feat(bb_metrics): Finally added BB-based on regime-as-signal. Also detected and *fixed* nastybug where slices during BB-calculations were corrupting data in code elswhere in execution path. Fixed by using only local variables and touching no object actributes in the _calculate_enhanced_* method.-

nautilus_actor_test_implementation_5x.py

@@ -1,4 +1,5 @@
 import time
+import sys
 import numpy as np
 import asyncio
 import json
@@ -927,6 +928,8 @@ class SILOQYMainActor(Actor):
                 
         except Exception as e:
             self.log.error(f"Nautilus ActorExecutor: Failed to publish tick: {e}")
+            self.log.error(f"{tick_tuple}")
+            sys.exit(2)
 
 class DOLPHINRegimeActor(Actor):    
     def __init__(self, config: DOLPHINRegimeActorConfig):

nautilus_actor_test_implementation_6x.py

@@ -39,6 +39,11 @@ CANDLES_INITIAL_TOPIC = "SILOQY.CANDLES.INITIAL"
 # ADDED LINE 18:
 TICK_SIZES_TOPIC = "SILOQY.TICK.SIZES"
 
+# NEW: Enhanced BB indicator topics for tuple-based publishing
+REGIME_INDICATORS_TOPIC = "DOLPHIN.REGIME.INDICATORS"
+BB_METRICS_TOPIC = "DOLPHIN.BB.METRICS"
+TEMPORAL_PATTERNS_TOPIC = "DOLPHIN.TEMPORAL.PATTERNS"
+
 # Rate limiting constant
 MIN_INTERVAL = 2.5  # seconds between API batches
 
@@ -53,6 +58,16 @@ class MarketRegime(Enum):
 # EXTRACTED FROM STANDALONE: REAL WEBSOCKET INFRASTRUCTURE
 # ============================================================================
 
+#TODO TODO: Manually added. Left for *future* re-integration of class instead of tuples (where appropriate)
+class SILOQYQuoteTick(Data):
+    def __init__(self, instrument_id, price, size, ts_event, ts_init=None):
+        super().__init__()
+        self.instrument_id = str(instrument_id)
+        self.price = float(price)
+        self.size = float(size)
+        self.ts_event = int(ts_event)
+        self.ts_init = int(ts_init if ts_init is not None else time.time_ns())
+
 @dataclass
 class TickData:
     """Universal tick data structure from standalone"""
@@ -868,21 +883,20 @@ class SILOQYMainActor(Actor):
                 
         # PRESERVED: Original high-performance tuple publishing
         try:
-            if hasattr(self, 'msgbus') and self.msgbus:
-                # STEP 3 FIX: Round all float values to 8 decimals in the tuple
             tick_tuple = (
-                    symbol,
-                    round(float(price), 8),
-                    round(float(quantity), 8),
+                str(symbol),
+                float(price), 
+                float(quantity),
                 int(timestamp),
-                    round(float(candle['open_price']), 8),
+                float(candle['open_price']),
                 int(candle['open_time'])
             )
            
             self.msgbus.publish(RAW_TOPIC, tick_tuple)
                 
         except Exception as e:
-            self.log.error(f"Nautilus ActorExecutor: Failed to publish tick: {e}")
+            self.log.error(f"Nautilus ActorExecutor: Faish tick: {e}")
+            print(f"EXCEPTION DETAILS: {type(e)} - {e}")
 
 class DOLPHINRegimeActor(Actor):    
     def __init__(self, config: DOLPHINRegimeActorConfig):
@@ -920,6 +934,13 @@ class DOLPHINRegimeActor(Actor):
         self.processed_ticks = 0
         self.regime_calculations = 0
         
+        # NEW: Enhanced indicator tracking for BB and temporal patterns
+        self.signal_history = deque(maxlen=100)  # For BB calculations
+        self.bb_period = 20  # BB calculation period
+        self.bb_std_dev = 2.0  # BB standard deviation multiplier
+        self.velocity_history = deque(maxlen=10)  # For regime velocity tracking
+        self.confidence_history = deque(maxlen=20)  # For confidence trend analysis
+        
         self.log.info(f"DOLPHINRegimeActor initialized with Nautilus ActorExecutor - max_symbols: {self.max_symbols}, "
                      f"ticks_per_analysis: {self.ticks_per_analysis}")
     
@@ -1041,6 +1062,132 @@ class DOLPHINRegimeActor(Actor):
         except Exception as e:
             self.log.error(f"Nautilus ActorExecutor: Regime detection error: {e}")
     
+    def _calculate_enhanced_indicators(self, bull_ratio, bear_ratio, confidence, analyzed, total_symbols):
+        """NEW: Calculate enhanced indicators including BB metrics and temporal patterns"""
+        
+        # Initialize history attributes if they don't exist
+        if not hasattr(self, 'signal_history'):
+            self.signal_history = deque(maxlen=50)
+        if not hasattr(self, 'velocity_history'):
+            self.velocity_history = deque(maxlen=50)
+        if not hasattr(self, 'confidence_history'):
+            self.confidence_history = deque(maxlen=50)
+        if not hasattr(self, 'bb_period'):
+            self.bb_period = 20
+        if not hasattr(self, 'bb_std_dev'):
+            self.bb_std_dev = 2.0
+        
+        # Calculate regime momentum signal
+        base_momentum = (bull_ratio - bear_ratio) * 100  # -100 to +100
+        sample_quality = min(analyzed / total_symbols, 1.0) if total_symbols > 0 else 0.0
+        signal = base_momentum * confidence * sample_quality
+        
+        # Add to signal history
+        self.signal_history.append(signal)
+        
+        # Calculate velocity (rate of change in signal)
+        velocity = 0.0
+        if len(self.signal_history) >= 2:
+            velocity = self.signal_history[-1] - self.signal_history[-2]
+        self.velocity_history.append(velocity)
+        
+        # Store confidence for trending
+        self.confidence_history.append(confidence)
+        
+        # Calculate Bollinger Bands if enough history
+        bb_metrics = {}
+        if len(self.signal_history) >= self.bb_period:
+            # REPLACE SLICE: recent_signals = list(self.signal_history)[-self.bb_period:]
+            recent_signals = []
+            start_index = len(self.signal_history) - self.bb_period
+            for i in range(start_index, len(self.signal_history)):
+                recent_signals.append(self.signal_history[i])
+            
+            sma = sum(recent_signals) / len(recent_signals)
+            
+            # Calculate standard deviation
+            variance = sum((x - sma) ** 2 for x in recent_signals) / len(recent_signals)
+            std_dev = variance ** 0.5
+            
+            upper_band = sma + (self.bb_std_dev * std_dev)
+            lower_band = sma - (self.bb_std_dev * std_dev)
+            
+            # Position within BBs (mean reversion interpretation)
+            if signal > upper_band:
+                bb_position = 'ABOVE_UPPER'
+            elif signal < lower_band:
+                bb_position = 'BELOW_LOWER'
+            elif signal >= sma:
+                bb_position = 'UPPER_HALF'
+            else:
+                bb_position = 'LOWER_HALF'
+            
+            # Momentum persistence interpretation 
+            if signal > upper_band:
+                momentum_signal = 'STRONG_BULL_BREAKOUT'
+            elif signal < lower_band:
+                momentum_signal = 'STRONG_BEAR_BREAKOUT'
+            elif signal > sma:
+                momentum_signal = 'MILD_BULLISH'
+            else:
+                momentum_signal = 'MILD_BEARISH'
+            
+            bb_metrics = {
+                'signal': signal,
+                'sma': sma,
+                'upper_band': upper_band,
+                'lower_band': lower_band,
+                'bb_position': bb_position,
+                'momentum_signal': momentum_signal,
+                'bb_ready': True
+            }
+        else:
+            bb_metrics = {
+                'signal': signal,
+                'sma': None,
+                'upper_band': None,
+                'lower_band': None,
+                'bb_position': 'INSUFFICIENT_DATA',
+                'momentum_signal': 'INSUFFICIENT_DATA',
+                'bb_ready': False
+            }
+        
+        # Calculate temporal patterns
+        temporal_metrics = {}
+        if len(self.velocity_history) >= 3:
+            avg_velocity = sum(self.velocity_history) / len(self.velocity_history)
+            velocity_trend = 'ACCELERATING' if velocity > avg_velocity else 'DECELERATING'
+        else:
+            avg_velocity = velocity
+            velocity_trend = 'BUILDING_HISTORY'
+            
+        if len(self.confidence_history) >= 5:
+            # REPLACE SLICE: confidence > sum(self.confidence_history[-5:-1])/4
+            confidence_sum = 0.0
+            count = 0
+            start_index = len(self.confidence_history) - 5
+            end_index = len(self.confidence_history) - 1  # exclude last element
+            for i in range(start_index, end_index):
+                confidence_sum += self.confidence_history[i]
+                count += 1
+            
+            if count > 0:
+                confidence_avg = confidence_sum / count
+                confidence_trend = 'RISING' if confidence > confidence_avg else 'FALLING'
+            else:
+                confidence_trend = 'BUILDING_HISTORY'
+        else:
+            confidence_trend = 'BUILDING_HISTORY'
+        
+        temporal_metrics = {
+            'velocity': velocity,
+            'avg_velocity': avg_velocity,
+            'velocity_trend': velocity_trend,
+            'confidence_trend': confidence_trend
+        }
+        
+        return bb_metrics, temporal_metrics
+        
     def _run_regime_detection(self):
         self.regime_calculations += 1
         
@@ -1065,7 +1212,6 @@ class DOLPHINRegimeActor(Actor):
             
             analyzed += 1
             
-
             # NEW: HFT-grade tick-size based comparison
             tick_size = self.tick_sizes[idx]
             equality_threshold = tick_size / 2  # Half tick size standard
@@ -1127,6 +1273,54 @@ class DOLPHINRegimeActor(Actor):
         except Exception as e:
             self.log.error(f"Nautilus ActorExecutor: Failed to publish regime result: {e}")
         
+        # NEW: Calculate enhanced indicators
+        bb_metrics, temporal_metrics = self._calculate_enhanced_indicators(
+            bull_ratio, bear_ratio, confidence, analyzed, total_symbols
+        )
+        
+        # NEW: Publish enhanced indicators to data bus as tuples
+        try:
+            if hasattr(self, 'msgbus') and self.msgbus:
+                timestamp = int(time.time() * 1000)
+                
+                # Publish regime indicators as tuple
+                indicator_tuple = (
+                    timestamp,
+                    float(bb_metrics['signal']),
+                    bool(bb_metrics['bb_ready']),
+                    float(temporal_metrics['velocity']),
+                    str(temporal_metrics['velocity_trend']),
+                    str(temporal_metrics['confidence_trend'])
+                )
+                self.msgbus.publish(REGIME_INDICATORS_TOPIC, indicator_tuple)
+                
+                # Publish BB metrics as tuple (only if BB is ready)
+                if bb_metrics['bb_ready']:
+                    bb_tuple = (
+                        timestamp,
+                        float(bb_metrics['signal']),
+                        float(bb_metrics['sma']),
+                        float(bb_metrics['upper_band']),
+                        float(bb_metrics['lower_band']),
+                        str(bb_metrics['bb_position']),
+                        str(bb_metrics['momentum_signal'])
+                    )
+                    self.msgbus.publish(BB_METRICS_TOPIC, bb_tuple)
+                    
+                    # Publish temporal patterns as tuple
+                    temporal_tuple = (
+                        timestamp,
+                        float(temporal_metrics['velocity']),
+                        float(temporal_metrics['avg_velocity']),
+                        str(temporal_metrics['velocity_trend']),
+                        str(temporal_metrics['confidence_trend']),
+                        int(len(self.signal_history))
+                    )
+                    self.msgbus.publish(TEMPORAL_PATTERNS_TOPIC, temporal_tuple)
+                    
+        except Exception as e:
+            self.log.error(f"Failed to publish enhanced indicators: {e}")
+        
         # Log regime changes
         if not self.regime_history or regime != self.regime_history[-1]:
             self.log.info(f"REGIME CHANGE: {regime.value} | Bull: {bull_ratio:.2%} "
@@ -1150,6 +1344,19 @@ class DOLPHINRegimeActor(Actor):
             self.log.info(f"{color_code}REGIME STATUS: {regime.value} | Bull: {bull_ratio:.2%} "
              f"Bear: {bear_ratio:.2%} ({bullish}/{bearish}) | Processed: {self.processed_ticks} ticks{reset_code}")
 
+            # Enhanced indicator line after regime status
+            if bb_metrics['bb_ready']:
+                self.log.info(f"{color_code}BB INDICATORS: Signal:{bb_metrics['signal']:.2f} | "
+                             f"SMA:{bb_metrics['sma']:.2f} | Upper:{bb_metrics['upper_band']:.2f} | "
+                             f"Lower:{bb_metrics['lower_band']:.2f} | Pos:{bb_metrics['bb_position']} | "
+                             f"Mom:{bb_metrics['momentum_signal']} | Vel:{temporal_metrics['velocity']:.2f} | "
+                             f"VelTrend:{temporal_metrics['velocity_trend']} | ConfTrend:{temporal_metrics['confidence_trend']}{reset_code}")
+            else:
+                self.log.info(f"{color_code}BB INDICATORS: Signal:{bb_metrics['signal']:.2f} | "
+                             f"Status:BUILDING_BB_HISTORY ({len(self.signal_history)}/{self.bb_period}) | "
+                             f"Vel:{temporal_metrics['velocity']:.2f} | VelTrend:{temporal_metrics['velocity_trend']} | "
+                             f"ConfTrend:{temporal_metrics['confidence_trend']}{reset_code}")
+
             # NEW: Log symbol pattern and counts
             if symbol_pattern:  # Only if we have symbols to show
                 pattern_str = " ".join(symbol_pattern) + " "  # Create pattern with spaces
@@ -1235,6 +1442,7 @@ class SILOQYNormalizerActor(Actor):
                 try:
                     # Publish directly to STRUCTURED_TOPIC to match original v3.28 behavior
                     self.msgbus.publish(STRUCTURED_TOPIC, tick)
+                    pass
                     
                 except Exception as e:
                      self.log.error(f"Nautilus ActorExecutor: Failed to publish to STRUCTURED_TOPIC: {e}")