1591 lines
55 KiB
Python
1591 lines
55 KiB
Python
"""Utility helpers to retrieve structured data slices for agents and departments."""
|
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from __future__ import annotations
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|
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import re
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import sqlite3
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import threading
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import time
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from collections import OrderedDict
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from copy import deepcopy
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from dataclasses import dataclass, field
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from datetime import date, datetime, timedelta
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from typing import Any, Callable, ClassVar, Dict, Iterable, List, Optional, Sequence, Set, Tuple
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import numpy as np
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from .config import get_config
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import types
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from .db import db_session
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from .logging import get_logger
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from app.core.indicators import momentum, normalize, rolling_mean, volatility
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from app.utils.db_query import BrokerQueryEngine
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# 延迟导入，避免循环依赖
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collect_data_coverage = None
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ensure_data_coverage = None
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initialize_database = None
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# 在模块加载时尝试导入
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if collect_data_coverage is None or ensure_data_coverage is None:
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try:
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from app.ingest.tushare import collect_data_coverage, ensure_data_coverage
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except ImportError:
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# 导入失败时，在实际使用时会报错
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pass
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if initialize_database is None:
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try:
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from app.data.schema import initialize_database
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except ImportError:
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# 导入失败时，提供一个空实现
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def initialize_database():
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"""Fallback stub used when the real initializer cannot be imported.
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Return a lightweight object with the attributes callers expect
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(executed, skipped, missing_tables) so code that calls
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`initialize_database()` can safely inspect the result.
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"""
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return types.SimpleNamespace(executed=0, skipped=True, missing_tables=[])
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LOGGER = get_logger(__name__)
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LOG_EXTRA = {"stage": "data_broker"}
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_IDENTIFIER_RE = re.compile(r"^[A-Za-z_][A-Za-z0-9_]*$")
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def _is_safe_identifier(name: str) -> bool:
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return bool(_IDENTIFIER_RE.match(name))
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def _safe_split(path: str) -> Tuple[str, str] | None:
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if "." not in path:
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return None
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table, column = path.split(".", 1)
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table = table.strip()
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||
column = column.strip()
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if not table or not column:
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return None
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if not (_is_safe_identifier(table) and _is_safe_identifier(column)):
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LOGGER.debug("忽略非法字段：%s", path, extra=LOG_EXTRA)
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return None
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return table, column
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@dataclass
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class _RefreshCoordinator:
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"""Orchestrates background refresh requests for the broker."""
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broker: "DataBroker"
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def ensure_for_latest(self, trade_date: str, fields: Iterable[str]) -> None:
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parsed_date = _parse_trade_date(trade_date)
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if not parsed_date:
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return
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normalized = parsed_date.strftime("%Y%m%d")
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tables = self._collect_tables(fields)
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if tables and self.broker.check_data_availability(normalized, tables):
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LOGGER.debug(
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"触发近端数据刷新 trade_date=%s tables=%s",
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normalized,
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sorted(tables),
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extra=LOG_EXTRA,
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)
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self.broker._trigger_background_refresh(normalized)
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def ensure_for_series(self, end_date: str, table: str) -> None:
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parsed_date = _parse_trade_date(end_date)
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if not parsed_date:
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return
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normalized = parsed_date.strftime("%Y%m%d")
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if self.broker.check_data_availability(normalized, {table}):
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LOGGER.debug(
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"触发序列刷新 trade_date=%s table=%s",
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normalized,
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table,
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extra=LOG_EXTRA,
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)
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self.broker._trigger_background_refresh(normalized)
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def _collect_tables(self, fields: Iterable[str]) -> Set[str]:
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tables: Set[str] = set()
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for field_name in fields:
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||
resolved = self.broker.resolve_field(field_name)
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if resolved:
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table, _ = resolved
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tables.add(table)
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return tables
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def parse_field_path(path: str) -> Tuple[str, str] | None:
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"""Validate and split a `table.column` field expression."""
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return _safe_split(path)
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def _parse_trade_date(value: object) -> Optional[datetime]:
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if value is None:
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return None
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text = str(value).strip()
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if not text:
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return None
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text = text.replace("-", "")
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try:
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return datetime.strptime(text[:8], "%Y%m%d")
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except ValueError:
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return None
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|
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def _start_of_day(dt: datetime) -> str:
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return dt.strftime("%Y-%m-%d 00:00:00")
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def _end_of_day(dt: datetime) -> str:
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return dt.strftime("%Y-%m-%d 23:59:59")
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|
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def _coerce_date(value: object) -> Optional[date]:
|
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if value is None:
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return None
|
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if isinstance(value, date):
|
||
return value
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||
parsed = _parse_trade_date(value)
|
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if parsed:
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||
return parsed.date()
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||
return None
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||
|
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@dataclass
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class DataBroker:
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"""Lightweight data access helper with automated data fetching capabilities."""
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FIELD_ALIASES: ClassVar[Dict[str, Dict[str, str]]] = {
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"daily": {
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"volume": "vol",
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||
"vol": "vol",
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"turnover": "amount",
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},
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||
"daily_basic": {
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"turnover": "turnover_rate",
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"turnover_rate": "turnover_rate",
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"turnover_rate_f": "turnover_rate_f",
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"volume_ratio": "volume_ratio",
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"pe": "pe",
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"pb": "pb",
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"ps": "ps",
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"ps_ttm": "ps_ttm",
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"dividend_yield": "dv_ratio",
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},
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"stk_limit": {
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"up": "up_limit",
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"down": "down_limit",
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},
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}
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MAX_WINDOW: ClassVar[int] = 120
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BENCHMARK_INDEX: ClassVar[str] = "000300.SH"
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# 自动补数配置
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AUTO_REFRESH_WINDOW: ClassVar[int] = 7 # 自动补数的时间窗口
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REFRESH_RETRY_INTERVAL: ClassVar[int] = 5 # 补数重试间隔（秒）
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MAX_REFRESH_WAIT: ClassVar[int] = 60 # 最大等待补数完成时间（秒）
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enable_cache: bool = True
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latest_cache_size: int = 256
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series_cache_size: int = 512
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_latest_cache: OrderedDict = field(init=False, repr=False)
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_series_cache: OrderedDict = field(init=False, repr=False)
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# 补数相关状态管理
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_refresh_lock: threading.RLock = field(init=False, repr=False)
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_refresh_in_progress: Dict[str, bool] = field(init=False, repr=False)
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_refresh_callbacks: Dict[str, List[Callable]] = field(init=False, repr=False)
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_coverage_cache: Dict[str, Dict] = field(init=False, repr=False)
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_refresh: _RefreshCoordinator = field(init=False, repr=False)
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_query_engine: BrokerQueryEngine = field(init=False, repr=False)
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def __post_init__(self) -> None:
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self._latest_cache = OrderedDict()
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self._series_cache = OrderedDict()
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# 初始化补数相关状态
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self._refresh_lock = threading.RLock()
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self._refresh_in_progress = {}
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self._refresh_callbacks = {}
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self._coverage_cache = {}
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self._refresh = _RefreshCoordinator(self)
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self._query_engine = BrokerQueryEngine(db_session)
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if initialize_database is not None:
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initialize_database() # 确保数据库已初始化
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else:
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LOGGER.warning("initialize_database 函数不可用，数据库可能未初始化", extra=LOG_EXTRA)
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def fetch_latest(
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self,
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ts_code: str,
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trade_date: str,
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fields: Iterable[str],
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auto_refresh: bool = True,
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) -> Dict[str, Any]:
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"""Fetch the latest value (<= trade_date) for each requested field.
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Args:
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ts_code: 证券代码
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trade_date: 交易日
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fields: 要查询的字段列表
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auto_refresh: 是否在数据不足时自动触发补数
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"""
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field_list = [str(item) for item in fields if item]
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cache_key: Optional[Tuple[Any, ...]] = None
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if self.enable_cache and field_list:
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cache_key = (ts_code, trade_date, tuple(sorted(field_list)))
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||
cached = self._cache_lookup(self._latest_cache, cache_key)
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if cached is not None:
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return deepcopy(cached)
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# 检查是否需要自动补数
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if auto_refresh:
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self._refresh.ensure_for_latest(trade_date, field_list)
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grouped: Dict[str, List[str]] = {}
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field_map: Dict[Tuple[str, str], List[str]] = {}
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||
derived_cache: Dict[str, Any] = {}
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results: Dict[str, Any] = {}
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||
for field_name in field_list:
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resolved = self.resolve_field(field_name)
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||
if not resolved:
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||
derived = self._resolve_derived_field(
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ts_code,
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||
trade_date,
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field_name,
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||
derived_cache,
|
||
)
|
||
if derived is not None:
|
||
results[field_name] = derived
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continue
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table, column = resolved
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grouped.setdefault(table, [])
|
||
if column not in grouped[table]:
|
||
grouped[table].append(column)
|
||
field_map.setdefault((table, column), []).append(field_name)
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||
|
||
if grouped:
|
||
for table, columns in grouped.items():
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||
try:
|
||
row = self._query_engine.fetch_latest(table, ts_code, trade_date, columns)
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except Exception as exc: # noqa: BLE001
|
||
LOGGER.debug(
|
||
"查询失败 table=%s fields=%s err=%s",
|
||
table,
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columns,
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exc,
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extra=LOG_EXTRA,
|
||
)
|
||
continue
|
||
if not row:
|
||
continue
|
||
for column in columns:
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||
value = row[column]
|
||
if value is None:
|
||
continue
|
||
for original in field_map.get((table, column), [f"{table}.{column}"]):
|
||
try:
|
||
results[original] = float(value)
|
||
except (TypeError, ValueError):
|
||
results[original] = value
|
||
|
||
if cache_key is not None and not results:
|
||
cached = self._cache_lookup(self._latest_cache, cache_key)
|
||
if cached is not None:
|
||
LOGGER.debug(
|
||
"使用缓存结果 ts_code=%s trade_date=%s",
|
||
ts_code,
|
||
trade_date,
|
||
extra=LOG_EXTRA,
|
||
)
|
||
return deepcopy(cached)
|
||
if cache_key is not None and results:
|
||
self._cache_store(
|
||
self._latest_cache,
|
||
cache_key,
|
||
deepcopy(results),
|
||
self.latest_cache_size,
|
||
)
|
||
return results
|
||
|
||
def fetch_series(
|
||
self,
|
||
table: str,
|
||
column: str,
|
||
ts_code: str,
|
||
end_date: str,
|
||
window: int,
|
||
auto_refresh: bool = True,
|
||
) -> List[Tuple[str, float]]:
|
||
"""Return descending time series tuples within the specified window.
|
||
|
||
Args:
|
||
table: 表名
|
||
column: 列名
|
||
ts_code: 证券代码
|
||
end_date: 结束日期
|
||
window: 时间窗口大小
|
||
auto_refresh: 是否在数据不足时自动触发补数
|
||
"""
|
||
|
||
if window <= 0:
|
||
return []
|
||
window = min(window, self.MAX_WINDOW)
|
||
resolved_field = self.resolve_field(f"{table}.{column}")
|
||
if not resolved_field:
|
||
LOGGER.debug(
|
||
"时间序列字段不存在 table=%s column=%s",
|
||
table,
|
||
column,
|
||
extra=LOG_EXTRA,
|
||
)
|
||
return []
|
||
table, resolved = resolved_field
|
||
|
||
# 检查是否需要自动补数
|
||
if auto_refresh:
|
||
self._refresh.ensure_for_series(end_date, table)
|
||
|
||
cache_key: Optional[Tuple[Any, ...]] = None
|
||
if self.enable_cache:
|
||
cache_key = (table, resolved, ts_code, end_date, window)
|
||
cached = self._cache_lookup(self._series_cache, cache_key)
|
||
if cached is not None:
|
||
return [tuple(item) for item in cached]
|
||
|
||
try:
|
||
rows = self._query_engine.fetch_series(table, resolved, ts_code, end_date, window)
|
||
except Exception as exc: # noqa: BLE001
|
||
LOGGER.debug(
|
||
"时间序列查询失败 table=%s column=%s err=%s",
|
||
table,
|
||
resolved,
|
||
exc,
|
||
extra=LOG_EXTRA,
|
||
)
|
||
if cache_key is not None:
|
||
cached = self._cache_lookup(self._series_cache, cache_key)
|
||
if cached is not None:
|
||
LOGGER.debug(
|
||
"使用缓存时间序列 table=%s column=%s ts_code=%s",
|
||
table,
|
||
resolved,
|
||
ts_code,
|
||
extra=LOG_EXTRA,
|
||
)
|
||
return [tuple(item) for item in cached]
|
||
return []
|
||
series: List[Tuple[str, float]] = []
|
||
for row in rows:
|
||
value = row[resolved]
|
||
trade_dt = row["trade_date"]
|
||
if value is None or trade_dt is None:
|
||
continue
|
||
try:
|
||
series.append((trade_dt, float(value)))
|
||
except (TypeError, ValueError):
|
||
continue
|
||
if cache_key is not None and series:
|
||
self._cache_store(
|
||
self._series_cache,
|
||
cache_key,
|
||
tuple(series),
|
||
self.series_cache_size,
|
||
)
|
||
return series
|
||
|
||
def fetch_batch_latest(
|
||
self,
|
||
ts_codes: List[str],
|
||
trade_date: str,
|
||
fields: Iterable[str],
|
||
auto_refresh: bool = True,
|
||
) -> Dict[str, Dict[str, Any]]:
|
||
"""批次化获取多个证券的最新字段数据
|
||
|
||
Args:
|
||
ts_codes: 证券代码列表
|
||
trade_date: 交易日
|
||
fields: 要查询的字段列表
|
||
auto_refresh: 是否在数据不足时自动触发补数
|
||
|
||
Returns:
|
||
证券代码到字段数据的映射
|
||
"""
|
||
if not ts_codes:
|
||
return {}
|
||
|
||
field_list = [str(item) for item in fields if item]
|
||
if not field_list:
|
||
return {}
|
||
|
||
# 检查是否需要自动补数
|
||
if auto_refresh:
|
||
self._refresh.ensure_for_latest(trade_date, field_list)
|
||
|
||
# 按表分组字段
|
||
field_groups = {}
|
||
for field_name in field_list:
|
||
resolved = self.resolve_field(field_name)
|
||
if not resolved:
|
||
continue
|
||
table, column = resolved
|
||
field_groups.setdefault(table, set()).add(column)
|
||
|
||
batch_data = {}
|
||
|
||
# 对每个表进行批量查询
|
||
for table, columns in field_groups.items():
|
||
if not ts_codes:
|
||
continue
|
||
|
||
# 构建批量查询SQL
|
||
placeholders = ','.join(['?'] * len(ts_codes))
|
||
columns_str = ', '.join(['ts_code', 'trade_date'] + list(columns))
|
||
|
||
query = f"""
|
||
SELECT {columns_str}
|
||
FROM (
|
||
SELECT {columns_str},
|
||
ROW_NUMBER() OVER (PARTITION BY ts_code ORDER BY trade_date DESC) as rn
|
||
FROM {table}
|
||
WHERE ts_code IN ({placeholders}) AND trade_date <= ?
|
||
) WHERE rn = 1
|
||
"""
|
||
|
||
try:
|
||
with db_session(read_only=True) as conn:
|
||
rows = conn.execute(query, (*ts_codes, trade_date)).fetchall()
|
||
for row in rows:
|
||
ts_code = row['ts_code']
|
||
batch_data.setdefault(ts_code, {})
|
||
|
||
for column in columns:
|
||
field_name = f"{table}.{column}"
|
||
try:
|
||
batch_data[ts_code][field_name] = float(row[column])
|
||
except (TypeError, ValueError):
|
||
batch_data[ts_code][field_name] = row[column]
|
||
except Exception as e:
|
||
LOGGER.warning(
|
||
"批次化字段查询失败 table=%s err=%s",
|
||
table, str(e),
|
||
extra=LOG_EXTRA
|
||
)
|
||
# 失败时回退到单条查询
|
||
for ts_code in ts_codes:
|
||
try:
|
||
latest_fields = self.fetch_latest(ts_code, trade_date, [f"{table}.{col}" for col in columns])
|
||
batch_data.setdefault(ts_code, {}).update(latest_fields)
|
||
except Exception as inner_e:
|
||
LOGGER.debug(
|
||
"单条字段查询失败 ts_code=%s table=%s err=%s",
|
||
ts_code, table, str(inner_e),
|
||
extra=LOG_EXTRA
|
||
)
|
||
|
||
return batch_data
|
||
|
||
def check_batch_data_sufficiency(
|
||
self,
|
||
ts_codes: List[str],
|
||
trade_date: str,
|
||
min_data_count: int = 60,
|
||
) -> Set[str]:
|
||
"""批次化检查多个证券的数据充分性
|
||
|
||
Args:
|
||
ts_codes: 证券代码列表
|
||
trade_date: 交易日
|
||
min_data_count: 最小数据条数要求
|
||
|
||
Returns:
|
||
数据充分的证券代码集合
|
||
"""
|
||
if not ts_codes:
|
||
return set()
|
||
|
||
sufficient_codes = set()
|
||
|
||
# 使用IN查询批量检查数据充分性
|
||
placeholders = ','.join(['?'] * len(ts_codes))
|
||
query = f"""
|
||
SELECT ts_code, COUNT(*) as data_count
|
||
FROM daily
|
||
WHERE ts_code IN ({placeholders}) AND trade_date <= ?
|
||
GROUP BY ts_code
|
||
HAVING COUNT(*) >= ?
|
||
"""
|
||
|
||
try:
|
||
with db_session(read_only=True) as conn:
|
||
rows = conn.execute(query, (*ts_codes, trade_date, min_data_count)).fetchall()
|
||
for row in rows:
|
||
ts_code = row['ts_code']
|
||
sufficient_codes.add(ts_code)
|
||
except Exception as e:
|
||
LOGGER.warning(
|
||
"批次化数据充分性检查失败 err=%s",
|
||
str(e),
|
||
extra=LOG_EXTRA
|
||
)
|
||
# 失败时回退到单条检查
|
||
for ts_code in ts_codes:
|
||
if check_data_sufficiency(ts_code, trade_date):
|
||
sufficient_codes.add(ts_code)
|
||
|
||
return sufficient_codes
|
||
|
||
def register_refresh_callback(
|
||
self,
|
||
start: date | str,
|
||
end: date | str,
|
||
callback: Callable[[], None],
|
||
) -> None:
|
||
"""Register a hook invoked after background refresh completes for the window."""
|
||
|
||
if callback is None:
|
||
return
|
||
start_date = _coerce_date(start)
|
||
end_date = _coerce_date(end)
|
||
if not start_date or not end_date:
|
||
LOGGER.debug(
|
||
"忽略无效补数回调窗口 start=%s end=%s",
|
||
start,
|
||
end,
|
||
extra=LOG_EXTRA,
|
||
)
|
||
return
|
||
key = f"{start_date}_{end_date}"
|
||
with self._refresh_lock:
|
||
bucket = self._refresh_callbacks.setdefault(key, [])
|
||
if callback not in bucket:
|
||
bucket.append(callback)
|
||
|
||
def get_news_data(
|
||
self,
|
||
ts_code: str,
|
||
trade_date: str,
|
||
limit: int = 30
|
||
) -> List[Dict[str, Any]]:
|
||
"""获取新闻数据（简化实现）
|
||
|
||
Args:
|
||
ts_code: 股票代码
|
||
trade_date: 交易日期
|
||
limit: 返回的新闻条数限制
|
||
|
||
Returns:
|
||
新闻数据列表，包含sentiment、heat、entities等字段
|
||
"""
|
||
# TODO: 使用真实新闻数据库替换随机生成的占位数据
|
||
return [
|
||
{
|
||
"sentiment": np.random.uniform(-1, 1),
|
||
"heat": np.random.uniform(0, 1),
|
||
"entities": "股票,市场,投资"
|
||
}
|
||
for _ in range(min(limit, 5))
|
||
]
|
||
|
||
def _lookup_industry(self, ts_code: str) -> Optional[str]:
|
||
"""查找股票所属行业
|
||
|
||
Args:
|
||
ts_code: 股票代码
|
||
|
||
Returns:
|
||
行业代码或名称，找不到时返回None
|
||
"""
|
||
# TODO: 替换为真实行业映射逻辑（当前仅为占位数据）
|
||
industry_mapping = {
|
||
"000001.SZ": "银行",
|
||
"000002.SZ": "房地产",
|
||
"000858.SZ": "食品饮料",
|
||
"000962.SZ": "医药生物",
|
||
}
|
||
return industry_mapping.get(ts_code, "其他")
|
||
|
||
def _derived_industry_sentiment(self, industry: str, trade_date: str) -> Optional[float]:
|
||
"""计算行业情绪得分
|
||
|
||
Args:
|
||
industry: 行业代码或名称
|
||
trade_date: 交易日期
|
||
|
||
Returns:
|
||
行业情绪得分，找不到时返回None
|
||
"""
|
||
# TODO: 接入行业情绪数据源，当前随机值仅用于占位显示
|
||
return np.random.uniform(-1, 1)
|
||
|
||
def get_industry_stocks(self, industry: str) -> List[str]:
|
||
"""获取同行业股票列表
|
||
|
||
Args:
|
||
industry: 行业代码或名称
|
||
|
||
Returns:
|
||
同行业股票代码列表
|
||
"""
|
||
# TODO: 使用实际行业成分数据替换占位列表
|
||
industry_stocks = {
|
||
"银行": ["000001.SZ", "002142.SZ", "600036.SH"],
|
||
"房地产": ["000002.SZ", "000402.SZ", "600048.SH"],
|
||
"食品饮料": ["000858.SZ", "600519.SH", "000568.SZ"],
|
||
"医药生物": ["000962.SZ", "600276.SH", "300003.SZ"],
|
||
}
|
||
return industry_stocks.get(industry, [])
|
||
|
||
def fetch_flags(
|
||
self,
|
||
table: str,
|
||
ts_code: str,
|
||
trade_date: str,
|
||
where_clause: str,
|
||
params: Sequence[object],
|
||
auto_refresh: bool = True,
|
||
) -> bool:
|
||
"""Generic helper to test if a record exists (used for limit/suspend lookups)."""
|
||
|
||
if not _is_safe_identifier(table):
|
||
return False
|
||
parsed_date = _parse_trade_date(trade_date)
|
||
trade_key = parsed_date.strftime("%Y%m%d") if parsed_date else str(trade_date)
|
||
|
||
if auto_refresh and parsed_date and self.check_data_availability(trade_key, {table}):
|
||
self._trigger_background_refresh(trade_key)
|
||
if hasattr(time, "sleep"):
|
||
time.sleep(0.5)
|
||
|
||
if table == "suspend":
|
||
query = (
|
||
"SELECT 1 FROM suspend "
|
||
"WHERE ts_code = ? "
|
||
"AND suspend_date <= ? "
|
||
"AND (resume_date IS NULL OR resume_date = '' OR resume_date > ?) "
|
||
"LIMIT 1"
|
||
)
|
||
bind_params = (ts_code, trade_key, trade_key)
|
||
else:
|
||
clauses = ["ts_code = ?"]
|
||
bind_params_list: List[object] = [ts_code]
|
||
clause_text = (where_clause or "").strip()
|
||
if clause_text:
|
||
clauses.append(clause_text)
|
||
bind_params_list.extend(params)
|
||
query = f"SELECT 1 FROM {table} WHERE {' AND '.join(clauses)} LIMIT 1"
|
||
bind_params = tuple(bind_params_list)
|
||
|
||
try:
|
||
with db_session(read_only=True) as conn:
|
||
try:
|
||
row = conn.execute(query, bind_params).fetchone()
|
||
except Exception as exc: # noqa: BLE001
|
||
LOGGER.debug(
|
||
"flag 查询失败 table=%s where=%s err=%s",
|
||
table,
|
||
where_clause,
|
||
exc,
|
||
extra=LOG_EXTRA,
|
||
)
|
||
return False
|
||
except sqlite3.OperationalError as exc:
|
||
LOGGER.debug(
|
||
"flag 查询连接失败 table=%s err=%s",
|
||
table,
|
||
exc,
|
||
extra=LOG_EXTRA,
|
||
)
|
||
return False
|
||
return row is not None
|
||
|
||
def fetch_table_rows(
|
||
self,
|
||
table: str,
|
||
ts_code: str,
|
||
trade_date: str,
|
||
window: int,
|
||
auto_refresh: bool = True,
|
||
) -> List[Dict[str, object]]:
|
||
if window <= 0:
|
||
return []
|
||
window = min(window, self.MAX_WINDOW)
|
||
|
||
# 检查是否需要自动补数
|
||
if auto_refresh:
|
||
parsed_date = _parse_trade_date(trade_date)
|
||
if parsed_date and self.check_data_availability(trade_date, {table}):
|
||
self._trigger_background_refresh(trade_date)
|
||
# 短暂等待以获取最新数据
|
||
if hasattr(time, 'sleep'):
|
||
time.sleep(0.5)
|
||
|
||
columns = self._get_table_columns(table)
|
||
if not columns:
|
||
LOGGER.debug("表不存在或无字段 table=%s", table, extra=LOG_EXTRA)
|
||
return []
|
||
|
||
try:
|
||
rows = self._query_engine.fetch_table(
|
||
table,
|
||
columns,
|
||
ts_code,
|
||
trade_date if "trade_date" in columns else None,
|
||
window,
|
||
)
|
||
except Exception as exc: # noqa: BLE001
|
||
LOGGER.debug("表查询失败 table=%s err=%s", table, exc, extra=LOG_EXTRA)
|
||
return []
|
||
|
||
return [{col: row[col] for col in columns} for row in rows]
|
||
|
||
def _resolve_derived_field(
|
||
self,
|
||
ts_code: str,
|
||
trade_date: str,
|
||
field: str,
|
||
cache: Dict[str, Any],
|
||
) -> Optional[Any]:
|
||
if field in cache:
|
||
return cache[field]
|
||
|
||
value: Optional[Any] = None
|
||
if field == "factors.mom_20":
|
||
value = self._derived_price_momentum(ts_code, trade_date, 20)
|
||
elif field == "factors.mom_60":
|
||
value = self._derived_price_momentum(ts_code, trade_date, 60)
|
||
elif field == "factors.volat_20":
|
||
value = self._derived_price_volatility(ts_code, trade_date, 20)
|
||
elif field == "factors.turn_20":
|
||
value = self._derived_turnover_mean(ts_code, trade_date, 20)
|
||
elif field == "news.sentiment_index":
|
||
rows = cache.get("__news_rows__")
|
||
if rows is None:
|
||
rows = self._fetch_recent_news(ts_code, trade_date)
|
||
cache["__news_rows__"] = rows
|
||
value = self._news_sentiment_from_rows(rows)
|
||
elif field == "news.heat_score":
|
||
rows = cache.get("__news_rows__")
|
||
if rows is None:
|
||
rows = self._fetch_recent_news(ts_code, trade_date)
|
||
cache["__news_rows__"] = rows
|
||
value = self._news_heat_from_rows(rows)
|
||
elif field == "macro.industry_heat":
|
||
value = self._derived_industry_heat(ts_code, trade_date)
|
||
elif field in {"macro.relative_strength", "index.performance_peers"}:
|
||
value = self._derived_relative_strength(ts_code, trade_date, cache)
|
||
|
||
cache[field] = value
|
||
return value
|
||
|
||
def _derived_price_momentum(
|
||
self,
|
||
ts_code: str,
|
||
trade_date: str,
|
||
window: int,
|
||
) -> Optional[float]:
|
||
series = self.fetch_series("daily", "close", ts_code, trade_date, window)
|
||
values = [value for _dt, value in series]
|
||
if not values:
|
||
return None
|
||
return momentum(values, window)
|
||
|
||
def _derived_price_volatility(
|
||
self,
|
||
ts_code: str,
|
||
trade_date: str,
|
||
window: int,
|
||
) -> Optional[float]:
|
||
series = self.fetch_series("daily", "close", ts_code, trade_date, window)
|
||
values = [value for _dt, value in series]
|
||
if len(values) < 2:
|
||
return None
|
||
return volatility(values, window)
|
||
|
||
def _derived_turnover_mean(
|
||
self,
|
||
ts_code: str,
|
||
trade_date: str,
|
||
window: int,
|
||
) -> Optional[float]:
|
||
series = self.fetch_series(
|
||
"daily_basic",
|
||
"turnover_rate",
|
||
ts_code,
|
||
trade_date,
|
||
window,
|
||
)
|
||
values = [value for _dt, value in series]
|
||
if not values:
|
||
return None
|
||
return rolling_mean(values, window)
|
||
|
||
def _fetch_recent_news(
|
||
self,
|
||
ts_code: str,
|
||
trade_date: str,
|
||
days: int = 3,
|
||
limit: int = 120,
|
||
) -> List[Dict[str, Any]]:
|
||
baseline = _parse_trade_date(trade_date)
|
||
if baseline is None:
|
||
return []
|
||
start = _start_of_day(baseline - timedelta(days=days))
|
||
end = _end_of_day(baseline)
|
||
query = (
|
||
"SELECT sentiment, heat FROM news "
|
||
"WHERE ts_code = ? AND pub_time BETWEEN ? AND ? "
|
||
"ORDER BY pub_time DESC LIMIT ?"
|
||
)
|
||
try:
|
||
with db_session(read_only=True) as conn:
|
||
rows = conn.execute(query, (ts_code, start, end, limit)).fetchall()
|
||
except sqlite3.OperationalError as exc:
|
||
LOGGER.debug(
|
||
"新闻查询连接失败 ts_code=%s err=%s",
|
||
ts_code,
|
||
exc,
|
||
extra=LOG_EXTRA,
|
||
)
|
||
return []
|
||
except Exception as exc: # noqa: BLE001
|
||
LOGGER.debug(
|
||
"新闻查询失败 ts_code=%s err=%s",
|
||
ts_code,
|
||
exc,
|
||
extra=LOG_EXTRA,
|
||
)
|
||
return []
|
||
return [dict(row) for row in rows]
|
||
|
||
@staticmethod
|
||
def _news_sentiment_from_rows(rows: List[Dict[str, Any]]) -> Optional[float]:
|
||
sentiments: List[float] = []
|
||
for row in rows:
|
||
value = row.get("sentiment")
|
||
if value is None:
|
||
continue
|
||
try:
|
||
sentiments.append(float(value))
|
||
except (TypeError, ValueError):
|
||
continue
|
||
if not sentiments:
|
||
return None
|
||
avg = sum(sentiments) / len(sentiments)
|
||
return max(-1.0, min(1.0, avg))
|
||
|
||
@staticmethod
|
||
def _news_heat_from_rows(rows: List[Dict[str, Any]]) -> Optional[float]:
|
||
if not rows:
|
||
return None
|
||
total_heat = 0.0
|
||
for row in rows:
|
||
value = row.get("heat")
|
||
if value is None:
|
||
continue
|
||
try:
|
||
total_heat += max(float(value), 0.0)
|
||
except (TypeError, ValueError):
|
||
continue
|
||
if total_heat > 0:
|
||
return normalize(total_heat, factor=100.0)
|
||
return normalize(len(rows), factor=20.0)
|
||
|
||
def _derived_industry_heat(self, ts_code: str, trade_date: str) -> Optional[float]:
|
||
industry = self._lookup_industry(ts_code)
|
||
if not industry:
|
||
return None
|
||
query = (
|
||
"SELECT heat FROM heat_daily "
|
||
"WHERE scope = ? AND key = ? AND trade_date <= ? "
|
||
"ORDER BY trade_date DESC LIMIT 1"
|
||
)
|
||
try:
|
||
with db_session(read_only=True) as conn:
|
||
row = conn.execute(query, ("industry", industry, trade_date)).fetchone()
|
||
except sqlite3.OperationalError as exc:
|
||
LOGGER.debug(
|
||
"行业热度查询失败 ts_code=%s err=%s",
|
||
ts_code,
|
||
exc,
|
||
extra=LOG_EXTRA,
|
||
)
|
||
return None
|
||
except Exception as exc: # noqa: BLE001
|
||
LOGGER.debug(
|
||
"行业热度读取异常 ts_code=%s err=%s",
|
||
ts_code,
|
||
exc,
|
||
extra=LOG_EXTRA,
|
||
)
|
||
return None
|
||
if not row:
|
||
return None
|
||
heat_value = row["heat"]
|
||
if heat_value is None:
|
||
return None
|
||
return normalize(heat_value, factor=100.0)
|
||
|
||
def _lookup_industry(self, ts_code: str) -> Optional[str]:
|
||
cache = getattr(self, "_industry_cache", None)
|
||
if cache is None:
|
||
cache = {}
|
||
self._industry_cache = cache
|
||
if ts_code in cache:
|
||
return cache[ts_code]
|
||
query = "SELECT industry FROM stock_basic WHERE ts_code = ?"
|
||
try:
|
||
with db_session(read_only=True) as conn:
|
||
row = conn.execute(query, (ts_code,)).fetchone()
|
||
except sqlite3.OperationalError as exc:
|
||
LOGGER.debug(
|
||
"行业查询连接失败 ts_code=%s err=%s",
|
||
ts_code,
|
||
exc,
|
||
extra=LOG_EXTRA,
|
||
)
|
||
cache[ts_code] = None
|
||
return None
|
||
except Exception as exc: # noqa: BLE001
|
||
LOGGER.debug(
|
||
"行业查询失败 ts_code=%s err=%s",
|
||
ts_code,
|
||
exc,
|
||
extra=LOG_EXTRA,
|
||
)
|
||
cache[ts_code] = None
|
||
return None
|
||
industry = None
|
||
if row:
|
||
industry = row["industry"]
|
||
cache[ts_code] = industry
|
||
return industry
|
||
|
||
def _derived_relative_strength(
|
||
self,
|
||
ts_code: str,
|
||
trade_date: str,
|
||
cache: Dict[str, Any],
|
||
) -> Optional[float]:
|
||
window = 20
|
||
series = self.fetch_series("daily", "close", ts_code, trade_date, max(window, 30))
|
||
values = [value for _dt, value in series]
|
||
if not values:
|
||
return None
|
||
stock_momentum = momentum(values, window)
|
||
bench_key = f"__benchmark_mom_{window}"
|
||
benchmark = cache.get(bench_key)
|
||
if benchmark is None:
|
||
benchmark = self._index_momentum(trade_date, window)
|
||
cache[bench_key] = benchmark
|
||
diff = stock_momentum if benchmark is None else stock_momentum - benchmark
|
||
diff = max(-0.2, min(0.2, diff))
|
||
return (diff + 0.2) / 0.4
|
||
|
||
def _index_momentum(self, trade_date: str, window: int) -> Optional[float]:
|
||
series = self.fetch_series(
|
||
"index_daily",
|
||
"close",
|
||
self.BENCHMARK_INDEX,
|
||
trade_date,
|
||
window,
|
||
)
|
||
values = [value for _dt, value in series]
|
||
if not values:
|
||
return None
|
||
return momentum(values, window)
|
||
|
||
def resolve_field(self, field: str) -> Optional[Tuple[str, str]]:
|
||
normalized = _safe_split(field)
|
||
if not normalized:
|
||
return None
|
||
table, column = normalized
|
||
resolved = self._resolve_column(table, column)
|
||
if not resolved:
|
||
# Certain fields are derived at runtime and intentionally
|
||
# do not require physical columns. Suppress noisy debug logs
|
||
# for those known derived fields so startup isn't spammy.
|
||
derived_fields = {
|
||
"macro.industry_heat",
|
||
"macro.relative_strength",
|
||
"index.performance_peers",
|
||
"news.heat_score",
|
||
"news.sentiment_index",
|
||
}
|
||
if f"{table}.{column}" in derived_fields:
|
||
return None
|
||
LOGGER.debug(
|
||
"字段不存在 table=%s column=%s",
|
||
table,
|
||
column,
|
||
extra=LOG_EXTRA,
|
||
)
|
||
return None
|
||
return table, resolved
|
||
|
||
def _get_table_columns(self, table: str) -> Optional[List[str]]:
|
||
if not _is_safe_identifier(table):
|
||
return None
|
||
cache = getattr(self, "_column_cache", None)
|
||
if cache is None:
|
||
cache = {}
|
||
self._column_cache = cache
|
||
if table in cache:
|
||
return cache[table]
|
||
try:
|
||
with db_session(read_only=True) as conn:
|
||
rows = conn.execute(f"PRAGMA table_info({table})").fetchall()
|
||
except Exception as exc: # noqa: BLE001
|
||
LOGGER.debug("获取表字段失败 table=%s err=%s", table, exc, extra=LOG_EXTRA)
|
||
cache[table] = None
|
||
return None
|
||
if not rows:
|
||
cache[table] = None
|
||
return None
|
||
columns = [row["name"] for row in rows if row["name"]]
|
||
cache[table] = columns
|
||
return columns
|
||
|
||
def _cache_lookup(self, cache: OrderedDict, key: Tuple[Any, ...]) -> Optional[Any]:
|
||
if key in cache:
|
||
cache.move_to_end(key)
|
||
return cache[key]
|
||
return None
|
||
|
||
def _cache_store(
|
||
self,
|
||
cache: OrderedDict,
|
||
key: Tuple[Any, ...],
|
||
value: Any,
|
||
limit: int,
|
||
) -> None:
|
||
if not self.enable_cache or limit <= 0:
|
||
return
|
||
cache[key] = value
|
||
cache.move_to_end(key)
|
||
while len(cache) > limit:
|
||
cache.popitem(last=False)
|
||
|
||
def check_data_availability(
|
||
self,
|
||
trade_date: str,
|
||
tables: Set[str] = None,
|
||
threshold: float = 0.8,
|
||
) -> bool:
|
||
"""检查指定交易日的数据是否可用，如不可用则返回True（需要补数）。
|
||
|
||
Args:
|
||
trade_date: 要检查的交易日
|
||
tables: 要检查的表集合，默认检查主要行情表
|
||
threshold: 数据覆盖率阈值，低于此值需要补数
|
||
|
||
Returns:
|
||
bool: True表示数据不足，需要补数
|
||
"""
|
||
# 如果配置了强制刷新，则始终返回需要补数
|
||
if get_config().force_refresh:
|
||
return True
|
||
|
||
# 如果未启用自动更新，则不进行补数
|
||
if not get_config().auto_update_data:
|
||
return False
|
||
|
||
# 默认检查的表
|
||
if tables is None:
|
||
tables = {"daily", "daily_basic", "stock_basic", "trade_cal"}
|
||
|
||
try:
|
||
# 解析交易日
|
||
parsed_date = _parse_trade_date(trade_date)
|
||
if not parsed_date:
|
||
LOGGER.debug("无法解析交易日: %s", trade_date, extra=LOG_EXTRA)
|
||
return False
|
||
|
||
# 计算检查窗口
|
||
end_date = parsed_date.strftime('%Y%m%d')
|
||
start_date = (parsed_date - timedelta(days=self.AUTO_REFRESH_WINDOW)).strftime('%Y%m%d')
|
||
|
||
# 构建缓存键
|
||
cache_key = f"{start_date}_{end_date}_{'_'.join(sorted(tables))}"
|
||
|
||
# 检查缓存
|
||
if cache_key in self._coverage_cache:
|
||
coverage = self._coverage_cache[cache_key]
|
||
current_time = time.time() if hasattr(time, 'time') else 0
|
||
if coverage.get('timestamp', 0) > current_time - 300: # 5分钟内有效
|
||
# 检查是否需要补数
|
||
for table in tables:
|
||
table_coverage = coverage.get(table, {})
|
||
if table_coverage.get('coverage', 0) < threshold:
|
||
return True
|
||
return False
|
||
|
||
# 收集数据覆盖情况
|
||
if collect_data_coverage is None:
|
||
LOGGER.error("collect_data_coverage 函数不可用，请检查导入配置", extra=LOG_EXTRA)
|
||
return False
|
||
|
||
coverage = collect_data_coverage(
|
||
date.fromisoformat(start_date[:4] + '-' + start_date[4:6] + '-' + start_date[6:8]),
|
||
date.fromisoformat(end_date[:4] + '-' + end_date[4:6] + '-' + end_date[6:8])
|
||
)
|
||
|
||
# 保存到缓存
|
||
coverage['timestamp'] = time.time() if hasattr(time, 'time') else 0
|
||
self._coverage_cache[cache_key] = coverage
|
||
|
||
# 检查是否需要补数
|
||
for table in tables:
|
||
table_coverage = coverage.get(table, {})
|
||
if table_coverage.get('coverage', 0) < threshold:
|
||
return True
|
||
|
||
except Exception as exc:
|
||
LOGGER.exception("检查数据可用性失败: %s", exc, extra=LOG_EXTRA)
|
||
# 出错时保守处理，不触发补数
|
||
return False
|
||
|
||
return False
|
||
|
||
def _trigger_background_refresh(self, target_date: str) -> None:
|
||
"""在后台线程触发数据补数。"""
|
||
parsed_date = _parse_trade_date(target_date)
|
||
if not parsed_date:
|
||
return
|
||
|
||
# 构建补数日期范围
|
||
end_date = parsed_date.date()
|
||
start_date = end_date - timedelta(days=self.AUTO_REFRESH_WINDOW)
|
||
refresh_key = f"{start_date}_{end_date}"
|
||
|
||
# 检查是否已经在补数中
|
||
with self._refresh_lock:
|
||
if self._refresh_in_progress.get(refresh_key, False):
|
||
LOGGER.debug("数据补数已经在进行中: %s", refresh_key, extra=LOG_EXTRA)
|
||
return
|
||
|
||
self._refresh_in_progress[refresh_key] = True
|
||
self._refresh_callbacks.setdefault(refresh_key, [])
|
||
|
||
def refresh_task():
|
||
try:
|
||
LOGGER.info("开始后台数据补数: %s 至 %s", start_date, end_date, extra=LOG_EXTRA)
|
||
|
||
# 执行补数
|
||
if ensure_data_coverage is None:
|
||
LOGGER.error("ensure_data_coverage 函数不可用，请检查导入配置", extra=LOG_EXTRA)
|
||
with self._refresh_lock:
|
||
self._refresh_in_progress[refresh_key] = False
|
||
return
|
||
|
||
ensure_data_coverage(
|
||
start_date,
|
||
end_date,
|
||
force=False,
|
||
progress_hook=None
|
||
)
|
||
|
||
LOGGER.info("后台数据补数完成: %s 至 %s", start_date, end_date, extra=LOG_EXTRA)
|
||
|
||
# 清除缓存，强制重新加载数据
|
||
self._latest_cache.clear()
|
||
self._series_cache.clear()
|
||
self._coverage_cache.clear()
|
||
|
||
# 执行回调
|
||
with self._refresh_lock:
|
||
callbacks = self._refresh_callbacks.pop(refresh_key, [])
|
||
self._refresh_in_progress[refresh_key] = False
|
||
|
||
if callbacks:
|
||
LOGGER.info(
|
||
"执行补数回调 count=%s key=%s",
|
||
len(callbacks),
|
||
refresh_key,
|
||
extra=LOG_EXTRA,
|
||
)
|
||
for callback in callbacks:
|
||
try:
|
||
callback()
|
||
except Exception as exc:
|
||
LOGGER.exception("补数回调执行失败: %s", exc, extra=LOG_EXTRA)
|
||
|
||
except Exception as exc:
|
||
LOGGER.exception("后台数据补数失败: %s", exc, extra=LOG_EXTRA)
|
||
with self._refresh_lock:
|
||
self._refresh_in_progress[refresh_key] = False
|
||
|
||
# 启动后台线程
|
||
thread = threading.Thread(target=refresh_task, daemon=True)
|
||
thread.start()
|
||
|
||
def is_refreshing(self, start_date: str = None, end_date: str = None) -> bool:
|
||
"""检查指定日期范围是否正在补数中。"""
|
||
with self._refresh_lock:
|
||
if not start_date and not end_date:
|
||
# 检查是否有任何补数正在进行
|
||
return any(self._refresh_in_progress.values())
|
||
|
||
# 检查指定日期范围
|
||
for key, in_progress in self._refresh_in_progress.items():
|
||
if in_progress and key.startswith(start_date or '') and key.endswith(end_date or ''):
|
||
return True
|
||
|
||
return False
|
||
|
||
def wait_for_refresh_complete(
|
||
self,
|
||
timeout: float = None,
|
||
start_date: str = None,
|
||
end_date: str = None
|
||
) -> bool:
|
||
"""等待数据补数完成。
|
||
|
||
Args:
|
||
timeout: 超时时间（秒），默认为MAX_REFRESH_WAIT
|
||
start_date: 开始日期
|
||
end_date: 结束日期
|
||
|
||
Returns:
|
||
bool: True表示补数已完成，False表示超时
|
||
"""
|
||
if timeout is None:
|
||
timeout = self.MAX_REFRESH_WAIT
|
||
|
||
start_time = time.time() if hasattr(time, 'time') else 0
|
||
current_time_func = time.time if hasattr(time, 'time') else lambda: 0
|
||
while current_time_func() - start_time < timeout:
|
||
if not self.is_refreshing(start_date, end_date):
|
||
return True
|
||
|
||
# 短暂休眠后再次检查
|
||
if hasattr(time, 'sleep'):
|
||
time.sleep(min(self.REFRESH_RETRY_INTERVAL, timeout / 10))
|
||
|
||
return False
|
||
|
||
def on_data_refresh(
|
||
self,
|
||
callback: Callable,
|
||
start_date: str = None,
|
||
end_date: str = None
|
||
) -> None:
|
||
"""注册数据补数完成的回调函数。"""
|
||
if start_date and end_date:
|
||
refresh_key = f"{start_date}_{end_date}"
|
||
with self._refresh_lock:
|
||
self._refresh_callbacks.setdefault(refresh_key, []).append(callback)
|
||
# 如果当前没有补数在进行，则直接调用回调
|
||
if not self._refresh_in_progress.get(refresh_key, False):
|
||
try:
|
||
callback()
|
||
except Exception as exc:
|
||
LOGGER.exception("补数回调执行失败: %s", exc, extra=LOG_EXTRA)
|
||
|
||
def set_auto_refresh_window(self, days: int) -> None:
|
||
"""设置自动补数的时间窗口。
|
||
|
||
Args:
|
||
days: 自动补数的天数窗口
|
||
"""
|
||
if days > 0:
|
||
self.AUTO_REFRESH_WINDOW = days
|
||
LOGGER.info("自动补数窗口已设置为 %d 天", days, extra=LOG_EXTRA)
|
||
|
||
def set_refresh_retry_interval(self, seconds: int) -> None:
|
||
"""设置补数检查的重试间隔。
|
||
|
||
Args:
|
||
seconds: 重试间隔（秒）
|
||
"""
|
||
if seconds > 0:
|
||
self.REFRESH_RETRY_INTERVAL = seconds
|
||
LOGGER.info("补数重试间隔已设置为 %d 秒", seconds, extra=LOG_EXTRA)
|
||
|
||
def set_max_refresh_wait(self, seconds: int) -> None:
|
||
"""设置最大等待补数完成时间。
|
||
|
||
Args:
|
||
seconds: 最大等待时间（秒）
|
||
"""
|
||
if seconds > 0:
|
||
self.MAX_REFRESH_WAIT = seconds
|
||
LOGGER.info("最大补数等待时间已设置为 %d 秒", seconds, extra=LOG_EXTRA)
|
||
|
||
def force_refresh_data(self, start_date: str, end_date: str) -> bool:
|
||
"""强制刷新指定日期范围内的数据。
|
||
|
||
Args:
|
||
start_date: 开始日期（格式：YYYYMMDD）
|
||
end_date: 结束日期（格式：YYYYMMDD）
|
||
|
||
Returns:
|
||
bool: 是否成功触发刷新
|
||
"""
|
||
try:
|
||
# 解析日期
|
||
start = _parse_trade_date(start_date)
|
||
end = _parse_trade_date(end_date)
|
||
if not start or not end:
|
||
LOGGER.error("日期格式不正确: %s, %s", start_date, end_date, extra=LOG_EXTRA)
|
||
return False
|
||
|
||
# 触发刷新
|
||
self._trigger_background_refresh(end_date)
|
||
return True
|
||
except Exception as exc:
|
||
LOGGER.exception("强制刷新数据失败: %s", exc, extra=LOG_EXTRA)
|
||
return False
|
||
|
||
def get_index_stocks(
|
||
self,
|
||
index_code: str,
|
||
trade_date: str,
|
||
min_weight: float = 0.0
|
||
) -> List[str]:
|
||
"""获取指数成分股列表。
|
||
|
||
Args:
|
||
index_code: 指数代码(如 000300.SH)
|
||
trade_date: 交易日期
|
||
min_weight: 最小权重筛选
|
||
|
||
Returns:
|
||
成分股代码列表
|
||
"""
|
||
try:
|
||
with db_session(read_only=True) as conn:
|
||
# 获取小于等于给定日期的最新一期成分股
|
||
rows = conn.execute(
|
||
"""
|
||
SELECT DISTINCT ts_code
|
||
FROM index_weight
|
||
WHERE index_code = ?
|
||
AND trade_date = (
|
||
SELECT MAX(trade_date)
|
||
FROM index_weight
|
||
WHERE index_code = ?
|
||
AND trade_date <= ?
|
||
)
|
||
AND weight >= ?
|
||
ORDER BY weight DESC
|
||
""",
|
||
(index_code, index_code, trade_date, min_weight)
|
||
).fetchall()
|
||
|
||
return [row["ts_code"] for row in rows if row and row["ts_code"]]
|
||
except Exception as exc:
|
||
LOGGER.exception(
|
||
"获取指数成分股失败 index=%s date=%s err=%s",
|
||
index_code,
|
||
trade_date,
|
||
exc,
|
||
extra=LOG_EXTRA
|
||
)
|
||
return []
|
||
|
||
def get_refresh_status(self) -> Dict[str, Dict[str, Any]]:
|
||
"""获取当前所有补数任务的状态。
|
||
|
||
Returns:
|
||
Dict: 包含所有补数任务状态的字典
|
||
"""
|
||
with self._refresh_lock:
|
||
status = {}
|
||
for key, in_progress in self._refresh_in_progress.items():
|
||
start, end = key.split('_')[:2] if '_' in key else (key, key)
|
||
status[key] = {
|
||
'start_date': start,
|
||
'end_date': end,
|
||
'in_progress': in_progress,
|
||
'callback_count': len(self._refresh_callbacks.get(key, []))
|
||
}
|
||
return status
|
||
|
||
def cancel_all_refresh_tasks(self) -> None:
|
||
"""取消所有正在等待的补数任务回调。
|
||
注意：已经开始执行的补数任务无法取消，但它们的结果将被忽略。
|
||
"""
|
||
with self._refresh_lock:
|
||
self._refresh_callbacks.clear()
|
||
# 保留刷新状态以避免立即重新触发
|
||
LOGGER.info("所有补数任务回调已取消", extra=LOG_EXTRA)
|
||
|
||
def clear_coverage_cache(self) -> None:
|
||
"""清除数据覆盖情况的缓存。"""
|
||
self._coverage_cache.clear()
|
||
LOGGER.info("数据覆盖缓存已清除", extra=LOG_EXTRA)
|
||
|
||
def get_stock_info(self, ts_code: str, trade_date: str = None) -> Optional[Dict[str, Any]]:
|
||
"""获取股票基本信息。
|
||
|
||
Args:
|
||
ts_code: 股票代码
|
||
trade_date: 交易日期，默认为最新日期
|
||
|
||
Returns:
|
||
Dict: 股票基本信息，包含名称、行业等
|
||
"""
|
||
if not trade_date:
|
||
# 如果没有提供交易日期，使用当前日期
|
||
trade_date = datetime.now().strftime("%Y%m%d")
|
||
|
||
try:
|
||
# 获取股票基本信息
|
||
raw_info = self.fetch_latest(
|
||
ts_code=ts_code,
|
||
trade_date=trade_date,
|
||
fields=["stock_basic.name", "stock_basic.industry"]
|
||
)
|
||
if not raw_info:
|
||
return None
|
||
|
||
info: Dict[str, Any] = {"ts_code": ts_code}
|
||
for key, value in raw_info.items():
|
||
if key == "ts_code":
|
||
continue
|
||
alias = key.split(".", 1)[-1] if isinstance(key, str) and "." in key else key
|
||
info[alias] = value
|
||
return info
|
||
except Exception as exc:
|
||
LOGGER.debug(
|
||
"获取股票信息失败 ts_code=%s err=%s",
|
||
ts_code,
|
||
exc,
|
||
extra=LOG_EXTRA
|
||
)
|
||
return None
|
||
|
||
def fetch_latest_factor(self, ts_code: str, factor: str, eval_date: date) -> Optional[float]:
|
||
"""获取指定股票的最新因子值。
|
||
|
||
Args:
|
||
ts_code: 股票代码
|
||
factor: 因子名称
|
||
eval_date: 评估日期
|
||
|
||
Returns:
|
||
float: 因子值，如果获取失败则返回None
|
||
"""
|
||
trade_date = eval_date.strftime("%Y%m%d")
|
||
|
||
try:
|
||
# 构建因子字段名称
|
||
factor_field = f"factors.{factor}"
|
||
|
||
# 获取因子值
|
||
result = self.fetch_latest(
|
||
ts_code=ts_code,
|
||
trade_date=trade_date,
|
||
fields=[factor_field]
|
||
)
|
||
|
||
if not result or factor_field not in result:
|
||
return None
|
||
|
||
return result[factor_field]
|
||
except Exception as exc:
|
||
LOGGER.debug(
|
||
"获取因子值失败 ts_code=%s factor=%s err=%s",
|
||
ts_code,
|
||
factor,
|
||
exc,
|
||
extra=LOG_EXTRA
|
||
)
|
||
return None
|
||
|
||
def get_all_stocks(self, trade_date: str) -> List[str]:
|
||
"""获取所有股票代码列表。
|
||
|
||
Args:
|
||
trade_date: 交易日期
|
||
|
||
Returns:
|
||
List[str]: 股票代码列表
|
||
"""
|
||
try:
|
||
with db_session(read_only=True) as conn:
|
||
# 获取指定日期之前所有有交易的股票
|
||
rows = conn.execute(
|
||
"""
|
||
SELECT DISTINCT ts_code
|
||
FROM daily
|
||
WHERE trade_date <= ?
|
||
ORDER BY ts_code
|
||
""",
|
||
(trade_date,)
|
||
).fetchall()
|
||
|
||
return [row["ts_code"] for row in rows if row and row["ts_code"]]
|
||
except Exception as exc:
|
||
LOGGER.exception(
|
||
"获取所有股票失败 date=%s err=%s",
|
||
trade_date,
|
||
exc,
|
||
extra=LOG_EXTRA
|
||
)
|
||
return []
|
||
|
||
def get_data_coverage(self, start_date: str, end_date: str) -> Dict:
|
||
"""获取指定日期范围内的数据覆盖情况。
|
||
|
||
Args:
|
||
start_date: 开始日期（格式：YYYYMMDD）
|
||
end_date: 结束日期（格式：YYYYMMDD）
|
||
|
||
Returns:
|
||
Dict: 数据覆盖情况的详细信息
|
||
"""
|
||
try:
|
||
# 解析日期
|
||
start = _parse_trade_date(start_date)
|
||
end = _parse_trade_date(end_date)
|
||
if not start or not end:
|
||
LOGGER.error("日期格式不正确: %s, %s", start_date, end_date, extra=LOG_EXTRA)
|
||
return {}
|
||
|
||
# 转换日期格式
|
||
start_d = date.fromisoformat(start.strftime('%Y-%m-%d'))
|
||
end_d = date.fromisoformat(end.strftime('%Y-%m-%d'))
|
||
|
||
# 收集数据覆盖情况
|
||
if collect_data_coverage is None:
|
||
LOGGER.error("collect_data_coverage 函数不可用，请检查导入配置", extra=LOG_EXTRA)
|
||
return {}
|
||
|
||
coverage = collect_data_coverage(start_d, end_d)
|
||
return coverage
|
||
except Exception as exc:
|
||
LOGGER.exception("获取数据覆盖情况失败: %s", exc, extra=LOG_EXTRA)
|
||
return {}
|
||
|
||
def _resolve_column(self, table: str, column: str) -> Optional[str]:
|
||
columns = self._get_table_columns(table)
|
||
if columns is None:
|
||
return None
|
||
alias_map = self.FIELD_ALIASES.get(table, {})
|
||
candidate = alias_map.get(column, column)
|
||
if candidate in columns:
|
||
return candidate
|
||
# Try lower-case or fallback alias normalization
|
||
lowered = candidate.lower()
|
||
for name in columns:
|
||
if name.lower() == lowered:
|
||
return name
|
||
return None
|
||
|
||
# 确保time模块可用
|
||
import sys
|
||
try:
|
||
import time
|
||
except ImportError:
|
||
# 创建一个简单的替代实现
|
||
class TimeStub:
|
||
def time(self):
|
||
return 0
|
||
def sleep(self, seconds):
|
||
pass
|
||
time = TimeStub()
|
||
LOGGER.warning("无法导入time模块，使用替代实现", extra=LOG_EXTRA)
|