知微
fa45d8aa5f
- health_checklist.json: 192.168.1.122→node122
- ocr_client.py: docstring IP→node122
- docs/market-data-requirements.md: IP→node122
- 所有API调用通过ProxyHandler({})绕过系统代理
Privoxy对node122:18003返回500，直连正常
673 lines
26 KiB
Python
673 lines
26 KiB
Python
#!/usr/bin/env python
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# -*- coding:utf-8 -*-
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"""
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Date: 2025/10/17 21:00
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Desc: 商品期权数据
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说明：
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(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
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(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
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(3) 成交量、持仓量：手（按双边计算）
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(4) 成交额：万元（按双边计算）
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(5) 涨跌＝收盘价－前结算价
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(6) 涨跌1=今结算价-前结算价
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(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
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(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
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"""
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import datetime
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import warnings
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from io import StringIO
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import pandas as pd
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import requests
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from akshare.option.cons import (
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get_calendar,
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convert_date,
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CZCE_DAILY_OPTION_URL_3,
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SHFE_HEADERS,
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)
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def option_hist_dce(
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symbol: str = "聚丙烯期权", trade_date: str = "20251016"
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) -> pd.DataFrame:
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"""
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大连商品交易所-期权-日频行情数据
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http://www.dce.com.cn/
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:param trade_date: 交易日
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:type trade_date: str
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: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",
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"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",
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"鸡蛋期权", "玉米淀粉期权", "生猪期权", "原木期权"}
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:type symbol: str
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:return: 日频行情数据
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:rtype: pandas.DataFrame
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"""
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option_code_map = {
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"玉米期权": "c",
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"豆粕期权": "m",
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"铁矿石期权": "i",
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"液化石油气期权": "pg",
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"聚乙烯期权": "l",
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"聚氯乙烯期权": "v",
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"聚丙烯期权": "pp",
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"棕榈油期权": "p",
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"黄大豆1号期权": "a",
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"黄大豆2号期权": "b",
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"豆油期权": "y",
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"乙二醇期权": "eg",
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"苯乙烯期权": "eb",
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"鸡蛋期权": "jd",
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"玉米淀粉期权": "cs",
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"生猪期权": "lh",
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"原木期权": "lg",
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}
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calendar = get_calendar()
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day = convert_date(trade_date) if trade_date is not None else datetime.date.today()
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if day.strftime("%Y%m%d") not in calendar:
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warnings.warn("%s非交易日" % day.strftime("%Y%m%d"))
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return pd.DataFrame()
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url = "http://www.dce.com.cn/dcereport/publicweb/dailystat/dayQuotes"
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payload = {
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"contractId": "",
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"lang": "zh",
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"optionSeries": "",
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"statisticsType": 0,
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"tradeDate": f"{trade_date}",
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"tradeType": "2",
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"varietyId": f"{option_code_map[symbol]}",
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}
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r = requests.post(url, json=payload)
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data_json = r.json()
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temp_df = pd.DataFrame(data_json["data"])
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temp_df.rename(
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columns={
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"variety": "品种名称",
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"contractId": "合约",
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"open": "开盘价",
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"high": "最高价",
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"low": "最低价",
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"close": "收盘价",
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"lastClear": "前结算价",
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"clearPrice": "结算价",
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"diff": "涨跌",
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"diff1": "涨跌1",
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"delta": "Delta",
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"volumn": "成交量", # 注意：你写的是“volumn”，可能是拼写错误，应为“volume”
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"openInterest": "持仓量",
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"diffI": "持仓量变化",
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"turnover": "成交额",
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"matchQtySum": "行权量",
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"impliedVolatility": "隐含波动率(%)",
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},
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inplace=True,
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)
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temp_df = temp_df[
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[
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"品种名称",
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"合约",
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"开盘价",
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"最高价",
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"最低价",
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"收盘价",
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"前结算价",
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"结算价",
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"涨跌",
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"涨跌1",
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"Delta",
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"隐含波动率(%)",
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"成交量",
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"持仓量",
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"持仓量变化",
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"成交额",
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"行权量",
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]
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]
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comma_cols = [
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"开盘价",
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"最高价",
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"最低价",
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"收盘价",
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"前结算价",
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"结算价",
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"涨跌",
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"涨跌1",
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"Delta",
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"隐含波动率(%)",
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"成交额",
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] # 需要处理的列
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for col in comma_cols:
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temp_df[col] = (
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temp_df[col]
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.astype(str)
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.str.replace(",", "")
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.pipe(pd.to_numeric, errors="coerce")
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)
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return temp_df
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def __option_czce_daily_convert_numeric_columns(df):
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# 定义要处理的列
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columns_to_convert = [
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"昨结算",
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"今开盘",
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"最高价",
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"最低价",
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"今收盘",
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"今结算",
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"涨跌1",
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"涨跌2",
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"成交量(手)",
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"持仓量",
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"增减量",
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"成交额(万元)",
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"DELTA",
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"隐含波动率",
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"行权量",
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]
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# 转换函数：去除逗号并转换为float
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def convert_to_float(x):
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try:
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return float(str(x).replace(",", ""))
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except: # noqa: E722
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return x
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# 创建 DataFrame 的副本以避免 SettingWithCopyWarning
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df_copy = df.copy()
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df_copy.columns = [item.strip() for item in df_copy]
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# 应用转换
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for col in columns_to_convert:
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df_copy[col] = df_copy[col].apply(convert_to_float)
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return df_copy
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def option_hist_czce(
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symbol: str = "白糖期权", trade_date: str = "20191017"
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) -> pd.DataFrame:
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"""
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郑州商品交易所-期权-日频行情数据
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http://www.czce.com.cn/cn/sspz/dejbqhqq/H770227index_1.htm#tabs-2
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:param trade_date: 交易日
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:type trade_date: str
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: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",
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"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",
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"玻璃期权", "瓶片期权", "丙烯期货"}
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:type symbol: str
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:return: 日频行情数据
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:rtype: pandas.DataFrame
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"""
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calendar = get_calendar()
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day = convert_date(trade_date) if trade_date is not None else datetime.date.today()
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if day.strftime("%Y%m%d") not in calendar:
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warnings.warn("{}非交易日".format(day.strftime("%Y%m%d")))
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return pd.DataFrame()
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if day > datetime.date(year=2010, month=8, day=24):
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url = CZCE_DAILY_OPTION_URL_3.format(day.strftime("%Y"), day.strftime("%Y%m%d"))
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try:
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r = requests.get(url)
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f = StringIO(r.text)
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table_df = pd.read_table(f, encoding="utf-8", skiprows=1, sep="|")
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table_df.columns = [
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"合约代码",
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"昨结算",
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"今开盘",
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"最高价",
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"最低价",
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"今收盘",
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"今结算",
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"涨跌1",
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"涨跌2",
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"成交量(手)",
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"持仓量",
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"增减量",
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"成交额(万元)",
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"DELTA",
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"隐含波动率",
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"行权量",
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]
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if symbol == "白糖期权":
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temp_df = table_df[table_df.iloc[:, 0].str.contains("SR")]
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temp_df.reset_index(inplace=True, drop=True)
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temp_df = temp_df.iloc[:-1, :].copy()
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new_df = __option_czce_daily_convert_numeric_columns(temp_df)
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return new_df
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elif symbol == "棉花期权":
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temp_df = table_df[table_df.iloc[:, 0].str.contains("CF")]
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temp_df.reset_index(inplace=True, drop=True)
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temp_df = temp_df.iloc[:-1, :].copy()
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new_df = __option_czce_daily_convert_numeric_columns(temp_df)
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return new_df
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elif symbol == "甲醇期权":
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temp_df = table_df[table_df.iloc[:, 0].str.contains("MA")]
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temp_df.reset_index(inplace=True, drop=True)
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temp_df = temp_df.iloc[:-1, :].copy()
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new_df = __option_czce_daily_convert_numeric_columns(temp_df)
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return new_df
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elif symbol == "PTA期权":
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temp_df = table_df[table_df.iloc[:, 0].str.contains("TA")]
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temp_df.reset_index(inplace=True, drop=True)
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temp_df = temp_df.iloc[:-1, :].copy()
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new_df = __option_czce_daily_convert_numeric_columns(temp_df)
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return new_df
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elif symbol == "动力煤期权":
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temp_df = table_df[table_df.iloc[:, 0].str.contains("ZC")]
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temp_df.reset_index(inplace=True, drop=True)
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temp_df = temp_df.iloc[:-1, :].copy()
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new_df = __option_czce_daily_convert_numeric_columns(temp_df)
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return new_df
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elif symbol == "菜籽粕期权":
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temp_df = table_df[table_df.iloc[:, 0].str.contains("RM")]
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temp_df.reset_index(inplace=True, drop=True)
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temp_df = temp_df.iloc[:-1, :].copy()
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new_df = __option_czce_daily_convert_numeric_columns(temp_df)
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return new_df
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elif symbol == "菜籽油期权":
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temp_df = table_df[table_df.iloc[:, 0].str.contains("OI")]
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temp_df.reset_index(inplace=True, drop=True)
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temp_df = temp_df.iloc[:-1, :].copy()
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new_df = __option_czce_daily_convert_numeric_columns(temp_df)
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return new_df
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elif symbol == "花生期权":
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temp_df = table_df[table_df.iloc[:, 0].str.contains("PK")]
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temp_df.reset_index(inplace=True, drop=True)
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temp_df = temp_df.iloc[:-1, :].copy()
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new_df = __option_czce_daily_convert_numeric_columns(temp_df)
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return new_df
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elif symbol == "短纤期权":
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temp_df = table_df[table_df.iloc[:, 0].str.contains("PF")]
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temp_df.reset_index(inplace=True, drop=True)
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temp_df = temp_df.iloc[:-1, :].copy()
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new_df = __option_czce_daily_convert_numeric_columns(temp_df)
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return new_df
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elif symbol == "对二甲苯期权":
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temp_df = table_df[table_df.iloc[:, 0].str.contains("PX")]
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temp_df.reset_index(inplace=True, drop=True)
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temp_df = temp_df.iloc[:-1, :].copy()
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new_df = __option_czce_daily_convert_numeric_columns(temp_df)
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return new_df
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elif symbol == "烧碱期权":
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temp_df = table_df[table_df.iloc[:, 0].str.contains("SH")]
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temp_df.reset_index(inplace=True, drop=True)
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temp_df = temp_df.iloc[:-1, :].copy()
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new_df = __option_czce_daily_convert_numeric_columns(temp_df)
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return new_df
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elif symbol == "纯碱期权":
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temp_df = table_df[table_df.iloc[:, 0].str.contains("SA")]
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temp_df.reset_index(inplace=True, drop=True)
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temp_df = temp_df.iloc[:-1, :].copy()
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new_df = __option_czce_daily_convert_numeric_columns(temp_df)
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return new_df
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elif symbol == "短纤期权":
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temp_df = table_df[table_df.iloc[:, 0].str.contains("PF")]
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temp_df.reset_index(inplace=True, drop=True)
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temp_df = temp_df.iloc[:-1, :].copy()
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new_df = __option_czce_daily_convert_numeric_columns(temp_df)
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return new_df
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elif symbol == "锰硅期权":
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temp_df = table_df[table_df.iloc[:, 0].str.contains("SM")]
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temp_df.reset_index(inplace=True, drop=True)
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temp_df = temp_df.iloc[:-1, :].copy()
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new_df = __option_czce_daily_convert_numeric_columns(temp_df)
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return new_df
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elif symbol == "硅铁期权":
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temp_df = table_df[table_df.iloc[:, 0].str.contains("SF")]
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temp_df.reset_index(inplace=True, drop=True)
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temp_df = temp_df.iloc[:-1, :].copy()
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new_df = __option_czce_daily_convert_numeric_columns(temp_df)
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return new_df
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elif symbol == "尿素期权":
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temp_df = table_df[table_df.iloc[:, 0].str.contains("UR")]
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temp_df.reset_index(inplace=True, drop=True)
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temp_df = temp_df.iloc[:-1, :].copy()
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new_df = __option_czce_daily_convert_numeric_columns(temp_df)
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return new_df
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elif symbol == "苹果期权":
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temp_df = table_df[table_df.iloc[:, 0].str.contains("AP")]
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temp_df.reset_index(inplace=True, drop=True)
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temp_df = temp_df.iloc[:-1, :].copy()
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new_df = __option_czce_daily_convert_numeric_columns(temp_df)
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return new_df
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elif symbol == "红枣期权":
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temp_df = table_df[table_df.iloc[:, 0].str.contains("CJ")]
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temp_df.reset_index(inplace=True, drop=True)
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temp_df = temp_df.iloc[:-1, :].copy()
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new_df = __option_czce_daily_convert_numeric_columns(temp_df)
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return new_df
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elif symbol == "玻璃期权":
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temp_df = table_df[table_df.iloc[:, 0].str.contains("FG")]
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temp_df.reset_index(inplace=True, drop=True)
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temp_df = temp_df.iloc[:-1, :].copy()
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new_df = __option_czce_daily_convert_numeric_columns(temp_df)
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return new_df
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elif symbol == "瓶片期权":
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temp_df = table_df[table_df.iloc[:, 0].str.contains("PR")]
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temp_df.reset_index(inplace=True, drop=True)
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temp_df = temp_df.iloc[:-1, :].copy()
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new_df = __option_czce_daily_convert_numeric_columns(temp_df)
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return new_df
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elif symbol == "丙烯期权":
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temp_df = table_df[table_df.iloc[:, 0].str.contains("PL")]
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temp_df.reset_index(inplace=True, drop=True)
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temp_df = temp_df.iloc[:-1, :].copy()
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new_df = __option_czce_daily_convert_numeric_columns(temp_df)
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return new_df
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else:
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return pd.DataFrame()
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except: # noqa: E722
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return pd.DataFrame()
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else:
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return pd.DataFrame()
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def option_hist_shfe(
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symbol: str = "铝期权", trade_date: str = "20250418"
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) -> pd.DataFrame:
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"""
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上海期货交易所-期权-日频行情数据
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https://www.shfe.com.cn/reports/tradedata/dailyandweeklydata/
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:param trade_date: 交易日
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:type trade_date: str
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: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',
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'黄金期权', '白银期权', '丁二烯橡胶期权', '天胶期权'}
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:type symbol: str
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:return: 日频行情数据
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:rtype: pandas.DataFrame
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"""
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calendar = get_calendar()
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day = convert_date(trade_date) if trade_date is not None else datetime.date.today()
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if day.strftime("%Y%m%d") not in calendar:
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warnings.warn("%s非交易日" % day.strftime("%Y%m%d"))
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return pd.DataFrame()
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if day > datetime.date(year=2010, month=8, day=24):
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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)
|