aszerW
ac022020c7
将分析/测试/实验脚本从核心目录移出： - enrich_etf_data.py → scripts/ - oil_tracking.py → analysis/ - tracking_error_full.py → analysis/ - tracking_error_validation.py → analysis/ - test_start_year_analysis.py → experiments/ - experiment_select_num.py → experiments/ rotation/ 目录现在只保留核心策略代码： - simple_rotation.py (策略主逻辑) - config_loader.py (配置加载) - config_simple.yaml (配置文件) - daily_scheduler.py (调度器)
290 lines
9.3 KiB
Python
290 lines
9.3 KiB
Python
"""
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ETF跟踪误差计算与校验
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- 使用Tushare数据计算ETF跟踪误差（基于NAV）
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- 与天天基金数据对比校验
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"""
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import os
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import sys
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import time
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import json
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import pandas as pd
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import numpy as np
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from pathlib import Path
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from datetime import datetime, timedelta
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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(PROJECT_ROOT / '.env')
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import tushare as ts
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# 轮动策略标的池
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POOL_INDEX_MAP = {
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'399006.SZ': {
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'name': '创业板指', 'current_etf': '159915.SZ', 'group': 'A',
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'index_code': '399006.SZ',
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},
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'H30269.CSI': {
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'name': '红利低波', 'current_etf': '512890.SH', 'group': 'A',
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'index_code': 'H30269.CSI',
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},
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}
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def get_etf_nav_data(pro, etf_code, start_date, end_date):
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"""
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获取ETF净值数据（使用fund_nav接口）
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注意：ETF应使用accum_nav（累计净值）而非unit_nav（单位净值）
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"""
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try:
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df = pro.fund_nav(
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ts_code=etf_code,
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start_date=start_date.replace('-', ''),
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end_date=end_date.replace('-', '')
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)
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if df is not None and len(df) > 0:
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df['date'] = pd.to_datetime(df['nav_date'])
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df = df.set_index('date').sort_index()
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# 使用累计净值
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return df['accum_nav'].astype(float)
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except Exception as e:
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print(f" 获取 {etf_code} NAV失败: {e}")
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return None
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def get_index_data(pro, index_code, start_date, end_date):
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"""获取指数日线数据"""
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try:
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df = pro.index_daily(
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ts_code=index_code,
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start_date=start_date.replace('-', ''),
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end_date=end_date.replace('-', '')
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)
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if df is not None and len(df) > 0:
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df['date'] = pd.to_datetime(df['trade_date'])
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df = df.set_index('date').sort_index()
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return df['close'].astype(float)
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except Exception as e:
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print(f" 获取指数 {index_code} 失败: {e}")
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return None
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def calculate_tracking_error(etf_nav, index_close):
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"""
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计算跟踪误差
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公式：STDEV(每日偏离度) × √252
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每日偏离度 = ETF净值收益率 - 指数收益率
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"""
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if etf_nav is None or index_close is None:
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return None
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# 计算收益率
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etf_ret = etf_nav.pct_change().dropna()
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idx_ret = index_close.pct_change().dropna()
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# 对齐日期
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common = etf_ret.index.intersection(idx_ret.index)
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if len(common) < 20:
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return None
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e = etf_ret.loc[common]
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i = idx_ret.loc[common]
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# 每日偏离度
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daily_deviation = e - i
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# 跟踪误差 = 标准差 × √252
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tracking_error = daily_deviation.std() * np.sqrt(252)
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# 其他指标
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correlation = e.corr(i)
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r_squared = correlation ** 2
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# 累计收益
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etf_cum = (1 + e).prod() - 1
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idx_cum = (1 + i).prod() - 1
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excess = etf_cum - idx_cum
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return {
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'annual_tracking_error': round(tracking_error * 100, 4), # %
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'correlation': round(correlation, 6),
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'r_squared': round(r_squared, 6),
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'etf_cum_return': round(etf_cum * 100, 2), # %
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'index_cum_return': round(idx_cum * 100, 2), # %
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'excess_return': round(excess * 100, 2), # %
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'common_days': len(common),
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}
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def main():
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print("=" * 80)
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print("ETF跟踪误差计算与校验")
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print(f"分析日期: {datetime.now().strftime('%Y-%m-%d')}")
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print("=" * 80)
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# 初始化
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pro = ts.pro_api(os.getenv('TUSHARE_TOKEN'))
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# 分析时间范围：最近1年
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end_date = datetime.now().strftime('%Y-%m-%d')
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start_date = (datetime.now() - timedelta(days=365)).strftime('%Y-%m-%d')
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print(f"计算区间: {start_date} ~ {end_date}")
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# 加载天天基金数据
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eastmoney_path = PROJECT_ROOT / 'rotation' / 'results' / 'etf_competitor_analysis.json'
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eastmoney_data = {}
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if eastmoney_path.exists():
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with open(eastmoney_path, 'r', encoding='utf-8') as f:
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eastmoney_data = json.load(f)
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print(f"已加载天天基金数据: {len(eastmoney_data)} 个指数")
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# 对每个指数计算跟踪误差
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print(f"\n开始计算跟踪误差...")
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results = {}
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for key, info in POOL_INDEX_MAP.items():
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index_name = info['name']
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index_code = info['index_code']
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current_etf = info['current_etf']
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print(f"\n{'='*60}")
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print(f"=== {index_name} ({key}) ===")
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print(f"{'='*60}")
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# 获取指数数据
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print(f" 获取指数数据: {index_code}")
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index_data = get_index_data(pro, index_code, start_date, end_date)
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if index_data is None:
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print(f" ✗ 指数数据获取失败")
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continue
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print(f" ✓ 指数数据: {len(index_data)} 天")
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# 获取该指数下所有ETF的NAV
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etf_list = []
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if key in eastmoney_data:
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for etf in eastmoney_data[key]['etfs']:
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etf_list.append({
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'code': etf['ts_code'],
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'name': etf['name'],
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'eastmoney_te': etf.get('annual_tracking_error', 'N/A'),
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})
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print(f" 共 {len(etf_list)} 只ETF需要计算")
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etf_results = []
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for etf_info in etf_list:
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etf_code = etf_info['code']
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etf_name = etf_info['name']
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# 获取ETF NAV
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etf_nav = get_etf_nav_data(pro, etf_code, start_date, end_date)
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if etf_nav is None or len(etf_nav) < 20:
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continue
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# 计算跟踪误差
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tracking = calculate_tracking_error(etf_nav, index_data)
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if tracking is None:
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continue
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result = {
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'ts_code': etf_code,
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'name': etf_name,
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'tushare_te': tracking['annual_tracking_error'],
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'tushare_r2': tracking['r_squared'],
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'tushare_correlation': tracking['correlation'],
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'tushare_excess_return': tracking['excess_return'],
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'tushare_common_days': tracking['common_days'],
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'eastmoney_te': etf_info['eastmoney_te'],
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'is_current': etf_code == current_etf,
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}
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etf_results.append(result)
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time.sleep(0.1)
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# 按跟踪误差排序
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etf_results.sort(key=lambda x: x['tushare_te'])
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results[key] = {
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'index_name': index_name,
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'index_code': index_code,
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'current_etf': current_etf,
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'etf_count': len(etf_results),
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'etfs': etf_results,
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}
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# 打印结果
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print(f"\n 计算完成: {len(etf_results)} 只ETF")
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print(f" {'代码':<12} {'名称':<20} {'Tushare TE':<12} {'天天基金 TE':<12} {'差异':<10} {'R²':<8}")
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print(f" {'-'*80}")
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for etf in etf_results[:10]:
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tushare_te = f"{etf['tushare_te']:.4f}%"
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eastmoney_te = etf['eastmoney_te']
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# 计算差异
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diff = 'N/A'
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if eastmoney_te and eastmoney_te != 'N/A' and eastmoney_te != '--':
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try:
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em_te = float(eastmoney_te.replace('%', ''))
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diff_val = etf['tushare_te'] - em_te
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diff = f"{diff_val:+.4f}%"
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except:
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pass
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marker = " ★" if etf['is_current'] else ""
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print(f" {etf['ts_code']:<12} {etf['name'][:20]:<20} {tushare_te:<12} {eastmoney_te:<12} {diff:<10} {etf['tushare_r2']:<8}{marker}")
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if len(etf_results) > 10:
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print(f" ... 还有 {len(etf_results) - 10} 只")
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# 保存结果
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output_dir = PROJECT_ROOT / 'rotation' / 'results'
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output_dir.mkdir(exist_ok=True)
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output_path = output_dir / 'tracking_error_validation.json'
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with open(output_path, 'w', encoding='utf-8') as f:
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json.dump(results, f, ensure_ascii=False, indent=2, default=str)
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print(f"\n{'='*80}")
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print(f"结果已保存: {output_path}")
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print(f"{'='*80}")
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# 汇总统计
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print(f"\n{'='*80}")
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print("校验汇总")
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print(f"{'='*80}")
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for key, data in results.items():
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print(f"\n--- {data['index_name']} ---")
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print(f" 指数代码: {data['index_code']}")
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print(f" 计算ETF数: {data['etf_count']}")
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# 统计有天天基金数据的ETF
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matched = [e for e in data['etfs'] if e['eastmoney_te'] and e['eastmoney_te'] not in ['N/A', '--']]
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print(f" 天天基金有数据: {len(matched)}")
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if matched:
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# 计算平均差异
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diffs = []
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for etf in matched:
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try:
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em_te = float(etf['eastmoney_te'].replace('%', ''))
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diff = etf['tushare_te'] - em_te
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diffs.append(diff)
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except:
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pass
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if diffs:
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avg_diff = np.mean(diffs)
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max_diff = max(diffs, key=abs)
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print(f" 平均差异: {avg_diff:+.4f}%")
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print(f" 最大差异: {max_diff:+.4f}%")
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if __name__ == '__main__':
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main()
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