768 lines
30 KiB
Python
768 lines
30 KiB
Python
#!/usr/bin/env python3
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"""
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CNN Model - PyTorch Implementation (Optimized for Short-Term High-Leverage Trading)
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This module implements an enhanced CNN model using PyTorch for time series analysis
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with a focus on detecting short-term high-leverage trading opportunities.
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Key improvements include attention mechanisms, rapid pattern detection,
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and optimized decision thresholds for trading signals.
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"""
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import os
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import logging
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import numpy as np
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import matplotlib.pyplot as plt
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from datetime import datetime
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import math
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import torch
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import torch.nn as nn
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import torch.optim as optim
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from torch.utils.data import DataLoader, TensorDataset
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from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
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import torch.nn.functional as F
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# Configure logging
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logger = logging.getLogger(__name__)
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class AttentionLayer(nn.Module):
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"""Self-attention layer for time series data"""
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def __init__(self, input_dim):
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super(AttentionLayer, self).__init__()
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self.query = nn.Linear(input_dim, input_dim)
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self.key = nn.Linear(input_dim, input_dim)
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self.value = nn.Linear(input_dim, input_dim)
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self.scale = math.sqrt(input_dim)
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def forward(self, x):
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# x shape: [batch, channels, seq_len]
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batch, channels, seq_len = x.size()
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# Reshape for attention computation
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x_reshaped = x.transpose(1, 2) # [batch, seq_len, channels]
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# Compute query, key, value
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q = self.query(x_reshaped) # [batch, seq_len, channels]
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k = self.key(x_reshaped) # [batch, seq_len, channels]
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v = self.value(x_reshaped) # [batch, seq_len, channels]
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# Compute attention scores
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attn_scores = torch.bmm(q, k.transpose(1, 2)) / self.scale # [batch, seq_len, seq_len]
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attn_weights = F.softmax(attn_scores, dim=2)
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# Apply attention
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out = torch.bmm(attn_weights, v) # [batch, seq_len, channels]
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out = out.transpose(1, 2) # [batch, channels, seq_len]
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return out
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class CNNPyTorch(nn.Module):
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"""
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CNN model for time series analysis using PyTorch.
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"""
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def __init__(self, input_shape, output_size=3):
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"""
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Initialize the CNN architecture.
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Args:
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input_shape (tuple): Shape of input data (window_size, features)
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output_size (int): Number of output classes
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"""
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super(CNNPyTorch, self).__init__()
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# Set device
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self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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window_size, num_features = input_shape
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self.window_size = window_size
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# Simpler architecture with fewer layers and dropout
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self.conv1 = nn.Sequential(
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nn.Conv1d(num_features, 32, kernel_size=3, padding=1),
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nn.BatchNorm1d(32),
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nn.ReLU(),
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nn.Dropout(0.2)
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)
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self.conv2 = nn.Sequential(
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nn.Conv1d(32, 64, kernel_size=3, padding=1),
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nn.BatchNorm1d(64),
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nn.ReLU(),
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nn.Dropout(0.2)
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)
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# Global average pooling to handle variable length sequences
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self.global_pool = nn.AdaptiveAvgPool1d(1)
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# Fully connected layers
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self.fc = nn.Sequential(
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nn.Linear(64, 32),
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nn.ReLU(),
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nn.Dropout(0.2),
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nn.Linear(32, output_size)
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)
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def forward(self, x):
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"""
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Forward pass through the network.
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Args:
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x: Input tensor of shape [batch_size, window_size, features]
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Returns:
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action_probs: Action probabilities
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"""
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# Transpose for conv1d: [batch, features, window]
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x = x.transpose(1, 2)
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# Convolutional layers
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x = self.conv1(x)
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x = self.conv2(x)
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# Global pooling
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x = self.global_pool(x)
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x = x.squeeze(-1)
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# Fully connected layers
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action_logits = self.fc(x)
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# Apply class weights to reduce HOLD bias
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# This helps overcome the dataset imbalance that often favors HOLD
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class_weights = torch.tensor([2.5, 0.4, 2.5], device=self.device) # Higher weights for BUY/SELL
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weighted_logits = action_logits * class_weights
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# Add random perturbation during training to encourage exploration
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if self.training:
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# Add small noise to encourage exploration
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noise = torch.randn_like(weighted_logits) * 0.3
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weighted_logits = weighted_logits + noise
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# Softmax to get probabilities
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action_probs = F.softmax(weighted_logits, dim=1)
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return action_probs, None # Return None for price_pred as we're focusing on actions
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class CNNModelPyTorch:
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"""
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High-level wrapper for the CNN model with training and evaluation functionality.
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"""
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def __init__(self, window_size=20, timeframes=None, output_size=3, num_pairs=3):
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"""
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Initialize the model.
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Args:
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window_size (int): Size of the input window
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timeframes (list): List of timeframes to use
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output_size (int): Number of output classes
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num_pairs (int): Number of trading pairs
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"""
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self.window_size = window_size
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self.timeframes = timeframes or ["1m", "5m", "15m"]
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self.output_size = output_size
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self.num_pairs = num_pairs
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# Set device
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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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# Initialize the underlying CNN model
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input_shape = (window_size, len(self.timeframes) * 5) # 5 features per timeframe
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self.model = CNNPyTorch(input_shape, output_size).to(self.device)
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# Initialize optimizer with lower learning rate for stability
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self.optimizer = optim.Adam(self.model.parameters(), lr=0.0001, weight_decay=0.01)
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# Initialize loss functions
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self.action_criterion = nn.CrossEntropyLoss()
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# Training history
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self.history = {
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'train_loss': [],
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'val_loss': [],
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'train_acc': [],
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'val_acc': []
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}
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# For compatibility with older code
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self.train_losses = []
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self.val_losses = []
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self.train_accuracies = []
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self.val_accuracies = []
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# Initialize action counts
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self.action_counts = {
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'BUY': [0, 0], # [total, correct]
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'SELL': [0, 0], # [total, correct]
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'HOLD': [0, 0] # [total, correct]
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}
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logger.info(f"Building PyTorch CNN model with window_size={window_size}, output_size={output_size}")
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# Learning rate scheduler
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self.scheduler = optim.lr_scheduler.ReduceLROnPlateau(
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self.optimizer,
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mode='min',
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factor=0.5,
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patience=5,
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verbose=True
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)
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# Sensitivity parameters for high-leverage trading
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self.confidence_threshold = 0.65
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self.max_consecutive_same_action = 3
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self.last_actions = [[] for _ in range(num_pairs)] # Track recent actions per pair
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def train_epoch(self, X_train, y_train, future_prices, batch_size):
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"""Train the model for one epoch with focus on short-term pattern recognition"""
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self.model.train()
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total_loss = 0
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total_correct = 0
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total_samples = 0
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# Convert inputs to tensors and create DataLoader
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X_train_tensor = torch.FloatTensor(X_train).to(self.device)
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y_train_tensor = torch.LongTensor(y_train).to(self.device)
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# Create dataset and dataloader
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dataset = TensorDataset(X_train_tensor, y_train_tensor)
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train_loader = DataLoader(dataset, batch_size=batch_size, shuffle=True)
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# Training loop
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for batch_X, batch_y in train_loader:
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self.optimizer.zero_grad()
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# Forward pass
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action_probs, _ = self.model(batch_X)
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# Calculate loss
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loss = self.action_criterion(action_probs, batch_y)
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# Backward pass and optimization
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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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# Update metrics
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total_loss += loss.item()
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predictions = torch.argmax(action_probs, dim=1)
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total_correct += (predictions == batch_y).sum().item()
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total_samples += batch_y.size(0)
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# Update action counts
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for i, (pred, target) in enumerate(zip(predictions, batch_y)):
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pred_action = ['SELL', 'HOLD', 'BUY'][pred.item()]
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self.action_counts[pred_action][0] += 1
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if pred.item() == target.item():
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self.action_counts[pred_action][1] += 1
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# Calculate average loss and accuracy
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avg_loss = total_loss / len(train_loader)
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accuracy = total_correct / total_samples
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# Update training history
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self.history['train_loss'].append(avg_loss)
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self.history['train_acc'].append(accuracy)
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self.train_losses.append(avg_loss)
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self.train_accuracies.append(accuracy)
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# Log trading signals
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for action in ['BUY', 'SELL', 'HOLD']:
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total = self.action_counts[action][0]
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correct = self.action_counts[action][1]
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precision = correct / total if total > 0 else 0
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logger.info(f"Trading signals - {action}: {total}, Precision: {precision:.4f}")
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return avg_loss, 0, accuracy # Return 0 for price_loss as we're not using it
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def evaluate(self, X_val, y_val, future_prices=None):
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"""Evaluate the model with focus on short-term trading performance metrics"""
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self.model.eval()
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total_loss = 0
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total_correct = 0
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total_samples = 0
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# Convert inputs to tensors
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X_val_tensor = torch.FloatTensor(X_val).to(self.device)
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y_val_tensor = torch.LongTensor(y_val).to(self.device)
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# Create dataset and dataloader
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dataset = TensorDataset(X_val_tensor, y_val_tensor)
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val_loader = DataLoader(dataset, batch_size=32)
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with torch.no_grad():
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for batch_X, batch_y in val_loader:
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# Forward pass
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action_probs, _ = self.model(batch_X)
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# Calculate loss
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loss = self.action_criterion(action_probs, batch_y)
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# Update metrics
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total_loss += loss.item()
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predictions = torch.argmax(action_probs, dim=1)
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total_correct += (predictions == batch_y).sum().item()
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total_samples += batch_y.size(0)
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# Calculate average loss and accuracy
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avg_loss = total_loss / len(val_loader)
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accuracy = total_correct / total_samples
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# Update validation history
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self.history['val_loss'].append(avg_loss)
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self.history['val_acc'].append(accuracy)
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self.val_losses.append(avg_loss)
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self.val_accuracies.append(accuracy)
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# Update learning rate scheduler
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self.scheduler.step(avg_loss)
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return avg_loss, 0, accuracy # Return 0 for price_loss as we're not using it
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def predict(self, X):
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"""Make predictions optimized for short-term high-leverage trading signals"""
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self.model.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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action_probs, price_pred = self.model(X_tensor)
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# Post-processing optimized for short-term trading signals
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action_probs_np = action_probs.cpu().numpy()
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# Apply more aggressive HOLD reduction for short-term trading
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action_probs_np[:, 1] *= 0.3 # More aggressive HOLD reduction
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# Apply boosting for BUY/SELL signals
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action_probs_np[:, 0] *= 2.0 # Boost SELL probabilities
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action_probs_np[:, 2] *= 2.0 # Boost BUY probabilities
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# Re-normalize
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action_probs_np = action_probs_np / action_probs_np.sum(axis=1, keepdims=True)
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# Store the predicted action for the most recent input
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if action_probs_np.shape[0] > 0:
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latest_action = np.argmax(action_probs_np[-1])
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self.last_actions[0].append(int(latest_action))
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# Keep only the most recent actions
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self.last_actions[0] = self.last_actions[0][-10:] # Store last 10 actions
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# Update action counts for stats
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actions = np.argmax(action_probs_np, axis=1)
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unique, counts = np.unique(actions, return_counts=True)
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action_dict = dict(zip(unique, counts))
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if 0 in action_dict:
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self.action_counts['SELL'][0] += action_dict[0]
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if 1 in action_dict:
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self.action_counts['HOLD'][0] += action_dict[1]
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if 2 in action_dict:
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self.action_counts['BUY'][0] += action_dict[2]
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# If price_pred is None, create a dummy array of zeros
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if price_pred is None:
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# Get the current close prices from the input if available
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current_prices = X_tensor[:, -1, 3].cpu().numpy() if X_tensor.shape[2] > 3 else np.zeros(X_tensor.shape[0])
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# Calculate price directions based on probabilities
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price_directions = action_probs_np[:, 2] - action_probs_np[:, 0] # BUY - SELL
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# Scale the price change based on signal strength
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price_preds = current_prices * (1 + price_directions * 0.002)
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return action_probs_np, price_preds.reshape(-1, 1)
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else:
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return action_probs_np, price_pred.cpu().numpy()
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def predict_next_candles(self, X, n_candles=3):
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"""
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Predict the next n candles with focus on short-term signals.
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Args:
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X: Input data of shape [batch_size, window_size, features]
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n_candles: Number of future candles to predict
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Returns:
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Dictionary of predictions for each timeframe
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"""
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self.model.eval()
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X_tensor = torch.tensor(X, dtype=torch.float32).to(self.device)
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with torch.no_grad():
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# Get initial predictions
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action_probs, price_pred = self.model(X_tensor)
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action_probs_np = action_probs.cpu().numpy()
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# Apply more aggressive processing for short-term signals
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action_probs_np[:, 1] *= 0.5 # Reduce HOLD
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action_probs_np[:, 0] *= 1.3 # Boost SELL
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action_probs_np[:, 2] *= 1.3 # Boost BUY
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# Re-normalize
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action_probs_np = action_probs_np / action_probs_np.sum(axis=1, keepdims=True)
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# For short-term predictions, implement decay of signal over time
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# First candle: full signal, then gradually decay
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predictions = {}
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for i, tf in enumerate(self.timeframes):
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tf_preds = np.zeros((n_candles, action_probs_np.shape[0], 3))
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for j in range(n_candles):
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# Apply decay factor to move signals toward HOLD over time
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# (short-term signals shouldn't persist too long)
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decay_factor = max(0.1, 1.0 - j * 0.3)
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# First, move probabilities toward HOLD with decay
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decayed_probs = action_probs_np.copy()
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decayed_probs[:, 0] = action_probs_np[:, 0] * decay_factor # Decay SELL
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decayed_probs[:, 2] = action_probs_np[:, 2] * decay_factor # Decay BUY
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# Increase HOLD probability to compensate
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hold_increase = (1.0 - decay_factor) * (action_probs_np[:, 0] + action_probs_np[:, 2])
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decayed_probs[:, 1] = action_probs_np[:, 1] + hold_increase
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# Re-normalize
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decayed_probs = decayed_probs / decayed_probs.sum(axis=1, keepdims=True)
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# Store in predictions array
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tf_preds[j] = decayed_probs
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# Store in output dictionary
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predictions[tf] = tf_preds
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return predictions
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def train(self, X_train, y_train, X_val=None, y_val=None, batch_size=32, epochs=100):
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"""
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Train the CNN model.
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Args:
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X_train: Training input data
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y_train: Training target data
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X_val: Validation input data
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y_val: Validation target data
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batch_size: Batch size for training
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epochs: Number of training epochs
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Returns:
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Training history
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"""
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logger.info(f"Training PyTorch CNN model with {len(X_train)} samples, "
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f"batch_size={batch_size}, epochs={epochs}")
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# Convert numpy arrays to PyTorch tensors
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X_train_tensor = torch.tensor(X_train, dtype=torch.float32).to(self.device)
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# Handle different output sizes for y_train
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if self.output_size == 1:
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y_train_tensor = torch.tensor(y_train, dtype=torch.float32).to(self.device)
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else:
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y_train_tensor = torch.tensor(y_train, dtype=torch.long).to(self.device)
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# Create DataLoader for training data
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train_dataset = TensorDataset(X_train_tensor, y_train_tensor)
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train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
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# Create DataLoader for validation data if provided
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if X_val is not None and y_val is not None:
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X_val_tensor = torch.tensor(X_val, dtype=torch.float32).to(self.device)
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if self.output_size == 1:
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y_val_tensor = torch.tensor(y_val, dtype=torch.float32).to(self.device)
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else:
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y_val_tensor = torch.tensor(y_val, dtype=torch.long).to(self.device)
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val_dataset = TensorDataset(X_val_tensor, y_val_tensor)
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val_loader = DataLoader(val_dataset, batch_size=batch_size)
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else:
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val_loader = None
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# Training loop
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for epoch in range(epochs):
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# Training phase
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self.model.train()
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running_loss = 0.0
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correct = 0
|
|
total = 0
|
|
|
|
for inputs, targets in train_loader:
|
|
# Zero the parameter gradients
|
|
self.optimizer.zero_grad()
|
|
|
|
# Forward pass
|
|
action_probs, price_pred = self.model(inputs)
|
|
|
|
# Calculate loss
|
|
if self.output_size == 1:
|
|
loss = self.criterion(action_probs, targets.unsqueeze(1))
|
|
else:
|
|
loss = self.criterion(action_probs, targets)
|
|
|
|
# Backward pass and optimize
|
|
loss.backward()
|
|
self.optimizer.step()
|
|
|
|
# Statistics
|
|
running_loss += loss.item()
|
|
_, predicted = torch.max(action_probs, 1)
|
|
total += targets.size(0)
|
|
correct += (predicted == targets).sum().item()
|
|
|
|
epoch_loss = running_loss / len(train_loader)
|
|
epoch_acc = correct / total if total > 0 else 0
|
|
|
|
# Validation phase
|
|
if val_loader is not None:
|
|
val_loss, val_acc = self.evaluate(X_val, y_val)
|
|
|
|
logger.info(f"Epoch {epoch+1}/{epochs} - "
|
|
f"loss: {epoch_loss:.4f} - acc: {epoch_acc:.4f} - "
|
|
f"val_loss: {val_loss:.4f} - val_acc: {val_acc:.4f}")
|
|
|
|
# Update history
|
|
self.train_losses.append(epoch_loss)
|
|
self.train_accuracies.append(epoch_acc)
|
|
self.val_losses.append(val_loss)
|
|
self.val_accuracies.append(val_acc)
|
|
else:
|
|
logger.info(f"Epoch {epoch+1}/{epochs} - "
|
|
f"loss: {epoch_loss:.4f} - acc: {epoch_acc:.4f}")
|
|
|
|
# Update history without validation
|
|
self.train_losses.append(epoch_loss)
|
|
self.train_accuracies.append(epoch_acc)
|
|
|
|
logger.info("Training completed")
|
|
return {
|
|
'loss': self.train_losses,
|
|
'accuracy': self.train_accuracies,
|
|
'val_loss': self.val_losses,
|
|
'val_accuracy': self.val_accuracies
|
|
}
|
|
|
|
def evaluate_metrics(self, X_test, y_test):
|
|
"""
|
|
Calculate and return comprehensive evaluation metrics as dict
|
|
"""
|
|
X_test_tensor = torch.tensor(X_test, dtype=torch.float32).to(self.device)
|
|
|
|
self.model.eval()
|
|
with torch.no_grad():
|
|
y_pred = self.model(X_test_tensor)
|
|
|
|
if self.output_size > 1:
|
|
_, y_pred_class = torch.max(y_pred, 1)
|
|
y_pred_class = y_pred_class.cpu().numpy()
|
|
else:
|
|
y_pred_class = (y_pred.cpu().numpy() > 0.5).astype(int).flatten()
|
|
|
|
metrics = {
|
|
'accuracy': accuracy_score(y_test, y_pred_class),
|
|
'precision': precision_score(y_test, y_pred_class, average='weighted', zero_division=0),
|
|
'recall': recall_score(y_test, y_pred_class, average='weighted', zero_division=0),
|
|
'f1_score': f1_score(y_test, y_pred_class, average='weighted', zero_division=0)
|
|
}
|
|
|
|
return metrics
|
|
|
|
def save(self, filepath):
|
|
"""
|
|
Save the model to a file with trading configuration.
|
|
|
|
Args:
|
|
filepath: Path to save the model
|
|
"""
|
|
# Create directory if it doesn't exist
|
|
os.makedirs(os.path.dirname(filepath), exist_ok=True)
|
|
|
|
# Save the model state with additional trading parameters
|
|
model_state = {
|
|
'model_state_dict': self.model.state_dict(),
|
|
'optimizer_state_dict': self.optimizer.state_dict(),
|
|
'history': self.history,
|
|
'window_size': self.window_size,
|
|
'num_features': len(self.timeframes) * 5, # 5 features per timeframe
|
|
'output_size': self.output_size,
|
|
'timeframes': self.timeframes,
|
|
# Save trading configuration
|
|
'confidence_threshold': self.confidence_threshold,
|
|
'max_consecutive_same_action': self.max_consecutive_same_action,
|
|
'action_counts': self.action_counts,
|
|
'last_actions': self.last_actions,
|
|
# Save model version information
|
|
'model_version': 'short_term_optimized_v2.0',
|
|
'timestamp': datetime.now().strftime('%Y%m%d_%H%M%S')
|
|
}
|
|
|
|
torch.save(model_state, f"{filepath}.pt")
|
|
logger.info(f"Model saved to {filepath}.pt with short-term trading optimizations")
|
|
|
|
# Save a backup of the model periodically
|
|
backup_dir = f"{filepath}_backup"
|
|
os.makedirs(backup_dir, exist_ok=True)
|
|
|
|
backup_path = os.path.join(backup_dir, f"model_{datetime.now().strftime('%Y%m%d_%H%M%S')}.pt")
|
|
torch.save(model_state, backup_path)
|
|
logger.info(f"Backup saved to {backup_path}")
|
|
|
|
def load(self, filepath):
|
|
"""Load model weights from file"""
|
|
if not os.path.exists(f"{filepath}.pt"):
|
|
logger.error(f"Model file {filepath}.pt not found")
|
|
return False
|
|
|
|
try:
|
|
# Load the model state
|
|
model_state = torch.load(f"{filepath}.pt", map_location=self.device)
|
|
|
|
# Update model parameters
|
|
self.window_size = model_state['window_size']
|
|
self.total_features = model_state['num_features']
|
|
self.output_size = model_state['output_size']
|
|
self.timeframes = model_state.get('timeframes', ["1m"])
|
|
|
|
# Load model state dict
|
|
self.model.load_state_dict(model_state['model_state_dict'])
|
|
|
|
# Load optimizer state if available
|
|
if 'optimizer_state_dict' in model_state:
|
|
self.optimizer.load_state_dict(model_state['optimizer_state_dict'])
|
|
|
|
# Load trading configuration if available
|
|
if 'confidence_threshold' in model_state:
|
|
self.confidence_threshold = model_state['confidence_threshold']
|
|
if 'max_consecutive_same_action' in model_state:
|
|
self.max_consecutive_same_action = model_state['max_consecutive_same_action']
|
|
|
|
# Log model version information if available
|
|
if 'model_version' in model_state:
|
|
logger.info(f"Model version: {model_state['model_version']}")
|
|
if 'timestamp' in model_state:
|
|
logger.info(f"Model timestamp: {model_state['timestamp']}")
|
|
|
|
return True
|
|
except Exception as e:
|
|
logger.error(f"Error loading model: {str(e)}")
|
|
return False
|
|
|
|
def plot_training_history(self, metrics_file="NN/models/saved/training_metrics.json"):
|
|
"""
|
|
Plot training history from saved metrics.
|
|
|
|
Args:
|
|
metrics_file: Path to the saved metrics JSON file
|
|
"""
|
|
try:
|
|
import json
|
|
import matplotlib.pyplot as plt
|
|
import matplotlib.dates as mdates
|
|
from datetime import datetime
|
|
|
|
# Load metrics
|
|
with open(metrics_file, 'r') as f:
|
|
metrics = json.load(f)
|
|
|
|
# Create plots directory
|
|
plots_dir = os.path.join(os.path.dirname(metrics_file), 'plots')
|
|
os.makedirs(plots_dir, exist_ok=True)
|
|
|
|
# Convert timestamps to datetime objects
|
|
timestamps = [datetime.fromisoformat(ts) for ts in metrics['timestamps']]
|
|
|
|
# 1. Plot Loss and Accuracy
|
|
fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(12, 10), sharex=True)
|
|
|
|
# Loss plot
|
|
ax1.plot(timestamps, metrics['train_loss'], 'b-', label='Training Loss')
|
|
ax1.plot(timestamps, metrics['val_loss'], 'r-', label='Validation Loss')
|
|
ax1.set_title('Model Loss Over Time')
|
|
ax1.set_ylabel('Loss')
|
|
ax1.legend()
|
|
ax1.grid(True)
|
|
|
|
# Accuracy plot
|
|
ax2.plot(timestamps, metrics['train_acc'], 'g-', label='Training Accuracy')
|
|
ax2.plot(timestamps, metrics['val_acc'], 'm-', label='Validation Accuracy')
|
|
ax2.set_title('Model Accuracy Over Time')
|
|
ax2.set_ylabel('Accuracy')
|
|
ax2.legend()
|
|
ax2.grid(True)
|
|
|
|
# Format x-axis
|
|
ax2.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d %H:%M'))
|
|
plt.xticks(rotation=45)
|
|
|
|
# Save the plot
|
|
plt.tight_layout()
|
|
plt.savefig(os.path.join(plots_dir, 'loss_accuracy.png'))
|
|
plt.close()
|
|
|
|
# 2. Plot PnL and Win Rate
|
|
fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(12, 10), sharex=True)
|
|
|
|
# PnL plot
|
|
ax1.plot(timestamps, metrics['train_pnl'], 'g-', label='Training PnL')
|
|
ax1.plot(timestamps, metrics['val_pnl'], 'r-', label='Validation PnL')
|
|
ax1.set_title('PnL Over Time')
|
|
ax1.set_ylabel('PnL')
|
|
ax1.legend()
|
|
ax1.grid(True)
|
|
|
|
# Win Rate plot
|
|
ax2.plot(timestamps, metrics['train_win_rate'], 'b-', label='Training Win Rate')
|
|
ax2.plot(timestamps, metrics['val_win_rate'], 'm-', label='Validation Win Rate')
|
|
ax2.set_title('Win Rate Over Time')
|
|
ax2.set_ylabel('Win Rate')
|
|
ax2.legend()
|
|
ax2.grid(True)
|
|
|
|
# Format x-axis
|
|
ax2.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d %H:%M'))
|
|
plt.xticks(rotation=45)
|
|
|
|
# Save the plot
|
|
plt.tight_layout()
|
|
plt.savefig(os.path.join(plots_dir, 'pnl_winrate.png'))
|
|
plt.close()
|
|
|
|
print(f"Performance visualizations saved to {plots_dir}")
|
|
return True
|
|
except Exception as e:
|
|
print(f"Error generating plots: {str(e)}")
|
|
import traceback
|
|
print(traceback.format_exc())
|
|
return False
|
|
|
|
def extract_hidden_features(self, X):
|
|
"""
|
|
Extract hidden features from the model - outputs from last dense layer before output.
|
|
|
|
Args:
|
|
X: Input data
|
|
|
|
Returns:
|
|
Hidden features (output from penultimate dense layer)
|
|
"""
|
|
# Convert to PyTorch tensor
|
|
X_tensor = torch.tensor(X, dtype=torch.float32).to(self.device)
|
|
|
|
# Forward pass through the model
|
|
self.model.eval()
|
|
with torch.no_grad():
|
|
# Get features through CNN layers
|
|
x_t = X_tensor.transpose(1, 2)
|
|
conv_out = self.model.conv_layers(x_t)
|
|
|
|
# Process through all dense layers except the output layer
|
|
features = conv_out
|
|
for layer in self.model.dense_block[:-2]: # Exclude last linear layer and dropout
|
|
features = layer(features)
|
|
|
|
return features.cpu().numpy()
|