device fix , TZ fix
This commit is contained in:
Dobromir Popov
@ -1478,7 +1478,11 @@ class DataProvider:
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# Check for cached data and determine what we need to fetch
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cached_data = self._load_monthly_data_from_cache(symbol)
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end_time = datetime.utcnow()
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import pytz
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utc = pytz.UTC
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sofia_tz = pytz.timezone('Europe/Sofia')
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end_time = datetime.utcnow().replace(tzinfo=utc).astimezone(sofia_tz)
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start_time = end_time - timedelta(days=30)
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if cached_data is not None and not cached_data.empty:
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@ -1496,6 +1500,12 @@ class DataProvider:
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# Check if we need to fill gaps
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gap_start = cache_end + timedelta(minutes=1)
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# Ensure gap_start has same timezone as end_time for comparison
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if gap_start.tzinfo is None:
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gap_start = sofia_tz.localize(gap_start)
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elif gap_start.tzinfo != sofia_tz:
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gap_start = gap_start.astimezone(sofia_tz)
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if gap_start < end_time:
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# Need to fill gap from cache_end to now
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logger.info(f"Filling gap from {gap_start} to {end_time}")
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@ -1573,8 +1583,10 @@ class DataProvider:
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'taker_buy_quote', 'ignore'
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])
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# Process columns
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df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms')
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# Process columns with proper timezone handling
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df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms', utc=True)
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# Convert from UTC to Europe/Sofia timezone to match cached data
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df['timestamp'] = df['timestamp'].dt.tz_convert('Europe/Sofia')
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for col in ['open', 'high', 'low', 'close', 'volume']:
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df[col] = df[col].astype(float)
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@ -1644,8 +1656,10 @@ class DataProvider:
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'taker_buy_quote', 'ignore'
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])
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# Process columns
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batch_df['timestamp'] = pd.to_datetime(batch_df['timestamp'], unit='ms')
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# Process columns with proper timezone handling
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batch_df['timestamp'] = pd.to_datetime(batch_df['timestamp'], unit='ms', utc=True)
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# Convert from UTC to Europe/Sofia timezone to match cached data
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batch_df['timestamp'] = batch_df['timestamp'].dt.tz_convert('Europe/Sofia')
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for col in ['open', 'high', 'low', 'close', 'volume']:
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batch_df[col] = batch_df[col].astype(float)
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@ -2377,11 +2377,10 @@ class TradingOrchestrator:
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return None
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async def _train_cnn_model(self, model, model_name: str, record: Dict, prediction: Dict, reward: float) -> bool:
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"""Train CNN model with training samples"""
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"""Train CNN model directly (no adapter)"""
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try:
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# Direct CNN model training (no adapter)
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if hasattr(self, 'cnn_model') and self.cnn_model and 'cnn' in model_name.lower():
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try:
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# Direct CNN model training (no adapter)
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if hasattr(self, 'cnn_model') and self.cnn_model and 'cnn' in model_name.lower():
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symbol = record.get('symbol', 'ETH/USDT')
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actual_action = prediction['action']
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@ -2441,47 +2440,36 @@ class TradingOrchestrator:
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else:
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logger.warning(f"No model input available for CNN training")
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return False
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except Exception as e:
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logger.error(f"Error in direct CNN training: {e}")
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return False
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# Try direct model training methods
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# Try model interface training methods
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elif hasattr(model, 'add_training_sample'):
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symbol = record.get('symbol', 'ETH/USDT')
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actual_action = prediction['action']
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model.add_training_sample(symbol, actual_action, reward)
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logger.debug(f"Added training sample to {model_name}: action={actual_action}, reward={reward:.3f}")
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# Trigger training if batch size is met
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if hasattr(model, 'train') and hasattr(model, 'training_data') and hasattr(model, 'batch_size'):
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if len(model.training_data) >= model.batch_size:
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# If model has train method, trigger training
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if hasattr(model, 'train') and callable(getattr(model, 'train')):
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try:
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training_start_time = time.time()
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training_results = model.train(epochs=1)
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training_duration_ms = (time.time() - training_start_time) * 1000
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if training_results and 'loss' in training_results:
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current_loss = training_results['loss']
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accuracy = training_results.get('accuracy', 0.0)
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# Validate training results
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if accuracy >= 0.99:
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logger.warning(f"CNN training shows suspiciously high accuracy: {accuracy:.4f} - possible overfitting")
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else:
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self.update_model_loss(model_name, current_loss)
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self.update_model_loss(model_name, current_loss)
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self._update_model_training_statistics(model_name, current_loss, training_duration_ms)
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logger.debug(f"CNN training completed: loss={current_loss:.4f}, time={training_duration_ms:.1f}ms")
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return True
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logger.debug(f"Model {model_name} training completed: loss={current_loss:.4f}")
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else:
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# Still update training statistics even if no loss returned
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self._update_model_training_statistics(model_name, training_duration_ms=training_duration_ms)
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return True # Sample added successfully
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except Exception as e:
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logger.error(f"Error training {model_name}: {e}")
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return True
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# Try basic training method for EnhancedCNN
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# Basic acknowledgment for other training methods
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elif hasattr(model, 'train'):
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logger.debug(f"Using basic train method for {model_name}")
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# For now, just acknowledge that training was attempted
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logger.debug(f"CNN model {model_name} training acknowledged (basic train method available)")
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return True
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@ -2622,7 +2610,8 @@ class TradingOrchestrator:
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# Convert to tensor and ensure proper device placement
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device = next(self.cnn_model.parameters()).device
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features_tensor = torch.tensor(features, dtype=torch.float32, device=device)
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import torch as torch_module # Explicit import to avoid scoping issues
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features_tensor = torch_module.tensor(features, dtype=torch_module.float32, device=device)
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# Ensure batch dimension
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if features_tensor.dim() == 1:
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@ -2632,12 +2621,12 @@ class TradingOrchestrator:
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self.cnn_model.eval()
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# Get prediction from CNN model
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with torch.no_grad():
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with torch_module.no_grad():
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q_values, extrema_pred, price_pred, features_refined, advanced_pred = self.cnn_model(features_tensor)
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# Convert to probabilities using softmax
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action_probs = torch.softmax(q_values, dim=1)
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action_idx = torch.argmax(action_probs, dim=1).item()
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action_probs = torch_module.softmax(q_values, dim=1)
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action_idx = torch_module.argmax(action_probs, dim=1).item()
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confidence = float(action_probs[0, action_idx].item())
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# Map action index to action string
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@ -2679,9 +2668,9 @@ class TradingOrchestrator:
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import traceback
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traceback.print_exc()
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# Fallback to direct model inference using BaseDataInput (unified approach)
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# Remove this fallback - direct CNN inference should work above
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if not predictions:
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logger.warning(f"CNN adapter failed for {symbol}, trying direct model inference with BaseDataInput")
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logger.debug(f"No CNN predictions generated for {symbol} - this is expected if CNN model is not properly initialized")
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try:
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# Use the already available base_data (no need to rebuild)
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@ -2694,10 +2683,9 @@ class TradingOrchestrator:
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# Use the model's act method with unified input
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if hasattr(model.model, 'act'):
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# Convert to tensor format expected by enhanced_cnn
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import torch
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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features_tensor = torch.tensor(feature_vector, dtype=torch.float32, device=device)
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# Convert to tensor format expected by enhanced_cnn
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device = torch_module.device('cuda' if torch_module.cuda.is_available() else 'cpu')
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features_tensor = torch_module.tensor(feature_vector, dtype=torch_module.float32, device=device)
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# Call the model's act method
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action_idx, confidence, action_probs = model.model.act(features_tensor, explore=False)
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@ -3263,9 +3251,6 @@ class TradingOrchestrator:
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if not self.decision_fusion_enabled:
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return
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import torch
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import torch.nn as nn
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# Create decision fusion network
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class DecisionFusionNet(nn.Module):
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def __init__(self, input_size=32, hidden_size=64):
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