""" COB RL Model - 1B Parameter Reinforcement Learning Network for COB Trading This module contains the massive 1B+ parameter RL network optimized for real-time Consolidated Order Book (COB) trading. The model processes COB features and performs inference every 200ms for ultra-low latency trading decisions. Architecture: - Input: 2000-dimensional COB features - Core: 12-layer transformer with 4096 hidden size (32 attention heads) - Output: Price direction (DOWN/SIDEWAYS/UP), value estimation, confidence - Parameters: ~1B total parameters for maximum market understanding """ import torch import torch.nn as nn import torch.nn.functional as F import numpy as np import logging from typing import Dict, List, Optional, Tuple, Any from abc import ABC, abstractmethod from models import ModelInterface logger = logging.getLogger(__name__) class MassiveRLNetwork(nn.Module): """ Massive 1B+ parameter RL network optimized for real-time COB trading This network processes consolidated order book data and makes predictions about future price movements with high confidence. Designed for 200ms inference cycles. """ def __init__(self, input_size: int = 2000, hidden_size: int = 2048, num_layers: int = 8): super(MassiveRLNetwork, self).__init__() self.input_size = input_size self.hidden_size = hidden_size self.num_layers = num_layers # Optimized input processing layers for 400M params self.input_projection = nn.Sequential( nn.Linear(input_size, hidden_size), nn.LayerNorm(hidden_size), nn.GELU(), nn.Dropout(0.1) ) # Efficient transformer-style encoder layers (400M target) self.encoder_layers = nn.ModuleList([ nn.TransformerEncoderLayer( d_model=hidden_size, nhead=16, # Reduced attention heads for efficiency dim_feedforward=hidden_size * 3, # 6K feedforward (reduced from 16K) dropout=0.1, activation='gelu', batch_first=True ) for _ in range(num_layers) ]) # Market regime understanding layers (optimized for 400M) self.regime_encoder = nn.Sequential( nn.Linear(hidden_size, hidden_size + 512), # Smaller expansion nn.LayerNorm(hidden_size + 512), nn.GELU(), nn.Dropout(0.1), nn.Linear(hidden_size + 512, hidden_size), nn.LayerNorm(hidden_size), nn.GELU() ) # Price prediction head (main RL objective) self.price_head = nn.Sequential( nn.Linear(hidden_size, hidden_size // 2), nn.LayerNorm(hidden_size // 2), nn.GELU(), nn.Dropout(0.2), nn.Linear(hidden_size // 2, hidden_size // 4), nn.LayerNorm(hidden_size // 4), nn.GELU(), nn.Linear(hidden_size // 4, 3) # DOWN, SIDEWAYS, UP ) # Value estimation head for RL self.value_head = nn.Sequential( nn.Linear(hidden_size, hidden_size // 2), nn.LayerNorm(hidden_size // 2), nn.GELU(), nn.Dropout(0.2), nn.Linear(hidden_size // 2, hidden_size // 4), nn.LayerNorm(hidden_size // 4), nn.GELU(), nn.Linear(hidden_size // 4, 1) ) # Confidence head self.confidence_head = nn.Sequential( nn.Linear(hidden_size, hidden_size // 4), nn.LayerNorm(hidden_size // 4), nn.GELU(), nn.Linear(hidden_size // 4, 1), nn.Sigmoid() ) # Initialize weights self.apply(self._init_weights) # Calculate total parameters total_params = sum(p.numel() for p in self.parameters()) logger.info(f"COB RL Network initialized with {total_params:,} parameters") def _init_weights(self, module): """Initialize weights with proper scaling for large models""" if isinstance(module, nn.Linear): torch.nn.init.xavier_uniform_(module.weight) if module.bias is not None: torch.nn.init.zeros_(module.bias) elif isinstance(module, nn.LayerNorm): torch.nn.init.ones_(module.weight) torch.nn.init.zeros_(module.bias) def forward(self, x): """ Forward pass through massive network Args: x: Input tensor of shape [batch_size, input_size] containing COB features Returns: Dict containing: - price_logits: Logits for price direction (DOWN/SIDEWAYS/UP) - value: Value estimation for RL - confidence: Confidence score [0, 1] - features: Hidden features for analysis """ batch_size = x.size(0) # Project input x = self.input_projection(x) # [batch, hidden_size] # Add sequence dimension for transformer x = x.unsqueeze(1) # [batch, 1, hidden_size] # Pass through transformer layers for layer in self.encoder_layers: x = layer(x) # Remove sequence dimension x = x.squeeze(1) # [batch, hidden_size] # Apply regime encoding x = self.regime_encoder(x) # Generate predictions price_logits = self.price_head(x) value = self.value_head(x) confidence = self.confidence_head(x) return { 'price_logits': price_logits, 'value': value, 'confidence': confidence, 'features': x # Hidden features for analysis } def predict(self, cob_features: np.ndarray) -> Dict[str, Any]: """ High-level prediction method for COB features Args: cob_features: COB features as numpy array [input_size] Returns: Dict containing prediction results """ self.eval() with torch.no_grad(): # Convert to tensor and add batch dimension if isinstance(cob_features, np.ndarray): x = torch.from_numpy(cob_features).float() else: x = cob_features.float() if x.dim() == 1: x = x.unsqueeze(0) # Add batch dimension # Move to device device = next(self.parameters()).device x = x.to(device) # Forward pass outputs = self.forward(x) # Process outputs price_probs = F.softmax(outputs['price_logits'], dim=1) predicted_direction = torch.argmax(price_probs, dim=1).item() confidence = outputs['confidence'].item() value = outputs['value'].item() return { 'predicted_direction': predicted_direction, # 0=DOWN, 1=SIDEWAYS, 2=UP 'confidence': confidence, 'value': value, 'probabilities': price_probs.cpu().numpy()[0], 'direction_text': ['DOWN', 'SIDEWAYS', 'UP'][predicted_direction] } def get_model_info(self) -> Dict[str, Any]: """Get model architecture information""" total_params = sum(p.numel() for p in self.parameters()) trainable_params = sum(p.numel() for p in self.parameters() if p.requires_grad) return { 'model_name': 'MassiveRLNetwork', 'total_parameters': total_params, 'trainable_parameters': trainable_params, 'input_size': self.input_size, 'hidden_size': self.hidden_size, 'num_layers': self.num_layers, 'architecture': 'Transformer-based RL Network', 'designed_for': 'Real-time COB trading (200ms inference)', 'output_classes': ['DOWN', 'SIDEWAYS', 'UP'] } class COBRLModelInterface(ModelInterface): """ Interface for the COB RL model that handles model management, training, and inference """ def __init__(self, model_checkpoint_dir: str = "models/realtime_rl_cob", device: str = None, name=None, **kwargs): super().__init__(name=name) # Initialize ModelInterface with a name self.model_checkpoint_dir = model_checkpoint_dir self.device = torch.device(device if device else ('cuda' if torch.cuda.is_available() else 'cpu')) # Initialize model self.model = MassiveRLNetwork().to(self.device) # Initialize optimizer self.optimizer = torch.optim.AdamW( self.model.parameters(), lr=1e-5, # Low learning rate for stability weight_decay=1e-6, betas=(0.9, 0.999) ) # Initialize scaler for mixed precision training self.scaler = torch.cuda.amp.GradScaler() if self.device.type == 'cuda' else None logger.info(f"COB RL Model Interface initialized on {self.device}") def predict(self, cob_features: np.ndarray) -> Dict[str, Any]: """Make prediction using the model""" self.model.eval() with torch.no_grad(): # Convert to tensor and add batch dimension if isinstance(cob_features, np.ndarray): x = torch.from_numpy(cob_features).float() else: x = cob_features.float() if x.dim() == 1: x = x.unsqueeze(0) # Add batch dimension # Move to device x = x.to(self.device) # Forward pass outputs = self.model(x) # Process outputs price_probs = F.softmax(outputs['price_logits'], dim=1) predicted_direction = torch.argmax(price_probs, dim=1).item() confidence = outputs['confidence'].item() value = outputs['value'].item() return { 'predicted_direction': predicted_direction, # 0=DOWN, 1=SIDEWAYS, 2=UP 'confidence': confidence, 'value': value, 'probabilities': price_probs.cpu().numpy()[0], 'direction_text': ['DOWN', 'SIDEWAYS', 'UP'][predicted_direction] } def train_step(self, features: torch.Tensor, targets: Dict[str, torch.Tensor]) -> float: """ Perform one training step Args: features: Input COB features [batch_size, input_size] targets: Dict containing 'direction', 'value', 'confidence' targets Returns: Training loss value """ self.model.train() self.optimizer.zero_grad() if self.scaler: with torch.cuda.amp.autocast(): outputs = self.model(features) loss = self._calculate_loss(outputs, targets) self.scaler.scale(loss).backward() self.scaler.unscale_(self.optimizer) torch.nn.utils.clip_grad_norm_(self.model.parameters(), max_norm=1.0) self.scaler.step(self.optimizer) self.scaler.update() else: outputs = self.model(features) loss = self._calculate_loss(outputs, targets) loss.backward() torch.nn.utils.clip_grad_norm_(self.model.parameters(), max_norm=1.0) self.optimizer.step() return loss.item() def _calculate_loss(self, outputs: Dict[str, torch.Tensor], targets: Dict[str, torch.Tensor]) -> torch.Tensor: """Calculate combined loss for RL training""" # Direction prediction loss (cross-entropy) direction_loss = F.cross_entropy(outputs['price_logits'], targets['direction']) # Value estimation loss (MSE) value_loss = F.mse_loss(outputs['value'].squeeze(), targets['value']) # Confidence loss (BCE) confidence_loss = F.binary_cross_entropy(outputs['confidence'].squeeze(), targets['confidence']) # Combined loss with weights total_loss = direction_loss + 0.5 * value_loss + 0.3 * confidence_loss return total_loss def save_model(self, filepath: str = None): """Save model checkpoint""" if filepath is None: import os os.makedirs(self.model_checkpoint_dir, exist_ok=True) filepath = f"{self.model_checkpoint_dir}/cob_rl_model_latest.pt" checkpoint = { 'model_state_dict': self.model.state_dict(), 'optimizer_state_dict': self.optimizer.state_dict(), 'model_info': self.model.get_model_info() } if self.scaler: checkpoint['scaler_state_dict'] = self.scaler.state_dict() torch.save(checkpoint, filepath) logger.info(f"COB RL model saved to {filepath}") def load_model(self, filepath: str = None): """Load model checkpoint""" if filepath is None: filepath = f"{self.model_checkpoint_dir}/cob_rl_model_latest.pt" try: checkpoint = torch.load(filepath, map_location=self.device) self.model.load_state_dict(checkpoint['model_state_dict']) self.optimizer.load_state_dict(checkpoint['optimizer_state_dict']) if self.scaler and 'scaler_state_dict' in checkpoint: self.scaler.load_state_dict(checkpoint['scaler_state_dict']) logger.info(f"COB RL model loaded from {filepath}") return True except Exception as e: logger.warning(f"Failed to load COB RL model from {filepath}: {e}") return False def get_model_stats(self) -> Dict[str, Any]: """Get model statistics""" return self.model.get_model_info() def get_memory_usage(self) -> float: """Estimate COBRLModel memory usage in MB""" # This is an estimation. For a more precise value, you'd inspect tensors. # A massive network might take hundreds of MBs or even GBs. # Let's use a more realistic estimate for a 1B parameter model. # Assuming float32 (4 bytes per parameter), 1B params = 4GB. # For a 400M parameter network (as mentioned in comments), it's 1.6GB. # Let's use a placeholder if it's too complex to calculate dynamically. try: # Calculate total parameters and convert to MB total_params = sum(p.numel() for p in self.model.parameters()) # Assuming float32 (4 bytes per parameter) and converting to MB memory_bytes = total_params * 4 memory_mb = memory_bytes / (1024 * 1024) return memory_mb except Exception as e: logger.debug(f"Could not estimate COBRLModel memory usage: {e}") return 1600.0 # Default to 1.6 GB as an estimate if calculation fails