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scalping dash also works initially

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This commit is contained in:
Dobromir Popov
2025-05-26 16:02:40 +03:00
parent 39942386b1
commit c97177aa88
39 changed files with 7272 additions and 1076 deletions
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102
NN/models/dqn_agent.py
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@ -143,6 +143,10 @@ class DQNAgent:
self.last_hidden_features = None # Store last extracted features
self.feature_history = [] # Store history of features for analysis
# Real-time tick features integration
self.realtime_tick_features = None # Latest tick features from tick processor
self.tick_feature_weight = 0.3 # Weight for tick features in decision making
# Check if mixed precision training should be used
self.use_mixed_precision = False
if torch.cuda.is_available() and hasattr(torch.cuda, 'amp') and 'DISABLE_MIXED_PRECISION' not in os.environ:
@ -163,6 +167,7 @@ class DQNAgent:
logger.info(f"DQN Agent using Enhanced CNN with device: {self.device}")
logger.info(f"Trade action fee set to {self.trade_action_fee}, minimum confidence: {self.minimum_action_confidence}")
logger.info(f"Real-time tick feature integration enabled with weight: {self.tick_feature_weight}")
# Log model parameters
total_params = sum(p.numel() for p in self.policy_net.parameters())
@ -291,8 +296,11 @@ class DQNAgent:
return random.randrange(self.n_actions)
with torch.no_grad():
# Enhance state with real-time tick features
enhanced_state = self._enhance_state_with_tick_features(state)
# Ensure state is normalized before inference
state_tensor = self._normalize_state(state)
state_tensor = self._normalize_state(enhanced_state)
state_tensor = torch.FloatTensor(state_tensor).unsqueeze(0).to(self.device)
# Get predictions using the policy network
@ -764,11 +772,14 @@ class DQNAgent:
# Calculate price change for different timeframes
immediate_changes = (next_prices - current_prices) / current_prices
# Get the actual batch size for this calculation
actual_batch_size = states.shape[0]
# Create price direction labels - simplified for training
# 0 = down, 1 = sideways, 2 = up
immediate_labels = torch.ones(min_size, dtype=torch.long, device=self.device) * 1 # Default: sideways
midterm_labels = torch.ones(min_size, dtype=torch.long, device=self.device) * 1
longterm_labels = torch.ones(min_size, dtype=torch.long, device=self.device) * 1
immediate_labels = torch.ones(actual_batch_size, dtype=torch.long, device=self.device) * 1 # Default: sideways
midterm_labels = torch.ones(actual_batch_size, dtype=torch.long, device=self.device) * 1
longterm_labels = torch.ones(actual_batch_size, dtype=torch.long, device=self.device) * 1
# Immediate term direction (1s, 1m)
immediate_up = (immediate_changes > 0.0005)
@ -794,19 +805,19 @@ class DQNAgent:
# Generate target values for price change regression
# For simplicity, we'll use the immediate change and scaled versions for longer timeframes
price_value_targets = torch.zeros((min_size, 4), device=self.device)
price_value_targets = torch.zeros((actual_batch_size, 4), device=self.device)
price_value_targets[:, 0] = immediate_changes
price_value_targets[:, 1] = immediate_changes * 2.0 # Approximate 1h change
price_value_targets[:, 2] = immediate_changes * 4.0 # Approximate 1d change
price_value_targets[:, 3] = immediate_changes * 6.0 # Approximate 1w change
# Calculate loss for price direction prediction (classification)
if len(current_price_pred['immediate'].shape) > 1 and current_price_pred['immediate'].shape[0] >= min_size:
if len(current_price_pred['immediate'].shape) > 1 and current_price_pred['immediate'].shape[0] >= actual_batch_size:
# Slice predictions to match the adjusted batch size
immediate_pred = current_price_pred['immediate'][:min_size]
midterm_pred = current_price_pred['midterm'][:min_size]
longterm_pred = current_price_pred['longterm'][:min_size]
price_values_pred = current_price_pred['values'][:min_size]
immediate_pred = current_price_pred['immediate'][:actual_batch_size]
midterm_pred = current_price_pred['midterm'][:actual_batch_size]
longterm_pred = current_price_pred['longterm'][:actual_batch_size]
price_values_pred = current_price_pred['values'][:actual_batch_size]
# Compute losses for each task
immediate_loss = nn.CrossEntropyLoss()(immediate_pred, immediate_labels)
@ -820,7 +831,7 @@ class DQNAgent:
price_loss = immediate_loss + 0.7 * midterm_loss + 0.5 * longterm_loss + 0.3 * price_value_loss
# Create extrema labels (same as before)
extrema_labels = torch.ones(min_size, dtype=torch.long, device=self.device) * 2 # Default: neither
extrema_labels = torch.ones(actual_batch_size, dtype=torch.long, device=self.device) * 2 # Default: neither
# Identify potential bottoms (significant negative change)
bottoms = (immediate_changes < -0.003)
@ -831,8 +842,8 @@ class DQNAgent:
extrema_labels[tops] = 1
# Calculate extrema prediction loss
if len(current_extrema_pred.shape) > 1 and current_extrema_pred.shape[0] >= min_size:
current_extrema_pred = current_extrema_pred[:min_size]
if len(current_extrema_pred.shape) > 1 and current_extrema_pred.shape[0] >= actual_batch_size:
current_extrema_pred = current_extrema_pred[:actual_batch_size]
extrema_loss = nn.CrossEntropyLoss()(current_extrema_pred, extrema_labels)
# Combined loss with all components
@ -1017,6 +1028,71 @@ class DQNAgent:
return normalized_state
def update_realtime_tick_features(self, tick_features):
"""Update with real-time tick features from tick processor"""
try:
if tick_features is not None:
self.realtime_tick_features = tick_features
# Log high-confidence tick features
if tick_features.get('confidence', 0) > 0.8:
logger.debug(f"High-confidence tick features updated: confidence={tick_features['confidence']:.3f}")
except Exception as e:
logger.error(f"Error updating real-time tick features: {e}")
def _enhance_state_with_tick_features(self, state: np.ndarray) -> np.ndarray:
"""Enhance state with real-time tick features if available"""
try:
if self.realtime_tick_features is None:
return state
# Extract neural features from tick processor
neural_features = self.realtime_tick_features.get('neural_features', np.array([]))
volume_features = self.realtime_tick_features.get('volume_features', np.array([]))
microstructure_features = self.realtime_tick_features.get('microstructure_features', np.array([]))
confidence = self.realtime_tick_features.get('confidence', 0.0)
# Combine tick features - make them compact to match state dimensions
tick_features = np.concatenate([
neural_features[:3] if len(neural_features) >= 3 else np.zeros(3), # Take first 3 neural features
volume_features[:1] if len(volume_features) >= 1 else np.zeros(1), # Take first volume feature
microstructure_features[:1] if len(microstructure_features) >= 1 else np.zeros(1), # Take first microstructure feature
])
# Weight the tick features
weighted_tick_features = tick_features * self.tick_feature_weight
# Enhance the state by adding tick features to each timeframe
if len(state.shape) == 1:
# 1D state - append tick features
enhanced_state = np.concatenate([state, weighted_tick_features])
else:
# 2D state - add tick features to each timeframe row
num_timeframes, num_features = state.shape
# Ensure tick features match the number of original features
if len(weighted_tick_features) != num_features:
# Pad or truncate tick features to match state feature dimension
if len(weighted_tick_features) < num_features:
# Pad with zeros
padded_features = np.zeros(num_features)
padded_features[:len(weighted_tick_features)] = weighted_tick_features
weighted_tick_features = padded_features
else:
# Truncate to match
weighted_tick_features = weighted_tick_features[:num_features]
# Add tick features to the last row (most recent timeframe)
enhanced_state = state.copy()
enhanced_state[-1, :] += weighted_tick_features # Add to last timeframe
return enhanced_state
except Exception as e:
logger.error(f"Error enhancing state with tick features: {e}")
return state
def update_learning_metrics(self, episode_reward, best_reward_threshold=0.01):
"""Update learning metrics and perform learning rate adjustments if needed"""
# Update average reward with exponential moving average

13
NN/models/enhanced_cnn.py
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@ -335,13 +335,14 @@ class EnhancedCNN(nn.Module):
# Process different input shapes
if len(x.shape) > 2:
# Handle 3D input [batch, timeframes, features]
# Handle 4D input [batch, timeframes, window, features] or 3D input [batch, timeframes, features]
if len(x.shape) == 4:
# Flatten window and features: [batch, timeframes, window*features]
x = x.view(batch_size, x.size(1), -1)
if self.conv_layers is not None:
# Reshape for 1D convolution:
# [batch, timeframes, features] -> [batch, timeframes, features*1]
if len(x.shape) == 3:
x = x.permute(0, 1, 2) # Ensure shape is [batch, timeframes, features]
x_reshaped = x.permute(0, 1, 2) # [batch, timeframes, features]
# Now x is 3D: [batch, timeframes, features]
x_reshaped = x
# Check if the feature dimension has changed and rebuild if necessary
if x_reshaped.size(1) * x_reshaped.size(2) != self.feature_dim: