--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/marking1/drumb.scala	Thu Mar 08 12:32:50 2018 +0000
@@ -0,0 +1,151 @@
+// Advanvced Part 3 about a really dumb investment strategy
+//==========================================================
+
+object CW6c {
+
+
+//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.
+
+
+import io.Source
+import scala.util._
+
+def get_january_data(symbol: String, year: Int) : List[String] = 
+  Source.fromFile(symbol ++ ".csv").getLines.toList.filter(_.startsWith(year.toString))
+
+
+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)
+
+
+// Complete the function below that obtains all first prices
+// for the stock symbols from a portfolio for the given
+// range of 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)
+
+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).
+
+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
+  }
+}
+
+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.
+
+
+def yearly_yield(data: List[List[Option[Double]]], balance: Long, year: Int): Long = {
+  val somes = data(year).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
+  }
+}
+
+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)
+  }
+}
+
+//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))
+
+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
+}