#!/usr/bin/env python3
"""batch_reassess.py — 批量补全12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, os
from datetime import datetime

# ── 共享 LLM 客户端 + DB 工具（profile-scripts 硬链到同目录）──
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
sys.path.insert(0, "/home/hmo/MoFin")
from llm_client import call_llm, REASSESS_MODEL, FALLBACK_MODEL, gateway_alive, ocg_alive
from mofin_db import snapshot_strategy_history, sync_recommend_tag

DB = "/home/hmo/MoFin/data/mofin.db"
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, timeout=30)
    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

FORCE_REASSESS = "--force" in sys.argv

def in_cooldown(code):
    """冷却期检查（--force 时全量强制重评）"""
    if FORCE_REASSESS:
        return False
    conn = sqlite3.connect(DB, timeout=30)
    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, timeout=30)
    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, timeout=30)
        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（含完整策略原文）"""
    data = {"code": code}
    
    # 从DB读策略（含 full_analysis / changelog_json / position_advice）
    conn = sqlite3.connect(DB, timeout=30)
    r = conn.execute("SELECT name, entry_low, entry_high, stop_loss, take_profit, timing_signal, action, rr_ratio, tech_snapshot, sector_context, stock_category, full_analysis, changelog_json, reassessed_at, position_advice, strategy_name 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 ""
        data["full_analysis"] = r[11] or ""
        data["changelog_json"] = r[12] or ""
        data["reassessed_at"] = r[13] or ""
        data["strategy_name"] = r[15] or ""  # 2026-08-18 来源策略
        # 2026-08-18 策略状态感知：switched → 用 strategy_attributed（切到的策略）的定义评估
        try:
            _st0 = conn.execute("SELECT strategy_state, strategy_attributed FROM holding_strategies WHERE code=? AND status='active'", (code,)).fetchone()
            _eval_strat = data["strategy_name"]
            if _st0 and _st0[0] == "switched" and _st0[1]:
                _eval_strat = _st0[1]
        except Exception:
            _eval_strat = data["strategy_name"]
        # 2026-08-18 策略语义注入：读 strategy_defs 定义卡 + 情势体检
        try:
            _sd = conn.execute("SELECT display_name, summary, entry_logic, exit_logic, review_focus, holding_style, status, retired_reason, superseded_by, regime FROM strategy_defs WHERE strategy_name=?", (_eval_strat,)).fetchone()
            if _sd:
                data["strategy_def"] = {
                    "display_name": _sd[0] or data["strategy_name"], "summary": _sd[1] or "",
                    "entry_logic": _sd[2] or "", "exit_logic": _sd[3] or "",
                    "review_focus": _sd[4] or "", "holding_style": _sd[5] or "",
                    "status": _sd[6] or "active", "retired_reason": _sd[7] or "",
                    "superseded_by": _sd[8] or "", "regime": _sd[9] or "",
                }
            else:
                data["strategy_def"] = None
        except Exception:
            data["strategy_def"] = None
        # ── 技术指标收集（策略输入数据）──
        # 从 stock_indicators 读取实时技术指标（由 price_monitor 联动更新）
        try:
            _si = _db2.execute(
                "SELECT ma5, ma10, ma20, ma60, rsi, bias60, dist_ma20, strong_support, weak_support, pivot, weak_resist, strong_resist, candle_pattern, vol_ratio "
                "FROM stock_indicators WHERE code=? ORDER BY date DESC LIMIT 1",
                (code,)).fetchone()
            if _si:
                data["ta_ma5"] = _si[0] if _si[0] else None
                data["ta_ma10"] = _si[1] if _si[1] else None
                data["ta_ma20"] = _si[2] if _si[2] else None
                data["ta_ma60"] = _si[3] if _si[3] else None
                data["ta_rsi"] = round(_si[4], 1) if _si[4] else None
                data["ta_dist_ma20"] = _si[6] if _si[6] else None
                data["ta_strong_support"] = _si[7] if _si[7] else None
                data["ta_weak_support"] = _si[8] if _si[8] else None
                data["ta_pivot"] = _si[9] if _si[9] else None
                data["ta_weak_resist"] = _si[10] if _si[10] else None
                data["ta_strong_resist"] = _si[11] if _si[11] else None
                data["ta_candle"] = _si[12] or ""
                data["ta_volume"] = ""
        except Exception:
            pass
        try:
            import multi_timeframe as _mtf
            _mtf_r = _mtf.full_multi_tf_analysis(code)
            if _mtf_r:
                _adj = _mtf_r.get("strategy_adjustment", {})
                data["mtf_trend_alignment"] = _adj.get("trend_alignment", "未知")
                data["mtf_daily_trend"] = _mtf_r.get("daily", {}).get("trend", {}).get("description", "")
                data["mtf_weekly_trend"] = _mtf_r.get("weekly", {}).get("trend", {}).get("description", "")
                data["mtf_monthly_trend"] = _mtf_r.get("monthly", {}).get("trend", {}).get("description", "")
                _rsi = _mtf_r.get("daily", {}).get("rsi")
                if _rsi is not None:
                    data["ta_rsi"] = round(_rsi, 1)
        except Exception:
            pass
        try:
            _db2 = sqlite3.connect(DB, timeout=30)
            _fi = _db2.execute("SELECT mcap_q, pe_q, bias60, rsi, r5f, dist_lo20, dist_ma20, vol_ratio FROM stock_indicators WHERE code=? ORDER BY date DESC LIMIT 1", (code,)).fetchone()
            if _fi:
                data["factor_mcap_q"] = _fi[0] if _fi[0] is not None else None
                data["factor_pe_q"] = _fi[1] if _fi[1] is not None else None
                data["factor_bias60"] = _fi[2] if _fi[2] is not None else None
                if data.get("ta_rsi") is None and _fi[3] is not None:
                    data["ta_rsi"] = round(_fi[3], 1)
                data["factor_ret5d"] = _fi[4] if _fi[4] is not None else None
                data["factor_dist_lo20"] = _fi[5] if _fi[5] is not None else None
                data["factor_dist_ma20"] = _fi[6] if _fi[6] is not None else None
                data["factor_vol_ratio"] = _fi[7] if _fi[7] is not None else None
            _db2.close()
        except Exception:
            pass
        
        # 行业强度（sector_snapshots: market_watch.py 写入的板块涨跌排名）
        try:
            _db3 = sqlite3.connect(DB, timeout=30)
            _sector = data.get("sector_context", "")
            if _sector:
                _ss = _db3.execute(
                    "SELECT change_pct, rank_in_market FROM sector_snapshots "
                    "WHERE sector_name LIKE ? ORDER BY date DESC LIMIT 1",
                    (f"%{_sector}%",)).fetchone()
                if _ss:
                    data["sector_change_pct"] = _ss[0] if _ss[0] is not None else None
                    data["sector_rank"] = _ss[1] if _ss[1] is not None else None
            _db3.close()
        except Exception:
            pass

        # 情势体检：温区 + 高风险消息 + 执行红线
        _sit = {"regime_a": "unknown", "regime_7d_ago": "unknown", "high_risk": "", "breach_stop": "否", "reach_tp": "否", "out_zone": "否", "over_hold": "否"}
        try:
            _m = conn.execute("SELECT regime, date FROM market_regime WHERE market='a' ORDER BY date DESC LIMIT 1").fetchone()
            if _m:
                _sit["regime_a"] = _m[0] or "unknown"
                _m7 = conn.execute("SELECT regime FROM market_regime WHERE market='a' AND date <= date(?, '-7 days') ORDER BY date DESC LIMIT 1", (_m[1],)).fetchone()
                if _m7: _sit["regime_7d_ago"] = _m7[0] or "unknown"
        except Exception:
            pass
        try:
            _hr = conn.execute("SELECT summary FROM signal_news WHERE overall_sentiment LIKE '%HIGH%' OR summary LIKE '%高风险%' ORDER BY id DESC LIMIT 1").fetchone()
            if _hr: _sit["high_risk"] = (_hr[0] or "")[:60]
        except Exception:
            pass
        try:
            _p = data.get("price") or 0
            _el2, _eh2 = data.get("entry_low") or 0, data.get("entry_high") or 0
            _sl2, _tp2 = data.get("stop_loss") or 0, data.get("take_profit") or 0
            if _p > 0 and _sl2 > 0 and _p < _sl2: _sit["breach_stop"] = "是"
            if _p > 0 and _tp2 > 0 and _p >= _tp2: _sit["reach_tp"] = "是"
            if _eh2 > 0 and _p > _eh2 * 1.05: _sit["out_zone"] = "是"
        except Exception:
            pass
        data["situation"] = _sit
        data["position_advice"] = r[14] or ""
    # 持仓状态（2026-07-22 老爸要求：LLM 必须知道是否持有/成本/股数）
    hr = conn.execute("SELECT shares, cost, price FROM holdings WHERE code=? AND is_active=1 AND shares>0", (code,)).fetchone()
    if hr and hr[0]:
        data["held"] = True
        data["held_shares"] = hr[0]
        data["held_cost"] = hr[1] or 0
    else:
        data["held"] = False
    # 2026-08-18 持仓处置决策：策略状态感知（invalidated/none → position_management）
    try:
        _st_r = conn.execute("SELECT strategy_state, strategy_attributed, strategy_provenance FROM holding_strategies WHERE code=? AND status='active'", (code,)).fetchone()
        data["strategy_state"] = _st_r[0] or "active" if _st_r else "active"
        data["strategy_attributed"] = _st_r[1] or "" if _st_r else ""
        # 无策略/失效 + 持仓 → 用 position_management 框架
        _need_pos_mgmt = (data.get("strategy_state") in ("invalidated", "none") or not data.get("strategy_def")) and data.get("held")
        if _need_pos_mgmt:
            _pm = conn.execute("SELECT display_name, summary, entry_logic, exit_logic, review_focus, holding_style, status, retired_reason, superseded_by, regime FROM strategy_defs WHERE strategy_name='position_management'").fetchone()
            if _pm:
                data["strategy_def"] = {
                    "display_name": _pm[0], "summary": _pm[1], "entry_logic": _pm[2],
                    "exit_logic": _pm[3], "review_focus": _pm[4], "holding_style": _pm[5],
                    "status": _pm[6], "retired_reason": _pm[7], "superseded_by": _pm[8], "regime": _pm[9],
                }
                data["pos_mgmt_triggered"] = True
        # current_regime_strategies 已移除（旧策略名与strategy_defs不匹配）
    except Exception:
        pass
    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:
        _pconn = sqlite3.connect(DB, timeout=30)
        # 价格: stock_daily(收盘价,全市场覆盖) 优先, live_prices(盘中) 其次
        _sd = _pconn.execute("SELECT close FROM stock_daily WHERE code=? ORDER BY date DESC LIMIT 1", (code,)).fetchone()
        if _sd and _sd[0]:
            data["price"] = float(_sd[0])
        else:
            _lp = _pconn.execute("SELECT price FROM live_prices WHERE code=?", (code,)).fetchone()
            data["price"] = float(_lp[0]) if _lp and _lp[0] else 0
        data["change_pct"] = "0"
        # PE/市值: 从stock_fundamentals读(fundamentals_full_refresh全市场覆盖)
        _ff = _pconn.execute("SELECT pe, pb, mcap_total FROM stock_fundamentals WHERE code=?", (code,)).fetchone()
        if _ff:
            data["pe"] = str(_ff[0]) if _ff[0] else ""
            data["mcap"] = str(_ff[1]) if _ff[1] else ""  # PB暂时不用,但mcap_total是市值
        else:
            data["pe"] = ""
            data["mcap"] = ""
        _pconn.close()
    except:
        data["price"] = 0
    
    # 行业上下文修正：sector_context 被"大盘上涨比"污染或为空时，用 stock_sectors 的行业名兜底；
    # 未映射的股票明确标注"行业未映射"（不让大盘指标伪装成行业信息）
    _sector_ctx = data.get('sector_context', '') or ''
    if (not _sector_ctx) or _sector_ctx.startswith('大盘上涨比') or len(_sector_ctx) < 4:
        _resolved = ""
        try:
            _sdb = sqlite3.connect(DB, timeout=30)
            _sr = _sdb.execute("SELECT sector_name FROM stock_sectors WHERE code=? LIMIT 1", (code,)).fetchone()
            if _sr and _sr[0]:
                _resolved = f"行业{_sr[0]}"
            else:
                # 2026-08-17 修复：stock_sectors 仅898只覆盖不全，回退 stock_sectors_em（5061只）
                _sr2 = _sdb.execute("SELECT sector FROM stock_sectors_em WHERE code=? LIMIT 1", (code,)).fetchone()
                if _sr2 and _sr2[0]:
                    _resolved = f"行业{_sr2[0]}"
            _sdb.close()
        except Exception:
            pass
        _sector_ctx = _resolved if _resolved else "行业未映射(仅大盘环境参考)"
    data['sector_context'] = _sector_ctx
    # 大盘
    try:
        conn = sqlite3.connect(DB, timeout=30)
        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"

    # ── 确定性技术位（2026-07-22 老爸：系统计算的技术位必须作为客观锚喂给LLM）──
    # 与技术路径(per_stock)同源: technical_analysis.full_analysis
    # 强撑/弱撑/枢轴/弱压/强压/有效区间 + 均线，全部确定性计算，非LLM估计。
    data["ta"] = {}
    try:
        import technical_analysis as ta_mod
        _ta = ta_mod.full_analysis(code)
        if _ta and "error" not in _ta:
            _sr = _ta.get("support_resistance", {}) or {}
            _mtf = _ta.get("multi_tf", {}) or {}
            _mas = (_mtf.get("mas") or {})
            data["ta"] = {
                "strong_support": _sr.get("strong_support"),
                "weak_support": _sr.get("weak_support"),
                "pivot": _sr.get("pivot"),
                "weak_resist": _sr.get("weak_resist"),
                "strong_resist": _sr.get("strong_resist"),
                "effective_range": _sr.get("effective_range"),
                "ma5": _mas.get("ma5"), "ma10": _mas.get("ma10"),
                "ma20": _mas.get("ma20"), "ma60": _mas.get("ma60"),
            }
    except Exception as _te:
        print(f"  ⚠️ 技术位计算失败({code}): {_te}", flush=True)

    # 深套重算（价格已拉取，与 strategy_lifecycle is_deep_loss 同口径 -20%）
    _cost3 = data.get("held_cost") or 0
    _px3 = data.get("price") or 0
    if data.get("held") and _cost3 > 0 and _px3 > 0:
        _pnl3 = (_px3 - _cost3) / _cost3 * 100
        data["deep_loss"] = _pnl3 < -20
        data["pnl_pct"] = round(_pnl3, 1)
    else:
        data["deep_loss"] = False
        data["pnl_pct"] = None

    return data

def build_prompt(data):
    """构建LLM prompt，先审阅原策略再结合实时数据输出修改判断+12维矩阵分析"""
    cash, total = get_portfolio()
    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 WHERE id=1 ORDER BY updated_at 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（不再要求情绪标签——原始新闻直接喂给12维LLM，由LLM自行判断情绪。
    # 2026-07-22：情绪分类器已退役，利好/利空标签停在07-09，强制过滤=自断新闻源）
    _news_note = "暂无近期消息"
    _news_items = []
    try:
        import sqlite3 as _sq
        _db = _sq.connect("/home/hmo/MoFin/data/mofin.db")
        # ① 个股直接相关（searched_stocks 含本代码 或 行业名匹配），不限情绪标签
        _sector_name = ""
        try:
            _sr = _db.execute(
                "SELECT sector_name FROM stock_sectors WHERE code=? LIMIT 1", (data['code'],)).fetchone()
            _sector_name = _sr[0] if _sr else ""
        except Exception:
            pass
        _nr = _db.execute(
            "SELECT summary, overall_sentiment, created_at FROM signal_news "
            "WHERE searched_stocks LIKE ? OR sector LIKE ? "
            "ORDER BY id DESC LIMIT 3",
            (f'%{data["code"]}%', f'%{_sector_name}%')).fetchall()
        _news_items.extend(_nr)
        # ② 大盘兜底（独立 try，不被①的失败拖累；取最新3条，不限标签）
        if not _news_items:
            _nr2 = _db.execute(
                "SELECT summary, overall_sentiment, created_at FROM signal_news "
                "ORDER BY id DESC LIMIT 3").fetchall()
            _news_items.extend(_nr2)
        _db.close()
    except:
        pass
    if _news_items:
        def _fmt(r):
            senti = r[1] if r[1] and r[1] != 'unknown' else '未标注'
            return f"{r[2][:10]} [{senti}] {r[0][:40]}"
        _news_note = " | ".join([_fmt(r) for r in _news_items])
    
    # ── 构建【原策略全文】section ──
    _params_parts = []
    if data.get('action'): _params_parts.append(f"当前策略: {data['action']}")
    if data.get('timing_signal'): _params_parts.append(f"信号: {data['timing_signal']}")
    if data.get('entry_low') or data.get('entry_high'):
        _params_parts.append(f"买入区间: {data.get('entry_low',0)}~{data.get('entry_high',0)}")
    if data.get('stop_loss'): _params_parts.append(f"止损: {data['stop_loss']}")
    if data.get('take_profit'): _params_parts.append(f"止盈: {data['take_profit']}")
    if data.get('position_advice'): _params_parts.append(f"仓位: {data['position_advice']}")
    _params_str = " | ".join(_params_parts) if _params_parts else "无策略参数"
    
    # 最近3条变更记录
    _changelog_str = "无变更记录"
    try:
        _cl_raw = data.get('changelog_json', '')
        if _cl_raw:
            _cl = json.loads(_cl_raw) if isinstance(_cl_raw, str) else _cl_raw
            if isinstance(_cl, list) and _cl:
                _recent = _cl[-3:] if len(_cl) > 3 else _cl
                _cl_lines = []
                for i, c in enumerate(_recent):
                    _act = c.get('action', c.get('reason', '')) if isinstance(c, dict) else str(c)
                    _ts = c.get('timestamp', '') if isinstance(c, dict) else ''
                    _cl_lines.append(f"  {i+1}. {_ts[:16]} {_act[:80]}")
                if _cl_lines:
                    _changelog_str = "\n".join(_cl_lines)
    except:
        pass
    
    # 完整分析原文（不截断）
    _full_analysis = data.get('full_analysis', '') or ''
    _fa_display = _full_analysis if _full_analysis else '（首次分析，无历史）'

    # ── 持仓上下文（2026-07-22 老爸要求：LLM 必须知道持有状态，建议不得两头都写）──
    if data.get('held'):
        _sh = data.get('held_shares', 0)
        _cost = data.get('held_cost', 0)
        _px = data.get('price', 0) or 0
        _pnl = ((_px - _cost) / _cost * 100) if _cost else 0
        _position_context = (f"⚠️ 我当前【已持有】{data['code']}：{_sh}股，成本{_cost:.2f}元，"
                             f"现价{_px}元（盈亏{_pnl:+.1f}%）。你的建议必须基于「已持有」状态给出"
                             f"（加减仓/止损止盈/持有观察），禁止给「未持有者」的建仓建议。")
    else:
        _position_context = (f"⚠️ 我当前【未持有】{data['code']}。你的建议必须基于「未持有」状态给出"
                             f"（是否建仓/什么价位建仓/仓位多大），禁止假设我有浮盈、"
                             f"禁止出现「已持仓者」视角的建议。")

    # ── 行业强度数据 ──
    _sector_extra = ""
    if data.get("sector_change_pct") is not None:
        _sector_extra = f" 行业涨跌={data['sector_change_pct']}%"
    if data.get("sector_rank") is not None:
        _sector_extra += f" 行业排名={data['sector_rank']}"

    _tech_parts = []
    if data.get("ta_strong_support"):
        _tech_parts.append(f"强支撑={data['ta_strong_support']} 弱支撑={data['ta_weak_support']} 枢轴={data['ta_pivot']} 弱压={data['ta_weak_resist']} 强压={data['ta_strong_resist']}")
    if data.get("ta_ma20"):
        _ma_info = f"MA5={data.get('ta_ma5','?')} MA10={data.get('ta_ma10','?')} MA20={data.get('ta_ma20','?')} MA60={data.get('ta_ma60','?')}"
        if data.get("ta_dist_ma20") is not None:
            _ma_info += f" 距MA20={data['ta_dist_ma20']}%"
        _tech_parts.append(_ma_info)
    if data.get("ta_rsi"):
        _tech_parts.append(f"RSI={data['ta_rsi']}")
    if data.get("ta_candle"):
        _tech_parts.append(f"K线形态={data['ta_candle']}")
    if data.get("mtf_trend_alignment"):
        _tech_parts.append(f"多周期趋势={data['mtf_trend_alignment']}")
    if data.get("mtf_daily_trend"):
        _tech_parts.append(f"日线={data['mtf_daily_trend']}")
    if data.get("mtf_weekly_trend"):
        _tech_parts.append(f"周线={data['mtf_weekly_trend']}")
    if data.get("mtf_monthly_trend"):
        _tech_parts.append(f"月线={data['mtf_monthly_trend']}")
    _factor_parts = []
    for _fk, _fl in [("factor_mcap_q", "市值分位"), ("factor_pe_q", "PE分位"), ("factor_bias60", "bias60"), ("factor_ret5d", "5日涨幅"), ("factor_dist_lo20", "距20日低点"), ("factor_dist_ma20", "距MA20"), ("factor_vol_ratio", "量比")]:
        if data.get(_fk) is not None:
            _factor_parts.append(f"{_fl}={data[_fk]}")
    if _factor_parts:
        _tech_parts.append("基本面分位: " + " ".join(_factor_parts))
    _tech_str = " | ".join(_tech_parts) if _tech_parts else "技术指标数据待刷新"

    # ── 换仓上下文（2026-07-24 老爸：现金不足时给出具体换股建议）──
    _rotation_context = ""
    if not data.get('held'):
        try:
            _rc = sqlite3.connect(DB, timeout=30)
            _all_holdings = _rc.execute("""
                SELECT hs.code, hs.name, hs.timing_signal, hs.rr_ratio,
                       h.position_pct, h.cost, lp.price, lp.change_pct,
                       hs.full_analysis
                FROM holding_strategies hs
                JOIN holdings h ON hs.code = h.code AND h.is_active = 1
                LEFT JOIN live_prices lp ON hs.code = lp.code
                WHERE hs.status='active' AND h.shares > 0
                  AND hs.timing_signal IN ('弱势持有','观望','持有')
                ORDER BY h.position_pct DESC""").fetchall()
            _rc.close()
            if _all_holdings:
                _hl = "；".join(f"{w[1]}({w[0]}){w[2]} RR={w[3]:.1f} 仓位{w[4]:.1f}% 浮盈{w[7]:+.1f}%" for w in _all_holdings)
                _rotation_context = (
                    f"\n【换仓分析】当前现金{cash:.0f}元不足以建仓，需换仓。"
                    f"以下持仓可供减持（代码/信号/RR/仓位%/浮盈）：{_hl}。"
                    f"\n请基于你对该股的12维分析结论，评估以上每只持仓的持有价值，"
                    f"推荐最应该卖出的一只（代码+理由+建议减持金额），在SIGNAL_JSON的rotation_candidate字段中明确输出。"
                    f"判断标准：因子评分低、技术面最弱、信号最差、浮亏最大、仓位可腾出的优先卖出。")
    _position_context += _rotation_context
    # 2026-08-18 换仓决策注入（老莫：需要资金时对比预期收益，卖E_hold最低的）
    # 触发：本票信号为买入/加仓 + 现金不足（cash < 建议仓位金额估算）
    try:
        from swap_decision import decide_swap, format_swap_advice, get_strategy_expected
        _sig_now = data.get("timing_signal") or ""
        _need_fund = _sig_now in ("买入", "可买入", "可加仓")
        if _need_fund:
            # 估算本票建议仓位金额（按 RR 5%-15%，取中 10%）
            _est_need = total * 0.10
            if cash < _est_need:
                # 拉当前持仓（深套判定：cost vs price）
                try:
                    import sqlite3 as _sq3
                    _c3 = _sq3.connect(DB, timeout=30)
                    _c3.row_factory = _sq3.Row
                    _holds = _c3.execute("SELECT code, name, shares, cost, price FROM holdings WHERE is_active=1 AND shares>0").fetchall()
                    _c3.close()
                    _hold_list = []
                    for _h in _holds:
                        _cost_v = _h["cost"] or 0; _price_v = _h["price"] or 0
                        _pnl_v = (_price_v - _cost_v) / _cost_v * 100 if _cost_v > 0 and _price_v > 0 else None
                        # 查该持仓的 strategy_name
                        _sn = ""
                        try:
                            _c4 = _sq3.connect(DB, timeout=30)
                            _sn_r = _c4.execute("SELECT strategy_name FROM holding_strategies WHERE code=? AND status='active'", (_h["code"],)).fetchone()
                            _c4.close()
                            _sn = _sn_r[0] if _sn_r else ""
                        except Exception:
                            pass
                        _hold_list.append({
                            "code": _h["code"], "name": _h["name"] or _h["code"],
                            "cost": _cost_v, "price": _price_v, "shares": _h["shares"] or 0,
                            "strategy_name": _sn, "pnl_pct": _pnl_v,
                        })
                    _sd = data.get("strategy_name") or data.get("strategy_attributed") or ""
                    _dec = decide_swap(need_cash=_est_need - cash, holdings=_hold_list,
                                       new_strategy=_sd, market="a")
                    _swap_advice = format_swap_advice(_dec)
                    _position_context += f"\n\n{_swap_advice}\n⚠️ 若你给出买入建议但现金不足，以上为系统算好的换仓方案（卖预期收益最低的持仓凑钱）；你只需确认是否采纳（可结合消息面/基本面修正），不要重新设计换仓逻辑。"
                    print(f"  [SWAP] 换仓决策已注入: {_dec.get('reason', '')[:80]}", flush=True)
                except Exception as _se:
                    print(f"  [SWAP] 换仓决策注入失败: {_se}", flush=True)
    except Exception:
        pass
    
    # ── 策略语义注入（2026-08-18 老莫：重评按策略定义，不是裸标签）──
    _sd = data.get("strategy_def")
    _sit = data.get("situation") or {}
    if _sd:
        _retired_note = ""
        if _sd.get("status") == "retired":
            _retired_note = (f"\n⚠️ 该策略已被系统标记【已淘汰】：{_sd.get('retired_reason') or ''}"
                             f" 不要沿用其入场/出场参数作为默认锚，按当前市场环境重新评估。")
        _strategy_sec = f"""【策略定义】（系统策略库 strategy_defs 提供，非LLM生成）
策略名: {_sd.get('display_name')}（{data.get('strategy_name') or 'unknown'}）
策略逻辑: {_sd.get('summary') or ''}
入场逻辑: {_sd.get('entry_logic') or ''}
出场规则: {_sd.get('exit_logic') or ''}
重评侧重: {_sd.get('review_focus') or ''}
持仓风格: {_sd.get('holding_style') or ''}{_retired_note}"""
    else:
        _strategy_sec = """【策略定义】（无策略记录）
来源策略未知。请基于技术形态/估值/资金特征判断该股当前最接近的策略画像，并在【策略判断】中说明；
若判断需要归类到某个策略，从以下候选中选择（禁止自创）：
accumulation(主力建仓) / b_td1_v3(超跌原池优选) / v_mr(弱市深超跌) / hk_pe_mom(港股动量) / hk_pe_oversold(港股超卖) / p_oversold(预测超跌) / s2_panic(恐慌买强势) / leader(龙头回调) / v_next(趋势龙头回调) / v8.1(顺势波段)"""
    _sit_sec = f"""【情势体检】（系统确定性计算，非LLM估计）
当前温区(A股): {_sit.get('regime_a','unknown')}（7天前: {_sit.get('regime_7d_ago','unknown')}）
温区突变: {'是' if _sit.get('regime_a') != _sit.get('regime_7d_ago') and _sit.get('regime_a') not in ('unknown','') else '否'}
高风险消息: {_sit.get('high_risk') or '无'}
执行红线: 破止损未执行={_sit.get('breach_stop')} 达止盈未执行={_sit.get('reach_tp')} 现价超买入区上沿={_sit.get('out_zone')} 超持有期={_sit.get('over_hold')}
⚠️ 若温区突变/高风险消息/执行红线任一命中，或重评侧重检查发现原入场逻辑不成立，【策略判断】必须考虑「策略失效重定」或「更换策略归类」。"""
    _orig_strategy_section = f"""{_strategy_sec}

{_sit_sec}

来源策略: {data.get("strategy_name") or "unknown"}（按此策略选股逻辑重评，可据最新情况调整参数）
当前策略参数: {_params_str}

变更记录（最近3条）:
{_changelog_str}

完整分析原文:
{_fa_display}"""
    
    # ── 技术位锚 section（2026-07-22 老爸：确定性计算值，LLM 必须尊重）──
    _ta = data.get("ta") or {}
    if _ta.get("weak_support"):
        _ma_parts = [f"{k.upper()}={_ta[k]}" for k in ("ma5", "ma10", "ma20", "ma60") if _ta.get(k)]
        _ma_line = ("  ".join(_ma_parts) + "\n") if _ma_parts else ""
        _ta_sec = f"""【技术位锚】（以下数值由系统基于K线/均线确定性计算，是客观事实，不是你的估计值）
强撑={_ta.get('strong_support')}  弱撑={_ta.get('weak_support')}  枢轴={_ta.get('pivot')}
弱压={_ta.get('weak_resist')}  强压={_ta.get('strong_resist')}  有效区间={_ta.get('effective_range')}
{_ma_line}⚠️ 参数锚定纪律（必须遵守，输出前自检）：
【趋势判断】（必须给出，影响信号方向）
- 上升趋势：MA5>MA10>MA20>MA60，价格在MA20上方 → 回调到支撑位可买入
- 下跌趋势：MA5<MA10<MA20<MA60，价格在MA20下方 → 反弹到阻力位应观望，禁止买入
- 下跌趋势中的反弹：价格从低点回升但未突破MA20 → 属于技术性反弹，不是反转，禁止追高买入
- 震荡：MA交织，方向不明 → 观望为主
- 波段出场形态（k线形态判断，2026-08-12 老莫：k线形态由LLM判断，不在代码硬编码）：价格连续2日收破MA10（站稳MA10下方）→ 趋势转弱，可波段先出/减仓；价格收回MA10上方且突破前一日高点（回稳+新高）→ 趋势回稳，可波段再进/接回
1. 买入区应落在技术位之间：下沿参考弱撑/强撑附近，上沿参考枢轴/弱压附近
2. 止损必须严格低于买入区下沿——放在弱撑下方1-3%或强撑附近；严禁止损≥区间下沿（等于把止损设在买入价上，下沿买入立即止损，RR恒为0）
3. 止盈应参考弱压/强压，不得明显高于强压
4. 止盈目标优先使用最近阻力位（20日新高/弱压/强压），不得超过最近阻力；若最近阻力使止盈空间压缩50%以上，直接降级为观望
5. 若按技术位计算的 RR(中值) < 2.0，信号必须降级为「关注」或「观望」，禁止给 RR<2.0 的买入建议
6. 现价已高于买入区上沿 5% 以上时，禁止追高推荐，信号降级为「观望」
7. 【关键】空仓/观望时买入区仍须填合理技术区间（供风报比计算），禁止填 0.0~0.0；
   空仓仅改信号/仓位建议，不影响区间参数——区间是参考值，不是"买不买"的判断
4. 若你判断技术位不适用（如突发重大消息/基本面剧变），必须在【修改点及理由】中明确写出偏离理由，禁止静默偏离"""
    else:
        _ta_sec = "【技术位锚】本次计算不可用，请基于价格行为谨慎给出参数，并仍须满足：止损<区间下沿<区间上沿<止盈。"

    # ── 市场状态标签（2026-07-27 老爸：盘前批次数据是昨收，不要骗LLM是"当日实时"）──
    _now = datetime.now()
    _h, _m, _w = _now.hour, _now.minute, _now.weekday()
    _is_market_open = _w < 5 and ((_h == 9 and _m >= 30) or (10 <= _h < 15))
    _price_label = "（盘中实时）" if _is_market_open else "（上次收盘/非交易时段）"
    _macro_label = "（盘中实时）" if _is_market_open else "（最近更新）"

    return f"""你是一个资深A股分析师。请先审阅以下【原策略全文】，判断是否需要修改策略，然后做出完整的12维矩阵分析。

【原策略全文】
{_orig_strategy_section}

── 以上是已有的策略，以下是当前实时数据，请结合两者做出判断 ──

⚠️ 重要：以下12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。

{_ta_sec}

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')}{_macro_label}
最新价：{data.get('price',0)}  涨跌：{data.get('change_pct','0')}%{_price_label}
PE={data.get('pe','?')}（最新财报）  市值={data.get('mcap','?')}亿
行业：{data.get('sector_context','?')}（近一个交易日）{(' 涨跌='+str(data.get('sector_change_pct',''))+'% 排名='+str(data.get('sector_rank',''))) if data.get('sector_change_pct') is not None else ''}{_sector_extra}
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）
{_tech_str}
{_tech_str}
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','?')}

我的总资产={total}元，可用现金={cash}元。
{_position_context}

请严格按以下格式输出（注意节标题不可省略）：

【策略判断】三选一：维持原策略 / 修改参数 / 策略失效需更换
- 默认倾向「维持原策略」；只有情势体检命中红线、或重评侧重检查发现原入场逻辑不成立时，才选「策略失效需更换」
- 「修改参数」= 策略框架仍适用，仅微调参数（止损/止盈/区间）
- 「策略失效需更换」= 原策略在当前情势下已不适合（温区切换/黑天鹅/入场逻辑不成立），必须换一个策略来评估
【策略失效理由】（策略判断≠维持原策略时必填，逐条对照重评侧重说明哪条不成立；维持时写"无需修改"）
【更换策略归类】策略失效需更换时【必须】写候选列表中的【策略代码名】（如 v_next、v8.1、leader），禁止写描述性文字（如"趋势回调""顺势追涨"）；候选列表：
accumulation / b_td1_v3 / v_mr / hk_pe_mom / hk_pe_oversold / p_oversold / s2_panic / leader / v_next / v8.1
若确实无合适策略，写"无合适策略"
【维持或修改】明确二选一判断：维持原策略 / 需要修改策略
【修改点及理由】
如果维持原策略 → 写"无需修改"
如果需要修改 → 逐条列出（每条格式："- 修改点名称：理由说明"）
【最终新策略】
用自然语言输出完整的最终策略全文（200-400字），自包含核心交易逻辑、买入区间价格、止损价、止盈价、仓位比例、风险提示。
⚠️ 本段不要使用【综合结论】【买入区间】等标签——用自然语言描述即可。

【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（锚定技术位：下沿参考弱撑/强撑，上沿参考枢轴/弱压）
【建议止损】数字（必须严格低于买入区下沿，放弱撑下方1-3%或强撑附近）
【建议止盈】数字（参考弱压/强压）

【参数自检】一行，格式"止损X < 区下沿Y < 区上沿Z < 止盈W：通过/不通过+原因"

【建议仓位】⚠️不可省略。综合结论非"买入"时写"不新建仓"；为"买入"时按以下公式：
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%
⚠️ 仓位硬约束（必须遵守）：
- 必须输出具体数字%（如"8%（理由）"），禁止"中等仓位/减仓或观望/轻仓/适量"等模糊表述——模糊仓位视为输出作废
- 必须结合可用现金{cash}元计算可买股数，仓位%对应的金额不得超过可用现金
- 若理想仓位超出现金，明确写出：实际可执行仓位X%（受现金限制），并给出换仓建议（减持哪只弱持仓换入本股）
输出格式："X%（理由：含现金可行性的一句话说明）"

⚠️ 输出纪律（必须遵守）：
1. 直接以【维持或修改】开头，禁止任何寒暄、开场白、分隔线
2. 禁止输出 <structured_data> 或任何 XML/代码块（唯一例外：末尾的 SIGNAL_JSON 行）
3. 所有【】节标题一个都不能少
4. 止损<区间下沿<区间上沿<止盈，违反任一条=输出作废重想
5. ⚠️ 最后一行必须是机器可读结论（单行，不加代码块标记）：
SIGNAL_JSON: {{"signal":"买入|可买入|可加仓|关注|观望|持有|弱势持有|卖出|止盈","actionable":true或false,"entry_low":数字或0,"entry_high":数字或0,"stop_loss":数字或0,"take_profit":数字或0,"position_pct":数字0到20,"reason":"一句话","rotation_candidate":{{"code":"代码或空","reason":"若现金不足需换仓，这里写最应卖出的持仓代码及理由，不需换仓则为空"}}}}
- signal 必须与【综合结论】完全一致
- actionable=true 仅当"当前价格下立即可执行的买入/卖出操作"；观望/等待/条件未满足/不符合建仓条件一律 false
- 数字字段与【买入区间】【建议止损】【建议止盈】【建议仓位】一致，无则0"""
def parse_response(text):
    """从LLM回复中提取策略参数。
    ⚠️ 节标题精确匹配：只认行首【买入区间】【综合结论】等节行。
    绝不用"包含关键词的第一行"——修改点段落会引用旧脏值（如"原买入区间95.0~99.0"），
    曾导致脏数据被反复写回（17只股票背着95~99区间，LLM新区间形同虚设）。"""
    result = {"signal": "", "entry_low": 0, "entry_high": 0, "stop_loss": 0, "take_profit": 0, "position": "",
              "zone_cleared": False, "action_advice": ""}

    def _section_line(name):
        """匹配节标题行：行首(可含空白)【名称】，返回该行内容"""
        for l in text.split("\n"):
            if re.match(r'^\s*【' + name + r'】', l):
                return l
        return ""

    # 信号（只认【综合结论】节行，且锚定】后的首个词，防"观望（不建议买入）"误判为买入）
    sl = _section_line("综合结论")
    if sl:
        m = re.search(r'综合结论】\s*[（(]?\s*(弱势持有|可加仓|可买入|买入|卖出|止盈|关注|观望|持有)', sl)
        if m:
            result["signal"] = m.group(1)

    # 买入区间（只认【买入区间】节行；"无"→显式清空，不保留旧值）
    zl = _section_line("买入区间")
    if zl:
        if re.search(r'】\s*(无|不设|不参与|空仓)', zl):
            result["zone_cleared"] = True
        elif len(re.findall(r'\d+\.?\d*', zl)) >= 2 and all(float(n) <= 0.01 for n in re.findall(r'\d+\.?\d*', zl)[:2]):
            # 2026-08-18 修复：LLM 输出"买入区0.0~0.0"也视为 zone_cleared（清空脏值）
            result["zone_cleared"] = True
        else:
            # 2026-08-18 修复：LLM 说"95~99（异常，应为14.87~15.01）"时，取"应为"后的正确区间
            # 否则取前两个数字（避免旧脏值被当推荐值写回，老莫 600262 教训）
            _corrected = re.search(r'(?:应为|修正为|改为|更正为)\s*[（(]?([\d.]+)\s*[~～-]\s*([\d.]+)', zl)
            if _corrected:
                result["entry_low"] = float(_corrected.group(1))
                result["entry_high"] = float(_corrected.group(2))
            else:
                nums = re.findall(r'\d+\.?\d*', zl)
                if len(nums) >= 2:
                    a, b = float(nums[0]), float(nums[1])
                    result["entry_low"] = min(a, b)
                    result["entry_high"] = max(a, b)

    # 止损（只认【建议止损】节行）
    for name in ("建议止损", "止损"):
        l = _section_line(name)
        if l:
            nums = re.findall(r'\d+\.?\d*', l)
            if nums:
                result["stop_loss"] = float(nums[0])
            break

    # 止盈（只认【建议止盈】节行）
    for name in ("建议止盈", "止盈"):
        l = _section_line(name)
        if l:
            nums = re.findall(r'\d+\.?\d*', l)
            if nums:
                result["take_profit"] = float(nums[0])
            break

    # 操作建议（只认【操作建议】节行）→ action 字段，前端"当前操作策略"列的唯一新鲜来源
    al = _section_line("操作建议")
    if al:
        result["action_advice"] = re.sub(r'^\s*【操作建议】\s*', '', al).strip()[:200]

    # 仓位：只有买入信号才需要，提取百分比数字（只认【建议仓位】节行）
    result["position"] = ""
    if result["signal"] == "买入":
        l = _section_line("建议仓位")
        if l:
            nums = re.findall(r'\d+\.?\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

    # 2026-08-18 策略判断（只认【策略判断】节行）
    # 老莫 2026-08-18：失效=需更换（同一决策），失效时必须给新归属；给不出才停在失效未归属
    result["strategy_judge"] = ""
    _sj = _section_line("策略判断")
    if _sj:
        _m = re.search(r'策略判断】\s*(维持原策略|修改参数|策略失效需更换|策略失效重定)', _sj)
        if _m:
            result["strategy_judge"] = _m.group(1)
    # 策略失效理由
    result["strategy_reason"] = ""
    _sr = _section_line("策略失效理由")
    if _sr:
        result["strategy_reason"] = re.sub(r'^\s*【策略失效理由】\s*', '', _sr).strip()[:200]
    # 更换策略归类（封闭集校验：必须在候选列表内；失效和更换都解析）
    result["strategy_switch_to"] = ""
    _CANDIDATES = ("accumulation", "b_td1_v3", "v_mr", "hk_pe_mom", "hk_pe_oversold", "p_oversold", "s2_panic", "leader", "v_next", "v8.1")
    _st = _section_line("更换策略归类")
    if _st:
        _body = re.sub(r'^\s*【更换策略归类】\s*', '', _st).strip().lower()
        if "无合适策略" in _body or "无合适" in _body:
            result["strategy_switch_to"] = ""  # 明确无合适 → 失效未归属
        else:
            for c in _CANDIDATES:
                if c in _body:
                    result["strategy_switch_to"] = c
                    break

    # ── 2026-08-25 机器可读结论优先（老莫：结构化标注,不靠关键词匹配散文）──
    _m_json = re.search(r'^SIGNAL_JSON:\s*(\{.*\})\s*$', text, re.M)
    if _m_json:
        try:
            import json as _js
            _sj = _js.loads(_m_json.group(1))
            result["signal_json"] = _sj
            if _sj.get("signal"):
                result["signal"] = _sj["signal"]
            for _k, _rk in (("entry_low","entry_low"),("entry_high","entry_high"),
                            ("stop_loss","stop_loss"),("take_profit","take_profit")):
                if _sj.get(_k):
                    result[_rk] = float(_sj[_k])
            if _sj.get("position_pct") is not None:
                result["position"] = f"{_sj['position_pct']}%（SIGNAL_JSON）"
            if result["signal"] in ("买入","可买入","可加仓") and _sj.get("actionable") is False:
                result["signal"] = "关注"
                result["actionable_downgraded"] = True
        except Exception:
            pass
    return result

def save_result(code, full_text, parsed, ta_levels=None):
    """保存LLM结果到DB（先快照再UPDATE）。空分析拒绝写入。
    ta_levels: collect_data 计算的确定性技术位，用于止损锚定校验。"""
    if not (full_text or "").strip():
        print(f"  \u274c 拒绝写入空分析（LLM输出为空，保护已有数据）")
        return
    conn = sqlite3.connect(DB, timeout=30)
    conn.execute("PRAGMA busy_timeout=30000")
    now = datetime.now().isoformat()
    
    # ── 修改前快照 ──
    snapshot_strategy_history(conn, code, 'batch_12d')
    
    updates = ["full_analysis=?", "reassessed_at=?"]
    params = [full_text, now]
    
    if parsed["signal"]:
        updates.append("timing_signal=?")
        params.append(parsed["signal"])
    # 区间写入门禁：上下沿都必须为正且 下沿<上沿<下沿x3，否则视为解析错误整体跳过
    # （防 214.68~2.52 类解析污染，与 GATE_ZONE_SANITY 同级防护）
    _el, _eh = parsed["entry_low"], parsed["entry_high"]
    if parsed.get("zone_cleared"):
        # LLM 显式输出【买入区间】无 → 清空区间（不再保留可能脏的旧值）
        updates.append("entry_low=0")
        updates.append("entry_high=0")
    elif _el > 0 and _eh > _el and _eh < _el * 3:
        # 区间-现价距离门禁：整体偏离现价过远（区上沿<现价0.5x 或 区下沿>现价1.5x）
        # → 判定脏数据/解析错误，拒写并清空（不再"保留原值"养脏，如95~99 vs 现价60）
        _px = 0.0
        try:
            _pr = conn.execute("SELECT price FROM live_prices WHERE code=?", (code,)).fetchone()
            _px = float(_pr[0]) if _pr and _pr[0] else 0.0
        except Exception:
            pass
        if _px > 0 and (_eh < _px * 0.5 or _el > _px * 1.5):
            print(f"  ⚠️ 买入区{_el}~{_eh}偏离现价{_px}过远，拒写并清空（防脏数据残留）", flush=True)
            updates.append("entry_low=0")
            updates.append("entry_high=0")
        else:
            updates.append("entry_low=?")
            params.append(_el)
            updates.append("entry_high=?")
            params.append(_eh)
    elif _el > 0 or _eh > 0:
        print(f"  ⚠️ 买入区解析异常({_el}~{_eh})，跳过区间写入（保留原值）", flush=True)
    # 止损/止盈一致性门禁：损>0 时必须在区间下沿之下(0.5x~1.0x)，盈>0 时必须在区间上沿之上
    _sl, _tp = parsed["stop_loss"], parsed["take_profit"]
    # ── 止损锚定门禁（2026-07-22 老爸：止损=区间下沿=把止损设在买入价上，RR恒0）──
    # LLM 输出 sl>=el 时：用确定性技术位自动修正（弱撑×0.985），无技术位则拒写止损。
    if _sl > 0 and _el > 0 and _sl >= _el:
        _ws = (ta_levels or {}).get("weak_support") or 0
        _ss = (ta_levels or {}).get("strong_support") or 0
        if _ws > 0 and _ws < _el:
            _fixed = round(_ws * 0.985, 2)
            print(f"  ⚠️ 止损{_sl}≥区下沿{_el}，按技术锚修正为 弱撑{_ws}×0.985={_fixed}", flush=True)
            _sl = _fixed
        elif _ss > 0 and _ss < _el:
            _fixed = round(_ss * 0.99, 2)
            print(f"  ⚠️ 止损{_sl}≥区下沿{_el}，按技术锚修正为 强撑{_ss}×0.99={_fixed}", flush=True)
            _sl = _fixed
        else:
            print(f"  ⚠️ 止损{_sl}≥区下沿{_el}且无可用技术位，拒写止损（保留原值）", flush=True)
            _sl = 0
    if _sl > 0 and (not _el or _sl < _el) and (not _tp or _sl < _tp):
        updates.append("stop_loss=?")
        params.append(_sl)
    elif _sl > 0:
        print(f"  ⚠️ 止损{_sl}与区间/止盈不一致，跳过写入（保留原值）", flush=True)
    if _tp > 0 and (not _eh or _tp > _eh) and (not _sl or _tp > _sl):
        updates.append("take_profit=?")
        params.append(_tp)
    elif _tp > 0:
        print(f"  ⚠️ 止盈{_tp}与区间/止损不一致，跳过写入（保留原值）", flush=True)
    # 2026-08-18 修复：LLM 算的 entry 区间也写回 entry_low/entry_high（纠正 promote 错误区间）
    # 例：promote 入库 entry 95~99 vs 现价 7.59 严重偏离，重评算正确 7.55~7.70 应覆盖
    _el_new = parsed.get("entry_low") or 0
    _eh_new = parsed.get("entry_high") or 0
    if _el_new > 0 and _eh_new > _el_new:
        # 校验 entry 与现 stop_loss/take_profit 一致（sl < el < eh < tp）
        _ok = True
        if "stop_loss=?" in updates:
            _cur_sl = params[updates.index("stop_loss=?")]
            if _cur_sl >= _el_new:
                _ok = False
        if "take_profit=?" in updates:
            _cur_tp = params[updates.index("take_profit=?")]
            if _cur_tp <= _eh_new:
                _ok = False
        if _ok:
            updates.append("entry_low=?")
            params.append(_el_new)
            updates.append("entry_high=?")
            params.append(_eh_new)
    if parsed["position"]:
        updates.append("position_advice=?")
        params.append(parsed["position"])
    if parsed.get("action_advice"):
        # 12维操作建议 → action（前端"当前操作策略"列；防技术路径旧值与分析矛盾）
        updates.append("action=?")
        params.append(parsed["action_advice"])
    
    # ── 2026-08-17 同步 trigger_json：price_monitor 的监控阈值来源 ──
    if updates:
        _cur = conn.execute(
            "SELECT stop_loss, take_profit, entry_low, entry_high FROM holding_strategies "
            "WHERE code=? AND status='active'", (code,)).fetchone()
        _sl = _tp = _el = _eh = 0
        if _cur:
            _sl, _tp, _el, _eh = _cur
        if "stop_loss=?" in updates:
            _sl = params[updates.index("stop_loss=?")]
        if "take_profit=?" in updates:
            _tp = params[updates.index("take_profit=?")]
        import json as _json
        _trigger = {"stop_loss": _sl, "entry_zone": f"{_el}~{_eh}" if _el and _eh else "",
                    "take_profit_zone": f"0~{_tp}" if _tp else ""}
        updates.append("trigger_json=?")
        params.append(_json.dumps(_trigger, ensure_ascii=False))
    params.append(code)
    sql = f"UPDATE holding_strategies SET {', '.join(updates)} WHERE code=? AND status='active'"
    conn.execute(sql, params)
    conn.commit()

    # 2026-08-18 策略状态落库（strategy_judge/switch_to，老莫：失效=需更换，同一决策）
    _j = parsed.get("strategy_judge", "")
    _st = parsed.get("strategy_switch_to", "")
    if _j in ("策略失效需更换", "策略失效重定"):
        if _st:
            # 失效+给归属 → 切换到新策略
            conn.execute("UPDATE holding_strategies SET strategy_attributed=?, strategy_state='switched', strategy_provenance='llm_attributed' WHERE code=? AND status='active'", (_st, code))
            conn.commit()
            print(f"  ✅ 策略切换: {_j} → 归属 {_st}（strategy_state=switched）")
        else:
            # 失效+无合适归属 → 标记失效
            conn.execute("UPDATE holding_strategies SET strategy_state='invalidated' WHERE code=? AND status='active'", (code,))
            conn.commit()
            print(f"  ✅ 策略失效: {_j} 无合适归属（strategy_state=invalidated）")

    # ── 信号以分析为唯一事实源（防信号/分析脱节）──
    from mofin_db import reconcile_signal_from_analysis
    final_sig = reconcile_signal_from_analysis(conn, code)
    # ── 推荐操作 tag 同步（跟随对齐后的信号）──
    sync_recommend_tag(conn, code, final_sig)
    
    # 买入信号推送已统一收拢到 sync_recommend_tag 的转场推送（防双重告警）。
    # 本路径只负责写库+tag，推送由 mofin_db.push_recommend_alert 在 tag 转场时触发。
    
    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"  \u23ed 冷却期内，跳过")
        return False
    
    # 有分析且未过期 \u2192 跳过（除非 force_today 且今早未评）
    if not FORCE_REASSESS and has_llm_analysis(code) and not analysis_stale(code, force_today):
        print(f"  \u23ed 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"  \u26a0\ufe0f 无价格数据，跳过")
        return False
    
    print(f"  调LLM生成12维分析...", flush=True)
    prompt = build_prompt(data)
    
    # ── 使用共享 LLM 客户端（替代 curl subprocess）──
    result = call_llm(prompt, model=REASSESS_MODEL, max_tokens=None, concurrent=True)  # 2026-08-24 并发模式: router round-robin分key(4worker不再全压同一key)
    
    if not result["ok"] or not (result.get("content") or "").strip():
        print(f"  \u274c LLM调用失败或空输出: {result.get('error') or 'empty content'}")
        return False
    
    full_text = result["content"]
    print(f"  \u2705 LLM返回({len(full_text)}字, {result['elapsed']:.1f}s, 尝试{result['attempts']}次)", flush=True)
    
    parsed = parse_response(full_text)
    
    # ── 截断保护：输出过短且无信号 = 低质输出，升级 pro 重试一次 ──
    if not parsed.get("signal") and len(full_text) < 1500:
        print(f"  ⚠️ 输出截断({len(full_text)}字)且无信号，升级 {FALLBACK_MODEL} 重试...", flush=True)
        result2 = call_llm(prompt, model=FALLBACK_MODEL, max_tokens=None, concurrent=True)  # 2026-08-24 并发模式
        if result2["ok"] and len((result2.get("content") or "").strip()) > len(full_text):
            full_text = result2["content"]
            parsed = parse_response(full_text)
            print(f"  \u2705 升级后({len(full_text)}字)", flush=True)
    
    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, ta_levels=data.get("ta"))
    print(f"  \u2705 已保存到DB")
    return True

def main():
    # ── 双通道预检：OCG直连 + hermes gateway 兜底，全挂才退出 ──
    _ocg_ok = ocg_alive()
    _gw_ok = gateway_alive()
    if not _ocg_ok and not _gw_ok:
        print("[FATAL] OCG上游与hermes gateway均不可用，退出")
        sys.exit(1)
    if not _ocg_ok:
        print("[WARN] OCG直连不可用，将使用gateway兜底（agent运行时，较慢）")
    if not _gw_ok:
        print("[WARN] hermes gateway不可用，仅使用OCG直连")
    
    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, timeout=30)
        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]

    # ── 分片并发（2026-07-24 老爸：这么多key不能并发？）──
    # --shard K/N：本 worker 只处理 index%N==K 的股票，N 个进程并发互不重叠。
    _shard_k, _shard_n = 0, 1
    if "--shard" in sys.argv:
        _sk = sys.argv[sys.argv.index("--shard") + 1]  # 格式 K/N
        _shard_k, _shard_n = int(_sk.split("/")[0]), int(_sk.split("/")[1])
    if _shard_n > 1:
        codes = [c for i, c in enumerate(codes) if i % _shard_n == _shard_k]

    print(f"待处理: {len(codes)}只 (type={dtype or 'all'}, force_today={force_today}"
          + (f", shard={_shard_k}/{_shard_n}" if _shard_n > 1 else "") + ")")
    
    ok = 0
    fail = 0
    skip = 0
    failed_codes = []
    for i, code in enumerate(codes):
        if not FORCE_REASSESS and has_llm_analysis(code) and not analysis_stale(code, force_today):
            print(f"  [{i+1}/{len(codes)}] \u23ed {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
            failed_codes.append(code)
        
        # 间隔8秒（pro model较重但gateway可承受；retry逻辑吸收瞬断）
        if i < len(codes) - 1:
            print(f"  等待8秒...", flush=True)
            time.sleep(8)
    
    # ── 失败二轮：主跑结束后休息 60s 让上游恢复，失败股整体重试一次 ──
    # （凌晨上游空输出高发，二轮可救回大半；仍失败的留给下一轮调度）
    if failed_codes:
        print(f"\n{'='*50}")
        print(f"失败二轮: {len(failed_codes)}只，休息60s后重试...")
        time.sleep(60)
        retry_ok = 0
        for code in failed_codes:
            print(f"  [retry] {code} ", end="", flush=True)
            if process_stock(code, force_today):
                retry_ok += 1
                ok += 1
                fail -= 1
            print(f"  等待8秒...", flush=True)
            time.sleep(8)
        print(f"失败二轮: {retry_ok}/{len(failed_codes)} 救回")
    
    print(f"\n{'='*50}")
    print(f"完成: {ok}成功, {fail}失败, {skip}跳过")
    print(f"{'='*50}")
    # ── 推荐摘要：本轮新增推荐聚成一条推送（防逐只轰炸）──
    # 并发分片模式（SKIP_FLUSH=1）下由 launcher 统一 flush，避免先到者发半成品摘要
    if not os.environ.get("SKIP_FLUSH"):
        try:
            from mofin_db import flush_rec_digest
            flush_rec_digest()
        except Exception as _e:
            print(f"  ⚠️ 推荐摘要发送失败: {_e}")

if __name__ == "__main__":
    main()