Calculate Mean Return and Variance matrix from the csv files downloaded from Yahoo Finance. Download the historical quotes data and update the tickers variable. Currently this program is just looking at last 200 days (one year). That parameter should be changed if analyzing for a different time horizon

meanVarianceFromYahooData.scala

package com.test
import scala.io.Source

object meanVarianceFromYahooData {
  val root = "/"
  def readData(f: String): List[Double] = {
    val iter = Source.fromFile(root + f + "csv").getLines().drop(1).map(_.split(","))

    def getClose: List[Double] = (iter map (_.last.toDouble)) toList

    def ret(a: Double, b: Double): Double = ((a - b) * 100 / b)

    def returnize(list: List[Double]): List[Double] = list match {
      case Nil => Nil
      case x :: Nil => Nil
      case x :: xs => ret(x, xs.head) :: returnize(xs)
    }
    returnize(getClose take 200)
  }

  def avg(numbers: List[Double]): Double = numbers.reduceLeft(_ + _) / numbers.length

  def stdDev(range: List[Double], domain: List[Double]): Double =
    if (range.length >= 2) ((range.map(_ - avg(range)) zip domain.map(_- avg(domain))).map { a => a._1 * a._2 }).reduceLeft(_ + _) / (range.length.toDouble - 1) else 0.0

  def varMat(lists: Tuple2[List[Double], List[Double]]) = { stdDev(lists._1, lists._2) }

  def main(args: Array[String]) {
    val tickers = List("DFCEX","VINIX","VBMFX","DODGX")
    val returns = for(tick <- tickers) yield readData(tick)
    println("+++ Expected Return Matrix ++++")
    println (for (a <- returns) yield avg(a))
    println("+++++++ Variance Matrix +++++++")
    (for (first <- returns; second <- returns) yield varMat((first, second))).grouped(tickers.length).toList foreach println
  }
}