def get_future_daily(start=None, end=None, market='CFFEX'):
"""
获取中金所日交易数据
Parameters
------
start: 开始日期 format：YYYY-MM-DD 或 YYYYMMDD 或 datetime.date对象 为空时为当天
end: 结束数据 format：YYYY-MM-DD 或 YYYYMMDD 或 datetime.date对象 为空时为当天
market: 'CFFEX' 中金所, 'CZCE' 郑商所, 'SHFE' 上期所, 'DCE' 大商所 之一。默认为中金所
Return
-------
DataFrame
中金所日交易数据(DataFrame):
symbol 合约代码
date 日期
open 开盘价
high 最高价
low 最低价
close 收盘价
pre_close 前收盘价
volume 成交量
open_interest 持仓量
turnover 成交额
settle 结算价
pre_settle 前结算价
variety 合约类别
或 None(给定日期没有交易数据)
"""
if market.upper() == 'CFFEX':
f = get_cffex_daily
elif market.upper() == 'CZCE':
f = get_czce_daily
elif market.upper() == 'SHFE':
f = get_shfe_daily
elif market.upper() == 'DCE':
f = get_dce_daily
else:
print('Invalid market.')
return
start = ct.convert_date(start) if start is not None else date.today()
end = ct.convert_date(end) if end is not None else date.today()
df_list = list()
while start <= end:
df = f(start)
if df is not None:
df_list.append(df)
start += timedelta(days=1)
if len(df_list) > 0:
return pd.concat(df_list)
def get_future_daily(start = None, end = None, market = 'CFFEX'):
"""
获取中金所日交易数据
Parameters
------
start: 开始日期 format：YYYY-MM-DD 或 YYYYMMDD 或 datetime.date对象 为空时为当天
end: 结束数据 format：YYYY-MM-DD 或 YYYYMMDD 或 datetime.date对象 为空时为当天
market: 'CFFEX' 中金所, 'CZCE' 郑商所, 'SHFE' 上期所, 'DCE' 大商所 之一。默认为中金所
Return
-------
DataFrame
中金所日交易数据(DataFrame):
symbol 合约代码
date 日期
open 开盘价
high 最高价
low 最低价
close 收盘价
pre_close 前收盘价
volume 成交量
open_interest 持仓量
turnover 成交额
settle 结算价
pre_settle 前结算价
variety 合约类别
或 None(给定日期没有交易数据)
"""
if market.upper() == 'CFFEX':
f = get_cffex_daily
elif market.upper() == 'CZCE':
f = get_czce_daily
elif market.upper() == 'SHFE':
f = get_shfe_daily
elif market.upper() == 'DCE':
f = get_dce_daily
else:
print('Invalid market.')
return
start = ct.convert_date(start) if start is not None else datetime.date.today()
end = ct.convert_date(end) if end is not None else datetime.date.today()
df_list = list()
while start <= end:
df = f(start)
if df is not None:
df_list.append(df)
start += datetime.timedelta(days = 1)
if len(df_list) > 0:
return pd.concat(df_list)
def get_shfe_vwap(date = None):
"""
获取上期所日成交均价数据
Parameters
------
date: 日期 format：YYYY-MM-DD 或 YYYYMMDD 或 datetime.date对象 为空时为当天
Return
-------
DataFrame
郑商所日交易数据(DataFrame):
symbol 合约代码
date 日期
time_range vwap时段，分09:00-10:15和09:00-15:00两类
vwap 加权平均成交均价
或 None(给定日期没有数据)
"""
day = ct.convert_date(date) if date is not None else datetime.date.today()
try:
json_data = json.loads(urlopen(Request(ct.SHFE_VWAP_URL % (day.strftime('%Y%m%d')),
headers=ct.SIM_HAEDERS)).read().decode('utf8'))
except HTTPError as reason:
if reason.code != 404:
print(ct.SHFE_DAILY_URL % (day.strftime('%Y%m%d')), reason)
return
if len(json_data['o_currefprice']) == 0:
return
df = pd.DataFrame(json_data['o_currefprice'])
df['INSTRUMENTID'] = df['INSTRUMENTID'].str.strip()
df[':B1'].astype('int16')
return df.rename(columns=ct.SHFE_VWAP_COLUMNS)[list(ct.SHFE_VWAP_COLUMNS.values())]
def get_dce_daily(day=None):
"""
获取大连商品交易所日交易数据
Parameters
------
day: 日期 format：YYYY-MM-DD 或 YYYYMMDD 或 datetime.date对象 为空时为当天
Return
-------
DataFrame
大商所日交易数据(DataFrame):
symbol 合约代码
date 日期
open 开盘价
high 最高价
low 最低价
close 收盘价
pre_close 前收盘价
volume 成交量
open_interest 持仓量
turnover 成交额
settle 结算价
pre_settle 前结算价
variety 合约类别
或 None(给定日期没有交易数据)
"""
day = ct.convert_date(day) if day is not None else date.today()
url = ct.DCE_DAILY_URL + '?' + urlencode({"currDate":day.strftime('%Y%m%d'),"year":day.strftime('%Y'), "month": str(int(day.strftime('%m'))-1), "day":day.strftime('%d')})
response = request.urlopen(request.Request(url, method='POST', headers=ct.DCE_HEADERS)).read().decode('utf8')
if u'错误：您所请求的网址（URL）无法获取' in response:
return
elif u'暂无数据' in response:
return
data = BeautifulSoup(response, 'html.parser').find_all('tr')
if len(data) == 0:
return
dict_data = list()
for idata in data[1:]:
if '小计' in idata.text or '总计' in idata.text:
continue
x = idata.find_all('td')
row_dict = dict()
for i,field in enumerate(ct.DCE_COLUMNS):
if i==0:
row_dict[field] = ct.DCE_MAP[x[i].text.strip()]
elif i>=2:
if '-' in x[i].text.strip():
row_dict[field] = 0.0
else:
row_dict[field] = float(x[i].text.strip().replace(',',''))
else:
row_dict[field] = x[i].text.strip()
dict_data.append(row_dict)
df = pd.DataFrame(dict_data)
df['date'] = day.strftime('%Y%m%d')
df['pre_close'] = df.close - df.change1
df['symbol'] = df.variety + df.month
return df[ct.OUTPUT_COLUMNS]
def get_shfe_vwap(day):
"""
获取上期所日成交均价数据
Parameters
------
day: 日期 format：YYYY-MM-DD 或 YYYYMMDD 或 datetime.date对象 为空时为当天
Return
-------
DataFrame
郑商所日交易数据(DataFrame):
symbol 合约代码
date 日期
time_range vwap时段，分09:00-10:15和09:00-15:00两类
vwap 加权平均成交均价
或 None(给定日期没有数据)
"""
day = ct.convert_date(day) if day is not None else date.today()
try:
json_data = json.loads(request.urlopen(request.Request(ct.SHFE_VWAP_URL % (day.strftime('%Y%m%d')), headers=ct.SIM_HAEDERS)).read().decode('utf8'))
except error.HTTPError as reason:
if reason.code != 404:
print(ct.SHFE_DAILY_URL % (day.strftime('%Y%m%d')), reason)
return
if len(json_data['o_currefprice']) == 0:
return
df = pd.DataFrame(json_data['o_currefprice'])
df['INSTRUMENTID'] = df['INSTRUMENTID'].str.strip()
df[':B1'].astype('int16')
return df.rename(columns=ct.SHFE_VWAP_COLUMNS)[list(ct.SHFE_VWAP_COLUMNS.values())]
def get_shfe_daily(date=None):
"""
获取上期所日交易数据
Parameters
------
date: 日期 format：YYYY-MM-DD 或 YYYYMMDD 或 datetime.date对象 为空时为当天
Return
-------
DataFrame
上期所日交易数据(DataFrame):
symbol 合约代码
date 日期
open 开盘价
high 最高价
low 最低价
close 收盘价
pre_close 前收盘价
volume 成交量
open_interest 持仓量
turnover 成交额
settle 结算价
pre_settle 前结算价
variety 合约类别
或 None(给定日期没有交易数据)
"""
day = ct.convert_date(date) if date is not None else date.today()
try:
json_data = json.loads(
urlopen(
Request(ct.SHFE_DAILY_URL % (day.strftime('%Y%m%d')),
headers=ct.SIM_HAEDERS)).read().decode('utf8'))
except HTTPError as reason:
if reason.code != 404:
print(ct.SHFE_DAILY_URL % (day.strftime('%Y%m%d')), reason)
return
if len(json_data['o_curinstrument']) == 0:
return
df = pd.DataFrame([
row for row in json_data['o_curinstrument']
if row['DELIVERYMONTH'] != u'小计' and row['DELIVERYMONTH'] != ''
])
df['variety'] = df.PRODUCTID.str.slice(0, -6)
df['symbol'] = df['variety'].str.upper() + df['DELIVERYMONTH']
df['date'] = day.strftime('%Y%m%d')
vwap_df = get_shfe_vwap(day)
if vwap_df is not None:
df = pd.merge(df,
vwap_df[vwap_df.time_range == '9:00-15:00'],
on=['date', 'symbol'],
how='left')
else:
print('Failed to fetch SHFE vwap.')
df.rename(columns=ct.SHFE_COLUMNS, inplace=True)
df['turnover'] = df.vwap * df.volume
df['pre_close'] = df['ZD2_CHG'] + df['close']
return df[ct.OUTPUT_COLUMNS]
def get_czce_daily(day=None):
"""
获取郑商所日交易数据
Parameters
------
day: 日期 format：YYYY-MM-DD 或 YYYYMMDD 或 datetime.date对象 为空时为当天
Return
-------
DataFrame
郑商所日交易数据(DataFrame):
symbol 合约代码
date 日期
open 开盘价
high 最高价
low 最低价
close 收盘价
pre_close 前收盘价
volume 成交量
open_interest 持仓量
turnover 成交额
settle 结算价
pre_settle 前结算价
variety 合约类别
或 None(给定日期没有交易数据)
"""
day = ct.convert_date(day) if day is not None else date.today()
try:
html = request.urlopen(request.Request(ct.CZCE_DAILY_URL % (day.strftime('%Y'),day.strftime('%Y%m%d')), headers=ct.SIM_HAEDERS)).read().decode('gbk','ignore')
except error.HTTPError as reason:
if reason.code != 404:
print(ct.CZCE_DAILY_URL % (day.strftime('%Y'),day.strftime('%Y%m%d')), reason)
return
if html.find('您的访问出错了') >= 0:
return
html = [i.replace(' ','').split('|') for i in html.split('\n')[:-4] if i[0][0] != '小']
if html[1][0]!='品种月份':
return
dict_data = list()
day_const = int(day.strftime('%Y%m%d'))
for ihtml in html[2:]:
m = ct.FUTURE_SYMBOL_PATTERN.match(ihtml[0])
if not m:
continue
row_dict = {'date': day_const, 'symbol': ihtml[0], 'variety': m.group(1)}
for i,field in enumerate(ct.CZCE_COLUMNS):
if ihtml[i+1] == "\r":
row_dict[field] = 0.0
else:
ihtml[i+1] = ihtml[i+1].replace(',','')
row_dict[field] = float(ihtml[i+1])
row_dict['pre_settle'] = row_dict['close'] - row_dict['change2']
dict_data.append(row_dict)
return pd.DataFrame(dict_data)[ct.OUTPUT_COLUMNS]
def get_shfe_daily(date = None):
"""
获取上期所日交易数据
Parameters
------
date: 日期 format：YYYY-MM-DD 或 YYYYMMDD 或 datetime.date对象 为空时为当天
Return
-------
DataFrame
上期所日交易数据(DataFrame):
symbol 合约代码
date 日期
open 开盘价
high 最高价
low 最低价
close 收盘价
pre_close 前收盘价
volume 成交量
open_interest 持仓量
turnover 成交额
settle 结算价
pre_settle 前结算价
variety 合约类别
或 None(给定日期没有交易数据)
"""
day = ct.convert_date(date) if date is not None else datetime.date.today()
try:
json_data = json.loads(urlopen(Request(ct.SHFE_DAILY_URL % (day.strftime('%Y%m%d')),
headers=ct.SIM_HAEDERS)).read().decode('utf8'))
except HTTPError as reason:
if reason.code != 404:
print(ct.SHFE_DAILY_URL % (day.strftime('%Y%m%d')), reason)
return
if len(json_data['o_curinstrument']) == 0:
return
df = pd.DataFrame([row for row in json_data['o_curinstrument'] if row['DELIVERYMONTH'] != u'小计' and row['DELIVERYMONTH'] != ''])
df['variety'] = df.PRODUCTID.str.slice(0, -6)
df['symbol'] = df['variety'].str.upper() + df['DELIVERYMONTH']
df['date'] = day.strftime('%Y%m%d')
vwap_df = get_shfe_vwap(day)
if vwap_df is not None:
df = pd.merge(df, vwap_df[vwap_df.time_range == '9:00-15:00'], on=['date', 'symbol'], how='left')
df['turnover'] = df.vwap * df.VOLUME
else:
print('Failed to fetch SHFE vwap.', day.strftime('%Y%m%d'))
df['turnover'] = .0
df.rename(columns=ct.SHFE_COLUMNS, inplace=True)
df['pre_close'] = df['ZD2_CHG'] + df['close']
return df[ct.OUTPUT_COLUMNS]
def get_czce_daily(date=None, type="future"):
"""
获取郑商所日交易数据
Parameters
------
date: 日期 format：YYYY-MM-DD 或 YYYYMMDD 或 datetime.date对象 为空时为当天
type: 数据类型, 为'future'期货 或 'option'期权二者之一
Return
-------
DataFrame
郑商所每日期货交易数据:
symbol 合约代码
date 日期
open 开盘价
high 最高价
low 最低价
close 收盘价
volume 成交量
open_interest 持仓量
turnover 成交额
settle 结算价
pre_settle 前结算价
variety 合约类别
或
DataFrame
郑商所每日期权交易数据
symbol 合约代码
date 日期
open 开盘价
high 最高价
low 最低价
close 收盘价
pre_settle 前结算价
settle 结算价
delta 对冲值
volume 成交量
open_interest 持仓量
oi_change 持仓变化
turnover 成交额
implied_volatility 隐含波动率
exercise_volume 行权量
variety 合约类别
None(类型错误或给定日期没有交易数据)
"""
if type == 'future':
url = ct.CZCE_DAILY_URL
listed_columns = ct.CZCE_COLUMNS
output_columns = ct.OUTPUT_COLUMNS
elif type == 'option':
url = ct.CZCE_OPTION_URL
listed_columns = ct.CZCE_OPTION_COLUMNS
output_columns = ct.OPTION_OUTPUT_COLUMNS
else:
print('invalid type :' + type +
',type should be one of "future" or "option"')
return
day = ct.convert_date(date) if date is not None else datetime.date.today()
try:
html = urlopen(
Request(url % (day.strftime('%Y'), day.strftime('%Y%m%d')),
headers=ct.SIM_HAEDERS)).read().decode('gbk', 'ignore')
except HTTPError as reason:
if reason.code != 404:
print(
ct.CZCE_DAILY_URL %
(day.strftime('%Y'), day.strftime('%Y%m%d')), reason)
return
if html.find(u'您的访问出错了') >= 0 or html.find(u'无期权每日行情交易记录') >= 0:
return
html = [
i.replace(' ', '').split('|') for i in html.split('\n')[:-4]
if i[0][0] != u'小'
]
if html[1][0] not in [u'品种月份', u'品种代码']:
return
dict_data = list()
day_const = int(day.strftime('%Y%m%d'))
for row in html[2:]:
m = ct.FUTURE_SYMBOL_PATTERN.match(row[0])
if not m:
continue
row_dict = {'date': day_const, 'symbol': row[0], 'variety': m.group(1)}
for i, field in enumerate(listed_columns):
if row[i + 1] == "\r":
row_dict[field] = 0.0
elif field in [
'volume', 'open_interest', 'oi_chg', 'exercise_volume'
]:
row[i + 1] = row[i + 1].replace(',', '')
row_dict[field] = int(row[i + 1])
else:
row[i + 1] = row[i + 1].replace(',', '')
row_dict[field] = float(row[i + 1])
dict_data.append(row_dict)
return pd.DataFrame(dict_data)[output_columns]
def get_dce_daily(date=None, type="future", retries=0):
"""
获取大连商品交易所日交易数据
Parameters
------
date: 日期 format：YYYY-MM-DD 或 YYYYMMDD 或 datetime.date对象 为空时为当天
type: 数据类型, 为'future'期货 或 'option'期权二者之一
retries: int, 当前重试次数，达到3次则获取数据失败
Return
-------
DataFrame
大商所日交易数据(DataFrame):
symbol 合约代码
date 日期
open 开盘价
high 最高价
low 最低价
close 收盘价
volume 成交量
open_interest 持仓量
turnover 成交额
settle 结算价
pre_settle 前结算价
variety 合约类别
或
DataFrame
郑商所每日期权交易数据
symbol 合约代码
date 日期
open 开盘价
high 最高价
low 最低价
close 收盘价
pre_settle 前结算价
settle 结算价
delta 对冲值
volume 成交量
open_interest 持仓量
oi_change 持仓变化
turnover 成交额
implied_volatility 隐含波动率
exercise_volume 行权量
variety 合约类别
或 None(给定日期没有交易数据)
"""
day = ct.convert_date(date) if date is not None else datetime.date.today()
if retries > 3:
print("maximum retires for DCE market data: ", day.strftime("%Y%m%d"))
return
if type == 'future':
url = ct.DCE_DAILY_URL + '?' + urlencode(
{
"currDate": day.strftime('%Y%m%d'),
"year": day.strftime('%Y'),
"month": str(int(day.strftime('%m')) - 1),
"day": day.strftime('%d')
})
listed_columns = ct.DCE_COLUMNS
output_columns = ct.OUTPUT_COLUMNS
elif type == 'option':
url = ct.DCE_DAILY_URL + '?' + urlencode(
{
"currDate": day.strftime('%Y%m%d'),
"year": day.strftime('%Y'),
"month": str(int(day.strftime('%m')) - 1),
"day": day.strftime('%d'),
"dayQuotes.trade_type": "1"
})
listed_columns = ct.DCE_OPTION_COLUMNS
output_columns = ct.OPTION_OUTPUT_COLUMNS
else:
print('invalid type :' + type +
', should be one of "future" or "option"')
return
try:
response = urlopen(Request(
url, method='POST', headers=ct.DCE_HEADERS)).read().decode('utf8')
except IncompleteRead as reason:
return get_dce_daily(day, retries=retries + 1)
except HTTPError as reason:
if reason.code == 504:
return get_dce_daily(day, retries=retries + 1)
elif reason.code != 404:
print(ct.DCE_DAILY_URL, reason)
return
if u'错误：您所请求的网址（URL）无法获取' in response:
return get_dce_daily(day, retries=retries + 1)
elif u'暂无数据' in response:
return
data = BeautifulSoup(response, 'html.parser').find_all('tr')
if len(data) == 0:
return
dict_data = list()
implied_data = list()
for idata in data[1:]:
if u'小计' in idata.text or u'总计' in idata.text:
continue
x = idata.find_all('td')
if type == 'future':
row_dict = {'variety': ct.DCE_MAP[x[0].text.strip()]}
row_dict['symbol'] = row_dict['variety'] + x[1].text.strip()
for i, field in enumerate(listed_columns):
field_content = x[i + 2].text.strip()
if '-' in field_content:
row_dict[field] = 0
elif field in ['volume', 'open_interest']:
row_dict[field] = int(field_content.replace(',', ''))
else:
row_dict[field] = float(field_content.replace(',', ''))
dict_data.append(row_dict)
elif len(x) == 16:
m = ct.FUTURE_SYMBOL_PATTERN.match(x[1].text.strip())
if not m:
continue
row_dict = {
'symbol': x[1].text.strip(),
'variety': m.group(1).upper(),
'contract_id': m.group(0)
}
for i, field in enumerate(listed_columns):
field_content = x[i + 2].text.strip()
if '-' in field_content:
row_dict[field] = 0
elif field in ['volume', 'open_interest']:
row_dict[field] = int(field_content.replace(',', ''))
else:
row_dict[field] = float(field_content.replace(',', ''))
dict_data.append(row_dict)
elif len(x) == 2:
implied_data.append({
'contract_id': x[0].text.strip(),
'implied_volatility': float(x[1].text.strip())
})
df = pd.DataFrame(dict_data)
df['date'] = day.strftime('%Y%m%d')
if type == 'future':
return df[output_columns]
else:
return pd.merge(df,
pd.DataFrame(implied_data),
on='contract_id',
how='left',
indicator=False)[output_columns]
def get_cffex_daily(date=None):
"""
获取中金所日交易数据
Parameters
------
date: 日期 format：YYYY-MM-DD 或 YYYYMMDD 或 datetime.date对象 为空时为当天
Return
-------
DataFrame
中金所日交易数据(DataFrame):
symbol 合约代码
date 日期
open 开盘价
high 最高价
low 最低价
close 收盘价
volume 成交量
open_interest 持仓量
turnover 成交额
settle 结算价
pre_settle 前结算价
variety 合约类别
或 None(给定日期没有交易数据)
"""
day = ct.convert_date(date) if date is not None else datetime.date.today()
try:
html = urlopen(
Request(ct.CFFEX_DAILY_URL %
(day.strftime('%Y%m'), day.strftime('%d'),
day.strftime('%Y%m%d')),
headers=ct.SIM_HAEDERS)).read().decode('gbk', 'ignore')
except HTTPError as reason:
if reason.code != 404:
print(
ct.CFFEX_DAILY_URL % (day.strftime('%Y%m'), day.strftime('%d'),
day.strftime('%Y%m%d')), reason)
return
if html.find(u'网页错误') >= 0:
return
html = [
i.replace(' ', '').split(',') for i in html.split('\n')[:-2]
if i[0][0] != u'小'
]
if html[0][0] != u'合约代码':
return
dict_data = list()
day_const = day.strftime('%Y%m%d')
for row in html[1:]:
m = ct.FUTURE_SYMBOL_PATTERN.match(row[0])
if not m:
continue
row_dict = {'date': day_const, 'symbol': row[0], 'variety': m.group(1)}
for i, field in enumerate(ct.CFFEX_COLUMNS):
if row[i + 1] == u"":
row_dict[field] = 0.0
elif field in ['volume', 'open_interest', 'oi_chg']:
row_dict[field] = int(row[i + 1])
else:
row_dict[field] = float(row[i + 1])
row_dict['pre_settle'] = row_dict['close'] - row_dict['change1']
dict_data.append(row_dict)
return pd.DataFrame(dict_data)[ct.OUTPUT_COLUMNS]
def get_czce_daily(date=None):
"""
获取郑商所日交易数据
Parameters
------
date: 日期 format：YYYY-MM-DD 或 YYYYMMDD 或 datetime.date对象 为空时为当天
Return
-------
DataFrame
郑商所日交易数据(DataFrame):
symbol 合约代码
date 日期
open 开盘价
high 最高价
low 最低价
close 收盘价
pre_close 前收盘价
volume 成交量
open_interest 持仓量
turnover 成交额
settle 结算价
pre_settle 前结算价
variety 合约类别
或 None(给定日期没有交易数据)
"""
day = ct.convert_date(date) if date is not None else datetime.date.today()
try:
html = urlopen(Request(ct.CZCE_DAILY_URL % (day.strftime('%Y'),
day.strftime('%Y%m%d')),
headers=ct.SIM_HAEDERS)).read().decode('gbk', 'ignore')
except HTTPError as reason:
if reason.code != 404:
print(ct.CZCE_DAILY_URL % (day.strftime('%Y'),
day.strftime('%Y%m%d')), reason)
return
if html.find(u'您的访问出错了') >= 0:
return
html = [i.replace(' ','').split('|') for i in html.split('\n')[:-4] if i[0][0] != u'小']
if html[1][0] != u'品种月份':
return
dict_data = list()
day_const = int(day.strftime('%Y%m%d'))
for ihtml in html[2:]:
m = ct.FUTURE_SYMBOL_PATTERN.match(ihtml[0])
if not m:
continue
row_dict = {'date': day_const, 'symbol': ihtml[0], 'variety': m.group(1)}
for i,field in enumerate(ct.CZCE_COLUMNS):
if ihtml[i+1] == "\r":
row_dict[field] = 0.0
else:
ihtml[i+1] = ihtml[i+1].replace(',','')
row_dict[field] = float(ihtml[i+1])
row_dict['pre_settle'] = row_dict['close'] - row_dict['change2']
dict_data.append(row_dict)
return pd.DataFrame(dict_data)[ct.OUTPUT_COLUMNS]
def get_dce_daily(date = None, retries=0):
"""
获取大连商品交易所日交易数据
Parameters
------
date: 日期 format：YYYY-MM-DD 或 YYYYMMDD 或 datetime.date对象 为空时为当天
Return
-------
DataFrame
大商所日交易数据(DataFrame):
symbol 合约代码
date 日期
open 开盘价
high 最高价
low 最低价
close 收盘价
pre_close 前收盘价
volume 成交量
open_interest 持仓量
turnover 成交额
settle 结算价
pre_settle 前结算价
variety 合约类别
或 None(给定日期没有交易数据)
"""
day = ct.convert_date(date) if date is not None else datetime.date.today()
if retries > 3:
print("maximum retires for DCE market data: ", day.strftime("%Y%m%d"))
return
url = ct.DCE_DAILY_URL + '?' + urlencode({"currDate":day.strftime('%Y%m%d'),
"year":day.strftime('%Y'),
"month": str(int(day.strftime('%m'))-1),
"day":day.strftime('%d')})
try:
response = urlopen(Request(url, method='POST', headers=ct.DCE_HEADERS)).read().decode('utf8')
except IncompleteRead as reason:
return get_dce_daily(day, retries+1)
except HTTPError as reason:
if reason.code == 504:
return get_dce_daily(day, retries+1)
elif reason.code != 404:
print(ct.DCE_DAILY_URL, reason)
return
if u'错误：您所请求的网址（URL）无法获取' in response:
return
elif u'暂无数据' in response:
return
data = BeautifulSoup(response, 'html.parser').find_all('tr')
if len(data) == 0:
return
dict_data = list()
for idata in data[1:]:
if u'小计' in idata.text or u'总计' in idata.text:
continue
x = idata.find_all('td')
row_dict = dict()
for i,field in enumerate(ct.DCE_COLUMNS):
if i==0:
row_dict[field] = ct.DCE_MAP[x[i].text.strip()]
elif i>=2:
if '-' in x[i].text.strip():
row_dict[field] = 0.0
else:
row_dict[field] = float(x[i].text.strip().replace(',',''))
else:
row_dict[field] = x[i].text.strip()
dict_data.append(row_dict)
df = pd.DataFrame(dict_data)
df['date'] = day.strftime('%Y%m%d')
df['pre_close'] = df.close - df.change1
df['symbol'] = df.variety + df.month
return df[ct.OUTPUT_COLUMNS]
def get_czce_daily(date=None, type="future"):
"""
获取郑商所日交易数据
Parameters
------
date: 日期 format：YYYY-MM-DD 或 YYYYMMDD 或 datetime.date对象 为空时为当天
type: 数据类型, 为'future'期货 或 'option'期权二者之一
Return
-------
DataFrame
郑商所每日期货交易数据:
symbol 合约代码
date 日期
open 开盘价
high 最高价
low 最低价
close 收盘价
volume 成交量
open_interest 持仓量
turnover 成交额
settle 结算价
pre_settle 前结算价
variety 合约类别
或
DataFrame
郑商所每日期权交易数据
symbol 合约代码
date 日期
open 开盘价
high 最高价
low 最低价
close 收盘价
pre_settle 前结算价
settle 结算价
delta 对冲值
volume 成交量
open_interest 持仓量
oi_change 持仓变化
turnover 成交额
implied_volatility 隐含波动率
exercise_volume 行权量
variety 合约类别
None(类型错误或给定日期没有交易数据)
"""
if type == 'future':
url = ct.CZCE_DAILY_URL
listed_columns = ct.CZCE_COLUMNS
output_columns = ct.OUTPUT_COLUMNS
elif type == 'option':
url = ct.CZCE_OPTION_URL
listed_columns = ct.CZCE_OPTION_COLUMNS
output_columns = ct.OPTION_OUTPUT_COLUMNS
else:
print('invalid type :' + type + ',type should be one of "future" or "option"')
return
day = ct.convert_date(date) if date is not None else datetime.date.today()
try:
html = urlopen(Request(url % (day.strftime('%Y'),
day.strftime('%Y%m%d')),
headers=ct.SIM_HAEDERS)).read().decode('gbk', 'ignore')
except HTTPError as reason:
if reason.code != 404:
print(ct.CZCE_DAILY_URL % (day.strftime('%Y'),
day.strftime('%Y%m%d')), reason)
return
if html.find(u'您的访问出错了') >= 0 or html.find(u'无期权每日行情交易记录') >= 0:
return
html = [i.replace(' ','').split('|') for i in html.split('\n')[:-4] if i[0][0] != u'小']
if html[1][0] not in [u'品种月份', u'品种代码']:
return
dict_data = list()
day_const = int(day.strftime('%Y%m%d'))
for row in html[2:]:
m = ct.FUTURE_SYMBOL_PATTERN.match(row[0])
if not m:
continue
row_dict = {'date': day_const, 'symbol': row[0], 'variety': m.group(1)}
for i,field in enumerate(listed_columns):
if row[i+1] == "\r":
row_dict[field] = 0.0
elif field in ['volume', 'open_interest', 'oi_chg', 'exercise_volume']:
row[i+1] = row[i+1].replace(',','')
row_dict[field] = int(row[i+1])
else:
row[i+1] = row[i+1].replace(',','')
row_dict[field] = float(row[i+1])
dict_data.append(row_dict)
return pd.DataFrame(dict_data)[output_columns]
def get_dce_daily(date = None, type="future", retries=0):
"""
获取大连商品交易所日交易数据
Parameters
------
date: 日期 format：YYYY-MM-DD 或 YYYYMMDD 或 datetime.date对象 为空时为当天
type: 数据类型, 为'future'期货 或 'option'期权二者之一
retries: int, 当前重试次数，达到3次则获取数据失败
Return
-------
DataFrame
大商所日交易数据(DataFrame):
symbol 合约代码
date 日期
open 开盘价
high 最高价
low 最低价
close 收盘价
volume 成交量
open_interest 持仓量
turnover 成交额
settle 结算价
pre_settle 前结算价
variety 合约类别
或
DataFrame
大商所每日期权交易数据
symbol 合约代码
date 日期
open 开盘价
high 最高价
low 最低价
close 收盘价
pre_settle 前结算价
settle 结算价
delta 对冲值
volume 成交量
open_interest 持仓量
oi_change 持仓变化
turnover 成交额
implied_volatility 隐含波动率
exercise_volume 行权量
variety 合约类别
或 None(给定日期没有交易数据)
"""
day = ct.convert_date(date) if date is not None else datetime.date.today()
if retries > 3:
print("maximum retires for DCE market data: ", day.strftime("%Y%m%d"))
return
if type == 'future':
url = ct.DCE_DAILY_URL + '?' + urlencode({"currDate":day.strftime('%Y%m%d'),
"year":day.strftime('%Y'),
"month": str(int(day.strftime('%m'))-1),
"day":day.strftime('%d')})
listed_columns = ct.DCE_COLUMNS
output_columns = ct.OUTPUT_COLUMNS
elif type == 'option':
url = ct.DCE_DAILY_URL + '?' + urlencode({"currDate":day.strftime('%Y%m%d'),
"year":day.strftime('%Y'),
"month": str(int(day.strftime('%m'))-1),
"day":day.strftime('%d'),
"dayQuotes.trade_type": "1"})
listed_columns = ct.DCE_OPTION_COLUMNS
output_columns = ct.OPTION_OUTPUT_COLUMNS
else:
print('invalid type :' + type + ', should be one of "future" or "option"')
return
try:
response = urlopen(Request(url, method='POST', headers=ct.DCE_HEADERS)).read().decode('utf8')
except IncompleteRead as reason:
return get_dce_daily(day, retries=retries+1)
except HTTPError as reason:
if reason.code == 504:
return get_dce_daily(day, retries=retries+1)
elif reason.code != 404:
print(ct.DCE_DAILY_URL, reason)
return
if u'错误：您所请求的网址（URL）无法获取' in response:
return get_dce_daily(day, retries=retries+1)
elif u'暂无数据' in response:
return
data = BeautifulSoup(response, 'html.parser').find_all('tr')
if len(data) == 0:
return
dict_data = list()
implied_data = list()
for idata in data[1:]:
if u'小计' in idata.text or u'总计' in idata.text:
continue
x = idata.find_all('td')
if type == 'future':
row_dict = {'variety': ct.DCE_MAP[x[0].text.strip()]}
row_dict['symbol'] = row_dict['variety'] + x[1].text.strip()
for i,field in enumerate(listed_columns):
field_content = x[i+2].text.strip()
if '-' in field_content:
row_dict[field] = 0
elif field in ['volume', 'open_interest']:
row_dict[field] = int(field_content.replace(',',''))
else:
row_dict[field] = float(field_content.replace(',',''))
dict_data.append(row_dict)
elif len(x) == 16:
m = ct.FUTURE_SYMBOL_PATTERN.match(x[1].text.strip())
if not m:
continue
row_dict = {'symbol': x[1].text.strip(), 'variety': m.group(1).upper(), 'contract_id': m.group(0)}
for i,field in enumerate(listed_columns):
field_content = x[i+2].text.strip()
if '-' in field_content:
row_dict[field] = 0
elif field in ['volume', 'open_interest']:
row_dict[field] = int(field_content.replace(',',''))
else:
row_dict[field] = float(field_content.replace(',',''))
dict_data.append(row_dict)
elif len(x) == 2:
implied_data.append({'contract_id': x[0].text.strip(), 'implied_volatility': float(x[1].text.strip())})
df = pd.DataFrame(dict_data)
df['date'] = day.strftime('%Y%m%d')
if type == 'future':
return df[output_columns]
else:
return pd.merge(df, pd.DataFrame(implied_data), on='contract_id', how='left', indicator=False)[output_columns]
