#!/usr/bin/env python # -*- coding:utf-8 -*- """ Date: 2025/11/12 15:20 Desc: 宏观数据-中国 """ import datetime import json import math import ssl import time import pandas as pd import requests from requests.adapters import HTTPAdapter from urllib3.poolmanager import PoolManager from akshare.economic.cons import ( JS_CHINA_ENERGY_DAILY_URL, ) from akshare.utils import demjson from akshare.utils.tqdm import get_tqdm class TLSAdapter(HTTPAdapter): def init_poolmanager(self, connections, maxsize, block=False): ctx = ssl.create_default_context() # 降低安全级别以兼容旧服务器 ctx.set_ciphers("DEFAULT@SECLEVEL=1") # 禁用不安全的协议 ctx.options |= ssl.OP_NO_SSLv2 ctx.options |= ssl.OP_NO_SSLv3 # 指定使用 TLSv1.2 self.poolmanager = PoolManager( num_pools=connections, maxsize=maxsize, block=block, ssl_version=ssl.PROTOCOL_TLSv1_2, ssl_context=ctx, ) def __macro_china_base_func(symbol: str, params: dict) -> pd.DataFrame: """ 金十数据中心-经济指标-美国-基础函数 https://datacenter.jin10.com/economic :return: 美国经济指标数据 :rtype: pandas.DataFrame """ import warnings warnings.filterwarnings(action="ignore", category=FutureWarning) headers = { "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) " "Chrome/107.0.0.0 Safari/537.36", "x-app-id": "rU6QIu7JHe2gOUeR", "x-csrf-token": "x-csrf-token", "x-version": "1.0.0", } url = "https://datacenter-api.jin10.com/reports/list_v2" params = params big_df = pd.DataFrame() while True: r = requests.get(url, params=params, headers=headers) data_json = r.json() if not data_json["data"]["values"]: break temp_df = pd.DataFrame(data_json["data"]["values"]) big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True) last_date_str = temp_df.iat[-1, 0] last_date_str = ( ( datetime.datetime.strptime(last_date_str, "%Y-%m-%d") - datetime.timedelta(days=1) ) .date() .isoformat() ) params.update({"max_date": f"{last_date_str}"}) big_df.columns = [ "日期", "今值", "预测值", "前值", ] big_df["商品"] = symbol big_df = big_df[ [ "商品", "日期", "今值", "预测值", "前值", ] ] big_df["日期"] = pd.to_datetime(big_df["日期"], errors="coerce").dt.date big_df["今值"] = pd.to_numeric(big_df["今值"], errors="coerce") big_df["预测值"] = pd.to_numeric(big_df["预测值"], errors="coerce") big_df["前值"] = pd.to_numeric(big_df["前值"], errors="coerce") big_df.sort_values(["日期"], inplace=True) big_df.reset_index(inplace=True, drop=True) return big_df # 企业商品价格指数 def macro_china_qyspjg() -> pd.DataFrame: """ 东方财富-经济数据一览-中国-企业商品价格指数 https://data.eastmoney.com/cjsj/qyspjg.html :return: 企业商品价格指数 :rtype: pandas.DataFrame """ url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "columns": "REPORT_DATE,TIME,BASE,BASE_SAME,BASE_SEQUENTIAL,FARM_BASE,FARM_BASE_SAME," "FARM_BASE_SEQUENTIAL,MINERAL_BASE,MINERAL_BASE_SAME,MINERAL_BASE_SEQUENTIAL," "ENERGY_BASE,ENERGY_BASE_SAME,ENERGY_BASE_SEQUENTIAL", "pageNumber": "1", "pageSize": "2000", "sortColumns": "REPORT_DATE", "sortTypes": "-1", "source": "WEB", "client": "WEB", "reportName": "RPT_ECONOMY_GOODS_INDEX", "p": "1", "pageNo": "1", "pageNum": "1", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) temp_df.rename( columns={ "REPORT_DATE": "-", "TIME": "月份", "BASE": "总指数-指数值", "BASE_SAME": "总指数-同比增长", "BASE_SEQUENTIAL": "总指数-环比增长", "FARM_BASE": "农产品-指数值", "FARM_BASE_SAME": "农产品-同比增长", "FARM_BASE_SEQUENTIAL": "农产品-环比增长", "MINERAL_BASE": "矿产品-指数值", "MINERAL_BASE_SAME": "矿产品-同比增长", "MINERAL_BASE_SEQUENTIAL": "矿产品-环比增长", "ENERGY_BASE": "煤油电-指数值", "ENERGY_BASE_SAME": "煤油电-同比增长", "ENERGY_BASE_SEQUENTIAL": "煤油电-环比增长", }, inplace=True, ) temp_df = temp_df[ [ "月份", "总指数-指数值", "总指数-同比增长", "总指数-环比增长", "农产品-指数值", "农产品-同比增长", "农产品-环比增长", "矿产品-指数值", "矿产品-同比增长", "矿产品-环比增长", "煤油电-指数值", "煤油电-同比增长", "煤油电-环比增长", ] ] temp_df["总指数-指数值"] = pd.to_numeric(temp_df["总指数-指数值"], errors="coerce") temp_df["总指数-同比增长"] = pd.to_numeric( temp_df["总指数-同比增长"], errors="coerce" ) temp_df["总指数-环比增长"] = pd.to_numeric( temp_df["总指数-环比增长"], errors="coerce" ) temp_df["农产品-指数值"] = pd.to_numeric(temp_df["农产品-指数值"], errors="coerce") temp_df["农产品-同比增长"] = pd.to_numeric( temp_df["农产品-同比增长"], errors="coerce" ) temp_df["农产品-环比增长"] = pd.to_numeric( temp_df["农产品-环比增长"], errors="coerce" ) temp_df["矿产品-指数值"] = pd.to_numeric(temp_df["矿产品-指数值"], errors="coerce") temp_df["矿产品-同比增长"] = pd.to_numeric( temp_df["矿产品-同比增长"], errors="coerce" ) temp_df["矿产品-环比增长"] = pd.to_numeric( temp_df["矿产品-环比增长"], errors="coerce" ) temp_df["煤油电-指数值"] = pd.to_numeric(temp_df["煤油电-指数值"], errors="coerce") temp_df["煤油电-同比增长"] = pd.to_numeric( temp_df["煤油电-同比增长"], errors="coerce" ) temp_df["煤油电-环比增长"] = pd.to_numeric( temp_df["煤油电-环比增长"], errors="coerce" ) return temp_df # 外商直接投资数据 def macro_china_fdi() -> pd.DataFrame: """ 东方财富-经济数据一览-中国-外商直接投资数据 https://data.eastmoney.com/cjsj/fdi.html :return: 外商直接投资数据 :rtype: pandas.DataFrame """ url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "columns": "REPORT_DATE,TIME,ACTUAL_FOREIGN,ACTUAL_FOREIGN_SAME,ACTUAL_FOREIGN_SEQUENTIAL," "ACTUAL_FOREIGN_ACCUMULATE,FOREIGN_ACCUMULATE_SAME", "pageNumber": "1", "pageSize": "2000", "sortColumns": "REPORT_DATE", "sortTypes": "-1", "source": "WEB", "client": "WEB", "reportName": "RPT_ECONOMY_FDI", "p": "1", "pageNo": "1", "pageNum": "1", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) temp_df.columns = [ "-", "月份", "当月", "当月-同比增长", "当月-环比增长", "累计", "累计-同比增长", ] temp_df = temp_df[ [ "月份", "当月", "当月-同比增长", "当月-环比增长", "累计", "累计-同比增长", ] ] temp_df["当月"] = pd.to_numeric(temp_df["当月"], errors="coerce") temp_df["当月-同比增长"] = pd.to_numeric(temp_df["当月-同比增长"], errors="coerce") temp_df["当月-环比增长"] = pd.to_numeric(temp_df["当月-环比增长"], errors="coerce") temp_df["累计"] = pd.to_numeric(temp_df["累计"], errors="coerce") temp_df["累计-同比增长"] = pd.to_numeric(temp_df["累计-同比增长"], errors="coerce") temp_df.sort_values(["月份"], ignore_index=True, inplace=True) return temp_df # 中国社会融资规模数据 def macro_china_shrzgm() -> pd.DataFrame: """ 商务数据中心-国内贸易-社会融资规模增量统计 https://data.mofcom.gov.cn/gnmy/shrzgm.shtml :return: 社会融资规模增量统计 :rtype: pandas.DataFrame """ session = requests.Session() session.mount(prefix="https://", adapter=TLSAdapter()) url = "https://data.mofcom.gov.cn/datamofcom/front/gnmy/shrzgmQuery" r = session.post(url) data_json = r.json() temp_df = pd.DataFrame(data_json) temp_df.columns = [ "月份", "其中-未贴现银行承兑汇票", "其中-委托贷款", "其中-委托贷款外币贷款", "其中-人民币贷款", "其中-企业债券", "社会融资规模增量", "其中-非金融企业境内股票融资", "其中-信托贷款", ] temp_df = temp_df[ [ "月份", "社会融资规模增量", "其中-人民币贷款", "其中-委托贷款外币贷款", "其中-委托贷款", "其中-信托贷款", "其中-未贴现银行承兑汇票", "其中-企业债券", "其中-非金融企业境内股票融资", ] ] temp_df["社会融资规模增量"] = pd.to_numeric( temp_df["社会融资规模增量"], errors="coerce" ) temp_df["其中-人民币贷款"] = pd.to_numeric( temp_df["其中-人民币贷款"], errors="coerce" ) temp_df["其中-委托贷款外币贷款"] = pd.to_numeric( temp_df["其中-委托贷款外币贷款"], errors="coerce" ) temp_df["其中-委托贷款"] = pd.to_numeric(temp_df["其中-委托贷款"], errors="coerce") temp_df["其中-信托贷款"] = pd.to_numeric(temp_df["其中-信托贷款"], errors="coerce") temp_df["其中-未贴现银行承兑汇票"] = pd.to_numeric( temp_df["其中-未贴现银行承兑汇票"], errors="coerce" ) temp_df["其中-企业债券"] = pd.to_numeric(temp_df["其中-企业债券"], errors="coerce") temp_df["其中-非金融企业境内股票融资"] = pd.to_numeric( temp_df["其中-非金融企业境内股票融资"], errors="coerce" ) temp_df.sort_values(["月份"], inplace=True) temp_df.reset_index(drop=True, inplace=True) return temp_df def macro_china_urban_unemployment() -> pd.DataFrame: """ 国家统计局-月度数据-城镇调查失业率 https://data.stats.gov.cn/dg/website/page.html#/pc/national/monthData :return: 城镇调查失业率 :rtype: pandas.DataFrame """ url = "https://data.stats.gov.cn/dg/website/publicrelease/web/external/getEsDataByCidAndDt" headers = { "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36", "Content-Type": "application/json", } payload = { "cid": "ee3b7046b390415b9b7745e3d16f6052", "indicatorIds": [ "3888eac6062945a79c8a27e5f13d4953", "1d550f3ec77a463bb607d4a3427e1465", "1c1b2d9ab24048bfadc5c7d9510dc663", "3921da310de24f14b6457c235657baf9", "bd6da1abb26046c2acb38aa701d90e86", "7bc1bd5daeac48ae8bb413c34ece1d08", "c03a36c9562246b6bc8aab010951ef1c", "1061f276ce354907b0b9900c266cf851", "40ab91b1ef4948e89633c5c7f55b9713" ], "daCatalogId": "", "das": [ { "text": "全国", "value": "000000000000" } ], "dts": ["199001MM-203601MM"], "showType": "1", "rootId": "fc982599aa684be7969d7b90b1bd0e84" } r = requests.post(url, json=payload, headers=headers, timeout=10) data_json = r.json() data_list = [] for month_item in data_json['data']: raw_month = month_item['name'] year_part = raw_month.split('年')[0] month_part = raw_month.split('年')[1].replace('月', '') month_clean = year_part + month_part.zfill(2) for value_item in month_item['values']: if value_item['_name'] == '城镇调查失业率': rate = value_item['value'] if rate: indicator_clean = value_item['i_showname'].replace(' (%)', '') data_list.append([month_clean, indicator_clean, rate]) temp_df = pd.DataFrame(data_list, columns=['date', 'item', 'value']) temp_df.sort_values(by=['date'], ascending=True, inplace=True) temp_df.reset_index(drop=True, inplace=True) return temp_df # 金十数据中心-经济指标-中国-国民经济运行状况-经济状况-中国GDP年率报告 def macro_china_gdp_yearly() -> pd.DataFrame: """ 金十数据中心-中国 GDP 年率报告, 数据区间从 20110120-至今 https://datacenter.jin10.com/reportType/dc_chinese_gdp_yoy :return: 中国 GDP 年率报告 :rtype: pandas.DataFrame """ t = time.time() params = { "max_date": "", "category": "ec", "attr_id": "57", "_": str(int(round(t * 1000))), } temp_df = __macro_china_base_func(symbol="中国GDP年率报告", params=params) return temp_df # 金十数据中心-经济指标-中国-国民经济运行状况-物价水平-中国CPI年率报告 def macro_china_cpi_yearly() -> pd.DataFrame: """ 中国年度 CPI 数据, 数据区间从 19860201-至今 https://datacenter.jin10.com/reportType/dc_chinese_cpi_yoy :return: 中国年度 CPI 数据 :rtype: pandas.DataFrame """ t = time.time() params = { "max_date": "", "category": "ec", "attr_id": "56", "_": str(int(round(t * 1000))), } temp_df = __macro_china_base_func(symbol="中国CPI年率报告", params=params) return temp_df # 金十数据中心-经济指标-中国-国民经济运行状况-物价水平-中国CPI月率报告 def macro_china_cpi_monthly() -> pd.DataFrame: """ 中国月度 CPI 数据, 数据区间从 19960201-至今 https://datacenter.jin10.com/reportType/dc_chinese_cpi_mom :return: 中国月度 CPI 数据 :rtype: pandas.DataFrame """ t = time.time() params = { "max_date": "", "category": "ec", "attr_id": "72", "_": str(int(round(t * 1000))), } temp_df = __macro_china_base_func(symbol="中国CPI月率报告", params=params) return temp_df # 金十数据中心-经济指标-中国-国民经济运行状况-物价水平-中国PPI年率报告 def macro_china_ppi_yearly() -> pd.DataFrame: """ 中国年度 PPI 数据, 数据区间从 19950801-至今 https://datacenter.jin10.com/reportType/dc_chinese_ppi_yoy :return: 中国年度 PPI 数据 :rtype: pandas.DataFrame """ t = time.time() params = { "max_date": "", "category": "ec", "attr_id": "60", "_": str(int(round(t * 1000))), } temp_df = __macro_china_base_func(symbol="中国PPI年率报告", params=params) return temp_df # 金十数据中心-经济指标-中国-贸易状况-以美元计算出口年率报告 def macro_china_exports_yoy() -> pd.DataFrame: """ 中国以美元计算出口年率报告, 数据区间从 19820201-至今 https://datacenter.jin10.com/reportType/dc_chinese_exports_yoy :return: 中国以美元计算出口年率报告 :rtype: pandas.DataFrame """ t = time.time() params = { "max_date": "", "category": "ec", "attr_id": "66", "_": str(int(round(t * 1000))), } temp_df = __macro_china_base_func( symbol="中国以美元计算出口年率报告", params=params ) return temp_df # 金十数据中心-经济指标-中国-贸易状况-以美元计算进口年率 def macro_china_imports_yoy() -> pd.DataFrame: """ 中国以美元计算进口年率报告, 数据区间从 19960201-至今 https://datacenter.jin10.com/reportType/dc_chinese_imports_yoy https://cdn.jin10.com/dc/reports/dc_chinese_imports_yoy_all.js?v=1578754588 :return: 中国以美元计算进口年率报告 :rtype: pandas.DataFrame """ t = time.time() params = { "max_date": "", "category": "ec", "attr_id": "77", "_": str(int(round(t * 1000))), } temp_df = __macro_china_base_func( symbol="中国以美元计算进口年率报告", params=params ) return temp_df # 金十数据中心-经济指标-中国-贸易状况-以美元计算贸易帐(亿美元) def macro_china_trade_balance() -> pd.DataFrame: """ 中国以美元计算贸易帐报告, 数据区间从 19810201-至今 https://datacenter.jin10.com/reportType/dc_chinese_trade_balance https://cdn.jin10.com/dc/reports/dc_chinese_trade_balance_all.js?v=1578754677 :return: 中国以美元计算贸易帐报告 :rtype: pandas.DataFrame """ t = time.time() params = { "max_date": "", "category": "ec", "attr_id": "61", "_": str(int(round(t * 1000))), } temp_df = __macro_china_base_func(symbol="中国以美元计算贸易帐报告", params=params) return temp_df # 金十数据中心-经济指标-中国-产业指标-规模以上工业增加值年率 def macro_china_industrial_production_yoy() -> pd.DataFrame: """ 中国规模以上工业增加值年率报告, 数据区间从19900301-至今 https://datacenter.jin10.com/reportType/dc_chinese_industrial_production_yoy https://cdn.jin10.com/dc/reports/dc_chinese_industrial_production_yoy_all.js?v=1578754779 :return: 中国规模以上工业增加值年率报告 :rtype: pandas.DataFrame """ t = time.time() params = { "max_date": "", "category": "ec", "attr_id": "58", "_": str(int(round(t * 1000))), } temp_df = __macro_china_base_func( symbol="中国规模以上工业增加值年率报告", params=params ) return temp_df # 金十数据中心-经济指标-中国-产业指标-官方制造业PMI def macro_china_pmi_yearly() -> pd.DataFrame: """ 中国年度 PMI 数据, 数据区间从 20050201-至今 https://datacenter.jin10.com/reportType/dc_chinese_manufacturing_pmi :return: 中国年度 PMI 数据 :return: pandas.DataFrame """ t = time.time() params = { "category": "ec", "attr_id": "65", "max_date": "", "_": str(int(round(t * 1000))), } temp_df = __macro_china_base_func(symbol="中国官方制造业PMI", params=params) return temp_df # 金十数据中心-经济指标-中国-产业指标-财新制造业PMI终值 def macro_china_cx_pmi_yearly() -> pd.DataFrame: """ 中国年度财新 PMI 数据, 数据区间从 20120120-至今 https://datacenter.jin10.com/reportType/dc_chinese_caixin_manufacturing_pmi :return: 中国年度财新 PMI 数据 :return: pandas.DataFrame """ t = time.time() params = { "max_date": "", "category": "ec", "attr_id": "73", "_": str(int(round(t * 1000))), } temp_df = __macro_china_base_func(symbol="中国财新制造业PMI终值报告", params=params) return temp_df # 金十数据中心-经济指标-中国-产业指标-财新服务业PMI def macro_china_cx_services_pmi_yearly() -> pd.DataFrame: """ 中国财新服务业PMI报告, 数据区间从 20120405-至今 https://datacenter.jin10.com/reportType/dc_chinese_caixin_services_pmi https://cdn.jin10.com/dc/reports/dc_chinese_caixin_services_pmi_all.js?v=1578818109 :return: pandas.DataFrame """ t = time.time() params = { "max_date": "", "category": "ec", "attr_id": "67", "_": str(int(round(t * 1000))), } temp_df = __macro_china_base_func(symbol="中国财新服务业PMI报告", params=params) return temp_df # 金十数据中心-经济指标-中国-产业指标-中国官方非制造业PMI def macro_china_non_man_pmi() -> pd.DataFrame: """ 中国官方非制造业 PMI, 数据区间从 20160101-至今 https://datacenter.jin10.com/reportType/dc_chinese_non_manufacturing_pmi :return: 中国官方非制造业 PMI :return: pandas.DataFrame """ t = time.time() params = { "max_date": "", "category": "ec", "attr_id": "75", "_": str(int(round(t * 1000))), } temp_df = __macro_china_base_func(symbol="中国官方非制造业PMI报告", params=params) return temp_df # 金十数据中心-经济指标-中国-金融指标-外汇储备(亿美元) def macro_china_fx_reserves_yearly() -> pd.DataFrame: """ 中国年度外汇储备数据, 数据区间从 20140115-至今 https://datacenter.jin10.com/reportType/dc_chinese_fx_reserves :return: 中国年度外汇储备数据 :return: pandas.DataFrame """ t = time.time() params = { "max_date": "", "category": "ec", "attr_id": "76", "_": str(int(round(t * 1000))), } temp_df = __macro_china_base_func(symbol="中国外汇储备报告", params=params) return temp_df # 金十数据中心-经济指标-中国-金融指标-M2货币供应年率 def macro_china_m2_yearly() -> pd.DataFrame: """ 中国年度 M2 数据, 数据区间从 19980201-至今 https://datacenter.jin10.com/reportType/dc_chinese_m2_money_supply_yoy :return: 中国年度 M2 数据 :return: pandas.DataFrame """ t = time.time() params = { "max_date": "", "category": "ec", "attr_id": "59", "_": str(int(round(t * 1000))), } temp_df = __macro_china_base_func(symbol="中国M2货币供应年率报告", params=params) return temp_df # 金十数据中心-经济指标-中国-金融指标-上海银行业同业拆借报告 def macro_china_shibor_all() -> pd.DataFrame: """ 上海银行业同业拆借报告, 数据区间从20170317-至今 https://datacenter.jin10.com/reportType/dc_shibor https://cdn.jin10.com/dc/reports/dc_shibor_all.js?v=1578755058 :return: 上海银行业同业拆借报告-今值(%) :rtype: pandas.DataFrame """ import numpy as np t = time.time() params = {"_": t} res = requests.get( url="https://cdn.jin10.com/data_center/reports/il_1.json", params=params ) json_data = res.json() temp_df = pd.DataFrame(json_data["values"]).T big_df = pd.DataFrame() temp_df.fillna(value="--", inplace=True) big_df["O/N-定价"] = temp_df["O/N"].apply(lambda x: x[0]) big_df["O/N-涨跌幅"] = temp_df["O/N"].apply(lambda x: x[1]) big_df["1W-定价"] = temp_df["1W"].apply(lambda x: x[0]) big_df["1W-涨跌幅"] = temp_df["1W"].apply(lambda x: x[1]) big_df["2W-定价"] = temp_df["2W"].apply(lambda x: x[0]) big_df["2W-涨跌幅"] = temp_df["2W"].apply(lambda x: x[1]) big_df["1M-定价"] = temp_df["1M"].apply(lambda x: x[0]) big_df["1M-涨跌幅"] = temp_df["1M"].apply(lambda x: x[1]) big_df["3M-定价"] = temp_df["3M"].apply(lambda x: x[0]) big_df["3M-涨跌幅"] = temp_df["3M"].apply(lambda x: x[1]) big_df["6M-定价"] = temp_df["6M"].apply(lambda x: x[0]) big_df["6M-涨跌幅"] = temp_df["6M"].apply(lambda x: x[1]) big_df["9M-定价"] = temp_df["9M"].apply(lambda x: x[0]) big_df["9M-涨跌幅"] = temp_df["9M"].apply(lambda x: x[1]) big_df["1Y-定价"] = temp_df["1Y"].apply(lambda x: x[0]) big_df["1Y-涨跌幅"] = temp_df["1Y"].apply(lambda x: x[1]) big_df = big_df.apply(lambda x: x.replace("-", np.nan)) big_df = big_df.apply(lambda x: x.replace([None], np.nan)) for item in big_df.columns: big_df[item] = pd.to_numeric(big_df[item], errors="coerce") big_df.sort_index(inplace=True) big_df.reset_index(inplace=True) big_df.rename(columns={"index": "日期"}, inplace=True) return big_df # 金十数据中心-经济指标-中国-金融指标-人民币香港银行同业拆息 def macro_china_hk_market_info() -> pd.DataFrame: """ 香港同业拆借报告, 数据区间从 20170320-至今 https://datacenter.jin10.com/reportType/dc_hk_market_info https://cdn.jin10.com/dc/reports/dc_hk_market_info_all.js?v=1578755471 :return: 香港同业拆借报告-今值(%) :rtype: pandas.DataFrame """ import numpy as np t = time.time() params = {"_": t} res = requests.get( url="https://cdn.jin10.com/data_center/reports/il_2.json", params=params ) json_data = res.json() temp_df = pd.DataFrame(json_data["values"]).T big_df = pd.DataFrame() temp_df.fillna(value="--", inplace=True) big_df["1W-定价"] = temp_df["1W"].apply(lambda x: x[0]) big_df["1W-涨跌幅"] = temp_df["1W"].apply(lambda x: x[1]) big_df["2W-定价"] = temp_df["2W"].apply(lambda x: x[0]) big_df["2W-涨跌幅"] = temp_df["2W"].apply(lambda x: x[1]) big_df["1M-定价"] = temp_df["1M"].apply(lambda x: x[0]) big_df["1M-涨跌幅"] = temp_df["1M"].apply(lambda x: x[1]) big_df["3M-定价"] = temp_df["3M"].apply(lambda x: x[0]) big_df["3M-涨跌幅"] = temp_df["3M"].apply(lambda x: x[1]) big_df["6M-定价"] = temp_df["6M"].apply(lambda x: x[0]) big_df["6M-涨跌幅"] = temp_df["6M"].apply(lambda x: x[1]) big_df["1Y-定价"] = temp_df["1Y"].apply(lambda x: x[0]) big_df["1Y-涨跌幅"] = temp_df["1Y"].apply(lambda x: x[1]) big_df["ON-定价"] = temp_df["ON"].apply(lambda x: x[0]) big_df["ON-涨跌幅"] = temp_df["ON"].apply(lambda x: x[1]) big_df["2M-定价"] = temp_df["2M"].apply(lambda x: x[0]) big_df["2M-涨跌幅"] = temp_df["2M"].apply(lambda x: x[1]) big_df = big_df.apply(lambda x: x.replace("-", np.nan)) big_df = big_df.apply(lambda x: x.replace([None], np.nan)) for item in big_df.columns: big_df[item] = pd.to_numeric(big_df[item], errors="coerce") big_df.sort_index(inplace=True) big_df.reset_index(inplace=True) big_df.rename(columns={"index": "日期"}, inplace=True) return big_df # 金十数据中心-经济指标-中国-其他-中国日度沿海六大电库存数据 def macro_china_daily_energy() -> pd.DataFrame: """ 中国日度沿海六大电库存数据, 数据区间从20160101-至今 https://datacenter.jin10.com/reportType/dc_qihuo_energy_report :return: pandas.DataFrame """ t = time.time() res = requests.get( JS_CHINA_ENERGY_DAILY_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"]["沿海六大电厂库存动态报告"] 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[["沿海六大电库存", "日耗", "存煤可用天数"]] temp_df.name = "energy" temp_df = temp_df.astype(float) temp_df.reset_index(inplace=True) temp_df.rename(columns={"index": "日期"}, inplace=True) temp_df["日期"] = pd.to_datetime(temp_df["日期"], errors="coerce").dt.date return temp_df # 金十数据中心-经济指标-中国-其他-中国人民币汇率中间价报告 def macro_china_rmb() -> pd.DataFrame: """ 中国人民币汇率中间价报告, 数据区间从 20170103-至今 https://datacenter.jin10.com/reportType/dc_rmb_data :return: 中国人民币汇率中间价报告 :rtype: pandas.DataFrame """ t = time.time() params = {"_": t} res = requests.get( "https://cdn.jin10.com/data_center/reports/exchange_rate.json", params=params, ) json_data = res.json() temp_df = pd.DataFrame(json_data["values"]).T big_df = pd.DataFrame() temp_df.fillna(value="--", inplace=True) big_df["美元/人民币_中间价"] = temp_df["美元/人民币"].apply(lambda x: x[0]) big_df["美元/人民币_涨跌幅"] = temp_df["美元/人民币"].apply(lambda x: x[1]) big_df["欧元/人民币_中间价"] = temp_df["欧元/人民币"].apply(lambda x: x[0]) big_df["欧元/人民币_涨跌幅"] = temp_df["欧元/人民币"].apply(lambda x: x[1]) big_df["100日元/人民币_中间价"] = temp_df["100日元/人民币"].apply(lambda x: x[0]) big_df["100日元/人民币_涨跌幅"] = temp_df["100日元/人民币"].apply(lambda x: x[1]) big_df["港元/人民币_中间价"] = temp_df["港元/人民币"].apply(lambda x: x[0]) big_df["港元/人民币_涨跌幅"] = temp_df["港元/人民币"].apply(lambda x: x[1]) big_df["英镑/人民币_中间价"] = temp_df["英镑/人民币"].apply(lambda x: x[0]) big_df["英镑/人民币_涨跌幅"] = temp_df["英镑/人民币"].apply(lambda x: x[1]) big_df["澳元/人民币_中间价"] = temp_df["澳元/人民币"].apply(lambda x: x[0]) big_df["澳元/人民币_涨跌幅"] = temp_df["澳元/人民币"].apply(lambda x: x[1]) big_df["新西兰元/人民币_中间价"] = temp_df["新西兰元/人民币"].apply(lambda x: x[0]) big_df["新西兰元/人民币_涨跌幅"] = temp_df["新西兰元/人民币"].apply(lambda x: x[1]) big_df["新加坡元/人民币_中间价"] = temp_df["新加坡元/人民币"].apply(lambda x: x[0]) big_df["新加坡元/人民币_涨跌幅"] = temp_df["新加坡元/人民币"].apply(lambda x: x[1]) big_df["瑞郎/人民币_中间价"] = temp_df["瑞郎/人民币"].apply(lambda x: x[0]) big_df["瑞郎/人民币_涨跌幅"] = temp_df["瑞郎/人民币"].apply(lambda x: x[1]) big_df["加元/人民币_中间价"] = temp_df["加元/人民币"].apply(lambda x: x[0]) big_df["加元/人民币_涨跌幅"] = temp_df["加元/人民币"].apply(lambda x: x[1]) big_df["人民币/马来西亚林吉特_中间价"] = temp_df["人民币/马来西亚林吉特"].apply( lambda x: x[0] ) big_df["人民币/马来西亚林吉特_涨跌幅"] = temp_df["人民币/马来西亚林吉特"].apply( lambda x: x[1] ) big_df["人民币/俄罗斯卢布_中间价"] = temp_df["人民币/俄罗斯卢布"].apply( lambda x: x[0] ) big_df["人民币/俄罗斯卢布_涨跌幅"] = temp_df["人民币/俄罗斯卢布"].apply( lambda x: x[1] ) big_df["人民币/南非兰特_中间价"] = temp_df["人民币/南非兰特"].apply(lambda x: x[0]) big_df["人民币/南非兰特_涨跌幅"] = temp_df["人民币/南非兰特"].apply(lambda x: x[1]) big_df["人民币/韩元_中间价"] = temp_df["人民币/韩元"].apply(lambda x: x[0]) big_df["人民币/韩元_涨跌幅"] = temp_df["人民币/韩元"].apply(lambda x: x[1]) big_df["人民币/阿联酋迪拉姆_中间价"] = temp_df["人民币/阿联酋迪拉姆"].apply( lambda x: x[0] ) big_df["人民币/阿联酋迪拉姆_涨跌幅"] = temp_df["人民币/阿联酋迪拉姆"].apply( lambda x: x[1] ) big_df["人民币/沙特里亚尔_中间价"] = temp_df["人民币/沙特里亚尔"].apply( lambda x: x[0] ) big_df["人民币/沙特里亚尔_涨跌幅"] = temp_df["人民币/沙特里亚尔"].apply( lambda x: x[1] ) big_df["人民币/匈牙利福林_中间价"] = temp_df["人民币/匈牙利福林"].apply( lambda x: x[0] ) big_df["人民币/匈牙利福林_涨跌幅"] = temp_df["人民币/匈牙利福林"].apply( lambda x: x[1] ) big_df["人民币/波兰兹罗提_中间价"] = temp_df["人民币/波兰兹罗提"].apply( lambda x: x[0] ) big_df["人民币/波兰兹罗提_涨跌幅"] = temp_df["人民币/波兰兹罗提"].apply( lambda x: x[1] ) big_df["人民币/丹麦克朗_中间价"] = temp_df["人民币/丹麦克朗"].apply(lambda x: x[0]) big_df["人民币/丹麦克朗_涨跌幅"] = temp_df["人民币/丹麦克朗"].apply(lambda x: x[1]) big_df["人民币/瑞典克朗_中间价"] = temp_df["人民币/瑞典克朗"].apply(lambda x: x[0]) big_df["人民币/瑞典克朗_涨跌幅"] = temp_df["人民币/瑞典克朗"].apply(lambda x: x[1]) big_df["人民币/挪威克朗_中间价"] = temp_df["人民币/挪威克朗"].apply(lambda x: x[0]) big_df["人民币/挪威克朗_涨跌幅"] = temp_df["人民币/挪威克朗"].apply(lambda x: x[1]) big_df["人民币/土耳其里拉_中间价"] = temp_df["人民币/土耳其里拉"].apply( lambda x: x[0] ) big_df["人民币/土耳其里拉_涨跌幅"] = temp_df["人民币/土耳其里拉"].apply( lambda x: x[1] ) big_df["人民币/墨西哥比索_中间价"] = temp_df["人民币/墨西哥比索"].apply( lambda x: x[0] ) big_df["人民币/墨西哥比索_涨跌幅"] = temp_df["人民币/墨西哥比索"].apply( lambda x: x[1] ) big_df["人民币/泰铢_定价"] = temp_df["人民币/泰铢"].apply(lambda x: x[0]) big_df["人民币/泰铢_涨跌幅"] = temp_df["人民币/泰铢"].apply(lambda x: x[1]) big_df = big_df.apply(lambda x: x.replace("-", pd.NA)) big_df = big_df.apply(lambda x: x.replace([None], pd.NA)) big_df.sort_index(inplace=True) big_df.fillna(0, inplace=True) big_df = big_df.astype("float") big_df.reset_index(inplace=True) big_df.rename(columns={"index": "日期"}, inplace=True) return big_df # 金十数据中心-经济指标-中国-其他-深圳融资融券报告 def macro_china_market_margin_sz() -> pd.DataFrame: """ 深圳融资融券报告, 数据区间从20100331-至今 https://datacenter.jin10.com/reportType/dc_market_margin_sz :return: pandas.DataFrame """ t = time.time() params = {"_": t} res = requests.get( url="https://cdn.jin10.com/data_center/reports/fs_2.json", params=params ) json_data = res.json() temp_df = pd.DataFrame(json_data["values"]).T 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.sort_index(inplace=True) temp_df.index = pd.to_datetime(temp_df.index) temp_df = temp_df.astype("float") temp_df.reset_index(inplace=True) temp_df.rename(columns={"index": "日期"}, inplace=True) temp_df["日期"] = pd.to_datetime(temp_df["日期"], errors="coerce").dt.date return temp_df # 金十数据中心-经济指标-中国-其他-上海融资融券报告 def macro_china_market_margin_sh() -> pd.DataFrame: """ 上海融资融券报告, 数据区间从 20100331-至今 https://datacenter.jin10.com/reportType/dc_market_margin_sse :return: pandas.DataFrame """ url = "https://cdn.jin10.com/data_center/reports/fs_1.json" t = time.time() params = {"_": t} r = requests.get(url, params=params) json_data = r.json() temp_df = pd.DataFrame(json_data["values"]).T temp_df.reset_index(inplace=True) temp_df.columns = [ "日期", "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.sort_values(by=["日期"], inplace=True, ignore_index=True) temp_df["日期"] = pd.to_datetime(temp_df["日期"], errors="coerce").dt.date 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"] = pd.to_numeric(temp_df["融资余额"], errors="coerce") 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"] = pd.to_numeric(temp_df["融券余量"], errors="coerce") 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"] = pd.to_numeric(temp_df["融资融券余额"], errors="coerce") return temp_df # 金十数据中心-经济指标-中国-其他-上海黄金交易所报告 def macro_china_au_report() -> pd.DataFrame: """ 上海黄金交易所报告, 数据区间从20100331-至今 https://datacenter.jin10.com/reportType/dc_sge_report :return: pandas.DataFrame """ t = time.time() params = {"_": t} res = requests.get( url="https://cdn.jin10.com/data_center/reports/sge.json", params=params ) json_data = res.json() big_df = pd.DataFrame() for item in json_data["values"].keys(): temp_df = pd.DataFrame(json_data["values"][item]) temp_df["date"] = item temp_df.columns = [ "商品", "开盘价", "最高价", "最低价", "收盘价", "涨跌", "涨跌幅", "加权平均价", "成交量", "成交金额", "持仓量", "交收方向", "交收量", "日期", ] big_df = pd.concat([big_df, temp_df], ignore_index=True) big_df = big_df[ [ "日期", "商品", "开盘价", "最高价", "最低价", "收盘价", "涨跌", "涨跌幅", "加权平均价", "成交量", "成交金额", "持仓量", "交收方向", "交收量", ] ] big_df["日期"] = pd.to_datetime(big_df["日期"]).dt.date big_df.sort_values(["日期"], inplace=True, ignore_index=True) big_df["持仓量"] = pd.to_numeric(big_df["持仓量"], errors="coerce") big_df["交收量"] = pd.to_numeric(big_df["交收量"], errors="coerce") return big_df # 中国-利率-贷款报价利率 def macro_china_lpr() -> pd.DataFrame: """ LPR品种详细数据 https://data.eastmoney.com/cjsj/globalRateLPR.html :return: LPR品种详细数据 :rtype: pandas.DataFrame """ import warnings warnings.filterwarnings(action="ignore", category=FutureWarning) url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "reportName": "RPTA_WEB_RATE", "columns": "ALL", "sortColumns": "TRADE_DATE", "sortTypes": "-1", "token": "894050c76af8597a853f5b408b759f5d", "pageNumber": "1", "pageSize": "500", "p": "1", "pageNo": "1", "pageNum": "1", } r = requests.get(url, params=params) data_json = r.json() total_page = data_json["result"]["pages"] big_df = pd.DataFrame() tqdm = get_tqdm() for page in tqdm(range(1, total_page + 1), leave=False): params.update({"pageNumber": page}) r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True) big_df["TRADE_DATE"] = pd.to_datetime(big_df["TRADE_DATE"], errors="coerce").dt.date big_df["LPR1Y"] = pd.to_numeric(big_df["LPR1Y"], errors="coerce") big_df["LPR5Y"] = pd.to_numeric(big_df["LPR5Y"], errors="coerce") big_df["RATE_1"] = pd.to_numeric(big_df["RATE_1"], errors="coerce") big_df["RATE_2"] = pd.to_numeric(big_df["RATE_2"], errors="coerce") big_df.sort_values(by=["TRADE_DATE"], inplace=True) big_df.reset_index(inplace=True, drop=True) return big_df # 中国-新房价指数 def macro_china_new_house_price( city_first: str = "北京", city_second: str = "上海" ) -> pd.DataFrame: """ 中国-新房价指数 https://data.eastmoney.com/cjsj/newhouse.html :param city_first: 城市; 城市列表见目标网站 :type city_first: str :param city_second: 城市; 城市列表见目标网站 :type city_second: str :return: 新房价指数 :rtype: pandas.DataFrame """ url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "reportName": "RPT_ECONOMY_HOUSE_PRICE", "columns": "REPORT_DATE,CITY,FIRST_COMHOUSE_SAME,FIRST_COMHOUSE_SEQUENTIAL,FIRST_COMHOUSE_BASE," "SECOND_HOUSE_SAME,SECOND_HOUSE_SEQUENTIAL,SECOND_HOUSE_BASE,REPORT_DAY", "filter": f'(CITY in ("{city_first}","{city_second}"))', "pageNumber": "1", "pageSize": "500", "sortColumns": "REPORT_DATE,CITY", "sortTypes": "-1,-1", "source": "WEB", "client": "WEB", "p": "1", "pageNo": "1", "pageNum": "1", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) temp_df.columns = [ "日期", "城市", "新建商品住宅价格指数-同比", "新建商品住宅价格指数-环比", "新建商品住宅价格指数-定基", "二手住宅价格指数-同比", "二手住宅价格指数-环比", "二手住宅价格指数-定基", "-", ] temp_df = temp_df[ [ "日期", "城市", "新建商品住宅价格指数-同比", "新建商品住宅价格指数-环比", "新建商品住宅价格指数-定基", "二手住宅价格指数-同比", "二手住宅价格指数-环比", "二手住宅价格指数-定基", ] ] temp_df["日期"] = pd.to_datetime(temp_df["日期"], errors="coerce").dt.date temp_df["新建商品住宅价格指数-同比"] = pd.to_numeric( temp_df["新建商品住宅价格指数-同比"], errors="coerce" ) temp_df["新建商品住宅价格指数-环比"] = pd.to_numeric( temp_df["新建商品住宅价格指数-环比"], errors="coerce" ) temp_df["新建商品住宅价格指数-定基"] = pd.to_numeric( temp_df["新建商品住宅价格指数-定基"], errors="coerce" ) temp_df["二手住宅价格指数-环比"] = pd.to_numeric( temp_df["二手住宅价格指数-环比"], errors="coerce" ) temp_df["二手住宅价格指数-同比"] = pd.to_numeric( temp_df["二手住宅价格指数-同比"], errors="coerce" ) temp_df["二手住宅价格指数-定基"] = pd.to_numeric( temp_df["二手住宅价格指数-定基"], errors="coerce" ) temp_df.sort_values(["日期"], ignore_index=True, inplace=True) return temp_df # 中国-企业景气及企业家信心指数 def macro_china_enterprise_boom_index() -> pd.DataFrame: """ https://data.eastmoney.com/cjsj/qyjqzs.html 中国-企业景气及企业家信心指数 :return: 企业景气及企业家信心指数 :rtype: pandas.DataFrame """ url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "columns": "REPORT_DATE,TIME,BOOM_INDEX,FAITH_INDEX,BOOM_INDEX_SAME,BOOM_INDEX_SEQUENTIAL," "FAITH_INDEX_SAME,FAITH_INDEX_SEQUENTIAL", "pageNumber": "1", "pageSize": "500", "sortColumns": "REPORT_DATE", "sortTypes": "-1", "source": "WEB", "client": "WEB", "reportName": "RPT_ECONOMY_BOOM_INDEX", "p": "1", "pageNo": "1", "pageNum": "1", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) temp_df.columns = [ "-", "季度", "企业景气指数-指数", "企业家信心指数-指数", "企业景气指数-同比", "企业景气指数-环比", "企业家信心指数-同比", "企业家信心指数-环比", ] temp_df = temp_df[ [ "季度", "企业景气指数-指数", "企业景气指数-同比", "企业景气指数-环比", "企业家信心指数-指数", "企业家信心指数-同比", "企业家信心指数-环比", ] ] temp_df["企业景气指数-指数"] = pd.to_numeric( temp_df["企业景气指数-指数"], errors="coerce" ) temp_df["企业家信心指数-指数"] = pd.to_numeric( temp_df["企业家信心指数-指数"], errors="coerce" ) temp_df["企业景气指数-同比"] = pd.to_numeric( temp_df["企业景气指数-同比"], errors="coerce" ) temp_df["企业景气指数-环比"] = pd.to_numeric( temp_df["企业景气指数-环比"], errors="coerce" ) temp_df["企业家信心指数-同比"] = pd.to_numeric( temp_df["企业家信心指数-同比"], errors="coerce" ) temp_df["企业家信心指数-环比"] = pd.to_numeric( temp_df["企业家信心指数-环比"], errors="coerce" ) return temp_df # 中国-全国税收收入 def macro_china_national_tax_receipts() -> pd.DataFrame: """ 中国-全国税收收入 https://data.eastmoney.com/cjsj/qgsssr.html :return: 全国税收收入 :rtype: pandas.DataFrame """ url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "columns": "REPORT_DATE,TIME,TAX_INCOME,TAX_INCOME_SAME,TAX_INCOME_SEQUENTIAL", "pageNumber": "1", "pageSize": "500", "sortColumns": "REPORT_DATE", "sortTypes": "-1", "source": "WEB", "client": "WEB", "reportName": "RPT_ECONOMY_TAX", "p": "1", "pageNo": "1", "pageNum": "1", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) temp_df.columns = ["-", "季度", "税收收入合计", "较上年同期", "季度环比"] temp_df = temp_df[["季度", "税收收入合计", "较上年同期", "季度环比"]] temp_df["税收收入合计"] = pd.to_numeric(temp_df["税收收入合计"], errors="coerce") temp_df["较上年同期"] = pd.to_numeric(temp_df["较上年同期"], errors="coerce") temp_df["季度环比"] = pd.to_numeric(temp_df["季度环比"], errors="coerce") return temp_df # 中国-银行理财产品发行数量 def macro_china_bank_financing() -> pd.DataFrame: """ 银行理财产品发行数量 https://data.eastmoney.com/cjsj/hyzs_list_EMI01516267.html :return: 银行理财产品发行数量 :rtype: pandas.DataFrame """ url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "sortColumns": "REPORT_DATE", "sortTypes": "-1", "pageSize": "1000", "pageNumber": "1", "reportName": "RPT_INDUSTRY_INDEX", "columns": "REPORT_DATE,INDICATOR_VALUE,CHANGE_RATE,CHANGERATE_3M,CHANGERATE_6M,CHANGERATE_1Y," "CHANGERATE_2Y,CHANGERATE_3Y", "filter": '(INDICATOR_ID="EMI01516267")', "source": "WEB", "client": "WEB", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) temp_df.columns = [ "日期", "最新值", "涨跌幅", "近3月涨跌幅", "近6月涨跌幅", "近1年涨跌幅", "近2年涨跌幅", "近3年涨跌幅", ] temp_df["日期"] = pd.to_datetime(temp_df["日期"]).dt.date temp_df["最新值"] = pd.to_numeric(temp_df["最新值"]) temp_df["涨跌幅"] = pd.to_numeric(temp_df["涨跌幅"]) temp_df["近3月涨跌幅"] = pd.to_numeric(temp_df["近3月涨跌幅"]) temp_df["近6月涨跌幅"] = pd.to_numeric(temp_df["近6月涨跌幅"]) temp_df["近1年涨跌幅"] = pd.to_numeric(temp_df["近1年涨跌幅"]) temp_df["近2年涨跌幅"] = pd.to_numeric(temp_df["近2年涨跌幅"]) temp_df["近3年涨跌幅"] = pd.to_numeric(temp_df["近3年涨跌幅"]) temp_df.sort_values(["日期"], inplace=True) temp_df.reset_index(inplace=True, drop=True) return temp_df def macro_china_insurance_income() -> pd.DataFrame: """ 原保险保费收入 https://data.eastmoney.com/cjsj/hyzs_list_EMM00088870.html :return: 原保险保费收入 :rtype: pandas.DataFrame """ url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "sortColumns": "REPORT_DATE", "sortTypes": "-1", "pageSize": "1000", "pageNumber": "1", "reportName": "RPT_INDUSTRY_INDEX", "columns": "REPORT_DATE,INDICATOR_VALUE,CHANGE_RATE,CHANGERATE_3M,CHANGERATE_6M,CHANGERATE_1Y," "CHANGERATE_2Y,CHANGERATE_3Y", "filter": '(INDICATOR_ID="EMM00088870")', "source": "WEB", "client": "WEB", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) temp_df.columns = [ "日期", "最新值", "涨跌幅", "近3月涨跌幅", "近6月涨跌幅", "近1年涨跌幅", "近2年涨跌幅", "近3年涨跌幅", ] temp_df["日期"] = pd.to_datetime(temp_df["日期"]).dt.date temp_df["最新值"] = pd.to_numeric(temp_df["最新值"]) temp_df["涨跌幅"] = pd.to_numeric(temp_df["涨跌幅"]) temp_df["近3月涨跌幅"] = pd.to_numeric(temp_df["近3月涨跌幅"]) temp_df["近6月涨跌幅"] = pd.to_numeric(temp_df["近6月涨跌幅"]) temp_df["近1年涨跌幅"] = pd.to_numeric(temp_df["近1年涨跌幅"]) temp_df["近2年涨跌幅"] = pd.to_numeric(temp_df["近2年涨跌幅"]) temp_df["近3年涨跌幅"] = pd.to_numeric(temp_df["近3年涨跌幅"]) temp_df.sort_values(["日期"], inplace=True) temp_df.reset_index(inplace=True, drop=True) return temp_df def macro_china_mobile_number() -> pd.DataFrame: """ 手机出货量 https://data.eastmoney.com/cjsj/hyzs_list_EMI00225823.html :return: 手机出货量 :rtype: pandas.DataFrame """ url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "sortColumns": "REPORT_DATE", "sortTypes": "-1", "pageSize": "1000", "pageNumber": "1", "reportName": "RPT_INDUSTRY_INDEX", "columns": "REPORT_DATE,INDICATOR_VALUE,CHANGE_RATE,CHANGERATE_3M,CHANGERATE_6M,CHANGERATE_1Y," "CHANGERATE_2Y,CHANGERATE_3Y", "filter": '(INDICATOR_ID="EMI00225823")', "source": "WEB", "client": "WEB", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) temp_df.drop_duplicates(inplace=True) temp_df.columns = [ "日期", "最新值", "涨跌幅", "近3月涨跌幅", "近6月涨跌幅", "近1年涨跌幅", "近2年涨跌幅", "近3年涨跌幅", ] temp_df["日期"] = pd.to_datetime(temp_df["日期"]).dt.date temp_df["最新值"] = pd.to_numeric(temp_df["最新值"]) temp_df["涨跌幅"] = pd.to_numeric(temp_df["涨跌幅"]) temp_df["近3月涨跌幅"] = pd.to_numeric(temp_df["近3月涨跌幅"]) temp_df["近6月涨跌幅"] = pd.to_numeric(temp_df["近6月涨跌幅"]) temp_df["近1年涨跌幅"] = pd.to_numeric(temp_df["近1年涨跌幅"]) temp_df["近2年涨跌幅"] = pd.to_numeric(temp_df["近2年涨跌幅"]) temp_df["近3年涨跌幅"] = pd.to_numeric(temp_df["近3年涨跌幅"]) temp_df.sort_values(["日期"], inplace=True) temp_df.reset_index(inplace=True, drop=True) return temp_df def macro_china_vegetable_basket() -> pd.DataFrame: """ 菜篮子产品批发价格指数 https://data.eastmoney.com/cjsj/hyzs_list_EMI00009275.html :return: 菜篮子产品批发价格指数 :rtype: pandas.DataFrame """ url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "sortColumns": "REPORT_DATE", "sortTypes": "-1", "pageSize": "500", "pageNumber": "1", "reportName": "RPT_INDUSTRY_INDEX", "columns": "REPORT_DATE,INDICATOR_VALUE,CHANGE_RATE,CHANGERATE_3M,CHANGERATE_6M,CHANGERATE_1Y," "CHANGERATE_2Y,CHANGERATE_3Y", "filter": '(INDICATOR_ID="EMI00009275")', "source": "WEB", "client": "WEB", } r = requests.get(url, params=params) data_json = r.json() total_page = data_json["result"]["pages"] big_df = pd.DataFrame() tqdm = get_tqdm() for page in tqdm(range(1, total_page + 1), leave=False): params.update({"pageNumber": page}) r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) big_df = pd.concat([big_df, temp_df], ignore_index=True) big_df.drop_duplicates(inplace=True) big_df.columns = [ "日期", "最新值", "涨跌幅", "近3月涨跌幅", "近6月涨跌幅", "近1年涨跌幅", "近2年涨跌幅", "近3年涨跌幅", ] big_df["日期"] = pd.to_datetime(big_df["日期"]).dt.date big_df["最新值"] = pd.to_numeric(big_df["最新值"]) big_df["涨跌幅"] = pd.to_numeric(big_df["涨跌幅"]) big_df["近3月涨跌幅"] = pd.to_numeric(big_df["近3月涨跌幅"]) big_df["近6月涨跌幅"] = pd.to_numeric(big_df["近6月涨跌幅"]) big_df["近1年涨跌幅"] = pd.to_numeric(big_df["近1年涨跌幅"]) big_df["近2年涨跌幅"] = pd.to_numeric(big_df["近2年涨跌幅"]) big_df["近3年涨跌幅"] = pd.to_numeric(big_df["近3年涨跌幅"]) big_df.sort_values(["日期"], inplace=True) big_df.reset_index(inplace=True, drop=True) return big_df def macro_china_agricultural_product() -> pd.DataFrame: """ 农产品批发价格总指数 https://data.eastmoney.com/cjsj/hyzs_list_EMI00009274.html :return: 农产品批发价格总指数 :rtype: pandas.DataFrame """ url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "sortColumns": "REPORT_DATE", "sortTypes": "-1", "pageSize": "500", "pageNumber": "1", "reportName": "RPT_INDUSTRY_INDEX", "columns": "REPORT_DATE,INDICATOR_VALUE,CHANGE_RATE,CHANGERATE_3M,CHANGERATE_6M,CHANGERATE_1Y," "CHANGERATE_2Y,CHANGERATE_3Y", "filter": '(INDICATOR_ID="EMI00009274")', "source": "WEB", "client": "WEB", } r = requests.get(url, params=params) data_json = r.json() total_page = data_json["result"]["pages"] big_df = pd.DataFrame() tqdm = get_tqdm() for page in tqdm(range(1, total_page + 1), leave=False): params.update({"pageNumber": page}) r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) big_df = pd.concat([big_df, temp_df], ignore_index=True) big_df.drop_duplicates(inplace=True) big_df.columns = [ "日期", "最新值", "涨跌幅", "近3月涨跌幅", "近6月涨跌幅", "近1年涨跌幅", "近2年涨跌幅", "近3年涨跌幅", ] big_df["日期"] = pd.to_datetime(big_df["日期"]).dt.date big_df["最新值"] = pd.to_numeric(big_df["最新值"]) big_df["涨跌幅"] = pd.to_numeric(big_df["涨跌幅"]) big_df["近3月涨跌幅"] = pd.to_numeric(big_df["近3月涨跌幅"]) big_df["近6月涨跌幅"] = pd.to_numeric(big_df["近6月涨跌幅"]) big_df["近1年涨跌幅"] = pd.to_numeric(big_df["近1年涨跌幅"]) big_df["近2年涨跌幅"] = pd.to_numeric(big_df["近2年涨跌幅"]) big_df["近3年涨跌幅"] = pd.to_numeric(big_df["近3年涨跌幅"]) big_df.sort_values(["日期"], inplace=True) big_df.reset_index(inplace=True, drop=True) return big_df def macro_china_agricultural_index() -> pd.DataFrame: """ 农副指数 https://data.eastmoney.com/cjsj/hyzs_list_EMI00662543.html :return: 农副指数 :rtype: pandas.DataFrame """ url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "sortColumns": "REPORT_DATE", "sortTypes": "-1", "pageSize": "500", "pageNumber": "1", "reportName": "RPT_INDUSTRY_INDEX", "columns": "REPORT_DATE,INDICATOR_VALUE,CHANGE_RATE,CHANGERATE_3M,CHANGERATE_6M,CHANGERATE_1Y," "CHANGERATE_2Y,CHANGERATE_3Y", "filter": '(INDICATOR_ID="EMI00662543")', "source": "WEB", "client": "WEB", } r = requests.get(url, params=params) data_json = r.json() total_page = data_json["result"]["pages"] big_df = pd.DataFrame() tqdm = get_tqdm() for page in tqdm(range(1, total_page + 1), leave=False): params.update({"pageNumber": page}) r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) big_df = pd.concat([big_df, temp_df], ignore_index=True) big_df.drop_duplicates(inplace=True) big_df.columns = [ "日期", "最新值", "涨跌幅", "近3月涨跌幅", "近6月涨跌幅", "近1年涨跌幅", "近2年涨跌幅", "近3年涨跌幅", ] big_df["日期"] = pd.to_datetime(big_df["日期"]).dt.date big_df["最新值"] = pd.to_numeric(big_df["最新值"]) big_df["涨跌幅"] = pd.to_numeric(big_df["涨跌幅"]) big_df["近3月涨跌幅"] = pd.to_numeric(big_df["近3月涨跌幅"]) big_df["近6月涨跌幅"] = pd.to_numeric(big_df["近6月涨跌幅"]) big_df["近1年涨跌幅"] = pd.to_numeric(big_df["近1年涨跌幅"]) big_df["近2年涨跌幅"] = pd.to_numeric(big_df["近2年涨跌幅"]) big_df["近3年涨跌幅"] = pd.to_numeric(big_df["近3年涨跌幅"]) big_df.sort_values(["日期"], inplace=True) big_df.reset_index(inplace=True, drop=True) return big_df def macro_china_energy_index() -> pd.DataFrame: """ 能源指数 https://data.eastmoney.com/cjsj/hyzs_list_EMI00662539.html :return: 能源指数 :rtype: pandas.DataFrame """ url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "sortColumns": "REPORT_DATE", "sortTypes": "-1", "pageSize": "500", "pageNumber": "1", "reportName": "RPT_INDUSTRY_INDEX", "columns": "REPORT_DATE,INDICATOR_VALUE,CHANGE_RATE,CHANGERATE_3M,CHANGERATE_6M,CHANGERATE_1Y," "CHANGERATE_2Y,CHANGERATE_3Y", "filter": '(INDICATOR_ID="EMI00662539")', "source": "WEB", "client": "WEB", } r = requests.get(url, params=params) data_json = r.json() total_page = data_json["result"]["pages"] big_df = pd.DataFrame() tqdm = get_tqdm() for page in tqdm(range(1, total_page + 1), leave=False): params.update({"pageNumber": page}) r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) big_df = pd.concat([big_df, temp_df], ignore_index=True) big_df.drop_duplicates(inplace=True) big_df.columns = [ "日期", "最新值", "涨跌幅", "近3月涨跌幅", "近6月涨跌幅", "近1年涨跌幅", "近2年涨跌幅", "近3年涨跌幅", ] big_df["日期"] = pd.to_datetime(big_df["日期"]).dt.date big_df["最新值"] = pd.to_numeric(big_df["最新值"]) big_df["涨跌幅"] = pd.to_numeric(big_df["涨跌幅"]) big_df["近3月涨跌幅"] = pd.to_numeric(big_df["近3月涨跌幅"]) big_df["近6月涨跌幅"] = pd.to_numeric(big_df["近6月涨跌幅"]) big_df["近1年涨跌幅"] = pd.to_numeric(big_df["近1年涨跌幅"]) big_df["近2年涨跌幅"] = pd.to_numeric(big_df["近2年涨跌幅"]) big_df["近3年涨跌幅"] = pd.to_numeric(big_df["近3年涨跌幅"]) big_df.sort_values(["日期"], inplace=True) big_df.reset_index(inplace=True, drop=True) return big_df def macro_china_commodity_price_index() -> pd.DataFrame: """ 大宗商品价格 https://data.eastmoney.com/cjsj/hyzs_list_EMI00662535.html :return: 大宗商品价格 :rtype: pandas.DataFrame """ url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "sortColumns": "REPORT_DATE", "sortTypes": "-1", "pageSize": "500", "pageNumber": "1", "reportName": "RPT_INDUSTRY_INDEX", "columns": "REPORT_DATE,INDICATOR_VALUE,CHANGE_RATE,CHANGERATE_3M,CHANGERATE_6M,CHANGERATE_1Y," "CHANGERATE_2Y,CHANGERATE_3Y", "filter": '(INDICATOR_ID="EMI00662535")', "source": "WEB", "client": "WEB", } r = requests.get(url, params=params) data_json = r.json() total_page = data_json["result"]["pages"] big_df = pd.DataFrame() tqdm = get_tqdm() for page in tqdm(range(1, total_page + 1), leave=False): params.update({"pageNumber": page}) r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) big_df = pd.concat([big_df, temp_df], ignore_index=True) big_df.drop_duplicates(inplace=True) big_df.columns = [ "日期", "最新值", "涨跌幅", "近3月涨跌幅", "近6月涨跌幅", "近1年涨跌幅", "近2年涨跌幅", "近3年涨跌幅", ] big_df["日期"] = pd.to_datetime(big_df["日期"]).dt.date big_df["最新值"] = pd.to_numeric(big_df["最新值"]) big_df["涨跌幅"] = pd.to_numeric(big_df["涨跌幅"]) big_df["近3月涨跌幅"] = pd.to_numeric(big_df["近3月涨跌幅"]) big_df["近6月涨跌幅"] = pd.to_numeric(big_df["近6月涨跌幅"]) big_df["近1年涨跌幅"] = pd.to_numeric(big_df["近1年涨跌幅"]) big_df["近2年涨跌幅"] = pd.to_numeric(big_df["近2年涨跌幅"]) big_df["近3年涨跌幅"] = pd.to_numeric(big_df["近3年涨跌幅"]) big_df.sort_values(["日期"], inplace=True) big_df.reset_index(inplace=True, drop=True) return big_df def macro_global_sox_index() -> pd.DataFrame: """ 费城半导体指数 https://data.eastmoney.com/cjsj/hyzs_list_EMI00055562.html :return: 费城半导体指数 :rtype: pandas.DataFrame """ url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "sortColumns": "REPORT_DATE", "sortTypes": "-1", "pageSize": "500", "pageNumber": "1", "reportName": "RPT_INDUSTRY_INDEX", "columns": "REPORT_DATE,INDICATOR_VALUE,CHANGE_RATE,CHANGERATE_3M,CHANGERATE_6M,CHANGERATE_1Y," "CHANGERATE_2Y,CHANGERATE_3Y", "filter": '(INDICATOR_ID="EMI00055562")', "source": "WEB", "client": "WEB", } r = requests.get(url, params=params) data_json = r.json() total_page = data_json["result"]["pages"] big_df = pd.DataFrame() tqdm = get_tqdm() for page in tqdm(range(1, total_page + 1), leave=False): params.update({"pageNumber": page}) r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) big_df = pd.concat([big_df, temp_df], ignore_index=True) big_df.drop_duplicates(inplace=True) big_df.columns = [ "日期", "最新值", "涨跌幅", "近3月涨跌幅", "近6月涨跌幅", "近1年涨跌幅", "近2年涨跌幅", "近3年涨跌幅", ] big_df["日期"] = pd.to_datetime(big_df["日期"]).dt.date big_df["最新值"] = pd.to_numeric(big_df["最新值"]) big_df["涨跌幅"] = pd.to_numeric(big_df["涨跌幅"]) big_df["近3月涨跌幅"] = pd.to_numeric(big_df["近3月涨跌幅"]) big_df["近6月涨跌幅"] = pd.to_numeric(big_df["近6月涨跌幅"]) big_df["近1年涨跌幅"] = pd.to_numeric(big_df["近1年涨跌幅"]) big_df["近2年涨跌幅"] = pd.to_numeric(big_df["近2年涨跌幅"]) big_df["近3年涨跌幅"] = pd.to_numeric(big_df["近3年涨跌幅"]) big_df.sort_values(["日期"], inplace=True) big_df.reset_index(inplace=True, drop=True) return big_df def macro_china_yw_electronic_index() -> pd.DataFrame: """ 义乌小商品指数-电子元器件 https://data.eastmoney.com/cjsj/hyzs_list_EMI00055551.html :return: 义乌小商品指数-电子元器件 :rtype: pandas.DataFrame """ url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "sortColumns": "REPORT_DATE", "sortTypes": "-1", "pageSize": "500", "pageNumber": "1", "reportName": "RPT_INDUSTRY_INDEX", "columns": "REPORT_DATE,INDICATOR_VALUE,CHANGE_RATE,CHANGERATE_3M,CHANGERATE_6M,CHANGERATE_1Y," "CHANGERATE_2Y,CHANGERATE_3Y", "filter": '(INDICATOR_ID="EMI00055551")', "source": "WEB", "client": "WEB", } r = requests.get(url, params=params) data_json = r.json() total_page = data_json["result"]["pages"] big_df = pd.DataFrame() tqdm = get_tqdm() for page in tqdm(range(1, total_page + 1), leave=False): params.update({"pageNumber": page}) r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) big_df = pd.concat([big_df, temp_df], ignore_index=True) big_df.drop_duplicates(inplace=True) big_df.columns = [ "日期", "最新值", "涨跌幅", "近3月涨跌幅", "近6月涨跌幅", "近1年涨跌幅", "近2年涨跌幅", "近3年涨跌幅", ] big_df["日期"] = pd.to_datetime(big_df["日期"]).dt.date big_df["最新值"] = pd.to_numeric(big_df["最新值"]) big_df["涨跌幅"] = pd.to_numeric(big_df["涨跌幅"]) big_df["近3月涨跌幅"] = pd.to_numeric(big_df["近3月涨跌幅"]) big_df["近6月涨跌幅"] = pd.to_numeric(big_df["近6月涨跌幅"]) big_df["近1年涨跌幅"] = pd.to_numeric(big_df["近1年涨跌幅"]) big_df["近2年涨跌幅"] = pd.to_numeric(big_df["近2年涨跌幅"]) big_df["近3年涨跌幅"] = pd.to_numeric(big_df["近3年涨跌幅"]) big_df.sort_values(["日期"], inplace=True) big_df.reset_index(inplace=True, drop=True) return big_df def macro_china_construction_index() -> pd.DataFrame: """ 建材指数 https://data.eastmoney.com/cjsj/hyzs_list_EMI00662541.html :return: 建材指数 :rtype: pandas.DataFrame """ import warnings warnings.filterwarnings(action="ignore", category=FutureWarning) url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "sortColumns": "REPORT_DATE", "sortTypes": "-1", "pageSize": "500", "pageNumber": "1", "reportName": "RPT_INDUSTRY_INDEX", "columns": "REPORT_DATE,INDICATOR_VALUE,CHANGE_RATE,CHANGERATE_3M,CHANGERATE_6M," "CHANGERATE_1Y,CHANGERATE_2Y,CHANGERATE_3Y", "filter": '(INDICATOR_ID="EMI00662541")', "source": "WEB", "client": "WEB", } r = requests.get(url, params=params) data_json = r.json() total_page = data_json["result"]["pages"] big_df = pd.DataFrame() tqdm = get_tqdm() for page in tqdm(range(1, total_page + 1), leave=False): params.update({"pageNumber": page}) r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True) big_df.drop_duplicates(inplace=True) big_df.columns = [ "日期", "最新值", "涨跌幅", "近3月涨跌幅", "近6月涨跌幅", "近1年涨跌幅", "近2年涨跌幅", "近3年涨跌幅", ] big_df["日期"] = pd.to_datetime(big_df["日期"], errors="coerce").dt.date big_df["最新值"] = pd.to_numeric(big_df["最新值"], errors="coerce") big_df["涨跌幅"] = pd.to_numeric(big_df["涨跌幅"], errors="coerce") big_df["近3月涨跌幅"] = pd.to_numeric(big_df["近3月涨跌幅"], errors="coerce") big_df["近6月涨跌幅"] = pd.to_numeric(big_df["近6月涨跌幅"], errors="coerce") big_df["近1年涨跌幅"] = pd.to_numeric(big_df["近1年涨跌幅"], errors="coerce") big_df["近2年涨跌幅"] = pd.to_numeric(big_df["近2年涨跌幅"], errors="coerce") big_df["近3年涨跌幅"] = pd.to_numeric(big_df["近3年涨跌幅"], errors="coerce") big_df.sort_values(by=["日期"], inplace=True) big_df.reset_index(inplace=True, drop=True) return big_df def macro_china_construction_price_index() -> pd.DataFrame: """ 建材价格指数 https://data.eastmoney.com/cjsj/hyzs_list_EMI00237146.html :return: 建材价格指数 :rtype: pandas.DataFrame """ url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "sortColumns": "REPORT_DATE", "sortTypes": "-1", "pageSize": "500", "pageNumber": "1", "reportName": "RPT_INDUSTRY_INDEX", "columns": "REPORT_DATE,INDICATOR_VALUE,CHANGE_RATE,CHANGERATE_3M,CHANGERATE_6M,CHANGERATE_1Y," "CHANGERATE_2Y,CHANGERATE_3Y", "filter": '(INDICATOR_ID="EMI00237146")', "source": "WEB", "client": "WEB", } r = requests.get(url, params=params) data_json = r.json() total_page = data_json["result"]["pages"] big_df = pd.DataFrame() tqdm = get_tqdm() for page in tqdm(range(1, total_page + 1), leave=False): params.update({"pageNumber": page}) r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) big_df = pd.concat([big_df, temp_df], ignore_index=True) big_df.drop_duplicates(inplace=True) big_df.columns = [ "日期", "最新值", "涨跌幅", "近3月涨跌幅", "近6月涨跌幅", "近1年涨跌幅", "近2年涨跌幅", "近3年涨跌幅", ] big_df["日期"] = pd.to_datetime(big_df["日期"]).dt.date big_df["最新值"] = pd.to_numeric(big_df["最新值"]) big_df["涨跌幅"] = pd.to_numeric(big_df["涨跌幅"]) big_df["近3月涨跌幅"] = pd.to_numeric(big_df["近3月涨跌幅"]) big_df["近6月涨跌幅"] = pd.to_numeric(big_df["近6月涨跌幅"]) big_df["近1年涨跌幅"] = pd.to_numeric(big_df["近1年涨跌幅"]) big_df["近2年涨跌幅"] = pd.to_numeric(big_df["近2年涨跌幅"]) big_df["近3年涨跌幅"] = pd.to_numeric(big_df["近3年涨跌幅"]) big_df.sort_values(["日期"], inplace=True) big_df.reset_index(inplace=True, drop=True) return big_df def macro_china_lpi_index() -> pd.DataFrame: """ 物流景气指数 https://data.eastmoney.com/cjsj/hyzs_list_EMI00352262.html :return: 物流景气指数 :rtype: pandas.DataFrame """ url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "sortColumns": "REPORT_DATE", "sortTypes": "-1", "pageSize": "500", "pageNumber": "1", "reportName": "RPT_INDUSTRY_INDEX", "columns": "REPORT_DATE,INDICATOR_VALUE,CHANGE_RATE,CHANGERATE_3M,CHANGERATE_6M,CHANGERATE_1Y," "CHANGERATE_2Y,CHANGERATE_3Y", "filter": '(INDICATOR_ID="EMI00352262")', "source": "WEB", "client": "WEB", } r = requests.get(url, params=params) data_json = r.json() total_page = data_json["result"]["pages"] big_df = pd.DataFrame() tqdm = get_tqdm() for page in tqdm(range(1, total_page + 1), leave=False): params.update({"pageNumber": page}) r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) big_df = pd.concat([big_df, temp_df], ignore_index=True) big_df.drop_duplicates(inplace=True) big_df.columns = [ "日期", "最新值", "涨跌幅", "近3月涨跌幅", "近6月涨跌幅", "近1年涨跌幅", "近2年涨跌幅", "近3年涨跌幅", ] big_df["日期"] = pd.to_datetime(big_df["日期"]).dt.date big_df["最新值"] = pd.to_numeric(big_df["最新值"]) big_df["涨跌幅"] = pd.to_numeric(big_df["涨跌幅"]) big_df["近3月涨跌幅"] = pd.to_numeric(big_df["近3月涨跌幅"]) big_df["近6月涨跌幅"] = pd.to_numeric(big_df["近6月涨跌幅"]) big_df["近1年涨跌幅"] = pd.to_numeric(big_df["近1年涨跌幅"]) big_df["近2年涨跌幅"] = pd.to_numeric(big_df["近2年涨跌幅"]) big_df["近3年涨跌幅"] = pd.to_numeric(big_df["近3年涨跌幅"]) big_df.sort_values(["日期"], inplace=True) big_df.reset_index(inplace=True, drop=True) return big_df def macro_china_bdti_index() -> pd.DataFrame: """ 原油运输指数 https://data.eastmoney.com/cjsj/hyzs_list_EMI00107668.html :return: 原油运输指数 :rtype: pandas.DataFrame """ url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "sortColumns": "REPORT_DATE", "sortTypes": "-1", "pageSize": "500", "pageNumber": "1", "reportName": "RPT_INDUSTRY_INDEX", "columns": "REPORT_DATE,INDICATOR_VALUE,CHANGE_RATE,CHANGERATE_3M,CHANGERATE_6M,CHANGERATE_1Y," "CHANGERATE_2Y,CHANGERATE_3Y", "filter": '(INDICATOR_ID="EMI00107668")', "source": "WEB", "client": "WEB", } r = requests.get(url, params=params) data_json = r.json() total_page = data_json["result"]["pages"] big_df = pd.DataFrame() tqdm = get_tqdm() for page in tqdm(range(1, total_page + 1), leave=False): params.update({"pageNumber": page}) r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) big_df = pd.concat([big_df, temp_df], ignore_index=True) big_df.drop_duplicates(inplace=True) big_df.columns = [ "日期", "最新值", "涨跌幅", "近3月涨跌幅", "近6月涨跌幅", "近1年涨跌幅", "近2年涨跌幅", "近3年涨跌幅", ] big_df["日期"] = pd.to_datetime(big_df["日期"]).dt.date big_df["最新值"] = pd.to_numeric(big_df["最新值"]) big_df["涨跌幅"] = pd.to_numeric(big_df["涨跌幅"]) big_df["近3月涨跌幅"] = pd.to_numeric(big_df["近3月涨跌幅"]) big_df["近6月涨跌幅"] = pd.to_numeric(big_df["近6月涨跌幅"]) big_df["近1年涨跌幅"] = pd.to_numeric(big_df["近1年涨跌幅"]) big_df["近2年涨跌幅"] = pd.to_numeric(big_df["近2年涨跌幅"]) big_df["近3年涨跌幅"] = pd.to_numeric(big_df["近3年涨跌幅"]) big_df.sort_values(["日期"], inplace=True) big_df.reset_index(inplace=True, drop=True) return big_df def macro_china_bsi_index() -> pd.DataFrame: """ 超灵便型船运价指数 https://data.eastmoney.com/cjsj/hyzs_list_EMI00107667.html :return: 超灵便型船运价指数 :rtype: pandas.DataFrame """ url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "sortColumns": "REPORT_DATE", "sortTypes": "-1", "pageSize": "500", "pageNumber": "1", "reportName": "RPT_INDUSTRY_INDEX", "columns": "REPORT_DATE,INDICATOR_VALUE,CHANGE_RATE,CHANGERATE_3M,CHANGERATE_6M,CHANGERATE_1Y," "CHANGERATE_2Y,CHANGERATE_3Y", "filter": '(INDICATOR_ID="EMI00107667")', "source": "WEB", "client": "WEB", } r = requests.get(url, params=params) data_json = r.json() total_page = data_json["result"]["pages"] big_df = pd.DataFrame() tqdm = get_tqdm() for page in tqdm(range(1, total_page + 1), leave=False): params.update({"pageNumber": page}) r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) big_df = pd.concat([big_df, temp_df], ignore_index=True) big_df.drop_duplicates(inplace=True) big_df.columns = [ "日期", "最新值", "涨跌幅", "近3月涨跌幅", "近6月涨跌幅", "近1年涨跌幅", "近2年涨跌幅", "近3年涨跌幅", ] big_df["日期"] = pd.to_datetime(big_df["日期"]).dt.date big_df["最新值"] = pd.to_numeric(big_df["最新值"]) big_df["涨跌幅"] = pd.to_numeric(big_df["涨跌幅"]) big_df["近3月涨跌幅"] = pd.to_numeric(big_df["近3月涨跌幅"]) big_df["近6月涨跌幅"] = pd.to_numeric(big_df["近6月涨跌幅"]) big_df["近1年涨跌幅"] = pd.to_numeric(big_df["近1年涨跌幅"]) big_df["近2年涨跌幅"] = pd.to_numeric(big_df["近2年涨跌幅"]) big_df["近3年涨跌幅"] = pd.to_numeric(big_df["近3年涨跌幅"]) big_df.sort_values(["日期"], inplace=True) big_df.reset_index(inplace=True, drop=True) return big_df def _em_macro_1(em_id) -> pd.DataFrame: """ 东财宏观数据的一种通用函数 :return: 处理后的数据 :rtype: pandas.DataFrame """ url = "https://datacenter-web.eastmoney.com/api/data/v1/get" ind_id = '"' + em_id + '"' params = { "sortColumns": "REPORT_DATE", "sortTypes": "-1", "pageSize": "500", "pageNumber": "1", "reportName": "RPT_INDUSTRY_INDEX", "columns": "REPORT_DATE,INDICATOR_VALUE,CHANGE_RATE,CHANGERATE_3M," "CHANGERATE_6M,CHANGERATE_1Y,CHANGERATE_2Y,CHANGERATE_3Y", "filter": "(INDICATOR_ID=" + ind_id + ")", "source": "WEB", "client": "WEB", } r = requests.get(url, params=params) data_json = r.json() total_page = data_json["result"]["pages"] big_df = pd.DataFrame() tqdm = get_tqdm() for page in tqdm(range(1, total_page + 1), leave=False): params.update({"pageNumber": page}) r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) big_df = pd.concat([big_df, temp_df], ignore_index=True) big_df.drop_duplicates(inplace=True) big_df.columns = [ "日期", "最新值", "涨跌幅", "近3月涨跌幅", "近6月涨跌幅", "近1年涨跌幅", "近2年涨跌幅", "近3年涨跌幅", ] big_df["日期"] = pd.to_datetime(big_df["日期"], errors="coerce").dt.date big_df["最新值"] = pd.to_numeric(big_df["最新值"], errors="coerce") big_df["涨跌幅"] = pd.to_numeric(big_df["涨跌幅"], errors="coerce") big_df["近3月涨跌幅"] = pd.to_numeric(big_df["近3月涨跌幅"], errors="coerce") big_df["近6月涨跌幅"] = pd.to_numeric(big_df["近6月涨跌幅"], errors="coerce") big_df["近1年涨跌幅"] = pd.to_numeric(big_df["近1年涨跌幅"], errors="coerce") big_df["近2年涨跌幅"] = pd.to_numeric(big_df["近2年涨跌幅"], errors="coerce") big_df["近3年涨跌幅"] = pd.to_numeric(big_df["近3年涨跌幅"], errors="coerce") big_df.sort_values(by=["日期"], inplace=True) big_df.reset_index(inplace=True, drop=True) return big_df def macro_shipping_bci() -> pd.DataFrame: """ 海岬型运费指数(BCI) https://data.eastmoney.com/cjsj/hyzs_list_EMI00107666.html :return: 海岬型运费指数 :rtype: pandas.DataFrame """ ts = _em_macro_1("EMI00107666") return ts def macro_shipping_bdi() -> pd.DataFrame: """ 波罗的海干散货指数(BDI) https://data.eastmoney.com/cjsj/hyzs_list_EMI00107664.html :return: 波罗的海干散货指数 :rtype: pandas.DataFrame """ ts = _em_macro_1("EMI00107664") return ts def macro_shipping_bpi() -> pd.DataFrame: """ 巴拿马型运费指数(BPI) https://data.eastmoney.com/cjsj/hyzs_list_EMI00107665.html :return: 巴拿马型运费指数 :rtype: pandas.DataFrame """ ts = _em_macro_1("EMI00107665") return ts def macro_shipping_bcti() -> pd.DataFrame: """ 成品油运输指数（BCTI） https://data.eastmoney.com/cjsj/hyzs_list_EMI00107669.html :return: 成品油运输指数 :rtype: pandas.DataFrame """ ts = _em_macro_1("EMI00107669") return ts def macro_china_new_financial_credit() -> pd.DataFrame: """ 中国-新增信贷数据 https://data.eastmoney.com/cjsj/xzxd.html :return: 新增信贷数据 :rtype: pandas.DataFrame """ url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "columns": "REPORT_DATE,TIME,RMB_LOAN,RMB_LOAN_SAME,RMB_LOAN_SEQUENTIAL," "RMB_LOAN_ACCUMULATE,LOAN_ACCUMULATE_SAME", "pageNumber": "1", "pageSize": "2000", "sortColumns": "REPORT_DATE", "sortTypes": "-1", "source": "WEB", "client": "WEB", "reportName": "RPT_ECONOMY_RMB_LOAN", "p": "1", "pageNo": "1", "pageNum": "1", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) temp_df.columns = [ "-", "月份", "当月", "当月-同比增长", "当月-环比增长", "累计", "累计-同比增长", ] temp_df = temp_df[ ["月份", "当月", "当月-同比增长", "当月-环比增长", "累计", "累计-同比增长"] ] temp_df["当月"] = pd.to_numeric(temp_df["当月"], errors="coerce") temp_df["当月-同比增长"] = pd.to_numeric(temp_df["当月-同比增长"], errors="coerce") temp_df["当月-环比增长"] = pd.to_numeric(temp_df["当月-环比增长"], errors="coerce") temp_df["累计"] = pd.to_numeric(temp_df["累计"], errors="coerce") temp_df["累计-同比增长"] = pd.to_numeric(temp_df["累计-同比增长"], errors="coerce") return temp_df def macro_china_fx_gold() -> pd.DataFrame: """ 东方财富-外汇和黄金储备 https://data.eastmoney.com/cjsj/hjwh.html :return: 外汇和黄金储备 :rtype: pandas.DataFrame """ url = "https://datacenter-web.eastmoney.com/api/data/v1/get" headers = { "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) " "Chrome/81.0.4044.138 Safari/537.36", } params = { "columns": "REPORT_DATE,TIME,GOLD_RESERVES,GOLD_RESERVES_SAME," "GOLD_RESERVES_SEQUENTIAL,FOREX,FOREX_SAME,FOREX_SEQUENTIAL", "pageNumber": "1", "pageSize": "1000", "sortColumns": "REPORT_DATE", "sortTypes": "-1", "source": "WEB", "client": "WEB", "reportName": "RPT_ECONOMY_GOLD_CURRENCY", "p": "1", "pageNo": "1", "pageNum": "1", } r = requests.get(url, params=params, headers=headers) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) temp_df.columns = [ "-", "月份", "黄金储备-数值", "黄金储备-同比", "黄金储备-环比", "国家外汇储备-数值", "国家外汇储备-同比", "国家外汇储备-环比", ] temp_df = temp_df[ [ "月份", "黄金储备-数值", "黄金储备-同比", "黄金储备-环比", "国家外汇储备-数值", "国家外汇储备-同比", "国家外汇储备-环比", ] ] temp_df["国家外汇储备-数值"] = pd.to_numeric( temp_df["国家外汇储备-数值"], errors="coerce" ) temp_df["国家外汇储备-同比"] = pd.to_numeric( temp_df["国家外汇储备-同比"], errors="coerce" ) temp_df["国家外汇储备-环比"] = pd.to_numeric( temp_df["国家外汇储备-环比"], errors="coerce" ) temp_df["黄金储备-数值"] = pd.to_numeric(temp_df["黄金储备-数值"], errors="coerce") temp_df["黄金储备-同比"] = pd.to_numeric(temp_df["黄金储备-同比"], errors="coerce") temp_df["黄金储备-环比"] = pd.to_numeric(temp_df["黄金储备-环比"], errors="coerce") temp_df.sort_values(by=["月份"], inplace=True, ignore_index=True) return temp_df def macro_china_stock_market_cap() -> pd.DataFrame: """ 东方财富-全国股票交易统计表 https://data.eastmoney.com/cjsj/gpjytj.html :return: 全国股票交易统计表 :rtype: pandas.DataFrame """ url = "https://datacenter-web.eastmoney.com/api/data/v1/get" headers = { "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) " "Chrome/81.0.4044.138 Safari/537.36", } params = { "reportName": "RPT_ECONOMY_STOCK_STATISTICS", "columns": "REPORT_DATE,TIME,TOTAL_SHARES_SH,TOTAL_MARKE_SH,DEAL_AMOUNT_SH,VOLUME_SH,HIGH_INDEX_SH," "LOW_INDEX_SH,TOTAL_SZARES_SZ,TOTAL_MARKE_SZ,DEAL_AMOUNT_SZ,VOLUME_SZ,HIGH_INDEX_SZ,LOW_INDEX_SZ", "sortColumns": "REPORT_DATE", "sortTypes": "-1", "pageNumber": "1", "pageSize": "1000", "source": "WEB", "client": "WEB", } r = requests.get(url, params=params, headers=headers) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) temp_df.columns = [ "-", "数据日期", "发行总股本-上海", "市价总值-上海", "成交金额-上海", "成交量-上海", "A股最高综合股价指数-上海", "A股最低综合股价指数-上海", "发行总股本-深圳", "市价总值-深圳", "成交金额-深圳", "成交量-深圳", "A股最高综合股价指数-深圳", "A股最低综合股价指数-深圳", ] temp_df = temp_df[ [ "数据日期", "发行总股本-上海", "发行总股本-深圳", "市价总值-上海", "市价总值-深圳", "成交金额-上海", "成交金额-深圳", "成交量-上海", "成交量-深圳", "A股最高综合股价指数-上海", "A股最高综合股价指数-深圳", "A股最低综合股价指数-上海", "A股最低综合股价指数-深圳", ] ] temp_df["发行总股本-上海"] = pd.to_numeric( temp_df["发行总股本-上海"], errors="coerce" ) temp_df["发行总股本-深圳"] = pd.to_numeric( temp_df["发行总股本-深圳"], errors="coerce" ) temp_df["市价总值-上海"] = pd.to_numeric(temp_df["市价总值-上海"], errors="coerce") temp_df["市价总值-深圳"] = pd.to_numeric(temp_df["市价总值-深圳"], errors="coerce") temp_df["成交金额-上海"] = pd.to_numeric(temp_df["成交金额-上海"], errors="coerce") temp_df["成交金额-深圳"] = pd.to_numeric(temp_df["成交金额-深圳"], errors="coerce") temp_df["成交量-上海"] = pd.to_numeric(temp_df["成交量-上海"], errors="coerce") temp_df["成交量-深圳"] = pd.to_numeric(temp_df["成交量-深圳"], errors="coerce") temp_df["A股最高综合股价指数-上海"] = pd.to_numeric( temp_df["A股最高综合股价指数-上海"], errors="coerce" ) temp_df["A股最高综合股价指数-深圳"] = pd.to_numeric( temp_df["A股最高综合股价指数-深圳"], errors="coerce" ) temp_df["A股最低综合股价指数-上海"] = pd.to_numeric( temp_df["A股最低综合股价指数-上海"], errors="coerce" ) temp_df["A股最低综合股价指数-深圳"] = pd.to_numeric( temp_df["A股最低综合股价指数-深圳"], errors="coerce" ) return temp_df def macro_china_money_supply() -> pd.DataFrame: """ 东方财富-货币供应量 https://data.eastmoney.com/cjsj/hbgyl.html :return: 货币供应量 :rtype: pandas.DataFrame """ url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "columns": "REPORT_DATE,TIME,BASIC_CURRENCY,BASIC_CURRENCY_SAME,BASIC_CURRENCY_SEQUENTIAL,CURRENCY," "CURRENCY_SAME,CURRENCY_SEQUENTIAL,FREE_CASH,FREE_CASH_SAME,FREE_CASH_SEQUENTIAL", "pageNumber": "1", "pageSize": "2000", "sortColumns": "REPORT_DATE", "sortTypes": "-1", "source": "WEB", "client": "WEB", "reportName": "RPT_ECONOMY_CURRENCY_SUPPLY", "p": "1", "pageNo": "1", "pageNum": "1", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) temp_df.columns = [ "-", "月份", "货币和准货币(M2)-数量(亿元)", "货币和准货币(M2)-同比增长", "货币和准货币(M2)-环比增长", "货币(M1)-数量(亿元)", "货币(M1)-同比增长", "货币(M1)-环比增长", "流通中的现金(M0)-数量(亿元)", "流通中的现金(M0)-同比增长", "流通中的现金(M0)-环比增长", ] temp_df = temp_df[ [ "月份", "货币和准货币(M2)-数量(亿元)", "货币和准货币(M2)-同比增长", "货币和准货币(M2)-环比增长", "货币(M1)-数量(亿元)", "货币(M1)-同比增长", "货币(M1)-环比增长", "流通中的现金(M0)-数量(亿元)", "流通中的现金(M0)-同比增长", "流通中的现金(M0)-环比增长", ] ] temp_df["货币和准货币(M2)-数量(亿元)"] = pd.to_numeric( temp_df["货币和准货币(M2)-数量(亿元)"], errors="coerce" ) temp_df["货币和准货币(M2)-同比增长"] = pd.to_numeric( temp_df["货币和准货币(M2)-同比增长"], errors="coerce" ) temp_df["货币和准货币(M2)-环比增长"] = pd.to_numeric( temp_df["货币和准货币(M2)-环比增长"], errors="coerce" ) temp_df["货币(M1)-数量(亿元)"] = pd.to_numeric( temp_df["货币(M1)-数量(亿元)"], errors="coerce" ) temp_df["货币(M1)-同比增长"] = pd.to_numeric( temp_df["货币(M1)-同比增长"], errors="coerce" ) temp_df["货币(M1)-环比增长"] = pd.to_numeric( temp_df["货币(M1)-环比增长"], errors="coerce" ) temp_df["流通中的现金(M0)-数量(亿元)"] = pd.to_numeric( temp_df["流通中的现金(M0)-数量(亿元)"], errors="coerce" ) temp_df["流通中的现金(M0)-同比增长"] = pd.to_numeric( temp_df["流通中的现金(M0)-同比增长"], errors="coerce" ) temp_df["流通中的现金(M0)-环比增长"] = pd.to_numeric( temp_df["流通中的现金(M0)-环比增长"], errors="coerce" ) return temp_df def macro_china_cpi() -> pd.DataFrame: """ 东方财富-中国居民消费价格指数 https://data.eastmoney.com/cjsj/cpi.html :return: 东方财富-中国居民消费价格指数 :rtype: pandas.DataFrame """ url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "columns": "REPORT_DATE,TIME,NATIONAL_SAME,NATIONAL_BASE,NATIONAL_SEQUENTIAL,NATIONAL_ACCUMULATE," "CITY_SAME,CITY_BASE,CITY_SEQUENTIAL,CITY_ACCUMULATE,RURAL_SAME," "RURAL_BASE,RURAL_SEQUENTIAL,RURAL_ACCUMULATE", "pageNumber": "1", "pageSize": "2000", "sortColumns": "REPORT_DATE", "sortTypes": "-1", "source": "WEB", "client": "WEB", "reportName": "RPT_ECONOMY_CPI", "p": "1", "pageNo": "1", "pageNum": "1", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) temp_df.columns = [ "-", "月份", "全国-同比增长", "全国-当月", "全国-环比增长", "全国-累计", "城市-同比增长", "城市-当月", "城市-环比增长", "城市-累计", "农村-同比增长", "农村-当月", "农村-环比增长", "农村-累计", ] temp_df = temp_df[ [ "月份", "全国-当月", "全国-同比增长", "全国-环比增长", "全国-累计", "城市-当月", "城市-同比增长", "城市-环比增长", "城市-累计", "农村-当月", "农村-同比增长", "农村-环比增长", "农村-累计", ] ] temp_df["全国-当月"] = pd.to_numeric(temp_df["全国-当月"], errors="coerce") temp_df["全国-同比增长"] = pd.to_numeric(temp_df["全国-同比增长"], errors="coerce") temp_df["全国-环比增长"] = pd.to_numeric(temp_df["全国-环比增长"], errors="coerce") temp_df["全国-累计"] = pd.to_numeric(temp_df["全国-累计"], errors="coerce") temp_df["城市-当月"] = pd.to_numeric(temp_df["城市-当月"], errors="coerce") temp_df["城市-同比增长"] = pd.to_numeric(temp_df["城市-同比增长"], errors="coerce") temp_df["城市-环比增长"] = pd.to_numeric(temp_df["城市-环比增长"], errors="coerce") temp_df["城市-累计"] = pd.to_numeric(temp_df["城市-累计"], errors="coerce") temp_df["农村-当月"] = pd.to_numeric(temp_df["农村-当月"], errors="coerce") temp_df["农村-同比增长"] = pd.to_numeric(temp_df["农村-同比增长"], errors="coerce") temp_df["农村-环比增长"] = pd.to_numeric(temp_df["农村-环比增长"], errors="coerce") temp_df["农村-累计"] = pd.to_numeric(temp_df["农村-累计"], errors="coerce") return temp_df def macro_china_gdp() -> pd.DataFrame: """ 东方财富-中国国内生产总值 https://data.eastmoney.com/cjsj/gdp.html :return: 东方财富中国国内生产总值 :rtype: pandas.DataFrame """ url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "columns": "REPORT_DATE,TIME,DOMESTICL_PRODUCT_BASE,FIRST_PRODUCT_BASE,SECOND_PRODUCT_BASE," "THIRD_PRODUCT_BASE,SUM_SAME,FIRST_SAME,SECOND_SAME,THIRD_SAME", "pageNumber": "1", "pageSize": "2000", "sortColumns": "REPORT_DATE", "sortTypes": "-1", "source": "WEB", "client": "WEB", "reportName": "RPT_ECONOMY_GDP", "p": "1", "pageNo": "1", "pageNum": "1", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) temp_df.columns = [ "-", "季度", "国内生产总值-绝对值", "第一产业-绝对值", "第二产业-绝对值", "第三产业-绝对值", "国内生产总值-同比增长", "第一产业-同比增长", "第二产业-同比增长", "第三产业-同比增长", ] temp_df = temp_df[ [ "季度", "国内生产总值-绝对值", "国内生产总值-同比增长", "第一产业-绝对值", "第一产业-同比增长", "第二产业-绝对值", "第二产业-同比增长", "第三产业-绝对值", "第三产业-同比增长", ] ] temp_df["国内生产总值-绝对值"] = pd.to_numeric( temp_df["国内生产总值-绝对值"], errors="coerce" ) temp_df["国内生产总值-同比增长"] = pd.to_numeric( temp_df["国内生产总值-同比增长"], errors="coerce" ) temp_df["第一产业-绝对值"] = pd.to_numeric( temp_df["第一产业-绝对值"], errors="coerce" ) temp_df["第一产业-同比增长"] = pd.to_numeric( temp_df["第一产业-同比增长"], errors="coerce" ) temp_df["第二产业-绝对值"] = pd.to_numeric( temp_df["第二产业-绝对值"], errors="coerce" ) temp_df["第二产业-同比增长"] = pd.to_numeric( temp_df["第二产业-同比增长"], errors="coerce" ) temp_df["第三产业-绝对值"] = pd.to_numeric( temp_df["第三产业-绝对值"], errors="coerce" ) temp_df["第三产业-同比增长"] = pd.to_numeric( temp_df["第三产业-同比增长"], errors="coerce" ) return temp_df def macro_china_ppi() -> pd.DataFrame: """ 东方财富-中国工业品出厂价格指数 https://data.eastmoney.com/cjsj/ppi.html :return: 东方财富中国工业品出厂价格指数 :rtype: pandas.DataFrame """ url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "columns": "REPORT_DATE,TIME,BASE,BASE_SAME,BASE_ACCUMULATE", "pageNumber": "1", "pageSize": "2000", "sortColumns": "REPORT_DATE", "sortTypes": "-1", "source": "WEB", "client": "WEB", "reportName": "RPT_ECONOMY_PPI", "p": "1", "pageNo": "1", "pageNum": "1", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) temp_df.columns = [ "-", "月份", "当月", "当月同比增长", "累计", ] temp_df = temp_df[ [ "月份", "当月", "当月同比增长", "累计", ] ] temp_df["当月"] = pd.to_numeric(temp_df["当月"], errors="coerce") temp_df["当月同比增长"] = pd.to_numeric(temp_df["当月同比增长"], errors="coerce") temp_df["累计"] = pd.to_numeric(temp_df["累计"], errors="coerce") return temp_df def macro_china_pmi() -> pd.DataFrame: """ 东方财富-中国采购经理人指数 https://data.eastmoney.com/cjsj/pmi.html :return: 东方财富中国采购经理人指数 :rtype: pandas.DataFrame """ url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "columns": "REPORT_DATE,TIME,MAKE_INDEX,MAKE_SAME,NMAKE_INDEX,NMAKE_SAME", "pageNumber": "1", "pageSize": "2000", "sortColumns": "REPORT_DATE", "sortTypes": "-1", "source": "WEB", "client": "WEB", "reportName": "RPT_ECONOMY_PMI", "p": "1", "pageNo": "1", "pageNum": "1", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) temp_df.columns = [ "-", "月份", "制造业-指数", "制造业-同比增长", "非制造业-指数", "非制造业-同比增长", ] temp_df = temp_df[ [ "月份", "制造业-指数", "制造业-同比增长", "非制造业-指数", "非制造业-同比增长", ] ] temp_df["制造业-指数"] = pd.to_numeric(temp_df["制造业-指数"], errors="coerce") temp_df["制造业-同比增长"] = pd.to_numeric( temp_df["制造业-同比增长"], errors="coerce" ) temp_df["非制造业-指数"] = pd.to_numeric(temp_df["非制造业-指数"], errors="coerce") temp_df["非制造业-同比增长"] = pd.to_numeric( temp_df["非制造业-同比增长"], errors="coerce" ) return temp_df def macro_china_gdzctz() -> pd.DataFrame: """ 东方财富-中国城镇固定资产投资 https://data.eastmoney.com/cjsj/gdzctz.html :return: 东方财富中国城镇固定资产投资 :rtype: pandas.DataFrame """ url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "columns": "REPORT_DATE,TIME,BASE,BASE_SAME,BASE_SEQUENTIAL,BASE_ACCUMULATE", "pageNumber": "1", "pageSize": "2000", "sortColumns": "REPORT_DATE", "sortTypes": "-1", "source": "WEB", "client": "WEB", "reportName": "RPT_ECONOMY_ASSET_INVEST", "p": "1", "pageNo": "1", "pageNum": "1", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) temp_df.columns = [ "-", "月份", "当月", "同比增长", "环比增长", "自年初累计", ] temp_df = temp_df[ [ "月份", "当月", "同比增长", "环比增长", "自年初累计", ] ] temp_df["当月"] = pd.to_numeric(temp_df["当月"], errors="coerce") temp_df["同比增长"] = pd.to_numeric(temp_df["同比增长"], errors="coerce") temp_df["环比增长"] = pd.to_numeric(temp_df["环比增长"], errors="coerce") temp_df["自年初累计"] = pd.to_numeric(temp_df["自年初累计"], errors="coerce") return temp_df def macro_china_hgjck() -> pd.DataFrame: """ 东方财富-海关进出口增减情况一览表 https://data.eastmoney.com/cjsj/hgjck.html :return: 东方财富-海关进出口增减情况一览表 :rtype: pandas.DataFrame """ url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "columns": "REPORT_DATE,TIME,EXIT_BASE,IMPORT_BASE,EXIT_BASE_SAME,IMPORT_BASE_SAME," "EXIT_BASE_SEQUENTIAL,IMPORT_BASE_SEQUENTIAL,EXIT_ACCUMULATE," "IMPORT_ACCUMULATE,EXIT_ACCUMULATE_SAME,IMPORT_ACCUMULATE_SAME", "pageNumber": "1", "pageSize": "2000", "sortColumns": "REPORT_DATE", "sortTypes": "-1", "source": "WEB", "client": "WEB", "reportName": "RPT_ECONOMY_CUSTOMS", "p": "1", "pageNo": "1", "pageNum": "1", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) temp_df.rename( columns={ "REPORT_DATE": "-", "TIME": "月份", "EXIT_BASE": "当月出口额-金额", "IMPORT_BASE": "当月进口额-金额", "EXIT_BASE_SAME": "当月出口额-同比增长", "IMPORT_BASE_SAME": "当月进口额-同比增长", "EXIT_BASE_SEQUENTIAL": "当月出口额-环比增长", "IMPORT_BASE_SEQUENTIAL": "当月进口额-环比增长", "EXIT_ACCUMULATE": "累计出口额-金额", "IMPORT_ACCUMULATE": "累计进口额-金额", "EXIT_ACCUMULATE_SAME": "累计出口额-同比增长", "IMPORT_ACCUMULATE_SAME": "累计进口额-同比增长", }, inplace=True, ) temp_df = temp_df[ [ "月份", "当月出口额-金额", "当月出口额-同比增长", "当月出口额-环比增长", "当月进口额-金额", "当月进口额-同比增长", "当月进口额-环比增长", "累计出口额-金额", "累计出口额-同比增长", "累计进口额-金额", "累计进口额-同比增长", ] ] temp_df["当月出口额-金额"] = pd.to_numeric( temp_df["当月出口额-金额"], errors="coerce" ) temp_df["当月出口额-同比增长"] = pd.to_numeric( temp_df["当月出口额-同比增长"], errors="coerce" ) temp_df["当月出口额-环比增长"] = pd.to_numeric( temp_df["当月出口额-环比增长"], errors="coerce" ) temp_df["当月进口额-金额"] = pd.to_numeric( temp_df["当月进口额-金额"], errors="coerce" ) temp_df["当月进口额-同比增长"] = pd.to_numeric( temp_df["当月进口额-同比增长"], errors="coerce" ) temp_df["当月进口额-环比增长"] = pd.to_numeric( temp_df["当月进口额-环比增长"], errors="coerce" ) temp_df["累计出口额-金额"] = pd.to_numeric( temp_df["累计出口额-金额"], errors="coerce" ) temp_df["累计出口额-同比增长"] = pd.to_numeric( temp_df["累计出口额-同比增长"], errors="coerce" ) temp_df["累计进口额-金额"] = pd.to_numeric( temp_df["累计进口额-金额"], errors="coerce" ) temp_df["累计进口额-同比增长"] = pd.to_numeric( temp_df["累计进口额-同比增长"], errors="coerce" ) return temp_df def macro_china_czsr() -> pd.DataFrame: """ 东方财富-财政收入 https://data.eastmoney.com/cjsj/czsr.html :return: 东方财富-财政收入 :rtype: pandas.DataFrame """ url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "columns": "REPORT_DATE,TIME,BASE,BASE_SAME,BASE_SEQUENTIAL,BASE_ACCUMULATE,ACCUMULATE_SAME", "pageNumber": "1", "pageSize": "2000", "sortColumns": "REPORT_DATE", "sortTypes": "-1", "source": "WEB", "client": "WEB", "reportName": "RPT_ECONOMY_INCOME", "p": "1", "pageNo": "1", "pageNum": "1", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) temp_df.columns = [ "-", "月份", "当月", "当月-同比增长", "当月-环比增长", "累计", "累计-同比增长", ] temp_df = temp_df[ [ "月份", "当月", "当月-同比增长", "当月-环比增长", "累计", "累计-同比增长", ] ] temp_df["当月"] = pd.to_numeric(temp_df["当月"], errors="coerce") temp_df["当月-同比增长"] = pd.to_numeric(temp_df["当月-同比增长"], errors="coerce") temp_df["当月-环比增长"] = pd.to_numeric(temp_df["当月-环比增长"], errors="coerce") temp_df["累计"] = pd.to_numeric(temp_df["累计"], errors="coerce") temp_df["累计-同比增长"] = pd.to_numeric(temp_df["累计-同比增长"], errors="coerce") temp_df.sort_values(by=["月份"], ignore_index=True, inplace=True) return temp_df def macro_china_whxd() -> pd.DataFrame: """ 东方财富-外汇贷款数据 https://data.eastmoney.com/cjsj/whxd.html :return: 东方财富-外汇贷款数据 :rtype: pandas.DataFrame """ url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "columns": "REPORT_DATE,TIME,BASE,BASE_SAME,BASE_SEQUENTIAL,BASE_ACCUMULATE", "pageNumber": "1", "pageSize": "2000", "sortColumns": "REPORT_DATE", "sortTypes": "-1", "source": "WEB", "client": "WEB", "reportName": "RPT_ECONOMY_FOREX_LOAN", "p": "1", "pageNo": "1", "pageNum": "1", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) temp_df.columns = [ "-", "月份", "当月", "同比增长", "环比增长", "累计", ] temp_df = temp_df[ [ "月份", "当月", "同比增长", "环比增长", "累计", ] ] temp_df["当月"] = pd.to_numeric(temp_df["当月"], errors="coerce") temp_df["同比增长"] = pd.to_numeric(temp_df["同比增长"], errors="coerce") temp_df["环比增长"] = pd.to_numeric(temp_df["环比增长"], errors="coerce") temp_df["累计"] = pd.to_numeric(temp_df["累计"], errors="coerce") temp_df.sort_values(by=["月份"], ignore_index=True, inplace=True) return temp_df def macro_china_wbck() -> pd.DataFrame: """ 东方财富-本外币存款 https://data.eastmoney.com/cjsj/wbck.html :return: 东方财富-本外币存款 :rtype: pandas.DataFrame """ url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "columns": "REPORT_DATE,TIME,BASE,BASE_SAME,BASE_SEQUENTIAL,BASE_ACCUMULATE", "pageNumber": "1", "pageSize": "2000", "sortColumns": "REPORT_DATE", "sortTypes": "-1", "source": "WEB", "client": "WEB", "reportName": "RPT_ECONOMY_FOREX_DEPOSIT", "p": "1", "pageNo": "1", "pageNum": "1", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) temp_df.columns = [ "-", "月份", "当月", "同比增长", "环比增长", "累计", ] temp_df = temp_df[ [ "月份", "当月", "同比增长", "环比增长", "累计", ] ] temp_df["当月"] = pd.to_numeric(temp_df["当月"], errors="coerce") temp_df["同比增长"] = pd.to_numeric(temp_df["同比增长"], errors="coerce") temp_df["环比增长"] = pd.to_numeric(temp_df["环比增长"], errors="coerce") temp_df["累计"] = pd.to_numeric(temp_df["累计"], errors="coerce") return temp_df def macro_china_xfzxx() -> pd.DataFrame: """ 东方财富网-经济数据一览-消费者信心指数 https://data.eastmoney.com/cjsj/xfzxx.html :return: 消费者信心指数 :rtype: pandas.DataFrame """ url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "columns": "REPORT_DATE,TIME,CONSUMERS_FAITH_INDEX,FAITH_INDEX_SAME,FAITH_INDEX_SEQUENTIAL," "CONSUMERS_ASTIS_INDEX,ASTIS_INDEX_SAME,ASTIS_INDEX_SEQUENTIAL,CONSUMERS_EXPECT_INDEX," "EXPECT_INDEX_SAME,EXPECT_INDEX_SEQUENTIAL", "pageNumber": "1", "pageSize": "2000", "sortColumns": "REPORT_DATE", "sortTypes": "-1", "source": "WEB", "client": "WEB", "reportName": "RPT_ECONOMY_FAITH_INDEX", "p": "1", "pageNo": "1", "pageNum": "1", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) temp_df.columns = [ "-", "月份", "消费者信心指数-指数值", "消费者信心指数-同比增长", "消费者信心指数-环比增长", "消费者满意指数-指数值", "消费者满意指数-同比增长", "消费者满意指数-环比增长", "消费者预期指数-指数值", "消费者预期指数-同比增长", "消费者预期指数-环比增长", ] temp_df = temp_df[ [ "月份", "消费者信心指数-指数值", "消费者信心指数-同比增长", "消费者信心指数-环比增长", "消费者满意指数-指数值", "消费者满意指数-同比增长", "消费者满意指数-环比增长", "消费者预期指数-指数值", "消费者预期指数-同比增长", "消费者预期指数-环比增长", ] ] temp_df["消费者信心指数-指数值"] = pd.to_numeric( temp_df["消费者信心指数-指数值"], errors="coerce" ) temp_df["消费者信心指数-同比增长"] = pd.to_numeric( temp_df["消费者信心指数-同比增长"], errors="coerce" ) temp_df["消费者信心指数-环比增长"] = pd.to_numeric( temp_df["消费者信心指数-环比增长"], errors="coerce" ) temp_df["消费者满意指数-指数值"] = pd.to_numeric( temp_df["消费者满意指数-指数值"], errors="coerce" ) temp_df["消费者满意指数-同比增长"] = pd.to_numeric( temp_df["消费者满意指数-同比增长"], errors="coerce" ) temp_df["消费者满意指数-环比增长"] = pd.to_numeric( temp_df["消费者满意指数-环比增长"], errors="coerce" ) temp_df["消费者预期指数-指数值"] = pd.to_numeric( temp_df["消费者满意指数-指数值"], errors="coerce" ) temp_df["消费者预期指数-同比增长"] = pd.to_numeric( temp_df["消费者预期指数-同比增长"], errors="coerce" ) temp_df["消费者预期指数-环比增长"] = pd.to_numeric( temp_df["消费者预期指数-环比增长"], errors="coerce" ) return temp_df def macro_china_gyzjz() -> pd.DataFrame: """ 东方财富网-经济数据-工业增加值增长 https://data.eastmoney.com/cjsj/gyzjz.html :return: 工业增加值增长 :rtype: pandas.DataFrame """ url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "columns": "REPORT_DATE,TIME,BASE_SAME,BASE_ACCUMULATE", "pageNumber": "1", "pageSize": "2000", "sortColumns": "REPORT_DATE", "sortTypes": "-1", "source": "WEB", "client": "WEB", "reportName": "RPT_ECONOMY_INDUS_GROW", "p": "1", "pageNo": "1", "pageNum": "1", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) temp_df.columns = [ "发布时间", "月份", "同比增长", "累计增长", ] temp_df = temp_df[ [ "月份", "同比增长", "累计增长", "发布时间", ] ] temp_df["同比增长"] = pd.to_numeric(temp_df["同比增长"], errors="coerce") temp_df["累计增长"] = pd.to_numeric(temp_df["累计增长"], errors="coerce") temp_df["发布时间"] = pd.to_datetime(temp_df["发布时间"], errors="coerce").dt.date temp_df.sort_values(["发布时间"], ignore_index=True, inplace=True) return temp_df def macro_china_reserve_requirement_ratio() -> pd.DataFrame: """ 存款准备金率 https://data.eastmoney.com/cjsj/ckzbj.html :return: 存款准备金率 :rtype: pandas.DataFrame """ url = "https://datacenter-web.eastmoney.com/api/data/v1/get" params = { "columns": "REPORT_DATE,PUBLISH_DATE,TRADE_DATE,INTEREST_RATE_BB,INTEREST_RATE_BA,CHANGE_RATE_B," "INTEREST_RATE_SB,INTEREST_RATE_SA,CHANGE_RATE_S,NEXT_SH_RATE,NEXT_SZ_RATE,REMARK", "pageNumber": "1", "pageSize": "2000", "sortColumns": "PUBLISH_DATE,TRADE_DATE", "sortTypes": "-1,-1", "source": "WEB", "client": "WEB", "reportName": "RPT_ECONOMY_DEPOSIT_RESERVE", "p": "1", "pageNo": "1", "pageNum": "1", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) temp_df.columns = [ "-", "公布时间", "生效时间", "大型金融机构-调整前", "大型金融机构-调整后", "大型金融机构-调整幅度", "中小金融机构-调整前", "中小金融机构-调整后", "中小金融机构-调整幅度", "消息公布次日指数涨跌-上证", "消息公布次日指数涨跌-深证", "备注", ] temp_df = temp_df[ [ "公布时间", "生效时间", "大型金融机构-调整前", "大型金融机构-调整后", "大型金融机构-调整幅度", "中小金融机构-调整前", "中小金融机构-调整后", "中小金融机构-调整幅度", "消息公布次日指数涨跌-上证", "消息公布次日指数涨跌-深证", "备注", ] ] temp_df["大型金融机构-调整前"] = pd.to_numeric( temp_df["大型金融机构-调整前"], errors="coerce" ) temp_df["大型金融机构-调整后"] = pd.to_numeric( temp_df["大型金融机构-调整后"], errors="coerce" ) temp_df["大型金融机构-调整幅度"] = pd.to_numeric( temp_df["大型金融机构-调整幅度"], errors="coerce" ) temp_df["大型金融机构-调整前"] = pd.to_numeric( temp_df["大型金融机构-调整前"], errors="coerce" ) temp_df["大型金融机构-调整后"] = pd.to_numeric( temp_df["大型金融机构-调整后"], errors="coerce" ) temp_df["大型金融机构-调整幅度"] = pd.to_numeric( temp_df["大型金融机构-调整幅度"], errors="coerce" ) temp_df["消息公布次日指数涨跌-上证"] = pd.to_numeric( temp_df["消息公布次日指数涨跌-上证"], errors="coerce" ) temp_df["消息公布次日指数涨跌-深证"] = pd.to_numeric( temp_df["消息公布次日指数涨跌-深证"], errors="coerce" ) temp_df["消息公布次日指数涨跌-深证"] = pd.to_numeric( temp_df["消息公布次日指数涨跌-深证"], errors="coerce" ) return temp_df def macro_china_consumer_goods_retail() -> pd.DataFrame: """ 东方财富-经济数据-社会消费品零售总额 https://data.eastmoney.com/cjsj/xfp.html :return: 社会消费品零售总额 :rtype: pandas.DataFrame """ url = "https://datacenter-web.eastmoney.com/api/data/v1/get" headers = { "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) " "Chrome/81.0.4044.138 Safari/537.36", } params = { "columns": "REPORT_DATE,TIME,RETAIL_TOTAL,RETAIL_TOTAL_SAME,RETAIL_TOTAL_SEQUENTIAL," "RETAIL_TOTAL_ACCUMULATE,RETAIL_ACCUMULATE_SAME", "pageNumber": "1", "pageSize": "1000", "sortColumns": "REPORT_DATE", "sortTypes": "-1", "source": "WEB", "client": "WEB", "reportName": "RPT_ECONOMY_TOTAL_RETAIL", "p": "1", "pageNo": "1", "pageNum": "1", } r = requests.get(url, params=params, headers=headers) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) temp_df.columns = [ "-", "月份", "当月", "同比增长", "环比增长", "累计", "累计-同比增长", ] temp_df = temp_df[ [ "月份", "当月", "同比增长", "环比增长", "累计", "累计-同比增长", ] ] temp_df["当月"] = pd.to_numeric(temp_df["当月"], errors="coerce") temp_df["同比增长"] = pd.to_numeric(temp_df["同比增长"], errors="coerce") temp_df["环比增长"] = pd.to_numeric(temp_df["环比增长"], errors="coerce") temp_df["累计"] = pd.to_numeric(temp_df["累计"], errors="coerce") temp_df["累计-同比增长"] = pd.to_numeric(temp_df["累计-同比增长"], errors="coerce") return temp_df def macro_china_society_electricity() -> pd.DataFrame: """ 新浪财经-中国宏观经济数据-全社会用电分类情况表 https://finance.sina.com.cn/mac/#industry-6-0-31-1 :return: 全社会用电分类情况表 :rtype: pandas.DataFrame """ url = "https://quotes.sina.cn/mac/api/jsonp_v3.php/SINAREMOTECALLCALLBACK1601557771972/MacPage_Service.get_pagedata" params = { "cate": "industry", "event": "6", "from": "0", "num": "31", "condition": "", } r = requests.get(url, params=params) data_text = r.text data_json = demjson.decode(data_text[data_text.find("{") : -3]) page_num = math.ceil(int(data_json["count"]) / 31) big_df = pd.DataFrame(data_json["data"]) for i in range(1, page_num): params.update({"from": i * 31}) r = requests.get(url, params=params) data_text = r.text data_json = demjson.decode(data_text[data_text.find("{") : -3]) temp_df = pd.DataFrame(data_json["data"]) big_df = pd.concat([big_df, temp_df], ignore_index=True) big_df.columns = [ "统计时间", "全社会用电量", "全社会用电量同比", "各行业用电量合计", "各行业用电量合计同比", "第一产业用电量", "第一产业用电量同比", "第二产业用电量", "第二产业用电量同比", "第三产业用电量", "第三产业用电量同比", "城乡居民生活用电量合计", "城乡居民生活用电量合计同比", "城镇居民用电量", "城镇居民用电量同比", "乡村居民用电量", "乡村居民用电量同比", ] for item in big_df.columns[1:]: big_df[item] = pd.to_numeric(big_df[item], errors="coerce") big_df.sort_values(["统计时间"], inplace=True, ignore_index=True) return big_df def macro_china_society_traffic_volume() -> pd.DataFrame: """ 新浪财经-中国宏观经济数据-全社会客货运输量 https://finance.sina.com.cn/mac/#industry-10-0-31-1 :return: 全社会客货运输量 :rtype: pandas.DataFrame """ url = "https://quotes.sina.cn/mac/api/jsonp_v3.php/SINAREMOTECALLCALLBACK1601559094538/MacPage_Service.get_pagedata" params = { "cate": "industry", "event": "10", "from": "0", "num": "31", "condition": "", } r = requests.get(url, params=params) data_text = r.text data_json = demjson.decode(data_text[data_text.find("{") : -3]) page_num = math.ceil(int(data_json["count"]) / 31) big_df = pd.DataFrame(data_json["data"]["非累计"]) tqdm = get_tqdm() for i in tqdm(range(1, page_num), leave=False): params.update({"from": i * 31}) r = requests.get(url, params=params) data_text = r.text data_json = demjson.decode(data_text[data_text.find("{") : -3]) temp_df = pd.DataFrame(data_json["data"]["非累计"]) big_df = pd.concat([big_df, temp_df], ignore_index=True) big_df.columns = [item[1] for item in data_json["config"]["all"]] big_df["货运量"] = pd.to_numeric(big_df["货运量"], errors="coerce") big_df["货运量同比增长"] = pd.to_numeric(big_df["货运量同比增长"], errors="coerce") big_df["货物周转量"] = pd.to_numeric(big_df["货物周转量"], errors="coerce") big_df["公里货物周转量同比增长"] = pd.to_numeric( big_df["公里货物周转量同比增长"], errors="coerce" ) big_df["客运量"] = pd.to_numeric(big_df["客运量"], errors="coerce") big_df["客运量同比增长"] = pd.to_numeric(big_df["客运量同比增长"], errors="coerce") big_df["旅客周转量"] = pd.to_numeric(big_df["旅客周转量"], errors="coerce") big_df["公里旅客周转量同比增长"] = pd.to_numeric( big_df["公里旅客周转量同比增长"], errors="coerce" ) big_df["沿海主要港口货物吞吐量"] = pd.to_numeric( big_df["沿海主要港口货物吞吐量"], errors="coerce" ) big_df["沿海主要港口货物吞吐量同比增长"] = pd.to_numeric( big_df["沿海主要港口货物吞吐量同比增长"], errors="coerce" ) big_df["其中:外贸货物吞吐量"] = pd.to_numeric( big_df["其中:外贸货物吞吐量"], errors="coerce" ) big_df["其中:外贸货物吞吐量同比增长"] = pd.to_numeric( big_df["其中:外贸货物吞吐量同比增长"], errors="coerce" ) big_df["民航总周转量"] = pd.to_numeric(big_df["民航总周转量"], errors="coerce") big_df["公里民航总周转"] = pd.to_numeric(big_df["公里民航总周转"], errors="coerce") return big_df def macro_china_postal_telecommunicational() -> pd.DataFrame: """ 新浪财经-中国宏观经济数据-邮电业务基本情况 https://finance.sina.com.cn/mac/#industry-11-0-31-1 :return: 邮电业务基本情况 :rtype: pandas.DataFrame """ url = "https://quotes.sina.cn/mac/api/jsonp_v3.php/SINAREMOTECALLCALLBACK1601624495046/MacPage_Service.get_pagedata" params = { "cate": "industry", "event": "11", "from": "0", "num": "31", "condition": "", } r = requests.get(url, params=params) data_text = r.text data_json = demjson.decode(data_text[data_text.find("{") : -3]) page_num = math.ceil(int(data_json["count"]) / 31) big_df = pd.DataFrame(data_json["data"]["非累计"]) tqdm = get_tqdm() for i in tqdm(range(1, page_num), leave=False): params.update({"from": i * 31}) r = requests.get(url, params=params) data_text = r.text data_json = demjson.decode(data_text[data_text.find("{") : -3]) temp_df = pd.DataFrame(data_json["data"]["非累计"]) big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True) big_df.columns = [item[1] for item in data_json["config"]["all"]] for item in big_df.columns[1:]: big_df[item] = pd.to_numeric(big_df[item], errors="coerce") return big_df def macro_china_international_tourism_fx() -> pd.DataFrame: """ 新浪财经-中国宏观经济数据-国际旅游外汇收入构成 https://finance.sina.com.cn/mac/#industry-15-0-31-3 :return: 国际旅游外汇收入构成 :rtype: pandas.DataFrame """ url = "https://quotes.sina.cn/mac/api/jsonp_v3.php/SINAREMOTECALLCALLBACK1601651495761/MacPage_Service.get_pagedata" params = { "cate": "industry", "event": "15", "from": "0", "num": "31", "condition": "", } r = requests.get(url, params=params) data_text = r.text data_json = demjson.decode(data_text[data_text.find("{") : -3]) page_num = math.ceil(int(data_json["count"]) / 31) big_df = pd.DataFrame(data_json["data"]) tqdm = get_tqdm() for i in tqdm(range(1, page_num)): params.update({"from": i * 31}) r = requests.get(url, params=params) data_text = r.text data_json = demjson.decode(data_text[data_text.find("{") : -3]) temp_df = pd.DataFrame(data_json["data"]) big_df = pd.concat([big_df, temp_df], ignore_index=True) big_df.columns = [item[1] for item in data_json["config"]["all"]] big_df["数量"] = pd.to_numeric(big_df["数量"], errors="coerce") big_df["比重"] = pd.to_numeric(big_df["比重"], errors="coerce") return big_df def macro_china_passenger_load_factor() -> pd.DataFrame: """ 新浪财经-中国宏观经济数据-民航客座率及载运率 https://finance.sina.com.cn/mac/#industry-20-0-31-1 :return: 民航客座率及载运率 :rtype: pandas.DataFrame """ url = "https://quotes.sina.cn/mac/api/jsonp_v3.php/SINAREMOTECALLCALLBACK1601651495761/MacPage_Service.get_pagedata" params = { "cate": "industry", "event": "20", "from": "0", "num": "31", "condition": "", } r = requests.get(url, params=params) data_text = r.text data_json = demjson.decode(data_text[data_text.find("{") : -3]) page_num = math.ceil(int(data_json["count"]) / 31) big_df = pd.DataFrame(data_json["data"]) tqdm = get_tqdm() for i in tqdm(range(1, page_num)): params.update({"from": i * 31}) r = requests.get(url, params=params) data_text = r.text data_json = demjson.decode(data_text[data_text.find("{") : -3]) temp_df = pd.DataFrame(data_json["data"]) big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True) big_df.columns = [item[1] for item in data_json["config"]["all"]] big_df["客座率"] = pd.to_numeric(big_df["客座率"], errors="coerce") big_df["载运率"] = pd.to_numeric(big_df["载运率"], errors="coerce") return big_df def _macro_china_freight_index() -> pd.DataFrame: """ 新浪财经-中国宏观经济数据-航贸运价指数 https://finance.sina.com.cn/mac/#industry-22-0-31-2 :return: 航贸运价指数 :rtype: pandas.DataFrame """ url = "https://quotes.sina.cn/mac/api/jsonp_v3.php/SINAREMOTECALLCALLBACK1601651495761/MacPage_Service.get_pagedata" params = { "cate": "industry", "event": "22", "from": "0", "num": "31", "condition": "", } r = requests.get(url, params=params) data_text = r.text data_json = demjson.decode(data_text[data_text.find("{") : -3]) page_num = math.ceil(int(data_json["count"]) / 31) big_df = pd.DataFrame(data_json["data"]) tqdm = get_tqdm() for i in tqdm(range(1, page_num)): params.update({"from": i * 31}) r = requests.get(url, params=params) data_text = r.text data_json = demjson.decode(data_text[data_text.find("{") : -3]) temp_df = pd.DataFrame(data_json["data"]) big_df = big_df.append(temp_df, ignore_index=True) big_df.columns = [item[1] for item in data_json["config"]["all"]] return big_df def macro_china_freight_index() -> pd.DataFrame: """ 新浪财经-中国宏观经济数据-航贸运价指数 https://finance.sina.com.cn/mac/#industry-22-0-31-2 :return: 航贸运价指数 :rtype: pandas.DataFrame """ url = "http://quotes.sina.cn/mac/view/vMacExcle.php" params = { "cate": "industry", "event": "22", "from": "0", "num": 5000, "condition": "", } r = requests.get(url, params=params) columns_list = r.content.decode("gbk").split("\n")[2].split(", ") columns_list = [item.strip() for item in columns_list] content_list = r.content.decode("gbk").split("\n")[3:] big_df = ( pd.DataFrame([item.split(", ") for item in content_list], columns=columns_list) .dropna(axis=1, how="all") .dropna(axis=0) .iloc[:, :-1] ) big_df["波罗的海好望角型船运价指数BCI"] = pd.to_numeric( big_df["波罗的海好望角型船运价指数BCI"] ) big_df["灵便型船综合运价指数BHMI"] = pd.to_numeric( big_df["灵便型船综合运价指数BHMI"] ) big_df["波罗的海超级大灵便型船BSI指数"] = pd.to_numeric( big_df["波罗的海超级大灵便型船BSI指数"] ) big_df["波罗的海综合运价指数BDI"] = pd.to_numeric(big_df["波罗的海综合运价指数BDI"]) big_df["HRCI国际集装箱租船指数"] = pd.to_numeric(big_df["HRCI国际集装箱租船指数"]) big_df["油轮运价指数成品油运价指数BCTI"] = pd.to_numeric( big_df["油轮运价指数成品油运价指数BCTI"] ) big_df["油轮运价指数原油运价指数BDTI"] = pd.to_numeric( big_df["油轮运价指数原油运价指数BDTI"] ) return big_df def macro_china_central_bank_balance() -> pd.DataFrame: """ 新浪财经-中国宏观经济数据-央行货币当局资产负债 https://finance.sina.com.cn/mac/#fininfo-8-0-31-2 :return: 央行货币当局资产负债 :rtype: pandas.DataFrame """ url = "https://quotes.sina.cn/mac/api/jsonp_v3.php/SINAREMOTECALLCALLBACK1601651495761/MacPage_Service.get_pagedata" params = { "cate": "fininfo", "event": "8", "from": "0", "num": "31", "condition": "", } r = requests.get(url, params=params) data_text = r.text data_json = demjson.decode(data_text[data_text.find("{") : -3]) page_num = math.ceil(int(data_json["count"]) / 31) big_df = pd.DataFrame(data_json["data"]) tqdm = get_tqdm() for i in tqdm(range(1, page_num)): params.update({"from": i * 31}) r = requests.get(url, params=params) data_text = r.text data_json = demjson.decode(data_text[data_text.find("{") : -3]) temp_df = pd.DataFrame(data_json["data"]) big_df = pd.concat([big_df, temp_df], ignore_index=True) big_df.columns = [item[1] for item in data_json["config"]["all"]] for item in big_df.columns[1:]: big_df[item] = pd.to_numeric(big_df[item], errors="coerce") return big_df def macro_china_insurance() -> pd.DataFrame: """ 新浪财经-中国宏观经济数据-保险业经营情况 https://finance.sina.com.cn/mac/#fininfo-19-0-31-3 :return: 保险业经营情况 :rtype: pandas.DataFrame """ url = "https://quotes.sina.cn/mac/api/jsonp_v3.php/SINAREMOTECALLCALLBACK1601651495761/MacPage_Service.get_pagedata" params = { "cate": "fininfo", "event": "19", "from": "0", "num": "31", "condition": "", } r = requests.get(url, params=params) data_text = r.text data_json = demjson.decode(data_text[data_text.find("{") : -3]) page_num = math.ceil(int(data_json["count"]) / 31) big_df = pd.DataFrame(data_json["data"]) tqdm = get_tqdm() for i in tqdm(range(1, page_num)): params.update({"from": i * 31}) r = requests.get(url, params=params) data_text = r.text data_json = demjson.decode(data_text[data_text.find("{") : -3]) temp_df = pd.DataFrame(data_json["data"]) big_df = pd.concat([big_df, temp_df], ignore_index=True) big_df.columns = [item[1] for item in data_json["config"]["all"]] for item in big_df.columns[2:]: big_df[item] = pd.to_numeric(big_df[item], errors="coerce") return big_df def macro_china_supply_of_money() -> pd.DataFrame: """ 新浪财经-中国宏观经济数据-货币供应量 https://finance.sina.com.cn/mac/#fininfo-1-0-31-1 :return: 货币供应量 :rtype: pandas.DataFrame """ url = "https://quotes.sina.cn/mac/api/jsonp_v3.php/SINAREMOTECALLCALLBACK1601651495761/MacPage_Service.get_pagedata" params = { "cate": "fininfo", "event": "1", "from": "0", "num": "31", "condition": "", } r = requests.get(url, params=params) data_text = r.text data_json = demjson.decode(data_text[data_text.find("{") : -3]) page_num = math.ceil(int(data_json["count"]) / 31) big_df = pd.DataFrame(data_json["data"]) tqdm = get_tqdm() for i in tqdm(range(1, page_num)): params.update({"from": i * 31}) r = requests.get(url, params=params) data_text = r.text data_json = demjson.decode(data_text[data_text.find("{") : -3]) temp_df = pd.DataFrame(data_json["data"]) big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True) big_df.columns = [item[1] for item in data_json["config"]["all"]] for item in big_df.columns[1:]: big_df[item] = pd.to_numeric(big_df[item], errors="coerce") return big_df def macro_china_foreign_exchange_gold() -> pd.DataFrame: """ 央行黄金和外汇储备 https://finance.sina.com.cn/mac/#fininfo-5-0-31-2 :return: 央行黄金和外汇储备 :rtype: pandas.DataFrame """ url = "https://quotes.sina.cn/mac/api/jsonp_v3.php/SINAREMOTECALLCALLBACK1601651495761/MacPage_Service.get_pagedata" params = { "cate": "fininfo", "event": "5", "from": "0", "num": "31", "condition": "", } r = requests.get(url, params=params) data_text = r.text data_json = demjson.decode(data_text[data_text.find("{") : -3]) page_num = math.ceil(int(data_json["count"]) / 31) big_df = pd.DataFrame(data_json["data"]) tqdm = get_tqdm() for i in tqdm(range(1, page_num), leave=False): params.update({"from": i * 31}) r = requests.get(url, params=params) data_text = r.text data_json = demjson.decode(data_text[data_text.find("{") : -3]) temp_df = pd.DataFrame(data_json["data"]) big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True) big_df.columns = [item[1] for item in data_json["config"]["all"]] big_df.sort_values(by=["统计时间"], ignore_index=True, inplace=True) big_df["黄金储备"] = pd.to_numeric(big_df["黄金储备"], errors="coerce") big_df["国家外汇储备"] = pd.to_numeric(big_df["国家外汇储备"], errors="coerce") return big_df def macro_china_retail_price_index() -> pd.DataFrame: """ 商品零售价格指数 https://finance.sina.com.cn/mac/#price-12-0-31-1 :return: 商品零售价格指数 :rtype: pandas.DataFrame """ url = "https://quotes.sina.cn/mac/api/jsonp_v3.php/SINAREMOTECALLCALLBACK1601651495761/MacPage_Service.get_pagedata" params = { "cate": "price", "event": "12", "from": "0", "num": "31", "condition": "", } r = requests.get(url, params=params) data_text = r.text data_json = demjson.decode(data_text[data_text.find("{") : -3]) page_num = math.ceil(int(data_json["count"]) / 31) big_df = pd.DataFrame(data_json["data"]) tqdm = get_tqdm() for i in tqdm(range(1, page_num), leave=False): params.update({"from": i * 31}) r = requests.get(url, params=params) data_text = r.text data_json = demjson.decode(data_text[data_text.find("{") : -3]) temp_df = pd.DataFrame(data_json["data"]) big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True) big_df.columns = [item[1] for item in data_json["config"]["all"]] big_df.sort_values(by=["统计月份"], ignore_index=True, inplace=True) big_df["零售商品价格指数"] = pd.to_numeric( big_df["零售商品价格指数"], errors="coerce" ) return big_df def macro_china_real_estate() -> pd.DataFrame: """ 国房景气指数 https://data.eastmoney.com/cjsj/hyzs_list_EMM00121987.html :return: 国房景气指数 :rtype: pandas.DataFrame """ url = "https://datacenter-web.eastmoney.com/api/data/v1/get" headers = { "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) " "Chrome/81.0.4044.138 Safari/537.36", } params = { "sortColumns": "REPORT_DATE", "sortTypes": "-1", "pageSize": "1000", "pageNumber": "1", "reportName": "RPT_INDUSTRY_INDEX", "columns": "REPORT_DATE,INDICATOR_VALUE,CHANGE_RATE,CHANGERATE_3M,CHANGERATE_6M,CHANGERATE_1Y," "CHANGERATE_2Y,CHANGERATE_3Y", "filter": '(INDICATOR_ID="EMM00121987")', "source": "WEB", "client": "WEB", } r = requests.get(url, params=params, headers=headers) data_json = r.json() total_page = data_json["result"]["pages"] big_df = pd.DataFrame() tqdm = get_tqdm() for page in tqdm(range(1, total_page + 1), leave=False): params.update({"pageNumber": page}) r = requests.get(url, params=params, headers=headers) 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 = [ "日期", "最新值", "涨跌幅", "近3月涨跌幅", "近6月涨跌幅", "近1年涨跌幅", "近2年涨跌幅", "近3年涨跌幅", ] big_df["日期"] = pd.to_datetime(big_df["日期"], errors="coerce").dt.date big_df["最新值"] = pd.to_numeric(big_df["最新值"], errors="coerce") big_df["涨跌幅"] = pd.to_numeric(big_df["涨跌幅"], errors="coerce") big_df["近3月涨跌幅"] = pd.to_numeric(big_df["近3月涨跌幅"], errors="coerce") big_df["近6月涨跌幅"] = pd.to_numeric(big_df["近6月涨跌幅"], errors="coerce") big_df["近1年涨跌幅"] = pd.to_numeric(big_df["近1年涨跌幅"], errors="coerce") big_df["近2年涨跌幅"] = pd.to_numeric(big_df["近2年涨跌幅"], errors="coerce") big_df["近3年涨跌幅"] = pd.to_numeric(big_df["近3年涨跌幅"], errors="coerce") big_df.sort_values(by=["日期"], inplace=True) big_df.drop_duplicates(inplace=True) big_df.reset_index(inplace=True, drop=True) return big_df if __name__ == "__main__": # 企业商品价格指数 macro_china_qyspjg_df = macro_china_qyspjg() print(macro_china_qyspjg_df) # 外商直接投资数据 macro_china_fdi_df = macro_china_fdi() print(macro_china_fdi_df) # 社会融资规模增量 macro_china_shrzgm_df = macro_china_shrzgm() print(macro_china_shrzgm_df) # 城镇调查失业率 macro_china_urban_unemployment_df = macro_china_urban_unemployment() print(macro_china_urban_unemployment_df) # 金十数据中心-经济指标-中国-国民经济运行状况-经济状况-中国GDP年率报告 macro_china_gdp_yearly_df = macro_china_gdp_yearly() print(macro_china_gdp_yearly_df) # 金十数据中心-经济指标-中国-国民经济运行状况-物价水平-中国CPI年率报告 macro_china_cpi_yearly_df = macro_china_cpi_yearly() print(macro_china_cpi_yearly_df) # 金十数据中心-经济指标-中国-国民经济运行状况-物价水平-中国CPI月率报告 macro_china_cpi_monthly_df = macro_china_cpi_monthly() print(macro_china_cpi_monthly_df) # 金十数据中心-经济指标-中国-国民经济运行状况-物价水平-中国PPI年率报告 macro_china_ppi_yearly_df = macro_china_ppi_yearly() print(macro_china_ppi_yearly_df) # 金十数据中心-经济指标-中国-贸易状况-以美元计算出口年率报告 macro_china_exports_yoy_df = macro_china_exports_yoy() print(macro_china_exports_yoy_df) # 金十数据中心-经济指标-中国-贸易状况-以美元计算进口年率 macro_china_imports_yoy_df = macro_china_imports_yoy() print(macro_china_imports_yoy_df) # 金十数据中心-经济指标-中国-贸易状况-以美元计算贸易帐(亿美元) macro_china_trade_balance_df = macro_china_trade_balance() print(macro_china_trade_balance_df) # 金十数据中心-经济指标-中国-产业指标-规模以上工业增加值年率 macro_china_industrial_production_yoy_df = macro_china_industrial_production_yoy() print(macro_china_industrial_production_yoy_df) # 金十数据中心-经济指标-中国-产业指标-官方制造业PMI macro_china_pmi_yearly_df = macro_china_pmi_yearly() print(macro_china_pmi_yearly_df) # 金十数据中心-经济指标-中国-产业指标-财新制造业PMI终值 macro_china_cx_pmi_yearly_df = macro_china_cx_pmi_yearly() print(macro_china_cx_pmi_yearly_df) # 金十数据中心-经济指标-中国-产业指标-财新服务业PMI macro_china_cx_services_pmi_yearly_df = macro_china_cx_services_pmi_yearly() print(macro_china_cx_services_pmi_yearly_df) # 金十数据中心-经济指标-中国-产业指标-中国官方非制造业PMI macro_china_non_man_pmi_df = macro_china_non_man_pmi() print(macro_china_non_man_pmi_df) # 金十数据中心-经济指标-中国-金融指标-外汇储备(亿美元) macro_china_fx_reserves_yearly_df = macro_china_fx_reserves_yearly() print(macro_china_fx_reserves_yearly_df) # 金十数据中心-经济指标-中国-金融指标-M2货币供应年率 macro_china_m2_yearly_df = macro_china_m2_yearly() print(macro_china_m2_yearly_df) # 金十数据中心-经济指标-中国-金融指标-上海银行业同业拆借报告 macro_china_shibor_all_df = macro_china_shibor_all() print(macro_china_shibor_all_df) # 金十数据中心-经济指标-中国-金融指标-人民币香港银行同业拆息 macro_china_hk_market_info_df = macro_china_hk_market_info() print(macro_china_hk_market_info_df) # 金十数据中心-经济指标-中国-其他-中国日度沿海六大电库存数据 macro_china_daily_energy_df = macro_china_daily_energy() print(macro_china_daily_energy_df) # 金十数据中心-经济指标-中国-其他-中国人民币汇率中间价报告 macro_china_rmb_df = macro_china_rmb() print(macro_china_rmb_df) # 金十数据中心-经济指标-中国-其他-深圳融资融券报告 macro_china_market_margin_sz_df = macro_china_market_margin_sz() print(macro_china_market_margin_sz_df) # 金十数据中心-经济指标-中国-其他-上海融资融券报告 macro_china_market_margin_sh_df = macro_china_market_margin_sh() print(macro_china_market_margin_sh_df) # 金十数据中心-经济指标-中国-其他-上海黄金交易所报告 macro_china_au_report_df = macro_china_au_report() print(macro_china_au_report_df) # 中国-新房价指数 macro_china_new_house_price_df = macro_china_new_house_price() print(macro_china_new_house_price_df) # 中国-企业景气及企业家信心指数 macro_china_enterprise_boom_index_df = macro_china_enterprise_boom_index() print(macro_china_enterprise_boom_index_df) # 中国-全国税收收入 macro_china_national_tax_receipts_df = macro_china_national_tax_receipts() print(macro_china_national_tax_receipts_df) # 中国-新增信贷数据 macro_china_new_financial_credit_df = macro_china_new_financial_credit() print(macro_china_new_financial_credit_df) # 中国-外汇和黄金储备 macro_china_fx_gold_df = macro_china_fx_gold() print(macro_china_fx_gold_df) macro_china_stock_market_cap_df = macro_china_stock_market_cap() print(macro_china_stock_market_cap_df) macro_china_money_supply_df = macro_china_money_supply() print(macro_china_money_supply_df) macro_china_cpi_df = macro_china_cpi() print(macro_china_cpi_df) macro_china_gdp_df = macro_china_gdp() print(macro_china_gdp_df) macro_china_ppi_df = macro_china_ppi() print(macro_china_ppi_df) macro_china_pmi_df = macro_china_pmi() print(macro_china_pmi_df) macro_china_gdzctz_df = macro_china_gdzctz() print(macro_china_gdzctz_df) macro_china_hgjck_df = macro_china_hgjck() print(macro_china_hgjck_df) macro_china_czsr_df = macro_china_czsr() print(macro_china_czsr_df) macro_china_whxd_df = macro_china_whxd() print(macro_china_whxd_df) macro_china_wbck_df = macro_china_wbck() print(macro_china_wbck_df) macro_china_xfzxx_df = macro_china_xfzxx() print(macro_china_xfzxx_df) macro_china_gyzjz_df = macro_china_gyzjz print(macro_china_gyzjz_df) macro_china_reserve_requirement_ratio_df = macro_china_reserve_requirement_ratio() print(macro_china_reserve_requirement_ratio_df) macro_china_consumer_goods_retail_df = macro_china_consumer_goods_retail() print(macro_china_consumer_goods_retail_df) macro_china_society_electricity_df = macro_china_society_electricity() print(macro_china_society_electricity_df) macro_china_society_traffic_volume_df = macro_china_society_traffic_volume() print(macro_china_society_traffic_volume_df) macro_china_postal_telecommunicational_df = macro_china_postal_telecommunicational() print(macro_china_postal_telecommunicational_df) macro_china_international_tourism_fx_df = macro_china_international_tourism_fx() print(macro_china_international_tourism_fx_df) macro_china_passenger_load_factor_df = macro_china_passenger_load_factor() print(macro_china_passenger_load_factor_df) macro_china_freight_index_df = macro_china_freight_index() print(macro_china_freight_index_df) macro_china_central_bank_balance_df = macro_china_central_bank_balance() print(macro_china_central_bank_balance_df) macro_china_insurance_df = macro_china_insurance() print(macro_china_insurance_df) macro_china_supply_of_money_df = macro_china_supply_of_money() print(macro_china_supply_of_money_df) macro_china_foreign_exchange_gold_df = macro_china_foreign_exchange_gold() print(macro_china_foreign_exchange_gold_df) macro_china_retail_price_index_df = macro_china_retail_price_index() print(macro_china_retail_price_index_df) macro_china_real_estate_df = macro_china_real_estate() print(macro_china_real_estate_df) macro_shipping_bci_df = macro_shipping_bci() print(macro_shipping_bci_df) macro_shipping_bdi_df = macro_shipping_bdi() print(macro_shipping_bdi_df) macro_shipping_bpi_df = macro_shipping_bpi() print(macro_shipping_bpi_df) macro_shipping_bcti_df = macro_shipping_bcti() print(macro_shipping_bcti_df)