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
174 lines
6.7 KiB
Python
174 lines
6.7 KiB
Python
"""
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补充 ETF 丰富信息到 etf_basic_full.csv（批量版）
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- fund_share: 最新基金份额
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- fund_daily: 近20日日均成交额
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- fund_nav: 最新累计净值
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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 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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pro = ts.pro_api(os.getenv('TUSHARE_TOKEN'))
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csv_path = PROJECT_ROOT / 'rotation' / 'results' / 'etf_basic_full.csv'
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df = pd.read_csv(csv_path)
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print(f"加载 {len(df)} 只ETF")
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# 初始化新列
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df['fund_scale_yi'] = np.nan
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df['avg_amount_wan'] = np.nan
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df['avg_vol_wan'] = np.nan
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df['latest_nav'] = np.nan
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df['nav_date'] = ''
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end_date = datetime.now().strftime('%Y%m%d')
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start_date = (datetime.now() - timedelta(days=60)).strftime('%Y%m%d')
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daily_start = (datetime.now() - timedelta(days=40)).strftime('%Y%m%d')
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all_codes = df['ts_code'].tolist()
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# ============================================================
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# 1. fund_share - 批量获取（每次最多100只）
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# ============================================================
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print(f"\n[1/3] 获取基金份额...")
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share_records = []
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batch_size = 100
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for i in range(0, len(all_codes), batch_size):
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batch = all_codes[i:i+batch_size]
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ts_codes_str = ','.join(batch)
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try:
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share_df = pro.fund_share(ts_code=ts_codes_str, start_date=start_date, end_date=end_date)
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if share_df is not None and len(share_df) > 0:
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share_records.append(share_df)
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except Exception as e:
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pass
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if (i + batch_size) % 500 == 0:
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print(f" 进度: {min(i+batch_size, len(all_codes))}/{len(all_codes)}")
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time.sleep(0.3)
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if share_records:
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all_shares = pd.concat(share_records, ignore_index=True)
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# 每只取最新一条
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latest_shares = all_shares.sort_values('trade_date', ascending=False).drop_duplicates(subset=['ts_code'], keep='first')
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share_map = dict(zip(latest_shares['ts_code'], latest_shares['fd_share']))
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df['fund_scale_yi'] = df['ts_code'].map(share_map) / 10000 # 万份→亿份
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print(f" ✓ 份额数据: {len(share_map)} 只")
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else:
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print(f" ✗ 份额数据获取失败")
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# ============================================================
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# 2. fund_daily - 批量获取
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# ============================================================
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print(f"\n[2/3] 获取日线行情...")
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daily_records = []
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for i in range(0, len(all_codes), batch_size):
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batch = all_codes[i:i+batch_size]
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ts_codes_str = ','.join(batch)
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try:
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daily_df = pro.fund_daily(ts_code=ts_codes_str, start_date=daily_start, end_date=end_date)
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if daily_df is not None and len(daily_df) > 0:
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daily_records.append(daily_df)
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except Exception as e:
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pass
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if (i + batch_size) % 500 == 0:
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print(f" 进度: {min(i+batch_size, len(all_codes))}/{len(all_codes)}")
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time.sleep(0.3)
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if daily_records:
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all_daily = pd.concat(daily_records, ignore_index=True)
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# 每只取最近20个交易日，算均值
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daily_stats = all_daily.groupby('ts_code').agg(
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avg_amount=('amount', 'mean'),
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avg_vol=('vol', 'mean')
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).reset_index()
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daily_map_amt = dict(zip(daily_stats['ts_code'], daily_stats['avg_amount']))
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daily_map_vol = dict(zip(daily_stats['ts_code'], daily_stats['avg_vol']))
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df['avg_amount_wan'] = df['ts_code'].map(daily_map_amt) / 10 # 千元→万元
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df['avg_vol_wan'] = df['ts_code'].map(daily_map_vol) / 10000 # 手→万手
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print(f" ✓ 行情数据: {len(daily_stats)} 只")
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else:
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print(f" ✗ 行情数据获取失败")
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# ============================================================
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# 3. fund_nav - 批量获取
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# ============================================================
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print(f"\n[3/3] 获取基金净值...")
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nav_records = []
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for i in range(0, len(all_codes), batch_size):
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batch = all_codes[i:i+batch_size]
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ts_codes_str = ','.join(batch)
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try:
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nav_df = pro.fund_nav(ts_code=ts_codes_str, start_date=start_date, end_date=end_date)
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if nav_df is not None and len(nav_df) > 0:
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nav_records.append(nav_df)
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except Exception as e:
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pass
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if (i + batch_size) % 500 == 0:
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print(f" 进度: {min(i+batch_size, len(all_codes))}/{len(all_codes)}")
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time.sleep(0.3)
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if nav_records:
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all_nav = pd.concat(nav_records, ignore_index=True)
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latest_nav = all_nav.sort_values('nav_date', ascending=False).drop_duplicates(subset=['ts_code'], keep='first')
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nav_map = dict(zip(latest_nav['ts_code'], latest_nav['accum_nav']))
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nav_date_map = dict(zip(latest_nav['ts_code'], latest_nav['nav_date'].astype(str)))
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df['latest_nav'] = df['ts_code'].map(nav_map)
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df['nav_date'] = df['ts_code'].map(nav_date_map)
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print(f" ✓ 净值数据: {len(nav_map)} 只")
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else:
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print(f" ✗ 净值数据获取失败")
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# 用 NAV 精确计算规模
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mask = df['fund_scale_yi'].notna() & df['latest_nav'].notna()
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df.loc[mask, 'fund_scale_yi'] = (df.loc[mask, 'fund_scale_yi'] * df.loc[mask, 'latest_nav']).round(2)
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# 保存
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df.to_csv(csv_path, index=False, encoding='utf-8-sig')
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print(f"\n已保存: {csv_path}")
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print(f"新增字段: fund_scale_yi, avg_amount_wan, avg_vol_wan, latest_nav, nav_date")
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# 统计
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print(f"\n{'='*80}")
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print("数据质量")
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print(f"{'='*80}")
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print(f"基金规模: {df['fund_scale_yi'].notna().sum()}/{len(df)} ({df['fund_scale_yi'].notna().mean()*100:.1f}%)")
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print(f"日均成交额: {df['avg_amount_wan'].notna().sum()}/{len(df)} ({df['avg_amount_wan'].notna().mean()*100:.1f}%)")
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print(f"最新净值: {df['latest_nav'].notna().sum()}/{len(df)} ({df['latest_nav'].notna().mean()*100:.1f}%)")
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# 轮动策略标的池
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print(f"\n{'='*80}")
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print("轮动策略标的池")
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print(f"{'='*80}")
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pool_etfs = {
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'159915.SZ': '创业板指', '512890.SH': '红利低波',
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'159920.SZ': '恒生指数', '513130.SH': '恒生科技',
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'513100.SH': '纳指100', '513520.SH': '日经225',
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'513030.SH': '德国DAX', '518880.SH': '黄金',
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'159980.SZ': '有色金属', '160723.SZ': '原油',
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}
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print(f"{'代码':<12} {'名称':<8} {'规模(亿)':>10} {'日成交额(万)':>14} {'净值':>8} {'费率':>6} {'类型':<6}")
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print('-' * 75)
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for code, name in pool_etfs.items():
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row = df[df['ts_code'] == code]
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if len(row) > 0:
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r = row.iloc[0]
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scale = f"{r['fund_scale_yi']:.1f}" if pd.notna(r['fund_scale_yi']) else 'N/A'
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amt = f"{r['avg_amount_wan']:,.0f}" if pd.notna(r['avg_amount_wan']) else 'N/A'
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nav = f"{r['latest_nav']:.4f}" if pd.notna(r['latest_nav']) else 'N/A'
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fee = f"{r['mgt_fee']}%" if pd.notna(r['mgt_fee']) else 'N/A'
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etype = str(r['etf_type'])
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print(f"{code:<12} {name:<8} {scale:>10} {amt:>14} {nav:>8} {fee:>6} {etype:<6}")
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