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feat: add get_board_ranking() for board change-pct ranking

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New MacClient/AsyncMacClient method that ranks all boards of a given
type (industry/concept) by change_pct, amount, main_net_amount, or vol.
Aggregates member quotes via get_board_summary() for each board.

Also bumps version to 1.3.0 and updates README + CLI version.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
This commit is contained in:
GitHub
2026-05-26 22:46:52 +08:00
co-authored by Claude Opus 4.7
parent 2ec36e01bf
commit 72652b9f8b
6 changed files with 210 additions and 15 deletions
Download Patch File Download Diff File Expand all files Collapse all files
src/easy_tdx/__init__.py
+1 -1
View File
@@ -107,4 +107,4 @@ __all__ = [
"save_best_ex_host",
]
__version__ = "1.0.0"
__version__ = "1.3.0"
src/easy_tdx/cli/__init__.py
+1 -1
View File
@@ -19,7 +19,7 @@ from .cmd_transaction import transaction
@click.group()
@click.version_option(version="1.1.0", prog_name="easy-tdx")
@click.version_option(version="1.3.0", prog_name="easy-tdx")
def cli() -> None:
"""easy-tdx -- 通达信行情数据 CLI（默认 JSON 输出，适合 Agent 使用）。
src/easy_tdx/mac/client.py
+153 -12
View File
@@ -76,6 +76,8 @@ def _convert_board_code(board_symbol: str) -> int:
if s.startswith("000"):
return 31000 + int(s)
return int(s)
_TRANSACTION_PAGE_SIZE = 1000
_T = TypeVar("_T")
@@ -393,8 +395,7 @@ class MacClient:
from datetime import date as date_cls
query_date = (
date_cls(date // 10000, (date % 10000) // 100, date % 100)
if date is not None else None
date_cls(date // 10000, (date % 10000) // 100, date % 100) if date is not None else None
)
chart = self._execute(SymbolTickChartCmd(market, code, query_date))
return pd.DataFrame(_flatten_tick_chart(chart))
@@ -417,8 +418,7 @@ class MacClient:
from datetime import date as date_cls
start_date = (
date_cls(date // 10000, (date % 10000) // 100, date % 100)
if date is not None else None
date_cls(date // 10000, (date % 10000) // 100, date % 100) if date is not None else None
)
chart = self._execute(TickChartsCmd(market, code, start_date, days))
return pd.DataFrame(_flatten_multi_tick_chart(chart))
@@ -457,8 +457,7 @@ class MacClient:
from datetime import date as date_cls
query_date = (
date_cls(date // 10000, (date % 10000) // 100, date % 100)
if date is not None else None
date_cls(date // 10000, (date % 10000) // 100, date % 100) if date is not None else None
)
all_items = self._execute(
SymbolTransactionCmd(
@@ -653,6 +652,80 @@ class MacClient:
"members": df,
}
def get_board_ranking(
self,
board_type: BoardType = BoardType.HY,
top_n: int = 50,
sort_by: str = "change_pct",
ascending: bool = False,
) -> pd.DataFrame:
"""获取板块涨跌幅排行榜（含成交额、成交量、资金流入流出、涨跌家数）。
先通过 ``get_board_list`` 获取全部板块，再逐个调用
``get_board_summary`` 聚合成分股数据，合并为排行榜 DataFrame。
Args:
board_type: 板块类型（``BoardType.HY`` 行业 / ``BoardType.GN`` 概念）。
top_n: 聚合的板块数量上限。概念板块有 300+ 个，
全部聚合网络开销大，建议按需限制。
sort_by: 排序字段，可选 ``change_pct`` / ``amount``
/ ``main_net_amount`` / ``vol``。
ascending: 排序方向，默认降序。
Returns:
DataFrame，列::
code 板块代码
name 板块名称
change_pct 涨跌幅%
amount 板块总成交额（元）
vol 板块总成交量（股）
main_net_amount 板块主力净流入（元）
up_count 上涨家数
down_count 下跌家数
member_count 成分股数量
"""
_VALID_SORT = {"change_pct", "amount", "main_net_amount", "vol"}
if sort_by not in _VALID_SORT:
raise ValueError(f"sort_by 必须是 {_VALID_SORT} 之一， got {sort_by!r}")
boards_df = self.get_board_list(board_type)
if boards_df.empty:
return pd.DataFrame()
# 从 board_list 的 price / pre_close 计算涨跌幅
if "price" in boards_df.columns and "pre_close" in boards_df.columns:
pre = boards_df["pre_close"].replace(0, float("nan"))
boards_df["change_pct"] = (boards_df["price"] - boards_df["pre_close"]) / pre * 100
else:
boards_df["change_pct"] = 0.0
# 按涨跌幅初排，取 top_n 减少后续聚合开销
boards_df = boards_df.sort_values("change_pct", ascending=ascending).head(top_n)
rows: list[dict[str, Any]] = []
for _, row in boards_df.iterrows():
code = str(row["code"])
summary = self.get_board_summary(code)
rows.append(
{
"code": code,
"name": row.get("name", ""),
"change_pct": round(float(row.get("change_pct", 0.0)), 2),
"amount": summary["amount"],
"vol": summary["vol"],
"main_net_amount": summary["main_net_amount"],
"up_count": summary["up_count"],
"down_count": summary["down_count"],
"member_count": summary["member_count"],
}
)
result = pd.DataFrame(rows)
if not result.empty:
result = result.sort_values(sort_by, ascending=ascending).reset_index(drop=True)
return result
# ------------------------------------------------------------------ #
# 资金流向
# ------------------------------------------------------------------ #
@@ -1080,8 +1153,7 @@ class AsyncMacClient:
from datetime import date as date_cls
query_date = (
date_cls(date // 10000, (date % 10000) // 100, date % 100)
if date is not None else None
date_cls(date // 10000, (date % 10000) // 100, date % 100) if date is not None else None
)
chart = await self._execute(SymbolTickChartCmd(market, code, query_date))
return pd.DataFrame(_flatten_tick_chart(chart))
@@ -1096,8 +1168,7 @@ class AsyncMacClient:
from datetime import date as date_cls
start_date = (
date_cls(date // 10000, (date % 10000) // 100, date % 100)
if date is not None else None
date_cls(date // 10000, (date % 10000) // 100, date % 100) if date is not None else None
)
chart = await self._execute(TickChartsCmd(market, code, start_date, days))
return pd.DataFrame(_flatten_multi_tick_chart(chart))
@@ -1121,8 +1192,7 @@ class AsyncMacClient:
from datetime import date as date_cls
query_date = (
date_cls(date // 10000, (date % 10000) // 100, date % 100)
if date is not None else None
date_cls(date // 10000, (date % 10000) // 100, date % 100) if date is not None else None
)
all_items = await self._execute(
SymbolTransactionCmd(
@@ -1291,6 +1361,77 @@ class AsyncMacClient:
"members": df,
}
async def get_board_ranking(
self,
board_type: BoardType = BoardType.HY,
top_n: int = 50,
sort_by: str = "change_pct",
ascending: bool = False,
) -> pd.DataFrame:
"""获取板块涨跌幅排行榜（含成交额、成交量、资金流入流出、涨跌家数）。
先通过 ``get_board_list`` 获取全部板块，再并发调用
``get_board_summary`` 聚合成分股数据，合并为排行榜 DataFrame。
Args:
board_type: 板块类型（``BoardType.HY`` 行业 / ``BoardType.GN`` 概念）。
top_n: 聚合的板块数量上限。概念板块有 300+ 个，
全部聚合网络开销大，建议按需限制。
sort_by: 排序字段，可选 ``change_pct`` / ``amount``
/ ``main_net_amount`` / ``vol``。
ascending: 排序方向，默认降序。
Returns:
DataFrame，列::
code 板块代码
name 板块名称
change_pct 涨跌幅%
amount 板块总成交额（元）
vol 板块总成交量（股）
main_net_amount 板块主力净流入（元）
up_count 上涨家数
down_count 下跌家数
member_count 成分股数量
"""
_VALID_SORT = {"change_pct", "amount", "main_net_amount", "vol"}
if sort_by not in _VALID_SORT:
raise ValueError(f"sort_by 必须是 {_VALID_SORT} 之一， got {sort_by!r}")
boards_df = await self.get_board_list(board_type)
if boards_df.empty:
return pd.DataFrame()
if "price" in boards_df.columns and "pre_close" in boards_df.columns:
pre = boards_df["pre_close"].replace(0, float("nan"))
boards_df["change_pct"] = (boards_df["price"] - boards_df["pre_close"]) / pre * 100
else:
boards_df["change_pct"] = 0.0
boards_df = boards_df.sort_values("change_pct", ascending=ascending).head(top_n)
async def _fetch_row(row: pd.Series) -> dict[str, Any]:
code = str(row["code"])
summary = await self.get_board_summary(code)
return {
"code": code,
"name": row.get("name", ""),
"change_pct": round(float(row.get("change_pct", 0.0)), 2),
"amount": summary["amount"],
"vol": summary["vol"],
"main_net_amount": summary["main_net_amount"],
"up_count": summary["up_count"],
"down_count": summary["down_count"],
"member_count": summary["member_count"],
}
rows = await asyncio.gather(*[_fetch_row(row) for _, row in boards_df.iterrows()])
result = pd.DataFrame(rows)
if not result.empty:
result = result.sort_values(sort_by, ascending=ascending).reset_index(drop=True)
return result
# ------------------------------------------------------------------ #
# 资金流向
# ------------------------------------------------------------------ #