wehub-resource-sync
348 lines
12 KiB
Python
348 lines
12 KiB
Python
from __future__ import annotations
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import json
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from dataclasses import dataclass
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from datetime import datetime, timezone
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from pathlib import Path
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from typing import Any
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import yfinance as yf
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from web.services.dashboard_store import append_history, load_latest, save_latest
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from web.services.dashboard_llm import build_llm_config, enrich_news_opinions, generate_market_digest
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from web.services.time_utils import format_et, market_status_text, now_et
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INDEX_TICKERS = {
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"SPY": "S&P 500 (SPY)",
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"QQQ": "Nasdaq 100 (QQQ)",
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"DIA": "Dow Jones (DIA)",
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"^VIX": "VIX",
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}
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INDEX_SUBTITLE_CN: dict[str, str] = {
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"SPY": "标普500指数 ETF",
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"QQQ": "纳斯达克100指数 ETF",
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"DIA": "道琼斯工业平均指数 ETF",
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"^VIX": "标普500波动率指数（恐慌指数）",
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}
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SECTOR_PROXIES = [
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"XLB", # Materials
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"XLC", # Communication Services
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"XLE", # Energy
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"XLF", # Financials
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"XLI", # Industrials
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"XLK", # Technology
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"XLP", # Consumer Staples
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"XLRE", # Real Estate
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"XLU", # Utilities
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"XLV", # Health Care
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"XLY", # Consumer Discretionary
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"SOXX", # Semiconductors (theme proxy)
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]
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SECTOR_SUBTITLE_CN: dict[str, str] = {
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"XLB": "原材料板块 ETF",
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"XLC": "通信服务板块 ETF",
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"XLE": "能源板块 ETF",
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"XLF": "金融板块 ETF",
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"XLI": "工业板块 ETF",
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"XLK": "信息技术板块 ETF",
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"XLP": "日常消费板块 ETF",
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"XLRE": "房地产板块 ETF",
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"XLU": "公用事业板块 ETF",
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"XLV": "医疗保健板块 ETF",
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"XLY": "可选消费板块 ETF",
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"SOXX": "半导体板块 ETF",
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}
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# Yahoo 可交易代理：美债收益率曲线、美元、通胀保值 ETF（非官方 CPI）
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MACRO_ENTRIES: list[tuple[str, str, str]] = [
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("^IRX", "美国13周国债收益率", "短期无风险利率锚，影响资金成本预期。"),
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("^FVX", "美国5年期国债收益率", "中期利率与增长预期参考。"),
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("^TNX", "美国10年期国债收益率", "全球资产定价常用贴现率与风险偏好锚。"),
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("^TYX", "美国30年期国债收益率", "长期通胀与期限溢价参考。"),
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("DX-Y.NYB", "美元指数", "美元相对一篮子货币强弱。"),
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("TIP", "通胀保值债券 ETF", "与通胀预期大致同向；非官方 CPI 读数。"),
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]
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CACHE_PATH = Path("data/cache/market_snapshot.json")
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@dataclass
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class QuoteRow:
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ticker: str
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label: str
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price: float | None
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prev_close: float | None
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change: float | None
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change_pct: float | None
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def _quote(symbol: str, label: str) -> QuoteRow:
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try:
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t = yf.Ticker(symbol)
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fast = t.fast_info
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price = float(fast.get("lastPrice")) if fast.get("lastPrice") is not None else None
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prev_close = (
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float(fast.get("previousClose")) if fast.get("previousClose") is not None else None
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)
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if price is not None and prev_close:
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change = price - prev_close
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change_pct = change / prev_close * 100
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else:
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change, change_pct = None, None
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return QuoteRow(symbol, label, price, prev_close, change, change_pct)
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except Exception:
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return QuoteRow(symbol, label, None, None, None, None)
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def get_macro_strip() -> list[dict[str, Any]]:
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rows: list[dict[str, Any]] = []
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for ticker, label, hint in MACRO_ENTRIES:
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q = _quote(ticker, label)
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if q.price is not None:
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if ticker.startswith("^"):
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display_value = f"{q.price:.2f}"
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else:
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display_value = f"{q.price:.2f}"
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else:
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display_value = "N/A"
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rows.append(
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{
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"ticker": ticker,
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"label": label,
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"hint": hint,
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"price": q.price,
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"change_pct": q.change_pct,
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"display_value": display_value,
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}
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)
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return rows
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def _format_published_at(item: dict[str, Any], content: dict[str, Any]) -> str:
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pub = content.get("pubDate")
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if pub:
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try:
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dt = datetime.fromisoformat(str(pub).replace("Z", "+00:00"))
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return dt.strftime("%Y-%m-%d %H:%M")
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except ValueError:
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pass
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ts = item.get("providerPublishTime")
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if ts is not None:
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try:
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dt = datetime.fromtimestamp(int(ts), tz=timezone.utc)
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return dt.strftime("%Y-%m-%d %H:%M")
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except (TypeError, ValueError, OSError):
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pass
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return ""
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def get_top_news(limit: int = 10) -> list[dict[str, Any]]:
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try:
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proxy = yf.Ticker("QQQ")
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raw = proxy.get_news(count=max(20, limit))
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except Exception:
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return []
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out: list[dict[str, Any]] = []
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for item in raw:
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content = item.get("content", {})
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title = content.get("title", "") or item.get("title", "")
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if not title:
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continue
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summary = content.get("summary", "")
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provider = (content.get("provider") or {}).get("displayName", "Unknown")
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url_obj = content.get("canonicalUrl") or content.get("clickThroughUrl") or {}
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link = url_obj.get("url", "") if isinstance(url_obj, dict) else ""
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published_at = _format_published_at(item, content)
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out.append(
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{
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"title": title,
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"summary": summary,
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"provider": provider,
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"link": link,
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"published_at": published_at,
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}
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)
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if len(out) >= limit:
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break
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return out
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def get_dashboard_market_snapshot() -> dict[str, Any]:
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index_rows = [_quote(tk, name) for tk, name in INDEX_TICKERS.items()]
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sectors = [_quote(tk, tk) for tk in SECTOR_PROXIES]
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sectors = [s for s in sectors if s.change_pct is not None]
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sectors.sort(key=lambda s: s.change_pct or -999, reverse=True)
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macro_strip = get_macro_strip()
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return {
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"indexes": index_rows,
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"top3_sectors": sectors[:3],
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"top6_sectors": sectors[:6],
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"macro_strip": macro_strip,
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"market_status": market_status_text(),
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"last_updated_et": format_et(now_et()),
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}
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def _quote_to_dict(q: QuoteRow) -> dict[str, Any]:
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return {
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"ticker": q.ticker,
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"label": q.label,
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"price": q.price,
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"prev_close": q.prev_close,
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"change": q.change,
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"change_pct": q.change_pct,
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}
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def _dict_to_quote(d: dict[str, Any]) -> QuoteRow:
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return QuoteRow(
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ticker=d.get("ticker", ""),
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label=d.get("label", ""),
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price=d.get("price"),
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prev_close=d.get("prev_close"),
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change=d.get("change"),
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change_pct=d.get("change_pct"),
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)
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def _snapshot_from_stored(snap: dict[str, Any]) -> dict[str, Any]:
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top6_raw = snap.get("top6_sectors")
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if top6_raw is None:
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top6_raw = snap.get("top3_sectors", [])
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return {
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"indexes": [_dict_to_quote(x) for x in snap.get("indexes", [])],
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"top3_sectors": [_dict_to_quote(x) for x in top6_raw[:3]],
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"top6_sectors": [_dict_to_quote(x) for x in top6_raw],
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"macro_strip": snap.get("macro_strip") or [],
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"market_status": snap.get("market_status", "N/A"),
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"last_updated_et": snap.get("last_updated_et", "N/A"),
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"market_digest_md": snap.get("market_digest_md") or "",
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"fetched_at_utc": snap.get("fetched_at_utc") or "",
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"llm_model": snap.get("llm_model") or "",
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"llm_provider": snap.get("llm_provider") or "",
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"llm_news_error": snap.get("llm_news_error") or "",
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"llm_digest_error": snap.get("llm_digest_error") or "",
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}
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def save_dashboard_cache(snapshot: dict[str, Any], news: list[dict[str, Any]]) -> None:
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CACHE_PATH.parent.mkdir(parents=True, exist_ok=True)
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payload = {
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"indexes": [_quote_to_dict(x) for x in snapshot.get("indexes", [])],
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"top3_sectors": [_quote_to_dict(x) for x in snapshot.get("top6_sectors", [])[:3]],
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"top6_sectors": [_quote_to_dict(x) for x in snapshot.get("top6_sectors", [])],
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"macro_strip": snapshot.get("macro_strip") or [],
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"market_digest_md": snapshot.get("market_digest_md") or "",
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"market_status": snapshot.get("market_status", "N/A"),
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"last_updated_et": snapshot.get("last_updated_et", "N/A"),
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"fetched_at_utc": snapshot.get("fetched_at_utc") or "",
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"llm_model": snapshot.get("llm_model") or "",
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"llm_provider": snapshot.get("llm_provider") or "",
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"llm_news_error": snapshot.get("llm_news_error") or "",
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"llm_digest_error": snapshot.get("llm_digest_error") or "",
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"news": news,
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}
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CACHE_PATH.write_text(json.dumps(payload, ensure_ascii=False, indent=2), encoding="utf-8")
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def load_dashboard_cache() -> tuple[dict[str, Any] | None, list[dict[str, Any]] | None]:
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if not CACHE_PATH.exists():
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return None, None
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try:
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raw = json.loads(CACHE_PATH.read_text(encoding="utf-8"))
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snapshot = _snapshot_from_stored(raw)
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return snapshot, raw.get("news", [])
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except Exception:
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return None, None
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def load_dashboard_display() -> tuple[dict[str, Any] | None, list[dict[str, Any]] | None]:
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"""优先 data/dashboard/latest.json，否则回退 data/cache/market_snapshot.json。"""
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latest = load_latest()
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if latest and latest.get("snapshot") is not None:
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snap = _snapshot_from_stored(latest["snapshot"])
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if not snap.get("fetched_at_utc") and latest.get("fetched_at_utc"):
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snap["fetched_at_utc"] = latest["fetched_at_utc"]
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if not snap.get("llm_model") and latest.get("llm_model"):
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snap["llm_model"] = latest["llm_model"]
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if not snap.get("llm_provider") and latest.get("llm_provider"):
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snap["llm_provider"] = latest["llm_provider"]
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return snap, latest.get("news", [])
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return load_dashboard_cache()
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def _build_store_payload(
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snapshot: dict[str, Any],
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news: list[dict[str, Any]],
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llm_model: str,
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fetched_at_utc: str,
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) -> dict[str, Any]:
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snap_out = {
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"indexes": [_quote_to_dict(x) for x in snapshot.get("indexes", [])],
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"top3_sectors": [_quote_to_dict(x) for x in snapshot.get("top6_sectors", [])[:3]],
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"top6_sectors": [_quote_to_dict(x) for x in snapshot.get("top6_sectors", [])],
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"macro_strip": snapshot.get("macro_strip") or [],
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"market_digest_md": snapshot.get("market_digest_md") or "",
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"market_status": snapshot.get("market_status", "N/A"),
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"last_updated_et": snapshot.get("last_updated_et", "N/A"),
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"fetched_at_utc": fetched_at_utc,
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"llm_model": llm_model,
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"llm_provider": snapshot.get("llm_provider") or "",
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"llm_news_error": snapshot.get("llm_news_error") or "",
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"llm_digest_error": snapshot.get("llm_digest_error") or "",
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}
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return {
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"snapshot": snap_out,
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"news": news,
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"llm_model": llm_model,
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"llm_provider": snapshot.get("llm_provider") or "",
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"fetched_at_utc": fetched_at_utc,
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}
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def refresh_dashboard_cache(
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limit: int = 10,
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) -> tuple[dict[str, Any], list[dict[str, Any]]]:
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"""拉取行情与新闻并调用仪表盘专用 LLM（始终 build_llm_config(None)，与个股分析 last_params 无关）。"""
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snapshot = get_dashboard_market_snapshot()
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news: list[dict[str, Any]] = get_top_news(limit=limit)
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cfg = build_llm_config(None)
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model_name = cfg.get("quick_think_llm", "")
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provider_name = str(cfg.get("llm_provider") or "")
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digest_md = ""
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news_err = ""
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digest_err = ""
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try:
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news = enrich_news_opinions(news, cfg)
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except Exception as e:
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news_err = f"{type(e).__name__}: {e}"[:500]
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for n in news:
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n.setdefault("stance", "unknown")
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n.setdefault("llm_summary", "")
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try:
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digest_md = generate_market_digest(snapshot, news, cfg)
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except Exception as e:
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digest_err = f"{type(e).__name__}: {e}"[:500]
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digest_md = ""
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fetched_at = datetime.now(timezone.utc).replace(microsecond=0).isoformat().replace("+00:00", "Z")
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snapshot["market_digest_md"] = digest_md
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snapshot["fetched_at_utc"] = fetched_at
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snapshot["llm_model"] = model_name
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snapshot["llm_provider"] = provider_name
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snapshot["llm_news_error"] = news_err
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snapshot["llm_digest_error"] = digest_err if not digest_md.strip() else ""
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save_dashboard_cache(snapshot, news)
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payload = _build_store_payload(snapshot, news, model_name, fetched_at)
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save_latest(payload)
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append_history(payload)
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return snapshot, news
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