wip

.kiro/specs/multi-exchange-data-aggregation/tasks.md

@@ -37,6 +37,9 @@
 
 - [ ] 4. Implement Binance exchange connector
   - Create Binance-specific WebSocket connector extending the base framework
+
+
+
   - Implement order book depth stream subscription and processing
   - Add trade stream subscription for volume analysis
   - Implement data normalization from Binance format to standard format

COBY/aggregation/__init__.py

@@ -0,0 +1,15 @@
+"""
+Data aggregation components for the COBY system.
+"""
+
+from .aggregation_engine import StandardAggregationEngine
+from .price_bucketer import PriceBucketer
+from .heatmap_generator import HeatmapGenerator
+from .cross_exchange_aggregator import CrossExchangeAggregator
+
+__all__ = [
+    'StandardAggregationEngine',
+    'PriceBucketer',
+    'HeatmapGenerator',
+    'CrossExchangeAggregator'
+]

COBY/aggregation/aggregation_engine.py

@@ -0,0 +1,338 @@
+"""
+Main aggregation engine implementation.
+"""
+
+from typing import Dict, List
+from ..interfaces.aggregation_engine import AggregationEngine
+from ..models.core import (
+    OrderBookSnapshot, PriceBuckets, HeatmapData, 
+    ImbalanceMetrics, ConsolidatedOrderBook
+)
+from ..utils.logging import get_logger, set_correlation_id
+from ..utils.exceptions import AggregationError
+from .price_bucketer import PriceBucketer
+from .heatmap_generator import HeatmapGenerator
+from .cross_exchange_aggregator import CrossExchangeAggregator
+from ..processing.metrics_calculator import MetricsCalculator
+
+logger = get_logger(__name__)
+
+
+class StandardAggregationEngine(AggregationEngine):
+    """
+    Standard implementation of aggregation engine interface.
+    
+    Provides:
+    - Price bucket creation with $1 USD buckets
+    - Heatmap generation
+    - Cross-exchange aggregation
+    - Imbalance calculations
+    - Support/resistance detection
+    """
+    
+    def __init__(self):
+        """Initialize aggregation engine with components"""
+        self.price_bucketer = PriceBucketer()
+        self.heatmap_generator = HeatmapGenerator()
+        self.cross_exchange_aggregator = CrossExchangeAggregator()
+        self.metrics_calculator = MetricsCalculator()
+        
+        # Processing statistics
+        self.buckets_created = 0
+        self.heatmaps_generated = 0
+        self.consolidations_performed = 0
+        
+        logger.info("Standard aggregation engine initialized")
+    
+    def create_price_buckets(self, orderbook: OrderBookSnapshot, 
+                           bucket_size: float = None) -> PriceBuckets:
+        """
+        Convert order book data to price buckets.
+        
+        Args:
+            orderbook: Order book snapshot
+            bucket_size: Size of each price bucket (uses $1 default)
+            
+        Returns:
+            PriceBuckets: Aggregated price bucket data
+        """
+        try:
+            set_correlation_id()
+            
+            # Use provided bucket size or default $1
+            if bucket_size:
+                bucketer = PriceBucketer(bucket_size)
+            else:
+                bucketer = self.price_bucketer
+            
+            buckets = bucketer.create_price_buckets(orderbook)
+            self.buckets_created += 1
+            
+            logger.debug(f"Created price buckets for {orderbook.symbol}@{orderbook.exchange}")
+            return buckets
+            
+        except Exception as e:
+            logger.error(f"Error creating price buckets: {e}")
+            raise AggregationError(f"Price bucket creation failed: {e}", "BUCKET_ERROR")
+    
+    def update_heatmap(self, symbol: str, buckets: PriceBuckets) -> HeatmapData:
+        """
+        Update heatmap data with new price buckets.
+        
+        Args:
+            symbol: Trading symbol
+            buckets: Price bucket data
+            
+        Returns:
+            HeatmapData: Updated heatmap visualization data
+        """
+        try:
+            set_correlation_id()
+            
+            heatmap = self.heatmap_generator.generate_heatmap(buckets)
+            self.heatmaps_generated += 1
+            
+            logger.debug(f"Generated heatmap for {symbol}: {len(heatmap.data)} points")
+            return heatmap
+            
+        except Exception as e:
+            logger.error(f"Error updating heatmap: {e}")
+            raise AggregationError(f"Heatmap update failed: {e}", "HEATMAP_ERROR")
+    
+    def calculate_imbalances(self, orderbook: OrderBookSnapshot) -> ImbalanceMetrics:
+        """
+        Calculate order book imbalance metrics.
+        
+        Args:
+            orderbook: Order book snapshot
+            
+        Returns:
+            ImbalanceMetrics: Calculated imbalance metrics
+        """
+        try:
+            set_correlation_id()
+            
+            return self.metrics_calculator.calculate_imbalance_metrics(orderbook)
+            
+        except Exception as e:
+            logger.error(f"Error calculating imbalances: {e}")
+            raise AggregationError(f"Imbalance calculation failed: {e}", "IMBALANCE_ERROR")
+    
+    def aggregate_across_exchanges(self, symbol: str, 
+                                 orderbooks: List[OrderBookSnapshot]) -> ConsolidatedOrderBook:
+        """
+        Aggregate order book data from multiple exchanges.
+        
+        Args:
+            symbol: Trading symbol
+            orderbooks: List of order book snapshots from different exchanges
+            
+        Returns:
+            ConsolidatedOrderBook: Consolidated order book data
+        """
+        try:
+            set_correlation_id()
+            
+            consolidated = self.cross_exchange_aggregator.aggregate_across_exchanges(
+                symbol, orderbooks
+            )
+            self.consolidations_performed += 1
+            
+            logger.debug(f"Consolidated {len(orderbooks)} order books for {symbol}")
+            return consolidated
+            
+        except Exception as e:
+            logger.error(f"Error aggregating across exchanges: {e}")
+            raise AggregationError(f"Cross-exchange aggregation failed: {e}", "CONSOLIDATION_ERROR")    
+   
+ def calculate_volume_weighted_price(self, orderbooks: List[OrderBookSnapshot]) -> float:
+        """
+        Calculate volume-weighted average price across exchanges.
+        
+        Args:
+            orderbooks: List of order book snapshots
+            
+        Returns:
+            float: Volume-weighted average price
+        """
+        try:
+            set_correlation_id()
+            
+            return self.cross_exchange_aggregator._calculate_weighted_mid_price(orderbooks)
+            
+        except Exception as e:
+            logger.error(f"Error calculating volume weighted price: {e}")
+            raise AggregationError(f"VWAP calculation failed: {e}", "VWAP_ERROR")
+    
+    def get_market_depth(self, orderbook: OrderBookSnapshot, 
+                        depth_levels: List[float]) -> Dict[float, Dict[str, float]]:
+        """
+        Calculate market depth at different price levels.
+        
+        Args:
+            orderbook: Order book snapshot
+            depth_levels: List of depth percentages (e.g., [0.1, 0.5, 1.0])
+            
+        Returns:
+            Dict: Market depth data {level: {'bid_volume': x, 'ask_volume': y}}
+        """
+        try:
+            set_correlation_id()
+            
+            depth_data = {}
+            
+            if not orderbook.mid_price:
+                return depth_data
+            
+            for level_pct in depth_levels:
+                # Calculate price range for this depth level
+                price_range = orderbook.mid_price * (level_pct / 100.0)
+                min_bid_price = orderbook.mid_price - price_range
+                max_ask_price = orderbook.mid_price + price_range
+                
+                # Calculate volumes within this range
+                bid_volume = sum(
+                    bid.size for bid in orderbook.bids 
+                    if bid.price >= min_bid_price
+                )
+                
+                ask_volume = sum(
+                    ask.size for ask in orderbook.asks 
+                    if ask.price <= max_ask_price
+                )
+                
+                depth_data[level_pct] = {
+                    'bid_volume': bid_volume,
+                    'ask_volume': ask_volume,
+                    'total_volume': bid_volume + ask_volume
+                }
+            
+            logger.debug(f"Calculated market depth for {len(depth_levels)} levels")
+            return depth_data
+            
+        except Exception as e:
+            logger.error(f"Error calculating market depth: {e}")
+            return {}
+    
+    def smooth_heatmap(self, heatmap: HeatmapData, smoothing_factor: float) -> HeatmapData:
+        """
+        Apply smoothing to heatmap data to reduce noise.
+        
+        Args:
+            heatmap: Raw heatmap data
+            smoothing_factor: Smoothing factor (0.0 to 1.0)
+            
+        Returns:
+            HeatmapData: Smoothed heatmap data
+        """
+        try:
+            set_correlation_id()
+            
+            return self.heatmap_generator.apply_smoothing(heatmap, smoothing_factor)
+            
+        except Exception as e:
+            logger.error(f"Error smoothing heatmap: {e}")
+            return heatmap  # Return original on error
+    
+    def calculate_liquidity_score(self, orderbook: OrderBookSnapshot) -> float:
+        """
+        Calculate liquidity score for an order book.
+        
+        Args:
+            orderbook: Order book snapshot
+            
+        Returns:
+            float: Liquidity score (0.0 to 1.0)
+        """
+        try:
+            set_correlation_id()
+            
+            return self.metrics_calculator.calculate_liquidity_score(orderbook)
+            
+        except Exception as e:
+            logger.error(f"Error calculating liquidity score: {e}")
+            return 0.0
+    
+    def detect_support_resistance(self, heatmap: HeatmapData) -> Dict[str, List[float]]:
+        """
+        Detect support and resistance levels from heatmap data.
+        
+        Args:
+            heatmap: Heatmap data
+            
+        Returns:
+            Dict: {'support': [prices], 'resistance': [prices]}
+        """
+        try:
+            set_correlation_id()
+            
+            return self.heatmap_generator.calculate_support_resistance(heatmap)
+            
+        except Exception as e:
+            logger.error(f"Error detecting support/resistance: {e}")
+            return {'support': [], 'resistance': []}
+    
+    def create_consolidated_heatmap(self, symbol: str, 
+                                  orderbooks: List[OrderBookSnapshot]) -> HeatmapData:
+        """
+        Create consolidated heatmap from multiple exchanges.
+        
+        Args:
+            symbol: Trading symbol
+            orderbooks: List of order book snapshots
+            
+        Returns:
+            HeatmapData: Consolidated heatmap data
+        """
+        try:
+            set_correlation_id()
+            
+            return self.cross_exchange_aggregator.create_consolidated_heatmap(
+                symbol, orderbooks
+            )
+            
+        except Exception as e:
+            logger.error(f"Error creating consolidated heatmap: {e}")
+            raise AggregationError(f"Consolidated heatmap creation failed: {e}", "CONSOLIDATED_HEATMAP_ERROR")
+    
+    def detect_arbitrage_opportunities(self, orderbooks: List[OrderBookSnapshot]) -> List[Dict]:
+        """
+        Detect arbitrage opportunities between exchanges.
+        
+        Args:
+            orderbooks: List of order book snapshots
+            
+        Returns:
+            List[Dict]: Arbitrage opportunities
+        """
+        try:
+            set_correlation_id()
+            
+            return self.cross_exchange_aggregator.detect_arbitrage_opportunities(orderbooks)
+            
+        except Exception as e:
+            logger.error(f"Error detecting arbitrage opportunities: {e}")
+            return []
+    
+    def get_processing_stats(self) -> Dict[str, any]:
+        """Get processing statistics"""
+        return {
+            'buckets_created': self.buckets_created,
+            'heatmaps_generated': self.heatmaps_generated,
+            'consolidations_performed': self.consolidations_performed,
+            'price_bucketer_stats': self.price_bucketer.get_processing_stats(),
+            'heatmap_generator_stats': self.heatmap_generator.get_processing_stats(),
+            'cross_exchange_stats': self.cross_exchange_aggregator.get_processing_stats()
+        }
+    
+    def reset_stats(self) -> None:
+        """Reset processing statistics"""
+        self.buckets_created = 0
+        self.heatmaps_generated = 0
+        self.consolidations_performed = 0
+        
+        self.price_bucketer.reset_stats()
+        self.heatmap_generator.reset_stats()
+        self.cross_exchange_aggregator.reset_stats()
+        
+        logger.info("Aggregation engine statistics reset")

COBY/aggregation/cross_exchange_aggregator.py

@@ -0,0 +1,390 @@
+"""
+Cross-exchange data aggregation and consolidation.
+"""
+
+from typing import List, Dict, Optional
+from collections import defaultdict
+from datetime import datetime
+from ..models.core import (
+    OrderBookSnapshot, ConsolidatedOrderBook, PriceLevel, 
+    PriceBuckets, HeatmapData, HeatmapPoint
+)
+from ..utils.logging import get_logger
+from ..utils.timing import get_current_timestamp
+from .price_bucketer import PriceBucketer
+from .heatmap_generator import HeatmapGenerator
+
+logger = get_logger(__name__)
+
+
+class CrossExchangeAggregator:
+    """
+    Aggregates data across multiple exchanges.
+    
+    Provides consolidated order books and cross-exchange heatmaps.
+    """
+    
+    def __init__(self):
+        """Initialize cross-exchange aggregator"""
+        self.price_bucketer = PriceBucketer()
+        self.heatmap_generator = HeatmapGenerator()
+        
+        # Exchange weights for aggregation
+        self.exchange_weights = {
+            'binance': 1.0,
+            'coinbase': 0.9,
+            'kraken': 0.8,
+            'bybit': 0.7,
+            'okx': 0.7,
+            'huobi': 0.6,
+            'kucoin': 0.6,
+            'gateio': 0.5,
+            'bitfinex': 0.5,
+            'mexc': 0.4
+        }
+        
+        # Statistics
+        self.consolidations_performed = 0
+        self.exchanges_processed = set()
+        
+        logger.info("Cross-exchange aggregator initialized")
+    
+    def aggregate_across_exchanges(self, symbol: str, 
+                                 orderbooks: List[OrderBookSnapshot]) -> ConsolidatedOrderBook:
+        """
+        Aggregate order book data from multiple exchanges.
+        
+        Args:
+            symbol: Trading symbol
+            orderbooks: List of order book snapshots from different exchanges
+            
+        Returns:
+            ConsolidatedOrderBook: Consolidated order book data
+        """
+        if not orderbooks:
+            raise ValueError("Cannot aggregate empty orderbook list")
+        
+        try:
+            # Track exchanges
+            exchanges = [ob.exchange for ob in orderbooks]
+            self.exchanges_processed.update(exchanges)
+            
+            # Calculate weighted mid price
+            weighted_mid_price = self._calculate_weighted_mid_price(orderbooks)
+            
+            # Consolidate bids and asks
+            consolidated_bids = self._consolidate_price_levels(
+                [ob.bids for ob in orderbooks], 
+                [ob.exchange for ob in orderbooks],
+                'bid'
+            )
+            
+            consolidated_asks = self._consolidate_price_levels(
+                [ob.asks for ob in orderbooks],
+                [ob.exchange for ob in orderbooks], 
+                'ask'
+            )
+            
+            # Calculate total volumes
+            total_bid_volume = sum(level.size for level in consolidated_bids)
+            total_ask_volume = sum(level.size for level in consolidated_asks)
+            
+            # Create consolidated order book
+            consolidated = ConsolidatedOrderBook(
+                symbol=symbol,
+                timestamp=get_current_timestamp(),
+                exchanges=exchanges,
+                bids=consolidated_bids,
+                asks=consolidated_asks,
+                weighted_mid_price=weighted_mid_price,
+                total_bid_volume=total_bid_volume,
+                total_ask_volume=total_ask_volume,
+                exchange_weights={ex: self.exchange_weights.get(ex, 0.5) for ex in exchanges}
+            )
+            
+            self.consolidations_performed += 1
+            
+            logger.debug(
+                f"Consolidated {len(orderbooks)} order books for {symbol}: "
+                f"{len(consolidated_bids)} bids, {len(consolidated_asks)} asks"
+            )
+            
+            return consolidated
+            
+        except Exception as e:
+            logger.error(f"Error aggregating across exchanges: {e}")
+            raise    
+
+    def create_consolidated_heatmap(self, symbol: str, 
+                                  orderbooks: List[OrderBookSnapshot]) -> HeatmapData:
+        """
+        Create consolidated heatmap from multiple exchanges.
+        
+        Args:
+            symbol: Trading symbol
+            orderbooks: List of order book snapshots
+            
+        Returns:
+            HeatmapData: Consolidated heatmap data
+        """
+        try:
+            # Create price buckets for each exchange
+            all_buckets = []
+            for orderbook in orderbooks:
+                buckets = self.price_bucketer.create_price_buckets(orderbook)
+                all_buckets.append(buckets)
+            
+            # Aggregate all buckets
+            if len(all_buckets) == 1:
+                consolidated_buckets = all_buckets[0]
+            else:
+                consolidated_buckets = self.price_bucketer.aggregate_buckets(all_buckets)
+            
+            # Generate heatmap from consolidated buckets
+            heatmap = self.heatmap_generator.generate_heatmap(consolidated_buckets)
+            
+            # Add exchange metadata to heatmap points
+            self._add_exchange_metadata(heatmap, orderbooks)
+            
+            logger.debug(f"Created consolidated heatmap for {symbol} from {len(orderbooks)} exchanges")
+            return heatmap
+            
+        except Exception as e:
+            logger.error(f"Error creating consolidated heatmap: {e}")
+            raise
+    
+    def _calculate_weighted_mid_price(self, orderbooks: List[OrderBookSnapshot]) -> float:
+        """Calculate volume-weighted mid price across exchanges"""
+        total_weight = 0.0
+        weighted_sum = 0.0
+        
+        for orderbook in orderbooks:
+            if orderbook.mid_price:
+                # Use total volume as weight
+                volume_weight = orderbook.bid_volume + orderbook.ask_volume
+                exchange_weight = self.exchange_weights.get(orderbook.exchange, 0.5)
+                
+                # Combined weight
+                weight = volume_weight * exchange_weight
+                
+                weighted_sum += orderbook.mid_price * weight
+                total_weight += weight
+        
+        return weighted_sum / total_weight if total_weight > 0 else 0.0
+    
+    def _consolidate_price_levels(self, level_lists: List[List[PriceLevel]], 
+                                exchanges: List[str], side: str) -> List[PriceLevel]:
+        """Consolidate price levels from multiple exchanges"""
+        # Group levels by price bucket
+        price_groups = defaultdict(lambda: {'size': 0.0, 'count': 0, 'exchanges': set()})
+        
+        for levels, exchange in zip(level_lists, exchanges):
+            exchange_weight = self.exchange_weights.get(exchange, 0.5)
+            
+            for level in levels:
+                # Round price to bucket
+                bucket_price = self.price_bucketer.get_bucket_price(level.price)
+                
+                # Add weighted volume
+                weighted_size = level.size * exchange_weight
+                price_groups[bucket_price]['size'] += weighted_size
+                price_groups[bucket_price]['count'] += level.count or 1
+                price_groups[bucket_price]['exchanges'].add(exchange)
+        
+        # Create consolidated price levels
+        consolidated_levels = []
+        for price, data in price_groups.items():
+            if data['size'] > 0:  # Only include non-zero volumes
+                level = PriceLevel(
+                    price=price,
+                    size=data['size'],
+                    count=data['count']
+                )
+                consolidated_levels.append(level)
+        
+        # Sort levels appropriately
+        if side == 'bid':
+            consolidated_levels.sort(key=lambda x: x.price, reverse=True)
+        else:
+            consolidated_levels.sort(key=lambda x: x.price)
+        
+        return consolidated_levels
+    
+    def _add_exchange_metadata(self, heatmap: HeatmapData, 
+                             orderbooks: List[OrderBookSnapshot]) -> None:
+        """Add exchange metadata to heatmap points"""
+        # Create exchange mapping by price bucket
+        exchange_map = defaultdict(set)
+        
+        for orderbook in orderbooks:
+            # Map bid prices to exchanges
+            for bid in orderbook.bids:
+                bucket_price = self.price_bucketer.get_bucket_price(bid.price)
+                exchange_map[bucket_price].add(orderbook.exchange)
+            
+            # Map ask prices to exchanges
+            for ask in orderbook.asks:
+                bucket_price = self.price_bucketer.get_bucket_price(ask.price)
+                exchange_map[bucket_price].add(orderbook.exchange)
+        
+        # Add exchange information to heatmap points
+        for point in heatmap.data:
+            bucket_price = self.price_bucketer.get_bucket_price(point.price)
+            # Store exchange info in a custom attribute (would need to extend HeatmapPoint)
+            # For now, we'll log it
+            exchanges_at_price = exchange_map.get(bucket_price, set())
+            if len(exchanges_at_price) > 1:
+                logger.debug(f"Price {point.price} has data from {len(exchanges_at_price)} exchanges")
+    
+    def calculate_exchange_dominance(self, orderbooks: List[OrderBookSnapshot]) -> Dict[str, float]:
+        """
+        Calculate which exchanges dominate at different price levels.
+        
+        Args:
+            orderbooks: List of order book snapshots
+            
+        Returns:
+            Dict[str, float]: Exchange dominance scores
+        """
+        exchange_volumes = defaultdict(float)
+        total_volume = 0.0
+        
+        for orderbook in orderbooks:
+            volume = orderbook.bid_volume + orderbook.ask_volume
+            exchange_volumes[orderbook.exchange] += volume
+            total_volume += volume
+        
+        # Calculate dominance percentages
+        dominance = {}
+        for exchange, volume in exchange_volumes.items():
+            dominance[exchange] = (volume / total_volume * 100) if total_volume > 0 else 0.0
+        
+        return dominance
+    
+    def detect_arbitrage_opportunities(self, orderbooks: List[OrderBookSnapshot], 
+                                     min_spread_pct: float = 0.1) -> List[Dict]:
+        """
+        Detect potential arbitrage opportunities between exchanges.
+        
+        Args:
+            orderbooks: List of order book snapshots
+            min_spread_pct: Minimum spread percentage to consider
+            
+        Returns:
+            List[Dict]: Arbitrage opportunities
+        """
+        opportunities = []
+        
+        if len(orderbooks) < 2:
+            return opportunities
+        
+        try:
+            # Find best bid and ask across exchanges
+            best_bids = []
+            best_asks = []
+            
+            for orderbook in orderbooks:
+                if orderbook.bids and orderbook.asks:
+                    best_bids.append({
+                        'exchange': orderbook.exchange,
+                        'price': orderbook.bids[0].price,
+                        'size': orderbook.bids[0].size
+                    })
+                    best_asks.append({
+                        'exchange': orderbook.exchange,
+                        'price': orderbook.asks[0].price,
+                        'size': orderbook.asks[0].size
+                    })
+            
+            # Sort to find best opportunities
+            best_bids.sort(key=lambda x: x['price'], reverse=True)
+            best_asks.sort(key=lambda x: x['price'])
+            
+            # Check for arbitrage opportunities
+            for bid in best_bids:
+                for ask in best_asks:
+                    if bid['exchange'] != ask['exchange'] and bid['price'] > ask['price']:
+                        spread = bid['price'] - ask['price']
+                        spread_pct = (spread / ask['price']) * 100
+                        
+                        if spread_pct >= min_spread_pct:
+                            opportunities.append({
+                                'buy_exchange': ask['exchange'],
+                                'sell_exchange': bid['exchange'],
+                                'buy_price': ask['price'],
+                                'sell_price': bid['price'],
+                                'spread': spread,
+                                'spread_percentage': spread_pct,
+                                'max_size': min(bid['size'], ask['size'])
+                            })
+            
+            # Sort by spread percentage
+            opportunities.sort(key=lambda x: x['spread_percentage'], reverse=True)
+            
+            if opportunities:
+                logger.info(f"Found {len(opportunities)} arbitrage opportunities")
+            
+            return opportunities
+            
+        except Exception as e:
+            logger.error(f"Error detecting arbitrage opportunities: {e}")
+            return []
+    
+    def get_exchange_correlation(self, orderbooks: List[OrderBookSnapshot]) -> Dict[str, Dict[str, float]]:
+        """
+        Calculate price correlation between exchanges.
+        
+        Args:
+            orderbooks: List of order book snapshots
+            
+        Returns:
+            Dict: Correlation matrix between exchanges
+        """
+        correlations = {}
+        
+        # Extract mid prices by exchange
+        exchange_prices = {}
+        for orderbook in orderbooks:
+            if orderbook.mid_price:
+                exchange_prices[orderbook.exchange] = orderbook.mid_price
+        
+        # Calculate simple correlation (would need historical data for proper correlation)
+        exchanges = list(exchange_prices.keys())
+        for i, exchange1 in enumerate(exchanges):
+            correlations[exchange1] = {}
+            for j, exchange2 in enumerate(exchanges):
+                if i == j:
+                    correlations[exchange1][exchange2] = 1.0
+                else:
+                    # Simple price difference as correlation proxy
+                    price1 = exchange_prices[exchange1]
+                    price2 = exchange_prices[exchange2]
+                    diff_pct = abs(price1 - price2) / max(price1, price2) * 100
+                    # Convert to correlation-like score (lower difference = higher correlation)
+                    correlation = max(0.0, 1.0 - (diff_pct / 10.0))
+                    correlations[exchange1][exchange2] = correlation
+        
+        return correlations
+    
+    def get_processing_stats(self) -> Dict[str, int]:
+        """Get processing statistics"""
+        return {
+            'consolidations_performed': self.consolidations_performed,
+            'unique_exchanges_processed': len(self.exchanges_processed),
+            'exchanges_processed': list(self.exchanges_processed),
+            'bucketer_stats': self.price_bucketer.get_processing_stats(),
+            'heatmap_stats': self.heatmap_generator.get_processing_stats()
+        }
+    
+    def update_exchange_weights(self, new_weights: Dict[str, float]) -> None:
+        """Update exchange weights for aggregation"""
+        self.exchange_weights.update(new_weights)
+        logger.info(f"Updated exchange weights: {new_weights}")
+    
+    def reset_stats(self) -> None:
+        """Reset processing statistics"""
+        self.consolidations_performed = 0
+        self.exchanges_processed.clear()
+        self.price_bucketer.reset_stats()
+        self.heatmap_generator.reset_stats()
+        logger.info("Cross-exchange aggregator statistics reset")

COBY/aggregation/heatmap_generator.py

@@ -0,0 +1,376 @@
+"""
+Heatmap data generation from price buckets.
+"""
+
+from typing import List, Dict, Optional, Tuple
+from ..models.core import PriceBuckets, HeatmapData, HeatmapPoint
+from ..config import config
+from ..utils.logging import get_logger
+
+logger = get_logger(__name__)
+
+
+class HeatmapGenerator:
+    """
+    Generates heatmap visualization data from price buckets.
+    
+    Creates intensity-based heatmap points for visualization.
+    """
+    
+    def __init__(self):
+        """Initialize heatmap generator"""
+        self.heatmaps_generated = 0
+        self.total_points_created = 0
+        
+        logger.info("Heatmap generator initialized")
+    
+    def generate_heatmap(self, buckets: PriceBuckets, 
+                        max_points: Optional[int] = None) -> HeatmapData:
+        """
+        Generate heatmap data from price buckets.
+        
+        Args:
+            buckets: Price buckets to convert
+            max_points: Maximum number of points to include (None = all)
+            
+        Returns:
+            HeatmapData: Heatmap visualization data
+        """
+        try:
+            heatmap = HeatmapData(
+                symbol=buckets.symbol,
+                timestamp=buckets.timestamp,
+                bucket_size=buckets.bucket_size
+            )
+            
+            # Calculate maximum volume for intensity normalization
+            all_volumes = list(buckets.bid_buckets.values()) + list(buckets.ask_buckets.values())
+            max_volume = max(all_volumes) if all_volumes else 1.0
+            
+            # Generate bid points
+            bid_points = self._create_heatmap_points(
+                buckets.bid_buckets, 'bid', max_volume
+            )
+            
+            # Generate ask points
+            ask_points = self._create_heatmap_points(
+                buckets.ask_buckets, 'ask', max_volume
+            )
+            
+            # Combine all points
+            all_points = bid_points + ask_points
+            
+            # Limit points if requested
+            if max_points and len(all_points) > max_points:
+                # Sort by volume and take top points
+                all_points.sort(key=lambda p: p.volume, reverse=True)
+                all_points = all_points[:max_points]
+            
+            heatmap.data = all_points
+            
+            self.heatmaps_generated += 1
+            self.total_points_created += len(all_points)
+            
+            logger.debug(
+                f"Generated heatmap for {buckets.symbol}: {len(all_points)} points "
+                f"(max_volume: {max_volume:.6f})"
+            )
+            
+            return heatmap
+            
+        except Exception as e:
+            logger.error(f"Error generating heatmap: {e}")
+            raise
+    
+    def _create_heatmap_points(self, bucket_dict: Dict[float, float], 
+                              side: str, max_volume: float) -> List[HeatmapPoint]:
+        """
+        Create heatmap points from bucket dictionary.
+        
+        Args:
+            bucket_dict: Dictionary of price -> volume
+            side: 'bid' or 'ask'
+            max_volume: Maximum volume for intensity calculation
+            
+        Returns:
+            List[HeatmapPoint]: List of heatmap points
+        """
+        points = []
+        
+        for price, volume in bucket_dict.items():
+            if volume > 0:  # Only include non-zero volumes
+                intensity = min(volume / max_volume, 1.0) if max_volume > 0 else 0.0
+                
+                point = HeatmapPoint(
+                    price=price,
+                    volume=volume,
+                    intensity=intensity,
+                    side=side
+                )
+                points.append(point)
+        
+        return points
+    
+    def apply_smoothing(self, heatmap: HeatmapData, 
+                       smoothing_factor: float = 0.3) -> HeatmapData:
+        """
+        Apply smoothing to heatmap data to reduce noise.
+        
+        Args:
+            heatmap: Original heatmap data
+            smoothing_factor: Smoothing factor (0.0 = no smoothing, 1.0 = maximum)
+            
+        Returns:
+            HeatmapData: Smoothed heatmap data
+        """
+        if smoothing_factor <= 0:
+            return heatmap
+        
+        try:
+            smoothed = HeatmapData(
+                symbol=heatmap.symbol,
+                timestamp=heatmap.timestamp,
+                bucket_size=heatmap.bucket_size
+            )
+            
+            # Separate bids and asks
+            bids = [p for p in heatmap.data if p.side == 'bid']
+            asks = [p for p in heatmap.data if p.side == 'ask']
+            
+            # Apply smoothing to each side
+            smoothed_bids = self._smooth_points(bids, smoothing_factor)
+            smoothed_asks = self._smooth_points(asks, smoothing_factor)
+            
+            smoothed.data = smoothed_bids + smoothed_asks
+            
+            logger.debug(f"Applied smoothing with factor {smoothing_factor}")
+            return smoothed
+            
+        except Exception as e:
+            logger.error(f"Error applying smoothing: {e}")
+            return heatmap  # Return original on error   
+ 
+    def _smooth_points(self, points: List[HeatmapPoint], 
+                      smoothing_factor: float) -> List[HeatmapPoint]:
+        """
+        Apply smoothing to a list of heatmap points.
+        
+        Args:
+            points: Points to smooth
+            smoothing_factor: Smoothing factor
+            
+        Returns:
+            List[HeatmapPoint]: Smoothed points
+        """
+        if len(points) < 3:
+            return points
+        
+        # Sort points by price
+        sorted_points = sorted(points, key=lambda p: p.price)
+        smoothed_points = []
+        
+        for i, point in enumerate(sorted_points):
+            # Calculate weighted average with neighbors
+            total_weight = 1.0
+            weighted_volume = point.volume
+            weighted_intensity = point.intensity
+            
+            # Add left neighbor
+            if i > 0:
+                left_point = sorted_points[i - 1]
+                weight = smoothing_factor
+                total_weight += weight
+                weighted_volume += left_point.volume * weight
+                weighted_intensity += left_point.intensity * weight
+            
+            # Add right neighbor
+            if i < len(sorted_points) - 1:
+                right_point = sorted_points[i + 1]
+                weight = smoothing_factor
+                total_weight += weight
+                weighted_volume += right_point.volume * weight
+                weighted_intensity += right_point.intensity * weight
+            
+            # Create smoothed point
+            smoothed_point = HeatmapPoint(
+                price=point.price,
+                volume=weighted_volume / total_weight,
+                intensity=min(weighted_intensity / total_weight, 1.0),
+                side=point.side
+            )
+            smoothed_points.append(smoothed_point)
+        
+        return smoothed_points
+    
+    def filter_by_intensity(self, heatmap: HeatmapData, 
+                           min_intensity: float = 0.1) -> HeatmapData:
+        """
+        Filter heatmap points by minimum intensity.
+        
+        Args:
+            heatmap: Original heatmap data
+            min_intensity: Minimum intensity threshold
+            
+        Returns:
+            HeatmapData: Filtered heatmap data
+        """
+        filtered = HeatmapData(
+            symbol=heatmap.symbol,
+            timestamp=heatmap.timestamp,
+            bucket_size=heatmap.bucket_size
+        )
+        
+        # Filter points by intensity
+        filtered.data = [
+            point for point in heatmap.data 
+            if point.intensity >= min_intensity
+        ]
+        
+        logger.debug(
+            f"Filtered heatmap: {len(heatmap.data)} -> {len(filtered.data)} points "
+            f"(min_intensity: {min_intensity})"
+        )
+        
+        return filtered
+    
+    def get_price_levels(self, heatmap: HeatmapData, 
+                        side: str = None) -> List[float]:
+        """
+        Get sorted list of price levels from heatmap.
+        
+        Args:
+            heatmap: Heatmap data
+            side: 'bid', 'ask', or None for both
+            
+        Returns:
+            List[float]: Sorted price levels
+        """
+        if side:
+            points = [p for p in heatmap.data if p.side == side]
+        else:
+            points = heatmap.data
+        
+        prices = [p.price for p in points]
+        return sorted(prices)
+    
+    def get_volume_profile(self, heatmap: HeatmapData) -> Dict[str, List[Tuple[float, float]]]:
+        """
+        Get volume profile from heatmap data.
+        
+        Args:
+            heatmap: Heatmap data
+            
+        Returns:
+            Dict: Volume profile with 'bids' and 'asks' as (price, volume) tuples
+        """
+        profile = {'bids': [], 'asks': []}
+        
+        # Extract bid profile
+        bid_points = [p for p in heatmap.data if p.side == 'bid']
+        profile['bids'] = [(p.price, p.volume) for p in bid_points]
+        profile['bids'].sort(key=lambda x: x[0], reverse=True)  # Highest price first
+        
+        # Extract ask profile
+        ask_points = [p for p in heatmap.data if p.side == 'ask']
+        profile['asks'] = [(p.price, p.volume) for p in ask_points]
+        profile['asks'].sort(key=lambda x: x[0])  # Lowest price first
+        
+        return profile
+    
+    def calculate_support_resistance(self, heatmap: HeatmapData, 
+                                   threshold: float = 0.7) -> Dict[str, List[float]]:
+        """
+        Identify potential support and resistance levels from heatmap.
+        
+        Args:
+            heatmap: Heatmap data
+            threshold: Intensity threshold for significant levels
+            
+        Returns:
+            Dict: Support and resistance levels
+        """
+        levels = {'support': [], 'resistance': []}
+        
+        # Find high-intensity bid levels (potential support)
+        bid_points = [p for p in heatmap.data if p.side == 'bid' and p.intensity >= threshold]
+        levels['support'] = sorted([p.price for p in bid_points], reverse=True)
+        
+        # Find high-intensity ask levels (potential resistance)
+        ask_points = [p for p in heatmap.data if p.side == 'ask' and p.intensity >= threshold]
+        levels['resistance'] = sorted([p.price for p in ask_points])
+        
+        logger.debug(
+            f"Identified {len(levels['support'])} support and "
+            f"{len(levels['resistance'])} resistance levels"
+        )
+        
+        return levels
+    
+    def get_heatmap_summary(self, heatmap: HeatmapData) -> Dict[str, float]:
+        """
+        Get summary statistics for heatmap data.
+        
+        Args:
+            heatmap: Heatmap data
+            
+        Returns:
+            Dict: Summary statistics
+        """
+        if not heatmap.data:
+            return {}
+        
+        # Separate bids and asks
+        bids = [p for p in heatmap.data if p.side == 'bid']
+        asks = [p for p in heatmap.data if p.side == 'ask']
+        
+        summary = {
+            'total_points': len(heatmap.data),
+            'bid_points': len(bids),
+            'ask_points': len(asks),
+            'total_volume': sum(p.volume for p in heatmap.data),
+            'bid_volume': sum(p.volume for p in bids),
+            'ask_volume': sum(p.volume for p in asks),
+            'max_intensity': max(p.intensity for p in heatmap.data),
+            'avg_intensity': sum(p.intensity for p in heatmap.data) / len(heatmap.data),
+            'price_range': 0.0,
+            'best_bid': 0.0,
+            'best_ask': 0.0
+        }
+        
+        # Calculate price range
+        all_prices = [p.price for p in heatmap.data]
+        if all_prices:
+            summary['price_range'] = max(all_prices) - min(all_prices)
+        
+        # Calculate best bid and ask
+        if bids:
+            summary['best_bid'] = max(p.price for p in bids)
+        if asks:
+            summary['best_ask'] = min(p.price for p in asks)
+        
+        # Calculate volume imbalance
+        total_volume = summary['total_volume']
+        if total_volume > 0:
+            summary['volume_imbalance'] = (
+                (summary['bid_volume'] - summary['ask_volume']) / total_volume
+            )
+        else:
+            summary['volume_imbalance'] = 0.0
+        
+        return summary
+    
+    def get_processing_stats(self) -> Dict[str, int]:
+        """Get processing statistics"""
+        return {
+            'heatmaps_generated': self.heatmaps_generated,
+            'total_points_created': self.total_points_created,
+            'avg_points_per_heatmap': (
+                self.total_points_created // max(self.heatmaps_generated, 1)
+            )
+        }
+    
+    def reset_stats(self) -> None:
+        """Reset processing statistics"""
+        self.heatmaps_generated = 0
+        self.total_points_created = 0
+        logger.info("Heatmap generator statistics reset")

COBY/aggregation/price_bucketer.py

@@ -0,0 +1,341 @@
+"""
+Price bucketing system for order book aggregation.
+"""
+
+import math
+from typing import Dict, List, Tuple, Optional
+from collections import defaultdict
+from ..models.core import OrderBookSnapshot, PriceBuckets, PriceLevel
+from ..config import config
+from ..utils.logging import get_logger
+from ..utils.validation import validate_price, validate_volume
+
+logger = get_logger(__name__)
+
+
+class PriceBucketer:
+    """
+    Converts order book data into price buckets for heatmap visualization.
+    
+    Uses universal $1 USD buckets for all symbols to simplify logic.
+    """
+    
+    def __init__(self, bucket_size: float = None):
+        """
+        Initialize price bucketer.
+        
+        Args:
+            bucket_size: Size of price buckets in USD (defaults to config value)
+        """
+        self.bucket_size = bucket_size or config.get_bucket_size()
+        
+        # Statistics
+        self.buckets_created = 0
+        self.total_volume_processed = 0.0
+        
+        logger.info(f"Price bucketer initialized with ${self.bucket_size} buckets")
+    
+    def create_price_buckets(self, orderbook: OrderBookSnapshot) -> PriceBuckets:
+        """
+        Convert order book data to price buckets.
+        
+        Args:
+            orderbook: Order book snapshot
+            
+        Returns:
+            PriceBuckets: Aggregated price bucket data
+        """
+        try:
+            # Create price buckets object
+            buckets = PriceBuckets(
+                symbol=orderbook.symbol,
+                timestamp=orderbook.timestamp,
+                bucket_size=self.bucket_size
+            )
+            
+            # Process bids (aggregate into buckets)
+            for bid in orderbook.bids:
+                if validate_price(bid.price) and validate_volume(bid.size):
+                    buckets.add_bid(bid.price, bid.size)
+                    self.total_volume_processed += bid.size
+            
+            # Process asks (aggregate into buckets)
+            for ask in orderbook.asks:
+                if validate_price(ask.price) and validate_volume(ask.size):
+                    buckets.add_ask(ask.price, ask.size)
+                    self.total_volume_processed += ask.size
+            
+            self.buckets_created += 1
+            
+            logger.debug(
+                f"Created price buckets for {orderbook.symbol}: "
+                f"{len(buckets.bid_buckets)} bid buckets, {len(buckets.ask_buckets)} ask buckets"
+            )
+            
+            return buckets
+            
+        except Exception as e:
+            logger.error(f"Error creating price buckets: {e}")
+            raise
+    
+    def aggregate_buckets(self, bucket_list: List[PriceBuckets]) -> PriceBuckets:
+        """
+        Aggregate multiple price buckets into a single bucket set.
+        
+        Args:
+            bucket_list: List of price buckets to aggregate
+            
+        Returns:
+            PriceBuckets: Aggregated buckets
+        """
+        if not bucket_list:
+            raise ValueError("Cannot aggregate empty bucket list")
+        
+        # Use first bucket as template
+        first_bucket = bucket_list[0]
+        aggregated = PriceBuckets(
+            symbol=first_bucket.symbol,
+            timestamp=first_bucket.timestamp,
+            bucket_size=self.bucket_size
+        )
+        
+        # Aggregate all bid buckets
+        for buckets in bucket_list:
+            for price, volume in buckets.bid_buckets.items():
+                bucket_price = aggregated.get_bucket_price(price)
+                aggregated.bid_buckets[bucket_price] = (
+                    aggregated.bid_buckets.get(bucket_price, 0) + volume
+                )
+        
+        # Aggregate all ask buckets
+        for buckets in bucket_list:
+            for price, volume in buckets.ask_buckets.items():
+                bucket_price = aggregated.get_bucket_price(price)
+                aggregated.ask_buckets[bucket_price] = (
+                    aggregated.ask_buckets.get(bucket_price, 0) + volume
+                )
+        
+        logger.debug(f"Aggregated {len(bucket_list)} bucket sets")
+        return aggregated
+    
+    def get_bucket_range(self, center_price: float, depth: int) -> Tuple[float, float]:
+        """
+        Get price range for buckets around a center price.
+        
+        Args:
+            center_price: Center price for the range
+            depth: Number of buckets on each side
+            
+        Returns:
+            Tuple[float, float]: (min_price, max_price)
+        """
+        half_range = depth * self.bucket_size
+        min_price = center_price - half_range
+        max_price = center_price + half_range
+        
+        return (max(0, min_price), max_price)
+    
+    def filter_buckets_by_range(self, buckets: PriceBuckets, 
+                               min_price: float, max_price: float) -> PriceBuckets:
+        """
+        Filter buckets to only include those within a price range.
+        
+        Args:
+            buckets: Original price buckets
+            min_price: Minimum price to include
+            max_price: Maximum price to include
+            
+        Returns:
+            PriceBuckets: Filtered buckets
+        """
+        filtered = PriceBuckets(
+            symbol=buckets.symbol,
+            timestamp=buckets.timestamp,
+            bucket_size=buckets.bucket_size
+        )
+        
+        # Filter bid buckets
+        for price, volume in buckets.bid_buckets.items():
+            if min_price <= price <= max_price:
+                filtered.bid_buckets[price] = volume
+        
+        # Filter ask buckets
+        for price, volume in buckets.ask_buckets.items():
+            if min_price <= price <= max_price:
+                filtered.ask_buckets[price] = volume
+        
+        return filtered
+    
+    def get_top_buckets(self, buckets: PriceBuckets, count: int) -> PriceBuckets:
+        """
+        Get top N buckets by volume.
+        
+        Args:
+            buckets: Original price buckets
+            count: Number of top buckets to return
+            
+        Returns:
+            PriceBuckets: Top buckets by volume
+        """
+        top_buckets = PriceBuckets(
+            symbol=buckets.symbol,
+            timestamp=buckets.timestamp,
+            bucket_size=buckets.bucket_size
+        )
+        
+        # Get top bid buckets
+        top_bids = sorted(
+            buckets.bid_buckets.items(),
+            key=lambda x: x[1],  # Sort by volume
+            reverse=True
+        )[:count]
+        
+        for price, volume in top_bids:
+            top_buckets.bid_buckets[price] = volume
+        
+        # Get top ask buckets
+        top_asks = sorted(
+            buckets.ask_buckets.items(),
+            key=lambda x: x[1],  # Sort by volume
+            reverse=True
+        )[:count]
+        
+        for price, volume in top_asks:
+            top_buckets.ask_buckets[price] = volume
+        
+        return top_buckets    
+
+    def calculate_bucket_statistics(self, buckets: PriceBuckets) -> Dict[str, float]:
+        """
+        Calculate statistics for price buckets.
+        
+        Args:
+            buckets: Price buckets to analyze
+            
+        Returns:
+            Dict[str, float]: Bucket statistics
+        """
+        stats = {
+            'total_bid_buckets': len(buckets.bid_buckets),
+            'total_ask_buckets': len(buckets.ask_buckets),
+            'total_bid_volume': sum(buckets.bid_buckets.values()),
+            'total_ask_volume': sum(buckets.ask_buckets.values()),
+            'bid_price_range': 0.0,
+            'ask_price_range': 0.0,
+            'max_bid_volume': 0.0,
+            'max_ask_volume': 0.0,
+            'avg_bid_volume': 0.0,
+            'avg_ask_volume': 0.0
+        }
+        
+        # Calculate bid statistics
+        if buckets.bid_buckets:
+            bid_prices = list(buckets.bid_buckets.keys())
+            bid_volumes = list(buckets.bid_buckets.values())
+            
+            stats['bid_price_range'] = max(bid_prices) - min(bid_prices)
+            stats['max_bid_volume'] = max(bid_volumes)
+            stats['avg_bid_volume'] = sum(bid_volumes) / len(bid_volumes)
+        
+        # Calculate ask statistics
+        if buckets.ask_buckets:
+            ask_prices = list(buckets.ask_buckets.keys())
+            ask_volumes = list(buckets.ask_buckets.values())
+            
+            stats['ask_price_range'] = max(ask_prices) - min(ask_prices)
+            stats['max_ask_volume'] = max(ask_volumes)
+            stats['avg_ask_volume'] = sum(ask_volumes) / len(ask_volumes)
+        
+        # Calculate combined statistics
+        stats['total_volume'] = stats['total_bid_volume'] + stats['total_ask_volume']
+        stats['volume_imbalance'] = (
+            (stats['total_bid_volume'] - stats['total_ask_volume']) / 
+            max(stats['total_volume'], 1e-10)
+        )
+        
+        return stats
+    
+    def merge_adjacent_buckets(self, buckets: PriceBuckets, merge_factor: int = 2) -> PriceBuckets:
+        """
+        Merge adjacent buckets to create larger bucket sizes.
+        
+        Args:
+            buckets: Original price buckets
+            merge_factor: Number of adjacent buckets to merge
+            
+        Returns:
+            PriceBuckets: Merged buckets with larger bucket size
+        """
+        merged = PriceBuckets(
+            symbol=buckets.symbol,
+            timestamp=buckets.timestamp,
+            bucket_size=buckets.bucket_size * merge_factor
+        )
+        
+        # Merge bid buckets
+        bid_groups = defaultdict(float)
+        for price, volume in buckets.bid_buckets.items():
+            # Calculate new bucket price
+            new_bucket_price = merged.get_bucket_price(price)
+            bid_groups[new_bucket_price] += volume
+        
+        merged.bid_buckets = dict(bid_groups)
+        
+        # Merge ask buckets
+        ask_groups = defaultdict(float)
+        for price, volume in buckets.ask_buckets.items():
+            # Calculate new bucket price
+            new_bucket_price = merged.get_bucket_price(price)
+            ask_groups[new_bucket_price] += volume
+        
+        merged.ask_buckets = dict(ask_groups)
+        
+        logger.debug(f"Merged buckets with factor {merge_factor}")
+        return merged
+    
+    def get_bucket_depth_profile(self, buckets: PriceBuckets, 
+                                center_price: float) -> Dict[str, List[Tuple[float, float]]]:
+        """
+        Get depth profile showing volume at different distances from center price.
+        
+        Args:
+            buckets: Price buckets
+            center_price: Center price for depth calculation
+            
+        Returns:
+            Dict: Depth profile with 'bids' and 'asks' lists of (distance, volume) tuples
+        """
+        profile = {'bids': [], 'asks': []}
+        
+        # Calculate bid depth profile
+        for price, volume in buckets.bid_buckets.items():
+            distance = abs(center_price - price)
+            profile['bids'].append((distance, volume))
+        
+        # Calculate ask depth profile
+        for price, volume in buckets.ask_buckets.items():
+            distance = abs(price - center_price)
+            profile['asks'].append((distance, volume))
+        
+        # Sort by distance
+        profile['bids'].sort(key=lambda x: x[0])
+        profile['asks'].sort(key=lambda x: x[0])
+        
+        return profile
+    
+    def get_processing_stats(self) -> Dict[str, float]:
+        """Get processing statistics"""
+        return {
+            'bucket_size': self.bucket_size,
+            'buckets_created': self.buckets_created,
+            'total_volume_processed': self.total_volume_processed,
+            'avg_volume_per_bucket': (
+                self.total_volume_processed / max(self.buckets_created, 1)
+            )
+        }
+    
+    def reset_stats(self) -> None:
+        """Reset processing statistics"""
+        self.buckets_created = 0
+        self.total_volume_processed = 0.0
+        logger.info("Price bucketer statistics reset")

COBY/config.py

@@ -51,9 +51,7 @@ class ExchangeConfig:
 @dataclass
 class AggregationConfig:
     """Data aggregation configuration"""
-    btc_bucket_size: float = float(os.getenv('BTC_BUCKET_SIZE', '10.0'))  # $10 USD buckets
-    eth_bucket_size: float = float(os.getenv('ETH_BUCKET_SIZE', '1.0'))   # $1 USD buckets
-    default_bucket_size: float = float(os.getenv('DEFAULT_BUCKET_SIZE', '1.0'))
+    bucket_size: float = float(os.getenv('BUCKET_SIZE', '1.0'))  # $1 USD buckets for all symbols
     heatmap_depth: int = int(os.getenv('HEATMAP_DEPTH', '50'))  # Number of price levels
     update_frequency: float = float(os.getenv('UPDATE_FREQUENCY', '0.5'))  # Seconds
     volume_threshold: float = float(os.getenv('VOLUME_THRESHOLD', '0.01'))  # Minimum volume
@@ -119,15 +117,9 @@ class Config:
         if self.aggregation.eth_bucket_size <= 0:
             raise ValueError("ETH bucket size must be positive")
     
-    def get_bucket_size(self, symbol: str) -> float:
-        """Get bucket size for a specific symbol"""
-        symbol_upper = symbol.upper()
-        if 'BTC' in symbol_upper:
-            return self.aggregation.btc_bucket_size
-        elif 'ETH' in symbol_upper:
-            return self.aggregation.eth_bucket_size
-        else:
-            return self.aggregation.default_bucket_size
+    def get_bucket_size(self, symbol: str = None) -> float:
+        """Get bucket size (now universal $1 for all symbols)"""
+        return self.aggregation.bucket_size
     
     def get_database_url(self) -> str:
         """Get database connection URL"""
@@ -158,8 +150,7 @@ class Config:
                 'symbols': self.exchanges.symbols,
             },
             'aggregation': {
-                'btc_bucket_size': self.aggregation.btc_bucket_size,
-                'eth_bucket_size': self.aggregation.eth_bucket_size,
+                'bucket_size': self.aggregation.bucket_size,
                 'heatmap_depth': self.aggregation.heatmap_depth,
             },
             'api': {