# -*- coding:utf-8 -*- # !/usr/bin/env python """ Date: 2024/4/22 14:00 Desc: 东方财富网-数据中心-龙虎榜单 https://data.eastmoney.com/stock/tradedetail.html """ import pandas as pd import requests from akshare.utils.tqdm import get_tqdm def stock_lhb_detail_em( start_date: str = "20230403", end_date: str = "20230417" ) -> pd.DataFrame: """ 东方财富网-数据中心-龙虎榜单-龙虎榜详情 https://data.eastmoney.com/stock/tradedetail.html :param start_date: 开始日期 :type start_date: str :param end_date: 结束日期 :type end_date: str :return: 龙虎榜详情 :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:]]) url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "sortColumns": "SECURITY_CODE,TRADE_DATE", "sortTypes": "1,-1", "pageSize": "5000", "pageNumber": "1", "reportName": "RPT_DAILYBILLBOARD_DETAILSNEW", "columns": "SECURITY_CODE,SECUCODE,SECURITY_NAME_ABBR,TRADE_DATE,EXPLAIN,CLOSE_PRICE,CHANGE_RATE," "BILLBOARD_NET_AMT,BILLBOARD_BUY_AMT,BILLBOARD_SELL_AMT,BILLBOARD_DEAL_AMT,ACCUM_AMOUNT," "DEAL_NET_RATIO,DEAL_AMOUNT_RATIO,TURNOVERRATE,FREE_MARKET_CAP,EXPLANATION,D1_CLOSE_ADJCHRATE," "D2_CLOSE_ADJCHRATE,D5_CLOSE_ADJCHRATE,D10_CLOSE_ADJCHRATE,SECURITY_TYPE_CODE", "source": "WEB", "client": "WEB", "filter": f"(TRADE_DATE<='{end_date}')(TRADE_DATE>='{start_date}')", } r = requests.get(url, params=params) data_json = r.json() total_page_num = data_json["result"]["pages"] big_df = pd.DataFrame() tqdm = get_tqdm() for page in tqdm(range(1, total_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([big_df, temp_df], ignore_index=True) big_df.reset_index(inplace=True) big_df["index"] = big_df.index + 1 big_df.rename( columns={ "index": "序号", "SECURITY_CODE": "代码", "SECUCODE": "-", "SECURITY_NAME_ABBR": "名称", "TRADE_DATE": "上榜日", "EXPLAIN": "解读", "CLOSE_PRICE": "收盘价", "CHANGE_RATE": "涨跌幅", "BILLBOARD_NET_AMT": "龙虎榜净买额", "BILLBOARD_BUY_AMT": "龙虎榜买入额", "BILLBOARD_SELL_AMT": "龙虎榜卖出额", "BILLBOARD_DEAL_AMT": "龙虎榜成交额", "ACCUM_AMOUNT": "市场总成交额", "DEAL_NET_RATIO": "净买额占总成交比", "DEAL_AMOUNT_RATIO": "成交额占总成交比", "TURNOVERRATE": "换手率", "FREE_MARKET_CAP": "流通市值", "EXPLANATION": "上榜原因", "D1_CLOSE_ADJCHRATE": "上榜后1日", "D2_CLOSE_ADJCHRATE": "上榜后2日", "D5_CLOSE_ADJCHRATE": "上榜后5日", "D10_CLOSE_ADJCHRATE": "上榜后10日", }, inplace=True, ) big_df = big_df[ [ "序号", "代码", "名称", "上榜日", "解读", "收盘价", "涨跌幅", "龙虎榜净买额", "龙虎榜买入额", "龙虎榜卖出额", "龙虎榜成交额", "市场总成交额", "净买额占总成交比", "成交额占总成交比", "换手率", "流通市值", "上榜原因", "上榜后1日", "上榜后2日", "上榜后5日", "上榜后10日", ] ] big_df["上榜日"] = pd.to_datetime(big_df["上榜日"], errors="coerce").dt.date big_df["收盘价"] = pd.to_numeric(big_df["收盘价"], errors="coerce") big_df["涨跌幅"] = pd.to_numeric(big_df["涨跌幅"], errors="coerce") big_df["龙虎榜净买额"] = pd.to_numeric(big_df["龙虎榜净买额"], errors="coerce") big_df["龙虎榜买入额"] = pd.to_numeric(big_df["龙虎榜买入额"], errors="coerce") big_df["龙虎榜卖出额"] = pd.to_numeric(big_df["龙虎榜卖出额"], errors="coerce") big_df["龙虎榜成交额"] = pd.to_numeric(big_df["龙虎榜成交额"], errors="coerce") big_df["市场总成交额"] = pd.to_numeric(big_df["市场总成交额"], errors="coerce") big_df["净买额占总成交比"] = pd.to_numeric( big_df["净买额占总成交比"], errors="coerce" ) big_df["成交额占总成交比"] = pd.to_numeric( big_df["成交额占总成交比"], errors="coerce" ) big_df["换手率"] = pd.to_numeric(big_df["换手率"], errors="coerce") big_df["流通市值"] = pd.to_numeric(big_df["流通市值"], errors="coerce") big_df["上榜后1日"] = pd.to_numeric(big_df["上榜后1日"], errors="coerce") big_df["上榜后2日"] = pd.to_numeric(big_df["上榜后2日"], errors="coerce") big_df["上榜后5日"] = pd.to_numeric(big_df["上榜后5日"], errors="coerce") big_df["上榜后10日"] = pd.to_numeric(big_df["上榜后10日"], errors="coerce") return big_df def stock_lhb_stock_statistic_em(symbol: str = "近一月") -> pd.DataFrame: """ 东方财富网-数据中心-龙虎榜单-个股上榜统计 https://data.eastmoney.com/stock/tradedetail.html :param symbol: choice of {"近一月", "近三月", "近六月", "近一年"} :type symbol: str :return: 个股上榜统计 :rtype: pandas.DataFrame """ symbol_map = { "近一月": "01", "近三月": "02", "近六月": "03", "近一年": "04", } url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "sortColumns": "BILLBOARD_TIMES,LATEST_TDATE,SECURITY_CODE", "sortTypes": "-1,-1,1", "pageSize": "5000", "pageNumber": "1", "reportName": "RPT_BILLBOARD_TRADEALL", "columns": "ALL", "source": "WEB", "client": "WEB", "filter": f'(STATISTICS_CYCLE="{symbol_map[symbol]}")', } 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", "-", "代码", "最近上榜日", "名称", "近1个月涨跌幅", "近3个月涨跌幅", "近6个月涨跌幅", "近1年涨跌幅", "涨跌幅", "收盘价", "-", "龙虎榜总成交额", "龙虎榜净买额", "-", "-", "机构买入净额", "上榜次数", "龙虎榜买入额", "龙虎榜卖出额", "机构买入总额", "机构卖出总额", "买方机构次数", "卖方机构次数", "-", ] temp_df = temp_df[ [ "序号", "代码", "名称", "最近上榜日", "收盘价", "涨跌幅", "上榜次数", "龙虎榜净买额", "龙虎榜买入额", "龙虎榜卖出额", "龙虎榜总成交额", "买方机构次数", "卖方机构次数", "机构买入净额", "机构买入总额", "机构卖出总额", "近1个月涨跌幅", "近3个月涨跌幅", "近6个月涨跌幅", "近1年涨跌幅", ] ] temp_df["最近上榜日"] = pd.to_datetime( temp_df["最近上榜日"], errors="coerce" ).dt.date return temp_df def stock_lhb_jgmmtj_em( start_date: str = "20240417", end_date: str = "20240430" ) -> pd.DataFrame: """ 东方财富网-数据中心-龙虎榜单-机构买卖每日统计 https://data.eastmoney.com/stock/jgmmtj.html :param start_date: 开始日期 :type start_date: str :param end_date: 结束日期 :type end_date: str :return: 机构买卖每日统计 :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:]]) url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "sortColumns": "NET_BUY_AMT,TRADE_DATE,SECURITY_CODE", "sortTypes": "-1,-1,1", "pageSize": "500", "pageNumber": "1", "reportName": "RPT_ORGANIZATION_TRADE_DETAILS", "columns": "ALL", "source": "WEB", "client": "WEB", "filter": f"(TRADE_DATE>='{start_date}')(TRADE_DATE<='{end_date}')", } 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, 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.reset_index(inplace=True) big_df["index"] = big_df.index + 1 big_df.columns = [ "序号", "-", "名称", "代码", "上榜日期", "收盘价", "涨跌幅", "买方机构数", "卖方机构数", "机构买入总额", "机构卖出总额", "机构买入净额", "市场总成交额", "机构净买额占总成交额比", "换手率", "流通市值", "上榜原因", "-", "-", "-", "-", "-", "-", "-", "-", "-", ] big_df = big_df[ [ "序号", "代码", "名称", "收盘价", "涨跌幅", "买方机构数", "卖方机构数", "机构买入总额", "机构卖出总额", "机构买入净额", "市场总成交额", "机构净买额占总成交额比", "换手率", "流通市值", "上榜原因", "上榜日期", ] ] big_df["上榜日期"] = pd.to_datetime(big_df["上榜日期"], errors="coerce").dt.date big_df["收盘价"] = pd.to_numeric(big_df["收盘价"], errors="coerce") big_df["涨跌幅"] = pd.to_numeric(big_df["涨跌幅"], errors="coerce") big_df["买方机构数"] = pd.to_numeric(big_df["买方机构数"], errors="coerce") big_df["卖方机构数"] = pd.to_numeric(big_df["卖方机构数"], errors="coerce") big_df["机构买入总额"] = pd.to_numeric(big_df["机构买入总额"], errors="coerce") big_df["机构卖出总额"] = pd.to_numeric(big_df["机构卖出总额"], errors="coerce") big_df["机构买入净额"] = pd.to_numeric(big_df["机构买入净额"], errors="coerce") big_df["市场总成交额"] = pd.to_numeric(big_df["市场总成交额"], errors="coerce") big_df["机构净买额占总成交额比"] = pd.to_numeric( big_df["机构净买额占总成交额比"], errors="coerce" ) big_df["换手率"] = pd.to_numeric(big_df["换手率"], errors="coerce") big_df["流通市值"] = pd.to_numeric(big_df["流通市值"], errors="coerce") return big_df def stock_lhb_jgstatistic_em(symbol: str = "近一月") -> pd.DataFrame: """ 东方财富网-数据中心-龙虎榜单-机构席位追踪 https://data.eastmoney.com/stock/jgstatistic.html :param symbol: choice of {"近一月", "近三月", "近六月", "近一年"} :type symbol: str :return: 机构席位追踪 :rtype: pandas.DataFrame """ symbol_map = { "近一月": "01", "近三月": "02", "近六月": "03", "近一年": "04", } url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "sortColumns": "ONLIST_TIMES,SECURITY_CODE", "sortTypes": "-1,1", "pageSize": "5000", "pageNumber": "1", "reportName": "RPT_ORGANIZATION_SEATNEW", "columns": "ALL", "source": "WEB", "client": "WEB", "filter": f'(STATISTICSCYCLE="{symbol_map[symbol]}")', } 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, 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.reset_index(inplace=True) big_df["index"] = big_df.index + 1 big_df.rename( columns={ "index": "序号", "SECURITY_CODE": "代码", "SECURITY_NAME_ABBR": "名称", "CLOSE_PRICE": "收盘价", "CHANGE_RATE": "涨跌幅", "AMOUNT": "龙虎榜成交金额", "ONLIST_TIMES": "上榜次数", "BUY_AMT": "机构买入额", "BUY_TIMES": "机构买入次数", "SELL_AMT": "机构卖出额", "SELL_TIMES": "机构卖出次数", "NET_BUY_AMT": "机构净买额", "M1_CLOSE_ADJCHRATE": "近1个月涨跌幅", "M3_CLOSE_ADJCHRATE": "近3个月涨跌幅", "M6_CLOSE_ADJCHRATE": "近6个月涨跌幅", "Y1_CLOSE_ADJCHRATE": "近1年涨跌幅", }, inplace=True, ) big_df = big_df[ [ "序号", "代码", "名称", "收盘价", "涨跌幅", "龙虎榜成交金额", "上榜次数", "机构买入额", "机构买入次数", "机构卖出额", "机构卖出次数", "机构净买额", "近1个月涨跌幅", "近3个月涨跌幅", "近6个月涨跌幅", "近1年涨跌幅", ] ] big_df["收盘价"] = pd.to_numeric(big_df["收盘价"], errors="coerce") big_df["涨跌幅"] = pd.to_numeric(big_df["涨跌幅"], errors="coerce") big_df["龙虎榜成交金额"] = pd.to_numeric(big_df["龙虎榜成交金额"], errors="coerce") big_df["上榜次数"] = pd.to_numeric(big_df["上榜次数"], errors="coerce") big_df["机构买入额"] = pd.to_numeric(big_df["机构买入额"], errors="coerce") big_df["机构买入次数"] = pd.to_numeric(big_df["机构买入次数"], errors="coerce") big_df["机构卖出额"] = pd.to_numeric(big_df["机构卖出额"], errors="coerce") big_df["机构卖出次数"] = pd.to_numeric(big_df["机构卖出次数"], errors="coerce") big_df["机构净买额"] = pd.to_numeric(big_df["机构净买额"], errors="coerce") big_df["近1个月涨跌幅"] = pd.to_numeric(big_df["近1个月涨跌幅"], errors="coerce") big_df["近3个月涨跌幅"] = pd.to_numeric(big_df["近3个月涨跌幅"], errors="coerce") big_df["近6个月涨跌幅"] = pd.to_numeric(big_df["近6个月涨跌幅"], errors="coerce") big_df["近1年涨跌幅"] = pd.to_numeric(big_df["近1年涨跌幅"], errors="coerce") return big_df def stock_lhb_hyyyb_em( start_date: str = "20220324", end_date: str = "20220324" ) -> pd.DataFrame: """ 东方财富网-数据中心-龙虎榜单-每日活跃营业部 https://data.eastmoney.com/stock/hyyyb.html :param start_date: 开始日期 :type start_date: str :param end_date: 结束日期 :type end_date: str :return: 每日活跃营业部 :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:]]) url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "sortColumns": "TOTAL_NETAMT,ONLIST_DATE,OPERATEDEPT_CODE", "sortTypes": "-1,-1,1", "pageSize": "5000", "pageNumber": "1", "reportName": "RPT_OPERATEDEPT_ACTIVE", "columns": "ALL", "source": "WEB", "client": "WEB", "filter": f"(ONLIST_DATE>='{start_date}')(ONLIST_DATE<='{end_date}')", } 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, 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.reset_index(inplace=True) big_df["index"] = big_df.index + 1 big_df.columns = [ "序号", "营业部名称", "上榜日", "买入个股数", "卖出个股数", "买入总金额", "卖出总金额", "总买卖净额", "-", "营业部代码", "买入股票", "-", "-", ] big_df = big_df[ [ "序号", "营业部名称", "上榜日", "买入个股数", "卖出个股数", "买入总金额", "卖出总金额", "总买卖净额", "买入股票", "营业部代码", ] ] big_df["上榜日"] = pd.to_datetime(big_df["上榜日"], errors="coerce").dt.date big_df["买入个股数"] = pd.to_numeric(big_df["买入个股数"], errors="coerce") big_df["卖出个股数"] = pd.to_numeric(big_df["卖出个股数"], errors="coerce") big_df["买入总金额"] = pd.to_numeric(big_df["买入总金额"], errors="coerce") big_df["卖出总金额"] = pd.to_numeric(big_df["卖出总金额"], errors="coerce") big_df["总买卖净额"] = pd.to_numeric(big_df["总买卖净额"], errors="coerce") return big_df def stock_lhb_yybph_em(symbol: str = "近一月") -> pd.DataFrame: """ 东方财富网-数据中心-龙虎榜单-营业部排行 https://data.eastmoney.com/stock/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 """ symbol_map = { "近一月": "01", "近三月": "02", "近六月": "03", "近一年": "04", } url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "sortColumns": "TOTAL_BUYER_SALESTIMES_1DAY,OPERATEDEPT_CODE", "sortTypes": "-1,1", "pageSize": "5000", "pageNumber": "1", "reportName": "RPT_RATEDEPT_RETURNT_RANKING", "columns": "ALL", "source": "WEB", "client": "WEB", "filter": f'(STATISTICSCYCLE="{symbol_map[symbol]}")', } 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, 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.reset_index(inplace=True) big_df["index"] = big_df.index + 1 big_df.rename( columns={ "index": "序号", "OPERATEDEPT_NAME": "营业部名称", "TOTAL_BUYER_SALESTIMES_1DAY": "上榜后1天-买入次数", "AVERAGE_INCREASE_1DAY": "上榜后1天-平均涨幅", "RISE_PROBABILITY_1DAY": "上榜后1天-上涨概率", "TOTAL_BUYER_SALESTIMES_2DAY": "上榜后2天-买入次数", "AVERAGE_INCREASE_2DAY": "上榜后2天-平均涨幅", "RISE_PROBABILITY_2DAY": "上榜后2天-上涨概率", "TOTAL_BUYER_SALESTIMES_3DAY": "上榜后3天-买入次数", "AVERAGE_INCREASE_3DAY": "上榜后3天-平均涨幅", "RISE_PROBABILITY_3DAY": "上榜后3天-上涨概率", "TOTAL_BUYER_SALESTIMES_5DAY": "上榜后5天-买入次数", "AVERAGE_INCREASE_5DAY": "上榜后5天-平均涨幅", "RISE_PROBABILITY_5DAY": "上榜后5天-上涨概率", "TOTAL_BUYER_SALESTIMES_10DAY": "上榜后10天-买入次数", "AVERAGE_INCREASE_10DAY": "上榜后10天-平均涨幅", "RISE_PROBABILITY_10DAY": "上榜后10天-上涨概率", }, inplace=True, ) 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2天-买入次数", "上榜后2天-平均涨幅", "上榜后2天-上涨概率", "上榜后3天-买入次数", "上榜后3天-平均涨幅", "上榜后3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", ] ] 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2天-买入次数"] = pd.to_numeric( big_df["上榜后2天-买入次数"], errors="coerce" ) big_df["上榜后2天-平均涨幅"] = pd.to_numeric( big_df["上榜后2天-平均涨幅"], errors="coerce" ) big_df["上榜后2天-上涨概率"] = pd.to_numeric( big_df["上榜后2天-上涨概率"], errors="coerce" ) big_df["上榜后3天-买入次数"] = pd.to_numeric( big_df["上榜后3天-买入次数"], errors="coerce" ) big_df["上榜后3天-平均涨幅"] = pd.to_numeric( big_df["上榜后3天-平均涨幅"], errors="coerce" ) big_df["上榜后3天-上涨概率"] = pd.to_numeric( big_df["上榜后3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" ) return big_df def stock_lhb_traderstatistic_em(symbol: str = "近一月") -> pd.DataFrame: """ 东方财富网-数据中心-龙虎榜单-营业部统计 https://data.eastmoney.com/stock/traderstatistic.html :param symbol: choice of {"近一月", "近三月", "近六月", "近一年"} :type symbol: str :return: 营业部统计 :rtype: pandas.DataFrame """ symbol_map = { "近一月": "01", "近三月": "02", "近六月": "03", "近一年": "04", } url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "sortColumns": "AMOUNT,OPERATEDEPT_CODE", "sortTypes": "-1,1", "pageSize": "5000", "pageNumber": "1", "reportName": "RPT_OPERATEDEPT_LIST_STATISTICS", "columns": "ALL", "source": "WEB", "client": "WEB", "filter": f'(STATISTICSCYCLE="{symbol_map[symbol]}")', } 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, 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.reset_index(inplace=True) big_df["index"] = big_df.index + 1 big_df.rename( columns={ "index": "序号", "OPERATEDEPT_NAME": "营业部名称", "AMOUNT": "龙虎榜成交金额", "SALES_ONLIST_TIMES": "上榜次数", "ACT_BUY": "买入额", "TOTAL_BUYER_SALESTIMES": "买入次数", "ACT_SELL": "卖出额", "TOTAL_SELLER_SALESTIMES": "卖出次数", }, inplace=True, ) big_df = big_df[ [ "序号", "营业部名称", "龙虎榜成交金额", "上榜次数", "买入额", "买入次数", "卖出额", "卖出次数", ] ] big_df["龙虎榜成交金额"] = pd.to_numeric(big_df["龙虎榜成交金额"], errors="coerce") big_df["上榜次数"] = pd.to_numeric(big_df["上榜次数"], errors="coerce") big_df["买入额"] = pd.to_numeric(big_df["买入额"], errors="coerce") big_df["买入次数"] = pd.to_numeric(big_df["买入次数"], errors="coerce") big_df["卖出额"] = pd.to_numeric(big_df["卖出额"], errors="coerce") big_df["卖出次数"] = pd.to_numeric(big_df["卖出次数"], errors="coerce") return big_df def stock_lhb_stock_detail_date_em(symbol: str = "600077") -> pd.DataFrame: """ 东方财富网-数据中心-龙虎榜单-个股龙虎榜详情-日期 https://data.eastmoney.com/stock/tradedetail.html :param symbol: 股票代码 :type symbol: str :return: 个股龙虎榜详情-日期 :rtype: pandas.DataFrame """ url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "reportName": "RPT_LHB_BOARDDATE", "columns": "SECURITY_CODE,TRADE_DATE,TR_DATE", "filter": f'(SECURITY_CODE="{symbol}")', "pageNumber": "1", "pageSize": "1000", "sortTypes": "-1", "sortColumns": "TRADE_DATE", "source": "WEB", "client": "WEB", } 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", "股票代码", "交易日", "-", ] temp_df = temp_df[ [ "序号", "股票代码", "交易日", ] ] temp_df["交易日"] = pd.to_datetime(temp_df["交易日"]).dt.date return temp_df def stock_lhb_stock_detail_em( symbol: str = "000788", date: str = "20220315", flag: str = "卖出" ) -> pd.DataFrame: """ 东方财富网-数据中心-龙虎榜单-个股龙虎榜详情 https://data.eastmoney.com/stock/lhb/600077.html :param symbol: 股票代码 :type symbol: str :param date: 查询日期; 需要通过 ak.stock_lhb_stock_detail_date_em(symbol="600077") 接口获取相应股票的有龙虎榜详情数据的日期 :type date: str :param flag: choice of {"买入", "卖出"} :type flag: str :return: 个股龙虎榜详情 :rtype: pandas.DataFrame """ flag_map = { "买入": "BUY", "卖出": "SELL", } report_map = { "买入": "RPT_BILLBOARD_DAILYDETAILSBUY", "卖出": "RPT_BILLBOARD_DAILYDETAILSSELL", } url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "reportName": report_map[flag], "columns": "ALL", "filter": f"""(TRADE_DATE='{"-".join([date[:4], date[4:6], date[6:]])}')(SECURITY_CODE="{symbol}")""", "pageNumber": "1", "pageSize": "500", "sortTypes": "-1", "sortColumns": flag_map[flag], "source": "WEB", "client": "WEB", } 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 if flag == "买入": temp_df.columns = [ "序号", "-", "-", "-", "-", "交易营业部名称", "类型", "-", "-", "-", "-", "买入金额", "卖出金额", "净额", "-", "-", "-", "-", "买入金额-占总成交比例", "卖出金额-占总成交比例", "-", ] temp_df = temp_df[ [ "序号", "交易营业部名称", "买入金额", "买入金额-占总成交比例", "卖出金额", "卖出金额-占总成交比例", "净额", "类型", ] ] temp_df["买入金额"] = pd.to_numeric(temp_df["买入金额"], errors="coerce") temp_df["买入金额-占总成交比例"] = pd.to_numeric( temp_df["买入金额-占总成交比例"], errors="coerce" ) temp_df["卖出金额"] = pd.to_numeric(temp_df["卖出金额"], errors="coerce") temp_df["卖出金额-占总成交比例"] = pd.to_numeric( temp_df["卖出金额-占总成交比例"], errors="coerce" ) temp_df.sort_values("类型", inplace=True, ignore_index=True) temp_df.reset_index(inplace=True, drop=True) temp_df["序号"] = range(1, len(temp_df["序号"]) + 1) else: temp_df.columns = [ "序号", "-", "-", "-", "-", "交易营业部名称", "类型", "-", "-", "-", "-", "买入金额", "卖出金额", "净额", "-", "-", "-", "-", "买入金额-占总成交比例", "卖出金额-占总成交比例", "-", ] temp_df = temp_df[ [ "序号", "交易营业部名称", "买入金额", "买入金额-占总成交比例", "卖出金额", "卖出金额-占总成交比例", "净额", "类型", ] ] temp_df["买入金额"] = pd.to_numeric(temp_df["买入金额"], errors="coerce") temp_df["买入金额-占总成交比例"] = pd.to_numeric( temp_df["买入金额-占总成交比例"], errors="coerce" ) temp_df["卖出金额"] = pd.to_numeric(temp_df["卖出金额"], errors="coerce") temp_df["卖出金额-占总成交比例"] = pd.to_numeric( temp_df["卖出金额-占总成交比例"], errors="coerce" ) temp_df.sort_values("类型", inplace=True, ignore_index=True) temp_df.reset_index(inplace=True, drop=True) temp_df["序号"] = range(1, len(temp_df["序号"]) + 1) return temp_df def stock_lhb_yyb_detail_em(symbol: str = "10188715") -> pd.DataFrame: """ 东方财富网-数据中心-龙虎榜单-营业部历史交易明细-营业部交易明细 https://data.eastmoney.com/stock/lhb/yyb/10188715.html :param symbol: 营业部代码, 如 "10188715", 通过 ak.stock_lhb_hyyyb_em() 接口获取 :type symbol: str :return: 营业部交易明细数据 :rtype: pandas.DataFrame """ url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "sortColumns": "TRADE_DATE,SECURITY_CODE", "sortTypes": "-1,1", "pageSize": "100", "pageNumber": "1", "reportName": "RPT_OPERATEDEPT_TRADE_DETAILSNEW", "columns": "ALL", "source": "WEB", "client": "WEB", "filter": f'(OPERATEDEPT_CODE="{symbol}")', } 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, 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) # 检查DataFrame是否为空 if big_df.empty: return pd.DataFrame() # 确保列名与实际返回的JSON数据结构一致 column_map = { "OPERATEDEPT_CODE": "营业部代码", "OPERATEDEPT_NAME": "营业部名称", "TRADE_DATE": "交易日期", "D1_CLOSE_ADJCHRATE": "1日后涨跌幅", "D2_CLOSE_ADJCHRATE": "2日后涨跌幅", "D3_CLOSE_ADJCHRATE": "3日后涨跌幅", "D5_CLOSE_ADJCHRATE": "5日后涨跌幅", "D10_CLOSE_ADJCHRATE": "10日后涨跌幅", "SECURITY_CODE": "股票代码", "SECURITY_NAME_ABBR": "股票名称", "ACT_BUY": "买入金额", "ACT_SELL": "卖出金额", "NET_AMT": "净额", "EXPLANATION": "上榜原因", "D20_CLOSE_ADJCHRATE": "20日后涨跌幅", "D30_CLOSE_ADJCHRATE": "30日后涨跌幅", "SECUCODE": "证券代码", "OPERATEDEPT_CODE_OLD": "营业部旧代码", "ORG_NAME_ABBR": "营业部简称", "CHANGE_RATE": "涨跌幅", } # 重命名列 big_df.rename(columns=column_map, inplace=True) # 添加序号列 big_df.reset_index(inplace=True) big_df["序号"] = big_df.index + 1 # 选择需要的列并排序 result_columns = [ "序号", "营业部代码", "营业部名称", "营业部简称", "交易日期", "股票代码", "股票名称", "涨跌幅", "买入金额", "卖出金额", "净额", "上榜原因", "1日后涨跌幅", "2日后涨跌幅", "3日后涨跌幅", "5日后涨跌幅", "10日后涨跌幅", "20日后涨跌幅", "30日后涨跌幅", ] # 确保所有列都存在 for col in result_columns: if col not in big_df.columns and col != "序号": big_df[col] = None big_df = big_df[result_columns] # 处理日期格式 big_df["交易日期"] = pd.to_datetime(big_df["交易日期"], errors="coerce").dt.date # 处理数值列 numeric_cols = [ "涨跌幅", "买入金额", "卖出金额", "净额", "1日后涨跌幅", "2日后涨跌幅", "3日后涨跌幅", "5日后涨跌幅", "10日后涨跌幅", "20日后涨跌幅", "30日后涨跌幅", ] for col in numeric_cols: big_df[col] = pd.to_numeric(big_df[col], errors="coerce") return big_df if __name__ == "__main__": stock_lhb_detail_em_df = stock_lhb_detail_em( start_date="20250201", end_date="20250228" ) print(stock_lhb_detail_em_df) stock_lhb_stock_statistic_em_df = stock_lhb_stock_statistic_em(symbol="近一月") print(stock_lhb_stock_statistic_em_df) stock_lhb_stock_statistic_em_df = stock_lhb_stock_statistic_em(symbol="近三月") print(stock_lhb_stock_statistic_em_df) stock_lhb_stock_statistic_em_df = stock_lhb_stock_statistic_em(symbol="近六月") print(stock_lhb_stock_statistic_em_df) stock_lhb_stock_statistic_em_df = stock_lhb_stock_statistic_em(symbol="近一年") print(stock_lhb_stock_statistic_em_df) stock_lhb_jgmmtj_em_df = stock_lhb_jgmmtj_em( start_date="20240417", end_date="20240430" ) print(stock_lhb_jgmmtj_em_df) stock_lhb_jgstatistic_em_df = stock_lhb_jgstatistic_em(symbol="近一月") print(stock_lhb_jgstatistic_em_df) stock_lhb_hyyyb_em_df = stock_lhb_hyyyb_em( start_date="20240401", end_date="20240430" ) print(stock_lhb_hyyyb_em_df) stock_lhb_yybph_em_df = stock_lhb_yybph_em(symbol="近一月") print(stock_lhb_yybph_em_df) stock_lhb_traderstatistic_em_df = stock_lhb_traderstatistic_em(symbol="近一月") print(stock_lhb_traderstatistic_em_df) stock_lhb_stock_detail_date_em_df = stock_lhb_stock_detail_date_em(symbol="002901") print(stock_lhb_stock_detail_date_em_df) stock_lhb_stock_detail_em_df = stock_lhb_stock_detail_em( symbol="002901", date="20221012", flag="买入" ) print(stock_lhb_stock_detail_em_df) stock_lhb_stock_detail_em_df = stock_lhb_stock_detail_em( symbol="600077", date="20070416", flag="买入" ) print(stock_lhb_stock_detail_em_df) stock_lhb_yyb_detail_em_df = stock_lhb_yyb_detail_em(symbol="10188715") print(stock_lhb_yyb_detail_em_df)