From 14086a898e73e906501556522099e32d6b797d3a Mon Sep 17 00:00:00 2001
From: Dobromir Popov <dobromir.popov@gateway.one>
Date: Wed, 30 Jul 2025 11:42:04 +0300
Subject: [PATCH] indents

---
 core/orchestrator.py | 76 ++++++++++++++++++++++----------------------
 1 file changed, 38 insertions(+), 38 deletions(-)

diff --git a/core/orchestrator.py b/core/orchestrator.py
index 4e8459e..8e33baa 100644
--- a/core/orchestrator.py
+++ b/core/orchestrator.py
@@ -4554,52 +4554,52 @@ class TradingOrchestrator:
                 memory_size = len(getattr(model, "memory", []))
                 batch_size = getattr(model, "batch_size", 32)
                 if memory_size >= batch_size:
-                                            self.training_logger.info(f"RL TRAINING STARTED for {model_name.upper()}:")
+                        self.training_logger.info(f"RL TRAINING STARTED for {model_name.upper()}:")
                         self.training_logger.info(f"  Experiences: {memory_size}")
                         self.training_logger.info(f"  Batch size: {batch_size}")
                         self.training_logger.info(f"  Action: {prediction['action']}")
                         self.training_logger.info(f"  Reward: {reward:.3f}")
 
-                    # Ensure model is in training mode
-                    if hasattr(model, "policy_net"):
-                        model.policy_net.train()
+                # Ensure model is in training mode
+                if hasattr(model, "policy_net"):
+                    model.policy_net.train()
 
-                    training_start_time = time.time()
-                    training_loss = model.replay()
-                    training_duration_ms = (time.time() - training_start_time) * 1000
+                training_start_time = time.time()
+                training_loss = model.replay()
+                training_duration_ms = (time.time() - training_start_time) * 1000
 
-                    if training_loss is not None and training_loss > 0:
-                        self.update_model_loss(model_name, training_loss)
-                        self._update_model_training_statistics(
-                            model_name, training_loss, training_duration_ms
-                        )
-                        self.training_logger.info(f"RL TRAINING COMPLETED for {model_name.upper()}:")
-                        self.training_logger.info(f"  Loss: {training_loss:.4f}")
-                        self.training_logger.info(f"  Training time: {training_duration_ms:.1f}ms")
-                        self.training_logger.info(f"  Experiences used: {memory_size}")
-                        self.training_logger.info(f"  Action: {prediction['action']}")
-                        self.training_logger.info(f"  Reward: {reward:.3f}")
-                        self.training_logger.info(f"  State shape: {state.shape}")
-                        return True
-                    elif training_loss == 0.0:
-                        logger.warning(
-                            f"RL training returned zero loss for {model_name} - possible gradient issue"
-                        )
-                        # Still update training statistics
-                        self._update_model_training_statistics(
-                            model_name, training_duration_ms=training_duration_ms
-                        )
-                        return False  # Training failed
-                    else:
-                        # Still update training statistics even if no loss returned
-                        self._update_model_training_statistics(
-                            model_name, training_duration_ms=training_duration_ms
-                        )
-                else:
-                    logger.debug(
-                        f"Not enough experiences for {model_name}: {memory_size}/{batch_size}"
+                if training_loss is not None and training_loss > 0:
+                    self.update_model_loss(model_name, training_loss)
+                    self._update_model_training_statistics(
+                        model_name, training_loss, training_duration_ms
                     )
-                    return True  # Experience added successfully, training will happen later
+                    self.training_logger.info(f"RL TRAINING COMPLETED for {model_name.upper()}:")
+                    self.training_logger.info(f"  Loss: {training_loss:.4f}")
+                    self.training_logger.info(f"  Training time: {training_duration_ms:.1f}ms")
+                    self.training_logger.info(f"  Experiences used: {memory_size}")
+                    self.training_logger.info(f"  Action: {prediction['action']}")
+                    self.training_logger.info(f"  Reward: {reward:.3f}")
+                    self.training_logger.info(f"  State shape: {state.shape}")
+                    return True
+                elif training_loss == 0.0:
+                    logger.warning(
+                        f"RL training returned zero loss for {model_name} - possible gradient issue"
+                    )
+                    # Still update training statistics
+                    self._update_model_training_statistics(
+                        model_name, training_duration_ms=training_duration_ms
+                    )
+                    return False  # Training failed
+                else:
+                    # Still update training statistics even if no loss returned
+                    self._update_model_training_statistics(
+                        model_name, training_duration_ms=training_duration_ms
+                    )
+            else:
+                logger.debug(
+                    f"Not enough experiences for {model_name}: {memory_size}/{batch_size}"
+                )
+                return True  # Experience added successfully, training will happen later
 
             return False