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remove dummy data, improve training , follow architecture

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This commit is contained in:
Dobromir Popov
2025-07-04 23:51:35 +03:00
parent e8b9c05148
commit ce8c00a9d1
13 changed files with 435 additions and 838 deletions
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8
NN/models/dqn_agent.py
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@ -451,7 +451,13 @@ class DQNAgent:
state_tensor = state.unsqueeze(0).to(self.device)
# Get Q-values
q_values = self.policy_net(state_tensor)
policy_output = self.policy_net(state_tensor)
if isinstance(policy_output, dict):
q_values = policy_output.get('q_values', policy_output.get('Q_values', list(policy_output.values())[0]))
elif isinstance(policy_output, tuple):
q_values = policy_output[0] # Assume first element is Q-values
else:
q_values = policy_output
action_values = q_values.cpu().data.numpy()[0]
# Calculate confidence scores

19
NN/models/enhanced_cnn_with_orderbook.py
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@ -161,7 +161,7 @@ class OrderBookEncoder(nn.Module):
attended_features, attention_weights = self.cross_attention(combined_seq)
# Flatten attended features
attended_flat = attended_features.view(attended_features.size(0), -1) # [batch, 512]
attended_flat = attended_features.reshape(attended_features.size(0), -1) # [batch, 512]
# Combine with microstructure features
combined_features = torch.cat([attended_flat, micro_encoded], dim=1) # [batch, 640]
@ -210,8 +210,7 @@ class VolumeProfileEncoder(nn.Module):
if isinstance(volume_profile_data, list):
if not volume_profile_data:
# Return zero features if no data
batch_size = 1
return torch.zeros(batch_size, self.aggregator[-1].out_features)
return torch.zeros(1, 256, device=torch.device('cpu')) # Hardcoded output dim as per hidden_dim in class init
# Convert to tensor
features = []
@ -239,7 +238,7 @@ class VolumeProfileEncoder(nn.Module):
# Encode each level
level_features = self.level_encoder(volume_tensor.view(-1, feature_dim))
level_features = level_features.view(batch_size, num_levels, -1)
level_features = level_features.reshape(batch_size, num_levels, -1)
# Apply attention across levels
attended_levels, _ = self.level_attention(level_features)
@ -423,14 +422,14 @@ class EnhancedCNNWithOrderBook(nn.Module):
Returns:
Dictionary with Q-values, confidence, regime, and auxiliary predictions
"""
batch_size = market_data.size(0)
# Process market data
# Process market data - ensure batch dimension first
if len(market_data.shape) == 2:
market_data = market_data.unsqueeze(0)
# Reshape for convolutional processing
market_reshaped = market_data.view(batch_size, -1, market_data.size(-1))
batch_size = market_data.size(0) # Get correct batch size after shape adjustment
# Reshape for convolutional processing with safe dimensions
market_reshaped = market_data.reshape(batch_size, -1, market_data.size(-1))
market_features = self.market_encoder(market_reshaped.transpose(1, 2))
# Process order book data
@ -440,7 +439,7 @@ class EnhancedCNNWithOrderBook(nn.Module):
if volume_profile_data is not None:
volume_features = self.volume_encoder(volume_profile_data)
else:
volume_features = torch.zeros(batch_size, 256, device=self.device)
volume_features = torch.zeros(batch_size, 256, device=market_data.device)
# Fuse all features
combined_features = torch.cat([

10
NN/models/saved/checkpoint_metadata.json
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@ -81,15 +81,15 @@
"wandb_artifact_name": null
},
{
"checkpoint_id": "decision_20250704_082452",
"checkpoint_id": "decision_20250704_214714",
"model_name": "decision",
"model_type": "decision_fusion",
"file_path": "NN\\models\\saved\\decision\\decision_20250704_082452.pt",
"created_at": "2025-07-04T08:24:52.949705",
"file_path": "NN\\models\\saved\\decision\\decision_20250704_214714.pt",
"created_at": "2025-07-04T21:47:14.427187",
"file_size_mb": 0.06720924377441406,
"performance_score": 102.79965677530546,
"performance_score": 102.79966325731509,
"accuracy": null,
"loss": 3.432258725613987e-06,
"loss": 3.3674381887394134e-06,
"val_accuracy": null,
"val_loss": null,
"reward": null,