stock-tracker

backend/.venv/lib/python3.9/site-packages/akshare/option/__init__.py

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+#!/usr/bin/env python
+# -*- coding:utf-8 -*-
+"""
+Date: 2019/9/30 13:58
+Desc:
+"""

backend/.venv/lib/python3.9/site-packages/akshare/option/cons.py

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+#!/usr/bin/env python
+# -*- coding:utf-8 -*-
+"""
+Date: 2019/9/30 13:58
+Desc: 期权配置文件
+"""
+
+import datetime
+import json
+import os
+import re
+
+# 中国金融期货交易所
+
+CFFEX_OPTION_URL_300 = "http://www.cffex.com.cn/quote_IO.txt"
+
+# 深圳证券交易所
+
+SZ_OPTION_URL_300 = "http://www.szse.cn/api/report/ShowReport?SHOWTYPE=xlsx&CATALOGID=ysplbrb&TABKEY=tab1&random=0.10432465776720479"
+
+# 上海证券交易所
+
+SH_OPTION_URL_50 = "http://yunhq.sse.com.cn:32041/v1/sh1/list/self/510050"
+SH_OPTION_URL_KING_50 = "http://yunhq.sse.com.cn:32041/v1/sho/list/tstyle/510050_{}"
+
+SH_OPTION_URL_300 = "http://yunhq.sse.com.cn:32041/v1/sh1/list/self/510300"
+SH_OPTION_URL_KING_300 = "http://yunhq.sse.com.cn:32041/v1/sho/list/tstyle/510300_{}"
+
+SH_OPTION_URL_500 = "http://yunhq.sse.com.cn:32041/v1/sh1/list/self/510500"
+SH_OPTION_URL_KING_500 = "http://yunhq.sse.com.cn:32041/v1/sho/list/tstyle/510500_{}"
+
+SH_OPTION_URL_KC_50 = "http://yunhq.sse.com.cn:32041/v1/sh1/list/self/588000"
+SH_OPTION_URL_KC_KING_50 = "http://yunhq.sse.com.cn:32041/v1/sho/list/tstyle/588000_{}"
+
+SH_OPTION_URL_KC_50_YFD = "http://yunhq.sse.com.cn:32041/v1/sh1/list/self/588080"
+SH_OPTION_URL_KING_50_YFD = "http://yunhq.sse.com.cn:32041/v1/sho/list/tstyle/588080_{}"
+
+SH_OPTION_PAYLOAD = {
+    "select": "select: code,name,last,change,chg_rate,amp_rate,volume,amount,prev_close"
+}
+
+SH_OPTION_PAYLOAD_OTHER = {"select": "contractid,last,chg_rate,presetpx,exepx"}
+
+
+# 大连商品交易所
+DCE_OPTION_URL = "http://portal.dce.com.cn/publicweb/quotesdata/dayQuotesCh.html"
+DCE_DAILY_OPTION_URL = (
+    "http://portal.dce.com.cn/publicweb/quotesdata/exportDayQuotesChData.html"
+)
+
+# 上海期货交易所
+SHFE_OPTION_URL = "https://tsite.shfe.com.cn/data/dailydata/option/kx/kx{}.dat"
+
+# 郑州商品交易所
+CZCE_DAILY_OPTION_URL_3 = (
+    "http://www.czce.com.cn/cn/DFSStaticFiles/Option/{}/{}/OptionDataDaily.txt"
+)
+
+# PAYLOAD
+SHFE_HEADERS = {"User-Agent": "Mozilla/4.0 (compatible; MSIE 5.5; Windows NT)"}
+
+DATE_PATTERN = re.compile(r"^([0-9]{4})[-/]?([0-9]{2})[-/]?([0-9]{2})")
+
+
+def convert_date(date):
+    """
+    transform a date string to datetime.date object
+    :param date, string, e.g. 2016-01-01, 20160101 or 2016/01/01
+    :return: object of datetime.date(such as 2016-01-01) or None
+    """
+    if isinstance(date, datetime.date):
+        return date
+    elif isinstance(date, str):
+        match = DATE_PATTERN.match(date)
+        if match:
+            groups = match.groups()
+            if len(groups) == 3:
+                return datetime.date(
+                    year=int(groups[0]), month=int(groups[1]), day=int(groups[2])
+                )
+    return None
+
+
+def get_json_path(name, module_file):
+    """
+    获取 JSON 配置文件的路径(从模块所在目录查找)
+    :param name: 文件名
+    :param module_file: filename
+    :return: str json_file_path
+    """
+    module_folder = os.path.abspath(os.path.dirname(os.path.dirname(module_file)))
+    module_json_path = os.path.join(module_folder, "file_fold", name)
+    return module_json_path
+
+
+def get_calendar():
+    """
+    获取交易日历至 2019 年结束, 这里的交易日历需要按年更新
+    :return: json
+    """
+    setting_file_name = "calendar.json"
+    setting_file_path = get_json_path(setting_file_name, __file__)
+    return json.load(open(setting_file_path, "r"))
+
+
+def last_trading_day(day):
+    """
+    获取前一个交易日
+    :param day: "%Y%m%d" or  datetime.date()
+    :return last_day: "%Y%m%d" or  datetime.date()
+    """
+    calendar = get_calendar()
+
+    if isinstance(day, str):
+        if day not in calendar:
+            print("Today is not trading day：" + day)
+            return False
+        pos = calendar.index(day)
+        last_day = calendar[pos - 1]
+        return last_day
+
+    elif isinstance(day, datetime.date):
+        d_str = day.strftime("%Y%m%d")
+        if d_str not in calendar:
+            print("Today is not working day：" + d_str)
+            return False
+        pos = calendar.index(d_str)
+        last_day = calendar[pos - 1]
+        last_day = datetime.datetime.strptime(last_day, "%Y%m%d").date()
+        return last_day
+
+
+def get_latest_data_date(day):
+    """
+    获取最新的有数据的交易日
+    :param day: datetime.datetime
+    :return string YYYYMMDD
+    """
+    calendar = get_calendar()
+    if day.strftime("%Y%m%d") in calendar:
+        if day.time() > datetime.time(17, 0, 0):
+            return day.strftime("%Y%m%d")
+        else:
+            return last_trading_day(day.strftime("%Y%m%d"))
+    else:
+        while day.strftime("%Y%m%d") not in calendar:
+            day = day - datetime.timedelta(days=1)
+        return day.strftime("%Y%m%d")
+
+
+if __name__ == "__main__":
+    d = datetime.datetime(2018, 10, 5, 17, 1, 0)
+    print(get_latest_data_date(d))

backend/.venv/lib/python3.9/site-packages/akshare/option/option_comm_qihuo.py

@@ -0,0 +1,86 @@
+#!/usr/bin/env python
+# -*- coding:utf-8 -*-
+"""
+Date: 2024/7/5 15:00
+Desc: 九期网-商品期权手续费
+https://www.9qihuo.com/qiquanshouxufei
+"""
+
+from functools import lru_cache
+from io import StringIO
+
+import pandas as pd
+import requests
+from bs4 import BeautifulSoup
+
+
+@lru_cache()
+def option_comm_symbol() -> pd.DataFrame:
+    import urllib3
+
+    urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
+    url = "https://www.9qihuo.com/qiquanshouxufei"
+    r = requests.get(url, verify=False)
+    soup = BeautifulSoup(r.text, features="lxml")
+    name = [
+        item.string.strip()
+        for item in soup.find(name="div", attrs={"id": "inst_list"}).find_all(name="a")
+    ]
+    code = [
+        item["href"].split("?")[1].split("=")[1]
+        for item in soup.find(name="div", attrs={"id": "inst_list"}).find_all(name="a")
+    ]
+    temp_df = pd.DataFrame([name, code]).T
+    temp_df.columns = ["品种名称", "品种代码"]
+    return temp_df
+
+
+def option_comm_info(symbol: str = "工业硅期权") -> pd.DataFrame:
+    """
+    九期网-商品期权手续费
+    https://www.9qihuo.com/qiquanshouxufei
+    :param symbol: choice of {"所有", "上海期货交易所", "大连商品交易所", "郑州商品交易所", "上海国际能源交易中心", "广州期货交易所"}
+    :type symbol: str
+    :return: 期权手续费
+    :rtype: pandas.DataFrame
+    """
+    import urllib3
+
+    urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
+    symbol_df = option_comm_symbol()
+    symbol_str = symbol_df[symbol_df["品种名称"].str.contains(symbol)][
+        "品种代码"
+    ].values[0]
+    params = {"heyue": symbol_str}
+    url = "https://www.9qihuo.com/qiquanshouxufei"
+    r = requests.get(url, params=params, verify=False)
+    temp_df = pd.read_html(StringIO(r.text))[0]
+    market_symbol = temp_df.iloc[0, 0]
+    columns = temp_df.iloc[2, :]
+    temp_df = temp_df.iloc[3:, :]
+    temp_df.columns = columns
+    temp_df["交易所"] = market_symbol
+    temp_df.reset_index(drop=True, inplace=True)
+    temp_df.index.name = None
+    temp_df.columns.name = None
+    temp_df["现价"] = pd.to_numeric(temp_df["现价"], errors="coerce")
+    temp_df["成交量"] = pd.to_numeric(temp_df["成交量"], errors="coerce")
+    temp_df["每跳毛利/元"] = pd.to_numeric(temp_df["每跳毛利/元"], errors="coerce")
+    temp_df["每跳净利/元"] = pd.to_numeric(temp_df["每跳净利/元"], errors="coerce")
+    soup = BeautifulSoup(r.text, features="lxml")
+    raw_date_text = soup.find(name="a", attrs={"id": "dlink"}).previous
+    comm_update_time = raw_date_text.split("，")[0].strip("（手续费更新时间：")
+    price_update_time = (
+        raw_date_text.split("，")[1].strip("价格更新时间：").strip("。）")
+    )
+    temp_df["手续费更新时间"] = comm_update_time
+    temp_df["价格更新时间"] = price_update_time
+    return temp_df
+
+
+if __name__ == "__main__":
+    option_comm_symbol_df = option_comm_symbol()
+    print(option_comm_symbol_df)
+
+    option_comm_info_df = option_comm_info(symbol="工业硅期权")
+    print(option_comm_info_df)

backend/.venv/lib/python3.9/site-packages/akshare/option/option_commodity.py

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+#!/usr/bin/env python
+# -*- coding:utf-8 -*-
+"""
+Date: 2025/10/17 21:00
+Desc: 商品期权数据
+说明：
+(1) 价格：自2019年12月02日起，纤维板报价单位由元/张改为元/立方米
+(2) 价格：元/吨，鸡蛋为元/500千克，纤维板为元/立方米，胶合板为元/张
+(3) 成交量、持仓量：手（按双边计算）
+(4) 成交额：万元（按双边计算）
+(5) 涨跌＝收盘价－前结算价
+(6) 涨跌1=今结算价-前结算价
+(7) 合约系列：具有相同月份标的期货合约的所有期权合约的统称
+(8) 隐含波动率：根据期权市场价格，利用期权定价模型计算的标的期货合约价格波动率
+"""
+
+import datetime
+import warnings
+from io import StringIO
+
+import pandas as pd
+import requests
+
+from akshare.option.cons import (
+    get_calendar,
+    convert_date,
+    CZCE_DAILY_OPTION_URL_3,
+    SHFE_HEADERS,
+)
+
+
+def option_hist_dce(
+    symbol: str = "聚丙烯期权", trade_date: str = "20251016"
+) -> pd.DataFrame:
+    """
+    大连商品交易所-期权-日频行情数据
+    http://www.dce.com.cn/
+    :param trade_date: 交易日
+    :type trade_date: str
+    :param symbol: choice of {"玉米期权", "豆粕期权", "铁矿石期权", "液化石油气期权", "聚乙烯期权", "聚氯乙烯期权",
+    "聚丙烯期权", "棕榈油期权", "黄大豆1号期权", "黄大豆2号期权", "豆油期权", "乙二醇期权", "苯乙烯期权",
+    "鸡蛋期权", "玉米淀粉期权", "生猪期权", "原木期权"}
+    :type symbol: str
+    :return: 日频行情数据
+    :rtype: pandas.DataFrame
+    """
+    option_code_map = {
+        "玉米期权": "c",
+        "豆粕期权": "m",
+        "铁矿石期权": "i",
+        "液化石油气期权": "pg",
+        "聚乙烯期权": "l",
+        "聚氯乙烯期权": "v",
+        "聚丙烯期权": "pp",
+        "棕榈油期权": "p",
+        "黄大豆1号期权": "a",
+        "黄大豆2号期权": "b",
+        "豆油期权": "y",
+        "乙二醇期权": "eg",
+        "苯乙烯期权": "eb",
+        "鸡蛋期权": "jd",
+        "玉米淀粉期权": "cs",
+        "生猪期权": "lh",
+        "原木期权": "lg",
+    }
+    calendar = get_calendar()
+    day = convert_date(trade_date) if trade_date is not None else datetime.date.today()
+    if day.strftime("%Y%m%d") not in calendar:
+        warnings.warn("%s非交易日" % day.strftime("%Y%m%d"))
+        return pd.DataFrame()
+    url = "http://www.dce.com.cn/dcereport/publicweb/dailystat/dayQuotes"
+    payload = {
+        "contractId": "",
+        "lang": "zh",
+        "optionSeries": "",
+        "statisticsType": 0,
+        "tradeDate": f"{trade_date}",
+        "tradeType": "2",
+        "varietyId": f"{option_code_map[symbol]}",
+    }
+    r = requests.post(url, json=payload)
+    data_json = r.json()
+    temp_df = pd.DataFrame(data_json["data"])
+    temp_df.rename(
+        columns={
+            "variety": "品种名称",
+            "contractId": "合约",
+            "open": "开盘价",
+            "high": "最高价",
+            "low": "最低价",
+            "close": "收盘价",
+            "lastClear": "前结算价",
+            "clearPrice": "结算价",
+            "diff": "涨跌",
+            "diff1": "涨跌1",
+            "delta": "Delta",
+            "volumn": "成交量",  # 注意：你写的是“volumn”，可能是拼写错误，应为“volume”
+            "openInterest": "持仓量",
+            "diffI": "持仓量变化",
+            "turnover": "成交额",
+            "matchQtySum": "行权量",
+            "impliedVolatility": "隐含波动率(%)",
+        },
+        inplace=True,
+    )
+    temp_df = temp_df[
+        [
+            "品种名称",
+            "合约",
+            "开盘价",
+            "最高价",
+            "最低价",
+            "收盘价",
+            "前结算价",
+            "结算价",
+            "涨跌",
+            "涨跌1",
+            "Delta",
+            "隐含波动率(%)",
+            "成交量",
+            "持仓量",
+            "持仓量变化",
+            "成交额",
+            "行权量",
+        ]
+    ]
+    comma_cols = [
+        "开盘价",
+        "最高价",
+        "最低价",
+        "收盘价",
+        "前结算价",
+        "结算价",
+        "涨跌",
+        "涨跌1",
+        "Delta",
+        "隐含波动率(%)",
+        "成交额",
+    ]  # 需要处理的列
+    for col in comma_cols:
+        temp_df[col] = (
+            temp_df[col]
+            .astype(str)
+            .str.replace(",", "")
+            .pipe(pd.to_numeric, errors="coerce")
+        )
+    return temp_df
+
+
+def __option_czce_daily_convert_numeric_columns(df):
+    # 定义要处理的列
+    columns_to_convert = [
+        "昨结算",
+        "今开盘",
+        "最高价",
+        "最低价",
+        "今收盘",
+        "今结算",
+        "涨跌1",
+        "涨跌2",
+        "成交量(手)",
+        "持仓量",
+        "增减量",
+        "成交额(万元)",
+        "DELTA",
+        "隐含波动率",
+        "行权量",
+    ]
+
+    # 转换函数：去除逗号并转换为float
+    def convert_to_float(x):
+        try:
+            return float(str(x).replace(",", ""))
+        except:  # noqa: E722
+            return x
+
+    # 创建 DataFrame 的副本以避免 SettingWithCopyWarning
+    df_copy = df.copy()
+    df_copy.columns = [item.strip() for item in df_copy]
+    # 应用转换
+    for col in columns_to_convert:
+        df_copy[col] = df_copy[col].apply(convert_to_float)
+
+    return df_copy
+
+
+def option_hist_czce(
+    symbol: str = "白糖期权", trade_date: str = "20191017"
+) -> pd.DataFrame:
+    """
+    郑州商品交易所-期权-日频行情数据
+    http://www.czce.com.cn/cn/sspz/dejbqhqq/H770227index_1.htm#tabs-2
+    :param trade_date: 交易日
+    :type trade_date: str
+    :param symbol: choice of {"白糖期权", "棉花期权", "甲醇期权", "PTA期权", "动力煤期权", "菜籽粕期权", "菜籽油期权",
+    "花生期权", "对二甲苯期权", "烧碱期权", "纯碱期权", "短纤期权", "锰硅期权", "硅铁期权", "尿素期权", "苹果期权", "红枣期权",
+    "玻璃期权", "瓶片期权", "丙烯期货"}
+    :type symbol: str
+    :return: 日频行情数据
+    :rtype: pandas.DataFrame
+    """
+    calendar = get_calendar()
+    day = convert_date(trade_date) if trade_date is not None else datetime.date.today()
+    if day.strftime("%Y%m%d") not in calendar:
+        warnings.warn("{}非交易日".format(day.strftime("%Y%m%d")))
+        return pd.DataFrame()
+    if day > datetime.date(year=2010, month=8, day=24):
+        url = CZCE_DAILY_OPTION_URL_3.format(day.strftime("%Y"), day.strftime("%Y%m%d"))
+        try:
+            r = requests.get(url)
+            f = StringIO(r.text)
+            table_df = pd.read_table(f, encoding="utf-8", skiprows=1, sep="|")
+            table_df.columns = [
+                "合约代码",
+                "昨结算",
+                "今开盘",
+                "最高价",
+                "最低价",
+                "今收盘",
+                "今结算",
+                "涨跌1",
+                "涨跌2",
+                "成交量(手)",
+                "持仓量",
+                "增减量",
+                "成交额(万元)",
+                "DELTA",
+                "隐含波动率",
+                "行权量",
+            ]
+            if symbol == "白糖期权":
+                temp_df = table_df[table_df.iloc[:, 0].str.contains("SR")]
+                temp_df.reset_index(inplace=True, drop=True)
+                temp_df = temp_df.iloc[:-1, :].copy()
+                new_df = __option_czce_daily_convert_numeric_columns(temp_df)
+                return new_df
+            elif symbol == "棉花期权":
+                temp_df = table_df[table_df.iloc[:, 0].str.contains("CF")]
+                temp_df.reset_index(inplace=True, drop=True)
+                temp_df = temp_df.iloc[:-1, :].copy()
+                new_df = __option_czce_daily_convert_numeric_columns(temp_df)
+                return new_df
+            elif symbol == "甲醇期权":
+                temp_df = table_df[table_df.iloc[:, 0].str.contains("MA")]
+                temp_df.reset_index(inplace=True, drop=True)
+                temp_df = temp_df.iloc[:-1, :].copy()
+                new_df = __option_czce_daily_convert_numeric_columns(temp_df)
+                return new_df
+            elif symbol == "PTA期权":
+                temp_df = table_df[table_df.iloc[:, 0].str.contains("TA")]
+                temp_df.reset_index(inplace=True, drop=True)
+                temp_df = temp_df.iloc[:-1, :].copy()
+                new_df = __option_czce_daily_convert_numeric_columns(temp_df)
+                return new_df
+            elif symbol == "动力煤期权":
+                temp_df = table_df[table_df.iloc[:, 0].str.contains("ZC")]
+                temp_df.reset_index(inplace=True, drop=True)
+                temp_df = temp_df.iloc[:-1, :].copy()
+                new_df = __option_czce_daily_convert_numeric_columns(temp_df)
+                return new_df
+            elif symbol == "菜籽粕期权":
+                temp_df = table_df[table_df.iloc[:, 0].str.contains("RM")]
+                temp_df.reset_index(inplace=True, drop=True)
+                temp_df = temp_df.iloc[:-1, :].copy()
+                new_df = __option_czce_daily_convert_numeric_columns(temp_df)
+                return new_df
+            elif symbol == "菜籽油期权":
+                temp_df = table_df[table_df.iloc[:, 0].str.contains("OI")]
+                temp_df.reset_index(inplace=True, drop=True)
+                temp_df = temp_df.iloc[:-1, :].copy()
+                new_df = __option_czce_daily_convert_numeric_columns(temp_df)
+                return new_df
+            elif symbol == "花生期权":
+                temp_df = table_df[table_df.iloc[:, 0].str.contains("PK")]
+                temp_df.reset_index(inplace=True, drop=True)
+                temp_df = temp_df.iloc[:-1, :].copy()
+                new_df = __option_czce_daily_convert_numeric_columns(temp_df)
+                return new_df
+            elif symbol == "短纤期权":
+                temp_df = table_df[table_df.iloc[:, 0].str.contains("PF")]
+                temp_df.reset_index(inplace=True, drop=True)
+                temp_df = temp_df.iloc[:-1, :].copy()
+                new_df = __option_czce_daily_convert_numeric_columns(temp_df)
+                return new_df
+            elif symbol == "对二甲苯期权":
+                temp_df = table_df[table_df.iloc[:, 0].str.contains("PX")]
+                temp_df.reset_index(inplace=True, drop=True)
+                temp_df = temp_df.iloc[:-1, :].copy()
+                new_df = __option_czce_daily_convert_numeric_columns(temp_df)
+                return new_df
+            elif symbol == "烧碱期权":
+                temp_df = table_df[table_df.iloc[:, 0].str.contains("SH")]
+                temp_df.reset_index(inplace=True, drop=True)
+                temp_df = temp_df.iloc[:-1, :].copy()
+                new_df = __option_czce_daily_convert_numeric_columns(temp_df)
+                return new_df
+            elif symbol == "纯碱期权":
+                temp_df = table_df[table_df.iloc[:, 0].str.contains("SA")]
+                temp_df.reset_index(inplace=True, drop=True)
+                temp_df = temp_df.iloc[:-1, :].copy()
+                new_df = __option_czce_daily_convert_numeric_columns(temp_df)
+                return new_df
+            elif symbol == "短纤期权":
+                temp_df = table_df[table_df.iloc[:, 0].str.contains("PF")]
+                temp_df.reset_index(inplace=True, drop=True)
+                temp_df = temp_df.iloc[:-1, :].copy()
+                new_df = __option_czce_daily_convert_numeric_columns(temp_df)
+                return new_df
+            elif symbol == "锰硅期权":
+                temp_df = table_df[table_df.iloc[:, 0].str.contains("SM")]
+                temp_df.reset_index(inplace=True, drop=True)
+                temp_df = temp_df.iloc[:-1, :].copy()
+                new_df = __option_czce_daily_convert_numeric_columns(temp_df)
+                return new_df
+            elif symbol == "硅铁期权":
+                temp_df = table_df[table_df.iloc[:, 0].str.contains("SF")]
+                temp_df.reset_index(inplace=True, drop=True)
+                temp_df = temp_df.iloc[:-1, :].copy()
+                new_df = __option_czce_daily_convert_numeric_columns(temp_df)
+                return new_df
+            elif symbol == "尿素期权":
+                temp_df = table_df[table_df.iloc[:, 0].str.contains("UR")]
+                temp_df.reset_index(inplace=True, drop=True)
+                temp_df = temp_df.iloc[:-1, :].copy()
+                new_df = __option_czce_daily_convert_numeric_columns(temp_df)
+                return new_df
+            elif symbol == "苹果期权":
+                temp_df = table_df[table_df.iloc[:, 0].str.contains("AP")]
+                temp_df.reset_index(inplace=True, drop=True)
+                temp_df = temp_df.iloc[:-1, :].copy()
+                new_df = __option_czce_daily_convert_numeric_columns(temp_df)
+                return new_df
+            elif symbol == "红枣期权":
+                temp_df = table_df[table_df.iloc[:, 0].str.contains("CJ")]
+                temp_df.reset_index(inplace=True, drop=True)
+                temp_df = temp_df.iloc[:-1, :].copy()
+                new_df = __option_czce_daily_convert_numeric_columns(temp_df)
+                return new_df
+            elif symbol == "玻璃期权":
+                temp_df = table_df[table_df.iloc[:, 0].str.contains("FG")]
+                temp_df.reset_index(inplace=True, drop=True)
+                temp_df = temp_df.iloc[:-1, :].copy()
+                new_df = __option_czce_daily_convert_numeric_columns(temp_df)
+                return new_df
+            elif symbol == "瓶片期权":
+                temp_df = table_df[table_df.iloc[:, 0].str.contains("PR")]
+                temp_df.reset_index(inplace=True, drop=True)
+                temp_df = temp_df.iloc[:-1, :].copy()
+                new_df = __option_czce_daily_convert_numeric_columns(temp_df)
+                return new_df
+            elif symbol == "丙烯期权":
+                temp_df = table_df[table_df.iloc[:, 0].str.contains("PL")]
+                temp_df.reset_index(inplace=True, drop=True)
+                temp_df = temp_df.iloc[:-1, :].copy()
+                new_df = __option_czce_daily_convert_numeric_columns(temp_df)
+                return new_df
+            else:
+                return pd.DataFrame()
+        except:  # noqa: E722
+            return pd.DataFrame()
+    else:
+        return pd.DataFrame()
+
+
+def option_hist_shfe(
+    symbol: str = "铝期权", trade_date: str = "20250418"
+) -> pd.DataFrame:
+    """
+    上海期货交易所-期权-日频行情数据
+    https://www.shfe.com.cn/reports/tradedata/dailyandweeklydata/
+    :param trade_date: 交易日
+    :type trade_date: str
+    :param symbol: choice of {'原油期权', '铜期权', '铝期权', '锌期权', '铅期权', '螺纹钢期权', '镍期权', '锡期权', '氧化铝期权',
+    '黄金期权', '白银期权', '丁二烯橡胶期权', '天胶期权'}
+    :type symbol: str
+    :return: 日频行情数据
+    :rtype: pandas.DataFrame
+    """
+    calendar = get_calendar()
+    day = convert_date(trade_date) if trade_date is not None else datetime.date.today()
+    if day.strftime("%Y%m%d") not in calendar:
+        warnings.warn("%s非交易日" % day.strftime("%Y%m%d"))
+        return pd.DataFrame()
+    if day > datetime.date(year=2010, month=8, day=24):
+        url = f"""https://www.shfe.com.cn/data/tradedata/option/dailydata/kx{day.strftime("%Y%m%d")}.dat"""
+        try:
+            r = requests.get(url, headers=SHFE_HEADERS)
+            json_data = r.json()
+            table_df = pd.DataFrame(
+                [
+                    row
+                    for row in json_data["o_curinstrument"]
+                    if row["INSTRUMENTID"] not in ["小计", "合计"]
+                    and row["INSTRUMENTID"] != ""
+                ]
+            )
+            contract_df = table_df[table_df["PRODUCTNAME"].str.strip() == symbol]
+            contract_df.rename(
+                columns={
+                    "INSTRUMENTID": "合约代码",
+                    "OPENPRICE": "开盘价",
+                    "HIGHESTPRICE": "最高价",
+                    "LOWESTPRICE": "最低价",
+                    "CLOSEPRICE": "收盘价",
+                    "PRESETTLEMENTPRICE": "前结算价",
+                    "SETTLEMENTPRICE": "结算价",
+                    "ZD1_CHG": "涨跌1",
+                    "ZD2_CHG": "涨跌2",
+                    "VOLUME": "成交量",
+                    "OPENINTEREST": "持仓量",
+                    "OPENINTERESTCHG": "持仓量变化",
+                    "TURNOVER": "成交额",
+                    "DELTA": "德尔塔",
+                    "EXECVOLUME": "行权量",
+                },
+                inplace=True,
+            )
+            contract_df = contract_df[
+                [
+                    "合约代码",
+                    "开盘价",
+                    "最高价",
+                    "最低价",
+                    "收盘价",
+                    "前结算价",
+                    "结算价",
+                    "涨跌1",
+                    "涨跌2",
+                    "成交量",
+                    "持仓量",
+                    "持仓量变化",
+                    "成交额",
+                    "德尔塔",
+                    "行权量",
+                ]
+            ]
+            contract_df.reset_index(inplace=True, drop=True)
+            return contract_df
+        except:  # noqa: E722
+            return pd.DataFrame()
+    else:
+        return pd.DataFrame()
+
+
+def option_vol_shfe(
+    symbol: str = "铝期权", trade_date: str = "20250418"
+) -> pd.DataFrame:
+    """
+    上海期货交易所-期权-日频行情数据
+    https://www.shfe.com.cn/reports/tradedata/dailyandweeklydata/
+    :param trade_date: 交易日
+    :type trade_date: str
+    :param symbol: choice of {'原油期权', '铜期权', '铝期权', '锌期权', '铅期权', '螺纹钢期权', '镍期权', '锡期权', '氧化铝期权',
+    '黄金期权', '白银期权', '丁二烯橡胶期权', '天胶期权'}
+    :type symbol: str
+    :return: 日频行情数据
+    :rtype: pandas.DataFrame
+    """
+    calendar = get_calendar()
+    day = convert_date(trade_date) if trade_date is not None else datetime.date.today()
+    if day.strftime("%Y%m%d") not in calendar:
+        warnings.warn("%s非交易日" % day.strftime("%Y%m%d"))
+        return pd.DataFrame()
+    if day > datetime.date(year=2010, month=8, day=24):
+        url = f"""https://www.shfe.com.cn/data/tradedata/option/dailydata/kx{day.strftime("%Y%m%d")}.dat"""
+        try:
+            r = requests.get(url, headers=SHFE_HEADERS)
+            json_data = r.json()
+            volatility_df = pd.DataFrame(json_data["o_cursigma"])
+            volatility_df = volatility_df[
+                volatility_df["PRODUCTNAME"].str.strip() == symbol
+            ]
+            volatility_df.rename(
+                columns={
+                    "INSTRUMENTID": "合约系列",
+                    "VOLUME": "成交量",
+                    "OPENINTEREST": "持仓量",
+                    "OPENINTERESTCHG": "持仓量变化",
+                    "TURNOVER": "成交额",
+                    "EXECVOLUME": "行权量",
+                    "SIGMA": "隐含波动率",
+                },
+                inplace=True,
+            )
+            volatility_df = volatility_df[
+                [
+                    "合约系列",
+                    "成交量",
+                    "持仓量",
+                    "持仓量变化",
+                    "成交额",
+                    "行权量",
+                    "隐含波动率",
+                ]
+            ]
+            volatility_df.reset_index(inplace=True, drop=True)
+            return volatility_df
+        except:  # noqa: E722
+            return pd.DataFrame()
+    else:
+        return pd.DataFrame()
+
+
+def option_hist_gfex(
+    symbol: str = "工业硅", trade_date: str = "20230724"
+) -> pd.DataFrame:
+    """
+    广州期货交易所-日频率-量价数据
+    http://www.gfex.com.cn/gfex/rihq/hqsj_tjsj.shtml
+    :param trade_date: 交易日
+    :type trade_date: str
+    :param symbol: choice of {"工业硅", "碳酸锂"}
+    :type symbol: str
+    :return: 日频行情数据
+    :rtype: pandas.DataFrame
+    """
+    calendar = get_calendar()
+    day = convert_date(trade_date) if trade_date is not None else datetime.date.today()
+    if day.strftime("%Y%m%d") not in calendar:
+        warnings.warn("%s非交易日" % day.strftime("%Y%m%d"))
+        return pd.DataFrame()
+    url = "http://www.gfex.com.cn/u/interfacesWebTiDayQuotes/loadList"
+    payload = {"trade_date": day.strftime("%Y%m%d"), "trade_type": "1"}
+    headers = {
+        "Accept": "application/json, text/javascript, */*; q=0.01",
+        "Accept-Encoding": "gzip, deflate",
+        "Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8",
+        "Cache-Control": "no-cache",
+        "Content-Length": "32",
+        "Content-Type": "application/x-www-form-urlencoded; charset=UTF-8",
+        "Host": "www.gfex.com.cn",
+        "Origin": "http://www.gfex.com.cn",
+        "Pragma": "no-cache",
+        "Proxy-Connection": "keep-alive",
+        "Referer": "http://www.gfex.com.cn/gfex/rihq/hqsj_tjsj.shtml",
+        "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",
+        "X-Requested-With": "XMLHttpRequest",
+        "content-type": "application/x-www-form-urlencoded",
+    }
+    r = requests.post(url, data=payload, headers=headers)
+    data_json = r.json()
+    temp_df = pd.DataFrame(data_json["data"])
+    temp_df.rename(
+        columns={
+            "variety": "商品名称",
+            "diffI": "持仓量变化",
+            "high": "最高价",
+            "turnover": "成交额",
+            "impliedVolatility": "隐含波动率",
+            "diff": "涨跌",
+            "delta": "Delta",
+            "close": "收盘价",
+            "diff1": "涨跌1",
+            "lastClear": "前结算价",
+            "open": "开盘价",
+            "matchQtySum": "行权量",
+            "delivMonth": "合约名称",
+            "low": "最低价",
+            "clearPrice": "结算价",
+            "varietyOrder": "品种代码",
+            "openInterest": "持仓量",
+            "volumn": "成交量",
+        },
+        inplace=True,
+    )
+    temp_df = temp_df[
+        [
+            "商品名称",
+            "合约名称",
+            "开盘价",
+            "最高价",
+            "最低价",
+            "收盘价",
+            "前结算价",
+            "结算价",
+            "涨跌",
+            "涨跌1",
+            "Delta",
+            "成交量",
+            "持仓量",
+            "持仓量变化",
+            "成交额",
+            "行权量",
+            "隐含波动率",
+        ]
+    ]
+    temp_df = temp_df[temp_df["商品名称"].str.contains(symbol)]
+    temp_df.reset_index(inplace=True, drop=True)
+    return temp_df
+
+
+def option_vol_gfex(symbol: str = "碳酸锂", trade_date: str = "20230724"):
+    """
+    广州期货交易所-日频率-合约隐含波动率
+    http://www.gfex.com.cn/gfex/rihq/hqsj_tjsj.shtml
+    :param symbol: choice of choice of {"工业硅", "碳酸锂"}
+    :type symbol: str
+    :param trade_date: 交易日
+    :type trade_date: str
+    :return: 日频行情数据
+    :rtype: pandas.DataFrame
+    """
+    symbol_code_map = {
+        "工业硅": "si",
+        "碳酸锂": "lc",
+        "多晶硅": "ps",
+    }
+    calendar = get_calendar()
+    day = convert_date(trade_date) if trade_date is not None else datetime.date.today()
+    if day.strftime("%Y%m%d") not in calendar:
+        warnings.warn("%s非交易日" % day.strftime("%Y%m%d"))
+        return
+    url = "http://www.gfex.com.cn/u/interfacesWebTiDayQuotes/loadListOptVolatility"
+    payload = {"trade_date": day.strftime("%Y%m%d")}
+    headers = {
+        "Accept": "application/json, text/javascript, */*; q=0.01",
+        "Accept-Encoding": "gzip, deflate",
+        "Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8",
+        "Cache-Control": "no-cache",
+        "Content-Length": "32",
+        "Content-Type": "application/x-www-form-urlencoded; charset=UTF-8",
+        "Host": "www.gfex.com.cn",
+        "Origin": "http://www.gfex.com.cn",
+        "Pragma": "no-cache",
+        "Proxy-Connection": "keep-alive",
+        "Referer": "http://www.gfex.com.cn/gfex/rihq/hqsj_tjsj.shtml",
+        "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",
+        "X-Requested-With": "XMLHttpRequest",
+        "content-type": "application/x-www-form-urlencoded",
+    }
+    r = requests.post(url, data=payload, headers=headers)
+    data_json = r.json()
+    temp_df = pd.DataFrame(data_json["data"])
+    temp_df.rename(
+        columns={
+            "seriesId": "合约系列",
+            "varietyId": "-",
+            "hisVolatility": "隐含波动率",
+        },
+        inplace=True,
+    )
+    temp_df = temp_df[
+        [
+            "合约系列",
+            "隐含波动率",
+        ]
+    ]
+    temp_df = temp_df[temp_df["合约系列"].str.contains(symbol_code_map[symbol])]
+    temp_df.reset_index(inplace=True, drop=True)
+    return temp_df
+
+
+if __name__ == "__main__":
+    option_hist_czce_df = option_hist_czce(symbol="白糖期权", trade_date="20250812")
+    print(option_hist_czce_df)
+
+    option_hist_dce_df = option_hist_dce(symbol="聚丙烯期权", trade_date="20250812")
+    print(option_hist_dce_df)
+
+    option_hist_shfe_df = option_hist_shfe(symbol="天胶期权", trade_date="20250418")
+    print(option_hist_shfe_df)
+
+    option_vol_shfe_df = option_vol_shfe(symbol="天胶期权", trade_date="20250418")
+    print(option_vol_shfe_df)
+
+    option_hist_gfex_df = option_hist_gfex(symbol="工业硅", trade_date="20250801")
+    print(option_hist_gfex_df)
+
+    option_vol_gfex_df = option_vol_gfex(symbol="多晶硅", trade_date="20250123")
+    print(option_vol_gfex_df)

backend/.venv/lib/python3.9/site-packages/akshare/option/option_commodity_sina.py

@@ -0,0 +1,177 @@
+#!/usr/bin/env python
+# -*- coding:utf-8 -*-
+"""
+Date: 2022/1/23 10:21
+Desc: 新浪财经-商品期权
+https://stock.finance.sina.com.cn/futures/view/optionsDP.php
+"""
+
+import pandas as pd
+import requests
+from bs4 import BeautifulSoup
+
+from akshare.utils import demjson
+
+
+def option_commodity_contract_sina(symbol: str = "玉米期权") -> pd.DataFrame:
+    """
+    当前可以查询的期权品种的合约日期
+    https://stock.finance.sina.com.cn/futures/view/optionsDP.php
+    :param symbol: choice of {"豆粕期权", "玉米期权", "铁矿石期权", "棉花期权", "白糖期权", "PTA期权", "甲醇期权", "橡胶期权", "沪铜期权", "黄金期权", "菜籽粕期权", "液化石油气期权", "动力煤期权", "菜籽油期权", "花生期权"}
+    :type symbol: str
+    :return: e.g., {'黄金期权': ['au2012', 'au2008', 'au2010', 'au2104', 'au2102', 'au2106', 'au2108']}
+    :rtype: dict
+    """
+    url = "https://stock.finance.sina.com.cn/futures/view/optionsDP.php/pg_o/dce"
+    r = requests.get(url)
+    soup = BeautifulSoup(r.text, "lxml")
+    url_list = [
+        item.find("a")["href"]
+        for item in soup.find_all("li", attrs={"class": "active"})
+        if item.find("a") is not None
+    ]
+    commodity_list = [
+        item.find("a").text
+        for item in soup.find_all("li", attrs={"class": "active"})
+        if item.find("a") is not None
+    ]
+    comm_list_dict = {key: value for key, value in zip(commodity_list, url_list)}
+    url = "https://stock.finance.sina.com.cn" + comm_list_dict[symbol]
+    r = requests.get(url)
+    soup = BeautifulSoup(r.text, "lxml")
+    symbol = (
+        soup.find(attrs={"id": "option_symbol"}).find(attrs={"class": "selected"}).text
+    )
+    contract = [
+        item.text for item in soup.find(attrs={"id": "option_suffix"}).find_all("li")
+    ]
+    temp_df = pd.DataFrame({symbol: contract})
+    temp_df.reset_index(inplace=True)
+    temp_df["index"] = temp_df.index + 1
+    temp_df.columns = ["序号", "合约"]
+    return temp_df
+
+
+def option_commodity_contract_table_sina(
+    symbol: str = "黄金期权", contract: str = "au2204"
+) -> pd.DataFrame:
+    """
+    当前所有期权合约, 包括看涨期权合约和看跌期权合约
+    https://stock.finance.sina.com.cn/futures/view/optionsDP.php
+    :param symbol: choice of {"豆粕期权", "玉米期权", "铁矿石期权", "棉花期权", "白糖期权", "PTA期权", "甲醇期权", "橡胶期权", "沪铜期权", "黄金期权", "菜籽粕期权", "液化石油气期权", "动力煤期权", "菜籽油期权", "花生期权"}
+    :type symbol: str
+    :param contract: e.g., 'au2012'
+    :type contract: str
+    :return: 合约实时行情
+    :rtype: pandas.DataFrame
+    """
+    url = "https://stock.finance.sina.com.cn/futures/view/optionsDP.php/pg_o/dce"
+    r = requests.get(url)
+    soup = BeautifulSoup(r.text, "lxml")
+    url_list = [
+        item.find("a")["href"]
+        for item in soup.find_all("li", attrs={"class": "active"})
+        if item.find("a") is not None
+    ]
+    commodity_list = [
+        item.find("a").text
+        for item in soup.find_all("li", attrs={"class": "active"})
+        if item.find("a") is not None
+    ]
+    comm_list_dict = {key: value for key, value in zip(commodity_list, url_list)}
+    url = "https://stock.finance.sina.com.cn/futures/api/openapi.php/OptionService.getOptionData"
+    params = {
+        "type": "futures",
+        "product": comm_list_dict[symbol].split("/")[-2],
+        "exchange": comm_list_dict[symbol].split("/")[-1],
+        "pinzhong": contract,
+    }
+    r = requests.get(url, params=params)
+    data_json = r.json()
+    up_df = pd.DataFrame(data_json["result"]["data"]["up"])
+    down_df = pd.DataFrame(data_json["result"]["data"]["down"])
+    temp_df = pd.concat([up_df, down_df], axis=1)
+    temp_df.columns = [
+        "看涨合约-买量",
+        "看涨合约-买价",
+        "看涨合约-最新价",
+        "看涨合约-卖价",
+        "看涨合约-卖量",
+        "看涨合约-持仓量",
+        "看涨合约-涨跌",
+        "行权价",
+        "看涨合约-看涨期权合约",
+        "看跌合约-买量",
+        "看跌合约-买价",
+        "看跌合约-最新价",
+        "看跌合约-卖价",
+        "看跌合约-卖量",
+        "看跌合约-持仓量",
+        "看跌合约-涨跌",
+        "看跌合约-看跌期权合约",
+    ]
+    temp_df["看涨合约-买量"] = pd.to_numeric(temp_df["看涨合约-买量"], errors="coerce")
+    temp_df["看涨合约-买价"] = pd.to_numeric(temp_df["看涨合约-买价"], errors="coerce")
+    temp_df["看涨合约-最新价"] = pd.to_numeric(
+        temp_df["看涨合约-最新价"], errors="coerce"
+    )
+    temp_df["看涨合约-卖价"] = pd.to_numeric(temp_df["看涨合约-卖价"], errors="coerce")
+    temp_df["看涨合约-卖量"] = pd.to_numeric(temp_df["看涨合约-卖量"], errors="coerce")
+    temp_df["看涨合约-持仓量"] = pd.to_numeric(
+        temp_df["看涨合约-持仓量"], errors="coerce"
+    )
+    temp_df["看涨合约-涨跌"] = pd.to_numeric(temp_df["看涨合约-涨跌"], errors="coerce")
+    temp_df["行权价"] = pd.to_numeric(temp_df["行权价"], errors="coerce")
+    temp_df["看跌合约-买量"] = pd.to_numeric(temp_df["看跌合约-买量"], errors="coerce")
+    temp_df["看跌合约-买价"] = pd.to_numeric(temp_df["看跌合约-买价"], errors="coerce")
+    temp_df["看跌合约-最新价"] = pd.to_numeric(
+        temp_df["看跌合约-最新价"], errors="coerce"
+    )
+    temp_df["看跌合约-卖价"] = pd.to_numeric(temp_df["看跌合约-卖价"], errors="coerce")
+    temp_df["看跌合约-卖量"] = pd.to_numeric(temp_df["看跌合约-卖量"], errors="coerce")
+    temp_df["看跌合约-持仓量"] = pd.to_numeric(
+        temp_df["看跌合约-持仓量"], errors="coerce"
+    )
+    temp_df["看跌合约-涨跌"] = pd.to_numeric(temp_df["看跌合约-涨跌"], errors="coerce")
+    return temp_df
+
+
+def option_commodity_hist_sina(symbol: str = "au2012C392") -> pd.DataFrame:
+    """
+    合约历史行情-日频
+    https://stock.finance.sina.com.cn/futures/view/optionsDP.php
+    :param symbol: return of option_sina_option_commodity_contract_list(symbol="黄金期权", contract="au2012"), 看涨期权合约 filed
+    :type symbol: str
+    :return: 合约历史行情-日频
+    :rtype: pandas.DataFrame
+    """
+    url = "https://stock.finance.sina.com.cn/futures/api/jsonp.php/var%20_m2009C30002020_7_17=/FutureOptionAllService.getOptionDayline"
+    params = {"symbol": symbol}
+    r = requests.get(url, params=params)
+    data_text = r.text
+    data_json = demjson.decode(data_text[data_text.find("[") : -2])
+    temp_df = pd.DataFrame(data_json)
+    temp_df.columns = ["open", "high", "low", "close", "volume", "date"]
+    temp_df = temp_df[["date", "open", "high", "low", "close", "volume"]]
+    temp_df["date"] = pd.to_datetime(temp_df["date"]).dt.date
+    temp_df["open"] = pd.to_numeric(temp_df["open"])
+    temp_df["high"] = pd.to_numeric(temp_df["high"])
+    temp_df["low"] = pd.to_numeric(temp_df["low"])
+    temp_df["close"] = pd.to_numeric(temp_df["close"])
+    temp_df["volume"] = pd.to_numeric(temp_df["volume"])
+    return temp_df
+
+
+if __name__ == "__main__":
+    option_commodity_contract_sina_df = option_commodity_contract_sina(
+        symbol="棉花期权"
+    )
+    print(option_commodity_contract_sina_df)
+
+    option_commodity_contract_table_sina_df = option_commodity_contract_table_sina(
+        symbol="棉花期权", contract="cf2301"
+    )
+    print(option_commodity_contract_table_sina_df)
+
+    option_commodity_hist_sina_df = option_commodity_hist_sina(symbol="cf2301P21600")
+    print(option_commodity_hist_sina_df)

backend/.venv/lib/python3.9/site-packages/akshare/option/option_contract_info_ctp.py

@@ -0,0 +1,63 @@
+#!/usr/bin/env python
+# -*- coding:utf-8 -*-
+"""
+Date: 2025/10/30 17:40
+Desc: openctp-合约信息接口
+http://openctp.cn/instruments.html
+"""
+
+import pandas as pd
+import requests
+
+
+def option_contract_info_ctp() -> pd.DataFrame:
+    """
+    openctp-合约信息接口-期权合约
+    http://openctp.cn/instruments.html
+    :return: 期权合约信息
+    :rtype: pandas.DataFrame
+    """
+    url = "http://dict.openctp.cn/instruments?types=option"
+    r = requests.get(url)
+    data_json = r.json()
+    temp_df = pd.DataFrame(data_json["data"])
+
+    # 字段映射：英文字段名 -> 中文字段名
+    column_mapping = {
+        "ExchangeID": "交易所ID",
+        "InstrumentID": "合约ID",
+        "InstrumentName": "合约名称",
+        "ProductClass": "商品类别",
+        "ProductID": "品种ID",
+        "VolumeMultiple": "合约乘数",
+        "PriceTick": "最小变动价位",
+        "LongMarginRatioByMoney": "做多保证金率",
+        "ShortMarginRatioByMoney": "做空保证金率",
+        "LongMarginRatioByVolume": "做多保证金/手",
+        "ShortMarginRatioByVolume": "做空保证金/手",
+        "OpenRatioByMoney": "开仓手续费率",
+        "OpenRatioByVolume": "开仓手续费/手",
+        "CloseRatioByMoney": "平仓手续费率",
+        "CloseRatioByVolume": "平仓手续费/手",
+        "CloseTodayRatioByMoney": "平今手续费率",
+        "CloseTodayRatioByVolume": "平今手续费/手",
+        "DeliveryYear": "交割年份",
+        "DeliveryMonth": "交割月份",
+        "OpenDate": "上市日期",
+        "ExpireDate": "最后交易日",
+        "DeliveryDate": "交割日",
+        "UnderlyingInstrID": "标的合约ID",
+        "UnderlyingMultiple": "标的合约乘数",
+        "OptionsType": "期权类型",
+        "StrikePrice": "行权价",
+        "InstLifePhase": "合约状态",
+    }
+    # 重命名列为中文
+    temp_df = temp_df.rename(columns=column_mapping)
+    return temp_df
+
+
+# 使用示例
+if __name__ == "__main__":
+    option_contract_info_ctp_df = option_contract_info_ctp()
+    print(option_contract_info_ctp_df)

backend/.venv/lib/python3.9/site-packages/akshare/option/option_current_sse.py

@@ -0,0 +1,61 @@
+# -*- coding:utf-8 -*-
+# !/usr/bin/env python
+"""
+Date: 2025/9/8 16:20
+Desc: 上海证券交易所-产品-股票期权-信息披露-当日合约
+http://www.sse.com.cn/assortment/options/disclo/preinfo/
+"""
+
+import pandas as pd
+import requests
+
+
+def option_current_day_sse() -> pd.DataFrame:
+    """
+    上海证券交易所-产品-股票期权-信息披露-当日合约
+    http://www.sse.com.cn/assortment/options/disclo/preinfo/
+    :return: 上交所期权当日合约
+    :rtype: pandas.DataFrame
+    """
+    url = "http://query.sse.com.cn/commonQuery.do"
+    params = {
+        "isPagination": "false",
+        "expireDate": "",
+        "securityId": "",
+        "sqlId": "SSE_ZQPZ_YSP_GGQQZSXT_XXPL_DRHY_SEARCH_L",
+    }
+    headers = {
+        "Accept": "*/*",
+        "Accept-Encoding": "gzip, deflate",
+        "Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8",
+        "Cache-Control": "no-cache",
+        "Connection": "keep-alive",
+        "Host": "query.sse.com.cn",
+        "Pragma": "no-cache",
+        "Referer": "http://www.sse.com.cn/",
+        "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) "
+        "Chrome/101.0.4951.67 Safari/537.36",
+    }
+    r = requests.get(url, params=params, headers=headers)
+    data_json = r.json()
+    temp_df = pd.DataFrame(data_json["result"])
+    dict_df = {
+        "SECURITY_ID": "合约编码",
+        "CONTRACT_ID": "合约交易代码",
+        "CONTRACT_SYMBOL": "合约简称",
+        "SECURITYNAMEBYID": "标的券名称及代码",
+        "CALL_OR_PUT": "类型",
+        "EXERCISE_PRICE": "行权价",
+        "CONTRACT_UNIT": "合约单位",
+        "END_DATE": "期权行权日",
+        "DELIVERY_DATE": "行权交收日",
+        "EXPIRE_DATE": "到期日",
+        "START_DATE": "开始日期",
+    }
+    temp_df = temp_df[dict_df.keys()].rename(columns=dict_df)
+    return temp_df
+
+
+if __name__ == "__main__":
+    option_current_day_sse_df = option_current_day_sse()
+    print(option_current_day_sse_df)

backend/.venv/lib/python3.9/site-packages/akshare/option/option_current_szse.py

@@ -0,0 +1,100 @@
+# -*- coding:utf-8 -*-
+# !/usr/bin/env python
+"""
+Date: 2025/9/13 16:00
+Desc: 深圳证券交易所-期权子网-行情数据-当日合约
+"""
+
+from io import BytesIO
+
+import pandas as pd
+import requests
+
+
+def option_current_day_szse() -> pd.DataFrame:
+    """
+    深圳证券交易所-期权子网-行情数据-当日合约
+    https://www.sse.org.cn/option/quotation/contract/daycontract/index.html
+    :return: 深圳期权当日合约
+    :rtype: pandas.DataFrame
+    """
+    import warnings
+
+    warnings.filterwarnings(
+        action="ignore", message="Workbook contains no default style"
+    )
+    url = "https://www.sse.org.cn/api/report/ShowReport"
+    params = {
+        "SHOWTYPE": "xlsx",
+        "CATALOGID": "option_drhy",
+        "TABKEY": "tab1",
+    }
+    r = requests.get(url, params=params)
+    temp_df = pd.read_excel(BytesIO(r.content))
+    temp_df["序号"] = pd.to_numeric(temp_df["序号"], errors="coerce")
+    temp_df["行权价"] = pd.to_numeric(temp_df["行权价"], errors="coerce")
+    temp_df["合约单位"] = pd.to_numeric(temp_df["合约单位"], errors="coerce")
+    temp_df["涨停价格"] = pd.to_numeric(temp_df["涨停价格"], errors="coerce")
+    temp_df["跌停价格"] = pd.to_numeric(temp_df["跌停价格"], errors="coerce")
+    temp_df["前结算价"] = pd.to_numeric(temp_df["前结算价"], errors="coerce")
+    temp_df["合约总持仓"] = pd.to_numeric(temp_df["合约总持仓"], errors="coerce")
+    temp_df["原行权价格"] = pd.to_numeric(temp_df["原行权价格"], errors="coerce")
+    temp_df["原合约单位"] = pd.to_numeric(temp_df["原合约单位"], errors="coerce")
+    temp_df["合约到期剩余交易天数"] = pd.to_numeric(
+        temp_df["合约到期剩余交易天数"], errors="coerce"
+    )
+    temp_df["合约到期剩余自然天数"] = pd.to_numeric(
+        temp_df["合约到期剩余自然天数"], errors="coerce"
+    )
+    temp_df["下次合约调整剩余交易天数"] = pd.to_numeric(
+        temp_df["下次合约调整剩余交易天数"], errors="coerce"
+    )
+    temp_df["下次合约调整剩余自然天数"] = pd.to_numeric(
+        temp_df["下次合约调整剩余自然天数"], errors="coerce"
+    )
+    temp_df["交易日期"] = pd.to_datetime(temp_df["交易日期"], errors="coerce").dt.date
+    temp_df["最后交易日"] = pd.to_datetime(
+        temp_df["最后交易日"], errors="coerce"
+    ).dt.date
+    temp_df["行权日"] = pd.to_datetime(temp_df["行权日"], errors="coerce").dt.date
+    temp_df["到期日"] = pd.to_datetime(temp_df["到期日"], errors="coerce").dt.date
+    temp_df["交收日"] = pd.to_datetime(temp_df["交收日"], errors="coerce").dt.date
+    temp_df = temp_df[
+        [
+            "序号",
+            "合约编码",
+            "合约代码",
+            "合约简称",
+            "标的证券简称(代码)",
+            "合约类型",
+            "行权价",
+            "合约单位",
+            "最后交易日",
+            "行权日",
+            "到期日",
+            "交收日",
+            "新挂",
+            "涨停价格",
+            "跌停价格",
+            "前结算价",
+            "合约调整",
+            "停牌",
+            "合约总持仓",
+            "挂牌原因",
+            "原合约代码",
+            "原合约简称",
+            "原行权价格",
+            "原合约单位",
+            "合约到期剩余交易天数",
+            "合约到期剩余自然天数",
+            "下次合约调整剩余交易天数",
+            "下次合约调整剩余自然天数",
+            "交易日期",
+        ]
+    ]
+    return temp_df
+
+
+if __name__ == "__main__":
+    option_current_day_szse_df = option_current_day_szse()
+    print(option_current_day_szse_df)

backend/.venv/lib/python3.9/site-packages/akshare/option/option_czce.py

@@ -0,0 +1,82 @@
+#!/usr/bin/env python
+# -*- coding:utf-8 -*-
+"""
+Date: 2025/10/17 16:30
+Desc: 郑州商品交易所-交易数据-历史行情下载-期权历史行情下载
+http://www.czce.com.cn/cn/jysj/lshqxz/H770319index_1.htm
+自 20200101 起，成交量、空盘量、成交额、行权量均为单边计算
+郑州商品交易所-期权上市时间表
+"SR": "20170419"
+"CF": "20190410"
+"TA": "20191216"
+"MA": "20191217"
+"RM": "20200116"
+"ZC": "20200630"
+"OI": "20220826"
+"PK": "20220826"
+"PX": "20230915"
+"SH": "20230915"
+"SA": "20231020"
+"PF": "20231020"
+"SM": "20231020"
+"SF": "20231020"
+"UR": "20231020"
+"AP": "20231020"
+"CJ": "20240621"
+"FG": "20240621"
+"PR": "20241227"
+"""
+
+import warnings
+from io import StringIO
+
+import pandas as pd
+import requests
+
+
+def option_hist_yearly_czce(symbol: str = "SR", year: str = "2021") -> pd.DataFrame:
+    """
+    郑州商品交易所-交易数据-历史行情下载-期权历史行情下载
+    http://www.czce.com.cn/cn/jysj/lshqxz/H770319index_1.htm
+    :param symbol: choice of {"白糖": "SR", "棉花": "CF", "PTA": "TA", "甲醇": "MA", "菜籽粕": "RM",
+    "动力煤": "ZC", "菜籽油": "OI", "花生": "PK", "对二甲苯": "PX", "烧碱": "SH", "纯碱": "SA", "短纤": "PF",
+    "锰硅": "SM", "硅铁": "SF", "尿素": "UR", "苹果": "AP", "红枣": "CJ", "玻璃": "FG", "瓶片": "PR"}
+    :type symbol: str
+    :param year: 需要获取数据的年份, 注意品种的上市时间
+    :type year: str
+    :return: 指定年份的日频期权数据
+    :rtype: pandas.DataFrame
+    """
+    symbol_year_dict = {
+        "SR": "2017",
+        "CF": "2019",
+        "TA": "2019",
+        "MA": "2019",
+        "RM": "2020",
+        "ZC": "2020",
+        "OI": "2022",
+        "PK": "2022",
+        "PX": "2023",
+        "SH": "2023",
+        "SA": "2023",
+        "PF": "2023",
+        "SM": "2023",
+        "SF": "2023",
+        "UR": "2023",
+        "AP": "2023",
+        "CJ": "2024",
+        "FG": "2024",
+        "PR": "2024",
+    }
+    if int(symbol_year_dict[symbol]) > int(year):
+        warnings.warn(f"{year} year, symbol {symbol} is not on trade")
+        return pd.DataFrame()
+    url = f"http://www.czce.com.cn/cn/DFSStaticFiles/Option/{year}/OptionDataAllHistory/{symbol}OPTIONS{year}.txt"
+    r = requests.get(url)
+    option_df = pd.read_table(StringIO(r.text), skiprows=1, sep="|", low_memory=False)
+    return option_df
+
+
+if __name__ == "__main__":
+    option_hist_yearly_czce_df = option_hist_yearly_czce(symbol="RM", year="2025")
+    print(option_hist_yearly_czce_df)

backend/.venv/lib/python3.9/site-packages/akshare/option/option_daily_stats_sse_szse.py

@@ -0,0 +1,145 @@
+# -*- coding:utf-8 -*-
+# !/usr/bin/env python
+"""
+Date: 2024/6/27 22:20
+Desc: 上海证券交易所-产品-股票期权-每日统计
+https://www.sse.com.cn/assortment/options/date/
+深圳证券交易所-市场数据-期权数据-日度概况
+https://investor.szse.cn/market/option/day/index.html
+"""
+
+import pandas as pd
+import requests
+
+
+def option_daily_stats_sse(date: str = "20240626") -> pd.DataFrame:
+    """
+    上海证券交易所-产品-股票期权-每日统计
+    https://www.sse.com.cn/assortment/options/date/
+    :param date: 交易日
+    :type date: str
+    :return: 每日统计
+    :rtype: pandas.DataFrame
+    """
+    url = "http://query.sse.com.cn/commonQuery.do"
+    params = {
+        "isPagination": "false",
+        "sqlId": "COMMON_SSE_ZQPZ_YSP_QQ_SJTJ_MRTJ_CX",
+        "tradeDate": date,
+    }
+    headers = {
+        "Accept": "*/*",
+        "Accept-Encoding": "gzip, deflate",
+        "Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8",
+        "Cache-Control": "no-cache",
+        "Connection": "keep-alive",
+        "Host": "query.sse.com.cn",
+        "Pragma": "no-cache",
+        "Referer": "https://www.sse.com.cn/",
+        "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) "
+        "Chrome/101.0.4951.67 Safari/537.36",
+    }
+    r = requests.get(url, params=params, headers=headers)
+    data_json = r.json()
+    temp_df = pd.DataFrame(data_json["result"])
+    temp_df.rename(
+        columns={
+            "CONTRACT_VOLUME": "合约数量",
+            "CALL_VOLUME": "认购成交量",
+            "LEAVES_QTY": "未平仓合约总数",
+            "CP_RATE": "认沽/认购",
+            "PUT_VOLUME": "认沽成交量",
+            "TRADE_DATE": "交易日",
+            "TOTAL_MONEY": "总成交额",
+            "TOTAL_VOLUME": "总成交量",
+            "SECURITY_CODE": "合约标的代码",
+            "LEAVES_CALL_QTY": "未平仓认购合约数",
+            "LEAVES_PUT_QTY": "未平仓认沽合约数",
+            "SECURITY_ABBR": "合约标的名称",
+        },
+        inplace=True,
+    )
+    temp_df = temp_df[
+        [
+            "合约标的代码",
+            "合约标的名称",
+            "合约数量",
+            "总成交额",
+            "总成交量",
+            "认购成交量",
+            "认沽成交量",
+            "认沽/认购",
+            "未平仓合约总数",
+            "未平仓认购合约数",
+            "未平仓认沽合约数",
+            "交易日",
+        ]
+    ]
+    temp_df["交易日"] = pd.to_datetime(temp_df["交易日"], errors="coerce").dt.date
+    for item in temp_df.columns[2:-1]:
+        temp_df[item] = temp_df[item].str.replace(",", "")
+        temp_df[item] = pd.to_numeric(temp_df[item], errors="coerce")
+    return temp_df
+
+
+def option_daily_stats_szse(date: str = "20240626") -> pd.DataFrame:
+    """
+    深圳证券交易所-市场数据-期权数据-日度概况
+    https://investor.szse.cn/market/option/day/index.html
+    :param date: 交易日
+    :type date: str
+    :return: 每日统计
+    :rtype: pandas.DataFrame
+    """
+    url = "https://investor.szse.cn/api/report/ShowReport/data"
+    params = {
+        "SHOWTYPE": "JSON",
+        "CATALOGID": "ysprdzb",
+        "TABKEY": "tab1",
+        "txtQueryDate": "-".join([date[:4], date[4:6], date[6:]]),
+        "random": "0.0652692406565949",
+    }
+    r = requests.get(url, params=params)
+    data_json = r.json()
+    temp_df = pd.DataFrame(data_json[0]["data"])
+    temp_df.rename(
+        columns={
+            "bddm": "合约标的代码",
+            "bdmc": "合约标的名称",
+            "cjl": "成交量",
+            "rccjl": "认购成交量",
+            "rpcjl": "认沽成交量",
+            "rcrpccb": "认沽/认购持仓比",
+            "wpchyzs": "未平仓合约总数",
+            "wpcrchys": "未平仓认购合约数",
+            "wpcrphys": "未平仓认沽合约数",
+        },
+        inplace=True,
+    )
+    temp_df = temp_df[
+        [
+            "合约标的代码",
+            "合约标的名称",
+            "成交量",
+            "认购成交量",
+            "认沽成交量",
+            "认沽/认购持仓比",
+            "未平仓合约总数",
+            "未平仓认购合约数",
+            "未平仓认沽合约数",
+        ]
+    ]
+    temp_df["交易日"] = "-".join([date[:4], date[4:6], date[6:]])
+    temp_df["交易日"] = pd.to_datetime(temp_df["交易日"], errors="coerce").dt.date
+    for item in temp_df.columns[2:-1]:
+        temp_df[item] = temp_df[item].str.replace(",", "")
+        temp_df[item] = pd.to_numeric(temp_df[item], errors="coerce")
+    return temp_df
+
+
+if __name__ == "__main__":
+    option_daily_stats_sse_df = option_daily_stats_sse(date="20240626")
+    print(option_daily_stats_sse_df)
+
+    option_daily_stats_szse_df = option_daily_stats_szse(date="20240626")
+    print(option_daily_stats_szse_df)

backend/.venv/lib/python3.9/site-packages/akshare/option/option_em.py

@@ -0,0 +1,188 @@
+#!/usr/bin/env python
+# -*- coding:utf-8 -*-
+"""
+Date: 2025/2/18 16:30
+Desc: 东方财富网-行情中心-期权市场
+https://quote.eastmoney.com/center/qqsc.html
+"""
+
+import pandas as pd
+import requests
+from akshare.utils.func import fetch_paginated_data
+
+
+def option_current_em() -> pd.DataFrame:
+    """
+    东方财富网-行情中心-期权市场
+    https://quote.eastmoney.com/center/qqsc.html
+    :return: 期权价格
+    :rtype: pandas.DataFrame
+    """
+    url = "https://23.push2.eastmoney.com/api/qt/clist/get"
+    params = {
+        "pn": "1",
+        "pz": "100",
+        "po": "1",
+        "np": "1",
+        "ut": "bd1d9ddb04089700cf9c27f6f7426281",
+        "fltt": "2",
+        "invt": "2",
+        "fid": "f3",
+        "fs": "m:10,m:12,m:140,m:141,m:151,m:163,m:226",
+        "fields": "f1,f2,f3,f4,f5,f6,f7,f8,f9,f10,f12,f13,f14,f15,f16,f17,f18,f20,f21,"
+        "f23,f24,f25,f22,f28,f11,f62,f128,f136,f115,f152,f133,f108,f163,f161,f162",
+    }
+    temp_df = fetch_paginated_data(url=url, base_params=params)
+    temp_df.columns = [
+        "序号",
+        "_",
+        "最新价",
+        "涨跌幅",
+        "涨跌额",
+        "成交量",
+        "成交额",
+        "_",
+        "_",
+        "_",
+        "_",
+        "_",
+        "代码",
+        "市场标识",
+        "名称",
+        "_",
+        "_",
+        "今开",
+        "_",
+        "_",
+        "_",
+        "_",
+        "_",
+        "_",
+        "_",
+        "昨结",
+        "_",
+        "持仓量",
+        "_",
+        "_",
+        "_",
+        "_",
+        "_",
+        "_",
+        "_",
+        "行权价",
+        "剩余日",
+        "日增",
+    ]
+    temp_df = temp_df[
+        [
+            "序号",
+            "代码",
+            "名称",
+            "最新价",
+            "涨跌额",
+            "涨跌幅",
+            "成交量",
+            "成交额",
+            "持仓量",
+            "行权价",
+            "剩余日",
+            "日增",
+            "昨结",
+            "今开",
+            "市场标识",
+        ]
+    ]
+    temp_df["最新价"] = pd.to_numeric(temp_df["最新价"], errors="coerce")
+    temp_df["涨跌额"] = pd.to_numeric(temp_df["涨跌额"], errors="coerce")
+    temp_df["涨跌幅"] = pd.to_numeric(temp_df["涨跌幅"], errors="coerce")
+    temp_df["成交量"] = pd.to_numeric(temp_df["成交量"], errors="coerce")
+    temp_df["成交额"] = pd.to_numeric(temp_df["成交额"], errors="coerce")
+    temp_df["持仓量"] = pd.to_numeric(temp_df["持仓量"], errors="coerce")
+    temp_df["行权价"] = pd.to_numeric(temp_df["行权价"], errors="coerce")
+    temp_df["剩余日"] = pd.to_numeric(temp_df["剩余日"], errors="coerce")
+    temp_df["日增"] = pd.to_numeric(temp_df["日增"], errors="coerce")
+    temp_df["昨结"] = pd.to_numeric(temp_df["昨结"], errors="coerce")
+    temp_df["今开"] = pd.to_numeric(temp_df["今开"], errors="coerce")
+    option_current_cffex_em_df = option_current_cffex_em()
+    big_df = pd.concat(objs=[temp_df, option_current_cffex_em_df], ignore_index=True)
+    big_df["序号"] = range(1, len(big_df) + 1)
+    return big_df
+
+
+def option_current_cffex_em() -> pd.DataFrame:
+    url = "https://futsseapi.eastmoney.com/list/option/221"
+    params = {
+        "orderBy": "zdf",
+        "sort": "desc",
+        "pageSize": "20000",
+        "pageIndex": "0",
+        "token": "58b2fa8f54638b60b87d69b31969089c",
+        "field": "dm,sc,name,p,zsjd,zde,zdf,f152,vol,cje,ccl,xqj,syr,rz,zjsj,o",
+        "blockName": "callback",
+        "_:": "1706689899924",
+    }
+    r = requests.get(url, params=params)
+    data_json = r.json()
+    temp_df = pd.DataFrame(data_json["list"])
+    temp_df.reset_index(inplace=True)
+    temp_df["index"] = temp_df["index"] + 1
+    temp_df.rename(
+        columns={
+            "index": "序号",
+            "rz": "日增",
+            "dm": "代码",
+            "zsjd": "-",
+            "ccl": "持仓量",
+            "syr": "剩余日",
+            "o": "今开",
+            "p": "最新价",
+            "sc": "市场标识",
+            "xqj": "行权价",
+            "vol": "成交量",
+            "name": "名称",
+            "zde": "涨跌额",
+            "zdf": "涨跌幅",
+            "zjsj": "昨结",
+            "cje": "成交额",
+        },
+        inplace=True,
+    )
+    temp_df = temp_df[
+        [
+            "序号",
+            "代码",
+            "名称",
+            "最新价",
+            "涨跌额",
+            "涨跌幅",
+            "成交量",
+            "成交额",
+            "持仓量",
+            "行权价",
+            "剩余日",
+            "日增",
+            "昨结",
+            "今开",
+            "市场标识",
+        ]
+    ]
+    temp_df["最新价"] = pd.to_numeric(temp_df["最新价"], errors="coerce")
+    temp_df["涨跌额"] = pd.to_numeric(temp_df["涨跌额"], errors="coerce")
+    temp_df["涨跌幅"] = pd.to_numeric(temp_df["涨跌幅"], errors="coerce")
+    temp_df["成交量"] = pd.to_numeric(temp_df["成交量"], errors="coerce")
+    temp_df["成交额"] = pd.to_numeric(temp_df["成交额"], errors="coerce")
+    temp_df["持仓量"] = pd.to_numeric(temp_df["持仓量"], errors="coerce")
+    temp_df["行权价"] = pd.to_numeric(temp_df["行权价"], errors="coerce")
+    temp_df["剩余日"] = pd.to_numeric(temp_df["剩余日"], errors="coerce")
+    temp_df["日增"] = pd.to_numeric(temp_df["日增"], errors="coerce")
+    temp_df["昨结"] = pd.to_numeric(temp_df["昨结"], errors="coerce")
+    temp_df["今开"] = pd.to_numeric(temp_df["今开"], errors="coerce")
+    return temp_df
+
+
+if __name__ == "__main__":
+    option_current_em_df = option_current_em()
+    print(option_current_em_df)
+
+    option_current_cffex_em_df = option_current_cffex_em()
+    print(option_current_cffex_em_df)

backend/.venv/lib/python3.9/site-packages/akshare/option/option_finance.py

@@ -0,0 +1,358 @@
+#!/usr/bin/env python
+# -*- coding:utf-8 -*-
+"""
+Date: 2024/2/24 15:18
+Desc: 金融期权数据
+http://www.sse.com.cn/assortment/options/price/
+http://www.szse.cn/market/product/option/index.html
+http://www.cffex.com.cn/hs300gzqq/
+http://www.cffex.com.cn/zz1000gzqq/
+"""
+
+from io import BytesIO
+
+import pandas as pd
+import requests
+
+from akshare.option.cons import (
+    SH_OPTION_PAYLOAD,
+    SH_OPTION_PAYLOAD_OTHER,
+    SH_OPTION_URL_50,
+    SH_OPTION_URL_KING_50,
+    SH_OPTION_URL_300,
+    SH_OPTION_URL_KING_300,
+    SH_OPTION_URL_500,
+    SH_OPTION_URL_KING_500,
+    SH_OPTION_URL_KC_50,
+    SH_OPTION_URL_KC_KING_50,
+    SH_OPTION_URL_KC_50_YFD,
+    SH_OPTION_URL_KING_50_YFD,
+    CFFEX_OPTION_URL_300,
+)
+
+
+def option_finance_sse_underlying(symbol: str = "华夏科创50ETF期权") -> pd.DataFrame:
+    """
+    期权标的当日行情
+    http://www.sse.com.cn/assortment/options/price/
+    :param symbol: choice of {"华夏上证50ETF期权", "华泰柏瑞沪深300ETF期权", "南方中证500ETF期权", "华夏科创50ETF期权", "易方达科创50ETF期权"}
+    :type symbol: str
+    :return: 期权标的当日行情
+    :rtype: pandas.DataFrame
+    """
+    symbol_map = {
+        "华夏上证50ETF期权": SH_OPTION_URL_50,
+        "华泰柏瑞沪深300ETF期权": SH_OPTION_URL_300,
+        "南方中证500ETF期权": SH_OPTION_URL_500,
+        "华夏科创50ETF期权": SH_OPTION_URL_KC_50,
+        "易方达科创50ETF期权": SH_OPTION_URL_KC_50_YFD,
+    }
+    r = requests.get(symbol_map[symbol], params=SH_OPTION_PAYLOAD)
+    data_json = r.json()
+    raw_data = pd.DataFrame(data_json["list"])
+    raw_data.at[0, 0] = "510300"
+    raw_data.at[0, 8] = pd.to_datetime(
+        str(data_json["date"]) + str(data_json["time"]),
+        format="%Y%m%d%H%M%S",
+    )
+    raw_data.columns = [
+        "代码",
+        "名称",
+        "当前价",
+        "涨跌",
+        "涨跌幅",
+        "振幅",
+        "成交量(手)",
+        "成交额(万元)",
+        "更新日期",
+    ]
+    return raw_data
+
+
+def option_finance_board(
+    symbol: str = "嘉实沪深300ETF期权", end_month: str = "2306"
+) -> pd.DataFrame:
+    """
+    期权当前交易日的行情数据
+    主要为三个: 华夏上证50ETF期权, 华泰柏瑞沪深300ETF期权, 嘉实沪深300ETF期权,
+    沪深300股指期权, 中证1000股指期权, 上证50股指期权, 华夏科创50ETF期权, 易方达科创50ETF期权
+    http://www.sse.com.cn/assortment/options/price/
+    http://www.szse.cn/market/product/option/index.html
+    http://www.cffex.com.cn/hs300gzqq/
+    http://www.cffex.com.cn/zz1000gzqq/
+    :param symbol: choice of {"华夏上证50ETF期权", "华泰柏瑞沪深300ETF期权", "南方中证500ETF期权",
+    "华夏科创50ETF期权", "易方达科创50ETF期权", "嘉实沪深300ETF期权", "沪深300股指期权", "中证1000股指期权", "上证50股指期权"}
+    :type symbol: str
+    :param end_month: 2003; 2020 年 3 月到期的期权
+    :type end_month: str
+    :return: 当日行情
+    :rtype: pandas.DataFrame
+    """
+    end_month = end_month[-2:]
+    if symbol == "华夏上证50ETF期权":
+        r = requests.get(
+            SH_OPTION_URL_KING_50.format(end_month),
+            params=SH_OPTION_PAYLOAD_OTHER,
+        )
+        data_json = r.json()
+        raw_data = pd.DataFrame(data_json["list"])
+        raw_data.index = [str(data_json["date"]) + str(data_json["time"])] * data_json[
+            "total"
+        ]
+        raw_data.columns = ["合约交易代码", "当前价", "涨跌幅", "前结价", "行权价"]
+        raw_data["数量"] = [data_json["total"]] * data_json["total"]
+        raw_data.reset_index(inplace=True)
+        raw_data.columns = [
+            "日期",
+            "合约交易代码",
+            "当前价",
+            "涨跌幅",
+            "前结价",
+            "行权价",
+            "数量",
+        ]
+        return raw_data
+    elif symbol == "华泰柏瑞沪深300ETF期权":
+        r = requests.get(
+            SH_OPTION_URL_KING_300.format(end_month),
+            params=SH_OPTION_PAYLOAD_OTHER,
+        )
+        data_json = r.json()
+        raw_data = pd.DataFrame(data_json["list"])
+        raw_data.index = [str(data_json["date"]) + str(data_json["time"])] * data_json[
+            "total"
+        ]
+        raw_data.columns = ["合约交易代码", "当前价", "涨跌幅", "前结价", "行权价"]
+        raw_data["数量"] = [data_json["total"]] * data_json["total"]
+        raw_data.reset_index(inplace=True)
+        raw_data.columns = [
+            "日期",
+            "合约交易代码",
+            "当前价",
+            "涨跌幅",
+            "前结价",
+            "行权价",
+            "数量",
+        ]
+        return raw_data
+    elif symbol == "南方中证500ETF期权":
+        r = requests.get(
+            SH_OPTION_URL_KING_500.format(end_month),
+            params=SH_OPTION_PAYLOAD_OTHER,
+        )
+        data_json = r.json()
+        raw_data = pd.DataFrame(data_json["list"])
+        raw_data.index = [str(data_json["date"]) + str(data_json["time"])] * data_json[
+            "total"
+        ]
+        raw_data.columns = ["合约交易代码", "当前价", "涨跌幅", "前结价", "行权价"]
+        raw_data["数量"] = [data_json["total"]] * data_json["total"]
+        raw_data.reset_index(inplace=True)
+        raw_data.columns = [
+            "日期",
+            "合约交易代码",
+            "当前价",
+            "涨跌幅",
+            "前结价",
+            "行权价",
+            "数量",
+        ]
+        return raw_data
+    elif symbol == "华夏科创50ETF期权":
+        r = requests.get(
+            SH_OPTION_URL_KC_KING_50.format(end_month),
+            params=SH_OPTION_PAYLOAD_OTHER,
+        )
+        data_json = r.json()
+        raw_data = pd.DataFrame(data_json["list"])
+        raw_data.index = [str(data_json["date"]) + str(data_json["time"])] * data_json[
+            "total"
+        ]
+        raw_data.columns = ["合约交易代码", "当前价", "涨跌幅", "前结价", "行权价"]
+        raw_data["数量"] = [data_json["total"]] * data_json["total"]
+        raw_data.reset_index(inplace=True)
+        raw_data.columns = [
+            "日期",
+            "合约交易代码",
+            "当前价",
+            "涨跌幅",
+            "前结价",
+            "行权价",
+            "数量",
+        ]
+        return raw_data
+    elif symbol == "易方达科创50ETF期权":
+        r = requests.get(
+            SH_OPTION_URL_KING_50_YFD.format(end_month),
+            params=SH_OPTION_PAYLOAD_OTHER,
+        )
+        data_json = r.json()
+        raw_data = pd.DataFrame(data_json["list"])
+        raw_data.index = [str(data_json["date"]) + str(data_json["time"])] * data_json[
+            "total"
+        ]
+        raw_data.columns = ["合约交易代码", "当前价", "涨跌幅", "前结价", "行权价"]
+        raw_data["数量"] = [data_json["total"]] * data_json["total"]
+        raw_data.reset_index(inplace=True)
+        raw_data.columns = [
+            "日期",
+            "合约交易代码",
+            "当前价",
+            "涨跌幅",
+            "前结价",
+            "行权价",
+            "数量",
+        ]
+        return raw_data
+    elif symbol == "嘉实沪深300ETF期权":
+        url = "http://www.szse.cn/api/report/ShowReport/data"
+        params = {
+            "SHOWTYPE": "JSON",
+            "CATALOGID": "ysplbrb",
+            "TABKEY": "tab1",
+            "PAGENO": "1",
+            "random": "0.10642298535346595",
+        }
+        r = requests.get(url, params=params)
+        data_json = r.json()
+        page_num = data_json[0]["metadata"]["pagecount"]
+        big_df = pd.DataFrame()
+        for page in range(1, page_num + 1):
+            params = {
+                "SHOWTYPE": "JSON",
+                "CATALOGID": "ysplbrb",
+                "TABKEY": "tab1",
+                "PAGENO": page,
+                "random": "0.10642298535346595",
+            }
+            r = requests.get(url, params=params)
+            data_json = r.json()
+            temp_df = pd.DataFrame(data_json[0]["data"])
+            big_df = pd.concat([big_df, temp_df], ignore_index=True)
+
+        big_df.columns = [
+            "合约编码",
+            "合约简称",
+            "标的名称",
+            "类型",
+            "行权价",
+            "合约单位",
+            "期权行权日",
+            "行权交收日",
+        ]
+        big_df["期权行权日"] = pd.to_datetime(big_df["期权行权日"])
+        big_df["end_month"] = big_df["期权行权日"].dt.month.astype(str).str.zfill(2)
+        big_df = big_df[big_df["end_month"] == end_month]
+        del big_df["end_month"]
+        big_df.reset_index(inplace=True, drop=True)
+        return big_df
+    elif symbol == "沪深300股指期权":
+        headers = {
+            "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/109.0.0.0 Safari/537.36"
+        }
+        r = requests.get(CFFEX_OPTION_URL_300, headers=headers)
+        raw_df = pd.read_table(BytesIO(r.content), sep=",")
+        raw_df["end_month"] = (
+            raw_df["instrument"]
+            .str.split("-", expand=True)
+            .iloc[:, 0]
+            .str.slice(
+                4,
+            )
+        )
+        raw_df = raw_df[raw_df["end_month"] == end_month]
+        del raw_df["end_month"]
+        raw_df.reset_index(inplace=True, drop=True)
+        return raw_df
+    elif symbol == "中证1000股指期权":
+        headers = {
+            "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/109.0.0.0 Safari/537.36"
+        }
+        url = "http://www.cffex.com.cn/quote_MO.txt"
+        r = requests.get(url, headers=headers)
+        raw_df = pd.read_table(BytesIO(r.content), sep=",")
+        raw_df["end_month"] = (
+            raw_df["instrument"]
+            .str.split("-", expand=True)
+            .iloc[:, 0]
+            .str.slice(
+                4,
+            )
+        )
+        raw_df = raw_df[raw_df["end_month"] == end_month]
+        del raw_df["end_month"]
+        raw_df.reset_index(inplace=True, drop=True)
+        return raw_df
+    elif symbol == "上证50股指期权":
+        headers = {
+            "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/109.0.0.0 Safari/537.36"
+        }
+        url = "http://www.cffex.com.cn/quote_HO.txt"
+        r = requests.get(url, headers=headers)
+        raw_df = pd.read_table(BytesIO(r.content), sep=",")
+        raw_df["end_month"] = (
+            raw_df["instrument"]
+            .str.split("-", expand=True)
+            .iloc[:, 0]
+            .str.slice(
+                4,
+            )
+        )
+        raw_df = raw_df[raw_df["end_month"] == end_month]
+        del raw_df["end_month"]
+        raw_df.reset_index(inplace=True, drop=True)
+        return raw_df
+    else:
+        return pd.DataFrame()
+
+
+if __name__ == "__main__":
+    option_finance_sse_underlying_df = option_finance_sse_underlying(
+        symbol="华夏科创50ETF期权"
+    )
+    print(option_finance_sse_underlying_df)
+
+    option_finance_board_df = option_finance_board(
+        symbol="华夏上证50ETF期权", end_month="2306"
+    )
+    print(option_finance_board_df)
+
+    option_finance_board_df = option_finance_board(
+        symbol="华泰柏瑞沪深300ETF期权", end_month="2306"
+    )
+    print(option_finance_board_df)
+
+    option_finance_board_df = option_finance_board(
+        symbol="南方中证500ETF期权", end_month="2306"
+    )
+    print(option_finance_board_df)
+
+    option_finance_board_df = option_finance_board(
+        symbol="华夏科创50ETF期权", end_month="2306"
+    )
+    print(option_finance_board_df)
+
+    option_finance_board_df = option_finance_board(
+        symbol="易方达科创50ETF期权", end_month="2306"
+    )
+    print(option_finance_board_df)
+
+    option_finance_board_df = option_finance_board(
+        symbol="嘉实沪深300ETF期权", end_month="2306"
+    )
+    print(option_finance_board_df)
+
+    option_finance_board_df = option_finance_board(
+        symbol="沪深300股指期权", end_month="2306"
+    )
+    print(option_finance_board_df)
+
+    option_finance_board_df = option_finance_board(
+        symbol="中证1000股指期权", end_month="2306"
+    )
+    print(option_finance_board_df)
+
+    option_finance_board_df = option_finance_board(
+        symbol="上证50股指期权", end_month="2306"
+    )
+    print(option_finance_board_df)

backend/.venv/lib/python3.9/site-packages/akshare/option/option_finance_sina.py

@@ -0,0 +1,979 @@
+#!/usr/bin/env python
+"""
+Date: 2024/6/21 18:00
+Desc: 新浪财经-股票期权
+https://stock.finance.sina.com.cn/option/quotes.html
+期权-中金所-沪深 300 指数
+https://stock.finance.sina.com.cn/futures/view/optionsCffexDP.php
+期权-上交所-50ETF
+期权-上交所-300ETF
+期权-上交所-500ETF
+https://stock.finance.sina.com.cn/option/quotes.html
+"""
+
+import datetime
+import json
+from functools import lru_cache
+from typing import Dict, List, Tuple
+
+import pandas as pd
+import requests
+from bs4 import BeautifulSoup
+
+from akshare.option.option_em import option_current_em
+from akshare.utils.func import set_df_columns
+
+
+# 期权-中金所-上证50指数
+def option_cffex_sz50_list_sina() -> Dict[str, List[str]]:
+    """
+    新浪财经-中金所-上证 50 指数-所有合约, 返回的第一个合约为主力合约
+    目前新浪财经-中金所有上证 50 指数，沪深 300 指数和中证 1000 指数
+    :return: 中金所-上证 50 指数-所有合约
+    :rtype: dict
+    """
+    url = "https://stock.finance.sina.com.cn/futures/view/optionsCffexDP.php/ho/cffex"
+    r = requests.get(url)
+    soup = BeautifulSoup(r.text, features="lxml")
+    symbol = soup.find(attrs={"id": "option_symbol"}).find_all("li")[0].text
+    temp_attr = soup.find(attrs={"id": "option_suffix"}).find_all("li")
+    contract = [item.text for item in temp_attr]
+    return {symbol: contract}
+
+
+# 期权-中金所-沪深300指数
+def option_cffex_hs300_list_sina() -> Dict[str, List[str]]:
+    """
+    新浪财经-中金所-沪深 300 指数-所有合约, 返回的第一个合约为主力合约
+    目前新浪财经-中金所有沪深 300 指数和中证 1000 指数
+    :return: 中金所-沪深300指数-所有合约
+    :rtype: dict
+    """
+    url = "https://stock.finance.sina.com.cn/futures/view/optionsCffexDP.php"
+    r = requests.get(url)
+    soup = BeautifulSoup(r.text, features="lxml")
+    symbol = soup.find(attrs={"id": "option_symbol"}).find_all("li")[1].text
+    temp_attr = soup.find(attrs={"id": "option_suffix"}).find_all("li")
+    contract = [item.text for item in temp_attr]
+    return {symbol: contract}
+
+
+def option_cffex_zz1000_list_sina() -> Dict[str, List[str]]:
+    """
+    新浪财经-中金所-中证 1000 指数-所有合约, 返回的第一个合约为主力合约
+    目前新浪财经-中金所有沪深 300 指数和中证 1000 指数
+    :return: 中金所-中证 1000 指数-所有合约
+    :rtype: dict
+    """
+    url = "https://stock.finance.sina.com.cn/futures/view/optionsCffexDP.php/mo/cffex"
+    r = requests.get(url)
+    soup = BeautifulSoup(r.text, features="lxml")
+    symbol = soup.find(attrs={"id": "option_symbol"}).find_all("li")[2].text
+    temp_attr = soup.find(attrs={"id": "option_suffix"}).find_all("li")
+    contract = [item.text for item in temp_attr]
+    return {symbol: contract}
+
+
+def option_cffex_sz50_spot_sina(symbol: str = "ho2303") -> pd.DataFrame:
+    """
+    中金所-上证 50 指数-指定合约-实时行情
+    https://stock.finance.sina.com.cn/futures/view/optionsCffexDP.php/ho/cffex
+    :param symbol: 合约代码; 用 ak.option_cffex_sz300_list_sina() 函数查看
+    :type symbol: str
+    :return: 中金所-上证 50 指数-指定合约-看涨看跌实时行情
+    :rtype: pandas.DataFrame
+    """
+    url = "https://stock.finance.sina.com.cn/futures/api/openapi.php/OptionService.getOptionData"
+    params = {
+        "type": "futures",
+        "product": "ho",
+        "exchange": "cffex",
+        "pinzhong": symbol,
+    }
+    r = requests.get(url, params=params)
+    data_text = r.text
+    data_json = json.loads(data_text[data_text.find("{") : data_text.rfind("}") + 1])
+    option_call_df = pd.DataFrame(
+        data_json["result"]["data"]["up"],
+        columns=[
+            "看涨合约-买量",
+            "看涨合约-买价",
+            "看涨合约-最新价",
+            "看涨合约-卖价",
+            "看涨合约-卖量",
+            "看涨合约-持仓量",
+            "看涨合约-涨跌",
+            "行权价",
+            "看涨合约-标识",
+        ],
+    )
+    option_put_df = pd.DataFrame(
+        data_json["result"]["data"]["down"],
+        columns=[
+            "看跌合约-买量",
+            "看跌合约-买价",
+            "看跌合约-最新价",
+            "看跌合约-卖价",
+            "看跌合约-卖量",
+            "看跌合约-持仓量",
+            "看跌合约-涨跌",
+            "看跌合约-标识",
+        ],
+    )
+    data_df = pd.concat(objs=[option_call_df, option_put_df], axis=1)
+    data_df["看涨合约-买量"] = pd.to_numeric(data_df["看涨合约-买量"], errors="coerce")
+    data_df["看涨合约-买价"] = pd.to_numeric(data_df["看涨合约-买价"], errors="coerce")
+    data_df["看涨合约-最新价"] = pd.to_numeric(
+        data_df["看涨合约-最新价"], errors="coerce"
+    )
+    data_df["看涨合约-卖价"] = pd.to_numeric(data_df["看涨合约-卖价"], errors="coerce")
+    data_df["看涨合约-卖量"] = pd.to_numeric(data_df["看涨合约-卖量"], errors="coerce")
+    data_df["看涨合约-持仓量"] = pd.to_numeric(
+        data_df["看涨合约-持仓量"], errors="coerce"
+    )
+    data_df["看涨合约-涨跌"] = pd.to_numeric(data_df["看涨合约-涨跌"], errors="coerce")
+    data_df["行权价"] = pd.to_numeric(data_df["行权价"], errors="coerce")
+    data_df["看跌合约-买量"] = pd.to_numeric(data_df["看跌合约-买量"], errors="coerce")
+    data_df["看跌合约-买价"] = pd.to_numeric(data_df["看跌合约-买价"], errors="coerce")
+    data_df["看跌合约-最新价"] = pd.to_numeric(
+        data_df["看跌合约-最新价"], errors="coerce"
+    )
+    data_df["看跌合约-卖价"] = pd.to_numeric(data_df["看跌合约-卖价"], errors="coerce")
+    data_df["看跌合约-卖量"] = pd.to_numeric(data_df["看跌合约-卖量"], errors="coerce")
+    data_df["看跌合约-持仓量"] = pd.to_numeric(
+        data_df["看跌合约-持仓量"], errors="coerce"
+    )
+    data_df["看跌合约-涨跌"] = pd.to_numeric(data_df["看跌合约-涨跌"], errors="coerce")
+    return data_df
+
+
+def option_cffex_hs300_spot_sina(symbol: str = "io2204") -> pd.DataFrame:
+    """
+    中金所-沪深 300 指数-指定合约-实时行情
+    https://stock.finance.sina.com.cn/futures/view/optionsCffexDP.php
+    :param symbol: 合约代码; 用 option_cffex_hs300_list_sina 函数查看
+    :type symbol: str
+    :return: 中金所-沪深300指数-指定合约-看涨看跌实时行情
+    :rtype: pandas.DataFrame
+    """
+    url = "https://stock.finance.sina.com.cn/futures/api/openapi.php/OptionService.getOptionData"
+    params = {
+        "type": "futures",
+        "product": "io",
+        "exchange": "cffex",
+        "pinzhong": symbol,
+    }
+    r = requests.get(url, params=params)
+    data_text = r.text
+    data_json = json.loads(data_text[data_text.find("{") : data_text.rfind("}") + 1])
+    option_call_df = pd.DataFrame(
+        data_json["result"]["data"]["up"],
+        columns=[
+            "看涨合约-买量",
+            "看涨合约-买价",
+            "看涨合约-最新价",
+            "看涨合约-卖价",
+            "看涨合约-卖量",
+            "看涨合约-持仓量",
+            "看涨合约-涨跌",
+            "行权价",
+            "看涨合约-标识",
+        ],
+    )
+    option_put_df = pd.DataFrame(
+        data_json["result"]["data"]["down"],
+        columns=[
+            "看跌合约-买量",
+            "看跌合约-买价",
+            "看跌合约-最新价",
+            "看跌合约-卖价",
+            "看跌合约-卖量",
+            "看跌合约-持仓量",
+            "看跌合约-涨跌",
+            "看跌合约-标识",
+        ],
+    )
+    data_df = pd.concat(objs=[option_call_df, option_put_df], axis=1)
+    data_df["看涨合约-买量"] = pd.to_numeric(data_df["看涨合约-买量"], errors="coerce")
+    data_df["看涨合约-买价"] = pd.to_numeric(data_df["看涨合约-买价"], errors="coerce")
+    data_df["看涨合约-最新价"] = pd.to_numeric(
+        data_df["看涨合约-最新价"], errors="coerce"
+    )
+    data_df["看涨合约-卖价"] = pd.to_numeric(data_df["看涨合约-卖价"], errors="coerce")
+    data_df["看涨合约-卖量"] = pd.to_numeric(data_df["看涨合约-卖量"], errors="coerce")
+    data_df["看涨合约-持仓量"] = pd.to_numeric(
+        data_df["看涨合约-持仓量"], errors="coerce"
+    )
+    data_df["看涨合约-涨跌"] = pd.to_numeric(data_df["看涨合约-涨跌"], errors="coerce")
+    data_df["行权价"] = pd.to_numeric(data_df["行权价"], errors="coerce")
+    data_df["看跌合约-买量"] = pd.to_numeric(data_df["看跌合约-买量"], errors="coerce")
+    data_df["看跌合约-买价"] = pd.to_numeric(data_df["看跌合约-买价"], errors="coerce")
+    data_df["看跌合约-最新价"] = pd.to_numeric(
+        data_df["看跌合约-最新价"], errors="coerce"
+    )
+    data_df["看跌合约-卖价"] = pd.to_numeric(data_df["看跌合约-卖价"], errors="coerce")
+    data_df["看跌合约-卖量"] = pd.to_numeric(data_df["看跌合约-卖量"], errors="coerce")
+    data_df["看跌合约-持仓量"] = pd.to_numeric(
+        data_df["看跌合约-持仓量"], errors="coerce"
+    )
+    data_df["看跌合约-涨跌"] = pd.to_numeric(data_df["看跌合约-涨跌"], errors="coerce")
+    return data_df
+
+
+def option_cffex_zz1000_spot_sina(symbol: str = "mo2208") -> pd.DataFrame:
+    """
+    中金所-中证 1000 指数-指定合约-实时行情
+    https://stock.finance.sina.com.cn/futures/view/optionsCffexDP.php
+    :param symbol: 合约代码; 用 option_cffex_zz1000_list_sina 函数查看
+    :type symbol: str
+    :return: 中金所-中证 1000 指数-指定合约-看涨看跌实时行情
+    :rtype: pandas.DataFrame
+    """
+    url = "https://stock.finance.sina.com.cn/futures/api/openapi.php/OptionService.getOptionData"
+    params = {
+        "type": "futures",
+        "product": "mo",
+        "exchange": "cffex",
+        "pinzhong": symbol,
+    }
+    r = requests.get(url, params=params)
+    data_text = r.text
+    data_json = json.loads(data_text[data_text.find("{") : data_text.rfind("}") + 1])
+    option_call_df = pd.DataFrame(
+        data_json["result"]["data"]["up"],
+        columns=[
+            "看涨合约-买量",
+            "看涨合约-买价",
+            "看涨合约-最新价",
+            "看涨合约-卖价",
+            "看涨合约-卖量",
+            "看涨合约-持仓量",
+            "看涨合约-涨跌",
+            "行权价",
+            "看涨合约-标识",
+        ],
+    )
+    option_put_df = pd.DataFrame(
+        data_json["result"]["data"]["down"],
+        columns=[
+            "看跌合约-买量",
+            "看跌合约-买价",
+            "看跌合约-最新价",
+            "看跌合约-卖价",
+            "看跌合约-卖量",
+            "看跌合约-持仓量",
+            "看跌合约-涨跌",
+            "看跌合约-标识",
+        ],
+    )
+    data_df = pd.concat(objs=[option_call_df, option_put_df], axis=1)
+    data_df["看涨合约-买量"] = pd.to_numeric(data_df["看涨合约-买量"], errors="coerce")
+    data_df["看涨合约-买价"] = pd.to_numeric(data_df["看涨合约-买价"], errors="coerce")
+    data_df["看涨合约-最新价"] = pd.to_numeric(
+        data_df["看涨合约-最新价"], errors="coerce"
+    )
+    data_df["看涨合约-卖价"] = pd.to_numeric(data_df["看涨合约-卖价"], errors="coerce")
+    data_df["看涨合约-卖量"] = pd.to_numeric(data_df["看涨合约-卖量"], errors="coerce")
+    data_df["看涨合约-持仓量"] = pd.to_numeric(
+        data_df["看涨合约-持仓量"], errors="coerce"
+    )
+    data_df["看涨合约-涨跌"] = pd.to_numeric(data_df["看涨合约-涨跌"], errors="coerce")
+    data_df["行权价"] = pd.to_numeric(data_df["行权价"], errors="coerce")
+    data_df["看跌合约-买量"] = pd.to_numeric(data_df["看跌合约-买量"], errors="coerce")
+    data_df["看跌合约-买价"] = pd.to_numeric(data_df["看跌合约-买价"], errors="coerce")
+    data_df["看跌合约-最新价"] = pd.to_numeric(
+        data_df["看跌合约-最新价"], errors="coerce"
+    )
+    data_df["看跌合约-卖价"] = pd.to_numeric(data_df["看跌合约-卖价"], errors="coerce")
+    data_df["看跌合约-卖量"] = pd.to_numeric(data_df["看跌合约-卖量"], errors="coerce")
+    data_df["看跌合约-持仓量"] = pd.to_numeric(
+        data_df["看跌合约-持仓量"], errors="coerce"
+    )
+    data_df["看跌合约-涨跌"] = pd.to_numeric(data_df["看跌合约-涨跌"], errors="coerce")
+    return data_df
+
+
+def option_cffex_sz50_daily_sina(symbol: str = "ho2303P2350") -> pd.DataFrame:
+    """
+    新浪财经-中金所-上证 50 指数-指定合约-日频行情
+    :param symbol: 具体合约代码(包括看涨和看跌标识), 可以通过 ak.option_cffex_sz50_spot_sina 中的 call-标识 获取
+    :type symbol: str
+    :return: 日频率数据
+    :rtype: pandas.DataFrame
+    """
+    year = datetime.datetime.now().year
+    month = datetime.datetime.now().month
+    day = datetime.datetime.now().day
+    url = (
+        f"https://stock.finance.sina.com.cn/futures/api/jsonp.php/var%20_{symbol}{year}_{month}_{day}"
+        f"=/FutureOptionAllService.getOptionDayline"
+    )
+    params = {"symbol": symbol}
+    r = requests.get(url, params=params)
+    data_text = r.text
+    data_df = pd.DataFrame(
+        eval(data_text[data_text.find("[") : data_text.rfind("]") + 1])
+    )
+    data_df.columns = ["open", "high", "low", "close", "volume", "date"]
+    data_df = data_df[
+        [
+            "date",
+            "open",
+            "high",
+            "low",
+            "close",
+            "volume",
+        ]
+    ]
+    data_df["date"] = pd.to_datetime(data_df["date"], errors="coerce").dt.date
+    data_df["open"] = pd.to_numeric(data_df["open"], errors="coerce")
+    data_df["high"] = pd.to_numeric(data_df["high"], errors="coerce")
+    data_df["low"] = pd.to_numeric(data_df["low"], errors="coerce")
+    data_df["close"] = pd.to_numeric(data_df["close"], errors="coerce")
+    data_df["volume"] = pd.to_numeric(data_df["volume"], errors="coerce")
+    return data_df
+
+
+def option_cffex_hs300_daily_sina(symbol: str = "io2202P4350") -> pd.DataFrame:
+    """
+    新浪财经-中金所-沪深300指数-指定合约-日频行情
+    :param symbol: 具体合约代码(包括看涨和看跌标识), 可以通过 ak.option_cffex_hs300_spot_sina 中的 call-标识 获取
+    :type symbol: str
+    :return: 日频率数据
+    :rtype: pandas.DataFrame
+    """
+    year = datetime.datetime.now().year
+    month = datetime.datetime.now().month
+    day = datetime.datetime.now().day
+    url = (
+        f"https://stock.finance.sina.com.cn/futures/api/jsonp.php/var%20_{symbol}{year}_{month}_{day}"
+        f"=/FutureOptionAllService.getOptionDayline"
+    )
+    params = {"symbol": symbol}
+    r = requests.get(url, params=params)
+    data_text = r.text
+    data_df = pd.DataFrame(
+        eval(data_text[data_text.find("[") : data_text.rfind("]") + 1])
+    )
+    data_df.columns = ["open", "high", "low", "close", "volume", "date"]
+    data_df = data_df[
+        [
+            "date",
+            "open",
+            "high",
+            "low",
+            "close",
+            "volume",
+        ]
+    ]
+    data_df["date"] = pd.to_datetime(data_df["date"], errors="coerce").dt.date
+    data_df["open"] = pd.to_numeric(data_df["open"], errors="coerce")
+    data_df["high"] = pd.to_numeric(data_df["high"], errors="coerce")
+    data_df["low"] = pd.to_numeric(data_df["low"], errors="coerce")
+    data_df["close"] = pd.to_numeric(data_df["close"], errors="coerce")
+    data_df["volume"] = pd.to_numeric(data_df["volume"], errors="coerce")
+    return data_df
+
+
+def option_cffex_zz1000_daily_sina(
+    symbol: str = "mo2208P6200",
+) -> pd.DataFrame:
+    """
+    新浪财经-中金所-中证 1000 指数-指定合约-日频行情
+    :param symbol: 具体合约代码(包括看涨和看跌标识), 可以通过 ak.option_cffex_zz1000_spot_sina 中的 call-标识 获取
+    :type symbol: str
+    :return: 日频率数据
+    :rtype: pandas.DataFrame
+    """
+    year = datetime.datetime.now().year
+    month = datetime.datetime.now().month
+    day = datetime.datetime.now().day
+    url = (
+        f"https://stock.finance.sina.com.cn/futures/api/jsonp.php/var%20_{symbol}{year}_{month}_{day}"
+        f"=/FutureOptionAllService.getOptionDayline"
+    )
+    params = {"symbol": symbol}
+    r = requests.get(url, params=params)
+    data_text = r.text
+    data_df = pd.DataFrame(
+        eval(data_text[data_text.find("[") : data_text.rfind("]") + 1])
+    )
+    data_df.columns = ["open", "high", "low", "close", "volume", "date"]
+    data_df = data_df[
+        [
+            "date",
+            "open",
+            "high",
+            "low",
+            "close",
+            "volume",
+        ]
+    ]
+    data_df["date"] = pd.to_datetime(data_df["date"], errors="coerce").dt.date
+    data_df["open"] = pd.to_numeric(data_df["open"], errors="coerce")
+    data_df["high"] = pd.to_numeric(data_df["high"], errors="coerce")
+    data_df["low"] = pd.to_numeric(data_df["low"], errors="coerce")
+    data_df["close"] = pd.to_numeric(data_df["close"], errors="coerce")
+    data_df["volume"] = pd.to_numeric(data_df["volume"], errors="coerce")
+    return data_df
+
+
+# 期权-上交所-50ETF
+def option_sse_list_sina(symbol: str = "50ETF", exchange: str = "null") -> List[str]:
+    """
+    新浪财经-期权-上交所-50ETF-合约到期月份列表
+    https://stock.finance.sina.com.cn/option/quotes.html
+    :param symbol: 50ETF or 300ETF
+    :type symbol: str
+    :param exchange: null
+    :type exchange: str
+    :return: 合约到期时间
+    :rtype: list
+    """
+    url = "https://stock.finance.sina.com.cn/futures/api/openapi.php/StockOptionService.getStockName"
+    params = {"exchange": f"{exchange}", "cate": f"{symbol}"}
+    r = requests.get(url, params=params)
+    data_json = r.json()
+    date_list = data_json["result"]["data"]["contractMonth"]
+    return ["".join(i.split("-")) for i in date_list][1:]
+
+
+def option_sse_expire_day_sina(
+    trade_date: str = "202102", symbol: str = "50ETF", exchange: str = "null"
+) -> Tuple[str, int]:
+    """
+    指定到期月份指定品种的剩余到期时间
+    :param trade_date: 到期月份: 202002, 20203, 20206, 20209
+    :type trade_date: str
+    :param symbol: 50ETF or 300ETF
+    :type symbol: str
+    :param exchange: null
+    :type exchange: str
+    :return: (到期时间, 剩余时间)
+    :rtype: tuple
+    """
+    url = "https://stock.finance.sina.com.cn/futures/api/openapi.php/StockOptionService.getRemainderDay"
+    params = {
+        "exchange": f"{exchange}",
+        "cate": f"{symbol}",
+        "date": f"{trade_date[:4]}-{trade_date[4:]}",
+    }
+    r = requests.get(url, params=params)
+    data_json = r.json()
+    data = data_json["result"]["data"]
+    if int(data["remainderDays"]) < 0:
+        url = "https://stock.finance.sina.com.cn/futures/api/openapi.php/StockOptionService.getRemainderDay"
+        params = {
+            "exchange": f"{exchange}",
+            "cate": f"{'XD' + symbol}",
+            "date": f"{trade_date[:4]}-{trade_date[4:]}",
+        }
+        r = requests.get(url, params=params)
+        data_json = r.json()
+        data = data_json["result"]["data"]
+    return data["expireDay"], int(data["remainderDays"])
+
+
+def option_sse_codes_sina(
+    symbol: str = "看涨期权",
+    trade_date: str = "202202",
+    underlying: str = "510050",
+) -> pd.DataFrame:
+    """
+    上海证券交易所-所有看涨和看跌合约的代码
+
+    :param symbol: choice of {"看涨期权", "看跌期权"}
+    :type symbol: str
+    :param trade_date: 期权到期月份
+    :type trade_date: "202002"
+    :param underlying: 标的产品代码 华夏上证 50ETF: 510050 or 华泰柏瑞沪深 300ETF: 510300
+    :type underlying: str
+    :return: 看涨看跌合约的代码
+    :rtype: Tuple[List, List]
+    """
+    if symbol == "看涨期权":
+        url = "".join(
+            [
+                "https://hq.sinajs.cn/list=OP_UP_",
+                underlying,
+                str(trade_date)[-4:],
+            ]
+        )
+    else:
+        url = "".join(
+            [
+                "https://hq.sinajs.cn/list=OP_DOWN_",
+                underlying,
+                str(trade_date)[-4:],
+            ]
+        )
+    headers = {
+        "Accept": "*/*",
+        "Accept-Encoding": "gzip, deflate, br",
+        "Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8",
+        "Cache-Control": "no-cache",
+        "Connection": "keep-alive",
+        "Host": "hq.sinajs.cn",
+        "Pragma": "no-cache",
+        "Referer": "https://stock.finance.sina.com.cn/",
+        "sec-ch-ua": '" Not;A Brand";v="99", "Google Chrome";v="97", "Chromium";v="97"',
+        "sec-ch-ua-mobile": "?0",
+        "sec-ch-ua-platform": '"Windows"',
+        "Sec-Fetch-Dest": "script",
+        "Sec-Fetch-Mode": "no-cors",
+        "Sec-Fetch-Site": "cross-site",
+        "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) "
+        "Chrome/97.0.4692.71 Safari/537.36",
+    }
+    r = requests.get(url, headers=headers)
+    data_text = r.text
+    data_temp = data_text.replace('"', ",").split(",")
+    temp_list = [i[7:] for i in data_temp if i.startswith("CON_OP_")]
+    temp_df = pd.DataFrame(temp_list)
+    temp_df.reset_index(inplace=True)
+    temp_df["index"] = temp_df.index + 1
+    temp_df.columns = [
+        "序号",
+        "期权代码",
+    ]
+    return temp_df
+
+
+def option_sse_spot_price_sina(symbol: str = "10003720") -> pd.DataFrame:
+    """
+    新浪财经-期权-期权实时数据
+    :param symbol: 期权代码
+    :type symbol: str
+    :return: 期权量价数据
+    :rtype: pandas.DataFrame
+    """
+    url = f"https://hq.sinajs.cn/list=CON_OP_{symbol}"
+    headers = {
+        "Accept": "*/*",
+        "Accept-Encoding": "gzip, deflate, br",
+        "Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8",
+        "Cache-Control": "no-cache",
+        "Connection": "keep-alive",
+        "Host": "hq.sinajs.cn",
+        "Pragma": "no-cache",
+        "Referer": "https://stock.finance.sina.com.cn/",
+        "sec-ch-ua": '" Not;A Brand";v="99", "Google Chrome";v="97", "Chromium";v="97"',
+        "sec-ch-ua-mobile": "?0",
+        "sec-ch-ua-platform": '"Windows"',
+        "Sec-Fetch-Dest": "script",
+        "Sec-Fetch-Mode": "no-cors",
+        "Sec-Fetch-Site": "cross-site",
+        "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) "
+        "Chrome/97.0.4692.71 Safari/537.36",
+    }
+    r = requests.get(url, headers=headers)
+    data_text = r.text
+    data_list = data_text[data_text.find('"') + 1 : data_text.rfind('"')].split(",")
+    field_list = [
+        "买量",
+        "买价",
+        "最新价",
+        "卖价",
+        "卖量",
+        "持仓量",
+        "涨幅",
+        "行权价",
+        "昨收价",
+        "开盘价",
+        "涨停价",
+        "跌停价",
+        "申卖价五",
+        "申卖量五",
+        "申卖价四",
+        "申卖量四",
+        "申卖价三",
+        "申卖量三",
+        "申卖价二",
+        "申卖量二",
+        "申卖价一",
+        "申卖量一",
+        "申买价一",
+        "申买量一 ",
+        "申买价二",
+        "申买量二",
+        "申买价三",
+        "申买量三",
+        "申买价四",
+        "申买量四",
+        "申买价五",
+        "申买量五",
+        "行情时间",
+        "主力合约标识",
+        "状态码",
+        "标的证券类型",
+        "标的股票",
+        "期权合约简称",
+        "振幅",
+        "最高价",
+        "最低价",
+        "成交量",
+        "成交额",
+    ]
+    data_df = pd.DataFrame(list(zip(field_list, data_list)), columns=["字段", "值"])
+    return data_df
+
+
+def option_sse_underlying_spot_price_sina(
+    symbol: str = "sh510300",
+) -> pd.DataFrame:
+    """
+    期权标的物的实时数据
+    :param symbol: sh510050 or sh510300
+    :type symbol: str
+    :return: 期权标的物的信息
+    :rtype: pandas.DataFrame
+    """
+    url = f"https://hq.sinajs.cn/list={symbol}"
+    headers = {
+        "Accept": "*/*",
+        "Accept-Encoding": "gzip, deflate",
+        "Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8",
+        "Cache-Control": "no-cache",
+        "Host": "hq.sinajs.cn",
+        "Pragma": "no-cache",
+        "Proxy-Connection": "keep-alive",
+        "Referer": "https://vip.stock.finance.sina.com.cn/",
+        "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) "
+        "Chrome/97.0.4692.71 Safari/537.36",
+    }
+    r = requests.get(url, headers=headers)
+    data_text = r.text
+    data_list = data_text[data_text.find('"') + 1 : data_text.rfind('"')].split(",")
+    field_list = [
+        "证券简称",
+        "今日开盘价",
+        "昨日收盘价",
+        "最近成交价",
+        "最高成交价",
+        "最低成交价",
+        "买入价",
+        "卖出价",
+        "成交数量",
+        "成交金额",
+        "买数量一",
+        "买价位一",
+        "买数量二",
+        "买价位二",
+        "买数量三",
+        "买价位三",
+        "买数量四",
+        "买价位四",
+        "买数量五",
+        "买价位五",
+        "卖数量一",
+        "卖价位一",
+        "卖数量二",
+        "卖价位二",
+        "卖数量三",
+        "卖价位三",
+        "卖数量四",
+        "卖价位四",
+        "卖数量五",
+        "卖价位五",
+        "行情日期",
+        "行情时间",
+        "停牌状态",
+    ]
+    data_df = pd.DataFrame(list(zip(field_list, data_list)), columns=["字段", "值"])
+    return data_df
+
+
+def option_sse_greeks_sina(symbol: str = "10003045") -> pd.DataFrame:
+    """
+    期权基本信息表
+    :param symbol: 合约代码
+    :type symbol: str
+    :return: 期权基本信息表
+    :rtype: pandas.DataFrame
+    """
+    url = f"https://hq.sinajs.cn/list=CON_SO_{symbol}"
+    headers = {
+        "Accept": "*/*",
+        "Accept-Encoding": "gzip, deflate",
+        "Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8",
+        "Cache-Control": "no-cache",
+        "Host": "hq.sinajs.cn",
+        "Pragma": "no-cache",
+        "Proxy-Connection": "keep-alive",
+        "Referer": "https://vip.stock.finance.sina.com.cn/",
+        "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) "
+        "Chrome/97.0.4692.71 Safari/537.36",
+    }
+    r = requests.get(url, headers=headers)
+    data_text = r.text
+    data_list = data_text[data_text.find('"') + 1 : data_text.rfind('"')].split(",")
+    field_list = [
+        "期权合约简称",
+        "成交量",
+        "Delta",
+        "Gamma",
+        "Theta",
+        "Vega",
+        "隐含波动率",
+        "最高价",
+        "最低价",
+        "交易代码",
+        "行权价",
+        "最新价",
+        "理论价值",
+    ]
+    data_df = pd.DataFrame(
+        list(zip(field_list, [data_list[0]] + data_list[4:])),
+        columns=["字段", "值"],
+    )
+    return data_df
+
+
+def option_sse_minute_sina(symbol: str = "10003720") -> pd.DataFrame:
+    """
+    指定期权品种在当前交易日的分钟数据, 只能获取当前交易日的数据, 不能获取历史分钟数据
+    https://stock.finance.sina.com.cn/option/quotes.html
+    :param symbol: 期权代码
+    :type symbol: str
+    :return: 指定期权的当前交易日的分钟数据
+    :rtype: pandas.DataFrame
+    """
+    url = "https://stock.finance.sina.com.cn/futures/api/openapi.php/StockOptionDaylineService.getOptionMinline"
+    params = {"symbol": f"CON_OP_{symbol}"}
+    headers = {
+        "accept": "*/*",
+        "accept-encoding": "gzip, deflate, br",
+        "accept-language": "zh-CN,zh;q=0.9,en;q=0.8",
+        "cache-control": "no-cache",
+        "pragma": "no-cache",
+        "referer": "https://stock.finance.sina.com.cn/option/quotes.html",
+        "sec-ch-ua": '" Not;A Brand";v="99", "Google Chrome";v="97", "Chromium";v="97"',
+        "sec-ch-ua-mobile": "?0",
+        "sec-ch-ua-platform": '"Windows"',
+        "sec-fetch-dest": "script",
+        "sec-fetch-mode": "no-cors",
+        "sec-fetch-site": "same-origin",
+        "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) "
+        "Chrome/97.0.4692.71 Safari/537.36",
+    }
+    r = requests.get(url, params=params, headers=headers)
+    data_json = r.json()
+    temp_df = data_json["result"]["data"]
+    data_df = pd.DataFrame(temp_df)
+    data_df = set_df_columns(
+        df=data_df, cols=["时间", "价格", "成交", "持仓", "均价", "日期"]
+    )
+    data_df = data_df[["日期", "时间", "价格", "成交", "持仓", "均价"]]
+    data_df["日期"] = pd.to_datetime(data_df["日期"], errors="coerce").dt.date
+    data_df["日期"] = data_df["日期"].ffill()
+    data_df["价格"] = pd.to_numeric(data_df["价格"], errors="coerce")
+    data_df["成交"] = pd.to_numeric(data_df["成交"], errors="coerce")
+    data_df["持仓"] = pd.to_numeric(data_df["持仓"], errors="coerce")
+    data_df["均价"] = pd.to_numeric(data_df["均价"], errors="coerce")
+    return data_df
+
+
+def option_sse_daily_sina(symbol: str = "10003889") -> pd.DataFrame:
+    """
+    指定期权的日频率数据
+    :param symbol: 期权代码
+    :type symbol: str
+    :return: 指定期权的所有日频率历史数据
+    :rtype: pandas.DataFrame
+    """
+    url = "https://stock.finance.sina.com.cn/futures/api/jsonp_v2.php//StockOptionDaylineService.getSymbolInfo"
+    params = {"symbol": f"CON_OP_{symbol}"}
+    headers = {
+        "accept": "*/*",
+        "accept-encoding": "gzip, deflate, br",
+        "accept-language": "zh-CN,zh;q=0.9,en;q=0.8",
+        "cache-control": "no-cache",
+        "pragma": "no-cache",
+        "referer": "https://stock.finance.sina.com.cn/option/quotes.html",
+        "sec-ch-ua": '" Not;A Brand";v="99", "Google Chrome";v="97", "Chromium";v="97"',
+        "sec-ch-ua-mobile": "?0",
+        "sec-ch-ua-platform": '"Windows"',
+        "sec-fetch-dest": "script",
+        "sec-fetch-mode": "no-cors",
+        "sec-fetch-site": "same-origin",
+        "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) "
+        "Chrome/97.0.4692.71 Safari/537.36",
+    }
+    r = requests.get(url, params=params, headers=headers)
+    data_text = r.text
+    data_json = json.loads(data_text[data_text.find("(") + 1 : data_text.rfind(")")])
+    temp_df = pd.DataFrame(data_json)
+    temp_df.columns = ["日期", "开盘", "最高", "最低", "收盘", "成交量"]
+    temp_df["日期"] = pd.to_datetime(temp_df["日期"], errors="coerce").dt.date
+    temp_df["开盘"] = pd.to_numeric(temp_df["开盘"], errors="coerce")
+    temp_df["最高"] = pd.to_numeric(temp_df["最高"], errors="coerce")
+    temp_df["最低"] = pd.to_numeric(temp_df["最低"], errors="coerce")
+    temp_df["收盘"] = pd.to_numeric(temp_df["收盘"], errors="coerce")
+    temp_df["成交量"] = pd.to_numeric(temp_df["成交量"], errors="coerce")
+    return temp_df
+
+
+def option_finance_minute_sina(symbol: str = "10002530") -> pd.DataFrame:
+    """
+    指定期权的分钟频率数据
+    https://stock.finance.sina.com.cn/option/quotes.html
+    :param symbol: 期权代码
+    :type symbol: str
+    :return: 指定期权的分钟频率数据
+    :rtype: pandas.DataFrame
+    """
+    url = "https://stock.finance.sina.com.cn/futures/api/openapi.php/StockOptionDaylineService.getFiveDayLine"
+    params = {
+        "symbol": f"CON_OP_{symbol}",
+    }
+    headers = {
+        "accept": "*/*",
+        "accept-encoding": "gzip, deflate, br",
+        "accept-language": "zh-CN,zh;q=0.9,en;q=0.8",
+        "cache-control": "no-cache",
+        "pragma": "no-cache",
+        "referer": "https://stock.finance.sina.com.cn/option/quotes.html",
+        "sec-ch-ua": '" Not;A Brand";v="99", "Google Chrome";v="97", "Chromium";v="97"',
+        "sec-ch-ua-mobile": "?0",
+        "sec-ch-ua-platform": '"Windows"',
+        "sec-fetch-dest": "script",
+        "sec-fetch-mode": "no-cors",
+        "sec-fetch-site": "same-origin",
+        "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) "
+        "Chrome/97.0.4692.71 Safari/537.36",
+    }
+    r = requests.get(url, params=params, headers=headers)
+    data_text = r.json()
+    temp_df = pd.DataFrame()
+    for item in data_text["result"]["data"]:
+        temp_df = pd.concat(objs=[temp_df, pd.DataFrame(item)], ignore_index=True)
+    temp_df.ffill(inplace=True)
+    temp_df.columns = ["time", "price", "volume", "_", "average_price", "date"]
+    temp_df = temp_df[["date", "time", "price", "average_price", "volume"]]
+    temp_df["price"] = pd.to_numeric(temp_df["price"], errors="coerce")
+    temp_df["average_price"] = pd.to_numeric(temp_df["average_price"], errors="coerce")
+    temp_df["volume"] = pd.to_numeric(temp_df["volume"], errors="coerce")
+    return temp_df
+
+
+@lru_cache()
+def __option_current_em() -> pd.DataFrame:
+    inner_option_current_em_df = option_current_em()
+    return inner_option_current_em_df
+
+
+def option_minute_em(symbol: str = "MO2404-P-4450") -> pd.DataFrame:
+    """
+    东方财富网-行情中心-期权市场-分时行情
+    https://wap.eastmoney.com/quote/stock/151.cu2404P61000.html
+    :param symbol: 期权代码; 通过调用 ak.option_current_em() 获取
+    :type symbol: str
+    :return: 指定期权的分钟频率数据
+    :rtype: pandas.DataFrame
+    """
+    inner_option_current_em_df = __option_current_em()
+    inner_option_current_em_df["标识"] = (
+        inner_option_current_em_df["市场标识"].astype(str)
+        + "."
+        + inner_option_current_em_df["代码"]
+    )
+    id_ = inner_option_current_em_df[inner_option_current_em_df["代码"] == symbol][
+        "标识"
+    ].values[0]
+    url = "https://push2.eastmoney.com/api/qt/stock/trends2/get"
+    params = {
+        "secid": id_,
+        "fields1": "f1,f2,f3,f4,f5,f6,f7,f8,f9,f10,f11,f12,f13,f14,f17",
+        "fields2": "f51,f53,f54,f55,f56,f57,f58",
+        "iscr": "0",
+        "iscca": "0",
+        "ut": "f057cbcbce2a86e2866ab8877db1d059",
+        "ndays": "1",
+        "cb": "quotepushdata1",
+    }
+    r = requests.get(url, params=params)
+    data_text = r.text
+    data_json = json.loads(data_text[data_text.find("(") + 1 : data_text.rfind(")")])
+    temp_df = pd.DataFrame([item.split(",") for item in data_json["data"]["trends"]])
+    temp_df.columns = ["time", "close", "high", "low", "volume", "amount", "-"]
+    temp_df = temp_df[["time", "close", "high", "low", "volume", "amount"]]
+    temp_df["close"] = pd.to_numeric(temp_df["close"], errors="coerce")
+    temp_df["high"] = pd.to_numeric(temp_df["high"], errors="coerce")
+    temp_df["low"] = pd.to_numeric(temp_df["low"], errors="coerce")
+    temp_df["volume"] = pd.to_numeric(temp_df["volume"], errors="coerce")
+    temp_df["amount"] = pd.to_numeric(temp_df["amount"], errors="coerce")
+    return temp_df
+
+
+if __name__ == "__main__":
+    option_cffex_sz50_list_sina_df = option_cffex_sz50_list_sina()
+    print(option_cffex_sz50_list_sina_df)
+
+    # 期权-中金所-沪深300指数
+    option_cffex_hs300_list_sina_df = option_cffex_hs300_list_sina()
+    print(option_cffex_hs300_list_sina_df)
+
+    option_cffex_zz1000_list_sina_df = option_cffex_zz1000_list_sina()
+    print(option_cffex_zz1000_list_sina_df)
+
+    option_cffex_sz50_spot_sina_df = option_cffex_sz50_spot_sina(symbol="ho2303")
+    print(option_cffex_sz50_spot_sina_df)
+
+    option_cffex_hs300_spot_sina_df = option_cffex_hs300_spot_sina(symbol="io2209")
+    print(option_cffex_hs300_spot_sina_df)
+
+    option_cffex_zz1000_spot_sina_df = option_cffex_zz1000_spot_sina(symbol="mo2209")
+    print(option_cffex_zz1000_spot_sina_df)
+
+    option_cffex_sz50_daily_sina_df = option_cffex_sz50_daily_sina(symbol="ho2303P2350")
+    print(option_cffex_sz50_daily_sina_df)
+
+    option_cffex_hs300_daily_sina_df = option_cffex_hs300_daily_sina(
+        symbol="io2202P4350"
+    )
+    print(option_cffex_hs300_daily_sina_df)
+
+    option_cffex_zz1000_daily_sina_df = option_cffex_zz1000_daily_sina(
+        symbol="mo2208P6200"
+    )
+    print(option_cffex_zz1000_daily_sina_df)
+
+    # 期权-上交所-50ETF
+    option_sse_list_sina_df = option_sse_list_sina(symbol="50ETF", exchange="null")
+    print(option_sse_list_sina_df)
+
+    option_sse_expire_day_sina_df = option_sse_expire_day_sina(
+        trade_date="202308", symbol="50ETF", exchange="null"
+    )
+    print(option_sse_expire_day_sina_df)
+
+    option_sse_codes_sina_df = option_sse_codes_sina(
+        symbol="看跌期权", trade_date="202209", underlying="510050"
+    )
+    print(option_sse_codes_sina_df)
+
+    option_sse_spot_price_sina_df = option_sse_spot_price_sina(symbol="10003686")
+    print(option_sse_spot_price_sina_df)
+
+    option_sse_underlying_spot_price_sina_df = option_sse_underlying_spot_price_sina(
+        symbol="sh510300"
+    )
+    print(option_sse_underlying_spot_price_sina_df)
+
+    option_sse_greeks_sina_df = option_sse_greeks_sina(symbol="10004023")
+    print(option_sse_greeks_sina_df)
+
+    option_sse_minute_sina_df = option_sse_minute_sina(symbol="10004023")
+    print(option_sse_minute_sina_df)
+
+    option_sse_daily_sina_df = option_sse_daily_sina(symbol="10004023")
+    print(option_sse_daily_sina_df)
+
+    option_finance_minute_sina_df = option_finance_minute_sina(symbol="10004023")
+    print(option_finance_minute_sina_df)
+
+    option_current_em_df = option_current_em()
+    print(option_current_em_df)
+
+    option_minute_em_df = option_minute_em(symbol="10008594")
+    print(option_minute_em_df)

backend/.venv/lib/python3.9/site-packages/akshare/option/option_lhb_em.py

@@ -0,0 +1,275 @@
+# -*- coding:utf-8 -*-
+# !/usr/bin/env python
+"""
+Date: 2024/7/30 17:00
+Desc: 东方财富网-数据中心-特色数据-期权龙虎榜单
+https://data.eastmoney.com/other/qqlhb.html
+"""
+
+import pandas as pd
+import requests
+
+
+def option_lhb_em(
+    symbol: str = "510050",
+    indicator: str = "期权交易情况-认沽交易量",
+    trade_date: str = "20220121",
+) -> pd.DataFrame:
+    """
+    东方财富网-数据中心-期货期权-期权龙虎榜单
+    https://data.eastmoney.com/other/qqlhb.html
+    :param symbol: 期权代码; choice of {"510050", "510300", "159919"}
+    :type symbol: str
+    :param indicator: 需要获取的指标; choice of {"期权交易情况-认沽交易量","期权持仓情况-认沽持仓量", "期权交易情况-认购交易量", "期权持仓情况-认购持仓量"}
+    :type indicator: str
+    :param trade_date: 交易日期
+    :type trade_date: str
+    :return: 期权龙虎榜单
+    :rtype: pandas.DataFrame
+    """
+    url = "https://datacenter-web.eastmoney.com/api/data/get"
+    params = {
+        "type": "RPT_IF_BILLBOARD_TD",
+        "sty": "ALL",
+        "filter": f"""(SECURITY_CODE="{symbol}")(TRADE_DATE='{
+            "-".join([trade_date[:4], trade_date[4:6], trade_date[6:]])
+        }')""",
+        "p": "1",
+        "pss": "200",
+        "source": "IFBILLBOARD",
+        "client": "WEB",
+        "ut": "b2884a393a59ad64002292a3e90d46a5",
+    }
+    r = requests.get(url, params=params)
+    data_json = r.json()
+    temp_df = pd.DataFrame(data_json["result"]["data"])
+    if indicator == "期权交易情况-认沽交易量":
+        temp_df = temp_df.iloc[:7, :]
+        temp_df.columns = [
+            "交易类型",
+            "交易日期",
+            "证券代码",
+            "标的名称",
+            "-",
+            "-",
+            "机构",
+            "名次",
+            "交易量",
+            "增减",
+            "净认沽量",
+            "占总交易量比例",
+            "-",
+            "-",
+            "-",
+            "-",
+            "-",
+            "-",
+            "-",
+            "-",
+            "-",
+            "-",
+            "-",
+            "-",
+            "-",
+        ]
+        temp_df = temp_df[
+            [
+                "交易类型",
+                "交易日期",
+                "证券代码",
+                "标的名称",
+                "名次",
+                "机构",
+                "交易量",
+                "增减",
+                "净认沽量",
+                "占总交易量比例",
+            ]
+        ]
+        temp_df["交易日期"] = pd.to_datetime(
+            temp_df["交易日期"], errors="coerce"
+        ).dt.date
+        temp_df["名次"] = pd.to_numeric(temp_df["名次"], errors="coerce")
+        temp_df["交易量"] = pd.to_numeric(temp_df["交易量"], errors="coerce")
+        temp_df["增减"] = pd.to_numeric(temp_df["增减"], errors="coerce")
+        temp_df["净认沽量"] = pd.to_numeric(temp_df["净认沽量"], errors="coerce")
+        temp_df["占总交易量比例"] = pd.to_numeric(
+            temp_df["占总交易量比例"], errors="coerce"
+        )
+        temp_df.reset_index(drop=True, inplace=True)
+        return temp_df
+    elif indicator == "期权持仓情况-认沽持仓量":
+        temp_df = temp_df.iloc[7:14, :]
+        temp_df.columns = [
+            "交易类型",
+            "交易日期",
+            "证券代码",
+            "标的名称",
+            "-",
+            "-",
+            "机构",
+            "名次",
+            "-",
+            "-",
+            "-",
+            "-",
+            "-",
+            "持仓量",
+            "增减",
+            "净持仓量",
+            "占总交易量比例",
+            "-",
+            "-",
+            "-",
+            "-",
+            "-",
+            "-",
+            "-",
+            "-",
+        ]
+        temp_df = temp_df[
+            [
+                "交易类型",
+                "交易日期",
+                "证券代码",
+                "标的名称",
+                "名次",
+                "机构",
+                "持仓量",
+                "增减",
+                "净持仓量",
+                "占总交易量比例",
+            ]
+        ]
+        temp_df["交易日期"] = pd.to_datetime(
+            temp_df["交易日期"], errors="coerce"
+        ).dt.date
+        temp_df["名次"] = pd.to_numeric(temp_df["名次"], errors="coerce")
+        temp_df["持仓量"] = pd.to_numeric(temp_df["持仓量"], errors="coerce")
+        temp_df["增减"] = pd.to_numeric(temp_df["增减"], errors="coerce")
+        temp_df["净持仓量"] = pd.to_numeric(temp_df["净持仓量"], errors="coerce")
+        temp_df["占总交易量比例"] = pd.to_numeric(
+            temp_df["占总交易量比例"], errors="coerce"
+        )
+        temp_df.reset_index(drop=True, inplace=True)
+        return temp_df
+    elif indicator == "期权交易情况-认购交易量":
+        temp_df = temp_df.iloc[14:21, :]
+        temp_df.columns = [
+            "交易类型",
+            "交易日期",
+            "证券代码",
+            "标的名称",
+            "-",
+            "-",
+            "机构",
+            "名次",
+            "-",
+            "-",
+            "-",
+            "-",
+            "-",
+            "-",
+            "-",
+            "-",
+            "-",
+            "交易量",
+            "增减",
+            "净交易量",
+            "占总交易量比例",
+            "-",
+            "-",
+            "-",
+            "-",
+        ]
+        temp_df = temp_df[
+            [
+                "交易类型",
+                "交易日期",
+                "证券代码",
+                "标的名称",
+                "名次",
+                "机构",
+                "交易量",
+                "增减",
+                "净交易量",
+                "占总交易量比例",
+            ]
+        ]
+        temp_df["交易日期"] = pd.to_datetime(
+            temp_df["交易日期"], errors="coerce"
+        ).dt.date
+        temp_df["名次"] = pd.to_numeric(temp_df["名次"], errors="coerce")
+        temp_df["交易量"] = pd.to_numeric(temp_df["交易量"], errors="coerce")
+        temp_df["增减"] = pd.to_numeric(temp_df["增减"], errors="coerce")
+        temp_df["净交易量"] = pd.to_numeric(temp_df["净交易量"], errors="coerce")
+        temp_df["占总交易量比例"] = pd.to_numeric(
+            temp_df["占总交易量比例"], errors="coerce"
+        )
+        temp_df.reset_index(drop=True, inplace=True)
+        return temp_df
+    elif indicator == "期权持仓情况-认购持仓量":
+        temp_df = temp_df.iloc[21:, :]
+        temp_df.rename(
+            columns={
+                "MEMBER_RANK": "名次",
+                "MEMBER_NAME_ABBR": "机构",
+                "BUY_POSITION": "持仓量",
+                "BUY_POSITION_CHANGE": "增减",
+                "NET_BUY_POSITION": "净持仓量",
+                "BUY_POSITION_RATIO": "占总交易量比例",
+                "TRADE_TYPE": "交易类型",
+                "TRADE_DATE": "交易日期",
+                "SECURITY_CODE": "证券代码",
+                "TARGET_NAME": "标的名称",
+            },
+            inplace=True,
+        )
+        temp_df = temp_df[
+            [
+                "交易类型",
+                "交易日期",
+                "证券代码",
+                "标的名称",
+                "名次",
+                "机构",
+                "持仓量",
+                "增减",
+                "净持仓量",
+                "占总交易量比例",
+            ]
+        ]
+        temp_df["交易日期"] = pd.to_datetime(
+            temp_df["交易日期"], errors="coerce"
+        ).dt.date
+        temp_df["名次"] = pd.to_numeric(temp_df["名次"], errors="coerce")
+        temp_df["持仓量"] = pd.to_numeric(temp_df["持仓量"], errors="coerce")
+        temp_df["增减"] = pd.to_numeric(temp_df["增减"], errors="coerce")
+        temp_df["净持仓量"] = pd.to_numeric(temp_df["净持仓量"], errors="coerce")
+        temp_df["占总交易量比例"] = pd.to_numeric(
+            temp_df["占总交易量比例"], errors="coerce"
+        )
+        temp_df.reset_index(drop=True, inplace=True)
+        return temp_df
+
+
+if __name__ == "__main__":
+    option_lhb_em_df = option_lhb_em(
+        symbol="510300", indicator="期权交易情况-认购交易量", trade_date="20220124"
+    )
+    print(option_lhb_em_df)
+
+    option_lhb_em_df = option_lhb_em(
+        symbol="510300", indicator="期权交易情况-认沽交易量", trade_date="20220124"
+    )
+    print(option_lhb_em_df)
+
+    option_lhb_em_df = option_lhb_em(
+        symbol="159919", indicator="期权持仓情况-认购持仓量", trade_date="20240712"
+    )
+    print(option_lhb_em_df)
+
+    option_lhb_em_df = option_lhb_em(
+        symbol="510300", indicator="期权持仓情况-认沽持仓量", trade_date="20220124"
+    )
+    print(option_lhb_em_df)

backend/.venv/lib/python3.9/site-packages/akshare/option/option_margin.py

@@ -0,0 +1,71 @@
+#!/usr/bin/env python
+# -*- coding:utf-8 -*-
+"""
+Date: 2025/6/16 18:00
+Desc: 唯爱期货-期权保证金
+https://www.iweiai.com/qihuo/yuanyou
+"""
+
+import requests
+import pandas as pd
+from io import StringIO
+
+from bs4 import BeautifulSoup
+from functools import lru_cache
+
+
+@lru_cache()
+def option_margin_symbol() -> pd.DataFrame:
+    """
+    获取商品期权品种代码和名称
+    :return: 商品期权品种代码和名称
+    :rtype: pandas.DataFrame
+    """
+    url = "https://www.iweiai.com/qiquan/yuanyou"
+    r = requests.get(url)
+    soup = BeautifulSoup(r.content, features="lxml")
+    symbol_text = [
+        item.get_text() for item in soup.find_all("a") if "qiquan" in item["href"]
+    ]
+    symbol_url = [
+        item["href"] for item in soup.find_all("a") if "qiquan" in item["href"]
+    ]
+    symbol_df = pd.DataFrame([symbol_text, symbol_url]).T
+    symbol_df.columns = ["symbol", "url"]
+    return symbol_df
+
+
+def option_margin(symbol: str = "原油期权") -> pd.DataFrame:
+    """
+    获取商品期权保证金
+    :param symbol: 商品期权品种名称, 如 "原油期权"，可以通过 ak.option_margin_symbol() 获取所有商品期权品种代码和名称
+    :type symbol: str
+    :return: 商品期权保证金
+    :rtype: pandas.DataFrame
+    """
+    option_margin_symbol_df = option_margin_symbol()
+    url = option_margin_symbol_df[option_margin_symbol_df["symbol"] == symbol][
+        "url"
+    ].values[0]
+    r = requests.get(url)
+    soup = BeautifulSoup(r.content, features="lxml")
+    updated_time = soup.find_all("small")[0].get_text().strip("最近更新：")
+    temp_df = pd.read_html(StringIO(r.text))[0]
+    temp_df["更新时间"] = updated_time
+
+    temp_df["结算价"] = pd.to_numeric(temp_df["结算价"], errors="coerce")
+    temp_df["交易乘数"] = pd.to_numeric(temp_df["交易乘数"], errors="coerce")
+    temp_df["买方权利金"] = pd.to_numeric(temp_df["买方权利金"], errors="coerce")
+    temp_df["卖方保证金"] = pd.to_numeric(temp_df["卖方保证金"], errors="coerce")
+    temp_df["开仓手续费"] = pd.to_numeric(temp_df["开仓手续费"], errors="coerce")
+    temp_df["平今手续费"] = pd.to_numeric(temp_df["平今手续费"], errors="coerce")
+    temp_df["平昨手续费"] = pd.to_numeric(temp_df["平昨手续费"], errors="coerce")
+    temp_df["手续费(开+平今)"] = pd.to_numeric(
+        temp_df["手续费(开+平今)"], errors="coerce"
+    )
+    return temp_df
+
+
+if __name__ == "__main__":
+    option_margin_df = option_margin(symbol="原油期权")
+    print(option_margin_df)

backend/.venv/lib/python3.9/site-packages/akshare/option/option_premium_analysis_em.py

@@ -0,0 +1,85 @@
+# -*- coding:utf-8 -*-
+# !/usr/bin/env python
+"""
+Date: 2025/3/11 17:00
+Desc: 东方财富网-数据中心-特色数据-期权折溢价
+https://data.eastmoney.com/other/premium.html
+"""
+
+import pandas as pd
+
+from akshare.utils.func import fetch_paginated_data
+
+
+def option_premium_analysis_em() -> pd.DataFrame:
+    """
+    东方财富网-数据中心-特色数据-期权折溢价
+    https://data.eastmoney.com/other/premium.html
+    :return: 期权折溢价
+    :rtype: pandas.DataFrame
+    """
+    url = "https://push2.eastmoney.com/api/qt/clist/get"
+    params = {
+        "fid": "f250",
+        "po": "1",
+        "pz": "100",
+        "pn": "1",
+        "np": "1",
+        "fltt": "2",
+        "invt": "2",
+        "ut": "b2884a393a59ad64002292a3e90d46a5",
+        "fields": "f1,f2,f3,f12,f13,f14,f161,f250,f330,f331,f332,f333,f334,f335,f337,f301,f152",
+        "fs": "m:10",
+    }
+    temp_df = fetch_paginated_data(url, params)
+    temp_df.columns = [
+        "-",
+        "-",
+        "最新价",
+        "涨跌幅",
+        "期权代码",
+        "-",
+        "期权名称",
+        "-",
+        "行权价",
+        "折溢价率",
+        "到期日",
+        "-",
+        "-",
+        "-",
+        "标的名称",
+        "标的最新价",
+        "标的涨跌幅",
+        "盈亏平衡价",
+    ]
+    temp_df = temp_df[
+        [
+            "期权代码",
+            "期权名称",
+            "最新价",
+            "涨跌幅",
+            "行权价",
+            "折溢价率",
+            "标的名称",
+            "标的最新价",
+            "标的涨跌幅",
+            "盈亏平衡价",
+            "到期日",
+        ]
+    ]
+    temp_df["最新价"] = pd.to_numeric(temp_df["最新价"], errors="coerce")
+    temp_df["涨跌幅"] = pd.to_numeric(temp_df["涨跌幅"], errors="coerce")
+    temp_df["行权价"] = pd.to_numeric(temp_df["行权价"], errors="coerce")
+    temp_df["折溢价率"] = pd.to_numeric(temp_df["折溢价率"], errors="coerce")
+    temp_df["标的最新价"] = pd.to_numeric(temp_df["标的最新价"], errors="coerce")
+    temp_df["标的涨跌幅"] = pd.to_numeric(temp_df["标的涨跌幅"], errors="coerce")
+    temp_df["盈亏平衡价"] = pd.to_numeric(temp_df["盈亏平衡价"], errors="coerce")
+    temp_df["到期日"] = pd.to_datetime(
+        temp_df["到期日"].astype(str), errors="coerce"
+    ).dt.date
+    return temp_df
+
+
+if __name__ == "__main__":
+    option_premium_analysis_em_df = option_premium_analysis_em()
+    print(option_premium_analysis_em_df)

backend/.venv/lib/python3.9/site-packages/akshare/option/option_risk_analysis_em.py

@@ -0,0 +1,87 @@
+# -*- coding:utf-8 -*-
+# !/usr/bin/env python
+"""
+Date: 2025/3/13 21:50
+Desc: 东方财富网-数据中心-特色数据-期权风险分析
+https://data.eastmoney.com/other/riskanal.html
+"""
+
+import pandas as pd
+
+from akshare.utils.func import fetch_paginated_data
+
+
+def option_risk_analysis_em() -> pd.DataFrame:
+    """
+    东方财富网-数据中心-特色数据-期权风险分析
+    https://data.eastmoney.com/other/riskanal.html
+    :return: 期权风险分析
+    :rtype: pandas.DataFrame
+    """
+    url = "https://push2.eastmoney.com/api/qt/clist/get"
+    params = {
+        "fid": "f12",
+        "po": "1",
+        "pz": "100",
+        "pn": "1",
+        "np": "1",
+        "fltt": "2",
+        "invt": "2",
+        "ut": "b2884a393a59ad64002292a3e90d46a5",
+        "fields": "f1,f2,f3,f12,f13,f14,f302,f303,f325,f326,f327,f329,f328,f301,f152,f154",
+        "fs": "m:10",
+    }
+    temp_df = fetch_paginated_data(url, params)
+    temp_df.columns = [
+        "-",
+        "-",
+        "最新价",
+        "涨跌幅",
+        "期权代码",
+        "-",
+        "期权名称",
+        "-",
+        "-",
+        "到期日",
+        "杠杆比率",
+        "实际杠杆比率",
+        "Delta",
+        "Gamma",
+        "Vega",
+        "Theta",
+        "Rho",
+    ]
+    temp_df = temp_df[
+        [
+            "期权代码",
+            "期权名称",
+            "最新价",
+            "涨跌幅",
+            "杠杆比率",
+            "实际杠杆比率",
+            "Delta",
+            "Gamma",
+            "Vega",
+            "Rho",
+            "Theta",
+            "到期日",
+        ]
+    ]
+    temp_df["最新价"] = pd.to_numeric(temp_df["最新价"], errors="coerce")
+    temp_df["涨跌幅"] = pd.to_numeric(temp_df["涨跌幅"], errors="coerce")
+    temp_df["杠杆比率"] = pd.to_numeric(temp_df["杠杆比率"], errors="coerce")
+    temp_df["实际杠杆比率"] = pd.to_numeric(temp_df["实际杠杆比率"], errors="coerce")
+    temp_df["Delta"] = pd.to_numeric(temp_df["Delta"], errors="coerce")
+    temp_df["Gamma"] = pd.to_numeric(temp_df["Gamma"], errors="coerce")
+    temp_df["Vega"] = pd.to_numeric(temp_df["Vega"], errors="coerce")
+    temp_df["Rho"] = pd.to_numeric(temp_df["Rho"], errors="coerce")
+    temp_df["Theta"] = pd.to_numeric(temp_df["Theta"], errors="coerce")
+    temp_df["到期日"] = pd.to_datetime(
+        temp_df["到期日"], format="%Y%m%d", errors="coerce"
+    ).dt.date
+    return temp_df
+
+
+if __name__ == "__main__":
+    option_risk_analysis_em_df = option_risk_analysis_em()
+    print(option_risk_analysis_em_df)

backend/.venv/lib/python3.9/site-packages/akshare/option/option_risk_indicator_sse.py

@@ -0,0 +1,73 @@
+# -*- coding:utf-8 -*-
+# !/usr/bin/env python
+"""
+Date: 2025/9/8 16:20
+Desc: 上海证券交易所-产品-股票期权-期权风险指标
+"""
+
+import pandas as pd
+import requests
+
+
+def option_risk_indicator_sse(date: str = "20240626") -> pd.DataFrame:
+    """
+    上海证券交易所-产品-股票期权-期权风险指标
+    http://www.sse.com.cn/assortment/options/risk/
+    :param date: 日期; 20150209 开始
+    :type date: str
+    :return: 期权风险指标
+    :rtype: pandas.DataFrame
+    """
+    url = "http://query.sse.com.cn/commonQuery.do"
+    params = {
+        "isPagination": "false",
+        "trade_date": date,
+        "sqlId": "SSE_ZQPZ_YSP_GGQQZSXT_YSHQ_QQFXZB_DATE_L",
+        "contractSymbol": "",
+    }
+    headers = {
+        "Accept": "*/*",
+        "Accept-Encoding": "gzip, deflate",
+        "Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8",
+        "Cache-Control": "no-cache",
+        "Connection": "keep-alive",
+        "Host": "query.sse.com.cn",
+        "Pragma": "no-cache",
+        "Referer": "http://www.sse.com.cn/",
+        "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) "
+        "Chrome/101.0.4951.67 Safari/537.36",
+    }
+    r = requests.get(url, params=params, headers=headers)
+    data_json = r.json()
+    temp_df = pd.DataFrame(data_json["result"])
+    temp_df = temp_df[
+        [
+            "TRADE_DATE",
+            "SECURITY_ID",
+            "CONTRACT_ID",
+            "CONTRACT_SYMBOL",
+            "DELTA_VALUE",
+            "THETA_VALUE",
+            "GAMMA_VALUE",
+            "VEGA_VALUE",
+            "RHO_VALUE",
+            "IMPLC_VOLATLTY",
+        ]
+    ]
+    temp_df["TRADE_DATE"] = pd.to_datetime(
+        temp_df["TRADE_DATE"], errors="coerce"
+    ).dt.date
+    temp_df["DELTA_VALUE"] = pd.to_numeric(temp_df["DELTA_VALUE"], errors="coerce")
+    temp_df["THETA_VALUE"] = pd.to_numeric(temp_df["THETA_VALUE"], errors="coerce")
+    temp_df["GAMMA_VALUE"] = pd.to_numeric(temp_df["GAMMA_VALUE"], errors="coerce")
+    temp_df["VEGA_VALUE"] = pd.to_numeric(temp_df["VEGA_VALUE"], errors="coerce")
+    temp_df["RHO_VALUE"] = pd.to_numeric(temp_df["RHO_VALUE"], errors="coerce")
+    temp_df["IMPLC_VOLATLTY"] = pd.to_numeric(
+        temp_df["IMPLC_VOLATLTY"], errors="coerce"
+    )
+    return temp_df
+
+
+if __name__ == "__main__":
+    option_risk_indicator_sse_df = option_risk_indicator_sse(date="20240626")
+    print(option_risk_indicator_sse_df)

backend/.venv/lib/python3.9/site-packages/akshare/option/option_value_analysis_em.py

@@ -0,0 +1,89 @@
+# -*- coding:utf-8 -*-
+# !/usr/bin/env python
+"""
+Date: 2025/3/11 17:00
+Desc: 东方财富网-数据中心-特色数据-期权价值分析
+https://data.eastmoney.com/other/valueAnal.html
+"""
+
+import pandas as pd
+
+from akshare.utils.func import fetch_paginated_data
+
+
+def option_value_analysis_em() -> pd.DataFrame:
+    """
+    东方财富网-数据中心-特色数据-期权价值分析
+    https://data.eastmoney.com/other/valueAnal.html
+    :return: 期权价值分析
+    :rtype: pandas.DataFrame
+    """
+    url = "https://push2.eastmoney.com/api/qt/clist/get"
+    params = {
+        "fid": "f301",
+        "po": "1",
+        "pz": "100",
+        "pn": "1",
+        "np": "1",
+        "fltt": "2",
+        "invt": "2",
+        "ut": "b2884a393a59ad64002292a3e90d46a5",
+        "fields": "f1,f2,f3,f12,f13,f14,f298,f299,f249,f300,f330,f331,f332,f333,f334,f335,f336,f301,f152",
+        "fs": "m:10",
+    }
+    temp_df = fetch_paginated_data(url, params)
+    temp_df.columns = [
+        "-",
+        "-",
+        "最新价",
+        "-",
+        "期权代码",
+        "-",
+        "期权名称",
+        "-",
+        "隐含波动率",
+        "时间价值",
+        "内在价值",
+        "理论价格",
+        "到期日",
+        "-",
+        "-",
+        "-",
+        "标的名称",
+        "标的最新价",
+        "-",
+        "标的近一年波动率",
+    ]
+    temp_df = temp_df[
+        [
+            "期权代码",
+            "期权名称",
+            "最新价",
+            "时间价值",
+            "内在价值",
+            "隐含波动率",
+            "理论价格",
+            "标的名称",
+            "标的最新价",
+            "标的近一年波动率",
+            "到期日",
+        ]
+    ]
+    temp_df["最新价"] = pd.to_numeric(temp_df["最新价"], errors="coerce")
+    temp_df["时间价值"] = pd.to_numeric(temp_df["时间价值"], errors="coerce")
+    temp_df["内在价值"] = pd.to_numeric(temp_df["内在价值"], errors="coerce")
+    temp_df["隐含波动率"] = pd.to_numeric(temp_df["隐含波动率"], errors="coerce")
+    temp_df["理论价格"] = pd.to_numeric(temp_df["理论价格"], errors="coerce")
+    temp_df["标的最新价"] = pd.to_numeric(temp_df["标的最新价"], errors="coerce")
+    temp_df["标的近一年波动率"] = pd.to_numeric(
+        temp_df["标的近一年波动率"], errors="coerce"
+    )
+    temp_df["到期日"] = pd.to_datetime(
+        temp_df["到期日"].astype(str), errors="coerce"
+    ).dt.date
+    return temp_df
+
+
+if __name__ == "__main__":
+    option_value_analysis_em_df = option_value_analysis_em()
+    print(option_value_analysis_em_df)