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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。
1463 lines
55 KiB
Python
1463 lines
55 KiB
Python
"""高层行情 API：TdxClient（同步）和 AsyncTdxClient（asyncio）。"""
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import asyncio
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import json
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import logging
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import time
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from collections.abc import Awaitable, Callable
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from dataclasses import asdict
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from datetime import datetime
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from pathlib import Path
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from types import TracebackType
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from typing import Any, TypeVar
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from zoneinfo import ZoneInfo
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import pandas as pd
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from ._df import (
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_add_minute_datetime,
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_apply_bar_time_align_df,
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_category_to_minutes,
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_merge_bar_datetime,
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_merge_txn_datetime,
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_to_df,
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)
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from ._reconnect import (
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_RETRY_DELAYS,
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AsyncHeartbeatMixin,
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find_working_host_async,
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find_working_host_sync,
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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 .codec.block import parse_block_dat
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from .codec.financial import parse_financial_dat, parse_financial_file_list
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from .codec.industry import parse_tdxhy_cfg
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from .codec.price_rules import compute_price_limits, get_no_limit_window_days
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from .commands.base import BaseCommand
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from .commands.block_info import GetBlockInfoCmd, GetBlockInfoMetaCmd
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from .commands.company_info import GetCompanyInfoCategoryCmd, GetCompanyInfoContentCmd
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from .commands.finance_info import GetFinanceInfoCmd
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from .commands.fund_flow import GetHistoryFundFlowCmd
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from .commands.minute_time import GetHistoryMinuteTimeDataCmd
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from .commands.report_file import GetReportFileCmd
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from .commands.security_bars import GetIndexBarsCmd, GetSecurityBarsCmd
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from .commands.security_count import GetSecurityCountCmd
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from .commands.security_list import GetSecurityListCmd
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from .commands.security_quotes import GetSecurityQuotesCmd
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from .commands.transaction import GetHistoryTransactionDataCmd, GetTransactionDataCmd
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from .commands.xdxr_info import GetXdxrInfoCmd
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from .config import (
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get_best_host,
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get_calc_hosts,
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get_known_hosts,
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get_port,
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get_timeout,
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save_best_host,
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)
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from .exceptions import TdxConnectionError
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from .models.bar import SecurityBar
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from .models.enums import KlineCategory, Market
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from .models.finance import (
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FinancialFileInfo,
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FinancialRecord,
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)
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from .models.quote import SecurityQuote
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from .models.security import SecurityInfo
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from .models.stats import FundFlow, HistoricalFundFlow, MarketStat
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from .models.timeseries import TransactionRecord
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from .transport.async_ import AsyncTdxConnection
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from .transport.sync import TdxConnection, ping_all
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_T = TypeVar("_T")
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_SHANGHAI_TZ = ZoneInfo("Asia/Shanghai")
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_DAILY_PLUS = frozenset(
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{
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KlineCategory.DAY,
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KlineCategory.WEEK,
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KlineCategory.MONTH,
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KlineCategory.YEAR,
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KlineCategory.YEAR_ALT,
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}
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)
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def _today_in_shanghai() -> int:
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return int(datetime.now(_SHANGHAI_TZ).strftime("%Y%m%d"))
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def _record_signature(
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record: TransactionRecord,
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) -> tuple[int, int, float, int, int, int]:
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return (
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record.hour,
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record.minute,
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record.price,
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record.vol,
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record.buyorsell,
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record.unknown_last,
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)
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def _page_signature(
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records: list[TransactionRecord],
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) -> tuple[tuple[int, int, float, int, int, int], tuple[int, int, float, int, int, int]]:
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return (_record_signature(records[0]), _record_signature(records[-1]))
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def _classify_fund_flow(records: list[TransactionRecord]) -> FundFlow:
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stats = {
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"super_in": 0.0,
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"large_in": 0.0,
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"medium_in": 0.0,
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"small_in": 0.0,
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"super_out": 0.0,
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"large_out": 0.0,
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"medium_out": 0.0,
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"small_out": 0.0,
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}
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for record in records:
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amount = record.price * record.vol * 100.0
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direction = "in" if record.buyorsell == 0 else "out" if record.buyorsell == 1 else None
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if not direction:
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continue
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if amount > 1_000_000:
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stats[f"super_{direction}"] += amount
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elif amount > 200_000:
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stats[f"large_{direction}"] += amount
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elif amount > 40_000:
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stats[f"medium_{direction}"] += amount
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else:
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stats[f"small_{direction}"] += amount
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return FundFlow(**stats)
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def _date_from_bar(bar: SecurityBar) -> int:
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return bar.year * 10000 + bar.month * 100 + bar.day
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def _historical_fund_flow_from_records(
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date: int, records: list[TransactionRecord]
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) -> HistoricalFundFlow:
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flow = _classify_fund_flow(records)
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year = date // 10000
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month = (date // 100) % 100
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day = date % 100
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return HistoricalFundFlow(
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year=year,
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month=month,
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day=day,
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super_in=flow.super_in,
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super_out=flow.super_out,
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large_in=flow.large_in,
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large_out=flow.large_out,
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medium_in=flow.medium_in,
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medium_out=flow.medium_out,
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small_in=flow.small_in,
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small_out=flow.small_out,
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)
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# ============================================================
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# 同步客户端
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# ============================================================
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_CACHE_DIR = Path.home() / ".easy_tdx" / "cache"
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_CACHE_MAX_AGE = 86400 # 1 天
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def _serialize_stocks(stocks: list[SecurityInfo]) -> list[dict[str, Any]]:
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return [{k: v for k, v in asdict(s).items() if k != "_raw"} for s in stocks]
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def _deserialize_stocks(data: list[dict[str, Any]]) -> list[SecurityInfo]:
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return [SecurityInfo(**{**d, "market": Market(d["market"])}) for d in data]
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def _load_cache() -> list[SecurityInfo] | None:
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path = _CACHE_DIR / "security_list_all.json"
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if not path.exists():
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return None
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try:
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raw = json.loads(path.read_text("utf-8"))
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updated = datetime.fromisoformat(raw["updated"])
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# 统一用 aware datetime 比较（审计 #18）：旧缓存可能写的是 naive datetime，
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# 此处 localize 到上海时区，避免跨时区机器（如 CI 的 UTC 与本地 +8）误判过期。
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if updated.tzinfo is None:
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updated = updated.replace(tzinfo=_SHANGHAI_TZ)
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if (datetime.now(_SHANGHAI_TZ) - updated).total_seconds() > _CACHE_MAX_AGE:
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return None
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return _deserialize_stocks(raw["data"])
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except Exception:
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return None
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def _save_cache(stocks: list[SecurityInfo]) -> None:
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_CACHE_DIR.mkdir(parents=True, exist_ok=True)
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data = {
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"updated": datetime.now(_SHANGHAI_TZ).isoformat(),
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"count": len(stocks),
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"data": _serialize_stocks(stocks),
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}
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(_CACHE_DIR / "security_list_all.json").write_text(
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json.dumps(data, ensure_ascii=False), "utf-8"
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)
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class TdxClient:
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"""同步通达信行情客户端，支持 IP 优选与断线自动重连。
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使用示例::
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# 单台服务器
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with TdxClient("180.153.18.170") as c:
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bars = c.get_security_bars(Market.SH, "600000", KlineCategory.DAY, 0, 100)
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# 自动从候选列表中选延迟最低的服务器
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with TdxClient.from_best_host() as c:
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count = c.get_security_count(Market.SH)
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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 | None = None,
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timeout: float | None = None,
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auto_reconnect: bool = True,
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heartbeat_interval: float = 15.0,
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) -> None:
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self._host = host if host is not None else get_best_host()
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self._port = port if port is not None else get_port()
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self._timeout = timeout if timeout is not None else get_timeout()
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self._auto_reconnect = auto_reconnect
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self._heartbeat_interval = heartbeat_interval
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self._conn = TdxConnection(host, port, timeout)
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# ------------------------------------------------------------------ #
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# 工厂方法：自动优选最低延迟服务器
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# ------------------------------------------------------------------ #
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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 | None = None,
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timeout: float | None = None,
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ping_timeout: float = 5.0,
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auto_reconnect: bool = True,
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heartbeat_interval: float = 15.0,
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) -> "TdxClient":
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"""测量 hosts 中所有服务器延迟，选最低延迟的建立连接。
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自动将最佳主机保存到 config.json，后续连接默认使用该主机。
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若所有服务器均不可达，回退到 hosts[0]。
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"""
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if hosts is None:
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hosts = get_known_hosts()
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if port is None:
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port = get_port()
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if timeout is None:
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timeout = get_timeout()
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ranked = ping_all(hosts, port, ping_timeout)
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best = ranked[0][0] if ranked else hosts[0]
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save_best_host(best)
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return cls(best, port, timeout, auto_reconnect, heartbeat_interval)
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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 | None = None,
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timeout: float = 5.0,
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) -> list[tuple[str, float]]:
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"""测量多台服务器延迟，返回按延迟排序的 (host, seconds) 列表。"""
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if hosts is None:
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hosts = get_known_hosts()
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if port is None:
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port = get_port()
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return ping_all(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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if self._heartbeat_interval > 0:
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self._conn.start_heartbeat(self._heartbeat_interval)
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def close(self) -> None:
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self._conn.stop_heartbeat()
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self._conn.close()
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def disconnect(self) -> None:
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"""Alias for close()."""
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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(GetSecurityCountCmd(Market.SH))
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except TdxConnectionError:
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self._reconnect()
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def _reconnect(self, host: str | None = None) -> None:
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"""关闭当前连接并重建（默认连 self._host；传入 host 则切换主机）。
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统一收敛所有"重建 TdxConnection + 起心跳"的副本：``_execute`` 同主机
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重试、``_execute`` 跨主机故障转移、``ensure_connected``、
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``get_market_stat`` 空数据重试都走这里，保证 4 处重建逻辑一致。
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"""
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target = host if host is not None else self._host
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if host is not None:
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self._host = host
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self._conn.stop_heartbeat()
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self._conn.close()
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self._conn = TdxConnection(target, self._port, self._timeout)
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self._conn.connect()
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if self._heartbeat_interval > 0:
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self._conn.start_heartbeat(self._heartbeat_interval)
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def __enter__(self) -> "TdxClient":
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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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# ------------------------------------------------------------------ #
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# 内部执行：含自动重连 + 跨主机故障转移
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# ------------------------------------------------------------------ #
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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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两阶段韧性：
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1. 同主机重试（``_RETRY_DELAYS``，4 次指数退避）——应对瞬时抖动。
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2. 跨主机故障转移——同主机重试仍失败时，重新测速选延迟最低的另
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一台服务器再试一轮。服务器连不上时用户无需手动 ``ping``。
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``auto_reconnect=False`` 时两阶段都不触发，直接抛出原异常。
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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._reconnect()
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try:
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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——重新测速切到另一台服务器再试一次
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new_host = select_best_host_sync(
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get_known_hosts(),
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ping_all,
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save_best_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._reconnect(new_host)
|
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try:
|
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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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# 市场信息
|
||
# ------------------------------------------------------------------ #
|
||
|
||
def get_security_count(self, market: Market) -> int:
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"""获取市场证券总数。"""
|
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return self._execute(GetSecurityCountCmd(market))
|
||
|
||
def get_security_list(self, market: Market, start: int) -> pd.DataFrame:
|
||
"""获取证券列表（每页约1000条，按 start 分页）。"""
|
||
return _to_df(self._execute(GetSecurityListCmd(market, start)))
|
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|
||
def get_security_list_all(self, pages: int | str = "all") -> pd.DataFrame:
|
||
"""获取沪深 A 股完整证券列表，并自动挂载行业信息。
|
||
|
||
Args:
|
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pages: 拉取页数。每个市场每页 1000 条。
|
||
"all" 拉取全部（默认，结果会缓存到本地文件）。
|
||
整数 N 表示每个市场只拉前 N 页，不缓存。
|
||
|
||
注意：
|
||
`Market.BJ` 的证券列表请求长期存在服务器超时问题，当前版本暂不纳入此方法。
|
||
"""
|
||
log = logging.getLogger(__name__)
|
||
|
||
if pages == "all":
|
||
cached = _load_cache()
|
||
if cached is not None:
|
||
log.info("从缓存加载沪深 A 股列表，共 %d 只", len(cached))
|
||
return _to_df(cached)
|
||
|
||
# 计算每个市场的最大起始偏移
|
||
def _max_start(count: int) -> int:
|
||
if pages == "all":
|
||
return count
|
||
return min(count, int(pages) * 1000)
|
||
|
||
# 尝试获取行业配置
|
||
industry_map: dict[str, tuple[str, str]] = {}
|
||
try:
|
||
cfg_data = self.get_report_file("tdxhy.cfg")
|
||
if cfg_data:
|
||
industry_map = parse_tdxhy_cfg(cfg_data)
|
||
log.info("行业配置已加载，共 %d 条映射", len(industry_map))
|
||
except Exception:
|
||
log.warning("无法获取 tdxhy.cfg，行业字段将为空")
|
||
|
||
all_stocks: list[SecurityInfo] = []
|
||
for market in [Market.SH, Market.SZ]:
|
||
count = self.get_security_count(market)
|
||
limit = _max_start(count)
|
||
total_pages = (limit + 999) // 1000
|
||
for page_idx, start in enumerate(range(0, limit, 1000)):
|
||
try:
|
||
stocks = self._execute(GetSecurityListCmd(market, start))
|
||
except Exception:
|
||
log.warning(
|
||
"%s 第 %d/%d 页获取失败，跳过", market.name, page_idx + 1, total_pages
|
||
)
|
||
continue
|
||
log.info(
|
||
"%s 第 %d/%d 页: %d 条", market.name, page_idx + 1, total_pages, len(stocks)
|
||
)
|
||
for s in stocks:
|
||
is_a_share = (market == Market.SH and s.code.startswith(("60", "68"))) or (
|
||
market == Market.SZ and s.code.startswith(("00", "30"))
|
||
)
|
||
if is_a_share:
|
||
if s.code in industry_map:
|
||
s.industry_tdx, s.industry_sw = industry_map[s.code]
|
||
all_stocks.append(s)
|
||
|
||
log.info("沪深 A 股总数: %d", len(all_stocks))
|
||
|
||
if pages == "all":
|
||
_save_cache(all_stocks)
|
||
|
||
return _to_df(all_stocks)
|
||
|
||
def get_security_quotes(self, stocks: list[tuple[Market, str]]) -> pd.DataFrame:
|
||
"""批量获取实时五档行情（最多80只/次）。"""
|
||
return _to_df(self._execute(GetSecurityQuotesCmd(stocks)))
|
||
|
||
def get_price_limits(
|
||
self, market: Market, code: str, name: str, pre_close: float
|
||
) -> tuple[float | None, float | None]:
|
||
"""按当前交易状态计算涨跌停价。
|
||
|
||
对上市初期不设涨跌幅限制的标的，会先用日 K 线条数估算已上市交易天数。
|
||
"""
|
||
listed_days: int | None = None
|
||
no_limit_window_days = get_no_limit_window_days(market, code, name)
|
||
if no_limit_window_days > 0:
|
||
try:
|
||
bars = self._execute(
|
||
GetSecurityBarsCmd(market, code, KlineCategory.DAY, 0, no_limit_window_days + 1)
|
||
)
|
||
listed_days = len(bars)
|
||
except Exception:
|
||
listed_days = None
|
||
|
||
return compute_price_limits(
|
||
market,
|
||
code,
|
||
name,
|
||
pre_close,
|
||
listed_days=listed_days,
|
||
)
|
||
|
||
# ------------------------------------------------------------------ #
|
||
# K 线
|
||
# ------------------------------------------------------------------ #
|
||
|
||
def get_security_bars(
|
||
self,
|
||
market: Market,
|
||
code: str,
|
||
category: KlineCategory,
|
||
start: int,
|
||
count: int = 800,
|
||
*,
|
||
bar_time: str = "start",
|
||
) -> pd.DataFrame:
|
||
"""获取 K 线数据（最多800条/次，按 start 分页）。
|
||
|
||
Args:
|
||
bar_time: 时间戳语义。 ``"start"``（默认）= bar 开始时间（通达信原始，
|
||
上午最后一根 5min 标 11:25、下午第一根标 13:00）；``"end"`` = bar 右端点
|
||
（= 开始 + 周期时长，与 Tushare/同花顺对齐，上午最后一根标 11:30）。
|
||
仅对分钟级周期生效；日线及以上不受影响。
|
||
"""
|
||
df = _to_df(self._execute(GetSecurityBarsCmd(market, code, category, start, count)))
|
||
delta = _category_to_minutes(int(category))
|
||
is_intraday = delta is not None
|
||
df = _apply_bar_time_align_df(
|
||
df,
|
||
is_intraday=is_intraday,
|
||
delta_minutes=delta,
|
||
bar_time=bar_time,
|
||
has_time_columns=True,
|
||
)
|
||
return _merge_bar_datetime(df, not is_intraday)
|
||
|
||
def get_index_bars(
|
||
self,
|
||
market: Market,
|
||
code: str,
|
||
category: KlineCategory,
|
||
start: int,
|
||
count: int = 800,
|
||
*,
|
||
bar_time: str = "start",
|
||
) -> pd.DataFrame:
|
||
"""获取指数 K 线数据。
|
||
|
||
Args:
|
||
bar_time: 见 :meth:`get_security_bars`，分钟级周期时间戳可对齐 Tushare 右端点。
|
||
"""
|
||
df = _to_df(self._execute(GetIndexBarsCmd(market, code, category, start, count)))
|
||
delta = _category_to_minutes(int(category))
|
||
is_intraday = delta is not None
|
||
df = _apply_bar_time_align_df(
|
||
df,
|
||
is_intraday=is_intraday,
|
||
delta_minutes=delta,
|
||
bar_time=bar_time,
|
||
has_time_columns=True,
|
||
)
|
||
return _merge_bar_datetime(df, not is_intraday)
|
||
|
||
# ------------------------------------------------------------------ #
|
||
# 分时
|
||
# ------------------------------------------------------------------ #
|
||
|
||
def get_minute_time_data(self, market: Market, code: str) -> pd.DataFrame:
|
||
"""获取今日分时数据（240条，走历史分时接口）。"""
|
||
today = _today_in_shanghai()
|
||
bars = self._execute(GetHistoryMinuteTimeDataCmd(market, code, today))
|
||
return _add_minute_datetime(_to_df(bars), today)
|
||
|
||
def get_history_minute_time_data(self, market: Market, code: str, date: int) -> pd.DataFrame:
|
||
"""获取历史某日分时数据（date: YYYYMMDD）。"""
|
||
bars = self._execute(GetHistoryMinuteTimeDataCmd(market, code, date))
|
||
return _add_minute_datetime(_to_df(bars), date)
|
||
|
||
# ------------------------------------------------------------------ #
|
||
# 逐笔成交
|
||
# ------------------------------------------------------------------ #
|
||
|
||
def get_transaction_data(
|
||
self, market: Market, code: str, start: int, count: int = 800
|
||
) -> pd.DataFrame:
|
||
"""获取当日逐笔成交（分页）。"""
|
||
df = _to_df(self._execute(GetTransactionDataCmd(market, code, start, count)))
|
||
return _merge_txn_datetime(df, _today_in_shanghai())
|
||
|
||
def get_history_transaction_data(
|
||
self, market: Market, code: str, date: int, start: int, count: int = 800
|
||
) -> pd.DataFrame:
|
||
"""获取历史逐笔成交（date: YYYYMMDD，分页）。"""
|
||
df = _to_df(self._execute(GetHistoryTransactionDataCmd(market, code, date, start, count)))
|
||
return _merge_txn_datetime(df, date)
|
||
|
||
# ------------------------------------------------------------------ #
|
||
# 财务 / 公司
|
||
# ------------------------------------------------------------------ #
|
||
|
||
def get_xdxr_info(self, market: Market, code: str) -> pd.DataFrame:
|
||
"""获取除权除息历史记录。"""
|
||
return _to_df(self._execute(GetXdxrInfoCmd(market, code)))
|
||
|
||
def get_finance_info(self, market: Market, code: str) -> pd.DataFrame:
|
||
"""获取最新财务数据。"""
|
||
return _to_df(self._execute(GetFinanceInfoCmd(market, code)))
|
||
|
||
def get_company_info_category(self, market: Market, code: str) -> pd.DataFrame:
|
||
"""获取公司信息文件目录。"""
|
||
return _to_df(self._execute(GetCompanyInfoCategoryCmd(market, code)))
|
||
|
||
def get_company_info_content(
|
||
self, market: Market, code: str, filename: str, offset: int, length: int
|
||
) -> str:
|
||
"""读取公司信息文本。"""
|
||
return self._execute(GetCompanyInfoContentCmd(market, code, filename, offset, length))
|
||
|
||
def get_block_info(self, filename: str) -> pd.DataFrame:
|
||
"""获取并解析板块文件（行业、概念、风格等）。
|
||
|
||
常用文件名：
|
||
'block_zs.dat' - 行业/指数板块
|
||
'block_gn.dat' - 概念板块
|
||
'block_fg.dat' - 风格板块
|
||
"""
|
||
size, _hash = self._execute(GetBlockInfoMetaCmd(filename))
|
||
full_data = bytearray()
|
||
pos = 0
|
||
chunk_size = 30000
|
||
while pos < size:
|
||
chunk = self._execute(GetBlockInfoCmd(filename, pos, chunk_size))
|
||
if not chunk:
|
||
break
|
||
full_data.extend(chunk)
|
||
pos += len(chunk)
|
||
return _to_df(parse_block_dat(bytes(full_data), filename))
|
||
|
||
def get_report_file(self, filename: str) -> bytes:
|
||
"""从服务器拉取大文件（如 'base_info.zip'）。"""
|
||
full_data = bytearray()
|
||
pos = 0
|
||
chunk_size = 30000
|
||
while True:
|
||
chunk = self._execute(GetReportFileCmd(filename, pos, chunk_size))
|
||
if not chunk:
|
||
break
|
||
full_data.extend(chunk)
|
||
pos += len(chunk)
|
||
if len(chunk) < chunk_size:
|
||
break
|
||
return bytes(full_data)
|
||
|
||
@staticmethod
|
||
def _download_from_host(
|
||
host: str, filename: str, port: int = 7709, timeout: float = 15.0
|
||
) -> bytes:
|
||
"""从指定服务器创建临时连接并下载文件。"""
|
||
conn = TdxConnection(host, port, timeout)
|
||
try:
|
||
conn.connect()
|
||
full_data = bytearray()
|
||
pos = 0
|
||
chunk_size = 30000
|
||
while True:
|
||
chunk = conn.execute(GetReportFileCmd(filename, pos, chunk_size))
|
||
if not chunk:
|
||
break
|
||
full_data.extend(chunk)
|
||
pos += len(chunk)
|
||
if len(chunk) < chunk_size:
|
||
break
|
||
return bytes(full_data)
|
||
finally:
|
||
conn.close()
|
||
|
||
def get_financial_file_list(self, host: str | None = None) -> pd.DataFrame:
|
||
"""获取可用的历史专业财报文件列表。
|
||
|
||
连接到计算服务器，下载 tdxfin/gpcw.txt 并解析。
|
||
"""
|
||
if host is None:
|
||
host = get_calc_hosts()[0]
|
||
data = self._download_from_host(host, "tdxfin/gpcw.txt")
|
||
raw_list = parse_financial_file_list(data)
|
||
return _to_df([FinancialFileInfo(filename=f, hash=h, filesize=s) for f, h, s in raw_list])
|
||
|
||
def get_financial_file(self, filename: str, host: str | None = None) -> bytes:
|
||
"""从计算服务器下载财报 zip 文件。
|
||
|
||
Args:
|
||
filename: 如 'tdxfin/gpcw20260331.zip'
|
||
"""
|
||
if host is None:
|
||
host = get_calc_hosts()[0]
|
||
return self._download_from_host(host, filename)
|
||
|
||
def get_financial_records(self, filename: str, host: str | None = None) -> pd.DataFrame:
|
||
"""下载财报 zip 并解析为每只股票的记录列表。
|
||
|
||
Args:
|
||
filename: 如 'tdxfin/gpcw20260331.zip'
|
||
"""
|
||
if host is None:
|
||
host = get_calc_hosts()[0]
|
||
import io
|
||
import re
|
||
import zipfile
|
||
|
||
zip_data = self.get_financial_file(filename, host)
|
||
if not zip_data:
|
||
return pd.DataFrame()
|
||
|
||
with zipfile.ZipFile(io.BytesIO(zip_data)) as zf:
|
||
dat_names = [n for n in zf.namelist() if n.endswith(".dat")]
|
||
if not dat_names:
|
||
return pd.DataFrame()
|
||
dat_data = zf.read(dat_names[0])
|
||
|
||
m = re.search(r"(\d{8})", filename)
|
||
report_date = int(m.group(1)) if m else 0
|
||
|
||
raw_records = parse_financial_dat(dat_data, report_date)
|
||
records: list[FinancialRecord] = []
|
||
for code, market_byte, rdate, fields in raw_records:
|
||
market = Market.SH if market_byte == 1 else Market.SZ
|
||
records.append(
|
||
FinancialRecord(code=code, market=market, report_date=rdate, fields=fields)
|
||
)
|
||
return _to_df(records)
|
||
|
||
def get_market_stat(self) -> pd.DataFrame:
|
||
"""获取 A 股全市场涨跌统计概况（基于 880005/880001/880006 统计指数）。
|
||
|
||
通达信这三个"统计指数"的计数类字段（涨/跌/平/总数/涨停/跌停家数）
|
||
返回的是真实家数的 1/10，需统一 ×10 还原。成交额/量/市值字段不受影响。
|
||
`suspended_count` 由 `total - up - down - neutral` 推得，用于保证计数守恒。
|
||
|
||
空数据容错：880005/880001/880006 并非所有服务器都提供，会返回空 quotes。
|
||
此时不只切换到延迟最低的一台（它可能也不提供），而是按延迟顺序逐台实测，
|
||
找到第一台返回有效数据的服务器，避免用户手动 ``easy-tdx ping``。
|
||
"""
|
||
# 通达信中 880005 是全市场行情统计，880001 是总市值指数，880006 是涨跌停统计
|
||
_cmd = GetSecurityQuotesCmd(
|
||
[(Market.SH, "880005"), (Market.SH, "880001"), (Market.SH, "880006")]
|
||
)
|
||
quotes = self._execute(_cmd)
|
||
if not quotes and self._auto_reconnect:
|
||
quotes = self._find_host_returning_quotes(_cmd)
|
||
if not quotes:
|
||
raise RuntimeError("无法获取市场统计数据")
|
||
q = quotes[0]
|
||
# 计数字段协议返回值为真实家数 / 10，这里 ×10 还原（见 docstring）
|
||
up = round(q.price * 10)
|
||
down = round(q.open * 10)
|
||
neutral = round(q.low * 10)
|
||
total = round(q.high * 10)
|
||
market_cap = quotes[1].price * 1e10 if len(quotes) > 1 else 0.0
|
||
limit_down = round(quotes[2].open * 10) if len(quotes) > 2 else 0
|
||
limit_up = round(quotes[2].price * 10) if len(quotes) > 2 else 0
|
||
return _to_df(
|
||
MarketStat(
|
||
up_count=up,
|
||
down_count=down,
|
||
neutral_count=neutral,
|
||
suspended_count=max(0, total - up - down - neutral),
|
||
total_count=total,
|
||
total_amount=q.amount,
|
||
total_volume=q.vol,
|
||
total_market_cap=market_cap,
|
||
limit_up_count=limit_up,
|
||
limit_down_count=limit_down,
|
||
)
|
||
)
|
||
|
||
def _find_host_returning_quotes(
|
||
self, cmd: "BaseCommand[list[SecurityQuote]]"
|
||
) -> list[SecurityQuote]:
|
||
"""空数据故障转移：测速后按延迟顺序逐台实测，返回首台有效数据的 quotes。
|
||
|
||
专供 ``get_market_stat`` 使用——统计指数并非所有服务器都提供，延迟最低
|
||
的不一定返回数据，故需逐台实际查询。最多尝试 ``_WORKING_HOST_MAX_ATTEMPTS``
|
||
台（见 ``_reconnect``）。找到后 client 停在该 host；全失败返回空。
|
||
"""
|
||
bad_host = self._host
|
||
ranked = ping_all(get_known_hosts(), self._port, 5.0)
|
||
|
||
def _try(host: str) -> bool:
|
||
# 切换到候选 host 并实测；非空即视为该 host 可用
|
||
self._reconnect(host)
|
||
return bool(self._execute(cmd))
|
||
|
||
new_host = find_working_host_sync(ranked, _try, save_best_host, bad_host)
|
||
if new_host is None:
|
||
# 全部候选都不可用，回退到原 host（保持状态可预测）
|
||
if self._host != bad_host:
|
||
self._reconnect(bad_host)
|
||
return []
|
||
# _try 已把 client 切到 new_host 并执行过 cmd，重新取一次拿结果
|
||
return self._execute(cmd)
|
||
|
||
def _collect_transaction_records(
|
||
self,
|
||
fetch_page: Callable[[int, int], list[TransactionRecord]],
|
||
page_size: int,
|
||
max_start: int = 10000,
|
||
) -> list[TransactionRecord]:
|
||
all_recs: list[TransactionRecord] = []
|
||
seen_sig: set[tuple[int, int, float, int, int, int]] = set()
|
||
seen_page_sigs: set[
|
||
tuple[
|
||
tuple[int, int, float, int, int, int],
|
||
tuple[int, int, float, int, int, int],
|
||
]
|
||
] = set()
|
||
start = 0
|
||
|
||
while start < max_start:
|
||
recs = fetch_page(start, page_size)
|
||
if not recs:
|
||
break
|
||
|
||
page_sig = _page_signature(recs)
|
||
if page_sig in seen_page_sigs:
|
||
break
|
||
seen_page_sigs.add(page_sig)
|
||
|
||
new_count = 0
|
||
for record in recs:
|
||
sig = _record_signature(record)
|
||
if sig not in seen_sig:
|
||
seen_sig.add(sig)
|
||
all_recs.append(record)
|
||
new_count += 1
|
||
|
||
if new_count == 0:
|
||
break
|
||
|
||
start += len(recs)
|
||
if len(recs) < 100:
|
||
break
|
||
|
||
return all_recs
|
||
|
||
def get_fund_flow(self, market: Market, code: str) -> pd.DataFrame:
|
||
"""获取个股当日资金流向分布（基于 L1 逐笔数据统计）。"""
|
||
records = self._collect_transaction_records(
|
||
lambda start, page_size: self._execute(
|
||
GetTransactionDataCmd(market, code, start, page_size)
|
||
),
|
||
2000,
|
||
)
|
||
return _to_df(_classify_fund_flow(records))
|
||
|
||
def get_history_fund_flow(
|
||
self, market: Market, code: str, start: int, count: int
|
||
) -> pd.DataFrame:
|
||
"""获取个股历史日线资金流向序列。
|
||
|
||
优先走 Category 22 直连接口；若服务器返回空列表，则自动回退为
|
||
"日 K 线取日期 + 历史逐笔成交重算资金流"的兼容实现。
|
||
"""
|
||
try:
|
||
direct = self._execute(GetHistoryFundFlowCmd(market, code, start, count))
|
||
except Exception:
|
||
direct = []
|
||
if direct:
|
||
return _to_df(direct)
|
||
|
||
bars = self._execute(GetSecurityBarsCmd(market, code, KlineCategory.DAY, start, count))
|
||
results: list[HistoricalFundFlow] = []
|
||
for bar in bars:
|
||
date = _date_from_bar(bar)
|
||
|
||
# 用闭包工厂立即绑定 date（审计 #10），避免 lambda 延迟绑定循环变量。
|
||
def _fetch_page(
|
||
page_start: int, page_size: int, _d: int = date
|
||
) -> list[TransactionRecord]:
|
||
return self._execute(
|
||
GetHistoryTransactionDataCmd(market, code, _d, page_start, page_size)
|
||
)
|
||
|
||
records = self._collect_transaction_records(_fetch_page, 800)
|
||
results.append(_historical_fund_flow_from_records(date, records))
|
||
return _to_df(results)
|
||
|
||
|
||
# ============================================================
|
||
# 异步客户端
|
||
# ============================================================
|
||
|
||
|
||
class AsyncTdxClient(AsyncHeartbeatMixin):
|
||
"""异步通达信行情客户端（asyncio）。
|
||
|
||
使用示例::
|
||
|
||
async with AsyncTdxClient("180.153.18.170") as c:
|
||
bars = await c.get_security_bars(Market.SH, "600000", KlineCategory.DAY, 0, 100)
|
||
|
||
注意：
|
||
单个 AsyncTdxClient 仅维护一条 TCP 连接；并发调用会在连接内串行执行。
|
||
"""
|
||
|
||
def __init__(
|
||
self,
|
||
host: str | None = None,
|
||
port: int | None = None,
|
||
timeout: float | None = None,
|
||
auto_reconnect: bool = True,
|
||
heartbeat_interval: float = 60.0,
|
||
) -> None:
|
||
self._host = host if host is not None else get_best_host()
|
||
self._port = port if port is not None else get_port()
|
||
self._timeout = timeout if timeout is not None else get_timeout()
|
||
self._auto_reconnect = auto_reconnect
|
||
self._heartbeat_interval = heartbeat_interval
|
||
self._conn = AsyncTdxConnection(self._host, self._port, self._timeout)
|
||
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 | None = None,
|
||
timeout: float | None = None,
|
||
ping_timeout: float = 5.0,
|
||
auto_reconnect: bool = True,
|
||
heartbeat_interval: float = 60.0,
|
||
) -> "AsyncTdxClient":
|
||
"""测量 hosts 中所有服务器延迟，选最低延迟的建立连接。
|
||
|
||
自动将最佳主机保存到 config.json。
|
||
"""
|
||
if hosts is None:
|
||
hosts = get_known_hosts()
|
||
if port is None:
|
||
port = get_port()
|
||
if timeout is None:
|
||
timeout = get_timeout()
|
||
ranked = ping_all(hosts, port, ping_timeout)
|
||
best = ranked[0][0] if ranked else hosts[0]
|
||
save_best_host(best)
|
||
return cls(best, port, timeout, auto_reconnect, heartbeat_interval)
|
||
|
||
@staticmethod
|
||
def ping_all(
|
||
hosts: list[str] | None = None,
|
||
port: int | None = None,
|
||
timeout: float = 5.0,
|
||
) -> list[tuple[str, float]]:
|
||
"""测量多台服务器延迟，返回按延迟排序的 (host, seconds) 列表。"""
|
||
if hosts is None:
|
||
hosts = get_known_hosts()
|
||
if port is None:
|
||
port = get_port()
|
||
return ping_all(hosts, port, timeout)
|
||
|
||
async def connect(self) -> None:
|
||
await self._conn.connect()
|
||
self._start_heartbeat()
|
||
|
||
async def close(self) -> None:
|
||
await self._stop_heartbeat()
|
||
await self._conn.close()
|
||
|
||
async def reconnect_to(self, host: str) -> None:
|
||
"""热切换到新 host：关旧连接 → 换 host → 建新连接。
|
||
|
||
用于 web UI 的"服务器设置"页面——用户点选一个 host 后，无需重启
|
||
服务即可切换。复用 ``_execute_lock`` 保证切换期间没有并发行情请求
|
||
撞到半开的连接。切换失败抛异常（旧连接已 close，client 处于断开
|
||
状态，调用方应捕获并提示用户选别的 host）。
|
||
"""
|
||
async with self._execute_lock:
|
||
await self._stop_heartbeat()
|
||
await self._conn.close()
|
||
self._host = host
|
||
self._conn = AsyncTdxConnection(host, self._port, self._timeout)
|
||
await self._conn.connect()
|
||
self._start_heartbeat()
|
||
|
||
async def __aenter__(self) -> "AsyncTdxClient":
|
||
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_security_count，复用 _execute 重连）。"""
|
||
return self.get_security_count(Market.SH)
|
||
|
||
async def _areconnect(self, host: str | None = None) -> None:
|
||
"""关闭当前连接并重建（默认连 self._host；传入 host 则切换主机）。
|
||
|
||
async 版的统一重建入口，与 sync ``_reconnect`` 对称，供 ``_execute``
|
||
同主机重试、跨主机故障转移、``get_market_stat`` 空数据重试复用。
|
||
"""
|
||
target = host if host is not None else self._host
|
||
if host is not None:
|
||
self._host = host
|
||
await self._stop_heartbeat()
|
||
await self._conn.close()
|
||
self._conn = AsyncTdxConnection(target, self._port, self._timeout)
|
||
await self._conn.connect()
|
||
self._start_heartbeat()
|
||
|
||
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。
|
||
|
||
两阶段韧性与 sync 版对称：先同主机重试（``_RETRY_DELAYS``），再跨主机
|
||
故障转移（重新测速切到另一台服务器）。整个流程在 ``_execute_lock`` 内
|
||
串行，避免并发请求触发多次故障转移抖动。``auto_reconnect=False`` 时
|
||
两阶段都不触发。
|
||
"""
|
||
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._areconnect()
|
||
try:
|
||
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_known_hosts(),
|
||
ping_all,
|
||
save_best_host,
|
||
self._port,
|
||
5.0,
|
||
self._host,
|
||
)
|
||
if new_host is not None:
|
||
await self._areconnect(new_host)
|
||
try:
|
||
return await self._conn.execute(cmd)
|
||
except TdxConnectionError as e:
|
||
last_exc = e
|
||
raise last_exc # type: ignore[misc]
|
||
|
||
async def get_security_count(self, market: Market) -> int:
|
||
return await self._execute(GetSecurityCountCmd(market))
|
||
|
||
async def get_security_list(self, market: Market, start: int) -> pd.DataFrame:
|
||
return _to_df(await self._execute(GetSecurityListCmd(market, start)))
|
||
|
||
async def get_security_list_all(self, pages: int | str = "all") -> pd.DataFrame:
|
||
"""获取沪深 A 股完整证券列表，并自动挂载行业信息。
|
||
|
||
Args:
|
||
pages: 拉取页数。每个市场每页 1000 条。
|
||
"all" 拉取全部（默认，结果会缓存到本地文件）。
|
||
整数 N 表示每个市场只拉前 N 页，不缓存。
|
||
|
||
注意：
|
||
`Market.BJ` 的证券列表请求长期存在服务器超时问题，当前版本暂不纳入此方法。
|
||
"""
|
||
log = logging.getLogger(__name__)
|
||
|
||
if pages == "all":
|
||
cached = _load_cache()
|
||
if cached is not None:
|
||
log.info("从缓存加载沪深 A 股列表，共 %d 只", len(cached))
|
||
return _to_df(cached)
|
||
|
||
def _max_start(count: int) -> int:
|
||
if pages == "all":
|
||
return count
|
||
return min(count, int(pages) * 1000)
|
||
|
||
industry_map: dict[str, tuple[str, str]] = {}
|
||
try:
|
||
cfg_data = await self.get_report_file("tdxhy.cfg")
|
||
if cfg_data:
|
||
industry_map = parse_tdxhy_cfg(cfg_data)
|
||
log.info("行业配置已加载，共 %d 条映射", len(industry_map))
|
||
except Exception:
|
||
log.warning("无法获取 tdxhy.cfg，行业字段将为空")
|
||
|
||
all_stocks: list[SecurityInfo] = []
|
||
for market in [Market.SH, Market.SZ]:
|
||
count = await self.get_security_count(market)
|
||
limit = _max_start(count)
|
||
total_pages = (limit + 999) // 1000
|
||
for page_idx, start in enumerate(range(0, limit, 1000)):
|
||
try:
|
||
stocks = await self._execute(GetSecurityListCmd(market, start))
|
||
except Exception:
|
||
log.warning(
|
||
"%s 第 %d/%d 页获取失败，跳过", market.name, page_idx + 1, total_pages
|
||
)
|
||
continue
|
||
log.info(
|
||
"%s 第 %d/%d 页: %d 条", market.name, page_idx + 1, total_pages, len(stocks)
|
||
)
|
||
for s in stocks:
|
||
is_a_share = (market == Market.SH and s.code.startswith(("60", "68"))) or (
|
||
market == Market.SZ and s.code.startswith(("00", "30"))
|
||
)
|
||
if is_a_share:
|
||
if s.code in industry_map:
|
||
s.industry_tdx, s.industry_sw = industry_map[s.code]
|
||
all_stocks.append(s)
|
||
|
||
log.info("沪深 A 股总数: %d", len(all_stocks))
|
||
if pages == "all":
|
||
_save_cache(all_stocks)
|
||
return _to_df(all_stocks)
|
||
|
||
async def get_security_quotes(self, stocks: list[tuple[Market, str]]) -> pd.DataFrame:
|
||
return _to_df(await self._execute(GetSecurityQuotesCmd(stocks)))
|
||
|
||
async def get_price_limits(
|
||
self, market: Market, code: str, name: str, pre_close: float
|
||
) -> tuple[float | None, float | None]:
|
||
"""按当前交易状态计算涨跌停价。"""
|
||
listed_days: int | None = None
|
||
no_limit_window_days = get_no_limit_window_days(market, code, name)
|
||
if no_limit_window_days > 0:
|
||
try:
|
||
bars = await self._execute(
|
||
GetSecurityBarsCmd(market, code, KlineCategory.DAY, 0, no_limit_window_days + 1)
|
||
)
|
||
listed_days = len(bars)
|
||
except Exception:
|
||
listed_days = None
|
||
|
||
return compute_price_limits(
|
||
market,
|
||
code,
|
||
name,
|
||
pre_close,
|
||
listed_days=listed_days,
|
||
)
|
||
|
||
async def get_security_bars(
|
||
self,
|
||
market: Market,
|
||
code: str,
|
||
category: KlineCategory,
|
||
start: int,
|
||
count: int = 800,
|
||
*,
|
||
bar_time: str = "start",
|
||
) -> pd.DataFrame:
|
||
"""获取 K 线数据。``bar_time`` 见同步版 :meth:`get_security_bars`。"""
|
||
df = _to_df(await self._execute(GetSecurityBarsCmd(market, code, category, start, count)))
|
||
delta = _category_to_minutes(int(category))
|
||
is_intraday = delta is not None
|
||
df = _apply_bar_time_align_df(
|
||
df,
|
||
is_intraday=is_intraday,
|
||
delta_minutes=delta,
|
||
bar_time=bar_time,
|
||
has_time_columns=True,
|
||
)
|
||
return _merge_bar_datetime(df, not is_intraday)
|
||
|
||
async def get_index_bars(
|
||
self,
|
||
market: Market,
|
||
code: str,
|
||
category: KlineCategory,
|
||
start: int,
|
||
count: int = 800,
|
||
*,
|
||
bar_time: str = "start",
|
||
) -> pd.DataFrame:
|
||
"""获取指数 K 线数据。``bar_time`` 见同步版 :meth:`get_index_bars`。"""
|
||
df = _to_df(await self._execute(GetIndexBarsCmd(market, code, category, start, count)))
|
||
delta = _category_to_minutes(int(category))
|
||
is_intraday = delta is not None
|
||
df = _apply_bar_time_align_df(
|
||
df,
|
||
is_intraday=is_intraday,
|
||
delta_minutes=delta,
|
||
bar_time=bar_time,
|
||
has_time_columns=True,
|
||
)
|
||
return _merge_bar_datetime(df, not is_intraday)
|
||
|
||
async def get_minute_time_data(self, market: Market, code: str) -> pd.DataFrame:
|
||
today = _today_in_shanghai()
|
||
bars = await self._execute(GetHistoryMinuteTimeDataCmd(market, code, today))
|
||
return _add_minute_datetime(_to_df(bars), today)
|
||
|
||
async def get_history_minute_time_data(
|
||
self, market: Market, code: str, date: int
|
||
) -> pd.DataFrame:
|
||
bars = await self._execute(GetHistoryMinuteTimeDataCmd(market, code, date))
|
||
return _add_minute_datetime(_to_df(bars), date)
|
||
|
||
async def get_transaction_data(
|
||
self, market: Market, code: str, start: int, count: int = 800
|
||
) -> pd.DataFrame:
|
||
df = _to_df(await self._execute(GetTransactionDataCmd(market, code, start, count)))
|
||
return _merge_txn_datetime(df, _today_in_shanghai())
|
||
|
||
async def get_history_transaction_data(
|
||
self, market: Market, code: str, date: int, start: int, count: int = 800
|
||
) -> pd.DataFrame:
|
||
df = _to_df(
|
||
await self._execute(GetHistoryTransactionDataCmd(market, code, date, start, count))
|
||
)
|
||
return _merge_txn_datetime(df, date)
|
||
|
||
async def get_xdxr_info(self, market: Market, code: str) -> pd.DataFrame:
|
||
return _to_df(await self._execute(GetXdxrInfoCmd(market, code)))
|
||
|
||
async def get_finance_info(self, market: Market, code: str) -> pd.DataFrame:
|
||
return _to_df(await self._execute(GetFinanceInfoCmd(market, code)))
|
||
|
||
async def get_company_info_category(self, market: Market, code: str) -> pd.DataFrame:
|
||
return _to_df(await self._execute(GetCompanyInfoCategoryCmd(market, code)))
|
||
|
||
async def get_company_info_content(
|
||
self, market: Market, code: str, filename: str, offset: int, length: int
|
||
) -> str:
|
||
return await self._execute(GetCompanyInfoContentCmd(market, code, filename, offset, length))
|
||
|
||
async def get_block_info(self, filename: str) -> pd.DataFrame:
|
||
"""获取并解析板块文件（行业、概念、风格等）。"""
|
||
size, _hash = await self._execute(GetBlockInfoMetaCmd(filename))
|
||
full_data = bytearray()
|
||
pos = 0
|
||
chunk_size = 30000
|
||
while pos < size:
|
||
chunk = await self._execute(GetBlockInfoCmd(filename, pos, chunk_size))
|
||
if not chunk:
|
||
break
|
||
full_data.extend(chunk)
|
||
pos += len(chunk)
|
||
return _to_df(parse_block_dat(bytes(full_data), filename))
|
||
|
||
async def get_report_file(self, filename: str) -> bytes:
|
||
"""从服务器拉取大文件。"""
|
||
full_data = bytearray()
|
||
pos = 0
|
||
chunk_size = 30000
|
||
while True:
|
||
chunk = await self._execute(GetReportFileCmd(filename, pos, chunk_size))
|
||
if not chunk:
|
||
break
|
||
full_data.extend(chunk)
|
||
pos += len(chunk)
|
||
if len(chunk) < chunk_size:
|
||
break
|
||
return bytes(full_data)
|
||
|
||
@staticmethod
|
||
async def _async_download_from_host(
|
||
host: str, filename: str, port: int = 7709, timeout: float = 15.0
|
||
) -> bytes:
|
||
"""从指定服务器创建临时异步连接并下载文件。"""
|
||
conn = AsyncTdxConnection(host, port, timeout)
|
||
try:
|
||
await conn.connect()
|
||
full_data = bytearray()
|
||
pos = 0
|
||
chunk_size = 30000
|
||
while True:
|
||
chunk = await conn.execute(GetReportFileCmd(filename, pos, chunk_size))
|
||
if not chunk:
|
||
break
|
||
full_data.extend(chunk)
|
||
pos += len(chunk)
|
||
if len(chunk) < chunk_size:
|
||
break
|
||
return bytes(full_data)
|
||
finally:
|
||
await conn.close()
|
||
|
||
async def get_financial_file_list(self, host: str | None = None) -> pd.DataFrame:
|
||
"""获取可用的历史专业财报文件列表（异步）。"""
|
||
if host is None:
|
||
host = get_calc_hosts()[0]
|
||
data = await self._async_download_from_host(host, "tdxfin/gpcw.txt")
|
||
raw_list = parse_financial_file_list(data)
|
||
return _to_df([FinancialFileInfo(filename=f, hash=h, filesize=s) for f, h, s in raw_list])
|
||
|
||
async def get_financial_file(self, filename: str, host: str | None = None) -> bytes:
|
||
"""从计算服务器下载财报 zip 文件（异步）。"""
|
||
if host is None:
|
||
host = get_calc_hosts()[0]
|
||
return await self._async_download_from_host(host, filename)
|
||
|
||
async def get_financial_records(self, filename: str, host: str | None = None) -> pd.DataFrame:
|
||
"""下载财报 zip 并解析为记录列表（异步）。"""
|
||
if host is None:
|
||
host = get_calc_hosts()[0]
|
||
import io
|
||
import re
|
||
import zipfile
|
||
|
||
zip_data = await self.get_financial_file(filename, host)
|
||
if not zip_data:
|
||
return pd.DataFrame()
|
||
|
||
with zipfile.ZipFile(io.BytesIO(zip_data)) as zf:
|
||
dat_names = [n for n in zf.namelist() if n.endswith(".dat")]
|
||
if not dat_names:
|
||
return pd.DataFrame()
|
||
dat_data = zf.read(dat_names[0])
|
||
|
||
m = re.search(r"(\d{8})", filename)
|
||
report_date = int(m.group(1)) if m else 0
|
||
|
||
raw_records = parse_financial_dat(dat_data, report_date)
|
||
records: list[FinancialRecord] = []
|
||
for code, market_byte, rdate, fields in raw_records:
|
||
market = Market.SH if market_byte == 1 else Market.SZ
|
||
records.append(
|
||
FinancialRecord(code=code, market=market, report_date=rdate, fields=fields)
|
||
)
|
||
return _to_df(records)
|
||
|
||
async def get_market_stat(self) -> pd.DataFrame:
|
||
"""获取 A 股全市场涨跌统计概况（基于 880005/880001/880006 统计指数）。
|
||
|
||
通达信这三个"统计指数"的计数类字段（涨/跌/平/总数/涨停/跌停家数）
|
||
返回的是真实家数的 1/10，需统一 ×10 还原。成交额/量/市值字段不受影响。
|
||
`suspended_count` 由 `total - up - down - neutral` 推得，用于保证计数守恒。
|
||
|
||
空数据容错：与 sync 版对称——空 quotes 时按延迟顺序逐台实测，找到首台
|
||
返回有效数据的服务器。
|
||
"""
|
||
# 通达信中 880005 是全市场行情统计，880001 是总市值指数，880006 是涨跌停统计
|
||
_cmd = GetSecurityQuotesCmd(
|
||
[(Market.SH, "880005"), (Market.SH, "880001"), (Market.SH, "880006")]
|
||
)
|
||
quotes = await self._execute(_cmd)
|
||
if not quotes and self._auto_reconnect:
|
||
quotes = await self._find_host_returning_quotes(_cmd)
|
||
if not quotes:
|
||
raise RuntimeError("无法获取市场统计数据")
|
||
q = quotes[0]
|
||
# 计数字段协议返回值为真实家数 / 10，这里 ×10 还原（见 docstring）
|
||
up = round(q.price * 10)
|
||
down = round(q.open * 10)
|
||
neutral = round(q.low * 10)
|
||
total = round(q.high * 10)
|
||
market_cap = quotes[1].price * 1e10 if len(quotes) > 1 else 0.0
|
||
limit_down = round(quotes[2].open * 10) if len(quotes) > 2 else 0
|
||
limit_up = round(quotes[2].price * 10) if len(quotes) > 2 else 0
|
||
return _to_df(
|
||
MarketStat(
|
||
up_count=up,
|
||
down_count=down,
|
||
neutral_count=neutral,
|
||
suspended_count=max(0, total - up - down - neutral),
|
||
total_count=total,
|
||
total_amount=q.amount,
|
||
total_volume=q.vol,
|
||
total_market_cap=market_cap,
|
||
limit_up_count=limit_up,
|
||
limit_down_count=limit_down,
|
||
)
|
||
)
|
||
|
||
async def _find_host_returning_quotes(
|
||
self, cmd: "BaseCommand[list[SecurityQuote]]"
|
||
) -> list[SecurityQuote]:
|
||
"""空数据故障转移（async）：与 sync ``_find_host_returning_quotes`` 对称。"""
|
||
bad_host = self._host
|
||
ranked = await asyncio.to_thread(ping_all, get_known_hosts(), self._port, 5.0)
|
||
|
||
async def _try(host: str) -> bool:
|
||
await self._areconnect(host)
|
||
# mypy 对 async 闭包内泛型参数的推断会宽化为 BaseCommand[object]
|
||
# （sync 同模式可正确推断），此处为已知 mypy 限制，非真实类型错误。
|
||
return bool(await self._execute(cmd)) # type: ignore[arg-type]
|
||
|
||
new_host = await find_working_host_async(ranked, _try, save_best_host, bad_host)
|
||
if new_host is None:
|
||
if self._host != bad_host:
|
||
await self._areconnect(bad_host)
|
||
return []
|
||
return await self._execute(cmd)
|
||
|
||
async def _collect_transaction_records(
|
||
self,
|
||
fetch_page: Callable[[int, int], Awaitable[list[TransactionRecord]]],
|
||
page_size: int,
|
||
max_start: int = 10000,
|
||
) -> list[TransactionRecord]:
|
||
all_recs: list[TransactionRecord] = []
|
||
seen_sig: set[tuple[int, int, float, int, int, int]] = set()
|
||
seen_page_sigs: set[
|
||
tuple[
|
||
tuple[int, int, float, int, int, int],
|
||
tuple[int, int, float, int, int, int],
|
||
]
|
||
] = set()
|
||
start = 0
|
||
|
||
while start < max_start:
|
||
recs = await fetch_page(start, page_size)
|
||
if not recs:
|
||
break
|
||
|
||
page_sig = _page_signature(recs)
|
||
if page_sig in seen_page_sigs:
|
||
break
|
||
seen_page_sigs.add(page_sig)
|
||
|
||
new_count = 0
|
||
for record in recs:
|
||
sig = _record_signature(record)
|
||
if sig not in seen_sig:
|
||
seen_sig.add(sig)
|
||
all_recs.append(record)
|
||
new_count += 1
|
||
|
||
if new_count == 0:
|
||
break
|
||
|
||
start += len(recs)
|
||
if len(recs) < 100:
|
||
break
|
||
|
||
return all_recs
|
||
|
||
async def get_fund_flow(self, market: Market, code: str) -> pd.DataFrame:
|
||
"""获取个股当日资金流向分布（基于 L1 逐笔数据统计）。"""
|
||
records = await self._collect_transaction_records(
|
||
lambda start, page_size: self._execute(
|
||
GetTransactionDataCmd(market, code, start, page_size)
|
||
),
|
||
2000,
|
||
)
|
||
return _to_df(_classify_fund_flow(records))
|
||
|
||
async def get_history_fund_flow(
|
||
self, market: Market, code: str, start: int, count: int
|
||
) -> pd.DataFrame:
|
||
"""获取个股历史日线资金流向序列。
|
||
|
||
优先走 Category 22 直连接口；若服务器返回空列表，则自动回退为
|
||
"日 K 线取日期 + 历史逐笔成交重算资金流"的兼容实现。
|
||
"""
|
||
try:
|
||
direct = await self._execute(GetHistoryFundFlowCmd(market, code, start, count))
|
||
except Exception:
|
||
direct = []
|
||
if direct:
|
||
return _to_df(direct)
|
||
|
||
bars = await self._execute(
|
||
GetSecurityBarsCmd(market, code, KlineCategory.DAY, start, count)
|
||
)
|
||
results: list[HistoricalFundFlow] = []
|
||
for bar in bars:
|
||
date = _date_from_bar(bar)
|
||
|
||
# 用闭包工厂立即绑定 date（审计 #10），避免 lambda 延迟绑定循环变量。
|
||
async def _fetch_page(
|
||
page_start: int, page_size: int, _d: int = date
|
||
) -> list[TransactionRecord]:
|
||
return await self._execute(
|
||
GetHistoryTransactionDataCmd(market, code, _d, page_start, page_size)
|
||
)
|
||
|
||
records = await self._collect_transaction_records(_fetch_page, 800)
|
||
results.append(_historical_fund_flow_from_records(date, records))
|
||
return _to_df(results)
|