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
137 lines
4.7 KiB
Python
137 lines
4.7 KiB
Python
#!/usr/bin/env python
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# -*- coding:utf-8 -*-
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"""
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Date: 2025/10/21 20:00
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Desc: 同花顺-数据中心-宏观数据-股票筹资
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https://data.10jqka.com.cn/macro/finance/
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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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def macro_stock_finance() -> pd.DataFrame:
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"""
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同花顺-数据中心-宏观数据-股票筹资
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https://data.10jqka.com.cn/macro/finance/
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:return: 股票筹资
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:rtype: pandas.DataFrame
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"""
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url = "https://data.10jqka.com.cn/macro/finance/"
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headers = {
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"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 "
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"(KHTML, like Gecko) Chrome/126.0.0.0 Safari/537.36"
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}
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r = requests.get(url, headers=headers)
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temp_df = pd.read_html(StringIO(r.text))[0]
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temp_df.rename(
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columns={
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"月份": "月份",
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"募集资金(亿元)": "募集资金",
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"首发募集资金(亿元)": "首发募集资金",
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"增发募集资金(亿元)": "增发募集资金",
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"配股募集资金(亿元)": "配股募集资金",
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},
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inplace=True,
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)
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temp_df = temp_df[
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["月份", "募集资金", "首发募集资金", "增发募集资金", "配股募集资金"]
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]
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temp_df["募集资金"] = pd.to_numeric(temp_df["募集资金"], errors="coerce")
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temp_df["首发募集资金"] = pd.to_numeric(temp_df["首发募集资金"], errors="coerce")
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temp_df["增发募集资金"] = pd.to_numeric(temp_df["增发募集资金"], errors="coerce")
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temp_df["配股募集资金"] = pd.to_numeric(temp_df["配股募集资金"], errors="coerce")
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temp_df.sort_values(by=["月份"], inplace=True, ignore_index=True)
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return temp_df
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def macro_rmb_loan() -> pd.DataFrame:
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"""
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同花顺-数据中心-宏观数据-新增人民币贷款
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https://data.10jqka.com.cn/macro/loan/
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:return: 新增人民币贷款
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:rtype: pandas.DataFrame
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"""
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url = "https://data.10jqka.com.cn/macro/loan/"
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headers = {
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"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 "
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"(KHTML, like Gecko) Chrome/126.0.0.0 Safari/537.36"
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}
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r = requests.get(url, headers=headers)
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temp_df = pd.read_html(StringIO(r.text), skiprows=0)[0]
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temp_df.columns = [
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"月份",
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"新增人民币贷款-总额",
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"新增人民币贷款-同比",
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"新增人民币贷款-环比",
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"累计人民币贷款-总额",
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"累计人民币贷款-同比",
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]
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temp_df["新增人民币贷款-总额"] = pd.to_numeric(
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temp_df["新增人民币贷款-总额"], errors="coerce"
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)
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temp_df["累计人民币贷款-总额"] = pd.to_numeric(
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temp_df["累计人民币贷款-总额"], errors="coerce"
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)
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temp_df.sort_values(by=["月份"], inplace=True, ignore_index=True)
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return temp_df
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def macro_rmb_deposit() -> pd.DataFrame:
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"""
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同花顺-数据中心-宏观数据-人民币存款余额
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https://data.10jqka.com.cn/macro/rmb/
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:return: 人民币存款余额
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:rtype: pandas.DataFrame
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"""
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url = "https://data.10jqka.com.cn/macro/rmb/"
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headers = {
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"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 "
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"(KHTML, like Gecko) Chrome/126.0.0.0 Safari/537.36"
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}
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r = requests.get(url, headers=headers)
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temp_df = pd.read_html(StringIO(r.text), skiprows=0)[0]
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temp_df.columns = [
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"月份",
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"新增存款-数量",
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"新增存款-同比",
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"新增存款-环比",
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"新增企业存款-数量",
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"新增企业存款-同比",
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"新增企业存款-环比",
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"新增储蓄存款-数量",
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"新增储蓄存款-同比",
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"新增储蓄存款-环比",
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"新增其他存款-数量",
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"新增其他存款-同比",
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"新增其他存款-环比",
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]
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temp_df["新增存款-数量"] = pd.to_numeric(temp_df["新增存款-数量"], errors="coerce")
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temp_df["新增企业存款-数量"] = pd.to_numeric(
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temp_df["新增企业存款-数量"], errors="coerce"
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)
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temp_df["新增企业存款-数量"] = pd.to_numeric(
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temp_df["新增企业存款-数量"], errors="coerce"
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)
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temp_df["新增储蓄存款-数量"] = pd.to_numeric(
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temp_df["新增储蓄存款-数量"], errors="coerce"
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)
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temp_df["新增其他存款-数量"] = pd.to_numeric(
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temp_df["新增其他存款-数量"], errors="coerce"
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)
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temp_df.sort_values(by=["月份"], inplace=True, ignore_index=True)
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return temp_df
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if __name__ == "__main__":
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macro_stock_finance_df = macro_stock_finance()
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print(macro_stock_finance_df)
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macro_rmb_loan_df = macro_rmb_loan()
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print(macro_rmb_loan_df)
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macro_rmb_deposit_df = macro_rmb_deposit()
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print(macro_rmb_deposit_df)
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