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/portfolio.py

@@ -95,7 +95,10 @@ def track_positions(
         # 多品种等权轮动
         current_signal = signals[0]
         entry_date = dates[0]
-        codes = current_signal.split(",")
+        codes = [c for c in current_signal.split(",") if c]  # 过滤空字符串
+        if not codes:
+            # 空信号，返回空结果
+            return pd.DataFrame(trades), pd.DataFrame()
         weight = 1.0 / len(codes)
         entry_prices = {c: data.loc[entry_date, c] for c in codes}
         entry_nav = data.loc[entry_date, "轮动策略净值"]
@@ -131,7 +134,9 @@ def track_positions(
 
                 current_signal = today_signal
                 entry_date = dates[i]
-                codes = current_signal.split(",")
+                codes = [c for c in current_signal.split(",") if c]  # 过滤空字符串
+                if not codes:
+                    break  # 空信号，结束循环
                 weight = 1.0 / len(codes)
                 entry_prices = {c: data.loc[entry_date, c] for c in codes}
                 entry_nav = data.loc[entry_date, "轮动策略净值"]
@@ -211,23 +216,37 @@ def save_trades(
     import os
     os.makedirs(os.path.dirname(save_path) if os.path.dirname(save_path) else ".", exist_ok=True)
 
-    trades_out = trades_df.copy()
-    trades_out["持仓收益"] = trades_out["持仓收益"].apply(lambda x: f"{x:.2%}")
-    trades_out["进场日期"] = trades_out["进场日期"].apply(
-        lambda x: x.strftime("%Y-%m-%d") if hasattr(x, "strftime") else str(x)[:10]
-    )
-    trades_out["出场日期"] = trades_out["出场日期"].apply(
-        lambda x: x.strftime("%Y-%m-%d") if hasattr(x, "strftime") else str(x)[:10]
-    )
-
+    # 保存调仓明细
     trades_path = f"{save_path}_trades.csv"
-    trades_out.to_csv(trades_path, index=False, encoding="utf-8-sig")
-    print(f"\n调仓明细已保存: {trades_path}")
-
-    summary_out = summary_df.copy()
-    for col in ["胜率", "平均收益", "累计收益", "最大单次收益", "最大单次亏损"]:
-        summary_out[col] = summary_out[col].apply(lambda x: f"{x:.2%}")
+    if not trades_df.empty:
+        trades_out = trades_df.copy()
+        if "持仓收益" in trades_out.columns:
+            trades_out["持仓收益"] = trades_out["持仓收益"].apply(lambda x: f"{x:.2%}")
+        if "进场日期" in trades_out.columns:
+            trades_out["进场日期"] = trades_out["进场日期"].apply(
+                lambda x: x.strftime("%Y-%m-%d") if hasattr(x, "strftime") else str(x)[:10]
+            )
+        if "出场日期" in trades_out.columns:
+            trades_out["出场日期"] = trades_out["出场日期"].apply(
+                lambda x: x.strftime("%Y-%m-%d") if hasattr(x, "strftime") else str(x)[:10]
+            )
+        trades_out.to_csv(trades_path, index=False, encoding="utf-8-sig")
+        print(f"\n调仓明细已保存: {trades_path}")
+    else:
+        # 创建空文件
+        pd.DataFrame().to_csv(trades_path, index=False, encoding="utf-8-sig")
+        print(f"\n调仓明细为空: {trades_path}")
 
+    # 保存品种汇总
     summary_path = f"{save_path}_summary.csv"
-    summary_out.to_csv(summary_path, index=False, encoding="utf-8-sig")
-    print(f"品种汇总已保存: {summary_path}")
+    if not summary_df.empty:
+        summary_out = summary_df.copy()
+        for col in ["胜率", "平均收益", "累计收益", "最大单次收益", "最大单次亏损"]:
+            if col in summary_out.columns:
+                summary_out[col] = summary_out[col].apply(lambda x: f"{x:.2%}")
+        summary_out.to_csv(summary_path, index=False, encoding="utf-8-sig")
+        print(f"品种汇总已保存: {summary_path}")
+    else:
+        # 创建空文件
+        pd.DataFrame().to_csv(summary_path, index=False, encoding="utf-8-sig")
+        print(f"品种汇总为空: {summary_path}")