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 call_money_supply():
'''
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)
qm_yoy:准货币同比增长(%)
ftd:定期存款(亿元)
ftd_yoy:定期存款同比增长(%)
sd:储蓄存款(亿元)
sd_yoy:储蓄存款同比增长(%)
rests:其他存款(亿元)
rests_yoy:其他存款同比增长(%)
'''
df = ts.get_money_supply()
return [df.loc[0, 'm0'], df.loc[0, 'm1'], df.loc[0, 'm2']]
def macro_info_to_sql():
create_classify_table()
a = ts.get_cpi()
b = ts.get_ppi()
c = ts.get_money_supply()
c = c.iloc[:, [0, 1, 3, 5]]
b = b.iloc[:, [0, 2]]
result = pd.merge(a,
b,
how='left',
on=None,
left_on=None,
right_on=None,
left_index=False,
right_index=False,
sort=False,
suffixes=('_x', '_y'),
copy=True,
indicator=False)
result = pd.merge(result,
c,
how='left',
on=None,
left_on=None,
right_on=None,
left_index=False,
right_index=False,
sort=False,
suffixes=('_x', '_y'),
copy=True,
indicator=False)
df_to_mysql('anack_macro_data', result)
def get_economics_state(s_date, e_date):
s_year = s_date.split('-')[0] # 起始年份
e_year = e_date.split('-')[0] # 结束年份
s_month = s_date.split('-')[1] # 起始月份
e_month = s_date.split('-')[1] # 结束月份
s_day = s_date.split('-')[2] # 开始日期
e_day = e_date.split('-')[2] # 结束日期
# 获取Shibor拆解利率数据
shibor_df = get_shibor(s_year)
for y in range(int(s_year)+1, int(e_year)+1):
shibor_df = pd.concat([shibor_df, get_shibor(y)])
return shibor_df
# 获取每月CPI数据
cpi_df = ts.get_cpi()
dates = pd.date_range(s_year + s_month + s_day, e_year + e_month + e_day) # 生成整个时间区间内的日期
cpi = pd.DataFrame(data={'cpi': 0}, index=dates) # 初始化处理后的CPI数据框
cpi.index.names = ['date']
for i in cpi_df.month: # 根据所需要的区间遍历每月CPI数据
year = i.split('.')[0]
month = i.split('.')[1]
if len(month) == 1: # 如果月份数小于10，则需要补0
month = '0' + month
cpi[year+'-'+month] = cpi_df[cpi_df['month'] == i].iloc[0, 1]
# 获取每月货币供应量数据
money_df = ts.get_money_supply()
money_supply = pd.DataFrame(data={'money': 0}, index=dates)
money_supply.index.names = ['date']
for i in money_df.month: # 根据所需要的区间遍历每月CPI数据
year = i.split('.')[0]
month = i.split('.')[1]
if len(month) == 1: # 如果月份数小于10，则需要补0
month = '0' + month
money_supply[year+'-'+month] = money_df[money_df['month'] == i].iloc[0, 2] # 广义货币M2同比增长(%)
def setMoneySupply(self,
isSave=False,
tableName=MACROECONOMIC_MONEY_SUPPLY):
df = ts.get_money_supply()
if isSave is True:
df.to_sql(tableName, self.engine_sql, if_exists='append')
return df
def get_money_supply_info():
df = ts.get_money_supply()
if df is not None:
res = df.to_sql(microE_money_supply, engine, if_exists='replace')
msg = 'ok' if res is None else res
print('获取货币供应量: ' + msg + '\n')
else:
print('获取货币供应量: ' + 'None' + '\n')
def get_money_supply():
try:
df = ts.get_money_supply()
engine = create_engine('mysql://*****:*****@127.0.0.1/stock?charset=utf8')
df.to_sql('money_supply', engine, if_exists='append')
print "message"
except Exception, e:
e.message
def get_last_12_month_m2_changes():
df = ts.get_money_supply()
df = df[0:12]
k = [i for i in df['month']]
v = [float(i) for i in df['m2_yoy']]
k.reverse()
v.reverse()
return [k, v]
def test():
ts.get_sz50s()
ts.get_hs300s()
ts.get_zz500s()
ts.realtime_boxoffice()
ts.get_latest_news()
ts.get_notices(tk)
ts.guba_sina()
ts.get_cpi()
ts.get_ppi()
ts.get_stock_basics()
ts.get_concept_classified()
ts.get_money_supply()
ts.get_gold_and_foreign_reserves()
ts.top_list() #每日龙虎榜列表
ts.cap_tops() #个股上榜统计
ts.broker_tops() #营业部上榜统计
ts.inst_tops() # 获取机构席位追踪统计数据
ts.inst_detail()
def return_money_supply(self):
'''
货币供应量
'''
df = ts.get_money_supply()
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 add_money_supply_aage(canvas_para):
"""
函数功能：在pdf中增加货币供应页
:param canvas_para:
:return:
"""
c = canvas_para
c.setFont("song", 10)
c.drawString(10, letter[1] - 20, '货币供应')
c.setLineWidth(3)
c.line(10, letter[1] - 24, letter[0] - 10, letter[1] - 24)
# 画货币供应量
money_supply = ts.get_money_supply().replace('--', np.nan)
money_supply['date'] = money_supply.apply(
lambda x: stdMonthDate2ISO(x['month']), axis=1)
# 画货币量曲线图
m0 = extract_point_from_df_date_x(money_supply, 'date', 'm0')
m1 = extract_point_from_df_date_x(money_supply, 'date', 'm1')
m2 = extract_point_from_df_date_x(money_supply, 'date', 'm2')
data_supply = [tuple(m0), tuple(m1), tuple(m2)]
data_supply_note = ['m0', 'm1', 'm2']
money_drawing = gen_lp_drawing(data=data_supply,
data_note=data_supply_note,
height=letter[1] * 0.2)
renderPDF.draw(drawing=money_drawing, canvas=c, x=10, y=letter[1] * 0.7)
# 画货币量增长率曲线图
m0_yoy = extract_point_from_df_date_x(money_supply, 'date', 'm0_yoy')
m1_yoy = extract_point_from_df_date_x(money_supply, 'date', 'm1_yoy')
m2_yoy = extract_point_from_df_date_x(money_supply, 'date', 'm2_yoy')
data_supply_yoy = [tuple(m0_yoy), tuple(m1_yoy), tuple(m2_yoy)]
data_supply_yoy_note = ['m0增长率', 'm1增长率', 'm2增长率']
money_yoy_drawing = gen_lp_drawing(data=data_supply_yoy,
data_note=data_supply_yoy_note,
height=letter[1] * 0.2)
renderPDF.draw(drawing=money_yoy_drawing,
canvas=c,
x=10,
y=letter[1] * 0.4)
c.showPage()
return c
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 cpi_vs_m2(self):
#此处的计算有误，获得的数据是同比，而本函数计算是环比增长的方法
dat_cpi = ts.get_cpi()
dat_m2 = ts.get_money_supply()
dat_cpi.index=pd.to_datetime(dat_cpi['month'])
dat_m2.index=pd.to_datetime(dat_m2['month'])
dat_cpi = dat_cpi['2018':'2008']
dat_m2 = dat_m2['2018':'2008']
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.plot()
plt.show()
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 get_money_supply():
"""得到货币供应量"""
logger.info('Begin get MoneySupply.')
try:
data_df = ts.get_money_supply()
except Exception as e:
logger.exception('Error get MoneySupply.')
return None
else:
data_dicts = []
if data_df.empty:
logger.warn('Empty get MoneySupply.')
else:
data_dicts = [{'month': row[0], 'm2': row[1],
'm2_yoy': row[2], 'm1': row[3], 'm1_yoy': row[4],
'm0': row[5], 'm0_yoy': row[6], 'cd': row[7], 'cd_yoy': row[8],
'qm': row[9], 'qm_yoy': row[10], 'ftd': row[11], 'ftd_yoy': row[12],
'sd':row[13], 'sd_yoy':row[14], 'rests': row[15], 'rests_yoy': row[16]}
for row in data_df.values]
logger.info('Success get MoneySupply.')
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 getMoneySupply(ann_dt_label):
raw_data = ts.get_money_supply()
year_month_label = 'year_month'
raw_data = raw_data.rename({'month': year_month_label}, axis=1)
raw_data.loc[:, 'year'] = raw_data[year_month_label].apply(
lambda x: x.split('.')[0]).astype('int')
raw_data.loc[:, 'month'] = raw_data[year_month_label].apply(
lambda x: x.split('.')[1]).astype('int')
raw_data = raw_data.drop(year_month_label, axis=1)
# ========= change datatype
raw_data = chgDataType(raw_data, ['year', 'month'])
# ========= change column names
raw_data = raw_data.rename({'qm': 'quasi_currency'}, axis=1)
# ========== announcement date
tmp_data = raw_data.loc[raw_data['month'] !=
12] # announced month is 1 month later
tmp_data.loc[:, 'announced_month'] = tmp_data['month'] + 1
tmp_data.loc[:, 'announced_year'] = tmp_data['year']
processed_data = tmp_data.copy()
tmp_data = raw_data.loc[raw_data[
'month'] == 12] # December, adding 1 month = January in the next year
tmp_data.loc[:, 'announced_month'] = 1
tmp_data.loc[:, 'announced_year'] = tmp_data['year'] + 1
processed_data = processed_data.append(tmp_data)
processed_data.loc[:, ann_dt_label] = processed_data.apply(
lambda x: datetime.strftime(
datetime(int(x['announced_year']), int(x['announced_month']), 15),
'%Y-%m-%d'),
axis=1)
processed_data = processed_data.drop(['announced_year', 'announced_month'],
axis=1)
return processed_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 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 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
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")
def money_supply():
return ts.get_money_supply()
def download_m2(self):
m2T = ts.get_money_supply()
self.__m2 = m2T[m2T.month >= START_MONTH]
self.__m2.to_csv(FILE_LOCATION, encoding='utf-8')
def getmoneysupplydb():
moneysupply = ts.get_money_supply()
moneysupply.to_sql('moneysupply_data',ENGINE,if_exists='append')
def getMoneySupply(self):
file_name = 'money_supply.csv'
path = self.index + self.index_money_supply + file_name
data = ts.get_money_supply()
data.to_csv(path, encoding='utf-8')
print(file_name)
#-*- coding: UTF-8 -*-
import pylab
import pandas as pd
import MySQLdb
import matplotlib.pyplot as plt
from sqlalchemy import create_engine
import tushare as ts
import numpy as np
import mylib as me
df = ts.get_money_supply()
df['m1dm2'] = 0.0
df['fm2'] = 0.0
df['fm1'] = 0.0
df['num'] = int(0)
df2 = df.head(12 * 15)
for i in range(df2.index.size):
m2 = float(df2.loc[i,'m2'])
m1 = float(df2.loc[i,'m1'])
df2.loc[i,'m1dm2'] = float(m1 / m2)
df2.loc[i,'fm2'] = float(df2.loc[i,'m2_yoy'])
df2.loc[i,'fm1'] = float(df2.loc[i,'m1_yoy'])
df2.loc[i,'num'] = i
#df2.sort_values('num', ascending = False)
print df2.head()
df2.to_csv('..\\..\\data\\view\\money.txt')
# encoding=utf-8
import matplotlib.pyplot as plt
import tushare as ts
from SDK.SDKHeader import *
money_supply_df = ts.get_money_supply()[0:30]
money_supply_df['month_std'] = money_supply_df.apply(lambda x:stdMonthDate(x['month']),axis=1)
money_supply_df = money_supply_df.sort_values(by='month_std',ascending=True)
sh_index = ts.get_hist_data('sh')
# trick to get the axes
fig, ax = plt.subplots(2,1)
# std month date str
month_date = list(map(lambda x:stdMonthDate(x),money_supply_df['month']))
x = range(0,len(money_supply_df['month']))
# plot data
ax[0].plot(x, list(map(lambda x:float(x),money_supply_df['m2_yoy'])), 'ro--', label='m2_yoy')
ax[0].plot(x, list(map(lambda x:float(x),money_supply_df['m1_yoy'])), 'b*--', label='m1_yoy')
ax[0].plot(x, list(map(lambda x:float(x),money_supply_df['m0_yoy'])), 'g*--', label='m0_yoy')
# ax[1].plot(sh_index.index,sh_index.open,'y*--',label='sh_index')
# make and set ticks and ticklabels
ticklabels_0 = month_date
ax[0].set_xticks(x)
ax[0].set_xticklabels(ticklabels_0,rotation=90)
ax[0].set_title('money_supply')
ax[0].legend(loc='best')
ax[0].grid()
def getMoneySupply(self):
logging.debug("begin getMoneySupply")
moneySupply=tushare.get_money_supply()
self.saveDb(moneySupply,"money_supply")
logging.debug("end getMoneySupply")
#-*- coding: UTF-8 -*-
import pylab
import pandas as pd
import MySQLdb
import matplotlib.pyplot as plt
from sqlalchemy import create_engine
import tushare as ts
import numpy as np
import mylib as me
import mydef as md
global G_DBengine
G_DBengine = create_engine('mysql://*****:*****@127.0.0.1/mystock?charset=utf8')
df = ts.get_money_supply()
df['m1dm2'] = 0.0
df['fm2'] = 0.0
df['fm1'] = 0.0
df['num'] = int(0)
df2 = df.head(12 * 15)
for i in range(df2.index.size):
m2 = float(df2.loc[i, 'm2'])
m1 = float(df2.loc[i, 'm1'])
df2.loc[i, 'm1dm2'] = float(m1 / m2)
df2.loc[i, 'fm2'] = float(df2.loc[i, 'm2_yoy'])
df2.loc[i, 'fm1'] = float(df2.loc[i, 'm1_yoy'])
df2.loc[i, 'num'] = i
df2.sort_values('num', ascending=False)
print df2.head()
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 Get_money_supply(self):
dt = ts.get_money_supply()
dt.to_csv('货币供应量.csv')
print(dt)
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")
