fix: xiaoguo_scanner 榜单更新+看空榜持仓预警

- 修bug：stock_rank_cxd_ths 实为'创新低'，改为 stock_rank_lxsz_ths '连续上涨'
- 新增6个看多榜（险资举牌）+ 5个看空榜（创新低/持续缩量/量价齐跌/连续下跌/向下突破）
- 看空榜自动比对持仓，命中写入 xiaoguo_risk 信号
- 东财热榜静默降级（502不可修）
- 看空榜不跳过已扫描，每轮全检

market_watch.py

@@ -117,6 +117,42 @@ def get_market_mood(sectors):
     return "bullish" if ratio > 0.7 else "neutral" if ratio > 0.4 else "bearish"
 
 
+def get_market_verdict(up_ratio, mood, sectors):
+    """Return (verdict, reason) based on sector data."""
+    if not sectors:
+        return "unknown", "数据不足"
+    if up_ratio < 25:
+        return "弱势", f"仅{up_ratio}%板块上涨，{mood}"
+    elif up_ratio < 40:
+        return "偏弱", f"{up_ratio}%板块上涨，结构分化"
+    elif up_ratio < 60:
+        return "均衡", f"{up_ratio}%板块上涨，涨跌均衡"
+    else:
+        return "强势", f"{up_ratio}%板块上涨，整体走强"
+
+
+def get_hot_sectors(sectors, top_n=3):
+    """Return sectors with highest positive change as hot sectors."""
+    hot = [s for s in sectors if s.get("change", 0) > 1.0]
+    hot.sort(key=lambda s: s.get("change", 0), reverse=True)
+    return [{
+        "name": s["name"],
+        "change": s.get("change", 0),
+        "reason": f"板块涨{s.get('change',0):.1f}%"
+    } for s in hot[:top_n]]
+
+
+def get_danger_sectors(sectors, top_n=3):
+    """Return sectors with lowest (negative) change as danger sectors."""
+    danger = [s for s in sectors if s.get("change", 0) < -1.0]
+    danger.sort(key=lambda s: s.get("change", 0))
+    return [{
+        "name": s["name"],
+        "change": s.get("change", 0),
+        "reason": f"板块跌{s.get('change',0):.1f}%"
+    } for s in danger[:top_n]]
+
+
 # ── 主流程 ──
 
 def main():
@@ -142,15 +178,24 @@ def main():
     top_gainers = [s for s in sorted_sectors if s.get("change", 0) > 0][:5]
     top_losers = [s for s in reversed(sorted_sectors) if s.get("change", 0) < 0][:3]
 
+    # 计算大盘数据
+    up_ratio = round(
+        sum(1 for s in sectors if s.get("change", 0) > 0) / max(len(sectors), 1) * 100, 1
+    )
+    mood = get_market_mood(sectors)
+    verdict, verdict_reason = get_market_verdict(up_ratio, mood, sectors)
+
     market_data = {
         "timestamp": datetime.now().strftime("%Y-%m-%d %H:%M"),
         "source": source,
         "concept_source": concept_source,
         "total_sectors": len(sectors),
-        "up_ratio": round(
-            sum(1 for s in sectors if s.get("change", 0) > 0) / max(len(sectors), 1) * 100, 1
-        ),
-        "mood": get_market_mood(sectors),
+        "up_ratio": up_ratio,
+        "mood": mood,
+        "market_verdict": verdict,
+        "verdict_reason": verdict_reason,
+        "hot_sectors": get_hot_sectors(sectors),
+        "danger_sectors": get_danger_sectors(sectors),
         "top_gainers": top_gainers,
         "top_losers": top_losers,
         "sectors": sectors,