crypto_predictorandClaude Sonnet 4.5
9bbdc8d42b
🎯 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>
234 lines
9.7 KiB
Python
234 lines
9.7 KiB
Python
"""方案A：使用2020-2025年BTC全部数据训练 - 准备实盘使用"""
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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("方案A：BTC全量数据训练 (2020-2025)")
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print("训练集：95% | 验证集：5%（仅用于early stopping）| 测试集：无")
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print("="*80)
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# ========== 步骤1: 获取全部BTC数据 ==========
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print("\n[1/7] 获取BTC 2020-2025年全部数据...")
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fetcher = BinanceDataFetcher()
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df = 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"✓ 已获取 {len(df):,} 行数据（约{len(df)/8760:.1f}年）")
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print(f"✓ 时间范围：{df['timestamp'].min()} 至 {df['timestamp'].max()}")
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print(f"✓ 价格范围：${df['close'].iloc[0]:,.2f} → ${df['close'].iloc[-1]:,.2f}")
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total_return = (df['close'].iloc[-1] - df['close'].iloc[0]) / df['close'].iloc[0]
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print(f"✓ 总收益率：{total_return*100:+.2f}%")
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# 市场周期分析
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df['year'] = df['timestamp'].dt.year
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print(f"\n✓ 市场周期覆盖：")
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for year in sorted(df['year'].unique()):
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year_df = df[df['year'] == year]
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if len(year_df) > 100:
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year_return = (year_df['close'].iloc[-1] - year_df['close'].iloc[0]) / year_df['close'].iloc[0]
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trend = "🚀 牛市" if year_return > 0.3 else ("🔴 熊市" if year_return < -0.3 else "📊 震荡")
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print(f" {year}年: {year_return*100:+7.2f}% {trend}")
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# ========== 步骤2: 数据预处理 ==========
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print("\n[2/7] 数据预处理（回归任务）...")
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preprocessor = CryptoPreprocessor(
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lookback=168, # 输入：过去7天
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forecast_horizon=1, # 预测：未来1小时
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price_change_threshold=0.0
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)
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X, y, processed_df = preprocessor.preprocess_pipeline(df, task='regression', fit=True)
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print(f"✓ 序列数量：{len(X):,}")
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print(f"✓ 序列形状：{X.shape}")
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print(f"✓ 特征数量：{X.shape[-1]}")
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print(f"\n✓ 标签统计（收益率）：")
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print(f" 最小值: {y.min()*100:.4f}%")
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print(f" 最大值: {y.max()*100:.4f}%")
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print(f" 均值: {y.mean()*100:.4f}%")
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print(f" 标准差: {y.std()*100:.4f}%")
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print(f" 中位数: {np.median(y)*100:.4f}%")
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n_positive = (y > 0).sum()
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n_negative = (y <= 0).sum()
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print(f"\n✓ 方向分布：")
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print(f" 上涨: {n_positive:,} ({n_positive/len(y)*100:.1f}%)")
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print(f" 下跌: {n_negative:,} ({n_negative/len(y)*100:.1f}%)")
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# ========== 步骤3: 划分数据集（95% 训练 + 5% 验证）==========
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print("\n[3/7] 划分数据集（95%训练 + 5%验证）...")
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print("⚠️ 注意：此次训练使用几乎全部数据，无独立测试集")
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print(" 验证集仅用于early stopping防止过拟合")
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n = len(X)
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train_end = int(n * 0.95)
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X_train = X[:train_end]
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y_train = y[:train_end]
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X_val = X[train_end:]
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y_val = y[train_end:]
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print(f"\n 训练集：{len(X_train):,} 样本 ({len(X_train)/n*100:.1f}%)")
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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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print(f" 验证集：{len(X_val):,} 样本 ({len(X_val)/n*100:.1f}%)")
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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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# 时间范围分析
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lookback = 168
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train_df = processed_df.iloc[lookback:train_end+lookback]
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val_df = processed_df.iloc[train_end+lookback:]
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print(f"\n 时间范围：")
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print(f" 训练集: {train_df['timestamp'].min()} 至 {train_df['timestamp'].max()}")
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train_return = (train_df['close'].iloc[-1] - train_df['close'].iloc[0]) / train_df['close'].iloc[0]
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print(f" ${train_df['close'].iloc[0]:,.0f} → ${train_df['close'].iloc[-1]:,.0f} ({train_return*100:+.1f}%)")
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print(f" 验证集: {val_df['timestamp'].min()} 至 {val_df['timestamp'].max()}")
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val_return = (val_df['close'].iloc[-1] - val_df['close'].iloc[0]) / val_df['close'].iloc[0]
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print(f" ${val_df['close'].iloc[0]:,.0f} → ${val_df['close'].iloc[-1]:,.0f} ({val_return*100:+.1f}%)")
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# ========== 步骤4: 创建数据加载器 ==========
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print("\n[4/7] 创建数据加载器...")
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train_loader, val_loader, _ = create_dataloaders(
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(X_train, y_train),
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(X_val, y_val),
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(X_val, y_val), # 测试集用验证集替代（不使用）
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task='regression',
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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回归模型...")
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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='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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print(f"✓ 任务类型：回归（预测未来1小时收益率）")
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print(f"✓ 损失函数：MSELoss")
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# ========== 步骤6: 训练模型 ==========
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print("\n[6/7] 开始全量数据训练...")
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print(f"✓ 训练样本：{len(X_train):,} （使用了{len(X_train)/len(X)*100:.1f}%的数据）")
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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}%）")
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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=15, # 增加patience，允许更充分训练
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lr_scheduler='plateau',
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save_best=True,
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checkpoint_dir='./models/checkpoints/dlinear_btc_full_data'
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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7: 保存最终模型 ==========
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print("\n[7/7] 保存最终模型...")
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# 在验证集上快速评估
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print("\n在验证集上进行sanity check...")
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val_metrics = trainer.evaluate(val_loader)
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results_path = './models/checkpoints/dlinear_btc_full_data/final_metrics.json'
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with open(results_path, 'w') as f:
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json.dump(val_metrics, f, indent=2)
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print(f"✓ 结果保存至：{results_path}")
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# 同时保存预处理器（实盘使用时需要）
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import pickle
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preprocessor_path = './models/checkpoints/dlinear_btc_full_data/preprocessor.pkl'
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with open(preprocessor_path, 'wb') as f:
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pickle.dump(preprocessor, f)
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print(f"✓ 预处理器保存至：{preprocessor_path}")
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# ========== 最终报告 ==========
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print("\n" + "="*80)
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print("全量数据训练完成 - 最终报告")
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print("="*80)
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print(f"\n📊 训练概况：")
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print(f" 数据时间范围：2020-01-01 至 2025-12-31 (6年)")
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print(f" 训练样本数量：{len(X_train):,} (95%)")
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print(f" 验证样本数量：{len(X_val):,} (5%)")
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print(f" 特征数量：{X.shape[-1]}")
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print(f" 模型参数：{model.count_parameters():,}")
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print(f"\n📈 验证集性能（仅供参考，非独立测试）：")
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print(f" RMSE: {val_metrics.get('rmse', 0)*100:.4f}%")
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print(f" MAE: {val_metrics.get('mae', 0)*100:.4f}%")
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print(f" 方向准确率: {val_metrics.get('directional_accuracy', 0)*100:.2f}%")
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print(f"\n💰 验证集回测（仅供参考）：")
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print(f" 总收益率: {val_metrics.get('backtest_total_return', 0)*100:.2f}%")
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print(f" 夏普比率: {val_metrics.get('backtest_sharpe_ratio', 0):.4f}")
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max_dd = val_metrics.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: {val_metrics.get('backtest_win_rate', 0)*100:.2f}%")
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print("\n" + "="*80)
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print("🎯 全量训练 vs 之前训练对比")
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print("="*80)
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print("\n之前训练（70/15/15分割）：")
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print(" 训练样本: 36,645")
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print(" 时间范围: 2020-01 至 2024-03")
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print(" 测试集方向准确率: 50.67%")
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print(" 测试集回测收益: -16.71%")
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print(f"\n当前全量训练（95/5分割）：")
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print(f" 训练样本: {len(X_train):,} (+{(len(X_train)-36645)/36645*100:.1f}%)")
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print(f" 时间范围: 2020-01 至 {train_df['timestamp'].max().strftime('%Y-%m')}")
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print(f" 验证集方向准确率: {val_metrics.get('directional_accuracy', 0)*100:.2f}%")
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print(f" 验证集回测收益: {val_metrics.get('backtest_total_return', 0)*100:.2f}%")
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print("\n" + "="*80)
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print("⚠️ 重要说明")
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print("="*80)
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print("\n1. 此模型使用了95%的历史数据训练")
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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")
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print("3. 模型已针对2020-2025年市场环境充分优化")
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print("4. 适用场景：")
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print(" ✅ 用于2026年未来数据的预测")
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print(" ✅ 用于实盘交易（假设未来市场特征与历史相似）")
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print(" ❌ 不适合评估在未见过数据上的泛化性能")
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print("\n💡 下一步建议：")
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print(" 1. 使用此模型预测2026年1月的BTC价格")
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print(" 2. 在实盘中小仓位测试策略")
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print(" 3. 定期（每月/季度）用新数据重新训练模型")
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print(" 4. 监控实盘性能，如果偏差大说明市场环境变化")
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print("\n" + "="*80)
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print("✓ 训练完成！模型已保存到 ./models/checkpoints/dlinear_btc_full_data/")
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print("="*80)
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if __name__ == "__main__":
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main()
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