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feat: merge datetime fields in DataFrame output, hide MinuteBar internal fields

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- K-line: daily+ periods output 'date' only, minute periods output 'datetime'
- Transactions (tick-by-tick): combine date param + hour/minute into 'datetime'
- XdxrRecord, HistoricalFundFlow: year/month/day merged to 'date'
- MinuteBar: rename unknown_1 to _unknown_1 (hidden from DataFrame)
- MinuteBar: add datetime column computed from bar index (A-share 240-bar pattern)
- get_minute_time_data: use history endpoint only (current-day endpoint broken in pytdx too)
- Update all examples to reflect new DataFrame column names
This commit is contained in:
Justin Gu
2026-05-22 04:19:07 +08:00
parent 0d7f7aead1
commit 00825eb24a
31 changed files with 720 additions and 663 deletions
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src/easy_tdx/_df.py
+111
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@@ -0,0 +1,111 @@
"""Dataclass → DataFrame 转换工具。"""
from __future__ import annotations
from dataclasses import asdict, is_dataclass
from typing import Any
import pandas as pd
def _to_df(data: Any) -> pd.DataFrame:
"""将 list[dataclass] 或单个 dataclass 转为 DataFrame。
自动丢弃以 ``_`` 开头的内部字段（如 ``_raw``）。
仅处理 year/month/day（无 hour/minute）→ date 的合并；
SecurityBar 的完整 datetime 合并由调用方按周期决定。
"""
if isinstance(data, list):
if not data:
return pd.DataFrame()
rows = []
for item in data:
d = _clean_dict(item)
rows.append(d)
return pd.DataFrame(rows)
if is_dataclass(data) and not isinstance(data, type):
return pd.DataFrame([_clean_dict(data)])
raise TypeError(f"不支持转换为 DataFrame 的类型: {type(data)}")
def _clean_dict(item: Any) -> dict[str, Any]:
d = asdict(item)
d = {k: v for k, v in d.items() if not k.startswith("_")}
return _merge_datetime_fields(d)
def _merge_datetime_fields(d: dict[str, Any]) -> dict[str, Any]:
"""将仅含 year/month/day（无 hour/minute）的模型合并为 date 列。"""
if all(k in d for k in ("year", "month", "day")) and not all(
k in d for k in ("hour", "minute")
):
dt = pd.Timestamp(year=d["year"], month=d["month"], day=d["day"])
result: dict[str, Any] = {"date": dt}
result.update({k: v for k, v in d.items() if k not in {"year", "month", "day"}})
return result
return d
def _merge_bar_datetime(df: pd.DataFrame, daily_plus: bool) -> pd.DataFrame:
"""根据 K 线周期将 SecurityBar 的分散字段合并为 date 或 datetime。
Args:
daily_plus: True 表示日线及以上周期（DAY/WEEK/MONTH/YEAR），只保留 date；
False 表示分钟线（MIN_1/5/15/30/60），保留完整 datetime。
"""
if df.empty or "year" not in df.columns:
return df
date_str = (
df["year"].astype(str)
+ "-"
+ df["month"].astype(str).str.zfill(2)
+ "-"
+ df["day"].astype(str).str.zfill(2)
)
if daily_plus:
df.insert(0, "date", pd.to_datetime(date_str))
else:
full_str = (
date_str
+ " "
+ df["hour"].astype(str).str.zfill(2)
+ ":"
+ df["minute"].astype(str).str.zfill(2)
)
df.insert(0, "datetime", pd.to_datetime(full_str))
df.drop(columns=["year", "month", "day", "hour", "minute"], inplace=True)
return df
def _merge_txn_datetime(df: pd.DataFrame, date_int: int) -> pd.DataFrame:
"""将逐笔成交的 date + hour:minute 合并为 datetime 列。"""
if df.empty or "hour" not in df.columns:
return df
year = date_int // 10000
month = (date_int // 100) % 100
day = date_int % 100
base = pd.Timestamp(year=year, month=month, day=day)
offsets = pd.to_timedelta(df["hour"] * 3600 + df["minute"] * 60, unit="s")
df.insert(0, "datetime", base + offsets)
df.drop(columns=["hour", "minute"], inplace=True)
return df
def _add_minute_datetime(df: pd.DataFrame, date_int: int) -> pd.DataFrame:
"""为分时 DataFrame 添加 datetime 列（从 bar 索引计算时间）。
A 股分时 240 条：0-119 = 9:30~11:29（上午），120-239 = 13:00~14:59（下午）。
"""
if df.empty:
return df
year = date_int // 10000
month = (date_int // 100) % 100
day = date_int % 100
base = pd.Timestamp(year=year, month=month, day=day)
n = len(df)
morning = list(range(9 * 60 + 30, 9 * 60 + 30 + 120))
afternoon = list(range(13 * 60, 13 * 60 + 120))
all_minutes = (morning + afternoon)[:n]
offsets = pd.to_timedelta(all_minutes, unit="m")
df.insert(0, "datetime", base + offsets)
return df
src/easy_tdx/client.py
+185 -194
View File
@@ -11,6 +11,9 @@ from types import TracebackType
from typing import TypeVar
from zoneinfo import ZoneInfo
import pandas as pd
from ._df import _add_minute_datetime, _merge_bar_datetime, _merge_txn_datetime, _to_df
from .codec.block import parse_block_dat
from .codec.financial import parse_financial_dat, parse_financial_file_list
from .codec.industry import parse_tdxhy_cfg
@@ -20,7 +23,7 @@ from .commands.block_info import GetBlockInfoCmd, GetBlockInfoMetaCmd
from .commands.company_info import GetCompanyInfoCategoryCmd, GetCompanyInfoContentCmd
from .commands.finance_info import GetFinanceInfoCmd
from .commands.fund_flow import GetHistoryFundFlowCmd
from .commands.minute_time import GetHistoryMinuteTimeDataCmd, GetMinuteTimeDataCmd
from .commands.minute_time import GetHistoryMinuteTimeDataCmd
from .commands.report_file import GetReportFileCmd
from .commands.security_bars import GetIndexBarsCmd, GetSecurityBarsCmd
from .commands.security_count import GetSecurityCountCmd
@@ -32,23 +35,27 @@ from .exceptions import TdxConnectionError
from .models.bar import SecurityBar
from .models.enums import KlineCategory, Market
from .models.finance import (
CompanyInfoCategory,
FinanceInfo,
FinancialFileInfo,
FinancialRecord,
TdxBlock,
XdxrRecord,
)
from .models.quote import SecurityQuote
from .models.security import SecurityInfo
from .models.stats import FundFlow, HistoricalFundFlow, MarketStat
from .models.timeseries import MinuteBar, TransactionRecord
from .models.timeseries import TransactionRecord
from .transport.async_ import AsyncTdxConnection
from .transport.sync import CALC_HOSTS, KNOWN_HOSTS, TdxConnection, ping_all
_DEFAULT_PORT = 7709
_T = TypeVar("_T")
_SHANGHAI_TZ = ZoneInfo("Asia/Shanghai")
_DAILY_PLUS = frozenset(
{
KlineCategory.DAY,
KlineCategory.WEEK,
KlineCategory.MONTH,
KlineCategory.YEAR,
KlineCategory.YEAR_ALT,
}
)
def _today_in_shanghai() -> int:
@@ -88,9 +95,7 @@ def _classify_fund_flow(records: list[TransactionRecord]) -> FundFlow:
for record in records:
amount = record.price * record.vol * 100.0
direction = (
"in" if record.buyorsell == 0 else "out" if record.buyorsell == 1 else None
)
direction = "in" if record.buyorsell == 0 else "out" if record.buyorsell == 1 else None
if not direction:
continue
@@ -278,11 +283,11 @@ class TdxClient:
"""获取市场证券总数。"""
return self._execute(GetSecurityCountCmd(market))
def get_security_list(self, market: Market, start: int) -> list[SecurityInfo]:
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 self._execute(GetSecurityListCmd(market, start))
return _to_df(self._execute(GetSecurityListCmd(market, start)))
def get_security_list_all(self, pages: int | str = "all") -> list[SecurityInfo]:
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:
@@ -299,7 +304,7 @@ class TdxClient:
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 cached
return _to_df(cached)
# 计算每个市场的最大起始偏移
def _max_start(count: int) -> int:
@@ -324,15 +329,18 @@ class TdxClient:
total_pages = (limit + 999) // 1000
for page_idx, start in enumerate(range(0, limit, 1000)):
try:
stocks = self.get_security_list(market, start)
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)
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))
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")))
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:
@@ -344,13 +352,11 @@ class TdxClient:
if pages == "all":
_save_cache(all_stocks)
return all_stocks
return _to_df(all_stocks)
def get_security_quotes(
self, stocks: list[tuple[Market, str]]
) -> list[SecurityQuote]:
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 self._execute(GetSecurityQuotesCmd(stocks))
return _to_df(self._execute(GetSecurityQuotesCmd(stocks)))
def get_price_limits(
self, market: Market, code: str, name: str, pre_close: float
@@ -363,8 +369,8 @@ class TdxClient:
no_limit_window_days = get_no_limit_window_days(market, code, name)
if no_limit_window_days > 0:
try:
bars = self.get_security_bars(
market, code, KlineCategory.DAY, 0, no_limit_window_days + 1
bars = self._execute(
GetSecurityBarsCmd(market, code, KlineCategory.DAY, 0, no_limit_window_days + 1)
)
listed_days = len(bars)
except Exception:
@@ -389,9 +395,10 @@ class TdxClient:
category: KlineCategory,
start: int,
count: int = 800,
) -> list[SecurityBar]:
) -> pd.DataFrame:
"""获取 K 线数据（最多800条/次，按 start 分页）。"""
return self._execute(GetSecurityBarsCmd(market, code, category, start, count))
df = _to_df(self._execute(GetSecurityBarsCmd(market, code, category, start, count)))
return _merge_bar_datetime(df, category in _DAILY_PLUS)
def get_index_bars(
self,
@@ -400,30 +407,25 @@ class TdxClient:
category: KlineCategory,
start: int,
count: int = 800,
) -> list[SecurityBar]:
) -> pd.DataFrame:
"""获取指数 K 线数据。"""
return self._execute(GetIndexBarsCmd(market, code, category, start, count))
df = _to_df(self._execute(GetIndexBarsCmd(market, code, category, start, count)))
return _merge_bar_datetime(df, category in _DAILY_PLUS)
# ------------------------------------------------------------------ #
# 分时
# ------------------------------------------------------------------ #
def get_minute_time_data(self, market: Market, code: str) -> list[MinuteBar]:
"""获取今日分时数据（240条）。"""
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()
try:
bars = self.get_history_minute_time_data(market, code, today)
if bars:
return bars
except Exception:
pass
return self._execute(GetMinuteTimeDataCmd(market, code))
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
) -> list[MinuteBar]:
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）。"""
return self._execute(GetHistoryMinuteTimeDataCmd(market, code, date))
bars = self._execute(GetHistoryMinuteTimeDataCmd(market, code, date))
return _add_minute_datetime(_to_df(bars), date)
# ------------------------------------------------------------------ #
# 逐笔成交
@@ -431,45 +433,41 @@ class TdxClient:
def get_transaction_data(
self, market: Market, code: str, start: int, count: int = 800
) -> list[TransactionRecord]:
) -> pd.DataFrame:
"""获取当日逐笔成交（分页）。"""
return self._execute(GetTransactionDataCmd(market, code, start, count))
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
) -> list[TransactionRecord]:
) -> pd.DataFrame:
"""获取历史逐笔成交（date: YYYYMMDD，分页）。"""
return self._execute(
GetHistoryTransactionDataCmd(market, code, date, start, count)
)
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) -> list[XdxrRecord]:
def get_xdxr_info(self, market: Market, code: str) -> pd.DataFrame:
"""获取除权除息历史记录。"""
return self._execute(GetXdxrInfoCmd(market, code))
return _to_df(self._execute(GetXdxrInfoCmd(market, code)))
def get_finance_info(self, market: Market, code: str) -> FinanceInfo:
def get_finance_info(self, market: Market, code: str) -> pd.DataFrame:
"""获取最新财务数据。"""
return self._execute(GetFinanceInfoCmd(market, code))
return _to_df(self._execute(GetFinanceInfoCmd(market, code)))
def get_company_info_category(
self, market: Market, code: str
) -> list[CompanyInfoCategory]:
def get_company_info_category(self, market: Market, code: str) -> pd.DataFrame:
"""获取公司信息文件目录。"""
return self._execute(GetCompanyInfoCategoryCmd(market, code))
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)
)
return self._execute(GetCompanyInfoContentCmd(market, code, filename, offset, length))
def get_block_info(self, filename: str) -> list[TdxBlock]:
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：
@@ -487,7 +485,7 @@ class TdxClient:
break
full_data.extend(chunk)
pos += len(chunk)
return parse_block_dat(bytes(full_data), filename)
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'）。"""
@@ -527,16 +525,14 @@ class TdxClient:
finally:
conn.close()
def get_financial_file_list(
self, host: str = CALC_HOSTS[0]
) -> list[FinancialFileInfo]:
def get_financial_file_list(self, host: str = CALC_HOSTS[0]) -> 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。
"""
data = self._download_from_host(host, "tdxfin/gpcw.txt")
raw_list = parse_financial_file_list(data)
return [FinancialFileInfo(filename=f, hash=h, filesize=s) for f, h, s in raw_list]
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 = CALC_HOSTS[0]) -> bytes:
"""从计算服务器下载财报 zip 文件。
@@ -546,9 +542,7 @@ class TdxClient:
"""
return self._download_from_host(host, filename)
def get_financial_records(
self, filename: str, host: str = CALC_HOSTS[0]
) -> list[FinancialRecord]:
def get_financial_records(self, filename: str, host: str = CALC_HOSTS[0]) -> pd.DataFrame:
"""下载财报 zip 并解析为每只股票的记录列表。
Args:
@@ -560,12 +554,12 @@ class TdxClient:
zip_data = self.get_financial_file(filename, host)
if not zip_data:
return []
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 []
return pd.DataFrame()
dat_data = zf.read(dat_names[0])
m = re.search(r"(\d{8})", filename)
@@ -576,13 +570,11 @@ class TdxClient:
for code, market_byte, rdate, fields in raw_records:
market = Market.SH if market_byte == b"\x01" else Market.SZ
records.append(
FinancialRecord(
code=code, market=market, report_date=rdate, fields=fields
)
FinancialRecord(code=code, market=market, report_date=rdate, fields=fields)
)
return records
return _to_df(records)
def get_market_stat(self) -> MarketStat:
def get_market_stat(self) -> pd.DataFrame:
"""获取 A 股全市场涨跌统计概况（基于 880005 行情统计）。
注意：
@@ -590,9 +582,11 @@ class TdxClient:
用于保证计数守恒，不应视为协议已明确验证的停牌字段。
"""
# 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
quotes = self.get_security_quotes([
(Market.SH, "880005"), (Market.SH, "880001"), (Market.SH, "880006"),
])
quotes = self._execute(
GetSecurityQuotesCmd(
[(Market.SH, "880005"), (Market.SH, "880001"), (Market.SH, "880006")]
)
)
if not quotes:
raise RuntimeError("无法获取市场统计数据")
q = quotes[0]
@@ -603,17 +597,19 @@ class TdxClient:
market_cap = quotes[1].price * 1e10 if len(quotes) > 1 else 0.0
limit_down = int(quotes[2].open) if len(quotes) > 2 else 0
limit_up = int(quotes[2].price) if len(quotes) > 2 else 0
return 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,
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 _collect_transaction_records(
@@ -659,41 +655,43 @@ class TdxClient:
return all_recs
def get_fund_flow(self, market: Market, code: str) -> FundFlow:
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.get_transaction_data(market, code, start, page_size),
lambda start, page_size: self._execute(
GetTransactionDataCmd(market, code, start, page_size)
),
2000,
)
return _classify_fund_flow(records)
return _to_df(_classify_fund_flow(records))
def get_history_fund_flow(
self, market: Market, code: str, start: int, count: int
) -> list[HistoricalFundFlow]:
) -> 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。
"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 direct
return _to_df(direct)
bars = self.get_security_bars(market, code, KlineCategory.DAY, start, count)
bars = self._execute(GetSecurityBarsCmd(market, code, KlineCategory.DAY, start, count))
results: list[HistoricalFundFlow] = []
for bar in bars:
date = _date_from_bar(bar)
records = self._collect_transaction_records(
lambda page_start, page_size: self.get_history_transaction_data(
market, code, date, page_start, page_size
lambda page_start, page_size: self._execute(
GetHistoryTransactionDataCmd(market, code, date, page_start, page_size)
),
800,
)
results.append(_historical_fund_flow_from_records(date, records))
return results
return _to_df(results)
# ============================================================
@@ -822,10 +820,10 @@ class AsyncTdxClient:
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) -> list[SecurityInfo]:
return await self._execute(GetSecurityListCmd(market, start))
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") -> list[SecurityInfo]:
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:
@@ -842,7 +840,7 @@ class AsyncTdxClient:
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 cached
return _to_df(cached)
def _max_start(count: int) -> int:
if pages == "all":
@@ -865,15 +863,18 @@ class AsyncTdxClient:
total_pages = (limit + 999) // 1000
for page_idx, start in enumerate(range(0, limit, 1000)):
try:
stocks = await self.get_security_list(market, start)
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)
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))
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")))
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:
@@ -883,12 +884,10 @@ class AsyncTdxClient:
log.info("沪深 A 股总数: %d", len(all_stocks))
if pages == "all":
_save_cache(all_stocks)
return all_stocks
return _to_df(all_stocks)
async def get_security_quotes(
self, stocks: list[tuple[Market, str]]
) -> list[SecurityQuote]:
return await self._execute(GetSecurityQuotesCmd(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
@@ -898,8 +897,8 @@ class AsyncTdxClient:
no_limit_window_days = get_no_limit_window_days(market, code, name)
if no_limit_window_days > 0:
try:
bars = await self.get_security_bars(
market, code, KlineCategory.DAY, 0, no_limit_window_days + 1
bars = await self._execute(
GetSecurityBarsCmd(market, code, KlineCategory.DAY, 0, no_limit_window_days + 1)
)
listed_days = len(bars)
except Exception:
@@ -920,10 +919,9 @@ class AsyncTdxClient:
category: KlineCategory,
start: int,
count: int = 800,
) -> list[SecurityBar]:
return await self._execute(
GetSecurityBarsCmd(market, code, category, start, count)
)
) -> pd.DataFrame:
df = _to_df(await self._execute(GetSecurityBarsCmd(market, code, category, start, count)))
return _merge_bar_datetime(df, category in _DAILY_PLUS)
async def get_index_bars(
self,
@@ -932,55 +930,50 @@ class AsyncTdxClient:
category: KlineCategory,
start: int,
count: int = 800,
) -> list[SecurityBar]:
return await self._execute(GetIndexBarsCmd(market, code, category, start, count))
) -> pd.DataFrame:
df = _to_df(await self._execute(GetIndexBarsCmd(market, code, category, start, count)))
return _merge_bar_datetime(df, category in _DAILY_PLUS)
async def get_minute_time_data(self, market: Market, code: str) -> list[MinuteBar]:
async def get_minute_time_data(self, market: Market, code: str) -> pd.DataFrame:
today = _today_in_shanghai()
try:
bars = await self.get_history_minute_time_data(market, code, today)
if bars:
return bars
except Exception:
pass
return await self._execute(GetMinuteTimeDataCmd(market, code))
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
) -> list[MinuteBar]:
return await self._execute(GetHistoryMinuteTimeDataCmd(market, code, date))
) -> 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
) -> list[TransactionRecord]:
return await self._execute(GetTransactionDataCmd(market, code, start, count))
) -> 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
) -> list[TransactionRecord]:
return await self._execute(
GetHistoryTransactionDataCmd(market, code, date, start, count)
) -> 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) -> list[XdxrRecord]:
return await self._execute(GetXdxrInfoCmd(market, code))
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) -> FinanceInfo:
return await self._execute(GetFinanceInfoCmd(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
) -> list[CompanyInfoCategory]:
return await self._execute(GetCompanyInfoCategoryCmd(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)
)
return await self._execute(GetCompanyInfoContentCmd(market, code, filename, offset, length))
async def get_block_info(self, filename: str) -> list[TdxBlock]:
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()
@@ -992,7 +985,7 @@ class AsyncTdxClient:
break
full_data.extend(chunk)
pos += len(chunk)
return parse_block_dat(bytes(full_data), filename)
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。"""
@@ -1032,23 +1025,17 @@ class AsyncTdxClient:
finally:
await conn.close()
async def get_financial_file_list(
self, host: str = CALC_HOSTS[0]
) -> list[FinancialFileInfo]:
async def get_financial_file_list(self, host: str = CALC_HOSTS[0]) -> pd.DataFrame:
"""获取可用的历史专业财报文件列表（异步）。"""
data = await self._async_download_from_host(host, "tdxfin/gpcw.txt")
raw_list = parse_financial_file_list(data)
return [FinancialFileInfo(filename=f, hash=h, filesize=s) for f, h, s in raw_list]
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 = CALC_HOSTS[0]
) -> bytes:
async def get_financial_file(self, filename: str, host: str = CALC_HOSTS[0]) -> bytes:
"""从计算服务器下载财报 zip 文件（异步）。"""
return await self._async_download_from_host(host, filename)
async def get_financial_records(
self, filename: str, host: str = CALC_HOSTS[0]
) -> list[FinancialRecord]:
async def get_financial_records(self, filename: str, host: str = CALC_HOSTS[0]) -> pd.DataFrame:
"""下载财报 zip 并解析为记录列表（异步）。"""
import io
import re
@@ -1056,12 +1043,12 @@ class AsyncTdxClient:
zip_data = await self.get_financial_file(filename, host)
if not zip_data:
return []
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 []
return pd.DataFrame()
dat_data = zf.read(dat_names[0])
m = re.search(r"(\d{8})", filename)
@@ -1072,13 +1059,11 @@ class AsyncTdxClient:
for code, market_byte, rdate, fields in raw_records:
market = Market.SH if market_byte == b"\x01" else Market.SZ
records.append(
FinancialRecord(
code=code, market=market, report_date=rdate, fields=fields
)
FinancialRecord(code=code, market=market, report_date=rdate, fields=fields)
)
return records
return _to_df(records)
async def get_market_stat(self) -> MarketStat:
async def get_market_stat(self) -> pd.DataFrame:
"""获取 A 股全市场涨跌统计概况（基于 880005 行情统计）。
注意：
@@ -1086,9 +1071,11 @@ class AsyncTdxClient:
用于保证计数守恒，不应视为协议已明确验证的停牌字段。
"""
# 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
quotes = await self.get_security_quotes([
(Market.SH, "880005"), (Market.SH, "880001"), (Market.SH, "880006"),
])
quotes = await self._execute(
GetSecurityQuotesCmd(
[(Market.SH, "880005"), (Market.SH, "880001"), (Market.SH, "880006")]
)
)
if not quotes:
raise RuntimeError("无法获取市场统计数据")
q = quotes[0]
@@ -1099,17 +1086,19 @@ class AsyncTdxClient:
market_cap = quotes[1].price * 1e10 if len(quotes) > 1 else 0.0
limit_down = int(quotes[2].open) if len(quotes) > 2 else 0
limit_up = int(quotes[2].price) if len(quotes) > 2 else 0
return 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,
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 _collect_transaction_records(
@@ -1155,40 +1144,42 @@ class AsyncTdxClient:
return all_recs
async def get_fund_flow(self, market: Market, code: str) -> FundFlow:
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.get_transaction_data(
market, code, start, page_size
lambda start, page_size: self._execute(
GetTransactionDataCmd(market, code, start, page_size)
),
2000,
)
return _classify_fund_flow(records)
return _to_df(_classify_fund_flow(records))
async def get_history_fund_flow(
self, market: Market, code: str, start: int, count: int
) -> list[HistoricalFundFlow]:
) -> 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。
"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 direct
return _to_df(direct)
bars = await self.get_security_bars(market, code, KlineCategory.DAY, start, count)
bars = await self._execute(
GetSecurityBarsCmd(market, code, KlineCategory.DAY, start, count)
)
results: list[HistoricalFundFlow] = []
for bar in bars:
date = _date_from_bar(bar)
records = await self._collect_transaction_records(
lambda page_start, page_size: self.get_history_transaction_data(
market, code, date, page_start, page_size
lambda page_start, page_size: self._execute(
GetHistoryTransactionDataCmd(market, code, date, page_start, page_size)
),
800,
)
results.append(_historical_fund_flow_from_records(date, records))
return results
return _to_df(results)
src/easy_tdx/commands/minute_time.py
+1 -1
View File
@@ -62,7 +62,7 @@ def _parse_minute_body(body: bytes, skip: int = 4) -> list[MinuteBar]:
MinuteBar(
price=last_price / 100.0,
vol=vol,
unknown_1=unknown_1,
_unknown_1=unknown_1,
_raw=body[record_start:pos],
)
)
src/easy_tdx/models/timeseries.py
+3 -3
View File
@@ -10,11 +10,11 @@ class MinuteBar:
unknown_1: 协议中第二个变长整数，含义未明（疑似均价的编码形式）。
"""
price: float # 价格
vol: int # 成交量
price: float # 价格
vol: int # 成交量
# pytdx 中被完全丢弃的字段，保留以供分析
unknown_1: int = field(default=0, repr=False) # 原 reversed1
_unknown_1: int = field(default=0, repr=False) # 原 reversed1
_raw: bytes = field(default=b"", repr=False, compare=False)