"""四模型集成预测：DLinear + N-BEATS + TTM-R2 + TimesFM 2.5""" 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 # ==================== 预处理器定义 ==================== class ClassificationPreprocessor: """DLinear分类预处理器""" def __init__(self, lookback=168, forecast_horizon=1): self.lookback = lookback self.forecast_horizon = forecast_horizon self.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['future_return'] = (df['future_price'] - df['close']) / df['close'] df['label'] = (df['future_return'] > 0).astype(int) 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]) self.is_fitted = True self.feature_columns = feature_cols else: scaled_features = self.scaler.transform(df[feature_cols]) X, y = [], [] for i in range(len(scaled_features) - self.lookback): X.append(scaled_features[i:i + self.lookback]) y.append(df['label'].iloc[i + self.lookback]) return np.array(X), np.array(y), df class NBeatsPreprocessor: """N-BEATS预处理器""" def __init__(self, lookback=168, forecast_horizon=1): self.lookback = lookback self.forecast_horizon = forecast_horizon self.price_scaler = StandardScaler() self.is_fitted = False def preprocess(self, df, fit=True): df = df.copy() df['future_price'] = df['close'].shift(-self.forecast_horizon) df = df[:-self.forecast_horizon].reset_index(drop=True) prices = df['close'].values.reshape(-1, 1) future_prices = df['future_price'].values.reshape(-1, 1) if fit: scaled_prices = self.price_scaler.fit_transform(prices) scaled_future = self.price_scaler.transform(future_prices) self.is_fitted = True else: scaled_prices = self.price_scaler.transform(prices) scaled_future = self.price_scaler.transform(future_prices) X, y = [], [] for i in range(len(scaled_prices) - self.lookback): X.append(scaled_prices[i:i + self.lookback, 0]) y.append(scaled_future[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("四模型集成预测：DLinear + N-BEATS + TTM-R2 + TimesFM 2.5") print("="*80) # ========== 步骤1: 加载DLinear和N-BEATS模型 ========== print("\n[1/7] 加载DLinear和N-BEATS模型...") device = torch.device('mps' if torch.backends.mps.is_available() else 'cpu') # 加载DLinear from models.dlinear.model import DLinear dlinear_checkpoint = torch.load( './models/checkpoints/dlinear_classification_2026/best_model.pt', map_location=device ) dlinear_model = DLinear(dlinear_checkpoint['config']) dlinear_model.load_state_dict(dlinear_checkpoint['model_state_dict']) dlinear_model = dlinear_model.to(device) dlinear_model.eval() with open('./models/checkpoints/dlinear_classification_2026/preprocessor.pkl', 'rb') as f: dlinear_preprocessor = pickle.load(f) print(f"✓ DLinear已加载") # 加载N-BEATS from models.nbeats.model import NBeats nbeats_checkpoint = torch.load( './models/checkpoints/nbeats_btc_1h/best_model.pt', map_location=device ) nbeats_model = NBeats(nbeats_checkpoint['config']) nbeats_model.load_state_dict(nbeats_checkpoint['model_state_dict']) nbeats_model = nbeats_model.to(device) nbeats_model.eval() with open('./models/checkpoints/nbeats_btc_1h/preprocessor.pkl', 'rb') as f: nbeats_preprocessor = pickle.load(f) print(f"✓ N-BEATS已加载") # ========== 步骤2: 加载TTM-R2 ========== print("\n[2/7] 加载TTM-R2零样本模型...") from tsfm_public.models.tinytimemixer import TinyTimeMixerForPrediction ttm_model = TinyTimeMixerForPrediction.from_pretrained( "ibm-granite/granite-timeseries-ttm-r2", trust_remote_code=True ) ttm_model = ttm_model.to(device) ttm_model.eval() print(f"✓ TTM-R2已加载 (参数量: {sum(p.numel() for p in ttm_model.parameters())})") # ========== 步骤3: 加载TimesFM 2.5 ========== print("\n[3/7] 加载TimesFM 2.5模型...") import timesfm timesfm_model = timesfm.TimesFM_2p5_200M_torch.from_pretrained( "google/timesfm-2.5-200m-pytorch", torch_compile=False # 推理时不使用torch.compile ) # 配置TimesFM用于BTC 1小时预测 timesfm_model.compile( timesfm.ForecastConfig( max_context=512, # 使用512小时历史数据 max_horizon=1, # 预测未来1小时 normalize_inputs=True, use_continuous_quantile_head=True, force_flip_invariance=True, infer_is_positive=True, fix_quantile_crossing=True, ) ) print(f"✓ TimesFM 2.5已加载并配置") print(f" 上下文长度: 512小时") print(f" 预测长度: 1小时") # ========== 步骤4: 获取测试数据 ========== print("\n[4/7] 获取2026年1月测试数据...") fetcher = BinanceDataFetcher() df_2026 = fetcher.fetch_ohlcv('BTC', '1h', start_date='2026-01-01', end_date='2026-01-30', use_cache=False) print(f"✓ 数据获取完成: {len(df_2026)} 行") print(f"✓ 时间范围: {df_2026['timestamp'].min()} 至 {df_2026['timestamp'].max()}") # ========== 步骤5: 预处理 ========== print("\n[5/7] 预处理数据...") X_dlinear, y_dlinear, df_dlinear = dlinear_preprocessor.preprocess(df_2026, fit=False) X_nbeats, y_nbeats, df_nbeats = nbeats_preprocessor.preprocess(df_2026, fit=False) # 确保样本数量一致 min_samples = min(len(X_dlinear), len(X_nbeats)) X_dlinear = X_dlinear[:min_samples] y_dlinear = y_dlinear[:min_samples] X_nbeats = X_nbeats[:min_samples] y_nbeats = y_nbeats[:min_samples] print(f"✓ 对齐样本数: {min_samples}") # ========== 步骤6: 四个模型分别预测 ========== print("\n[6/7] 四个模型分别预测...") # 6.1 DLinear预测 X_dlinear_tensor = torch.FloatTensor(X_dlinear).to(device) with torch.no_grad(): dlinear_logits = dlinear_model(X_dlinear_tensor).cpu().numpy() dlinear_probs = 1 / (1 + np.exp(-dlinear_logits)) # sigmoid dlinear_preds = (dlinear_probs > 0.5).astype(int).flatten() # 1=涨, 0=跌 print(f"✓ DLinear预测完成") print(f" 预测上涨: {(dlinear_preds==1).sum()} ({(dlinear_preds==1).sum()/len(dlinear_preds)*100:.1f}%)") print(f" 预测下跌: {(dlinear_preds==0).sum()} ({(dlinear_preds==0).sum()/len(dlinear_preds)*100:.1f}%)") # 6.2 N-BEATS预测 X_nbeats_tensor = torch.FloatTensor(X_nbeats).to(device) with torch.no_grad(): nbeats_pred_scaled = nbeats_model(X_nbeats_tensor).cpu().numpy() # 还原真实价格并使用当前K线open价格判断 nbeats_pred_prices = nbeats_preprocessor.inverse_transform_price(nbeats_pred_scaled) current_open_prices = df_nbeats['open'].iloc[168:168+len(nbeats_pred_prices)].values nbeats_preds = (nbeats_pred_prices > current_open_prices).astype(int) # 1=涨, 0=跌 print(f"✓ N-BEATS预测完成 (使用当前K线open价格判断)") print(f" 预测上涨: {(nbeats_preds==1).sum()} ({(nbeats_preds==1).sum()/len(nbeats_preds)*100:.1f}%)") print(f" 预测下跌: {(nbeats_preds==0).sum()} ({(nbeats_preds==0).sum()/len(nbeats_preds)*100:.1f}%)") # 6.3 TTM-R2预测 print(f"✓ TTM-R2预测中...") context_length = 512 ttm_preds_list = [] all_prices = df_2026['close'].values all_open_prices = df_2026['open'].values for i in range(min_samples): start_idx = i end_idx = start_idx + context_length if end_idx > len(all_prices): context_data = all_prices[-context_length:].reshape(1, -1, 1) current_open_price = all_open_prices[min(168 + i, len(all_open_prices) - 1)] else: context_data = all_prices[start_idx:end_idx].reshape(1, -1, 1) current_open_price = all_open_prices[end_idx] if end_idx < len(all_open_prices) else all_open_prices[-1] context_tensor = torch.FloatTensor(context_data).to(device) with torch.no_grad(): output = ttm_model(context_tensor) pred_price = output.prediction_outputs.cpu().numpy()[0, 0, 0] ttm_preds_list.append(1 if pred_price > current_open_price else 0) ttm_preds = np.array(ttm_preds_list) print(f"✓ TTM-R2预测完成") print(f" 预测上涨: {(ttm_preds==1).sum()} ({(ttm_preds==1).sum()/len(ttm_preds)*100:.1f}%)") print(f" 预测下跌: {(ttm_preds==0).sum()} ({(ttm_preds==0).sum()/len(ttm_preds)*100:.1f}%)") # 6.4 TimesFM 2.5预测 print(f"✓ TimesFM 2.5预测中...") timesfm_preds_list = [] for i in range(min_samples): # TimesFM使用512小时上下文 start_idx = i end_idx = start_idx + 512 if end_idx > len(all_prices): # 如果数据不足，使用最后512个数据点 context_prices = all_prices[-512:] current_open_price = all_open_prices[min(168 + i, len(all_open_prices) - 1)] else: context_prices = all_prices[start_idx:end_idx] current_open_price = all_open_prices[end_idx] if end_idx < len(all_open_prices) else all_open_prices[-1] # TimesFM的forecast方法接受list of arrays作为inputs try: point_forecast, _ = timesfm_model.forecast( horizon=1, inputs=[context_prices], # list包含单个序列 ) # point_forecast形状: (1, 1) - (num_series, horizon) pred_price = point_forecast[0, 0] # 与当前K线open价格比较 timesfm_preds_list.append(1 if pred_price > current_open_price else 0) except Exception as e: print(f" ⚠️ TimesFM预测第{i}个样本失败: {e}") # 使用随机预测作为fallback timesfm_preds_list.append(np.random.randint(0, 2)) timesfm_preds = np.array(timesfm_preds_list) print(f"✓ TimesFM 2.5预测完成") print(f" 预测上涨: {(timesfm_preds==1).sum()} ({(timesfm_preds==1).sum()/len(timesfm_preds)*100:.1f}%)") print(f" 预测下跌: {(timesfm_preds==0).sum()} ({(timesfm_preds==0).sum()/len(timesfm_preds)*100:.1f}%)") # ========== 步骤7: 集成策略评估 ========== print("\n[7/7] 集成策略评估...") true_labels = y_dlinear # 策略1: 双模型交集（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_correct = (two_model_preds == true_labels) & two_model_valid two_model_acc = two_model_correct.sum() / two_model_valid.sum() * 100 if two_model_valid.sum() > 0 else 0 two_model_coverage = two_model_valid.sum() / len(true_labels) * 100 # 策略2: 三模型多数投票（DLinear + N-BEATS + TTM-R2） vote_sum_3 = dlinear_preds + nbeats_preds + ttm_preds three_model_preds = np.where(vote_sum_3 >= 2, 1, 0) three_model_acc = (three_model_preds == true_labels).mean() * 100 # 策略3: 四模型多数投票（至少3个模型同意） vote_sum_4 = dlinear_preds + nbeats_preds + ttm_preds + timesfm_preds four_model_preds = np.where(vote_sum_4 >= 3, 1, 0) four_model_acc = (four_model_preds == true_labels).mean() * 100 # 策略4: 四模型半数以上投票（至少2个模型同意） four_model_preds_half = np.where(vote_sum_4 >= 2, 1, 0) four_model_acc_half = (four_model_preds_half == true_labels).mean() * 100 # ========== 结果展示 ========== print("\n" + "="*80) print("📊 集成策略对比结果") print("="*80) print(f"\n单模型性能：") print(f" DLinear: {(dlinear_preds == true_labels).mean() * 100:.2f}%") print(f" N-BEATS: {(nbeats_preds == true_labels).mean() * 100:.2f}%") print(f" TTM-R2: {(ttm_preds == true_labels).mean() * 100:.2f}%") print(f" TimesFM 2.5: {(timesfm_preds == true_labels).mean() * 100:.2f}%") print(f"\n集成策略性能：") print(f" 双模型交集 (DL+NB): {two_model_acc:.2f}% (覆盖率: {two_model_coverage:.2f}%)") print(f" 三模型投票 (DL+NB+TTM, ≥2票): {three_model_acc:.2f}% (覆盖率: 100%)") print(f" 四模型强投票 (所有, ≥3票): {four_model_acc:.2f}% (覆盖率: 100%)") print(f" 四模型弱投票 (所有, ≥2票): {four_model_acc_half:.2f}% (覆盖率: 100%)") # 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()