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fix model mappings,dash updates, trading

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This commit is contained in:
Dobromir Popov
2025-07-22 15:44:59 +03:00
parent 3e35b9cddb
commit 1a54fb1d56
32 changed files with 6168 additions and 857 deletions
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25
NN/models/cnn_model.py
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@ -111,6 +111,9 @@ class SpatialAttentionBlock(nn.Module):
# Avoid in-place operation by creating new tensor
return torch.mul(x, attention)
#Todo:
#1. Add pivot points array as input
#2. change output to be next pivot point (we'll need to adjust training as well)
class EnhancedCNNModel(nn.Module):
"""
Much larger and more sophisticated CNN architecture for trading
@ -125,7 +128,7 @@ class EnhancedCNNModel(nn.Module):
def __init__(self,
input_size: int = 60,
feature_dim: int = 50,
output_size: int = 2, # BUY/SELL for 2-action system
output_size: int = 3, # BUY/SELL/HOLD for 3-action system
base_channels: int = 256, # Increased from 128 to 256
num_blocks: int = 12, # Increased from 6 to 12
num_attention_heads: int = 16, # Increased from 8 to 16
@ -479,9 +482,13 @@ class EnhancedCNNModel(nn.Module):
action = int(np.argmax(probs))
action_confidence = float(probs[action])
# FIXED ACTION MAPPING: 0=BUY, 1=SELL, 2=HOLD
action_names = ['BUY', 'SELL', 'HOLD']
action_name = action_names[action] if action < len(action_names) else 'HOLD'
return {
'action': action,
'action_name': 'BUY' if action == 0 else 'SELL',
'action_name': action_name,
'confidence': float(confidence),
'action_confidence': action_confidence,
'probabilities': probs.tolist(),
@ -965,21 +972,21 @@ class CNNModel:
if len(trend_data) > 1:
trend = (trend_data[-1] - trend_data[0]) / trend_data[0] if trend_data[0] != 0 else 0
# Map trend to action
# Map trend to action - FIXED ACTION MAPPING: 0=BUY, 1=SELL
if trend > 0.001: # Upward trend > 0.1%
action = 1 # BUY
action = 0 # BUY (action 0)
confidence = min(0.9, 0.5 + abs(trend) * 10)
elif trend < -0.001: # Downward trend < -0.1%
action = 0 # SELL
action = 1 # SELL (action 1)
confidence = min(0.9, 0.5 + abs(trend) * 10)
else:
action = 0 # Default to SELL for unclear trend
action = 2 # Default to HOLD for unclear trend
confidence = 0.3
else:
action = 0
action = 2 # HOLD for unknown trend
confidence = 0.3
else:
action = 0
action = 2 # HOLD for insufficient data
confidence = 0.3
# Create probabilities
@ -1000,7 +1007,7 @@ class CNNModel:
except Exception as e:
logger.error(f"Error in fallback prediction: {e}")
# Final fallback - conservative prediction
pred_class = np.array([0]) # SELL
pred_class = np.array([2]) # HOLD (safe default)
proba = np.ones(self.output_size) / self.output_size # Equal probabilities
pred_proba = np.array([proba])
return pred_class, pred_proba