diff --git a/nautilus_actor_test_implementation_5x.py b/nautilus_actor_test_implementation_5x.py
index ded83bf..529efac 100644
--- a/nautilus_actor_test_implementation_5x.py
+++ b/nautilus_actor_test_implementation_5x.py
@@ -1126,25 +1126,37 @@ class DOLPHINRegimeActor(Actor):
                 
         except Exception as e:
             self.log.error(f"Nautilus ActorExecutor: Failed to publish regime result: {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:.1%} "
-                         f"Bear: {bear_ratio:.1%} Sideways: {sideways_ratio:.1%} | "
-                         f"Confidence: {confidence:.1%} | Analyzed: {analyzed}/{total_symbols}")
+            self.log.info(f"REGIME CHANGE: {regime.value} | Bull: {bull_ratio:.2%} "
+                     f"Bear: {bear_ratio:.2%} Sideways: {sideways_ratio:.2%} ({bullish}/{bearish}) | "
+                     f"Confidence: {confidence:.2%} | Analyzed: {analyzed}/{total_symbols}")
             self.regime_history.append(regime)
         
         # Periodic regime status (even without changes)
-        if self.regime_calculations % 1 == 0:  # Every calculation
-            self.log.info(f"REGIME STATUS: {regime.value} | Bull: {bull_ratio:.1%} "
-                         f"Bear: {bear_ratio:.1%} | Processed: {self.processed_ticks} ticks")
+        if self.regime_calculations % 50 == 0:  # Every second, approx, given avg. tick rate
+            # Determine color based on regime
+            if regime == MarketRegime.BULL:
+                color_code = "\033[92m"  # Green
+            elif regime == MarketRegime.BEAR:
+                color_code = "\033[91m"  # Red
+            else:  # SIDEWAYS
+                color_code = "\033[93m"  # Yellow
+            
+            # Reset color code
+            reset_code = "\033[0m"
+            
+            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: Log symbol pattern and counts
             if symbol_pattern:  # Only if we have symbols to show
                 pattern_str = " ".join(symbol_pattern) + " "  # Create pattern with spaces
                 bull_count = sum(1 for s in symbol_pattern if s.startswith("B"))
                 bear_count = sum(1 for s in symbol_pattern if s.startswith("X"))
-                self.log.info(f"{pattern_str} and totals: BULLS:{bull_count}/BEARS:{bear_count}")
+               
+                self.log.debug(f"{pattern_str} and totals: BULLS:{bull_count}/BEARS:{bear_count}")
     
     def _calculate_confidence(self, bull_ratio: float, bear_ratio: float, 
                              analyzed: int, total: int) -> float:
@@ -1259,7 +1271,7 @@ def test_siloqy_actors_with_nautilus_process_management():
             "candle_interval_ms": 15 * 60 * 1000,
             "throttle_mode": True,  # ENABLED: Safe for dual instance testing
             "throttle_rate_limit_seconds": 10.0,  # 10s between batches (vs 2.5s)
-            "max_symbols_throttled": 100  # Only 100 symbols (vs 2000+)
+            "max_symbols_throttled": 414  # Only 100 symbols (vs 2000+)
         }
     )
     
@@ -1269,7 +1281,7 @@ def test_siloqy_actors_with_nautilus_process_management():
         config={
             "component_id": "SILOQY-MAIN-ACTOR",
             "candle_interval_ms": 15 * 60 * 1000,
-            "throttle_mode": True,  # ENABLED: Reduced tick generation
+            "throttle_mode": False,  # ENABLED: Reduced tick generation
             "enable_real_data": True  # CHANGE TO True for real WebSocket data
         }
     )