--- a/testing1/drumb.scala Sun Nov 01 01:21:31 2020 +0000
+++ /dev/null Thu Jan 01 00:00:00 1970 +0000
@@ -1,178 +0,0 @@
-// 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))
-
-}
-
-
-