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
191 lines
6.4 KiB
Python
191 lines
6.4 KiB
Python
#!/usr/bin/env python
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# -*- coding:utf-8 -*-
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"""
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Date: 2024/10/1 17:00
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Desc: 中国外汇交易中心暨全国银行间同业拆借中心
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中国外汇交易中心暨全国银行间同业拆借中心-市场数据-债券市场行情-现券市场做市报价
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中国外汇交易中心暨全国银行间同业拆借中心-市场数据-债券市场行情-现券市场成交行情
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https://www.chinamoney.com.cn/chinese/mkdatabond/
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"""
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from io import StringIO
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import pandas as pd
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import requests
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from akshare.bond.bond_china_money import bond_china_close_return_map
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from akshare.utils.cons import headers
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def bond_spot_quote() -> pd.DataFrame:
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"""
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中国外汇交易中心暨全国银行间同业拆借中心-市场数据-债券市场行情-现券市场做市报价
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https://www.chinamoney.com.cn/chinese/mkdatabond/
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:return: 现券市场做市报价
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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-md-bond/CbMktMakQuot"
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payload = {
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"flag": "1",
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"lang": "cn",
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}
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r = requests.post(url=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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temp_df.columns = [
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"_",
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"_",
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"报价机构",
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"_",
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"_",
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"_",
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"债券简称",
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"_",
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"_",
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"_",
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"_",
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"买入/卖出收益率",
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"_",
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"买入/卖出净价",
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"_",
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"_",
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"_",
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]
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temp_df = temp_df[
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[
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"报价机构",
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"债券简称",
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"买入/卖出净价",
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"买入/卖出收益率",
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]
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]
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temp_df["买入净价"] = (
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temp_df["买入/卖出净价"].str.split("/", expand=True).iloc[:, 0]
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)
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temp_df["卖出净价"] = (
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temp_df["买入/卖出净价"].str.split("/", expand=True).iloc[:, 1]
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)
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temp_df["买入收益率"] = (
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temp_df["买入/卖出收益率"].str.split("/", expand=True).iloc[:, 0]
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)
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temp_df["卖出收益率"] = (
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temp_df["买入/卖出收益率"].str.split("/", expand=True).iloc[:, 1]
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)
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del temp_df["买入/卖出净价"]
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del temp_df["买入/卖出收益率"]
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temp_df["买入净价"] = pd.to_numeric(temp_df["买入净价"], errors="coerce")
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temp_df["卖出净价"] = pd.to_numeric(temp_df["卖出净价"], errors="coerce")
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temp_df["买入收益率"] = pd.to_numeric(temp_df["买入收益率"], errors="coerce")
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temp_df["卖出收益率"] = pd.to_numeric(temp_df["卖出收益率"], errors="coerce")
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return temp_df
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def bond_spot_deal() -> pd.DataFrame:
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"""
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中国外汇交易中心暨全国银行间同业拆借中心-市场数据-债券市场行情-现券市场成交行情
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https://www.chinamoney.com.cn/chinese/mkdatabond/
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:return: 现券市场成交行情
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:rtype: pandas.DataFrame
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"""
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url = "https://www.chinamoney.com.cn/ags/ms/cm-u-md-bond/CbtPri"
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payload = {
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"flag": "1",
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"lang": "cn",
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"bondName": "",
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}
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r = requests.post(url=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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temp_df.columns = [
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"_",
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"_",
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"债券简称",
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"_",
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"_",
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"_",
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"_",
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"涨跌",
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"_",
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"_",
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"_",
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"加权收益率",
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"成交净价",
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"_",
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"_",
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"最新收益率",
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"-",
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"交易量",
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"_",
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"_",
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"_",
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"_",
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]
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temp_df = temp_df[
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[
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"债券简称",
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"成交净价",
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"最新收益率",
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"涨跌",
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"加权收益率",
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"交易量",
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]
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]
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temp_df["成交净价"] = pd.to_numeric(temp_df["成交净价"], errors="coerce")
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temp_df["最新收益率"] = pd.to_numeric(temp_df["最新收益率"], errors="coerce")
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temp_df["涨跌"] = pd.to_numeric(temp_df["涨跌"], errors="coerce")
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temp_df["加权收益率"] = pd.to_numeric(temp_df["加权收益率"], errors="coerce")
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temp_df["交易量"] = pd.to_numeric(temp_df["交易量"], errors="coerce")
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return temp_df
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def bond_china_yield(
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start_date: str = "20200204", end_date: str = "20210124"
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) -> pd.DataFrame:
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"""
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中国债券信息网-国债及其他债券收益率曲线
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https://www.chinabond.com.cn/
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https://yield.chinabond.com.cn/cbweb-pbc-web/pbc/historyQuery?startDate=2019-02-07&endDate=2020-02-04&gjqx=0&qxId=ycqx&locale=cn_ZH
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注意: end_date - start_date 应该小于一年
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:param start_date: 需要查询的日期, 返回在该日期之后一年内的数据
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:type start_date: str
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:param end_date: 需要查询的日期, 返回在该日期之前一年内的数据
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:type end_date: str
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:return: 返回在指定日期之间之前一年内的数据
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:rtype: pandas.DataFrame
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"""
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url = "https://yield.chinabond.com.cn/cbweb-pbc-web/pbc/historyQuery"
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params = {
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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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"gjqx": "0",
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"qxId": "ycqx",
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"locale": "cn_ZH",
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}
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res = requests.get(url, params=params, headers=headers)
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data_text = res.text.replace(" ", "")
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data_df = pd.read_html(StringIO(data_text), header=0)[1]
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data_df["日期"] = pd.to_datetime(data_df["日期"], errors="coerce").dt.date
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data_df["3月"] = pd.to_numeric(data_df["3月"], errors="coerce")
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data_df["6月"] = pd.to_numeric(data_df["6月"], errors="coerce")
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data_df["1年"] = pd.to_numeric(data_df["1年"], errors="coerce")
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data_df["3年"] = pd.to_numeric(data_df["3年"], errors="coerce")
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data_df["5年"] = pd.to_numeric(data_df["5年"], errors="coerce")
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data_df["7年"] = pd.to_numeric(data_df["7年"], errors="coerce")
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data_df["10年"] = pd.to_numeric(data_df["10年"], errors="coerce")
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data_df["30年"] = pd.to_numeric(data_df["30年"], errors="coerce")
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data_df.sort_values(by="日期", inplace=True)
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data_df.reset_index(inplace=True, drop=True)
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return data_df
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if __name__ == "__main__":
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bond_spot_quote_df = bond_spot_quote()
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print(bond_spot_quote_df)
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bond_spot_deal_df = bond_spot_deal()
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print(bond_spot_deal_df)
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bond_china_yield_df = bond_china_yield(start_date="20210201", end_date="20220201")
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print(bond_china_yield_df)
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