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
143 lines
4.8 KiB
Python
143 lines
4.8 KiB
Python
# !/usr/bin/env python
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# -*- coding:utf-8 -*-
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"""
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Date: 2026/1/13 15:00
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Desc: 雪球-行情中心-个股
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https://xueqiu.com/S/SH513520
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"""
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import re
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from datetime import datetime
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import pandas as pd
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import requests
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def _convert_timestamp(timestamp_ms: int) -> str:
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"""
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时间戳转换为字符串时间
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:param timestamp_ms: 时间戳
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:type timestamp_ms: int
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:return: 字符串
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:rtype: str
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"""
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timestamp_s = timestamp_ms / 1000
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datetime_obj = datetime.fromtimestamp(timestamp_s)
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return datetime_obj.strftime("%Y-%m-%d %H:%M:%S")
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def stock_individual_spot_xq(
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symbol: str = "SH600000",
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token: str = None,
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timeout: float = None,
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) -> pd.DataFrame:
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"""
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雪球-行情中心-个股
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https://xueqiu.com/S/SH600000
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:param symbol: 证券代码，可以是 A 股代码，A 股场内基金代码，A 股指数，美股代码, 美股指数
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:type symbol: str
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:param token: set xueqiu token
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:type token: str
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:param timeout: choice of None or a positive float number
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:type timeout: float
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:return: 证券最新行情
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:rtype: pandas.DataFrame
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"""
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from akshare.stock.cons import xq_a_token
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session = requests.Session()
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xq_a_token = token or xq_a_token
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headers = {
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"cookie": f"xq_a_token={xq_a_token};",
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"User-Agent": "Mozilla/5.0 (iPhone; CPU iPhone OS 16_6 like Mac OS X) AppleWebKit/605.1.15 "
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"(KHTML, like Gecko) Version/16.6 Mobile/15E148 Safari/604.1",
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}
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session.get(url="https://xueqiu.com", headers=headers)
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url = f"https://stock.xueqiu.com/v5/stock/quote.json?symbol={symbol}&extend=detail"
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r = session.get(url, headers=headers, timeout=timeout)
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column_name_map = {
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"acc_unit_nav": "累计净值",
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"amount": "成交额",
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"amplitude": "振幅",
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"avg_price": "均价",
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"chg": "涨跌",
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"currency": "货币",
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"current": "现价",
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"current_year_percent": "今年以来涨幅",
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"dividend": "股息(TTM)",
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"dividend_yield": "股息率(TTM)",
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"eps": "每股收益",
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"exchange": "交易所",
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"float_market_capital": "流通值",
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"float_shares": "流通股",
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"found_date": "成立日期",
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"goodwill_in_net_assets": "净资产中的商誉",
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"high": "最高",
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"high52w": "52周最高",
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"iopv": "参考净值",
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"issue_date": "发行日期",
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"last_close": "昨收",
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"limit_down": "跌停",
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"limit_up": "涨停",
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"lot_size": "最小交易单位",
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"low": "最低",
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"low52w": "52周最低",
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"market_capital": "资产净值/总市值",
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"name": "名称",
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"nav_date": "净值日期",
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"navps": "每股净资产",
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"open": "今开",
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"pb": "市净率",
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"pe_forecast": "市盈率(动)",
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"pe_lyr": "市盈率(静)",
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"pe_ttm": "市盈率(TTM)",
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"percent": "涨幅",
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"premium_rate": "溢价率",
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"psr": "市销率",
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"symbol": "代码",
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"total_shares": "基金份额/总股本",
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"turnover_rate": "周转率",
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"unit_nav": "单位净值",
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"volume": "成交量",
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"time": "时间",
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}
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json_data = r.json()
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temp_df = pd.json_normalize(json_data["data"]["quote"])
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temp_df.columns = [
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*map(
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lambda x: column_name_map[x] if x in column_name_map.keys() else x,
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temp_df.columns,
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) # 由于传入的 symbol 可能是个股，可能是指数，也可能是基金，所以这里取列的最大公约数，没有数据的列内容为 None
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]
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temp_df = temp_df[
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list(
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filter(
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lambda x: re.search(pattern="[\u4e00-\u9fa5]", string=x),
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temp_df.columns,
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) # 过滤 temp_df，留下包含汉字的列
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)
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]
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temp_df = temp_df.T.reset_index()
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temp_df.columns = ["item", "value"]
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temp_df.loc[temp_df["item"] == "时间", "value"] = temp_df.loc[
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temp_df["item"] == "时间", "value"
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].apply(lambda x: _convert_timestamp(int(x)) if x and not pd.isna(x) else None)
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temp_df.loc[temp_df["item"] == "发行日期", "value"] = temp_df.loc[
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temp_df["item"] == "发行日期", "value"
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].apply(lambda x: _convert_timestamp(int(x)) if x and not pd.isna(x) else None)
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return temp_df
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if __name__ == "__main__":
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stock_individual_spot_xq_df = stock_individual_spot_xq(symbol="BJ430139")
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print(stock_individual_spot_xq_df)
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stock_individual_spot_xq_df = stock_individual_spot_xq(symbol="SH600000")
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print(stock_individual_spot_xq_df)
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stock_individual_spot_xq_df = stock_individual_spot_xq(symbol="SPY")
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print(stock_individual_spot_xq_df)
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stock_individual_spot_xq_df = stock_individual_spot_xq(symbol=".INX")
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print(stock_individual_spot_xq_df)
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