"""2026年2月BTC数据 - DLinear, N-BEATS, TTM-R2 准确率测试 (使用统一推理管理器) 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） 价格对比：预测结果与预测K线(t+1)的收盘价做对比 时间：币安数据UTC，输出转为北京时间(UTC+8)，按北京时间分天 """ import sys import os import pandas as pd import numpy as np from datetime import timedelta from typing import Dict, List, Any # 添加项目路径 sys.path.insert(0, os.path.dirname(os.path.abspath(__file__))) from data.fetcher import BinanceDataFetcher from models.inference import get_inference_manager # ==================== 时区与美股判断 ==================== def utc_to_beijing(ts): """UTC时间转北京时间(UTC+8)""" return ts + timedelta(hours=8) def is_us_stock_open_by_utc(utc_ts): """根据UTC时间判断美股当天是否开盘""" et_ts = utc_ts - timedelta(hours=5) et_date = pd.Timestamp(et_ts.date()) us_holidays_feb_2026 = { pd.Timestamp('2026-02-16'), # 总统日 Presidents' Day } if et_date.weekday() >= 5: return False if et_date in us_holidays_feb_2026: return False return True def fmt_pred(val): """格式化预测结果""" if val is None or (isinstance(val, float) and np.isnan(val)): return "N/A" return "涨" if int(val) == 1 else "跌" def fmt_result(pred, actual): """格式化对错""" if pred is None or (isinstance(pred, float) and np.isnan(pred)): return " " return "✓" if int(pred) == int(actual) else "✗" def main(): print("=" * 110) print("2026年2月 BTC 1h 涨跌预测准确率测试 (DLinear, N-BEATS, TTM-R2)") print("基于统一推理管理器 (models.inference)") print("=" * 110) # ========== 步骤1: 加载模型 ========== print("\n[1/4] 加载模型...") manager = get_inference_manager() manager.load_all_models() # ========== 步骤2: 获取测试数据 ========== print("\n[2/4] 获取2026年2月数据...") fetcher = BinanceDataFetcher() # 获取足够长的数据以供lookback df = fetcher.fetch_ohlcv('BTC', '1h', start_date='2025-12-01', end_date='2026-03-01', use_cache=False) print(f"数据范围(UTC): {df['timestamp'].min()} -> {df['timestamp'].max()}") print(f"总行数: {len(df)}") # ========== 步骤3: 运行预测 ========== print("\n[3/4] 运行预测 (逐小时回测)...") # 找出2月（北京时间）的所有预测目标K线索引 jan_targets = [] for target_idx in range(200, len(df)): # 从200开始确保有足够lookback target_time_utc = df.iloc[target_idx]['timestamp'] target_time_bj = utc_to_beijing(target_time_utc) if target_time_bj.year == 2026 and target_time_bj.month == 2: jan_targets.append(target_idx) print(f"2月（北京时间）目标K线数: {len(jan_targets)}") results = [] for i, target_idx in enumerate(jan_targets): if i % 50 == 0: print(f" 处理进度: {i}/{len(jan_targets)}...") target_time_utc = df.iloc[target_idx]['timestamp'] target_time_bj = utc_to_beijing(target_time_utc) # 输入数据：截止到 target_idx - 1 # 注意：predict_all 需要传入DataFrame，它会取最后一行作为 "当前" # 所以我们需要传入 df.iloc[:target_idx] (不包含 target_idx) # Python切片是 [start:end)，所以 [:target_idx] 包含 0 到 target_idx-1 df_input = df.iloc[:target_idx].copy() # 实际结果 current_close = df.iloc[target_idx-1]['close'] target_close = df.iloc[target_idx]['close'] actual_label = 1 if target_close > current_close else 0 # 预测 preds = manager.predict_all(df_input) dl_pred = preds.get('dlinear', {}).get('prediction') nb_pred = preds.get('nbeats', {}).get('prediction') ttm_pred = preds.get('ttm', {}).get('prediction') ens_pred = preds.get('ensemble', {}).get('prediction') results.append({ 'time_utc': target_time_utc, 'time_bj': target_time_bj, 'current_close': current_close, 'target_close': target_close, 'actual': actual_label, 'dlinear': dl_pred, 'nbeats': nb_pred, 'ttm': ttm_pred, 'ensemble': ens_pred, 'us_open': is_us_stock_open_by_utc(target_time_utc) }) # ========== 步骤4: 输出结果 ========== if not results: print("没有找到符合条件的测试样本") return df_res = pd.DataFrame(results) df_res['date_bj'] = df_res['time_bj'].dt.date total_samples = len(df_res) print(f"\n[4/4] 准确率统计 (2026年2月，北京时间)") print(f"总样本数: {total_samples}") # ========== 每小时详细数据 ========== print("\n" + "=" * 110) print("每小时详细数据") print("=" * 110) hourly_header = ( f"{'北京时间':<18} {'前收盘':>10} {'现收盘':>10} {'实际':>4} " f"{'DL':>3} {'':>1} {'NB':>3} {'':>1} {'TTM':>3} {'':>1} {'集成':>4} {'':<1}" ) for date_bj, day_group in df_res.groupby('date_bj'): us_open = day_group['us_open'].iloc[0] us_str = "美股开盘" if us_open else "美股休市" dl_day_acc = (day_group['dlinear'] == day_group['actual']).mean() nb_valid = day_group.dropna(subset=['nbeats']) nb_day_acc = (nb_valid['nbeats'] == nb_valid['actual']).mean() if len(nb_valid) > 0 else None ttm_valid = day_group.dropna(subset=['ttm']) ttm_day_acc = (ttm_valid['ttm'] == ttm_valid['actual']).mean() if len(ttm_valid) > 0 else None ens_valid = day_group.dropna(subset=['ensemble']) ens_day_acc = (ens_valid['ensemble'] == ens_valid['actual']).mean() if len(ens_valid) > 0 else None nb_acc_str = f"{nb_day_acc:.1%}" if nb_day_acc is not None else "N/A" ttm_acc_str = f"{ttm_day_acc:.1%}" if ttm_day_acc is not None else "N/A" ens_acc_str = f"{ens_day_acc:.1%}" if ens_day_acc is not None else "N/A" print(f"\n--- {date_bj} ({us_str}) --- DL:{dl_day_acc:.1%} | NB:{nb_acc_str} | TTM:{ttm_acc_str} | 集成:{ens_acc_str}") print(hourly_header) print("-" * 110) for _, row in day_group.iterrows(): time_str = row['time_bj'].strftime('%m-%d %H:%M') actual_str = fmt_pred(row['actual']) dl_str = fmt_pred(row['dlinear']) dl_r = fmt_result(row['dlinear'], row['actual']) nb_str = fmt_pred(row['nbeats']) nb_r = fmt_result(row['nbeats'], row['actual']) ttm_str = fmt_pred(row['ttm']) ttm_r = fmt_result(row['ttm'], row['actual']) ens_str = fmt_pred(row['ensemble']) ens_r = fmt_result(row['ensemble'], row['actual']) print( f"{time_str:<18} {row['current_close']:>10.2f} {row['target_close']:>10.2f} {actual_str:>4} " f"{dl_str:>3} {dl_r:>1} {nb_str:>3} {nb_r:>1} {ttm_str:>3} {ttm_r:>1} {ens_str:>4} {ens_r:<1}" ) # ========== 每日准确率汇总表 ========== print("\n\n" + "=" * 110) print("每日准确率汇总 (北京时间)") print("=" * 110) header = f"{'日期':<14} {'样本':>4} {'美股':>6} {'DLinear':>8} {'N-BEATS':>8} {'TTM-R2':>8} {'集成(投票)':>10}" print(header) print("-" * 110) for date_bj, group in df_res.groupby('date_bj'): total = len(group) us_open = group['us_open'].iloc[0] us_str = "开盘" if us_open else "休市" dl_acc = (group['dlinear'] == group['actual']).mean() nb_valid = group.dropna(subset=['nbeats']) nb_acc = (nb_valid['nbeats'] == nb_valid['actual']).mean() if len(nb_valid) > 0 else None ttm_valid = group.dropna(subset=['ttm']) ttm_acc = (ttm_valid['ttm'] == ttm_valid['actual']).mean() if len(ttm_valid) > 0 else None ens_valid = group.dropna(subset=['ensemble']) ens_acc = (ens_valid['ensemble'] == ens_valid['actual']).mean() if len(ens_valid) > 0 else None dl_str = f"{dl_acc:.1%}" nb_str = f"{nb_acc:.1%}" if nb_acc is not None else "N/A" ttm_str = f"{ttm_acc:.1%}" if ttm_acc is not None else "N/A" ens_str = f"{ens_acc:.1%}" if ens_acc is not None else "N/A" print(f"{str(date_bj):<14} {total:>4} {us_str:>6} {dl_str:>8} {nb_str:>8} {ttm_str:>8} {ens_str:>10}") print("-" * 110) # --- 总准确率 --- dl_total = (df_res['dlinear'] == df_res['actual']).mean() nb_all = df_res.dropna(subset=['nbeats']) nb_total = (nb_all['nbeats'] == nb_all['actual']).mean() if len(nb_all) > 0 else None ttm_all = df_res.dropna(subset=['ttm']) ttm_total = (ttm_all['ttm'] == ttm_all['actual']).mean() if len(ttm_all) > 0 else None ens_all = df_res.dropna(subset=['ensemble']) ens_total = (ens_all['ensemble'] == ens_all['actual']).mean() if len(ens_all) > 0 else None nb_t_str = f"{nb_total:.2%}" if nb_total is not None else "N/A" ttm_t_str = f"{ttm_total:.2%}" if ttm_total is not None else "N/A" ens_t_str = f"{ens_total:.2%}" if ens_total is not None else "N/A" print(f"{'总计':<14} {total_samples:>4} {'':>6} {dl_total:.2%} {nb_t_str:>8} {ttm_t_str:>8} {ens_t_str:>10}") print("=" * 110) if __name__ == "__main__": main()