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
101 lines
4.2 KiB
Python
101 lines
4.2 KiB
Python
#!/usr/bin/env python
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# -*- coding:utf-8 -*-
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"""
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Date: 2024/1/20 23:04
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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-回购定盘利率-历史数据
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"""
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import pandas as pd
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import requests
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def repo_rate_query(symbol: str = "回购定盘利率") -> 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-回购定盘利率-历史数据
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https://www.chinamoney.com.cn/chinese/bkfrr/
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:param symbol: choice of {"回购定盘利率", "银银间回购定盘利率"}
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:type symbol: str
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:return: 回购定盘利率-历史数据
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:rtype: pandas.DataFrame
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"""
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if symbol == "回购定盘利率":
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url = "https://www.chinamoney.com.cn/r/cms/www/chinamoney/data/currency/frr-chrt.csv"
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temp_df = pd.read_csv(url, header=None)
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temp_df.dropna(axis=1, inplace=True)
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temp_df.columns = ["date", "FR001", "FR007", "FR014"]
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temp_df["date"] = pd.to_datetime(temp_df["date"], errors="coerce").dt.date
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temp_df["FR001"] = pd.to_numeric(temp_df["FR001"], errors="coerce")
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temp_df["FR007"] = pd.to_numeric(temp_df["FR007"], errors="coerce")
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temp_df["FR014"] = pd.to_numeric(temp_df["FR014"], errors="coerce")
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temp_df.sort_values(by=["date"], ignore_index=True, inplace=True)
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return temp_df
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else:
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url = "https://www.chinamoney.com.cn/r/cms/www/chinamoney/data/currency/fdr-chrt.csv"
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temp_df = pd.read_csv(url, header=None)
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temp_df.dropna(axis=1, inplace=True)
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temp_df.columns = ["date", "FDR001", "FDR007", "FDR014"]
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temp_df["date"] = pd.to_datetime(temp_df["date"], errors="coerce").dt.date
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temp_df["FDR001"] = pd.to_numeric(temp_df["FDR001"], errors="coerce")
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temp_df["FDR007"] = pd.to_numeric(temp_df["FDR007"], errors="coerce")
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temp_df["FDR014"] = pd.to_numeric(temp_df["FDR014"], errors="coerce")
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temp_df.sort_values(by=["date"], ignore_index=True, inplace=True)
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return temp_df
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def repo_rate_hist(
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start_date: str = "20200930", end_date: str = "20201029"
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) -> 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-回购定盘利率-历史数据
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https://www.chinamoney.com.cn/chinese/bkfrr/
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:param start_date: 开始时间, 开始时间与结束时间需要在一个月内
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:type start_date: str
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:param end_date: 结束时间, 开始时间与结束时间需要在一个月内
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:type end_date: str
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:return: 回购定盘利率-历史数据
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:rtype: pandas.DataFrame
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"""
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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-currency/FrrHis"
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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) 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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"startDate": start_date,
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"endDate": end_date,
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}
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r = requests.post(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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temp_df = pd.DataFrame([item for item in temp_df["frValueMap"].to_list()])
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temp_df = temp_df[
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[
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"date",
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"FR001",
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"FR007",
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"FR014",
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"FDR001",
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"FDR007",
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"FDR014",
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]
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]
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temp_df["date"] = pd.to_datetime(temp_df["date"], errors="coerce").dt.date
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temp_df["FR001"] = pd.to_numeric(temp_df["FR001"], errors="coerce")
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temp_df["FR007"] = pd.to_numeric(temp_df["FR007"], errors="coerce")
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temp_df["FR014"] = pd.to_numeric(temp_df["FR014"], errors="coerce")
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temp_df["FDR001"] = pd.to_numeric(temp_df["FDR001"], errors="coerce")
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temp_df["FDR007"] = pd.to_numeric(temp_df["FDR007"], errors="coerce")
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temp_df["FDR014"] = pd.to_numeric(temp_df["FDR014"], errors="coerce")
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temp_df.sort_values(["date"], ignore_index=True, inplace=True)
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return temp_df
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if __name__ == "__main__":
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repo_rate_query_df = repo_rate_query(symbol="回购定盘利率")
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print(repo_rate_query_df)
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repo_rate_hist_df = repo_rate_hist(start_date="20231001", end_date="20240101")
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print(repo_rate_hist_df)
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