dash and training wip

.vscode/launch.json

@@ -47,6 +47,9 @@
             "env": {
                 "PYTHONUNBUFFERED": "1",
                 "ENABLE_REALTIME_CHARTS": "1"
+            },
+            "linux": {
+                "python": "${workspaceFolder}/venv/bin/python"
             }
         },
         {
@@ -156,6 +159,7 @@
             "type": "python",
             "request": "launch",
             "program": "run_clean_dashboard.py",
+            "python": "${workspaceFolder}/venv/bin/python",
             "console": "integratedTerminal",
             "justMyCode": false,
             "env": {

NN/models/saved/checkpoint_metadata.json

@@ -1,104 +1,3 @@
 {
-  "decision": [
+  "decision": []
-    {
-      "checkpoint_id": "decision_20250704_082022",
-      "model_name": "decision",
-      "model_type": "decision_fusion",
-      "file_path": "NN\\models\\saved\\decision\\decision_20250704_082022.pt",
-      "created_at": "2025-07-04T08:20:22.416087",
-      "file_size_mb": 0.06720924377441406,
-      "performance_score": 102.79971076963062,
-      "accuracy": null,
-      "loss": 2.8923120591883844e-06,
-      "val_accuracy": null,
-      "val_loss": null,
-      "reward": null,
-      "pnl": null,
-      "epoch": null,
-      "training_time_hours": null,
-      "total_parameters": null,
-      "wandb_run_id": null,
-      "wandb_artifact_name": null
-    },
-    {
-      "checkpoint_id": "decision_20250704_082021",
-      "model_name": "decision",
-      "model_type": "decision_fusion",
-      "file_path": "NN\\models\\saved\\decision\\decision_20250704_082021.pt",
-      "created_at": "2025-07-04T08:20:21.900854",
-      "file_size_mb": 0.06720924377441406,
-      "performance_score": 102.79970038321,
-      "accuracy": null,
-      "loss": 2.996176877014177e-06,
-      "val_accuracy": null,
-      "val_loss": null,
-      "reward": null,
-      "pnl": null,
-      "epoch": null,
-      "training_time_hours": null,
-      "total_parameters": null,
-      "wandb_run_id": null,
-      "wandb_artifact_name": null
-    },
-    {
-      "checkpoint_id": "decision_20250704_082022",
-      "model_name": "decision",
-      "model_type": "decision_fusion",
-      "file_path": "NN\\models\\saved\\decision\\decision_20250704_082022.pt",
-      "created_at": "2025-07-04T08:20:22.294191",
-      "file_size_mb": 0.06720924377441406,
-      "performance_score": 102.79969219038436,
-      "accuracy": null,
-      "loss": 3.0781056310808756e-06,
-      "val_accuracy": null,
-      "val_loss": null,
-      "reward": null,
-      "pnl": null,
-      "epoch": null,
-      "training_time_hours": null,
-      "total_parameters": null,
-      "wandb_run_id": null,
-      "wandb_artifact_name": null
-    },
-    {
-      "checkpoint_id": "decision_20250704_134829",
-      "model_name": "decision",
-      "model_type": "decision_fusion",
-      "file_path": "NN\\models\\saved\\decision\\decision_20250704_134829.pt",
-      "created_at": "2025-07-04T13:48:29.903250",
-      "file_size_mb": 0.06720924377441406,
-      "performance_score": 102.79967532851693,
-      "accuracy": null,
-      "loss": 3.2467253719811344e-06,
-      "val_accuracy": null,
-      "val_loss": null,
-      "reward": null,
-      "pnl": null,
-      "epoch": null,
-      "training_time_hours": null,
-      "total_parameters": null,
-      "wandb_run_id": null,
-      "wandb_artifact_name": null
-    },
-    {
-      "checkpoint_id": "decision_20250704_214714",
-      "model_name": "decision",
-      "model_type": "decision_fusion",
-      "file_path": "NN\\models\\saved\\decision\\decision_20250704_214714.pt",
-      "created_at": "2025-07-04T21:47:14.427187",
-      "file_size_mb": 0.06720924377441406,
-      "performance_score": 102.79966325731509,
-      "accuracy": null,
-      "loss": 3.3674381887394134e-06,
-      "val_accuracy": null,
-      "val_loss": null,
-      "reward": null,
-      "pnl": null,
-      "epoch": null,
-      "training_time_hours": null,
-      "total_parameters": null,
-      "wandb_run_id": null,
-      "wandb_artifact_name": null
-    }
-  ]
 }

NN/training/enhanced_realtime_training.py

@@ -1969,7 +1969,17 @@ class EnhancedRealtimeTrainingSystem:
             
             self.last_prediction_time[symbol] = int(current_time)
             
-            logger.info(f"Forward DQN prediction: {symbol} action={['BUY','SELL','HOLD'][action]} confidence={confidence:.2f} target={target_time.strftime('%H:%M:%S')}")
+            # Robust action labeling
+            if action is None:
+                action_label = 'HOLD'
+            elif action == 0:
+                action_label = 'SELL'
+            elif action == 1:
+                action_label = 'BUY'
+            else:
+                action_label = 'UNKNOWN'
+            
+            logger.info(f"Forward DQN prediction: {symbol} action={action_label} confidence={confidence:.2f} target={target_time.strftime('%H:%M:%S')}")
             
         except Exception as e:
             logger.error(f"Error generating forward DQN prediction: {e}")

config.yaml

@@ -81,8 +81,8 @@ orchestrator:
   # Model weights for decision combination
   cnn_weight: 0.7      # Weight for CNN predictions
   rl_weight: 0.3       # Weight for RL decisions
-  confidence_threshold: 0.15
+  confidence_threshold: 0.45
-  confidence_threshold_close: 0.08
+  confidence_threshold_close: 0.30
   decision_frequency: 30
   
   # Multi-symbol coordination

core/orchestrator.py

@@ -349,7 +349,8 @@ class TradingOrchestrator:
                     try:
                         self.cob_rl_agent.load_model() # This loads the state into the model
                         from utils.checkpoint_manager import load_best_checkpoint
-                        result = load_best_checkpoint("cob_rl_model")
+                        # Use consistent model name with checkpoint manager and get_model_states
+                        result = load_best_checkpoint("cob_rl")
                         if result:
                             file_path, metadata = result
                             self.model_states['cob_rl']['initial_loss'] = getattr(metadata, 'initial_loss', None)
@@ -1592,13 +1593,16 @@ class TradingOrchestrator:
                 logger.warning("EnhancedRealtimeTrainingSystem not available - training disabled")
                 self.training_enabled = False
                 return
-            
+                    # Initialize unified training manager
-            # Initialize the enhanced training system
+            from utils.training_integration import get_unified_training_manager
-            self.enhanced_training_system = EnhancedRealtimeTrainingSystem(
+            self.training_manager = get_unified_training_manager(
                 orchestrator=self,
                 data_provider=self.data_provider,
-                dashboard=None  # Will be set by dashboard when available
+                dashboard=None
             )
+            self.training_manager.initialize()
+            # Keep backward-compatible attribute
+            self.enhanced_training_system = getattr(self.training_manager, 'training_system', None)
             
             logger.info("Enhanced real-time training system initialized")
             logger.info("  - Real-time model training: ENABLED")
@@ -1614,11 +1618,11 @@ class TradingOrchestrator:
     def start_enhanced_training(self):
         """Start the enhanced real-time training system"""
         try:
-            if not self.training_enabled or not self.enhanced_training_system:
+            if not self.training_enabled or not getattr(self, 'training_manager', None):
                 logger.warning("Enhanced training system not available")
                 return False
             
-            self.enhanced_training_system.start_training()
+            self.training_manager.start()
             logger.info("Enhanced real-time training started")
             return True
             
@@ -1629,8 +1633,8 @@ class TradingOrchestrator:
     def stop_enhanced_training(self):
         """Stop the enhanced real-time training system"""
         try:
-            if self.enhanced_training_system:
+            if getattr(self, 'training_manager', None):
-                self.enhanced_training_system.stop_training()
+                self.training_manager.stop()
                 logger.info("Enhanced real-time training stopped")
                 return True
             return False

core/realtime_rl_cob_trader.py

@@ -731,7 +731,8 @@ class RealtimeRLCOBTrader:
             with self.training_lock:
                 # Check if we have enough data for training
                 predictions = list(self.prediction_history[symbol])
-                if len(predictions) < 10:
+                # Train with fewer samples to kickstart learning
+                if len(predictions) < 6:
                     return
                 
                 # Calculate rewards for recent predictions
@@ -739,11 +740,11 @@ class RealtimeRLCOBTrader:
                 
                 # Filter predictions with calculated rewards
                 training_predictions = [p for p in predictions if p.reward is not None]
-                if len(training_predictions) < 5:
+                if len(training_predictions) < 3:
                     return
                 
                 # Prepare training batch
-                batch_size = min(32, len(training_predictions))
+                batch_size = min(16, len(training_predictions))
                 batch_predictions = training_predictions[-batch_size:]
                 
                 # Train model

enhanced_realtime_training.py

@@ -0,0 +1,8 @@
+"""
+Shim module to expose EnhancedRealtimeTrainingSystem at project root.
+This avoids import issues when modules do `from enhanced_realtime_training import EnhancedRealtimeTrainingSystem`.
+"""
+
+from NN.training.enhanced_realtime_training import EnhancedRealtimeTrainingSystem
+
+__all__ = ["EnhancedRealtimeTrainingSystem"]

requirements.txt

@@ -10,7 +10,6 @@ tensorboard>=2.15.0
 scikit-learn>=1.3.0
 matplotlib>=3.7.0
 seaborn>=0.12.0
-wandb>=0.16.0
 
 ta>=0.11.0
 ccxt>=4.0.0

run_clean_dashboard.py

@@ -3,6 +3,34 @@
 Clean Trading Dashboard Runner with Enhanced Stability and Error Handling
 """
 
+# Ensure we run with the project's virtual environment Python
+try:
+    import os
+    import sys
+    from pathlib import Path
+    import platform
+
+    def _ensure_project_venv():
+        try:
+            project_root = Path(__file__).resolve().parent
+            if platform.system().lower().startswith('win'):
+                venv_python = project_root / 'venv' / 'Scripts' / 'python.exe'
+            else:
+                venv_python = project_root / 'venv' / 'bin' / 'python'
+
+            if venv_python.exists():
+                current = Path(sys.executable).resolve()
+                target = venv_python.resolve()
+                if current != target:
+                    os.execv(str(target), [str(target), *sys.argv])
+        except Exception:
+            # If anything goes wrong, continue with current interpreter
+            pass
+
+    _ensure_project_venv()
+except Exception:
+    pass
+
 import sys
 import logging
 import traceback

utils/checkpoint_manager.py

@@ -14,11 +14,7 @@ from collections import defaultdict
 import torch
 import random
 
-try:
+WANDB_AVAILABLE = False
-    import wandb
-    WANDB_AVAILABLE = True
-except ImportError:
-    WANDB_AVAILABLE = False
 
 logger = logging.getLogger(__name__)
 
@@ -58,13 +54,13 @@ class CheckpointManager:
                  base_checkpoint_dir: str = "NN/models/saved",
                  max_checkpoints_per_model: int = 5,
                  metadata_file: str = "checkpoint_metadata.json",
-                 enable_wandb: bool = True):
+                 enable_wandb: bool = False):
         self.base_dir = Path(base_checkpoint_dir)
         self.base_dir.mkdir(parents=True, exist_ok=True)
         
         self.max_checkpoints = max_checkpoints_per_model
         self.metadata_file = self.base_dir / metadata_file
-        self.enable_wandb = enable_wandb and WANDB_AVAILABLE
+        self.enable_wandb = False
         
         self.checkpoints: Dict[str, List[CheckpointMetadata]] = defaultdict(list)
         self._load_metadata()
@@ -115,10 +111,7 @@ class CheckpointManager:
                 total_parameters=training_metadata.get('total_parameters') if training_metadata else None
             )
             
-            if self.enable_wandb and wandb.run is not None:
+            # W&B disabled
-                artifact_name = self._upload_to_wandb(checkpoint_path, metadata)
-                metadata.wandb_run_id = wandb.run.id
-                metadata.wandb_artifact_name = artifact_name
             
             self.checkpoints[model_name].append(metadata)
             self._rotate_checkpoints(model_name)
@@ -273,19 +266,7 @@ class CheckpointManager:
                 logger.error(f"Error removing rotated checkpoint {checkpoint.checkpoint_id}: {e}")
     
     def _upload_to_wandb(self, file_path: Path, metadata: CheckpointMetadata) -> Optional[str]:
-        try:
+        return None
-            if not self.enable_wandb or wandb.run is None:
-                return None
-            
-            artifact_name = f"{metadata.model_name}_checkpoint"
-            artifact = wandb.Artifact(artifact_name, type="model")
-            artifact.add_file(str(file_path))
-            wandb.log_artifact(artifact)
-            
-            return artifact_name
-        except Exception as e:
-            logger.error(f"Error uploading to W&B: {e}")
-            return None
     
     def _load_metadata(self):
         try:
@@ -404,6 +385,56 @@ class CheckpointManager:
                         logger.debug(f"Found legacy model file in subdirectory: {candidate_path}")
                         return candidate_path
         
+        # Extended search: scan common project model directories for best checkpoints
+        try:
+            # Attempt to infer project root from base_dir (NN/models/saved -> root)
+            project_root = base_dir.resolve().parent.parent.parent
+        except Exception:
+            project_root = Path(".").resolve()
+        additional_dirs = [
+            project_root / "models",
+            project_root / "models" / "archive",
+            project_root / "models" / "backtest",
+        ]
+
+        def _match_legacy_name(candidate: Path, model: str) -> bool:
+            name = candidate.name.lower()
+            model_keys = {
+                'dqn_agent': ['dqn', 'agent', 'policy'],
+                'enhanced_cnn': ['cnn', 'optimized_short_term'],
+                'extrema_trainer': ['supervised', 'extrema'],
+                'cob_rl': ['cob', 'rl', 'policy'],
+                'decision': ['decision', 'transformer']
+            }.get(model, [model])
+            return any(k in name for k in model_keys)
+
+        candidates: List[Path] = []
+        for adir in additional_dirs:
+            if not adir.exists():
+                continue
+            try:
+                for pt in adir.rglob('*.pt'):
+                    # Prefer files that indicate "best" and match model hints
+                    lname = pt.name.lower()
+                    if 'best' in lname and _match_legacy_name(pt, model_name):
+                        candidates.append(pt)
+                # Do not add generic fallbacks to avoid mismatched model types
+            except Exception:
+                # Ignore directory traversal issues
+                pass
+
+        if candidates:
+            # Pick the most recently modified candidate
+            try:
+                best = max(candidates, key=lambda p: p.stat().st_mtime)
+                logger.debug(f"Found legacy model file in project models dir: {best}")
+                return best
+            except Exception:
+                # If stat fails, just return the first one deterministically
+                candidates.sort()
+                logger.debug(f"Found legacy model file in project models dir: {candidates[0]}")
+                return candidates[0]
+
         return None
     
     def _create_legacy_metadata(self, model_name: str, file_path: Path) -> CheckpointMetadata:

utils/reward_calculator.py

@@ -75,15 +75,18 @@ class RewardCalculator:
     def calculate_basic_reward(self, pnl, confidence):
         """Calculate basic training reward based on P&L and confidence"""
         try:
+            # Reward based on net PnL after fees and confidence alignment
             base_reward = pnl
-            if pnl < 0 and confidence > 0.7:
+            # Stronger penalty for confident wrong decisions
-                confidence_adjustment = -confidence * 2
+            if pnl < 0 and confidence >= 0.6:
-            elif pnl > 0 and confidence > 0.7:
+                confidence_adjustment = -confidence * 3.0
-                confidence_adjustment = confidence * 1.5
+            elif pnl > 0 and confidence >= 0.6:
+                confidence_adjustment = confidence * 1.0
             else:
-                confidence_adjustment = 0
+                confidence_adjustment = 0.0
             final_reward = base_reward + confidence_adjustment
-            normalized_reward = np.tanh(final_reward / 10.0)
+            # Reduce tanh compression so small PnL changes are not flattened
+            normalized_reward = np.tanh(final_reward / 2.5)
             logger.debug(f"Basic reward calculation: P&L={pnl:.4f}, confidence={confidence:.2f}, reward={normalized_reward:.4f}")
             return float(normalized_reward)
         except Exception as e:

utils/training_integration.py

@@ -14,7 +14,7 @@ from .checkpoint_manager import get_checkpoint_manager, save_checkpoint, load_be
 logger = logging.getLogger(__name__)
 
 class TrainingIntegration:
-    def __init__(self, enable_wandb: bool = True):
+    def __init__(self, enable_wandb: bool = False):
         self.checkpoint_manager = get_checkpoint_manager()
         self.enable_wandb = enable_wandb
         
@@ -22,24 +22,8 @@ class TrainingIntegration:
             self._init_wandb()
     
     def _init_wandb(self):
-        try:
+        # Disabled by default to avoid CLI prompts
-            import wandb
+        pass
-            
-            if wandb.run is None:
-                wandb.init(
-                    project="gogo2-trading",
-                    name=f"training_{datetime.now().strftime('%Y%m%d_%H%M%S')}",
-                    config={
-                        "max_checkpoints_per_model": self.checkpoint_manager.max_checkpoints,
-                        "checkpoint_dir": str(self.checkpoint_manager.base_dir)
-                    }
-                )
-                logger.info(f"Initialized W&B run: {wandb.run.id}")
-            
-        except ImportError:
-            logger.warning("W&B not available - checkpoint management will work without it")
-        except Exception as e:
-            logger.error(f"Error initializing W&B: {e}")
     
     def save_cnn_checkpoint(self, 
                            cnn_model, 
@@ -64,19 +48,7 @@ class TrainingIntegration:
                 'total_parameters': self._count_parameters(cnn_model)
             }
             
-            if self.enable_wandb:
+            # W&B disabled
-                try:
-                    import wandb
-                    if wandb.run is not None:
-                        wandb.log({
-                            f"{model_name}/train_accuracy": train_accuracy,
-                            f"{model_name}/val_accuracy": val_accuracy,
-                            f"{model_name}/train_loss": train_loss,
-                            f"{model_name}/val_loss": val_loss,
-                            f"{model_name}/epoch": epoch
-                        })
-                except Exception as e:
-                    logger.warning(f"Error logging to W&B: {e}")
             
             metadata = save_checkpoint(
                 model=cnn_model,
@@ -120,22 +92,7 @@ class TrainingIntegration:
                 'total_parameters': self._count_parameters(rl_agent)
             }
             
-            if self.enable_wandb:
+            # W&B disabled
-                try:
-                    import wandb
-                    if wandb.run is not None:
-                        wandb.log({
-                            f"{model_name}/avg_reward": avg_reward,
-                            f"{model_name}/best_reward": best_reward,
-                            f"{model_name}/epsilon": epsilon,
-                            f"{model_name}/episode": episode
-                        })
-                        
-                        if total_pnl is not None:
-                            wandb.log({f"{model_name}/total_pnl": total_pnl})
-                            
-                except Exception as e:
-                    logger.warning(f"Error logging to W&B: {e}")
             
             metadata = save_checkpoint(
                 model=rl_agent,
@@ -202,3 +159,75 @@ def get_training_integration() -> TrainingIntegration:
     if _training_integration is None:
         _training_integration = TrainingIntegration()
     return _training_integration
+
+# ---------------- Unified Training Manager ----------------
+
+class UnifiedTrainingManager:
+    """Single entry point to manage all training in the system.
+
+    Coordinates EnhancedRealtimeTrainingSystem and provides start/stop/status.
+    """
+
+    def __init__(self, orchestrator, data_provider, dashboard=None):
+        self.orchestrator = orchestrator
+        self.data_provider = data_provider
+        self.dashboard = dashboard
+        self.training_system = None
+        self.started = False
+
+    def initialize(self) -> bool:
+        try:
+            # Import via project root shim to avoid path issues
+            from enhanced_realtime_training import EnhancedRealtimeTrainingSystem
+            self.training_system = EnhancedRealtimeTrainingSystem(
+                orchestrator=self.orchestrator,
+                data_provider=self.data_provider,
+                dashboard=self.dashboard
+            )
+            return True
+        except Exception as e:
+            logger.error(f"UnifiedTrainingManager: failed to initialize training system: {e}")
+            self.training_system = None
+            return False
+
+    def start(self) -> bool:
+        try:
+            if self.training_system is None:
+                if not self.initialize():
+                    return False
+            self.training_system.start_training()
+            self.started = True
+            logger.info("UnifiedTrainingManager: training started")
+            return True
+        except Exception as e:
+            logger.error(f"UnifiedTrainingManager: error starting training: {e}")
+            return False
+
+    def stop(self) -> bool:
+        try:
+            if self.training_system and self.started:
+                self.training_system.stop_training()
+                self.started = False
+                logger.info("UnifiedTrainingManager: training stopped")
+            return True
+        except Exception as e:
+            logger.error(f"UnifiedTrainingManager: error stopping training: {e}")
+            return False
+
+    def get_stats(self) -> Dict[str, Any]:
+        try:
+            if self.training_system and hasattr(self.training_system, 'get_training_stats'):
+                return self.training_system.get_training_stats()
+            return {}
+        except Exception:
+            return {}
+
+_unified_training_manager = None
+
+def get_unified_training_manager(orchestrator=None, data_provider=None, dashboard=None) -> UnifiedTrainingManager:
+    global _unified_training_manager
+    if _unified_training_manager is None:
+        if orchestrator is None or data_provider is None:
+            raise ValueError("orchestrator and data_provider are required for first-time initialization")
+        _unified_training_manager = UnifiedTrainingManager(orchestrator, data_provider, dashboard)
+    return _unified_training_manager

web/clean_dashboard.py

@@ -642,6 +642,35 @@ class CleanTradingDashboard:
                 logger.error(f"Error updating trades table: {e}")
                 return html.P(f"Error: {str(e)}", className="text-danger")
         
+        @self.app.callback(
+            Output('training-status', 'children'),
+            [Input('start-training-btn', 'n_clicks'),
+             Input('stop-training-btn', 'n_clicks')],
+            prevent_initial_call=True
+        )
+        def control_training(start_clicks, stop_clicks):
+            try:
+                from utils.training_integration import get_unified_training_manager
+                manager = get_unified_training_manager(
+                    orchestrator=self.orchestrator,
+                    data_provider=self.data_provider,
+                    dashboard=self
+                )
+                ctx = dash.callback_context
+                if not ctx.triggered:
+                    raise PreventUpdate
+                trigger_id = ctx.triggered[0]['prop_id'].split('.')[0]
+                if trigger_id == 'start-training-btn':
+                    ok = manager.start()
+                    return 'Running' if ok else 'Error'
+                elif trigger_id == 'stop-training-btn':
+                    ok = manager.stop()
+                    return 'Stopped' if ok else 'Error'
+                return 'Idle'
+            except Exception as e:
+                logger.error(f"Training control error: {e}")
+                return 'Error'
+
         @self.app.callback(
             [Output('eth-cob-content', 'children'),
              Output('btc-cob-content', 'children')],
@@ -5215,7 +5244,12 @@ class CleanTradingDashboard:
         """Start the Dash server"""
         try:
             logger.info(f"TRADING: Starting Clean Dashboard at http://{host}:{port}")
-            self.app.run(host=host, port=port, debug=debug)
+            # Run the Dash app normally; launch/activation is handled by the runner
+            if hasattr(self, 'app') and self.app is not None:
+                # Dash 3.x: use app.run
+                self.app.run(host=host, port=port, debug=debug)
+            else:
+                logger.error("Dash app is not initialized")
         except Exception as e:
             logger.error(f"Error starting dashboard server: {e}")
             raise

web/layout_manager.py

@@ -153,6 +153,29 @@ class DashboardLayoutManager:
                             tooltip={"placement": "bottom", "always_visible": False}
                         )
                     ], className="mb-2"),
+                    # Training Controls
+                    html.Div([
+                        html.Label([
+                            html.I(className="fas fa-play me-1"),
+                            "Training Controls"
+                        ], className="form-label small mb-1"),
+                        html.Div([
+                            html.Button([
+                                html.I(className="fas fa-play me-1"),
+                                "Start Training"
+                            ], id="start-training-btn", className="btn btn-success btn-sm me-2", 
+                               style={"fontSize": "10px", "padding": "2px 8px"}),
+                            html.Button([
+                                html.I(className="fas fa-stop me-1"),
+                                "Stop Training"
+                            ], id="stop-training-btn", className="btn btn-danger btn-sm", 
+                               style={"fontSize": "10px", "padding": "2px 8px"})
+                        ], className="d-flex align-items-center mb-1"),
+                        html.Div([
+                            html.Span("Training:", className="small me-1"),
+                            html.Span(id="training-status", children="Idle", className="badge bg-secondary small")
+                        ])
+                    ], className="mb-2"),
                     
                     # Entry Aggressiveness Control
                     html.Div([