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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"""分类任务：使用2020-2025全部数据训练，2026年测试"""
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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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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 ClassificationPreprocessor:
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"""分类任务预处理器：预测涨(1)或跌(0)"""
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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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"""添加技术指标"""
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df = df.copy()
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df['rsi'] = ta.rsi(df['close'], length=14)
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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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"""创建二分类标签：1=上涨, 0=下跌"""
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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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# 二分类：大于0为上涨(1)，否则为下跌(0)
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df['label'] = (df['future_return'] > 0).astype(int)
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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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df = self.add_features(df)
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df = self.create_labels(df)
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df = df.dropna().reset_index(drop=True)
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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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if fit:
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scaled_features = 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_features = self.scaler.transform(df[feature_cols])
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X, y = [], []
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for i in range(len(scaled_features) - self.lookback):
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X.append(scaled_features[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("分类任务：2020-2025全量训练 → 2026测试")
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print("="*80)
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# ========== 步骤1: 获取训练数据（2020-2025） ==========
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print("\n[1/6] 获取2020-2025年训练数据...")
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fetcher = BinanceDataFetcher()
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df_train = 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"✓ 训练数据量: {len(df_train):,} 行")
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print(f"✓ 时间范围: {df_train['timestamp'].min()} 至 {df_train['timestamp'].max()}")
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print(f"✓ 价格: ${df_train['close'].iloc[0]:,.2f} → ${df_train['close'].iloc[-1]:,.2f}")
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# ========== 步骤2: 预处理训练数据 ==========
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print("\n[2/6] 预处理训练数据（分类任务）...")
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preprocessor = ClassificationPreprocessor(lookback=168, forecast_horizon=1)
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X_train, y_train, processed_train = preprocessor.preprocess(df_train, fit=True)
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print(f"✓ 训练序列: {len(X_train):,}")
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print(f"✓ 特征数量: {len(preprocessor.feature_columns)}")
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print(f"✓ 特征列表: {preprocessor.feature_columns}")
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# 标签分布
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n_up = (y_train == 1).sum()
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n_down = (y_train == 0).sum()
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print(f"\n✓ 标签分布:")
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print(f" 上涨(1): {n_up:,} ({n_up/len(y_train)*100:.1f}%)")
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print(f" 下跌(0): {n_down:,} ({n_down/len(y_train)*100:.1f}%)")
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# 计算类别权重
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pos_weight = n_down / n_up if n_up > 0 else 1.0
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print(f" pos_weight: {pos_weight:.3f}")
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# ========== 步骤3: 创建数据加载器 ==========
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print("\n[3/6] 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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# 用10%作为验证集（内部分割）
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train_size = int(0.9 * len(train_dataset))
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val_size = len(train_dataset) - train_size
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train_subset, val_subset = torch.utils.data.random_split(
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train_dataset, [train_size, val_size]
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)
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train_loader = DataLoader(train_subset, batch_size=64, shuffle=True)
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val_loader = DataLoader(val_subset, batch_size=64, shuffle=False)
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print(f" 训练样本: {train_size:,}")
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print(f" 验证样本: {val_size:,}")
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# ========== 步骤4: 创建模型 ==========
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print("\n[4/6] 创建DLinear分类模型...")
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model_config = DLinearConfig(
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input_dim=9,
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lookback=168,
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task='classification', # 分类任务
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hidden_dim=256,
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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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print(f"✓ 任务类型: 二分类（预测涨/跌）")
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print(f"✓ 损失函数: BCEWithLogitsLoss")
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# ========== 步骤5: 训练 ==========
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print("\n[5/6] 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_classification_2026'
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)
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trainer = Trainer(
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model,
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train_config,
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task='classification',
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pos_weight=pos_weight # 使用类别权重
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)
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history = trainer.fit(train_loader, val_loader)
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# ========== 步骤6: 保存 ==========
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print("\n[6/6] 保存模型...")
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with open('./models/checkpoints/dlinear_classification_2026/preprocessor.pkl', 'wb') as f:
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pickle.dump(preprocessor, f)
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print(f"✓ 模型保存至: ./models/checkpoints/dlinear_classification_2026/")
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print("\n" + "="*80)
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print("✓ 训练完成！准备在2026年数据上测试...")
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print("="*80)
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if __name__ == "__main__":
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main()
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