claudios/dypybench_functions · Datasets at Hugging Face

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock_feature/stock_yjyg_em.py

stock_yjkb_em

(date: str = "20211231")

return big_df

东方财富-数据中心-年报季报-业绩快报 https://data.eastmoney.com/bbsj/202003/yjkb.html :param date: "20200331", "20200630", "20200930", "20201231"; 从 20100331 开始 :type date: str :return: 业绩快报 :rtype: pandas.DataFrame

16

116

def stock_yjkb_em(date: str = "20211231") -> pd.DataFrame: """ 东方财富-数据中心-年报季报-业绩快报 https://data.eastmoney.com/bbsj/202003/yjkb.html :param date: "20200331", "20200630", "20200930", "20201231"; 从 20100331 开始 :type date: str :return: 业绩快报 :rtype: pandas.DataFrame """ url = "https://datacenter.eastmoney.com/securities/api/data/v1/get" params = { "sortColumns": "UPDATE_DATE,SECURITY_CODE", "sortTypes": "-1,-1", "pageSize": "500", "pageNumber": "1", "reportName": "RPT_FCI_PERFORMANCEE", "columns": "ALL", "filter": f"""(SECURITY_TYPE_CODE in ("058001001","058001008"))(TRADE_MARKET_CODE!="069001017")(REPORT_DATE='{'-'.join([date[:4], date[4:6], date[6:]])}')""", } r = requests.get(url, params=params) data_json = r.json() big_df = pd.DataFrame() total_page = data_json["result"]["pages"] 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([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", "股票代码", "股票简称", "市场板块", "_", "证券类型", "_", "公告日期", "_", "每股收益", "营业收入-营业收入", "营业收入-去年同期", "净利润-净利润", "净利润-去年同期", "每股净资产", "净资产收益率", "营业收入-同比增长", "净利润-同比增长", "营业收入-季度环比增长", "净利润-季度环比增长", "所处行业", "_", "_", "_", "_", "_", "_", "_", "_", ] big_df = big_df[ [ "序号", "股票代码", "股票简称", "每股收益", "营业收入-营业收入", "营业收入-去年同期", "营业收入-同比增长", "营业收入-季度环比增长", "净利润-净利润", "净利润-去年同期", "净利润-同比增长", "净利润-季度环比增长", "每股净资产", "净资产收益率", "所处行业", "公告日期", ] ] big_df["每股收益"] = pd.to_numeric(big_df["每股收益"], errors="coerce") big_df["营业收入-营业收入"] = pd.to_numeric(big_df["营业收入-营业收入"], errors="coerce") big_df["营业收入-去年同期"] = pd.to_numeric(big_df["营业收入-去年同期"], errors="coerce") big_df["营业收入-同比增长"] = pd.to_numeric(big_df["营业收入-同比增长"], errors="coerce") big_df["营业收入-季度环比增长"] = pd.to_numeric(big_df["营业收入-季度环比增长"], errors="coerce") big_df["净利润-净利润"] = pd.to_numeric(big_df["净利润-净利润"], errors="coerce") big_df["净利润-去年同期"] = pd.to_numeric(big_df["净利润-去年同期"], errors="coerce") big_df["净利润-同比增长"] = pd.to_numeric(big_df["净利润-同比增长"], errors="coerce") big_df["净利润-季度环比增长"] = pd.to_numeric(big_df["净利润-季度环比增长"], errors="coerce") big_df["每股净资产"] = pd.to_numeric(big_df["每股净资产"], errors="coerce") big_df["净资产收益率"] = pd.to_numeric(big_df["净资产收益率"], errors="coerce") big_df["净资产收益率"] = pd.to_numeric(big_df["净资产收益率"], errors="coerce") big_df["公告日期"] = pd.to_datetime(big_df["公告日期"]).dt.date return big_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock_feature/stock_yjyg_em.py#L16-L116

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false

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def stock_yjkb_em(date: str = "20211231") -> pd.DataFrame: url = "https://datacenter.eastmoney.com/securities/api/data/v1/get" params = { "sortColumns": "UPDATE_DATE,SECURITY_CODE", "sortTypes": "-1,-1", "pageSize": "500", "pageNumber": "1", "reportName": "RPT_FCI_PERFORMANCEE", "columns": "ALL", "filter": f"""(SECURITY_TYPE_CODE in ("058001001","058001008"))(TRADE_MARKET_CODE!="069001017")(REPORT_DATE='{'-'.join([date[:4], date[4:6], date[6:]])}')""", } r = requests.get(url, params=params) data_json = r.json() big_df = pd.DataFrame() total_page = data_json["result"]["pages"] 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([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", "股票代码", "股票简称", "市场板块", "_", "证券类型", "_", "公告日期", "_", "每股收益", "营业收入-营业收入", "营业收入-去年同期", "净利润-净利润", "净利润-去年同期", "每股净资产", "净资产收益率", "营业收入-同比增长", "净利润-同比增长", "营业收入-季度环比增长", "净利润-季度环比增长", "所处行业", "_", "_", "_", "_", "_", "_", "_", "_", ] big_df = big_df[ [ "序号", "股票代码", "股票简称", "每股收益", "营业收入-营业收入", "营业收入-去年同期", "营业收入-同比增长", "营业收入-季度环比增长", "净利润-净利润", "净利润-去年同期", "净利润-同比增长", "净利润-季度环比增长", "每股净资产", "净资产收益率", "所处行业", "公告日期", ] ] big_df["每股收益"] = pd.to_numeric(big_df["每股收益"], errors="coerce") big_df["营业收入-营业收入"] = pd.to_numeric(big_df["营业收入-营业收入"], errors="coerce") big_df["营业收入-去年同期"] = pd.to_numeric(big_df["营业收入-去年同期"], errors="coerce") big_df["营业收入-同比增长"] = pd.to_numeric(big_df["营业收入-同比增长"], errors="coerce") big_df["营业收入-季度环比增长"] = pd.to_numeric(big_df["营业收入-季度环比增长"], errors="coerce") big_df["净利润-净利润"] = pd.to_numeric(big_df["净利润-净利润"], errors="coerce") big_df["净利润-去年同期"] = pd.to_numeric(big_df["净利润-去年同期"], errors="coerce") big_df["净利润-同比增长"] = pd.to_numeric(big_df["净利润-同比增长"], errors="coerce") big_df["净利润-季度环比增长"] = pd.to_numeric(big_df["净利润-季度环比增长"], errors="coerce") big_df["每股净资产"] = pd.to_numeric(big_df["每股净资产"], errors="coerce") big_df["净资产收益率"] = pd.to_numeric(big_df["净资产收益率"], errors="coerce") big_df["净资产收益率"] = pd.to_numeric(big_df["净资产收益率"], errors="coerce") big_df["公告日期"] = pd.to_datetime(big_df["公告日期"]).dt.date return big_df

17,934

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock_feature/stock_yjyg_em.py

stock_yjyg_em

(date: str = "20200331")

return big_df

东方财富-数据中心-年报季报-业绩预告 https://data.eastmoney.com/bbsj/202003/yjyg.html :param date: "2020-03-31", "2020-06-30", "2020-09-30", "2020-12-31"; 从 2008-12-31 开始 :type date: str :return: 业绩预告 :rtype: pandas.DataFrame

119

204

def stock_yjyg_em(date: str = "20200331") -> pd.DataFrame: """ 东方财富-数据中心-年报季报-业绩预告 https://data.eastmoney.com/bbsj/202003/yjyg.html :param date: "2020-03-31", "2020-06-30", "2020-09-30", "2020-12-31"; 从 2008-12-31 开始 :type date: str :return: 业绩预告 :rtype: pandas.DataFrame """ url = "https://datacenter.eastmoney.com/securities/api/data/v1/get" params = { "sortColumns": "NOTICE_DATE,SECURITY_CODE", "sortTypes": "-1,-1", "pageSize": "500", "pageNumber": "1", "reportName": "RPT_PUBLIC_OP_NEWPREDICT", "columns": "ALL", "filter": f" (REPORT_DATE='{'-'.join([date[:4], date[4:6], date[6:]])}')", } r = requests.get(url, params=params) data_json = r.json() big_df = pd.DataFrame() total_page = data_json["result"]["pages"] 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([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", "_", "股票代码", "股票简称", "_", "公告日期", "报告日期", "_", "预测指标", "_", "_", "_", "_", "业绩变动", "业绩变动原因", "预告类型", "上年同期值", "_", "_", "_", "_", "业绩变动幅度", "预测数值", "_", "_", "_", "_", "_", ] big_df = big_df[ [ "序号", "股票代码", "股票简称", "预测指标", "业绩变动", "预测数值", "业绩变动幅度", "业绩变动原因", "预告类型", "上年同期值", "公告日期", ] ] big_df["公告日期"] = pd.to_datetime(big_df["公告日期"]).dt.date big_df["业绩变动幅度"] = pd.to_numeric(big_df["业绩变动幅度"], errors="coerce") big_df["预测数值"] = pd.to_numeric(big_df["预测数值"], errors="coerce") big_df["上年同期值"] = pd.to_numeric(big_df["上年同期值"], errors="coerce") return big_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock_feature/stock_yjyg_em.py#L119-L204

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def stock_yjyg_em(date: str = "20200331") -> pd.DataFrame: url = "https://datacenter.eastmoney.com/securities/api/data/v1/get" params = { "sortColumns": "NOTICE_DATE,SECURITY_CODE", "sortTypes": "-1,-1", "pageSize": "500", "pageNumber": "1", "reportName": "RPT_PUBLIC_OP_NEWPREDICT", "columns": "ALL", "filter": f" (REPORT_DATE='{'-'.join([date[:4], date[4:6], date[6:]])}')", } r = requests.get(url, params=params) data_json = r.json() big_df = pd.DataFrame() total_page = data_json["result"]["pages"] 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([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", "_", "股票代码", "股票简称", "_", "公告日期", "报告日期", "_", "预测指标", "_", "_", "_", "_", "业绩变动", "业绩变动原因", "预告类型", "上年同期值", "_", "_", "_", "_", "业绩变动幅度", "预测数值", "_", "_", "_", "_", "_", ] big_df = big_df[ [ "序号", "股票代码", "股票简称", "预测指标", "业绩变动", "预测数值", "业绩变动幅度", "业绩变动原因", "预告类型", "上年同期值", "公告日期", ] ] big_df["公告日期"] = pd.to_datetime(big_df["公告日期"]).dt.date big_df["业绩变动幅度"] = pd.to_numeric(big_df["业绩变动幅度"], errors="coerce") big_df["预测数值"] = pd.to_numeric(big_df["预测数值"], errors="coerce") big_df["上年同期值"] = pd.to_numeric(big_df["上年同期值"], errors="coerce") return big_df

17,935

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock_feature/stock_yjyg_em.py

stock_yysj_em

(symbol: str = "沪深A股", date: str = "20200331") -> pd

return big_df

东方财富-数据中心-年报季报-预约披露时间 https://data.eastmoney.com/bbsj/202003/yysj.html :param symbol: choice of {'沪深A股', '沪市A股', '科创板', '深市A股', '创业板', '京市A股', 'ST板'} :type symbol: str :param date: "20190331", "20190630", "20190930", "20191231"; 从 20081231 开始 :type date: str :return: 指定时间的上市公司预约披露时间数据 :rtype: pandas.DataFrame

207

322

def stock_yysj_em(symbol: str = "沪深A股", date: str = "20200331") -> pd.DataFrame: """ 东方财富-数据中心-年报季报-预约披露时间 https://data.eastmoney.com/bbsj/202003/yysj.html :param symbol: choice of {'沪深A股', '沪市A股', '科创板', '深市A股', '创业板', '京市A股', 'ST板'} :type symbol: str :param date: "20190331", "20190630", "20190930", "20191231"; 从 20081231 开始 :type date: str :return: 指定时间的上市公司预约披露时间数据 :rtype: pandas.DataFrame """ url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "sortColumns": "FIRST_APPOINT_DATE,SECURITY_CODE", "sortTypes": "1,1", "pageSize": "500", "pageNumber": "1", "reportName": "RPT_PUBLIC_BS_APPOIN", "columns": "ALL", "filter": f"""(SECURITY_TYPE_CODE in ("058001001","058001008"))(TRADE_MARKET_CODE!="069001017")(REPORT_DATE='{'-'.join([date[:4], date[4:6], date[6:]])}')""", } if symbol == "沪市A股": params.update( { "filter": f"""(SECURITY_TYPE_CODE in ("058001001","058001008"))(TRADE_MARKET_CODE in ("069001001001","069001001003","069001001006"))(REPORT_DATE='{'-'.join([date[:4], date[4:6], date[6:]])}')""" } ) elif symbol == "科创板": params.update( { "filter": f"""(SECURITY_TYPE_CODE in ("058001001","058001008"))(TRADE_MARKET_CODE="069001001006")(REPORT_DATE='{'-'.join([date[:4], date[4:6], date[6:]])}')""" } ) elif symbol == "深市A股": params.update( { "filter": f"""(SECURITY_TYPE_CODE="058001001")(TRADE_MARKET_CODE in ("069001002001","069001002002","069001002003","069001002005"))(REPORT_DATE='{'-'.join([date[:4], date[4:6], date[6:]])}')""" } ) elif symbol == "创业板": params.update( { "filter": f"""(SECURITY_TYPE_CODE="058001001")(TRADE_MARKET_CODE="069001002002")(REPORT_DATE='{'-'.join([date[:4], date[4:6], date[6:]])}')""" } ) elif symbol == "京市A股": params.update( { "filter": f"""(TRADE_MARKET_CODE="069001017")(REPORT_DATE='{'-'.join([date[:4], date[4:6], date[6:]])}')""" } ) elif symbol == "ST板": params.update( { "filter": f"""(TRADE_MARKET_CODE in("069001001003","069001002005"))(REPORT_DATE='{'-'.join([date[:4], date[4:6], date[6:]])}')""" } ) r = requests.get(url, params=params) data_json = r.json() total_page = data_json["result"]["pages"] big_df = pd.DataFrame() 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([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", "股票代码", "股票简称", "_", "_", "首次预约时间", "一次变更日期", "二次变更日期", "三次变更日期", "实际披露时间", "_", "_", "_", "_", "_", "_", "_", "_", "_", "_", "_", "_", ] big_df = big_df[ [ "序号", "股票代码", "股票简称", "首次预约时间", "一次变更日期", "二次变更日期", "三次变更日期", "实际披露时间", ] ] big_df["首次预约时间"] = pd.to_datetime(big_df["首次预约时间"]).dt.date big_df["一次变更日期"] = pd.to_datetime(big_df["一次变更日期"]).dt.date big_df["二次变更日期"] = pd.to_datetime(big_df["二次变更日期"]).dt.date big_df["三次变更日期"] = pd.to_datetime(big_df["三次变更日期"]).dt.date big_df["实际披露时间"] = pd.to_datetime(big_df["实际披露时间"]).dt.date return big_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock_feature/stock_yjyg_em.py#L207-L322

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70.689655

8

def stock_yysj_em(symbol: str = "沪深A股", date: str = "20200331") -> pd.DataFrame: url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "sortColumns": "FIRST_APPOINT_DATE,SECURITY_CODE", "sortTypes": "1,1", "pageSize": "500", "pageNumber": "1", "reportName": "RPT_PUBLIC_BS_APPOIN", "columns": "ALL", "filter": f"""(SECURITY_TYPE_CODE in ("058001001","058001008"))(TRADE_MARKET_CODE!="069001017")(REPORT_DATE='{'-'.join([date[:4], date[4:6], date[6:]])}')""", } if symbol == "沪市A股": params.update( { "filter": f"""(SECURITY_TYPE_CODE in ("058001001","058001008"))(TRADE_MARKET_CODE in ("069001001001","069001001003","069001001006"))(REPORT_DATE='{'-'.join([date[:4], date[4:6], date[6:]])}')""" } ) elif symbol == "科创板": params.update( { "filter": f"""(SECURITY_TYPE_CODE in ("058001001","058001008"))(TRADE_MARKET_CODE="069001001006")(REPORT_DATE='{'-'.join([date[:4], date[4:6], date[6:]])}')""" } ) elif symbol == "深市A股": params.update( { "filter": f"""(SECURITY_TYPE_CODE="058001001")(TRADE_MARKET_CODE in ("069001002001","069001002002","069001002003","069001002005"))(REPORT_DATE='{'-'.join([date[:4], date[4:6], date[6:]])}')""" } ) elif symbol == "创业板": params.update( { "filter": f"""(SECURITY_TYPE_CODE="058001001")(TRADE_MARKET_CODE="069001002002")(REPORT_DATE='{'-'.join([date[:4], date[4:6], date[6:]])}')""" } ) elif symbol == "京市A股": params.update( { "filter": f"""(TRADE_MARKET_CODE="069001017")(REPORT_DATE='{'-'.join([date[:4], date[4:6], date[6:]])}')""" } ) elif symbol == "ST板": params.update( { "filter": f"""(TRADE_MARKET_CODE in("069001001003","069001002005"))(REPORT_DATE='{'-'.join([date[:4], date[4:6], date[6:]])}')""" } ) r = requests.get(url, params=params) data_json = r.json() total_page = data_json["result"]["pages"] big_df = pd.DataFrame() 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([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", "股票代码", "股票简称", "_", "_", "首次预约时间", "一次变更日期", "二次变更日期", "三次变更日期", "实际披露时间", "_", "_", "_", "_", "_", "_", "_", "_", "_", "_", "_", "_", ] big_df = big_df[ [ "序号", "股票代码", "股票简称", "首次预约时间", "一次变更日期", "二次变更日期", "三次变更日期", "实际披露时间", ] ] big_df["首次预约时间"] = pd.to_datetime(big_df["首次预约时间"]).dt.date big_df["一次变更日期"] = pd.to_datetime(big_df["一次变更日期"]).dt.date big_df["二次变更日期"] = pd.to_datetime(big_df["二次变更日期"]).dt.date big_df["三次变更日期"] = pd.to_datetime(big_df["三次变更日期"]).dt.date big_df["实际披露时间"] = pd.to_datetime(big_df["实际披露时间"]).dt.date return big_df

17,936

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock_feature/stock_jgdy_em.py

stock_jgdy_tj_em

(date: str = "20220101")

return big_df

东方财富网-数据中心-特色数据-机构调研-机构调研统计 http://data.eastmoney.com/jgdy/tj.html :param date: 开始时间 :type date: str :return: 机构调研统计 :rtype: pandas.DataFrame

15

104

def stock_jgdy_tj_em(date: str = "20220101") -> pd.DataFrame: """ 东方财富网-数据中心-特色数据-机构调研-机构调研统计 http://data.eastmoney.com/jgdy/tj.html :param date: 开始时间 :type date: str :return: 机构调研统计 :rtype: pandas.DataFrame """ url = "http://datacenter-web.eastmoney.com/api/data/v1/get" params = { 'sortColumns': 'NOTICE_DATE,SUM,RECEIVE_START_DATE,SECURITY_CODE', 'sortTypes': '-1,-1,-1,1', 'pageSize': '500', 'pageNumber': '1', 'reportName': 'RPT_ORG_SURVEYNEW', 'columns': 'ALL', 'quoteColumns': 'f2~01~SECURITY_CODE~CLOSE_PRICE,f3~01~SECURITY_CODE~CHANGE_RATE', 'source': 'WEB', 'client': 'WEB', 'filter': f"""(NUMBERNEW="1")(IS_SOURCE="1")(RECEIVE_START_DATE>'{'-'.join([date[:4], date[4:6], date[6:]])}')""" } r = requests.get(url, params=params) data_json = r.json() total_page = data_json['result']['pages'] big_df = pd.DataFrame() 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([big_df, temp_df]) big_df.reset_index(inplace=True) big_df["index"] = list(range(1, len(big_df) + 1)) big_df.columns = [ "序号", "_", "代码", "名称", "_", "公告日期", "接待日期", "_", "_", "_", "_", "_", "_", "_", "接待地点", "_", "接待方式", "_", "接待人员", "_", "_", "_", "_", "_", "接待机构数量", "_", "_", "_", "_", "_", "_", "最新价", "涨跌幅", ] big_df = big_df[ [ "序号", "代码", "名称", "最新价", "涨跌幅", "接待机构数量", "接待方式", "接待人员", "接待地点", "接待日期", "公告日期", ] ] 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['接待机构数量'] = pd.to_numeric(big_df['接待机构数量'], errors="coerce") big_df['接待日期'] = pd.to_datetime(big_df['接待日期']).dt.date big_df['公告日期'] = pd.to_datetime(big_df['公告日期']).dt.date return big_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock_feature/stock_jgdy_em.py#L15-L104

25

[ 0, 1, 2, 3, 4, 5, 6, 7, 8 ]

10

[ 9, 10, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 69, 84, 85, 86, 87, 88, 89 ]

24.444444

false

12.962963

90

2

75.555556

6

def stock_jgdy_tj_em(date: str = "20220101") -> pd.DataFrame: url = "http://datacenter-web.eastmoney.com/api/data/v1/get" params = { 'sortColumns': 'NOTICE_DATE,SUM,RECEIVE_START_DATE,SECURITY_CODE', 'sortTypes': '-1,-1,-1,1', 'pageSize': '500', 'pageNumber': '1', 'reportName': 'RPT_ORG_SURVEYNEW', 'columns': 'ALL', 'quoteColumns': 'f2~01~SECURITY_CODE~CLOSE_PRICE,f3~01~SECURITY_CODE~CHANGE_RATE', 'source': 'WEB', 'client': 'WEB', 'filter': f"""(NUMBERNEW="1")(IS_SOURCE="1")(RECEIVE_START_DATE>'{'-'.join([date[:4], date[4:6], date[6:]])}')""" } r = requests.get(url, params=params) data_json = r.json() total_page = data_json['result']['pages'] big_df = pd.DataFrame() 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([big_df, temp_df]) big_df.reset_index(inplace=True) big_df["index"] = list(range(1, len(big_df) + 1)) big_df.columns = [ "序号", "_", "代码", "名称", "_", "公告日期", "接待日期", "_", "_", "_", "_", "_", "_", "_", "接待地点", "_", "接待方式", "_", "接待人员", "_", "_", "_", "_", "_", "接待机构数量", "_", "_", "_", "_", "_", "_", "最新价", "涨跌幅", ] big_df = big_df[ [ "序号", "代码", "名称", "最新价", "涨跌幅", "接待机构数量", "接待方式", "接待人员", "接待地点", "接待日期", "公告日期", ] ] 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['接待机构数量'] = pd.to_numeric(big_df['接待机构数量'], errors="coerce") big_df['接待日期'] = pd.to_datetime(big_df['接待日期']).dt.date big_df['公告日期'] = pd.to_datetime(big_df['公告日期']).dt.date return big_df

17,937

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock_feature/stock_jgdy_em.py

stock_jgdy_detail_em

(date: str = "20220101")

return big_df

东方财富网-数据中心-特色数据-机构调研-机构调研详细 http://data.eastmoney.com/jgdy/xx.html :param date: 开始时间 :type date: str :return: 机构调研详细 :rtype: pandas.DataFrame

107

178

def stock_jgdy_detail_em(date: str = "20220101") -> pd.DataFrame: """ 东方财富网-数据中心-特色数据-机构调研-机构调研详细 http://data.eastmoney.com/jgdy/xx.html :param date: 开始时间 :type date: str :return: 机构调研详细 :rtype: pandas.DataFrame """ url = "http://datacenter-web.eastmoney.com/api/data/v1/get" params = { 'sortColumns': 'NOTICE_DATE,RECEIVE_START_DATE,SECURITY_CODE,NUMBERNEW', 'sortTypes': '-1,-1,1,-1', 'pageSize': '50000', 'pageNumber': '1', 'reportName': 'RPT_ORG_SURVEY', 'columns': 'SECUCODE,SECURITY_CODE,SECURITY_NAME_ABBR,NOTICE_DATE,RECEIVE_START_DATE,RECEIVE_OBJECT,RECEIVE_PLACE,RECEIVE_WAY_EXPLAIN,INVESTIGATORS,RECEPTIONIST,ORG_TYPE', 'quoteColumns': 'f2~01~SECURITY_CODE~CLOSE_PRICE,f3~01~SECURITY_CODE~CHANGE_RATE', 'source': 'WEB', 'client': 'WEB', 'filter': f"""(IS_SOURCE="1")(RECEIVE_START_DATE>'{'-'.join([date[:4], date[4:6], date[6:]])}')""" } r = requests.get(url, params=params) data_json = r.json() total_page = data_json['result']['pages'] big_df = pd.DataFrame() 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([big_df, temp_df]) big_df.reset_index(inplace=True) big_df["index"] = list(range(1, len(big_df) + 1)) big_df.columns = [ "序号", "_", "代码", "名称", "公告日期", "调研日期", "调研机构", "接待地点", "接待方式", "调研人员", "接待人员", "机构类型", "最新价", "涨跌幅", ] big_df = big_df[ [ "序号", "代码", "名称", "最新价", "涨跌幅", "调研机构", "机构类型", "调研人员", "接待方式", "接待人员", "接待地点", "调研日期", "公告日期", ] ] 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['调研日期'] = pd.to_datetime(big_df['调研日期']).dt.date big_df['公告日期'] = pd.to_datetime(big_df['公告日期']).dt.date return big_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock_feature/stock_jgdy_em.py#L107-L178

25

[ 0, 1, 2, 3, 4, 5, 6, 7, 8 ]

12.5

[ 9, 10, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 50, 67, 68, 69, 70, 71 ]

29.166667

false

12.962963

72

2

70.833333

6

def stock_jgdy_detail_em(date: str = "20220101") -> pd.DataFrame: url = "http://datacenter-web.eastmoney.com/api/data/v1/get" params = { 'sortColumns': 'NOTICE_DATE,RECEIVE_START_DATE,SECURITY_CODE,NUMBERNEW', 'sortTypes': '-1,-1,1,-1', 'pageSize': '50000', 'pageNumber': '1', 'reportName': 'RPT_ORG_SURVEY', 'columns': 'SECUCODE,SECURITY_CODE,SECURITY_NAME_ABBR,NOTICE_DATE,RECEIVE_START_DATE,RECEIVE_OBJECT,RECEIVE_PLACE,RECEIVE_WAY_EXPLAIN,INVESTIGATORS,RECEPTIONIST,ORG_TYPE', 'quoteColumns': 'f2~01~SECURITY_CODE~CLOSE_PRICE,f3~01~SECURITY_CODE~CHANGE_RATE', 'source': 'WEB', 'client': 'WEB', 'filter': f"""(IS_SOURCE="1")(RECEIVE_START_DATE>'{'-'.join([date[:4], date[4:6], date[6:]])}')""" } r = requests.get(url, params=params) data_json = r.json() total_page = data_json['result']['pages'] big_df = pd.DataFrame() 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([big_df, temp_df]) big_df.reset_index(inplace=True) big_df["index"] = list(range(1, len(big_df) + 1)) big_df.columns = [ "序号", "_", "代码", "名称", "公告日期", "调研日期", "调研机构", "接待地点", "接待方式", "调研人员", "接待人员", "机构类型", "最新价", "涨跌幅", ] big_df = big_df[ [ "序号", "代码", "名称", "最新价", "涨跌幅", "调研机构", "机构类型", "调研人员", "接待方式", "接待人员", "接待地点", "调研日期", "公告日期", ] ] 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['调研日期'] = pd.to_datetime(big_df['调研日期']).dt.date big_df['公告日期'] = pd.to_datetime(big_df['公告日期']).dt.date return big_df

17,938

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock_feature/stock_fund_flow.py

_get_file_content_ths

(file: str = "ths.js")

return file_data

获取 JS 文件的内容 :param file: JS 文件名 :type file: str :return: 文件内容 :rtype: str

25

36

def _get_file_content_ths(file: str = "ths.js") -> str: """ 获取 JS 文件的内容 :param file: JS 文件名 :type file: str :return: 文件内容 :rtype: str """ setting_file_path = get_ths_js(file) with open(setting_file_path) as f: file_data = f.read() return file_data

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock_feature/stock_fund_flow.py#L25-L36

25

[ 0, 1, 2, 3, 4, 5, 6, 7 ]

66.666667

[ 8, 9, 10, 11 ]

33.333333

false

6.878307

12

2

66.666667

5

def _get_file_content_ths(file: str = "ths.js") -> str: setting_file_path = get_ths_js(file) with open(setting_file_path) as f: file_data = f.read() return file_data

17,939

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock_feature/stock_fund_flow.py

stock_fund_flow_individual

(symbol: str = "即时") ->

return big_df

同花顺-数据中心-资金流向-个股资金流 http://data.10jqka.com.cn/funds/ggzjl/#refCountId=data_55f13c2c_254 :param symbol: choice of {“即时”, "3日排行", "5日排行", "10日排行", "20日排行"} :type symbol: str :return: 个股资金流 :rtype: pandas.DataFrame

39

129

def stock_fund_flow_individual(symbol: str = "即时") -> pd.DataFrame: """ 同花顺-数据中心-资金流向-个股资金流 http://data.10jqka.com.cn/funds/ggzjl/#refCountId=data_55f13c2c_254 :param symbol: choice of {“即时”, "3日排行", "5日排行", "10日排行", "20日排行"} :type symbol: str :return: 个股资金流 :rtype: pandas.DataFrame """ js_code = py_mini_racer.MiniRacer() js_content = _get_file_content_ths("ths.js") js_code.eval(js_content) v_code = js_code.call("v") headers = { "Accept": "text/html, */*; q=0.01", "Accept-Encoding": "gzip, deflate", "Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8", "Cache-Control": "no-cache", "Connection": "keep-alive", "hexin-v": v_code, "Host": "data.10jqka.com.cn", "Pragma": "no-cache", "Referer": "http://data.10jqka.com.cn/funds/hyzjl/", "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/90.0.4430.85 Safari/537.36", "X-Requested-With": "XMLHttpRequest", } url = "http://data.10jqka.com.cn/funds/ggzjl/field/zdf/order/desc/ajax/1/free/1/" r = requests.get(url, headers=headers) soup = BeautifulSoup(r.text, "lxml") raw_page = soup.find("span", attrs={"class": "page_info"}).text page_num = raw_page.split("/")[1] if symbol == "3日排行": url = "http://data.10jqka.com.cn/funds/ggzjl/board/3/field/zdf/order/desc/page/{}/ajax/1/free/1/" elif symbol == "5日排行": url = "http://data.10jqka.com.cn/funds/ggzjl/board/5/field/zdf/order/desc/page/{}/ajax/1/free/1/" elif symbol == "10日排行": url = "http://data.10jqka.com.cn/funds/ggzjl/board/10/field/zdf/order/desc/page/{}/ajax/1/free/1/" elif symbol == "20日排行": url = "http://data.10jqka.com.cn/funds/ggzjl/board/20/field/zdf/order/desc/page/{}/ajax/1/free/1/" else: url = "http://data.10jqka.com.cn/funds/ggzjl/field/zdf/order/desc/page/{}/ajax/1/free/1/" big_df = pd.DataFrame() for page in tqdm(range(1, int(page_num) + 1)): js_code = py_mini_racer.MiniRacer() js_content = _get_file_content_ths("ths.js") js_code.eval(js_content) v_code = js_code.call("v") headers = { "Accept": "text/html, */*; q=0.01", "Accept-Encoding": "gzip, deflate", "Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8", "Cache-Control": "no-cache", "Connection": "keep-alive", "hexin-v": v_code, "Host": "data.10jqka.com.cn", "Pragma": "no-cache", "Referer": "http://data.10jqka.com.cn/funds/hyzjl/", "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/90.0.4430.85 Safari/537.36", "X-Requested-With": "XMLHttpRequest", } r = requests.get(url.format(page), headers=headers) temp_df = pd.read_html(r.text)[0] big_df = pd.concat([big_df, temp_df], ignore_index=True) del big_df["序号"] big_df.reset_index(inplace=True) big_df["index"] = range(1, len(big_df) + 1) if symbol == "即时": big_df.columns = [ "序号", "股票代码", "股票简称", "最新价", "涨跌幅", "换手率", "流入资金", "流出资金", "净额", "成交额", ] else: big_df.columns = [ "序号", "股票代码", "股票简称", "最新价", "阶段涨跌幅", "连续换手率", "资金流入净额", ] return big_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock_feature/stock_fund_flow.py#L39-L129

25

[ 0, 1, 2, 3, 4, 5, 6, 7, 8 ]

9.89011

[ 9, 10, 11, 12, 13, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 40, 41, 42, 43, 44, 45, 46, 47, 60, 61, 62, 64, 65, 66, 67, 68, 81, 90 ]

39.56044

false

6.878307

91

7

60.43956

6

def stock_fund_flow_individual(symbol: str = "即时") -> pd.DataFrame: js_code = py_mini_racer.MiniRacer() js_content = _get_file_content_ths("ths.js") js_code.eval(js_content) v_code = js_code.call("v") headers = { "Accept": "text/html, */*; q=0.01", "Accept-Encoding": "gzip, deflate", "Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8", "Cache-Control": "no-cache", "Connection": "keep-alive", "hexin-v": v_code, "Host": "data.10jqka.com.cn", "Pragma": "no-cache", "Referer": "http://data.10jqka.com.cn/funds/hyzjl/", "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/90.0.4430.85 Safari/537.36", "X-Requested-With": "XMLHttpRequest", } url = "http://data.10jqka.com.cn/funds/ggzjl/field/zdf/order/desc/ajax/1/free/1/" r = requests.get(url, headers=headers) soup = BeautifulSoup(r.text, "lxml") raw_page = soup.find("span", attrs={"class": "page_info"}).text page_num = raw_page.split("/")[1] if symbol == "3日排行": url = "http://data.10jqka.com.cn/funds/ggzjl/board/3/field/zdf/order/desc/page/{}/ajax/1/free/1/" elif symbol == "5日排行": url = "http://data.10jqka.com.cn/funds/ggzjl/board/5/field/zdf/order/desc/page/{}/ajax/1/free/1/" elif symbol == "10日排行": url = "http://data.10jqka.com.cn/funds/ggzjl/board/10/field/zdf/order/desc/page/{}/ajax/1/free/1/" elif symbol == "20日排行": url = "http://data.10jqka.com.cn/funds/ggzjl/board/20/field/zdf/order/desc/page/{}/ajax/1/free/1/" else: url = "http://data.10jqka.com.cn/funds/ggzjl/field/zdf/order/desc/page/{}/ajax/1/free/1/" big_df = pd.DataFrame() for page in tqdm(range(1, int(page_num) + 1)): js_code = py_mini_racer.MiniRacer() js_content = _get_file_content_ths("ths.js") js_code.eval(js_content) v_code = js_code.call("v") headers = { "Accept": "text/html, */*; q=0.01", "Accept-Encoding": "gzip, deflate", "Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8", "Cache-Control": "no-cache", "Connection": "keep-alive", "hexin-v": v_code, "Host": "data.10jqka.com.cn", "Pragma": "no-cache", "Referer": "http://data.10jqka.com.cn/funds/hyzjl/", "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/90.0.4430.85 Safari/537.36", "X-Requested-With": "XMLHttpRequest", } r = requests.get(url.format(page), headers=headers) temp_df = pd.read_html(r.text)[0] big_df = pd.concat([big_df, temp_df], ignore_index=True) del big_df["序号"] big_df.reset_index(inplace=True) big_df["index"] = range(1, len(big_df) + 1) if symbol == "即时": big_df.columns = [ "序号", "股票代码", "股票简称", "最新价", "涨跌幅", "换手率", "流入资金", "流出资金", "净额", "成交额", ] else: big_df.columns = [ "序号", "股票代码", "股票简称", "最新价", "阶段涨跌幅", "连续换手率", "资金流入净额", ] return big_df

17,940

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock_feature/stock_fund_flow.py

stock_fund_flow_concept

(symbol: str = "即时") ->

return big_df

同花顺-数据中心-资金流向-概念资金流 http://data.10jqka.com.cn/funds/gnzjl/#refCountId=data_55f13c2c_254 :param symbol: choice of {“即时”, "3日排行", "5日排行", "10日排行", "20日排行"} :type symbol: str :return: 概念资金流 :rtype: pandas.DataFrame

132

230

def stock_fund_flow_concept(symbol: str = "即时") -> pd.DataFrame: """ 同花顺-数据中心-资金流向-概念资金流 http://data.10jqka.com.cn/funds/gnzjl/#refCountId=data_55f13c2c_254 :param symbol: choice of {“即时”, "3日排行", "5日排行", "10日排行", "20日排行"} :type symbol: str :return: 概念资金流 :rtype: pandas.DataFrame """ js_code = py_mini_racer.MiniRacer() js_content = _get_file_content_ths("ths.js") js_code.eval(js_content) v_code = js_code.call("v") headers = { "Accept": "text/html, */*; q=0.01", "Accept-Encoding": "gzip, deflate", "Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8", "Cache-Control": "no-cache", "Connection": "keep-alive", "hexin-v": v_code, "Host": "data.10jqka.com.cn", "Pragma": "no-cache", "Referer": "http://data.10jqka.com.cn/funds/gnzjl/", "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/90.0.4430.85 Safari/537.36", "X-Requested-With": "XMLHttpRequest", } url = ( "http://data.10jqka.com.cn/funds/gnzjl/field/tradezdf/order/desc/ajax/1/free/1/" ) r = requests.get(url, headers=headers) soup = BeautifulSoup(r.text, "lxml") raw_page = soup.find("span", attrs={"class": "page_info"}).text page_num = raw_page.split("/")[1] if symbol == "3日排行": url = "http://data.10jqka.com.cn/funds/gnzjl/board/3/field/tradezdf/order/desc/page/{}/ajax/1/free/1/" elif symbol == "5日排行": url = "http://data.10jqka.com.cn/funds/gnzjl/board/5/field/tradezdf/order/desc/page/{}/ajax/1/free/1/" elif symbol == "10日排行": url = "http://data.10jqka.com.cn/funds/gnzjl/board/10/field/tradezdf/order/desc/page/{}/ajax/1/free/1/" elif symbol == "20日排行": url = "http://data.10jqka.com.cn/funds/gnzjl/board/20/field/tradezdf/order/desc/page/{}/ajax/1/free/1/" else: url = "http://data.10jqka.com.cn/funds/gnzjl/field/tradezdf/order/desc/page/{}/ajax/1/free/1/" big_df = pd.DataFrame() for page in tqdm(range(1, int(page_num) + 1)): js_code = py_mini_racer.MiniRacer() js_content = _get_file_content_ths("ths.js") js_code.eval(js_content) v_code = js_code.call("v") headers = { "Accept": "text/html, */*; q=0.01", "Accept-Encoding": "gzip, deflate", "Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8", "Cache-Control": "no-cache", "Connection": "keep-alive", "hexin-v": v_code, "Host": "data.10jqka.com.cn", "Pragma": "no-cache", "Referer": "http://data.10jqka.com.cn/funds/gnzjl/", "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/90.0.4430.85 Safari/537.36", "X-Requested-With": "XMLHttpRequest", } r = requests.get(url.format(page), headers=headers) temp_df = pd.read_html(r.text)[0] big_df = pd.concat([big_df, temp_df], ignore_index=True) del big_df["序号"] big_df.reset_index(inplace=True) big_df["index"] = range(1, len(big_df) + 1) if symbol == "即时": big_df.columns = [ "序号", "行业", "行业指数", "行业-涨跌幅", "流入资金", "流出资金", "净额", "公司家数", "领涨股", "领涨股-涨跌幅", "当前价", ] big_df["行业-涨跌幅"] = big_df["行业-涨跌幅"].str.strip("%") big_df["领涨股-涨跌幅"] = big_df["领涨股-涨跌幅"].str.strip("%") big_df["行业-涨跌幅"] = pd.to_numeric(big_df["行业-涨跌幅"], errors="coerce") big_df["领涨股-涨跌幅"] = pd.to_numeric(big_df["领涨股-涨跌幅"], errors="coerce") else: big_df.columns = [ "序号", "行业", "公司家数", "行业指数", "阶段涨跌幅", "流入资金", "流出资金", "净额", ] return big_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock_feature/stock_fund_flow.py#L132-L230

25

[ 0, 1, 2, 3, 4, 5, 6, 7, 8 ]

9.090909

[ 9, 10, 11, 12, 13, 26, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 47, 48, 49, 62, 63, 64, 66, 67, 68, 69, 70, 83, 84, 85, 86, 88, 98 ]

40.40404

false

6.878307

99

7

59.59596

6

def stock_fund_flow_concept(symbol: str = "即时") -> pd.DataFrame: js_code = py_mini_racer.MiniRacer() js_content = _get_file_content_ths("ths.js") js_code.eval(js_content) v_code = js_code.call("v") headers = { "Accept": "text/html, */*; q=0.01", "Accept-Encoding": "gzip, deflate", "Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8", "Cache-Control": "no-cache", "Connection": "keep-alive", "hexin-v": v_code, "Host": "data.10jqka.com.cn", "Pragma": "no-cache", "Referer": "http://data.10jqka.com.cn/funds/gnzjl/", "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/90.0.4430.85 Safari/537.36", "X-Requested-With": "XMLHttpRequest", } url = ( "http://data.10jqka.com.cn/funds/gnzjl/field/tradezdf/order/desc/ajax/1/free/1/" ) r = requests.get(url, headers=headers) soup = BeautifulSoup(r.text, "lxml") raw_page = soup.find("span", attrs={"class": "page_info"}).text page_num = raw_page.split("/")[1] if symbol == "3日排行": url = "http://data.10jqka.com.cn/funds/gnzjl/board/3/field/tradezdf/order/desc/page/{}/ajax/1/free/1/" elif symbol == "5日排行": url = "http://data.10jqka.com.cn/funds/gnzjl/board/5/field/tradezdf/order/desc/page/{}/ajax/1/free/1/" elif symbol == "10日排行": url = "http://data.10jqka.com.cn/funds/gnzjl/board/10/field/tradezdf/order/desc/page/{}/ajax/1/free/1/" elif symbol == "20日排行": url = "http://data.10jqka.com.cn/funds/gnzjl/board/20/field/tradezdf/order/desc/page/{}/ajax/1/free/1/" else: url = "http://data.10jqka.com.cn/funds/gnzjl/field/tradezdf/order/desc/page/{}/ajax/1/free/1/" big_df = pd.DataFrame() for page in tqdm(range(1, int(page_num) + 1)): js_code = py_mini_racer.MiniRacer() js_content = _get_file_content_ths("ths.js") js_code.eval(js_content) v_code = js_code.call("v") headers = { "Accept": "text/html, */*; q=0.01", "Accept-Encoding": "gzip, deflate", "Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8", "Cache-Control": "no-cache", "Connection": "keep-alive", "hexin-v": v_code, "Host": "data.10jqka.com.cn", "Pragma": "no-cache", "Referer": "http://data.10jqka.com.cn/funds/gnzjl/", "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/90.0.4430.85 Safari/537.36", "X-Requested-With": "XMLHttpRequest", } r = requests.get(url.format(page), headers=headers) temp_df = pd.read_html(r.text)[0] big_df = pd.concat([big_df, temp_df], ignore_index=True) del big_df["序号"] big_df.reset_index(inplace=True) big_df["index"] = range(1, len(big_df) + 1) if symbol == "即时": big_df.columns = [ "序号", "行业", "行业指数", "行业-涨跌幅", "流入资金", "流出资金", "净额", "公司家数", "领涨股", "领涨股-涨跌幅", "当前价", ] big_df["行业-涨跌幅"] = big_df["行业-涨跌幅"].str.strip("%") big_df["领涨股-涨跌幅"] = big_df["领涨股-涨跌幅"].str.strip("%") big_df["行业-涨跌幅"] = pd.to_numeric(big_df["行业-涨跌幅"], errors="coerce") big_df["领涨股-涨跌幅"] = pd.to_numeric(big_df["领涨股-涨跌幅"], errors="coerce") else: big_df.columns = [ "序号", "行业", "公司家数", "行业指数", "阶段涨跌幅", "流入资金", "流出资金", "净额", ] return big_df

17,941

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock_feature/stock_fund_flow.py

stock_fund_flow_industry

(symbol: str = "即时") ->

return big_df

同花顺-数据中心-资金流向-行业资金流 http://data.10jqka.com.cn/funds/hyzjl/#refCountId=data_55f13c2c_254 :param symbol: choice of {“即时”, "3日排行", "5日排行", "10日排行", "20日排行"} :type symbol: str :return: 行业资金流 :rtype: pandas.DataFrame

233

331

def stock_fund_flow_industry(symbol: str = "即时") -> pd.DataFrame: """ 同花顺-数据中心-资金流向-行业资金流 http://data.10jqka.com.cn/funds/hyzjl/#refCountId=data_55f13c2c_254 :param symbol: choice of {“即时”, "3日排行", "5日排行", "10日排行", "20日排行"} :type symbol: str :return: 行业资金流 :rtype: pandas.DataFrame """ js_code = py_mini_racer.MiniRacer() js_content = _get_file_content_ths("ths.js") js_code.eval(js_content) v_code = js_code.call("v") headers = { "Accept": "text/html, */*; q=0.01", "Accept-Encoding": "gzip, deflate", "Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8", "Cache-Control": "no-cache", "Connection": "keep-alive", "hexin-v": v_code, "Host": "data.10jqka.com.cn", "Pragma": "no-cache", "Referer": "http://data.10jqka.com.cn/funds/hyzjl/", "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/90.0.4430.85 Safari/537.36", "X-Requested-With": "XMLHttpRequest", } url = ( "http://data.10jqka.com.cn/funds/hyzjl/field/tradezdf/order/desc/ajax/1/free/1/" ) r = requests.get(url, headers=headers) soup = BeautifulSoup(r.text, "lxml") raw_page = soup.find("span", attrs={"class": "page_info"}).text page_num = raw_page.split("/")[1] if symbol == "3日排行": url = "http://data.10jqka.com.cn/funds/hyzjl/board/3/field/tradezdf/order/desc/page/{}/ajax/1/free/1/" elif symbol == "5日排行": url = "http://data.10jqka.com.cn/funds/hyzjl/board/5/field/tradezdf/order/desc/page/{}/ajax/1/free/1/" elif symbol == "10日排行": url = "http://data.10jqka.com.cn/funds/hyzjl/board/10/field/tradezdf/order/desc/page/{}/ajax/1/free/1/" elif symbol == "20日排行": url = "http://data.10jqka.com.cn/funds/hyzjl/board/20/field/tradezdf/order/desc/page/{}/ajax/1/free/1/" else: url = "http://data.10jqka.com.cn/funds/hyzjl/field/tradezdf/order/desc/page/{}/ajax/1/free/1/" big_df = pd.DataFrame() for page in tqdm(range(1, int(page_num) + 1)): js_code = py_mini_racer.MiniRacer() js_content = _get_file_content_ths("ths.js") js_code.eval(js_content) v_code = js_code.call("v") headers = { "Accept": "text/html, */*; q=0.01", "Accept-Encoding": "gzip, deflate", "Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8", "Cache-Control": "no-cache", "Connection": "keep-alive", "hexin-v": v_code, "Host": "data.10jqka.com.cn", "Pragma": "no-cache", "Referer": "http://data.10jqka.com.cn/funds/hyzjl/", "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/90.0.4430.85 Safari/537.36", "X-Requested-With": "XMLHttpRequest", } r = requests.get(url.format(page), headers=headers) temp_df = pd.read_html(r.text)[0] big_df = pd.concat([big_df, temp_df], ignore_index=True) del big_df["序号"] big_df.reset_index(inplace=True) big_df["index"] = range(1, len(big_df) + 1) if symbol == "即时": big_df.columns = [ "序号", "行业", "行业指数", "行业-涨跌幅", "流入资金", "流出资金", "净额", "公司家数", "领涨股", "领涨股-涨跌幅", "当前价", ] big_df["行业-涨跌幅"] = big_df["行业-涨跌幅"].str.strip("%") big_df["领涨股-涨跌幅"] = big_df["领涨股-涨跌幅"].str.strip("%") big_df["行业-涨跌幅"] = pd.to_numeric(big_df["行业-涨跌幅"], errors="coerce") big_df["领涨股-涨跌幅"] = pd.to_numeric(big_df["领涨股-涨跌幅"], errors="coerce") else: big_df.columns = [ "序号", "行业", "公司家数", "行业指数", "阶段涨跌幅", "流入资金", "流出资金", "净额", ] return big_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock_feature/stock_fund_flow.py#L233-L331

25

[ 0, 1, 2, 3, 4, 5, 6, 7, 8 ]

9.090909

[ 9, 10, 11, 12, 13, 26, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 42, 43, 44, 45, 46, 47, 48, 49, 62, 63, 64, 66, 67, 68, 69, 70, 83, 84, 85, 86, 88, 98 ]

40.40404

false

6.878307

99

7

59.59596

6

def stock_fund_flow_industry(symbol: str = "即时") -> pd.DataFrame: js_code = py_mini_racer.MiniRacer() js_content = _get_file_content_ths("ths.js") js_code.eval(js_content) v_code = js_code.call("v") headers = { "Accept": "text/html, */*; q=0.01", "Accept-Encoding": "gzip, deflate", "Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8", "Cache-Control": "no-cache", "Connection": "keep-alive", "hexin-v": v_code, "Host": "data.10jqka.com.cn", "Pragma": "no-cache", "Referer": "http://data.10jqka.com.cn/funds/hyzjl/", "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/90.0.4430.85 Safari/537.36", "X-Requested-With": "XMLHttpRequest", } url = ( "http://data.10jqka.com.cn/funds/hyzjl/field/tradezdf/order/desc/ajax/1/free/1/" ) r = requests.get(url, headers=headers) soup = BeautifulSoup(r.text, "lxml") raw_page = soup.find("span", attrs={"class": "page_info"}).text page_num = raw_page.split("/")[1] if symbol == "3日排行": url = "http://data.10jqka.com.cn/funds/hyzjl/board/3/field/tradezdf/order/desc/page/{}/ajax/1/free/1/" elif symbol == "5日排行": url = "http://data.10jqka.com.cn/funds/hyzjl/board/5/field/tradezdf/order/desc/page/{}/ajax/1/free/1/" elif symbol == "10日排行": url = "http://data.10jqka.com.cn/funds/hyzjl/board/10/field/tradezdf/order/desc/page/{}/ajax/1/free/1/" elif symbol == "20日排行": url = "http://data.10jqka.com.cn/funds/hyzjl/board/20/field/tradezdf/order/desc/page/{}/ajax/1/free/1/" else: url = "http://data.10jqka.com.cn/funds/hyzjl/field/tradezdf/order/desc/page/{}/ajax/1/free/1/" big_df = pd.DataFrame() for page in tqdm(range(1, int(page_num) + 1)): js_code = py_mini_racer.MiniRacer() js_content = _get_file_content_ths("ths.js") js_code.eval(js_content) v_code = js_code.call("v") headers = { "Accept": "text/html, */*; q=0.01", "Accept-Encoding": "gzip, deflate", "Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8", "Cache-Control": "no-cache", "Connection": "keep-alive", "hexin-v": v_code, "Host": "data.10jqka.com.cn", "Pragma": "no-cache", "Referer": "http://data.10jqka.com.cn/funds/hyzjl/", "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/90.0.4430.85 Safari/537.36", "X-Requested-With": "XMLHttpRequest", } r = requests.get(url.format(page), headers=headers) temp_df = pd.read_html(r.text)[0] big_df = pd.concat([big_df, temp_df], ignore_index=True) del big_df["序号"] big_df.reset_index(inplace=True) big_df["index"] = range(1, len(big_df) + 1) if symbol == "即时": big_df.columns = [ "序号", "行业", "行业指数", "行业-涨跌幅", "流入资金", "流出资金", "净额", "公司家数", "领涨股", "领涨股-涨跌幅", "当前价", ] big_df["行业-涨跌幅"] = big_df["行业-涨跌幅"].str.strip("%") big_df["领涨股-涨跌幅"] = big_df["领涨股-涨跌幅"].str.strip("%") big_df["行业-涨跌幅"] = pd.to_numeric(big_df["行业-涨跌幅"], errors="coerce") big_df["领涨股-涨跌幅"] = pd.to_numeric(big_df["领涨股-涨跌幅"], errors="coerce") else: big_df.columns = [ "序号", "行业", "公司家数", "行业指数", "阶段涨跌幅", "流入资金", "流出资金", "净额", ] return big_df

17,942

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock_feature/stock_fund_flow.py

stock_fund_flow_big_deal

()

return big_df

同花顺-数据中心-资金流向-大单追踪 http://data.10jqka.com.cn/funds/ddzz/### :return: 大单追踪 :rtype: pandas.DataFrame

334

400

def stock_fund_flow_big_deal() -> pd.DataFrame: """ 同花顺-数据中心-资金流向-大单追踪 http://data.10jqka.com.cn/funds/ddzz/### :return: 大单追踪 :rtype: pandas.DataFrame """ js_code = py_mini_racer.MiniRacer() js_content = _get_file_content_ths("ths.js") js_code.eval(js_content) v_code = js_code.call("v") headers = { "Accept": "text/html, */*; q=0.01", "Accept-Encoding": "gzip, deflate", "Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8", "Cache-Control": "no-cache", "Connection": "keep-alive", "hexin-v": v_code, "Host": "data.10jqka.com.cn", "Pragma": "no-cache", "Referer": "http://data.10jqka.com.cn/funds/hyzjl/", "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/90.0.4430.85 Safari/537.36", "X-Requested-With": "XMLHttpRequest", } url = "http://data.10jqka.com.cn/funds/ddzz/order/desc/ajax/1/free/1/" r = requests.get(url, headers=headers) soup = BeautifulSoup(r.text, "lxml") raw_page = soup.find("span", attrs={"class": "page_info"}).text page_num = raw_page.split("/")[1] url = "http://data.10jqka.com.cn/funds/ddzz/order/asc/page/{}/ajax/1/free/1/" big_df = pd.DataFrame() for page in tqdm(range(1, int(page_num) + 1)): js_code = py_mini_racer.MiniRacer() js_content = _get_file_content_ths("ths.js") js_code.eval(js_content) v_code = js_code.call("v") headers = { "Accept": "text/html, */*; q=0.01", "Accept-Encoding": "gzip, deflate", "Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8", "Cache-Control": "no-cache", "Connection": "keep-alive", "hexin-v": v_code, "Host": "data.10jqka.com.cn", "Pragma": "no-cache", "Referer": "http://data.10jqka.com.cn/funds/hyzjl/", "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/90.0.4430.85 Safari/537.36", "X-Requested-With": "XMLHttpRequest", } r = requests.get(url.format(page), headers=headers) temp_df = pd.read_html(r.text)[0] big_df = pd.concat([big_df, temp_df], ignore_index=True) big_df.columns = [ "成交时间", "股票代码", "股票简称", "成交价格", "成交量", "成交额", "大单性质", "涨跌幅", "涨跌额", "详细", ] del big_df["详细"] return big_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock_feature/stock_fund_flow.py#L334-L400

25

[ 0, 1, 2, 3, 4, 5, 6 ]

10.447761

[ 7, 8, 9, 10, 11, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 49, 50, 51, 53, 65, 66 ]

35.820896

false

6.878307

67

2

64.179104

4

def stock_fund_flow_big_deal() -> pd.DataFrame: js_code = py_mini_racer.MiniRacer() js_content = _get_file_content_ths("ths.js") js_code.eval(js_content) v_code = js_code.call("v") headers = { "Accept": "text/html, */*; q=0.01", "Accept-Encoding": "gzip, deflate", "Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8", "Cache-Control": "no-cache", "Connection": "keep-alive", "hexin-v": v_code, "Host": "data.10jqka.com.cn", "Pragma": "no-cache", "Referer": "http://data.10jqka.com.cn/funds/hyzjl/", "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/90.0.4430.85 Safari/537.36", "X-Requested-With": "XMLHttpRequest", } url = "http://data.10jqka.com.cn/funds/ddzz/order/desc/ajax/1/free/1/" r = requests.get(url, headers=headers) soup = BeautifulSoup(r.text, "lxml") raw_page = soup.find("span", attrs={"class": "page_info"}).text page_num = raw_page.split("/")[1] url = "http://data.10jqka.com.cn/funds/ddzz/order/asc/page/{}/ajax/1/free/1/" big_df = pd.DataFrame() for page in tqdm(range(1, int(page_num) + 1)): js_code = py_mini_racer.MiniRacer() js_content = _get_file_content_ths("ths.js") js_code.eval(js_content) v_code = js_code.call("v") headers = { "Accept": "text/html, */*; q=0.01", "Accept-Encoding": "gzip, deflate", "Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8", "Cache-Control": "no-cache", "Connection": "keep-alive", "hexin-v": v_code, "Host": "data.10jqka.com.cn", "Pragma": "no-cache", "Referer": "http://data.10jqka.com.cn/funds/hyzjl/", "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/90.0.4430.85 Safari/537.36", "X-Requested-With": "XMLHttpRequest", } r = requests.get(url.format(page), headers=headers) temp_df = pd.read_html(r.text)[0] big_df = pd.concat([big_df, temp_df], ignore_index=True) big_df.columns = [ "成交时间", "股票代码", "股票简称", "成交价格", "成交量", "成交额", "大单性质", "涨跌幅", "涨跌额", "详细", ] del big_df["详细"] return big_df

17,943

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock_feature/stock_szse_margin.py

stock_margin_underlying_info_szse

(date: str = "20221129")

return temp_df

深圳证券交易所-融资融券数据-标的证券信息 https://www.szse.cn/disclosure/margin/object/index.html :param date: 交易日 :type date: str :return: 标的证券信息 :rtype: pandas.DataFrame

14

40

def stock_margin_underlying_info_szse(date: str = "20221129") -> pd.DataFrame: """ 深圳证券交易所-融资融券数据-标的证券信息 https://www.szse.cn/disclosure/margin/object/index.html :param date: 交易日 :type date: str :return: 标的证券信息 :rtype: pandas.DataFrame """ url = "http://www.szse.cn/api/report/ShowReport" params = { "SHOWTYPE": "xlsx", "CATALOGID": "1834_xxpl", "txtDate": "-".join([date[:4], date[4:6], date[6:]]), "tab1PAGENO": "1", "random": "0.7425245522795993", 'TABKEY': 'tab1', } headers = { "Referer": "http://www.szse.cn/disclosure/margin/object/index.html", "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/88.0.4324.150 Safari/537.36", } r = requests.get(url, params=params, headers=headers) with warnings.catch_warnings(record=True): warnings.simplefilter("always") temp_df = pd.read_excel(r.content, engine="openpyxl", dtype={"证券代码": str}) return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock_feature/stock_szse_margin.py#L14-L40

25

[ 0, 1, 2, 3, 4, 5, 6, 7, 8 ]

33.333333

[ 9, 10, 18, 22, 23, 24, 25, 26 ]

29.62963

false

12.5

27

2

70.37037

6

def stock_margin_underlying_info_szse(date: str = "20221129") -> pd.DataFrame: url = "http://www.szse.cn/api/report/ShowReport" params = { "SHOWTYPE": "xlsx", "CATALOGID": "1834_xxpl", "txtDate": "-".join([date[:4], date[4:6], date[6:]]), "tab1PAGENO": "1", "random": "0.7425245522795993", 'TABKEY': 'tab1', } headers = { "Referer": "http://www.szse.cn/disclosure/margin/object/index.html", "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/88.0.4324.150 Safari/537.36", } r = requests.get(url, params=params, headers=headers) with warnings.catch_warnings(record=True): warnings.simplefilter("always") temp_df = pd.read_excel(r.content, engine="openpyxl", dtype={"证券代码": str}) return temp_df

17,944

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock_feature/stock_szse_margin.py

stock_margin_szse

(date: str = "20221129")

return temp_df

深圳证券交易所-融资融券数据-融资融券汇总 https://www.szse.cn/disclosure/margin/margin/index.html :param date: 交易日 :type date: str :return: 融资融券汇总 :rtype: pandas.DataFrame

43

87

def stock_margin_szse(date: str = "20221129") -> pd.DataFrame: """ 深圳证券交易所-融资融券数据-融资融券汇总 https://www.szse.cn/disclosure/margin/margin/index.html :param date: 交易日 :type date: str :return: 融资融券汇总 :rtype: pandas.DataFrame """ url = "http://www.szse.cn/api/report/ShowReport/data" params = { "SHOWTYPE": "JSON", "CATALOGID": "1837_xxpl", "txtDate": "-".join([date[:4], date[4:6], date[6:]]), "tab1PAGENO": "1", "random": "0.7425245522795993", } headers = { "Referer": "http://www.szse.cn/disclosure/margin/object/index.html", "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/88.0.4324.150 Safari/537.36", } r = requests.get(url, params=params, headers=headers) data_json = r.json() temp_df = pd.DataFrame(data_json[0]["data"]) temp_df.columns = [ "融资买入额", "融资余额", "融券卖出量", "融券余量", "融券余额", "融资融券余额", ] temp_df["融资买入额"] = temp_df["融资买入额"].str.replace(",", "") temp_df["融资买入额"] = pd.to_numeric(temp_df["融资买入额"]) temp_df["融资余额"] = temp_df["融资余额"].str.replace(",", "") temp_df["融资余额"] = pd.to_numeric(temp_df["融资余额"]) temp_df["融券卖出量"] = temp_df["融券卖出量"].str.replace(",", "") temp_df["融券卖出量"] = pd.to_numeric(temp_df["融券卖出量"]) temp_df["融券余量"] = temp_df["融券余量"].str.replace(",", "") temp_df["融券余量"] = pd.to_numeric(temp_df["融券余量"]) temp_df["融券余额"] = temp_df["融券余额"].str.replace(",", "") temp_df["融券余额"] = pd.to_numeric(temp_df["融券余额"]) temp_df["融资融券余额"] = temp_df["融资融券余额"].str.replace(",", "") temp_df["融资融券余额"] = pd.to_numeric(temp_df["融资融券余额"]) return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock_feature/stock_szse_margin.py#L43-L87

25

[ 0, 1, 2, 3, 4, 5, 6, 7, 8 ]

20

[ 9, 10, 17, 21, 22, 23, 24, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44 ]

44.444444

false

12.5

45

1

55.555556

6

def stock_margin_szse(date: str = "20221129") -> pd.DataFrame: url = "http://www.szse.cn/api/report/ShowReport/data" params = { "SHOWTYPE": "JSON", "CATALOGID": "1837_xxpl", "txtDate": "-".join([date[:4], date[4:6], date[6:]]), "tab1PAGENO": "1", "random": "0.7425245522795993", } headers = { "Referer": "http://www.szse.cn/disclosure/margin/object/index.html", "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/88.0.4324.150 Safari/537.36", } r = requests.get(url, params=params, headers=headers) data_json = r.json() temp_df = pd.DataFrame(data_json[0]["data"]) temp_df.columns = [ "融资买入额", "融资余额", "融券卖出量", "融券余量", "融券余额", "融资融券余额", ] temp_df["融资买入额"] = temp_df["融资买入额"].str.replace(",", "") temp_df["融资买入额"] = pd.to_numeric(temp_df["融资买入额"]) temp_df["融资余额"] = temp_df["融资余额"].str.replace(",", "") temp_df["融资余额"] = pd.to_numeric(temp_df["融资余额"]) temp_df["融券卖出量"] = temp_df["融券卖出量"].str.replace(",", "") temp_df["融券卖出量"] = pd.to_numeric(temp_df["融券卖出量"]) temp_df["融券余量"] = temp_df["融券余量"].str.replace(",", "") temp_df["融券余量"] = pd.to_numeric(temp_df["融券余量"]) temp_df["融券余额"] = temp_df["融券余额"].str.replace(",", "") temp_df["融券余额"] = pd.to_numeric(temp_df["融券余额"]) temp_df["融资融券余额"] = temp_df["融资融券余额"].str.replace(",", "") temp_df["融资融券余额"] = pd.to_numeric(temp_df["融资融券余额"]) return temp_df

17,945

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock_feature/stock_szse_margin.py

stock_margin_detail_szse

(date: str = "20221128")

return temp_df

深证证券交易所-融资融券数据-融资融券交易明细 https://www.szse.cn/disclosure/margin/margin/index.html :param date: 交易日期 :type date: str :return: 融资融券明细 :rtype: pandas.DataFrame

90

140

def stock_margin_detail_szse(date: str = "20221128") -> pd.DataFrame: """ 深证证券交易所-融资融券数据-融资融券交易明细 https://www.szse.cn/disclosure/margin/margin/index.html :param date: 交易日期 :type date: str :return: 融资融券明细 :rtype: pandas.DataFrame """ url = "http://www.szse.cn/api/report/ShowReport" params = { "SHOWTYPE": "xlsx", "CATALOGID": "1837_xxpl", "txtDate": "-".join([date[:4], date[4:6], date[6:]]), "tab2PAGENO": "1", "random": "0.24279342734085696", "TABKEY": "tab2", } headers = { "Referer": "http://www.szse.cn/disclosure/margin/margin/index.html", "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/88.0.4324.150 Safari/537.36", } r = requests.get(url, params=params, headers=headers) with warnings.catch_warnings(record=True): warnings.simplefilter("always") temp_df = pd.read_excel(r.content, engine="openpyxl", dtype={"证券代码": str}) temp_df.columns = [ "证券代码", "证券简称", "融资买入额", "融资余额", "融券卖出量", "融券余量", "融券余额", "融资融券余额", ] temp_df["证券简称"] = temp_df["证券简称"].str.replace(" ", "") temp_df["融资买入额"] = temp_df["融资买入额"].str.replace(",", "") temp_df["融资买入额"] = pd.to_numeric(temp_df["融资买入额"], errors="coerce") temp_df["融资余额"] = temp_df["融资余额"].str.replace(",", "") temp_df["融资余额"] = pd.to_numeric(temp_df["融资余额"], errors="coerce") temp_df["融券卖出量"] = temp_df["融券卖出量"].str.replace(",", "") temp_df["融券卖出量"] = pd.to_numeric(temp_df["融券卖出量"], errors="coerce") temp_df["融券余量"] = temp_df["融券余量"].str.replace(",", "") temp_df["融券余量"] = pd.to_numeric(temp_df["融券余量"], errors="coerce") temp_df["融券余额"] = temp_df["融券余额"].str.replace(",", "") temp_df["融券余额"] = pd.to_numeric(temp_df["融券余额"], errors="coerce") temp_df["融资融券余额"] = temp_df["融资融券余额"].str.replace(",", "") temp_df["融资融券余额"] = pd.to_numeric(temp_df["融资融券余额"], errors="coerce") return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock_feature/stock_szse_margin.py#L90-L140

25

[ 0, 1, 2, 3, 4, 5, 6, 7, 8 ]

17.647059

[ 9, 10, 18, 23, 24, 25, 26, 27, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50 ]

43.137255

false

12.5

51

2

56.862745

6

def stock_margin_detail_szse(date: str = "20221128") -> pd.DataFrame: url = "http://www.szse.cn/api/report/ShowReport" params = { "SHOWTYPE": "xlsx", "CATALOGID": "1837_xxpl", "txtDate": "-".join([date[:4], date[4:6], date[6:]]), "tab2PAGENO": "1", "random": "0.24279342734085696", "TABKEY": "tab2", } headers = { "Referer": "http://www.szse.cn/disclosure/margin/margin/index.html", "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/88.0.4324.150 Safari/537.36", } r = requests.get(url, params=params, headers=headers) with warnings.catch_warnings(record=True): warnings.simplefilter("always") temp_df = pd.read_excel(r.content, engine="openpyxl", dtype={"证券代码": str}) temp_df.columns = [ "证券代码", "证券简称", "融资买入额", "融资余额", "融券卖出量", "融券余量", "融券余额", "融资融券余额", ] temp_df["证券简称"] = temp_df["证券简称"].str.replace(" ", "") temp_df["融资买入额"] = temp_df["融资买入额"].str.replace(",", "") temp_df["融资买入额"] = pd.to_numeric(temp_df["融资买入额"], errors="coerce") temp_df["融资余额"] = temp_df["融资余额"].str.replace(",", "") temp_df["融资余额"] = pd.to_numeric(temp_df["融资余额"], errors="coerce") temp_df["融券卖出量"] = temp_df["融券卖出量"].str.replace(",", "") temp_df["融券卖出量"] = pd.to_numeric(temp_df["融券卖出量"], errors="coerce") temp_df["融券余量"] = temp_df["融券余量"].str.replace(",", "") temp_df["融券余量"] = pd.to_numeric(temp_df["融券余量"], errors="coerce") temp_df["融券余额"] = temp_df["融券余额"].str.replace(",", "") temp_df["融券余额"] = pd.to_numeric(temp_df["融券余额"], errors="coerce") temp_df["融资融券余额"] = temp_df["融资融券余额"].str.replace(",", "") temp_df["融资融券余额"] = pd.to_numeric(temp_df["融资融券余额"], errors="coerce") return temp_df

17,946

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock_feature/stock_us_hist_futunn.py

stock_us_code_table_fu

()

return big_df

富途牛牛-行情-美股-美股代码表 https://www.futunn.com/stock/HON-US :return: 美股代码表 :rtype: pandas.DataFrame

16

97

def stock_us_code_table_fu() -> pd.DataFrame: """ 富途牛牛-行情-美股-美股代码表 https://www.futunn.com/stock/HON-US :return: 美股代码表 :rtype: pandas.DataFrame """ url = "https://www.futunn.com/quote/list/us/1/694" headers = { "accept": "application/json, text/plain, */*", "accept-encoding": "gzip, deflate, br", "accept-language": "zh-CN,zh;q=0.9,en;q=0.8", "cache-control": "no-cache", "futu-x-csrf-token": "FM5ZhxYFQsZM4k9rXk3TMA==-O4oTw/tuNRp5DlJNWo/TNEEfMt8=", "pragma": "no-cache", "referer": "https://www.futunn.com/stock/HON-US?seo_redirect=1&channel=1244&subchannel=2&from=BaiduAladdin&utm_source=alading_user&utm_medium=website_growth", "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/96.0.4664.45 Safari/537.36", } r = requests.get(url, headers=headers) data_text = r.text data_json = json.loads( data_text[data_text.find('{"prefetch') : data_text.find(",window._params")] ) pd.DataFrame(data_json["prefetch"]["stockList"]["list"]) total_page = data_json["prefetch"]["stockList"]["page"]["page_count"] big_df = pd.DataFrame() for page in tqdm(range(1, total_page + 1), leave=False): url = f"https://www.futunn.com/quote/list/us/1/{page}" headers = { "accept": "application/json, text/plain, */*", "accept-encoding": "gzip, deflate, br", "accept-language": "zh-CN,zh;q=0.9,en;q=0.8", "cache-control": "no-cache", "futu-x-csrf-token": "FM5ZhxYFQsZM4k9rXk3TMA==-O4oTw/tuNRp5DlJNWo/TNEEfMt8=", "pragma": "no-cache", "referer": "https://www.futunn.com/stock/HON-US?seo_redirect=1&channel=1244&subchannel=2&from=BaiduAladdin&utm_source=alading_user&utm_medium=website_growth", "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/96.0.4664.45 Safari/537.36", } r = requests.get(url, headers=headers) data_text = r.text data_json = json.loads( data_text[data_text.find('{"prefetch') : data_text.find(",window._params")] ) temp_df = pd.DataFrame(data_json["prefetch"]["stockList"]["list"]) big_df = pd.concat([big_df, temp_df], ignore_index=True) big_df.columns = [ "代码", "最新价", "成交额", "成交量", "换手率", "振幅", "涨跌额", "涨跌幅", "-", "股票名称", "股票简称", "-", ] big_df = big_df[ [ "代码", "股票名称", "股票简称", "最新价", "涨跌额", "涨跌幅", "成交量", "成交额", "换手率", "振幅", ] ] big_df["最新价"] = pd.to_numeric(big_df["最新价"]) big_df["涨跌额"] = pd.to_numeric(big_df["涨跌额"]) big_df["涨跌幅"] = big_df["涨跌幅"].str.strip("%") big_df["涨跌幅"] = pd.to_numeric(big_df["涨跌幅"]) big_df["换手率"] = big_df["换手率"].str.strip("%") big_df["换手率"] = pd.to_numeric(big_df["换手率"]) big_df["振幅"] = big_df["振幅"].str.strip("%") big_df["振幅"] = pd.to_numeric(big_df["振幅"]) return big_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock_feature/stock_us_hist_futunn.py#L16-L97

25

[ 0, 1, 2, 3, 4, 5, 6 ]

8.536585

[ 7, 8, 18, 19, 20, 23, 24, 25, 26, 27, 28, 38, 39, 40, 43, 44, 45, 59, 73, 74, 75, 76, 77, 78, 79, 80, 81 ]

32.926829

false

14.285714

82

2

67.073171

4

def stock_us_code_table_fu() -> pd.DataFrame: url = "https://www.futunn.com/quote/list/us/1/694" headers = { "accept": "application/json, text/plain, */*", "accept-encoding": "gzip, deflate, br", "accept-language": "zh-CN,zh;q=0.9,en;q=0.8", "cache-control": "no-cache", "futu-x-csrf-token": "FM5ZhxYFQsZM4k9rXk3TMA==-O4oTw/tuNRp5DlJNWo/TNEEfMt8=", "pragma": "no-cache", "referer": "https://www.futunn.com/stock/HON-US?seo_redirect=1&channel=1244&subchannel=2&from=BaiduAladdin&utm_source=alading_user&utm_medium=website_growth", "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/96.0.4664.45 Safari/537.36", } r = requests.get(url, headers=headers) data_text = r.text data_json = json.loads( data_text[data_text.find('{"prefetch') : data_text.find(",window._params")] ) pd.DataFrame(data_json["prefetch"]["stockList"]["list"]) total_page = data_json["prefetch"]["stockList"]["page"]["page_count"] big_df = pd.DataFrame() for page in tqdm(range(1, total_page + 1), leave=False): url = f"https://www.futunn.com/quote/list/us/1/{page}" headers = { "accept": "application/json, text/plain, */*", "accept-encoding": "gzip, deflate, br", "accept-language": "zh-CN,zh;q=0.9,en;q=0.8", "cache-control": "no-cache", "futu-x-csrf-token": "FM5ZhxYFQsZM4k9rXk3TMA==-O4oTw/tuNRp5DlJNWo/TNEEfMt8=", "pragma": "no-cache", "referer": "https://www.futunn.com/stock/HON-US?seo_redirect=1&channel=1244&subchannel=2&from=BaiduAladdin&utm_source=alading_user&utm_medium=website_growth", "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/96.0.4664.45 Safari/537.36", } r = requests.get(url, headers=headers) data_text = r.text data_json = json.loads( data_text[data_text.find('{"prefetch') : data_text.find(",window._params")] ) temp_df = pd.DataFrame(data_json["prefetch"]["stockList"]["list"]) big_df = pd.concat([big_df, temp_df], ignore_index=True) big_df.columns = [ "代码", "最新价", "成交额", "成交量", "换手率", "振幅", "涨跌额", "涨跌幅", "-", "股票名称", "股票简称", "-", ] big_df = big_df[ [ "代码", "股票名称", "股票简称", "最新价", "涨跌额", "涨跌幅", "成交量", "成交额", "换手率", "振幅", ] ] big_df["最新价"] = pd.to_numeric(big_df["最新价"]) big_df["涨跌额"] = pd.to_numeric(big_df["涨跌额"]) big_df["涨跌幅"] = big_df["涨跌幅"].str.strip("%") big_df["涨跌幅"] = pd.to_numeric(big_df["涨跌幅"]) big_df["换手率"] = big_df["换手率"].str.strip("%") big_df["换手率"] = pd.to_numeric(big_df["换手率"]) big_df["振幅"] = big_df["振幅"].str.strip("%") big_df["振幅"] = pd.to_numeric(big_df["振幅"]) return big_df

17,947

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock_feature/stock_us_hist_futunn.py

stock_us_hist_fu

( symbol: str = "202545", start_date: str = "19700101", end_date: str = "22220101", )

return temp_df

富途牛牛-行情-美股-每日行情 https://www.futunn.com/stock/HON-US :param symbol: 股票代码; 此股票代码可以通过调用 ak.stock_us_code_table_fu() 的 `代码` 字段获取 :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: 每日行情 :rtype: pandas.DataFrame

100

187

def stock_us_hist_fu( symbol: str = "202545", start_date: str = "19700101", end_date: str = "22220101", ) -> pd.DataFrame: """ 富途牛牛-行情-美股-每日行情 https://www.futunn.com/stock/HON-US :param symbol: 股票代码; 此股票代码可以通过调用 ak.stock_us_code_table_fu() 的 `代码` 字段获取 :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: 每日行情 :rtype: pandas.DataFrame """ url = "https://www.futunn.com/stock/HON-US?seo_redirect=1&channel=1244&subchannel=2&from=BaiduAladdin&utm_source=alading_user&utm_medium=website_growth" headers = { "accept": "application/json, text/plain, */*", "accept-encoding": "gzip, deflate, br", "accept-language": "zh-CN,zh;q=0.9,en;q=0.8", "cache-control": "no-cache", "futu-x-csrf-token": "FM5ZhxYFQsZM4k9rXk3TMA==-O4oTw/tuNRp5DlJNWo/TNEEfMt8=", "pragma": "no-cache", "referer": "https://www.futunn.com/stock/HON-US?seo_redirect=1&channel=1244&subchannel=2&from=BaiduAladdin&utm_source=alading_user&utm_medium=website_growth", "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/96.0.4664.45 Safari/537.36", } session_ = requests.session() r = session_.get(url, headers=headers) soup = BeautifulSoup(r.text, "lxml") url = "https://www.futunn.com/quote-api/get-kline" params = { "stock_id": symbol, "market_type": "2", "type": "2", } headers = { "accept": "application/json, text/plain, */*", "accept-encoding": "gzip, deflate, br", "accept-language": "zh-CN,zh;q=0.9,en;q=0.8", "cache-control": "no-cache", "futu-x-csrf-token": soup.find("meta")["content"], "pragma": "no-cache", "referer": "https://www.futunn.com/stock/HON-US?seo_redirect=1&channel=1244&subchannel=2&from=BaiduAladdin&utm_source=alading_user&utm_medium=website_growth", "sec-fetch-dest": "empty", "sec-fetch-mode": "cors", "sec-fetch-site": "same-origin", "user-agent": "Mozilla/5.0 (iPhone; CPU iPhone OS 13_2_3 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/13.0.3 Mobile/15E148 Safari/604.1", } r = session_.get(url, params=params, headers=headers) data_json = r.json() temp_df = pd.DataFrame(data_json["data"]["list"]) temp_df.columns = [ "日期", "今开", "今收", "最高", "最低", "成交量", "成交额", "换手率", "-", "昨收", ] temp_df = temp_df[ [ "日期", "今开", "今收", "最高", "最低", "成交量", "成交额", "换手率", "昨收", ] ] temp_df.index = pd.to_datetime(temp_df["日期"], unit="s") temp_df = temp_df[start_date:end_date] temp_df.reset_index(inplace=True, drop=True) temp_df["日期"] = pd.to_datetime(temp_df["日期"], unit="s").dt.date temp_df["今开"] = pd.to_numeric(temp_df["今开"]) / 100 temp_df["最高"] = pd.to_numeric(temp_df["最高"]) / 100 temp_df["最低"] = pd.to_numeric(temp_df["最低"]) / 100 temp_df["今收"] = pd.to_numeric(temp_df["今收"]) / 100 temp_df["昨收"] = pd.to_numeric(temp_df["昨收"]) / 100 return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock_feature/stock_us_hist_futunn.py#L100-L187

25

[ 0 ]

1.136364

[ 17, 18, 28, 29, 30, 31, 32, 37, 50, 51, 52, 53, 65, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87 ]

26.136364

false

14.285714

88

1

73.863636

10

def stock_us_hist_fu( symbol: str = "202545", start_date: str = "19700101", end_date: str = "22220101", ) -> pd.DataFrame: url = "https://www.futunn.com/stock/HON-US?seo_redirect=1&channel=1244&subchannel=2&from=BaiduAladdin&utm_source=alading_user&utm_medium=website_growth" headers = { "accept": "application/json, text/plain, */*", "accept-encoding": "gzip, deflate, br", "accept-language": "zh-CN,zh;q=0.9,en;q=0.8", "cache-control": "no-cache", "futu-x-csrf-token": "FM5ZhxYFQsZM4k9rXk3TMA==-O4oTw/tuNRp5DlJNWo/TNEEfMt8=", "pragma": "no-cache", "referer": "https://www.futunn.com/stock/HON-US?seo_redirect=1&channel=1244&subchannel=2&from=BaiduAladdin&utm_source=alading_user&utm_medium=website_growth", "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/96.0.4664.45 Safari/537.36", } session_ = requests.session() r = session_.get(url, headers=headers) soup = BeautifulSoup(r.text, "lxml") url = "https://www.futunn.com/quote-api/get-kline" params = { "stock_id": symbol, "market_type": "2", "type": "2", } headers = { "accept": "application/json, text/plain, */*", "accept-encoding": "gzip, deflate, br", "accept-language": "zh-CN,zh;q=0.9,en;q=0.8", "cache-control": "no-cache", "futu-x-csrf-token": soup.find("meta")["content"], "pragma": "no-cache", "referer": "https://www.futunn.com/stock/HON-US?seo_redirect=1&channel=1244&subchannel=2&from=BaiduAladdin&utm_source=alading_user&utm_medium=website_growth", "sec-fetch-dest": "empty", "sec-fetch-mode": "cors", "sec-fetch-site": "same-origin", "user-agent": "Mozilla/5.0 (iPhone; CPU iPhone OS 13_2_3 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/13.0.3 Mobile/15E148 Safari/604.1", } r = session_.get(url, params=params, headers=headers) data_json = r.json() temp_df = pd.DataFrame(data_json["data"]["list"]) temp_df.columns = [ "日期", "今开", "今收", "最高", "最低", "成交量", "成交额", "换手率", "-", "昨收", ] temp_df = temp_df[ [ "日期", "今开", "今收", "最高", "最低", "成交量", "成交额", "换手率", "昨收", ] ] temp_df.index = pd.to_datetime(temp_df["日期"], unit="s") temp_df = temp_df[start_date:end_date] temp_df.reset_index(inplace=True, drop=True) temp_df["日期"] = pd.to_datetime(temp_df["日期"], unit="s").dt.date temp_df["今开"] = pd.to_numeric(temp_df["今开"]) / 100 temp_df["最高"] = pd.to_numeric(temp_df["最高"]) / 100 temp_df["最低"] = pd.to_numeric(temp_df["最低"]) / 100 temp_df["今收"] = pd.to_numeric(temp_df["今收"]) / 100 temp_df["昨收"] = pd.to_numeric(temp_df["昨收"]) / 100 return temp_df

17,948

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/stock_feature/stock_inner_trade_xq.py

stock_inner_trade_xq

()

return temp_df

雪球-行情中心-沪深股市-内部交易 https://xueqiu.com/hq/insider :return: 内部交易 :rtype: pandas.DataFrame

12

74

def stock_inner_trade_xq() -> pd.DataFrame: """ 雪球-行情中心-沪深股市-内部交易 https://xueqiu.com/hq/insider :return: 内部交易 :rtype: pandas.DataFrame """ url = "https://xueqiu.com/service/v5/stock/f10/cn/skholderchg" params = { 'size': '100000', 'page': '1', 'extend': 'true', '_': '1651223013040', } headers = { "Accept": "*/*", "Accept-Encoding": "gzip, deflate, br", "Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8", "Cache-Control": "no-cache", "Connection": "keep-alive", "Host": "xueqiu.com", "Pragma": "no-cache", "Referer": "https://xueqiu.com/hq", "sec-ch-ua": '" Not A;Brand";v="99", "Chromium";v="100", "Google Chrome";v="100"', "sec-ch-ua-mobile": "?0", "sec-ch-ua-platform": '"Windows"', "Sec-Fetch-Dest": "empty", "Sec-Fetch-Mode": "cors", "Sec-Fetch-Site": "same-origin", "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/100.0.4896.127 Safari/537.36", "X-Requested-With": "XMLHttpRequest", } r = requests.get(url, params=params, headers=headers) data_json = r.json() temp_df = pd.DataFrame(data_json["data"]["items"]) temp_df.columns = [ '股票代码', '股票名称', '变动人', '-', '变动日期', '变动股数', '成交均价', '变动后持股数', '与董监高关系', '董监高职务', ] temp_df = temp_df[[ '股票代码', '股票名称', '变动日期', '变动人', '变动股数', '成交均价', '变动后持股数', '与董监高关系', '董监高职务', ]] temp_df['变动日期'] = pd.to_datetime(temp_df['变动日期'], unit="ms").dt.date temp_df['变动股数'] = pd.to_numeric(temp_df['变动股数']) temp_df['成交均价'] = pd.to_numeric(temp_df['成交均价']) temp_df['变动后持股数'] = pd.to_numeric(temp_df['变动后持股数']) return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/stock_feature/stock_inner_trade_xq.py#L12-L74

25

[ 0, 1, 2, 3, 4, 5, 6 ]

11.111111

[ 7, 8, 14, 32, 33, 34, 35, 47, 58, 59, 60, 61, 62 ]

20.634921

false

25

63

1

79.365079

4

def stock_inner_trade_xq() -> pd.DataFrame: url = "https://xueqiu.com/service/v5/stock/f10/cn/skholderchg" params = { 'size': '100000', 'page': '1', 'extend': 'true', '_': '1651223013040', } headers = { "Accept": "*/*", "Accept-Encoding": "gzip, deflate, br", "Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8", "Cache-Control": "no-cache", "Connection": "keep-alive", "Host": "xueqiu.com", "Pragma": "no-cache", "Referer": "https://xueqiu.com/hq", "sec-ch-ua": '" Not A;Brand";v="99", "Chromium";v="100", "Google Chrome";v="100"', "sec-ch-ua-mobile": "?0", "sec-ch-ua-platform": '"Windows"', "Sec-Fetch-Dest": "empty", "Sec-Fetch-Mode": "cors", "Sec-Fetch-Site": "same-origin", "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/100.0.4896.127 Safari/537.36", "X-Requested-With": "XMLHttpRequest", } r = requests.get(url, params=params, headers=headers) data_json = r.json() temp_df = pd.DataFrame(data_json["data"]["items"]) temp_df.columns = [ '股票代码', '股票名称', '变动人', '-', '变动日期', '变动股数', '成交均价', '变动后持股数', '与董监高关系', '董监高职务', ] temp_df = temp_df[[ '股票代码', '股票名称', '变动日期', '变动人', '变动股数', '成交均价', '变动后持股数', '与董监高关系', '董监高职务', ]] temp_df['变动日期'] = pd.to_datetime(temp_df['变动日期'], unit="ms").dt.date temp_df['变动股数'] = pd.to_numeric(temp_df['变动股数']) temp_df['成交均价'] = pd.to_numeric(temp_df['成交均价']) temp_df['变动后持股数'] = pd.to_numeric(temp_df['变动后持股数']) return temp_df

17,949

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/sport/sport_olympic_winter.py

sport_olympic_winter_hist

()

return temp_df

腾讯运动-冬奥会-历届奖牌榜 :return: 历届奖牌榜 :rtype: pandas.DataFrame

12

34

def sport_olympic_winter_hist() -> pd.DataFrame: """ 腾讯运动-冬奥会-历届奖牌榜 :return: 历届奖牌榜 :rtype: pandas.DataFrame """ url = "https://app.sports.qq.com/m/oly/historyMedal" r = requests.get(url) data_json = r.json() temp_df = pd.DataFrame(data_json["data"]["list"]) temp_df = temp_df.explode("list") temp_df["国家及地区"] = temp_df["list"].apply(lambda x: (x["noc"])) temp_df["金牌数"] = temp_df["list"].apply(lambda x: (int(x["gold"]))) temp_df["总奖牌数"] = temp_df["list"].apply(lambda x: (int(x["total"]))) temp_df["举办年份"] = temp_df["year"].astype("str") temp_df["届数"] = temp_df["no"].astype("str") temp_df["举办地点"] = temp_df["country"] temp_df = temp_df[["举办年份", "届数", "举办地点", "国家及地区", "金牌数", "总奖牌数"]] temp_df = temp_df.replace("俄罗斯奥委会", "俄罗斯") temp_df.reset_index(inplace=True) temp_df["index"] = range(1, len(temp_df) + 1) temp_df.rename(columns={"index": "序号"}, inplace=True) return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/sport/sport_olympic_winter.py#L12-L34

25

[ 0, 1, 2, 3, 4, 5 ]

26.086957

[ 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22 ]

73.913043

false

20.833333

23

1

26.086957

3

def sport_olympic_winter_hist() -> pd.DataFrame: url = "https://app.sports.qq.com/m/oly/historyMedal" r = requests.get(url) data_json = r.json() temp_df = pd.DataFrame(data_json["data"]["list"]) temp_df = temp_df.explode("list") temp_df["国家及地区"] = temp_df["list"].apply(lambda x: (x["noc"])) temp_df["金牌数"] = temp_df["list"].apply(lambda x: (int(x["gold"]))) temp_df["总奖牌数"] = temp_df["list"].apply(lambda x: (int(x["total"]))) temp_df["举办年份"] = temp_df["year"].astype("str") temp_df["届数"] = temp_df["no"].astype("str") temp_df["举办地点"] = temp_df["country"] temp_df = temp_df[["举办年份", "届数", "举办地点", "国家及地区", "金牌数", "总奖牌数"]] temp_df = temp_df.replace("俄罗斯奥委会", "俄罗斯") temp_df.reset_index(inplace=True) temp_df["index"] = range(1, len(temp_df) + 1) temp_df.rename(columns={"index": "序号"}, inplace=True) return temp_df

17,950

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/sport/sport_olympic.py

sport_olympic_hist

()

return temp_df

运动-奥运会-奖牌数据 https://www.kaggle.com/marcogdepinto/let-s-discover-more-about-the-olympic-games :return: 奥运会-奖牌数据 :rtype: pandas.DataFrame

11

22

def sport_olympic_hist() -> pd.DataFrame: """ 运动-奥运会-奖牌数据 https://www.kaggle.com/marcogdepinto/let-s-discover-more-about-the-olympic-games :return: 奥运会-奖牌数据 :rtype: pandas.DataFrame """ url = "https://jfds-1252952517.cos.ap-chengdu.myqcloud.com/akshare/data/data_olympic/athlete_events.zip" temp_df = pd.read_csv(url) columns_list = [item.lower() for item in temp_df.columns.tolist()] temp_df.columns = columns_list return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/sport/sport_olympic.py#L11-L22

25

[ 0, 1, 2, 3, 4, 5, 6 ]

58.333333

[ 7, 8, 9, 10, 11 ]

41.666667

false

36.363636

12

2

58.333333

4

def sport_olympic_hist() -> pd.DataFrame: url = "https://jfds-1252952517.cos.ap-chengdu.myqcloud.com/akshare/data/data_olympic/athlete_events.zip" temp_df = pd.read_csv(url) columns_list = [item.lower() for item in temp_df.columns.tolist()] temp_df.columns = columns_list return temp_df

17,951

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/option/option_em.py

option_current_em

()

return temp_df

东方财富网-行情中心-期权市场 http://quote.eastmoney.com/center :return: 期权价格 :rtype: pandas.DataFrame

14

105

def option_current_em() -> pd.DataFrame: """ 东方财富网-行情中心-期权市场 http://quote.eastmoney.com/center :return: 期权价格 :rtype: pandas.DataFrame """ url = 'http://23.push2.eastmoney.com/api/qt/clist/get' params = { 'cb': 'jQuery112409395946290628259_1606225274048', 'pn': '1', 'pz': '200000', 'po': '1', 'np': '1', 'ut': 'bd1d9ddb04089700cf9c27f6f7426281', 'fltt': '2', 'invt': '2', 'fid': 'f3', 'fs': 'm:10,m:140,m:141,m:151', 'fields': 'f1,f2,f3,f4,f5,f6,f7,f8,f9,f10,f12,f13,f14,f15,f16,f17,f18,f20,f21,f23,f24,f25,f22,f28,f11,f62,f128,f136,f115,f152,f133,f108,f163,f161,f162', '_': '1606225274063', } r = requests.get(url, params=params) data_text = r.text data_json = json.loads(data_text[data_text.find('{'):-2]) temp_df = pd.DataFrame(data_json['data']['diff']) temp_df.columns = [ '_', '最新价', '涨跌幅', '涨跌额', '成交量', '成交额', '_', '_', '_', '_', '_', '代码', '_', '名称', '_', '_', '今开', '_', '_', '_', '_', '_', '_', '_', '昨结', '_', '持仓量', '_', '_', '_', '_', '_', '_', '_', '行权价', '剩余日', '日增' ] temp_df = temp_df[[ '代码', '名称', '最新价', '涨跌额', '涨跌幅', '成交量', '成交额', '持仓量', '行权价', '剩余日', '日增', '昨结', '今开' ]] temp_df['最新价'] = pd.to_numeric(temp_df['最新价'], errors='coerce') temp_df['涨跌额'] = pd.to_numeric(temp_df['涨跌额'], errors='coerce') temp_df['涨跌幅'] = pd.to_numeric(temp_df['涨跌幅'], 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['持仓量'] = pd.to_numeric(temp_df['持仓量'], errors='coerce') temp_df['行权价'] = pd.to_numeric(temp_df['行权价'], errors='coerce') temp_df['剩余日'] = pd.to_numeric(temp_df['剩余日'], errors='coerce') temp_df['日增'] = pd.to_numeric(temp_df['日增'], errors='coerce') temp_df['昨结'] = pd.to_numeric(temp_df['昨结'], errors='coerce') temp_df['今开'] = pd.to_numeric(temp_df['今开'], errors='coerce') return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/option/option_em.py#L14-L105

25

[ 0, 1, 2, 3, 4, 5, 6 ]

7.608696

[ 7, 8, 22, 23, 24, 25, 26, 65, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91 ]

21.73913

false

21.428571

92

1

78.26087

4

def option_current_em() -> pd.DataFrame: url = 'http://23.push2.eastmoney.com/api/qt/clist/get' params = { 'cb': 'jQuery112409395946290628259_1606225274048', 'pn': '1', 'pz': '200000', 'po': '1', 'np': '1', 'ut': 'bd1d9ddb04089700cf9c27f6f7426281', 'fltt': '2', 'invt': '2', 'fid': 'f3', 'fs': 'm:10,m:140,m:141,m:151', 'fields': 'f1,f2,f3,f4,f5,f6,f7,f8,f9,f10,f12,f13,f14,f15,f16,f17,f18,f20,f21,f23,f24,f25,f22,f28,f11,f62,f128,f136,f115,f152,f133,f108,f163,f161,f162', '_': '1606225274063', } r = requests.get(url, params=params) data_text = r.text data_json = json.loads(data_text[data_text.find('{'):-2]) temp_df = pd.DataFrame(data_json['data']['diff']) temp_df.columns = [ '_', '最新价', '涨跌幅', '涨跌额', '成交量', '成交额', '_', '_', '_', '_', '_', '代码', '_', '名称', '_', '_', '今开', '_', '_', '_', '_', '_', '_', '_', '昨结', '_', '持仓量', '_', '_', '_', '_', '_', '_', '_', '行权价', '剩余日', '日增' ] temp_df = temp_df[[ '代码', '名称', '最新价', '涨跌额', '涨跌幅', '成交量', '成交额', '持仓量', '行权价', '剩余日', '日增', '昨结', '今开' ]] temp_df['最新价'] = pd.to_numeric(temp_df['最新价'], errors='coerce') temp_df['涨跌额'] = pd.to_numeric(temp_df['涨跌额'], errors='coerce') temp_df['涨跌幅'] = pd.to_numeric(temp_df['涨跌幅'], 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['持仓量'] = pd.to_numeric(temp_df['持仓量'], errors='coerce') temp_df['行权价'] = pd.to_numeric(temp_df['行权价'], errors='coerce') temp_df['剩余日'] = pd.to_numeric(temp_df['剩余日'], errors='coerce') temp_df['日增'] = pd.to_numeric(temp_df['日增'], errors='coerce') temp_df['昨结'] = pd.to_numeric(temp_df['昨结'], errors='coerce') temp_df['今开'] = pd.to_numeric(temp_df['今开'], errors='coerce') return temp_df

17,952

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/option/option_value_analysis_em.py

option_value_analysis_em

()

return temp_df

东方财富网-数据中心-特色数据-期权价值分析 https://data.eastmoney.com/other/valueAnal.html :return: 期权价值分析 :rtype: pandas.DataFrame

12

77

def option_value_analysis_em() -> pd.DataFrame: """ 东方财富网-数据中心-特色数据-期权价值分析 https://data.eastmoney.com/other/valueAnal.html :return: 期权价值分析 :rtype: pandas.DataFrame """ url = "https://push2.eastmoney.com/api/qt/clist/get" params = { 'fid': 'f301', 'po': '1', 'pz': '5000', 'pn': '1', 'np': '1', 'fltt': '2', 'invt': '2', 'ut': 'b2884a393a59ad64002292a3e90d46a5', 'fields': 'f1,f2,f3,f12,f13,f14,f298,f299,f249,f300,f330,f331,f332,f333,f334,f335,f336,f301,f152', 'fs': 'm:10' } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["data"]["diff"]) temp_df.columns = [ '-', '最新价', '-', '期权代码', '-', '期权名称', '-', '隐含波动率', '时间价值', '内在价值', '理论价格', '到期日', '-', '-', '-', '标的名称', '标的最新价', '-', '标的近一年波动率', ] temp_df = temp_df[[ '期权代码', '期权名称', '最新价', '时间价值', '内在价值', '隐含波动率', '理论价格', '标的名称', '标的最新价', '标的近一年波动率', '到期日', ]] temp_df['最新价'] = pd.to_numeric(temp_df['最新价'], errors="coerce") temp_df['时间价值'] = pd.to_numeric(temp_df['时间价值']) temp_df['内在价值'] = pd.to_numeric(temp_df['内在价值']) temp_df['隐含波动率'] = pd.to_numeric(temp_df['隐含波动率']) temp_df['理论价格'] = pd.to_numeric(temp_df['理论价格'], errors="coerce") temp_df['标的最新价'] = pd.to_numeric(temp_df['标的最新价']) temp_df['标的近一年波动率'] = pd.to_numeric(temp_df['标的近一年波动率']) temp_df['到期日'] = pd.to_datetime(temp_df['到期日'].astype(str)).dt.date return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/option/option_value_analysis_em.py#L12-L77

25

[ 0, 1, 2, 3, 4, 5, 6 ]

10.606061

[ 7, 8, 20, 21, 22, 23, 44, 57, 58, 59, 60, 61, 62, 63, 64, 65 ]

24.242424

false

21.73913

66

1

75.757576

4

def option_value_analysis_em() -> pd.DataFrame: url = "https://push2.eastmoney.com/api/qt/clist/get" params = { 'fid': 'f301', 'po': '1', 'pz': '5000', 'pn': '1', 'np': '1', 'fltt': '2', 'invt': '2', 'ut': 'b2884a393a59ad64002292a3e90d46a5', 'fields': 'f1,f2,f3,f12,f13,f14,f298,f299,f249,f300,f330,f331,f332,f333,f334,f335,f336,f301,f152', 'fs': 'm:10' } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["data"]["diff"]) temp_df.columns = [ '-', '最新价', '-', '期权代码', '-', '期权名称', '-', '隐含波动率', '时间价值', '内在价值', '理论价格', '到期日', '-', '-', '-', '标的名称', '标的最新价', '-', '标的近一年波动率', ] temp_df = temp_df[[ '期权代码', '期权名称', '最新价', '时间价值', '内在价值', '隐含波动率', '理论价格', '标的名称', '标的最新价', '标的近一年波动率', '到期日', ]] temp_df['最新价'] = pd.to_numeric(temp_df['最新价'], errors="coerce") temp_df['时间价值'] = pd.to_numeric(temp_df['时间价值']) temp_df['内在价值'] = pd.to_numeric(temp_df['内在价值']) temp_df['隐含波动率'] = pd.to_numeric(temp_df['隐含波动率']) temp_df['理论价格'] = pd.to_numeric(temp_df['理论价格'], errors="coerce") temp_df['标的最新价'] = pd.to_numeric(temp_df['标的最新价']) temp_df['标的近一年波动率'] = pd.to_numeric(temp_df['标的近一年波动率']) temp_df['到期日'] = pd.to_datetime(temp_df['到期日'].astype(str)).dt.date return temp_df

17,953

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/option/option_finance_sina.py

option_cffex_sz50_list_sina

()

return {symbol: contract}

新浪财经-中金所-上证 50 指数-所有合约, 返回的第一个合约为主力合约目前新浪财经-中金所有上证 50 指数，沪深 300 指数和中证 1000 指数 :return: 中金所-上证 50 指数-所有合约 :rtype: dict

23

36

def option_cffex_sz50_list_sina() -> Dict[str, List[str]]: """ 新浪财经-中金所-上证 50 指数-所有合约, 返回的第一个合约为主力合约目前新浪财经-中金所有上证 50 指数，沪深 300 指数和中证 1000 指数 :return: 中金所-上证 50 指数-所有合约 :rtype: dict """ url = "https://stock.finance.sina.com.cn/futures/view/optionsCffexDP.php/ho/cffex" r = requests.get(url) soup = BeautifulSoup(r.text, "lxml") symbol = soup.find(attrs={"id": "option_symbol"}).find_all("li")[0].text temp_attr = soup.find(attrs={"id": "option_suffix"}).find_all("li") contract = [item.text for item in temp_attr] return {symbol: contract}

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/option/option_finance_sina.py#L23-L36

25

[ 0, 1, 2, 3, 4, 5, 6 ]

50

[ 7, 8, 9, 10, 11, 12, 13 ]

50

false

8.469055

14

2

50

4

def option_cffex_sz50_list_sina() -> Dict[str, List[str]]: url = "https://stock.finance.sina.com.cn/futures/view/optionsCffexDP.php/ho/cffex" r = requests.get(url) soup = BeautifulSoup(r.text, "lxml") symbol = soup.find(attrs={"id": "option_symbol"}).find_all("li")[0].text temp_attr = soup.find(attrs={"id": "option_suffix"}).find_all("li") contract = [item.text for item in temp_attr] return {symbol: contract}

17,954

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/option/option_finance_sina.py

option_cffex_hs300_list_sina

()

return {symbol: contract}

新浪财经-中金所-沪深 300 指数-所有合约, 返回的第一个合约为主力合约目前新浪财经-中金所有沪深 300 指数和中证 1000 指数 :return: 中金所-沪深300指数-所有合约 :rtype: dict

40

53

def option_cffex_hs300_list_sina() -> Dict[str, List[str]]: """ 新浪财经-中金所-沪深 300 指数-所有合约, 返回的第一个合约为主力合约目前新浪财经-中金所有沪深 300 指数和中证 1000 指数 :return: 中金所-沪深300指数-所有合约 :rtype: dict """ url = "https://stock.finance.sina.com.cn/futures/view/optionsCffexDP.php" r = requests.get(url) soup = BeautifulSoup(r.text, "lxml") symbol = soup.find(attrs={"id": "option_symbol"}).find_all("li")[1].text temp_attr = soup.find(attrs={"id": "option_suffix"}).find_all("li") contract = [item.text for item in temp_attr] return {symbol: contract}

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/option/option_finance_sina.py#L40-L53

25

[ 0, 1, 2, 3, 4, 5, 6 ]

50

[ 7, 8, 9, 10, 11, 12, 13 ]

50

false

8.469055

14

2

50

4

def option_cffex_hs300_list_sina() -> Dict[str, List[str]]: url = "https://stock.finance.sina.com.cn/futures/view/optionsCffexDP.php" r = requests.get(url) soup = BeautifulSoup(r.text, "lxml") symbol = soup.find(attrs={"id": "option_symbol"}).find_all("li")[1].text temp_attr = soup.find(attrs={"id": "option_suffix"}).find_all("li") contract = [item.text for item in temp_attr] return {symbol: contract}

17,955

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/option/option_finance_sina.py

option_cffex_zz1000_list_sina

()

return {symbol: contract}

新浪财经-中金所-中证 1000 指数-所有合约, 返回的第一个合约为主力合约目前新浪财经-中金所有沪深 300 指数和中证 1000 指数 :return: 中金所-中证 1000 指数-所有合约 :rtype: dict

56

69

def option_cffex_zz1000_list_sina() -> Dict[str, List[str]]: """ 新浪财经-中金所-中证 1000 指数-所有合约, 返回的第一个合约为主力合约目前新浪财经-中金所有沪深 300 指数和中证 1000 指数 :return: 中金所-中证 1000 指数-所有合约 :rtype: dict """ url = "https://stock.finance.sina.com.cn/futures/view/optionsCffexDP.php/mo/cffex" r = requests.get(url) soup = BeautifulSoup(r.text, "lxml") symbol = soup.find(attrs={"id": "option_symbol"}).find_all("li")[2].text temp_attr = soup.find(attrs={"id": "option_suffix"}).find_all("li") contract = [item.text for item in temp_attr] return {symbol: contract}

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/option/option_finance_sina.py#L56-L69

25

[ 0, 1, 2, 3, 4, 5, 6 ]

50

[ 7, 8, 9, 10, 11, 12, 13 ]

50

false

8.469055

14

2

50

4

def option_cffex_zz1000_list_sina() -> Dict[str, List[str]]: url = "https://stock.finance.sina.com.cn/futures/view/optionsCffexDP.php/mo/cffex" r = requests.get(url) soup = BeautifulSoup(r.text, "lxml") symbol = soup.find(attrs={"id": "option_symbol"}).find_all("li")[2].text temp_attr = soup.find(attrs={"id": "option_suffix"}).find_all("li") contract = [item.text for item in temp_attr] return {symbol: contract}

17,956

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/option/option_finance_sina.py

option_cffex_sz50_spot_sina

(symbol: str = "ho2303")

return data_df

中金所-上证 50 指数-指定合约-实时行情 https://stock.finance.sina.com.cn/futures/view/optionsCffexDP.php/ho/cffex :param symbol: 合约代码; 用 ak.option_cffex_sz300_list_sina() 函数查看 :type symbol: str :return: 中金所-上证 50 指数-指定合约-看涨看跌实时行情 :rtype: pd.DataFrame

72

136

def option_cffex_sz50_spot_sina(symbol: str = "ho2303") -> pd.DataFrame: """ 中金所-上证 50 指数-指定合约-实时行情 https://stock.finance.sina.com.cn/futures/view/optionsCffexDP.php/ho/cffex :param symbol: 合约代码; 用 ak.option_cffex_sz300_list_sina() 函数查看 :type symbol: str :return: 中金所-上证 50 指数-指定合约-看涨看跌实时行情 :rtype: pd.DataFrame """ url = "https://stock.finance.sina.com.cn/futures/api/openapi.php/OptionService.getOptionData" params = { "type": "futures", "product": "ho", "exchange": "cffex", "pinzhong": symbol, } r = requests.get(url, params=params) data_text = r.text data_json = json.loads( data_text[data_text.find("{") : data_text.rfind("}") + 1] ) option_call_df = pd.DataFrame( data_json["result"]["data"]["up"], columns=[ "看涨合约-买量", "看涨合约-买价", "看涨合约-最新价", "看涨合约-卖价", "看涨合约-卖量", "看涨合约-持仓量", "看涨合约-涨跌", "行权价", "看涨合约-标识", ], ) option_put_df = pd.DataFrame( data_json["result"]["data"]["down"], columns=[ "看跌合约-买量", "看跌合约-买价", "看跌合约-最新价", "看跌合约-卖价", "看跌合约-卖量", "看跌合约-持仓量", "看跌合约-涨跌", "看跌合约-标识", ], ) data_df = pd.concat([option_call_df, option_put_df], axis=1) data_df["看涨合约-买量"] = pd.to_numeric(data_df["看涨合约-买量"], errors="coerce") data_df["看涨合约-买价"] = pd.to_numeric(data_df["看涨合约-买价"], errors="coerce") data_df["看涨合约-最新价"] = pd.to_numeric(data_df["看涨合约-最新价"], errors="coerce") data_df["看涨合约-卖价"] = pd.to_numeric(data_df["看涨合约-卖价"], errors="coerce") data_df["看涨合约-卖量"] = pd.to_numeric(data_df["看涨合约-卖量"], errors="coerce") data_df["看涨合约-持仓量"] = pd.to_numeric(data_df["看涨合约-持仓量"], errors="coerce") data_df["看涨合约-涨跌"] = pd.to_numeric(data_df["看涨合约-涨跌"], errors="coerce") data_df["行权价"] = pd.to_numeric(data_df["行权价"], errors="coerce") data_df["看跌合约-买量"] = pd.to_numeric(data_df["看跌合约-买量"], errors="coerce") data_df["看跌合约-买价"] = pd.to_numeric(data_df["看跌合约-买价"], errors="coerce") data_df["看跌合约-最新价"] = pd.to_numeric(data_df["看跌合约-最新价"], errors="coerce") data_df["看跌合约-卖价"] = pd.to_numeric(data_df["看跌合约-卖价"], errors="coerce") data_df["看跌合约-卖量"] = pd.to_numeric(data_df["看跌合约-卖量"], errors="coerce") data_df["看跌合约-持仓量"] = pd.to_numeric(data_df["看跌合约-持仓量"], errors="coerce") data_df["看跌合约-涨跌"] = pd.to_numeric(data_df["看跌合约-涨跌"], errors="coerce") return data_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/option/option_finance_sina.py#L72-L136

25

[ 0, 1, 2, 3, 4, 5, 6, 7, 8 ]

13.846154

[ 9, 10, 16, 17, 18, 21, 35, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64 ]

36.923077

false

8.469055

65

1

63.076923

6

def option_cffex_sz50_spot_sina(symbol: str = "ho2303") -> pd.DataFrame: url = "https://stock.finance.sina.com.cn/futures/api/openapi.php/OptionService.getOptionData" params = { "type": "futures", "product": "ho", "exchange": "cffex", "pinzhong": symbol, } r = requests.get(url, params=params) data_text = r.text data_json = json.loads( data_text[data_text.find("{") : data_text.rfind("}") + 1] ) option_call_df = pd.DataFrame( data_json["result"]["data"]["up"], columns=[ "看涨合约-买量", "看涨合约-买价", "看涨合约-最新价", "看涨合约-卖价", "看涨合约-卖量", "看涨合约-持仓量", "看涨合约-涨跌", "行权价", "看涨合约-标识", ], ) option_put_df = pd.DataFrame( data_json["result"]["data"]["down"], columns=[ "看跌合约-买量", "看跌合约-买价", "看跌合约-最新价", "看跌合约-卖价", "看跌合约-卖量", "看跌合约-持仓量", "看跌合约-涨跌", "看跌合约-标识", ], ) data_df = pd.concat([option_call_df, option_put_df], axis=1) data_df["看涨合约-买量"] = pd.to_numeric(data_df["看涨合约-买量"], errors="coerce") data_df["看涨合约-买价"] = pd.to_numeric(data_df["看涨合约-买价"], errors="coerce") data_df["看涨合约-最新价"] = pd.to_numeric(data_df["看涨合约-最新价"], errors="coerce") data_df["看涨合约-卖价"] = pd.to_numeric(data_df["看涨合约-卖价"], errors="coerce") data_df["看涨合约-卖量"] = pd.to_numeric(data_df["看涨合约-卖量"], errors="coerce") data_df["看涨合约-持仓量"] = pd.to_numeric(data_df["看涨合约-持仓量"], errors="coerce") data_df["看涨合约-涨跌"] = pd.to_numeric(data_df["看涨合约-涨跌"], errors="coerce") data_df["行权价"] = pd.to_numeric(data_df["行权价"], errors="coerce") data_df["看跌合约-买量"] = pd.to_numeric(data_df["看跌合约-买量"], errors="coerce") data_df["看跌合约-买价"] = pd.to_numeric(data_df["看跌合约-买价"], errors="coerce") data_df["看跌合约-最新价"] = pd.to_numeric(data_df["看跌合约-最新价"], errors="coerce") data_df["看跌合约-卖价"] = pd.to_numeric(data_df["看跌合约-卖价"], errors="coerce") data_df["看跌合约-卖量"] = pd.to_numeric(data_df["看跌合约-卖量"], errors="coerce") data_df["看跌合约-持仓量"] = pd.to_numeric(data_df["看跌合约-持仓量"], errors="coerce") data_df["看跌合约-涨跌"] = pd.to_numeric(data_df["看跌合约-涨跌"], errors="coerce") return data_df

17,957

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/option/option_finance_sina.py

option_cffex_hs300_spot_sina

(symbol: str = "io2204")

return data_df

中金所-沪深 300 指数-指定合约-实时行情 https://stock.finance.sina.com.cn/futures/view/optionsCffexDP.php :param symbol: 合约代码; 用 option_cffex_hs300_list_sina 函数查看 :type symbol: str :return: 中金所-沪深300指数-指定合约-看涨看跌实时行情 :rtype: pd.DataFrame

139

203

def option_cffex_hs300_spot_sina(symbol: str = "io2204") -> pd.DataFrame: """ 中金所-沪深 300 指数-指定合约-实时行情 https://stock.finance.sina.com.cn/futures/view/optionsCffexDP.php :param symbol: 合约代码; 用 option_cffex_hs300_list_sina 函数查看 :type symbol: str :return: 中金所-沪深300指数-指定合约-看涨看跌实时行情 :rtype: pd.DataFrame """ url = "https://stock.finance.sina.com.cn/futures/api/openapi.php/OptionService.getOptionData" params = { "type": "futures", "product": "io", "exchange": "cffex", "pinzhong": symbol, } r = requests.get(url, params=params) data_text = r.text data_json = json.loads( data_text[data_text.find("{") : data_text.rfind("}") + 1] ) option_call_df = pd.DataFrame( data_json["result"]["data"]["up"], columns=[ "看涨合约-买量", "看涨合约-买价", "看涨合约-最新价", "看涨合约-卖价", "看涨合约-卖量", "看涨合约-持仓量", "看涨合约-涨跌", "行权价", "看涨合约-标识", ], ) option_put_df = pd.DataFrame( data_json["result"]["data"]["down"], columns=[ "看跌合约-买量", "看跌合约-买价", "看跌合约-最新价", "看跌合约-卖价", "看跌合约-卖量", "看跌合约-持仓量", "看跌合约-涨跌", "看跌合约-标识", ], ) data_df = pd.concat([option_call_df, option_put_df], axis=1) data_df["看涨合约-买量"] = pd.to_numeric(data_df["看涨合约-买量"], errors="coerce") data_df["看涨合约-买价"] = pd.to_numeric(data_df["看涨合约-买价"], errors="coerce") data_df["看涨合约-最新价"] = pd.to_numeric(data_df["看涨合约-最新价"], errors="coerce") data_df["看涨合约-卖价"] = pd.to_numeric(data_df["看涨合约-卖价"], errors="coerce") data_df["看涨合约-卖量"] = pd.to_numeric(data_df["看涨合约-卖量"], errors="coerce") data_df["看涨合约-持仓量"] = pd.to_numeric(data_df["看涨合约-持仓量"], errors="coerce") data_df["看涨合约-涨跌"] = pd.to_numeric(data_df["看涨合约-涨跌"], errors="coerce") data_df["行权价"] = pd.to_numeric(data_df["行权价"], errors="coerce") data_df["看跌合约-买量"] = pd.to_numeric(data_df["看跌合约-买量"], errors="coerce") data_df["看跌合约-买价"] = pd.to_numeric(data_df["看跌合约-买价"], errors="coerce") data_df["看跌合约-最新价"] = pd.to_numeric(data_df["看跌合约-最新价"], errors="coerce") data_df["看跌合约-卖价"] = pd.to_numeric(data_df["看跌合约-卖价"], errors="coerce") data_df["看跌合约-卖量"] = pd.to_numeric(data_df["看跌合约-卖量"], errors="coerce") data_df["看跌合约-持仓量"] = pd.to_numeric(data_df["看跌合约-持仓量"], errors="coerce") data_df["看跌合约-涨跌"] = pd.to_numeric(data_df["看跌合约-涨跌"], errors="coerce") return data_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/option/option_finance_sina.py#L139-L203

25

[ 0, 1, 2, 3, 4, 5, 6, 7, 8 ]

13.846154

[ 9, 10, 16, 17, 18, 21, 35, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64 ]

36.923077

false

8.469055

65

1

63.076923

6

def option_cffex_hs300_spot_sina(symbol: str = "io2204") -> pd.DataFrame: url = "https://stock.finance.sina.com.cn/futures/api/openapi.php/OptionService.getOptionData" params = { "type": "futures", "product": "io", "exchange": "cffex", "pinzhong": symbol, } r = requests.get(url, params=params) data_text = r.text data_json = json.loads( data_text[data_text.find("{") : data_text.rfind("}") + 1] ) option_call_df = pd.DataFrame( data_json["result"]["data"]["up"], columns=[ "看涨合约-买量", "看涨合约-买价", "看涨合约-最新价", "看涨合约-卖价", "看涨合约-卖量", "看涨合约-持仓量", "看涨合约-涨跌", "行权价", "看涨合约-标识", ], ) option_put_df = pd.DataFrame( data_json["result"]["data"]["down"], columns=[ "看跌合约-买量", "看跌合约-买价", "看跌合约-最新价", "看跌合约-卖价", "看跌合约-卖量", "看跌合约-持仓量", "看跌合约-涨跌", "看跌合约-标识", ], ) data_df = pd.concat([option_call_df, option_put_df], axis=1) data_df["看涨合约-买量"] = pd.to_numeric(data_df["看涨合约-买量"], errors="coerce") data_df["看涨合约-买价"] = pd.to_numeric(data_df["看涨合约-买价"], errors="coerce") data_df["看涨合约-最新价"] = pd.to_numeric(data_df["看涨合约-最新价"], errors="coerce") data_df["看涨合约-卖价"] = pd.to_numeric(data_df["看涨合约-卖价"], errors="coerce") data_df["看涨合约-卖量"] = pd.to_numeric(data_df["看涨合约-卖量"], errors="coerce") data_df["看涨合约-持仓量"] = pd.to_numeric(data_df["看涨合约-持仓量"], errors="coerce") data_df["看涨合约-涨跌"] = pd.to_numeric(data_df["看涨合约-涨跌"], errors="coerce") data_df["行权价"] = pd.to_numeric(data_df["行权价"], errors="coerce") data_df["看跌合约-买量"] = pd.to_numeric(data_df["看跌合约-买量"], errors="coerce") data_df["看跌合约-买价"] = pd.to_numeric(data_df["看跌合约-买价"], errors="coerce") data_df["看跌合约-最新价"] = pd.to_numeric(data_df["看跌合约-最新价"], errors="coerce") data_df["看跌合约-卖价"] = pd.to_numeric(data_df["看跌合约-卖价"], errors="coerce") data_df["看跌合约-卖量"] = pd.to_numeric(data_df["看跌合约-卖量"], errors="coerce") data_df["看跌合约-持仓量"] = pd.to_numeric(data_df["看跌合约-持仓量"], errors="coerce") data_df["看跌合约-涨跌"] = pd.to_numeric(data_df["看跌合约-涨跌"], errors="coerce") return data_df

17,958

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/option/option_finance_sina.py

option_cffex_zz1000_spot_sina

(symbol: str = "mo2208")

return data_df

中金所-中证 1000 指数-指定合约-实时行情 https://stock.finance.sina.com.cn/futures/view/optionsCffexDP.php :param symbol: 合约代码; 用 option_cffex_zz1000_list_sina 函数查看 :type symbol: str :return: 中金所-中证 1000 指数-指定合约-看涨看跌实时行情 :rtype: pd.DataFrame

206

270

def option_cffex_zz1000_spot_sina(symbol: str = "mo2208") -> pd.DataFrame: """ 中金所-中证 1000 指数-指定合约-实时行情 https://stock.finance.sina.com.cn/futures/view/optionsCffexDP.php :param symbol: 合约代码; 用 option_cffex_zz1000_list_sina 函数查看 :type symbol: str :return: 中金所-中证 1000 指数-指定合约-看涨看跌实时行情 :rtype: pd.DataFrame """ url = "https://stock.finance.sina.com.cn/futures/api/openapi.php/OptionService.getOptionData" params = { "type": "futures", "product": "mo", "exchange": "cffex", "pinzhong": symbol, } r = requests.get(url, params=params) data_text = r.text data_json = json.loads( data_text[data_text.find("{") : data_text.rfind("}") + 1] ) option_call_df = pd.DataFrame( data_json["result"]["data"]["up"], columns=[ "看涨合约-买量", "看涨合约-买价", "看涨合约-最新价", "看涨合约-卖价", "看涨合约-卖量", "看涨合约-持仓量", "看涨合约-涨跌", "行权价", "看涨合约-标识", ], ) option_put_df = pd.DataFrame( data_json["result"]["data"]["down"], columns=[ "看跌合约-买量", "看跌合约-买价", "看跌合约-最新价", "看跌合约-卖价", "看跌合约-卖量", "看跌合约-持仓量", "看跌合约-涨跌", "看跌合约-标识", ], ) data_df = pd.concat([option_call_df, option_put_df], axis=1) data_df["看涨合约-买量"] = pd.to_numeric(data_df["看涨合约-买量"], errors="coerce") data_df["看涨合约-买价"] = pd.to_numeric(data_df["看涨合约-买价"], errors="coerce") data_df["看涨合约-最新价"] = pd.to_numeric(data_df["看涨合约-最新价"], errors="coerce") data_df["看涨合约-卖价"] = pd.to_numeric(data_df["看涨合约-卖价"], errors="coerce") data_df["看涨合约-卖量"] = pd.to_numeric(data_df["看涨合约-卖量"], errors="coerce") data_df["看涨合约-持仓量"] = pd.to_numeric(data_df["看涨合约-持仓量"], errors="coerce") data_df["看涨合约-涨跌"] = pd.to_numeric(data_df["看涨合约-涨跌"], errors="coerce") data_df["行权价"] = pd.to_numeric(data_df["行权价"], errors="coerce") data_df["看跌合约-买量"] = pd.to_numeric(data_df["看跌合约-买量"], errors="coerce") data_df["看跌合约-买价"] = pd.to_numeric(data_df["看跌合约-买价"], errors="coerce") data_df["看跌合约-最新价"] = pd.to_numeric(data_df["看跌合约-最新价"], errors="coerce") data_df["看跌合约-卖价"] = pd.to_numeric(data_df["看跌合约-卖价"], errors="coerce") data_df["看跌合约-卖量"] = pd.to_numeric(data_df["看跌合约-卖量"], errors="coerce") data_df["看跌合约-持仓量"] = pd.to_numeric(data_df["看跌合约-持仓量"], errors="coerce") data_df["看跌合约-涨跌"] = pd.to_numeric(data_df["看跌合约-涨跌"], errors="coerce") return data_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/option/option_finance_sina.py#L206-L270

25

[ 0, 1, 2, 3, 4, 5, 6, 7, 8 ]

13.846154

[ 9, 10, 16, 17, 18, 21, 35, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64 ]

36.923077

false

8.469055

65

1

63.076923

6

def option_cffex_zz1000_spot_sina(symbol: str = "mo2208") -> pd.DataFrame: url = "https://stock.finance.sina.com.cn/futures/api/openapi.php/OptionService.getOptionData" params = { "type": "futures", "product": "mo", "exchange": "cffex", "pinzhong": symbol, } r = requests.get(url, params=params) data_text = r.text data_json = json.loads( data_text[data_text.find("{") : data_text.rfind("}") + 1] ) option_call_df = pd.DataFrame( data_json["result"]["data"]["up"], columns=[ "看涨合约-买量", "看涨合约-买价", "看涨合约-最新价", "看涨合约-卖价", "看涨合约-卖量", "看涨合约-持仓量", "看涨合约-涨跌", "行权价", "看涨合约-标识", ], ) option_put_df = pd.DataFrame( data_json["result"]["data"]["down"], columns=[ "看跌合约-买量", "看跌合约-买价", "看跌合约-最新价", "看跌合约-卖价", "看跌合约-卖量", "看跌合约-持仓量", "看跌合约-涨跌", "看跌合约-标识", ], ) data_df = pd.concat([option_call_df, option_put_df], axis=1) data_df["看涨合约-买量"] = pd.to_numeric(data_df["看涨合约-买量"], errors="coerce") data_df["看涨合约-买价"] = pd.to_numeric(data_df["看涨合约-买价"], errors="coerce") data_df["看涨合约-最新价"] = pd.to_numeric(data_df["看涨合约-最新价"], errors="coerce") data_df["看涨合约-卖价"] = pd.to_numeric(data_df["看涨合约-卖价"], errors="coerce") data_df["看涨合约-卖量"] = pd.to_numeric(data_df["看涨合约-卖量"], errors="coerce") data_df["看涨合约-持仓量"] = pd.to_numeric(data_df["看涨合约-持仓量"], errors="coerce") data_df["看涨合约-涨跌"] = pd.to_numeric(data_df["看涨合约-涨跌"], errors="coerce") data_df["行权价"] = pd.to_numeric(data_df["行权价"], errors="coerce") data_df["看跌合约-买量"] = pd.to_numeric(data_df["看跌合约-买量"], errors="coerce") data_df["看跌合约-买价"] = pd.to_numeric(data_df["看跌合约-买价"], errors="coerce") data_df["看跌合约-最新价"] = pd.to_numeric(data_df["看跌合约-最新价"], errors="coerce") data_df["看跌合约-卖价"] = pd.to_numeric(data_df["看跌合约-卖价"], errors="coerce") data_df["看跌合约-卖量"] = pd.to_numeric(data_df["看跌合约-卖量"], errors="coerce") data_df["看跌合约-持仓量"] = pd.to_numeric(data_df["看跌合约-持仓量"], errors="coerce") data_df["看跌合约-涨跌"] = pd.to_numeric(data_df["看跌合约-涨跌"], errors="coerce") return data_df

17,959

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/option/option_finance_sina.py

option_cffex_sz50_daily_sina

(symbol: str = "ho2303P2350")

return data_df

新浪财经-中金所-上证 50 指数-指定合约-日频行情 :param symbol: 具体合约代码(包括看涨和看跌标识), 可以通过 ak.option_cffex_sz50_spot_sina 中的 call-标识获取 :type symbol: str :return: 日频率数据 :rtype: pd.DataFrame

273

308

def option_cffex_sz50_daily_sina(symbol: str = "ho2303P2350") -> pd.DataFrame: """ 新浪财经-中金所-上证 50 指数-指定合约-日频行情 :param symbol: 具体合约代码(包括看涨和看跌标识), 可以通过 ak.option_cffex_sz50_spot_sina 中的 call-标识获取 :type symbol: str :return: 日频率数据 :rtype: pd.DataFrame """ year = datetime.datetime.now().year month = datetime.datetime.now().month day = datetime.datetime.now().day url = f"https://stock.finance.sina.com.cn/futures/api/jsonp.php/var%20_{symbol}{year}_{month}_{day}=/FutureOptionAllService.getOptionDayline" params = {"symbol": symbol} r = requests.get(url, params=params) data_text = r.text data_df = pd.DataFrame( eval(data_text[data_text.find("[") : data_text.rfind("]") + 1]) ) data_df.columns = ["open", "high", "low", "close", "volume", "date"] data_df = data_df[ [ "date", "open", "high", "low", "close", "volume", ] ] data_df["date"] = pd.to_datetime(data_df["date"]).dt.date data_df["open"] = pd.to_numeric(data_df["open"]) data_df["high"] = pd.to_numeric(data_df["high"]) data_df["low"] = pd.to_numeric(data_df["low"]) data_df["close"] = pd.to_numeric(data_df["close"]) data_df["volume"] = pd.to_numeric(data_df["volume"]) return data_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/option/option_finance_sina.py#L273-L308

25

[ 0, 1, 2, 3, 4, 5, 6, 7 ]

22.222222

[ 8, 9, 10, 11, 12, 13, 14, 15, 18, 19, 29, 30, 31, 32, 33, 34, 35 ]

47.222222

false

8.469055

36

1

52.777778

5

def option_cffex_sz50_daily_sina(symbol: str = "ho2303P2350") -> pd.DataFrame: year = datetime.datetime.now().year month = datetime.datetime.now().month day = datetime.datetime.now().day url = f"https://stock.finance.sina.com.cn/futures/api/jsonp.php/var%20_{symbol}{year}_{month}_{day}=/FutureOptionAllService.getOptionDayline" params = {"symbol": symbol} r = requests.get(url, params=params) data_text = r.text data_df = pd.DataFrame( eval(data_text[data_text.find("[") : data_text.rfind("]") + 1]) ) data_df.columns = ["open", "high", "low", "close", "volume", "date"] data_df = data_df[ [ "date", "open", "high", "low", "close", "volume", ] ] data_df["date"] = pd.to_datetime(data_df["date"]).dt.date data_df["open"] = pd.to_numeric(data_df["open"]) data_df["high"] = pd.to_numeric(data_df["high"]) data_df["low"] = pd.to_numeric(data_df["low"]) data_df["close"] = pd.to_numeric(data_df["close"]) data_df["volume"] = pd.to_numeric(data_df["volume"]) return data_df

17,960

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/option/option_finance_sina.py

option_cffex_hs300_daily_sina

(symbol: str = "io2202P4350")

return data_df

新浪财经-中金所-沪深300指数-指定合约-日频行情 :param symbol: 具体合约代码(包括看涨和看跌标识), 可以通过 ak.option_cffex_hs300_spot_sina 中的 call-标识获取 :type symbol: str :return: 日频率数据 :rtype: pd.DataFrame

311

346

def option_cffex_hs300_daily_sina(symbol: str = "io2202P4350") -> pd.DataFrame: """ 新浪财经-中金所-沪深300指数-指定合约-日频行情 :param symbol: 具体合约代码(包括看涨和看跌标识), 可以通过 ak.option_cffex_hs300_spot_sina 中的 call-标识获取 :type symbol: str :return: 日频率数据 :rtype: pd.DataFrame """ year = datetime.datetime.now().year month = datetime.datetime.now().month day = datetime.datetime.now().day url = f"https://stock.finance.sina.com.cn/futures/api/jsonp.php/var%20_{symbol}{year}_{month}_{day}=/FutureOptionAllService.getOptionDayline" params = {"symbol": symbol} r = requests.get(url, params=params) data_text = r.text data_df = pd.DataFrame( eval(data_text[data_text.find("[") : data_text.rfind("]") + 1]) ) data_df.columns = ["open", "high", "low", "close", "volume", "date"] data_df = data_df[ [ "date", "open", "high", "low", "close", "volume", ] ] data_df["date"] = pd.to_datetime(data_df["date"]).dt.date data_df["open"] = pd.to_numeric(data_df["open"]) data_df["high"] = pd.to_numeric(data_df["high"]) data_df["low"] = pd.to_numeric(data_df["low"]) data_df["close"] = pd.to_numeric(data_df["close"]) data_df["volume"] = pd.to_numeric(data_df["volume"]) return data_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/option/option_finance_sina.py#L311-L346

25

[ 0, 1, 2, 3, 4, 5, 6, 7 ]

22.222222

[ 8, 9, 10, 11, 12, 13, 14, 15, 18, 19, 29, 30, 31, 32, 33, 34, 35 ]

47.222222

false

8.469055

36

1

52.777778

5

def option_cffex_hs300_daily_sina(symbol: str = "io2202P4350") -> pd.DataFrame: year = datetime.datetime.now().year month = datetime.datetime.now().month day = datetime.datetime.now().day url = f"https://stock.finance.sina.com.cn/futures/api/jsonp.php/var%20_{symbol}{year}_{month}_{day}=/FutureOptionAllService.getOptionDayline" params = {"symbol": symbol} r = requests.get(url, params=params) data_text = r.text data_df = pd.DataFrame( eval(data_text[data_text.find("[") : data_text.rfind("]") + 1]) ) data_df.columns = ["open", "high", "low", "close", "volume", "date"] data_df = data_df[ [ "date", "open", "high", "low", "close", "volume", ] ] data_df["date"] = pd.to_datetime(data_df["date"]).dt.date data_df["open"] = pd.to_numeric(data_df["open"]) data_df["high"] = pd.to_numeric(data_df["high"]) data_df["low"] = pd.to_numeric(data_df["low"]) data_df["close"] = pd.to_numeric(data_df["close"]) data_df["volume"] = pd.to_numeric(data_df["volume"]) return data_df

17,961

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/option/option_finance_sina.py

option_cffex_zz1000_daily_sina

( symbol: str = "mo2208P6200", )

return data_df

新浪财经-中金所-中证 1000 指数-指定合约-日频行情 :param symbol: 具体合约代码(包括看涨和看跌标识), 可以通过 ak.option_cffex_zz1000_spot_sina 中的 call-标识获取 :type symbol: str :return: 日频率数据 :rtype: pd.DataFrame

349

386

def option_cffex_zz1000_daily_sina( symbol: str = "mo2208P6200", ) -> pd.DataFrame: """ 新浪财经-中金所-中证 1000 指数-指定合约-日频行情 :param symbol: 具体合约代码(包括看涨和看跌标识), 可以通过 ak.option_cffex_zz1000_spot_sina 中的 call-标识获取 :type symbol: str :return: 日频率数据 :rtype: pd.DataFrame """ year = datetime.datetime.now().year month = datetime.datetime.now().month day = datetime.datetime.now().day url = f"https://stock.finance.sina.com.cn/futures/api/jsonp.php/var%20_{symbol}{year}_{month}_{day}=/FutureOptionAllService.getOptionDayline" params = {"symbol": symbol} r = requests.get(url, params=params) data_text = r.text data_df = pd.DataFrame( eval(data_text[data_text.find("[") : data_text.rfind("]") + 1]) ) data_df.columns = ["open", "high", "low", "close", "volume", "date"] data_df = data_df[ [ "date", "open", "high", "low", "close", "volume", ] ] data_df["date"] = pd.to_datetime(data_df["date"]).dt.date data_df["open"] = pd.to_numeric(data_df["open"]) data_df["high"] = pd.to_numeric(data_df["high"]) data_df["low"] = pd.to_numeric(data_df["low"]) data_df["close"] = pd.to_numeric(data_df["close"]) data_df["volume"] = pd.to_numeric(data_df["volume"]) return data_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/option/option_finance_sina.py#L349-L386

25

[ 0 ]

2.631579

[ 10, 11, 12, 13, 14, 15, 16, 17, 20, 21, 31, 32, 33, 34, 35, 36, 37 ]

44.736842

false

8.469055

38

1

55.263158

5

def option_cffex_zz1000_daily_sina( symbol: str = "mo2208P6200", ) -> pd.DataFrame: year = datetime.datetime.now().year month = datetime.datetime.now().month day = datetime.datetime.now().day url = f"https://stock.finance.sina.com.cn/futures/api/jsonp.php/var%20_{symbol}{year}_{month}_{day}=/FutureOptionAllService.getOptionDayline" params = {"symbol": symbol} r = requests.get(url, params=params) data_text = r.text data_df = pd.DataFrame( eval(data_text[data_text.find("[") : data_text.rfind("]") + 1]) ) data_df.columns = ["open", "high", "low", "close", "volume", "date"] data_df = data_df[ [ "date", "open", "high", "low", "close", "volume", ] ] data_df["date"] = pd.to_datetime(data_df["date"]).dt.date data_df["open"] = pd.to_numeric(data_df["open"]) data_df["high"] = pd.to_numeric(data_df["high"]) data_df["low"] = pd.to_numeric(data_df["low"]) data_df["close"] = pd.to_numeric(data_df["close"]) data_df["volume"] = pd.to_numeric(data_df["volume"]) return data_df

17,962

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/option/option_finance_sina.py

option_sse_list_sina

( symbol: str = "50ETF", exchange: str = "null" )

return ["".join(i.split("-")) for i in date_list][1:]

新浪财经-期权-上交所-50ETF-合约到期月份列表 https://stock.finance.sina.com.cn/option/quotes.html :param symbol: 50ETF or 300ETF :type symbol: str :param exchange: null :type exchange: str :return: 合约到期时间 :rtype: list

390

408

def option_sse_list_sina( symbol: str = "50ETF", exchange: str = "null" ) -> List[str]: """ 新浪财经-期权-上交所-50ETF-合约到期月份列表 https://stock.finance.sina.com.cn/option/quotes.html :param symbol: 50ETF or 300ETF :type symbol: str :param exchange: null :type exchange: str :return: 合约到期时间 :rtype: list """ url = "http://stock.finance.sina.com.cn/futures/api/openapi.php/StockOptionService.getStockName" params = {"exchange": f"{exchange}", "cate": f"{symbol}"} r = requests.get(url, params=params) data_json = r.json() date_list = data_json["result"]["data"]["contractMonth"] return ["".join(i.split("-")) for i in date_list][1:]

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/option/option_finance_sina.py#L390-L408

25

[ 0 ]

5.263158

[ 13, 14, 15, 16, 17, 18 ]

31.578947

false

8.469055

19

2

68.421053

8

def option_sse_list_sina( symbol: str = "50ETF", exchange: str = "null" ) -> List[str]: url = "http://stock.finance.sina.com.cn/futures/api/openapi.php/StockOptionService.getStockName" params = {"exchange": f"{exchange}", "cate": f"{symbol}"} r = requests.get(url, params=params) data_json = r.json() date_list = data_json["result"]["data"]["contractMonth"] return ["".join(i.split("-")) for i in date_list][1:]

17,963

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/option/option_finance_sina.py

option_sse_expire_day_sina

( trade_date: str = "202102", symbol: str = "50ETF", exchange: str = "null" )

return data["expireDay"], int(data["remainderDays"])

指定到期月份指定品种的剩余到期时间 :param trade_date: 到期月份: 202002, 20203, 20206, 20209 :type trade_date: str :param symbol: 50ETF or 300ETF :type symbol: str :param exchange: null :type exchange: str :return: (到期时间, 剩余时间) :rtype: tuple

411

444

def option_sse_expire_day_sina( trade_date: str = "202102", symbol: str = "50ETF", exchange: str = "null" ) -> Tuple[str, int]: """ 指定到期月份指定品种的剩余到期时间 :param trade_date: 到期月份: 202002, 20203, 20206, 20209 :type trade_date: str :param symbol: 50ETF or 300ETF :type symbol: str :param exchange: null :type exchange: str :return: (到期时间, 剩余时间) :rtype: tuple """ url = "http://stock.finance.sina.com.cn/futures/api/openapi.php/StockOptionService.getRemainderDay" params = { "exchange": f"{exchange}", "cate": f"{symbol}", "date": f"{trade_date[:4]}-{trade_date[4:]}", } r = requests.get(url, params=params) data_json = r.json() data = data_json["result"]["data"] if int(data["remainderDays"]) < 0: url = "http://stock.finance.sina.com.cn/futures/api/openapi.php/StockOptionService.getRemainderDay" params = { "exchange": f"{exchange}", "cate": f"{'XD' + symbol}", "date": f"{trade_date[:4]}-{trade_date[4:]}", } r = requests.get(url, params=params) data_json = r.json() data = data_json["result"]["data"] return data["expireDay"], int(data["remainderDays"])

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/option/option_finance_sina.py#L411-L444

25

[ 0 ]

2.941176

[ 14, 15, 20, 21, 22, 23, 24, 25, 30, 31, 32, 33 ]

35.294118

false

8.469055

34

2

64.705882

9

def option_sse_expire_day_sina( trade_date: str = "202102", symbol: str = "50ETF", exchange: str = "null" ) -> Tuple[str, int]: url = "http://stock.finance.sina.com.cn/futures/api/openapi.php/StockOptionService.getRemainderDay" params = { "exchange": f"{exchange}", "cate": f"{symbol}", "date": f"{trade_date[:4]}-{trade_date[4:]}", } r = requests.get(url, params=params) data_json = r.json() data = data_json["result"]["data"] if int(data["remainderDays"]) < 0: url = "http://stock.finance.sina.com.cn/futures/api/openapi.php/StockOptionService.getRemainderDay" params = { "exchange": f"{exchange}", "cate": f"{'XD' + symbol}", "date": f"{trade_date[:4]}-{trade_date[4:]}", } r = requests.get(url, params=params) data_json = r.json() data = data_json["result"]["data"] return data["expireDay"], int(data["remainderDays"])

17,964

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/option/option_finance_sina.py

option_sse_codes_sina

( symbol: str = "看涨期权", trade_date: str = "202202", underlying: str = "510050", )

return temp_df

上海证券交易所-所有看涨和看跌合约的代码 :param symbol: choice of {"看涨期权", "看跌期权"} :type symbol: str :param trade_date: 期权到期月份 :type trade_date: "202002" :param underlying: 标的产品代码华夏上证 50ETF: 510050 or 华泰柏瑞沪深 300ETF: 510300 :type underlying: str :return: 看涨看跌合约的代码 :rtype: Tuple[List, List]

上海证券交易所-所有看涨和看跌合约的代码

447

508

def option_sse_codes_sina( symbol: str = "看涨期权", trade_date: str = "202202", underlying: str = "510050", ) -> pd.DataFrame: """ 上海证券交易所-所有看涨和看跌合约的代码 :param symbol: choice of {"看涨期权", "看跌期权"} :type symbol: str :param trade_date: 期权到期月份 :type trade_date: "202002" :param underlying: 标的产品代码华夏上证 50ETF: 510050 or 华泰柏瑞沪深 300ETF: 510300 :type underlying: str :return: 看涨看跌合约的代码 :rtype: Tuple[List, List] """ if symbol == "看涨期权": url = "".join( [ "http://hq.sinajs.cn/list=OP_UP_", underlying, str(trade_date)[-4:], ] ) else: url = "".join( [ "http://hq.sinajs.cn/list=OP_DOWN_", underlying, str(trade_date)[-4:], ] ) headers = { "Accept": "*/*", "Accept-Encoding": "gzip, deflate, br", "Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8", "Cache-Control": "no-cache", "Connection": "keep-alive", "Host": "hq.sinajs.cn", "Pragma": "no-cache", "Referer": "https://stock.finance.sina.com.cn/", "sec-ch-ua": '" Not;A Brand";v="99", "Google Chrome";v="97", "Chromium";v="97"', "sec-ch-ua-mobile": "?0", "sec-ch-ua-platform": '"Windows"', "Sec-Fetch-Dest": "script", "Sec-Fetch-Mode": "no-cors", "Sec-Fetch-Site": "cross-site", "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/97.0.4692.71 Safari/537.36", } r = requests.get(url, headers=headers) data_text = r.text data_temp = data_text.replace('"', ",").split(",") temp_list = [i[7:] for i in data_temp if i.startswith("CON_OP_")] temp_df = pd.DataFrame(temp_list) temp_df.reset_index(inplace=True) temp_df["index"] = temp_df.index + 1 temp_df.columns = [ "序号", "期权代码", ] return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/option/option_finance_sina.py#L447-L508

25

[ 0 ]

1.612903

[ 17, 18, 26, 33, 50, 51, 52, 53, 54, 55, 56, 57, 61 ]

20.967742

false

8.469055

62

3

79.032258

10

def option_sse_codes_sina( symbol: str = "看涨期权", trade_date: str = "202202", underlying: str = "510050", ) -> pd.DataFrame: if symbol == "看涨期权": url = "".join( [ "http://hq.sinajs.cn/list=OP_UP_", underlying, str(trade_date)[-4:], ] ) else: url = "".join( [ "http://hq.sinajs.cn/list=OP_DOWN_", underlying, str(trade_date)[-4:], ] ) headers = { "Accept": "*/*", "Accept-Encoding": "gzip, deflate, br", "Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8", "Cache-Control": "no-cache", "Connection": "keep-alive", "Host": "hq.sinajs.cn", "Pragma": "no-cache", "Referer": "https://stock.finance.sina.com.cn/", "sec-ch-ua": '" Not;A Brand";v="99", "Google Chrome";v="97", "Chromium";v="97"', "sec-ch-ua-mobile": "?0", "sec-ch-ua-platform": '"Windows"', "Sec-Fetch-Dest": "script", "Sec-Fetch-Mode": "no-cors", "Sec-Fetch-Site": "cross-site", "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/97.0.4692.71 Safari/537.36", } r = requests.get(url, headers=headers) data_text = r.text data_temp = data_text.replace('"', ",").split(",") temp_list = [i[7:] for i in data_temp if i.startswith("CON_OP_")] temp_df = pd.DataFrame(temp_list) temp_df.reset_index(inplace=True) temp_df["index"] = temp_df.index + 1 temp_df.columns = [ "序号", "期权代码", ] return temp_df

17,965

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/option/option_finance_sina.py

option_sse_spot_price_sina

(symbol: str = "10003720")

return data_df

新浪财经-期权-期权实时数据 :param symbol: 期权代码 :type symbol: str :return: 期权量价数据 :rtype: pandas.DataFrame

511

590

def option_sse_spot_price_sina(symbol: str = "10003720") -> pd.DataFrame: """ 新浪财经-期权-期权实时数据 :param symbol: 期权代码 :type symbol: str :return: 期权量价数据 :rtype: pandas.DataFrame """ url = f"http://hq.sinajs.cn/list=CON_OP_{symbol}" headers = { "Accept": "*/*", "Accept-Encoding": "gzip, deflate, br", "Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8", "Cache-Control": "no-cache", "Connection": "keep-alive", "Host": "hq.sinajs.cn", "Pragma": "no-cache", "Referer": "https://stock.finance.sina.com.cn/", "sec-ch-ua": '" Not;A Brand";v="99", "Google Chrome";v="97", "Chromium";v="97"', "sec-ch-ua-mobile": "?0", "sec-ch-ua-platform": '"Windows"', "Sec-Fetch-Dest": "script", "Sec-Fetch-Mode": "no-cors", "Sec-Fetch-Site": "cross-site", "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/97.0.4692.71 Safari/537.36", } r = requests.get(url, headers=headers) data_text = r.text data_list = data_text[ data_text.find('"') + 1 : data_text.rfind('"') ].split(",") field_list = [ "买量", "买价", "最新价", "卖价", "卖量", "持仓量", "涨幅", "行权价", "昨收价", "开盘价", "涨停价", "跌停价", "申卖价五", "申卖量五", "申卖价四", "申卖量四", "申卖价三", "申卖量三", "申卖价二", "申卖量二", "申卖价一", "申卖量一", "申买价一", "申买量一 ", "申买价二", "申买量二", "申买价三", "申买量三", "申买价四", "申买量四", "申买价五", "申买量五", "行情时间", "主力合约标识", "状态码", "标的证券类型", "标的股票", "期权合约简称", "振幅", "最高价", "最低价", "成交量", "成交额", ] data_df = pd.DataFrame( list(zip(field_list, data_list)), columns=["字段", "值"] ) return data_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/option/option_finance_sina.py#L511-L590

25

[ 0, 1, 2, 3, 4, 5, 6, 7 ]

10

[ 8, 9, 26, 27, 28, 31, 76, 79 ]

10

false

8.469055

80

1

90

5

def option_sse_spot_price_sina(symbol: str = "10003720") -> pd.DataFrame: url = f"http://hq.sinajs.cn/list=CON_OP_{symbol}" headers = { "Accept": "*/*", "Accept-Encoding": "gzip, deflate, br", "Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8", "Cache-Control": "no-cache", "Connection": "keep-alive", "Host": "hq.sinajs.cn", "Pragma": "no-cache", "Referer": "https://stock.finance.sina.com.cn/", "sec-ch-ua": '" Not;A Brand";v="99", "Google Chrome";v="97", "Chromium";v="97"', "sec-ch-ua-mobile": "?0", "sec-ch-ua-platform": '"Windows"', "Sec-Fetch-Dest": "script", "Sec-Fetch-Mode": "no-cors", "Sec-Fetch-Site": "cross-site", "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/97.0.4692.71 Safari/537.36", } r = requests.get(url, headers=headers) data_text = r.text data_list = data_text[ data_text.find('"') + 1 : data_text.rfind('"') ].split(",") field_list = [ "买量", "买价", "最新价", "卖价", "卖量", "持仓量", "涨幅", "行权价", "昨收价", "开盘价", "涨停价", "跌停价", "申卖价五", "申卖量五", "申卖价四", "申卖量四", "申卖价三", "申卖量三", "申卖价二", "申卖量二", "申卖价一", "申卖量一", "申买价一", "申买量一 ", "申买价二", "申买量二", "申买价三", "申买量三", "申买价四", "申买量四", "申买价五", "申买量五", "行情时间", "主力合约标识", "状态码", "标的证券类型", "标的股票", "期权合约简称", "振幅", "最高价", "最低价", "成交量", "成交额", ] data_df = pd.DataFrame( list(zip(field_list, data_list)), columns=["字段", "值"] ) return data_df

17,966

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/option/option_finance_sina.py

option_sse_underlying_spot_price_sina

( symbol: str = "sh510300", )

return data_df

期权标的物的实时数据 :param symbol: sh510050 or sh510300 :type symbol: str :return: 期权标的物的信息 :rtype: pandas.DataFrame

593

658

def option_sse_underlying_spot_price_sina( symbol: str = "sh510300", ) -> pd.DataFrame: """ 期权标的物的实时数据 :param symbol: sh510050 or sh510300 :type symbol: str :return: 期权标的物的信息 :rtype: pandas.DataFrame """ url = f"http://hq.sinajs.cn/list={symbol}" headers = { "Accept": "*/*", "Accept-Encoding": "gzip, deflate", "Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8", "Cache-Control": "no-cache", "Host": "hq.sinajs.cn", "Pragma": "no-cache", "Proxy-Connection": "keep-alive", "Referer": "http://vip.stock.finance.sina.com.cn/", "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/97.0.4692.71 Safari/537.36", } r = requests.get(url, headers=headers) data_text = r.text data_list = data_text[ data_text.find('"') + 1 : data_text.rfind('"') ].split(",") field_list = [ "证券简称", "今日开盘价", "昨日收盘价", "最近成交价", "最高成交价", "最低成交价", "买入价", "卖出价", "成交数量", "成交金额", "买数量一", "买价位一", "买数量二", "买价位二", "买数量三", "买价位三", "买数量四", "买价位四", "买数量五", "买价位五", "卖数量一", "卖价位一", "卖数量二", "卖价位二", "卖数量三", "卖价位三", "卖数量四", "卖价位四", "卖数量五", "卖价位五", "行情日期", "行情时间", "停牌状态", ] data_df = pd.DataFrame( list(zip(field_list, data_list)), columns=["字段", "值"] ) return data_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/option/option_finance_sina.py#L593-L658

25

[ 0 ]

1.515152

[ 10, 11, 22, 23, 24, 27, 62, 65 ]

12.121212

false

8.469055

66

1

87.878788

5

def option_sse_underlying_spot_price_sina( symbol: str = "sh510300", ) -> pd.DataFrame: url = f"http://hq.sinajs.cn/list={symbol}" headers = { "Accept": "*/*", "Accept-Encoding": "gzip, deflate", "Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8", "Cache-Control": "no-cache", "Host": "hq.sinajs.cn", "Pragma": "no-cache", "Proxy-Connection": "keep-alive", "Referer": "http://vip.stock.finance.sina.com.cn/", "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/97.0.4692.71 Safari/537.36", } r = requests.get(url, headers=headers) data_text = r.text data_list = data_text[ data_text.find('"') + 1 : data_text.rfind('"') ].split(",") field_list = [ "证券简称", "今日开盘价", "昨日收盘价", "最近成交价", "最高成交价", "最低成交价", "买入价", "卖出价", "成交数量", "成交金额", "买数量一", "买价位一", "买数量二", "买价位二", "买数量三", "买价位三", "买数量四", "买价位四", "买数量五", "买价位五", "卖数量一", "卖价位一", "卖数量二", "卖价位二", "卖数量三", "卖价位三", "卖数量四", "卖价位四", "卖数量五", "卖价位五", "行情日期", "行情时间", "停牌状态", ] data_df = pd.DataFrame( list(zip(field_list, data_list)), columns=["字段", "值"] ) return data_df

17,967

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/option/option_finance_sina.py

option_sse_greeks_sina

(symbol: str = "10003045")

return data_df

期权基本信息表 :param symbol: 合约代码 :type symbol: str :return: 期权基本信息表 :rtype: pandas.DataFrame

661

705

def option_sse_greeks_sina(symbol: str = "10003045") -> pd.DataFrame: """ 期权基本信息表 :param symbol: 合约代码 :type symbol: str :return: 期权基本信息表 :rtype: pandas.DataFrame """ url = f"http://hq.sinajs.cn/list=CON_SO_{symbol}" headers = { "Accept": "*/*", "Accept-Encoding": "gzip, deflate", "Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8", "Cache-Control": "no-cache", "Host": "hq.sinajs.cn", "Pragma": "no-cache", "Proxy-Connection": "keep-alive", "Referer": "http://vip.stock.finance.sina.com.cn/", "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/97.0.4692.71 Safari/537.36", } r = requests.get(url, headers=headers) data_text = r.text data_list = data_text[ data_text.find('"') + 1 : data_text.rfind('"') ].split(",") field_list = [ "期权合约简称", "成交量", "Delta", "Gamma", "Theta", "Vega", "隐含波动率", "最高价", "最低价", "交易代码", "行权价", "最新价", "理论价值", ] data_df = pd.DataFrame( list(zip(field_list, [data_list[0]] + data_list[4:])), columns=["字段", "值"], ) return data_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/option/option_finance_sina.py#L661-L705

25

[ 0, 1, 2, 3, 4, 5, 6, 7 ]

17.777778

[ 8, 9, 20, 21, 22, 25, 40, 44 ]

17.777778

false

8.469055

45

1

82.222222

5

def option_sse_greeks_sina(symbol: str = "10003045") -> pd.DataFrame: url = f"http://hq.sinajs.cn/list=CON_SO_{symbol}" headers = { "Accept": "*/*", "Accept-Encoding": "gzip, deflate", "Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8", "Cache-Control": "no-cache", "Host": "hq.sinajs.cn", "Pragma": "no-cache", "Proxy-Connection": "keep-alive", "Referer": "http://vip.stock.finance.sina.com.cn/", "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/97.0.4692.71 Safari/537.36", } r = requests.get(url, headers=headers) data_text = r.text data_list = data_text[ data_text.find('"') + 1 : data_text.rfind('"') ].split(",") field_list = [ "期权合约简称", "成交量", "Delta", "Gamma", "Theta", "Vega", "隐含波动率", "最高价", "最低价", "交易代码", "行权价", "最新价", "理论价值", ] data_df = pd.DataFrame( list(zip(field_list, [data_list[0]] + data_list[4:])), columns=["字段", "值"], ) return data_df

17,968

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/option/option_finance_sina.py

option_sse_minute_sina

(symbol: str = "10003720")

return data_df

指定期权品种在当前交易日的分钟数据, 只能获取当前交易日的数据, 不能获取历史分钟数据 https://stock.finance.sina.com.cn/option/quotes.html :param symbol: 期权代码 :type symbol: str :return: 指定期权的当前交易日的分钟数据 :rtype: pandas.DataFrame

708

746

def option_sse_minute_sina(symbol: str = "10003720") -> pd.DataFrame: """ 指定期权品种在当前交易日的分钟数据, 只能获取当前交易日的数据, 不能获取历史分钟数据 https://stock.finance.sina.com.cn/option/quotes.html :param symbol: 期权代码 :type symbol: str :return: 指定期权的当前交易日的分钟数据 :rtype: pandas.DataFrame """ url = "https://stock.finance.sina.com.cn/futures/api/openapi.php/StockOptionDaylineService.getOptionMinline" params = {"symbol": f"CON_OP_{symbol}"} headers = { "accept": "*/*", "accept-encoding": "gzip, deflate, br", "accept-language": "zh-CN,zh;q=0.9,en;q=0.8", "cache-control": "no-cache", "pragma": "no-cache", "referer": "https://stock.finance.sina.com.cn/option/quotes.html", "sec-ch-ua": '" Not;A Brand";v="99", "Google Chrome";v="97", "Chromium";v="97"', "sec-ch-ua-mobile": "?0", "sec-ch-ua-platform": '"Windows"', "sec-fetch-dest": "script", "sec-fetch-mode": "no-cors", "sec-fetch-site": "same-origin", "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/97.0.4692.71 Safari/537.36", } r = requests.get(url, params=params, headers=headers) data_json = r.json() temp_df = data_json["result"]["data"] data_df = pd.DataFrame(temp_df) data_df.columns = ["时间", "价格", "成交", "持仓", "均价", "日期"] data_df = data_df[["日期", "时间", "价格", "成交", "持仓", "均价"]] data_df["日期"] = pd.to_datetime(data_df["日期"]).dt.date data_df["日期"].ffill(inplace=True) data_df["价格"] = pd.to_numeric(data_df["价格"]) data_df["成交"] = pd.to_numeric(data_df["成交"]) data_df["持仓"] = pd.to_numeric(data_df["持仓"]) data_df["均价"] = pd.to_numeric(data_df["均价"]) return data_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/option/option_finance_sina.py#L708-L746

25

[ 0, 1, 2, 3, 4, 5, 6, 7, 8 ]

23.076923

[ 9, 10, 11, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38 ]

41.025641

false

8.469055

39

1

58.974359

6

def option_sse_minute_sina(symbol: str = "10003720") -> pd.DataFrame: url = "https://stock.finance.sina.com.cn/futures/api/openapi.php/StockOptionDaylineService.getOptionMinline" params = {"symbol": f"CON_OP_{symbol}"} headers = { "accept": "*/*", "accept-encoding": "gzip, deflate, br", "accept-language": "zh-CN,zh;q=0.9,en;q=0.8", "cache-control": "no-cache", "pragma": "no-cache", "referer": "https://stock.finance.sina.com.cn/option/quotes.html", "sec-ch-ua": '" Not;A Brand";v="99", "Google Chrome";v="97", "Chromium";v="97"', "sec-ch-ua-mobile": "?0", "sec-ch-ua-platform": '"Windows"', "sec-fetch-dest": "script", "sec-fetch-mode": "no-cors", "sec-fetch-site": "same-origin", "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/97.0.4692.71 Safari/537.36", } r = requests.get(url, params=params, headers=headers) data_json = r.json() temp_df = data_json["result"]["data"] data_df = pd.DataFrame(temp_df) data_df.columns = ["时间", "价格", "成交", "持仓", "均价", "日期"] data_df = data_df[["日期", "时间", "价格", "成交", "持仓", "均价"]] data_df["日期"] = pd.to_datetime(data_df["日期"]).dt.date data_df["日期"].ffill(inplace=True) data_df["价格"] = pd.to_numeric(data_df["价格"]) data_df["成交"] = pd.to_numeric(data_df["成交"]) data_df["持仓"] = pd.to_numeric(data_df["持仓"]) data_df["均价"] = pd.to_numeric(data_df["均价"]) return data_df

17,969

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/option/option_finance_sina.py

option_sse_daily_sina

(symbol: str = "10003889")

return temp_df

指定期权的日频率数据 :param symbol: 期权代码 :type symbol: str :return: 指定期权的所有日频率历史数据 :rtype: pandas.DataFrame

749

787

def option_sse_daily_sina(symbol: str = "10003889") -> pd.DataFrame: """ 指定期权的日频率数据 :param symbol: 期权代码 :type symbol: str :return: 指定期权的所有日频率历史数据 :rtype: pandas.DataFrame """ url = "http://stock.finance.sina.com.cn/futures/api/jsonp_v2.php//StockOptionDaylineService.getSymbolInfo" params = {"symbol": f"CON_OP_{symbol}"} headers = { "accept": "*/*", "accept-encoding": "gzip, deflate, br", "accept-language": "zh-CN,zh;q=0.9,en;q=0.8", "cache-control": "no-cache", "pragma": "no-cache", "referer": "https://stock.finance.sina.com.cn/option/quotes.html", "sec-ch-ua": '" Not;A Brand";v="99", "Google Chrome";v="97", "Chromium";v="97"', "sec-ch-ua-mobile": "?0", "sec-ch-ua-platform": '"Windows"', "sec-fetch-dest": "script", "sec-fetch-mode": "no-cors", "sec-fetch-site": "same-origin", "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/97.0.4692.71 Safari/537.36", } r = requests.get(url, params=params, headers=headers) data_text = r.text data_json = json.loads( data_text[data_text.find("(") + 1 : data_text.rfind(")")] ) temp_df = pd.DataFrame(data_json) temp_df.columns = ["日期", "开盘", "最高", "最低", "收盘", "成交量"] temp_df["日期"] = pd.to_datetime(temp_df["日期"]).dt.date temp_df["开盘"] = pd.to_numeric(temp_df["开盘"]) temp_df["最高"] = pd.to_numeric(temp_df["最高"]) temp_df["最低"] = pd.to_numeric(temp_df["最低"]) temp_df["收盘"] = pd.to_numeric(temp_df["收盘"]) temp_df["成交量"] = pd.to_numeric(temp_df["成交量"]) return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/option/option_finance_sina.py#L749-L787

25

[ 0, 1, 2, 3, 4, 5, 6, 7 ]

20.512821

[ 8, 9, 10, 25, 26, 27, 30, 31, 32, 33, 34, 35, 36, 37, 38 ]

38.461538

false

8.469055

39

1

61.538462

5

def option_sse_daily_sina(symbol: str = "10003889") -> pd.DataFrame: url = "http://stock.finance.sina.com.cn/futures/api/jsonp_v2.php//StockOptionDaylineService.getSymbolInfo" params = {"symbol": f"CON_OP_{symbol}"} headers = { "accept": "*/*", "accept-encoding": "gzip, deflate, br", "accept-language": "zh-CN,zh;q=0.9,en;q=0.8", "cache-control": "no-cache", "pragma": "no-cache", "referer": "https://stock.finance.sina.com.cn/option/quotes.html", "sec-ch-ua": '" Not;A Brand";v="99", "Google Chrome";v="97", "Chromium";v="97"', "sec-ch-ua-mobile": "?0", "sec-ch-ua-platform": '"Windows"', "sec-fetch-dest": "script", "sec-fetch-mode": "no-cors", "sec-fetch-site": "same-origin", "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/97.0.4692.71 Safari/537.36", } r = requests.get(url, params=params, headers=headers) data_text = r.text data_json = json.loads( data_text[data_text.find("(") + 1 : data_text.rfind(")")] ) temp_df = pd.DataFrame(data_json) temp_df.columns = ["日期", "开盘", "最高", "最低", "收盘", "成交量"] temp_df["日期"] = pd.to_datetime(temp_df["日期"]).dt.date temp_df["开盘"] = pd.to_numeric(temp_df["开盘"]) temp_df["最高"] = pd.to_numeric(temp_df["最高"]) temp_df["最低"] = pd.to_numeric(temp_df["最低"]) temp_df["收盘"] = pd.to_numeric(temp_df["收盘"]) temp_df["成交量"] = pd.to_numeric(temp_df["成交量"]) return temp_df

17,970

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/option/option_finance_sina.py

option_finance_minute_sina

(symbol: str = "10002530")

return temp_df

指定期权的分钟频率数据 https://stock.finance.sina.com.cn/option/quotes.html :param symbol: 期权代码 :type symbol: str :return: 指定期权的分钟频率数据 :rtype: pandas.DataFrame

790

829

def option_finance_minute_sina(symbol: str = "10002530") -> pd.DataFrame: """ 指定期权的分钟频率数据 https://stock.finance.sina.com.cn/option/quotes.html :param symbol: 期权代码 :type symbol: str :return: 指定期权的分钟频率数据 :rtype: pandas.DataFrame """ url = "https://stock.finance.sina.com.cn/futures/api/openapi.php/StockOptionDaylineService.getFiveDayLine" params = { "symbol": f"CON_OP_{symbol}", } headers = { "accept": "*/*", "accept-encoding": "gzip, deflate, br", "accept-language": "zh-CN,zh;q=0.9,en;q=0.8", "cache-control": "no-cache", "pragma": "no-cache", "referer": "https://stock.finance.sina.com.cn/option/quotes.html", "sec-ch-ua": '" Not;A Brand";v="99", "Google Chrome";v="97", "Chromium";v="97"', "sec-ch-ua-mobile": "?0", "sec-ch-ua-platform": '"Windows"', "sec-fetch-dest": "script", "sec-fetch-mode": "no-cors", "sec-fetch-site": "same-origin", "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/97.0.4692.71 Safari/537.36", } r = requests.get(url, params=params, headers=headers) data_text = r.json() temp_df = pd.DataFrame() for item in data_text["result"]["data"]: temp_df = pd.concat([temp_df, pd.DataFrame(item)], ignore_index=True) temp_df.fillna(method="ffill", inplace=True) temp_df.columns = ["time", "price", "volume", "_", "average_price", "date"] temp_df = temp_df[["date", "time", "price", "average_price", "volume"]] temp_df["price"] = pd.to_numeric(temp_df["price"]) temp_df["average_price"] = pd.to_numeric(temp_df["average_price"]) temp_df["volume"] = pd.to_numeric(temp_df["volume"]) return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/option/option_finance_sina.py#L790-L829

25

[ 0, 1, 2, 3, 4, 5, 6, 7, 8 ]

22.5

[ 9, 10, 13, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39 ]

37.5

false

8.469055

40

2

62.5

6

def option_finance_minute_sina(symbol: str = "10002530") -> pd.DataFrame: url = "https://stock.finance.sina.com.cn/futures/api/openapi.php/StockOptionDaylineService.getFiveDayLine" params = { "symbol": f"CON_OP_{symbol}", } headers = { "accept": "*/*", "accept-encoding": "gzip, deflate, br", "accept-language": "zh-CN,zh;q=0.9,en;q=0.8", "cache-control": "no-cache", "pragma": "no-cache", "referer": "https://stock.finance.sina.com.cn/option/quotes.html", "sec-ch-ua": '" Not;A Brand";v="99", "Google Chrome";v="97", "Chromium";v="97"', "sec-ch-ua-mobile": "?0", "sec-ch-ua-platform": '"Windows"', "sec-fetch-dest": "script", "sec-fetch-mode": "no-cors", "sec-fetch-site": "same-origin", "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/97.0.4692.71 Safari/537.36", } r = requests.get(url, params=params, headers=headers) data_text = r.json() temp_df = pd.DataFrame() for item in data_text["result"]["data"]: temp_df = pd.concat([temp_df, pd.DataFrame(item)], ignore_index=True) temp_df.fillna(method="ffill", inplace=True) temp_df.columns = ["time", "price", "volume", "_", "average_price", "date"] temp_df = temp_df[["date", "time", "price", "average_price", "volume"]] temp_df["price"] = pd.to_numeric(temp_df["price"]) temp_df["average_price"] = pd.to_numeric(temp_df["average_price"]) temp_df["volume"] = pd.to_numeric(temp_df["volume"]) return temp_df

17,971

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/option/option_finance.py

option_finance_underlying

(symbol: str = "50ETF")

期权标的当日行情, 目前只有华夏上证 50 ETF, 华泰柏瑞沪深 300 ETF 两个产品 http://www.sse.com.cn/assortment/options/price/ :param symbol: 50ETF 或 300ETF :type symbol: str :return: 期权标的当日行情 :rtype: pandas.DataFrame

22

72

def option_finance_underlying(symbol: str = "50ETF") -> pd.DataFrame: """ 期权标的当日行情, 目前只有华夏上证 50 ETF, 华泰柏瑞沪深 300 ETF 两个产品 http://www.sse.com.cn/assortment/options/price/ :param symbol: 50ETF 或 300ETF :type symbol: str :return: 期权标的当日行情 :rtype: pandas.DataFrame """ if symbol == "50ETF": res = requests.get(SH_OPTION_URL_50, params=SH_OPTION_PAYLOAD) data_json = res.json() raw_data = pd.DataFrame(data_json["list"]) raw_data.at[0, 0] = "510050" raw_data.at[0, 8] = pd.to_datetime( str(data_json["date"]) + str(data_json["time"]), format="%Y%m%d%H%M%S", ) raw_data.columns = [ "代码", "名称", "当前价", "涨跌", "涨跌幅", "振幅", "成交量(手)", "成交额(万元)", "更新日期", ] return raw_data else: res = requests.get(SH_OPTION_URL_300, params=SH_OPTION_PAYLOAD) data_json = res.json() raw_data = pd.DataFrame(data_json["list"]) raw_data.at[0, 0] = "510300" raw_data.at[0, 8] = pd.to_datetime( str(data_json["date"]) + str(data_json["time"]), format="%Y%m%d%H%M%S", ) raw_data.columns = [ "代码", "名称", "当前价", "涨跌", "涨跌幅", "振幅", "成交量(手)", "成交额(万元)", "更新日期", ] return raw_data

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/option/option_finance.py#L22-L72

25

[ 0, 1, 2, 3, 4, 5, 6, 7, 8 ]

17.647059

[ 9, 10, 11, 12, 13, 14, 18, 29, 31, 32, 33, 34, 35, 39, 50 ]

29.411765

false

7.777778

51

2

70.588235

6

def option_finance_underlying(symbol: str = "50ETF") -> pd.DataFrame: if symbol == "50ETF": res = requests.get(SH_OPTION_URL_50, params=SH_OPTION_PAYLOAD) data_json = res.json() raw_data = pd.DataFrame(data_json["list"]) raw_data.at[0, 0] = "510050" raw_data.at[0, 8] = pd.to_datetime( str(data_json["date"]) + str(data_json["time"]), format="%Y%m%d%H%M%S", ) raw_data.columns = [ "代码", "名称", "当前价", "涨跌", "涨跌幅", "振幅", "成交量(手)", "成交额(万元)", "更新日期", ] return raw_data else: res = requests.get(SH_OPTION_URL_300, params=SH_OPTION_PAYLOAD) data_json = res.json() raw_data = pd.DataFrame(data_json["list"]) raw_data.at[0, 0] = "510300" raw_data.at[0, 8] = pd.to_datetime( str(data_json["date"]) + str(data_json["time"]), format="%Y%m%d%H%M%S", ) raw_data.columns = [ "代码", "名称", "当前价", "涨跌", "涨跌幅", "振幅", "成交量(手)", "成交额(万元)", "更新日期", ] return raw_data

17,972

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/option/option_finance.py

option_finance_board

( symbol: str = "嘉实沪深300ETF期权", end_month: str = "2212" )

期权的当日具体的行情数据, 主要为三个: 华夏上证50ETF期权, 华泰柏瑞沪深300ETF期权, 嘉实沪深300ETF期权, 沪深300股指期权, 中证1000股指期权 http://www.sse.com.cn/assortment/options/price/ http://www.szse.cn/market/product/option/index.html http://www.cffex.com.cn/hs300gzqq/ http://www.cffex.com.cn/zz1000gzqq/ :param symbol: choice of {"华夏上证50ETF期权", "华泰柏瑞沪深300ETF期权", "嘉实沪深300ETF期权", "沪深300股指期权", "中证1000股指期权"} :type symbol: str :param end_month: 2003; 2020年3月到期的期权 :type end_month: str :return: 当日行情 :rtype: pandas.DataFrame

75

192

def option_finance_board( symbol: str = "嘉实沪深300ETF期权", end_month: str = "2212" ) -> pd.DataFrame: """ 期权的当日具体的行情数据, 主要为三个: 华夏上证50ETF期权, 华泰柏瑞沪深300ETF期权, 嘉实沪深300ETF期权, 沪深300股指期权, 中证1000股指期权 http://www.sse.com.cn/assortment/options/price/ http://www.szse.cn/market/product/option/index.html http://www.cffex.com.cn/hs300gzqq/ http://www.cffex.com.cn/zz1000gzqq/ :param symbol: choice of {"华夏上证50ETF期权", "华泰柏瑞沪深300ETF期权", "嘉实沪深300ETF期权", "沪深300股指期权", "中证1000股指期权"} :type symbol: str :param end_month: 2003; 2020年3月到期的期权 :type end_month: str :return: 当日行情 :rtype: pandas.DataFrame """ end_month = end_month[-2:] if symbol == "华夏上证50ETF期权": r = requests.get( SH_OPTION_URL_KING_50.format(end_month), params=SH_OPTION_PAYLOAD_OTHER, ) data_json = r.json() raw_data = pd.DataFrame(data_json["list"]) raw_data.index = [ str(data_json["date"]) + str(data_json["time"]) ] * data_json["total"] raw_data.columns = ["合约交易代码", "当前价", "涨跌幅", "前结价", "行权价"] raw_data["数量"] = [data_json["total"]] * data_json["total"] raw_data.reset_index(inplace=True) raw_data.columns = ["日期", "合约交易代码", "当前价", "涨跌幅", "前结价", "行权价", "数量"] return raw_data elif symbol == "华泰柏瑞沪深300ETF期权": r = requests.get( SH_OPTION_URL_KING_300.format(end_month), params=SH_OPTION_PAYLOAD_OTHER, ) data_json = r.json() raw_data = pd.DataFrame(data_json["list"]) raw_data.index = [ str(data_json["date"]) + str(data_json["time"]) ] * data_json["total"] raw_data.columns = ["合约交易代码", "当前价", "涨跌幅", "前结价", "行权价"] raw_data["数量"] = [data_json["total"]] * data_json["total"] raw_data.reset_index(inplace=True) raw_data.columns = ["日期", "合约交易代码", "当前价", "涨跌幅", "前结价", "行权价", "数量"] return raw_data elif symbol == "嘉实沪深300ETF期权": url = "http://www.szse.cn/api/report/ShowReport/data" params = { "SHOWTYPE": "JSON", "CATALOGID": "ysplbrb", "TABKEY": "tab1", "PAGENO": "1", "random": "0.10642298535346595", } r = requests.get(url, params=params) data_json = r.json() page_num = data_json[0]["metadata"]["pagecount"] big_df = pd.DataFrame() for page in range(1, page_num + 1): params = { "SHOWTYPE": "JSON", "CATALOGID": "ysplbrb", "TABKEY": "tab1", "PAGENO": page, "random": "0.10642298535346595", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json[0]["data"]) big_df = pd.concat([big_df, temp_df], ignore_index=True) big_df.columns = [ "合约编码", "合约简称", "标的名称", "类型", "行权价", "合约单位", "期权行权日", "行权交收日", ] big_df["期权行权日"] = pd.to_datetime(big_df["期权行权日"]) big_df["end_month"] = big_df["期权行权日"].dt.month.astype(str).str.zfill(2) big_df = big_df[big_df["end_month"] == end_month] del big_df["end_month"] big_df.reset_index(inplace=True, drop=True) return big_df elif symbol == "沪深300股指期权": raw_df = pd.read_table(CFFEX_OPTION_URL_300, sep=",") raw_df["end_month"] = ( raw_df["instrument"] .str.split("-", expand=True) .iloc[:, 0] .str.slice( 4, ) ) raw_df = raw_df[raw_df["end_month"] == end_month] del raw_df["end_month"] raw_df.reset_index(inplace=True, drop=True) return raw_df elif symbol == "中证1000股指期权": url = "http://www.cffex.com.cn/quote_MO.txt" raw_df = pd.read_table(url, sep=",") raw_df["end_month"] = ( raw_df["instrument"] .str.split("-", expand=True) .iloc[:, 0] .str.slice( 4, ) ) raw_df = raw_df[raw_df["end_month"] == end_month] del raw_df["end_month"] raw_df.reset_index(inplace=True, drop=True) return raw_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/option/option_finance.py#L75-L192

25

[ 0 ]

0.847458

[ 16, 17, 18, 22, 23, 24, 27, 28, 29, 30, 31, 32, 33, 37, 38, 39, 42, 43, 44, 45, 46, 47, 48, 49, 56, 57, 58, 59, 60, 61, 68, 69, 70, 71, 73, 83, 84, 85, 86, 87, 88, 89, 90, 91, 99, 100, 101, 102, 103, 104, 105, 106, 114, 115, 116, 117 ]

47.457627

false

7.777778

118

7

52.542373

11

def option_finance_board( symbol: str = "嘉实沪深300ETF期权", end_month: str = "2212" ) -> pd.DataFrame: end_month = end_month[-2:] if symbol == "华夏上证50ETF期权": r = requests.get( SH_OPTION_URL_KING_50.format(end_month), params=SH_OPTION_PAYLOAD_OTHER, ) data_json = r.json() raw_data = pd.DataFrame(data_json["list"]) raw_data.index = [ str(data_json["date"]) + str(data_json["time"]) ] * data_json["total"] raw_data.columns = ["合约交易代码", "当前价", "涨跌幅", "前结价", "行权价"] raw_data["数量"] = [data_json["total"]] * data_json["total"] raw_data.reset_index(inplace=True) raw_data.columns = ["日期", "合约交易代码", "当前价", "涨跌幅", "前结价", "行权价", "数量"] return raw_data elif symbol == "华泰柏瑞沪深300ETF期权": r = requests.get( SH_OPTION_URL_KING_300.format(end_month), params=SH_OPTION_PAYLOAD_OTHER, ) data_json = r.json() raw_data = pd.DataFrame(data_json["list"]) raw_data.index = [ str(data_json["date"]) + str(data_json["time"]) ] * data_json["total"] raw_data.columns = ["合约交易代码", "当前价", "涨跌幅", "前结价", "行权价"] raw_data["数量"] = [data_json["total"]] * data_json["total"] raw_data.reset_index(inplace=True) raw_data.columns = ["日期", "合约交易代码", "当前价", "涨跌幅", "前结价", "行权价", "数量"] return raw_data elif symbol == "嘉实沪深300ETF期权": url = "http://www.szse.cn/api/report/ShowReport/data" params = { "SHOWTYPE": "JSON", "CATALOGID": "ysplbrb", "TABKEY": "tab1", "PAGENO": "1", "random": "0.10642298535346595", } r = requests.get(url, params=params) data_json = r.json() page_num = data_json[0]["metadata"]["pagecount"] big_df = pd.DataFrame() for page in range(1, page_num + 1): params = { "SHOWTYPE": "JSON", "CATALOGID": "ysplbrb", "TABKEY": "tab1", "PAGENO": page, "random": "0.10642298535346595", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json[0]["data"]) big_df = pd.concat([big_df, temp_df], ignore_index=True) big_df.columns = [ "合约编码", "合约简称", "标的名称", "类型", "行权价", "合约单位", "期权行权日", "行权交收日", ] big_df["期权行权日"] = pd.to_datetime(big_df["期权行权日"]) big_df["end_month"] = big_df["期权行权日"].dt.month.astype(str).str.zfill(2) big_df = big_df[big_df["end_month"] == end_month] del big_df["end_month"] big_df.reset_index(inplace=True, drop=True) return big_df elif symbol == "沪深300股指期权": raw_df = pd.read_table(CFFEX_OPTION_URL_300, sep=",") raw_df["end_month"] = ( raw_df["instrument"] .str.split("-", expand=True) .iloc[:, 0] .str.slice( 4, ) ) raw_df = raw_df[raw_df["end_month"] == end_month] del raw_df["end_month"] raw_df.reset_index(inplace=True, drop=True) return raw_df elif symbol == "中证1000股指期权": url = "http://www.cffex.com.cn/quote_MO.txt" raw_df = pd.read_table(url, sep=",") raw_df["end_month"] = ( raw_df["instrument"] .str.split("-", expand=True) .iloc[:, 0] .str.slice( 4, ) ) raw_df = raw_df[raw_df["end_month"] == end_month] del raw_df["end_month"] raw_df.reset_index(inplace=True, drop=True) return raw_df

17,973

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/option/option_risk_analysis_em.py

option_risk_analysis_em

()

return temp_df

东方财富网-数据中心-特色数据-期权风险分析 https://data.eastmoney.com/other/riskanal.html :return: 期权风险分析 :rtype: pandas.DataFrame

12

77

def option_risk_analysis_em() -> pd.DataFrame: """ 东方财富网-数据中心-特色数据-期权风险分析 https://data.eastmoney.com/other/riskanal.html :return: 期权风险分析 :rtype: pandas.DataFrame """ url = "https://push2.eastmoney.com/api/qt/clist/get" params = { 'fid': 'f3', 'po': '1', 'pz': '5000', 'pn': '1', 'np': '1', 'fltt': '2', 'invt': '2', 'ut': 'b2884a393a59ad64002292a3e90d46a5', 'fields': 'f1,f2,f3,f12,f13,f14,f302,f303,f325,f326,f327,f329,f328,f301,f152,f154', 'fs': 'm:10' } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["data"]["diff"]) temp_df.columns = [ '-', '最新价', '涨跌幅', '期权代码', '-', '期权名称', '-', '-', '到期日', '杠杆比率', '实际杠杆比率', 'Delta', 'Gamma', 'Vega', 'Theta', 'Rho', ] temp_df = temp_df[[ '期权代码', '期权名称', '最新价', '涨跌幅', '杠杆比率', '实际杠杆比率', 'Delta', 'Gamma', 'Vega', 'Rho', 'Theta', '到期日', ]] temp_df['最新价'] = pd.to_numeric(temp_df['最新价'], errors="coerce") temp_df['涨跌幅'] = pd.to_numeric(temp_df['涨跌幅'], errors="coerce") temp_df['杠杆比率'] = pd.to_numeric(temp_df['杠杆比率'], errors="coerce") temp_df['实际杠杆比率'] = pd.to_numeric(temp_df['实际杠杆比率'], errors="coerce") temp_df['Delta'] = pd.to_numeric(temp_df['Delta'], errors="coerce") temp_df['Gamma'] = pd.to_numeric(temp_df['Gamma'], errors="coerce") temp_df['Vega'] = pd.to_numeric(temp_df['Vega'], errors="coerce") temp_df['Rho'] = pd.to_numeric(temp_df['Rho'], errors="coerce") temp_df['Theta'] = pd.to_numeric(temp_df['Theta'], errors="coerce") temp_df['到期日'] = pd.to_datetime(temp_df['到期日'].astype(str)).dt.date return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/option/option_risk_analysis_em.py#L12-L77

25

[ 0, 1, 2, 3, 4, 5, 6 ]

10.606061

[ 7, 8, 20, 21, 22, 23, 41, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65 ]

27.272727

false

20

66

1

72.727273

4

def option_risk_analysis_em() -> pd.DataFrame: url = "https://push2.eastmoney.com/api/qt/clist/get" params = { 'fid': 'f3', 'po': '1', 'pz': '5000', 'pn': '1', 'np': '1', 'fltt': '2', 'invt': '2', 'ut': 'b2884a393a59ad64002292a3e90d46a5', 'fields': 'f1,f2,f3,f12,f13,f14,f302,f303,f325,f326,f327,f329,f328,f301,f152,f154', 'fs': 'm:10' } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["data"]["diff"]) temp_df.columns = [ '-', '最新价', '涨跌幅', '期权代码', '-', '期权名称', '-', '-', '到期日', '杠杆比率', '实际杠杆比率', 'Delta', 'Gamma', 'Vega', 'Theta', 'Rho', ] temp_df = temp_df[[ '期权代码', '期权名称', '最新价', '涨跌幅', '杠杆比率', '实际杠杆比率', 'Delta', 'Gamma', 'Vega', 'Rho', 'Theta', '到期日', ]] temp_df['最新价'] = pd.to_numeric(temp_df['最新价'], errors="coerce") temp_df['涨跌幅'] = pd.to_numeric(temp_df['涨跌幅'], errors="coerce") temp_df['杠杆比率'] = pd.to_numeric(temp_df['杠杆比率'], errors="coerce") temp_df['实际杠杆比率'] = pd.to_numeric(temp_df['实际杠杆比率'], errors="coerce") temp_df['Delta'] = pd.to_numeric(temp_df['Delta'], errors="coerce") temp_df['Gamma'] = pd.to_numeric(temp_df['Gamma'], errors="coerce") temp_df['Vega'] = pd.to_numeric(temp_df['Vega'], errors="coerce") temp_df['Rho'] = pd.to_numeric(temp_df['Rho'], errors="coerce") temp_df['Theta'] = pd.to_numeric(temp_df['Theta'], errors="coerce") temp_df['到期日'] = pd.to_datetime(temp_df['到期日'].astype(str)).dt.date return temp_df

17,974

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/option/option_czce.py

option_czce_hist

(symbol: str = "SR", year: str = "2021")

return option_df

郑州商品交易所-交易数据-历史行情下载-期权历史行情下载 http://www.czce.com.cn/cn/jysj/lshqxz/H770319index_1.htm :param symbol: choice of {"白糖": "SR", "棉花": "CF", "PTA": "TA", "甲醇": "MA", "菜籽粕": "RM", "动力煤": "ZC"} :type symbol: str :param year: 需要获取数据的年份, 注意品种的上市时间 :type year: str :return: 指定年份的日频期权数据 :rtype: pandas.DataFrame

23

49

def option_czce_hist(symbol: str = "SR", year: str = "2021") -> pd.DataFrame: """ 郑州商品交易所-交易数据-历史行情下载-期权历史行情下载 http://www.czce.com.cn/cn/jysj/lshqxz/H770319index_1.htm :param symbol: choice of {"白糖": "SR", "棉花": "CF", "PTA": "TA", "甲醇": "MA", "菜籽粕": "RM", "动力煤": "ZC"} :type symbol: str :param year: 需要获取数据的年份, 注意品种的上市时间 :type year: str :return: 指定年份的日频期权数据 :rtype: pandas.DataFrame """ symbol_year_dict = { "SR": "2017", "CF": "2019", "TA": "2019", "MA": "2019", "RM": "2020", "ZC": "2020", } if int(symbol_year_dict[symbol]) > int(year): warnings.warn(f"{year} year, symbol {symbol} is not on trade") return None warnings.warn("正在下载中, 请稍等") url = f'http://www.czce.com.cn/cn/DFSStaticFiles/Option/2021/OptionDataAllHistory/{symbol}OPTIONS{year}.txt' r = requests.get(url) option_df = pd.read_table(StringIO(r.text), skiprows=1, sep="|", low_memory=False) return option_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/option/option_czce.py#L23-L49

25

[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 ]

40.740741

[ 11, 19, 20, 21, 22, 23, 24, 25, 26 ]

33.333333

false

38.888889

27

2

66.666667

8

def option_czce_hist(symbol: str = "SR", year: str = "2021") -> pd.DataFrame: symbol_year_dict = { "SR": "2017", "CF": "2019", "TA": "2019", "MA": "2019", "RM": "2020", "ZC": "2020", } if int(symbol_year_dict[symbol]) > int(year): warnings.warn(f"{year} year, symbol {symbol} is not on trade") return None warnings.warn("正在下载中, 请稍等") url = f'http://www.czce.com.cn/cn/DFSStaticFiles/Option/2021/OptionDataAllHistory/{symbol}OPTIONS{year}.txt' r = requests.get(url) option_df = pd.read_table(StringIO(r.text), skiprows=1, sep="|", low_memory=False) return option_df

17,975

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/option/option_qvix.py

option_50etf_qvix

()

return temp_df

50ETF 期权波动率指数 QVIX http://1.optbbs.com/s/vix.shtml?50ETF :return: 50ETF 期权波动率指数 QVIX :rtype: pandas.DataFrame

13

30

def option_50etf_qvix() -> pd.DataFrame: """ 50ETF 期权波动率指数 QVIX http://1.optbbs.com/s/vix.shtml?50ETF :return: 50ETF 期权波动率指数 QVIX :rtype: pandas.DataFrame """ url = "http://1.optbbs.com/d/csv/d/k.csv" temp_df = pd.read_csv(url).iloc[:, :5] temp_df.columns = [ "date", "open", "high", "low", "close", ] temp_df["date"] = pd.to_datetime(temp_df["date"]).dt.date return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/option/option_qvix.py#L13-L30

25

[ 0, 1, 2, 3, 4, 5, 6 ]

38.888889

[ 7, 8, 9, 16, 17 ]

27.777778

false

21.212121

18

1

72.222222

4

def option_50etf_qvix() -> pd.DataFrame: url = "http://1.optbbs.com/d/csv/d/k.csv" temp_df = pd.read_csv(url).iloc[:, :5] temp_df.columns = [ "date", "open", "high", "low", "close", ] temp_df["date"] = pd.to_datetime(temp_df["date"]).dt.date return temp_df

17,976

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/option/option_qvix.py

option_50etf_min_qvix

()

return temp_df

50 ETF 期权波动率指数 QVIX http://1.optbbs.com/s/vix.shtml?50ETF :return: 50 ETF 期权波动率指数 QVIX :rtype: pandas.DataFrame

33

46

def option_50etf_min_qvix() -> pd.DataFrame: """ 50 ETF 期权波动率指数 QVIX http://1.optbbs.com/s/vix.shtml?50ETF :return: 50 ETF 期权波动率指数 QVIX :rtype: pandas.DataFrame """ url = "http://1.optbbs.com/d/csv/d/vix50.csv" temp_df = pd.read_csv(url).iloc[:, :2] temp_df.columns = [ "time", "qvix", ] return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/option/option_qvix.py#L33-L46

25

[ 0, 1, 2, 3, 4, 5, 6 ]

50

[ 7, 8, 9, 13 ]

28.571429

false

21.212121

14

1

71.428571

4

def option_50etf_min_qvix() -> pd.DataFrame: url = "http://1.optbbs.com/d/csv/d/vix50.csv" temp_df = pd.read_csv(url).iloc[:, :2] temp_df.columns = [ "time", "qvix", ] return temp_df

17,977

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/option/option_qvix.py

option_300etf_qvix

()

return temp_df

300 ETF 期权波动率指数 QVIX http://1.optbbs.com/s/vix.shtml?300ETF :return: 300 ETF 期权波动率指数 QVIX :rtype: pandas.DataFrame

49

66

def option_300etf_qvix() -> pd.DataFrame: """ 300 ETF 期权波动率指数 QVIX http://1.optbbs.com/s/vix.shtml?300ETF :return: 300 ETF 期权波动率指数 QVIX :rtype: pandas.DataFrame """ url = "http://1.optbbs.com/d/csv/d/k.csv" temp_df = pd.read_csv(url).iloc[:, [0, 9, 10, 11, 12]] temp_df.columns = [ "date", "open", "high", "low", "close", ] temp_df["date"] = pd.to_datetime(temp_df["date"]).dt.date return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/option/option_qvix.py#L49-L66

25

[ 0, 1, 2, 3, 4, 5, 6 ]

38.888889

[ 7, 8, 9, 16, 17 ]

27.777778

false

21.212121

18

1

72.222222

4

def option_300etf_qvix() -> pd.DataFrame: url = "http://1.optbbs.com/d/csv/d/k.csv" temp_df = pd.read_csv(url).iloc[:, [0, 9, 10, 11, 12]] temp_df.columns = [ "date", "open", "high", "low", "close", ] temp_df["date"] = pd.to_datetime(temp_df["date"]).dt.date return temp_df

17,978

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/option/option_qvix.py

option_300etf_min_qvix

()

return temp_df

300 ETF 期权波动率指数 QVIX-分时 http://1.optbbs.com/s/vix.shtml?300ETF :return: 300 ETF 期权波动率指数 QVIX-分时 :rtype: pandas.DataFrame

69

82

def option_300etf_min_qvix() -> pd.DataFrame: """ 300 ETF 期权波动率指数 QVIX-分时 http://1.optbbs.com/s/vix.shtml?300ETF :return: 300 ETF 期权波动率指数 QVIX-分时 :rtype: pandas.DataFrame """ url = "http://1.optbbs.com/d/csv/d/vix300.csv" temp_df = pd.read_csv(url).iloc[:, :2] temp_df.columns = [ "time", "qvix", ] return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/option/option_qvix.py#L69-L82

25

[ 0, 1, 2, 3, 4, 5, 6 ]

50

[ 7, 8, 9, 13 ]

28.571429

false

21.212121

14

1

71.428571

4

def option_300etf_min_qvix() -> pd.DataFrame: url = "http://1.optbbs.com/d/csv/d/vix300.csv" temp_df = pd.read_csv(url).iloc[:, :2] temp_df.columns = [ "time", "qvix", ] return temp_df

17,979

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/option/option_risk_indicator_sse.py

option_risk_indicator_sse

(date: str = "20220516")

return temp_df

上海证券交易所-产品-股票期权-期权风险指标 http://www.sse.com.cn/assortment/options/risk/ :param date: 日期; 20150209 开始 :type date: str :return: 期权风险指标 :rtype: pandas.DataFrame

11

63

def option_risk_indicator_sse(date: str = "20220516") -> pd.DataFrame: """ 上海证券交易所-产品-股票期权-期权风险指标 http://www.sse.com.cn/assortment/options/risk/ :param date: 日期; 20150209 开始 :type date: str :return: 期权风险指标 :rtype: pandas.DataFrame """ url = "http://query.sse.com.cn/commonQuery.do" params = { "isPagination": "false", "trade_date": date, "sqlId": "SSE_ZQPZ_YSP_GGQQZSXT_YSHQ_QQFXZB_DATE_L", "contractSymbol": "", "_": "1652877575590", } headers = { "Accept": "*/*", "Accept-Encoding": "gzip, deflate", "Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8", "Cache-Control": "no-cache", "Connection": "keep-alive", "Host": "query.sse.com.cn", "Pragma": "no-cache", "Referer": "http://www.sse.com.cn/", "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/101.0.4951.67 Safari/537.36", } r = requests.get(url, params=params, headers=headers) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]) temp_df = temp_df[ [ "TRADE_DATE", "SECURITY_ID", "CONTRACT_ID", "CONTRACT_SYMBOL", "DELTA_VALUE", "THETA_VALUE", "GAMMA_VALUE", "VEGA_VALUE", "RHO_VALUE", "IMPLC_VOLATLTY", ] ] temp_df["TRADE_DATE"] = pd.to_datetime(temp_df["TRADE_DATE"]).dt.date temp_df["DELTA_VALUE"] = pd.to_numeric(temp_df["DELTA_VALUE"]) temp_df["THETA_VALUE"] = pd.to_numeric(temp_df["THETA_VALUE"]) temp_df["GAMMA_VALUE"] = pd.to_numeric(temp_df["GAMMA_VALUE"]) temp_df["VEGA_VALUE"] = pd.to_numeric(temp_df["VEGA_VALUE"]) temp_df["RHO_VALUE"] = pd.to_numeric(temp_df["RHO_VALUE"]) temp_df["IMPLC_VOLATLTY"] = pd.to_numeric(temp_df["IMPLC_VOLATLTY"]) return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/option/option_risk_indicator_sse.py#L11-L63

25

[ 0, 1, 2, 3, 4, 5, 6, 7, 8 ]

16.981132

[ 9, 10, 17, 28, 29, 30, 31, 45, 46, 47, 48, 49, 50, 51, 52 ]

28.301887

false

20.833333

53

1

71.698113

6

def option_risk_indicator_sse(date: str = "20220516") -> pd.DataFrame: url = "http://query.sse.com.cn/commonQuery.do" params = { "isPagination": "false", "trade_date": date, "sqlId": "SSE_ZQPZ_YSP_GGQQZSXT_YSHQ_QQFXZB_DATE_L", "contractSymbol": "", "_": "1652877575590", } headers = { "Accept": "*/*", "Accept-Encoding": "gzip, deflate", "Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8", "Cache-Control": "no-cache", "Connection": "keep-alive", "Host": "query.sse.com.cn", "Pragma": "no-cache", "Referer": "http://www.sse.com.cn/", "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/101.0.4951.67 Safari/537.36", } r = requests.get(url, params=params, headers=headers) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]) temp_df = temp_df[ [ "TRADE_DATE", "SECURITY_ID", "CONTRACT_ID", "CONTRACT_SYMBOL", "DELTA_VALUE", "THETA_VALUE", "GAMMA_VALUE", "VEGA_VALUE", "RHO_VALUE", "IMPLC_VOLATLTY", ] ] temp_df["TRADE_DATE"] = pd.to_datetime(temp_df["TRADE_DATE"]).dt.date temp_df["DELTA_VALUE"] = pd.to_numeric(temp_df["DELTA_VALUE"]) temp_df["THETA_VALUE"] = pd.to_numeric(temp_df["THETA_VALUE"]) temp_df["GAMMA_VALUE"] = pd.to_numeric(temp_df["GAMMA_VALUE"]) temp_df["VEGA_VALUE"] = pd.to_numeric(temp_df["VEGA_VALUE"]) temp_df["RHO_VALUE"] = pd.to_numeric(temp_df["RHO_VALUE"]) temp_df["IMPLC_VOLATLTY"] = pd.to_numeric(temp_df["IMPLC_VOLATLTY"]) return temp_df

17,980

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/option/cons.py

convert_date

(date)

return None

transform a date string to datetime.date object :param date, string, e.g. 2016-01-01, 20160101 or 2016/01/01 :return: object of datetime.date(such as 2016-01-01) or None

52

70

def convert_date(date): """ transform a date string to datetime.date object :param date, string, e.g. 2016-01-01, 20160101 or 2016/01/01 :return: object of datetime.date(such as 2016-01-01) or None """ if isinstance(date, datetime.date): return date elif isinstance(date, str): match = DATE_PATTERN.match(date) if match: groups = match.groups() if len(groups) == 3: return datetime.date( year=int( groups[0]), month=int( groups[1]), day=int( groups[2])) return None

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/option/cons.py#L52-L70

25

[ 0, 1, 2, 3, 4, 5 ]

31.578947

[ 6, 7, 8, 9, 10, 11, 12, 13, 18 ]

47.368421

false

37.313433

19

5

52.631579

3

def convert_date(date): if isinstance(date, datetime.date): return date elif isinstance(date, str): match = DATE_PATTERN.match(date) if match: groups = match.groups() if len(groups) == 3: return datetime.date( year=int( groups[0]), month=int( groups[1]), day=int( groups[2])) return None

17,981

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/option/cons.py

get_json_path

(name, module_file)

return module_json_path

获取 JSON 配置文件的路径(从模块所在目录查找) :param name: 文件名 :param module_file: filename :return: str json_file_path

73

82

def get_json_path(name, module_file): """ 获取 JSON 配置文件的路径(从模块所在目录查找) :param name: 文件名 :param module_file: filename :return: str json_file_path """ module_folder = os.path.abspath(os.path.dirname(os.path.dirname(module_file))) module_json_path = os.path.join(module_folder, "file_fold", name) return module_json_path

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/option/cons.py#L73-L82

25

[ 0, 1, 2, 3, 4, 5, 6 ]

70

[ 7, 8, 9 ]

30

false

37.313433

10

1

70

4

def get_json_path(name, module_file): module_folder = os.path.abspath(os.path.dirname(os.path.dirname(module_file))) module_json_path = os.path.join(module_folder, "file_fold", name) return module_json_path

17,982

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/option/cons.py

get_calendar

()

return json.load(open(setting_file_path, "r"))

获取交易日历至 2019 年结束, 这里的交易日历需要按年更新 :return: json

85

92

def get_calendar(): """ 获取交易日历至 2019 年结束, 这里的交易日历需要按年更新 :return: json """ setting_file_name = "calendar.json" setting_file_path = get_json_path(setting_file_name, __file__) return json.load(open(setting_file_path, "r"))

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/option/cons.py#L85-L92

25

[ 0, 1, 2, 3, 4 ]

62.5

[ 5, 6, 7 ]

37.5

false

37.313433

8

1

62.5

2

def get_calendar(): setting_file_name = "calendar.json" setting_file_path = get_json_path(setting_file_name, __file__) return json.load(open(setting_file_path, "r"))

17,983

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/option/cons.py

last_trading_day

(day)

获取前一个交易日 :param day: "%Y%m%d" or datetime.date() :return last_day: "%Y%m%d" or datetime.date()

95

119

def last_trading_day(day): """ 获取前一个交易日 :param day: "%Y%m%d" or datetime.date() :return last_day: "%Y%m%d" or datetime.date() """ calendar = get_calendar() if isinstance(day, str): if day not in calendar: print("Today is not trading day：" + day) return False pos = calendar.index(day) last_day = calendar[pos - 1] return last_day elif isinstance(day, datetime.date): d_str = day.strftime("%Y%m%d") if d_str not in calendar: print("Today is not working day：" + d_str) return False pos = calendar.index(d_str) last_day = calendar[pos - 1] last_day = datetime.datetime.strptime(last_day, "%Y%m%d").date() return last_day

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/option/cons.py#L95-L119

25

[ 0, 1, 2, 3, 4, 5 ]

24

[ 6, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24 ]

68

false

37.313433

25

5

32

3

def last_trading_day(day): calendar = get_calendar() if isinstance(day, str): if day not in calendar: print("Today is not trading day：" + day) return False pos = calendar.index(day) last_day = calendar[pos - 1] return last_day elif isinstance(day, datetime.date): d_str = day.strftime("%Y%m%d") if d_str not in calendar: print("Today is not working day：" + d_str) return False pos = calendar.index(d_str) last_day = calendar[pos - 1] last_day = datetime.datetime.strptime(last_day, "%Y%m%d").date() return last_day

17,984

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/option/option_premium_analysis_em.py

option_premium_analysis_em

()

return temp_df

东方财富网-数据中心-特色数据-期权折溢价 https://data.eastmoney.com/other/premium.html :return: 期权折溢价 :rtype: pandas.DataFrame

12

75

def option_premium_analysis_em() -> pd.DataFrame: """ 东方财富网-数据中心-特色数据-期权折溢价 https://data.eastmoney.com/other/premium.html :return: 期权折溢价 :rtype: pandas.DataFrame """ url = "https://push2.eastmoney.com/api/qt/clist/get" params = { 'fid': 'f250', 'po': '1', 'pz': '5000', 'pn': '1', 'np': '1', 'fltt': '2', 'invt': '2', 'ut': 'b2884a393a59ad64002292a3e90d46a5', 'fields': 'f1,f2,f3,f12,f13,f14,f161,f250,f330,f331,f332,f333,f334,f335,f337,f301,f152', 'fs': 'm:10' } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["data"]["diff"]) temp_df.columns = [ '-', '最新价', '涨跌幅', '期权代码', '-', '期权名称', '-', '行权价', '折溢价率', '到期日', '-', '-', '-', '标的名称', '标的最新价', '标的涨跌幅', '盈亏平衡价', ] temp_df = temp_df[[ '期权代码', '期权名称', '最新价', '涨跌幅', '行权价', '折溢价率', '标的名称', '标的最新价', '标的涨跌幅', '盈亏平衡价', '到期日', ]] temp_df['最新价'] = pd.to_numeric(temp_df['最新价'], errors="coerce") temp_df['涨跌幅'] = pd.to_numeric(temp_df['涨跌幅'], errors="coerce") temp_df['行权价'] = pd.to_numeric(temp_df['行权价'], errors="coerce") temp_df['折溢价率'] = pd.to_numeric(temp_df['折溢价率'], errors="coerce") temp_df['标的最新价'] = pd.to_numeric(temp_df['标的最新价'], errors="coerce") temp_df['标的涨跌幅'] = pd.to_numeric(temp_df['标的涨跌幅'], errors="coerce") temp_df['盈亏平衡价'] = pd.to_numeric(temp_df['盈亏平衡价'], errors="coerce") temp_df['到期日'] = pd.to_datetime(temp_df['到期日'].astype(str)).dt.date return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/option/option_premium_analysis_em.py#L12-L75

25

[ 0, 1, 2, 3, 4, 5, 6 ]

10.9375

[ 7, 8, 20, 21, 22, 23, 42, 55, 56, 57, 58, 59, 60, 61, 62, 63 ]

25

false

21.73913

64

1

75

4

def option_premium_analysis_em() -> pd.DataFrame: url = "https://push2.eastmoney.com/api/qt/clist/get" params = { 'fid': 'f250', 'po': '1', 'pz': '5000', 'pn': '1', 'np': '1', 'fltt': '2', 'invt': '2', 'ut': 'b2884a393a59ad64002292a3e90d46a5', 'fields': 'f1,f2,f3,f12,f13,f14,f161,f250,f330,f331,f332,f333,f334,f335,f337,f301,f152', 'fs': 'm:10' } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["data"]["diff"]) temp_df.columns = [ '-', '最新价', '涨跌幅', '期权代码', '-', '期权名称', '-', '行权价', '折溢价率', '到期日', '-', '-', '-', '标的名称', '标的最新价', '标的涨跌幅', '盈亏平衡价', ] temp_df = temp_df[[ '期权代码', '期权名称', '最新价', '涨跌幅', '行权价', '折溢价率', '标的名称', '标的最新价', '标的涨跌幅', '盈亏平衡价', '到期日', ]] temp_df['最新价'] = pd.to_numeric(temp_df['最新价'], errors="coerce") temp_df['涨跌幅'] = pd.to_numeric(temp_df['涨跌幅'], errors="coerce") temp_df['行权价'] = pd.to_numeric(temp_df['行权价'], errors="coerce") temp_df['折溢价率'] = pd.to_numeric(temp_df['折溢价率'], errors="coerce") temp_df['标的最新价'] = pd.to_numeric(temp_df['标的最新价'], errors="coerce") temp_df['标的涨跌幅'] = pd.to_numeric(temp_df['标的涨跌幅'], errors="coerce") temp_df['盈亏平衡价'] = pd.to_numeric(temp_df['盈亏平衡价'], errors="coerce") temp_df['到期日'] = pd.to_datetime(temp_df['到期日'].astype(str)).dt.date return temp_df

17,985

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/option/option_commodity.py

option_dce_daily

( symbol: str = "聚乙烯期权", trade_date: str = "20210728" )

大连商品交易所-期权-日频行情数据 :param trade_date: 交易日 :type trade_date: str :param symbol: choice of {"玉米期权", "豆粕期权", "铁矿石期权", "液化石油气期权", "聚乙烯期权", "聚氯乙烯期权", "聚丙烯期权", "棕榈油期权", "黄大豆1号期权", "黄大豆2号期权", "豆油期权"} :type symbol: str :return: 日频行情数据 :rtype: pandas.DataFrame

34

161

def option_dce_daily( symbol: str = "聚乙烯期权", trade_date: str = "20210728" ) -> Tuple[Any, Any]: """ 大连商品交易所-期权-日频行情数据 :param trade_date: 交易日 :type trade_date: str :param symbol: choice of {"玉米期权", "豆粕期权", "铁矿石期权", "液化石油气期权", "聚乙烯期权", "聚氯乙烯期权", "聚丙烯期权", "棕榈油期权", "黄大豆1号期权", "黄大豆2号期权", "豆油期权"} :type symbol: str :return: 日频行情数据 :rtype: pandas.DataFrame """ calendar = get_calendar() day = ( convert_date(trade_date) if trade_date is not None else datetime.date.today() ) if day.strftime("%Y%m%d") not in calendar: warnings.warn("%s非交易日" % day.strftime("%Y%m%d")) return url = DCE_DAILY_OPTION_URL payload = { "dayQuotes.variety": "all", "dayQuotes.trade_type": "1", "year": str(day.year), "month": str(day.month - 1), "day": str(day.day), "exportFlag": "excel", } res = requests.post(url, data=payload) table_df = pd.read_excel(BytesIO(res.content), header=0) another_df = table_df.iloc[ table_df[table_df.iloc[:, 0].str.contains("合约")].iloc[-1].name :, [0, 1], ] another_df.reset_index(inplace=True, drop=True) another_df.iloc[0] = another_df.iat[0, 0].split("\t") another_df.columns = another_df.iloc[0] another_df = another_df.iloc[1:, :] if symbol == "豆粕期权": result_one_df, result_two_df = ( table_df[table_df["商品名称"] == "豆粕"], another_df[another_df.iloc[:, 0].str.contains("m")], ) result_one_df.reset_index(inplace=True, drop=True) result_two_df.reset_index(inplace=True, drop=True) return result_one_df, result_two_df elif symbol == "玉米期权": result_one_df, result_two_df = ( table_df[table_df["商品名称"] == "玉米"], another_df[another_df.iloc[:, 0].str.contains("c")], ) result_one_df.reset_index(inplace=True, drop=True) result_two_df.reset_index(inplace=True, drop=True) return result_one_df, result_two_df elif symbol == "铁矿石期权": result_one_df, result_two_df = ( table_df[table_df["商品名称"] == "铁矿石"], another_df[another_df.iloc[:, 0].str.contains("i")], ) result_one_df.reset_index(inplace=True, drop=True) result_two_df.reset_index(inplace=True, drop=True) return result_one_df, result_two_df elif symbol == "液化石油气期权": result_one_df, result_two_df = ( table_df[table_df["商品名称"] == "液化石油气"], another_df[another_df.iloc[:, 0].str.contains("pg")], ) result_one_df.reset_index(inplace=True, drop=True) result_two_df.reset_index(inplace=True, drop=True) return result_one_df, result_two_df elif symbol == "聚乙烯期权": result_one_df, result_two_df = ( table_df[table_df["商品名称"] == "聚乙烯"], another_df[another_df.iloc[:, 0].str.contains("l")], ) result_one_df.reset_index(inplace=True, drop=True) result_two_df.reset_index(inplace=True, drop=True) return result_one_df, result_two_df elif symbol == "聚氯乙烯期权": result_one_df, result_two_df = ( table_df[table_df["商品名称"] == "聚氯乙烯"], another_df[another_df.iloc[:, 0].str.contains("v")], ) result_one_df.reset_index(inplace=True, drop=True) result_two_df.reset_index(inplace=True, drop=True) return result_one_df, result_two_df elif symbol == "聚丙烯期权": result_one_df, result_two_df = ( table_df[table_df["商品名称"] == "聚丙烯"], another_df[another_df.iloc[:, 0].str.contains("pp")], ) result_one_df.reset_index(inplace=True, drop=True) result_two_df.reset_index(inplace=True, drop=True) return result_one_df, result_two_df elif symbol == "棕榈油期权": result_one_df, result_two_df = ( table_df[table_df["商品名称"] == "棕榈油"], another_df[another_df.iloc[:, 0].str.contains(r"^p\d")], ) result_one_df.reset_index(inplace=True, drop=True) result_two_df.reset_index(inplace=True, drop=True) return result_one_df, result_two_df elif symbol == "黄大豆1号期权": result_one_df, result_two_df = ( table_df[table_df["商品名称"] == "黄大豆1号"], another_df[another_df.iloc[:, 0].str.contains("a")], ) result_one_df.reset_index(inplace=True, drop=True) result_two_df.reset_index(inplace=True, drop=True) return result_one_df, result_two_df elif symbol == "黄大豆2号期权": result_one_df, result_two_df = ( table_df[table_df["商品名称"] == "黄大豆2号"], another_df[another_df.iloc[:, 0].str.contains("b")], ) result_one_df.reset_index(inplace=True, drop=True) result_two_df.reset_index(inplace=True, drop=True) return result_one_df, result_two_df elif symbol == "豆油期权": result_one_df, result_two_df = ( table_df[table_df["商品名称"] == "豆油"], another_df[another_df.iloc[:, 0].str.contains("y")], ) result_one_df.reset_index(inplace=True, drop=True) result_two_df.reset_index(inplace=True, drop=True) return result_one_df, result_two_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/option/option_commodity.py#L34-L161

25

[ 0 ]

0.78125

[ 12, 13, 18, 19, 20, 21, 22, 30, 31, 32, 36, 37, 38, 39, 40, 41, 45, 46, 47, 48, 49, 53, 54, 55, 56, 57, 61, 62, 63, 64, 65, 69, 70, 71, 72, 73, 77, 78, 79, 80, 81, 85, 86, 87, 88, 89, 93, 94, 95, 96, 97, 101, 102, 103, 104, 105, 109, 110, 111, 112, 113, 117, 118, 119, 120, 121, 125, 126, 127 ]

53.90625

false

7.692308

128

13

46.09375

7

def option_dce_daily( symbol: str = "聚乙烯期权", trade_date: str = "20210728" ) -> Tuple[Any, Any]: calendar = get_calendar() day = ( convert_date(trade_date) if trade_date is not None else datetime.date.today() ) if day.strftime("%Y%m%d") not in calendar: warnings.warn("%s非交易日" % day.strftime("%Y%m%d")) return url = DCE_DAILY_OPTION_URL payload = { "dayQuotes.variety": "all", "dayQuotes.trade_type": "1", "year": str(day.year), "month": str(day.month - 1), "day": str(day.day), "exportFlag": "excel", } res = requests.post(url, data=payload) table_df = pd.read_excel(BytesIO(res.content), header=0) another_df = table_df.iloc[ table_df[table_df.iloc[:, 0].str.contains("合约")].iloc[-1].name :, [0, 1], ] another_df.reset_index(inplace=True, drop=True) another_df.iloc[0] = another_df.iat[0, 0].split("\t") another_df.columns = another_df.iloc[0] another_df = another_df.iloc[1:, :] if symbol == "豆粕期权": result_one_df, result_two_df = ( table_df[table_df["商品名称"] == "豆粕"], another_df[another_df.iloc[:, 0].str.contains("m")], ) result_one_df.reset_index(inplace=True, drop=True) result_two_df.reset_index(inplace=True, drop=True) return result_one_df, result_two_df elif symbol == "玉米期权": result_one_df, result_two_df = ( table_df[table_df["商品名称"] == "玉米"], another_df[another_df.iloc[:, 0].str.contains("c")], ) result_one_df.reset_index(inplace=True, drop=True) result_two_df.reset_index(inplace=True, drop=True) return result_one_df, result_two_df elif symbol == "铁矿石期权": result_one_df, result_two_df = ( table_df[table_df["商品名称"] == "铁矿石"], another_df[another_df.iloc[:, 0].str.contains("i")], ) result_one_df.reset_index(inplace=True, drop=True) result_two_df.reset_index(inplace=True, drop=True) return result_one_df, result_two_df elif symbol == "液化石油气期权": result_one_df, result_two_df = ( table_df[table_df["商品名称"] == "液化石油气"], another_df[another_df.iloc[:, 0].str.contains("pg")], ) result_one_df.reset_index(inplace=True, drop=True) result_two_df.reset_index(inplace=True, drop=True) return result_one_df, result_two_df elif symbol == "聚乙烯期权": result_one_df, result_two_df = ( table_df[table_df["商品名称"] == "聚乙烯"], another_df[another_df.iloc[:, 0].str.contains("l")], ) result_one_df.reset_index(inplace=True, drop=True) result_two_df.reset_index(inplace=True, drop=True) return result_one_df, result_two_df elif symbol == "聚氯乙烯期权": result_one_df, result_two_df = ( table_df[table_df["商品名称"] == "聚氯乙烯"], another_df[another_df.iloc[:, 0].str.contains("v")], ) result_one_df.reset_index(inplace=True, drop=True) result_two_df.reset_index(inplace=True, drop=True) return result_one_df, result_two_df elif symbol == "聚丙烯期权": result_one_df, result_two_df = ( table_df[table_df["商品名称"] == "聚丙烯"], another_df[another_df.iloc[:, 0].str.contains("pp")], ) result_one_df.reset_index(inplace=True, drop=True) result_two_df.reset_index(inplace=True, drop=True) return result_one_df, result_two_df elif symbol == "棕榈油期权": result_one_df, result_two_df = ( table_df[table_df["商品名称"] == "棕榈油"], another_df[another_df.iloc[:, 0].str.contains(r"^p\d")], ) result_one_df.reset_index(inplace=True, drop=True) result_two_df.reset_index(inplace=True, drop=True) return result_one_df, result_two_df elif symbol == "黄大豆1号期权": result_one_df, result_two_df = ( table_df[table_df["商品名称"] == "黄大豆1号"], another_df[another_df.iloc[:, 0].str.contains("a")], ) result_one_df.reset_index(inplace=True, drop=True) result_two_df.reset_index(inplace=True, drop=True) return result_one_df, result_two_df elif symbol == "黄大豆2号期权": result_one_df, result_two_df = ( table_df[table_df["商品名称"] == "黄大豆2号"], another_df[another_df.iloc[:, 0].str.contains("b")], ) result_one_df.reset_index(inplace=True, drop=True) result_two_df.reset_index(inplace=True, drop=True) return result_one_df, result_two_df elif symbol == "豆油期权": result_one_df, result_two_df = ( table_df[table_df["商品名称"] == "豆油"], another_df[another_df.iloc[:, 0].str.contains("y")], ) result_one_df.reset_index(inplace=True, drop=True) result_two_df.reset_index(inplace=True, drop=True) return result_one_df, result_two_df

17,986

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/option/option_commodity.py

option_czce_daily

( symbol: str = "白糖期权", trade_date: str = "20191017" )

郑州商品交易所-期权-日频行情数据 :param trade_date: 交易日 :type trade_date: str :param symbol: choice of {"白糖期权", "棉花期权", "甲醇期权", "PTA期权", "菜籽粕期权", "动力煤期权", "菜籽油期权", "花生期权"} :type symbol: str :return: 日频行情数据 :rtype: pandas.DataFrame

164

226

def option_czce_daily( symbol: str = "白糖期权", trade_date: str = "20191017" ) -> pd.DataFrame: """ 郑州商品交易所-期权-日频行情数据 :param trade_date: 交易日 :type trade_date: str :param symbol: choice of {"白糖期权", "棉花期权", "甲醇期权", "PTA期权", "菜籽粕期权", "动力煤期权", "菜籽油期权", "花生期权"} :type symbol: str :return: 日频行情数据 :rtype: pandas.DataFrame """ calendar = get_calendar() day = ( convert_date(trade_date) if trade_date is not None else datetime.date.today() ) if day.strftime("%Y%m%d") not in calendar: warnings.warn("{}非交易日".format(day.strftime("%Y%m%d"))) return if day > datetime.date(2010, 8, 24): url = CZCE_DAILY_OPTION_URL_3.format( day.strftime("%Y"), day.strftime("%Y%m%d") ) try: r = requests.get(url) f = StringIO(r.text) table_df = pd.read_table(f, encoding="utf-8", skiprows=1, sep="|") if symbol == "白糖期权": temp_df = table_df[table_df.iloc[:, 0].str.contains("SR")] temp_df.reset_index(inplace=True, drop=True) return temp_df.iloc[:-1, :] elif symbol == "PTA期权": temp_df = table_df[table_df.iloc[:, 0].str.contains("TA")] temp_df.reset_index(inplace=True, drop=True) return temp_df.iloc[:-1, :] elif symbol == "甲醇期权": temp_df = table_df[table_df.iloc[:, 0].str.contains("MA")] temp_df.reset_index(inplace=True, drop=True) return temp_df.iloc[:-1, :] elif symbol == "菜籽粕期权": temp_df = table_df[table_df.iloc[:, 0].str.contains("RM")] temp_df.reset_index(inplace=True, drop=True) return temp_df.iloc[:-1, :] elif symbol == "动力煤期权": temp_df = table_df[table_df.iloc[:, 0].str.contains("ZC")] temp_df.reset_index(inplace=True, drop=True) return temp_df.iloc[:-1, :] elif symbol == "菜籽油期权": temp_df = table_df[table_df.iloc[:, 0].str.contains("OI")] temp_df.reset_index(inplace=True, drop=True) return temp_df.iloc[:-1, :] elif symbol == "花生期权": temp_df = table_df[table_df.iloc[:, 0].str.contains("PK")] temp_df.reset_index(inplace=True, drop=True) return temp_df.iloc[:-1, :] else: temp_df = table_df[table_df.iloc[:, 0].str.contains("CF")] temp_df.reset_index(inplace=True, drop=True) return temp_df.iloc[:-1, :] except: return

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/option/option_commodity.py#L164-L226

25

[ 0 ]

1.587302

[ 12, 13, 18, 19, 20, 21, 22, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 58, 59, 60, 61, 62 ]

69.84127

false

7.692308

63

11

30.15873

7

def option_czce_daily( symbol: str = "白糖期权", trade_date: str = "20191017" ) -> pd.DataFrame: calendar = get_calendar() day = ( convert_date(trade_date) if trade_date is not None else datetime.date.today() ) if day.strftime("%Y%m%d") not in calendar: warnings.warn("{}非交易日".format(day.strftime("%Y%m%d"))) return if day > datetime.date(2010, 8, 24): url = CZCE_DAILY_OPTION_URL_3.format( day.strftime("%Y"), day.strftime("%Y%m%d") ) try: r = requests.get(url) f = StringIO(r.text) table_df = pd.read_table(f, encoding="utf-8", skiprows=1, sep="|") if symbol == "白糖期权": temp_df = table_df[table_df.iloc[:, 0].str.contains("SR")] temp_df.reset_index(inplace=True, drop=True) return temp_df.iloc[:-1, :] elif symbol == "PTA期权": temp_df = table_df[table_df.iloc[:, 0].str.contains("TA")] temp_df.reset_index(inplace=True, drop=True) return temp_df.iloc[:-1, :] elif symbol == "甲醇期权": temp_df = table_df[table_df.iloc[:, 0].str.contains("MA")] temp_df.reset_index(inplace=True, drop=True) return temp_df.iloc[:-1, :] elif symbol == "菜籽粕期权": temp_df = table_df[table_df.iloc[:, 0].str.contains("RM")] temp_df.reset_index(inplace=True, drop=True) return temp_df.iloc[:-1, :] elif symbol == "动力煤期权": temp_df = table_df[table_df.iloc[:, 0].str.contains("ZC")] temp_df.reset_index(inplace=True, drop=True) return temp_df.iloc[:-1, :] elif symbol == "菜籽油期权": temp_df = table_df[table_df.iloc[:, 0].str.contains("OI")] temp_df.reset_index(inplace=True, drop=True) return temp_df.iloc[:-1, :] elif symbol == "花生期权": temp_df = table_df[table_df.iloc[:, 0].str.contains("PK")] temp_df.reset_index(inplace=True, drop=True) return temp_df.iloc[:-1, :] else: temp_df = table_df[table_df.iloc[:, 0].str.contains("CF")] temp_df.reset_index(inplace=True, drop=True) return temp_df.iloc[:-1, :] except: return

17,987

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/option/option_commodity.py

option_shfe_daily

( symbol: str = "铝期权", trade_date: str = "20200827" )

上海期货交易所-期权-日频行情数据 :param trade_date: 交易日 :type trade_date: str :param symbol: choice of {"铜期权", "天胶期权", "黄金期权", "铝期权", "锌期权"} :type symbol: str :return: 日频行情数据 :rtype: pandas.DataFrame

229

346

def option_shfe_daily( symbol: str = "铝期权", trade_date: str = "20200827" ) -> pd.DataFrame: """ 上海期货交易所-期权-日频行情数据 :param trade_date: 交易日 :type trade_date: str :param symbol: choice of {"铜期权", "天胶期权", "黄金期权", "铝期权", "锌期权"} :type symbol: str :return: 日频行情数据 :rtype: pandas.DataFrame """ calendar = get_calendar() day = ( convert_date(trade_date) if trade_date is not None else datetime.date.today() ) if day.strftime("%Y%m%d") not in calendar: warnings.warn("%s非交易日" % day.strftime("%Y%m%d")) return if day > datetime.date(2010, 8, 24): url = SHFE_OPTION_URL.format(day.strftime("%Y%m%d")) try: r = requests.get(url, headers=SHFE_HEADERS) json_data = r.json() table_df = pd.DataFrame( [ row for row in json_data["o_curinstrument"] if row["INSTRUMENTID"] not in ["小计", "合计"] and row["INSTRUMENTID"] != "" ] ) contract_df = table_df[ table_df["PRODUCTNAME"].str.strip() == symbol ] product_df = pd.DataFrame(json_data["o_curproduct"]) product_df = product_df[ product_df["PRODUCTNAME"].str.strip() == symbol ] volatility_df = pd.DataFrame(json_data["o_cursigma"]) volatility_df = volatility_df[ volatility_df["PRODUCTNAME"].str.strip() == symbol ] contract_df.columns = [ "_", "_", "_", "合约代码", "前结算价", "开盘价", "最高价", "最低价", "收盘价", "结算价", "涨跌1", "涨跌2", "成交量", "持仓量", "持仓量变化", "_", "行权量", "成交额", "德尔塔", "_", "_", "_", "_", ] contract_df = contract_df[ [ "合约代码", "开盘价", "最高价", "最低价", "收盘价", "前结算价", "结算价", "涨跌1", "涨跌2", "成交量", "持仓量", "持仓量变化", "成交额", "德尔塔", "行权量", ] ] volatility_df.columns = [ "_", "_", "_", "合约系列", "成交量", "持仓量", "持仓量变化", "行权量", "成交额", "隐含波动率", "_", ] volatility_df = volatility_df[ [ "合约系列", "成交量", "持仓量", "持仓量变化", "成交额", "行权量", "隐含波动率", ] ] return contract_df, volatility_df except: return

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/option/option_commodity.py#L229-L346

25

[ 0 ]

0.847458

[ 12, 13, 18, 19, 20, 21, 22, 23, 24, 25, 26, 34, 37, 38, 41, 42, 45, 70, 90, 104, 115, 116, 117 ]

19.491525

false

7.692308

118

6

80.508475

7

def option_shfe_daily( symbol: str = "铝期权", trade_date: str = "20200827" ) -> pd.DataFrame: calendar = get_calendar() day = ( convert_date(trade_date) if trade_date is not None else datetime.date.today() ) if day.strftime("%Y%m%d") not in calendar: warnings.warn("%s非交易日" % day.strftime("%Y%m%d")) return if day > datetime.date(2010, 8, 24): url = SHFE_OPTION_URL.format(day.strftime("%Y%m%d")) try: r = requests.get(url, headers=SHFE_HEADERS) json_data = r.json() table_df = pd.DataFrame( [ row for row in json_data["o_curinstrument"] if row["INSTRUMENTID"] not in ["小计", "合计"] and row["INSTRUMENTID"] != "" ] ) contract_df = table_df[ table_df["PRODUCTNAME"].str.strip() == symbol ] product_df = pd.DataFrame(json_data["o_curproduct"]) product_df = product_df[ product_df["PRODUCTNAME"].str.strip() == symbol ] volatility_df = pd.DataFrame(json_data["o_cursigma"]) volatility_df = volatility_df[ volatility_df["PRODUCTNAME"].str.strip() == symbol ] contract_df.columns = [ "_", "_", "_", "合约代码", "前结算价", "开盘价", "最高价", "最低价", "收盘价", "结算价", "涨跌1", "涨跌2", "成交量", "持仓量", "持仓量变化", "_", "行权量", "成交额", "德尔塔", "_", "_", "_", "_", ] contract_df = contract_df[ [ "合约代码", "开盘价", "最高价", "最低价", "收盘价", "前结算价", "结算价", "涨跌1", "涨跌2", "成交量", "持仓量", "持仓量变化", "成交额", "德尔塔", "行权量", ] ] volatility_df.columns = [ "_", "_", "_", "合约系列", "成交量", "持仓量", "持仓量变化", "行权量", "成交额", "隐含波动率", "_", ] volatility_df = volatility_df[ [ "合约系列", "成交量", "持仓量", "持仓量变化", "成交额", "行权量", "隐含波动率", ] ] return contract_df, volatility_df except: return

17,988

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/option/option_lhb_em.py

option_lhb_em

( symbol: str = "510050", indicator: str = "期权交易情况-认沽交易量", trade_date: str = "20220121", )

东方财富网-数据中心-特色数据-期权龙虎榜单 https://data.eastmoney.com/other/qqlhb.html :param symbol: 期权代码; choice of {"510050", "510300", "159919"} :type symbol: str :param indicator: 需要获取的指标; choice of {"期权交易情况-认沽交易量","期权持仓情况-认沽持仓量", "期权交易情况-认购交易量", "期权持仓情况-认购持仓量"} :type indicator: str :param trade_date: 交易日期 :type trade_date: str :return: 期权龙虎榜单 :rtype: pandas.DataFrame

12

247

def option_lhb_em( symbol: str = "510050", indicator: str = "期权交易情况-认沽交易量", trade_date: str = "20220121", ) -> pd.DataFrame: """ 东方财富网-数据中心-特色数据-期权龙虎榜单 https://data.eastmoney.com/other/qqlhb.html :param symbol: 期权代码; choice of {"510050", "510300", "159919"} :type symbol: str :param indicator: 需要获取的指标; choice of {"期权交易情况-认沽交易量","期权持仓情况-认沽持仓量", "期权交易情况-认购交易量", "期权持仓情况-认购持仓量"} :type indicator: str :param trade_date: 交易日期 :type trade_date: str :return: 期权龙虎榜单 :rtype: pandas.DataFrame """ url = "https://datacenter-web.eastmoney.com/api/data/get" params = { "type": "RPT_IF_BILLBOARD_TD", "sty": "ALL", "filter": f"""(SECURITY_CODE="{symbol}")(TRADE_DATE='{'-'.join([trade_date[:4], trade_date[4:6], trade_date[6:]])}')""", "p": "1", "pss": "200", "source": "IFBILLBOARD", "client": "WEB", "ut": "b2884a393a59ad64002292a3e90d46a5", "_": "1642904215146", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) if indicator == "期权交易情况-认沽交易量": temp_df = temp_df.iloc[:7, :] temp_df.columns = [ "交易类型", "交易日期", "证券代码", "标的名称", "-", "-", "机构", "名次", "交易量", "增减", "净认沽量", "占总交易量比例", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", ] temp_df = temp_df[ [ "交易类型", "交易日期", "证券代码", "标的名称", "名次", "机构", "交易量", "增减", "净认沽量", "占总交易量比例", ] ] temp_df["交易日期"] = pd.to_datetime(temp_df["交易日期"]).dt.date temp_df["名次"] = pd.to_numeric(temp_df["名次"]) temp_df["交易量"] = pd.to_numeric(temp_df["交易量"]) temp_df["增减"] = pd.to_numeric(temp_df["增减"]) temp_df["净认沽量"] = pd.to_numeric(temp_df["净认沽量"]) temp_df["占总交易量比例"] = pd.to_numeric(temp_df["占总交易量比例"]) temp_df.reset_index(drop=True, inplace=True) return temp_df elif indicator == "期权持仓情况-认沽持仓量": temp_df = temp_df.iloc[7:14, :] temp_df.columns = [ "交易类型", "交易日期", "证券代码", "标的名称", "-", "-", "机构", "名次", "-", "-", "-", "-", "-", "持仓量", "增减", "净持仓量", "占总交易量比例", "-", "-", "-", "-", "-", "-", "-", "-", ] temp_df = temp_df[ [ "交易类型", "交易日期", "证券代码", "标的名称", "名次", "机构", "持仓量", "增减", "净持仓量", "占总交易量比例", ] ] temp_df["交易日期"] = pd.to_datetime(temp_df["交易日期"]).dt.date temp_df["名次"] = pd.to_numeric(temp_df["名次"]) temp_df["持仓量"] = pd.to_numeric(temp_df["持仓量"]) temp_df["增减"] = pd.to_numeric(temp_df["增减"]) temp_df["净持仓量"] = pd.to_numeric(temp_df["净持仓量"]) temp_df["占总交易量比例"] = pd.to_numeric(temp_df["占总交易量比例"]) temp_df.reset_index(drop=True, inplace=True) return temp_df elif indicator == "期权交易情况-认购交易量": temp_df = temp_df.iloc[14:21, :] temp_df.columns = [ "交易类型", "交易日期", "证券代码", "标的名称", "-", "-", "机构", "名次", "-", "-", "-", "-", "-", "-", "-", "-", "-", "交易量", "增减", "净交易量", "占总交易量比例", "-", "-", "-", "-", ] temp_df = temp_df[ [ "交易类型", "交易日期", "证券代码", "标的名称", "名次", "机构", "交易量", "增减", "净交易量", "占总交易量比例", ] ] temp_df["交易日期"] = pd.to_datetime(temp_df["交易日期"]).dt.date temp_df["名次"] = pd.to_numeric(temp_df["名次"]) temp_df["交易量"] = pd.to_numeric(temp_df["交易量"]) temp_df["增减"] = pd.to_numeric(temp_df["增减"]) temp_df["净交易量"] = pd.to_numeric(temp_df["净交易量"]) temp_df["占总交易量比例"] = pd.to_numeric(temp_df["占总交易量比例"]) temp_df.reset_index(drop=True, inplace=True) return temp_df elif indicator == "期权持仓情况-认购持仓量": temp_df = temp_df.iloc[21:, :] temp_df.columns = [ "交易类型", "交易日期", "证券代码", "标的名称", "-", "-", "机构", "名次", "-", "-", "-" "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", "持仓量", "增减", "净持仓量", "占总交易量比例", ] temp_df = temp_df[ [ "交易类型", "交易日期", "证券代码", "标的名称", "名次", "机构", "持仓量", "增减", "净持仓量", "占总交易量比例", ] ] temp_df["交易日期"] = pd.to_datetime(temp_df["交易日期"]).dt.date temp_df["名次"] = pd.to_numeric(temp_df["名次"]) temp_df["持仓量"] = pd.to_numeric(temp_df["持仓量"]) temp_df["增减"] = pd.to_numeric(temp_df["增减"]) temp_df["净持仓量"] = pd.to_numeric(temp_df["净持仓量"]) temp_df["占总交易量比例"] = pd.to_numeric(temp_df["占总交易量比例"]) temp_df.reset_index(drop=True, inplace=True) return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/option/option_lhb_em.py#L12-L247

25

[ 0 ]

0.423729

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22.457627

false

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def option_lhb_em( symbol: str = "510050", indicator: str = "期权交易情况-认沽交易量", trade_date: str = "20220121", ) -> pd.DataFrame: url = "https://datacenter-web.eastmoney.com/api/data/get" params = { "type": "RPT_IF_BILLBOARD_TD", "sty": "ALL", "filter": f"""(SECURITY_CODE="{symbol}")(TRADE_DATE='{'-'.join([trade_date[:4], trade_date[4:6], trade_date[6:]])}')""", "p": "1", "pss": "200", "source": "IFBILLBOARD", "client": "WEB", "ut": "b2884a393a59ad64002292a3e90d46a5", "_": "1642904215146", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) if indicator == "期权交易情况-认沽交易量": temp_df = temp_df.iloc[:7, :] temp_df.columns = [ "交易类型", "交易日期", "证券代码", "标的名称", "-", "-", "机构", "名次", "交易量", "增减", "净认沽量", "占总交易量比例", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", ] temp_df = temp_df[ [ "交易类型", "交易日期", "证券代码", "标的名称", "名次", "机构", "交易量", "增减", "净认沽量", "占总交易量比例", ] ] temp_df["交易日期"] = pd.to_datetime(temp_df["交易日期"]).dt.date temp_df["名次"] = pd.to_numeric(temp_df["名次"]) temp_df["交易量"] = pd.to_numeric(temp_df["交易量"]) temp_df["增减"] = pd.to_numeric(temp_df["增减"]) temp_df["净认沽量"] = pd.to_numeric(temp_df["净认沽量"]) temp_df["占总交易量比例"] = pd.to_numeric(temp_df["占总交易量比例"]) temp_df.reset_index(drop=True, inplace=True) return temp_df elif indicator == "期权持仓情况-认沽持仓量": temp_df = temp_df.iloc[7:14, :] temp_df.columns = [ "交易类型", "交易日期", "证券代码", "标的名称", "-", "-", "机构", "名次", "-", "-", "-", "-", "-", "持仓量", "增减", "净持仓量", "占总交易量比例", "-", "-", "-", "-", "-", "-", "-", "-", ] temp_df = temp_df[ [ "交易类型", "交易日期", "证券代码", "标的名称", "名次", "机构", "持仓量", "增减", "净持仓量", "占总交易量比例", ] ] temp_df["交易日期"] = pd.to_datetime(temp_df["交易日期"]).dt.date temp_df["名次"] = pd.to_numeric(temp_df["名次"]) temp_df["持仓量"] = pd.to_numeric(temp_df["持仓量"]) temp_df["增减"] = pd.to_numeric(temp_df["增减"]) temp_df["净持仓量"] = pd.to_numeric(temp_df["净持仓量"]) temp_df["占总交易量比例"] = pd.to_numeric(temp_df["占总交易量比例"]) temp_df.reset_index(drop=True, inplace=True) return temp_df elif indicator == "期权交易情况-认购交易量": temp_df = temp_df.iloc[14:21, :] temp_df.columns = [ "交易类型", "交易日期", "证券代码", "标的名称", "-", "-", "机构", "名次", "-", "-", "-", "-", "-", "-", "-", "-", "-", "交易量", "增减", "净交易量", "占总交易量比例", "-", "-", "-", "-", ] temp_df = temp_df[ [ "交易类型", "交易日期", "证券代码", "标的名称", "名次", "机构", "交易量", "增减", "净交易量", "占总交易量比例", ] ] temp_df["交易日期"] = pd.to_datetime(temp_df["交易日期"]).dt.date temp_df["名次"] = pd.to_numeric(temp_df["名次"]) temp_df["交易量"] = pd.to_numeric(temp_df["交易量"]) temp_df["增减"] = pd.to_numeric(temp_df["增减"]) temp_df["净交易量"] = pd.to_numeric(temp_df["净交易量"]) temp_df["占总交易量比例"] = pd.to_numeric(temp_df["占总交易量比例"]) temp_df.reset_index(drop=True, inplace=True) return temp_df elif indicator == "期权持仓情况-认购持仓量": temp_df = temp_df.iloc[21:, :] temp_df.columns = [ "交易类型", "交易日期", "证券代码", "标的名称", "-", "-", "机构", "名次", "-", "-", "-" "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-", "持仓量", "增减", "净持仓量", "占总交易量比例", ] temp_df = temp_df[ [ "交易类型", "交易日期", "证券代码", "标的名称", "名次", "机构", "持仓量", "增减", "净持仓量", "占总交易量比例", ] ] temp_df["交易日期"] = pd.to_datetime(temp_df["交易日期"]).dt.date temp_df["名次"] = pd.to_numeric(temp_df["名次"]) temp_df["持仓量"] = pd.to_numeric(temp_df["持仓量"]) temp_df["增减"] = pd.to_numeric(temp_df["增减"]) temp_df["净持仓量"] = pd.to_numeric(temp_df["净持仓量"]) temp_df["占总交易量比例"] = pd.to_numeric(temp_df["占总交易量比例"]) temp_df.reset_index(drop=True, inplace=True) return temp_df

17,989

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/option/option_commodity_sina.py

option_commodity_contract_sina

(symbol: str = "玉米期权") -> pd.D

return temp_df

当前可以查询的期权品种的合约日期 https://stock.finance.sina.com.cn/futures/view/optionsDP.php :param symbol: choice of {"豆粕期权", "玉米期权", "铁矿石期权", "棉花期权", "白糖期权", "PTA期权", "甲醇期权", "橡胶期权", "沪铜期权", "黄金期权", "菜籽粕期权", "液化石油气期权", "动力煤期权", "菜籽油期权", "花生期权"} :type symbol: str :return: e.g., {'黄金期权': ['au2012', 'au2008', 'au2010', 'au2104', 'au2102', 'au2106', 'au2108']} :rtype: dict

15

58

def option_commodity_contract_sina(symbol: str = "玉米期权") -> pd.DataFrame: """ 当前可以查询的期权品种的合约日期 https://stock.finance.sina.com.cn/futures/view/optionsDP.php :param symbol: choice of {"豆粕期权", "玉米期权", "铁矿石期权", "棉花期权", "白糖期权", "PTA期权", "甲醇期权", "橡胶期权", "沪铜期权", "黄金期权", "菜籽粕期权", "液化石油气期权", "动力煤期权", "菜籽油期权", "花生期权"} :type symbol: str :return: e.g., {'黄金期权': ['au2012', 'au2008', 'au2010', 'au2104', 'au2102', 'au2106', 'au2108']} :rtype: dict """ url = ( "https://stock.finance.sina.com.cn/futures/view/optionsDP.php/pg_o/dce" ) r = requests.get(url) soup = BeautifulSoup(r.text, "lxml") url_list = [ item.find("a")["href"] for item in soup.find_all("li", attrs={"class": "active"}) if item.find("a") is not None ] commodity_list = [ item.find("a").text for item in soup.find_all("li", attrs={"class": "active"}) if item.find("a") is not None ] comm_list_dict = { key: value for key, value in zip(commodity_list, url_list) } url = "https://stock.finance.sina.com.cn" + comm_list_dict[symbol] r = requests.get(url) soup = BeautifulSoup(r.text, "lxml") symbol = ( soup.find(attrs={"id": "option_symbol"}) .find(attrs={"class": "selected"}) .text ) contract = [ item.text for item in soup.find(attrs={"id": "option_suffix"}).find_all("li") ] temp_df = pd.DataFrame({symbol: contract}) temp_df.reset_index(inplace=True) temp_df["index"] = temp_df.index + 1 temp_df.columns = ["序号", "合约"] return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/option/option_commodity_sina.py#L15-L58

25

[ 0, 1, 2, 3, 4, 5, 6, 7, 8 ]

20.454545

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36.363636

false

11.842105

44

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63.636364

6

def option_commodity_contract_sina(symbol: str = "玉米期权") -> pd.DataFrame: url = ( "https://stock.finance.sina.com.cn/futures/view/optionsDP.php/pg_o/dce" ) r = requests.get(url) soup = BeautifulSoup(r.text, "lxml") url_list = [ item.find("a")["href"] for item in soup.find_all("li", attrs={"class": "active"}) if item.find("a") is not None ] commodity_list = [ item.find("a").text for item in soup.find_all("li", attrs={"class": "active"}) if item.find("a") is not None ] comm_list_dict = { key: value for key, value in zip(commodity_list, url_list) } url = "https://stock.finance.sina.com.cn" + comm_list_dict[symbol] r = requests.get(url) soup = BeautifulSoup(r.text, "lxml") symbol = ( soup.find(attrs={"id": "option_symbol"}) .find(attrs={"class": "selected"}) .text ) contract = [ item.text for item in soup.find(attrs={"id": "option_suffix"}).find_all("li") ] temp_df = pd.DataFrame({symbol: contract}) temp_df.reset_index(inplace=True) temp_df["index"] = temp_df.index + 1 temp_df.columns = ["序号", "合约"] return temp_df

17,990

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/option/option_commodity_sina.py

option_commodity_contract_table_sina

( symbol: str = "黄金期权", contract: str = "au2204" )

return temp_df

当前所有期权合约, 包括看涨期权合约和看跌期权合约 https://stock.finance.sina.com.cn/futures/view/optionsDP.php :param symbol: choice of {"豆粕期权", "玉米期权", "铁矿石期权", "棉花期权", "白糖期权", "PTA期权", "甲醇期权", "橡胶期权", "沪铜期权", "黄金期权", "菜籽粕期权", "液化石油气期权", "动力煤期权", "菜籽油期权", "花生期权"} :type symbol: str :param contract: e.g., 'au2012' :type contract: str :return: 合约实时行情 :rtype: pandas.DataFrame

61

138

def option_commodity_contract_table_sina( symbol: str = "黄金期权", contract: str = "au2204" ) -> pd.DataFrame: """ 当前所有期权合约, 包括看涨期权合约和看跌期权合约 https://stock.finance.sina.com.cn/futures/view/optionsDP.php :param symbol: choice of {"豆粕期权", "玉米期权", "铁矿石期权", "棉花期权", "白糖期权", "PTA期权", "甲醇期权", "橡胶期权", "沪铜期权", "黄金期权", "菜籽粕期权", "液化石油气期权", "动力煤期权", "菜籽油期权", "花生期权"} :type symbol: str :param contract: e.g., 'au2012' :type contract: str :return: 合约实时行情 :rtype: pandas.DataFrame """ url = ( "https://stock.finance.sina.com.cn/futures/view/optionsDP.php/pg_o/dce" ) r = requests.get(url) soup = BeautifulSoup(r.text, "lxml") url_list = [ item.find("a")["href"] for item in soup.find_all("li", attrs={"class": "active"}) if item.find("a") is not None ] commodity_list = [ item.find("a").text for item in soup.find_all("li", attrs={"class": "active"}) if item.find("a") is not None ] comm_list_dict = { key: value for key, value in zip(commodity_list, url_list) } url = "https://stock.finance.sina.com.cn/futures/api/openapi.php/OptionService.getOptionData" params = { "type": "futures", "product": comm_list_dict[symbol].split("/")[-2], "exchange": comm_list_dict[symbol].split("/")[-1], "pinzhong": contract, } r = requests.get(url, params=params) data_json = r.json() up_df = pd.DataFrame(data_json["result"]["data"]["up"]) down_df = pd.DataFrame(data_json["result"]["data"]["down"]) temp_df = pd.concat([up_df, down_df], axis=1) temp_df.columns = [ "看涨合约-买量", "看涨合约-买价", "看涨合约-最新价", "看涨合约-卖价", "看涨合约-卖量", "看涨合约-持仓量", "看涨合约-涨跌", "行权价", "看涨合约-看涨期权合约", "看跌合约-买量", "看跌合约-买价", "看跌合约-最新价", "看跌合约-卖价", "看跌合约-卖量", "看跌合约-持仓量", "看跌合约-涨跌", "看跌合约-看跌期权合约", ] temp_df["看涨合约-买量"] = pd.to_numeric(temp_df["看涨合约-买量"], errors="coerce") temp_df["看涨合约-买价"] = pd.to_numeric(temp_df["看涨合约-买价"], errors="coerce") temp_df["看涨合约-最新价"] = pd.to_numeric(temp_df["看涨合约-最新价"], errors="coerce") temp_df["看涨合约-卖价"] = pd.to_numeric(temp_df["看涨合约-卖价"], errors="coerce") temp_df["看涨合约-卖量"] = pd.to_numeric(temp_df["看涨合约-卖量"], errors="coerce") temp_df["看涨合约-持仓量"] = pd.to_numeric(temp_df["看涨合约-持仓量"], errors="coerce") temp_df["看涨合约-涨跌"] = pd.to_numeric(temp_df["看涨合约-涨跌"], errors="coerce") temp_df["行权价"] = pd.to_numeric(temp_df["行权价"], errors="coerce") temp_df["看跌合约-买量"] = pd.to_numeric(temp_df["看跌合约-买量"], errors="coerce") temp_df["看跌合约-买价"] = pd.to_numeric(temp_df["看跌合约-买价"], errors="coerce") temp_df["看跌合约-最新价"] = pd.to_numeric(temp_df["看跌合约-最新价"], errors="coerce") temp_df["看跌合约-卖价"] = pd.to_numeric(temp_df["看跌合约-卖价"], errors="coerce") temp_df["看跌合约-卖量"] = pd.to_numeric(temp_df["看跌合约-卖量"], errors="coerce") temp_df["看跌合约-持仓量"] = pd.to_numeric(temp_df["看跌合约-持仓量"], errors="coerce") temp_df["看跌合约-涨跌"] = pd.to_numeric(temp_df["看跌合约-涨跌"], errors="coerce") return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/option/option_commodity_sina.py#L61-L138

25

[ 0 ]

1.282051

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38.461538

false

11.842105

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3

61.538462

8

def option_commodity_contract_table_sina( symbol: str = "黄金期权", contract: str = "au2204" ) -> pd.DataFrame: url = ( "https://stock.finance.sina.com.cn/futures/view/optionsDP.php/pg_o/dce" ) r = requests.get(url) soup = BeautifulSoup(r.text, "lxml") url_list = [ item.find("a")["href"] for item in soup.find_all("li", attrs={"class": "active"}) if item.find("a") is not None ] commodity_list = [ item.find("a").text for item in soup.find_all("li", attrs={"class": "active"}) if item.find("a") is not None ] comm_list_dict = { key: value for key, value in zip(commodity_list, url_list) } url = "https://stock.finance.sina.com.cn/futures/api/openapi.php/OptionService.getOptionData" params = { "type": "futures", "product": comm_list_dict[symbol].split("/")[-2], "exchange": comm_list_dict[symbol].split("/")[-1], "pinzhong": contract, } r = requests.get(url, params=params) data_json = r.json() up_df = pd.DataFrame(data_json["result"]["data"]["up"]) down_df = pd.DataFrame(data_json["result"]["data"]["down"]) temp_df = pd.concat([up_df, down_df], axis=1) temp_df.columns = [ "看涨合约-买量", "看涨合约-买价", "看涨合约-最新价", "看涨合约-卖价", "看涨合约-卖量", "看涨合约-持仓量", "看涨合约-涨跌", "行权价", "看涨合约-看涨期权合约", "看跌合约-买量", "看跌合约-买价", "看跌合约-最新价", "看跌合约-卖价", "看跌合约-卖量", "看跌合约-持仓量", "看跌合约-涨跌", "看跌合约-看跌期权合约", ] temp_df["看涨合约-买量"] = pd.to_numeric(temp_df["看涨合约-买量"], errors="coerce") temp_df["看涨合约-买价"] = pd.to_numeric(temp_df["看涨合约-买价"], errors="coerce") temp_df["看涨合约-最新价"] = pd.to_numeric(temp_df["看涨合约-最新价"], errors="coerce") temp_df["看涨合约-卖价"] = pd.to_numeric(temp_df["看涨合约-卖价"], errors="coerce") temp_df["看涨合约-卖量"] = pd.to_numeric(temp_df["看涨合约-卖量"], errors="coerce") temp_df["看涨合约-持仓量"] = pd.to_numeric(temp_df["看涨合约-持仓量"], errors="coerce") temp_df["看涨合约-涨跌"] = pd.to_numeric(temp_df["看涨合约-涨跌"], errors="coerce") temp_df["行权价"] = pd.to_numeric(temp_df["行权价"], errors="coerce") temp_df["看跌合约-买量"] = pd.to_numeric(temp_df["看跌合约-买量"], errors="coerce") temp_df["看跌合约-买价"] = pd.to_numeric(temp_df["看跌合约-买价"], errors="coerce") temp_df["看跌合约-最新价"] = pd.to_numeric(temp_df["看跌合约-最新价"], errors="coerce") temp_df["看跌合约-卖价"] = pd.to_numeric(temp_df["看跌合约-卖价"], errors="coerce") temp_df["看跌合约-卖量"] = pd.to_numeric(temp_df["看跌合约-卖量"], errors="coerce") temp_df["看跌合约-持仓量"] = pd.to_numeric(temp_df["看跌合约-持仓量"], errors="coerce") temp_df["看跌合约-涨跌"] = pd.to_numeric(temp_df["看跌合约-涨跌"], errors="coerce") return temp_df

17,991

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/option/option_commodity_sina.py

option_commodity_hist_sina

(symbol: str = "au2012C392")

return temp_df

合约历史行情-日频 https://stock.finance.sina.com.cn/futures/view/optionsDP.php :param symbol: return of option_sina_option_commodity_contract_list(symbol="黄金期权", contract="au2012"), 看涨期权合约 filed :type symbol: str :return: 合约历史行情-日频 :rtype: pandas.DataFrame

141

164

def option_commodity_hist_sina(symbol: str = "au2012C392") -> pd.DataFrame: """ 合约历史行情-日频 https://stock.finance.sina.com.cn/futures/view/optionsDP.php :param symbol: return of option_sina_option_commodity_contract_list(symbol="黄金期权", contract="au2012"), 看涨期权合约 filed :type symbol: str :return: 合约历史行情-日频 :rtype: pandas.DataFrame """ url = "https://stock.finance.sina.com.cn/futures/api/jsonp.php/var%20_m2009C30002020_7_17=/FutureOptionAllService.getOptionDayline" params = {"symbol": symbol} r = requests.get(url, params=params) data_text = r.text data_json = demjson.decode(data_text[data_text.find("[") : -2]) temp_df = pd.DataFrame(data_json) temp_df.columns = ["open", "high", "low", "close", "volume", "date"] temp_df = temp_df[["date", "open", "high", "low", "close", "volume"]] temp_df["date"] = pd.to_datetime(temp_df["date"]).dt.date temp_df["open"] = pd.to_numeric(temp_df["open"]) temp_df["high"] = pd.to_numeric(temp_df["high"]) temp_df["low"] = pd.to_numeric(temp_df["low"]) temp_df["close"] = pd.to_numeric(temp_df["close"]) temp_df["volume"] = pd.to_numeric(temp_df["volume"]) return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/option/option_commodity_sina.py#L141-L164

25

[ 0, 1, 2, 3, 4, 5, 6, 7, 8 ]

37.5

[ 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 ]

62.5

false

11.842105

24

1

37.5

6

def option_commodity_hist_sina(symbol: str = "au2012C392") -> pd.DataFrame: url = "https://stock.finance.sina.com.cn/futures/api/jsonp.php/var%20_m2009C30002020_7_17=/FutureOptionAllService.getOptionDayline" params = {"symbol": symbol} r = requests.get(url, params=params) data_text = r.text data_json = demjson.decode(data_text[data_text.find("[") : -2]) temp_df = pd.DataFrame(data_json) temp_df.columns = ["open", "high", "low", "close", "volume", "date"] temp_df = temp_df[["date", "open", "high", "low", "close", "volume"]] temp_df["date"] = pd.to_datetime(temp_df["date"]).dt.date temp_df["open"] = pd.to_numeric(temp_df["open"]) temp_df["high"] = pd.to_numeric(temp_df["high"]) temp_df["low"] = pd.to_numeric(temp_df["low"]) temp_df["close"] = pd.to_numeric(temp_df["close"]) temp_df["volume"] = pd.to_numeric(temp_df["volume"]) return temp_df

17,992

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/crypto/crypto_bitcoin_cme.py

crypto_bitcoin_cme

(date: str = "20210609")

return temp_df

芝加哥商业交易所-比特币成交量报告 https://datacenter.jin10.com/reportType/dc_cme_btc_report :return: 比特币成交量报告 :rtype: pandas.DataFrame

12

50

def crypto_bitcoin_cme(date: str = "20210609") -> pd.DataFrame: """ 芝加哥商业交易所-比特币成交量报告 https://datacenter.jin10.com/reportType/dc_cme_btc_report :return: 比特币成交量报告 :rtype: pandas.DataFrame """ url = "https://datacenter-api.jin10.com/reports/list" params = { "category": "cme", "date": "-".join([date[:4], date[4:6], date[6:]]), "attr_id": "4", "_": "1624354777843", } headers = { "accept": "*/*", "accept-encoding": "gzip, deflate, br", "accept-language": "zh-CN,zh;q=0.9,en;q=0.8", "cache-control": "no-cache", "origin": "https://datacenter.jin10.com", "pragma": "no-cache", "referer": "https://datacenter.jin10.com/", "sec-ch-ua": '" Not;A Brand";v="99", "Google Chrome";v="91", "Chromium";v="91"', "sec-ch-ua-mobile": "?0", "sec-fetch-dest": "empty", "sec-fetch-mode": "cors", "sec-fetch-site": "same-site", "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.106 Safari/537.36", "x-app-id": "rU6QIu7JHe2gOUeR", "x-csrf-token": "", "x-version": "1.0.0", } r = requests.get(url, params=params, headers=headers) data_json = r.json() temp_df = pd.DataFrame( [item for item in data_json["data"]["values"]], columns=[item["name"] for item in data_json["data"]["keys"]], ) return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/crypto/crypto_bitcoin_cme.py#L12-L50

25

[ 0, 1, 2, 3, 4, 5, 6 ]

17.948718

[ 7, 8, 14, 32, 33, 34, 38 ]

17.948718

false

35.714286

39

3

82.051282

4

def crypto_bitcoin_cme(date: str = "20210609") -> pd.DataFrame: url = "https://datacenter-api.jin10.com/reports/list" params = { "category": "cme", "date": "-".join([date[:4], date[4:6], date[6:]]), "attr_id": "4", "_": "1624354777843", } headers = { "accept": "*/*", "accept-encoding": "gzip, deflate, br", "accept-language": "zh-CN,zh;q=0.9,en;q=0.8", "cache-control": "no-cache", "origin": "https://datacenter.jin10.com", "pragma": "no-cache", "referer": "https://datacenter.jin10.com/", "sec-ch-ua": '" Not;A Brand";v="99", "Google Chrome";v="91", "Chromium";v="91"', "sec-ch-ua-mobile": "?0", "sec-fetch-dest": "empty", "sec-fetch-mode": "cors", "sec-fetch-site": "same-site", "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.106 Safari/537.36", "x-app-id": "rU6QIu7JHe2gOUeR", "x-csrf-token": "", "x-version": "1.0.0", } r = requests.get(url, params=params, headers=headers) data_json = r.json() temp_df = pd.DataFrame( [item for item in data_json["data"]["values"]], columns=[item["name"] for item in data_json["data"]["keys"]], ) return temp_df

17,993

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/crypto/crypto_hist_investing.py

crypto_name_url_table

(symbol: str = "web")

加密货币名称、代码和 ID，每次更新较慢 https://cn.investing.com/crypto/ethereum/historical-data :param symbol: choice of {"web", "local"}; web 表示从网页获取最新，local 表示利用本地本文件 :type symbol: str :return: 加密货币名称、代码和 ID :rtype: pandas.DataFrame

19

128

def crypto_name_url_table(symbol: str = "web") -> pd.DataFrame: """ 加密货币名称、代码和 ID，每次更新较慢 https://cn.investing.com/crypto/ethereum/historical-data :param symbol: choice of {"web", "local"}; web 表示从网页获取最新，local 表示利用本地本文件 :type symbol: str :return: 加密货币名称、代码和 ID :rtype: pandas.DataFrame """ if symbol == "web": headers = { "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/86.0.4240.111 Safari/537.36", "X-Requested-With": "XMLHttpRequest", } url = "https://cn.investing.com/crypto/Service/LoadCryptoCurrencies" payload = { 'draw': '14', 'columns[0][data]': 'currencies_order', 'columns[0][name]': 'currencies_order', 'columns[0][searchable]': 'true', 'columns[0][orderable]': 'true', 'columns[0][search][value]': '', 'columns[0][search][regex]': 'false', 'columns[1][data]': 'function', 'columns[1][name]': 'crypto_id', 'columns[1][searchable]': 'true', 'columns[1][orderable]': 'false', 'columns[1][search][value]': '', 'columns[1][search][regex]': 'false', 'columns[2][data]': 'function', 'columns[2][name]': 'name', 'columns[2][searchable]': 'true', 'columns[2][orderable]': 'true', 'columns[2][search][value]': '', 'columns[2][search][regex]': 'false', 'columns[3][data]': 'symbol', 'columns[3][name]': 'symbol', 'columns[3][searchable]': 'true', 'columns[3][orderable]': 'true', 'columns[3][search][value]': '', 'columns[3][search][regex]': 'false', 'columns[4][data]': 'function', 'columns[4][name]': 'price_usd', 'columns[4][searchable]': 'true', 'columns[4][orderable]': 'true', 'columns[4][search][value]': '', 'columns[4][search][regex]': 'false', 'columns[5][data]': 'market_cap_formatted', 'columns[5][name]': 'market_cap_usd', 'columns[5][searchable]': 'true', 'columns[5][orderable]': 'true', 'columns[5][search][value]': '', 'columns[5][search][regex]': 'false', 'columns[6][data]': '24h_volume_formatted', 'columns[6][name]': '24h_volume_usd', 'columns[6][searchable]': 'true', 'columns[6][orderable]': 'true', 'columns[6][search][value]': '', 'columns[6][search][regex]': 'false', 'columns[7][data]': 'total_volume', 'columns[7][name]': 'total_volume', 'columns[7][searchable]': 'true', 'columns[7][orderable]': 'true', 'columns[7][search][value]': '', 'columns[7][search][regex]': 'false', 'columns[8][data]': 'change_percent_formatted', 'columns[8][name]': 'change_percent', 'columns[8][searchable]': 'true', 'columns[8][orderable]': 'true', 'columns[8][search][value]': '', 'columns[8][search][regex]': 'false', 'columns[9][data]': 'percent_change_7d_formatted', 'columns[9][name]': 'percent_change_7d', 'columns[9][searchable]': 'true', 'columns[9][orderable]': 'true', 'columns[9][search][value]': '', 'columns[9][search][regex]': 'false', 'order[0][column]': 'currencies_order', 'order[0][dir]': 'asc', 'start': '0', 'length': '100', 'search[value]': '', 'search[regex]': 'false', 'currencyId': '12', } r = requests.post(url, data=payload, headers=headers) data_json = r.json() total_page = math.ceil(int(data_json['recordsTotal']) / 100) big_df = pd.DataFrame() for page in tqdm(range(1, total_page+1), leave=False): payload.update({ "start": (page-1)*100, 'length': 100 }) r = requests.post(url, data=payload, headers=headers) data_json = r.json() temp_df = pd.DataFrame(data_json['data']) big_df = pd.concat([big_df, temp_df], ignore_index=True) big_df = big_df[[ 'symbol', 'name', 'name_trans', 'sml_id', 'related_pair_ID', ]] return big_df else: get_crypto_info_csv_path = get_crypto_info_csv() name_url_df = pd.read_csv(get_crypto_info_csv_path) return name_url_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/crypto/crypto_hist_investing.py#L19-L128

25

[ 0, 1, 2, 3, 4, 5, 6, 7, 8 ]

8.181818

[ 9, 10, 14, 15, 85, 86, 87, 88, 89, 90, 94, 95, 96, 97, 98, 105, 107, 108, 109 ]

17.272727

false

12.5

110

3

82.727273

6

def crypto_name_url_table(symbol: str = "web") -> pd.DataFrame: if symbol == "web": headers = { "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/86.0.4240.111 Safari/537.36", "X-Requested-With": "XMLHttpRequest", } url = "https://cn.investing.com/crypto/Service/LoadCryptoCurrencies" payload = { 'draw': '14', 'columns[0][data]': 'currencies_order', 'columns[0][name]': 'currencies_order', 'columns[0][searchable]': 'true', 'columns[0][orderable]': 'true', 'columns[0][search][value]': '', 'columns[0][search][regex]': 'false', 'columns[1][data]': 'function', 'columns[1][name]': 'crypto_id', 'columns[1][searchable]': 'true', 'columns[1][orderable]': 'false', 'columns[1][search][value]': '', 'columns[1][search][regex]': 'false', 'columns[2][data]': 'function', 'columns[2][name]': 'name', 'columns[2][searchable]': 'true', 'columns[2][orderable]': 'true', 'columns[2][search][value]': '', 'columns[2][search][regex]': 'false', 'columns[3][data]': 'symbol', 'columns[3][name]': 'symbol', 'columns[3][searchable]': 'true', 'columns[3][orderable]': 'true', 'columns[3][search][value]': '', 'columns[3][search][regex]': 'false', 'columns[4][data]': 'function', 'columns[4][name]': 'price_usd', 'columns[4][searchable]': 'true', 'columns[4][orderable]': 'true', 'columns[4][search][value]': '', 'columns[4][search][regex]': 'false', 'columns[5][data]': 'market_cap_formatted', 'columns[5][name]': 'market_cap_usd', 'columns[5][searchable]': 'true', 'columns[5][orderable]': 'true', 'columns[5][search][value]': '', 'columns[5][search][regex]': 'false', 'columns[6][data]': '24h_volume_formatted', 'columns[6][name]': '24h_volume_usd', 'columns[6][searchable]': 'true', 'columns[6][orderable]': 'true', 'columns[6][search][value]': '', 'columns[6][search][regex]': 'false', 'columns[7][data]': 'total_volume', 'columns[7][name]': 'total_volume', 'columns[7][searchable]': 'true', 'columns[7][orderable]': 'true', 'columns[7][search][value]': '', 'columns[7][search][regex]': 'false', 'columns[8][data]': 'change_percent_formatted', 'columns[8][name]': 'change_percent', 'columns[8][searchable]': 'true', 'columns[8][orderable]': 'true', 'columns[8][search][value]': '', 'columns[8][search][regex]': 'false', 'columns[9][data]': 'percent_change_7d_formatted', 'columns[9][name]': 'percent_change_7d', 'columns[9][searchable]': 'true', 'columns[9][orderable]': 'true', 'columns[9][search][value]': '', 'columns[9][search][regex]': 'false', 'order[0][column]': 'currencies_order', 'order[0][dir]': 'asc', 'start': '0', 'length': '100', 'search[value]': '', 'search[regex]': 'false', 'currencyId': '12', } r = requests.post(url, data=payload, headers=headers) data_json = r.json() total_page = math.ceil(int(data_json['recordsTotal']) / 100) big_df = pd.DataFrame() for page in tqdm(range(1, total_page+1), leave=False): payload.update({ "start": (page-1)*100, 'length': 100 }) r = requests.post(url, data=payload, headers=headers) data_json = r.json() temp_df = pd.DataFrame(data_json['data']) big_df = pd.concat([big_df, temp_df], ignore_index=True) big_df = big_df[[ 'symbol', 'name', 'name_trans', 'sml_id', 'related_pair_ID', ]] return big_df else: get_crypto_info_csv_path = get_crypto_info_csv() name_url_df = pd.read_csv(get_crypto_info_csv_path) return name_url_df

17,994

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/crypto/crypto_hist_investing.py

crypto_hist

( symbol: str = "BTC", period: str = "每日", start_date: str = "20191020", end_date: str = "20201020", )

return df_data

加密货币历史数据 https://cn.investing.com/crypto/ethereum/historical-data :param symbol: 货币名称 :type symbol: str :param period: choice of {"每日", "每周", "每月"} :type period: str :param start_date: '20151020', 注意格式 :type start_date: str :param end_date: '20201020', 注意格式 :type end_date: str :return: 加密货币历史数据获取 :rtype: pandas.DataFrame

131

239

def crypto_hist( symbol: str = "BTC", period: str = "每日", start_date: str = "20191020", end_date: str = "20201020", ): """ 加密货币历史数据 https://cn.investing.com/crypto/ethereum/historical-data :param symbol: 货币名称 :type symbol: str :param period: choice of {"每日", "每周", "每月"} :type period: str :param start_date: '20151020', 注意格式 :type start_date: str :param end_date: '20201020', 注意格式 :type end_date: str :return: 加密货币历史数据获取 :rtype: pandas.DataFrame """ import warnings warnings.filterwarnings('ignore') headers = { "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/86.0.4240.111 Safari/537.36", "X-Requested-With": "XMLHttpRequest", } period_map = {"每日": "Daily", "每周": "Weekly", "每月": "Monthly"} 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:]]) name_url_df = crypto_name_url_table(symbol='local') curr_id = name_url_df[name_url_df["symbol"] == symbol]["related_pair_ID"].values[0] sml_id = name_url_df[name_url_df["symbol"] == symbol]["sml_id"].values[0] url = "https://cn.investing.com/instruments/HistoricalDataAjax" payload = { "curr_id": curr_id, "smlID": sml_id, "header": "null", "st_date": start_date, "end_date": end_date, "interval_sec": period_map[period], "sort_col": "date", "sort_ord": "DESC", "action": "historical_data", } r = requests.post(url, data=payload, headers=headers) temp_df = pd.read_html(r.text)[0] df_data = temp_df.copy() if period == "每月": df_data.index = pd.to_datetime(df_data["日期"], format="%Y年%m月") else: df_data.index = pd.to_datetime(df_data["日期"], format="%Y年%m月%d日") if any(df_data["交易量"].astype(str).str.contains("-")): df_data["交易量"][df_data["交易量"].str.contains("-")] = df_data["交易量"][ df_data["交易量"].str.contains("-") ].replace("-", 0) if any(df_data["交易量"].astype(str).str.contains("B")): df_data["交易量"][df_data["交易量"].str.contains("B").fillna(False)] = ( df_data["交易量"][df_data["交易量"].str.contains("B").fillna(False)] .str.replace("B", "") .str.replace(",", "") .astype(float) * 1000000000 ) if any(df_data["交易量"].astype(str).str.contains("M")): df_data["交易量"][df_data["交易量"].str.contains("M").fillna(False)] = ( df_data["交易量"][df_data["交易量"].str.contains("M").fillna(False)] .str.replace("M", "") .str.replace(",", "") .astype(float) * 1000000 ) if any(df_data["交易量"].astype(str).str.contains("K")): df_data["交易量"][df_data["交易量"].str.contains("K").fillna(False)] = ( df_data["交易量"][df_data["交易量"].str.contains("K").fillna(False)] .str.replace("K", "") .str.replace(",", "") .astype(float) * 1000 ) df_data["交易量"] = df_data["交易量"].astype(float) df_data["涨跌幅"] = pd.DataFrame( round( df_data["涨跌幅"].str.replace(",", "").str.replace("%", "").astype(float) / 100, 6, ) ) del df_data["日期"] df_data.reset_index(inplace=True) df_data = df_data[[ "日期", "收盘", "开盘", "高", "低", "交易量", "涨跌幅", ]] df_data['日期'] = pd.to_datetime(df_data['日期']).dt.date df_data['收盘'] = pd.to_numeric(df_data['收盘']) df_data['开盘'] = pd.to_numeric(df_data['开盘']) df_data['高'] = pd.to_numeric(df_data['高']) df_data['低'] = pd.to_numeric(df_data['低']) df_data['交易量'] = pd.to_numeric(df_data['交易量']) df_data['涨跌幅'] = pd.to_numeric(df_data['涨跌幅']) df_data.sort_values('日期', inplace=True) df_data.reset_index(inplace=True, drop=True) return df_data

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/crypto/crypto_hist_investing.py#L131-L239

25

[ 0 ]

0.917431

[ 20, 21, 22, 26, 27, 28, 29, 30, 31, 32, 33, 44, 46, 47, 48, 49, 51, 52, 53, 56, 57, 64, 65, 72, 73, 80, 81, 88, 89, 90, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108 ]

36.697248

false

12.5

109

6

63.302752

12

def crypto_hist( symbol: str = "BTC", period: str = "每日", start_date: str = "20191020", end_date: str = "20201020", ): import warnings warnings.filterwarnings('ignore') headers = { "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/86.0.4240.111 Safari/537.36", "X-Requested-With": "XMLHttpRequest", } period_map = {"每日": "Daily", "每周": "Weekly", "每月": "Monthly"} 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:]]) name_url_df = crypto_name_url_table(symbol='local') curr_id = name_url_df[name_url_df["symbol"] == symbol]["related_pair_ID"].values[0] sml_id = name_url_df[name_url_df["symbol"] == symbol]["sml_id"].values[0] url = "https://cn.investing.com/instruments/HistoricalDataAjax" payload = { "curr_id": curr_id, "smlID": sml_id, "header": "null", "st_date": start_date, "end_date": end_date, "interval_sec": period_map[period], "sort_col": "date", "sort_ord": "DESC", "action": "historical_data", } r = requests.post(url, data=payload, headers=headers) temp_df = pd.read_html(r.text)[0] df_data = temp_df.copy() if period == "每月": df_data.index = pd.to_datetime(df_data["日期"], format="%Y年%m月") else: df_data.index = pd.to_datetime(df_data["日期"], format="%Y年%m月%d日") if any(df_data["交易量"].astype(str).str.contains("-")): df_data["交易量"][df_data["交易量"].str.contains("-")] = df_data["交易量"][ df_data["交易量"].str.contains("-") ].replace("-", 0) if any(df_data["交易量"].astype(str).str.contains("B")): df_data["交易量"][df_data["交易量"].str.contains("B").fillna(False)] = ( df_data["交易量"][df_data["交易量"].str.contains("B").fillna(False)] .str.replace("B", "") .str.replace(",", "") .astype(float) * 1000000000 ) if any(df_data["交易量"].astype(str).str.contains("M")): df_data["交易量"][df_data["交易量"].str.contains("M").fillna(False)] = ( df_data["交易量"][df_data["交易量"].str.contains("M").fillna(False)] .str.replace("M", "") .str.replace(",", "") .astype(float) * 1000000 ) if any(df_data["交易量"].astype(str).str.contains("K")): df_data["交易量"][df_data["交易量"].str.contains("K").fillna(False)] = ( df_data["交易量"][df_data["交易量"].str.contains("K").fillna(False)] .str.replace("K", "") .str.replace(",", "") .astype(float) * 1000 ) df_data["交易量"] = df_data["交易量"].astype(float) df_data["涨跌幅"] = pd.DataFrame( round( df_data["涨跌幅"].str.replace(",", "").str.replace("%", "").astype(float) / 100, 6, ) ) del df_data["日期"] df_data.reset_index(inplace=True) df_data = df_data[[ "日期", "收盘", "开盘", "高", "低", "交易量", "涨跌幅", ]] df_data['日期'] = pd.to_datetime(df_data['日期']).dt.date df_data['收盘'] = pd.to_numeric(df_data['收盘']) df_data['开盘'] = pd.to_numeric(df_data['开盘']) df_data['高'] = pd.to_numeric(df_data['高']) df_data['低'] = pd.to_numeric(df_data['低']) df_data['交易量'] = pd.to_numeric(df_data['交易量']) df_data['涨跌幅'] = pd.to_numeric(df_data['涨跌幅']) df_data.sort_values('日期', inplace=True) df_data.reset_index(inplace=True, drop=True) return df_data

17,995

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/crypto/crypto_hold.py

crypto_bitcoin_hold_report

()

return temp_df

金十数据-比特币持仓报告 https://datacenter.jin10.com/dc_report?name=bitcoint :return: 比特币持仓报告 :rtype: pandas.DataFrame

12

70

def crypto_bitcoin_hold_report(): """ 金十数据-比特币持仓报告 https://datacenter.jin10.com/dc_report?name=bitcoint :return: 比特币持仓报告 :rtype: pandas.DataFrame """ url = "https://datacenter-api.jin10.com/bitcoin_treasuries/list" headers = { "accept": "*/*", "accept-encoding": "gzip, deflate, br", "accept-language": "zh-CN,zh;q=0.9,en;q=0.8", "cache-control": "no-cache", "origin": "https://datacenter.jin10.com", "pragma": "no-cache", "referer": "https://datacenter.jin10.com/", "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/89.0.4389.128 Safari/537.36", "x-app-id": "rU6QIu7JHe2gOUeR", "x-version": "1.0.0", } params = {"_": "1618902583006"} r = requests.get(url, params=params, headers=headers) data_json = r.json() temp_df = pd.DataFrame(data_json["data"]["values"]) temp_df.columns = [ '代码', '公司名称-英文', '国家/地区', '市值', '比特币占市值比重', '持仓成本', '持仓占比', '持仓量', '当日持仓市值', '查询日期', '公告链接', '_', '分类', '倍数', '_', '公司名称-中文', ] temp_df = temp_df[[ '代码', '公司名称-英文', '公司名称-中文', '国家/地区', '市值', '比特币占市值比重', '持仓成本', '持仓占比', '持仓量', '当日持仓市值', '查询日期', '公告链接', '分类', '倍数', ]] return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/crypto/crypto_hold.py#L12-L70

25

[ 0, 1, 2, 3, 4, 5, 6 ]

11.864407

[ 7, 8, 20, 21, 22, 23, 24, 42, 58 ]

15.254237

false

23.076923

59

1

84.745763

4

def crypto_bitcoin_hold_report(): url = "https://datacenter-api.jin10.com/bitcoin_treasuries/list" headers = { "accept": "*/*", "accept-encoding": "gzip, deflate, br", "accept-language": "zh-CN,zh;q=0.9,en;q=0.8", "cache-control": "no-cache", "origin": "https://datacenter.jin10.com", "pragma": "no-cache", "referer": "https://datacenter.jin10.com/", "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/89.0.4389.128 Safari/537.36", "x-app-id": "rU6QIu7JHe2gOUeR", "x-version": "1.0.0", } params = {"_": "1618902583006"} r = requests.get(url, params=params, headers=headers) data_json = r.json() temp_df = pd.DataFrame(data_json["data"]["values"]) temp_df.columns = [ '代码', '公司名称-英文', '国家/地区', '市值', '比特币占市值比重', '持仓成本', '持仓占比', '持仓量', '当日持仓市值', '查询日期', '公告链接', '_', '分类', '倍数', '_', '公司名称-中文', ] temp_df = temp_df[[ '代码', '公司名称-英文', '公司名称-中文', '国家/地区', '市值', '比特币占市值比重', '持仓成本', '持仓占比', '持仓量', '当日持仓市值', '查询日期', '公告链接', '分类', '倍数', ]] return temp_df

17,996

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/crypto/crypto_hold.py

crypto_bitcoin_hold_report

()

return temp_df

金十数据-比特币持仓报告 https://datacenter.jin10.com/dc_report?name=bitcoint :return: 比特币持仓报告 :rtype: pandas.DataFrame

73

131

def crypto_bitcoin_hold_report(): """ 金十数据-比特币持仓报告 https://datacenter.jin10.com/dc_report?name=bitcoint :return: 比特币持仓报告 :rtype: pandas.DataFrame """ url = "https://datacenter-api.jin10.com/bitcoin_treasuries/list" headers = { "accept": "*/*", "accept-encoding": "gzip, deflate, br", "accept-language": "zh-CN,zh;q=0.9,en;q=0.8", "cache-control": "no-cache", "origin": "https://datacenter.jin10.com", "pragma": "no-cache", "referer": "https://datacenter.jin10.com/", "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/89.0.4389.128 Safari/537.36", "x-app-id": "rU6QIu7JHe2gOUeR", "x-version": "1.0.0", } params = {"_": "1618902583006"} r = requests.get(url, params=params, headers=headers) data_json = r.json() temp_df = pd.DataFrame(data_json["data"]["values"]) temp_df.columns = [ '代码', '公司名称-英文', '国家/地区', '市值', '比特币占市值比重', '持仓成本', '持仓占比', '持仓量', '当日持仓市值', '查询日期', '公告链接', '_', '分类', '倍数', '_', '公司名称-中文', ] temp_df = temp_df[[ '代码', '公司名称-英文', '公司名称-中文', '国家/地区', '市值', '比特币占市值比重', '持仓成本', '持仓占比', '持仓量', '当日持仓市值', '查询日期', '公告链接', '分类', '倍数', ]] return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/crypto/crypto_hold.py#L73-L131

25

[ 0, 1, 2, 3, 4, 5, 6 ]

11.864407

[ 7, 8, 20, 21, 22, 23, 24, 42, 58 ]

15.254237

false

23.076923

59

1

84.745763

4

def crypto_bitcoin_hold_report(): url = "https://datacenter-api.jin10.com/bitcoin_treasuries/list" headers = { "accept": "*/*", "accept-encoding": "gzip, deflate, br", "accept-language": "zh-CN,zh;q=0.9,en;q=0.8", "cache-control": "no-cache", "origin": "https://datacenter.jin10.com", "pragma": "no-cache", "referer": "https://datacenter.jin10.com/", "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/89.0.4389.128 Safari/537.36", "x-app-id": "rU6QIu7JHe2gOUeR", "x-version": "1.0.0", } params = {"_": "1618902583006"} r = requests.get(url, params=params, headers=headers) data_json = r.json() temp_df = pd.DataFrame(data_json["data"]["values"]) temp_df.columns = [ '代码', '公司名称-英文', '国家/地区', '市值', '比特币占市值比重', '持仓成本', '持仓占比', '持仓量', '当日持仓市值', '查询日期', '公告链接', '_', '分类', '倍数', '_', '公司名称-中文', ] temp_df = temp_df[[ '代码', '公司名称-英文', '公司名称-中文', '国家/地区', '市值', '比特币占市值比重', '持仓成本', '持仓占比', '持仓量', '当日持仓市值', '查询日期', '公告链接', '分类', '倍数', ]] return temp_df

17,997

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/crypto/crypto_crix.py

crypto_crix

(symbol: str = "CRIX")

CRIX 和 VCRIX 指数 https://thecrix.de/ :param symbol: choice of {"CRIX", "VCRIX"} :type symbol: str :return: CRIX 和 VCRIX 指数 :rtype: pandas.DataFrame

13

60

def crypto_crix(symbol: str = "CRIX") -> pd.DataFrame: """ CRIX 和 VCRIX 指数 https://thecrix.de/ :param symbol: choice of {"CRIX", "VCRIX"} :type symbol: str :return: CRIX 和 VCRIX 指数 :rtype: pandas.DataFrame """ from requests.packages.urllib3.exceptions import InsecureRequestWarning requests.packages.urllib3.disable_warnings(InsecureRequestWarning) url: str = "https://thecrix.de/" r = requests.get(url, verify=False) soup = BeautifulSoup(r.text, "lxml") data_text = soup.find_all("script")[12].string if symbol == "CRIX": inner_text = data_text[data_text.find("series") : data_text.find("CRIX")] temp_df = pd.DataFrame( list( eval( inner_text[ inner_text.find("data") + 5 : inner_text.find("name") ].strip() ) )[0] ) temp_df.columns = ["date", "value"] temp_df["date"] = pd.to_datetime(temp_df["date"], unit="ms").dt.date return temp_df else: data_text = data_text[ data_text.find("VCRIX IndeX") : data_text.find("2014-11-28") ] inner_text = data_text[data_text.find("series") : data_text.find('"VCRIX"')] temp_df = pd.DataFrame( list( eval( inner_text[ inner_text.find("data") + 5 : inner_text.find("name") ].strip() ) )[0] ) temp_df.columns = ["date", "value"] temp_df["date"] = pd.to_datetime(temp_df["date"], unit="ms").dt.date return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/crypto/crypto_crix.py#L13-L60

25

[ 0, 1, 2, 3, 4, 5, 6, 7, 8 ]

18.75

[ 9, 10, 12, 13, 14, 15, 16, 17, 18, 27, 28, 29, 32, 35, 36, 45, 46, 47 ]

37.5

false

23.076923

48

2

62.5

6

def crypto_crix(symbol: str = "CRIX") -> pd.DataFrame: from requests.packages.urllib3.exceptions import InsecureRequestWarning requests.packages.urllib3.disable_warnings(InsecureRequestWarning) url: str = "https://thecrix.de/" r = requests.get(url, verify=False) soup = BeautifulSoup(r.text, "lxml") data_text = soup.find_all("script")[12].string if symbol == "CRIX": inner_text = data_text[data_text.find("series") : data_text.find("CRIX")] temp_df = pd.DataFrame( list( eval( inner_text[ inner_text.find("data") + 5 : inner_text.find("name") ].strip() ) )[0] ) temp_df.columns = ["date", "value"] temp_df["date"] = pd.to_datetime(temp_df["date"], unit="ms").dt.date return temp_df else: data_text = data_text[ data_text.find("VCRIX IndeX") : data_text.find("2014-11-28") ] inner_text = data_text[data_text.find("series") : data_text.find('"VCRIX"')] temp_df = pd.DataFrame( list( eval( inner_text[ inner_text.find("data") + 5 : inner_text.find("name") ].strip() ) )[0] ) temp_df.columns = ["date", "value"] temp_df["date"] = pd.to_datetime(temp_df["date"], unit="ms").dt.date return temp_df

17,998

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/interest_rate/interbank_rate_em.py

rate_interbank

( market: str = "上海银行同业拆借市场", symbol: str = "Shibor人民币", indicator: str = "隔夜", )

return big_df

东方财富-拆借利率一览-具体市场的具体品种的具体指标的拆借利率数据具体 market 和 symbol 参见: http://data.eastmoney.com/shibor/shibor.aspx?m=sg&t=88&d=99333&cu=sgd&type=009065&p=79 :param market: choice of {"上海银行同业拆借市场", "中国银行同业拆借市场", "伦敦银行同业拆借市场", "欧洲银行同业拆借市场", "香港银行同业拆借市场", "新加坡银行同业拆借市场"} :type market: str :param symbol: choice of {"Shibor人民币", "Chibor人民币", "Libor英镑", "***", "Sibor美元"} :type symbol: str :param indicator: choice of {"隔夜", "1周", "2周", "***", "1年"} :type indicator: str :return: 具体市场的具体品种的具体指标的拆借利率数据 :rtype: pandas.DataFrame

12

127

def rate_interbank( market: str = "上海银行同业拆借市场", symbol: str = "Shibor人民币", indicator: str = "隔夜", ): """ 东方财富-拆借利率一览-具体市场的具体品种的具体指标的拆借利率数据具体 market 和 symbol 参见: http://data.eastmoney.com/shibor/shibor.aspx?m=sg&t=88&d=99333&cu=sgd&type=009065&p=79 :param market: choice of {"上海银行同业拆借市场", "中国银行同业拆借市场", "伦敦银行同业拆借市场", "欧洲银行同业拆借市场", "香港银行同业拆借市场", "新加坡银行同业拆借市场"} :type market: str :param symbol: choice of {"Shibor人民币", "Chibor人民币", "Libor英镑", "***", "Sibor美元"} :type symbol: str :param indicator: choice of {"隔夜", "1周", "2周", "***", "1年"} :type indicator: str :return: 具体市场的具体品种的具体指标的拆借利率数据 :rtype: pandas.DataFrame """ market_map = { "上海银行同业拆借市场": "001", "中国银行同业拆借市场": "002", "伦敦银行同业拆借市场": "003", "欧洲银行同业拆借市场": "004", "香港银行同业拆借市场": "005", "新加坡银行同业拆借市场": "006", } symbol_map = { "Shibor人民币": "CNY", "Chibor人民币": "CNY", "Libor英镑": "GBP", "Libor欧元": "EUR", "Libor美元": "USD", "Libor日元": "JPY", "Euribor欧元": "EUR", "Hibor美元": "USD", "Hibor人民币": "CNH", "Hibor港币": "HKD", "Sibor星元": "SGD", "Sibor美元": "USD", } indicator_map = { "隔夜": "001", "1周": "101", "2周": "102", "3周": "103", "1月": "201", "2月": "202", "3月": "203", "4月": "204", "5月": "205", "6月": "206", "7月": "207", "8月": "208", "9月": "209", "10月": "210", "11月": "211", "1年": "301", } url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "reportName": "RPT_IMP_INTRESTRATEN", "columns": "REPORT_DATE,REPORT_PERIOD,IR_RATE,CHANGE_RATE,INDICATOR_ID,LATEST_RECORD,MARKET,MARKET_CODE,CURRENCY,CURRENCY_CODE", "quoteColumns": "", "filter": f"""(MARKET_CODE="{market_map[market]}")(CURRENCY_CODE="{symbol_map[symbol]}")(INDICATOR_ID="{indicator_map[indicator]}")""", "pageNumber": "1", "pageSize": "500", "sortTypes": "-1", "sortColumns": "REPORT_DATE", "source": "WEB", "client": "WEB", "p": "1", "pageNo": "1", "pageNum": "1", "_": "1653376974939", } r = requests.get(url, params=params) data_json = r.json() total_page = data_json["result"]["pages"] big_df = pd.DataFrame() for page in tqdm(range(1, total_page + 1), leave=False): params.update( { "pageNumber": page, "p": page, "pageNo": page, "pageNum": 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.columns = [ "报告日", "-", "利率", "涨跌", "-", "-", "-", "-", "-", "-", ] big_df = big_df[ [ "报告日", "利率", "涨跌", ] ] big_df["报告日"] = pd.to_datetime(big_df["报告日"]).dt.date big_df["利率"] = pd.to_numeric(big_df["利率"]) big_df["涨跌"] = pd.to_numeric(big_df["涨跌"]) big_df.sort_values(["报告日"], inplace=True) big_df.reset_index(inplace=True, drop=True) return big_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/interest_rate/interbank_rate_em.py#L12-L127

25

[ 0 ]

0.862069

[ 17, 25, 39, 57, 58, 74, 75, 76, 77, 78, 79, 87, 88, 89, 90, 91, 103, 110, 111, 112, 113, 114, 115 ]

19.827586

false

19.354839

116

2

80.172414

10

def rate_interbank( market: str = "上海银行同业拆借市场", symbol: str = "Shibor人民币", indicator: str = "隔夜", ): market_map = { "上海银行同业拆借市场": "001", "中国银行同业拆借市场": "002", "伦敦银行同业拆借市场": "003", "欧洲银行同业拆借市场": "004", "香港银行同业拆借市场": "005", "新加坡银行同业拆借市场": "006", } symbol_map = { "Shibor人民币": "CNY", "Chibor人民币": "CNY", "Libor英镑": "GBP", "Libor欧元": "EUR", "Libor美元": "USD", "Libor日元": "JPY", "Euribor欧元": "EUR", "Hibor美元": "USD", "Hibor人民币": "CNH", "Hibor港币": "HKD", "Sibor星元": "SGD", "Sibor美元": "USD", } indicator_map = { "隔夜": "001", "1周": "101", "2周": "102", "3周": "103", "1月": "201", "2月": "202", "3月": "203", "4月": "204", "5月": "205", "6月": "206", "7月": "207", "8月": "208", "9月": "209", "10月": "210", "11月": "211", "1年": "301", } url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "reportName": "RPT_IMP_INTRESTRATEN", "columns": "REPORT_DATE,REPORT_PERIOD,IR_RATE,CHANGE_RATE,INDICATOR_ID,LATEST_RECORD,MARKET,MARKET_CODE,CURRENCY,CURRENCY_CODE", "quoteColumns": "", "filter": f"""(MARKET_CODE="{market_map[market]}")(CURRENCY_CODE="{symbol_map[symbol]}")(INDICATOR_ID="{indicator_map[indicator]}")""", "pageNumber": "1", "pageSize": "500", "sortTypes": "-1", "sortColumns": "REPORT_DATE", "source": "WEB", "client": "WEB", "p": "1", "pageNo": "1", "pageNum": "1", "_": "1653376974939", } r = requests.get(url, params=params) data_json = r.json() total_page = data_json["result"]["pages"] big_df = pd.DataFrame() for page in tqdm(range(1, total_page + 1), leave=False): params.update( { "pageNumber": page, "p": page, "pageNo": page, "pageNum": 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.columns = [ "报告日", "-", "利率", "涨跌", "-", "-", "-", "-", "-", "-", ] big_df = big_df[ [ "报告日", "利率", "涨跌", ] ] big_df["报告日"] = pd.to_datetime(big_df["报告日"]).dt.date big_df["利率"] = pd.to_numeric(big_df["利率"]) big_df["涨跌"] = pd.to_numeric(big_df["涨跌"]) big_df.sort_values(["报告日"], inplace=True) big_df.reset_index(inplace=True, drop=True) return big_df

17,999

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/hf/hf_sp500.py

hf_sp_500

(year: str = "2017")

return temp_df

S&P 500 minute data from 2012-2018 :param year: from 2012-2018 :type year: str :return: specific year dataframe :rtype: pandas.DataFrame

13

30

def hf_sp_500(year: str = "2017") -> pd.DataFrame: """ S&P 500 minute data from 2012-2018 :param year: from 2012-2018 :type year: str :return: specific year dataframe :rtype: pandas.DataFrame """ url = f"https://github.com/FutureSharks/financial-data/raw/master/pyfinancialdata/data/stocks/histdata/SPXUSD/DAT_ASCII_SPXUSD_M1_{year}.csv" temp_df = pd.read_table(url, header=None, sep=";") temp_df.columns = ["date", "open", "high", "low", "close", "price"] temp_df['date'] = pd.to_datetime(temp_df['date']).dt.date temp_df['open'] = pd.to_numeric(temp_df['open']) temp_df['high'] = pd.to_numeric(temp_df['high']) temp_df['low'] = pd.to_numeric(temp_df['low']) temp_df['close'] = pd.to_numeric(temp_df['close']) temp_df['price'] = pd.to_numeric(temp_df['price']) return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/hf/hf_sp500.py#L13-L30

25

[ 0, 1, 2, 3, 4, 5, 6, 7 ]

44.444444

[ 8, 9, 10, 11, 12, 13, 14, 15, 16, 17 ]

55.555556

false

25

18

1

44.444444

5

def hf_sp_500(year: str = "2017") -> pd.DataFrame: url = f"https://github.com/FutureSharks/financial-data/raw/master/pyfinancialdata/data/stocks/histdata/SPXUSD/DAT_ASCII_SPXUSD_M1_{year}.csv" temp_df = pd.read_table(url, header=None, sep=";") temp_df.columns = ["date", "open", "high", "low", "close", "price"] temp_df['date'] = pd.to_datetime(temp_df['date']).dt.date temp_df['open'] = pd.to_numeric(temp_df['open']) temp_df['high'] = pd.to_numeric(temp_df['high']) temp_df['low'] = pd.to_numeric(temp_df['low']) temp_df['close'] = pd.to_numeric(temp_df['close']) temp_df['price'] = pd.to_numeric(temp_df['price']) return temp_df

18,000

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/economic/macro_australia.py

macro_australia_retail_rate_monthly

()

return temp_df

东方财富-经济数据-澳大利亚-零售销售月率 https://data.eastmoney.com/cjsj/foreign_5_0.html :return: 零售销售月率 :rtype: pandas.DataFrame

15

62

def macro_australia_retail_rate_monthly() -> pd.DataFrame: """ 东方财富-经济数据-澳大利亚-零售销售月率 https://data.eastmoney.com/cjsj/foreign_5_0.html :return: 零售销售月率 :rtype: pandas.DataFrame """ url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "reportName": "RPT_ECONOMICVALUE_AUSTRALIA", "columns": "ALL", "filter": '(INDICATOR_ID="EMG00152903")', "pageNumber": "1", "pageSize": "2000", "sortColumns": "REPORT_DATE", "sortTypes": "-1", "source": "WEB", "client": "WEB", "p": "1", "pageNo": "1", "pageNum": "1", "_": "1669047266881", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) temp_df.columns = [ "-", "-", "-", "时间", "-", "发布日期", "现值", "前值", ] temp_df = temp_df[[ "时间", "前值", "现值", "发布日期", ]] temp_df["前值"] = pd.to_numeric(temp_df["前值"], errors="coerce") temp_df["现值"] = pd.to_numeric(temp_df["现值"], errors="coerce") temp_df['发布日期'] = pd.to_datetime(temp_df['发布日期']).dt.date return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/economic/macro_australia.py#L15-L62

25

[ 0, 1, 2, 3, 4, 5, 6 ]

14.583333

[ 7, 8, 23, 24, 25, 26, 38, 44, 45, 46, 47 ]

22.916667

false

11.650485

48

1

77.083333

4

def macro_australia_retail_rate_monthly() -> pd.DataFrame: url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "reportName": "RPT_ECONOMICVALUE_AUSTRALIA", "columns": "ALL", "filter": '(INDICATOR_ID="EMG00152903")', "pageNumber": "1", "pageSize": "2000", "sortColumns": "REPORT_DATE", "sortTypes": "-1", "source": "WEB", "client": "WEB", "p": "1", "pageNo": "1", "pageNum": "1", "_": "1669047266881", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) temp_df.columns = [ "-", "-", "-", "时间", "-", "发布日期", "现值", "前值", ] temp_df = temp_df[[ "时间", "前值", "现值", "发布日期", ]] temp_df["前值"] = pd.to_numeric(temp_df["前值"], errors="coerce") temp_df["现值"] = pd.to_numeric(temp_df["现值"], errors="coerce") temp_df['发布日期'] = pd.to_datetime(temp_df['发布日期']).dt.date return temp_df

18,001

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/economic/macro_australia.py

macro_australia_trade

()

return temp_df

东方财富-经济数据-澳大利亚-贸易帐 https://data.eastmoney.com/cjsj/foreign_5_1.html :return: 贸易帐 :rtype: pandas.DataFrame

66

112

def macro_australia_trade() -> pd.DataFrame: """ 东方财富-经济数据-澳大利亚-贸易帐 https://data.eastmoney.com/cjsj/foreign_5_1.html :return: 贸易帐 :rtype: pandas.DataFrame """ url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "reportName": "RPT_ECONOMICVALUE_AUSTRALIA", "columns": "ALL", "filter": '(INDICATOR_ID="EMG00152793")', "pageNumber": "1", "pageSize": "2000", "sortColumns": "REPORT_DATE", "sortTypes": "-1", "source": "WEB", "client": "WEB", "p": "1", "pageNo": "1", "pageNum": "1", "_": "1669047266881", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) temp_df.columns = [ "-", "-", "-", "时间", "-", "发布日期", "现值", "前值", ] temp_df = temp_df[[ "时间", "前值", "现值", "发布日期", ]] temp_df["前值"] = pd.to_numeric(temp_df["前值"], errors="coerce") temp_df["现值"] = pd.to_numeric(temp_df["现值"], errors="coerce") temp_df['发布日期'] = pd.to_datetime(temp_df['发布日期']).dt.date return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/economic/macro_australia.py#L66-L112

25

[ 0, 1, 2, 3, 4, 5, 6 ]

14.893617

[ 7, 8, 23, 24, 25, 26, 37, 43, 44, 45, 46 ]

23.404255

false

11.650485

47

1

76.595745

4

def macro_australia_trade() -> pd.DataFrame: url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "reportName": "RPT_ECONOMICVALUE_AUSTRALIA", "columns": "ALL", "filter": '(INDICATOR_ID="EMG00152793")', "pageNumber": "1", "pageSize": "2000", "sortColumns": "REPORT_DATE", "sortTypes": "-1", "source": "WEB", "client": "WEB", "p": "1", "pageNo": "1", "pageNum": "1", "_": "1669047266881", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) temp_df.columns = [ "-", "-", "-", "时间", "-", "发布日期", "现值", "前值", ] temp_df = temp_df[[ "时间", "前值", "现值", "发布日期", ]] temp_df["前值"] = pd.to_numeric(temp_df["前值"], errors="coerce") temp_df["现值"] = pd.to_numeric(temp_df["现值"], errors="coerce") temp_df['发布日期'] = pd.to_datetime(temp_df['发布日期']).dt.date return temp_df

18,002

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/economic/macro_australia.py

macro_australia_unemployment_rate

()

return temp_df

东方财富-经济数据-澳大利亚-失业率 https://data.eastmoney.com/cjsj/foreign_5_2.html :return: 失业率 :rtype: pandas.DataFrame

116

162

def macro_australia_unemployment_rate() -> pd.DataFrame: """ 东方财富-经济数据-澳大利亚-失业率 https://data.eastmoney.com/cjsj/foreign_5_2.html :return: 失业率 :rtype: pandas.DataFrame """ url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "reportName": "RPT_ECONOMICVALUE_AUSTRALIA", "columns": "ALL", "filter": '(INDICATOR_ID="EMG00101141")', "pageNumber": "1", "pageSize": "2000", "sortColumns": "REPORT_DATE", "sortTypes": "-1", "source": "WEB", "client": "WEB", "p": "1", "pageNo": "1", "pageNum": "1", "_": "1669047266881", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) temp_df.columns = [ "-", "-", "-", "时间", "-", "发布日期", "现值", "前值", ] temp_df = temp_df[[ "时间", "前值", "现值", "发布日期", ]] temp_df["前值"] = pd.to_numeric(temp_df["前值"], errors="coerce") temp_df["现值"] = pd.to_numeric(temp_df["现值"], errors="coerce") temp_df['发布日期'] = pd.to_datetime(temp_df['发布日期']).dt.date return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/economic/macro_australia.py#L116-L162

25

[ 0, 1, 2, 3, 4, 5, 6 ]

14.893617

[ 7, 8, 23, 24, 25, 26, 37, 43, 44, 45, 46 ]

23.404255

false

11.650485

47

1

76.595745

4

def macro_australia_unemployment_rate() -> pd.DataFrame: url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "reportName": "RPT_ECONOMICVALUE_AUSTRALIA", "columns": "ALL", "filter": '(INDICATOR_ID="EMG00101141")', "pageNumber": "1", "pageSize": "2000", "sortColumns": "REPORT_DATE", "sortTypes": "-1", "source": "WEB", "client": "WEB", "p": "1", "pageNo": "1", "pageNum": "1", "_": "1669047266881", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) temp_df.columns = [ "-", "-", "-", "时间", "-", "发布日期", "现值", "前值", ] temp_df = temp_df[[ "时间", "前值", "现值", "发布日期", ]] temp_df["前值"] = pd.to_numeric(temp_df["前值"], errors="coerce") temp_df["现值"] = pd.to_numeric(temp_df["现值"], errors="coerce") temp_df['发布日期'] = pd.to_datetime(temp_df['发布日期']).dt.date return temp_df

18,003

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/economic/macro_australia.py

macro_australia_ppi_quarterly

()

return temp_df

东方财富-经济数据-澳大利亚-生产者物价指数季率 https://data.eastmoney.com/cjsj/foreign_5_3.html :return: 生产者物价指数季率 :rtype: pandas.DataFrame

166

212

def macro_australia_ppi_quarterly() -> pd.DataFrame: """ 东方财富-经济数据-澳大利亚-生产者物价指数季率 https://data.eastmoney.com/cjsj/foreign_5_3.html :return: 生产者物价指数季率 :rtype: pandas.DataFrame """ url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "reportName": "RPT_ECONOMICVALUE_AUSTRALIA", "columns": "ALL", "filter": '(INDICATOR_ID="EMG00152722")', "pageNumber": "1", "pageSize": "2000", "sortColumns": "REPORT_DATE", "sortTypes": "-1", "source": "WEB", "client": "WEB", "p": "1", "pageNo": "1", "pageNum": "1", "_": "1669047266881", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) temp_df.columns = [ "-", "-", "-", "时间", "-", "发布日期", "现值", "前值", ] temp_df = temp_df[[ "时间", "前值", "现值", "发布日期", ]] temp_df["前值"] = pd.to_numeric(temp_df["前值"], errors="coerce") temp_df["现值"] = pd.to_numeric(temp_df["现值"], errors="coerce") temp_df['发布日期'] = pd.to_datetime(temp_df['发布日期']).dt.date return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/economic/macro_australia.py#L166-L212

25

[ 0, 1, 2, 3, 4, 5, 6 ]

14.893617

[ 7, 8, 23, 24, 25, 26, 37, 43, 44, 45, 46 ]

23.404255

false

11.650485

47

1

76.595745

4

def macro_australia_ppi_quarterly() -> pd.DataFrame: url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "reportName": "RPT_ECONOMICVALUE_AUSTRALIA", "columns": "ALL", "filter": '(INDICATOR_ID="EMG00152722")', "pageNumber": "1", "pageSize": "2000", "sortColumns": "REPORT_DATE", "sortTypes": "-1", "source": "WEB", "client": "WEB", "p": "1", "pageNo": "1", "pageNum": "1", "_": "1669047266881", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) temp_df.columns = [ "-", "-", "-", "时间", "-", "发布日期", "现值", "前值", ] temp_df = temp_df[[ "时间", "前值", "现值", "发布日期", ]] temp_df["前值"] = pd.to_numeric(temp_df["前值"], errors="coerce") temp_df["现值"] = pd.to_numeric(temp_df["现值"], errors="coerce") temp_df['发布日期'] = pd.to_datetime(temp_df['发布日期']).dt.date return temp_df

18,004

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/economic/macro_australia.py

macro_australia_cpi_quarterly

()

return temp_df

东方财富-经济数据-澳大利亚-消费者物价指数季率 http://data.eastmoney.com/cjsj/foreign_5_4.html :return: 消费者物价指数季率 :rtype: pandas.DataFrame

216

262

def macro_australia_cpi_quarterly() -> pd.DataFrame: """ 东方财富-经济数据-澳大利亚-消费者物价指数季率 http://data.eastmoney.com/cjsj/foreign_5_4.html :return: 消费者物价指数季率 :rtype: pandas.DataFrame """ url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "reportName": "RPT_ECONOMICVALUE_AUSTRALIA", "columns": "ALL", "filter": '(INDICATOR_ID="EMG00101104")', "pageNumber": "1", "pageSize": "2000", "sortColumns": "REPORT_DATE", "sortTypes": "-1", "source": "WEB", "client": "WEB", "p": "1", "pageNo": "1", "pageNum": "1", "_": "1669047266881", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) temp_df.columns = [ "-", "-", "-", "时间", "-", "发布日期", "现值", "前值", ] temp_df = temp_df[[ "时间", "前值", "现值", "发布日期", ]] temp_df["前值"] = pd.to_numeric(temp_df["前值"], errors="coerce") temp_df["现值"] = pd.to_numeric(temp_df["现值"], errors="coerce") temp_df['发布日期'] = pd.to_datetime(temp_df['发布日期']).dt.date return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/economic/macro_australia.py#L216-L262

25

[ 0, 1, 2, 3, 4, 5, 6 ]

14.893617

[ 7, 8, 23, 24, 25, 26, 37, 43, 44, 45, 46 ]

23.404255

false

11.650485

47

1

76.595745

4

def macro_australia_cpi_quarterly() -> pd.DataFrame: url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "reportName": "RPT_ECONOMICVALUE_AUSTRALIA", "columns": "ALL", "filter": '(INDICATOR_ID="EMG00101104")', "pageNumber": "1", "pageSize": "2000", "sortColumns": "REPORT_DATE", "sortTypes": "-1", "source": "WEB", "client": "WEB", "p": "1", "pageNo": "1", "pageNum": "1", "_": "1669047266881", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) temp_df.columns = [ "-", "-", "-", "时间", "-", "发布日期", "现值", "前值", ] temp_df = temp_df[[ "时间", "前值", "现值", "发布日期", ]] temp_df["前值"] = pd.to_numeric(temp_df["前值"], errors="coerce") temp_df["现值"] = pd.to_numeric(temp_df["现值"], errors="coerce") temp_df['发布日期'] = pd.to_datetime(temp_df['发布日期']).dt.date return temp_df

18,005

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/economic/macro_australia.py

macro_australia_cpi_yearly

()

return temp_df

东方财富-经济数据-澳大利亚-消费者物价指数年率 https://data.eastmoney.com/cjsj/foreign_5_5.html :return: 消费者物价指数年率 :rtype: pandas.DataFrame

266

312

def macro_australia_cpi_yearly() -> pd.DataFrame: """ 东方财富-经济数据-澳大利亚-消费者物价指数年率 https://data.eastmoney.com/cjsj/foreign_5_5.html :return: 消费者物价指数年率 :rtype: pandas.DataFrame """ url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "reportName": "RPT_ECONOMICVALUE_AUSTRALIA", "columns": "ALL", "filter": '(INDICATOR_ID="EMG00101093")', "pageNumber": "1", "pageSize": "2000", "sortColumns": "REPORT_DATE", "sortTypes": "-1", "source": "WEB", "client": "WEB", "p": "1", "pageNo": "1", "pageNum": "1", "_": "1669047266881", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) temp_df.columns = [ "-", "-", "-", "时间", "-", "发布日期", "现值", "前值", ] temp_df = temp_df[[ "时间", "前值", "现值", "发布日期", ]] temp_df["前值"] = pd.to_numeric(temp_df["前值"], errors="coerce") temp_df["现值"] = pd.to_numeric(temp_df["现值"], errors="coerce") temp_df['发布日期'] = pd.to_datetime(temp_df['发布日期']).dt.date return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/economic/macro_australia.py#L266-L312

25

[ 0, 1, 2, 3, 4, 5, 6 ]

14.893617

[ 7, 8, 23, 24, 25, 26, 37, 43, 44, 45, 46 ]

23.404255

false

11.650485

47

1

76.595745

4

def macro_australia_cpi_yearly() -> pd.DataFrame: url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "reportName": "RPT_ECONOMICVALUE_AUSTRALIA", "columns": "ALL", "filter": '(INDICATOR_ID="EMG00101093")', "pageNumber": "1", "pageSize": "2000", "sortColumns": "REPORT_DATE", "sortTypes": "-1", "source": "WEB", "client": "WEB", "p": "1", "pageNo": "1", "pageNum": "1", "_": "1669047266881", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) temp_df.columns = [ "-", "-", "-", "时间", "-", "发布日期", "现值", "前值", ] temp_df = temp_df[[ "时间", "前值", "现值", "发布日期", ]] temp_df["前值"] = pd.to_numeric(temp_df["前值"], errors="coerce") temp_df["现值"] = pd.to_numeric(temp_df["现值"], errors="coerce") temp_df['发布日期'] = pd.to_datetime(temp_df['发布日期']).dt.date return temp_df

18,006

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/economic/macro_australia.py

macro_australia_bank_rate

()

return temp_df

东方财富-经济数据-澳大利亚-央行公布利率决议 https://data.eastmoney.com/cjsj/foreign_5_6.html :return: 央行公布利率决议 :rtype: pandas.DataFrame

316

362

def macro_australia_bank_rate() -> pd.DataFrame: """ 东方财富-经济数据-澳大利亚-央行公布利率决议 https://data.eastmoney.com/cjsj/foreign_5_6.html :return: 央行公布利率决议 :rtype: pandas.DataFrame """ url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "reportName": "RPT_ECONOMICVALUE_AUSTRALIA", "columns": "ALL", "filter": '(INDICATOR_ID="EMG00342255")', "pageNumber": "1", "pageSize": "2000", "sortColumns": "REPORT_DATE", "sortTypes": "-1", "source": "WEB", "client": "WEB", "p": "1", "pageNo": "1", "pageNum": "1", "_": "1669047266881", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) temp_df.columns = [ "-", "-", "-", "时间", "-", "发布日期", "现值", "前值", ] temp_df = temp_df[[ "时间", "前值", "现值", "发布日期", ]] temp_df["前值"] = pd.to_numeric(temp_df["前值"], errors="coerce") temp_df["现值"] = pd.to_numeric(temp_df["现值"], errors="coerce") temp_df['发布日期'] = pd.to_datetime(temp_df['发布日期']).dt.date return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/economic/macro_australia.py#L316-L362

25

[ 0, 1, 2, 3, 4, 5, 6 ]

14.893617

[ 7, 8, 23, 24, 25, 26, 37, 43, 44, 45, 46 ]

23.404255

false

11.650485

47

1

76.595745

4

def macro_australia_bank_rate() -> pd.DataFrame: url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "reportName": "RPT_ECONOMICVALUE_AUSTRALIA", "columns": "ALL", "filter": '(INDICATOR_ID="EMG00342255")', "pageNumber": "1", "pageSize": "2000", "sortColumns": "REPORT_DATE", "sortTypes": "-1", "source": "WEB", "client": "WEB", "p": "1", "pageNo": "1", "pageNum": "1", "_": "1669047266881", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) temp_df.columns = [ "-", "-", "-", "时间", "-", "发布日期", "现值", "前值", ] temp_df = temp_df[[ "时间", "前值", "现值", "发布日期", ]] temp_df["前值"] = pd.to_numeric(temp_df["前值"], errors="coerce") temp_df["现值"] = pd.to_numeric(temp_df["现值"], errors="coerce") temp_df['发布日期'] = pd.to_datetime(temp_df['发布日期']).dt.date return temp_df

18,007

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/economic/macro_china_hk.py

macro_china_hk_cpi

()

return temp_df

东方财富-经济数据一览-中国香港-消费者物价指数 https://data.eastmoney.com/cjsj/foreign_8_0.html :return: 消费者物价指数 :rtype: pandas.DataFrame

14

48

def macro_china_hk_cpi() -> pd.DataFrame: """ 东方财富-经济数据一览-中国香港-消费者物价指数 https://data.eastmoney.com/cjsj/foreign_8_0.html :return: 消费者物价指数 :rtype: pandas.DataFrame """ url = "https://datainterface.eastmoney.com/EM_DataCenter/JS.aspx" params = { "type": "GJZB", "sty": "HKZB", "js": "({data:[(x)],pages:(pc)})", "p": "1", "ps": "2000", "mkt": "8", "stat": "0", "pageNo": "1", "pageNum": "1", "_": "1621332091873", } r = requests.get(url, params=params) data_text = r.text data_json = demjson.decode(data_text[1:-1]) temp_df = pd.DataFrame([item.split(",") for item in data_json["data"]]) temp_df.columns = [ "时间", "前值", "现值", "发布日期", ] temp_df['前值'] = pd.to_numeric(temp_df['前值']) temp_df['现值'] = pd.to_numeric(temp_df['现值']) temp_df['时间'] = pd.to_datetime(temp_df['时间']).dt.date temp_df['发布日期'] = pd.to_datetime(temp_df['发布日期']).dt.date return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/economic/macro_china_hk.py#L14-L48

25

[ 0, 1, 2, 3, 4, 5, 6 ]

20

[ 7, 8, 20, 21, 22, 23, 24, 30, 31, 32, 33, 34 ]

34.285714

false

10

35

2

65.714286

4

def macro_china_hk_cpi() -> pd.DataFrame: url = "https://datainterface.eastmoney.com/EM_DataCenter/JS.aspx" params = { "type": "GJZB", "sty": "HKZB", "js": "({data:[(x)],pages:(pc)})", "p": "1", "ps": "2000", "mkt": "8", "stat": "0", "pageNo": "1", "pageNum": "1", "_": "1621332091873", } r = requests.get(url, params=params) data_text = r.text data_json = demjson.decode(data_text[1:-1]) temp_df = pd.DataFrame([item.split(",") for item in data_json["data"]]) temp_df.columns = [ "时间", "前值", "现值", "发布日期", ] temp_df['前值'] = pd.to_numeric(temp_df['前值']) temp_df['现值'] = pd.to_numeric(temp_df['现值']) temp_df['时间'] = pd.to_datetime(temp_df['时间']).dt.date temp_df['发布日期'] = pd.to_datetime(temp_df['发布日期']).dt.date return temp_df

18,008

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/economic/macro_china_hk.py

macro_china_hk_cpi_ratio

()

return temp_df

东方财富-经济数据一览-中国香港-消费者物价指数年率 https://data.eastmoney.com/cjsj/foreign_8_1.html :return: 消费者物价指数年率 :rtype: pandas.DataFrame

51

85

def macro_china_hk_cpi_ratio() -> pd.DataFrame: """ 东方财富-经济数据一览-中国香港-消费者物价指数年率 https://data.eastmoney.com/cjsj/foreign_8_1.html :return: 消费者物价指数年率 :rtype: pandas.DataFrame """ url = "https://datainterface.eastmoney.com/EM_DataCenter/JS.aspx" params = { "type": "GJZB", "sty": "HKZB", "js": "({data:[(x)],pages:(pc)})", "p": "1", "ps": "2000", "mkt": "8", "stat": "1", "pageNo": "1", "pageNum": "1", "_": "1621332091873", } r = requests.get(url, params=params) data_text = r.text data_json = demjson.decode(data_text[1:-1]) temp_df = pd.DataFrame([item.split(",") for item in data_json["data"]]) temp_df.columns = [ "时间", "前值", "现值", "发布日期", ] temp_df['前值'] = pd.to_numeric(temp_df['前值']) temp_df['现值'] = pd.to_numeric(temp_df['现值']) temp_df['时间'] = pd.to_datetime(temp_df['时间']).dt.date temp_df['发布日期'] = pd.to_datetime(temp_df['发布日期']).dt.date return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/economic/macro_china_hk.py#L51-L85

25

[ 0, 1, 2, 3, 4, 5, 6 ]

20

[ 7, 8, 20, 21, 22, 23, 24, 30, 31, 32, 33, 34 ]

34.285714

false

10

35

2

65.714286

4

def macro_china_hk_cpi_ratio() -> pd.DataFrame: url = "https://datainterface.eastmoney.com/EM_DataCenter/JS.aspx" params = { "type": "GJZB", "sty": "HKZB", "js": "({data:[(x)],pages:(pc)})", "p": "1", "ps": "2000", "mkt": "8", "stat": "1", "pageNo": "1", "pageNum": "1", "_": "1621332091873", } r = requests.get(url, params=params) data_text = r.text data_json = demjson.decode(data_text[1:-1]) temp_df = pd.DataFrame([item.split(",") for item in data_json["data"]]) temp_df.columns = [ "时间", "前值", "现值", "发布日期", ] temp_df['前值'] = pd.to_numeric(temp_df['前值']) temp_df['现值'] = pd.to_numeric(temp_df['现值']) temp_df['时间'] = pd.to_datetime(temp_df['时间']).dt.date temp_df['发布日期'] = pd.to_datetime(temp_df['发布日期']).dt.date return temp_df

18,009

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/economic/macro_china_hk.py

macro_china_hk_rate_of_unemployment

()

return temp_df

东方财富-经济数据一览-中国香港-失业率 https://data.eastmoney.com/cjsj/foreign_8_2.html :return: 失业率 :rtype: pandas.DataFrame

88

122

def macro_china_hk_rate_of_unemployment() -> pd.DataFrame: """ 东方财富-经济数据一览-中国香港-失业率 https://data.eastmoney.com/cjsj/foreign_8_2.html :return: 失业率 :rtype: pandas.DataFrame """ url = "https://datainterface.eastmoney.com/EM_DataCenter/JS.aspx" params = { "type": "GJZB", "sty": "HKZB", "js": "({data:[(x)],pages:(pc)})", "p": "1", "ps": "2000", "mkt": "8", "stat": "2", "pageNo": "1", "pageNum": "1", "_": "1621332091873", } r = requests.get(url, params=params) data_text = r.text data_json = demjson.decode(data_text[1:-1]) temp_df = pd.DataFrame([item.split(",") for item in data_json["data"]]) temp_df.columns = [ "时间", "前值", "现值", "发布日期", ] temp_df['前值'] = pd.to_numeric(temp_df['前值']) temp_df['现值'] = pd.to_numeric(temp_df['现值']) temp_df['时间'] = pd.to_datetime(temp_df['时间']).dt.date temp_df['发布日期'] = pd.to_datetime(temp_df['发布日期']).dt.date return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/economic/macro_china_hk.py#L88-L122

25

[ 0, 1, 2, 3, 4, 5, 6 ]

20

[ 7, 8, 20, 21, 22, 23, 24, 30, 31, 32, 33, 34 ]

34.285714

false

10

35

2

65.714286

4

def macro_china_hk_rate_of_unemployment() -> pd.DataFrame: url = "https://datainterface.eastmoney.com/EM_DataCenter/JS.aspx" params = { "type": "GJZB", "sty": "HKZB", "js": "({data:[(x)],pages:(pc)})", "p": "1", "ps": "2000", "mkt": "8", "stat": "2", "pageNo": "1", "pageNum": "1", "_": "1621332091873", } r = requests.get(url, params=params) data_text = r.text data_json = demjson.decode(data_text[1:-1]) temp_df = pd.DataFrame([item.split(",") for item in data_json["data"]]) temp_df.columns = [ "时间", "前值", "现值", "发布日期", ] temp_df['前值'] = pd.to_numeric(temp_df['前值']) temp_df['现值'] = pd.to_numeric(temp_df['现值']) temp_df['时间'] = pd.to_datetime(temp_df['时间']).dt.date temp_df['发布日期'] = pd.to_datetime(temp_df['发布日期']).dt.date return temp_df

18,010

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/economic/macro_china_hk.py

macro_china_hk_gbp

()

return temp_df

东方财富-经济数据一览-中国香港-香港 GDP https://data.eastmoney.com/cjsj/foreign_8_3.html :return: 香港 GDP :rtype: pandas.DataFrame

125

159

def macro_china_hk_gbp() -> pd.DataFrame: """ 东方财富-经济数据一览-中国香港-香港 GDP https://data.eastmoney.com/cjsj/foreign_8_3.html :return: 香港 GDP :rtype: pandas.DataFrame """ url = "https://datainterface.eastmoney.com/EM_DataCenter/JS.aspx" params = { "type": "GJZB", "sty": "HKZB", "js": "({data:[(x)],pages:(pc)})", "p": "1", "ps": "2000", "mkt": "8", "stat": "3", "pageNo": "1", "pageNum": "1", "_": "1621332091873", } r = requests.get(url, params=params) data_text = r.text data_json = demjson.decode(data_text[1:-1]) temp_df = pd.DataFrame([item.split(",") for item in data_json["data"]]) temp_df.columns = [ "时间", "前值", "现值", "发布日期", ] temp_df['前值'] = pd.to_numeric(temp_df['前值']) / 100 temp_df['现值'] = pd.to_numeric(temp_df['现值']) / 100 temp_df['时间'] = pd.to_datetime(temp_df['时间']).dt.date temp_df['发布日期'] = pd.to_datetime(temp_df['发布日期']).dt.date return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/economic/macro_china_hk.py#L125-L159

25

[ 0, 1, 2, 3, 4, 5, 6 ]

20

[ 7, 8, 20, 21, 22, 23, 24, 30, 31, 32, 33, 34 ]

34.285714

false

10

35

2

65.714286

4

def macro_china_hk_gbp() -> pd.DataFrame: url = "https://datainterface.eastmoney.com/EM_DataCenter/JS.aspx" params = { "type": "GJZB", "sty": "HKZB", "js": "({data:[(x)],pages:(pc)})", "p": "1", "ps": "2000", "mkt": "8", "stat": "3", "pageNo": "1", "pageNum": "1", "_": "1621332091873", } r = requests.get(url, params=params) data_text = r.text data_json = demjson.decode(data_text[1:-1]) temp_df = pd.DataFrame([item.split(",") for item in data_json["data"]]) temp_df.columns = [ "时间", "前值", "现值", "发布日期", ] temp_df['前值'] = pd.to_numeric(temp_df['前值']) / 100 temp_df['现值'] = pd.to_numeric(temp_df['现值']) / 100 temp_df['时间'] = pd.to_datetime(temp_df['时间']).dt.date temp_df['发布日期'] = pd.to_datetime(temp_df['发布日期']).dt.date return temp_df

18,011

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/economic/macro_china_hk.py

macro_china_hk_gbp_ratio

()

return temp_df

东方财富-经济数据一览-中国香港-香港 GDP 同比 https://data.eastmoney.com/cjsj/foreign_8_4.html :return: 香港 GDP 同比 :rtype: pandas.DataFrame

162

196

def macro_china_hk_gbp_ratio() -> pd.DataFrame: """ 东方财富-经济数据一览-中国香港-香港 GDP 同比 https://data.eastmoney.com/cjsj/foreign_8_4.html :return: 香港 GDP 同比 :rtype: pandas.DataFrame """ url = "https://datainterface.eastmoney.com/EM_DataCenter/JS.aspx" params = { "type": "GJZB", "sty": "HKZB", "js": "({data:[(x)],pages:(pc)})", "p": "1", "ps": "2000", "mkt": "8", "stat": "4", "pageNo": "1", "pageNum": "1", "_": "1621332091873", } r = requests.get(url, params=params) data_text = r.text data_json = demjson.decode(data_text[1:-1]) temp_df = pd.DataFrame([item.split(",") for item in data_json["data"]]) temp_df.columns = [ "时间", "前值", "现值", "发布日期", ] temp_df['前值'] = pd.to_numeric(temp_df['前值']) temp_df['现值'] = pd.to_numeric(temp_df['现值']) temp_df['时间'] = pd.to_datetime(temp_df['时间']).dt.date temp_df['发布日期'] = pd.to_datetime(temp_df['发布日期']).dt.date return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/economic/macro_china_hk.py#L162-L196

25

[ 0, 1, 2, 3, 4, 5, 6 ]

20

[ 7, 8, 20, 21, 22, 23, 24, 30, 31, 32, 33, 34 ]

34.285714

false

10

35

2

65.714286

4

def macro_china_hk_gbp_ratio() -> pd.DataFrame: url = "https://datainterface.eastmoney.com/EM_DataCenter/JS.aspx" params = { "type": "GJZB", "sty": "HKZB", "js": "({data:[(x)],pages:(pc)})", "p": "1", "ps": "2000", "mkt": "8", "stat": "4", "pageNo": "1", "pageNum": "1", "_": "1621332091873", } r = requests.get(url, params=params) data_text = r.text data_json = demjson.decode(data_text[1:-1]) temp_df = pd.DataFrame([item.split(",") for item in data_json["data"]]) temp_df.columns = [ "时间", "前值", "现值", "发布日期", ] temp_df['前值'] = pd.to_numeric(temp_df['前值']) temp_df['现值'] = pd.to_numeric(temp_df['现值']) temp_df['时间'] = pd.to_datetime(temp_df['时间']).dt.date temp_df['发布日期'] = pd.to_datetime(temp_df['发布日期']).dt.date return temp_df

18,012

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/economic/macro_china_hk.py

macro_china_hk_building_volume

()

return temp_df

东方财富-经济数据一览-中国香港-香港楼宇买卖合约数量 https://data.eastmoney.com/cjsj/foreign_8_5.html :return: 香港楼宇买卖合约数量 :rtype: pandas.DataFrame

199

233

def macro_china_hk_building_volume() -> pd.DataFrame: """ 东方财富-经济数据一览-中国香港-香港楼宇买卖合约数量 https://data.eastmoney.com/cjsj/foreign_8_5.html :return: 香港楼宇买卖合约数量 :rtype: pandas.DataFrame """ url = "https://datainterface.eastmoney.com/EM_DataCenter/JS.aspx" params = { "type": "GJZB", "sty": "HKZB", "js": "({data:[(x)],pages:(pc)})", "p": "1", "ps": "2000", "mkt": "8", "stat": "5", "pageNo": "1", "pageNum": "1", "_": "1621332091873", } r = requests.get(url, params=params) data_text = r.text data_json = demjson.decode(data_text[1:-1]) temp_df = pd.DataFrame([item.split(",") for item in data_json["data"]]) temp_df.columns = [ "时间", "前值", "现值", "发布日期", ] temp_df['前值'] = pd.to_numeric(temp_df['前值']) temp_df['现值'] = pd.to_numeric(temp_df['现值']) temp_df['时间'] = pd.to_datetime(temp_df['时间']).dt.date temp_df['发布日期'] = pd.to_datetime(temp_df['发布日期']).dt.date return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/economic/macro_china_hk.py#L199-L233

25

[ 0, 1, 2, 3, 4, 5, 6 ]

20

[ 7, 8, 20, 21, 22, 23, 24, 30, 31, 32, 33, 34 ]

34.285714

false

10

35

2

65.714286

4

def macro_china_hk_building_volume() -> pd.DataFrame: url = "https://datainterface.eastmoney.com/EM_DataCenter/JS.aspx" params = { "type": "GJZB", "sty": "HKZB", "js": "({data:[(x)],pages:(pc)})", "p": "1", "ps": "2000", "mkt": "8", "stat": "5", "pageNo": "1", "pageNum": "1", "_": "1621332091873", } r = requests.get(url, params=params) data_text = r.text data_json = demjson.decode(data_text[1:-1]) temp_df = pd.DataFrame([item.split(",") for item in data_json["data"]]) temp_df.columns = [ "时间", "前值", "现值", "发布日期", ] temp_df['前值'] = pd.to_numeric(temp_df['前值']) temp_df['现值'] = pd.to_numeric(temp_df['现值']) temp_df['时间'] = pd.to_datetime(temp_df['时间']).dt.date temp_df['发布日期'] = pd.to_datetime(temp_df['发布日期']).dt.date return temp_df

18,013

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/economic/macro_china_hk.py

macro_china_hk_building_amount

()

return temp_df

东方财富-经济数据一览-中国香港-香港楼宇买卖合约成交金额 https://data.eastmoney.com/cjsj/foreign_8_6.html :return: 香港楼宇买卖合约成交金额 :rtype: pandas.DataFrame

236

270

def macro_china_hk_building_amount() -> pd.DataFrame: """ 东方财富-经济数据一览-中国香港-香港楼宇买卖合约成交金额 https://data.eastmoney.com/cjsj/foreign_8_6.html :return: 香港楼宇买卖合约成交金额 :rtype: pandas.DataFrame """ url = "https://datainterface.eastmoney.com/EM_DataCenter/JS.aspx" params = { "type": "GJZB", "sty": "HKZB", "js": "({data:[(x)],pages:(pc)})", "p": "1", "ps": "2000", "mkt": "8", "stat": "6", "pageNo": "1", "pageNum": "1", "_": "1621332091873", } r = requests.get(url, params=params) data_text = r.text data_json = demjson.decode(data_text[1:-1]) temp_df = pd.DataFrame([item.split(",") for item in data_json["data"]]) temp_df.columns = [ "时间", "前值", "现值", "发布日期", ] temp_df['前值'] = pd.to_numeric(temp_df['前值']) / 100 temp_df['现值'] = pd.to_numeric(temp_df['现值']) / 100 temp_df['时间'] = pd.to_datetime(temp_df['时间']).dt.date temp_df['发布日期'] = pd.to_datetime(temp_df['发布日期']).dt.date return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/economic/macro_china_hk.py#L236-L270

25

[ 0, 1, 2, 3, 4, 5, 6 ]

20

[ 7, 8, 20, 21, 22, 23, 24, 30, 31, 32, 33, 34 ]

34.285714

false

10

35

2

65.714286

4

def macro_china_hk_building_amount() -> pd.DataFrame: url = "https://datainterface.eastmoney.com/EM_DataCenter/JS.aspx" params = { "type": "GJZB", "sty": "HKZB", "js": "({data:[(x)],pages:(pc)})", "p": "1", "ps": "2000", "mkt": "8", "stat": "6", "pageNo": "1", "pageNum": "1", "_": "1621332091873", } r = requests.get(url, params=params) data_text = r.text data_json = demjson.decode(data_text[1:-1]) temp_df = pd.DataFrame([item.split(",") for item in data_json["data"]]) temp_df.columns = [ "时间", "前值", "现值", "发布日期", ] temp_df['前值'] = pd.to_numeric(temp_df['前值']) / 100 temp_df['现值'] = pd.to_numeric(temp_df['现值']) / 100 temp_df['时间'] = pd.to_datetime(temp_df['时间']).dt.date temp_df['发布日期'] = pd.to_datetime(temp_df['发布日期']).dt.date return temp_df

18,014

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/economic/macro_china_hk.py

macro_china_hk_trade_diff_ratio

()

return temp_df

东方财富-经济数据一览-中国香港-香港商品贸易差额年率 https://data.eastmoney.com/cjsj/foreign_8_7.html :return: 香港商品贸易差额年率 :rtype: pandas.DataFrame

273

307

def macro_china_hk_trade_diff_ratio() -> pd.DataFrame: """ 东方财富-经济数据一览-中国香港-香港商品贸易差额年率 https://data.eastmoney.com/cjsj/foreign_8_7.html :return: 香港商品贸易差额年率 :rtype: pandas.DataFrame """ url = "https://datainterface.eastmoney.com/EM_DataCenter/JS.aspx" params = { "type": "GJZB", "sty": "HKZB", "js": "({data:[(x)],pages:(pc)})", "p": "1", "ps": "2000", "mkt": "8", "stat": "7", "pageNo": "1", "pageNum": "1", "_": "1621332091873", } r = requests.get(url, params=params) data_text = r.text data_json = demjson.decode(data_text[1:-1]) temp_df = pd.DataFrame([item.split(",") for item in data_json["data"]]) temp_df.columns = [ "时间", "前值", "现值", "发布日期", ] temp_df['前值'] = pd.to_numeric(temp_df['前值']) temp_df['现值'] = pd.to_numeric(temp_df['现值']) temp_df['时间'] = pd.to_datetime(temp_df['时间']).dt.date temp_df['发布日期'] = pd.to_datetime(temp_df['发布日期']).dt.date return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/economic/macro_china_hk.py#L273-L307

25

[ 0, 1, 2, 3, 4, 5, 6 ]

20

[ 7, 8, 20, 21, 22, 23, 24, 30, 31, 32, 33, 34 ]

34.285714

false

10

35

2

65.714286

4

def macro_china_hk_trade_diff_ratio() -> pd.DataFrame: url = "https://datainterface.eastmoney.com/EM_DataCenter/JS.aspx" params = { "type": "GJZB", "sty": "HKZB", "js": "({data:[(x)],pages:(pc)})", "p": "1", "ps": "2000", "mkt": "8", "stat": "7", "pageNo": "1", "pageNum": "1", "_": "1621332091873", } r = requests.get(url, params=params) data_text = r.text data_json = demjson.decode(data_text[1:-1]) temp_df = pd.DataFrame([item.split(",") for item in data_json["data"]]) temp_df.columns = [ "时间", "前值", "现值", "发布日期", ] temp_df['前值'] = pd.to_numeric(temp_df['前值']) temp_df['现值'] = pd.to_numeric(temp_df['现值']) temp_df['时间'] = pd.to_datetime(temp_df['时间']).dt.date temp_df['发布日期'] = pd.to_datetime(temp_df['发布日期']).dt.date return temp_df

18,015

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/economic/macro_china_hk.py

macro_china_hk_ppi

()

return temp_df

东方财富-经济数据一览-中国香港-香港制造业 PPI 年率 https://data.eastmoney.com/cjsj/foreign_8_8.html :return: 香港制造业 PPI 年率 :rtype: pandas.DataFrame

310

344

def macro_china_hk_ppi() -> pd.DataFrame: """ 东方财富-经济数据一览-中国香港-香港制造业 PPI 年率 https://data.eastmoney.com/cjsj/foreign_8_8.html :return: 香港制造业 PPI 年率 :rtype: pandas.DataFrame """ url = "https://datainterface.eastmoney.com/EM_DataCenter/JS.aspx" params = { "type": "GJZB", "sty": "HKZB", "js": "({data:[(x)],pages:(pc)})", "p": "1", "ps": "2000", "mkt": "8", "stat": "8", "pageNo": "1", "pageNum": "1", "_": "1621332091873", } r = requests.get(url, params=params) data_text = r.text data_json = demjson.decode(data_text[1:-1]) temp_df = pd.DataFrame([item.split(",") for item in data_json["data"]]) temp_df.columns = [ "时间", "前值", "现值", "发布日期", ] temp_df['前值'] = pd.to_numeric(temp_df['前值']) temp_df['现值'] = pd.to_numeric(temp_df['现值']) temp_df['时间'] = pd.to_datetime(temp_df['时间']).dt.date temp_df['发布日期'] = pd.to_datetime(temp_df['发布日期']).dt.date return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/economic/macro_china_hk.py#L310-L344

25

[ 0, 1, 2, 3, 4, 5, 6 ]

20

[ 7, 8, 20, 21, 22, 23, 24, 30, 31, 32, 33, 34 ]

34.285714

false

10

35

2

65.714286

4

def macro_china_hk_ppi() -> pd.DataFrame: url = "https://datainterface.eastmoney.com/EM_DataCenter/JS.aspx" params = { "type": "GJZB", "sty": "HKZB", "js": "({data:[(x)],pages:(pc)})", "p": "1", "ps": "2000", "mkt": "8", "stat": "8", "pageNo": "1", "pageNum": "1", "_": "1621332091873", } r = requests.get(url, params=params) data_text = r.text data_json = demjson.decode(data_text[1:-1]) temp_df = pd.DataFrame([item.split(",") for item in data_json["data"]]) temp_df.columns = [ "时间", "前值", "现值", "发布日期", ] temp_df['前值'] = pd.to_numeric(temp_df['前值']) temp_df['现值'] = pd.to_numeric(temp_df['现值']) temp_df['时间'] = pd.to_datetime(temp_df['时间']).dt.date temp_df['发布日期'] = pd.to_datetime(temp_df['发布日期']).dt.date return temp_df

18,016

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/economic/macro_usa.py

macro_usa_phs

()

return temp_df

东方财富-经济数据一览-美国-未决房屋销售月率 https://data.eastmoney.com/cjsj/foreign_0_5.html :return: 未决房屋销售月率 :rtype: pandas.DataFrame

28

75

def macro_usa_phs() -> pd.DataFrame: """ 东方财富-经济数据一览-美国-未决房屋销售月率 https://data.eastmoney.com/cjsj/foreign_0_5.html :return: 未决房屋销售月率 :rtype: pandas.DataFrame """ url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "reportName": "RPT_ECONOMICVALUE_USA", "columns": "ALL", "filter": '(INDICATOR_ID="EMG00342249")', "pageNumber": "1", "pageSize": "2000", "sortColumns": "REPORT_DATE", "sortTypes": "-1", "source": "WEB", "client": "WEB", "p": "1", "pageNo": "1", "pageNum": "1", "_": "1669047266881", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) temp_df.columns = [ "-", "-", "-", "时间", "-", "发布日期", "现值", "前值", ] temp_df = temp_df[ [ "时间", "前值", "现值", "发布日期", ] ] temp_df["前值"] = pd.to_numeric(temp_df["前值"], errors="coerce") temp_df["现值"] = pd.to_numeric(temp_df["现值"], errors="coerce") temp_df["发布日期"] = pd.to_datetime(temp_df["发布日期"]).dt.date return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/economic/macro_usa.py#L28-L75

25

[ 0, 1, 2, 3, 4, 5, 6 ]

14.583333

[ 7, 8, 23, 24, 25, 26, 36, 44, 45, 46, 47 ]

22.916667

false

4.118993

48

1

77.083333

4

def macro_usa_phs() -> pd.DataFrame: url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "reportName": "RPT_ECONOMICVALUE_USA", "columns": "ALL", "filter": '(INDICATOR_ID="EMG00342249")', "pageNumber": "1", "pageSize": "2000", "sortColumns": "REPORT_DATE", "sortTypes": "-1", "source": "WEB", "client": "WEB", "p": "1", "pageNo": "1", "pageNum": "1", "_": "1669047266881", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) temp_df.columns = [ "-", "-", "-", "时间", "-", "发布日期", "现值", "前值", ] temp_df = temp_df[ [ "时间", "前值", "现值", "发布日期", ] ] temp_df["前值"] = pd.to_numeric(temp_df["前值"], errors="coerce") temp_df["现值"] = pd.to_numeric(temp_df["现值"], errors="coerce") temp_df["发布日期"] = pd.to_datetime(temp_df["发布日期"]).dt.date return temp_df

18,017

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/economic/macro_usa.py

macro_usa_gdp_monthly

()

return temp_df

金十数据-美国国内生产总值(GDP)报告, 数据区间从 20080228-至今 https://datacenter.jin10.com/reportType/dc_usa_gdp :return: pandas.Series

79

135

def macro_usa_gdp_monthly() -> pd.DataFrame: """ 金十数据-美国国内生产总值(GDP)报告, 数据区间从 20080228-至今 https://datacenter.jin10.com/reportType/dc_usa_gdp :return: pandas.Series """ t = time.time() res = requests.get( JS_USA_GDP_MONTHLY_URL.format( str(int(round(t * 1000))), str(int(round(t * 1000)) + 90) ) ) json_data = json.loads(res.text[res.text.find("{") : res.text.rfind("}") + 1]) date_list = [item["date"] for item in json_data["list"]] value_list = [item["datas"]["美国国内生产总值(GDP)"] for item in json_data["list"]] value_df = pd.DataFrame(value_list) value_df.columns = json_data["kinds"] value_df.index = pd.to_datetime(date_list) temp_df = value_df["今值(%)"] url = "https://datacenter-api.jin10.com/reports/list_v2" params = { "max_date": "", "category": "ec", "attr_id": "53", "_": str(int(round(t * 1000))), } headers = { "accept": "*/*", "accept-encoding": "gzip, deflate, br", "accept-language": "zh-CN,zh;q=0.9,en;q=0.8", "cache-control": "no-cache", "origin": "https://datacenter.jin10.com", "pragma": "no-cache", "referer": "https://datacenter.jin10.com/reportType/dc_usa_michigan_consumer_sentiment", "sec-fetch-dest": "empty", "sec-fetch-mode": "cors", "sec-fetch-site": "same-site", "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/80.0.3987.149 Safari/537.36", "x-app-id": "rU6QIu7JHe2gOUeR", "x-csrf-token": "", "x-version": "1.0.0", } r = requests.get(url, params=params, headers=headers) temp_se = pd.DataFrame(r.json()["data"]["values"]).iloc[:, :2] temp_se.index = pd.to_datetime(temp_se.iloc[:, 0]) temp_se = temp_se.iloc[:, 1] temp_df = temp_df.append(temp_se) temp_df.dropna(inplace=True) temp_df.sort_index(inplace=True) temp_df = temp_df.reset_index() temp_df.drop_duplicates(subset="index", inplace=True) temp_df.set_index("index", inplace=True) temp_df = temp_df.squeeze() temp_df.index.name = None temp_df.name = "gdp" temp_df = temp_df.astype("float") return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/economic/macro_usa.py#L79-L135

25

[ 0, 1, 2, 3, 4, 5 ]

10.526316

[ 6, 7, 12, 13, 14, 15, 16, 17, 18, 19, 20, 26, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56 ]

47.368421

false

4.118993

57

3

52.631579

3

def macro_usa_gdp_monthly() -> pd.DataFrame: t = time.time() res = requests.get( JS_USA_GDP_MONTHLY_URL.format( str(int(round(t * 1000))), str(int(round(t * 1000)) + 90) ) ) json_data = json.loads(res.text[res.text.find("{") : res.text.rfind("}") + 1]) date_list = [item["date"] for item in json_data["list"]] value_list = [item["datas"]["美国国内生产总值(GDP)"] for item in json_data["list"]] value_df = pd.DataFrame(value_list) value_df.columns = json_data["kinds"] value_df.index = pd.to_datetime(date_list) temp_df = value_df["今值(%)"] url = "https://datacenter-api.jin10.com/reports/list_v2" params = { "max_date": "", "category": "ec", "attr_id": "53", "_": str(int(round(t * 1000))), } headers = { "accept": "*/*", "accept-encoding": "gzip, deflate, br", "accept-language": "zh-CN,zh;q=0.9,en;q=0.8", "cache-control": "no-cache", "origin": "https://datacenter.jin10.com", "pragma": "no-cache", "referer": "https://datacenter.jin10.com/reportType/dc_usa_michigan_consumer_sentiment", "sec-fetch-dest": "empty", "sec-fetch-mode": "cors", "sec-fetch-site": "same-site", "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/80.0.3987.149 Safari/537.36", "x-app-id": "rU6QIu7JHe2gOUeR", "x-csrf-token": "", "x-version": "1.0.0", } r = requests.get(url, params=params, headers=headers) temp_se = pd.DataFrame(r.json()["data"]["values"]).iloc[:, :2] temp_se.index = pd.to_datetime(temp_se.iloc[:, 0]) temp_se = temp_se.iloc[:, 1] temp_df = temp_df.append(temp_se) temp_df.dropna(inplace=True) temp_df.sort_index(inplace=True) temp_df = temp_df.reset_index() temp_df.drop_duplicates(subset="index", inplace=True) temp_df.set_index("index", inplace=True) temp_df = temp_df.squeeze() temp_df.index.name = None temp_df.name = "gdp" temp_df = temp_df.astype("float") return temp_df

18,018

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/economic/macro_usa.py

macro_usa_cpi_monthly

()

return temp_df

美国CPI月率报告, 数据区间从19700101-至今 https://datacenter.jin10.com/reportType/dc_usa_cpi https://cdn.jin10.com/dc/reports/dc_usa_cpi_all.js?v=1578741110 :return: 美国CPI月率报告-今值(%) :rtype: pandas.Series

139

197

def macro_usa_cpi_monthly() -> pd.DataFrame: """ 美国CPI月率报告, 数据区间从19700101-至今 https://datacenter.jin10.com/reportType/dc_usa_cpi https://cdn.jin10.com/dc/reports/dc_usa_cpi_all.js?v=1578741110 :return: 美国CPI月率报告-今值(%) :rtype: pandas.Series """ t = time.time() res = requests.get( JS_USA_CPI_MONTHLY_URL.format( str(int(round(t * 1000))), str(int(round(t * 1000)) + 90) ) ) json_data = json.loads(res.text[res.text.find("{") : res.text.rfind("}") + 1]) date_list = [item["date"] for item in json_data["list"]] value_list = [item["datas"]["美国居民消费价格指数(CPI)(月环比)"] for item in json_data["list"]] value_df = pd.DataFrame(value_list) value_df.columns = json_data["kinds"] value_df.index = pd.to_datetime(date_list) temp_df = value_df["今值(%)"] url = "https://datacenter-api.jin10.com/reports/list_v2" params = { "max_date": "", "category": "ec", "attr_id": "9", "_": str(int(round(t * 1000))), } headers = { "accept": "*/*", "accept-encoding": "gzip, deflate, br", "accept-language": "zh-CN,zh;q=0.9,en;q=0.8", "cache-control": "no-cache", "origin": "https://datacenter.jin10.com", "pragma": "no-cache", "referer": "https://datacenter.jin10.com/reportType/dc_usa_michigan_consumer_sentiment", "sec-fetch-dest": "empty", "sec-fetch-mode": "cors", "sec-fetch-site": "same-site", "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/80.0.3987.149 Safari/537.36", "x-app-id": "rU6QIu7JHe2gOUeR", "x-csrf-token": "", "x-version": "1.0.0", } r = requests.get(url, params=params, headers=headers) temp_se = pd.DataFrame(r.json()["data"]["values"]).iloc[:, :2] temp_se.index = pd.to_datetime(temp_se.iloc[:, 0]) temp_se = temp_se.iloc[:, 1] temp_df = temp_df.append(temp_se) temp_df.dropna(inplace=True) temp_df.sort_index(inplace=True) temp_df = temp_df.reset_index() temp_df.drop_duplicates(subset="index", inplace=True) temp_df.set_index("index", inplace=True) temp_df = temp_df.squeeze() temp_df.index.name = None temp_df.name = "cpi_monthly" temp_df = temp_df.astype("float") return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/economic/macro_usa.py#L139-L197

25

[ 0, 1, 2, 3, 4, 5, 6, 7 ]

13.559322

[ 8, 9, 14, 15, 16, 17, 18, 19, 20, 21, 22, 28, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58 ]

45.762712

false

4.118993

59

3

54.237288

5

def macro_usa_cpi_monthly() -> pd.DataFrame: t = time.time() res = requests.get( JS_USA_CPI_MONTHLY_URL.format( str(int(round(t * 1000))), str(int(round(t * 1000)) + 90) ) ) json_data = json.loads(res.text[res.text.find("{") : res.text.rfind("}") + 1]) date_list = [item["date"] for item in json_data["list"]] value_list = [item["datas"]["美国居民消费价格指数(CPI)(月环比)"] for item in json_data["list"]] value_df = pd.DataFrame(value_list) value_df.columns = json_data["kinds"] value_df.index = pd.to_datetime(date_list) temp_df = value_df["今值(%)"] url = "https://datacenter-api.jin10.com/reports/list_v2" params = { "max_date": "", "category": "ec", "attr_id": "9", "_": str(int(round(t * 1000))), } headers = { "accept": "*/*", "accept-encoding": "gzip, deflate, br", "accept-language": "zh-CN,zh;q=0.9,en;q=0.8", "cache-control": "no-cache", "origin": "https://datacenter.jin10.com", "pragma": "no-cache", "referer": "https://datacenter.jin10.com/reportType/dc_usa_michigan_consumer_sentiment", "sec-fetch-dest": "empty", "sec-fetch-mode": "cors", "sec-fetch-site": "same-site", "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/80.0.3987.149 Safari/537.36", "x-app-id": "rU6QIu7JHe2gOUeR", "x-csrf-token": "", "x-version": "1.0.0", } r = requests.get(url, params=params, headers=headers) temp_se = pd.DataFrame(r.json()["data"]["values"]).iloc[:, :2] temp_se.index = pd.to_datetime(temp_se.iloc[:, 0]) temp_se = temp_se.iloc[:, 1] temp_df = temp_df.append(temp_se) temp_df.dropna(inplace=True) temp_df.sort_index(inplace=True) temp_df = temp_df.reset_index() temp_df.drop_duplicates(subset="index", inplace=True) temp_df.set_index("index", inplace=True) temp_df = temp_df.squeeze() temp_df.index.name = None temp_df.name = "cpi_monthly" temp_df = temp_df.astype("float") return temp_df

18,019

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/economic/macro_canada.py

macro_canada_new_house_rate

()

return temp_df

东方财富-经济数据-加拿大-新屋开工 https://data.eastmoney.com/cjsj/foreign_7_0.html :return: 新屋开工 :rtype: pandas.DataFrame

13

59

def macro_canada_new_house_rate() -> pd.DataFrame: """ 东方财富-经济数据-加拿大-新屋开工 https://data.eastmoney.com/cjsj/foreign_7_0.html :return: 新屋开工 :rtype: pandas.DataFrame """ url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "reportName": "RPT_ECONOMICVALUE_CA", "columns": "ALL", "filter": '(INDICATOR_ID="EMG00342247")', "pageNumber": "1", "pageSize": "2000", "sortColumns": "REPORT_DATE", "sortTypes": "-1", "source": "WEB", "client": "WEB", "p": "1", "pageNo": "1", "pageNum": "1", "_": "1669047266881", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) temp_df.columns = [ "-", "-", "-", "-", "时间", "-", "发布日期", "现值", "前值", ] temp_df = temp_df[[ "时间", "前值", "现值", "发布日期", ]] temp_df["前值"] = pd.to_numeric(temp_df["前值"], errors="coerce") temp_df["现值"] = pd.to_numeric(temp_df["现值"], errors="coerce") temp_df['发布日期'] = pd.to_datetime(temp_df['发布日期']).dt.date return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/economic/macro_canada.py#L13-L59

25

[ 0, 1, 2, 3, 4, 5, 6 ]

14.893617

[ 7, 8, 23, 24, 25, 26, 37, 43, 44, 45, 46 ]

23.404255

false

9.722222

47

1

76.595745

4

def macro_canada_new_house_rate() -> pd.DataFrame: url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "reportName": "RPT_ECONOMICVALUE_CA", "columns": "ALL", "filter": '(INDICATOR_ID="EMG00342247")', "pageNumber": "1", "pageSize": "2000", "sortColumns": "REPORT_DATE", "sortTypes": "-1", "source": "WEB", "client": "WEB", "p": "1", "pageNo": "1", "pageNum": "1", "_": "1669047266881", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) temp_df.columns = [ "-", "-", "-", "-", "时间", "-", "发布日期", "现值", "前值", ] temp_df = temp_df[[ "时间", "前值", "现值", "发布日期", ]] temp_df["前值"] = pd.to_numeric(temp_df["前值"], errors="coerce") temp_df["现值"] = pd.to_numeric(temp_df["现值"], errors="coerce") temp_df['发布日期'] = pd.to_datetime(temp_df['发布日期']).dt.date return temp_df

18,020

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/economic/macro_canada.py

macro_canada_unemployment_rate

()

return temp_df

东方财富-经济数据-加拿大-失业率 https://data.eastmoney.com/cjsj/foreign_7_1.html :return: 失业率 :rtype: pandas.DataFrame

63

109

def macro_canada_unemployment_rate() -> pd.DataFrame: """ 东方财富-经济数据-加拿大-失业率 https://data.eastmoney.com/cjsj/foreign_7_1.html :return: 失业率 :rtype: pandas.DataFrame """ url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "reportName": "RPT_ECONOMICVALUE_CA", "columns": "ALL", "filter": '(INDICATOR_ID="EMG00157746")', "pageNumber": "1", "pageSize": "2000", "sortColumns": "REPORT_DATE", "sortTypes": "-1", "source": "WEB", "client": "WEB", "p": "1", "pageNo": "1", "pageNum": "1", "_": "1669047266881", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) temp_df.columns = [ "-", "-", "-", "-", "时间", "-", "发布日期", "现值", "前值", ] temp_df = temp_df[[ "时间", "前值", "现值", "发布日期", ]] temp_df["前值"] = pd.to_numeric(temp_df["前值"], errors="coerce") temp_df["现值"] = pd.to_numeric(temp_df["现值"], errors="coerce") temp_df['发布日期'] = pd.to_datetime(temp_df['发布日期']).dt.date return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/economic/macro_canada.py#L63-L109

25

[ 0, 1, 2, 3, 4, 5, 6 ]

14.893617

[ 7, 8, 23, 24, 25, 26, 37, 43, 44, 45, 46 ]

23.404255

false

9.722222

47

1

76.595745

4

def macro_canada_unemployment_rate() -> pd.DataFrame: url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "reportName": "RPT_ECONOMICVALUE_CA", "columns": "ALL", "filter": '(INDICATOR_ID="EMG00157746")', "pageNumber": "1", "pageSize": "2000", "sortColumns": "REPORT_DATE", "sortTypes": "-1", "source": "WEB", "client": "WEB", "p": "1", "pageNo": "1", "pageNum": "1", "_": "1669047266881", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) temp_df.columns = [ "-", "-", "-", "-", "时间", "-", "发布日期", "现值", "前值", ] temp_df = temp_df[[ "时间", "前值", "现值", "发布日期", ]] temp_df["前值"] = pd.to_numeric(temp_df["前值"], errors="coerce") temp_df["现值"] = pd.to_numeric(temp_df["现值"], errors="coerce") temp_df['发布日期'] = pd.to_datetime(temp_df['发布日期']).dt.date return temp_df

18,021

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/economic/macro_canada.py

macro_canada_trade

()

return temp_df

东方财富-经济数据-加拿大-贸易帐 https://data.eastmoney.com/cjsj/foreign_7_2.html :return: 贸易帐 :rtype: pandas.DataFrame

113

159

def macro_canada_trade() -> pd.DataFrame: """ 东方财富-经济数据-加拿大-贸易帐 https://data.eastmoney.com/cjsj/foreign_7_2.html :return: 贸易帐 :rtype: pandas.DataFrame """ url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "reportName": "RPT_ECONOMICVALUE_CA", "columns": "ALL", "filter": '(INDICATOR_ID="EMG00102022")', "pageNumber": "1", "pageSize": "2000", "sortColumns": "REPORT_DATE", "sortTypes": "-1", "source": "WEB", "client": "WEB", "p": "1", "pageNo": "1", "pageNum": "1", "_": "1669047266881", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) temp_df.columns = [ "-", "-", "-", "-", "时间", "-", "发布日期", "现值", "前值", ] temp_df = temp_df[[ "时间", "前值", "现值", "发布日期", ]] temp_df["前值"] = pd.to_numeric(temp_df["前值"], errors="coerce") temp_df["现值"] = pd.to_numeric(temp_df["现值"], errors="coerce") temp_df['发布日期'] = pd.to_datetime(temp_df['发布日期']).dt.date return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/economic/macro_canada.py#L113-L159

25

[ 0, 1, 2, 3, 4, 5, 6 ]

14.893617

[ 7, 8, 23, 24, 25, 26, 37, 43, 44, 45, 46 ]

23.404255

false

9.722222

47

1

76.595745

4

def macro_canada_trade() -> pd.DataFrame: url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "reportName": "RPT_ECONOMICVALUE_CA", "columns": "ALL", "filter": '(INDICATOR_ID="EMG00102022")', "pageNumber": "1", "pageSize": "2000", "sortColumns": "REPORT_DATE", "sortTypes": "-1", "source": "WEB", "client": "WEB", "p": "1", "pageNo": "1", "pageNum": "1", "_": "1669047266881", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) temp_df.columns = [ "-", "-", "-", "-", "时间", "-", "发布日期", "现值", "前值", ] temp_df = temp_df[[ "时间", "前值", "现值", "发布日期", ]] temp_df["前值"] = pd.to_numeric(temp_df["前值"], errors="coerce") temp_df["现值"] = pd.to_numeric(temp_df["现值"], errors="coerce") temp_df['发布日期'] = pd.to_datetime(temp_df['发布日期']).dt.date return temp_df

18,022

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/economic/macro_canada.py

macro_canada_retail_rate_monthly

()

return temp_df

东方财富-经济数据-加拿大-零售销售月率 https://data.eastmoney.com/cjsj/foreign_7_3.html :return: 零售销售月率 :rtype: pandas.DataFrame

163

209

def macro_canada_retail_rate_monthly() -> pd.DataFrame: """ 东方财富-经济数据-加拿大-零售销售月率 https://data.eastmoney.com/cjsj/foreign_7_3.html :return: 零售销售月率 :rtype: pandas.DataFrame """ url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "reportName": "RPT_ECONOMICVALUE_CA", "columns": "ALL", "filter": '(INDICATOR_ID="EMG01337094")', "pageNumber": "1", "pageSize": "2000", "sortColumns": "REPORT_DATE", "sortTypes": "-1", "source": "WEB", "client": "WEB", "p": "1", "pageNo": "1", "pageNum": "1", "_": "1669047266881", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) temp_df.columns = [ "-", "-", "-", "-", "时间", "-", "发布日期", "现值", "前值", ] temp_df = temp_df[[ "时间", "前值", "现值", "发布日期", ]] temp_df["前值"] = pd.to_numeric(temp_df["前值"], errors="coerce") temp_df["现值"] = pd.to_numeric(temp_df["现值"], errors="coerce") temp_df['发布日期'] = pd.to_datetime(temp_df['发布日期']).dt.date return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/economic/macro_canada.py#L163-L209

25

[ 0, 1, 2, 3, 4, 5, 6 ]

14.893617

[ 7, 8, 23, 24, 25, 26, 37, 43, 44, 45, 46 ]

23.404255

false

9.722222

47

1

76.595745

4

def macro_canada_retail_rate_monthly() -> pd.DataFrame: url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "reportName": "RPT_ECONOMICVALUE_CA", "columns": "ALL", "filter": '(INDICATOR_ID="EMG01337094")', "pageNumber": "1", "pageSize": "2000", "sortColumns": "REPORT_DATE", "sortTypes": "-1", "source": "WEB", "client": "WEB", "p": "1", "pageNo": "1", "pageNum": "1", "_": "1669047266881", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) temp_df.columns = [ "-", "-", "-", "-", "时间", "-", "发布日期", "现值", "前值", ] temp_df = temp_df[[ "时间", "前值", "现值", "发布日期", ]] temp_df["前值"] = pd.to_numeric(temp_df["前值"], errors="coerce") temp_df["现值"] = pd.to_numeric(temp_df["现值"], errors="coerce") temp_df['发布日期'] = pd.to_datetime(temp_df['发布日期']).dt.date return temp_df

18,023

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/economic/macro_canada.py

macro_canada_bank_rate

()

return temp_df

东方财富-经济数据-加拿大-央行公布利率决议 https://data.eastmoney.com/cjsj/foreign_7_4.html :return: 央行公布利率决议 :rtype: pandas.DataFrame

213

259

def macro_canada_bank_rate() -> pd.DataFrame: """ 东方财富-经济数据-加拿大-央行公布利率决议 https://data.eastmoney.com/cjsj/foreign_7_4.html :return: 央行公布利率决议 :rtype: pandas.DataFrame """ url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "reportName": "RPT_ECONOMICVALUE_CA", "columns": "ALL", "filter": '(INDICATOR_ID="EMG00342248")', "pageNumber": "1", "pageSize": "2000", "sortColumns": "REPORT_DATE", "sortTypes": "-1", "source": "WEB", "client": "WEB", "p": "1", "pageNo": "1", "pageNum": "1", "_": "1669047266881", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) temp_df.columns = [ "-", "-", "-", "-", "时间", "-", "发布日期", "现值", "前值", ] temp_df = temp_df[[ "时间", "前值", "现值", "发布日期", ]] temp_df["前值"] = pd.to_numeric(temp_df["前值"], errors="coerce") temp_df["现值"] = pd.to_numeric(temp_df["现值"], errors="coerce") temp_df['发布日期'] = pd.to_datetime(temp_df['发布日期']).dt.date return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/economic/macro_canada.py#L213-L259

25

[ 0, 1, 2, 3, 4, 5, 6 ]

14.893617

[ 7, 8, 23, 24, 25, 26, 37, 43, 44, 45, 46 ]

23.404255

false

9.722222

47

1

76.595745

4

def macro_canada_bank_rate() -> pd.DataFrame: url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "reportName": "RPT_ECONOMICVALUE_CA", "columns": "ALL", "filter": '(INDICATOR_ID="EMG00342248")', "pageNumber": "1", "pageSize": "2000", "sortColumns": "REPORT_DATE", "sortTypes": "-1", "source": "WEB", "client": "WEB", "p": "1", "pageNo": "1", "pageNum": "1", "_": "1669047266881", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) temp_df.columns = [ "-", "-", "-", "-", "时间", "-", "发布日期", "现值", "前值", ] temp_df = temp_df[[ "时间", "前值", "现值", "发布日期", ]] temp_df["前值"] = pd.to_numeric(temp_df["前值"], errors="coerce") temp_df["现值"] = pd.to_numeric(temp_df["现值"], errors="coerce") temp_df['发布日期'] = pd.to_datetime(temp_df['发布日期']).dt.date return temp_df

18,024

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/economic/macro_canada.py

macro_canada_core_cpi_yearly

()

return temp_df

东方财富-经济数据-加拿大-核心消费者物价指数年率 https://data.eastmoney.com/cjsj/foreign_7_5.html :return: 核心消费者物价指数年率 :rtype: pandas.DataFrame

263

309

def macro_canada_core_cpi_yearly() -> pd.DataFrame: """ 东方财富-经济数据-加拿大-核心消费者物价指数年率 https://data.eastmoney.com/cjsj/foreign_7_5.html :return: 核心消费者物价指数年率 :rtype: pandas.DataFrame """ url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "reportName": "RPT_ECONOMICVALUE_CA", "columns": "ALL", "filter": '(INDICATOR_ID="EMG00102030")', "pageNumber": "1", "pageSize": "2000", "sortColumns": "REPORT_DATE", "sortTypes": "-1", "source": "WEB", "client": "WEB", "p": "1", "pageNo": "1", "pageNum": "1", "_": "1669047266881", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) temp_df.columns = [ "-", "-", "-", "-", "时间", "-", "发布日期", "现值", "前值", ] temp_df = temp_df[[ "时间", "前值", "现值", "发布日期", ]] temp_df["前值"] = pd.to_numeric(temp_df["前值"], errors="coerce") temp_df["现值"] = pd.to_numeric(temp_df["现值"], errors="coerce") temp_df['发布日期'] = pd.to_datetime(temp_df['发布日期']).dt.date return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/economic/macro_canada.py#L263-L309

25

[ 0, 1, 2, 3, 4, 5, 6 ]

14.893617

[ 7, 8, 23, 24, 25, 26, 37, 43, 44, 45, 46 ]

23.404255

false

9.722222

47

1

76.595745

4

def macro_canada_core_cpi_yearly() -> pd.DataFrame: url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "reportName": "RPT_ECONOMICVALUE_CA", "columns": "ALL", "filter": '(INDICATOR_ID="EMG00102030")', "pageNumber": "1", "pageSize": "2000", "sortColumns": "REPORT_DATE", "sortTypes": "-1", "source": "WEB", "client": "WEB", "p": "1", "pageNo": "1", "pageNum": "1", "_": "1669047266881", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) temp_df.columns = [ "-", "-", "-", "-", "时间", "-", "发布日期", "现值", "前值", ] temp_df = temp_df[[ "时间", "前值", "现值", "发布日期", ]] temp_df["前值"] = pd.to_numeric(temp_df["前值"], errors="coerce") temp_df["现值"] = pd.to_numeric(temp_df["现值"], errors="coerce") temp_df['发布日期'] = pd.to_datetime(temp_df['发布日期']).dt.date return temp_df

18,025

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/economic/macro_canada.py

macro_canada_core_cpi_monthly

()

return temp_df

东方财富-经济数据-加拿大-核心消费者物价指数月率 https://data.eastmoney.com/cjsj/foreign_7_6.html :return: 核心消费者物价指数月率 :rtype: pandas.DataFrame

313

359

def macro_canada_core_cpi_monthly() -> pd.DataFrame: """ 东方财富-经济数据-加拿大-核心消费者物价指数月率 https://data.eastmoney.com/cjsj/foreign_7_6.html :return: 核心消费者物价指数月率 :rtype: pandas.DataFrame """ url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "reportName": "RPT_ECONOMICVALUE_CA", "columns": "ALL", "filter": '(INDICATOR_ID="EMG00102044")', "pageNumber": "1", "pageSize": "2000", "sortColumns": "REPORT_DATE", "sortTypes": "-1", "source": "WEB", "client": "WEB", "p": "1", "pageNo": "1", "pageNum": "1", "_": "1669047266881", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) temp_df.columns = [ "-", "-", "-", "-", "时间", "-", "发布日期", "现值", "前值", ] temp_df = temp_df[[ "时间", "前值", "现值", "发布日期", ]] temp_df["前值"] = pd.to_numeric(temp_df["前值"], errors="coerce") temp_df["现值"] = pd.to_numeric(temp_df["现值"], errors="coerce") temp_df['发布日期'] = pd.to_datetime(temp_df['发布日期']).dt.date return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/economic/macro_canada.py#L313-L359

25

[ 0, 1, 2, 3, 4, 5, 6 ]

14.893617

[ 7, 8, 23, 24, 25, 26, 37, 43, 44, 45, 46 ]

23.404255

false

9.722222

47

1

76.595745

4

def macro_canada_core_cpi_monthly() -> pd.DataFrame: url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "reportName": "RPT_ECONOMICVALUE_CA", "columns": "ALL", "filter": '(INDICATOR_ID="EMG00102044")', "pageNumber": "1", "pageSize": "2000", "sortColumns": "REPORT_DATE", "sortTypes": "-1", "source": "WEB", "client": "WEB", "p": "1", "pageNo": "1", "pageNum": "1", "_": "1669047266881", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) temp_df.columns = [ "-", "-", "-", "-", "时间", "-", "发布日期", "现值", "前值", ] temp_df = temp_df[[ "时间", "前值", "现值", "发布日期", ]] temp_df["前值"] = pd.to_numeric(temp_df["前值"], errors="coerce") temp_df["现值"] = pd.to_numeric(temp_df["现值"], errors="coerce") temp_df['发布日期'] = pd.to_datetime(temp_df['发布日期']).dt.date return temp_df

18,026

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/economic/macro_canada.py

macro_canada_cpi_yearly

()

return temp_df

东方财富-经济数据-加拿大-消费者物价指数年率 https://data.eastmoney.com/cjsj/foreign_7_7.html :return: 消费者物价指数年率 :rtype: pandas.DataFrame

363

409

def macro_canada_cpi_yearly() -> pd.DataFrame: """ 东方财富-经济数据-加拿大-消费者物价指数年率 https://data.eastmoney.com/cjsj/foreign_7_7.html :return: 消费者物价指数年率 :rtype: pandas.DataFrame """ url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "reportName": "RPT_ECONOMICVALUE_CA", "columns": "ALL", "filter": '(INDICATOR_ID="EMG00102029")', "pageNumber": "1", "pageSize": "2000", "sortColumns": "REPORT_DATE", "sortTypes": "-1", "source": "WEB", "client": "WEB", "p": "1", "pageNo": "1", "pageNum": "1", "_": "1669047266881", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) temp_df.columns = [ "-", "-", "-", "-", "时间", "-", "发布日期", "现值", "前值", ] temp_df = temp_df[[ "时间", "前值", "现值", "发布日期", ]] temp_df["前值"] = pd.to_numeric(temp_df["前值"], errors="coerce") temp_df["现值"] = pd.to_numeric(temp_df["现值"], errors="coerce") temp_df['发布日期'] = pd.to_datetime(temp_df['发布日期']).dt.date return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/economic/macro_canada.py#L363-L409

25

[ 0, 1, 2, 3, 4, 5, 6 ]

14.893617

[ 7, 8, 23, 24, 25, 26, 37, 43, 44, 45, 46 ]

23.404255

false

9.722222

47

1

76.595745

4

def macro_canada_cpi_yearly() -> pd.DataFrame: url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "reportName": "RPT_ECONOMICVALUE_CA", "columns": "ALL", "filter": '(INDICATOR_ID="EMG00102029")', "pageNumber": "1", "pageSize": "2000", "sortColumns": "REPORT_DATE", "sortTypes": "-1", "source": "WEB", "client": "WEB", "p": "1", "pageNo": "1", "pageNum": "1", "_": "1669047266881", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) temp_df.columns = [ "-", "-", "-", "-", "时间", "-", "发布日期", "现值", "前值", ] temp_df = temp_df[[ "时间", "前值", "现值", "发布日期", ]] temp_df["前值"] = pd.to_numeric(temp_df["前值"], errors="coerce") temp_df["现值"] = pd.to_numeric(temp_df["现值"], errors="coerce") temp_df['发布日期'] = pd.to_datetime(temp_df['发布日期']).dt.date return temp_df

18,027

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/economic/macro_canada.py

macro_canada_cpi_monthly

()

return temp_df

东方财富-经济数据-加拿大-消费者物价指数月率 https://data.eastmoney.com/cjsj/foreign_7_8.html :return: 消费者物价指数月率 :rtype: pandas.DataFrame

413

459

def macro_canada_cpi_monthly() -> pd.DataFrame: """ 东方财富-经济数据-加拿大-消费者物价指数月率 https://data.eastmoney.com/cjsj/foreign_7_8.html :return: 消费者物价指数月率 :rtype: pandas.DataFrame """ url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "reportName": "RPT_ECONOMICVALUE_CA", "columns": "ALL", "filter": '(INDICATOR_ID="EMG00158719")', "pageNumber": "1", "pageSize": "2000", "sortColumns": "REPORT_DATE", "sortTypes": "-1", "source": "WEB", "client": "WEB", "p": "1", "pageNo": "1", "pageNum": "1", "_": "1669047266881", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) temp_df.columns = [ "-", "-", "-", "-", "时间", "-", "发布日期", "现值", "前值", ] temp_df = temp_df[[ "时间", "前值", "现值", "发布日期", ]] temp_df["前值"] = pd.to_numeric(temp_df["前值"], errors="coerce") temp_df["现值"] = pd.to_numeric(temp_df["现值"], errors="coerce") temp_df['发布日期'] = pd.to_datetime(temp_df['发布日期']).dt.date return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/economic/macro_canada.py#L413-L459

25

[ 0, 1, 2, 3, 4, 5, 6 ]

14.893617

[ 7, 8, 23, 24, 25, 26, 37, 43, 44, 45, 46 ]

23.404255

false

9.722222

47

1

76.595745

4

def macro_canada_cpi_monthly() -> pd.DataFrame: url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "reportName": "RPT_ECONOMICVALUE_CA", "columns": "ALL", "filter": '(INDICATOR_ID="EMG00158719")', "pageNumber": "1", "pageSize": "2000", "sortColumns": "REPORT_DATE", "sortTypes": "-1", "source": "WEB", "client": "WEB", "p": "1", "pageNo": "1", "pageNum": "1", "_": "1669047266881", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) temp_df.columns = [ "-", "-", "-", "-", "时间", "-", "发布日期", "现值", "前值", ] temp_df = temp_df[[ "时间", "前值", "现值", "发布日期", ]] temp_df["前值"] = pd.to_numeric(temp_df["前值"], errors="coerce") temp_df["现值"] = pd.to_numeric(temp_df["现值"], errors="coerce") temp_df['发布日期'] = pd.to_datetime(temp_df['发布日期']).dt.date return temp_df

18,028

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/economic/macro_canada.py

macro_canada_gdp_monthly

()

return temp_df

东方财富-经济数据-加拿大-GDP 月率 https://data.eastmoney.com/cjsj/foreign_7_9.html :return: GDP 月率 :rtype: pandas.DataFrame

463

509

def macro_canada_gdp_monthly() -> pd.DataFrame: """ 东方财富-经济数据-加拿大-GDP 月率 https://data.eastmoney.com/cjsj/foreign_7_9.html :return: GDP 月率 :rtype: pandas.DataFrame """ url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "reportName": "RPT_ECONOMICVALUE_CA", "columns": "ALL", "filter": '(INDICATOR_ID="EMG00159259")', "pageNumber": "1", "pageSize": "2000", "sortColumns": "REPORT_DATE", "sortTypes": "-1", "source": "WEB", "client": "WEB", "p": "1", "pageNo": "1", "pageNum": "1", "_": "1669047266881", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) temp_df.columns = [ "-", "-", "-", "-", "时间", "-", "发布日期", "现值", "前值", ] temp_df = temp_df[[ "时间", "前值", "现值", "发布日期", ]] temp_df["前值"] = pd.to_numeric(temp_df["前值"], errors="coerce") temp_df["现值"] = pd.to_numeric(temp_df["现值"], errors="coerce") temp_df['发布日期'] = pd.to_datetime(temp_df['发布日期']).dt.date return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/economic/macro_canada.py#L463-L509

25

[ 0, 1, 2, 3, 4, 5, 6 ]

14.893617

[ 7, 8, 23, 24, 25, 26, 37, 43, 44, 45, 46 ]

23.404255

false

9.722222

47

1

76.595745

4

def macro_canada_gdp_monthly() -> pd.DataFrame: url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "reportName": "RPT_ECONOMICVALUE_CA", "columns": "ALL", "filter": '(INDICATOR_ID="EMG00159259")', "pageNumber": "1", "pageSize": "2000", "sortColumns": "REPORT_DATE", "sortTypes": "-1", "source": "WEB", "client": "WEB", "p": "1", "pageNo": "1", "pageNum": "1", "_": "1669047266881", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) temp_df.columns = [ "-", "-", "-", "-", "时间", "-", "发布日期", "现值", "前值", ] temp_df = temp_df[[ "时间", "前值", "现值", "发布日期", ]] temp_df["前值"] = pd.to_numeric(temp_df["前值"], errors="coerce") temp_df["现值"] = pd.to_numeric(temp_df["现值"], errors="coerce") temp_df['发布日期'] = pd.to_datetime(temp_df['发布日期']).dt.date return temp_df

18,029

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/economic/macro_uk.py

macro_uk_core

(symbol: str = "EMG00010348")

return temp_df

东方财富-数据中心-经济数据一览-宏观经济-英国-核心代码 https://data.eastmoney.com/cjsj/foreign_4_0.html :param symbol: 代码 :type symbol: str :return: 指定 symbol 的数据 :rtype: pandas.DataFrame

12

66

def macro_uk_core(symbol: str = "EMG00010348") -> pd.DataFrame: """ 东方财富-数据中心-经济数据一览-宏观经济-英国-核心代码 https://data.eastmoney.com/cjsj/foreign_4_0.html :param symbol: 代码 :type symbol: str :return: 指定 symbol 的数据 :rtype: pandas.DataFrame """ url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "reportName": "RPT_ECONOMICVALUE_BRITAIN", "columns": "ALL", "filter": f'(INDICATOR_ID="{symbol}")', "pageNumber": "1", "pageSize": "5000", "sortColumns": "REPORT_DATE", "sortTypes": "-1", "source": "WEB", "client": "WEB", "p": "1", "pageNo": "1", "pageNum": "1", "_": "1667639896816", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) temp_df.rename( columns={ "COUNTRY": "-", "INDICATOR_ID": "-", "INDICATOR_NAME": "-", "REPORT_DATE_CH": "时间", "REPORT_DATE": "-", "PUBLISH_DATE": "发布日期", "VALUE": "现值", "PRE_VALUE": "前值", "INDICATOR_IDOLD": "-", }, inplace=True, ) temp_df = temp_df[ [ "时间", "前值", "现值", "发布日期", ] ] temp_df["前值"] = pd.to_numeric(temp_df["前值"]) temp_df["现值"] = pd.to_numeric(temp_df["现值"]) temp_df["发布日期"] = pd.to_datetime(temp_df["发布日期"]).dt.date temp_df.sort_values(["发布日期"], inplace=True, ignore_index=True) return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/economic/macro_uk.py#L12-L66

25

[ 0, 1, 2, 3, 4, 5, 6, 7, 8 ]

16.363636

[ 9, 10, 25, 26, 27, 28, 42, 50, 51, 52, 53, 54 ]

21.818182

false

21.73913

55

1

78.181818

6

def macro_uk_core(symbol: str = "EMG00010348") -> pd.DataFrame: url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "reportName": "RPT_ECONOMICVALUE_BRITAIN", "columns": "ALL", "filter": f'(INDICATOR_ID="{symbol}")', "pageNumber": "1", "pageSize": "5000", "sortColumns": "REPORT_DATE", "sortTypes": "-1", "source": "WEB", "client": "WEB", "p": "1", "pageNo": "1", "pageNum": "1", "_": "1667639896816", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) temp_df.rename( columns={ "COUNTRY": "-", "INDICATOR_ID": "-", "INDICATOR_NAME": "-", "REPORT_DATE_CH": "时间", "REPORT_DATE": "-", "PUBLISH_DATE": "发布日期", "VALUE": "现值", "PRE_VALUE": "前值", "INDICATOR_IDOLD": "-", }, inplace=True, ) temp_df = temp_df[ [ "时间", "前值", "现值", "发布日期", ] ] temp_df["前值"] = pd.to_numeric(temp_df["前值"]) temp_df["现值"] = pd.to_numeric(temp_df["现值"]) temp_df["发布日期"] = pd.to_datetime(temp_df["发布日期"]).dt.date temp_df.sort_values(["发布日期"], inplace=True, ignore_index=True) return temp_df

18,030

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/economic/macro_uk.py

macro_uk_halifax_monthly

()

return temp_df

东方财富-经济数据-英国-Halifax 房价指数月率 https://data.eastmoney.com/cjsj/foreign_4_0.html :return: Halifax 房价指数月率 :rtype: pandas.DataFrame

70

78

def macro_uk_halifax_monthly() -> pd.DataFrame: """ 东方财富-经济数据-英国-Halifax 房价指数月率 https://data.eastmoney.com/cjsj/foreign_4_0.html :return: Halifax 房价指数月率 :rtype: pandas.DataFrame """ temp_df = macro_uk_core(symbol="EMG00342256") return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/economic/macro_uk.py#L70-L78

25

[ 0, 1, 2, 3, 4, 5, 6 ]

77.777778

[ 7, 8 ]

22.222222

false

21.73913

9

1

77.777778

4

def macro_uk_halifax_monthly() -> pd.DataFrame: temp_df = macro_uk_core(symbol="EMG00342256") return temp_df

18,031

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/economic/macro_uk.py

macro_uk_halifax_yearly

()

return temp_df

东方财富-经济数据-英国-Halifax 房价指数年率 https://data.eastmoney.com/cjsj/foreign_4_1.html :return: Halifax房价指数年率 :rtype: pandas.DataFrame

82

90

def macro_uk_halifax_yearly() -> pd.DataFrame: """ 东方财富-经济数据-英国-Halifax 房价指数年率 https://data.eastmoney.com/cjsj/foreign_4_1.html :return: Halifax房价指数年率 :rtype: pandas.DataFrame """ temp_df = macro_uk_core(symbol="EMG00010370") return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/economic/macro_uk.py#L82-L90

25

[ 0, 1, 2, 3, 4, 5, 6 ]

77.777778

[ 7, 8 ]

22.222222

false

21.73913

9

1

77.777778

4

def macro_uk_halifax_yearly() -> pd.DataFrame: temp_df = macro_uk_core(symbol="EMG00010370") return temp_df

18,032

akfamily/akshare

087025d8d6f799b30ca114013e82c1ad22dc9294

akshare/economic/macro_uk.py

macro_uk_trade

()

return temp_df

东方财富-经济数据-英国-贸易帐 https://data.eastmoney.com/cjsj/foreign_4_2.html :return: 贸易帐 :rtype: pandas.DataFrame

94

102

def macro_uk_trade() -> pd.DataFrame: """ 东方财富-经济数据-英国-贸易帐 https://data.eastmoney.com/cjsj/foreign_4_2.html :return: 贸易帐 :rtype: pandas.DataFrame """ temp_df = macro_uk_core(symbol="EMG00158309") return temp_df

https://github.com/akfamily/akshare/blob/087025d8d6f799b30ca114013e82c1ad22dc9294/project25/akshare/economic/macro_uk.py#L94-L102

25

[ 0, 1, 2, 3, 4, 5, 6 ]

77.777778

[ 7, 8 ]

22.222222

false

21.73913

9

1

77.777778

4

def macro_uk_trade() -> pd.DataFrame: temp_df = macro_uk_core(symbol="EMG00158309") return temp_df

18,033