// Main Part 1 about a really dumb investment strategy//=====================================================// generate jar with// > scala -d drumb.jar drumb.scalaobject M1 { //two test portfoliosval blchip_portfolio = List("GOOG", "AAPL", "MSFT", "IBM", "FB", "AMZN", "BIDU")val rstate_portfolio = List("PLD", "PSA", "AMT", "AIV", "AVB", "BXP", "CCI", "DLR", "EQIX", "EQR", "ESS", "EXR", "FRT", "HCP") import io.Sourceimport scala.util._// (1) The function below takes a stock symbol and a year as arguments.// It should read the corresponding CSV-file and reads the January // data from the given year. The data should be collected in a list of// strings for each line in the CSV-file.def get_january_data(symbol: String, year: Int) : List[String] = Source.fromFile(symbol ++ ".csv")("ISO-8859-1").getLines().toList.filter(_.startsWith(year.toString))//test cases//blchip_portfolio.map(get_january_data(_, 2018))//rstate_portfolio.map(get_january_data(_, 2018))//get_january_data("GOOG", 1980)//get_january_data("GOOG", 2010)//get_january_data("FB", 2014)//get_january_data("PLD", 1980)//get_january_data("EQIX", 2010)//get_january_data("ESS", 2014)// (2) From the output of the get_january_data function, the next function // should extract the first line (if it exists) and the corresponding// first trading price in that year with type Option[Double]. If no line // is generated by get_january_data then the result is None; Some if // there is a price.def get_first_price(symbol: String, year: Int) : Option[Double] = { val data = Try(Some(get_january_data(symbol, year).head)) getOrElse None data.map(_.split(",").toList(1).toDouble)}//test cases//get_first_price("GOOG", 1980)//get_first_price("GOOG", 2010)//get_first_price("FB", 2014)/*for (i <- 1978 to 2018) { println(blchip_portfolio.map(get_first_price(_, i)))}for (i <- 1978 to 2018) { println(rstate_portfolio.map(get_first_price(_, i)))}*/ // (3) Complete the function below that obtains all first prices// for the stock symbols from a portfolio (list of strings) and // for the given range of years. The inner lists are for the// stock symbols and the outer list for the years.def get_prices(portfolio: List[String], years: Range): List[List[Option[Double]]] = for (year <- years.toList) yield for (symbol <- portfolio) yield get_first_price(symbol, year)//test cases//println("Task 3 data from Google and Apple in 2010 to 2012")//val goog_aapl_prices = get_prices(List("GOOG", "AAPL"), 2010 to 2012)//println(goog_aapl_prices.toString ++ "\n")//val p_fb = get_prices(List("FB"), 2012 to 2014)//val tt = get_prices(List("BIDU"), 2004 to 2008)// (4) The function below calculates the change factor (delta) between// a price in year n and a price in year n + 1. def get_delta(price_old: Option[Double], price_new: Option[Double]) : Option[Double] = { (price_old, price_new) match { case (Some(x), Some(y)) => Some((y - x) / x) case _ => None }}// (5) The next function calculates all change factors for all prices (from a // portfolio). The input to this function are the nested lists created by // get_prices above.def get_deltas(data: List[List[Option[Double]]]): List[List[Option[Double]]] = for (i <- (0 until (data.length - 1)).toList) yield for (j <- (0 until (data(0).length)).toList) yield get_delta(data(i)(j), data(i + 1)(j))// test case using the prices calculated above//println("Task 5 change prices from Google and Apple in 2010 and 2011")//val goog_aapl_deltas = get_deltas(goog_aapl_prices)//println(goog_aapl_deltas.toString ++ "\n")//val ttd = get_deltas(tt)// (6) Write a function that given change factors, a starting balance and an index,// calculates the yearly yield, i.e. new balance, according to our dumb investment // strategy. Index points to a year in the data list.def yearly_yield(data: List[List[Option[Double]]], balance: Long, index: Int): Long = { val somes = data(index).flatten val somes_length = somes.length if (somes_length == 0) balance else { val portion: Double = balance.toDouble / somes_length.toDouble balance + (for (x <- somes) yield (x * portion)).sum.toLong }}// test case using the deltas calculated above//println("Task 6 yield from Google and Apple in 2010 with balance 100")//val d0 = goog_aapl_deltas(0)(0)//val d1 = goog_aapl_deltas(0)(1)//println(s"50 * ${d0.get} + 50 * ${d1.get} = ${50.toDouble * d0.get + 50.toDouble * d1.get}")//val goog_aapl_yield = yearly_yield(goog_aapl_deltas, 100, 0)//println("Rounded yield: " ++ goog_aapl_yield.toString ++ "\n")//yearly_yield(get_prices(rstate_portfolio, 2016 to 2018), 100, 2) //get_prices(rstate_portfolio, 2016 to 2018)(2).flatten.sum// (7) Write a function compound_yield that calculates the overall balance for a // range of years where in each year the yearly profit is compounded to the new // balances and then re-invested into our portfolio. For this use the function and // results generated under (6). The function investment calls compound_yield// with the appropriate deltas and the first index.def compound_yield(data: List[List[Option[Double]]], balance: Long, index: Int): Long = { if (index >= data.length) balance else { val new_balance = yearly_yield(data, balance, index) compound_yield(data, new_balance, index + 1) }}def investment(portfolio: List[String], years: Range, start_balance: Long): Long = { compound_yield(get_deltas(get_prices(portfolio, years)), start_balance, 0)}//test cases for the two portfolios given above println("Real data: " + investment(rstate_portfolio, 1978 to 2019, 100)) println("Blue data: " + investment(blchip_portfolio, 1978 to 2019, 100))}