feat(data): Added BB and other indicators to output, based on an 'single point representation' of the regime value and BULL-BEAR percentages

nautilus_actor_test_implementation_5x.py

@@ -38,6 +38,10 @@ SYMBOLS_DISCOVERED_TOPIC = "SILOQY.SYMBOLS.DISCOVERED"
 CANDLES_INITIAL_TOPIC = "SILOQY.CANDLES.INITIAL"
 # ADDED LINE 18:
 TICK_SIZES_TOPIC = "SILOQY.TICK.SIZES"
+# NEW: Enhanced indicator topics for data bus 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
@@ -920,6 +924,13 @@ 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 +1052,101 @@ 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"""
+        # 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:
+            recent_signals = list(self.signal_history)[-self.bb_period:]
+            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:
+            confidence_trend = 'RISING' if confidence > sum(self.confidence_history[-5:-1])/4 else 'FALLING'
+        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
         
@@ -1106,6 +1212,11 @@ class DOLPHINRegimeActor(Actor):
         # PRESERVED: Original confidence calculation
         confidence = self._calculate_confidence(bull_ratio, bear_ratio, analyzed, total_symbols)
         
+        # NEW: Calculate enhanced indicators
+        bb_metrics, temporal_metrics = self._calculate_enhanced_indicators(
+            bull_ratio, bear_ratio, confidence, analyzed, total_symbols
+        )
+        
         self.previous_bull_ratio = bull_ratio
         
         # Publish regime result using Nautilus message bus
@@ -1124,6 +1235,41 @@ class DOLPHINRegimeActor(Actor):
                 
                 self.msgbus.publish(REGIME_TOPIC, regime_tuple)
                 
+                # NEW: Publish enhanced indicators to data bus
+                indicator_data = {
+                    'timestamp': int(time.time() * 1000),
+                    'regime_momentum_signal': bb_metrics['signal'],
+                    'bb_ready': bb_metrics['bb_ready'],
+                    'velocity': temporal_metrics['velocity'],
+                    'velocity_trend': temporal_metrics['velocity_trend'],
+                    'confidence_trend': temporal_metrics['confidence_trend']
+                }
+                self.msgbus.publish(REGIME_INDICATORS_TOPIC, indicator_data)
+                
+                # Publish BB metrics separately for specialized consumers
+                if bb_metrics['bb_ready']:
+                    bb_data = {
+                        'timestamp': int(time.time() * 1000),
+                        'signal': bb_metrics['signal'],
+                        'sma': bb_metrics['sma'],
+                        'upper_band': bb_metrics['upper_band'],
+                        'lower_band': bb_metrics['lower_band'],
+                        'bb_position': bb_metrics['bb_position'],
+                        'momentum_signal': bb_metrics['momentum_signal']
+                    }
+                    self.msgbus.publish(BB_METRICS_TOPIC, bb_data)
+                
+                # Publish temporal patterns
+                temporal_data = {
+                    'timestamp': int(time.time() * 1000),
+                    'velocity': temporal_metrics['velocity'],
+                    'avg_velocity': temporal_metrics['avg_velocity'],
+                    'velocity_trend': temporal_metrics['velocity_trend'],
+                    'confidence_trend': temporal_metrics['confidence_trend'],
+                    'signal_history_length': len(self.signal_history)
+                }
+                self.msgbus.publish(TEMPORAL_PATTERNS_TOPIC, temporal_data)
+                
         except Exception as e:
             self.log.error(f"Nautilus ActorExecutor: Failed to publish regime result: {e}")
 
@@ -1150,6 +1296,19 @@ 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}")
             
+            # NEW: Enhanced indicator line after regime status
+            if bb_metrics['bb_ready']:
+                self.log.info(f"{color_code}INDICATORS: Signal:{bb_metrics['signal']:.1f} | "
+                             f"SMA:{bb_metrics['sma']:.1f} | Upper:{bb_metrics['upper_band']:.1f} | "
+                             f"Lower:{bb_metrics['lower_band']:.1f} | Pos:{bb_metrics['bb_position']} | "
+                             f"Mom:{bb_metrics['momentum_signal']} | Vel:{temporal_metrics['velocity']:.1f} | "
+                             f"VelTrend:{temporal_metrics['velocity_trend']} | ConfTrend:{temporal_metrics['confidence_trend']}{reset_code}")
+            else:
+                self.log.info(f"{color_code}INDICATORS: Signal:{bb_metrics['signal']:.1f} | "
+                             f"Status:BUILDING_BB_HISTORY ({len(self.signal_history)}/{self.bb_period}) | "
+                             f"Vel:{temporal_metrics['velocity']:.1f} | 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