rebuild(stage-10): deliver mentor and unified llm streaming
This commit is contained in:
leefer
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from __future__ import annotations
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import json
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import re
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from typing import Any
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from backend.data.gateway import DataGateway
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from backend.data.repository import MarketRepository
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from backend.database.connection import Database
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from backend.features.mentor.skills import MentorSkill
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from backend.features.screener.repository import ScreenerRepository
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INDEX_UNIVERSE = (
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("index", "000001.SH", "上证指数"),
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("index", "399001.SZ", "深证成指"),
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("index", "399006.SZ", "创业板指"),
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("index", "000016.SH", "上证50"),
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("index", "000300.SH", "沪深300"),
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("index", "000905.SH", "中证500"),
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("index", "000852.SH", "中证1000"),
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("index", "932000.CSI", "中证2000"),
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)
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ETF_UNIVERSE = (
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("stock", "510050.SH", "上证50ETF"),
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("stock", "510300.SH", "沪深300ETF"),
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("stock", "510500.SH", "中证500ETF"),
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("stock", "512100.SH", "中证1000ETF"),
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)
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class MentorContextBuilder:
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def __init__(
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self,
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database: Database,
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market_repository: MarketRepository,
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screener_repository: ScreenerRepository,
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gateway: DataGateway,
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) -> None:
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self._database = database
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self._market = market_repository
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self._screener = screener_repository
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self._gateway = gateway
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def build(self, requested_date: str, question: str, skill: MentorSkill) -> dict[str, Any]:
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context = self._gateway.trade_context(requested_date)
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trade_date = context.actual_date or requested_date
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with self._database.read() as connection:
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summary_row = self._market.latest_summary(connection, trade_date)
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summary = _payload(summary_row)
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history = [
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_summary_item(row)
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for row in self._market.summaries(connection, trade_date, 10)
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]
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popularity = _payload(
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self._market.latest_insight_snapshot(connection, "popularity", trade_date)
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)
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dragon = _payload(
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self._market.latest_insight_snapshot(connection, "dragon-list", trade_date)
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)
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matched = self._matched_stocks(connection, trade_date, question)
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indexes = self._market_matrix(connection, INDEX_UNIVERSE)
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etfs = self._market_matrix(connection, ETF_UNIVERSE)
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result: dict[str, Any] = {
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"data_trade_date": str(summary_row["trade_date"]) if summary_row else trade_date,
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"data_profile": skill.profile,
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"overview": summary.get("overview") or {},
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"sentiment": summary.get("sentiment") or {},
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"recent_market_history": history,
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"question_matched_stocks": matched,
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}
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self._apply_profile(result, skill.profile, summary, popularity, indexes, etfs)
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if _requires_dragon_context(question):
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result["dragon_list"] = _dragon_context(dragon, matched)
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return result
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def _matched_stocks(
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self, connection, trade_date: str, question: str
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) -> list[dict[str, Any]]:
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directory = self._gateway.stock_directory()
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identifiers = _matched_identifiers(directory, question)
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snapshot = self._screener.latest_factor_snapshot(connection, trade_date)
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factor_rows = (
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{
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row["identifier"]: row
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for row in self._screener.factor_rows(connection, int(snapshot["id"]))
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}
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if snapshot
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else {}
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)
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result = []
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for identifier in identifiers[:2]:
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row = dict(factor_rows.get(identifier) or directory.get(identifier) or {})
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chart = self._market.chart(connection, "stock", identifier, "day")
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row["recent_prices"] = (_payload(chart).get("points") or [])[-20:]
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result.append(row)
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return result
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def _market_matrix(
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self, connection, universe: tuple[tuple[str, str, str], ...]
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) -> list[dict[str, Any]]:
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result = []
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for entity_type, identifier, name in universe:
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row = self._market.chart(connection, entity_type, identifier, "day")
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points = _payload(row).get("points") or []
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closes = [float(item["close"]) for item in points if item.get("close")]
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result.append(
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{
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"code": identifier,
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"name": name,
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"available": bool(closes),
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"close": closes[-1] if closes else None,
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"change": _return(closes, 1),
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"return_5d": _return(closes, 5),
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"return_10d": _return(closes, 10),
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"return_20d": _return(closes, 20),
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"amount": points[-1].get("amount") if points else None,
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}
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)
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return result
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@staticmethod
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def _apply_profile(
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result: dict[str, Any],
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profile: str,
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summary: dict[str, Any],
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popularity: dict[str, Any],
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indexes: list[dict[str, Any]],
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etfs: list[dict[str, Any]],
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) -> None:
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limits = list(summary.get("limits") or [])
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broken = list(summary.get("broken") or [])
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yesterday = list(summary.get("yesterday_limits") or [])
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rotation = list(summary.get("sector_rotation") or [])
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ordered = sorted(
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limits,
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key=lambda row: (int(row.get("streak") or 0), float(row.get("amount") or 0)),
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reverse=True,
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)
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if profile == "first_board":
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result["first_board_environment"] = {
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"seal_rate": (summary.get("overview") or {}).get("seal_rate"),
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"first_boards": [row for row in ordered if int(row.get("streak") or 1) == 1][:35],
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"broken_stocks": broken[:30],
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"hot_sectors": rotation[:12],
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}
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elif profile == "leader":
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result.update(
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{
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"limit_ladder": summary.get("ladders") or [],
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"multi_board_leaders": [
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row for row in ordered if int(row.get("streak") or 0) >= 2
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][:25],
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"sector_rotation": rotation[:12],
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"popularity_core": (popularity.get("combined") or [])[:10],
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}
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)
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elif profile == "trend":
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result.update(
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{
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"index_momentum": indexes,
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"sector_rotation": rotation[:20],
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"market_breadth": summary.get("overview") or {},
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}
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)
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elif profile == "low_absorption":
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result.update(
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{
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"yesterday_limit_performance": yesterday[:35],
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"broken_stocks": broken[:20],
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"hot_sectors": rotation[:12],
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}
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)
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elif profile == "macro":
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result.update(
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{
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"broad_indexes": indexes,
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"core_etfs": etfs,
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"market_style": {
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"overview": summary.get("overview") or {},
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"top_sectors": rotation[:15],
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},
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"unavailable_data": [
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"政策原文与隔夜资讯尚未接入",
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"汇率、利率和商品宏观序列当前不可用",
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],
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}
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)
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else:
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result.update(
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{
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"limit_ladder": summary.get("ladders") or [],
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"limit_performance": summary.get("limit_performance") or [],
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"sector_rotation": rotation[:15],
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"limit_up_stocks": ordered[:30],
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"broken_stocks": broken[:20],
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"limit_down_stocks": (summary.get("down_limits") or [])[:20],
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"yesterday_limit_performance": yesterday[:20],
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}
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)
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def _payload(row) -> dict[str, Any]:
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if row is None:
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return {}
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try:
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value = json.loads(str(row["payload_json"]))
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except (KeyError, TypeError, json.JSONDecodeError):
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return {}
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return value if isinstance(value, dict) else {}
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def _summary_item(row) -> dict[str, Any]:
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payload = _payload(row)
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return {
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"trade_date": str(row["trade_date"]),
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"overview": payload.get("overview") or {},
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"sentiment": payload.get("sentiment") or {},
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}
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def _return(closes: list[float], days: int) -> float | None:
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if len(closes) <= days or closes[-days - 1] <= 0:
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return None
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return round((closes[-1] / closes[-days - 1] - 1) * 100, 2)
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def _dragon_context(payload: dict[str, Any], matched: list[dict[str, Any]]) -> dict[str, Any]:
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codes = {
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str(item.get("code") or item.get("identifier") or "").split(".")[0]
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for item in matched
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}
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operations = payload.get("stocks") or payload.get("operations") or []
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return {
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"matched": [
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row
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for row in operations
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if str(row.get("ts_code") or row.get("code") or "").split(".")[0] in codes
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][:20],
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"largest_net_flows": sorted(
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operations,
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key=lambda row: abs(float(row.get("net_buy") or row.get("net_million") or 0)),
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reverse=True,
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)[:12],
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}
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def _requires_dragon_context(question: str) -> bool:
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return any(word in question for word in ("龙虎榜", "席位", "机构", "游资"))
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def _matched_identifiers(directory: dict[str, dict[str, Any]], question: str) -> list[str]:
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codes = set(re.findall(r"(?<!\d)\d{6}(?!\d)", question))
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result = []
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for identifier, row in directory.items():
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code = str(row.get("symbol") or identifier.split(".")[0])
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name = str(row.get("name") or "")
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if code in codes or (len(name) >= 2 and name in question):
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result.append(identifier)
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if len(result) == 2:
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break
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return result
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