stock-tracker

2026-07-04 00:17:11 +08:00
commit 6087341a48
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+#!/usr/bin/env python
+# -*- coding:utf-8 -*-
+"""
+Date: 2020/10/29 13:03
+Desc:
+"""
@@ -0,0 +1,100 @@
+#!/usr/bin/env python
+# -*- coding:utf-8 -*-
+"""
+Date: 2024/1/20 23:04
+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
+"""
+
+import pandas as pd
+import requests
+
+
+def repo_rate_query(symbol: str = "回购定盘利率") -> pd.DataFrame:
+    """
+    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
+    https://www.chinamoney.com.cn/chinese/bkfrr/
+    :param symbol: choice of {"回购定盘利率", "银银间回购定盘利率"}
+    :type symbol: str
+    :return: 回购定盘利率-历史数据
+    :rtype: pandas.DataFrame
+    """
+    if symbol == "回购定盘利率":
+        url = "https://www.chinamoney.com.cn/r/cms/www/chinamoney/data/currency/frr-chrt.csv"
+        temp_df = pd.read_csv(url, header=None)
+        temp_df.dropna(axis=1, inplace=True)
+        temp_df.columns = ["date", "FR001", "FR007", "FR014"]
+        temp_df["date"] = pd.to_datetime(temp_df["date"], errors="coerce").dt.date
+        temp_df["FR001"] = pd.to_numeric(temp_df["FR001"], errors="coerce")
+        temp_df["FR007"] = pd.to_numeric(temp_df["FR007"], errors="coerce")
+        temp_df["FR014"] = pd.to_numeric(temp_df["FR014"], errors="coerce")
+        temp_df.sort_values(by=["date"], ignore_index=True, inplace=True)
+        return temp_df
+    else:
+        url = "https://www.chinamoney.com.cn/r/cms/www/chinamoney/data/currency/fdr-chrt.csv"
+        temp_df = pd.read_csv(url, header=None)
+        temp_df.dropna(axis=1, inplace=True)
+        temp_df.columns = ["date", "FDR001", "FDR007", "FDR014"]
+        temp_df["date"] = pd.to_datetime(temp_df["date"], errors="coerce").dt.date
+        temp_df["FDR001"] = pd.to_numeric(temp_df["FDR001"], errors="coerce")
+        temp_df["FDR007"] = pd.to_numeric(temp_df["FDR007"], errors="coerce")
+        temp_df["FDR014"] = pd.to_numeric(temp_df["FDR014"], errors="coerce")
+        temp_df.sort_values(by=["date"], ignore_index=True, inplace=True)
+        return temp_df
+
+
+def repo_rate_hist(
+    start_date: str = "20200930", end_date: str = "20201029"
+) -> pd.DataFrame:
+    """
+    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
+    https://www.chinamoney.com.cn/chinese/bkfrr/
+    :param start_date: 开始时间, 开始时间与结束时间需要在一个月内
+    :type start_date: str
+    :param end_date: 结束时间, 开始时间与结束时间需要在一个月内
+    :type end_date: str
+    :return: 回购定盘利率-历史数据
+    :rtype: pandas.DataFrame
+    """
+    start_date = "-".join([start_date[:4], start_date[4:6], start_date[6:]])
+    end_date = "-".join([end_date[:4], end_date[4:6], end_date[6:]])
+    url = "https://www.chinamoney.com.cn/ags/ms/cm-u-bk-currency/FrrHis"
+    headers = {
+        "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",
+    }
+    params = {
+        "lang": "CN",
+        "startDate": start_date,
+        "endDate": end_date,
+    }
+    r = requests.post(url, params=params, headers=headers)
+    data_json = r.json()
+    temp_df = pd.DataFrame(data_json["records"])
+    temp_df = pd.DataFrame([item for item in temp_df["frValueMap"].to_list()])
+    temp_df = temp_df[
+        [
+            "date",
+            "FR001",
+            "FR007",
+            "FR014",
+            "FDR001",
+            "FDR007",
+            "FDR014",
+        ]
+    ]
+    temp_df["date"] = pd.to_datetime(temp_df["date"], errors="coerce").dt.date
+    temp_df["FR001"] = pd.to_numeric(temp_df["FR001"], errors="coerce")
+    temp_df["FR007"] = pd.to_numeric(temp_df["FR007"], errors="coerce")
+    temp_df["FR014"] = pd.to_numeric(temp_df["FR014"], errors="coerce")
+    temp_df["FDR001"] = pd.to_numeric(temp_df["FDR001"], errors="coerce")
+    temp_df["FDR007"] = pd.to_numeric(temp_df["FDR007"], errors="coerce")
+    temp_df["FDR014"] = pd.to_numeric(temp_df["FDR014"], errors="coerce")
+    temp_df.sort_values(["date"], ignore_index=True, inplace=True)
+    return temp_df
+
+
+if __name__ == "__main__":
+    repo_rate_query_df = repo_rate_query(symbol="回购定盘利率")
+    print(repo_rate_query_df)
+
+    repo_rate_hist_df = repo_rate_hist(start_date="20231001", end_date="20240101")
+    print(repo_rate_hist_df)