4建模分析股票市场数据.rar_r语言股票分析_建模分析股票市场数据_数据分析_股票_股票分析

library(ggplot2) install.packages("plyr") library(plyr) library(reshape2) library(zoo) library(XML) setwd("E:\\Program Files\\RStudio\\work\\4建模分析股票市场数据") finviz<-read.csv("finviz.csv") head(finviz) summary(finviz) clean_numeric<-function(s){ s<-gsub("%|\\$|,|\\)|\\(","",s) s<-as.numeric(s) } finviz<-cbind(finviz[,1:6],apply(finviz[,7:68],2,clean_numeric)) hist(finviz$Price,breaks=100,main="Price Distribution",xlab="Price") hist(finviz$Price[finviz$Price<150],breaks=100,main="PriceDistribution",xlab="Price") sector_avg_prices<-aggregate(Price~Sector,data=finviz,FUN="mean") colnames(sector_avg_prices)[2]<-"Sector_Avg_Price" ggplot(sector_avg_prices,aes(x=Sector,y=Sector_Avg_Price,fill=Sector))+geom_bar(stat="identity")+ggtitle("Sector Avg Prices")+ theme(axis.text.x=element_text(angle=90,hjust=1)) industry_avg_prices<-aggregate(Price~Sector+Industry,data=finviz,FUN="mean") industry_avg_prices<-industry_avg_prices[order(industry_avg_prices$Sector,industry_avg_prices$Industry),] colnames(industry_avg_prices)[3]<-"Industry_Avg_Price" industry_chart<-subset(industry_avg_prices,Sector=="Financial") ggplot(industry_chart,aes(x=Industry,y=Industry_Avg_Price,fill=Industry))+geom_bar(stat="identity")+theme(legend.position="none")+ ggtitle("Industry Avg Prices")+ theme(axis.text.x=element_text(angle=90,hjust=1)) company_chart<-subset(finviz,Industry=="Property & Casualty Insurance") ggplot(company_chart,aes(x=Company,y=Price,fill=Company))+geom_bar(stat="identity")+theme(legend.position="none")+ggtitle("Company Avg Prices")+ theme(axis.text.x=element_text(angle=90,hjust=1)) finviz<-subset(finviz,Ticker!="BRK-A") sector_avg_prices<-aggregate(Price~Sector,data=finviz,FUN="mean") colnames(sector_avg_prices)[2]<-"Sector_Avg_Price" ggplot(sector_avg_prices,aes(x=Sector,y=Sector_Avg_Price,fill=Sector))+geom_bar(stat="identity")+ggtitle("Sector_Avg_Price")+ theme(axis.text.x=element_text(angle=90,hjust=1)) sector_avg<-melt(finviz,id="Sector") sector_avg<-subset(sector_avg,variable%in%c("Price","P.E","PEG","P.S","P.B")) sector_avg<-(na.omit(sector_avg)) sector_avg$value<-as.numeric(sector_avg$value) sector_avg<-dcast(sector_avg,Sector~variable,mean) colnames(sector_avg)[2:6]<-c("SAvgPE","SAvgPEG","SAvgPS","SAvgPB","SAvgPrice") industry_avg<-melt(finviz,id=c("Sector","Industry")) industry_avg<-subset(industry_avg,variable%in%c("Price","P.E","PEG","P.S","P.B")) industry_avg<-(na.omit(industry_avg)) industry_avg$value<-as.numeric(industry_avg$value) industry_avg<-dcast(industry_avg,Sector+Industry~variable,mean) industry_avg<-(na.omit(industry_avg)) colnames(industry_avg)[3:7]<-c("IAvgPE","IAvgPEG","IAvgPS","IAvgPB","IAvgPrice") finviz<-merge(finviz,sector_avg,by.x="Sector",by.y="Sector") finviz<-merge(finviz,industry_avg,by.x=c("Sector","Industry"),by.y=c("Sector","Industry")) finviz$SPEUnder<-0 finviz$SPEGUnder<-0 finviz$SPSUnder<-0 finviz$SPBUnder<-0 finviz$SPriceUnder<-0 finviz$IPEUnder<-0 finviz$IPEGUnder<-0 finviz$IPSUnder<-0 finviz$SPEUnder[finviz$P.E<finviz$SAvgPE]<-1 finviz$SPEGUnder[finviz$PEG<finviz$SAvgPEG]<-1 finviz$SPSUnder[finviz$P.S<finviz$SAvgPS]<-1 finviz$SPBUnder[finviz$P.B<finviz$SAvgPB]<-1 finviz$SPriceUnder[finviz$Price<finviz$SAvgPrice]<-1 finviz$IPEUnder[finviz$P.E<finviz$IAvgPE]<-1 finviz$IPEGUnder[finviz$PEG<finviz$IAvgPEG]<-1 finviz$IPSUnder[finviz$P.S<finviz$IAvgPS]<-1 finviz$IPBUnder[finviz$P.B<finviz$IAvgPB]<-1 finviz$IPriceUnder[finviz$Price<finviz$IAvgPrice]<-1 finviz$RelValIndex<-apply(finviz[79:88],1,sum) potentially_undervalued<-subset(finviz,RelValIndex>=8) target_stocks<-subset(finviz,Price>20&Price<100&Volume>10000&Country=="USA"& EPS..ttm.>0& EPS.growth.next.year>0& EPS.growth.next.5.years>0& Total.Debt.Equity<1&Beta<1.5& Institutional.Ownership<30& RelValIndex>8) stock0<-read.csv("NRCIB.CSV") stock0<-cbind(symbol="NRCIB",stock0) stock1<-read.csv("WPZ.csv") stock1<-cbind(symbol="WPZ",stock1) stock<-rbind(stock0,stock1) stock2<-read.csv("TCAP.csv") stock2<-cbind(symbol="TCAP",stock2) stock<-rbind(stock,stock2) stock3<-read.csv("AAN.csv") stock3<-cbind(symbol="AAN",stock3) stock<-rbind(stock,stock3) stock4<-read.csv("OMI.csv") stock4<-cbind(symbol="OMI",stock4) stock<-rbind(stock,stock4) stock5<-read.csv("MAIN.csv") stock5<-cbind(symbol="MAIN",stock5) stock<-rbind(stock,stock5) maxrow<-nrow(stock5)-49 ma50<-cbind(stock5[1:maxrow,1:2],rollmean(stock5$Adj.Close,50,align="right")) maxrow<-nrow(stock5)-199 ma200<-cbind(stock5[1:maxrow,1:2],rollmean(stock5$Adj.Close,200,align="right")) stock5<-merge(stock5,ma50,by.x=c("symbol","Date"),by.y=c("symbol","Date"),all.x=TRUE) colnames(stock5)[9]<-"MovAvg50" stock5<-merge(stock5,ma200,by.x=c("symbol","Date"),by.y=c("symbol","Date"),all.x=TRUE) colnames(stock5)[10]<-"MovAvg200" stock5[,2]<-as.Date(stock5[,2]) price_chart<-melt(stock5[,c(1,2,7,9,10)],id=c("symbol","Date")) qplot(Date,value,data=price_chart,geom="line",color=variable,main=paste("OMI","Daily Stock Price"),ylab="Price") ggsave(filename=paste0("stock_price_",5,".png"))