1 // Core Part 6 about a really dumb investment strategy

     2 //=====================================================

     3 

     4 

     5 // generate jar with

     6 //   > scala -d drumb.jar  drumb.scala

     7 

     8 

     9 object CW6b { 

    10 

    11 

    12 //two test portfolios

    13 

    14 val blchip_portfolio = List("GOOG", "AAPL", "MSFT", "IBM", "FB", "AMZN", "BIDU")

    15 val rstate_portfolio = List("PLD", "PSA", "AMT", "AIV", "AVB", "BXP", "CCI", 

    16                             "DLR", "EQIX", "EQR", "ESS", "EXR", "FRT", "HCP") 

    17 

    18 import io.Source

    19 import scala.util._

    20 

    21 // (1) The function below takes a stock symbol and a year as arguments.

    22 //     It should read the corresponding CSV-file and reads the January 

    23 //     data from the given year. The data should be collected in a list of

    24 //     strings for each line in the CSV-file.

    25 

    26 def get_january_data(symbol: String, year: Int) : List[String] = 

    27   Source.fromFile(symbol ++ ".csv")("ISO-8859-1").getLines.toList.filter(_.startsWith(year.toString))

    28 

    29 

    30 //test cases

    31 //blchip_portfolio.map(get_january_data(_, 2018))

    32 //rstate_portfolio.map(get_january_data(_, 2018))

    33 

    34 //get_january_data("GOOG", 1980)

    35 //get_january_data("GOOG", 2010)

    36 //get_january_data("FB", 2014)

    37 

    38 //get_january_data("PLD", 1980)

    39 //get_january_data("EQIX", 2010)

    40 //get_january_data("ESS", 2014)

    41 

    42 

    43 // (2) From the output of the get_january_data function, the next function 

    44 //     should extract the first line (if it exists) and the corresponding

    45 //     first trading price in that year with type Option[Double]. If no line 

    46 //     is generated by get_january_data then the result is None; Some if 

    47 //     there is a price.

    48 

    49 def get_first_price(symbol: String, year: Int) : Option[Double] = {

    50   val data = Try(Some(get_january_data(symbol, year).head)) getOrElse None 

    51   data.map(_.split(",").toList(1).toDouble)

    52 }

    53 

    54 //test cases

    55 //get_first_price("GOOG", 1980)

    56 //get_first_price("GOOG", 2010)

    57 //get_first_price("FB", 2014)

    58 

    59 /*

    60 for (i <- 1978 to 2018) {

    61   println(blchip_portfolio.map(get_first_price(_, i)))

    62 }

    63 

    64 for (i <- 1978 to 2018) {

    65   println(rstate_portfolio.map(get_first_price(_, i)))

    66 }

    67 */ 

    68 

    69 

    70 // (3) Complete the function below that obtains all first prices

    71 //     for the stock symbols from a portfolio (list of strings) and 

    72 //     for the given range of years. The inner lists are for the

    73 //     stock symbols and the outer list for the years.

    74 

    75 def get_prices(portfolio: List[String], years: Range): List[List[Option[Double]]] = 

    76   for (year <- years.toList) yield

    77     for (symbol <- portfolio) yield get_first_price(symbol, year)

    78 

    79 

    80 //test cases

    81 

    82 //println("Task 3 data from Google and Apple in 2010 to 2012")

    83 //val goog_aapl_prices = get_prices(List("GOOG", "AAPL"), 2010 to 2012)

    84 //println(goog_aapl_prices.toString ++ "\n")

    85 

    86 //val p_fb = get_prices(List("FB"), 2012 to 2014)

    87 //val tt = get_prices(List("BIDU"), 2004 to 2008)

    88 

    89 

    90 // (4) The function below calculates the change factor (delta) between

    91 //     a price in year n and a price in year n + 1. 

    92 

    93 def get_delta(price_old: Option[Double], price_new: Option[Double]) : Option[Double] = {

    94   (price_old, price_new) match {

    95     case (Some(x), Some(y)) => Some((y - x) / x)

    96     case _ => None

    97   }

    98 }

    99 

   100 

   101 // (5) The next function calculates all change factors for all prices (from a 

   102 //     portfolio). The input to this function are the nested lists created by 

   103 //     get_prices above.

   104 

   105 def get_deltas(data: List[List[Option[Double]]]):  List[List[Option[Double]]] =

   106   for (i <- (0 until (data.length - 1)).toList) yield 

   107     for (j <- (0 until (data(0).length)).toList) yield get_delta(data(i)(j), data(i + 1)(j))

   108 

   109 

   110 

   111 // test case using the prices calculated above

   112 

   113 //println("Task 5 change prices from Google and Apple in 2010 and 2011")

   114 //val goog_aapl_deltas = get_deltas(goog_aapl_prices)

   115 //println(goog_aapl_deltas.toString ++ "\n")

   116 

   117 //val ttd = get_deltas(tt)

   118 

   119 

   120 // (6) Write a function that given change factors, a starting balance and an index,

   121 //     calculates the yearly yield, i.e. new balance, according to our dumb investment 

   122 //     strategy. Index points to a year in the data list.

   123 

   124 def yearly_yield(data: List[List[Option[Double]]], balance: Long, index: Int): Long = {

   125   val somes = data(index).flatten

   126   val somes_length = somes.length

   127   if (somes_length == 0) balance

   128   else {

   129     val portion: Double = balance.toDouble / somes_length.toDouble

   130     balance + (for (x <- somes) yield (x * portion)).sum.toLong

   131   }

   132 }

   133 

   134 // test case using the deltas calculated above

   135 //println("Task 6 yield from Google and Apple in 2010 with  balance 100")

   136 

   137 //val d0 = goog_aapl_deltas(0)(0)

   138 //val d1 = goog_aapl_deltas(0)(1)

   139 //println(s"50 * ${d0.get} + 50 * ${d1.get} = ${50.toDouble * d0.get + 50.toDouble * d1.get}")

   140 

   141 

   142 //val goog_aapl_yield = yearly_yield(goog_aapl_deltas, 100, 0)

   143 //println("Rounded yield: " ++ goog_aapl_yield.toString ++ "\n")

   144 

   145 

   146 //yearly_yield(get_prices(rstate_portfolio, 2016 to 2018), 100, 2) 

   147 //get_prices(rstate_portfolio, 2016 to 2018)(2).flatten.sum

   148 

   149 

   150 // (7) Write a function compound_yield that calculates the overall balance for a 

   151 //     range of years where in each year the yearly profit is compounded to the new 

   152 //     balances and then re-invested into our portfolio. For this use the function and 

   153 //     results generated under (6). The function investment calls compound_yield

   154 //     with the appropriate deltas and the first index.

   155 

   156 

   157 def compound_yield(data: List[List[Option[Double]]], balance: Long, index: Int): Long = {

   158   if (index >= data.length) balance else {

   159     val new_balance = yearly_yield(data, balance, index)

   160     compound_yield(data, new_balance, index + 1)

   161   }

   162 }

   163 

   164 def investment(portfolio: List[String], years: Range, start_balance: Long): Long = {

   165   compound_yield(get_deltas(get_prices(portfolio, years)), start_balance, 0)

   166 }

   167 

   168 

   169 

   170 //test cases for the two portfolios given above

   171 

   172 //println("Real data: " + investment(rstate_portfolio, 1978 to 2019, 100))

   173 //println("Blue data: " + investment(blchip_portfolio, 1978 to 2019, 100))

   174 

   175 }

   176 

   177 

   178