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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"""使用真实Binance数据训练DLinear模型"""
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import sys
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import os
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sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
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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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from models.dlinear.model import DLinear, DLinearConfig
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from training.trainer import Trainer
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from training.config import TrainingConfig
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import json
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def main():
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print("="*80)
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print("DLinear模型训练 - 真实Binance数据")
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print("="*80)
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# 步骤1: 获取真实数据
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print("\n[1/7] 从Binance获取BTC 1h真实数据...")
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fetcher = BinanceDataFetcher()
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df = fetcher.fetch_ohlcv('BTC', '1h', use_cache=True)
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print(f"✓ 已获取 {len(df)} 行真实数据")
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print(f"✓ 时间范围：{df['timestamp'].min()} 至 {df['timestamp'].max()}")
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# 步骤2: 数据预处理
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print("\n[2/7] 数据预处理（技术指标 + 特征工程）...")
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preprocessor = CryptoPreprocessor(lookback=168, forecast_horizon=24)
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X, y, processed_df = preprocessor.preprocess_pipeline(df, task='classification', fit=True)
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print(f"✓ 已创建序列：X形状 = {X.shape}, y形状 = {y.shape}")
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print(f"✓ 特征数量：{X.shape[-1]}")
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print(f"✓ 标签分布：下跌(0) = {(y==0).sum()}, 上涨(1) = {(y==1).sum()}")
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# 步骤3: 划分数据集
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print("\n[3/7] 划分训练/验证/测试集 (70/15/15)...")
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train_data, val_data, test_data = split_data(X, y, train_split=0.7, val_split=0.15)
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# 步骤4: 创建数据加载器
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print("\n[4/7] 创建PyTorch数据加载器...")
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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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task='classification',
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batch_size=64
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)
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print(f"✓ 训练批次：{len(train_loader)}")
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print(f"✓ 验证批次：{len(val_loader)}")
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print(f"✓ 测试批次：{len(test_loader)}")
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# 步骤5: 创建模型
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print("\n[5/7] 创建DLinear模型...")
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model_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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hidden_dim=512,
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dropout=0.1,
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moving_avg_kernel=25
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)
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model = DLinear(model_config)
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print(f"✓ 模型已创建，可训练参数：{model.count_parameters():,} 个")
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# 步骤6: 训练模型
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print("\n[6/7] 开始训练模型...")
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train_config = TrainingConfig(
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batch_size=64,
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max_epochs=50,
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learning_rate=0.001,
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early_stopping_patience=10,
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lr_scheduler='plateau',
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save_best=True,
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checkpoint_dir='./models/checkpoints/dlinear_btc_1h_real'
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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)
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# 步骤7: 评估模型
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print("\n[7/7] 在测试集上评估模型...")
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metrics = trainer.evaluate(test_loader)
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# 保存评估结果
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results_path = './models/checkpoints/dlinear_btc_1h_real/test_metrics.json'
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with open(results_path, 'w') as f:
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json.dump(metrics, f, indent=2)
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print(f"\n✓ 评估结果已保存至：{results_path}")
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# 打印结果摘要
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print("\n" + "="*80)
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print("训练完成 - 最终测试结果")
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print("="*80)
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print(f"\n分类指标:")
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print(f" 准确率: {metrics.get('accuracy', 0):.4f}")
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print(f" F1分数: {metrics.get('f1', 0):.4f}")
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print(f" 精确率: {metrics.get('precision', 0):.4f}")
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print(f" 召回率: {metrics.get('recall', 0):.4f}")
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if 'true_positives' in metrics:
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print(f"\n混淆矩阵:")
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print(f" 真阳性: {metrics['true_positives']}")
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print(f" 真阴性: {metrics['true_negatives']}")
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print(f" 假阳性: {metrics['false_positives']}")
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print(f" 假阴性: {metrics['false_negatives']}")
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print(f"\n回测结果:")
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print(f" 总收益率: {metrics.get('backtest_total_return', 0)*100:.2f}%")
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print(f" 最终资金: ${metrics.get('backtest_final_capital', 0):,.2f}")
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print(f" 夏普比率: {metrics.get('backtest_sharpe_ratio', 0):.4f}")
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print(f" 最大回撤: {metrics.get('backtest_max_drawdown', 0)*100:.2f}%")
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print(f" 胜率: {metrics.get('backtest_win_rate', 0)*100:.2f}%")
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print(f" 交易次数: {metrics.get('backtest_num_trades', 0)}")
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# ONNX导出测试
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print("\n" + "="*80)
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print("测试ONNX导出")
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print("="*80)
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try:
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import onnx
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import onnxruntime as ort
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import torch
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import numpy as np
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onnx_path = './models/checkpoints/dlinear_btc_1h_real/model.onnx'
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print("\n[1/3] 导出模型到ONNX格式...")
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model.to_onnx(onnx_path)
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print(f"✓ ONNX模型已保存至：{onnx_path}")
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print("\n[2/3] 验证ONNX模型...")
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onnx_model = onnx.load(onnx_path)
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onnx.checker.check_model(onnx_model)
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print("✓ ONNX模型有效")
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print("\n[3/3] 测试ONNX推理与PyTorch对比...")
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test_input = torch.randn(1, 168, X.shape[-1])
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model.cpu()
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model.eval()
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with torch.no_grad():
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pytorch_output = model(test_input).numpy()
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ort_session = ort.InferenceSession(onnx_path)
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onnx_output = ort_session.run(None, {'input': test_input.numpy()})[0]
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diff = np.abs(pytorch_output - onnx_output).max()
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print(f"✓ PyTorch与ONNX的最大差异：{diff:.8f}")
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if diff < 1e-5:
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print("✓ ONNX导出成功！输出在容差范围内匹配。")
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else:
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print("⚠ 警告：ONNX输出与PyTorch有差异")
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except Exception as e:
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print(f"✗ ONNX导出失败：{e}")
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print("\n" + "="*80)
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print("✓ DLinear模型训练完成（真实数据）")
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print("="*80)
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if __name__ == "__main__":
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main()
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