Py: Adjust Splits and Dividends from Real Prices using Quandl Finance Data

pythonActualPrice.ipynb

Reproduced in R

library(data.table)

calculateAdjustedPrices <- function(df){  
  
  # Adapted from:
  # https://joshschertz.com/2016/08/27/Vectorizing-Adjusted-Close-with-Python/
  
  df <- df[rev(order(index)),]
  
  adjColumn = 'Adj_Close_Check'
  
  priceCol = df$Close
  splitCol = df$Split
  dividendCol = df$Dividend
  adjPriceCol = NULL 
  adjPriceCol[1] = priceCol[1]
  
  for(i in 2:length(priceCol)){
    adjPriceCol[i] = 
      round((adjPriceCol[i - 1] + adjPriceCol[i - 1] *
               (((priceCol[i] * splitCol[i - 1]) -
                   priceCol[i - 1] -
                   dividendCol[i - 1]) / priceCol[i - 1])), 4)}
  
  df$adj_Test = adjPriceCol
  
  df <- df[order(index),]
  
  return(df)
  
}

ticker <- "AAPL"
securityX <- Quandl(paste0("EOD/",ticker), api_key="xxxxxxx", type = "xts")
securityX <- as.data.table(securityX)
securityX <- securityX[index >= "1980-12-12" & index <= "2016-08-26",]

(securityX <- calculateAdjustedPrices(securityX))
# getSplits("AAPL") # Doesn't include Reverse Splits!
plot(securityX$index, securityX$Close/securityX$adj_Test, type = "l")