--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/main_testing1/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))
+
+}
+
+
+