130 lines
4.2 KiB
Python
130 lines
4.2 KiB
Python
import torch
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import torch.nn as nn
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import torch.optim as optim
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import numpy as np
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import os
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import logging
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import torch.nn.functional as F
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from typing import List, Tuple
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# Configure logger
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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class CNNModelPyTorch(nn.Module):
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"""
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CNN model for trading signals
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Simplified version for RL training
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"""
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def __init__(self, window_size: int, num_features: int, output_size: int, timeframes: List[str]):
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super(CNNModelPyTorch, self).__init__()
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self.window_size = window_size
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self.num_features = num_features
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self.output_size = output_size
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self.timeframes = timeframes
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# Device configuration
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self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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logger.info(f"Using device: {self.device}")
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# Build model
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self.build_model()
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# Initialize optimizer and scheduler
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self.optimizer = optim.Adam(self.parameters(), lr=0.001)
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self.scheduler = optim.lr_scheduler.ReduceLROnPlateau(
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self.optimizer, mode='max', factor=0.5, patience=5, verbose=True
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)
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# Move model to device
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self.to(self.device)
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def build_model(self):
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"""Build the CNN architecture"""
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# First Convolutional Layer
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self.conv1 = nn.Conv1d(
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in_channels=self.num_features * len(self.timeframes),
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out_channels=32,
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kernel_size=3,
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padding=1
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)
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self.bn1 = nn.BatchNorm1d(32)
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# Second Convolutional Layer
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self.conv2 = nn.Conv1d(32, 64, kernel_size=3, padding=1)
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self.bn2 = nn.BatchNorm1d(64)
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# Third Convolutional Layer
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self.conv3 = nn.Conv1d(64, 128, kernel_size=3, padding=1)
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self.bn3 = nn.BatchNorm1d(128)
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# Calculate size after convolutions
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conv_out_size = self.window_size * 128
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# Fully connected layers
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self.fc1 = nn.Linear(conv_out_size, 512)
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self.fc2 = nn.Linear(512, 256)
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self.fc3 = nn.Linear(256, self.output_size)
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# Additional output for value estimation
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self.value_fc = nn.Linear(256, 1)
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def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
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"""Forward pass through the network"""
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# Ensure input is on the correct device
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x = x.to(self.device)
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# Reshape input: [batch, window_size, features] -> [batch, channels, window_size]
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batch_size = x.size(0)
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x = x.permute(0, 2, 1)
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# Convolutional layers
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x = F.relu(self.bn1(self.conv1(x)))
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x = F.relu(self.bn2(self.conv2(x)))
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x = F.relu(self.bn3(self.conv3(x)))
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# Flatten
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x = x.view(batch_size, -1)
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# Fully connected layers
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x = F.relu(self.fc1(x))
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x = F.relu(self.fc2(x))
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# Split into advantage and value streams
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advantage = self.fc3(x)
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value = self.value_fc(x)
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# Combine value and advantage
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q_values = value + (advantage - advantage.mean(dim=1, keepdim=True))
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return q_values, value
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def predict(self, X):
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"""Make predictions"""
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self.eval()
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# Convert to tensor if not already
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if not isinstance(X, torch.Tensor):
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X_tensor = torch.tensor(X, dtype=torch.float32).to(self.device)
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else:
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X_tensor = X.to(self.device)
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with torch.no_grad():
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q_values, value = self(X_tensor)
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q_values_np = q_values.cpu().numpy()
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actions = np.argmax(q_values_np, axis=1)
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return actions, q_values_np
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def save(self, path: str):
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"""Save model weights"""
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os.makedirs(os.path.dirname(path), exist_ok=True)
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torch.save(self.state_dict(), f"{path}.pt")
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logger.info(f"Model saved to {path}.pt")
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def load(self, path: str):
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"""Load model weights"""
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self.load_state_dict(torch.load(f"{path}.pt", map_location=self.device))
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self.eval()
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logger.info(f"Model loaded from {path}.pt") |