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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"""简化特征训练：只使用OHLCV + RSI + MACD"""
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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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import numpy as np
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import pandas as pd
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import pandas_ta as ta
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from sklearn.preprocessing import StandardScaler
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import torch
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import torch.nn as nn
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from torch.utils.data import DataLoader, TensorDataset
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from data.fetcher import BinanceDataFetcher
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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 pickle
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class SimplePreprocessor:
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"""简化的预处理器：只使用OHLCV + RSI + MACD"""
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def __init__(self, lookback=168, forecast_horizon=1):
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self.lookback = lookback
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self.forecast_horizon = forecast_horizon
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self.scaler = StandardScaler()
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self.is_fitted = False
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def add_features(self, df):
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"""添加RSI和MACD特征"""
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df = df.copy()
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# RSI (14 periods)
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df['rsi'] = ta.rsi(df['close'], length=14)
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# MACD
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macd = ta.macd(df['close'])
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if macd is not None and not macd.empty:
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df['macd'] = macd.iloc[:, 0]
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df['macd_hist'] = macd.iloc[:, 1]
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df['macd_signal'] = macd.iloc[:, 2]
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return df
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def create_labels(self, df):
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"""创建标签：未来1小时的收益率"""
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df = df.copy()
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df['future_price'] = df['close'].shift(-self.forecast_horizon)
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df['future_return'] = (df['future_price'] - df['close']) / df['close']
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df['label'] = df['future_return']
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return df[:-self.forecast_horizon]
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def preprocess(self, df, fit=True):
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"""完整预处理流程"""
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# 添加技术指标
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df = self.add_features(df)
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df = self.create_labels(df)
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# 删除NaN
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df = df.dropna().reset_index(drop=True)
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# 选择特征列：OHLCV + RSI + MACD
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feature_cols = ['open', 'high', 'low', 'close', 'volume',
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'rsi', 'macd', 'macd_hist', 'macd_signal']
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# 标准化
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if fit:
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scaled = self.scaler.fit_transform(df[feature_cols])
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self.is_fitted = True
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self.feature_columns = feature_cols
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else:
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if not self.is_fitted:
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raise ValueError("Must fit before transform")
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scaled = self.scaler.transform(df[feature_cols])
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# 创建序列
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X, y = [], []
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for i in range(len(scaled) - self.lookback):
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X.append(scaled[i:i + self.lookback])
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y.append(df['label'].iloc[i + self.lookback])
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return np.array(X), np.array(y), df
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def main():
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print("="*80)
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print("简化特征训练：OHLCV + RSI + MACD")
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print("="*80)
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# ========== 步骤1: 获取数据 ==========
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print("\n[1/7] 获取BTC 2020-2025年数据...")
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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"✓ 数据量: {len(df):,} 行")
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print(f"✓ 时间: {df['timestamp'].min()} 至 {df['timestamp'].max()}")
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print(f"✓ 价格: ${df['close'].iloc[0]:,.2f} → ${df['close'].iloc[-1]:,.2f}")
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# ========== 步骤2: 预处理 ==========
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print("\n[2/7] 数据预处理（只使用9个特征）...")
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preprocessor = SimplePreprocessor(lookback=168, forecast_horizon=1)
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X, y, processed_df = preprocessor.preprocess(df, 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"✓ 特征列表:")
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for i, col in enumerate(preprocessor.feature_columns, 1):
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print(f" {i}. {col}")
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print(f"\n✓ 标签统计:")
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print(f" 收益率范围: {y.min()*100:.4f}% ~ {y.max()*100:.4f}%")
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print(f" 均值: {y.mean()*100:.4f}%")
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print(f" 上涨样本: {(y>0).sum()} ({(y>0).sum()/len(y)*100:.1f}%)")
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print(f" 下跌样本: {(y<=0).sum()} ({(y<=0).sum()/len(y)*100:.1f}%)")
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# ========== 步骤3: 划分数据集（95/5）==========
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print("\n[3/7] 划分数据集（95%训练 + 5%验证）...")
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n = len(X)
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train_end = int(n * 0.95)
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X_train, y_train = X[:train_end], y[:train_end]
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X_val, y_val = X[train_end:], y[train_end:]
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print(f" 训练集: {len(X_train):,} 样本")
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print(f" 验证集: {len(X_val):,} 样本")
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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_dataset = TensorDataset(
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torch.FloatTensor(X_train),
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torch.FloatTensor(y_train)
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)
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val_dataset = TensorDataset(
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torch.FloatTensor(X_val),
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torch.FloatTensor(y_val)
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)
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train_loader = DataLoader(train_dataset, batch_size=64, shuffle=False)
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val_loader = DataLoader(val_dataset, batch_size=64, shuffle=False)
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# ========== 步骤5: 创建模型 ==========
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print("\n[5/7] 创建DLinear模型...")
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model_config = DLinearConfig(
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input_dim=9, # OHLCV + RSI + MACD(3个)
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lookback=168,
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task='regression',
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hidden_dim=256, # 减小隐藏层
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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():,}")
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# ========== 步骤6: 训练 ==========
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print("\n[6/7] 开始训练...")
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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,
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lr_scheduler='plateau',
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save_best=True,
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checkpoint_dir='./models/checkpoints/dlinear_simple_features'
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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: 评估并保存 ==========
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print("\n[7/7] 评估并保存...")
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val_metrics = trainer.evaluate(val_loader)
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# 保存结果
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with open('./models/checkpoints/dlinear_simple_features/metrics.json', 'w') as f:
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json.dump(val_metrics, f, indent=2)
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# 保存预处理器
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with open('./models/checkpoints/dlinear_simple_features/preprocessor.pkl', 'wb') as f:
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pickle.dump(preprocessor, f)
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print("\n" + "="*80)
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print("训练完成")
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print("="*80)
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print(f"\n📊 验证集性能:")
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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:")
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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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print(f"\n✓ 模型保存至: ./models/checkpoints/dlinear_simple_features/")
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print(f"✓ 特征数量: 9 (OHLCV + RSI + MACD)")
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print("="*80)
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if __name__ == "__main__":
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main()
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