def option_dce_daily(symbol: str = "聚乙烯期权",
trade_date: str = "20210728") -> Tuple[Any, Any]:
"""
大连商品交易所-期权-日频行情数据
: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"}
: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
url = DCE_DAILY_OPTION_URL
payload = {
"dayQuotes.variety": "all",
"dayQuotes.trade_type": "1",
"year": str(day.year),
"month": str(day.month - 1),
"day": str(day.day),
"exportFlag": "excel",
}
res = requests.post(url, data=payload)
table_df = pd.read_excel(BytesIO(res.content), header=0)
another_df = table_df.iloc[
table_df[table_df.iloc[:,
0].str.contains("合约")].iloc[-1].name:, [0, 1]]
another_df.reset_index(inplace=True, drop=True)
another_df.iloc[0] = another_df.iat[0, 0].split("\t")
another_df.columns = another_df.iloc[0]
another_df = another_df.iloc[1:, :]
if symbol == "豆粕期权":
return table_df[table_df["商品名称"] == "豆粕"], another_df[
another_df.iloc[:, 0].str.contains("m")]
elif symbol == "玉米期权":
return table_df[table_df["商品名称"] == "玉米"], another_df[
another_df.iloc[:, 0].str.contains("c")]
elif symbol == "铁矿石期权":
return table_df[table_df["商品名称"] == "铁矿石"], another_df[
another_df.iloc[:, 0].str.contains("i")]
elif symbol == "液化石油气期权":
return table_df[table_df["商品名称"] == "液化石油气"], another_df[
another_df.iloc[:, 0].str.contains("pg")]
elif symbol == "聚乙烯期权":
return table_df[table_df["商品名称"] == "聚乙烯"], another_df[
another_df.iloc[:, 0].str.contains("l")]
elif symbol == "聚氯乙烯期权":
return table_df[table_df["商品名称"] == "聚氯乙烯"], another_df[
another_df.iloc[:, 0].str.contains("v")]
elif symbol == "聚丙烯期权":
return table_df[table_df["商品名称"] == "聚丙烯"], another_df[
another_df.iloc[:, 0].str.contains("pp")]
elif symbol == "棕榈油期权":
return table_df[table_df["商品名称"] == "棕榈油"], another_df[
another_df.iloc[:, 0].str.contains(r'^p\d')]
def option_czce_daily(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
: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", "菜籽粕期权", "动力煤期权"}
: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("{}非交易日".format(day.strftime("%Y%m%d")))
return
if day > datetime.date(2010, 8, 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="|")
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)
return temp_df.iloc[:-1, :]
elif symbol == "PTA期权":
temp_df = table_df[table_df.iloc[:, 0].str.contains("TA")]
temp_df.reset_index(inplace=True, drop=True)
return temp_df.iloc[:-1, :]
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)
return temp_df.iloc[:-1, :]
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)
return temp_df.iloc[:-1, :]
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)
return temp_df.iloc[:-1, :]
else:
temp_df = table_df[table_df.iloc[:, 0].str.contains("CF")]
temp_df.reset_index(inplace=True, drop=True)
return temp_df.iloc[:-1, :]
except:
return
def get_dce_option_daily(trade_date="20200817", symbol="聚丙烯期权"):
"""
大连商品交易所-期权-日频行情数据
:param trade_date: str format："20191017"
:param symbol: str "玉米期权" or "豆粕期权" or "铁矿石期权", or "液化石油气期权" or "聚乙烯期权" or "聚氯乙烯期权" or "聚丙烯期权"
:return: pandas.DataFrame
part-1:
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 \
0 玉米 c2001-C-1680 168.5 168.5 168.5 168.5 168.0 167.5 0.5 -0.5
1 玉米 c2001-C-1700 0 0.0 0.0 148.0 148.0 148.0 0.0 0.0
2 玉米 c2001-C-1720 0 0.0 0.0 129.0 128.0 129.0 1.0 1.0
3 玉米 c2001-C-1740 115 115.0 115.0 115.0 108.0 111.0 7.0 3.0
4 玉米 c2001-C-1760 89 95.5 89.0 95.5 89.0 93.5 6.5 4.5
.. ... ... ... ... ... ... ... ... ... ...
239 玉米 c2009-P-2040 0 0.0 0.0 91.0 88.5 91.0 2.5 2.5
240 玉米 c2009-P-2060 0 0.0 0.0 106.0 104.0 106.0 2.0 2.0
241 玉米 c2009-P-2080 0 0.0 0.0 121.5 120.5 121.5 1.0 1.0
242 玉米 c2009-P-2100 0 0.0 0.0 138.5 137.5 138.5 1.0 1.0
243 玉米 c2009-P-2120 0 0.0 0.0 155.5 155.5 155.5 0.0 0.0
Delta 成交量 持仓量 持仓量变化 成交额 行权量
0 0.98 2 236 0 0.34 0.0
1 0.96 0 236 0 0 0.0
2 0.94 0 210 0 0 0.0
3 0.90 20 1,040 0 2.3 0.0
4 0.85 12 680 0 1.11 0.0
.. ... .. ... ... ... ...
239 -0.70 0 30 0 0 0.0
240 -0.75 0 50 0 0 0.0
241 -0.80 0 20 0 0 0.0
242 -0.84 0 10 0 0 0.0
243 -0.88 0 0 0 0 0.0
part-2:
0 合约系列 隐含波动率(%)
1 c2001 12.95
2 c2003 8.74
3 c2005 8.75
4 c2007 7.7
5 c2009 6.85
"""
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 None
url = DCE_DAILY_OPTION_URL
payload = {
"dayQuotes.variety": "all",
"dayQuotes.trade_type": "1",
"year": str(day.year),
"month": str(day.month - 1),
"day": str(day.day),
"exportFlag": "excel",
}
res = requests.post(url, data=payload)
table_df = pd.read_excel(BytesIO(res.content), header=0)
another_df = table_df.iloc[
table_df[table_df.iloc[:,
0].str.contains("合约")].iloc[-1].name:, [0, 1]]
another_df.reset_index(inplace=True, drop=True)
another_df.iloc[0] = another_df.iat[0, 0].split("\t")
another_df.columns = another_df.iloc[0]
another_df = another_df.iloc[1:, :]
if symbol == "豆粕期权":
return table_df[table_df["商品名称"] == "豆粕"], another_df[
another_df.iloc[:, 0].str.contains("m")]
elif symbol == "玉米期权":
return table_df[table_df["商品名称"] == "玉米"], another_df[
another_df.iloc[:, 0].str.contains("c")]
elif symbol == "铁矿石期权":
return table_df[table_df["商品名称"] == "铁矿石"], another_df[
another_df.iloc[:, 0].str.contains("i")]
elif symbol == "液化石油气期权":
return table_df[table_df["商品名称"] == "液化石油气"], another_df[
another_df.iloc[:, 0].str.contains("pg")]
elif symbol == "聚乙烯期权":
return table_df[table_df["商品名称"] == "聚乙烯"], another_df[
another_df.iloc[:, 0].str.contains("i")]
elif symbol == "聚氯乙烯期权":
return table_df[table_df["商品名称"] == "聚氯乙烯"], another_df[
another_df.iloc[:, 0].str.contains("v")]
elif symbol == "聚丙烯期权":
return table_df[table_df["商品名称"] == "聚丙烯"], another_df[
another_df.iloc[:, 0].str.contains("pp")]
def get_shfe_option_daily(trade_date="20200827", symbol="铝期权"):
"""
上海期货交易所-期权-日频行情数据
:param trade_date: str "20191017"
:param symbol: str "铜期权" or "天胶期权" or "黄金期权" or "铝期权" or "锌期权"
:return: tuple(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 None
if day > datetime.date(2010, 8, 24):
url = SHFE_OPTION_URL.format(day.strftime("%Y%m%d"))
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]
product_df = pd.DataFrame(json_data["o_curproduct"])
product_df = product_df[product_df["PRODUCTNAME"].str.strip() ==
symbol]
volatility_df = pd.DataFrame(json_data["o_cursigma"])
volatility_df = volatility_df[
volatility_df["PRODUCTNAME"].str.strip() == symbol]
contract_df.columns = [
"_",
"_",
"_",
"合约代码",
"前结算价",
"开盘价",
"最高价",
"最低价",
"收盘价",
"结算价",
"涨跌1",
"涨跌2",
"成交量",
"持仓量",
"持仓量变化",
"_",
"行权量",
"成交额",
"德尔塔",
"_",
"_",
"_",
"_",
]
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",
]]
volatility_df.columns = [
"_",
"_",
"_",
"合约系列",
"成交量",
"持仓量",
"持仓量变化",
"行权量",
"成交额",
"隐含波动率",
"_",
]
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",
]]
return contract_df, volatility_df
except:
return None
def get_czce_option_daily(trade_date="20191017", symbol="白糖期权"):
"""
郑州商品交易所-期权-日频行情数据
说明：
(1) 价格：元/吨
(2) 成交量、空盘量：手
(3) 成交额：万元
(4) 涨跌一：今收盘-昨结算
(5) 涨跌二：今结算-昨结算
(6) 隐含波动率：将当日期权合约的结算价代入期权定价模型，反推出来的波动率数值
:param trade_date: str "20191017"
:param symbol: str "白糖期权", "棉花期权", "甲醇期权", "PTA期权", "菜籽粕期权"
:return: pandas.DataFrame
郑商所每日期权交易数据
品种代码 昨结算 今开盘 最高价 最低价 今收盘 \
0 CF001C10800 1,579.00 0.00 0.00 0.00 0.00
1 CF001C11000 1,392.00 0.00 0.00 0.00 0.00
2 CF001C11200 1,211.00 0.00 0.00 0.00 0.00
3 CF001C11400 1,038.00 1,396.00 1,396.00 1,396.00 1,396.00
4 CF001C11600 874.00 0.00 0.00 0.00 0.00
.. ... ... ... ... ... ...
398 SR009P5900 576.00 0.00 0.00 0.00 0.00
399 SR009P6000 653.00 0.00 0.00 0.00 0.00
400 小计
401 SR合计
402 总计
今结算 涨跌1 涨跌2 成交量(手) 空盘量 增减量 \
0 1,866.00 287.00 287.00 0 0 0
1 1,672.00 280.00 280.00 0 0 0
2 1,481.00 270.00 270.00 0 4 0
3 1,295.00 358.00 257.00 2 68 0
4 1,114.00 240.00 240.00 0 224 0
.. ... ... ... ... ... ...
398 580.00 4.00 4.00 0 0 0
399 658.00 5.00 5.00 0 0 0
400 656 860 400
401 32,098 276,900 2252
402 110,664 474,154 14770
成交额(万元) DELTA 隐含波动率 行权量
0 0.00 0.9765 22.29 0
1 0.00 0.9621 21.84 0
2 0.00 0.9423 21.38 0
3 1.40 0.9155 20.91 0
4 0.00 0.8800 20.45 0
.. ... ... ... ...
398 0.00 -0.6639 16.24 0
399 0.00 -0.7007 16.58 0
400 97.28 0
401 2138.41 0
402 8769.52 2
"""
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 None
if day > datetime.date(2010, 8, 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="|")
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)
return temp_df.iloc[:-1, :]
elif symbol == "PTA期权":
temp_df = table_df[table_df.iloc[:, 0].str.contains("TA")]
temp_df.reset_index(inplace=True, drop=True)
return temp_df.iloc[:-1, :]
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)
return temp_df.iloc[:-1, :]
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)
return temp_df.iloc[:-1, :]
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)
return temp_df.iloc[:-1, :]
else:
temp_df = table_df[table_df.iloc[:, 0].str.contains("CF")]
temp_df.reset_index(inplace=True, drop=True)
return temp_df.iloc[:-1, :]
except:
return None
def get_dce_option_daily(trade_date="20191017", symbol="玉米期权"):
"""
获取大连商品交易所-期权-日频行情数据
:param trade_date: str format："20191017"
:param symbol: str "玉米期权" or "豆粕期权"
:return: pandas.DataFrame
part-1:
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 \
0 玉米 c2001-C-1680 168.5 168.5 168.5 168.5 168.0 167.5 0.5 -0.5
1 玉米 c2001-C-1700 0 0.0 0.0 148.0 148.0 148.0 0.0 0.0
2 玉米 c2001-C-1720 0 0.0 0.0 129.0 128.0 129.0 1.0 1.0
3 玉米 c2001-C-1740 115 115.0 115.0 115.0 108.0 111.0 7.0 3.0
4 玉米 c2001-C-1760 89 95.5 89.0 95.5 89.0 93.5 6.5 4.5
.. ... ... ... ... ... ... ... ... ... ...
239 玉米 c2009-P-2040 0 0.0 0.0 91.0 88.5 91.0 2.5 2.5
240 玉米 c2009-P-2060 0 0.0 0.0 106.0 104.0 106.0 2.0 2.0
241 玉米 c2009-P-2080 0 0.0 0.0 121.5 120.5 121.5 1.0 1.0
242 玉米 c2009-P-2100 0 0.0 0.0 138.5 137.5 138.5 1.0 1.0
243 玉米 c2009-P-2120 0 0.0 0.0 155.5 155.5 155.5 0.0 0.0
Delta 成交量 持仓量 持仓量变化 成交额 行权量
0 0.98 2 236 0 0.34 0.0
1 0.96 0 236 0 0 0.0
2 0.94 0 210 0 0 0.0
3 0.90 20 1,040 0 2.3 0.0
4 0.85 12 680 0 1.11 0.0
.. ... .. ... ... ... ...
239 -0.70 0 30 0 0 0.0
240 -0.75 0 50 0 0 0.0
241 -0.80 0 20 0 0 0.0
242 -0.84 0 10 0 0 0.0
243 -0.88 0 0 0 0 0.0
part-2:
0 合约系列 隐含波动率(%)
1 c2001 12.95
2 c2003 8.74
3 c2005 8.75
4 c2007 7.7
5 c2009 6.85
"""
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 None
url = DCE_DAILY_OPTION_URL
payload = {
"dayQuotes.variety": "all",
"dayQuotes.trade_type": "1",
"year": str(day.year),
"month": str(day.month - 1),
"day": str(day.day),
"exportFlag": "txt"
}
res = requests.post(url, data=payload)
f = StringIO(res.text)
table_df = pd.read_table(f,
encoding="gbk",
skiprows=2,
header=None,
sep=r"\t\t",
engine="python")
another_df = table_df.iloc[
table_df[table_df.iloc[:,
0].str.contains("合约")].iloc[-1].name:, [0, 1]]
another_df.reset_index(inplace=True, drop=True)
another_df.iloc[0] = another_df.iat[0, 0].split("\t")
another_df.columns = another_df.iloc[0]
another_df = another_df.iloc[1:, :]
table_df = table_df.join(table_df.iloc[:, 1].str.split(r"\t", expand=True),
lsuffix="l")
table_df.columns = [
"商品名称", "_", "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", "合约名称", "开盘价"
]
table_df = table_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"
]]
table_df.dropna(axis=1, how="all", inplace=True)
product_one_df = table_df.iloc[:table_df[table_df.iloc[:, 0].str.
contains("小计")].iloc[0].name, :]
product_two_df = table_df.iloc[
table_df[table_df.iloc[:, 0].str.contains("小计")].iloc[0].name +
1:table_df[table_df.iloc[:, 0].str.contains("小计")].iloc[1].name, :]
product_three_df = table_df.iloc[
table_df[table_df.iloc[:, 0].str.contains("小计")].iloc[1].name +
1:table_df[table_df.iloc[:, 0].str.contains("小计")].iloc[2].name, :]
if symbol == "玉米期权":
return product_one_df, another_df[another_df.iloc[:,
0].str.contains("c")]
elif symbol == "铁矿石期权":
return product_two_df, another_df[another_df.iloc[:,
0].str.contains("i")]
else:
return product_three_df, another_df[another_df.iloc[:, 0].str.contains(
"m")]
def get_shfe_option_daily(trade_date="20191220", symbol="黄金期权"):
"""
获取上海期货交易所-期权-日频行情数据
:param trade_date: str "20191017"
:param symbol: str "铜期权" or "天胶期权" or "黄金期权"
:return: pandas.DataFrame
part-1:
PRODUCTID PRODUCTSORTNO PRODUCTNAME \
288 ru_o 100 天胶期权
289 ru_o 100 天胶期权
290 ru_o 100 天胶期权
291 ru_o 100 天胶期权
292 ru_o 100 天胶期权
.. ... ... ...
789 ru_o 100 天胶期权
790 ru_o 100 天胶期权
791 ru_o 100 天胶期权
792 ru_o 100 天胶期权
793 ru_o 100 天胶期权
INSTRUMENTID PRESETTLEMENTPRICE OPENPRICE \
288 ru1911C10000 729
289 ru1911C10250 495
290 ru1911C10500 293
291 ru1911C10750 146
292 ru1911C11000 58
.. ... ... ...
789 ru2010P9500 155
790 ru2010P9600 172
791 ru2010P9700 189
792 ru2010P9800 209
793 ru2010P9900 229
HIGHESTPRICE LOWESTPRICE CLOSEPRICE SETTLEMENTPRICE ZD1_CHG ZD2_CHG \
288 778 778 49 49
289 542 542 47 47
290 334 334 41 41
291 176 176 30 30
292 76 76 18 18
.. ... ... ... ... ... ...
789 151 151 -4 -4
790 167 167 -5 -5
791 184 184 -5 -5
792 204 204 -5 -5
793 224 224 -5 -5
VOLUME OPENINTEREST OPENINTERESTCHG ORDERNO EXECVOLUME TURNOVER \
288 0 0 0 0 0 0.0
289 0 0 0 0 0 0.0
290 0 0 0 0 0 0.0
291 0 0 0 0 0 0.0
292 0 4 0 0 0 0.0
.. ... ... ... ... ... ...
789 0 0 0 0 0 0.0
790 0 0 0 0 0 0.0
791 0 0 0 0 0 0.0
792 0 0 0 0 0 0.0
793 0 0 0 0 0 0.0
DELTA
288 0.976387
289 0.908465
290 0.757436
291 0.531736
292 0.299911
.. ...
789 -0.112120
790 -0.122028
791 -0.131944
792 -0.142837
793 -0.154073
part-2:
PRODUCTID PRODUCTSORTNO PRODUCTNAME HIGHESTPRICE LOWESTPRICE \
1 ru_o 100 天胶期权 2774 2
AVGPRICE VOLUME TURNOVER YEARVOLUME YEARTURNOVER EXECVOLUME \
1 148.573 8290 0.125033 112.5122 34.062215 0
YEAREXECVOLUME
1 1.0624
part-3:
PRODUCTID PRODUCTSORTNO PRODUCTNAME INSTRUMENTID \
12 ru_o 100 天胶期权 ru1911
13 ru_o 100 天胶期权 ru2001
14 ru_o 100 天胶期权 ru2003
15 ru_o 100 天胶期权 ru2004
16 ru_o 100 天胶期权 ru2005
17 ru_o 100 天胶期权 ru2006
18 ru_o 100 天胶期权 ru2007
19 ru_o 100 天胶期权 ru2008
20 ru_o 100 天胶期权 ru2009
21 ru_o 100 天胶期权 ru2010
SIGMA
12 0.242419
13 0.234428
14 0.218916
15 0.208057
16 0.205821
17 0.205821
18 0.240689
19 0.240689
20 0.216861
21 0.216861
"""
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 None
if day > datetime.date(2010, 8, 24):
url = SHFE_OPTION_URL.format(day.strftime('%Y%m%d'))
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]
product_df = pd.DataFrame(json_data['o_curproduct'])
product_df = product_df[product_df["PRODUCTNAME"].str.strip() ==
symbol]
volatility_df = pd.DataFrame(json_data['o_cursigma'])
volatility_df = volatility_df[
volatility_df["PRODUCTNAME"].str.strip() == symbol]
return contract_df, product_df, volatility_df
except:
return None
