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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"""跨资产迁移学习实验 - 用BTC训练，在ETH上验证和测试"""
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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("跨资产迁移学习实验")
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print("训练：BTC 2020-2025 | 验证+测试：ETH 2020-2025")
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print("="*80)
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fetcher = BinanceDataFetcher()
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# ========== 步骤1: 获取BTC训练数据 ==========
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print("\n[1/10] 获取BTC数据（用于训练）...")
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df_btc = fetcher.fetch_ohlcv(
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symbol='BTC',
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timeframe='1h',
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start_date='2020-01-01',
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end_date='2025-12-31',
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use_cache=True
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)
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print(f"✓ BTC数据: {len(df_btc)} 行")
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print(f" 时间: {df_btc['timestamp'].min()} 至 {df_btc['timestamp'].max()}")
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print(f" 价格: ${df_btc['close'].iloc[0]:,.0f} → ${df_btc['close'].iloc[-1]:,.0f}")
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btc_return = (df_btc['close'].iloc[-1] - df_btc['close'].iloc[0]) / df_btc['close'].iloc[0]
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print(f" 收益率: {btc_return*100:+.1f}%")
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# ========== 步骤2: 获取ETH验证/测试数据 ==========
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print("\n[2/10] 获取ETH数据（用于验证和测试）...")
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df_eth = fetcher.fetch_ohlcv(
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symbol='ETH',
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timeframe='1h',
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start_date='2020-01-01',
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end_date='2025-12-31',
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use_cache=True
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)
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print(f"✓ ETH数据: {len(df_eth)} 行")
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print(f" 时间: {df_eth['timestamp'].min()} 至 {df_eth['timestamp'].max()}")
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print(f" 价格: ${df_eth['close'].iloc[0]:,.2f} → ${df_eth['close'].iloc[-1]:,.2f}")
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eth_return = (df_eth['close'].iloc[-1] - df_eth['close'].iloc[0]) / df_eth['close'].iloc[0]
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print(f" 收益率: {eth_return*100:+.1f}%")
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# ========== 步骤3: 预处理BTC数据 ==========
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print("\n[3/10] 预处理BTC数据（回归任务）...")
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preprocessor_btc = CryptoPreprocessor(
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lookback=168,
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forecast_horizon=1,
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price_change_threshold=0.0
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)
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X_btc, y_btc, processed_btc = preprocessor_btc.preprocess_pipeline(
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df_btc, task='regression', fit=True
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)
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print(f"✓ BTC序列: X={X_btc.shape}, y={y_btc.shape}")
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# ========== 步骤4: 预处理ETH数据 ==========
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print("\n[4/10] 预处理ETH数据（使用BTC的scaler）...")
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# 重要：复用BTC的preprocessor（已fit的scaler）来处理ETH数据
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# 添加技术指标和时间特征
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df_eth_processed = preprocessor_btc.add_technical_indicators(df_eth)
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df_eth_processed = preprocessor_btc.add_time_features(df_eth_processed)
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df_eth_processed = preprocessor_btc.create_labels(df_eth_processed, task='regression')
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# 删除NaN行
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processed_eth = df_eth_processed.dropna().reset_index(drop=True)
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# 使用BTC训练的scaler来标准化ETH特征（不重新fit）
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scaled_features = preprocessor_btc.transform(processed_eth)
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# 创建序列
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X_eth, y_eth = preprocessor_btc.create_sequences(
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scaled_features,
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processed_eth['label'].values
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)
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print(f"✓ ETH序列: X={X_eth.shape}, y={y_eth.shape}")
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# ========== 步骤5: 划分数据集 ==========
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print("\n[5/10] 划分数据集...")
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# BTC: 只用前70%作为训练集
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n_btc = len(X_btc)
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train_end = int(n_btc * 0.7)
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X_train = X_btc[:train_end]
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y_train = y_btc[:train_end]
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print(f" 训练集（BTC）: {len(X_train)} 样本")
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print(f" 收益率范围: {y_train.min()*100:.4f}% ~ {y_train.max()*100:.4f}%")
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# ETH: 70%验证，30%测试
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n_eth = len(X_eth)
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val_end = int(n_eth * 0.7)
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X_val = X_eth[:val_end]
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y_val = y_eth[:val_end]
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X_test = X_eth[val_end:]
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y_test = y_eth[val_end:]
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print(f" 验证集（ETH）: {len(X_val)} 样本")
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print(f" 收益率范围: {y_val.min()*100:.4f}% ~ {y_val.max()*100:.4f}%")
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print(f" 测试集（ETH）: {len(X_test)} 样本")
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print(f" 收益率范围: {y_test.min()*100:.4f}% ~ {y_test.max()*100:.4f}%")
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# 分析时间范围
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lookback = 168
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train_df = processed_btc.iloc[lookback:train_end+lookback]
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val_df = processed_eth.iloc[lookback:val_end+lookback]
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test_df = processed_eth.iloc[val_end+lookback:]
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print(f"\n 时间范围分析:")
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print(f" 训练（BTC）: {train_df['timestamp'].min()} 至 {train_df['timestamp'].max()}")
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print(f" 验证（ETH）: {val_df['timestamp'].min()} 至 {val_df['timestamp'].max()}")
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print(f" 测试（ETH）: {test_df['timestamp'].min()} 至 {test_df['timestamp'].max()}")
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# ========== 步骤6: 创建数据加载器 ==========
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print("\n[6/10] 创建数据加载器...")
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train_loader, val_loader, test_loader = create_dataloaders(
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(X_train, y_train),
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(X_val, y_val),
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(X_test, y_test),
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task='regression',
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batch_size=64
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)
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# ========== 步骤7: 创建模型 ==========
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print("\n[7/10] 创建DLinear回归模型...")
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model_config = DLinearConfig(
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input_dim=X_btc.shape[-1],
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lookback=168,
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task='regression',
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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"✓ 模型参数: {model.count_parameters():,} 个")
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# ========== 步骤8: 训练模型 ==========
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print("\n[8/10] 训练模型（BTC训练，ETH验证）...")
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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/cross_asset_btc_to_eth'
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)
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trainer = Trainer(model, train_config, task='regression')
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history = trainer.fit(train_loader, val_loader)
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# ========== 步骤9: 在ETH测试集上评估 ==========
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print("\n[9/10] 在ETH测试集上评估...")
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metrics_eth = trainer.evaluate(test_loader)
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results_path = './models/checkpoints/cross_asset_btc_to_eth/test_metrics.json'
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with open(results_path, 'w') as f:
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json.dump(metrics_eth, f, indent=2)
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print(f"✓ 结果保存至: {results_path}")
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# ========== 步骤10: 对比分析 ==========
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print("\n[10/10] 性能分析...")
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print("\n" + "="*80)
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print("跨资产迁移学习实验结果")
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print("="*80)
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print("\n📋 实验设置:")
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print(f" 训练集: BTC 2020-2024 ({len(X_train):,} 样本)")
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print(f" 验证集: ETH 2020-2024 ({len(X_val):,} 样本)")
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print(f" 测试集: ETH 2024-2025 ({len(X_test):,} 样本)")
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print("\n📊 ETH测试集性能:")
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print(f" RMSE: {metrics_eth.get('rmse', 0)*100:.4f}%")
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print(f" MAE: {metrics_eth.get('mae', 0)*100:.4f}%")
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print(f" 方向准确率: {metrics_eth.get('directional_accuracy', 0)*100:.2f}%")
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print(f"\n💰 ETH回测结果:")
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print(f" 总收益率: {metrics_eth.get('backtest_total_return', 0)*100:.2f}%")
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print(f" 夏普比率: {metrics_eth.get('backtest_sharpe_ratio', 0):.4f}")
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max_dd = metrics_eth.get('backtest_max_drawdown', 0)
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if not np.isnan(max_dd):
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print(f" 最大回撤: {max_dd*100:.2f}%")
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print(f" 胜率: {metrics_eth.get('backtest_win_rate', 0)*100:.2f}%")
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print(f" 交易次数: {metrics_eth.get('backtest_num_trades', 0):.0f}")
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# 对比之前的结果
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print("\n" + "="*80)
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print("📊 性能对比")
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print("="*80)
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print("\n实验1：BTC训练 → BTC测试（之前结果）")
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print(" 方向准确率: 50.67%")
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print(" RMSE: 0.4881%")
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print(" 回测收益: -16.71%")
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print(f"\n实验2：BTC训练 → ETH测试（当前结果）")
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print(f" 方向准确率: {metrics_eth.get('directional_accuracy', 0)*100:.2f}%")
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print(f" RMSE: {metrics_eth.get('rmse', 0)*100:.4f}%")
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print(f" 回测收益: {metrics_eth.get('backtest_total_return', 0)*100:.2f}%")
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# 性能评估
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print("\n🎯 迁移学习效果评估:")
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dir_acc = metrics_eth.get('directional_accuracy', 0)
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if dir_acc > 0.52:
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print(f" ✅ 方向准确率 {dir_acc*100:.1f}% > 52%，跨资产迁移有效")
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elif dir_acc > 0.50:
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print(f" ⚠️ 方向准确率 {dir_acc*100:.1f}% 略优于随机")
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else:
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print(f" ❌ 方向准确率 {dir_acc*100:.1f}% ≤ 50%，迁移失败")
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total_return = metrics_eth.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，策略盈利")
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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💡 结论:")
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if dir_acc > 0.52 and total_return > 0:
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print(" ✅ 跨资产迁移学习成功！BTC学到的模式可应用于ETH")
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elif dir_acc > 0.50:
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print(" ⚠️ 跨资产迁移部分有效，但收益不佳")
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else:
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print(" ❌ 跨资产迁移失败，BTC和ETH市场动态差异太大")
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print("\n" + "="*80)
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print("✓ 实验完成！")
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print("="*80)
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if __name__ == "__main__":
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main()
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