// 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))


}






















import CW6b._ 
investment(rstate_portfolio, 1978 to 2019, 100)