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 prepare_data():
deposit_rate = ts.get_gdp_year()
record_json = deposit_rate.to_json(orient='records')
print(record_json)
collection = mongoConfig.get_collection_default("gdp_year")
mongoConfig.clear_collection(collection)
mongoConfig.insert_json(collection, json.loads(record_json))
def get_gdp_year():
try:
df = ts.get_gdp_year()
engine = create_engine('mysql://*****:*****@127.0.0.1/stock?charset=utf8')
df.to_sql('gdp_year', engine, if_exists='append')
print "message"
except Exception, e:
e.message
def get_gdp_year_info():
df = ts.get_gdp_year()
if df is not None:
res = df.to_sql(microE_gdp_year, engine, if_exists='replace')
msg = 'ok' if res is None else res
print('获取国内生产总值(年度): ' + msg + '\n')
else:
print('获取国内生产总值(年度): ' + 'None' + '\n')
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 return_gdp_year(self):
'''
国内生产总值(年度)
'''
df = ts.get_gdp_year()
df = df[df['year'] >= 1978]
detail = {}
for col in df.columns:
lt = df[col].values.tolist()
lt.reverse()
for idx in xrange(0, len(lt)):
if math.isnan(lt[idx]):
lt[idx] = None
detail[col] = lt
self.reply(detail=detail)
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_gdp_year():
"""国内生产总值(年度)"""
logger.info('Begin get GrossDomesticProductYear.')
try:
data_df = ts.get_gdp_year()
except Exception as e:
logger.exception('Error get GrossDomesticProductYear.')
return None
else:
data_dicts = []
if data_df.empty:
logger.warn('Empty get GrossDomesticProductYear.')
else:
data_dicts = [{'year': row[0], 'gdp': row[1],
'pc_gdp': row[2], 'gnp': row[3], 'pi': row[4],
'si': row[5], 'industry': row[6], 'cons_industry': row[7], 'ti': row[8],
'trans_industry': row[9], 'lbdy': row[10]}
for row in data_df.values]
logger.info('Success get GrossDomesticProductYear.')
return data_dicts
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)
def gdp_vs_m2(self):
dat_cpi = ts.get_cpi()
dat_m2 = ts.get_money_supply()
dat_gdp = ts.get_gdp_year()
dat_gdp.index =pd.to_datetime(dat_gdp['year'], format='%Y')
dat_gdp=dat_gdp.to_period('A')
dat_gdp = dat_gdp['gdp']
dat_gdp = dat_gdp['2017':'2000']
dat_gdp=dat_gdp.sort_index()
#dat_gdp = (dat_gdp - dat_gdp.min()) / (dat_gdp.max() - dat_gdp.min())
dat_cpi.index=pd.to_datetime(dat_cpi['month'])
dat_m2.index=pd.to_datetime(dat_m2['month'])
dat_cpi = dat_cpi['2017':'2000']
dat_m2 = dat_m2['2017':'2000']
dat_m2['cpi']=dat_cpi['cpi']
dat = dat_m2[['m0','m1','m2','cpi']]
dat = dat.sort_index()
dat['cpi']= dat['cpi']/100
dat['cpi'] = dat['cpi'].cumprod(axis=0)
dat = dat.astype(dtype='float64')
#dat = (dat - dat.min()) / (dat.max() - dat.min())
dat=dat.resample("AS").first()
dat= dat.to_period('A')
#dat=dat.resample("AS").sum()
dat['gdp']=dat_gdp
#dat['m2-gdp']=dat['m2']-dat['gdp']
#dat = (dat - dat.min()) / (dat.max() - dat.min())
#dat['m2-gdp']=dat['m2']-dat['gdp']
dat['m0']=dat['m0']/dat['m0']['2000']
dat['m1']=dat['m1']/dat['m1']['2000']
dat['m2']=dat['m2']/dat['m2']['2000']
dat['cpi']=dat['cpi']/dat['cpi']['2000']
dat['gdp']=dat['gdp']/dat['gdp']['2000']
dat['m2-gdp']=dat['m2']-dat['gdp']
dat=dat.drop(['cpi','m0','m1'], axis=1)
print(dat)
#dat['m2-gdp'].plot()
dat.plot()
plt.show()
def gdp():
return ts.get_gdp_year()
def getGDPYear(self):
file_name = 'gdp_year.csv'
path = self.index + self.index_gdp_year + file_name
data = ts.get_gdp_year()
data.to_csv(path, encoding='utf-8')
print(file_name)
# 练习题 # 利用TuShare接口，获取近5年的GDP信息，绘制柱状图。 # 再抓取这5年的单季度GDP，绘制折线图。 from matplotlib.font_manager import FontProperties import matplotlib.pyplot as plt import numpy as np import tushare as ts # 获取年度gdp信息 gdp_years = ts.get_gdp_year().head(5) # 绘制年度gdp图片所用数据存储 years = [] year_gdps = [] # 保存最近5年的GDP数据 for i in range(5): years.append(int(gdp_years.iloc[i]['year'])) year_gdps.append(int(gdp_years.iloc[i]['gdp'])) # 查询季度GDP记录 gdp_quarters = ts.get_gdp_quarter().head(25) quarters = [] quarter_gdps = [] # 保存最近5年的GDP的季度信息 for i in range(25): quarter_information = gdp_quarters.iloc[i] # 剔除掉超出这5年内的信息 if int(quarter_information.quarter) > years[0]: continue elif int(quarter_information.quarter) < years[-1]:
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()
gdp_for.to_csv('D:\\ts\\macro\\gdp_for.csv', encoding='gbk')
gdp_pull=ts.get_gdp_pull()
gdp_pull.to_csv('D:\\ts\\macro\\gdp_pull.csv', encoding='gbk')
gdp_contrib=ts.get_gdp_contrib()
gdp_contrib.to_csv('D:\\ts\\macro\\gdp_contrib.csv', encoding='gbk')
cpi=ts.get_cpi()
def getgdpyeardb():
gdpyear = ts.get_gdp_year()
gdpyear.to_sql('gdpyear_db',ENGINE,if_exists='append')
def capture_stock_data():
capture_date = datetime.datetime.now().strftime("%Y%m%d")
save_dir = "/home/dandelion/stock_data/" + capture_date
if not os.path.exists(save_dir):
os.mkdir(save_dir)
print("The save directory is created successfully!\n", save_dir)
print("The save directory is already exist!\n", save_dir)
# ======================Daily Command================================================================
# get the boxoffcie data of the last day and save as csvfile named as the capture command
ts.day_boxoffice().to_csv(
save_dir + "/" + capture_date + "_day_boxoffice.csv",
header=True,
sep=",",
index=False,
)
print("day_boxoffice data capture completed!")
# get the cinema data of the last day and save as csvfile named as the capture command
ts.day_cinema().to_csv(
save_dir + "/" + capture_date + "_day_cinema.csv",
header=True,
sep=",",
index=False,
)
print("day_cinema data capture completed!")
ts.month_boxoffice().to_csv(
save_dir + "/" + capture_date + "_month_boxoffice.csv",
header=True,
sep=",",
index=False,
)
print("month_boxoffice data capture completed!")
ts.realtime_boxoffice().to_csv(
save_dir + "/" + capture_date + "_realtime_boxoffice.csv",
header=True,
sep=",",
index=False,
)
print("realtime_boxoffice data capture completed!")
# get the stock data index of the last day and save as csvfile named as the capture command
ts.get_index().to_csv(
save_dir + "/" + capture_date + "_get_index.csv",
header=True,
sep=",",
index=False,
)
print("get_index data capture completed!")
# get the history cpi data and save as csvfile named as the capture command
ts.get_cpi().to_csv(
save_dir + "/" + capture_date + "_get_cpi.csv",
header=True,
sep=",",
index=False,
)
print("get_cpi data capture completed!")
# get the history gdp data by month and save as csvfile named as the capture command
ts.get_gdp_year().to_csv(
save_dir + "/" + capture_date + "_get_gdp_year.csv",
header=True,
sep=",",
index=False,
)
print("get_gdp_year data capture completed!")
# get today all stock data and save as csvfile named as the capture command
# ts.get_today_all().to_csv(save_dir+'/'+capture_date+'_get_today_all.csv',header=True,sep=',',index=False)
# get detail information of the top brokers today and save as csvfile named as the capture command
ts.broker_tops().to_csv(
save_dir + "/" + capture_date + "_broker_tops.csv",
header=True,
sep=",",
index=False,
)
print("broker_tops data capture completed!")
# get detail information of the top brokers today and save as csvfile named as the capture command
ts.cap_tops().to_csv(
save_dir + "/" + capture_date + "_cap_tops.csv",
header=True,
sep=",",
index=False,
)
print("cap_tops data capture completed!")
ts.get_area_classified().to_csv(
save_dir + "/" + capture_date + "_get_area_classified.csv",
header=True,
sep=",",
index=False,
)
print("get_area_classified data capture completed!")
# ts.get_balance_sheet(code='').to_csv(save_dir+'/'+capture_date+'_get_balance_sheet.csv',header=True,sep=',',index=False)
# print('get_balance_sheet data capture completed!')
# ts.get_cash_flow(code='').to_csv(save_dir+'/'+capture_date+'_get_cash_flow.csv',header=True,sep=',',index=False)
# print('get_cash_flow data capture completed!')
ts.get_day_all().to_csv(
save_dir + "/" + capture_date + "_get_day_all.csv",
header=True,
sep=",",
index=False,
)
print("get_day_all data capture completed!")
ts.get_cashflow_data(2018, 3).to_csv(
save_dir + "/" + capture_date + "_get_cashflow_data.csv",
header=True,
sep=",",
index=False,
)
print("get_cashflow_data data capture completed!")
ts.get_concept_classified().to_csv(
save_dir + "/" + capture_date + "_get_concept_classified.csv",
header=True,
sep=",",
index=False,
)
print("get_concept_classified data capture completed!")
ts.get_debtpaying_data(2018, 3).to_csv(
save_dir + "/" + capture_date + "_get_debtpaying_data.csv",
header=True,
sep=",",
index=False,
)
print("get_debtpaying_data data capture completed!")
ts.get_deposit_rate().to_csv(
save_dir + "/" + capture_date + "_get_deposit_rate.csv",
header=True,
sep=",",
index=False,
)
print("get_deposit_rate data capture completed!")
ts.get_gdp_contrib().to_csv(
save_dir + "/" + capture_date + "_get_gdp_contrib.csv",
header=True,
sep=",",
index=False,
)
ts.get_gdp_for().to_csv(
save_dir + "/" + capture_date + "_get_gdp_for.csv",
header=True,
sep=",",
index=False,
)
ts.get_gdp_pull().to_csv(
save_dir + "/" + capture_date + "_get_gdp_pull.csv",
header=True,
sep=",",
index=False,
)
ts.get_gdp_quarter().to_csv(
save_dir + "/" + capture_date + "_get_gdp_quarter.csv",
header=True,
sep=",",
index=False,
)
print("get_gdp_ data capture completed!")
# ts.get_gdp_year().to_csv(save_dir+'/'+capture_date+'_get_gdp_year.csv',header=True,sep=',',index=False)
ts.get_gem_classified().to_csv(
save_dir + "/" + capture_date + "_get_gem_classified.csv",
header=True,
sep=",",
index=False,
)
ts.get_gold_and_foreign_reserves().to_csv(
save_dir + "/" + capture_date + "_get_gold_and_foreign_reserves.csv",
header=True,
sep=",",
index=False,
)
ts.get_growth_data(2018, 3).to_csv(
save_dir + "/" + capture_date + "_get_growth_data.csv",
header=True,
sep=",",
index=False,
)
ts.get_industry_classified().to_csv(
save_dir + "/" + capture_date + "_get_industry_classified.csv",
header=True,
sep=",",
index=False,
)
ts.get_hs300s().to_csv(
save_dir + "/" + capture_date + "_get_hs300s.csv",
header=True,
sep=",",
index=False,
)
ts.get_sz50s().to_csv(
save_dir + "/" + capture_date + "_get_sz50s.csv",
header=True,
sep=",",
index=False,
)
ts.get_zz500s().to_csv(
save_dir + "/" + capture_date + "_get_zz500s.csv",
header=True,
sep=",",
index=False,
)
ts.get_operation_data(2018, 3).to_csv(
save_dir + "/" + capture_date + "_get_operation_data.csv",
header=True,
sep=",",
index=False,
)
ts.get_stock_basics().to_csv(
save_dir + "/" + capture_date + "_get_stock_basics.csv",
header=True,
sep=",",
index=False,
)
ts.get_report_data(2018, 3).to_csv(
save_dir + "/" + capture_date + "_get_report_data.csv",
header=True,
sep=",",
index=False,
)
ts.inst_detail().to_csv(
save_dir + "/" + capture_date + "_inst_detail.csv",
header=True,
sep=",",
index=False,
)
ts.inst_tops().to_csv(
save_dir + "/" + capture_date + "_inst_tops.csv",
header=True,
sep=",",
index=False,
)
print("inst_tops data capture completed!")
ts.new_stocks().to_csv(
save_dir + "/" + capture_date + "_new_stocks.csv",
header=True,
sep=",",
index=False,
)
print("new_stocks data capture completed!")
ts.top_list().to_csv(
save_dir + "/" + capture_date + "_top_list.csv",
header=True,
sep=",",
index=False,
)
print("top_list data capture completed!")
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')
def Get_gdp_year(self):
dt = ts.get_gdp_year()
dt.to_csv('年度GDP.csv')
print(dt)
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 setGdpYear(self, isSave=False, tableName=MACROECONOMIC_GDP_YEAR):
df = ts.get_gdp_year()
if isSave is True:
df.to_sql(tableName, self.engine_sql, if_exists='append')
return df
d_r=ts.get_deposit_rate() d_r.info() d_r d_r.deposit_type.value_counts() dr1=d_r[d_r.deposit_type=='活期存款(不定期)'].sort_values(by='date'); dr1.index=dr1.date.str[:7];dr1 dr2=d_r[d_r.deposit_type=='定期存款整存整取(一年)'].sort_values(by='date'); dr2.index=dr2.date.str[:7];dr2 dr3=pd.concat([dr1.rate.astype(float),dr2.rate.astype(float)],axis=1); dr3.columns=['活期存款(不定期)','整存整取(一年)'];dr3 dr3.plot(); dr3.plot(secondary_y='整存整取(一年)'); ###10.3.2 国内生产总值GDP分析 g_y=ts.get_gdp_year() ## 国内生产总值(年度) g_y.info() g_y.head() #g_y.index=g_y.year #g_y.drop(['year'],axis=1,inplace=True) #g_y.sort_index(inplace=True) # g_y.sort_values(by='year',inplace=True) g_y.head() plt.plot(g_y.year,g_y.gdp) g_y1=g_y[g_y.year>=1990];g_y1 plt.plot(g_y1.year,g_y1.gdp) g_y2=g_y1[['pi','si','ti']] g_y2.index=g_y1.year; g_y2
import tushare as ts
import csv
from matplotlib.font_manager import FontProperties
import matplotlib.pyplot as plt
import numpy as np
gdp_years = ts.get_gdp_year()
gdp_quarters = ts.get_gdp_quarter().head(25)
years = []
yearsgdp = []
for i in range(5):
years.append(gdp_years.iloc[i]['year'])
yearsgdp.append(gdp_years.iloc[i]['gdp'])
quarters = []
quartersgdp = []
for i in range(25):
qinfo = gdp_quarters.iloc[i]
if qinfo.quarter > years[0]:
continue
elif qinfo.quarter < years[-1]:
break
quarters.append(qinfo.quarter)
quartersgdp.append(qinfo.gdp)
years.reverse()
yearsgdp.reverse()
quarters.reverse()
quartersgdp.reverse()
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")
__author__ = 'Shawn Li'
import tushare as ts
from sklearn.neural_network import MLPRegressor
from sklearn.preprocessing import MinMaxScaler
import pandas as pd
from pandas import Series, DataFrame
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
money_supply = ts.get_money_supply_bal()
gdp_year = ts.get_gdp_year()
gdp_for = ts.get_gdp_for()
length = len(money_supply)
def float_array(array):
float_arr = np.array(array)
float_arr = float_arr.astype(np.float64)
return float_arr
m2 = float_array(money_supply['m2'])[0:length]
m1 = float_array(money_supply['m1'])[0:length]
m0 = float_array(money_supply['m0'])[0:length]
cd = float_array(money_supply['cd'])[0:length]
gdp = float_array(gdp_year['gdp'])[0:length]
pi = float_array(gdp_year['pi'])[0:length]
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")
