# -*- coding:utf-8 -*- # !/usr/bin/env python """ Date: 2025/3/4 23:00 Desc: 东方财富网-数据中心-沪深港通持股 https://data.eastmoney.com/hsgtcg/ 沪深港通详情: https://finance.eastmoney.com/news/1622,20161118685370149.html """ import pandas as pd import requests from bs4 import BeautifulSoup from akshare.utils.func import fetch_paginated_data from akshare.utils.tqdm import get_tqdm def stock_hsgt_fund_flow_summary_em() -> pd.DataFrame: """ 东方财富网-数据中心-资金流向-沪深港通资金流向 https://data.eastmoney.com/hsgt/index.html#lssj :return: 沪深港通资金流向 :rtype: pandas.DataFrame """ url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "reportName": "RPT_MUTUAL_QUOTA", "columns": "TRADE_DATE,MUTUAL_TYPE,BOARD_TYPE,MUTUAL_TYPE_NAME,FUNDS_DIRECTION," "INDEX_CODE,INDEX_NAME,BOARD_CODE", "quoteColumns": "status~07~BOARD_CODE,dayNetAmtIn~07~BOARD_CODE,dayAmtRemain~07~BOARD_CODE," "dayAmtThreshold~07~BOARD_CODE,f104~07~BOARD_CODE,f105~07~BOARD_CODE," "f106~07~BOARD_CODE,f3~03~INDEX_CODE~INDEX_f3,netBuyAmt~07~BOARD_CODE", "quoteType": "0", "pageNumber": "1", "pageSize": "2000", "sortTypes": "1", "sortColumns": "MUTUAL_TYPE", "source": "WEB", "client": "WEB", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) temp_df.columns = [ "交易日", "-", "类型", "板块", "资金方向", "-", "相关指数", "-", "交易状态", "资金净流入", "当日资金余额", "-", "上涨数", "下跌数", "持平数", "指数涨跌幅", "成交净买额", ] temp_df = temp_df[ [ "交易日", "类型", "板块", "资金方向", "交易状态", "成交净买额", "资金净流入", "当日资金余额", "上涨数", "持平数", "下跌数", "相关指数", "指数涨跌幅", ] ] temp_df["交易日"] = pd.to_datetime(temp_df["交易日"], errors="coerce").dt.date temp_df["成交净买额"] = pd.to_numeric(temp_df["成交净买额"], errors="coerce") temp_df["资金净流入"] = pd.to_numeric(temp_df["资金净流入"], errors="coerce") temp_df["当日资金余额"] = pd.to_numeric(temp_df["当日资金余额"], errors="coerce") temp_df["上涨数"] = pd.to_numeric(temp_df["上涨数"], errors="coerce") temp_df["持平数"] = pd.to_numeric(temp_df["持平数"], errors="coerce") temp_df["下跌数"] = pd.to_numeric(temp_df["下跌数"], errors="coerce") temp_df["指数涨跌幅"] = pd.to_numeric(temp_df["指数涨跌幅"], errors="coerce") temp_df["成交净买额"] = temp_df["成交净买额"] / 10000 temp_df["资金净流入"] = temp_df["资金净流入"] / 10000 temp_df["当日资金余额"] = temp_df["当日资金余额"] / 10000 return temp_df def stock_hk_ggt_components_em() -> pd.DataFrame: """ 东方财富网-行情中心-港股市场-港股通成份股 https://quote.eastmoney.com/center/gridlist.html#hk_components :return: 港股通成份股 :rtype: pandas.DataFrame """ url = "https://33.push2.eastmoney.com/api/qt/clist/get" params = { "pn": "1", "pz": "100", "po": "1", "np": "1", "ut": "bd1d9ddb04089700cf9c27f6f7426281", "fltt": "2", "fid": "f12", "fs": "b:DLMK0146,b:DLMK0144", "fields": "f1,f2,f3,f4,f5,f6,f7,f8,f9,f10,f12,f13,f14,f15,f16,f17,f18,f19,f20,f21,f23,f24," "f25,f26,f22,f33,f11,f62,f128,f136,f115,f152", } temp_df = fetch_paginated_data(url=url, base_params=params) temp_df.columns = [ "序号", "-", "最新价", "涨跌幅", "涨跌额", "成交量", "成交额", "-", "-", "-", "-", "-", "代码", "-", "名称", "最高", "最低", "今开", "昨收", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", ] temp_df = temp_df[ [ "序号", "代码", "名称", "最新价", "涨跌额", "涨跌幅", "今开", "最高", "最低", "昨收", "成交量", "成交额", ] ] return temp_df def stock_hsgt_hold_stock_em( market: str = "沪股通", indicator: str = "5日排行" ) -> pd.DataFrame: """ 东方财富-数据中心-沪深港通持股-个股排行 https://data.eastmoney.com/hsgtcg/list.html :param market: choice of {"北向", "沪股通", "深股通"} :type market: str :param indicator: choice of {"今日排行", "3日排行", "5日排行", "10日排行", "月排行", "季排行", "年排行"} :type indicator: str :return: 指定 sector 和 indicator 的数据 :rtype: pandas.DataFrame """ url = "https://data.eastmoney.com/hsgtcg/list.html" r = requests.get(url) soup = BeautifulSoup(r.text, features="lxml") date = ( soup.find(name="div", attrs={"class": "title"}) .find("span") .text.strip("（") .strip("）") ) url = "https://datacenter-web.eastmoney.com/api/data/v1/get" indicator_type = "" filter_str = "" if indicator == "今日排行": indicator_type = "1" if indicator == "3日排行": indicator_type = "3" if indicator == "5日排行": indicator_type = "5" if indicator == "10日排行": indicator_type = "10" if indicator == "月排行": indicator_type = "M" if indicator == "季排行": indicator_type = "Q" if indicator == "年排行": indicator_type = "Y" if market == "北向": filter_str = f"""(TRADE_DATE='{date}')(INTERVAL_TYPE="{indicator_type}")""" elif market == "沪股通": filter_str = f"""(TRADE_DATE='{date}')(INTERVAL_TYPE="{indicator_type}")(MUTUAL_TYPE="001")""" elif market == "深股通": filter_str = f"""(TRADE_DATE='{date}')(INTERVAL_TYPE="{indicator_type}")(MUTUAL_TYPE="003")""" params = { "sortColumns": "ADD_MARKET_CAP", "sortTypes": "-1", "pageSize": "50000", "pageNumber": "1", "reportName": "RPT_MUTUAL_STOCK_NORTHSTA", "columns": "ALL", "source": "WEB", "client": "WEB", "filter": filter_str, } r = requests.get(url, params=params) data_json = r.json() page_num = data_json["result"]["pages"] big_df = pd.DataFrame() tqdm = get_tqdm() for page in tqdm(range(1, page_num + 1), leave=False): params.update({"pageNumber": page}) r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True) big_df.reset_index(inplace=True) big_df["index"] = range(1, len(big_df) + 1) big_df.columns = [ "序号", "_", "_", "日期", "_", "名称", "_", "_", "代码", "_", "_", "_", "_", "今日持股-股数", "今日持股-市值", "今日持股-占流通股比", "今日持股-占总股本比", "今日收盘价", "今日涨跌幅", "_", "所属板块", "_", "_", "_", "_", "_", "_", "_", "_", "_", "_", f"{indicator.split('排')[0]}增持估计-市值", f"{indicator.split('排')[0]}增持估计-股数", f"{indicator.split('排')[0]}增持估计-市值增幅", f"{indicator.split('排')[0]}增持估计-占流通股比", f"{indicator.split('排')[0]}增持估计-占总股本比", "_", "_", "_", "_", "_", "_", "_", "_", ] big_df = big_df[ [ "序号", "代码", "名称", "今日收盘价", "今日涨跌幅", "今日持股-股数", "今日持股-市值", "今日持股-占流通股比", "今日持股-占总股本比", f"{indicator.split('排')[0]}增持估计-股数", f"{indicator.split('排')[0]}增持估计-市值", f"{indicator.split('排')[0]}增持估计-市值增幅", f"{indicator.split('排')[0]}增持估计-占流通股比", f"{indicator.split('排')[0]}增持估计-占总股本比", "所属板块", "日期", ] ] big_df["今日收盘价"] = pd.to_numeric(big_df["今日收盘价"], errors="coerce") big_df["今日涨跌幅"] = pd.to_numeric(big_df["今日涨跌幅"], errors="coerce") big_df["今日持股-股数"] = pd.to_numeric(big_df["今日持股-股数"], errors="coerce") big_df["今日持股-市值"] = pd.to_numeric(big_df["今日持股-市值"], errors="coerce") big_df["今日持股-占流通股比"] = pd.to_numeric( big_df["今日持股-占流通股比"], errors="coerce" ) big_df["今日持股-占总股本比"] = pd.to_numeric( big_df["今日持股-占总股本比"], errors="coerce" ) big_df[f"{indicator.split('排')[0]}增持估计-股数"] = pd.to_numeric( big_df[f"{indicator.split('排')[0]}增持估计-股数"], errors="coerce" ) big_df[f"{indicator.split('排')[0]}增持估计-市值"] = pd.to_numeric( big_df[f"{indicator.split('排')[0]}增持估计-市值"], errors="coerce" ) big_df[f"{indicator.split('排')[0]}增持估计-市值增幅"] = pd.to_numeric( big_df[f"{indicator.split('排')[0]}增持估计-市值增幅"], errors="coerce" ) big_df[f"{indicator.split('排')[0]}增持估计-占流通股比"] = pd.to_numeric( big_df[f"{indicator.split('排')[0]}增持估计-占流通股比"], errors="coerce" ) big_df[f"{indicator.split('排')[0]}增持估计-占总股本比"] = pd.to_numeric( big_df[f"{indicator.split('排')[0]}增持估计-占总股本比"], errors="coerce" ) big_df["日期"] = pd.to_datetime(big_df["日期"], errors="coerce").dt.date return big_df def stock_hsgt_stock_statistics_em( symbol: str = "北向持股", start_date: str = "20240110", end_date: str = "20240110", ): """ 东方财富网-数据中心-沪深港通-沪深港通持股-每日个股统计 https://data.eastmoney.com/hsgtcg/StockStatistics.aspx market=001, 沪股通持股 market=003, 深股通持股 :param symbol: choice of {"北向持股", "南向持股"} :type symbol: str :param start_date: 指定数据获取开始的时间, e.g., "20200713" :type start_date: str :param end_date: 指定数据获取结束的时间, e.g., "20200715" :type end_date:str :return: 指定市场和指定时间段的每日个股统计数据 :rtype: pandas.DataFrame """ start_date = "-".join([start_date[:4], start_date[4:6], start_date[6:]]) end_date = "-".join([end_date[:4], end_date[4:6], end_date[6:]]) big_df = pd.DataFrame() if symbol == "南向持股": params = { "sortColumns": "TRADE_DATE", "sortTypes": "-1", "pageSize": "1000", "pageNumber": "1", "columns": "ALL", "source": "WEB", "client": "WEB", "filter": f"""(INTERVAL_TYPE="1")(RN=1)(TRADE_DATE>='{start_date}')(TRADE_DATE<='{end_date}')""", "rt": "53160469", "reportName": "RPT_MUTUAL_STOCK_HOLDRANKS", } if start_date == end_date: params.update( {"filter": f"""(INTERVAL_TYPE="1")(RN=1)(TRADE_DATE='{start_date}')"""} ) url = "https://datacenter-web.eastmoney.com/api/data/v1/get" r = requests.get(url, params=params) data_json = r.json() total_page = data_json["result"]["pages"] big_df = pd.DataFrame() tqdm = get_tqdm() for page in tqdm(range(1, int(total_page) + 1), leave=False): params.update({"pageNumber": page}) r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True) big_df.columns = [ "-", "持股日期", "-", "-", "股票简称", "股票代码", "-", "-", "-", "-", "持股市值", "持股数量", "-", "-", "-", "-", "当日收盘价", "当日涨跌幅", "-", "-", "-", "-", "-", "持股数量占发行股百分比", "-", "持股市值变化-1日", "持股市值变化-5日", "持股市值变化-10日", "-", ] big_df = big_df[ [ "持股日期", "股票代码", "股票简称", "当日收盘价", "当日涨跌幅", "持股数量", "持股市值", "持股数量占发行股百分比", "持股市值变化-1日", "持股市值变化-5日", "持股市值变化-10日", ] ] big_df["持股日期"] = pd.to_datetime(big_df["持股日期"], errors="coerce").dt.date big_df["当日收盘价"] = pd.to_numeric(big_df["当日收盘价"], errors="coerce") big_df["当日涨跌幅"] = pd.to_numeric(big_df["当日涨跌幅"], errors="coerce") big_df["持股数量"] = pd.to_numeric(big_df["持股数量"], errors="coerce") big_df["持股市值"] = pd.to_numeric(big_df["持股市值"], errors="coerce") big_df["持股数量占发行股百分比"] = pd.to_numeric( big_df["持股数量占发行股百分比"], errors="coerce" ) big_df["持股市值变化-1日"] = pd.to_numeric( big_df["持股市值变化-1日"], errors="coerce" ) big_df["持股市值变化-5日"] = pd.to_numeric( big_df["持股市值变化-5日"], errors="coerce" ) big_df["持股市值变化-10日"] = pd.to_numeric( big_df["持股市值变化-10日"], errors="coerce" ) elif symbol == "北向持股": params = { "sortColumns": "TRADE_DATE", "sortTypes": "-1", "pageSize": "1000", "pageNumber": "1", "columns": "ALL", "source": "WEB", "client": "WEB", "filter": f"""(INTERVAL_TYPE="1")(MUTUAL_TYPE in ("001","003"))(TRADE_DATE>='{start_date}')(TRADE_DATE<='{end_date}')""", "rt": "53160469", "reportName": "RPT_MUTUAL_STOCK_NORTHSTA", } if start_date == end_date: params.update( { "filter": f"""(INTERVAL_TYPE="1")(MUTUAL_TYPE in ("001","003"))(TRADE_DATE='{start_date}')""" } ) url = "http://datacenter-web.eastmoney.com/api/data/v1/get" r = requests.get(url, params=params) data_json = r.json() total_page = data_json["result"]["pages"] big_df = pd.DataFrame() tqdm = get_tqdm() for page in tqdm(range(1, int(total_page) + 1), leave=False): params.update({"pageNumber": page}) r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True) big_df.columns = [ "-", "-", "持股日期", "-", "股票简称", "-", "-", "股票代码", "-", "-", "-", "-", "持股数量", "持股市值", "-", "持股数量占发行股百分比", "当日收盘价", "当日涨跌幅", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", "持股市值变化-1日", "持股市值变化-5日", "持股市值变化-10日", "-", "-", "-", ] big_df = big_df[ [ "持股日期", "股票代码", "股票简称", "当日收盘价", "当日涨跌幅", "持股数量", "持股市值", "持股数量占发行股百分比", "持股市值变化-1日", "持股市值变化-5日", "持股市值变化-10日", ] ] big_df["持股日期"] = pd.to_datetime(big_df["持股日期"], errors="coerce").dt.date big_df["当日收盘价"] = pd.to_numeric(big_df["当日收盘价"], errors="coerce") big_df["当日涨跌幅"] = pd.to_numeric(big_df["当日涨跌幅"], errors="coerce") big_df["持股数量"] = pd.to_numeric(big_df["持股数量"], errors="coerce") big_df["持股市值"] = pd.to_numeric(big_df["持股市值"], errors="coerce") big_df["持股数量占发行股百分比"] = pd.to_numeric( big_df["持股数量占发行股百分比"], errors="coerce" ) big_df["持股市值变化-1日"] = pd.to_numeric( big_df["持股市值变化-1日"], errors="coerce" ) big_df["持股市值变化-5日"] = pd.to_numeric( big_df["持股市值变化-5日"], errors="coerce" ) big_df["持股市值变化-10日"] = pd.to_numeric( big_df["持股市值变化-10日"], errors="coerce" ) elif symbol == "沪股通持股": params = { "sortColumns": "TRADE_DATE", "sortTypes": "-1", "pageSize": "1000", "pageNumber": "1", "columns": "ALL", "source": "WEB", "client": "WEB", "filter": f"""(INTERVAL_TYPE="1")(MUTUAL_TYPE="001")(TRADE_DATE>='{start_date}')(TRADE_DATE<='{end_date}')""", "reportName": "RPT_MUTUAL_STOCK_NORTHSTA", } if start_date == end_date: params.update( { "filter": f"""(INTERVAL_TYPE="1")(MUTUAL_TYPE="001")(TRADE_DATE='{start_date}')""" } ) url = "https://datacenter-web.eastmoney.com/api/data/v1/get" r = requests.get(url, params=params) data_json = r.json() total_page = data_json["result"]["pages"] big_df = pd.DataFrame() tqdm = get_tqdm() for page in tqdm(range(1, int(total_page) + 1), leave=False): params.update({"pageNumber": page}) r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True) big_df.columns = [ "-", "-", "持股日期", "-", "股票简称", "-", "-", "股票代码", "-", "-", "-", "-", "持股数量", "持股市值", "-", "持股数量占发行股百分比", "当日收盘价", "当日涨跌幅", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", "持股市值变化-1日", "持股市值变化-5日", "持股市值变化-10日", "-", "-", "-", ] big_df = big_df[ [ "持股日期", "股票代码", "股票简称", "当日收盘价", "当日涨跌幅", "持股数量", "持股市值", "持股数量占发行股百分比", "持股市值变化-1日", "持股市值变化-5日", "持股市值变化-10日", ] ] big_df["持股日期"] = pd.to_datetime(big_df["持股日期"], errors="coerce").dt.date big_df["当日收盘价"] = pd.to_numeric(big_df["当日收盘价"], errors="coerce") big_df["当日涨跌幅"] = pd.to_numeric(big_df["当日涨跌幅"], errors="coerce") big_df["持股数量"] = pd.to_numeric(big_df["持股数量"], errors="coerce") big_df["持股市值"] = pd.to_numeric(big_df["持股市值"], errors="coerce") big_df["持股数量占发行股百分比"] = pd.to_numeric( big_df["持股数量占发行股百分比"], errors="coerce" ) big_df["持股市值变化-1日"] = pd.to_numeric( big_df["持股市值变化-1日"], errors="coerce" ) big_df["持股市值变化-5日"] = pd.to_numeric( big_df["持股市值变化-5日"], errors="coerce" ) big_df["持股市值变化-10日"] = pd.to_numeric( big_df["持股市值变化-10日"], errors="coerce" ) elif symbol == "深股通持股": params = { "sortColumns": "TRADE_DATE", "sortTypes": "-1", "pageSize": "1000", "pageNumber": "1", "columns": "ALL", "source": "WEB", "client": "WEB", "filter": f"""(INTERVAL_TYPE="1")(MUTUAL_TYPE="003")(TRADE_DATE >='{start_date}')(TRADE_DATE<='{end_date}')""", "reportName": "RPT_MUTUAL_STOCK_NORTHSTA", } if start_date == end_date: params.update( { "filter": f"""(INTERVAL_TYPE="1")(MUTUAL_TYPE="003")(TRADE_DATE='{start_date}')""" } ) url = "https://datacenter-web.eastmoney.com/api/data/v1/get" r = requests.get(url, params=params) data_json = r.json() total_page = data_json["result"]["pages"] big_df = pd.DataFrame() tqdm = get_tqdm() for page in tqdm(range(1, int(total_page) + 1), leave=False): params.update({"pageNumber": page}) r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True) big_df.columns = [ "-", "-", "持股日期", "-", "股票简称", "-", "-", "股票代码", "-", "-", "-", "-", "持股数量", "持股市值", "-", "持股数量占发行股百分比", "当日收盘价", "当日涨跌幅", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", "持股市值变化-1日", "持股市值变化-5日", "持股市值变化-10日", "-", "-", "-", ] big_df = big_df[ [ "持股日期", "股票代码", "股票简称", "当日收盘价", "当日涨跌幅", "持股数量", "持股市值", "持股数量占发行股百分比", "持股市值变化-1日", "持股市值变化-5日", "持股市值变化-10日", ] ] big_df["持股日期"] = pd.to_datetime(big_df["持股日期"], errors="coerce").dt.date big_df["当日收盘价"] = pd.to_numeric(big_df["当日收盘价"], errors="coerce") big_df["当日涨跌幅"] = pd.to_numeric(big_df["当日涨跌幅"], errors="coerce") big_df["持股数量"] = pd.to_numeric(big_df["持股数量"], errors="coerce") big_df["持股市值"] = pd.to_numeric(big_df["持股市值"], errors="coerce") big_df["持股数量占发行股百分比"] = pd.to_numeric( big_df["持股数量占发行股百分比"], errors="coerce" ) big_df["持股市值变化-1日"] = pd.to_numeric( big_df["持股市值变化-1日"], errors="coerce" ) big_df["持股市值变化-5日"] = pd.to_numeric( big_df["持股市值变化-5日"], errors="coerce" ) big_df["持股市值变化-10日"] = pd.to_numeric( big_df["持股市值变化-10日"], errors="coerce" ) return big_df def stock_hsgt_institution_statistics_em( market: str = "北向持股", start_date: str = "20220601", end_date: str = "20220609", ): """ 东方财富网-数据中心-沪深港通-沪深港通持股-每日机构统计 https://data.eastmoney.com/hsgtcg/InstitutionStatistics.aspx :param market: choice of {"北向持股", "南向持股", "沪股通持股", "深股通持股"} :type market: str :param start_date: 指定数据获取开始的时间, e.g., "20200713" :type start_date: str :param end_date: 指定数据获取结束的时间, e.g., "20200715" :type end_date:str :return: 指定市场和指定时间段的每日个股统计数据 :rtype: pandas.DataFrame """ start_date = "-".join([start_date[:4], start_date[4:6], start_date[6:]]) end_date = "-".join([end_date[:4], end_date[4:6], end_date[6:]]) headers = { "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) " "Chrome/114.0.0.0 Safari/537.36" } if market == "南向持股": params = { "sortColumns": "HOLD_DATE", "sortTypes": "-1", "pageSize": "500", "pageNumber": "1", "reportName": "PRT_MUTUAL_ORG_STA", "columns": "ALL", "source": "WEB", "client": "WEB", "filter": f"""(MARKET_TYPE="S")(HOLD_DATE>='{start_date}')(HOLD_DATE<='{end_date}')""", } url = "https://datacenter-web.eastmoney.com/api/data/v1/get" r = requests.get(url, params=params, headers=headers) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) temp_df.columns = [ "持股日期", "_", "持股只数", "_", "持股市值", "持股市值变化-1日", "持股市值变化-5日", "持股市值变化-10日", "_", "机构名称", "_", "_", "_", "_", "_", "_", "_", "_", ] temp_df = temp_df[ [ "持股日期", "机构名称", "持股只数", "持股市值", "持股市值变化-1日", "持股市值变化-5日", "持股市值变化-10日", ] ] temp_df["持股日期"] = pd.to_datetime( temp_df["持股日期"], errors="coerce" ).dt.date temp_df["持股只数"] = pd.to_numeric(temp_df["持股只数"], errors="coerce") temp_df["持股市值"] = pd.to_numeric(temp_df["持股市值"], errors="coerce") temp_df["持股市值变化-1日"] = pd.to_numeric( temp_df["持股市值变化-1日"], errors="coerce" ) temp_df["持股市值变化-5日"] = pd.to_numeric( temp_df["持股市值变化-5日"], errors="coerce" ) temp_df["持股市值变化-10日"] = pd.to_numeric( temp_df["持股市值变化-10日"], errors="coerce" ) return temp_df elif market == "北向持股": params = { "sortColumns": "HOLD_DATE", "sortTypes": "-1", "pageSize": "500", "pageNumber": "1", "reportName": "PRT_MUTUAL_ORG_STA", "columns": "ALL", "source": "WEB", "client": "WEB", "filter": f"""(MARKET_TYPE="N")(HOLD_DATE>='{start_date}')(HOLD_DATE<='{end_date}')""", } url = "https://datacenter-web.eastmoney.com/api/data/v1/get" r = requests.get(url, params=params, headers=headers) data_json = r.json() total_page = data_json["result"]["pages"] big_df = pd.DataFrame() tqdm = get_tqdm() for page in tqdm(range(1, total_page + 1), leave=False): params.update({"pageNumber": page}) r = requests.get(url, params=params, headers=headers) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True) big_df.columns = [ "持股日期", "_", "持股只数", "_", "持股市值", "持股市值变化-1日", "持股市值变化-5日", "持股市值变化-10日", "_", "机构名称", "_", "_", "_", "_", "_", "_", "_", "_", ] big_df = big_df[ [ "持股日期", "机构名称", "持股只数", "持股市值", "持股市值变化-1日", "持股市值变化-5日", "持股市值变化-10日", ] ] big_df["持股日期"] = pd.to_datetime(big_df["持股日期"], errors="coerce").dt.date big_df["持股只数"] = pd.to_numeric(big_df["持股只数"], errors="coerce") big_df["持股市值"] = pd.to_numeric(big_df["持股市值"], errors="coerce") big_df["持股市值变化-1日"] = pd.to_numeric( big_df["持股市值变化-1日"], errors="coerce" ) big_df["持股市值变化-5日"] = pd.to_numeric( big_df["持股市值变化-5日"], errors="coerce" ) big_df["持股市值变化-10日"] = pd.to_numeric( big_df["持股市值变化-10日"], errors="coerce" ) return big_df elif market == "沪股通持股": params = { "sortColumns": "HOLD_DATE", "sortTypes": "-1", "pageSize": "500", "pageNumber": "1", "reportName": "PRT_MUTUAL_ORG_STA", "columns": "ALL", "source": "WEB", "client": "WEB", "filter": f"""(MARKET_TYPE="001")(HOLD_DATE>='{start_date}')(HOLD_DATE<='{end_date}')""", } url = "https://datacenter-web.eastmoney.com/api/data/v1/get" r = requests.get(url, params=params, headers=headers) data_json = r.json() total_page = data_json["result"]["pages"] big_df = pd.DataFrame() tqdm = get_tqdm() for page in tqdm(range(1, total_page + 1), leave=False): params.update({"pageNumber": page}) r = requests.get(url, params=params, headers=headers) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True) big_df.columns = [ "持股日期", "_", "持股只数", "_", "持股市值", "持股市值变化-1日", "持股市值变化-5日", "持股市值变化-10日", "_", "机构名称", "_", "_", "_", "_", "_", "_", "_", "_", ] big_df = big_df[ [ "持股日期", "机构名称", "持股只数", "持股市值", "持股市值变化-1日", "持股市值变化-5日", "持股市值变化-10日", ] ] big_df["持股日期"] = pd.to_datetime(big_df["持股日期"], errors="coerce").dt.date big_df["持股只数"] = pd.to_numeric(big_df["持股只数"], errors="coerce") big_df["持股市值"] = pd.to_numeric(big_df["持股市值"], errors="coerce") big_df["持股市值变化-1日"] = pd.to_numeric( big_df["持股市值变化-1日"], errors="coerce" ) big_df["持股市值变化-5日"] = pd.to_numeric( big_df["持股市值变化-5日"], errors="coerce" ) big_df["持股市值变化-10日"] = pd.to_numeric( big_df["持股市值变化-10日"], errors="coerce" ) return big_df elif market == "深股通持股": params = { "sortColumns": "HOLD_DATE", "sortTypes": "-1", "pageSize": "500", "pageNumber": "1", "reportName": "PRT_MUTUAL_ORG_STA", "columns": "ALL", "source": "WEB", "client": "WEB", "filter": f"""(MARKET_TYPE="003")(HOLD_DATE>='{start_date}')(HOLD_DATE<='{end_date}')""", } url = "https://datacenter-web.eastmoney.com/api/data/v1/get" r = requests.get(url, params=params, headers=headers) data_json = r.json() total_page = data_json["result"]["pages"] big_df = pd.DataFrame() tqdm = get_tqdm() for page in tqdm(range(1, total_page + 1), leave=False): params.update({"pageNumber": page}) r = requests.get(url, params=params, headers=headers) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True) big_df.columns = [ "持股日期", "_", "持股只数", "_", "持股市值", "持股市值变化-1日", "持股市值变化-5日", "持股市值变化-10日", "_", "机构名称", "_", "_", "_", "_", "_", "_", "_", "_", ] big_df = big_df[ [ "持股日期", "机构名称", "持股只数", "持股市值", "持股市值变化-1日", "持股市值变化-5日", "持股市值变化-10日", ] ] big_df["持股日期"] = pd.to_datetime(big_df["持股日期"], errors="coerce").dt.date big_df["持股只数"] = pd.to_numeric(big_df["持股只数"], errors="coerce") big_df["持股市值"] = pd.to_numeric(big_df["持股市值"], errors="coerce") big_df["持股市值变化-1日"] = pd.to_numeric( big_df["持股市值变化-1日"], errors="coerce" ) big_df["持股市值变化-5日"] = pd.to_numeric( big_df["持股市值变化-5日"], errors="coerce" ) big_df["持股市值变化-10日"] = pd.to_numeric( big_df["持股市值变化-10日"], errors="coerce" ) return big_df def stock_hsgt_hist_em(symbol: str = "北向资金") -> pd.DataFrame: """ 东方财富网-数据中心-资金流向-沪深港通资金流向-沪深港通历史数据 https://data.eastmoney.com/hsgt/index.html :param symbol: choice of {"北向资金", "沪股通", "深股通", "南向资金", "港股通沪", "港股通深"} :type symbol: str :return: 沪深港通历史数据 :rtype: pandas.DataFrame """ import warnings warnings.filterwarnings(action="ignore", category=FutureWarning) url = "https://datacenter-web.eastmoney.com/api/data/v1/get" symbol_map = { "北向资金": "5", "沪股通": "1", "深股通": "3", "南向资金": "6", "港股通沪": "2", "港股通深": "4", } params = { "sortColumns": "TRADE_DATE", "sortTypes": "-1", "pageSize": "1000", "pageNumber": "1", "reportName": "RPT_MUTUAL_DEAL_HISTORY", "columns": "ALL", "source": "WEB", "client": "WEB", "filter": f'(MUTUAL_TYPE="00{symbol_map[symbol]}")', } r = requests.get(url, params=params) data_json = r.json() total_page = data_json["result"]["pages"] temp_list = [] tqdm = get_tqdm() for page in tqdm(range(1, int(total_page) + 1), leave=False): params.update({"pageNumber": page}) r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) temp_list.append(temp_df) big_df = pd.concat(objs=temp_list, ignore_index=True) if symbol == "北向资金": index_name = "沪深300" elif symbol == "沪股通": index_name = "上证指数" elif symbol == "深股通": index_name = "深证指数" elif symbol == "南向资金": index_name = "沪深300" else: index_name = "恒生指数" big_df.rename( columns={ "MUTUAL_TYPE": "-", "TRADE_DATE": "日期", "FUND_INFLOW": "当日资金流入", "NET_DEAL_AMT": "当日成交净买额", "QUOTA_BALANCE": "当日余额", "ACCUM_DEAL_AMT": "历史累计净买额", "BUY_AMT": "买入成交额", "SELL_AMT": "卖出成交额", "LEAD_STOCKS_CODE": "领涨股-代码", "LEAD_STOCKS_NAME": "领涨股", "LS_CHANGE_RATE": "领涨股-涨跌幅", "INDEX_CLOSE_PRICE": index_name, "INDEX_CHANGE_RATE": f"{index_name}-涨跌幅", "HOLD_MARKET_CAP": "持股市值", }, inplace=True, ) big_df = big_df[ [ "日期", "当日成交净买额", "买入成交额", "卖出成交额", "历史累计净买额", "当日资金流入", "当日余额", "持股市值", "领涨股", "领涨股-涨跌幅", index_name, f"{index_name}-涨跌幅", "领涨股-代码", ] ] big_df["日期"] = pd.to_datetime(big_df["日期"], errors="coerce").dt.date big_df.sort_values(["日期"], inplace=True, ignore_index=True) big_df["当日资金流入"] = ( pd.to_numeric(big_df["当日资金流入"], errors="coerce") / 100 ) big_df["当日余额"] = pd.to_numeric(big_df["当日余额"], errors="coerce") / 100 if symbol == "沪股通" or symbol == "深股通": big_df["历史累计净买额"] = ( pd.to_numeric(big_df["历史累计净买额"], errors="coerce") / 100 ) else: big_df["历史累计净买额"] = ( pd.to_numeric(big_df["历史累计净买额"], errors="coerce") / 100 / 10000 ) big_df["当日成交净买额"] = ( pd.to_numeric(big_df["当日成交净买额"], errors="coerce") / 100 ) big_df["买入成交额"] = pd.to_numeric(big_df["买入成交额"], errors="coerce") / 100 big_df["卖出成交额"] = pd.to_numeric(big_df["卖出成交额"], errors="coerce") / 100 big_df["领涨股-涨跌幅"] = pd.to_numeric(big_df["领涨股-涨跌幅"], errors="coerce") big_df[index_name] = pd.to_numeric(big_df[index_name], errors="coerce") big_df[f"{index_name}-涨跌幅"] = pd.to_numeric( big_df[f"{index_name}-涨跌幅"], errors="coerce" ) big_df["持股市值"] = pd.to_numeric(big_df["持股市值"], errors="coerce") return big_df def stock_hsgt_board_rank_em( symbol: str = "北向资金增持行业板块排行", indicator: str = "今日" ) -> pd.DataFrame: """ 东方财富网-数据中心-沪深港通持股-行业板块排行-北向资金增持行业板块排行 https://data.eastmoney.com/hsgtcg/bk.html :param symbol: choice of {"北向资金增持行业板块排行", "北向资金增持概念板块排行", "北向资金增持地域板块排行"} :type symbol: str :param indicator: choice of {"今日", "3日", "5日", "10日", "1月", "1季", "1年"} :type indicator: str :return: 北向资金增持行业板块排行 :rtype: pandas.DataFrame """ url = "https://data.eastmoney.com/hsgtcg/hy.html" r = requests.get(url) soup = BeautifulSoup(r.text, features="lxml") current_date = soup.find(attrs={"id": "bkph_date"}).text.strip("（").strip("）") symbol_map = { "北向资金增持行业板块排行": "5", "北向资金增持概念板块排行": "4", "北向资金增持地域板块排行": "3", } indicator_map = { "今日": "1", "3日": "3", "5日": "5", "10日": "10", "1月": "M", "1季": "Q", "1年": "Y", } url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "sortColumns": "ADD_MARKET_CAP", "sortTypes": "-1", "pageSize": "500", "pageNumber": "1", "reportName": "RPT_MUTUAL_BOARD_HOLDRANK_WEB", "columns": "ALL", "quoteColumns": "f3~05~SECURITY_CODE~INDEX_CHANGE_RATIO", "source": "WEB", "client": "WEB", "filter": f"""(BOARD_TYPE="{symbol_map[symbol]}")(TRADE_DATE='{current_date}')(INTERVAL_TYPE="{indicator_map[indicator]}")""", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) temp_df.reset_index(inplace=True) temp_df["index"] = temp_df.index + 1 temp_df.columns = [ "序号", "_", "_", "名称", "_", "最新涨跌幅", "报告时间", "_", "_", "北向资金今日增持估计-股票只数", "北向资金今日持股-股票只数", "北向资金今日增持估计-市值", "北向资金今日增持估计-市值增幅", "-", "北向资金今日增持估计-占板块比", "北向资金今日增持估计-占北向资金比", "北向资金今日持股-市值", "北向资金今日持股-占北向资金比", "北向资金今日持股-占板块比", "_", "_", "今日增持最大股-市值", "_", "_", "今日减持最大股-市值", "今日增持最大股-占总市值比", "_", "_", "今日减持最大股-占总市值比", "_", "_", "_", "_", "_", "_", "_", ] temp_df = temp_df[ [ "序号", "名称", "最新涨跌幅", "北向资金今日持股-股票只数", "北向资金今日持股-市值", "北向资金今日持股-占板块比", "北向资金今日持股-占北向资金比", "北向资金今日增持估计-股票只数", "北向资金今日增持估计-市值", "北向资金今日增持估计-市值增幅", "北向资金今日增持估计-占板块比", "北向资金今日增持估计-占北向资金比", "今日增持最大股-市值", "今日增持最大股-占总市值比", "今日减持最大股-市值", "今日减持最大股-占总市值比", "报告时间", ] ] temp_df["最新涨跌幅"] = pd.to_numeric(temp_df["最新涨跌幅"], errors="coerce") temp_df["北向资金今日持股-股票只数"] = pd.to_numeric( temp_df["北向资金今日持股-股票只数"], errors="coerce" ) temp_df["北向资金今日持股-市值"] = pd.to_numeric( temp_df["北向资金今日持股-市值"], errors="coerce" ) temp_df["北向资金今日持股-占板块比"] = pd.to_numeric( temp_df["北向资金今日持股-占板块比"], errors="coerce" ) temp_df["北向资金今日持股-占北向资金比"] = pd.to_numeric( temp_df["北向资金今日持股-占北向资金比"], errors="coerce" ) temp_df["北向资金今日增持估计-股票只数"] = pd.to_numeric( temp_df["北向资金今日增持估计-股票只数"], errors="coerce" ) temp_df["北向资金今日增持估计-市值"] = pd.to_numeric( temp_df["北向资金今日增持估计-市值"], errors="coerce" ) temp_df["北向资金今日增持估计-市值增幅"] = pd.to_numeric( temp_df["北向资金今日增持估计-市值增幅"], errors="coerce" ) temp_df["北向资金今日增持估计-占板块比"] = pd.to_numeric( temp_df["北向资金今日增持估计-占板块比"], errors="coerce" ) temp_df["北向资金今日增持估计-占北向资金比"] = pd.to_numeric( temp_df["北向资金今日增持估计-占北向资金比"], errors="coerce" ) temp_df["报告时间"] = pd.to_datetime(temp_df["报告时间"], errors="coerce").dt.date return temp_df def __stock_hsgt_individual_zh_hk_em(symbol: str = "00700") -> pd.DataFrame: """ 东方财富-数据中心-沪深港通-沪深港通持股-具体股票 https://data.eastmoney.com/hsgt/StockHdDetail/00700.html :param symbol: 股票代码 :type symbol: str :return: 具体股票-沪深港通持股 :rtype: pandas.DataFrame """ url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "sortColumns": "TRADE_DATE", "sortTypes": "-1", "pageSize": "500", "pageNumber": "1", "reportName": "RPT_MUTUAL_STOCK_HOLDRANKS", "columns": "ALL", "source": "WEB", "client": "WEB", "filter": f"""(SECUCODE="{symbol}.HK")(MUTUAL_TYPE="002")""", } r = requests.get(url, params=params) data_json = r.json() total_page = data_json["result"]["pages"] temp_df = pd.DataFrame(data_json["result"]["data"]) for page in range(1, total_page + 1): params.update({"pageNumber": str(page)}) r = requests.get(url, params=params) data_json = r.json() if page == 1: temp_df = pd.DataFrame(data_json["result"]["data"]) else: temp_df = pd.concat( objs=[temp_df, pd.DataFrame(data_json["result"]["data"])], ignore_index=True, ) temp_df.rename( columns={ "SECURITY_INNER_CODE": "-", "SECUCODE": "-", "TRADE_DATE": "持股日期", "SECURITY_CODE": "-", "SECURITY_NAME": "-", "MUTUAL_TYPE": "-", "CHANGE_RATE": "当日涨跌幅", "CLOSE_PRICE": "当日收盘价", "HOLD_SHARES": "持股数量", "HOLD_MARKET_CAP": "持股市值", "A_SHARES_RATIO": "-", "HOLD_SHARES_RATIO": "持股数量占A股百分比", "FREE_SHARES_RATIO": "-", "TOTAL_SHARES_RATIO": "-", "HOLD_MARKETCAP_CHG1": "持股市值变化-1日", "HOLD_MARKETCAP_CHG5": "持股市值变化-5日", "HOLD_MARKETCAP_CHG10": "持股市值变化-10日", }, inplace=True, ) temp_df = temp_df[ [ "持股日期", "当日收盘价", "当日涨跌幅", "持股数量", "持股市值", "持股数量占A股百分比", "持股市值变化-1日", "持股市值变化-5日", "持股市值变化-10日", ] ] temp_df["持股日期"] = pd.to_datetime(temp_df["持股日期"], errors="coerce").dt.date temp_df["当日收盘价"] = pd.to_numeric(temp_df["当日收盘价"], errors="coerce") temp_df["当日涨跌幅"] = pd.to_numeric(temp_df["当日涨跌幅"], errors="coerce") temp_df["持股数量"] = pd.to_numeric(temp_df["持股数量"], errors="coerce") temp_df["持股市值"] = pd.to_numeric(temp_df["持股市值"], errors="coerce") temp_df["持股数量占A股百分比"] = pd.to_numeric( temp_df["持股数量占A股百分比"], errors="coerce" ) temp_df["持股市值变化-1日"] = pd.to_numeric( temp_df["持股市值变化-1日"], errors="coerce" ) temp_df["持股市值变化-5日"] = pd.to_numeric( temp_df["持股市值变化-5日"], errors="coerce" ) temp_df["持股市值变化-10日"] = pd.to_numeric( temp_df["持股市值变化-10日"], errors="coerce" ) temp_df.sort_values("持股日期", ignore_index=True, inplace=True) return temp_df def __stock_hsgt_individual_zh_a_em(symbol: str = "002008") -> pd.DataFrame: """ 东方财富-数据中心-沪深港通-沪深港通持股-具体股票 https://data.eastmoney.com/hsgt/StockHdStatistics/002008.html :param symbol: 股票代码 :type symbol: str :return: 具体股票-沪深港通持股 :rtype: pandas.DataFrame """ url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "sortColumns": "TRADE_DATE", "sortTypes": "-1", "pageSize": "500", "pageNumber": "1", "reportName": "RPT_MUTUAL_HOLDSTOCKNDATE_STA", "columns": "ALL", "source": "WEB", "client": "WEB", "filter": f"""(SECURITY_CODE="{symbol}")(INTERVAL_TYPE="1")""", } r = requests.get(url, params=params) data_json = r.json() total_page = data_json["result"]["pages"] temp_df = pd.DataFrame(data_json["result"]["data"]) for page in range(1, total_page + 1): params.update({"pageNumber": str(page)}) r = requests.get(url, params=params) data_json = r.json() if page == 1: temp_df = pd.DataFrame(data_json["result"]["data"]) else: temp_df = pd.concat( objs=[temp_df, pd.DataFrame(data_json["result"]["data"])], ignore_index=True, ) temp_df.rename( columns={ "SECURITY_INNER_CODE": "-", "SECUCODE": "-", "TRADE_DATE": "持股日期", "SECURITY_CODE": "-", "SECURITY_NAME": "-", "MUTUAL_TYPE": "-", "CHANGE_RATE": "当日涨跌幅", "CLOSE_PRICE": "当日收盘价", "HOLD_SHARES": "持股数量", "HOLD_MARKET_CAP": "持股市值", "A_SHARES_RATIO": "-", "HOLD_SHARES_RATIO": "持股数量占A股百分比", "FREE_SHARES_RATIO": "-", "TOTAL_SHARES_RATIO": "-", "HMC_CHANGE": "今日持股市值变化", "ADD_SHARES_REPAIR": "今日增持股数", "PREDICT_AMC": "今日增持资金", }, inplace=True, ) temp_df = temp_df[ [ "持股日期", "当日收盘价", "当日涨跌幅", "持股数量", "持股市值", "持股数量占A股百分比", "今日增持股数", "今日增持资金", "今日持股市值变化", ] ] temp_df["持股日期"] = pd.to_datetime(temp_df["持股日期"], errors="coerce").dt.date temp_df["当日收盘价"] = pd.to_numeric(temp_df["当日收盘价"], errors="coerce") temp_df["当日涨跌幅"] = pd.to_numeric(temp_df["当日涨跌幅"], errors="coerce") temp_df["持股数量"] = pd.to_numeric(temp_df["持股数量"], errors="coerce") temp_df["持股市值"] = pd.to_numeric(temp_df["持股市值"], errors="coerce") temp_df["持股数量占A股百分比"] = pd.to_numeric( temp_df["持股数量占A股百分比"], errors="coerce" ) temp_df["今日增持股数"] = pd.to_numeric(temp_df["今日增持股数"], errors="coerce") temp_df["今日增持资金"] = pd.to_numeric(temp_df["今日增持资金"], errors="coerce") temp_df["今日持股市值变化"] = pd.to_numeric( temp_df["今日持股市值变化"], errors="coerce" ) temp_df.sort_values("持股日期", ignore_index=True, inplace=True) return temp_df def stock_hsgt_individual_em(symbol: str = "002008") -> pd.DataFrame: """ 东方财富-数据中心-沪深港通-沪深港通持股-具体股票 https://data.eastmoney.com/hsgt/StockHdDetail/002008.html :param symbol: 股票代码 :type symbol: str :return: 具体股票-沪深港通持股 :rtype: pandas.DataFrame """ if len(symbol) == 6: return __stock_hsgt_individual_zh_a_em(symbol=symbol) else: return __stock_hsgt_individual_zh_hk_em(symbol=symbol) def stock_hsgt_individual_detail_em( symbol: str = "002008", start_date: str = "20220130", end_date: str = "20220330", ) -> pd.DataFrame: """ 东方财富-数据中心-沪深港通-沪深港通持股-具体股票详情 https://data.eastmoney.com/hsgtcg/StockHdStatistics/002008.html :param symbol: 股票代码 :type symbol: str :param start_date: 开始时间 :type start_date: str :param end_date: 结束时间 :type end_date: str :return: 沪深港通持股-具体股票详情 :rtype: pandas.DataFrame """ url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "sortColumns": "HOLD_DATE", "sortTypes": "-1", "pageSize": "500", "pageNumber": "1", "reportName": "RPT_MUTUAL_HOLD_DET", "columns": "ALL", "source": "WEB", "client": "WEB", "filter": f"""(SECURITY_CODE="{symbol}")(MARKET_CODE="003")(HOLD_DATE >='{"-".join([start_date[:4], start_date[4:6], start_date[6:]])}')(HOLD_DATE <='{"-".join([end_date[:4], end_date[4:6], end_date[6:]])}')""", } r = requests.get(url, params=params) data_json = r.json() try: data_json["result"]["pages"] except TypeError: params.update( { "filter": f"""(SECURITY_CODE="{symbol}")(MARKET_CODE="001")(HOLD_DATE >='{"-".join([start_date[:4], start_date[4:6], start_date[6:]])}')(HOLD_DATE <='{"-".join([end_date[:4], end_date[4:6], end_date[6:]])}')""", } ) r = requests.get(url, params=params) data_json = r.json() total_page = data_json["result"]["pages"] big_df = pd.DataFrame() tqdm = get_tqdm() for page in tqdm(range(1, int(total_page) + 1), leave=False): params.update({"pageNumber": page}) r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True) big_df.rename( columns={ "SECUCODE": "-", "SECURITY_CODE": "-", "SECURITY_INNER_CODE": "-", "SECURITY_NAME_ABBR": "-", "HOLD_DATE": "持股日期", "ORG_CODE": "-", "ORG_NAME": "机构名称", "HOLD_NUM": "持股数量", "MARKET_CODE": "-", "HOLD_SHARES_RATIO": "持股数量占A股百分比", "HOLD_MARKET_CAP": "持股市值", "CLOSE_PRICE": "当日收盘价", "CHANGE_RATE": "当日涨跌幅", "HOLD_MARKET_CAPONE": "持股市值变化-1日", "HOLD_MARKET_CAPFIVE": "持股市值变化-5日", "HOLD_MARKET_CAPTEN": "持股市值变化-10日", "PARTICIPANT_CODE": "-", }, inplace=True, ) big_df = big_df[ [ "持股日期", "当日收盘价", "当日涨跌幅", "机构名称", "持股数量", "持股市值", "持股数量占A股百分比", "持股市值变化-1日", "持股市值变化-5日", "持股市值变化-10日", ] ] big_df["持股日期"] = pd.to_datetime(big_df["持股日期"], errors="coerce").dt.date big_df["当日收盘价"] = pd.to_numeric(big_df["当日收盘价"], errors="coerce") big_df["当日涨跌幅"] = pd.to_numeric(big_df["当日涨跌幅"], errors="coerce") big_df["持股数量"] = pd.to_numeric(big_df["持股数量"], errors="coerce") big_df["持股市值"] = pd.to_numeric(big_df["持股市值"], errors="coerce") big_df["持股数量占A股百分比"] = pd.to_numeric( big_df["持股数量占A股百分比"], errors="coerce" ) big_df["持股市值变化-1日"] = pd.to_numeric( big_df["持股市值变化-1日"], errors="coerce" ) big_df["持股市值变化-5日"] = pd.to_numeric( big_df["持股市值变化-5日"], errors="coerce" ) big_df["持股市值变化-10日"] = pd.to_numeric( big_df["持股市值变化-10日"], errors="coerce" ) return big_df if __name__ == "__main__": stock_hsgt_fund_flow_summary_em_df = stock_hsgt_fund_flow_summary_em() print(stock_hsgt_fund_flow_summary_em_df) stock_hk_ggt_components_em_df = stock_hk_ggt_components_em() print(stock_hk_ggt_components_em_df) stock_hsgt_hold_stock_em_df = stock_hsgt_hold_stock_em( market="北向", indicator="今日排行" ) print(stock_hsgt_hold_stock_em_df) stock_hsgt_hold_stock_em_df = stock_hsgt_hold_stock_em( market="沪股通", indicator="5日排行" ) print(stock_hsgt_hold_stock_em_df) stock_hsgt_hold_stock_em_df = stock_hsgt_hold_stock_em( market="深股通", indicator="5日排行" ) print(stock_hsgt_hold_stock_em_df) stock_hsgt_hold_stock_em_df = stock_hsgt_hold_stock_em( market="沪股通", indicator="10日排行" ) print(stock_hsgt_hold_stock_em_df) stock_hsgt_stock_statistics_em_df = stock_hsgt_stock_statistics_em( symbol="北向持股", start_date="20240110", end_date="20240110" ) print(stock_hsgt_stock_statistics_em_df) stock_hsgt_stock_statistics_em_df = stock_hsgt_stock_statistics_em( symbol="南向持股", start_date="20250314", end_date="20250314" ) print(stock_hsgt_stock_statistics_em_df) stock_hsgt_stock_statistics_em_df = stock_hsgt_stock_statistics_em( symbol="沪股通持股", start_date="20240110", end_date="20240110" ) print(stock_hsgt_stock_statistics_em_df) stock_hsgt_stock_statistics_em_df = stock_hsgt_stock_statistics_em( symbol="深股通持股", start_date="20240110", end_date="20240110" ) print(stock_hsgt_stock_statistics_em_df) stock_hsgt_institution_statistics_em_df = stock_hsgt_institution_statistics_em( market="北向持股", start_date="20240110", end_date="20240110" ) print(stock_hsgt_institution_statistics_em_df) stock_hsgt_institution_statistics_em_df = stock_hsgt_institution_statistics_em( market="南向持股", start_date="20240110", end_date="20240110" ) print(stock_hsgt_institution_statistics_em_df) stock_hsgt_institution_statistics_em_df = stock_hsgt_institution_statistics_em( market="沪股通持股", start_date="20240110", end_date="20240110" ) print(stock_hsgt_institution_statistics_em_df) stock_hsgt_institution_statistics_em_df = stock_hsgt_institution_statistics_em( market="深股通持股", start_date="20240110", end_date="20240110" ) print(stock_hsgt_institution_statistics_em_df) stock_hsgt_hist_em_df = stock_hsgt_hist_em(symbol="港股通沪") print(stock_hsgt_hist_em_df) stock_hsgt_board_rank_em_df = stock_hsgt_board_rank_em( symbol="北向资金增持行业板块排行", indicator="今日" ) print(stock_hsgt_board_rank_em_df) stock_hsgt_individual_em_df = stock_hsgt_individual_em(symbol="00700") print(stock_hsgt_individual_em_df) stock_hsgt_individual_detail_em_df = stock_hsgt_individual_detail_em( symbol="002008", start_date="20240110", end_date="20240110" ) print(stock_hsgt_individual_detail_em_df)