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
195 lines
5.8 KiB
Python
195 lines
5.8 KiB
Python
#!/usr/bin/env python
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# -*- coding:utf-8 -*-
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"""
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Date: 2024/4/3 16:21
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Desc: 中国-香港-宏观指标
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https://data.eastmoney.com/cjsj/foreign_8_0.html
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"""
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import pandas as pd
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import requests
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def macro_china_hk_core(symbol: str = "EMG00341602") -> pd.DataFrame:
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"""
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东方财富-数据中心-经济数据一览-宏观经济-日本-核心代码
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https://data.eastmoney.com/cjsj/foreign_1_0.html
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:param symbol: 代码
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:type symbol: str
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:return: 指定 symbol 的数据
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:rtype: pandas.DataFrame
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"""
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url = "https://datacenter-web.eastmoney.com/api/data/v1/get"
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params = {
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"reportName": "RPT_ECONOMICVALUE_HK",
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"columns": "ALL",
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"filter": f'(INDICATOR_ID="{symbol}")',
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"pageNumber": "1",
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"pageSize": "5000",
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"sortColumns": "REPORT_DATE",
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"sortTypes": "-1",
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"source": "WEB",
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"client": "WEB",
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"p": "1",
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"pageNo": "1",
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"pageNum": "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["result"]["data"])
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temp_df.rename(
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columns={
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"COUNTRY": "-",
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"INDICATOR_ID": "-",
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"INDICATOR_NAME": "-",
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"REPORT_DATE_CH": "时间",
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"REPORT_DATE": "-",
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"PUBLISH_DATE": "发布日期",
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"VALUE": "现值",
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"PRE_VALUE": "前值",
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"INDICATOR_IDOLD": "-",
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},
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inplace=True,
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)
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temp_df = temp_df[
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[
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"时间",
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"前值",
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"现值",
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"发布日期",
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]
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]
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temp_df["前值"] = pd.to_numeric(temp_df["前值"], errors="coerce")
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temp_df["现值"] = pd.to_numeric(temp_df["现值"], errors="coerce")
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temp_df["发布日期"] = pd.to_datetime(temp_df["发布日期"], errors="coerce").dt.date
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temp_df.sort_values(["发布日期"], inplace=True, ignore_index=True)
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return temp_df
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def macro_china_hk_cpi() -> pd.DataFrame:
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"""
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东方财富-经济数据一览-中国香港-消费者物价指数
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https://data.eastmoney.com/cjsj/foreign_8_0.html
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:return: 消费者物价指数
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:rtype: pandas.DataFrame
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"""
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temp_df = macro_china_hk_core(symbol="EMG01336996")
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return temp_df
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def macro_china_hk_cpi_ratio() -> pd.DataFrame:
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"""
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东方财富-经济数据一览-中国香港-消费者物价指数年率
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https://data.eastmoney.com/cjsj/foreign_8_1.html
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:return: 消费者物价指数年率
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:rtype: pandas.DataFrame
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"""
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temp_df = macro_china_hk_core(symbol="EMG00059282")
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return temp_df
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def macro_china_hk_rate_of_unemployment() -> pd.DataFrame:
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"""
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东方财富-经济数据一览-中国香港-失业率
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https://data.eastmoney.com/cjsj/foreign_8_2.html
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:return: 失业率
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:rtype: pandas.DataFrame
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"""
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temp_df = macro_china_hk_core(symbol="EMG00059647")
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return temp_df
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def macro_china_hk_gbp() -> pd.DataFrame:
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"""
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东方财富-经济数据一览-中国香港-香港 GDP
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https://data.eastmoney.com/cjsj/foreign_8_3.html
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:return: 香港 GDP
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:rtype: pandas.DataFrame
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"""
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temp_df = macro_china_hk_core(symbol="EMG01337008")
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return temp_df
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def macro_china_hk_gbp_ratio() -> pd.DataFrame:
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"""
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东方财富-经济数据一览-中国香港-香港 GDP 同比
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https://data.eastmoney.com/cjsj/foreign_8_4.html
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:return: 香港 GDP 同比
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:rtype: pandas.DataFrame
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"""
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temp_df = macro_china_hk_core(symbol="EMG01337009")
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return temp_df
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def macro_china_hk_building_volume() -> pd.DataFrame:
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"""
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东方财富-经济数据一览-中国香港-香港楼宇买卖合约数量
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https://data.eastmoney.com/cjsj/foreign_8_5.html
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:return: 香港楼宇买卖合约数量
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:rtype: pandas.DataFrame
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"""
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temp_df = macro_china_hk_core(symbol="EMG00158055")
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return temp_df
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def macro_china_hk_building_amount() -> pd.DataFrame:
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"""
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东方财富-经济数据一览-中国香港-香港楼宇买卖合约成交金额
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https://data.eastmoney.com/cjsj/foreign_8_6.html
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:return: 香港楼宇买卖合约成交金额
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:rtype: pandas.DataFrame
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"""
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temp_df = macro_china_hk_core(symbol="EMG00158066")
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return temp_df
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def macro_china_hk_trade_diff_ratio() -> pd.DataFrame:
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"""
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东方财富-经济数据一览-中国香港-香港商品贸易差额年率
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https://data.eastmoney.com/cjsj/foreign_8_7.html
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:return: 香港商品贸易差额年率
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:rtype: pandas.DataFrame
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"""
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temp_df = macro_china_hk_core(symbol="EMG00157898")
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return temp_df
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def macro_china_hk_ppi() -> pd.DataFrame:
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"""
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东方财富-经济数据一览-中国香港-香港制造业 PPI 年率
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https://data.eastmoney.com/cjsj/foreign_8_8.html
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:return: 香港制造业 PPI 年率
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:rtype: pandas.DataFrame
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"""
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temp_df = macro_china_hk_core(symbol="EMG00157818")
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return temp_df
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if __name__ == "__main__":
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macro_china_hk_cpi_df = macro_china_hk_cpi()
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print(macro_china_hk_cpi_df)
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macro_china_hk_cpi_ratio_df = macro_china_hk_cpi_ratio()
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print(macro_china_hk_cpi_ratio_df)
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macro_china_hk_rate_of_unemployment_df = macro_china_hk_rate_of_unemployment()
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print(macro_china_hk_rate_of_unemployment_df)
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macro_china_hk_gbp_df = macro_china_hk_gbp()
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print(macro_china_hk_gbp_df)
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macro_china_hk_gbp_ratio_df = macro_china_hk_gbp_ratio()
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print(macro_china_hk_gbp_ratio_df)
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marco_china_hk_building_volume_df = macro_china_hk_building_volume()
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print(marco_china_hk_building_volume_df)
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macro_china_hk_building_amount_df = macro_china_hk_building_amount()
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print(macro_china_hk_building_amount_df)
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macro_china_hk_trade_diff_ratio_df = macro_china_hk_trade_diff_ratio()
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print(macro_china_hk_trade_diff_ratio_df)
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macro_china_hk_ppi_df = macro_china_hk_ppi()
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print(macro_china_hk_ppi_df)
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