williams data structure in data provider
This commit is contained in:
Dobromir Popov
@ -2378,14 +2378,14 @@ class TradingDashboard:
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net_pnl = leveraged_pnl - leveraged_fee - self.current_position['fees']
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self.total_realized_pnl += net_pnl
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self.total_fees += fee
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self.total_fees += leveraged_fee
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# Record the close trade
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close_record = decision.copy()
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close_record['position_action'] = 'CLOSE_SHORT'
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close_record['entry_price'] = entry_price
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close_record['pnl'] = net_pnl
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close_record['fees'] = fee
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close_record['fees'] = leveraged_fee
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close_record['fee_type'] = fee_type
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close_record['fee_rate'] = fee_rate
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close_record['size'] = size # Use original position size for close
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@ -2434,7 +2434,7 @@ class TradingDashboard:
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# Now open long position (regardless of previous position)
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if self.current_position is None:
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# Open long position with confidence-based size
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fee = decision['price'] * decision['size'] * fee_rate * self.leverage_multiplier # Leverage affects fees
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fee = decision['price'] * decision['size'] * fee_rate # ✅ FIXED: No leverage on fees
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self.current_position = {
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'side': 'LONG',
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'price': decision['price'],
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@ -2471,14 +2471,14 @@ class TradingDashboard:
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net_pnl = leveraged_pnl - leveraged_fee - self.current_position['fees']
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self.total_realized_pnl += net_pnl
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self.total_fees += fee
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self.total_fees += leveraged_fee
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# Record the close trade
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close_record = decision.copy()
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close_record['position_action'] = 'CLOSE_SHORT'
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close_record['entry_price'] = entry_price
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close_record['pnl'] = net_pnl
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close_record['fees'] = fee
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close_record['fees'] = leveraged_fee
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close_record['fee_type'] = fee_type
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close_record['fee_rate'] = fee_rate
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self.session_trades.append(close_record)
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@ -2539,14 +2539,14 @@ class TradingDashboard:
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net_pnl = leveraged_pnl - leveraged_fee - self.current_position['fees']
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self.total_realized_pnl += net_pnl
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self.total_fees += fee
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self.total_fees += leveraged_fee
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# Record the close trade
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close_record = decision.copy()
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close_record['position_action'] = 'CLOSE_LONG'
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close_record['entry_price'] = entry_price
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close_record['pnl'] = net_pnl
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close_record['fees'] = fee
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close_record['fees'] = leveraged_fee
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close_record['fee_type'] = fee_type
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close_record['fee_rate'] = fee_rate
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close_record['size'] = size # Use original position size for close
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@ -2583,7 +2583,7 @@ class TradingDashboard:
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# Now open short position (regardless of previous position)
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if self.current_position is None:
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# Open short position with confidence-based size
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fee = decision['price'] * decision['size'] * fee_rate * self.leverage_multiplier # Leverage affects fees
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fee = decision['price'] * decision['size'] * fee_rate # ✅ FIXED: No leverage on fees
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self.current_position = {
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'side': 'SHORT',
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'price': decision['price'],
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@ -2625,16 +2625,16 @@ class TradingDashboard:
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else:
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return 0.0, 0.0
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# Apply leverage amplification
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# Apply leverage amplification ONLY to P&L
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leveraged_pnl = base_pnl * self.leverage_multiplier
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# Calculate fees with leverage (higher position value = higher fees)
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position_value = exit_price * size * self.leverage_multiplier
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leveraged_fee = position_value * fee_rate
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# Calculate fees WITHOUT leverage (normal position value)
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position_value = exit_price * size # ✅ FIXED: No leverage multiplier
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normal_fee = position_value * fee_rate # ✅ FIXED: Normal fees
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logger.info(f"[LEVERAGE] {side} PnL: Base=${base_pnl:.2f} x {self.leverage_multiplier}x = ${leveraged_pnl:.2f}, Fee=${leveraged_fee:.4f}")
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logger.info(f"[LEVERAGE] {side} PnL: Base=${base_pnl:.2f} x {self.leverage_multiplier}x = ${leveraged_pnl:.2f}, Fee=${normal_fee:.4f}")
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return leveraged_pnl, leveraged_fee
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return leveraged_pnl, normal_fee # ✅ FIXED: Return normal fee
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except Exception as e:
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logger.warning(f"Error calculating leveraged PnL and fees: {e}")
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@ -3013,7 +3013,7 @@ class TradingDashboard:
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logger.info(f"Using best {model_type} model: {model_info.model_name} "
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f"(Score: {model_info.metrics.get_composite_score():.3f})")
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else:
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else:
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logger.info("No managed models available, falling back to legacy loading")
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# Fallback to original model loading logic
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self._load_legacy_models()
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@ -3021,7 +3021,7 @@ class TradingDashboard:
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except ImportError:
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logger.warning("ModelManager not available, using legacy model loading")
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self._load_legacy_models()
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except Exception as e:
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except Exception as e:
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logger.error(f"Error loading models via ModelManager: {e}")
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self._load_legacy_models()
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@ -3048,12 +3048,12 @@ class TradingDashboard:
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def __init__(self, model):
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self.model = model
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self.model.eval()
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def predict(self, feature_matrix):
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with torch.no_grad():
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if hasattr(feature_matrix, 'shape') and len(feature_matrix.shape) == 2:
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feature_tensor = torch.FloatTensor(feature_matrix).unsqueeze(0)
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else:
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else:
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feature_tensor = torch.FloatTensor(feature_matrix)
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prediction = self.model(feature_tensor)
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@ -3073,10 +3073,10 @@ class TradingDashboard:
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prediction = exp_pred / np.sum(exp_pred)
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return prediction
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def get_memory_usage(self):
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return 50 # MB estimate
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def to_device(self, device):
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self.model = self.model.to(device)
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return self
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@ -3090,7 +3090,7 @@ class TradingDashboard:
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})
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logger.info(f"Loaded CNN model: {model_file}")
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except Exception as e:
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except Exception as e:
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logger.warning(f"Failed to load CNN model {model_file}: {e}")
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# Check for RL models
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@ -3101,12 +3101,12 @@ class TradingDashboard:
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try:
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checkpoint_path = os.path.join(rl_models_dir, model_file)
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class RLWrapper:
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def __init__(self, checkpoint_path):
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class RLWrapper:
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def __init__(self, checkpoint_path):
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self.checkpoint_path = checkpoint_path
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self.checkpoint = torch.load(checkpoint_path, map_location='cpu')
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def predict(self, feature_matrix):
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def predict(self, feature_matrix):
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# Mock RL prediction
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if hasattr(feature_matrix, 'shape'):
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state_sum = np.sum(feature_matrix) % 100
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@ -3117,17 +3117,17 @@ class TradingDashboard:
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action_probs = [0.1, 0.1, 0.8] # BUY
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elif state_sum < 30:
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action_probs = [0.8, 0.1, 0.1] # SELL
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else:
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else:
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action_probs = [0.2, 0.6, 0.2] # HOLD
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return np.array(action_probs)
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def get_memory_usage(self):
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return 75 # MB estimate
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def to_device(self, device):
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return self
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wrapper = RLWrapper(checkpoint_path)
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self.available_models['rl'].append({
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'name': model_file,
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@ -3137,12 +3137,12 @@ class TradingDashboard:
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})
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logger.info(f"Loaded RL model: {model_file}")
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except Exception as e:
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except Exception as e:
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logger.warning(f"Failed to load RL model {model_file}: {e}")
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total_models = sum(len(models) for models in self.available_models.values())
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logger.info(f"Legacy model loading complete. Total models: {total_models}")
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except Exception as e:
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logger.error(f"Error in legacy model loading: {e}")
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# Initialize empty model structure
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