#!/usr/bin/env python # -*- coding:utf-8 -*- """ Date: 2025/3/10 19:30 Desc: 东方财富-沪深板块-行业板块 https://quote.eastmoney.com/center/boardlist.html#industry_board """ import re from functools import lru_cache import pandas as pd import requests from akshare.utils.func import fetch_paginated_data @lru_cache() def __stock_board_industry_name_em() -> pd.DataFrame: """ 东方财富网-沪深板块-行业板块-名称 https://quote.eastmoney.com/center/boardlist.html#industry_board :return: 行业板块-名称 :rtype: pandas.DataFrame """ url = "https://17.push2.eastmoney.com/api/qt/clist/get" params = { "pn": "1", "pz": "100", "po": "1", "np": "1", "ut": "bd1d9ddb04089700cf9c27f6f7426281", "fltt": "2", "invt": "2", "fid": "f3", "fs": "m:90 t:2 f:!50", "fields": "f1,f2,f3,f4,f5,f6,f7,f8,f9,f10,f12,f13,f14,f15,f16,f17,f18,f20,f21," "f23,f24,f25,f26,f22,f33,f11,f62,f128,f136,f115,f152,f124,f107,f104,f105," "f140,f141,f207,f208,f209,f222", } temp_df = fetch_paginated_data(url, params) temp_df.columns = [ "排名", "-", "最新价", "涨跌幅", "涨跌额", "-", "_", "-", "换手率", "-", "-", "-", "板块代码", "-", "板块名称", "-", "-", "-", "-", "总市值", "-", "-", "-", "-", "-", "-", "-", "-", "上涨家数", "下跌家数", "-", "-", "-", "领涨股票", "-", "-", "领涨股票-涨跌幅", "-", "-", "-", "-", "-", ] temp_df = temp_df[ [ "排名", "板块名称", "板块代码", "最新价", "涨跌额", "涨跌幅", "总市值", "换手率", "上涨家数", "下跌家数", "领涨股票", "领涨股票-涨跌幅", ] ] 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["总市值"] = pd.to_numeric(temp_df["总市值"], errors="coerce") temp_df["换手率"] = pd.to_numeric(temp_df["换手率"], errors="coerce") temp_df["上涨家数"] = pd.to_numeric(temp_df["上涨家数"], errors="coerce") temp_df["下跌家数"] = pd.to_numeric(temp_df["下跌家数"], errors="coerce") temp_df["领涨股票-涨跌幅"] = pd.to_numeric( temp_df["领涨股票-涨跌幅"], errors="coerce" ) return temp_df def stock_board_industry_name_em() -> pd.DataFrame: """ 东方财富网-沪深板块-行业板块-名称 https://quote.eastmoney.com/center/boardlist.html#industry_board :return: 行业板块-名称 :rtype: pandas.DataFrame """ url = "https://17.push2.eastmoney.com/api/qt/clist/get" params = { "pn": "1", "pz": "100", "po": "1", "np": "1", "ut": "bd1d9ddb04089700cf9c27f6f7426281", "fltt": "2", "invt": "2", "fid": "f3", "fs": "m:90 t:2 f:!50", "fields": "f1,f2,f3,f4,f5,f6,f7,f8,f9,f10,f12,f13,f14,f15,f16,f17,f18,f20,f21," "f23,f24,f25,f26,f22,f33,f11,f62,f128,f136,f115,f152,f124,f107,f104,f105," "f140,f141,f207,f208,f209,f222", } temp_df = fetch_paginated_data(url, params) temp_df.columns = [ "排名", "-", "最新价", "涨跌幅", "涨跌额", "-", "_", "-", "换手率", "-", "-", "-", "板块代码", "-", "板块名称", "-", "-", "-", "-", "总市值", "-", "-", "-", "-", "-", "-", "-", "-", "上涨家数", "下跌家数", "-", "-", "-", "领涨股票", "-", "-", "领涨股票-涨跌幅", "-", "-", "-", "-", "-", ] temp_df = temp_df[ [ "排名", "板块名称", "板块代码", "最新价", "涨跌额", "涨跌幅", "总市值", "换手率", "上涨家数", "下跌家数", "领涨股票", "领涨股票-涨跌幅", ] ] 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["总市值"] = pd.to_numeric(temp_df["总市值"], errors="coerce") temp_df["换手率"] = pd.to_numeric(temp_df["换手率"], errors="coerce") temp_df["上涨家数"] = pd.to_numeric(temp_df["上涨家数"], errors="coerce") temp_df["下跌家数"] = pd.to_numeric(temp_df["下跌家数"], errors="coerce") temp_df["领涨股票-涨跌幅"] = pd.to_numeric( temp_df["领涨股票-涨跌幅"], errors="coerce" ) return temp_df def stock_board_industry_spot_em(symbol: str = "小金属") -> pd.DataFrame: """ 东方财富网-沪深板块-行业板块-实时行情 https://quote.eastmoney.com/bk/90.BK1027.html :param symbol: 板块名称 or 东财板块代码 :type symbol: str :return: 实时行情 :rtype: pandas.DataFrame """ url = "https://91.push2.eastmoney.com/api/qt/stock/get" field_map = { "f43": "最新", "f44": "最高", "f45": "最低", "f46": "开盘", "f47": "成交量", "f48": "成交额", "f170": "涨跌幅", "f171": "振幅", "f168": "换手率", "f169": "涨跌额", } if re.match(pattern=r"^BK\d+", string=symbol): em_code = symbol else: industry_listing = __stock_board_industry_name_em() em_code = industry_listing.query("板块名称 == @symbol")["板块代码"].values[0] params = dict( fields=",".join(field_map.keys()), mpi="1000", invt="2", fltt="1", secid=f"90.{em_code}", ) r = requests.get(url, params=params) data_dict = r.json() result = pd.DataFrame.from_dict(data_dict["data"], orient="index") result.rename(field_map, inplace=True) result.reset_index(inplace=True) result.columns = ["item", "value"] result["value"] = pd.to_numeric(result["value"], errors="coerce") # 各项转换成正常单位. 除了成交量与成交额, 原始数据中已是正常单位(元) result["value"] = result["value"] * 1e-2 result.iloc[4, 1] = result.iloc[4, 1] * 1e2 result.iloc[5, 1] = result.iloc[5, 1] * 1e2 return result def stock_board_industry_hist_em( symbol: str = "小金属", start_date: str = "20211201", end_date: str = "20220401", period: str = "日k", adjust: str = "", ) -> pd.DataFrame: """ 东方财富网-沪深板块-行业板块-历史行情 https://quote.eastmoney.com/bk/90.BK1027.html :param symbol: 板块名称 :type symbol: str :param start_date: 开始时间 :type start_date: str :param end_date: 结束时间 :type end_date: str :param period: 周期; choice of {"日k", "周k", "月k"} :type period: str :param adjust: choice of {'': 不复权, "qfq": 前复权, "hfq": 后复权} :type adjust: str :return: 历史行情 :rtype: pandas.DataFrame """ if re.match(pattern=r"^BK\d+", string=symbol): em_code = symbol else: industry_listing = __stock_board_industry_name_em() em_code = industry_listing.query("板块名称 == @symbol")["板块代码"].values[0] period_map = { "日k": "101", "周k": "102", "月k": "103", } adjust_map = {"": "0", "qfq": "1", "hfq": "2"} url = "http://7.push2his.eastmoney.com/api/qt/stock/kline/get" params = { "secid": f"90.{em_code}", "fields1": "f1,f2,f3,f4,f5,f6", "fields2": "f51,f52,f53,f54,f55,f56,f57,f58,f59,f60,f61", "klt": period_map[period], "fqt": adjust_map[adjust], "beg": start_date, "end": end_date, "smplmt": "10000", "lmt": "1000000", } r = requests.get(url, params=params) data_json = r.json() 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 = temp_df[ [ "日期", "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", ] ] 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["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["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["换手率"] = pd.to_numeric(temp_df["换手率"], errors="coerce") return temp_df def stock_board_industry_hist_min_em( symbol: str = "小金属", period: str = "5" ) -> pd.DataFrame: """ 东方财富网-沪深板块-行业板块-分时历史行情 https://quote.eastmoney.com/bk/90.BK1027.html :param symbol: 板块名称 :type symbol: str :param period: choice of {"1", "5", "15", "30", "60"} :type period: str :return: 分时历史行情 :rtype: pandas.DataFrame """ if re.match(pattern=r"^BK\d+", string=symbol): em_code = symbol else: industry_listing = __stock_board_industry_name_em() em_code = industry_listing.query("板块名称 == @symbol")["板块代码"].values[0] 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", "iscr": "0", "ndays": "1", "secid": f"90.{em_code}", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame( [item.split(",") for item in data_json["data"]["trends"]] ) temp_df.columns = [ "日期时间", "开盘", "收盘", "最高", "最低", "成交量", "成交额", "最新价", ] 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["最新价"] = pd.to_numeric(temp_df["最新价"], errors="coerce") return temp_df else: url = "https://7.push2his.eastmoney.com/api/qt/stock/kline/get" params = { "secid": f"90.{em_code}", "fields1": "f1,f2,f3,f4,f5,f6", "fields2": "f51,f52,f53,f54,f55,f56,f57,f58,f59,f60,f61", "klt": period, "fqt": "1", "beg": "0", "end": "20500101", "smplmt": "10000", "lmt": "1000000", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame( [item.split(",") for item in data_json["data"]["klines"]] ) temp_df.columns = [ "日期时间", "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 = temp_df[ [ "日期时间", "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", ] ] 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["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["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["换手率"] = pd.to_numeric(temp_df["换手率"], errors="coerce") return temp_df def stock_board_industry_cons_em(symbol: str = "小金属") -> pd.DataFrame: """ 东方财富网-沪深板块-行业板块-板块成份 https://data.eastmoney.com/bkzj/BK1027.html :param symbol: 板块名称或者板块代码 :type symbol: str :return: 板块成份 :rtype: pandas.DataFrame """ if re.match(pattern=r"^BK\d+", string=symbol): stock_board_code = symbol else: stock_board_concept_em_map = __stock_board_industry_name_em() stock_board_code = stock_board_concept_em_map[ stock_board_concept_em_map["板块名称"] == symbol ]["板块代码"].values[0] url = "https://29.push2.eastmoney.com/api/qt/clist/get" params = { "pn": "1", "pz": "100", "po": "1", "np": "1", "ut": "bd1d9ddb04089700cf9c27f6f7426281", "fltt": "2", "invt": "2", "fid": "f3", "fs": f"b:{stock_board_code} f:!50", "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,f62,f128,f136,f115,f152,f45", } temp_df = fetch_paginated_data(url, params) temp_df.columns = [ "序号", "_", "最新价", "涨跌幅", "涨跌额", "成交量", "成交额", "振幅", "换手率", "市盈率-动态", "_", "_", "代码", "_", "名称", "最高", "最低", "今开", "昨收", "_", "_", "_", "市净率", "_", "_", "_", "_", "_", "_", "_", "_", "_", "_", ] temp_df = temp_df[ [ "序号", "代码", "名称", "最新价", "涨跌幅", "涨跌额", "成交量", "成交额", "振幅", "最高", "最低", "今开", "昨收", "换手率", "市盈率-动态", "市净率", ] ] 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["涨跌额"] = pd.to_numeric(temp_df["涨跌额"], 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["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["今开"] = pd.to_numeric(temp_df["今开"], 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["市盈率-动态"] = pd.to_numeric(temp_df["市盈率-动态"], errors="coerce") temp_df["市净率"] = pd.to_numeric(temp_df["市净率"], errors="coerce") return temp_df if __name__ == "__main__": stock_board_industry_name_em_df = stock_board_industry_name_em() print(stock_board_industry_name_em_df) stock_board_industry_spot_em_df = stock_board_industry_spot_em(symbol="小金属") print(stock_board_industry_spot_em_df) stock_board_industry_hist_em_df = stock_board_industry_hist_em( symbol="小金属", start_date="20211201", end_date="20240222", period="日k", adjust="", ) print(stock_board_industry_hist_em_df) stock_board_industry_hist_min_em_df = stock_board_industry_hist_min_em( symbol="小金属", period="1" ) print(stock_board_industry_hist_min_em_df) stock_board_industry_cons_em_df = stock_board_industry_cons_em(symbol="互联网服务") print(stock_board_industry_cons_em_df)