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
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selfrelease
@@ -193,6 +193,35 @@ def deep_analyze():
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# 其他情况保持技术面推荐，但更新评分为综合评分
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else:
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report['recommend']['rate'] = final_score
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# ---- 追加三项得分汇总到 AI 解说 ----
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tech_score = comprehensive.get('technical_score', 0)
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ext_score = comprehensive.get('external_score', 0)
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fin_score = comprehensive.get('final_score', 0)
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fin_verdict = comprehensive.get('verdict', '')
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ext_summary = comprehensive.get('summary', '')
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score_line = (
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f'综合评分汇总：技术得分{tech_score:.0f}分，'
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f'外部得分{"+" if ext_score >= 0 else ""}{ext_score}分，'
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f'综合得分{fin_score}分（{fin_verdict}）。'
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)
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if ext_summary:
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score_line += f'外部因素：{ext_summary}。'
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if report.get('ai_summary'):
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report['ai_summary']['text'] += score_line
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# 更新 action_tip 以综合得分为准
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if fin_score >= 80:
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report['ai_summary']['action_tip'] = '综合评级强烈看多，技术面与外部因素共振看好，可以考虑积极参与。'
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elif fin_score >= 65:
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report['ai_summary']['action_tip'] = '综合评级看多，整体偏积极，可以逢低关注。'
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elif fin_score >= 50:
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report['ai_summary']['action_tip'] = '综合评级中性偏多，多空均衡，建议观望为主。'
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elif fin_score >= 35:
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report['ai_summary']['action_tip'] = '综合评级中性偏空，外部因素拖累，不建议急于买入。'
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else:
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report['ai_summary']['action_tip'] = '综合评级看空，外部因素重大利空，建议回避或减仓。'
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report['ai_summary']['confidence'] = '高' if fin_score >= 70 or fin_score <= 30 else '中'
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except Exception as e:
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print(f"综合评分引擎计算失败，使用技术面评分: {e}")
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@@ -655,6 +684,7 @@ def get_scan_results():
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page = int(request.args.get('page', 1))
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per_page = int(request.args.get('per_page', 50))
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sort_by = request.args.get('sort', 'triggered_count')
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with_scores = request.args.get('with_scores', 'false').lower() == 'true'
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holding_codes_str = request.args.get('holding_codes', '')
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holding_set = set(c.strip() for c in holding_codes_str.split(',') if c.strip())
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recommend_text = (request.args.get('recommend_text') or '').strip()
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@@ -777,6 +807,29 @@ def get_scan_results():
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item['holding_note'] = f"若已持仓：{dh}（{rh}）"
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results.append(item)
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# ---- 批量计算综合评分，按综合得分重排序 ----
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if with_scores and results:
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try:
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from services.score_engine import compute_comprehensive_score_batch
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stocks_input = [
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{'stock_code': r['code'], 'stock_name': r.get('name', ''),
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'technical_score': r.get('recommend_rate', 50)}
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for r in results
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]
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scores_map = compute_comprehensive_score_batch(stocks_input)
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for r in results:
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sc = scores_map.get(r['code'])
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if sc:
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r['technical_score'] = sc['technical_score']
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r['external_score'] = sc['external_score']
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r['final_score'] = sc['final_score']
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r['verdict'] = sc['verdict']
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r['recommend_rate'] = sc['final_score']
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# 按综合得分降序重排当前页
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results.sort(key=lambda x: (-x.get('final_score', 0), -x.get('triggered_count', 0)))
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except Exception as e:
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print(f'批量综合评分计算失败: {e}')
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if codes_for_page is not None:
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if codes_for_page:
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placeholders = ','.join(['%s'] * len(codes_for_page))
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@@ -808,6 +861,28 @@ def get_scan_results():
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by_code[code] = item
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results = [by_code[c] for c in codes_for_page if c in by_code]
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# ---- 批量计算综合评分（recommend_text 筛选路径）----
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if with_scores and results and codes_for_page is not None:
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try:
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from services.score_engine import compute_comprehensive_score_batch
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stocks_input = [
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{'stock_code': r['code'], 'stock_name': r.get('name', ''),
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'technical_score': r.get('recommend_rate', 50)}
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for r in results
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]
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scores_map = compute_comprehensive_score_batch(stocks_input)
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for r in results:
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sc = scores_map.get(r['code'])
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if sc:
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r['technical_score'] = sc['technical_score']
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r['external_score'] = sc['external_score']
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r['final_score'] = sc['final_score']
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r['verdict'] = sc['verdict']
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r['recommend_rate'] = sc['final_score']
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results.sort(key=lambda x: (-x.get('final_score', 0), -x.get('triggered_count', 0)))
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except Exception as e:
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print(f'批量综合评分计算失败(recommend_text路径): {e}')
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cur.execute("""
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SELECT
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s.value->>'name' as signal_name,
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@@ -1276,8 +1351,29 @@ def get_bull_stocks():
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conn.close()
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# 使用统一算法找牛股
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result = find_bull_stocks(scan_rows, holding_codes)
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# ---- 批量计算综合评分 ----
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scores_map = None
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try:
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from services.score_engine import compute_comprehensive_score_batch
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# 先用 compute_recommend 算出技术面基础分
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stocks_input = []
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for row in scan_rows:
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sig_status = row.get('signal_status') or []
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indicators = row.get('indicators') or {}
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tc = row.get('triggered_count') or 0
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is_holding = row.get('code', '') in holding_codes
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_, _, _, rate = compute_recommend(sig_status, indicators, tc, is_holding)
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stocks_input.append({
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'stock_code': row.get('code', ''),
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'stock_name': row.get('name', ''),
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'technical_score': rate,
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})
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scores_map = compute_comprehensive_score_batch(stocks_input)
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except Exception as e:
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print(f'牛股筛选综合评分计算失败: {e}')
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# 使用统一算法找牛股（传入综合评分）
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result = find_bull_stocks(scan_rows, holding_codes, scores_map=scores_map)
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# 为每只股票附加价格信息
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for stage_num, stocks in result['stages'].items():
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