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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"""四模型集成预测：DLinear + N-BEATS + TTM-R2 + TimesFM 2.5"""
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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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# ==================== 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("四模型集成预测：DLinear + N-BEATS + TTM-R2 + TimesFM 2.5")
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print("="*80)
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# ========== 步骤1: 加载DLinear和N-BEATS模型 ==========
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print("\n[1/7] 加载DLinear和N-BEATS模型...")
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device = torch.device('mps' if torch.backends.mps.is_available() else 'cpu')
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# 加载DLinear
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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已加载")
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# 加载N-BEATS
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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已加载")
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# ========== 步骤2: 加载TTM-R2 ==========
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print("\n[2/7] 加载TTM-R2零样本模型...")
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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已加载 (参数量: {sum(p.numel() for p in ttm_model.parameters())})")
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# ========== 步骤3: 加载TimesFM 2.5 ==========
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print("\n[3/7] 加载TimesFM 2.5模型...")
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import timesfm
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timesfm_model = timesfm.TimesFM_2p5_200M_torch.from_pretrained(
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"google/timesfm-2.5-200m-pytorch",
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torch_compile=False # 推理时不使用torch.compile
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)
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# 配置TimesFM用于BTC 1小时预测
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timesfm_model.compile(
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timesfm.ForecastConfig(
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max_context=512, # 使用512小时历史数据
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max_horizon=1, # 预测未来1小时
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normalize_inputs=True,
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use_continuous_quantile_head=True,
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force_flip_invariance=True,
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infer_is_positive=True,
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fix_quantile_crossing=True,
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)
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)
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print(f"✓ TimesFM 2.5已加载并配置")
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print(f" 上下文长度: 512小时")
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print(f" 预测长度: 1小时")
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# ========== 步骤4: 获取测试数据 ==========
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print("\n[4/7] 获取2026年1月测试数据...")
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fetcher = BinanceDataFetcher()
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df_2026 = fetcher.fetch_ohlcv('BTC', '1h', start_date='2026-01-01',
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end_date='2026-01-30', use_cache=False)
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print(f"✓ 数据获取完成: {len(df_2026)} 行")
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print(f"✓ 时间范围: {df_2026['timestamp'].min()} 至 {df_2026['timestamp'].max()}")
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# ========== 步骤5: 预处理 ==========
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print("\n[5/7] 预处理数据...")
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X_dlinear, y_dlinear, df_dlinear = dlinear_preprocessor.preprocess(df_2026, fit=False)
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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6: 四个模型分别预测 ==========
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print("\n[6/7] 四个模型分别预测...")
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# 6.1 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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# 6.2 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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# 还原真实价格并使用当前K线open价格判断
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nbeats_pred_prices = nbeats_preprocessor.inverse_transform_price(nbeats_pred_scaled)
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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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# 6.3 TTM-R2预测
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print(f"✓ TTM-R2预测中...")
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context_length = 512
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ttm_preds_list = []
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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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for i in range(min_samples):
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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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context_data = all_prices[-context_length:].reshape(1, -1, 1)
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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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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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pred_price = output.prediction_outputs.cpu().numpy()[0, 0, 0]
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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")
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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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# 6.4 TimesFM 2.5预测
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print(f"✓ TimesFM 2.5预测中...")
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timesfm_preds_list = []
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for i in range(min_samples):
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# TimesFM使用512小时上下文
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start_idx = i
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end_idx = start_idx + 512
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if end_idx > len(all_prices):
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# 如果数据不足，使用最后512个数据点
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context_prices = all_prices[-512:]
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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_prices = all_prices[start_idx:end_idx]
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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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# TimesFM的forecast方法接受list of arrays作为inputs
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try:
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point_forecast, _ = timesfm_model.forecast(
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horizon=1,
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inputs=[context_prices], # list包含单个序列
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)
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# point_forecast形状: (1, 1) - (num_series, horizon)
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pred_price = point_forecast[0, 0]
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# 与当前K线open价格比较
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timesfm_preds_list.append(1 if pred_price > current_open_price else 0)
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except Exception as e:
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print(f" ⚠️ TimesFM预测第{i}个样本失败: {e}")
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# 使用随机预测作为fallback
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timesfm_preds_list.append(np.random.randint(0, 2))
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timesfm_preds = np.array(timesfm_preds_list)
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print(f"✓ TimesFM 2.5预测完成")
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print(f" 预测上涨: {(timesfm_preds==1).sum()} ({(timesfm_preds==1).sum()/len(timesfm_preds)*100:.1f}%)")
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print(f" 预测下跌: {(timesfm_preds==0).sum()} ({(timesfm_preds==0).sum()/len(timesfm_preds)*100:.1f}%)")
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# ========== 步骤7: 集成策略评估 ==========
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print("\n[7/7] 集成策略评估...")
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true_labels = y_dlinear
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# 策略1: 双模型交集（DLinear + N-BEATS）
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two_model_agree_up = (dlinear_preds == 1) & (nbeats_preds == 1)
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two_model_agree_down = (dlinear_preds == 0) & (nbeats_preds == 0)
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two_model_preds = np.full(len(dlinear_preds), -1)
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two_model_preds[two_model_agree_up] = 1
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two_model_preds[two_model_agree_down] = 0
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two_model_valid = two_model_preds != -1
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two_model_correct = (two_model_preds == true_labels) & two_model_valid
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two_model_acc = two_model_correct.sum() / two_model_valid.sum() * 100 if two_model_valid.sum() > 0 else 0
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two_model_coverage = two_model_valid.sum() / len(true_labels) * 100
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# 策略2: 三模型多数投票（DLinear + N-BEATS + TTM-R2）
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vote_sum_3 = dlinear_preds + nbeats_preds + ttm_preds
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three_model_preds = np.where(vote_sum_3 >= 2, 1, 0)
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three_model_acc = (three_model_preds == true_labels).mean() * 100
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# 策略3: 四模型多数投票（至少3个模型同意）
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vote_sum_4 = dlinear_preds + nbeats_preds + ttm_preds + timesfm_preds
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four_model_preds = np.where(vote_sum_4 >= 3, 1, 0)
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four_model_acc = (four_model_preds == true_labels).mean() * 100
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# 策略4: 四模型半数以上投票（至少2个模型同意）
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four_model_preds_half = np.where(vote_sum_4 >= 2, 1, 0)
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four_model_acc_half = (four_model_preds_half == true_labels).mean() * 100
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# ========== 结果展示 ==========
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print("\n" + "="*80)
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print("📊 集成策略对比结果")
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print("="*80)
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print(f"\n单模型性能：")
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print(f" DLinear: {(dlinear_preds == true_labels).mean() * 100:.2f}%")
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print(f" N-BEATS: {(nbeats_preds == true_labels).mean() * 100:.2f}%")
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print(f" TTM-R2: {(ttm_preds == true_labels).mean() * 100:.2f}%")
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print(f" TimesFM 2.5: {(timesfm_preds == true_labels).mean() * 100:.2f}%")
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print(f"\n集成策略性能：")
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print(f" 双模型交集 (DL+NB): {two_model_acc:.2f}% (覆盖率: {two_model_coverage:.2f}%)")
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print(f" 三模型投票 (DL+NB+TTM, ≥2票): {three_model_acc:.2f}% (覆盖率: 100%)")
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print(f" 四模型强投票 (所有, ≥3票): {four_model_acc:.2f}% (覆盖率: 100%)")
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print(f" 四模型弱投票 (所有, ≥2票): {four_model_acc_half:.2f}% (覆盖率: 100%)")
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|
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# 投票分布分析
|
||||
print(f"\n四模型投票分布：")
|
||||
for votes in range(5):
|
||||
count = (vote_sum_4 == votes).sum()
|
||||
pct = count / len(vote_sum_4) * 100
|
||||
bars = "█" * int(pct / 2)
|
||||
print(f" {votes}票同意: {count:3d}次 ({pct:5.1f}%) {bars}")
|
||||
|
||||
# 模型一致性分析
|
||||
print(f"\n模型一致性分析：")
|
||||
all_agree_up = (dlinear_preds == 1) & (nbeats_preds == 1) & (ttm_preds == 1) & (timesfm_preds == 1)
|
||||
all_agree_down = (dlinear_preds == 0) & (nbeats_preds == 0) & (ttm_preds == 0) & (timesfm_preds == 0)
|
||||
all_agree = all_agree_up | all_agree_down
|
||||
|
||||
print(f" 四模型完全一致: {all_agree.sum()}次 ({all_agree.sum()/len(all_agree)*100:.1f}%)")
|
||||
if all_agree.sum() > 0:
|
||||
all_agree_acc = ((four_model_preds[all_agree] == true_labels[all_agree]).mean() * 100)
|
||||
print(f" 一致时准确率: {all_agree_acc:.2f}%")
|
||||
|
||||
print("\n" + "="*80)
|
||||
print("✓ 四模型集成预测完成!")
|
||||
print("="*80)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in new issue
Block a user