403 lines
20 KiB
Python
403 lines
20 KiB
Python
"""AI 分析层：基于中台数据构造上下文，调用大模型生成点评；无 key 时规则降级。"""
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from __future__ import annotations
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import datetime as dt
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from sqlalchemy import select, func, desc
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import llm
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import signals
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import rag
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from db import get_session
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from models import (SectorDaily, FundFlowDaily, SentimentDaily, DragonTiger,
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StockMetric, DailyQuote, Security)
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DISCLAIMER = "（注：以上为基于数据的程序化分析，不构成投资建议，市场有风险。）"
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# ============ 每日复盘点评 ============
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def _daily_context(date=None):
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with get_session() as s:
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d = dt.date.fromisoformat(date) if date else s.execute(select(func.max(SectorDaily.date))).scalar()
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if not d:
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return None
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secs = s.execute(select(SectorDaily).where(SectorDaily.date == d).order_by(SectorDaily.pct.desc())).scalars().all()
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flows = s.execute(select(FundFlowDaily).where(FundFlowDaily.date == d).order_by(FundFlowDaily.net.desc())).scalars().all()
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senti = s.execute(select(SentimentDaily).where(SentimentDaily.date == d)).scalar_one_or_none()
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lhb = s.execute(select(DragonTiger).where(DragonTiger.date == d).order_by(DragonTiger.net.desc()).limit(6)).scalars().all()
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return {
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"date": d, "senti": senti,
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"top": [(x.name, x.pct) for x in secs[:6]],
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"bot": [(x.name, x.pct) for x in secs[-4:]],
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"inflow": [(x.name, x.net) for x in flows[:6]],
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"outflow": [(x.name, x.net) for x in flows[-4:][::-1]],
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"lhb": [(x.name, x.net) for x in lhb],
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}
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def review_daily_comment(date=None):
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ctx = _daily_context(date)
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if not ctx:
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return {"ok": False, "msg": "暂无入库数据，请先到数据中台入库"}
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s = ctx["senti"]
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senti_line = (f"上涨{s.up}家/下跌{s.down}家，涨停{s.limit_up}家/跌停{s.limit_down}家" if s else "无情绪数据")
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facts = (
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f"日期：{ctx['date']}\n"
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f"市场情绪：{senti_line}\n"
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f"领涨板块：{', '.join(f'{n}({p:+.2f}%)' for n,p in ctx['top'])}\n"
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f"领跌板块：{', '.join(f'{n}({p:+.2f}%)' for n,p in ctx['bot'])}\n"
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f"主力净流入前列：{', '.join(f'{n}({v:+.1f}亿)' for n,v in ctx['inflow'])}\n"
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f"主力净流出前列：{', '.join(f'{n}({v:+.1f}亿)' for n,v in ctx['outflow'])}\n"
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f"龙虎榜净买额居前：{', '.join(f'{n}({v:+.2f}亿)' for n,v in ctx['lhb'])}\n"
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)
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if llm.enabled():
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try:
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prompt = ("请根据以下当日A股盘面数据，撰写一篇 250 字以内的收盘复盘点评，"
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"包含：① 今日市场情绪与赚钱效应判断；② 资金与题材主线；③ 明日需关注的方向与风险。"
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"分段清晰。\n\n" + facts)
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text = llm.ask(prompt, temperature=0.6)
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return {"ok": True, "source": "llm", "date": ctx["date"].isoformat(), "facts": facts, "text": text}
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except Exception as e:
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pass
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# 规则降级
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tone = "情绪偏暖、赚钱效应尚可" if (s and s.up > s.down) else "情绪偏弱、需控制仓位"
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text = (f"今日{tone}。{senti_line}。\n"
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f"领涨主线集中在 {', '.join(n for n,_ in ctx['top'][:3])}，"
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f"主力资金净流入居前的是 {', '.join(n for n,_ in ctx['inflow'][:3])}；"
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f"而 {', '.join(n for n,_ in ctx['outflow'][:2])} 遭资金流出，需回避。\n"
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f"明日关注领涨板块的持续性与量能配合，注意高位股退潮风险。\n{DISCLAIMER}")
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return {"ok": True, "source": "rule", "date": ctx["date"].isoformat(), "facts": facts, "text": text}
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# ============ 个股诊断 ============
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def _stock_context(symbol):
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with get_session() as s:
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m = s.get(StockMetric, symbol)
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sec = s.get(Security, symbol)
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rows = s.execute(select(DailyQuote).where(DailyQuote.code == symbol)
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.order_by(DailyQuote.date.desc()).limit(60)).scalars().all()
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return m, sec, list(reversed(rows))
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def _build_evidence(m, stats, news_tone):
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"""构造证据链：每条含 维度/事实/方向/历史命中率。"""
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def hr(key):
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s = stats.get(key)
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return (s["win_rate"], s["avg_ret"], s["samples"]) if s else (None, None, None)
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ev = []
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# 趋势
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wr, ar, ns = hr("ma_bull")
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ev.append({"dim": "趋势", "fact": f"均线{'多头排列(MA5>MA10>MA20)' if m.ma_bull else '未呈多头'}，20日{m.ret20:+.1f}%",
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"signal": "bull" if m.ma_bull else ("bear" if m.ret20 < -3 else "neutral"),
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"win_rate": wr, "avg_ret": ar, "samples": ns, "weight": 1.2})
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# 技术(MACD)
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wr, ar, ns = hr("macd_gold")
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ev.append({"dim": "技术", "fact": f"{'MACD金叉' if m.macd_gold else 'MACD未金叉'}，RSI14={m.rsi14:.0f}"
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+ ("（超买）" if m.rsi14 >= 80 else ("（超卖）" if m.rsi14 < 30 else "")),
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"signal": "bull" if m.macd_gold else ("bear" if m.rsi14 >= 80 else "neutral"),
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"win_rate": wr, "avg_ret": ar, "samples": ns, "weight": 1.0})
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# 动量
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wr, ar, ns = hr("up_streak3")
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streak_sig = "bull" if m.up_streak >= 3 else ("bear" if m.ret5 < -5 else "neutral")
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ev.append({"dim": "动量", "fact": f"连涨{m.up_streak}日，5日{m.ret5:+.1f}%",
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"signal": streak_sig, "win_rate": wr if m.up_streak >= 3 else None,
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"avg_ret": ar if m.up_streak >= 3 else None, "samples": ns if m.up_streak >= 3 else None, "weight": 1.0})
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# 资金
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wr, ar, ns = hr("vol_breakout")
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vol_sig = "bull" if (m.vol_ratio > 2 and m.pct > 0) else ("bear" if (m.vol_ratio > 2 and m.pct < 0) else "neutral")
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ev.append({"dim": "资金", "fact": f"量比{m.vol_ratio}，当日{m.pct:+.2f}%，成交额{m.amount}亿",
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"signal": vol_sig, "win_rate": wr if vol_sig != "neutral" else None,
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"avg_ret": ar if vol_sig != "neutral" else None, "samples": ns if vol_sig != "neutral" else None, "weight": 1.1})
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# 位置
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key = "new_high60" if m.pos60 >= 0.99 else ("rsi_oversold" if m.rsi14 < 30 else None)
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wr, ar, ns = hr(key) if key else (None, None, None)
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pos_sig = "bear" if m.pos60 > 0.92 else ("bull" if m.pos60 < 0.2 else "neutral")
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ev.append({"dim": "位置", "fact": f"处于60日 {m.pos60*100:.0f}% 分位" + ("（创新高）" if m.pos60 >= 0.99 else ("（低位）" if m.pos60 < 0.2 else "")),
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"signal": pos_sig, "win_rate": wr, "avg_ret": ar, "samples": ns, "weight": 0.8})
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# 消息面（RAG）
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ev.append({"dim": "消息", "fact": f"近期资讯情绪：{news_tone}",
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"signal": {"利好": "bull", "利空": "bear"}.get(news_tone, "neutral"),
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"win_rate": None, "avg_ret": None, "samples": None, "weight": 0.9})
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return ev
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def _confidence_direction(ev):
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sval = {"bull": 1, "bear": -1, "neutral": 0}
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net = sum(sval[e["signal"]] * e["weight"] for e in ev)
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tw = sum(e["weight"] for e in ev if e["signal"] != "neutral")
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agreement = abs(net) / tw if tw else 0.0
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conf = int(max(5, min(96, 35 + agreement * 55 + (8 if tw >= 3 else 0))))
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thr = 0.15 * sum(e["weight"] for e in ev)
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direction = "up" if net > thr else ("down" if net < -thr else "flat")
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return conf, direction, round(net, 2)
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def diagnose(symbol):
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m, sec, rows = _stock_context(symbol)
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if not m:
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return {"ok": False, "msg": "该股票无因子数据，请先入库（数据中台→全市场回填或指定入库）"}
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name = (sec.name if sec else m.name) or symbol
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# 规则打分（0~100）
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trend = 50 + (15 if m.ma_bull else -10) + min(20, max(-20, m.ret20))
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momentum = 50 + min(25, max(-25, m.ret5)) + (10 if 50 < m.rsi14 < 75 else (-10 if m.rsi14 >= 80 else 0))
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capital = 50 + min(30, (m.vol_ratio - 1) * 20) * (1 if m.pct > 0 else -1)
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position = 100 - abs(m.pos60 * 100 - 45) # 价格分位适中得分高
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clamp = lambda x: int(max(5, min(98, x)))
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scores = {"趋势": clamp(trend), "动量": clamp(momentum), "资金": clamp(capital), "位置": clamp(position)}
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total = clamp(sum(scores.values()) / 4)
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# 证据链 + 历史命中率 + RAG 资讯
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stats = signals.get_stats(horizon=5)
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rctx = rag.stock_context(symbol, limit=5)
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evidence = _build_evidence(m, stats, rctx["tone"])
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confidence, direction, net = _confidence_direction(evidence)
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# 留痕：本次诊断写入预测，N 日后核验
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try:
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signals.record_prediction(symbol, name, m.date, total, confidence, direction, m.close, horizon=5)
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except Exception:
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pass
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facts = (
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f"股票：{name}({symbol})\n"
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f"最新价：{m.close}，当日{m.pct:+.2f}%\n"
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f"均线：MA5={m.ma5} MA10={m.ma10} MA20={m.ma20} MA60={m.ma60}，{'多头排列' if m.ma_bull else '非多头'}\n"
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f"涨幅：5日{m.ret5:+.2f}% / 20日{m.ret20:+.2f}% / 60日{m.ret60:+.2f}%\n"
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f"量比：{m.vol_ratio}，RSI14：{m.rsi14}，60日价格分位：{m.pos60*100:.0f}%，"
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f"{'MACD金叉' if m.macd_gold else 'MACD未金叉'}，连涨{m.up_streak}日，成交额{m.amount}亿\n"
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)
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hit_lines = "\n".join(
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f"- {e['dim']}信号「{e['fact']}」历史5日上涨概率 {e['win_rate']}%、平均{e['avg_ret']:+.2f}%（样本{e['samples']}）"
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for e in evidence if e.get("win_rate") is not None)
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base = {"ok": True, "symbol": symbol, "name": name, "scores": scores, "total": total,
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"confidence": confidence, "direction": direction, "evidence": evidence,
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"news": rctx["items"], "news_tone": rctx["tone"], "facts": facts}
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if llm.enabled():
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try:
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prompt = ("请基于以下个股量化数据、信号历史命中率与相关资讯，输出结构化诊断：\n"
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"1) 一句话结论（含方向与置信判断）；2) 技术面/资金面/趋势面分别点评（引用历史命中率）；"
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"3) 操作建议（含参考性关注位与止损思路）；4) 主要风险。200~320字。\n\n"
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f"{facts}\n历史命中率：\n{hit_lines or '（暂无回测数据，请先在AI准确率页计算）'}\n\n"
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f"{rctx['block'] or '近期无相关资讯。'}\n")
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text = llm.ask(prompt, temperature=0.4)
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base.update({"source": "llm", "text": text})
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return base
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except Exception:
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pass
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judge = "偏强" if total >= 60 else ("偏弱" if total < 45 else "中性")
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dir_cn = {"up": "看多", "down": "看空", "flat": "中性观望"}[direction]
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text = (f"综合评分 {total}（{judge}），方向{dir_cn}，置信度 {confidence}%。"
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f"趋势{'多头向上' if m.ma_bull else '尚未走强'}，20日{m.ret20:+.1f}%；"
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f"动量 RSI {m.rsi14:.0f}{'，超买防回调' if m.rsi14>=80 else ''}；"
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f"资金{'放量' if m.vol_ratio>=1.5 else '量能一般'}（量比{m.vol_ratio}）；"
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f"价格处于60日 {m.pos60*100:.0f}% 分位；消息面{rctx['tone']}。\n"
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+ (f"依据信号历史表现：\n{hit_lines}\n" if hit_lines else "")
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+ f"操作：{'回踩不破MA20可关注' if m.ma_bull else '等待站稳均线再观察'}，跌破MA20或前低则减仓。"
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f"风险：{'高位放量谨防见顶' if m.pos60>0.9 else '大盘系统性波动'}。\n{DISCLAIMER}")
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base.update({"source": "rule", "text": text})
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return base
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# ============ 走势分析（右键K线） ============
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def trend_analysis(symbol: str, date: str, period: str = "daily"):
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"""分析某只股票在指定日期（或最新）附近暴涨/暴跌的原因。
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period: daily / weekly / monthly
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"""
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with get_session() as s:
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sec = s.get(Security, symbol)
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# 取该日期前后一段数据作为上下文
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if date:
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try:
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target_date = dt.date.fromisoformat(date)
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except Exception:
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target_date = None
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else:
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target_date = None
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# 取最近60根K线
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rows = s.execute(
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select(DailyQuote).where(DailyQuote.code == symbol)
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.order_by(DailyQuote.date.desc()).limit(60)
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).scalars().all()
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rows = list(reversed(rows))
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# 当日及相邻数据
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target_row = None
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if target_date and rows:
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# 找最近的一根
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closest = min(rows, key=lambda r: abs((r.date - target_date).days))
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if abs((closest.date - target_date).days) <= 7:
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target_row = closest
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if not target_row and rows:
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target_row = rows[-1]
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m = s.get(StockMetric, symbol)
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name = (sec.name if sec else (m.name if m else symbol)) or symbol
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# 计算目标K线的涨跌幅
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pct = 0.0
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if target_row and rows:
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idx = rows.index(target_row)
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if idx > 0:
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prev_close = rows[idx - 1].close
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if prev_close:
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pct = round((target_row.close - prev_close) / prev_close * 100, 2)
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# 拉取相关新闻
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import akshare_service as svc
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try:
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news_data = svc.get_stock_news(symbol, limit=10)
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news_items = news_data.get("list", [])
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except Exception:
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news_items = []
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# 拉取RAG上下文
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import rag
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rctx = rag.stock_context(symbol, limit=6)
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# 构造上下文数据
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period_cn = {"daily": "日K", "weekly": "周K", "monthly": "月K"}.get(period, "K线")
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date_str = target_row.date.isoformat() if target_row else (date or "最新")
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# 当日技术面
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if target_row:
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tech_line = (
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f"目标K线：{date_str}，开{target_row.open} 收{target_row.close} "
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f"高{target_row.high} 低{target_row.low}，"
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f"涨跌幅{pct:+.2f}%，成交量{target_row.volume:,}"
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)
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else:
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tech_line = f"目标日期：{date_str}，暂无日线数据"
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# 前后走势（最近5根）
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if target_row and rows:
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idx = rows.index(target_row)
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window = rows[max(0, idx-4):idx+2]
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trend_line = "前后走势：" + " → ".join(
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f"{r.date.strftime('%m/%d')}({'↑' if i == 0 or r.close >= rows[rows.index(r)-1].close else '↓'}{abs(round((r.close/rows[rows.index(r)-1].close-1)*100,1)) if rows.index(r) > 0 else 0}%)"
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for i, r in enumerate(window)
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)
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else:
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trend_line = ""
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# 均线状态
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ma_line = ""
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if m:
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ma_line = (f"均线状态：MA5={m.ma5} MA10={m.ma10} MA20={m.ma20} MA60={m.ma60}，"
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f"{'多头排列' if m.ma_bull else '非多头'}，"
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f"量比{m.vol_ratio}，RSI14={m.rsi14}")
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# 新闻摘要
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news_block = ""
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if news_items:
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news_block = "相关新闻（近期）：\n" + "\n".join(
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f"- [{n.get('time','')[:10]}] {n.get('title','')}" for n in news_items[:6]
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)
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# 判断是否暴涨/暴跌
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move_desc = ""
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if abs(pct) >= 5:
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move_desc = f"该股{'暴涨' if pct > 0 else '暴跌'} {abs(pct):.2f}%（{'接近/涨停' if pct >= 9.5 else '显著上涨' if pct > 0 else '接近/跌停' if pct <= -9.5 else '显著下跌'}）"
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elif abs(pct) >= 2:
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move_desc = f"该股{'上涨' if pct > 0 else '下跌'} {abs(pct):.2f}%"
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else:
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move_desc = f"该股小幅变动 {pct:+.2f}%"
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facts = f"""{name}（{symbol}）{period_cn}走势分析
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分析日期：{date_str}
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{move_desc}
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{tech_line}
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{trend_line}
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{ma_line}
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{news_block}
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消息面情绪：{rctx['tone']}
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{rctx['block'] or ''}"""
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if llm.enabled():
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try:
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prompt = (
|
||
f"请分析 {name}（{symbol}）在 {date_str} 前后{period_cn}的走势，"
|
||
f"重点解释：① 为什么{'暴涨' if pct >= 5 else ('暴跌' if pct <= -5 else '出现此走势')}（从技术面、资金面、政策面、新闻事件等维度）；"
|
||
f"② 背后的主要驱动逻辑是什么；③ 后续需关注的信号或风险。250字以内，分点清晰。\n\n{facts}"
|
||
)
|
||
text = llm.ask(prompt, temperature=0.5, max_tokens=600)
|
||
return {"ok": True, "source": "llm", "symbol": symbol, "name": name,
|
||
"date": date_str, "period": period, "pct": pct, "facts": facts, "text": text}
|
||
except Exception:
|
||
pass
|
||
|
||
# 规则降级
|
||
reasons = []
|
||
if m:
|
||
if m.ma_bull and pct > 0:
|
||
reasons.append("均线多头排列，趋势向上")
|
||
if m.vol_ratio >= 2 and pct > 0:
|
||
reasons.append(f"成交量显著放大（量比{m.vol_ratio}），主力资金介入")
|
||
if m.vol_ratio >= 2 and pct < 0:
|
||
reasons.append(f"放量下跌（量比{m.vol_ratio}），资金出逃信号")
|
||
if m.macd_gold and pct > 0:
|
||
reasons.append("MACD金叉，动能转强")
|
||
if m.rsi14 >= 80:
|
||
reasons.append(f"RSI超买（{m.rsi14}），注意回调风险")
|
||
if m.rsi14 < 30:
|
||
reasons.append(f"RSI超卖（{m.rsi14}），存在超跌反弹机会")
|
||
if m.pos60 >= 0.95 and pct > 0:
|
||
reasons.append("突破60日新高，动量突破")
|
||
if m.pos60 <= 0.1 and pct > 0:
|
||
reasons.append("低位反弹，超跌修复")
|
||
if rctx['tone'] == '利好':
|
||
reasons.append("近期资讯面偏利好")
|
||
elif rctx['tone'] == '利空':
|
||
reasons.append("近期资讯面偏利空")
|
||
if news_items:
|
||
hot_news = news_items[0]['title'][:40]
|
||
reasons.append(f"最新消息：{hot_news}…")
|
||
if not reasons:
|
||
reasons.append("暂无明确技术或消息面驱动，可能为市场情绪或板块联动")
|
||
|
||
text = (
|
||
f"{name} 在 {date_str} {move_desc}。\n"
|
||
f"主要原因分析：\n" +
|
||
"\n".join(f"{i+1}. {r}" for i, r in enumerate(reasons)) +
|
||
f"\n\n建议：{'关注量能是否持续配合，谨防高位回调。' if pct >= 5 else ('关注是否企稳止跌，底部确认前谨慎抄底。' if pct <= -5 else '走势相对平稳，跟踪板块动向。')}"
|
||
f"\n{DISCLAIMER}"
|
||
)
|
||
return {"ok": True, "source": "rule", "symbol": symbol, "name": name,
|
||
"date": date_str, "period": period, "pct": pct, "facts": facts, "text": text}
|
||
|
||
|
||
# ============ 今日策略 ============
|
||
def today_strategy():
|
||
with get_session() as s:
|
||
d = s.execute(select(func.max(SectorDaily.date))).scalar()
|
||
secs = s.execute(select(SectorDaily).where(SectorDaily.date == d).order_by(SectorDaily.pct.desc()).limit(5)).scalars().all() if d else []
|
||
flows = s.execute(select(FundFlowDaily).where(FundFlowDaily.date == d).order_by(FundFlowDaily.net.desc()).limit(5)).scalars().all() if d else []
|
||
strong = s.execute(select(StockMetric).where(StockMetric.up_streak >= 3).order_by(StockMetric.up_streak.desc()).limit(8)).scalars().all()
|
||
macd = s.execute(select(StockMetric).where(StockMetric.macd_gold.is_(True)).order_by(StockMetric.ret5.desc()).limit(8)).scalars().all()
|
||
facts = (
|
||
f"领涨板块：{', '.join(f'{x.name}({x.pct:+.2f}%)' for x in secs)}\n"
|
||
f"主力净流入板块：{', '.join(f'{x.name}({x.net:+.1f}亿)' for x in flows)}\n"
|
||
f"强势连涨个股：{', '.join(f'{x.name}({x.up_streak}连阳)' for x in strong)}\n"
|
||
f"MACD金叉个股：{', '.join(x.name for x in macd)}\n"
|
||
)
|
||
if llm.enabled():
|
||
try:
|
||
prompt = ("请基于以下盘面数据，给出『今天/接下来怎么做』的策略观点："
|
||
"① 当前资金与题材主线；② 值得关注的方向（给理由）；③ 需要回避的风险。"
|
||
"220字以内。\n\n" + facts)
|
||
text = llm.ask(prompt, temperature=0.6)
|
||
return {"ok": True, "source": "llm", "facts": facts, "text": text}
|
||
except Exception:
|
||
pass
|
||
main = "、".join(x.name for x in secs[:3]) or "暂无数据"
|
||
text = (f"当前资金主线集中在 {main}。"
|
||
f"可重点跟踪净流入居前的 {', '.join(x.name for x in flows[:3])} 板块内的强势个股；"
|
||
f"强势连涨股需注意分歧与退潮节奏，避免追高。\n{DISCLAIMER}")
|
||
return {"ok": True, "source": "rule", "facts": facts, "text": text}
|