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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"""简化预测脚本：只判断涨跌方向正确性"""
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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 torch
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import pickle
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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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from data.fetcher import BinanceDataFetcher
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from models.dlinear.model import DLinear
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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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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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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 = 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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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("2026年BTC涨跌方向预测")
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print("="*80)
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# ========== 步骤1: 加载模型 ==========
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print("\n[1/5] 加载模型...")
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model_path = './models/checkpoints/dlinear_simple_features/best_model.pt'
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preprocessor_path = './models/checkpoints/dlinear_simple_features/preprocessor.pkl'
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device = torch.device('mps' if torch.backends.mps.is_available() else 'cpu')
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checkpoint = torch.load(model_path, map_location=device, weights_only=False)
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model = DLinear(checkpoint['config'])
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model.load_state_dict(checkpoint['model_state_dict'])
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model = model.to(device)
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model.eval()
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with open(preprocessor_path, 'rb') as f:
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preprocessor = pickle.load(f)
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print(f"✓ 模型加载成功")
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print(f"✓ 特征数量: {len(preprocessor.feature_columns)}")
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# ========== 步骤2: 获取2026年数据 ==========
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print("\n[2/5] 获取2026年数据...")
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fetcher = BinanceDataFetcher()
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try:
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df_2026 = fetcher.fetch_ohlcv(
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symbol='BTC',
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timeframe='1h',
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start_date='2026-01-01',
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end_date='2026-12-31',
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use_cache=False
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)
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except:
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print("⚠️ 获取最新可用数据...")
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df_2026 = fetcher.fetch_ohlcv(
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symbol='BTC',
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timeframe='1h',
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start_date='2025-12-01',
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use_cache=False
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)
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print(f"✓ 数据量: {len(df_2026)} 行")
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print(f"✓ 时间: {df_2026['timestamp'].min()} 至 {df_2026['timestamp'].max()}")
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print(f"✓ 价格: ${df_2026['close'].iloc[0]:,.2f} → ${df_2026['close'].iloc[-1]:,.2f}")
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# ========== 步骤3: 预处理 ==========
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print("\n[3/5] 预处理数据...")
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X, y, processed_df = preprocessor.preprocess(df_2026, fit=False)
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print(f"✓ 预测序列: {len(X)} 个")
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print(f"✓ 实际涨跌: 上涨{(y>0).sum()}次, 下跌{(y<=0).sum()}次")
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if len(X) == 0:
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print("❌ 数据不足")
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return
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# ========== 步骤4: 预测 ==========
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print("\n[4/5] 模型预测...")
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X_tensor = torch.FloatTensor(X).to(device)
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with torch.no_grad():
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predictions = model(X_tensor).squeeze().cpu().numpy()
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print(f"✓ 预测完成")
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print(f"✓ 预测收益率范围: {predictions.min()*100:.4f}% ~ {predictions.max()*100:.4f}%")
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# ========== 步骤5: 方向判断 ==========
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print("\n[5/5] 涨跌方向评估...")
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# 预测方向：正值=上涨，负值=下跌
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pred_direction = (predictions > 0).astype(int) # 1=涨, 0=跌
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true_direction = (y > 0).astype(int)
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# 统计
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n_pred_up = (pred_direction == 1).sum()
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n_pred_down = (pred_direction == 0).sum()
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n_true_up = (true_direction == 1).sum()
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n_true_down = (true_direction == 0).sum()
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# 准确率
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correct = (pred_direction == true_direction).sum()
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accuracy = correct / len(predictions)
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# 混淆矩阵
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from sklearn.metrics import confusion_matrix
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cm = confusion_matrix(true_direction, pred_direction)
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tn, fp, fn, tp = cm.ravel()
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print("\n" + "="*80)
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print("📊 涨跌方向预测结果")
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print("="*80)
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print(f"\n预测统计:")
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print(f" 预测上涨: {n_pred_up} ({n_pred_up/len(predictions)*100:.1f}%)")
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print(f" 预测下跌: {n_pred_down} ({n_pred_down/len(predictions)*100:.1f}%)")
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print(f"\n实际统计:")
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print(f" 实际上涨: {n_true_up} ({n_true_up/len(y)*100:.1f}%)")
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print(f" 实际下跌: {n_true_down} ({n_true_down/len(y)*100:.1f}%)")
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print(f"\n准确率:")
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print(f" 方向正确: {correct}/{len(predictions)} ({accuracy*100:.2f}%)")
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print(f"\n详细分析:")
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print(f" ✓ 预测涨且真涨: {tp} ({tp/len(y)*100:.1f}%)")
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print(f" ✓ 预测跌且真跌: {tn} ({tn/len(y)*100:.1f}%)")
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print(f" ✗ 预测涨但真跌: {fp} ({fp/len(y)*100:.1f}%)")
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print(f" ✗ 预测跌但真涨: {fn} ({fn/len(y)*100:.1f}%)")
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# 预测偏差
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bias = n_pred_up - n_true_up
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print(f"\n预测偏差:")
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if abs(bias) < len(predictions) * 0.05:
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print(f" ✅ 预测平衡（偏差{bias}个）")
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elif bias > 0:
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print(f" ⚠️ 偏向预测上涨（多预测{bias}次上涨）")
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else:
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print(f" ⚠️ 偏向预测下跌（多预测{-bias}次下跌）")
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# 性能评估
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print("\n" + "="*80)
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print("🎯 性能评估")
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print("="*80)
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if accuracy > 0.55:
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print(f"\n ✅ 方向准确率 {accuracy*100:.2f}% > 55%")
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print(f" 模型有预测能力，可考虑实盘测试")
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elif accuracy > 0.52:
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print(f"\n ⚠️ 方向准确率 {accuracy*100:.2f}% > 52%")
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print(f" 模型略优于随机，谨慎使用")
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elif accuracy >= 0.48:
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print(f"\n ⚠️ 方向准确率 {accuracy*100:.2f}% ≈ 50%")
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print(f" 接近随机猜测，不建议实盘")
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else:
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print(f"\n ❌ 方向准确率 {accuracy*100:.2f}% < 48%")
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print(f" 低于随机，模型失效")
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# 预测样本展示
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print("\n" + "="*80)
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print("📋 预测样本展示（前30个）")
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print("="*80)
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print(f"\n{'时间':<20} {'预测':<6} {'实际':<6} {'结果':<4} {'预测值':<10}")
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print("-"*60)
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for i in range(min(30, len(predictions))):
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timestamp = processed_df['timestamp'].iloc[i + 168]
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pred = "上涨" if pred_direction[i] == 1 else "下跌"
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true = "上涨" if true_direction[i] == 1 else "下跌"
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result = "✓" if pred_direction[i] == true_direction[i] else "✗"
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pred_val = predictions[i]
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print(f"{str(timestamp):<20} {pred:<6} {true:<6} {result:<4} {pred_val*100:>8.4f}%")
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print("\n" + "="*80)
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print(f"总结: 在{len(predictions)}次预测中，方向正确{correct}次 ({accuracy*100:.2f}%)")
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print("="*80)
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if __name__ == "__main__":
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main()
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