535 lines
23 KiB
Python
535 lines
23 KiB
Python
# -*- coding: utf-8 -*-
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"""
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持仓跟踪智能体（AI Agent + 工作流）
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对持仓/自选股票进行定期深度跟踪，工作流：
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1. 上下文构建 读取产业链知识库（上游/下游/同业 + 关键词）
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2. 数据采集 个股动态 + 上游产业链 + 下游产业链 + 同业动态（DB + RAG 向量检索）
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3. 智能体分析 大模型扮演产业链跟踪分析师，输出深度专业分析 + 影响度判定
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4. 沉淀与通知 结果入库 tracking_reports；重大变化（impact_score 达阈值）邮件通知
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「智能体」体现在：大模型自主综合个股与产业链多环节信息，输出结构化的
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个股动态 / 上游供给成本 / 下游需求景气 / 同业竞争 / 传导影响 / 风险与关注要点，
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并给出 0-100 影响度评分与变化性质判定。
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"""
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import json
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import logging
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import re
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import threading
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import time
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from database import query, query_one, execute
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from settings import tracking_config, tracking_state, set_tracking_state, mail_config
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from engine.chain_data import get_chain
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from engine.indicators import compute_indicators
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from rag.vector_store import query_vectors
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from config import CHROMA_NEWS_COLLECTION
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from engine.analyst import llm_chat
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log = logging.getLogger("agent")
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_track_lock = threading.Lock()
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_cycle_running = False
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_jobs = {} # code -> {status, error, ts}
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# ===================================================================== 数据采集
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def _db_news(codes, days=40, limit=8):
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"""按关联股票代码取新闻（近 days 天）"""
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if not codes:
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return []
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conds, args = [], []
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for c in codes:
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conds.append("(related_stocks=? OR related_stocks LIKE ? OR related_stocks LIKE ?)")
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args += [c, f"%,{c}", f"{c},%"]
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args.append(limit)
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return query(
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f"SELECT id,title,content,source,category,publish_date,sentiment FROM news "
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f"WHERE ({' OR '.join(conds)}) AND publish_date >= date('now','-{days} day') "
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f"ORDER BY publish_date DESC LIMIT ?", args)
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def _rag_news_text(question, where=None, top_k=4):
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"""向量语义检索，返回紧凑文本"""
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try:
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hits = query_vectors(question, n_results=top_k, where=where, name=CHROMA_NEWS_COLLECTION)
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out = []
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for h in hits:
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m = h.get("metadata", {})
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out.append(f" [{m.get('date','')}] {m.get('title','')} (情感{m.get('sentiment',0):+.2f}) "
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f"{h.get('document','')[:90]}")
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return out
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except Exception as e:
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log.warning("rag fail: %s", e)
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return []
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def _fmt_news(items):
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return "\n".join(
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f" [{n['publish_date']}] {n['title']} (情感{n['sentiment']:+.2f}) {n['content'][:90]}"
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for n in items) or " （暂无）"
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def collect_chain(code):
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"""采集个股 + 产业链各环节资讯，返回结构化 dict"""
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stock = query_one("SELECT * FROM stocks WHERE code=?", (code,))
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if not stock:
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return None
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chain = get_chain(stock["industry"])
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seg = {}
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# 1. 个股直接动态
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direct = _db_news([code], days=40, limit=8)
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direct_rag = _rag_news_text(f"{stock['name']} 最新动态 业绩 公告 重大事项", where={"code": code}, top_k=4)
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seg["direct"] = {"db": _fmt_news(direct), "rag": "\n".join(direct_rag) or " （暂无）"}
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# 2. 上游
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up_db = _db_news(chain["upstream"]["codes"], days=40, limit=6)
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up_rag = _rag_news_text(f"{stock['industry']} 上游 {chain['upstream']['keywords']}", top_k=4)
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seg["upstream"] = {"codes": chain["upstream"]["codes"],
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"keywords": chain["upstream"]["keywords"],
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"db": _fmt_news(up_db), "rag": "\n".join(up_rag) or " （暂无）"}
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# 3. 下游
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dn_db = _db_news(chain["downstream"]["codes"], days=40, limit=6)
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dn_rag = _rag_news_text(f"{stock['industry']} 下游需求 景气 {chain['downstream']['keywords']}", top_k=4)
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seg["downstream"] = {"codes": chain["downstream"]["codes"],
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"keywords": chain["downstream"]["keywords"],
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"db": _fmt_news(dn_db), "rag": "\n".join(dn_rag) or " （暂无）"}
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# 4. 同业
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peer_db = _db_news(chain["peers"], days=40, limit=5)
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peer_rag = _rag_news_text(f"{stock['industry']} 竞争格局 同业 {chain['peers']}", top_k=3)
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seg["peers"] = {"codes": chain["peers"],
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"db": _fmt_news(peer_db), "rag": "\n".join(peer_rag) or " （暂无）"}
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# 5. 技术面 + 机构
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ind = compute_indicators(query("SELECT date,open,high,low,close,volume FROM stock_daily "
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"WHERE code=? ORDER BY date ASC", (code,)))
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ratings = query("SELECT inst_name, rating, target_price, rating_date FROM inst_ratings "
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"WHERE stock_code=? ORDER BY rating_date DESC LIMIT 5", (code,))
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holdings = query("SELECT inst_name, quarter, hold_value, change_pct FROM fund_holdings "
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"WHERE stock_code=? ORDER BY quarter DESC LIMIT 5", (code,))
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seg["stock"] = stock
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seg["chain"] = chain
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seg["indicators"] = ind
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seg["ratings"] = ratings
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seg["holdings"] = holdings
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return seg
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# ===================================================================== 分析提示词
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def _fmt_ind(ind):
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return (f"最新价 {ind.get('close')}（{ind.get('change_pct',0):+.2f}%），5日{ind.get('chg_5d',0):+.2f}% / "
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f"20日{ind.get('chg_20d',0):+.2f}%，RSI={ind.get('rsi')}，量比{ind.get('vol_ratio')}，"
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f"MA20={ind.get('ma20')}")
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def build_prompt(seg):
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s = seg["stock"]
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chain = seg["chain"]
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rated = "、".join(f"{r['inst_name']}({r['rating']},目标{r['target_price']})" for r in seg["ratings"]) or "暂无"
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held = "；".join(f"{h['inst_name']} {h['quarter']}持仓{h['hold_value']:.0f}万 环比{h['change_pct']:+.1f}%"
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for h in seg["holdings"]) or "暂无"
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return f"""你是资深产业链跟踪分析师，正在对【持仓标的】{s['name']}({s['code']}) 进行深度跟踪。请综合【个股】与【产业链上下游/同业】的全部动态，输出一份专业、有洞察的产业链跟踪分析。
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【个股基本面】
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{s.get('description','')}
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【技术面】{_fmt_ind(seg['indicators'])}
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【机构动向】评级：{rated} ｜ 持仓：{held}
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【一、个股直接动态】（公告/新闻/机构观点）
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{seg['direct']['db']}
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{seg['direct']['rag']}
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【二、上游产业链】（供给/原材料/成本端；关联股票 {seg['upstream']['codes'] or '无'}，关键词：{seg['upstream']['keywords']}）
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{seg['upstream']['db']}
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{seg['upstream']['rag']}
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【三、下游产业链】（需求/客户/景气端；关联股票 {seg['downstream']['codes'] or '无'}，关键词：{seg['downstream']['keywords']}）
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{seg['downstream']['db']}
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{seg['downstream']['rag']}
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【四、同业动态】（竞争格局；{seg['peers']['codes'] or '无'}）
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{seg['peers']['db']}
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{seg['peers']['rag']}
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【输出要求】
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第一步，先输出一个 json 代码块（必须最先输出，内容为本次判定，不要包含其他内容）：
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```json
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{{"significance":"high|medium|low","impact_score":0到100的整数,"change_kind":"利好/利空/中性/震荡","summary":"一句话总结","chain_trend":"产业链趋势判断"}}
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```
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第二步，再输出 Markdown 分析报告，结构如下：
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## 一、个股最新动态
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## 二、上游产业链分析（供给、原材料、成本端变化及其传导）
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## 三、下游产业链分析（需求、客户、景气度变化及其传导）
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## 四、同行业竞争格局
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## 五、产业链传导与投资启示（上游→中游→下游，对{ s['name']}的影响路径）
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## 六、风险提示
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## 关注要点（3-5条）
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分析须严格基于提供的资讯，避免编造。impact_score 反映本次跟踪发现的动态对股价的潜在影响程度：>=65 视为重大变化。"""
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def parse_judge(text):
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"""从容错地从 LLM 输出中提取 JSON 判定（支持 json 代码块/截断/缺失字段）"""
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t = text.strip()
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# 0) 优先取 json 代码块
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m = re.search(r"```json\s*(.*?)\s*```", t, re.S)
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if m:
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try:
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j = json.loads(m.group(1))
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if "significance" in j or "impact_score" in j:
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return j
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except Exception:
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pass
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# 1) 整体 JSON 对象解析
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for m in re.finditer(r"\{[^{}]*\}", t, re.S):
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try:
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j = json.loads(m.group(0))
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if "significance" in j or "impact_score" in j:
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return j
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except Exception:
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continue
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# 2) 逐字段容错提取（末尾被截断时也能拿到已输出字段）
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out = {}
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m = re.search(r'"significance"\s*:\s*"(high|medium|low)"', t)
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if m:
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out["significance"] = m.group(1)
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m = re.search(r'"impact_score"\s*:\s*(\d+)', t)
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if m:
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out["impact_score"] = int(m.group(1))
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m = re.search(r'"change_kind"\s*:\s*"([^"]{1,20})"', t)
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if m:
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out["change_kind"] = m.group(1)
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m = re.search(r'"summary"\s*:\s*"((?:[^"\\]|\\.){1,200})"', t)
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if m:
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out["summary"] = m.group(1)
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m = re.search(r'"chain_trend"\s*:\s*"((?:[^"\\]|\\.){1,120})"', t)
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if m:
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out["chain_trend"] = m.group(1)
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return out or None
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def strip_json_block(text):
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"""从报告文本中剥离最前面的 json 代码块，保留纯 Markdown"""
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m = re.search(r"```json\s*.*?```\s*", text, re.S)
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if m:
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return text[m.end():].strip()
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return text
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# ===================================================================== 执行
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def track_stock(code, focus=""):
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"""执行一次跟踪，返回 {ok, report_id, meta, ...}"""
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with _track_lock:
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seg = collect_chain(code)
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if not seg:
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return {"error": "股票不存在"}
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s = seg["stock"]
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prompt = build_prompt(seg)
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try:
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reply = llm_chat([
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{"role": "system", "content": "你是一名严谨专业的产业链跟踪分析师，输出结构化、有数据支撑的分析。"},
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{"role": "user", "content": prompt},
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]).strip()
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if not reply:
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raise RuntimeError("LLM 返回为空")
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judge = parse_judge(reply)
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# 摘要兜底：解析不到则取报告首个标题；并剥离 json 块保留纯净 Markdown
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clean_report = strip_json_block(reply)
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summary = (judge or {}).get("summary") or _first_heading(clean_report)
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meta = {
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"significance": (judge or {}).get("significance", "medium"),
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"impact_score": int((judge or {}).get("impact_score", 50)),
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"change_kind": (judge or {}).get("change_kind", "中性"),
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"summary": summary,
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"chain_trend": (judge or {}).get("chain_trend", ""),
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"news_counts": {
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"direct": _count(seg["direct"]),
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"upstream": _count(seg["upstream"]),
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"downstream": _count(seg["downstream"]),
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"peers": _count(seg["peers"]),
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},
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"focus": focus,
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}
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sources = {
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"direct": seg["direct"], "upstream": seg["upstream"],
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"downstream": seg["downstream"], "peers": seg["peers"],
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"indicators": _fmt_ind(seg["indicators"]),
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"ratings": seg["ratings"], "holdings": seg["holdings"],
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}
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execute(
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"INSERT INTO tracking_reports(target_type, code, stock_name, industry, report, meta, sources, status, created_at) "
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"VALUES('stock',?,?,?,?,?,?,'done',datetime('now','localtime'))",
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(code, s["name"], s["industry"], clean_report, json.dumps(meta, ensure_ascii=False),
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json.dumps(sources, ensure_ascii=False)))
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rid = query_one("SELECT MAX(id) id FROM tracking_reports")["id"]
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_notify_if_significant(rid, s, meta)
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return {"ok": True, "report_id": rid, "meta": meta}
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except Exception as e:
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log.exception("track %s fail", code)
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return {"error": str(e)}
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def _count(seg):
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return (seg["db"].count("[") + seg["rag"].count("[")) // 1
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def _first_heading(text):
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"""取报告第一行非空文本作为摘要兜底"""
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for line in (text or "").splitlines():
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line = line.strip().lstrip("#* ").strip()
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if line:
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return line[:60]
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return ""
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def _notify_if_significant(rid, stock, meta):
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"""影响度达阈值且开启通知 → 邮件（股票/概念通用）"""
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cfg = tracking_config()
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try:
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if cfg["notify"] and int(meta.get("impact_score", 0)) >= int(cfg["impact_threshold"]):
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from engine.notifier import send_email
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mc = mail_config()
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nc = meta.get("news_counts") or {}
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detail = (f"个股资讯 {nc.get('direct', 0)} 条 / 上游 {nc.get('upstream', 0)} 条 / "
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f"下游 {nc.get('downstream', 0)} 条 / 同业 {nc.get('peers', 0)} 条"
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if "upstream" in nc else
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f"相关资讯 {nc.get('direct', 0)} 条 / 语义检索 {nc.get('rag', 0)} 条 / "
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f"受益个股 {nc.get('related', 0)} 只")
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send_email(
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f"[持仓跟踪] {stock['name']} 出现{meta.get('change_kind', '')}动态（影响度{meta.get('impact_score', 0)}）",
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f"""<html><body style="font-family:Microsoft YaHei;padding:20px;background:#f5f6f8;">
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<div style="max-width:640px;margin:auto;background:#fff;border-radius:8px;border:1px solid #e5e7eb;overflow:hidden;">
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<div style="background:#1e293b;color:#fff;padding:14px 20px;font-size:17px;font-weight:bold;">🧭 智能体跟踪 · {stock['name']}（{stock['code']}）</div>
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<div style="padding:16px 20px;">
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<p><b>影响度：</b>{meta.get('impact_score', 0)}/100（{'🔴 重大' if int(meta.get('impact_score', 0)) >= 65 else '🟡 关注'}）<br>
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<b>性质：</b>{meta.get('change_kind', '')} ｜ <b>显著性：</b>{meta.get('significance', '')}</p>
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<p style="font-size:15px;"><b>摘要：</b>{meta.get('summary', '')}</p>
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<p style="color:#555;"><b>趋势判断：</b>{meta.get('chain_trend', '')}</p>
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<p style="color:#888;font-size:12px;">{detail}</p>
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</div></div></body></html>""",
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cfg=mc)
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set_tracking_state(last_alert=int(meta.get("impact_score", 0)))
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except Exception as e:
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log.warning("track notify fail: %s", e)
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# ===================================================================== 概念/主题跟踪
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def track_concept(name, keywords="", focus=""):
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"""跟踪一个概念/主题：采集相关新闻 → 受益个股梳理 → LLM 深度分析 → 影响度判定"""
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kw = keywords.strip() or name
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kw_list = [k.strip() for k in kw.replace("，", ",").split(",") if k.strip()]
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kws = " ".join(kw_list)
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# 1) 采集：DB 关键词检索 + RAG 向量检索
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conds, args = [], []
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for k in kw_list[:5]:
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conds.append("(title LIKE ? OR content LIKE ?)")
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args += [f"%{k}%", f"%{k}%"]
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args.append(15)
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db_news = query(
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f"SELECT id,title,content,source,category,publish_date,sentiment,related_stocks FROM news "
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f"WHERE ({' OR '.join(conds)}) AND publish_date >= date('now','-45 day') "
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f"ORDER BY publish_date DESC LIMIT ?", args)
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rag_hits = _rag_news_text(f"{name} 概念主题 {kws} 最新动态 政策 催化", top_k=8)
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db_text = _fmt_news(db_news)
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rag_text = "\n".join(rag_hits) or " （暂无）"
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# 2) 受益个股：从 DB 新闻 related_stocks + RAG 命中元数据 code 汇总
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codes = set()
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for n in db_news:
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for c in (n["related_stocks"] or "").split(","):
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if c:
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codes.add(c)
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try:
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for h in query_vectors(f"{name} {kws} 受益个股", n_results=8, name=CHROMA_NEWS_COLLECTION):
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c = (h.get("metadata") or {}).get("code")
|
||
if c:
|
||
codes.add(c)
|
||
except Exception:
|
||
pass
|
||
related = query("SELECT code, name, industry FROM stocks WHERE code IN (%s)" %
|
||
",".join(["?"] * len(codes)), tuple(codes)) if codes else []
|
||
related_txt = "、".join(f"{r['name']}({r['code']},{r['industry']})" for r in related) or "暂无"
|
||
# 3) 提示词
|
||
prompt = f"""你是资深题材/概念跟踪分析师，正在深度跟踪【{name}】这一概念主题。
|
||
|
||
【概念/主题】{name}
|
||
【检索关键词】{kws}
|
||
|
||
【最新相关资讯】
|
||
{db_text}
|
||
{rag_text}
|
||
|
||
【关联受益个股】{related_txt}
|
||
|
||
【输出要求】
|
||
第一步，先输出一个 json 代码块（必须最先输出）：
|
||
```json
|
||
{{"significance":"high|medium|low","impact_score":0到100的整数,"change_kind":"利好/利空/中性/震荡","summary":"一句话总结","chain_trend":"题材趋势判断"}}
|
||
```
|
||
第二步，输出 Markdown 分析报告，结构如下：
|
||
## 一、概念主题最新动态
|
||
## 二、核心驱动与催化（政策/产业/事件）
|
||
## 三、受益标的梳理（关联个股及逻辑）
|
||
## 四、市场情绪与资金动向
|
||
## 五、风险提示
|
||
## 关注要点（3-5条）
|
||
|
||
分析须严格基于资讯，避免编造。impact_score >=65 视为重大变化。"""
|
||
try:
|
||
reply = llm_chat([
|
||
{"role": "system", "content": "你是一名严谨专业的题材与概念跟踪分析师。"},
|
||
{"role": "user", "content": prompt},
|
||
]).strip()
|
||
if not reply:
|
||
raise RuntimeError("LLM 返回为空")
|
||
judge = parse_judge(reply)
|
||
clean_report = strip_json_block(reply)
|
||
summary = (judge or {}).get("summary") or _first_heading(clean_report)
|
||
meta = {
|
||
"significance": (judge or {}).get("significance", "medium"),
|
||
"impact_score": int((judge or {}).get("impact_score", 50)),
|
||
"change_kind": (judge or {}).get("change_kind", "中性"),
|
||
"summary": summary,
|
||
"chain_trend": (judge or {}).get("chain_trend", ""),
|
||
"news_counts": {"direct": len(db_news), "rag": len(rag_hits), "related": len(related)},
|
||
"keywords": kws,
|
||
}
|
||
sources = {"keywords": kws, "db_news": db_text, "rag_news": rag_text,
|
||
"related": related_txt}
|
||
cid = f"CONCEPT:{name}"
|
||
execute(
|
||
"INSERT INTO tracking_reports(target_type, code, stock_name, industry, report, meta, sources, status, created_at) "
|
||
"VALUES('concept',?,?,?,?,?,?,'done',datetime('now','localtime'))",
|
||
(cid, name, "概念/主题", clean_report, json.dumps(meta, ensure_ascii=False),
|
||
json.dumps(sources, ensure_ascii=False)))
|
||
rid = query_one("SELECT MAX(id) id FROM tracking_reports")["id"]
|
||
_notify_if_significant(rid, {"name": name, "code": cid}, meta)
|
||
return {"ok": True, "report_id": rid, "meta": meta}
|
||
except Exception as e:
|
||
log.exception("track concept %s fail", name)
|
||
return {"error": str(e)}
|
||
|
||
|
||
# ===================================================================== 目标管理
|
||
def list_targets():
|
||
return query("SELECT * FROM watch_targets ORDER BY type, id")
|
||
|
||
|
||
def add_target(ttype, name, code="", keywords=""):
|
||
name = name.strip()
|
||
if not name:
|
||
return {"error": "名称不能为空"}
|
||
execute("INSERT INTO watch_targets(type, code, name, keywords) VALUES(?,?,?,?)",
|
||
(ttype, code, name, keywords))
|
||
return {"ok": True}
|
||
|
||
|
||
def delete_target(tid):
|
||
execute("DELETE FROM watch_targets WHERE id=?", (tid,))
|
||
return {"ok": True}
|
||
|
||
|
||
# ===================================================================== 批量与调度
|
||
def track_all(progress=None):
|
||
"""跟踪全部目标：持仓（自选股）+ 目标(概念/主题/股票)"""
|
||
global _cycle_running
|
||
if _cycle_running:
|
||
return {"tracked": 0, "msg": "已有跟踪任务进行中，请稍后再试"}
|
||
_cycle_running = True
|
||
try:
|
||
targets = []
|
||
# 持仓（自选股）
|
||
for w in query("SELECT w.code, s.name FROM watchlist w JOIN stocks s ON s.code=w.code ORDER BY w.added_at"):
|
||
targets.append(("stock", w["code"], w["name"]))
|
||
# 目标（股票/概念）
|
||
for t in query("SELECT id, type, code, name, keywords FROM watch_targets WHERE enabled=1 ORDER BY id"):
|
||
if t["type"] == "stock" and t["code"]:
|
||
targets.append(("stock", t["code"], t["name"]))
|
||
else:
|
||
targets.append(("concept", t["name"], t["keywords"] or t["name"]))
|
||
# 去重
|
||
seen, uniq = set(), []
|
||
for typ, key, name in targets:
|
||
u = (typ, key)
|
||
if u in seen:
|
||
continue
|
||
seen.add(u)
|
||
uniq.append((typ, key, name))
|
||
if not uniq:
|
||
return {"tracked": 0, "msg": "暂无跟踪目标：请添加持仓/自选股或概念主题目标"}
|
||
results = []
|
||
for i, (typ, key, name) in enumerate(uniq):
|
||
if typ == "stock":
|
||
r = track_stock(key)
|
||
else:
|
||
r = track_concept(key, name)
|
||
results.append({"type": typ, "name": name, **r})
|
||
set_tracking_state(last_run=time.strftime("%Y-%m-%d %H:%M:%S"), last_stock=name)
|
||
if progress:
|
||
progress(i + 1, len(uniq))
|
||
return {"tracked": len(results), "results": results}
|
||
finally:
|
||
_cycle_running = False
|
||
|
||
|
||
def latest_reports(code, limit=5):
|
||
return query("SELECT id, target_type, code, stock_name, industry, meta, status, created_at "
|
||
"FROM tracking_reports WHERE code=? ORDER BY id DESC LIMIT ?", (code, limit))
|
||
|
||
|
||
def list_reports(limit=30):
|
||
return query("SELECT id, target_type, code, stock_name, industry, meta, status, created_at "
|
||
"FROM tracking_reports ORDER BY id DESC LIMIT ?", (limit,))
|
||
|
||
|
||
def get_report(rid):
|
||
return query_one("SELECT * FROM tracking_reports WHERE id=?", (rid,))
|
||
|
||
|
||
class TrackingThread(threading.Thread):
|
||
"""后台调度：定期跟踪全部持仓股票"""
|
||
|
||
def __init__(self):
|
||
super().__init__(daemon=True, name="tracking")
|
||
self._stop = threading.Event()
|
||
|
||
def stop(self):
|
||
self._stop.set()
|
||
|
||
def run(self):
|
||
log.info("持仓跟踪调度器启动")
|
||
first = True
|
||
while not self._stop.is_set():
|
||
try:
|
||
cfg = tracking_config()
|
||
if first:
|
||
# 启动后等待一个完整间隔再首跑，避免重启即烧一轮 LLM、与手动操作冲突
|
||
self._stop.wait(cfg.get("interval_min", 60) * 60)
|
||
first = False
|
||
continue
|
||
if cfg["enabled"]:
|
||
try:
|
||
r = track_all()
|
||
log.info("tracking cycle: %s", r)
|
||
except Exception as e:
|
||
log.warning("tracking cycle error: %s", e)
|
||
except Exception as e:
|
||
log.warning("tracking loop error: %s", e)
|
||
self._stop.wait(cfg.get("interval_min", 60) * 60)
|
||
log.info("持仓跟踪调度器停止")
|
||
|
||
|
||
_tracking = None
|
||
|
||
|
||
def start_tracking():
|
||
global _tracking
|
||
if _tracking and _tracking.is_alive():
|
||
return _tracking
|
||
_tracking = TrackingThread()
|
||
_tracking.start()
|
||
return _tracking
|