refactor: per_stock统一使用batch的collect_data+build_prompt
- 删除per_stock内160行独立prompt及数据装配(宏观/PE/资金流/新闻/变更记录/技术位锚) - 单一prompt源头: 以后改prompt只改batch_reassess.build_prompt一处, 不再漂移 - per_stock免费获得: 技术位锚/持仓上下文(持有vs未持有)/组合现金/参数自检/统一输出纪律
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@@ -390,179 +390,28 @@ def main():
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if _fa and _fa[0]: print(f" ✅ full_analysis已写入({len(_fa[0])}字)")
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else: print(f" ⚠️ full_analysis为空")
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_v.close()
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# LLM生成完整12维分析
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_macro_desc = ""
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_pe_val = ""
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_pb_val = ""
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# LLM生成完整12维分析（2026-07-23：统一走 batch 的 collect_data+build_prompt。
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# 单一 prompt 源头，根治双 prompt 漂移——per_stock 曾缺技术位锚/持仓上下文/参数自检）
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import sys as _sys2
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if '/home/hmo/MoFin/deploy/profile-scripts' not in _sys2.path:
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_sys2.path.insert(0, '/home/hmo/MoFin/deploy/profile-scripts')
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try:
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_mdb = __import__('sqlite3').connect("/home/hmo/MoFin/data/mofin.db")
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_mr = _mdb.execute("SELECT structure FROM macro_context_log ORDER BY id DESC LIMIT 1").fetchone()
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if _mr and _mr[0]:
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_st = __import__('json').loads(_mr[0])
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_macro_desc = _st.get("description", "")
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_mood = _mr[1] if len(_mr) > 1 else ""
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if _mood: _macro_desc += f" 情绪={_mood}"
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# 基本面从腾讯API
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_p = "sh" if str(code).startswith(("6","9")) else "sz"
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_cr = __import__('subprocess').run(["curl","-s",f"http://qt.gtimg.cn/q={_p}{code}"], capture_output=True, timeout=10)
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_ct = _cr.stdout.decode("gbk", errors="ignore").split("~")
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if len(_ct) > 39 and _ct[39]: _pe_val = f"PE={_ct[39]}"
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if len(_ct) > 44 and _ct[44]: _pb_val = f"PB≈{float(_ct[44])/10000:.1f}亿"
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_mdb.close()
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except:
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pass
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# 拉取资金流数据
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_flow_note = "暂无资金流数据"
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try:
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_fdb = __import__('sqlite3').connect("/home/hmo/MoFin/data/mofin.db")
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_fr = _fdb.execute("SELECT cache_json FROM capital_flow_cache ORDER BY id DESC LIMIT 1").fetchone()
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if _fr and _fr[0]:
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_fc = __import__('json').loads(_fr[0])
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_s = _fc.get("stocks", {}).get(code, {})
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if _s and _s.get("analysis"):
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_a = _s["analysis"]
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_flow_note = f"净流入{_a.get('net_flow',0):.0f}万 主力{_a.get('main_force',0):.0f}万 趋势{_a.get('trend','中性')}"
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_fdb.close()
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except:
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pass
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# 拉取近期消息面（不限情绪标签）
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_news_note = "暂无近期消息"
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try:
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_ndb = __import__('sqlite3').connect("/home/hmo/MoFin/data/mofin.db")
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_nr2 = _ndb.execute(
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"SELECT summary, overall_sentiment, created_at FROM signal_news "
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"WHERE searched_stocks LIKE ? OR sector LIKE ? "
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"ORDER BY id DESC LIMIT 3",
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(f'%{code}%', f'%{entry.get("name","")[:4]}%')
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).fetchall()
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if not _nr2:
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_nr2 = _ndb.execute(
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"SELECT summary, overall_sentiment, created_at FROM signal_news "
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"ORDER BY id DESC LIMIT 3").fetchall()
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if _nr2:
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_news_note = " | ".join([f"{r[2][:10]} [{r[1] or '未标注'}] {r[0][:40]}" for r in _nr2])
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_ndb.close()
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except:
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pass
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# ── 拉取已有策略全文 + 最近变更 ──
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_existing_full_analysis = ""
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_existing_changelog_text = "无变更记录"
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try:
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_edb = __import__('sqlite3').connect("/home/hmo/MoFin/data/mofin.db")
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_er = _edb.execute(
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"SELECT full_analysis, changelog_json FROM holding_strategies "
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"WHERE code=? AND status='active'", (code,)
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).fetchone()
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if _er:
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_existing_full_analysis = _er[0] or ""
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_cl_raw = _er[1] or ""
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if _cl_raw:
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_cl = __import__('json').loads(_cl_raw) if isinstance(_cl_raw, str) else _cl_raw
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if isinstance(_cl, list) and _cl:
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_recent = _cl[-3:]
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_existing_changelog_text = "\n".join(
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[f" [{c.get('timestamp','?')}] {c.get('action','?')}: {c.get('reason','')}"[:120]
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for c in reversed(_recent)]
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)
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_edb.close()
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except:
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pass
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# ── 确定性技术位锚（与 batch 同源，2026-07-23：双 prompt 曾漂移导致per_stock无锚）──
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_ta_sec_ps = "【技术位锚】本次计算不可用，仍须满足：止损<区间下沿<区间上沿<止盈。"
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try:
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import technical_analysis as _ta_mod
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_ta_ps = _ta_mod.full_analysis(code)
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if _ta_ps and "error" not in _ta_ps:
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_sr_ps = _ta_ps.get("support_resistance", {}) or {}
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if _sr_ps.get("weak_support"):
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_ta_sec_ps = f"""【技术位锚】（系统基于K线确定性计算，客观事实，非估计值）
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强撑={_sr_ps.get('strong_support')} 弱撑={_sr_ps.get('weak_support')} 枢轴={_sr_ps.get('pivot')}
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弱压={_sr_ps.get('weak_resist')} 强压={_sr_ps.get('strong_resist')} 有效区间={_sr_ps.get('effective_range')}
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⚠️ 参数锚定纪律：买入区下沿参考弱撑/强撑；止损必须严格低于买入区下沿（放弱撑下方1-3%或强撑附近，严禁止损≥区间下沿）；止盈参考弱压/强压；偏离须在【修改点及理由】明确说明。"""
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except Exception:
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pass
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_prompt = f"""你是一个资深股票分析师。请对股票{code}评估现有策略是否仍然有效，并输出完整的新策略。
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╔══════════════════════════════════════════════╗
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║ 📋 第一步：审阅原策略 ║
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╚══════════════════════════════════════════════╝
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【原策略全文】（上次完整分析）：
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{_existing_full_analysis or '暂无完整策略分析'}
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【当前策略参数】：
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价格={price} 信号={result.get("timing_signal") or entry.get("timing_signal","")}
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买入区间={entry.get("entry_low",0)}~{entry.get("entry_high",0)}
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止损={entry.get("stop_loss",0)} 止盈={entry.get("take_profit",0)}
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RR={result.get("rr_ratio", entry.get("rr_ratio", 0))}
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策略={result.get("action") or entry.get("action","")}
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行业={(result.get("sector_context") or entry.get("sector_context",""))[:50]}（当日实时）
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技术={(result.get("tech_snapshot") or entry.get("tech_snapshot",""))[:200]}（MA=5/10/20/60日 支撑阻力=近20日 量价=当日+近5日趋势）
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【最近变更记录】：
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{_existing_changelog_text}
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╔══════════════════════════════════════════════╗
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║ 📊 第二步：12维矩阵交叉分析 ║
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╚══════════════════════════════════════════════╝
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⚠️ 重要：12个维度必须交叉对比，找出矛盾/共振点，给出综合判断。
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{_ta_sec_ps}
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当前实时数据（每条标注时间窗口，禁止使用模型训练数据）：
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大盘={_macro_desc or "震荡"}（当日实时） | PE/市值={_pe_val} {_pb_val}（最新财报）
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资金流={_flow_note}（近5日累计）
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消息面={_news_note}（最近3条，自动标注抓取时间）
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╔══════════════════════════════════════════════╗
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║ 📝 第三步：决策输出 ║
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╚══════════════════════════════════════════════╝
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请严格按以下顺序输出：
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【维持或修改】判断当前策略是否仍然有效，回答「维持」或「修改」。
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【修改点及理由】（如果维持，写「无需修改」；如果修改，逐条列出）：
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- 修改什么参数/方向
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- 理由（引用具体维度矛盾或共振）
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【最终新策略】（完整策略全文，self-contained，可直接存入DB）
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【交叉分析】哪些维度矛盾/共振，关键信号
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① 大盘×基本面 ② 大盘×消息面 ③ 大盘×技术面 ④ 大盘×资金面
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⑤ 行业×基本面 ⑥ 行业×消息面 ⑦ 行业×技术面 ⑧ 行业×资金面
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⑨ 个股×基本面 ⑩ 个股×消息面 ⑪ 个股×技术面 ⑫ 个股×资金面
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最后必须输出：
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【综合结论】(买入/关注/观望/卖出)
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【操作建议】
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【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）
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【建议止损】（必须严格低于买入区下沿，放弱撑下方1-3%或强撑附近）
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【建议止盈】（参考弱压/强压）
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【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"
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【建议仓位】⚠️不可省略，非"买入"时写"不新建仓"
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⚠️ 输出纪律（必须遵守）：
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1. 直接以【维持或修改】开头，禁止任何寒暄、开场白、分隔线
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2. 禁止输出 <structured_data> 或任何 XML/JSON/代码块
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3. 所有【】节标题一个都不能少
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4. 止损<区间下沿<区间上沿<止盈，违反任一条=输出作废重想"""
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from batch_reassess import collect_data as _cd, build_prompt as _bp
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_prompt = _bp(_cd(code))
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except Exception as _pe:
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print(f" ⚠️ 统一prompt构建失败: {_pe}", flush=True)
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_prompt = None
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_full_analysis_text = None
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try:
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_llm_result = call_llm(_prompt, max_tokens=4096, timeout=150, retries=1, backoff=20)
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if _llm_result["ok"]:
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_full_analysis_text = _llm_result["content"]
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print(f" ✅ LLM12维分析完成({len(_full_analysis_text)}字, {_llm_result['elapsed']:.1f}s)", flush=True)
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else:
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print(f" ❌ LLM12维分析失败({_llm_result['attempts']}次): {_llm_result['error'][:200]}", flush=True)
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except Exception as _e:
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print(f" ❌ LLM12维分析异常: {_e}", flush=True)
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if _prompt:
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try:
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_llm_result = call_llm(_prompt, max_tokens=4096, timeout=150, retries=1, backoff=20)
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if _llm_result["ok"]:
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_full_analysis_text = _llm_result["content"]
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print(f" ✅ LLM12维分析完成({len(_full_analysis_text)}字, {_llm_result['elapsed']:.1f}s)", flush=True)
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else:
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print(f" ❌ LLM12维分析失败({_llm_result['attempts']}次): {_llm_result['error'][:200]}", flush=True)
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except Exception as _e:
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print(f" ❌ LLM12维分析异常: {_e}", flush=True)
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# ── 保存到DB（覆写前先快照）──
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_fa_conn = __import__('sqlite3').connect("/home/hmo/MoFin/data/mofin.db")
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