def rates_us():
name = 'rates_us'
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("G0000886,G0000887,G0000891,G8455661,M0000185,G0000898",
"2010-06-21",
"2021-06-18",
usedf=True)
if df.shape[1] == 1:
return [], name, []
df.columns = ['美债1年', '美债2年', '美债10年', '美债10-2', '美元兑人民币', 'libor_3m']
df['date'] = df.index
df = df.loc[(df.date > last_date) & (df.date < today_date.date())]
columns_type = [
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
DateTime()
]
dtypelist = dict(zip(df.columns, columns_type))
return df, name, dtypelist
def main():
w.start()
# @ 读取db.txt内的邮箱信息
conn, engine = do.get_db_conn()
l = [
daily_fig_bond_leverage(),
daily_fig_credit_premium(),
daily_fig_liquidity_premium(),
daily_fig_rates(),industial_premium(),
cash_cost(),policy_rate(),monetary_policy_tools(),\
repo_volume(),interbank_deposit(),rates(),
cash_amt_prc(),spreads()]
for a, b, c in l:
if len(np.array(a)) == 0:
print(b, '已是最新，无需更新')
continue
a.to_sql(name=b,
con=engine,
schema='finance',
if_exists='append',
index=False,
dtype=c)
print('成功更新表', b, '至', do.get_latest_date(b))
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
def daily_fig_rates():
name = 'fig_rates'
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(
'S0059744,S0059746,S0059747,S0059749,M1004263,M1004265,M1004267,M1004271',
last_date,
today_date,
usedf=True)
if df.shape[1] == 1:
return [], name, []
df.columns = [
"1年国债", "3年国债", "5年国债", "10年国债", "1年国开", "3年国开", "5年国开", "10年国开"
]
df = df.dropna(axis=0)
df['date'] = df.index
df = df.loc[df.date > last_date]
columns_type = [
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
DateTime()
]
dtypelist = dict(zip(df.columns, columns_type))
return df, name, dtypelist
def pmy_rate_sec():
name = 'primary_rate_sec'
last_date = do.get_latest_date(name)
df = pd.read_excel('/Users/wdt/Desktop/tpy/Data_Tools/【数据库更新】/专题数据/一级发行数据/利率债一级.xlsx',\
sheet_name = '数据（导入）')
df = df[cols]
df['date'] = df['发行起始日']
df = df.loc[df.date > last_date]
columns_type = [
VARCHAR(30),
VARCHAR(30),
VARCHAR(30),
DateTime(),
DateTime(),
Float(),
Float(),
VARCHAR(30),
VARCHAR(30),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
DateTime()
]
dtypelist = dict(zip(df.columns, columns_type))
return df, name, dtypelist
def policy_rate():
name = 'policy_rate'
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('M0041371,M0041373,M0041377,M0329656,\
M0329543,M0329544,M0329545',
last_date,
today_date,
usedf=True)
if df.shape[1] == 1:
return [], name, []
df.columns = ['逆回购利率：7天', '逆回购利率：14天', '逆回购利率：28天',\
'逆回购利率：63天', 'MLF：3m', 'MLF：6m',
'MLF：1y']
df['date'] = df.index
df = df.loc[df.date > last_date]
columns_type = [
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
DateTime()
]
dtypelist = dict(zip(df.columns, columns_type))
return df, name, dtypelist
def fig_industrial_production():
name = 'fig_industrial_production'
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("S5704502,S5715680,S5708175,S5715660,S5417017,S5914175",
last_date,
today_date,
usedf=True)
if df.shape[1] == 1:
return [], name, []
df.columns = [
"日均产量：粗钢：国内", "日均产量：焦炭：重点企业(旬)", "高炉开工率(163家):全国", "产能利用率:电炉:全国",
"PTA产业链负荷率:PTA工厂", "浮法玻璃:产能利用率"
]
df['date'] = df.index
df = df.loc[(df.date > last_date) & (df.date < dt.datetime.now().date())]
columns_type = [
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 spreads():
# 息差与杠杆
name = 'spreads'
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("M0220162,M0220163,M1004515,M0048486,M0048490,M1004007,M1004900,S0059722,S0059724,S0059725,M1004271,M1004300", \
last_date, today_date, usedf=True)
if df.shape[1] == 1:
return [], name, []
df.columns = ['DR001','DR007','GC007','IRS_1y_FR007','IRS_5y_FR007',\
'IRS_5y_shibor3m','cd_AAA_6m',\
'中短票_AA+_1y','中短票_AA+_3y','中短票_AA+_5y',\
'国开10年','地方债_AAA_3y']
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(),
Float(),
Float(),
DateTime()
]
dtypelist = dict(zip(df.columns, columns_type))
return df, name, dtypelist
def daily_fig_credit_premium():
name = 'fig_credit_premium'
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("M0048432,M0048434,M0048435, \
M0048422,M0048424,M0048425, \
M0048412,M0048414,M0048415, \
M1004265,S0059746, \
M1010704,M1010706,M1010708, \
M1015080,S0059738",
last_date,
today_date,
usedf=True)
if df.shape[1] == 1:
return [], name, []
df.columns = [
"中债城投债到期收益率(AAA):1年", "中债城投债到期收益率(AAA):3年", "中债城投债到期收益率(AAA):5年",
"中债城投债到期收益率(AA+):1年", "中债城投债到期收益率(AA+):3年", "中债城投债到期收益率(AA+):5年",
"中债城投债到期收益率(AA):1年", "中债城投债到期收益率(AA):3年", "中债城投债到期收益率(AA):5年",
"中债国开债到期收益率:3年", "中债国债到期收益率:3年", "中债商业银行二级资本债到期收益率(AAA-):1年",
"中债商业银行二级资本债到期收益率(AAA-):3年", "中债商业银行二级资本债到期收益率(AAA-):5年",
"中债可续期产业债到期收益率(AAA):3年", "中债中短期票据到期收益率(AAA):3年"
]
df['date'] = df.index
df = df.loc[df.date > last_date]
columns_type = [
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
DateTime()
]
dtypelist = dict(zip(df.columns, columns_type))
return df, name, dtypelist
def download_certain_mail(msg_list,download_io=' ',lastday=' '):
try:
subject_list=[]
date_list=[]
for msg in msg_list:
# 获取可读的附件名
subject=decode_str(msg.get("subject"))
# print(subject)# 附件名
if '现券市场交易情况总结日报' in subject:
lastday = do.get_latest_date('Net_buy_bond')
download_io="/Users/wdt/Desktop/tpy/raw_data_pool/现券市场交易情况总结/日报/"
# 查找邮件名中的八位数字
date= re.search(r"\d{8}",subject)[0]
if date > lastday.strftime("%Y%m%d") :
subject_list.append(subject)
date_list.append(date)
get_att(msg,download_io)
print("已经下载附件："+subject)
else:
pass
elif '质押式回购市场交易情况总结日报' in subject:
lastday=do.get_latest_date('Repo_amt_prc_for_collateral')
download_io = '/Users/wdt/Desktop/tpy/raw_data_pool/质押式回购市场交易情况总结/'
date= re.search(r"\d{8}",subject)[0]
if date > lastday.strftime("%Y%m%d") :
subject_list.append(subject)
date_list.append(date)
get_att(msg,download_io)
print("已经下载附件："+subject)
else:
pass
print("##########")
except BaseException as e:
print('fail error:',e)
def interbank_deposit():
name = 'interbank_deposit'
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('M1006645,M0329545', last_date, today_date, usedf=True)
if df.shape[1] == 1:
return [], name, []
df.columns = ['存单_股份行_1y', 'MLF：1y']
df['date'] = df.index
df = df.loc[df.date > last_date]
columns_type = [Float(), Float(), DateTime()]
dtypelist = dict(zip(df.columns, columns_type))
return df, name, dtypelist
def monetary_policy_tools():
name = 'monetary_policy_tools'
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('M0061614,M0061615,M0061616,\
M0329540,M0329541,M0329542,M5596597,\
M0041372,M0041374,M0041378,M0329655,\
M0062600,M0060446,M0096197,\
M0134555,M0150207',
last_date,
today_date,
usedf=True)
if df.shape[1] == 1:
return [], name, []
df.columns = ['OMO：净投放', 'OMO：投放', 'OMO：回笼',\
'MLF_数量_3m','MLF_数量_6m','MLF_数量_1y','MLF_到期',\
'逆回购_数量_7d','逆回购_数量_14d','逆回购_数量_28d','逆回购_数量_63d',\
'逆回购_到期','国库现金：中标量','国库现金：到期量',\
'SLO_投放','SLO_回笼']
df['date'] = df.index
df = df.loc[(df.date > last_date) & (df.date <= dt.datetime.now().date())]
columns_type = [
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
DateTime()
]
dtypelist = dict(zip(df.columns, columns_type))
return df, name, dtypelist
def cash_cost():
name = 'cash_cost'
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('M1006336,M1006337,M1004515,M0017142',
last_date,
today_date,
usedf=True)
if df.shape[1] == 1:
return [], name, []
df.columns = ['DR001', 'DR007', 'GC007', 'shibor_3m']
df['date'] = df.index
df = df.loc[df.date > last_date]
columns_type = [Float(2), Float(2), Float(2), Float(2), DateTime()]
dtypelist = dict(zip(df.columns, columns_type))
return df, name, dtypelist
def fig_downstream():
name = 'fig_downstream'
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("S2707379,S2707380,S2726996,S6126413, \
S0049599,S0000293,S6500614,S6424740, \
S6604459,S6604460,S0000066,S0237842,S0031550",
last_date,
today_date,
usedf=True)
if df.shape[1] == 1:
return [], name, []
df.columns = [
'30大中城市:商品房成交套数', '30大中城市:商品房成交面积', '100大中城市:成交土地溢价率:当周值',
'当周日均销量:乘用车:厂家零售', '柯桥纺织:价格指数:总类', '义乌中国小商品指数:总价格指数', '中关村电子价格产品指数',
'中国公路物流运价指数', '电影票房收入', '电影观影人次', 'CCFI:综合指数', 'CICFI:综合指数',
'波罗的海干散货指数(BDI)'
]
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(),
Float(),
Float(),
Float(),
DateTime()
]
dtypelist = dict(zip(df.columns, columns_type))
return df, name, dtypelist
def daily_fig_bond_leverage():
name = 'fig_bond_leverage'
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('M0041739,M5639029', last_date, today_date, usedf=True)
if df.shape[1] == 1:
return [], name, []
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
df = df.loc[df.date > last_date]
columns_type = [Float(4), Float(1), DateTime()]
dtypelist = dict(zip(df.columns, columns_type))
return df, name, dtypelist
def fig_cpi_ppi_related():
name = 'fig_cpi_ppi_related'
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("S0000241,S0000242,S0000240,S0000236, \
S5065111,S5065112,S0143884,S5042881,S0105896,S0031505,\
S0248945,M6424471",
last_date,
today_date,
usedf=True)
if df.shape[1] == 1:
return [], name, []
df.columns = [
"食用农产品价格指数:蛋类:周环比", "食用农产品价格指数:蔬菜类:周环比", "食用农产品价格指数:禽类:周环比",
"食用农产品价格指数", '平均批发价:28种重点监测蔬菜', '平均批发价:7种重点监测水果', '平均价:猪肉:全国',
'中国大宗商品价格指数:总指数', '南华综合指数', 'CRB现货指数:综合', '农产品批发价格200指数',
'iCPI:总指数:日环比'
]
df['date'] = df.index
df = df.loc[(df.date > last_date) & (df.date < dt.datetime.now().date())]
columns_type = [
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
DateTime()
]
dtypelist = dict(zip(df.columns, columns_type))
return df, name, dtypelist
def fig_upstream():
name = 'fig_upstream'
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("S5104570,S5125686,S5111905,S5111903,\
S5705040,S5705131,S0031648,S0031645,\
M0066355,M0066356,S0049493,S0049494,S0200868",
last_date,
today_date,
usedf=True)
if df.shape[1] == 1:
return [], name, []
df.columns = [
'综合平均价格指数:环渤海动力煤', '炼焦煤库存:六港口合计', '现货价:原油:英国布伦特Dtd',
'现货价:原油:美国西德克萨斯中级轻质原油(WTI)', 'Mylpic矿价指数:综合', '国内铁矿石港口库存量',
'伦敦现货白银:以美元计价', '伦敦现货黄金:以美元计价', '期货收盘价(活跃合约):阴极铜', '期货收盘价(活跃合约):铝',
'库存期货:阴极铜', '库存期货:铝', '南华焦炭指数'
]
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(),
Float(),
Float(),
Float(),
DateTime()
]
dtypelist = dict(zip(df.columns, columns_type))
return df, name, dtypelist
def industial_premium():
name = 'fig_industries_premium'
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("M1008950,M1008953,M1008973,M1008971,M1008964",
last_date,
today_date,
usedf=True)
if df.shape[1] == 1:
return [], name, []
df.columns = ["信用利差_地产","信用利差_钢铁","信用利差_煤炭",\
"信用利差_有色","信用利差_汽车"]
df['date'] = df.index
df = df.loc[df.date > last_date]
columns_type = [Float(), Float(), Float(), Float(), Float(), DateTime()]
dtypelist = dict(zip(df.columns, columns_type))
return df, name, dtypelist
def main():
# 附件下载路径
download_io = "/Users/wdt/Desktop/tpy/raw_data_pool/现券市场交易情况总结/日报/"
# @ 读取mail_config.txt内的邮箱信息
io = os.getcwd() + "/mail_config.txt"
with open(io, 'r') as f1:
config = f1.readlines()
for i in range(0, len(config)):
config[i] = config[i].rstrip('\n')
host = config[0] # pop.163.com
username = config[1] # 用户名
password = config[2] # 密码
# 获取数据库内最后更新时间
last_date = do.get_latest_date('Net_buy_bond')
#获取邮件池子
msg_list = get_mail_pool(host, username, password)
#下载邮件
download_certain_mail(msg_list, download_io, last_date)
def repo_volume():
name = 'repo_volume'
last_date = do.get_latest_date(name)
today_date = dt.datetime.now().date()
print('表{}的最近更新日期为{}'.format(name, last_date))
err, df=w.edb('M1004529,M0330244,M0330245,M0330246,\
M0330247,M0330248,M0330249,M0330250,\
M0330251,M0330252,M0330253,M0330254,\
M0041739' ,\
last_date,today_date.strftime("%Y-%m-%d"), "Fill=Previous",usedf=True)
if df.shape[1] == 1:
return [], name, []
df.columns = ['加权利率:R001','成交量:R001','成交量:R007','成交量:R014',
'成交量:R021','成交量:R1M', '成交量:R2M','成交量:R3M',
'成交量:R4M', '成交量:R6M', '成交量:R9M','成交量:R1Y',\
'成交量:银行间质押式回购']
df['date'] = df.index
df = df.loc[df.date > last_date]
columns_type = [
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
DateTime()
]
dtypelist = dict(zip(df.columns, columns_type))
return df, name, dtypelist
def main():
conn, engine = do.get_db_conn()
w.start()
# 宏观周报数据
l = [
fig_industrial_production(),
fig_cpi_ppi_related(),
fig_upstream(),
fig_downstream(),
fig_midstream()
]
for a, b, c in l:
if len(np.array(a)) == 0:
print(b, '已是最新，无需更新')
continue
a.to_sql(name=b,
con=engine,
schema='finance',
if_exists='append',
index=False,
dtype=c)
print('成功更新表', b, '至', do.get_latest_date(b))
def upload_date_list(name):
'''
输出一个待更新日期的列表
最后依照此列表逐个上传
'''
dir_names=[]
latest_date = do.get_latest_date(name)
if name == 'Net_buy_bond':
path = input_path+"/现券市场交易情况总结/日报"
elif 'Repo' in name :
path = input_path+'/质押式回购市场交易情况总结/日报'
for dir in os.listdir(path ):
if 'DS' in dir or 'xlsx' in dir :
continue
# print(dir)
attach_time = re.match(".*\\d{8}\\.",dir).group()[-9:-1]
attach_datetime = pd.to_datetime(attach_time)
if attach_datetime > latest_date:
dir_names.append(dir)
else:
pass
return dir_names
def daily_fig_liquidity_premium():
name = 'fig_liquidity_premium'
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('M0017139,M0041653,M0220163,\
M0017142,M0048486,M1010889,M1010892,M0329545,\
M1011048' , \
last_date,today_date,usedf=True)
if df.shape[1] == 1:
return [], name, []
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.loc[df.date > last_date]
columns_type = [
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
DateTime()
]
dtypelist = dict(zip(df.columns, columns_type))
return df, name, dtypelist
def rates():
name = 'rates'
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("S0059744,S0059746,S0059747,S0059748,S0059749,\
M1004298,M1004300,M1004302,M1004304,M1004306,\
M1004263,M1004265,M1004267,M1004269,M1004271,\
M0048432,M0048434,M0048435,M0048436,\
M0048422,M0048424,M0048425,M0048426,\
M0048412,M0048414,M0048415,M0048416,\
M1002654,M1002656,M1002658,\
M1003631,M1003633,M1003635,\
M1003639,M1003641,M1003643,\
M1003623,M1003625,M1003627" ,\
last_date,today_date,usedf = True)
if df.shape[1] == 1:
return [], name, []
df.columns=['国债1年','国债3年','国债5年','国债7年','国债10年',\
'地方1年','地方3年','地方5年','地方7年','地方10年',\
'国开1年','国开3年','国开5年','国开7年','国开10年',\
'城投_AAA_1y','城投_AAA_3y','城投_AAA_5y','城投_AAA_7y',\
'城投_AA+_1y','城投_AA+_3y','城投_AA+_5y','城投_AA+_7y',\
'城投_AA_1y','城投_AA_3y','城投_AA_5y','城投_AA_7y',\
'中票_AAA_1y','中票_AAA_3y','中票_AAA_5y',\
'中票_AA+_1y','中票_AA+_3y','中票_AA+_5y',\
'中票_AA_1y','中票_AA_3y','中票_AA_5y',\
'中票_AA-_1y','中票_AA-_3y','中票_AA-_5y']
df['date'] = df.index
df = df.loc[df.date > last_date]
columns_type = [
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
Float(),
DateTime()
]
dtypelist = dict(zip(df.columns, columns_type))
return df, name, dtypelist
