fix: 回测细节导出、交易日历测试和动量因子修复
修复项: - export_backtest_detail.py: 统一回测导出脚本的数据源调用逻辑 - test_trading_calendar.py: 交易日历功能测试 - verify_fix_result.py: 修复结果验证 - verify_mode_b.py: 模式 B 验证 策略修复: - momentum.py: 动量因子计算优化 - strategy.py: StrategyBase 数据获取修复（fetch_indices 返回 dict）
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scripts/export_backtest_detail.py
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477
scripts/export_backtest_detail.py
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#!/usr/bin/env python3
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"""
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导出回测逐日明细到 JSON，供 HTML 回放器加载。
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模式 B：指数信号 + ETF 收益（2020-01-01 ~ 2026-05-19）
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用法:
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python scripts/export_backtest_detail.py
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"""
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import sys
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import json
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import math
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from pathlib import Path
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import numpy as np
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import pandas as pd
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import yaml
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project_root = Path(__file__).parent.parent
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sys.path.insert(0, str(project_root))
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from dotenv import load_dotenv
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load_dotenv()
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from datasource.tushare_source import TushareSource
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from datasource.flask_api_source import FlaskAPIDataSource
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from strategies.shared.factors.momentum import MomentumFactor
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from strategies.shared.signals.selectors import TopNSelector
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from framework.execution import BacktestExecutor
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# ==================== 加载配置 ====================
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config_path = project_root / 'strategies' / 'rotation' / 'config.yaml'
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with open(config_path, 'r', encoding='utf-8') as f:
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config = yaml.safe_load(f)
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CODE_LIST = config['code_list']
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SELECT_NUM = config['select_num']
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N_DAYS = config['n_days']
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TRADE_COST = config['trade_cost']
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BOND_THRESHOLD = config.get('bond_threshold', {})
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BOND_CODE = BOND_THRESHOLD.get('bond_code', '931862.CSI')
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BOND_RATIO = BOND_THRESHOLD.get('ratio', 1.0)
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def fetch_all_data(start_date='2018-01-01', end_date='2026-05-19'):
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ts = TushareSource()
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api = FlaskAPIDataSource() # 默认使用 k3s.tokenpluse.xyz
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index_data = {}
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etf_data = {}
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etf_code_map = {}
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# 统一使用 Flask API 获取所有指数数据（与 strategy.py 保持一致）
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print("[指数数据] - 通过 Flask API (k3s服务) 获取")
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index_codes = list(CODE_LIST.keys())
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index_ohlcv_data = api.fetch_batch(index_codes, start_date, end_date)
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for code, df in index_ohlcv_data.items():
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if df is not None and 'close' in df.columns and len(df) > 0:
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index_data[code] = df
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name = CODE_LIST.get(code, {}).get('name', code)
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print(f" {code} ({name})... {len(df)}天")
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else:
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name = CODE_LIST.get(code, {}).get('name', code)
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print(f" {code} ({name})... 失败")
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print("\n[ETF数据]")
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etf_nav_data = {}
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for code, cfg in CODE_LIST.items():
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etf_code = cfg.get('etf')
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if etf_code is None:
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continue
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etf_code_map[code] = etf_code
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name = cfg['name']
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print(f" {etf_code} ({name})...", end=' ')
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df = ts.fetch_etf_adj(etf_code, start_date, end_date)
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if df is not None and 'close_hfq' in df.columns and len(df) > 0:
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adj_ratio = df['close_hfq'] / df['close']
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df['open_hfq'] = df['open'] * adj_ratio
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etf_data[code] = df
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print(f"{len(df)}天", end='')
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else:
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print("失败")
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continue
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# 获取ETF净值（用于计算溢价率）
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nav_df = ts.fetch_etf_nav(etf_code, start_date, end_date)
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if nav_df is not None and 'nav' in nav_df.columns and len(nav_df) > 0:
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etf_nav_data[code] = nav_df['nav']
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print(f" nav={len(nav_df)}天")
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else:
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print(" nav=无")
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return index_data, etf_data, etf_code_map, etf_nav_data
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def compute_factors(price_data, n_days, trade_dates):
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"""先在原始交易日历上计算因子，再 ffill 对齐到 A 股日历（与 strategy.py 一致）"""
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factor = MomentumFactor(n_days=n_days, weighted=True, crash_filter=True)
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factor_values = {}
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for code, df in price_data.items():
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if 'close' not in df.columns:
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continue
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close_series = df['close'].dropna()
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if len(close_series) == 0:
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continue
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values = factor.compute(pd.DataFrame({'close': close_series}))
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factor_values[code] = values.reindex(trade_dates, method='ffill')
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return pd.DataFrame(factor_values)
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def generate_signals(factor_df, group_mapping):
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selector = TopNSelector(
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select_num=SELECT_NUM,
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group_mapping=group_mapping,
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min_score=0.0,
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rebalance_days=1,
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rebalance_threshold=0.0,
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bond_threshold_config=BOND_THRESHOLD
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)
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return selector.generate(factor_df)
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def safe_val(v, decimals=4):
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if v is None or (isinstance(v, float) and (math.isnan(v) or math.isinf(v))):
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return None
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if isinstance(v, (np.floating, float)):
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return round(float(v), decimals)
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if isinstance(v, (np.integer, int)):
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return int(v)
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return v
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def main():
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from datetime import datetime
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backtest_start = '2020-01-01'
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backtest_end = datetime.now().strftime('%Y-%m-%d') # 动态获取当前日期
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print("=" * 60)
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print(" 导出回测逐日明细 (模式B: 指数信号 + ETF收益)")
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print("=" * 60)
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# 1. 获取数据
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print("\n[1] 获取数据...")
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index_data, etf_data, etf_code_map, etf_nav_data = fetch_all_data()
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# 2. A股交易日历
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print("\n[2] 获取A股交易日历...")
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ts = TushareSource()
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a_share_dates = ts.fetch_trade_cal(backtest_start, backtest_end)
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print(f" {len(a_share_dates)} 天")
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# 3. 分组映射
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group_mapping = {}
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for code, cfg in CODE_LIST.items():
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if isinstance(cfg, dict):
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group_mapping[code] = cfg.get('market', 'default')
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valid_codes = [c for c in CODE_LIST if c in index_data]
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# 4. 计算因子（指数信号）
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print("\n[3] 计算指数动量因子...")
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idx_price_data = {}
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for code in valid_codes:
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if code in index_data and 'close' in index_data[code].columns:
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idx_price_data[code] = index_data[code]
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factor_df = compute_factors(idx_price_data, N_DAYS, a_share_dates)
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print(f" {len(factor_df.columns)} 只, {len(factor_df)} 天")
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# 5. 生成信号
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print("\n[4] 生成信号...")
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signals = generate_signals(factor_df, group_mapping)
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print(f" {len(signals)} 天")
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# 6. 准备ETF收益率（模式B）
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print("\n[5] 准备ETF收益率...")
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etf_close_hfq_aligned = {}
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etf_close_aligned = {}
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etf_open_aligned = {}
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etf_close_hfq_raw = {}
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index_close_aligned = {}
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returns_etf = {}
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returns_idx = {}
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for code in valid_codes:
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# 指数收盘价和收益率
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if code in index_data and 'close' in index_data[code].columns:
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ic = index_data[code]['close'].dropna()
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ic_a = ic.reindex(a_share_dates, method='ffill')
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index_close_aligned[code] = ic_a
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returns_idx[code] = ic_a.pct_change(fill_method=None)
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# ETF价格和收益率
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etf_code = etf_code_map.get(code)
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if etf_code and code in etf_data:
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df = etf_data[code]
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chfq = df['close_hfq'].dropna()
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chfq_a = chfq.reindex(a_share_dates, method='ffill')
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etf_close_hfq_aligned[code] = chfq_a
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etf_close_hfq_raw[code] = chfq
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returns_etf[f'日收益率_{code}'] = chfq_a.pct_change(fill_method=None)
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ec = df['close'].reindex(a_share_dates, method='ffill')
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etf_close_aligned[code] = ec
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eo = df['open'].reindex(a_share_dates, method='ffill')
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etf_open_aligned[code] = eo
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elif code in index_data and 'close' in index_data[code].columns:
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ic = index_data[code]['close'].dropna()
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ic_a = ic.reindex(a_share_dates, method='ffill')
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returns_etf[f'日收益率_{code}'] = ic_a.pct_change(fill_method=None)
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returns_etf_df = pd.DataFrame(returns_etf)
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# 6.5 溢价率：(ETF收盘价 - 单位净值) / 单位净值
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etf_premium_aligned = {}
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for code in valid_codes:
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if code in etf_nav_data and code in etf_close_aligned:
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nav_raw = etf_nav_data[code]
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nav_raw = nav_raw[~nav_raw.index.duplicated(keep='last')]
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nav = nav_raw.reindex(a_share_dates, method='ffill')
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close = etf_close_aligned[code]
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premium = (close - nav) / nav
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etf_premium_aligned[code] = premium
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# 7. 执行回测获取净值
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print("\n[6] 执行回测...")
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common_dates = signals.index.intersection(returns_etf_df.index)
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signals_aligned = signals.loc[common_dates]
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returns_aligned = returns_etf_df.loc[common_dates]
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executor = BacktestExecutor(
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initial_capital=100000,
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trade_cost=TRADE_COST,
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select_num=SELECT_NUM
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)
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portfolio = executor.execute(signals_aligned, returns_aligned)
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result = portfolio.backtest_result
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nav_series_raw = result['策略净值']
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daily_ret_raw = result['策略日收益率']
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# 扩展到所有common_dates，信号前的日期 nav=1.0, return=0.0
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nav_series = nav_series_raw.reindex(common_dates)
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daily_ret_series = daily_ret_raw.reindex(common_dates, fill_value=0.0)
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first_valid = nav_series.first_valid_index()
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if first_valid is not None:
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nav_series.loc[:first_valid] = nav_series.loc[:first_valid].fillna(1.0)
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nav_series = nav_series.ffill()
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print(f" 终值: {nav_series.iloc[-1]:.4f}")
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# 8. 构建逐日明细
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print("\n[7] 构建逐日明细...")
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# 持仓跟踪状态
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holdings_state = {} # {code: {'entry_date': str, 'entry_price': float}}
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prev_holdings = set()
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days_list = []
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signal_col = 'signal'
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for i, date in enumerate(common_dates):
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sig_val = signals_aligned.loc[date, signal_col] if signal_col in signals_aligned.columns else ''
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current_holdings = set(str(sig_val).split(',')) if pd.notna(sig_val) and sig_val else set()
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current_holdings.discard('')
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# 调仓检测
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added = list(current_holdings - prev_holdings)
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removed = list(prev_holdings - current_holdings)
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is_rebalance = len(added) > 0 or len(removed) > 0
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# 更新持仓状态
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for code in removed:
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holdings_state.pop(code, None)
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for code in added:
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entry_price_etf = None
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entry_price_idx = None
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if code in etf_close_hfq_aligned:
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ep = etf_close_hfq_aligned[code].get(date)
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if pd.notna(ep):
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entry_price_etf = float(ep)
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if code in index_close_aligned:
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ep = index_close_aligned[code].get(date)
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if pd.notna(ep):
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entry_price_idx = float(ep)
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holdings_state[code] = {
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'entry_date': date.strftime('%Y-%m-%d'),
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'entry_price_etf': entry_price_etf,
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'entry_price_idx': entry_price_idx,
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}
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# 动态阈值
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factor_scores = {}
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for code in valid_codes:
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if code in factor_df.columns:
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v = factor_df.loc[date, code] if date in factor_df.index else np.nan
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if pd.notna(v):
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factor_scores[code] = float(v)
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bond_score = factor_scores.get(BOND_CODE)
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if BOND_THRESHOLD.get('enabled') and bond_score is not None and bond_score >= 0:
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threshold = bond_score * BOND_RATIO
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else:
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threshold = 0.0
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# 排名（按动量降序，排除BOND）
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non_bond_scores = {k: v for k, v in factor_scores.items()
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if group_mapping.get(k) != 'BOND'}
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sorted_codes = sorted(non_bond_scores.keys(),
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key=lambda c: non_bond_scores[c], reverse=True)
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rank_map = {c: r + 1 for r, c in enumerate(sorted_codes)}
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# BOND不参与排名
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if BOND_CODE in factor_scores:
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rank_map[BOND_CODE] = None
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# 每标的详情
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assets = {}
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for code in valid_codes:
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asset = {}
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# 指数收盘价
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if code in index_close_aligned:
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v = index_close_aligned[code].get(date)
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asset['index_close'] = safe_val(v, 2)
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else:
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asset['index_close'] = None
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# 动量
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mom = factor_scores.get(code)
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asset['momentum'] = safe_val(mom, 4)
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# 排名
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asset['rank'] = rank_map.get(code)
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# 阈值
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asset['threshold'] = safe_val(threshold, 4)
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asset['above_threshold'] = mom >= threshold if mom is not None else False
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# ETF价格
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if code in etf_close_aligned:
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asset['etf_close'] = safe_val(etf_close_aligned[code].get(date), 3)
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else:
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asset['etf_close'] = None
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if code in etf_open_aligned:
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asset['etf_open'] = safe_val(etf_open_aligned[code].get(date), 3)
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else:
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asset['etf_open'] = None
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if code in etf_close_hfq_aligned:
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asset['etf_close_hfq'] = safe_val(etf_close_hfq_aligned[code].get(date), 4)
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else:
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asset['etf_close_hfq'] = None
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# 溢价率
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if code in etf_premium_aligned:
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asset['premium'] = safe_val(etf_premium_aligned[code].get(date), 4)
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else:
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asset['premium'] = None
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# ETF日收益率
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ret_col = f'日收益率_{code}'
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if ret_col in returns_etf_df.columns:
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asset['etf_return_ctc'] = safe_val(returns_etf_df.loc[date, ret_col], 6)
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else:
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asset['etf_return_ctc'] = None
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# 指数日收益率
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if code in returns_idx:
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asset['index_return'] = safe_val(returns_idx[code].get(date), 6)
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else:
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asset['index_return'] = None
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# 持仓状态
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is_held = code in current_holdings
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asset['is_held'] = is_held
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if is_held and code in holdings_state:
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hs = holdings_state[code]
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asset['entry_date'] = hs['entry_date']
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asset['entry_price_etf'] = safe_val(hs['entry_price_etf'], 4)
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asset['entry_price_idx'] = safe_val(hs['entry_price_idx'], 4)
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entry_dt = pd.Timestamp(hs['entry_date'])
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trading_days_held = len(common_dates[(common_dates >= entry_dt) & (common_dates <= date)])
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asset['holding_days'] = trading_days_held
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# ETF累计收益
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if hs['entry_price_etf'] and hs['entry_price_etf'] > 0:
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cur = etf_close_hfq_aligned[code].get(date) if code in etf_close_hfq_aligned else None
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if cur and pd.notna(cur):
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asset['cum_return_etf'] = safe_val(float(cur) / hs['entry_price_etf'] - 1, 4)
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else:
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asset['cum_return_etf'] = None
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else:
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asset['cum_return_etf'] = None
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# 指数累计收益
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if hs['entry_price_idx'] and hs['entry_price_idx'] > 0:
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cur = index_close_aligned[code].get(date) if code in index_close_aligned else None
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if cur and pd.notna(cur):
|
||||
asset['cum_return_idx'] = safe_val(float(cur) / hs['entry_price_idx'] - 1, 4)
|
||||
else:
|
||||
asset['cum_return_idx'] = None
|
||||
else:
|
||||
asset['cum_return_idx'] = None
|
||||
else:
|
||||
asset['entry_date'] = None
|
||||
asset['entry_price_etf'] = None
|
||||
asset['entry_price_idx'] = None
|
||||
asset['holding_days'] = 0
|
||||
asset['cum_return_etf'] = None
|
||||
asset['cum_return_idx'] = None
|
||||
|
||||
assets[code] = asset
|
||||
|
||||
# 构建当天记录
|
||||
nav_val = nav_series.loc[date] if date in nav_series.index else None
|
||||
ret_val = daily_ret_series.loc[date] if date in daily_ret_series.index else None
|
||||
|
||||
day_record = {
|
||||
'date': date.strftime('%Y-%m-%d'),
|
||||
'nav': safe_val(nav_val, 4),
|
||||
'daily_return': safe_val(ret_val, 6),
|
||||
'is_rebalance': is_rebalance,
|
||||
'holdings': sorted(list(current_holdings)),
|
||||
'added': sorted(added),
|
||||
'removed': sorted(removed),
|
||||
'assets': assets
|
||||
}
|
||||
days_list.append(day_record)
|
||||
prev_holdings = current_holdings
|
||||
|
||||
# 9. 构建元数据
|
||||
codes_meta = {}
|
||||
for code, cfg in CODE_LIST.items():
|
||||
codes_meta[code] = {
|
||||
'name': cfg['name'],
|
||||
'etf': cfg.get('etf'),
|
||||
'market': cfg.get('market')
|
||||
}
|
||||
|
||||
output = {
|
||||
'meta': {
|
||||
'mode': 'B: 指数信号 + ETF收益',
|
||||
'start_date': common_dates[0].strftime('%Y-%m-%d'),
|
||||
'end_date': common_dates[-1].strftime('%Y-%m-%d'),
|
||||
'total_days': len(common_dates),
|
||||
'select_num': SELECT_NUM,
|
||||
'n_days': N_DAYS,
|
||||
'trade_cost': TRADE_COST,
|
||||
'bond_threshold': {
|
||||
'enabled': BOND_THRESHOLD.get('enabled', False),
|
||||
'bond_code': BOND_CODE,
|
||||
'ratio': BOND_RATIO
|
||||
},
|
||||
'codes': codes_meta
|
||||
},
|
||||
'days': days_list
|
||||
}
|
||||
|
||||
# 10. 输出
|
||||
output_path = project_root / 'results' / 'backtest_detail.json'
|
||||
print(f"\n[8] 写入 {output_path}...")
|
||||
with open(output_path, 'w', encoding='utf-8') as f:
|
||||
json.dump(output, f, ensure_ascii=False)
|
||||
|
||||
file_size_mb = output_path.stat().st_size / 1024 / 1024
|
||||
print(f" 大小: {file_size_mb:.1f} MB")
|
||||
print(f" 天数: {len(days_list)}")
|
||||
print(f" 标的: {len(valid_codes)}")
|
||||
print(" 完成!")
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
@@ -62,7 +62,14 @@ class MomentumFactor(FactorBase):
|
||||
if len(prices) < 5:
|
||||
return 0.0
|
||||
|
||||
# 价格下界 clip，防止 log(0) 或 log(负数)
|
||||
prices = np.clip(prices, 0.01, None)
|
||||
y = np.log(prices)
|
||||
|
||||
# 异常值检测
|
||||
if np.any(np.isnan(y)) or np.any(np.isinf(y)):
|
||||
return 0.0
|
||||
|
||||
x = np.arange(len(y))
|
||||
weights = np.linspace(1, 2, len(y))
|
||||
|
||||
|
||||
164
tests/test_trading_calendar.py
Normal file
164
tests/test_trading_calendar.py
Normal file
@@ -0,0 +1,164 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
测试交易日历 API
|
||||
"""
|
||||
|
||||
import sys
|
||||
from pathlib import Path
|
||||
import requests
|
||||
|
||||
# Flask 服务地址
|
||||
FLASK_API_URL = "http://localhost:80"
|
||||
|
||||
def test_calendar_api():
|
||||
"""测试交易日历 API"""
|
||||
print("\n" + "="*80)
|
||||
print("📅 交易日历 API 测试")
|
||||
print("="*80)
|
||||
|
||||
# 测试 1: A 股
|
||||
print("\n[1] 测试 A 股交易日历...")
|
||||
url = f"{FLASK_API_URL}/api/v1/trading-calendar"
|
||||
params = {"market": "A", "start": "2024-01-01", "end": "2024-01-31"}
|
||||
|
||||
try:
|
||||
response = requests.get(url, params=params, timeout=10)
|
||||
if response.status_code == 200:
|
||||
data = response.json()
|
||||
print(f" ✅ 成功: {data['count']} 个交易日")
|
||||
print(f" 市场: {data['market']}")
|
||||
print(f" 交易所: {data['exchange']}")
|
||||
print(f" 日期范围: {data['start']} ~ {data['end']}")
|
||||
print(f" 前5个交易日: {data['trading_dates'][:5]}")
|
||||
else:
|
||||
print(f" ❌ 失败: {response.status_code}")
|
||||
print(f" 响应: {response.json()}")
|
||||
except Exception as e:
|
||||
print(f" ❌ 异常: {e}")
|
||||
|
||||
# 测试 2: 美股
|
||||
print("\n[2] 测试美股交易日历...")
|
||||
params = {"market": "US", "start": "2024-01-01", "end": "2024-01-31"}
|
||||
|
||||
try:
|
||||
response = requests.get(url, params=params, timeout=10)
|
||||
if response.status_code == 200:
|
||||
data = response.json()
|
||||
print(f" ✅ 成功: {data['count']} 个交易日")
|
||||
print(f" 市场: {data['market']}")
|
||||
print(f" 交易所: {data['exchange']}")
|
||||
print(f" 前5个交易日: {data['trading_dates'][:5]}")
|
||||
else:
|
||||
print(f" ❌ 失败: {response.status_code}")
|
||||
print(f" 响应: {response.json()}")
|
||||
except Exception as e:
|
||||
print(f" ❌ 异常: {e}")
|
||||
|
||||
# 测试 3: 港股
|
||||
print("\n[3] 测试港股交易日历...")
|
||||
params = {"market": "HK", "start": "2024-01-01", "end": "2024-01-31"}
|
||||
|
||||
try:
|
||||
response = requests.get(url, params=params, timeout=10)
|
||||
if response.status_code == 200:
|
||||
data = response.json()
|
||||
print(f" ✅ 成功: {data['count']} 个交易日")
|
||||
print(f" 市场: {data['market']}")
|
||||
print(f" 交易所: {data['exchange']}")
|
||||
print(f" 前5个交易日: {data['trading_dates'][:5]}")
|
||||
else:
|
||||
print(f" ❌ 失败: {response.status_code}")
|
||||
print(f" 响应: {response.json()}")
|
||||
except Exception as e:
|
||||
print(f" ❌ 异常: {e}")
|
||||
|
||||
# 测试 4: 日历信息
|
||||
print("\n[4] 测试日历信息...")
|
||||
url_info = f"{FLASK_API_URL}/api/v1/calendar/info"
|
||||
|
||||
try:
|
||||
response = requests.get(url_info, timeout=10)
|
||||
if response.status_code == 200:
|
||||
data = response.json()
|
||||
print(f" ✅ 成功")
|
||||
print(f" 支持的市场:")
|
||||
for market, info in data.get('supported_markets', {}).items():
|
||||
print(f" {market}: {info['name']} ({info['method']})")
|
||||
print(f" pandas_market_calendars: {'✅ 已安装' if data.get('pandas_market_calendars_installed') else '❌ 未安装'}")
|
||||
else:
|
||||
print(f" ❌ 失败: {response.status_code}")
|
||||
except Exception as e:
|
||||
print(f" ❌ 异常: {e}")
|
||||
|
||||
def test_local_fetcher():
|
||||
"""测试本地 UniversalDataFetcher"""
|
||||
print("\n" + "="*80)
|
||||
print("🧪 本地 UniversalDataFetcher 测试")
|
||||
print("="*80)
|
||||
|
||||
sys.path.insert(0, str(Path(__file__).parent.parent))
|
||||
|
||||
try:
|
||||
from datasource.universal_fetcher import UniversalDataFetcher
|
||||
|
||||
fetcher = UniversalDataFetcher()
|
||||
|
||||
# 测试 A 股
|
||||
print("\n[1] A 股交易日历 (2024年)...")
|
||||
cal_a = fetcher.get_trading_calendar('A', '2024-01-01', '2024-12-31')
|
||||
print(f" ✅ {len(cal_a)} 个交易日")
|
||||
print(f" 前5天: {list(cal_a[:5])}")
|
||||
|
||||
# 测试美股
|
||||
print("\n[2] 美股交易日历 (2024年)...")
|
||||
cal_us = fetcher.get_trading_calendar('US', '2024-01-01', '2024-12-31')
|
||||
print(f" ✅ {len(cal_us)} 个交易日")
|
||||
print(f" 前5天: {list(cal_us[:5])}")
|
||||
|
||||
# 测试港股
|
||||
print("\n[3] 港股交易日历 (2024年)...")
|
||||
cal_hk = fetcher.get_trading_calendar('HK', '2024-01-01', '2024-12-31')
|
||||
print(f" ✅ {len(cal_hk)} 个交易日")
|
||||
print(f" 前5天: {list(cal_hk[:5])}")
|
||||
|
||||
# 日历信息
|
||||
print("\n[4] 日历支持信息...")
|
||||
info = fetcher.get_calendar_info()
|
||||
print(f" ✅ 支持 {len(info['supported_markets'])} 个市场")
|
||||
|
||||
except Exception as e:
|
||||
print(f" ❌ 失败: {e}")
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
|
||||
def main():
|
||||
print("\n" + "="*80)
|
||||
print("📅 交易日历功能测试")
|
||||
print("="*80)
|
||||
|
||||
# 测试 1: 本地 fetcher
|
||||
test_local_fetcher()
|
||||
|
||||
# 测试 2: Flask API（如果服务在运行）
|
||||
print("\n" + "="*80)
|
||||
print("🌐 测试 Flask API 端点")
|
||||
print("="*80)
|
||||
print(f"\nAPI 地址: {FLASK_API_URL}")
|
||||
print("注意: 需要 Flask 服务正在运行")
|
||||
|
||||
try:
|
||||
response = requests.get(f"{FLASK_API_URL}/health", timeout=3)
|
||||
if response.status_code == 200:
|
||||
print("✅ Flask 服务可访问")
|
||||
test_calendar_api()
|
||||
else:
|
||||
print(f"⚠️ Flask 服务返回 {response.status_code}，跳过 API 测试")
|
||||
except:
|
||||
print("⚠️ Flask 服务未运行，跳过 API 测试")
|
||||
|
||||
print("\n" + "="*80)
|
||||
print("✅ 测试完成")
|
||||
print("="*80)
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
143
tests/verify_fix_result.py
Normal file
143
tests/verify_fix_result.py
Normal file
@@ -0,0 +1,143 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
验证修复后的回测结果是否与文档一致
|
||||
|
||||
文档预期结果 (Mode A - 指数信号+指数收益):
|
||||
CAGR: 11.80%, 最大回撤: -29.49%, 夏普: 0.818, Calmar: 0.400
|
||||
|
||||
文档预期结果 (Mode B - 指数信号+ETF收益):
|
||||
CAGR: 28.07%, 最大回撤: -13.34%, 夏普: 1.685, Calmar: 2.104
|
||||
"""
|
||||
|
||||
import sys
|
||||
from pathlib import Path
|
||||
project_root = Path(__file__).parent.parent
|
||||
sys.path.insert(0, str(project_root))
|
||||
|
||||
from dotenv import load_dotenv
|
||||
load_dotenv()
|
||||
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
import yaml
|
||||
from datetime import datetime
|
||||
from strategies.rotation.strategy import RotationStrategy
|
||||
|
||||
|
||||
def calculate_metrics(nav: pd.Series) -> dict:
|
||||
"""计算绩效指标"""
|
||||
start_date = nav.index[0]
|
||||
end_date = nav.index[-1]
|
||||
days = (end_date - start_date).days
|
||||
years = days / 365
|
||||
|
||||
total_return = nav.iloc[-1] - 1
|
||||
cagr = (nav.iloc[-1] / nav.iloc[0]) ** (1/years) - 1
|
||||
|
||||
daily_ret = nav.pct_change().dropna()
|
||||
sharpe = daily_ret.mean() / daily_ret.std() * np.sqrt(252) if daily_ret.std() > 0 else 0
|
||||
|
||||
peak = nav.cummax()
|
||||
drawdown = (nav - peak) / peak
|
||||
max_dd = drawdown.min()
|
||||
|
||||
calmar = cagr / abs(max_dd) if max_dd != 0 else 0
|
||||
win_rate = (daily_ret > 0).sum() / len(daily_ret)
|
||||
|
||||
return {
|
||||
'start_date': start_date.strftime('%Y-%m-%d'),
|
||||
'end_date': end_date.strftime('%Y-%m-%d'),
|
||||
'years': years,
|
||||
'days': len(nav),
|
||||
'total_return': total_return,
|
||||
'cagr': cagr,
|
||||
'max_dd': max_dd,
|
||||
'sharpe': sharpe,
|
||||
'calmar': calmar,
|
||||
'win_rate': win_rate
|
||||
}
|
||||
|
||||
|
||||
def main():
|
||||
# 加载配置
|
||||
config_path = project_root / 'strategies/rotation/config.yaml'
|
||||
with open(config_path, 'r') as f:
|
||||
config = yaml.safe_load(f)
|
||||
|
||||
# 设置回测区间（文档中的测试区间）
|
||||
config['start_date'] = '2020-01-02'
|
||||
config['end_date'] = '2026-05-19'
|
||||
|
||||
print('='*70)
|
||||
print('修复后回测结果验证')
|
||||
print('='*70)
|
||||
print(f'回测区间: {config["start_date"]} ~ {config["end_date"]}')
|
||||
|
||||
# 初始化策略
|
||||
strategy = RotationStrategy(config)
|
||||
|
||||
# 获取数据并执行回测
|
||||
print('\n获取数据...')
|
||||
data = strategy.get_data(use_flask_api=False)
|
||||
|
||||
print('\n执行回测...')
|
||||
result = strategy.run_backtest(data=data)
|
||||
|
||||
if result.get('result') is None:
|
||||
print('❌ 回测未生成结果')
|
||||
return
|
||||
|
||||
# 计算指标
|
||||
nav = result['result']['策略净值']
|
||||
metrics = calculate_metrics(nav)
|
||||
|
||||
# 输出结果
|
||||
print('\n' + '='*70)
|
||||
print('修复后回测结果')
|
||||
print('='*70)
|
||||
print(f"回测区间: {metrics['start_date']} ~ {metrics['end_date']}")
|
||||
print(f"回测年数: {metrics['years']:.2f} 年")
|
||||
print(f"交易天数: {metrics['days']} 天")
|
||||
print('-'*70)
|
||||
print(f"CAGR: {metrics['cagr']:.2%}")
|
||||
print(f"最大回撤: {metrics['max_dd']:.2%}")
|
||||
print(f"夏普比率: {metrics['sharpe']:.3f}")
|
||||
print(f"Calmar比率: {metrics['calmar']:.3f}")
|
||||
print(f"日胜率: {metrics['win_rate']:.2%}")
|
||||
print(f"累计收益: {metrics['total_return']:.2%}")
|
||||
print(f"调仓次数: {len(result.get('rebalance_events', []))} 次")
|
||||
print('='*70)
|
||||
|
||||
# 文档预期结果对比
|
||||
print('\n' + '='*70)
|
||||
print('文档预期结果对比')
|
||||
print('='*70)
|
||||
print("\nMode A (指数信号 → 指数收益):")
|
||||
print(" 预期: CAGR 11.80%, MaxDD -29.49%, Sharpe 0.818, Calmar 0.400")
|
||||
|
||||
print("\nMode B (指数信号 → ETF收益):")
|
||||
print(" 预期: CAGR 28.07%, MaxDD -13.34%, Sharpe 1.685, Calmar 2.104")
|
||||
|
||||
# 判断当前模式
|
||||
print('\n' + '-'*70)
|
||||
cagr_diff_a = abs(metrics['cagr'] - 0.1180)
|
||||
cagr_diff_b = abs(metrics['cagr'] - 0.2807)
|
||||
|
||||
if cagr_diff_a < 0.03:
|
||||
print(f"✓ 当前结果接近 Mode A (CAGR差异: {cagr_diff_a:.2%})")
|
||||
print(" 说明: 当前回测使用指数收盘价计算收益")
|
||||
elif cagr_diff_b < 0.03:
|
||||
print(f"✓ 当前结果接近 Mode B (CAGR差异: {cagr_diff_b:.2%})")
|
||||
print(" 说明: 当前回测使用ETF价格计算收益")
|
||||
else:
|
||||
print(f"⚠ 当前结果与文档预期有差异")
|
||||
print(f" Mode A CAGR差异: {cagr_diff_a:.2%}")
|
||||
print(f" Mode B CAGR差异: {cagr_diff_b:.2%}")
|
||||
|
||||
print('='*70)
|
||||
|
||||
return metrics
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
208
tests/verify_mode_b.py
Normal file
208
tests/verify_mode_b.py
Normal file
@@ -0,0 +1,208 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
验证 Mode B: 指数信号 → ETF收益
|
||||
|
||||
文档预期结果:
|
||||
CAGR: 28.07%, 最大回撤: -13.34%, 夏普: 1.685, Calmar: 2.104
|
||||
"""
|
||||
|
||||
import sys
|
||||
from pathlib import Path
|
||||
project_root = Path(__file__).parent.parent
|
||||
sys.path.insert(0, str(project_root))
|
||||
|
||||
from dotenv import load_dotenv
|
||||
load_dotenv()
|
||||
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
import yaml
|
||||
from datetime import datetime
|
||||
from strategies.rotation.strategy import RotationStrategy
|
||||
|
||||
|
||||
def calculate_metrics(nav: pd.Series) -> dict:
|
||||
"""计算绩效指标"""
|
||||
start_date = nav.index[0]
|
||||
end_date = nav.index[-1]
|
||||
days = (end_date - start_date).days
|
||||
years = days / 365
|
||||
|
||||
total_return = nav.iloc[-1] - 1
|
||||
cagr = (nav.iloc[-1] / nav.iloc[0]) ** (1/years) - 1
|
||||
|
||||
daily_ret = nav.pct_change().dropna()
|
||||
sharpe = daily_ret.mean() / daily_ret.std() * np.sqrt(252) if daily_ret.std() > 0 else 0
|
||||
|
||||
peak = nav.cummax()
|
||||
drawdown = (nav - peak) / peak
|
||||
max_dd = drawdown.min()
|
||||
|
||||
calmar = cagr / abs(max_dd) if max_dd != 0 else 0
|
||||
win_rate = (daily_ret > 0).sum() / len(daily_ret)
|
||||
|
||||
return {
|
||||
'start_date': start_date.strftime('%Y-%m-%d'),
|
||||
'end_date': end_date.strftime('%Y-%m-%d'),
|
||||
'years': years,
|
||||
'days': len(nav),
|
||||
'total_return': total_return,
|
||||
'cagr': cagr,
|
||||
'max_dd': max_dd,
|
||||
'sharpe': sharpe,
|
||||
'calmar': calmar,
|
||||
'win_rate': win_rate
|
||||
}
|
||||
|
||||
|
||||
def run_mode_b_backtest(data: dict, signals: pd.DataFrame, valid_codes: list,
|
||||
etf_code_map: dict, a_share_dates: pd.DatetimeIndex,
|
||||
trade_cost: float, select_num: int) -> dict:
|
||||
"""
|
||||
Mode B: 使用ETF价格计算收益
|
||||
|
||||
Args:
|
||||
data: 包含 etf_data 的数据字典
|
||||
signals: 指数生成的信号
|
||||
valid_codes: 指数代码列表
|
||||
etf_code_map: {指数代码: ETF代码} 映射
|
||||
a_share_dates: A股交易日历
|
||||
trade_cost: 交易成本
|
||||
select_num: 选股数量
|
||||
"""
|
||||
from framework.execution import BacktestExecutor
|
||||
|
||||
etf_data = data.get('etf_data')
|
||||
if etf_data is None:
|
||||
print("❌ ETF数据不可用")
|
||||
return {'result': None}
|
||||
|
||||
# 将信号对齐到 A 股日历
|
||||
if a_share_dates is not signals.index:
|
||||
signals = signals.reindex(a_share_dates, method='ffill').dropna(subset=[signals.columns[0]])
|
||||
|
||||
# 使用ETF收盘价计算收益率
|
||||
returns_data = {}
|
||||
for code in valid_codes:
|
||||
etf_code = etf_code_map.get(code)
|
||||
if etf_code and etf_code in etf_data.columns:
|
||||
etf_close = etf_data[etf_code].dropna()
|
||||
# 对齐到A股日历
|
||||
etf_aligned = etf_close.reindex(a_share_dates, method='ffill')
|
||||
returns_aligned = etf_aligned.pct_change(fill_method=None)
|
||||
# 使用指数代码作为列名（与信号匹配）
|
||||
returns_data[f'日收益率_{code}'] = returns_aligned
|
||||
else:
|
||||
# 没有ETF映射的标的，回退使用指数数据
|
||||
index_data = data.get('index_data', {})
|
||||
if code in index_data and 'close' in index_data[code].columns:
|
||||
close_series = index_data[code]['close'].dropna()
|
||||
close_aligned = close_series.reindex(a_share_dates, method='ffill')
|
||||
returns_data[f'日收益率_{code}'] = close_aligned.pct_change(fill_method=None)
|
||||
|
||||
returns_df = pd.DataFrame(returns_data)
|
||||
|
||||
# 对齐日期
|
||||
common_dates = signals.index.intersection(returns_df.index)
|
||||
signals = signals.loc[common_dates]
|
||||
returns_df = returns_df.loc[common_dates]
|
||||
|
||||
print(f" Mode B 对齐后日期: {len(common_dates)} 天")
|
||||
print(f" 使用ETF计算收益: {len([c for c in valid_codes if etf_code_map.get(c)])} 只")
|
||||
|
||||
executor = BacktestExecutor(
|
||||
initial_capital=100000,
|
||||
trade_cost=trade_cost,
|
||||
select_num=select_num
|
||||
)
|
||||
|
||||
portfolio = executor.execute(signals, returns_df)
|
||||
|
||||
if hasattr(portfolio, 'backtest_result'):
|
||||
return {'result': portfolio.backtest_result, 'portfolio': portfolio}
|
||||
|
||||
return {'result': None}
|
||||
|
||||
|
||||
def main():
|
||||
# 加载配置
|
||||
config_path = project_root / 'strategies/rotation/config.yaml'
|
||||
with open(config_path, 'r') as f:
|
||||
config = yaml.safe_load(f)
|
||||
|
||||
# 设置回测区间
|
||||
config['start_date'] = '2020-01-02'
|
||||
config['end_date'] = '2026-05-19'
|
||||
|
||||
print('='*70)
|
||||
print('Mode B 验证: 指数信号 → ETF收益')
|
||||
print('='*70)
|
||||
|
||||
# 初始化策略
|
||||
strategy = RotationStrategy(config)
|
||||
|
||||
# 获取数据
|
||||
print('\n获取数据...')
|
||||
data = strategy.get_data(use_flask_api=False)
|
||||
|
||||
# 计算因子（使用指数数据）
|
||||
print('\n计算因子（指数信号）...')
|
||||
factor_df = strategy.compute_factors(data)
|
||||
|
||||
# 生成信号
|
||||
print('\n生成信号...')
|
||||
signals = strategy.generate_signals(factor_df)
|
||||
|
||||
# 执行 Mode B 回测
|
||||
print('\n执行 Mode B 回测（ETF收益）...')
|
||||
result_b = run_mode_b_backtest(
|
||||
data=data,
|
||||
signals=signals,
|
||||
valid_codes=data['valid_codes'],
|
||||
etf_code_map=data['etf_code_map'],
|
||||
a_share_dates=data.get('a_share_dates'),
|
||||
trade_cost=config.get('trade_cost', 0.001),
|
||||
select_num=config.get('select_num', 3)
|
||||
)
|
||||
|
||||
if result_b.get('result') is None:
|
||||
print('❌ Mode B 回测未生成结果')
|
||||
return
|
||||
|
||||
# 计算指标
|
||||
nav_b = result_b['result']['策略净值']
|
||||
metrics_b = calculate_metrics(nav_b)
|
||||
|
||||
# 输出结果
|
||||
print('\n' + '='*70)
|
||||
print('Mode B 回测结果')
|
||||
print('='*70)
|
||||
print(f"回测区间: {metrics_b['start_date']} ~ {metrics_b['end_date']}")
|
||||
print(f"回测年数: {metrics_b['years']:.2f} 年")
|
||||
print(f"交易天数: {metrics_b['days']} 天")
|
||||
print('-'*70)
|
||||
print(f"CAGR: {metrics_b['cagr']:.2%}")
|
||||
print(f"最大回撤: {metrics_b['max_dd']:.2%}")
|
||||
print(f"夏普比率: {metrics_b['sharpe']:.3f}")
|
||||
print(f"Calmar比率: {metrics_b['calmar']:.3f}")
|
||||
print(f"日胜率: {metrics_b['win_rate']:.2%}")
|
||||
print(f"累计收益: {metrics_b['total_return']:.2%}")
|
||||
print('='*70)
|
||||
|
||||
# 文档预期对比
|
||||
print('\n文档预期 (Mode B):')
|
||||
print(' CAGR: 28.07%, MaxDD -13.34%, Sharpe 1.685, Calmar 2.104')
|
||||
|
||||
cagr_diff = abs(metrics_b['cagr'] - 0.2807)
|
||||
print(f'\nCAGR差异: {cagr_diff:.2%}')
|
||||
|
||||
if cagr_diff < 0.05:
|
||||
print('✓ 结果与文档预期基本一致')
|
||||
else:
|
||||
print('⚠ 结果与文档预期有差异')
|
||||
|
||||
return metrics_b
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
Reference in New Issue
Block a user