// Advanced Part 3 about a really dumb investment strategy

val rstate_portfolio = List("PLD", "PSA", "AMT", "AIV", "AVB", "BXP", "CCI","DLR", "EQIX", "EQR", "ESS", "EXR", "FRT", "GGP", "HCP") 

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

//       It should read the corresponding CSV-file and read 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] = {

	val file = symbol + ".csv"

	val list = scala.io.Source.fromFile(file).mkString.split("\n").toList

	val rx = (year.toString + ".*")

	(for(n <- 1 to list.length -1 if(list(n) matches rx)) yield list(n)).toList

}

// (1.b) 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 as Option[Double]. If no line is 

//       generated by get_january_data then the result is None

	val first_line = get_january_data(symbol, year)

	if(first_line.length == 0 ){

		None

	} else {

	Option((first_line(0).split(",")(1)).toDouble)

	}

// (1.c) 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(y <- years) yield (for(n <- 0 to portfolio.length-1) yield get_first_price(portfolio(n), y)).toList).toList

}

// (2) The first function below calculates the change factor (dta) 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). The input to this

//     function are the nested lists created by get_prices above.

	for( x <- price_old; y <- price_new) yield (y-x)/x

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

	(for( n <- 1 to data.length-1) yield (for(i <- 0 to data(n).length-1) yield  get_delta(data(n-1)(i), data(n)(i))).toList).toList

}

//     calculates the yearly yield, i.e. new balance, 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 increments = (for(n <- 0 to data(year).length-1 if(!(data(year)(n) == None))) yield (data(year)(n).getOrElse(0.0))).toList

	val sumi = (increments.sum).toDouble

	if(increments.length == 0){

		balance

	}else{

		val il = (increments.length).toDouble

		val averag = sumi/il

		val i = (balance + (balance*averag))

		i.toLong

	}

def compound_yield(data: List[List[Option[Double]]], balance: Long, ye: Int) : Long = {//if(year == 0) yearly_yield(data, balance, 0) else compound_yield(data, yearly_yield(data, balance, year), year-1)

	val increments_py = (for(year <- 0 to ye) yield {

		val increments = (for(n <- 0 to data(year).length-1 if(!(data(year)(n) == None))) yield (data(year)(n).getOrElse(0.0))).toList

		val sum_of = (increments.sum).toDouble

		val number_of = (increments.length).toDouble

		sum_of/number_of + 1.0

	}).toList

	val mul_factor = increments_py.reduceLeft(_*_)

	(balance*mul_factor).toLong

}

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

	val p = get_prices(portfolio, years)

	val d = get_deltas(p)

	compound_yield(d, start_balance, d.length-1)

investment(rstate_portfolio, 1978 to 2017, 100)

investment(blchip_portfolio, 1978 to 2017, 100)