fixes

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

@@ -1,7 +1,7 @@
 # Design Document
 
 ## Overview
-
+ 
 The Multi-Exchange Data Aggregation System is a comprehensive data collection and processing subsystem designed to serve as the foundational data layer for the trading orchestrator. The system will collect real-time order book and OHLCV data from the top 10 cryptocurrency exchanges, aggregate it into standardized formats, store it in a TimescaleDB time-series database, and provide both live data feeds and historical replay capabilities.
 
 The system follows a microservices architecture with containerized components, ensuring scalability, maintainability, and seamless integration with the existing trading infrastructure.

web/clean_dashboard.py

@@ -293,7 +293,9 @@ class CleanTradingDashboard:
         # WebSocket streaming
         self.ws_price_cache: dict = {}
         self.is_streaming = False
-        self.tick_cache: list = []
+        # Use bounded deque to avoid unbounded growth
+        from collections import deque as _deque
+        self.tick_cache: _deque = _deque(maxlen=3600)  # keep last ~1h of 1s ticks
         
         # COB data cache - enhanced with price buckets and memory system
         self.cob_cache: dict = {
@@ -301,14 +303,14 @@ class CleanTradingDashboard:
                 'last_update': 0, 
                 'data': None, 
                 'updates_count': 0,
-                'update_times': [],
+                'update_times': _deque(maxlen=7200),  # cap ~2h of 1s timestamps
                 'update_rate': 0.0
             },
             'BTC/USDT': {
                 'last_update': 0, 
                 'data': None, 
                 'updates_count': 0,
-                'update_times': [],
+                'update_times': _deque(maxlen=7200),
                 'update_rate': 0.0
             }
         }
@@ -5795,9 +5797,9 @@ class CleanTradingDashboard:
             # Add to recent decisions for display
             self.recent_decisions.append(signal)
             
-            # Keep more decisions for longer history - extend to 200 decisions
+            # Keep decisions bounded to prevent growth
             if len(self.recent_decisions) > 200:
-                self.recent_decisions = self.recent_decisions[-200:]
+                del self.recent_decisions[:-200]
             
             # Train ALL models on EVERY prediction result (not just executed ones)
             # This ensures models learn from all predictions, not just successful trades
@@ -8866,7 +8868,7 @@ class CleanTradingDashboard:
                 # Add to recent decisions
                 self.recent_decisions.append(signal)
                 if len(self.recent_decisions) > 200:
-                    self.recent_decisions.pop(0)
+                    del self.recent_decisions[:-200]
                 
                 logger.info(f"COB SIGNAL: {symbol} {signal['action']} signal generated - imbalance: {imbalance:.3f}, confidence: {signal['confidence']:.3f}")