crypto_predictorandClaude Sonnet 4.5
9bbdc8d42b
🎯 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>
59 lines
1.3 KiB
Python
59 lines
1.3 KiB
Python
"""Training configuration."""
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from dataclasses import dataclass
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from typing import Literal
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@dataclass
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class TrainingConfig:
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"""Configuration for model training."""
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# Training parameters
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batch_size: int = 64
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max_epochs: int = 100
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learning_rate: float = 0.001
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weight_decay: float = 1e-5
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# Early stopping
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early_stopping_patience: int = 10
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early_stopping_min_delta: float = 1e-4
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# Learning rate scheduler
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lr_scheduler: Literal['plateau', 'cosine', 'none'] = 'plateau'
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lr_patience: int = 5
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lr_factor: float = 0.5
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# Device
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device: str = 'auto' # 'auto', 'mps', 'cuda', or 'cpu'
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# Logging
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log_interval: int = 10 # Log every N batches
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# Checkpointing
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save_best: bool = True
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checkpoint_dir: str = './models/checkpoints'
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def get_device(self) -> str:
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"""Get the device to use for training.
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Returns:
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Device string ('mps', 'cuda', or 'cpu')
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"""
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import torch
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if self.device != 'auto':
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return self.device
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# Auto-detect best available device
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if torch.backends.mps.is_available():
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return 'mps'
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elif torch.cuda.is_available():
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return 'cuda'
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else:
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return 'cpu'
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if __name__ == "__main__":
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config = TrainingConfig()
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print(f"Using device: {config.get_device()}")
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