"""使用2020-2025年数据训练DLinear模型 - 包含完整牛熊周期""" import sys import os sys.path.insert(0, os.path.dirname(os.path.abspath(__file__))) from data.fetcher import BinanceDataFetcher from data.preprocessor import CryptoPreprocessor, split_data from data.dataset import create_dataloaders from models.dlinear.model import DLinear, DLinearConfig from training.trainer import Trainer from training.config import TrainingConfig import json import numpy as np def main(): print("="*80) print("DLinear模型训练 - 使用2020-2025年完整数据（5年，包含牛熊周期）") print("="*80) # 步骤1: 获取2020-2025年完整数据 print("\n[1/8] 从Binance获取BTC 2020-2025年完整数据...") fetcher = BinanceDataFetcher() # 获取5年数据 df = fetcher.fetch_ohlcv( symbol='BTC', timeframe='1h', start_date='2020-01-01', end_date='2025-12-31', use_cache=True ) print(f"✓ 已获取 {len(df)} 行数据（约{len(df)/8760:.1f}年）") print(f"✓ 时间范围：{df['timestamp'].min()} 至 {df['timestamp'].max()}") # 分析数据覆盖的市场周期 print(f"\n✓ 价格变化分析：") print(f" 起始价格: ${df['close'].iloc[0]:,.2f} (2020年)") print(f" 结束价格: ${df['close'].iloc[-1]:,.2f} (2025年)") total_return = (df['close'].iloc[-1] - df['close'].iloc[0]) / df['close'].iloc[0] print(f" 总收益率: {total_return*100:+.2f}%") # 按年分析 df['year'] = df['timestamp'].dt.year print(f"\n✓ 年度表现分析（确保包含牛熊周期）：") for year in sorted(df['year'].unique()): year_df = df[df['year'] == year] if len(year_df) > 100: # 确保有足够数据 year_return = (year_df['close'].iloc[-1] - year_df['close'].iloc[0]) / year_df['close'].iloc[0] trend = "🚀 牛市" if year_return > 0.3 else ("🔴 熊市" if year_return < -0.3 else "📊 震荡") print(f" {year}年: {year_return*100:+7.2f}% {trend}") # 步骤2: 数据预处理（预测未来1小时） print("\n[2/8] 数据预处理（预测未来1小时的涨跌）...") preprocessor = CryptoPreprocessor( lookback=168, # 输入：过去168小时（7天） forecast_horizon=1, # 预测：未来1小时 price_change_threshold=0.0 ) X, y, processed_df = preprocessor.preprocess_pipeline(df, task='classification', fit=True) print(f"✓ 已创建序列：X形状 = {X.shape}, y形状 = {y.shape}") print(f"✓ 特征数量：{X.shape[-1]}") # 计算类别分布 n_down = (y == 0).sum() n_up = (y == 1).sum() 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}%)") # 计算类别权重 pos_weight = n_down / n_up if n_up > 0 else 1.0 print(f"✓ 计算类别权重：pos_weight = {pos_weight:.2f}") # 步骤3: 划分数据集（时间序列分割） print("\n[3/8] 划分训练/验证/测试集 (70/15/15)...") train_data, val_data, test_data = split_data(X, y, train_split=0.7, val_split=0.15) print(f" 训练集：{len(train_data[0])} 样本") print(f" 下跌: {(train_data[1]==0).sum()}, 上涨: {(train_data[1]==1).sum()}") print(f" 验证集：{len(val_data[0])} 样本") print(f" 下跌: {(val_data[1]==0).sum()}, 上涨: {(val_data[1]==1).sum()}") print(f" 测试集：{len(test_data[0])} 样本") print(f" 下跌: {(test_data[1]==0).sum()}, 上涨: {(test_data[1]==1).sum()}") # 分析各数据集对应的时间范围 lookback = 168 train_end_idx = int(len(processed_df) * 0.7) - lookback val_end_idx = int(len(processed_df) * 0.85) - lookback train_df = processed_df.iloc[lookback:train_end_idx+lookback] val_df = processed_df.iloc[train_end_idx+lookback:val_end_idx+lookback] test_df = processed_df.iloc[val_end_idx+lookback:] print(f"\n 训练集时间范围: {train_df['timestamp'].min()} 至 {train_df['timestamp'].max()}") train_return = (train_df['close'].iloc[-1] - train_df['close'].iloc[0]) / train_df['close'].iloc[0] print(f" 价格: ${train_df['close'].iloc[0]:,.0f} → ${train_df['close'].iloc[-1]:,.0f} ({train_return*100:+.1f}%)") print(f" 验证集时间范围: {val_df['timestamp'].min()} 至 {val_df['timestamp'].max()}") val_return = (val_df['close'].iloc[-1] - val_df['close'].iloc[0]) / val_df['close'].iloc[0] print(f" 价格: ${val_df['close'].iloc[0]:,.0f} → ${val_df['close'].iloc[-1]:,.0f} ({val_return*100:+.1f}%)") print(f" 测试集时间范围: {test_df['timestamp'].min()} 至 {test_df['timestamp'].max()}") test_return = (test_df['close'].iloc[-1] - test_df['close'].iloc[0]) / test_df['close'].iloc[0] print(f" 价格: ${test_df['close'].iloc[0]:,.0f} → ${test_df['close'].iloc[-1]:,.0f} ({test_return*100:+.1f}%)") # 步骤4: 创建数据加载器 print("\n[4/8] 创建PyTorch数据加载器...") train_loader, val_loader, test_loader = create_dataloaders( train_data, val_data, test_data, task='classification', batch_size=64 ) # 步骤5: 创建模型 print("\n[5/8] 创建DLinear模型...") model_config = DLinearConfig( input_dim=X.shape[-1], lookback=168, task='classification', hidden_dim=512, dropout=0.1, moving_avg_kernel=25 ) model = DLinear(model_config) print(f"✓ 模型已创建，可训练参数：{model.count_parameters():,} 个") # 步骤6: 训练模型 print("\n[6/8] 开始训练模型（数据量大，预计需要更长时间）...") train_config = TrainingConfig( batch_size=64, max_epochs=50, learning_rate=0.001, early_stopping_patience=10, lr_scheduler='plateau', save_best=True, checkpoint_dir='./models/checkpoints/dlinear_btc_2020_2025' ) trainer = Trainer( model, train_config, task='classification', pos_weight=pos_weight ) print(f"✓ 训练数据规模：{len(train_data[0]):,} 样本（是之前的 {len(train_data[0])/5990:.1f}x）") history = trainer.fit(train_loader, val_loader) # 步骤7: 评估模型 print("\n[7/8] 在测试集上评估模型...") metrics = trainer.evaluate(test_loader) # 保存评估结果 results_path = './models/checkpoints/dlinear_btc_2020_2025/test_metrics.json' with open(results_path, 'w') as f: json.dump(metrics, f, indent=2) print(f"\n✓ 评估结果已保存至：{results_path}") # 步骤8: 对比分析 print("\n[8/8] 性能对比分析...") print("\n" + "="*80) print("训练完成 - 最终测试结果（使用2020-2025年数据）") print("="*80) print(f"\n📊 分类指标:") print(f" 准确率: {metrics.get('accuracy', 0):.4f} ({metrics.get('accuracy', 0)*100:.2f}%)") print(f" F1分数: {metrics.get('f1', 0):.4f}") print(f" 精确率: {metrics.get('precision', 0):.4f}") print(f" 召回率: {metrics.get('recall', 0):.4f}") if 'true_positives' in metrics: tp = metrics['true_positives'] tn = metrics['true_negatives'] fp = metrics['false_positives'] fn = metrics['false_negatives'] print(f"\n📈 混淆矩阵:") print(f" 真阳性（预测涨且真涨）: {tp}") print(f" 真阴性（预测跌且真跌）: {tn}") print(f" 假阳性（预测涨但真跌）: {fp}") print(f" 假阴性（预测跌但真涨）: {fn}") total = tp + tn + fp + fn if total > 0: print(f"\n 预测分布:") print(f" 预测上涨: {tp + fp} ({(tp+fp)/total*100:.1f}%)") print(f" 预测下跌: {tn + fn} ({(tn+fn)/total*100:.1f}%)") print(f"\n💰 回测结果:") print(f" 总收益率: {metrics.get('backtest_total_return', 0)*100:.2f}%") print(f" 最终资金: ${metrics.get('backtest_final_capital', 0):,.2f}") print(f" 夏普比率: {metrics.get('backtest_sharpe_ratio', 0):.4f}") max_dd = metrics.get('backtest_max_drawdown', 0) if not np.isnan(max_dd): print(f" 最大回撤: {max_dd*100:.2f}%") else: print(f" 最大回撤: N/A") print(f" 胜率: {metrics.get('backtest_win_rate', 0)*100:.2f}%") print(f" 交易次数: {metrics.get('backtest_num_trades', 0)}") # 性能评估 print(f"\n🎯 性能评估:") acc = metrics.get('accuracy', 0) if acc > 0.55: print(f" ✅ 准确率 {acc*100:.1f}% > 55%，模型有一定预测能力") elif acc > 0.50: print(f" ⚠️ 准确率 {acc*100:.1f}% 略优于随机（50%），预测能力较弱") else: print(f" ❌ 准确率 {acc*100:.1f}% ≤ 50%，模型预测能力不足") total_return = metrics.get('backtest_total_return', 0) if total_return > 0.05: print(f" ✅ 回测收益 {total_return*100:.2f}% > 5%，策略有较好盈利潜力") elif total_return > 0: print(f" ⚠️ 回测收益 {total_return*100:.2f}% > 0，策略有微弱盈利") else: print(f" ❌ 回测收益 {total_return*100:.2f}% ≤ 0，策略亏损") # 对比之前的结果 print(f"\n📊 与之前结果对比:") print(f" 使用1年数据（2025年）:") print(f" - 训练集: 2025.02-10 (牛市+16%)") print(f" - 测试集: 2025.12-2026.01 (熊市-2%)") print(f" - 准确率: 50.7%") print(f" - 预测上涨: 65.8% (偏向上涨)") print(f" - 问题: 训练测试分布不一致") print(f"\n 使用5年数据（2020-2025年）:") print(f" - 训练集: 包含牛市(2020-2021)和熊市(2022)") print(f" - 测试集: 2024-2025数据") print(f" - 准确率: {acc*100:.1f}%") if total > 0: print(f" - 预测上涨: {(tp+fp)/total*100:.1f}%") print(f" - 改进: 见过完整市场周期，泛化更好") print("\n" + "="*80) print("✓ 训练完成！") print("="*80) if __name__ == "__main__": main()