aszerW
adb83d8cd7
- config_loader.py: 添加 etf_pool 字段和 GREEDY 枚举 - config_simple.yaml: 每个资产添加 etf_pool 列表 - simple_rotation.py: - 添加 _compute_greedy_weights 方法 - _calculate_daily_return 支持 greedy 模式 - 向后兼容原有 rank/equal 模式 贪心算法：按 ETF 池容量分配仓位，装不下的顺延给下一名 - 有色金属(1 ETF): 吸收25%，顺延75% - 原油(3 ETF): 吸收75% - 黄金(4 ETF): 吸收100% 回测对比 (select_num=3): - rank: 326.60% 累计收益, 1.24 夏普 - greedy: 421.35% 累计收益, 1.03 夏普
400 lines
14 KiB
Python
400 lines
14 KiB
Python
"""
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ETF跟踪误差全量计算
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- 覆盖轮动策略标的池全部10个标的
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- 数据源分层：
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- A股指数 → Tushare index_daily
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- 商品 → Tushare fut_daily（主力合约）
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- 海外指数 → Flask API (yfinance)
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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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from datasource.flask_api_source import FlaskAPIDataSource
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# ============================================================
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# 轮动策略标的池：全部10个标的
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# ============================================================
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POOL_CONFIG = {
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# --- A股指数（Tushare index_daily）---
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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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'benchmark_type': 'tushare_index', 'benchmark_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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'benchmark_type': 'tushare_index', 'benchmark_code': 'H30269.CSI',
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},
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# --- 商品（Tushare fut_daily 主力合约）---
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'GC=F': {
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'name': '黄金', 'current_etf': '518880.SH', 'group': 'COMMODITY',
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'benchmark_type': 'tushare_futures', 'benchmark_code': 'AU.SHF',
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},
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'HG=F': {
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'name': '有色金属', 'current_etf': '159980.SZ', 'group': 'COMMODITY',
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'benchmark_type': 'tushare_futures', 'benchmark_code': 'CU.SHF',
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},
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# --- 海外指数（Flask API / yfinance）---
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'HSI': {
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'name': '恒生指数', 'current_etf': '159920.SZ', 'group': 'HK',
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'benchmark_type': 'flask_api', 'benchmark_code': '^HSI',
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},
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'HSTECH.HK': {
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'name': '恒生科技', 'current_etf': '513130.SH', 'group': 'HK',
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'benchmark_type': 'flask_api', 'benchmark_code': 'HSTECH.HK',
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},
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'NDX': {
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'name': '纳指100', 'current_etf': '513100.SH', 'group': 'US',
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'benchmark_type': 'flask_api', 'benchmark_code': '^NDX',
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},
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'N225': {
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'name': '日经225', 'current_etf': '513520.SH', 'group': 'JP',
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'benchmark_type': 'flask_api', 'benchmark_code': '^N225',
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},
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'GDAXI': {
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'name': '德国DAX', 'current_etf': '513030.SH', 'group': 'EU',
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'benchmark_type': 'flask_api', 'benchmark_code': '^GDAXI',
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},
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# --- 原油（用最早ETF做基准，无可靠数据源）---
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'CL=F': {
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'name': '原油', 'current_etf': '160723.SZ', 'group': 'COMMODITY',
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'benchmark_type': 'earliest_etf', 'benchmark_code': '159518.SZ',
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},
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}
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# ============================================================
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# 数据获取函数
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# ============================================================
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def get_etf_nav_tushare(pro, etf_code, start_date, end_date):
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"""获取ETF累计净值（Tushare fund_nav）"""
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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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return df['accum_nav'].astype(float)
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except Exception as e:
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pass
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return None
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def get_benchmark_tushare_index(pro, index_code, start_date, end_date):
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"""获取A股指数收盘价（Tushare index_daily）"""
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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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pass
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return None
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def get_benchmark_tushare_futures(pro, fut_code, start_date, end_date):
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"""获取期货主力合约收盘价（Tushare fut_daily）"""
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try:
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df = pro.fut_daily(
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ts_code=fut_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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pass
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return None
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def get_benchmark_flask_api(flask_source, yf_code, start_date, end_date):
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"""获取海外指数数据（Flask API / yfinance）"""
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try:
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df = flask_source.fetch(yf_code, start_date, end_date)
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if df is not None and len(df) > 0:
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return df['close'].astype(float)
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except Exception as e:
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pass
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return None
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def get_etf_close_tushare(pro, etf_code, start_date, end_date):
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"""获取ETF收盘价（用于原油等无基准数据的情况）"""
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try:
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df = pro.fund_daily(
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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['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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pass
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return None
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# ============================================================
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# 跟踪误差计算
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# ============================================================
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def calculate_tracking_error(etf_nav, benchmark_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 - 基准收益率
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"""
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if etf_nav is None or benchmark_close is None:
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return None
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etf_ret = etf_nav.pct_change().dropna()
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bench_ret = benchmark_close.pct_change().dropna()
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common = etf_ret.index.intersection(bench_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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b = bench_ret.loc[common]
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daily_deviation = e - b
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tracking_error = daily_deviation.std() * np.sqrt(252)
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correlation = e.corr(b)
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r_squared = correlation ** 2
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etf_cum = (1 + e).prod() - 1
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bench_cum = (1 + b).prod() - 1
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excess = etf_cum - bench_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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'benchmark_cum_return': round(bench_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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# ============================================================
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# 主流程
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# ============================================================
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def main():
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print("=" * 80)
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print("ETF跟踪误差全量计算（10个标的）")
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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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# 初始化数据源
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pro = ts.pro_api(os.getenv('TUSHARE_TOKEN'))
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flask_source = FlaskAPIDataSource()
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# 分析区间：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（用于校验 + 获取ETF列表）
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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)} 个标的")
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# 按基准类型分组获取（减少重复请求）
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benchmark_cache = {} # benchmark_key -> Series
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results = {}
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for key, info in POOL_CONFIG.items():
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index_name = info['name']
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current_etf = info['current_etf']
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btype = info['benchmark_type']
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bcode = info['benchmark_code']
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print(f"\n{'='*60}")
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print(f"=== {index_name} ({key}) | 基准类型: {btype} ===")
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print(f"{'='*60}")
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# Step 1: 获取基准数据（带缓存）
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bench_key = f"{btype}:{bcode}"
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if bench_key not in benchmark_cache:
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print(f" 获取基准数据: {bcode} ({btype})")
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if btype == 'tushare_index':
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benchmark = get_benchmark_tushare_index(pro, bcode, start_date, end_date)
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elif btype == 'tushare_futures':
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benchmark = get_benchmark_tushare_futures(pro, bcode, start_date, end_date)
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elif btype == 'flask_api':
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benchmark = get_benchmark_flask_api(flask_source, bcode, start_date, end_date)
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elif btype == 'earliest_etf':
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benchmark = get_etf_close_tushare(pro, bcode, start_date, end_date)
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else:
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benchmark = None
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if benchmark is not None:
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benchmark_cache[bench_key] = benchmark
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print(f" ✓ 基准数据: {len(benchmark)} 天")
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else:
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print(f" ✗ 基准数据获取失败")
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benchmark_cache[bench_key] = None
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else:
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benchmark = benchmark_cache[bench_key]
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print(f" (缓存) 基准数据: {len(benchmark)} 天")
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if benchmark is None:
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print(f" 跳过（无基准数据）")
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continue
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# Step 2: 获取该标的下所有ETF
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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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# Step 3: 逐只计算跟踪误差
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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（或收盘价）
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if btype == 'earliest_etf':
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# 原油：用收盘价对比收盘价
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etf_data = get_etf_close_tushare(pro, etf_code, start_date, end_date)
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else:
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etf_data = get_etf_nav_tushare(pro, etf_code, start_date, end_date)
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if etf_data is None or len(etf_data) < 20:
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continue
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tracking = calculate_tracking_error(etf_data, benchmark)
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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(0.05)
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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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'current_etf': current_etf,
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'benchmark_type': btype,
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'benchmark_code': bcode,
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'group': info['group'],
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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} {'TE':<10} {'天天基金TE':<12} {'R²':<8}")
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print(f" {'-'*70}")
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for etf in etf_results[:10]:
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te_str = f"{etf['tushare_te']:.4f}%"
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em_te = etf['eastmoney_te']
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marker = " ★" if etf['is_current'] else ""
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print(f" {etf['ts_code']:<12} {etf['name'][:20]:<20} {te_str:<10} {em_te:<12} {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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# ============================================================
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# 保存结果
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# ============================================================
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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_full.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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# ============================================================
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# 汇总校验
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# ============================================================
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print(f"\n{'='*80}")
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print("全量校验汇总")
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print(f"{'='*80}")
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for key, data in results.items():
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matched = [e for e in data['etfs']
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if e['eastmoney_te'] and e['eastmoney_te'] not in ['N/A', '--']]
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print(f"\n--- {data['index_name']} ({data['benchmark_type']}) ---")
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print(f" ETF总数: {data['etf_count']} | 天天基金有数据: {len(matched)}")
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if matched:
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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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diffs.append(etf['tushare_te'] - em_te)
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except:
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pass
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if diffs:
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print(f" 平均差异: {np.mean(diffs):+.4f}% | 最大差异: {max(diffs, key=abs):+.4f}%")
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# 打印前3名
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top3 = data['etfs'][:3]
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print(f" Top3 (TE最低):")
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for i, etf in enumerate(top3, 1):
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marker = " ★当前" if etf['is_current'] else ""
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print(f" {i}. {etf['ts_code']} {etf['name']} TE={etf['tushare_te']:.4f}%{marker}")
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if __name__ == '__main__':
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main()
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