def download_economy():
#货币供应量
ts.get_money_supply().to_csv(path + 'money_supply.csv')
#季度GDP
ts.get_gdp_quarter().to_csv(path + 'gdp_quarter.csv')
#年度GDP
ts.get_gdp_year().to_csv(path + 'gdp_year.csv')
#CPI
ts.get_cpi().to_csv(path + 'cpi.csv')
#存款准备金率
ts.get_rrr().to_csv(path + 'rrr.csv')
def add_reserve_base_rate_page(canvas_para):
"""
函数功能：在pdf中增加准备金基率
:param canvas_para:
:return:
"""
c = canvas_para
c.setFont("song", 10)
c.drawString(10, letter[1] - 20, '存款准备金基率')
c.setLineWidth(3)
c.line(10, letter[1] - 24, letter[0] - 10, letter[1] - 24)
# 画银行准备金基率
df_rbr = ts.get_rrr().replace('--', np.nan)
# df_rbr['date'] = df_rbr.apply(lambda x: stdMonthDate2ISO(x['month']), axis=1)
# 提取相关数据
pot_before = extract_point_from_df_date_x(df_rbr, 'date', 'before')
pot_now = extract_point_from_df_date_x(df_rbr, 'date', 'now')
pot_changed = extract_point_from_df_date_x(df_rbr, 'date', 'changed')
data_rbr = [tuple(pot_now)]
data_rbr_note = ['准备金基率']
money_drawing = gen_lp_drawing(data=data_rbr,
data_note=data_rbr_note,
height=letter[1] * 0.2)
renderPDF.draw(drawing=money_drawing, canvas=c, x=10, y=letter[1] * 0.7)
c.showPage()
return c
def get_rrr(year, quarter):
try:
df = ts.get_rrr()
engine = create_engine('mysql://*****:*****@127.0.0.1/stock?charset=utf8')
df.to_sql('rrr', engine, if_exists='append')
print "message"
except Exception, e:
e.message
def get_rrr_info():
df = ts.get_rrr()
if df is not None:
res = df.to_sql(microE_rrr, engine, if_exists='replace')
msg = 'ok' if res is None else res
print('获取存款准备金率: ' + msg + '\n')
else:
print('获取存款准备金率: ' + 'None' + '\n')
def return_rrr(self):
'''
存款准备金率
'''
df = ts.get_rrr()
detail = {}
for col in df.columns:
lt = df[col].values.tolist()
lt.reverse()
detail[col] = lt
self.reply(detail=detail)
def stat_all(tmp_datetime):
# 存款利率
data = ts.get_deposit_rate()
common.insert_db(data, "ts_deposit_rate", False, "`date`,`deposit_type`")
# 贷款利率
data = ts.get_loan_rate()
common.insert_db(data, "ts_loan_rate", False, "`date`,`loan_type`")
# 存款准备金率
data = ts.get_rrr()
common.insert_db(data, "ts_rrr", False, "`date`")
# 货币供应量
data = ts.get_money_supply()
common.insert_db(data, "ts_money_supply", False, "`month`")
# 货币供应量(年底余额)
data = ts.get_money_supply_bal()
common.insert_db(data, "ts_money_supply_bal", False, "`year`")
# 国内生产总值(年度)
data = ts.get_gdp_year()
common.insert_db(data, "ts_gdp_year", False, "`year`")
# 国内生产总值(季度)
data = ts.get_gdp_quarter()
common.insert_db(data, "ts_get_gdp_quarter", False, "`quarter`")
# 三大需求对GDP贡献
data = ts.get_gdp_for()
common.insert_db(data, "ts_gdp_for", False, "`year`")
# 三大产业对GDP拉动
data = ts.get_gdp_pull()
common.insert_db(data, "ts_gdp_pull", False, "`year`")
# 三大产业贡献率
data = ts.get_gdp_contrib()
common.insert_db(data, "ts_gdp_contrib", False, "`year`")
# 居民消费价格指数
data = ts.get_cpi()
common.insert_db(data, "ts_cpi", False, "`month`")
# 工业品出厂价格指数
data = ts.get_ppi()
common.insert_db(data, "ts_ppi", False, "`month`")
#############################基本面数据 http://tushare.org/fundamental.html
# 股票列表
data = ts.get_stock_basics()
print(data.index)
common.insert_db(data, "ts_stock_basics", True, "`code`")
def download_economy():
path = './data/'
df = ts.get_money_supply()
df.to_csv(path+'money_supply.csv')
ts.get_gdp_quarter().to_csv(path+'gdp_quarter.csv')
ts.get_gdp_year().to_csv(path + 'gdp_year.csv')
ts.get_cpi().to_csv(path+'cpi.csv')
# ts.get_hist_data('sz').to_csv(path + 'sz.csv')
# ts.get_hist_data('sh').to_csv(path + 'sh.csv')
# import time
import datetime
# now_year = time.localtime().tm_year
# now_mon = time.localtime().tm_mon
# now_day = time.localtime().tm_mday
years = 3
start = datetime.datetime.today().date() + datetime.timedelta(-365*years)
end = datetime.datetime.today().date()
ts.get_k_data('399001', start=str(start), index=True).to_csv(path + 'sz.csv') #默认2年 ,
ts.get_k_data('000001', start=str(start), index=True).to_csv(path + 'sh.csv')
#
ts.get_rrr().to_csv(path + 'rrr.csv')
def download_economy():
import tushare as ts
path = 'C:/Users/Administrator/stockPriditionProjects/data/'
ts.get_money_supply().to_csv(path + 'money_supply.csv')
ts.get_gdp_quarter().to_csv(path + 'gdp_quarter.csv')
ts.get_gdp_year().to_csv(path + 'gdp_year.csv')
ts.get_cpi().to_csv(path + 'cpi.csv')
# ts.get_hist_data('sz').to_csv(path + 'sz.csv')
# ts.get_hist_data('sh').to_csv(path + 'sh.csv')
# import time
import datetime
# now_year = time.localtime().tm_year
# now_mon = time.localtime().tm_mon
# now_day = time.localtime().tm_mday
years = 3
start = datetime.datetime.today().date() + datetime.timedelta(-365 * years)
end = datetime.datetime.today().date()
ts.get_k_data('399001', start=str(start),
index=True).to_csv(path + 'sz.csv') #默认2年 ,
ts.get_k_data('000001', start=str(start),
index=True).to_csv(path + 'sh.csv')
#存款准备金率
ts.get_rrr().to_csv(path + 'rrr.csv')
def get_macro():
Macro={}
Macro['Depo']=ts.get_deposit_rate()
Macro['Loan']=ts.get_loan_rate()
Macro['RRR']=ts.get_rrr()
Macro['MoneySupply']=ts.get_money_supply()
Macro['MoneyBalance']=ts.get_money_supply_bal()
Macro['GDPYOY']=ts.get_gdp_year()
Macro['GDPQOQ']=ts.get_gdp_quarter()
Macro['GDPFOR']=ts.get_gdp_for()
Macro['GDPPULL']=ts.get_gdp_pull()
Macro['GDPCON']=ts.get_gdp_contrib()
Macro['CPI']=ts.get_cpi()
Macro['PPI']=ts.get_ppi()
Macro['SHIBO']=ts.shibor_data()
return Macro
def get_required_reserves_rate():
"""得到存款准备金率"""
logger.info('Begin get RequiredReservesRate.')
try:
data_df = ts.get_rrr()
except Exception as e:
logger.exception('Error get RequiredReservesRate.')
return None
else:
data_dicts = []
if data_df.empty:
logger.warn('Empty get RequiredReservesRate.')
else:
data_dicts = [{'date': row[0], 'before': row[1], 'now': row[2], 'changed': row[3]}
for row in data_df.values]
logger.info('Success get RequiredReservesRate.')
return data_dicts
def core_function(self, type):
self.set_data()
print(type)
mongo = MongoClient("127.0.0.1", 27017)
if (type == 'gdp_year'):
print("gdp_year")
df = fd.get_gdp_year()
elif (type == 'gdp_quarter'):
print("gdp_quarter")
df = fd.get_gdp_quarter()
elif (type == 'gdp_for'):
print("gdp_for")
df = fd.get_gdp_for()
elif (type == 'gdp_pull'):
print("gdp_pull")
df = fd.get_gdp_pull()
elif (type == 'get_money_supply_bal'):
print("get_money_supply_bal")
df = fd.get_money_supply_bal()
elif (type == 'gdp_contrib'):
print("gdp_contrib")
df = fd.get_gdp_contrib()
elif (type == 'get_cpi'):
print("get_cpi")
df = ts.get_cpi()
elif (type == 'get_ppi'):
print("get_ppi")
df = ts.get_ppi()
elif (type == 'get_rrr'):
print("get_rrr")
df = ts.get_rrr()
elif (type == 'money_supply'):
print("money_supply")
df = ts.get_money_supply()
elif (type == 'money_supply_bal'):
print("money_supply_bal")
df = ts.get_money_supply_bal()
else:
df = {}
print(df)
insert_string = df.to_json(orient='records')
items = json.loads(insert_string)
mongo.macro.gdp_year.insert(items)
def __init__(self, instrument, timewindow):
self.__instrument = instrument
self.__timewindow = int(timewindow)
self.__starttime = (
datetime.datetime.now() -
datetime.timedelta(days=self.__timewindow)).strftime('%Y-%m-%d')
# __starttime is max time of entry point
self.__endtime = datetime.datetime.now().strftime('%Y-%m-%d')
basics = ts.get_stock_basics()
profit = ts.get_profit_data(2014, 3) #code is not the index
report = ts.get_report_data(2014, 3)
operation = ts.get_operation_data(2014, 3)
growth = ts.get_growth_data(2014, 3)
debtpay = ts.get_debtpaying_data(2014, 3)
cashflow = ts.get_cashflow_data(2014, 3)
# country wise
rrr = ts.get_rrr()
money_supply = ts.get_money_supply()
cpi = ts.get_cpi()
def __call__(self, conns):
self.base = Base()
self.financial_data = conns['financial_data']
'''存款利率'''
deposit_rate = ts.get_deposit_rate()
self.base.batchwri(deposit_rate, 'deposit_rate', self.financial_data)
'''贷款利率'''
loan_rate = ts.get_loan_rate()
self.base.batchwri(loan_rate, 'loan_rate', self.financial_data)
'''存款准备金率'''
rrr = ts.get_rrr()
self.base.batchwri(rrr, 'RatioOfDeposit', self.financial_data)
'''货币供应量'''
money_supply = ts.get_money_supply()
self.base.batchwri(money_supply, 'money_supply', self.financial_data)
'''货币供应量（年底余额)'''
money_supply_bal = ts.get_money_supply_bal()
self.base.batchwri(money_supply_bal, 'money_supply_bal',
self.financial_data)
'''国内生产总值(年度）'''
gdp_year = ts.get_gdp_year()
self.base.batchwri(gdp_year, 'gdp_year', self.financial_data)
'''国内生产总值（季度）'''
gdp_quarter = ts.get_gdp_quarter()
self.base.batchwri(gdp_quarter, 'gdp_quarter', self.financial_data)
'''三大需求对GDP贡献'''
gdp_for = ts.get_gdp_for()
self.base.batchwri(gdp_for, 'gdp_for', self.financial_data)
'''三大产业对GDP拉动'''
gdp_pull = ts.get_gdp_pull()
self.base.batchwri(gdp_pull, 'gdp_pull', self.financial_data)
'''三大产业贡献率'''
gdp_contrib = ts.get_gdp_contrib()
self.base.batchwri(gdp_contrib, 'gdp_contrib', self.financial_data)
'''居民消费价格指数'''
cpi = ts.get_cpi()
self.base.batchwri(cpi, 'cpi', self.financial_data)
'''工业品出场价格指数'''
ppi = ts.get_ppi()
self.base.batchwri(ppi, 'ppi', self.financial_data)
import tushare as ts # 存款利率 # 结构不规整 dr = ts.get_deposit_rate() print(dr) # 贷款利率 # 结构不规整 lr = ts.get_loan_rate() print(lr) # 存款准备金率 rrr = ts.get_rrr() print(rrr) # 货币供应量 ms = ts.get_money_supply() print(ms) # 货币供应量(年底余额) ms = ts.get_money_supply_bal() print(ms) # 国内生产总值(年度) gy = ts.get_gdp_year() print(gy)
def rrr():
return ts.get_rrr()
# -*- coding: utf-8 -*- import tushare as ts # 宏观经济数据 #存款利率 saving_rate = ts.get_deposit_rate() #贷款利率 loan_rate = ts.get_loan_rate() #存款准备金率 reserve_rate = ts.get_rrr() #货币供应量 m_supply = ts.get_money_supply() #货币供应量(年底余额) m_supply_y = ts.get_money_supply_bal() #GDP gdp_y = ts.get_gdp_year() gdp_q = ts.get_gdp_quarter() #三大需求对GDP贡献 gdp_for = ts.get_gdp_for() #三大产业对GDP拉动 gdp_pull = ts.get_gdp_pull() #三大产业贡献率 gdp_contrib = ts.get_gdp_contrib() cpi = ts.get_cpi()
def getRRRRate(self):
file_name = 'rrr_rate.csv'
path = self.index + self.index_rrr + file_name
data = ts.get_rrr()
data.to_csv(path, encoding='utf-8')
print(file_name)
def macro_type(macros_type):
if macros_type == 'deposit_rate':
deposit_rate = ts.get_deposit_rate()
if deposit_rate is not None:
deposit_rate.to_sql('macros_deposit_rate',
engine,
flavor='mysql',
if_exists='replace')
elif macros_type == 'loan_rate':
loan_rate = ts.get_loan_rate()
if loan_rate is not None:
loan_rate.to_sql('macros_loan_rate',
engine,
flavor='mysql',
if_exists='replace')
elif macros_type == 'rrr':
rrr = ts.get_rrr()
if rrr is not None:
rrr.to_sql('macros_rrr',
engine,
flavor='mysql',
if_exists='replace')
elif macros_type == 'money_supply':
money_supply = ts.get_money_supply()
if money_supply is not None:
money_supply.to_sql('macros_money_supply',
engine,
flavor='mysql',
if_exists='replace')
elif macros_type == 'money_supply_bal':
money_supply_bal = ts.get_money_supply_bal()
if money_supply_bal is not None:
money_supply_bal.to_sql('macros_money_supply_bal',
engine,
flavor='mysql',
if_exists='replace')
elif macros_type == 'gdp_year':
gdp_year = ts.get_gdp_year()
if gdp_year is not None:
gdp_year.to_sql('macros_gdp_year',
engine,
flavor='mysql',
if_exists='replace')
elif macros_type == 'gdp_quater':
gdp_quater = ts.get_gdp_quarter()
if gdp_quater is not None:
gdp_quater.to_sql('macros_gdp_quater',
engine,
flavor='mysql',
if_exists='replace')
elif macros_type == 'gdp_for':
gdp_for = ts.get_gdp_for()
if gdp_for is not None:
gdp_for.to_sql('macros_gdp_for',
engine,
flavor='mysql',
if_exists='replace')
elif macros_type == 'gdp_pull':
gdp_pull = ts.get_gdp_pull()
if gdp_pull is not None:
gdp_pull.to_sql('macros_gdp_pull',
engine,
flavor='mysql',
if_exists='replace')
elif macros_type == 'gdp_contrib':
gdp_contrib = ts.get_gdp_contrib()
if gdp_contrib is not None:
gdp_contrib.to_sql('macros_gdp_contrib',
engine,
flavor='mysql',
if_exists='replace')
elif macros_type == 'cpi':
cpi = ts.get_cpi()
if cpi is not None:
cpi.to_sql('macros_cpi',
engine,
flavor='mysql',
if_exists='replace')
elif macros_type == 'ppi':
ppi = ts.get_ppi()
if ppi is not None:
ppi.to_sql('macros_ppi',
engine,
flavor='mysql',
if_exists='replace')
'''
i didn't multiprocess this one since the performance seems acceptable
'''
import tushare as ts
import pandas as pd
if __name__ =='__main__':
deposit_rate=ts.get_deposit_rate()
deposit_rate .to_csv('D:\\ts\\macro\\deposit_rate.csv',encoding='gbk')
loan_rate=ts.get_loan_rate()
loan_rate.to_csv('D:\\ts\\macro\\loan_rate.csv', encoding='gbk')
rrr=ts.get_rrr()
rrr.to_csv('D:\\ts\\macro\\rrr.csv', encoding='gbk')
money_supply=ts.get_money_supply()
money_supply.to_csv('D:\\ts\\macro\\money_supply.csv', encoding='gbk')
money_supply_bal=ts.get_money_supply_bal()
money_supply_bal.to_csv('D:\\ts\\macro\\money_supply_bal.csv', encoding='gbk')
gdp_year=ts.get_gdp_year()
gdp_year.to_csv('D:\\ts\\macro\\gdp_year.csv', encoding='gbk')
gdp_quater=ts.get_gdp_quarter()
gdp_quater.to_csv('D:\\ts\\macro\\gdp_quater.csv', encoding='gbk')
gdp_for=ts.get_gdp_for()
def getrrrdb():
rrr = ts.get_rrr()
rrr.to_sql('rrr_data',ENGINE,if_exists='append')
def setRRR(self, isSave=False, tableName=MACROECONOMIC_RRR):
df = ts.get_rrr()
if isSave is True:
df.to_sql(tableName, self.engine_sql, if_exists='append')
return df
def init(engine, session): tbl = "macro_deposit" tsl.log(tbl + " start...") df = ts.get_deposit_rate() df.to_sql(tbl,engine,if_exists='replace') tsl.log(tbl + " done") tbl = "macro_loan" tsl.log(tbl + " start...") df = ts.get_loan_rate() df.to_sql(tbl,engine,if_exists='replace') tsl.log(tbl + " done") tbl = "macro_rrr" tsl.log(tbl + " start...") df = ts.get_rrr() df.to_sql(tbl,engine,if_exists='replace') tsl.log(tbl + " done") tbl = "macro_money_supply" tsl.log(tbl + " start...") df = ts.get_money_supply() df.to_sql(tbl,engine,if_exists='replace') tsl.log(tbl + " done") tbl = "macro_money_supply_year" tsl.log(tbl + " start...") df = ts.get_money_supply_bal() df.to_sql(tbl,engine,if_exists='replace') tsl.log(tbl + " done") tbl = "macro_gdp_year" tsl.log(tbl + " start...") df = ts.get_gdp_year() df.to_sql(tbl,engine,if_exists='replace') tsl.log(tbl + " done") tbl = "macro_gdp_quarter" tsl.log(tbl + " start...") df = ts.get_gdp_quarter() df.to_sql(tbl,engine,if_exists='replace') tsl.log(tbl + " done") tbl = "macro_gdp_for" tsl.log(tbl + " start...") df = ts.get_gdp_for() df.to_sql(tbl,engine,if_exists='replace') tsl.log(tbl + " done") tbl = "macro_gdp_pull" tsl.log(tbl + " start...") df = ts.get_gdp_pull() df.to_sql(tbl,engine,if_exists='replace') tsl.log(tbl + " done") tbl = "macro_gdp_contrib" tsl.log(tbl + " start...") df = ts.get_gdp_contrib() df.to_sql(tbl,engine,if_exists='replace') tsl.log(tbl + " done") tbl = "macro_cpi" tsl.log(tbl + " start...") df = ts.get_cpi() df.to_sql(tbl,engine,if_exists='replace') tsl.log(tbl + " done") tbl = "macro_ppi" tsl.log(tbl + " start...") df = ts.get_ppi() df.to_sql(tbl,engine,if_exists='replace') tsl.log(tbl + " done") tbl = "gold_and_foreign_reserves" tsl.log(tbl + " start...") df = ts.get_gold_and_foreign_reserves() df.to_sql(tbl,engine,if_exists='replace') tsl.log(tbl + " done")
import tushare as ts import matplotlib.pyplot as plt import matplotlib as mpl import mpl_finance as mpf import numpy as np import pandas as pd lra = ts.get_rrr() print(lra[:1]) c = lra.iloc[:, 2] print(c.values) n = np.shape(c.values) print(n[0]) t = np.linspace(1, n[0], n[0]) plt.plot(t, c.values) plt.show()
rate:利率（%）
'''
df = ts.get_loan_rate()
print(df)
# 存款准备金率
'''
返回值说明：
date :变动日期
before :调整前存款准备金率(%)
now:调整后存款准备金率(%)
changed:调整幅度(%)
'''
df = ts.get_rrr()
#货币供应量
'''
返回值说明：
month :统计时间
m2 :货币和准货币（广义货币M2）(亿元)
m2_yoy:货币和准货币（广义货币M2）同比增长(%)
m1:货币(狭义货币M1)(亿元)
m1_yoy:货币(狭义货币M1)同比增长(%)
m0:流通中现金(M0)(亿元)
m0_yoy:流通中现金(M0)同比增长(%)
cd:活期存款(亿元)
cd_yoy:活期存款同比增长(%)
qm:准货币(亿元)
def job_5():
try:
print("I'm working......宏观经济数据")
# 存款利率
deposit_rate = ts.get_deposit_rate()
data = pd.DataFrame(deposit_rate)
data.to_sql('deposit_rate',engine,index=True,if_exists='replace')
print("存款利率......done")
# 贷款利率
loan_rate = ts.get_loan_rate()
data = pd.DataFrame(loan_rate)
data.to_sql('loan_rate',engine,index=True,if_exists='replace')
print("贷款利率......done")
# 存款准备金率
rrr = ts.get_rrr()
data = pd.DataFrame(rrr)
data.to_sql('rrr',engine,index=True,if_exists='replace')
print("存款准备金率......done")
# 货币供应量
money_supply = ts.get_money_supply()
data = pd.DataFrame(money_supply)
data.to_sql('money_supply',engine,index=True,if_exists='replace')
print("货币供应量......done")
# 货币供应量(年底余额)
money_supply_bal = ts.get_money_supply_bal()
data = pd.DataFrame(money_supply_bal)
data.to_sql('money_supply_bal',engine,index=True,if_exists='replace')
print("货币供应量(年底余额)......done")
# 国内生产总值(年度)
gdp_year = ts.get_gdp_year()
data = pd.DataFrame(gdp_year)
data.to_sql('gdp_year',engine,index=True,if_exists='replace')
print("国内生产总值(年度)......done")
# 国内生产总值(季度)
gdp_quarter = ts.get_gdp_quarter()
data = pd.DataFrame(gdp_quarter)
data.to_sql('gdp_quarter',engine,index=True,if_exists='replace')
print("国内生产总值(季度)......done")
# 三大需求对GDP贡献
gdp_for = ts.get_gdp_for()
data = pd.DataFrame(gdp_for)
data.to_sql('gdp_for',engine,index=True,if_exists='replace')
print("三大需求对GDP贡献......done")
# 三大产业对GDP拉动
gdp_pull = ts.get_gdp_pull()
data = pd.DataFrame(gdp_pull)
data.to_sql('gdp_pull',engine,index=True,if_exists='replace')
print("三大产业对GDP拉动......done")
# 三大产业贡献率
gdp_contrib = ts.get_gdp_contrib()
data = pd.DataFrame(gdp_contrib)
data.to_sql('gdp_contrib',engine,index=True,if_exists='replace')
print("三大产业贡献率......done")
# 居民消费价格指数
cpi = ts.get_cpi()
data = pd.DataFrame(cpi)
data.to_sql('cpi',engine,index=True,if_exists='replace')
print("居民消费价格指数......done")
# 工业品出厂价格指数
ppi = ts.get_ppi()
data = pd.DataFrame(ppi)
data.to_sql('ppi',engine,index=True,if_exists='replace')
print("工业品出厂价格指数......done")
except Exception as e:
print(e)
def load_macro_economy():
# 下载存款利率
try:
rs = ts.get_deposit_rate()
pd.DataFrame.to_sql(rs, "deposit_rate", con=conn_macro_economy, flavor="mysql", if_exists="replace", index=True)
print("下载存款利率ok")
except:
print("下载存款利率出错")
# 下载贷款利率
try:
rs = ts.get_loan_rate()
pd.DataFrame.to_sql(rs, "loan_rate", con=conn_macro_economy, flavor="mysql", if_exists="replace", index=True)
print("下载贷款利率ok")
except:
print("下载贷款利率出错")
# 下载存款准备金率
try:
rs = ts.get_rrr()
pd.DataFrame.to_sql(rs, "rrr", con=conn_macro_economy, flavor="mysql", if_exists="replace", index=True)
print("下载存款准备金率ok")
except:
print("下载存款准备金率出错")
# 下载货币供应量
try:
rs = ts.get_money_supply()
pd.DataFrame.to_sql(rs, "money_supply", con=conn_macro_economy, flavor="mysql", if_exists="replace", index=True)
print("下载货币供应量ok")
except:
print("下载货币供应量出错")
# 下载货币供应量(年底余额)
try:
rs = ts.get_money_supply_bal()
pd.DataFrame.to_sql(
rs, "money_supply_bal", con=conn_macro_economy, flavor="mysql", if_exists="replace", index=True
)
print("下载货币供应量(年底余额)ok")
except:
print("下载货币供应量(年底余额)出错")
# 下载国内生产总值(年度)
try:
rs = ts.get_gdp_year()
pd.DataFrame.to_sql(rs, "gdp_year", con=conn_macro_economy, flavor="mysql", if_exists="replace", index=True)
print("下载国内生产总值(年度)ok")
except:
print("下载国内生产总值(年度)出错")
# 下载国内生产总值(季度)
try:
rs = ts.get_gdp_quarter()
pd.DataFrame.to_sql(rs, "gdp_quarter", con=conn_macro_economy, flavor="mysql", if_exists="replace", index=True)
print("下载国内生产总值(季度)ok")
except:
print("下载国内生产总值(季度)出错")
# 下载三大需求对GDP贡献
try:
rs = ts.get_gdp_for()
pd.DataFrame.to_sql(rs, "gdp_for", con=conn_macro_economy, flavor="mysql", if_exists="replace", index=True)
print("下载三大需求对GDP贡献ok")
except:
print("下载三大需求对GDP贡献出错")
# 下载三大产业对GDP拉动
try:
rs = ts.get_gdp_pull()
pd.DataFrame.to_sql(rs, "gdp_pull", con=conn_macro_economy, flavor="mysql", if_exists="replace", index=True)
print("下载三大产业对GDP拉动ok")
except:
print("下载三大产业对GDP拉动出错")
# 下载三大产业贡献率
try:
rs = ts.get_gdp_contrib()
pd.DataFrame.to_sql(rs, "gdp_contrib", con=conn_macro_economy, flavor="mysql", if_exists="replace", index=True)
print("下载三大产业贡献率ok")
except:
print("下载三大产业贡献率出错")
# 下载居民消费价格指数
try:
rs = ts.get_cpi()
pd.DataFrame.to_sql(rs, "gdp_cpi", con=conn_macro_economy, flavor="mysql", if_exists="replace", index=True)
print("下载居民消费价格指数ok")
except:
print("下载居民消费价格指数出错")
# 下载工业品出厂价格指数
try:
rs = ts.get_ppi()
pd.DataFrame.to_sql(rs, "gdp_ppi", con=conn_macro_economy, flavor="mysql", if_exists="replace", index=True)
print("下载工业品出厂价格指数ok")
except:
print("下载工业品出厂价格指数出错")
def macrodata(start=None, end=None):
"""macroeconomics data : Shibor | Reserve Ratio | M2 | GDP | CPI | Loan Rate.
the data start from 2006-10-08. Cause it is when the shibor data start on Tushare.
parameters:
---------
start: a string present a date indicates the return data start from. for example: '2011-01-22'
end : refer to start
return:
--------
pandas.DataFrame
"""
T = __T
T.rename(columns={'calendarDate': 'date'}, inplace=True)
today = dt.today()
today = dt.strftime(today, '%Y-%m-%d')
T = T[T.date > '2006-04-01']
T = T[T.date <= today]
loan = ts.get_loan_rate() #date loan_type rate
loan = loan[loan.date > '2006-06-01']
rate = pd.DataFrame()
for i in range(len(loan)):
if loan.iloc[i].loan_type == '短期贷款(六个月以内)':
rate = rate.append(loan.iloc[i][['date', 'rate']])
rrr = ts.get_rrr()[['date', 'now']] # 准备金率
rrr.rename(columns={'now': 'rrr'}, inplace=True)
rrr = rrr[rrr.date > '2006-08-01']
dataScale = ['15', '09']
dataindex = 0
def makeDate(raw_date):
raw_date = str(raw_date)
raw_date = raw_date.split('.')
if int(raw_date[1]) <= 9:
raw_date[1] = '0' + raw_date[1]
return raw_date[0] + '-' + raw_date[1] + '-' + dataScale[dataindex]
def makeQuarter(raw_date):
raw_date = str(raw_date)
raw_date = raw_date.split('.')
p = raw_date[1]
q = {'1': '01', '2': '04', '3': '07', '4': '10'}
return raw_date[0] + '-' + q[p] + '-' + '15'
supply = ts.get_money_supply()[['month', 'm2_yoy']] #15th.
supply.rename(columns={'month': 'date', 'm2_yoy': 'm2'}, inplace=True)
if type(supply.iloc[1]['date']) == type(''):
supply = supply[supply.date > '2006.6']
else:
supply = supply[supply.date > 2006.6]
supply['date'] = supply['date'].apply(makeDate)
dataindex = dataindex + 1
m2 = supply
gdp = ts.get_gdp_quarter()[['quarter',
'gdp_yoy']] # gdp will available at 15th.
gdp.rename(columns={'quarter': 'date', 'gdp_yoy': 'gdp'}, inplace=True)
if type(gdp.iloc[1]['date']) == type(''):
gdp = gdp[gdp.date > '2006.1']
else:
gdp = gdp[gdp.date > 2006.1]
gdp['date'] = gdp['date'].apply(makeQuarter)
cpi = ts.get_cpi() #month , cpi, available at 9th.
cpi.rename(columns={'month': 'date'}, inplace=True)
if type(cpi.iloc[1]['date']) == type(''):
cpi = cpi[cpi.date > '2006.6']
else:
cpi = cpi[cpi.date > 2006.6]
cpi['date'] = cpi['date'].apply(makeDate)
lastvalue = 0.0
def setValue(v):
nonlocal lastvalue
if pd.isnull(v) or v == 'None':
return lastvalue
else:
lastvalue = v
return lastvalue
T = T.merge(cpi, on='date', how='left')
lastvalue = cpi.iloc[-1]['cpi']
T['cpi'] = T['cpi'].apply(setValue)
T = T.merge(gdp, on='date', how='left')
lastvalue = gdp.iloc[-1]['gdp']
T['gdp'] = T['gdp'].apply(setValue)
T = T.merge(m2, on='date', how='left')
lastvalue = m2.iloc[-1]['m2']
T['m2'] = T['m2'].apply(setValue)
T = T.merge(rrr, on='date', how='left')
lastvalue = rrr.iloc[-1]['rrr']
T['rrr'] = T['rrr'].apply(setValue)
T = T.merge(rate, on='date', how='left')
lastvalue = rate.iloc[-1]['rate']
T['rate'] = T['rate'].apply(setValue)
# T=shibor.merge(T,on='date',how='left')
T = T[T.isOpen > 0.5]
T = T[T.date >= '2006-10-08']
del T['isOpen']
if start and end:
T = T[T.date >= start]
T = T[T.date <= end]
T[['rrr']] = T[['rrr']].astype(float)
T[['m2']] = T[['m2']].astype(float)
T[['rate']] = T[['rate']].astype(float)
T[['gdp']] = T[['gdp']].astype(float)
T[['cpi']] = T[['cpi']].astype(float)
T = T.reset_index()
del T['index']
return T
