我的任务是采用股票预测模型并将其置于Shiny中。我对R的成功起伏不定。代码有点混乱，但希望能够超越这一点，并尝试将其清理为更好的代码。
我收到错误
Error: could not find function "myATR"
这似乎是来自这一行，我正在构建一个公式字符串以赋予指定模型。
这是
行formula <- as.formula(paste0("Next(Cl(",symbol(),"),",i,") ~ Delt(Cl(",symbol(),"),k=1)+myATR(",symbol(),")+myADX(",symbol(),")+myEMV(",symbol(),")+myVolat(",symbol(),")+myMACD(",symbol(),")+mySAR(",symbol(),")+runMean(Cl(",symbol(),"))"))
公式myATV在代码中定义为
myATR <- function(x) ATR(HLC(x))[,'atr']
我很难过，不知道怎么回答这个问题。我已将我的ui.R和server.R包括在内。
ui.R
# Stock Symbols
# #####
# AAPL - Apple Inc.
# GOOGL - Alphabet Inc.
# ORCL - Oracle Corporation
# FB - Facebook, Inc.
# TWTR - Twitter Inc
shinyUI(fluidPage(
titlePanel("Stock Tickers and Prediction Models, Predicting Future Close Prices"),
sidebarLayout(
sidebarPanel(
selectInput("symbolID", label= "Select ticker",
choices = list("XXII" = 1, "GOOGL" = 2, "ORCL" = 3, "MSFT" = 4, "AIG" = 5)),
selectInput("modelID", label= "Select ticker",
choices = list("svm" = 1, "random forest" = 2, "neural network" = 3))
),
mainPanel(
plotOutput("Graph"),
tableOutput("getsymbols"),
tableOutput("Predictions")
)
)
))
server.R
library(shiny)
library(quantmod)
library(e1071)
library(nlme)
library(earth)
library(xts)
# for stock symbols
symbols = {}
# for model symbols
models = {}
# end date and start dates based on system time
endDate = as.Date(Sys.Date() - 1, format = "%Y-%M-%D")
startDate = as.Date(endDate - 1000, format = "%Y-%M-%D")
myATR <- function(x) ATR(HLC(x))[,'atr']
myADX <- function(x) ADX(HLC(x))[,'ADX']
myEMV <- function(x) EMV(cbind(Hi(x),Lo(x)),Vo(x))[,2]
myMACD <- function(x) MACD(Cl(x))[,2]
mySAR <- function(x) SAR(cbind(Hi(x),Cl(x))) [,1]
myVolat <- function(x) volatility(OHLC(x),calc="garman")[,1]
shinyServer(function(input, output){
inputSymbols <- reactive({
if (input$symbolID == 1) {
symbols = 'XXII'
} else if (input$symbolID == 2) {
symbols = 'GOOGL'
} else if (input$symbolID == 3) {
symbols = 'ORCL'
} else if (input$symbolID == 4) {
symbols = 'MSFT'
} else if (input$symbolID == 5) {
symbols = 'AIG'
}
getSymbols(symbols, src = "yahoo", from = startDate, to = endDate, auto.assign = FALSE)
})
symbol <- reactive({
if (input$symbolID == 1) {
symbols = 'XXII'
} else if (input$symbolID == 2) {
symbols = 'GOOGL'
} else if (input$symbolID == 3) {
symbols = 'ORCL'
} else if (input$symbolID == 4) {
symbols = 'MSFT'
} else if (input$symbolID == 5) {
symbols = 'AIG'
}
symbols
})
model <- reactive({
if (input$modelID == 1) {
models = 1
} else if (input$modelID == 2) {
models = 2
} else if (input$modelID == 3) {
models = 3
}
models
})
#model <- reactive(input@model)
# graph output
output$Graph <- renderPlot({
chartSeries(inputSymbols(), subset = paste(endDate - 30, endDate, sep = "/"), type = 'line', theme = 'white')
})
#output$getsymbols = renderPrint({symbol()})
output$Predictions <- renderTable({
getSymbols(symbol())
#Obtain prediction model for a given iteration
GetPredModel <- function(i){
formula <- as.formula(paste0("Next(Cl(",symbol(),"),",i,") ~ Delt(Cl(",symbol(),"),k=1)+myATR(",symbol(),")+myADX(",symbol(),")+myEMV(",symbol(),")+myVolat(",symbol(),")+myMACD(",symbol(),")+mySAR(",symbol(),")+runMean(Cl(",symbol(),"))"))
#formula <- as.formula(paste0("Next(Cl(",symbol(),")",",",i,") ~ Delt(Cl(",symbol(),"),k=1)+myATR(",symbol(),")+myADX(",symbol(),")+myEMV(",symbol(),")+myVolat(",symbol(),")+myMACD(",symbol(),")+mySAR(",symbol(),")+runMean(Cl(",symbol(),"))"))
m <- specifyModel(formula)
data <- as.data.frame(modelData(m))
return(data)
}
#Derive max data available
maxdate<-max(index(get(symbol())))
#Define prediction as xts object
prediction<-xts()
n=1
while(n<=5){
data<-GetPredModel(n)
formula <- as.formula(paste0("Next.Cl.",symbol(),".",n," ~ ."))
set.seed(1234)
# pick model
if (model() == 1) {
res <- svm(formula,data,cost=10,gamma=0.01)
} else if (model() == 2) {
res<- randomForest(formula,data,na.action = na.omit, ntree=200)
} else if (model() == 3) {
res<-nnet(formula,data, size=10, decay=0.01, maxit=1000, linout=T, trace=F)
}
pred_date<-as.Date(maxdate) + n
p <- predict(res,data[as.character(maxdate),])
#print(pred_date)
names(p)<-c(as.character(pred_date))
prediction<-rbind(as.xts(cbind(p)),prediction)
n = n + 1
}
})
})
答案 0 :(得分：0)
当我尝试使用应用功能nnet时，无法找到randomForest。你错过了这两个套餐吗？
答案 1 :(得分：0)
以下是按工作顺序提交的内容。根据mRcSchwering，范围问题很重要。希望这有助于某人。
library(shiny)
library(quantmod)
library(e1071)
library(nlme)
library(xts)
library(DMwR)
library(TTR)
library(nnet)
library(randomForest)
library(gridExtra)
myATR <- function(x) ATR(HLC(x))[,'atr']
myADX <- function(x) ADX(HLC(x))[,'ADX']
myEMV <- function(x) EMV(cbind(Hi(x),Lo(x)),Vo(x))[,2]
myMACD <- function(x) MACD(Cl(x))[,2]
mySAR <- function(x) SAR(cbind(Hi(x),Cl(x))) [,1]
myVolat <- function(x) volatility(OHLC(x),calc="garman")[,1]
assign("myATR", myATR, envir = .GlobalEnv)
assign("myADX", myADX, envir = .GlobalEnv)
assign("myEMV", myEMV, envir = .GlobalEnv)
assign("myMACD", myMACD, envir = .GlobalEnv)
assign("mySAR", mySAR, envir = .GlobalEnv)
assign("myVolat", myVolat, envir = .GlobalEnv)
#Obtain prediction model for a given iteration
GetPredModel <- function(i, stockName){
formula <- as.formula(paste0("Next(Cl(",stockName,")",",",i,") ~ Delt(Cl(",stockName,"),k=1)+myATR(",stockName,")+myADX(",stockName,")+myEMV(",stockName,")+myVolat(",stockName,")+myMACD(",stockName,")+mySAR(",stockName,")+runMean(Cl(",stockName,"))"))
m <- specifyModel(formula)
data <- as.data.frame(modelData(m))
return(data)
}
shinyServer(function(input, output){
# graph output
output$Graph <- renderPlot({
stockName = input$symbolID
modelID = input$modelID
# end date and start dates based on system time
endDate = as.Date(Sys.Date() - 1, format = "%Y-%M-%D")
startDate = as.Date(endDate - 180, format = "%Y-%M-%D")
# Plot the actual stock price
chartData <- getSymbols(stockName, src = "yahoo", from = startDate, to = endDate, auto.assign = FALSE)
chartSeries(chartData, subset = paste(endDate - 30, endDate, sep = "/"), type = 'line', theme = 'white')
})
# Calculate forecast and plot forecast
output$Predictions <- renderPlot({
stockName = input$symbolID
modelID = input$modelID
# end date and start dates based on system time
endDate = as.Date(Sys.Date() - 1, format = "%Y-%M-%D")
startDate = as.Date(endDate - 180, format = "%Y-%M-%D")
#Derive max data available
maxdate<-max(index(getSymbols(stockName, auto.assign = FALSE)))
#Define prediction as xts object
prediction<-xts()
n=1
while(n<=5){
data<-GetPredModel(n, stockName)
formula <- as.formula(paste0("Next.Cl.",stockName,".",n," ~ ."))
set.seed(1234)
# pick model
if (modelID == 1) {
res <- svm(formula,data,cost=10,gamma=0.01)
} else if (modelID == 2) {
res<- randomForest(formula,data,na.action = na.omit, ntree=200)
} else if (modelID == 3) {
res<-nnet(formula,data, size=10, decay=0.01, maxit=1000, linout=T, trace=F)
}
pred_date<-as.Date(maxdate) + n
p <- predict(res,data[as.character(maxdate),])
if (modelID == 3){
p <- p[,1]
}
#print(pred_date)
names(p)<-c(as.character(pred_date))
prediction<-rbind(as.xts(cbind(p)),prediction)
n = n + 1
}
plot(prediction)
#grid.arrange(pActual, pPredict, ncol= 1)
})
})