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  1. library(ggplot2)
  2. library(plyr)
  3. library(moments)
  4. library(lattice)
  5. library(corrplot)
  6. library(Rmisc)
  7. library(xts)
  8. library(zoo)
  9. library(dplyr)
  10. library(skimr)
  11. library(magrittr)
  12. library(ggpubr)
  13. library(GGally)
  14. library(PerformanceAnalytics)
  15. library(forecast)
  16. library(tseries)
  17. library(aTSA)
  18. # Google Trends -----------------------------------------------------------
  19. date<-read.csv("cautari.csv")
  20. View(date)
  21. attach(date)
  22. fix(date)
  23. Zi<-as.Date(date$Zi, "%d-%m-%Y")
  24. date<-data.frame(Zi, date$Apple, date$Microsoft)
  25. fix(date)
  26. attach(date)
  27. stat<-summary(date[-1])
  28. stat
  29. h1<-ggplot(date, aes(x=Apple))+geom_histogram(col="deepskyblue")
  30. h1
  31. h2<-ggplot(date, aes(x=Microsoft))+geom_histogram(col="gold1")
  32. h2
  33. multiplot(h1,h2, cols=2)
  34. skewness(date[-1])
  35. kurtosis(date[-1])
  36. b1<-boxplot(date$Apple, col="deepskyblue", main="Apple")
  37. b1$out
  38. b2<-boxplot(date$Microsoft, col="gold1", main="Microsoft")
  39. b2$out
  40. ggpairs(date[,2:3],
  41.         axisLabels = "show",
  42.         diag = list(continuous="bar", discrete="bar"),
  43.         upper=list(continuous="points", discrete="box"),
  44.         lower=list(continuous="cor", combo="facehist"))
  45. ggplot(date)+geom_point(aes(x=Zi, y=Apple), color="red")+
  46.   geom_point(aes(x=Zi, y=Microsoft), color="blue")
  47. g1=ggplot(date)+geom_point(aes(x=Zi, y=Apple), color="red")
  48. g1
  49. g2=ggplot(date)+geom_point(aes(x=Zi, y=Microsoft), color="blue")
  50. g2
  51. multiplot(g1,g2, cols=2)
  52. #analizam raspunsul respondentilor
  53. date<-read.csv("tema.csv")
  54. View(date)
  55. attach(date)
  56. summary(date)
  57. #reprezentare grafica pentru notele acordate de fiecare persoane pentru apple la momentele 0 si 1
  58. g1=ggplot(date) + geom_point(aes(x=INITIALE, y=APPLE0, color="red", size=APPLE0)) +
  59.   geom_point(aes(x=INITIALE), y=APPLE1, color="green", size=APPLE1) +
  60.   labs(subtitle="APPLE0 si APPLE1",x="INITIALE", y="APPLE", title="Scatterplot Apple",
  61.        caption="Punctaje acordate") +
  62. coord_flip()
  63. g1
  64. #reprezentare grafica pentru notele acordate de fiecare persoane pentru microsoft la momentele 0 si 1
  65. g2=ggplot(date) + geom_point(aes(x=INITIALE, y=MICROSOFT0, color="red", size=MICROSOFT0)) +
  66.   geom_point(aes(x=INITIALE), y=MICROSOFT1, color="green", size=MICROSOFT1) +
  67.   labs(subtitle="MICROSOFT0, MICROSOFT1",x="INITIALE", y="MICROSOFT", title="Scatterplot Microsoft",
  68.        caption="Punctaje acordate") +
  69.   coord_flip()
  70. g2
  71. #reprezentare in acelasi grafic punctajele date pentru toate cele 2 branduri  la momentul 0
  72. g3=ggplot(date) + geom_point(aes(x=INITIALE, y=APPLE0, color="APPLE", size=APPLE0)) +
  73.   geom_point(aes(x=INITIALE, y=MICROSOFT0, color="MICROSOFT", size=MICROSOFT0)) +
  74.   labs(subtitle="BRANDURILE LA MOMENTUL 0",x="INITIALE", y="Cele 2 brand-uri", title="Scatterplot",
  75.        caption="Punctaj acordate")+coord_flip()
  76. g3
  77. #analog momentul 1
  78. g4=ggplot(date) + geom_point(aes(x=INITIALE, y=APPLE2, color="APPLE", size=APPLE2)) +
  79.   geom_point(aes(x=INITIALE, y=MICROSOFT2, color="MICROSOFT", size=MICROSOFT2)) +
  80.   labs(subtitle="BRANDURI LA MOMENTUL 2",x="INITIALE", y="Cele 2 brand-uri", title="Scatterplot",
  81.        caption="Punctaj acordate")+coord_flip()
  82. g4
  83. #barchart apple 0
  84. APPLE0m<-aggregate(date$APPLE0, by=list(date$INITIALE), FUN=mean)
  85. colnames(APPLE0m)<-c("Nume" , "Note")
  86. View(APPLE0m)
  87. APPLE0m<-APPLE0m[order(APPLE0m$Note),] #sortare dupa note
  88. APPLE0m$Nume<-factor(APPLE0m$Nume, levels=APPLE0m$Nume)
  89. head(APPLE0m)
  90. graf1<-ggplot(APPLE0m, aes(x=Nume, y=Note))+
  91.   geom_bar(stat="identity", width=0.5, fill="blue")+
  92.   labs(title="Bar Chart APPLE0",
  93.        subtitle = "Note APPLE0",
  94.        caption="sursa:note")
  95. theme(axis.text.x = element_text(angle = 75, vjust = 0.5))
  96. plot(graf1)
  97. #bar chart apple 1
  98. APPLE1m<-aggregate(date$APPLE1, by=list(date$INITIALE), FUN=mean)
  99. colnames(APPLE1m)<-c("Nume" , "Note")
  100. View(APPLE1m)
  101. APPLE1m<-APPLE1m[order(APPLE1m$Note),] #sortare dupa note
  102. APPLE1m$Nume<-factor(APPLE1m$Nume, levels=APPLE1m$Nume)
  103. head(APPLE0m)
  104. graf2<-ggplot(APPLE1m, aes(x=Nume, y=Note))+
  105.   geom_bar(stat="identity", width=0.5, fill="blue")+
  106.   labs(title="Bar Chart APPLE1",
  107.        subtitle = "Note APPLE1",
  108.        caption="sursa:note")
  109. theme(axis.text.x = element_text(angle = 75, vjust = 0.5))
  110. plot(graf2)
  111. #barchart microsoft 0
  112. MICROSOFT0m<-aggregate(date$MICROSOFT0, by=list(date$INITIALE), FUN=mean)
  113. colnames(MICROSOFT0m)<-c("Nume" , "Note")
  114. View(MICROSOFT0m)
  115. MICROSOFT0m<-MICROSOFT0m[order(MICROSOFT0m$Note),] #sortare dupa note
  116. MICROSOFT0m$Nume<-factor(MICROSOFT0m$Nume, levels=MICROSOFT0m$Nume)
  117. head(MICROSOFT0m)
  118. graf3<-ggplot(MICROSOFT0m, aes(x=Nume, y=Note))+
  119.   geom_bar(stat="identity", width=0.5, fill="blue")+
  120.   labs(title="Bar Chart MICROSOFT0",
  121.        subtitle = "Note MICROSOFT0",
  122.        caption="sursa:note")
  123. theme(axis.text.x = element_text(angle = 75, vjust = 0.5))
  124. plot(graf3)
  125. #bar chart microsoft 1
  126. MICROSOFT1m<-aggregate(date$MICROSOFT1, by=list(date$INITIALE), FUN=mean)
  127. colnames(MICROSOFT1m)<-c("Nume" , "Note")
  128. View(MICROSOFT1m)
  129. MICROSOFT1m<-MICROSOFT1m[order(MICROSOFT1m$Note),] #sortare dupa note
  130. MICROSOFT1m$Nume<-factor(MICROSOFT1m$Nume, levels=MICROSOFT1m$Nume)
  131. head(MICROSOFT0m)
  132. graf4<-ggplot(MICROSOFT1m, aes(x=Nume, y=Note))+
  133.   geom_bar(stat="identity", width=0.5, fill="blue")+
  134.   labs(title="Bar Chart MICROSOFT1",
  135.        subtitle = "Note MICROSOFT1",
  136.        caption="sursa:note")
  137. theme(axis.text.x = element_text(angle = 75, vjust = 0.5))
  138. plot(graf4)
  139. #analiza cu media grupului- apple 0
  140. APPLE0_norm<-round((date$APPLE0- mean(date$APPLE0))/sd(date$APPLE0),2)
  141. APPLE0_norm
  142. APPLE0_type<-ifelse(APPLE0_norm<0, "sub medie", "peste medie")
  143. APPLE0_type
  144. date<-date[order(APPLE0_type),]
  145. graf5<-ggplot(date, aes(x=INITIALE, y=APPLE0_norm, label=APPLE0_norm))+
  146.   geom_bar(stat="identity", aes(fill=APPLE0_type), width = 0.5)+
  147.   scale_fill_manual(name="Apple 0",
  148.                     labels=c("peste medie", "sub medie"),
  149.                     values=c("sub medie"="red", "peste medie"="green"))+
  150.   labs(subtitle="Abaterie fata de medie Apple 0",
  151.        title="Apple0")+
  152.   coord_flip()
  153. plot(graf5)
  154. #graficul ne arata abateria fata de media grupului, pers cu nota cea mai mica va avea o medie cea mai mare
  155. #apple 1
  156. APPLE1_norm<-round((date$APPLE1- mean(date$APPLE1))/sd(date$APPLE1),2)
  157. APPLE1_norm
  158. APPLE1_type<-ifelse(APPLE1_norm<0, "sub medie", "peste medie")
  159. APPLE1_type
  160. date<-date[order(APPLE1_type),]
  161. graf6<-ggplot(date, aes(x=INITIALE, y=APPLE1_norm, label=APPLE1_norm))+
  162.   geom_bar(stat="identity", aes(fill=APPLE1_type), width = 0.5)+
  163.   scale_fill_manual(name="Apple 0",
  164.                     labels=c("peste medie", "sub medie"),
  165.                     values=c("sub medie"="red", "peste medie"="green"))+
  166.   labs(subtitle="Abaterie fata de medie Apple 1",
  167.        title="Apple1")+
  168.   coord_flip()
  169. plot(graf6)
  170. #analiza cu media grupului- microsoft 0
  171. MICROSOFT0_norm<-round((date$MICROSOFT0- mean(date$MICROSOFT0))/sd(date$MICROSOFT0),2)
  172. MICROSOFT0_norm
  173. MICROSOFT0_type<-ifelse(MICROSOFT0_norm<0, "sub medie", "peste medie")
  174. MICROSOFT0_type
  175. date<-date[order(MICROSOFT0_type),]
  176. graf6<-ggplot(date, aes(x=INITIALE, y=MICROSOFT0_norm, label=MICROSOFT0_norm))+
  177.   geom_bar(stat="identity", aes(fill=MICROSOFT0_type), width = 0.5)+
  178.   scale_fill_manual(name="Microsoft 0",
  179.                     labels=c("peste medie", "sub medie"),
  180.                     values=c("sub medie"="red", "peste medie"="green"))+
  181.   labs(subtitle="Abaterie fata de medie Microsoft 0",
  182.        title="MICROSOFT0")+
  183.   coord_flip()
  184. plot(graf6)
  185. #microsoft 1
  186. MICROSOFT1_norm<-round((date$MICROSOFT1- mean(date$MICROSOFT1))/sd(date$MICROSOFT1),2)
  187. MICROSOFT1_norm
  188. MICROSOFT1_type<-ifelse(MICROSOFT1_norm<0, "sub medie", "peste medie")
  189. MICROSOFT1_type
  190. date<-date[order(MICROSOFT1_type),]
  191. graf7<-ggplot(date, aes(x=INITIALE, y=MICROSOFT1_norm, label=MICROSOFT1_norm))+
  192.   geom_bar(stat="identity", aes(fill=MICROSOFT1_type), width = 0.5)+
  193.   scale_fill_manual(name="Microsoft 0",
  194.                     labels=c("peste medie", "sub medie"),
  195.                     values=c("sub medie"="red", "peste medie"="green"))+
  196.   labs(subtitle="Abaterie fata de medie Microsoft 1",
  197.        title="MICROSOFT1")+
  198.   coord_flip()
  199. plot(graf7)
  200. # activele ----------------------------------------------------------------
  201. date<-read.csv("nasdaq.csv")
  202. View(date)
  203. fix(date)
  204. attach(date)
  205. library(DataExplorer)
  206. DataExplorer::create_report(date[,-1])#i-am scos prima coloana
  207. price<-xts(date[,-1], order.by = as.Date(date[,1],"%d-%m-%Y"))
  208. View(price)
  209. summary(price)
  210. #analizam prezenta outliers pentru preturi folosind performance analitycs (boxploturi)
  211. chart.Boxplot(price[,1], main="Boxplot pret Apple", colorset = rich10equal)
  212. chart.Boxplot(price[,2], main="Boxplot pret Microsoft", colorset = rich10equal)
  213. chart.Boxplot(price[,3], main="Boxplot pret IXIC", colorset = rich10equal)
  214. chart.Boxplot(price[,4], main="Boxplot pret FBND", colorset = rich10equal)
  215. RApple<-na.omit(Return.calculate(price[,1], method = "discrete"))
  216. View(RApple)
  217. RMicrosoft<-na.omit(Return.calculate(price[,2], method = "discrete"))
  218. View(RMicrosoft)
  219. RIxic<-na.omit(Return.calculate(price[,3], method = "discrete"))
  220. View(RIxic)
  221. Rfbnd<-na.omit(Return.calculate(price[,4], method = "discrete"))
  222. View(Rfbnd)
  223. stocks=na.omit((CalculateReturns(price)))
  224. View(stocks)
  225. colnames(stocks)=c("RAPPLE", "RMICROSOFT", "RIXIC", "RFBND")
  226. summary(stocks)
  227. chart.Boxplot(RApple, main="Boxplot return Apple", colorset = rich10equal)
  228. chart.Boxplot(RMicrosoft, main="Boxplot return Microsoft", colorset = rich10equal)
  229. chart.Boxplot(RIxic, main="Boxplot return Ixic", colorset = rich10equal)
  230. chart.Boxplot(Rfbnd, main="Boxplot return Fbnd", colorset = rich10equal)
  231. chart.Histogram(RApple, main="Histograma returns Apple",colorset=rich10equal, methods = c("add.density", "add.normal", "add.risk"))
  232. chart.Histogram(RMicrosoft, main="Histograma returns Microsoft",colorset=rich10equal, methods = c("add.density", "add.normal"))
  233. chart.Histogram(RIxic, main="Histograma returns Ixic",colorset=rich10equal, methods = c("add.density", "add.normal"))
  234. chart.Histogram(Rfbnd, main="Histograma returns Rfnbd",colorset=rich10equal, methods = c("add.density", "add.normal"))
  235. chart.Correlation(stocks,histogram = T)
  236. #evolutia activelor
  237. chart.RollingPerformance(stocks[,1], Rf=0, main="Performanta pe 1 an APPLE", colorset=tim8equal)
  238. chart.RollingPerformance(stocks[,1:2], main="Performanta 2018 Apple & Microsoft", colorset=tim8equal,legend.loc="topleft")
  239. chart.Drawdown(stocks[,1],colorset = rich8equal, main="valori negative pentru apple")
  240. chart.Drawdown(stocks[,2],colorset = rich8equal, main="valori negative pentru microsoft")
  241. chart.Drawdown(stocks[,3],colorset = rich8equal, main="valori negative pentru ixic")
  242. chart.Drawdown(stocks[,4],colorset = rich8equal, main="valori negative pentru fbnd")
  243. chart.Drawdown(stocks[,1:3],colorset = rich8equal, main="Valori negative pentru Apple, Microsoft & Ixic",legend.loc="bottomleft")
  244. # evolutia activelor fata de indicele de piata: apple si microsoft, comparat cu ixic
  245. chart.RelativePerformance(stocks[,1:2],stocks[,3], main="Performanta relativa Apple & Microsoft fata de IXIC", legend.loc = "bottomright")
  246. table.Stats(stocks,ci=0.95, digits=4)
  247. SharpeRatio(stocks, Rf=0,p=0.95, FUN="StdDev")
  248. t(table.SpecificRisk(stocks[,1:2], Rb=stocks[,3], Rf=stocks[,4]))
  249. monthplot(stocks[,1], col="black", main="Sezonalitate RApple")
  250. monthplot(stocks[,2], col="black", main="Sezonalitate RMicrosoft")
  251. adf.test(stocks[,1])
  252. adf.test(stocks[,2]) #stationara
  253. sfmAPPLE=lm(stocks[,1]~stocks[,3])
  254. summary(sfmAPPLE)
  255. table.SFM(Ra=stocks[,1,drop=F], Rb=stocks[,3,drop=F], Rf=0, digits=4)
  256. sfmMICROSOFT=lm(stocks[,2]~stocks[,3])
  257. summary(sfmMICROSOFT)
  258. table.SFM(Ra=stocks[,2,drop=F], Rb=stocks[,3,drop=F], Rf=0, digits=4)
  259. plot.ts(stocks[,3],stocks[,1], col="black", main="sfm pt rentabilitatea apple")
  260. abline(lm(stocks[,1]~stocks[,3]))
  261.  
  262. # camp --------------------------------------------------------------------
  263. table.SFM(Ra=stocks[,1,drop=F], Rb=stocks[,3,drop=F], Rf=stocks[,4,drop=F], digits=4)
  264. chart.Regression(stocks[,1,drop=F], stocks[,3,drop=F], Rf=stocks[,4,drop=F], excess.returns=T, fit=c("linear"), co="blue")
  265. table.CAPM(Ra=stocks[,1], Rb=stocks[,3], Rf=stocks[,4], scale=232, digits=4)
  266. APPLE_CAPM=mean(stocks[,4])+(CAPM.beta(Ra=stocks[,1], Rb=stocks[,3], Rf=stocks[,4])) *mean(stocks[,3]-stocks[,4])
  267. APPLE_CAPM
  268. table.CAPM(Ra=stocks[,2], Rb=stocks[,3], Rf=stocks[,4], scale=232, digits=4)
  269. MICROSOFT_CAPM=mean(stocks[,4])+(CAPM.beta(Ra=stocks[,2], Rb=stocks[,3], Rf=stocks[,4])) *mean(stocks[,3]-stocks[,4])
  270. MICROSOFT_CAPM
  271.  
  272. # functia valoare ---------------------------------------------------------
  273. a=0.88
  274. b=0.88
  275. l=2.25
  276. #Apple functia valoare
  277. val_RAPPLE=ifelse(stocks[,1]>=0,(stocks[,1])^a,(-l)*((-stocks[,1])^b))
  278. mean(val_RAPPLE)
  279. mean(stocks[,1])
  280. APPLE=merge.zoo(stocks[,1], val_RAPPLE)
  281. View(APPLE)
  282. colnames(APPLE)=c("RAPPLE","Val_RAPPLE")
  283. summary(APPLE)
  284. plot(APPLE)
  285. plot(x=APPLE[,1], y=APPLE[,2],xlab="Rentabilitate APPLE", ylab="Valoare APPLE", main="Functia valoare pentru APPLE")
  286. #Microsoft functia valoare
  287. val_RMICROSOFT=ifelse(stocks[,2]>=0,(stocks[,2])^a,(-l)*((-stocks[,2])^b))
  288. View(val_RMICROSOFT)
  289. mean(val_RMICROSOFT)
  290. mean(stocks[,2])
  291. MICROSOFT=merge.zoo(stocks[,2], val_RMICROSOFT)
  292. colnames(MICROSOFT)=c("RMICROSOFT","Val_RMICROSOFT")
  293. View(MICROSOFT)
  294. summary(MICROSOFT)
  295. plot(MICROSOFT)
  296. plot(x=MICROSOFT[,1], y=MICROSOFT[,2],xlab="Rentabilitate MICROSOFT", ylab="Valoare MICROOSFT", main="Functia valoare pentru MICROSOFT")
  297.  
  298. # Time Value of Money -----------------------------------------------------
  299. #AS MOM 0
  300. Return.cumulative(APPLE[,1],geometric=T)
  301. val2_APPLE=700*(Return.cumulative(APPLE[,1],geometric=T))
  302. val2_APPLE
  303. TVM_APPLE=700+val2_APPLE
  304. TVM_APPLE
  305. Return.cumulative(MICROSOFT[,1],geometric=T)
  306. val2_MICROSOFT=300*(Return.cumulative(MICROSOFT[,1],geometric=T))
  307. val2_MICROSOFT
  308. TVM_MICROSOFT=300+val2_MICROSOFT
  309. TVM_MICROSOFT
  310. #AS MOM 1
  311. Return.cumulative(APPLE[,1],geometric=T)
  312. val2_APPLE=800*(Return.cumulative(APPLE[,1],geometric=T))
  313. val2_APPLE
  314. TVM_APPLE=800+val2_APPLE
  315. TVM_APPLE
  316. Return.cumulative(MICROSOFT[,1],geometric=T)
  317. val2_MICROSOFT=200*(Return.cumulative(MICROSOFT[,1],geometric=T))
  318. val2_MICROSOFT
  319. TVM_MICROSOFT=200+val2_MICROSOFT
  320. TVM_MICROSOFT
  321. #AS MOM 2
  322. Return.cumulative(APPLE[,1],geometric=T)
  323. val2_APPLE=600*(Return.cumulative(APPLE[,1],geometric=T))
  324. val2_APPLE
  325. TVM_APPLE=600+val2_APPLE
  326. TVM_APPLE
  327. Return.cumulative(MICROSOFT[,1],geometric=T)
  328. val2_MICROSOFT=400*(Return.cumulative(MICROSOFT[,1],geometric=T))
  329. val2_MICROSOFT
  330. TVM_MICROSOFT=400+val2_MICROSOFT
  331. TVM_MICROSOFT
  332.  
  333. #IV MOM 0
  334. Return.cumulative(APPLE[,1],geometric=T)
  335. val2_APPLE=600*(Return.cumulative(APPLE[,1],geometric=T))
  336. val2_APPLE
  337. TVM_APPLE=600+val2_APPLE
  338. TVM_APPLE
  339. Return.cumulative(MICROSOFT[,1],geometric=T)
  340. val2_MICROSOFT=400*(Return.cumulative(MICROSOFT[,1],geometric=T))
  341. val2_MICROSOFT
  342. TVM_MICROSOFT=400+val2_MICROSOFT
  343. TVM_MICROSOFT
  344. #IV MOM 1
  345. Return.cumulative(APPLE[,1],geometric=T)
  346. val2_APPLE=700*(Return.cumulative(APPLE[,1],geometric=T))
  347. val2_APPLE
  348. TVM_APPLE=700+val2_APPLE
  349. TVM_APPLE
  350. Return.cumulative(MICROSOFT[,1],geometric=T)
  351. val2_MICROSOFT=300*(Return.cumulative(MICROSOFT[,1],geometric=T))
  352. val2_MICROSOFT
  353. TVM_MICROSOFT=300+val2_MICROSOFT
  354. TVM_MICROSOFT
  355. #IV MOM 2
  356. Return.cumulative(APPLE[,1],geometric=T)
  357. val2_APPLE=600*(Return.cumulative(APPLE[,1],geometric=T))
  358. val2_APPLE
  359. TVM_APPLE=600+val2_APPLE
  360. TVM_APPLE
  361. Return.cumulative(MICROSOFT[,1],geometric=T)
  362. val2_MICROSOFT=400*(Return.cumulative(MICROSOFT[,1],geometric=T))
  363. val2_MICROSOFT
  364. TVM_MICROSOFT=400+val2_MICROSOFT
  365. TVM_MICROSOFT
  366.  
  367. #CB MOM 0
  368. Return.cumulative(APPLE[,1],geometric=T)
  369. val2_APPLE=900*(Return.cumulative(APPLE[,1],geometric=T))
  370. val2_APPLE
  371. TVM_APPLE=900+val2_APPLE
  372. TVM_APPLE
  373. Return.cumulative(MICROSOFT[,1],geometric=T)
  374. val2_MICROSOFT=100*(Return.cumulative(MICROSOFT[,1],geometric=T))
  375. val2_MICROSOFT
  376. TVM_MICROSOFT=100+val2_MICROSOFT
  377. TVM_MICROSOFT
  378. #CB MOM 1
  379. Return.cumulative(APPLE[,1],geometric=T)
  380. val2_APPLE=900*(Return.cumulative(APPLE[,1],geometric=T))
  381. val2_APPLE
  382. TVM_APPLE=900+val2_APPLE
  383. TVM_APPLE
  384. Return.cumulative(MICROSOFT[,1],geometric=T)
  385. val2_MICROSOFT=100*(Return.cumulative(MICROSOFT[,1],geometric=T))
  386. val2_MICROSOFT
  387. TVM_MICROSOFT=100+val2_MICROSOFT
  388. TVM_MICROSOFT
  389. #CB MOM 2
  390. Return.cumulative(APPLE[,1],geometric=T)
  391. val2_APPLE=500*(Return.cumulative(APPLE[,1],geometric=T))
  392. val2_APPLE
  393. TVM_APPLE=500+val2_APPLE
  394. TVM_APPLE
  395. Return.cumulative(MICROSOFT[,1],geometric=T)
  396. val2_MICROSOFT=500*(Return.cumulative(MICROSOFT[,1],geometric=T))
  397. val2_MICROSOFT
  398. TVM_MICROSOFT=500+val2_MICROSOFT
  399. TVM_MICROSOFT
  400.  
  401. #SS mom 0
  402. Return.cumulative(APPLE[,1],geometric=T)
  403. val2_APPLE=700*(Return.cumulative(APPLE[,1],geometric=T))
  404. val2_APPLE
  405. TVM_APPLE=700+val2_APPLE
  406. TVM_APPLE
  407. Return.cumulative(MICROSOFT[,1],geometric=T)
  408. val2_MICROSOFT=300*(Return.cumulative(MICROSOFT[,1],geometric=T))
  409. val2_MICROSOFT
  410. TVM_MICROSOFT=300+val2_MICROSOFT
  411. TVM_MICROSOFT
  412. #SS mom 1
  413. Return.cumulative(APPLE[,1],geometric=T)
  414. val2_APPLE=800*(Return.cumulative(APPLE[,1],geometric=T))
  415. val2_APPLE
  416. TVM_APPLE=800+val2_APPLE
  417. TVM_APPLE
  418. Return.cumulative(MICROSOFT[,1],geometric=T)
  419. val2_MICROSOFT=200*(Return.cumulative(MICROSOFT[,1],geometric=T))
  420. val2_MICROSOFT
  421. TVM_MICROSOFT=200+val2_MICROSOFT
  422. TVM_MICROSOFT
  423. #SS mom 2
  424. Return.cumulative(APPLE[,1],geometric=T)
  425. val2_APPLE=400*(Return.cumulative(APPLE[,1],geometric=T))
  426. val2_APPLE
  427. TVM_APPLE=400+val2_APPLE
  428. TVM_APPLE
  429. Return.cumulative(MICROSOFT[,1],geometric=T)
  430. val2_MICROSOFT=600*(Return.cumulative(MICROSOFT[,1],geometric=T))
  431. val2_MICROSOFT
  432. TVM_MICROSOFT=600+val2_MICROSOFT
  433. TVM_MICROSOFT
  434.  
  435. #construim prospecte
  436. #AS la momanetul 0
  437. #AS imparte suma in Apple 700$, iar in Microsoft 300$, notele fiind 8, respectiv 5
  438. Val_700<-700^0.88
  439. Val_300<-300^0.88
  440. Val_700
  441. Val_300
  442. #Luam notele de la momentul 0 si le impartim la 10
  443. p=8/10 #Apple
  444. k<-5/10 #Microsoft
  445. #Transformam p si k in ponderi decizionale
  446. pp<-(p^0.61)/(p^0.61 + (1-p)^0.61)
  447. pq<-(k^0.61)/(k^0.61 + (1-k)^0.61)
  448. Val_pr_AS_0<-pp*Val_700+pq*Val_300
  449. Val_pr_AS_0
  450.  
  451. #AS la momentul 1
  452. #AS imparte suma in Apple 800$, iar in Microsoft 200$, notele fiind 9, respectiv 5
  453. Val_800<-800^0.88
  454. Val_200<-200^0.88
  455. Val_800
  456. Val_200
  457. #Luam notele de la momentul 0 si le impartim la 10
  458. p=9/10 #Apple
  459. k<-5/10 #Microsoft
  460. #Transformam p si k in ponderi decizionale
  461. pp<-(p^0.61)/(p^0.61 + (1-p)^0.61)
  462. pq<-(k^0.61)/(k^0.61 + (1-k)^0.61)
  463. Val_pr_AS_1<-pp*Val_800+pq*Val_200
  464. Val_pr_AS_1
  465.  
  466. #AS la momentul 2
  467. #AS investeste in Apple 600$, iar in Microsoft 400$, notele fiind 8, respectiv 7
  468. val2_APPLE_t2=600*Return.cumulative(APPLE[,1], geometric = T)
  469. val2_APPLE_t2 #castigul la momentul prezent
  470. tvm_APPLE_t2 <- 600 + val2_APPLE_t2
  471. tvm_APPLE_t2
  472. val2_MICROSOFT_t2=400*Return.cumulative(MICROSOFT[,1], geometric = T)
  473. val2_MICROSOFT_t2 #castigul la momentul prezent
  474. tvm_MICROSOFT_t2 <- 400 + val2_MICROSOFT_t2
  475. tvm_MICROSOFT_t2
  476. Val_tvm_APPLE_t2 = tvm_APPLE_t2^0.88
  477. Val_tvm_APPLE_t2
  478. Val_tvm_MICROSOFT_t2 = tvm_MICROSOFT_t2^0.88
  479. Val_tvm_MICROSOFT_t2
  480. p=8/10 #Apple
  481. k<-7/10 #Microsoft
  482. pp<-(p^0.61)/(p^0.61 + (1-p)^0.61)
  483. pq<-(k^0.61)/(k^0.61 + (1-k)^0.61)
  484. Val_pr_AS_2<-pp*Val_tvm_APPLE_t2+pq*Val_tvm_MICROSOFT_t2
  485. Val_pr_AS_2
  486.  
  487. #IV la momanetul 0
  488. #IV imparte suma in Apple 600$, iar in Microsoft 400$, notele fiind 7, respectiv 6
  489. Val_600<-600^0.88
  490. Val_400<-400^0.88
  491. Val_600
  492. Val_400
  493. #Luam notele de la momentul 0 si le impartim la 10
  494. p=7/10 #Apple
  495. k<-6/10 #Microsoft
  496. #Transformam p si k in ponderi decizionale
  497. pp<-(p^0.61)/(p^0.61 + (1-p)^0.61)
  498. pq<-(k^0.61)/(k^0.61 + (1-k)^0.61)
  499. Val_pr_IV_0<-pp*Val_600+pq*Val_400
  500. Val_pr_IV_0
  501.  
  502. #IV la momentul 1
  503. #IV imparte suma in Apple 700$, iar in Microsoft 300$, notele fiind 8, respectiv 6
  504. Val_700<-700^0.88
  505. Val_300<-300^0.88
  506. Val_700
  507. Val_300
  508. #Luam notele de la momentul 0 si le impartim la 10
  509. p=8/10 #Apple
  510. k<-6/10 #Microsoft
  511. #Transformam p si k in ponderi decizionale
  512. pp<-(p^0.61)/(p^0.61 + (1-p)^0.61)
  513. pq<-(k^0.61)/(k^0.61 + (1-k)^0.61)
  514. Val_pr_IV_1<-pp*Val_700+pq*Val_300
  515. Val_pr_IV_1
  516.  
  517. #IV la momentul 2
  518. #IV investeste in Apple 600$, iar in Microsoft 400$, notele fiind 7, respectiv 8
  519. val2_APPLE_t2=600*Return.cumulative(APPLE[,1], geometric = T)
  520. val2_APPLE_t2 #castigul la momentul prezent
  521. tvm_APPLE_t2 <- 600 + val2_APPLE_t2
  522. tvm_APPLE_t2
  523. val2_MICROSOFT_t2=400*Return.cumulative(MICROSOFT[,1], geometric = T)
  524. val2_MICROSOFT_t2 #castigul la momentul prezent
  525. tvm_MICROSOFT_t2 <- 400 + val2_MICROSOFT_t2
  526. tvm_MICROSOFT_t2
  527. Val_tvm_APPLE_t2 = tvm_APPLE_t2^0.88
  528. Val_tvm_APPLE_t2
  529. Val_tvm_MICROSOFT_t2 = tvm_MICROSOFT_t2^0.88
  530. Val_tvm_MICROSOFT_t2
  531. p<-7/10 #Apple
  532. k<-8/10 #Microsoft
  533. pp<-(p^0.61)/(p^0.61 + (1-p)^0.61)
  534. pq<-(k^0.61)/(k^0.61 + (1-k)^0.61)
  535. Val_pr_IV_2<-pp*Val_tvm_APPLE_t2+pq*Val_tvm_MICROSOFT_t2
  536. Val_pr_IV_2
  537.  
  538. #CB la momanetul 0
  539. #CB imparte suma in Apple 900$, iar in Microsoft 100$, notele fiind 8, respectiv 3
  540. Val_900<-900^0.88
  541. Val_100<-100^0.88
  542. Val_900
  543. Val_100
  544. #Luam notele de la momentul 0 si le impartim la 10
  545. p=8/10 #Apple
  546. k<-3/10 #Microsoft
  547. #Transformam p si k in ponderi decizionale
  548. pp<-(p^0.61)/(p^0.61 + (1-p)^0.61)
  549. pq<-(k^0.61)/(k^0.61 + (1-k)^0.61)
  550. Val_pr_CB_0<-pp*Val_900+pq*Val_100
  551. Val_pr_CB_0
  552.  
  553. #CB la momentul 1
  554. #CB imparte suma in Apple 900$, iar in Microsoft 100$, notele fiind 10, respectiv 3
  555. Val_900<-900^0.88
  556. Val_100<-100^0.88
  557. Val_900
  558. Val_100
  559. #Luam notele de la momentul 0 si le impartim la 10
  560. p<-10/10 #Apple
  561. k<-3/10 #Microsoft
  562. #Transformam p si k in ponderi decizionale
  563. pp<-(p^0.61)/(p^0.61 + (1-p)^0.61)
  564. pq<-(k^0.61)/(k^0.61 + (1-k)^0.61)
  565. Val_pr_CB_1<-pp*Val_900+pq*Val_100
  566. Val_pr_CB_1
  567.  
  568. #CB la momentul 2
  569. #CB investeste in Apple 500$, iar in Microsoft 500$, notele fiind 7, respectiv 6
  570. val2_APPLE_t2=500*Return.cumulative(APPLE[,1], geometric = T)
  571. val2_APPLE_t2 #castigul la momentul prezent
  572. tvm_APPLE_t2 <- 500 + val2_APPLE_t2
  573. tvm_APPLE_t2
  574. val2_MICROSOFT_t2=500*Return.cumulative(MICROSOFT[,1], geometric = T)
  575. val2_MICROSOFT_t2 #castigul la momentul prezent
  576. tvm_MICROSOFT_t2 <- 500 + val2_MICROSOFT_t2
  577. tvm_MICROSOFT_t2
  578. Val_tvm_APPLE_t2 = tvm_APPLE_t2^0.88
  579. Val_tvm_APPLE_t2
  580. Val_tvm_MICROSOFT_t2 = tvm_MICROSOFT_t2^0.88
  581. Val_tvm_MICROSOFT_t2
  582. p<-7/10 #Apple
  583. k<-6/10 #Microsoft
  584. pp<-(p^0.61)/(p^0.61 + (1-p)^0.61)
  585. pq<-(k^0.61)/(k^0.61 + (1-k)^0.61)
  586. Val_pr_CB_2<-pp*Val_tvm_APPLE_t2+pq*Val_tvm_MICROSOFT_t2
  587. Val_pr_CB_2
  588.  
  589. #SS la momanetul 0
  590. #SS imparte suma in Apple 700$, iar in Microsoft 300$, notele fiind 9, respectiv 7
  591. Val_700<-700^0.88
  592. Val_300<-300^0.88
  593. Val_700
  594. Val_300
  595. #Luam notele de la momentul 0 si le impartim la 10
  596. p=9/10 #Apple
  597. k<-7/10 #Microsoft
  598. #Transformam p si k in ponderi decizionale
  599. pp<-(p^0.61)/(p^0.61 + (1-p)^0.61)
  600. pq<-(k^0.61)/(k^0.61 + (1-k)^0.61)
  601. Val_pr_SS_0<-pp*Val_700+pq*Val_300
  602. Val_pr_SS_0
  603.  
  604. #SS la momentul 1
  605. #SS imparte suma in Apple 800$, iar in Microsoft 200$, notele fiind 9, respectiv 6
  606. Val_800<-800^0.88
  607. Val_200<-200^0.88
  608. Val_800
  609. Val_200
  610. #Luam notele de la momentul 0 si le impartim la 10
  611. p<-9/10 #Apple
  612. k<-6/10 #Microsoft
  613. #Transformam p si k in ponderi decizionale
  614. pp<-(p^0.61)/(p^0.61 + (1-p)^0.61)
  615. pq<-(k^0.61)/(k^0.61 + (1-k)^0.61)
  616. Val_pr_SS_1<-pp*Val_800+pq*Val_200
  617. Val_pr_SS_1
  618.  
  619. #SS la momentul 2
  620. #SS investeste in Apple 400$, iar in Microsoft 600$, notele fiind 5, respectiv 5
  621. val2_APPLE_t2=400*Return.cumulative(APPLE[,1], geometric = T)
  622. val2_APPLE_t2 #castigul la momentul prezent
  623. tvm_APPLE_t2 <- 400 + val2_APPLE_t2
  624. tvm_APPLE_t2
  625. val2_MICROSOFT_t2=600*Return.cumulative(MICROSOFT[,1], geometric = T)
  626. val2_MICROSOFT_t2 #castigul la momentul prezent
  627. tvm_MICROSOFT_t2 <- 600 + val2_MICROSOFT_t2
  628. tvm_MICROSOFT_t2
  629. Val_tvm_APPLE_t2 = tvm_APPLE_t2^0.88
  630. Val_tvm_APPLE_t2
  631. Val_tvm_MICROSOFT_t2 = tvm_MICROSOFT_t2^0.88
  632. Val_tvm_MICROSOFT_t2
  633. p<-5/10 #Apple
  634. k<-5/10 #Microsoft
  635. pp<-(p^0.61)/(p^0.61 + (1-p)^0.61)
  636. pq<-(k^0.61)/(k^0.61 + (1-k)^0.61)
  637. Val_pr_SS_2<-pp*Val_tvm_APPLE_t2+pq*Val_tvm_MICROSOFT_t2
  638. Val_pr_SS_2
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