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

rotation/oil_tracking.py

@@ -0,0 +1,129 @@
+"""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）"""
+import os, sys
+sys.path.insert(0, '/app')
+from dotenv import load_dotenv
+load_dotenv('/app/.env')
+
+import pandas as pd
+import numpy as np
+import requests
+import time
+
+# 绕过系统代理避免SSL EOF
+_session = requests.Session()
+_session.trust_env = False
+
+BASE_URL = os.getenv('FLASK_API_URL', 'https://k3s.tokenpluse.xyz')
+
+def fetch_ohlcv(code, start='2020-01-01', end='2026-06-20'):
+    """通过Flask API获取OHLCV数据"""
+    url = f"{BASE_URL}/api/v1/ohlcv"
+    params = {'code': code, 'start': start, 'end': end}
+    for attempt in range(3):
+        try:
+            resp = _session.get(url, params=params, timeout=120)
+            if resp.status_code == 200:
+                data = resp.json()
+                if 'error' in data or not data.get('data'):
+                    print(f"  {code}: 无数据 - {data.get('error', 'empty')}")
+                    return None
+                df = pd.DataFrame(data['data'])
+                df['date'] = pd.to_datetime(df['date'])
+                df = df.set_index('date').sort_index()
+                df['ret'] = df['close'].pct_change()
+                cnt = data.get('count', len(df))
+                dr = data.get('date_range', {})
+                print(f"  {code}: {cnt} 条 ({dr.get('start','?')} ~ {dr.get('end','?')})")
+                return df
+            else:
+                print(f"  {code}: HTTP {resp.status_code}")
+                return None
+        except Exception as e:
+            if attempt < 2:
+                time.sleep(2)
+                continue
+            print(f"  {code}: 失败 - {e}")
+            return None
+    return None
+
+# 1. 获取WTI原油价格
+print("=" * 90)
+print("获取WTI原油价格 (CL=F)")
+print("=" * 90)
+cl_df = fetch_ohlcv('CL=F')
+
+if cl_df is None or len(cl_df) < 10:
+    print("WTI数据获取失败，退出")
+    sys.exit(1)
+
+# 2. 获取3只原油ETF价格
+etf_codes = {
+    '160723.SZ': '嘉实原油(WTI*100%)',
+    '161129.SZ': '易方达原油(标普高盛原油*100%)',
+    '501018.SH': '南方原油(WTI*60%+BRENT*40%)',
+}
+
+print("\n" + "=" * 90)
+print("获取原油ETF价格")
+print("=" * 90)
+etf_dfs = {}
+for code, name in etf_codes.items():
+    df = fetch_ohlcv(code)
+    if df is not None:
+        etf_dfs[code] = name
+
+# 3. 计算跟踪准确率
+print("\n" + "=" * 90)
+print("跟踪准确率（基于ETF收盘价收益率 vs WTI收盘价日收益率）")
+print("=" * 90)
+
+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)