data in python MT maketime1 ability1 cost1 maketime2 ability2 cost2 maketime3 ability3 cost3 maketime4 ability4 cost4 1 6 60 5000 12 24 20000 9 36 8000 11 6 15000 10 2 24 66.5 10000 9 24 10000 10 48 13000 10 6 10000 10 3 12 58 3000 11 24 3000 12 56 9000 12 6 8000 11 4 24 49.5 6000 10 24 6000 11 30 10000 11 6 7000 11 5 12 60 2000 11 24 0 0 0 0 0 0 0 0 6 24 60 9000 11 24 0 0 0 0 0 0 0 0 Command Mode (press Esc to enable) F : find and replace Ctrl-Shift-F : open the command palette Ctrl-Shift-P : open the command palette Enter : enter edit mode P : open the command palette Shift-Enter : run cell, select below Ctrl-Enter : run selected cells Alt-Enter : run cell and insert below Y : change cell to code M : change cell to markdown R : change cell to raw 1 : change cell to heading 1 2 : change cell to heading 2 3 : change cell to heading 3 4 : change cell to heading 4 5 : change cell to heading 5 6 : change cell to heading 6 K : select cell above Up : select cell above Down : select cell below J : select cell below Shift-K : extend selected cells above Shift-Up : extend selected cells above Shift-Down : extend selected cells below Shift-J : extend selected cells below A : insert cell above B : insert cell below X : cut selected cells C : copy selected cells Shift-V : paste cells above V : paste cells below Z : undo cell deletion D,D : delete selected cells Shift-M : merge selected cells, or current cell with cell below if only one cell is selected Ctrl-S : Save and Checkpoint S : Save and Checkpoint L : toggle line numbers O : toggle output of selected cells Shift-O : toggle output scrolling of selected cells H : show keyboard shortcuts I,I : interrupt the kernel 0,0 : restart the kernel (with dialog) Esc : close the pager Q : close the pager Shift-L : toggles line numbers in all cells, and persist the setting Shift-Space : scroll notebook up Space : scroll notebook down Edit Mode (press Enter to enable) Tab : code completion or indent Shift-Tab : tooltip Ctrl-] : indent Ctrl-[ : dedent Ctrl-A : select all Ctrl-Z : undo Ctrl-/ : comment Ctrl-D : delete whole line Ctrl-U : undo selection Insert : toggle overwrite flag Ctrl-Home : go to cell start Ctrl-Up : go to cell start Ctrl-End : go to cell end Ctrl-Down : go to cell end Ctrl-Left : go one word left Ctrl-Right : go one word right Ctrl-Backspace : delete word before Ctrl-Delete : delete word after Ctrl-Y : redo Alt-U : redo selection Ctrl-M : enter command mode Ctrl-Shift-F : open the command palette Ctrl-Shift-P : open the command palette Esc : enter command mode Shift-Enter : run cell, select below Ctrl-Enter : run selected cells Alt-Enter : run cell and insert below Ctrl-Shift-Minus : split cell at cursor Ctrl-S : Save and Checkpoint Down : move cursor down Up : move cursor up
S1=[] S2=[] S3=[] listcolumn=data1.columns for name1 in listcolumn: length=len(data1.columns) #A = data1.corr() #L = scipy.linalg.cholesky(A, lower=True) #U = scipy.linalg.cholesky(A, lower=False) s=[] s1=[] s2=[] for i in range(1000): b=0 matrixfinal=np.random.normal(loc=100, scale=1, size=250).reshape(250,1) for name in data1.columns: #a=np.random.normal(loc=data1[name].mean(), scale=data1[name].std(), size=250) a=stock_monte_carlo(data1.loc['2017-09-01'][name],250,data1[name].pct_change(1).mean(),data1[name].pct_change(1).std()) b=b+a[0]*C[name]/100 matrixfinal=np.hstack((matrixfinal,a.reshape(250,1))) matrixfinal=np.delete(matrixfinal,0,axis=1) data3=pd.DataFrame(matrixfinal) #data3=pd.DataFrame(matrixfinal.dot(U/np.abs(U.sum(0)))) #data3.drop([0],axis=1,inplace=True) data3.columns=data1.columns#获取模拟数据 data3['combineindex']=data3.sum(axis=1) yearincomerate=((data3.loc[249]['combineindex']+b)/data3.loc[0]['combineindex'])**(250/data3['combineindex'].count())-1 varofincomerate=data3['combineindex'].pct_change(1).std()*np.sqrt(250)#收益波动率 sharpratio=(yearincomerate-0.015)/varofincomerate# sharpratio1=(yearincomerate-0.015)/(data3['combineindex'].pct_change(1).cumsum().std()*np.sqrt(250)) sharpratio2=(yearincomerate-0.015)/(data3['combineindex'].std()*np.sqrt(250)) s.append(sharpratio) s1.append(sharpratio1) s2.append(sharpratio2) S1.append(np.mean(s)) S2.append(np.mean(s1)) S3.append(np.mean(s2)) data1.drop(name1,axis=1,inplace=True) #####获取票息数据 C={} for num in c.columns: datalilv1=w.wsd( num, "couponrate2", "2017-09-01","2018-09-01","credibility=1") C[num]=np.mean(datalilv1.Data[0])
datalilv=w.wsd("011801582.IB", "net_cnbd", "2017-09-01", "2018-09-01", "credibility=1") c=pd.DataFrame([datalilv.Times,datalilv.Data[0]]).T c.columns=['date','101351029.IB'] c.index=pd.to_datetime(c.date) c.drop(['date'],axis=1,inplace=True)
import random list1=set(data[data['债券等级']=='AAA']['债券代码']) #[random.randrange(1000) for i in range(100)] list2=random.sample(list1, 120)