feat: 实现贪心分配模式(greedy)
- 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
This commit is contained in:
@@ -56,6 +56,7 @@ class WeightType(str, Enum):
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EQUAL = "equal" # 等权
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RANK = "rank" # 按排名加权 (slot i gets (N-i)/triangular(N))
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KELLY = "kelly" # Kelly准则近似 (score-proportional weighting)
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GREEDY = "greedy" # 贪心分配（按ETF池容量吸收仓位）
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class DataSourceType(str, Enum):
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@@ -82,6 +83,7 @@ class AssetConfig(BaseModel):
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signal_source: str = Field(..., description="信号来源代码")
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trade_source: str = Field(..., description="交易来源代码")
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etf_pool: List[str] = Field(default_factory=list, description="可交易ETF池（按规模排序）")
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description: Optional[str] = Field(None, description="标的描述")
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etf: Optional[str] = Field(None, description="ETF代码（兼容旧配置）")
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@@ -179,6 +181,7 @@ class RotationConfig(BaseModel):
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diversified: bool = Field(default=True)
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threshold: ThresholdConfig = Field(default_factory=ThresholdConfig)
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weight: WeightType = Field(default=WeightType.EQUAL)
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etf_max_weight: float = Field(default=0.25, ge=0.01, le=1.0)
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class RebalanceConfig(BaseModel):
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@@ -6,66 +6,77 @@ asset_pools:
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name: 创业板指
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signal_source: 399006.SZ
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trade_source: 159915.SZ
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etf_pool: [159915.SZ, 159952.SZ, 159957.SZ, 159948.SZ]
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931862.CSI:
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description: 中证0-9个月国债指数，久期<1年，防御配置
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group: BOND
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name: 短债指数
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signal_source: 931862.CSI
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trade_source: 931862.CSI
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etf_pool: [931862.CSI]
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CL=F:
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description: WTI原油期货（2000年至今）
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group: COMMODITY
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name: 原油
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signal_source: CL=F
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trade_source: 160723.SZ
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etf_pool: [160723.SZ, 501018.SH, 161129.SZ]
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GC=F:
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description: COMEX黄金期货（2000年至今）
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group: COMMODITY
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name: 黄金
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signal_source: GC=F
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trade_source: 518880.SH
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etf_pool: [518880.SH, 159937.SZ, 159934.SZ, 518800.SH]
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GDAXI:
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description: 德国DAX指数
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group: EU
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name: 德国DAX
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signal_source: GDAXI
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trade_source: 513030.SH
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etf_pool: [159561.SZ, 513030.SH]
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H30269.CSI:
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description: 红利低波指数
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group: A
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name: 中证红利低波
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signal_source: H30269.CSI
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trade_source: 512890.SH
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etf_pool: [512890.SH, 563020.SH, 159547.SZ, 560150.SH]
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HG=F:
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description: COMEX铜期货（2000年至今）
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group: COMMODITY
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name: 有色金属
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signal_source: HG=F
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trade_source: 159980.SZ
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etf_pool: [159980.SZ]
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HSI:
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description: 恒生指数
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group: HK
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name: 恒生指数
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signal_source: HSI
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trade_source: 159920.SZ
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etf_pool: [159920.SZ, 513600.SH, 159271.SZ, 513210.SH]
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HSTECH.HK:
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description: 恒生科技指数
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group: HK
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name: 恒生科技
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signal_source: HSTECH.HK
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trade_source: 513130.SH
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etf_pool: [513180.SH, 513130.SH, 513010.SH, 159740.SZ]
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N225:
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description: 日经225指数
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group: JP
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name: 日经225
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signal_source: N225
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trade_source: 513520.SH
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etf_pool: [513880.SH, 513520.SH, 513000.SH, 159866.SZ]
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NDX:
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description: 纳斯达克100指数
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group: US
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name: 纳指100
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signal_source: NDX
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trade_source: 513100.SH
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etf_pool: [159941.SZ, 513100.SH, 513300.SH, 159501.SZ]
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backtest:
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start_date: '2020-01-10'
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benchmark:
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@@ -109,6 +120,7 @@ rotation:
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diversified: true
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select_num: 3
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weight: rank
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etf_max_weight: 0.25
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threshold:
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dynamic:
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fallback_enabled: true
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173
rotation/enrich_etf_data.py
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173
rotation/enrich_etf_data.py
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@@ -0,0 +1,173 @@
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"""
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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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129
rotation/oil_tracking.py
Normal file
129
rotation/oil_tracking.py
Normal file
@@ -0,0 +1,129 @@
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"""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():
|
||||
etf = etf_dfs[code][['ret']].copy()
|
||||
etf.columns = ['etf_ret']
|
||||
etf.index = etf.index.normalize()
|
||||
|
||||
cl = cl_df[['ret']].copy()
|
||||
cl.columns = ['cl_ret']
|
||||
cl.index = cl.index.normalize()
|
||||
|
||||
m = pd.merge(etf.reset_index(), cl.reset_index(), on='date', how='inner', suffixes=('', '_cl'))
|
||||
if 'ret' in m.columns and 'ret_cl' in m.columns:
|
||||
m = m.rename(columns={'ret': 'etf_ret', 'ret_cl': 'cl_ret'})
|
||||
m = m[['date', 'etf_ret', 'cl_ret']].dropna()
|
||||
|
||||
if len(m) < 10:
|
||||
print(f"\n{code} {name}: 数据不足 ({len(m)} 天)")
|
||||
continue
|
||||
|
||||
corr = m['etf_ret'].corr(m['cl_ret'])
|
||||
r2 = corr ** 2
|
||||
diff = m['etf_ret'] - m['cl_ret']
|
||||
te_annual = diff.std() * np.sqrt(252)
|
||||
cum_etf = (1 + m['etf_ret']).prod() - 1
|
||||
cum_cl = (1 + m['cl_ret']).prod() - 1
|
||||
bias = diff.mean()
|
||||
|
||||
# 分段: 2024至今
|
||||
recent = m[m['date'] >= '2024-01-01']
|
||||
if len(recent) > 20:
|
||||
r2_recent = recent['etf_ret'].corr(recent['cl_ret']) ** 2
|
||||
else:
|
||||
r2_recent = np.nan
|
||||
|
||||
print(f"\n{code} {name}")
|
||||
print(f" 重叠交易日: {len(m)} 天")
|
||||
print(f" 全区间 R²: {r2:.4f} ({r2*100:.1f}%)")
|
||||
if not np.isnan(r2_recent):
|
||||
print(f" 2024至今 R²: {r2_recent:.4f} ({r2_recent*100:.1f}%)")
|
||||
else:
|
||||
print(f" 2024至今 R²: 数据不足")
|
||||
print(f" 年化跟踪误差: {te_annual*100:.2f}%")
|
||||
print(f" 日均偏差: {bias*100:.4f}%")
|
||||
print(f" ETF累计收益: {cum_etf*100:.1f}%")
|
||||
print(f" WTI累计收益: {cum_cl*100:.1f}%")
|
||||
print(f" 累计收益差: {(cum_etf-cum_cl)*100:.1f}%")
|
||||
|
||||
print("\n" + "=" * 90)
|
||||
print("注: 原油ETF为QDII-LOF，净值披露有T+1~T+2延迟，")
|
||||
print(" 且需通过期货合约展期，与WTI现货价格存在结构性偏差。")
|
||||
print("=" * 90)
|
||||
@@ -469,6 +469,10 @@ class SimpleRotationStrategy:
|
||||
self.min_hold_days = self.config.rebalance.min_hold_days
|
||||
self.weight_type = self.config.rotation.weight.value # 'equal' or 'rank'
|
||||
|
||||
# ETF expansion: max weight per individual ETF
|
||||
self.etf_max_weight = getattr(self.config.rotation, 'etf_max_weight', 0.25)
|
||||
self.min_slots = math.ceil(1.0 / self.etf_max_weight) # e.g. 4 for 25%
|
||||
|
||||
# Dynamic threshold
|
||||
threshold = self.config.rotation.threshold
|
||||
self.use_dynamic_threshold = (threshold.mode.value == 'dynamic')
|
||||
@@ -487,6 +491,21 @@ class SimpleRotationStrategy:
|
||||
self.code_to_group[asset.signal_source] = asset.group
|
||||
self.trade_code_to_group[asset.trade_source] = asset.group
|
||||
|
||||
# ETF expansion pool: signal_code -> [etf_code, ...] from config
|
||||
self.signal_to_etfs: Dict[str, List[str]] = {}
|
||||
for code, asset in self.config.asset_pools.assets.items():
|
||||
if asset.etf_pool:
|
||||
self.signal_to_etfs[asset.signal_source] = asset.etf_pool
|
||||
else:
|
||||
# Fallback: use trade_source as single ETF
|
||||
self.signal_to_etfs[asset.signal_source] = [asset.trade_source]
|
||||
|
||||
# Log ETF pool summary
|
||||
for signal_code, etfs in self.signal_to_etfs.items():
|
||||
asset = self.config.asset_pools.assets.get(signal_code)
|
||||
name = asset.name if asset else signal_code
|
||||
print(f" ETF pool [{name}]: {len(etfs)} ETFs {etfs}")
|
||||
|
||||
# Data source
|
||||
data_source = self.config.data.sources[0]
|
||||
base_url = data_source.url or 'https://k3s.tokenpluse.xyz'
|
||||
@@ -508,6 +527,55 @@ class SimpleRotationStrategy:
|
||||
# Position weights: code -> weight (updated each day by _generate_signals)
|
||||
self._position_weights: Dict[str, float] = {}
|
||||
|
||||
def _compute_greedy_weights(self, holdings: List[str], factors: Dict[str, float]) -> Dict[str, float]:
|
||||
"""Compute greedy weights for signal-level holdings.
|
||||
|
||||
Greedy Algorithm:
|
||||
1. Each index has absorption capacity = min(n_etfs, ceil(1/max_weight)) × max_weight
|
||||
2. Iterate through holdings in order (sorted by momentum)
|
||||
3. Each index absorbs up to its capacity
|
||||
4. Remaining weight flows to next index
|
||||
|
||||
Example (select_num=1, max_weight=0.25):
|
||||
- 有色金属(1 ETF): capacity=25%, absorbs 25%, remaining=75%
|
||||
- 原油(3 ETFs): capacity=75%, absorbs 75%, remaining=0%
|
||||
- Total: 100%
|
||||
|
||||
Args:
|
||||
holdings: List of signal codes (sorted by momentum desc)
|
||||
factors: Dict of signal_code -> momentum score
|
||||
|
||||
Returns:
|
||||
signal_weights: Dict mapping signal_code -> weight
|
||||
"""
|
||||
if not holdings:
|
||||
return {}
|
||||
|
||||
signal_weights = {}
|
||||
remaining_weight = 1.0
|
||||
|
||||
for signal_code in holdings:
|
||||
if remaining_weight <= 0:
|
||||
break
|
||||
|
||||
# Get ETF pool size for this index
|
||||
etf_pool = self.signal_to_etfs.get(signal_code, [])
|
||||
n_etfs = len(etf_pool) if etf_pool else 1
|
||||
|
||||
# Calculate absorption capacity
|
||||
max_etfs_can_use = math.ceil(1.0 / self.etf_max_weight) # e.g. 4 for 25%
|
||||
n_to_use = min(n_etfs, max_etfs_can_use)
|
||||
capacity = n_to_use * self.etf_max_weight
|
||||
|
||||
# Absorb up to capacity (but not more than remaining)
|
||||
absorb = min(capacity, remaining_weight)
|
||||
remaining_weight -= absorb
|
||||
|
||||
# Assign weight to this signal
|
||||
signal_weights[signal_code] = absorb
|
||||
|
||||
return signal_weights
|
||||
|
||||
def _preload_data(self):
|
||||
"""Preload all historical data"""
|
||||
start_date = self.config.backtest.start_date
|
||||
@@ -728,13 +796,69 @@ class SimpleRotationStrategy:
|
||||
return self._position_weights[code]
|
||||
return 1.0 / n_unique if n_unique > 0 else 0.0
|
||||
|
||||
def _calculate_daily_return(self, old_holdings, new_holdings, date, is_rebalance):
|
||||
def _calculate_daily_return(self, old_holdings, new_holdings, date, is_rebalance, factors=None):
|
||||
"""
|
||||
Compute daily return (T+1 execution) with configurable position weighting:
|
||||
- Hold: close-to-close, weighted by today's position weight
|
||||
- Sell: close-to-open (sold at open), weighted by today's position weight
|
||||
- Buy: open-to-close (intraday), weighted by today's position weight
|
||||
|
||||
When weight=greedy, computes signal-level weights based on ETF pool capacity.
|
||||
Otherwise, uses signal_to_trade mapping with position_weights.
|
||||
"""
|
||||
# Greedy mode: compute signal-level weights based on ETF pool capacity
|
||||
if self.weight_type == 'greedy':
|
||||
factors = factors or {}
|
||||
old_weights = self._compute_greedy_weights(old_holdings, factors) if old_holdings else {}
|
||||
new_weights = self._compute_greedy_weights(new_holdings, factors) if new_holdings else {}
|
||||
|
||||
# Use signal-level codes with trade_source prices
|
||||
old_set = set(old_holdings) if old_holdings else set()
|
||||
new_set = set(new_holdings) if new_holdings else set()
|
||||
|
||||
if not old_set:
|
||||
if not new_set:
|
||||
return 0.0
|
||||
ret = 0.0
|
||||
for code in new_set:
|
||||
tc = self.signal_to_trade.get(code, code)
|
||||
p = self._get_etf_prices(tc, date)
|
||||
w = new_weights.get(code, 0.0)
|
||||
if p and p['open'] > 0:
|
||||
ret += w * (p['close'] - p['open']) / p['open']
|
||||
if is_rebalance:
|
||||
ret -= self.trade_cost
|
||||
return ret
|
||||
|
||||
daily_return = 0.0
|
||||
|
||||
for code in old_set:
|
||||
tc = self.signal_to_trade.get(code, code)
|
||||
p = self._get_etf_prices(tc, date)
|
||||
if p is None or p['prev_close'] == 0:
|
||||
continue
|
||||
w = old_weights.get(code, 0.0)
|
||||
if code in new_set:
|
||||
r = (p['close'] - p['prev_close']) / p['prev_close']
|
||||
else:
|
||||
r = (p['open'] - p['prev_close']) / p['prev_close']
|
||||
if not math.isnan(r):
|
||||
daily_return += w * r
|
||||
|
||||
for code in new_set - old_set:
|
||||
tc = self.signal_to_trade.get(code, code)
|
||||
p = self._get_etf_prices(tc, date)
|
||||
w = new_weights.get(code, 0.0)
|
||||
if p and p['open'] > 0 and not math.isnan(p['close']):
|
||||
r = (p['close'] - p['open']) / p['open']
|
||||
if not math.isnan(r):
|
||||
daily_return += w * r
|
||||
|
||||
if is_rebalance:
|
||||
daily_return -= self.trade_cost
|
||||
return daily_return
|
||||
|
||||
# Original mode: use signal_to_trade mapping
|
||||
if not old_holdings:
|
||||
if not new_holdings:
|
||||
return 0.0
|
||||
@@ -862,7 +986,7 @@ class SimpleRotationStrategy:
|
||||
|
||||
# Return uses T's ETF prices (open for buy/sell, close for hold)
|
||||
daily_return = self._calculate_daily_return(
|
||||
current_holdings, new_holdings, date, is_rebalance
|
||||
current_holdings, new_holdings, date, is_rebalance, factors=factors
|
||||
)
|
||||
nav *= (1 + daily_return)
|
||||
|
||||
@@ -887,6 +1011,9 @@ class SimpleRotationStrategy:
|
||||
for code in removed:
|
||||
entry_info.pop(code, None)
|
||||
|
||||
# Compute greedy weights for signal-level holdings
|
||||
greedy_weights = self._compute_greedy_weights(new_holdings, factors) if self.weight_type == 'greedy' else {}
|
||||
|
||||
# Compute bond threshold value for detail record
|
||||
threshold_val = 0.0
|
||||
if self.use_dynamic_threshold and bond_momentum is not None:
|
||||
@@ -898,6 +1025,7 @@ class SimpleRotationStrategy:
|
||||
'daily_return': round(daily_return, 6),
|
||||
'is_rebalance': is_rebalance,
|
||||
'holdings': sorted(new_holdings),
|
||||
'greedy_weights': {k: round(v, 6) for k, v in greedy_weights.items()} if greedy_weights else None,
|
||||
'added': sorted(added),
|
||||
'removed': sorted(removed),
|
||||
'factors': {k: round(v, 6) for k, v in factors.items()},
|
||||
|
||||
399
rotation/tracking_error_full.py
Normal file
399
rotation/tracking_error_full.py
Normal file
@@ -0,0 +1,399 @@
|
||||
"""
|
||||
ETF跟踪误差全量计算
|
||||
- 覆盖轮动策略标的池全部10个标的
|
||||
- 数据源分层：
|
||||
- A股指数 → Tushare index_daily
|
||||
- 商品 → Tushare fut_daily（主力合约）
|
||||
- 海外指数 → Flask API (yfinance)
|
||||
- 与天天基金数据对比校验
|
||||
"""
|
||||
import os
|
||||
import sys
|
||||
import time
|
||||
import json
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
from pathlib import Path
|
||||
from datetime import datetime, timedelta
|
||||
|
||||
PROJECT_ROOT = Path(__file__).parent.parent
|
||||
sys.path.insert(0, str(PROJECT_ROOT))
|
||||
from dotenv import load_dotenv
|
||||
load_dotenv(PROJECT_ROOT / '.env')
|
||||
|
||||
import tushare as ts
|
||||
from datasource.flask_api_source import FlaskAPIDataSource
|
||||
|
||||
|
||||
# ============================================================
|
||||
# 轮动策略标的池：全部10个标的
|
||||
# ============================================================
|
||||
POOL_CONFIG = {
|
||||
# --- A股指数（Tushare index_daily）---
|
||||
'399006.SZ': {
|
||||
'name': '创业板指', 'current_etf': '159915.SZ', 'group': 'A',
|
||||
'benchmark_type': 'tushare_index', 'benchmark_code': '399006.SZ',
|
||||
},
|
||||
'H30269.CSI': {
|
||||
'name': '红利低波', 'current_etf': '512890.SH', 'group': 'A',
|
||||
'benchmark_type': 'tushare_index', 'benchmark_code': 'H30269.CSI',
|
||||
},
|
||||
# --- 商品（Tushare fut_daily 主力合约）---
|
||||
'GC=F': {
|
||||
'name': '黄金', 'current_etf': '518880.SH', 'group': 'COMMODITY',
|
||||
'benchmark_type': 'tushare_futures', 'benchmark_code': 'AU.SHF',
|
||||
},
|
||||
'HG=F': {
|
||||
'name': '有色金属', 'current_etf': '159980.SZ', 'group': 'COMMODITY',
|
||||
'benchmark_type': 'tushare_futures', 'benchmark_code': 'CU.SHF',
|
||||
},
|
||||
# --- 海外指数（Flask API / yfinance）---
|
||||
'HSI': {
|
||||
'name': '恒生指数', 'current_etf': '159920.SZ', 'group': 'HK',
|
||||
'benchmark_type': 'flask_api', 'benchmark_code': '^HSI',
|
||||
},
|
||||
'HSTECH.HK': {
|
||||
'name': '恒生科技', 'current_etf': '513130.SH', 'group': 'HK',
|
||||
'benchmark_type': 'flask_api', 'benchmark_code': 'HSTECH.HK',
|
||||
},
|
||||
'NDX': {
|
||||
'name': '纳指100', 'current_etf': '513100.SH', 'group': 'US',
|
||||
'benchmark_type': 'flask_api', 'benchmark_code': '^NDX',
|
||||
},
|
||||
'N225': {
|
||||
'name': '日经225', 'current_etf': '513520.SH', 'group': 'JP',
|
||||
'benchmark_type': 'flask_api', 'benchmark_code': '^N225',
|
||||
},
|
||||
'GDAXI': {
|
||||
'name': '德国DAX', 'current_etf': '513030.SH', 'group': 'EU',
|
||||
'benchmark_type': 'flask_api', 'benchmark_code': '^GDAXI',
|
||||
},
|
||||
# --- 原油（用最早ETF做基准，无可靠数据源）---
|
||||
'CL=F': {
|
||||
'name': '原油', 'current_etf': '160723.SZ', 'group': 'COMMODITY',
|
||||
'benchmark_type': 'earliest_etf', 'benchmark_code': '159518.SZ',
|
||||
},
|
||||
}
|
||||
|
||||
|
||||
# ============================================================
|
||||
# 数据获取函数
|
||||
# ============================================================
|
||||
|
||||
def get_etf_nav_tushare(pro, etf_code, start_date, end_date):
|
||||
"""获取ETF累计净值（Tushare fund_nav）"""
|
||||
try:
|
||||
df = pro.fund_nav(
|
||||
ts_code=etf_code,
|
||||
start_date=start_date.replace('-', ''),
|
||||
end_date=end_date.replace('-', '')
|
||||
)
|
||||
if df is not None and len(df) > 0:
|
||||
df['date'] = pd.to_datetime(df['nav_date'])
|
||||
df = df.set_index('date').sort_index()
|
||||
return df['accum_nav'].astype(float)
|
||||
except Exception as e:
|
||||
pass
|
||||
return None
|
||||
|
||||
|
||||
def get_benchmark_tushare_index(pro, index_code, start_date, end_date):
|
||||
"""获取A股指数收盘价（Tushare index_daily）"""
|
||||
try:
|
||||
df = pro.index_daily(
|
||||
ts_code=index_code,
|
||||
start_date=start_date.replace('-', ''),
|
||||
end_date=end_date.replace('-', '')
|
||||
)
|
||||
if df is not None and len(df) > 0:
|
||||
df['date'] = pd.to_datetime(df['trade_date'])
|
||||
df = df.set_index('date').sort_index()
|
||||
return df['close'].astype(float)
|
||||
except Exception as e:
|
||||
pass
|
||||
return None
|
||||
|
||||
|
||||
def get_benchmark_tushare_futures(pro, fut_code, start_date, end_date):
|
||||
"""获取期货主力合约收盘价（Tushare fut_daily）"""
|
||||
try:
|
||||
df = pro.fut_daily(
|
||||
ts_code=fut_code,
|
||||
start_date=start_date.replace('-', ''),
|
||||
end_date=end_date.replace('-', '')
|
||||
)
|
||||
if df is not None and len(df) > 0:
|
||||
df['date'] = pd.to_datetime(df['trade_date'])
|
||||
df = df.set_index('date').sort_index()
|
||||
return df['close'].astype(float)
|
||||
except Exception as e:
|
||||
pass
|
||||
return None
|
||||
|
||||
|
||||
def get_benchmark_flask_api(flask_source, yf_code, start_date, end_date):
|
||||
"""获取海外指数数据（Flask API / yfinance）"""
|
||||
try:
|
||||
df = flask_source.fetch(yf_code, start_date, end_date)
|
||||
if df is not None and len(df) > 0:
|
||||
return df['close'].astype(float)
|
||||
except Exception as e:
|
||||
pass
|
||||
return None
|
||||
|
||||
|
||||
def get_etf_close_tushare(pro, etf_code, start_date, end_date):
|
||||
"""获取ETF收盘价（用于原油等无基准数据的情况）"""
|
||||
try:
|
||||
df = pro.fund_daily(
|
||||
ts_code=etf_code,
|
||||
start_date=start_date.replace('-', ''),
|
||||
end_date=end_date.replace('-', '')
|
||||
)
|
||||
if df is not None and len(df) > 0:
|
||||
df['date'] = pd.to_datetime(df['trade_date'])
|
||||
df = df.set_index('date').sort_index()
|
||||
return df['close'].astype(float)
|
||||
except Exception as e:
|
||||
pass
|
||||
return None
|
||||
|
||||
|
||||
# ============================================================
|
||||
# 跟踪误差计算
|
||||
# ============================================================
|
||||
|
||||
def calculate_tracking_error(etf_nav, benchmark_close):
|
||||
"""
|
||||
计算跟踪误差
|
||||
公式：STDEV(每日偏离度) × √252
|
||||
每日偏离度 = ETF净值收益率 - 基准收益率
|
||||
"""
|
||||
if etf_nav is None or benchmark_close is None:
|
||||
return None
|
||||
|
||||
etf_ret = etf_nav.pct_change().dropna()
|
||||
bench_ret = benchmark_close.pct_change().dropna()
|
||||
|
||||
common = etf_ret.index.intersection(bench_ret.index)
|
||||
if len(common) < 20:
|
||||
return None
|
||||
|
||||
e = etf_ret.loc[common]
|
||||
b = bench_ret.loc[common]
|
||||
|
||||
daily_deviation = e - b
|
||||
tracking_error = daily_deviation.std() * np.sqrt(252)
|
||||
correlation = e.corr(b)
|
||||
r_squared = correlation ** 2
|
||||
|
||||
etf_cum = (1 + e).prod() - 1
|
||||
bench_cum = (1 + b).prod() - 1
|
||||
excess = etf_cum - bench_cum
|
||||
|
||||
return {
|
||||
'annual_tracking_error': round(tracking_error * 100, 4),
|
||||
'correlation': round(correlation, 6),
|
||||
'r_squared': round(r_squared, 6),
|
||||
'etf_cum_return': round(etf_cum * 100, 2),
|
||||
'benchmark_cum_return': round(bench_cum * 100, 2),
|
||||
'excess_return': round(excess * 100, 2),
|
||||
'common_days': len(common),
|
||||
}
|
||||
|
||||
|
||||
# ============================================================
|
||||
# 主流程
|
||||
# ============================================================
|
||||
|
||||
def main():
|
||||
print("=" * 80)
|
||||
print("ETF跟踪误差全量计算（10个标的）")
|
||||
print(f"分析日期: {datetime.now().strftime('%Y-%m-%d')}")
|
||||
print("=" * 80)
|
||||
|
||||
# 初始化数据源
|
||||
pro = ts.pro_api(os.getenv('TUSHARE_TOKEN'))
|
||||
flask_source = FlaskAPIDataSource()
|
||||
|
||||
# 分析区间：最近1年
|
||||
end_date = datetime.now().strftime('%Y-%m-%d')
|
||||
start_date = (datetime.now() - timedelta(days=365)).strftime('%Y-%m-%d')
|
||||
print(f"计算区间: {start_date} ~ {end_date}")
|
||||
|
||||
# 加载天天基金数据（用于校验 + 获取ETF列表）
|
||||
eastmoney_path = PROJECT_ROOT / 'rotation' / 'results' / 'etf_competitor_analysis.json'
|
||||
eastmoney_data = {}
|
||||
if eastmoney_path.exists():
|
||||
with open(eastmoney_path, 'r', encoding='utf-8') as f:
|
||||
eastmoney_data = json.load(f)
|
||||
print(f"已加载天天基金数据: {len(eastmoney_data)} 个标的")
|
||||
|
||||
# 按基准类型分组获取（减少重复请求）
|
||||
benchmark_cache = {} # benchmark_key -> Series
|
||||
|
||||
results = {}
|
||||
|
||||
for key, info in POOL_CONFIG.items():
|
||||
index_name = info['name']
|
||||
current_etf = info['current_etf']
|
||||
btype = info['benchmark_type']
|
||||
bcode = info['benchmark_code']
|
||||
|
||||
print(f"\n{'='*60}")
|
||||
print(f"=== {index_name} ({key}) | 基准类型: {btype} ===")
|
||||
print(f"{'='*60}")
|
||||
|
||||
# Step 1: 获取基准数据（带缓存）
|
||||
bench_key = f"{btype}:{bcode}"
|
||||
if bench_key not in benchmark_cache:
|
||||
print(f" 获取基准数据: {bcode} ({btype})")
|
||||
if btype == 'tushare_index':
|
||||
benchmark = get_benchmark_tushare_index(pro, bcode, start_date, end_date)
|
||||
elif btype == 'tushare_futures':
|
||||
benchmark = get_benchmark_tushare_futures(pro, bcode, start_date, end_date)
|
||||
elif btype == 'flask_api':
|
||||
benchmark = get_benchmark_flask_api(flask_source, bcode, start_date, end_date)
|
||||
elif btype == 'earliest_etf':
|
||||
benchmark = get_etf_close_tushare(pro, bcode, start_date, end_date)
|
||||
else:
|
||||
benchmark = None
|
||||
|
||||
if benchmark is not None:
|
||||
benchmark_cache[bench_key] = benchmark
|
||||
print(f" ✓ 基准数据: {len(benchmark)} 天")
|
||||
else:
|
||||
print(f" ✗ 基准数据获取失败")
|
||||
benchmark_cache[bench_key] = None
|
||||
else:
|
||||
benchmark = benchmark_cache[bench_key]
|
||||
print(f" (缓存) 基准数据: {len(benchmark)} 天")
|
||||
|
||||
if benchmark is None:
|
||||
print(f" 跳过（无基准数据）")
|
||||
continue
|
||||
|
||||
# Step 2: 获取该标的下所有ETF
|
||||
etf_list = []
|
||||
if key in eastmoney_data:
|
||||
for etf in eastmoney_data[key]['etfs']:
|
||||
etf_list.append({
|
||||
'code': etf['ts_code'],
|
||||
'name': etf['name'],
|
||||
'eastmoney_te': etf.get('annual_tracking_error', 'N/A'),
|
||||
})
|
||||
|
||||
print(f" 共 {len(etf_list)} 只ETF需要计算")
|
||||
|
||||
# Step 3: 逐只计算跟踪误差
|
||||
etf_results = []
|
||||
|
||||
for etf_info in etf_list:
|
||||
etf_code = etf_info['code']
|
||||
etf_name = etf_info['name']
|
||||
|
||||
# 获取ETF NAV（或收盘价）
|
||||
if btype == 'earliest_etf':
|
||||
# 原油：用收盘价对比收盘价
|
||||
etf_data = get_etf_close_tushare(pro, etf_code, start_date, end_date)
|
||||
else:
|
||||
etf_data = get_etf_nav_tushare(pro, etf_code, start_date, end_date)
|
||||
|
||||
if etf_data is None or len(etf_data) < 20:
|
||||
continue
|
||||
|
||||
tracking = calculate_tracking_error(etf_data, benchmark)
|
||||
if tracking is None:
|
||||
continue
|
||||
|
||||
result = {
|
||||
'ts_code': etf_code,
|
||||
'name': etf_name,
|
||||
'tushare_te': tracking['annual_tracking_error'],
|
||||
'tushare_r2': tracking['r_squared'],
|
||||
'tushare_correlation': tracking['correlation'],
|
||||
'tushare_excess_return': tracking['excess_return'],
|
||||
'tushare_common_days': tracking['common_days'],
|
||||
'eastmoney_te': etf_info['eastmoney_te'],
|
||||
'is_current': etf_code == current_etf,
|
||||
}
|
||||
|
||||
etf_results.append(result)
|
||||
time.sleep(0.05)
|
||||
|
||||
# 按跟踪误差排序
|
||||
etf_results.sort(key=lambda x: x['tushare_te'])
|
||||
|
||||
results[key] = {
|
||||
'index_name': index_name,
|
||||
'current_etf': current_etf,
|
||||
'benchmark_type': btype,
|
||||
'benchmark_code': bcode,
|
||||
'group': info['group'],
|
||||
'etf_count': len(etf_results),
|
||||
'etfs': etf_results,
|
||||
}
|
||||
|
||||
# 打印结果
|
||||
print(f"\n 计算完成: {len(etf_results)} 只ETF")
|
||||
print(f" {'代码':<12} {'名称':<20} {'TE':<10} {'天天基金TE':<12} {'R²':<8}")
|
||||
print(f" {'-'*70}")
|
||||
|
||||
for etf in etf_results[:10]:
|
||||
te_str = f"{etf['tushare_te']:.4f}%"
|
||||
em_te = etf['eastmoney_te']
|
||||
marker = " ★" if etf['is_current'] else ""
|
||||
print(f" {etf['ts_code']:<12} {etf['name'][:20]:<20} {te_str:<10} {em_te:<12} {etf['tushare_r2']:<8}{marker}")
|
||||
|
||||
if len(etf_results) > 10:
|
||||
print(f" ... 还有 {len(etf_results) - 10} 只")
|
||||
|
||||
# ============================================================
|
||||
# 保存结果
|
||||
# ============================================================
|
||||
output_dir = PROJECT_ROOT / 'rotation' / 'results'
|
||||
output_dir.mkdir(exist_ok=True)
|
||||
output_path = output_dir / 'tracking_error_full.json'
|
||||
|
||||
with open(output_path, 'w', encoding='utf-8') as f:
|
||||
json.dump(results, f, ensure_ascii=False, indent=2, default=str)
|
||||
|
||||
print(f"\n{'='*80}")
|
||||
print(f"结果已保存: {output_path}")
|
||||
print(f"{'='*80}")
|
||||
|
||||
# ============================================================
|
||||
# 汇总校验
|
||||
# ============================================================
|
||||
print(f"\n{'='*80}")
|
||||
print("全量校验汇总")
|
||||
print(f"{'='*80}")
|
||||
|
||||
for key, data in results.items():
|
||||
matched = [e for e in data['etfs']
|
||||
if e['eastmoney_te'] and e['eastmoney_te'] not in ['N/A', '--']]
|
||||
|
||||
print(f"\n--- {data['index_name']} ({data['benchmark_type']}) ---")
|
||||
print(f" ETF总数: {data['etf_count']} | 天天基金有数据: {len(matched)}")
|
||||
|
||||
if matched:
|
||||
diffs = []
|
||||
for etf in matched:
|
||||
try:
|
||||
em_te = float(etf['eastmoney_te'].replace('%', ''))
|
||||
diffs.append(etf['tushare_te'] - em_te)
|
||||
except:
|
||||
pass
|
||||
if diffs:
|
||||
print(f" 平均差异: {np.mean(diffs):+.4f}% | 最大差异: {max(diffs, key=abs):+.4f}%")
|
||||
|
||||
# 打印前3名
|
||||
top3 = data['etfs'][:3]
|
||||
print(f" Top3 (TE最低):")
|
||||
for i, etf in enumerate(top3, 1):
|
||||
marker = " ★当前" if etf['is_current'] else ""
|
||||
print(f" {i}. {etf['ts_code']} {etf['name']} TE={etf['tushare_te']:.4f}%{marker}")
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
289
rotation/tracking_error_validation.py
Normal file
289
rotation/tracking_error_validation.py
Normal file
@@ -0,0 +1,289 @@
|
||||
"""
|
||||
ETF跟踪误差计算与校验
|
||||
- 使用Tushare数据计算ETF跟踪误差（基于NAV）
|
||||
- 与天天基金数据对比校验
|
||||
"""
|
||||
import os
|
||||
import sys
|
||||
import time
|
||||
import json
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
from pathlib import Path
|
||||
from datetime import datetime, timedelta
|
||||
|
||||
PROJECT_ROOT = Path(__file__).parent.parent
|
||||
sys.path.insert(0, str(PROJECT_ROOT))
|
||||
from dotenv import load_dotenv
|
||||
load_dotenv(PROJECT_ROOT / '.env')
|
||||
|
||||
import tushare as ts
|
||||
|
||||
|
||||
# 轮动策略标的池
|
||||
POOL_INDEX_MAP = {
|
||||
'399006.SZ': {
|
||||
'name': '创业板指', 'current_etf': '159915.SZ', 'group': 'A',
|
||||
'index_code': '399006.SZ',
|
||||
},
|
||||
'H30269.CSI': {
|
||||
'name': '红利低波', 'current_etf': '512890.SH', 'group': 'A',
|
||||
'index_code': 'H30269.CSI',
|
||||
},
|
||||
}
|
||||
|
||||
|
||||
def get_etf_nav_data(pro, etf_code, start_date, end_date):
|
||||
"""
|
||||
获取ETF净值数据（使用fund_nav接口）
|
||||
注意：ETF应使用accum_nav（累计净值）而非unit_nav（单位净值）
|
||||
"""
|
||||
try:
|
||||
df = pro.fund_nav(
|
||||
ts_code=etf_code,
|
||||
start_date=start_date.replace('-', ''),
|
||||
end_date=end_date.replace('-', '')
|
||||
)
|
||||
if df is not None and len(df) > 0:
|
||||
df['date'] = pd.to_datetime(df['nav_date'])
|
||||
df = df.set_index('date').sort_index()
|
||||
# 使用累计净值
|
||||
return df['accum_nav'].astype(float)
|
||||
except Exception as e:
|
||||
print(f" 获取 {etf_code} NAV失败: {e}")
|
||||
return None
|
||||
|
||||
|
||||
def get_index_data(pro, index_code, start_date, end_date):
|
||||
"""获取指数日线数据"""
|
||||
try:
|
||||
df = pro.index_daily(
|
||||
ts_code=index_code,
|
||||
start_date=start_date.replace('-', ''),
|
||||
end_date=end_date.replace('-', '')
|
||||
)
|
||||
if df is not None and len(df) > 0:
|
||||
df['date'] = pd.to_datetime(df['trade_date'])
|
||||
df = df.set_index('date').sort_index()
|
||||
return df['close'].astype(float)
|
||||
except Exception as e:
|
||||
print(f" 获取指数 {index_code} 失败: {e}")
|
||||
return None
|
||||
|
||||
|
||||
def calculate_tracking_error(etf_nav, index_close):
|
||||
"""
|
||||
计算跟踪误差
|
||||
公式：STDEV(每日偏离度) × √252
|
||||
每日偏离度 = ETF净值收益率 - 指数收益率
|
||||
"""
|
||||
if etf_nav is None or index_close is None:
|
||||
return None
|
||||
|
||||
# 计算收益率
|
||||
etf_ret = etf_nav.pct_change().dropna()
|
||||
idx_ret = index_close.pct_change().dropna()
|
||||
|
||||
# 对齐日期
|
||||
common = etf_ret.index.intersection(idx_ret.index)
|
||||
if len(common) < 20:
|
||||
return None
|
||||
|
||||
e = etf_ret.loc[common]
|
||||
i = idx_ret.loc[common]
|
||||
|
||||
# 每日偏离度
|
||||
daily_deviation = e - i
|
||||
|
||||
# 跟踪误差 = 标准差 × √252
|
||||
tracking_error = daily_deviation.std() * np.sqrt(252)
|
||||
|
||||
# 其他指标
|
||||
correlation = e.corr(i)
|
||||
r_squared = correlation ** 2
|
||||
|
||||
# 累计收益
|
||||
etf_cum = (1 + e).prod() - 1
|
||||
idx_cum = (1 + i).prod() - 1
|
||||
excess = etf_cum - idx_cum
|
||||
|
||||
return {
|
||||
'annual_tracking_error': round(tracking_error * 100, 4), # %
|
||||
'correlation': round(correlation, 6),
|
||||
'r_squared': round(r_squared, 6),
|
||||
'etf_cum_return': round(etf_cum * 100, 2), # %
|
||||
'index_cum_return': round(idx_cum * 100, 2), # %
|
||||
'excess_return': round(excess * 100, 2), # %
|
||||
'common_days': len(common),
|
||||
}
|
||||
|
||||
|
||||
def main():
|
||||
print("=" * 80)
|
||||
print("ETF跟踪误差计算与校验")
|
||||
print(f"分析日期: {datetime.now().strftime('%Y-%m-%d')}")
|
||||
print("=" * 80)
|
||||
|
||||
# 初始化
|
||||
pro = ts.pro_api(os.getenv('TUSHARE_TOKEN'))
|
||||
|
||||
# 分析时间范围：最近1年
|
||||
end_date = datetime.now().strftime('%Y-%m-%d')
|
||||
start_date = (datetime.now() - timedelta(days=365)).strftime('%Y-%m-%d')
|
||||
print(f"计算区间: {start_date} ~ {end_date}")
|
||||
|
||||
# 加载天天基金数据
|
||||
eastmoney_path = PROJECT_ROOT / 'rotation' / 'results' / 'etf_competitor_analysis.json'
|
||||
eastmoney_data = {}
|
||||
if eastmoney_path.exists():
|
||||
with open(eastmoney_path, 'r', encoding='utf-8') as f:
|
||||
eastmoney_data = json.load(f)
|
||||
print(f"已加载天天基金数据: {len(eastmoney_data)} 个指数")
|
||||
|
||||
# 对每个指数计算跟踪误差
|
||||
print(f"\n开始计算跟踪误差...")
|
||||
|
||||
results = {}
|
||||
|
||||
for key, info in POOL_INDEX_MAP.items():
|
||||
index_name = info['name']
|
||||
index_code = info['index_code']
|
||||
current_etf = info['current_etf']
|
||||
|
||||
print(f"\n{'='*60}")
|
||||
print(f"=== {index_name} ({key}) ===")
|
||||
print(f"{'='*60}")
|
||||
|
||||
# 获取指数数据
|
||||
print(f" 获取指数数据: {index_code}")
|
||||
index_data = get_index_data(pro, index_code, start_date, end_date)
|
||||
if index_data is None:
|
||||
print(f" ✗ 指数数据获取失败")
|
||||
continue
|
||||
print(f" ✓ 指数数据: {len(index_data)} 天")
|
||||
|
||||
# 获取该指数下所有ETF的NAV
|
||||
etf_list = []
|
||||
if key in eastmoney_data:
|
||||
for etf in eastmoney_data[key]['etfs']:
|
||||
etf_list.append({
|
||||
'code': etf['ts_code'],
|
||||
'name': etf['name'],
|
||||
'eastmoney_te': etf.get('annual_tracking_error', 'N/A'),
|
||||
})
|
||||
|
||||
print(f" 共 {len(etf_list)} 只ETF需要计算")
|
||||
|
||||
etf_results = []
|
||||
|
||||
for etf_info in etf_list:
|
||||
etf_code = etf_info['code']
|
||||
etf_name = etf_info['name']
|
||||
|
||||
# 获取ETF NAV
|
||||
etf_nav = get_etf_nav_data(pro, etf_code, start_date, end_date)
|
||||
if etf_nav is None or len(etf_nav) < 20:
|
||||
continue
|
||||
|
||||
# 计算跟踪误差
|
||||
tracking = calculate_tracking_error(etf_nav, index_data)
|
||||
if tracking is None:
|
||||
continue
|
||||
|
||||
result = {
|
||||
'ts_code': etf_code,
|
||||
'name': etf_name,
|
||||
'tushare_te': tracking['annual_tracking_error'],
|
||||
'tushare_r2': tracking['r_squared'],
|
||||
'tushare_correlation': tracking['correlation'],
|
||||
'tushare_excess_return': tracking['excess_return'],
|
||||
'tushare_common_days': tracking['common_days'],
|
||||
'eastmoney_te': etf_info['eastmoney_te'],
|
||||
'is_current': etf_code == current_etf,
|
||||
}
|
||||
|
||||
etf_results.append(result)
|
||||
time.sleep(0.1)
|
||||
|
||||
# 按跟踪误差排序
|
||||
etf_results.sort(key=lambda x: x['tushare_te'])
|
||||
|
||||
results[key] = {
|
||||
'index_name': index_name,
|
||||
'index_code': index_code,
|
||||
'current_etf': current_etf,
|
||||
'etf_count': len(etf_results),
|
||||
'etfs': etf_results,
|
||||
}
|
||||
|
||||
# 打印结果
|
||||
print(f"\n 计算完成: {len(etf_results)} 只ETF")
|
||||
print(f" {'代码':<12} {'名称':<20} {'Tushare TE':<12} {'天天基金 TE':<12} {'差异':<10} {'R²':<8}")
|
||||
print(f" {'-'*80}")
|
||||
|
||||
for etf in etf_results[:10]:
|
||||
tushare_te = f"{etf['tushare_te']:.4f}%"
|
||||
eastmoney_te = etf['eastmoney_te']
|
||||
|
||||
# 计算差异
|
||||
diff = 'N/A'
|
||||
if eastmoney_te and eastmoney_te != 'N/A' and eastmoney_te != '--':
|
||||
try:
|
||||
em_te = float(eastmoney_te.replace('%', ''))
|
||||
diff_val = etf['tushare_te'] - em_te
|
||||
diff = f"{diff_val:+.4f}%"
|
||||
except:
|
||||
pass
|
||||
|
||||
marker = " ★" if etf['is_current'] else ""
|
||||
print(f" {etf['ts_code']:<12} {etf['name'][:20]:<20} {tushare_te:<12} {eastmoney_te:<12} {diff:<10} {etf['tushare_r2']:<8}{marker}")
|
||||
|
||||
if len(etf_results) > 10:
|
||||
print(f" ... 还有 {len(etf_results) - 10} 只")
|
||||
|
||||
# 保存结果
|
||||
output_dir = PROJECT_ROOT / 'rotation' / 'results'
|
||||
output_dir.mkdir(exist_ok=True)
|
||||
output_path = output_dir / 'tracking_error_validation.json'
|
||||
|
||||
with open(output_path, 'w', encoding='utf-8') as f:
|
||||
json.dump(results, f, ensure_ascii=False, indent=2, default=str)
|
||||
|
||||
print(f"\n{'='*80}")
|
||||
print(f"结果已保存: {output_path}")
|
||||
print(f"{'='*80}")
|
||||
|
||||
# 汇总统计
|
||||
print(f"\n{'='*80}")
|
||||
print("校验汇总")
|
||||
print(f"{'='*80}")
|
||||
|
||||
for key, data in results.items():
|
||||
print(f"\n--- {data['index_name']} ---")
|
||||
print(f" 指数代码: {data['index_code']}")
|
||||
print(f" 计算ETF数: {data['etf_count']}")
|
||||
|
||||
# 统计有天天基金数据的ETF
|
||||
matched = [e for e in data['etfs'] if e['eastmoney_te'] and e['eastmoney_te'] not in ['N/A', '--']]
|
||||
print(f" 天天基金有数据: {len(matched)}")
|
||||
|
||||
if matched:
|
||||
# 计算平均差异
|
||||
diffs = []
|
||||
for etf in matched:
|
||||
try:
|
||||
em_te = float(etf['eastmoney_te'].replace('%', ''))
|
||||
diff = etf['tushare_te'] - em_te
|
||||
diffs.append(diff)
|
||||
except:
|
||||
pass
|
||||
|
||||
if diffs:
|
||||
avg_diff = np.mean(diffs)
|
||||
max_diff = max(diffs, key=abs)
|
||||
print(f" 平均差异: {avg_diff:+.4f}%")
|
||||
print(f" 最大差异: {max_diff:+.4f}%")
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
Reference in New Issue
Block a user