crypto_predictor
1bd9d25f60
- 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
407 lines
15 KiB
Python
407 lines
15 KiB
Python
"""分析DLinear输出概率值的偏向性及其对集成策略的影响"""
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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("DLinear输出概率值分布分析 - 以0.1阶梯统计")
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print("="*80)
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# ========== 加载模型 ==========
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print("\n[1/4] 加载模型...")
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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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# 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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# 加载TTM-R2
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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"✓ 三个模型已加载")
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# ========== 获取数据 ==========
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print("\n[2/4] 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='2026-12-31', 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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# ========== 预处理 ==========
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print("\n[3/4] 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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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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# ========== 预测并获取DLinear概率值 ==========
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print("\n[4/4] 执行预测并分析DLinear输出概率...")
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# DLinear预测（保留概率值）
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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.flatten())) # sigmoid概率
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dlinear_preds = (dlinear_probs > 0.5).astype(int)
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# 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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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)
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# TTM-R2预测
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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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# 双模型交集
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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_valid = two_model_agree_up | two_model_agree_down
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# 三模型多数投票
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vote_sum = dlinear_preds + nbeats_preds + ttm_preds
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three_model_preds = np.where(vote_sum >= 2, 1, 0)
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# 真实标签
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true_labels = y_dlinear
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print(f"✓ 预测完成，开始分析DLinear概率分布...")
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# ========== 按0.1阶梯分析 ==========
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print("\n" + "="*80)
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print("📊 DLinear输出概率分布分析（0.1阶梯）")
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print("="*80)
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# 定义概率区间
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prob_bins = np.arange(0, 1.1, 0.1)
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prob_labels = [f"{i/10:.1f}-{(i+1)/10:.1f}" for i in range(10)]
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# 为每个样本分配到概率区间
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prob_bin_indices = np.digitize(dlinear_probs, prob_bins) - 1
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prob_bin_indices = np.clip(prob_bin_indices, 0, 9) # 确保在0-9范围内
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# 初始化统计
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stats = []
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for i, bin_label in enumerate(prob_labels):
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mask = prob_bin_indices == i
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count = mask.sum()
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if count == 0:
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continue
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# 该区间的真实标签
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bin_true = true_labels[mask]
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# 该区间DLinear的预测
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bin_dlinear_pred = dlinear_preds[mask]
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# 该区间在双模型交集中的表现
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bin_two_model_valid = two_model_valid[mask]
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bin_two_model_pred = np.full(count, -1)
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bin_two_model_pred[two_model_agree_up[mask]] = 1
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bin_two_model_pred[two_model_agree_down[mask]] = 0
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# 该区间在三模型投票中的表现
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bin_three_model_pred = three_model_preds[mask]
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# 计算统计
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dlinear_acc = (bin_dlinear_pred == bin_true).mean() * 100 if count > 0 else 0
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two_model_valid_count = bin_two_model_valid.sum()
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two_model_acc = ((bin_two_model_pred == bin_true) & (bin_two_model_pred != -1)).sum() / two_model_valid_count * 100 if two_model_valid_count > 0 else 0
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two_model_coverage = two_model_valid_count / count * 100
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three_model_acc = (bin_three_model_pred == bin_true).mean() * 100 if count > 0 else 0
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# 平均概率值
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avg_prob = dlinear_probs[mask].mean()
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# 预测倾向
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pred_up = bin_dlinear_pred.mean() * 100
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stats.append({
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'bin': bin_label,
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'count': count,
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'avg_prob': avg_prob,
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'pred_up_pct': pred_up,
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'dlinear_acc': dlinear_acc,
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'two_model_acc': two_model_acc,
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'two_model_cov': two_model_coverage,
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'three_model_acc': three_model_acc
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})
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# 转换为DataFrame
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stats_df = pd.DataFrame(stats)
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# 显示统计表
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print("\n概率区间分析：")
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print(f"{'区间':<10} {'样本数':>6} {'平均概率':>8} {'预测↑%':>7} "
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f"{'DL准确率':>9} {'双模准确率':>10} {'双模覆盖':>8} {'三模准确率':>10}")
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print("-" * 95)
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for _, row in stats_df.iterrows():
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# 根据准确率添加标记
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dl_mark = "🔥" if row['dlinear_acc'] >= 70 else ("✅" if row['dlinear_acc'] >= 60 else ("⚠️" if row['dlinear_acc'] < 45 else ""))
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two_mark = "🔥" if row['two_model_acc'] >= 70 else ("✅" if row['two_model_acc'] >= 60 else ("⚠️" if row['two_model_acc'] < 45 else ""))
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three_mark = "🔥" if row['three_model_acc'] >= 70 else ("✅" if row['three_model_acc'] >= 60 else ("⚠️" if row['three_model_acc'] < 45 else ""))
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print(f"{row['bin']:<10} {row['count']:>6} {row['avg_prob']:>8.3f} {row['pred_up_pct']:>6.1f}% "
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f"{row['dlinear_acc']:>8.1f}{dl_mark} "
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f"{row['two_model_acc']:>9.1f}{two_mark} "
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f"{row['two_model_cov']:>7.1f}% "
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f"{row['three_model_acc']:>9.1f}{three_mark}")
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# ========== 关键发现 ==========
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print("\n" + "="*80)
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print("🔍 关键发现")
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print("="*80)
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# 极端概率区间（高置信度）
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high_conf_up = stats_df[stats_df['avg_prob'] >= 0.65]
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high_conf_down = stats_df[stats_df['avg_prob'] <= 0.35]
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print(f"\n📈 高置信度看涨区间 (概率≥0.65):")
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if len(high_conf_up) > 0:
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total_samples = high_conf_up['count'].sum()
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avg_dl_acc = (high_conf_up['dlinear_acc'] * high_conf_up['count']).sum() / total_samples
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avg_two_acc = (high_conf_up['two_model_acc'] * high_conf_up['count']).sum() / total_samples
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avg_three_acc = (high_conf_up['three_model_acc'] * high_conf_up['count']).sum() / total_samples
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print(f" 样本数: {total_samples} ({total_samples/len(dlinear_probs)*100:.1f}%)")
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print(f" DLinear平均准确率: {avg_dl_acc:.2f}%")
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print(f" 双模型平均准确率: {avg_two_acc:.2f}%")
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print(f" 三模型平均准确率: {avg_three_acc:.2f}%")
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else:
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print(" 无数据")
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print(f"\n📉 高置信度看跌区间 (概率≤0.35):")
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if len(high_conf_down) > 0:
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total_samples = high_conf_down['count'].sum()
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avg_dl_acc = (high_conf_down['dlinear_acc'] * high_conf_down['count']).sum() / total_samples
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avg_two_acc = (high_conf_down['two_model_acc'] * high_conf_down['count']).sum() / total_samples
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avg_three_acc = (high_conf_down['three_model_acc'] * high_conf_down['count']).sum() / total_samples
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print(f" 样本数: {total_samples} ({total_samples/len(dlinear_probs)*100:.1f}%)")
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print(f" DLinear平均准确率: {avg_dl_acc:.2f}%")
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print(f" 双模型平均准确率: {avg_two_acc:.2f}%")
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print(f" 三模型平均准确率: {avg_three_acc:.2f}%")
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else:
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print(" 无数据")
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# 中等置信度区间
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mid_conf = stats_df[(stats_df['avg_prob'] > 0.45) & (stats_df['avg_prob'] < 0.55)]
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print(f"\n⚖️ 中等置信度区间 (0.45-0.55):")
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if len(mid_conf) > 0:
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total_samples = mid_conf['count'].sum()
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avg_dl_acc = (mid_conf['dlinear_acc'] * mid_conf['count']).sum() / total_samples
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avg_two_acc = (mid_conf['two_model_acc'] * mid_conf['count']).sum() / total_samples if total_samples > 0 else 0
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avg_three_acc = (mid_conf['three_model_acc'] * mid_conf['count']).sum() / total_samples
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print(f" 样本数: {total_samples} ({total_samples/len(dlinear_probs)*100:.1f}%)")
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print(f" DLinear平均准确率: {avg_dl_acc:.2f}%")
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print(f" 双模型平均准确率: {avg_two_acc:.2f}%")
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print(f" 三模型平均准确率: {avg_three_acc:.2f}%")
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print(f" ⚠️ 建议: 低置信度区间，考虑弃权或减仓")
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else:
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print(" 无数据")
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# 找出表现最好的概率区间
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best_bin = stats_df.loc[stats_df['dlinear_acc'].idxmax()]
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worst_bin = stats_df.loc[stats_df['dlinear_acc'].idxmin()]
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print(f"\n🏆 DLinear表现最佳区间:")
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print(f" 区间: {best_bin['bin']}")
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print(f" 准确率: {best_bin['dlinear_acc']:.1f}%")
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print(f" 样本数: {best_bin['count']}")
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print(f"\n💀 DLinear表现最差区间:")
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print(f" 区间: {worst_bin['bin']}")
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print(f" 准确率: {worst_bin['dlinear_acc']:.1f}%")
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print(f" 样本数: {worst_bin['count']}")
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# ========== 概率分布可视化 ==========
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print("\n" + "="*80)
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print("📊 DLinear概率分布直方图")
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print("="*80)
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print("\n样本数分布:")
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max_count = stats_df['count'].max()
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for _, row in stats_df.iterrows():
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bar_length = int(row['count'] / max_count * 40)
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bar = "█" * bar_length
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print(f"{row['bin']:<10} {bar} {row['count']}")
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print("\n" + "="*80)
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print("✓ DLinear概率分布分析完成!")
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print("="*80)
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if __name__ == "__main__":
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main()
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