Initial commit: BTC 1h prediction model (53.10% accuracy)
🎯 项目成果： - 准确率: 53.10% (2026年测试集) - 任务类型: 二分类 (预测涨/跌) - 特征: OHLCV + RSI + MACD (9个特征) - 训练数据: 2020-2025年BTC小时数据 (52,351序列) - 测试数据: 2026年1-29日 (484序列) 📊 性能指标： - 精确率: 51.79% - 召回率: 49.36% - F1分数: 0.5054 - 预测分布: 非常平衡 (偏差仅-11个) 🔬 实验历史： - 尝试了5种方案 (回归/分类, 不同数据划分) - 分类任务优于回归任务 (53.10% vs 49.5%) - 全量训练避免了过拟合问题 📦 主要模型文件： - models/checkpoints/dlinear_classification_2026/ - best_model.pt (最佳模型) - preprocessor.pkl (预处理器) - test_2026_results.json (测试结果) Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
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"""Training orchestration and utilities."""
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import os
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from pathlib import Path
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from typing import Optional, Dict
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import torch
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import torch.nn as nn
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from torch.utils.data import DataLoader
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from tqdm import tqdm
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import json
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from .config import TrainingConfig
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from .evaluator import evaluate_model
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class EarlyStopping:
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"""Early stopping to stop training when validation loss doesn't improve."""
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def __init__(self, patience: int = 10, min_delta: float = 1e-4):
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"""Initialize early stopping.
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Args:
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patience: Number of epochs to wait before stopping
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min_delta: Minimum change to qualify as improvement
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"""
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self.patience = patience
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self.min_delta = min_delta
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self.counter = 0
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self.best_loss = None
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self.should_stop = False
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def __call__(self, val_loss: float) -> bool:
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"""Check if training should stop.
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Args:
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val_loss: Current validation loss
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Returns:
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True if training should stop
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"""
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if self.best_loss is None:
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self.best_loss = val_loss
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elif val_loss > self.best_loss - self.min_delta:
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self.counter += 1
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if self.counter >= self.patience:
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self.should_stop = True
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else:
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self.best_loss = val_loss
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self.counter = 0
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return self.should_stop
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class Trainer:
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"""Model trainer with support for MPS/CUDA/CPU."""
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def __init__(
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self,
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model: nn.Module,
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config: TrainingConfig,
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task: str = 'classification',
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pos_weight: float = None # 添加类别权重参数
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):
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"""Initialize trainer.
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Args:
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model: PyTorch model to train
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config: Training configuration
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task: Task type ('classification' or 'regression')
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pos_weight: Positive class weight for imbalanced data
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"""
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self.model = model
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self.config = config
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self.task = task
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self.pos_weight = pos_weight
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# Set device
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self.device = config.get_device()
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self.model = self.model.to(self.device)
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print(f"Training on device: {self.device}")
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# Loss function (with class weight if provided)
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if hasattr(model, 'get_loss_function'):
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self.criterion = model.get_loss_function(pos_weight=pos_weight, device=self.device)
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else:
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if task == 'classification':
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if pos_weight is not None:
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# 使用float32以兼容MPS
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pos_weight_tensor = torch.tensor([pos_weight], dtype=torch.float32).to(self.device)
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self.criterion = nn.BCEWithLogitsLoss(pos_weight=pos_weight_tensor)
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else:
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self.criterion = nn.BCELoss()
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else:
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self.criterion = nn.MSELoss()
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if pos_weight is not None:
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print(f"Using weighted loss - positive class weight: {pos_weight:.2f}")
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# Optimizer
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self.optimizer = torch.optim.AdamW(
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model.parameters(),
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lr=config.learning_rate,
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weight_decay=config.weight_decay
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)
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# Learning rate scheduler
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if config.lr_scheduler == 'plateau':
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self.scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
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self.optimizer,
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mode='min',
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factor=config.lr_factor,
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patience=config.lr_patience,
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verbose=True
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)
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elif config.lr_scheduler == 'cosine':
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self.scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
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self.optimizer,
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T_max=config.max_epochs
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)
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else:
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self.scheduler = None
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# Early stopping
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self.early_stopping = EarlyStopping(
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patience=config.early_stopping_patience,
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min_delta=config.early_stopping_min_delta
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)
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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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'learning_rates': []
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}
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# Best model tracking
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self.best_val_loss = float('inf')
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self.best_model_path = None
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def train_epoch(self, train_loader: DataLoader) -> float:
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"""Train for one epoch.
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Args:
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train_loader: Training data loader
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Returns:
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Average training loss
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"""
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self.model.train()
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total_loss = 0.0
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num_batches = 0
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pbar = tqdm(train_loader, desc='Training')
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for batch_X, batch_y in pbar:
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batch_X = batch_X.to(self.device)
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batch_y = batch_y.to(self.device)
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# Forward pass
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self.optimizer.zero_grad()
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outputs = self.model(batch_X).squeeze()
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# Handle different output shapes
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if self.task == 'classification':
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batch_y = batch_y.float()
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loss = self.criterion(outputs, batch_y)
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# Backward pass
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loss.backward()
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self.optimizer.step()
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# Track loss
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total_loss += loss.item()
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num_batches += 1
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# Update progress bar
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pbar.set_postfix({'loss': f'{loss.item():.4f}'})
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return total_loss / num_batches
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def validate(self, val_loader: DataLoader) -> float:
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"""Validate the model.
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Args:
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val_loader: Validation data loader
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Returns:
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Average validation loss
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"""
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self.model.eval()
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total_loss = 0.0
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num_batches = 0
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with torch.no_grad():
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for batch_X, batch_y in val_loader:
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batch_X = batch_X.to(self.device)
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batch_y = batch_y.to(self.device)
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outputs = self.model(batch_X).squeeze()
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if self.task == 'classification':
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batch_y = batch_y.float()
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loss = self.criterion(outputs, batch_y)
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total_loss += loss.item()
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num_batches += 1
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return total_loss / num_batches
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def fit(
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self,
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train_loader: DataLoader,
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val_loader: DataLoader,
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save_dir: Optional[str] = None
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) -> Dict:
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"""Train the model.
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Args:
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train_loader: Training data loader
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val_loader: Validation data loader
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save_dir: Directory to save checkpoints
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Returns:
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Training history
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"""
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if save_dir is None:
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save_dir = self.config.checkpoint_dir
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save_dir = Path(save_dir)
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save_dir.mkdir(parents=True, exist_ok=True)
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print(f"Starting training for {self.config.max_epochs} epochs...")
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print(f"Model parameters: {self.model.count_parameters()}")
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for epoch in range(self.config.max_epochs):
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# Train
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train_loss = self.train_epoch(train_loader)
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# Validate
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val_loss = self.validate(val_loader)
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# Update history
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self.history['train_loss'].append(train_loss)
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self.history['val_loss'].append(val_loss)
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current_lr = self.optimizer.param_groups[0]['lr']
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self.history['learning_rates'].append(current_lr)
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# Print progress
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print(f"Epoch {epoch + 1}/{self.config.max_epochs}")
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print(f" Train loss: {train_loss:.4f}")
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print(f" Val loss: {val_loss:.4f}")
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print(f" LR: {current_lr:.6f}")
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# Save best model
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if val_loss < self.best_val_loss:
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self.best_val_loss = val_loss
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if self.config.save_best:
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self.best_model_path = save_dir / 'best_model.pt'
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self.model.save(str(self.best_model_path))
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print(f" Saved best model (val_loss: {val_loss:.4f})")
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# Learning rate scheduling
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if self.scheduler is not None:
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if self.config.lr_scheduler == 'plateau':
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self.scheduler.step(val_loss)
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else:
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self.scheduler.step()
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# Early stopping
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if self.early_stopping(val_loss):
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print(f"Early stopping at epoch {epoch + 1}")
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break
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# Save final model
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final_model_path = save_dir / 'final_model.pt'
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self.model.save(str(final_model_path))
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# Save training history
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history_path = save_dir / 'training_history.json'
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with open(history_path, 'w') as f:
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json.dump(self.history, f, indent=2)
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print(f"\nTraining completed!")
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print(f"Best validation loss: {self.best_val_loss:.4f}")
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print(f"Best model saved to: {self.best_model_path}")
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return self.history
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def evaluate(self, test_loader: DataLoader) -> Dict[str, float]:
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"""Evaluate the model on test data.
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Args:
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test_loader: Test data loader
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Returns:
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Dictionary of evaluation metrics
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"""
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# Load best model if available
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if self.best_model_path is not None and self.best_model_path.exists():
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print(f"Loading best model from {self.best_model_path}")
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checkpoint = torch.load(self.best_model_path, map_location=self.device)
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self.model.load_state_dict(checkpoint['model_state_dict'])
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# Evaluate
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metrics = evaluate_model(self.model, test_loader, self.task, self.device)
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print("\nTest Metrics:")
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for key, value in metrics.items():
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print(f" {key}: {value:.4f}")
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return metrics
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if __name__ == "__main__":
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# Test the trainer
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from ..models.dlinear.model import DLinear, DLinearConfig
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from ..data.fetcher import BinanceDataFetcher
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from ..data.preprocessor import CryptoPreprocessor, split_data
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from ..data.dataset import create_dataloaders
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# Fetch data
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print("Fetching data...")
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fetcher = BinanceDataFetcher()
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df = fetcher.fetch_ohlcv('BTC', '1h')
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# Preprocess
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print("\nPreprocessing...")
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preprocessor = CryptoPreprocessor(lookback=168)
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X, y, _ = preprocessor.preprocess_pipeline(df, task='classification')
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# Split
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train_data, val_data, test_data = split_data(X, y)
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# Create dataloaders
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train_loader, val_loader, test_loader = create_dataloaders(
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train_data, val_data, test_data,
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batch_size=32
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)
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# Create model
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print("\nCreating model...")
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config = DLinearConfig(
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input_dim=X.shape[-1],
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lookback=168,
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task='classification'
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)
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model = DLinear(config)
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# Train
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train_config = TrainingConfig(
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batch_size=32,
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max_epochs=5, # Short test
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early_stopping_patience=3
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)
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trainer = Trainer(model, train_config, task='classification')
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history = trainer.fit(train_loader, val_loader, save_dir='/tmp/test_training')
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# Evaluate
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metrics = trainer.evaluate(test_loader)
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