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MoFin/venv/lib/python3.12/site-packages/akshare/economic/macro_euro.py
T
知微 fa45d8aa5f fix: 小果地址统一node122(兼容LAN+EasyTier) 
- health_checklist.json: 192.168.1.122→node122
- ocr_client.py: docstring IP→node122
- docs/market-data-requirements.md: IP→node122
- 所有API调用通过ProxyHandler({})绕过系统代理
  Privoxy对node122:18003返回500，直连正常
2026-06-30 02:56:35 +08:00

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#!/usr/bin/env python
# -*- coding:utf-8 -*-
"""
Date: 2024/4/3 17:08
Desc: 金十数据中心-经济指标-欧元区
金十数据中心-经济指标-欧元区-国民经济运行状况-经济状况
金十数据中心-经济指标-欧元区-国民经济运行状况-物价水平
金十数据中心-经济指标-欧元区-国民经济运行状况-劳动力市场
金十数据中心-经济指标-欧元区-贸易状况
金十数据中心-经济指标-欧元区-产业指标
金十数据中心-经济指标-欧元区-领先指标
"""
import time
import pandas as pd
import requests
from tqdm import tqdm
# 金十数据中心-经济指标-欧元区-国民经济运行状况
# 金十数据中心-经济指标-欧元区-国民经济运行状况-经济状况
# 金十数据中心-经济指标-欧元区-国民经济运行状况-经济状况-欧元区季度GDP年率报告
def macro_euro_gdp_yoy() -> pd.DataFrame:
"""
欧元区季度 GDP 年率报告, 数据区间从 20131114-至今
https://datacenter.jin10.com/reportType/dc_eurozone_gdp_yoy
:return: 欧元区季度 GDP 年率报告
:rtype: pandas.DataFrame
"""
ec = 84
url = "https://datacenter-api.jin10.com/reports/dates"
params = {"category": "ec", "attr_id": ec}
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",
}
r = requests.get(url, headers=headers, params=params)
data_json = r.json()
date_list = data_json["data"]
date_point_list = [item for num, item in enumerate(date_list) if num % 20 == 0]
big_df = pd.DataFrame()
for date in tqdm(date_point_list, leave=False):
url = "https://datacenter-api.jin10.com/reports/list_v2"
params = {
"max_date": f"{date}",
"category": "ec",
"attr_id": ec,
}
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",
}
r = requests.get(url, headers=headers, params=params)
data_json = r.json()
temp_df = pd.DataFrame(
data_json["data"]["values"],
columns=[item["name"] for item in data_json["data"]["keys"]],
)
big_df = pd.concat([big_df, temp_df], ignore_index=True)
big_df["商品"] = "欧元区季度GDP年率"
big_df = big_df[["商品", "日期", "今值", "预测值", "前值"]]
big_df["今值"] = pd.to_numeric(big_df["今值"])
big_df["预测值"] = pd.to_numeric(big_df["预测值"])
big_df["前值"] = pd.to_numeric(big_df["前值"])
big_df["日期"] = pd.to_datetime(big_df["日期"]).dt.date
big_df.sort_values(["日期"], ignore_index=True, inplace=True)
return big_df
# 金十数据中心-经济指标-欧元区-国民经济运行状况-物价水平-欧元区CPI月率报告
def macro_euro_cpi_mom() -> pd.DataFrame:
"""
欧元区 CPI 月率报告, 数据区间从 19900301-至今
https://datacenter.jin10.com/reportType/dc_eurozone_cpi_mom
https://cdn.jin10.com/dc/reports/dc_eurozone_cpi_mom_all.js?v=1578578318
:return: 欧元区CPI月率报告
:rtype: pandas.Series
"""
ec = 84
url = "https://datacenter-api.jin10.com/reports/dates"
params = {"category": "ec", "attr_id": ec}
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",
}
r = requests.get(url, headers=headers, params=params)
data_json = r.json()
date_list = data_json["data"]
date_point_list = [item for num, item in enumerate(date_list) if num % 20 == 0]
big_df = pd.DataFrame()
for date in tqdm(date_point_list, leave=False):
url = "https://datacenter-api.jin10.com/reports/list_v2"
params = {
"max_date": f"{date}",
"category": "ec",
"attr_id": ec,
}
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",
}
r = requests.get(url, headers=headers, params=params)
data_json = r.json()
temp_df = pd.DataFrame(
data_json["data"]["values"],
columns=[item["name"] for item in data_json["data"]["keys"]],
)
big_df = pd.concat([big_df, temp_df], ignore_index=True)
big_df["商品"] = "欧元区CPI月率"
big_df = big_df[["商品", "日期", "今值", "预测值", "前值"]]
big_df["今值"] = pd.to_numeric(big_df["今值"])
big_df["预测值"] = pd.to_numeric(big_df["预测值"])
big_df["前值"] = pd.to_numeric(big_df["前值"])
big_df["日期"] = pd.to_datetime(big_df["日期"]).dt.date
big_df.sort_values(["日期"], ignore_index=True, inplace=True)
return big_df
# 金十数据中心-经济指标-欧元区-国民经济运行状况-物价水平-欧元区CPI年率报告
def macro_euro_cpi_yoy() -> pd.DataFrame:
"""
欧元区CPI年率报告, 数据区间从19910201-至今
https://datacenter.jin10.com/reportType/dc_eurozone_cpi_yoy
https://cdn.jin10.com/dc/reports/dc_eurozone_cpi_yoy_all.js?v=1578578404
:return: 欧元区CPI年率报告-今值(%)
:rtype: pandas.Series
"""
ec = 8
url = "https://datacenter-api.jin10.com/reports/dates"
params = {
"category": "ec",
"attr_id": ec,
}
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",
}
r = requests.get(url, headers=headers, params=params)
data_json = r.json()
date_list = data_json["data"]
date_point_list = [item for num, item in enumerate(date_list) if num % 20 == 0]
big_df = pd.DataFrame()
for date in tqdm(date_point_list, leave=False):
url = "https://datacenter-api.jin10.com/reports/list_v2"
params = {
"max_date": f"{date}",
"category": "ec",
"attr_id": ec,
}
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",
}
r = requests.get(url, headers=headers, params=params)
data_json = r.json()
temp_df = pd.DataFrame(
data_json["data"]["values"],
columns=[item["name"] for item in data_json["data"]["keys"]],
)
big_df = pd.concat([big_df, temp_df], ignore_index=True)
big_df["商品"] = "欧元区CPI年率"
big_df = big_df[["商品", "日期", "今值", "预测值", "前值"]]
big_df["今值"] = pd.to_numeric(big_df["今值"])
big_df["预测值"] = pd.to_numeric(big_df["预测值"])
big_df["前值"] = pd.to_numeric(big_df["前值"])
big_df["日期"] = pd.to_datetime(big_df["日期"]).dt.date
big_df.sort_values(["日期"], ignore_index=True, inplace=True)
return big_df
# 金十数据中心-经济指标-欧元区-国民经济运行状况-物价水平-欧元区PPI月率报告
def macro_euro_ppi_mom() -> pd.DataFrame:
"""
欧元区PPI月率报告, 数据区间从19810301-至今
https://datacenter.jin10.com/reportType/dc_eurozone_ppi_mom
https://cdn.jin10.com/dc/reports/dc_eurozone_ppi_mom_all.js?v=1578578493
:return: 欧元区PPI月率报告-今值(%)
:rtype: pandas.Series
"""
ec = 36
url = "https://datacenter-api.jin10.com/reports/dates"
params = {
"category": "ec",
"attr_id": ec,
}
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",
}
r = requests.get(url, headers=headers, params=params)
data_json = r.json()
date_list = data_json["data"]
date_point_list = [item for num, item in enumerate(date_list) if num % 20 == 0]
big_df = pd.DataFrame()
for date in tqdm(date_point_list, leave=False):
url = "https://datacenter-api.jin10.com/reports/list_v2"
params = {
"max_date": f"{date}",
"category": "ec",
"attr_id": ec,
}
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",
}
r = requests.get(url, headers=headers, params=params)
data_json = r.json()
temp_df = pd.DataFrame(
data_json["data"]["values"],
columns=[item["name"] for item in data_json["data"]["keys"]],
)
big_df = pd.concat([big_df, temp_df], ignore_index=True)
big_df["商品"] = "欧元区PPI月率"
big_df = big_df[["商品", "日期", "今值", "预测值", "前值"]]
big_df["今值"] = pd.to_numeric(big_df["今值"])
big_df["预测值"] = pd.to_numeric(big_df["预测值"])
big_df["前值"] = pd.to_numeric(big_df["前值"])
big_df["日期"] = pd.to_datetime(big_df["日期"]).dt.date
big_df.sort_values(["日期"], ignore_index=True, inplace=True)
return big_df
# 金十数据中心-经济指标-欧元区-国民经济运行状况-物价水平-欧元区零售销售月率报告
def macro_euro_retail_sales_mom() -> pd.DataFrame:
"""
欧元区零售销售月率报告, 数据区间从20000301-至今
https://datacenter.jin10.com/reportType/dc_eurozone_retail_sales_mom
https://cdn.jin10.com/dc/reports/dc_eurozone_retail_sales_mom_all.js?v=1578578576
:return: 欧元区零售销售月率报告-今值(%)
:rtype: pandas.Series
"""
ec = 38
url = "https://datacenter-api.jin10.com/reports/dates"
params = {
"category": "ec",
"attr_id": ec,
}
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",
}
r = requests.get(url, headers=headers, params=params)
data_json = r.json()
date_list = data_json["data"]
date_point_list = [item for num, item in enumerate(date_list) if num % 20 == 0]
big_df = pd.DataFrame()
for date in tqdm(date_point_list, leave=False):
url = "https://datacenter-api.jin10.com/reports/list_v2"
params = {
"max_date": f"{date}",
"category": "ec",
"attr_id": ec,
}
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",
}
r = requests.get(url, headers=headers, params=params)
data_json = r.json()
temp_df = pd.DataFrame(
data_json["data"]["values"],
columns=[item["name"] for item in data_json["data"]["keys"]],
)
big_df = pd.concat([big_df, temp_df], ignore_index=True)
big_df["商品"] = "欧元区零售销售月率"
big_df = big_df[["商品", "日期", "今值", "预测值", "前值"]]
big_df["今值"] = pd.to_numeric(big_df["今值"])
big_df["预测值"] = pd.to_numeric(big_df["预测值"])
big_df["前值"] = pd.to_numeric(big_df["前值"])
big_df["日期"] = pd.to_datetime(big_df["日期"]).dt.date
big_df.sort_values(["日期"], ignore_index=True, inplace=True)
return big_df
# 金十数据中心-经济指标-欧元区-国民经济运行状况-劳动力市场-欧元区季调后就业人数季率报告
def macro_euro_employment_change_qoq() -> pd.DataFrame:
"""
欧元区季调后就业人数季率报告, 数据区间从20083017-至今
https://datacenter.jin10.com/reportType/dc_eurozone_employment_change_qoq
https://cdn.jin10.com/dc/reports/dc_eurozone_employment_change_qoq_all.js?v=1578578699
:return: 欧元区季调后就业人数季率报告-今值(%)
:rtype: pandas.Series
"""
ec = 14
url = "https://datacenter-api.jin10.com/reports/dates"
params = {"category": "ec", "attr_id": ec}
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",
}
r = requests.get(url, headers=headers, params=params)
data_json = r.json()
date_list = data_json["data"]
date_point_list = [item for num, item in enumerate(date_list) if num % 20 == 0]
big_df = pd.DataFrame()
for date in tqdm(date_point_list, leave=False):
url = "https://datacenter-api.jin10.com/reports/list_v2"
params = {
"max_date": f"{date}",
"category": "ec",
"attr_id": ec,
}
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",
}
r = requests.get(url, headers=headers, params=params)
data_json = r.json()
temp_df = pd.DataFrame(
data_json["data"]["values"],
columns=[item["name"] for item in data_json["data"]["keys"]],
)
big_df = pd.concat([big_df, temp_df], ignore_index=True)
big_df["商品"] = "欧元区季调后就业人数季率"
big_df = big_df[["商品", "日期", "今值", "预测值", "前值"]]
big_df["今值"] = pd.to_numeric(big_df["今值"])
big_df["预测值"] = pd.to_numeric(big_df["预测值"])
big_df["前值"] = pd.to_numeric(big_df["前值"])
big_df["日期"] = pd.to_datetime(big_df["日期"]).dt.date
big_df.sort_values(["日期"], ignore_index=True, inplace=True)
return big_df
# 金十数据中心-经济指标-欧元区-国民经济运行状况-劳动力市场-欧元区失业率报告
def macro_euro_unemployment_rate_mom() -> pd.DataFrame:
"""
欧元区失业率报告, 数据区间从19980501-至今
https://datacenter.jin10.com/reportType/dc_eurozone_unemployment_rate_mom
https://cdn.jin10.com/dc/reports/dc_eurozone_unemployment_rate_mom_all.js?v=1578578767
:return: 欧元区失业率报告-今值(%)
:rtype: pandas.Series
"""
ec = 46
url = "https://datacenter-api.jin10.com/reports/dates"
params = {
"category": "ec",
"attr_id": ec,
}
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",
}
r = requests.get(url, headers=headers, params=params)
data_json = r.json()
date_list = data_json["data"]
date_point_list = [item for num, item in enumerate(date_list) if num % 20 == 0]
big_df = pd.DataFrame()
for date in tqdm(date_point_list, leave=False):
url = "https://datacenter-api.jin10.com/reports/list_v2"
params = {
"max_date": f"{date}",
"category": "ec",
"attr_id": ec,
}
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",
}
r = requests.get(url, headers=headers, params=params)
data_json = r.json()
temp_df = pd.DataFrame(
data_json["data"]["values"],
columns=[item["name"] for item in data_json["data"]["keys"]],
)
big_df = pd.concat([big_df, temp_df], ignore_index=True)
big_df["商品"] = "欧元区失业率"
big_df = big_df[["商品", "日期", "今值", "预测值", "前值"]]
big_df["今值"] = pd.to_numeric(big_df["今值"])
big_df["预测值"] = pd.to_numeric(big_df["预测值"])
big_df["前值"] = pd.to_numeric(big_df["前值"])
big_df["日期"] = pd.to_datetime(big_df["日期"]).dt.date
big_df.sort_values(["日期"], ignore_index=True, inplace=True)
return big_df
# 金十数据中心-经济指标-欧元区-贸易状况-欧元区未季调贸易帐报告
def macro_euro_trade_balance() -> pd.DataFrame:
"""
欧元区未季调贸易帐报告, 数据区间从19990201-至今
https://datacenter.jin10.com/reportType/dc_eurozone_trade_balance_mom
https://cdn.jin10.com/dc/reports/dc_eurozone_trade_balance_mom_all.js?v=1578577862
:return: 欧元区未季调贸易帐报告-今值(亿欧元)
:rtype: pandas.Series
"""
ec = 43
url = "https://datacenter-api.jin10.com/reports/dates"
params = {
"category": "ec",
"attr_id": ec,
}
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",
}
r = requests.get(url, headers=headers, params=params)
data_json = r.json()
date_list = data_json["data"]
date_point_list = [item for num, item in enumerate(date_list) if num % 20 == 0]
big_df = pd.DataFrame()
for date in tqdm(date_point_list, leave=False):
url = "https://datacenter-api.jin10.com/reports/list_v2"
params = {
"max_date": f"{date}",
"category": "ec",
"attr_id": ec,
}
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",
}
r = requests.get(url, headers=headers, params=params)
data_json = r.json()
temp_df = pd.DataFrame(
data_json["data"]["values"],
columns=[item["name"] for item in data_json["data"]["keys"]],
)
big_df = pd.concat([big_df, temp_df], ignore_index=True)
big_df["商品"] = "欧元区未季调贸易帐"
big_df = big_df[["商品", "日期", "今值", "预测值", "前值"]]
big_df["今值"] = pd.to_numeric(big_df["今值"])
big_df["预测值"] = pd.to_numeric(big_df["预测值"])
big_df["前值"] = pd.to_numeric(big_df["前值"])
big_df["日期"] = pd.to_datetime(big_df["日期"]).dt.date
big_df.sort_values(["日期"], ignore_index=True, inplace=True)
return big_df
# 金十数据中心-经济指标-欧元区-贸易状况-欧元区经常帐报告
def macro_euro_current_account_mom() -> pd.DataFrame:
"""
欧元区经常帐报告, 数据区间从20080221-至今, 前两个值需要去掉
https://datacenter.jin10.com/reportType/dc_eurozone_current_account_mom
https://cdn.jin10.com/dc/reports/dc_eurozone_current_account_mom_all.js?v=1578577976
:return: 欧元区经常帐报告-今值(亿欧元)
:rtype: pandas.Series
"""
ec = 11
url = "https://datacenter-api.jin10.com/reports/dates"
params = {
"category": "ec",
"attr_id": ec,
}
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",
}
r = requests.get(url, headers=headers, params=params)
data_json = r.json()
date_list = data_json["data"]
date_point_list = [item for num, item in enumerate(date_list) if num % 20 == 0]
big_df = pd.DataFrame()
for date in tqdm(date_point_list, leave=False):
url = "https://datacenter-api.jin10.com/reports/list_v2"
params = {
"max_date": f"{date}",
"category": "ec",
"attr_id": ec,
}
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",
}
r = requests.get(url, headers=headers, params=params)
data_json = r.json()
temp_df = pd.DataFrame(
data_json["data"]["values"],
columns=[item["name"] for item in data_json["data"]["keys"]],
)
big_df = pd.concat([big_df, temp_df], ignore_index=True)
big_df["商品"] = "欧元区经常帐"
big_df = big_df[["商品", "日期", "今值", "预测值", "前值"]]
big_df["今值"] = pd.to_numeric(big_df["今值"])
big_df["预测值"] = pd.to_numeric(big_df["预测值"])
big_df["前值"] = pd.to_numeric(big_df["前值"])
big_df["日期"] = pd.to_datetime(big_df["日期"]).dt.date
big_df.sort_values(["日期"], ignore_index=True, inplace=True)
return big_df
# 金十数据中心-经济指标-欧元区-产业指标-欧元区工业产出月率报告
def macro_euro_industrial_production_mom() -> pd.DataFrame:
"""
欧元区工业产出月率报告, 数据区间从19910301-至今
https://datacenter.jin10.com/reportType/dc_eurozone_industrial_production_mom
https://cdn.jin10.com/dc/reports/dc_eurozone_industrial_production_mom_all.js?v=1578577377
:return: 欧元区工业产出月率报告-今值(%)
:rtype: pandas.Series
"""
ec = 19
url = "https://datacenter-api.jin10.com/reports/dates"
params = {
"category": "ec",
"attr_id": ec,
}
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",
}
r = requests.get(url, headers=headers, params=params)
data_json = r.json()
date_list = data_json["data"]
date_point_list = [item for num, item in enumerate(date_list) if num % 20 == 0]
big_df = pd.DataFrame()
for date in tqdm(date_point_list, leave=False):
url = "https://datacenter-api.jin10.com/reports/list_v2"
params = {
"max_date": f"{date}",
"category": "ec",
"attr_id": ec,
}
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",
}
r = requests.get(url, headers=headers, params=params)
data_json = r.json()
temp_df = pd.DataFrame(
data_json["data"]["values"],
columns=[item["name"] for item in data_json["data"]["keys"]],
)
big_df = pd.concat([big_df, temp_df], ignore_index=True)
big_df["商品"] = "欧元区工业产出月率"
big_df = big_df[["商品", "日期", "今值", "预测值", "前值"]]
big_df["今值"] = pd.to_numeric(big_df["今值"])
big_df["预测值"] = pd.to_numeric(big_df["预测值"])
big_df["前值"] = pd.to_numeric(big_df["前值"])
big_df["日期"] = pd.to_datetime(big_df["日期"]).dt.date
big_df.sort_values(["日期"], ignore_index=True, inplace=True)
return big_df
# 金十数据中心-经济指标-欧元区-产业指标-欧元区制造业PMI初值报告
def macro_euro_manufacturing_pmi() -> pd.DataFrame:
"""
欧元区制造业PMI初值报告, 数据区间从20080222-至今
https://datacenter.jin10.com/reportType/dc_eurozone_manufacturing_pmi
https://cdn.jin10.com/dc/reports/dc_eurozone_manufacturing_pmi_all.js?v=1578577537
:return: 欧元区制造业PMI初值报告-今值
:rtype: pandas.Series
"""
ec = 30
url = "https://datacenter-api.jin10.com/reports/dates"
params = {
"category": "ec",
"attr_id": ec,
}
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",
}
r = requests.get(url, headers=headers, params=params)
data_json = r.json()
date_list = data_json["data"]
date_point_list = [item for num, item in enumerate(date_list) if num % 20 == 0]
big_df = pd.DataFrame()
for date in tqdm(date_point_list, leave=False):
url = "https://datacenter-api.jin10.com/reports/list_v2"
params = {
"max_date": f"{date}",
"category": "ec",
"attr_id": ec,
}
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",
}
r = requests.get(url, headers=headers, params=params)
data_json = r.json()
temp_df = pd.DataFrame(
data_json["data"]["values"],
columns=[item["name"] for item in data_json["data"]["keys"]],
)
big_df = pd.concat([big_df, temp_df], ignore_index=True)
big_df["商品"] = "欧元区制造业PMI初值"
big_df = big_df[["商品", "日期", "今值", "预测值", "前值"]]
big_df["今值"] = pd.to_numeric(big_df["今值"])
big_df["预测值"] = pd.to_numeric(big_df["预测值"])
big_df["前值"] = pd.to_numeric(big_df["前值"])
big_df["日期"] = pd.to_datetime(big_df["日期"]).dt.date
big_df.sort_values(["日期"], ignore_index=True, inplace=True)
return big_df
# 金十数据中心-经济指标-欧元区-产业指标-欧元区服务业PMI终值报告
def macro_euro_services_pmi() -> pd.DataFrame:
"""
欧元区服务业PMI终值报告, 数据区间从 20080222-至今
https://datacenter.jin10.com/reportType/dc_eurozone_services_pmi
https://cdn.jin10.com/dc/reports/dc_eurozone_services_pmi_all.js?v=1578577639
:return: 欧元区服务业PMI终值报告-今值
:rtype: pandas.Series
"""
ec = 41
url = "https://datacenter-api.jin10.com/reports/dates"
params = {
"category": "ec",
"attr_id": ec,
}
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",
}
r = requests.get(url, headers=headers, params=params)
data_json = r.json()
date_list = data_json["data"]
date_point_list = [item for num, item in enumerate(date_list) if num % 20 == 0]
big_df = pd.DataFrame()
for date in tqdm(date_point_list, leave=False):
url = "https://datacenter-api.jin10.com/reports/list_v2"
params = {
"max_date": f"{date}",
"category": "ec",
"attr_id": ec,
}
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",
}
r = requests.get(url, headers=headers, params=params)
data_json = r.json()
temp_df = pd.DataFrame(
data_json["data"]["values"],
columns=[item["name"] for item in data_json["data"]["keys"]],
)
big_df = pd.concat([big_df, temp_df], ignore_index=True)
big_df["商品"] = "欧元区服务业PMI终值"
big_df = big_df[["商品", "日期", "今值", "预测值", "前值"]]
big_df["今值"] = pd.to_numeric(big_df["今值"])
big_df["预测值"] = pd.to_numeric(big_df["预测值"])
big_df["前值"] = pd.to_numeric(big_df["前值"])
big_df["日期"] = pd.to_datetime(big_df["日期"]).dt.date
big_df.sort_values(["日期"], ignore_index=True, inplace=True)
return big_df
# 金十数据中心-经济指标-欧元区-领先指标-欧元区ZEW经济景气指数报告
def macro_euro_zew_economic_sentiment() -> pd.DataFrame:
"""
欧元区ZEW经济景气指数报告, 数据区间从20080212-至今
https://datacenter.jin10.com/reportType/dc_eurozone_zew_economic_sentiment
https://cdn.jin10.com/dc/reports/dc_eurozone_zew_economic_sentiment_all.js?v=1578577013
:return: 欧元区ZEW经济景气指数报告-今值
:rtype: pandas.Series
"""
ec = 48
url = "https://datacenter-api.jin10.com/reports/dates"
params = {
"category": "ec",
"attr_id": ec,
}
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",
}
r = requests.get(url, headers=headers, params=params)
data_json = r.json()
date_list = data_json["data"]
date_point_list = [item for num, item in enumerate(date_list) if num % 20 == 0]
big_df = pd.DataFrame()
for date in tqdm(date_point_list, leave=False):
url = "https://datacenter-api.jin10.com/reports/list_v2"
params = {
"max_date": f"{date}",
"category": "ec",
"attr_id": ec,
}
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",
}
r = requests.get(url, headers=headers, params=params)
data_json = r.json()
temp_df = pd.DataFrame(
data_json["data"]["values"],
columns=[item["name"] for item in data_json["data"]["keys"]],
)
big_df = pd.concat([big_df, temp_df], ignore_index=True)
big_df["商品"] = "欧元区ZEW经济景气指数"
big_df = big_df[["商品", "日期", "今值", "预测值", "前值"]]
big_df["今值"] = pd.to_numeric(big_df["今值"])
big_df["预测值"] = pd.to_numeric(big_df["预测值"])
big_df["前值"] = pd.to_numeric(big_df["前值"])
big_df["日期"] = pd.to_datetime(big_df["日期"]).dt.date
big_df.sort_values(["日期"], ignore_index=True, inplace=True)
return big_df
# 金十数据中心-经济指标-欧元区-领先指标-欧元区Sentix投资者信心指数报告
def macro_euro_sentix_investor_confidence() -> pd.DataFrame:
"""
欧元区Sentix投资者信心指数报告, 数据区间从20020801-至今
https://datacenter.jin10.com/reportType/dc_eurozone_sentix_investor_confidence
https://cdn.jin10.com/dc/reports/dc_eurozone_sentix_investor_confidence_all.js?v=1578577195
:return: 欧元区Sentix投资者信心指数报告-今值
:rtype: pandas.Series
"""
ec = 40
url = "https://datacenter-api.jin10.com/reports/dates"
params = {
"category": "ec",
"attr_id": ec,
}
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",
}
r = requests.get(url, headers=headers, params=params)
data_json = r.json()
date_list = data_json["data"]
date_point_list = [item for num, item in enumerate(date_list) if num % 20 == 0]
big_df = pd.DataFrame()
for date in tqdm(date_point_list, leave=False):
url = "https://datacenter-api.jin10.com/reports/list_v2"
params = {
"max_date": f"{date}",
"category": "ec",
"attr_id": ec,
}
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",
}
r = requests.get(url, headers=headers, params=params)
data_json = r.json()
temp_df = pd.DataFrame(
data_json["data"]["values"],
columns=[item["name"] for item in data_json["data"]["keys"]],
)
big_df = pd.concat([big_df, temp_df], ignore_index=True)
big_df["商品"] = "欧元区Sentix投资者信心指数"
big_df = big_df[["商品", "日期", "今值", "预测值", "前值"]]
big_df["今值"] = pd.to_numeric(big_df["今值"])
big_df["预测值"] = pd.to_numeric(big_df["预测值"])
big_df["前值"] = pd.to_numeric(big_df["前值"])
big_df["日期"] = pd.to_datetime(big_df["日期"]).dt.date
big_df.sort_values(["日期"], ignore_index=True, inplace=True)
return big_df
# 金十数据中心-伦敦金属交易所(LME)-持仓报告
def macro_euro_lme_holding() -> pd.DataFrame:
"""
伦敦金属交易所(LME)-持仓报告, 数据区间从 20151022-至今
https://datacenter.jin10.com/reportType/dc_lme_traders_report
https://cdn.jin10.com/data_center/reports/lme_position.json?_=1591533934658
:return: 伦敦金属交易所(LME)-持仓报告
:rtype: pandas.DataFrame
"""
t = time.time()
params = {"_": str(int(round(t * 1000)))}
r = requests.get(
url="https://cdn.jin10.com/data_center/reports/lme_position.json", params=params
)
json_data = r.json()
temp_df = pd.DataFrame(json_data["values"]).T
temp_df.fillna(value="[0, 0, 0]", inplace=True)
big_df = pd.DataFrame()
for item in temp_df.columns:
for i in range(3):
inner_temp_df = temp_df.loc[:, item].apply(lambda x: eval(str(x))[i])
inner_temp_df.name = inner_temp_df.name + "-" + json_data["keys"][i]["name"]
big_df = pd.concat(objs=[big_df, inner_temp_df], axis=1)
big_df = big_df.astype("float")
big_df = big_df.iloc[:-1, :].copy()
big_df.reset_index(inplace=True)
big_df.rename(columns={"index": "日期"}, inplace=True)
big_df.sort_values(by=["日期"], ignore_index=True, inplace=True)
return big_df
# 金十数据中心-伦敦金属交易所(LME)-库存报告
def macro_euro_lme_stock() -> pd.DataFrame:
"""
伦敦金属交易所(LME)-库存报告, 数据区间从 20140702-至今
https://datacenter.jin10.com/reportType/dc_lme_report
https://cdn.jin10.com/data_center/reports/lme_stock.json?_=1591535304783
:return: 伦敦金属交易所(LME)-库存报告
:rtype: pandas.DataFrame
"""
t = time.time()
params = {"_": str(int(round(t * 1000)))}
r = requests.get(
url="https://cdn.jin10.com/data_center/reports/lme_stock.json", params=params
)
json_data = r.json()
temp_df = pd.DataFrame(json_data["values"]).T
big_df = pd.DataFrame()
for item in temp_df.columns:
for i in range(3):
inner_temp_df = temp_df.loc[:, item].apply(lambda x: eval(str(x))[i])
inner_temp_df.name = inner_temp_df.name + "-" + json_data["keys"][i]["name"]
big_df = pd.concat(objs=[big_df, inner_temp_df], axis=1)
big_df.sort_index(inplace=True)
big_df.reset_index(inplace=True)
big_df.rename(columns={"index": "日期"}, inplace=True)
return big_df
if __name__ == "__main__":
# 金十数据中心-经济指标-欧元区-国民经济运行状况
# 金十数据中心-经济指标-欧元区-国民经济运行状况-经济状况
# 金十数据中心-经济指标-欧元区-国民经济运行状况-经济状况-欧元区季度GDP年率报告
macro_euro_gdp_yoy_df = macro_euro_gdp_yoy()
print(macro_euro_gdp_yoy_df)
# 金十数据中心-经济指标-欧元区-国民经济运行状况-物价水平
# 金十数据中心-经济指标-欧元区-国民经济运行状况-物价水平-欧元区CPI月率报告
macro_euro_cpi_mom_df = macro_euro_cpi_mom()
print(macro_euro_cpi_mom_df)
# 金十数据中心-经济指标-欧元区-国民经济运行状况-物价水平-欧元区CPI年率报告
macro_euro_cpi_yoy_df = macro_euro_cpi_yoy()
print(macro_euro_cpi_yoy_df)
# 金十数据中心-经济指标-欧元区-国民经济运行状况-物价水平-欧元区PPI月率报告
macro_euro_ppi_mom_df = macro_euro_ppi_mom()
print(macro_euro_ppi_mom_df)
# 金十数据中心-经济指标-欧元区-国民经济运行状况-物价水平-欧元区零售销售月率报告
macro_euro_retail_sales_mom_df = macro_euro_retail_sales_mom()
print(macro_euro_retail_sales_mom_df)
# 金十数据中心-经济指标-欧元区-国民经济运行状况-劳动力市场
# 金十数据中心-经济指标-欧元区-国民经济运行状况-劳动力市场-欧元区季调后就业人数季率报告
macro_euro_employment_change_qoq_df = macro_euro_employment_change_qoq()
print(macro_euro_employment_change_qoq_df)
# 金十数据中心-经济指标-欧元区-国民经济运行状况-劳动力市场-欧元区失业率报告
macro_euro_unemployment_rate_mom_df = macro_euro_unemployment_rate_mom()
print(macro_euro_unemployment_rate_mom_df)
# 金十数据中心-经济指标-欧元区-贸易状况
# 金十数据中心-经济指标-欧元区-贸易状况-欧元区未季调贸易帐报告
macro_euro_trade_balance_df = macro_euro_trade_balance()
print(macro_euro_trade_balance_df)
# 金十数据中心-经济指标-欧元区-贸易状况-欧元区经常帐报告
macro_euro_current_account_mom_df = macro_euro_current_account_mom()
print(macro_euro_current_account_mom_df)
# 金十数据中心-经济指标-欧元区-产业指标
# 金十数据中心-经济指标-欧元区-产业指标-欧元区工业产出月率报告
macro_euro_industrial_production_mom_df = macro_euro_industrial_production_mom()
print(macro_euro_industrial_production_mom_df)
# 金十数据中心-经济指标-欧元区-产业指标-欧元区制造业PMI初值报告
macro_euro_manufacturing_pmi_df = macro_euro_manufacturing_pmi()
print(macro_euro_manufacturing_pmi_df)
# 金十数据中心-经济指标-欧元区-产业指标-欧元区服务业PMI终值报告
macro_euro_services_pmi_df = macro_euro_services_pmi()
print(macro_euro_services_pmi_df)
# 金十数据中心-经济指标-欧元区-领先指标
# 金十数据中心-经济指标-欧元区-领先指标-欧元区ZEW经济景气指数报告
macro_euro_zew_economic_sentiment_df = macro_euro_zew_economic_sentiment()
print(macro_euro_zew_economic_sentiment_df)
# 金十数据中心-经济指标-欧元区-领先指标-欧元区Sentix投资者信心指数报告
macro_euro_sentix_investor_confidence_df = macro_euro_sentix_investor_confidence()
print(macro_euro_sentix_investor_confidence_df)
# 金十数据中心-伦敦金属交易所(LME)-持仓报告
macro_euro_lme_holding_df = macro_euro_lme_holding()
print(macro_euro_lme_holding_df)
# 金十数据中心-伦敦金属交易所(LME)-库存报告
macro_euro_lme_stock_df = macro_euro_lme_stock()
print(macro_euro_lme_stock_df)
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