refactor: 清理测试文件和2019相关代码
- 删除测试脚本: test_2026_classification.py, test_lagllama.py, test_timesfm.py, test_ttm.py - 删除2019相关文件: analyze_2019_*, apply_2019_*, dlinear_2019_*, three_models_2019_* - 添加集成模型相关代码和分析脚本 - 添加N-BEATS模型和CatBoost分类模型
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"""三模型集成预测 - DLinear + N-BEATS + TTM-R2 交集策略"""
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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 sklearn.metrics import confusion_matrix
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from data.fetcher import BinanceDataFetcher
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# ==================== 预处理器定义 ====================
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class ClassificationPreprocessor:
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"""DLinear分类预处理器"""
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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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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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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'] > 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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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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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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class NBeatsPreprocessor:
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"""N-BEATS预处理器"""
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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.price_scaler = StandardScaler()
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self.is_fitted = False
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def preprocess(self, df, fit=True):
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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 = df[:-self.forecast_horizon].reset_index(drop=True)
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prices = df['close'].values.reshape(-1, 1)
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future_prices = df['future_price'].values.reshape(-1, 1)
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if fit:
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scaled_prices = self.price_scaler.fit_transform(prices)
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scaled_future = self.price_scaler.transform(future_prices)
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self.is_fitted = True
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else:
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scaled_prices = self.price_scaler.transform(prices)
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scaled_future = self.price_scaler.transform(future_prices)
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X, y = [], []
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for i in range(len(scaled_prices) - self.lookback):
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X.append(scaled_prices[i:i + self.lookback, 0])
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y.append(scaled_future[i + self.lookback, 0])
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return np.array(X), np.array(y), df
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def inverse_transform_price(self, scaled_price):
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return self.price_scaler.inverse_transform(scaled_price.reshape(-1, 1)).flatten()
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def main():
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print("="*80)
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print("三模型集成预测：DLinear + N-BEATS + TTM-R2 交集策略")
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print("="*80)
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# ========== 步骤1: 加载三个模型 ==========
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print("\n[1/6] 加载三个模型...")
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device = torch.device('mps' if torch.backends.mps.is_available() else 'cpu')
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# 1. 加载DLinear分类模型
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from models.dlinear.model import DLinear
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dlinear_checkpoint = torch.load(
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'./models/checkpoints/dlinear_classification_2026/best_model.pt',
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map_location=device
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)
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dlinear_model = DLinear(dlinear_checkpoint['config'])
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dlinear_model.load_state_dict(dlinear_checkpoint['model_state_dict'])
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dlinear_model = dlinear_model.to(device)
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dlinear_model.eval()
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with open('./models/checkpoints/dlinear_classification_2026/preprocessor.pkl', 'rb') as f:
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dlinear_preprocessor = pickle.load(f)
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print(f"✓ DLinear分类模型已加载 (准确率: 53.10%)")
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# 2. 加载N-BEATS回归模型
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from models.nbeats.model import NBeats
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nbeats_checkpoint = torch.load(
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'./models/checkpoints/nbeats_btc_1h/best_model.pt',
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map_location=device
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)
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nbeats_model = NBeats(nbeats_checkpoint['config'])
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nbeats_model.load_state_dict(nbeats_checkpoint['model_state_dict'])
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nbeats_model = nbeats_model.to(device)
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nbeats_model.eval()
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with open('./models/checkpoints/nbeats_btc_1h/preprocessor.pkl', 'rb') as f:
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nbeats_preprocessor = pickle.load(f)
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print(f"✓ N-BEATS回归模型已加载 (准确率: 50.83%)")
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# 3. 加载TTM-R2模型
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from tsfm_public.models.tinytimemixer import TinyTimeMixerForPrediction
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ttm_model = TinyTimeMixerForPrediction.from_pretrained(
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"ibm-granite/granite-timeseries-ttm-r2",
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trust_remote_code=True
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)
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ttm_model = ttm_model.to(device)
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ttm_model.eval()
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print(f"✓ TTM-R2模型已加载 (准确率: 37.50%)")
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# ========== 步骤2: 获取2026年测试数据 ==========
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print("\n[2/6] 获取2026年测试数据...")
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fetcher = BinanceDataFetcher()
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try:
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df_2026 = fetcher.fetch_ohlcv('BTC', '1h', start_date='2026-01-01',
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end_date='2026-02-02', use_cache=False)
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except:
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df_2026 = fetcher.fetch_ohlcv('BTC', '1h', start_date='2025-12-01', use_cache=False)
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print(f"✓ 测试数据: {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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# ========== 步骤3: 预处理数据 ==========
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print("\n[3/6] 预处理数据...")
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# DLinear预处理
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X_dlinear, y_dlinear, df_dlinear = dlinear_preprocessor.preprocess(df_2026, fit=False)
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# N-BEATS预处理
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X_nbeats, y_nbeats, df_nbeats = nbeats_preprocessor.preprocess(df_2026, fit=False)
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# 确保样本数量一致
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min_samples = min(len(X_dlinear), len(X_nbeats))
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X_dlinear = X_dlinear[:min_samples]
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y_dlinear = y_dlinear[:min_samples]
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X_nbeats = X_nbeats[:min_samples]
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y_nbeats = y_nbeats[:min_samples]
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print(f"✓ 对齐样本数: {min_samples}")
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# ========== 步骤4: 三个模型分别预测 ==========
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print("\n[4/6] 三个模型分别预测...")
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# 4.1 DLinear预测
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X_dlinear_tensor = torch.FloatTensor(X_dlinear).to(device)
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with torch.no_grad():
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dlinear_logits = dlinear_model(X_dlinear_tensor).cpu().numpy()
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dlinear_probs = 1 / (1 + np.exp(-dlinear_logits)) # sigmoid
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dlinear_preds = (dlinear_probs > 0.5).astype(int).flatten() # 1=涨, 0=跌
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print(f"✓ DLinear预测完成")
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print(f" 预测上涨: {(dlinear_preds==1).sum()} ({(dlinear_preds==1).sum()/len(dlinear_preds)*100:.1f}%)")
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print(f" 预测下跌: {(dlinear_preds==0).sum()} ({(dlinear_preds==0).sum()/len(dlinear_preds)*100:.1f}%)")
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# 4.2 N-BEATS预测
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X_nbeats_tensor = torch.FloatTensor(X_nbeats).to(device)
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with torch.no_grad():
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nbeats_pred_scaled = nbeats_model(X_nbeats_tensor).cpu().numpy()
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# 还原真实价格
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nbeats_pred_prices = nbeats_preprocessor.inverse_transform_price(nbeats_pred_scaled)
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# 【修改】获取当前K线的open价格（未来可交易价格）用于判断方向
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# 第i个样本对应的是第168+i根K线，其open价格是真正可以交易的价格
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current_open_prices = df_nbeats['open'].iloc[168:168+len(nbeats_pred_prices)].values
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nbeats_preds = (nbeats_pred_prices > current_open_prices).astype(int) # 1=涨, 0=跌
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print(f"✓ N-BEATS预测完成 (使用当前K线open价格判断)")
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print(f" 预测上涨: {(nbeats_preds==1).sum()} ({(nbeats_preds==1).sum()/len(nbeats_preds)*100:.1f}%)")
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print(f" 预测下跌: {(nbeats_preds==0).sum()} ({(nbeats_preds==0).sum()/len(nbeats_preds)*100:.1f}%)")
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# 4.3 TTM-R2预测
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print(f"✓ TTM-R2预测中...")
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# TTM-R2需要特定格式: (batch_size, context_length, num_features)
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context_length = 512
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ttm_preds_list = []
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# 从df_2026获取价格数据
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all_prices = df_2026['close'].values
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all_open_prices = df_2026['open'].values
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# 为每个样本创建预测
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for i in range(min_samples):
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# 计算当前样本在原始数据中的位置
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# DLinear的lookback是168，所以第i个样本对应df_2026的第168+i根K线
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start_idx = i
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end_idx = start_idx + context_length
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if end_idx > len(all_prices):
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# 如果超出范围，使用最后512个价格
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context_data = all_prices[-context_length:].reshape(1, -1, 1)
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# 【修改】使用对应K线的open价格作为当前可交易价格
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current_open_price = all_open_prices[min(168 + i, len(all_open_prices) - 1)]
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else:
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context_data = all_prices[start_idx:end_idx].reshape(1, -1, 1)
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# 【修改】使用第end_idx根K线的open价格（即当前K线的开盘价）
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current_open_price = all_open_prices[end_idx] if end_idx < len(all_open_prices) else all_open_prices[-1]
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context_tensor = torch.FloatTensor(context_data).to(device)
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with torch.no_grad():
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output = ttm_model(context_tensor)
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# 预测第一个时间步的值
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pred_price = output.prediction_outputs.cpu().numpy()[0, 0, 0]
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# 【修改】判断方向: 预测价格 vs 当前K线open价格
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ttm_preds_list.append(1 if pred_price > current_open_price else 0)
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ttm_preds = np.array(ttm_preds_list)
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print(f"✓ TTM-R2预测完成 (使用当前K线open价格判断)")
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print(f" 预测上涨: {(ttm_preds==1).sum()} ({(ttm_preds==1).sum()/len(ttm_preds)*100:.1f}%)")
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print(f" 预测下跌: {(ttm_preds==0).sum()} ({(ttm_preds==0).sum()/len(ttm_preds)*100:.1f}%)")
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# ========== 步骤5: 三模型多数投票策略（两方相同） ==========
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print("\n[5/6] 应用三模型多数投票策略（两方相同）...")
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# 真实标签
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true_labels = y_dlinear
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# 多数投票：至少两个模型预测上涨 → 上涨，至少两个预测下跌 → 下跌
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vote_sum = dlinear_preds + nbeats_preds + ttm_preds
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# vote_sum >= 2 表示至少两个模型预测上涨（1）
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# vote_sum <= 1 表示至少两个模型预测下跌（0）
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majority_up = vote_sum >= 2
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majority_down = vote_sum <= 1
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# 统计各种情况
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all_agree_up = (dlinear_preds == 1) & (nbeats_preds == 1) & (ttm_preds == 1)
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all_agree_down = (dlinear_preds == 0) & (nbeats_preds == 0) & (ttm_preds == 0)
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# 恰好两方同意（不包括三方都同意）
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exactly_two_up = majority_up & ~all_agree_up
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exactly_two_down = majority_down & ~all_agree_down
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n_all_agree_up = all_agree_up.sum()
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n_all_agree_down = all_agree_down.sum()
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n_exactly_two_up = exactly_two_up.sum()
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n_exactly_two_down = exactly_two_down.sum()
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print(f"\n📊 预测投票分析:")
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print(f" 三方都预测上涨: {n_all_agree_up} ({n_all_agree_up/len(dlinear_preds)*100:.1f}%)")
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print(f" 三方都预测下跌: {n_all_agree_down} ({n_all_agree_down/len(dlinear_preds)*100:.1f}%)")
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print(f" 恰好两方预测上涨: {n_exactly_two_up} ({n_exactly_two_up/len(dlinear_preds)*100:.1f}%)")
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print(f" 恰好两方预测下跌: {n_exactly_two_down} ({n_exactly_two_down/len(dlinear_preds)*100:.1f}%)")
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# 详细显示是哪两方同意
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dl_nb_up = (dlinear_preds == 1) & (nbeats_preds == 1) & (ttm_preds == 0)
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dl_ttm_up = (dlinear_preds == 1) & (nbeats_preds == 0) & (ttm_preds == 1)
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nb_ttm_up = (dlinear_preds == 0) & (nbeats_preds == 1) & (ttm_preds == 1)
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dl_nb_down = (dlinear_preds == 0) & (nbeats_preds == 0) & (ttm_preds == 1)
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dl_ttm_down = (dlinear_preds == 0) & (nbeats_preds == 1) & (ttm_preds == 0)
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nb_ttm_down = (dlinear_preds == 1) & (nbeats_preds == 0) & (ttm_preds == 0)
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print(f"\n 详细组合:")
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print(f" DLinear+NBEATS同意看涨: {dl_nb_up.sum()} ({dl_nb_up.sum()/len(dlinear_preds)*100:.1f}%)")
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print(f" DLinear+TTM同意看涨: {dl_ttm_up.sum()} ({dl_ttm_up.sum()/len(dlinear_preds)*100:.1f}%)")
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print(f" NBEATS+TTM同意看涨: {nb_ttm_up.sum()} ({nb_ttm_up.sum()/len(dlinear_preds)*100:.1f}%)")
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print(f" DLinear+NBEATS同意看跌: {dl_nb_down.sum()} ({dl_nb_down.sum()/len(dlinear_preds)*100:.1f}%)")
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print(f" DLinear+TTM同意看跌: {dl_ttm_down.sum()} ({dl_ttm_down.sum()/len(dlinear_preds)*100:.1f}%)")
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print(f" NBEATS+TTM同意看跌: {nb_ttm_down.sum()} ({nb_ttm_down.sum()/len(dlinear_preds)*100:.1f}%)")
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# 多数投票结果
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ensemble_preds = np.where(majority_up, 1, 0)
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print(f"\n📊 三模型多数投票策略结果:")
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print(f" 最终预测上涨: {(ensemble_preds==1).sum()} ({(ensemble_preds==1).sum()/len(dlinear_preds)*100:.1f}%)")
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print(f" 最终预测下跌: {(ensemble_preds==0).sum()} ({(ensemble_preds==0).sum()/len(dlinear_preds)*100:.1f}%)")
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print(f" 覆盖率: 100% (多数投票无弃权)")
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# ========== 步骤6: 性能对比 ==========
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print("\n[6/6] 性能对比分析...")
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# 计算多数投票准确率
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ensemble_accuracy = (ensemble_preds == true_labels).mean()
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print(f"\n✨ 三模型多数投票准确率: {ensemble_accuracy*100:.2f}%")
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# 混淆矩阵
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cm = confusion_matrix(true_labels, ensemble_preds)
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tn, fp, fn, tp = cm.ravel()
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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(ensemble_preds)*100:.1f}%)")
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print(f" ✓ 预测跌且真跌: {tn} ({tn/len(ensemble_preds)*100:.1f}%)")
|
||||
print(f" ✗ 预测涨但真跌: {fp} ({fp/len(ensemble_preds)*100:.1f}%)")
|
||||
print(f" ✗ 预测跌但真涨: {fn} ({fn/len(ensemble_preds)*100:.1f}%)")
|
||||
|
||||
# 对比分析
|
||||
print("\n" + "="*80)
|
||||
print("📊 性能对比")
|
||||
print("="*80)
|
||||
|
||||
dlinear_acc = (dlinear_preds == true_labels).mean()
|
||||
nbeats_acc = (nbeats_preds == true_labels).mean()
|
||||
ttm_acc = (ttm_preds == true_labels).mean()
|
||||
|
||||
print(f"\n单模型性能:")
|
||||
print(f" DLinear分类: {dlinear_acc*100:.2f}%")
|
||||
print(f" N-BEATS回归: {nbeats_acc*100:.2f}%")
|
||||
print(f" TTM-R2零样本: {ttm_acc*100:.2f}%")
|
||||
|
||||
# 两模型交集（DLinear + N-BEATS）
|
||||
two_model_agree_up = (dlinear_preds == 1) & (nbeats_preds == 1)
|
||||
two_model_agree_down = (dlinear_preds == 0) & (nbeats_preds == 0)
|
||||
two_model_preds = np.full(len(dlinear_preds), -1)
|
||||
two_model_preds[two_model_agree_up] = 1
|
||||
two_model_preds[two_model_agree_down] = 0
|
||||
two_model_valid = two_model_preds != -1
|
||||
two_model_acc = (two_model_preds[two_model_valid] == true_labels[two_model_valid]).mean()
|
||||
|
||||
print(f"\n集成策略性能:")
|
||||
print(f" 双模型交集 (DLinear+NBEATS): {two_model_acc*100:.2f}% (覆盖率: {two_model_valid.sum()/len(dlinear_preds)*100:.1f}%)")
|
||||
|
||||
if len(valid_preds) > 0:
|
||||
print(f" 三模型交集 (ALL): {ensemble_accuracy*100:.2f}% (覆盖率: {valid_mask.sum()/len(dlinear_preds)*100:.1f}%)")
|
||||
|
||||
# 性能评估
|
||||
print("\n" + "="*80)
|
||||
print("🎯 策略评估")
|
||||
print("="*80)
|
||||
|
||||
best_single = max(dlinear_acc, nbeats_acc, ttm_acc)
|
||||
|
||||
if ensemble_accuracy > best_single:
|
||||
improvement = ensemble_accuracy - best_single
|
||||
print(f"\n ✅ 三模型交集优于所有单模型!")
|
||||
print(f" 准确率提升: {improvement*100:.2f}个百分点")
|
||||
print(f" 代价: 弃权{(~valid_mask).sum()}次预测 ({(~valid_mask).sum()/len(dlinear_preds)*100:.1f}%)")
|
||||
elif ensemble_accuracy > two_model_acc:
|
||||
improvement = ensemble_accuracy - two_model_acc
|
||||
print(f"\n ✅ 三模型交集优于双模型交集!")
|
||||
print(f" 准确率提升: {improvement*100:.2f}个百分点")
|
||||
print(f" 但覆盖率下降: {(two_model_valid.sum() - valid_mask.sum())/len(dlinear_preds)*100:.1f}%")
|
||||
else:
|
||||
print(f"\n ⚠️ 三模型交集未能提升准确率")
|
||||
print(f" 可能原因: TTM-R2表现太差({ttm_acc*100:.2f}%)，拖累了整体性能")
|
||||
print(f" 建议: 使用双模型交集(DLinear+NBEATS)")
|
||||
|
||||
# 实际收益分析
|
||||
print(f"\n💰 实际收益分析:")
|
||||
print(f" 如果只在三个模型都同意时交易:")
|
||||
print(f" - 交易次数: {valid_mask.sum()} ({valid_mask.sum()/len(dlinear_preds)*100:.1f}%)")
|
||||
print(f" - 准确率: {ensemble_accuracy*100:.2f}%")
|
||||
print(f" - 避免交易: {(~valid_mask).sum()}次 (规避不确定性)")
|
||||
|
||||
print("\n" + "="*80)
|
||||
print("✓ 三模型集成预测分析完成!")
|
||||
print("="*80)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in new issue
Block a user