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