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>

training/config.py

@@ -0,0 +1,58 @@
+"""Training configuration."""
+
+from dataclasses import dataclass
+from typing import Literal
+
+
+@dataclass
+class TrainingConfig:
+    """Configuration for model training."""
+
+    # Training parameters
+    batch_size: int = 64
+    max_epochs: int = 100
+    learning_rate: float = 0.001
+    weight_decay: float = 1e-5
+
+    # Early stopping
+    early_stopping_patience: int = 10
+    early_stopping_min_delta: float = 1e-4
+
+    # Learning rate scheduler
+    lr_scheduler: Literal['plateau', 'cosine', 'none'] = 'plateau'
+    lr_patience: int = 5
+    lr_factor: float = 0.5
+
+    # Device
+    device: str = 'auto'  # 'auto', 'mps', 'cuda', or 'cpu'
+
+    # Logging
+    log_interval: int = 10  # Log every N batches
+
+    # Checkpointing
+    save_best: bool = True
+    checkpoint_dir: str = './models/checkpoints'
+
+    def get_device(self) -> str:
+        """Get the device to use for training.
+
+        Returns:
+            Device string ('mps', 'cuda', or 'cpu')
+        """
+        import torch
+
+        if self.device != 'auto':
+            return self.device
+
+        # Auto-detect best available device
+        if torch.backends.mps.is_available():
+            return 'mps'
+        elif torch.cuda.is_available():
+            return 'cuda'
+        else:
+            return 'cpu'
+
+
+if __name__ == "__main__":
+    config = TrainingConfig()
+    print(f"Using device: {config.get_device()}")