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StockpricePrototype_2.py
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import sys
import tushare as ts
import time
import pandas
import math
from datetime import date
RDate=time.strftime("%Y-%m-%d",time.gmtime())
RYear=time.strftime("%Y",time.gmtime())
RMonth=time.strftime("%m",time.gmtime())
RQuarter=str(math.ceil( int(RMonth)/4 ) +1)
class HDR:
'to creat a class hold basic infomation for the handler'
Hancount=0
def __init__(self,name,display,defaultPara=''):
self.name=name
self.defaultPara=defaultPara
self.Hancount+=1
self.display=display
def __del__(self):
print('delete '+self.__class__.__name__)
def _Modifydpara(self,para):
self.defaultPara=para
def _display(self):
return self.display
def _Han(self,RecentPara=''):
if RecentPara:
return self.name+'('+RecentPara+')'
elif not self.defaultPara:
return self.name+'()'
else:
return self.name+'('+self.defaultPara+')'
class PST:
def __init__(self,hanset,itera,para,display):
self.hanset=hanset
self.iter=itera
self.para=para
self.display=display
def __del__(self):
print("del "+self.__class__.__name__)
def _display(self):
return (self.display)
def Pind(self,Para):
if not Para:
return
ParaArr=Para.split('|')
PArr2=[];
con=0
Mainco=-1
for PCell in ParaArr:
ParaArr[con]=PCell.split(',')
if len(ParaArr[con])>1:
Mainco=con
con+=1
RE=[]
con=0
self.NameSet=ParaArr[Mainco]
while con<len(ParaArr[Mainco]):
i=0
str=''
while i<len(ParaArr):
if i != Mainco:
str=str+"'"+ParaArr[i][0]+"'"
else:
str=str+"'"+ParaArr[i][con]+"'"
if i != len(ParaArr)-1:
str=str+","
i+=1
RE.append(str)
con+=1
# print (RE)
return RE
def sets(self,Para=''):
REArr=[]
# print(self)
Para=self.Pind(Para)
if Para:
if isinstance(Para,list):
con=0
while con<self.iter and con<len(Para):
REArr.append(self.hanset+'('+ Para[con]+')')
con+=1
else:
con=0
while con<self.iter:
REArr.append(self.hanset+'()' )
con+=1
# print (REArr)
return REArr
S_todayall= HDR('get_today_all','今日股票')
S_Srltequote= HDR('get_realtime_quotes','实时五档',"['sh','sz','hs300','sz50','zxb','cyb'] ")
S_Stodaytick= HDR('get_today_ticks','个股详情','000826')
H_all= HDR('get_hist_data','个股历史快捷','000826')
H_F_all= HDR('get_h_data','个股历史',"'000826','2010-01-01'" )
H_Stick= HDR('get_tick_data','个股大盘历史详细'," '000826',"+"'"+RDate+ "'")
H_F_all_handy= PST('get_h_data',1000,'','个股历史批量')
I_today= HDR('get_index','今日大盘')
B_S= HDR('get_sina_dd','大笔交易','000826,'+RDate )
IR_D= HDR('get_deposit_rate','存款利率')
IR_L= HDR('get_loan_rate','贷款利率')
IR_RRR= HDR('get_rrr','准备金利率')
IR_Shi= HDR('shibor_data','SHIBOR')
IR_SHiQuo= HDR('shibor_quote_data','SHIBOR_问询')
IR_SHiMa= HDR('shibor_ma_data','SHIBOR_MA')
IR_LPR= HDR('lpr_data','LPR')
IR_LPRMa= HDR('lpr_ma_data','LPR_MA')
M_ms= HDR('get_money_supply','货币供给')
M_msbal= HDR('get_money_supply_bal','货币供给余量')
GDP_Y= HDR('get_gdp_year','GDP_Year')
GDP_Q= HDR('get_gdp_quarter','GDP_Quarter')
GDP_Y_for= HDR('get_gdp_for','GDP_Year_For')
GDP_Y_pull= HDR('get_gdp_pull','GDP_Year_Pull')
GDP_Y_ctb= HDR('get_gdp_contrib','GDP_Year_Contribution')
CPI_M= HDR('get_cpi','CPI_Month')
PPI_M= HDR('get_ppi','PPI_Month')
SR_Basic= HDR('get_stock_basics','基本面')
SR_Report= HDR('get_report_data','财务',RYear+','+RQuarter)
SR_Profit= HDR('get_profit_data','盈利',RYear+','+RQuarter)
SR_Opera= HDR('get_operation_data','运营',RYear+','+RQuarter)
SR_Growth= HDR('get_growth_data','成长',RYear+','+RQuarter)
SR_Debtpay= HDR('get_debtpaying_data','偿付',RYear+','+RQuarter)
SR_CashFlow= HDR('get_cashflow_data','现金流',RYear+','+RQuarter)
SR_Forcast= HDR('forecast_data','业绩预报',RYear+','+RQuarter)
MG_SH= HDR('sh_margins','融资融券_SH', "'"+RYear+"-01-01'" )
MG_SZ= HDR('sz_margins','融资融券_SZ',"'"+RYear+"-01-01'")
def allnum_sub(PDD,conj,imax):
i=0
while i<imax:
S=PDD.ix[i,conj]
if S!='--':
PDD.ix[i,conj]=float(S)
i+=1
return PDD
def allnum(PDD):
PDD=PDD[0]
#PDDBac=PDD
imax=PDD.shape[0]
jmax=PDD.shape[1]
conj=1
print(imax)
print(jmax)
while conj<jmax:
PDD=allnum_sub(PDD,conj,imax)
conj+=1
return [PDD]
def timefix(PDDataF):
i=0
Ti={'quarter':0,'month':1,'date':2,'opDate':3}
try:
N =Ti[ PDDataF.columns[0] ]
except:
return PDDataF
for M in PDDataF.ix[:,0]:
PDDataF.ix[i,0]=timefix_single(PDDataF.ix[i,0],N)
i+=1
return PDDataF
def timefix_single(STR,N):
if type(STR) == pandas.tslib.Timestamp:
return STR
if isinstance(STR,float):
STR=str(STR)
if(STR.find('.')!=-1 and N==1):
TS=STR.split('.')
STR+='/30'
if(TS[1]!='2' and TS[1]!='02'):
return pandas.to_datetime('30' + TS[1] + TS[0], format="%d%m%Y")
else:
return pandas.to_datetime('28' + TS[1] + TS[0], format="%d%m%Y")
if(STR.find('.')!=-1 and N==0):
B= STR.split('.')
B[1]=str( int(B[1])*3 )
return pandas.to_datetime('30' + B[1] + B[0], format="%d%m%Y")
if(STR.find('-')!=-1 and N==2 or N==3):
TS=STR.split('-')
return pandas.to_datetime(TS[2] + TS[1] + TS[0], format="%d%m%Y")
def savname():
# 要判断开市还是收市了 不用了没收市前都没数据
return time.strftime("%Y-%m-%d",time.gmtime())
def to_mongodb():
import pymongo
import json
conn=pymongo.Connection ('127.0.0.1',port =27017)
df=ts.get_tick_data('600848',date='2014-12-22')
conn.db.tickdata.insert(json.loads(df.to_json(orient ='records')))
def getallkeys(sdir):
dkeystr=str(sdir.keys() )
allkeys=[]
end=0
con=-1
Ocon=0
while(end!=200):
con=dkeystr.find("'",con+1)
if con==-1:
break
Ocon=con+1
con=dkeystr.find("'",con+1)
allkeys.append(dkeystr[Ocon:con])
end+=1
return allkeys
def _menu(lib):
keys=getallkeys(lib)
j=0
for key in keys:
lib_list=lib[key]
print(str(j)+'. '+key+':')
i=0
for hdr in lib_list:
print('\t'+str(i)+'. '+hdr._display())
i+=1
j=j+1
def _SAVE(Type='excel',Path='C:\\Users\\macbook\Desktop\\',NA='TEST',para=''):
Type=Type.lower()
SAVEHDL={'excel':['to_excel','xlsx'],'csv':['to_csv','csv'],'hdf5':['to_hdf','h5'],'json':['to_json'],'mysql':['to_sql'],'mongodb':['to_mongodb']}
SAVEPATH= Path+NA
#Active the handler
if para!='':
para=','+para
try:
if len(SAVEHDL[Type])>1:
ST= SAVEHDL[Type][0] + "(" + "'" + SAVEPATH + "." + SAVEHDL[Type][1] +"'" + para + ")"
else:
ST= SAVEHDL[Type][0] + "(" + SAVEPATH + para + ")"
except:
print('jj')
else:
if not ST:
return 'ERROR'
else:
return ST
def _getdata(s_handle=[],para='',columns=['']): #colums should be keys arrays
dataset=[]
if isinstance(s_handle , HDR):
s_handle=[s_handle]
for part_han in s_handle:
data= pandas.DataFrame()
#handle=eval('ts.'+part_han)
#print('ts.'+part_han.han() )
if para=='':
data=eval( 'ts.'+part_han._Han() )
else:
#data=handle(para)
data=eval('ts.'+part_han._Han(para) )
if not columns[0] :
dataset.append( timefix(data) )
else:
dataset.append( timefix( data[columns] ) ) #columns is array
return dataset
if isinstance(s_handle, PST):
data= pandas.DataFrame()
if para=='':
Hans= s_handle.sets()
else:
Hans= s_handle.sets(para)
print(Hans) #test
for Han in Hans:
data=eval('ts.'+Han )
if not columns[0] :
dataset.append( data )
else:
dataset.append( data[columns] ) #columns is array
return dataset
def _intergralsheet(dataset):
'''
a = Series(range(5))
b = Series(np.linspace(4, 20, 5))
df = pd.concat([a, b], axis=1)
print df
df = DataFrame()
index = ['alpha', 'beta', 'gamma', 'delta', 'eta']
for i in range(5):
a = DataFrame([np.linspace(i, 5*i, 5)], index=[index[i]])
df = pd.concat([df, a], axis=0)
print df
'''
finaldata=pandas.DataFrame()
for datasheet in dataset:
finaldata=pandas.concat([finaldata,datasheet],axis=1)
#其中的axis=1表示按列进行合并，axis=0表示按行合并，并且，Series都处理成一列，所以这里如果选axis=0的话，将得到一个10×1的DataFrame。下面这个例子展示了如何按行合并DataFrame成一个大的DataFrame：
return [finaldata]
def main():
print("--------------Game Starting------------")
# print(dir(ts))
NameSpace=dir(ts)
# Make a self lib, Good for search
HTFlib={'TodayStock':[S_todayall,S_Srltequote,S_Stodaytick],'HistoryStock':[H_all,H_Stick,H_F_all,H_F_all_handy],'TodayIndex':[I_today],'TodayBig':[B_S],'InterestRate':[IR_D,IR_L,IR_RRR,IR_Shi,IR_SHiQuo,IR_SHiMa,IR_LPR,IR_LPRMa],'Monetory':[M_ms,M_msbal],'GDP':[GDP_Y,GDP_Q,GDP_Y_for,GDP_Y_pull,GDP_Y_ctb],'CPI':[CPI_M],'PPI':[PPI_M],'StockReport':[SR_Basic,SR_Report,SR_Profit,SR_Opera,SR_Growth,SR_Debtpay,SR_CashFlow,SR_Forcast],'Margin':[MG_SH,MG_SZ]}
while( input (" type 'R' to start everyday work ").lower()== 'r'):
routine_Bool=int( input('for stock or macro: 0 or 1 ' ) )
if(routine_Bool):
savingcont=0
Path='C:\\\\Users\\\\macbook\\\\Desktop\\\\研究数据\\\\个股\\\\AutoMacro\\\\'
NA=['GDP','CPI','SHIBOR','LPR','PPI','MS','RRR','MG_SH']
Type='excel'
Rou=[]
i=0
Rou.append(_getdata(GDP_Q,'',['quarter','gdp_yoy','pi_yoy','si_yoy','ti_yoy']) )
Rou.append(_getdata(CPI_M,'',['month','cpi']) )
Rou.append(_getdata(IR_Shi,'',['date','ON','1W','2W','1M','3M','6M','9M','1Y']) )
Rou.append(_getdata(IR_LPR,'',['date','1Y']) )
Rou.append(_getdata(PPI_M,'',['month','ppiip','ppi','qm','rmi','pi','cg','food','clothing','roeu','dcg']) )
Rou.append( allnum( _getdata(M_ms,'',['month','m2_yoy','m1_yoy','m0_yoy','cd','qm','ftd','sd','rests']) ) )
Rou.append( allnum(_getdata(IR_RRR,'',['date','now']) ) )
Rou.append(_getdata(MG_SH,'',['opDate','rzye','rzmre','rqyl','rqylje','rqmcl','rzrqjyzl']) )
for N in Rou:
if type(N[0])==pandas.DataFrame:
STail=_SAVE(Type,Path,NA[i],'')
print('Rou['+str(i)+'][0]'+'.'+STail)
eval('Rou['+str(i)+'][0]'+'.'+STail)
i+=1
else:
savingcont=0
Path='C:\\\\Users\\\\macbook\\\\Desktop\\\\研究数据\\\\股票历史\\\\' #change this
NA=['000826启迪桑德','000959首钢股份','300187永清环保','300284苏交所','600428中远航运','600896中海海盛','601808中海油服','601866中海集运','601919中国远洋','600276恒瑞医药','002004华邦健康','600470六国化工','600576万家文化','002432九安医疗']
#change this
Type='excel'
Rou=[]
i=0
Rou=_getdata(H_F_all_handy,'000826,000959,300187,300284,600428,600896,601808,601866,601919,600276,002004,600470,600576,002432|2013-01-01',['']) #change this
for na in NA:
STail=_SAVE(Type,Path,na,para="")
print('Rou['+ str(i)+']'+'.'+STail)
eval('Rou['+str(i)+']'+'.'+STail)
i+=1
if i == len(NA):
break
while( input(" type 'end' to end the program ").lower() !='end'):
_menu(HTFlib)
keys=getallkeys(HTFlib)
Key=input("--Key--: ")
if Key.isdigit():
Key=keys[int(Key) ]
Sub=(input("\t--Subscript--: ") )
Columns=input("--ColumnsKey like 'a,b,c,d'--: ").split(',')
para=input("--Para--")
Han=eval( 'HTFlib[Key]['+Sub+']')
dataset=_getdata(Han,para,Columns)
#dataset=_intergralsheet(dataset)
defaultNA=[]
if isinstance(Han, HDR):
defaultNA.append(savname()+Han.display)
elif isinstance(Han,PST):
for Han_NA in Han.NameSet:
defaultNA.append( savname()+Han_NA )
else:
for P_Han in Han:
defaultNA.append( savname()+P_Han.display )
Type='excel'
#Path='C:\\\\Users\\\\macbook\\\\Desktop\\\\'
Path='C:\\\\Users\\\\macbook\\\\Desktop\\\\研究数据\\\\股票历史\\\\'
NA_SET=input("type your nameset like 'a,b,c' :" ).split(',')
if not NA_SET[0]:
NA_SET=defaultNA
i=0
for NA in NA_SET:
STail=_SAVE(Type,Path,NA,para="")
print('dataset['+ str(i)+']'+'.'+STail)
eval('dataset['+str(i)+']'+'.'+STail)
i+=1
if i == len(NA_SET):
break
#TuHandle=eval('ts.'+NameSpace[N])
#Active
#TuHandle()
#eval('ts.'+NameSpace[N]+'()')
input("--------------End----------------")
main()