chore: scripts/大收尸——178个影子副本/零引用工具归档至archive/20260722-scripts-cleanup（红线#6/#8，git mv保历史）
This commit is contained in:
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#!/usr/bin/env python3
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"""batch_reassess.py — 批量补全九维分析（逐只处理，间隔防限流）
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用法: python3 batch_reassess.py [--all] [--code XXXXXX]
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流程：收集最新数据 → 调LLM(gateway)写九维分析+策略 → 保存到DB
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"""
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import sys, json, subprocess, sqlite3, re, time
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from datetime import datetime
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DB = "/home/hmo/MoFin/data/mofin.db"
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GATEWAY = "http://127.0.0.1:8643/v1/chat/completions"
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COOLDOWN_HOURS = 1
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def has_llm_analysis(code):
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"""检查是否为LLM生成的九维分析（>500字）"""
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conn = sqlite3.connect(DB)
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r = conn.execute("SELECT LENGTH(full_analysis) FROM holding_strategies WHERE code=? AND status='active'", (code,)).fetchone()
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conn.close()
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return r and r[0] and r[0] > 500
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def in_cooldown(code):
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"""冷却期检查"""
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conn = sqlite3.connect(DB)
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r = conn.execute("SELECT reassessed_at FROM holding_strategies WHERE code=? AND status='active'", (code,)).fetchone()
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conn.close()
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if not r or not r[0]:
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return False
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try:
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last = datetime.fromisoformat(r[0])
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diff = (datetime.now() - last).total_seconds() / 3600
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return diff < COOLDOWN_HOURS
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except:
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return False
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def collect_data(code):
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"""收集最新数据"""
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data = {"code": code}
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# 从DB读策略
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conn = sqlite3.connect(DB)
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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()
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if r:
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data["name"] = r[0]
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data["entry_low"] = r[1] or 0
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data["entry_high"] = r[2] or 0
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data["stop_loss"] = r[3] or 0
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data["take_profit"] = r[4] or 0
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data["timing_signal"] = r[5] or ""
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data["action"] = r[6] or ""
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data["rr_ratio"] = r[7] or 0
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data["tech_snapshot"] = r[8] or ""
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data["sector_context"] = r[9] or ""
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data["stock_category"] = r[10] or ""
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conn.close()
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# 从腾讯API拉最新价和基本面
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prefix = "sh" if str(code).startswith(("6","9")) else "sz"
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try:
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r = subprocess.run(["curl", "-s", f"http://qt.gtimg.cn/q={prefix}{code}"], capture_output=True, timeout=10)
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parts = r.stdout.decode("gbk", errors="ignore").split("~")
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data["price"] = float(parts[3]) if len(parts) > 3 and parts[3] else 0
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data["pe"] = parts[39] if len(parts) > 39 and parts[39] else ""
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data["mcap"] = parts[44] if len(parts) > 44 and parts[44] else ""
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data["change_pct"] = parts[32] if len(parts) > 32 and parts[32] else "0"
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except:
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data["price"] = 0
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# 大盘
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try:
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conn = sqlite3.connect(DB)
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mr = conn.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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s = json.loads(mr[0])
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data["macro"] = s.get("description", "大盘震荡")
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conn.close()
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except:
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data["macro"] = "大盘震荡"
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return data
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def build_prompt(data):
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"""构建LLM prompt，要求输出完整策略"""
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cash = 321271 # 可用现金（从DB读取）
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total = 952879 # 总资产
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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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import sqlite3 as _sq, json as _j
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_db = _sq.connect("/home/hmo/MoFin/data/mofin.db")
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_fr = _db.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 = _j.loads(_fr[0])
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_stocks = _fc.get("stocks", {})
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_s = _stocks.get(data['code'], {})
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if _s and _s.get("analysis"):
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_a = _s["analysis"]
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_net = _a.get("net_flow", 0)
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_main = _a.get("main_force", 0)
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_retail = _a.get("retail_flow", 0)
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_trend = _a.get("trend", "中性")
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_flow_note = f"净流入{_net:.0f}万 主力{_main:.0f}万 散户{_retail:.0f}万 趋势{_trend}"
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_db.close()
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except:
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pass
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# 拉取近期消息面
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_news_note = "暂无近期消息"
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try:
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import sqlite3 as _sq
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_db = _sq.connect("/home/hmo/MoFin/data/mofin.db")
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_nr = _db.execute(
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"SELECT summary, overall_sentiment, created_at FROM signal_news "
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"WHERE (code=? OR sector LIKE ?) AND overall_sentiment IN ('利好','利空') "
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"ORDER BY id DESC LIMIT 3",
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(data['code'], f'%{data.get("name","")[:4]}%')
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).fetchall()
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if _nr:
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_news_note = " | ".join([f"{r[2][:10]} {r[1]} {r[0][:40]}" for r in _nr])
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_db.close()
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except:
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pass
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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。
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⚠️ 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。
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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。
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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）：
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大盘：{data.get('macro','震荡')}（当日实时）
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最新价：{data.get('price',0)} 涨跌：{data.get('change_pct','0')}%（当日实时）
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PE={data.get('pe','?')}（最新财报） 市值={data.get('mcap','?')}亿
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行业：{data.get('sector_context','?')}（当日实时）
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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）
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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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当前信号：{data.get('timing_signal','?')} 分类：{data.get('stock_category','?')}
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原策略：{(data.get('action','') or '')[:200]}
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我的总资产={total}元，可用现金={cash}元。
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请严格按以下格式输出：
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【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
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① 大盘×基本面 [一句话，说明矛盾关系]
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② 大盘×消息面 [一句话]
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③ 大盘×技术面 [一句话]
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④ 大盘×资金面 [一句话]
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⑤ 行业×基本面 [一句话]
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⑥ 行业×消息面 [一句话]
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⑦ 行业×技术面 [一句话]
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⑧ 行业×资金面 [一句话]
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⑨ 个股×基本面 [一句话]
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⑩ 个股×消息面 [一句话]
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⑪ 个股×技术面 [一句话]
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⑫ 个股×资金面 [一句话]
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【综合结论】（买入/关注/观望/卖出）
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【操作建议】具体操作建议
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【买入区间】最低价~最高价
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【建议止损】数字
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【建议止盈】数字
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【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：
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基础仓位按RR确定：RR<1.5→不推荐，RR1.5~3→8%，RR3~5→12%，RR5+→15%
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大盘偏弱×0.8，大盘偏强×1.15
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蓝筹/白马×1.2，成长×0.85，题材/短线×0.6
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最终仓位范围：5%~20%
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同时考虑：现金{cash}元足够买多少手。
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输出格式："X%（理由：一句话说明为什么这个仓位）"""
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def parse_response(text):
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"""从LLM回复中提取策略参数"""
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result = {"signal": "", "entry_low": 0, "entry_high": 0, "stop_loss": 0, "take_profit": 0, "position": ""}
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# 信号
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sl = [l for l in text.split("\n") if "综合结论" in l]
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if sl:
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for kw in ["买入","关注","观望","卖出"]:
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if kw in sl[0]:
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result["signal"] = kw
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break
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# 买入区间
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zl = [l for l in text.split("\n") if "买入区间" in l]
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if zl:
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nums = re.findall(r'[\d.]+', zl[0])
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if len(nums) >= 2:
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result["entry_low"] = float(nums[0])
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result["entry_high"] = float(nums[1])
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# 止损
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for l in text.split("\n"):
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if "建议止损" in l:
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nums = re.findall(r'[\d.]+', l)
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if nums: result["stop_loss"] = float(nums[0])
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# 止盈
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for l in text.split("\n"):
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if "建议止盈" in l:
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nums = re.findall(r'[\d.]+', l)
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if nums: result["take_profit"] = float(nums[0])
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# 仓位：只有买入信号才需要，提取百分比数字
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result["position"] = ""
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if result["signal"] == "买入":
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for l in text.split("\n"):
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if "建议仓位" in l:
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nums = re.findall(r'[\d.]+', l)
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for n in nums:
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f = float(n)
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if 1 <= f <= 30: # 合理的仓位范围
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result["position"] = f"{f:.0f}%"
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break
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break
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return result
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def save_result(code, full_text, parsed):
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"""保存LLM结果到DB"""
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conn = sqlite3.connect(DB)
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now = datetime.now().isoformat()
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updates = ["full_analysis=?", "reassessed_at=?"]
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params = [full_text, now]
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if parsed["signal"]:
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updates.append("timing_signal=?")
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params.append(parsed["signal"])
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if parsed["entry_low"] > 0:
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updates.append("entry_low=?")
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params.append(parsed["entry_low"])
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if parsed["entry_high"] > 0:
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updates.append("entry_high=?")
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params.append(parsed["entry_high"])
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if parsed["stop_loss"] > 0:
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updates.append("stop_loss=?")
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params.append(parsed["stop_loss"])
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if parsed["take_profit"] > 0:
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updates.append("take_profit=?")
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params.append(parsed["take_profit"])
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if parsed["position"]:
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updates.append("position_advice=?")
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params.append(parsed["position"])
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params.append(code)
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sql = f"UPDATE holding_strategies SET {', '.join(updates)} WHERE code=? AND status='active'"
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conn.execute(sql, params)
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conn.commit()
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# 买入信号→推XMPP通知（在conn close前执行）
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if parsed.get("signal") == "买入":
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try:
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_nr = conn.execute("SELECT name, price FROM holding_strategies WHERE code=? AND status='active'", (code,)).fetchone()
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_name = _nr[0] if _nr else code
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_p = _nr[1] if _nr else 0
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_el = parsed.get("entry_low", 0)
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_eh = parsed.get("entry_high", 0)
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_sl = parsed.get("stop_loss", 0)
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_tp = parsed.get("take_profit", 0)
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_pos = parsed.get("position", "")
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_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}"
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import urllib.request, json as _jj
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_req = urllib.request.Request("http://127.0.0.1:5805/",
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data=_jj.dumps({"body": _msg, "to": "hmo@yoin.fun", "type": "chat"}).encode(),
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headers={"Content-Type": "application/json"})
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urllib.request.urlopen(_req, timeout=5)
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print(f" 📨 XMPP推送成功: {_msg[:60]}")
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except Exception as _e:
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print(f" ⚠️ XMPP推送失败: {_e}")
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conn.close()
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def process_stock(code):
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"""处理单只股票"""
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print(f"\n{'='*50}")
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print(f"处理: {code}")
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print(f"{'='*50}")
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if has_llm_analysis(code):
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print(f" ⏭ 已有LLM九维分析，跳过")
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return False
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if in_cooldown(code):
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print(f" ⏭ 冷却期内，跳过")
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return False
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print(f" 收集数据...", flush=True)
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data = collect_data(code)
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if not data.get("price"):
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print(f" ⚠️ 无价格数据，跳过")
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return False
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print(f" 调LLM生成九维分析...", flush=True)
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prompt = build_prompt(data)
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try:
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r = subprocess.run(["curl", "-s", "--max-time", "300",
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"-H", "Content-Type: application/json",
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"-H", "Authorization: Bearer hermes123",
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"-d", json.dumps({"model":"deepseek-v4-flash","messages":[{"role":"user","content":prompt}],"max_tokens":2048}),
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GATEWAY], capture_output=True, timeout=310)
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if r.returncode != 0:
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print(f" ❌ curl失败: {r.stderr.decode()[:100]}")
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return False
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resp = json.loads(r.stdout)
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if "choices" not in resp:
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print(f" ❌ API异常: {str(resp)[:200]}")
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return False
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full_text = resp["choices"][0]["message"]["content"]
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print(f" ✅ LLM返回({len(full_text)}字)", flush=True)
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parsed = parse_response(full_text)
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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']}")
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save_result(code, full_text, parsed)
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print(f" ✅ 已保存到DB")
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return True
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except subprocess.TimeoutExpired:
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print(f" ❌ 超时")
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return False
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except Exception as e:
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print(f" ❌ 错误: {e}")
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return False
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def main():
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codes = []
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if "--code" in sys.argv:
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idx = sys.argv.index("--code")
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codes = [sys.argv[idx+1]]
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else:
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# 所有自选策略
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conn = sqlite3.connect(DB)
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rows = conn.execute("SELECT code FROM holding_strategies WHERE status='active' AND decision_type='自选策略' ORDER BY code").fetchall()
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conn.close()
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codes = [r[0] for r in rows]
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print(f"待处理: {len(codes)}只")
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ok = 0
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fail = 0
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skip = 0
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for i, code in enumerate(codes):
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if has_llm_analysis(code):
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print(f" [{i+1}/{len(codes)}] ⏭ {code} 已有LLM分析")
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skip += 1
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continue
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print(f" [{i+1}/{len(codes)}] ", end="", flush=True)
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if process_stock(code):
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ok += 1
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else:
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fail += 1
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# 间隔15秒（防gateway过载）
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if i < len(codes) - 1:
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print(f" 等待15秒...", flush=True)
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time.sleep(15)
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print(f"\n{'='*50}")
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print(f"完成: {ok}成功, {fail}失败, {skip}跳过")
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print(f"{'='*50}")
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if __name__ == "__main__":
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main()
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Reference in New Issue
Block a user