fix(core): 修复计算与数据对齐等多处逻辑问题

- 修正CAGR计算，去除NaN并检查起始值有效性以避免异常结果
- 优化混合数据源的数据对齐逻辑，使用配置结束日期与A股最新数据日期的较早者
- 计算因子时对齐A股交易日历，重新基于对齐价格计算日收益率，改进因子对齐准确度
- 轮动策略中跳过空信号，避免空信号影响持仓和调仓逻辑
- 调整信号处理，过滤空字符串和NaN，保证轮动信号数据有效性
- 多品种轮动持仓中加入空信号判断，避免无效信号导致错误
- 调整调仓明细和品种汇总保存逻辑，增加空文件创建以保证输出路径文件稳定生成
- 完善多处打印信息和注释，增强代码可读性与调试便利性

strategies/rotation/engine.py

@@ -115,6 +115,10 @@ class RotationStrategy(BacktestStrategy):
             target = daily_target.iloc[i]
 
             if current_held is None:
+                # 跳过空信号，直到找到第一个有效信号
+                if not target:
+                    held_signals.append(None)  # 添加None占位，保持长度一致
+                    continue
                 current_held = target
                 last_rebalance_idx = i
                 held_signals.append(current_held)
@@ -122,20 +126,24 @@ class RotationStrategy(BacktestStrategy):
 
             days_since = i - last_rebalance_idx
             if days_since >= rebalance_days:
-                should = self._check_rebalance(
-                    result.iloc[i], current_held, target,
-                    select_num, rebalance_threshold
-                )
-                if should:
-                    current_held = target
-                    last_rebalance_idx = i
+                # 目标信号为空时不调仓
+                if target:  # 只在目标有效时才检查是否调仓
+                    should = self._check_rebalance(
+                        result.iloc[i], current_held, target,
+                        select_num, rebalance_threshold
+                    )
+                    if should:
+                        current_held = target
+                        last_rebalance_idx = i
 
             held_signals.append(current_held)
 
         result["信号_raw"] = held_signals
         result["信号"] = result["信号_raw"].shift(1)
         result = result.drop(columns=["信号_raw"])
+        # 删除信号为 NaN 或空字符串的行
         result = result.dropna(subset=["信号"])
+        result = result[result["信号"] != ""]
 
         self.signals = result
         self._print_signal_stats(result, select_num, rebalance_days, rebalance_threshold)
@@ -152,12 +160,14 @@ class RotationStrategy(BacktestStrategy):
                 return (new_score / old_score - 1) >= threshold
             return new_score > 0
         else:
-            new_codes = target.split(",")
-            old_codes = current_held.split(",")
+            new_codes = [c for c in target.split(",") if c]  # 过滤空字符串
+            old_codes = [c for c in current_held.split(",") if c]  # 过滤空字符串
+            if not new_codes or not old_codes:
+                return True  # 有空持仓，需要调仓
             if set(new_codes) == set(old_codes):
                 return False
-            new_total = sum(float(row[f"得分_{c}"]) for c in new_codes)
-            old_total = sum(float(row[f"得分_{c}"]) for c in old_codes)
+            new_total = sum(float(row.get(f"得分_{c}", 0)) for c in new_codes)
+            old_total = sum(float(row.get(f"得分_{c}", 0)) for c in old_codes)
             if old_total > 0:
                 return (new_total / old_total - 1) >= threshold
             return new_total > 0
@@ -206,12 +216,17 @@ class RotationStrategy(BacktestStrategy):
         # 计算策略日收益率
         if select_num == 1:
             def calc_return(row):
-                return row[f"日收益率_{row['信号']}"]
+                signal = row['信号']
+                if not signal or pd.isna(signal):
+                    return 0.0
+                return row.get(f"日收益率_{signal}", 0.0)
             result["轮动策略日收益率"] = result.apply(calc_return, axis=1)
         else:
             def calc_multi_return(row):
-                codes = row["信号"].split(",")
-                returns = [row[f"日收益率_{c}"] for c in codes]
+                codes = [c for c in row["信号"].split(",") if c]  # 过滤空字符串
+                if not codes:
+                    return 0.0
+                returns = [row.get(f"日收益率_{c}", 0.0) for c in codes]
                 return np.mean(returns)
             result["轮动策略日收益率"] = result.apply(calc_multi_return, axis=1)