dash working

web/templated_dashboard.py

@@ -986,33 +986,71 @@ class TemplatedTradingDashboard:
             logger.debug(f"TEMPLATED DASHBOARD: Error generating bucketed COB data: {e}")
 
     def _calculate_cumulative_imbalance(self, symbol: str) -> Dict[str, float]:
-        """Calculate average imbalance over multiple time windows."""
+        """Calculate Moving Averages (MA) of imbalance over different periods."""
         stats = {}
-        now = time.time()
         history = self.cob_data_history.get(symbol)
 
         if not history:
             return {'1s': 0.0, '5s': 0.0, '15s': 0.0, '60s': 0.0}
 
-        periods = {'1s': 1, '5s': 5, '15s': 15, '60s': 60}
-        
-        for name, duration in periods.items():
-            recent_imbalances = []
-            for snap in history:
-                # Check if snap is a valid dict with timestamp and stats
-                if isinstance(snap, dict) and 'timestamp' in snap and (now - snap['timestamp'] <= duration) and 'stats' in snap and snap['stats']:
-                    imbalance = snap['stats'].get('imbalance')
-                    if imbalance is not None:
-                        recent_imbalances.append(imbalance)
+        # Convert history to list and get recent snapshots
+        history_list = list(history)
+        if not history_list:
+            return {'1s': 0.0, '5s': 0.0, '15s': 0.0, '60s': 0.0}
 
-            if recent_imbalances:
-                stats[name] = sum(recent_imbalances) / len(recent_imbalances)
-            else:
-                stats[name] = 0.0
+        # Extract imbalance values from recent snapshots
+        imbalances = []
+        for snap in history_list:
+            if isinstance(snap, dict) and 'stats' in snap and snap['stats']:
+                imbalance = snap['stats'].get('imbalance')
+                if imbalance is not None:
+                    imbalances.append(imbalance)
+
+        if not imbalances:
+            return {'1s': 0.0, '5s': 0.0, '15s': 0.0, '60s': 0.0}
+
+        # Calculate Moving Averages over different periods
+        # MA periods: 1s=1 period, 5s=5 periods, 15s=15 periods, 60s=60 periods
+        ma_periods = {'1s': 1, '5s': 5, '15s': 15, '60s': 60}
         
-        # Debug logging to verify cumulative imbalance calculation
+        for name, period in ma_periods.items():
+            if len(imbalances) >= period:
+                # Calculate SMA over the last 'period' values
+                recent_imbalances = imbalances[-period:]
+                sma_value = sum(recent_imbalances) / len(recent_imbalances)
+                
+                # Also calculate EMA for better responsiveness
+                if period > 1:
+                    # EMA calculation with alpha = 2/(period+1)
+                    alpha = 2.0 / (period + 1)
+                    ema_value = recent_imbalances[0]  # Start with first value
+                    for value in recent_imbalances[1:]:
+                        ema_value = alpha * value + (1 - alpha) * ema_value
+                    # Use EMA for better responsiveness
+                    stats[name] = ema_value
+                else:
+                    # For 1s, use SMA (no EMA needed)
+                    stats[name] = sma_value
+            else:
+                # If not enough data, use available data
+                available_imbalances = imbalances[-min(period, len(imbalances)):]
+                if available_imbalances:
+                    if len(available_imbalances) > 1:
+                        # Calculate EMA for available data
+                        alpha = 2.0 / (len(available_imbalances) + 1)
+                        ema_value = available_imbalances[0]
+                        for value in available_imbalances[1:]:
+                            ema_value = alpha * value + (1 - alpha) * ema_value
+                        stats[name] = ema_value
+                    else:
+                        # Single value, use as is
+                        stats[name] = available_imbalances[0]
+                else:
+                    stats[name] = 0.0
+        
+        # Debug logging to verify MA calculation
         if any(value != 0.0 for value in stats.values()):
-            logger.debug(f"TEMPLATED DASHBOARD: [CUMULATIVE-IMBALANCE] {symbol}: {stats}")
+            logger.debug(f"TEMPLATED DASHBOARD: [MOVING-AVERAGE-IMBALANCE] {symbol}: {stats} (from {len(imbalances)} snapshots)")
         
         return stats