500 lines
21 KiB
Python
500 lines
21 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 PricePatternAttention(nn.Module):
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"""
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Attention mechanism specifically designed to focus on price patterns
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that might indicate local extrema or trend reversals
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"""
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def __init__(self, input_dim, hidden_dim=64):
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super(PricePatternAttention, self).__init__()
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self.query = nn.Linear(input_dim, hidden_dim)
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self.key = nn.Linear(input_dim, hidden_dim)
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self.value = nn.Linear(input_dim, hidden_dim)
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self.scale = torch.sqrt(torch.tensor(hidden_dim, dtype=torch.float32))
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def forward(self, x):
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"""Apply attention to input sequence"""
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# x shape: [batch_size, seq_len, features]
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batch_size, seq_len, _ = x.size()
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# Project input to query, key, value
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q = self.query(x) # [batch_size, seq_len, hidden_dim]
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k = self.key(x) # [batch_size, seq_len, hidden_dim]
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v = self.value(x) # [batch_size, seq_len, hidden_dim]
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# Calculate attention scores
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scores = torch.matmul(q, k.transpose(-2, -1)) / self.scale # [batch_size, seq_len, seq_len]
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# Apply softmax to get attention weights
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attn_weights = F.softmax(scores, dim=-1) # [batch_size, seq_len, seq_len]
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# Apply attention to values
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output = torch.matmul(attn_weights, v) # [batch_size, seq_len, hidden_dim]
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return output, attn_weights
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class AdaptiveNorm(nn.Module):
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"""
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Adaptive normalization layer that chooses between different normalization
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methods based on input dimensions
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"""
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def __init__(self, num_features):
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super(AdaptiveNorm, self).__init__()
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self.batch_norm = nn.BatchNorm1d(num_features, affine=True)
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self.group_norm = nn.GroupNorm(min(32, num_features), num_features)
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self.layer_norm = nn.LayerNorm([num_features, 1])
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def forward(self, x):
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# Check input dimensions
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batch_size, channels, seq_len = x.size()
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# Choose normalization method:
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# - Batch size > 1 and seq_len > 1: BatchNorm
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# - Batch size == 1 or seq_len == 1: GroupNorm
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# - Fallback for extreme cases: LayerNorm
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if batch_size > 1 and seq_len > 1:
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return self.batch_norm(x)
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elif seq_len > 1:
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return self.group_norm(x)
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else:
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# For 1D inputs (seq_len=1), we need to adjust the layer norm
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# to the actual input size
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if not hasattr(self, 'layer_norm_1d') or self.layer_norm_1d.normalized_shape[0] != channels:
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self.layer_norm_1d = nn.LayerNorm([channels, seq_len]).to(x.device)
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return self.layer_norm_1d(x)
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class SimpleCNN(nn.Module):
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"""
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Simple CNN model for reinforcement learning with image-like state inputs
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"""
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def __init__(self, input_shape, n_actions):
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super(SimpleCNN, self).__init__()
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# Store dimensions
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self.input_shape = input_shape
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self.n_actions = n_actions
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# Calculate input dimensions
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if len(input_shape) == 3: # [channels, height, width]
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self.channels, self.height, self.width = input_shape
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self.feature_dim = self.height * self.width
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elif len(input_shape) == 2: # [timeframes, features]
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self.channels = input_shape[0]
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self.features = input_shape[1]
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self.feature_dim = self.features
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elif len(input_shape) == 1: # [features]
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self.channels = 1
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self.features = input_shape[0]
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self.feature_dim = self.features
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else:
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raise ValueError(f"Unsupported input shape: {input_shape}")
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# Build network
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self._build_network()
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# Initialize device
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self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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self.to(self.device)
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logger.info(f"SimpleCNN initialized with input shape: {input_shape}, actions: {n_actions}")
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def _build_network(self):
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"""Build the neural network with current feature dimensions"""
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# Create a flexible architecture that adapts to input dimensions
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# Increased complexity
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self.fc_layers = nn.Sequential(
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nn.Linear(self.feature_dim, 512), # Increased size
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nn.ReLU(),
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nn.Dropout(0.2), # Added dropout
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nn.Linear(512, 512), # Increased size
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nn.ReLU(),
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nn.Dropout(0.2), # Added dropout
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nn.Linear(512, 512), # Added layer
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nn.ReLU(),
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nn.Dropout(0.2) # Added dropout
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)
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# Output heads (Dueling DQN architecture)
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self.advantage_head = nn.Linear(512, self.n_actions) # Updated input size
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self.value_head = nn.Linear(512, 1) # Updated input size
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# Extrema detection head
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self.extrema_head = nn.Linear(512, 3) # 0=bottom, 1=top, 2=neither, Updated input size
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# Price prediction heads for different timeframes
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self.price_pred_immediate = nn.Linear(512, 3) # Updated input size
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self.price_pred_midterm = nn.Linear(512, 3) # Updated input size
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self.price_pred_longterm = nn.Linear(512, 3) # Updated input size
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# Regression heads for exact price prediction
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self.price_pred_value = nn.Linear(512, 4) # Updated input size
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def _check_rebuild_network(self, features):
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"""Check if network needs to be rebuilt for different feature dimensions"""
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if features != self.feature_dim:
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logger.info(f"Rebuilding network for new feature dimension: {features} (was {self.feature_dim})")
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self.feature_dim = features
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self._build_network()
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# Move to device after rebuilding
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self.to(self.device)
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return True
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return False
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def forward(self, x):
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"""Forward pass through the network"""
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# Flatten input if needed to ensure it matches the expected feature dimension
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batch_size = x.size(0)
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# Reshape input if needed
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if len(x.shape) > 2: # Handle multi-dimensional input
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# For 3D input: [batch, seq_len, features] or [batch, channels, features]
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x = x.reshape(batch_size, -1) # Flatten to [batch, seq_len*features]
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# Check if the feature dimension matches and rebuild if necessary
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if x.size(1) != self.feature_dim:
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self._check_rebuild_network(x.size(1))
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# Apply fully connected layers with ReLU activation
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x = self.fc_layers(x)
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# Branch 1: Action values (Q-values)
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action_values = self.advantage_head(x)
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# Branch 2: Extrema detection (market top/bottom classification)
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extrema_pred = self.extrema_head(x)
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# Branch 3: Price movement prediction over different timeframes
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# Split into three timeframes: immediate, midterm, longterm
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price_immediate = self.price_pred_immediate(x)
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price_midterm = self.price_pred_midterm(x)
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price_longterm = self.price_pred_longterm(x)
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# Branch 4: Value prediction (regression for expected price changes)
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price_values = self.price_pred_value(x)
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# Package price predictions
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price_predictions = {
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'immediate': price_immediate, # Classification (up/down/sideways)
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'midterm': price_midterm, # Classification (up/down/sideways)
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'longterm': price_longterm, # Classification (up/down/sideways)
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'values': price_values # Regression (expected % change)
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}
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# Return all outputs and the hidden feature representation
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return action_values, extrema_pred, price_predictions, x
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def extract_features(self, x):
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"""Extract hidden features from the input and return both action values and features"""
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# Flatten input if needed to ensure it matches the expected feature dimension
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batch_size = x.size(0)
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# Reshape input if needed
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if len(x.shape) > 2: # Handle multi-dimensional input
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# For 3D input: [batch, seq_len, features] or [batch, channels, features]
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x = x.reshape(batch_size, -1) # Flatten to [batch, seq_len*features]
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# Check if the feature dimension matches and rebuild if necessary
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if x.size(1) != self.feature_dim:
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self._check_rebuild_network(x.size(1))
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# Apply fully connected layers with ReLU activation
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x_features = self.fc_layers(x)
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# Branch 1: Action values (Q-values)
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action_values = self.advantage_head(x_features)
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# Return action values and the hidden feature representation
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return action_values, x_features
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def save(self, path):
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"""Save model weights and architecture"""
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os.makedirs(os.path.dirname(path), exist_ok=True)
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torch.save({
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'state_dict': self.state_dict(),
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'input_shape': self.input_shape,
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'n_actions': self.n_actions,
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'feature_dim': self.feature_dim
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}, f"{path}.pt")
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logger.info(f"Model saved to {path}.pt")
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def load(self, path):
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"""Load model weights and architecture"""
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try:
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checkpoint = torch.load(f"{path}.pt", map_location=self.device)
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self.input_shape = checkpoint['input_shape']
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self.n_actions = checkpoint['n_actions']
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self.feature_dim = checkpoint['feature_dim']
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self._build_network()
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self.load_state_dict(checkpoint['state_dict'])
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self.to(self.device)
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logger.info(f"Model loaded from {path}.pt")
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return True
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except Exception as e:
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logger.error(f"Error loading model: {str(e)}")
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return False
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class CNNModelPyTorch(nn.Module):
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"""
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CNN model for trading with multiple timeframes
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"""
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def __init__(self, window_size=20, num_features=5, output_size=3, timeframes=None):
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super(CNNModelPyTorch, self).__init__()
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if timeframes is None:
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timeframes = [1]
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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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# num_features should already be the total features across all timeframes
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self.total_features = num_features
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logger.info(f"CNNModelPyTorch initialized with window_size={window_size}, num_features={num_features}, "
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f"total_features={self.total_features}, output_size={output_size}, 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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# Create model architecture
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self._create_layers()
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# Move model to device
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self.to(self.device)
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def _create_layers(self):
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"""Create all model layers with current feature dimensions"""
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# Convolutional layers - use total_features as input channels
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self.conv1 = nn.Conv1d(self.total_features, 64, kernel_size=3, padding=1)
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self.norm1 = AdaptiveNorm(64)
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self.dropout1 = nn.Dropout(0.2)
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self.conv2 = nn.Conv1d(64, 128, kernel_size=3, padding=1)
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self.norm2 = AdaptiveNorm(128)
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self.dropout2 = nn.Dropout(0.3)
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self.conv3 = nn.Conv1d(128, 256, kernel_size=3, padding=1)
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self.norm3 = AdaptiveNorm(256)
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self.dropout3 = nn.Dropout(0.4)
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# Add price pattern attention layer
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self.attention = PricePatternAttention(256)
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# Extrema detection specialized convolutional layer
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self.extrema_conv = nn.Conv1d(256, 128, kernel_size=3, padding=1) # Smaller kernel for small inputs
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self.extrema_norm = AdaptiveNorm(128)
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# Fully connected layers - input size will be determined dynamically
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self.fc1 = None # Will be initialized in forward pass
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self.fc2 = nn.Linear(512, 256)
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self.dropout_fc = nn.Dropout(0.5)
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# Advantage and Value streams (Dueling DQN architecture)
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self.fc3 = nn.Linear(256, self.output_size) # Advantage stream
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self.value_fc = nn.Linear(256, 1) # Value stream
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# Additional prediction head for extrema detection (tops/bottoms)
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self.extrema_fc = nn.Linear(256, 3) # 0=bottom, 1=top, 2=neither
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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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def rebuild_conv_layers(self, input_channels):
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"""
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Rebuild convolutional layers for different input dimensions
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Args:
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input_channels: Number of input channels (features) in the data
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"""
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logger.info(f"Rebuilding convolutional layers for {input_channels} input channels")
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# Update total features
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self.total_features = input_channels
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# Recreate all layers with new dimensions
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self._create_layers()
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# Move layers to device
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self.to(self.device)
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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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# Log input tensor shape for debugging
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input_shape = x.size()
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logger.debug(f"Input tensor shape: {input_shape}")
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# Check input dimensions and reshape as needed
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if len(x.size()) == 2:
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# If input is [batch_size, features], reshape to [batch_size, features, 1]
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batch_size, feature_dim = x.size()
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# Check and handle if input features don't match model expectations
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if feature_dim != self.total_features:
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logger.warning(f"Input features ({feature_dim}) don't match model features ({self.total_features})")
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if not hasattr(self, 'rebuild_warning_shown'):
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logger.error(f"Dimension mismatch: Expected {self.total_features} features but got {feature_dim}")
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self.rebuild_warning_shown = True
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# Don't rebuild - instead adapt the input
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# If features are fewer, pad with zeros. If more, truncate
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if feature_dim < self.total_features:
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padding = torch.zeros(batch_size, self.total_features - feature_dim, device=self.device)
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x = torch.cat([x, padding], dim=1)
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else:
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x = x[:, :self.total_features]
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# For 1D input, use a sequence length of 1
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seq_len = 1
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x = x.unsqueeze(2) # Reshape to [batch, features, 1]
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elif len(x.size()) == 3:
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# Standard case: [batch_size, window_size, features]
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batch_size, seq_len, feature_dim = x.size()
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# Check and handle if input dimensions don't match model expectations
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if feature_dim != self.total_features:
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logger.warning(f"Input features ({feature_dim}) don't match model features ({self.total_features})")
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if not hasattr(self, 'rebuild_warning_shown'):
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logger.error(f"Dimension mismatch: Expected {self.total_features} features but got {feature_dim}")
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self.rebuild_warning_shown = True
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# Don't rebuild - instead adapt the input
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# If features are fewer, pad with zeros. If more, truncate
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if feature_dim < self.total_features:
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padding = torch.zeros(batch_size, seq_len, self.total_features - feature_dim, device=self.device)
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x = torch.cat([x, padding], dim=2)
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else:
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x = x[:, :, :self.total_features]
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# Reshape input: [batch, window_size, features] -> [batch, features, window_size]
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x = x.permute(0, 2, 1)
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else:
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raise ValueError(f"Unexpected input shape: {x.size()}, expected 2D or 3D tensor")
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# Log reshaped tensor for debugging
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logger.debug(f"Reshaped tensor for convolution: {x.size()}")
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# Convolutional layers with dropout - safely handle small spatial dimensions
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try:
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x = self.dropout1(F.relu(self.norm1(self.conv1(x))))
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x = self.dropout2(F.relu(self.norm2(self.conv2(x))))
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x = self.dropout3(F.relu(self.norm3(self.conv3(x))))
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except Exception as e:
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logger.warning(f"Error in convolutional layers: {str(e)}")
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# Fallback for very small inputs: skip some convolutions
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if seq_len < 3:
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# Apply a simpler convolution for very small inputs
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x = F.relu(self.conv1(x))
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x = F.relu(self.conv2(x))
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# Skip last conv if we get dimension errors
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try:
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x = F.relu(self.conv3(x))
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except:
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pass
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# Store conv features for extrema detection
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conv_features = x
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# Get the current shape after convolutions
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_, channels, conv_seq_len = x.size()
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# Initialize fc1 if not created yet or if the shape has changed
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if self.fc1 is None:
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flattened_size = channels * conv_seq_len
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logger.info(f"Initializing fc1 with input size {flattened_size}")
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self.fc1 = nn.Linear(flattened_size, 512).to(self.device)
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# Apply extrema detection safely
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try:
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extrema_features = F.relu(self.extrema_norm(self.extrema_conv(conv_features)))
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except Exception as e:
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logger.warning(f"Error in extrema detection: {str(e)}")
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extrema_features = conv_features # Fallback
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# Handle attention for small sequence lengths
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if conv_seq_len > 1:
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# Reshape for attention: [batch, channels, seq_len] -> [batch, seq_len, channels]
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x_attention = x.permute(0, 2, 1)
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# Apply attention
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try:
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attention_output, attention_weights = self.attention(x_attention)
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except Exception as e:
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logger.warning(f"Error in attention layer: {str(e)}")
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# Fallback: don't use attention
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# Flatten - get the actual shape for this batch
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flattened_size = channels * conv_seq_len
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x = x.view(batch_size, flattened_size)
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# Check if we need to recreate fc1 with the correct size
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if self.fc1.in_features != flattened_size:
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logger.info(f"Recreating fc1 layer to match input size {flattened_size}")
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self.fc1 = nn.Linear(flattened_size, 512).to(self.device)
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# Reinitialize optimizer after changing the model
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self.optimizer = optim.Adam(self.parameters(), lr=0.001)
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# Fully connected layers with dropout
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x = F.relu(self.fc1(x))
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x = self.dropout_fc(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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# Also compute extrema prediction from the same features
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extrema_flat = extrema_features.view(batch_size, -1)
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extrema_pred = self.extrema_fc(x) # Use the same features for extrema prediction
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return q_values, extrema_pred
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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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|
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with torch.no_grad():
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q_values, extrema_pred = 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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|
|
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# Also return extrema predictions
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extrema_np = extrema_pred.cpu().numpy()
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extrema_classes = np.argmax(extrema_np, axis=1)
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|
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return actions, q_values_np, extrema_classes
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|
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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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|
|
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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") |