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_cpi_page(canvas_para, length):
"""
函数功能：增加CPI页
:param canvas_para:
:return:
"""
c = canvas_para
cpi_df = ts.get_cpi()
cpi_df['month'] = cpi_df.apply(lambda x: stdMonthDate(x['month']), axis=1)
cpi_df = cpi_df.sort_values(
by='month', ascending=False).head(length).sort_values(by='month',
ascending=True)
cpi = extract_point_from_df_date_x(df_origin=cpi_df,
date_col='month',
y_col='cpi',
timeAxis='month')
gdp_pull_drawing = gen_lp_drawing([tuple(cpi)],
data_note=['CPI增长率'],
time_axis='month')
renderPDF.draw(drawing=gdp_pull_drawing, canvas=c, x=10, y=letter[1] * 0.6)
c.showPage()
return c
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 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_cpi():
try:
df = ts.get_cpi()
engine = create_engine('mysql://*****:*****@127.0.0.1/stock?charset=utf8')
df.to_sql('cpi', engine, if_exists='append')
print "message"
except Exception, e:
e.message
def get_gdp_cpi_info():
df = ts.get_cpi()
if df is not None:
res = df.to_sql(microE_cpi, engine, if_exists='replace')
msg = 'ok' if res is None else res
print('获取居民消费价格指数: ' + msg + '\n')
else:
print('获取居民消费价格指数: ' + 'None' + '\n')
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 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 get_cpi(self):
dat=ts.get_cpi()
dat.index = pd.to_datetime(dat['month'])
dat = dat.drop(['month'], axis=1)
dat['cpi']=dat['cpi']/100
dat=dat['2018':'2008'] #选取时间片
dat=dat.sort_index() #倒序
dat['cpi2']=dat['cpi'].cumprod(axis=0) #计算累乘
dat.to_csv("cpi.csv")
dat.plot()
plt.show()
def return_cpi(self):
'''
居民消费价格指数
'''
df = ts.get_cpi()
detail = {}
for col in df.columns:
lt = df[col].values.tolist()
lt.reverse()
detail[col] = lt
self.reply(detail=detail)
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_cpi():
"""居民消费价格指数"""
logger.info('Begin get CPI.')
try:
data_df = ts.get_cpi()
except Exception as e:
logger.exception('Error get CPI.')
return None
else:
data_dicts = []
if data_df.empty:
logger.warn('Empty get CPI.')
else:
data_dicts = [{'month': row[0], 'cpi': row[1]} for row in data_df.values]
logger.info('Success get CPI.')
return data_dicts
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 get_all_price(code_list):
'''''process all stock'''
df = ts.get_realtime_quotes(STOCK)
print df
df = ts.get_latest_news()
print df
df = ts.get_cpi()
print df
df = ts.get_stock_basics()
print df
df = ts.get_sz50s()
print df
df = ts.get_hist_data('600848')
print df
def get_cpi(start_month, end_month):
"""
取得居民消费价格指数
@param start_month: 开始月份YYYY.M
@param end_month: 结束月份YYYY.M
@return: 指定月份范围的CPI数据集
"""
# 日期格式
date_format = '%Y.%m'
# 文件路径
file_path = './cache/ts/ts_cpi.csv'
# 如果存在数据文件，则直接读取
if os.path.exists(file_path):
logcm.print_info('读取缓存数据...')
# 读取文件
df_cpi = pd.read_csv(file_path,
dtype={
'month': str,
'cpi': float,
'month_num': int
})
else:
# 取得CPI数据
df_cpi = ts.get_cpi()
# 把日期转成Num
month_num_list = datecm.date_list_to_num(df_cpi["month"], date_format)
# 插入新的列
df_cpi.insert(2, 'month_num', month_num_list)
# 保存到文件
df_cpi.to_csv(file_path, index=False)
# 对开始结束日期计算
start = datecm.date_to_num(start_month, date_format)
end = datecm.date_to_num(end_month, date_format)
# 根据开始结束时间数据筛选
df_result = df_cpi.ix[df_cpi.month_num >= start, :]
df_result = df_result.ix[df_cpi.month_num <= end, :]
# 按照日期数值排正序
df_result = df_result.sort_values(by=['month_num'])
# 返回数据集
return df_result
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)
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()
pi :第一产业献率(%)
si :第二产业献率(%)
industry:其中工业献率(%)
ti :第三产业献率(%)
'''
ts.get_gdp_contrib()
# 居民消费价格指数
'''
返回值说明：
month :统计月份
cpi :价格指数
'''
ts.get_cpi()
# 工业品出厂价格指数
'''
返回值说明：
month :统计月份
ppiip :工业品出厂价格指数
ppi :生产资料价格指数
qm:采掘工业价格指数
rmi:原材料工业价格指数
pi:加工工业价格指数
cg:生活资料价格指数
food:食品类价格指数
clothing:衣着类价格指数
roeu:一般日用品价格指数
import tushare as ts print(ts.get_cpi())
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!")
plt.show() ## 相关指标 short_indices(cum_delta ,0.03) ## 计算收益 gain = 1+signal1.shift(1)*df[0] ## 画图 sz.close.plot() (sz.close[0]*gain.cumprod()).plot() plt.show() short_indices(gain.cumprod(),0.03) cpi = tushare.get_cpi() cpi.index = cpi["month"].apply(pandas.to_datetime) dff = pandas.DataFrame() dff["cpi"] = cpi["cpi"].shift(1) dff["index_gain"] = delta dff = dff.dropna().sort_index() dff.corr() signal= dff["cpi"]>(dff["cpi"].shift(1)) gain = 1+dff["index_gain"]*(signal.shift(1)) sz.close.plot() (sz.close[0]*gain.cumprod()).plot() plt.show()
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat Feb 2 20:29:25 2019
@author: xsxsz
"""
import pandas as pd
import tushare as ts
df = ts.realtime_boxoffice()
print(df)
print('--------------')
df = ts.get_cpi()
print(df)
print('--------------')
df = ts.get_stock_basics()
print(df)
print('--------------')
df.to_csv('./stock.csv')
def call_cpi():
df = ts.get_cpi()
return df.loc[0, 'cpi']
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()
cpi.to_csv('D:\\ts\\macro\\cpi.csv', encoding='gbk')
ppi=ts.get_ppi()
ppi.to_csv('D:\\ts\\macro\\ppi.csv', encoding='gbk')
print 'all done'
def cpi():
return ts.get_cpi()
#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()
ppi = ts.get_ppi()
df = ts.shibor_data() #取当前年份的数据
#df = ts.shibor_data(2014) #取2014年的数据
df.sort('date', ascending=False).head(10)
df = ts.shibor_quote_data() #取当前年份的数据
#df = ts.shibor_quote_data(2014) #取2014年的数据
df.sort('date', ascending=False).head(10)
#shibo均值
df = ts.shibor_ma_data() #取当前年份的数据
#df = ts.shibor_ma_data(2014) #取2014年的数据
df.sort('date', ascending=False).head(10)
"""
# @File : macro_eco_data.py
# @Author: Chen Zhen
# python version: 3.7
# @Date : 2020/3/28
# @Desc : use tushare to crawl maro economics data
"""
import tushare as ts
print(ts.get_cpi()) # 得到cpi数据
print(ts.get_hist_data('600519')) # 得到茅台的股票交易数据
def getCPI(self):
file_name = 'cpi.csv'
path = self.index + self.index_cpi + file_name
data = ts.get_cpi()
data.to_csv(path, encoding='utf-8')
print(file_name)
def Get_cpi(self):
dt = ts.get_cpi()
dt.to_csv('居民消费价格指数cpi.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")
