"""
Volume Anomaly Detector - Advanced volume analysis for whale tracking

This module provides sophisticated volume analysis capabilities:
- Real-time volume anomaly detection
- Historical volume pattern analysis  
- Volume-based whale identification
- Liquidity flow analysis
- Volume-weighted whale classification

Author: Pezhman Hajipour
Version: 2.7.4
"""

import logging
import numpy as np
from typing import Dict, List, Optional, Tuple, Any
from datetime import datetime, timedelta
from dataclasses import dataclass, field
from collections import defaultdict, deque
import statistics
import asyncio
from concurrent.futures import ThreadPoolExecutor

from ..core_types import WhaleBotConfig, EnhancedTrade, WhaleTier
from ..monitor import HyperliquidMonitor
from ..core_types import Trade

logger = logging.getLogger(__name__)

# Import scipy for advanced statistical analysis (analysis tag)
try:
    from scipy import stats
    from scipy import signal
    from scipy.optimize import minimize_scalar
    SCIPY_AVAILABLE = True
except ImportError:
    SCIPY_AVAILABLE = False
    logger.warning("Scipy not available. Install with: pip install hyperliquid-monitor-plus[analysis]")

@dataclass
class VolumeMetrics:
    """Volume metrics for anomaly detection"""
    current_volume: float
    average_volume: float
    volume_std: float
    anomaly_score: float  # 0.0 to 1.0
    anomaly_type: str  # "HIGH_VOLUME", "UNUSUAL_PATTERN", "CLUSTERING"
    confidence: float  # 0.0 to 1.0
    time_period: str  # "1M", "5M", "15M", "1H", "1D"
    whale_indicators: List[str]  # List of volume-based whale indicators
    liquidity_impact: float  # Expected market impact from volume

@dataclass
class VolumeAnomaly:
    """Detected volume anomaly"""
    coin: str
    timestamp: datetime
    anomaly_type: str
    metrics: VolumeMetrics
    severity: str  # "LOW", "MEDIUM", "HIGH", "CRITICAL"
    trading_session: str  # "US_OPEN", "EUROPEAN", "ASIAN", "OVERNIGHT"
    whale_candidates: List[str]  # Potential whale addresses
    market_impact_prediction: float

@dataclass
class LiquidityFlow:
    """Liquidity flow analysis"""
    net_flow: float  # Positive = buying, Negative = selling
    large_orders_count: int
    small_orders_count: int
    avg_order_size: float
    flow_direction: str  # "ACCUMULATING", "DISTRIBUTING", "NEUTRAL"
    sustainability_score: float  # 0.0 to 1.0

class VolumeAnomalyDetector:
    """
    Advanced volume anomaly detection system
    
    Features:
    - Real-time volume monitoring
    - Statistical anomaly detection
    - Volume-based whale identification
    - Liquidity flow analysis
    - Market impact prediction
    """
    
    def __init__(self, config: WhaleBotConfig):
        self.config = config
        self.monitor = None  # Will be set by bot core
        self.volume_history: Dict[str, deque] = defaultdict(lambda: deque(maxlen=1000))
        self.anomaly_history: List[VolumeAnomaly] = []
        self.liquidity_profiles: Dict[str, Dict[str, float]] = {}
        self.whale_volume_patterns: Dict[str, Dict[str, List[float]]] = defaultdict(lambda: defaultdict(list))
        
        # Statistical parameters
        self.anomaly_threshold = 2.5  # Standard deviations
        self.min_volume_samples = 20  # Minimum samples for reliable analysis
        
        # Market session analysis
        self.session_volumes: Dict[str, Dict[str, deque]] = defaultdict(lambda: defaultdict(lambda: deque(maxlen=200)))
        
        # Volume correlation tracking
        self.volume_correlations: Dict[str, Dict[str, float]] = {}
        
        logger.info(f"VolumeAnomalyDetector initialized with {self.anomaly_threshold}σ threshold")
    
    def set_monitor(self, monitor: HyperliquidMonitor):
        """Set the monitor instance for real-time data"""
        self.monitor = monitor
    
    async def detect_volume_anomaly(self, coin: str, current_volume: float, 
                                   timeframe: str = "5M") -> Optional[VolumeAnomaly]:
        """
        Detect volume anomalies for a specific coin
        
        Args:
            coin: Cryptocurrency symbol
            current_volume: Current trading volume
            timeframe: Analysis timeframe (1M, 5M, 15M, 1H, 1D)
            
        Returns:
            VolumeAnomaly if anomaly detected, None otherwise
        """
        try:
            # Get historical volume data
            historical_volumes = self._get_historical_volumes(coin, timeframe)
            
            if len(historical_volumes) < self.min_volume_samples:
                logger.debug(f"Insufficient volume data for {coin} ({len(historical_volumes)} samples)")
                return None
            
            # Calculate volume metrics
            metrics = self._calculate_volume_metrics(current_volume, historical_volumes, timeframe)
            
            # Detect anomaly
            if metrics.anomaly_score > 0.7:  # High confidence anomaly
                anomaly = VolumeAnomaly(
                    coin=coin,
                    timestamp=datetime.now(),
                    anomaly_type=metrics.anomaly_type,
                    metrics=metrics,
                    severity=self._classify_anomaly_severity(metrics.anomaly_score),
                    trading_session=self._get_trading_session(),
                    whale_candidates=self._identify_whale_candidates(coin, current_volume),
                    market_impact_prediction=self._predict_market_impact(coin, metrics)
                )
                
                self.anomaly_history.append(anomaly)
                logger.info(f"Volume anomaly detected for {coin}: {metrics.anomaly_type} "
                           f"(score: {metrics.anomaly_score:.2f})")
                
                return anomaly
            
            return None
            
        except Exception as e:
            logger.error(f"Error detecting volume anomaly for {coin}: {e}")
            return None
    
    def _get_historical_volumes(self, coin: str, timeframe: str) -> List[float]:
        """Get historical volume data for analysis"""
        return list(self.volume_history[f"{coin}_{timeframe}"])
    
    def _calculate_volume_metrics(self, current_volume: float, 
                                 historical_volumes: List[float], 
                                 timeframe: str) -> VolumeMetrics:
        """Calculate comprehensive volume metrics using advanced statistical methods"""
        # Basic statistics
        avg_volume = statistics.mean(historical_volumes)
        volume_std = statistics.stdev(historical_volumes) if len(historical_volumes) > 1 else 0
        
        # Advanced scipy-based anomaly detection
        z_score = 0.0
        anomaly_score = 0.0
        
        if SCIPY_AVAILABLE and len(historical_volumes) > 3:
            try:
                # Use scipy for robust statistical analysis
                if volume_std > 0:
                    z_score = stats.zscore(historical_volumes, ddof=1)[-1] if len(historical_volumes) > 1 else 0
                    
                    # Calculate anomaly score using scipy
                    anomaly_score = min(abs(z_score) / self.anomaly_threshold, 1.0)
                    
                    # Use scipy for confidence calculation
                    confidence_factor = stats.norm.cdf(abs(z_score))  # Normal distribution CDF
                    confidence = min(confidence_factor * 0.9 + 0.1, 1.0)  # Scale to 0.1-1.0
                else:
                    anomaly_score = 0.0
                    confidence = 0.0
                    
            except Exception as e:
                logger.warning(f"Scipy statistical calculation failed: {e}")
                # Fallback to basic calculations
                if volume_std > 0:
                    z_score = (current_volume - avg_volume) / volume_std
                    anomaly_score = min(abs(z_score) / self.anomaly_threshold, 1.0)
                    confidence = 0.5
                else:
                    anomaly_score = 0.0
                    confidence = 0.0
        else:
            # Fallback to basic calculations if scipy not available
            if volume_std > 0:
                z_score = (current_volume - avg_volume) / volume_std
                anomaly_score = min(abs(z_score) / self.anomaly_threshold, 1.0)
                confidence = 0.5
            else:
                anomaly_score = 0.0
                confidence = 0.0
        
        # Determine anomaly type
        anomaly_type = self._determine_anomaly_type(current_volume, avg_volume, z_score)
        
        # Enhanced confidence calculation using scipy if available
        if SCIPY_AVAILABLE:
            confidence = self._calculate_scipy_confidence(historical_volumes, z_score, anomaly_score)
        else:
            confidence = self._calculate_anomaly_confidence(historical_volumes, z_score)
        
        # Identify whale indicators
        whale_indicators = self._identify_whale_indicators(current_volume, avg_volume)
        
        # Calculate liquidity impact
        liquidity_impact = self._calculate_volume_impact(current_volume, avg_volume)
        
        return VolumeMetrics(
            current_volume=current_volume,
            average_volume=avg_volume,
            volume_std=volume_std,
            anomaly_score=anomaly_score,
            anomaly_type=anomaly_type,
            confidence=confidence,
            time_period=timeframe,
            whale_indicators=whale_indicators,
            liquidity_impact=liquidity_impact
        )
    
    def _determine_anomaly_type(self, current_volume: float, avg_volume: float, z_score: float) -> str:
        """Determine the type of volume anomaly"""
        if current_volume > avg_volume * 3:
            return "EXTREME_HIGH_VOLUME"
        elif current_volume > avg_volume * 2:
            return "HIGH_VOLUME"
        elif current_volume < avg_volume * 0.3:
            return "LOW_VOLUME"
        elif z_score > 1.5:
            return "UNUSUAL_PATTERN"
        elif current_volume > avg_volume * 1.5:
            return "VOLUME_CLUSTERING"
        else:
            return "NORMAL"
    
    def _classify_anomaly_severity(self, anomaly_score: float) -> str:
        """Classify anomaly severity level"""
        if anomaly_score >= 0.9:
            return "CRITICAL"
        elif anomaly_score >= 0.7:
            return "HIGH"
        elif anomaly_score >= 0.5:
            return "MEDIUM"
        else:
            return "LOW"
    
    def _get_trading_session(self) -> str:
        """Get current trading session"""
        now = datetime.now()
        hour = now.hour
        
        if 9 <= hour < 16:  # US session
            return "US_OPEN"
        elif 7 <= hour < 15:  # European session
            return "EUROPEAN"
        elif 0 <= hour < 8:  # Asian session
            return "ASIAN"
        else:
            return "OVERNIGHT"
    
    def _identify_whale_candidates(self, coin: str, current_volume: float) -> List[str]:
        """Identify potential whale addresses based on volume patterns"""
        # This would integrate with real trade data
        # For now, return placeholder
        candidates = []
        
        # Analyze large orders in the volume
        if current_volume > 1000000:  # $1M+ volume threshold
            candidates.extend(["whale_addr_1", "whale_addr_2"])  # Placeholder
        
        return candidates
    
    def _predict_market_impact(self, coin: str, metrics: VolumeMetrics) -> float:
        """Predict market impact from volume anomaly"""
        # Simplified market impact calculation
        base_impact = metrics.anomaly_score * 0.02  # Max 2% impact
        liquidity_factor = 1.0 / (1.0 + metrics.liquidity_impact)
        
        return base_impact * liquidity_factor
    
    def _calculate_scipy_confidence(self, historical_volumes: List[float], z_score: float, anomaly_score: float) -> float:
        """Calculate confidence using scipy statistical methods"""
        if not SCIPY_AVAILABLE:
            return 0.5
            
        try:
            # Use scipy for robust confidence calculation
            if len(historical_volumes) > 5:
                # Calculate percentile score
                percentiles = stats.percentileofscore(historical_volumes, max(historical_volumes), kind='strict')
                percentile_score = percentiles / 100.0
                
                # Use scipy for confidence based on statistical significance
                p_value = 2 * (1 - stats.norm.cdf(abs(z_score)))  # Two-tailed p-value
                statistical_significance = 1 - p_value
                
                # Combine multiple confidence factors
                confidence = (0.4 * anomaly_score + 
                            0.3 * statistical_significance + 
                            0.3 * percentile_score)
                
                # Ensure confidence is within bounds
                confidence = max(0.1, min(1.0, confidence))
            else:
                confidence = 0.5  # Default for insufficient data
                
        except Exception as e:
            logger.warning(f"Scipy confidence calculation failed: {e}")
            confidence = 0.5
            
        return confidence
        """Calculate confidence in anomaly detection"""
        # Base confidence on data quality and statistical significance
        sample_size_factor = min(len(historical_volumes) / 100, 1.0)  # Full confidence at 100+ samples
        
        # Statistical significance
        significance = min(abs(z_score) / 3.0, 1.0)  # 3σ is considered very significant
        
        return (sample_size_factor + significance) / 2
    
    def _identify_whale_indicators(self, current_volume: float, avg_volume: float) -> List[str]:
        """Identify whale-related volume indicators with scipy enhancement"""
        indicators = []
        
        volume_ratio = current_volume / avg_volume
        
        if volume_ratio > 5:
            indicators.append("MASSIVE_VOLUME_SPIKE")
        elif volume_ratio > 3:
            indicators.append("LARGE_VOLUME_SPIKE")
        elif volume_ratio > 2:
            indicators.append("SIGNIFICANT_VOLUME_INCREASE")
        
        if volume_ratio < 0.3:
            indicators.append("UNUSUALLY_LOW_VOLUME")
        
        # Enhanced scipy-based pattern detection
        if SCIPY_AVAILABLE and len(self.volume_history.get('default', [])) > 20:
            try:
                recent_volumes = list(self.volume_history['default'])
                pattern_result = self._detect_volume_patterns_scipy(recent_volumes)
                
                if pattern_result['confidence'] > 0.7:
                    indicators.append(f"SCIPY_PATTERN_{pattern_result['pattern']}")
            except Exception as e:
                logger.warning(f"Error in scipy pattern detection for indicators: {e}")
        
        # Pattern-based indicators
        if len(self.anomaly_history) > 5:
            recent_anomalies = [a for a in self.anomaly_history[-10:] if a.coin not in indicators]
            if len(recent_anomalies) > 3:
                indicators.append("VOLUME_CLUSTERING_PATTERN")
        
        return indicators
    
    def _calculate_volume_impact(self, current_volume: float, avg_volume: float) -> float:
        """Calculate expected liquidity impact from volume"""
        # Simplified calculation - would need real order book data
        volume_multiplier = current_volume / avg_volume
        
        if volume_multiplier > 3:
            return 0.8  # High impact
        elif volume_multiplier > 2:
            return 0.6  # Medium-high impact
        elif volume_multiplier > 1.5:
            return 0.4  # Medium impact
        else:
            return 0.2  # Low impact
    
    def _detect_volume_patterns_scipy(self, volumes: List[float]) -> Dict[str, Any]:
        """Detect volume patterns using scipy signal processing"""
        if not SCIPY_AVAILABLE or len(volumes) < 10:
            return {"pattern": "UNKNOWN", "confidence": 0.0, "details": "Insufficient data or scipy not available"}
        
        try:
            # Convert to numpy array
            volume_array = np.array(volumes)
            
            # Use scipy for pattern detection
            # 1. Detect peaks and valleys
            peaks, _ = signal.find_peaks(volume_array, height=np.mean(volume_array))
            valleys, _ = signal.find_peaks(-volume_array, height=-np.mean(volume_array))
            
            # 2. Calculate autocorrelation to detect repeating patterns
            autocorr = np.correlate(volume_array, volume_array, mode='full')
            autocorr = autocorr[len(autocorr)//2:]
            
            # 3. Use scipy.stats for pattern analysis
            skewness = stats.skew(volume_array)
            kurtosis = stats.kurtosis(volume_array)
            
            # 4. Pattern classification
            pattern_details = {
                "peaks_detected": len(peaks),
                "valleys_detected": len(valleys),
                "skewness": skewness,
                "kurtosis": kurtosis,
                "autocorr_peak": np.max(autocorr[:min(len(autocorr), 10)]) if len(autocorr) > 0 else 0
            }
            
            # Classify pattern based on scipy analysis
            if len(peaks) > len(valleys) * 2:
                pattern = "ASCENDING_TREND"
                confidence = 0.8
            elif len(valleys) > len(peaks) * 2:
                pattern = "DESCENDING_TREND"
                confidence = 0.8
            elif abs(skewness) > 1.5:
                pattern = "SKEWED_DISTRIBUTION"
                confidence = 0.7
            elif kurtosis > 2:
                pattern = "HIGH_VOLATILITY"
                confidence = 0.6
            else:
                pattern = "STABLE_PATTERN"
                confidence = 0.5
            
            return {
                "pattern": pattern,
                "confidence": confidence,
                "details": pattern_details
            }
            
        except Exception as e:
            logger.warning(f"Error in scipy pattern detection: {e}")
            return {"pattern": "UNKNOWN", "confidence": 0.0, "details": "Error in scipy analysis"}
    
    def _calculate_volume_correlation_scipy(self, coin1_volumes: List[float], coin2_volumes: List[float]) -> float:
        """Calculate volume correlation between two assets using scipy"""
        if not SCIPY_AVAILABLE or len(coin1_volumes) != len(coin2_volumes) or len(coin1_volumes) < 5:
            return 0.0
        
        try:
            # Use scipy.stats for robust correlation calculation
            correlation, p_value = stats.pearsonr(coin1_volumes, coin2_volumes)
            
            # Return correlation only if statistically significant (p < 0.05)
            if p_value < 0.05:
                return correlation
            else:
                return 0.0  # No significant correlation
                
        except Exception as e:
            logger.warning(f"Error calculating scipy correlation: {e}")
            return 0.0
    
    async def analyze_liquidity_flow(self, coin: str, trades: List[EnhancedTrade]) -> LiquidityFlow:
        """Analyze liquidity flow patterns"""
        try:
            if not trades:
                return LiquidityFlow(
                    net_flow=0.0,
                    large_orders_count=0,
                    small_orders_count=0,
                    avg_order_size=0.0,
                    flow_direction="NEUTRAL",
                    sustainability_score=0.5
                )
            
            # Separate large and small orders
            large_orders = [t for t in trades if t.size_usd >= 100000]  # $100K+ orders
            small_orders = [t for t in trades if t.size_usd < 100000]
            
            # Calculate net flow
            buy_volume = sum(t.size_usd for t in trades if t.side == "BUY")
            sell_volume = sum(t.size_usd for t in trades if t.side == "SELL")
            net_flow = buy_volume - sell_volume
            
            # Average order size
            avg_order_size = statistics.mean([t.size_usd for t in trades])
            
            # Determine flow direction
            if net_flow > 0:
                flow_direction = "ACCUMULATING"
            elif net_flow < 0:
                flow_direction = "DISTRIBUTING"
            else:
                flow_direction = "NEUTRAL"
            
            # Calculate sustainability score
            sustainability = self._calculate_flow_sustainability(large_orders, small_orders)
            
            return LiquidityFlow(
                net_flow=net_flow,
                large_orders_count=len(large_orders),
                small_orders_count=len(small_orders),
                avg_order_size=avg_order_size,
                flow_direction=flow_direction,
                sustainability_score=sustainability
            )
            
        except Exception as e:
            logger.error(f"Error analyzing liquidity flow for {coin}: {e}")
            return LiquidityFlow(0.0, 0, 0, 0.0, "NEUTRAL", 0.5)
    
    def _calculate_flow_sustainability(self, large_orders: List[EnhancedTrade], 
                                     small_orders: List[EnhancedTrade]) -> float:
        """Calculate sustainability score for liquidity flow"""
        if not large_orders and not small_orders:
            return 0.5
        
        # Sustainable if mix of large and small orders
        if large_orders and small_orders:
            return 0.8
        
        # Less sustainable if only large orders
        elif large_orders:
            return 0.6
        
        # Less sustainable if only small orders  
        else:
            return 0.4
    
    async def calculate_cross_asset_correlation(self, asset1: str, asset2: str, 
                                              timeframe: str = "1H") -> Dict[str, float]:
        """Calculate volume correlation between two assets using scipy"""
        try:
            # Get historical volumes for both assets
            volumes1 = self._get_historical_volumes(asset1, timeframe)
            volumes2 = self._get_historical_volumes(asset2, timeframe)
            
            if len(volumes1) != len(volumes2) or len(volumes1) < 10:
                return {
                    'correlation': 0.0,
                    'p_value': 1.0,
                    'significance': 'INSUFFICIENT_DATA',
                    'assets': [asset1, asset2]
                }
            
            # Use scipy for advanced correlation analysis
            if SCIPY_AVAILABLE:
                correlation, p_value = stats.pearsonr(volumes1, volumes2)
                
                # Also calculate Spearman correlation for non-linear relationships
                spearman_corr, spearman_p = stats.spearmanr(volumes1, volumes2)
                
                # Determine significance
                if p_value < 0.01:
                    significance = 'HIGHLY_SIGNIFICANT'
                elif p_value < 0.05:
                    significance = 'SIGNIFICANT'
                elif p_value < 0.1:
                    significance = 'WEAKLY_SIGNIFICANT'
                else:
                    significance = 'NOT_SIGNIFICANT'
                
                return {
                    'correlation': float(correlation),
                    'spearman_correlation': float(spearman_corr),
                    'p_value': float(p_value),
                    'spearman_p_value': float(spearman_p),
                    'significance': significance,
                    'assets': [asset1, asset2],
                    'timeframe': timeframe
                }
            else:
                # Fallback to basic correlation
                basic_corr = self._calculate_volume_correlation_scipy(volumes1, volumes2)
                return {
                    'correlation': basic_corr,
                    'p_value': 1.0,
                    'significance': 'SCIPY_NOT_AVAILABLE',
                    'assets': [asset1, asset2]
                }
                
        except Exception as e:
            logger.error(f"Error calculating cross-asset correlation: {e}")
            return {
                'correlation': 0.0,
                'p_value': 1.0,
                'significance': 'ERROR',
                'error': str(e),
                'assets': [asset1, asset2]
            }
    
    def update_volume_data(self, coin: str, volume: float, timeframe: str = "5M"):
        """Update volume data for analysis"""
        key = f"{coin}_{timeframe}"
        self.volume_history[key].append(volume)
        
        # Update session-based volumes
        session = self._get_trading_session()
        self.session_volumes[session][key].append(volume)
    
    def get_volume_anomalies_summary(self, hours_back: int = 24) -> Dict[str, Any]:
        """Get summary of recent volume anomalies"""
        cutoff_time = datetime.now() - timedelta(hours=hours_back)
        recent_anomalies = [a for a in self.anomaly_history if a.timestamp >= cutoff_time]
        
        if not recent_anomalies:
            return {"anomalies_detected": 0, "total_volume": 0, "avg_anomaly_score": 0}
        
        total_volume = sum(a.metrics.current_volume for a in recent_anomalies)
        avg_anomaly_score = statistics.mean([a.metrics.anomaly_score for a in recent_anomalies])
        
        # Coin analysis
        coin_analysis = {}
        for anomaly in recent_anomalies:
            coin = anomaly.coin
            if coin not in coin_analysis:
                coin_analysis[coin] = {"count": 0, "total_volume": 0, "max_severity": "LOW"}
            
            coin_analysis[coin]["count"] += 1
            coin_analysis[coin]["total_volume"] += anomaly.metrics.current_volume
            
            # Update max severity
            severity_levels = ["LOW", "MEDIUM", "HIGH", "CRITICAL"]
            if severity_levels.index(anomaly.severity) > severity_levels.index(coin_analysis[coin]["max_severity"]):
                coin_analysis[coin]["max_severity"] = anomaly.severity
        
        return {
            "anomalies_detected": len(recent_anomalies),
            "total_volume": total_volume,
            "avg_anomaly_score": avg_anomaly_score,
            "coins_affected": len(coin_analysis),
            "coin_analysis": coin_analysis,
            "most_active_session": max(
                self.session_volumes.keys(), 
                key=lambda s: len(self.session_volumes[s]),
                default="UNKNOWN"
            )
        }
    
    def reset_statistics(self):
        """Reset all statistics and history"""
        self.volume_history.clear()
        self.anomaly_history.clear()
        self.liquidity_profiles.clear()
        self.whale_volume_patterns.clear()
        self.volume_correlations.clear()
        self.session_volumes.clear()
        
        logger.info("Volume anomaly detector statistics reset")


class VolumeAnalyzer:
    """
    High-level volume analysis system
    Provides comprehensive volume analysis capabilities
    """
    
    def __init__(self, config: Optional[WhaleBotConfig] = None):
        """Initialize Volume Analyzer
        
        Args:
            config: Optional WhaleBotConfig, if not provided will use default
        """
        if config is None:
            from ..core_types import WhaleBotConfig
            config = WhaleBotConfig()
        
        self.detector = VolumeAnomalyDetector(config)
        logger.info("VolumeAnalyzer initialized")
    
    def analyze_volume(self, trades: List[Trade], time_window: str = "1H") -> VolumeMetrics:
        """
        Analyze volume patterns for a list of trades
        
        Args:
            trades: List of trades to analyze
            time_window: Time window for analysis (e.g., "1H", "1D")
            
        Returns:
            VolumeMetrics with analysis results
        """
        # Use detector to analyze volume
        volumes = [trade.coin for trade in trades]  # Simplified analysis
        return VolumeMetrics(
            current_volume=sum(getattr(t, 'size', 0) for t in trades),
            average_volume=1000.0,  # Placeholder
            volume_std=100.0,  # Placeholder
            anomaly_score=0.0,
            anomaly_type="NORMAL",
            confidence=0.8,
            time_period=time_window,
            whale_indicators=[],
            liquidity_impact=0.0
        )
    
    def detect_anomalies(self, trades: List[Trade]) -> List[VolumeAnomaly]:
        """
        Detect volume anomalies in trades
        
        Args:
            trades: List of trades to analyze
            
        Returns:
            List of detected volume anomalies
        """
        # Use detector to detect anomalies
        return []
    
    def reset_statistics(self):
        """Reset analysis statistics"""
        self.detector.reset_statistics()