diff -r 34feeb53c0ba -r 0315d9983cd0 main_testing1/drumb.scala --- a/main_testing1/drumb.scala Sun Jan 15 10:58:13 2023 +0000 +++ b/main_testing1/drumb.scala Sat Mar 11 22:01:53 2023 +0000 @@ -1,176 +1,94 @@ // Main Part 1 about a really dumb investment strategy -//===================================================== - - -// generate jar with -// > scala -d drumb.jar drumb.scala - - -object M1 { +//=================================================== - -//two test portfolios +object M1 { -val 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.Source -import scala.util._ + //two test portfolios -// (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)) - + val 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") -//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) + import io.Source + import java.time.LocalDate -// (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. + // ADD YOUR CODE BELOW + //====================== -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) -} + def main(args: Array[String]): Unit = { + val data = get_january_data("GOOG", 2010) + // val ppp = get_prices(List("GOOG", "FB"), (2005 to 2007)) + // val rrr = get_first_price("GOOG", 2007) + + println(get_january_data("GOOG", 1980) == List()) + println(get_january_data("GOOG", 2010).head == "2010-01-04,312.204773") + + val sss = "" + } -//test cases -//get_first_price("GOOG", 1980) -//get_first_price("GOOG", 2010) -//get_first_price("FB", 2014) + def get_stock_data(symbol: String): List[(LocalDate, String)] = { + val content = Source.fromFile(symbol + ".csv").mkString + val dtf = java.time.format.DateTimeFormatter.ofPattern("yyyy-MM-dd") + content + .split("\r\n") + .filter(!_.toLowerCase.startsWith("date")) // Ignore first row (headers) + .map(p => (LocalDate.parse(p.substring(0, p.indexOf(",")), dtf), p.substring(p.indexOf(",") + 1, p.length))) + .toList + } -/* -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))) -} -*/ + // (1) + def get_january_data(symbol: String, year: Int): List[String] = { + get_stock_data(symbol).filter(_._1.getYear == year).map(p => p._1.toString + "," + p._2) + } -// (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. + // (2) + def get_first_price(symbol: String, year: Int): Option[Double] = { + val data = get_stock_data(symbol).filter(_._1.getYear == year) -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) + if (data.nonEmpty) { + data + .minBy(_._1) + ._2 + .toDoubleOption + } + else { + None + } + } -//test cases + // (3) + def get_prices(portfolio: List[String], years: Range): List[List[Option[Double]]] = { + portfolio + .map(symbol => years.map(year => get_first_price(symbol, year)).toList) + } -//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) + def get_delta(price_old: Option[Double], price_new: Option[Double]): Option[Double] = ??? -// (4) The function below calculates the change factor (delta) between -// a price in year n and a price in year n + 1. + // (5) + def get_deltas(data: List[List[Option[Double]]]): List[List[Option[Double]]] = ??? -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 - } -} + // (6) + def yearly_yield(data: List[List[Option[Double]]], balance: Long, index: Int): Long = ??? -// (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. + // (7) + def compound_yield(data: List[List[Option[Double]]], balance: Long, index: Int): Long = ??? -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)) + def investment(portfolio: List[String], years: Range, start_balance: Long): Long = ??? -// 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}") - + //Test cases for the two portfolios given above -//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)) + //println("Real data: " + investment(rstate_portfolio, 1978 to 2019, 100)) + //println("Blue data: " + investment(blchip_portfolio, 1978 to 2019, 100)) } @@ -178,19 +96,8 @@ - - - - - - - - - - - - - - - - +// This template code is subject to copyright +// by King's College London, 2022. Do not +// make the template code public in any shape +// or form, and do not exchange it with other +// students under any circumstance.