知微
fa45d8aa5f
- 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
622 lines
16 KiB
Python
622 lines
16 KiB
Python
# -*- coding:utf-8 -*-
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# !/usr/bin/env python
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"""
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Date: 2025/8/7 18:30
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Desc: 财新数据-指数报告-数字经济指数
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https://yun.ccxe.com.cn/indices/dei
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"""
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import pandas as pd
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import requests
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def index_pmi_com_cx() -> pd.DataFrame:
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"""
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财新数据-指数报告-财新中国 PMI-综合 PMI
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https://yun.ccxe.com.cn/indices/pmi
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:return: 财新中国 PMI-综合 PMI
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:rtype: pandas.DataFrame
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"""
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url = "https://yun.ccxe.com.cn/api/index/pro/cxIndexTrendInfo"
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params = {"type": "com"}
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r = requests.get(url, params=params)
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data_json = r.json()
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temp_df = pd.DataFrame(data_json["data"])
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temp_df.columns = ["变化值", "综合PMI", "日期"]
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temp_df = temp_df[
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[
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"日期",
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"综合PMI",
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"变化值",
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]
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]
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temp_df["日期"] = (
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pd.to_datetime(temp_df["日期"], unit="ms", utc=True)
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.dt.tz_convert("Asia/Shanghai")
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.dt.date
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)
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return temp_df
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def index_pmi_man_cx() -> pd.DataFrame:
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"""
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财新数据-指数报告-财新中国 PMI-制造业 PMI
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https://yun.ccxe.com.cn/indices/pmi
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:return: 财新中国 PMI-制造业 PMI
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:rtype: pandas.DataFrame
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"""
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url = "https://yun.ccxe.com.cn/api/index/pro/cxIndexTrendInfo"
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params = {"type": "man"}
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r = requests.get(url, params=params)
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data_json = r.json()
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temp_df = pd.DataFrame(data_json["data"])
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temp_df.columns = ["变化值", "制造业PMI", "日期"]
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temp_df = temp_df[
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[
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"日期",
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"制造业PMI",
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"变化值",
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]
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]
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temp_df["日期"] = (
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pd.to_datetime(temp_df["日期"], unit="ms", utc=True)
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.dt.tz_convert("Asia/Shanghai")
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.dt.date
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)
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return temp_df
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def index_pmi_ser_cx() -> pd.DataFrame:
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"""
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财新数据-指数报告-财新中国 PMI-服务业 PMI
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https://yun.ccxe.com.cn/indices/pmi
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:return: 财新中国 PMI-服务业 PMI
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:rtype: pandas.DataFrame
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"""
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url = "https://yun.ccxe.com.cn/api/index/pro/cxIndexTrendInfo"
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params = {"type": "ser"}
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r = requests.get(url, params=params)
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data_json = r.json()
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temp_df = pd.DataFrame(data_json["data"])
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temp_df.columns = ["变化值", "服务业PMI", "日期"]
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temp_df = temp_df[
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[
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"日期",
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"服务业PMI",
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"变化值",
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]
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]
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temp_df["日期"] = (
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pd.to_datetime(temp_df["日期"], unit="ms", utc=True)
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.dt.tz_convert("Asia/Shanghai")
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.dt.date
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)
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return temp_df
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def index_dei_cx() -> pd.DataFrame:
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"""
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财新数据-指数报告-数字经济指数
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https://yun.ccxe.com.cn/indices/dei
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:return: 数字经济指数
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:rtype: pandas.DataFrame
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"""
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url = "https://yun.ccxe.com.cn/api/index/pro/cxIndexTrendInfo"
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params = {"type": "dei"}
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r = requests.get(url, params=params)
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data_json = r.json()
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temp_df = pd.DataFrame(data_json["data"])
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temp_df.columns = ["变化值", "数字经济指数", "日期"]
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temp_df = temp_df[
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[
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"日期",
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"数字经济指数",
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"变化值",
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]
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]
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temp_df["日期"] = (
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pd.to_datetime(temp_df["日期"], unit="ms", utc=True)
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.dt.tz_convert("Asia/Shanghai")
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.dt.date
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)
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return temp_df
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def index_ii_cx() -> pd.DataFrame:
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"""
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财新数据-指数报告-产业指数
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https://yun.ccxe.com.cn/indices/dei
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:return: 产业指数
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:rtype: pandas.DataFrame
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"""
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url = "https://yun.ccxe.com.cn/api/index/pro/cxIndexTrendInfo"
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params = {"type": "ii"}
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r = requests.get(url, params=params)
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data_json = r.json()
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temp_df = pd.DataFrame(data_json["data"])
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temp_df.columns = ["变化值", "产业指数", "日期"]
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temp_df = temp_df[
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[
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"日期",
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"产业指数",
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"变化值",
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]
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]
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temp_df["日期"] = (
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pd.to_datetime(temp_df["日期"], unit="ms", utc=True)
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.dt.tz_convert("Asia/Shanghai")
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.dt.date
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)
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return temp_df
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def index_si_cx() -> pd.DataFrame:
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"""
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财新数据-指数报告-溢出指数
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https://yun.ccxe.com.cn/indices/dei
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:return: 溢出指数
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:rtype: pandas.DataFrame
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"""
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url = "https://yun.ccxe.com.cn/api/index/pro/cxIndexTrendInfo"
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params = {"type": "si"}
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r = requests.get(url, params=params)
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data_json = r.json()
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temp_df = pd.DataFrame(data_json["data"])
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temp_df.columns = ["变化值", "溢出指数", "日期"]
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temp_df = temp_df[
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[
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"日期",
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"溢出指数",
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"变化值",
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]
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]
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temp_df["日期"] = (
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pd.to_datetime(temp_df["日期"], unit="ms", utc=True)
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.dt.tz_convert("Asia/Shanghai")
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.dt.date
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)
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return temp_df
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def index_fi_cx() -> pd.DataFrame:
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"""
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财新数据-指数报告-融合指数
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https://yun.ccxe.com.cn/indices/dei
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:return: 融合指数
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:rtype: pandas.DataFrame
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"""
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url = "https://yun.ccxe.com.cn/api/index/pro/cxIndexTrendInfo"
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params = {"type": "fi"}
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r = requests.get(url, params=params)
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data_json = r.json()
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temp_df = pd.DataFrame(data_json["data"])
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temp_df.columns = ["变化值", "融合指数", "日期"]
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temp_df = temp_df[
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[
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"日期",
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"融合指数",
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"变化值",
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]
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]
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temp_df["日期"] = (
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pd.to_datetime(temp_df["日期"], unit="ms", utc=True)
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.dt.tz_convert("Asia/Shanghai")
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.dt.date
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)
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return temp_df
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def index_bi_cx() -> pd.DataFrame:
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"""
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财新数据-指数报告-基础指数
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https://yun.ccxe.com.cn/indices/dei
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:return: 基础指数
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:rtype: pandas.DataFrame
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"""
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url = "https://yun.ccxe.com.cn/api/index/pro/cxIndexTrendInfo"
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params = {"type": "bi"}
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r = requests.get(url, params=params)
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data_json = r.json()
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temp_df = pd.DataFrame(data_json["data"])
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temp_df.columns = ["变化值", "基础指数", "日期"]
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temp_df = temp_df[
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[
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"日期",
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"基础指数",
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"变化值",
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]
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]
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temp_df["日期"] = (
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pd.to_datetime(temp_df["日期"], unit="ms", utc=True)
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.dt.tz_convert("Asia/Shanghai")
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.dt.date
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)
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return temp_df
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def index_nei_cx() -> pd.DataFrame:
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"""
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财新数据-指数报告-中国新经济指数
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https://yun.ccxe.com.cn/indices/nei
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:return: 中国新经济指数
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:rtype: pandas.DataFrame
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"""
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url = "https://yun.ccxe.com.cn/api/index/pro/cxIndexTrendInfo"
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params = {"type": "nei"}
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r = requests.get(url, params=params)
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data_json = r.json()
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temp_df = pd.DataFrame(data_json["data"])
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temp_df.columns = ["变化值", "中国新经济指数", "日期"]
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temp_df = temp_df[
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[
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"日期",
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"中国新经济指数",
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"变化值",
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]
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]
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temp_df["日期"] = (
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pd.to_datetime(temp_df["日期"], unit="ms", utc=True)
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.dt.tz_convert("Asia/Shanghai")
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.dt.date
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)
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return temp_df
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def index_li_cx() -> pd.DataFrame:
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"""
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财新数据-指数报告-劳动力投入指数
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https://yun.ccxe.com.cn/indices/nei
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:return: 劳动力投入指数
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:rtype: pandas.DataFrame
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"""
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url = "https://yun.ccxe.com.cn/api/index/pro/cxIndexTrendInfo"
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params = {"type": "li"}
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r = requests.get(url, params=params)
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data_json = r.json()
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temp_df = pd.DataFrame(data_json["data"])
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temp_df.columns = ["变化值", "劳动力投入指数", "日期"]
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temp_df = temp_df[
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[
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"日期",
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"劳动力投入指数",
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"变化值",
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]
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]
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temp_df["日期"] = (
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pd.to_datetime(temp_df["日期"], unit="ms", utc=True)
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.dt.tz_convert("Asia/Shanghai")
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.dt.date
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)
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return temp_df
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def index_ci_cx() -> pd.DataFrame:
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"""
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财新数据-指数报告-资本投入指数
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https://yun.ccxe.com.cn/indices/nei
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:return: 资本投入指数
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:rtype: pandas.DataFrame
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"""
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url = "https://yun.ccxe.com.cn/api/index/pro/cxIndexTrendInfo"
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params = {"type": "ci"}
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r = requests.get(url, params=params)
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data_json = r.json()
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temp_df = pd.DataFrame(data_json["data"])
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temp_df.columns = ["变化值", "资本投入指数", "日期"]
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temp_df = temp_df[
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[
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"日期",
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"资本投入指数",
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"变化值",
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]
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]
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temp_df["日期"] = (
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pd.to_datetime(temp_df["日期"], unit="ms", utc=True)
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.dt.tz_convert("Asia/Shanghai")
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.dt.date
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)
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return temp_df
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def index_ti_cx() -> pd.DataFrame:
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"""
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财新数据-指数报告-科技投入指数
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https://yun.ccxe.com.cn/indices/nei
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:return: 科技投入指数
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:rtype: pandas.DataFrame
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"""
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url = "https://yun.ccxe.com.cn/api/index/pro/cxIndexTrendInfo"
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params = {"type": "ti"}
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r = requests.get(url, params=params)
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data_json = r.json()
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temp_df = pd.DataFrame(data_json["data"])
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temp_df.columns = ["变化值", "科技投入指数", "日期"]
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temp_df = temp_df[
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[
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"日期",
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"科技投入指数",
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"变化值",
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]
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]
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temp_df["日期"] = (
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pd.to_datetime(temp_df["日期"], unit="ms", utc=True)
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.dt.tz_convert("Asia/Shanghai")
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.dt.date
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)
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return temp_df
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def index_neaw_cx() -> pd.DataFrame:
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"""
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财新数据-指数报告-新经济行业入职平均工资水平
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https://yun.ccxe.com.cn/indices/nei
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:return: 新经济行业入职平均工资水平
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:rtype: pandas.DataFrame
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"""
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url = "https://yun.ccxe.com.cn/api/index/pro/cxIndexTrendInfo"
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params = {"type": "neaw"}
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r = requests.get(url, params=params)
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data_json = r.json()
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temp_df = pd.DataFrame(data_json["data"])
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temp_df.columns = ["变化值", "新经济行业入职平均工资水平", "日期"]
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temp_df = temp_df[
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[
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"日期",
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"新经济行业入职平均工资水平",
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"变化值",
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]
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]
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temp_df["日期"] = (
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pd.to_datetime(temp_df["日期"], unit="ms", utc=True)
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.dt.tz_convert("Asia/Shanghai")
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.dt.date
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)
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return temp_df
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def index_awpr_cx() -> pd.DataFrame:
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"""
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财新数据-指数报告-新经济入职工资溢价水平
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https://yun.ccxe.com.cn/indices/nei
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:return: 新经济入职工资溢价水平
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:rtype: pandas.DataFrame
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"""
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url = "https://yun.ccxe.com.cn/api/index/pro/cxIndexTrendInfo"
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params = {"type": "awpr"}
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r = requests.get(url, params=params)
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data_json = r.json()
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temp_df = pd.DataFrame(data_json["data"])
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temp_df.columns = ["变化值", "新经济入职工资溢价水平", "日期"]
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temp_df = temp_df[
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[
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"日期",
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"新经济入职工资溢价水平",
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"变化值",
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]
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]
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temp_df["日期"] = (
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pd.to_datetime(temp_df["日期"], unit="ms", utc=True)
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.dt.tz_convert("Asia/Shanghai")
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.dt.date
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)
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return temp_df
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def index_cci_cx() -> pd.DataFrame:
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"""
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财新数据-指数报告-大宗商品指数
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https://yun.ccxe.com.cn/indices/nei
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:return: 大宗商品指数
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:rtype: pandas.DataFrame
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"""
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url = "https://yun.ccxe.com.cn/api/index/pro/cxIndexTrendInfo"
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params = {
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"type": "cci",
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"code": "1000050",
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"month": "-1",
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}
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r = requests.get(url, params=params)
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data_json = r.json()
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temp_df = pd.DataFrame(data_json["data"])
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temp_df.columns = ["变化值", "大宗商品指数", "日期"]
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temp_df = temp_df[
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[
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"日期",
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"大宗商品指数",
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"变化值",
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]
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]
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temp_df["日期"] = (
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pd.to_datetime(temp_df["日期"], unit="ms", utc=True)
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.dt.tz_convert("Asia/Shanghai")
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.dt.date
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)
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return temp_df
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def index_qli_cx() -> pd.DataFrame:
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"""
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财新数据-指数报告-高质量因子
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https://yun.ccxe.com.cn/indices/qli
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:return: 高质量因子
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:rtype: pandas.DataFrame
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"""
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url = "https://yun.ccxe.com.cn/api/index/pro/cxIndexTrendInfo"
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params = {
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"type": "qli",
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"code": "1000050",
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"month": "-1",
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}
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r = requests.get(url, params=params)
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data_json = r.json()
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temp_df = pd.DataFrame(data_json["data"])
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temp_df.columns = ["变化幅度", "高质量因子指数", "日期"]
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temp_df = temp_df[
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[
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"日期",
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"高质量因子指数",
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"变化幅度",
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]
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]
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temp_df["日期"] = (
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pd.to_datetime(temp_df["日期"], unit="ms", utc=True)
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.dt.tz_convert("Asia/Shanghai")
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.dt.date
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)
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return temp_df
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def index_ai_cx() -> pd.DataFrame:
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"""
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财新数据-指数报告-AI策略指数
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https://yun.ccxe.com.cn/indices/ai
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:return: AI策略指数
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:rtype: pandas.DataFrame
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"""
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url = "https://yun.ccxe.com.cn/api/index/pro/cxIndexTrendInfo"
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params = {
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"type": "ai",
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"code": "1000050",
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"month": "-1",
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}
|
|
r = requests.get(url, params=params)
|
|
data_json = r.json()
|
|
temp_df = pd.DataFrame(data_json["data"])
|
|
temp_df.columns = ["变化幅度", "AI策略指数", "日期"]
|
|
temp_df = temp_df[
|
|
[
|
|
"日期",
|
|
"AI策略指数",
|
|
"变化幅度",
|
|
]
|
|
]
|
|
temp_df["日期"] = (
|
|
pd.to_datetime(temp_df["日期"], unit="ms", utc=True)
|
|
.dt.tz_convert("Asia/Shanghai")
|
|
.dt.date
|
|
)
|
|
return temp_df
|
|
|
|
|
|
def index_bei_cx() -> pd.DataFrame:
|
|
"""
|
|
财新数据-指数报告-基石经济指数
|
|
https://yun.ccxe.com.cn/indices/bei
|
|
:return: 基石经济指数
|
|
:rtype: pandas.DataFrame
|
|
"""
|
|
url = "https://yun.ccxe.com.cn/api/index/pro/cxIndexTrendInfo"
|
|
params = {
|
|
"type": "ind",
|
|
"code": "930927",
|
|
"month": "-1",
|
|
}
|
|
r = requests.get(url, params=params)
|
|
data_json = r.json()
|
|
temp_df = pd.DataFrame(data_json["data"])
|
|
temp_df.columns = ["变化幅度", "基石经济指数", "日期"]
|
|
temp_df = temp_df[
|
|
[
|
|
"日期",
|
|
"基石经济指数",
|
|
"变化幅度",
|
|
]
|
|
]
|
|
temp_df["日期"] = (
|
|
pd.to_datetime(temp_df["日期"], unit="ms", utc=True)
|
|
.dt.tz_convert("Asia/Shanghai")
|
|
.dt.date
|
|
)
|
|
return temp_df
|
|
|
|
|
|
def index_neei_cx() -> pd.DataFrame:
|
|
"""
|
|
财新数据-指数报告-新动能指数
|
|
https://yun.ccxe.com.cn/indices/neei
|
|
:return: 新动能指数
|
|
:rtype: pandas.DataFrame
|
|
"""
|
|
url = "https://yun.ccxe.com.cn/api/index/pro/cxIndexTrendInfo"
|
|
params = {
|
|
"type": "ind",
|
|
"code": "930928",
|
|
"month": "1",
|
|
}
|
|
r = requests.get(url, params=params)
|
|
data_json = r.json()
|
|
temp_df = pd.DataFrame(data_json["data"])
|
|
temp_df.columns = ["变化幅度", "新动能指数", "日期"]
|
|
temp_df = temp_df[
|
|
[
|
|
"日期",
|
|
"新动能指数",
|
|
"变化幅度",
|
|
]
|
|
]
|
|
temp_df["日期"] = (
|
|
pd.to_datetime(temp_df["日期"], unit="ms", utc=True)
|
|
.dt.tz_convert("Asia/Shanghai")
|
|
.dt.date
|
|
)
|
|
return temp_df
|
|
|
|
|
|
if __name__ == "__main__":
|
|
index_pmi_com_cx_df = index_pmi_com_cx()
|
|
print(index_pmi_com_cx_df)
|
|
|
|
index_pmi_man_cx_df = index_pmi_man_cx()
|
|
print(index_pmi_man_cx_df)
|
|
|
|
index_pmi_ser_cx_df = index_pmi_ser_cx()
|
|
print(index_pmi_ser_cx_df)
|
|
|
|
index_dei_cx_df = index_dei_cx()
|
|
print(index_dei_cx_df)
|
|
|
|
index_ii_cx_df = index_ii_cx()
|
|
print(index_ii_cx_df)
|
|
|
|
index_si_cx_df = index_si_cx()
|
|
print(index_si_cx_df)
|
|
|
|
index_fi_cx_df = index_fi_cx()
|
|
print(index_fi_cx_df)
|
|
|
|
index_bi_cx_df = index_bi_cx()
|
|
print(index_bi_cx_df)
|
|
|
|
index_nei_cx_df = index_nei_cx()
|
|
print(index_nei_cx_df)
|
|
|
|
index_li_cx_df = index_li_cx()
|
|
print(index_li_cx_df)
|
|
|
|
index_ci_cx_df = index_ci_cx()
|
|
print(index_ci_cx_df)
|
|
|
|
index_ti_cx_df = index_ti_cx()
|
|
print(index_ti_cx_df)
|
|
|
|
index_neaw_cx_df = index_neaw_cx()
|
|
print(index_neaw_cx_df)
|
|
|
|
index_awpr_cx_df = index_awpr_cx()
|
|
print(index_awpr_cx_df)
|
|
|
|
index_cci_cx_df = index_cci_cx()
|
|
print(index_cci_cx_df)
|
|
|
|
index_qli_cx_df = index_qli_cx()
|
|
print(index_qli_cx_df)
|
|
|
|
index_ai_cx_df = index_ai_cx()
|
|
print(index_ai_cx_df)
|
|
|
|
index_bei_cx_df = index_bei_cx()
|
|
print(index_bei_cx_df)
|
|
|
|
index_neei_cx_df = index_neei_cx()
|
|
print(index_neei_cx_df)
|