#!/usr/bin/env python # -*- coding:utf-8 -*- """ Date: 2025/1/13 22:30 Desc: 东方财富网-数据中心-大宗交易-市场统计 https://data.eastmoney.com/dzjy/ """ import pandas as pd import requests def stock_dzjy_sctj() -> pd.DataFrame: """ 东方财富网-数据中心-大宗交易-市场统计 https://data.eastmoney.com/dzjy/dzjy_sctj.html :return: 市场统计表 :rtype: pandas.DataFrame """ url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "sortColumns": "TRADE_DATE", "sortTypes": "-1", "pageSize": "500", "pageNumber": "1", "reportName": "PRT_BLOCKTRADE_MARKET_STA", "columns": "TRADE_DATE,SZ_INDEX,SZ_CHANGE_RATE,BLOCKTRADE_DEAL_AMT,PREMIUM_DEAL_AMT," "PREMIUM_RATIO,DISCOUNT_DEAL_AMT,DISCOUNT_RATIO", "source": "WEB", "client": "WEB", } r = requests.get(url, params=params) data_json = r.json() total_page = int(data_json["result"]["pages"]) big_df = pd.DataFrame() for page in range(1, total_page + 1): 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"] = big_df["index"] + 1 big_df.columns = [ "序号", "交易日期", "上证指数", "上证指数涨跌幅", "大宗交易成交总额", "溢价成交总额", "溢价成交总额占比", "折价成交总额", "折价成交总额占比", ] big_df["交易日期"] = pd.to_datetime(big_df["交易日期"], errors="coerce").dt.date big_df["上证指数"] = pd.to_numeric(big_df["上证指数"], errors="coerce") big_df["上证指数涨跌幅"] = pd.to_numeric(big_df["上证指数涨跌幅"], errors="coerce") big_df["大宗交易成交总额"] = pd.to_numeric( big_df["大宗交易成交总额"], errors="coerce" ) big_df["溢价成交总额"] = pd.to_numeric(big_df["溢价成交总额"], errors="coerce") big_df["溢价成交总额占比"] = pd.to_numeric( big_df["溢价成交总额占比"], errors="coerce" ) big_df["折价成交总额"] = pd.to_numeric(big_df["折价成交总额"], errors="coerce") big_df["折价成交总额占比"] = pd.to_numeric( big_df["折价成交总额占比"], errors="coerce" ) return big_df def stock_dzjy_mrmx( symbol: str = "基金", start_date: str = "20220104", end_date: str = "20220104" ) -> pd.DataFrame: """ 东方财富网-数据中心-大宗交易-每日明细 https://data.eastmoney.com/dzjy/dzjy_mrmx.html :param symbol: choice of {'A股', 'B股', '基金', '债券'} :type symbol: str :param start_date: 开始日期 :type start_date: str :param end_date: 结束日期 :type end_date: str :return: 每日明细 :rtype: pandas.DataFrame """ symbol_map = { "A股": "1", "B股": "2", "基金": "3", "债券": "4", } url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "sortColumns": "SECURITY_CODE", "sortTypes": "1", "pageSize": "5000", "pageNumber": "1", "reportName": "RPT_DATA_BLOCKTRADE", "columns": "TRADE_DATE,SECURITY_CODE,SECUCODE,SECURITY_NAME_ABBR,CHANGE_RATE,CLOSE_PRICE," "DEAL_PRICE,PREMIUM_RATIO,DEAL_VOLUME,DEAL_AMT,TURNOVER_RATE,BUYER_NAME,SELLER_NAME," "CHANGE_RATE_1DAYS,CHANGE_RATE_5DAYS,CHANGE_RATE_10DAYS,CHANGE_RATE_20DAYS,BUYER_CODE,SELLER_CODE", "source": "WEB", "client": "WEB", "filter": f"""(SECURITY_TYPE_WEB={symbol_map[symbol]})(TRADE_DATE>= '{"-".join([start_date[:4], start_date[4:6], start_date[6:]])}')(TRADE_DATE<= '{"-".join([end_date[:4], end_date[4:6], end_date[6:]])}')""", } r = requests.get(url, params=params) data_json = r.json() if not data_json["result"]["data"]: return pd.DataFrame() temp_df = pd.DataFrame(data_json["result"]["data"]) temp_df.reset_index(inplace=True) temp_df["index"] = temp_df.index + 1 if symbol in {"A股"}: temp_df.columns = [ "序号", "交易日期", "证券代码", "-", "证券简称", "涨跌幅", "收盘价", "成交价", "折溢率", "成交量", "成交额", "成交额/流通市值", "买方营业部", "卖方营业部", "_", "_", "_", "_", "_", "_", ] temp_df["交易日期"] = pd.to_datetime( temp_df["交易日期"], errors="coerce" ).dt.date temp_df = temp_df[ [ "序号", "交易日期", "证券代码", "证券简称", "涨跌幅", "收盘价", "成交价", "折溢率", "成交量", "成交额", "成交额/流通市值", "买方营业部", "卖方营业部", ] ] temp_df["涨跌幅"] = pd.to_numeric(temp_df["涨跌幅"], errors="coerce") temp_df["收盘价"] = pd.to_numeric(temp_df["收盘价"], errors="coerce") temp_df["成交价"] = pd.to_numeric(temp_df["成交价"], errors="coerce") temp_df["折溢率"] = pd.to_numeric(temp_df["折溢率"], errors="coerce") temp_df["成交量"] = pd.to_numeric(temp_df["成交量"], errors="coerce") temp_df["成交额"] = pd.to_numeric(temp_df["成交额"], errors="coerce") temp_df["成交额/流通市值"] = pd.to_numeric( temp_df["成交额/流通市值"], errors="coerce" ) if symbol in {"B股", "基金", "债券"}: temp_df.columns = [ "序号", "交易日期", "证券代码", "-", "证券简称", "-", "-", "成交价", "-", "成交量", "成交额", "-", "买方营业部", "卖方营业部", "_", "_", "_", "_", "_", "_", ] temp_df["交易日期"] = pd.to_datetime( temp_df["交易日期"], errors="coerce" ).dt.date temp_df = temp_df[ [ "序号", "交易日期", "证券代码", "证券简称", "成交价", "成交量", "成交额", "买方营业部", "卖方营业部", ] ] temp_df["成交价"] = pd.to_numeric(temp_df["成交价"], errors="coerce") temp_df["成交量"] = pd.to_numeric(temp_df["成交量"], errors="coerce") temp_df["成交额"] = pd.to_numeric(temp_df["成交额"], errors="coerce") return temp_df def stock_dzjy_mrtj( start_date: str = "20220105", end_date: str = "20220105" ) -> pd.DataFrame: """ 东方财富网-数据中心-大宗交易-每日统计 https://data.eastmoney.com/dzjy/dzjy_mrtj.html :param start_date: 开始日期 :type start_date: str :param end_date: 结束日期 :type end_date: str :return: 每日统计 :rtype: pandas.DataFrame """ url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "sortColumns": "TURNOVERRATE", "sortTypes": "-1", "pageSize": "5000", "pageNumber": "1", "reportName": "RPT_BLOCKTRADE_STA", "columns": "TRADE_DATE,SECURITY_CODE,SECUCODE,SECURITY_NAME_ABBR,CHANGE_RATE," "CLOSE_PRICE,AVERAGE_PRICE,PREMIUM_RATIO,DEAL_NUM,VOLUME,DEAL_AMT," "TURNOVERRATE,D1_CLOSE_ADJCHRATE,D5_CLOSE_ADJCHRATE,D10_CLOSE_ADJCHRATE,D20_CLOSE_ADJCHRATE", "source": "WEB", "client": "WEB", "filter": f"(TRADE_DATE>='{'-'.join([start_date[:4], start_date[4:6], start_date[6:]])}')(TRADE_DATE<=" f"'{'-'.join([end_date[:4], end_date[4:6], end_date[6:]])}')", } 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", "交易日期", "证券代码", "-", "证券简称", "涨跌幅", "收盘价", "成交价", "折溢率", "成交笔数", "成交总量", "成交总额", "成交总额/流通市值", "_", "_", "_", "_", ] temp_df["交易日期"] = pd.to_datetime(temp_df["交易日期"], errors="coerce").dt.date temp_df = temp_df[ [ "序号", "交易日期", "证券代码", "证券简称", "涨跌幅", "收盘价", "成交价", "折溢率", "成交笔数", "成交总量", "成交总额", "成交总额/流通市值", ] ] temp_df["涨跌幅"] = pd.to_numeric(temp_df["涨跌幅"], errors="coerce") temp_df["收盘价"] = pd.to_numeric(temp_df["收盘价"], errors="coerce") temp_df["成交价"] = pd.to_numeric(temp_df["成交价"], errors="coerce") temp_df["折溢率"] = pd.to_numeric(temp_df["折溢率"], errors="coerce") temp_df["成交笔数"] = pd.to_numeric(temp_df["成交笔数"], errors="coerce") temp_df["成交总量"] = pd.to_numeric(temp_df["成交总量"], errors="coerce") temp_df["成交总额"] = pd.to_numeric(temp_df["成交总额"], errors="coerce") temp_df["成交总额/流通市值"] = pd.to_numeric( temp_df["成交总额/流通市值"], errors="coerce" ) return temp_df def stock_dzjy_hygtj(symbol: str = "近三月") -> pd.DataFrame: """ 东方财富网-数据中心-大宗交易-活跃 A 股统计 https://data.eastmoney.com/dzjy/dzjy_hygtj.html :param symbol: choice of {'近一月', '近三月', '近六月', '近一年'} :type symbol: str :return: 活跃 A 股统计 :rtype: pandas.DataFrame """ period_map = { "近一月": "1", "近三月": "3", "近六月": "6", "近一年": "12", } url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "sortColumns": "DEAL_NUM,SECURITY_CODE", "sortTypes": "-1,-1", "pageSize": "5000", "pageNumber": "1", "reportName": "RPT_BLOCKTRADE_ACSTA", "columns": "SECURITY_CODE,SECUCODE,SECURITY_NAME_ABBR,CLOSE_PRICE,CHANGE_RATE,TRADE_DATE," "DEAL_AMT,PREMIUM_RATIO,SUM_TURNOVERRATE,DEAL_NUM,PREMIUM_TIMES,DISCOUNT_TIMES," "D1_AVG_ADJCHRATE,D5_AVG_ADJCHRATE,D10_AVG_ADJCHRATE,D20_AVG_ADJCHRATE,DATE_TYPE_CODE", "source": "WEB", "client": "WEB", "filter": f"(DATE_TYPE_CODE={period_map[symbol]})", } r = requests.get(url, params=params) data_json = r.json() total_page = data_json["result"]["pages"] big_df = pd.DataFrame() for page in range(1, int(total_page) + 1): 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"] = big_df.index + 1 big_df.columns = [ "序号", "证券代码", "_", "证券简称", "最新价", "涨跌幅", "最近上榜日", "总成交额", "折溢率", "成交总额/流通市值", "上榜次数-总计", "上榜次数-溢价", "上榜次数-折价", "上榜日后平均涨跌幅-1日", "上榜日后平均涨跌幅-5日", "上榜日后平均涨跌幅-10日", "上榜日后平均涨跌幅-20日", "_", ] big_df = big_df[ [ "序号", "证券代码", "证券简称", "最新价", "涨跌幅", "最近上榜日", "上榜次数-总计", "上榜次数-溢价", "上榜次数-折价", "总成交额", "折溢率", "成交总额/流通市值", "上榜日后平均涨跌幅-1日", "上榜日后平均涨跌幅-5日", "上榜日后平均涨跌幅-10日", "上榜日后平均涨跌幅-20日", ] ] big_df["最近上榜日"] = pd.to_datetime(big_df["最近上榜日"], errors="coerce").dt.date big_df["最新价"] = pd.to_numeric(big_df["最新价"], errors="coerce") big_df["涨跌幅"] = pd.to_numeric(big_df["涨跌幅"], errors="coerce") big_df["上榜次数-总计"] = pd.to_numeric(big_df["上榜次数-总计"], errors="coerce") big_df["上榜次数-溢价"] = pd.to_numeric(big_df["上榜次数-溢价"], errors="coerce") big_df["上榜次数-折价"] = pd.to_numeric(big_df["上榜次数-折价"], errors="coerce") big_df["总成交额"] = pd.to_numeric(big_df["总成交额"], errors="coerce") big_df["折溢率"] = pd.to_numeric(big_df["折溢率"], errors="coerce") big_df["成交总额/流通市值"] = pd.to_numeric( big_df["成交总额/流通市值"], errors="coerce" ) big_df["上榜日后平均涨跌幅-1日"] = pd.to_numeric( big_df["上榜日后平均涨跌幅-1日"], errors="coerce" ) big_df["上榜日后平均涨跌幅-5日"] = pd.to_numeric( big_df["上榜日后平均涨跌幅-5日"], errors="coerce" ) big_df["上榜日后平均涨跌幅-10日"] = pd.to_numeric( big_df["上榜日后平均涨跌幅-10日"], errors="coerce" ) big_df["上榜日后平均涨跌幅-20日"] = pd.to_numeric( big_df["上榜日后平均涨跌幅-20日"], errors="coerce" ) return big_df def stock_dzjy_hyyybtj(symbol: str = "近3日") -> pd.DataFrame: """ 东方财富网-数据中心-大宗交易-活跃营业部统计 https://data.eastmoney.com/dzjy/dzjy_hyyybtj.html :param symbol: choice of {'当前交易日', '近3日', '近5日', '近10日', '近30日'} :type symbol: str :return: 活跃营业部统计 :rtype: pandas.DataFrame """ period_map = { "当前交易日": "1", "近3日": "3", "近5日": "5", "近10日": "10", "近30日": "30", } url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "sortColumns": "BUYER_NUM,TOTAL_BUYAMT", "sortTypes": "-1,-1", "pageSize": "5000", "pageNumber": "1", "reportName": "RPT_BLOCKTRADE_OPERATEDEPTSTATISTICS", "columns": "OPERATEDEPT_CODE,OPERATEDEPT_NAME,ONLIST_DATE,STOCK_DETAILS," "BUYER_NUM,SELLER_NUM,TOTAL_BUYAMT,TOTAL_SELLAMT,TOTAL_NETAMT,N_DATE", "source": "WEB", "client": "WEB", "filter": f"(N_DATE=-{period_map[symbol]})", } r = requests.get(url, params=params) data_json = r.json() total_page = data_json["result"]["pages"] big_df = pd.DataFrame() for page in range(1, int(total_page) + 1): 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"] = big_df.index + 1 big_df.columns = [ "序号", "_", "营业部名称", "最近上榜日", "买入的股票", "次数总计-买入", "次数总计-卖出", "成交金额统计-买入", "成交金额统计-卖出", "成交金额统计-净买入额", "_", ] big_df = big_df[ [ "序号", "最近上榜日", "营业部名称", "次数总计-买入", "次数总计-卖出", "成交金额统计-买入", "成交金额统计-卖出", "成交金额统计-净买入额", "买入的股票", ] ] big_df["最近上榜日"] = pd.to_datetime(big_df["最近上榜日"], errors="coerce").dt.date big_df["次数总计-买入"] = pd.to_numeric(big_df["次数总计-买入"], errors="coerce") big_df["次数总计-卖出"] = pd.to_numeric(big_df["次数总计-卖出"], errors="coerce") big_df["成交金额统计-买入"] = pd.to_numeric( big_df["成交金额统计-买入"], errors="coerce" ) big_df["成交金额统计-卖出"] = pd.to_numeric( big_df["成交金额统计-卖出"], errors="coerce" ) big_df["成交金额统计-净买入额"] = pd.to_numeric( big_df["成交金额统计-净买入额"], errors="coerce" ) return big_df def stock_dzjy_yybph(symbol: str = "近三月") -> pd.DataFrame: """ 东方财富网-数据中心-大宗交易-营业部排行 https://data.eastmoney.com/dzjy/dzjy_yybph.html :param symbol: choice of {'近一月', '近三月', '近六月', '近一年'} :type symbol: str :return: 营业部排行 :rtype: pandas.DataFrame """ period_map = { "近一月": "30", "近三月": "90", "近六月": "180", "近一年": "360", } url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "sortColumns": "D5_BUYER_NUM,D1_AVERAGE_INCREASE", "sortTypes": "-1,-1", "pageSize": "5000", "pageNumber": "1", "reportName": "RPT_BLOCKTRADE_OPERATEDEPT_RANK", "columns": "OPERATEDEPT_CODE,OPERATEDEPT_NAME,D1_BUYER_NUM,D1_AVERAGE_INCREASE," "D1_RISE_PROBABILITY,D5_BUYER_NUM,D5_AVERAGE_INCREASE,D5_RISE_PROBABILITY," "D10_BUYER_NUM,D10_AVERAGE_INCREASE,D10_RISE_PROBABILITY,D20_BUYER_NUM," "D20_AVERAGE_INCREASE,D20_RISE_PROBABILITY,N_DATE,RELATED_ORG_CODE", "source": "WEB", "client": "WEB", "filter": f"(N_DATE=-{period_map[symbol]})", } r = requests.get(url, params=params) data_json = r.json() total_page = data_json["result"]["pages"] big_df = pd.DataFrame() for page in range(1, int(total_page) + 1): 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"] = big_df.index + 1 big_df.columns = [ "序号", "_", "营业部名称", "上榜后1天-买入次数", "上榜后1天-平均涨幅", "上榜后1天-上涨概率", "上榜后5天-买入次数", "上榜后5天-平均涨幅", "上榜后5天-上涨概率", "上榜后10天-买入次数", "上榜后10天-平均涨幅", "上榜后10天-上涨概率", "上榜后20天-买入次数", "上榜后20天-平均涨幅", "上榜后20天-上涨概率", "_", "_", ] big_df = big_df[ [ "序号", "营业部名称", "上榜后1天-买入次数", "上榜后1天-平均涨幅", "上榜后1天-上涨概率", "上榜后5天-买入次数", "上榜后5天-平均涨幅", "上榜后5天-上涨概率", "上榜后10天-买入次数", "上榜后10天-平均涨幅", "上榜后10天-上涨概率", "上榜后20天-买入次数", "上榜后20天-平均涨幅", "上榜后20天-上涨概率", ] ] big_df["上榜后1天-买入次数"] = pd.to_numeric( big_df["上榜后1天-买入次数"], errors="coerce" ) big_df["上榜后1天-平均涨幅"] = pd.to_numeric( big_df["上榜后1天-平均涨幅"], errors="coerce" ) big_df["上榜后1天-上涨概率"] = pd.to_numeric( big_df["上榜后1天-上涨概率"], errors="coerce" ) big_df["上榜后5天-买入次数"] = pd.to_numeric( big_df["上榜后5天-买入次数"], errors="coerce" ) big_df["上榜后5天-平均涨幅"] = pd.to_numeric( big_df["上榜后5天-平均涨幅"], errors="coerce" ) big_df["上榜后5天-上涨概率"] = pd.to_numeric( big_df["上榜后5天-上涨概率"], errors="coerce" ) big_df["上榜后10天-买入次数"] = pd.to_numeric( big_df["上榜后10天-买入次数"], errors="coerce" ) big_df["上榜后10天-平均涨幅"] = pd.to_numeric( big_df["上榜后10天-平均涨幅"], errors="coerce" ) big_df["上榜后10天-上涨概率"] = pd.to_numeric( big_df["上榜后10天-上涨概率"], errors="coerce" ) big_df["上榜后20天-买入次数"] = pd.to_numeric( big_df["上榜后20天-买入次数"], errors="coerce" ) big_df["上榜后20天-平均涨幅"] = pd.to_numeric( big_df["上榜后20天-平均涨幅"], errors="coerce" ) big_df["上榜后20天-上涨概率"] = pd.to_numeric( big_df["上榜后20天-上涨概率"], errors="coerce" ) return big_df if __name__ == "__main__": stock_dzjy_sctj_df = stock_dzjy_sctj() print(stock_dzjy_sctj_df) stock_dzjy_mrmx_df = stock_dzjy_mrmx( symbol="债券", start_date="20220104", end_date="20220104" ) print(stock_dzjy_mrmx_df) stock_dzjy_mrtj_df = stock_dzjy_mrtj(start_date="20220105", end_date="20220105") print(stock_dzjy_mrtj_df) stock_dzjy_hygtj_df = stock_dzjy_hygtj(symbol="近三月") print(stock_dzjy_hygtj_df) stock_dzjy_hyyybtj_df = stock_dzjy_hyyybtj(symbol="近3日") print(stock_dzjy_hyyybtj_df) stock_dzjy_yybph_df = stock_dzjy_yybph(symbol="近三月") print(stock_dzjy_yybph_df)