feat: UI/UX全面优化 — CSS变量体系/a11y无障碍/Admin角色化/骨架屏/打印样式

stock-html/services/stock_algorithms.py

@@ -830,13 +830,15 @@ def compute_bull_stage(signal_status):
     }
 
 
-def find_bull_stocks(scan_rows, holding_codes=None):
+def find_bull_stocks(scan_rows, holding_codes=None, scores_map=None):
     """
     从扫描结果中找出潜在牛股，按阶段分组排序。
     
     参数:
         scan_rows: list[dict] 扫描结果列表 (含 code, name, signal_status, indicators, triggered_count)
         holding_codes: set 持仓代码集合
+        scores_map: dict 综合评分映射 {code: {technical_score, external_score, final_score, verdict}}
+                    当提供时，每只股票附加三项得分，并按综合得分排序
     
     返回:
         dict: {
@@ -872,8 +874,21 @@ def find_bull_stocks(scan_rows, holding_codes=None):
             row.get('triggered_count'), is_holding,
         )
 
+        # 附加综合评分（如果提供了 scores_map）
+        code = row.get('code', '')
+        technical_score = rate
+        external_score = 0
+        final_score = rate
+        verdict = ''
+        if scores_map and code in scores_map:
+            sc = scores_map[code]
+            technical_score = sc.get('technical_score', rate)
+            external_score = sc.get('external_score', 0)
+            final_score = sc.get('final_score', rate)
+            verdict = sc.get('verdict', '')
+
         item = {
-            'code': row.get('code', ''),
+            'code': code,
             'name': row.get('name', ''),
             'stage': stage,
             'stage_name': bull['stage_name'],
@@ -887,16 +902,26 @@ def find_bull_stocks(scan_rows, holding_codes=None):
             'recommend_type': st,
             'recommend_text': disp,
             'recommend_reason': reason,
-            'recommend_rate': rate,
+            'recommend_rate': final_score,
             'is_holding': is_holding,
             'triggered_count': row.get('triggered_count', 0),
+            'technical_score': technical_score,
+            'external_score': external_score,
+            'final_score': final_score,
+            'verdict': verdict,
         }
 
         stages[stage].append(item)
 
-    # 每个阶段内按推荐评分降序排序
-    for stage_num in stages:
-        stages[stage_num].sort(key=lambda x: (-x['recommend_rate'], -x['progress']))
+    # 每个阶段内排序：有综合评分时按综合得分→技术得分→进度，否则按推荐评分→进度
+    if scores_map:
+        for stage_num in stages:
+            stages[stage_num].sort(
+                key=lambda x: (-x.get('final_score', 0), -x.get('technical_score', 0), -x['progress'])
+            )
+    else:
+        for stage_num in stages:
+            stages[stage_num].sort(key=lambda x: (-x['recommend_rate'], -x['progress']))
 
     total = sum(len(v) for v in stages.values())