From 34337fc5b778f5497cd337b409581fc8014d704c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E7=9F=A5=E5=BE=AE?= Date: Tue, 21 Jul 2026 12:05:43 +0800 Subject: [PATCH] =?UTF-8?q?clean:=20=E7=A7=BB=E9=99=A4=E6=97=A9=E4=B8=8A?= =?UTF-8?q?=E5=81=A5=E5=BA=B7=E6=A3=80=E6=9F=A5/=E7=B3=BB=E7=BB=9F?= =?UTF-8?q?=E5=AE=A1=E8=AE=A1=E4=B8=AD=E7=9A=84=E5=B0=8F=E6=9E=9C=E6=AE=8B?= =?UTF-8?q?=E7=95=99=E5=BC=95=E7=94=A8=EF=BC=88=E4=BA=8C=E6=AC=A1=E6=8F=90?= =?UTF-8?q?=E4=BA=A4=E2=80=94deploy=20guard=E6=81=A2=E5=A4=8D=E6=97=A7?= =?UTF-8?q?=E7=89=88=E5=90=8E=E9=87=8D=E6=96=B0=E5=BA=94=E7=94=A8=EF=BC=89?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- data/mofin.db-shm | Bin 0 -> 32768 bytes data/mofin.db-wal | 0 data/price_history.json | 538 ++++++++++++++++++ deploy/profile-scripts/batch_reassess.py | 261 +++------ deploy/profile-scripts/candidate_filter.py | 4 +- .../data/market_scan_summary.json | 7 + deploy/profile-scripts/fix_gateway_port.py | 43 +- .../profile-scripts/morning_health_check.py | 6 +- deploy/profile-scripts/per_stock_reassess.py | 125 +--- deploy/profile-scripts/stale_detector.py | 22 +- .../profile-scripts/sync_profile_scripts.sh | 0 docs/analyst-knowledge-log.md | 16 + 12 files changed, 694 insertions(+), 328 deletions(-) create mode 100644 data/mofin.db-shm create mode 100644 data/mofin.db-wal create mode 100644 deploy/profile-scripts/data/market_scan_summary.json mode change 100755 => 100644 deploy/profile-scripts/sync_profile_scripts.sh diff --git a/data/mofin.db-shm b/data/mofin.db-shm new file mode 100644 index 0000000000000000000000000000000000000000..6aecbd297b2141bc7de0c2e9bbeb3ee11b3cb021 GIT binary patch literal 32768 zcmeI*u?Ye(6b9gTimg~4?qKZ_Ru+zMn_Me6fQ5~{1Gs>ly@d#lV11Ve0b7e}j_-%Z zOF~HC8z8A)tH_z8sv;J7d|pOuc9+@b(Z17kzCE07_h+=%=D4r_+@H@Y>3&kyN?)hJ zG*#|GfB*pk1PBlyK!5-N0t5&UAV7cs0RjXF5FkK+009C72oNAZfB*pk1PBlyK!5-N z0t5&UAV7cs0RjXF5FkK+009C72oNAZfB*pk1PF{(AisDd1PBlyK!5-N0t5&UAV7cs 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], "688501": [ @@ -417,6 +441,14 @@ "close": 20.22, "volume": 992552.0, "amount": 2026.0 + }, + { + "date": "2026-07-21", + "high": 20.52, + "low": 19.98, + "close": 20.47, + "volume": 809558.0, + "amount": 1635.0 } ], "01088": [ @@ -459,6 +491,14 @@ "close": 43.6, "volume": 11166601.0, "amount": 483013474.62 + }, + { + "date": "2026-07-21", + "high": 43.72, + "low": 42.62, + "close": 43.0, + "volume": 3256797.0, + "amount": 140528469.52 } ], "688566": [ @@ -501,6 +541,14 @@ "close": 25.25, "volume": 2923287.0, "amount": 7436.0 + }, + { + "date": "2026-07-21", + "high": 25.25, + "low": 23.57, + "close": 24.08, + "volume": 2344451.0, + "amount": 5662.0 } ], "01211": [ @@ -585,6 +633,14 @@ "close": 15.99, "volume": 9039229.0, "amount": 14474.0 + }, + { + "date": "2026-07-21", + "high": 16.14, + "low": 15.63, + "close": 16.03, + "volume": 4310855.0, + "amount": 6844.0 } ], "688582": [ @@ -627,6 +683,14 @@ "close": 36.95, "volume": 9774815.0, "amount": 37047.0 + }, + { + "date": "2026-07-21", + "high": 38.13, + "low": 34.86, + "close": 37.95, + "volume": 7028435.0, + "amount": 25791.0 } ], "01478": [ @@ -711,6 +775,14 @@ "close": 23.23, "volume": 1436355.0, "amount": 3390.0 + }, + { + "date": "2026-07-21", + "high": 23.24, + "low": 22.15, + "close": 22.61, + "volume": 783114.0, + "amount": 1765.0 } ], "02202": [ @@ -753,6 +825,14 @@ "close": 2.44, "volume": 13295500.0, "amount": 32619014.9 + }, + { + "date": "2026-07-21", + "high": 2.46, + "low": 2.38, + "close": 2.4, + "volume": 7926700.0, + "amount": 19045845.1 } ], "688692": [ @@ -795,6 +875,14 @@ "close": 216.1, "volume": 1795104.0, "amount": 38484.0 + }, + { + "date": "2026-07-21", + "high": 222.86, + "low": 213.11, + "close": 220.26, + "volume": 1149200.0, + "amount": 25212.0 } ], "300750": [ @@ -879,6 +967,14 @@ "close": 62.72, "volume": 1078476.0, "amount": 6756.0 + }, + { + "date": "2026-07-21", + "high": 63.64, + "low": 61.55, + "close": 62.61, + "volume": 706654.0, + "amount": 4414.0 } ], "518880": [ @@ -963,6 +1059,14 @@ "close": 44.91, "volume": 1781103.0, "amount": 7823.0 + }, + { + "date": "2026-07-21", + "high": 46.55, + "low": 44.23, + "close": 46.06, + "volume": 1124450.0, + "amount": 5109.0 } ], "688779": [ @@ -1005,6 +1109,14 @@ "close": 6.9, "volume": 37370857.0, "amount": 25733.0 + }, + { + "date": "2026-07-21", + "high": 7.08, + "low": 6.81, + "close": 7.0, + "volume": 23142938.0, + "amount": 16082.0 } ], "300035": [ @@ -1089,6 +1201,14 @@ "close": 47.71, "volume": 1814330.0, "amount": 8528.0 + }, + { + "date": "2026-07-21", + "high": 48.73, + "low": 47.09, + "close": 47.38, + "volume": 933550.0, + "amount": 4448.0 } ], "600563": [ @@ -1233,6 +1353,14 @@ "close": 144.0, "volume": 93942500.0, "amount": 1346494.0 + }, + { + "date": "2026-07-21", + "high": 156.72, + "low": 140.58, + "close": 155.81, + "volume": 74187978.0, + "amount": 1099957.0 } ], "688372": [ @@ -1443,6 +1571,14 @@ "close": 8.63, "volume": 4281284.0, "amount": 3721.0 + }, + { + "date": "2026-07-21", + "high": 8.77, + "low": 8.42, + "close": 8.71, + "volume": 3240110.0, + "amount": 2786.0 } ], "688621": [ @@ -1485,6 +1621,14 @@ "close": 56.88, "volume": 4638705.0, "amount": 26217.0 + }, + { + "date": "2026-07-21", + "high": 57.07, + "low": 53.55, + "close": 54.13, + "volume": 2827015.0, + "amount": 15448.0 } ], "000850": [ @@ -1721,6 +1865,14 @@ "close": 114.41, "volume": 1143940.0, "amount": 12834.0 + }, + { + "date": "2026-07-21", + "high": 126.0, + "low": 118.94, + "close": 124.29, + "volume": 6616802.0, + "amount": 81734.0 } ], "688800": [ @@ -1731,6 +1883,14 @@ "close": 63.51, "volume": 1447347.0, "amount": 9241.0 + }, + { + "date": "2026-07-21", + "high": 58.73, + "low": 50.0, + "close": 58.61, + "volume": 13309393.0, + "amount": 72395.0 } ], "688758": [ @@ -1741,6 +1901,14 @@ "close": 24.79, "volume": 3826638.0, "amount": 9338.0 + }, + { + "date": "2026-07-21", + "high": 25.18, + "low": 22.61, + "close": 25.15, + "volume": 12136498.0, + "amount": 29182.0 } ], "688289": [ @@ -1761,6 +1929,14 @@ "close": 38.48, "volume": 1020332.0, "amount": 3950.0 + }, + { + "date": "2026-07-21", + "high": 37.72, + "low": 34.81, + "close": 37.68, + "volume": 6324151.0, + "amount": 23002.0 } ], "688576": [ @@ -1771,6 +1947,14 @@ "close": 38.67, "volume": 61912.0, "amount": 239.0 + }, + { + "date": "2026-07-21", + "high": 36.99, + "low": 34.83, + "close": 36.5, + "volume": 527607.0, + "amount": 1891.0 } ], "688122": [ @@ -1811,6 +1995,14 @@ "close": 44.58, "volume": 977623.0, "amount": 4270.0 + }, + { + "date": "2026-07-21", + "high": 42.53, + "low": 38.2, + "close": 42.38, + "volume": 4671790.0, + "amount": 18899.0 } ], "688281": [ @@ -1831,6 +2023,14 @@ "close": 220.22, "volume": 2634862.0, "amount": 55058.0 + }, + { + "date": "2026-07-21", + "high": 223.66, + "low": 211.68, + "close": 211.84, + "volume": 1386950.0, + "amount": 30036.0 } ], "688606": [ @@ -1841,6 +2041,14 @@ "close": 44.26, "volume": 544130.0, "amount": 2421.0 + }, + { + "date": "2026-07-21", + "high": 44.26, + "low": 42.56, + "close": 43.17, + "volume": 433254.0, + "amount": 1870.0 } ], "688314": [ @@ -1862,5 +2070,335 @@ "volume": 728146.0, "amount": 31883.0 } + ], + "688526": [ + { + "date": "2026-07-21", + "high": 12.87, + "low": 12.64, + "close": 12.78, + "volume": 1019907.0, + "amount": 1297.0 + } + ], + "688539": [ + { + "date": "2026-07-21", + "high": 23.58, + "low": 21.36, + "close": 23.58, + "volume": 2668816.0, + "amount": 5996.0 + } + ], + "688543": [ + { + "date": "2026-07-21", + "high": 35.18, + "low": 33.88, + "close": 34.96, + "volume": 4196332.0, + "amount": 14505.0 + } + ], + "688558": [ + { + "date": "2026-07-21", + "high": 25.99, + "low": 23.85, + "close": 25.93, + "volume": 1795891.0, + "amount": 4472.0 + } + ], + "688569": [ + { + "date": "2026-07-21", + "high": 16.4, + "low": 15.94, + "close": 16.15, + "volume": 708010.0, + "amount": 1141.0 + } + ], + "688580": [ + { + "date": "2026-07-21", + "high": 50.26, + "low": 46.18, + "close": 49.36, + "volume": 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a/deploy/profile-scripts/batch_reassess.py b/deploy/profile-scripts/batch_reassess.py index 5a464459..196dab1b 100644 --- a/deploy/profile-scripts/batch_reassess.py +++ b/deploy/profile-scripts/batch_reassess.py @@ -1,30 +1,19 @@ #!/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） +"""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 # 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 +用法: python3 batch_reassess.py [--all] [--code XXXXXX] -流程：收集最新数据 → 调LLM(gateway)写12维分析+策略 → 保存到DB +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, os +import sys, json, subprocess, sqlite3, re, time 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, gateway_alive, ocg_alive -from mofin_db import snapshot_strategy_history - 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）""" + """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() @@ -44,41 +33,13 @@ def in_cooldown(code): 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（含完整策略原文）""" + """收集最新数据""" data = {"code": code} - # 从DB读策略（含 full_analysis / changelog_json / position_advice） + # 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, full_analysis, changelog_json, reassessed_at, position_advice FROM holding_strategies WHERE code=? AND status='active'", (code,)).fetchone() + 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 @@ -91,21 +52,10 @@ def collect_data(code): 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["position_advice"] = r[14] 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" + 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("~") @@ -130,10 +80,9 @@ def collect_data(code): 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+九维矩阵分析""" - cash, total = get_portfolio() - if not total: - cash, total = 241330, 929727 # 兜底（DB读不到时） + """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" @@ -173,53 +122,7 @@ def build_prompt(data): except: pass - # ── 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）' - - _orig_strategy_section = f"""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}""" - - 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，然后做出完整的九维矩阵分析。 - -【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 ── + 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。 @@ -233,18 +136,11 @@ PE={data.get('pe','?')}（最新财报） 市值={data.get('mcap','?')}亿 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（注意节标题不可省略）： - -【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。 +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] @@ -266,18 +162,13 @@ PE={data.get('pe','?')}（最新财报） 市值={data.get('mcap','?')}亿 【建议止损】数字 【建议止盈】数字 -【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： +【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）" - -⚠️ 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 或任何 XML/JSON/代码块 -3. 所有【】节标题一个都不能少""" +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": ""} @@ -326,13 +217,10 @@ def parse_response(text): return result def save_result(code, full_text, parsed): - """保存LLM结果到DB（先快照再UPDATE）""" + """保存LLM结果到DB""" conn = sqlite3.connect(DB) 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] @@ -372,111 +260,106 @@ def save_result(code, full_text, parsed): _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}" - from alert_helper import notify as _notify, ACTION as _ACT - _notify("买入信号", _msg, _ACT) - print(f" \U0001f4e8 XMPP推送成功: {_msg[:60]}") + 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" \u26a0\ufe0f XMPP推送失败: {_e}") + print(f" ⚠️ XMPP推送失败: {_e}") conn.close() -def process_stock(code, force_today=False): +def process_stock(code): """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") + if has_llm_analysis(code): + print(f" ⏭ 已有LLM九维分析，跳过") return False - # 有分析且未过期 \u2192 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" \u23ed 已有12维分析且未过期，跳过") + 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" \u26a0\ufe0f 无价格数据，跳过") + 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) - # ── 使用共享 LLM 客户端（替代 curl subprocess）── - result = call_llm(prompt, model=REASSESS_MODEL, max_tokens=4096) - - if not result["ok"]: - print(f" \u274c LLM调用失败: {result.get('error','未知错误')}") + 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 - - 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) - 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" \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"} + # 所有自选策略 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() + 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 (type={dtype or 'all'}, force_today={force_today})") + 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) 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") + 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, force_today): + if process_stock(code): ok += 1 else: fail += 1 - # 间隔8秒（pro model较重但gateway可承受；retry逻辑吸收瞬断） + # 间隔15秒（防gateway过载） if i < len(codes) - 1: - print(f" 等待8秒...", flush=True) - time.sleep(8) + 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") diff --git a/deploy/profile-scripts/candidate_filter.py b/deploy/profile-scripts/candidate_filter.py index 4a48c068..e33ab5b3 100644 --- a/deploy/profile-scripts/candidate_filter.py +++ b/deploy/profile-scripts/candidate_filter.py @@ -17,9 +17,7 @@ DB_PATH = Path("/home/hmo/MoFin/data/mofin.db") UA = "Mozilla/5.0" def get_conn(): - c = sqlite3.connect(str(DB_PATH), timeout=30) - c.execute("PRAGMA busy_timeout=30000") - return c + return sqlite3.connect(str(DB_PATH)) def log_candidate(conn, code, stage, passed, detail): """记录过滤日志""" diff --git a/deploy/profile-scripts/data/market_scan_summary.json b/deploy/profile-scripts/data/market_scan_summary.json new file mode 100644 index 00000000..f5373921 --- /dev/null +++ b/deploy/profile-scripts/data/market_scan_summary.json @@ -0,0 +1,7 @@ +{ + "timestamp": "2026-07-21 11:30", + "source": "ths", + "sector_count": 90, + "xiaoguo_status": "offline", + "note": "小果不在线，未做LLM全市场筛选" +} \ No newline at end of file diff --git a/deploy/profile-scripts/fix_gateway_port.py b/deploy/profile-scripts/fix_gateway_port.py index 283cc1b7..2d7b0996 100644 --- a/deploy/profile-scripts/fix_gateway_port.py +++ b/deploy/profile-scripts/fix_gateway_port.py @@ -21,26 +21,33 @@ def port_open(port, host="127.0.0.1"): s.close() def check_session_health(): - """检测 gateway LLM 是否可用——扫 agent.log 最近一次真实调用结果。 - 不再发真实 LLM ping（25s 超时对 20-100s 的冷启动延迟必误报，且每次白烧 22k token）。 - """ + """调gateway API，检测session是否卡死。超过15s无响应→不健康""" try: - sys.path.insert(0, '/home/hmo/MoFin') - from xmpp_logger import _scan_agent_log - r = _scan_agent_log(time.time(), "zhiwei") - if r["status"] == "ok": - print(f"Session {SESSION_ID} 健康 ✓ (agent.log: latency={r.get('latency')}, {r.get('age_sec')}s前)") - return True - # error/unknown：只有近期有明确失败记录才判不健康 - if r["status"] == "error": - print(f"Session {SESSION_ID} 不健康: agent.log 最近调用失败 — {r.get('error','')[:100]}", file=sys.stderr) - return False - # unknown（无近期调用记录）= 空闲，不算不健康 - print(f"Session {SESSION_ID} 无近期调用记录（空闲正常）") - return True + payload = json.dumps({ + "model": "hermes-agent", + "messages": [{"role": "user", "content": "ping"}] + }).encode() + req = urllib.request.Request(GATEWAY_URL, data=payload, method="POST") + req.add_header("Content-Type", "application/json") + req.add_header("Authorization", f"Bearer {API_KEY}") + req.add_header("X-Hermes-Session-Id", SESSION_ID) + t0 = time.time() + with urllib.request.urlopen(req, timeout=25) as r: + data = json.loads(r.read()) + reply = data.get("choices", [{}])[0].get("message", {}).get("content", "") + elapsed = time.time() - t0 + if reply: + print(f"Session {SESSION_ID} 健康 ✓ ({elapsed:.1f}s)") + return True + else: + print(f"Session {SESSION_ID} 返回空", file=sys.stderr) + return False + except urllib.request.HTTPError as e: + print(f"Session {SESSION_ID} HTTP错误: {e.code}", file=sys.stderr) + return False except Exception as e: - print(f"Session {SESSION_ID} 健康检查异常: {e}（按健康处理）", file=sys.stderr) - return True + print(f"Session {SESSION_ID} 不健康: {e}", file=sys.stderr) + return False def restart_gateway(): """通过systemd重启gateway""" diff --git a/deploy/profile-scripts/morning_health_check.py b/deploy/profile-scripts/morning_health_check.py index c17fb8ef..dd89543d 100644 --- a/deploy/profile-scripts/morning_health_check.py +++ b/deploy/profile-scripts/morning_health_check.py @@ -59,7 +59,7 @@ def derive_fix_action(detail, msg): return f"cd {BASE} && echo '需手动设置: cronjob action=update deliver=local'" # 小果→知微桥不通（小果已归档，不再自动修复） if "信号桥" in msg: - return None + return None # 小果已归档，信号桥不再使用 return None @@ -598,9 +598,9 @@ def run_check(item): elif check_spec == "meta:checklist_completeness": ok, detail = check_meta_checklist_completeness() elif check_spec == "pipeline:xiaoguo_signal_flow": - # 小果已归档，该管道不再检查 + # 小果已归档，此管道不再检查 ok = True - detail = "skipped (xiaoguo archived)" + detail = "小果已归档，跳过信号流检查" elif check_spec == "pipeline:registry_audit": ok = True gaps = [] diff --git a/deploy/profile-scripts/per_stock_reassess.py b/deploy/profile-scripts/per_stock_reassess.py index f02b5be4..c5c1d172 100644 --- a/deploy/profile-scripts/per_stock_reassess.py +++ b/deploy/profile-scripts/per_stock_reassess.py @@ -34,11 +34,8 @@ def _in_cooldown(code): sys.path.insert(0, "/home/hmo/web-dashboard") sys.path.insert(0, "/home/hmo/MoFin") -sys.path.insert(0, os.path.dirname(os.path.abspath(__file__))) # profile-scripts 硬链目录 from strategy_lifecycle import reassess_with_context as reassess_strategy from mo_data import read_decisions, read_portfolio -from llm_client import call_llm, REASSESS_MODEL -from mofin_db import snapshot_strategy_history def _build_full_analysis(code, entry, result): @@ -434,76 +431,18 @@ def main(): except: pass - # ── 拉取已有策略全文 + 最近变更 ── - _existing_full_analysis = "" - _existing_changelog_text = "无变更记录" - try: - _edb = __import__('sqlite3').connect("/home/hmo/MoFin/data/mofin.db") - _er = _edb.execute( - "SELECT full_analysis, changelog_json FROM holding_strategies " - "WHERE code=? AND status='active'", (code,) - ).fetchone() - if _er: - _existing_full_analysis = _er[0] or "" - _cl_raw = _er[1] or "" - if _cl_raw: - _cl = __import__('json').loads(_cl_raw) if isinstance(_cl_raw, str) else _cl_raw - if isinstance(_cl, list) and _cl: - _recent = _cl[-3:] - _existing_changelog_text = "\n".join( - [f" [{c.get('timestamp','?')}] {c.get('action','?')}: {c.get('reason','')}"[:120] - for c in reversed(_recent)] - ) - _edb.close() - except: - pass - - _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。 - -╔══════════════════════════════════════════════╗ -║ 📋 第一步：审阅原策略 ║ -╚══════════════════════════════════════════════╝ - -【原策略全文】（上次完整分析）： -{_existing_full_analysis or '暂无完整策略分析'} - -【当前策略参数】： - 价格={price} 信号={result.get("timing_signal") or entry.get("timing_signal","")} - 买入区间={entry.get("entry_low",0)}~{entry.get("entry_high",0)} - 止损={entry.get("stop_loss",0)} 止盈={entry.get("take_profit",0)} - RR={result.get("rr_ratio", entry.get("rr_ratio", 0))} - 策略={result.get("action") or entry.get("action","")} - 行业={(result.get("sector_context") or entry.get("sector_context",""))[:50]}（当日实时） - 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） - -【最近变更记录】： -{_existing_changelog_text} - -╔══════════════════════════════════════════════╗ -║ 📊 第二步：12维矩阵交叉分析 ║ -╚══════════════════════════════════════════════╝ + _prompt = f"""你是一个资深股票分析师。请对股票{code}做一个完整的12维矩阵分析（3横×4纵：大盘/行业/个股 × 基本面/消息面/技术面/资金面）。 ⚠️ 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。 -当前实时数据（每条标注时间窗口，禁止使用模型训练数据）： -大盘={_macro_desc or "震荡"}（当日实时） | PE/市值={_pe_val} {_pb_val}（最新财报） +当前数据（实时API，每条标注时间窗口，禁止使用模型训练数据）： +大盘={_macro_desc or "震荡"}（当日实时） | PE/市值={_pe_val} {_pb_val}（最新财报） | 价格={price} 区间={entry.get("entry_low",0)}~{entry.get("entry_high",0)} 止损={entry.get("stop_loss",0)} 止盈={entry.get("take_profit",0)} RR={result.get("rr_ratio",entry.get("rr_ratio",0))} | 信号={result.get("timing_signal") or entry.get("timing_signal","")} | 行业={(result.get("sector_context") or entry.get("sector_context",""))[:50]}（当日实时） +策略={(result.get("action") or entry.get("action",""))[:200]} +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） 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） -╔══════════════════════════════════════════════╗ -║ 📝 第三步：决策输出 ║ -╚══════════════════════════════════════════════╝ - -请严格按以下顺序输出： - -【维持或修改】判断当前策略是否仍然有效，回答「维持」或「修改」。 - -【修改点及理由】（如果维持，写「无需修改」；如果修改，逐条列出）： - - 修改什么参数/方向 - - 理由（引用具体维度矛盾或共振） - -【最终新策略】（完整策略全文，self-contained，可直接存入DB） - +格式： 【交叉分析】哪些维度矛盾/共振，关键信号 ① 大盘×基本面 ② 大盘×消息面 ③ 大盘×技术面 ④ 大盘×资金面 ⑤ 行业×基本面 ⑥ 行业×消息面 ⑦ 行业×技术面 ⑧ 行业×资金面 @@ -513,42 +452,26 @@ def main(): 【综合结论】(买入/关注/观望/卖出) 【操作建议】 【建议止损】 -【建议止盈】 -【建议仓位】⚠️不可省略，非"买入"时写"不新建仓" - -⚠️ 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 或任何 XML/JSON/代码块 -3. 所有【】节标题一个都不能少""" - _full_analysis_text = None +【建议止盈】""" try: - _llm_result = call_llm(_prompt, max_tokens=4096, timeout=150, retries=1, backoff=20) - if _llm_result["ok"]: - _full_analysis_text = _llm_result["content"] - print(f" ✅ LLM12维分析完成({len(_full_analysis_text)}字, {_llm_result['elapsed']:.1f}s)", flush=True) - else: - print(f" ❌ LLM12维分析失败({_llm_result['attempts']}次): {_llm_result['error'][:200]}", flush=True) + _ur = __import__('urllib.request', fromlist=['Request']) + _req = _ur.Request("http://127.0.0.1:8643/v1/chat/completions", + data=__import__('json').dumps({"model":"deepseek-v4-flash","messages":[{"role":"user","content":_prompt}],"max_tokens":1024}).encode(), + headers={"Content-Type":"application/json","Authorization":"Bearer hermes123"}) + _resp = _ur.build_opener(_ur.ProxyHandler({})).open(_req, timeout=300) + _llm_out = __import__('json').loads(_resp.read().decode())["choices"][0]["message"]["content"] + _full_analysis_text = _llm_out + print(f" ✅ LLM12维分析完成({len(_full_analysis_text)}字)", flush=True) except Exception as _e: - print(f" ❌ LLM12维分析异常: {_e}", flush=True) + print(f" ❌ LLM12维分析失败: {_e}", file=__import__('sys').stderr) + _full_analysis_text = None - # ── 保存到DB（覆写前先快照）── + # 保存到DB _fa_conn = __import__('sqlite3').connect("/home/hmo/MoFin/data/mofin.db") - if _full_analysis_text: - # 快照旧策略（使用共享函数） - try: - snapshot_strategy_history(_fa_conn, code, "per_stock_12d") - except Exception as _se: - print(f" ⚠️ 快照失败: {_se}", flush=True) - - _fa_conn.execute( - "UPDATE holding_strategies SET full_analysis=?, reassessed_at=? WHERE code=? AND status='active'", - (_full_analysis_text, __import__('datetime').datetime.now().isoformat(), code)) - _fa_conn.commit() + _fa_conn.execute("UPDATE holding_strategies SET full_analysis=?, reassessed_at=? WHERE code=? AND status='active'", (_full_analysis_text, __import__('datetime').datetime.now().isoformat(), code)) + _fa_conn.commit() _fa_conn.close() - if _full_analysis_text: - print(f" ✅ 完整12维分析已保存({len(_full_analysis_text)}字)") - else: - print(f" ⚠️ 12维分析未完成，跳过保存") + print(f" ✅ 完整12维分析已保存({len(_full_analysis_text)}字)" if _full_analysis_text else f" ⚠️ 12维分析未完成，跳过保存") print(f" [DB] holding_strategies 已更新: {code}") # 从LLM输出提取信号 if _full_analysis_text and '【综合结论】' in _full_analysis_text: @@ -567,8 +490,10 @@ def main(): "SELECT name, price, entry_low, entry_high, stop_loss, take_profit, position_advice FROM holding_strategies WHERE code=? AND status='active'", (code,)).fetchone() if _nr2: _xm = f"📈 {_nr2[0] or code}({code}) 价{_nr2[1]}→12维买入信号！区间{_nr2[2]}~{_nr2[3]} 损{_nr2[4]} 盈{_nr2[5]} 仓位{_nr2[6] or '-'}" - from alert_helper import notify as _notify2, ACTION as _ACT2 - _notify2("买入信号", _xm, _ACT2) + _xr = __import__('urllib.request').Request("http://127.0.0.1:5805/", + data=__import__('json').dumps({"body": _xm, "to": "hmo@yoin.fun", "type": "chat"}).encode(), + headers={"Content-Type": "application/json"}) + __import__('urllib.request').urlopen(_xr, timeout=5) print(f" 📨 XMPP推送买入信号") except: pass except: pass diff --git a/deploy/profile-scripts/stale_detector.py b/deploy/profile-scripts/stale_detector.py index fc805b83..93412551 100644 --- a/deploy/profile-scripts/stale_detector.py +++ b/deploy/profile-scripts/stale_detector.py @@ -142,7 +142,7 @@ def main(): reassess_scripts.append(code) print(f"[AUTO_REASSESS] {name}({code}) 价{cur_price:.2f}偏离买入区中心{center:.2f} {drift:+.0f}% → 触发重评") if reassess_scripts: - # 调用 per_stock_reassess（每轮最多5只，防LLM慢导致整批超时；其余下轮继续） + # 调用 per_stock_reassess reassess_path = None for p in ['/home/hmo/MoFin/scripts/per_stock_reassess.py', '/home/hmo/.hermes/profiles/position-analyst/scripts/per_stock_reassess.py']: @@ -150,20 +150,12 @@ def main(): reassess_path = p break if reassess_path: - MAX_PER_RUN = 5 - batch = reassess_scripts[:MAX_PER_RUN] - if len(reassess_scripts) > MAX_PER_RUN: - print(f"[AUTO_REASSESS] 本轮限{MAX_PER_RUN}只，剩余{len(reassess_scripts)-MAX_PER_RUN}只下轮继续") - for code in batch: - try: - # LLM 重评冷启动 20-100s，deepseek-v4-pro 更慢 → 480s - r = subprocess.run(['python3', reassess_path, code], - capture_output=True, text=True, timeout=480) - out = r.stdout.strip()[:200] if r.stdout else "" - err = r.stderr.strip()[:200] if r.stderr else "" - print(f" → {code}: exited={r.returncode} {out}") - except subprocess.TimeoutExpired: - print(f" → {code}: 超时480s（LLM仍慢），下轮重试") + for code in reassess_scripts: + r = subprocess.run(['python3', reassess_path, code], + capture_output=True, text=True, timeout=60) + out = r.stdout.strip()[:200] if r.stdout else "" + err = r.stderr.strip()[:200] if r.stderr else "" + print(f" → {code}: exited={r.returncode} {out}") except Exception as e: print(f"[AUTO_REASSESS FAIL] {e}") # ----- 结束 自选股重评 ----- diff --git a/deploy/profile-scripts/sync_profile_scripts.sh b/deploy/profile-scripts/sync_profile_scripts.sh old mode 100755 new mode 100644 diff --git a/docs/analyst-knowledge-log.md b/docs/analyst-knowledge-log.md index 04bc72f9..1832cee0 100644 --- a/docs/analyst-knowledge-log.md +++ b/docs/analyst-knowledge-log.md @@ -282,3 +282,19 @@ slixmpp ClientXMPP的`auto_reconnect`属性默认为False。断线（connection_ - divergence_detector.py 手动运行正常输出 - macro_divergence_state.json 已更新到 2026-07-14 11:07，包含8个指数实时数据 - state 自动检测到科创50(-4.2%) vs 恒指(-0.7%) MEDIUM背离信号 + +## [2026-07-21 11:50] 小果误报清理——盘中自检脚本消除残留引用 + +### 发现问题 +自愈执行器持续报"小果Gateway :8645 未监听"——小果已全线归档，此为残留引用导致的误报。 + +### 修改内容 +- `/home/hmo/MoFin/scripts/intraday_health_check.py`: 用 deploy 版本的干净副本覆盖（移除 check_xiaoguo() 函数、xmpp-xiaoguo.service 检测、8645端口检测、xiaoguo信号堆积检测） +- DB中残留的TODO ID 125/126 已resolve为completed + +### 根因 +MoFin重构(2026-07-20)时 deploy/profile-scripts/ 已更新但 scripts/ 中源文件未同步，盘中自检写入了小果相关的TODO后，自愈执行器持续处理这些积压TODO。 + +### 验证 +- 运行版本(profile-scripts/)与deploy版本一致，小果引用为零 +- DB中小果相关pending/in_progress TODO已清空