"""2026年价格预测脚本：直接预测价格并判断涨跌方向""" import sys import os sys.path.insert(0, os.path.dirname(os.path.abspath(__file__))) import torch import pickle import numpy as np import pandas as pd import pandas_ta as ta from sklearn.preprocessing import StandardScaler from data.fetcher import BinanceDataFetcher from models.dlinear.model import DLinear class PricePreprocessor: """预测未来价格的预处理器""" def __init__(self, lookback=168, forecast_horizon=1): self.lookback = lookback self.forecast_horizon = forecast_horizon self.scaler = StandardScaler() self.price_scaler = StandardScaler() self.is_fitted = False def add_features(self, df): df = df.copy() df['rsi'] = ta.rsi(df['close'], length=14) macd = ta.macd(df['close']) if macd is not None and not macd.empty: df['macd'] = macd.iloc[:, 0] df['macd_hist'] = macd.iloc[:, 1] df['macd_signal'] = macd.iloc[:, 2] return df def create_labels(self, df): df = df.copy() df['future_price'] = df['close'].shift(-self.forecast_horizon) df['label'] = df['future_price'] return df[:-self.forecast_horizon] def preprocess(self, df, fit=True): df = self.add_features(df) df = self.create_labels(df) df = df.dropna().reset_index(drop=True) feature_cols = ['open', 'high', 'low', 'close', 'volume', 'rsi', 'macd', 'macd_hist', 'macd_signal'] if fit: scaled_features = self.scaler.fit_transform(df[feature_cols]) scaled_labels = self.price_scaler.fit_transform(df[['label']]) self.is_fitted = True self.feature_columns = feature_cols else: if not self.is_fitted: raise ValueError("Must fit before transform") scaled_features = self.scaler.transform(df[feature_cols]) scaled_labels = self.price_scaler.transform(df[['label']]) X, y = [], [] for i in range(len(scaled_features) - self.lookback): X.append(scaled_features[i:i + self.lookback]) y.append(scaled_labels[i + self.lookback, 0]) return np.array(X), np.array(y), df def inverse_transform_price(self, scaled_price): return self.price_scaler.inverse_transform(scaled_price.reshape(-1, 1)).flatten() def main(): print("="*80) print("2026年BTC价格预测 - 直接预测未来价格") print("="*80) # ========== 加载模型 ========== print("\n[1/4] 加载模型...") model_path = './models/checkpoints/dlinear_predict_price/best_model.pt' preprocessor_path = './models/checkpoints/dlinear_predict_price/preprocessor.pkl' device = torch.device('mps' if torch.backends.mps.is_available() else 'cpu') checkpoint = torch.load(model_path, map_location=device, weights_only=False) model = DLinear(checkpoint['config']) model.load_state_dict(checkpoint['model_state_dict']) model = model.to(device) model.eval() with open(preprocessor_path, 'rb') as f: preprocessor = pickle.load(f) print(f"✓ 模型加载成功") # ========== 获取2026年数据 ========== print("\n[2/4] 获取2026年数据...") fetcher = BinanceDataFetcher() try: df_2026 = fetcher.fetch_ohlcv('BTC', '1h', start_date='2026-01-01', end_date='2026-12-31', use_cache=False) except: df_2026 = fetcher.fetch_ohlcv('BTC', '1h', start_date='2025-12-01', use_cache=False) print(f"✓ 数据量: {len(df_2026)} 行") print(f"✓ 时间: {df_2026['timestamp'].min()} 至 {df_2026['timestamp'].max()}") print(f"✓ 价格: ${df_2026['close'].iloc[0]:,.2f} → ${df_2026['close'].iloc[-1]:,.2f}") # ========== 预处理 ========== print("\n[3/4] 预处理并预测...") X, y, processed_df = preprocessor.preprocess(df_2026, fit=False) if len(X) == 0: print("❌ 数据不足") return # 模型预测 X_tensor = torch.FloatTensor(X).to(device) with torch.no_grad(): predictions = model(X_tensor).squeeze().cpu().numpy() # 还原为真实价格 pred_prices = preprocessor.inverse_transform_price(predictions) true_prices = preprocessor.inverse_transform_price(y) # 获取当前价格 current_prices = processed_df['close'].iloc[168:168+len(pred_prices)].values # ========== 方向判断 ========== print("\n[4/4] 涨跌方向评估...") pred_direction = (pred_prices > current_prices).astype(int) true_direction = (true_prices > current_prices).astype(int) correct = (pred_direction == true_direction).sum() accuracy = correct / len(predictions) from sklearn.metrics import confusion_matrix cm = confusion_matrix(true_direction, pred_direction) tn, fp, fn, tp = cm.ravel() print("\n" + "="*80) print("📊 2026年价格预测结果") print("="*80) print(f"\n价格预测误差:") price_mae = np.abs(pred_prices - true_prices).mean() price_rmse = np.sqrt(((pred_prices - true_prices) ** 2).mean()) price_mape = np.mean(np.abs((true_prices - pred_prices) / true_prices)) * 100 print(f" MAE: ${price_mae:,.2f}") print(f" RMSE: ${price_rmse:,.2f}") print(f" MAPE: {price_mape:.2f}%") print(f"\n方向预测统计:") print(f" 预测上涨: {(pred_direction==1).sum()} / 实际上涨: {(true_direction==1).sum()}") print(f" 预测下跌: {(pred_direction==0).sum()} / 实际下跌: {(true_direction==0).sum()}") print(f"\n方向准确率:") print(f" 正确: {correct}/{len(predictions)} ({accuracy*100:.2f}%)") print(f"\n详细分析:") print(f" ✓ 预测涨且真涨: {tp} ({tp/len(predictions)*100:.1f}%)") print(f" ✓ 预测跌且真跌: {tn} ({tn/len(predictions)*100:.1f}%)") print(f" ✗ 预测涨但真跌: {fp} ({fp/len(predictions)*100:.1f}%)") print(f" ✗ 预测跌但真涨: {fn} ({fn/len(predictions)*100:.1f}%)") # 性能评估 print("\n" + "="*80) print("🎯 性能评估") print("="*80) if accuracy > 0.55: print(f"\n ✅ 方向准确率 {accuracy*100:.2f}% > 55%，模型有预测能力") elif accuracy > 0.52: print(f"\n ⚠️ 方向准确率 {accuracy*100:.2f}% > 52%，略优于随机") elif accuracy >= 0.48: print(f"\n ⚠️ 方向准确率 {accuracy*100:.2f}% ≈ 50%，接近随机") else: print(f"\n ❌ 方向准确率 {accuracy*100:.2f}% < 48%，低于随机") # 预测样本展示 print("\n" + "="*80) print("📋 预测样本展示（前30个）") print("="*80) print(f"\n{'时间':<20} {'当前价格':<12} {'预测价格':<12} {'实际价格':<12} {'预测':<6} {'实际':<6} {'结果':<4}") print("-"*90) for i in range(min(30, len(predictions))): timestamp = processed_df['timestamp'].iloc[i + 168] curr = current_prices[i] pred = pred_prices[i] true = true_prices[i] pred_dir = "上涨" if pred > curr else "下跌" true_dir = "上涨" if true > curr else "下跌" result = "✓" if pred_direction[i] == true_direction[i] else "✗" print(f"{str(timestamp):<20} ${curr:>10,.2f} ${pred:>10,.2f} ${true:>10,.2f} " f"{pred_dir:<6} {true_dir:<6} {result:<4}") print("\n" + "="*80) print(f"总结: 在{len(predictions)}次预测中，方向正确{correct}次 ({accuracy*100:.2f}%)") print("="*80) if __name__ == "__main__": main()