fix: generate_report now uses actual position_weights from daily_records

Previously hardcoded equal weight (1/select_num), ignoring config weight type.
Now reads position_weights from last daily_record, correctly showing rank-based weights.

rotation/experiments/debug_clf_2022.py

@@ -0,0 +1,128 @@
+"""分析 2022年4月底~5月初 CL=F 入选原因"""
+import os, sys, math
+from pathlib import Path
+import numpy as np
+import pandas as pd
+
+PROJECT_ROOT = Path(__file__).parent.parent.parent
+sys.path.insert(0, str(PROJECT_ROOT))
+
+from rotation.simple_rotation import SimpleRotationStrategy, slope_r2_score
+
+if 'FLASK_API_URL' not in os.environ:
+    os.environ['FLASK_API_URL'] = 'https://k3s.tokenpluse.xyz'
+
+strategy = SimpleRotationStrategy()
+strategy._preload_data()
+
+# 分析日期范围
+start = pd.Timestamp('2022-04-15')
+end = pd.Timestamp('2022-05-10')
+n_days = strategy.config.factor.n_days  # 25
+
+print("=" * 80)
+print(f"  分析 CL=F 动量信号 ({start.date()} ~ {end.date()})")
+print(f"  窗口长度: {n_days} 天")
+print("=" * 80)
+
+# 获取所有 signal_codes 的 score
+signal_codes = strategy.signal_codes
+date_range = pd.bdate_range(start, end)
+
+for date in date_range:
+    scores = {}
+    for code in signal_codes:
+        if code not in strategy.index_data:
+            continue
+        df = strategy.index_data[code]
+        mask = df.index <= date
+        recent = df.loc[mask]
+        if len(recent) < n_days:
+            continue
+        prices = recent['close'].values[-n_days:]
+        score = slope_r2_score(prices)
+        scores[code] = (score, prices[-1])
+    
+    if not scores:
+        continue
+    
+    # 排序
+    ranked = sorted(scores.items(), key=lambda x: x[1][0], reverse=True)
+    cl_rank = None
+    for i, (code, (score, price)) in enumerate(ranked):
+        if code == 'CL=F':
+            cl_rank = i + 1
+            break
+    
+    cl_score = scores.get('CL=F', (None, None))[0]
+    cl_price = scores.get('CL=F', (None, None))[1]
+    
+    print(f"\n{date.strftime('%Y-%m-%d')} | CL=F score={cl_score:.4f}, price={cl_price:.2f}, rank={cl_rank}/{len(ranked)}")
+    print(f"  Top 5:")
+    for i, (code, (score, price)) in enumerate(ranked[:5]):
+        marker = " <<<" if code == 'CL=F' else ""
+        print(f"    #{i+1} {code:<15} score={score:>10.4f}  price={price:.2f}{marker}")
+
+# 详细分析 CL=F 价格走势
+print(f"\n{'='*80}")
+print(f"  CL=F 价格走势 (2022年3月~5月)")
+print(f"{'='*80}")
+
+df_cl = strategy.index_data['CL=F']
+mask = (df_cl.index >= '2022-03-01') & (df_cl.index <= '2022-05-15')
+cl_prices = df_cl.loc[mask, 'close']
+
+for date, price in cl_prices.items():
+    # 计算25天窗口的score
+    mask2 = df_cl.index <= date
+    recent = df_cl.loc[mask2]
+    if len(recent) < n_days:
+        continue
+    prices = recent['close'].values[-n_days:]
+    score = slope_r2_score(prices)
+    normalized = prices / prices[0]
+    slope, intercept = np.polyfit(np.arange(len(normalized)), normalized, 1)
+    y_pred = slope * np.arange(len(normalized)) + intercept
+    ss_res = np.sum((normalized - y_pred) ** 2)
+    ss_tot = np.sum((normalized - np.mean(normalized)) ** 2)
+    r2 = 1 - ss_res / ss_tot if ss_tot > 0 else 0
+    
+    flag = ""
+    if date.strftime('%Y-%m-%d') in ('2022-04-29', '2022-05-05'):
+        flag = " <<< 入选日"
+    
+    print(f"  {date.strftime('%Y-%m-%d')}  price={price:>8.2f}  score={score:>10.4f}  "
+          f"slope={slope:>8.5f}  R²={r2:.4f}{flag}")
+
+# 分析 CL=F 的组内竞争
+print(f"\n{'='*80}")
+print(f"  CL=F 所在组: 查看组内竞争")
+print(f"{'='*80}")
+
+groups = strategy.config.asset_pools.by_group
+for group_name, assets in groups.items():
+    group_codes = [a.signal_source for a in assets.values()]
+    if 'CL=F' in group_codes:
+        print(f"  组名: {group_name}")
+        print(f"  组成员: {group_codes}")
+        
+        # 4/29 和 5/5 的组内得分
+        for target_date_str in ['2022-04-29', '2022-05-05']:
+            target_date = pd.Timestamp(target_date_str)
+            print(f"\n  {target_date_str} 组内得分:")
+            for code in group_codes:
+                if code not in strategy.index_data:
+                    continue
+                df = strategy.index_data[code]
+                mask = df.index <= target_date
+                recent = df.loc[mask]
+                if len(recent) < n_days:
+                    print(f"    {code:<15} 数据不足")
+                    continue
+                prices = recent['close'].values[-n_days:]
+                score = slope_r2_score(prices)
+                marker = " <<< TOP1" if score == max(
+                    slope_r2_score(strategy.index_data[c].loc[strategy.index_data[c].index <= target_date]['close'].values[-n_days:])
+                    for c in group_codes if c in strategy.index_data and len(strategy.index_data[c].loc[strategy.index_data[c].index <= target_date]) >= n_days
+                ) else ""
+                print(f"    {code:<15} score={score:>10.4f}{marker}")