fix: 修复 auto_trade 推理预处理逻辑，新增 create_labels 版对比测试脚本
auto_trade_polymarket.py: 新增 preprocess_for_inference 方法，推理时不调用 create_labels（避免 df[:-1] 砍掉输入K线），与 test 脚本逻辑一致。 test_dlinear_jan_2026_create_labels.py: 使用 create_labels 方式的 DLinear 测试 脚本，用于对比两种预处理方式的准确率差异。 Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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@@ -141,6 +141,26 @@ class ClassificationPreprocessor:
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return np.array(X), np.array(y), df
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def preprocess_for_inference(self, df: pd.DataFrame):
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"""推理专用预处理：不调用create_labels，整个df一次性处理
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与test_dlinear_jan_2026.py逻辑一致：
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加特征 -> dropna -> scaler.transform -> 构建窗口
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"""
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df = self.add_features(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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scaled_features = self.scaler.transform(df[feature_cols])
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X = []
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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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return np.array(X), df
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class PolymarketBTCTrader:
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"""Polymarket BTC Hourly交易器"""
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@@ -616,9 +636,17 @@ class PolymarketBTCTrader:
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logger.info(f" 当前K线时间: {current_time} (预测目标)")
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logger.info(f" 当前收盘价: ${current_close:,.2f} (实际结果)")
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# 用除最后一根外的数据进行预测
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# 用除最后一根外的数据进行预测（无create_labels，与test逻辑一致）
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df_for_prediction = df.iloc[:-1]
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X, _, _ = self.preprocessor.preprocess(df_for_prediction, fit=False)
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X, processed_df = self.preprocessor.preprocess_for_inference(df_for_prediction)
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# 打印模型输入的最后一根K线数据
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last_input_row = df_for_prediction.iloc[-1]
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logger.info(f" 📌 模型输入末根K线: {last_input_row['timestamp']} "
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f"O:{last_input_row['open']:.2f} H:{last_input_row['high']:.2f} "
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f"L:{last_input_row['low']:.2f} C:{last_input_row['close']:.2f} "
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f"V:{last_input_row['volume']:.0f}")
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X_last = torch.FloatTensor(X[-1:]).to(self.device)
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with torch.no_grad():
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@@ -632,14 +660,6 @@ class PolymarketBTCTrader:
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logger.info(f"🤖 DLinear预测: {direction}")
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logger.info(f" 置信度: {confidence:.2%}")
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# 显示预测是否正确 (用于验证)
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actual_up = current_close > prev_close
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if prediction == 1:
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correct = actual_up
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else:
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correct = not actual_up
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logger.info(f" 实际涨跌: {'📈 涨' if actual_up else '📉 跌'} ({'✅ 正确' if correct else '❌ 错误'})")
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return prediction, probability
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def run_once(self) -> Dict:
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@@ -784,9 +804,9 @@ class PolymarketBTCTrader:
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logger.info(f" 上一K线: {prev_time} 收盘价: ${prev_close:,.2f}")
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logger.info(f" 当前K线: {current_time} 收盘价: ${current_close:,.2f}")
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# 进行预测
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# 进行预测（无create_labels，与test逻辑一致）
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df_for_prediction = df.iloc[:-1]
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X, _, _ = self.preprocessor.preprocess(df_for_prediction, fit=False)
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X, _ = self.preprocessor.preprocess_for_inference(df_for_prediction)
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X_last = torch.FloatTensor(X[-1:]).to(self.device)
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with torch.no_grad():
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@@ -0,0 +1,305 @@
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"""2026年1月BTC数据 - DLinear 准确率测试 (create_labels版)
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与test_dlinear_jan_2026.py对比：
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- 本脚本使用create_labels预处理方式（auto_trade旧逻辑）
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- 每个目标K线截取到input_idx的df，调用create_labels后取X[-1]预测
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- 用于验证create_labels对预测结果的影响
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输入数据：预测K线的前一根（即用截止到t的数据预测t+1）
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价格对比：预测结果与预测K线(t+1)的收盘价做对比
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时间：币安数据UTC，输出转为北京时间(UTC+8)，按北京时间分天
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"""
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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 numpy as np
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import pandas as pd
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import pickle
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import pandas_ta as ta
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from datetime import timedelta
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from sklearn.preprocessing import StandardScaler
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from data.fetcher import BinanceDataFetcher
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# ==================== 时区与美股判断 ====================
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def utc_to_beijing(ts):
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"""UTC时间转北京时间(UTC+8)"""
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return ts + timedelta(hours=8)
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def is_us_stock_open_by_utc(utc_ts):
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"""根据UTC时间判断美股当天是否开盘
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美股交易日基于美东时间(ET)，UTC-5(冬令时)
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2026年1月在冬令时范围内（夏令时3月第二个周日开始）
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所以美东 = UTC - 5h
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"""
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et_ts = utc_ts - timedelta(hours=5)
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et_date = pd.Timestamp(et_ts.date())
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us_holidays_jan_2026 = {
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pd.Timestamp('2026-01-01'), # 元旦 New Year's Day
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pd.Timestamp('2026-01-19'), # 马丁·路德·金日 MLK Day
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}
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if et_date.weekday() >= 5:
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return False
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if et_date in us_holidays_jan_2026:
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return False
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return True
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# ==================== pickle反序列化占位类 ====================
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class ClassificationPreprocessor:
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"""pickle.load时需要能找到这个类名"""
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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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# ==================== create_labels预处理 ====================
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def preprocess_with_create_labels(df, scaler, lookback=168, forecast_horizon=1):
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"""使用create_labels方式预处理（auto_trade旧逻辑）
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步骤：加特征 -> create_labels(shift + 砍尾) -> dropna -> scaler.transform -> 构建窗口
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"""
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df = df.copy()
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# 加特征
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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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# create_labels: shift + 砍最后一行
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df['future_price'] = df['close'].shift(-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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df = df[:-forecast_horizon]
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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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scaled_features = scaler.transform(df[feature_cols])
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X = []
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for i in range(len(scaled_features) - lookback):
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X.append(scaled_features[i:i + lookback])
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return np.array(X), df
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# ==================== 工具函数 ====================
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def fmt_pred(val):
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"""格式化预测结果"""
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if val is None or (isinstance(val, float) and np.isnan(val)):
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return "N/A"
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return "涨" if int(val) == 1 else "跌"
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def fmt_result(pred, actual):
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"""格式化对错"""
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if pred is None or (isinstance(pred, float) and np.isnan(pred)):
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return " "
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return "✓" if int(pred) == int(actual) else "✗"
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def main():
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print("=" * 90)
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print("2026年1月 BTC 1h 涨跌预测准确率测试 (DLinear - create_labels版)")
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print("输入：预测K线的前一根 | 对比：预测结果 vs 预测K线收盘价 | 时间：北京时间(UTC+8)")
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print("预处理：截取到input的df，调用create_labels后取X[-1]")
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print("=" * 90)
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device = torch.device('mps' if torch.backends.mps.is_available() else 'cpu')
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print(f"使用设备: {device}")
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# ========== 步骤1: 加载模型 ==========
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print("\n[1/4] 加载模型...")
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from models.dlinear.model import DLinear
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checkpoint = torch.load('./models/checkpoints/dlinear_classification_2026/best_model.pt',
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map_location=device, weights_only=False)
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model = DLinear(checkpoint['config'])
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model.load_state_dict(checkpoint['model_state_dict'])
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model = model.to(device).eval()
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with open('./models/checkpoints/dlinear_classification_2026/preprocessor.pkl', 'rb') as f:
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preprocessor = pickle.load(f)
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print(" ✓ DLinear已加载")
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# ========== 步骤2: 获取测试数据 ==========
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print("\n[2/4] 获取数据...")
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fetcher = BinanceDataFetcher()
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df = fetcher.fetch_ohlcv('BTC', '1h', start_date='2025-10-01', end_date='2026-02-01', use_cache=False)
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print(f"数据范围(UTC): {df['timestamp'].min()} -> {df['timestamp'].max()}")
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print(f"总行数: {len(df)}")
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# ========== 步骤3: 预处理与预测 ==========
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print("\n[3/4] 运行预测...")
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lookback = preprocessor.lookback
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scaler = preprocessor.scaler
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# 找出1月（北京时间）的所有预测目标K线索引
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jan_targets = []
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for target_idx in range(1, len(df)):
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target_time_utc = df.iloc[target_idx]['timestamp']
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target_time_bj = utc_to_beijing(target_time_utc)
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if target_time_bj.year == 2026 and target_time_bj.month == 1:
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jan_targets.append(target_idx)
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print(f"1月（北京时间）目标K线数: {len(jan_targets)}")
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results = []
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for target_idx in jan_targets:
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target_time_utc = df.iloc[target_idx]['timestamp']
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target_time_bj = utc_to_beijing(target_time_utc)
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# 输入K线索引（前一根）
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input_idx = target_idx - 1
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current_close = df.iloc[input_idx]['close']
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target_close = df.iloc[target_idx]['close']
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actual_label = 1 if target_close > current_close else 0
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# --- DLinear 预测（create_labels方式：截取到input_idx，取X[-1]） ---
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dl_pred = None
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dl_prob = None
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df_up_to_input = df.iloc[:input_idx + 1]
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X, _ = preprocess_with_create_labels(df_up_to_input, scaler, lookback)
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if len(X) > 0:
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x_tensor = torch.FloatTensor(X[-1:]).to(device)
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with torch.no_grad():
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logits = model(x_tensor).cpu().numpy()
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prob = 1 / (1 + np.exp(-logits))
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dl_pred = int(prob.item() > 0.5)
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dl_prob = float(prob.item())
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results.append({
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'time_utc': target_time_utc,
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'time_bj': target_time_bj,
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'current_close': current_close,
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'target_close': target_close,
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'actual': actual_label,
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'dlinear': dl_pred,
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'dl_prob': dl_prob,
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})
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# ========== 步骤4: 输出结果 ==========
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if not results:
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print("没有找到符合条件的测试样本")
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return
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df_res = pd.DataFrame(results)
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df_res['date_bj'] = df_res['time_bj'].dt.date
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df_res['us_open'] = df_res['time_utc'].apply(is_us_stock_open_by_utc)
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total_samples = len(df_res)
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print(f"\n[4/4] 准确率统计 (2026年1月，北京时间)")
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print(f"总样本数: {total_samples}")
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# ========== 每小时详细数据 ==========
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print("\n" + "=" * 90)
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print("每小时详细数据")
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print("=" * 90)
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hourly_header = (
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f"{'北京时间':<18} {'前收盘':>10} {'现收盘':>10} {'实际':>4} "
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f"{'DL':>3} {'':>1} {'概率':>6}"
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)
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for date_bj, day_group in df_res.groupby('date_bj'):
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us_open = day_group['us_open'].iloc[0]
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us_str = "美股开盘" if us_open else "美股休市"
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dl_day_correct = (day_group['dlinear'] == day_group['actual']).sum()
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dl_day_total = day_group['dlinear'].notna().sum()
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dl_day_acc = dl_day_correct / dl_day_total if dl_day_total > 0 else 0
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print(f"\n--- {date_bj} ({us_str}) --- DL准确率:{dl_day_acc:.1%} ({dl_day_correct}/{dl_day_total})")
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print(hourly_header)
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print("-" * 90)
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for _, row in day_group.iterrows():
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time_str = row['time_bj'].strftime('%m-%d %H:%M')
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actual_str = fmt_pred(row['actual'])
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dl_str = fmt_pred(row['dlinear'])
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dl_r = fmt_result(row['dlinear'], row['actual'])
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prob_str = f"{row['dl_prob']:.3f}" if row['dl_prob'] is not None else " N/A"
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print(
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f"{time_str:<18} {row['current_close']:>10.2f} {row['target_close']:>10.2f} {actual_str:>4} "
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f"{dl_str:>3} {dl_r:>1} {prob_str:>6}"
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)
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# ========== 每日准确率汇总表 ==========
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print("\n\n" + "=" * 90)
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print("每日准确率汇总 (北京时间)")
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print("=" * 90)
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header = f"{'日期':<14} {'样本':>4} {'美股':>6} {'DLinear':>8}"
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print(header)
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print("-" * 90)
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for date_bj, group in df_res.groupby('date_bj'):
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total = len(group)
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us_open = group['us_open'].iloc[0]
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us_str = "开盘" if us_open else "休市"
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dl_valid = group.dropna(subset=['dlinear'])
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dl_acc = (dl_valid['dlinear'] == dl_valid['actual']).mean() if len(dl_valid) > 0 else 0
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dl_str = f"{dl_acc:.1%}"
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print(f"{str(date_bj):<14} {total:>4} {us_str:>6} {dl_str:>8}")
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print("-" * 90)
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# --- 总准确率 ---
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dl_valid_all = df_res.dropna(subset=['dlinear'])
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dl_total = (dl_valid_all['dlinear'] == dl_valid_all['actual']).mean() if len(dl_valid_all) > 0 else 0
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print(f"{'总计':<14} {total_samples:>4} {'':>6} {dl_total:.2%}")
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print("=" * 90)
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# --- 美股开盘日 vs 休市日对比 ---
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print("\n[美股开盘日 vs 休市日 准确率对比]")
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print("-" * 60)
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for label, flag in [("开盘日", True), ("休市日", False)]:
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subset = df_res[df_res['us_open'] == flag]
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if len(subset) == 0:
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continue
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n = len(subset)
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dl_s = subset.dropna(subset=['dlinear'])
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dl_a = (dl_s['dlinear'] == dl_s['actual']).mean() if len(dl_s) > 0 else 0
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print(f" {label} ({n}样本): DLinear={dl_a:.2%}")
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print("-" * 60)
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# --- 预测分布统计 ---
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print("\n[预测分布统计]")
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print("-" * 60)
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actual_up = (df_res['actual'] == 1).sum()
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actual_down = (df_res['actual'] == 0).sum()
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print(f" 实际: 上涨={actual_up} ({actual_up/total_samples:.1%}) 下跌={actual_down} ({actual_down/total_samples:.1%})")
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dl_valid = df_res['dlinear'].dropna()
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||||
if len(dl_valid) > 0:
|
||||
up = (dl_valid == 1).sum()
|
||||
down = (dl_valid == 0).sum()
|
||||
print(f" DLinear: 预测上涨={up} ({up/len(dl_valid):.1%}) 预测下跌={down} ({down/len(dl_valid):.1%})")
|
||||
|
||||
print("=" * 90)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in new issue
Block a user