#!/usr/bin/env python # -*- coding:utf-8 -*- """ Date: 2025/3/10 18:00 Desc: 东方财富网-行情首页-沪深京 A 股 https://quote.eastmoney.com/ """ import pandas as pd import requests from akshare.utils.func import fetch_paginated_data def stock_zh_a_spot_em() -> pd.DataFrame: """ 东方财富网-沪深京 A 股-实时行情 https://quote.eastmoney.com/center/gridlist.html#hs_a_board :return: 实时行情 :rtype: pandas.DataFrame """ url = "https://82.push2.eastmoney.com/api/qt/clist/get" params = { "pn": "1", "pz": "100", "po": "1", "np": "1", "ut": "bd1d9ddb04089700cf9c27f6f7426281", "fltt": "2", "invt": "2", "fid": "f12", "fs": "m:0 t:6,m:0 t:80,m:1 t:2,m:1 t:23,m:0 t:81 s:2048", "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", } temp_df = fetch_paginated_data(url, params) temp_df.columns = [ "index", "_", "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", "量比", "5分钟涨跌", "代码", "_", "名称", "最高", "最低", "今开", "昨收", "总市值", "流通市值", "涨速", "市净率", "60日涨跌幅", "年初至今涨跌幅", "-", "-", "-", "-", "-", "-", "-", ] temp_df.rename(columns={"index": "序号"}, inplace=True) 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", "量比", "换手率", "市盈率-动态", "市净率", "总市值", "流通市值", "涨速", "5分钟涨跌", "60日涨跌幅", "年初至今涨跌幅", ] ] 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["量比"] = pd.to_numeric(temp_df["量比"], 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") temp_df["总市值"] = pd.to_numeric(temp_df["总市值"], errors="coerce") temp_df["流通市值"] = pd.to_numeric(temp_df["流通市值"], errors="coerce") temp_df["涨速"] = pd.to_numeric(temp_df["涨速"], errors="coerce") temp_df["5分钟涨跌"] = pd.to_numeric(temp_df["5分钟涨跌"], errors="coerce") temp_df["60日涨跌幅"] = pd.to_numeric(temp_df["60日涨跌幅"], errors="coerce") temp_df["年初至今涨跌幅"] = pd.to_numeric( temp_df["年初至今涨跌幅"], errors="coerce" ) return temp_df def stock_sh_a_spot_em() -> pd.DataFrame: """ 东方财富网-沪 A 股-实时行情 https://quote.eastmoney.com/center/gridlist.html#hs_a_board :return: 实时行情 :rtype: pandas.DataFrame """ url = "https://82.push2.eastmoney.com/api/qt/clist/get" params = { "pn": "1", "pz": "100", "po": "1", "np": "1", "ut": "bd1d9ddb04089700cf9c27f6f7426281", "fltt": "2", "invt": "2", "fid": "f12", "fs": "m:1 t:2,m:1 t:23", "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", } 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", "量比", "5分钟涨跌", "代码", "_", "名称", "最高", "最低", "今开", "昨收", "总市值", "流通市值", "涨速", "市净率", "60日涨跌幅", "年初至今涨跌幅", "-", "-", "-", "-", "-", "-", "-", ] 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", "量比", "换手率", "市盈率-动态", "市净率", "总市值", "流通市值", "涨速", "5分钟涨跌", "60日涨跌幅", "年初至今涨跌幅", ] ] 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["量比"] = pd.to_numeric(temp_df["量比"], 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") temp_df["总市值"] = pd.to_numeric(temp_df["总市值"], errors="coerce") temp_df["流通市值"] = pd.to_numeric(temp_df["流通市值"], errors="coerce") temp_df["涨速"] = pd.to_numeric(temp_df["涨速"], errors="coerce") temp_df["5分钟涨跌"] = pd.to_numeric(temp_df["5分钟涨跌"], errors="coerce") temp_df["60日涨跌幅"] = pd.to_numeric(temp_df["60日涨跌幅"], errors="coerce") temp_df["年初至今涨跌幅"] = pd.to_numeric( temp_df["年初至今涨跌幅"], errors="coerce" ) return temp_df def stock_sz_a_spot_em() -> pd.DataFrame: """ 东方财富网-深 A 股-实时行情 https://quote.eastmoney.com/center/gridlist.html#hs_a_board :return: 实时行情 :rtype: pandas.DataFrame """ url = "https://82.push2.eastmoney.com/api/qt/clist/get" params = { "pn": "1", "pz": "100", "po": "1", "np": "1", "ut": "bd1d9ddb04089700cf9c27f6f7426281", "fltt": "2", "invt": "2", "fid": "f12", "fs": "m:0 t:6,m:0 t:80", "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", } 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", "量比", "5分钟涨跌", "代码", "_", "名称", "最高", "最低", "今开", "昨收", "总市值", "流通市值", "涨速", "市净率", "60日涨跌幅", "年初至今涨跌幅", "-", "-", "-", "-", "-", "-", "-", ] 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", "量比", "换手率", "市盈率-动态", "市净率", "总市值", "流通市值", "涨速", "5分钟涨跌", "60日涨跌幅", "年初至今涨跌幅", ] ] 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["量比"] = pd.to_numeric(temp_df["量比"], 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") temp_df["总市值"] = pd.to_numeric(temp_df["总市值"], errors="coerce") temp_df["流通市值"] = pd.to_numeric(temp_df["流通市值"], errors="coerce") temp_df["涨速"] = pd.to_numeric(temp_df["涨速"], errors="coerce") temp_df["5分钟涨跌"] = pd.to_numeric(temp_df["5分钟涨跌"], errors="coerce") temp_df["60日涨跌幅"] = pd.to_numeric(temp_df["60日涨跌幅"], errors="coerce") temp_df["年初至今涨跌幅"] = pd.to_numeric( temp_df["年初至今涨跌幅"], errors="coerce" ) return temp_df def stock_bj_a_spot_em() -> pd.DataFrame: """ 东方财富网-京 A 股-实时行情 https://quote.eastmoney.com/center/gridlist.html#hs_a_board :return: 实时行情 :rtype: pandas.DataFrame """ url = "https://82.push2.eastmoney.com/api/qt/clist/get" params = { "pn": "1", "pz": "100", "po": "1", "np": "1", "ut": "bd1d9ddb04089700cf9c27f6f7426281", "fltt": "2", "invt": "2", "fid": "f12", "fs": "m:0 t:81 s:2048", "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", } 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", "量比", "5分钟涨跌", "代码", "_", "名称", "最高", "最低", "今开", "昨收", "总市值", "流通市值", "涨速", "市净率", "60日涨跌幅", "年初至今涨跌幅", "-", "-", "-", "-", "-", "-", "-", ] 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", "量比", "换手率", "市盈率-动态", "市净率", "总市值", "流通市值", "涨速", "5分钟涨跌", "60日涨跌幅", "年初至今涨跌幅", ] ] 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["量比"] = pd.to_numeric(temp_df["量比"], 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") temp_df["总市值"] = pd.to_numeric(temp_df["总市值"], errors="coerce") temp_df["流通市值"] = pd.to_numeric(temp_df["流通市值"], errors="coerce") temp_df["涨速"] = pd.to_numeric(temp_df["涨速"], errors="coerce") temp_df["5分钟涨跌"] = pd.to_numeric(temp_df["5分钟涨跌"], errors="coerce") temp_df["60日涨跌幅"] = pd.to_numeric(temp_df["60日涨跌幅"], errors="coerce") temp_df["年初至今涨跌幅"] = pd.to_numeric( temp_df["年初至今涨跌幅"], errors="coerce" ) return temp_df def stock_new_a_spot_em() -> pd.DataFrame: """ 东方财富网-新股-实时行情 https://quote.eastmoney.com/center/gridlist.html#newshares :return: 实时行情 :rtype: pandas.DataFrame """ url = "https://82.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": "f26", "fs": "m:0 f:8,m:1 f:8", "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,f11,f62,f128,f136,f115,f152", } 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", "量比", "5分钟涨跌", "代码", "_", "名称", "最高", "最低", "今开", "昨收", "总市值", "流通市值", "涨速", "市净率", "60日涨跌幅", "年初至今涨跌幅", "上市日期", "-", "-", "-", "-", "-", "-", "-", ] 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", "量比", "换手率", "市盈率-动态", "市净率", "上市日期", "总市值", "流通市值", "涨速", "5分钟涨跌", "60日涨跌幅", "年初至今涨跌幅", ] ] 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["量比"] = pd.to_numeric(temp_df["量比"], 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") temp_df["总市值"] = pd.to_numeric(temp_df["总市值"], errors="coerce") temp_df["流通市值"] = pd.to_numeric(temp_df["流通市值"], errors="coerce") temp_df["涨速"] = pd.to_numeric(temp_df["涨速"], errors="coerce") temp_df["5分钟涨跌"] = pd.to_numeric(temp_df["5分钟涨跌"], errors="coerce") temp_df["60日涨跌幅"] = pd.to_numeric(temp_df["60日涨跌幅"], errors="coerce") temp_df["年初至今涨跌幅"] = pd.to_numeric( temp_df["年初至今涨跌幅"], errors="coerce" ) temp_df["上市日期"] = pd.to_datetime(temp_df["上市日期"], format="%Y%m%d").dt.date return temp_df def stock_cy_a_spot_em() -> pd.DataFrame: """ 东方财富网-创业板-实时行情 https://quote.eastmoney.com/center/gridlist.html#gem_board :return: 实时行情 :rtype: pandas.DataFrame """ url = "https://7.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": "m:0 t:80", "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", } 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", "量比", "5分钟涨跌", "代码", "_", "名称", "最高", "最低", "今开", "昨收", "总市值", "流通市值", "涨速", "市净率", "60日涨跌幅", "年初至今涨跌幅", "-", "-", "-", "-", "-", "-", "-", ] 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", "量比", "换手率", "市盈率-动态", "市净率", "总市值", "流通市值", "涨速", "5分钟涨跌", "60日涨跌幅", "年初至今涨跌幅", ] ] 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["量比"] = pd.to_numeric(temp_df["量比"], 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") temp_df["总市值"] = pd.to_numeric(temp_df["总市值"], errors="coerce") temp_df["流通市值"] = pd.to_numeric(temp_df["流通市值"], errors="coerce") temp_df["涨速"] = pd.to_numeric(temp_df["涨速"], errors="coerce") temp_df["5分钟涨跌"] = pd.to_numeric(temp_df["5分钟涨跌"], errors="coerce") temp_df["60日涨跌幅"] = pd.to_numeric(temp_df["60日涨跌幅"], errors="coerce") temp_df["年初至今涨跌幅"] = pd.to_numeric( temp_df["年初至今涨跌幅"], errors="coerce" ) return temp_df def stock_kc_a_spot_em() -> pd.DataFrame: """ 东方财富网-科创板-实时行情 https://quote.eastmoney.com/center/gridlist.html#kcb_board :return: 实时行情 :rtype: pandas.DataFrame """ url = "https://7.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": "m:1 t:23", "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", } 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", "量比", "5分钟涨跌", "代码", "_", "名称", "最高", "最低", "今开", "昨收", "总市值", "流通市值", "涨速", "市净率", "60日涨跌幅", "年初至今涨跌幅", "-", "-", "-", "-", "-", "-", "-", ] 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", "量比", "换手率", "市盈率-动态", "市净率", "总市值", "流通市值", "涨速", "5分钟涨跌", "60日涨跌幅", "年初至今涨跌幅", ] ] 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["量比"] = pd.to_numeric(temp_df["量比"], 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") temp_df["总市值"] = pd.to_numeric(temp_df["总市值"], errors="coerce") temp_df["流通市值"] = pd.to_numeric(temp_df["流通市值"], errors="coerce") temp_df["涨速"] = pd.to_numeric(temp_df["涨速"], errors="coerce") temp_df["5分钟涨跌"] = pd.to_numeric(temp_df["5分钟涨跌"], errors="coerce") temp_df["60日涨跌幅"] = pd.to_numeric(temp_df["60日涨跌幅"], errors="coerce") temp_df["年初至今涨跌幅"] = pd.to_numeric( temp_df["年初至今涨跌幅"], errors="coerce" ) return temp_df def stock_zh_ab_comparison_em() -> pd.DataFrame: """ 东方财富网-行情中心-沪深京个股-AB股比价-全部AB股比价 https://quote.eastmoney.com/center/gridlist.html#ab_comparison :return: 实时行情 :rtype: pandas.DataFrame """ url = "https://push2.eastmoney.com/api/qt/clist/get" params = { "np": "1", "fltt": "1", "invt": "2", "fs": "m:1+b:BK0498,m:0+b:BK0498", "fields": "f201,f202,f203,f196,f200,f197,f152,f12,f13,f14,f1,f2,f4,f3,f199", "fid": "f199", "pn": "1", "pz": "100", "po": "1", "dect": "1", "wbp2u": "|0|0|0|web", } columns_map = { "index": "序号", "f1": "-", "f2": "最新价B", "f3": "涨跌幅B", "f4": "-", "f12": "A股代码", "f13": "-", "f14": "A股名称", "f152": "-", "f196": "最新价A", "f197": "涨跌幅A", "f199": "比价", "f200": "-", "f201": "B股代码", "f202": "-", "f203": "B股名称", } temp_df = fetch_paginated_data(url, params) temp_df = temp_df.rename(columns=columns_map) list_name = [value for key, value in columns_map.items() if value != "_"] temp_df = temp_df[list_name] temp_df = temp_df[ [ "序号", "B股代码", "B股名称", "最新价B", "涨跌幅B", "A股代码", "A股名称", "最新价A", "涨跌幅A", "比价", ] ] temp_df["最新价B"] = pd.to_numeric(temp_df["最新价B"], errors="coerce") / 100 temp_df["涨跌幅B"] = pd.to_numeric(temp_df["涨跌幅B"], errors="coerce") / 100 temp_df["最新价A"] = pd.to_numeric(temp_df["最新价A"], errors="coerce") / 100 temp_df["涨跌幅A"] = pd.to_numeric(temp_df["涨跌幅A"], errors="coerce") / 100 temp_df["比价"] = pd.to_numeric(temp_df["比价"], errors="coerce") / 100 return temp_df def stock_zh_b_spot_em() -> pd.DataFrame: """ 东方财富网- B 股-实时行情 https://quote.eastmoney.com/center/gridlist.html#hs_a_board :return: 实时行情 :rtype: pandas.DataFrame """ url = "https://28.push2.eastmoney.com/api/qt/clist/get" params = { "pn": "1", "pz": "100", "po": "1", "np": "1", "ut": "bd1d9ddb04089700cf9c27f6f7426281", "fltt": "2", "invt": "2", "fid": "f12", "fs": "m:0 t:7,m:1 t:3", "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", } 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", "量比", "5分钟涨跌", "代码", "_", "名称", "最高", "最低", "今开", "昨收", "总市值", "流通市值", "涨速", "市净率", "60日涨跌幅", "年初至今涨跌幅", "-", "-", "-", "-", "-", "-", "-", ] 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", "量比", "换手率", "市盈率-动态", "市净率", "总市值", "流通市值", "涨速", "5分钟涨跌", "60日涨跌幅", "年初至今涨跌幅", ] ] 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["量比"] = pd.to_numeric(temp_df["量比"], 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") temp_df["总市值"] = pd.to_numeric(temp_df["总市值"], errors="coerce") temp_df["流通市值"] = pd.to_numeric(temp_df["流通市值"], errors="coerce") temp_df["涨速"] = pd.to_numeric(temp_df["涨速"], errors="coerce") temp_df["5分钟涨跌"] = pd.to_numeric(temp_df["5分钟涨跌"], errors="coerce") temp_df["60日涨跌幅"] = pd.to_numeric(temp_df["60日涨跌幅"], errors="coerce") temp_df["年初至今涨跌幅"] = pd.to_numeric( temp_df["年初至今涨跌幅"], errors="coerce" ) return temp_df def stock_zh_a_hist( symbol: str = "000001", period: str = "daily", start_date: str = "19700101", end_date: str = "20500101", adjust: str = "", timeout: float = None, ) -> pd.DataFrame: """ 东方财富网-行情首页-沪深京 A 股-每日行情 https://quote.eastmoney.com/concept/sh603777.html?from=classic :param symbol: 股票代码 :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 :param timeout: choice of None or a positive float number :type timeout: float :return: 每日行情 :rtype: pandas.DataFrame """ market_code = 1 if symbol.startswith("6") else 0 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], "secid": f"{market_code}.{symbol}", "beg": start_date, "end": end_date, } r = requests.get(url, params=params, timeout=timeout) 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["股票代码"] = symbol 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", "股票代码", ] temp_df["日期"] = pd.to_datetime(temp_df["日期"], errors="coerce").dt.date 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 = temp_df[ [ "日期", "股票代码", "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", ] ] return temp_df def stock_zh_a_hist_min_em( symbol: str = "000001", 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: """ 东方财富网-行情首页-沪深京 A 股-每日分时行情 https://quote.eastmoney.com/concept/sh603777.html?from=classic :param symbol: 股票代码 :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 """ market_code = 1 if symbol.startswith("6") else 0 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"{market_code}.{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"{market_code}.{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 def stock_zh_a_hist_pre_min_em( symbol: str = "000001", start_time: str = "09:00:00", end_time: str = "15:50:00", ) -> pd.DataFrame: """ 东方财富网-行情首页-沪深京 A 股-每日分时行情包含盘前数据 https://quote.eastmoney.com/concept/sh603777.html?from=classic :param symbol: 股票代码 :type symbol: str :param start_time: 开始时间 :type start_time: str :param end_time: 结束时间 :type end_time: str :return: 每日分时行情包含盘前数据 :rtype: pandas.DataFrame """ market_code = 1 if symbol.startswith("6") else 0 url = "https://push2.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", "ndays": "1", "iscr": "1", "iscca": "0", "secid": f"{market_code}.{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", "最新价", ] temp_df.index = pd.to_datetime(temp_df["时间"]) date_format = temp_df.index[0].date().isoformat() temp_df = temp_df[date_format + " " + start_time : date_format + " " + end_time] 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["最新价"] = pd.to_numeric(temp_df["最新价"], errors="coerce") temp_df["时间"] = pd.to_datetime(temp_df["时间"]).astype(str) return temp_df def stock_hk_spot_em() -> pd.DataFrame: """ 东方财富网-港股-实时行情 https://quote.eastmoney.com/center/gridlist.html#hk_stocks :return: 港股-实时行情 :rtype: pandas.DataFrame """ url = "https://72.push2.eastmoney.com/api/qt/clist/get" params = { "pn": "1", "pz": "100", "po": "1", "np": "1", "ut": "bd1d9ddb04089700cf9c27f6f7426281", "fltt": "2", "invt": "2", "fid": "f12", "fs": "m:128 t:3,m:128 t:4,m:128 t:1,m:128 t:2", "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", } 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", "量比", "_", "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", "涨跌额", "涨跌幅", "今开", "最高", "最低", "昨收", "成交量", "成交额", ] ] temp_df["序号"] = pd.to_numeric(temp_df["序号"], errors="coerce") 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["今开"] = pd.to_numeric(temp_df["今开"], 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["昨收"] = pd.to_numeric(temp_df["昨收"], errors="coerce") temp_df["成交量"] = pd.to_numeric(temp_df["成交量"], errors="coerce") temp_df["成交额"] = pd.to_numeric(temp_df["成交额"], errors="coerce") return temp_df def stock_hk_main_board_spot_em() -> pd.DataFrame: """ 东方财富网-港股-主板-实时行情 https://quote.eastmoney.com/center/gridlist.html#hk_mainboard :return: 港股-主板-实时行情 :rtype: pandas.DataFrame """ url = "https://81.push2.eastmoney.com/api/qt/clist/get" params = { "pn": "1", "pz": "100", "po": "1", "np": "1", "ut": "bd1d9ddb04089700cf9c27f6f7426281", "fltt": "2", "invt": "2", "fid": "f12", "fs": "m:128 t:3", "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", } 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", "量比", "_", "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", "涨跌额", "涨跌幅", "今开", "最高", "最低", "昨收", "成交量", "成交额", ] ] temp_df["序号"] = pd.to_numeric(temp_df["序号"], errors="coerce") 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["今开"] = pd.to_numeric(temp_df["今开"], 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["昨收"] = pd.to_numeric(temp_df["昨收"], errors="coerce") temp_df["成交量"] = pd.to_numeric(temp_df["成交量"], errors="coerce") temp_df["成交额"] = pd.to_numeric(temp_df["成交额"], errors="coerce") return temp_df def stock_hk_hist( symbol: str = "00593", period: str = "daily", start_date: str = "19700101", end_date: str = "22220101", adjust: str = "", ) -> pd.DataFrame: """ 东方财富网-行情-港股-每日行情 https://quote.eastmoney.com/hk/08367.html :param symbol: 港股-每日行情 :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": "1", "hfq": "2", "": "不复权"} :type adjust: str :return: 每日行情 :rtype: pandas.DataFrame """ adjust_dict = {"qfq": "1", "hfq": "2", "": "0"} period_dict = {"daily": "101", "weekly": "102", "monthly": "103"} url = "https://33.push2his.eastmoney.com/api/qt/stock/kline/get" params = { "secid": f"116.{symbol}", "fields1": "f1,f2,f3,f4,f5,f6", "fields2": "f51,f52,f53,f54,f55,f56,f57,f58,f59,f60,f61", "klt": period_dict[period], "fqt": adjust_dict[adjust], "end": "20500000", "lmt": "1000000", } 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"]]) if temp_df.empty: return pd.DataFrame() 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 = temp_df[start_date:end_date] if temp_df.empty: return pd.DataFrame() 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") temp_df["日期"] = pd.to_datetime(temp_df["日期"], errors="coerce").dt.date return temp_df def stock_hk_hist_min_em( symbol: str = "01611", period: str = "1", adjust: str = "", start_date: str = "1979-09-01 09:32:00", end_date: str = "2222-01-01 09:32:00", ) -> pd.DataFrame: """ 东方财富网-行情-港股-每日分时行情 https://quote.eastmoney.com/hk/00948.html :param symbol: 股票代码 :type symbol: str :param period: choice of {'1', '5', '15', '30', '60'} :type period: str :param adjust: choice of {'', 'qfq', 'hfq'} :type adjust: str :param start_date: 开始日期 :type start_date: str :param end_date: 结束日期 :type end_date: str :return: 每日分时行情 :rtype: pandas.DataFrame """ 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", "iscr": "0", "ndays": "5", "secid": f"116.{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", "最新价", ] 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["最新价"] = pd.to_numeric(temp_df["最新价"], 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": "bd1d9ddb04089700cf9c27f6f7426281", "klt": period, "fqt": adjust_map[adjust], "secid": f"116.{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 def stock_us_spot_em() -> pd.DataFrame: """ 东方财富网-美股-实时行情 https://quote.eastmoney.com/center/gridlist.html#us_stocks :return: 美股-实时行情; 延迟 15 min :rtype: pandas.DataFrame """ url = "https://72.push2.eastmoney.com/api/qt/clist/get" params = { "pn": "1", "pz": "100", "po": "1", "np": "1", "ut": "bd1d9ddb04089700cf9c27f6f7426281", "fltt": "2", "invt": "2", "fid": "f12", "fs": "m:105,m:106,m:107", "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", } 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", "_", "_", "_", "简称", "编码", "名称", "最高价", "最低价", "开盘价", "昨收价", "总市值", "_", "_", "_", "_", "_", "_", "_", "_", "市盈率", "_", "_", "_", "_", "_", ] temp_df["代码"] = temp_df["编码"].astype(str) + "." + temp_df["简称"] temp_df = temp_df[ [ "序号", "名称", "最新价", "涨跌额", "涨跌幅", "开盘价", "最高价", "最低价", "昨收价", "总市值", "市盈率", "成交量", "成交额", "振幅", "换手率", "代码", ] ] 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["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["昨收价"] = pd.to_numeric(temp_df["昨收价"], errors="coerce") temp_df["总市值"] = pd.to_numeric(temp_df["总市值"], errors="coerce") temp_df["市盈率"] = pd.to_numeric(temp_df["市盈率"], errors="coerce") temp_df["成交量"] = pd.to_numeric(temp_df["成交量"], errors="coerce") temp_df["成交额"] = pd.to_numeric(temp_df["成交额"], errors="coerce") temp_df["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_us_hist( symbol: str = "105.MSFT", period: str = "daily", start_date: str = "19700101", end_date: str = "22220101", adjust: str = "", ) -> pd.DataFrame: """ 东方财富网-行情-美股-每日行情 https://quote.eastmoney.com/us/ENTX.html#fullScreenChart :param symbol: 股票代码; 此股票代码需要通过调用 ak.stock_us_spot_em() 的 `代码` 字段获取 :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": "1", "hfq": "2", "": "不复权"} :type adjust: str :return: 每日行情 :rtype: pandas.DataFrame """ period_dict = {"daily": "101", "weekly": "102", "monthly": "103"} adjust_dict = {"qfq": "1", "hfq": "2", "": "0"} url = "https://63.push2his.eastmoney.com/api/qt/stock/kline/get" params = { "secid": f"{symbol}", "fields1": "f1,f2,f3,f4,f5,f6", "fields2": "f51,f52,f53,f54,f55,f56,f57,f58,f59,f60,f61", "klt": period_dict[period], "fqt": adjust_dict[adjust], "end": "20500000", "lmt": "1000000", } r = requests.get(url, timeout=15, params=params) data_json = r.json() if not 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 = temp_df[start_date:end_date] 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") temp_df.sort_values(["日期"], inplace=True, ignore_index=True) return temp_df def stock_us_hist_min_em( symbol: str = "105.ATER", start_date: str = "1979-09-01 09:32:00", end_date: str = "2222-01-01 09:32:00", ) -> pd.DataFrame: """ 东方财富网-行情首页-美股-每日分时行情 https://quote.eastmoney.com/us/ATER.html :param symbol: 股票代码 :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 :return: 每日分时行情 :rtype: pandas.DataFrame """ 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": "5", "secid": f"{symbol.split('.')[0]}.{symbol.split('.')[1]}", } r = requests.get(url, params=params, timeout=15) data_json = r.json() if not data_json["data"]["trends"]: return pd.DataFrame() 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", "最新价", ] temp_df.index = pd.to_datetime(temp_df["时间"], errors="coerce") 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["最新价"] = pd.to_numeric(temp_df["最新价"], errors="coerce") temp_df["时间"] = pd.to_datetime(temp_df["时间"], errors="coerce").astype(str) return temp_df if __name__ == "__main__": stock_zh_a_spot_em_df = stock_zh_a_spot_em() print(stock_zh_a_spot_em_df) stock_sh_a_spot_em_df = stock_sh_a_spot_em() print(stock_sh_a_spot_em_df) stock_sz_a_spot_em_df = stock_sz_a_spot_em() print(stock_sz_a_spot_em_df) stock_bj_a_spot_em_df = stock_bj_a_spot_em() print(stock_bj_a_spot_em_df) stock_new_a_spot_em_df = stock_new_a_spot_em() print(stock_new_a_spot_em_df) stock_cy_a_spot_em_df = stock_cy_a_spot_em() print(stock_cy_a_spot_em_df) stock_kc_a_spot_em_df = stock_kc_a_spot_em() print(stock_kc_a_spot_em_df) stock_zh_b_spot_em_df = stock_zh_b_spot_em() print(stock_zh_b_spot_em_df) stock_hk_spot_em_df = stock_hk_spot_em() print(stock_hk_spot_em_df) stock_hk_main_board_spot_em_df = stock_hk_main_board_spot_em() print(stock_hk_main_board_spot_em_df) stock_zh_a_hist_df = stock_zh_a_hist( symbol="600734", period="daily", start_date="20050501", end_date="20250304", adjust="hfq", ) print(stock_zh_a_hist_df) stock_zh_a_hist_min_em_df = stock_zh_a_hist_min_em(symbol="603777", period="1") print(stock_zh_a_hist_min_em_df) stock_zh_a_hist_pre_min_em_df = stock_zh_a_hist_pre_min_em(symbol="000001") print(stock_zh_a_hist_pre_min_em_df) stock_hk_hist_df = stock_hk_hist( symbol="00593", period="daily", start_date="19700101", end_date="22220101", adjust="", ) print(stock_hk_hist_df) stock_hk_hist_qfq_df = stock_hk_hist( symbol="00593", period="daily", start_date="19700101", end_date="22220101", adjust="qfq", ) print(stock_hk_hist_qfq_df) stock_hk_hist_hfq_df = stock_hk_hist( symbol="00593", period="daily", start_date="19700101", end_date="22220101", adjust="hfq", ) print(stock_hk_hist_hfq_df) stock_us_spot_em_df = stock_us_spot_em() print(stock_us_spot_em_df) stock_us_hist_df = stock_us_hist( symbol="106.TTE", period="daily", start_date="20200101", end_date="20240414", adjust="qfq", ) print(stock_us_hist_df) stock_zh_a_spot_em_df = stock_zh_a_spot_em() print(stock_zh_a_spot_em_df) stock_hk_hist_min_em_df = stock_hk_hist_min_em(symbol="01611") print(stock_hk_hist_min_em_df) stock_us_spot_em_df = stock_us_spot_em() print(stock_us_spot_em_df) stock_us_hist_min_em_df = stock_us_hist_min_em(symbol="106.TTE") print(stock_us_hist_min_em_df) stock_us_hist_min_em_df = stock_us_hist_min_em(symbol="105.ATER") print(stock_us_hist_min_em_df) stock_zh_a_hist_min_em_df = stock_zh_a_hist_min_em( symbol="300364", start_date="2025-03-07 09:30:00", end_date="2025-03-07 15:00:00", period="5", adjust="hfq", ) print(stock_zh_a_hist_min_em_df) stock_zh_a_hist_df = stock_zh_a_hist( symbol="833454", period="daily", start_date="20170301", end_date="20241115", adjust="hfq", ) print(stock_zh_a_hist_df) stock_hk_hist_min_em_df = stock_hk_hist_min_em( symbol="01611", period="1", adjust="", start_date="2025-03-07 09:30:00", end_date="2025-03-07 18:32:00", ) print(stock_hk_hist_min_em_df) stock_zh_b_spot_em_df = stock_zh_b_spot_em() print(stock_zh_b_spot_em_df)