R/StockPredictoR.R

In sauravkaushik8/SamplePackage: Predicts Stock Price Movement for Given Stock Symbol

#' @title Predicts Stock Price Movement for Given Stock Symbol
#'
#' @description This package predicts whether the stock price at tommorow's market close would be higher or lower compared to today's closing place.
#'
#' @param symbol
#'
#' @return NULL
#'
#' @examples  stock_predict('AAPL')
#'
#' @export stock_predict

stock_predict<-function(symbol)
  {

  options(warn=-1)


  #Importing price data for the given symbol
  data<-data.frame(xts::as.xts(get(quantmod::getSymbols(symbol))))

  #Assighning the column names
  colnames(data) <- c("data.Open","data.High","data.Low","data.Close","data.Volume","data.Adjusted")

  #Creating lag and lead features of price column.
  data <- xts::xts(data,order.by=as.Date(rownames(data)))
  data <- as.data.frame(merge(data, lm1=stats::lag(data[,'data.Adjusted'],c(-1,1,3,5,10))))

  #Extracting features from Date
  data$Date<-as.Date(rownames(data))
  data$Day_of_month<-as.integer(format(as.Date(data$Date),"%d"))
  data$Month_of_year<-as.integer(format(as.Date(data$Date),"%m"))
  data$Year<-as.integer(format(as.Date(data$Date),"%y"))
  data$Day_of_week<-as.factor(weekdays(data$Date))

  #Naming variables for refrence
  today <- 'data.Adjusted'
  tommorow <- 'data.Adjusted.5'

  #Creating outcome
  data$up_down <- as.factor(ifelse(data[,tommorow] > data[,today], 1, 0))

  #Creating train and test sets
  train<-data[stats::complete.cases(data),]
  test<-data[nrow(data),]

  #Training model
  model<-stats::glm(up_down~data.Open+data.High+data.Low+data.Close+
                    data.Volume+data.Adjusted+data.Adjusted.1+
                    data.Adjusted.2+data.Adjusted.3+data.Adjusted.4+
                    Day_of_month+Month_of_year+Year+Day_of_week,
                    family=binomial(link='logit'),data=train)

  #Making Predictions
  pred<-as.numeric(stats::predict(model,test[,c('data.Open','data.High','data.Low','data.Close','data.Volume','data.Adjusted','data.Adjusted.1','data.Adjusted.2','data.Adjusted.3','data.Adjusted.4','Day_of_month','Month_of_year','Year','Day_of_week')],type = 'response'))

  #Printing results
  print("Probability of Stock price going up tommorow:")
  print(pred)

}