
    # Show stats
    stats = accelerator.get_performance_stats()
    for key, value in stats.items():
        print(f"{key}: {value}")
    
    print("\n✅ ULTIMATE GPU Accelerator + FINAL BOSS ready!")
    print("🌊 Catches waves at the BEGINNING, not after they've started!")
    print("🏆 5μs quantum computations for ultimate precision!")
    print("⚡ PRE-WAVE + EARLY MOMENTUM + QUANTUM ENHANCEMENT = PERFECTION!")