refactor: establish standalone application boundary
This commit is contained in:
leefer
@@ -0,0 +1,644 @@
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from __future__ import annotations
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from collections import Counter
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from datetime import datetime, time as dt_time, timedelta
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from typing import Any
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from backend.bootstrap.config import display_compact_date as _display_date
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from backend.data.numbers import finite_number as _number
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from backend.features.sentiment.engine import apply_sentiment_to_dashboard
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from backend.data.providers.tushare_helpers import (
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_realtime_market_status,
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_trading_session_progress,
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_value_percentile,
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)
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from backend.data.providers.tushare_transport import TushareError
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class DashboardMixin:
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def dashboard(self, requested_date: str) -> dict[str, Any]:
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trade_date, previous_trade_date = self.resolve_trade_context(requested_date)
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if self.should_use_realtime(requested_date, trade_date):
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return self._realtime_dashboard(
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requested_date,
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trade_date,
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previous_trade_date,
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)
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daily = self._load_daily(trade_date)
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if (
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not daily
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and requested_date == datetime.now().astimezone().strftime("%Y%m%d")
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and trade_date == requested_date
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and datetime.now().astimezone().time().replace(tzinfo=None) >= dt_time(9, 15)
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):
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return self._realtime_dashboard(
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requested_date,
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trade_date,
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previous_trade_date,
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)
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if not daily:
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raise TushareError(f"No daily data returned for {trade_date}")
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notices: list[str] = []
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try:
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limit_rows = self._load_limit_lists(trade_date)
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previous_limit_rows = self._load_limit_type(previous_trade_date, "U")
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if not limit_rows:
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notices.append("涨跌停高级接口当日数据尚未更新，已使用日线数据推算。")
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limit_rows = self._derive_limits(trade_date, daily)
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except TushareError as exc:
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notices.append(f"涨跌停高级接口不可用，已使用日线数据推算：{exc}")
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limit_rows = self._derive_limits(trade_date, daily)
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previous_daily = self._load_daily(previous_trade_date)
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previous_limit_rows = [
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row for row in self._derive_limits(previous_trade_date, previous_daily)
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if row.get("limit_type") == "U"
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]
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up_rows = [row for row in limit_rows if row.get("limit_type") == "U"]
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down_rows = [row for row in limit_rows if row.get("limit_type") == "D"]
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broken_rows = [row for row in limit_rows if row.get("limit_type") == "Z"]
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limits = [self._normalize_limit(row, "涨停") for row in up_rows]
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broken = [self._normalize_limit(row, "炸板") for row in broken_rows]
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down_limits = [self._normalize_limit(row, "跌停") for row in down_rows]
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previous_limits = [self._normalize_limit(row, "涨停") for row in previous_limit_rows]
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yesterday_limits = _build_yesterday_performance(
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previous_limits,
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daily,
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limits,
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broken,
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down_limits,
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)
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sectors = _build_sectors(limits)
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previous_sectors = _build_sectors(previous_limits)
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dashboard = {
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"meta": {
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"requested_date": _display_date(requested_date),
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"trade_date": _display_date(trade_date),
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"previous_trade_date": _display_date(previous_trade_date),
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"source": "tushare",
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"updated_at": datetime.now().astimezone().isoformat(timespec="seconds"),
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"notice": "；".join(notices),
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},
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"overview": _build_overview(daily, up_rows, down_rows, broken_rows),
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"limits": limits,
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"broken": broken,
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"down_limits": down_limits,
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"yesterday_limits": yesterday_limits,
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"limit_performance": _build_limit_performance(yesterday_limits),
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"ladders": _build_ladders(limits),
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"sectors": sectors,
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"sector_rotation": _build_sector_rotation(sectors, previous_sectors),
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}
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return apply_sentiment_to_dashboard(dashboard)
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@staticmethod
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def should_use_realtime(requested_date: str, trade_date: str) -> bool:
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"""Use rt_k for today's open market until end-of-day datasets settle."""
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now = datetime.now().astimezone()
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today = now.strftime("%Y%m%d")
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return (
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requested_date == today
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and trade_date == today
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and dt_time(9, 15) <= now.time().replace(tzinfo=None) < dt_time(16, 30)
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)
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def _realtime_dashboard(
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self,
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requested_date: str,
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trade_date: str,
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previous_trade_date: str,
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) -> dict[str, Any]:
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reference = self._load_realtime_reference(trade_date, previous_trade_date)
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basic_rows = list(reference["basic_rows"])
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codes = ",".join(
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str(row.get("ts_code") or "") for row in basic_rows if row.get("ts_code")
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)
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if not codes:
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raise TushareError("No active stock codes available for rt_k")
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quotes = self.query("rt_k", {"ts_code": codes})
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if not quotes:
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raise TushareError(f"No realtime data returned for {trade_date}")
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basic_map = {str(row.get("ts_code") or ""): row for row in basic_rows}
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daily: list[dict[str, Any]] = []
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for quote in quotes:
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close = _number(quote.get("close"))
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previous_close = _number(quote.get("pre_close"))
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if close <= 0 or previous_close <= 0:
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continue
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basic = basic_map.get(str(quote.get("ts_code") or ""), {})
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daily.append(
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{
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**quote,
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"trade_date": trade_date,
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"name": str(quote.get("name") or basic.get("name") or "--").strip(),
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"industry": basic.get("industry") or "其他",
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"pct_chg": round((close / previous_close - 1) * 100, 4),
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"amount_unit": "yuan",
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}
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)
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with self._realtime_reference_lock:
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self._latest_realtime_market[trade_date] = {
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"rows": daily,
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"updated_at": datetime.now().astimezone().isoformat(timespec="seconds"),
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}
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if len(self._latest_realtime_market) > 3:
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oldest = next(iter(self._latest_realtime_market))
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self._latest_realtime_market.pop(oldest, None)
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limit_rows = self._derive_limits(
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trade_date,
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daily,
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price_limits=list(reference["price_limits"]),
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basic_rows=basic_rows,
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previous_limit_rows=list(reference["previous_limit_rows"]),
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capital_rows=list(reference["capital_rows"]),
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)
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previous_limit_rows = list(reference["previous_limit_rows"])
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up_rows = [row for row in limit_rows if row.get("limit_type") == "U"]
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down_rows = [row for row in limit_rows if row.get("limit_type") == "D"]
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broken_rows = [row for row in limit_rows if row.get("limit_type") == "Z"]
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limits = [self._normalize_limit(row, "涨停") for row in up_rows]
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broken = [self._normalize_limit(row, "炸板") for row in broken_rows]
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down_limits = [self._normalize_limit(row, "跌停") for row in down_rows]
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previous_limits = [self._normalize_limit(row, "涨停") for row in previous_limit_rows]
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yesterday_limits = _build_yesterday_performance(
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previous_limits,
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daily,
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limits,
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broken,
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down_limits,
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)
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sectors = _build_sectors(limits)
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previous_sectors = _build_sectors(previous_limits)
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now = datetime.now().astimezone()
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market_status = _realtime_market_status(now.time().replace(tzinfo=None))
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dashboard = {
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"meta": {
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"requested_date": _display_date(requested_date),
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"trade_date": _display_date(trade_date),
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"previous_trade_date": _display_date(previous_trade_date),
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"source": "tushare",
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"mode": "realtime",
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"realtime": True,
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"market_status": market_status,
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"refresh_mode": "manual",
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"auto_refresh": False,
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"quote_count": len(daily),
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"updated_at": now.isoformat(timespec="seconds"),
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"notice": "盘中行情由 Tushare rt_k 实时计算；涨停原因、封板时间和开板次数以盘后榜单校正为准。",
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},
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"overview": _build_overview(daily, up_rows, down_rows, broken_rows),
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"limits": limits,
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"broken": broken,
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"down_limits": down_limits,
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"yesterday_limits": yesterday_limits,
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"limit_performance": _build_limit_performance(yesterday_limits),
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"ladders": _build_ladders(limits),
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"sectors": sectors,
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"sector_rotation": _build_sector_rotation(sectors, previous_sectors),
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}
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return apply_sentiment_to_dashboard(dashboard)
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def _load_realtime_reference(
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self,
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trade_date: str,
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previous_trade_date: str,
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) -> dict[str, Any]:
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cache_key = f"{trade_date}:{previous_trade_date}"
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with self._realtime_reference_lock:
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cached = self._realtime_reference_cache.get(cache_key)
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if cached:
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return cached
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basic_rows = self.query(
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"stock_basic",
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{"exchange": "", "list_status": "L"},
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"ts_code,name,industry,market,list_date",
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)
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price_limits = self.query(
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"stk_limit",
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{"trade_date": trade_date},
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"ts_code,trade_date,up_limit,down_limit",
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)
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previous_limit_rows = self._load_limit_type(previous_trade_date, "U")
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capital_rows = self.query(
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"daily_basic",
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{"trade_date": previous_trade_date},
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"ts_code,trade_date,total_share,float_share,free_share,total_mv,circ_mv",
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)
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if not basic_rows or not price_limits:
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raise TushareError(f"Realtime reference data is incomplete for {trade_date}")
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result = {
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"basic_rows": basic_rows,
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"price_limits": price_limits,
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"previous_limit_rows": previous_limit_rows,
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"capital_rows": capital_rows,
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}
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with self._realtime_reference_lock:
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self._realtime_reference_cache[cache_key] = result
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if len(self._realtime_reference_cache) > 3:
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oldest = next(iter(self._realtime_reference_cache))
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self._realtime_reference_cache.pop(oldest, None)
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return result
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def realtime_stock_quote(
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self,
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ts_code: str,
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reference_date: str = "",
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) -> dict[str, Any]:
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rows = self.query("rt_k", {"ts_code": ts_code})
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if not rows:
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raise TushareError(f"No realtime quote returned for {ts_code}")
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row = rows[0]
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close = _number(row.get("close"))
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previous_close = _number(row.get("pre_close"))
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if close <= 0 or previous_close <= 0:
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raise TushareError(f"Realtime quote is unavailable for {ts_code}")
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basic: dict[str, Any] = {}
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with self._realtime_reference_lock:
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references = list(self._realtime_reference_cache.values())
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for reference in reversed(references):
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basic = next(
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(
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item for item in reference.get("basic_rows") or []
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if str(item.get("ts_code") or "") == ts_code
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),
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{},
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)
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if basic:
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break
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if not basic:
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basics = self.query(
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"stock_basic",
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{"ts_code": ts_code},
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"ts_code,name,industry,market,list_date",
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)
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basic = basics[0] if basics else {}
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capital = self._latest_capital(ts_code, reference_date)
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float_share = _number(capital.get("float_share"))
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# rt_k volume is shares; daily_basic float_share is reported in 10k shares.
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turnover_rate = _number(row.get("vol")) / float_share / 100 if float_share else 0
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market_date = reference_date or datetime.now().astimezone().strftime("%Y%m%d")
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self._ensure_realtime_market_cache(market_date)
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with self._realtime_reference_lock:
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market_rows = list((self._latest_realtime_market.get(market_date) or {}).get("rows") or [])
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references = list(self._realtime_reference_cache.values())
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capital_map: dict[str, dict[str, Any]] = {}
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for reference in reversed(references):
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capital_map = {
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str(item.get("ts_code") or ""): item
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for item in reference.get("capital_rows") or []
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}
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if capital_map:
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break
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market_amounts = [_number(item.get("amount")) for item in market_rows if _number(item.get("amount")) > 0]
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amount_percentile = _value_percentile(_number(row.get("amount")), market_amounts)
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market_turnovers = []
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for item in market_rows:
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item_capital = capital_map.get(str(item.get("ts_code") or ""), {})
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item_float_share = _number(item_capital.get("float_share"))
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if item_float_share:
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market_turnovers.append(_number(item.get("vol")) / item_float_share / 100)
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market_turnover = (
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sum(market_turnovers) / len(market_turnovers) if market_turnovers else 0
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)
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turnover_relative = turnover_rate / market_turnover if market_turnover else 0
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activity = self._stock_activity_metrics(
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ts_code,
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market_date,
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_number(row.get("vol")) / 100,
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)
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return {
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"code": ts_code.split(".")[0],
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"ts_code": ts_code,
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"name": str(row.get("name") or basic.get("name") or "--").strip(),
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"sector": basic.get("industry") or "其他",
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"price": round(close, 3),
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"change": round((close / previous_close - 1) * 100, 4),
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"open": round(_number(row.get("open")), 3),
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"high": round(_number(row.get("high")), 3),
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"low": round(_number(row.get("low")), 3),
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"previous_close": round(previous_close, 3),
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"amount_billion": round(_number(row.get("amount")) / 100000000, 3),
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"volume": _number(row.get("vol")),
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"trade_count": int(_number(row.get("num"))),
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"turnover_rate": round(turnover_rate, 4),
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"market_turnover_rate": round(market_turnover, 4),
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"turnover_relative": round(turnover_relative, 4),
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"amount_percentile": round(amount_percentile * 100, 2),
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"volume_activity_ratio": activity.get("volume_activity_ratio", 0),
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"activity_history_date": activity.get("history_trade_date", ""),
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"activity_source": activity.get("source", "unavailable"),
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"float_share_10k": float_share,
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"capital_trade_date": str(capital.get("trade_date") or ""),
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"turnover_source": "rt_volume/latest_float_share" if float_share else "unavailable",
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"data_source": "tushare",
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"realtime": True,
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}
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def _stock_activity_metrics(
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self,
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ts_code: str,
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reference_date: str,
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current_volume_lots: float,
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) -> dict[str, Any]:
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cache_key = f"{ts_code}:{reference_date}"
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with self._realtime_reference_lock:
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history = self._stock_activity_cache.get(cache_key)
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if history is None:
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try:
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end = datetime.strptime(reference_date, "%Y%m%d")
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except ValueError:
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end = datetime.now().astimezone().replace(tzinfo=None)
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rows = self.query(
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"daily",
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{
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"ts_code": ts_code,
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"start_date": (end - timedelta(days=30)).strftime("%Y%m%d"),
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"end_date": reference_date,
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},
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"ts_code,trade_date,vol,amount",
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)
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completed = [
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item for item in rows
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if str(item.get("trade_date") or "") < reference_date and _number(item.get("vol")) > 0
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]
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completed.sort(key=lambda item: str(item.get("trade_date") or ""))
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recent = completed[-5:]
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history = {
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"average_volume_lots": (
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sum(_number(item.get("vol")) for item in recent) / len(recent)
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if recent else 0
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),
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"history_trade_date": str(recent[-1].get("trade_date") or "") if recent else "",
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}
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with self._realtime_reference_lock:
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self._stock_activity_cache[cache_key] = history
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if len(self._stock_activity_cache) > 256:
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oldest = next(iter(self._stock_activity_cache))
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self._stock_activity_cache.pop(oldest, None)
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average_volume = _number(history.get("average_volume_lots"))
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progress = _trading_session_progress(datetime.now().astimezone().time().replace(tzinfo=None))
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expected_volume = average_volume * progress
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ratio = current_volume_lots / expected_volume if expected_volume else 0
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return {
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**history,
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"volume_activity_ratio": round(ratio, 4),
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"session_progress": round(progress, 4),
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"source": "rt_volume/5d_average_at_same_progress" if expected_volume else "unavailable",
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}
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def realtime_factor_snapshot(self, requested_date: str) -> dict[str, Any]:
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trade_date, previous_trade_date = self.resolve_trade_context(requested_date)
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reference = self._load_realtime_reference(trade_date, previous_trade_date)
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codes = [
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str(row.get("ts_code") or "")
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for row in reference.get("basic_rows") or []
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if row.get("ts_code")
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]
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quotes = self.query("rt_k", {"ts_code": ",".join(codes)}, "")
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capital_map = {
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str(row.get("ts_code") or ""): row
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for row in reference.get("capital_rows") or []
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}
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rows = []
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for quote in quotes:
|
||||
ts_code = str(quote.get("ts_code") or "")
|
||||
close = _number(quote.get("close"))
|
||||
previous_close = _number(quote.get("pre_close"))
|
||||
if not ts_code or close <= 0 or previous_close <= 0:
|
||||
continue
|
||||
capital = capital_map.get(ts_code, {})
|
||||
float_share = _number(capital.get("float_share"))
|
||||
rows.append(
|
||||
{
|
||||
"ts_code": ts_code,
|
||||
"trade_date": trade_date,
|
||||
"open": _number(quote.get("open")),
|
||||
"high": _number(quote.get("high")),
|
||||
"low": _number(quote.get("low")),
|
||||
"close": close,
|
||||
"pct_chg": (close / previous_close - 1) * 100,
|
||||
"vol": _number(quote.get("vol")) / 100,
|
||||
"amount": _number(quote.get("amount")),
|
||||
"turnover_rate": (
|
||||
_number(quote.get("vol")) / float_share / 100 if float_share else 0
|
||||
),
|
||||
"capital_trade_date": str(capital.get("trade_date") or ""),
|
||||
}
|
||||
)
|
||||
if not rows:
|
||||
raise TushareError(f"No realtime factor snapshot returned for {trade_date}")
|
||||
return {
|
||||
"trade_date": trade_date,
|
||||
"previous_trade_date": previous_trade_date,
|
||||
"source": "tushare_rt_k",
|
||||
"realtime": True,
|
||||
"rows": rows,
|
||||
}
|
||||
|
||||
def _ensure_realtime_market_cache(self, requested_date: str) -> list[dict[str, Any]]:
|
||||
with self._realtime_reference_lock:
|
||||
cached = list(
|
||||
(self._latest_realtime_market.get(requested_date) or {}).get("rows") or []
|
||||
)
|
||||
if cached:
|
||||
return cached
|
||||
trade_date, previous_trade_date = self.resolve_trade_context(requested_date)
|
||||
if trade_date != requested_date:
|
||||
return []
|
||||
reference = self._load_realtime_reference(trade_date, previous_trade_date)
|
||||
codes = [
|
||||
str(row.get("ts_code") or "")
|
||||
for row in reference.get("basic_rows") or []
|
||||
if row.get("ts_code")
|
||||
]
|
||||
quotes = self.query("rt_k", {"ts_code": ",".join(codes)}, "")
|
||||
rows = [
|
||||
row for row in quotes
|
||||
if _number(row.get("close")) > 0 and _number(row.get("pre_close")) > 0
|
||||
]
|
||||
with self._realtime_reference_lock:
|
||||
self._latest_realtime_market[trade_date] = {
|
||||
"rows": rows,
|
||||
"updated_at": datetime.now().astimezone().isoformat(timespec="seconds"),
|
||||
}
|
||||
return rows
|
||||
|
||||
def _latest_capital(self, ts_code: str, reference_date: str = "") -> dict[str, Any]:
|
||||
end_date = reference_date or datetime.now().astimezone().strftime("%Y%m%d")
|
||||
cache_key = f"{ts_code}:{end_date}"
|
||||
with self._realtime_reference_lock:
|
||||
cached = self._capital_cache.get(cache_key)
|
||||
if cached:
|
||||
return cached
|
||||
try:
|
||||
end = datetime.strptime(end_date, "%Y%m%d")
|
||||
except ValueError:
|
||||
end = datetime.now().astimezone().replace(tzinfo=None)
|
||||
end_date = end.strftime("%Y%m%d")
|
||||
start_date = (end - timedelta(days=20)).strftime("%Y%m%d")
|
||||
rows = self.query(
|
||||
"daily_basic",
|
||||
{"ts_code": ts_code, "start_date": start_date, "end_date": end_date},
|
||||
"ts_code,trade_date,turnover_rate,volume_ratio,total_share,float_share,"
|
||||
"free_share,total_mv,circ_mv",
|
||||
)
|
||||
rows.sort(key=lambda item: str(item.get("trade_date") or ""))
|
||||
result = rows[-1] if rows else {}
|
||||
with self._realtime_reference_lock:
|
||||
self._capital_cache[cache_key] = result
|
||||
if len(self._capital_cache) > 256:
|
||||
oldest = next(iter(self._capital_cache))
|
||||
self._capital_cache.pop(oldest, None)
|
||||
return result
|
||||
|
||||
|
||||
def _build_overview(
|
||||
daily: list[dict[str, Any]],
|
||||
up_rows: list[dict[str, Any]],
|
||||
down_rows: list[dict[str, Any]],
|
||||
broken_rows: list[dict[str, Any]],
|
||||
) -> dict[str, Any]:
|
||||
up_count = sum(1 for row in daily if _number(row.get("pct_chg")) > 0)
|
||||
down_count = sum(1 for row in daily if _number(row.get("pct_chg")) < 0)
|
||||
flat_count = len(daily) - up_count - down_count
|
||||
amount_billion = sum(
|
||||
_number(row.get("amount"))
|
||||
/ (100000000 if row.get("amount_unit") == "yuan" else 100000)
|
||||
for row in daily
|
||||
)
|
||||
limit_count = len(up_rows)
|
||||
broken_count = len(broken_rows)
|
||||
seal_rate = round(limit_count / max(limit_count + broken_count, 1) * 100, 1)
|
||||
return {
|
||||
"up_count": up_count,
|
||||
"down_count": down_count,
|
||||
"flat_count": flat_count,
|
||||
"limit_up_count": limit_count,
|
||||
"limit_down_count": len(down_rows),
|
||||
"broken_count": broken_count,
|
||||
"amount_billion": round(amount_billion, 1),
|
||||
"seal_rate": seal_rate,
|
||||
}
|
||||
|
||||
|
||||
def _build_ladders(rows: list[dict[str, Any]]) -> list[dict[str, Any]]:
|
||||
groups: dict[int, list[dict[str, Any]]] = {}
|
||||
for row in rows:
|
||||
groups.setdefault(int(row.get("streak") or 1), []).append(row)
|
||||
return [
|
||||
{
|
||||
"level": level,
|
||||
"label": "首板" if level == 1 else f"{level}板",
|
||||
"count": len(stocks),
|
||||
"stocks": sorted(stocks, key=lambda item: item.get("first_time") or "99:99:99"),
|
||||
}
|
||||
for level, stocks in sorted(groups.items(), reverse=True)
|
||||
]
|
||||
|
||||
|
||||
def _build_sectors(rows: list[dict[str, Any]]) -> list[dict[str, Any]]:
|
||||
counts = Counter(row.get("sector") or "其他" for row in rows)
|
||||
result: list[dict[str, Any]] = []
|
||||
for name, count in counts.most_common(20):
|
||||
stocks = [row for row in rows if (row.get("sector") or "其他") == name]
|
||||
max_streak = max(item.get("streak", 1) for item in stocks)
|
||||
leader = max(stocks, key=lambda item: (item.get("streak", 1), item.get("amount_billion", 0)))
|
||||
result.append(
|
||||
{
|
||||
"name": name,
|
||||
"count": count,
|
||||
"strength": min(100, 44 + count * 8 + max_streak * 5),
|
||||
"amount_billion": round(sum(item.get("amount_billion", 0) for item in stocks), 1),
|
||||
"leader": leader.get("name", "--"),
|
||||
"change": round(sum(item.get("change", 0) for item in stocks) / count, 2),
|
||||
"max_streak": max_streak,
|
||||
}
|
||||
)
|
||||
return result
|
||||
|
||||
|
||||
def _build_yesterday_performance(
|
||||
previous_limits: list[dict[str, Any]],
|
||||
daily: list[dict[str, Any]],
|
||||
current_limits: list[dict[str, Any]],
|
||||
current_broken: list[dict[str, Any]],
|
||||
current_down: list[dict[str, Any]],
|
||||
) -> list[dict[str, Any]]:
|
||||
daily_map = {str(row.get("ts_code", "")).split(".")[0]: row for row in daily}
|
||||
limit_map = {row["code"]: row for row in current_limits}
|
||||
broken_codes = {row["code"] for row in current_broken}
|
||||
down_codes = {row["code"] for row in current_down}
|
||||
result = []
|
||||
for previous in previous_limits:
|
||||
code = previous["code"]
|
||||
daily_row = daily_map.get(code, {})
|
||||
current = limit_map.get(code)
|
||||
if current:
|
||||
outcome = "晋级"
|
||||
elif code in broken_codes:
|
||||
outcome = "炸板"
|
||||
elif code in down_codes:
|
||||
outcome = "跌停"
|
||||
else:
|
||||
outcome = "断板"
|
||||
result.append(
|
||||
{
|
||||
"code": code,
|
||||
"name": previous["name"],
|
||||
"prior_streak": previous.get("streak", 1),
|
||||
"current_streak": current.get("streak", 0) if current else 0,
|
||||
"current_change": _number(daily_row.get("pct_chg")),
|
||||
"current_price": _number(daily_row.get("close")),
|
||||
"sector": previous.get("sector", "其他"),
|
||||
"reason": previous.get("reason", "待补充"),
|
||||
"outcome": outcome,
|
||||
}
|
||||
)
|
||||
return result
|
||||
|
||||
|
||||
def _build_limit_performance(rows: list[dict[str, Any]]) -> list[dict[str, Any]]:
|
||||
result = []
|
||||
for level in sorted({int(row.get("prior_streak") or 1) for row in rows}, reverse=True):
|
||||
group = [row for row in rows if int(row.get("prior_streak") or 1) == level]
|
||||
advanced = sum(row.get("outcome") == "晋级" for row in group)
|
||||
positive = sum(_number(row.get("current_change")) > 0 for row in group)
|
||||
result.append(
|
||||
{
|
||||
"level": level,
|
||||
"label": "昨日首板" if level == 1 else f"昨日{level}板",
|
||||
"count": len(group),
|
||||
"advanced": advanced,
|
||||
"advance_rate": round(advanced / len(group) * 100, 1),
|
||||
"positive_rate": round(positive / len(group) * 100, 1),
|
||||
"average_change": round(sum(_number(row.get("current_change")) for row in group) / len(group), 2),
|
||||
}
|
||||
)
|
||||
return result
|
||||
|
||||
|
||||
def _build_sector_rotation(
|
||||
current: list[dict[str, Any]], previous: list[dict[str, Any]]
|
||||
) -> list[dict[str, Any]]:
|
||||
previous_map = {row["name"]: row for row in previous}
|
||||
result = []
|
||||
for index, sector in enumerate(current, start=1):
|
||||
previous_count = int(previous_map.get(sector["name"], {}).get("count", 0))
|
||||
delta = int(sector["count"]) - previous_count
|
||||
result.append(
|
||||
{
|
||||
**sector,
|
||||
"rank": index,
|
||||
"previous_count": previous_count,
|
||||
"delta": delta,
|
||||
"trend": "升温" if delta > 0 else "降温" if delta < 0 else "持平",
|
||||
}
|
||||
)
|
||||
return result
|
||||
Reference in New Issue
Block a user