diff -r 663c2a9108d1 -r 4de31fdc0d67 main_solution1/drumb.scala --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/main_solution1/drumb.scala Mon Nov 02 02:31:44 2020 +0000 @@ -0,0 +1,178 @@ +// Core Part 6 about a really dumb investment strategy +//===================================================== + + +// generate jar with +// > scala -d drumb.jar drumb.scala + + +object CW6b { + + +//two test portfolios + +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._ + +// (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)) + +} + + +