A shiny app that helps you explore stock returns (by way of yahoo finance and quantmod)

server.R

library(shiny)
library(quantmod)
library(VGAM)

# Define server logic for random distribution application
shinyServer(function(input, output) {
  
  # acquiring data
  dataInput <- reactive({
    if (input$get == 0)
      return(NULL)

    return(isolate({
      getSymbols(input$symb,src="yahoo", auto.assign = FALSE)
    }))
  })
  
  datesInput <- reactive({
    if (input$get == 0)
      return(NULL)
    
    return(isolate({
      paste0(input$dates[1], "::",  input$dates[2])
    }))
  })
  
  returns <- reactive({ 
    if (input$get == 0)
      return(NULL)
    
    dailyReturn(dataInput())
  })
  
  xs <- reactive({ 
    if (input$get == 0)
      return(NULL)
    
    span <- range(returns())
    seq(span[1], span[2], by = diff(span) / 100)
  })
  
  # tab based controls
  output$newBox <- renderUI({
    switch(input$tab,
           "Charts" = chartControls,
           "Model" = modelControls,
           "VaR" = helpText("VaR")
    )
  })
  
  # Charts tab
  chartControls <- div(
    wellPanel(
      selectInput("chart_type",
        label = "Chart type",
        choices = c("Candlestick" = "candlesticks", 
          "Matchstick" = "matchsticks",
          "Bar" = "bars",
          "Line" = "line"),
        selected = "Line"
      ),
      checkboxInput(inputId = "log_y", label = "log y axis", 
                    value = FALSE)
    ),
    
    wellPanel(
      p(strong("Technical Analysis")),
      checkboxInput("ta_vol", label = "Volume", value = FALSE),
      checkboxInput("ta_sma", label = "Simple Moving Average", 
                    value = FALSE),
      checkboxInput("ta_ema", label = "Exponential Moving Average", 
                    value = FALSE),
      checkboxInput("ta_wma", label = "Weighted Moving Average", 
                    value = FALSE),
      checkboxInput("ta_bb", label = "Bolinger Bands", 
                    value = FALSE),
      checkboxInput("ta_momentum", label = "Momentum", 
                    value = FALSE),
      
      br(),
      
      actionButton("chart_act", "Add Technical Analysis")
    )
  )
  
  TAInput <- reactive({
    if (input$chart_act == 0)
      return("NULL")
    
    tas <- isolate({c(input$ta_vol, input$ta_sma, input$ta_ema, 
      input$ta_wma,input$ta_bb, input$ta_momentum)})
    funcs <- c(addVo(), addSMA(), addEMA(), addWMA(), 
      addBBands(), addMomentum())
    
    if (any(tas)) funcs[tas]
    else "NULL"
  })
  
  output$chart <- renderPlot({
    chartSeries(dataInput(),
                name = input$symb,
                type = input$chart_type,
                subset = datesInput(),
                log.scale = input$log_y,
                theme = "white",
                TA = TAInput())
  })
  
  # Model tab
  modelControls <- div(
      br(),
      
      sliderInput("n", "Number of bins in histogram",
        min = 1, max = 250, value = 30
      ),
      
      br(),
    
    wellPanel(
      selectInput("family", "Model returns as",
                choices = c("normal", "double exponential", "t"),
                selected = "normal"
      ),
      
      sliderInput("mu", "Mean",
        min = -1, max = 1, value = 0, step = 0.01
      ), 
      
      sliderInput("sigma", "Standard Deviation",
        min = 0, max = 0.1, value = 0.05, step = 0.001
      ),
      conditionalPanel(condition = "input.family == 't'",
        sliderInput("df", "Degrees of freedom",
          min = 2, max = 1000, value = 10
        )
      )
      
    )
  )
    
  ys <- reactive({ 
    if (input$get == 0)
      return(NULL)
    
    switch(input$family,
           "double exponential" = dlaplace(xs(), 
                                location = input$mu, 
                                scale = input$sigma
                                ),
           "normal" = dnorm(xs(), 
                             mean = input$mu, 
                             sd = input$sigma
                             ),
           "t" = dt((xs() - input$mu) / input$sigma,
                    df = input$df) * sqrt(2 * length(returns()))
    )
  })
    
  ks <- reactive({
    switch(input$family,
           "double exponential" = ks.test(returns(), "plaplace", 
                                          input$mu, input$sigma),
           "normal" = ks.test(returns(), "pnorm", 
                              input$mu, input$sigma),
           "t" = ks.test((returns() - input$mu) / input$sigma, "pt", 
                         input$df)
    )
  })
  
  output$hist <- renderPlot({
    hist(returns(), xlab = "returns", freq = FALSE,
      main = paste(input$symb, "Daily Returns:", 
        input$dates[1], "-", input$dates[2], sep = " "),
      breaks = input$n)
   lines(xs(), ys(), col = "red")
  })
  
  
  output$ks <- renderText({
    paste0("Kolmogorv-Smirnoff statistic: ", ks()$statistic)
  })
  output$ksp <- renderText({
    paste0("P-value for model: ", ks()$p.value)
  })
  
  # VaR tab
  output$text3 <- renderText({paste0(input$symb, " 3: ", input$tab)})
                           
})

ui.R

library(shiny)

# Define UI for random distribution application 
shinyUI(pageWithSidebar(

  headerPanel("Stock Explorer"),

  sidebarPanel(
    
    helpText("Select a stock to examine. 
      Information will be collected from yahoo finance."),
    
    textInput("symb", "Symbol", "GOOG"),
    
    dateRangeInput("dates", 
      "Compare to historic returns from",
      start = "2013-01-01", end = "2013-09-05"),
    
    actionButton("get", "Get Stock"),
    
    br(),
    br(),
    
    uiOutput("newBox")
    
  ),

  # Show a tabset that includes a plot, summary, and table view
  # of the generated distribution
  mainPanel(
    tabsetPanel(
      tabPanel("Charts", plotOutput("chart")), 
      tabPanel("Model", div(h3(textOutput("ks"))), 
               div(h3(textOutput("ksp"))), 
                   plotOutput("hist")), 
      tabPanel("VaR", h3(textOutput("text3"))),
      id = "tab"
    )
  )
))