device fix , TZ fix
This commit is contained in:
Dobromir Popov
@ -1056,7 +1056,55 @@ class DQNAgent:
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if isinstance(state, torch.Tensor):
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state = state.detach().cpu().numpy()
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elif not isinstance(state, np.ndarray):
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# Check if state is a dict or complex object
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if isinstance(state, dict):
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logger.error(f"State is a dict: {state}")
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# Extract numerical values from dict if possible
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if 'features' in state:
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state = state['features']
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elif 'state' in state:
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state = state['state']
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else:
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# Try to extract all numerical values
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numerical_values = []
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for key, value in state.items():
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if isinstance(value, (int, float)):
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numerical_values.append(float(value))
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elif isinstance(value, (list, np.ndarray)):
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try:
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# Handle nested structures safely
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flattened = np.array(value).flatten()
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for x in flattened:
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if isinstance(x, (int, float)):
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numerical_values.append(float(x))
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elif hasattr(x, 'item'): # numpy scalar
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numerical_values.append(float(x.item()))
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except (ValueError, TypeError):
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continue
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elif isinstance(value, dict):
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# Recursively extract from nested dicts
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try:
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nested_values = self._extract_numeric_from_dict(value)
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numerical_values.extend(nested_values)
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except Exception:
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continue
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if numerical_values:
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state = np.array(numerical_values, dtype=np.float32)
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else:
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logger.error("No numerical values found in state dict, using default state")
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expected_size = getattr(self, 'state_size', 403)
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if isinstance(expected_size, tuple):
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expected_size = np.prod(expected_size)
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return np.zeros(int(expected_size), dtype=np.float32)
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else:
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try:
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state = np.array(state, dtype=np.float32)
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except (ValueError, TypeError) as e:
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logger.error(f"Cannot convert state to numpy array: {type(state)}, {e}")
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expected_size = getattr(self, 'state_size', 403)
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if isinstance(expected_size, tuple):
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expected_size = np.prod(expected_size)
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return np.zeros(int(expected_size), dtype=np.float32)
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# Flatten if multi-dimensional
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if state.ndim > 1:
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@ -1762,3 +1810,33 @@ class DQNAgent:
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except:
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return 0.0
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def _extract_numeric_from_dict(self, data_dict):
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"""Recursively extract all numeric values from a dictionary"""
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numeric_values = []
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try:
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for key, value in data_dict.items():
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if isinstance(value, (int, float)):
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numeric_values.append(float(value))
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elif isinstance(value, (list, np.ndarray)):
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try:
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flattened = np.array(value).flatten()
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for x in flattened:
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if isinstance(x, (int, float)):
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numeric_values.append(float(x))
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elif hasattr(x, 'item'): # numpy scalar
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numeric_values.append(float(x.item()))
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except (ValueError, TypeError):
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continue
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elif isinstance(value, dict):
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# Recursively extract from nested dicts
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nested_values = self._extract_numeric_from_dict(value)
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numeric_values.extend(nested_values)
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elif isinstance(value, torch.Tensor):
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try:
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numeric_values.append(float(value.item()))
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except Exception:
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continue
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except Exception as e:
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logger.debug(f"Error extracting numeric values from dict: {e}")
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return numeric_values
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@ -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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symbol = record.get('symbol', 'ETH/USDT')
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actual_action = prediction['action']
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@ -2442,46 +2441,35 @@ class TradingOrchestrator:
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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_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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# Try basic training method for EnhancedCNN
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return True
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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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@ -2695,9 +2684,8 @@ 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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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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@ -11,7 +11,7 @@ import os
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# Add the project root to the path
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sys.path.append(os.path.dirname(os.path.abspath(__file__)))
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from core.enhanced_cnn_adapter import EnhancedCNNAdapter
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from NN.models.enhanced_cnn import EnhancedCNN
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from core.data_models import BaseDataInput, OHLCVBar
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# Configure logging
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@ -1,102 +0,0 @@
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#!/usr/bin/env python3
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"""
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Test Timezone Fix
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This script tests that historical data timestamps are properly converted to Europe/Sofia timezone.
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"""
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import asyncio
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import pandas as pd
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from datetime import datetime
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from core.data_provider import DataProvider
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async def test_timezone_fix():
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"""Test the timezone conversion fix"""
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print("=== Testing Timezone Fix ===")
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# Initialize data provider
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print("1. Initializing data provider...")
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data_provider = DataProvider()
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# Wait for initialization
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await asyncio.sleep(2)
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# Test different timeframes
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timeframes = ['1m', '1h', '1d']
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symbol = 'ETH/USDT'
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for timeframe in timeframes:
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print(f"\n2. Testing {timeframe} data for {symbol}:")
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# Get historical data
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df = data_provider.get_historical_data(symbol, timeframe, limit=5)
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if df is not None and not df.empty:
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print(f" ✅ Got {len(df)} candles")
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# Check timezone
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if 'timestamp' in df.columns:
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first_timestamp = df['timestamp'].iloc[0]
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last_timestamp = df['timestamp'].iloc[-1]
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print(f" First timestamp: {first_timestamp}")
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print(f" Last timestamp: {last_timestamp}")
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# Check if timezone is Europe/Sofia
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if hasattr(first_timestamp, 'tz') and first_timestamp.tz is not None:
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timezone_str = str(first_timestamp.tz)
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if 'Europe/Sofia' in timezone_str or 'EET' in timezone_str or 'EEST' in timezone_str:
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print(f" ✅ Timezone is correct: {timezone_str}")
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else:
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print(f" ❌ Timezone is incorrect: {timezone_str}")
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else:
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print(" ❌ No timezone information found")
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# Show time difference from UTC
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if hasattr(first_timestamp, 'utcoffset') and first_timestamp.utcoffset() is not None:
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offset_hours = first_timestamp.utcoffset().total_seconds() / 3600
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print(f" UTC offset: {offset_hours:+.0f} hours")
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if offset_hours == 2 or offset_hours == 3: # EET (+2) or EEST (+3)
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print(" ✅ UTC offset is correct for Europe/Sofia")
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else:
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print(f" ❌ UTC offset is incorrect: {offset_hours:+.0f} hours")
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# Show sample data
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print(" Sample data:")
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for i in range(min(3, len(df))):
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row = df.iloc[i]
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print(f" {row['timestamp']}: O={row['open']:.2f} H={row['high']:.2f} L={row['low']:.2f} C={row['close']:.2f}")
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else:
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print(" ❌ No timestamp column found")
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else:
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print(f" ❌ No data available for {timeframe}")
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# Test current time comparison
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print(f"\n3. Current time comparison:")
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current_utc = datetime.utcnow()
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current_sofia = datetime.now()
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print(f" Current UTC time: {current_utc}")
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print(f" Current local time: {current_sofia}")
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# Calculate expected offset
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import pytz
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sofia_tz = pytz.timezone('Europe/Sofia')
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current_sofia_tz = datetime.now(sofia_tz)
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offset_hours = current_sofia_tz.utcoffset().total_seconds() / 3600
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print(f" Europe/Sofia current time: {current_sofia_tz}")
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print(f" Current UTC offset: {offset_hours:+.0f} hours")
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if offset_hours == 2:
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print(" ✅ Currently in EET (Eastern European Time)")
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elif offset_hours == 3:
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print(" ✅ Currently in EEST (Eastern European Summer Time)")
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else:
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print(f" ❌ Unexpected offset: {offset_hours:+.0f} hours")
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print("\n✅ Timezone fix test completed!")
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if __name__ == "__main__":
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asyncio.run(test_timezone_fix())
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@ -5851,7 +5851,63 @@ class CleanTradingDashboard:
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logger.debug(f"Base data input created successfully for {symbol}")
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# Make prediction using CNN adapter
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# Make prediction using CNN model directly (EnhancedCNN uses act method)
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if hasattr(self.cnn_adapter, 'act'):
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# Use the act method for EnhancedCNN
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features = base_data_input.get_feature_vector()
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# Convert to tensor and ensure proper device placement
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import torch
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device = next(self.cnn_adapter.parameters()).device
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features_tensor = torch.tensor(features, dtype=torch.float32, device=device)
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# Ensure batch dimension
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if features_tensor.dim() == 1:
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features_tensor = features_tensor.unsqueeze(0)
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# Set model to evaluation mode
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self.cnn_adapter.eval()
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# Get prediction from CNN model
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with torch.no_grad():
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q_values, extrema_pred, price_pred, features_refined, advanced_pred = self.cnn_adapter(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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confidence = float(action_probs[0, action_idx].item())
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# Map action index to action string
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actions = ['BUY', 'SELL', 'HOLD']
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action = actions[action_idx]
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# Create probabilities dictionary
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probabilities = {
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'BUY': float(action_probs[0, 0].item()),
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'SELL': float(action_probs[0, 1].item()),
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'HOLD': float(action_probs[0, 2].item())
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}
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# Extract price predictions if available
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price_prediction = None
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if price_pred is not None:
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price_prediction = price_pred.squeeze(0).cpu().numpy().tolist()
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prediction = {
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'action': action,
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'confidence': confidence,
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'buy_probability': probabilities['BUY'],
|
||||
'sell_probability': probabilities['SELL'],
|
||||
'hold_probability': probabilities['HOLD'],
|
||||
'timestamp': datetime.now(),
|
||||
'hidden_states': features_refined.squeeze(0).cpu().numpy().tolist() if features_refined is not None else None,
|
||||
'metadata': {
|
||||
'price_prediction': price_prediction,
|
||||
'extrema_prediction': extrema_pred.squeeze(0).cpu().numpy().tolist() if extrema_pred is not None else None
|
||||
}
|
||||
}
|
||||
else:
|
||||
# Fallback for other CNN models that might have predict method
|
||||
model_output = self.cnn_adapter.predict(base_data_input)
|
||||
|
||||
# Convert to dictionary for dashboard use
|
||||
|
||||
Reference in New Issue
Block a user