refactoring

NN/models/checkpoints/registry_metadata.json

@@ -0,0 +1,25 @@
+{
+  "models": {
+    "test_model": {
+      "type": "cnn",
+      "latest_path": "models/cnn/saved/test_model_latest.pt",
+      "last_saved": "20250908_132919",
+      "save_count": 1
+    },
+    "audit_test_model": {
+      "type": "cnn",
+      "latest_path": "models/cnn/saved/audit_test_model_latest.pt",
+      "last_saved": "20250908_142204",
+      "save_count": 2,
+      "checkpoints": [
+        {
+          "id": "audit_test_model_20250908_142204_0.8500",
+          "path": "models/cnn/checkpoints/audit_test_model_20250908_142204_0.8500.pt",
+          "performance_score": 0.85,
+          "timestamp": "20250908_142204"
+        }
+      ]
+    }
+  },
+  "last_updated": "2025-09-08T14:22:04.917612"
+}

NN/models/checkpoints/saved/session_metadata.json

@@ -0,0 +1,17 @@
+{
+  "timestamp": "2025-08-30T01:03:28.549034",
+  "session_pnl": 0.9740795673949083,
+  "trade_count": 44,
+  "stored_models": [
+    [
+      "DQN",
+      null
+    ],
+    [
+      "CNN",
+      null
+    ]
+  ],
+  "training_iterations": 0,
+  "model_performance": {}
+}

NN/models/checkpoints/saved/test_simple_model/test_simple_model_metadata.json

@@ -0,0 +1,8 @@
+{
+  "model_name": "test_simple_model",
+  "model_type": "test",
+  "saved_at": "2025-09-02T15:30:36.295046",
+  "save_method": "improved_model_saver",
+  "test": true,
+  "accuracy": 0.95
+}

NN/models/cnn_model.py

@@ -20,8 +20,8 @@ import torch.nn.functional as F
 from typing import Dict, Any, Optional, Tuple
 
 # Import checkpoint management
-from utils.checkpoint_manager import save_checkpoint, load_best_checkpoint
-from utils.model_registry import get_model_registry
+from NN.training.model_manager import save_checkpoint, load_best_checkpoint
+from NN.training.model_manager import create_model_manager
 
 # Configure logging
 logger = logging.getLogger(__name__)
@@ -778,7 +778,7 @@ class CNNModelTrainer:
     def save_model(self, filepath: str = None, metadata: Optional[Dict] = None):
         """Save model with metadata using unified registry"""
         try:
-            from utils.model_registry import save_model
+            from NN.training.model_manager import save_model
 
             # Prepare model data
             model_data = {
@@ -826,7 +826,7 @@ class CNNModelTrainer:
     def load_model(self, filepath: str = None) -> Dict:
         """Load model from unified registry or file"""
         try:
-            from utils.model_registry import load_model
+            from NN.training.model_manager import load_model
 
             # Use unified registry if no filepath or if it's a models/ path
             if filepath is None or filepath.startswith('models/'):

NN/models/dqn_agent.py

@@ -15,8 +15,8 @@ import time
 sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
 
 # Import checkpoint management
-from utils.checkpoint_manager import save_checkpoint, load_best_checkpoint
-from utils.model_registry import get_model_registry
+from NN.training.model_manager import save_checkpoint, load_best_checkpoint
+from NN.training.model_manager import create_model_manager
 
 # Configure logger
 logger = logging.getLogger(__name__)
@@ -1333,7 +1333,7 @@ class DQNAgent:
     def save(self, path: str = None):
         """Save model and agent state using unified registry"""
         try:
-            from utils.model_registry import save_model
+            from NN.training.model_manager import save_model
 
             # Use unified registry if no path or if it's a models/ path
             if path is None or path.startswith('models/'):
@@ -1393,7 +1393,7 @@ class DQNAgent:
     def load(self, path: str = None):
         """Load model and agent state from unified registry or file"""
         try:
-            from utils.model_registry import load_model
+            from NN.training.model_manager import load_model
 
             # Use unified registry if no path or if it's a models/ path
             if path is None or path.startswith('models/'):