popov/gogo2
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

improved model loading and training

Browse Source
This commit is contained in:
Dobromir Popov
2025-10-31 01:22:49 +02:00
parent 7ddf98bf18
commit ba91740e4c
7 changed files with 745 additions and 186 deletions
Download Patch File Download Diff File Expand all files Collapse all files

28
NN/models/advanced_transformer_trading.py
View File

@@ -625,7 +625,7 @@ class AdvancedTradingTransformer(nn.Module):
'calculated_angle': trend_angles.unsqueeze(-1), # (batch, 1)
'calculated_steepness': trend_steepness.unsqueeze(-1), # (batch, 1)
'calculated_direction': trend_direction.unsqueeze(-1), # (batch, 1)
'vector': torch.stack([price_deltas.squeeze(), time_deltas.squeeze()], dim=1) # (batch, 2) - [price_delta, time_delta]
'vector': torch.stack([price_deltas.squeeze(), time_deltas.squeeze()], dim=0).unsqueeze(0) if batch_size == 1 else torch.stack([price_deltas.squeeze(), time_deltas.squeeze()], dim=1) # (batch, 2) - [price_delta, time_delta]
}
else:
outputs['trend_vector'] = {
@@ -663,8 +663,13 @@ class AdvancedTradingTransformer(nn.Module):
# Calculate action probabilities based on trend
for i in range(batch_size):
angle = trend_angle[i].item() if batch_size > 0 else 0.0
steep = trend_steepness_val[i].item() if batch_size > 0 else 0.0
# Handle both 0-dim and 1-dim tensors
if trend_angle.dim() == 0:
angle = trend_angle.item()
steep = trend_steepness_val.item()
else:
angle = trend_angle[i].item()
steep = trend_steepness_val[i].item()
# Normalize steepness to [0, 1] range (assuming max steepness of 10 units)
normalized_steepness = min(steep / 10.0, 1.0) if steep > 0 else 0.0
@@ -964,10 +969,19 @@ class TradingTransformerTrainer:
# Add confidence loss if available
if 'confidence' in outputs and 'trade_success' in batch:
confidence_loss = self.confidence_criterion(
outputs['confidence'].squeeze(),
batch['trade_success'].float()
)
# Ensure both tensors have compatible shapes
# confidence: [batch_size, 1] -> squeeze last dim to [batch_size]
# trade_success: [batch_size] - ensure same shape
confidence_pred = outputs['confidence'].squeeze(-1) # Only remove last dimension
trade_target = batch['trade_success'].float()
# Ensure shapes match (handle edge case where batch_size=1)
if confidence_pred.dim() == 0: # scalar case
confidence_pred = confidence_pred.unsqueeze(0)
if trade_target.dim() == 0: # scalar case
trade_target = trade_target.unsqueeze(0)
confidence_loss = self.confidence_criterion(confidence_pred, trade_target)
total_loss += 0.1 * confidence_loss
# Backward pass