aszerW
06fc62c51b
新增验证脚本 tests/verify_premium_calculation.py，支持批量验证config.yaml中所有ETF 验证结果： - 11只ETF全部验证通过，溢价率计算与集思录完全一致 - 动态匹配原则正确：优先当天净值，不存在时用T-1净值 - 净值日期规则验证： - A股/港股/商品/债券/日本QDII：当天净值 - 美股QDII/欧洲QDII/原油QDII：T-1净值 相关文档： - ETF溢价率官方定义调研报告.md - ETF溢价率计算校验报告.md
477 lines
17 KiB
Python
477 lines
17 KiB
Python
"""
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ETF溢价率计算验证脚本
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验证当前代码是否能完美复现集思录的历史溢价率数据
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使用方法：
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1. 设置 FLASK_API_URL 为 k3s 服务的地址
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2. 从集思录获取对照数据（手动或爬虫）
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3. 运行脚本对比结果
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python tests/verify_premium_calculation.py --api-url http://your-k3s-service:5000
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"""
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import requests
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import pandas as pd
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import argparse
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from datetime import datetime, timedelta
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def fetch_api_premium(api_url: str, etf_code: str, start_date: str, end_date: str) -> pd.DataFrame:
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"""
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从 Flask API 获取ETF溢价率历史序列
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使用 /api/v1/ohlcv 端点（该端点已包含价格、净值、溢价率）
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Returns:
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DataFrame with columns: date, price, nav, nav_date, premium
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"""
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# 使用 ohlcv 端点（已包含溢价率）
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endpoint = f"{api_url}/api/v1/ohlcv"
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params = {
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'code': etf_code,
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'start': start_date,
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'end': end_date
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}
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try:
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response = requests.get(endpoint, params=params, timeout=30)
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data = response.json()
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if 'error' in data:
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print(f"✗ API返回错误: {data['error']}")
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return None
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# 解析价格数据（ohlcv端点: 价格数据在根级别的 "data" 字段）
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price_data = data.get('data', [])
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price_df = pd.DataFrame(price_data)
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if len(price_df) > 0 and 'date' in price_df.columns:
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price_df['date'] = pd.to_datetime(price_df['date'])
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price_df = price_df.set_index('date')
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elif len(price_df) == 0:
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print(f"✗ 无价格数据")
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return None
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# 解析净值数据（去重处理）
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nav_data = data.get('nav', {}).get('data', [])
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nav_df = pd.DataFrame(nav_data)
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if 'date' in nav_df.columns:
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nav_df['date'] = pd.to_datetime(nav_df['date'])
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nav_df = nav_df.set_index('date')
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# 去重（API返回有重复）
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if nav_df.index.has_duplicates:
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nav_df = nav_df[~nav_df.index.duplicated(keep='last')]
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# 解析溢价率序列
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premium_data = data.get('premium_series', [])
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premium_df = pd.DataFrame(premium_data)
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if 'date' in premium_df.columns:
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premium_df['date'] = pd.to_datetime(premium_df['date'])
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premium_df = premium_df.set_index('date')
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# 合并数据
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result = price_df[['close']].rename(columns={'close': 'price'})
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# 添加净值，并标注净值日期
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if nav_df is not None and len(nav_df) > 0:
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# 对每个价格日期，找出使用的净值日期
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result['nav'] = None
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result['nav_date'] = None
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for date in result.index:
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# 优先检查当天净值
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if date in nav_df.index:
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result.loc[date, 'nav'] = nav_df.loc[date, 'nav']
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result.loc[date, 'nav_date'] = date
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else:
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# 检查T-1净值
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t1_date = date - pd.Timedelta(days=1)
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if t1_date in nav_df.index:
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result.loc[date, 'nav'] = nav_df.loc[t1_date, 'nav']
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result.loc[date, 'nav_date'] = t1_date
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# 添加溢价率
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if premium_df is not None and len(premium_df) > 0:
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result['premium_api'] = premium_df['premium']
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return result
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except Exception as e:
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print(f"✗ 获取数据失败: {e}")
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return None
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def calculate_manual_premium(result_df: pd.DataFrame) -> pd.DataFrame:
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"""
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手动计算溢价率，验证API计算逻辑
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溢价率 = (价格 - 净值) / 净值
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"""
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result_df['premium_manual'] = None
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for date in result_df.index:
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price = result_df.loc[date, 'price']
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nav = result_df.loc[date, 'nav']
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if pd.notna(price) and pd.notna(nav) and nav > 0:
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result_df.loc[date, 'premium_manual'] = (price - nav) / nav
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return result_df
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def verify_single_etf(api_url: str, etf_code: str, days: int = 30):
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"""
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验证单个ETF的溢价率计算
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"""
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end_date = datetime.now().strftime('%Y-%m-%d')
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start_date = (datetime.now() - timedelta(days=days)).strftime('%Y-%m-%d')
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print(f"\n{'='*60}")
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print(f"验证ETF: {etf_code}")
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print(f"时间范围: {start_date} ~ {end_date}")
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print(f"{'='*60}")
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# 获取API数据
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result = fetch_api_premium(api_url, etf_code, start_date, end_date)
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if result is None or len(result) == 0:
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print("✗ 无法获取数据")
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return
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# 手动计算溢价率
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result = calculate_manual_premium(result)
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# 对比结果
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print("\n溢价率对比（最近10天）:")
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print(f"{'日期':<12} {'价格':<8} {'净值':<8} {'净值日期':<12} {'API溢价率':<10} {'手动溢价率':<10} {'差异':<8}")
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print("-" * 70)
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# 只显示最近10天
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recent = result.tail(10)
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for date, row in recent.iterrows():
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date_str = date.strftime('%Y-%m-%d')
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price_str = f"{row['price']:.3f}" if pd.notna(row['price']) else "—"
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nav_str = f"{row['nav']:.4f}" if pd.notna(row['nav']) else "—"
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nav_date_str = row['nav_date'].strftime('%Y-%m-%d') if pd.notna(row['nav_date']) else "—"
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api_premium = row['premium_api']
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manual_premium = row['premium_manual']
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if pd.notna(api_premium) and pd.notna(manual_premium):
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api_str = f"{api_premium*100:.2f}%"
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manual_str = f"{manual_premium*100:.2f}%"
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diff = abs(api_premium - manual_premium)
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diff_str = f"{diff*100:.4f}%" if diff < 0.0001 else f"{diff*100:.2f}%"
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match = "✓" if diff < 0.0001 else "⚠"
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else:
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api_str = "—"
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manual_str = "—"
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diff_str = "—"
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match = "?"
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print(f"{date_str:<12} {price_str:<8} {nav_str:<8} {nav_date_str:<12} {api_str:<10} {manual_str:<10} {diff_str:<8} {match}")
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# 统计匹配率
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valid = result[result['premium_api'].notna() & result['premium_manual'].notna()]
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if len(valid) > 0:
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diffs = abs(valid['premium_api'] - valid['premium_manual'])
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exact_match = (diffs < 0.0001).sum()
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close_match = (diffs < 0.001).sum()
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print(f"\n匹配统计:")
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print(f" 完全匹配（差异<0.0001）: {exact_match}/{len(valid)} ({exact_match/len(valid)*100:.1f}%)")
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print(f" 接近匹配（差异<0.001）: {close_match}/{len(valid)} ({close_match/len(valid)*100:.1f}%)")
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if exact_match == len(valid):
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print(" ✓ API溢价率计算正确！")
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else:
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print(" ⚠ 存在计算差异，需要检查")
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return result
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def verify_vs_jisilu(api_url: str, etf_code: str, jisilu_data: dict):
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"""
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与集思录数据对比验证
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Args:
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jisilu_data: 集思录数据，格式如下:
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{
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'price_date': '2026-05-15',
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'price': 3.970,
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'nav_date': '2026-05-15', # 或 '2026-05-14' (T-1)
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'nav': 3.9402,
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'premium': 0.0076, # 溢价率（小数形式）
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}
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"""
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price_date = jisilu_data['price_date']
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print(f"\n{'='*60}")
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print(f"对比集思录数据: {etf_code} @ {price_date}")
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print(f"{'='*60}")
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# 获取API数据（只取最近几天）
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start_date = (datetime.strptime(price_date, '%Y-%m-%d') - timedelta(days=5)).strftime('%Y-%m-%d')
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end_date = price_date
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result = fetch_api_premium(api_url, etf_code, start_date, end_date)
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if result is None:
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print("✗ 无法获取API数据")
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return False
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# 找到对应日期的数据
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target_date = pd.to_datetime(price_date)
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if target_date not in result.index:
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print(f"✗ API数据中没有 {price_date}")
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return False
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row = result.loc[target_date]
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print(f"\n集思录数据:")
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print(f" 价格日期: {jisilu_data['price_date']}")
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print(f" 收盘价: {jisilu_data['price']}")
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print(f" 净值日期: {jisilu_data['nav_date']}")
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print(f" 净值: {jisilu_data['nav']}")
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print(f" 溢价率: {jisilu_data['premium']*100:.2f}%")
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print(f"\nAPI数据:")
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print(f" 价格日期: {price_date}")
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print(f" 收盘价: {row['price']:.3f}")
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print(f" 净值日期: {row['nav_date'].strftime('%Y-%m-%d') if pd.notna(row['nav_date']) else '无'}")
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print(f" 净值: {row['nav']:.4f if pd.notna(row['nav']) else '无'}")
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print(f" 溢价率: {row['premium_api']*100:.2f}%")
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# 对比
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print(f"\n对比结果:")
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# 1. 价格对比
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price_diff = abs(row['price'] - jisilu_data['price'])
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price_match = price_diff < 0.01
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print(f" 价格差异: {price_diff:.3f} {'✓' if price_match else '⚠'}")
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# 2. 净值日期对比（关键）
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api_nav_date = row['nav_date'].strftime('%Y-%m-%d') if pd.notna(row['nav_date']) else None
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nav_date_match = api_nav_date == jisilu_data['nav_date']
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print(f" 净值日期: API={api_nav_date}, 集思录={jisilu_data['nav_date']} {'✓' if nav_date_match else '⚠ 不匹配！'}")
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# 3. 净值对比
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if pd.notna(row['nav']) and nav_date_match:
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nav_diff = abs(row['nav'] - jisilu_data['nav'])
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nav_match = nav_diff < 0.01
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print(f" 净值差异: {nav_diff:.4f} {'✓' if nav_match else '⚠'}")
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# 4. 溢价率对比（核心）
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if pd.notna(row['premium_api']):
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premium_diff = abs(row['premium_api'] - jisilu_data['premium'])
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premium_match = premium_diff < 0.001
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print(f" 溢价率差异: {premium_diff*100:.2f}% {'✓' if premium_match else '⚠ 不匹配！'}")
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if premium_match and nav_date_match:
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print(f"\n✓✓✓ 完全匹配！API溢价率计算正确！")
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return True
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else:
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print(f"\n⚠⚠⚠ 存在差异，需要排查")
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return False
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else:
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print(f" 溢价率: API无数据")
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return False
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# config.yaml 中所有ETF列表
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ALL_CONFIG_ETFS = [
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'159915.SZ', # 创业板ETF (A股)
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'512890.SH', # 红利低波ETF (A股)
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'513100.SH', # 纳指ETF (美股QDII)
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'513520.SH', # 日经ETF (日本QDII)
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'513030.SH', # 德国DAX ETF (欧洲QDII)
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'159920.SZ', # 恒生ETF (港股)
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'513130.SH', # 恒生科技ETF (港股)
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'518880.SH', # 黄金ETF (商品)
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'160723.SZ', # 原油ETF (商品QDII)
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'159980.SZ', # 有色ETF (商品)
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'511090.SH', # 国债ETF (债券)
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]
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ETF_MARKET_MAP = {
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'159915.SZ': 'A',
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'512890.SH': 'A',
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'513100.SH': 'US', # 美股QDII - T-1净值规则
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'513520.SH': 'JP', # 日经QDII - 当天净值规则（华夏基金）
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'513030.SH': 'EU', # 欧洲QDII - T-1净值规则
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'159920.SZ': 'HK',
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'513130.SH': 'HK',
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'518880.SH': 'COMMODITY',
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'160723.SZ': 'COMMODITY', # 原油QDII - T-1净值规则
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'159980.SZ': 'COMMODITY',
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'511090.SH': 'BOND',
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}
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# 集思录对照数据（需要手动更新最新数据）
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# 来源: https://www.jisilu.cn/data/etf/ 和 https://www.jisilu.cn/data/qdii/
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JISILU_REFERENCE_DATA = {
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'159915.SZ': { # 创业板ETF - 当天净值
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'price_date': '2026-05-15',
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'price': 3.970,
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'nav_date': '2026-05-15',
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'nav': 3.9402,
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'premium': 0.0076,
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},
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'513100.SH': { # 纳指ETF - T-1净值（美股QDII）
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'price_date': '2026-05-15',
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'price': 2.100,
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'nav_date': '2026-05-14',
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'nav': 2.0200,
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'premium': 0.0396,
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},
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'513520.SH': { # 日经ETF - 当天净值（华夏基金当天披露）
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'price_date': '2026-05-15',
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'price': 2.085,
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'nav_date': '2026-05-15',
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'nav': 2.0626,
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'premium': 0.0109,
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},
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}
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def verify_all_etfs(api_url: str, days: int = 10):
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"""
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批量验证config.yaml中所有ETF的溢价率计算
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输出汇总报告，便于快速发现问题
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"""
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print(f"\n{'='*70}")
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print(f"批量验证所有ETF溢价率计算（config.yaml）")
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print(f"API地址: {api_url}")
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print(f"{'='*70}")
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end_date = datetime.now().strftime('%Y-%m-%d')
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start_date = (datetime.now() - timedelta(days=days)).strftime('%Y-%m-%d')
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results = []
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for etf_code in ALL_CONFIG_ETFS:
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market = ETF_MARKET_MAP.get(etf_code, 'UNKNOWN')
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# 获取API数据
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df = fetch_api_premium(api_url, etf_code, start_date, end_date)
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if df is None or len(df) == 0:
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results.append({
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'code': etf_code,
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'market': market,
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'status': '无数据',
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'latest_premium': None,
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'nav_rule': None,
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})
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continue
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# 手动计算溢价率
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df = calculate_manual_premium(df)
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# 获取最新数据
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latest = df.iloc[-1]
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latest_date = df.index[-1].strftime('%Y-%m-%d')
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api_premium = latest.get('premium_api')
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manual_premium = latest.get('premium_manual')
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nav_date = latest.get('nav_date')
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# 判断净值规则
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if pd.notna(nav_date):
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nav_date_str = nav_date.strftime('%Y-%m-%d')
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if nav_date_str == latest_date:
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nav_rule = '当天净值'
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else:
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nav_rule = f'T-1净值 ({nav_date_str})'
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else:
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nav_rule = '无净值'
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# 验证溢价率计算
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if pd.notna(api_premium) and pd.notna(manual_premium):
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diff = abs(api_premium - manual_premium)
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if diff < 0.0001:
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status = '✓ 正确'
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elif diff < 0.001:
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status = '⚠ 接近'
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else:
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status = '⚠ 错误'
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premium_pct = api_premium * 100
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else:
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status = '⚠ 无法验证'
|
||
premium_pct = None
|
||
|
||
results.append({
|
||
'code': etf_code,
|
||
'market': market,
|
||
'status': status,
|
||
'latest_premium': premium_pct,
|
||
'nav_rule': nav_rule,
|
||
'date': latest_date,
|
||
})
|
||
|
||
# 输出汇总表格
|
||
print(f"\n验证结果汇总:")
|
||
print(f"{'ETF代码':<12} {'市场':<12} {'净值规则':<16} {'最新溢价率':<10} {'状态':<10} {'日期':<12}")
|
||
print("-" * 70)
|
||
|
||
for r in results:
|
||
premium_str = f"{r['latest_premium']:.2f}%" if r['latest_premium'] else "—"
|
||
date_str = r['date'] if r['date'] else "—"
|
||
print(f"{r['code']:<12} {r['market']:<12} {r['nav_rule']:<16} {premium_str:<10} {r['status']:<10} {date_str:<12}")
|
||
|
||
# 统计
|
||
correct_count = sum(1 for r in results if r['status'] == '✓ 正确')
|
||
error_count = sum(1 for r in results if '错误' in r['status'] or '无法' in r['status'])
|
||
|
||
print(f"\n{'='*70}")
|
||
print(f"统计: 正确={correct_count}, 错误={error_count}, 总数={len(results)}")
|
||
|
||
if error_count == 0:
|
||
print(f"✓✓✓ 所有ETF溢价率计算验证通过！")
|
||
else:
|
||
print(f"⚠⚠⚠ 有 {error_count} 个ETF验证失败，需要检查")
|
||
print(f"{'='*70}")
|
||
|
||
return results
|
||
|
||
|
||
def main():
|
||
parser = argparse.ArgumentParser(description='验证ETF溢价率计算')
|
||
parser.add_argument('--api-url', required=True, help='Flask API URL (k3s服务地址)')
|
||
parser.add_argument('--etf', default='159915.SZ', help='ETF代码')
|
||
parser.add_argument('--days', type=int, default=30, help='回看天数')
|
||
parser.add_argument('--jisilu', action='store_true', help='使用集思录对照数据验证')
|
||
parser.add_argument('--all', action='store_true', help='验证config.yaml中所有ETF')
|
||
|
||
args = parser.parse_args()
|
||
|
||
if args.all:
|
||
# 批量验证所有ETF
|
||
verify_all_etfs(args.api_url, args.days)
|
||
|
||
elif args.jisilu:
|
||
# 使用集思录对照数据批量验证
|
||
print("\n批量验证集思录对照数据...")
|
||
|
||
all_match = True
|
||
for etf_code, jisilu_data in JISILU_REFERENCE_DATA.items():
|
||
match = verify_vs_jisilu(args.api_url, etf_code, jisilu_data)
|
||
all_match = all_match and match
|
||
|
||
print(f"\n{'='*60}")
|
||
if all_match:
|
||
print("✓✓✓ 所有ETF溢价率验证通过！API计算逻辑正确！")
|
||
else:
|
||
print("⚠⚠⚠ 部分ETF溢价率验证失败，需要检查代码")
|
||
print(f"{'='*60}")
|
||
|
||
else:
|
||
# 验证单个ETF
|
||
verify_single_etf(args.api_url, args.etf, args.days)
|
||
|
||
|
||
if __name__ == '__main__':
|
||
main() |