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
391 lines
13 KiB
Python
391 lines
13 KiB
Python
#!/usr/bin/env python
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# -*- coding:utf-8 -*-
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"""
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Date: 2024/6/27 16:00
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Desc: 收盘收益率曲线历史数据
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https://www.chinamoney.com.cn/chinese/bkcurvclosedyhis/?bondType=CYCC000&reference=1
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"""
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from functools import lru_cache
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import pandas as pd
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import requests
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from akshare.utils.tqdm import get_tqdm
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def __bond_register_service() -> requests.Session:
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"""
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将服务注册到网站中，则该 IP 在 24 小时内可以直接访问
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https://www.chinamoney.com.cn
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:return: 访问过的 Session
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:rtype: requests.Session
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"""
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session = requests.Session()
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headers = {
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"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) "
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"Chrome/108.0.0.0 Safari/537.36",
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}
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session.get(
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url="https://www.chinamoney.com.cn/chinese/bkcurvclosedyhis/?bondType=CYCC000&reference=1",
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headers=headers,
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)
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cookies_dict = session.cookies.get_dict()
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cookies_str = "; ".join(f"{k}={v}" for k, v in cookies_dict.items())
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# 此处需要通过未访问的游览器，首次打开
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# https://www.chinamoney.com.cn/chinese/bkcurvclosedyhis/?bondType=CYCC000&reference=1
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# 页面进行人工获取
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data = {"key": "TThwSjc2NWkzV0VSOVRzOA=="}
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headers = {
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"Accept": "application/json, text/javascript, */*; q=0.01",
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"Accept-Encoding": "gzip, deflate, br",
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"Accept-Language": "en",
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"Cache-Control": "no-cache",
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"Connection": "keep-alive",
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"Content-Length": "22",
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"Cookie": cookies_str,
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"Content-Type": "application/x-www-form-urlencoded; charset=UTF-8",
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"Host": "www.chinamoney.com.cn",
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"Origin": "https://www.chinamoney.com.cn",
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"Pragma": "no-cache",
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"Referer": "https://www.chinamoney.com.cn/chinese/bkcurvclosedyhis/?bondType=CYCC000&reference=1",
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"Sec-Fetch-Dest": "empty",
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"Sec-Fetch-Mode": "cors",
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"Sec-Fetch-Site": "same-origin",
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"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) "
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"Chrome/108.0.0.0 Safari/537.36",
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"X-Requested-With": "XMLHttpRequest",
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}
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session.post(
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url="https://www.chinamoney.com.cn/dqs/rest/cm-u-rbt/apply",
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data=data,
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headers=headers,
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)
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# 20231127 新增部分 https://github.com/akfamily/akshare/issues/4299
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cookies_dict = session.cookies.get_dict()
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cookies_str = "; ".join(f"{k}={v}" for k, v in cookies_dict.items())
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headers = {
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"Accept": "application/json, text/javascript, /; q=0.01",
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"Accept-Encoding": "gzip, deflate, br",
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"Accept-Language": "en",
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"Connection": "keep-alive",
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"Content-Length": "0",
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"Cookie": cookies_str,
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"Content-Type": "application/x-www-form-urlencoded; charset=UTF-8",
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"Host": "www.chinamoney.com.cn",
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"Origin": "https://www.chinamoney.com.cn",
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"Referer": "https://www.chinamoney.com.cn/chinese/bkcurvclosedyhis/?bondType=CYCC000&reference=1",
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"Sec-Fetch-Dest": "empty",
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"Sec-Fetch-Mode": "cors",
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"Sec-Fetch-Site": "same-origin",
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"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) "
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"Chrome/108.0.0.0 Safari/537.36",
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"X-Requested-With": "XMLHttpRequest",
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}
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session.post(
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url="https://www.chinamoney.com.cn/lss/rest/cm-s-account/getSessionUser",
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headers=headers,
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)
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return session
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@lru_cache()
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def bond_china_close_return_map() -> pd.DataFrame:
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"""
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收盘收益率曲线历史数据
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https://www.chinamoney.com.cn/chinese/bkcurvclosedyhis/?bondType=CYCC000&reference=1
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:return: 收盘收益率曲线历史数据
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:rtype: pandas.DataFrame
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"""
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headers = {
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"Accept": "application/json, text/javascript, */*; q=0.01",
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"Accept-Encoding": "gzip, deflate, br",
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"Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8",
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"Cache-Control": "no-cache",
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"Connection": "keep-alive",
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"Content-Length": "0",
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"Host": "www.chinamoney.com.cn",
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"Origin": "https://www.chinamoney.com.cn",
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"Pragma": "no-cache",
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"Referer": "https://www.chinamoney.com.cn/chinese/bkcurvclosedyhis/?bondType=CYCC000&reference=1",
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"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) "
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"Chrome/108.0.0.0 Safari/537.36",
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"X-Requested-With": "XMLHttpRequest",
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}
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url = "https://www.chinamoney.com.cn/ags/ms/cm-u-bk-currency/ClsYldCurvCurvGO"
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try:
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r = requests.get(url, headers=headers)
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data_json = r.json()
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except: # noqa: E722
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session = __bond_register_service()
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r = session.get(url, headers=headers)
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data_json = r.json()
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temp_df = pd.DataFrame(data_json["records"])
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return temp_df
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def bond_china_close_return(
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symbol: str = "国债",
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period: str = "1",
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start_date: str = "20231101",
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end_date: str = "20231101",
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) -> pd.DataFrame:
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"""
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收盘收益率曲线历史数据
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https://www.chinamoney.com.cn/chinese/bkcurvclosedyhis/?bondType=CYCC000&reference=1
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:param symbol: 需要获取的指标
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:type period: choice of {'0.1', '0.5', '1'}
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:param period: 期限间隔
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:type symbol: str
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:param start_date: 开始日期, 结束日期和开始日期不要超过 1 个月
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:type start_date: str
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:param end_date: 结束日期, 结束日期和开始日期不要超过 1 个月
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:type end_date: str
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:return: 收盘收益率曲线历史数据
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:rtype: pandas.DataFrame
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"""
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name_code_df = bond_china_close_return_map()
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symbol_code = name_code_df[name_code_df["cnLabel"] == symbol]["value"].values[0]
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url = "https://www.chinamoney.com.cn/ags/ms/cm-u-bk-currency/ClsYldCurvHis"
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headers = {
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"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) "
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"Chrome/108.0.0.0 Safari/537.36",
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}
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params = {
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"lang": "CN",
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"reference": "1,2,3",
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"bondType": symbol_code,
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"startDate": "-".join([start_date[:4], start_date[4:6], start_date[6:]]),
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"endDate": "-".join([end_date[:4], end_date[4:6], end_date[6:]]),
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"termId": period,
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"pageNum": "1",
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"pageSize": "50",
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}
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r = requests.get(url, params=params, headers=headers)
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data_json = r.json()
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temp_df = pd.DataFrame(data_json["records"])
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del temp_df["newDateValue"]
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temp_df.columns = [
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"日期",
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"期限",
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"到期收益率",
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"即期收益率",
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"远期收益率",
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]
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temp_df = temp_df[
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[
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"日期",
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"期限",
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"到期收益率",
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"即期收益率",
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"远期收益率",
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]
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]
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temp_df["日期"] = pd.to_datetime(temp_df["日期"], errors="coerce").dt.date
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temp_df["期限"] = pd.to_numeric(temp_df["期限"], errors="coerce")
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temp_df["到期收益率"] = pd.to_numeric(temp_df["到期收益率"], errors="coerce")
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temp_df["即期收益率"] = pd.to_numeric(temp_df["即期收益率"], errors="coerce")
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temp_df["远期收益率"] = pd.to_numeric(temp_df["远期收益率"], errors="coerce")
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return temp_df
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def macro_china_swap_rate(
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start_date: str = "20231101", end_date: str = "20231204"
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) -> pd.DataFrame:
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"""
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FR007 利率互换曲线历史数据; 只能获取近一年的数据
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https://www.chinamoney.com.cn/chinese/bkcurvfxhis/?cfgItemType=72&curveType=FR007
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:param start_date: 开始日期, 开始和结束日期不得超过一个月
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:type start_date: str
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:param end_date: 结束日期, 开始和结束日期不得超过一个月
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:type end_date: str
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:return: FR007利率互换曲线历史数据
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:rtype: pandas.DataFrame
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"""
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bond_china_close_return_map()
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start_date = "-".join([start_date[:4], start_date[4:6], start_date[6:]])
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end_date = "-".join([end_date[:4], end_date[4:6], end_date[6:]])
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url = "https://www.chinamoney.com.cn/ags/ms/cm-u-bk-shibor/IfccHis"
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params = {
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"cfgItemType": "72",
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"interestRateType": "0",
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"startDate": start_date,
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"endDate": end_date,
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"bidAskType": "",
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"lang": "CN",
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"quoteTime": "全部",
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"pageSize": "5000",
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"pageNum": "1",
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}
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headers = {
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"Accept": "application/json, text/javascript, */*; q=0.01",
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"Accept-Encoding": "gzip, deflate, br",
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"Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8",
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"Cache-Control": "no-cache",
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"Connection": "keep-alive",
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"Content-Length": "0",
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"Host": "www.chinamoney.com.cn",
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"Origin": "https://www.chinamoney.com.cn",
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"Pragma": "no-cache",
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"Referer": "https://www.chinamoney.com.cn/chinese/bkcurvfxhis/?cfgItemType=72&curveType=FR007",
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"sec-ch-ua": '"Google Chrome";v="107", "Chromium";v="107", "Not=A?Brand";v="24"',
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"sec-ch-ua-mobile": "?0",
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"sec-ch-ua-platform": '"Windows"',
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"Sec-Fetch-Dest": "empty",
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"Sec-Fetch-Mode": "cors",
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"Sec-Fetch-Site": "same-origin",
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"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) "
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"Chrome/107.0.0.0 Safari/537.36",
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"X-Requested-With": "XMLHttpRequest",
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}
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r = requests.post(url, data=params, headers=headers)
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data_json = r.json()
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temp_df = pd.DataFrame(data_json["records"])
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temp_df.columns = [
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"日期",
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"_",
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"_",
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"时刻",
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"_",
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"_",
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"_",
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"_",
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"_",
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"价格类型",
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"_",
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"曲线名称",
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"_",
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"_",
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"_",
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"_",
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"data",
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]
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price_df = pd.DataFrame([item for item in temp_df["data"]])
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price_df.columns = [
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"1M",
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"3M",
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"6M",
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"9M",
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"1Y",
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"2Y",
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"3Y",
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"4Y",
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"5Y",
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"7Y",
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"10Y",
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]
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big_df = pd.concat(objs=[temp_df, price_df], axis=1)
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big_df = big_df[
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[
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"日期",
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"曲线名称",
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"时刻",
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"价格类型",
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"1M",
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"3M",
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"6M",
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"9M",
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"1Y",
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"2Y",
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"3Y",
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"4Y",
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"5Y",
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"7Y",
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"10Y",
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]
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]
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big_df["日期"] = pd.to_datetime(big_df["日期"], errors="coerce").dt.date
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big_df["1M"] = pd.to_numeric(big_df["1M"], errors="coerce")
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big_df["3M"] = pd.to_numeric(big_df["3M"], errors="coerce")
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big_df["6M"] = pd.to_numeric(big_df["6M"], errors="coerce")
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big_df["9M"] = pd.to_numeric(big_df["9M"], errors="coerce")
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big_df["1Y"] = pd.to_numeric(big_df["1Y"], errors="coerce")
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big_df["2Y"] = pd.to_numeric(big_df["2Y"], errors="coerce")
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big_df["3Y"] = pd.to_numeric(big_df["3Y"], errors="coerce")
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big_df["4Y"] = pd.to_numeric(big_df["4Y"], errors="coerce")
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big_df["5Y"] = pd.to_numeric(big_df["5Y"], errors="coerce")
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big_df["7Y"] = pd.to_numeric(big_df["7Y"], errors="coerce")
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big_df["10Y"] = pd.to_numeric(big_df["10Y"], errors="coerce")
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big_df.sort_values(["日期"], inplace=True, ignore_index=True)
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return big_df
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def macro_china_bond_public() -> pd.DataFrame:
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"""
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中国-债券信息披露-债券发行
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https://www.chinamoney.com.cn/chinese/xzjfx/
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:return: 债券发行
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:rtype: pandas.DataFrame
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"""
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bond_china_close_return_map()
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url = "https://www.chinamoney.com.cn/ags/ms/cm-u-bond-an/bnBondEmit"
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headers = {
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"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) "
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"Chrome/107.0.0.0 Safari/537.36",
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}
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payload = {
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"enty": "",
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"bondType": "",
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"bondNameCode": "",
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"leadUnderwriter": "",
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"pageNo": "1",
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"pageSize": "10",
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"limit": "1",
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}
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r = requests.post(url, data=payload, headers=headers)
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data_json = r.json()
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total_page = int(data_json["data"]["pageTotalSize"]) + 1
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big_df = pd.DataFrame()
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tqdm = get_tqdm()
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for page in tqdm(range(1, total_page), leave=False):
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payload.update({"pageNo": page})
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r = requests.post(url, data=payload, headers=headers)
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data_json = r.json()
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temp_df = pd.DataFrame(data_json["records"])
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big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True)
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big_df.columns = [
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"债券全称",
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"债券类型",
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"-",
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"发行日期",
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"-",
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"计息方式",
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"-",
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"债券期限",
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"-",
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"债券评级",
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"-",
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"价格",
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"计划发行量",
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]
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big_df = big_df[
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[
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"债券全称",
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"债券类型",
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"发行日期",
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"计息方式",
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"价格",
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"债券期限",
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"计划发行量",
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"债券评级",
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]
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]
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big_df["价格"] = pd.to_numeric(big_df["价格"], errors="coerce")
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big_df["计划发行量"] = pd.to_numeric(big_df["计划发行量"], errors="coerce")
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return big_df
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if __name__ == "__main__":
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bond_china_close_return_df = bond_china_close_return(
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symbol="同业存单(AAA)", period="1", start_date="20240607", end_date="20240607"
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)
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print(bond_china_close_return_df)
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macro_china_swap_rate_df = macro_china_swap_rate(
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start_date="20251010", end_date="20251208"
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)
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print(macro_china_swap_rate_df)
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macro_china_bond_public_df = macro_china_bond_public()
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print(macro_china_bond_public_df)
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