diff --git a/app/ui/views/backtest.py b/app/ui/views/backtest.py
index 8b27923..1b205b4 100644
--- a/app/ui/views/backtest.py
+++ b/app/ui/views/backtest.py
@@ -381,7 +381,7 @@ def render_backtest_review() -> None:
                                     title="风险事件分布",
                                 )
                                 agg_fig.update_layout(height=320, margin=dict(l=10, r=10, t=40, b=20))
-                                st.plotly_chart(agg_fig, use_container_width=True)
+                                st.plotly_chart(agg_fig, width="stretch")
                         except Exception:  # noqa: BLE001
                             LOGGER.debug("绘制风险事件分布失败", extra=LOG_EXTRA)
                 except Exception:  # noqa: BLE001
diff --git a/app/ui/views/factor_calculation.py b/app/ui/views/factor_calculation.py
index 0c30482..ab787ff 100644
--- a/app/ui/views/factor_calculation.py
+++ b/app/ui/views/factor_calculation.py
@@ -274,7 +274,7 @@ def render_factor_calculation() -> None:
                 
                 if df_data:
                     df = pd.DataFrame(df_data)
-                    st.dataframe(df.head(100), use_container_width=True)  # 只显示前100条
+                    st.dataframe(df.head(100), width="stretch")  # 只显示前100条
                     st.info(f"共 {len(df_data)} 条因子记录（显示前100条）")
             else:
                 st.info("没有找到因子计算结果")
diff --git a/app/ui/views/market.py b/app/ui/views/market.py
index 0ea1778..de6a08d 100644
--- a/app/ui/views/market.py
+++ b/app/ui/views/market.py
@@ -194,10 +194,10 @@ def render_market_visualization() -> None:
         ]
     )
     fig.update_layout(title=f"{ts_code} K线图", xaxis_title="日期", yaxis_title="价格")
-    st.plotly_chart(fig, use_container_width=True)
+    st.plotly_chart(fig, width="stretch")
 
     fig_vol = px.bar(df, x="trade_date", y="vol", title="成交量")
-    st.plotly_chart(fig_vol, use_container_width=True)
+    st.plotly_chart(fig_vol, width="stretch")
 
     df_ma = df.copy()
     df_ma["MA5"] = df_ma["close"].rolling(window=5).mean()
@@ -205,6 +205,6 @@ def render_market_visualization() -> None:
     df_ma["MA60"] = df_ma["close"].rolling(window=60).mean()
 
     fig_ma = px.line(df_ma, x="trade_date", y=["close", "MA5", "MA20", "MA60"], title="均线对比")
-    st.plotly_chart(fig_ma, use_container_width=True)
+    st.plotly_chart(fig_ma, width="stretch")
 
     st.dataframe(df, hide_index=True, width='stretch')
diff --git a/app/ui/views/stock_eval.py b/app/ui/views/stock_eval.py
index e681bea..537c5d2 100644
--- a/app/ui/views/stock_eval.py
+++ b/app/ui/views/stock_eval.py
@@ -208,7 +208,7 @@ def render_stock_evaluation() -> None:
         st.dataframe(
             result_df,
             hide_index=True,
-            use_container_width=True
+            width="stretch"
         )
         
         # 绘制IC均值分布
@@ -237,7 +237,7 @@ def render_stock_evaluation() -> None:
                 st.dataframe(
                     score_df,
                     hide_index=True,
-                    use_container_width=True
+                    width="stretch"
                 )
                 
                 # 添加入池功能
@@ -260,7 +260,12 @@ def _calculate_stock_scores(
     
     # 标准化权重
     weights = np.array(factor_weights)
-    weights = weights / np.sum(np.abs(weights))
+    abs_sum = np.sum(np.abs(weights))
+    if abs_sum > 0:  # 避免除以零
+        weights = weights / abs_sum
+    else:
+        # 如果所有权重都是零，则使用均匀分布
+        weights = np.ones_like(weights) / len(weights)
     
     # 获取所有股票的因子值
     stocks = universe or broker.get_all_stocks(eval_date.strftime("%Y%m%d"))
diff --git a/app/utils/data_access.py b/app/utils/data_access.py
index f75a3de..18794fe 100644
--- a/app/utils/data_access.py
+++ b/app/utils/data_access.py
@@ -1397,6 +1397,83 @@ class DataBroker:
         self._coverage_cache.clear()
         LOGGER.info("数据覆盖缓存已清除", extra=LOG_EXTRA)
 
+    def get_stock_info(self, ts_code: str, trade_date: str = None) -> Optional[Dict[str, Any]]:
+        """获取股票基本信息。
+
+        Args:
+            ts_code: 股票代码
+            trade_date: 交易日期，默认为最新日期
+
+        Returns:
+            Dict: 股票基本信息，包含名称、行业等
+        """
+        if not trade_date:
+            # 如果没有提供交易日期，使用当前日期
+            trade_date = datetime.now().strftime("%Y%m%d")
+        
+        try:
+            # 获取股票基本信息
+            info = self.fetch_latest(
+                ts_code=ts_code,
+                trade_date=trade_date,
+                fields=["stock_basic.name", "stock_basic.industry"]
+            )
+            
+            if not info:
+                return None
+                
+            # 添加股票代码
+            result = {"ts_code": ts_code}
+            result.update(info)
+            
+            return result
+        except Exception as exc:
+            LOGGER.debug(
+                "获取股票信息失败 ts_code=%s err=%s",
+                ts_code,
+                exc,
+                extra=LOG_EXTRA
+            )
+            return None
+
+    def fetch_latest_factor(self, ts_code: str, factor: str, eval_date: date) -> Optional[float]:
+        """获取指定股票的最新因子值。
+
+        Args:
+            ts_code: 股票代码
+            factor: 因子名称
+            eval_date: 评估日期
+
+        Returns:
+            float: 因子值，如果获取失败则返回None
+        """
+        trade_date = eval_date.strftime("%Y%m%d")
+        
+        try:
+            # 构建因子字段名称
+            factor_field = f"factors.{factor}"
+            
+            # 获取因子值
+            result = self.fetch_latest(
+                ts_code=ts_code,
+                trade_date=trade_date,
+                fields=[factor_field]
+            )
+            
+            if not result or factor_field not in result:
+                return None
+                
+            return result[factor_field]
+        except Exception as exc:
+            LOGGER.debug(
+                "获取因子值失败 ts_code=%s factor=%s err=%s",
+                ts_code,
+                factor,
+                exc,
+                extra=LOG_EXTRA
+            )
+            return None
+
     def get_data_coverage(self, start_date: str, end_date: str) -> Dict:
         """获取指定日期范围内的数据覆盖情况。