Simon
93e32f4e63
- MarketDataProvider.get_index_weight（协议）；Tushare 实现 normalize_index_weight + get_index_weight（ts_code=... 全历史成分权重）；Sina 抛 DataSourceNotSupported； FailoverProvider 代理并审计每次尝试 - CLI：sync index_weight --code 000300.SH（拉取→幂等落库 index_weight→打印最新快照； 失败走 sync_log 审计并返回非零） - tests：Tushare 映射与调用（FakePro）、Failover 主源单源语义（新浪不支持被审计）； 全量 pytest 通过
295 lines
10 KiB
Python
295 lines
10 KiB
Python
"""Tushare Provider —— 首选数据源实现。
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依赖注入：pro 客户端（tushare.pro.client 或测试 Fake）。真实运行时惰性加载
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tushare 库（pyproject optional：uv sync --extra datasource-tushare）。
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归一化函数只依赖 list[dict]，便于无 pandas 环境下单测。
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"""
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from __future__ import annotations
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import importlib
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import logging
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import time
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from datetime import date, datetime, timedelta
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from decimal import Decimal
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from typing import Any
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from app.domain.entities.index import IndexWeight
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from app.domain.entities.market import (
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AdjustFactor,
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DailyBar,
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FinancialIndicator,
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Stock,
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TradingCalendar,
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)
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from app.infrastructure.data_sources.errors import (
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DataSourceAuthenticationError,
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DataSourceError,
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)
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logger = logging.getLogger(__name__)
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_TS_DATE = "%Y%m%d"
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def _to_date(value: str | None) -> date | None:
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if value is None or value == "":
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return None
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return datetime.strptime(str(value)[:10], _TS_DATE).date()
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def _to_decimal(value) -> Decimal | None:
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if value is None:
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return None
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try:
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num = float(value)
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except (ValueError, TypeError):
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return None
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if num != num: # NaN
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return None
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return Decimal(str(num))
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class TushareProvider:
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"""封装 Tushare Pro（ts.pro_api）。所有输出已归一化为领域实体。"""
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name = "tushare"
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def __init__(
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self,
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token: str = "",
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*,
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pro: object | None = None,
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max_retries: int = 3,
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rate_limit_wait: float = 30.0,
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) -> None:
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self._pro = pro if pro is not None else _build_pro(token)
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self._max_retries = max_retries
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self._rate_limit_wait = rate_limit_wait
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# ---- 归一化（纯函数，输入 list[dict]，可单测） ----
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@staticmethod
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def normalize_stock(records: list[dict[str, Any]]) -> list[Stock]:
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stocks: list[Stock] = []
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for rec in records:
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stocks.append(
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Stock(
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symbol=str(rec.get("ts_code") or rec.get("symbol") or ""),
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name=str(rec.get("name") or ""),
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industry=rec.get("industry"),
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area=rec.get("area"),
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market=rec.get("market"),
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exchange=rec.get("exchange"),
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list_date=_to_date(rec.get("list_date")) or date.min,
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delist_date=_to_date(rec.get("delist_date")),
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status=str(rec.get("status") or "L"),
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)
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)
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return stocks
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@staticmethod
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def normalize_calendar(records: list[dict[str, Any]]) -> list[TradingCalendar]:
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return [
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TradingCalendar(
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calendar_date=_to_date(rec.get("cal_date")) or date.min,
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is_open=bool(rec.get("is_open")),
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)
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for rec in records
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]
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@staticmethod
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def normalize_index_weight(
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records: list[dict[str, Any]], index_code_fallback: str = ""
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) -> list[IndexWeight]:
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"""index_weight 接口行 → IndexWeight（index_code/con_code/trade_date/weight）。"""
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out: list[IndexWeight] = []
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for rec in records:
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code = str(rec.get("index_code") or index_code_fallback or "")
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symbol = str(rec.get("con_code") or "")
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if not code or not symbol:
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continue
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out.append(
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IndexWeight(
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index_code=code,
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index_name=rec.get("index_name"),
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trade_date=_to_date(rec.get("trade_date")) or date.min,
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symbol=symbol,
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weight=_to_decimal(rec.get("weight")),
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)
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)
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return out
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@staticmethod
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def normalize_daily(records: list[dict[str, Any]]) -> list[DailyBar]:
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bars: list[DailyBar] = []
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for rec in records:
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vol = _to_decimal(rec.get("vol"))
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amount = _to_decimal(rec.get("amount"))
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bars.append(
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DailyBar(
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symbol=str(rec.get("ts_code") or ""),
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trade_date=_to_date(rec.get("trade_date")) or date.min,
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source="tushare",
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adjust="none",
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open=_to_decimal(rec.get("open")),
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high=_to_decimal(rec.get("high")),
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low=_to_decimal(rec.get("low")),
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close=_to_decimal(rec.get("close")),
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volume=vol * 100 if vol is not None else None,
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amount=amount * 1000 if amount is not None else None,
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)
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)
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return bars
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@staticmethod
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def normalize_adj_factor(records: list[dict[str, Any]]) -> list[AdjustFactor]:
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return [
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AdjustFactor(
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symbol=str(rec.get("ts_code") or ""),
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trade_date=_to_date(rec.get("trade_date")) or date.min,
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factor=_to_decimal(rec.get("adj_factor")) or Decimal(1),
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)
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for rec in records
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]
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@staticmethod
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def normalize_financial(records: list[dict[str, Any]]) -> list[FinancialIndicator]:
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rows: list[FinancialIndicator] = []
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for rec in records:
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rows.append(
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FinancialIndicator(
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symbol=str(rec.get("ts_code") or ""),
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report_date=_to_date(rec.get("end_date")) or date.min,
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announce_date=_to_date(rec.get("ann_date")) or date.min,
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eps=_to_decimal(rec.get("eps")),
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roe=_to_decimal(rec.get("roe")),
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net_profit=_to_decimal(rec.get("n_income_attr_p")),
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gross_margin=_to_decimal(rec.get("grossprofit_margin")),
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)
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)
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return rows
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# ---- 接口调用 ----
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def get_stock_basic(self) -> list[Stock]:
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records = self._call(
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"stock_basic",
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fields="ts_code,symbol,name,area,industry,market,exchange,list_date,delist_date,status",
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)
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return self.normalize_stock(records)
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def get_trade_cal(self, start: date, end: date) -> list[TradingCalendar]:
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records = self._call(
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"trade_cal",
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exchange="SSE",
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start_date=start.strftime(_TS_DATE),
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end_date=end.strftime(_TS_DATE),
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is_open="",
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)
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return self.normalize_calendar(records)
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def get_daily(self, symbol: str, start: date, end: date) -> list[DailyBar]:
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records = self._call(
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"daily",
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ts_code=symbol,
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start_date=start.strftime(_TS_DATE),
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end_date=end.strftime(_TS_DATE),
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)
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return self.normalize_daily(records)
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def get_adjust_factor(self, symbol: str, start: date, end: date) -> list[AdjustFactor]:
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records = self._call(
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"adj_factor",
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ts_code=symbol,
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start_date=start.strftime(_TS_DATE),
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end_date=end.strftime(_TS_DATE),
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)
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return self.normalize_adj_factor(records)
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def get_financial(
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self,
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symbol: str,
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start: date | None = None,
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end: date | None = None,
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) -> list[FinancialIndicator]:
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"""fina_indicator：报告期窗口 + 100 条/请求上限自动分页。
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Tushare 单次请求最多返回 100 条（超出按最新 100 条截断），因此
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全量历史必须按报告期窗口回卷分页，否则老报告期会被静默丢弃。
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"""
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lo = start or date(1990, 1, 1)
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hi = end or date.today()
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raw: list[dict[str, Any]] = []
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while lo <= hi:
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batch = self._call(
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"fina_indicator",
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ts_code=symbol,
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start_date=lo.strftime(_TS_DATE),
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end_date=hi.strftime(_TS_DATE),
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)
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raw += batch
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if len(batch) < 100:
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break
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ends = [
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datetime.strptime(str(r["end_date"])[:8], _TS_DATE).date()
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for r in batch
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if r.get("end_date")
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]
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if not ends:
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break
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next_hi = min(ends) - timedelta(days=1)
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if next_hi < lo: # 无进展保护（边界簇被截断等极端情况）
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break
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hi = next_hi
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return self.normalize_financial(raw)
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# ---- 内部 ----
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_RATE_LIMIT_MARKERS = ("频率超限", "每分钟", "frequenc", "too many")
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def get_index_weight(self, index_code: str) -> list[IndexWeight]:
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"""指数成分（Tushare index_weight 全历史，ts_code 过滤）。"""
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records = self._call("index_weight", ts_code=index_code)
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return self.normalize_index_weight(records, index_code_fallback=index_code)
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def _call(self, api: str, **kwargs) -> list[dict[str, Any]]:
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"""带限速退避的调用：频率超限按指数退避（最长 _rate_limit_wait）等待后重试。"""
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last_error: Exception | None = None
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for attempt in range(self._max_retries):
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try:
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fn = getattr(self._pro, api)
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result = fn(**kwargs)
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if result is None:
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return []
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if hasattr(result, "to_dict"):
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return result.to_dict("records")
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if isinstance(result, list):
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return result
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return []
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except Exception as exc: # noqa: BLE001 —— tushare 异常无统一类型，逐一归类
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last_error = exc
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msg = str(exc)
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if "权限" in msg or "积分" in msg or "token" in msg.lower():
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raise DataSourceAuthenticationError(msg) from exc
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if any(marker in msg for marker in self._RATE_LIMIT_MARKERS):
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wait = min(self._rate_limit_wait, 2 ** (attempt + 1))
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logger.warning("tushare.%s 频率超限，退避 %.1fs 后重试", api, wait)
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time.sleep(wait)
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raise DataSourceError(
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f"tushare.{api} 重试 {self._max_retries} 次仍失败: {last_error}"
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) from last_error
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def _build_pro(token: str):
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if not token:
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raise DataSourceAuthenticationError(
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"缺少 TUSHARE_TOKEN：请 cp .env.example .env 并填入 Tushare Pro token"
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)
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try:
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ts = importlib.import_module("tushare")
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except ImportError as exc: # pragma: no cover —— 环境相关
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raise DataSourceError(
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"未安装 tushare 客户端：cd backend && uv sync --extra datasource-tushare"
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) from exc
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return ts.pro_api(token)
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