#!/usr/bin/env python # -*- coding:utf-8 -*- """ Date: 2025/2/15 22:00 Desc: 东方财富-ETF行情 https://quote.eastmoney.com/sh513500.html """ from functools import lru_cache import pandas as pd import requests from akshare.utils.func import fetch_paginated_data @lru_cache() def _fund_etf_code_id_map_em() -> dict: """ 东方财富-ETF代码和市场标识映射 https://quote.eastmoney.com/center/gridlist.html#fund_etf :return: ETF 代码和市场标识映射 :rtype: dict """ url = "https://88.push2.eastmoney.com/api/qt/clist/get" params = { "pn": "1", "pz": "100", "po": "1", "np": "1", "ut": "bd1d9ddb04089700cf9c27f6f7426281", "fltt": "2", "invt": "2", "wbp2u": "|0|0|0|web", "fid": "f3", "fs": "b:MK0021,b:MK0022,b:MK0023,b:MK0024", "fields": "f3,f12,f13", } temp_df = fetch_paginated_data(url, params) temp_dict = dict(zip(temp_df["f12"], temp_df["f13"])) return temp_dict def fund_etf_spot_em() -> pd.DataFrame: """ 东方财富-ETF 实时行情 https://quote.eastmoney.com/center/gridlist.html#fund_etf :return: ETF 实时行情 :rtype: pandas.DataFrame """ url = "https://88.push2.eastmoney.com/api/qt/clist/get" params = { "pn": "1", "pz": "100", "po": "1", "np": "1", "ut": "bd1d9ddb04089700cf9c27f6f7426281", "fltt": "2", "invt": "2", "wbp2u": "|0|0|0|web", "fid": "f12", "fs": "b:MK0021,b:MK0022,b:MK0023,b:MK0024,b:MK0827", "fields": ( "f1,f2,f3,f4,f5,f6,f7,f8,f9,f10," "f12,f13,f14,f15,f16,f17,f18,f20,f21," "f23,f24,f25,f22,f11,f30,f31,f32,f33," "f34,f35,f38,f62,f63,f64,f65,f66,f69," "f72,f75,f78,f81,f84,f87,f115,f124,f128," "f136,f152,f184,f297,f402,f441" ), } temp_df = fetch_paginated_data(url, params) temp_df.rename( columns={ "f12": "代码", "f14": "名称", "f2": "最新价", "f4": "涨跌额", "f3": "涨跌幅", "f5": "成交量", "f6": "成交额", "f7": "振幅", "f17": "开盘价", "f15": "最高价", "f16": "最低价", "f18": "昨收", "f8": "换手率", "f10": "量比", "f30": "现手", "f31": "买一", "f32": "卖一", "f33": "委比", "f34": "外盘", "f35": "内盘", "f62": "主力净流入-净额", "f184": "主力净流入-净占比", "f66": "超大单净流入-净额", "f69": "超大单净流入-净占比", "f72": "大单净流入-净额", "f75": "大单净流入-净占比", "f78": "中单净流入-净额", "f81": "中单净流入-净占比", "f84": "小单净流入-净额", "f87": "小单净流入-净占比", "f38": "最新份额", "f21": "流通市值", "f20": "总市值", "f402": "基金折价率", "f441": "IOPV实时估值", "f297": "数据日期", "f124": "更新时间", }, inplace=True, ) temp_df = temp_df[ [ "代码", "名称", "最新价", "IOPV实时估值", "基金折价率", "涨跌额", "涨跌幅", "成交量", "成交额", "开盘价", "最高价", "最低价", "昨收", "振幅", "换手率", "量比", "委比", "外盘", "内盘", "主力净流入-净额", "主力净流入-净占比", "超大单净流入-净额", "超大单净流入-净占比", "大单净流入-净额", "大单净流入-净占比", "中单净流入-净额", "中单净流入-净占比", "小单净流入-净额", "小单净流入-净占比", "现手", "买一", "卖一", "最新份额", "流通市值", "总市值", "数据日期", "更新时间", ] ] 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["开盘价"] = pd.to_numeric(temp_df["开盘价"], errors="coerce") temp_df["最高价"] = pd.to_numeric(temp_df["最高价"], errors="coerce") temp_df["最低价"] = pd.to_numeric(temp_df["最低价"], errors="coerce") temp_df["昨收"] = pd.to_numeric(temp_df["昨收"], errors="coerce") temp_df["换手率"] = pd.to_numeric(temp_df["换手率"], errors="coerce") temp_df["量比"] = pd.to_numeric(temp_df["量比"], errors="coerce") temp_df["委比"] = pd.to_numeric(temp_df["委比"], errors="coerce") temp_df["外盘"] = pd.to_numeric(temp_df["外盘"], errors="coerce") temp_df["内盘"] = pd.to_numeric(temp_df["内盘"], errors="coerce") temp_df["流通市值"] = pd.to_numeric(temp_df["流通市值"], errors="coerce") temp_df["总市值"] = pd.to_numeric(temp_df["总市值"], errors="coerce") temp_df["振幅"] = pd.to_numeric(temp_df["振幅"], errors="coerce") temp_df["现手"] = pd.to_numeric(temp_df["现手"], errors="coerce") temp_df["买一"] = pd.to_numeric(temp_df["买一"], errors="coerce") temp_df["卖一"] = pd.to_numeric(temp_df["卖一"], errors="coerce") temp_df["最新份额"] = pd.to_numeric(temp_df["最新份额"], errors="coerce") temp_df["IOPV实时估值"] = pd.to_numeric(temp_df["IOPV实时估值"], errors="coerce") temp_df["基金折价率"] = pd.to_numeric(temp_df["基金折价率"], errors="coerce") temp_df["主力净流入-净额"] = pd.to_numeric( temp_df["主力净流入-净额"], errors="coerce" ) temp_df["主力净流入-净占比"] = pd.to_numeric( temp_df["主力净流入-净占比"], errors="coerce" ) temp_df["超大单净流入-净额"] = pd.to_numeric( temp_df["超大单净流入-净额"], errors="coerce" ) temp_df["超大单净流入-净占比"] = pd.to_numeric( temp_df["超大单净流入-净占比"], errors="coerce" ) temp_df["大单净流入-净额"] = pd.to_numeric( temp_df["大单净流入-净额"], errors="coerce" ) temp_df["大单净流入-净占比"] = pd.to_numeric( temp_df["大单净流入-净占比"], errors="coerce" ) temp_df["中单净流入-净额"] = pd.to_numeric( temp_df["中单净流入-净额"], errors="coerce" ) temp_df["中单净流入-净占比"] = pd.to_numeric( temp_df["中单净流入-净占比"], errors="coerce" ) temp_df["小单净流入-净额"] = pd.to_numeric( temp_df["小单净流入-净额"], errors="coerce" ) temp_df["小单净流入-净占比"] = pd.to_numeric( temp_df["小单净流入-净占比"], errors="coerce" ) temp_df["数据日期"] = pd.to_datetime( temp_df["数据日期"], format="%Y%m%d", errors="coerce" ) temp_df["更新时间"] = ( pd.to_datetime(temp_df["更新时间"], unit="s", errors="coerce") .dt.tz_localize("UTC") .dt.tz_convert("Asia/Shanghai") ) return temp_df def get_market_id(symbol: str) -> int: """ 东方财富-ETF市场标识判断 :param symbol: ETF 代码 :type symbol: str :return: ETF 代码和市场标识（1:上证 0:深证） :rtype: int """ if symbol.startswith(("0", "1", "3", "2", "5", "6")): if symbol.startswith(("5", "6")): return 1 else: return 0 else: return 1 def fund_etf_hist_em( symbol: str = "159707", period: str = "daily", start_date: str = "19700101", end_date: str = "20500101", adjust: str = "", ) -> pd.DataFrame: """ 东方财富-ETF行情 https://quote.eastmoney.com/sz159707.html :param symbol: ETF 代码 :type symbol: str :param period: choice of {'daily', 'weekly', 'monthly'} :type period: str :param start_date: 开始日期 :type start_date: str :param end_date: 结束日期 :type end_date: str :param adjust: choice of {"qfq": "前复权", "hfq": "后复权", "": "不复权"} :type adjust: str :return: 每日行情 :rtype: pandas.DataFrame """ # code_id_dict = _fund_etf_code_id_map_em() adjust_dict = {"qfq": "1", "hfq": "2", "": "0"} period_dict = {"daily": "101", "weekly": "102", "monthly": "103"} url = "https://push2his.eastmoney.com/api/qt/stock/kline/get" params = { "fields1": "f1,f2,f3,f4,f5,f6", "fields2": "f51,f52,f53,f54,f55,f56,f57,f58,f59,f60,f61,f116", "ut": "7eea3edcaed734bea9cbfc24409ed989", "klt": period_dict[period], "fqt": adjust_dict[adjust], "beg": start_date, "end": end_date, } try: # market_id = code_id_dict[symbol] market_id = get_market_id(symbol) params.update({"secid": f"{market_id}.{symbol}"}) r = requests.get(url, timeout=15, params=params) data_json = r.json() except KeyError: market_id = 1 params.update({"secid": f"{market_id}.{symbol}"}) r = requests.get(url, timeout=15, params=params) data_json = r.json() if not data_json["data"]: market_id = 0 params.update({"secid": f"{market_id}.{symbol}"}) r = requests.get(url, timeout=15, params=params) data_json = r.json() if not (data_json["data"] and data_json["data"]["klines"]): return pd.DataFrame() temp_df = pd.DataFrame([item.split(",") for item in data_json["data"]["klines"]]) temp_df.columns = [ "日期", "开盘", "收盘", "最高", "最低", "成交量", "成交额", "振幅", "涨跌幅", "涨跌额", "换手率", ] temp_df.index = pd.to_datetime(temp_df["日期"], errors="coerce") temp_df.reset_index(inplace=True, drop=True) temp_df["开盘"] = pd.to_numeric(temp_df["开盘"], errors="coerce") temp_df["收盘"] = pd.to_numeric(temp_df["收盘"], errors="coerce") temp_df["最高"] = pd.to_numeric(temp_df["最高"], errors="coerce") temp_df["最低"] = pd.to_numeric(temp_df["最低"], 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["振幅"] = pd.to_numeric(temp_df["振幅"], errors="coerce") temp_df["涨跌幅"] = pd.to_numeric(temp_df["涨跌幅"], errors="coerce") temp_df["涨跌额"] = pd.to_numeric(temp_df["涨跌额"], errors="coerce") temp_df["换手率"] = pd.to_numeric(temp_df["换手率"], errors="coerce") return temp_df def fund_etf_hist_min_em( symbol: str = "159707", start_date: str = "1979-09-01 09:32:00", end_date: str = "2222-01-01 09:32:00", period: str = "5", adjust: str = "", ) -> pd.DataFrame: """ 东方财富-ETF 行情 https://quote.eastmoney.com/sz159707.html :param symbol: ETF 代码 :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 :param period: choice of {"1", "5", "15", "30", "60"} :type period: str :param adjust: choice of {'', 'qfq', 'hfq'} :type adjust: str :return: 每日分时行情 :rtype: pandas.DataFrame """ # code_id_dict = _fund_etf_code_id_map_em() # 商品期货类 ETF # code_id_dict.update( # { # "159980": "0", # "159981": "0", # "159985": "0", # "511090": "1", # "511220": "1", # "511380": "1", # } # ) adjust_map = { "": "0", "qfq": "1", "hfq": "2", } if period == "1": url = "https://push2his.eastmoney.com/api/qt/stock/trends2/get" params = { "fields1": "f1,f2,f3,f4,f5,f6,f7,f8,f9,f10,f11,f12,f13", "fields2": "f51,f52,f53,f54,f55,f56,f57,f58", "ut": "7eea3edcaed734bea9cbfc24409ed989", "ndays": "5", "iscr": "0", "secid": f"{get_market_id(symbol)}.{symbol}", } r = requests.get(url, timeout=15, params=params) data_json = r.json() temp_df = pd.DataFrame( [item.split(",") for item in data_json["data"]["trends"]] ) temp_df.columns = [ "时间", "开盘", "收盘", "最高", "最低", "成交量", "成交额", "均价", ] temp_df.index = pd.to_datetime(temp_df["时间"]) temp_df = temp_df[start_date:end_date] temp_df.reset_index(drop=True, inplace=True) temp_df["开盘"] = pd.to_numeric(temp_df["开盘"], errors="coerce") temp_df["收盘"] = pd.to_numeric(temp_df["收盘"], errors="coerce") temp_df["最高"] = pd.to_numeric(temp_df["最高"], errors="coerce") temp_df["最低"] = pd.to_numeric(temp_df["最低"], 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["均价"] = pd.to_numeric(temp_df["均价"], errors="coerce") temp_df["时间"] = pd.to_datetime(temp_df["时间"]).astype(str) return temp_df else: url = "https://push2his.eastmoney.com/api/qt/stock/kline/get" params = { "fields1": "f1,f2,f3,f4,f5,f6", "fields2": "f51,f52,f53,f54,f55,f56,f57,f58,f59,f60,f61", "ut": "7eea3edcaed734bea9cbfc24409ed989", "klt": period, "fqt": adjust_map[adjust], "secid": f"{get_market_id(symbol)}.{symbol}", "beg": "0", "end": "20500000", } r = requests.get(url, timeout=15, params=params) data_json = r.json() temp_df = pd.DataFrame( [item.split(",") for item in data_json["data"]["klines"]] ) temp_df.columns = [ "时间", "开盘", "收盘", "最高", "最低", "成交量", "成交额", "振幅", "涨跌幅", "涨跌额", "换手率", ] temp_df.index = pd.to_datetime(temp_df["时间"]) temp_df = temp_df[start_date:end_date] temp_df.reset_index(drop=True, inplace=True) temp_df["开盘"] = pd.to_numeric(temp_df["开盘"], errors="coerce") temp_df["收盘"] = pd.to_numeric(temp_df["收盘"], errors="coerce") temp_df["最高"] = pd.to_numeric(temp_df["最高"], errors="coerce") temp_df["最低"] = pd.to_numeric(temp_df["最低"], 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["振幅"] = pd.to_numeric(temp_df["振幅"], errors="coerce") temp_df["涨跌幅"] = pd.to_numeric(temp_df["涨跌幅"], errors="coerce") temp_df["涨跌额"] = pd.to_numeric(temp_df["涨跌额"], errors="coerce") temp_df["换手率"] = pd.to_numeric(temp_df["换手率"], errors="coerce") temp_df["时间"] = pd.to_datetime(temp_df["时间"]).astype(str) temp_df = temp_df[ [ "时间", "开盘", "收盘", "最高", "最低", "涨跌幅", "涨跌额", "成交量", "成交额", "振幅", "换手率", ] ] return temp_df if __name__ == "__main__": fund_etf_spot_em_df = fund_etf_spot_em() print(fund_etf_spot_em_df) fund_etf_hist_hfq_em_df = fund_etf_hist_em( symbol="513500", period="daily", start_date="20000101", end_date="20230201", adjust="hfq", ) print(fund_etf_hist_hfq_em_df) fund_etf_hist_qfq_em_df = fund_etf_hist_em( symbol="511010", period="daily", start_date="20000101", end_date="20230718", adjust="", ) print(fund_etf_hist_qfq_em_df) fund_etf_hist_em_df = fund_etf_hist_em( symbol="159985", period="daily", start_date="20000101", end_date="20231211", adjust="", ) print(fund_etf_hist_em_df) fund_etf_hist_min_em_df = fund_etf_hist_min_em( symbol="511380", period="1", adjust="", start_date="2025-03-10 09:30:00", end_date="2025-03-10 17:40:00", ) print(fund_etf_hist_min_em_df)