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Dobromir Popov
@@ -143,7 +143,7 @@ class EnhancedCNNModel(nn.Module):
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def __init__(self,
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input_size: int = 60,
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feature_dim: int = 50,
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output_size: int = 2, # BUY/SELL for 2-action system
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output_size: int = 5, # OHLCV prediction (Open, High, Low, Close, Volume)
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base_channels: int = 256, # Increased from 128 to 256
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num_blocks: int = 12, # Increased from 6 to 12
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num_attention_heads: int = 16, # Increased from 8 to 16
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@@ -416,39 +416,40 @@ class EnhancedCNNModel(nn.Module):
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volatility_pred = self._memory_barrier(self.volatility_predictor(processed_features))
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confidence = self._memory_barrier(self.confidence_head(processed_features))
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# Combine all features for final decision (8 regime classes + 1 volatility)
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# Combine all features for OHLCV prediction
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# Create completely independent tensors for concatenation
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vol_pred_flat = self._memory_barrier(volatility_pred.reshape(volatility_pred.shape[0], -1)) # Flatten instead of squeeze
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combined_features = torch.cat([processed_features, regime_probs, vol_pred_flat], dim=1)
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combined_features = self._memory_barrier(combined_features)
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trading_logits = self._memory_barrier(self.decision_head(combined_features))
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# Apply temperature scaling for better calibration - create new tensor
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temperature = 1.5
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scaled_logits = trading_logits / temperature
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trading_probs = self._memory_barrier(F.softmax(scaled_logits, dim=1))
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# Flatten confidence to ensure consistent shape
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# OHLCV prediction (Open, High, Low, Close, Volume)
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ohlcv_pred = self._memory_barrier(self.decision_head(combined_features))
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# Generate confidence based on prediction stability
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confidence_flat = self._memory_barrier(confidence.reshape(confidence.shape[0], -1))
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volatility_flat = self._memory_barrier(volatility_pred.reshape(volatility_pred.shape[0], -1))
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# Calculate prediction confidence based on volatility and regime stability
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regime_stability = torch.std(regime_probs, dim=1, keepdim=True)
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prediction_confidence = 1.0 / (1.0 + regime_stability + volatility_flat * 0.1)
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prediction_confidence = self._memory_barrier(prediction_confidence.squeeze(-1))
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return {
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'logits': self._memory_barrier(trading_logits),
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'probabilities': self._memory_barrier(trading_probs),
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'confidence': confidence_flat[:, 0] if confidence_flat.shape[1] > 0 else confidence_flat.reshape(-1)[0],
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'ohlcv': self._memory_barrier(ohlcv_pred), # [batch_size, 5] - OHLCV predictions
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'confidence': prediction_confidence,
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'regime': self._memory_barrier(regime_probs),
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'volatility': volatility_flat[:, 0] if volatility_flat.shape[1] > 0 else volatility_flat.reshape(-1)[0],
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'features': self._memory_barrier(processed_features)
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'features': self._memory_barrier(processed_features),
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'regime_stability': self._memory_barrier(regime_stability.squeeze(-1))
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}
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def predict(self, feature_matrix) -> Dict[str, Any]:
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"""
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Make predictions on feature matrix
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Make OHLCV predictions on feature matrix
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Args:
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feature_matrix: tensor or numpy array of shape [sequence_length, features]
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Returns:
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Dictionary with prediction results
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Dictionary with OHLCV prediction results and trading signals
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"""
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self.eval()
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@@ -468,17 +469,13 @@ class EnhancedCNNModel(nn.Module):
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# Forward pass
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outputs = self.forward(x)
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# Extract results with proper shape handling
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if HAS_NUMPY:
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probs = outputs['probabilities'].cpu().numpy()[0]
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confidence_tensor = outputs['confidence'].cpu().numpy()
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regime = outputs['regime'].cpu().numpy()[0]
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volatility = outputs['volatility'].cpu().numpy()
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else:
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probs = outputs['probabilities'].cpu().tolist()[0]
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confidence_tensor = outputs['confidence'].cpu().tolist()
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regime = outputs['regime'].cpu().tolist()[0]
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volatility = outputs['volatility'].cpu().tolist()
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# Extract OHLCV predictions
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ohlcv_pred = outputs['ohlcv'].cpu().numpy()[0] if HAS_NUMPY else outputs['ohlcv'].cpu().tolist()[0]
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# Extract other outputs
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confidence_tensor = outputs['confidence'].cpu().numpy() if HAS_NUMPY else outputs['confidence'].cpu().tolist()
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regime = outputs['regime'].cpu().numpy()[0] if HAS_NUMPY else outputs['regime'].cpu().tolist()[0]
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volatility = outputs['volatility'].cpu().numpy() if HAS_NUMPY else outputs['volatility'].cpu().tolist()
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# Handle confidence shape properly
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if HAS_NUMPY and isinstance(confidence_tensor, np.ndarray):
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@@ -490,7 +487,7 @@ class EnhancedCNNModel(nn.Module):
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confidence = float(confidence_tensor[0] if len(confidence_tensor) > 0 else 0.7)
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else:
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confidence = float(confidence_tensor)
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# Handle volatility shape properly
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if HAS_NUMPY and isinstance(volatility, np.ndarray):
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if volatility.ndim == 0:
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@@ -502,28 +499,68 @@ class EnhancedCNNModel(nn.Module):
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else:
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volatility = float(volatility)
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# Determine action (0=BUY, 1=SELL for 2-action system)
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if HAS_NUMPY:
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action = int(np.argmax(probs))
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else:
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action = int(torch.argmax(torch.tensor(probs)).item())
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action_confidence = float(probs[action])
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# Extract OHLCV values
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open_price, high_price, low_price, close_price, volume = ohlcv_pred
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# Convert logits to list
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if HAS_NUMPY:
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raw_logits = outputs['logits'].cpu().numpy()[0].tolist()
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else:
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raw_logits = outputs['logits'].cpu().tolist()[0]
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# Calculate price movement and direction
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price_change = close_price - open_price
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price_change_pct = (price_change / open_price) * 100 if open_price != 0 else 0
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# Calculate candle characteristics
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body_size = abs(close_price - open_price)
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upper_wick = high_price - max(open_price, close_price)
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lower_wick = min(open_price, close_price) - low_price
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total_range = high_price - low_price
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# Determine trading action based on predicted candle
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if price_change_pct > 0.1: # Bullish candle (>0.1% gain)
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action = 0 # BUY
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action_name = 'BUY'
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action_confidence = min(0.95, confidence * (1 + abs(price_change_pct) * 10))
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elif price_change_pct < -0.1: # Bearish candle (<-0.1% loss)
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action = 1 # SELL
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action_name = 'SELL'
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action_confidence = min(0.95, confidence * (1 + abs(price_change_pct) * 10))
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else: # Sideways/neutral candle
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# Use body vs wick analysis for weak signals
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if body_size / total_range > 0.7: # Strong directional body
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action = 0 if price_change > 0 else 1
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action_name = 'BUY' if action == 0 else 'SELL'
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action_confidence = confidence * 0.6 # Reduce confidence for weak signals
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else:
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action = 2 # HOLD
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action_name = 'HOLD'
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action_confidence = confidence * 0.3 # Very low confidence
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# Adjust confidence based on volatility
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if volatility > 0.5: # High volatility
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action_confidence *= 0.8 # Reduce confidence in volatile conditions
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elif volatility < 0.2: # Low volatility
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action_confidence *= 1.2 # Increase confidence in stable conditions
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action_confidence = min(0.95, action_confidence) # Cap at 95%
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return {
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'action': action,
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'action_name': 'BUY' if action == 0 else 'SELL',
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'action_name': action_name,
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'confidence': float(confidence),
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'action_confidence': action_confidence,
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'probabilities': probs if isinstance(probs, list) else probs.tolist(),
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'ohlcv_prediction': {
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'open': float(open_price),
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'high': float(high_price),
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'low': float(low_price),
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'close': float(close_price),
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'volume': float(volume)
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},
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'price_change_pct': price_change_pct,
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'candle_characteristics': {
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'body_size': body_size,
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'upper_wick': upper_wick,
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'lower_wick': lower_wick,
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'total_range': total_range
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},
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'regime_probabilities': regime if isinstance(regime, list) else regime.tolist(),
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'volatility_prediction': float(volatility),
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'raw_logits': raw_logits
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'prediction_quality': 'high' if action_confidence > 0.8 else 'medium' if action_confidence > 0.6 else 'low'
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}
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def get_memory_usage(self) -> Dict[str, Any]:
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