diff --git a/data/mofin.db-shm b/data/mofin.db-shm
index fe9ac284..9b17ac65 100644
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diff --git a/data/mofin.db-wal b/data/mofin.db-wal
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diff --git a/deploy/profile-scripts/batch_reassess.py b/deploy/profile-scripts/batch_reassess.py
index 4a4f52c2..196dab1b 100644
--- a/deploy/profile-scripts/batch_reassess.py
+++ b/deploy/profile-scripts/batch_reassess.py
@@ -1,424 +1,369 @@
-#!/usr/bin/env python3
-"""batch_reassess.py — 批量补全12维(九维矩阵)LLM分析（逐只处理，间隔防限流）
-
-用法:
-  python3 batch_reassess.py                          # 所有缺分析/过期的 active 策略
-  python3 batch_reassess.py --type holding           # 只处理持仓策略
-  python3 batch_reassess.py --type watchlist         # 只处理自选策略
-  python3 batch_reassess.py --type holding --today   # 持仓每日刷新（今早未评过的强制重评）
-  python3 batch_reassess.py --code XXXXXX            # 单只
-
-流程：收集最新数据 → 调LLM(gateway)写12维分析+策略 → 保存到DB
-"""
-import sys, json, subprocess, sqlite3, re, time
-from datetime import datetime
-
-DB = "/home/hmo/MoFin/data/mofin.db"
-GATEWAY = "http://127.0.0.1:8643/v1/chat/completions"
-COOLDOWN_HOURS = 1
-STALE_HOURS = 20  # 分析超过20小时视为过期，需要重评
-
-def has_llm_analysis(code):
-    """检查是否为LLM生成的12维分析（>500字）"""
-    conn = sqlite3.connect(DB)
-    r = conn.execute("SELECT LENGTH(full_analysis) FROM holding_strategies WHERE code=? AND status='active'", (code,)).fetchone()
-    conn.close()
-    return r and r[0] and r[0] > 500
-
-def in_cooldown(code):
-    """冷却期检查"""
-    conn = sqlite3.connect(DB)
-    r = conn.execute("SELECT reassessed_at FROM holding_strategies WHERE code=? AND status='active'", (code,)).fetchone()
-    conn.close()
-    if not r or not r[0]:
-        return False
-    try:
-        last = datetime.fromisoformat(r[0])
-        diff = (datetime.now() - last).total_seconds() / 3600
-        return diff < COOLDOWN_HOURS
-    except:
-        return False
-
-def analysis_stale(code, force_today=False):
-    """分析是否过期（>STALE_HOURS 或 force_today 时今早4点前未重评）"""
-    conn = sqlite3.connect(DB)
-    r = conn.execute("SELECT reassessed_at FROM holding_strategies WHERE code=? AND status='active'", (code,)).fetchone()
-    conn.close()
-    if not r or not r[0]:
-        return True
-    try:
-        last = datetime.fromisoformat(r[0])
-        if force_today:
-            today4am = datetime.now().replace(hour=4, minute=0, second=0, microsecond=0)
-            return last < today4am
-        return (datetime.now() - last).total_seconds() / 3600 > STALE_HOURS
-    except:
-        return True
-
-def get_portfolio():
-    """从 portfolio_summary 读实时现金/总资产（不再硬编码）"""
-    try:
-        conn = sqlite3.connect(DB)
-        r = conn.execute("SELECT cash, total_assets FROM portfolio_summary WHERE id=1").fetchone()
-        conn.close()
-        if r and r[1]:
-            return int(r[0] or 0), int(r[1])
-    except Exception:
-        pass
-    return 0, 0
-
-def collect_data(code):
-    """收集最新数据"""
-    data = {"code": code}
-    
-    # 从DB读策略
-    conn = sqlite3.connect(DB)
-    r = conn.execute("SELECT name, entry_low, entry_high, stop_loss, take_profit, timing_signal, action, rr_ratio, tech_snapshot, sector_context, stock_category FROM holding_strategies WHERE code=? AND status='active'", (code,)).fetchone()
-    if r:
-        data["name"] = r[0]
-        data["entry_low"] = r[1] or 0
-        data["entry_high"] = r[2] or 0
-        data["stop_loss"] = r[3] or 0
-        data["take_profit"] = r[4] or 0
-        data["timing_signal"] = r[5] or ""
-        data["action"] = r[6] or ""
-        data["rr_ratio"] = r[7] or 0
-        data["tech_snapshot"] = r[8] or ""
-        data["sector_context"] = r[9] or ""
-        data["stock_category"] = r[10] or ""
-    conn.close()
-    
-    # 从腾讯API拉最新价和基本面
-    # 代码前缀：5位=港股(hk)，6/9开头=沪(sh)，其他=深(sz)
-    _c = str(code)
-    if len(_c) == 5:
-        prefix = "hk"
-    elif _c.startswith(("6", "9")):
-        prefix = "sh"
-    else:
-        prefix = "sz"
-    try:
-        r = subprocess.run(["curl", "-s", f"http://qt.gtimg.cn/q={prefix}{code}"], capture_output=True, timeout=10)
-        parts = r.stdout.decode("gbk", errors="ignore").split("~")
-        data["price"] = float(parts[3]) if len(parts) > 3 and parts[3] else 0
-        data["pe"] = parts[39] if len(parts) > 39 and parts[39] else ""
-        data["mcap"] = parts[44] if len(parts) > 44 and parts[44] else ""
-        data["change_pct"] = parts[32] if len(parts) > 32 and parts[32] else "0"
-    except:
-        data["price"] = 0
-    
-    # 大盘
-    try:
-        conn = sqlite3.connect(DB)
-        mr = conn.execute("SELECT structure FROM macro_context_log ORDER BY id DESC LIMIT 1").fetchone()
-        if mr and mr[0]:
-            s = json.loads(mr[0])
-            data["macro"] = s.get("description", "大盘震荡")
-        conn.close()
-    except:
-        data["macro"] = "大盘震荡"
-    
-    return data
-
-def build_prompt(data):
-    """构建LLM prompt，要求输出完整策略"""
-    cash, total = get_portfolio()  # 实时从 portfolio_summary 读
-    if not total:
-        cash, total = 241330, 929727  # 兜底（DB读不到时）
-    
-    # 拉取资金流数据
-    _flow_note = "暂无资金流数据"
-    try:
-        import sqlite3 as _sq, json as _j
-        _db = _sq.connect("/home/hmo/MoFin/data/mofin.db")
-        _fr = _db.execute("SELECT cache_json FROM capital_flow_cache ORDER BY id DESC LIMIT 1").fetchone()
-        if _fr and _fr[0]:
-            _fc = _j.loads(_fr[0])
-            _stocks = _fc.get("stocks", {})
-            _s = _stocks.get(data['code'], {})
-            if _s and _s.get("analysis"):
-                _a = _s["analysis"]
-                _net = _a.get("net_flow", 0)
-                _main = _a.get("main_force", 0)
-                _retail = _a.get("retail_flow", 0)
-                _trend = _a.get("trend", "中性")
-                _flow_note = f"净流入{_net:.0f}万 主力{_main:.0f}万 散户{_retail:.0f}万 趋势{_trend}"
-        _db.close()
-    except:
-        pass
-    
-    # 拉取近期消息面
-    _news_note = "暂无近期消息"
-    try:
-        import sqlite3 as _sq
-        _db = _sq.connect("/home/hmo/MoFin/data/mofin.db")
-        _nr = _db.execute(
-            "SELECT summary, overall_sentiment, created_at FROM signal_news "
-            "WHERE (code=? OR sector LIKE ?) AND overall_sentiment IN ('利好','利空') "
-            "ORDER BY id DESC LIMIT 3",
-            (data['code'], f'%{data.get("name","")[:4]}%')
-        ).fetchall()
-        if _nr:
-            _news_note = " | ".join([f"{r[2][:10]} {r[1]} {r[0][:40]}" for r in _nr])
-        _db.close()
-    except:
-        pass
-    
-    return f"""你是一个资深A股分析师。请对{data['code']} {data.get('name','')}做一个完整的九维矩阵分析，并输出策略参数。
-
-⚠️ 重要：以下9个维度不是独立分析的，你必须交叉对比后给出综合结论。
-例如：如果消息面利好但资金流在流出，说明利好可能是出货；如果基本面强但技术面破位，说明估值可能还没到底。
-
-当前数据（以下数据均来自实时API，每条标注时间窗口，禁止使用模型内部训练数据）：
-大盘：{data.get('macro','震荡')}（当日实时）
-最新价：{data.get('price',0)}  涨跌：{data.get('change_pct','0')}%（当日实时）
-PE={data.get('pe','?')}（最新财报）  市值={data.get('mcap','?')}亿
-行业：{data.get('sector_context','?')}（当日实时）
-技术面：{data.get('tech_snapshot','')[:300]}（MA=5/10/20/60日 支撑阻力=近20日 量价=当日+近5日趋势）
-资金流：{_flow_note}（近5日累计）
-消息面：{_news_note}（最近3条，自动标注抓取时间）
-当前信号：{data.get('timing_signal','?')}  分类：{data.get('stock_category','?')}
-原策略：{(data.get('action','') or '')[:200]}
-
-我的总资产={total}元，可用现金={cash}元。
-
-请严格按以下格式输出：
-
-【交叉分析】用2-3句话说明哪些维度出现矛盾/共振，最关键的信号是什么
-① 大盘×基本面 [一句话，说明矛盾关系]
-② 大盘×消息面 [一句话]
-③ 大盘×技术面 [一句话]
-④ 大盘×资金面 [一句话]
-⑤ 行业×基本面 [一句话]
-⑥ 行业×消息面 [一句话]
-⑦ 行业×技术面 [一句话]
-⑧ 行业×资金面 [一句话]
-⑨ 个股×基本面 [一句话]
-⑩ 个股×消息面 [一句话]
-⑪ 个股×技术面 [一句话]
-⑫ 个股×资金面 [一句话]
-
-【综合结论】（买入/关注/观望/卖出）
-【操作建议】具体操作建议
-【买入区间】最低价~最高价
-【建议止损】数字
-【建议止盈】数字
-
-【建议仓位】只有综合结论为"买入"时才输出此项。仓位计算公式：
-基础仓位按RR确定：RR<1.5→不推荐，RR1.5~3→8%，RR3~5→12%，RR5+→15%
-大盘偏弱×0.8，大盘偏强×1.15
-蓝筹/白马×1.2，成长×0.85，题材/短线×0.6
-最终仓位范围：5%~20%
-同时考虑：现金{cash}元足够买多少手。
-输出格式："X%（理由：一句话说明为什么这个仓位）"""
-def parse_response(text):
-    """从LLM回复中提取策略参数"""
-    result = {"signal": "", "entry_low": 0, "entry_high": 0, "stop_loss": 0, "take_profit": 0, "position": ""}
-    
-    # 信号
-    sl = [l for l in text.split("\n") if "综合结论" in l]
-    if sl:
-        for kw in ["买入","关注","观望","卖出"]:
-            if kw in sl[0]:
-                result["signal"] = kw
-                break
-    
-    # 买入区间
-    zl = [l for l in text.split("\n") if "买入区间" in l]
-    if zl:
-        nums = re.findall(r'[\d.]+', zl[0])
-        if len(nums) >= 2:
-            result["entry_low"] = float(nums[0])
-            result["entry_high"] = float(nums[1])
-    
-    # 止损
-    for l in text.split("\n"):
-        if "建议止损" in l:
-            nums = re.findall(r'[\d.]+', l)
-            if nums: result["stop_loss"] = float(nums[0])
-    
-    # 止盈
-    for l in text.split("\n"):
-        if "建议止盈" in l:
-            nums = re.findall(r'[\d.]+', l)
-            if nums: result["take_profit"] = float(nums[0])
-    
-    # 仓位：只有买入信号才需要，提取百分比数字
-    result["position"] = ""
-    if result["signal"] == "买入":
-        for l in text.split("\n"):
-            if "建议仓位" in l:
-                nums = re.findall(r'[\d.]+', l)
-                for n in nums:
-                    f = float(n)
-                    if 1 <= f <= 30:  # 合理的仓位范围
-                        result["position"] = f"{f:.0f}%"
-                        break
-                break
-    
-    return result
-
-def save_result(code, full_text, parsed):
-    """保存LLM结果到DB"""
-    conn = sqlite3.connect(DB)
-    now = datetime.now().isoformat()
-    
-    updates = ["full_analysis=?", "reassessed_at=?"]
-    params = [full_text, now]
-    
-    if parsed["signal"]:
-        updates.append("timing_signal=?")
-        params.append(parsed["signal"])
-    if parsed["entry_low"] > 0:
-        updates.append("entry_low=?")
-        params.append(parsed["entry_low"])
-    if parsed["entry_high"] > 0:
-        updates.append("entry_high=?")
-        params.append(parsed["entry_high"])
-    if parsed["stop_loss"] > 0:
-        updates.append("stop_loss=?")
-        params.append(parsed["stop_loss"])
-    if parsed["take_profit"] > 0:
-        updates.append("take_profit=?")
-        params.append(parsed["take_profit"])
-    if parsed["position"]:
-        updates.append("position_advice=?")
-        params.append(parsed["position"])
-    
-    params.append(code)
-    sql = f"UPDATE holding_strategies SET {', '.join(updates)} WHERE code=? AND status='active'"
-    conn.execute(sql, params)
-    conn.commit()
-    
-    # 买入信号→推XMPP通知（在conn close前执行）
-    if parsed.get("signal") == "买入":
-        try:
-            _nr = conn.execute("SELECT name, price FROM holding_strategies WHERE code=? AND status='active'", (code,)).fetchone()
-            _name = _nr[0] if _nr else code
-            _p = _nr[1] if _nr else 0
-            _el = parsed.get("entry_low", 0)
-            _eh = parsed.get("entry_high", 0)
-            _sl = parsed.get("stop_loss", 0)
-            _tp = parsed.get("take_profit", 0)
-            _pos = parsed.get("position", "")
-            _msg = f"📈 {_name}({code}) 价{_p}→12维分析生成买入信号！区间{_el}~{_eh} 损{_sl} 盈{_tp} 仓位{_pos}"
-            import urllib.request, json as _jj
-            _req = urllib.request.Request("http://127.0.0.1:5805/",
-                data=_jj.dumps({"body": _msg, "to": "hmo@yoin.fun", "type": "chat"}).encode(),
-                headers={"Content-Type": "application/json"})
-            urllib.request.urlopen(_req, timeout=5)
-            print(f"  📨 XMPP推送成功: {_msg[:60]}")
-        except Exception as _e:
-            print(f"  ⚠️ XMPP推送失败: {_e}")
-    
-    conn.close()
-
-def process_stock(code, force_today=False):
-    """处理单只股票"""
-    print(f"\n{'='*50}")
-    print(f"处理: {code}")
-    print(f"{'='*50}")
-    
-    if in_cooldown(code):
-        print(f"  ⏭ 冷却期内，跳过")
-        return False
-    
-    # 有分析且未过期 → 跳过（除非 force_today 且今早未评）
-    if has_llm_analysis(code) and not analysis_stale(code, force_today):
-        print(f"  ⏭ 已有12维分析且未过期，跳过")
-        return False
-    
-    print(f"  收集数据...", flush=True)
-    data = collect_data(code)
-    if not data.get("price"):
-        print(f"  ⚠️ 无价格数据，跳过")
-        return False
-    
-    print(f"  调LLM生成九维分析...", flush=True)
-    prompt = build_prompt(data)
-    
-    try:
-        r = subprocess.run(["curl", "-s", "--max-time", "300",
-            "-H", "Content-Type: application/json",
-            "-H", "Authorization: Bearer hermes123",
-            "-d", json.dumps({"model":"deepseek-v4-flash","messages":[{"role":"user","content":prompt}],"max_tokens":2048}),
-            GATEWAY], capture_output=True, timeout=310)
-        
-        if r.returncode != 0:
-            print(f"  ❌ curl失败: {r.stderr.decode()[:100]}")
-            return False
-        
-        resp = json.loads(r.stdout)
-        if "choices" not in resp:
-            print(f"  ❌ API异常: {str(resp)[:200]}")
-            return False
-        
-        full_text = resp["choices"][0]["message"]["content"]
-        print(f"  ✅ LLM返回({len(full_text)}字)", flush=True)
-        
-        parsed = parse_response(full_text)
-        print(f"  信号={parsed['signal']} 区间={parsed['entry_low']}~{parsed['entry_high']} 损={parsed['stop_loss']} 盈={parsed['take_profit']} 仓位={parsed['position']}")
-        
-        save_result(code, full_text, parsed)
-        print(f"  ✅ 已保存到DB")
-        return True
-        
-    except subprocess.TimeoutExpired:
-        print(f"  ❌ 超时")
-        return False
-    except Exception as e:
-        print(f"  ❌ 错误: {e}")
-        return False
-
-def main():
-    codes = []
-    force_today = "--today" in sys.argv
-    dtype = None
-    if "--type" in sys.argv:
-        idx = sys.argv.index("--type")
-        dtype = sys.argv[idx + 1]  # holding | watchlist | all
-    if "--code" in sys.argv:
-        idx = sys.argv.index("--code")
-        codes = [sys.argv[idx+1]]
-    else:
-        # 按类型筛选 active 策略
-        type_map = {"holding": "持仓策略", "watchlist": "自选策略"}
-        conn = sqlite3.connect(DB)
-        if dtype in type_map:
-            rows = conn.execute(
-                "SELECT code FROM holding_strategies WHERE status='active' AND decision_type=? ORDER BY code",
-                (type_map[dtype],)).fetchall()
-        else:
-            rows = conn.execute(
-                "SELECT code FROM holding_strategies WHERE status='active' ORDER BY decision_type, code").fetchall()
-        conn.close()
-        codes = [r[0] for r in rows]
-    
-    print(f"待处理: {len(codes)}只 (type={dtype or 'all'}, force_today={force_today})")
-    
-    ok = 0
-    fail = 0
-    skip = 0
-    for i, code in enumerate(codes):
-        if has_llm_analysis(code) and not analysis_stale(code, force_today):
-            print(f"  [{i+1}/{len(codes)}] ⏭ {code} 已有12维分析且未过期")
-            skip += 1
-            continue
-        
-        print(f"  [{i+1}/{len(codes)}] ", end="", flush=True)
-        if process_stock(code, force_today):
-            ok += 1
-        else:
-            fail += 1
-        
-        # 间隔15秒（防gateway过载）
-        if i < len(codes) - 1:
-            print(f"  等待15秒...", flush=True)
-            time.sleep(15)
-    
-    print(f"\n{'='*50}")
-    print(f"完成: {ok}成功, {fail}失败, {skip}跳过")
-    print(f"{'='*50}")
-
-if __name__ == "__main__":
-    main()
+#!/usr/bin/env python3
+"""batch_reassess.py — 批量补全九维分析（逐只处理，间隔防限流）
+
+用法: python3 batch_reassess.py [--all] [--code XXXXXX]
+
+流程：收集最新数据 → 调LLM(gateway)写九维分析+策略 → 保存到DB
+"""
+import sys, json, subprocess, sqlite3, re, time
+from datetime import datetime
+
+DB = "/home/hmo/MoFin/data/mofin.db"
+GATEWAY = "http://127.0.0.1:8643/v1/chat/completions"
+COOLDOWN_HOURS = 1
+
+def has_llm_analysis(code):
+    """检查是否为LLM生成的九维分析（>500字）"""
+    conn = sqlite3.connect(DB)
+    r = conn.execute("SELECT LENGTH(full_analysis) FROM holding_strategies WHERE code=? AND status='active'", (code,)).fetchone()
+    conn.close()
+    return r and r[0] and r[0] > 500
+
+def in_cooldown(code):
+    """冷却期检查"""
+    conn = sqlite3.connect(DB)
+    r = conn.execute("SELECT reassessed_at FROM holding_strategies WHERE code=? AND status='active'", (code,)).fetchone()
+    conn.close()
+    if not r or not r[0]:
+        return False
+    try:
+        last = datetime.fromisoformat(r[0])
+        diff = (datetime.now() - last).total_seconds() / 3600
+        return diff < COOLDOWN_HOURS
+    except:
+        return False
+
+def collect_data(code):
+    """收集最新数据"""
+    data = {"code": code}
+    
+    # 从DB读策略
+    conn = sqlite3.connect(DB)
+    r = conn.execute("SELECT name, entry_low, entry_high, stop_loss, take_profit, timing_signal, action, rr_ratio, tech_snapshot, sector_context, stock_category FROM holding_strategies WHERE code=? AND status='active'", (code,)).fetchone()
+    if r:
+        data["name"] = r[0]
+        data["entry_low"] = r[1] or 0
+        data["entry_high"] = r[2] or 0
+        data["stop_loss"] = r[3] or 0
+        data["take_profit"] = r[4] or 0
+        data["timing_signal"] = r[5] or ""
+        data["action"] = r[6] or ""
+        data["rr_ratio"] = r[7] or 0
+        data["tech_snapshot"] = r[8] or ""
+        data["sector_context"] = r[9] or ""
+        data["stock_category"] = r[10] or ""
+    conn.close()
+    
+    # 从腾讯API拉最新价和基本面
+    prefix = "sh" if str(code).startswith(("6","9")) else "sz"
+    try:
+        r = subprocess.run(["curl", "-s", f"http://qt.gtimg.cn/q={prefix}{code}"], capture_output=True, timeout=10)
+        parts = r.stdout.decode("gbk", errors="ignore").split("~")
+        data["price"] = float(parts[3]) if len(parts) > 3 and parts[3] else 0
+        data["pe"] = parts[39] if len(parts) > 39 and parts[39] else ""
+        data["mcap"] = parts[44] if len(parts) > 44 and parts[44] else ""
+        data["change_pct"] = parts[32] if len(parts) > 32 and parts[32] else "0"
+    except:
+        data["price"] = 0
+    
+    # 大盘
+    try:
+        conn = sqlite3.connect(DB)
+        mr = conn.execute("SELECT structure FROM macro_context_log ORDER BY id DESC LIMIT 1").fetchone()
+        if mr and mr[0]:
+            s = json.loads(mr[0])
+            data["macro"] = s.get("description", "大盘震荡")
+        conn.close()
+    except:
+        data["macro"] = "大盘震荡"
+    
+    return data
+
+def build_prompt(data):
+    """构建LLM prompt，要求输出完整策略"""
+    cash = 321271  # 可用现金（从DB读取）
+    total = 952879  # 总资产
+    
+    # 拉取资金流数据
+    _flow_note = "暂无资金流数据"
+    try:
+        import sqlite3 as _sq, json as _j
+        _db = _sq.connect("/home/hmo/MoFin/data/mofin.db")
+        _fr = _db.execute("SELECT cache_json FROM capital_flow_cache ORDER BY id DESC LIMIT 1").fetchone()
+        if _fr and _fr[0]:
+            _fc = _j.loads(_fr[0])
+            _stocks = _fc.get("stocks", {})
+            _s = _stocks.get(data['code'], {})
+            if _s and _s.get("analysis"):
+                _a = _s["analysis"]
+                _net = _a.get("net_flow", 0)
+                _main = _a.get("main_force", 0)
+                _retail = _a.get("retail_flow", 0)
+                _trend = _a.get("trend", "中性")
+                _flow_note = f"净流入{_net:.0f}万 主力{_main:.0f}万 散户{_retail:.0f}万 趋势{_trend}"
+        _db.close()
+    except:
+        pass
+    
+    # 拉取近期消息面
+    _news_note = "暂无近期消息"
+    try:
+        import sqlite3 as _sq
+        _db = _sq.connect("/home/hmo/MoFin/data/mofin.db")
+        _nr = _db.execute(
+            "SELECT summary, overall_sentiment, created_at FROM signal_news "
+            "WHERE (code=? OR sector LIKE ?) AND overall_sentiment IN ('利好','利空') "
+            "ORDER BY id DESC LIMIT 3",
+            (data['code'], f'%{data.get("name","")[:4]}%')
+        ).fetchall()
+        if _nr:
+            _news_note = " | ".join([f"{r[2][:10]} {r[1]} {r[0][:40]}" for r in _nr])
+        _db.close()
+    except:
+        pass
+    
+    return f"""你是一个资深A股分析师。请对{data['code']} {data.get('name','')}做一个完整的九维矩阵分析，并输出策略参数。
+
+⚠️ 重要：以下9个维度不是独立分析的，你必须交叉对比后给出综合结论。
+例如：如果消息面利好但资金流在流出，说明利好可能是出货；如果基本面强但技术面破位，说明估值可能还没到底。
+
+当前数据（以下数据均来自实时API，每条标注时间窗口，禁止使用模型内部训练数据）：
+大盘：{data.get('macro','震荡')}（当日实时）
+最新价：{data.get('price',0)}  涨跌：{data.get('change_pct','0')}%（当日实时）
+PE={data.get('pe','?')}（最新财报）  市值={data.get('mcap','?')}亿
+行业：{data.get('sector_context','?')}（当日实时）
+技术面：{data.get('tech_snapshot','')[:300]}（MA=5/10/20/60日 支撑阻力=近20日 量价=当日+近5日趋势）
+资金流：{_flow_note}（近5日累计）
+消息面：{_news_note}（最近3条，自动标注抓取时间）
+当前信号：{data.get('timing_signal','?')}  分类：{data.get('stock_category','?')}
+原策略：{(data.get('action','') or '')[:200]}
+
+我的总资产={total}元，可用现金={cash}元。
+
+请严格按以下格式输出：
+
+【交叉分析】用2-3句话说明哪些维度出现矛盾/共振，最关键的信号是什么
+① 大盘×基本面 [一句话，说明矛盾关系]
+② 大盘×消息面 [一句话]
+③ 大盘×技术面 [一句话]
+④ 大盘×资金面 [一句话]
+⑤ 行业×基本面 [一句话]
+⑥ 行业×消息面 [一句话]
+⑦ 行业×技术面 [一句话]
+⑧ 行业×资金面 [一句话]
+⑨ 个股×基本面 [一句话]
+⑩ 个股×消息面 [一句话]
+⑪ 个股×技术面 [一句话]
+⑫ 个股×资金面 [一句话]
+
+【综合结论】（买入/关注/观望/卖出）
+【操作建议】具体操作建议
+【买入区间】最低价~最高价
+【建议止损】数字
+【建议止盈】数字
+
+【建议仓位】只有综合结论为"买入"时才输出此项。仓位计算公式：
+基础仓位按RR确定：RR<1.5→不推荐，RR1.5~3→8%，RR3~5→12%，RR5+→15%
+大盘偏弱×0.8，大盘偏强×1.15
+蓝筹/白马×1.2，成长×0.85，题材/短线×0.6
+最终仓位范围：5%~20%
+同时考虑：现金{cash}元足够买多少手。
+输出格式："X%（理由：一句话说明为什么这个仓位）"""
+def parse_response(text):
+    """从LLM回复中提取策略参数"""
+    result = {"signal": "", "entry_low": 0, "entry_high": 0, "stop_loss": 0, "take_profit": 0, "position": ""}
+    
+    # 信号
+    sl = [l for l in text.split("\n") if "综合结论" in l]
+    if sl:
+        for kw in ["买入","关注","观望","卖出"]:
+            if kw in sl[0]:
+                result["signal"] = kw
+                break
+    
+    # 买入区间
+    zl = [l for l in text.split("\n") if "买入区间" in l]
+    if zl:
+        nums = re.findall(r'[\d.]+', zl[0])
+        if len(nums) >= 2:
+            result["entry_low"] = float(nums[0])
+            result["entry_high"] = float(nums[1])
+    
+    # 止损
+    for l in text.split("\n"):
+        if "建议止损" in l:
+            nums = re.findall(r'[\d.]+', l)
+            if nums: result["stop_loss"] = float(nums[0])
+    
+    # 止盈
+    for l in text.split("\n"):
+        if "建议止盈" in l:
+            nums = re.findall(r'[\d.]+', l)
+            if nums: result["take_profit"] = float(nums[0])
+    
+    # 仓位：只有买入信号才需要，提取百分比数字
+    result["position"] = ""
+    if result["signal"] == "买入":
+        for l in text.split("\n"):
+            if "建议仓位" in l:
+                nums = re.findall(r'[\d.]+', l)
+                for n in nums:
+                    f = float(n)
+                    if 1 <= f <= 30:  # 合理的仓位范围
+                        result["position"] = f"{f:.0f}%"
+                        break
+                break
+    
+    return result
+
+def save_result(code, full_text, parsed):
+    """保存LLM结果到DB"""
+    conn = sqlite3.connect(DB)
+    now = datetime.now().isoformat()
+    
+    updates = ["full_analysis=?", "reassessed_at=?"]
+    params = [full_text, now]
+    
+    if parsed["signal"]:
+        updates.append("timing_signal=?")
+        params.append(parsed["signal"])
+    if parsed["entry_low"] > 0:
+        updates.append("entry_low=?")
+        params.append(parsed["entry_low"])
+    if parsed["entry_high"] > 0:
+        updates.append("entry_high=?")
+        params.append(parsed["entry_high"])
+    if parsed["stop_loss"] > 0:
+        updates.append("stop_loss=?")
+        params.append(parsed["stop_loss"])
+    if parsed["take_profit"] > 0:
+        updates.append("take_profit=?")
+        params.append(parsed["take_profit"])
+    if parsed["position"]:
+        updates.append("position_advice=?")
+        params.append(parsed["position"])
+    
+    params.append(code)
+    sql = f"UPDATE holding_strategies SET {', '.join(updates)} WHERE code=? AND status='active'"
+    conn.execute(sql, params)
+    conn.commit()
+    
+    # 买入信号→推XMPP通知（在conn close前执行）
+    if parsed.get("signal") == "买入":
+        try:
+            _nr = conn.execute("SELECT name, price FROM holding_strategies WHERE code=? AND status='active'", (code,)).fetchone()
+            _name = _nr[0] if _nr else code
+            _p = _nr[1] if _nr else 0
+            _el = parsed.get("entry_low", 0)
+            _eh = parsed.get("entry_high", 0)
+            _sl = parsed.get("stop_loss", 0)
+            _tp = parsed.get("take_profit", 0)
+            _pos = parsed.get("position", "")
+            _msg = f"📈 {_name}({code}) 价{_p}→12维分析生成买入信号！区间{_el}~{_eh} 损{_sl} 盈{_tp} 仓位{_pos}"
+            import urllib.request, json as _jj
+            _req = urllib.request.Request("http://127.0.0.1:5805/",
+                data=_jj.dumps({"body": _msg, "to": "hmo@yoin.fun", "type": "chat"}).encode(),
+                headers={"Content-Type": "application/json"})
+            urllib.request.urlopen(_req, timeout=5)
+            print(f"  📨 XMPP推送成功: {_msg[:60]}")
+        except Exception as _e:
+            print(f"  ⚠️ XMPP推送失败: {_e}")
+    
+    conn.close()
+
+def process_stock(code):
+    """处理单只股票"""
+    print(f"\n{'='*50}")
+    print(f"处理: {code}")
+    print(f"{'='*50}")
+    
+    if has_llm_analysis(code):
+        print(f"  ⏭ 已有LLM九维分析，跳过")
+        return False
+    
+    if in_cooldown(code):
+        print(f"  ⏭ 冷却期内，跳过")
+        return False
+    
+    print(f"  收集数据...", flush=True)
+    data = collect_data(code)
+    if not data.get("price"):
+        print(f"  ⚠️ 无价格数据，跳过")
+        return False
+    
+    print(f"  调LLM生成九维分析...", flush=True)
+    prompt = build_prompt(data)
+    
+    try:
+        r = subprocess.run(["curl", "-s", "--max-time", "300",
+            "-H", "Content-Type: application/json",
+            "-H", "Authorization: Bearer hermes123",
+            "-d", json.dumps({"model":"deepseek-v4-flash","messages":[{"role":"user","content":prompt}],"max_tokens":2048}),
+            GATEWAY], capture_output=True, timeout=310)
+        
+        if r.returncode != 0:
+            print(f"  ❌ curl失败: {r.stderr.decode()[:100]}")
+            return False
+        
+        resp = json.loads(r.stdout)
+        if "choices" not in resp:
+            print(f"  ❌ API异常: {str(resp)[:200]}")
+            return False
+        
+        full_text = resp["choices"][0]["message"]["content"]
+        print(f"  ✅ LLM返回({len(full_text)}字)", flush=True)
+        
+        parsed = parse_response(full_text)
+        print(f"  信号={parsed['signal']} 区间={parsed['entry_low']}~{parsed['entry_high']} 损={parsed['stop_loss']} 盈={parsed['take_profit']} 仓位={parsed['position']}")
+        
+        save_result(code, full_text, parsed)
+        print(f"  ✅ 已保存到DB")
+        return True
+        
+    except subprocess.TimeoutExpired:
+        print(f"  ❌ 超时")
+        return False
+    except Exception as e:
+        print(f"  ❌ 错误: {e}")
+        return False
+
+def main():
+    codes = []
+    if "--code" in sys.argv:
+        idx = sys.argv.index("--code")
+        codes = [sys.argv[idx+1]]
+    else:
+        # 所有自选策略
+        conn = sqlite3.connect(DB)
+        rows = conn.execute("SELECT code FROM holding_strategies WHERE status='active' AND decision_type='自选策略' ORDER BY code").fetchall()
+        conn.close()
+        codes = [r[0] for r in rows]
+    
+    print(f"待处理: {len(codes)}只")
+    
+    ok = 0
+    fail = 0
+    skip = 0
+    for i, code in enumerate(codes):
+        if has_llm_analysis(code):
+            print(f"  [{i+1}/{len(codes)}] ⏭ {code} 已有LLM分析")
+            skip += 1
+            continue
+        
+        print(f"  [{i+1}/{len(codes)}] ", end="", flush=True)
+        if process_stock(code):
+            ok += 1
+        else:
+            fail += 1
+        
+        # 间隔15秒（防gateway过载）
+        if i < len(codes) - 1:
+            print(f"  等待15秒...", flush=True)
+            time.sleep(15)
+    
+    print(f"\n{'='*50}")
+    print(f"完成: {ok}成功, {fail}失败, {skip}跳过")
+    print(f"{'='*50}")
+
+if __name__ == "__main__":
+    main()
diff --git a/deploy/profile-scripts/premarket_full_review.py b/deploy/profile-scripts/premarket_full_review.py
index e777a3f9..1cebcd1c 100644
--- a/deploy/profile-scripts/premarket_full_review.py
+++ b/deploy/profile-scripts/premarket_full_review.py
@@ -1,64 +1,40 @@
-#!/usr/bin/env python3
-"""premarket_full_review.py — 盘前全量重评
-
-执行顺序：
-1. regenerate_all() 全量技术参数重评（持仓+自选）
-2. batch_reassess.py --type holding --today 持仓12维LLM分析（每日强制刷新）
-3. watchlist_auto_exit() 自选退出检查
-4. 输出摘要
-
-调度：交易日 08:10（A股09:30开盘）
-"""
-import sys, os, json
-sys.path.insert(0, '/home/hmo/MoFin')
-
-# Step 1: 全量技术参数重评
-print("=" * 50)
-print("📊 盘前全量重评开始")
-print("=" * 50)
-from strategy_lifecycle import regenerate_all
-result = regenerate_all(stdout=True)
-print(f"\n重评完成: {result.get('ok',0)}/{result.get('total',0)}成功")
-
-# Step 1.5: 持仓 12 维 LLM 深度分析（每日强制，14只约8-10分钟）
-print("\n" + "=" * 50)
-print("🧠 持仓12维LLM分析（每日强制刷新）")
-print("=" * 50)
-import subprocess as _sp
-analysis_result = {"ok": 0, "fail": 0, "skip": 0}
-try:
-    r = _sp.run(
-        ["python3", "/home/hmo/.hermes/profiles/position-analyst/scripts/batch_reassess.py",
-         "--type", "holding", "--today"],
-        capture_output=True, text=True, timeout=3600)
-    print(r.stdout[-2000:] if len(r.stdout) > 2000 else r.stdout)
-    if r.returncode != 0 and r.stderr:
-        print(f"⚠️ stderr: {r.stderr[:300]}")
-    # 从输出尾部解析统计
-    import re as _re
-    m = _re.search(r"完成: (\d+)成功, (\d+)失败, (\d+)跳过", r.stdout)
-    if m:
-        analysis_result = {"ok": int(m.group(1)), "fail": int(m.group(2)), "skip": int(m.group(3))}
-except Exception as e:
-    print(f"⚠️ 12维分析步骤异常: {e}")
-
-# Step 2: 自选退出
-print("\n" + "=" * 50)
-print("🔍 自选退出检查")
-print("=" * 50)
-from scripts.watchlist_auto_exit import main as auto_exit
-exited = auto_exit(dry_run=False)
-
-# Step 3: 写入摘要供开盘简报引用
-summary = {
-    "premarket_at": __import__('datetime').datetime.now().isoformat(),
-    "reassess": result,
-    "llm_analysis_12d": analysis_result,
-    "auto_exit": [{"code": c, "name": n, "reason": r} for c, n, s, r in exited],
-    "total_kept": result.get('total', 0) - len(exited),
-}
-os.makedirs("/tmp/mofin_premarket", exist_ok=True)
-with open("/tmp/mofin_premarket/summary.json", "w") as f:
-    json.dump(summary, f, ensure_ascii=False, indent=2)
-
-print(f"\n✅ 盘前重评完毕")
+#!/usr/bin/env python3
+"""premarket_full_review.py — 盘前全量重评
+
+执行顺序：
+1. regenerate_all() 全量技术分析重评（持仓+自选）
+2. watchlist_auto_exit() 自选退出检查
+3. 输出摘要
+
+调度：交易日 08:10（A股09:30开盘）
+"""
+import sys, os, json
+sys.path.insert(0, '/home/hmo/MoFin')
+
+# Step 1: 全量重评
+print("=" * 50)
+print("📊 盘前全量重评开始")
+print("=" * 50)
+from strategy_lifecycle import regenerate_all
+result = regenerate_all(stdout=True)
+print(f"\n重评完成: {result.get('ok',0)}/{result.get('total',0)}成功")
+
+# Step 2: 自选退出
+print("\n" + "=" * 50)
+print("🔍 自选退出检查")
+print("=" * 50)
+from scripts.watchlist_auto_exit import main as auto_exit
+exited = auto_exit(dry_run=False)
+
+# Step 3: 写入摘要供开盘简报引用
+summary = {
+    "premarket_at": __import__('datetime').datetime.now().isoformat(),
+    "reassess": result,
+    "auto_exit": [{"code": c, "name": n, "reason": r} for c, n, s, r in exited],
+    "total_kept": result.get('total', 0) - len(exited),
+}
+os.makedirs("/tmp/mofin_premarket", exist_ok=True)
+with open("/tmp/mofin_premarket/summary.json", "w") as f:
+    json.dump(summary, f, ensure_ascii=False, indent=2)
+
+print(f"\n✅ 盘前重评完毕")