#!/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)