Wednesday, 13 February 2013

12'Feb Lab Assignment

Question 1 :- 




Create log of the returns data ,use log return and calculate historical volatility



Commands used:-

                             acf(ts.closingprice
                             acf(returns)
                             T=(252)^0.5
                             historicalvolitility<-sd(returns)*T
                             historicalvolitility
                             [1] 0.1475815



Fig :- 1


Fig:-2


Fig:-3

Fig:-4




Question 2:-



create acf plot for log returns data and do an adf test and interpret it.


Fig:-5

Fig:-6




Thursday, 7 February 2013

Assignment -05'Feb2012

Assignment 1 question



1. Find returns of NSE data of greater than 6 months having selected the 10th data point as start and 95th data point as end.

2. Find plot of that return




Solution:-




> z<-read.csv(file.choose(),header=T)
> head(z)
         Date    Open    High     Low   Close Shares.Traded Turnover..Rs..Cr.
1 02-Jul-2012 5283.85 5302.15 5263.35 5278.60     126161441           4991.57
2 03-Jul-2012 5298.85 5317.00 5265.95 5287.95     133117055           5161.82
3 04-Jul-2012 5310.40 5317.65 5273.30 5302.55     155995887           5750.10
4 05-Jul-2012 5297.05 5333.65 5288.85 5327.30     118915392           4709.79
5 06-Jul-2012 5324.70 5327.20 5287.75 5316.95     113300726           4760.51
6 09-Jul-2012 5283.70 5300.60 5257.75 5275.15     101169926           4189.25
> open<-z$Open[10:95]
> open.ts<-ts(open,deltat=1/252)
> open.ts
Time Series:
Start = c(1, 1)
End = c(1, 86)
Frequency = 252
 [1] 5242.75 5232.35 5228.05 5199.10 5249.85 5233.55 5163.25 5128.80 5118.40
[10] 5126.30 5124.30 5129.75 5214.85 5220.70 5233.10 5195.60 5260.85 5295.40
[19] 5345.25 5348.30 5308.20 5316.35 5343.25 5385.95 5368.60 5368.70 5395.75
[28] 5426.15 5392.60 5387.85 5348.05 5343.85 5268.60 5298.20 5276.50 5249.15
[37] 5243.90 5217.65 5309.45 5343.65 5361.90 5336.10 5404.45 5435.20 5528.35
[46] 5631.75 5602.40 5536.95 5577.00 5691.95 5674.90 5653.40 5673.75 5684.80
[55] 5704.75 5727.70 5751.55 5815.00 5751.85 5708.15 5671.15 5663.50 5681.70
[64] 5674.25 5705.60 5681.10 5675.30 5703.30 5667.60 5715.65 5688.80 5683.55
[73] 5665.20 5656.35 5596.75 5609.85 5696.35 5693.05 5694.10 5718.60 5709.00
[82] 5731.10 5688.45 5689.70 5650.35 5624.80
> summary(open.ts)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
   5118    5281    5431    5474    5682    5815
> z.diff<-diff(open.ts)
> z.diff
Time Series:
Start = c(1, 2)
End = c(1, 86)
Frequency = 252
 [1] -10.40  -4.30 -28.95  50.75 -16.30 -70.30 -34.45 -10.40   7.90  -2.00
[11]   5.45  85.10   5.85  12.40 -37.50  65.25  34.55  49.85   3.05 -40.10
[21]   8.15  26.90  42.70 -17.35   0.10  27.05  30.40 -33.55  -4.75 -39.80
[31]  -4.20 -75.25  29.60 -21.70 -27.35  -5.25 -26.25  91.80  34.20  18.25
[41] -25.80  68.35  30.75  93.15 103.40 -29.35 -65.45  40.05 114.95 -17.05
[51] -21.50  20.35  11.05  19.95  22.95  23.85  63.45 -63.15 -43.70 -37.00
[61]  -7.65  18.20  -7.45  31.35 -24.50  -5.80  28.00 -35.70  48.05 -26.85
[71]  -5.25 -18.35  -8.85 -59.60  13.10  86.50  -3.30   1.05  24.50  -9.60
[81]  22.10 -42.65   1.25 -39.35 -25.55
> returns<-cbind(open.ts,z.diff,lag(open.ts,k=-1))
> returns
Time Series:
Start = c(1, 1)
End = c(1, 87)
Frequency = 252
         open.ts z.diff lag(open.ts, k = -1)
1.000000 5242.75     NA                   NA
1.003968 5232.35 -10.40              5242.75
1.007937 5228.05  -4.30              5232.35
1.011905 5199.10 -28.95              5228.05
1.015873 5249.85  50.75              5199.10
1.019841 5233.55 -16.30              5249.85
1.023810 5163.25 -70.30              5233.55
1.027778 5128.80 -34.45              5163.25
1.031746 5118.40 -10.40              5128.80
1.035714 5126.30   7.90              5118.40
1.039683 5124.30  -2.00              5126.30
1.043651 5129.75   5.45              5124.30
1.047619 5214.85  85.10              5129.75
1.051587 5220.70   5.85              5214.85
1.055556 5233.10  12.40              5220.70
1.059524 5195.60 -37.50              5233.10
1.063492 5260.85  65.25              5195.60
1.067460 5295.40  34.55              5260.85
1.071429 5345.25  49.85              5295.40
1.075397 5348.30   3.05              5345.25
1.079365 5308.20 -40.10              5348.30
1.083333 5316.35   8.15              5308.20
1.087302 5343.25  26.90              5316.35
1.091270 5385.95  42.70              5343.25
1.095238 5368.60 -17.35              5385.95
1.099206 5368.70   0.10              5368.60
1.103175 5395.75  27.05              5368.70
1.107143 5426.15  30.40              5395.75
1.111111 5392.60 -33.55              5426.15
1.115079 5387.85  -4.75              5392.60
1.119048 5348.05 -39.80              5387.85
1.123016 5343.85  -4.20              5348.05
1.126984 5268.60 -75.25              5343.85
1.130952 5298.20  29.60              5268.60
1.134921 5276.50 -21.70              5298.20
1.138889 5249.15 -27.35              5276.50
1.142857 5243.90  -5.25              5249.15
1.146825 5217.65 -26.25              5243.90
1.150794 5309.45  91.80              5217.65
1.154762 5343.65  34.20              5309.45
1.158730 5361.90  18.25              5343.65
1.162698 5336.10 -25.80              5361.90
1.166667 5404.45  68.35              5336.10
1.170635 5435.20  30.75              5404.45
1.174603 5528.35  93.15              5435.20
1.178571 5631.75 103.40              5528.35
1.182540 5602.40 -29.35              5631.75
1.186508 5536.95 -65.45              5602.40
1.190476 5577.00  40.05              5536.95
1.194444 5691.95 114.95              5577.00
1.198413 5674.90 -17.05              5691.95
1.202381 5653.40 -21.50              5674.90
1.206349 5673.75  20.35              5653.40
1.210317 5684.80  11.05              5673.75
1.214286 5704.75  19.95              5684.80
1.218254 5727.70  22.95              5704.75
1.222222 5751.55  23.85              5727.70
1.226190 5815.00  63.45              5751.55
1.230159 5751.85 -63.15              5815.00
1.234127 5708.15 -43.70              5751.85
1.238095 5671.15 -37.00              5708.15
1.242063 5663.50  -7.65              5671.15
1.246032 5681.70  18.20              5663.50
1.250000 5674.25  -7.45              5681.70
1.253968 5705.60  31.35              5674.25
1.257937 5681.10 -24.50              5705.60
1.261905 5675.30  -5.80              5681.10
1.265873 5703.30  28.00              5675.30
1.269841 5667.60 -35.70              5703.30
1.273810 5715.65  48.05              5667.60
1.277778 5688.80 -26.85              5715.65
1.281746 5683.55  -5.25              5688.80
1.285714 5665.20 -18.35              5683.55
1.289683 5656.35  -8.85              5665.20
1.293651 5596.75 -59.60              5656.35
1.297619 5609.85  13.10              5596.75
1.301587 5696.35  86.50              5609.85
1.305556 5693.05  -3.30              5696.35
1.309524 5694.10   1.05              5693.05
1.313492 5718.60  24.50              5694.10
1.317460 5709.00  -9.60              5718.60
1.321429 5731.10  22.10              5709.00
1.325397 5688.45 -42.65              5731.10
1.329365 5689.70   1.25              5688.45
1.333333 5650.35 -39.35              5689.70
1.337302 5624.80 -25.55              5650.35
1.341270      NA     NA              5624.80
> plot(returns)
> returns<-z.diff/lag(open.ts,k=-1)
> returns
Time Series:
Start = c(1, 2)
End = c(1, 86)
Frequency = 252
 [1] -1.983692e-03 -8.218105e-04 -5.537437e-03  9.761305e-03 -3.104851e-03
 [6] -1.343256e-02 -6.672154e-03 -2.027765e-03  1.543451e-03 -3.901449e-04
[11]  1.063560e-03  1.658950e-02  1.121796e-03  2.375160e-03 -7.165925e-03
[16]  1.255870e-02  6.567380e-03  9.413831e-03  5.706001e-04 -7.497710e-03
[21]  1.535360e-03  5.059862e-03  7.991391e-03 -3.221344e-03  1.862683e-05
[26]  5.038464e-03  5.634064e-03 -6.183021e-03 -8.808367e-04 -7.386991e-03
[31] -7.853330e-04 -1.408161e-02  5.618191e-03 -4.095731e-03 -5.183360e-03
[36] -1.000162e-03 -5.005816e-03  1.759413e-02  6.441345e-03  3.415269e-03
[41] -4.811727e-03  1.280898e-02  5.689756e-03  1.713828e-02  1.870359e-02
[46] -5.211524e-03 -1.168249e-02  7.233224e-03  2.061144e-02 -2.995458e-03
[51] -3.788613e-03  3.599604e-03  1.947566e-03  3.509358e-03  4.022963e-03
[56]  4.163975e-03  1.103181e-02 -1.085985e-02 -7.597556e-03 -6.481960e-03
[61] -1.348933e-03  3.213561e-03 -1.311227e-03  5.524959e-03 -4.294027e-03
[66] -1.020929e-03  4.933660e-03 -6.259534e-03  8.478015e-03 -4.697628e-03
[71] -9.228660e-04 -3.228616e-03 -1.562169e-03 -1.053683e-02  2.340644e-03
[76]  1.541931e-02 -5.793183e-04  1.844354e-04  4.302699e-03 -1.678733e-03
[81]  3.871081e-03 -7.441852e-03  2.197435e-04 -6.916006e-03 -4.521844e-03
> plot(returns)







Assignment 2:-


1-700 data is available, Predict the data from 701-850, use the GLM estimation using LOGIT Analysis for the same.


Solution:-

z<-read.csv(file.choose(),header=T)

head(z)

z.data<-z[1:700,1:9]

 
sapply(z.data,mean)

z.data$ed<-factor(z.data$ed)

logit.est<-glm(default~age+employ+address+income+debtinc+creddebt+othdebt,data=z.data,family="binomial")

summary(logit.est)

confint.default(logit.est)

logit.eg2<-with(z[701:850,1:8],data.frame(age=mean(age),employ=mean(employ),address=mean(address),income=mean(income),debtinc=mean(debtinc),creddebt=mean(creddebt),othdebt=mean(othdebt),ed=factor(1:3)))

logit.eg2$prob<-predict(logit.est,newdata=logit.eg2,type="response")


head(logit.eg2)


                                                                     Fig:-1




                                                                       Fig:-2




Tuesday, 22 January 2013

Day3-IT Lab



Assignment 1:-

  independent:- groovee  dependent:- mileage.  Fit data in  lm model  and comment on the applicability of lm.










We see here that cluster plot is having a parabolic pattern, therefore we cannot  go for linear regression here.

Assignment :-2



 alpha -independent,
 pluto:- dependent
            res/ind
            sres/ind
            qqplot and add a qline










Assignment :-3

Anova hypothesis test:-check if comfort level of the three chairs is  same.






p value=0.687

Since p-value is greater than 5%, we cannot reject the null hypothesis. 


Wednesday, 16 January 2013

Tuesday, 15 January 2013

Blog post for submission no 1 for Business Applications Lab

                     Submission- 1 Business Applications Lab




Assignment 0:-


Line









Assignment 1:-

Plot zcol1 as histogram














Assignment 2:-

Plot both lines and points






Assignment 3:-

Scatter plot














Assignment 4:-

Max volatility in the data





Day 2-IT Lab

                                    Day 2:IT Lab assignments








Assignment :- 1


Commands :-

z1<-c(1,2,3,4,5,6,7,8,9)

dim(z1)<-c(3,3)

z2<-c(32,48,01,05,10,12,15,18,23)

dim(z2)<-c(3,3)

x<-z1[,3]

y<-z2[,1]

z3<-cbind(x,y)







Assignment 2:-


Command:-

mul<-z1%*%z2

mul



Assignment :-3


Command:-
z6<-read.csv(file.choose(),header=T)
reg2<-lm(High~Open,data=z6)
reg2








Assignment :-4
Command :-

x<-seq(0,200)
y<-dnorm(x,mean=100,sd=20)
plot(x,y,type="l")




                                    

Thursday, 18 October 2012

Website configuration for TTK P restige






I have built a HR system for TTK Prestige company. TTK Prestige Limited is the largest provider of kitchen appliances in the country.
There are two roles in my software
1) Employee
2) HR (admin)
Employee:-
Userid:- nishant.k
Pwd:-pass
Following are functions of employee module:-
1) Add or edit personal information.
2) Fill your timesheet and submit it.

HR (admin):-
Userid: admin
Pwd: pass
1) It can add an employee on joining or delete an employee on exit.
2) Add new clients and projects.
3) Assign project heads.
4) Assign supervisors to the existing active employees.
5) Approve the submitted timesheet of an employee.
6) Generate timesheet report.
7)  Add if there are any vacancies for job in the company.
8) See the submitted resume against any open vacancy.

Reason why I choose this software:-
 1 )       It enables easier access to all employee information at one platform.
 2 )       It’s highly flexible in deployment options mean that our Human Resource and Payroll solutions is      defined completely as our need.
 3)       Better project management in the sense of project deadline.
 4 )      Skill set can be managed well.
 5 )      Different userid types can be configured.
 6 )      Visibility of different HR functions can be controlled at each level.

Other software explored was SugarCRM.
Reasons  to prefer OrangeCRM over SugarCRM.
1)OrangeCRM is highly user friendly.
2)Better employee information management

Link of application
:- http://goo.gl/BlJGr