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https://ghfast.top/https://github.com/aeroxw/easy-tdx.git
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feat: add get_board_summary() for board capital flow aggregation
New MacClient/AsyncMacClient method that aggregates board member quotes into total amount, main force net inflow (1d/3d/5d), and up/down counts. Includes example demo. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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co-authored by
Claude Opus 4.7
Claude Opus 4.7
parent
4820b4a049
commit
ba032da9ed
+139
-1
@@ -11,6 +11,7 @@ from typing import Any, TypeVar
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import pandas as pd
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from .._df import _to_df
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from ..codec.bitmap import Fields, PresetField
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from ..commands.base import BaseCommand
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from ..config import get_best_host, get_mac_hosts, get_port, get_timeout, save_best_host
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from ..exceptions import TdxConnectionError
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@@ -35,7 +36,6 @@ from .commands import (
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from .commands.chart_sampling import ChartSamplingCmd
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from .commands.file_query import FileDownloadCmd, FileListCmd
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from .commands.goods_list import GoodsListCmd
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from ..codec.bitmap import Fields, PresetField
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from .enums import Adjust, BoardType, Category, FilterType, Period, SortOrder, SortType
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from .models import (
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MacBar,
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@@ -584,6 +584,75 @@ class MacClient:
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items = self._execute(SymbolBelongBoardCmd(market, code))
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return _to_df(items)
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def get_board_summary(
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self,
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board_symbol: str,
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sort_type: SortType = SortType.CHANGE_PCT,
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sort_order: SortOrder = SortOrder.DESC,
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) -> dict[str, Any]:
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"""获取板块汇总：总成交金额、主力资金流向等（聚合成分股数据）。
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基于 ``get_board_members`` 获取全部成分股报价，对成交额和资金流字段求和。
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Args:
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board_symbol: 板块代码（如 "881001"）。
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sort_type: 排序字段。
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sort_order: 排序方向。
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Returns:
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包含以下键的字典::
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member_count 成分股数量
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amount 板块总成交额（元）
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vol 板块总成交量（股）
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main_net_amount 板块主力净流入（元）
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main_net_3d 板块近3日主力净流入（元）
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main_net_5d 板块近5日主力净流入（元）
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up_count 上涨家数
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down_count 下跌家数
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members 成分股明细 DataFrame
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"""
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from ..codec.bitmap import FieldBit, PresetField
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fields = (
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PresetField.BASIC
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+ FieldBit.AMOUNT
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+ FieldBit.MAIN_NET_AMOUNT
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+ FieldBit.MAIN_NET_3D_AMOUNT
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+ FieldBit.MAIN_NET_5D_AMOUNT
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)
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df = self.get_board_members(
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board_symbol,
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sort_type=sort_type,
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sort_order=sort_order,
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fields=fields,
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)
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agg_keys = ("amount", "main_net_amount", "main_net_3d_amount", "main_net_5d_amount")
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numeric_cols = [c for c in agg_keys if c in df.columns]
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sums = df[numeric_cols].sum() if numeric_cols else pd.Series(dtype=float)
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close_col = "close" if "close" in df.columns else None
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pre_close_col = "pre_close" if "pre_close" in df.columns else None
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if close_col and pre_close_col:
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diff = df[close_col] - df[pre_close_col]
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up_count = int((diff > 0).sum())
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down_count = int((diff < 0).sum())
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else:
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up_count = down_count = 0
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return {
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"member_count": len(df),
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"amount": float(sums.get("amount", 0.0)),
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"vol": int(df["vol"].sum()) if "vol" in df.columns else 0,
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"main_net_amount": float(sums.get("main_net_amount", 0.0)),
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"main_net_3d": float(sums.get("main_net_3d_amount", 0.0)),
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"main_net_5d": float(sums.get("main_net_5d_amount", 0.0)),
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"up_count": up_count,
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"down_count": down_count,
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"members": df,
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}
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# ------------------------------------------------------------------ #
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# 资金流向
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# ------------------------------------------------------------------ #
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@@ -1153,6 +1222,75 @@ class AsyncMacClient:
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items = await self._execute(SymbolBelongBoardCmd(market, code))
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return _to_df(items)
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async def get_board_summary(
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self,
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board_symbol: str,
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sort_type: SortType = SortType.CHANGE_PCT,
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sort_order: SortOrder = SortOrder.DESC,
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) -> dict[str, Any]:
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"""获取板块汇总：总成交金额、主力资金流向等（聚合成分股数据）。
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基于 ``get_board_members`` 获取全部成分股报价，对成交额和资金流字段求和。
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Args:
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board_symbol: 板块代码（如 "881001"）。
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sort_type: 排序字段。
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sort_order: 排序方向。
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Returns:
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包含以下键的字典::
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member_count 成分股数量
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amount 板块总成交额（元）
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vol 板块总成交量（股）
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main_net_amount 板块主力净流入（元）
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main_net_3d 板块近3日主力净流入（元）
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main_net_5d 板块近5日主力净流入（元）
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up_count 上涨家数
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down_count 下跌家数
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members 成分股明细 DataFrame
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"""
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from ..codec.bitmap import FieldBit, PresetField
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fields = (
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PresetField.BASIC
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+ FieldBit.AMOUNT
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+ FieldBit.MAIN_NET_AMOUNT
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+ FieldBit.MAIN_NET_3D_AMOUNT
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+ FieldBit.MAIN_NET_5D_AMOUNT
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)
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df = await self.get_board_members(
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board_symbol,
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sort_type=sort_type,
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sort_order=sort_order,
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fields=fields,
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)
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agg_keys = ("amount", "main_net_amount", "main_net_3d_amount", "main_net_5d_amount")
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numeric_cols = [c for c in agg_keys if c in df.columns]
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sums = df[numeric_cols].sum() if numeric_cols else pd.Series(dtype=float)
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close_col = "close" if "close" in df.columns else None
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pre_close_col = "pre_close" if "pre_close" in df.columns else None
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if close_col and pre_close_col:
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diff = df[close_col] - df[pre_close_col]
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up_count = int((diff > 0).sum())
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down_count = int((diff < 0).sum())
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else:
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up_count = down_count = 0
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return {
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"member_count": len(df),
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"amount": float(sums.get("amount", 0.0)),
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"vol": int(df["vol"].sum()) if "vol" in df.columns else 0,
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"main_net_amount": float(sums.get("main_net_amount", 0.0)),
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"main_net_3d": float(sums.get("main_net_3d_amount", 0.0)),
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"main_net_5d": float(sums.get("main_net_5d_amount", 0.0)),
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"up_count": up_count,
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"down_count": down_count,
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"members": df,
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}
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# ------------------------------------------------------------------ #
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# 资金流向
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# ------------------------------------------------------------------ #
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