"""使用回归任务训练DLinear模型 - 预测未来1小时的实际价格变化百分比""" 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回归模型训练 - 预测未来1小时价格变化百分比") 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() 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: 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（回归任务） print("\n[2/8] 数据预处理（回归任务 - 预测未来1小时的收益率）...") 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 # 回归任务不使用阈值 ) X, y, processed_df = preprocessor.preprocess_pipeline(df, task='regression', 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]}") # 分析标签分布（收益率） print(f"\n✓ 标签统计（收益率）：") print(f" 最小值: {y.min()*100:.4f}%") print(f" 最大值: {y.max()*100:.4f}%") print(f" 均值: {y.mean()*100:.4f}%") print(f" 标准差: {y.std()*100:.4f}%") print(f" 中位数: {np.median(y)*100:.4f}%") # 方向统计 n_positive = (y > 0).sum() n_negative = (y <= 0).sum() print(f"\n✓ 方向分布：") print(f" 正收益（上涨）: {n_positive} ({n_positive/len(y)*100:.1f}%)") print(f" 负收益（下跌）: {n_negative} ({n_negative/len(y)*100:.1f}%)") # 步骤3: 划分数据集 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" 收益率范围: {train_data[1].min()*100:.4f}% ~ {train_data[1].max()*100:.4f}%") print(f" 均值: {train_data[1].mean()*100:.4f}%") print(f" 验证集：{len(val_data[0])} 样本") print(f" 收益率范围: {val_data[1].min()*100:.4f}% ~ {val_data[1].max()*100:.4f}%") print(f" 均值: {val_data[1].mean()*100:.4f}%") print(f" 测试集：{len(test_data[0])} 样本") print(f" 收益率范围: {test_data[1].min()*100:.4f}% ~ {test_data[1].max()*100:.4f}%") print(f" 均值: {test_data[1].mean()*100:.4f}%") # 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='regression', # 回归任务 batch_size=64 ) # 步骤5: 创建模型 print("\n[5/8] 创建DLinear回归模型...") model_config = DLinearConfig( input_dim=X.shape[-1], lookback=168, task='regression', # 回归任务 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") print(f"✓ 任务类型：回归（预测收益率百分比）") print(f"✓ 损失函数：MSELoss") # 步骤6: 训练模型 print("\n[6/8] 开始训练回归模型...") 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_regression' ) trainer = Trainer( model, train_config, task='regression' # 回归任务，不需要pos_weight ) print(f"✓ 训练数据规模：{len(train_data[0]):,} 样本") 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_regression/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: 结果分析 print("\n[8/8] 回归模型性能分析...") print("\n" + "="*80) print("训练完成 - 回归模型测试结果") print("="*80) print(f"\n📊 回归指标:") print(f" RMSE (均方根误差): {metrics.get('rmse', 0):.6f} ({metrics.get('rmse', 0)*100:.4f}%)") print(f" MAE (平均绝对误差): {metrics.get('mae', 0):.6f} ({metrics.get('mae', 0)*100:.4f}%)") print(f" MAPE (平均绝对百分比误差): {metrics.get('mape', 0):.2f}%") print(f" 方向准确率: {metrics.get('directional_accuracy', 0)*100:.2f}%") 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)}") # 与分类任务对比 print("\n" + "="*80) print("📊 回归 vs 分类任务对比") print("="*80) print(f"\n二分类模型（之前的结果）:") print(f" 准确率: 49.76%") print(f" F1分数: 0.3898") print(f" 预测偏差: 68.1%预测下跌（过度悲观）") print(f" 回测收益: -3.29%") print(f"\n回归模型（当前结果）:") print(f" 方向准确率: {metrics.get('directional_accuracy', 0)*100:.2f}%") print(f" RMSE: {metrics.get('rmse', 0)*100:.4f}%") print(f" MAE: {metrics.get('mae', 0)*100:.4f}%") print(f" 回测收益: {metrics.get('backtest_total_return', 0)*100:.2f}%") # 性能评估 print(f"\n🎯 回归模型性能评估:") dir_acc = metrics.get('directional_accuracy', 0) if dir_acc > 0.55: print(f" ✅ 方向准确率 {dir_acc*100:.1f}% > 55%，模型有预测能力") elif dir_acc > 0.50: print(f" ⚠️ 方向准确率 {dir_acc*100:.1f}% 略优于随机，预测能力较弱") else: print(f" ❌ 方向准确率 {dir_acc*100:.1f}% ≤ 50%，方向预测能力不足") rmse = metrics.get('rmse', 0) mae = metrics.get('mae', 0) print(f"\n 误差分析:") print(f" RMSE = {rmse*100:.4f}% - 平均预测误差的平方根") print(f" MAE = {mae*100:.4f}% - 平均绝对误差") if rmse < 0.01: # 小于1% print(f" ✅ RMSE < 1%，预测精度较高") elif rmse < 0.02: # 小于2% print(f" ⚠️ RMSE在1-2%之间，预测精度中等") else: print(f" ❌ RMSE > 2%，预测误差较大") total_return = metrics.get('backtest_total_return', 0) if total_return > 0.05: print(f"\n ✅ 回测收益 {total_return*100:.2f}% > 5%，策略有盈利潜力") elif total_return > 0: print(f"\n ⚠️ 回测收益 {total_return*100:.2f}% > 0，策略微弱盈利") else: print(f"\n ❌ 回测收益 {total_return*100:.2f}% ≤ 0，策略亏损") print("\n💡 回归任务的优势:") print(" ✅ 提供预测幅度信息（可用于动态仓位管理）") print(" ✅ 评估风险收益比") print(" ✅ 更适合量化交易策略") print("\n💡 回归任务的挑战:") print(" ❌ 预测具体数值比预测方向更难") print(" ❌ 对异常值敏感") print(" ❌ 需要更多训练数据才能稳定") print("\n" + "="*80) print("✓ 回归模型训练完成！") print("="*80) if __name__ == "__main__": main()