diff --git a/deploy/profile-scripts/market_watch.py b/deploy/profile-scripts/market_watch.py
index 74dd77b7..cc8ad153 100644
--- a/deploy/profile-scripts/market_watch.py
+++ b/deploy/profile-scripts/market_watch.py
@@ -1,257 +1,278 @@
-#!/usr/bin/env python3
-"""market_watch.py — 行業熱點數據採集，寫入 dashboard data/market.json
-
-數據源優先級：
-  後端A：東方財富 push2 API（首選，有板塊代碼+實時指數）
-  後端B：同花順 THS / akshare（降級，有漲跌家數+資金流向）
-
-注意：當前服務器無法連通東方財富API（已被封禁/域名不可達），
-實際運行時自動降級到同花順 THS 後端。THS 提供90+行業板塊的
-實時漲跌、上漲/下跌家數、淨流入資金等數據，足以滿足需求。
-
-輸出：data/market.json → MoFin Dashboard 市場數據展示
-"""
-
-import json
-from datetime import datetime
-from pathlib import Path
-
-from mofin_db import get_conn, init_all_tables, write_market_snapshot
-
-DATA_DIR = Path(__file__).parent.parent / "data"
-
-
-# ── 後端A：東方財富 push2 API（首選，有板塊代碼+實時指數） ──
-
-def _fetch_em(url):
-    """通用 EM API 請求"""
-    import urllib.request
-    req = urllib.request.Request(
-        url,
-        headers={"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"}
-    )
-    resp = urllib.request.urlopen(req, timeout=10)
-    return json.loads(resp.read().decode("utf-8"))
-
-
-def fetch_sector_em():
-    """東方財富行業板塊"""
-    try:
-        data = _fetch_em(
-            "https://push2.eastmoney.com/api/qt/clist/get?"
-            "pn=1&pz=60&po=1&np=1&fields=f2,f3,f4,f12,f14&fs=m:90+t:2"
-        )
-        return [{
-            "name": i["f14"],
-            "code": i["f12"],
-            "price": i.get("f2", 0),
-            "change": i.get("f3", 0),
-        } for i in data.get("data", {}).get("diff", [])]
-    except Exception:
-        return None
-
-
-def fetch_concept_em():
-    """東方財富概念板塊"""
-    try:
-        data = _fetch_em(
-            "https://push2.eastmoney.com/api/qt/clist/get?"
-            "pn=1&pz=30&po=1&np=1&fields=f2,f3,f4,f12,f14&fs=m:90+t:3"
-        )
-        return [{
-            "name": i["f14"],
-            "code": i["f12"],
-            "change": i.get("f3", 0),
-        } for i in data.get("data", {}).get("diff", [])]
-    except Exception:
-        return None
-
-
-# ── 後端B：同花順 THS / akshare（降級） ──
-
-def fetch_sector_ths():
-    """THS 行業板塊（含漲跌家數、資金流向、領漲股）"""
-    try:
-        import akshare as ak
-        df = ak.stock_board_industry_summary_ths()
-        return [{
-            "name": r["板块"],
-            "code": "",
-            "price": 0,
-            "change": float(r.get("涨跌幅", 0)),
-            "volume": float(r.get("总成交量", 0)),
-            "turnover": float(r.get("总成交额", 0)),
-            "net_inflow": float(r.get("净流入", 0)),
-            "up_count": int(r.get("上涨家数", 0)),
-            "down_count": int(r.get("下跌家数", 0)),
-            "avg_price": float(r.get("均价", 0)),
-            "lead_stock": r.get("领涨股", ""),
-            "lead_stock_change": float(r.get("领涨股-涨跌幅", 0)),
-        } for _, r in df.iterrows()]
-    except Exception as e:
-        print(f"THS行業失敗: {e}", flush=True)
-        return []
-
-
-def fetch_concept_ths():
-    """THS 概念板塊（僅名稱，無實時漲跌）"""
-    try:
-        import akshare as ak
-        df = ak.stock_board_concept_name_ths()
-        return [{
-            "name": r["name"],
-            "code": str(r.get("code", "")),
-            "change": 0,
-        } for _, r in df.iterrows()]
-    except Exception as e:
-        print(f"THS概念失敗: {e}", flush=True)
-        return []
-
-
-# ── 輔助函數 ──
-
-def get_market_mood(sectors):
-    if not sectors:
-        return "unknown"
-    ratio = sum(1 for s in sectors if s.get("change", 0) > 0) / len(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():
-    # 行業板塊：EM → THS → 兜底
-    sectors = fetch_sector_em()
-    source = "eastmoney"
-    if sectors is None:
-        sectors = fetch_sector_ths()
-        source = "ths"
-
-    # 概念板塊：EM → THS → 空
-    concepts = fetch_concept_em()
-    concept_source = "eastmoney"
-    if concepts is None:
-        concepts = fetch_concept_ths()
-        concept_source = "ths"
-    if not concepts:
-        concepts = []
-        concept_source = "unavailable"
-
-    # 排序
-    sorted_sectors = sorted(sectors, key=lambda s: s.get("change", 0), reverse=True)
-    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": 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,
-        "concepts": concepts,
-    }
-
-    DATA_DIR.mkdir(parents=True, exist_ok=True)
-
-    # ── SQLite 写入（替代 market.json）──
-    conn = get_conn()
-    init_all_tables(conn)
-    ok, msg, sid = write_market_snapshot(conn, market_data)
-    if ok:
-        print(f"[DB] {msg}", flush=True)
-    else:
-        print(f"[DB] 写入失败（JSON 不受影响）: {msg}", flush=True)
-    conn.close()
-
-    # ── 大盘市场阶段（market_regime）：与回测 _load_index_ctx 同算法 ──
-    # 回测用 above_ma20 + adx 做市场过滤（v_next4 要求趋势市才出手），
-    # 实盘此前只有当日涨跌幅 mood，缺 MA20+ADX 的市场阶段判断 —— 此处补齐。
-    try:
-        import market_regime as _mr
-        _regime = _mr.compute_regime()
-        if _regime:
-            _mr.save_regime(_regime)
-            print(f"[regime] {_regime['date']} → {_regime['regime']} "
-                  f"(above_ma20={_regime['above_ma20']} adx={_regime['adx']})", flush=True)
-        else:
-            print("[regime] sh000001 数据不足，跳过", flush=True)
-    except Exception as _e:
-        print(f"[regime] 计算失败: {_e}", flush=True)
-
-    # ── v_mr 均值回复扫描（每天最多一次，regime 门控内置）──
-    # trend_down/choppy 时扫描深超跌小盘票，写 candidates(sector='v_mr')
-    # mr_scanner 自带幂等保护：当天已有 v_mr 候选则跳过，避免重复全扫描
-    try:
-        import subprocess as _sp
-        import sys as _sys
-        _script = Path(__file__).parent / "mr_scanner.py"
-        if _script.exists():
-            _r = _sp.run(
-                [_sys.executable, str(_script), "--top", "15"],
-                capture_output=True, text=True, timeout=1500)
-            if _r.stdout:
-                for _line in _r.stdout.strip().splitlines():
-                    print(f"[mr_scanner] {_line}", flush=True)
-            if _r.returncode != 0 and _r.stderr:
-                print(f"[mr_scanner] stderr: {_r.stderr[:500]}", flush=True)
-        else:
-            print("[mr_scanner] 脚本不存在，跳过", flush=True)
-    except Exception as _e:
-        print(f"[mr_scanner] 调用失败: {_e}", flush=True)
-
-    # 靜默：只寫文件，不輸出到stdout，避免cron推送
-
-
-if __name__ == "__main__":
-    main()
+#!/usr/bin/env python3
+"""market_watch.py — 行業熱點數據採集，寫入 dashboard data/market.json
+
+數據源優先級：
+  後端A：東方財富 push2 API（首選，有板塊代碼+實時指數）
+  後端B：同花順 THS / akshare（降級，有漲跌家數+資金流向）
+
+注意：當前服務器無法連通東方財富API（已被封禁/域名不可達），
+實際運行時自動降級到同花順 THS 後端。THS 提供90+行業板塊的
+實時漲跌、上漲/下跌家數、淨流入資金等數據，足以滿足需求。
+
+輸出：data/market.json → MoFin Dashboard 市場數據展示
+"""
+
+import json
+from datetime import datetime
+from pathlib import Path
+
+from mofin_db import get_conn, init_all_tables, write_market_snapshot
+
+DATA_DIR = Path(__file__).parent.parent / "data"
+
+
+# ── 後端A：東方財富 push2 API（首選，有板塊代碼+實時指數） ──
+
+def _fetch_em(url):
+    """通用 EM API 請求"""
+    import urllib.request
+    req = urllib.request.Request(
+        url,
+        headers={"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"}
+    )
+    resp = urllib.request.urlopen(req, timeout=10)
+    return json.loads(resp.read().decode("utf-8"))
+
+
+def fetch_sector_em():
+    """東方財富行業板塊"""
+    try:
+        data = _fetch_em(
+            "https://push2.eastmoney.com/api/qt/clist/get?"
+            "pn=1&pz=60&po=1&np=1&fields=f2,f3,f4,f12,f14&fs=m:90+t:2"
+        )
+        return [{
+            "name": i["f14"],
+            "code": i["f12"],
+            "price": i.get("f2", 0),
+            "change": i.get("f3", 0),
+        } for i in data.get("data", {}).get("diff", [])]
+    except Exception:
+        return None
+
+
+def fetch_concept_em():
+    """東方財富概念板塊"""
+    try:
+        data = _fetch_em(
+            "https://push2.eastmoney.com/api/qt/clist/get?"
+            "pn=1&pz=30&po=1&np=1&fields=f2,f3,f4,f12,f14&fs=m:90+t:3"
+        )
+        return [{
+            "name": i["f14"],
+            "code": i["f12"],
+            "change": i.get("f3", 0),
+        } for i in data.get("data", {}).get("diff", [])]
+    except Exception:
+        return None
+
+
+# ── 後端B：同花順 THS / akshare（降級） ──
+
+def fetch_sector_ths():
+    """THS 行業板塊（含漲跌家數、資金流向、領漲股）"""
+    try:
+        import akshare as ak
+        df = ak.stock_board_industry_summary_ths()
+        return [{
+            "name": r["板块"],
+            "code": "",
+            "price": 0,
+            "change": float(r.get("涨跌幅", 0)),
+            "volume": float(r.get("总成交量", 0)),
+            "turnover": float(r.get("总成交额", 0)),
+            "net_inflow": float(r.get("净流入", 0)),
+            "up_count": int(r.get("上涨家数", 0)),
+            "down_count": int(r.get("下跌家数", 0)),
+            "avg_price": float(r.get("均价", 0)),
+            "lead_stock": r.get("领涨股", ""),
+            "lead_stock_change": float(r.get("领涨股-涨跌幅", 0)),
+        } for _, r in df.iterrows()]
+    except Exception as e:
+        print(f"THS行業失敗: {e}", flush=True)
+        return []
+
+
+def fetch_concept_ths():
+    """THS 概念板塊（僅名稱，無實時漲跌）"""
+    try:
+        import akshare as ak
+        df = ak.stock_board_concept_name_ths()
+        return [{
+            "name": r["name"],
+            "code": str(r.get("code", "")),
+            "change": 0,
+        } for _, r in df.iterrows()]
+    except Exception as e:
+        print(f"THS概念失敗: {e}", flush=True)
+        return []
+
+
+# ── 輔助函數 ──
+
+def get_market_mood(sectors):
+    if not sectors:
+        return "unknown"
+    ratio = sum(1 for s in sectors if s.get("change", 0) > 0) / len(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():
+    # 行業板塊：EM → THS → 兜底
+    sectors = fetch_sector_em()
+    source = "eastmoney"
+    if sectors is None:
+        sectors = fetch_sector_ths()
+        source = "ths"
+
+    # 概念板塊：EM → THS → 空
+    concepts = fetch_concept_em()
+    concept_source = "eastmoney"
+    if concepts is None:
+        concepts = fetch_concept_ths()
+        concept_source = "ths"
+    if not concepts:
+        concepts = []
+        concept_source = "unavailable"
+
+    # 排序
+    sorted_sectors = sorted(sectors, key=lambda s: s.get("change", 0), reverse=True)
+    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": 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,
+        "concepts": concepts,
+    }
+
+    DATA_DIR.mkdir(parents=True, exist_ok=True)
+
+    # ── SQLite 写入（替代 market.json）──
+    conn = get_conn()
+    init_all_tables(conn)
+    ok, msg, sid = write_market_snapshot(conn, market_data)
+    if ok:
+        print(f"[DB] {msg}", flush=True)
+    else:
+        print(f"[DB] 写入失败（JSON 不受影响）: {msg}", flush=True)
+    conn.close()
+
+    # ── 大盘市场阶段（market_regime）：与回测 _load_index_ctx 同算法 ──
+    # 回测用 above_ma20 + adx 做市场过滤（v_next4 要求趋势市才出手），
+    # 实盘此前只有当日涨跌幅 mood，缺 MA20+ADX 的市场阶段判断 —— 此处补齐。
+    try:
+        import market_regime as _mr
+        _regime = _mr.compute_regime()
+        if _regime:
+            _mr.save_regime(_regime)
+            print(f"[regime] {_regime['date']} → {_regime['regime']} "
+                  f"(above_ma20={_regime['above_ma20']} adx={_regime['adx']})", flush=True)
+        else:
+            print("[regime] sh000001 数据不足，跳过", flush=True)
+    except Exception as _e:
+        print(f"[regime] 计算失败: {_e}", flush=True)
+
+    # ── v_mr 均值回复扫描（每天最多一次，regime 门控内置）──
+    # trend_down/choppy 时扫描深超跌小盘票，写 candidates(sector='v_mr')
+    # mr_scanner 自带幂等保护：当天已有 v_mr 候选则跳过，避免重复全扫描
+    try:
+        import subprocess as _sp
+        import sys as _sys
+        _script = Path(__file__).parent / "mr_scanner.py"
+        if _script.exists():
+            _r = _sp.run(
+                [_sys.executable, str(_script), "--top", "15"],
+                capture_output=True, text=True, timeout=1500)
+            if _r.stdout:
+                for _line in _r.stdout.strip().splitlines():
+                    print(f"[mr_scanner] {_line}", flush=True)
+            if _r.returncode != 0 and _r.stderr:
+                print(f"[mr_scanner] stderr: {_r.stderr[:500]}", flush=True)
+        else:
+            print("[mr_scanner] 脚本不存在，跳过", flush=True)
+    except Exception as _e:
+        print(f"[mr_scanner] 调用失败: {_e}", flush=True)
+
+    # ── S2 恐慌买超跌扫描（每天最多一次，大盘RSI<25门控内置）──
+    # 策略家族(§39): S1(v_weak甜区)+S2(恐慌买)同时运行零重叠
+    # s2_scanner 自带幂等：当天已有 s2_panic 候选则跳过
+    try:
+        import subprocess as _sp2
+        import sys as _sys2
+        _script2 = Path(__file__).parent / "s2_scanner.py"
+        if _script2.exists():
+            _r2 = _sp2.run(
+                [_sys2.executable, str(_script2), "--top", "15"],
+                capture_output=True, text=True, timeout=1500)
+            if _r2.stdout:
+                for _line in _r2.stdout.strip().splitlines():
+                    print(f"[s2_scanner] {_line}", flush=True)
+            if _r2.returncode != 0 and _r2.stderr:
+                print(f"[s2_scanner] stderr: {_r2.stderr[:500]}", flush=True)
+        else:
+            print("[s2_scanner] 脚本不存在，跳过", flush=True)
+    except Exception as _e2:
+        print(f"[s2_scanner] 调用失败: {_e2}", flush=True)
+
+    # 靜默：只寫文件，不輸出到stdout，避免cron推送
+
+
+if __name__ == "__main__":
+    main()