def daily_Repo_amt_prc_for_collateral(dir_name):
"""
机构类型-债券类型 ——
"""
date = re.match(".*\\d{8}\\.",dir_name).group()[-9:-1]
excel_io= input_path+"/质押式回购市场交易情况总结/日报/" + dir_name
tmp = pd.read_excel(excel_io)
# print(tmp.shape,date)
if tmp.shape[0] == 206:
h = 132; nr = 33
elif tmp.shape[0] == 178:
h = 110; nr = 27
df=pd.read_excel(excel_io,header=h,nrows =nr).iloc[:,:6]
df["date"]=date
df["机构类型"].fillna(method="ffill",inplace=True)
df.rename(columns={'债券类型':'抵押品类型'},inplace=True)
df=df.replace("-",0)
name = "Repo_amt_prc_for_collateral"
columns_type=[#图表的数据口径
String(30),
String(30),
Float(4),
Float(2),
Float(4),
Float(2),
DateTime()]
dtypelist = dict(zip(df.columns,columns_type))#变成字典形式
return df,name,dtypelist
def init_db():
metadata = MetaData()
persons = Table(
'person', metadata,
Column('id', Integer, Sequence('person_id_seq'), primary_key=True),
Column('name', String(64)), Column('nickName', String(64)),
Column('location', String(128)))
goods = Table(
'goods', metadata,
Column('id', Integer, Sequence('good_id_seq'), primary_key=True),
Column('category', ARRAY(String())), Column('detail', String()),
Column('name', String(128)), Column('price', Float()),
Column('description', String()), Column('brand', String(64)))
reviews = Table(
'reviews', metadata,
Column('id', Integer, Sequence('review_id_seq'), primary_key=True),
Column('review', String()), Column('stars', Float()),
Column('title', String()), Column('date_review', DateTime()),
Column('helpful', Float()), Column('person_id',
ForeignKey('person.id')),
Column('product_id', ForeignKey('goods.id')))
mapper(Persons, persons)
mapper(Goods, goods)
mapper(Reviews, reviews)
metadata.create_all(bind=engine)
def futureGame2mysql(game):
game = pd.DataFrame(game).T
game['awayTeam'] = "nan"
game['homeTeam'] = "nan"
game['away_win_rate'] = 0.0
game['home_win_rate'] = 0.0
gameTeam = game.iloc[0]['gameTeam']
gameTeam = gameTeam.split("vs")
game['awayTeam'] = gameTeam[0]
game['homeTeam'] = gameTeam[1]
game.drop(columns=['gameOver','gameTeam'], axis=1, inplace=True)
game['gameId'] = game['gameId'].astype(int)
dtype_dict = {
game.columns[0]: Integer(),
game.columns[1]: NVARCHAR(length=32),
game.columns[2]: NVARCHAR(length=3),
game.columns[3]: NVARCHAR(length=3),
game.columns[4]: Float(precision=6, asdecimal=True),
game.columns[5]: Float(precision=6, asdecimal=True),
}
try:
game.to_sql(name='future_game', con=con, if_exists='append', index=False, dtype=dtype_dict)
except:
print('[futureGame2mysql] game ' + str(game['gameId']) + ' has existed, skip.')
class Donation(Base, CRUDMixin):
__tablename__ = 'donations'
id = Column('id', Integer, primary_key=True)
name = Column('name', String())
url = Column('url', String())
twitter = Column('twitter', String())
comment = Column('comment', String())
amount = Column('amount', Float(precision=2))
amount_one = Column('amount_one', Float(precision=2))
amount_two = Column('amount_two', Float(precision=2))
amount_three = Column('amount_three', Float(precision=2))
time = Column('time', DateTime())
time_approved = Column('time_approved', DateTime())
approved = Column('approved', Boolean, default=False)
ipn_hash = Column('ipn_hash', String())
user_id = Column('user_id', Integer, ForeignKey('users.id'))
user = relationship('User')
prize_id = Column('prize_id', Integer, ForeignKey('prizes.id'))
prize = relationship('Prize')
challenge_id = Column('challenge_id', Integer, ForeignKey('challenges.id'))
challenge = relationship('Challenge')
game_id = Column('game_id', Integer, ForeignKey('games.id'))
game = relationship('Game')
def __unicode__(self):
return self.name
class User(Base):
__tablename__ = 'users'
id = Column(Integer, primary_key=True)
rolename = Column(String(), ForeignKey("userroles.rolename"),
nullable=False)
role = relationship("UserRole", innerjoin=False)
templates = relationship("Template",
backref='users',
primaryjoin="User.id==Template.user_id",
order_by="Template.create_date",
lazy='joined')
username = Column(String(50), nullable=False, unique=True)
password = Column(String(), nullable=False, default='')
twitter_id = Column(String(), nullable=False)
facebook_id = Column(String(), nullable=False)
quote = Column(Float(), nullable=False, default=10.0)
datasize = Column(Float(), nullable=False, default=0.0)
email = Column(String(120), nullable=False)#, unique=True)
create_date = Column(String(10), nullable=False)
def __init__(self, username, role, password='', twitter_id='', facebook_id='', email=''):
super(User, self).__init__()
self.username = username
self.role = role
self.set_password(password)
self.twitter_id = twitter_id
self.facebook_id = facebook_id
self.email = email
self.create_date = datetime.datetime.now()
def set_password(self, password):
self.password = get_password_hash(SECRET_KEY, self.username, password)
def cash_amt_prc():
# 资金现券与成交量
name = 'cash_amt_prc'
last_date = do.get_latest_date(name)
today_date = dt.datetime.now()
print('表{}的最近更新日期为{}'.format(name, last_date))
err, df = w.edb(
"M0041652,M0041653,M0041655,M1004511,M1004515,M0220162,M0220163,M0330244,M0041739,M0041740",
last_date,
today_date,
usedf=True)
if df.shape[1] == 1:
return [], name, []
df.columns = ['R001','R007','R021','GC001','GC007','DR001','DR007',\
'成交量:R001','成交量:银行间质押式回购','成交量:银行间债券现券']
df['date'] = df.index
df = df.loc[(df.date > last_date) & (df.date < today_date.date())]
columns_type = [
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
DateTime()
]
dtypelist = dict(zip(df.columns, columns_type))
return df, name, dtypelist
class Grb(Base):
__tablename__ = 'grb'
tar_id = Column(Integer, ForeignKey('targets.tar_id'), nullable=False)
grb_id = Column(Integer, primary_key=True, nullable=False)
grb_seqn = Column(Integer, primary_key=True, nullable=False)
grb_type = Column(Integer, primary_key=True, nullable=False)
grb_ra = Column(Float(Precision=64))
grb_dec = Column(Float(Precision=64))
grb_is_grb = Column(Boolean, nullable=False)
grb_date = Column(DateTime, nullable=False)
grb_last_update = Column(DateTime, nullable=False)
grb_errorbox = Column(Float(Precision=64))
grb_autodisabled = Column(Boolean, nullable=False)
def __init__(self, tar_id=None, grb_ra=None, grb_dec=None, grb_is_grb=None, grb_date=None, grb_last_update=None, grb_errorbox=None, grb_autodisabled=None):
self.tar_id = tar_id
self.grb_ra = grb_ra
self.grb_dec = grb_dec
self.grb_is_grb = grb_is_grb
self.grb_date = grb_date
self.grb_last_update = grb_last_update
self.grb_errorbox = grb_errorbox
self.grb_autodisabled = grb_autodisabled
def __repr__(self):
return '<Grb({0},{1},{2},{3})>'.format(self.tar_id, self.grb_id, self.grb_seqn, self.grb_type)
class Relay(Base):
"Relay settings"
__tablename__ = 'relaysettings'
__table_args__ = (UniqueConstraint('username', 'address'), {})
id = Column(Integer, primary_key=True)
address = Column(Unicode(255), index=True)
username = Column(Unicode(255))
__password = Column('password', Unicode(255))
enabled = Column(Boolean, default=True)
description = Column(Unicode(255))
low_score = Column(Float(), default=0.0)
high_score = Column(Float(), default=0.0)
spam_actions = Column(SmallInteger, default=2)
highspam_actions = Column(SmallInteger, default=2)
ratelimit = Column(SmallInteger, default=250, server_default='250')
org_id = Column(Integer, ForeignKey('organizations.id'))
org = relationship('Group', backref=backref('relaysettings', order_by=id))
__mapper_args__ = {'order_by': id}
def set_password(self, password):
"sets the password to a hash"
self.__password = bcrypt.hashpw(password, bcrypt.gensalt())
def apijson(self):
"""Return JSON for the API"""
mdict = {}
for attr in [
'id', 'address', 'username', 'enabled', 'description',
'low_score', 'high_score', 'spam_actions', 'highspam_actions'
]:
mdict[attr] = getattr(self, attr)
return mdict
def fig_midstream():
# 中游
err,df = w.edb("S5705039,S0247603,S0181750,S5914515,S5907373,S5416650,M0067419,M0066359,\
M0066348,M0066350" , \
start, end, usedf = True)
df.columns = [
'Mylpic综合钢价指数', '库存:主要钢材品种:合计', '库存:螺纹钢(含上海全部仓库)', '水泥价格指数:全国',
'中国玻璃价格指数', '中国盛泽化纤价格指数', '期货收盘价(活跃合约):PVC', '期货收盘价(活跃合约):天然橡胶',
'期货收盘价(活跃合约):黄大豆1号', '期货收盘价(活跃合约):黄玉米'
]
df['date'] = df.index
name = 'fig_midstream'
columns_type = [
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
DateTime()
]
dtypelist = dict(zip(df.columns, columns_type))
return df, name, dtypelist
def fig_midstream():
name = 'fig_midstream'
last_date = do.get_latest_date(name)
today_date = dt.datetime.now()
print('表{}的最近更新日期为{}'.format(name, last_date))
err,df = w.edb("S5705039,S0247603,S0181750,S5914515,S5907373,S5416650,M0067419,M0066359,\
M0066348,M0066350" , \
last_date, today_date, usedf = True)
if df.shape[1] == 1:
return [], name, []
df.columns = [
'Mylpic综合钢价指数', '库存:主要钢材品种:合计', '库存:螺纹钢(含上海全部仓库)', '水泥价格指数:全国',
'中国玻璃价格指数', '中国盛泽化纤价格指数', '期货收盘价(活跃合约):PVC', '期货收盘价(活跃合约):天然橡胶',
'期货收盘价(活跃合约):黄大豆1号', '期货收盘价(活跃合约):黄玉米'
]
df['date'] = df.index
df = df.loc[(df.date > last_date) & (df.date < today_date.date())]
columns_type = [
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
DateTime()
]
dtypelist = dict(zip(df.columns, columns_type))
return df, name, dtypelist
def daily_Repo_price_for_investors(dir_name):
"""
From质押式回购市场交易情况总结日报
正/逆回购方-利率
"""
date = re.match(".*\\d{8}\\.",dir_name).group()[-9:-1]
excel_io= input_path+"/质押式回购市场交易情况总结/日报/" + dir_name
df=pd.read_excel(excel_io,header=1,nrows = 5).iloc[:,1:7]
df.columns.name="正回购方"
df.rename(columns={'Unnamed: 1':'机构名称'},inplace=True)
df["date"]=date
df.reset_index(drop=True)
df=df.replace('-',0)
name = "Repo_price_for_investors" # 为了对应数据库table名字
columns_type=[#图表的数据口径
String(30),
Float(),
Float(),
Float(),
Float(),
Float(),
DateTime()]
dtypelist = dict(zip(df.columns,columns_type))#变成字典形式
return df,name,dtypelist
class GridBasedData(Base):
__tablename__ = 'grid_based_data'
id = Column(UUID(as_uuid=True),
primary_key=True,
server_default=sqlalchemy.text("uuid_generate_v4()"))
created_at = Column(DateTime, nullable=False)
modified_at = Column(DateTime, nullable=False)
date_time = Column(DateTime, nullable=False)
epw_date_time = Column(DateTime, nullable=False)
point_id = Column(UUID(as_uuid=True),
ForeignKey('grid_points.id'),
nullable=False)
simulation_id = Column(UUID(as_uuid=True),
ForeignKey('simulations.id'),
nullable=False)
job_id = Column(UUID(as_uuid=True), ForeignKey('jobs.id'), nullable=False)
window_surface_id = Column(UUID(as_uuid=True),
ForeignKey('analysis_surfaces.id'),
nullable=False)
state_name = Column(String, nullable=False) # is window surface state name
unit = Column(String, nullable=False)
is_direct = Column(Boolean, nullable=False)
sky_total = Column(Float(precision=2))
sky_direct = Column(Float(precision=2))
sun = Column(Float(precision=2))
total = Column(Float(precision=2))
point = relationship('GridPoint', backref='datums')
simulation = relationship('Simulation', backref='datums')
window_surface = relationship('AnalysisSurface', backref='datums')
class Cpu(Base):
__tablename__ = 'cpu_moniter'
id = Column(Integer, primary_key=True, autoincrement=True)
time = Column(DateTime, nullable=False)
cpu1 = Column(Float(precision=2), nullable=False)
cpu2 = Column(Float(precision=2), nullable=False)
cpu3 = Column(Float(precision=2), nullable=False)
cpu4 = Column(Float(precision=2), nullable=False)
def test_divide_columns_into_type_of_filters():
column_types_dict = {
"col_1": Integer(),
"col_2": Text(),
"col_3": Float(),
"col_4": DateTime(),
"col_5": ARRAY("string"),
"col_6": Boolean(),
"col_7": Integer(),
"col_8": Float(),
"col_9": DateTime(),
"col_10": Text(),
"col_11": Boolean(),
"col_12": ARRAY("string"),
}
unique_entries = {
"col_1": [1 for i in range(MAX_ENTRIES_FOR_FILTER_SELECTOR + 1)],
"col_2": ["Dream" for i in range(MAX_ENTRIES_FOR_FILTER_SELECTOR - 1)],
"col_3": [2.5 for i in range(MAX_ENTRIES_FOR_FILTER_SELECTOR + 1)],
"col_4":
["11/10/2013" for i in range(MAX_ENTRIES_FOR_FILTER_SELECTOR + 1)],
"col_5": [[1, 4, 5]
for i in range(MAX_ENTRIES_FOR_FILTER_SELECTOR - 1)],
"col_6": [True for i in range(MAX_ENTRIES_FOR_FILTER_SELECTOR - 1)],
"col_7": [1 for i in range(MAX_ENTRIES_FOR_FILTER_SELECTOR - 1)],
"col_8": [3.5 for i in range(MAX_ENTRIES_FOR_FILTER_SELECTOR - 1)],
"col_9":
["11/10/2014" for i in range(MAX_ENTRIES_FOR_FILTER_SELECTOR - 1)],
"col_10":
["Dream" for i in range(MAX_ENTRIES_FOR_FILTER_SELECTOR + 1)],
"col_11": [True for i in range(MAX_ENTRIES_FOR_FILTER_SELECTOR + 1)],
"col_12": [[1, 4, 5]
for i in range(MAX_ENTRIES_FOR_FILTER_SELECTOR + 1)],
}
(
filter_column_names,
numerical_filter_column_names,
unique_entries_dict,
) = divide_columns_into_type_of_filters(unique_entries, column_types_dict)
assert set(numerical_filter_column_names) == {
"col_1",
"col_3",
"col_4",
"col_7",
"col_8",
"col_9",
}
filter_column_names_set = {
"col_2", "col_5", "col_6", "col_7", "col_8", "col_9"
}
assert set(filter_column_names) == filter_column_names_set
assert set(unique_entries_dict.keys()) == filter_column_names_set
assert unique_entries_dict["col_2"] == unique_entries["col_2"]
assert unique_entries_dict["col_5"] == unique_entries["col_5"]
assert unique_entries_dict["col_6"] == unique_entries["col_6"]
assert unique_entries_dict["col_7"] == unique_entries["col_7"]
assert unique_entries_dict["col_8"] == unique_entries["col_8"]
assert unique_entries_dict["col_9"] == unique_entries["col_9"]
class Academia(connector.Manager.Base):
__tablename__ = 'academia'
id = Column(Integer, Sequence('academia_id_seq'), primary_key=True)
Nombre = Column(String(60), nullable=False)
Direccion = Column(String(80), nullable=False)
Descripcion = Column(String(80), nullable=False)
Distrito = Column(String(40), nullable=False)
Geo_x = Column(Float(53, 32), nullable=False)
Geo_y = Column(Float(53, 32), nullable=False)
class Sentiments(Base):
__tablename__ = 'sentiments'
site = Column(String(500), primary_key=True)
city = Column(String(100), primary_key=True)
state = Column(String(50), primary_key=True)
postTime = Column(DateTime(timezone=True), primary_key=True)
NaiveBayes = Column(Float(precision=5))
Vader = Column(Float(precision=5))
class WEAData(Base):
__tablename__ = 'wea_data'
id = Column(UUID(as_uuid=True),
primary_key=True,
server_default=sqlalchemy.text("uuid_generate_v4()"))
wea_id = Column(UUID(as_uuid=True), ForeignKey('weas.id'), nullable=False)
date_time = Column(DateTime, nullable=False)
direct_normal_radiation = Column(Float(precision=2))
diffuse_horizontal_radiation = Column(Float(precision=2))
def daily_fig_bond_leverage():
err, df = w.edb('M0041739,M5639029', start, end, usedf=True)
df.columns = ['成交量:银行间质押式回购', '债券市场托管余额']
# df = df.dropna(axis = 0)
df['date'] = df.index
name = 'fig_bond_leverage'
columns_type = [Float(4), Float(1), DateTime()]
dtypelist = dict(zip(df.columns, columns_type))
return df, name, dtypelist
def mkt_rates():
# TODO 货币市场利率
err,df=w.edb("M1006336,M1006337,M0017142,M1006645",\
"2000-06-17", "2021-06-16",usedf=True)
df.columns = ['DR001', 'DR007', 'shibor_3m', '存单_1y']
df['date'] = df.index
name = 'mkt_rates'
columns_type = [Float(), Float(), Float(), Float(), DateTime()]
dtypelist = dict(zip(df.columns, columns_type))
return df, name, dtypelist
def daily_fig_liquidity_premium():
err, df = w.edb('M0017139,M0041653,M0220163,\
M0017142,M0048486,M1010889,M1010892,M0329545,\
M1011048',
start,
end,
"Fill=Previous",
usedf=True)
df.columns = [
"shibor_7d", "质押回购利率_7天", "存款类质押回购利率_7天", "shibor_3m", "IRS：FR007：1y",
"存单_AAA_3m", "存单_AAA_1y", "MLF：1年", "国股银票转贴现收益率_3m"
]
df['date'] = df.index
# df = df.dropna(axis = 0)
name = 'fig_liquidity_premium'
columns_type = [
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
DateTime()
]
dtypelist = dict(zip(df.columns, columns_type))
return df, name, dtypelist
def ccl():
# 超储率
## 2015年前无政府存款记录
err,df = w.edb("M0001528,M0062047,M0251905,M0043821,M0061518,M0043823,M0010096,\
M0001690,M0001380" ,\
"2010-01-01", "2021-06-16", usedf=True)
df.columns=['住户存款','非金融企业存款','政府存款',\
'中小型准备金率','大型准备金率','超额准备金率','超储率_季度',\
'基础货币','M0']
df['date'] = df.index
name = 'ccl_related'
columns_type = [
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
DateTime()
]
dtypelist = dict(zip(df.columns, columns_type))
return df, name, dtypelist
def pro_cache_data_daily(self, df, index, cached_end):
dtypedict = {
'ts_code': NVARCHAR(length=10),
'trade_date': NVARCHAR(length=8),
'open': Float(),
'high': Float(),
'low': Float(),
'close': Float(),
'pre_close': Float(),
'change': Float(),
'pct_chg': Float(),
'vol': Float(),
'amount': Float()
}
if cached_end is None:
print('######## cache new trade daily ############')
new = df
else:
new = df[df.trade_date > cached_end]
if index:
new.to_sql(self.tables['index_trade_daily'],
con=self.conn,
if_exists='append',
index=False,
dtype=dtypedict)
else:
new.to_sql(self.tables['stock_trade_daily'],
con=self.conn,
if_exists='append',
index=False,
dtype=dtypedict)
def save_probability_to_sql(df_probability, name):
dtypedict = {'draw': Float(), 'lose': Float(), 'win': Float()}
yconnect = create_engine(
'mysql+mysqldb://root:@127.0.0.1:3306/zucai?charset=utf8')
pd.io.sql.to_sql(df_probability,
name,
yconnect,
schema='zucai',
if_exists='replace',
dtype=dtypedict)
def save_dataframe_to_db(df, table_name):
""" Save data to the table
"""
df1 = df[(df['question_id_1'] == 1) & (df['question_id_2'] == 2) &
(df['answer_id_1'] == 1)]
#print(df)
pd.options.display.float_format = '{:,.9f}'.format
print(df1[['answer_id_1', 'answer_id_2', 'abs_coeff', 'abs_test_coeff']])
#sys.exit(0)
if NO_WRITING_DB:
print("Data wasn't saved to BD, because the option -nw is set.")
return
print(df.info())
df_no_nan = df.dropna()
CONFIG = database_connect.CONFIG
from sqlalchemy.pool import NullPool
from sqlalchemy.orm.session import sessionmaker
#sudo apt install python3-mysqldb
#str_connect = 'mysql+mysqlconnector://{0}:{1}@{2}/{3}'\
# .format(CONFIG['user'], CONFIG['password'], CONFIG['host'], CONFIG['database'])
str_connect = 'mysql+mysqldb://{0}:{1}@{2}/{3}'\
.format(CONFIG['user'], CONFIG['password'], CONFIG['host'], CONFIG['database'])
engine = create_engine(str_connect, echo=DEBUG, poolclass=NullPool)
Session = sessionmaker()
Session.configure(bind=engine)
session = Session() # создаем объект сессии
logging.debug('Try to write to sql {0} rows (from df)'.format(
len(df_no_nan)))
#if DEBUG: print(df_no_nan)
dtype_dict = {
'coeff': Float(),
'abs_coeff': Float(),
'abs_test_coeff': Float()
}
df_no_nan.to_sql(name=table_name,
con=engine,
index=False,
dtype=dtype_dict,
if_exists='append',
chunksize=20000)
#df.to_sql(name='matrix_corr_qa', con=conn, if_exists = 'replace', index=False)
session.close()
engine.dispose()
logging.info('Data was written in table {0}'.format(table_name))
def define_columns(data_dict, class_name):
"""Dynamically define the class attributes for the ORM
Parameters
----------
data_dict : dict
A dictionary containing the ORM definitions
class_name : str
The name of the class/ORM.
Returns
-------
data_dict : dict
A dictionary containing the ORM definitions, now with header
definitions added.
"""
special_keywords = [
'RULEFILE', 'FWERROR', 'FW2ERROR', 'PROPTTL1', 'TARDESCR', 'QUALCOM2'
]
with open(
os.path.join(
os.path.split(__file__)[0], 'table_definitions',
class_name.lower() + '.txt'), 'r') as f:
data = f.readlines()
keywords = [item.strip().split(', ') for item in data]
for keyword in keywords:
if keyword[0] in special_keywords:
data_dict[keyword[0].lower()] = get_special_column(keyword[0])
elif keyword[1] == 'Integer':
data_dict[keyword[0].lower()] = Column(Integer())
elif keyword[1] == 'String':
data_dict[keyword[0].lower()] = Column(String(50))
elif keyword[1] == 'Float':
data_dict[keyword[0].lower()] = Column(Float(precision=32))
elif keyword[1] == 'Decimal':
data_dict[keyword[0].lower()] = Column(Float(precision='13,8'))
elif keyword[1] == 'Date':
data_dict[keyword[0].lower()] = Column(Date())
elif keyword[1] == 'Time':
data_dict[keyword[0].lower()] = Column(Time())
elif keyword[1] == 'DateTime':
data_dict[keyword[0].lower()] = Column(DateTime)
elif keyword[1] == 'Bool':
data_dict[keyword[0].lower()] = Column(Boolean)
else:
raise ValueError('unrecognized header keyword type: {}:{}'.format(
keyword[0], keyword[1]))
if 'aperture' in data_dict:
data_dict['aperture'] = Column(String(50), index=True)
return data_dict
def interbank_deposit():
# 同业存单价格与净融资量
err,df = w.edb('M1006645,M0329545', start,end,usedf=True)
df.columns = ['存单_股份行_1y', 'MLF：1y']
df['date'] = df.index
name = 'interbank_deposit'
columns_type=[Float(),Float(),
DateTime()]
dtypelist = dict(zip(df.columns,columns_type))
return df, name , dtypelist
def local():
name = 'localbond_issue'
err, df = w.edb("M5658453,M6191591",
"2003-12-01",
"2021-06-30",
usedf=True)
df.columns = ['地方专项债限额', '地方专项债累计发行额']
df['date'] = df.index
columns_type = [Float(), Float(), DateTime()]
dtypelist = dict(zip(df.columns, columns_type))
return df, name, dtypelist
class Targets(Base):
__tablename__ = 'targets'
tar_id = Column(Integer, primary_key=True)
tar_name = Column(String(150))
tar_ra = Column(Float(Precision=64))
tar_dec = Column(Float(Precision=64))
tar_comment = Column(Text)
tar_enabled = Column(Boolean)
tar_priority = Column(Integer)
tar_bonus = Column(Integer)
tar_bonus_time = Column(DateTime)
tar_next_observable = Column(DateTime)
tar_info = Column(String(2000))
interruptible = Column(Boolean)
tar_pm_ra = Column(Float(Precision=64))
tar_pm_dec = Column(Float(Precision=64))
tar_telescope_mode = Column(Integer)
def __init__(self,
tar_name=None,
tar_ra=None,
tar_dec=None,
tar_comment=None,
tar_enabled=None,
tar_priority=None,
tar_bonus=None,
tar_bonus_time=None,
tar_next_observable=None,
tar_info=None,
interruptible=None,
tar_pm_ra=None,
tar_pm_dec=None,
tar_telescope_mode=None):
self.tar_name = tar_name
self.tar_ra = tar_ra
self.tar_dec = tar_dec
self.tar_comment = tar_comment
self.tar_enabled = tar_enabled
self.tar_priority = tar_priority
self.tar_bonus = tar_bonus
self.tar_bonus_time = tar_bonus_time
self.tar_next_observable = tar_next_observable
self.tar_info = tar_info
self.interruptible = interruptible
self.tar_pm_ra = tar_pm_ra
self.tar_pm_dec = tar_pm_dec
self.tar_telescope_mode = tar_telescope_mode
def __repr__(self):
return '<Target({0},{1},{2},{3})>'.format(self.tar_id, self.tar_name,
self.tar_ra, self.tar_dec)
def cash_cost():
err, df=w.edb('M1006336,M1006337,M1004515,M0017142',
start,end,usedf=True)
df.columns = ['DR001','DR007','GC007','shibor_3m']
df['date'] = df.index
df = df.dropna(axis = 0)
name = 'cash_cost'
columns_type=[Float(2),Float(2),Float(2),Float(2),
DateTime()]
dtypelist = dict(zip(df.columns,columns_type))
return df, name, dtypelist
def hs300():
err,df1 = w.wsd("000300.SH", "dividendyield2", \
"2002-01-01", "2021-06-30", usedf=True)
df1.columns = ['股息率']
err, df2 = w.edb("M0058003", "2010-01-01", "2021-06-30", usedf=True)
df2.columns = ['一般贷款']
df1['一般贷款'] = df2['一般贷款']
df1['date'] = df1.index
name = 'hs300Div'
df = df1
columns_type = [Float(), Float(), DateTime()]
dtypelist = dict(zip(df.columns, columns_type))
return df, name, dtypelist
