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app.R
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library(shiny)
library(quantmod)
library(xts)
library(tseries)
library(forecast)
library(timeSeries)
library(tidyverse)
library(dplyr)
library(tsfknn)
library(ggplot2 )
source("fonctions1.R")
# Define UI for application that draws a histogram
ui <- fluidPage(
titlePanel("Stock Prediction Application"),
tabsetPanel(type="tabs",
tabPanel("Data",br(),
sidebarLayout(
sidebarPanel(
selectInput("share", "Please select the market share",
choices = c('BTC-USD','ETH-USD','DOGE-USD','XRP-USD','ADA-USD')),
dateRangeInput("dates",
"Date range",
start = "2020-02-01",
end = as.character(Sys.Date())),
selectInput("variable_choice", "Please select what to represent",
choices = c('All','Open','High','Low','Close')),
selectInput("TA_choice", "Please select a technical indicator",
choices = c('Nothing','Volume','Moving Average Convergence Divergence',
'Bollinger Bands','Relative Strength Indicator',
"Commodity Channel Index")),
br(),
br(),
actionButton("zoomIN", "Zoom in last week"),
actionButton("zoomOUT", "Zoom out"),
br(),
br()
),
mainPanel(
tabsetPanel(type="tabs",
tabPanel("visualization",br(),plotOutput("plot")
),
tabPanel("Data & Summary",br(),
tableOutput("dataset"),
verbatimTextOutput("summaryDset") )
)
)#main panel
)#side bar layout
),#tab Data
tabPanel("ARIMA",br(),
sidebarLayout(
sidebarPanel(
helpText("Please select your timeseries and ARIMA parameters"),
selectInput("variable_ARIMA", "Please select what to represent",
choices = c('Open','High','Low','Close'),
selected = "Close"),
selectInput("timeseries", "Please select your time series",
choices = c('Actual','Differenced')),
checkboxInput("log_arima", "Log tranformation",
value = TRUE),
# Horizontal line ----
tags$hr(),
helpText("ARIMA (P,D,Q)"),
# Input: Enter P,D,Q value ----
numericInput("pval", "P", value=1),
numericInput("dval", "D", value=1),
numericInput("qval", "Q", value=1),
checkboxInput("auto_arima", "Auto ARIMA",
value = FALSE),
numericInput("forecast_period", "Forecasting Period", value=10)
),#sidebarPanel ARIMA
mainPanel(
tabsetPanel(type="tabs",
tabPanel("Pre-analysis",br(),
plotOutput("plot_arima1"),
verbatimTextOutput("Fuller_Test"),
plotOutput("ACF_plot"),
plotOutput("PACF_plot")
),
tabPanel("ARIMA",br(),
verbatimTextOutput("summary_arima"),
verbatimTextOutput("aic_bic"),
plotOutput("multiple_plot")
),
tabPanel("Forecasting",br(),
plotOutput("arima_forecast_plot"),
verbatimTextOutput("forecasting_values")
)
)# tabsetpanel
)#main panel arima
)#sidebarLayout ARIMA
),#Menu ARIMA
tabPanel("KNN",br(),
sidebarLayout(
sidebarPanel(
helpText("Forecasting Using K Nearest Neighbours"),
selectInput("variable_KNN", "Please select what to predict",
choices = c('Open','High','Low','Close'),
selected = "Close"),
checkboxInput("log_knn", "Log tranformation",
value = TRUE),
numericInput("K", "K", value=10),
numericInput("KNN_period", "Forecasting period", value=30)
),
mainPanel(
plotOutput("KNN_plot"),
verbatimTextOutput("KNN_accuracy")
)
)
), # menu knn
tabPanel("Neural Networks",br(),
sidebarLayout(
sidebarPanel(
selectInput("variable_NN", "Please select what to predict",
choices = c('Open','High','Low','Close'),
selected = "Close"),
checkboxInput("log_knn", "Log tranformation",
value = TRUE),
numericInput("period_NN", "Forecasting Period", value=20),
br(),
actionButton("execute_nn", "Run Model"),
helpText("NB : The execution takes some time, please wait until the plot appears")
),
mainPanel(plotOutput("NN"))
)
)# menu NN
)# tabsetpanel les menus
)
# Define server logic required to draw a histogram
server <- function(input, output) {
dataInput <- reactive({
return(data_function(input$share,input$dates,input$variable_choice) )
})
dataInput_arima <- reactive({
data<-data_function(input$share,input$dates,input$variable_ARIMA)
if(input$log_arima){
return(log(data))
}
else {return(data)}
})
dataInput_knn <- reactive({
data<-data_function(input$share,input$dates,input$variable_KNN)
if(input$log_knn){
return(log(data))
}
else {return(data)}
})
dataInput_nn <- reactive({
data<-data_function(input$share,input$dates,input$variable_NN)
if(input$log_knn){
return(log(data))
}
else {return(data)}
})
output$plot <- renderPlot({
chartSeries(dataInput(),multi.col = FALSE,theme = 'white',
type = "candles",
TA=technical_indicators(input$TA_choice) )
})
observeEvent(input$zoomIN, {
output$plot<-renderPlot({
chartSeries(dataInput(),multi.col = FALSE,theme = "white",
type = "candles",subset = "last 1 week",
TA=technical_indicators(input$TA_choice) )
})
})
observeEvent(input$zoomOUT, {
output$plot<-renderPlot({
chartSeries(dataInput(),multi.col = FALSE,theme = 'white',
type = "candles",
TA=technical_indicators(input$TA_choice) )
})
})
output$dataset <- renderTable(head(dataInput()))
output$summaryDset <- renderPrint({
summary(dataInput())
})
output$plot_arima1 <- renderPlot({
if(input$timeseries == "Actual"){
plot(dataInput_arima(), main = "Time-Series plot", col = "blue")
}
else if (input$timeseries == "Differenced"){
diff_price <- diff(dataInput_arima(),1)[-1,]
plot(diff_price)
}
})
output$Fuller_Test <- renderPrint({
print(adf.test(actual_or_diff(dataInput_arima(),input$timeseries)))
})
output$ACF_plot <- renderPlot({
if(input$timeseries == "Actual"){
acf(dataInput_arima(),main=paste(c('ACF for actual data of',input$share),
collapse=' '),lag.max = 320)
}
if(input$timeseries == "Differenced"){
diff_price <- diff(dataInput_arima(),1)[-1,]
acf(diff_price,main=paste(c('ACF for differenced data of',input$share),
collapse=' '),lag.max = 320)
}
})
output$PACF_plot <- renderPlot({
if(input$timeseries == "Actual"){
pacf(dataInput_arima(),main=paste(c('PACF for actual data of',input$share),
collapse=' '),lag.max = 320)
}
if(input$timeseries == "Differenced"){
diff_price <- diff(dataInput_arima(),1)[-1,]
pacf(diff_price,main=paste(c('PACF for differenced data of',input$share),
collapse=' '),lag.max = 320)
}
})
#------------------------------- ARIMA Model -----------------------------------
# get the model
model_ARIMA<-reactive({
arima_model(dataInput_arima(),input$pval,input$dval,input$qval,input$auto_arima)
})
output$multiple_plot <- renderPlot({
arima_model_outputs(model_ARIMA(),input$pval,input$dval,input$qval,choice="plot",input$auto_arima)
})
output$summary_arima<-renderPrint({
arima_model_outputs(model_ARIMA(),input$pval,input$dval,input$qval,choice="summary",input$auto_arima)
})
output$aic_bic <- renderPrint({
arima_model_outputs(model_ARIMA(),input$pval,input$dval,input$qval,choice="aic_bic",input$auto_arima)
})
# Forecasting
output$arima_forecast_plot <- renderPlot({
price_forecast<-arima_forecast(model_ARIMA(),input$forecast_period)
plot(price_forecast)
})
output$forecasting_values <- renderPrint({
price_forecast<-arima_forecast(model_ARIMA(),input$forecast_period)
mean_forecast <- price_forecast$mean
values <- data.frame(ForecastedPrice = c(mean_forecast))
print(values)
})
# ------------------------------- KNN ----------------------------
output$KNN_plot <- renderPlot({
KNN_model(dataInput_knn(),input$K,input$KNN_period,"plot")
})
output$KNN_accuracy <- renderPrint(
KNN_model(dataInput_knn(),input$K,input$KNN_period,"accuracy")
)
# ---------------------------- Neural Networks ----------------------
observeEvent(input$execute_nn, {
output$NN <- renderPlot({
NN_model(dataInput_nn(),input$period_NN)
})
})
}
# Run the application
shinyApp(ui = ui, server = server)