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  1. library(ggplot2)
  2. library(Rmisc)
  3. library(lattice)
  4. library(plyr)
  5. library(corrplot)
  6. library(moments)
  7. library(xts)
  8. library(gridExtra)
  9. library(ggalt)
  10. library(scales)
  11. #Partea 3 Google Trends
  12. date<-read.csv("multiTimeline (1).csv", header=TRUE)
  13. View(date)
  14. attach(date)
  15. fix(date)
  16. ziua<-as.Date(date$Day, "%d-%m-%Y")
  17. View(date)
  18. a<-data.frame(date$Day,date$Microsoft, date$Amazon)
  19. attach(a)
  20. fix(a)
  21. View(a)
  22. a<-na.omit(a)
  23. View(a)
  24. attach(a)
  25. # statistici descriptive --------------------------------------------------
  26. stat<-summary(a[-1])
  27. stat
  28. output = function() {
  29.   fisier.input = choose.files(caption = "Selectie fisier de date",
  30.                               filters = matrix(
  31.                                 data = c("Fisiere csv", "*.csv"),
  32.                                 nrow = 1,
  33.                                 ncol = 2
  34.                               ))
  35.   tabel.date = read.csv(file = fisier.input, row.names = 1)
  36.   tabel.indicatori = sapply(
  37.     X = tabel.date,
  38.     FUN = function(coloana) {
  39.       if (is.numeric(coloana)) {
  40.         f_media = mean(x = coloana)
  41.         f_mediana = median(x = coloana)
  42.         f_std = sd(x = coloana)
  43.         f_cuart = quantile(x= coloana , c(.25, .75))
  44.         f_apl = moments::kurtosis(x = coloana)
  45.         f_sim = moments::skewness(x = coloana)
  46.         return(
  47.           c(
  48.             Media = f_media,
  49.             Mediana = f_mediana,
  50.             Std = f_std,
  51.             Cuartile = f_cuart,
  52.             Simetria = f_sim,
  53.             Aplatizarea = f_apl
  54.           )
  55.         )
  56.       }
  57.     }
  58.   )
  59.  
  60.   print(tabel.indicatori)
  61.   write.csv(tabel.indicatori,paste("Statistici.csv",sep = "_"))
  62.   grid.arrange(tableGrob(round(as.matrix(tabel.indicatori),2)))
  63. }
  64. output()
  65.  
  66. h1<-ggplot(date, aes(x=Microsoft))+geom_histogram(col="#7cbb00")
  67. h1
  68. h2<-ggplot(date, aes(x=Amazon))+geom_histogram(col="#ff9900")
  69. h2
  70. multiplot(h1,h2, cols=2)
  71. b1<-boxplot(Microsoft, col="#7cbb00", main="Microsoft")
  72. b1$out
  73. b2<-boxplot(Amazon, col="#ff9900", main="Amazon")
  74. b2$out
  75. corrplot(cor(a[-1]),method="number", type="upper")
  76.  
  77. install.packages("dplyr",dependencies = T)
  78. install.packages("GGally",dependencies = T)
  79. library(dplyr)
  80. library(GGally)
  81. ggpairs(a[-1],
  82.         axisLabels = "show",
  83.         diag = list(continuous="bar", discrete="bar"),
  84.         upper=list(continuous="points", discrete="box"),
  85.         lower=list(continuous="cor",combo="facehist"))
  86. ggplot(a)+ geom_point( aes(x=a$date.Day, y=a$date.Microsoft), color="#7cbb00") +
  87.   geom_point( aes(x=a$date.Day, y=a$date.Amazon), color="#ff9900")
  88. g1<-ggplot(a) +geom_point( aes(x=a$date.Day, y=a$date.Microsoft), color="#7cbb00")
  89. g2<-ggplot(a) +geom_point( aes(x=a$date.Day, y=a$date.Amazon), color="#ff9900")
  90. multiplot(g1,g2, cols=2)
  91. library(zoo)
  92. library(xts)
  93. a<-na.omit(a)
  94. attach(a)
  95. fix(a)
  96. View(a)
  97. sem= xts(a[-1], order.by = as.Date(a[,1],"%d-%m-%y"))
  98. plot(as.xts(sem),plot,type="s",at="pretty", col=heat.colors(n=2, alpha=1),main="Evolutia cautarilor pe Google pentru Microsoft si Amazon in anul 2018")
  99. View(sem)
  100.  
  101. note<-read.csv("Note t0.csv")
  102. View(note)
  103. attach(note)
  104. fix(note)
  105. g1=ggplot(note, aes(x=Initiale, y=Microsoft0)) + geom_point(aes(col=Microsoft0, size=Microsoft0))
  106. g1
  107. g2=ggplot(note, aes(x=Initiale, y=Amazon0)) + geom_point(aes(col=Amazon0, size=Amazon0))
  108. g2
  109.  
  110. g3=ggplot(note) + geom_point(aes(x=Initiale, y=Microsoft0, color="Microsoft0", size=Microsoft0)) +
  111.   geom_point(aes(x=Initiale, y=Amazon0, color="Amazon0", size=Amazon0)) +
  112.   labs(subtitle="BRANDURILE LA MOMENTUL 0",x="INITIALE", y="2 branduri", title="Scatterplot",
  113.        caption="Punctaj")
  114. g3
  115. g4=ggplot(note) + geom_point(aes(x=Initiale, y=Microsoft1, color="Microsoft1", size=Microsoft1)) +
  116.   geom_point(aes(x=Initiale, y=Amazon1, color="Amazon1", size=Amazon1)) +
  117.   labs(subtitle="BRANDURILE LA MOMENTUL 1",x="INITIALE", y="2 branduri", title="Scatterplot",
  118.        caption="Punctaj")
  119. g4
  120. Microsoft0m<-aggregate(note$Microsoft0, by=list(note$Initiale), FUN=mean)
  121. colnames(Microsoft0m)<-c("Nume" , "Note")
  122. View(Microsoft0m)
  123. Microsoft0m<-Microsoft0m[order(Microsoft0m$Note),]
  124. Microsoft0m$Nume<-factor(Microsoft0m$Nume, levels=Microsoft0m$Nume)
  125. note$Microsoft0norm<-round((note$Microsoft0- mean(note$Microsoft0))/sd(note$Microsoft0),2)
  126. note$Microsoft0norm
  127. note$Microsoft0type<-ifelse(note$Microsoft0norm<0, "sub medie", "peste medie")
  128. note$Microsoft0type
  129. note<-note[order(note$Microsoft0type),]
  130. View(note)
  131. note
  132. grafic1<-ggplot(note, aes(x=Initiale, y=note$Microsoft0norm, label=note$Microsoft0norm))+
  133.   geom_bar(stat="identity", aes(fill=note$Microsoft0type), width = 0.5)+
  134.   scale_fill_manual(name="Microsoft 0",
  135.                     labels=c("peste medie", "sub medie"),
  136.                     values=c("sub medie"="red", "peste medie"="green"))+
  137.   labs(subtitle="Abaterie fata de medie Microsoft 0",
  138.        title="Microsoft 0")
  139. plot(grafic1)
  140.  
  141. Amazon0m<-aggregate(note$Amazon0, by=list(note$Initiale), FUN=mean)
  142. colnames(Amazon0m)<-c("Nume" , "Note")
  143. View(Amazon0m)
  144. Amazon0m<-Amazon0m[order(Amazon0m$Note),]
  145. Amazon0m$Nume<-factor(Amazon0m$Nume, levels=Amazon0m$Nume)
  146. note$Amazon0norm<-round((note$Amazon0- mean(note$Amazon0))/sd(note$Amazon0),2)
  147. note$Amazon0norm
  148. note$Amazon0type<-ifelse(note$Amazon0norm<0, "sub medie", "peste medie")
  149. note$Amazon0type
  150. note<-note[order(note$Amazon0type),]
  151. View(note)
  152. note
  153. grafic2<-ggplot(note, aes(x=Initiale, y=note$Amazon0norm, label=note$Amazon0norm))+
  154.   geom_bar(stat="identity", aes(fill=note$Amazon0type), width = 0.5)+
  155.   scale_fill_manual(name="Amazon 0",
  156.                     labels=c("peste medie", "sub medie"),
  157.                     values=c("sub medie"="red", "peste medie"="green"))+
  158.   labs(subtitle="Abaterie fata de medie Amazon 0",
  159.        title="Amazon 0")
  160. plot(grafic2)
  161.  
  162. Microsoft1m<-aggregate(note$Microsoft1, by=list(note$Initiale), FUN=mean)
  163. colnames(Microsoft1m)<-c("Nume" , "Note")
  164. View(Microsoft1m)
  165. Microsoft1m<-Microsoft1m[order(Microsoft1m$Note),]
  166. Microsoft1m$Nume<-factor(Microsoft1m$Nume, levels=Microsoft1m$Nume)
  167. note$Microsoft1norm<-round((note$Microsoft1- mean(note$Microsoft1))/sd(note$Microsoft1),2)
  168. note$Microsoft1norm
  169. note$Microsoft1type<-ifelse(note$Microsoft1norm<0, "sub medie", "peste medie")
  170. note$Microsoft1type
  171. note<-note[order(note$Microsoft1type),]
  172. View(note)
  173. note
  174. grafic3<-ggplot(note, aes(x=Initiale, y=note$Microsoft1norm, label=note$Microsoft1norm))+
  175.   geom_bar(stat="identity", aes(fill=note$Microsoft1type), width = 0.5)+
  176.   scale_fill_manual(name="Microsoft 1",
  177.                     labels=c("peste medie", "sub medie"),
  178.                     values=c("sub medie"="red", "peste medie"="green"))+
  179.   labs(subtitle="Abaterie fata de medie Microsoft 1",
  180.        title="Microsoft 1")
  181. plot(grafic3)
  182.  
  183. Amazon1m<-aggregate(note$Amazon1, by=list(note$Initiale), FUN=mean)
  184. colnames(Amazon1m)<-c("Nume" , "Note")
  185. View(Amazon1m)
  186. Amazon1m<-Amazon1m[order(Amazon1m$Note),]
  187. Amazon1m$Nume<-factor(Amazon1m$Nume, levels=Amazon1m$Nume)
  188. note$Amazon1norm<-round((note$Amazon1- mean(note$Amazon1))/sd(note$Amazon1),2)
  189. note$Amazon1norm
  190. note$Amazon1type<-ifelse(note$Amazon1norm<0, "sub medie", "peste medie")
  191. note$Amazon1type
  192. note<-note[order(note$Amazon1type),]
  193. View(note)
  194. note
  195. grafic4<-ggplot(note, aes(x=Initiale, y=note$Amazon1norm, label=note$Amazon1norm))+
  196.   geom_bar(stat="identity", aes(fill=note$Amazon1type), width = 0.5)+
  197.   scale_fill_manual(name="Amazon 1",
  198.                     labels=c("peste medie", "sub medie"),
  199.                     values=c("sub medie"="red", "peste medie"="green"))+
  200.   labs(subtitle="Abaterie fata de medie Amazon 0",
  201.        title="Amazon 1")
  202. plot(grafic4)
  203. theme_set(theme_classic())
  204. grafic5<-ggplot(note, aes(x=Microsoft0, xend=Microsoft1, y=Initiale, group=Initiale)) +
  205.   geom_dumbbell(color="red",
  206.                 size=0.5,
  207.                 colour_xend = "blue") +
  208.   labs(x="Note",
  209.        y="Nume",
  210.        title="Microsoft 0 vs Microsoft 1")
  211. plot(grafic5)
  212.  
  213. grafic6<-ggplot(note, aes(x=Amazon0, xend=Amazon1, y=Initiale, group=Initiale)) +
  214.   geom_dumbbell(color="red",
  215.                 size=0.5,
  216.                 colour_xend = "blue") +
  217.   labs(x="Note",
  218.        y="Nume",
  219.        title="Amazon 0 vs Amazon 1")
  220. plot(grafic6)
  221.  
  222. preturi<-read.csv("MSFT.csv")
  223. View(preturi)
  224. preturi<-na.omit(preturi)
  225. stat<-summary(preturi[-1])
  226. stat
  227. output = function() {
  228.   fisier.input = choose.files(caption = "Selectie fisier de date",
  229.                               filters = matrix(
  230.                                 data = c("Fisiere csv", "*.csv"),
  231.                                 nrow = 1,
  232.                                 ncol = 2
  233.                               ))
  234.   tabel.date = read.csv(file = fisier.input, row.names = 1)
  235.   tabel.indicatori = sapply(
  236.     X = tabel.date,
  237.     FUN = function(coloana) {
  238.       if (is.numeric(coloana)) {
  239.         f_media = mean(x = coloana)
  240.         f_mediana = median(x = coloana)
  241.         f_std = sd(x = coloana)
  242.         f_cuart = quantile(x= coloana , c(.25, .75))
  243.         f_apl = moments::kurtosis(x = coloana)
  244.         f_sim = moments::skewness(x = coloana)
  245.         return(
  246.           c(
  247.             Media = f_media,
  248.             Mediana = f_mediana,
  249.             Std = f_std,
  250.             Cuartile = f_cuart,
  251.             Simetria = f_sim,
  252.             Aplatizarea = f_apl
  253.           )
  254.         )
  255.       }
  256.     }
  257.   )
  258.  
  259.   print(tabel.indicatori)
  260.   write.csv(tabel.indicatori,paste("Statistici.csv",sep = "_"))
  261.   grid.arrange(tableGrob(round(as.matrix(tabel.indicatori),2)))
  262. }
  263. output()
  264. library(dplyr)
  265. library(skimr)
  266. skim(preturi)
  267. library(DataExplorer)
  268. library(ggplot2)
  269. DataExplorer::create_report(preturi[,-1])
  270.  
  271. library(xts)
  272. library(zoo)
  273. library(PerformanceAnalytics)
  274. View(preturi)
  275. st<-xts(preturi[,-1], order.by = as.Date(preturi[,1],"%d-%m-%y"))
  276. View(st)
  277. summary(st)
  278. library(sfsmisc)
  279. chart.Boxplot(st[,1], main="Boxplot pret Microsoft", colorset = rich10equal)
  280. chart.Boxplot(st[,2], main="Boxplot pret Amazon", colorset = rich10equal)
  281. chart.Boxplot(st[,3], main="Boxplot pret S&P500", colorset = rich10equal)
  282.  
  283. chart.Histogram(st[,1], main="Histograma Microsoft", colorset=rich12equal)
  284. chart.Histogram(st[,2], main="Histograma Amazon", colorset=rich12equal)
  285. chart.Histogram(st[,3], main="Histograma S&P500", colorset=rich12equal)
  286.  
  287. MSFT<-Return.calculate(st[,1], method = "discrete")
  288. RMSFT<-MSFT[-1]
  289. plot(RMSFT)
  290. plot(st$Microsoft)
  291.  
  292. AMZ<-Return.calculate(st[,2], method = "discrete")
  293. RAMZ<-AMZ[-1]
  294. plot(RAMZ)
  295. plot(st$Amazon)
  296.  
  297. RSP<-Return.calculate(st[,3], method = "discrete")
  298. RSP<-RSP[-1]
  299. plot(RSP)
  300. plot(st$S.P.500)
  301.  
  302. Ractiv<-Return.calculate(st[,4], method="discrete")
  303. Ractiv<-Ractiv[-1]
  304. plot(Ractiv)
  305. plot(st$Activ)
  306.  
  307. chart.Boxplot(RMSFT, main="Boxplot rentabilitate Microsoft", colorset = rich10equal)
  308. chart.Boxplot(RAMZ, main="Boxplot rentabilitate Amazon", colorset = rich10equal)
  309. chart.Boxplot(RSP, main="Boxplot rentabilitate S&P 500", colorset = rich10equal)
  310. chart.Boxplot(Ractiv,main="Boxplot rentabilitate Activ", colorset = rich6equal)
  311.  
  312. chart.Histogram(RMSFT, main="Histograma Rentabilitatii Microsoft", colorset=rich6equal,methods = c("add.density","add.normal","add.risk"))
  313. chart.Histogram(RAMZ, main="Histograma Rentabilitatii Amazon", colorset=rich8equal,methods = c("add.density","add.normal","add.risk"))
  314. chart.Histogram(RSP, main="Histograma Rentabilitatii S&P 500", colorset=rich6equal,methods = c("add.density","add.normal","add.risk"))
  315. chart.Histogram(Ractiv, main="Histograma Rentabilitatii Activ", colorset=rich6equal,methods = c("add.density","add.normal","add.risk"))
  316.  
  317. rentab<-data.frame(RMSFT, RAMZ, RSP)
  318. chart.Correlation(rentab, histogram=T)
  319.  
  320. chart.Drawdown(a,colorset=rich10equal, legend.loc="bottomleft")
  321.  
  322. chart.RollingPerformance(RMSFT, Rf=0, colorset=tim8equal, main="Performanta pe un an a actiunilor Microsoft",
  323.                          legend.loc = "topright")
  324. chart.RollingPerformance(RAMZ, Rf=0, colorset=tim8equal, main="Performanta pe un an a actiunilor Amazon",
  325.                          legend.loc = "topright")
  326.  
  327. tol3qualitative=c("#4477AA", "#DDCC77", "#CC6677")
  328. chart.Drawdown(rentab, legend.loc="bottomleft", main="Rentabilitati negative", colorset = tol3qualitative)
  329.  
  330. chart.RelativePerformance(RMSFT,RSP, colorset="lawngreen", main="Perfomanta Microsoft/S&P 500",
  331.                           legend.loc = "topright")
  332. chart.RelativePerformance(RAMZ,RSP, colorset="lawngreen", main="Perfomanta Amazon/S&P 500",
  333.                           legend.loc = "topright")
  334. chart.RelativePerformance(rentab[,1:2],rentab[,3], colorset=tol3qualitative, main="Perfomanta Microsoft, Amazon/S&P 500",
  335.                           legend.loc = "topright")
  336.  
  337. st2<-merge.zoo(RMSFT, RAMZ, RSP, Ractiv)
  338. st2<-na.omit(st2)
  339. View(st2)
  340. Rmedii<-colMeans(st2)
  341. Rmedii
  342. mcov<-cov(st2)
  343. mcov
  344. table.Stats(st2[,1:3],ci=0.95, digits = 4)
  345.  
  346. SharpeRatio(st[,1:3], Rf=0, p=0.95, FUN = c("StdDev"))
  347. st3<-CalculateReturns(st)
  348. View(st3)
  349. st3<-na.omit(st3)
  350. summary(st3)
  351.  
  352. rentab_medie=colMeans(st3)
  353. rentab_medie
  354. charts.PerformanceSummary(st3[,1:3], Rf=st3[,4], methods = "StdDev",main="Performanta actiunilor si a S&P 500 fata de US Bond",colorset=tol3qualitative,
  355.                           legend.loc = "topleft")
  356.  
  357. t(table.SpecificRisk(st3[,1:2],Rb=st3[,3], Rf=st3[,4]))
  358. chart.Correlation(st3, histogram = T)
  359. library("forecast")
  360. library(tseries)
  361. library(aTSA)
  362. monthplot(st3[,1],col="#7cbb00", main="Sezonalitatea pentru seria de timp Microsoft")
  363. monthplot(st3[,2],col="#ff9900", main="sSezonalitatea pentru seria de timp Amazon")
  364. adf.test(st3[,1])
  365. adf.test(st3[,2])
  366.  
  367. pp.test(st3[,1])
  368. pp.test(st3[,2])
  369.  
  370. chart.Regression(st3[,1,drop=FALSE],st3[,3,drop=FALSE], Rf=0, fit=c("linear"),col="#7cbb00",main="Microsoft")
  371. table.SFM(Ra=st3[,1,drop=FALSE], Rb=st3[,3,drop=FALSE],Rf=0,digits=4)
  372.  
  373. chart.Regression(st3[,2,drop=FALSE],st3[,3,drop=FALSE], Rf=0, fit=c("linear"),col="#ff9900",main="Amazon")
  374. table.SFM(Ra=st3[,2,drop=FALSE], Rb=st3[,3,drop=FALSE],Rf=0,digits=4)
  375.  
  376. table.SFM(Ra=st3[,1,drop=FALSE], Rb=st3[,3,drop=FALSE],Rf=st3[,4,drop=F],digits=4)
  377. chart.Regression(st3[,1,drop=FALSE],st3[,3,drop=FALSE], Rf=st3[,4,drop=F],excess.returns = TRUE, fit=c("linear"),col="#7cbb00")
  378. table.CAPM(Ra=st3[,1,drop=FALSE], Rb=st3[,3,drop=FALSE],Rf=st3[,4,drop=F],scale=248, digits=4)
  379. CAPM.beta(Ra=st3[,1,drop=FALSE], Rb=st3[,3,drop=FALSE],Rf=st3[,4,drop=F])
  380. MSFT_CAPM=mean(st3[,4])+(CAPM.beta(Ra=st3[,1,drop=FALSE], Rb=st3[,3,drop=FALSE],Rf=st3[,4,drop=F]))*mean(st3[,3]-st3[,4])
  381. MSFT_CAPM
  382. View(st3)
  383. table.SFM(Ra=st3[,2,drop=FALSE], Rb=st3[,3,drop=FALSE],Rf=st3[,4,drop=F],digits=4)
  384. chart.Regression(st3[,2,drop=FALSE],st3[,3,drop=FALSE], Rf=st3[,4,drop=F],excess.returns = TRUE, fit=c("linear"),col="#ff9900")
  385. table.CAPM(Ra=st3[,2,drop=FALSE], Rb=st3[,3,drop=FALSE],Rf=st3[,4,drop=F],scale=248, digits=4)
  386. CAPM.beta(Ra=st3[,2,drop=FALSE], Rb=st3[,3,drop=FALSE],Rf=st3[,4,drop=F])
  387. AMZ_CAPM=mean(st3[,4])+(CAPM.beta(Ra=st3[,2,drop=FALSE], Rb=st3[,3,drop=FALSE],Rf=st3[,4,drop=F]))*mean(st3[,3]-st3[,4])
  388. AMZ_CAPM
  389.  
  390. preturi<-read.csv("MSFT.csv")
  391. View(preturi)
  392. preturi=na.omit(preturi)
  393. fix(preturi)
  394. attach(preturi)
  395. library(xts)
  396. library(zoo)
  397. library(PerformanceAnalytics)
  398.  
  399. stocks<-xts(preturi[,-1], order.by = as.Date(preturi[,1],"%d-%m-%y"))
  400. stocks
  401.  
  402. stocks3=CalculateReturns(stocks)
  403. View(stocks3)
  404. stocks3=na.omit(stocks3)
  405. colnames(stocks3)=c("RMSFT", "RAMZ", "RS.P500", "Ractiv")
  406. summary(stocks3)
  407. a=0.88
  408. b=0.88
  409. l=2.25
  410. val_RMSFT=ifelse(stocks3[,1]>=0,(stocks3[,1])^a,(-l)*((-stocks3[,1])^b))
  411. mean(val_RMSFT)
  412. mean(stocks3[,1])
  413.  
  414. val_RAMZ=ifelse(stocks3[,2]>=0,(stocks3[,2])^a,(-l)*((-stocks3[,2])^b))
  415. mean(val_RAMZ)
  416. mean(stocks3[,2])
  417.  
  418. MSFT1=merge.zoo(stocks3[,1], val_RMSFT)
  419. View(MSFT1)
  420. colnames(MSFT1)=c("RMSFT","Val_RMSFT")
  421. View(MSFT1)
  422. summary(MSFT1)
  423.  
  424. AMZ1=merge.zoo(stocks3[,2], val_RAMZ)
  425. View(AMZ1)
  426. colnames(AMZ1)=c("RAMZ","Val_RAMZ")
  427. View(AMZ1)
  428. summary(AMZ1)
  429.  
  430. plot(MSFT1)
  431. plot(AMZ1)
  432. plot(x=MSFT1[,1], y=MSFT1[,2],xlab="Rentabilitate MSFT", ylab="Valoare MSFT", main="Functia valoare pentru MSFT")
  433.  
  434. obiect1=as.data.frame(MSFT1)
  435. View(obiect1)
  436. obiect1=transform(obiect1, Dates=as.Date(rownames(obiect1)))
  437. View(obiect1)
  438. ord=obiect1[order(obiect1[,1]),]
  439. ord=ord[,-3]
  440. plot(ord,type="l",main="Functia valoare pentru Microsoft", col="#7cbb00")
  441. library(ggplot2)
  442. ggplot() +geom_line(data = ord, aes(x=ord[,1],y=ord[,2]),color="#7cbb00")
  443.  
  444. obiect2=as.data.frame(AMZ1)
  445. View(obiect2)
  446. obiect2=transform(obiect2, Dates=as.Date(rownames(obiect2)))
  447. View(obiect2)
  448. ord=obiect2[order(obiect1[,1]),]
  449. ord=ord[,-3]
  450. plot(ord,type="l",main="Functia valoare pentru Amazon", col="#ff9900")
  451. library(ggplot2)
  452. ggplot() +geom_line(data = ord, aes(x=ord[,1],y=ord[,2]),color="#ff9900")
  453.  
  454. Return.cumulative(MSFT1[,1],geometric=T)
  455. val2_MSFT=0*(Return.cumulative(MSFT1[,1],geometric=T))
  456. val2_MSFT
  457. TVM_MSFT=200+val2_MSFT
  458. TVM_MSFT
  459.  
  460. Return.cumulative(AMZ1[,1],geometric=T)
  461. val2_AMZ=300*(Return.cumulative(AMZ1[,1],geometric=T))
  462. val2_AMZ
  463. TVM_AMZ=200+val2_AMZ
  464. TVM_AMZ
  465.  
  466. v_TVM_MSFT=TVM_MSFT^0.88
  467. v_TVM_MSFT
  468.  
  469. v_TVM_AMZ=TVM_AMZ^0.88
  470. v_TVM_AMZ
  471.  
  472. Val_1<-0^0.88
  473. Val_2<-300^0.88
  474. Val_1
  475. Val_2
  476. p=5/10
  477. k<-10/10
  478.  
  479. pp<-(p^0.65)/(p^0.65 + (1-p)^0.65)
  480. pp
  481. pq<-(k^0.65)/(k^0.65 + (1-k)^0.65)
  482. pq
  483. Val_pr_0<-pp*Val_1+pq*Val_2
  484. Val_pr_0
  485.  
  486. Val_tvm_MSFT_t2 = 94.25654935
  487.  
  488. Val_tvm_AMZ_t2 = 99.56707449
  489.  
  490. Val_pr_0<-pp*Val_tvm_MSFT_t2+pq*Val_tvm_AMZ_t2
  491. Val_pr_0
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