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
553 lines
15 KiB
Python
553 lines
15 KiB
Python
#!/usr/bin/env python
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# -*- coding:utf-8 -*-
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"""
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Date: 2022/11/27 20:30
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Desc: 东方财富-经济数据-加拿大
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https://data.eastmoney.com/cjsj/foreign_5_0.html
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"""
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import pandas as pd
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import requests
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# 新屋开工
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def macro_canada_new_house_rate() -> pd.DataFrame:
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"""
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东方财富-经济数据-加拿大-新屋开工
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https://data.eastmoney.com/cjsj/foreign_7_0.html
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:return: 新屋开工
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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_CA",
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"columns": "ALL",
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"filter": '(INDICATOR_ID="EMG00342247")',
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"pageNumber": "1",
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"pageSize": "2000",
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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.columns = [
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"-",
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"-",
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"-",
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"-",
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"时间",
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"-",
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"发布日期",
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"现值",
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"前值",
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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"]).dt.date
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return temp_df
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# 失业率
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def macro_canada_unemployment_rate() -> pd.DataFrame:
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"""
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东方财富-经济数据-加拿大-失业率
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https://data.eastmoney.com/cjsj/foreign_7_1.html
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:return: 失业率
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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_CA",
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"columns": "ALL",
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"filter": '(INDICATOR_ID="EMG00157746")',
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"pageNumber": "1",
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"pageSize": "2000",
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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.columns = [
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"-",
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"-",
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"-",
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"-",
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"时间",
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"-",
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"发布日期",
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"现值",
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"前值",
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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"]).dt.date
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return temp_df
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# 贸易帐
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def macro_canada_trade() -> pd.DataFrame:
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"""
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东方财富-经济数据-加拿大-贸易帐
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https://data.eastmoney.com/cjsj/foreign_7_2.html
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:return: 贸易帐
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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_CA",
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"columns": "ALL",
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"filter": '(INDICATOR_ID="EMG00102022")',
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"pageNumber": "1",
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"pageSize": "2000",
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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.columns = [
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"-",
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"-",
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"-",
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"-",
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"时间",
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"-",
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"发布日期",
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"现值",
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"前值",
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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"]).dt.date
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return temp_df
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# 零售销售月率
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def macro_canada_retail_rate_monthly() -> pd.DataFrame:
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"""
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东方财富-经济数据-加拿大-零售销售月率
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https://data.eastmoney.com/cjsj/foreign_7_3.html
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:return: 零售销售月率
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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_CA",
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"columns": "ALL",
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"filter": '(INDICATOR_ID="EMG01337094")',
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"pageNumber": "1",
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"pageSize": "2000",
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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.columns = [
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"-",
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"-",
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"-",
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"-",
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"时间",
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"-",
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"发布日期",
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"现值",
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"前值",
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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"]).dt.date
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return temp_df
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# 央行公布利率决议
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def macro_canada_bank_rate() -> pd.DataFrame:
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"""
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东方财富-经济数据-加拿大-央行公布利率决议
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https://data.eastmoney.com/cjsj/foreign_7_4.html
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:return: 央行公布利率决议
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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_CA",
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"columns": "ALL",
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"filter": '(INDICATOR_ID="EMG00342248")',
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"pageNumber": "1",
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"pageSize": "2000",
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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.columns = [
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"-",
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"-",
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"-",
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"-",
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"时间",
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"-",
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"发布日期",
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"现值",
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"前值",
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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"]).dt.date
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return temp_df
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# 核心消费者物价指数年率
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def macro_canada_core_cpi_yearly() -> pd.DataFrame:
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"""
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东方财富-经济数据-加拿大-核心消费者物价指数年率
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https://data.eastmoney.com/cjsj/foreign_7_5.html
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:return: 核心消费者物价指数年率
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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_CA",
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"columns": "ALL",
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"filter": '(INDICATOR_ID="EMG00102030")',
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"pageNumber": "1",
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"pageSize": "2000",
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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.columns = [
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"-",
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"-",
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"-",
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"-",
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"时间",
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"-",
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"发布日期",
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"现值",
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"前值",
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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"]).dt.date
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return temp_df
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# 核心消费者物价指数月率
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def macro_canada_core_cpi_monthly() -> pd.DataFrame:
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"""
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东方财富-经济数据-加拿大-核心消费者物价指数月率
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https://data.eastmoney.com/cjsj/foreign_7_6.html
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:return: 核心消费者物价指数月率
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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_CA",
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"columns": "ALL",
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"filter": '(INDICATOR_ID="EMG00102044")',
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"pageNumber": "1",
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"pageSize": "2000",
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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.columns = [
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"-",
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"-",
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"-",
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"-",
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"时间",
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"-",
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"发布日期",
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"现值",
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"前值",
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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"]).dt.date
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return temp_df
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# 消费者物价指数年率
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def macro_canada_cpi_yearly() -> pd.DataFrame:
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"""
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东方财富-经济数据-加拿大-消费者物价指数年率
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https://data.eastmoney.com/cjsj/foreign_7_7.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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url = "https://datacenter-web.eastmoney.com/api/data/v1/get"
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params = {
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"reportName": "RPT_ECONOMICVALUE_CA",
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"columns": "ALL",
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"filter": '(INDICATOR_ID="EMG00102029")',
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"pageNumber": "1",
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"pageSize": "2000",
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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.columns = [
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"-",
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"-",
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"-",
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"-",
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"时间",
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"-",
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"发布日期",
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"现值",
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"前值",
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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"]).dt.date
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return temp_df
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# 消费者物价指数月率
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def macro_canada_cpi_monthly() -> pd.DataFrame:
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"""
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东方财富-经济数据-加拿大-消费者物价指数月率
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https://data.eastmoney.com/cjsj/foreign_7_8.html
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:return: 消费者物价指数月率
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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_CA",
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"columns": "ALL",
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"filter": '(INDICATOR_ID="EMG00158719")',
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"pageNumber": "1",
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"pageSize": "2000",
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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.columns = [
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"-",
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"-",
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"-",
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"-",
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"时间",
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"-",
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"发布日期",
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"现值",
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"前值",
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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"]).dt.date
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return temp_df
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# GDP 月率
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def macro_canada_gdp_monthly() -> pd.DataFrame:
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"""
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东方财富-经济数据-加拿大-GDP 月率
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https://data.eastmoney.com/cjsj/foreign_7_9.html
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:return: GDP 月率
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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_CA",
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"columns": "ALL",
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"filter": '(INDICATOR_ID="EMG00159259")',
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"pageNumber": "1",
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"pageSize": "2000",
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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",
|
|
"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 = [
|
|
"-",
|
|
"-",
|
|
"-",
|
|
"-",
|
|
"时间",
|
|
"-",
|
|
"发布日期",
|
|
"现值",
|
|
"前值",
|
|
]
|
|
temp_df = temp_df[
|
|
[
|
|
"时间",
|
|
"前值",
|
|
"现值",
|
|
"发布日期",
|
|
]
|
|
]
|
|
temp_df["前值"] = pd.to_numeric(temp_df["前值"], errors="coerce")
|
|
temp_df["现值"] = pd.to_numeric(temp_df["现值"], errors="coerce")
|
|
temp_df["发布日期"] = pd.to_datetime(temp_df["发布日期"]).dt.date
|
|
return temp_df
|
|
|
|
|
|
if __name__ == "__main__":
|
|
macro_canada_new_house_rate_df = macro_canada_new_house_rate()
|
|
print(macro_canada_new_house_rate_df)
|
|
|
|
macro_canada_unemployment_rate_df = macro_canada_unemployment_rate()
|
|
print(macro_canada_unemployment_rate_df)
|
|
|
|
macro_canada_trade_df = macro_canada_trade()
|
|
print(macro_canada_trade_df)
|
|
|
|
macro_canada_retail_rate_monthly_df = macro_canada_retail_rate_monthly()
|
|
print(macro_canada_retail_rate_monthly_df)
|
|
|
|
macro_canada_bank_rate_df = macro_canada_bank_rate()
|
|
print(macro_canada_bank_rate_df)
|
|
|
|
macro_canada_core_cpi_yearly_df = macro_canada_core_cpi_yearly()
|
|
print(macro_canada_core_cpi_yearly_df)
|
|
|
|
macro_canada_core_cpi_monthly_df = macro_canada_core_cpi_monthly()
|
|
print(macro_canada_core_cpi_monthly_df)
|
|
|
|
macro_canada_cpi_yearly_df = macro_canada_cpi_yearly()
|
|
print(macro_canada_cpi_yearly_df)
|
|
|
|
macro_canada_cpi_monthly_df = macro_canada_cpi_monthly()
|
|
print(macro_canada_cpi_monthly_df)
|
|
|
|
macro_canada_gdp_monthly_df = macro_canada_gdp_monthly()
|
|
print(macro_canada_gdp_monthly_df)
|