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
206 lines
6.9 KiB
Python
206 lines
6.9 KiB
Python
#!/usr/bin/env python
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# -*- coding:utf-8 -*-
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"""
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Date: 2025/11/10 15:30
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Desc: 新浪财经-基金行情
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https://vip.stock.finance.sina.com.cn/fund_center/index.html#jjhqetf
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"""
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import pandas as pd
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import py_mini_racer
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import requests
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from akshare.stock.cons import hk_js_decode
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from akshare.utils import demjson
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def fund_etf_category_sina(symbol: str = "LOF基金") -> pd.DataFrame:
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"""
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新浪财经-基金列表
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https://vip.stock.finance.sina.com.cn/fund_center/index.html#jjhqetf
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:param symbol: choice of {"封闭式基金", "ETF基金", "LOF基金"}
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:type symbol: str
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:return: 指定 symbol 的基金列表
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:rtype: pandas.DataFrame
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"""
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fund_map = {
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"封闭式基金": "close_fund",
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"ETF基金": "etf_hq_fund",
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"LOF基金": "lof_hq_fund",
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}
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url = (
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"https://vip.stock.finance.sina.com.cn/quotes_service/api/jsonp.php/"
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"IO.XSRV2.CallbackList['da_yPT46_Ll7K6WD']/Market_Center.getHQNodeDataSimple"
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)
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params = {
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"page": "1",
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"num": "5000",
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"sort": "symbol",
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"asc": "0",
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"node": fund_map[symbol],
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"[object HTMLDivElement]": "qvvne",
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}
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r = requests.get(url, params=params)
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data_text = r.text
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data_json = demjson.decode(data_text[data_text.find("([") + 1 : -2])
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temp_df = pd.DataFrame(data_json)
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if symbol == "封闭式基金":
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temp_df.columns = [
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"代码",
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"名称",
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"最新价",
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"涨跌额",
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"涨跌幅",
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"买入",
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"卖出",
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"昨收",
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"今开",
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"最高",
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"最低",
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"成交量",
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"成交额",
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"_",
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"_",
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]
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else:
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temp_df.columns = [
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"代码",
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"名称",
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"最新价",
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"涨跌额",
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"涨跌幅",
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"买入",
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"卖出",
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"昨收",
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"今开",
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"最高",
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"最低",
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"成交量",
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"成交额",
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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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"代码",
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"名称",
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"最新价",
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"涨跌额",
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"涨跌幅",
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"买入",
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"卖出",
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"昨收",
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"今开",
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"最高",
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"最低",
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"成交量",
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"成交额",
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]
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]
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temp_df["最新价"] = pd.to_numeric(temp_df["最新价"], errors="coerce")
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temp_df["涨跌额"] = pd.to_numeric(temp_df["涨跌额"], errors="coerce")
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temp_df["涨跌幅"] = pd.to_numeric(temp_df["涨跌幅"], errors="coerce")
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temp_df["买入"] = pd.to_numeric(temp_df["买入"], errors="coerce")
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temp_df["卖出"] = pd.to_numeric(temp_df["卖出"], errors="coerce")
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temp_df["昨收"] = pd.to_numeric(temp_df["昨收"], errors="coerce")
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temp_df["今开"] = pd.to_numeric(temp_df["今开"], errors="coerce")
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temp_df["最高"] = pd.to_numeric(temp_df["最高"], errors="coerce")
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temp_df["最低"] = pd.to_numeric(temp_df["最低"], errors="coerce")
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temp_df["成交量"] = pd.to_numeric(temp_df["成交量"], errors="coerce")
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temp_df["成交额"] = pd.to_numeric(temp_df["成交额"], errors="coerce")
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return temp_df
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def fund_etf_hist_sina(symbol: str = "sh510050") -> pd.DataFrame:
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"""
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新浪财经-基金-ETF 基金-日行情数据
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https://finance.sina.com.cn/fund/quotes/159996/bc.shtml
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:param symbol: 基金名称, 可以通过 ak.fund_etf_category_sina() 函数获取
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:type symbol: str
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:return: 日行情数据
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:rtype: pandas.DataFrame
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"""
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url = (
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f"https://finance.sina.com.cn/realstock/company/{symbol}/hisdata_klc2/klc_kl.js"
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)
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r = requests.get(url)
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js_code = py_mini_racer.MiniRacer()
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js_code.eval(hk_js_decode)
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dict_list = js_code.call(
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"d", r.text.split("=")[1].split(";")[0].replace('"', "")
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) # 执行js解密代码
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temp_df = pd.DataFrame(dict_list)
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if temp_df.empty: # 处理获取数据为空的问题
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return pd.DataFrame()
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temp_df["date"] = pd.to_datetime(temp_df["date"], errors="coerce").dt.tz_localize(
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None
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)
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temp_df["open"] = pd.to_numeric(temp_df["open"], errors="coerce")
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temp_df["high"] = pd.to_numeric(temp_df["high"], errors="coerce")
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temp_df["low"] = pd.to_numeric(temp_df["low"], errors="coerce")
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temp_df["close"] = pd.to_numeric(temp_df["close"], errors="coerce")
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temp_df["volume"] = pd.to_numeric(temp_df["volume"], errors="coerce")
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# 转换日期列为日期类型
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temp_df["date"] = temp_df["date"].dt.date
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temp_df = temp_df.sort_values(by="date", ascending=True)
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return temp_df
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def fund_etf_dividend_sina(symbol: str = "sh510050") -> pd.DataFrame:
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"""
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新浪财经-基金-ETF 基金-累计分红
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https://finance.sina.com.cn/fund/quotes/510050/bc.shtml
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:param symbol: 基金名称, 可以通过 ak.fund_etf_category_sina() 函数获取
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:type symbol: str
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:return: 累计分红
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:rtype: pandas.DataFrame
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"""
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# 构建复权数据URL
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factor_url = f"https://finance.sina.com.cn/realstock/company/{symbol}/hfq.js"
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r = requests.get(factor_url)
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text = r.text
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if text.startswith("var"):
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json_str = text.split("=")[1].strip().rsplit("}", maxsplit=1)[0].strip()
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data = eval(json_str + "}") # 这里使用eval而不是json.loads因为数据格式特殊
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if isinstance(data, dict) and "data" in data:
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df = pd.DataFrame(data["data"])
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# 重命名列
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df.columns = ["date", "f", "s", "u"] if len(df.columns) == 4 else df.columns
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# 移除1900-01-01的数据
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df = df[df["date"] != "1900-01-01"]
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# 转换日期
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df["date"] = pd.to_datetime(df["date"])
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# 转换数值类型
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df[["f", "s", "u"]] = df[["f", "s", "u"]].astype(float)
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# 按日期排序
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df = df.sort_values(by="date", ascending=True, ignore_index=True)
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temp_df = df[["date", "u"]].copy()
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temp_df.columns = ["日期", "累计分红"]
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temp_df["日期"] = pd.to_datetime(temp_df["日期"], errors="coerce").dt.date
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return temp_df
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else:
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return pd.DataFrame()
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else:
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return pd.DataFrame()
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if __name__ == "__main__":
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fund_etf_category_sina_df = fund_etf_category_sina(symbol="封闭式基金")
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print(fund_etf_category_sina_df)
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fund_etf_category_sina_df = fund_etf_category_sina(symbol="ETF基金")
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print(fund_etf_category_sina_df)
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fund_etf_category_sina_df = fund_etf_category_sina(symbol="LOF基金")
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print(fund_etf_category_sina_df)
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fund_etf_hist_sina_df = fund_etf_hist_sina(symbol="sh510050")
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print(fund_etf_hist_sina_df)
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fund_etf_dividend_sina_df = fund_etf_dividend_sina(symbol="sh510050")
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print(fund_etf_dividend_sina_df)
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