// Main Part 1 about a really dumb investment strategy+ −
//=====================================================+ −
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// generate jar with+ −
// > scala -d drumb.jar drumb.scala+ −
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object M1 { + −
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//two test portfolios+ −
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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") + −
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import io.Source+ −
import scala.util._+ −
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// (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.+ −
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def get_january_data(symbol: String, year: Int) : List[String] = + −
Source.fromFile(symbol ++ ".csv")("ISO-8859-1").getLines().toList.filter(_.startsWith(year.toString))+ −
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//test cases+ −
//blchip_portfolio.map(get_january_data(_, 2018))+ −
//rstate_portfolio.map(get_january_data(_, 2018))+ −
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//get_january_data("GOOG", 1980)+ −
//get_january_data("GOOG", 2010)+ −
//get_january_data("FB", 2014)+ −
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//get_january_data("PLD", 1980)+ −
//get_january_data("EQIX", 2010)+ −
//get_january_data("ESS", 2014)+ −
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// (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.+ −
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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)+ −
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//test cases+ −
//get_first_price("GOOG", 1980)+ −
//get_first_price("GOOG", 2010)+ −
//get_first_price("FB", 2014)+ −
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/*+ −
for (i <- 1978 to 2018) {+ −
println(blchip_portfolio.map(get_first_price(_, i)))+ −
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for (i <- 1978 to 2018) {+ −
println(rstate_portfolio.map(get_first_price(_, i)))+ −
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*/ + −
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// (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.+ −
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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)+ −
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//test cases+ −
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//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")+ −
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//val p_fb = get_prices(List("FB"), 2012 to 2014)+ −
//val tt = get_prices(List("BIDU"), 2004 to 2008)+ −
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// (4) The function below calculates the change factor (delta) between+ −
// a price in year n and a price in year n + 1. + −
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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+ −
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// (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.+ −
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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))+ −
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// test case using the prices calculated above+ −
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//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")+ −
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//val ttd = get_deltas(tt)+ −
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// (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.+ −
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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+ −
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// test case using the deltas calculated above+ −
//println("Task 6 yield from Google and Apple in 2010 with balance 100")+ −
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//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}")+ −
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//val goog_aapl_yield = yearly_yield(goog_aapl_deltas, 100, 0)+ −
//println("Rounded yield: " ++ goog_aapl_yield.toString ++ "\n")+ −
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//yearly_yield(get_prices(rstate_portfolio, 2016 to 2018), 100, 2) + −
//get_prices(rstate_portfolio, 2016 to 2018)(2).flatten.sum+ −
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// (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.+ −
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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)+ −
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def investment(portfolio: List[String], years: Range, start_balance: Long): Long = {+ −
compound_yield(get_deltas(get_prices(portfolio, years)), start_balance, 0)+ −
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//test cases for the two portfolios given above+ −
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println("Real data: " + investment(rstate_portfolio, 1978 to 2019, 100))+ −
println("Blue data: " + investment(blchip_portfolio, 1978 to 2019, 100))+ −
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