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)
130 lines
4.2 KiB
Python
130 lines
4.2 KiB
Python
"""计算3只原油ETF跟踪WTI原油价格的准确率（使用Flask API数据源）"""
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import os, sys
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sys.path.insert(0, '/app')
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from dotenv import load_dotenv
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load_dotenv('/app/.env')
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import pandas as pd
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import numpy as np
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import requests
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import time
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# 绕过系统代理避免SSL EOF
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_session = requests.Session()
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_session.trust_env = False
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BASE_URL = os.getenv('FLASK_API_URL', 'https://k3s.tokenpluse.xyz')
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def fetch_ohlcv(code, start='2020-01-01', end='2026-06-20'):
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"""通过Flask API获取OHLCV数据"""
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url = f"{BASE_URL}/api/v1/ohlcv"
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params = {'code': code, 'start': start, 'end': end}
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for attempt in range(3):
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try:
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resp = _session.get(url, params=params, timeout=120)
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if resp.status_code == 200:
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data = resp.json()
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if 'error' in data or not data.get('data'):
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print(f" {code}: 无数据 - {data.get('error', 'empty')}")
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return None
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df = pd.DataFrame(data['data'])
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df['date'] = pd.to_datetime(df['date'])
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df = df.set_index('date').sort_index()
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df['ret'] = df['close'].pct_change()
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cnt = data.get('count', len(df))
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dr = data.get('date_range', {})
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print(f" {code}: {cnt} 条 ({dr.get('start','?')} ~ {dr.get('end','?')})")
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return df
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else:
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print(f" {code}: HTTP {resp.status_code}")
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return None
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except Exception as e:
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if attempt < 2:
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time.sleep(2)
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continue
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print(f" {code}: 失败 - {e}")
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return None
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return None
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# 1. 获取WTI原油价格
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print("=" * 90)
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print("获取WTI原油价格 (CL=F)")
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print("=" * 90)
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cl_df = fetch_ohlcv('CL=F')
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if cl_df is None or len(cl_df) < 10:
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print("WTI数据获取失败，退出")
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sys.exit(1)
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# 2. 获取3只原油ETF价格
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etf_codes = {
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'160723.SZ': '嘉实原油(WTI*100%)',
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'161129.SZ': '易方达原油(标普高盛原油*100%)',
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'501018.SH': '南方原油(WTI*60%+BRENT*40%)',
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}
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print("\n" + "=" * 90)
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print("获取原油ETF价格")
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print("=" * 90)
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etf_dfs = {}
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for code, name in etf_codes.items():
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df = fetch_ohlcv(code)
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if df is not None:
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etf_dfs[code] = name
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# 3. 计算跟踪准确率
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print("\n" + "=" * 90)
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print("跟踪准确率（基于ETF收盘价收益率 vs WTI收盘价日收益率）")
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print("=" * 90)
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for code, name in etf_dfs.items():
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etf = etf_dfs[code][['ret']].copy()
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etf.columns = ['etf_ret']
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etf.index = etf.index.normalize()
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cl = cl_df[['ret']].copy()
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cl.columns = ['cl_ret']
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cl.index = cl.index.normalize()
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m = pd.merge(etf.reset_index(), cl.reset_index(), on='date', how='inner', suffixes=('', '_cl'))
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if 'ret' in m.columns and 'ret_cl' in m.columns:
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m = m.rename(columns={'ret': 'etf_ret', 'ret_cl': 'cl_ret'})
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m = m[['date', 'etf_ret', 'cl_ret']].dropna()
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if len(m) < 10:
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print(f"\n{code} {name}: 数据不足 ({len(m)} 天)")
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continue
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corr = m['etf_ret'].corr(m['cl_ret'])
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r2 = corr ** 2
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diff = m['etf_ret'] - m['cl_ret']
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te_annual = diff.std() * np.sqrt(252)
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cum_etf = (1 + m['etf_ret']).prod() - 1
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cum_cl = (1 + m['cl_ret']).prod() - 1
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bias = diff.mean()
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# 分段: 2024至今
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recent = m[m['date'] >= '2024-01-01']
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if len(recent) > 20:
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r2_recent = recent['etf_ret'].corr(recent['cl_ret']) ** 2
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else:
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r2_recent = np.nan
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print(f"\n{code} {name}")
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print(f" 重叠交易日: {len(m)} 天")
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print(f" 全区间 R²: {r2:.4f} ({r2*100:.1f}%)")
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if not np.isnan(r2_recent):
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print(f" 2024至今 R²: {r2_recent:.4f} ({r2_recent*100:.1f}%)")
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else:
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print(f" 2024至今 R²: 数据不足")
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print(f" 年化跟踪误差: {te_annual*100:.2f}%")
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print(f" 日均偏差: {bias*100:.4f}%")
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print(f" ETF累计收益: {cum_etf*100:.1f}%")
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print(f" WTI累计收益: {cum_cl*100:.1f}%")
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print(f" 累计收益差: {(cum_etf-cum_cl)*100:.1f}%")
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print("\n" + "=" * 90)
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print("注: 原油ETF为QDII-LOF，净值披露有T+1~T+2延迟，")
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print(" 且需通过期货合约展期，与WTI现货价格存在结构性偏差。")
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print("=" * 90)
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