def lpr_ma_data(year=None):
"""
获取贷款基础利率均值数据
Parameters
------
year:年份(int)
Return
------
date:日期
1Y_5:5日均值
1Y_10:10日均值
1Y_20:20日均值
"""
year = du.get_year() if year is None else year
lab = ct.SHIBOR_TYPE['LPR_Tendency']
lab = lab.encode('utf-8') if ct.PY3 else lab
try:
clt = Client(url=ct.SHIBOR_DATA_URL %
(ct.P_TYPE['http'], ct.DOMAINS['shibor'], ct.PAGES['dw'],
'LPR_Tendency', year, lab, year))
content = clt.gvalue()
df = pd.read_excel(StringIO(content), skiprows=[0])
df.columns = ct.LPR_MA_COLS
df['date'] = df['date'].map(lambda x: x.date())
if pd.__version__ < '0.21':
df['date'] = df['date'].astype(np.datetime64)
else:
df['date'] = df['date'].astype('datetime64[D]')
return df
except:
return None
def lpr_data(year=None):
"""
获取贷款基础利率（LPR）
Parameters
------
year:年份(int)
Return
------
date:日期
1Y:1年贷款基础利率
"""
year = du.get_year() if year is None else year
lab = ct.SHIBOR_TYPE['LPR']
lab = lab.encode(shibor_encoding) if ct.PY3 else lab
try:
df = pd.read_excel(ct.SHIBOR_DATA_URL %
(ct.P_TYPE['http'], ct.DOMAINS['shibor'],
ct.PAGES['dw'], 'LPR', year, lab, year))
df.columns = ct.LPR_COLS
df['date'] = df['date'].map(lambda x: x.date())
df['date'] = df['date'].astype(np.datetime64)
return df
except:
return None
def shibor_data(year=None):
"""
获取上海银行间同业拆放利率（Shibor）
Parameters
------
year:年份(int)
Return
------
date:日期
ON:隔夜拆放利率
1W:1周拆放利率
2W:2周拆放利率
1M:1个月拆放利率
3M:3个月拆放利率
6M:6个月拆放利率
9M:9个月拆放利率
1Y:1年拆放利率
"""
year = du.get_year() if year is None else year
lab = ct.SHIBOR_TYPE['Shibor']
lab = lab.encode(shibor_encoding) if ct.PY3 else lab
try:
df = pd.read_excel(ct.SHIBOR_DATA_URL %
(ct.P_TYPE['http'], ct.DOMAINS['shibor'],
ct.PAGES['dw'], 'Shibor', year, lab, year))
df.columns = ct.SHIBOR_COLS
df['date'] = df['date'].map(lambda x: x.date())
df['date'] = df['date'].astype(np.datetime64)
return df
except:
return None
def lpr_ma_data(year=None):
"""
获取贷款基础利率均值数据
Parameters
------
year:年份(int)
Return
------
date:日期
1Y_5:5日均值
1Y_10:10日均值
1Y_20:20日均值
"""
year = du.get_year() if year is None else year
lab = ct.SHIBOR_TYPE['LPR_Tendency']
lab = lab.encode('utf-8') if ct.PY3 else lab
try:
clt = Client(url=ct.SHIBOR_DATA_URL%(ct.P_TYPE['http'], ct.DOMAINS['shibor'],
ct.PAGES['dw'], 'LPR_Tendency',
year, lab,
year))
content = clt.gvalue()
df = pd.read_excel(StringIO(content), skiprows=[0])
df.columns = ct.LPR_MA_COLS
df['date'] = df['date'].map(lambda x: x.date())
if pd.__version__ < '0.21':
df['date'] = df['date'].astype(np.datetime64)
else:
df['date'] = df['date'].astype('datetime64[D]')
return df
except:
return None
def lpr_data(year=None):
"""
获取贷款基础利率（LPR）
Parameters
------
year:年份(int)
Return
------
date:日期
1Y:1年贷款基础利率
"""
year = du.get_year() if year is None else year
lab = ct.SHIBOR_TYPE['LPR']
lab = lab.encode('utf-8') if ct.PY3 else lab
try:
df = pd.read_excel(ct.SHIBOR_DATA_URL%(ct.P_TYPE['http'], ct.DOMAINS['shibor'],
ct.PAGES['dw'], 'LPR',
year, lab,
year))
df.columns = ct.LPR_COLS
df['date'] = df['date'].map(lambda x: x.date())
df['date'] = df['date'].astype(np.datetime64)
return df
except:
return None
def shibor_ma_data(year=None):
"""
获取Shibor均值数据
Parameters
------
year:年份(int)
Return
------
date:日期
其它分别为各周期5、10、20均价
"""
year = du.get_year() if year is None else year
lab = ct.SHIBOR_TYPE['Tendency']
lab = lab.encode('utf-8') if ct.PY3 else lab
try:
df = pd.read_excel(ct.SHIBOR_DATA_URL%(ct.P_TYPE['http'], ct.DOMAINS['shibor'],
ct.PAGES['dw'], 'Shibor_Tendency',
year, lab,
year), skiprows=[0])
df.columns = ct.SHIBOR_MA_COLS
df['date'] = df['date'].map(lambda x: x.date())
df['date'] = df['date'].astype(np.datetime64)
return df
except:
return None
def shibor_data(year=None):
"""
获取上海银行间同业拆放利率（Shibor）
Parameters
------
year:年份(int)
Return
------
date:日期
ON:隔夜拆放利率
1W:1周拆放利率
2W:2周拆放利率
1M:1个月拆放利率
3M:3个月拆放利率
6M:6个月拆放利率
9M:9个月拆放利率
1Y:1年拆放利率
"""
year = du.get_year() if year is None else year
lab = ct.SHIBOR_TYPE['Shibor']
lab = lab.encode('utf-8') if ct.PY3 else lab
try:
df = pd.read_excel(ct.SHIBOR_DATA_URL%(ct.P_TYPE['http'], ct.DOMAINS['shibor'],
ct.PAGES['dw'], 'Shibor',
year, lab,
year))
df.columns = ct.SHIBOR_COLS
df['date'] = df['date'].map(lambda x: x.date())
df['date'] = df['date'].astype(np.datetime64)
return df
except:
return None
def shibor_ma_data(year=None):
"""
获取Shibor均值数据
Parameters
------
year:年份(int)
Return
------
date:日期
其它分别为各周期5、10、20均价
"""
year = du.get_year() if year is None else year
lab = ct.SHIBOR_TYPE['Tendency']
lab = lab.encode(shibor_encoding) if ct.PY3 else lab
try:
df = pd.read_excel(
ct.SHIBOR_DATA_URL %
(ct.P_TYPE['http'], ct.DOMAINS['shibor'], ct.PAGES['dw'],
'Shibor_Tendency', year, lab, year),
skiprows=[0])
df.columns = ct.SHIBOR_MA_COLS
df['date'] = df['date'].map(lambda x: x.date())
df['date'] = df['date'].astype(np.datetime64)
return df
except:
return None
def shibor_quote_data(year=None):
"""
获取Shibor银行报价数据
Parameters
------
year:年份(int)
Return
------
date:日期
bank:报价银行名称
ON:隔夜拆放利率
ON_B:隔夜拆放买入价
ON_A:隔夜拆放卖出价
1W_B:1周买入
1W_A:1周卖出
2W_B:买入
2W_A:卖出
1M_B:买入
1M_A:卖出
3M_B:买入
3M_A:卖出
6M_B:买入
6M_A:卖出
9M_B:买入
9M_A:卖出
1Y_B:买入
1Y_A:卖出
"""
year = du.get_year() if year is None else year
lab = ct.SHIBOR_TYPE['Quote']
lab = lab.encode('utf-8') if ct.PY3 else lab
try:
clt = Client(url=ct.SHIBOR_DATA_URL%(ct.P_TYPE['http'], ct.DOMAINS['shibor'],
ct.PAGES['dw'], 'Quote',
year, lab,
year))
content = clt.gvalue()
df = pd.read_excel(StringIO(content), skiprows=[0])
# df.columns = ct.QUOTE_COLS
df.columns = ct.SHIBOR_Q_COLS
df['date'] = df['date'].map(lambda x: x.date())
if pd.__version__ < '0.21':
df['date'] = df['date'].astype(np.datetime64)
else:
df['date'] = df['date'].astype('datetime64[D]')
return df
except:
return None
def xsg_data(year=None, month=None, retry_count=3, pause=0.001):
"""
获取限售股解禁数据
Parameters
--------
year:年份,默认为当前年
month:解禁月份，默认为当前月
retry_count : int, 默认 3
如遇网络等问题重复执行的次数
pause : int, 默认 0
重复请求数据过程中暂停的秒数，防止请求间隔时间太短出现的问题
Return
------
DataFrame
code:股票代码
name:名称
date:解禁日期
count:解禁数量（万股）
ratio:占总盘比率
"""
year = du.get_year() if year is None else year
month = du.get_month() if month is None else month
for _ in range(retry_count):
time.sleep(pause)
try:
request = Request(rv.XSG_URL %
(ct.P_TYPE['http'], ct.DOMAINS['em'],
ct.PAGES['emxsg'], year, month))
lines = urlopen(request, timeout=10).read()
lines = lines.decode('utf-8') if ct.PY3 else lines
except Exception as e:
print(e)
else:
da = lines[3:len(lines) - 3]
list = []
for row in da.split('","'):
list.append([data for data in row.split(',')])
df = pd.DataFrame(list)
df = df[[1, 3, 4, 5, 6]]
for col in [5, 6]:
df[col] = df[col].astype(float)
df[5] = df[5] / 10000
df[6] = df[6] * 100
df[5] = df[5].map(ct.FORMAT)
df[6] = df[6].map(ct.FORMAT)
df.columns = rv.XSG_COLS
return df
raise IOError(ct.NETWORK_URL_ERROR_MSG)
def shibor_quote_data(year=None):
"""
获取Shibor银行报价数据
Parameters
------
year:年份(int)
Return
------
date:日期
bank:报价银行名称
ON:隔夜拆放利率
ON_B:隔夜拆放买入价
ON_A:隔夜拆放卖出价
1W_B:1周买入
1W_A:1周卖出
2W_B:买入
2W_A:卖出
1M_B:买入
1M_A:卖出
3M_B:买入
3M_A:卖出
6M_B:买入
6M_A:卖出
9M_B:买入
9M_A:卖出
1Y_B:买入
1Y_A:卖出
"""
year = du.get_year() if year is None else year
lab = ct.SHIBOR_TYPE['Quote']
lab = lab.encode('utf-8') if ct.PY3 else lab
try:
clt = Client(url=ct.SHIBOR_DATA_URL%(ct.P_TYPE['http'], ct.DOMAINS['shibor'],
ct.PAGES['dw'], 'Quote',
year, lab,
year))
content = clt.gvalue()
df = pd.read_excel(StringIO(content), skiprows=[0])
# df.columns = ct.QUOTE_COLS
df.columns = ct.SHIBOR_Q_COLS
df['date'] = df['date'].map(lambda x: x.date())
if pd.__version__ < '0.21':
df['date'] = df['date'].astype(np.datetime64)
else:
df['date'] = df['date'].astype('datetime64[D]')
return df
except:
return None
def xsg_data(year=None, month=None,
retry_count=3, pause=0.001):
"""
获取限售股解禁数据
Parameters
--------
year:年份,默认为当前年
month:解禁月份，默认为当前月
retry_count : int, 默认 3
如遇网络等问题重复执行的次数
pause : int, 默认 0
重复请求数据过程中暂停的秒数，防止请求间隔时间太短出现的问题
Return
------
DataFrame
code:股票代码
name:名称
date:解禁日期
count:解禁数量（万股）
ratio:占总盘比率
"""
year = du.get_year() if year is None else year
month = du.get_month() if month is None else month
for _ in range(retry_count):
time.sleep(pause)
try:
request = Request(rv.XSG_URL%(ct.P_TYPE['http'], ct.DOMAINS['em'],
ct.PAGES['emxsg'], year, month))
lines = urlopen(request, timeout = 10).read()
lines = lines.decode('utf-8') if ct.PY3 else lines
except Exception as e:
print(e)
else:
da = lines[3:len(lines)-3]
list = []
for row in da.split('","'):
list.append([data for data in row.split(',')])
df = pd.DataFrame(list)
df = df[[1, 3, 4, 5, 6]]
for col in [5, 6]:
df[col] = df[col].astype(float)
df[5] = df[5]/10000
df[6] = df[6]*100
df[5] = df[5].map(ct.FORMAT)
df[6] = df[6].map(ct.FORMAT)
df.columns = rv.XSG_COLS
return df
raise IOError(ct.NETWORK_URL_ERROR_MSG)
def xsg_data(year=None, month=None,
retry_count=3, pause=0.001):
"""
获取限售股解禁数据
Parameters
--------
year:年份,默认为当前年
month:解禁月份，默认为当前月
retry_count : int, 默认 3
如遇网络等问题重复执行的次数
pause : int, 默认 0
重复请求数据过程中暂停的秒数，防止请求间隔时间太短出现的问题
Return
------
DataFrame
code:股票代码
name:名称
date:解禁日期
count:解禁数量（万股）
ratio:占总盘比率
"""
year = dt.get_year() if year is None else year
month = dt.get_month() if month is None else month
for _ in range(retry_count):
time.sleep(pause)
try:
with urlopen(rv.XSG_URL%(ct.P_TYPE['http'], ct.DOMAINS['em'],
ct.PAGES['emxsg'], year, month)) as resp:
lines = resp.read()
except _network_error_classes:
pass
else:
da = lines[3:len(lines)-3]
list = []
for row in da.split('","'):
list.append([data for data in row.split(',')])
df = pd.DataFrame(list)
df = df[[1, 3, 4, 5, 6]]
for col in [5, 6]:
df[col] = df[col].astype(float)
df[5] = df[5]/10000
df[6] = df[6]*100
df[5] = df[5].map(ct.FORMAT)
df[6] = df[6].map(ct.FORMAT)
df.columns = rv.XSG_COLS
return df
raise IOError("获取失败，请检查网络和URL")
def xsg_data(year=None, month=None,
retry_count=3, pause=0.001):
"""
获取限售股解禁数据
Parameters
--------
year:年份,默认为当前年
month:解禁月份，默认为当前月
retry_count : int, 默认 3
如遇网络等问题重复执行的次数
pause : int, 默认 0
重复请求数据过程中暂停的秒数，防止请求间隔时间太短出现的问题
Return
------
DataFrame
code:股票代码
name:名称
date:解禁日期
count:解禁数量（万股）
ratio:占总盘比率
"""
year = dt.get_year() if year is None else year
month = dt.get_month() if month is None else month
for _ in range(retry_count):
time.sleep(pause)
try:
with urlopen(rv.XSG_URL%(ct.P_TYPE['http'], ct.DOMAINS['em'],
ct.PAGES['emxsg'], year, month)) as resp:
lines = resp.read()
except _network_error_classes:
pass
else:
da = lines[3:len(lines)-3]
list = []
for row in da.split('","'):
list.append([data for data in row.split(',')])
df = pd.DataFrame(list)
df = df[[1, 3, 4, 5, 6]]
for col in [5, 6]:
df[col] = df[col].astype(float)
df[5] = df[5]/10000
df[6] = df[6]*100
df[5] = df[5].map(ct.FORMAT)
df[6] = df[6].map(ct.FORMAT)
df.columns = rv.XSG_COLS
return df
raise IOError("获取失败，请检查网络和URL")
def shibor_quote_data(year=None):
"""
获取Shibor银行报价数据
Parameters
------
year:年份(int)
Return
------
date:日期
bank:报价银行名称
ON:隔夜拆放利率
ON_B:隔夜拆放买入价
ON_A:隔夜拆放卖出价
1W_B:1周买入
1W_A:1周卖出
2W_B:买入
2W_A:卖出
1M_B:买入
1M_A:卖出
3M_B:买入
3M_A:卖出
6M_B:买入
6M_A:卖出
9M_B:买入
9M_A:卖出
1Y_B:买入
1Y_A:卖出
"""
year = du.get_year() if year is None else year
lab = ct.SHIBOR_TYPE['Quote']
lab = lab.encode(shibor_encoding) if ct.PY3 else lab
try:
df = pd.read_excel(ct.SHIBOR_DATA_URL %
(ct.P_TYPE['http'], ct.DOMAINS['shibor'],
ct.PAGES['dw'], 'Quote', year, lab, year),
skiprows=[0])
df.columns = ct.QUOTE_COLS
df['date'] = df['date'].map(lambda x: x.date())
df['date'] = df['date'].astype(np.datetime64)
return df
except:
return None
def shibor_data(year=None):
"""
获取上海银行间同业拆放利率（Shibor）
Parameters
------
year:年份(int)
Return
------
date:日期
ON:隔夜拆放利率
1W:1周拆放利率
2W:2周拆放利率
1M:1个月拆放利率
3M:3个月拆放利率
6M:6个月拆放利率
9M:9个月拆放利率
1Y:1年拆放利率
"""
year = du.get_year() if year is None else year
lab = ct.SHIBOR_TYPE['Shibor']
lab = lab.encode('utf-8') if ct.PY3 else lab
try:
clt = Client(url=ct.SHIBOR_DATA_URL%(ct.P_TYPE['http'], ct.DOMAINS['shibor'],
ct.PAGES['dw'], 'Shibor',
year, lab,
year))
content = clt.gvalue()
df = pd.read_excel(StringIO(content))
df.columns = ct.SHIBOR_COLS
df['date'] = df['date'].map(lambda x: x.date())
if pd.__version__ < '0.21':
df['date'] = df['date'].astype(np.datetime64)
else:
df['date'] = df['date'].astype('datetime64[D]')
return df
except:
return None
# -*- coding:utf-8 -*-
"""
@author: wenhq
"""
import os
import tushare as ts
import tushare.util.dateu as dateu
from tushare.util.dateu import is_holiday
tradeyear = str(dateu.get_year())
tradeyear_path = os.path.join(os.getcwd(), 'DataYes', tradeyear)
# 按年创建每天行情数据文件
if not os.path.exists(tradeyear_path):
os.makedirs(tradeyear_path)
# 保存文件
if not is_holiday(dateu.today()):
df = ts.get_today_all()
df.to_csv(os.path.join(tradeyear_path,
''.join([dateu.today().replace('-', ''), '.csv'])),
encoding='gb18030')
