v1.7.0: 每日定时报告(工作日9:00盘前分析/15:30盘后总结) - 全球市场数据+报告引擎(简版邮件正文+详细版HTML附件)+cron定时+手动触发+发送日志; 修复模拟数据换手率失真
This commit is contained in:
+17
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@@ -30,11 +30,25 @@ STRONG_WORDS = ["回购", "中标", "减持", "问询", "停牌", "重组", "预
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# ===================================================================== 邮件
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def send_email(subject, html_body, to=None, cfg=None, sender_name=None):
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"""发送 HTML 邮件。cfg 来自设置；失败抛异常（调用方捕获）"""
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def send_email(subject, html_body, to=None, cfg=None, sender_name=None, attachments=None):
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"""发送 HTML 邮件。cfg 来自设置；attachments: [{filename, content(bytes)}]；失败抛异常"""
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from email.mime.multipart import MIMEMultipart
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from email.mime.base import MIMEBase
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from email import encoders
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cfg = cfg or mail_config()
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to = to or cfg["email_to"]
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msg = MIMEText(html_body, "html", "utf-8")
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if attachments:
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msg = MIMEMultipart()
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msg.attach(MIMEText(html_body, "html", "utf-8"))
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for att in attachments:
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part = MIMEBase("application", "octet-stream")
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part.set_payload(att.get("content") or b"")
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encoders.encode_base64(part)
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part.add_header("Content-Disposition", "attachment",
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filename=("utf-8", "", att.get("filename", "report.html")))
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msg.attach(part)
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else:
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msg = MIMEText(html_body, "html", "utf-8")
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msg["From"] = formataddr((sender_name or cfg["sender_name"], cfg["smtp_user"]))
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msg["To"] = to
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msg["Subject"] = subject
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@@ -0,0 +1,463 @@
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# -*- coding: utf-8 -*-
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"""
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每日行情报告引擎（盘前 / 盘后）
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- premarket : 工作日 9:00 —— 昨日市场回顾 / 昨日至今要闻 / 全球市场 / 持仓与关注目标 / 盘前研判
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- postmarket : 交易日 15:30 —— 今日市场总结 / 今日要闻 / 全球市场 / 持仓表现 / 盘后研判
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每期输出两份报告：
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简单版 —— 邮件正文（HTML，快速浏览）
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详细版 —— HTML 附件（完整结构化 + AI 深度解读）
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"""
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import datetime as dt
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import html as html_mod
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import json
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import logging
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import time
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from database import query, query_one, execute
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from settings import mail_config
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from engine.analyst import llm_chat
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log = logging.getLogger("report")
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GLOBAL_ORDER = ["dji", "nasdaq", "sp500", "hsi", "nikkei", "kospi", "dax", "cac", "ftse"]
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KIND_META = {
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"premarket": {"name": "盘前分析", "scope": "昨日与今日", "title": "盘前 · 昨日市场回顾与今日展望"},
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"postmarket": {"name": "盘后总结", "scope": "今日", "title": "盘后 · 今日市场总结"},
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}
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# ===================================================================== 数据采集
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def latest_trading_day():
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r = query_one("SELECT MAX(date) d FROM stock_daily")
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return r["d"] if r else dt.date.today().isoformat()
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def collect_market(day):
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"""指数 / 涨跌 / 量能 / 行业 / 个股"""
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idx = query("SELECT * FROM market_index WHERE date<=? ORDER BY date DESC LIMIT 2", (day,))
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latest = idx[0] if idx else {}
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prev = idx[1] if len(idx) > 1 else latest
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inds = []
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for k, label in (("sh", "上证指数"), ("sz", "深证成指"), ("cy", "创业板指")):
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cur, old = latest.get(k, 0), prev.get(k, 0) or 1
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inds.append({"key": k, "label": label, "value": cur,
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"chg": round((cur - old) / old * 100, 2)})
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stat = query_one(
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"SELECT COUNT(*) total, SUM(CASE WHEN change_pct>0 THEN 1 ELSE 0 END) up,"
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"SUM(CASE WHEN change_pct<0 THEN 1 ELSE 0 END) down,"
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"SUM(CASE WHEN change_pct>=9.8 THEN 1 ELSE 0 END) limit_up,"
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"SUM(CASE WHEN change_pct<=-9.8 THEN 1 ELSE 0 END) limit_down,"
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"ROUND(SUM(amount)/10000,2) amount_yi "
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"FROM stock_daily WHERE date=?", (day,))
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heat = query(
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"SELECT s.industry, ROUND(AVG(d.change_pct),2) chg, COUNT(*) cnt "
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"FROM stock_daily d JOIN stocks s ON s.code=d.code WHERE d.date=? "
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"GROUP BY s.industry ORDER BY chg DESC", (day,))
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gainers = query(
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"SELECT s.name, s.code, s.industry, d.change_pct FROM stock_daily d "
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"JOIN stocks s ON s.code=d.code WHERE d.date=? ORDER BY d.change_pct DESC LIMIT 8", (day,))
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losers = query(
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"SELECT s.name, s.code, s.industry, d.change_pct FROM stock_daily d "
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"JOIN stocks s ON s.code=d.code WHERE d.date=? ORDER BY d.change_pct ASC LIMIT 8", (day,))
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return {"date": day, "indexes": inds, "stat": stat, "heat": heat,
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"gainers": gainers, "losers": losers}
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def collect_news(since_date, limit=20):
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rows = query(
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"SELECT id,title,content,source,category,publish_date,sentiment,related_stocks FROM news "
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"WHERE publish_date>=? ORDER BY publish_date DESC, id DESC LIMIT ?", (since_date, limit))
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# 按重要度排序（类别权重 + 情感强度）
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w = {"公司": 3, "业绩": 3, "机构观点": 2, "行业": 2, "市场": 1}
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for n in rows:
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n["_score"] = w.get(n["category"], 1) * 10 + abs(n["sentiment"]) * 5
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rows.sort(key=lambda x: x["_score"], reverse=True)
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return rows
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def collect_positions():
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rows = query(
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"SELECT w.code, s.name, s.industry, s.market_cap, d.close, d.change_pct "
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"FROM watchlist w JOIN stocks s ON s.code=w.code "
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"LEFT JOIN stock_daily d ON d.code=s.code AND d.date=(SELECT MAX(date) FROM stock_daily) "
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"ORDER BY w.added_at")
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out = []
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for r in rows:
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sc = query_one(
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"SELECT AVG(sentiment) s FROM news WHERE (related_stocks=? OR related_stocks LIKE ? OR related_stocks LIKE ?) "
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"AND publish_date>=date('now','-7 day')", (r["code"], f"%,{r['code']}", f"{r['code']},%"))
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out.append({**r, "news_score": round(sc["s"], 2) if sc and sc["s"] is not None else 0})
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return out
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def collect_targets():
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tgts = query("SELECT id, type, code, name, keywords FROM watch_targets WHERE enabled=1")
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out = []
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for t in tgts:
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if t["type"] == "stock" and t["code"]:
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latest = query_one(
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"SELECT meta, created_at FROM tracking_reports WHERE code=? ORDER BY id DESC LIMIT 1", (t["code"],))
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else:
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latest = query_one(
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"SELECT meta, created_at FROM tracking_reports WHERE code=? ORDER BY id DESC LIMIT 1",
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(f"CONCEPT:{t['name']}",))
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m = json.loads(latest["meta"]) if latest else {}
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out.append({"type": t["type"], "name": t["name"],
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"impact": m.get("impact_score"), "change_kind": m.get("change_kind"),
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"summary": m.get("summary", ""), "tracked_at": latest["created_at"] if latest else None})
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return out
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def collect_global():
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r = query_one("SELECT date, data FROM global_markets ORDER BY date DESC LIMIT 1")
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if not r:
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return []
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try:
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data = json.loads(r["data"])
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except Exception:
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return []
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items = []
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for k in GLOBAL_ORDER:
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if k in data:
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items.append(data[k])
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return items
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# ===================================================================== 文本渲染
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def fmt_market(mkt):
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s = mkt["stat"] or {}
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idx_txt = " ".join(f"{i['label']} {i['value']:.2f} ({i['chg']:+.2f}%)" for i in mkt["indexes"])
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heat_txt = "、".join(f"{h['industry']}({h['chg']:+.2f}%)" for h in mkt["heat"][:6]) or "无"
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g_txt = "、".join(f"{g['name']}({g['change_pct']:+.2f}%)" for g in mkt["gainers"][:5])
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l_txt = "、".join(f"{g['name']}({g['change_pct']:+.2f}%)" for g in mkt["losers"][:5])
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return {
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"idx": idx_txt,
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"breadth": (f"上涨 {s.get('up',0)} / 下跌 {s.get('down',0)} 家，"
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f"涨停 {s.get('limit_up',0)} / 跌停 {s.get('limit_down',0)}，"
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f"两市成交 {s.get('amount_yi',0)} 亿"),
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"heat": heat_txt,
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"gainers": g_txt or "无",
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"losers": l_txt or "无",
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}
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def fmt_news(news, top=8):
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lines = []
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for n in news[:top]:
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tone = "利好" if n["sentiment"] > 0 else ("利空" if n["sentiment"] < 0 else "中性")
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lines.append(f"- [{n['publish_date']}] {n['title']}（{n['category']}·{tone}{n['sentiment']:+.2f}）{n['content'][:60]}")
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return "\n".join(lines) or "（暂无）"
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def fmt_positions(pos):
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if not pos:
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return "（当前无持仓/自选股）"
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return "\n".join(
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f"- {p['name']}({p['code']}) {p['industry']} 收盘{p['close']} ({p['change_pct']:+.2f}%) 市值{p['market_cap']:.0f}亿 近7日消息面{p['news_score']:+.2f}"
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for p in pos)
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def fmt_targets(tgts):
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if not tgts:
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return "（当前无跟踪目标）"
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return "\n".join(
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f"- [{t['type']}] {t['name']} 影响度{t['impact'] or '--'}/100 {t['change_kind'] or ''} {t['summary'][:50]}"
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for t in tgts)
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def fmt_global(items):
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return " ".join(f"{g.get('label','')} {g.get('value',0):.2f} ({g.get('chg',0):+.2f}%)" for g in items) or "（暂无）"
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# ===================================================================== 生成报告
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def _build_context(kind):
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day = latest_trading_day()
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mkt = collect_market(day)
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fm = fmt_market(mkt)
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if kind == "premarket":
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news = collect_news(day, limit=24)
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scope_txt = "昨日/最近交易日"
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else:
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news = collect_news(day, limit=24)
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scope_txt = "今日"
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pos = collect_positions()
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tgts = collect_targets()
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glob = collect_global()
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ctx = {
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"kind_name": KIND_META[kind]["name"],
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"date": day,
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"scope": scope_txt,
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"mkt": mkt, "fm": fm,
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"news": news, "news_txt": fmt_news(news, 10),
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"pos": pos, "pos_txt": fmt_positions(pos),
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"tgts": tgts, "tgts_txt": fmt_targets(tgts),
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"global_txt": fmt_global(glob),
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"global": glob,
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}
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return ctx
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def _base_prompt(ctx, detailed):
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d = ctx["date"]
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title = KIND_META[ctx["kind_name"] if ctx["kind_name"] in KIND_META else "premarket"]["title"] if False else ""
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kind = "盘前分析" if "盘前" in ctx["kind_name"] else "盘后总结"
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return f"""你是资深A股市场分析师，请基于下方【数据】生成一份{kind}报告。
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【报告日期】{d}
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【指数】{ctx['fm']['idx']}
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【涨跌结构】{ctx['fm']['breadth']}
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【领涨行业】{ctx['fm']['heat']}
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【领涨个股】{ctx['fm']['gainers']}
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【领跌个股】{ctx['fm']['losers']}
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【重点要闻】
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{ctx['news_txt']}
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【全球市场】
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{ctx['global_txt']}
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【持仓/自选股】
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{ctx['pos_txt']}
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【关注目标/主题】
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{ctx['tgts_txt']}
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"""
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def _brief_prompt(ctx):
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return _base_prompt(ctx, False) + """
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【输出要求】输出一份精炼的盘前/盘后速览（约 200-300 字），Markdown 格式，包含：
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1. 一句话大盘研判
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2. 3-5 条关键要点（行情/消息/持仓/主题）
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3. 今日关注提示
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要求信息密集、数据准确，不要编造数据。"""
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def _detail_prompt(ctx):
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return _base_prompt(ctx, True) + """
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【输出要求】输出一份完整的盘前/盘后分析报告（Markdown），结构如下：
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## 一、市场概览（指数表现/涨跌结构/量能/领涨领跌板块个股解读）
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## 二、消息面解析（分类解读重点要闻及影响：政策/行业/公司/机构观点）
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## 三、全球市场联动（外围市场表现及对A股的传导）
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## 四、持仓表现（逐只点评：涨跌、评分依据、近期消息面）
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## 五、关注目标/主题（各主题/个股的最新动态与影响度解读）
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## 六、操作策略与风险提示
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数据须严格来自上文【数据】，可补充合理分析逻辑，不得编造数字。"""
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def generate_reports(kind):
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"""生成 (brief_html, detail_html)"""
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ctx = _build_context(kind)
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brief_md = ""
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detail_md = ""
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try:
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brief_md = llm_chat([
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{"role": "system", "content": "你是一名严谨专业的A股市场分析师。"},
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{"role": "user", "content": _brief_prompt(ctx)},
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]).strip()
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except Exception as e:
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log.warning("brief llm fail: %s", e)
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try:
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detail_md = llm_chat([
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{"role": "system", "content": "你是一名严谨专业的A股市场分析师。"},
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{"role": "user", "content": _detail_prompt(ctx)},
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]).strip()
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except Exception as e:
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log.warning("detail llm fail: %s", e)
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brief_html = _render_brief(ctx, brief_md)
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detail_html = _render_detail(ctx, detail_md)
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return brief_html, detail_html
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# ===================================================================== 渲染
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def _render_brief(ctx, brief_md):
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kind = KIND_META[ctx["kind_name"] if ctx["kind_name"] in KIND_META else "premarket"]
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rows = []
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for i in ctx["mkt"]["indexes"]:
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rows.append(f"<b style='color:{'#e03e3e' if i['chg']>=0 else '#17a34a'}'>{i['label']} {i['value']:.2f} ({i['chg']:+.2f}%)</b>")
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news_li = "".join(f"<li>[{n['publish_date']}] {html_mod.escape(n['title'])} <span style='color:#888'>({n['category']})</span></li>"
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for n in ctx["news"][:6]) or "<li>暂无</li>"
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pos_li = "".join(f"<li><b>{p['name']}</b>({p['code']}) 收{p['close']} "
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f"<b style='color:{'#e03e3e' if p['change_pct']>=0 else '#17a34a'}'>{p['change_pct']:+.2f}%</b> · {p['industry']}</li>"
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for p in ctx["pos"]) or "<li>暂无持仓</li>"
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tgt_li = "".join(f"<li>{html_mod.escape(t['name'])}（影响度{t['impact'] or '--'}，{t['change_kind'] or '—'}）</li>"
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for t in ctx["tgts"][:5]) or "<li>暂无目标</li>"
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gb = " ｜ ".join(f"{g.get('label','')} {g.get('value',0):.2f} "
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f"<b style='color:{'#e03e3e' if g.get('chg',0)>=0 else '#17a34a'}'>({g.get('chg',0):+.2f}%)</b>" for g in ctx["global"][:6])
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ai = html_mod.escape(brief_md) if brief_md else "（AI 简评生成失败，请查看附件详细版）"
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return f"""<html><body style="font-family:Microsoft YaHei,Arial;background:#f5f6f8;padding:20px;">
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<div style="max-width:680px;margin:auto;background:#fff;border-radius:8px;border:1px solid #e5e7eb;overflow:hidden;">
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<div style="background:#1e293b;color:#fff;padding:16px 22px;">
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<div style="font-size:20px;font-weight:bold;">📊 智能荐股 · {kind['name']}</div>
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<div style="font-size:12px;opacity:.8;margin-top:4px;">{ctx['date']} · 简版速览 · 详细版见附件</div>
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</div>
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<div style="padding:18px 22px;">
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<div style="font-size:15px;color:#333;margin-bottom:6px;">🔎 大盘：</div>
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<div style="font-size:15px;">{' '.join(rows)}</div>
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<div style="color:#666;font-size:13px;margin-top:4px;">{ctx['fm']['breadth']}</div>
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<div style="color:#666;font-size:13px;margin-top:4px;"><b>领涨行业：</b>{ctx['fm']['heat']}</div>
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<div style="margin:14px 0;border-top:1px solid #eee;"></div>
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<div style="font-size:14px;color:#333;margin-bottom:6px;">📰 重点要闻：</div>
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||||
<ul style="color:#444;font-size:13px;padding-left:20px;line-height:1.8;">{news_li}</ul>
|
||||
<div style="margin:14px 0;border-top:1px solid #eee;"></div>
|
||||
<div style="font-size:14px;color:#333;margin-bottom:6px;">🌏 全球市场：</div>
|
||||
<div style="color:#444;font-size:13px;">{gb}</div>
|
||||
<div style="margin:14px 0;border-top:1px solid #eee;"></div>
|
||||
<div style="font-size:14px;color:#333;margin-bottom:6px;">💼 持仓：</div>
|
||||
<ul style="color:#444;font-size:13px;padding-left:20px;line-height:1.8;">{pos_li}</ul>
|
||||
<div style="margin:14px 0;border-top:1px solid #eee;"></div>
|
||||
<div style="font-size:14px;color:#333;margin-bottom:6px;">🎯 关注目标/主题：</div>
|
||||
<ul style="color:#444;font-size:13px;padding-left:20px;line-height:1.8;">{tgt_li}</ul>
|
||||
<div style="margin:14px 0;border-top:1px solid #eee;"></div>
|
||||
<div style="font-size:14px;color:#333;margin-bottom:6px;">🤖 AI 研判：</div>
|
||||
<div style="color:#333;font-size:13px;line-height:1.8;white-space:pre-wrap;">{ai}</div>
|
||||
</div>
|
||||
<div style="background:#f8fafc;padding:10px 22px;color:#94a3b8;font-size:11px;text-align:center;">
|
||||
智能荐股系统自动生成 · 内容基于模拟数据，仅供演示，不构成投资建议
|
||||
</div></div></body></html>"""
|
||||
|
||||
|
||||
def _md_to_html(md):
|
||||
"""极简 Markdown → HTML（用于附件详细版）"""
|
||||
md = html_mod.escape(md or "")
|
||||
out, in_list = [], False
|
||||
for line in md.splitlines():
|
||||
line = line.rstrip()
|
||||
if not line:
|
||||
if in_list:
|
||||
out.append("</ul>"); in_list = False
|
||||
continue
|
||||
if line.startswith("## "):
|
||||
if in_list:
|
||||
out.append("</ul>"); in_list = False
|
||||
out.append(f"<h2>{line[3:]}</h2>")
|
||||
elif line.startswith("### "):
|
||||
if in_list:
|
||||
out.append("</ul>"); in_list = False
|
||||
out.append(f"<h3>{line[4:]}</h3>")
|
||||
elif line.startswith("##"):
|
||||
if in_list:
|
||||
out.append("</ul>"); in_list = False
|
||||
out.append(f"<h2>{line[2:].strip()}</h2>")
|
||||
elif line.startswith("- "):
|
||||
if not in_list:
|
||||
out.append("<ul>"); in_list = True
|
||||
out.append(f"<li>{line[2:]}</li>")
|
||||
elif line.startswith("# "):
|
||||
if in_list:
|
||||
out.append("</ul>"); in_list = False
|
||||
out.append(f"<h1>{line[2:]}</h1>")
|
||||
else:
|
||||
if in_list:
|
||||
out.append("</ul>"); in_list = False
|
||||
out.append(f"<p>{line}</p>")
|
||||
if in_list:
|
||||
out.append("</ul>")
|
||||
return "".join(out)
|
||||
|
||||
|
||||
def _render_detail(ctx, detail_md):
|
||||
kind = KIND_META[ctx["kind_name"] if ctx["kind_name"] in KIND_META else "premarket"]
|
||||
idx_rows = "".join(
|
||||
f"<tr><td>{i['label']}</td><td>{i['value']:.2f}</td>"
|
||||
f"<td style='color:{'#e03e3e' if i['chg']>=0 else '#17a34a'}'>{i['chg']:+.2f}%</td></tr>"
|
||||
for i in ctx["mkt"]["indexes"])
|
||||
stat = ctx["mkt"]["stat"] or {}
|
||||
heat_rows = "".join(f"<tr><td>{h['industry']}</td><td>{h['cnt']}</td>"
|
||||
f"<td style='color:{'#e03e3e' if h['chg']>=0 else '#17a34a'}'>{h['chg']:+.2f}%</td></tr>"
|
||||
for h in ctx["mkt"]["heat"])
|
||||
g_rows = "".join(f"<tr><td>{g['name']}</td><td>{g['code']}</td>"
|
||||
f"<td style='color:{'#e03e3e' if g['change_pct']>=0 else '#17a34a'}'>{g['change_pct']:+.2f}%</td></tr>"
|
||||
for g in ctx["mkt"]["gainers"])
|
||||
l_rows = "".join(f"<tr><td>{g['name']}</td><td>{g['code']}</td>"
|
||||
f"<td style='color:{'#e03e3e' if g['change_pct']>=0 else '#17a34a'}'>{g['change_pct']:+.2f}%</td></tr>"
|
||||
for g in ctx["mkt"]["losers"])
|
||||
news_rows = "".join(
|
||||
f"<tr><td>{n['publish_date']}</td><td>{n['category']}</td><td>{html_mod.escape(n['title'])}</td>"
|
||||
f"<td style='color:{'#e03e3e' if n['sentiment']>=0 else '#17a34a'}'>{n['sentiment']:+.2f}</td></tr>"
|
||||
for n in ctx["news"][:15])
|
||||
pos_rows = "".join(
|
||||
f"<tr><td><b>{p['name']}</b>{p['code']}</td><td>{p['industry']}</td><td>{p['close']}</td>"
|
||||
f"<td style='color:{'#e03e3e' if p['change_pct']>=0 else '#17a34a'}'>{p['change_pct']:+.2f}%</td>"
|
||||
f"<td>{p['market_cap']:.0f}亿</td><td>{p['news_score']:+.2f}</td></tr>"
|
||||
for p in ctx["pos"]) or "<tr><td colspan='6'>暂无持仓</td></tr>"
|
||||
gb_rows = "".join(f"<tr><td>{g.get('label','')}</td><td>{g.get('value',0):.2f}</td>"
|
||||
f"<td style='color:{'#e03e3e' if g.get('chg',0)>=0 else '#17a34a'}'>{g.get('chg',0):+.2f}%</td></tr>"
|
||||
for g in ctx["global"])
|
||||
body = _md_to_html(detail_md) if detail_md else "<p>（AI 分析生成失败）</p>"
|
||||
return f"""<!DOCTYPE html><html lang="zh-CN"><head><meta charset="UTF-8">
|
||||
<title>智能荐股 · {kind['name']} {ctx['date']}</title>
|
||||
<style>
|
||||
body{{font-family:Microsoft YaHei,Arial,sans-serif;background:#f5f6f8;padding:24px;color:#333;line-height:1.8;}}
|
||||
.wrap{{max-width:820px;margin:auto;background:#fff;border:1px solid #e5e7eb;border-radius:10px;overflow:hidden;}}
|
||||
.head{{background:#1e293b;color:#fff;padding:20px 28px;}}
|
||||
.head h1{{margin:0;font-size:22px;}}
|
||||
.head .sub{{font-size:12px;opacity:.8;margin-top:4px;}}
|
||||
.body{{padding:20px 28px;}}
|
||||
h2{{border-bottom:2px solid #eef2f7;padding-bottom:8px;margin-top:28px;color:#1e293b;font-size:18px;}}
|
||||
h3{{color:#334155;margin-top:18px;}}
|
||||
table{{width:100%;border-collapse:collapse;margin:10px 0;font-size:13px;}}
|
||||
th,td{{border:1px solid #e5e7eb;padding:7px 10px;text-align:left;}}
|
||||
th{{background:#f8fafc;color:#475569;}}
|
||||
.up{{color:#e03e3e;}}.down{{color:#17a34a;}}
|
||||
.card{{background:#f8fafc;border:1px solid #e5e7eb;border-radius:8px;padding:14px 16px;margin:12px 0;font-size:13px;}}
|
||||
.foot{{background:#f8fafc;padding:12px 28px;color:#94a3b8;font-size:11px;text-align:center;}}
|
||||
</style></head><body><div class="wrap">
|
||||
<div class="head">
|
||||
<h1>📊 智能荐股 · {kind['name']}（{ctx['date']}）</h1>
|
||||
<div class="sub">市场/要闻/全球/持仓/主题 全景分析 · 详细版报告 · 自动生成</div>
|
||||
</div>
|
||||
<div class="body">
|
||||
<h2>〇、数据总览</h2>
|
||||
<div class="card"><b>指数</b><table><tr><th>指数</th><th>收盘</th><th>涨跌</th></tr>{idx_rows}</table>
|
||||
<b>涨跌结构</b>：{ctx['fm']['breadth']}</div>
|
||||
<div class="card"><b>行业热度</b><table><tr><th>行业</th><th>家数</th><th>平均涨跌</th></tr>{heat_rows}</table></div>
|
||||
<div class="card"><b>领涨个股</b><table><tr><th>名称</th><th>代码</th><th>涨跌</th></tr>{g_rows}</table>
|
||||
<b>领跌个股</b><table><tr><th>名称</th><th>代码</th><th>涨跌</th></tr>{l_rows}</table></div>
|
||||
<h2>重点要闻</h2>
|
||||
<table><tr><th>日期</th><th>分类</th><th>标题</th><th>情感</th></tr>{news_rows}</table>
|
||||
<h2>全球市场</h2>
|
||||
<table><tr><th>指数</th><th>点位</th><th>涨跌</th></tr>{gb_rows}</table>
|
||||
<h2>持仓 / 自选股</h2>
|
||||
<table><tr><th>股票</th><th>行业</th><th>收盘</th><th>涨跌</th><th>市值</th><th>消息面</th></tr>{pos_rows}</table>
|
||||
<h2>关注目标 / 主题</h2>
|
||||
{html_mod.escape(ctx['tgts_txt']).replace(chr(10), '<br>')}
|
||||
<h2>AI 深度分析</h2>
|
||||
{body}
|
||||
</div>
|
||||
<div class="foot">智能荐股系统自动生成 · 内容基于模拟数据，仅供演示，不构成投资建议</div>
|
||||
</div></body></html>"""
|
||||
|
||||
|
||||
# ===================================================================== 发送
|
||||
def send_daily_report(kind="premarket"):
|
||||
"""生成并发送报告：正文=简版，附件=详细版 HTML。返回 dict 状态"""
|
||||
from engine.notifier import send_email
|
||||
mc = mail_config()
|
||||
brief_html, detail_html = generate_reports(kind)
|
||||
meta = KIND_META.get(kind, KIND_META["premarket"])
|
||||
subject = f"[智能荐股] {meta['name']} {time.strftime('%Y-%m-%d')}"
|
||||
detail_file = f"智能荐股_{meta['name']}_{time.strftime('%Y%m%d')}.html"
|
||||
try:
|
||||
send_email(subject, brief_html, cfg=mc,
|
||||
attachments=[{"filename": detail_file, "content": detail_html.encode("utf-8")}])
|
||||
execute("INSERT INTO report_log(kind, subject, brief_len, detail_len, status, message) "
|
||||
"VALUES(?,?,?,?,'sent','附件: '||?)",
|
||||
(kind, subject, len(brief_html), len(detail_html), detail_file))
|
||||
return {"ok": True, "subject": subject, "detail_file": detail_file}
|
||||
except Exception as e:
|
||||
execute("INSERT INTO report_log(kind, subject, brief_len, detail_len, status, message) "
|
||||
"VALUES(?,?,?,?,'failed',?)",
|
||||
(kind, subject, len(brief_html), len(detail_html), str(e)))
|
||||
return {"ok": False, "error": str(e)}
|
||||
|
||||
|
||||
def report_log(limit=20):
|
||||
return query("SELECT * FROM report_log ORDER BY id DESC LIMIT ?", (limit,))
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
import sys
|
||||
kind = sys.argv[1] if len(sys.argv) > 1 else "premarket"
|
||||
if kind not in ("premarket", "postmarket"):
|
||||
kind = "premarket"
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
r = send_daily_report(kind)
|
||||
print(r)
|
||||
Reference in New Issue
Block a user