"""方案A：使用2020-2025年BTC全部数据训练 - 准备实盘使用""" 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("方案A：BTC全量数据训练 (2020-2025)") print("训练集：95% | 验证集：5%（仅用于early stopping）| 测试集：无") print("="*80) # ========== 步骤1: 获取全部BTC数据 ========== print("\n[1/7] 获取BTC 2020-2025年全部数据...") 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()}") print(f"✓ 价格范围：${df['close'].iloc[0]:,.2f} → ${df['close'].iloc[-1]:,.2f}") total_return = (df['close'].iloc[-1] - df['close'].iloc[0]) / df['close'].iloc[0] print(f"✓ 总收益率：{total_return*100:+.2f}%") # 市场周期分析 df['year'] = df['timestamp'].dt.year print(f"\n✓ 市场周期覆盖：") 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 ========== print("\n[2/7] 数据预处理（回归任务）...") preprocessor = CryptoPreprocessor( lookback=168, # 输入：过去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='regression', fit=True) print(f"✓ 序列数量：{len(X):,}") print(f"✓ 序列形状：{X.shape}") print(f"✓ 特征数量：{X.shape[-1]}") 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}%") n_positive = (y > 0).sum() n_negative = (y <= 0).sum() print(f"\n✓ 方向分布：") print(f" 上涨: {n_positive:,} ({n_positive/len(y)*100:.1f}%)") print(f" 下跌: {n_negative:,} ({n_negative/len(y)*100:.1f}%)") # ========== 步骤3: 划分数据集（95% 训练 + 5% 验证）========== print("\n[3/7] 划分数据集（95%训练 + 5%验证）...") print("⚠️ 注意：此次训练使用几乎全部数据，无独立测试集") print(" 验证集仅用于early stopping防止过拟合") n = len(X) train_end = int(n * 0.95) X_train = X[:train_end] y_train = y[:train_end] X_val = X[train_end:] y_val = y[train_end:] print(f"\n 训练集：{len(X_train):,} 样本 ({len(X_train)/n*100:.1f}%)") print(f" 收益率范围: {y_train.min()*100:.4f}% ~ {y_train.max()*100:.4f}%") print(f" 验证集：{len(X_val):,} 样本 ({len(X_val)/n*100:.1f}%)") print(f" 收益率范围: {y_val.min()*100:.4f}% ~ {y_val.max()*100:.4f}%") # 时间范围分析 lookback = 168 train_df = processed_df.iloc[lookback:train_end+lookback] val_df = processed_df.iloc[train_end+lookback:] print(f"\n 时间范围：") print(f" 训练集: {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" ${train_df['close'].iloc[0]:,.0f} → ${train_df['close'].iloc[-1]:,.0f} ({train_return*100:+.1f}%)") print(f" 验证集: {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" ${val_df['close'].iloc[0]:,.0f} → ${val_df['close'].iloc[-1]:,.0f} ({val_return*100:+.1f}%)") # ========== 步骤4: 创建数据加载器 ========== print("\n[4/7] 创建数据加载器...") train_loader, val_loader, _ = create_dataloaders( (X_train, y_train), (X_val, y_val), (X_val, y_val), # 测试集用验证集替代（不使用） task='regression', batch_size=64 ) # ========== 步骤5: 创建模型 ========== print("\n[5/7] 创建DLinear回归模型...") model_config = DLinearConfig( input_dim=X.shape[-1], lookback=168, task='regression', hidden_dim=512, dropout=0.1, moving_avg_kernel=25 ) model = DLinear(model_config) print(f"✓ 模型参数：{model.count_parameters():,} 个") print(f"✓ 任务类型：回归（预测未来1小时收益率）") print(f"✓ 损失函数：MSELoss") # ========== 步骤6: 训练模型 ========== print("\n[6/7] 开始全量数据训练...") print(f"✓ 训练样本：{len(X_train):,} （使用了{len(X_train)/len(X)*100:.1f}%的数据）") print(f"✓ 对比之前：之前训练集36,645样本，现在{len(X_train):,}样本（+{(len(X_train)-36645)/36645*100:.1f}%）") train_config = TrainingConfig( batch_size=64, max_epochs=50, learning_rate=0.001, early_stopping_patience=15, # 增加patience，允许更充分训练 lr_scheduler='plateau', save_best=True, checkpoint_dir='./models/checkpoints/dlinear_btc_full_data' ) trainer = Trainer(model, train_config, task='regression') history = trainer.fit(train_loader, val_loader) # ========== 步骤7: 保存最终模型 ========== print("\n[7/7] 保存最终模型...") # 在验证集上快速评估 print("\n在验证集上进行sanity check...") val_metrics = trainer.evaluate(val_loader) results_path = './models/checkpoints/dlinear_btc_full_data/final_metrics.json' with open(results_path, 'w') as f: json.dump(val_metrics, f, indent=2) print(f"✓ 结果保存至：{results_path}") # 同时保存预处理器（实盘使用时需要） import pickle preprocessor_path = './models/checkpoints/dlinear_btc_full_data/preprocessor.pkl' with open(preprocessor_path, 'wb') as f: pickle.dump(preprocessor, f) print(f"✓ 预处理器保存至：{preprocessor_path}") # ========== 最终报告 ========== print("\n" + "="*80) print("全量数据训练完成 - 最终报告") print("="*80) print(f"\n📊 训练概况：") print(f" 数据时间范围：2020-01-01 至 2025-12-31 (6年)") print(f" 训练样本数量：{len(X_train):,} (95%)") print(f" 验证样本数量：{len(X_val):,} (5%)") print(f" 特征数量：{X.shape[-1]}") print(f" 模型参数：{model.count_parameters():,}") print(f"\n📈 验证集性能（仅供参考，非独立测试）：") print(f" RMSE: {val_metrics.get('rmse', 0)*100:.4f}%") print(f" MAE: {val_metrics.get('mae', 0)*100:.4f}%") print(f" 方向准确率: {val_metrics.get('directional_accuracy', 0)*100:.2f}%") print(f"\n💰 验证集回测（仅供参考）：") print(f" 总收益率: {val_metrics.get('backtest_total_return', 0)*100:.2f}%") print(f" 夏普比率: {val_metrics.get('backtest_sharpe_ratio', 0):.4f}") max_dd = val_metrics.get('backtest_max_drawdown', 0) if not np.isnan(max_dd): print(f" 最大回撤: {max_dd*100:.2f}%") print(f" 胜率: {val_metrics.get('backtest_win_rate', 0)*100:.2f}%") print("\n" + "="*80) print("🎯 全量训练 vs 之前训练对比") print("="*80) print("\n之前训练（70/15/15分割）：") print(" 训练样本: 36,645") print(" 时间范围: 2020-01 至 2024-03") print(" 测试集方向准确率: 50.67%") print(" 测试集回测收益: -16.71%") print(f"\n当前全量训练（95/5分割）：") print(f" 训练样本: {len(X_train):,} (+{(len(X_train)-36645)/36645*100:.1f}%)") print(f" 时间范围: 2020-01 至 {train_df['timestamp'].max().strftime('%Y-%m')}") print(f" 验证集方向准确率: {val_metrics.get('directional_accuracy', 0)*100:.2f}%") print(f" 验证集回测收益: {val_metrics.get('backtest_total_return', 0)*100:.2f}%") print("\n" + "="*80) print("⚠️ 重要说明") print("="*80) print("\n1. 此模型使用了95%的历史数据训练") print("2. 验证集仅占5%，性能指标仅供参考，不代表真实泛化能力") print("3. 模型已针对2020-2025年市场环境充分优化") print("4. 适用场景：") print(" ✅ 用于2026年未来数据的预测") print(" ✅ 用于实盘交易（假设未来市场特征与历史相似）") print(" ❌ 不适合评估在未见过数据上的泛化性能") print("\n💡 下一步建议：") print(" 1. 使用此模型预测2026年1月的BTC价格") print(" 2. 在实盘中小仓位测试策略") print(" 3. 定期（每月/季度）用新数据重新训练模型") print(" 4. 监控实盘性能，如果偏差大说明市场环境变化") print("\n" + "="*80) print("✓ 训练完成！模型已保存到 ./models/checkpoints/dlinear_btc_full_data/") print("="*80) if __name__ == "__main__": main()