300 lines
10 KiB
Python
300 lines
10 KiB
Python
#!/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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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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当前数据：
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大盘：{data.get('macro','震荡')}
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最新价：{data.get('price',0)} 涨跌：{data.get('change_pct','0')}%
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PE={data.get('pe','?')} 市值={data.get('mcap','?')}亿
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行业：{data.get('sector_context','?')}
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技术面：{data.get('tech_snapshot','')[:200]}
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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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请严格按以下格式输出：
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① 大盘×基本面 [一句话]
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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×资金流 [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 个股×基本面 [一句话]
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⑨ 个股×消息面 [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】总资产的百分之几（如8%），只写数字+%号，不要写文字描述"""
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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（提取百分比）
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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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pct = ""
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if nums:
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# 取第一个合理的百分比（1-100之间）
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for n in nums:
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f = float(n)
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if 1 <= f <= 100:
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pct = f"{f:.0f}%"
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break
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# 如果没有百分比，用文字描述映射到百分比
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raw = l.replace("建议仓位","").strip()
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if not pct:
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if "轻仓" in raw: pct = "3%"
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elif "中" in raw and "仓" in raw: pct = "5%"
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elif "重仓" in raw: pct = "10%"
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elif "清仓" in raw or "零仓" in raw: pct = "0%"
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else: pct = "5%"
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result["position"] = pct
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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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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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