--- a/marking1/drumb.scala Sat Jun 22 08:39:52 2019 +0100
+++ b/marking1/drumb.scala Wed Jul 24 14:22:06 2019 +0100
@@ -1,58 +1,89 @@
-// Advanvced Part 3 about a really dumb investment strategy
-//==========================================================
+// Main Part about a really dumb investment strategy
+//======================================================
-//object CW6c {
+//object CW6b { // for purposes of generating a jar
//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", "GGP", "HCP")
-
-// (1) The function below should obtain the first trading price
-// for a stock symbol by using the query
-//
-// http://ichart.yahoo.com/table.csv?s=<<symbol>>&a=0&b=1&c=<<year>>&d=1&e=1&f=<<year>>
-//
-// and extracting the first January Adjusted Close price in a year.
-
+ "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").getLines.toList.filter(_.startsWith(year.toString))
+ 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)
}
-get_first_price("GOOG", 1980)
-get_first_price("GOOG", 2010)
-get_first_price("FB", 2014)
+//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)))
+}
+*/
-// Complete the function below that obtains all first prices
-// for the stock symbols from a portfolio for the given
-// range of years
+// (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 case
-val p_fb = get_prices(List("FB"), 2012 to 2014)
-val p = get_prices(List("GOOG", "AAPL"), 2010 to 2012)
+//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 tt = get_prices(List("BIDU"), 2004 to 2008)
+//val p_fb = get_prices(List("FB"), 2012 to 2014)
+//val tt = get_prices(List("BIDU"), 2004 to 2008)
-// (2) The first function below calculates the change factor (delta) between
-// a price in year n and a price in year n+1. The second function calculates
-// all change factors for all prices (from a portfolio).
+
+// (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 {
@@ -61,25 +92,32 @@
}
}
+
+// (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
-val d = get_deltas(p)
-val ttd = get_deltas(tt)
-// (3) Write a function that given change factors, a starting balance and a year
-// calculates the yearly yield, i.e. new balanace, according to our dump investment
-// strategy. Another function calculates given the same data calculates the
-// compound yield up to a given year. Finally a function combines all
-// calculations by taking a portfolio, a range of years and a start balance
-// as arguments.
+//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)
-def yearly_yield(data: List[List[Option[Double]]], balance: Long, year: Int): Long = {
- val somes = data(year).flatten
+// (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 {
@@ -88,64 +126,48 @@
}
}
-def compound_yield(data: List[List[Option[Double]]], balance: Long, year: Int): Long = {
- if (year >= data.length) balance else {
- val new_balance = yearly_yield(data, balance, year)
- compound_yield(data, new_balance, year + 1)
+// 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)
}
}
-//yearly_yield(d, 100, 0)
-//compound_yield(d.take(6), 100, 0)
-
-//test case
-//yearly_yield(d, 100, 0)
-//yearly_yield(d, 225, 1)
-//yearly_yield(d, 246, 2)
-//yearly_yield(d, 466, 3)
-//yearly_yield(d, 218, 4)
-
-//yearly_yield(d, 100, 0)
-//yearly_yield(d, 125, 1)
-
def investment(portfolio: List[String], years: Range, start_balance: Long): Long = {
compound_yield(get_deltas(get_prices(portfolio, years)), start_balance, 0)
}
-/*
-val q1 = get_deltas(get_prices(List("GOOG", "AAPL", "BIDU"), 2000 to 2017))
-yearly_yield(q1, 100, 0)
-yearly_yield(q1, 100, 1)
-yearly_yield(q1, 100, 2)
-yearly_yield(q1, 100, 3)
-yearly_yield(q1, 100, 4)
-yearly_yield(q1, 100, 5)
-yearly_yield(q1, 100, 6)
-investment(List("GOOG", "AAPL", "BIDU"), 2004 to 2017, 100)
-val one = get_deltas(get_prices(rstate_portfolio, 1978 to 1984))
-val two = get_deltas(get_prices(blchip_portfolio, 1978 to 1984))
-
-val one_full = get_deltas(get_prices(rstate_portfolio, 1978 to 2017))
-val two_full = get_deltas(get_prices(blchip_portfolio, 1978 to 2017))
-
-one_full.map(_.flatten).map(_.sum).sum
-two_full.map(_.flatten).map(_.sum).sum
//test cases for the two portfolios given above
-//println("Real data: " + investment(rstate_portfolio, 1978 to 2017, 100))
-//println("Blue data: " + investment(blchip_portfolio, 1978 to 2017, 100))
+//println("Real data: " + investment(rstate_portfolio, 1978 to 2019, 100))
+//println("Blue data: " + investment(blchip_portfolio, 1978 to 2019, 100))
-for (i <- 2000 to 2017) {
- println("Year " + i)
- //println("Real data: " + investment(rstate_portfolio, 1978 to i, 100))
- //println("Blue data: " + investment(blchip_portfolio, 1978 to i, 100))
- println("test: " + investment(List("GOOG", "AAPL", "BIDU"), 2000 to i, 100))
-}
+//}
-*/
-//1984
-//1992
-//}
+