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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"""训练DLinear模型 - 预测未来1小时涨跌（已修复双重Sigmoid Bug）"""
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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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import numpy as np
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def main():
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print("="*80)
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print("DLinear模型训练 - 预测未来1小时涨跌（已修复双重Sigmoid Bug）")
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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（预测未来1小时）
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print("\n[2/7] 数据预处理（预测未来1小时的涨跌）...")
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preprocessor = CryptoPreprocessor(
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lookback=168, # 输入：过去168小时（7天）
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forecast_horizon=1, # ← 改为预测未来1小时
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price_change_threshold=0.0 # 任何涨幅>0都算上涨
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)
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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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# 计算类别分布
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n_down = (y == 0).sum()
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n_up = (y == 1).sum()
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print(f"✓ 标签分布：下跌(0) = {n_down} ({n_down/len(y)*100:.1f}%), 上涨(1) = {n_up} ({n_up/len(y)*100:.1f}%)")
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# 计算类别权重
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pos_weight = n_down / n_up if n_up > 0 else 1.0
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print(f"✓ 计算类别权重：pos_weight = {pos_weight:.2f}")
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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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print(f" 训练集：下跌 {(train_data[1]==0).sum()}, 上涨 {(train_data[1]==1).sum()}")
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print(f" 验证集：下跌 {(val_data[1]==0).sum()}, 上涨 {(val_data[1]==1).sum()}")
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print(f" 测试集：下跌 {(test_data[1]==0).sum()}, 上涨 {(test_data[1]==1).sum()}")
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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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# 步骤5: 创建模型
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print("\n[5/7] 创建DLinear模型（已修复双重Sigmoid Bug）...")
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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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print(f"✓ Bug修复：输出层使用Identity（输出logits），由BCEWithLogitsLoss处理Sigmoid")
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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_forecast'
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)
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trainer = Trainer(
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model,
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train_config,
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task='classification',
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pos_weight=pos_weight
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)
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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_forecast/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（预测未来1小时涨跌）")
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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} ({metrics.get('accuracy', 0)*100:.2f}%)")
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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（预测涨且真涨）: {metrics['true_positives']}")
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print(f" 真阴性（预测跌且真跌）: {metrics['true_negatives']}")
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print(f" 假阳性（预测涨但真跌）: {metrics['false_positives']}")
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print(f" 假阴性（预测跌但真涨）: {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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# 性能分析
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print(f"\n🎯 性能评估:")
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acc = metrics.get('accuracy', 0)
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if acc > 0.55:
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print(f" ✅ 准确率 {acc*100:.1f}% > 55%，模型有一定预测能力")
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elif acc > 0.50:
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print(f" ⚠️ 准确率 {acc*100:.1f}% 略优于随机（50%），预测能力较弱")
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else:
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print(f" ❌ 准确率 {acc*100:.1f}% ≤ 50%，模型预测能力不足")
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total_return = metrics.get('backtest_total_return', 0)
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if total_return > 0:
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print(f" ✅ 回测收益 {total_return*100:.2f}% > 0，策略有盈利潜力")
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else:
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print(f" ❌ 回测收益 {total_return*100:.2f}% ≤ 0，策略亏损")
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print("\n" + "="*80)
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print("✓ 训练完成！")
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print("="*80)
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if __name__ == "__main__":
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main()
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