predict price direction
This commit is contained in:
Dobromir Popov
@ -265,8 +265,9 @@ class EnhancedCNN(nn.Module):
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nn.Linear(256, 3) # 0=bottom, 1=top, 2=neither
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)
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# ULTRA MASSIVE multi-timeframe price prediction heads
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self.price_pred_immediate = nn.Sequential(
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# ULTRA MASSIVE price direction prediction head
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# Outputs single direction and confidence values
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self.price_direction_head = nn.Sequential(
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nn.Linear(1024, 1024), # Increased from 512
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nn.ReLU(),
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nn.Dropout(0.3),
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@ -275,32 +276,13 @@ class EnhancedCNN(nn.Module):
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nn.Dropout(0.3),
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nn.Linear(512, 256), # Increased from 128
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nn.ReLU(),
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nn.Linear(256, 3) # Up, Down, Sideways
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nn.Linear(256, 2) # [direction, confidence]
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)
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self.price_pred_midterm = nn.Sequential(
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nn.Linear(1024, 1024), # Increased from 512
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nn.ReLU(),
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nn.Dropout(0.3),
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nn.Linear(1024, 512), # Increased from 256
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nn.ReLU(),
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nn.Dropout(0.3),
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nn.Linear(512, 256), # Increased from 128
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nn.ReLU(),
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nn.Linear(256, 3) # Up, Down, Sideways
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)
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self.price_pred_longterm = nn.Sequential(
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nn.Linear(1024, 1024), # Increased from 512
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nn.ReLU(),
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nn.Dropout(0.3),
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nn.Linear(1024, 512), # Increased from 256
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nn.ReLU(),
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nn.Dropout(0.3),
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nn.Linear(512, 256), # Increased from 128
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nn.ReLU(),
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nn.Linear(256, 3) # Up, Down, Sideways
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)
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# Direction activation (tanh for -1 to 1)
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self.direction_activation = nn.Tanh()
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# Confidence activation (sigmoid for 0 to 1)
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self.confidence_activation = nn.Sigmoid()
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# ULTRA MASSIVE value prediction with ensemble approaches
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self.price_pred_value = nn.Sequential(
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@ -490,10 +472,14 @@ class EnhancedCNN(nn.Module):
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# Extrema predictions (bottom/top/neither detection)
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extrema_pred = self.extrema_head(features_refined)
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# Multi-timeframe price movement predictions
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price_immediate = self.price_pred_immediate(features_refined)
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price_midterm = self.price_pred_midterm(features_refined)
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price_longterm = self.price_pred_longterm(features_refined)
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# Price direction predictions
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price_direction_raw = self.price_direction_head(features_refined)
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# Apply separate activations to direction and confidence
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direction = self.direction_activation(price_direction_raw[:, 0:1]) # -1 to 1
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confidence = self.confidence_activation(price_direction_raw[:, 1:2]) # 0 to 1
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price_direction_pred = torch.cat([direction, confidence], dim=1) # [batch, 2]
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price_values = self.price_pred_value(features_refined)
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# Additional specialized predictions for enhanced accuracy
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@ -502,15 +488,14 @@ class EnhancedCNN(nn.Module):
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market_regime_pred = self.market_regime_head(features_refined)
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risk_pred = self.risk_head(features_refined)
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# Package all price predictions into a single tensor (use immediate as primary)
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# For compatibility with DQN agent, we return price_immediate as the price prediction tensor
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price_pred_tensor = price_immediate
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# Use the price direction prediction directly (already [batch, 2])
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price_direction_tensor = price_direction_pred
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# Package additional predictions into a single tensor (use volatility as primary)
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# For compatibility with DQN agent, we return volatility_pred as the advanced prediction tensor
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advanced_pred_tensor = volatility_pred
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return q_values, extrema_pred, price_pred_tensor, features_refined, advanced_pred_tensor
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return q_values, extrema_pred, price_direction_tensor, features_refined, advanced_pred_tensor
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def act(self, state, explore=True) -> Tuple[int, float, List[float]]:
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"""Enhanced action selection with ultra massive model predictions"""
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@ -528,7 +513,11 @@ class EnhancedCNN(nn.Module):
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state_tensor = state_tensor.unsqueeze(0)
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with torch.no_grad():
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q_values, extrema_pred, price_predictions, features, advanced_predictions = self(state_tensor)
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q_values, extrema_pred, price_direction_predictions, features, advanced_predictions = self(state_tensor)
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# Process price direction predictions
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if price_direction_predictions is not None:
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self.process_price_direction_predictions(price_direction_predictions)
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# Apply softmax to get action probabilities
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action_probs_tensor = torch.softmax(q_values, dim=1)
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@ -565,6 +554,100 @@ class EnhancedCNN(nn.Module):
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logger.info(f" Risk Level: {risk_labels[risk_class]} ({risk[risk_class]:.3f})")
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return action_idx, confidence, action_probs
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def process_price_direction_predictions(self, price_direction_pred: torch.Tensor) -> Dict[str, float]:
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"""
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Process price direction predictions and convert to standardized format
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Args:
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price_direction_pred: Tensor of shape (batch_size, 2) containing [direction, confidence]
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Returns:
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Dict with direction (-1 to 1) and confidence (0 to 1)
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"""
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try:
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if price_direction_pred is None or price_direction_pred.numel() == 0:
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return {}
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# Extract direction and confidence values
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direction_value = float(price_direction_pred[0, 0].item()) # -1 to 1
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confidence_value = float(price_direction_pred[0, 1].item()) # 0 to 1
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processed_directions = {
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'direction': direction_value,
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'confidence': confidence_value
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}
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# Store for later access
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self.last_price_direction = processed_directions
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return processed_directions
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except Exception as e:
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logger.error(f"Error processing price direction predictions: {e}")
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return {}
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def get_price_direction_vector(self) -> Dict[str, float]:
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"""
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Get the current price direction and confidence
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Returns:
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Dict with direction (-1 to 1) and confidence (0 to 1)
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"""
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return getattr(self, 'last_price_direction', {})
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def get_price_direction_summary(self) -> Dict[str, Any]:
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"""
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Get a summary of price direction prediction
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Returns:
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Dict containing direction and confidence information
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"""
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try:
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last_direction = getattr(self, 'last_price_direction', {})
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if not last_direction:
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return {
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'direction_value': 0.0,
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'confidence_value': 0.0,
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'direction_label': "SIDEWAYS",
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'discrete_direction': 0,
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'strength': 0.0,
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'weighted_strength': 0.0
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}
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direction_value = last_direction['direction']
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confidence_value = last_direction['confidence']
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# Convert to discrete direction
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if direction_value > 0.1:
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direction_label = "UP"
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discrete_direction = 1
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elif direction_value < -0.1:
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direction_label = "DOWN"
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discrete_direction = -1
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else:
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direction_label = "SIDEWAYS"
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discrete_direction = 0
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return {
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'direction_value': float(direction_value),
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'confidence_value': float(confidence_value),
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'direction_label': direction_label,
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'discrete_direction': discrete_direction,
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'strength': abs(float(direction_value)),
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'weighted_strength': abs(float(direction_value)) * float(confidence_value)
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}
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except Exception as e:
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logger.error(f"Error calculating price direction summary: {e}")
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return {
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'direction_value': 0.0,
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'confidence_value': 0.0,
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'direction_label': "SIDEWAYS",
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'discrete_direction': 0,
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'strength': 0.0,
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'weighted_strength': 0.0
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}
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def save(self, path):
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"""Save model weights and architecture"""
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