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服务器连不上或返回空数据时，自动测速切到延迟最低的可用服务器再试， Python API / CLI / Web API 三入口全部自动生效。 核心改动： - _reconnect.py 新增 select_best_host_sync/async（连接失败 failover） 和 find_working_host_sync/async（空数据逐台实测） - 8 个 client 的 _execute 注入跨主机故障转移（复用 auto_reconnect 开关） - get_market_stat 空数据时按延迟顺序逐台实测找返回数据的服务器 - 新增 _reconnect/_areconnect helper 收敛重建连接副本 - MacClient failover 用 save_best_mac_host（延续 v1.19.4 不污染 best_host） - 顺手修复 test_commands_offline 未使用 import（main CI failure 根因） 测试：925 passed（新增 18 个 failover 测试），ruff/mypy 零新增错误。
864 lines
30 KiB
Python
864 lines
30 KiB
Python
"""MAC 协议扩展市场高层 API：MacExClient（同步）和 AsyncMacExClient（asyncio）。
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期货/港股/美股等扩展市场通过 MAC 协议命令（0x122B/0x122E/0x122D/0x122F/0x2562）
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获取数据，使用 ExTdxConnection（端口 7727，单包握手）。
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"""
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import asyncio
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import logging
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import time
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from collections.abc import Awaitable
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from datetime import date
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from types import TracebackType
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from typing import Any, TypeVar
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import pandas as pd
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from .._df import _to_df
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from .._reconnect import (
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_RETRY_DELAYS,
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AsyncHeartbeatMixin,
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select_best_host_async,
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select_best_host_sync,
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)
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from ..commands.base import BaseCommand
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from ..config import get_best_mac_ex_host, get_mac_ex_hosts, save_best_mac_ex_host
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from ..exceptions import TdxConnectionError
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from ..mac.commands.chart_sampling import ChartSamplingCmd
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from ..mac.commands.symbol_bar import SymbolBarCmd
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from ..mac.commands.symbol_quotes import SymbolQuotesCmd
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from ..mac.commands.symbol_tick_chart import SymbolTickChartCmd
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from ..mac.commands.symbol_transaction import SymbolTransactionCmd
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from ..mac.enums import Adjust, Period, SortOrder, SortType
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from ..mac.models import MacQuoteField
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from ._hk_transaction import (
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_fetch_all_hk_transactions_async,
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_fetch_all_hk_transactions_sync,
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_fetch_hk_transactions_async,
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_fetch_hk_transactions_sync,
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is_hk_stock_market,
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)
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from .commands.get_instrument_count import GetExInstrumentCountCmd
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from .commands.get_instrument_info import GetExInstrumentInfoCmd
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from .commands.login import MacExLoginCmd
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from .transport.async_ import AsyncExTdxConnection
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from .transport.sync import ExTdxConnection, ping_ex_all
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_DEFAULT_PORT = 7727
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_T = TypeVar("_T")
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logger = logging.getLogger(__name__)
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def _quotes_to_df(result: list[MacQuoteField]) -> pd.DataFrame:
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"""将 MacQuoteField 列表展开为 DataFrame。"""
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rows: list[dict[str, Any]] = []
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for item in result:
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row: dict[str, Any] = {"market": item.market, "code": item.code, "name": item.name}
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row.update(item.fields)
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rows.append(row)
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return pd.DataFrame(rows) if rows else pd.DataFrame()
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# ============================================================
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# 同步客户端
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# ============================================================
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class MacExClient:
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"""同步 MAC 协议扩展市场客户端（期货/港股/美股，端口 7727）。
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使用示例::
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with MacExClient() as c:
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df = c.goods_kline(ExMarket.CFFEX_FUTURES, "IFL0", Period.DAILY)
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df = c.goods_quotes([(ExMarket.HK_MAIN_BOARD, "00700")])
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"""
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def __init__(
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self,
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host: str | None = None,
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port: int = _DEFAULT_PORT,
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timeout: float = 15.0,
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auto_reconnect: bool = True,
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) -> None:
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self._host = host if host is not None else get_best_mac_ex_host()
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self._port = port
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self._timeout = timeout
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self._auto_reconnect = auto_reconnect
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self._conn = ExTdxConnection(self._host, port, timeout, mac_ex_mode=True)
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@classmethod
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def from_best_host(
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cls,
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hosts: list[str] | None = None,
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port: int = _DEFAULT_PORT,
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timeout: float = 15.0,
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ping_timeout: float = 5.0,
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auto_reconnect: bool = True,
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) -> "MacExClient":
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"""测量所有 MAC 扩展行情服务器延迟，选最低延迟建立连接。"""
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candidates = hosts or get_mac_ex_hosts()
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ranked = ping_ex_all(candidates, port, ping_timeout)
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best = ranked[0][0] if ranked else candidates[0]
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save_best_mac_ex_host(best)
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return cls(best, port, timeout, auto_reconnect)
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@staticmethod
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def ping_all(
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hosts: list[str] | None = None,
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port: int = _DEFAULT_PORT,
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timeout: float = 5.0,
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) -> list[tuple[str, float]]:
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return ping_ex_all(hosts or get_mac_ex_hosts(), port, timeout)
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# ------------------------------------------------------------------ #
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# 连接管理
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# ------------------------------------------------------------------ #
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def connect(self) -> None:
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self._conn.connect()
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self._login()
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def close(self) -> None:
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self._conn.close()
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def disconnect(self) -> None:
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self.close()
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def ensure_connected(self) -> None:
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"""验证连接存活，断线则自动重建。"""
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try:
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self._execute(GetExInstrumentCountCmd())
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except TdxConnectionError:
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self._conn.close()
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self._conn = ExTdxConnection(self._host, self._port, self._timeout, mac_ex_mode=True)
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self._conn.connect()
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self._login()
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def __enter__(self) -> "MacExClient":
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self.connect()
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return self
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def __exit__(
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self,
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exc_type: type[BaseException] | None,
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exc_val: BaseException | None,
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exc_tb: TracebackType | None,
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) -> None:
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self.close()
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def _login(self) -> None:
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"""执行 MAC EX 登录命令。"""
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self._conn.execute(MacExLoginCmd())
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def _execute(self, cmd: "BaseCommand[_T]") -> _T:
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"""执行命令；断线时指数退避重试，同主机耗尽则跨主机故障转移。
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每次重连后必须重新 ``_login()``（MAC 协议扩展行情特有）。登录握手期的
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``TdxConnectionError`` 与业务请求一样计入退避重试；``TdxCommandError``
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（登录被拒等确定性失败）不重试，直接抛出。跨主机故障转移阶段同样遵循
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``connect + login`` 纳入重试的语义。
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"""
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try:
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return self._conn.execute(cmd)
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except TdxConnectionError:
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if not self._auto_reconnect:
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raise
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last_exc: TdxConnectionError | None = None
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for delay in _RETRY_DELAYS:
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time.sleep(delay)
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self._conn.close()
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self._conn = ExTdxConnection(
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self._host, self._port, self._timeout, mac_ex_mode=True
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)
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# connect + login 纳入重试：登录握手期连接再次断开属可重试语义。
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try:
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self._conn.connect()
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self._login()
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return self._conn.execute(cmd)
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except TdxConnectionError as e:
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last_exc = e
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# 第二阶段：跨主机故障转移——测速切到另一台 MAC 扩展行情服务器
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new_host = select_best_host_sync(
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get_mac_ex_hosts(),
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ping_ex_all,
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save_best_mac_ex_host,
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self._port,
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5.0,
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self._host,
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)
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if new_host is not None:
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self._host = new_host
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self._conn.close()
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self._conn = ExTdxConnection(
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self._host, self._port, self._timeout, mac_ex_mode=True
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)
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try:
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self._conn.connect()
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self._login()
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return self._conn.execute(cmd)
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except TdxConnectionError as e:
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last_exc = e
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raise last_exc # type: ignore[misc]
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# ------------------------------------------------------------------ #
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# 商品列表
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# ------------------------------------------------------------------ #
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def goods_count(self, market: int | None = None) -> int:
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"""获取商品总数。market=None 时返回全市场总数，否则返回指定市场的数量。"""
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if market is None:
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return self._execute(GetExInstrumentCountCmd())
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# 需要二分查找定位市场边界来计数
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offset = self._find_market_offset(market)
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if offset < 0:
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return 0
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total = self._execute(GetExInstrumentCountCmd())
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# 从 offset 开始扫描计数
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n = 0
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page = 1000
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pos = offset
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while pos < total:
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batch = self._execute(GetExInstrumentInfoCmd(start=pos, count=page))
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if not batch:
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break
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for item in batch:
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if item.market == market:
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n += 1
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elif item.market > market:
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return n
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pos += page
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return n
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def goods_list(self, market: int, start: int = 0, count: int = 600) -> pd.DataFrame:
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"""获取扩展市场商品列表（期货合约/港股/美股等）。
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通过 EX 协议的 GetInstrumentInfo 命令获取，按 market 过滤。
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Parameters
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----------
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market : int
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ExMarket 枚举值，如 ExMarket.HK_MAIN_BOARD。
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start : int
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市场内起始偏移。
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count : int
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请求数量。
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"""
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offset = self._find_market_offset(market)
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if offset < 0:
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return pd.DataFrame()
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total = self._execute(GetExInstrumentCountCmd())
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page_size = 1000
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collected: list[Any] = []
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skipped = 0
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pos = offset
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while pos < total and len(collected) < count:
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batch = self._execute(GetExInstrumentInfoCmd(start=pos, count=page_size))
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if not batch:
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break
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for item in batch:
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if item.market == market:
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if skipped < start:
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skipped += 1
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else:
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collected.append(item)
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if len(collected) >= count:
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break
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elif item.market > market:
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break
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else:
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pos += page_size
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continue
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break
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return _to_df(collected)
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def _find_market_offset(self, market: int) -> int:
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"""二分查找定位指定市场在全局商品列表中的起始偏移。"""
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total = self._execute(GetExInstrumentCountCmd())
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if total == 0:
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return -1
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lo, hi = 0, total
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while lo < hi:
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mid = (lo + hi) // 2
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items = self._execute(GetExInstrumentInfoCmd(start=mid, count=1))
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if not items:
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hi = mid
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continue
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m = items[0].market
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if m < market:
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lo = mid + 1
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else:
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hi = mid
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return lo
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# ------------------------------------------------------------------ #
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# 行情
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# ------------------------------------------------------------------ #
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def goods_quotes(
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self,
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stocks: list[tuple[int, str]],
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fields: Any = None,
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) -> pd.DataFrame:
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"""批量获取扩展市场自定义字段报价。
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Parameters
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----------
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stocks : list[tuple[int, str]]
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[(ExMarketcode, code), ...] 列表，最多 80 只。
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fields : Fields | None
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字段选择，默认 PresetField.COMMON。
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"""
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cmd = SymbolQuotesCmd(stocks, fields)
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result: list[MacQuoteField] = self._execute(cmd)
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return _quotes_to_df(result)
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def goods_quotes_list(
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self,
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market: int,
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start: int = 0,
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count: int = 100,
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sort_type: SortType = SortType.CODE,
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sort_order: SortOrder = SortOrder.NONE,
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) -> pd.DataFrame:
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"""获取扩展市场排序报价列表（通过 GoodsList + Quotes 组合）。
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先获取商品列表，再批量查询报价。
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Parameters
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----------
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market : int
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ExMarket 枚举值。
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start : int
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起始偏移。
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count : int
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返回条数（最大 80，受报价批量限制）。
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sort_type : SortType
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排序字段（暂未实现排序，预留接口）。
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sort_order : SortOrder
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排序方向（暂未实现排序，预留接口）。
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"""
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page_size = min(count, 80)
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items_df = self.goods_list(market, start=start, count=page_size)
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if items_df.empty:
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return pd.DataFrame()
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stocks: list[tuple[int, str]] = []
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for _, row in items_df.iterrows():
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stocks.append((market, row["code"]))
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cmd = SymbolQuotesCmd(stocks)
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result: list[MacQuoteField] = self._execute(cmd)
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return _quotes_to_df(result)
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def goods_kline(
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self,
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market: int,
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code: str,
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period: Period = Period.DAILY,
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start: int = 0,
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count: int = 800,
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adjust: Adjust = Adjust.NONE,
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) -> pd.DataFrame:
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"""获取扩展市场 K 线数据（支持复权）。
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Parameters
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----------
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market : int
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ExMarket 枚举值。
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code : str
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证券代码。
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period : Period
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K 线周期。
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start : int
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起始偏移（0=最新）。
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count : int
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返回条数。
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adjust : Adjust
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复权方式（NONE/QFQ/HFQ）。
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"""
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cmd = SymbolBarCmd(
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market=market,
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code=code,
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period=period,
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start=start,
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count=count,
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fq=adjust,
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)
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result = self._execute(cmd)
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return _to_df(result)
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# ------------------------------------------------------------------ #
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# 分时
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# ------------------------------------------------------------------ #
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def goods_tick_chart(
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self,
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market: int,
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code: str,
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query_date: date | None = None,
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) -> pd.DataFrame:
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"""获取单日分时图。
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Parameters
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----------
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market : int
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ExMarket 枚举值。
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code : str
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证券代码。
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query_date : date | None
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查询日期，None 表示今天。
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"""
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cmd = SymbolTickChartCmd(market=market, code=code, query_date=query_date)
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result = self._execute(cmd)
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return _to_df(result)
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def goods_chart_sampling(
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self,
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market: int,
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code: str,
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) -> pd.DataFrame:
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"""获取分时缩略采样价格点（约 240 个点）。
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Parameters
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----------
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market : int
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ExMarket 枚举值。
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code : str
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证券代码。
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"""
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cmd = ChartSamplingCmd(market=market, code=code)
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prices: list[float] = self._execute(cmd)
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if not prices:
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return pd.DataFrame()
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return pd.DataFrame({"price": prices})
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# ------------------------------------------------------------------ #
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# 成交
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# ------------------------------------------------------------------ #
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def goods_transaction(
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self,
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market: int,
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code: str,
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query_date: date | None = None,
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start: int = 0,
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count: int = 2000,
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) -> pd.DataFrame:
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"""获取逐笔成交数据。
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Parameters
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----------
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market : int
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ExMarket 枚举值。
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code : str
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证券代码。
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query_date : date | None
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查询日期，None 表示今天。
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start : int
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起始偏移。**注意：通达信逐笔协议为倒序**——``start=0`` 指向最新一笔
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（收盘方向），``start`` 越大越早。A 股 0x122F 与港股 ex 协议语义一致。
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count : int
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返回条数。港股单日成交常达数万笔（如 02715 约 1.3 万笔/日），默认
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``count=2000`` 只取最近 2000 笔，会集中在尾盘时段。若需全天全部成交，
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请改用 :meth:`goods_transaction_all`。
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Note
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----
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港股股票类市场（HK_MAIN_BOARD/HK_GEM/HK_INDEX/HK_FUND/HK_STOCK_GGT/HK_DARK_POOL，
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见 :data:`easy_tdx.ex._hk_transaction.HK_STOCK_MARKETS`）走 ex 扩展行情协议
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（当日 0x23FC / 历史 0x2406），返回价格单位为港元（浮点）。其余扩展市场
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（美股 / 期货等）走 MAC 协议 0x122F。原因：0x122F 的数据源未接入港股，
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||
对港股请求会返回空（issue #14）。不确定市场归属时，可先用
|
||
:meth:`goods_kline` 探测哪个 market 能取到 K 线。
|
||
"""
|
||
if is_hk_stock_market(market):
|
||
result = _fetch_hk_transactions_sync(
|
||
self._execute, market, code, query_date, start, count
|
||
)
|
||
return _to_df(result)
|
||
cmd = SymbolTransactionCmd(
|
||
market=market,
|
||
code=code,
|
||
query_date=query_date,
|
||
start=start,
|
||
count=count,
|
||
)
|
||
result = self._execute(cmd)
|
||
return _to_df(result)
|
||
|
||
def goods_transaction_all(
|
||
self,
|
||
market: int,
|
||
code: str,
|
||
query_date: date | None = None,
|
||
) -> pd.DataFrame:
|
||
"""获取港股某日**全部**逐笔成交（仅港股股票类市场，自动翻页取全天）。
|
||
|
||
与 :meth:`goods_transaction` 的区别：不受 ``count`` 上限约束，自动翻页直至
|
||
末页，返回当日所有逐笔成交（港股单日常 1~5 万笔）。返回顺序仍为协议原生
|
||
倒序（最新在前）；如需正序展示，调用方自行 ``df.iloc[::-1]`` 反转。
|
||
|
||
Parameters
|
||
----------
|
||
market : int
|
||
ExMarket 枚举值（须为港股股票类市场，见
|
||
:data:`easy_tdx.ex._hk_transaction.HK_STOCK_MARKETS`）。
|
||
code : str
|
||
证券代码。
|
||
query_date : date | None
|
||
查询日期，None 表示今天。
|
||
|
||
Raises
|
||
------
|
||
ValueError
|
||
``market`` 不属于港股股票类市场时抛出（本方法专为港股设计；其他扩展
|
||
市场请用 :meth:`goods_transaction`）。
|
||
"""
|
||
if not is_hk_stock_market(market):
|
||
raise ValueError(
|
||
f"goods_transaction_all 仅支持港股股票类市场（HK_STOCK_MARKETS），"
|
||
f"收到 market={market}；其他市场请用 goods_transaction。"
|
||
)
|
||
result = _fetch_all_hk_transactions_sync(self._execute, market, code, query_date)
|
||
return _to_df(result)
|
||
|
||
|
||
# ============================================================
|
||
# 异步客户端
|
||
# ============================================================
|
||
|
||
|
||
class AsyncMacExClient(AsyncHeartbeatMixin):
|
||
"""异步 MAC 协议扩展市场客户端（asyncio，端口 7727）。
|
||
|
||
使用示例::
|
||
|
||
async with AsyncMacExClient() as c:
|
||
df = await c.goods_kline(ExMarket.CFFEX_FUTURES, "IFL0", Period.DAILY)
|
||
"""
|
||
|
||
def __init__(
|
||
self,
|
||
host: str | None = None,
|
||
port: int = _DEFAULT_PORT,
|
||
timeout: float = 15.0,
|
||
auto_reconnect: bool = True,
|
||
heartbeat_interval: float = 60.0,
|
||
) -> None:
|
||
self._host = host if host is not None else get_best_mac_ex_host()
|
||
self._port = port
|
||
self._timeout = timeout
|
||
self._auto_reconnect = auto_reconnect
|
||
self._heartbeat_interval = heartbeat_interval
|
||
self._conn = AsyncExTdxConnection(self._host, port, timeout, mac_ex_mode=True)
|
||
self._execute_lock = asyncio.Lock()
|
||
self._heartbeat_task: asyncio.Task[None] | None = None
|
||
|
||
@classmethod
|
||
def from_best_host(
|
||
cls,
|
||
hosts: list[str] | None = None,
|
||
port: int = _DEFAULT_PORT,
|
||
timeout: float = 15.0,
|
||
ping_timeout: float = 5.0,
|
||
auto_reconnect: bool = True,
|
||
heartbeat_interval: float = 60.0,
|
||
) -> "AsyncMacExClient":
|
||
candidates = hosts or get_mac_ex_hosts()
|
||
ranked = ping_ex_all(candidates, port, ping_timeout)
|
||
best = ranked[0][0] if ranked else candidates[0]
|
||
save_best_mac_ex_host(best)
|
||
return cls(best, port, timeout, auto_reconnect, heartbeat_interval)
|
||
|
||
@staticmethod
|
||
def ping_all(
|
||
hosts: list[str] | None = None,
|
||
port: int = _DEFAULT_PORT,
|
||
timeout: float = 5.0,
|
||
) -> list[tuple[str, float]]:
|
||
return ping_ex_all(hosts or get_mac_ex_hosts(), port, timeout)
|
||
|
||
# ------------------------------------------------------------------ #
|
||
# 连接管理
|
||
# ------------------------------------------------------------------ #
|
||
|
||
async def connect(self) -> None:
|
||
await self._conn.connect()
|
||
await self._login()
|
||
self._start_heartbeat()
|
||
|
||
async def close(self) -> None:
|
||
await self._stop_heartbeat()
|
||
await self._conn.close()
|
||
|
||
async def __aenter__(self) -> "AsyncMacExClient":
|
||
await self.connect()
|
||
return self
|
||
|
||
async def __aexit__(
|
||
self,
|
||
exc_type: type[BaseException] | None,
|
||
exc_val: BaseException | None,
|
||
exc_tb: TracebackType | None,
|
||
) -> None:
|
||
await self.close()
|
||
|
||
def _heartbeat_cmd(self) -> Awaitable[object]:
|
||
"""心跳使用的轻量请求（get_instrument_count，复用 _execute 重连）。"""
|
||
return self._execute(GetExInstrumentCountCmd())
|
||
|
||
async def _login(self) -> None:
|
||
"""执行 MAC EX 登录命令。"""
|
||
await self._conn.execute(MacExLoginCmd())
|
||
|
||
async def _execute(self, cmd: "BaseCommand[_T]") -> _T:
|
||
"""执行命令；断线时指数退避重试，同主机耗尽则跨主机故障转移。
|
||
|
||
每次重连后必须重新 ``_login()``（MAC 协议扩展行情特有）。登录握手期的
|
||
``TdxConnectionError`` 与业务请求一样计入退避重试；``TdxCommandError``
|
||
（登录被拒等确定性失败）不重试，直接抛出。
|
||
"""
|
||
async with self._execute_lock:
|
||
try:
|
||
return await self._conn.execute(cmd)
|
||
except TdxConnectionError:
|
||
if not self._auto_reconnect:
|
||
raise
|
||
last_exc: TdxConnectionError | None = None
|
||
for delay in _RETRY_DELAYS:
|
||
await asyncio.sleep(delay)
|
||
await self._conn.close()
|
||
self._conn = AsyncExTdxConnection(
|
||
self._host, self._port, self._timeout, mac_ex_mode=True
|
||
)
|
||
# connect + login 纳入重试：登录握手期连接再次断开属可重试语义。
|
||
try:
|
||
await self._conn.connect()
|
||
await self._login()
|
||
return await self._conn.execute(cmd)
|
||
except TdxConnectionError as e:
|
||
last_exc = e
|
||
# 第二阶段：跨主机故障转移
|
||
new_host = await select_best_host_async(
|
||
get_mac_ex_hosts(),
|
||
ping_ex_all,
|
||
save_best_mac_ex_host,
|
||
self._port,
|
||
5.0,
|
||
self._host,
|
||
)
|
||
if new_host is not None:
|
||
self._host = new_host
|
||
await self._conn.close()
|
||
self._conn = AsyncExTdxConnection(
|
||
self._host, self._port, self._timeout, mac_ex_mode=True
|
||
)
|
||
try:
|
||
await self._conn.connect()
|
||
await self._login()
|
||
return await self._conn.execute(cmd)
|
||
except TdxConnectionError as e:
|
||
last_exc = e
|
||
raise last_exc # type: ignore[misc]
|
||
|
||
# ------------------------------------------------------------------ #
|
||
# 商品列表
|
||
# ------------------------------------------------------------------ #
|
||
|
||
async def goods_count(self, market: int | None = None) -> int:
|
||
"""获取商品总数。market=None 时返回全市场总数，否则返回指定市场的数量。"""
|
||
if market is None:
|
||
return await self._execute(GetExInstrumentCountCmd())
|
||
offset = await self._find_market_offset(market)
|
||
if offset < 0:
|
||
return 0
|
||
total = await self._execute(GetExInstrumentCountCmd())
|
||
n = 0
|
||
page = 1000
|
||
pos = offset
|
||
while pos < total:
|
||
batch = await self._execute(GetExInstrumentInfoCmd(start=pos, count=page))
|
||
if not batch:
|
||
break
|
||
for item in batch:
|
||
if item.market == market:
|
||
n += 1
|
||
elif item.market > market:
|
||
return n
|
||
pos += page
|
||
return n
|
||
|
||
async def goods_list(self, market: int, start: int = 0, count: int = 600) -> pd.DataFrame:
|
||
"""获取扩展市场商品列表（期货合约/港股/美股等）。"""
|
||
offset = await self._find_market_offset(market)
|
||
if offset < 0:
|
||
return pd.DataFrame()
|
||
total = await self._execute(GetExInstrumentCountCmd())
|
||
page_size = 1000
|
||
collected: list[Any] = []
|
||
skipped = 0
|
||
pos = offset
|
||
while pos < total and len(collected) < count:
|
||
batch = await self._execute(GetExInstrumentInfoCmd(start=pos, count=page_size))
|
||
if not batch:
|
||
break
|
||
for item in batch:
|
||
if item.market == market:
|
||
if skipped < start:
|
||
skipped += 1
|
||
else:
|
||
collected.append(item)
|
||
if len(collected) >= count:
|
||
break
|
||
elif item.market > market:
|
||
break
|
||
else:
|
||
pos += page_size
|
||
continue
|
||
break
|
||
return _to_df(collected)
|
||
|
||
async def _find_market_offset(self, market: int) -> int:
|
||
"""二分查找定位指定市场在全局商品列表中的起始偏移。"""
|
||
total = await self._execute(GetExInstrumentCountCmd())
|
||
if total == 0:
|
||
return -1
|
||
lo, hi = 0, total
|
||
while lo < hi:
|
||
mid = (lo + hi) // 2
|
||
items = await self._execute(GetExInstrumentInfoCmd(start=mid, count=1))
|
||
if not items:
|
||
hi = mid
|
||
continue
|
||
m = items[0].market
|
||
if m < market:
|
||
lo = mid + 1
|
||
else:
|
||
hi = mid
|
||
return lo
|
||
|
||
# ------------------------------------------------------------------ #
|
||
# 行情
|
||
# ------------------------------------------------------------------ #
|
||
|
||
async def goods_quotes(
|
||
self,
|
||
stocks: list[tuple[int, str]],
|
||
fields: Any = None,
|
||
) -> pd.DataFrame:
|
||
cmd = SymbolQuotesCmd(stocks, fields)
|
||
result: list[MacQuoteField] = await self._execute(cmd)
|
||
return _quotes_to_df(result)
|
||
|
||
async def goods_quotes_list(
|
||
self,
|
||
market: int,
|
||
start: int = 0,
|
||
count: int = 100,
|
||
sort_type: SortType = SortType.CODE,
|
||
sort_order: SortOrder = SortOrder.NONE,
|
||
) -> pd.DataFrame:
|
||
page_size = min(count, 80)
|
||
items_df = await self.goods_list(market, start=start, count=page_size)
|
||
if items_df.empty:
|
||
return pd.DataFrame()
|
||
stocks: list[tuple[int, str]] = [(market, row["code"]) for _, row in items_df.iterrows()]
|
||
cmd = SymbolQuotesCmd(stocks)
|
||
result: list[MacQuoteField] = await self._execute(cmd)
|
||
return _quotes_to_df(result)
|
||
|
||
# ------------------------------------------------------------------ #
|
||
# K 线
|
||
# ------------------------------------------------------------------ #
|
||
|
||
async def goods_kline(
|
||
self,
|
||
market: int,
|
||
code: str,
|
||
period: Period = Period.DAILY,
|
||
start: int = 0,
|
||
count: int = 800,
|
||
adjust: Adjust = Adjust.NONE,
|
||
) -> pd.DataFrame:
|
||
cmd = SymbolBarCmd(
|
||
market=market,
|
||
code=code,
|
||
period=period,
|
||
start=start,
|
||
count=count,
|
||
fq=adjust,
|
||
)
|
||
result = await self._execute(cmd)
|
||
return _to_df(result)
|
||
|
||
# ------------------------------------------------------------------ #
|
||
# 分时
|
||
# ------------------------------------------------------------------ #
|
||
|
||
async def goods_tick_chart(
|
||
self,
|
||
market: int,
|
||
code: str,
|
||
query_date: date | None = None,
|
||
) -> pd.DataFrame:
|
||
cmd = SymbolTickChartCmd(market=market, code=code, query_date=query_date)
|
||
result = await self._execute(cmd)
|
||
return _to_df(result)
|
||
|
||
async def goods_chart_sampling(
|
||
self,
|
||
market: int,
|
||
code: str,
|
||
) -> pd.DataFrame:
|
||
cmd = ChartSamplingCmd(market=market, code=code)
|
||
prices: list[float] = await self._execute(cmd)
|
||
if not prices:
|
||
return pd.DataFrame()
|
||
return pd.DataFrame({"price": prices})
|
||
|
||
# ------------------------------------------------------------------ #
|
||
# 成交
|
||
# ------------------------------------------------------------------ #
|
||
|
||
async def goods_transaction(
|
||
self,
|
||
market: int,
|
||
code: str,
|
||
query_date: date | None = None,
|
||
start: int = 0,
|
||
count: int = 2000,
|
||
) -> pd.DataFrame:
|
||
"""获取逐笔成交数据（异步）。
|
||
|
||
路由与 ``start`` 倒序语义见同步版 :meth:`goods_transaction`。
|
||
"""
|
||
if is_hk_stock_market(market):
|
||
result = await _fetch_hk_transactions_async(
|
||
self._execute, market, code, query_date, start, count
|
||
)
|
||
return _to_df(result)
|
||
cmd = SymbolTransactionCmd(
|
||
market=market,
|
||
code=code,
|
||
query_date=query_date,
|
||
start=start,
|
||
count=count,
|
||
)
|
||
result = await self._execute(cmd)
|
||
return _to_df(result)
|
||
|
||
async def goods_transaction_all(
|
||
self,
|
||
market: int,
|
||
code: str,
|
||
query_date: date | None = None,
|
||
) -> pd.DataFrame:
|
||
"""获取港股某日全部逐笔成交（异步）。语义见同步版 :meth:`goods_transaction_all`。"""
|
||
if not is_hk_stock_market(market):
|
||
raise ValueError(
|
||
f"goods_transaction_all 仅支持港股股票类市场（HK_STOCK_MARKETS），"
|
||
f"收到 market={market}；其他市场请用 goods_transaction。"
|
||
)
|
||
result = await _fetch_all_hk_transactions_async(self._execute, market, code, query_date)
|
||
return _to_df(result)
|